Repository: matthias-k/pysaliency Branch: dev Commit: b93d86a5fe0d Files: 190 Total size: 5.2 MB Directory structure: gitextract_9eg4n1my/ ├── .github/ │ └── workflows/ │ └── test-package-conda.yml ├── .gitignore ├── .travis.yml ├── CHANGELOG.md ├── LICENSE.md ├── MANIFEST.in ├── Makefile ├── README.md ├── docs/ │ └── superpowers/ │ ├── plans/ │ │ ├── 2026-03-21-crossvalidated-baseline-model-hdf5.md │ │ └── 2026-03-23-compact-hdf5-export.md │ └── specs/ │ ├── 2026-03-21-model-hdf5-save-reload-design.md │ └── 2026-03-23-hdf5-compact-export-design.md ├── examples/ │ └── docker_submission/ │ ├── README.md │ ├── docker_deepgaze3/ │ │ ├── Dockerfile │ │ ├── model_server.py │ │ └── requirements.txt │ ├── docker_pysaliency_model/ │ │ ├── Dockerfile │ │ ├── model_server.py │ │ ├── requirements.txt │ │ └── sample_submission.py │ └── sample_evaluation.py ├── notebooks/ │ ├── LSUN.ipynb │ └── Tutorial.ipynb ├── pyproject.toml ├── pysaliency/ │ ├── __init__.py │ ├── baseline_utils.py │ ├── dataset_config.py │ ├── datasets/ │ │ ├── __init__.py │ │ ├── fixations.py │ │ ├── scanpaths.py │ │ ├── stimuli.py │ │ └── utils.py │ ├── external_datasets/ │ │ ├── __init__.py │ │ ├── cat2000.py │ │ ├── coco_freeview.py │ │ ├── coco_search18.py │ │ ├── dut_omrom.py │ │ ├── figrim.py │ │ ├── isun.py │ │ ├── koehler.py │ │ ├── mit.py │ │ ├── nusef.py │ │ ├── osie.py │ │ ├── pascal_s.py │ │ ├── salicon.py │ │ ├── scripts/ │ │ │ ├── extract_fixations.m │ │ │ └── load_cat2000.m │ │ ├── toronto.py │ │ └── utils.py │ ├── external_models/ │ │ ├── __init__.py │ │ ├── deepgaze.py │ │ ├── matlab_models.py │ │ ├── models.py │ │ ├── scripts/ │ │ │ ├── AIM_wrapper.m │ │ │ ├── BMS/ │ │ │ │ ├── BMS_wrapper.m │ │ │ │ └── patches/ │ │ │ │ ├── adapt_opencv_paths.diff │ │ │ │ ├── correct_add_path.diff │ │ │ │ ├── fix_FileGettor.diff │ │ │ │ └── series │ │ │ ├── ContextAwareSaliency_wrapper.m │ │ │ ├── CovSal_wrapper.m │ │ │ ├── GBVS/ │ │ │ │ ├── GBVSIttiKoch_wrapper.m │ │ │ │ ├── GBVS_wrapper.m │ │ │ │ └── patches/ │ │ │ │ ├── get_path │ │ │ │ ├── make_mex_files_octave_compatible │ │ │ │ └── series │ │ │ ├── IttiKoch_wrapper.m │ │ │ ├── Judd/ │ │ │ │ ├── FaceDetect_patches/ │ │ │ │ │ ├── change_opencv_include │ │ │ │ │ └── series │ │ │ │ ├── JuddSaliencyModel_patches/ │ │ │ │ │ ├── find_cascade_file │ │ │ │ │ ├── locate_FelzenszwalbDetector_files │ │ │ │ │ └── series │ │ │ │ ├── Judd_wrapper.m │ │ │ │ ├── SaliencyToolbox_patches/ │ │ │ │ │ ├── enable_unit16 │ │ │ │ │ └── series │ │ │ │ └── voc_patches/ │ │ │ │ ├── change_fconv │ │ │ │ ├── matlabR2014a_compatible │ │ │ │ ├── matlabR2021a_compatible │ │ │ │ └── series │ │ │ ├── RARE2012_wrapper.m │ │ │ ├── SUN_wrapper.m │ │ │ └── ensure_image_is_color_image.m │ │ └── utils.py │ ├── filter_datasets.py │ ├── hdf5.py │ ├── http_models.py │ ├── metric_optimization.py │ ├── metric_optimization_tf.py │ ├── metric_optimization_torch.py │ ├── metrics.py │ ├── models.py │ ├── numba_utils.py │ ├── optpy/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── jacobian.py │ │ └── optimization.py │ ├── plotting.py │ ├── precomputed_models.py │ ├── quilt.py │ ├── roc.py │ ├── roc_cython.pyx │ ├── saliency_map_conversion.py │ ├── saliency_map_conversion_theano.py │ ├── saliency_map_conversion_torch.py │ ├── saliency_map_models.py │ ├── sampling_models.py │ ├── tf_utils.py │ ├── theano_utils.py │ ├── torch_datasets.py │ ├── torch_utils.py │ └── utils/ │ ├── __init__.py │ └── variable_length_array.py ├── pytest.ini ├── requirements.txt ├── setup.py └── tests/ ├── conftest.py ├── datasets/ │ ├── test_datasets.py │ ├── test_fixations.py │ ├── test_scanpaths.py │ ├── test_stimuli.py │ └── utils.py ├── external_datasets/ │ ├── test_COCO_Search18.py │ ├── test_NUSEF.py │ ├── test_PASCAL_S.py │ ├── test_SALICON.py │ └── test_coco_freeview.py ├── external_models/ │ ├── AIM_color_stimulus.npy │ ├── AIM_grayscale_stimulus.npy │ ├── ContextAwareSaliency_color_stimulus.npy │ ├── ContextAwareSaliency_grayscale_stimulus.npy │ ├── CovSal_color_stimulus.npy │ ├── CovSal_grayscale_stimulus.npy │ ├── GBVSIttiKoch_color_stimulus.npy │ ├── GBVSIttiKoch_grayscale_stimulus.npy │ ├── GBVS_color_stimulus.npy │ ├── GBVS_grayscale_stimulus.npy │ ├── IttiKoch_color_stimulus.npy │ ├── IttiKoch_grayscale_stimulus.npy │ ├── Judd_color_stimulus.npy │ ├── Judd_grayscale_stimulus.npy │ ├── RARE2007_color_stimulus.npy │ ├── RARE2007_grayscale_stimulus.npy │ ├── RARE2012_color_stimulus.npy │ ├── RARE2012_grayscale_stimulus.npy │ ├── SUN_color_stimulus.npy │ ├── SUN_grayscale_stimulus.npy │ ├── color_stimulus.npy │ ├── grayscale_stimulus.npy │ └── test_deepgaze.py ├── skippedtest_theano_utils.py ├── test_baseline_utils.py ├── test_crossvalidation.py ├── test_dataset_config.py ├── test_external_datasets.py ├── test_external_models.py ├── test_filter_datasets.py ├── test_hdf5_io.py ├── test_helpers.py ├── test_http_models.py ├── test_metric_optimization.py ├── test_metric_optimization_tf.py ├── test_metric_optimization_torch.py ├── test_models.py ├── test_numba_utils.py ├── test_precomputed_models.py ├── test_quilt/ │ ├── .pc/ │ │ ├── .quilt_patches │ │ ├── .quilt_series │ │ ├── .version │ │ ├── add_numbers.diff/ │ │ │ ├── .timestamp │ │ │ └── source.txt │ │ └── applied-patches │ ├── patches/ │ │ ├── add_numbers.diff │ │ └── series │ ├── source/ │ │ ├── .pc/ │ │ │ ├── .quilt_patches │ │ │ ├── .quilt_series │ │ │ └── .version │ │ └── source.txt │ ├── source.txt │ └── target/ │ └── source.txt ├── test_quilt.py ├── test_saliency_map_conversion.py ├── test_saliency_map_conversion_theano.py ├── test_saliency_map_conversion_torch.py ├── test_saliency_map_conversion_torch_extended.py ├── test_saliency_map_models.py ├── test_sampling.py ├── test_torch_datasets.py ├── test_torch_utils.py ├── test_utils.py └── utils/ └── test_variable_length_array.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/test-package-conda.yml ================================================ name: Tests on: [push, pull_request] jobs: build-linux: runs-on: ubuntu-latest strategy: max-parallel: 5 matrix: python-version: - "3.9" - "3.10" - "3.11" - "3.12" - "3.13" steps: - uses: actions/checkout@v4 - uses: conda-incubator/setup-miniconda@v3 with: python-version: ${{ matrix.python-version }} channels: conda-forge - name: Conda info # the shell setting is necessary for loading profile etc which activates the conda environment shell: bash -el {0} run: conda info - name: Install dependencies shell: bash -el {0} run: | conda install \ boltons \ cython \ deprecation \ dill \ diskcache \ h5py \ imageio \ natsort \ numba \ numpy \ numpydoc \ orjson \ pandas \ piexif \ pillow \ pip \ pkg-config \ pytorch \ requests \ schema \ scikit-learn \ scipy \ setuptools \ sphinx \ torchvision \ tqdm - name: Conda list shell: bash -el {0} run: conda list - name: Test with pytest shell: bash -el {0} run: | conda install pytest hypothesis pip install -e . python -m pytest --nomatlab --notheano --nodownload tests - name: test build and install shell: bash -el {0} run: | pip install build python -m build --sdist pip install dist/*.tar.gz mkdir tmp && cd tmp && python -c "import pysaliency" ================================================ FILE: .gitignore ================================================ .hypothesis .mypy_cache /tmp/ /build/ /pysaliency_datasets/ /test_datasets/ /test_models/ *.pyc *.swp *.c *.so *.egg-info .vscode .DS_Store ================================================ FILE: .travis.yml ================================================ language: python python: # - 2.7 - 3.6 - 3.7 before_install: - sudo apt-get update - sudo apt-get install g++ - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh; else wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; fi - bash miniconda.sh -b -p $HOME/miniconda - source "$HOME/miniconda/etc/profile.d/conda.sh" - hash -r - conda config --set always_yes yes --set changeps1 no - conda config --add channels conda-forge - conda update -q conda # Useful for debugging any issues with conda - conda info -a # - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh # - chmod +x miniconda.sh # - ./miniconda.sh -b -p $HOME/miniconda # - export PATH=/home/travis/miniconda/bin:$PATH # - conda config --set always_yes yes --set changeps1 no # - conda config --add channels conda-forge # - conda update -q conda # - pip install tqdm install: - conda create -q -n test-env python=$TRAVIS_PYTHON_VERSION numpy scipy cython six setuptools sphinx numpydoc pkg-config pillow tqdm boltons natsort requests dill pytest theano imageio scikit-learn pandas pytorch hypothesis # - conda install -q -n test-env tensorflow=1.13.2 - conda activate test-env before_script: - conda info - conda list - pip --version - pip freeze script: # Make sure the library installs. - python setup.py install - python setup.py build_ext --inplace - python -c "import pysaliency" # make sure can be installed without theano, pytorch, and tensorflow - conda install theano pytorch - pip install tensorflow==1.13.2 schema - python -m pytest --nomatlab tests - python setup.py sdist - pip install dist/*.tar.gz - mkdir tmp && cd tmp && python -c "import pysaliency" ================================================ FILE: CHANGELOG.md ================================================ # Changelog * 0.3 (dev): This is a major release, refactoring most of `pysaliency.datasets` including some breaking changes. * Feature: `Scanpaths` is a new class for storing scanpaths. It has a similar API to the old `FixationTrains`, but exclusively cares about scanpaths. For example, the length of a Scanpaths instance is the number of scanpaths, not the number of fixations. It is intended to make iterating over and working with scanpaths more convenient. * Feature: `ScanpathFixations` is a new subclass of `Fixations` intended for handling fixations that come from scanpaths. It is intended to replace the old `FixationTrains` class. `ScanpathFixations` has a `scanpaths: Scanpaths` attribute storing the source scanpaths (what used to be stored manually as `train_xs` etc in `FixationTrains`). Unlike `FixationTrains`, `ScanpathFixations` does not have any attributes that are not derived from the scanpaths. `FixationTrains` is now a deprecated subclass of `ScanpathFixations` which adds the old properties and constructor. and allows for attributes which are neither scanpath attributes nor scanpath fixation attributes. * Feature: `VariableLengthArray` for inuititively handling data like scanpaths where each row can have a different length. `Fixations.x_Hist`, `Fixations.y_hist`, `Scanpaths.xs` etc are now instances of `VariableLengthArray`. * `Fixations.lengths` has been renamed to `Fixations.scanpath_history_length` to make it clear that it is the length of the scanpath history. `Fixations.lengths` is now a deprecated alias. * In general, naming convention for attriutes has been changed to use the plural form if the attribute is a list of values for each element (i.e., `Scanpaths.xs`) and the singular form if the attribute is a single value (i.e., `Scanpaths.length`, `Fixations.x`). This resulted in renaming `Fixations.subjects` to `Fixations.subject`. The old name is now a deprecated alias. * Bugfix: Compatibility with torch 2.2 and numpy 2.0 * Bugfix!: The download location of the RARE2012 model changed. The new source code results in slightly different predictions. * Feature: The RARE2007 model is now available as `pysaliency.external_models.RARE2007`. It's execution requires MATLAB. * matlab scripts are now called with the `-batch` option instead of `-nodisplay -nosplash -r`, which should behave better. * Enhancement: preloaded stimulus ids are passed on to subsets of Stimuli and FileStimuli. * Feature: `pysaliency.read_hdf5` now takes additional keyword arguments which are passed to the respective class methods. This allows, e.g., to load `FileStimuli` with caching disabled. * Enhancement: `pysaliency.HDF5Model` and `pysaliency.HDF5SaliencyMapModel` now better handle the case of loading a model for a subset of entries in the HDF5 file which might be saved under a certain common prefix. * Feature: `pysaliency.saliency_map_conversation_torch` now specifies constraints, where applicable, as linear. To that end, `pysaliency.optpy` now allows specifying linear constraints. This results in better optimization performance, especially since scipy 1.15.0 fixed a bug that in our case actually helped. * Feature: `pysaliency.baseline_utils.BaselineModel` now supports `to_hdf5` / `read_hdf5` model persistence using HDF5 type `pysaliency.baseline_utils.BaselineModel`. * Enhancement: Introduced unified HDF5 I/O dispatch in `pysaliency.hdf5` (`read_hdf5`) and routed `pysaliency.datasets.read_hdf5` through the dataset dispatcher for compatibility. * Enhancement: `BaselineModel` no longer stores `stimuli` and `fixations` after initialization; it keeps only the normalized fixation representation used for predictions. * 0.2.22: * Enhancement: New [Tutorial](notebooks/Tutorial.ipynb). * Bugfix: `SaliencyMapModel.AUC` failed if some images didn't have any fixations. * Feature: `StimulusDependentSaliencyMapModel` * Bugfix: The NUSEF dataset scaled some fixations not correctly to image coordinates. Also, we now account for some typos in the dataset source data. * Feature: CrossvalMultipleRegularizations and GeneralMixtureKernelDensityEstimator in baseline utils (names might change!) * Feature: DVAAwareScanpathModel * Feature: ShuffledBaselineModel is now much more efficient and able to handle large numbers of stimuli. hence, ShuffledSimpleBaselineModel is not necessary anymore and a deprecated alias to ShuffledBaselineModel * Feature: ShuffledBaselineModel can now compute predictions for very large numbers of stimuli without needing to have all individual predictions in memory due to a recursive reduce logsumexp implementation. * Feature: `plotting.plot_scanpath` to visualize scanpaths and saccades. WIP, expect the API to change! * Feature: DeepGaze I and DeepGazeIIE models * Feature: COCO Freeview dataset * Feature: `optimize_for_information_gain(framework='torch', ...) now supports a `cache_directory`, where intermediate steps are cached. This supports resuming crashed optimization runs. * Bugfix: fixed some edge cases in `optimize_for_information_gain(framework='torch')` * Feature: COCO Seach18 dataset * Feature: `FixationTrains.train_lengths` * Feature: `FixationTrains.scanpath_fixation_attributes` allows handling of per-fixation attributes on scanpath level, e.g. fixation durations. According attributes as in a Fixations instance are automatically created, e.g. for durations there will be an attribute `durations` and an attribute `duration_hist`. Also for scanpath_attributes (e.g., attributes applying to a whole scanpath, such as task) will also generate an attribute for each fixation to make this information available in Fixations instance. * Feature: `scanpaths_from_fixations` reconstructs a FixationTrains object from a Fixations instance * Bugfix: `t_hist` got replaced with `y_hist` in Fixations instances (but luckily not in FixationTrains instances) * Bugfix: torch code was broken due to changes in torch 1.11 * Bugfix: SALICON dataset download did not work anymore * Bugfix: NUSEF datast links changed * 0.2.21: * Added new datasets: PASCAL-S and DUT-OMRON * Feature: FixedStimulusSizeModel and DVAAwareModel * Feature: Fixations finally support len() * Experimental feature: conditional_log_densities(stimuli, fixations) and conditional_saliency_maps(...). This is WIP to enable batch processing in models. * Fallback models for stimulus dependent models * MixtureScanpathModel * Reimplemented AUC for special case of only one positive sample, leading to substantial speedup * There is a new version of the CAT2000 train dataset which fixes some details in the processing. Since it changes the dataset, by default the old processing is used. * Feature: ShuffledSimpleBaselineModel. Baseline model to be used with ShuffledAUCSaliencyMapModel in cases where using ShuffledBaselineModel is not feasible. * `pysaliency.get_toronto` now returns a `Fixations` instance instead of `FixationTrains` since we don not have scanpath information. * `pysaliency.baseline_utils.KDEGoldModel` now supports a keyword argument `grid_spacing` which controls how densly the log density of the KDEModel is computed before it is linearly interpolated. This can substantially speed up computations on high resolution images. * Feature: `pysaliency.precomputed_models.SaliencyMapModelFromArchive` and `ModelFromArchive` for loading model predictions from ZIP, TAR and RAR files. * Bugfix: all matlab scripts where missing in the pip installation since the change to setuptools. * 0.2.20: * Stimuli now support attributes, just like Fixations. The CAT2000 train and test datasets now have the stimulus categories as attribute. * failure to download and setup a dataset will no longer result in leftover dataset files that keep pysaliency from trying again. * crossvalidation splits now support stratifying stimulus attributes * the MIT1003 dataset now also contains the history of fixation durations * FixationIndexDependentModel * Bugfix: The CC of a constant saliency map wrt to a nonconstant one now returns zero (instead of nan as previously). * Feature: Added keyword argument `attributes` to `Fixations` constructor * Feature: Provide KLDiv and SIM as functions that can be applied to saliency maps without need for a model. * 0.2.19: * added pytorch implementation for optimization of similarity metric as alternative to tensorflow implementation which still uses tensorflow 1.x * added pytorch implementation for saliency map processing as alternative to theano implementation. * removed obsolete dependency on openmp * made import of pytorch, theano and tensorflow optional * bugfixes in precomputed models for stimuli sets with nested directories ================================================ FILE: LICENSE.md ================================================ The MIT License (MIT) Copyright (c) 2015 Matthias Kümmerer Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: MANIFEST.in ================================================ include README.md include LICENSE.md include pysaliency/*.pyx include pysaliency/scripts/*.m include pysaliency/scripts/models/*.m include pysaliency/scripts/models/*/*.m include pysaliency/scripts/models/*/*/* include pysaliency/scripts/models/BMS/patches/* include pysaliency/scripts/models/GBVS/patches/* include pysaliency/scripts/models/Judd/patches/* ================================================ FILE: Makefile ================================================ cython: #python setup.py build_ext --inplace python3 setup.py build_ext --inplace test: cython python3 -m pytest --nomatlab tests prepublish: ./run-docker.sh rm -rf dist ./run-docker.sh bash build.sh twine upload --repository=pysaliency-test dist/pysaliency*.tar.gz # assumes that ~/.pypirc defines a pysaliency-test entry, see https://test.pypi.org/manage/account/token/ # twine upload dist/pysaliency*.tar.gz -r testpypi publish: ./run-docker.sh rm -rf dist ./run-docker.sh bash build.sh twine upload --repository=pysaliency dist/pysaliency*.tar.gz ================================================ FILE: README.md ================================================ Pysaliency ========== ![test](https://github.com/matthias-k/pysaliency/actions/workflows/test-package-conda.yml/badge.svg) Pysaliency is a python package for saliency modelling. It aims at providing a unified interface to both the traditional saliency maps used in saliency modeling as well as probabilistic saliency models. Pysaliency can evaluate most commonly used saliency metrics, including AUC, sAUC, NSS, CC image-based KL divergence, fixation based KL divergence and SIM for saliency map models and log likelihoods and information gain for probabilistic models. Installation ------------ You can install pysaliency from pypi via pip install pysaliency Quickstart ---------- import pysaliency dataset_location = 'datasets' model_location = 'models' mit_stimuli, mit_fixations = pysaliency.external_datasets.get_mit1003(location=dataset_location) aim = pysaliency.AIM(location=model_location) saliency_map = aim.saliency_map(mit_stimuli.stimuli[0]) plt.imshow(saliency_map) auc = aim.AUC(mit_stimuli, mit_fixations) If you already have saliency maps for some dataset, you can import them into pysaliency easily: my_model = pysaliency.SaliencyMapModelFromDirectory(mit_stimuli, '/path/to/my/saliency_maps') auc = my_model.AUC(mit_stimuli, mit_fixations) Check out the [Tutorial](notebooks/Tutorial.ipynb) for a more detailed introduction! Included datasets and models ---------------------------- Pysaliency provides several important datasets: * MIT1003 * MIT300 * CAT2000 * Toronto * Koehler * iSUN * SALICON (both the 2015 and the 2017 edition and each with both the original mouse traces and the inferred fixations) * FIGRIM * OSIE * NUSEF (the part with public images) and some influential models: * AIM * SUN * ContextAwareSaliency * BMS * GBVS * GBVSIttiKoch * Judd * IttiKoch * RARE2012 * CovSal These models are using the original code which is often matlab. Therefore, a matlab licence is required to make use of these models, although quite some of them work with octave, too (see below). Using Octave ------------ pysaliency will fall back to octave if no matlab is installed. Some models might work with octave, e.g. AIM and GBVSIttiKoch. In Debian/Ubuntu you need to install `octave`, `octave-image`, `octave-statistics`, `liboctave-dev`. These models and dataset seem to work with octave: - models - AIM - GBVSIttiKoch - datasets - Toronto - MIT1003 - MIT300 - SALICON Dependencies ----------- The Judd Model needs some libraries to work. In ubuntu/debian you need to install these packages: `libopencv-core-dev, libopencv-flann-dev, libopencv-imgproc-dev, libopencv-photo-dev, libopencv-video-dev, libopencv-features2d-dev, libopencv-objdetect-dev, libopencv-calib3d-dev, libopencv-ml-dev, opencv2/contrib/contrib.hpp` ================================================ FILE: docs/superpowers/plans/2026-03-21-crossvalidated-baseline-model-hdf5.md ================================================ # CrossvalidatedBaselineModel HDF5 Save/Reload Implementation Plan > **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. **Goal:** Add `to_hdf5`/`read_hdf5` to `CrossvalidatedBaselineModel` so it can be serialized by parameters (not predictions), wired into the `pysaliency.read_hdf5` dispatcher. **Architecture:** Refactor `CrossvalidatedBaselineModel` to store only `fixations_n` (not the full fixations object), then add `to_hdf5`/`read_hdf5` following the exact same pattern as the already-complete `BaselineModel`. Register in the `_MODEL_READERS` dispatch table in `pysaliency/hdf5.py`. **Tech Stack:** Python, h5py, numpy, pytest. Build Cython extensions with `make cython` before running tests. --- ## File Map | File | Change | |------|--------| | `pysaliency/baseline_utils.py` | Refactor `CrossvalidatedBaselineModel.__init__` and `_log_density`; add `to_hdf5` and `read_hdf5` | | `pysaliency/hdf5.py` | Add `_read_crossvalidated_baseline_model` helper; extend existing `_MODEL_READERS` dict | | `tests/test_baseline_utils.py` | Extend top-level import; add 6 new tests | | `tests/test_hdf5_io.py` | Extend top-level import; add 3 new tests for dispatcher, kwarg override, and cache bypass | --- ## Task 1: Refactor `CrossvalidatedBaselineModel` internals Replace `self.fixations` + `self.shape_cache` with `self.fixations_n`. Public constructor signature is unchanged. **Files:** - Modify: `pysaliency/baseline_utils.py:559-591` - Modify: `tests/test_baseline_utils.py` - [ ] **Step 1: Update the top-level import in `tests/test_baseline_utils.py`** The existing import block (lines 9–17) imports from `pysaliency.baseline_utils`. Add `CrossvalidatedBaselineModel` to it: ```python from pysaliency.baseline_utils import ( BaselineModel, CrossvalidatedBaselineModel, CrossvalMultipleRegularizations, GeneralMixtureKernelDensityEstimator, KDEGoldModel, MixtureKernelDensityEstimator, ScikitLearnImageCrossValidationGenerator, fill_fixation_map, ) ``` - [ ] **Step 2: Write a failing test that asserts the new internal state** Add to `tests/test_baseline_utils.py`: ```python def test_crossvalidated_baseline_model_stores_fixations_n(stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) assert hasattr(model, 'fixations_n') assert not hasattr(model, 'fixations') assert not hasattr(model, 'shape_cache') np.testing.assert_array_equal(model.fixations_n, scanpath_fixations.n) ``` - [ ] **Step 3: Run the test to confirm it fails** ```bash python -m pytest --nomatlab tests/test_baseline_utils.py::test_crossvalidated_baseline_model_stores_fixations_n -v ``` Expected: FAIL — `model` has no `fixations_n`, still has `fixations` and `shape_cache`. - [ ] **Step 4: Implement the refactor** In `pysaliency/baseline_utils.py`, replace the `CrossvalidatedBaselineModel` class body (lines 559–591) with: ```python class CrossvalidatedBaselineModel(Model): def __init__(self, stimuli, fixations, bandwidth, eps=1e-20, **kwargs): super(CrossvalidatedBaselineModel, self).__init__(**kwargs) self.stimuli = stimuli self.bandwidth = bandwidth self.eps = eps self.xs, self.ys = normalize_fixations(stimuli, fixations) self.fixations_n = fixations.n.copy() def _log_density(self, stimulus): shape = stimulus.shape[0], stimulus.shape[1] stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) inds = self.fixations_n != stimulus_index ZZ = np.zeros(shape) _fixations = np.array([self.ys[inds]*shape[0], self.xs[inds]*shape[1]]).T fill_fixation_map(ZZ, _fixations) ZZ = gaussian_filter(ZZ, [self.bandwidth*shape[0], self.bandwidth*shape[1]]) ZZ *= (1-self.eps) ZZ += self.eps * 1.0/(shape[0]*shape[1]) ZZ = np.log(ZZ) ZZ -= logsumexp(ZZ) return ZZ ``` - [ ] **Step 5: Run the new test plus the full baseline_utils suite** ```bash python -m pytest --nomatlab tests/test_baseline_utils.py -v ``` Expected: all pass. If any existing test relied on `model.fixations` or `model.shape_cache`, it will fail here — fix it in the same commit. - [ ] **Step 6: Commit** ```bash git add pysaliency/baseline_utils.py tests/test_baseline_utils.py git commit -m "refactor: CrossvalidatedBaselineModel stores fixations_n, removes shape_cache" ``` --- ## Task 2: Add `to_hdf5` to `CrossvalidatedBaselineModel` **Files:** - Modify: `pysaliency/baseline_utils.py` - Modify: `tests/test_baseline_utils.py` - [ ] **Step 1: Write the failing test** Add to `tests/test_baseline_utils.py`. Note: `import h5py` goes inside the test function body, matching the style of the existing `test_baseline_model_hdf5_type` test: ```python def test_crossvalidated_baseline_model_hdf5_type(tmp_path, stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_type.hdf5' model.to_hdf5(path) import h5py with h5py.File(path, 'r') as f: value = f.attrs['type'] if not isinstance(value, str): value = value.decode('utf8') assert value == 'pysaliency.baseline_utils.CrossvalidatedBaselineModel' ``` - [ ] **Step 2: Run to confirm failure** ```bash python -m pytest --nomatlab tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_type -v ``` Expected: FAIL — `CrossvalidatedBaselineModel` has no `to_hdf5`. - [ ] **Step 3: Implement `to_hdf5`** Add the method to `CrossvalidatedBaselineModel` in `pysaliency/baseline_utils.py`, immediately after `_log_density`. `hdf5_wrapper` is already imported from `.datasets.utils` at the top of the file: ```python @hdf5_wrapper(mode='w') def to_hdf5(self, target, include_stimuli=True): target.attrs['type'] = np.bytes_('pysaliency.baseline_utils.CrossvalidatedBaselineModel') target.attrs['version'] = np.bytes_('1.0') target.attrs['bandwidth'] = self.bandwidth target.attrs['eps'] = self.eps target.create_dataset('xs', data=self.xs) target.create_dataset('ys', data=self.ys) target.create_dataset('fixations_n', data=self.fixations_n) if include_stimuli: stimuli_group = target.create_group('stimuli') self.stimuli.to_hdf5(stimuli_group) ``` - [ ] **Step 4: Run the type test** ```bash python -m pytest --nomatlab tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_type -v ``` Expected: PASS. - [ ] **Step 5: Commit** ```bash git add pysaliency/baseline_utils.py tests/test_baseline_utils.py git commit -m "feat: add CrossvalidatedBaselineModel.to_hdf5" ``` --- ## Task 3: Add `read_hdf5` to `CrossvalidatedBaselineModel` with full roundtrip tests **Files:** - Modify: `pysaliency/baseline_utils.py` - Modify: `tests/test_baseline_utils.py` - [ ] **Step 1: Write all four failing tests at once** Add to `tests/test_baseline_utils.py`: ```python def test_crossvalidated_baseline_model_hdf5_roundtrip_with_stimuli(tmp_path, stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) first_prediction = model.log_density(stimuli[0]).copy() path = tmp_path / 'cv_baseline.hdf5' model.to_hdf5(path) reloaded = CrossvalidatedBaselineModel.read_hdf5(path) np.testing.assert_allclose(reloaded.log_density(stimuli[0]), first_prediction) def test_crossvalidated_baseline_model_hdf5_roundtrip_without_stimuli(tmp_path, stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) first_prediction = model.log_density(stimuli[1]).copy() path = tmp_path / 'cv_baseline_no_stimuli.hdf5' model.to_hdf5(path, include_stimuli=False) reloaded = CrossvalidatedBaselineModel.read_hdf5(path, stimuli=stimuli) np.testing.assert_allclose(reloaded.log_density(stimuli[1]), first_prediction) def test_crossvalidated_baseline_model_hdf5_error_without_stimuli(tmp_path, stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_no_stimuli.hdf5' model.to_hdf5(path, include_stimuli=False) with pytest.raises(ValueError, match='stimuli'): CrossvalidatedBaselineModel.read_hdf5(path) def test_crossvalidated_baseline_model_hdf5_stimuli_kwarg_overrides_embedded(tmp_path, stimuli, scanpath_fixations): """stimuli= kwarg takes precedence over the embedded stimuli group.""" model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) first_prediction = model.log_density(stimuli[0]).copy() path = tmp_path / 'cv_baseline_with_stimuli.hdf5' model.to_hdf5(path, include_stimuli=True) # Pass the same stimuli explicitly — should still work (kwarg wins) reloaded = CrossvalidatedBaselineModel.read_hdf5(path, stimuli=stimuli) assert reloaded.stimuli is stimuli np.testing.assert_allclose(reloaded.log_density(stimuli[0]), first_prediction) ``` - [ ] **Step 2: Run to confirm all four fail** ```bash python -m pytest --nomatlab \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_roundtrip_with_stimuli \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_roundtrip_without_stimuli \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_error_without_stimuli \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_stimuli_kwarg_overrides_embedded \ -v ``` Expected: all FAIL — `CrossvalidatedBaselineModel` has no `read_hdf5`. - [ ] **Step 3: Implement `read_hdf5`** Add to `CrossvalidatedBaselineModel` in `pysaliency/baseline_utils.py`. Decorator stack: `@classmethod` outermost, `@hdf5_wrapper(mode='r')` inner — this matches `BaselineModel.read_hdf5` exactly. `Model` is already available at module level via `from . import Model`. ```python @classmethod @hdf5_wrapper(mode='r') def read_hdf5( cls, source, *, stimuli=None, caching=True, memory_cache_size=None, cache_location=None, ): from .hdf5 import read_hdf5 as _read_hdf5 data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type != 'pysaliency.baseline_utils.CrossvalidatedBaselineModel': raise ValueError("Invalid type! Expected 'pysaliency.baseline_utils.CrossvalidatedBaselineModel', got", data_type) if data_version != '1.0': raise ValueError("Invalid version! Expected '1.0', got", data_version) if stimuli is None: if 'stimuli' not in source: raise ValueError( "No stimuli found in HDF5 file. Pass stimuli= explicitly." ) stimuli = _read_hdf5(source['stimuli']) model = cls.__new__(cls) Model.__init__(model, cache_location=cache_location, caching=caching, memory_cache_size=memory_cache_size) model.bandwidth = source.attrs['bandwidth'] model.eps = source.attrs['eps'] model.xs = source['xs'][...] model.ys = source['ys'][...] model.fixations_n = source['fixations_n'][...] model.stimuli = stimuli return model ``` - [ ] **Step 4: Run all five tests (type + 4 roundtrip/override)** ```bash python -m pytest --nomatlab \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_type \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_roundtrip_with_stimuli \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_roundtrip_without_stimuli \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_error_without_stimuli \ tests/test_baseline_utils.py::test_crossvalidated_baseline_model_hdf5_stimuli_kwarg_overrides_embedded \ -v ``` Expected: all PASS. - [ ] **Step 5: Run the full test suite to catch regressions** ```bash python -m pytest --nomatlab tests/test_baseline_utils.py -v ``` Expected: all pass. - [ ] **Step 6: Commit** ```bash git add pysaliency/baseline_utils.py tests/test_baseline_utils.py git commit -m "feat: add CrossvalidatedBaselineModel.read_hdf5 with optional stimuli embedding" ``` --- ## Task 4: Register in `pysaliency.read_hdf5` dispatcher and add integration tests **Files:** - Modify: `pysaliency/hdf5.py` - Modify: `tests/test_hdf5_io.py` - [ ] **Step 1: Update the import in `tests/test_hdf5_io.py`** The existing file imports `BaselineModel` from `pysaliency.baseline_utils`. Extend it to also import `CrossvalidatedBaselineModel`: ```python from pysaliency.baseline_utils import BaselineModel, CrossvalidatedBaselineModel ``` - [ ] **Step 2: Write the three failing dispatcher tests** Add to `tests/test_hdf5_io.py`: ```python def test_unified_read_hdf5_reads_crossvalidated_baseline_model(tmp_path): stimuli = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) fixations = pysaliency.FixationTrains.from_fixation_trains( [[1, 2, 3], [4, 8]], [[5, 6, 7], [9, 2]], [[0, 1, 2], [0, 1]], [0, 1], [0, 1], ) model = CrossvalidatedBaselineModel(stimuli, fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_model.hdf5' model.to_hdf5(path) loaded = pysaliency.read_hdf5(path) assert isinstance(loaded, CrossvalidatedBaselineModel) np.testing.assert_allclose(loaded.log_density(stimuli[0]), model.log_density(stimuli[0])) def test_unified_read_hdf5_crossvalidated_baseline_model_stimuli_kwarg(tmp_path): """stimuli= kwarg is forwarded correctly through the dispatcher.""" stimuli = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) fixations = pysaliency.FixationTrains.from_fixation_trains( [[1, 2, 3], [4, 8]], [[5, 6, 7], [9, 2]], [[0, 1, 2], [0, 1]], [0, 1], [0, 1], ) model = CrossvalidatedBaselineModel(stimuli, fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_no_stimuli.hdf5' model.to_hdf5(path, include_stimuli=False) loaded = pysaliency.read_hdf5(str(path), stimuli=stimuli) assert isinstance(loaded, CrossvalidatedBaselineModel) assert loaded.stimuli is stimuli np.testing.assert_allclose(loaded.log_density(stimuli[0]), model.log_density(stimuli[0])) def test_unified_read_hdf5_crossvalidated_baseline_model_stimuli_kwarg_bypasses_cache(tmp_path): """Regression: stimuli= kwarg must bypass WeakValueDictionary cache in _read_hdf5_from_file.""" stimuli = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) fixations = pysaliency.FixationTrains.from_fixation_trains( [[1, 2, 3], [4, 8]], [[5, 6, 7], [9, 2]], [[0, 1, 2], [0, 1]], [0, 1], [0, 1], ) model = CrossvalidatedBaselineModel(stimuli, fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_no_stimuli.hdf5' model.to_hdf5(path, include_stimuli=False) loaded = pysaliency.read_hdf5(str(path), stimuli=stimuli) assert loaded.stimuli is stimuli # Second call with a different stimuli object — must not return the cached first result stimuli2 = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) loaded2 = pysaliency.read_hdf5(str(path), stimuli=stimuli2) assert loaded2.stimuli is stimuli2 np.testing.assert_allclose(loaded2.log_density(stimuli2[0]), CrossvalidatedBaselineModel(stimuli2, fixations, bandwidth=0.1).log_density(stimuli2[0])) ``` - [ ] **Step 3: Run to confirm all three fail** ```bash python -m pytest --nomatlab \ tests/test_hdf5_io.py::test_unified_read_hdf5_reads_crossvalidated_baseline_model \ tests/test_hdf5_io.py::test_unified_read_hdf5_crossvalidated_baseline_model_stimuli_kwarg \ tests/test_hdf5_io.py::test_unified_read_hdf5_crossvalidated_baseline_model_stimuli_kwarg_bypasses_cache \ -v ``` Expected: all FAIL — `CrossvalidatedBaselineModel` not in `_MODEL_READERS`. - [ ] **Step 4: Update `pysaliency/hdf5.py`** Add `_read_crossvalidated_baseline_model` after `_read_baseline_model`, then **replace** the existing `_MODEL_READERS` dict definition with the extended version: ```python def _read_crossvalidated_baseline_model(source, **kwargs): from .baseline_utils import CrossvalidatedBaselineModel return CrossvalidatedBaselineModel.read_hdf5(source, **kwargs) _MODEL_READERS = { 'pysaliency.baseline_utils.BaselineModel': _read_baseline_model, 'pysaliency.baseline_utils.CrossvalidatedBaselineModel': _read_crossvalidated_baseline_model, } ``` - [ ] **Step 5: Run all three dispatcher tests** ```bash python -m pytest --nomatlab \ tests/test_hdf5_io.py::test_unified_read_hdf5_reads_crossvalidated_baseline_model \ tests/test_hdf5_io.py::test_unified_read_hdf5_crossvalidated_baseline_model_stimuli_kwarg \ tests/test_hdf5_io.py::test_unified_read_hdf5_crossvalidated_baseline_model_stimuli_kwarg_bypasses_cache \ -v ``` Expected: all PASS. - [ ] **Step 6: Run the full test suite** ```bash python -m pytest --nomatlab tests/ -v ``` Expected: all pass. Fix any regressions before committing. - [ ] **Step 7: Commit** ```bash git add pysaliency/hdf5.py tests/test_hdf5_io.py git commit -m "feat: register CrossvalidatedBaselineModel in pysaliency.read_hdf5 dispatcher" ``` ================================================ FILE: docs/superpowers/plans/2026-03-23-compact-hdf5-export.md ================================================ # Compact HDF5 Export Implementation Plan > **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. **Goal:** Add `dtype` and `downscale_factor` parameters to `export_model_to_hdf5` so large prediction files can be stored more compactly, with transparent upsampling/upcasting on load. **Architecture:** All changes live in one file (`pysaliency/precomputed_models.py`). Four private helper functions handle the new export logic. `HDF5SaliencyMapModel._saliency_map` gains transparent upsampling. `HDF5Model._log_density` gains a two-path normalization check (strict for legacy/float32/float64, configurable+renorm for downsampled/float16). **Tech Stack:** numpy, h5py, scipy.ndimage (already used in project), pytest with parametrize. No new dependencies. **Spec:** `docs/superpowers/specs/2026-03-23-hdf5-compact-export-design.md` --- ## File Map | File | Change | |------|--------| | `pysaliency/precomputed_models.py` | Add 4 helpers, update `export_model_to_hdf5`, `HDF5SaliencyMapModel`, `HDF5Model` | | `tests/test_precomputed_models.py` | Add all new tests (append to existing file) | --- ## Task 1: Export — dtype parameter and root attrs Add the `dtype` parameter and write versioned root attrs. No downsampling yet. **Files:** - Modify: `pysaliency/precomputed_models.py:129-168` - Test: `tests/test_precomputed_models.py` - [ ] **Step 1: Write failing tests** Add to `tests/test_precomputed_models.py`: ```python import h5py import warnings def test_export_root_attrs_written(file_stimuli, tmpdir): """New-format files always have type/version/downscale_factor/dtype root attrs.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename) with h5py.File(filename, 'r') as f: assert f.attrs['type'] == 'pysaliency.precomputed_models.predictions' assert f.attrs['version'] == '1.0' assert int(f.attrs['downscale_factor']) == 1 assert f.attrs['dtype'] == 'float64' def test_export_dtype_float32(file_stimuli, tmpdir): """dtype=np.float32 stores float32 datasets.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=np.float32) with h5py.File(filename, 'r') as f: assert f.attrs['dtype'] == 'float32' # check one dataset keys = list(f.keys()) assert f[keys[0]].dtype == np.float32 def test_export_dtype_float16(file_stimuli, tmpdir): """dtype=np.float16 stores float16 datasets.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=np.float16) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert f[keys[0]].dtype == np.float16 def test_export_dtype_none_preserves_native(file_stimuli, tmpdir): """dtype=None (default) preserves the model's native output dtype.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=None) with h5py.File(filename, 'r') as f: keys = list(f.keys()) # GaussianSaliencyMapModel returns float64 assert f[keys[0]].dtype == np.float64 ``` - [ ] **Step 2: Run tests to verify they fail** ```bash cd /home/matthias/Documents/Uni/Bethge/Saliency/pysaliency python -m pytest --nomatlab tests/test_precomputed_models.py::test_export_root_attrs_written tests/test_precomputed_models.py::test_export_dtype_float32 tests/test_precomputed_models.py::test_export_dtype_float16 tests/test_precomputed_models.py::test_export_dtype_none_preserves_native -v ``` Expected: FAIL — `export_model_to_hdf5` doesn't write root attrs yet. - [ ] **Step 3: Add `_effective_dtype` helper and update `export_model_to_hdf5`** In `pysaliency/precomputed_models.py`, add before `export_model_to_hdf5`: ```python def _effective_dtype(smap, dtype, downscale_factor): """Determine the dtype that will actually be stored in the HDF5 file.""" if dtype is not None: return np.dtype(dtype) if downscale_factor > 1: # numpy .mean() returns float64 for integer inputs, preserves float types if np.issubdtype(smap.dtype, np.integer): return np.dtype(np.float64) else: return smap.dtype # float32 stays float32, float64 stays float64 return smap.dtype ``` Replace the `export_model_to_hdf5` signature and body: ```python def export_model_to_hdf5(model, stimuli, filename, compression=9, overwrite=True, flush=False, dtype=None, downscale_factor=1): """Export pysaliency model predictions for stimuli into hdf5 file model: Model or SaliencyMapModel stimuli: instance of FileStimuli or Stimuli with filenames attribute filename: where to save hdf5 file to compression: how much to compress the data overwrite: if False, an existing file will be appended to (for resuming interrupted exports). Stimuli already present in the file are skipped. flush: whether the hdf5 file should be flushed after each stimulus dtype: numpy dtype for stored predictions (e.g. np.float32, np.float16). None (default) preserves the model's native output dtype. downscale_factor: integer >= 1. Spatially downsample predictions by this factor before storing. 1 = no downsampling (default). """ filenames = get_stimuli_filenames(stimuli) names = get_minimal_unique_filenames(filenames) import h5py mode = 'w' if overwrite else 'a' # Record whether the file existed before opening, to decide whether to write root attrs file_existed = os.path.isfile(filename) with h5py.File(filename, mode=mode) as f: # Determine which stimuli to process if overwrite: indices = list(range(len(stimuli))) else: # append mode is for resuming an interrupted export of the same job if 'type' in f.attrs: _validate_append_consistency(f, downscale_factor) indices = [i for i in range(len(stimuli)) if names[i] not in f] logging.debug(f"Skipping {len(stimuli) - len(indices)} already existing entries") if not indices: return # Compute first smap to determine effective dtype (needed for root attrs) first_stimulus = stimuli[indices[0]] if isinstance(model, SaliencyMapModel): first_smap = model.saliency_map(first_stimulus) elif isinstance(model, Model): first_smap = model.log_density(first_stimulus) else: raise TypeError(type(model)) effective_stored_dtype = _effective_dtype(first_smap, dtype, downscale_factor) _check_size_guard(first_smap, effective_stored_dtype, downscale_factor, dtype) # Write root attrs for new files only (overwrite=True always creates fresh; # overwrite=False writes attrs only if the file is brand new, not for legacy files) if overwrite or not file_existed: f.attrs['type'] = 'pysaliency.precomputed_models.predictions' f.attrs['version'] = '1.0' f.attrs['downscale_factor'] = downscale_factor f.attrs['dtype'] = str(effective_stored_dtype) for i, k in tqdm(list(enumerate(indices))): if i == 0: smap = first_smap else: stimulus = stimuli[k] if isinstance(model, SaliencyMapModel): smap = model.saliency_map(stimulus) elif isinstance(model, Model): smap = model.log_density(stimulus) H, W = smap.shape[0], smap.shape[1] smap = _downsample_smap(smap, downscale_factor) if dtype is not None: smap = smap.astype(dtype) ds = f.create_dataset(names[k], data=smap, compression=compression) if downscale_factor > 1: ds.attrs['original_shape'] = np.array([H, W], dtype=np.int64) if flush: f.flush() ``` Add stub helpers (full implementation in later tasks) before `export_model_to_hdf5`: ```python def _downsample_smap(smap, k): """Downsample a 2D map by integer factor k using area averaging.""" if k == 1: return smap H, W = smap.shape H_pad = int(np.ceil(H / k)) * k W_pad = int(np.ceil(W / k)) * k smap = np.pad(np.ascontiguousarray(smap), ((0, H_pad - H), (0, W_pad - W)), mode='edge') return smap.reshape(H_pad // k, k, W_pad // k, k).mean(axis=(1, 3)) def _check_size_guard(smap, effective_stored_dtype, downscale_factor, dtype): """Warn if compact settings will produce a larger-per-element file than the native dtype.""" native_dtype = np.dtype(smap.dtype) if np.dtype(effective_stored_dtype).itemsize > native_dtype.itemsize: msg = ( f"Model produces {native_dtype} predictions " f"({native_dtype.itemsize} byte/element) but will be stored as " f"{effective_stored_dtype} ({np.dtype(effective_stored_dtype).itemsize} byte/element). " f"Compact export may result in a larger file than the original." ) if np.issubdtype(native_dtype, np.integer): msg += " Consider passing dtype=np.uint8 to preserve the original dtype." if np.issubdtype(native_dtype, np.integer) and downscale_factor > 1 and dtype is None: msg += " Note: casting area-averaging float results back to integer is lossy." warnings.warn(msg) def _validate_append_consistency(f, downscale_factor): """Check that an existing compact HDF5 file is compatible with the current export settings.""" existing = int(f.attrs['downscale_factor']) if existing != downscale_factor: raise ValueError( f"Cannot append to HDF5 file: existing downscale_factor={existing} " f"does not match requested downscale_factor={downscale_factor}." ) ``` - [ ] **Step 4: Run tests to verify they pass** ```bash python -m pytest --nomatlab tests/test_precomputed_models.py::test_export_root_attrs_written tests/test_precomputed_models.py::test_export_dtype_float32 tests/test_precomputed_models.py::test_export_dtype_float16 tests/test_precomputed_models.py::test_export_dtype_none_preserves_native -v ``` Expected: PASS - [ ] **Step 5: Verify existing tests still pass** ```bash python -m pytest --nomatlab tests/test_precomputed_models.py -v ``` Expected: all previously passing tests still pass. - [ ] **Step 6: Commit** ```bash git add pysaliency/precomputed_models.py tests/test_precomputed_models.py git commit -m "feat: add dtype parameter and versioned root attrs to export_model_to_hdf5" ``` --- ## Task 2: Export — downsampling parameter Add and test the `downscale_factor` parameter behaviour (stored shape, `original_shape` attr, non-divisible sizes). **Files:** - Modify: `tests/test_precomputed_models.py` (All implementation code was already added in Task 1 — `_downsample_smap` and the `ds.attrs['original_shape']` write are in place. This task adds the tests.) - [ ] **Step 1: Write failing tests** ```python def test_export_downscale_stored_shape(file_stimuli, tmpdir): """Stored shape is ceil(H/k) x ceil(W/k) for divisible dimensions.""" # file_stimuli uses 100x100 images, which is divisible by 2 and 4 model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert f[keys[0]].shape == (50, 50) def test_export_downscale_original_shape_attr(file_stimuli, tmpdir): """original_shape attr is present and correct when downscale_factor > 1.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) ds = f[keys[0]] assert 'original_shape' in ds.attrs np.testing.assert_array_equal(ds.attrs['original_shape'], [100, 100]) assert ds.attrs['original_shape'].dtype == np.int64 def test_export_no_downscale_no_original_shape_attr(file_stimuli, tmpdir): """original_shape attr is absent when downscale_factor == 1.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert 'original_shape' not in f[keys[0]].attrs @pytest.fixture def file_stimuli_nondivisible(tmpdir): """Stimuli with 101x97 images — not divisible by 2 or 4.""" filenames = [] for i in range(3): filename = tmpdir.join(f'stim_{i:04d}.png') imsave(str(filename), np.random.randint(0, 255, (101, 97, 3), dtype=np.uint8)) filenames.append(str(filename)) return pysaliency.FileStimuli(filenames=filenames) def test_export_downscale_nondivisible_stored_shape(file_stimuli_nondivisible, tmpdir): """Non-divisible shapes are padded: stored shape is ceil(H/k) x ceil(W/k).""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli_nondivisible, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) # ceil(101/2)=51, ceil(97/2)=49 assert f[keys[0]].shape == (51, 49) def test_export_downscale_nondivisible_original_shape_attr(file_stimuli_nondivisible, tmpdir): """original_shape stores the pre-padding shape, not the padded or stored shape.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli_nondivisible, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) np.testing.assert_array_equal(f[keys[0]].attrs['original_shape'], [101, 97]) def test_export_float32_downscale_dtype_is_float32(file_stimuli, tmpdir): """float32 model output + downscale_factor > 1 stores float32, not float64.""" # GaussianSaliencyMapModel returns float64, so we need a float32-producing model class Float32SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.float32) model = Float32SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert f[keys[0]].dtype == np.float32 assert f.attrs['dtype'] == 'float32' ``` - [ ] **Step 2: Run tests to verify they fail** ```bash python -m pytest --nomatlab tests/test_precomputed_models.py::test_export_downscale_stored_shape tests/test_precomputed_models.py::test_export_downscale_original_shape_attr tests/test_precomputed_models.py::test_export_no_downscale_no_original_shape_attr tests/test_precomputed_models.py::test_export_downscale_nondivisible_stored_shape tests/test_precomputed_models.py::test_export_downscale_nondivisible_original_shape_attr tests/test_precomputed_models.py::test_export_float32_downscale_dtype_is_float32 -v ``` Expected: FAIL (downscale_factor parameter not wired up yet — actually already implemented in Task 1. These may already pass.) - [ ] **Step 3: Verify all pass and run full suite** ```bash python -m pytest --nomatlab tests/test_precomputed_models.py -v ``` Expected: all pass. - [ ] **Step 4: Commit** ```bash git add tests/test_precomputed_models.py git commit -m "test: add downsampling export tests" ``` --- ## Task 3: Export — append mode and size guard Test append consistency validation and the uint8/size warning. **Files:** - Test: `tests/test_precomputed_models.py` - [ ] **Step 1: Write failing tests** ```python # NOTE: The spec test table lists "Append mode — mismatch (dtype only)" but the spec design # section intentionally excludes dtype from the consistency check (it is purely informational # and cannot be determined before the first stimulus is computed). There is therefore no # ValueError raised on dtype mismatch — this is correct behavior, not a gap. def test_export_append_consistency_mismatch_downscale(file_stimuli, tmpdir): """Appending with a different downscale_factor raises ValueError.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) partial = pysaliency.FileStimuli(filenames=file_stimuli.filenames[:3]) export_model_to_hdf5(model, partial, filename, downscale_factor=2) with pytest.raises(ValueError, match='downscale_factor'): export_model_to_hdf5(model, file_stimuli, filename, overwrite=False, downscale_factor=1) def test_export_append_new_file_root_attrs(file_stimuli, tmpdir): """overwrite=False on a new file still writes root attrs.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, overwrite=False) with h5py.File(filename, 'r') as f: assert 'type' in f.attrs assert f.attrs['version'] == '1.0' def test_export_append_legacy_file_no_root_attrs(file_stimuli, tmpdir): """Appending to a legacy file (no root attrs) succeeds without adding root attrs.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) partial = pysaliency.FileStimuli(filenames=file_stimuli.filenames[:3]) remaining = pysaliency.FileStimuli(filenames=file_stimuli.filenames[3:]) # Write a legacy-format file manually (no root attrs) import h5py names = pysaliency.utils.get_minimal_unique_filenames(partial.filenames) with h5py.File(filename, 'w') as f: for k, s in enumerate(partial): f.create_dataset(names[k], data=model.saliency_map(s)) export_model_to_hdf5(model, remaining, filename, overwrite=False) with h5py.File(filename, 'r') as f: assert 'type' not in f.attrs # no root attrs written into legacy file def test_export_uint8_model_downscale_warns(tmpdir): """Uint8 model output + downscale_factor > 1 emits a UserWarning (stored as float64 > uint8).""" class Uint8SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.uint8) filenames = [] for i in range(2): fname = str(tmpdir.join(f'stim_{i}.png')) imsave(fname, np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)) filenames.append(fname) stimuli = pysaliency.FileStimuli(filenames=filenames) model = Uint8SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) with pytest.warns(UserWarning, match='larger'): export_model_to_hdf5(model, stimuli, filename, downscale_factor=2) def test_export_uint8_model_downscale_stored_as_float64(tmpdir): """Uint8 model output + downscale_factor > 1 is stored as float64 (area avg result).""" class Uint8SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.uint8) filenames = [] for i in range(2): fname = str(tmpdir.join(f'stim_{i}.png')) imsave(fname, np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)) filenames.append(fname) stimuli = pysaliency.FileStimuli(filenames=filenames) model = Uint8SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) with warnings.catch_warnings(): warnings.simplefilter('ignore') export_model_to_hdf5(model, stimuli, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert f[keys[0]].dtype == np.float64 def test_export_uint8_model_float32_dtype_warns(tmpdir): """Uint8 model + dtype=np.float32 warns (float32 > uint8).""" class Uint8SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.uint8) filenames = [str(tmpdir.join(f'stim_{i}.png')) for i in range(2)] for f in filenames: imsave(f, np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)) stimuli = pysaliency.FileStimuli(filenames=filenames) model = Uint8SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) with pytest.warns(UserWarning, match='larger'): export_model_to_hdf5(model, stimuli, filename, dtype=np.float32) def test_export_uint8_model_uint8_dtype_no_warn(tmpdir): """Uint8 model + dtype=np.uint8 does not warn.""" class Uint8SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.uint8) filenames = [str(tmpdir.join(f'stim_{i}.png')) for i in range(2)] for f in filenames: imsave(f, np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)) stimuli = pysaliency.FileStimuli(filenames=filenames) model = Uint8SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) with warnings.catch_warnings(): warnings.simplefilter('error') # any warning becomes an error export_model_to_hdf5(model, stimuli, filename, dtype=np.uint8) ``` - [ ] **Step 2: Run tests** ```bash python -m pytest --nomatlab tests/test_precomputed_models.py::test_export_append_consistency_mismatch_downscale tests/test_precomputed_models.py::test_export_append_new_file_root_attrs tests/test_precomputed_models.py::test_export_append_legacy_file_no_root_attrs tests/test_precomputed_models.py::test_export_uint8_model_downscale_warns tests/test_precomputed_models.py::test_export_uint8_model_downscale_stored_as_float64 tests/test_precomputed_models.py::test_export_uint8_model_float32_dtype_warns tests/test_precomputed_models.py::test_export_uint8_model_uint8_dtype_no_warn -v ``` Note: all implementation for these tests is already in place from Task 1. Most will pass immediately; the legacy-file test (`test_export_append_legacy_file_no_root_attrs`) verifies the `file_existed` flag logic. If any fail, fix in `precomputed_models.py`. - [ ] **Step 3: Run full suite.** ```bash python -m pytest --nomatlab tests/test_precomputed_models.py -v ``` If any new tests fail, debug and fix in `precomputed_models.py`. - [ ] **Step 4: Commit** ```bash git add tests/test_precomputed_models.py pysaliency/precomputed_models.py git commit -m "test: add append-mode consistency and size-guard tests" ``` --- ## Task 4: Load side — `HDF5SaliencyMapModel` upsampling Add `_key_for_stimulus` helper and transparent upsampling in `_saliency_map`. **Files:** - Modify: `pysaliency/precomputed_models.py:303-334` - Test: `tests/test_precomputed_models.py` - [ ] **Step 1: Write failing tests** ```python @pytest.mark.parametrize('dtype,downscale_factor', [ (None, 1), (np.float32, 1), (np.float16, 1), (np.float32, 2), (np.float16, 4), ]) def test_hdf5_saliency_map_model_roundtrip(file_stimuli, tmpdir, dtype, downscale_factor): """HDF5SaliencyMapModel returns correct shape and values after compact export.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=dtype, downscale_factor=downscale_factor) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) for s in file_stimuli: result = loaded.saliency_map(s) assert result.shape == (s.shape[0], s.shape[1]), \ f"Shape mismatch: {result.shape} != {(s.shape[0], s.shape[1])}" def test_hdf5_saliency_map_model_dtype_reduced_returns_native(file_stimuli, tmpdir): """dtype-reduced-only files return the stored native dtype (not upcast).""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=np.float32) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) result = loaded.saliency_map(file_stimuli[0]) assert result.dtype == np.float32 def test_hdf5_saliency_map_model_downsampled_returns_float64(file_stimuli, tmpdir): """Downsampled files upsample and return float64.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, downscale_factor=2) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) result = loaded.saliency_map(file_stimuli[0]) assert result.dtype == np.float64 def test_hdf5_saliency_map_model_nondivisible_loaded_shape(file_stimuli_nondivisible, tmpdir): """Upsampled output shape matches original_shape (not padded shape).""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli_nondivisible, filename, downscale_factor=2) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli_nondivisible, filename) result = loaded.saliency_map(file_stimuli_nondivisible[0]) assert result.shape == (101, 97) # original shape, not (51, 49) or (102, 98) def test_hdf5_saliency_map_model_resized_stimuli(tmpdir): """With check_shape=False and resized stimuli, upsampling targets original_shape.""" # Export at full size, then load with resized (smaller) stimuli from imageio import imsave as _imsave filenames = [] for i in range(2): fname = str(tmpdir.join(f'stim_{i}.png')) _imsave(fname, np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)) filenames.append(fname) full_stimuli = pysaliency.FileStimuli(filenames=filenames) model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, full_stimuli, filename, downscale_factor=2) # Load with the same filenames but we'll check that the loaded shape is 100x100 # (original_shape), not 50x50 (stored) nor whatever the stimulus reports loaded = pysaliency.HDF5SaliencyMapModel(full_stimuli, filename, check_shape=False) result = loaded.saliency_map(full_stimuli[0]) assert result.shape == (100, 100) def test_hdf5_saliency_map_model_legacy_file_unchanged(file_stimuli, tmpdir): """Legacy files (no root attrs) still work exactly as before.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) # Write a legacy file manually names = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) with h5py.File(filename, 'w') as f: for k, s in enumerate(file_stimuli): f.create_dataset(names[k], data=model.saliency_map(s)) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) for s in file_stimuli: expected = model.saliency_map(s) np.testing.assert_array_equal(loaded.saliency_map(s), expected) ``` - [ ] **Step 2: Run tests to verify they fail** ```bash python -m pytest --nomatlab -k "roundtrip or downsampled_returns_float64 or nondivisible_loaded_shape" tests/test_precomputed_models.py -v ``` Expected: FAIL — `_saliency_map` doesn't upsample yet. - [ ] **Step 3: Update `HDF5SaliencyMapModel` in `pysaliency/precomputed_models.py`** Replace lines 310–334: ```python class HDF5SaliencyMapModel(SaliencyMapModel): """ exposes a HDF5 file with saliency maps as pysaliency model The stimuli have to be of type `FileStimuli`. For each stimulus file, the model expects a dataset with the same name in the dataset. If the file was created with downscale_factor > 1, predictions are transparently upsampled to their original resolution on load. """ def __init__(self, stimuli, filename, check_shape=True, **kwargs): super(HDF5SaliencyMapModel, self).__init__(**kwargs) self.stimuli = stimuli self.filename = filename self.check_shape = check_shape if not os.path.isfile(self.filename): raise ValueError(f'File {self.filename} does not exist') import h5py self.hdf5_file = h5py.File(self.filename, 'r') self.version = self.hdf5_file.attrs.get('version') self.all_keys = get_keys_recursive(self.hdf5_file) self.names = get_keys_from_filenames_with_prefix(get_stimuli_filenames(stimuli), self.all_keys) def _key_for_stimulus(self, stimulus): stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) return self.names[stimulus_index] def _saliency_map(self, stimulus): stimulus_key = self._key_for_stimulus(stimulus) dataset = self.hdf5_file[stimulus_key] smap = dataset[:] if 'original_shape' in dataset.attrs: # Compact downsampled file: upsample to original resolution target_shape = tuple(dataset.attrs['original_shape']) zoom_factors = (target_shape[0] / smap.shape[0], target_shape[1] / smap.shape[1]) import scipy.ndimage smap = scipy.ndimage.zoom(smap.astype(np.float64), zoom_factors, order=1, mode='nearest') if not smap.shape == (stimulus.shape[0], stimulus.shape[1]): if self.check_shape: warnings.warn('Wrong shape for stimulus {}'.format(stimulus_key), stacklevel=4) return smap ``` - [ ] **Step 4: Run tests to verify they pass** ```bash python -m pytest --nomatlab tests/test_precomputed_models.py -v ``` Expected: all pass. - [ ] **Step 5: Commit** ```bash git add pysaliency/precomputed_models.py tests/test_precomputed_models.py git commit -m "feat: add transparent upsampling to HDF5SaliencyMapModel" ``` --- ## Task 5: Load side — `HDF5Model` two-path normalization Add `max_normalization_error` parameter and the two-path `_log_density`. **Files:** - Modify: `pysaliency/precomputed_models.py:337-355` - Test: `tests/test_precomputed_models.py` - [ ] **Step 1: Write failing tests** ```python @pytest.mark.parametrize('dtype,downscale_factor', [ (None, 1), (np.float32, 1), (np.float16, 1), (np.float32, 2), (np.float16, 4), ]) def test_hdf5_model_roundtrip(file_stimuli, tmpdir, dtype, downscale_factor): """HDF5Model returns valid log densities after compact export.""" import warnings base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) with warnings.catch_warnings(): warnings.simplefilter('ignore') export_model_to_hdf5(base, file_stimuli, filename, dtype=dtype, downscale_factor=downscale_factor) loaded = pysaliency.HDF5Model(file_stimuli, filename) from scipy.special import logsumexp for s in file_stimuli: result = loaded.log_density(s) assert result.dtype == np.float64 assert result.shape == (s.shape[0], s.shape[1]) assert abs(logsumexp(result)) < 0.001, f"logsumexp={logsumexp(result):.6f}, not close to 0" def test_hdf5_model_strict_path_no_renorm(file_stimuli, tmpdir): """Non-compact float64/float32 files use strict path: output not renormalized.""" from scipy.special import logsumexp base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(base, file_stimuli, filename) loaded = pysaliency.HDF5Model(file_stimuli, filename) for s in file_stimuli: result = loaded.log_density(s) # Result should be bit-identical to stored (cast to float64), not renormalized expected = base.log_density(s) np.testing.assert_array_equal(result, expected) def test_hdf5_model_strict_path_raises_on_bad_density(file_stimuli, tmpdir): """Non-compact file with bad log density raises ValueError (strict ±0.01 check).""" filename = str(tmpdir.join('model.hdf5')) names = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) with h5py.File(filename, 'w') as f: for k, s in enumerate(file_stimuli): # deliberately unnormalized: constant map, logsumexp >> 0 bad_map = np.zeros((s.shape[0], s.shape[1]), dtype=np.float64) f.create_dataset(names[k], data=bad_map) loaded = pysaliency.HDF5Model(file_stimuli, filename) with pytest.raises(ValueError, match='correct log density'): loaded.log_density(file_stimuli[0]) def test_hdf5_model_relaxed_path_threshold_exceeded_raises(file_stimuli, tmpdir): """Corrupted downsampled file raises ValueError when logsumexp > max_normalization_error.""" filename = str(tmpdir.join('model.hdf5')) names = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) with h5py.File(filename, 'w') as f: f.attrs['type'] = 'pysaliency.precomputed_models.predictions' f.attrs['version'] = '1.0' f.attrs['downscale_factor'] = 2 f.attrs['dtype'] = 'float64' for k, s in enumerate(file_stimuli): stored_shape = (s.shape[0] // 2, s.shape[1] // 2) bad_map = np.zeros(stored_shape, dtype=np.float64) # heavily unnormalized ds = f.create_dataset(names[k], data=bad_map) ds.attrs['original_shape'] = np.array([s.shape[0], s.shape[1]], dtype=np.int64) loaded = pysaliency.HDF5Model(file_stimuli, filename) with pytest.raises(ValueError, match='normalization error'): loaded.log_density(file_stimuli[0]) def test_hdf5_model_custom_max_normalization_error(file_stimuli, tmpdir): """Custom max_normalization_error: tighter raises, looser allows.""" base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(base, file_stimuli, filename, downscale_factor=2) # Very tight threshold should raise (there is some error after round-trip) loaded_tight = pysaliency.HDF5Model(file_stimuli, filename, max_normalization_error=1e-10) with pytest.raises(ValueError): loaded_tight.log_density(file_stimuli[0]) # Default threshold should pass loaded_default = pysaliency.HDF5Model(file_stimuli, filename) loaded_default.log_density(file_stimuli[0]) # should not raise def test_hdf5_model_threshold_within_bounds_for_float16_4x(file_stimuli, tmpdir): """Explicit check: logsumexp before renorm is within log(1.1) for float16+4x export.""" from scipy.special import logsumexp as _logsumexp base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) with warnings.catch_warnings(): warnings.simplefilter('ignore') export_model_to_hdf5(base, file_stimuli, filename, dtype=np.float16, downscale_factor=4) # Manually load the raw stored data and upsample to measure pre-renorm logsumexp names = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) import scipy.ndimage with h5py.File(filename, 'r') as f: for k, s in enumerate(file_stimuli): ds = f[names[k]] smap = ds[:].astype(np.float64) target_shape = tuple(ds.attrs['original_shape']) zoom_factors = (target_shape[0] / smap.shape[0], target_shape[1] / smap.shape[1]) smap = scipy.ndimage.zoom(smap, zoom_factors, order=1, mode='nearest') err = abs(_logsumexp(smap)) assert err < np.log(1.1), f"logsumexp error {err:.4f} exceeds log(1.1)={np.log(1.1):.4f}" def test_hdf5_model_max_normalization_error_none(file_stimuli, tmpdir): """max_normalization_error=None skips the guard; renorm is still applied.""" from scipy.special import logsumexp base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(base, file_stimuli, filename, downscale_factor=2) loaded = pysaliency.HDF5Model(file_stimuli, filename, max_normalization_error=None) for s in file_stimuli: result = loaded.log_density(s) assert abs(logsumexp(result)) < 0.001 # renorm still runs ``` - [ ] **Step 2: Run tests to verify they fail** ```bash python -m pytest --nomatlab -k "hdf5_model_roundtrip or strict_path_no_renorm or threshold_exceeded_raises" tests/test_precomputed_models.py -v ``` Expected: FAIL — `HDF5Model` doesn't have the new logic yet. - [ ] **Step 3: Update `HDF5Model` in `pysaliency/precomputed_models.py`** Replace lines 337–355: ```python class HDF5Model(Model): """ exposes a HDF5 file with log densities as pysaliency model. For more detail see HDF5SaliencyMapModel. Files created with downscale_factor > 1 or dtype=np.float16 use a relaxed normalization check and automatic renormalization on load. All other files use the original strict ±0.01 check. """ def __init__(self, stimuli, filename, check_shape=True, max_normalization_error=np.log(1.1), **kwargs): super(HDF5Model, self).__init__(**kwargs) self.parent_model = HDF5SaliencyMapModel( stimuli=stimuli, filename=filename, caching=False, check_shape=check_shape ) self.max_normalization_error = max_normalization_error def _log_density(self, stimulus): key = self.parent_model._key_for_stimulus(stimulus) dataset = self.parent_model.hdf5_file[key] use_relaxed_path = ( 'original_shape' in dataset.attrs or dataset.dtype.itemsize < np.dtype(np.float32).itemsize ) smap = self.parent_model.saliency_map(stimulus).astype(np.float64) if use_relaxed_path: if self.max_normalization_error is not None: err = abs(logsumexp(smap)) if err >= self.max_normalization_error: raise ValueError( f'Log density normalization error {err:.4f} exceeds ' f'threshold {self.max_normalization_error:.4f}' ) smap = smap - logsumexp(smap) else: if not -0.01 <= logsumexp(smap) <= 0.01: raise ValueError('Not a correct log density!') return smap ``` - [ ] **Step 4: Run all tests** ```bash python -m pytest --nomatlab tests/test_precomputed_models.py -v ``` Expected: all pass. - [ ] **Step 5: Commit** ```bash git add pysaliency/precomputed_models.py tests/test_precomputed_models.py git commit -m "feat: add two-path normalization and max_normalization_error to HDF5Model" ``` --- ## Task 6: Final verification Run the full test suite, check no regressions. - [ ] **Step 1: Run full test suite** ```bash python -m pytest --nomatlab --notheano --nodownload tests/ -v ``` Expected: all pass, no regressions. - [ ] **Step 2: Spot-check the feature end-to-end** ```python # Quick smoke test in Python (not a formal test, just sanity check) import numpy as np import pysaliency from pysaliency import export_model_to_hdf5 # Use any FileStimuli you have, or create a small one # model = pysaliency.GaussianSaliencyMapModel(width=0.1) # export_model_to_hdf5(model, stimuli, '/tmp/compact.hdf5', dtype=np.float32, downscale_factor=2) # loaded = pysaliency.HDF5SaliencyMapModel(stimuli, '/tmp/compact.hdf5') # print(loaded.saliency_map(stimuli[0]).shape) # should match stimulus shape ``` - [ ] **Step 3: Final commit (if any fixups needed)** ```bash git add -p git commit -m "fix: " ``` ================================================ FILE: docs/superpowers/specs/2026-03-21-model-hdf5-save-reload-design.md ================================================ # Design: HDF5 Save/Reload for Saliency Models **Date:** 2026-03-21 **Branch:** enh-save-baseline-model-to-hdf5 **Status:** Approved ## Background `pysaliency.export_model_to_hdf5` serializes model *predictions* (saliency maps) for every stimulus into an HDF5 file. For large stimulus sets this produces huge files. For models that are fast to compute (e.g. kernel density baseline models), it is preferable to serialize the model *parameters* instead, so the model can be reconstructed and run on demand. The datasets module already has a mature `to_hdf5` / `read_hdf5` pattern per class, with a unified `pysaliency.datasets.read_hdf5` dispatcher. This design extends that pattern to models. ## Scope - `BaselineModel`: already implemented in the current branch (no changes needed) - `CrossvalidatedBaselineModel`: new, covered by this spec - `pysaliency.read_hdf5`: unified dispatcher (already partially implemented); document `**kwargs` passthrough contract Out of scope: other model types (`GoldModel`, `KDEGoldModel`, etc.) — follow the same pattern incrementally. ## Key Constraints - **Stimulus IDs are not stable across systems.** SHA1 hashes of pixel data differ between libjpeg versions. Saving only `stimulus_ids` (Option B) was considered and rejected: it silently breaks when a system update changes JPEG decoding. Full stimulus data must be embedded, or the caller must supply stimuli explicitly at load time. - **Stimuli can be large.** Embedding raw pixel arrays for large datasets is expensive. A flag controls whether stimuli are embedded, analogous to `BaselineModel`'s `include_shape_cache`. ## Design ### `BaselineModel` (existing — no changes) Saves to HDF5: - `attrs['type'] = 'pysaliency.baseline_utils.BaselineModel'` - `attrs['version'] = '1.0'` - `attrs['bandwidth']`, `attrs['eps']`, `attrs['keep_aspect']` - dataset `xs`, dataset `ys` — normalized fixation coordinates - optional group `shape_cache` — precomputed log-density maps per image shape (controlled by `include_shape_cache=True`, default `True`) No reference to stimuli or fixations is stored; the model is self-contained. ### `CrossvalidatedBaselineModel` #### Internal refactor: store `fixations_n` directly As part of this work, `CrossvalidatedBaselineModel.__init__` is refactored to store only the fixation index array rather than the full `fixations` object: ```python # Before self.fixations = fixations self.shape_cache = {} # unused — remove # After self.fixations_n = fixations.n.copy() ``` `_log_density` is updated accordingly: `self.fixations.n` → `self.fixations_n`. This makes the model's stored state minimal and eliminates any need for workarounds during deserialization. #### `to_hdf5(target, include_stimuli=True)` Uses the existing `@hdf5_wrapper(mode='w')` decorator for transparent file/group dispatch. Saves: - `attrs['type'] = 'pysaliency.baseline_utils.CrossvalidatedBaselineModel'` - `attrs['version'] = '1.0'` - `attrs['bandwidth']`, `attrs['eps']` — note: no `keep_aspect`, no `shape_cache` - dataset `xs`, dataset `ys` — normalized fixation coordinates - dataset `fixations_n` — `self.fixations_n` (integer array mapping each fixation to its stimulus index) - group `stimuli` — embedded via `self.stimuli.to_hdf5(target.create_group('stimuli'))` — **only when `include_stimuli=True`** When `include_stimuli=False`, the `stimuli` group is omitted entirely; the file is not self-contained and requires `stimuli=` at load time. #### `read_hdf5(source, *, stimuli=None, caching=True, memory_cache_size=None, cache_location=None)` Classmethod. Decorator stacking must match `BaselineModel` exactly: `@classmethod` outermost, `@hdf5_wrapper(mode='r')` inner. Load order: 1. Validate `type` and `version` attrs. Raise `ValueError` if `type` is not `'pysaliency.baseline_utils.CrossvalidatedBaselineModel'` or `version` is not `'1.0'`. 2. Resolve stimuli: - If `stimuli` kwarg is provided → use it (ignore embedded group even if present) - Else if `'stimuli'` group exists in file → load via `pysaliency.hdf5.read_hdf5(source['stimuli'])` - Else → raise `ValueError("No stimuli found in HDF5 file. Pass stimuli= explicitly.")` 3. Reconstruct model via `cls.__new__(cls)` + `Model.__init__(...)` (same pattern as `BaselineModel`) 4. Restore fields: - `model.bandwidth = source.attrs['bandwidth']` - `model.eps = source.attrs['eps']` - `model.xs = source['xs'][...]` - `model.ys = source['ys'][...]` - `model.fixations_n = source['fixations_n'][...]` - `model.stimuli = stimuli` (resolved above) #### Registration `pysaliency/hdf5.py` `_MODEL_READERS`: ```python _MODEL_READERS = { 'pysaliency.baseline_utils.BaselineModel': _read_baseline_model, 'pysaliency.baseline_utils.CrossvalidatedBaselineModel': _read_crossvalidated_baseline_model, } ``` where `_read_crossvalidated_baseline_model` is a thin wrapper that delegates to `CrossvalidatedBaselineModel.read_hdf5`. ### `pysaliency.read_hdf5` dispatcher The dispatcher already accepts and passes through `**kwargs` to the class-specific reader. This is the correct contract — `read_hdf5` remains a thin dispatcher with no knowledge of model-specific parameters. Usage: ```python # Self-contained file — no kwargs needed model = pysaliency.read_hdf5('model.hdf5') # File saved without stimuli — pass them through **kwargs model = pysaliency.read_hdf5('model.hdf5', stimuli=my_stimuli) # Canonical class-level API (always available) model = CrossvalidatedBaselineModel.read_hdf5('model.hdf5', stimuli=my_stimuli) ``` ### `pysaliency.__init__` exports `read_hdf5` is added to the top-level `pysaliency` namespace (already in progress on this branch). ## Implementation Notes - **`CrossvalidatedBaselineModel` refactor** is an internal change. The public constructor signature `__init__(self, stimuli, fixations, bandwidth, eps)` is unchanged; only the stored attributes change (`self.fixations_n` replaces `self.fixations`, `self.shape_cache` is removed). This is not a breaking change. - **`hdf5_wrapper` + `@classmethod` ordering:** `@classmethod` must be the outermost decorator and `@hdf5_wrapper(mode='r')` the inner one — exactly as in `BaselineModel.read_hdf5`. Reversing the order will fail. - **WeakValueDictionary cache in `pysaliency.read_hdf5`:** The top-level `_read_hdf5_from_file` is cached via `boltons.cacheutils.cached`. Since `boltons` does not include `**kwargs` in the cache key, passing `stimuli=` via `pysaliency.read_hdf5('model.hdf5', stimuli=X)` on a second call would return the cached model from the first call. The existing fallback path (lines 43–45 of `hdf5.py`) bypasses the cache when `kwargs` are not hashable — but a numpy array for `stimuli` is not hashable, so the fallback triggers correctly. This is worth an explicit test to guard against regressions. - **Stimuli hash stability:** Hash consistency requires that the embedded stimuli and the stimuli passed to `_log_density` are decoded via the same code path. If both are `FileStimuli` pointing to the same JPEG files, hashes will match even across libjpeg updates (both sides re-decode with the same updated library). A mismatch only arises when the two sides use different decoding paths — e.g., stimuli embedded as raw numpy arrays in HDF5 but evaluated with freshly JPEG-decoded `FileStimuli`, or vice versa. In such cases `stimuli=` must be passed explicitly on reload. - **Unknown stimulus at inference time:** If a stimulus is passed to `_log_density` that was not in the training set, `self.stimuli.stimulus_ids.index(stimulus_id)` raises `ValueError`. This is existing behavior and is not changed by this design. ## Tests All tests go in `tests/test_baseline_utils.py` (existing file) and `tests/test_hdf5_io.py`: | Test | File | |------|------| | Roundtrip `CrossvalidatedBaselineModel` with `include_stimuli=True` — numerical equality of log-density | `test_baseline_utils.py` | | Roundtrip `CrossvalidatedBaselineModel` with `include_stimuli=False` + explicit `stimuli=` — numerical equality | `test_baseline_utils.py` | | `ValueError` when `include_stimuli=False` and no `stimuli=` on reload | `test_baseline_utils.py` | | `type` attr is `'pysaliency.baseline_utils.CrossvalidatedBaselineModel'` | `test_baseline_utils.py` | | `pysaliency.read_hdf5` dispatcher round-trips `CrossvalidatedBaselineModel` | `test_hdf5_io.py` | | `pysaliency.read_hdf5` with `stimuli=` kwarg passthrough bypasses WeakValueDictionary cache | `test_hdf5_io.py` | ================================================ FILE: docs/superpowers/specs/2026-03-23-hdf5-compact-export-design.md ================================================ # Design: Compact HDF5 Export for Model Predictions **Date:** 2026-03-23 **Status:** Approved ## Background `pysaliency.export_model_to_hdf5` serializes model predictions (saliency maps or log-density maps) for every stimulus into an HDF5 file. For large stimulus sets these files reach 20–30 GB. Two independently usable optimizations can reduce this significantly with minimal impact on downstream metric scores: 1. **Dtype reduction** — storing predictions as float32 or float16 instead of float64. Pilot experiments show float16 has essentially zero effect on average metric values and only negligible effects on worst-case per-stimulus changes. Float32 is even safer. (Sub-byte float formats such as float8 are out of scope as they are not natively supported by numpy.) 2. **Spatial downsampling** — storing predictions at 1/2, 1/4, or 1/8 of the original resolution. Most saliency models produce smooth outputs. Even 2× downsampling has a noticeably larger effect than dtype reduction, though in practice it is often still acceptable; 4× is larger still; 8× introduces degradation that becomes clearly visible in metric scores. Both optimizations are independent and can be combined. ## Scope - `export_model_to_hdf5`: two new parameters (`dtype`, `downscale_factor`) - `HDF5SaliencyMapModel`: transparent upsampling and upcast on load - `HDF5Model`: transparent upsampling and renormalization for downsampled or float16 files; strict legacy behavior preserved for float32/float64 non-downsampled files - New tests in `tests/test_precomputed_models.py` Out of scope: per-stimulus dtype/downscale selection; formats other than HDF5. ## Design ### Export API ```python def export_model_to_hdf5( model, stimuli, filename, compression=9, overwrite=True, flush=False, dtype=None, # e.g. np.float32, np.float16; None = preserve native dtype downscale_factor=1, # integer >= 1; 1 = no downsampling ): ``` Both new parameters default to today's behavior — fully backward compatible. #### Export pseudocode The sequencing below is normative; it resolves the ordering of root-attr writing versus stimulus processing: ```python mode = 'w' if overwrite else 'a' with h5py.File(filename, mode=mode) as f: # Determine which stimuli to process if overwrite: indices = list(range(len(stimuli))) else: # append mode is for resuming an interrupted export of the same job, # not for combining data from different export runs if 'type' in f.attrs: # Validate consistency with existing compact file _validate_append_consistency(f, downscale_factor) # raises ValueError on mismatch indices = [i for i in range(len(stimuli)) if names[i] not in f] if not indices: return # nothing to do # Process first stimulus to determine effective_stored_dtype (needed for root attrs) first_smap = _compute_smap(model, stimuli[indices[0]]) effective_stored_dtype = _effective_dtype(first_smap, dtype, downscale_factor) # Emit uint8/size guard warning based on first stimulus (once only) _check_size_guard(first_smap, effective_stored_dtype) # Write root attrs if this is a new file (overwrite=True or file had no attrs) if overwrite or 'type' not in f.attrs: f.attrs['type'] = 'pysaliency.precomputed_models.predictions' f.attrs['version'] = '1.0' f.attrs['downscale_factor'] = downscale_factor f.attrs['dtype'] = str(effective_stored_dtype) # informational; based on first stimulus # Process all stimuli (reuse already-computed first_smap) for i, k in enumerate(indices): smap = first_smap if i == 0 else _compute_smap(model, stimuli[k]) smap = _downsample(smap, downscale_factor) # step 3; no-op if downscale_factor == 1 smap = smap.astype(dtype) if dtype is not None else smap # step 4 ds = f.create_dataset(names[k], data=smap, compression=compression) if downscale_factor > 1: ds.attrs['original_shape'] = np.array([H, W], dtype=np.int64) # pre-padding H, W from step 3 if flush: f.flush() ``` Note on the root-attrs `dtype` field: it is **purely informational**, computed from the first stimulus only. If stimuli have heterogeneous native dtypes (rare in practice), the field may not reflect later stimuli — this is acceptable given it is informational and the loader does not depend on it. #### `_effective_dtype(smap, dtype, downscale_factor)` ```python def _effective_dtype(smap, dtype, downscale_factor): if dtype is not None: return np.dtype(dtype) if downscale_factor > 1: # numpy .mean() returns float64 for integer inputs, preserves float types if np.issubdtype(smap.dtype, np.integer): return np.dtype(np.float64) else: return smap.dtype # float32 stays float32, float64 stays float64 return smap.dtype ``` #### `_downsample(smap, downscale_factor)` ```python def _downsample(smap, k): if k == 1: return smap H, W = smap.shape H_pad = int(np.ceil(H / k)) * k W_pad = int(np.ceil(W / k)) * k smap = np.pad(np.ascontiguousarray(smap), ((0, H_pad - H), (0, W_pad - W)), mode='edge') # np.ascontiguousarray ensures C-order before reshape; np.pad preserves C-order return smap.reshape(H_pad // k, k, W_pad // k, k).mean(axis=(1, 3)) # Note: when H is not divisible by k, the last bin is biased toward the # border value (edge-padded rows are copies). This is a minor, acceptable # artefact for the 2× and 4× factors targeted by this feature. ``` #### `_validate_append_consistency(f, downscale_factor)` Checks only `int(f.attrs['downscale_factor']) == downscale_factor`. Raises `ValueError` with a descriptive message on mismatch. The `dtype` root attr is purely informational and is intentionally excluded from this check: determining `effective_stored_dtype` requires computing the first stimulus, which has not happened yet at the point the validator is called. The `downscale_factor` check is sufficient to catch the most dangerous mismatch (spatial layout of stored data). #### Uint8 / size guard Implemented in `_check_size_guard(smap, effective_stored_dtype)`: If `effective_stored_dtype.itemsize > np.dtype(smap.dtype).itemsize`, emit `warnings.warn` explaining: - The native dtype and its size in bytes - The effective stored dtype and its size in bytes - A suggestion to pass `dtype=np.uint8` (if native is integer) or to omit compact options - Additionally, if native is integer and `downscale_factor > 1` and `dtype` is None: note that casting the float result back to the original integer type would be lossy ### HDF5 File Format ``` / (root) attrs: type = 'pysaliency.precomputed_models.predictions' version = '1.0' downscale_factor = 2 dtype = 'float32' # informational; from first stimulus images/cat.jpg (dataset shape ceil(H/2) × ceil(W/2), dtype float32) attrs: original_shape = np.array([H, W], dtype=np.int64) # pre-padding; present only when downscale_factor > 1 images/dog.jpg (dataset shape ceil(H'/2) × ceil(W'/2), dtype float32) attrs: original_shape = np.array([H', W'], dtype=np.int64) ``` **Overwritten files** (`overwrite=True`): h5py opens with `mode='w'`, which truncates the file completely before writing begins. The resulting file is always internally consistent. **Append-mode files** (`overwrite=False`): intended only for resuming an interrupted export. All datasets in the file originate from the same export job with the same settings; mixing data from different jobs is not a supported use case. **Legacy files** (written by current code) have no root attrs and no `original_shape` on datasets. The loader handles them identically to today. **Non-compact new files** (`dtype=None`, `downscale_factor=1`) have root attrs but no `original_shape` on datasets — the loader skips upsampling. Root attrs are purely informational. The loader derives behavior entirely from per-dataset `original_shape` (whether to upsample) and the dataset's native HDF5 dtype (which normalization path to use in `HDF5Model`). ### Load Side #### `HDF5SaliencyMapModel` `__init__` reads and stores `f.attrs.get('version')` for future compat; no other init changes. A new private helper is added: ```python def _key_for_stimulus(self, stimulus): stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) # raises ValueError if not found return self.names[stimulus_index] ``` `_saliency_map` is refactored to call `_key_for_stimulus` internally, replacing the inline index lookup currently at lines 328–329. `_saliency_map` updated logic: 1. `stimulus_key = self._key_for_stimulus(stimulus)` 2. `dataset = self.hdf5_file[stimulus_key]`; `smap = dataset[:]` 3. If `'original_shape'` in `dataset.attrs`: cast `smap` to float64, then upsample: ```python target_shape = tuple(dataset.attrs['original_shape']) zoom_factors = (target_shape[0] / smap.shape[0], target_shape[1] / smap.shape[1]) smap = scipy.ndimage.zoom(smap.astype(np.float64), zoom_factors, order=1, mode='nearest') ``` 4. Otherwise: return `smap` with native dtype unchanged. Shape check (`check_shape`) runs against the post-upsampling shape, as today. When using downsampled exports with resized stimuli and `check_shape=False`, upsampling targets `original_shape` (the shape at export time), not the (resized) stimulus shape. **Dtype behavior:** | File type | `original_shape` present? | `_saliency_map` returns | |---|---|---| | Legacy / uint8 / non-compact | no | native dtype (unchanged) | | Compact, dtype-reduced only | no | native dtype (float16/32) | | Compact, downsampled | yes | float64 | The dtype-reduced-only path intentionally returns the native stored dtype. Downstream code that requires float64 (e.g. `HDF5Model`) is responsible for casting. #### `HDF5Model` Constructor gains a new parameter and explicitly stores it: ```python class HDF5Model(Model): def __init__(self, stimuli, filename, check_shape=True, max_normalization_error=np.log(1.1), **kwargs): super().__init__(**kwargs) self.parent_model = HDF5SaliencyMapModel( stimuli=stimuli, filename=filename, caching=False, check_shape=check_shape, ) self.max_normalization_error = max_normalization_error # on HDF5Model, not parent_model ``` `max_normalization_error` can be updated after construction. Setting it to `None` disables the tolerance guard on the relaxed path only — the strict ±0.01 check on the legacy path is always applied regardless. `_log_density` updated logic: ```python def _log_density(self, stimulus): key = self.parent_model._key_for_stimulus(stimulus) dataset = self.parent_model.hdf5_file[key] use_relaxed_path = ( 'original_shape' in dataset.attrs # spatially downsampled or dataset.dtype.itemsize < np.dtype(np.float32).itemsize # float16 or narrower ) # Note: this attr lookup is cheap (HDF5 metadata); parent_model has caching=False # so the subsequent saliency_map call also reads from disk each time. smap = self.parent_model.saliency_map(stimulus).astype(np.float64) if use_relaxed_path: if self.max_normalization_error is not None: if abs(logsumexp(smap)) >= self.max_normalization_error: raise ValueError( f'Log density normalization error {abs(logsumexp(smap)):.4f} ' f'exceeds threshold {self.max_normalization_error:.4f}' ) smap -= logsumexp(smap) else: # Legacy path: strict check, no renormalization if not -0.01 <= logsumexp(smap) <= 0.01: raise ValueError('Not a correct log density!') return smap ``` ### Interpolation Choices Motivated by pilot experiments comparing several strategies (always using bilinear upsampling in log space): - **Downsampling**: area averaging in log/value space via edge-pad then reshape + mean. Non-divisible shapes are handled by padding to the next multiple of `k` with edge values; `original_shape` stores the pre-padding shape so the padding region is implicitly removed during upsampling. Known minor limitation: the last bin in non-divisible dimensions is biased toward the border value (edge-padded rows). This is acceptable for the 2× and 4× factors targeted. Empirically outperforms subsampling and probability-space pooling. - **Upsampling**: bilinear interpolation in log/value space via `scipy.ndimage.zoom(order=1, mode='nearest')`. `mode='nearest'` matches the existing project convention and avoids reflect-padding edge artefacts. The upsampling strategy has negligible empirical effect on metric scores. - **Renormalization**: not applied at export; applied at load time only on the relaxed path in `HDF5Model`, after verifying the residual is within the configured tolerance. ## Tests All new tests in `tests/test_precomputed_models.py`. Roundtrip tests are parametrized over `(dtype, downscale_factor)` combinations: `(None, 1)`, `(np.float32, 1)`, `(np.float16, 1)`, `(np.float32, 2)`, `(np.float16, 4)` for both `HDF5SaliencyMapModel` and `HDF5Model`. The `(None, 1)` case is a regression test (no new code paths). For `HDF5Model`: - `(np.float32, 1)` and `(None, 1)`: exercise the strict legacy path — no renormalization, strict ±0.01 check - `(np.float16, 1)`: exercises the relaxed path triggered by dtype (float16 itemsize < float32 itemsize) - `(np.float32, 2)` and `(np.float16, 4)`: exercise the relaxed path triggered by `original_shape` | Test | Description | |------|-------------| | Root attrs | Correct `type`, `version`, `dtype` (using `effective_stored_dtype`), `downscale_factor` written | | Dataset dtype | Stored dtype matches `effective_stored_dtype` | | Dataset shape | Stored shape is `ceil(original_shape / downscale_factor)` | | `original_shape` attr | Present iff `downscale_factor > 1`; `np.int64` array with correct pre-padding shape | | Non-divisible shape — stored | Stored shape is `ceil(H/k) × ceil(W/k)` | | Non-divisible shape — loaded | Upsampled output shape matches `original_shape` exactly | | Append mode — consistent | Appending with matching settings succeeds | | Append mode — mismatch (downscale only) | `ValueError` on mismatched `downscale_factor` | | Append mode — mismatch (dtype only) | `ValueError` on mismatched `dtype` | | Append mode — new file created | Root attrs written correctly when `overwrite=False` and file is new | | Append mode — legacy file | Append to legacy file succeeds; no root attrs written | | uint8 + `downscale_factor>1` — warning | `UserWarning` emitted | | uint8 + `downscale_factor>1` — stored dtype | Actual stored dtype is float64 | | uint8 + `dtype=np.float32` — warning | `UserWarning` emitted | | uint8 + `dtype=np.uint8` | No warning | | float32 + `downscale_factor>1` + `dtype=None` | Stored dtype is float32 (not float64); root attr `dtype` is `'float32'` | | Legacy file load | Native dtype returned, shape matches stimulus — no behavioral change | | float32 load, no downsampling | float32 returned (native dtype preserved) | | Downsampled load | float64 returned, shape matches `original_shape` | | Resized stimuli + `check_shape=False` | Stimulus presented at size S ≠ `original_shape`; upsamples to `original_shape`, not S | | `HDF5Model` float16 relaxed path | float16 file triggers relaxed path; output float64, logsumexp ≈ 0 | | `HDF5Model` float32 strict path | float32 non-downsampled file uses strict path; logsumexp outside ±0.01 raises `ValueError` | | `HDF5Model` non-compact no renorm | Non-compact float32/float64 output is bit-identical to stored values (cast to float64) | | `HDF5Model` downsampled roundtrip | float64 output, logsumexp ≈ 0 after renorm (parametrized) | | `HDF5Model` threshold | logsumexp before renorm within `log(1.1)` for float16+4× | | `HDF5Model` corrupted file | `ValueError` raised when logsumexp exceeds `max_normalization_error` | | Custom `max_normalization_error` | Tighter threshold triggers `ValueError`; looser does not | | `max_normalization_error=None` | Relaxed path skips guard; output is float64, logsumexp ≈ 0 (renorm still applied) | ================================================ FILE: examples/docker_submission/README.md ================================================ # Submission This directory contains an example of how to create a Docker container for submitting a scanpath model to the MIT/Tübingen Saliency Benchmark. You'll need to build a docker or singularity container that offers a json API for requesting model predictions. The benchmark will use `pysaliency.http_models.HTTPScanpathModel` to interact with your model. ## Preparing the submission 1. Create a docker or singularity container that exposes your model as an API compatible with `pysaliency.http_models.HTTPScanpathModel`. There are two different examples contained here: - `docker_pysaliency`: A docker container exposing a pysaliency model (which is implemented in `sample_submission.py`). Use this if you already have a pysaliency implementation of your model. - `docker_deepgaze`: A docker container exposing the DeepGaze model. It demonstrates how to implement the API for an arbitrary model. 2. Build the Docker container as described in the "Launching the submission container" section. ## Launching the submission container In this example, we will use the `docker_pysaliency` directory to create a Docker container that exposes a pysaliency model. The container will run a Flask server that listens for HTTP requests and responds with model predictions. First we have to build the container ```bash docker build -t sample_pysaliency docker ``` Then we can start it ```bash docker run --rm -it -p 4000:4000 sample_pysaliency ``` The above command will launch the image as interactive container in the foregroun and expose the port `4000` to the host machine. If you prefer to run it in the background, use ```bash docker run --name sample_pysaliency -dp 4000:4000 sample_pysaliency ``` which will launch a container named `sample_pysaliency`. The container will be running in the background. To test the model server, run the sample_evaluation script. This script will evaluate the model on the MIT1003 dataset. Make sure to have the `pysaliency` package installed: ```bash python ./sample_evaluation.py ``` To delete the background container, run the following command: ```bash docker stop sample_pysaliency && docker rm sample_pysaliency ``` # TODOs - [ ] Establish and discuss how arguments can be passed to the model server, e.g. information about the image resolution in dva or other parameters. ================================================ FILE: examples/docker_submission/docker_deepgaze3/Dockerfile ================================================ # Specify a base image depending on the project. FROM bitnami/python:3.8 # For more complex examples, might need to use a different base image. # FROM pytorch/pytorch:1.9.1-cuda11.1-cudnn8-runtime WORKDIR /app ENV HTTP_PORT=4000 RUN apt-get update \ && apt-get -y install gcc \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* /var/cache/apt/* COPY ./requirements.txt ./ RUN python -m pip install --no-cache -U pip \ && python -m pip install --no-cache -r requirements.txt COPY ./model_server.py ./ # COPY ./sample_submission.py ./ # This is needed for Singularity builds. EXPOSE $HTTP_PORT # The entrypoint for a container, CMD ["gunicorn", "-w", "1", "-b", "0.0.0.0:4000", "--pythonpath", ".", "--access-logfile", "-", "model_server:app"] ================================================ FILE: examples/docker_submission/docker_deepgaze3/model_server.py ================================================ from flask import Flask, request import numpy as np import json from PIL import Image from io import BytesIO import orjson from scipy.ndimage import zoom from scipy.special import logsumexp import torch # Import your model here import deepgaze_pytorch # Flask server app = Flask("saliency-model-server") app.logger.setLevel("DEBUG") # # TODO - replace this with your model model = deepgaze_pytorch.DeepGazeIII(pretrained=True) def get_fixation_history(fixation_coordinates, model): history = [] for index in model.included_fixations: try: history.append(fixation_coordinates[index]) except IndexError: # for early fixations, not all previous fixations exist history.append(np.nan) return np.array(history) @app.route('/conditional_log_density', methods=['POST']) def conditional_log_density(): # get data data = json.loads(request.form['json_data']) # extract scanpath history x_hist = np.array(data['x_hist']) y_hist = np.array(data['y_hist']) print(x_hist) x_hist = get_fixation_history(x_hist, model) print(x_hist) y_hist = get_fixation_history(y_hist, model) # t_hist = np.array(data['t_hist']) # attributes = data.get('attributes', {}) # extract stimulus image_bytes = request.files['stimulus'].read() image = Image.open(BytesIO(image_bytes)) stimulus = np.array(image) # centerbias for deepgaze3 model centerbias_template = np.zeros((1024, 1024)) centerbias = zoom(centerbias_template, (stimulus.shape[0]/centerbias_template.shape[0], stimulus.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest' ) centerbias -= logsumexp(centerbias) # make tensors for deepgaze3 model image_tensor = torch.tensor([stimulus.transpose(2, 0, 1)]) centerbias_tensor = torch.tensor([centerbias]) x_hist_tensor = torch.tensor([x_hist[model.included_fixations]]) y_hist_tensor = torch.tensor([y_hist[model.included_fixations]]) # return model response log_density = model(image_tensor, centerbias_tensor, x_hist_tensor, y_hist_tensor) log_density_list = log_density.tolist() response = orjson.dumps({'log_density': log_density_list}) return response @app.route('/type', methods=['GET']) def type(): type = "ScanpathModel" version = "v1.0.0" return orjson.dumps({'type': type, 'version': version}) def main(): app.run(host="localhost", port="4000", debug="True", threaded=True) if __name__ == "__main__": main() ================================================ FILE: examples/docker_submission/docker_deepgaze3/requirements.txt ================================================ cython flask gunicorn numpy # Add additional dependencies here pysaliency scipy torch flask_orjson git+https://github.com/matthias-k/deepgaze ================================================ FILE: examples/docker_submission/docker_pysaliency_model/Dockerfile ================================================ # Specify a base image depending on the project. FROM bitnami/python:3.8 # For more complex examples, might need to use a different base image. # FROM pytorch/pytorch:1.9.1-cuda11.1-cudnn8-runtime WORKDIR /app ENV HTTP_PORT=4000 RUN apt-get update \ && apt-get -y install gcc \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* /var/cache/apt/* COPY ./requirements.txt ./ RUN python -m pip install --no-cache -U pip \ && python -m pip install --no-cache -r requirements.txt COPY ./model_server.py ./ COPY ./sample_submission.py ./ # This is needed for Singularity builds. EXPOSE $HTTP_PORT # The entrypoint for a container, CMD ["gunicorn", "-w", "1", "-b", "0.0.0.0:4000", "--pythonpath", ".", "--access-logfile", "-", "model_server:app"] ================================================ FILE: examples/docker_submission/docker_pysaliency_model/model_server.py ================================================ from flask import Flask, request, jsonify from flask_orjson import OrjsonProvider import numpy as np import json from PIL import Image from io import BytesIO import orjson # Import your model here from sample_submission import MySimpleScanpathModel app = Flask("saliency-model-server") app.json_provider = OrjsonProvider(app) app.logger.setLevel("DEBUG") # # TODO - replace this with your model model = MySimpleScanpathModel() @app.route('/conditional_log_density', methods=['POST']) def conditional_log_density(): data = json.loads(request.form['json_data']) x_hist = np.array(data['x_hist']) y_hist = np.array(data['y_hist']) t_hist = np.array(data['t_hist']) attributes = data.get('attributes', {}) image_bytes = request.files['stimulus'].read() image = Image.open(BytesIO(image_bytes)) stimulus = np.array(image) log_density = model.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes) log_density_list = log_density.tolist() response = orjson.dumps({'log_density': log_density_list}) return response @app.route('/type', methods=['GET']) def type(): type = "ScanpathModel" version = "v1.0.0" return orjson.dumps({'type': type, 'version': version}) def main(): app.run(host="localhost", port="4000", debug="True", threaded=True) if __name__ == "__main__": main() ================================================ FILE: examples/docker_submission/docker_pysaliency_model/requirements.txt ================================================ cython flask gunicorn numpy # Add additional dependencies here pysaliency scipy torch flask_orjson ================================================ FILE: examples/docker_submission/docker_pysaliency_model/sample_submission.py ================================================ """ This file implements a very simple scanpath model using local constrast and a saccadic prior. """ import numpy as np import sys from typing import Union from scipy.ndimage import gaussian_filter import pysaliency class LocalContrastModel(pysaliency.Model): def __init__(self, bandwidth=0.05, **kwargs): super().__init__(**kwargs) self.bandwidth = bandwidth def _log_density(self, stimulus: Union[pysaliency.datasets.Stimulus, np.ndarray]): # _log_density can either take pysaliency Stimulus objects, or, for convenience, simply numpy arrays # `as_stimulus` ensures that we have a Stimulus object stimulus_object = pysaliency.datasets.as_stimulus(stimulus) # grayscale image gray_stimulus = np.mean(stimulus_object.stimulus_data, axis=2) # size contains the height and width of the image, but not potential color channels height, width = stimulus_object.size # define kernel size based on image size kernel_size = np.round(self.bandwidth * max(width, height)).astype(int) sigma = (kernel_size - 1) / 6 # apply Gausian blur and calculate squared difference between blurred and original image blurred_stimulus = gaussian_filter(gray_stimulus, sigma) prediction = gaussian_filter((gray_stimulus - blurred_stimulus)**2, sigma) # normalize to [1, 255] prediction = (254 * (prediction / prediction.max())).astype(int) + 1 density = prediction / prediction.sum() return np.log(density) class MySimpleScanpathModel(pysaliency.ScanpathModel): def __init__(self, spatial_model_bandwidth: float=0.05, saccade_width: float=0.1): self.spatial_model_bandwidth = spatial_model_bandwidth self.saccade_width = saccade_width self.spatial_model = LocalContrastModel(spatial_model_bandwidth) # self.spatial_model = pysaliency.UniformModel() def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None,): stimulus_object = pysaliency.datasets.as_stimulus(stimulus) # size contains the height and width of the image, but not potential color channels height, width = stimulus_object.size spatial_prior_log_density = self.spatial_model.log_density(stimulus) spatial_prior_density = np.exp(spatial_prior_log_density) # compute saccade bias last_x = x_hist[-1] last_y = y_hist[-1] xs = np.arange(width, dtype=float) ys = np.arange(height, dtype=float) XS, YS = np.meshgrid(xs, ys) XS -= last_x YS -= last_y # compute prior max_size = max(width, height) actual_kernel_size = self.saccade_width * max_size saccade_bias = np.exp(-0.5 * (XS ** 2 + YS ** 2) / actual_kernel_size ** 2) prediction = spatial_prior_density * saccade_bias density = prediction / prediction.sum() return np.log(density) ================================================ FILE: examples/docker_submission/sample_evaluation.py ================================================ import numpy as np import sys from pysaliency.http_models import HTTPScanpathModel sys.path.insert(0, '..') import pysaliency from tqdm import tqdm import deepgaze_pytorch def get_fixation_history(fixation_coordinates, model): history = [] for index in model.included_fixations: try: history.append(fixation_coordinates[index]) except IndexError: history.append(np.nan) return history if __name__ == "__main__": # initialize HTTPScanpathModel http_model = HTTPScanpathModel("http://localhost:4000") http_model.check_type() # for testing # test_model = deepgaze_pytorch.DeepGazeIII(pretrained=True) # get MIT1003 dataset stimuli, fixations = pysaliency.get_mit1003(location='pysaliency_datasets') # only use first 1000 fixations for testing eval_fixations = fixations[fixations.scanpath_history_length > 0][:1000] # error if no history information_gain = http_model.information_gain(stimuli, eval_fixations, average="image", verbose=True) print("IG:", information_gain) # for fixation_index in tqdm(range(len(eval_fixations))): # get server response for one stimulus # server_density = http_model.conditional_log_density( # stimulus=stimuli.stimuli[eval_fixations.n[fixation_index]], # x_hist=eval_fixations.x_hist[fixation_index], # y_hist=eval_fixations.y_hist[fixation_index], # t_hist=eval_fixations.t_hist[fixation_index] # ) # get test model response # test_model_density = test_model( # stimulus=stimuli.stimuli[eval_fixations.n[fixation_index]], # x_hist=eval_fixations.x_hist[fixation_index], # y_hist=eval_fixations.y_hist[fixation_index], # t_hist=eval_fixations.t_hist[fixation_index] # ) # Testing # test = np.testing.assert_allclose(server_density, test_model_density) ================================================ FILE: notebooks/LSUN.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LSUN Challenge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This document describes how to setup and run the python code for the LSUN saliency evaluation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With your favorite python package management tool, install the needed libraries (here shown for `pip`):\n", "\n", " pip install numpy scipy theano Cython natsort dill hdf5storage\n", " pip install git+https://github.com/matthias-k/optpy\n", " pip install git+https://github.com/matthias-k/pysaliency\n", "\n", "If you want to use the SALICON dataset, you also need to install the\n", "[SALICON API](https://github.com/NUS-VIP/salicon-api)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Usage\n", "\n", "start by importing pysaliency:\n", "\n", " import pysaliency\n", "\n", "you probably also want to load the LSUN datasets:\n", "\n", " dataset_location = 'datasets' # where to cache datasets\n", " stimuli_salicon_train, fixations_salicon_train = pysaliency.get_SALICON_train(location=dataset_location)\n", " stimuli_salicon_val, fixations_salicon_val = pysaliency.get_SALICON_val(location=dataset_location)\n", " stimuli_salicon_test = pysaliency.get_SALICON_test(location=dataset_location)\n", " \n", " stimuli_isun_train, stimuli_isun_val, stimuli_isun_test, fixations_isun_train, fixations_isun_val = pysaliency.get_iSUN(location=dataset_location)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Add ModelFromDirectory for log densities\n", "# TODO: Change defaults for saliency map convertor (at least in LSUN subclass)\n", "# TODO: Write fit functions optimize_for_information_gain(model, stimuli, fixations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import your saliency model into pysaliency\n", "\n", "If you did not develop your model in the pysaliency framework, you have to import the generated saliencymaps or log-densities into pysaliency. If you have the saliency maps saved to an directory with names corresponding to the stimuli\n", "filenames, use `pysaliency.SaliencyMapModelFromDirectory`. You can save your saliency maps as png, jpg, tiff, mat or npy files." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "my_model = pysaliency.SaliencyMapModelFromDirectory(stimuli_salicon_train, \"my_model/saliency_maps/SALICON_TRAIN\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you have an LSUN submission file prepared, you can load it with `pysaliency.SaliencyMapModelFromDirectory`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "my_model = pysaliency.SaliencyMapModelFromFile(stimuli_salicon_train, \"my_model/salicon_train.mat\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate your model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluating your model with pysaliency is fairly easy. In general, the evaluation functions take the stimuli and fixations to evaluate on, and maybe some additional configuration parameters. The following metrics are used in the LSUN saliency challenge (additionaly, the information gain metric is used, see below):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "my_model.AUC(stimuli_salicon_train, fixations_salicon_train, nonfixations='uniform')\n", "my_model.AUC(stimuli_salicon_train, fixations_salicon_train, nonfixations='shuffled')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optimize your model for information gain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you wish to hand in a probabilistic model, you might wish to optimize the model for the nonlinearity and centerbiases\n", "of the datasets. Otherwise we will optimize all saliency map models for information gain using a subset of the iSUN dataset using the following code. Feel free to adapt it to your needs (for example, use more images for fitting)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "my_probabilistic_model = pysaliency.SaliencyMapConvertor(my_model, ...)\n", "fit_stimuli, fit_fixations = pysaliency.create_subset(stimuli_isun_train, fixations_isun_train, range(0, 500))\n", "my_probabilistic_model = pysaliency.optimize_for_information_gain\n", " my_model, fit_stimuli, fit_fixations,\n", " num_nonlinearity=20,\n", " num_centerbias=12,\n", " optimize=[\n", " 'nonlinearity',\n", " 'centerbias',\n", " 'alpha',\n", " #'blurradius', # we do not optimize the bluring.\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### hand in your model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1+" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: notebooks/Tutorial.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "18dc5e5a-ece3-477c-9090-b4265ba04942", "metadata": {}, "source": [ "# Pysaliency: A short tutorial\n", "\n", "`pysaliency` is a python library which aims at making analyzing and modeling of eye movement data convenient. It was build and extended over the course of multiple papers which are reflected in its structure, mainly:\n", "\n", "* [Kümmerer, Wallis & Bethge: Information-theoretic model comparison unifies saliency metrics. PNAS 2015](http://www.pnas.org/content/112/52/16054)\n", "* [Kümmerer, Wallis & Bethge: Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics. ECCV 2018](http://openaccess.thecvf.com/content_ECCV_2018/html/Matthias_Kummerer_Saliency_Benchmarking_Made_ECCV_2018_paper.html)\n", "* [Kümmerer & Bethge: Predicting Visual Fixations, Annual Reviews in Vision Science 2023](https://www.annualreviews.org/doi/10.1146/annurev-vision-120822-072528)" ] }, { "cell_type": "code", "execution_count": 1, "id": "08c68bf6-8d6d-4c13-934d-8555bf7ca985", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "from typing import Union\n", "\n", "\n", "import sys\n", "\n", "# only needed if running notebook from within the pysaliency repository\n", "sys.path.insert(0, '..')\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "import pysaliency\n", "\n", "from scipy.ndimage import gaussian_filter\n", "\n", "# change path to your local matlab installation\n", "# os.environ['PATH'] = f\"/usr/local/MATLAB/R2024a/bin/:{os.environ['PATH']}\"\n", "os.environ['PATH'] = f\"/Applications/MATLAB_R2024b.app//bin/:{os.environ['PATH']}\"\n" ] }, { "cell_type": "markdown", "id": "06a8bc27-b407-4844-b381-8bd22e3a8f72", "metadata": {}, "source": [ "## Datasets\n", "\n", "`pysaliency` has to main classes for handling data. `pysaliency.Stimuli` contains images which have been shown to a subject, `pysaliency.Fixations` keeps track of recorded fixations. For the purpose of this tutorial, we'll use the MIT1003 dataset, which `pysaliency` can download and import on it's own." ] }, { "cell_type": "code", "execution_count": 2, "id": "c0ec6a86-bebe-4cdb-b863-1557905c1026", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading file: 235MB [01:49, 2.14MB/s] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Checking md5 sum...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Downloading file: 47.1MB [00:21, 2.22MB/s] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Checking md5 sum...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Downloading file: 32.8kB [00:00, 346kB/s] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Checking md5 sum...\n", "Creating stimuli\n", "Creating fixations\n", "Running original code to extract fixations. This can take some minutes.\n", "Warning: In the IPython Notebook, the output is shown on the console instead of the notebook.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: package sun.awt.X11 not in java.desktop\n", "WARNING: package sun.awt.X11 not in java.desktop\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processing scanpath 15044/15045\r" ] } ], "source": [ "stimuli, fixations = pysaliency.get_mit1003(location='pysaliency_datasets')" ] }, { "cell_type": "markdown", "id": "b31fae21-1642-4041-9cd7-76b4a2ef5298", "metadata": {}, "source": [ "`Stimuli` hold the images in `Stimuli.stimuli` as numpy array. In the case of large datasets, the subclass `FileStimuli` (which is used here) will make sure that the images are only loaded once they are needed." ] }, { "cell_type": "code", "execution_count": 3, "id": "343bd172-bb86-4e23-897e-0c6d60042de0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "00: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010764.jpeg)\n", "01: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010785.jpeg)\n", "02: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010800.jpeg)\n", "03: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010806.jpeg)\n", "04: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010808.jpeg)\n", "05: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010816.jpeg)\n", "06: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010855.jpeg)\n", "07: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010885.jpeg)\n", "08: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i05june05_static_street_boston_p1010907.jpeg)\n", "09: (768, 1024, 3) (pysaliency_datasets/MIT1003/stimuli/i10feb04_static_cars_highland_img_0843.jpeg)\n", "Total number of stimuli in dataset: 1003\n" ] } ], "source": [ "for image_index in range(10):\n", " print(f\"{image_index:02d}: {stimuli.stimuli[image_index].shape} ({stimuli.filenames[image_index]})\")\n", "\n", "print(f\"Total number of stimuli in dataset: {len(stimuli)}\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "ed51a9e1-0cfa-4258-8ae5-195e45ec5053", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axes = plt.subplots(4, 4, figsize=(12, 8))\n", "\n", "for k, ax in enumerate(axes.flatten()):\n", " ax.imshow(stimuli.stimuli[k])\n", " ax.set_axis_off()\n", " ax.set_title(str(k))" ] }, { "cell_type": "markdown", "id": "b7ee2535-3201-4d83-b5e3-1d33d53dc4f0", "metadata": {}, "source": [ "`Stimuli` instances implement the `collections.abs.Sequence` interface and hence behave mostly like normal lists, e.g. you can slice them with `stimuli[10:20]`." ] }, { "cell_type": "markdown", "id": "42cfde1a-a30d-4fa0-b396-9934bea9f7df", "metadata": {}, "source": [ "`Fixations` hold the fixations make on images. Fixations also behave mostly like a list, where each list item is a fixation made on an image. This also\n", "holds for nearly all attributes, which are usually numpy arrays with one row per fixation. The most important attributes are `Fixations.x` and `Fixations.y` which\n", "contain the x and y positions of the fixations in pixels. `Fixations` are always meant to be used together with a `Stimuli` object, where `Fixations.n` indicates for each fixation on which image it was made:" ] }, { "cell_type": "code", "execution_count": 5, "id": "a4cb1ba4-062d-48df-8cdd-4d41167d5591", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGzCAYAAADuc1ebAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9ebRlWV3Y/znzOXce3zxUvZqrq7obGppZWmkGoxEQlZgYRaNhKWgiy4WQtQySn5FEY+LAEKIRNUuXCMaI0aiIiNDQQM/dNQ9vHu6783DmYf/+uK9e1+uu7q6Ghq5u7qfX6Xp3333O2Wfffc7+nu+0JSGEYMSIESNGjBgx4gZFfrYbMGLEiBEjRowY8WSMhJURI0aMGDFixA3NSFgZMWLEiBEjRtzQjISVESNGjBgxYsQNzUhYGTFixIgRI0bc0IyElREjRowYMWLEDc1IWBkxYsSIESNG3NCMhJURI0aMGDFixA3NSFgZMWLEiBEjRtzQjISVEd+SLC0tIUkSv/d7v/esnF+SJH7xF3/xWTn3txo3el//4i/+IpIkPdvNGDHihmYkrIx4XvJ7v/d7SJJ0ze0973nPN6UNf/VXf3VDT5IjRowY8VxBGq0NNOL5yO/93u/xoz/6o/yH//Af2L9//57vTpw4wS233ILv+2iahqIo35A2vPOd7+RDH/oQ17rFPM9DVVVUVf2GnHvEo9zofR1FEVEUYZrms92UESNuWG7Mu3fEiGeI7/zO7+RFL3rRNb97NieH0cT0zeNG7+sbWZAaMeJGYWQGGvEtyWN9Vra3t6lWq9xxxx17NCEXL14knU7z1re+dbfs85//PN///d/P3NwchmEwOzvLz/7sz+K67m6dt73tbXzoQx8C2GOCusK1/Cjuv/9+vvM7v5NcLkcmk+E1r3kNd9999546V8xbd911F+9617uoVquk02ne/OY3U6/X99S95557eP3rX0+lUsGyLPbv38+P/diPXVf/fPjDH+amm27CMAympqZ4xzveQafT2VPnjjvu4MSJE5w+fZpv//ZvJ5VKMT09za/8yq9c1zk+9rGP8R3f8R2MjY1hGAbHjx/nIx/5yHXtC/CJT3yC48ePY5omJ06c4M/+7M9429vexr59+/bUu7qvP/nJTyJJEp/73Oced7yPfvSjSJLEI488slt29uxZvu/7vo9SqYRpmrzoRS/iU5/61J79ns5vci2u5bMiSRLvfOc7d6/Rsixe9rKX8fDDD++29eDBg5imyR133MHS0tKe/a9njD7dfkyShF//9V/npptuwjRNxsfHefvb30673X7Kaxwx4utlJM6PeF7T7XZpNBp7yiqVyuPqjY2N8ZGPfITv//7v57d+67f4mZ/5GZIk4W1vexvZbJYPf/jDu3U/8YlP4DgOP/mTP0m5XOYrX/kKv/Vbv8Xa2hqf+MQnAHj729/OxsYGn/70p/lf/+t/PWU7T506xate9SpyuRzvfve70TSNj370o9xxxx187nOf4yUvecme+j/90z9NsVjkfe97H0tLS/z6r/8673znO/n4xz8ODIWv173udVSrVd7znvdQKBRYWlrif//v//2UbfnFX/xF3v/+93PnnXfykz/5k5w7d46PfOQjfPWrX+Wuu+5C07Tduu12mze84Q187/d+Lz/wAz/AJz/5SX7+53+ekydP8p3f+Z1Pep6PfOQj3HTTTXzP93wPqqryF3/xF/zUT/0USZLwjne840n3/cu//Eve+ta3cvLkST7wgQ/Qbrf5V//qXzE9Pf2k+33Xd30XmUyGP/mTP+HVr371nu8+/vGPc9NNN3HixAlg+Ju84hWvYHp6mve85z2k02n+5E/+hDe96U386Z/+KW9+85v37P9Uv8nT5fOf/zyf+tSndvviAx/4AN/93d/Nu9/9bj784Q/zUz/1U7TbbX7lV36FH/uxH+Pv//7vd/e9njH6dPvx7W9/+6559Wd+5mdYXFzkgx/8IPfff//jxsWIEc84YsSI5yEf+9jHBHDNTQghFhcXBSA+9rGP7dnvB3/wB0UqlRLnz58Xv/qrvyoA8X/+z//ZU8dxnMed7wMf+ICQJEksLy/vlr3jHe8QT3SLAeJ973vf7uc3velNQtd1cenSpd2yjY0Nkc1mxbd927c97rruvPNOkSTJbvnP/uzPCkVRRKfTEUII8Wd/9mcCEF/96lefoqf2sr29LXRdF6973etEHMe75R/84AcFIH73d393t+zVr361AMQf/MEf7Jb5vi8mJibEW97ylqc817X68fWvf71YWFh4yn1PnjwpZmZmRL/f3y37h3/4BwGI+fn5PXUf29c/+IM/KMbGxkQURbtlm5ubQpZl8R/+w3/YLXvNa14jTp48KTzP2y1LkkS8/OUvF4cOHdotu97f5Il43/ve97hxAgjDMMTi4uJu2Uc/+lEBiImJCdHr9XbL3/ve9wpgT93rHaPX24+f//znBSD+8A//cM8x//qv//qa5SNGPNOMzEAjntd86EMf4tOf/vSe7cn44Ac/SD6f5/u+7/v4hV/4Bf7lv/yXvPGNb9xTx7Ks3b9t26bRaPDyl78cIQT333//025jHMf87d/+LW9605tYWFjYLZ+cnOSf//N/zhe+8AV6vd6eff71v/7Xe0wHr3rVq4jjmOXlZQAKhQIA//f//l/CMLzutvzd3/0dQRDwb//tv0WWH308/MRP/AS5XI6//Mu/3FM/k8nwQz/0Q7ufdV3n9ttv5/Lly095rqv78YoG7NWvfjWXL1+m2+0+4X4bGxs8/PDD/PAP/zCZTGa3/NWvfjUnT558yvO+9a1vZXt7m3/4h3/YLfvkJz9JkiS75r5Wq8Xf//3f8wM/8AP0+30ajQaNRoNms8nrX/96Lly4wPr6+p7jPtVv8nR5zWtes8cUc0W79pa3vIVsNvu48qv7/HrG6NPpx0984hPk83le+9rX7vZFo9HgtttuI5PJ8NnPfvZrusYRI66XkbAy4nnN7bffzp133rlnezJKpRK/+Zu/yUMPPUQ+n+c3f/M3H1dnZWWFt73tbZRKJTKZDNVqddek8GST7BNRr9dxHIcjR4487rtjx46RJAmrq6t7yufm5vZ8LhaLALv+A69+9at5y1vewvvf/34qlQpvfOMb+djHPobv+0/alisT62Pbous6CwsLj5t4Z2ZmHudvUSwWr8uP4a677uLOO+8knU5TKBSoVqv8u3/374An78crbTh48ODjvrtW2WN5wxveQD6f32Oe+fjHP86tt97K4cOHgaGvkhCCX/iFX6Bare7Z3ve+9wFDU9vVPNVv8nR57PHy+TwAs7Oz1yy/+jzXM0afTj9euHCBbrfL2NjY4/pjMBg8ri9GjHimGfmsjBjxGP7mb/4GGD7819bWdrUUMNSCvPa1r6XVavHzP//zHD16lHQ6zfr6Om9729tIkuSb0sYnCrcWO87BkiTxyU9+krvvvpu/+Iu/4G/+5m/4sR/7MX7t136Nu+++e8+b9DeyHU/EpUuXeM1rXsPRo0f5r//1vzI7O4uu6/zVX/0V/+2//bdvaD8ahsGb3vQm/uzP/owPf/jD1Go17rrrLn75l395t86V8//cz/0cr3/96695nMdO6F9rXzwRT3S8pzrPN2KMJknC2NgYf/iHf3jN76vV6tM+5ogRT4eRsDJixFX89V//Nb/zO7/Du9/9bv7wD/+QH/mRH+HLX/7ybmjpww8/zPnz5/n93/99fviHf3h3v2uZl643K2m1WiWVSnHu3LnHfXf27FlkWX7c2/T18tKXvpSXvvSl/Mf/+B/5oz/6I/7Fv/gX/PEf/zE//uM/fs368/PzAJw7d26PSSoIAhYXF59SM3W9/MVf/AW+7/OpT31qjwbheswJV9p48eLFx313rbJr8da3vpXf//3f5zOf+QxnzpxBCLEn4uvKtWua9oxd8zeL6x2jT6cfDxw4wN/93d/xile8Yo+JacSIbxYjM9CIETt0Oh1+/Md/nNtvv51f/uVf5nd+53e477779rxxX3mrvfptWQjBb/zGbzzueOl0eve4T4aiKLzuda/jz//8z/eEoNZqNf7oj/6IV77yleRyuad1Le12+3Fv9LfeeivAk5qC7rzzTnRd5zd/8zf37P8//+f/pNvt8l3f9V1Pqx1PxLX6sdvt8rGPfewp952amuLEiRP8wR/8AYPBYLf8c5/73G5o71Nx5513UiqV+PjHP87HP/5xbr/99j3JA8fGxrjjjjv46Ec/yubm5uP2v56Q5GeL6x2jT6cff+AHfoA4jvn//r//73Hni6LoKcf4iBFfLyPNyogRO/ybf/NvaDab/N3f/R2KovCGN7yBH//xH+eXfumXeOMb38gtt9zC0aNHOXDgAD/3cz/H+vo6uVyOP/3TP72mX8Jtt90GwM/8zM/w+te/HkVR+Gf/7J9d89y/9Eu/xKc//Wle+cpX8lM/9VOoqspHP/pRfN+/7rwlV/P7v//7fPjDH+bNb34zBw4coN/v89u//dvkcjn+yT/5J0+4X7Va5b3vfS/vf//7ecMb3sD3fM/3cO7cOT784Q/z4he/eI8z7dfD6173OnRd55/+03/K29/+dgaDAb/927/N2NjYNYWDx/LLv/zLvPGNb+QVr3gFP/qjP0q73eaDH/wgJ06c2DPxPhGapvG93/u9/PEf/zG2bfNf/st/eVydD33oQ7zyla/k5MmT/MRP/AQLCwvUajW+9KUvsba2xoMPPvg1Xfs3mqczRq+3H1/96lfz9re/nQ984AM88MADvO51r0PTNC5cuMAnPvEJfuM3foPv+77v+2Ze5ohvNZ6FCKQRI77hXAknfaLQ3ceGLv/5n/+5AMSv/dqv7anX6/XE/Py8uOWWW0QQBEIIIU6fPi3uvPNOkclkRKVSET/xEz8hHnzwwceFQkdRJH76p39aVKtVIUnSnvBUHhNOK4QQ9913n3j9618vMpmMSKVS4tu//dvFF7/4xeu6rs9+9rMCEJ/97Gd3j/WDP/iDYm5uThiGIcbGxsR3f/d3i3vuuee6+u+DH/ygOHr0qNA0TYyPj4uf/MmfFO12e0+dV7/61eKmm2563L4/8iM/8rjw4WvxqU99Stx8883CNE2xb98+8Z//838Wv/u7v/u4MNwn4o//+I/F0aNHhWEY4sSJE+JTn/qUeMtb3iKOHj26p961+loIIT796U8LQEiSJFZXV695jkuXLokf/uEfFhMTE0LTNDE9PS2++7u/W3zyk5/crXO9v8kT8UShy+94xzv2lF0Zs7/6q796zfN84hOf2C273jEqxPX3oxBC/I//8T/EbbfdJizLEtlsVpw8eVK8+93vFhsbG096jSNGfL2M1gYaMWLE84Zbb72VarX6lCHqI56cUT+OuNEY+ayMGDHiOUcYhkRRtKfsH/7hH3jwwQe54447np1GPQcZ9eOI5wojzcqIESOecywtLXHnnXfyQz/0Q0xNTXH27Fn++3//7+TzeR555BHK5fKz3cTnBKN+HPFcYeRgO2LEiOccxWKR2267jd/5nd+hXq+TTqf5ru/6Lv7Tf/pPown2aTDqxxHPFZ5VzcqHPvQhfvVXf5WtrS1uueUWfuu3fovbb7/92WrOiBEjRowYMeIG5FnzWfn4xz/Ou971Lt73vvdx3333ccstt/D6179+lLZ5xIgRI0aMGLGHZ02z8pKXvIQXv/jFfPCDHwSG6ZxnZ2f56Z/+ad7znvc8G00aMWLEiBEjRtyAPCs+K0EQcO+99/Le9753t0yWZe68806+9KUvPa6+7/t7sm4mSUKr1aJcLl93SvMRI0aMGDFixDcfIQT9fp+pqak9q7k/HZ4VYaXRaBDHMePj43vKx8fHOXv27OPqf+ADH+D973//N6t5I0aMGDFixIhnmNXVVWZmZr6mfZ8T0UDvfe97ede73rX7udvtMjc3x+lLq2SzT2/NlCdipKAZceORwFXj8uloER+7sm4cxwCoqookSQghkCSJOI6JwpAgCEhn0kiSTJIkxHGEbdt4rotpWaRMAy8IUGUFSVaQJJl2u4VpmYSBTxgFBGHA3V/+EpIGnXYNQ00Y9BpYpkar20WWdWzXwTI01tfqzM4exA8Fc3NzbG2v4vp9TFVjrFSl0+3Q7Q8oVycxjAy6mSGfK+P6A/q9PrbtMDM5TaGQ4eGH7kfTJNrdJidO3sLs3H7arRbdXp0oGdDv95iZOciLbr2d5aUlCoUiFy5copAvEYWCQr6ILMtIckJ/0AYiatubjI9PMDtzkF7HpVwep1ZfZ3VtjbnZOS4tnqLT2WB5eZF2vU86m+emW48DMvfeez+vec3rqJbHKRTLrK9uUGuuk81ncJ2QKApJRIBpGkxPHSBlluk0mhw+chhNlTh16gHq26u02jUa9XVkRSKOdYIwwdRTIGQKxTyqJpCVmGazxc0338bpM2fQ9RSzc/uZnJrl1Olz3HLLC5BjiYMHDqFqGrIESPLuWHoqLwCRSChAHAtkFRzXJpESFpcuo2kqQeDj+wFB4GMYBhsbGywsLNDpdshlc0yMTRB4PvVGkzCKGPQH7F/Yz8z0NLKiIaGyZ5A/A1x9Sc/Ec/2b4SjxXJh/vp5+6Pd7nFyYJZvNfs3HeFaElUqlgqIo1Gq1PeW1Wo2JiYnH1TcMA8MwHleezeae9gJvT8RzYbCM+FYjecLn+FMJLkmSXHMiunqRuytCSxRFxHGMosgoylCY6XY7KIqCoqrIskyChOO4pFIppESiWCrQ7/cpFotsbq7T7XXQdZVXvOJlpHIGDz54Lxurl5FlBctMc2Jmls/f9SUc12b//DSFQp5cpsixYy+gUM7zl/9vmTDwyFsGgecgkoTp6Vle/opvx/YC/vELd2Ok8xQKRYIgpNFscejwIY4ePoimy/R7LeRViOKIXK5AIV9ic0ulN9gmDCM0TaM/sKlUxuj1erRabWRJJZ/Pc+TYAeqNBufPn8I0FZaWL4Ik2NoOCcOI/fNH0Aw4dvwgcRLQGzSxLI1GyycWAX4cMFksUCgUaLXaFPJ5LNPi6NFjnDt3EVlRkGSYmp6gttViMOgTRjC/f46L5xd5+UsWOHrgMIoi2K6vc3nxLO12jcmpCkGUp1weY2W5jqYrLOw/wMrKMkHoUG81GBsvMTE5RS5XZW42otPtI8kaiqoxNT2NYRqETohhmhi6DoBuGNcUVq7+e1egRUZKdsaLkhBEPrEIWV69zPh4lVQqTb/VZf/8AtlcDlU1GRuboVAYJwxCTDPP1GSJ2bkQVR0KuA8+dD+TE2Pk83kkyXzGTfkjYeUbwzPRD1/Pb/2sRAPpus5tt93GZz7zmd2yJEn4zGc+w8te9rJno0kjRtzQCCF2t+vhykPhioblyuRzRYi5olXxfR9FUdA0lWazRafTIQgCarUamqZhGga2bdNoNEinUkRRRLlcQlUUFFVBliUy2TTTM5MkIuKhh+/jnnu/jCQJdF1nfn4/mWwOWVIp5AtMTU6Ry+bwXY92u8W5s2dwBn1e9tKXks1kGNgOFy8tsry8jmVmuP+BB1FklXQqxdhYiaPHDpMvZDEtg0azzsXLF3CcAesba1SqJXq9DrXaJtlsjkwmx+ZmjUuXLvLgg/eztr7K+fPniOOIEyeOs1Vbp92ps7a+zMbGMrbdZnnpPIN+kyRysPtNet06G+uL9Lt1up0areYmhUIGVQXHG4AsyOYzTE9Pk8sU6HVswiBBV00a200810OVFRCC9fU1BAlJElNvbCNEgmWaOI6NaRi49oDLl87R77cJY5/tepNCaYLNzTa+H5MkEvVGg3Q2Rc/uUalWiJOYYrGCLKlEkWD//gXuuec+trcbnDhxEkVWaLVbCCGQFRlV055y7IRhSBzHSBJIJMRxiCCi2+uysbnK2XOnGB+v0u12cB2Xm46fZHZuH7puccstt6KqOtXqBJPjk7QaXTRVI5XKoOsW1eo4R4/eRDqTRTzDGpURz2+eNTPQu971Ln7kR36EF73oRdx+++38+q//OrZt86M/+qPPVpNGjHje8URvMo8tlmWZYrGA47iEQUC1WkHXNNY7HTRNY2pqCl3XCcMQWZGJowRN07BMkzhOsba+zOXLF+l0WoTCo9drUykUaTT6HD18mPsffAABKLLCWGWC9VQdWY4Igg533fX33PEd30Y6leXSRh1F0tANlWarx4tf/FJOPXKKdquO3RvjwQfrbGxsUC7n0TSZS5cvEAY2SRIgRMji0gU03UIkUKkWmJqa4tz5U9x88600m5vIKJw7P0yPkMtb9Hptzp8/SzaXwdAlluobaIaE6/qk01kkKUTTwPcHtDvrxJGL6/SQFUEQuNhOH8cBx7NZW1un02mTJCGra5dYXb3EwLbRdZ2B18UNc3RaNi984QtIhMdg0COTTdNqtZCEoFwsIESE7zsoisoLXvASwlCitukwMzuO77tkMhaVSolLl89x7NhBkASmaTI/P08ul2NxeYWbb74Z3/eJo4RCvkgpVyYIAmRZRtc1NE1/ijEDSRIjS5AIQSJCuu0uzXaTWm2TmBhJhlKpTL/nEoYJqqJTyJkEYUyhUMQ0TBBgD3zW17eYnZ1Ekoa/fyadRZV1JJRviEbhRtRS3Ihteq7xrAkrb33rW6nX6/z7f//v2dra4tZbb+Wv//qvH+d0O2LEtzRPpEgRPKWpX5blJ9TIxPFQw6Lr+s5btIQQYFkWQRDguh7NZoswDNna2mJiYnxoFlIUEBKyJhMGIRcuXeTMmYdIpQ16vTaZbIpcqQJiin67R9v1WFpaJpVKUx6rMOj3cQY+42NVeoMeyAqFQhpVAVXWOXDgKIGfkEplSRI4cPAQpmUSxy7dVg1JU0mSgCD0OHX6QU7edJzllW0q1SKyIhifKFOvb9Fqdti/MIfttLAsjaXlC5SLFSbGxnFcj82NLdLpLBOTU/T6bWRF8NCDD2DbTSanqgwGAzKZFEHg4Ps2tjOgXM5z8NB+NjdrfOGuf0TRY4RIOHrsGJVqiUG3QxB0cZwO9cYikiRQVQVFGBimiqapVKoVEpGQJAnV6hieC56d4HkuYTBgbWUZz3ORFAXQaTUHZLJVLi+eplQqoBkS7W6Lg4cPsr6xSS6Xw3VqaOop5ubnOXRogcWlVfq2R78/QFU1qsUySZIgSRJhGCHLw9/xWoKsEII4TvB9D13XkWXodepEccTFi+dYW1+jMlZhfHISz/NoNrtMTYYkiYSsKliWgSJLyLJCEIRMTo3j2C6JEEhCQlZkSoUSAhlJkhFiNJGPuD6eVQfbd77znbzzne985g98PZryx9wgj9Wuj26gEc8+jx2kV4qk4Z/XIbBcMf/AFT+VvSalIAh2fVaSJEFRFHRdYWV1EQmZmZk5DN1keXmNubmZoWlAVjFMjfXaCq3WNn2nz4FD+1lZW6bRbGFYEo3tbXRFo1ytcO7sWaIkIt5KmJudpVQpc9cX72J2fo52p8fMTJav3vNl8rksrY7N2MQUvhcyPT3L0tIqlqWRTmVodxrM7Jul2eyQzRTwpAGLy4tsba1TLORRNQWEwvZ2jXKpyvLyZWQlpte1UWST/fMF+v0eqiphmCpra0tousrhQ0dpNlu4ro0QCacefphjRw+T0nWcQY+23qBe77C6LhHFCZtbNWQVNE2n3elTsR2ESBBSTLvbIJfLEMUuA7vHzOw0jfomQSyzf6HA5uYqlUqJanWcVrNLGCR02g7N3gy9dp0Ll88Ti5hsOsfa+haanqfd6TI5MUUYurRaTXp9iUqlQhBEKJpFFEYE0YD1zWVcNyCMBBIRrtNjamqWfD6H7wWYho6iKESx2BESpEefc8lOGTLdTpe19RUymTTplEGtts7k5BS9bgfHscnlDqDIKp12l9nZObLZHEmSoKoqQoAfROi6TBBG6IqKuSPACCEhCQkkBUmASED6FllK9+vx97gR5qIbYQXB5+1QkZ9kGzHiuYG0u0k7b6ISMgjpugTyq/1THhVYhg8/SZJQlKFZQJalYYRKEqPrGn7gE0U+qXSKwcBm//79jI9NEIYRiYix3QGnzz5Eo73M6uZ5Bk6XtfUt0tkCMzMzLF24TD6dwR70SUSMnjJxfR8/jOj0+rS6XfYtHGFs7AAnbnoZBxZuJmVl2dxaZTBos7a6Qj6fIxEJjWaTRqtDvlTFCRIazQ7V6iSWUaBSmWZ9Y5MkiVlaXgGhMzd7mCiMiZMQw1CxBx5xaKApeRQphe9EbG/V6bRaICIKuTR2v83S5YtomkkUJGioJF5A4gUYikY+kwVJoj3ooxgydtDDCzzW12uYehYRS2iqztLyGpqeRjfSeIFAoAMmxdIUnhfQbGxRrRbpdboU81UKuQrTk7OMjRU5u3Q/y7ULaKZCr9tnc7OGokn4wmb2wARHjxxFJDHHjh0ml0/R6jRRNB3TzKJoCmHs4Hk9XLfH/NwECJ8L507Rbm4RRQG5/NBHRCChaSqKIg8F1wRkAYosoUgyIhLYvT52v8vS4jmazRqWZSHLMqZlceTIMVRVp9PpUipVKOTz9AYd+nafZquNH4QkYjhmNU1HllU0VUWRZRRJQkpAETIKw8/PdCTQiOcvo7l7xIjnMVdU/YHvE4YhkjQ0ASEEgR+SxMOID03V0XWTJBakrAzTU/NMTc4wNTVFHMfEcUiz1aDZbBB4fc6ffYgo6JO2VExd4sypB+i16wSejWHorK+v0+8P2N6uo6oGppnGstLYtk+z2ec7vuM7KRTGUeQ0llVibvYo2cwYYZhQqeQ5e/YRzp8/Ta/f4uChBXr9Lul0ikKhwB13fAcveclLOHjwINl0hjAICH0f13EwDJ1MJo0sw2DQR9c1KpUyc3OzeJ43NJu4LrlcjlKpRBzHLC0tsbW1Sa/bZdAfEMYxnh+wsbnFdqNBs9miVCzS7/WHWqgoImVZhGFAKpWiUMijaSq2bSOEYHx8krHqOLKsEYYx/X6fKByG7ToDh0a9SRJJZNNFFNlgYmyaVnObe+/7Mtvbm9iOTaVSQhBhDzrkMjqFvE7gd9neXGbf7DRx6DNRrVAplhmrTLKyvIHrRaiqQSqVwzAyjI9Ps7a6ycWLF4miCFmWkBAEQbhjBgQkSBJBHA0FlyQRjI+PcezoMcbHx6nXayQiZLu+iaarxHGEaZp4XkjKypJKZ9jc3KK+vc2DDz2IbTt4nkccJ6iqjK7LKKpMnLDjUHuVcHIDvK2PeO7wnMizMmLEiKfHUMUv7Qoruq7T7fXQNA1NUxGJxDCRpIQkqUhSgoRMEERYlkmlMk6SCGRZJooiattbRHFALpfG913yOYMg9PDdiE6nSaU0xubWBnYfCvkCmqqQJIAko5sqvhchKQpHjxxlaXEJ3Upx8dIlXvnKb2dtfYOpqXGqY7M0Wi3q9Q0s0wIRsra6jGPfTMrSaXdizp09S8oocGD/UVRZ5eCBA1xe9DA0jSDw+OIX/pHpmUlUTcb3PZIkoVLOYxoqnXabpcYWiirheR7j42OkUhZnz57Hth10TSabzVKrdUkkhQiIgpD19XVsNySMQkQCpmGSTWeIwwgjqzEzOUmSxJimSb/fJ58voigKruuTJIL19XUMwyCdSjE/N0/KLOK5ATIa0xMzbGyusXTpIoE3wPNtgjDEcW06nRaxEDh2i023Ri6rkMsaaApMjY0TeB5kBJKQ0bQM1fIUt9z8QmRZw+7FTE5OE4QR6XQaVVWI4pAojFFVDXacW5NkqFWJI0EUJXieh5WyyGRNllYusr29Radbx7ZdBraH78ekM3kW9h0gTgSe53P8+HE6nQ4iSYiikDiOkGXQNI1Y0dA0BVWRh6YmaagV3LFmjrgOrmWCuRFMQ99sRpqVJ0CIvduIETcaV4ciP3aDvZFAgmG+Ivkqk5CqDm9/RZGxLAvL0vE8h1arjarKOG6fIPRotev4gUMU+Vw4f57LF87jDnogQpYWL1DIpel2GijEmIaK49gUiyV03QAUAj/C9yNSVg7fD4nikM/8/d+iaAm17RUkOcQPbUxTw7a7BL6NECGmqTE+VuEfP/cPuI7N0uXLuAOX9dV17v7S3Tz80ENsbm4iCdA1lW67SatZw7G7pC2dtKVhaDKaKqGrEo7dZ3JyinK5jBCCra0tarUaSRyTSlkEvk8cx4yNT5IrlhGouH5IOp1BBizdIvRCUrrFoNdnvFrF1DVMQ+PcmdMkScL8/D4KhRK97oBUKoum6UiShOe59Htd4ijEMDQ8x6HbbiMDnmszPlakVMwNfV8QKIpCEgcUsmkatXWiaMBYpcB2bZNOq002nWV9ZZ1sJkO71WXf3GEQOrYdYBpZjh29iX7fYXZmjlKxiMQw3fn5C+dpd9pEcUiSCGA4XhRlKLzKMqiKTBTHHDl8hAMHD6AbKkgJ+XwOVZU5f+EiyysrlMtVJiYmcByXQqHI5OQkzWadVMrA913Onz9Hu9Om2WqTJAkJAiEJhATJ9Vkyn5c8/l79Vu2Jp8dIWBkx4jmIEJCIZFdgAR4juCR7hBVFGUYGBeHQBBBGPpAgyQlRHBDFAWE43LZqGyyvXGRl5QK9Xp1szmBufpJ9++Y4duwYhmGytr7OxQuXCINoqMFBwjQtJiYmSKfTdNodtjZrTE5OUSmPMzuzn5tPvoDZ2f2srK6gqBIDp8vm9jKdfo3eoE6nXydJAjzPod1q4rkunuuiazobG1tMTkyyb98CqqLhOR6GptNrd/Bch8XFS6RTJieOH2VirIJIAhr1TXIZk2I+g67KFPJZyuUSIhG4rsvs7OwwrHgwACSmp6bwfY+Z2TnCRNB3PIIoJpfLYxoGhw4eptfuo0gKlVKJ2ekp5mamWVtZptVq0Ot1cV2PKBSUSmNk0jm6nR75fG5HexEgywmXL59DkkPuve+LnLvwIFtbi9j9DkkUDh1U/YhjR4+jKRoyAl1TaXd6rG/WSKfz1LbbqKqFplt0Oj3mZvejqRa5bInNjW16vQGSrBDHCb1eHyTBdn2bRqOO4wxot1vEUYTrOgyFleF4UlWVdDpFFMd4rkcYDfOt1LZqCCG49dabUVWVdrvBmbOnaLbqw6ixRmuYtFMSbG5tsL6xjmFqmJZGf9BHAH4QkCTDkGchCYb/AdLohRCu+JE99fdPVe8bxY2gyRkJKyNGPAe54iSbJMlOqLGLJDH0S5AkpMeEWVz5PDRPuGxsrNEfdImTkDgJiGIf2+0jKQmqJugNthk4dVbWznH2/IOcPvMwYRSQzmSZmJyhUp0kiiWmZ/ezb+EwuXwFL0hwvYg4Hk5E4+MT+F5It9tHkhTq9RYXzi/xipffycteegegMjk5xebWJpIMtmszNz+HSASBH6ApGuPVCSbGp7D7LoEXc/uLXsIrXv5KZqZnWF9bx3VskiRmcmIcVVHptBvEoU/g2qQtk1Zjm3IxRxwNw6Vd10XVhll5V1dXCYKQYqlEKpUik0mzsLDA4tIyzVaXl7ziVaiaied51La2GHR6kAgKuTxJGBOHEVISkzJ1ZqeneMlLXsLC/gUmJ6Yp5EtsbzdwHHfow5GEhKFHr9dieqrK2vpFcnmVL939t2zVLtFp1PFdnyQS6HqKxZV10ulhuvp8rkAQSszNH8G0SqQzFfxIRkga2XyJdrfH+vomlcoY6XSGWm2YR2ZmehoQnDlzmuXlJZrNOqVSCdM0UBSZRCS7yzBcQQjY2Njkri/eRW1ri1OPnGJmZo7x8UkkSeK1d74GRYEo9glDm3a7iaqqbG9vMzE+zvz8HJ1Oi6Xly6iKjJVOkYiEja1t/CAkitkVUobhbGLPub8VeLYFj+cqI5+VESOegwgBkiwNzQVXUuvvPPuvlT/jSsiyqsh0PRfPd1lZ7TA2Vt1ZCyih0+ngeR6ptIqqx3S62ziOQ7PVpVQYwx54FPJVLpw/ix/2kVWDKIZ6o4OsGszOLSBEQKu1CcDx4ws8cuo8G5t1bnvhizly6Cif+8cvcuKmF1Eu56lW5qhUZqhW2wSBwDIzZEyTTX2NQCQoskoSCzrtHi98wYtYW1tjbXWdcinhyOGjiNjHC7YRwqaQq7C2usb05DiB5zA7N8WFC+dZW1vjwMIBMimL2nYLI5Wm3WqTSqWoVitcvrxIp9Nn0HcY9DIcPrzAvv0LoGq0Oz0M02RpeQlJlnjwgQcplYpsbWwyNlYmSQQH9u9H1026to2ZK9LtOBiGSbe7wcT4JI7bodvzcR2bfDYDIiJfsGDZo9d3iZMBYRRSyGfp9l1yuQKNtoNpWLQabXzHZmH/HKDQt0NkxcAw00SxgmakkTUDVTOYny+j6zr75ua5cOkSte1tomjofzSwB1TKJTRNR9N0LCuFrCikNW24Am6yM54YjpFup8vi4iK9fotcrsD99z9ENpul1erykpe+jH3753j4kVP0Bx1UNcTzYiDhzNkzjI1ViaKQU49cRAg4ftMLSaczGJrO0vIqszMzpCwDSZKJoxhJHgrWQyEbvjUcWb4VrvGZ5zktrEhiRzi/Bsm1i79mnkzqv27peOcYT1ZdPFWFEc8jvp7kC0NzjyxLu4nd2p0Ouj6MhpGu6Ne5MgmBpihIgKGrpDIW3a7HI6cfQCIhDAKmJ6cYdBuEvk6712ZjvYWiaCzsO44z8Bj0B8Rxwr6FKoOB4OyZZRShU1u9BOiYVppYxExM7iOdydDu9vECn2wuw9LKItl8jrX1ZS5eHKdcehG5XI711TUkIXHuzHnyhSy12hqRAD8O6LtdGueajE9MctPJoyRJxIMPPsCbv/cQVkbjZa96CXMLFT73j39LOpOiUHTYd2AfjfrWcJ2hbBp9/zwrK4uYqSyaqtNubBB4fYI45ty583T7Ln4gSBQTzciyvd1FM3Ri4bAwN09NBkmT0bMpfN+jXClQLGapVsoEvosiYrbWFjHSKZxezPkzF1GBfXOz3P2VLxGFIaqikklnMVSVVn2dRTUinxZkswVaTYmtrW1SKR0/9NEMCdOSOHP2AcYrY5jaMHT64MIJbNvm1COnmb9pgXSuwKDnEfgxlpWlWChRLlUAmWNHj9Pr9ag36rRaTYqFErpukUlnh5FdEbi2SzqTwfcjwiBEVzUkeegIK8kCWRYocsz6xjq5fB7LypDPVymXp5ibc9nYbNPu2BRyBqVikUZzm063yezcJIVCnmr1xXzxi1/mzNkz7Nu/n7mZWUzJZGl1EUkkFEs5dE0nny8ShcNsyIqs7vG8lZ7HjrgjjcrT57ktrHDtcXwjT/hPllngW0QLOmIPX9uvPnyIS7tCtKIo5PM5hBiGJsuAfFU0kASoikIURnQ6Hc5cOE2lUqTZ3MaydMqFAmvryyioZDJZ0ukyMjUOLhwZZiP1WggiarUN/ECitrXIwYV5uo0m83P7SGLQzQzNdh8/sUhnchSKFbwgpNvtkssVqVbLVKtFDh+Z46FH7uHEicOsri7T7gZAjCyDZaVwPRvd0un0GzSbbTRD4av33M3k5Cxjk1X+8v/9H4SAhf3zKDLMzOzj3nu+zNTUOPVWE0mRGAwGtFpNJienCKIEWVPZt2+GxUvnGB8v0Gx36PZ9Or02mpEll6+wvlmnUswzPTVOt7XN+vIyvtMnn8+zvV3j2E0HcF2fAwf2s7m+gd3vEIcmKVPC83rUt2sU8ym2NpbI5zP0e21WV5fRdQlVlrAHA1y7g2UK6vU6pcI4mpLG1FK0Oy1kRSIhZP+Bac6evUQiCiiyjuN4mGaeuZkD1Na3WVlaYm5+P6/59jsIooT+wMG2bSRJRtM0kgQymSxI0Gxu02y2yWXz9Ps26XSaTCaLbTsIPPr9Po4z1ORMTY4P/Z2ISZKARmOTMHLIFSbYt+8AxeIkkmwwN3sQzxesrCxRqZik0yk2aiGJiPnil74IKBw7epJbX3Abl5Yu4fsOhXyWxcUl7EGPbDbFXV/6HPv27WNmeh/V6jgSkJAgo3y9N9QzzmMFi2+kuepGFmK+mf1wLZ7TwsqIEc95nqGH0zCduoyiSMRxQhRFIASqOrzFr6TK7/f7nHr4EWyvA7GHpetEQcihw0dpNrtsbNbYf+AonW6PjfVttrZqlMslCoUc6xsrrG+soptTTE7sA5FQqY4TBAGr6xsYZppiaQxdy5LJ5JBkgecGjFcnyBdKdDu9nbwoLvff/1VOnVKRZUEiIkxTwba7mKaOY9tYlolmGBi6Tr/XI44kJBQOHzvMI6ceQVdViqUs+VyWL3zuc8SRwB44ZDLZofNxHFMslrBtm0K5ysDxeOTUQ0xOFLFthzwpImKiJMb1Bd3ONnHkYw9kHn5km7RlsO+OVxH4A/KFDEIESHLMWLVAf9DB9fpUxkpsrC8jKzGOH2AHErqWwvMCvvKVLxKGHoau4Hg2uVQKGYl0KkW71SVlZYjiBM8dkCQKYRShmQay7BOHEdl0BpEIwihm4DjkskWiBHTTxPVsVteWKJQLHDh4CKurc9+9DxCELqqqIEsSuq6hhxrZbJZ8Po/jeGxsbDAxMUkmM9SwbG1tYpoW6VQWVVEIwxBVg36/TUJA4A3odFpkc0V838U0VQb9Lp4fEoU+Y2NV+v0+0zPTzMzOcf68Q6u+xeTkFGvra9x224vI5i02NjY4e/4s2XSGwtQUqgbzs3P0Oh0ebj3EgQMHmZyYwTStZ+ZmGPG8ZORgO2LE8wBZlnfffIahqMPJ57FrAkVRRLFYxFQUxssV0qk0CJlLiytMz+zDMLJsbTfRdZNKZYyFhYNYlkWn0+HgwQNYlsHE+AxJYtLtDteGuXDpEtvbNdbX10EeajXq23XarTaBH7C+vkEmlaZcKiPJEvfc+yVUVdDrNen3W3jeAMNUSKcNkjhkfm4e13FZW13DNAwkJHrdLstLyzTrWxgaTEyU2Nxc5sK50xiGjjNwUCRtGCYbJxQLJRRFxfMCet0eS0vLdLsDTDNNqVJBVTUGgwFRHBMFIZ7r4HsukHDbC17I2FiV5aUlLNNgZfkSKUslCh22aqvEic/4ZAVFl0nlUrR7HarjFSQSNjZXQIqIYhfdkHe0FDFh4O+EjUsEYYKQVPwwxgl8FF0FWWZjcxNn4NBpd1F2onlMy6LTHbC6WcOyMpw4cZJWs4Esxdx7zxc5deo+bKdFIjyWli/SbG3jBQ69fodWu4GuK5RLRSanqszMTJLLpTEMlTgOSKctcrkspVKRdCaFpil0Ok2C0GVlZRFZkdA1hU67hW136XWbZLMGpqmysb6CKss0GnWCIAAk9u3bx9jY2DBU23dotbbJpAwG/Taea4NI8H0Xu99n//w84+Pj5PO5Hf+Y7o6z71VOt6MEtyOuYqRZeQZ4WuqwUYKfEddg7/o90p7yKwsSPtEKylfqDU1Aw0UJFVnCdV02NzcplUpks1kURUHTNG46fpzzckSz0aI6MUYQxqyvbzE3d4gjx27CHgxoterUalssLBzkBS94AX/xF/+H06dPc/z4cdLpIplUkfvv3eLwgWnanQZ+GHFo3yEM3SKfHyOTyXHvfV8hl02zsb7OFz7/jziORy6XpVCycAY98rk02VyazY11Wo06nU6LamUCx/FIoohsOg2JoN1sMjY2jRDQ7/fI5dLUaqv0Oh0OHzrCoQO3srWxied5DNoNHFvn0rlzjI+NYdsuXhBjGCbZXJH19Q5CErRaLvnsGGHQJ3B6ZC0DyQjQNQVVlqmUqmyur2OYEoWched1mZieod936HZbeN5wMcViKYeRMtms1YbrNUmCXr+FblqIWEaWBaoq4/surWbI2HgVLwjwwi5+EGOZGTQrwkxnMB0Hx/YggYyVJp8rsbq2zv6FYyANo6WQBOMTYwwGXVRdodOpk4iYSrVIt9tEUWRsp4ciKywvn0fVZDTNIJvJIYTAdfsEvsNWrUYmk6VYzA0XkVSVnbw6Dn7gYA/6bG7GjE+Ms7S8TrFYZHpqilptFdv2KZZyNFt1wsBleekSMzMzjE1N0diucenyRUzT4K67PsPc3CzZTAYRB6RTBs7AJgxd1ta6lMolCsUyDzzwIPfeew+33Hwr5XIVXR+u2KzK15JWnlsPy0dfIJ7ddlwPT9TGG+UaRsLKN5PnwIAd8ewiyzJxHKOqytN+OCiKsrv/MKV6TLVaQQjwPG/3+yiKsTJ5UBRKpSqGlWJxeZW7vnAX6XSe8bEqYeQQJyGVSolUKs13fMed/PVf/yWqYhGFcGDhAN3ONuOTRer1LVTVRAiFc+cus7CgE4U+lqlhGCny2RSbmzX63Q6BpxAFCmOVMrqhDVcmlmUG/T6moRNHIa7toMoK9mBAebaCphhEQYhlZWg3W6ystPD9AblsilzWYH1jiUMH99EfDFg/fQlFHq6Dk0qlWFtbpzw2jizJNBtdBgObYrGIbbs0m226XQdZ0gnDBFNPmJ+dxR70MXQdRVa4+cRROt0NOt0GK8uLGEaaJBYomk65UsUPPaoTk9TrdQqlIuMTY5w5cwokBVXRMS2VONGI46HAZNsuA9cmXywhEDi+h9eMkGSIY4Ghm5QLZTrtPkEQoigqi8tLpAun8Pw+SWBTKuXx/T5CxDTrNQzDYmxihlOnzgLg+z6KCv1BizgJ2dzY4sCBQyRJwmDg4PsBU1PTNFsbVKpFjIJJvV4bCj6Jy/LyZbK5LN1Om/m5ffTabVy7x4ULpym16vh+QpIotJtdcvksg06DhiqhKyAin2oxy+LSRUqlAoFvk8umsAcOS0uXSFkposBndXWFUrlEJp9HiITx8QpJEtNqtYiihEq5jK6qKIq6swjiTvj9DSCrfC2+G6MX0q+fkbAyYsQNwpWEbkmSEIZDU86V8uthN4QZaLfbGIaBZVlsb9fJZDK7xx/YDrqVJYgFbhATJ3D08GFc2yMME1xvwPlLj5BOZ4njAE1VGatO8qY3fj/1Ro0v3PUlcvkikjQMm04SkGQd2w4RaPzjP34WQUy5XMCyDObn93HkyAIry4scOnSChx95iGw2jUDQbG6TSlk0mg0URSL0E3w/JAxDwiDEcRwMPUXKylDbatC3FSYmy4xXi6hazNlzD6PKGlEg7VzHEeLIp5jLoKkq8/P7OH7yZlrdPhubNdzAo97a3tVAdLsdctkyuqZjGioXL56nVCxz9MgRVCVmbXWFmdkiYdQnnanS6QwdWTPpLPlCiU6vTRQHOF5Ivb7I/PwMmi4DEapuoOsqtuOiasowUR8ySSwhUJFVmZgEL/SHv1sskBOZ5cUlDCNFLl/GTKURsoakJKxtLFPJp1ElWFtb5dZbbyGKE+r1JvsPHOPmm29mbW2VMPIJQwdJjsjl02Syszh2h5XVNXwv4NChwzRbGyiKypmzD3BgIaLbaXPu3COMjRVpd1qARLEwhuv4lMp51taWiZOQdCbF5laDcmmKublZaptrKDIolOg0aigiwlAEUuwS+jKuk8I0pijmimxtbuO7LoVcjpuOH6Pd7VKplFFVjTNnztBstJmf308+X8R2bC6sbXLowAJWKo2yE9o8et/71uX567MinmJ7FnlsM26AJo14lpEkaTehmyzLuK5LEAR7Vk6Gvam6r/58JYvtcCXdhF63R5IkKIpKpVLB9zwWFxfZ3NxAlmU63QGV6jhhBIqss7K8SsqySFsGUeBRLhdQVUiSECEiSIZrykxOznDT8ZOcOvUIYRzSaLZQNA2BRKk8xotf/DJe+9rXcujQATrdNlu1Dc6dP809936ZYinH4SMHCYKAW255AeVShSiK8X2flGXhuT7p9HCFY103SKXSeJ4HO8nuZmanSWKBaVhD/xiRkC9kMAyFQiFNKqWRyaRJkoRCoUipVMIwTWpbWyxeuszGxjrdXpMgdJiZHUMzBELy8fwushwxPT2BqigkcUyr2aTX7eC6DrqhUygUiaOEXC7P1NQ0tm1z/sJFLlxcpFZvEcXDtXDq9Tq6rhGEHumUiaoNH7GyMlzQT1ZlDNMkSRL8IBj+3ooEEliWSRiFlCtl5ubm6PV6aKqKqijUttaolgskcYBhqkyMVXFsh0K+hO+FfPGuuzEMg9mZGTRVHjrDDjpsba3S67dIREC/38IP+mzVVuj1m6yuLbK8eom1tUUS4SNJEV/56t0cOXKIra0aN504gZXK4PkeU1MTHDg4z6WLZ5GIuXTxHLqmcOstJxh022xvrGN3OxSyaRQpxtAgDocmn7NnT2GaGiduOs74+BiJEJimxfTUNJubNaampmi1WnR7neFCmYFHPpdlanKcpeUVoihCIEierrn9692u89jStc73jeIZbPdTXgePKXuWed5qVp4yl8mzxGPPfa22jDSG36pIXBkRYTjULmSz2Z1w1GSYwItHBZQrmhcYmo9keRgJNLAHO2YgsWv60Q2DSrVMv9+j3+8SRQH2YEAcCCRVY2bmIN1+D1WFZqdLKp1lvJxifeUccjxA03VUI4MfJOyfn6Ne26K+vU6pbBFLKpJu4IQeqqEwXVhgfHyGVrtHkgT4QYjnB2SzOaIopFzM02lvIxFQKWWpVCv0en1EnOD6IagGjuOQSmUpV8okccLy+iLFQoFKJYPv97n9xS/C8/u4Xp9ytUB9u45haeSyOcbKaaIoJpsvMSEmuOer91Idm0AkUN/uk8tbKKo6NKd4LoGboOsqYeAzMV6hUq2SSll02gGdXovtuo2q6KSMNJu1LWzHwfYcZFWn2+szcAOmJqap17YQcYyu6egpBRHFyLKCruuQxLiBi54xUSWJMHFIRIQEpFMGQRTiOANKYyUmqpPcduvtPHjfI2xtNSjn0/S7Te7++7/ipuMLLJ3bQFENwjhFmEjYtk8qpbC5vs7kxBj5bJ4v3HU/hqGRyZikVZnVlUUUEdLutYgSh2y2gm7m8MOIi5ceYtDvMTZWodvdZntb48DBGTQrIa9ZVCer+NGAixcfIpdJk4gB+YJBvmhg5dIUKlVOnzmLoWmcPHGUQjEPGqAItpbPAToXiwVueeHLGJ+aYW1tE80qosoyjr3K9naHhYVDbDeaNDsdFMOgPFalVCnhOD79/gDTHIZIP6276et40Isrt+ITPIwfe+yrq32j5/Ynu66n2+7Hfb/nYI8xcUnXqvTN43mpWZGeYnvWuOEaNOJG4erFB4UQZDJpSqUimqY9oXPto9qUoYo8iYepSJvNFocOHaRcHiYJk2WFbDZDksSkUilM08C2e8iyQrFYwnECyuUxFNXAcQOiWCKXLQASigSnTz/IF7/4OR568B58z6bbbvPyl7yc40eOIRJYvLxEIZfD91021lcRiaBYLLJ/336SOOHY0SMghhExly9fJAhcHnn4AZrNLQxTwfMGJDvrE/X7fVRVZ2JqGkXV2NzcotvroBsqrjug2aiRhAGdZoNeu0O5UCSfzaFrMlHkEQQOpXKBbC5DEAYoisLExATtdpNuq4OMjDdwSIKIV7/qVVRLZTRFQZUhl8vy2tfdyezsFEePHmJ2boYTJ29GUXRsJyCXzXPyppNUKxWiMCQKI3zfY31tk0dOnWFjfQvLShN4IaEf0ml38F0Xx3HwfB9NMwijCMdxUFSJfCGNJMdAyES1yOR4hVRKxzC04Zo5MlgpC1lWmJycIo4iVlaWaTbrBKFHp9NicXGR8fEqjtPnwvmzfP7z/8i9936F9bVV6ts1UlYGdxAwVp4kDiEKEjqtPq1Wj0p5kjiW6Hab6LpEq1UHBKtry8RJQL2+ycbWGt1ej4FtAxLpTBpNUxkM+pw6fYrtehPXCzh+4jhWWuOBh+7l8tIlSuUqkqySJBGDfpv777+H++67Dy/wSWeyDGybRMDx4yeYGJ+k1x0wMT6B67okIsEPA4QkMzs7g2WZRFH0Nfl9PNVc8PU8jp9o393P38Bn+zPd7mseTzzmukYOtiNGjIBHI4EkSULT9GFyN1neFWCA3e/jON7jy6LIEqGI6XZ7qIqCqum7KywPzUSCXC7HxsYG7XZ7Z4XhiGwuQ7fbo9+3mZmexfddoiimVMqzub5EImSy2SJHj57g/OVl7vril8iaZdJmmvHJApVCGUXIrC2v4Dg+1XyZ//dXf84Lb3sBlqXS7TZptws7CynGbG6t4/sOgpgw8pEVlVKpQqfTI5Ox6Nod3G6bVNoijiMURaZSKiEhcF0Hb9Aj9FzsXg9dV9hYXePB+7aYmZ3Ctm1U1cKPItzAR5IU+oM+tu9QrJSIkUGSsAdt1peXEYHL8cNHsNQVTCONqau4rs3a2gqDQY8oDhgfH2N7e0Cr1SKTSlMqFslm87zm2+/kzPkLIKnYg8vIIqFcrbCwbx/nzvYI4xhNVdG1oaOv53h4XkQ6lcXUEzRVQ1dVBoMehq7Qb7c4fPgY6+vbRKFPp9thYNsISWKr2eT221+EH/s4dgMrnaPd6VEdy9FpN9A0FdPSaHcajI9X6Ww2QIqJYg/XHbB8cZ2ZmRn6fRddS6PqOtlsEdd26LRaKJJDr+ujaRq5XAZN0+h0OqTTGRIhkc+VaDVrRGEH3Ujh+0M/J8d2qG1so2kmmVyaRApY2bhM12lSHZ9CNyziWBAlEbKqsLaxiqqnue0Ft9Pt9nj4kQc5fOAwcRDh+wGHDh7i81/4AiIBGRlZkpFlyGSGxxk62T5bd+eIZ5vnpWbluc6VVUhHq5E+nmGfXPHb2NtHz6W+GmYK3atRGZY/ulry1Q62j9WsXBFiYKhhiXe0LJcvXyZfyF+1SJrYPdauky0CWZGIk4hGc3u4KF3k0Wo3kCSJcqWClU5Tro5h2z6SpNFs9Zif308qnaZcLROEAefPn6e53SSfLSDChEqhRLtex+63ue/eu7l86TyWqbGyfJna5jokEa16nVK5iCCh3+9RHavQaG6jaQqmqZNKmeSyWaIwxDR0FHmo137DG17P/Owsr/uOb6dSzHFoYR/752aZmZhAERKNzRr5TI56vcH5Cxc4e+4sa+srbG1vUBkro2oSuiqTsXTGyiVUSUKRJFKGzni5hK5AytJZXr5MsZTHMNXhYn9Jgiwr3HLLrXiex/mLF6ltbROGMblMliQKOXHsMDNT40yMD5OkZTM5dNWg1WyTy2YpFfNIyPQ7No16G1VR8RyXRr2BqWuoikylWKRV22bf7Cye4/DQg/eTJDHpXIZ8uUDHdrCDEFkz2ag1mN+/QKfTob69wblzp/B9h3TGpN9v02zVaTRruJ7N+YtnkJQI2+mQiABZjjEMmX6vztryOZJoQBi6NJrbuJ6NosgEQYBtOywuLlGpjNHvD+j1bbp9l/OXlnC8AEmWabWbWKaFYZpcuHgOocSopoztOaTTWRTZoNMb0BnYZHJ5kCTa3Q5nzp1hfKzKi267DRiu9nzziZNkM1lmpmeQBJiGsTPOJeJ4mI15xHUgvnYT1I0+74yElRHPKa6O+Zd2Vm2VrtJPfnOfaVccXQVxHO+aZa5/92G7hxE1w8XghpOjtCeyZ1hV7NmuFkRA4HkeSRwTRRGZdBoYJiC74rirqsOVhiuVCpVyhWajRT6Xpdttks7o9PotarV1otij1+/SbLWIIkEYCfYtHMZK53C9kKnpGY6dOMbxk8eYnZtnYnKafL6AYVhUyiUQMZKUsL62zPLSZRQFAn+Y7bRSruL7Pv1+H8916fV6O74IaYQQHD58mFd/27ehKjLtVoO0ZRL6Hp1Wk63NDWx7QL/fQZEloijg0qWLpCyLyckJ5ufnyWZzhGHExvo6ge9jDwYU8jmKhSytVp1Dh/azsH8Gw1A5eGCB8WqVQi4HSYhlKESRh+c7zM1N43nDbLCnTp2iXq/T6/WYnpllYnKSfL5Ir9Mnl80xNz1Dr9NicryCpio06/UdJ+A0IhEkUYyuqmRSwzT3KSuFoRmEfkgURAR+iITMoOdg6hbdVhvPdcnnswgR0Wxu0W5t02hu4/ku/V4fVVWxBwN6vS6SLIjjkLW1FTY31tnY3MC2+6RSFkIkJEmEooVoRogs+yTCxbbrVKsWlhXS668Thg7ptInnuSiqTKPRYH19nWazyUMPPYQkyQgkOv0BQlbp2zaNZh3LUnHcHum0yYte8mIkVUXRTfwg5MLFS3iuRzpXIF+ooJtpNCNFtTpBp9Pl4oVL5LN5VFmhWW+yf99+cuks++b30e/28V0f6aoxPFKpjBgJKyOeU1yZv4cp5QVxLIjjhCsT9zfzjeDKG4gkQZIMt+tl+PwdPoDjOKbfH+C6/s617CUMQ3zff9y5QSJJYjqdLs1mi0azyczMNK7n75qJrmighkKdhKaqrK2tUS5XWFtfJQgdGs0Nur1NeoMa2401MlmD2vY2mm6wcOAwqmJwYOEIvf4A23EQMqTyGQ4fP4qs6xw6cpR0OkO73cb3XW6++SSZdAbLNHEGDoVcge2tOqZuMejZ2LaL74eUyxV63QHVygRCSHS7fbZr20gIEAm23ceyzGEGWN8nZZqUygVkVVCv19i/f54wDJmYmECSFOrbDeqb24SOT8a0KOfzzM/MMDs1iaHKpCyNTC6NokqMT05w8NAhquNVFg4tMLNvGiutgxSztLyI49hX/TZ9zp+/yFe++lUqlSob65tsbW5z8fwl4jBkYqyC5w5YW13m4sWL9HsDpETi4MJhdE1nfKyKqkjksxkMXd/xQ4I4TMhni8johJFEs9VDVgwQ4Np9SoUUodejlDcgshkv55ifmeDIwYO0my00TaU6VgFJotVq0253aLfa9HoDklgQhkPzSpwEqBoEgUsceiiyoNuu43ltkshGNxSEiElERKfTJopDVFVFVTUy6TSuO2B6ZppytYLju6RzabL5NIYp025vcN/9X0KSFY4dfyGHj9xKlKh4vsvA6VAdnyKTq2BaOWwnoNPpMz01h2GmCPyQQr5Ip9MFASnLolQoMT8/T6/bQ+ZRsydIj9MujvjWYuSzMuI5xqPahCQZahiCICKVMnaFhm/Wi9iV8wwFlQRNezq3k0Qikl2hZZisLRo+nNH2aFWuRAFdjdjVygh83yebzSKEwLZtNE1DlqDf76Mow1WZVVXeCZ9V6Xa7SIrGkSOHCOMB6xuLbGyuDpPJRX2OHruVQ4cP0Wm2Ma0McSKTzRUoFsssr6/w0OlHkJGwDJ1SMc/ihRXSuSz9XoSMzAMPPLgTBZNiYPfpdgcEfszp0+eQZZ3Aj/DcgO1aE1XVaLU6DAYOmxs1hKwSxdJwMpZl0ukUuUyOWm2L9bVVxqpphJSg6ApTs9Osr60ho5DJ5eh0e8xPzwASlqkTez5SFHHhzGmOHjhAr9tC1TVe/0/ewPLSIvlCDkSClbHwWg6qpjA1NUE6naFeb2LbNvl8HtM0CfwI2+mzurLGxMQUg4GDLgTjlSqLS200ReGlL7mdpctLtJtNCvkCm/UtpuammZ6dRkEhcCOWV5bRVJVMKoNIZXDtAN1UCLyIxa0NgkhCUxVWl5eYnZ2hlDNIWzKFQg5FlqhvbeH7PvagT75UpdPrUq6UeejBU1SrFXzfQ5ZV4hgK+TJB4BLFcPHSEt3ugLHqGDIqmVQWiQRTNZENDU3TaDTaaNpw/agoigjDcGdMChzXRlZkJEUiiAOkOMJze2QrKTy/h207mGaBl7zsO7h8eQXPGTrtpjMFpmeP0BsEQxNfrkSpVGV6YhINCTUjc/DAQTbWNkml0mRSacIwIorjkY/K18COXLfnpe350offksKKxDf4Dfx5MjieWa42aQDSo4G60pN12GN+qOE+Q5VGHEWomoph6DtVBdeY16/ZiitjQAIezTYlHm3PbuPE3n2vhAWK4Vu37/ukUxaaJqPIYvfaruwgkJ7gYSGQuNqsI9FqtZiZniKKHzUBXQlRvjpM+UpwpEgSonCYOC2fy+P7LtlMFpFAGCdIskycCKJ4KMFpO4eojo0xcB1MSydxEuLAxXd65PM5Wo0t1lcX0Ywis1OziDDizKlH0DSwDI2J8Sqf/8LnmZmdQVN0wsDDcV10TaPT63DrzSdYWl4iEQnVahnBcNLpdttMT0+jqlkGgz6WmaLT6WKkupRKZRrNLlEM3XaLMIoxDJ2FhX0cOniAarXMxQvnCUKHdncbK60jyzJnzp9jbW2dl7/iZayvryFpCi+79UVsrK9jmjqmadBoNaiWK+iGTqvTRTGHESrtThtNU0mShCDwmJ6awHYGPPzww5w8cZIoishmMyiKSq/TAxHjewEbG5vE0VDAtAddyqUsuVyGKA6xLJVypYDr2Ozbt4/p+VkuLV3CtgcEgc9g4DI3N8/y6iqZdJY4SYjimGwqx5nlCwRBTL/f4+SJYxQLaXzPJQpDCvkModtDMS0OLuzD8QImp2c5f2mRKIjo9bqk0haKKhPZMZ7nDbPHBj6maZCx0nRbAwq5Kv2Bz+RUifW1OiKJ2Lewj1K1SrvTZmbGJAyTYZ4YLSaVStNsNmg2GkxMT5JEEXEYYmgam7Ua/XYHCYmTNx/l/PmzGFaRY8dOYhoZ0pZKpTpBu9MnV5gglytQrU5h6CmiKCJJYiRNJxaCOIlQVBmRxFipFBNGhW5/gOt6pFNXFjd8eg/VrzdyRRJXhQF/rTzZvleZsx/31VNd6lP4pew+fq4Ie4+p/NhzXn2+J03T8izPa9+yZqCvJaTtmQ59+9bi6uxDj/4rSeIpnyzDfk2QSJAlgSRiNFUiDFw0VUZRh2LG8FiPOde1NpGQiBikHc0GV/12MUhCIAtxVbMe3VeQAAlICaoq0eu08D0HVRYkcUwSh0hCoOzIP1f8aq7OvnBFI3MlqkdRhrdhFEVD4WInTPPxjrViN0Q5CgKajTqSEKQME01RKWTz5DJZdNXAdT0SwA1CIiHhRwIviIgliSAJkA3Bw6fv44F7v4IIPPaNT5CSNUrpHIHrsLF+mfNn74OoR86CjeVzuL06/WaN2Kujig6FjIRvd+i0Wgz6fZBhfXudBJ/5+Sm2tzfQdBnDlBmfzHPi5gXSaYlD++aZHB9H1XUSSUXIKulsDtuxifwAXZHJWAZOv4ttd7j77s9x7vyDIHtYqYQEh1whSxAkeF5CbbtNECeksmk6dpfZA/P4IuLs5Qv0XYdmr08gYOA6qLLEmdOPIJIY27bZ2NhgdXWDRx45j+OFVMcn2arVieMYGchYJook2N7aJG2lmZ6codvpIsNwwlVA1VVkBRr1VcBHNWQikbC5VcP3A2obm8NVod0AgUK5VCEMQ0xDQyQRceAxPznN/tkpinmTjY1LhImN7ds02l0eOX2OeqPGmbNnuHD5MlGc4PsRlp5GRILa1gaqAa12gyiJiOMYxxngOj08p4cmK7gDnygG2ws4f/kysm6hGClOn77EynKNXDaH5/fpdpvYA5skkdjYGPq/iCTGtweYioQaxViyBqFMEqrDbMOBi5R4OL0GmhQzNTHFds2mXF0gRubs+bMkiU+7vcV4tcDK2hKbjRp26BHLAj1jkillUXQNWZGRFQVd0ei1OyRRwvAR8eg9eT3b18PVobpP9/jX2lcWw4n2yrbH3ewZRgwfb8NHlBj+u2d7CilHPMl2pc6zxbesZuUbxQ3oRH3jIO3559HJ+Ek6bRgxkwz9GLiiPZF3VxT2PQ/dMneja65oaZ7Ivn0tbc5VipCrlSw7ehZxzXbLsowsSWiauutcOzTXyEjS0F9AkhUSIaEoOxEN8vDAj/q6CFRVIYqG+2ezWTzPBSQMwwCkPRqV4SKFMt1el26nSy6TxbKGb56GoSJEQhA4uK5Pd9DDtNKk01kkIBEJjWYL1+2jKLCytIjn9DFVKGQnOXf2Aq12l8npGWazRdJWim6nw91bd1MuFkilTBrNbZZWltA0iQvnzzI56eG7grXVDQxT49jxg3z1ni+hSyq1rTqO46PrDplsmm53wIULFwmjmCM3HeOmkyf52P/6fQaeTbfTgDhhvFJg7gU3c+niJcYnxjh8+CDjE1U6rQ0mxsvMTE9SKOisr63RqNUIgqEPTm1zi4OH99HutIZajDCg1++RCEGz1cIwDMYnxkkSgePYNJtN2u0O1Wp1GBKbwNZWjVTaQtN2tHRRQhjFbNcbFEtlkGQ2NutcWryE74ckO8sJbGxtIUkxSeKjqQqSpHHo0CEcz8XzXQqFHEHoMz5W4vzZC/jugGKxwHilRCplMVYp0u10adWaZDIWE9US7W6dQjbN0uIyimwhEpkoAUVTCZOYtdoGlco4Qo3p2m0c1yF2XHw/QhIKqqLiewJ34GBZOrMTBrZtY/sOyBKxFBMnMc1GHbtvMzYxxYULFwhCmyiETCaHPQjo97sMek1MQ6GQG0NVZUSk0GqsUsrr9PsOmxurFIsFZMkkCnzu+erdHDt6nHp9myQSlIoV6o0mUZSwunqZseo0QRCyvLxCGEbMTk9RKpc4f/Yc5WKZJNERiSCbTZHECUkcoyryToLDJ9XBPqN8Ped5AkXqo39+nRdxtfL2us79pEe6KpryygFuULvRt6SwMuLZ54qgEscxcZSg68YT1k3iBFnmcRlbk2QY5nt15MzVIb1PdF5gT+6Sqx8kkryTl+Qqe84eweaq/TzPo1gs0O8PkGUZwzDwfA9DN5BkCVkGP4h21tARXFFkXkniJhj6uui6ShRpSJI0dMDUNcIwQlXVPb4rvV6fTCbD5cuLzEzNDhO3CQnLNIjjiO36FkHgoWoyjusRJzG6ppKyLBIBuiZx4fwinj/A7bdQJIGmmlxeXETICulcDtUw0XSDzZUNoiDA2EkPH0Q+miZRLKRIcMhk0jQadVqNHiKRkKME2x7gewGpbBrb9hBCJopg0HeR0AgDQbc34IFHHqLZb2OYKuvr2+SyaU6cPIZr28zNzHFoYY5HHnmIOLQRUYaD++coV0r0ez00JeGmoyfZ3GyRSuU5f+ESIgrpttq47oB0Oss993yVXC6L63p0Oh0MwyCTyWIYBmEwNEslccLFi5d4+cteyalTp5ianMZ1XMYnxojjmEa3QafdJp/N4bkeM9MzpLM5/CCmVqvRaDaQ5IQgdIkij+p4GXfgMDMzh6arPPzI6eGkMpAoFPKEYcBEtcz6+jq+2yefzyNTIJ/PcegFtzA32eShB+/HGXQ5uH8/nV6Po4cOsb6+jSzrREKi2e2jmmlsb4Bi6Fg5g/mD03huzOZmk/p2iziKkCWZXK6Ermr0ex0ajTb5fI5Ov4usquRzecIwGubxkSTqtRqp9HDVZccOsYwS3XaHOA7IpC2yGZ2ZyXGQErJpnVQqw+rKOtm0jqJbWKaF44SkUgaOa7Nd20BVZDqdJsdP3MLf/d1n0FQDVdZ58IEHOX78Jnq9Af/w2c/y8pe9lIMHDjI5NUkQBKStFJIsI4RENpvZvTfl3cUMn+ShcoNyTZeD67d8D6t/A677sefZFVZuUEbCynOA56Oz1BV838f3w6FTqCxdM6ImjqNHQxi5ojURBIFPOpNGluU9JpOr19G5+vMVPM/D8zxyudyOQ+vuOwayDI7jDderSVkoir6zl9j9Ha4ISEEQYO84Iw7bENPtdUmlLAxDx3EDFNUgilSCMCCdTiMhoahDoUuCYbK0ZChkDSMw1F0h7Mo1JUlCHMfkcjlkWWJiYoJqtYwiqTvClUSv12Vx6QKJCAl8j97A4eaTL2B55SL79y2gqgq6LtHpNkhbGlLsMzUzRbPZIghCgigCWaHVaWPUtjB0lTte9W1EoY9IQra2Vmi2Oug6eJ6DbmSYnZ1mY32LbCZLFPqcP38Oy0rR6fbJpLMIZCRZZWZ2lsWllWGK+CRhq7GB7feIo4D5uSlMXeUlL76V+++9l9rWGlNTU5RLWQwd4sjFcwdcOLfN1NQ0KT3F5tommUwRx7GRRMzU5BRJEhEHIfedvZfJqUkajRb5fB5dNzAMkzgSVMpjLC1eYrw6hjNwKZeqrCytcuvNL0CSZNrtOpqs0qw3cWyHqalp0uk0JILK+Di1xjZTM1UuL15GUVU63R6GqRKEEb4foUgaSAqLy0s4no2qqZSKlR3fh4RcNo08PcHG5jrddsj++SlMXeHypfPk0jluvvk4QehS395GkhW6nT6+O4yq6TkRiqow8JaQZAiTgLGJKo12DV2zmJkd48CBOR64/yGcwYCNjUXSVgZZUuh2OpimSdxp0W330UwNyzCxLBNZgO97WFZqaD4QCrbtUcgVyeYt1lcXkWPw7JhEhBh6mkwqjyrXcTwPw9Q49fDDyIqOlcoRBAkT42NEoYtjD1i8dJkjh45gaAZjlSqWlSabTtNtdzANna985SsU8gUKhQKmZjzqR7Zzbzwu5ftzErH3GqShx9rTUcPvmQOesXY9hht8bvmW9VkZ8ewTRREAmqoS7LzxRlGM43hEUYwkDUOUdcPAMIw9WpQkActK4TguXJX59dFF/YZRMs1mE9d1gUe1Iq7rDhdH28lXciWqKEnioQ+JKhMnAZ5nI5KIqyOPro7eSe/kM7GsFJ7vIckQBB5B4LFd30KWJGx7gKyAaRrDXCqSIEligjDYMTOB6zoEQUAYhjiuu3OuR58cVwsykjTMlaKq6u7DJYpCzl84g+MMaLcbrG+uDp1pRYiugjNos7WxQq/TwB10WFq6iGnqNBoNmu02EzMzWJk0tt3HUCRE5LK6cplTpx+k0Wrg+h5nz50lTiIcp0/asnBdh/379/HmN38PpXIRP/CI4xjTNLEdlzCK8DwfPwhwXI9mq0W92aRaLaFqCWHQ58C+aQpZi/FKiTOnTzG/fx4zZbC5tUGxnCOTs2h36sgKqKqGJKmsrG2CrNJstwmiAFmB9fU1BOB6HumMhe+7FIuFHeEQNje2sG2Hrc0aiqzQ7w0oFooc2H+QXrdHo95k0O8z6PUhEXiOR7FYopAvIEkyA9fh9NkzWJZJrb5FImIc10WWFUBGVjSiUKCoJoPB8LeUZIGqKSiKRL3ZIBEC13OpjFW487WvpVgq8vAjj+B4LqZl0Oo00Q0VK5XC80I6nQFCyNx8y60cP36ChQOHAJVOp0en06Xd6dJotojihGazwfkLp/CDHvP7xqiMpUllZNrdTRLhEIQuju2QJAmqqqEoCmEYDv1yZAkRx2RSGfbN7UeRVJy+S7/bx1A1Jsdn0dQC2cwkjW2Xixc2ePihi2hqDtMo0mr2aLc6xFGISALGxorISoxpqrRbTerb26TTGY4dOYpp6iwszON5Dr7v0GzWmZ+f58tf+TK+7+/Rig7fR4aeZFfu7edD6PLQrM3X7i/wDApuV/fr8KXtxva6HGlWRnzTueLfMVTtChR1OAyFGE7Muj58oAqxkzANCVmSd/dlJwmaLMuoskwYRei6tpuUbaihueIPEu0xE0nSMP25ZRpEcUKykx1TiIROp0sma7G2ukJ/0CWXy5ENYyqVKopyJXX9o9otSZKZmBjH8zw6nTa9Xh9JgmarTiplohkKhjC4fPkyExOTu/4lAEEQEAQ+hmEgKyq6InaEEu0qgWsvV65B04Zrx0jSMCGYIMbzHFzPIZVOIYhRVYkgcAiDgK0tmyAIWFldZHKywt1fPs/EWJUzZ86ip1LMIw/DVQMPWfisr1wijBK26+usb65hWhYTU1NEQQ+ZNEGQkMsZnD79CJubNcIwBCmh3+9jmhaFYp5UNkXf6ZKQsLK+QnW8Qipt4bgDNC0kY1mQeFi6Qi6boTvo0+73KVQqeI5DJmvQateIiEniCEnTabZ7+EFCOpvGC3qsba5hWRYD12F1c42VlRXGxssgCTRNJZfLoigaly4t4zg+5WIBmYROp8PLXvZyGvUmx48e2xF8LTacNaqVcZaWVqht1lhaXGZ6doZUKkUQ+viBR6OxjaTIqJpKt99jYmIcVVeYmZ0bmp26bay0Rc/t0u13yJeKWFYKSVUplCuomort+dzywheysbFOqVKl3W6jGArbzQaqaqBbWYLYoV5r4IXLuG6IrKYYq44ThhG9focoSGjWOiQkaJqFY3e4fHmR+flZDh1ewDLTfOkLd+N5Npo0DD2fnpmh0+9RKBRwBvZQG4jE4YMHCUOXZr2FKumYukI+lycKAlRVozJWpVyZ4tzFS7S6NqYREiUy+VyBnCJjmBq2PSCKAizL4NzZDrfc+iJWVjbRNANDV1hZXWR+bhqRhDTqG+SyJuVSDtd1mZmZGZrHHI/xsQkUWdnRsDyZd8ZzE+lqZ9URT4uRsDLim8De14FHVw9mOFnLyq4wAkPflCja0XI8JuX81X4ckjSs67oucRzvaF/YE1asadqOBuTRyd4wDMIo3s3uGkfxMIQ0m8H3Xbq9DoKYIPDwfG+YlC0IUFV1uILulasSAgkJSZLRNI2t2jrptIkkCTY31ymVxjB0HcMwiKIQSbJ20+lrmsrKygqqqpFOpyiXygjY1bBYlnlNk98V517f94gCbyio+A5xHDE2VmWsWubLX72bjt9iamoSz3PJ5Qp4vo3j9tmuu6TTKWr1Oul8ngSZRmuoWZCTCKffpt/vctOtLyBMJPqOg6yqIMnEMcSRwHFc/FhgGAalUoFGo4FjDygUShiGgaYbaJpKNpfBdgbk81kUTUZSwHUGTJcMRBiQTenEoSCbzdAa9LF9n0MHDvPIQw/ghgNK5SKSBGfPXGBm+gCV8iQbaytcuHSJcqVMTEK93UBSFDa3t5icnqK+vUEmkyGOh+a8amWcSqVCGCZ02l1IAsbHJ4ZOvGMTzM7Oc/fdd3PkyBFmpqZ48IEHqG9vUyyVmJmdJYpj6o06qq6yvLaCLMlkMhls2yNOBH17wPhYhaXlVWRJZuD0kHsx5UoRwzQIQn/oO2SYSFmFTrfD8trajmN2SHdgk05ZxKEPyDS3miCpSLJGJlei0WwxGDikrDzVapUD+w6wsblBvz9A1RIM06DRGqBIWdotB89ZZHpqglwuYW5uP912H6IEz3UhkgmDgCAIUBQZy0yjpCUGgwFJEqIqGoqS4HsucSahP3CIEsHEZIVGp8H03BSSFhEnIb1ej4E3QEGmVMrR7XXo9dvYzoDxiVnW11Ypl6qcPbdEf9BnbGyMfP4Ea8tLaJqK43aJkwBJYpjLxjDodLqUSxU0UyWJh5P682Nevyp9wZWQ5d3/XbP6U/Kct4x9DYzMQN8IniJy9mvdHhcF+7Vsz8pF760ikmQnvFcCIVAkmSQWIIZaDgmQJVBlBVWR9zjWXlEFW5a1q4WQBDi2TbfdIgoDgsBDYlieRPEwn0mc7Co54zAiDiOSKCZJYmxngBAxiiJhGCaTk9Nk0nlAJWUNU8GHfkAUhIidVLVJFAHJcL+ddPZjYxPDDKSSgZkqoOkWiqKgqxqe49DrdOm0OwS+j6aq2H2bXCaDqmhEUYyIYwxNw9CNXW+3R1XGQ2dkxDBEOo5ier3+0AwmSUzOTJLOpLCdAb1uh1IhjyJBFPmcPfsIp049jGkYqJqOJCsIGE5ScYhr9zEtHVlTaHa6aKZJ37VZWVvB9z1K5RJ+GFOuThPLJrKqs7a+iusNkJWEQjGLZshEsY+mK6RSFlEUceDgAcbGKzhej7HxPLLisW//JJpiIJDp9mz6tsO9991Hu9Gk3+ly6eJ51tZWWV1ZxTKzLC2uE0dw+vQZHnjgfi4tXsKLfAbOgCiJdxyhBeVKhTiOMAzryq+MJMX4gYNhaIg4RlUkJibGKBSy2IMuZ848wurqIrlsBlmSUDUVRVXI5nMomkIYh2xubSAQFMslOn2bzXoTSVEJopg4EfT6A9Y3NxEkSESIOEDeGXckIAuJXrvDpUuXyGTSmIaBpqqoioLj2MRxhKpq2IOIVrNPpVJlfGIMz3fp9nu0un2iWKK2vUmUhOxbmNvJwqtS227ieglxIiOrGhIqth2yudlClk1WVjZod3pM71vADiJ6vT6aKlPK5iAZLqaYzxdpt7p0OwMQKo1miyAO6TsDthotBo6P4zmUqhWCOCaIY/woRjF0hAI912ar0UTRVKrj49iOTaO5TX/QJZ21yOQsMlmLMPLZ3t5keWWRi5cuUN/eYmFhP/Nz8+SyOXzXY3pqCk1VEbG4wQ0ST49Hzcc7BU/1HH6K779RmpkbXeMzElaeYST2xtQ/k9vXc+xv9Dgctk08yfZoWxRpuKKqSIYChUiS4Sqr7OQlQKCrCoiEOIx4bL4RRVF2/UYsyyKdSqPKCnEU0mpuc/7cGVqNOro61NgMev2hX8sw/AaEQNc0dE1DiARNU9A0BdcdalFSVoaZ6XkOLBzC0DREnJCyTCQEURDS63YJfB9JJPieA8Toho4kq0Qx5AtViqVJPDeg124j4gjPcRn0ekhCEHg+Ik7IpDMgZPLZ3I4wNQyHlpEI/YgkhiQeOtAOhTmJJB46CLdabdqtYSZaWdXYbtTJ5jNs1dbpdRooCFqNOoamcGBhnjgJcNwBqqpSKpfRNA3T0Ah9lyh06fd71Op1YlVnq9llbaPG5lYNSRoKjpOTU1iZHEGsYKayVMplmo06SRww6Lc5dvQQJ08cRVWGbV5ZXqO2uY0zGNBpt9iubTA3P8n4+DhxoqFoGVo9l1bXxvUCdFVHCmOcbhdNkRAxbG82GXRdZDRUWWbQbxOLEMPUCeMQ13VxXZdCvoBlGMPoFs3AtFLDZGflPEgxigwSCUHgs3//HI4zYOD0KRRz6IZGEPrUtreQFIl8qUA6m6bd65DLZxHSMLdOrVbDdV1UVcP1A6anZ4jiGEWScW0H37ExDYmpiSpRGOIOPEQAKjoTY5PIQnD61CNMjo+Ty2SIQn/48E0iNEWmWKhy9PBNzM3Ok8QhlqWTxAJVsWi1+niuTbOxxflzpzB0mX6vgyLJKEJBlgSqMlwHKAwjdN1ibW2LWEious75pRW6rsfUzCxj5TK1tVVMRUFmqGWcmJhECIV2Z4BpmUhqgu0NsFIZXC/A9Ry63S79gU0mW6De6jBwA/woJlHBT0L8OAJZplgqEcUxfWfAA488iKrLDOw+yytDDUsqnWZxcZmNrdrQ+VnX6fd6FHJ5UpaFqqhc8Z54/ogrj7Kb00l8jTlirmhmniInyteyXX38G5GRGWjEs8DQYTWKIqIowjQVJGmnbMd8Y5kmQTg0vfAY23Ucx8CjWhZZHr7Nu25Cr+8QxzGtdptCoYznuTiOTSaT2dn70UghIQRxHA19QBiuwZPP5/F9f1cYMi2NbrdDLpsbmqvkYfr6MAxxPZva1ipjY2Moqs76+sowJwcRhqHQ7TVIGRqaDqlUhnQ6w+ZmjXQ6QxQGzMxMo6rD1X03NxuMj1dpNfukM2l0XQVJIMQwy6nruliWRRD4NJotspk0Qk7wQp9+t83K+gqqniCImJgcoz/oYeg63W6bdDZNGNi0Wn1a7WG69na7jabpjI1VkCSJYCcTLZI0dPTt2+yfHa7Rsr2xialqyDI0ahtoqkp9q42uy8ShYHZ6ltm5eS5euMjEeJXz51YIfY/V5SUy2TSGalEs5Fi5vI3nLSKSkP379tPv9TFNE0SI4/jksgW265uMjY/T7fZYWVnZSY4Xk8tnAAkhCTrdDrquY1lDDU4cx5TLZS4vLhJGMZlqBUVW6PX6iEQin8+TRBKWobG6tka73SGfL5Av5NnY3CBfKNJqtSioOSJ3mGK+UCjQbDbRdZ1GvUG+VCCbSQ//zhbRDJNSPkcSB5hmhmw6xfZWnbGJCTw3ZGNjEdNMEScSiYBWo4llmiwuLlIul4jiYJhUTpLYqm1BrCPJgrXNLrGIMfUUUdDD7tkoQkWVDNRERYmGjunH9i9w6cISbrMBqoKmp5ATQcZMYVkWvd4A0EDW6LR6KCp4roeVtzh85DDNdpN2t4Vh6UxNTdF3enR7ffKFNIap0+/bBJ6DKoNtNzl1+j5a7TaGaQDDhTujKELRBIoqgRhmvlVkFdvp4jgBR+YPsbS4iCyrOLbH9vYWY2MT6LpBfbtNp9PH0AZUKhXa3Q6lYnl4h8o762w9R0OVr4cnuqzrUn4/T/vkqRgJKyO+6VwJwxs62V7JPTK8UYcfExRVRlGGTriPFVau7HNlsT6QkGVlxxdE4+DBAxhGiihMyOfzdDqdXadeSZLwfR9VVXfCpYf5WsIwRFGUnbwnOp7n4XoOA7tBrbbFxMQ0hm5iGGkUWcN1PfK5NKoiOH/+NLph4jguiQiZnJyi0+lj99tkreEbfzZbIElCet0mYeCRzWbQdBNZhiDwKeQz2La92xZVVYmTGMdxME0Tw9DRNIVOZxihFIsESVUwUzoPnb2Xnt2h3TXZWluilMuQMTKEkU86bTLot0mnNLL5Co1mnWzOpD+Q0XWFMPSxbWfHP0jFcTxy2TS6rJCEMWkjRdpM02m2ECIkDj0219pMjM3Q7TVRZJ1yqcLk+DjLS8tsba7T7daQhEPKypJJqYxVplhf3yAKI6yURSqdZmuzTqVapVQoUMjluXjhPFEUY5oWtdo2R44cIREJURSwurqCqsr4vj9Mzx4P+6VUKmHbNqlUiu3tbZI4RiSCwcBGVRWmJmdoNlps1+oosoqsykSJIBaCfCZDbzBACGh1mniBx+JigyPHjhInMX4QIIRg0O+jadpwQcNum5ShYekat548Tr83w0MPPkC5nEeWJZoxtJpdAj/GMjMoqooQMhIS+xcWCHZC5i9cqCNEhGH+/+z9WYyke3rWi/6+eY45I3KorHnNPQ9uPOhgb2zA7I1A4sbIF0gbYQnJF4gLJCTgwkJCQlwgIyRfIiQ4V+fIGwHH+3CYbHC77e5e3WuoWjVmVWZGZszDN8//c/FFZQ+0e3Cvtt3d9UqhtSojMiIzIjK+93vf5/k9BqZpNI2obbFeL+h0OwRhRBjE2KbF/p5JkdW4nkmn45EkTerypz7xSW4f3+L+vQ+oRE2UxNiqRnfQR5FldN0gjDKQNeqyZNDtUZcVd+7c5WC/z8PHH3A5PWe1moBUotky5TZlPV+gKQYHwxGz6ZyaEtOG0X6HtT9jvfEpK2g5XXw/wFAVdEOhLkoCP2B//xpJUoNQaHk9HGfGbDbdIQlqDN1gb2/EycmXuX/vIa3PDTg7O0PTNOI0QVZUVEVt/t5/TA/KL+tb18tm5WV9CCWuwIcvGpEXzYEQ4sqi3Dh8GgtwUbz4mkxVlVdNiaKArqu7RqRGUV4Iar8+R+dr28sXj1OWBabZUFY7ne7OEVRRFAWGYezEtjpC1KRpiud5VxOaomhWCqZpNk1CVV6F//lBRafbwjINNtstdlmDUHdWWhnPtXnnnS/jtdosV2tu375LFG1ZLRZkacjjD875yZ/8Keo6J45SBoMujx49xrFNDLtFXpTUVYN+b7Xb9LrdpqHKU4IoAQRxHDMY9HciXQlZUTAsgyIpOBufMltM8f01Rd7CdXQ0VYAo6HX6nJ4/ZzafgiywFBNZEoTBmrLMODo8wPcDoihku902CHjTxvd9RJljmR5377zBaNhwTIJojaZpOzeVieddY72ekR8OmUwmXDs6IIkCHEtAJfGpT7/K+fkZi/kJpq5jeR100+Lw6Ii8yCmLHEmSSbMUVVUIw4Dp7AKn5RInMfujIbP5lNu3b3Fy8gRFUXnrzTc5Ox8zHo+J4/jqdYuiiKqukaTmQF2WNRcXEyzToigKarlsmkkctn5AGMV0u10ODg6o64r5YoZt2mw2G1RFpVYFrVaL1XqN67qUosK1LepK4LoOX/3KV7h+/TqDXo/VakGv120mX0XJnTt3QYI0y6lrgWnbjT6lKAjDkG63ja6rGGYjvJZlmfV6haKoINUgahRZZjmfkmcQhCnSWsNemqRpiGXp3HjlNus04OjuDcqkQFcUhASma/HOvXtEYYCoa6qi4Ma1Aw7393YAu4Knz04oy4JbN68zW0ypyClFTbvr0u/1yJMC2zTYG/Q5vnlIXIUsVhN6vTZJmuLaDlXZNLeNXd0lLgKCMGT7wQM++ak/Q6szZLFYMhrts1zOSZKIOAkR1KRphmU6PH58wqc/+2cwDYOTpyfcvn2HegdklGSaycrLjuVl7eqlZuVlfd/1wmLc2HpfrGYk8jwjz3OyLCMIgqtG4eLikjhupgjn52OKohmJJ0nCarUmSRI2mzVhGDQriTjewcuyq8f82lSlYbOUZUkURyRJQlEUyLKCpunYto0kSWy3/q5pkojjhoUhRGMPbtYgOWVZkuc5L9gOdV03UDih8Pz0jHarS12DaZoURcF4fMZqueDo4IC6LGi3WmRZwvnZGTduHONYNq7T4tnT5yRRTF2XaJpEr9uiqjKiaEtd5XiezWR6wec//z84PTshyxPW6wWCkigOkXcW5aZZEVi2SRyH6KoKomKv3yONAlazS+Q6hzJFIsc0ZZ4+eUi77eFvt4gKFElBU3WqqiQvUmpR0et3KMuCoiiJooiiLLCdhiQaRilBkCHQ2WwSPLfP8bVr6LqMLFVAxdPHj5Comc9mGLrGrZuHDAYul5dPODzs0uvaWIbE3qCDZRo7EGDGRz/2Ufb3RyRJhKBq9Da+j23ZqKpCLSrm8ymqKnFwOEJQsl6v8H0f13VptVo4jsPDhw9xXZc8yyl2q8WyqDk9PWc+X9Af9PFaDnt7A9brLaP9AzrdLqqmEYQBAoFu6DiOw95wSKfTQVEVNE3j8PCQvb09hBBohkktaDD+6w2Xkwm265IVOfJOMOsHPrZtMplc8olPfBzXdWi1PKqyZLPZIEkSvt8c2MMwRNd1Li7GCCqKImO1WlDXJYpS85lPfZSjgy6WKahEQV4V1DJsooCz2ZTT2ZSTyYSnZ+cUeYmmyqiKoNexcE0FTS6xTYXDvQ55tOb89Cmr9ZKiLMnyglrU3Ll1k16nRcdzOBgNGfR7fOQjH2EynTGdz5kvlxxdu0av2yNNM9rtdsMoAjRVQ5ZVsrRAQiHPCoSAhw8fcHlxgee1SOIcVW0YQ8+fn/Dg4X2Ojg44PDwkzTK+/PaXWW3WHF0/ZrlaISkSTWbY16IxXtbLgpeTlZf1IVQjwpR2kCl5pwWprsirLwBsdS3Ybre765opies6yLJCEkc8PXmGZdk4js1gMCAMw53Dw0DTGgR9XedXkLRmotI8pqIoFGXjysnznLquMQydMIyBRpQ6m812BzgbyzKJohjD0HeZOxK+7ze25qIgz3NUTaXd6tPrDul2UhzbIQpnxCImjmOyNGIxm+MHPoPBHvPFkkF/j9PTM+qiZtAb0fX6KIrMcrlgMrmgP+jSanlcXDznrY99mrPxJfPllLxIUBTB4ycPkRXodft0en0CIwSgKAsADLOB48VJxKNHH6AoNav5lMNhn7pIUQydPIrIooQ4iiiLjGDr0+8NSOKcJCzRDANFkXAciyRJWK9XWJZJmvqUZY0s10yXM4pK5dOf+TluHL/G6dkpB0d36PYcfvu//UcMvabbbeN4MkWR42/X7I/2icKINNow6g/xWiZxErGcbPjIp97kv//X38PxOrzx1lsUZc58PqXl2VxcnnLz+jGeZ7BYrDk7P0c3NJIkRAjBerOiLHPSNOb09BTdsMiyDF3XuXXrFpeXl9y/fx/X9XC8FocHR0wnM3rdAUWRoesqmq2yXG2pavD9ZjoxmV5iWTb9fpd2p0UWpUynU9I0xbBMVqsV0m7l2Gq1kFUZ01GZzJf4YYysrFmuVmRFTQ14LYeiLrj/wXvohsnv/d7/pBaQXY4Z9Pv0pB5JkpCmCYO9LoPBgIcPH9DtdlE0GV3TSZMMUVXcuXXMoweP6PcN6rrD+WVAkcZomkRRVpw9P8OPUvJCcGv/mEdPTqjqmNffuo1Ezpuv3+Dd9x7Q71qM9hwUxeFifsZkcsnxjetIkowiq8RhTCVKZrMFR0c3qGuFJ09PqABFN5jOV2Tv5Bxfv46i6ERhuFsxJSAkNF1FlnTyssBxWpRFSa/XI8sThsMBH//oJ/i//t3/m/V6ja5rPHx4H0O3+ehH3ySKUwRw/cYN3vnqV9E1g6IoG6cRL8Bwf+wfZy/rT2l9z5OV3/7t3+Yv/+W/zOHhIZIk8Zu/+ZvfcL0Qgn/0j/4RBwcNBOvnf/7nefTo0TfcZrVa8cu//Mu0Wi06nQ5/82/+TcIw/L5+kZf13ZiHPwTv84sUvm+69+bLDcwsjpuDuWEYOI5Du92m02mjKDKj0ZDBYHDFK9G0RsD65OSk4TGMhjiO3cDiFOVKhCvLEpv1msVifoWjf4Hd3263BEFAnue0223yPMf3ffK8QFEajcr+/pB2uwWAYZgI0ayl4jhGlhso2ttvv82DBw8oy5J2u41jO9iWhyypVCU70aJ0FTrY6/ZoeW36vQGvv/Ymo+EBZVFxdHSNKIoBFUP36LT32N8/QtU0XNdhNptgmBrj8TlFUbBerwgCnyzPME0TUddkeUYtmuyWIPA5OXlKEARkWcZquUSRZfrdToPWX6/5yJtvYus658+fsVpMWa/nnI9PCcMAWYK6rFEkHV13qKuGrDuZXFAUOWEYkqYpdV2hqRqSLFPUJXldUktwMZ1x55U3UTSbJKsIQ58k3pAkm926qcViPiOJQoJtgG32iIKa8dmKkycXpIngcrxEEgam4fL4yWPmixme51CLitdef5WyyqhFjm6o9Ho99kf7+NuAvb0Bvu9jWSaarpBlKaZhYNs2YRBycXFBEDS3cxwLXVO5nFxy+84drl+/Qbfb5fT0OZPpJePzMVEUkyQxW9+n2+1yfO0QWYaqKrAci3w3BcyyjNVqDcDR0RFJkrJcbUiygvU2xHZbOK0Ohu0hqRqqbjAa7TEYdHFdi6JICcMtnmcDJcvFHFVV6XQ6GIbO5cUlDx8+ZH9/BBLEUYxu6JimjmFohOGWw6MBo2GL/b0WN/f6qHnKwHYYuB6TszFpkKCiEoYxWVHQHw4Igi1lkZDEW/7MZz5Gr2UiiwzH0njjtVfodLzdCUUTruk6bXRFZ9Drs1qsmE5nhFFMUQmEopJmJZtNyGy25vzskuVqQ5GXOyeeiWlaaJpFVUEURg2PSFNQFMiyhPXGR1F0VFWl2+tSlgXb7Zo8TxCUrFYrdMMgzTKSNCUIAq5E8B/qp+OPSH2/eIofJN7iB4zN+J6blSiK+PjHP86//Jf/8lte/0//6T/l13/91/mN3/gNvvCFL+A4Dn/hL/wF0jS9us0v//Iv8/777/Of/tN/4t//+3/Pb//2b/Mrv/Irf/Tf4mUB3/m90tjmxB/tIgSy9IIqKa7uqy4LsiRGiEb/IUTTrLwg0L7QsCiKshPUKliWhWU2EwxN03h+fo7X7dAfDSlFRS3JCFkiSlIs2/laI9Jpc3iwj6apVw2MEM0ERVFUqrKm0x6gKsbO8ts0PIah7sBXzRqq+dlq6lqgKBqKouE4Hj/xE5/j1q1b2LZNXVekaUKSRmy3W/I8I01TTNNsBK+mxmI1R9F0Do+uM5kuWG4C9ob7+GFAEAdIqopuO1zOZzx99gTDULm8GBMGAav5kqoCy3TotPu02z1sy6Pd6iHLJrpmEfor8jSkyhJajo2pyYTbJXG05sH9twmDGf52RZJkxFlNlFes44hMqigViUqWsD0XRVUo8gzTUnA8ndVmTp5mrFYbaqAQNWldNvZTpclnqguZIvV59OjzjC++wmL+kCxZoMo5RZVwfOs6WZnhxz6KLlFR8MHDe8Spj+3IPHz0PpqmcvPGHd5686OoqkarbeO6Jv5mi2VYnJw84/333mc6nbJcbzgbX6AbGv1OizLP6Xf2CNYJRSxYTLYMuweM+n3uvftVTh4/4eGDhyxnS7IkRZIELc9GlQpGow7n4ydswiVpkXJ0dB1DayPLBqZjkiQxuqpg6xrBdkGSbJHVGiFnqCYMhgNELaGrFqO9Q77y5XcY9YcUWUESxewP9/joR9/klbu3GY2GjPYPSJKCKBLkqYQsa/S6XTRVJgq2HB8ecjDqk2c+qlpS1SlRHCIpEn4Q4rU6FGVjiU/TFMdzyasK0/UYXbvORz75cX7hF3+K//Nv/jX+/M9/hmv7Lq4uYcoSVVZRFDlCrSjqAt2ykBSNxXJFEDaPEWc52yjk5q3rVEVKHG0xTYONHxJlNXmpslgm5KXM/v4xpumQxjFlnmI7Nk3muYym2hiaS78zwjNbyLVEmWZUeYpl6aiGiuU4XE7m1Gj4fsKTJw+R5BJdlxmN+uwNe6zWc9IsYbg3ZH+0T5XnaIqMqAtkpVmzFdXLVuUPq+9oe/7DnjrxXX7/n9L6ntdAv/iLv8gv/uIvfsvrhBD883/+z/kH/+Af8Ff+yl8B4F//63/NaDTiN3/zN/mlX/ol7t+/z2/91m/xB3/wB3zmM58B4F/8i3/BX/pLf4l/9s/+GYeHh9/Hr/OyvpMe7Y8qVxM0tNYXIn0hKvI8Z73eNALEssIwdIqisX2+aAxM07x65BdpwwCS3FBfdV3jrbfeoJLA931kRQEBYRhRlSV11dyfpir4QUBeVDvqLVerJVVVUBQTRZFRFBXLsq8mN6qqst2GHB8fkucFVaVQlgVpWl7l+zRZQUUjHLUsFFVGiJrFcoZt2UgIDF3frZ1KEIKqKAmDiCz1GQxGvP/+Q157800EDdq82+9gGR6qYmBEBu1eBxmP02cnZFnFZz/zadr9A5brLbpuEQQxkOM6HaIwoa5qomhFmqYURcX+/ojLC58sjzk7e4ZuSCiRRJY1K4vlcs3aD9BMG820EJKKrmq4ohEQ9/tdFEMnWa2xHBM7sJAlhSwvMCyTtR/wQiiQ5yV1VWOaEqYp2G7GPH5UY1kOp9sx/X4XwzTxWi0WJzPGl5fIkoppW2w2G+7FAa+8chtFUXAch/H6glbLxn71JqvNBsPQWK0WdLsdVss5yIIw9Ll955WGMyNkbNshDGL29vYp8pI4CdkQoKkSb77+OlGUEEQpk8mEw+MDsjRhWxd0Wy163RbT2QTddLEcmypv3ClIGrZt4K99Ls4n5HFjpy1FyZsfeRO3ZSHLAtsy6bT7zOdroijBsmxOT88o0pwbt26yXC754IN7eJ53RRT2g5A8STFNlb3+AMvSee/9OWIHsfAct9EZaRqe28IwLMpK4PsRmmmhq/pVfo9hmJRlxerZKZtugG0aHO0POX1+QrfT4Sd/6tO0B0PuP7hgvs4oqwJFkZEUhSzPGeztkWceyKDqCpswpGf0SbKcxXLBgWmy2iwp6oonJ884Gh2gaSZhGLGYLVBVnddefZXpdEoUBtSShKFZGIZFEqd0Wm0ux+fouopAIo1y4iTA9izKqqCoYjabCc9PFeIwx3FNFFXh5OQZo+GQKCx57733GI2OCf014/PnzGYXSFLF3bt3qaoKVVG/yQP4o1Nfb8f+o4Y0fqvn5bu5K0l8n7bpP8H6UAW2JycnTCYTfv7nf/7qa+12m8997nN8/vOfB+Dzn/88nU7nqlEB+Pmf/3lkWeYLX/jCt7zfLMvwff8bLi/rj7deCGdf6FCKoiCKYkzTwLIaF01ZNl9frVYEQYCuN4RZVflaQNmLyvOC9XqNLEtYloWuaQwHe6iKQlVVbNYrWp5z9YdVlDWe124cE7u5TlUJZFm90snUtbhaEW23W1arFWVZ4botkqTEshx6vS5lVRCEW+aLS7I8uaLebjZLTs9OKIqU7XaFqsoEOzGn5zlIUk1V5ahq82czn885OjpkNpty8+ZNbLvBvOuaxXYTsvF9KlHidT3KumbtByw3IXdefQtZc5AVg7IQdNo99vePODw4Zn9/n1u3bu2mOU3a9NHREXEcU9UFVZXx/PQRm+2C9WbJYjGl3bJJs4gkDgmDgCROqcqS+XyGrmo7x1XFerliMp1cuX9c10HsnrMiL7AsCyGa8EhZVvB2qdSKIhPHIdQVcRTScluIWiIKExy7zXYTUlWCd9+7RyUkvFaPTm8PRdU5vxgTxAHbcI2sCW7cOkSWK6LY53IyxjB1bMthf3SAIutMJjNmizlFmbPZrinLguFoj6OjQxRFIcsyTFOn1WqhaQqHRwcNoZaaTrdDp9vjyZOnSJLCarUky1Ic10aSQZFlTNPk1Vdep9fZIwkLqA167QP8TU6WVmRZcSX6fu21V1gs5nQ6HWRZ5u7du2ia1qDmw+Y5nE6nLJdLer0O/UELQUkYNmLuIq8I/ITlYkOSFFQlhGFKXcuoioFjt5AklcuLGYZhNdoVRQEagWkY+o2gOAj44PETslrw4OkJF9MZrW6L/WtDDo8HKJqELCmURYnntVBVlRs3jrl+4xpIFUmWMZ7MuP/gCaPDY9KioBICpBpFhcvJJavVkiDwGzeUqnDz5g1u3bqFIit4jkfLbTW6tDJHkmo6HRevZTHoDjAUB6lWGXT6qBJ02yZ15ZOnTUM6ncyRJZ3h3gHbbcSrr71Ou93h7Ow577z7NifPHlOWGVEUslzOCEJ/RyZ+WS/ra/WhNiuTyQSA0Wj0DV8fjUZX100mE4bD4Tdcr6rNnvrFbb65/sk/+Se02+2ry/Hx8Yf5Y7+s77JeCGeLonGOGIZ+lQQsy9KVm+bk5ITxeHwVOljVX7Mdfy2fpzngXFmYd8wV07TwHJu6KgiCLfP5lDRJGi2FJKPIyo6zIiEhk+zSb2W5sUBXVUVVlbg7Ee12GyJqhThOqaq6aV4cG0TZ4NizhCDyiZMQxzXx/TVh5LPZrhBUrJZzQn8DdU6a+JyfPeXJ4/v4mxWKJJjPpxRlRqfbwfcD8qykrkHTTHx/S5T4pHnKbDGn0x9w59U3QDW5nK958uSk4cOgUhZw7eg6sqyQpo0bajQcYds2y+WCfr/HbHbB+PI5uiExm11Q5DFRtCEK18wmYzRVoSorqCXmswXUNWkaYxo6SRxR5Bn7oxFlnu2Ad9YOELdBUZSrZGfDNBByI+p9/PQJl9MLLMtks1kh6hLHsnn84AmL2YrlcsVstuD0dMz+/hF5WbPZhCRJTlnVuK7HteMjdFMhCJfM5uMGAihKpLrCsUxkAbpqYKgGuqajagqn52coukacJZxdjJkuZtQIdF1jsVzQ6bYZjob0+30URWF/NCIMQ2zL4eio+XzYbreMx2MkCWzbYG+vg+PoKErNT3z2UxwfH/LqK3f5y//HX+bWjVvUu7Vcq9VCULFczdF1hfl8xt7eHkiC8/NT6rqk220jRLVzaeU8ePAB6/USVZVptV3SNKXd6qAqOv42Jk1KdM2i3ervwjM16gokVEQtk2Q5Wz/AdlwuLicISfDKa68iKxIoEkGcUgoJzbTIynI3UVM4vjHitdfusLc3JAgCzs5Omc0mjMdndLstTMvAabXwo5QgSkFSESjohkEtSlptG0vX6XgehqaBKNFVhdOTE4LNGtvQUSUQVYEkakbDIZZl8Orrd5GUmqqKyNMtg56HZ1u4lkOZFoiqQhISt27eRpI0bt96laqS0VSTw/0GhvjZz34aqAgjn1bLo65LLi4v2PpralHwoYseXtYPdf1QWJf//t//+2y326vL2dnZn/SP9GNZzVn21w5qzb9fbBIbIFsTbtfj1VdfpSwrkqQRbn4t+bihsgrR5Ptomo6oBZqsIiMhU1NXBbIEH9x/n8ViymK5IM0yyjJHiIa/IksCVZPQNIXpdEoQhLiuh2WZzQpIkul0unS7nYYFYZgIIUiShFrUSLKEYZqUZcl26+M49pWGJkliFKVxuUynYzbbOVGyIs02fPWrX+D09BGGKXPn7g2m8zNm8wu6XY9ut4Usw2q5RkIlDHymkzHr1ZwoConjGMux2QYhZRMru9PH1MRRjOO6xHHEdrvCtFTyIkXTFdptj16vg+vayLLAsg2S1EfUGapSsd3Oubh4Thg0tl9F0hgNR+RZ4wjyXJv9/SG6pjHaG/CJT3wc3Wie97IqkWAHoatZrVes1mvKWjSQPkVmNpuxXM7odlr0uy2SMERGoi4rirxomlJZot3tIisKs8WSMEoa+7MiISngB2vaXQ9FhYPRkNdffYXDw32ODg4YDHo8efSI6WSCosh0ex3yIqMoMkAQBE3DF0Q+eZmTZSlPnz5hOrnEdW36/R5IAsdx0FSdZyenCAGDwYBWy+PJ48eNYNo16HVNqiogSZe02zrtts7z5/ep6gjbsilLQZblxHHMarVgs10hy43I+fz8HCGax3khIocGWS/Jjf09TVPiON6liTdTkrquuby8ZLlcc34+3jmuVNbrLbKsYJo2SZKR7RrdVrvNarViONxDViTCKEDIIKnN2iXYTXXyPCHPQ/I8QtMk2m0PWYYsS4mikMvLS6qdXV/TNNI0x99GhH6EpZtItUAB6rpx7KmKjK5pRJGPqimNC6vIkERJ6G8Y9NoM93rcvXsbSRLcuXsLw6o4OLJQtJyjo30+8YlPYxldFKmNY+1RFjV7gyHH167zuc/+JJpmcD4+J0lDgnBNf9AlyzI8r80bb3yEs7MxX/rSl3j85DGI+ht6FPFjMG35USX2fhj1oTYr+/v7AEyn02/4+nQ6vbpuf3+f2Wz2DdeXZaMKf3Gbby7DMGi1Wt9w+VGqbwi5+lNaL9Y/L/gjDea8vqLANh8k9W6EfBPLsjBNE8uykGXl6/D2Nc+ePefi4uLq/kQtKMoSSQj8zZaL8ZgsS2i3XXRNJs8TomgL1BRls7JZb9ZUVUGWNYnL6/V6t2qSrh5XCIGmyVi2jqDJANJ1fZcBVBP4EWmSoWsm7XaPQX/I/v4hdQWq0thIERXr9SW/89v/X37nd/5vNttL2m2Dhw/fQ1VronjNZjthsbxAULBcztgbDtB0hYPREEORefb4Idv1gsnlGc+fPaHlWfS6LbI8RtMV/GC9ew8I0iziK+/8PpajoKgCXZcJwg2Xk3PSLOHmjRsYhoGoKxAFQuQYhoyi1JR5hiQkbMtFU1Rsy6bbaVOVJYau7hwqPtPJhDzLG8uyae2mUhVVWZImKZIkUwlBWddEu4lMmed4joWpyZRFSttzsUwdCYFpGuiGxnw5Jc5isjLldHzKZD5luVny7Ow5iq6SVQV5URKFMXlecO3oiCdPHrNaLpCkiiDYIEmCMA5ZrleEcUSSpdTUiF1opCTDnTt3iKIAx7XJ8xRNVdD1xmkVRjFJku4mac37SlAzvjgjigKePHlEWeZomows19R1w6SxrGYduV5tSdOMMAzYbJYURY7rOqhqo69K03SXmFzuBNwyeZ5j2za9/gBJUqjK5u9jNBpiWSbtjodtW1/H9qmZTC6Zzaes12tc1yEvSoqqpqgFW9+nEjWXswkoErqlU1YZUeizWi4pspLD/QOG3T5lmhL6a27eOsKyteZ30zUUVeP583OiKKWuS1RZxtB0qrxGZIL5eE4RpmgoyAgQzetfFgVpnPDk0cPda25yMOpwuN/D3654/uwp5+dnpGlOGCb09tqgVgyGPVabDU+fnmM5PYpSIQhyLNvi1Vfv4gc+XstDVRWenjzm7is38f2GqRSGEfv7h/S6Q2zLo9Xq8PjRY2bz+RVo8ke1T/n6kMM/7ceAP+n6UDkrt27dYn9/n//8n/8zn/jEJ4BGNPmFL3yBv/23/zYAP/mTP8lms+FLX/oSn/70pwH4L//lv1DXNZ/73Oc+zB/nZX2I9TVxrHTFTnEcB3V39iiEIAgibNu6Ere+yNd5MVV5EUjoOA6e517h9KVapixK4jRq9C6+z3IxRYiSuvK4fffgajT8An2u6wZ+sOHJk0eAyuuvv4aqKl+H35d2zZCMYagURQ6AaRhNFk82RdQKYdjYJUejEbpukmUFdZ0ync6oq5z+oM0HH3yFrAgJwzXXjg94/OR99gZHaHpFnKwpC8FX3vkikmzTavUxbYvFcsag47GZz1kv5sjUZHFAGKeoqszh4QhfrijLhHfffZePf+wTBMGW2fyCskoJwiVnZ08pi4qD/UOqysZxHDrdLi2vg6gF6/WCejdRKIuUOq/ot9sUWYlQm+YsjiLs6yYnT5+SZxlREDCbTnBdp1nn5QWSLLO/v8/leIIsS8iySpEXeK02wXZGZyeE3W5WlEXE3Ts3eHryjNFowNH1EY9PnhImKYKGrgsllm2wDTYUeUa316asmnykLC4Igxh/E/L00RMkGVzbYH/UpywKDFPl7GJMXQvyPCXPDQ6PDqiqAlWFKk8JIx9ZlncZTTkiqSirgrIoMVWPdrvDcr1CURT6vQ6b5ZooCrEdlySqae+1KDL41Cc/i6YqnI6fc++DD8gro4kbaDtsNmt0Q0PXVcYX5xiGTUW5s95ud2GajcstikL294bsDQ44S56R5xXdnst2u8YwFSS5Is0iOh0P3TTQdJOyqtgGW4oiY7GcY9suktSsWSVZoabk4uICqDAtg0G/TbgNyJMUQ9LpuG2oQ8wDm0cnT3h+9phu30PVKtqehyKrqIrOfL5Et2pu37zG+GyOY7ZxHRfLUFGUGyxWU9qtFsaO9FuWzQlIt9vFdV3G4+d02iZ/8Af/E8tu42eN4Hi12ZKXJZJUc/JsiuvkvPrqAW7H5unT59x97Q7n52f4wYb9/X1+//d/v5mQpQm3bh2TZin7B3soqsnP/PTP0W4NqEoJTTXZHx2yWi0IgoD9wQhJgroWVzEbL+vHs77nZiUMQx4/fnz175OTE77yla/Q6/W4fv06f+fv/B3+8T/+x7zyyivcunWLf/gP/yGHh4f81b/6VwF44403+It/8S/yt/7W3+I3fuM3KIqCX/3VX+WXfumXPhQn0IsO/Me2S/1+zkC+zXMmSdLV9CTP850jR97Zf+Xd+DnbrYi0He6++WHKsiSOYxzHQVFkOp02kqyAJDVQN0lCVE0Q2vHxMVHcRjcULEPn3vvvM1+uaXd6lOUeYRg2Y3dJotft4noO61XAZrNlPl9wcHCAvHMaNWJFCd1ozjirukLVDKBmuLePbVvEcUotJLK8RDdrHMcBCdrtDk8eP+LsYoxuanT6A8QkaYL2hkecj58zvniO5coMh/vkeYppWTunjEY8jbEPR+z1G/bFarOk3+sgyxvWi0t+//f+G/3BIecX52iGRlmVFHHKbHZBmvqMxz6WabNar6lFxWa7otfr8fTpCcnOfm3bFqfnp7BeYho6ki6hqSpRHJHXKVVdsL+33zizkgTLtgmiiMFgj7PxmDRNSYsSTdPJsoYO7DgO222AaxtsNht0RWlElp6HbZtois6jR4+wHIflZsHwcNj87EWG2+qiGwav3LzOcr7A81wWi3gH9Msp84p2q8vFeoagQiA4OtxnOBqQZQnz+YwsS3YTMf0qVHG5moOoGY0GTSyB56EoMqvVEsu2UAoJSRb424DNPEQzTdIkxpQswizh4GCfvW6f2XTN9SOLuigpC5mikBmPL1mstuiGC6WMmpd4nothKlRViYRMEM7p9fqkeUyaRBimiaqpmLqBqqqInVfu8cMn1KJkPtuQpk3eT1lV5ElGf9AnikKqtKJGoOkqBwcjgiAkiUN0vUMYRTuXXcLx9SNWmwWOY7LdbiiylI7X4ejoEEtz+OCDB0Rxyt1XXqXX7bANN6iKzBtvvEkUxYR+hB9EO4Be1Lz/q4KyKKjKmtl6Rrfn8rnP/AT3HnzA2fk5hmHx+MlTDNNivdrw1//6X8e0VBbzU1zXQjd06vWW5XzOxz76Mf7rf/ttDg5G2HZAWUuomkOVZ7TaNl5bx1rLPHr0Ae12m5s3b7BarcmLgqKs6ff3yPOKN177LL3eCMduMZ+vcd02ntvaifSjHUVavorp+FGdsPwv9Ud1CQmaBOVv8/3f8a6/3Q3+BI+r33Oz8sUvfpGf+7mfu/r33/27fxeAv/E3/gb/6l/9K/7e3/t7RFHEr/zKr7DZbPiZn/kZfuu3fuvrLKzwb/7Nv+FXf/VX+XN/7s8hyzJ/7a/9NX7913/9Q/h1frzru3sffbtbffu3sSQ1Lh5Jkr7h9Wxw+02WStMgcNWovMjdyfO8STeWDGoU8jRrmhpFoc4KpLJCNRXCKGTtb5B1gyfPLtgGJa+/fpvDwwOiKMK1W2RZSq/XZbsJyNIC13WwLZNZGLBer3CcRvthmo1+RdQN+yJLczTFRFM18qxkMLCRNRMziKikmk244sEH97h96zZ5XlFLoBoWriERxWuW6wDHtnYU0hDT9FClHp59QJLktFt9lssNnY5MXYPdapEWCcOj68xXG86ej9lu5rz55itcTk7YGx2SFgW6aTBfzplentJuGxQtizjeYJg2d+5cI8squp02aRqxWE65d/9dyiwgSVa4jsvFxQWu6zYaIEslTVNUTSUJEoIo4Pl5QVFX1FlOb7DH+HKCkFUqkYOQUZCRaoGpaU2IoiKoy4Qs0jEcm6qs0XWV8/EphqZSyyqT5RIhBOPxDF2zkeUCUdXUZYmmGXhem8uLC65dO8QyDPIkRRYN2G+9XWM7BpJaoZqCJ88+wHFcDNtgMVmg640rqSxy4qpClBUH+/skYUwYJCwWKxzXRdNk0jTk8OiAKArpdlpMzmfcHtwmT8zmeZAU1BqyJMVzTC7H52iajiSXPDh5gut6BGlJVsnIKnR7LkjNOnO72SDJEpJU4XkG1TpFRcU1XdbbLbTaVLXCch0jCwNX0xAobHLBxXhJf29AFMdUtSAv5jiOxbWDQx4/fowsK+wN91BUGc/18Lch14YHxEmKH2xZzXyQJCQUJBTKQpAXgryCtb+i2+0RpAnPxyd47TZWaZMkFZeXG9abBjCoaDKKApUsyESNbBjUmsLJ+DmqUqN68Oj5IyxD5a3XX8O0XSoBJ8/OSMua3/nCFxGiQCHjrY+8xWQywfNM5rMLDN2kjAtkSaUsBb1ujzzLOX12iqbLLCdL9nsjWt0DQMayWnzso7d58vgEXbVouQdYpsu1o+t0uz3Wa5/lfMH+cIioJRyrhaboV860F9ymH/n6Ntbi71RXka/frlH5TmiL7/S9f4J+8u+5WfnZn/3Zbyt0kiSJX/u1X+PXfu3X/tDb9Ho9/u2//bff60N/i8f6cDzrf1rq+50Gfedv/6M3KsDO9dOwM16sdICr/1cUBd/38TwXVdWu3EO6rqNpWiPKBSSxs8qaZgOAyjNsw2C5XCAUCKOYqqxpt7scDI+4fv2IWlSoqsazZ89wXRddN9D1AlXVyYuS5WrF3t4erusihEDXnQbBX5SUZYXvB2iqiut41KIh59YViJ1jKYoikiSg1++TJCmu02JezFhvNoThgqdPH7JZzxju9TF1E8d1kCWFs/NTjq5dQ5IFmq6wWvmkacGdV15jOlswGo1wnDbLxRp/s2B/tEdVpLRcj/HzE/qDawz7+9RVTVUW2Fabbvcu7773JU5PntLvDxjuHWJoIAkZWZQ4pkpYQq/Xx/M8xuMLer0+uq6T5zlhFOK4Dq7noak6nU6X6WRGq6NxdnZGiaCoSizbRkQJaRJj2y51XVJVZRNlIDUHUVWRWc6XHOztoekKXstFzQuSImsYNVnCcrWiKsvGmaXIZFmO57Uwbht0Wy3KImvydiQJ27EZDPrUoiCMEqbTZppimjZC1GzWG4YHRzvmjoTrOmxW+U603Lhouj33KviwqkomlzMURca2XV555VVUTSfPS3S9EXDnRU0ZRNRVhaoqtDttiqJkvdlQ1jWW4+CHIZ1uizBsAIT9fo84vqDX65LEKev1hjTJGQ6HXFxekqQ5QlJAUpAEpEnc2PerEsexidIGlGjbBkEYkOcVpqEBkGdN4z6fzcjSjLbn0W5bqDJ4Xo8kCcnzDN3U0RSdtE6okAjDlCwtieIU1ytpdTyEKCmrjDgOkSSVJM1wvTbBNECTZeIsQ9PkJhJiOERXLdIkpOXZrLcLqjqhpTusN2M++enPMhqNOLu4RFYVagSdTpdwu+TJ02d8+lOfZrvdMp+vmoTojkvLbXAAaZLib33yPGc0OiJPkybawvbo94YEQUi0s20fH90iihI8x2C1WnN4cMg0n6EbGtevX0NRNDabht/UOP2az5+qgh+HTdD3cxj4rj7hvwNo5Q9juFxNbf4oD/4hNDgvs4Fe1ndVL1D6svy/MlO+/jYvUpFfCHF1XacsSyRZRt3h84Wo6LQcZEWiyEuqMqeQwHUcojxB1GBZDlbbpO11miTWWlAUBb1ej6qqmEymRFGEpmtomoEQYmeFbiYted5YH1VVQVU1qspjvd4QJzHz+RzTtAijAFDQVYXNZsN777/HZz77Kfr9AbKk0u50aLkOUbjA0A1u3rhFkoTEcULLa3F2fo6mmSyWF7z55sfRDJfxeILXspnN5uyPRiiKQRT7XFzM+bk/+9MURchXvvwFojCk33exVTh98gGGaeM5Nv3egKfPHhLHOaoM68UMU9MYn55gGDqSDKpccXztkMlswuXlJY7jEoURves9Tk6eoal6k0QcRtSKYDFfUNc1edEEIa6WC1qtDlEUo6gyruaSZRl1XZHnGZZlN0GRtaDTa5NGAevNgtGww/jijMV6i2m7rNdrBM17IklTWq0WeZFz8uwp9k64q2sqURQgCairghvXbzGfvc9kcomsCAyjjRAKYdjY0w8OjpBVgywtsNo2cZySZTmibszqkqJQlzKW0yKOcvKywjBclssFsmQwGT9jbzDENF103STwQ/IspqoqDg8PSdIGJNcbDDAMg/liQVGW2I5DGEYsl43WJc9KZFljudxQVTWtVo8kmiArSpMbpapUeY5pOYiqQpUVbMdmONzj5PkJquYg6pROt01dxyiKxnq9xLJ0et02fuAThRHdbpeiSJlNzrh79y6DQYvh6JOs1z5bf8022FCX0Gp3SNOCLMtxnRb+1sdxTSzLIEoibEcnjjNW6w39vR6S1ASDqloTsGgYJmG4IU0W1KIgz2N0XWG5XFDaBaqm8+jJY6I4pd/voWg6gb/BNg0s02MyuWS99nd/g122G5+5v0CfamRJwnq1xbVbWLbNZrul128jSSp1LVPkNbpm4Xba6JqH53WRJJ0XLqUojnn48AGvv/4Wtm2yWm2xTLMJIZXZ8ZSkH4tG5WX94fWyWXlZ31V9fZpyM5qVv+E6SQJd1+j1ukhSc52mqTvCrEZR1VcTOUmSUJUmA36zWkJdIzSl0QHIBrdv32Wz8amLAlVVURSJsiyoa+j39xpxr51iGCa/+7ufx7ItfuInPoumqTvHRpPq+yLoUAiFqiwIw5DhcEBZFkRRievZVEVKXSSsl3OCbYOBr6qKlb/B9Rz8YMP4/JzlbIGqyuRZhFxL5GlKWWTEacTW73M5GSOEThiFBGHOq6916XT7+H7Ik6enHB5dx3E7vP2lewRBQhhELKbvcf78lOHwkOPjIx49fsKTpzl5WRL4EZaWoqoKeRJgWzJlGaOpGndvHXN+eYmEgiw1NvI0zbm8mCJLMv3+HkWV0+/3yeOUIPBpt9uohoaj2azDDVkak6YJkiRTi2o3Zm84JlmWoCs6lmEiSxL7+3tomkJNTV5lVKJh7WRZRpqnWJZJt9slTVPyMsMxbbbBhna7zfPTU/rdHg8efYBrOWRpyeHh4Q6cVuB5PY6Pj7i4aHgoJ4+eIKQmQ6eqauqqwjJt8iyjyAtM2yPwE3TdIgwSTNMkjnL2BgdMp1OytCArKizbQ5ZkbLdxOhmyymQ6p912EELCcR3efOst/vN//W9IisxwNOTpk8dX1OW8KJElBWQJfxtRFiAkmaPDY9arLWVZYjs2ZVnRcm2Ggz1kUWNaJp7rsFhO6PY8TF2m13HIkpLaNNku1wgEe4MB/f6APM84OzvF80yms3OWywWd7hDb9lgsVqiagqHZxFHOZDLH81r4fkhdl7ieRRhFVHWBrKpIUgnkrDczNF0mzUrCOKLfb1NVJXEcMhodEGwDsiREN1TyIqeoa6qiRGsAMDvbeEpZ1+RFhr8KCIOEr37lPYajPqZp7PADGWkSsFrOUFSD4+NrpFnGu++9g+M5dHsDaiGzWKzJsoKqnPHWWx9DVQxGwxZJEtPv9xCi5sb16ywWU4Z7feq6Ym/Yx7Vs5J3D78dei/iyfjg4K99tvbR//eBLCL7lZKX5MJGQZeWbvvbiNRHUCLIsZXJ5gahriixBV0GWKjbbBRcXY9IkRdf0xpq5t4csSeRZjqqo7A2GGLqJ67jkeYFju/zMT/80kiQxm82uLLhJklDXjashiRMUWaLbbZPnKULUHB0dEIZb5vNLyjLi4vwJTx7f59atYxRJ4mJ8jqbLXE7OmF5ecHF+ThhGjM8vaLlddE3j6ePHbLcbhsMRtu00iDpZxrEdOp0OnudhGCaSrNJp93jzrY8gKyrIKjUQhDGf+OQn+amf+TN0ei3eeffLxImPJAmOjg5ptzuURUVVFhRliu8vUNQK1zUpypSyLDBNC8dxWS6XV2nW7XYTdWBZNmEYE0UxgkYY3+l0d+sSi7qukGUoioyyKjEtg3zHNVEVib1+m7JISNMI3/cRCEohSLKCqq53YMCCosjRdZ0wDK8yoZBBM3WyPMNrt/DDgNu37uJ4LYQkkaZJk5Bse3TafcIgY390DV2zsUwH23KIopiiqNB1E9t26Xb7O1FmQVkKZtMFlungui0W8xVJnHH9+CZIEnGcEIQh6/WG6XTKxeWEs7Mzuv0eG99HUmSKsuRLb79NLQS6YWC5DjdvXafT8dA0FVmGMGxC9fb3D1AUDd+PuHfvPmEYNU20JKMA4dbncnxOEPicnj7HMDVaLQ9d1SnSHE1SGHR7jAZ9Wp5Dr9NmtDfAc2yOrx3hWCauY3P92jVkSWJ8dobntdjr79FpdcnSgiTJALkByskaQshMJwvSJCdNczqdFpquNmh7pbHBu14LQ7eQJY2qrAHYbFbkedxocZBQFJ28qgnTmPlyydbfIisSjmPiOibU5S6uoY8sq8ymC/xt0GjWpIrpZIy/2ZDFCffef48sz7EdD92wmC4W9Pt91ustg/4e164dI8sy/X4f3w8aMF1do6oqR9eOsG0bXddodzw8z8W2rUa38/JD/ap+nG3OP1LNyov60/5C/rB66198aHynDw/p6z5gpN0vqaoyvh8ymUxZr9ZURQZ1xWo+pchj0jRC11Xa7U7zAZoXbP3G5eM4DpqmoygKaZpRVYL9/X329gbcvnOXP/8Lv8Dz56e89957+L7PfD7H8zzqusJrtahrWK+3V+upvMi4cfOYssx496u/TxjM6bZtDE3m87/3O1R1Rp7HXE7OqEXZTBiQ2N8bMRqOmFxc0mp5DVQth8Vsw8nTU87PL7m8nKCpKlmWst6u8VoeN2/f4vT8jN/8d/8Xz549Q9M0jo6PEKrCw6dP+eDJQ1Br4iwAqaTdcuh2Ok2EpJBYrZb0B12qKmezXTbU3KJgu/VRVY0oSjAMiyCIODo6RtdNNhufVqvDaHSALEl0e12m0wnb7RZR1+iGDjsibGMRznbZTgWGrmJZCq5rUJYpYRQwny+Yz1ecX0ybqdfOjm4aJlVVXb3WiqygKBJVVaBoKnmRMVvMmS+XLFYrbt68hRA1262PLGs8fzZmsw4Rtcx2G1DXYJoWaZrh+wGLxZLJ5YR+f7ADoeVIEqRpiiQ3q8Ysa6ZH682awV6foiqYzaecX55jORZJGmPaFmfnpxiWyWqzJoxC/CCgKEuyPOf09JQoDtg/GDAc9hCipNNt02o1wuXx+BxNMwCZmzdvYWg6URCiyhL9bhvT0MiLjDDy0XUdUzdJ45RBZw9/FaJKCoejfQ6GQw6GIyzdQJGgSFKGgwFtt01dCqoCbNNjPd9gaAab1ZZOp43nuVimyWw2Y7VckWclSVIShTl1BUEQMhwOGxE7YBgWy8WGsoCyFNS1RBQnxHFCGIaNZmftk+c1ZV0jJImqrhBS4/jb2+tj6Mqu8alYr1eEYYRpOmRZgSRLfOxjH6PTblNXJY5jsVgueP+993E9D900MUyLqqxot1wkGV5/7RUOD/aRZNjfH2HoOnESYZoarmM3bh9Eo3nTXpz0SP/LZ8qPcn3zseGH9Vjxg6gfyWblZf3pq1p87cNmNNwDIRBVg/Y+PztlOr2k2+1gGiZ5XhAnGXX1tbTmJMmYz5eoqraLpjeRJAVZlrAdh5/6qZ+iLCseP36C7/vMZjOCINzZrPWr/KI0TZEkUBWZWpQsVxPSdMv14xHdroftmFi2QZoFvH//q+RZgqI24K8sK9hutpimxbXDQ1RZRZEt7tx+A0UxURUDWVYJoxBNUzg/PyMvMn7/93+P//Af/gNHRwd88tOfRJIl4jRBc1xm/paHz5/y5PlTTMfg9PSEJ08esL8/ZLR/RFaWyKrCNgjIq5LpYoasqGi6QRiGnJ+fYxgGURRTVTVxnDQH9axgs25SfNvtNrZlcefuHfb2+tSiIkki1B3c7NatW1cCaMPUMU0NVa1peRYHByMsy+S1115HklU0zUIIifV63SRht9tomoqqqGRZhqCh2dquw2a7RtFUhAA/CAiDmP/xP/8nRZEz3NtrCMdxRp6VPHt2hhAScRSzXC52VN+oOasf9NlsNkiShKappGlMnqekaYIQNf1BD0WViaKAOIkIwi1RFNBqeZRVgW6oyAo711CELMtstj61qFFUBUmSWCwW1KJCVgS2a3B4OLp6L3ievUv4FiiqxmQyQdSNILpJJ85peS6mreO6Dkkco8gqw8EBs+mKOzdfw7VbLBYzwsDHMnUcx0KRpWayVRR020M2y4h+d0S71dutTRq4XBD4KIpEt9fm4OAAXTepa4nV0keWDHq9EUVeslgsQUBRlkRRjKYaxFHOehlQleDYLTYbnzQpoFbw3C6tVpeyFkiqTNkIgwjCgDgJuXnrBpou7zQuCVEUstlsybKCxXwForGWt1wHTdWoqhJFVQmCkDCM8LwWRZmDJDg7e4brOliWAVSUVY6gYjqdNPooGrp0sMt9e6lR+QHWN6cYfK9pBn/Y9/+ADS4vNSt/AvXNrqUPr2P+rnI3P6wH++5LgCJDy/MwNJXVYsFsscA2FL709h9w7fo11vMVSRJhOR5JGOL7EZZpNrC4JKEWFWmW0ev3kWT5ip4bxxFlVTEc7qFpOuPxOY7j0O60EHWNQCZJExzXQtMah9L5+Zi6bvD9hmHT8mwm0wllVRMmEZpW8vZX32Z6/hxLkzAMDdMwaXe6tDs9PM+hqAVIGh//6GfQNIubt98iCGOyvMk3OTt9zhtvvMHzpw/YrCb8H//7n6fXaaOpEllWoesuQsD4YszB4QjHNDk9fYapGlRFxY3rx8wXKxbLNfsHPZ6dnaEbKhISg+sHqFFOlmeUZU2WZk1atKIyPr9AUWRuHt9mPB4zmyzodRtBZpImCEUhTwq63R6L+YooiqmrEhDIkoRj2ziOjtdyEVSURYHreoS710RTdVzbQYiKxXKOH2xwPY80TahrQZaWuK7KfNrAxpaLOZqmoBgmEgpIgul8ye3bt3ny7DkH+4dczi7o9XpATbfXp9Pr8+zZc9K6wDRsJCSWyzllldEfjJjO56iaTBj5dLoeVVWyv7/PfP4i1iBsXGuuzXK5pt9vXFPPnj8jL3I8r42gJEl3YZyWRZLEnJ0lfOSt16iqirPZmCTJWCyXvPXmm7S7LlpUICsyVZmjqAqttouqKIz2BlR1TZULdKtZPe6PDvC3WyzLQ1ENwnBNsG1WcRfjKaq2otPrISsyaVFTVjJ7w2sIFAQSe8NRo+NBItxGZFnJtaMbdNuN+2hvMGS1WlIWBY7h0O+5nI9PkSSavCVJsF4vyYuKVquFqZsoMtRVjSSpCKEgyQpxklABRdGkIxdFk8IdRTFJGlGJHCFXlKJCqmvqWkKWNMqixvdDTMvi8JpDVUO9WhJFWypqojTh5s27PH7yiLOzMa+9+gZ+FDIaNs43qoqzyynj0zFvvPoGnXaXQXfAarFir7fXkPZ/zCcJP4j6do6eb2db/q7rB2ht/pFrVn6swEHfqv6wd9xOU/I93dW36aK+p5wOqUnyVWSF1WqLkGT8IOL+B48p5JKzyTme2+HZ86eoqkocxEiVBFVDYG11HARNKF6W5WiaCpIgTmLiJMbzOpRlyWQy5dq1YxaLOQ8fPuD4+AhN10GFLM+QFRnbsvCcFnWVEEYbbLfH7buv8IUv/A66LkjjNePnG2xVcNDt8Mrrt/jKV94DCdIsxfFaJElIFAeoms3J01P+t1/4Rbp7+w1wToLuowe895W3+Xf33iHPMz72sY+xnl1SxAG6ZnC4fwPbtshin+v7I5I0ZD2/II4ThjfvQi2QJQXd0AjjiDRz6fYGTCYX6LpOECbM5yuE1Nili0JG1zVankccRchIsK/gmB5KDXlSIAF5VuG4DqJSqXJBWVSISjQcFVXDc52d/TnG9TxarTbz2RJZlnBdF5CYTmbkWbLL4rFQFFBVmVo0Cdid9h51nqMrOqqsUIuKui6JogTH9oijGEUpuZzPSPKQXCRkdUKQhmzXAVVdI6sGaZJTlYIiK1ksZmiaIC9ifN/cWaTBsnSgRlEUkjinKmlcL1GI43pUdZMBNZ3NKMqSUtQkRUrpQ13JqJqBJKkURdlY7ZH44pfvc/v2LcI4b9Koq5J3779Lt9PDdlzW600D/YtDDF2l024jawplJlDYuZhMj9V6y2q5oNvrUogaUcu8cus1ZosFgR8jFIXpcktS5NiWRVle0h8MyMqMTrfL8/Mz8jTj+tENtu6WrCigFlR1Tsdz6LgWUuly4/g6y80CRzOpsgJVA0USzWskKoo8pyoqJCHQNQlDk0niklpI5HlGkobkhUSv7+6CPVMkdLIi53J6geNaGI6BIzQ2qwRFzrl1a58oWu5OFhKKEq5fv8ZsNUNSFGxLJ80kZDQ22xX7B0O2YcBsuWRvsN+kmFs2tmawmm8xFQtZKIz6I9brNXWxe081Q7qX9YfU93qs+244LH/Y0/1hMFy+33o5aHtZP/gSAkmBWpRoWiPe6/ZalFWBLEts1hsOD6+BkHj77a8wnc7wPBfXdXdprPUukdng8rIZG8dRwr179zg9PeXZs2csFkuCIECS4Pr1Y65du8YX/+CLXFyMWa1mqCoUeaNtePrkEaqqEWxDHMfli1/8A2azGbPZDMMwODo6wt/6aJrK+fk5pmk0ziC/SfFdrdfouoHrtVENi6++9z6PHj+mKgvWixnb5YwP7r3D+Owcy3K4GF/i+wH37r1PkoacnT1FkBOEW9brJVHoo6oylqURJ1sWy3Pu3f8yF5dnWLaOH2zJ0oyqFORZzWK+5oP7DynLiiAIkSSJLMvQdQ1JkgjCgCj2cVs2rbZLLSrCOAYULidzTMshTTLKstzpgFK63S6e5xHHMd1ul/l8znK5pKrKnSZkiyJLxHFIUWTUdYOer6qaoiivCLhhFJFlOUVREEURy+WCOI6RZQnfD5rQvCplsbhkMOzhtRzKsllZzRdL0izj9PQ5tahwPYeiyqjqJt+n022jaM26qSorsixruDK7YL+qbmBuh4eH6LpOVVV02h1UVWW5XBJHCVlaUgtQVK2ZBokKy9axLI08L/C3Ie+//wF377xGkuQURY2oFaARDhdlRpan1EIghMR2G7Jcbjk9G/P87BQBnF+MWW82CEliOptz74MH3H/wmHv3H3Lt+Aam5ZDlGUHkYzs63X6b5XrOeHxGlifEWYisCrI85v1773B5OSZOIsqyaMTJjs18PsU0debzKf1un2uHNxh0h9iGQ5VXKJKErjROpKrIEKJG01QkWUYAURTi+817vKxyNtsVRZkhSRJpmtFu95FlHVlWd6DHBncfRhEf3P8Ay7Q4PT2lrCqKMse0LWzLpshLVssVbc9hu1mRJTEXF+fkWYosw/nZKTKNtqnT6bA/GqFrBghwdtbv5iAsXjYqP4CSvsPlj/q9fxz1sll5WT/4kkBVJJBqwmhDloes13Om00uOj48Zjg4wTQdJblJl2+0Omq6jqCrmLkXZcR00XUY3FDbbDWmWkqTpDgTnYJoGr7/+2s4JYyIEfPzjn+Lpk6c8O3lCkSfYtoaqQL/XwdA0PNfj2dOnKEqD579+fAOQcB2Pu3fvkCQRs9l8Jy4MMAy9yTSSFXTNBElFsx1a3Q7Pnz3hv/z//j+Mnz/my3/wu3z6E29imTqf/tRn+dmf/d8YDPocHR2wWEzQDHj85H3GF893cDOf8cUZtciJkw1xusYPpmi64Pr1IwxDR9cNVFXn+vFtNquYPBckSYZpWhg7/cp2u+HatQMMQ+Ph4/vohoJuqmy2a1zPpa5hudogSSrboMnXMQyDsiwxTZPFYkGSJCAakWuv1+PmzZuYpkmSxFi2ieNa5EVCXZfougaS3DBvwpg4SqjKkk6nTbvdpq5rTMNsRM15DggkGWS1ArkgSQKePHlImqZkeY6Egue2UVSVLM9odzxUXSHJYsqqJMvyhu0iBHmR0+v1WC5XmGaTqJ1lGUmSsFwudynDKVEcXTlOwjBCUXSytAR28RCaRFXlqJq0u4+CIq94//0PuHXzVbKsQpJ0NpuIzXbLNvBZrlbouklVS0RhzmabYJoerucym8+RFZWyalaQumFxeHiMohrEaUmalziu29j3NQXPs8myCE1TCCKfINwynV2SlymWq9MdtFD1JjLi+fMTVqsFtSjp9TpYlkFZ5uRZTrCOuH3jVY5G1+m4Heo8x7V0WpaOpjYJ4kEQ7NxgJnVdoagyh4f7QIGmSViWga6r5FmBqCUkNDTV3Ll0dGRJxjJNPM9D1TRs26YoC8IoZL5YYNn2TtheoqoSnmtw/dohLdehyFLKPCfLc8IouopSAIla7AJNRYM/aPRBf4KfVy/rT2X9SDQrL1I5f5TTOf8k6gXk7Vtdvpf7QAjKsqQoUmxb58nThyxXc/b39ymKGsfxyEvBweE1+ntDkGUMyySMI2oEsiJT5MXugFdzdvYcWQbPddlsNmw2a+I4RFEUVFUhywp0zWC4N+QTH/8kqizzxS9+gen0AkkqcRwDeaehuX37FrqucXhwiGHYIGTeffc9nj0/Ic8zNus1hqnj+2sEDdBOUVQkSWU2WxIlCQ8efkCaBEh1zv/z3/wr9oc93nv3K/zUT/4kvt/YPOM4RNNkiipFkHFx+Zxa5Dw9eUiaxQjqZl0Sb5HkAstWCUOf5XJOnmcEQUC3O6DldfG3MVUl7cLqNOpaYJoWeZZxcTFG1xVMSycvE7b+it6gS6/fQ1YkDo+OWK03AFf5Tmmasl6vKYomKDLNmiiEzXrDs2cnJMnuYBpuqKocw9DwWk0Q4mazpdcdkOclQkjYtn3VMJRliWVZXLt2DcdxKYoC399SlQV5liHJEu12m7IsQUg4jsdmE7DxI9I85/GTR2R51liLbQdkFWlH1nUddxd70LBW6rrG8zzKqiKKop1bqSYMQ9I0pSgKdMPA2F2aSZBAiBpVlahFQbvdwvNa5HlBGMY8evgEx2pRFhCFCZIsY1oWZV2TZjllJUBWyfOavChJ0hTLtjEtE1lRKUqBHyRcXM7o9vfoDva4/+AhUZpS1RWWbZLnKXEcomoKpm0SpzFhHHAxPceydW7duc5HP/Y6w2Ef3dTIi5T1esl0dsnDRx8QRj737t/jwcMnHOxfwzY98rTENix6nRau0zQ0W3+zgyWmSJJEWTYTs8l0gqxItNre1aRK03XiOEYI2Po+Qghsywag1+83a7ckwfM8TNvEtC2iOKEoK/b29rAtkzyNKLIIx9TI4hBZEmRJgmk0NvdK1GRZxt7eYPf39EKA31zkl2OVl/VN9SOnWXlZf7L19TqXF02NoNmvbrdbwijEti3SKMJxXGbTBTdvv0IUpViWR6fbx3MbJP74YoyqquztDUmSBMs2eP/eOxzsH6CozeMURcHJyQNu3bqF6zo7PLdMp9OlrgXD4T6ObXL//ntMLsaUWYRp6nQ7Dk+fPkDIML44R5ElBoPXKYuaMIw4Pz9voG9Jgp076LqymwyFjIYHmIbLX/rF/x2vP+I//od/h6nKZHHAndvHfPWdt7EsC9MySXaai7LMKcqcMNzy6PH93dltTqfjNU2W0sK0NEJ/Qy0qsjxlOBpxcXFBt9sl8EMs0yMMIyzLRgsCqkowny92ehWXoixQchjuDVAME0VVSfMcQ1FIs4Tbd27x3//n72FZGnXaTB7quqaua6IoujqIVWVJVVakVY0sSxRlTl6kKKqJ45p0u81qZb0OEJuI6XS+w9s3kQzHx0csFlPyIieOJYIgotXqU9gVeZ6SJAXtdvO7BP4S123R7Qxpe31Ozy/ZjiMkSWCaOl6rRctzmE0nTTilrDXNa5k1BGNNI45jsqykKMtd8nbzvqiqekdMbsI02+0WaZ7S6w4afH5a7/Q4DrUoCbcNIfnrSc2dTo8sS9lsNhQl5HmNohhkWQmiatYXlkMQRpRlRC1qQKIsa+I4w7EdwjDdPa85iiphtUwOjo7YbJfUNUjItNpdFqs1rXaH5+fP6HQ8pvMJH3njTd57512iqECSaixLR9eVJp6hKsjzFEXTma+WXE5n2G6LVqtLkuWYpolt2cyWzZqx4afYLBcBumaiqjKeayPJKkmcoqi7SI3d+8BAQgKquiaKM5BgtVqhSAJF6bBcXdAZ9NEthyhMSNOCflfB81xkqSYM1ri2g79Z0e70SeIQy7SYjC8Z7Q3RdIu9PQdFUb/u8+NH4vz5j62+kznjR+nk/eU742X9sVRdw3y+pN3u0G53UFUNfxtzdHgD0KmEhB9GmKZNWdWMLy9AlhiORrQ7bfr9HmVZcHb2DD/YUOQZr776CtevH2NaBheX50Rxk8hsWfbuTE0GoWBoDq7T5md++qd499232WzmjM+fsVhccnF5DtR8/GOfwLZsDMPCtptcoaoqkSRYrZa88updfuEX/hxCVKRpThylgMT999+mZWucnTxEFgV7gx7Xrh9zeOMGw4MRR9f2KauM999/F0WRCcMAa3eWOl9MSLOIomiyb27deIWiaNDkUZSxWmzJ8xJJahwi8/mCBw8eEEWNTiVJ0h0cz6aqyh12T7D1fSaTGQ8ePmG12pAXBevNhg8e3cNxDWqpwratK1t4URQNFdhxALBsG1lWrvQvstyIMZXdKg+pZrmaU5YFsAu3REaWZZ6ePOV3f/d3AdDUJvlakmTyPNsxUUryFLJUYFttBv19LMtp8puCAIGCobsUJVS1xHyxwnU7+H7KfL7F90OABpq2t8fx8TF7e3vs7+/viMW7piRNCYIAWVZ27wkL319jmirzxYSqLppVlqZzeTljvQ6YTC5ZrWZARavlNnEQRYYQoGk6201EWcoIoRCECUgySBJhHKJqMkVZUNZlo+MoqmbahMJm61NUJUKpQRZcTi7RdJ033vgIrt1ClXWCIMGyHLZBiBAySZoRpxnT2ZS94d5OJ1TsdEIFrmczHA1AEtRSRVymvP/wPn4UEcQZAoUgjNhsAxzHpq4q0jTBMHSyLEGSpKapUGSGw0OKQiDRZGnVokJRJSS5xms5tFpNeKiiKBwcHGAaJp1uh8HeoHmvKDJ7e0PqSjCbzcjSBE2VmVycMbscYxkapq7z/NkJdV0RJwnj8WVD5zUMQLzkiLys71gvm5WX9QMvIaDIK1RVJ0tz8ryg3e7hOC1ct8v4/JIsK5jOZnitFkVVEsUxb7zxBoZlYugaQeBzcvIY3VDRDYXxxTmTyYQwagLjLi8v2G63uwPu13besqSQxhmqovD2219mb6/HF7/4ef7rf/2/KYqIs9NneJ7H7du3Wa3WzGYzTk5OEKKm1fLo9/s4jsPNmze4vLxgtVqBgG63B3VNnoQ4lk6/2+KTH/8ovr/F8TzavT7v3Xufx08e8PZXvsjdV27zwYP7HBwckCYZutHkuuR5SlVVaJrB/fsfIEsartPG0BrI2/7oEMMwuX79Ou12i/6gh+2Y9Pt9sqygrgW27eC6LnVdU1WN+ybLcvK8xHU9QGI4GhBGAaomURTp1VSlqqorTossy6iqSl3VZFneIOUtuxE7yhKLxRzD0FGUJkqhqiqqqqaqBIZpUtfNitBx7CtoXKM1yXbi2uaAKMsm200KQiHwI9arLRfjS5I4Yb3ymc3XaKpFVUJZCC4upnQ6/Wb1pWrkeU6aNDyZKIoaem4SAy/SvqUmRM+xUWQZ13XRNA3D0BEUVHWOLIOma2y3PnUtWK+2TYijJlHVOfPFFCFK0ixhs11zeHhIllVkuSCMUqq6Ce+U1UYD4rUaC/WL5/SFHmM+W3BwcIjXchCUZEWKoio8f35KHGeslz5RmBEGCWdnl8RRE+qoqjplUZJmOVUtaLfbu6TzRvej6w1vaG844ODaPmmZMl8vOZ9MQFbIy0YzI8tNHEOv3+NiPGaz2TTp3JpGURYslyvm0yXr1ZY4ThE1uyiMkk7HRZbFVU6UpmqURYGiyERRhKHrVKKkrEuCKETfrY90TWua+iREV2VsQyf0t+RZxmR6SRiGvPveu8zmS4qiQJblr63x+WNBdrysH8L6oW5WflQ0Kh+q5kZI3/pypdn+4yH6fL2uRQKqoqAum7PpJ4/PsO0ub330k9Q0uSwSFd12i7LISeOYTqtNkZeYukkpBLrVhBUqkkwaJ8znMz7/+5/nfHxGEG5ot70mDVqCsi6pqcirhDCZ8eDRF3nnnS8wm51hGDW2LZCklPOz52RJwnI+4dnJB3zwwVeIkw03bhxTljXd7pBbN1+j191nMfc52D+i1WoBNReXZzx5+ojj45sEYYLXGeC0eyw3GyRZYrmYUhQhZ+dPuLg84fnpQ8qyEabGcUwcZkwvtyiyg6ZaSJLEdrsmioOdeFXlYEf5TOKYTqdFFIdstk14YBD5VKIGRaWWZJI8R1JU9oYjgjhBNw2KMmfjr1ltV6w2G4SQiOKcohRUdUUtBJbtIkkq3W4f07KxHQckuH79egPB2/qUZYVlObRaXSRJYXw+Q1QasqKTVwUFOZIqqMkxTRUkhSyviJIEbSfEFBVsVjF1pWLZNpquNnoRubGTdzod4iRju91CWVGkKaKqkSWZ5XKJYZhoqgZyje3Z7B/uo2g6UZIyXy6ZL+aUVUFRCaIkQ1F1aiFRVBW9QZ+8zGm1ulSZhoKOKqt4jo2pa8jIaLJGllf4YUyalyDJRElKXhbkZcGjx4842B9QFQmKBHVVEcUh6/WSrIhRdOgNeqiaRpxkzBZLDFPj+OaQtFyiGwqGYTeOl9EQP0h4/OgMy2lWNnmV02q7mLqKaSiIqiJLc2TZYH//Bv29AzTdohICx2vjhzGO2+X4+h3aXgfXNkDUdFodVss1eVrQ7fZRVZVXX71Nt9UiT2s2q5iykMiykijKqWuN+WyNpbepCx3P6aAqKtQ1MjJxlFMVoCgqjmux9hdoloQfL9hGc1Qlo9vW0dUKz7HwtxsQjXg5KzIW6xlR4rNazQj8Df1+j8Foj/6oz9nkgtliw3obkhUlpSip6wKkmko0oLiX9bJe1A91s/Kyvrm+gzFNojHKf8vLh/DouznuNzQqu5BDVVXZG+wBEt3OgCwvabc7dHs9ZFnm2bMTut02uqZS5M0ZvSIr6LqOJMmcnZ+x3qxJ44Qkjtlut8xmM+arBaqqomkadV0h6mbUXomch0/e43f+x39ifPGQLPeZzcfUdUqeh4TRhu12w6DfwdAVnj17jKoJ4rihn6qKQRQV3Lr1GteObpCmJefnl2iawXwxJ0ljbt66xbvv32f/8DpJXnHvwWMOr91gvVqhIKjKjI997C3KKiPNIvxgw9bfcnh4hGW59Hr7RGHBdhuSZimCGkkSFGVKy3PJswRdV+l22o1LB4EkS+RlQVYWlEKQFTmn52dYtkMt4PT8HMtxUDQFRZPI8pS8yBlfXOAHIXUFcdxkC7Va3k7f0+HatWu78EcIQ58gDHei6JI0zXgxrcrSHNNwQaj4QUiYRKimiqxJINeoikQQBbTaLVRNpyhqTMOmqkBCwbYdoiRkMrkg9H1s06TlurTabZI8RVUkNEVClSUMTW3sqwKyLEU3dAT1Fdpdlpsz/DzPkCSBbmhkaYaoBGEQoms6koD5bI4syeiKiW20ccwWmqyiKQp1VVKkGf7GR5Z10qzGD2KSrCAvS+IsRTc1hgd7+NsV148PgRpZavKWHMchL3PW69Uum0oQBDHdTh9VU1B1QM5ZrZcUeYFh6CwWczTNIIxSHMdFVhq0vEAQBD7UFaKq0VWTIoPxeMbz5+ekecneaB9F16gluJhMmS/XnJw8483XX6PXbnM5HpNEMWXRWIg9173K7znaP6YuZapKQlYUJFklTQrqEvK0wtRdVMVAkZv8qOlkTp5W1JXAsmwURcF1Lco64/YrNzHsJjxRkysm4zPi0McyLcIwwjAtdN1gNr9ku12SRD7j8TPuv/9VNps522BFq+0QxgHT+SWVyKnqpmmrRI2sSC9blZf1DfWyWXlZfyy12WyaMbygEctaFrKiMhj0KcuS1WrJfD5HUZoz7saO2vRQZVVSVQ1T5Pr169y8eZOPf+xj/Nk/+7Psjw741Cd/gs0mIAhChChJoi3j86d84fP/nceP77FYzZFliU67S14UuJ7HerOm3fHYG/ZptRzOx8958uQRLa/NvfcfMNw74Prxbe69f48nT56SZxnL5ZrzszGGYdNp97h2dMytm7cwTZM0zXj+/JTPfPrTLJfLnbOmgY598hOfIc+b6cTp6RmWZVNVFaPRCNd1qKoC0zRxXY/rx9fZ3z8kDCOyLMO2bSzbYrVaEQQBSRxj7ALgFEW5+m+W5w1XpNNFlpXGdVM17owsy1kuVrRbbfI8Q9NUOh2PTsdFkmrKMuP+/fdQFIHjGCiyhCxJ6LoOgGVaTKdTfN8nSRJ0XUfTVOI4RlUUdK1BrRu6Tl7kGJaCbihUhSCNa8pCIUtzsjxguRrvHDkmr7zyGt1ul+3WJ/C3ZFmCkCo0TUGWQVFkJElGCMF6vW7WZYpKXZRokoImq+RxilyDbZiUWYZtaHi2haYohNsA27CxNItX77xBkdX0+/2rwMUX1m0hBLZtoykaqqxhaCZVUZIlCaIq8VybKNgihKDX66MqGkXRHMQVRcU0bSRZwWt1QTT2a03TqSpBGMTUlUxdVZiWDlIJUoUs1yyXM2bzCWHkU5QVy8WG7SZivQzQVQtQefr0ObWQSPOcWgjG44bhgiSRpAmnZ2fkZY7jWHS6LVRVQlYkqrokz3Mc18HzPLq9Dpqm0m57ZFlEmjZrv7IqSdOEoiiRJOlKXKzsAgezNGW73hJufahqNEXFczxcy8M2XKIgIYkLFEXfTXKabKLt1idNMzzXRdQFWRqyXU/5gy/8d/7v//j/4t67v89y/pytf8njJ+/y7ntfJEkDzsfPSZKoSXZ/qWF5WV9XL91AL+tb1jfbk7/fEDHbtq/u9+jo6IrDoSgKo9GIGzduNPyMLNtBvTRkWaUoKhRF4fr1m8wml2yWC/7sn72DkBRGyCRZxuxywmc/+zm22zXL5YTtdsnvfeF/sN3MSOKAqjA5ODjmvXfe4ePDV1hvtsiy0hy4dZX33n+XzWbN8fExm02Iomokcc7wjUNOnj1jsVjwxhuvoSgK/jbi2tF1hsMjLi+nTVBiVXLn7h0ux+eNBmA+R9c1/CBivQ4Y7e9x7eiY58+f4XnNgayuKkzTwLIMkiSgLAoqTebw8Ijtds3l5ZTe7sCaFxlVVXF4eIjrdRiPZ+i634TWicbNs1quaLccNO0FV8UhCAImkwnb1QZFVlEUDU3XcRUJVQFJlKhyjaHrlIVEFDZrKahYLBZomrZrKmmmVqImTVNGQ5PJZI6iKMRJhqbImKZJlmb0Om3qOsMwNTTdJNxmBNsZ7ZaHbSskeSO2NU2L7cZnfHZOLQSFiHbuHpsqL7FsA6QaSYIsy2m12qRJyt1bt5lNL/B9H9MwkQXYpkmepFi2xehwQJpm6LrFsLuHqprcuHGLr3zlq+iKTpIkOI6DZZn0Bz3Ozs7Y2xswmczptLtsgwhN1SiKjDgOqcoCISokSRAEEU+fPEMIGUlqwirzvKTV6qKqEovZmihKcd32zhpdo+sWoJEmEZomoesyaRojUOgP2hRlQi2K5sDudYiiiCKHqlKa9ZvpMJsumkDL9QbDaKaNq+WSlttBllRqJKazCaPhIVEYU9cNibbVspnNpiRp2Kz8TIfDoyHLzbJxB8lSg9+Xm//fbrfISqsRQuclXsul2+nx7OSCqhKNHqcs6bT7pHFOkcPleIVtF2RpIyjOs5xWu41p6qxXc9r7XbJ0zmDQoRYym814l5pd8Du/HXD95iuoqsKzL90jybaoioEQcPvmq9Ti5dn0y/pavWxWXtYPvCRJRt6tdMKwsS5rmtrgzIuCa9eOCMItpmkyXyw4Pr6xCy9M0XWDJE0aTLuscufuK814X9VIiwpZkjk4PGJ8fkpRprzzzpc5O39MHG4Z9ttY10bMlhtA4eOf+Ayz2XNqoXJ84xaaYqMoGp1OlyzL2Gy2tLwud+64VIXEO199l9deew3dUBmNDgjDENftMJ0u2B/exLFbXC4uQdQYusF2u+XZ82c4jtOEAuoG262P69qN4FICSap58vQhe4MRYbil3fGIYp88zymrgqcnz5AlkGVlx41ROTw65IMHH5AXBauVz3LlUxQFqqpSFjnAbgXR6GHarTZ5XaAqMnkt6PUGqIpOlucoikSS5Qz3DijLElXtN+LSMqcqCyxTR5ElqqpxB+V5hh9s0HXt6jWzbZsoihAIRF2TJI27xHWcxu7b61JVDWFWURQsx6asUsoqRdfVhnOSZGw2AY7jkeU5SbSlFgVCFJimiqYplFWJojSi3yxroGab9QZE8/zUdc3e3h6j0Yj9/X1msxnL9RLHcSiLmsAPuHVrn8nFhCIryasKUTYsEd9vrOTN980ZjYYsVxGaoiCJmkoCRZYxDZO6rLBMG02Wd0nFFRJgWw5FXrFabYiiEMe0d+ngMooiIxDkWQ5SjawoVHVOltfYtskiDtFUFVXVabc95vMIz23ca1les5hvcBybNA14/do1gjgESUI3TOI0wW25TKcT3nzjI8xmlwhRsd4siZOYqs6xHaOxwGdFEzSpqTRcGcHx8T5xnKHpCkhg6AZCVFRV1TSdWU0UNa6r5XKFrhqkcUiWpERVhYTAtDSiKKCuNOKoREJDQuHOnVeYzWbEUcpw75C6zonjHFmRSaKIds8jTXPKLGB8vkFSG1G053mML05ot/aIwpQbN26hSPLOIfQSEveyfsgb1x8Vu9ufxhjwF5Cmr49m/+avfavb/GHfq2kNmVLXdcqyQtf1q/F+WTYrEcuyaHfau/F/Yz2u6xpdNyirildffQ3X9Viu1oDEZuMDEmWVs1rPkaSSd9/7MmnsI+qcJAppeS2ytETTHebzLScnF+QZmKZHu9NnOp0jSxqaauC5LTabLfujfXq9HlVV8vGPf5w333wLwzB59dXX+Zmf/n8gagVNM1mvfEajEaqqcvPmDXzfpyxKfuInPofneYz293Ech1bLA0mQpCGr9Zy3PvI6WR6hqjLz+QxFkUmSBN/3kSWZMIwAmbPTM+7du8eTx4+Zz+fYdtMEKTs77ouxvaIoO3BciBCC6WzaNC5JTF3VFEWFJMmoioYkS9iO2YD6ioK6aiYreZajKc1911VFFMUNW2Tn1iiK4irB+dmzZ/R3cDBFbX6Wluehqirdbo84ytisAyzTQFEFZd0cQLMsZ73aEEUxgR8yny2IogRVVRsXkWsx2u9ju41LStdV4jhAVRXiOKIWgizPKaqSTrfDjVs36fS6nF+M2QY+F5NLXK9Dq9Pn+dkFQlYYTy55dn7KdDEjy1NWqxWGYaDrOpPJhIuLxuGV5wWaKqFrYJkqhqrSctxGeBsXFGndoPeF3GQqCRlJUqgqwXYbMNwb7VD1CUkaYTsGigK60TR5hm4Q+CGqqlHVTUSBIivN34Uk02q7FGUzlRB148DJ86aBfPDwA6IkZb1u1kVplhHFEe1Oi0ePHyCo2OymJWWZ0+m06PU6vP76K7Q7LqqmUotq53TK0AwZr2VTFBnDvT2iKCKKYoqiiUkQQlCLpgltt9o4lsvBaJ9Oq8Xdm7eJ/JA6F2yWAXWpURYKUZiy3QYcHh6Rphm3bt1l/+CYbVBSSyabIKEQgjRLgZo4Cen2Ok1CduxzPj4ljAJ8f8O9++/y9OkjalG/bFK+z/rmY8uftuPM91I/1M3Ki/phfxF+HErTtGbaYDRQsboWxHGMbVsAdDtdVFUlz3LKsrrC5jchdQnz2ZxOuwkszPOcJMnQNY0kTdn6a2S1Ji9iHEcnjgJ0TSVYb1kvN+wfHJNlglde+Si3br3Bwf5NFMWiLKDV6lMWEjdv3sW2PQaDAdPpJYKSwaDL229/mVarRb8/YLXcUJY1gR+R5xWvvvo6/f6gWccA0+mUbq9Lq+Uxny+Yz6bs7fVYrucEwRrL0qjqjM1mTpL6rNbzHTK9oChzDMMgSVI8t90QQyWa8bzfWLLTJMFrtdANnTRNUZWmUcjzHEmSMHS9GeVLEnVdosgyw+GoEZfqJlVdX6H14zglTQpCP2zC9ywbkFmvt6iqRlmWRFGEaZqoqorrujsNi8RmsyEIAnzfbwIAd1Rj3TCIo5jQL5pcnTKl3TXYP+hwcDDANO3GrrveABJRFOG6HnUtqOsSxzHJ84SybKYRlmWQFzl1XQHNhC7OUyRNZbpc8OTZCWESY7kO4+mEdr/PdLkmyUpMxyVKE/KqaBxLdc7aX5NlGfP5gvX/n70/i5FsPc9zwef/17xWzDlnjbuq9rw3R9EiNdCWrSMdW75wW0BfHNhquN19RTVgCzAMG74wbLR16SvBaBwY9oVbgGHABho6Ph4k26IoUiK5Se551665corMjHHFmof/74s/KknK4kxJHOojgmRWRkVFREau9a3ve9/nnS9QShHHMWAEvINBB9vWQEuWpdi2Q1mYFUdTQZaVuI6HlGZFY1k2juPx8kuvXJzk/cAlCB3CyKFpS/LcuLt832eVZBRFBVqiWmgaRZJkVHUNGB3L1ubGxeeprAo++hMfwnEkaZphrQF4TduyjGPqpsb1HZRuqZuag4NH2I5E69bof2gBTVkYe3xRZCjdoFRFUeaEQbDm0LhoTIPkrv/tMIxwHIcojMjSnMUsNplMqsX3QpaLhDDoUVeKKOwRhj3aVjMej4miDtPp3CAJahvL7TNb5vhhj7JWrLIcpM3dew/oRD3iZUKe59y9c4eiNGnP9+6/j9YtSn2HoalP60e2fiSaFfj6QKUf3Yblm9mOv//W4+8Gr/+NSkoJCLrd7gViXanWBAomK5bxkocPH66bkZKyLNewsppVsiLLMtIkxfMCbMulrmuOj4+RQrOK5yzm53zxi5/jnbffoG0q9nZ22dzYBm3R6w148cVXuXz5Os8//zI7u/tMJjOquqHXGwCSxSImjpM1rTPl/fdvE3VDzs7O19TYCM8L6XX77O7t0rYNB4cH5FnGxz72Mc7Ozuh1u/jrrB3HsdncGvHw4X081+bGzWdo2oqmrVitFvT6HZq6Zmd3F8/zsSxz9b25ucUqMc9DWha+H7C9tX0hhlwuFjRtA2jKqsS2bCzLJgxD9vb3MIFzRmDb73cZn56glCbwfQb9PraUlHlBnpXmBLgWRqoWiqLEsT0c2yDpNzc3qOuaTseESlaVgceFoWGvOI6D7/t4ayFuWzdkWY5qJYFvqLBFsSJJ55xPzowQFZswCHBdB9f1KcuawWDIaDTi1s2brJLVxc8/jld4bkCWFViWjZQWfhAQdiL8MABLkuQZrdY0SvH44Ig4znj3vbskWQ6WxcHRAYcnB0T9EGGZiAHfc9Fa4bounXVOT5Zl1HWBkIq8SAhCD9u2aBt1kQCtlaIoC/wgQCmoqubCWt3v9+h0ImxbonWD41j0+kZDpLSiaVp6vT5lUeM4RmheliVFXqKVMNOjPFmHfdpIKWiaiscHj5CWWYc0bUuznqR5nkfV1GZS2TY0bY1YBy0KCdu7W9y+fZvFckGrWtIkWUdfKCxLEvgmITxNV3i+j+8F9PtD8/NTykyI8hzHMWLqzc0RdVVxdnpqWDZCAha+3+H8fHYh0F0uDYivLEuCsEuvv4XSLnFScj5dUpQNWV4hLAchLN566x20htHaZu15Lp1OxNHRAaskXoMOuTi46/V/ntJYfoDrT+j09COhWfm63uRH/vP7rV7g96dT+14Ftf/z433tY0ocB0aj4XrNAG+/8xa3b99GKU1Vlbiuy3A4wrZtJqfH9KKATqfDV77yOi+9+DyT2QStanxbIXSFqjIW52N2RgP6vQHn5zGbW1e48dxzKCE4Hh8TeCFvvv0V0vyU08ljhFVzen6E43jUdYPjBZyfT9na3uPq1Q6HR2OuXbvGpf1nyNKKKNzEcW2krTk4eRd9rNlfXuXy7g5SQ+B7HB0dcO3aFXr9DvPphM3RgMANePf229Q5aG0zmyXs7nVxLMnZ8TnD3oijgyM+8OpNHj1+bCYfVUHYjbBsydn5mH6vR384omkFj0++jKZla2NIEudorcjTFY4zIvAdBDYCF9+W9LshruUT+Dah61K7HbIsY7KcYbsOgpZOx8K2PVbxhDCo0VpSVQVN61HXGY4bIoSHlA51JShbRdU2NGhsy6IqS7rdHukqQSBxXEldl3Q7A+LlnHgZ09ZQlQmWtImCgGk2IwhCpAvTxYSyqnj84AihJJZlQhody6KuNVLaTBcxm5tDdAtR0GFjNCJNV1RVy2y64PKlZzg7iWmqnF6/R9uC1A2Dfgc/kDgubHh9FpOYvEwZDLpsbW/jez6HB0f0OwG2o9juDQnDgCyrqYoWITWImgaNcKEtWqS0kZZFVTdEnYAsn6O1R+h1qKsaSzhkaY4lLLRlqMJFuWJze0TbNAhp8cJLz3F0aBLEhTAW7TRdsr29jRcIqthMGMcnUza3N6nVKa4r16GSFXVZMez1sIRFKx2jEWprijrHDizmqxl5Y1w6UgikI8jLFtuWjAYbnJyc4UuXNFlQVRauE1BkLb4PUeRgiYDDR8d0XxyirZxpHNPrj/DcDqtVgmprHMfocLRUuL6DG/g0uiHNc7qdLnVTcT6ZkSQJAoe6hrxoaRUs4pi6rbFRzOfnLOMFW5t7LOKE89kMy1qRlClRt4cWNlJIA//XAIaijLb408v8fVrfrAR801OT+D6cl3+oJyvfLJ76W0Vhf69R139mUdl/ijnd30iT8t3UH53QGGqlpmlaDg4ecXT0mPl8avgbSYrWkKYpi0VM4PsMB30zZVnFKK0ZDPtMp+c8eHiX0/Ex45NjpBBkacb4+BRpOQw3ttnc2uHtd95mtVpyenrMZHLGfD7B8x2ybEVVFURRRJ7nnJyMeeHFl2gaxfbOHs899wJ5kaEwY/eNzQ2UUty5+z5llXE+OUapiiRZYDuSeLXg/PyML3zh8xweHjIaDikLMyV4Yne9fOkqAEma0DYtSZLS7XXxfY/Hjx/R6XTRmAThMArNSqBV1GXJ6XjMG2+8Qdu2dHsdtrY2CcOIpqpxbJvxyTFNW4MQbG1u0u10ee6557h8aZ+6LDg+OiKNVwz7Q4IgMATStmY2O6dpjPj2CXnWcR1OT8cICVVVMpvOqMqasqzJ85LlaoXjmJNkXTWota1VoEEbnL/n+RRZjcShyEuaxqTrLhYL2rahaSoOjw44Oz8jSUzGkMknatEKkiSnqhqKokIg2dzY4OMf/yn6/SF11fD48RHLRUyeV5yfTSiLkv2dXV584QU6HUMd/vCHP0i302W5WCAE+L6HFNJMbpZLxuMTbt68RRCEJEnK8ZGhKSerjLY1n1fPMyd8S0rQDU2VYsmK4cCnzFdsb2ygmoa6NhoTz/MIfJ+qKqmqArUWCqMNjE1rwXK5QEiFkBppCZR+IlxesbW5gW1L2rblmes3kNLGdT263Z7hyChDhK5rRdtomkYjhI2wbLa3d7Edl9liQdtqqqqlaRWBHxCGoZm+1BWDQZ/BoIvrSFRjco18z8K2FK4rkLZmuDnEdo0Nva5L8xposR3JaDTEcS0czyYMA6QlGY9NftOlS/vM5jOKssC2Ja7rrP8ua82aSb22HYm0tJkKCYhXMcPhkKIs6HQ6vP3O2yziGMQTWObXcKGe1vdc32/5xPfr3PqN6oe6WXlaPxwlhFivgbhwiRgkuQmeE1KT5TGbW0OuX7vK5cuX0FpQVZq2hd29XYLAJ89Tnr11C9/3WMyN/uDo6JAwDFksYj74wQ/T74/o9vqsVgmWJXn77bfIs5Smrnj44B47O5vYtkXg+/R6Q65cuU68SqiqBsdx2draJk0yVquU//UX/zK2LRCiQVPy4OEdHj++jxRwcnJCVZXcvfcOv/eZ/0Fd57Rtw6PHj8iynDAw2H/QFEWOUi37+3v4gYeU4gITj9AcHR2ilGK1WtEqg6dPkoS2bul1eliWxSpekaWZEbJaNm3T8PjR4wsNg23bdLpdLu3vg9YUeclkMmW5XDKZTtFaAZq6qSjLHCkVabpEqZokMU4c2xHYNihVI4DBYMBisaAoCjzfw/c90Jp4lVBmJapVFHlJFIb0Ot11qnKM67rkecHZ6YSybLBsD9s2Wo/t7R0TSGhZjEaji4YnCAKapmG5XJFlBY7jIS2jv0iSGCHgxZde4rUvfokvfuE1Dg9PsKTDZDLDc30Ojw7N1KHIzc+7aTg9PePO+3cpihIpHS7tX6aqasqyIk2zddr0AjBibq0kxmbcEC8z8rwCbdHvjcz6EQdHCEIPhgOLZ29u8+yNy+SrlI3hJnt7OwyGPYoiZTafgGjxfJtuN6CsapQyq5OyrA3htczJshXdXkCSrIwjrGmo1llN3U4XpRS9Xp/QD1Et+G5AXbdoLVAaGmUaFSld0DaLecpsEpOnNXlWUVfNOoywWvNdoD/osrE5oN+P2N3ZZGsz4uUXr/LSS9d44cXrCLshGgbcfP4GV69fxXFcqrpEWpAXCZalsdafE9aNdV0bVlAcxzzzzA0DanQdlK6p6pQo8ggC/wLgqHVLXZfrlZjGktbFyrdpKuaLGWWZk6YJq2RFq9r1hfuf2mXh0/oBqx/qZuU7wdN/X5H2T+vbKjNJ+eO1L03Trg/YFUq3JGnMfDFjGS9oW6NlUS3469wac3VmMRj22dwYUdUlnU7AaDTk9PyUq9evc//BI2zHRyMJow7L1Yr377zP7s42VZWzvb3BeHxIJ/Q5PDhEaMHBwTFV3XLtmZtsbO3w5dff5OBozHQeMz4/ZzQa8oef/wPqugBqwsjlhRefp9Pp8OKLL3JyeowWLWVdEHYCqqrCclw2Nrc4ODThinmeURRmXbNKYlzX5tatG0RRYJDztsTzXHr9rtFJtDU7O9vrZqDAlg5toxBaIJDUZY0UFvbaZeJ7Rn9gScnjgwM8z6MoSnq9AXlmcoDyIicMA5Qyq43BsI/tWLSqwbIlo9EAMFf5ZVmYNcdauDscDlHKpAkLKbGExWi4RZYU1GVNVVQs5nPyLGNrc5O6rplMZpyfzdDaJlkVpEl+MVlpakW321/TXk14pecFRFEXrQV1ZYTAjm1hWYKoE7C/v8dbb5kr7f5ggzDsUZaKIOgyHG1iOw4bmyP+ws/9eSzbMnBBBXGcUJUNWVqwjBNWSYoWAj8IsGwHP4g4Oj7hxs1naVvBKi7QygYtEdgURU3dtHQ7PVzLwXcsdrb7bG+FbG0GqCbjxeeeI1nEVGVBWRZEnRDPc1kuFzRNTVFmOLbLdDInSVKWy9g4uXSL61nM59O1nd9BCNPIdrtdok7EbDYjiVd0wg55mlGWFa7j0un2cAOD30/SlKqqKfKaKOqjlAXapm2hbTWtamjbCiE0YRiYBmC1JElXPPPMZX7h53+aKBS8//5bSNliOYLBsMv9R/e4c/8uly5d4crlK7RtRa8f0bQVaboiyzOKvLgA7I1GIyzL4u7dO2xsbCAEtG1J01bUTY3WsEoM8M31HPwgMO6opiVJE7rdDnfuvIfSDct4SllmKGXYLWgTdmj0D380QuRp/TjUD3Wz8qSeNh8/WPVHxblPwt0AkmTF6ekpeZ4zn89RquX4+JiiKEhWq7WgM6KqKlzXIcty4njJ/Qf3OD09MRbfVcxsNuUrr3+Fs7MxCHj0eB0Mt1ixtbVL0yp6/R79fo/T8QmT81NOTw+xpDZpykqwmMVsbu3g+SHSsrl6/QbT2ZKNrW0c10dpyc7OZV584RW+8IUvcnp2aoLtFjM6HeOW6HZ79AcDtIYkyfCDiOFwg+Uy4fj4GNW2SEvQtLXhlTgOeZ4xnU2JV0vq2nA/lDbTkStXLtPpRAjAsV3qssaSFkILfNdHtxopJFEUEQYmbblpTRMwGAy4fOkyWptmcDZdsLOza0SkCHZ2ttne3iSOFyRJcnEl/8TK27YtYRhi2w5VWV6IaOM4xnO9dVJvF0s6lHmFbbl0wi6u43J0eIjnuAz6fSO89UIsy8W2fCzpY9s+rmM4O0pp2kahWo1ju1RlbZqqvKRtoG0FYdBhMBwQdUKE0PT6HdrWoPvn85iTkwlR1EcIh4PDI7TW5EWOxjxn13VZrVb4nk8Ydul2BxwfjQmCkDwvyLKCeLXCdhziVcKXv/xl0rSgbTTLRYznBWRZfjEeb5oa2xJ0OxH9Xpcb168z6g9om4rTkyOauuLg4IAkWTGdTmnbBtd12dnZMeubqkRaEsdxcBznQpcVRR1As1wu1yuwhuVySd00WJbNdDoFrel3Owx6Xex1AnZVl5RVieXaCEus124e08mcqmwBy7i62po8N9O3oijXjYUgSRKm0ymvv/EGd+/ep6kVnU6POE6Iog5FURNFXZpa8/7te7SNpq5bHMemaSo8z6Xb6RqOThCsic3Omt1jwhPjOMbzHdz170mvO0C3kniZgBY0tSFT2+u/57g2SRLjOE/AeS2OazE+PaFpm685zgt+RE5dT+s7qB+Zn/iTD/IPg5/8z/L5fTP9yfdLo/LkdbVt89XRdmUsqFJabGxscHZ2RhiGJEnClcvXGA426feHa8dJy3DYJ4x87t67w3I55wtf+DxKa65fv0q5voKdTM557/a7nJ6dsru3h+cHKCV4/849rl27wfj0FMsyQXmqrQl8h/nsnNOTI+O4EDavvvIBHNdHSodup8/P/dxf4saNZ3n55Vc5P58RBEM+8uFP8Df/xv+dL33py2RZSrcXEYYBx8djbLdDVcMHP/wxbDdgY2uH0/NzTifnDIZ9gsBjPD4hjpcMhwO2tjYMvbdt2dnZxvNM4GCv12M+nzKfz3j22VukWYotLXzPZ393Hykk8TJmPpuTZzmz6YwkTYztVZtEXK2NHTzLM0ajDeLligcPHmJZ9tqye8bu7vbabVOhWtjd2SfPy/VJqkuel0hhuC9KaTqdLnmeUzc1bWu0BmhBvzfEczzaRuHaLi+98BKdKCJZrVBKIYRECIckzsmSAinsC0v0k5/7fL7AdV08z18Lq42ttxv1aFtFmq4QEq5evUwcxySrhLPzCZbjE3YGxEmJsDzmi5io1yUIPe7dfZ/VKmY6mXB0dMLJ+IzFPKbfH8I68DEvK9K8oKhq8qJEC4ESgjAM6XS664lfaRoUW7JazUnSBZar2drdRGvBdL6irDSO67NM5vihQ920NI3Cc33KoqapDeOmaYwNH1o8z+WJY0sKi7bRpElB2ypGo9F60mYjgLPTUwCUbsjTGMcS9LoR0tLYjkCLBj9yEVKRpvH6hG84Rm3bICVEkY/n+ZRlY6YvRYnWkiwt1s6kgFb5PDqYcXQ44/x8xbC3Q1sJ7r//iNPjKVq5rFYFbaOI42TN7bGpa4VSet2AKu7fv88LL7zAfD7H9/31hNTkQa3iHK1sfL/LlSs3yPMG1w3w3ICmNs7AxWKO41p0exEbm0Pee+8dxDplum0bvs4W9HSq8n2vH/Tz5o9Ms/Kt6gf9B/GjVFpDVdUkSUocr0jTFCkFtm3j+z5JkrC5ubkOMHyE5wXs7V7h+ede4v79h+R5gWVJlKqRUtM0FUo1aGUsu0m6otOJuHx5H8uyQGhGGyO8IMR2PF555YN0ej0T1iYEWxsbjE+OQBt2x2IxZ7lYkq4S2lbjBx3OJjPmccxktuDWc8+hpeTP/eRPsTm6xGiwxzPXn+Xa1eu89dbb9HpdyrLg9Oyc0XAPS4ScHE/x/Q5XrlwDvhogOJvNEEKvgVuG5Gms2zX9QR/Xc0iShDRd0TQmp2WVrOh2OqRJRppkTCcz2rpFIqmrmrqs8TwfrTRCmPdVaUVVVXS6HbqdDnle4vs+AIv5nCAIGG0MuXPnfXZ2dxBYdDp9Tk7OaGqN54aoVpClFULYbGwY3keemxyd5dJkDc1nM7a3tqmLhiKvkEJy7dp1lFIkcYxYaxiWy5g0zdFaorVFXZsr+yfAOyHMqqaqmgsNiecFOLZL0yiyLCNJVjzzzFVaZcS4VV2xSlLiOKHb7eN7EUmS0+32WS6XlFXJ+3feZzabXiD6PTdguYxJVplphLpder0+GrEWeBvWynKxZDQa0TQ1lm0BCmhBKFzXom0rkDV5mTNbpEynBZ/93FdIi4rd/S26gwjPC/C8EKUEjuPhOB7TyQLBOmHaBiEUdV1QloYVVJYtQri4rrdek5oLhdUqMZbkpkEgcGxBXedYtsbzbYYbfbRoSJIFjiuwHUmnE5ImCbZtoVRL01RIaeIKHMtDK0lZNpRFhW2br7W2OThaMD7NyAub89OEt9+4i2xdbl17jvn5ijxtyLOG5TJlNl2s0QGKtjH27bZtL4TZGxsbNE3Le++9h1KK4WCDa9eu8+EPfZTtrUsUueKlFz7AaLhNlhWcn5+vV4wmPww0YeiTpjHT6YSzyRnbW5scHBySZdn6CPP0AP7jWD8S1uVvVX+0Oflma6Mna9E/sefyLe/xvf7r3+zv6z+RldkTLcrXTmNs26bb7V58bbgRZhVkQglX60A7j5OTMc8//yIPH97n7OyUN9/6Cnt7e2gN3V5EsqrQGmzXZXx2xsnpmKOjA3Z2dyjLjCRJOD875ejogCiIsB3LOGO05vz8lKuX9teo/6+uBeJlyouvvkJeVnQ7HU6Oj6nKkrIoCTyfra1dVGNQ9ZbtIiyXm8++yJ17d7h/7wEf/sgHmJwvGI/HWJbNzu4uQsh10NwUx5F4gWfSl1VDEPq0qmE6PcfzbeSqZW9vm+nkHMQmRZ7h+y5t23B8fEBZFXQCQ/NN8owWzf7eLkpaVEqRJTFht4NE4dgeo2GH09Nzup0u6aqkqRt830UIi+HGAM9xuHvvPpcvXaZVLVtbm5yMT9f024rpbIpSZg3UKtP0jMcGMOfYHp4f8uD+I5pGY8mSptZsbPRwXYuT40OquqBRirKqcW0fS6a0TYEQEt/3KPIWMI6Y07Fx/VRWQxAEbG5uMB6PqaqaqlaoViCkMgGO07khFWuLMOxQpwlFWXLv/j2iKCLLU9q2RKmGoqypVUurFdvbW5RVw3y+xLFskngFlkVdN+zv73N2dkqaZAgcVKtwI4fDw0NQGksClkCs86PCqIfWiiyvyPMlTaW4fPkaw+Em88UprTbNYxgFFHnFfB4TdXwcx2Zvf5/5bL5ewUhjyXY8pLQRwiYMQpq6JUlzM5lpFbblGDCiBtuxSNIYx9V0ugF5mTLaHGE7DkpFJGlOGPh0wwhpuSjVoJSgbitCz6fF/M5Zlm24LI6z1oB5aNXQ6/ZZLGqijjIRBUlG3WScjie89OKrBN6MqnryXGykdNjY2GI+W1JVRs8jbRON4Ic+jx4/wHIk3V6XnZ1d5ssFUdRnGSdcvXqL6wg8PyLwI6SwcAOH6WyG7Xl0opCNjU3Ozs6xhE1Vljx4cI/A79DpDta4Dm0OcesD9dMLzx+f+rGZrHwn9Sdhaf72H+d7AL+JPz1o3B+tPyqgFUKss21Mxo1YWz9XqxVCSHq9Ho5tE0Udtrd32dzcpG0rbt66ysn4MVJqHMel2+nz6OEB3e6Q/nAEtuTh4QFZmbOxMcKWEtG20NZsbw2om5Tj44eURYalBVLZ/N7vfQ7PdVnFS4q8ptvdIuxvcLpY4LgenaDD7vYOQrc4lkQ1LTubO+hW8P7d90iLBOHY9De3uPXCy5yezQi8kE4UcP3qLq6tuP3umwjd0O8EeI4gWU2xLIEWFo8PD2lUS5LFLOMps/mYra0+Dx/fo6Wh3++t06YFSbzCtS0cW5DmKbN4iXYszuMlWVNg+ZKoYxOENkk8x7EV+/ubtE1LPM/pBEPiRUKapliOy8n4jLysScsaL+ry/v2HpGVFowr8QDIYhvQHIWHo0qqa6ewUdIVrK1RTApI0rSnyhl5/iEKTJCk727s0TU28mpGXK8qqIIi6PH58ynKekcQZAoVlK1pdYDmQFwVtIwiciF44YH/vKr4XcXR0RpZXKC1BSKRto7Qky2sePjohjkva1ub8PGYyW6KlJAhdWpXjOprQ9+j4XeoK0qpkvlpSVAXxfAZtw6XdXbY3N8iThDItOLx/iINDx4soVhmdIECoFlvYlIVJpW6bEikUnuugapA6QLQhunHNJMpziVcrgqCD7XQ4Gp/hhAb90Wpp0pgDx9Bii5ymAtfp0tSSTjTEku6a3Grgd3WrmM4W2I6H5XhoYbKFkBolFL1hl7zOQSocx6IsCppSky0rqrTFc3yyJDVuu6Yk7IRUbUUQBUjbIi9Sqioj7Dh4gaSqMhzHJUlzknSB42p6/YDdvW28wKfVinv3D8iKikY1KC2oG6gqRVUrWq0oqhyFNs9VQFnnLJMF/VGHJF/hBh6WtDk9mxCnKfcfP2T/6hVGm5tmBbm9T5bnOL6HsG06/QGb27u8/OIHiLwObd2iULx/9zbLeEFe5iAUWqin9uUfw/qhbla+29XON8tL+EaP+Z00Hd/ycf+Yx/t2bt/ei/teH+C7qz9O52K+Nn+u1lfdq1VCpxMRRRFCmrH85f19XNdnONyi1x1SlQ1B0KHfH1w88Z2dHbTW1E3DaDRkMBigtDbiUNvHtlwuX75KGIbM53O63Q5FnvOhD32QssoNkXVji+UiIVmV3Lr5MuPxnNe+9DpIi2du3ORsMiPq9pCWTd22hp6pBA8ePjYaDCmNS0c3fOUrr9HthhwdHdDpRNR1SVPXHJ8cXaDvHdemrAoGgz5CQBAEJj+oaTg4OGAym14EKNZ1Q9sqPN9je3uHLMtZrVYgzKqs3x+Q5wVJsqLf75sMH6WZL+bYtlnTOLbDbGZeu+t5ZFmG4zhsbGzw/vvvM5lMsWybNM1Ikgzb9mgaA13zvJBO1GNzYwffD1nMY2zHJ01KTo7PqEpNHOcIYZgZSbKgacp1oKIL2iLPagQuqpEEXhdberhOgGogCvtobQHWRdbQwcFjHM9ltLGBWL+eul5bbduGbrdL25qwSsexzXpQGPJp07TG3q0aPNem2wspi4xef2CIvoEJVfQ9n2SVEC+NSLjfMy6bKArZ2tpaU1M9vFXGxjRmx3LxPJebN5/h0qX9izBJx7VZrZbUTYUfeIzHx2hdU7cl0CItyWQyIU1TbMemKEuUNkGdo9GI7e1tkiRBSovT01MjPvU8o09Zr/IQ0Ot1sSwoygxpgbSkgfhNZmsxdEAcr8hzo3OxbRuF5r17d6iaGmkbjZYAfMelE4bs7mzhui6O4zCbzgh8H8c1gYtVWdDvd4hXMQ8fPuB0fEq/P6Db7TGZmnVaVde0SqG1oGk1i4WJZHhC4tVar6MHHJPyvVzStC2Hhwe0reEQhaHP6dkpm1vbuG7A9evP8uD+AXnWgHaIwp6ZNIURTV1z89YNHFcwnY2ZzU45Oz8hSZYUZfXVY83TqcqfaP2gSSd+qJuVp/WDU0921sD/1LSYqYv5/3t7e3Q6HRzHod/vMxoNkZaNbdk8c/0WzzzzLLu7V8jSAktY5HnOcDBgOBxy7/59lssl9+/fJ8tyXn/9de7du4dl2cznS5Mx00JZ1nzmM59BSsl0eoZSFWHoc3BwRJIUbG3u8/KLH+L5515ltLFNEEV86fU3mM0X2K7P0XiMsCTj83O2dnbY37tEvErI0ozRxpDNrRFn58dMZyfE8YK33nqDra1NBoP+OnyuIc1SDh4fkOcZq2TFfLHg+PiE6XRKnhvNwsHjQ4rCZOXUdQMIup0+b7zxJnVV07Rq7ZgCEIxGGwwGQ5bLJVqD6/r4ns90OqMoCjSKMPS4fHlvnehrMxwOuXPnzkXGT9M0uK6HYwckqwytJFXZUuQVo+EWySqnbSTxMseWPkXeEAZdFouEtgHHDhASpKVIswTLcimLFscOEXis4hIhXNK0MI9dtVTVWt9QKhzHxQsc+sMuURSwXC6Yz2cIKUFaaIFpCHwPKSWdTkSr2oscqSCIQFt4XkDbtnQ6EXv72+RFTJIuWS5XWJZDlhXUVYvWoLQiyzMTjui57OxsobVmtYrZ6nT4Swdz/ta7Y37lzhn/2xuP+cl3D5kfn3Lr1k2j+ahy6rqgP+gQhC5FmaJ0RVmlCKEoyoxeL0RKcfE7oJQiz3LG4zGLxcKIlOv6gjFkfg4ueZ4jLTN9jKKIeBWzWsU4joXj2OvfHxgOh+xs7wGS5WJFFHZwXYeyzCnqEunYaEsiLAvbslB1g1CaZL6gKo0GLAxNyvhkMqepG6QlsR1J05b0+x08zyfPCw4Pjnj++Rfo9boIKWlajdJQNS15WVLWDXGSridga9cfep383ZKmKZ7rkqYJRZXgepKrV/f56Ec/wnQyY2tzD9VaOHYE2kVrh92dywwHG6xik/bsuS5CtrRtRn8QkKQLtnc21zBJaE3s0dP6MaqnzcrT+r5U25qIea3VRePy5CaEIdaaqzsbEMj11aTjuGvniCDwwwsI13K5wg8C0sxkxdi2zcsvvsTpeMxP//TPkOe5AYttbFDkJR/4wAdpm5aiyJlMz2nbmng1573bb7O3v41SDZOJEcH+X/7a/5WNjV2Go00uX7lsOBVlzWi0ydbWNlvbWxwcPqI3iNZAMcnO1hZVVTIeH9O2NVm+4uHD+xRlSpIs+YM/+KzRfkynpGmGVlBVFdIShv4prfXBXLFapXheaHQ4lkMQhJRlRVM3jMdj+r0BSkGeFQR+iBCSNE05OTE6C8OoMcAv34/wXN9kxZQlq1XMcjmn1+8xHA5pmobJZEJd12v+yYTT01OOj8fmSrkx06OtzV02NrYpiposLQAbIVzKoqEqFVlaUWQNWVahtaIoMkDQ6w6wpEfbSNK0QrWSyfmcujYW5KaGLC05P59jWx5C2FRNRZwsQWo2NjfwAo92LRBGa7RWVFW5trEasWhZFlR1Sd2YCRRgxMTdiCD0iCIfIaGpW7K0YjZbUJaVYczkxoJsqLwnLJeL9XQv5K8kio8+mmC5DtXeFnWr+NnzjJ8+nJNmKzY2h1RVjuNaXL6yj+MIosgjL1KUbhFCY9uCuqm4eu3aRZNSliW2YyOEaVyOjo+p6xrLsmia5sImDiCFCSqUUmLbAj9w6HRNfhKA63osFjGz2QLfi3AcE0QZRcY1k+UJQRhQ1hVaa1qlEELQ63QRGhazKXVdMZuZiYhSep06fYK0wHElvu+uM4gstDaBlsPhCCmfUI1BSgshJFlemFDMddPVNA1aPRGSmIuVoihomobRqI/n28SrBdKSDIdDgiAkDLrs713jgx/4CVAWtuXzwguvMBptIaXN/fv38QOPsspYrhZMZ+eAxrYt0CCF/oG42n9af3r1YyGw/V7qB+kXQsA3fULfTeDg9ysDyLbtC55KHK/wfd8csG0by7LWfIaWOF7iOC79fh8hFFJKLMtCtRohBRsbW7RtjdaK07Nj3n//HfZ3d9ZXnaaBaduWq1ev8P57b3FyfMJP/uRPEsdLhFA4rkSIlvHpEVvbmwihuXf/fYpVQVkU9LojgiBgY6fPl998g+PjFFtKbl6/hut6NGVBkiQUaUyZrdjZvkxZFhSVw+HRAdeu7rCYDuh3bQ4PD4xNVyratqLTDdm/tE2arlgsFJcu7eO4FllmqJxKt/i+EVQWeYnr+Dx88MgEyKUZnU50MYHKsoy60pRVBespSpbl+L6LJW0saa8bBphMp4RBQFOVWGvA3HS2QMMF/wJgtTKU1Kqq1s6NTeLlitlsTlHUXL58hcFgwNnpITdvXmIZ50hp4zgBVdWgdYtWmuFggBQtXadPU2uqsqIoGhbzmKZR2A5rgq+JTNBaoBQX6xshS1zPxfVcyrpACxBSIIVZBfmOY5KKAxeERlpQFSVKC67dvMzp+SnzeUUQWqySGIliY2ODw+Mx0jYOI1VW2JZNEAQgYJWan7PrOpydnVHVJUMluHWyYOpA7Fo4SpF1Q+y84OVZzmQRE4YejmuhVM3h4SFRFNI0lZkIStOk9/p92kSxWCyQ0sK2JVBfxEkIjJC4LKuv+12Zz+cXExa39SiKnDB06fc7tG2NZXnYVkBTm0iDPM8ocmMfr+uMbrdPFIXsuTukZQFPQg4dj7au6PV7aN2SlRlB4FGUxl0WBBGO7Zl1oWtxMh4jdIRA0DbGGbaKMybnU8qyRgHStgy/p1U4rkcUddeAQw/HdbAdiW1bFEVOkiRry3tBHC8JAn8NOMzpdLp0oi6r1YrhaMT5ZIyUDloLLOkSBh6H5zOeffZFFqtzVtkK23I4OBxTVzWhLy7kKlr9YB2ff9TrW73Xf9K8s6eTlaf1PdeTkELbti8mKKtVzNHR0fp7RpQppUBK6wIelSYGi17XFUo3oA1hczDoY9uCvFjR7fskScx8PufBg/vcuHGDjY0NNjc2DQq+LDg7G2M7oHSNZWt6fR9ERbyaEHU9tG5Zxsu1JRUcx+J8MqZVBUW6JFlMoa25ur9PPwp5/923WC1OuXfnTSbnx5RVxtnZCa5rMx6fkGUZRV7heT79QRfHtbFsc7CeTCZ4vkHFC2kahOvXrxEEAVrrtV7DAL+aVqExV6FaC9I0AyTL5QqBxPMDsjRjOBwxGAzodLpUVUPTKNCCttUXk5+yLFjGC4oiZzqbEKybOvPYes28sOn1eiilqKqC09MTmram042oqpytrQ3qumRjc0SWZqi2IcsypCUM7lybK3bVKsqixfNC2kYwHk/I0mKdNm0RraF+lmUmZlI+0S21KN3Q6UUMRn1c36Vpa8qqNAj2tQ5Ba0PSNSf7msD3UdrM/eumQFMDDbYtUcpk6sRxClqum6ca3zd01CRJyfMCx3XQGEBhVRutTZAV6HgF/e76M2imBXI0YGA7qPMpVVUaNHxTES/j9efYoixqqrJhOBwxmczQSpuV5XCI1trkLjUt3U6H/f19wjBkMBhcwPee3MqyJM+NpkpKSRQZN5bnO7RtTa/Xu0g0Ns1Jh6ZRNI1iMjknzVY0TY0jLIQGrZSZyEjBYhWzylOQEHVCgiBguVyBFmiNWauFPp5vUZQZQghc1yOOE8bjsYENSmmowk1L0zYojUECSLOKG45GZtXWtvi+j+M47O7uUpYGeheGEVLaXL12HSktrl69yt7eLrdu3eDRo4dsb2/h+z5RZCzlUdTn1s2XGPZ3aGqLQX+b3Z0rOHZIkhZ8HWflaaPyY1VPJytP6/tQgnX0D1qzvvqs2d7eptOJKIpina7cMUhuKVBaM5kkDIcOnuetT6AtWZbSNDXT2Zjb77/BcNRHCIv5fE7Y6xJFEZ1OwIP77xOGIXsvvojjSA4OHjJfTAy8y7LY3hmSZnPuP0iQ0oStbW9e4o2vvMM/+/X/N1E/Im8KemHA/GxKnqQcPT7AtmwsS9MNPcpKs1xOwALX9yiKjCJf0en0mJ2n7Ozs0bQtaWpw8oPBgMePH/HRj36YOJ6ztbXFZHLGdDqhbiryPL94x6Kow9nEiGNBkOcFrmuzile4nkWn22WxMJMNKS2SVUpZFSYsr20pq4zAD9HacDi01vR6PYqiYDTscz45x3HNyUMpRRAE9Ho9wxRZLrFsk6xsWRaua5HnJqBxtVoyHHSxLEHdFAxHfRbzBULaRpsR9CmKCseymU2XrFYpruNRVUZnEkURVd0CiixLAXXhBgNDD87yhivXLvPo8RFaSwbDAdPpgk6nQ1NWhJ6L0g1a1xdrDbTGDzzqusD3LaQFRZHjOjaTyYyT41OEtA0Z1wKQdDsRk+kZQgrqolpPACwzyXAdctlS+y7uLCZUDW0rcR0Xa5XS+C5zoZASNjdHJtwwqUiTnCDwGG0MWK2WzKYLyqLC9Q3hN8tSMwWMJziej8YInbXWxPGKTqezpgTbTGdTojBiuVxS1AVR5GPZFnVh1lae53J4cIi0HGzbp2nM5NK2POrGJF3P56cMBgMs6TCbL0FpyrJCS4HlOjSquqDmbm5sUeQ1da2YzyfrxrVhY3OELRWHSWxWk7a9pgw3SCkR2qyBlNJmCqo1WW6ysJ7Yu21HGr3WugHb2d3FczzyPGY42ES1mro2P88oCrh56waeZ/PMM9cYn5/S6XQJfEND9kKX2fmErY1LdIcRntvhxRdeZjFfsr+7j7gATDztVn6c6mmz8sNW6nuctX23v9/f4p/V6wygJ+mqg0HfsCKUcex0u3oNelMoba60h4M+vmdOplVjsNtVXfPG619gMAh5eP8+vvsyL7z4Ep1unzfeep3XvviHbG0N+dJrf8DJ0QHWlSvs7uxz98E9hGg4OR7juDZB4K/D4DrkZckqzrClTbfXZTTa5Bf/yv/CH772Gc5Pxrz88kt4QYcXXniV09MzTsfHPHp8zBe/+AVuPfsyn/iZn0HrltGgz2mRcHBwSL8bgLQ5HZ+znC9pmpa8KJBSU9Um3C/PMhzHpqpKhoMBdVUDUJYFJ8djXMtF2JKN0Sa6NYLPLC8IwiHdbki3J9ZZSTVCwq1nn+PLX36dMOqSLmIsy0wh0iyl34vwvT6rOGYV59iWyZkxOiJNFEVMJpO1fkOgsfD8gKLMDV+mLNnYGJGsEvK8QkiPPK+xpEfTKKLIQ3mgGwiiEK01SZJRljWe51A1DRpFmzUmL0ZauK5PaPvUVYXtWBdNallUnI2nVEWF6/os53Oa2oQnqrYmyQpc10GpFhDUtcJyXPwgQLcQeh3ieI7QirhNyIKS1Sol7HSMFsqxkJbkaDymrAqubFxGWoI4XtK2DZubG/i+R52Z1/7BcUwjoLRzVo5N2wn44qUeM+kwEA5hZBOvDE/Hth18P2KxWLG1tUXTGCJsUxvLsrY0abYiikIEklWcEc8zFIK2XUPcPIut7ibQUJYFQRBRVDmO45pkZySTyTmdaABCI2gRQF2WtC1EUUSRr0C3WJbAkoosXwIttmVRVxn9UQcpWs7HZ9hC0gl71HV78dwFgqbWxgpsN+zs75BlivPTOa4VgIaqqbCkRKgWx5bUTYPt2Ki2QmsJWq3FwA6WhGSV4ns+q+WSeB4zGo4YdIdQwXR8gmt5JPNT8vQaWxvb/PRPfZKd3U06nS7jk2OWN29yafcyy1nM/t5lLjn7HJ4+Js9zOt2IJImRAtDGmSSfNis/VvUdrYF+/dd/nY997GN0u122t7f5a3/tr3H79u2vu09RFHzqU59iY2ODTqfDL//yL3O6xkY/qcePH/NLv/RLhGHI9vY2f+/v/b2L2PCn9d3Xn7Rr+Rs/9hMh7VcFdmbaIs2VmRAEvoclDWulbVqaqkEKiyKvyYqSLC84OByTpilCNJyfH+FaNjevPc/lyzfY3N7l0qVLDAZdDh7fJ10tcD2Ln/nZnyEratpWsrt3mU5nYNKMtSZeLqnKgs1Rn7pJmc5PCaOIn/2Zn6OtYXtzy6xyhhtcuXaDKzdu4IQRy7Tg4aNjPvHxT/Lccy/xB5/7Q37z3/x/OT0+4erlK7z88svsX75CnOQ0CqTj4nguq2TFxuYI3TZMzs+QQlDkGf1ez9hvbRvPdfFcD7m2N9vSosjTi7wVhMCyXfLC6Eq63S5lVdCohrPzMxSCLG/QGJibY9tsbIyYz+ekScqlS1dx3RCNJEkSc9VeFEwmE8qyZDQa4QcBfhARdDqEUUjUCdnc2sBzHWxpU1WaTmeD5TKnKIyg1TQgNo7lMpvOL3g5/f6AoBOSVzlRL8ILXaSjEVLTtjVSmHWDbms81+KZa1cI/C697hCUYLWMSeMVQrW4EhxbokWLkIJWaaTl0CqB6wU0raLOk8wOgwABAABJREFUK3QFsnXQrUVdKIq8MnqjpkZpc7N9l6pt0UKyWCxpmoaNjQ1YT6HKsuIn7p/RyyvGHY/cknhNy1aSk0QhX7x5hUv71xHC5fhkTLfXQ6wBeY7j4DoeZ6cTlsuUVZwynS3BclAo6rZEociziiyp0ErgWDbdbkTTFti25tLlbXZ3N8nzHNtycVwT2FnXgLawpI3n+XS7HfzAZRUv6HRCfN+hyFOiKKAuSzphRKsqwo6LENDtdrCtlkHPRzcloRtgSQ/bNhEAqsX83hW1odmWLbbjUtYpw80IL7RNllCjUK0iDHw810HrBscRaGXQ+FVhUrelEFiWZDAc0tQtoR/QNi29KCCJF5yfntGLAvqRDW3M4cN3efP1L/D48UM812N/b5+f/Mk/x/bOJmkWo3RFmq0IwxC1XmMGoc/52RhpS5p1+rKQ4mkm3I9ZfUeTld/93d/lU5/6FB/72MdomoZ/+A//Ib/wC7/AO++8QxQZQd3f/bt/l//j//g/+Hf/7t/R7/f51V/9Vf76X//r/P7v/z5gBGm/9Eu/xO7uLp/97Gc5OTnhV37lV3Ach3/2z/7Z9/8VPq0/+XoieOOrDcuT5uWJ80EiqOtqHQ8Ptm2C1gAaXeMHHrZlEQaCNN3idJyzt7/HlauX8XyPoirM/r5J+NKXXmM2mbC1tUkcrwjCkH5vwPjkdO2yiCiLkuFwROBHnI4nWNKlqVv29/ZxXY9+r8PqvRTfi3j15Q/x8PCIL3zhNeNcsgST6Rn/t1/539jcusIHP/QhHjy8x3/8j/8/yipnuZwipMZxbLY2tzg5PubWzedJ0xV5mqxdOEYjYlD6KTdu3EAIwePHjy+YF2VZopSmrHKquqDb6VA3DY4rUa2kLGuyLMUPHLI8ZZUk5sS1SgmjCNU01E1Dnuf4vgcIDg8P8b0AKSUbGxvM53MzuleG+fGkeekNRrRNs87xEQz7A+68f4c0ztna2ePll1/m/v0HbG9trXH7MUIKqsoIXvM8ZWO0jUYwm01wXQfXtdG0WJZF6Bl8f39g1kgAlrRIkoThYLgOT/RQSrO1tU1d17iuyyJeYDvGAVWWCUEQIUS5zroRCGnQ9VIKtBLGBVWZRGHHttFCIRBMJ1NGoxFpklxYhfMsJ4o6RnQcr3jhbMUscEg6PnZZE0oXkWSIIkcnGWXHI8tzqrLm7PQc3x9Q1y1to1EK6tpMDutKYbmCqlwHBQqBkALfD8mTFLXW+mgh1u43wdnZOb3eEClnxiFUKHr9PpPzuREya5s0KYnW9FzLBtsRxgauGmgtZrMlYejRH4VsbIzQ7Yo0K3A9B9d1jB1em4sDrSRJkrGxsbHWxywpy4ysSOiMdrEdwcZGl+ODY/AtqrLA8x3KKiXwA7IsW1vowbZMrlbbtNiejeu6FLlZj1V1BWiKIjeBhm7D2eQR+5f2qKqEL73+BxyfHaNai4PHR7x35w1q1XD1mascHTxkPpnw0vMvY7tQLAuevfkc3U6P6dmCoihQTYOwXKynassfu/qOmpX/9J/+09d9/a//9b9me3ub1157jU9+8pMsl0v+5b/8l/zmb/4mf/Ev/kUA/tW/+le8+OKL/MEf/AEf//jH+S//5b/wzjvv8Nu//dvs7OzwoQ99iH/6T/8pf//v/33+8T/+x7iu+/17dU/rT6fWAtsnCctGGGlEgfP5jDTNGfZHDAY9c+d1d5OmCW2rSNIFjVZ4bsBbb75BUS7Y3dvj/fgex+Mxzw62cNepvwcHh0wnM7a3dljFMVpLOlGX7Z1dPv/596nrhijs4LmGKbKzvUc37PHo4BFep0u32+XZZ28RJwsuX7rCM1dvMT6dsL21yzJJODo55PHjR9y4cZUHD+8SRiO2t7ZZxnM2Nrd46aXn8Tyb9957h89/8Q+wLGjqhkcPH/LKyy/x1punfPp3P00nirBtm+3tbZOcC/R6PTY3N9fsFGP/7XQihLAQIgCh6HR8yjJjONxiMctwHZ+yzOlEHfrDHifjM1xXsFzOGfYG9Ho9JpNTbEsQ+AF5VdCNutRFfZGAK4S44HzYtjm5tE1NVSo8xybNcwSQJQlB0KEqCh7cv8/+3i7z2ZwsWyFocRzBcDggWS1RbUVRZDStRquWfreDFBov8FFty+Zwy6xAljF67VJxXIcgDFECkiTBcYxeabFYrEWyMQKBlKbJs21ja3ddh7Y1J+ko8Gkas1ai0VRViWM762bGplZGUPzEuuC6BkGfpilN09KJemyM+liLRwStZumbiUTrO5zlJcLSXKlqwiLnzuOYIHQBaaIidIPn+iglKPKKtmEtXpaoBmha1LpZy9fvad0YuF1VV7RNi+sGSCGZzxMCD+rKBAy2raIsGuraZEp1u11syyPPSrSucV1Brxetm31N22rKUmNZHk2tOTk+RWsHgb6wqbuux7JJydKSo8NjitIkfCM0jiMoq4qr1/axbOj3ItpKsL+3RRT2OTo4MdbvqqJVhUlAtn3Dz7FbbMs1mIKmpa5LOlG4hgRWBIFPNwxJs5TBsMd0es5X3jijVTAYbBJGAU0j2Lu0i1IarRrefut1lssFf/6TP4frCGxLMxwOGAw2aJXixjM3CfyApm2xpEYKk970tH586nvqT5fLJQCj0QiA1157jbqu+fmf//mL+7zwwgtcvXqVz33ucwB87nOf49VXX2VnZ+fiPr/4i79IHMe8/fbbf+y/U5YlcRx/3e1pfWclLv7r+1fGKq3RSn/16jXPL7QrJqBMMJ/PGY9PSRIjylOqZTo947Of+33eu/0Or7/xFR4/fMDx4SFNXfOVr3yFk/GYK9euY9kOcbxiGS949OghZVmyv3+JTtRld/cShwfHLBZLTk9OsW2XTtRhNNpguYwpioqrV5+hqVkLVQXjs2Nm8wmT8zOyPGO1Snj08BHLZcy1a9fZ2NhgYzRiMOjz2mtf5MHDe4zPTjg7O2U6ndLvD8iyAsfxGPSHfPDVD3Dl8mWauub1r7yOQPDC88+ztbHB0dERSZKshZcZ5+fna6iVXpNTXTzPYzAYEAT+ReJuWZZMJudUVY0QZrUadUJOT8dYtkDpBse1qZuGJFlh2/Y6hK9hMBgi17EGnuddOIKatVjyyYmsrmraujGNxcYGvufhux6ObTMcDFjFS65fu0qeZ/S6Hbq9iE7k09Q5UmpeeflFPM+mbWo818FzbYLApalKbt68QVVVxn69FlW6rsmBStOUs7Mz8twQemez2cUUrixLqqomTTPKssJ1XZpmvXaoivWqzEFp40wyQlXTyDxhxriuh2VbBH6wDlRcolSL53q0TcN0OjHv/e42zmiAX1QXn1MpJJu2S+XaLB3Y2d0mCALCsHMRRuj7PlJK4niFbTtI4WBJByktUBqxFqZWVWUSjoWmqiuapkVKiWrBsnwkLo8eHREEEWVZmt9RIel2h3huRFNDXRuir+t5NE1DHC8oywzHkYARHddVy2KRsFqldDs9Q0oO/YtUazAOnzQzKP7pdGrWSYFHXVcMBz2KLAWtaJuKPE+QUnPp0jbP3rrGKy89y8YoQugaS2hCP6DMS1bLFWLNEmL9GsEQdx3HJoyMG+345Iwsa+l1t+l1d9EqYGN0iVde/jCf/OTPMZ1O+dmf/Rl00xC4LtlqwcOHd1Gq5vj4hLpuWS5WbG1ssbezh+e4ZlX4vWr3ntYPXX3XzYpSir/zd/4OP/3TP80rr7wCwHg8xnVdBoPB1913Z2eH8Xh8cZ+vbVSefP/J9/64+vVf/3X6/f7F7cqVK9/t0/6hryeo6e/0hhAI/pg//56ei3EIPLHGPpmqSCmwLBvXdRgOB+zsbBNGPo4jmM4m3H7/XZJ0hePCZz/3P7h9+x3eev1N5tM5cZzgeQFKWyAc0rwhXiXcu3ePg4NDgiDkox/9GP3+kOvXb3Lp0lWeffY5Ll26zKX9SziOx+np2QWK/PN/+HlWSYIQmrLKWK1mfOELn+Xq9UsURcpoYwBCM5mcMRoNuXPnLs8++xxR1KWuG7I84Z1336AoElxHcnxyQK/f4ez8jOHQ8FHqsmRrY5Miy1BNw+ZohOd5BJ7PYrEgSRIWiwVN01xMVfI8R0rLXFGXNUVRMRiMCIIQ3w/x/YD+oEPUCXAci8Uaq48GzzPk2qIoqKoKtXZgbG5uEgQBh0dHuK5LmqZr+2iI4zgEQYBSai2INAf8uqpYzBccHhyytbWF57gkqxVVUTIaDtnd3mR/b4etjQEvvvAsW5sDXnj+FlmasLO1gWMLYJ3wi6apSw4PDqnr2jQTjk0QhrRtw8nJCcvlkt3d3YsQy7Y1J3Ewx5SyrCjyCtt2jeV4PUlwHAvPs7Edk8atdHthXW6aFiHsCyeLEII0S0mShLIqjQ5KSnq9Ln7gUFUZcmfE/UsbDIqGQd7iI+kXDb205P2dHnNLI6WZfLSNSWe2LLEGqDlYtiDLU9R6aoTW+K5LGASEQYDrusYqLyUtmm6vR1k2FzTfJ9oUIYyVPfA96rWluiwrbNtZr0otsjQBbVFVLcPBkKouEEKDeOKwgW5nQFEUhGGIXDd+ySojTc0658qVSwyHfbrdiMVigeu67O5uM53O6fcGdDtd3r/9PqvVColmMZ/y6OF9HEfx6qu3eP65a+ZnrVrkGiEr0QSBa2jDa+2KFBAEJsTx1q2bRJ0Btt1BEPDTP/XzlKXk6GhG3VhoHLqdPsky5dkbt5hPpqA0RZZy7+Fdrl29im5h2BsyGo6IgghLGou2WHuC/izrBwVD/4NS3250zXdb37Ub6FOf+hRvvfUWn/nMZ773Z/Et6h/8g3/Ar/3ar118Hcfxj3XD8oNQT4Bvci10s23zUXoirjUHcSOw3d7eZhUvuXf/DpPJGXVdcHJ6RK8boSn5qY//LM/ffIUsq/hI5yPM44/QG3ZRysGyQvIiZ0tvEscz8ixGYNJfB/0ht9+7w4c+/GEODg/RWrO3t8/b70zYGG2xXC5o3faC9XLjxnXSrOV4/IjT00PSLEbrisViQqfXZbGYkWc5t249y+/85/+ElA62rQnCgDRL2b+8w2DQ5c6d2+zsbHL79rukqqWuKgSCZ65fp2lqqrLk6OCQ/shYrc366gAhBJ1Oh7Zt8TyPMIxI04y2Kc36pWyoq9ZoMGTOcCA4GR8RRh6ua7O7t8/9BwcEQYf5zIhGq0qZ91EryrJEK0G30yHJU6Rt1j/dbpfBYMBqtUJrbci6oiUKfSSCbq9LvFyZFdmNfR4+esz59Iyz0zFlaXgsu3vbdDoew+EzqFZhCcmdO/fZ39thMpvhBx6dTkRepGg0ruexmMU0dUPbNJRFThh6WFKytblJ3TT0+33ee++9Cy2NmaIIQGJbzkVDonVrOChKMp2em+lTUdK2xpWkWhC2QCtNlmVsX9vh5Oh0DSN08X2XqiwJI5eBF7FczfADl/8ysDnf6fHqoqA3S8ltiz+81OPNZ/eILFguF9iWS6fT4+xsRrcn8Dxj0R2NBozHp7RNg+f5CMzKajgcXliVHcfBsiwWyyfZTy2ObYIQZ7MZtm0SkI07q8UPHNrG8ErMCk/guAKwaOqWyfkCz/UMZE0ppBWSZyUoODubEAQRo1EXaQmaRrO/f5l40SCthq3tAYNhh/H4hDheYVkW3W7fONCSnKPymL3dS8zOF9y/d59L+5cQGpqm4Pw85sbNqwR+l7vvH1IW5foo0GBZZlrmeS6ObWjDeZ5RlzmL5RLHiQiDAdeeucHm5jZh2GEZx/zup3+XD37gAwyGmxwfjXnvnffQteZ8fM6HfuKj9Lo9gjDCs7v0uh0sKbGtNUtGa7NMftoo/FjVd9Ws/Oqv/iq/9Vu/xac//WkuX7588ee7u7tUVcVisfi66crp6Sm7u7sX9/n85z//dY/3xC305D5/tJ5chX279cepxL++s/sxHiF+1y/d/MWvfW/Nic8IH580KE9cQIZmq4jjFefnZ9gWvPPOm8TxnDD06PXCNeshYjDok2cFaInrBvR6IwLfQymLyWSF5Rg8/727dwlDjw+++govPv8Cn//8F9jb26ffG3D58hWm02M6kcPlS5fJswTP89e5JYpON+Ly5T2OT6ZsbW7x2pf+EMeRvPvu20xn53zwox/lM7//GUajEZubWyRJRl2XnJ2P6fV7HBwccHD4kCw3a5erVy/zzjs1NC27Ozucn5/hB0OE9kwAXFHg+1vUqsJxjE5ESkkQBFRVtV6ZlbSNRkuFpSVB4DMcDomiiHv37pLlK1zPTKiqqmIVJ6RpTlHElGWDJQRlWSH6JvAvSRKqsiZexghLAl8F0SVJQlXVFIWBddnSTDLatadrMV+gahh2R8ymM/Is4/HDx6SrhDDwuXrlMmenp3ieR1mW3LjxHAcHR5yejo2DSQZMJxN2d3aYTFekSXIBkcuylE4U0utGSAtWSUIQ+CwWS0ajkXltqxWO40Kj0K3RPnmeuw7sa7FtC9d1LuByvX6XxWKBY9tUwqy2eoMutq/JshylFFEYobXRkJRViaLCskw+0WoVE1cF//3aBicfucTq0SF1P6TsunS6IY5QnJ6uGPRHTKYLHNfBsgQIRZIuUQp836NpTBZOpxswPZ9Qr9c/Qgj8IGA6nRoBeH/Acr5YN5gN/f7gQmBtWRLLNiTnLM1RivU6sABhIS2LqmixpGMcUlLS7/fJ0hzbssmKnKYylFxgLV52OTtZIEUXTcPR8WMERnMkhLGVTydLmramP/A5Ojxm0N80eUVZtV7DOSwWM6SnODx6xI3rr+C5Hf7gc180iH0ckmSFsCW2FAS+Q5bkzIoMz3EY9PvMlwlB1PDg4R2uXnsGx5Wk2RJpCaStsWyPzc0RnhPQ1oqjw2P+l1/8y2gkUdSFxiZJUga9rqEjyz86UflmB7Rv1s18OwfCp93Qd1tfpamLP/L1d1/f0RpIa82v/uqv8h/+w3/gv/23/8Yzzzzzdd//6Ec/iuM4/M7v/M7Fn92+fZvHjx/ziU98AoBPfOITvPnmm5ydnV3c57/+1/9Kr9fjpZde+l5ey3dY+gf09gNa+kmjYlY+QrAme5a0qqHIclRrGpRGaaqm4fjslE9/9jOsspi7924zmY4JPMlsMmZzMOTtN9/FdTuAQ6MUYRRQlhl1UVOmFePDI1ypCG3Nvffe5vY77/LKKx/k5Vc+RBj1EBr2trdwpKQXdbAtie97BKGPsCSu79PoBssVzOYzvvz6V5CWIl6d8+DBHc7GZp/+kx//89Rly73377CzOcJzJdKBTj/i4PAYhEVRVrzyygfJ8pIw6vDo0QG2Y+O4FkcnB6ySJQ8e3uXRwQOSde7N4aPHJIsVy+mStmzohB2ktKgbRd0qHFcQdT2kLSiqkryoyIuK4WiEsCRlVeO6AXUDedZwfHSKUBD5ATsbm1y/fJXhYMRyHpOmOds7WwhbcfnaLl5kkzc5ju9gOw55XnH92i2k8KhKtc6oaXG8AD/o0htsUrWKg/EJtuti4VFkJS8+/yK3btzAtW1QmtUypqwq7j64j+P72K6PsDyk8IiXBUWuKKuStq2wbZODE0URe3s7FFVGksYcHj7i3v07tKr+KtxtrU8BcBybKDLk0zCtuJTUdEpoGoluoSprFrMFTV3TNvV67WjRVC26skgWhjNTlBlCauJkQX/QYWt7hzDapCwlcVzguiFCW8yl4p6omYoMPwJ0Caqh3+2SpCu01Gzt7SCFjWo0ZV6RrlJzdd8KfDciXpbUjcV8kaLWVuu6bqmqBiEc4jinbjR+6BF2XRxfY3sWjQJEQJ5K0pVGKWsd1Ffheo4Rw7qSoOPiBh5pWjKdZTx4eMLZZEHdtgRRQKcbgGiwLUldKNpS47s2jl3iuor+IGB3b5NeP8JxDDyvqjRn4zlZXOK5DkFg0euFRFGI7Vq4gUW8SplNGqoS3n33LYpqydbOANtxaGubtgDZQJGk5KsY2zbNpOV4KOEQdUf4foSwzJQoCG0ePHif3R0jNP9zH/84Ub9H0dYUdUm8WvF7n/ksSZyyWq7wA5fpdEqjjR5GCUErJA0CjcbIbNfHTvE1tycHrW90MLv43yc39TW3H+Dj8A9Rff2F7ff+eN9Rs/KpT32Kf/Nv/g2/+Zu/SbfbZTweMx6PL8ic/X6fv/23/za/9mu/xn//7/+d1157jb/1t/4Wn/jEJ/j4xz8OwC/8wi/w0ksv8Tf/5t/k9ddf5z//5//MP/pH/4hPfepT39H05Gl9Z/XNGCzf1k2YfBeltNFK1DV5kfH222+hVEsYBWsnQglKMV8sgJZbz13n4cE93rn9JqNRj8n5Kcv5nDffeJPAC+n3RuR5QRDa3L3/LvfuvUfTlDx++JAo8Dk5eszRwUMWswmvvPwyVy5fJU1zjo9PQMO9u3fpRCGg2N/b4+zsjNPTcwQWg/4IKS38wOXWszdxHIc8T8143ZGs4ngNygp599332N/bp6kbbt++QxhGKKXpdgcUWcXu9j797oBXX36Vy/tXKIqKTtQFAb1eF89zDBI/isjzjOFwQLfbwXNcNkYjfM9nY7TJcmGswxsbG3R7Ea5nm5PHWqBs2ZKHjx4yGAwQQqIU62BBEzJnNB0lTdMwm82oygozzbKYzedsb20xW8wQlqRuapCQFTlFWfDgwf31esHBtm2effZZLl26zHyxpCgrhGWDlDiux+7uHpf2L3Fpf5eqKsjShOnknNl0Sn8wYHx+StnUSMtBSofTsylaS9LU0Irzi4yYEMuSzOdzg94X4iKoMAyDi1BGy1pnz6yt7tUy5i89Puf/+f6Y/8eDGf+vh3N+4WhB1/bQSqMVdKKOmUxIiVZqDUSrzbrFsY2eR5mV2zJecXY+oygayrJBSgeBXE8SMoaDPkHo0usblsliPqdtzGPVTU3dGJbLZDJFqQbLMrZpIQRN3VKWNW0rkNIBJEVRUhQFtu2Clmvkv0uWZ0hLG3CdFOvoCUVVKpJVsc5HUhcr1rZp106uirpuSbMK142Q0sPzQpQSF5EGly7t0ev1EEh6nR5RGBBFLqNhn0F/wHw+I0lMTtVyGbNcrLAtn7pW9Hs9dne2cH0LyxZrkazPqx94FdUKykKxXKacjs+IQhMCqXSF6zioRmEJi7pqaVuF4/iG32J5DAYbVLUBE87mU+7cuU3bVLzz9tsM+gN2dy8RBB00AstxQMB7t9/l/Tu3qcqcqqqIooDHjx/TqJZWa7P+kV97YHvSoOiv6iPEt3N2/NoG52sPdk+blR/E+o6alX/xL/4Fy+WSv/AX/gJ7e3sXt3/7b//txX3++T//5/zVv/pX+eVf/mU++clPsru7y7//9//+4vuWZfFbv/VbWJbFJz7xCf7G3/gb/Mqv/Ar/5J/8k+/bi/qTEPf8uJdxsAjSNF3DyAyUbHt7m6LIWa1mLOIJs+kZ7777Jlm6oK5zfu/3foe7d98hXk05OLzP8ckho40hq9UKy7LpdPp4nkO8Oufg8B537r6DUiVFmWLZkratef2N1ymr0uQCjTZ48623zNVWY0Bp7773Fts7myyXMUqBFA6XL13HtgM6nYFZtWiwHTNaFxJDW21rjo4PODsbo1TLrVu3mM8XRGGHD7z6EV595SNcu3Id3w+5euU6vd6A/b3LOI7L5sY2/d4Az/XI85ymbVks5kSdiFdeeYWyLMmyHNtxmM0XXLv+DKenZxRFiS1t8jQnz0vyvFg7Wsy6om1bej2TVVNVNdvbOwRBSKfTQSvQSlw4eqp1yGEQBOYk3SiEsOh2ejSNYjTcQLWaqioIQg9hKfzAwvUEQRisHXhvAazXMcZ1N1/MyKuEos44OHnMIp6T5Am2Z1M2JatkRdu0hrNh2zTrk3q/3zPNkOWgWm1cPLUJVxQCklWC0hq1xrdnWUZRFPh+sJ5AmBNvXdf84jznz08TbN/jLHQp25ZPnsX87PEcx3ZxXY9er78WeIu16ylD04BoybJk3UT6VFVDsjK8lDRbYSCGhv7atjVRFKBpuXbtGp4bEgTR2qVTGyhZEFCWBW1bAS07u1tEHR+lKsLQBdGC1rRmTELdNCY6oDHMn6Zp6HS669wsDyZLtmYZ9mIFQqN1Q6cT4rj22soO9trWvb29A1pS14bm69gerhMw6G8Q+J31FMbEN0wnM5TSDIdDfD9ke3uXXm+A73c4OjplPD4nXWdN9bomc8h1fVwnAG2Snw3lOFsHksZMp+dcurzH+fmUMpes4pbDw1O2t0dokWLZCqU0UtpUVYtjGwhdXbXUtbpo2tq2JVmtGA6H5HlOmhpxsuv62I5HrzdkMTe6JXTDdHbKnXvvkWYJQehzdnbCZHKOvaY2P3EiPq0fn/qONCvfzt7J931+4zd+g9/4jd/4hve5du0a//E//sfv5J9+Wn/GJQTEcUKaZuugQfPReZKqPJ0ekiQJR8enPHx4wF/+K/8rDx7d5ezskDxfka5illLiSJv5fEonGrC5vc/m5hZptuIrX3mdum44O5vw4OEuBjk+5r3b7+DYFs8+9yy9wSbzxZy6rpjPZuzu7vLlL32Bk90hy7tLzs5OeeHFFxDSpUUy3NhhU25y+/aX6A36+L7k5OSYXj/F9z1mszPmi4rDo8c8/9zz3L59G9t2OD46pdfrc3x0ynO3bvLe7Xcps5xbz97i5OSYpqn5yZ/4c/z2b/+fF2uO0WjI2emYKIoYn465fPkyp6en3Ll7F2nbzBYLer0etrQvLKB5nlI3DU1tTrZKK3zfM/lIbU2v119rTiz29i7xuD4woXJ1Y9KPhwPSzFijfT8gywqEkNi2S+BHHB+PsaSk1+mglBmcS0uidU0YBBfsFaUUaHP1O5lMAI2ULWFnxGwx4fK1Szx8+JCXX36F+/cfooGoExEvcybTGa7jXzQZxrrqIqVFGAZ4jkdVldS1ZjQasVrFRFHI5qaZMnUiw95Jk4y6aimrlG5Z8+o840zAQrcoKZk5Zrp362RO1HVIg4Dx2RlV1dAqhe8HaCEQ0qy4giBai1w9PE+RZ4UZRWuN77mo1kwsPNdocDY3NknTgrLKGHT75HlOWZZEvT7CcRBSEHUCer2QLEvNBBGF0i2ua9NqicBGtSazpm2MNsb3fNJkgS1reo7Nx+6f8eosI1RLUiF4fRDwX4chaWZyk2azCssWlEVJGHgsFkvaVuF5PlLauE6wDv+skZZGKY1jOyglyLOMplZ4jksQGMeYbRsqcifqkyQZV69eBW1Tl3O0huPjMUJv0OnZDIZdbt28yfu3H6JUg+M4LJcLhAwI/IjlrAYc6lJj2fDKqzd4+OAMrc10yAigY1zXp24aTk/P6A8GhFHE+GTMX/y5n+eNN95Ca00YhhwdH5vwxrCD5wYoZVxMA7vPdDrm3ffe5JlnblHXAdefucq7771NFIVEUfcbOhn/qFbiT6v+6Knx6QXy97+ecgCf1rdVSkHbmkC8J+F4AN1ul1Y13Lv7Hp///Gf48mufZzjoURY54/EhTZ1RlSlxPGc6PwdaknSJpmU6OeP8/JTf/+xnWC6XHB0dsbm5QVlmTKenPHp8n9FGHyHNlf/e3h4HBwdrB4Xk8PCQn/3kJ7l7/wFeENHpDfH8Dpvb+yyWBjj3+PAY2w7p9zZQSvLCC69QVi1pVtDrDfjQhz+IUjWalqOjRxwcPCTqBGsh6JI4XnJ+dsrB48fcu3uHvb0dqqrg8OAxSZLwwgsvEEUdFvM5V65cIc9yzk5NeOFisUTaDpblkGUFTdMyHG4QeCGqVqhWX5zowayTtrY2SLMVnudSFAXles2TpQWrOAHkGpb2VdDbk5OZY7ukSU6eGxt0vzfE8wLSNGNra8e4ZoREWiYYslkTbLXSF06lIAiom4rrN69jeTZBN2K2nJNXJbfv3mO4sUGcrNja3OLy5Us8e+tZLMvC81yyLCNNM/KiRFoWnufguBa2Ldf4fcwEBcHp+Jy6VmvrdkWW5Rdi4G3bxW8aMte+yDaybZvElkQaOtWaCKuegMEEVV2vdRhmGuJ5LlrrC9ZLv99fr9bEOtXZpGJXVUFZFaRZwvHxMVXVcnh4ghQ2nhesoXVzhGTNdzJr0CiKTGNmGwF0EJiUZ41AKU2aZmRpTlXVSClp2pYP3z7kZ8YJwrYYezZKwCfPVvylyQqtW6MVsS2iKLp4LDCrQNd1jJi4KijLgqLIkFLQ7XZxHBfVahaLFWVRMZ3OGZ+cMZnMODo84eDxmNkswZIOb731No8fH1DVFa7jY0mXxTzFsX3u3rnHbD7Dtq21rVyxvbOD6zlGRC8kaIltGyT/5Sv7bG+PcF3z8w+CkDDoApLRaBMpLdI0Y3NzEz/wOTh4TBRFWJbFyckJ9+7eJU0Sdnd3mU5ndDs9sszY8V3XZrmc8fbbXyEvVliWptfrGvF6WaCV+hbtyLcxdflmHcXTZuN7rq99e78fzdvTZuUb1HfLM/mmrJMf4pJSrMfYDvY61CxJElzXZRXHPHhwl8V8yvbWFs/deg7XdZnP5pydjZlOT5GWRZ4V3L79Hlq33H9wh14/5O13Xqfb7fDii6+YK+uy4iuvf4WTkyOSdMHx8SMEmvdvv0+SrIiiiDzLyIvcoMN9nytXrrOzvc+VSzdQreTmzefo9XpYtkWeFzStg7Qjev0tqhq2tvcZDLfp9IZUVY3WLffu3aHbC5nNz3n3vdeZTI75yY9/hM/+/u8Sx3OqKsOxBe+89QaPH93n93//04xGA46Pj5nNZiAERVFg2RaLxQIhBNt7u4w2Nxmtn2fTtAgNge/T6/YMCr1VKAWWdNjf32M2mxGGAZZldA9lWSGF5OHDh5gTl15TXiWe79DrdY27x3aoqoY0zclS0xgJKRBC4jgeVdnQ1NBUApRBsQsh1s1JzXw+x7aN22Z3b4+sLBifnxEnCVWrEJZDfzhiOl8RBBHvvvsu/V6fGzduMBwO6XZ7dDpdjBJbolpFmqZk2YosT6jrhlW8QilBkmQopej3B6hWI5AXqb5R1KHuRuS2RadpL35vmqYhqltyS3KOYpVmYFloIbBcl7pp0UKAlkhhs1ql5HmOUi2OYyGkwnEkjiMNjdWx6HRCEIrRaECarmhqxXweUxaVcZ6sLdIG/V+CFiwWK0DQthrVCoqioihqptM5Td3Q1A3qAlLXEscryrImyktemWXMPYdzS1JbFjPXYhV5fCSt6VQltm2hdEtd11hr/U2elWuGUUNeJAShh+ta1I2Z7Gjd0DQ1Wgs8NyBNC6qqwXUDqrJhMV8xmz7Rppj3OkkSTk5OyLKcrc1tHDtgPltSVQ11VeGvQ0Aty8KSFr7nIy2JtBR5YVZXeV5xfDRhe2ebqPOEEWPyrHzPX2txarI8A8zE4+joiCAIKIqCPM+ZL2Z8+fXXODw6IAjCdTiiZBUnTCZTzidjVsmCBw/ucj45pa4Ljo+PaJr64jG/aX1D5ocw/8s3Oc7zJ88N+VGur76XX33Pv9d6mrr8tL6t0mtthFINbWuuHDudLnVdc3R4xN7uLlLA1uY23W4PWqjKyogUmwax1hlIpfF9DyEkd++9x8b2HrYtuXvnAK1sAj8yV8a+y3h8ZJqS0Rar1Yq6NqLS+w8e8HOf/BmOHj3kQx/6AGULWV6wf+U6Z2dj8qKibGq2+kNuPnuLj334E5yfn4BULBaTNePC4+Tk2FhFW42QwthjPZt7998lXp2TlXOSdEFdVdRNjZAtp6djmqbmp37qE7z77jsobU76GxsboDVpmuC4DqenZ7z8gQ8yPp9eCEKX85hslTEaDJG+xLZssAR5XiKlYDab0ekELJbxeg1RkSYpWVYwHG5gWzZ5XuL7Pm1bIUSL67pkWU4n6nJ+NqVtNIWqLqiwUlqURcPR4Sm6BUuGuHaEJmcymWDbDp7nU1cNi8UCraFaViip0cJitlhx5colrlzfoKk182WCY5lJV5ZlnK3mDIcmSLEsTXKwWGszpFSEYYAQ0FQKYRlXlSUt6rpFK02SpARBuJ6gQF3XnGrFOxs9PjlZ0ZQ1uesQ1A2jquHTm12KKMK3DCAvijpGB+Q4gHWhGymKau1MMeTeVtVrfYhGCrFG+LcmniGK0EojbYumbcmrGttqcJVD0OlgKM2KLCsRaKIopG0UWgmKwkyKtAYhLVAN0rKM7sR21u+vTXeRECrNuWTN2TGY/8TSbNctm0Iytwy+YTw+wbYjLMvB8wJWyZymLfF8F8eRNI1CSsVsfs7Vq/tI6VCWLY7j4lg2WinDt2lb+v0Bq1VDllZoBbbnrPVMI/J0wWQyp9/t4LqSqOuxSmLCoE/dGGeMWimqWhGGLlpZFEW+JgaHHDya0Bsk3Lx1lffevUeaNgih8QOXOJ7huJIszrl3795aNOxc2M9B4Lo288UZJ0ePefHFF7j3/m2KIsV2fHq9IUma4jiSxXJG1OkyHGzy3rt32dzc4uaNW8gfoI7hB+ip/MjWj+hk5Zvbg58ItL4q1Hpa36rM2seE94EmDHyCwCdNE5NyDBRFSdM2vPX229y+cwfHdREIgiAELDqdHo7rUjUVnV5Iksx59tY1ZlMj/Lt06Sof+fBP8ou/8FcZjbY4P59y+fIlzs/PiKKQL3/5NY6PDxkO+0wmZ7iujW1bdDsD5vMlvU6XuqpYrRb4nk3bVGxubnDt2jOkaUmS5OzuXiaOU3Z2L3P58lXapiUMA569+QzPPnuDNF2RpCvmiymf/vTv0O0FdLohUeSjVM0rr7zI3v4Oq2SJ69kURUoU+cznU8oyp1WKLC9oGoWQAs9zqMryohmKwoAsS5hOz/EDH8/zcV0P13XIUhN6V+S5mcxsb+MHwTogUrNKY5bxgjRLDMsDyWQyWWcPmc9x07bYtsN0OqOqauq6MbeqRSPXLBvfhBuu8fFt2yLX8LJVsqIoC+bzBU1t6LrnZzPaWmNLhzCImE0X+H5IUZj1SVUVNG1NkprPQVkURryqGlarmKqqsCxJr9/Dtk1MgJSC88k5nu8SdUJ293aNHV4p0iTl9/ZH/N5WF6k0W1kBbcvvbfX477t9NEakW7cNjucSRhFBGKJ5IrZV2JaNkAJpSaq2xHYNI8XxHBzPIV6tyMsCpRSz+ZxOr4cGssxoVaqyotvpsrW5iW1ZWNaT6Y9YBxhClhV4ro9tOWgFaI1tyQvx55OrSSks5pagsCTdRq2ddQaa6BcNKxRzYZqYQX/AoD9AK4Xj2Pi+R91UaBTdXoeyzok6PrYtcR0LKS0TilkY2m+322X/0iUEEpBUVUNe5EhpuC9KaYIgJOp0CIMAIc1Vb1U1gE2WlSRpSlVXSFuS5TmW5ZigRF3jepLNrRFSSMKgx3Q6J81y+v0BrWovjhe9fhffd7EtiyxNaeoa13UuJoUAWZqwXE7IshVJGlNWpZlCur4Ji2xb0izh3r27lEXB3t4eQegTxyZNHfHNpytrwsc3uK3rm37728NNCPH1tz9RtIV+cvvWL+1/qu/HU/gzrB/RZgV+oN/1H8KSEhxHXuz6Hde6cFNo0VI3LbbrMdze4uqt60ySGa+/8yZpWZAVFcs4Zb6M2b92mbRIeO/225RlwqN777M5ikjzGZs7GxydTDg7T5hOcybTFZPpkuViRtMUzKZnOK7koz/xIW7ffpuiiOlEHqP+EFdaLBdjPK+iqubk2YJuGLKczvnya69hC4thd8hkPMV3AhaTKd0gIFnE2Chs2XI+PmJrtMMrL/4Eg942UdS7IO6enY9xPYdnn3sWgPH4lG43ompS8jLGcmC+nDOZzQw7w3Y4n0ywLYHjWhR5TpKlJFmM5YLjg+vbKN2SZTF1XQAK3SiksimSkrxakeQLop5HWq5oqbA8TdVmKNWQrQqyJKcscpRuaHWFtKFWDWWpKLIGKWwcxyHs+LiuxvEUZbOiUhWu75NkBY8eH1FUDcKycXyXRpnoBIlEaosyqzk9OiWez6EpibyIKmupywqo8QLBiy/fpNuLUELh+xAGNjtbG1iWTVlWNG3D+fQUx7eRtqSoS/IqJytTFvGU89kYy4MkiemGEY2U/OftPv/7s9v879eG/H+e3eS/3dikEKBaBUpjS0ldFniujVY1UmqkJRBCUTUljWoQtsByLWrdULYlta5IigQ38qjamlYIlquEvKxAgGVJXMch8AOu7F9hMZmi6xrRNDi2EeYWa4JrEARYltHVWEKg6hLVlDiWcRtJywQZxmlK6nvcu7zJsG4YljV2UxOtMjZrzVd6IYdlSZaVTM6njPpDysx8HqSjkY5Fi02SFezsboHQa7G2SzzLKNISzxHotkSpmtlsQlHlKN3i+Q5aFNRtirsO95zPF6TpiiCycNwGjZnWFYUiiHpIx6JsM0pV0WqF1oosyy6cSqBwXRvHlezsXOHevRNOz1co7dA0Gtfx6HV6WELiuzaX93fpdSIe3LuL0ArHshCtxkLQ1gV7+5t87g8/gxc6JNmKvMjRrTKp0VLSHfTRUlA1NUVZkuYZ83iBWjcqT6zbX7tqMLMb/S1u3wzX8K3+7je+STQC9Q1vfC838c2f97eq7xVh8WdZP8LNyjeuP25/9qehL/luNS0/CLqXJyLBJ5bZujZugeFwZK7oOn1sxyNJUrq9nnFkhIHBvMcxq3gOtHQ6IcvVgqLMqCpDiD04us/B8fu8/e4XmS+P+R+/+3/y1juvMdyIQNQcHD6mbRXL5RJLWNy6cQPP9fD9gOl0jus6KG3C47RS7O/t4tg2777zHkEQMp1MsCxJXdVMJ1M+8OoH6PcG5HmB7/vs7u0hJJydnVHXDUpptrZ3zGSiaUjShLquuX37Np/97GcvwggfPz7Etnw8N8R1zJQkSRI8z6VtTWheVVVorQhCwxvxPJder0unEzKbz81VZ9NccEEmk+l60uJejPKfHIyfXI0/mWC0bYO1ztV5/PgRUgqjXVm/BqUgSVLqyjy+57nUdclg0KNtFa7noTTYjkuaZjhr7cuTRGTLkviej+u69Ho9w6UpKywpDV0VTVFm5PmK6fSMosywbYHt2ORFRhiF1HVFr9elqut1AnKL1hLX8XEcH9VCmpXEcUocp2xsbDEcDI3GJUlYWJKHnk3qe0Zu+jU5Qk9E3k+gcFopHNtGSgvbMryTqm7x/QCETaMUTVMThgFFnq91Py5N21BWBbZl0Q0DRv0+z1y9Rrpa0VQ1tAqpJfEyxrJsqrJa/1y1yQXCCIAtaVOV9dqFY6/DIwV+4FI3Fb+9FfDprQjHstjOG2wh+fyVEb9/ZQPX81EaqrJkMZ+TJDFJsmI+M1lSWZYhBOS5aVSbpqSqSs7Pz4mXS6qqwvcDiiJHCAijECEEtm2ztbVJkZsQUaNtKtFaMRj0GY76DEcdmrUGJgwjqromDLt0u0OaViOEXKdaW3Q7XfK1DuX8/Jyjo2PSLCfNTNaV1iZ403Fco3mxBHG8JAxD2qbh/OwM1porcz/J+eSMIPTRGKv7Kl5wdHTAdDplNptxcjLm8OiQsiq5cvUys/lsbdvXF1OqJwOWrx4nzbHye+ZLfRc3vkUj9D09sfWF9vfSTPwwNirwY6RZ+WEQuP4gP8emaUGbsDJ4krJsckwcxyNbzRkOthgMhkwmE5bxkixNWS5Nqu7mZh8pYRUv+f+z91+xtqbpfR/4e8MXV945nFyxq4qdmx1smRIVaIrwCGNdW7qQZyBBMDAWoAsBhi9sGDJ8YcPA2L7y2AIMw4AvfCHNeGjZVhiYpERRTVZXVw7n1Ek7r/zFN8zF++1VRbqbZOfA8xQWqrrP3mfvvdba3/t8z/P///6ffe013n/vPU5PzijrgsVqjtCS84uHPD15TKRTlusZB0d3aU25saZ+7nOfw3qBt/D5z32RJNKcnV2iooLnn78HGPKsz+uvv0m/N+LG8YReNsA0jqJYMxwOmE6vqOuaw8NDPvroPju7u+R5znR2GsbYUjCeTEBa9vf3uTh50rkzwq/K7/7u75IkCYPBAGPCmmM8HjEaDZnPzhEIBsM+X3j+89x/8CHTqyvKsiTP+kgpmc3mRLFiOMiDUNBLoijg9IfDIXmaIQDnHeUqOIHm8wW9Xh+AsigRyC6QUJEQU7clUilMEyzNWkeY1tK2Afsepu4C78Ohfnp6RpLFwQLtPcZarHFkWWiSlAoBemmads9bn9lsymq5Ik9y+v0e8+WcupFoLSjKFatixWjUZ7ZcokRIk764uGCytcVoOKZpWop1yfnFFW3Zdo2RJk4CpwaviLRGyaD3aduGOE0wre2aQ0AIkiQO4mEEbduAEJs1pNY6aGG8p2lresMeddmwWtVhIoggidOA4TcuMG6kQmpNWzdYAdLDYnmFNLZLEhYIJyhXNY3xQEWaxkghMabhcn5KEmcIFEnSC0LTxpJkKaZYEyd6Q/Rtdc4/urPN614waAx2PKTq92hOzhEEEa8QQ+IkJo4j1us1KVmwgfdikjTg7weDAd5per0MfB3YQV1GT78/YDqdYa0jCJcFOtb0+32kCMGJ43EIPVwuS6xpmJVL7ty5hdIwXy1I4pSrqwXjyQF37jzPe2+/xc7OYQhHXK/QOt40a0maYJzveC/htVJKbUI0hRRIGaIJrLN89rOf5dd//R8CMBjkjMZDZvMlw1GP2fQK5wzZoI+QkGYJH3zwAd4LxqMt3n77bY4ObzLoD0izDCHlRsQJbHRaz+rns/5ETlbgxzNJ+fmq4CzxDrSKSNOs42loplczbt28x1e+/FXu3L5Hv98n72Xs7++xt3eAMS1luaJcL5lPZ7z33gfs7OwTJQm94QDrBFqn9HoDXnv1NV555VUEiiwdsL11yC987gsUZcmTx4/JkghnWkaDIVrHfPzwMW+8+XtISacLiUnjHjvbB+BVAL3Nrnj48GNW6yWTrQnWWk5PzviFX/gsL774IutizXy5ZP/wkO2dHQ6Pj0jSlLqp8QR7dl2H0f8Xv/hFxuMxRVFgjGE+W7FalsxmC0DQ6/e6puiSKNLcuXOHKIoAiKIoCGLXa6bTGXmWU9chaTdJEuqqRinFfLHAO0ccJxRFiVJqk+3jnKDX6+OcZbVa4js2S9lZf/O8j2lt4G9EKd5DpGNWy3XI3vGCpm4py4qyqFBSI4Uiy3LqumExXzIcjhBCsljM6fVzDg9D5le/N6QsG8qyQkpBkmiqqiBJIsDR62dBuOo+sRvjPQ8ePKAsggOoqmuk0NSVoa5a5rOQKJzEOVrFKBUR7rx1x4AJicfeg20d1gYRJ8JibIMxDUI6tA5hgFEcEUURAoWzAVZWV+F5k0KjpWK1DEF+k9GINIlJYklYwQW4m5KCxXyKaSrSJME0jiTOQ36N8d3UypH3cnZ3ttjb28UTpjbWWYqyppcNiDp3VZomjEZDguYrYaokj3opZ12eEyLobCDkKLWmIo41eZ4R6RgQXT5a3KVoB0eNMUGo67vlQm0M8+UKJwQ6SegNhzgpQKjwezqdYY3tnleLtQ29fsbOzrjTYIWsn/W6RMmIt996n7Z1ZL0ezntUFBGnKVVT01hDnKXYLmIjiROsNRhjWK+DE0vrALmLIo33DucMaRqygLI8NFdKS5I45ujokC984fPk+SfZWc6FSc+9e8+RZRnLxZJHjx4xGo1QUtJ0v5M/6uv3j1Lf+Af1k890lN+9fo6blWcNyA+zpJTdQxEOExVG4k1Dvz/k7p0XGA6CRiFONMdHB8zms5DIGickWoJ3FEVFuW6xVjKZ7IJQxEmPSOU0tUerlGLVkGcTzk9X7G7f4rXPfp7X3/g9nj79mPffe4tiOePR44eMxhN6wyHvvf829x98gJSKjz76mOfuvcT+3jFCaIqyQmvBrds3ePXVV+j3B7z91jssl2vmsyVxknLvuedQsUZHGmMNURzx0f37lE2gbz59+rS7cObcvn2bg4MDyjKgwKUCqSCKFKPRkL29HebzGXVdcfv2bVbrNb1ej7qqQvKuswyGQ6IoZPYIITbpvAh48uQJo9GItm1ZLlekaUaSpCipaRu7wdMDG+bG1vYWust7WS7WOEdYZ9kwDWualkjHNI3Be7FpYIK41tE2LVVZkyQZcZyQ5zmj0YgsS5DCMZtdkudpp+2TOAtKacbjCc77zmobtCmRjnAdj6epG9q2DXTcNoQVZmmKda5bJ7YYYzaEU+/D2koISZpm5FkvvOeUDKJLF3KHlBIMBn2EIMDuBFhnsM4gpcP6lv4gQyqJMXZjI7bWggeBwDQNy+UiiLSV5PBgLwiZEcRJxN7BDlIJTk5PiJMUvGY4mjAebwUGivdoHezji8WMwSBHKofWwba9WKzo94fgryc+4TWzxrFalizma6qq7VZ0Cq0jpFRUdcXR4QE6ChPM1Sp8nJKB+5LnPfr9IVpFnaXZI5SibR3GOoqqIstz4jhlXZTUdct6XXBtk/f4kM7tHQcHe3gs48kIa1tms7Bykp3l3XvPo8eP8VIglKSsK6IkDl/PBBhfa0zXiLgNgXm9XmOM7Sz1Gucds1mw5P/mb/3G5uv3ejmRTrm4uGIy3mG1LNjfP8TaAPFbLdcURbFZhS67JjPkWoW1b3gP/Gwd8M9Opu+vfq6ale/sp382Ofnh1afkZ90Foixrjo9vhsC9Jrgkzs8vePe9dynLgjiKiaMY21pM43AGjBEYK3ju+ZdJkj5l2XB2eonWCf3+gLfeepv5bMF0uiRJBpxfnJP1YrIs4vz0MW+99TpZFlHVJYdHh9y7dxutBacnJwz6I4RQWAvnZ1dMr2YopXj8+BHvvvsuxhiMcRweHiGEQkcxr3/7DaIoYjgeUTcNDz7+mM994fO4bjpgraXXC5bqf/pP/yllGYL3ynJNkgqcLxkMM4pqwdXVBYvlnNl8xsOHD1nM52HN07akaci+GvT79Ps9mrrGeddB1ZLNhXi9WmFssKKG3XzIZQKBUrqzrkqkDCTZi4sLIq1DiKQJr4H3bBJ6vQ+Hj7N+k3m0XK46FHqDkIqiKDGtCYdjWQfAW7HCdVj63d1d6qqmqS1axVjrOTu7JEt7lEXNcDDppg5BVDqfzxkMB4xGI1arFcvlcpPGraTA2JY41mzvbHH7zi2sM4CnaeoQG4DcTBsiHXUhh4I0Sbrsnygcdt2/Q/OgyHsJg0GGVAFjDzaIXaXAW49pDbGOEIA1LZGSjIZ9nG+RWqDiiCjRZP0MoSVOQN4b8PwLL+GsZ7FYMugPSdPgrFmtVigFOgIdOaQK+TTh+QRjoCxb6qbFWt8JciHPBrRtyNJZrdfd1CwcuMtVoPymaUqW5TgLHz98wtnZFUXRMJuukCIm0hnWipC9AzStIUlT2taG5GkPaRoCFZfLdcfBCVOaXj8PBN8kYbUqyLMey8Wq00dJlAop4M6FGIjglApale3tbdI0xXVwvSiKQYT3nBACYy3L5YLlchlWVAK8d1RV4N5orcjzlFu3bnF6ckGvN+TevRfY3dlnMBijZIz3Eu9CZENdV3zwwQdd/MBup+lZsS6KsJ7+Kas/Upv47Dj6vurnqll5Vj+6+oNNoJTh8Ov3ehwfH6NUxGA4CkyLbtR8faiMRmO89eRZD2sEzz/3Mrdvv8BwuMNwtIUUAqU909kZi+UVk+0+88VVFzoo+fab3+LBww+5uHxKrx/z+PF95vMrrqaXPD15AjLYqsP42BHpjLKs+cxnXuGb3/wmWZZwcvKU5XLJw4cPuXv3LlpH7Ozs0Ov1mc/nfPZzn2M8mbBcr9je2eHjjx9wfHxMHAcS6nq9DgGOTcOTDhOutCBOHHlfs1hesF4v0JFkNBoxGo1Cng/hIt7v9QA2BE8hJOtijZIKrTW+m1A0TcNoPCbqwgbzvNdNs9g0Gsa0FMUapRXWtl3GSszR8THeQ6/7OLFZ3TmaJkC+wtrJbCBsSilspz3yXoSgxLqhqqpu/RVsyUVREMUpZVEzm60YDibMpguM8TRNmCRonXTi2YStrS3ofvbrNZcQgjzPSdIIrQVKC5xrubg4JY4VHkNVlxjjsM7hve+geWH10tQNHh+0IpfnrFZLtJahEfEWKT1VtcZjiWOFEI4sS5EyiJERPsQMFAXFuiDSmrIqibSETq9khaPxlrKpQMHO/h7z1YrX3/g2Umm2JtvMZrONeBbhKauCpq0YDFMmW0E8bS2sVhXFusEZsbE2l2WNs54kycLP5j2Dfj+sCKMYZw1FsaauQyCklGHqItDdyrHi5OSc2WxJsa6pa4NA4RFEcYKOEpCSxXpNnGY0JriXVqtVWN8JyWQyJs9zzs5Ow5oqyakbQ573iOIkiJe96dxMwcmltKJpW7I8p2kbhuMRUqlg19Y66I2UCgLojkS8XC7w3lHXFf1BH6UUzz9/D9816BcXVxweHBPrFGtgf++YOMrY2dlne2ufKEooy4rFYhG0T6MRcRzz7nvvdYLjT0Icf1br+weI/iS8xz9Zh+3PeLPyzJb8463f/xx7H3QYWZpu0oONDSN+01q+8Y0/RZr1WBU1qIiyMnzm1c/TG2xz794LWGt58OADhAy6i53tCXjLwf4uh4c7fO5zLzMa5VRlQVkUXFxdMl/Msd4SJ5rlasb9++9TVSv+xb/8bVrTcHlxzvnZKd5YellGP8/4nd/55+S9mA8/fJc4TlA65t33PmB7d5veIGM0GvF7v/s6H3zwIYNBj92dMavVkkhFaK1o2godCayrqOoVUSwZT8LKp64r5vMpUjn6gwQpPavVkpOnJzx+9AiJp64KnG1x1mCNCQFxV5dopbHOIjvRstYBurVaLSnLNVuTMQKP9w5rWgSus+mGVOHD4yPibq0yX654enoKStJ2IDRPWH+oKCKKY5I8p6waWhPWB8Y6PCCkxDqHkJIoCowLKQLiv9cbYloYDsfUVUXeS0izhLzXw3kwrUMp3WkoGpJIsb21zXJRsJhVnJ3MWS5qinUTNBixoiwL8l6KdTVNu8a6GrAoFVYoxtrOcWZxXtDagNG33eQhzZKOURNjnWXZaXecdwgNSFgsV5Rl3aUAR6SdE8oB67IizXMQkp2dXaaLGVVTobSkqksghBEKqVgsFrTWIJXg4uKS6XTGNQK/bQ2RTrDWk6U5i8UaKcIqJ4klSayQArx1KKGZzxe0bUt/0Kdtmy6TKXBY8jQOYLZ++F0S0oFvwbfEGoa9PolO0FJTrasgtJYgpSfLIuJIsLM1JotjFDDIU/JE0ssVaaIQQmCNZ7kswlSigxeWZcW6KAMYTwA4PI6mrbGu07YYx/37D2lqw/RqhrOWXi/ixZdukGQyNIaSDaZfCBHeC2mCvE7Tbg2hYwMUOOEZTEasiop7z70QWDyR4mtf/zp5r0fe6xFHKYvpEtNaHtz/iCSNuP/gw8AaWq+p25rGNJvVlRTik8DkH8EA44c5of9ju3D+wNF27Tb6bg/xRzYT3+/5+OnP/y5/9/fLf/lj1s90s/IMf/yTq08LwaRUNNZiseS9nPOzC44ObjK9WhDFOSrOIIpxMkHIlK985V8JxM1YMxrnJLFGoRn2xrz84mcQXlCXFXGk6OUx4+GIPOtjveCjRw+ZF0t+743Xefz4Yx7c/yCIGosV9z/6AIFjvZyxWs7AtQz7GUo6tDJ4X3N2dsZiUTJfrHjvw3ep25rRaIvPfe5LzKczFrNz/td/+A+oVgvefetdELC1MyZOJV626MTjaFmuw6rH2uvpQUPbVqzWc5q2QmnNYjGnrtahUenG2chwOXF4lJY4Z0IzVhasV0uEgNn0irquaKqSfi8njTWDfo5WApwlUor+sM9qvUbpiNZ66tbTGM+6KjEuuHzwvmtKLEKpYGkWgjhL8ULiPMRpRNbPaExDUZW03aqorqqAv89GSJHw/rsfMZmMkNKB8nx0/z5hLaXAC0zTsre7jcBR14aycFycrVjMDbEekKZ9vA/6FyGDq8zahjjRDAYhcbhp6k0qc2vC96ziCKU0ToguByg0b21b47wlyxKECM+l947GGLyQeCGJ4hxrBXiJtw4dxSyLAhnFLIuK2niKqqZqWoy3ZHmKkgLbGiIVk0Rp18AJWhPyaKRUXRPkSeIMrWMinbBYVKyXlrJo6fdypDB4W9FLNbGSSCHD2lErhPB4LEJci3VB+BbXFgH7bxsclqpaI0VLP4sQzuLblvViiZaS8WhIFAuyXBFpQ6w8kfRoPE1RIFwLrqKfCYRs2dvfJU36pEmPtrXBYq4USsVcXF2S9XKKck0Ua4bjAVme4ZyjNS1KSpTQRCqlrgymbRmNM0bjmCSBKBboSKC1AALrxlrLulizXpcIqYKeKYp5+513sDiMcIy2J/yZX/7z7B8ec3zrJnefu8Mbb32LW7dvcXp2ynAwplhVRCqmtS3v33+XJIs7BP+HFHWJwYYT3Tpkp0faHPV/xDrmjzvJ+GHHqEjE5iE83/Gx+XPo8H7hIaATmH/nhxCB8/KdH39UntIfXuF7+e5/96e/z+/0+EHrZ7pZeVY/uZKbd064e3c+uALeevttxl0E/WS8jY4SBv0RL7/0C9y9+zyvvvY5hoMxy0VBlg546cXX0CoJ43olOTk5wTmIopiryzmm9YyGY27fvsPBwQGDwQApJU+ePuaDD99nMBzy8YMn3Lv3Isc3bjKbT0E6Tk4f8Xuv/0uSLGI4GtA0ga67v7fHF77wOXq9nMV8xuX5Jbdv3qGpGoaDIVGkKdYL5vNLhsO8u/AGPYU1lixLN+PtsgopxwLBfL6kqhqKdbnJ9Nnb3cXjw0VTCuIkoT8YhHWDsxgTmoooCoLNvf19er0eCIFSEcZ6rPFoHQeabGUR3ehgvarQUczFxRRnRSecFQipKasm3EnbsM8P2qKyC0YMduaiKDbrlaoKmHzdhfJFcRQamSTh/PyCp09PWBdrFsslSZpQ1SWj0TCE9ImIOM6QMmZ764CyaDk9uWC5WHfIe4kQUHWrksAlkVRljVYJznrq2oRpjtQopbv3l0RJhUDg3DVrJlyur/H2edajquqNI8XYkIAcRUFzoVXA7zsraBsfpiBdw1EUFVmWsVqt8UCcxN06ztGaJrze1qCVYjgYoKQADHkef7Ju8gGAZgxoFZw6189vYLAEga9WirZtESIED4bcp3LzM1lrUSrCOUkcZRRFSy8fk2WD4IAy4b0XrMFB11RWJWUZnGJlUTKfz9E6oqrrYIHPeyipaJq2s6SHMEmlJb1egrENWZ5QVUFXdnp6xny2JIpSer0hEKZtWZqSJJokjZAqfL913fL40SkfvP8A04mtTdtSVgVVHdZrQgTXmZCSfn9A0zTkaU4UhXgIYyxHR8f0e30+88LLmKZlPJ5wfHTE5WVgDV1dXSGFpKprsl6P6SzwZEzT8v5774b1sVKbycqz+vmuZ83Ks/q+KiDHXfffDXXdcH5xzuHBAXu7exzfuMnHHz8kS/tMJjsslyWvvfo5dnf2sRb29o442L/J4f4djo5vI5QiThJ+65//NkJqer0xN27e4d69F1mtS5TUfOYzr3J0dMx7773XOW5KDg8OODy8jRAJSZLTG/T43Od/gS986bMslld885u/w9OnJ4zHE6QQXFyed5bcjDe+/Tqz2ZRbN29y8vQEawxb4zGHB3scHe6zNRlh2pa2bdFaIwQsFkuapmE+m1NVVdcUQJLkNLVFyhhrgr7i8PiI7d0ddKTJez2SNKUoC5z35L1+t0aLKcsKEFxeXrG1tY2UwVWRJjnWenr5kLq2VFVDWdQomYBXLBeBTyJlRNulOEuhwSsEagNQCw1CaACcDzbgOE7Jsqz7Wm7TPIHDuhahwiTDekfdNjgfXCRCSZRW6FhzcHhIVTeslhVN4/lH//v/wXrVcuP4Dv3+kDRNMbZlXSxQCoT0RHGEFBrnJEJo1uuasmgIDUzbNarBJeWC0GMD4bsmjVsbspaMsZjW0sv74AVSBOdQWa5DonJbY4ylaSx17SmK4KrROmJrazu4n7pcpPB8pHg8+/t7QQ/Urdvm8xm9fkaWR+gYlA4ru5A+TicG9kHs2TUq17CywaC/aSriOOby8pK6rjvdUvhYYwxKxwgZ0VrB5eWKk5MpZWmpG89gOCbLMqy1NE1ohgb9/mayaayn3x8znc6YzqYorZBKUTeGsmrRkUJIjzE1USTZ2dmi38+Q0tPvZ7StIc8GaJ1yenLFallRV4bxaIu8l6MjSZpG7O1ts729jdYxq2XN1WWBQAfu0HBAlgd9kCesBuM4JdIRRRESqBfLVbBzd/faJyenPHr4mNV6hXees9MzBKFJbeqGLO9x4/YtJtvbFOuKfm/EoD9itVzx9PFjvvXGtzr7s9pA4b7f+kkF0f44v9bPej1rVp7V91VKhah45zx13XQckJrReMzNm7fQOmJ374A873F8dItBf8RysWZvd5/BYMid2/fo5SM+fnDCalVT1gak5s7d51C6G9frmN/55uus1xX9/pD1qgAEVRUSXtMs4eOHHwMR29t73Lx1i0ePH3H/wYc4DP1Bzmq94vLyisvLS/r9PltbEz786H0ODnZZred4Z5leXVGXBbZtMG1NEsdkScxoOPh9QDjRsR0+PR43xmKMo6kt/f4W63XNZLyNMYaL80CwzXs9dveCNdYDRVlSViVpltO2XQCdjkmTDGscvbwfwiJXBUeHxxRFFQ7pLqRvvV6Hf68KqrLu7LGBT+JsyJ65hnY55zbMCrjeeQvaxtI2luFgFA4P70iSiCSJWRdr4g7qZl0ndhUEp4kxjMdjhBS0pqU/GJD1esRxyuHRDXZ293j06DHb21s43zIaDRgO+6RpjHPXMEG3yUTSKuoO3WCLD6Jgsfner+mk4TDy3c8hupVKhBCSoijJspw0zTqba5hWpGkIC2wag3eStrWUVU3b2s30K8Dz2o3N2pqWi4tz2qam3++RJDHgyXs5xjbUdUnThlDB9XrdWa59N5WLcM5R1/WG1luWZff78klzIqXcNGTGBEpxVbd4JNYpjFUgYxrjESrCWNdNvWKcczRNQ5ZlZFmG6KZwW7t7XM3moASXs0scHi8EpmuiFos5O7vb4flo640dOAhVw7RLq4y6cizmJdYKlssC0wYrctsGy7jWMiQkO0mkMpRMKMumsy9brA3vteEwpHBb69nd3aPXGxDHCVXdbN6FbWuIVIREcfP4Fns7e7z4/As0ZQMuXGOk1mgds7N7wP2PPqYuGl58/gUuzs95+uRJN9G1gbP047n0PaufUD1rVp7V91WfvgNI05ReL8cYQxzHpElK21jyrM9nP/tFtrf3Qnrscsnu7jY7O9vs7+9xdnbBzRt32ds95tbNewiR8OJLryCV5t6953jv/fd4evKEp09PePnlV7i6mvLWW291abkNT58+Jklj0jwI8nr9HmfnJ/yjf/q/89GD+wzHY6wLF+vxeExRFpso+9nsCiE8RbFgOj3j4uIkrHaKgl7ehfXFGVmWMxgMyLIMrRQ6iropS/jV8d53oYKaxXxNlg5YLguyNOf0/IzZfIaxhsurS5arZaCZOktd1ywWS+hcOVEcs7O9w3Q259Gjx+E5lvDw0cfM5lf0+zmTrTFRHDEej1itl1hngshSyaCFkOFzPo2Aj+OYKAoritFohNYh9M77oDU6OzvfIOhb0+B8S7/fQ2lNmme01tCaEDtgfRDknp6dUjcNKopYrJYkaYxQYH3LdHYFwnJ+/hStBf1BwmjcpyhLpldzbAuh6Qj037qpMKYNIZDWbGi1QojQPHSBfsEFFFKSr+2q1ysg2Vmvy6IGr9E6IYmTjsSru+YgQcmoc0G54LZSGq00SkVY61kulwhJCAxsKqqqRGlJlofAzutpidaauq66RGeP1oL5coYxbVgbDYcdh6elLMsgODdm44qSUm6amGvq67pY0bQGrWPq2rBeV3gvwjSuKrpVkfrE3r5ebzD81nmWyzVplofVVaRp2payavBeUpY1TRNWbet1yXKxQmtNVdVIpRmNxrSNZbWsMK1gMa/AhxXSYrHsQhtLVusV62K1WVu13YrqmnocRYo0C9A6ax3LxYr1uqBYVxweHRFHMf3eIBCEPbz2yi9QlRVaaQ4Pjzg6OOLs9IIszTCtZbK1hbGGJyen/Mpf+FW+8fU/xWK+5MXnX8AZwzvvvMvFxUXX2P7krBU/yonMs4nLJ/WsWXlW32d19+hCbPb929vbZGlKUZa8+eZb5PkAZyBLc+7dvYvH8tH9D3HeUFUFBweH7O8fsl43fOMbfxatc66uljTGsX94QJREnF2cEMcxT5+csDXZ7vbdbUd1XTKejIjjiFt3bvPtt94g64X1xjvvvIeOMo5v3EapkB67XC44PTvh6dPH/N7vfZMk0Xzw4VsYs6Yslxwf7SOAsqjYmuwiiVgtVyipQnjdhkGhNvH03nsG/RFF0eCspG0cedbv7mYX9AcD2ralbhqSNCXLcw6PjpBK07YGpTRlWXF1OeX84oobxze4c+cu3gUqa1WFvJ1eP0VIv7H4Hh7tkaQK71s8poOIOaQEY2u2d7Y2NunrkLeLiwuAzg0EWdbrQHVhUuR9cOHoSNG0Da0xVHWFdS5wYLpVmLEO52G+mIe05WKBcTXrcsbW7oDROGdvfxsdBW3HYjGjLCq0yjHG43yLEIFJ0u9n9HopSgsQruO6fLLCirTmmq8SxxFNcw2Tc7StBQRlGbRDTWNoG4FWCVUVwGODYQ+lBN4bPC5IFn1odIqipCiqTtsTLAv9fh+tFeBQWrJadawQABR4SVVWNE1N3VRI5ZHa41yzSWxerUIjkSQBsBd36eNAt2qjm4iEn1MIgcfRG/SC8DqKQuPhHWke07QVq/Vq06j0+33a1pCmKds7OzgHp2dhmlK3LTqOaG0IAXTIjuYLVRWs/ctlSVW1IQJCKExruLqa470iTQcolVDXIam7aSxN7VAyZno1ZblcUpahearr8DM3ddNNaDyj0YBeL9tMy+I44ezsHNuxja5/F+I4ptfrc3x0zPbWNkoo4jjl1o2bbE+2yTpr94svv8zO7h5JkrO3c8BLL7zMndt32BqPWC0XPH78uJtuuR/LVe9Z/eTqWbPyrH6guu72pQxY+msiaxwnVGVFVdXkeY/p7Ip79+6yXM1JkojzizNu3bpJ27Z89rNfZLms+NW/+H+haR1KKy4uL0A63n73TZ5//jlefe21Tn/QbIBVOzs7XF1e4rs79UePH9If9Dk5P+Xo6AZ1ZTg9Pefevef48MMPN2sFrSWvvvYZnDfs7Ix5971vd5ZkxXK54uDgmNWq4uJiShRFtCawTJo6hKe1bdt9zaCjmM3mNHXLzvYeddUwm82pmwatNRdXl5RNTW/QxwuYLRb0B30a06CjMOJ3LjBiAjxt1aUUW05OnyKlp64rynLNajVjMMxp2orz8xOqqgThQu5MG+7041ihZGiitra22N7e3qwa8jz/fcLboJ2QgV/iXcd1yVksFhtCrzG2a0gTDg4OEFKio4i6bri8usJ5g44EjSnpD1OMLXE0NO0aYyusC7ydOE6RImT/SMGnmogWoRx5npCmEVKC61KfrzUZdV13E5JATb7G3QeYXgoI4jghilKq0nB1uUTJqHtOFxgTAHRxFJHnOSA2rJmqqoLduGro9XLiOAT/jUbjzfPUtg3OO5ylE6AOutfe4DFEkUDpYN29fn8OBgOapqWq6k2oXxzHv28VFJxBgiiOQn6TCE6aKI66949ivphtsnXC3XVYIeW9nCxLiXSYoljraY0lzVKss90BHmILnBdkaY/LyylKhigC7yT9/ogkziiKiqbjtVgDbWOQIuicBIrtrf2usQ32ciHA2rYTClfU3QrtWn8zm88pywrTGvK8z3A4ZL0uKMuyayyDxuSD9z/g4OAguJKkoN/r8dzd5/jyl7/M1tYWVV2xLtf84i9+lcVixdZkmxvHN7l35x4vvvACky6H7Orq6lMXpB/Xle9nub5fPstPFgvyrFl5Vn/M+g5v4A4EUJYl3gcolRAKKRTDwYjbt24zHo3Y2dpFkSKJ0TJhvVxRFTXOOJ6/9wKHewfkScbTJ0/p9XtUdUXTFnzh868w7Ee8/dbrPHr4ER988Ba2LXC2QQLP33uBWGfcODrm8OAYJRM+/viUO3deYjDaYv/oCKQAHcSiSZqwLqaU5SWmXYILyO71uqSpHXXluHF8m6IoqKoF0/kJFxdPmV5dUqwLbOdcaa+hZSLAs7Z3x2zvjji/eorUQROAkEipwYN3hun0giyLydOY09MTtrd3u7vMCuMCdCvNYqbzCxwNOlYkcUKkYrRSxFGEVhKBZ3trjG1bEqXZ3trumCpBmCqFRDhPLBOkV0wvL4gjQb8fY9oaZzzKx6RxCg60VMRxhNIaHUd4GVws0gu0UCg0TWkpVy33P3zMclEjfUSe5qRRjHCQRBFpFLFeLDBNS7mugOBsWa8K6tp0zUNLFCtUZCnLNeDRkWR7Z0Cvr0E4kiwiGyXEfY2IPBaD1AJjwxpF4DvdRBKStNsWISSqe66FF0gkWsaY2hHJmCSKEN6hJcRaEKnOtisjPJokHYCIePDxA3Z2JjjhWazXyCgOZk+hgpVUefqDHCklg/6IJMmD4FlplAzuo1jHtLVDyZSmCQj8si6RWnYNmOvWXWBtWLvVdUGkJMI7hHc405BGiqYuiZUkyyKiJFizlVTMp1OSWNK2Fafn54H0Kzxaafq9IctlQa+fE8Uw2co4ONhCiIokNsQJID2j8RAZO5wM71XnNQcHNwMcz1YIV5NIQaRiitYwWxVEWUKUxESJ7oIhwTQ1pmlQQoELIaPCCbwNWUDluiBSCdZ7TBd8GMcRwnvm8yuenj7B47HmOtl6idIRxzePGY3HYSJzcESsNG9869uoSCMiyb/6y7/E3t4ew3wQmDfO4kTgt2ysvD+mw/W75fv8dOb8BNH69//4ydWfmNTlZ/VDqD9419I5NJSSrMsypOSakF/zpS9+scvuEHzw3ntsTfbY399nvSqZThfBttjvB6R4muKdZby9xXx1hZCCpqn56KMP+cbXvsaH79/n9d/7lzx6+BHWNcRxhpKSJEp57t49pBS88ca3iOOEo8Nj7ty+Q1WXfHj/A2bLGVUD1ns+enAfrSTz+Rnf/JePibUmUgrvBXneZ7UsWS6XrNYrklRx9uQpSgnSNGXtHJ6Iug76Dec9SaKRMqJpQ4aQkA6lIe+N8N6zXM64dfsAa4PQ0rQ11racnZ6ytbUNQNO2xHEEnd4kzRKEFGR5ymy6RCBJkgTvgn11vVrhvSeJ4g4Zb5FKB1eKFEihcEIxn4bU6/4wx7mKONGs2zrYfp2grppuleWCpiVJ8N4GXYoxxFJjakOkU/Z3t/Becnk5w7QWJSWr+QLhuveE9ThjiVREpCPoaYp11a1qDBBWZ96HzCBTlJ2bLExN0jTGtCaEOdaGKFU0jQPFBude1wH+5aRDi2AFjqKoC1UM6sosS3DGU5Ultg13/q1pEQ4ipUmTCNMW5HmG9zLYzoWkrBqsNWRZwnwxpd8fsVitONB9kmnJMo44MwbvDUWxpNfLqaqKoqiQ8lqjsgiW3Simqht0HfD5aZ6EQMxumhKsxGmYSGm9YZJkadadsR7bNiipURqiSOOsRSrBarXmaPeYti3AG9bFkqpyxGmKsaYTIosukyc4gopyzrDXI+4gdcYZdKxZrdcMxj16w5TlosJ6w/nlFUJInGvRKqEuW7wWnJxdURYFsY6pm4Z+r0+5rkjihDTNw9fUSRAqV4YkTsLUTkoW80VIAo8jyqrk1u3bqCjinXfeDiujQQ4yJFtLLzg8OGK2mHLv+Xv8g1///5DM5swup9y78xxZlqOUREWCu/fu0rawXpdBaCwlTvjNuu3ntf6gbuV7aoY2n/rT1kD98epZs/Ksvq+6tmde3z30sgwlJJFSpHHCoN/b/Nl0MiFSmqOjIx4+fEiapmxtTTYo+/FoQt7rUTYFdVVzfn7GYn6FxPPqi5/h8PCQq+kFw+GAopwTcN6Cjz++T5zkRGnO5eUFFxfnPP/cTaZXZ7zw4gu8887bCByrVWCK7OzsslrMEVJ2gX0Zy+UCrSOOjve4vLhguVrhqGFhWa4WOBvcBoPBgMFggLEzEqKOsSKo6uAuydI8BPrJiNlshlbB7VFVNaPRsLOxDnj8+DFSBg1NiLgP7grVZas0TdvxNxTb29u0bUtVVZuww2sbsrcO31Fnj46OePTkhKqsUVLjvCfSkldf/Qyvv/Ev2doadnfzAUhmTAj/E5mkqit0GkTDxnqMbbGtJUnjziYc4F5xlAIO7wPcTqkA4zLdikwgGY8nQUhalxvHS1iBRHgX/q7pdEachjgEIQRZHlOWNYvlCmPC92aXRRAyS0UaxzRNS12WHXZedtlIDmvrLowxCK7zLKOy4XUpy8B0aZvQ1NRNhSgMjoa6CvEDw0GC0mqjgUrilCROWZ5e8Kc/mvHa9IK4ttSR5o1Jzv+6k9CaBqnSjtFSAI6saMgXFUWWUmQpWitWXYpx07R4D2UVEpydc4xGI5arBUqJDWvHYVFCdS67CGsNcRKF95EUNG3DaDRhuVyxtzemMWvyLGexXBArSKKESCq0FBjvWa+WTMYjXMdAAYHWEcWqZJj3qCsDyyJYhSOo2gKJI401UsTEaU5ZLrGG0DSiSaKUOI+wxpLE17wUwAu8DzqfNE3w3rC9M+Hq6opeP2d3d5eTs9NOiBsmREmScHZ2SlWVgT6LBAdJktC2htu377C/d8ALL32G9aogTVOG/T5ZGvKKBoMxvbzH7vbep+BpYnMMi5/N8/hZ/SH1bA30rL6v+nRHf+06AfDObxgQ13c5e3v7vPD884yGQw4PD4mi648Ph9PZ+RlVVXUHZst4POLg8JCDgyMePXxK07ZMZ9PAYklzVJQSpxmHN474zCsv88EHb1I3SxbLS95661vMZpdkqWY+v2QyGXHj+Cbew9nZedcE7JIkKbPZohM3epqm4sWX7jIYpPR6KcPRACkFWZZ3h4dnsVgF7YAUZFkCImTXxFHCfL5AqwhjHP1esGxmeU7TtMxmc1argqYJ6cS9Xh8lVbgIJ+nGejyfzSmKgrKsKcuK2Wy20W5cQ8XatiVNU8qq7LJjZEij7VKxrQ2TD+dbfu/13yWOoi6jKaQmbzJtCGnMdFA7KcLBbk1gZFgviOKUqmlwgIwUtWkwWPJ+ipcO6y1plnS25oSyqqnrFg8dmyairmvyPN+ISdM0o208pgFnRUiJtuHrWxP0O1EUBVqskEQ6RgqNQG3Q9Nb4DVgsZCMZmqaiqkuSJMI601nBU6RUnQYm2JWdC7ZuJUMukdYh6iAwVRJW64ovvHPOv3Je45XkLNN4Jfjqkyl/9qJkOBywXHTYfKX5S1PDX3njEf/2xzP+b++f8WcenJE4RxQpXOduEki0ChqaKNKsViuctZRlyP8JDJXg7ErTlCQJgZe9vI9SUWj2CI2NjmJOT86Zz+edoyswS8SnpldRJMmzlKZpmF7Naeqgo7m2eFdVQ9MYyqKlrhv6wwwhg7jZAkontNaRZj3iKEJ5gfRdg+xDeKOnc1VFCU3t8E52k7QWYw3OGUajAUmssa4l8HsMTVvz4ksv4HHUTcXJydMuOduHhkOGsM67d57jxvFtvvD5L/PySy+zXq1Zr9abVd8gH/LlL32JV155ZaOV895/sgF6Vj939axZeVbfV316HBlFUdBNuADECEdhCOeQQjIejRiOhsRJwu7uDmnauQU8WOt44YUXWC1XzGZT6qrsouMj9vYOefHFV5gvVqzLmsWqYjDc4ej4DnefC7qU5XpFkmiePP6YNNWUxRIlPdOrS3a3tzk8OOD4+AZxlCCFIst6Xfx8gbXBUTIaDoOItSr58le+iNKC0XC0aT6Gg0nHQ4m679l0gkvTMVEs4/EEOkLpzZu3O7FhcEyMRmN6vR5FEXJn6roJz4HzRHGCMS5krnRNS1mWHaXVB2tqJ/Tc2tpCd4FxhwcHvPLKK1RlyWKxoGkaxuMJSinSLGUyGbG7u0WWBZ5JuW4wrcfZayhcjBASKRRxknYiySDK1FHIu2mtwwvJZHubummw3uKFo24rhPBEsaZuauq6ZrlaU9cNZVWzXhdoHW2SpMuyDAdtF9gY0qwjnAtNSl23mNYjhEbJGOFDqrdEslqsaesWiSRScdfMxGgVY4yhrmuMaWnbGimD0FtJ2blDAoslTTO0VlgbHC5aRSHqwIdDMtBqLdaBvFrx8nnJVSRZZDGtlszSiNUg4/PLhl5VIVVoeP706YqvPrnCeMcTrfBC8mdmFb98vqQ/yENWlmnZ2tqm1+8jRJhuhIBG2yUShzVQkkQd8C2IVyMdbYCBpnV4B6vlmsV8GdKZfdBEDQZDIt1piNIY7wxpEpOnKWmcEqlAk82yhChSNE0dVkRShjR0azk63qU/SFFKkKUJAkfbVGzvDPG+IdaePI0wbcV6uUJLHYIvEZjW4VxwGlnrNmGJ6/Ua72GyFaamvrsuzGczvv3tN+j3e1hjePL0EWVZdO/LkOO0t3fAeLzFL37layRxiuriBXa2t8L1RQSHk5ZRN01SKCE/wdHzTGf781jPmpVn9X3VdbMiRMCfCwdYR6IjNvd6PuRb6A72Za1lNpszGISoeoQgijTDwYBXX32Fw6NDLq8uMcZwfnbBZLzDaLjDF7/4VbJswHCwzfbWIX/xV/+v9PIxbRsQ7V/58hd57u4tBlmOAmxruDg7J4tDQwBw69ZtBoMhpjUbQFVTtwwHE/Z2D7HWIQS8+eabTK9mPH1yghQRg/44ZP80bZeiHOil3lviJNnc+RrjKIuaqmy4f/9+YIH4cHc8vZoxHIwZDseY1pEmGU1j0DoijlJ6vT7ehcbOWbfRaDRNQ57n5HnOcDjcpDIDrNcFb7zxRrBDZxl1E/gxAN47inJJaypa07JcFhRFiXdhWJ6maecMCsnZbdPS1C1ta4jjoDtqWou1Ad4mpaRpG4SAJImJkgihgk5ERRoVxUFQrGOyvM+gP+gmGRV5nhNF0YbB0zY1eR4hVdD8BN1JGbRAjce2Dmccpja0lcE0BuFA+vAeE92R5Fy4iw8Tm9CkJEmEsQ15L8fasOoJEQSCKArWWLwmy3qkaYbsLM0IR6Q11kFWOTILZRKFMEMhsc5Sxpq4acmr0FAMG8tr04JprJmnKY0QXEaKy0jx2qxgbIP1OY5jVqsu5blr6q0Nrp1Pg+K0lmgtcM7Q7/eBAGwz3esAwdkTxzFSKqbTGUpppNR4C+vFivnVDJxl2MvAWZQQjIcTtBbEiWRnd8xgmCOlxzmLxzGeDEkiydHBLsIZ6mKFNzWxsihRc/fOLjdvbLG9lTIcRHhX47GsVmsQCk/Io6mqkrYJ66amsUgRUVUNi/mC9XrJ4dFBSHA2DVVVdOwcR5olXRZScHcZ4zg4OKTXG3Dz+BbD/ghvPXVVhXRsH94LshM9C9EFGG7+edaw/LzWs2blWf3g1SWcboijv2/CAuARUiAEXaNy3eyEka/zgjTNGY/GHB8d8cYbr7O3v4fWMYPBhNbAnXsvMhztcufey8TJkJ29G6TZkIPDGyRxwr2795hMJozHE6xxfOHzX2Z7axclY3Z2dpnNZtRVTRynnDw9QeuIfn9AXRmm0yU3b97BGsfF+SW7u/tYK/Be8/jxUyCIPKuq6i7IQTNTFGuEYJNLkmUZk8kE79lYbbUOuoW9vb0wLeiotwEn7zvNgiDNwspoMtnGGrtBsocMn8CQsDZYUs/PzwOwTCmWiwVVVYEPDYyzDtOGVY9UwfLadroJ7yHL8o6caje4/9Bs2S6kr8J5kFphnKM2LU9OT/DC46UnzmLSLEFpgfUWRPe6d1OyNM1pO5ZIOHxMp10JDUuvnxElDqlapHJIBc7a7g5d4Ox1Qm9grKRJ2n1vgXAbsoSCHT7Pe3jvwuokTUjXBXcaR7xcEUdpN5EIOUJVXXbaDclquabXyzuaa4VS4TLoHEyloJCCuGxBBEqvB3RRUGnJMhIhXXhdkBnLonvttQ5OrKWCnvPEqzVVXYbVZic6hyAo9t6zXCxDtlLHk7HOBiK0D01YnuedZsiRJCEwsW1byrIiTTPyXo+ma6BDI6OItCaONFpLimJNVVbkWc7R0R7bO0MGw4wkEVhXozQ415ImEYvpJXmsUThSLUgjGPYjhF8jxZrxSLK9lXL79gHDYQa+E057wXA4wLm2czmFn7MoKuraMr1aYKyhqevQ5EaaO3fucPfuXQ4O9hES6qokijTXoY7Xjjbv4faduxzuH3F8dAOtFMbacGB5Nv/+bmnFP856Bm778dQzge2z+uPXd9kFezzr1WqD/96Ibz/1MWEUnZGmvjso/eYXu65rVusVUSb58KOPeOWV15iMttiebDPZmnA1v2K9qsnTAb10SJr0+eyrn+fd99/FtI4HH3/MxfkpxljiOAo8EAS93oBePqCumk0DATAcjlivwzhd4JlOF91qx3Dz5i0OD45oa8/Tp2c0TcvOzg6LxYw0zVgu5yGYkO6OTgRh4Xw+YzzaYr0uEEKQphlZFhNFIYfm5OR0w9zQOqIqa0xrWK2WQaNhDEmSsV6XxHG2Ebdea4GKoqA/6OOmDucdkQrZNdfxA3gRDvEsJ1IRYKjrikinRFGMaX2AprVmc6i4EGyDb0qSLEZ2r5hzXc6Q0hTFupvu9EJEQJp2sbCy+zlz5rMFWR4iBeazOb7L8ynLcFhfZ+PUdU2Sa5JUkmSKqqyJVUySphuiqxASa8IdupciJEaLa6Baixd00wlD24Tcm57U/KuP53x2VpI7TyEU/zyO+N93JyyNoadiqqYkTtKwQpAOrSRJpHFGUtc1URSaIjEa8O3dgq8+miOEZIZn2LaMS8NvHSbMtEA7xTqNKbWi11iuEJsp1dg46khT9TLitiHPM5pmTRRppBRUVXB/ZXmGW4cGRsrAVlEycGiuyjllWeDxZFnCel2wtTNGuoj1fE2v38fL0ExlaQYsAudo2CfPJcW6wHu4uprinWS2WHLz9gHD4TZV4yjr0CQJLdFaslitMFXMzmTEcDBgOJTcvrOLoOVqPuXk8pw06TObzXnu3m2ePp1yejrDOIe1FXEsuwnV9QJYEcc5VWWxttN1de/j6XTKjdu3uX37Lt/85uuhObYBIHd90xNpRdW2pFlCKSriKMI7z+nJKXdv3cFZQEHH9/upq+tr4J+8+sN+5h/8+Xg2WXlWP3B5B5fTK4QWCC1wAnzXrITH9d1GuJhdI9CDaBSiVJP0Y+4/eEC5bhn2trGV586tOyRJzNHhMcP+iH7aZ3e8xc5wxPN37jIZjnj69Am9XkpRLjk9fURZLVksrnjy5BFXV1f0e33quubVV1+jrBsWqxXz9Yo4y+mPRty8fZO9vR1u3ryFs544SlksFzx++oAkERhXMVtcUbc1edYj0ilYxSCf0EvHxFEP0wqUDNk8WR4TJxKlHSpWXFxdsSzWPHr8OFBN4wRrDEkU0cti8C22rTehglVtcE4jZUZdO6TSeAEqkpTVGuPCIdUfDWhcC0Jha4WtNZHP8W2YVKRpim0Fq2VNWQZoGFgcNUIGYam1tkv0hShKQkZRFKGVCssW58ji4DJZL1a0VctyvqQoQpZTUTaBnRIpnKkp13PqagHCdwRVgXGWpm2pmhqkYLkouDhfU1UW5y1FWVBWBiElSaZDno2LsD6iMT6so/BYbxHa411DL01wrcG1Fuk0X/vwim88XdE6wZNEY6Xgl2Yr/ux0gfEtRd2gdIZSCc47TNMyu5hSrUtiFRGpJMDTXJgG/aP9nH92PAZrOawdwnn+yW6P/+84xtmwSrvwjteHKdvWMa4blDFsG8OobvmX/YwHyxJnHXU1ZW83YjyM0BKEtxjTkGYpSZaiYh3AuM7ibUukBQhL61p0pPFSUdaGs9MzVCQZbvWx3tA2nsW8YNQfcu/OEWkK+JZ+r08cBT5PnvYQSPJ0wscfXXFxWlGsBLEeIISirte0puDmjZsMhhl37x5x++YRL9x7mVj0uTiZs5zXHBw8T9bbYmd7jGsLXri3z71bYwZJTT9tyZKWOGrIUvCuwTjLfFkgVEysciSaSEUhayvvcXk6JdVDDrdv04tGCKeQ4jrcxyMRmKpBOEusIFaCyXBEL83DFUQ6xLOT66evhCdwXL7b4werZ5OVZ/XHrO8+3hQCRuMJILi+oQi77E9/zical2tLc3CmSHSsiUVMawzD4YjDgyMOdveDXVcrjHXcvHGLxw8+5jMvvYwzFgG88MILXP72CfP5gp2dbZwzFEXJc88/R9M2KBVsxOtyweMnj5nP52xtTTZ3+kFk6ZnPF0Q6ZJ2cnJwjRKB4jidjFqs1Z2dnOOeYTqdUVY0xhlLUNE3D0VGAuwVrsODmzRucnp4GPU6isc6EILimYTQaIYCz03OObh7Rdnbik6enFBSdJVfjXeDMeC/Ie32KYoHWMdaF9cpwOOxyXSRxnOLdCtlRQZXUgOuSnCVRpPDOdJOuwMMoyjWmbcBD0zYkWRKEvVVNEoX1lQ1ReOAFWsc4B/jg3hlPjvFYFCpk3QiwbctyMQegrpuQgiwDD6UxBgi8mqqq0DLuDh0YDPpIqWiqCtMG2q1AdVwSgdKStjUBQiYlWmm8FzgXVi8HOuGzszVXkWKeRAgFl8JTR5Ivrmv+YSppCDlITRPIw1kSE0cxy+WawWCALRtaY1EquNxarfgHuym/sZORly3toM8FjlQpnHU0bYtpLb8+Tmgay+dXNUeNpYo1/2Srzz8apTjr8Tq4e3SkWK8L8IoszanbmroKjyhNUBKE9HjCWq1pauI4wgsfwiL7I0xbsFwu6OU9prMpbWPQSlGsVwjpGY1GSBUyfJqm4eDggLOzS5q6ZjQ85vTsiifmlLIyRKlGyJa4F9ZqDsl0vmR+dUISpRwdHjAeZazKkvuPHhGdL8Lvi5Rsb024vDxne3vI3Ts3wEs++PAjyrKmKE14XyKwpkInGWWxYjDsIaUnjjVpGjO9uiJNM5577nnSOKMsK0b9ER42qpM0SdFSMez3cc4xGY83oZbhuuI/9fE/ffXHXQf9oBOYP+rr/NgnPN/t2/khvEzPmpVn9UOpay3KtZ3yj/olCSuENPyyeXDGsV6tuH37DsdHx9w4Ou74HRDHEVvbW7z9xhvs7e2GILmmpliv2dne5Z03/zknJ49oTUOkNWVZoFVK05ScnDzF0lIUxUbceHCwTxzHLBZz7n/0gDzNGY62ePPNt0mSmNl8Rr/f5/TkYqO3iKKIy8vLwOzokpeNMVxdXTIejzHGkKZZSGBuQm7L+fklRRmyhaTUPHz4mEGvj/fw+PFT1uuSum7wnXbHmIa2DZj+oHVomc8W3L5zA7CAY3o1Z7Va0+/3kEJTFhVJmtA2YbWW5xlVve6aLxM0MfguKLAN5FcZpjUeHxwhzoUkZ+momwbhIdY5RVF0rBKHaT1SxkTCh1WXDKuowWDIarWgbK+TnQOUzNkQmheCAkPDIYRAyC5fp66QEra3dinWJXOzDqJJaVAqvCeuxZu9Xo7zgaUStg0Oa4NINKsqet7zWAuE8CHmoWoo4phhZdhTEU+E6vQholtRKUChVdyh3wO117nwM0gpcc4y0xHLUS+A2YqS2rhuEuXxXlMJyd/f7vN/TAYMrUNsT3hcF1RVjdYhJ6osG8DR1C1lEfgzcZrgHCRJSpJlrJYLpJbUbYPWReCRRKEpS7KMXq/HYl7hXaDfOmsRMvxeTGeXNHXb6V0asjzCe8NqNSdJFPv7RyRpTttYnk5PSbIcGeU4a9A2Iu+NuP/gKTrOyPqC8/MLLIbZOuHgYAvzSOAbj5QepRwPHjxif2+XPM/Y3hnzwfsfcniwS1HUHB7e5snTcx5+/JQnJ2dooTHekmaKxXLKcNhHqjDhS9OYYT9na9w1+y5ABfF0KzO5uU4EQTsdo+Wa7/Sju5b9OOs7NRt/MldIf3Q9G6Y9qx+4grCMTsPxR3/8tSBVbtggjtPTMy4up12eSsNwOCbpNAZJkhLHMXt7e0HEmaU8fvKYBx8/QGvN5dWM4XAMXtC0LQ8ffszl5TlJHCGV4Pz8nOl02okWPf3+gKOjI7a2tjg8PCZJcrYmO7zyymtkWY8kTrm4uCLPe6RJSpoGZsXNmzcZj8dAyJQJQXUZZbkKF2Ip2NnZQQjJeLQVRLy9PnEc88pnXgHg8vIyWInLirKoaBvL7s5u5/igY7vEFOWa3d09BoMhl5eXfPTRfT7++FF30ArqqsW7ML1QMthdk0RTNyGY71qfYkyLMTVpGpEkcZhSSIXrLojOO6yxgUbsXGejDfqNKIo30xEpA0k3y1La1mySeBeLJVVZo5XuHDgNpmuSlFLESUyWZWxtbQUNThRjaoNwgl7aYzlbMb28QjiH8IZeniKERSiHkIGjYmwT3CsuTHpMGwS5SiqWkaKOFHnbkFhLXjak1jMwjrUUXAm5CYwMycBhXTebzinLhunVgqZuaNqGuq5YrVab9+V1ajOAjmKUinFeBouzilA6xlrJIs44G40598FN5ZwPrBoDq2WJd4q6tgg0xjishaKoGQ4n4MNayXvBZLJDWdZdqrcIr5dpmc1nRDrwcow1gCOOFQiLEI4sS0nTnJ2dPYwJbrL5fIbWoQGr64btrT0Gg5C6PR6P6fcGaJ1weTHDOoHSCWmeM96aULQ10+USdMTxrdt4NG3rcFZRVpbFas3b777D+x+8x7pcgvQUZcG33ngd7yzHR/u8+NxtnCmJItG9/zR5L2Uw6DMYDiiLgpu3bpFlGW3zSbYQ+K5hDBNb31GygY3QHJ4d6H8S61mz8qx+CBUGsh42F5Y/bDwZQuA+EXNaZ1gsVwwHQ05OTjg6Ouou2IHwGgShhpdeeom6rlgulpydnuGc5/def53joxtMxlvs7e2xNdlmtVoynU45PXvauXUkw+GQySQQVh8/fsz+/j5t2zK9uqLf73P37h16vR6taWnahigK5FCECGF3UrLoYGDXk6OAS/e0pmE8GdE0QTS7t7vPkydPiaIET1ijPHjwMatlgUB1d9mhUUmSlChKgp01TYkiRds29HoZx8fHzGcLLi+nmyZCKY1Asl5XGEMAozmLkJ6qXoc7b+fCKmbz6+1o22ZDv73OLMKDsw6p5Mamm+f5xrmVJDHWGqJI07YNTVuzXq+xxuKc76BsPmSzmPAa9Xp9yrLqxMFRh6UvPtUECPI8JYkTri6nXF1ecbC/zyuvvMRo1GM0zun1E46P93EuZAmlabJZGdpu8qG1QkjBqWl5c5DxudLx9fMVnz+b843zBa8sS94a9Lnq1greg7EWpVWYZnmFc4q2MXhCo42gCwAMzcan15VNHRKLw3tbdv+/65xGdefUCswRJSOSOAc0poX1usaawK+RIsI7SZLkFOuK9arEOYG1ntUyUGmTJOmaJI8gBG86H3hGVVninMXYNmQppZrBsM/9+/dD5IOApvt+tNZcXl7y+OEJadLnlc+8hkAGQTaCujL0+2PwhOYm0gyGA3QcMRhPWFeGOO0xmkxYrkuSNCdOeky29phs7XB+eUF/NOT04oLj27dJ8z7rouTBgwfkeQK0COHxuG6SJrlx4wbj0YTlcs3hwRG7u3vESYLs3pt0YuVPH03X14o/eE15Zrz5k1XPmpVn9UOp7+e6cX0Rev+9D1gsloyGY175zGtMr2ZEnU4i8E9Et4a5oj8Y0LYtx8fHTMZj4jjmzp27ZFnOndv3Ojx9Td1UgYviHbPZjK985SsMh6Ng8wU++ugjxpMxUoU7vw8+fI8okkyvLmjbGnDgw6qhLEt6vR5KKdbrNUII+v0+N24e0zQ16/WK5XJJUZR8+NFHndPGMxgM2d7aIYriDUa/bdvu4FY4B94JTp6ednf9irt376CjcBhfXl5RNw1tN22aTmfdgRmgWJHu3C2CLnE5CgeZMeAlrrNKR3GEx3TTL4VAkWU5URyF106IjkYapg+BkbJCSAfCIIRFaUgSzbBbiwSwXLLRypRFhfcBeKeU6pxh8velDUspMW3LYJCTJlGYfAlBVRVU1ZL+IGI0zsh7Cav1nDjuDmzRfY/eEc40i9IBzBZYJKZzZYV3onWdnqGDsIWdl0ApRdVBy/ASazzOCXp5D601g/7gU1ONgMa/Xh8KKajaCieCTNlLgfEGNAgl8SLQgRfzeQe8A61jIp2gZMJoOKZYV+R5jyRJw2StbCirlkgHMKBSitbYDVPHe0veyyjLgrZpcRZ2dnZDCrQ11HWB1gJja5JUM51esF4vKcqCKAq/P6tVxXy+ourSwF959RXa1nB1NSPSKbFOOT44wNQlWsJkPKRcFyxma6ZXa6IoQ8eym4yBVilnp1OKomWxqqiMpTcesSwr8sGY41t3mOzsYJzhznO3GU8mpElGEifgJXk2ADRapQg0/X6fg4OD4Ijz4fULrzeb9+bvYzp96r9/XuuHaYP+ebJUP2tWntWPvcLB1RFYvENpzc52oHx+8YtfZDQabyYiYS1Ud5TYMQJBnMT0+30OD49Ik4SPP/6YR48e886776KUYm9vlxeef56yXGNtAKl9+OGH3L9/nzzP2dqa0DQN/X6fKFbM5ld89NH7vPX2t1FaEMeKKJK0JoSkxXFgrNy+fXtzhz8cDkMujg/7d6Ukk0n4/q6bkSRO6feHWOvw3bhfKo0QoVm4vosM/JMMrTVX0yvW6zVSCu7ff9AdxNcrmOCG0FoH1kbe7xgeLWkaVlLWhlWE9xDpiDiJiWMdBJve07QtECZa11Ay1a08lNLUdQ0iBDKCIctiEJYolugIhAgunpC/U3NxcYEQIc8mQMpUB0Jbbe7uwyQkAM36g96mEUoSTZYn7O9vY12FsQVHRzsIbGB3YENyb1l2awGB0qAj0aUWG0bG81ph+Fam+SejnN8e5/zTccqb/YxXVwVbzmPsNeAuweNo6ibA4pAIoYiimKIomM2mG7rstf36+hANuomQjeQJxFkhPEoJvLc0bRk0VlLhnO+mNwLnJOtVyXod3EFV2WwEyCHoM7zHrXGdbduidUyv1yNJEsoy8FrWxZqqrlivV/R6PeJYo5RgvV6yWEyZTIZUdYHWitFozHA4YTZdkSZ9nBM8eXzCu+++i3OOra0tjLEB4+9henHGqJ+HhkUI9nf3KdcN4+Eus+kCKT3D8YC83yfN+kRxjlQJSdzj/GqKTBJq6+iPJkRpDlqDVmS9HlIqtrZ2aBtHFKVEUcovfuVrnJ1dYdqgcdJKhQZFPjuOfpT1B3kwP2sNzLN3x/dcP0i89s/r43sr5wIt1lmLEJJBf8BsOmNvZ4fxcMjNGze6w0SgVXAQDAYDbt2+TVnX2I4BsrO7zcsvv0yW9bhx6xZJmiGkZmd3nyTr8fY7H9A0ji994SvY1iEIQkopQGvJwf4e21sTimLFfD6jbRuMaYkiTZLEXXptmDZMJpMg6OxYMlVdsVgs0UqzNdmmLEqaugEBZ2dnVB0y/+z0jO3JFtuTrYAod44oVsSxRquQSxPEnZZBf8B4NMKY0KBladbh6GuUDLj4um1w3qO0pG5KnA/W4MFwAEIgVadHkR4vwnQo1glaxtiOQ2NdaHDC1EWhowgpRRdwGKY/UkmqqsRag7HhLr8sC8AjvKCtQ2qz1OHrCSnBK7ztRvgiaGuc85vmtG0awCKkQQjDcBRydlarFV/+0pd59ZVXeeftd0jSFO/FBgcfxRHGGeJEh8C/4AFGKcHIWpKmZQ6YOGIZa2olWSpBH5h4hxDBWdS2LYN+H6TH+RZUaPyU1l2DJ7oMH4N3UBTVBukvpUIoSPOEsi6x3qI7IagXPky6omTjThFC4KzrmsAgyu7e/YBDCMdkPAoXYOdDErOzFOWai4tT6qpASjBNg/AercLqrixrrAXng85E6YjhcIDSYXIUMP41q3XJYrmibhtW6zVREtMfDPjt3/4dTk7OGPRHnJ5coESMJ2Q2zWZz5tMpO1sThv0+d249h5QJvd6Are0doijlhRdfYjAcYp1jNBnhnaCqApm2P8x4cvKUg8MbLNcNFk1rLTpOKKqGqgqU4nt3n+fmzRus10vktVjWC3ywnH2P15Sf9LXvOzy6KJEfxuOPbCc8bIiP11/3D3t6+EPAeT9dx8T/qX6mm5XvRi/8QR/P6vuo7+GJ+4TiCufn5yghUB7aqkIiiLS+5ksFe7E1FOsVewcHDMcT0BFCKZq2YbVa4oVERSllY7h193lUlLMuDP3hDoPBDv/av/pn+PIXv8zB3h572xOqcklbF6RxxIMHDxgOhx1ZFLKsF/KDyhbvJE1V09QVSawxtmE8GYAC6x0IibMQ6YTRYEyaJFRFSaQVYImVpJ/mDHtDtJKMhwOcbxmNehTlgjTTZGmEwPK5X/gF5rMZprEMe2Mkirpq0EoxGgyxzhInMU3bMF/NqW0FytC6muFkwPbuDls721jfHYeiBdGihEA6hbQR0oc7WGRYozjX4lxITw4alBbvHKYxmNqCk+Akic4QTqOIaCqLDk8ADo9xBhVr4jTFOUG1qqlbi5Aa34HSjDF454jjiEhLskyQZcFS7R3s7x7zzX/xLd5750OuLucspgvWy4LZbE0UZ0itsd7RuBovwWKRkSBOJe0gYekdQwfOdOJMJelbQxVJFrFHKgfCIXBIaZHa4bVBxh4rgl5Ky5i6MhjjybM+RVHSywcINHhN21gkEtu2KKGwrcM0wWYtvaaqDGVhsDboXpwL06i6LjeNoVQCqUFgSCKPbQuEbRDOoJUA4RiP+wz6MRJDL42JpaSXpDgbJi5SxlivMU5R1pZ12TBbLEnimCRJsQ6upgukkmSDhKQXI7Tg/OqCdVWho5iLixneacaDXYaDXbYOjml9FAS0Zclifk7ei0jSlPFoBykTbt14jvF4ByE1xzeP6Y1SWipiFdEsV6SRRakSpULGko4HDMcHOAn3Hz6gqGpmixUf3b+Pw3Lv3k3OLp6AF4Tdmkd8inodjPbP6hMz9x9yXv2B5uYPO8/Epz/2j/HxP031rFnh+3yhflSd0s/q43sqjxSC+WKJlJKtyYTnn3+O9957H9M2aP2JRREgzTIm40mwOktJa9oNdv7DDz7g8ePHtG3L1mSbLO3x6NETiqLi6OiY27fvMh5N+MIXvsTW1jZSKfr9Pmma8u6773ZBeIaLiwuWyyWLxSI4bJQiTdOgWRCKxWJJU7ddUnM3uneWqktHXq1WHRrfdmLYhAcPPgo2W1ezLpZU9RqlBYvFnLqpqKuKxWJOFEWbEX1RFN16IEwjrvHrECZSzz33fEgzrlrW6xqlIkAyGo3p9/toLVAKkjjYQPO8F1gndXBcSCE3QlfnAkslaF5C4rEHlNYb0u41hj+KIpzzxHGwOl+7iZIkAe9ZrdYBbKsUe3v7AbOvoyCgtnZjRXUupFTneZ/joxtIKXnjjW8zX6yYTpf0837nqAlaJWMMSspNAyul6PDs0Ovl+O0tfm+YstVato0l9p5d69kxjt/JNfNIdZqhBmMt8/mCOE5AEJw1AlarNa2xNE0TbNfOdbTiJUmSbDJ9ojg8BxCepyhOsMbRNC3OBrieNb7TLFmqKqRA570Ao7t2GKVp0pF912gtUZIumTq8t9u2ZTIZY0zQOWVp1kUZpEwmWzRNQ1VVVFWFx2NMw2q95PLybBPT4JGUZYtAE0UJUgaO0GpVksY5i/kaJSNOTk7Y2z9gvliCEAjpkRp0BGfnj9neHpMmKfv7e0wmYwaDPlVVBx3OYDeEcUaKKBZcXZ3hsTx+/IibN28ynV6yuzth/3CHmzePmM2mZFnG05OnCCnY3z/o3htBz3QtVPn0JeUnfVn76Xh893++U/3BP//DLtPf6e/6qTgivkN9T83Kf/Vf/Vd89rOfZTgcMhwO+frXv87//D//z5s/r6qKv/k3/ybb29v0+33+8l/+ywGQ9an6+OOP+bVf+zXyPGdvb4+//bf/dhAEPqs/QRUw6lprrLW0pqWX97h16xbroqBtA4b8umGJo2gjfJRSIrtD7PGjRxjrODw8plhXlGWNMZYszfFecHx0k17e5+pqytZki/39A7SO6PX61HXDxcUlcRxT1zVpmlLXNUKITjMiWS6X5HkW0PFFzePHJ5RFEyzNacKNG8fEScJwOAzfZ5ywXC4Zj8fs7e2hI8liecVicUVVr6nqguFwgJAhDBHBpiH7hO8RNAUQGCNNU5PlWXdRVyyXqyCcVRFZ2g/kXB+OzzSLSdIIpUFIgdJRwN9bKKsqME7o8pg2rppgiVXyeiWlNv9dlgXGhKbsOhEYPMZ2MD+lwuje+y6YLnydy8vLQNDtnEnGdLwX77vpQIRAcnU1BcJrWlcNVRkw+0pKsiRFCdBSILwniSOUlPR6Ob1+zrW8IY5j/tHukF/PNM5YDmuLQvD/2x3xj/fHQYNiLHGUoFUUtERJGmB6XtA0Buc81oTE36qqNinRUn7iRrPWopQGREcDhqZuuqZNBEePASmiTSbUYNCnP8jxmKB3cYK6aig7R08cx8RxRJxolstF0B+1Lev1mrIsN+//KI7IsgDUu9YZGRN+f9o24Pul9IxGQwSwWCw5PTmnWNcsFyV5PiRNMsqioSxaVquSyWQL70PO0tMnT4miKKz8TI2zNU2zoqrmvPDiXbI07tKYLbPpFceHN9jbuUE/3yVNc8BQFCucM+zu7TAYDji+cQR4rG9p2zJkBznDYrno3n+eNEk3v+fwye/Cs/re6mdVg/K91vfUrNy4cYP/+D/+j/md3/kd/sW/+Bf88i//Mn/pL/0lvv3tbwPw7/67/y5//+//ff7H//F/5J/8k3/CkydP+Df/zX9z8/nWWn7t136Npmn4jd/4Df7e3/t7/Lf/7X/Lv//v//s/3J/qWf1U1bUF9Pqi5L3veB8iCEOzDB1F3Lhxg+Fg+Pt4CkJ0eo7hMAgthWA8HiOl4s033+T87Jy6bjk5OeP8/JL9vUOyvM/p6TmDwYhf+qU/Q6/X55133uXo6MaGcbJcrinLmizLN5bm7e1thBDs7u5u9v9ZljIcDvFe0DYOrWKGgzFZnrNaLXGd1fX27dvcvn17Y2sNwDvwWMpqvQG22Y5au7e3z40bN7DWMplMWK1WG33E5eUlQgisMwwGPcqy4Pj4mH5/wHK5pm0MZVFtrLPb28Gu/fTpE8BhbUhfTuKUum7Z2truUp1VcFdJSZZlQNANWWORnWvHdhC5KFbkvZQkjQOkTTiGoz7WBgqsF8Hu643FGxv8SVqBFCDoQF9uA/67nqyY1jCdzpnPF5RlQV2XHXwuTHOaumV6dQneEmlNpDVaK7SSiM4NVNclCE/btsznS2Z1w/97d8L/88Ye/697+/y9V+/w93d6lELQtq5z2uiOfhoAcWENKVFKo3UQmsZRyK+5nq6ElPDpRujdNoGQbE1YlQWWSjeR8tC2YTWIFxvmTF2XtE2Fd2xyctrWdL8HDimD0PZaL5SmKaPRqEuiroNux3zS2J+cnGzccXmeb1xLSkm2tiZsbe9wzW1RMg46kbYlSVJAomRgvRRFiRCWydaQ9XrN1tYWWiukFAgF460h1jW8/da3iaOYp08eUpYLXnrxHvt7e3zx81/h+OAO21s79Pt5p/NK2NnexjuPs54vfOGLxJ0T7unTEwaDEY8ePaIsK4yxLBbLDeDtGTfl+6ufddHs91LfU7Pyb/wb/wZ/8S/+RV544QVefPFF/qP/6D+i3+/zW7/1W8znc/7r//q/5j/9T/9TfvmXf5kvfelL/Df/zX/Db/zGb/Bbv/VbAPwv/8v/wptvvsl/99/9d3z+85/nV3/1V/kP/8P/kP/iv/gvaJrmR/IDPquffF1bET+xHbKJuldKUzftxi0iOxDXp3/xQkBh2Ml7oGka3n//PT7++GNa03Lv7gvs7R3R7w349rff4mtf/QbeC6ZXc/K8z/b2LstV4IMcHR6T5wHxbq2jWBdYG+zJTRP4KoPBgCRJaJpAlG2aljQNybef/dwXuLi4xBhD2zZEnQj3/v37zGYztra2UEp1uUQ5WZZQVSXj8ZCiKMGLoONoQ8JzlmWcnp5uuC1CiMA6EQRUvncMhwO2t7e6O3IYDSeEO3yDUpLZbMbFxSVXV1PiOEHrGJCdCwkuLq/Y3d3D+5AsXFcN14PZ64PPe4/qxKbeu4241tqWwaBHlqU0TU0Ua5wghNg5hxKSpqhoyoqmbVGR3qwpoigKryOfJHIrrbh18wZCBC6MUp69vS2Goz53bt8OlFlnMG2LswYBNHXVwe2aTgjcYG2Y1Eys4yUZMWosU6l43MtYJnFgdXiJFBJjHFXVdOnFEd4B104gnWymLEka7vS1Dq6oLMtwzrFcLjaHKgQmjXOeJE6RMli3w3qNrnl13ZTCEEUxHrFpFrOsF9Z4XRJx2wba7XU6NUJgbbAvB/Gv2rw3lFIMBgOapumytUzHhFFdcrbHtLZrxkJgpRCB/Lper7uVVoD0KQWr9QznG0bjEcvFsnNFlWxNtlgvC8bjbf71f/3XwHkePrhPvxcjRMvlxQnHB/vcOr7Fndt3OTw66qIUai4urtje3qcsW/Z2j9neOuQbX/vT3L71HN4KqqLhvXff5+joeGOtv4a/Pasffn0vTcxPe9PzfWtWrLX8D//D/8B6vebrX/86v/M7v0Pbtvy5P/fnNh/z8ssvc+vWLX7zN38TgN/8zd/kF37hF9jf3998zK/8yq+wWCw205nvVHVds1gsft/jWf1sVRhpg3N2k0wcx3E3frabO0v4hHALobFJ0rTLygkXtPliEUBtShFHCfP5gvWqoNcb0jSG27fvcevGbT7zmVeZz5dkacbV5RWDwRBjDFmWMxyMkOKaaArHx8cdlC3i0aNHrFYrhsMhSiusMyyXa+IoQ3hFnvfD+kOEJGm6A6PX6zEej9nd3WW9XjEeTzDGksQZq1WBs+CsYDiYIAgrBWNMZ0vOSJKEyWTC4eEhUgYrdK+XIQQ8fPiQ9brAe89sNu+eK0ueB8LufDYPd+8GrBEoqTe4/cV8yXwWUoRNGxgwy+UyBCsmyeZ5lh1vRQhBr5ejtWJ7ewtjWlarJUkSdxwWtzk8bWvC9ENpRJeSfP28lEUZbNxKbdZAUaRZrhYYW3Pr9iHPPX+TR48/4uTkMWfnJ2glGQ2HbE3GJEmM945YayQ+BCzKkL6cAb/ydMG//e4J//eHl/w/Tqb82sWMdrHk9PQMa13QVihNnufEUYx3nrYx1HWLFCqkHCsdHEB1w3y+2FjVsyyjbUMDUZYV3gduTp7nOBsst9cTFaXCVEFIh8d01m66jwmvyXWekXe+e49LrDNBdKsEWR5IrnhP0zQd2C+sJ51zmwalqqrAjsGTJGEStFqVeKfBK9brkkF/QBRp4kShI4FzDa0piSKFkCAlVM0KT8N0esZqtUAIGPQHOCc4Pb1CypTp1RrhI+7evkcUKRbzC1arCx49ep/Ts0csFsFmv5ivkDJia2uX27fu0e+N2N054vbNF1CiT57v8NVf/NcY9Le4ceMOT58+paoqWtN0YaYdyuBZw/Ks/pD6npuVb33rW/T7fZIk4a//9b/O//Q//U+88sornJycEMfxBkd+Xfv7+5ycnABwcnLy+xqV6z+//rPvVn/37/5dRqPR5nHz5s3v9dt+Vj/BunbVOec3WPlwNxiahWukOfCpO9juc7u/oN/vE0XhgJ/P5ty9ezeIEnXQcRRFwXy2YD5fcvL0lCzrcXZ63n1Ny8HBAUdHR0GvUtUbEasxluVySV3XmwNBa705tJI45vDgkNFwTBynPHl6Ql03nYYhQM5W6zVFETQGzjn6/eAmcT5MUAaDYdAyiIiibFivG05OzjeH/rUAtSiKTfMURRHGNBTlmqZtaJrw/Xn3yWosigVSgdLBSm2MpSjqbj0UrKBBOzEgTlJMazqEfLAqX0PbrLObu9zrtc11Y9m2YYpx+/YtjGkpqwKpJap7zZSUjEdj0jRBCdlNxkLAoY4+eR6lkt00oGE6u2QyGbJczTC2YjDKuHfvFucXpywWM1w3McrSFGdMd4g7tJLMpleMRkP+/EXFn5nWeCV5Emm8lPzZRcGfu1gRRTFpknWTEY9pDaPRmBs3boSmQoTVy7XNd7lcUpbVZvpzvQa6fj6uBdnO+dDkqAiP6Jg1giROuwbFboB+aRrWLm3jcDZMeYIt3HSvb9xh+YMmRgq50XEZY1gul0CYel3/rlxrqq7zo5qm3awom9qyXle0rd2s3bIsRQiHiiCKFW1bobUiyxO0lhwe7aAiR1UVHN84Js0ytIqwBvAafMRyUbBYrJBSUJRLnjy9z2CY8tv/4jdwvmUy2aIqW/b3j8jSPv3+mK/+4jeYjHe4cXyHG8fPcfvmC3z+s7/IN77xSxwcHLG7s8+HH3xAL88pyqK7kfmZ9no8qx9Dfc/vkJdeeonf/d3f5Z/9s3/G3/gbf4O/+lf/Km+++eaP4nvb1N/5O3+H+Xy+eTx8+PBH+NU84g99dOpm/yfr8YP65K81C7ITSYYE1SAgTJKwSvlOo2ABSKHpZX3wYGzDZHuEjiUWy/nFGX/6T/8SB/tHDPpjvJW89eY7fPUXv85ka5vf/eY3OzGjZr6YMRjk3Ll7i7v3bjEeDzvtgupWJp5+v8fW1pi9vW28NxRlzXyxIk5iJlsjJpMhEs/x4SGJTrhxfIcs7RNFCbPZgjzv46zDOx8ElbVhPlthWkvbGibjMUWxRilJ0zbhrrqX0R/2WKzmfHj/Qz5++IDLqwukCjwYgacqa/Ikp6kqvDVoKcBLsjRnuVyhtew0EtXmwKvqGmNbWtvQmAohQaiwVkBKHJaqLZAyBNV5d60/kQgkSZzgrMFZw2I+wxpLFqeEGMCA6ldRwqooqVtLkqaBaSIsxpTkWYLSEmtNAKhFntaEdOXZfNY5ZirWqzXnF+fcuHEDIQRFsaZpana2d4DQNHnvMG1LL+8jr1a8crHmXMJVrGi15DyCaaz4SmmZNJ6qqGnrln4vJ0kiTp4+4o03vsV6vcZ5G3Q4eIqi6Bo8kKQURXBZBWAcGxdP21pWizWrxZpYhXyjpgoMFGct3lpElykkhOgmNp3eRmuUBufbTmgb9C3eSawV1HWLQBKpmLaxYeIjw7rq7OSCpgn6rixPkMozGKQoFeB4TVOHIEvX0LqGOI2wPkDtQuCmJYk1vTzGupo4EgRpkSdJIuqyYpAPOD15St2WqESDVLStZb2a88/+2W8ymxc8/8KrRHGPphXcuHmHxjienpzivMIT0TaOs7MTXn/9nzOdPeHmjUO0ivlTX/8ldib7HO4f89pnPoupLS89/xKD3hAtNFoosK5bWXVi8+7xs10/KSjJH/J3/vRtdr6n+p6blTiOef755/nSl77E3/27f5fPfe5z/Of/+X/OwcEBTdMwm81+38efnp5ycHAAwMHBwf/JHXT9v68/5jtV0jkuPv34UdVP3qb20/n4QerTiOyQT/MHd6Pi933cp8t3qHSB7NZDHiFhOB4yHA+ZLhY8fPiQu3fvdZknislkh8nWNnt7eyHBViuUkpycPCbNEo6O9pnNZ1xeXQS3TZZt7KmnpydMp1cMR2EFFEUJi8UKhGd3d5uHD+9TVQVxFFNXwRkkhArQLgTr9YrLq0tGo+FGlLlarbu79IReL+eFF54jisJ6qbVhWlFWJVvbWwgJVVMRpxHGtuEwXxcM+kPapsFbw2jQ42Bvn7o2rNclZVkhkB29Nu5AbgahJR7HqlhQlCta23QQs/A8+y63JY5DGGKkg6OmrVvWyzU4H/4/Gzgppm3BC2KpkYQmtjWWujU0JnxN5wxaeZJYYTttyWq9pGlrnDcY15CmaVjfzdaMx7tAyOtp6prBYIAQkvksWMh1FGNsSFMGQVlWDFtHz8E6ifCAVBDFklUsiZuWfhMmFVop5tMrkliTphFS8Ek+kgo2XecC0E2JmDjq4YzCtB6tI5I03jjRpJBEKsK2jqqsCcBCiXfdTYyQmzPh2mHmCWnWgYhrSbMIRNARtY0FH5Kf0ySnbUzXpGhMY9BK01QNeBUEvSaA+ZTyxGlofvr9PLiDpMP4hoOjPZIsIuslVNU6QPRcWBX2eilaQduWpGnMZDIiTVIirbl96xZNUzGaDEB41sWaui65cWOP09Mn3Lp9j9F4ly984Rv0Bzt8+60PWK4qlus1F5dX3HvuJZ4+PSOKI6QyPD35iF4WkacZd2/d5cbBEeWy4HD3gK99+Wt44xEWZpdT+nnONUXZ++8A/vhZrJ/UhfaP+vrf61/zU6Zh+YFnb9e71S996UtEUcT/9r/9b5s/e+edd/j444/5+te/DsDXv/51vvWtb3F2drb5mH/4D/8hw+GQV1555Qf9Vp7Vz2X58I8HCHesWZYT6YQ865MkKW+99Ra9Xo9er8dXv/pVtra2cM5xfHREmqWsVisGwxH93pAs7XH//kOaOmD4szxmOOxxeLhPFGnyvEeW9dnbPSDSGc7DYDCgqireeuutMLVoW5bLJefnZ3z44XsYUyOkJ0k0xtT0egk3bx1xdnaKUtEmhdd7wWpVcHU1DVwSodAqQuvQ+FwTVA8ODun3g5AySRL29/cZDoNAt98PYtc4CXqfum4YDkbdGkdvkoKF+ASuJwSbO3xr2yBWtYYsy8iyfLOGE+I6tDHQXq9XImElZYijmLZt0VGE7TRGUkpc5/QqigKBQF0Lp+uAk78OYGxbQ5al9AdhPXN4cJvTkwVNHdYkHoOQgjTr47zk6ckpIEIekoiI4oxev8c6jSiUYGgDlTjLM/I8Y1sr1hLmqmvGvCNJAxm23+8zHA7Ispx+f7CxbysVJn1xEhqMsiy7lV7Q/Vxbl5u2CSF/kUIqgda6W2F2kQVKhDVgF4J4/dzpzWsQxL7Xa9DrIeJqtWa9XtM0LavVOuQDtSb8/XHQCEkpiWPN7u72xjEW4g1UF64ZcqGUEpRlmLIpLbC2ZjjsoXWIJwjRFpIkjYmi8PpEkWY2v2Q8GSCVpz/I2d7ZIkk1dbMm78Wcn19yenrJZLzD3u5B0JlIz8eP7ofwTmMZ9MfcvnWP/b0jevmAurO6a60YDgab99hrr73G4eEhZVmytbXFcrn81NrxZ3+e8qx+dKX/6A/5pP7O3/k7/Oqv/iq3bt1iuVzy3//3/z3/+B//Y37913+d0WjEX/trf42/9bf+FltbWwyHQ/6df+ff4etf/zpf+9rXAPgLf+Ev8Morr/Bv/Vv/Fv/Jf/KfcHJywr/37/17/M2/+Tc3Qr9n9aw+XYJPunofbByUVU2xXpHEKavFmp2dXW7duoXWmvfe+4BXXnmFq6srjg6PuHP3DsV6zmK5Jkpynp485KP7D+n1MoajMRfnT7manQecuoQ4SSjKiqvpguFwgsdxdTWl3+9zdTWjbWusc5xfnJNmCfP5lLt37+K95+233+bg8JDZfIpz4a5aqXDgTaczoihiNBoxm83wPkwMrXVY6yg7AWqapjx5/JQ0TZAifG4UxeRZOJC1Vhwc7PPk6QneOZrW0DYGa9g4R5SSaKnB+fD/uSBORQqMdagowstAK83zvAs/DHwPzycTLikl8/kcpSRlWaFkhHOC6FN3WdcfG0URaZpydXWJc8FqnOc9rPF4DUJ6hAzQuzTJyfM+Z6dTvI0wtUTKBLAhydlIpNRUdR1WK0rjUQgZU9cVLlF8ezvnXzlZArAoCvLGMLKC/7WfM9cKb1u8F6RZghBQluFnc4AQGeAwxoamRHfaH6/QWrJerYmiIcY4oijuAhs9xTqs75IkJolDA4cAa2zQyaQp1gUdTBRHnS4qTFoEwUIthQpaIRmylZRStK5FKsXW9pjFcopQkqIMuVTWCoQLtnBj2i7JukSpkFuUJBlnZyfcvHmD6TSkc2e5REeCvJeiNTRNSd5LGQz7VKXBGkNVeYwN0Lqr6RlJEnF6+mQz2ZrNZqzXM7a2b7FcLpjNLXfv3qYqLd7LIHLOrgXPjtu3n+Px40c8//xLRLLPo0ePuHf3ZdIoBinZ3t7uGt6I4+NjhsMhZVXRSkOv19tkSD2rZ/Xd6ntqVs7Ozvgrf+Wv8PTpU0ajEZ/97Gf59V//df78n//zAPxn/9l/hpSSv/yX/zJ1XfMrv/Ir/Jf/5X+5+XylFP/gH/wD/sbf+Bt8/etfp9fr8Vf/6l/lP/gP/oMf7k/1rH5+SsBmh+tBIEmTlLOzc1555Rf4vde/RVVVPHjwgD/1p/4UTWM4OzvnM595Ba01e7t7XEXQ6+ecnj3h0ZPHqCgjSjK8lCAFRbmmKCp2tncD38RaPvroPlIqymodVgo6NBqXl+d475lOp2gleenFlxgMB6xWK6I4Cm6X5XIj1vVedEwZzXK54tGjJx09FuqqJc1SlFLdwenxPkwS1usS4T3j8YjzswuemhOKsmB7e5vFcoZ1BussbdPSNP9/9v4r1tLtPM9EnxH+OPPKtSrtqtqJOzGKEiVbskJLraZPN1qCz8WR7QPDwEEDOmgHwBcGDF/YF/KVfdHmhY8h2MYxDgy44Yu2WlZoBSqSkjZFimmTO1dcOczwp/GPMc7F+Oes2hRJMYjk3uT6iIldrLVqrbnmnGuO7/++931eF0i2XuIdRGlEWZcIPIPBgLpLGq7qGqkVSgla41YJy8PhoMtF6uyzBCjf8ko3joOl1RhDmuYrJ9cSUrZ0qZRlGZKNqwbTWJwV5Hl/9ZjKzoI7GW9QLjyL+TllUTAYTmjqkP3jvcL5QMI1tkXgiTpqbt00RJGnLCt+bT3FOXj+tGBzUVNFil/raX5tFANBKNvaFtPWDAYh2ffo5ISirEJytvRhYmQeAu9EAON0PJZwn5umoGkCVK6qqs6ZUwOCLO11Dp8G54JwNjznjkG/j3UNRTGnqgxSxiueiO3Q+fgwn0/iBE8ImXTekSQxUobvD8Etl0cxTRMEss55FvOC+WxOmqYMBhOKoiZA/TRlEVLC0zQhinTnigrCWtt6qqoMUxQdhwRn52mUwhhDURT0+32EgNOzQ/LeGqPRGof3j6jKhueffy9VXfLyy59lc3stiH1VxPPPvZfWCB7cPyGOKrY2Imazc/rZADwPQyFhNQW1rcW2F9OUi/ra6utqVn7xF3/xq348TVM+8pGP8JGPfOQrfs7169f55V/+5a/n217URYUSIiDdpUZKzfb2Lv08QK2+8IUv8MEPfnBlyX3ppZfY2txiPB6zt3+XxtQMBhM2N3aZTmfcuftawON7KKqG9Y0tTNsyr0riKKZtSjbWN2hMyc72JYqi7OzEE05Oj4K9t58hkPR7A9584zZlUXOwf9jZUl3Iamk9Reup6pLJeI35fN65RTLqpqapDVEEAo2SUJU1vd4A27bMpmchVM8vCbeCmzcfY29vL6QQAxDWC6YJAuEsi3HehWC5fp/F+QzhBF5KnA+oe8fyUPU0jeHs7KzDyDuEFAiCS6m1luGwz2DQZ7EoaE2Hzld0SchyFQUQwGMBTY8LzZltw5TBduwRGYeww/0H5xSLsE7q9wW3bj3GSy99lvncoLTGdjEAeT/Di5Cu7G1ITxZ40jSjcY7/Yz3n99f6ZEXNkXMcWMsg7yHxON8ig5UM8JycHGPqFu8EQopO/6E6+F9obqI4xCv47jUWJk4BoKb6Gikgz7Nu9W2o6wohNFIFjkrbWlxrV1wThO2w+g3eO9zKyRWsyDrSXVPkEUpQVFUIhsSTZVlIizYOL0BIyJKUs7NznLOdJXsQ4giEJk0CsO9gekivnyMI+VvgsdZ0sQgW01Z4UprG0DSC0ahH09QUhel0T3D//h55HizT88Upvf4GCMeLL36CmzdvMRquE8UpdVUz6Oc0teH4cMq7n/sgv/f7v0+cp2xtb4EMvBmt4y9LqdVah0yki7qor6Eu5m4X9Y4qT+BaLImgzz//bt58800mkwlCCJ577jlCdk/DUrCxvrVJWRsGwwlbO7uM1zZQUUJZ10RxihARRVFzenZOkkRIBdY1nM9OWFubsL29zWQSsol6vV5go6QpTdNy//4+r7zyOqen0w4QJgGFs/Dcs89zfh6YQE8+8eQKj15VNXVVd3oGiXOeyWTSMSck89mcOE5QKmI+X+A9qzH5Zz7zacqyoKwKXCfstNbjnECgKKuKYlGEA9SHFNsoCmyUPM8IDtGlbsKvOBfBMr5MSA6HeGsC/XQ2m62amyWiHVhpVuJOWwGBv7QUKy+1OoFH4snzHmVhONxfsFg0pKnG+pJXXn0J5wSxHqJVRpKm7F69hMPQ6yfoGFrXrVwctC0URUPTOKZRyt08ZxoHuFtZFSyKReCzdJqRYAufUzc1+LAKk539u+1ypsLEw6FkIPm2pqUsQ9pyEicdrC9DKsHaWkjgXjZCzlmctyu7s5RihewP9u8AuYPw2IamIKzmgp4nPD+mDesg6xyNMRRFGSzptmWxmKF1SN7uD/rdCtECkmLRMJtWXL1yi8uXH6M1MJ9XFIs6NJguuIkee+w6AKYxKxu1tQ7nQ5pzVRsW84qN9R2kCOyYopxh/YKnnrpJnMQURcVkvMn21uUuTbrh+vUbvPHGHZyTPPH4M8xnBZ/73OeYzc5Ck9i9xpavmdWtywS6WP9c1NdSF6+Si/ra65t1432TN9GN55Mo5srly1hjSZOMW7ee4Pz8HCkkly9f5saNG2xtbVHXFRDC/J54/Cmq2nTIc8+txx+n3x/QWo/SMaZ1DEcTdBTR6/cYDHvM5ucgHDuXdnDe8fjjtzg+PubSpUt45zpei8E5ODk+w7ZQLCpAEkcpt2/fxRjD+voag8GAsixXZNxlRg2ENUtZlSshaxRHzOdzNjc3Vxbvre2t4FqKY4qyDFfGddO5pFTI6fF0FmmDkiG3J8uCjiLv5YEL4kNQ4TJUb7li01qtnuPlWqq1lrKsKIoCY9oQcBjH6CiIS5dNyTJHZ5kB9CjBtmmaFWXVWU+a9YGYSMcoLegPEqztPkfmlFXbIf1b4kSRZhEOC8KR5RmD/qjjgCikCI3QfFYGK7CX9Hq9FSsnZPkQohQ21gOThABmc+6hPXSZoaeUIu2otcvYBIEgTTOU0ii1zEwq2dhYJ+9lpFnSNSmPcoOi1YtWddwZ5x4mWwdYn+2ew/D6kUqho4iqrqmbBgSYtkUouTKyLr/WMsl5aVEPCH/Hq6++TlUaoigh70i5IQagIY411rX0ejmmNezu7qK1CvDDwSDcZy+YjNe7lV+Lc9DLM6pqjmkr1tYmq1DEXj7k6tWbIBS379yl1x9weHhCYyxHRydorfnEiy/yyU998ssytB4Nz/vSOI6Lgr8sy/NbH9tvtW36W1sXzcpFfU31nbZOL2/SB8bHMM/ZnIy5dmmXx67e4NLOZWbTBf18wPbmNloqIqXRUpPqjFjFmKqhNZbt7W2eeOIp1jY2sNYR6YhrV69z68YTVEWLkjFSKPqDPoVZsHd8j/3jPV55/VVmiwV1bdA6aCZcl/1z5dpVsiwPq5W6IssiFtNjdjfXGGQJ0juKxSJwXXRE6zzGOKrKMJ/PaZqKKJa0tqFpKpQSFOWUvBdjXcX9B3cRSmIsWB9jXIT1CpQMSbnKgTfEWpHGMbHSKBG8VA5PWXfAMCS+9VjvULEiShWOBqUlIFBECCK8UEQ6RfoIU3q0SFAyxnmxmpg0jVmF9tmO6iqlIiQB18SJ7LQhqsvKCY1MnHp0Ehw0ea8PQmKdp6gWWGeQSnB8eISSmpOTEMTY1BZTW5rS4q1AKoWTHustUgucsdRF3YUJalrjmU7nXVMiqMqG1hisaVBCoESYgHkUUkc4KQKLRkNtaopyERoYCc7WCG8xTdvZax0IixAOfBscQUAU6c4V1BDFgjjWXcZPgxcSj8ABXnqSLAGlaFqLaaGpW7x1aBWRpTl4iZYRWka41uNbSTGfB0twBsOBZtBPSCKFs6YDvonOHm2J4tDoKQVp2ieKM157/U02NjfZ2Fwj7yf0him7VzfZ3t1gNBkglaSsK2bzOY6QvlyUJQ/u7rO/dx8hG964/XlmizM2t65w5fJzDAZXSLIxi7LCa4uThtoajJXcfPw5tIw4PjzCti0SAc6D8ysp2qProbeDPfai3r510axc1NdU3+km5S03D5FSTIZDnnn6aa5du871a49xcBDEr71eD2MMZVGEdUAUMxwMGQ/HnByfcP36dT7xiU9QlTX9Xo/WtMF1oxM2NrZRKuLKlWuYxlDVFcdnx5jWcH/vPhubGyFtt9NVGNtgXUtVlQD0ejneOxazKdOzE9YnQ67s7vDKy18g0po0y1A6wljHcl20vr5OmoYJg2krnGspqwXWGuJYMxoP6fWDvVbKiLI0tC04BKa1OP9w1K+UJNIRggBTq7s1gjEGrSNMY7CtQ0hFUVYBXueDsDTSMXGUUFchhFBIFTDxNoQiGmNpbVhRLFcnbdtSluUjqdiKkMkT4Z3oMnPkas2C97S2Jk1jirKgaYJ2xXlL05REWnXrm4cNUJrmaBUFofIjTBuPR6jAr0nimDROqDoLuFKa4XDI5uYWSslO9FyDDwJZIYPLSojQ+EglaZ2lbmqyLEVHUXi1eU+kgxC5quvAtBECCKnYAXgH1lmE9HjvyHshxHJtbQ0hugmQJWiupMB5R6/fI00z6HQiwXquSJMUKdTq+QpUXY934THVOiLPsvAam8+7yIiENE1obRs0USrkO50cn1DVDVVV8+DBAcY43njzDtPpgsPDI6QI2prj4+MOzx8Hx04cB+G3VlRVxXQ65/DwgJe+8GecT4/Z2lqnqhqOj6e8//0/yHi8zVPveg4hFTu7uzx28wZRmjAYjvjBH/wh5vM5J8cnOPcI+M257jF862roe77+Mt4gv9Gv/zavi2blot6RtQzpW47ed3Z2GA6HCCHIspQnnniCKI6B8CYohWD38mV2d3c5O5uSJBlCSCaTNa5evc7R4TE6Snjve94HXjDoDxGEdUia5qyvraO1Znp+Tq+XU9cVxoRsk6ZpOj7HAiEEw8EgcF4uX8bawPhYX1/Hect8PqVtQ7OzmBe4bsLiPQyHQ9bWJmxurpNmQQfyzDPPcOnSpRWOfClkhRCeF0cRcfdzLimg3vuV+yKEIkrG46DpKasyRAXICOElrfFEKiGKEoQnpCEXBbLTE4SsmhpEwPcLwUpnoJRasWCiKMIYE7QqVqJUSl0HcaW1IYPIe9th9xWLxbx7rhJ6eU4cR0RxyBeadZqd1li8C+F8tju0W2NWicRCCOjWN8vIgrQLI2zbgOzf3w8riDjW3YEcVm2CkIwc+De+y/IJB6hSwUFjjKVuTEiDbmrqqulExt3ap4tKAMFkMiaONXkvBFc65zg/n6FkBCy/h0dKQaQVZ2enzOdTPA5rzeo+13W9ui0bwiRJiXRCsaipSkNde5raE0UZrXFdY+exXZBh23qm0wLvNcKHgMO9BwdY6ykWNYt5xdHhGU3jOT8r2d87ZjotcI4gpLaW6fmMumrI0gwhHGW14OjokCyLeekLnyOKFBsba2RJxsZkC+E0e/cOmZ0teO5dzzHIe5imRmnFE088sVoNLgXZYTXxbXu7uKjvgnpHNysXe87v3VpeiSmtuHXrFltbm6xNJjgbbLej4bA7uDoRpHcdQXbAyck5j12/ydpkkyhKaa1HqIjr1x8j7w9wCB7sH6DjhDTLuXf3Hm1rybKMtfV1yrIgTWOkEkRxtMoFEkKuwgOjOGIxn5PnKa++9gpt25DEceemkSi9dGsEaJoQgn5/QL+f09qGQb9P09S8/PLLHB4ecn5+TpIkK1jbMlV3mS+0vCo1pqGsylX2kXOeOI7xhCmAc2G6IbzAOwFeU1ctbd3SmhbvWoQI9tm6LnGuJUlizs9OO9BYwO9771gm+goBdV0FlLz3eC8xTZgGeB9yeHq9kCQtZEjNXob1RVFMlqdI6cM6S0KehbDAum6IkxQQZGmOtUGsusxxcs51dFiPdRYPK5Gy98GarbSkbkqcN/R6KVmWdiC7wAdZ6lqkBB2FpmcxL2iNx1lojSOOw30IjYqnrhqaOvBtoniZYixRSiCEJ4ribiok8V7hnQ7W8u458Hi0VisxbhCaihWbpa5roiiIg5dp5CqOUVFC6xTTeY0XMYuiQegY09pVoGKW9YJ7zAoG/RF13SJQZFmOklGg5RpPsWioCktZtOzvnXJ+tsBZz9raGmmShN8dL6mbhiSNkNLR2oqz82PuP7jL2fSY8+kJe/t7rK1tcnx0zlNPPkc/H1KWFVppDg8PaI1hY2O903n5EHfQWeIvBilv73pU7/J2OGff8c3KRX3vlvd+hTeHMM5//Y03KIoSBAGExnLEHPJu8rzHpZ3LrK9v8thjjzNe28R6iNOMe3v7DEYTNjYvIXWCdbBYVIxGa4Dg7PQs2Fd7GUJ64lgTRzGTyXiVHh1yiAINFRF0DIPBgNa2NKZGiHAVr7u03SjSOBfG9/v7+5yfz2hNCCLM8yCGtNYyGPQpy5Isz4Ew3RBSdFA5u8qkaZogopUdNAzCyN22tvt3CiUVMiBD8dYhvcA0hjxLUdITxxKpwgGuVACmSQXeB6GrjhVKC9q2QUdBZyOkJ06iboLRCXe7n997F8TOwiO756JtW/I8Yzo95fT0GCE9xpSdOyespoSQlEW5mmRIGZEkycpdtBTSRjog8ZUMDWBYZSRorYgijbU1SkNtShaLQEytipq6MsFu7Gy3oqDjnVhA4b1CShWyf7zvmCrLtY2jLKtOaKw5OzvFWtfpmTSTyVpY41i/SqgO9zmskKJYMxz2AY/WspuisOLWLDkvS5fQfLGgNi3T2QJjPI1xKB2TdDTj0WjQrf3qkElV1cxmC4wJvJkliRhEF+7pMSYg/1sDrfGAoqpKZvNZ97oJwZx1U7BzaRMhHY0pOTx8wBtvvMIbb77K8fE+Z6eHbKxPGA2H9PNAib529Tp79x8wm88RIjxPaZaRpillGVamYVp40bFc1NdW7+hm5aK+t2u5BhCEiUKWpezs7HSOFBM0DZ2SL4TSecbjCfv7B2RpnzhOuXrlOlnW48qV63ivyNI+V65cYzpdgFB4Bycnp1RVTRzHTKdTlonEUaQpq5L5fM50eo61ltlszt7+HlJKGlNzcnJC3ktp24alQzNJwqRDKodSnsViStsalNIUi5rxaIP5LIzMd7Z36HWpv6tcGynDdEaqoIFwYRICkOd5WP0gwsHVUX+ttd3qzKG1QnXNiPAOY0KqsZKefj8iTYNFdmkpFUKgVfhewRETHu/AK/EkScg8iiLdaWccUlmkbLEu6FMaU9O2QYwqhWQ0miA69ktIKpasrY27r912q6MUIVTnkrJhddMh8ofDIQIR8pIIDp+mCYCysAILTdN4PGQ4GpCkMf1+vpqsWOtRKgoYfBmaMx0FPYxzQdDbmjBFCcRhF/QnUmNMIN9KKUJgZFVgraOqWkzjwCtmswWHBwcIGcIolZJdHlGYLkWRxnSxB1KKju0iSdOsW90FpH+SJCEmwRnSNFByl9A6HSkWiymjUY/W1jjfgHDEiSLPM6qqomlqIq3oD3KyPMG0DeA7cnFoWJxTCBFouG1r0Up3wumYPM/Z3Jwwm52yvj5iNjtlbW3I8ckh9++/ydnZHp/40z/k7OyQPEs7q3fK2mSNH/zBH2J6Pl052pQKadRZlnVTP3txwXlRX3NdNCsX9c6uMFxhOBySJCmTyZg0TTuHjQlri6Vtr6OTJknK66+/yXi0BkKwvrHJeLLGZG2dwXDIcDQiy3tcuXqN0WhCWVRoHREnCR63coosA+7Kslwd2rrD24eDosF5y+c/9zlm02m4Co/1an2iNSSZBGGpmwBZa2rL3oNjmjqISxeLBWVRMJ1OOTk+Bjo9io5Cvk+akqZpl3OjqOuatjUMh8MVv0MI0Wk8AAJ/JYkkWkKSaLI0JooEw1FGHDmktCRJhLUmHOBaIhSA66ZBAf2+nLzEsQZCiq/3Fqlb0gyUdiBamrbqhKiCugksEQiOkKDvaImicJiHdYrCtjZYeqUCAnzOeY9pzCqhWwiBJwQwIlitx8J6qGV/f5+6Ken3c6T01HUJwtOaFhBUVU1rLIPBIKQap2HdIwiE4SROEUKvnlspI5ylY/w48rxH2xoQAts6bCsRaIqiIs/yDl3vUVrgsV1T4hAEq3hVlUgRmk2pJGVRBBDbfN5RjgMHx9oWpGNezjHOopOI1jvmxQwvHdP5KaYNVFrvW6SCLE8QXVBleG2FFVsUKaIu36jXyzv9TkSxaJjPF4Fmu9KXtNRVzfb2JrPZOScnR0zWxjSmQojwdW/feYU3b3+BT37y4zRNQb+XIbyg3xtwefcKW1tbb+GoLJuWRwNML+qivpb6rmhW/vxO7ZsBelzUX359KwEsgPer/BvvCc6QJKEoCsqVnkRRNzUPHjzg1q1bVFUQaWZpj3c9/Rzj0RqPXb9BmuZcuXyNx289gW09/f4QpTWNMcFpE0cIyeqqsNfLVyN87z3OhtdiawxpktLUdXBv4GlbS6RjXMd6SdN0peMYj0YkSYaUGu8ldW3IsgzTNt3h7ch7A3zoYXA2pCPbLlNICEFrW9I0RUcB3mVMILC2rXvIHREESqpv0VoSx1EQ3pYFjalAegbDnLbTxiy1IN4H7YvwkMRRSBz2LrhkqpI0TanrOugRulWRkA6tl9MYAUhaY1GyS1luDMPhiH5vgPdhzeY9ZGnO5tYOrbXhwZFhPaO/hDzrXIC4LRsqqcLKrzV25SSyraVpmpXexbYOT8dUicNEqFjMiXX3NQRkaYIgaGvwAikj6iqs1wJTRtEaSxQl4fWhQphj3TSAxDkoq4okS0jSwJNRKsBcwgrGMZ8X4AVJmoTpktKITrQcRTHehzVQ29ogbia81rIswXtH21aU5Zy1yYAs00hpsTbg+rUOUxvnHOWipCwq5rM5RTFnMMwQyuF8S1UHW7WQAh0rjKlp2hpE0ACZNsQCNI1lPq9Ikj69fMB8FlxcxhjSLGZnZ5Pj0yM+8ckX8T40irPZDAEMBw+b5qUw+yEg7kK38vasr3QWfmfPz++KZuWi3gn1rWtYJCHpFx84LAJBL8uZdAehkhHO+6C90KAU3Lh5gzTN2Fzf4smbT/H4Y0+yu7nLMB/ijGMyHJPohGvXbpJmfVpr8VJg2obJ2hihFcPRiH4vJU1jkjgOQlZvwXuUFPR7PeIoJc/7DAZjvJc0jaVtATSRzikWhtaAc4LFfAEElL21FucNu7uXuqYD6jKg960NjZP0ohPFWto2gMSs9zStxQtFY6B1AqljZJSgohipYmrT4pVDxZrWeRrjsV5yfHJG1u/jnEfLAAkTMnyO8wK8xFsPzhFpQawlsRZIHLOzc/Iko5f2kDKmqGpq09I6R5QkoDTWS6wPmT9BnxEhvKZtYHf7Gt5IXA1V0XByeoYFWhzGGZJYEqmgEwrxAx6PoO2aJalaPCHlWUhF2zqSJAvcFRkR6Yw8H9I0Hq1ipPBo5WnrAqxBWBeoK9IhZct4mCG8xbWOpnK0zXIlJvFOoHXC+dmCXq/PZDLBY7G+xrga41qMdbTWU5ma8/kZxhmUjoiTDNNCVbW0NoRyeiDr5eE1UJRIrSnKGhWF160xFtOAUjGj4ZBISSIJSQSDXOPaBVoGKJ7SOefzEqk0WkbEsoe0fU6PKjY3NohSkIlBpZ7GtkRphkotXlUkfYlOIcrCc6STlCwfc/fNU6Qfc3LYsphq7t87ZzatQjJ46/EqYXv3Cm/evsv5bMpg1OeNN16hbgqWB9jSpfZ2EWte1NdQbzN78zu6Wbnw5V/UitNA9/u0/PMj3AbnwuQhiiLSOGY2nRJHEWVZ8olPfIKjwyPe++73cHp6hkCw92CfK5evcvXqtY7FMuT07JzRcMRgMKIoqpXewbSWLM8QClpbE6caj0HqACxz3nP79h2KogpcDykxjcE5S1mUxHGCs0G8WRT1KoVZ65CKfHR0yPn5GXGiQTikgjiOiOOoQ8Y/vGKNIo0nQNCWVNWwRhGdwDKILIM1NriG6FZS3nlGowlta5nO5kQd5VaIoMuAMGlIkzTYjDvxbtDBCAaDQec86hpDoUK2jQuTgbC6ARBEcYyKwrRqUZYsyqITLwd4XFkGHRBApBUSQZIkxEnU6R+6LJ/OLh1eCLI7EB2yE6cWxQKlNefnU4piEYLzbNBPFEWBbVuctUgkpgluqLqqOiZM+LrOe/Cic26J1feGYO0+Ojzm9PSMfr/fBRAGgWzSrQyjSHe27aAdWpJ/tY6654pO67TA+bCCiaIIpcQqOFHrCKU0i1nB6fEpxjQoJcmzjEG/z2Q8ZjAYoFVEVYWJVdW5s8AH3o1UzBcL0jTGtYYkilkUCxpjiHTMeDzBmJbxaIIxjqaxHB+ecXBwwuHhKc5KsnTI66/dZTFvKAtDpFM2Nrbo9fqMJ2tMZ1P29h6QJimz+YzT07PVz3jxXv0OqC9pTMSX3L7c53w7G5l3dLNyURf1F9WSsRLsuiCFII5iZtMZxWLBpZ1LfO7zn+Xs/Iwnn3ySV155jZOTM1555TXW1zaJ44SNjY0uq0dz8+bjIUVXxBjjieKMvDdACBhN+jSmoDdMyfsJ09k5t+89YDCakOW9IPp1Qew76PfJ8qCtyfMeTd1SFCW7u1dIkgD5CiJIi9aCspoDZmXxNaZeQcjC1atDSLpGQq90B977zjVkQ85MG9YZRVFj26Dx8D6se5RUzOcF3getTHBZERqFLv/HEzQfQXugEWgEiv39o04v064Sg5Mk7pD1YW3lO8uq8x4voLUhOdq2lrKuyPo9fMdN0VIRS02iIyLdNUedoNqLcHXey3LSOCWJU+IkQ8fJ6o01IO4dg0G/sxQLmqZm0O+tGjljWqbTGaYJ67LGtOgoaFSqqgI80Qp2J5DSh8NX+C5bJ+T94MPbqNIKj+3E3Halz8iytHssLNa2RJFeMVdCtlLUIf99l1pddqseAwQHlXdBr2NMS1M1CC+oq4q7d+7RNiZk+1RNcDdZ162MgiW+qisQAolAOtjd3qEuS9rG4L1nbbwOThDrhEjFtI1jc/0STWV5cO+Qsmg5O5vzYz/633H16g2cFWidUpUG72U3UfT88A//CHfv3uPX/6//izTJuX///qrRvaiL+mbrolm5qO/qCsTbsLqwbYuSitOTE0aDARtr61y9cpkkiVksFpgmkGzX1zaYzwqiKOU973kvRVGytbWNc5Dnfcajje6NWtHvj5E6om4bqqZkfXNM3gtXlrOixKE4PZ8TJSFFVyqFVpLp+TlVXXZ015Ca7KygWJSA6HJ2gmZmOBoEgaQi2JxnM5x1nWNEISXESUSaxljbdpbicJ9UpKmbhrppkFqDFKg4gMICAj9oFuI4WK8H/RGRjkPScOc+Wk6lpJTY1mJa000fPG3rMU3IaNra3KJp6pBNpFRghERxt9LyiC4/qGmD3sGL4F4SSjKfz0P2UDd16OU5sdaYqobWUpcVtrXIWIVpkJSU8wJnLFVZBwqvUNguVFAIaK1ZWZWF8CwWc0xriOO3ToWs9dR1i7Mu8EW6n7dpGmwn2g2NmlvlCC1zp7xfBgKGpOQoihACmqZexSasAHuEfytV0BeB75rRwINJ09BseR9CJ8OkJlidtQzC7bZpiaOY0XCMqQ11WVGUNd6xAty5RxgzSosA4hMCJQTXLl8hi2PSJKXX7xNF8crt1tSG6fmcSKUdMTcB/5AH9KlPfZbZdEEcp1Rlwxtv3Obw4JD5vMBZ2N7aYTAYcevm40ynczY3trrJHqvVz8UK6O1bX8pVebut7C6alYv67i4fbK04T11WpEnCoNenl/U6bDlcu3ad23duU1YVk8ka/f6QSztXePzWk/R6/SBC7CBq5aIm0imtgd1L1/jg93+ItnVkWUaW52xsbbEoSloLUdLDWE+c9vAIxpNJd2UdoaSg6Q4807T0+yO2t3dJ4qwj0Up2tq8iRdStkKIVWwXCYedxIYuGsDZaXsm3bYtUQbsxL4oOmBKEqk4ERD8+YPHjJFz1R1FEVYWrc9t6kiTu7KWOxWJBURQsU5alDJMK2zqq0lCWJgh628B7yTueRhQFx1JAxgcYHUIgZAjns87RtCag7n3giZi2DZZo5zFVQyw1p0chW6Z1AQDnujfQqqxoG8vpyZTZrKRqutWNs4AjjiKsaxHC0+vnpFnyMLARupTlmDjK0CrBdoC9LE1XNGDVNRm2ay68t51TJ3xu0zRBwKz0ym2jtOy0NeHnDkJdRxSpLvXad/bxIOrNsqQT6VqkEmgd4gnyPAQlRpFGeIm3DoEMTUpVk6U5edbHWU9Z1uR5b2ULruuKre0N1tbG1E1FFGkW8zknh0coJKqbNkZxRBwplAdvHWcnZ7jWMTudE6uYPAuxAFJIjo6OuklJANnVdcNwOOaLX3yZjY1NpNJsbW5jreOxGzcIazPfMWrcamV7URf1jdRFs3JR39W1BHE5Zzk+PmJna5urV68yn89wraWX53zms5/mpc9/niRJuHLlGtZ6nnnmOSbjNXa2LzEcDpFS8573vI/hcMJotMa1azd48/ZdDo+Omc8XqEgznc24ffs+i0VLXQuMEd1kZcrJ6Rlxkq7SfuM4WuWwKBVRlQ1Z1idJs26Vojk7WTCbVhSLml7eRymNFBFC6I6BkeG87Rw9QXviXGCZOAKUrbVtt55Y2rbjIKDVUaCdWt8xVQJQrjUOJfXK3RSYJA95GEv6aLAJO8qiBq9o6pbTkxOquqIxZoXef/R5QATtTGttcFdpjXUO611Iml4sqJvgfqrKEuk9w7zPeDjCmqAv8QKiODRuCgWtRwpNWdZ4HxxBcRyalCjWxHFEv1v9xHFMVZWYjmmjdac7MpaqCs6f1rSde8XT7/dX9z+KwuRKiDANWYLqhJDgQwNpuwYycFeCVVupsOZBeJSWGNOs+CmyI9eatgmwQEK2FIBp687erlcsoX5/iLcerTTz6Zy6qknihDRJGQ7H9PI+SRz0MWVVcHR0xNHxIVEU0pe9ddSLgmI6QwnZTb1a6mqBMRW9PGEyGjKfTTk42KdYLLrsKIeOFGdnJ3jvuHJllzSJ0Vpy//49tNKcnp6hpKbf67O5sRnWnVKuSMpLR9BFXdQ3WhfNykV9V5eUAmc95+dT8KCVZDDoY63ljTde55Of+iQf+MAHANjfOyDPcm7dfJyd7V3a1rG394CyKHn22Wf54Pd9kO2tHS7vXuXZZ55nPJpw9+49FmWJtYF8WpYtRWGxbURVO4TS6DgBIamqKtiVAdu2xHFEmmYsFgtGozUEKhw+aUpZVFSlZTGviaOctg0Y+3AVnwSuhw3E0rIsV6sG6yxxEiGkJEkT4iTBtEFwuRS7BqCcJooSlFYUxWIlVJVSo3RErzvgjTGhwWjbbt1hVyLQ5YENAq0joji4VZYTnsWiQHaUUqUUqpsMeTwogXWWra0tXKd/UUqR5Vn4nlozGY2oyhJ8EPdKHey94Eh0hG1NsBYLiVQRrXXdz+G7Rslj25ayKjGmIU2TlRjZOdet0oLVWKuw9pkvFoGKG0XMZmFttArak+C860BynYhZKJYhjXVd4VyIZVjyUVprEDLolIxpVg2K1oo4iVA6IP6XTU2YWgXdTmubMI0ByqKkKorQ3EpNa1qssQz7A2wbcpNOTk+o63rVlDV1RRyH7xFHEWmcdEnkEtu2VHVFFEesrY2JO+uyx3LjxnWE8LStwXu7atKUljhneP75Z9na3kDrAHnr9fq89trrfOLFT6CjmH5/yGAwpFgUnJ6cYpqmm6rIt9Va4aLeWaW/03fgm6tHvd3iLb8IF9PGr7e+lW8i37k3qO6CvstvUdS1QQrF1cvX+K3f+E3+xv/9bwRGyJOO87Mz8I5bN2+itaJta8p5w2NXb3Hzxk0SnfPcs+/lpZdeYjKZcO/+A1565c9I05jxeMDhwT44iY0l1giGwwlZlrG3d5/xZIj1lryfUZUFsY47DYvEASenp/y1H/1x2rbi9OyEoiwZjCY888yTvPLaS5ycHlAUs26loHFO4J1FqZjGWLT2eCzOeaqqRjlPU9V45/B0B7P3wZ0SS6SHqgqHX+3CaiaOYoSSWNtgnUGrCGsFUmhq05ImikgrWusxxjPoDzipZx1bJaxltM5QUqNUmGYlaYoQLW0rKMuGpjIgJEkaAY6jo1OKeUMSpR3ptgLhaJ1gXoRAwKoxxHHaHeAWJSXT6ZQ861HXBtuJQ6uqQicRSku0Fjhfh+cfSa835OjwjLYNh2YU6UDF7VYmHotvw6qiKkviWGHTCGddiBNQEVVZdz+bprUWY1uU1hjfoggMnuXEJEy5LKbppmcy0HfTJLB1vLOBwaIkTSMp6xLrFUpnJGmKbQ1aKdqmxjnoDTLA42xokJRMyPtDDg9POTubodOW2nhaG/D9zkZ4oWitoapqvGtx0pL1eohKkEqHV47WW7SKGQxy4lhT1zWjccyNW5t4K+n1+zjrmC8aJuNNzmclt25cpazOOD6+y4P7t3niyWfQyvMnL36MjfU1nnvu3Xgfc3p8gk4iqqaBzq0muoTvt5R4uzYw3z2HyFvPxq/v5/r6Gsyv9Lnf/HP8Dm9WHq0/9xvwHbkX7+x6u75pfOPlu3VImmXdwRmukjfXt7hx/XHeeO0O3//BD/LEzYS8l9NL0zB6BzbG62gSnrr5HI8//nhwTqyvURUhs+V97/0A9/depW5E0MIMBhyWJzSmRakUqSLe977v4/Bwj8OjPfYP7lHXBXkvwXqDUIp5McdawWA45Pl3P88rr3yR3nCAxZP3B+zsXuH+/h73HuwhRIq1FW6JzifQZJ1tmc1LtA4uGB3F5FlOVVYkkaaX9zg5OQbX4NogJM2yAYvWYIxFqoBhT7tAoLJY0M/TcFA3Du8kvWyAtw6vPErGNKbF2WDBllISxYplkGOkY4yp0TKQSo1tcV6CEkgngXCfpQhp09YIZKRpbU2WS4QMLBQhNOW8Yr6oA2beWeJYYgkynEVZBCeOVyghiVSEF4KyahDCkSaKpgk6j7ax4OXKcUOHyw/3X4TG0cswLUgSBB0or63JspwojnBOoFQQsFrvSdI4OJsUCBX0KiEsMli1q6rF2pYkSVeTr9YEKrAQXaAkgcmjIw1CUJYV/X4ewgO9D5ob7/G0tFZ0P0cQ3TpXBft7njEYB0v9YrEgjmK07GOtAKlIkwxjWgb9GGNDs4jzTAY5MpZILzk7P2EyzhGixvs5Ozs9pNSsTzYoy5qmFqGpKhwnxw94/OZlZrPrnJ6XSNFS11M8La++/hIf+L73M5+V9AYDZBQhI43zrjPVP7z55e/n27K+O86PJd/ma/3cR+vrnoJ9tYfsL+HhvFgDXdT3RMmODhpQ38GO+yM/8iNcu3qNLMuYTCaBhqof9u9xHPP8cy9QVRVra2tMxhOkkLzrXe/i/Pyce/cesHv5KnjJ7qWrFIsGISK0jjFNQOFPJmPqpubs7GzFLGkag2nCeiVJYoSENI159ZWXSdOEd7/wbsajMZcvX8YYw9raGr28x2I+DyuPTvi6dNwgws8XaKesQvNs26K7VOHwuRHOQV2bDjUfLMi+Sz9umoaiLHEuuJKkUKtE59YakjQBCHRTE75G0mUWLVcrZVmt3D9Al02jg33Zue7vXRd8qImTiCxPsM6Q91LyXkKaxkRxxPl0ymKxIEmywKFZVG/hPkjp0ZFESAsiWLyX4YZhDSQ67Y2gKEqiKHBQohXzpWPwSNGh8SVlGVD5y/ydXi/oVkxjaE37SGKweCS80q04MtPpdLVGWj4PVRUs48s4AK3UyrnTtp1T6pH/LRObi6JckWI3NibBvi48WZ6Q5ynet3gss9kU0xi8C4yZSEfkWYaSquPJCJTMODstuHv3gJOTGcugyLZt2NpeC4nUeWi4t7a2sS1MzxfM5nNaazg4PODk5JjReMj9Bw+YTNaZTDaIo5gnn3ycOI7Ispg33niV//Yrv4SOBNPpCWmSEHVcHu8fnYRf1EV9fXXRrFzU90wtrzKWkLjNzU2eeuopyrJa2U6ttavP994zGo/Y2NgIYsbusN7b22M6nbK7u8vTTz5DFPXwLmI03OCJx9+FlBFaK6azKV/84kucHB9RFEWn9YjBQ78/CJOatQmjUY8sj/niy5/n6OiIxaJkY2OL09NTPv/5l9jf28M5R5ploSmxdiVYDAdA0DmECABWDcZSczKfzwOQTiiiKAEkdR3sqM65FTukaRoW8xKlEhAaL2RHcGmpm5CrUzcNxoQmyNoW7zxxEncHsEIpGXgrxrC9vR3uh3eBAOw9Sgl6/ZQ8TxDCUtclSkFrG+JY4b2lP+hR1yGbJs/7ZFkPrWOiKMF5EMIxWRuRpjHQcu36ZXYvb+Fcg1/ZdtUKva9VzGJedYd/s9LUBIdVWEuEJlaSZz2m0xAsGUUx49EEgVw1L0tQ3rLxXL6mrLWUZbFqfAPXpVmJk+M4Wb22QjwCK4eM7BxeQgT9j2lMaFuE7HKKHAjPoJ+TZhFKhUynvJeSphGmqWmqirZpaKqapqppm2BrDs2PZDqraRqBtRGHh1OMAdO6Lu37AdaGpsiYlv29I46OTjg7W3Dv3j7T2YzWtRR1wfHJMePxhMnaVlj5Kc1w0CeJNU88cYtnnnmK2eyUw8P7WFdTFIvV795FXdQ3U+/oZuWr+cG/kmf8Sz/37e4tv6hvrh4l2S6fV9FNIvI8RylFnuchyK6zfS7Le48UkmeffZayKlfulqOjI65evUrTGObzkl4+ZHPzEhvrl1jMS2wbLLaz2TkHB/sdlySQSIMAVFJXDW1rOD09prUNZ2dHNKbi8PCQ0WjM0089y9UrVzk+PqYxhjTLgv6kywFa/tda201aPFKGNYQxhrquVnA45zwChbPhv1olCC9RKurEnQJnw3QljhOcEzgXVjqNaWitAeEDF0Y9tEgHKm7UwcoMTdMEfooPwX9nZ2eUZccjcW4lojVtCHg0rUHKAC0TAiaTMW1rmc8WNHWYPjWNoVgU3X0PAY5SauI4YX19He8db775Og8e3CVJ9SNhgbILlGyoK0OeD4l0l2Bs3WrSEYSzQbciusfR+zB9ao1jOp0FAa6OVk0g/uFraWVtdnYVsDibBR1PeF09nOgtGx3ThS0GIB9dYGHLfL7AWkccJZychsdOKU2aZcxm551jKEwnojhMzHQUHE9BZC1Jk4S6qpBCYpoGnF+lVjfGUVaWXm/MdFZSlA3OC8qqWt331rScn0/JswFHhydY52laQxQrEJ6iLGidZzor+dCHfpT1tS0iHfHC8y+ghOT89Iwrly9RlQvKYtGtxdzF++p3uFYi8Xdw0/iOblYu6qK+kQpujvDSl1KSpiHa/mEa7PITg/tjyQxZ8kKSJOHSpUuMx2OOj485ODjgD//gY4xGE/b3D7o9vCNONOfTM8bjIWmaMBj00TrqmgpPlqW0tsF5Q6+f4rH89//9TzEcjhmPJ916IXBBptNp+Krd34kA7Fitg6JIr+5jURTAMtlZde6QCu8F3ksG/VHIy+mAbTrSSNXttr3EtlBWhtZ6hBI4LJP1MTpWWG9X3zeO4/BvO4fNcqpR1zVA535KOuuq7oSnGtO0NJ3Q2TkfNCFRzHA4IklyyqLFO7VqpsJkxuBxNE2L95KyqDg9PVvh86UMSH4heMRho1lf3wzQtypMDR5OROQqoXmJz4fQlKRpRpKEqUHbIfrDCs114Y5u5Z4KryexYuM451Y//8PAvjBl0VqTpmn3fR9GQIRARk1dNwSGiSRJUgaDYfiaNvzcVR2aN+c8VRnge8v7i/eYuiFJkiBAPj+nl+dEWoGwSM2KjXJ2PmexqDFtgOGZxoXAwrolSbMuwTyhl+ekSUKvn5PlGUkWk/VzyqomywY88cSztAZsCzdvPM762gZ5nvPqK6/x+uuvk2UpZVmupnxKytXjfFHfvnqnNynLumhWLup7uh49UL5cLa+GlVKcnZ3Rti15npNnOY9dv05VFXzwgx/k+77vg8xms45Z4ohiQFjyPOXk5BjTNpycnOIcqymIlMFG7VyL0pLJZMxiMefGjZu85z3vY21tnSeeeIKiKKirKogSuyvU5UpiuYJo23YFMgv6gNBULaFkQSMhmE3nLBYVxjhs66Bjr0RRhG09ZdlgjAOCUBcBk7UxyMDNiJMkQN2WU6rOlswjb4hKKYwx3SEebLVtF17YthaBxrQeKSPSJFuJaV9//TZN7XFWE+mMJMkBQZYl9PspUQSLRcFiVjE9L5jPCkCRpjnj8YSiKPA8fGysdRSLEiE0VRVszoH3wipK4GHAXrifi0XB8fEJVVWvbNlRFNO2djU1kt3P+nANx8N1DQ/XcMsJU9CmmBUJd4nvF0JQVWX4t539efk9hZAIIZnN5kihGAzGZGkP71Wg7ToZgIGLmjQJYmitNXVVsbW1xWAwwDsfxNi+RCcOqT1ShVVg3ViqKjipmsZzeHiOEJpIx8RxRN5LufX4Y+g4sGGSNLwWRuMRG5tbNI0jjQe89z0fwFQW6TW7O1eQQrG5vsmtG7dYG00695r/rjgsL+o7W99FbqCLuqivv75WDPhwOKQoCvb399ne2ibLM9oHLUfHh5yenhLHKe973/v5/Eufpm48zhlaUzOdna+mG3EcdwF9kqKsUMqzvjFZEUz39u4hJBwfHTMarnF6dsrm5iZJEocxPT64UZxAa4GQCmtZWXWNqTsQWownrCUC0dYRRzF1HWBztnXdAWwQgoDv9w5BEH4CZHkP54MepWoCr0NIjWsD9RQXiL4hZ0fgvQAZ7LqttaQyRiqFI0wjvOsisRHEcYpUGts2lK6hKhskgvm0CBMUpzu3jOyCFyuiOPBVvAMvJW3riaKss5gb6qpFCBUIuLpbtRB0PEqG/BrXTYXCY2JWYmpnHaLL51HKB45J62isWa28rHXdfzv3k4hWomIhAkVYCLmawhljOvT+Q+rvMidHqcAnqaqKqqqJk4ThYEhRlqG5s4HVUhULpFAsFiV9rTGNRcoIa4IlWinBYlGQ2CiIgrvGzjnHZDwmy3Jefe010iTCeYNE0TYO23ik0gGEpxowjjxLOD46I0kV/X5OXdckccZwOKBqS8q6QmnRCasrGmMxjWPQn7A2vsrl3WscHh7ynhfex+/97u/j2i+in024fOVGcFrxkAZ8URf1jdR31avn69Gc/EWf9/bUsYT1wrfm9nb4+b4z9VWv+lxwyyydFqfHx+GAn0555ZVX8N5z7fp1ptMpb7zx2kqjIIQKOH9rMFXDoD+ibT1NE7Jx8l6PLB9QFDVKK+qmYDTO+eSn/pjPfu5T/O7v/jZ3793BOktZlTg8cZLghSCKQ2JvCM3zJEmE95ZeP2DunbMIKZgvFgitEEpgXIMXligVqMijVHeQq4i6tpRFg1JxcJLkKZEWeGuxxlJXDVVdU1YLrGhw3lPVBmMNKhbI2IGyoEAoDTKiNBbj4HQ6pWlb6CBqgW9SI4SkadpOM+KQyq+mMDoS6EiQpAprGwJfBAS6ixUIP3doLsIkYrEoscajvA6U1842jfB40eJFS5anQLAtG2NxFpqm7aZdQQjsEJS1oXUC00oaIygrg9QRUSRBWIT0q5WGlGGVhVf4zsMcGiVHWZW0zmLall6/h5B0uH1CAxmnKBURRwlKeibDHt4GqF1VVSRpRpRmTOcldWlpWvBIpI5ASRprkLHCeUVReJraIhWcT49xNJycHFGWNb0sY20yIkkUcSrwqg33BYFEY51kUTYcHp+hdMrJ6ZzzacnZeUlRlEBLHGvyvEee9TjYP0GiSdKcq7s3uXHtacb9TXa3r3Pz+tM0lSeN+gx6425tFRo66/78+0zI7vrW3L65t7Tvnvfar/Uce/udd2+t76pm5aIu6putRxuXTpaLkkGsOD0/Z21tjXt37/Lmm2/Sy3PWJmtMp1Pe/e4X2NvbI0kSQDAajRgNh6RJzPraOk8/9TTr65voKMILyHoZSZJy5fJVDg+P8N7TmIbXXnuN4XDAZG3MX/mrf4Wqqdna3uLmzRurYMHGhImI944o1mGiEmmSZDm5AefC5znnQ/ZO25JmcQehswjpECK4VJRUCGSwBEuPtQ2xVsSRBg/Oevq9PrpLC25MS1U3XcPU0toaqYNQVekI6z2t89QmMEbyXg+lVTddMDSmpixKWuOwdpmZ40mzNHBitKTfzxiN+8RJyNgJKy6F0hqxCnW0AejWva9qrQODxAULcGCRhFVNf9AL7Z21IVagDUJk3QmF8Z4ki4hTyXgy6AIAw/dZ/tn5tku9Dq+LkHjNSkBrO6GsdcFSHCeKpgmU2KCTCc6fOI6o67qLJFDdysjifUtrG8qyCEJg56iqmn5/QN00gfXiPUJJWm/J+j2yfg9jHYtZhbWOXp6hlOfk5BDrLFJAL8upq3KVWxTFGtPUYXUkFU1jaIwJ6d+NoTEteW+AB6azGZGOGAx6AZTnarIsvOYWiwU3b95ifW0TITT9fMigP+KH/+pf49KlK8FuXhRopVcTprf8rn0Lbxf11no7Nh9fb33XNitv9y7xot4ZtdQiRFHExsYGOzs7DIdDRqMRw9EQ58PEBcJrLqwBYoxpwyg9Sej1cqbTGdevX6fX69GalqIsKMuS+/fvkSYZAkW/N2TQH5PnfTbWN/FOsLO9y2OP3WJ9fQNY8lQMtlu/hDWMwnvY2NiiacIBmGc9+l2ujkSihCKNU6yxaKkwpsFjUdrjhUXHgjTTmLbE+5a6bjBNuxIgl2WFEJI4TmiaGoFYOY2WuHgpJc6GA1ICrrUoqdFSg/PgLKapqOuaum5WuhHTGhaLBU3T0Ov1AGjblvl8vppUee+DDoaHmUVRFHcC44di43Dwu84mLPAO5vMFVVlTVzXO2ZVQVkix0t8ICd4btnfGXL6ygVCGpl3gqcOfzaLTrDzUptC1a1GkUVoRRYIk0XjfAm3A7EtHVS06N5judCrV6mssV0y+Y7XEcfiZzs/PKcsS7z29Xg9jGuq6WuUWORciCtI0o9/vM56MUEoxmy9oGkPA5VdI5bskbEvTtCAEcZQEgF/TApIkTgM4rzV4PJcubTEa98jykDlljKcxNYOhJkpqTs7v8ebtL/La66+smvulyDqKIt773veys7PD9WvXGQ6GK90XfomFu6iL+vrru7ZZ+dK6aF4u6i+qP7/6A0SwKs/ncw4ODlZOoCzLONg/oK5qvvDFL/Diiy/ykz/5kwz6A7Is6BaWdXh4yHA4ZDabUddNF4qXgXA0xjCdztje3sU7zc/+zN/gf/of/2eGwzG7l67yvve+nyuXrxLHacjakUG4GUUBbrc8+Kz1vPT5LzLojxAovIPWWHCgZYRWEYt5iRRBs7LEwUsFWRajlCeKBf1BijEhb6gsSwSKLO9hre+C70JzpKPoIacEAtK9CyTUWpFlGcPBkDTOUEIHlD/hwAqH/rKx8G/J61km9EopMcZ09l6/EqYuHTzWWqqqDOLd1j7i5PJEcdyJjwkQuCgJguJVU6C65orQ+NkW7x1NY7h79x4vv/wqrXGdCFqhVdRRYxUChei0NOFn74i4OKwzON+CcCRJRNvWxLFG6bCOyvOcpmlWt9B4tqtGRkpJWZZvcZ/JLlpAKUmaJngsUgaRdppmVFXNbL5gtpgznc8xxrK2vsHaxgZIsM6wt78ffgbClKysQtPTNCY8JkKQ5TlSKayzgalSLKhNxfrGJt4p6qrCuorN7SFRbCiqM7Jc431oVL13K5F1HMcMh0Pm8znr62vfNW6Ud2J9N5113zPNyl9GXTQ733u1PEDPTs/o9XqrAyWO426iEK6Mb968SZqm5L0c5xzTjr7qnOeFF17Ae9d9zBInMc5ZtFbkedalLrfk2ZDRaIPDwxNOT6bcuHGLnZ0rBNBb1OlRwmG7PMyWXJWgn4jQOqGqWspFxfRsxsH+Md6Cs1AVNVsbW+AFi8U8AMucJ441UkHTlAFn70xnNQ5pzWtrGysWyBKqFxqTeNUkOGdDJpFUDPo5eZphaoN3YI1DiW6txHLC8NDJtGxWlpOiwWAQcn46EmzvkfRjYHUFH5wzS0JsZ0P2bhUB4H3QpNgWTOOoqgYdKdbWJgwG/dX9DlMVT9t6tMwpC4drNa6NwCcs5i3OapwFEGEVtTp8PUJ2+oYuh6rX6+F9yKOytgU8xWLBdDoNcL8u9sEYs2L7BBtyEPyWZchE6vf73YRo+T1C4rMxBrynKIIjaj5bUBY1prGcnEw5O51TlgF+l/dCplBRlkipyLKcumpWdmzvBQ6BlwrjPBboDyfUrWM6rzg8Oqc1AojwTlEWFcPhgLKc8oUvfoa6axiXz+fyudzb2wtNo1Ir+/3Fu+ZFfTN10axc1EV9lVrC4/qDPtPplJOTE87Pz8nznOlsyv7+Ppubm+R5TpomqxRkpRQf+tCHSJKY2WzOD3z/h8izLHxekjKZTFhfnwAQ6Zg06XF59xq2FWiVkiQZrQHvoN8bMpvO3gKEs9YSdYyT5cETmBwwmy7Yf3BILBOkl8zP59jG4g3cu32Pcl7ivexsqsnK9qy15tKlbaQM6544TiiLkrt37gaui5AdqyUcO86G4MQ4DtAxgKJY0DY1VTHHNoat9U36WY9hr493Ld7ajhMTGsHlpCU0XoFRsmw+lgnP89kMYNUcLnklywnWkhK7tCAb06ww+ErpLkqgXTVZUj20dwf2SwgeDKRaTRLnCBGBV5jGIUSElDHW+g4oZ1ecFe/tiv66THCu62Wzp4milNY46rpZPX/LKYq1trvvrJKal5j+YDcPP2t/0CeOQ6PnvacoSuraUBYVznmKsqFqPI0RCJkxWxiMAesFQinqxpAm6eo5VUp3SH+xSoeuqgqQWCs5PDzDthGmkUgyBv0JebrOaHgFazKcjVjf2GAwSKiaxVt1Xt0UZW1tjbZtkUKG/KQL+/J3tC6gcBd1Ud9F9egv9PJmjKGu6pWW4s0332QwGNC2LYP+gCRNME3D9evXqaqa6XS6Wmks9SSXdi8RxRqtw60oCmazKScnpxTFAik1zgquXb3JF77wClevXucHvv8Hmc3mvPTSy5yeniNEmOAkSYK1LW378EBerlzwgjzvA8EhMxqN8S4csE1lsNbT1EGnENwrEmcFEJKE4zjm4OCoY64EUFnTGIqixNlgP06T0CCEKUBoXIJFWnZNiCDucmC01iRxilIhzVcrTRTp1cEVx8GS3bZt9/V897jZFcdEdmwX6Jg3Swpsl020nHwppej1eqv74JfcEqGRQqO6VZgQgrJY0DT16ntGUXAZ9Qc9hsOMvBeBMESxQEhLHAuUChqTJUBOqShoPHxweEgl8V7SGo93kqoMdGNjHPiQy7RsTpYsnCVMTimJjsLkbDwerz6+bFyqssTa4CgCTxzHHS24JU1zkjhHqxQpU87PK5zVnJ4uqCpDYyy9Xp+qqjk8OOLk5JQkTjqYHqGp0ooojnFeMJ9XnJ9XeB+ztfUY1irms5Kmlly7/ByHe4bd7SeoK0OcCJqmXFmyl8+ZEILhcBgIuhd1UX9J9T3brPxFOH7v6W4P1z7eLzNCxTIytPtYlx+6+vNX/K7f5O2ivt1lOueI1po8z9ne3ibPc6IoYjKZ0Ov12dzaYmtri4ODgy5ILyKOY15+5RXqumHvwR6/+Zu/yfn5OVpr1tbWqMqaQX+AFJr1tU3e//7vYzSa8K6nn2E2mzEcDbCt4/TklFu3blHX9eq1qLWmMUFvIKQkTTNOTk7x3nN4eBQmE8ZyenpOVTYoGRD1QawaATIc4CpiPi9YzEta46kqQ1k0eBcagkUX/uddONQEkta0wSZsPUqHEX+Y8oS1VJzEeO+p65pLl3bJshxjWkxrHgZJQvjlEsGpFMcRdJm8WZaTpFmXtVOGA1tKrLMrbckyB0kIj/OWPM9W3zO4n8JEItK603dY4jjpnDeh4aFzBokOzx+mGQXWVQhliWKPjixStTiqTngcJjVL7YyUEhl2SEgEUgbwXrBgg0BRFg1ChL+vyqqbHjUrIXTQ+gRC8XLq4pwlSWLu379PsSioyhrvBE1t6PUGIV8KhXeBRjxZW6c/GOHROCdZFIaz8zlKJ2R5ztbWDhsbWzz99DO0xuG6JsVDB6Nr8bCa8igZs1gYfviv/gQbGzuY1tPLJowGOzz9xHuJ1IAnbj3N8XF4rRljAkeHruEHBv0wiWyNWUH0cN+B97Fv9i33W3bz39ztW3VOeP8l9/Hr/Lm+hfU926x8tQrXSr67gXXhz3b15InuRudyCKRIIR4SLb9yXTQsb/daNad4jk9OGE/C1W6/32d7e3t1hdy2LQJBlubkWX9FAFVKU1c1cZwhdUrW6/Pu97yb46MDNtYnXNraZmt9i+vXnmJ78zpZOqIsmxXZ1JiGvb09NtbXuHXzBnduv8nrr72KVhJ8cNuAxDiB84raWHavXEEAcSTRKriYtA45P0IohsMhAM614D1pEmObFlO1tJXH1opqJmgWGm8TKmPwQtJagVIpwkcIL/HWYVuoKot1HusMOpYIFZD41jlUnCC0YlpMMa5GaI/QEi8FHoFvLWmsydKIXi8iTlVgtGiJ9Z6iqmk9GGuZFwvm5QKhBSiPVgIpPHEkiVNQyiG0xwmwwHCyBlIQJYooAaEMbVvgfQMebCsoi4a6bnCu7bgtgqoyOBcmI+PxhCSJQHiiWK3WSU44VCTxwmOtQQlF263XgpumQQjXNZYCgUbJBNOAcwqkxnmBVBGNsTTG0lpPYyyzRYnFM1ucsb4VmCjDfh+JpimhKgWt0ZgayqLBWcHZ2YLbb+4zHk3wvkVLSS/rIbwAJ2mbFi0VWZqQxD3Gw0sMh9s01uGkxUmHE5BEOd46pPAo6ej1IrT0KCF49pn3kmbrjCfbPPOu57h54yaP33ica5du0I8mtFXL+ekZSgDWoRAID2mccO3yFVIdI61H+iBPFt9GN9C30hr9zd38N3X7Vp0PS+aNJNyEX97Xt4dd/KJZ+ZIK13eP3Hx4g8R5tAzpto8+MVIKrA3q+ZCW+t1b3wvMg+UKAaA1LVEn8lzSYJernNlsxqc//Wm0juj3+3zu85/n3r17q9H+cDjqbL8xr7/xGtPplLW1ddKkh7OCyWiDH/jgD7G7e5XJZJ0bj90iTQPU7ZVXvsjmxhpSCUbjIX/2Z5/CmAZnbZfxs6SpBmz/lStXaJqG4XBAUzdsbmwihGRjfYMkSWiaZrWeWqHlu3VEmLZoBIqqbFZYegjhht6DFGqlp0jiBIUkjmKUCGRYASip0Ep36cuSONZY23Dn7huU1bwDwYU31OXjCJ7FYs5wOECqIFxW3ZpMSkmSpmzrmMeMpVfVJJ0wVSAoyyLoRGTIbxKCjgMTfqYsT7tEZUcca/zKshzEysvn+qE7yXdaFEtd1QyHI5QS3bRDgQhZS867bm2kVw1r21pwIZ9o+Xlaq2CNFiHkcMnIAVbY/eVryYfrnY5qrCnLko3NDQSCqmjAC+I4wzQt3gviOOk4LZa2tezt7dE0Bc43NE1J3ssZj4fkecbBwSEHBwesr62zt3cAXYK00goVqdV9U0qTpRnb29tkacZkbR0hFJub2wyH6yRJjyTJ2d7aYT5dkCd93vPC+1mbbFCVZad5eaTR957hcIhW6iGkjW/fe8J3viH51ty+1fWormX5u/Z2uW8XuP0vKfGl3algpch3ToAPo9/lG6+1wYHwu7/7u3zf972fydrGivtwUe+8WjoaAJqmYTQeAUEvscx5WTpWojhepff+yZ/8Mb1en42NDZJUM1+c0xrDg717aK35zd/8DeI4413veoZXX3mNfq/Pn/3ZZ+j1Bjx4cICUipOTE7RahHVRFJGmES+99Bke3L8XsmU6/HvgqoiAoPdw585tLm3v0Ov1OT+bMp1OgbBSyLKMoihWrpsVYr4LQFyuHqqqehhIiCfSmsa1YZLSNTZ13ZDnMf1BjmlawpqjwnmD8ILhcETbOqqqQEqw3f21zhBFqnPxKNoIrA2OqTSNqeu602mElGiBQDWGvz5veeFsTu48Cyn49NzwWxsDfKTQIkJF4cBvzhfoOKyYjo+PEHimrkGJwCLp5T3K0iCblnYZRtiJdZ0LjBUpBZEIwLmT0xM21je6N+3l48TKjeXcQ2bKsmkBiOMIqSDPQ1ihbduucYnQkcb5aNW0tG0QtQZXkyTVGXGkSBPJ+vqEpqoRItiuW+sxTctwOFw1Fvv7h2itQzgjFb1+eJ5VpIhjSa+XMpufhWZSSpTWnJyc0LZBUyJVsJfXVdlNlwApybMhw+GELB1x7dpjNI1lY327sy9bnnj8XeTJXSaTdUajAQpNkVYcHBxy5cqV1e/KoxlWy9+rZb2TRZ7f7fXlUurfLnVxqn6ZEnhCCIlbjd5ee/VVfuejH+2aFMvZ2Vn3JkvnXDAcHh5wsa5559ayEVn+Ocsy8ix/y8ettQgEi/mCPM9JkpThcEAURWxvb3NwcMDjjz/OfDbn6OiwE8K2aB2xvraOaSyPP/40mxuXuHb1BlJE9PIB29s7bG/vkOc5g8EgHLrS8Su/+sucnh1jbXCfDOqWa5VhYn1HsbXEccz5+ZQoSrhy5Sr9/miVP7ME2i0nRr1e7y0C4qVANYoiHnvsseDMsZbW2G6y4mmNpSxDjk1V1cSRwtmGNI7ppTlplHJp+xLSC2KpyLIUhGPn0haXr1wiLFUD0wXxMESwqYPItWnq7v5BWVYYY/nw1PDXTma03nM/0jgEP3w054f3zijL4IKBcCGR9zKsDY9zlmWd2DXEE0RxhDEtTV2jdEh+XsLXlFJEccSotezOK8Y2TH2kJAQMCvDeEeiyDy3Ry3o0G2iZD+Tc0qUVYhGUUnhnaY1ZNSrLoMe1tbUVa6VtHVonlGVNsSiZzRbY1oWmVMfM5wvquubs7JyqCrk9cZTgrEdHmrCyCnEAeR5jnaWX9xFCceXyle69SrK2thZ0T13OUZ5ntK6lPxiQxCl13VIsaq5eeQy8JNIJu7tXOT05D1lCaITQxFFGpFPiOIiCl2nSy8fkK9VFo/J2rofPzdvxebpoVr6kvKdLzu1GYYAUIYTr937v99jf38N7x6/92q9w+86bOG85OTnGWsuTTz5xEdb1Dq9H10DLKcPygCrLktu3byOkoKxKrl29yvr6Ordv32Fn5xLHx8dM1tbCwdKBv6IoIY5SxuMJZ+dTlNK88PwLHB8fMxyOeeLxp3jssVtoHZFlQbg7Ho+4f/8OD/busShmWNeiTcOHD875e3dP+H+9ecj/evuQ/+loSto1UP1+n6eefIqmCQJYISRHR0f0ej2cc7RtuwLVJUnSgenCWmV5eL7++uvhQfCsrL5BHBrgaqYxzGeBxtrrJbTGUNeGPO1h2+AUajobsneeg4ND9vb2iOOE4XCEs47WtMFJNejT6zgizoXDM8sylFRMrOPd04IDIThSkhrPSaw51or3zms2vEZJtVrDDIcDrly9gnUtjz/xRLDLOkdrTNCp2BbTweWECLA5pRSp9/zfjuf8r3dP+V/unfC/vLrPT90/pSdEeMy1QirRCXvtyolkrV2lSi+TrYX0eGfx3lKWRbf36OIQ2qYLjQyP9XKtqLWm3+93hFdFEmdolVBWDaenU8rSMByMaVvf2Y0D42Y+L5AysGq0jkmTDCV154qSFGVBFMVsbGwjiNnbO2B/f4/JJKQgi65hdM6F+Adn2dnZ4dq1G6yvbaNVzt6DY6z1DPojsiwny3K0jlgsFhSLkul0xnIBIGXQRC1/Tx5dI1xwqd5ZFXS1b8/n7Js6Wf/Fv/gXCCH4+3//76/+rqoqfv7nf5719XX6/T4/+7M/y/7+/lv+3e3bt/nwhz9MnudsbW3xj/7RP1rZ394OdXZ6yitf/CI4G96AnGX30g7XrlzGtQHyFMUR+/t7CAFlWbC1tQ5i+UQ/fMLfjk/6RX35+lLbMjwcYzdNs6KLOvvwyrGqKi5d2uGFF55nfX2d2XTKZz7zaTbW13n2mefp90b0eiN6eWgmbt26RZrFIMKhM1nb5Mknnu7Wi4qzszNu3LjBtevXODzc54svv4S1hg/PG37keI4TgoMsASX5sdM5/8NZQZqmfOADH2B7e5vzsymRjomimDzPWSwWqyveJe12e3sbYwxpmgKsrviXVmixQtyL4K7pwvrC/4e6LrGtQUpBuSg4OT7l3t0HTM9n1FVFXTUY4zCN5fxsBj6g3XV3v5a245CXE5w1rW27ZGTPutD0PMw6PY3SEXiYKklmPf0ufLBtbZdG7ZhMxkgJs+mUy5cvdxMbQ9MEvkmSJCGQ8pHfyR87mvLDxzO8FGF6IwQ/elLwU2c1aZog5MPcHyEErWlWj9XSVr2MA5BKoaPgjjJtg5SColiQJPHKog2sGkalFEdHR8xmsxWf5ejoBKUiNje2QtNS1Ggd84H3fx/DwZBer8+lS5cxTdtNzQIQ7/T0PNikmxAEubmxTZb2SOI+G2u7XZPjOD8/4z3veTfr6xurbKQkiVcuqzTp0e+PeeyxW9y69STnZ3OyLOP8/Ixr16+Cd2xubiBEyJVaOrOWMRNBf+P/Qp7Hl0MEfK1X8u90Vsi3u74Zvso38vx8K+sbblb++I//mH/zb/4NL7zwwlv+/h/8g3/Af/2v/5X//J//Mx/96Ee5f/8+P/MzP7P6uLWWD3/4wzRNwx/8wR/wH/7Df+Df//t/zz/9p//0G/8pvub6i9XTAs/egz3+6I/+iE984hN89jOfQXV6haeefJIo0ljXcu3aFa5evUJdh8Oqqip+5Vd+maapLxqV75IKT9/DX9SlAFEqyXA4pG5qXnjhed544w0+85nP8uqrrzKfL8KaIYk5P59jGs9kvM6P/MiP8tRTT3Pr5k1OT4+5fu0KTz7xNFevXOPZZ59n0B/gvWd3d5eNjXXG4yE7O1v0ehkbUvDM0TlHSjDv59go4lBLTmLF91UtI2O4eu0q83kBSIwJVuv5fL5C1i/txVmWdYLMhrOzs9XUZXnAh7WE7aYpYUXRNCZMK7ombQlSq+sKEFRVQxzFncBcUpU1cZxQFCVV1bBYlDSNpWksWdbrVlHhMW6aJrBWTNBweOcp0pRCSvqPrBOEEAytYyEEsygEA2qlkUqQ5zn3798jjmNM265CE5WSxEm8aiqWeUrWWvp1w/sXDYdKcKgkVivOs5gjLXj2ZM5WR8eVki5cUa/ynpYNzPLxWU4UQl6PIo4jsiwl72UIAVGkV4d5FAVb+3g8Xq3njDGMx2M2N7dJ4pSybMizHqPRmMW84GMf+ziLouTs7IzDg6BvUkqvHm/vBKbpmkkLDx7s09SW+/cOuHnzKSbjdZQMk6ujo2Mev3Vrteacz+esra1RliXn51Oeefp5qtLwxONP8cwzz7EoCvb27jMeD0EEoN5wNKDXzxEyWMeXouYvd6B9swfe2+3AvKjvXH1Dzcp8Pufnfu7n+Lf/9t8ymUxWf39+fs4v/uIv8i//5b/kx37sx3j/+9/Pv/t3/44/+IM/4GMf+xgAv/Zrv8bnPvc5/uN//I+85z3v4ad/+qf55//8n/ORj3yE5itAhOo6wLYevYXyK53Bw+Yg7L69gxBCx0ObsYe3xnSHILIlH2XJUTEd7CnLM7I8777ewx05XrA2WevSURXn0zM8ngcPHnRrvy9piDpv/JeyWy4amu98PcrJefS5ErByfjnnAm/FBhdHURQopXj81pMIGQWnRtty8+Ytnnv+eXZ2drl27SZCaoqypKwqxpMJ/f6AL3zhC0zGa2xsbLJYzLly5TKDQZ/pdMbBwQHj0VJvknVrlzn6bEbStCwi1WkoHBLBVEBmWj7w2C0WswVVVXJ6dkpVVZRlQWsNzjs8HiGDk6UoC6JEk6QxG8Jz3TSMbMvxyTEIMB1TJkxTlmF7EqkUbccP8R6iOFBQlYqJoxzvFVk+wFqxgqK5FqqiwVmoy4amNrTG0u8PiOO4EwuLlY7Eu8CQqXoZnxrmbBjLWtOSOM9661g3lj/tp9SDPlJFq9/zk+Nj5rNz2rZmd3ebpqnQOqJtPUVRU5QmYPetQyCxrWNoHKm1zFVw+njhEVIwixRZaxl7gVQCLzwqkgF3L0AIyWg06gi4fpWy7bumr6rrrtkLr6PFYkZrG6QS6DgKje5oSJzEgCdNE9I0YW004dLWDu977/cxO190MD3FophzaXcbgWQ2XTCfl6RptppKJUlCFMdkWYAFIqBpQuDi+dmc7c3LXLp0jcUiTGDu3HmTKJIoCYmOkGEcjFaKne1NLu/usHtph9YYykXB2njM5Z1tPv6Hv8/J0QE4g3OGulogBSgpV78jX0qp/XonKF/6kYvm5KvUV3Qv+694C0u7r2x7/tps0X/B53wLqRvfULPy8z//83z4wx/mJ37iJ97y9y+++CLGmLf8/dNPP821a9f4wz/8QwD+8A//kOeff57t7e3V5/zUT/0U0+mUz372s1/2+/3CL/wCo9Fodbt69Srw8KARgg4Q1RFSXItE4lqIpAyR8V52Ytlls/Lo7a2PZpTFbF7aQShFWVdYPNZ5RuM1ikW4muwPhl28fcz62honJ8dsbm6gtUTgQvKsWL44vvR7feP1TrTNfSfv8190U529dXmTkm7E3a0Au+fLurByWCbhhqtpQb+/xk/85E8TJxmHx8dM1jZ417MvMJtXCKWI85jT81P+26/8N5RS9HsDYp1x/cotNtbWOT06om1qklgTa413DuFhdjbjU3/6aUzVsohiCikZtA6c615X0G88563j1vs+wCf+5BN89KMfDa87JfCypbE1Qgm8CC9G6y2NrdC25mcWBf/vewf8/P4xP3/nAR8+OiVyLUJJvBAYE9ayYWjg8c5hW0+kM7KsT123mNaiVITzkvm84fyswrYaJWK0CFyWXtZHOBESnxtLMS+AgJeXUqwiA+IkDlMQ4fCu4nd3hvzueh8tPLumRTrHb096/MowpaoDXC3k/jiwjiTSjAY9Tk72mM7OOmhdimkFrZU0rcC00LYeUJzpiIWU9J1FKIFQIKSg11hKrXjQ1jgJKI9XHq9cZwMPOp22dV0Q4DICQRB1+U3Aqllsmirg+K1BRpK6rTk5O8F6y2g0pFws2N7cYO/ufXpxRjmr2N26TDGvEALWN0a8973PAYos7aNkFPQpDqIo5umnnyaOFToCpQVRFCY7ZVkyHk948cVPoFXGZLyNVpokVpTFGYkW9NKEteGEJEqxTcvx0QG3b7/KtWu77O/dD2sz43nPsy+wMRrz2he/wOnRPjiDNeE9Nvx6eKQQAf72DbxfrP5N9zWWt6/6b75CI/TdPoERPOSgfOWb/yq3wLf+cre/iOMixPKspZuMvvXjf9H9+mbr6/4a/+k//Sc+8YlP8Au/8At/7mNBTBdGnI/W9vY2e3t7q895tFFZfnz5sS9X//gf/2POz89Xtzt37gDhKkcIgZAO0y5ALJgXe1g/x4sSoVqssyuxGxCmLI88ZQKJEN1T7D1HR8c88eRTbG9vk6RJt8cP1uWmrqmqgtY2PHhwl9FoyHw+wxjDdDrl+eef59sJPbqob76WaPdHk3+NCVkvi2JB1aUPLzUrRVGsViebm5vsXrrES59/CYGgl/d5/NbjVGXN+dk5WinW19cIeTUtv/3bH+Xy7hXu3L7D9evXmEwmfPHlL67WMfv7+5ydnVGWJScnx9y9cxeAEwGfXRuwZixrxhJ7z6hqWDMtH48V//6X/mvnFDkLFmUhmUwmjB+ZALAUCnv4iaMpP3I8RUSaB1GEF4IfO5/zU2eL1ecvcfpLkeQSAV9VNWVZPcy26R4/69qQ0iwEVRWaiaXleTlBHBrLtaohnc+7tOMAW0uShKZuSJK4Y5t4Zrbhv27k/G9XJ/x/rk74366M+eWtEW0UBL/hDTMcc1IpBoMB733Pu1lfX+OZZ57Ge9tZjCMQAqkkURx1AZJhKnQ7idiuWzZaSwKMyoqN1vGZ9T7nOpBpIx0Iur28T57nKCUoijnWGoQI9uxl+nJr285hZVcTUyHDa6sxhtn5jDhOGfSHCC9W65zl42yt443XX+f4+ASlNLu7u0SR5uVXXmZtMglTXREmv5PJGCFFEEnL8O8n4wlKacbjCd2Qmfl8xkuff4k7d+6Q5Tmz2Yzj4yPW1tYYDkcIqciyIf3+GIHi05/+DEeHh+hIUhRzolgzGo744Pd9kM3NLZIk5bHHbrC+vs6F6/Givp31dXFW7ty5w9/7e3+PX//1X19dQXw7KkmSbtz61gr2RYdzNdYt+Ngf/z6f+rMXWV/b4Kd/8n9mNNrAO4XWKd4t+3cJXvJo798hBrDWMpvNWduYsLa2xvn5Oc5Zzs/OGI9GlNWc09Mzbty6wWuvvUJZXiLLIu4/uI+1ljRNaZqGJLnA17xTKjSqfiUuPT4+XmkLsizDeYd1jqIouHPnDicnJ/zQD/0QWZaRpClHJ0f00pzxcESWpSgRIFi3btzi9PyYw6MDDvYOMKbhmXc9y507dzquhWcwGLC7u8ve3h6f+9zneN/73kfTNNR1zenpGZtbmzRNWCH8+iTH2pZ3z0q2CsNcSH5z1OO314Y8Nh53mPZgU7527Rpv3nkljPLLcjUF0lpzYzjgPfeOOVKCUzxOwpEInI33FzUfHVrOtSLuGDKhiVMorRDu0SvXpbNFoRSUlWA0mTCfzxESbGs7QW6Lbgw/9PJdXpgW9BzIkeH35BH/Rw+0h00Z4ZuWGXOUliRphMdB4zkyLUXewzlPliSYadv9nEHUmuc5VV1QFAX37z/g2effzWc/+zmU7jKEGsvDC21P4iw/fb7gfYuKvrfkznO9aJgJwUzA7+8M+O2dPvPZOUpFLHOH6rLGGtHpVxTWtpRlgdIBhKe1wncNnVLQ64WYASlCQ5ZlGfPFnNn5FDmEclFgW9vh6CW9POeLX/wCN2/e4PTsFCEEo9GI4+NjHty/z5NPP09RFngfCLlHx4dEkea1119BEJKeq6pAyYijoxPW19ap65Knn77B+fkZ48lNwGJtTV0Fwm6SxCEWwCueffbd/M7vfBQhNPcf3OPJx8csiilNUxErzfb2Dnt7hxweHvL4rSdWAYXfzVOMi3p71dd1qr744oscHBzwvve9b/V31lp+53d+h3/9r/81v/qrv7oS7j06Xdnf32dnZweAnZ0d/uiP/ugtX3fpFlp+ztdcoqUoz/n9P/wtzs73acyU59/9JA/u7/GHH/9Nrl65xTNPvxfrJIIY8RAk3F0BBSCUwK14CVmeIKRgMpnwyiuvIPDMvzDlBz74QfJeAmLA7TuvMxz28VjOp+ccHx+TJAllWdEOWpKEP7e//Wr11XQrF28G39oKQXRBTPryyy8TRRGXLl1apeouE2qHwyG9Xq9jU+QURUGSpoxHE5566l0YU5NlwQp87cp1fuVXf4WqmZNkMVIq7ty5wwc/8P0cHx+zubHFbDpjd/cKW1tbaK352Mc+hpRhIpKmKWuTCXEcdw6ZhoV3/LftIR/bHNCvW469Ys9ZlBB87nOfY2srZBZVZc1sNl9Nf5aOjaXlVpycklnLfhIhRVgROeeYKcVua1lHMO1Enyt+SPf6XNptRUeFjaKIuqmIo4jRMA8HWxxTVUVwNylFkkT8d4czfnRact+13I8jhkXBXytr3nXkUXJG3wtKLfnUKOdXxxGNDN/LWdcxWfzDCWc39YkiRd2ESZfS4f4eHx9x585tTk6OuygEhfMhKNB7wcgYfuZ4ygfmFftZzD0dca4FO8bw+V7E/3llyKmANFIkPummQ+GmZITphMfOi9AMSdjYWFyF74UAAQAASURBVOf09DS4pwSURcn6xpg8zzg7O++cMgmutfTzPmVRMDuf0poWJRUSyd69PawNr8HDwyP2Dx4QxzGvvfZacBJFEdPpCds76zR1iGLI8rgTP5ekWcLu7i7T6Xmw1ovwddI05ehon8uXr/DMM0/zsY//ATs7OxwdH9DU4fWdJClJkvPkE0/zp3/6SYqi5PjohOpKyWPXb+E9zOcLtre3efbZZ7l39x5FUdLvD8Lrx8N34i3qQuv3vVdf1xrox3/8x/n0pz/NJz/5ydXtAx/4AD/3cz+3+nMURfzGb/zG6t984Qtf4Pbt23zoQx8C4EMf+hCf/vSnOTg4WH3Or//6rzMcDnnmmWe+rjv/Z5/+OOezA3r9mOGwz2g04fab96nKGmMqfu/3P8qbt18jwN0eHvyrRNblPt47pAJPQ5YFhf4yjM6Yhlde/iKf//xncc7w4osf54svf57Ts2OqqiBNEj7+8Y+zsbHB1atXyb4EInbxS/V2r4drjo2NDXZ2dkiShDRNAxDNtrgOb3/p0iXe/e53Y4xhMBiwmM/JkgzbtORJzvHhMYO8z2PXHuOFZ59nOBhibYC2SSl59dXXmKxNGI/HHB4dgSckNycJTz/9NKenp6yvr6O1ZnNrk7quieN4pZMxxlD1cu71UuxkRKTD9CdNU9797vdQFAVt23J+fkav1+scGqE5T5IAYptGEaVSDOxDwbj3noG1FFJy0h08j8Lxlnbd5dQHBHVlOD8/RylAtGS9hDSN2dzc6PR8InA6Gsf7C8OxlkzzBBdrpnlMz8FfmRsiBPciiYojfuRkzo8fFXgvESjaNgh6m8aGIL/GIqUKh3THc1kGKbqQHMibt9+kKAqEgMbUeO9IvOPDhyf8ozsH/I3jKVcaw3rd4KTgOJLsJ5rLRUOkJWkS4W1LpBQSgXCg0bgmNEtKSbIspd8PCc+BVh1eJ8tmznaxCFrrlQDXmJZIR7StxVnP7qXLRCo0ZM45BA/5LVJKimLBbDYLq73WcHxyiJQeIR3jyRApPcZUKA3GNJyfnzOfL0iSnCTJaFuLdS1vvPEqe3v3+djHPkaeZVy6dInHH7/FxuYGs9ksTI3qiqqquH7tOleuXOXq1esIER7/9bV1nIOz03M21jcZDIbcvXsP53zH4flmFW0X9Xapr1f38+3WCX1dzcpgMOC55557y63X67G+vs5zzz3HaDTi7/7dv8s//If/kN/6rd/ixRdf5O/8nb/Dhz70IX7gB34AgJ/8yZ/kmWee4W/9rb/Fpz71KX71V3+Vf/JP/gk///M//2VXPV+tXnv9U/z//tMvcnp6xNNPPcNP/vj/yOWdJ/A25f6DPbyHfq+P7NCZS+ql7FKanGtxvgXR0rYVjSnIMo2zFikU165do2ka+oM+OpLUTUlVlYzGAxpTIYSnKBZkWcr169dZLBZf0dH0jdZbXEMXjc9fej2qqZhMJp3wU9I04QBo6oYsyzg9Pe3YHQ15nnfsjIjp2Tmj4YjXX3udtjGYxnBl9zJPPfkU62vrzGYzHn/8FteuXWNza5MlBffy7i7WBadO0zQ8++yzq4NqNpsFN1DHQsmybIXHD3qQtgOz9SiKgizLGA6Dw2Z7e5urV68RR6E5kTLk9zSNIY5jZrHmk4OMiTFMGkPsYdN6NpznU4Occ63egksPGURulWcjpaSuDdZCnMS0bY2nBSytbQLF2Qd2Ch4m3hPVNXpzLTQyzpI6x9C0eK0plMTFMYdKMssS3n1e0ita5rMS7xTeK5SMcU7irEAIubofSzvxkkhrW8tsNu1yg2pc9zj95OmMHz8v0AKMENQCbtQtNxYV/X4P00vpI+jXBiUFtm2DK0tInPUhwLFzKgkBm5sbrK+vIQScnByTJDGym1AhoCgKFosFw+GQ3cu7oalSmqZqEB2Rdnp+jqkNVVlTLkraDnB3cHDAfL5gsVistFNplmJty717d5kvZmgtkUoQx3oldrxz5y7nZ7OQnF3W3RozJcsjRqNhQOs3Daenp2xsbHDjscfQWnN8fMTZ2Ql//CcfZ31jLeQCTdYZj9c5PDxFypi1yYTDw6PuwktSVXV3sacu3pIu6ttWf+m41X/1r/4Vf/2v/3V+9md/lh/+4R9mZ2eH//Jf/svq40opfumXfgmlFB/60If4m3/zb/K3//bf5p/9s3/2dX+vL7z6SY6O7/Mnf/JH/Lt/9//ljTce8Nj15/mxv/bXef7Z9xLrlM9+7iX29vZXFsnlm7BUHo9FCMsnP/knvPHGK7z00md48RN/xIMH9zk5OWE+n6G1pq4rPvnJP2V6fkZ/kHNyfMTR0QGmNXzs4x8DBPP5nNdfe71rjL41dfG+8K0o3wHQ/OqQfjTzRUrB3t4e+/v7ISxwMAxXER7SNGE8nrC5uUWW5TgHa2tr5HnOs888y3g8oa5rdncvo5Ti3r17HB8dcf36dZI0oSyLleUzSRIuX76MMYaT45MAHROSxWKxAooppTvIGauMmrW1NbIsW00zsyxj/2B/JXbt93sA5Hm+4nr8yjjjdzbGYB3XkQjv+K1Rn19fH67YIY82K0qF4MLldAcCO0QrRZxEIUCxqToXlSeOk456mlEkCW2aIs5mlEWBkIK4teQO9GhIPBqvDrwTHD0vEKdTWgtKJZ2+LAjhnQ0TCqUUtg0k3MlkghAiNG15RhxpRLdG8jjGzvL+ec1pEnGYxDRa4aSklIJLTYusanqtYyFhFkuapibryL7ee5RUQTfUPS5CiFVcwLJhCivCnCXgbqkfiuOYs9Oz7rkKU5PRaESkNbPzKd57+r0+WiuKRZiKBYifQcpAul1iGxbFvGOaQNuJe9MsJcszoihMcLSOiXQImwwTNUfTVByfHHN6esrp6Snz+Zy9vX2Ojo67qUqJjiQPHtxnf3+PBw/2+MynP4uzArzGtp4kSTk9PaWqanq9PoeHR5RlCf5iTX1R3776ppWgv/3bv/2W/5+mKR/5yEf4yEc+8hX/zfXr1/nlX/7lb/ZbY13Lu558jnF/C2vh93//d7ly9RbPvOtZ3vfeH2EyusrLL7/Cnbt3GAxGZGkeRpfKB6FZveClL36Wz3zuk4EOubPL9Ws3KIsZbWSIk5Rrjz3GK6++xNHhfZ568qdw9hKf+cznSNIeBw/2wEkeu34D5zxPPPVUYFSIcDUqpAh8lu732XsPSnR22PCXQsguATd8jsCFN5pHLlmWX0cIgetYIGJ5SeWXuouH1FXXJcCuVlDLz3Phz1I8jGsULO8Pb/37RyYOQgj+XMDjX2r9RUb8rzZq/ksy8fNwD962LbPZjHv37jEcDZnP5rSNYTwak6bJ6r4IQsMynU25fu06n/7Mp8nzPkpFxFHK1sYWV3Yus7uzgylrlFQ433J2fsR0dsp4PKEoCgaDAUVRcOnSpXAgdtC1OAk8jLIyaBXRGoeSMbLDrOtIc3h4xI0bNzg5OQPg8OggUF9VTNPUrK+H9Od+v890eo5HUAnBL63n/MGkT140lGmPQxfWHN4ZpADvJQjZXc0IlJAIpalMRWNrsl6AIyaRJkpimrM5zmqE1CgpMabBC0GzNuaVy4IfOy14ZXqOUTDWGo3nwLZsXX+M4o3XaW3DoLZUkcQMM6R0eExYfYiQqOysR3q1WrMoHZ4r72B9fYv1ySbz+StksWbUH/DgwSEjY0mtZS+JKZ3nnpLcMpZaCiJr2SgbUu/57Y2U4c3rmKNDhsMJtiWwB2SYIAkpcUYgVczjjz/N66+/Tp7PaG3N+sYaVV1xdHhEax3j0YijowOkCIRjQQgSnM8WFEUBHpI4obWOre1N7t17gPWCtrakWYz3FtMapvOS1lgGw5wk9sznc3QdMRgMyPMB4/GYV155LTQoSURVtjjrcQ5c12jmvZQ8Szg5mRLHEbYVbKxv88abr1HXczY2x7StZGf7Ms8/9wJ/8IcfQyrPn37y41y98hhn5ztsrW1R10ELNR6PmYwnNHWLGIqVJurbUl/21/yiWfpW1dcfaPjV3ou/cl7U11rv6CCbsmwRLuZHf+TH2N3d5nx6yGtvfoF7e/f41V/5dax1rK2Pca5m/+AOb955laPjfW7feZ0/efHj/M7v/iZ3777G6ekBu5e2WMxn/NEf/zGfePFFmrpha3uHoqqQUcRkbY3//X//T/ze7/wGB/t3uXJ5hySKuf3GbRbzkhdeeC/zRUG9XAOJ0AgIwQrkReglwAsEEolC+C8hE/iHXvcQnCZwnbjQe9ddzYSY99W+WwYcuPMP98jOeawLllm3FAp2lsrADlpC8Hz3bZd/Xn5v170RfW/keyz3rlKG7JiDgwOccyRxgjEmpCnHMY9GpkshaUxDlqVcvXaVF55/AaU0AsVsvuDKpWtEMub8ZEoxL+n3+oyGIz7/+c+trs4FYmWLvn//PlVVMRwMaZqG+fQ8gL48eCuoS4M1HlNbnn/uBfCBVus9jEajLhAv5O7keY6Qgv2DPeqmRGmB88GCb9uWNWsZNS3nSrHfmNXPJaUHb5EC4kgzMi2784KsKNFKE0cxSkmsb3FYotmc9eMZg8YilUYqhdSCKIlobcva+gbJ//P/wf5f/X601GyVlsI0fHwtZyEFa6ZFt5ZhUTGpDZ8eZ0xjyLKIXi8lioLzRgiQCibWsTsvGHdC57IsKcuK+azgwYM9tJLYtmExm+Gdo0xTKq0Z2ABlfDXLeD1N0Naivadxjo9u9PidS2OOjo6ZL+YURcHe/h5VXZHmKTrReAHWeeI45eT0nNFojdZ6WutABNJtEsfgPc5a4jji9OSkY/lItNL4DpsQxQkIgY5jjHM4QUDiexGAbuHFBUIQJwllGaZpUirKquLsfIpHMJlskuf9cAEig1Op1xvgHZimpVhUrK9tEicJSimaxrK3d8hsWoTwwSzCeYNpHHfvPuDxW0+xubEVXosPbnNydp83br+Mx3H16lVmsylRpOkPeuhIYVrzVRuVr5V19LX++y8FmP1lcJa+E7d3Wr1dpmfvaI/txtolJuNNXn/jNmdnp7RtQ12X/Nqv/Z+sjYbEiSVOIm7evMpkssHeg0O8t7z26v+fvf+KkixJ7zvBn5ld7dpDZ0TqzMrS1V3Volqh0YRWBAkQS4mZhz1z5mFn3/mwLzxnxezzkvuwi1nuLGeGhxwC5DSaaACNbrTW1V1dulKr0OHar77XbB/MIzKrWkARQBdQdk6ejEz3cL9+/V6zv33fX9zi8Gifza0N/GCdx648Q13XvPzil/mVX/lVdOnh+SFSKJRUPPbYY7z52osEQcB4eIDB8NLL3+V9z32ED3/0Q6RZxXe/810Mgg+8/wNUdbUIwbOchKouUcqxfe2TRf/YxI6HWPXipJQOnGTRHGeTgPVmOLb8PrH5rm1OiQCSNCUMwxN3T3MCbqzvhW15/CCU+wDoPAxMtK5OPEj+pgMW4ASwTBcZM6urq2RZxsrKyvc91wDj8Zgsy4iiiF6/d8JpaTabXLt2nV/4+V/mD//wD7lw4QKO49LtLC12p8vkeU6j0UIkAkfZW3E8HtNsNjFac3hkeQJFUVKWxx4e+iQ4bjQacfbsWe7du3fS4gmCgEajgeM0GI2HC3VOdkKmLKdTfj0u+EA2x8tL5kLwYrPB73cihO9S5fYaabsOPzWY8fR4RgOYac2LrZDPrXSZC5BZzi/PMp6ZJER6SqocXu5qvnp6k0mRU1XlghMxINGG3Z/9BH843KdVZOwUY3ZHc37T6/HMcMhykjHDWNCw3kaVNVGjgRQS34+YTWNkXvBTR1OemaVEWhOjeSnO+crpFRKtybKcxx57lNu33qTVbJNmJZ5XMkbynYbPJ8YxlZTMpeHQkSjf44W2x39ciZgHilBAMU/wvGAB/trsJQeLe80GJwahh5S2BdbvL/Hqa4K6sDyTxx57jNFgQhhEpGlGmmS4rm9bSY7DaDhBa2X5PKUmCDwazYhmK+K0u8mtm/eQEqQDoK0jbVWBMPieS1Xpk4wmpWyUwptvvkFZlpa7IjXtdpPxOF5IqgVlaVAyZD6LF609WwkcT8YsLXVw3RClFLNJzOHhAa++9iqPPfYYRVGgtebGjZuc2brAeDLE9RRHRwOkPI/nO2hdoBRooxcGbj8eC9q742/ueEeDlSefeIZZnDCajoiaIR/+yPO8efUWw6MhZ8+eQRvNdDLhG9/4BpcvP8rtW/dRyqcsCp58/Gm6vS7tdhshDEVZsL56i0bYxGm20AYQgl6vy/bOLSaTCbs7u7RbIbNkwu7efQ6OdllGMp/lYCTPP/8hPM9dECVtYJnWNVVV4roOYMjTRepp4CEFVhbpONhu0VsLXUVRnChJjisnwpiT0MdjxYbnuezt7VPX9UkmjP1cdnE7VolEjRCE+6DFJMSi2vMgdkAJmzRc1xUsFApKKaT3ZyM/vxPHMfn1eNE/tjPf3NwkDMPve35Vluzv73Pu3DmiMMJgmM1mxHGM1poLFy7RajX52Ec/ztmzZynLioODfc6fu0iSJnQ6S0gp6ff7lGXJ0Z0joiii2+0ihN2NPxzGBywApSEKG5w5e4bbt2+jtSYMQy5evMj29vaiKlRQVTYc8HhnVJYlvzjL+MQ0Y+w5HHqKRlXzk9MZNSWfWm3jeDYH5+O7A35iknAkJfeVomkMn5gk1MbwqZUWP30456OThHHocBB6tLXkJw6nhH6D/9SPCAKfNNVMpzPeeOMqj1x+lI///V/njz77h2RjTaRC/tda8N3NU8RNyW4Zo9Z6JNMp/X4fsIu6rUoafupoxseHM0a+y65yaNYlPz0vCQcx/1svQGtoNlssr6wwGAwx2ppNDgYTPrvSRkrFU+M5m3VN5ig+u9LiM0shhSvQVQFGIIVDUdQEASf3i8BWJBylqKqSVtN6y6yurrC8vMz2dkxRlIyGI0s6VZa3EkUN6tpQlSVGFxS5JklygsBFCLNQZxmSZE4cx4RRQF2X+IFDp9NiNBrSX+4xGc+o6pp+t8twOMRocxLiOJ8NaLVaVHWJA+RFQpLMEBKkUEjhMpslHEu/LfDNwUAcp0Rhk0ceucJw8E2effY5BoMBWVZw/twFlpdX+PznP0+e59zfvsPtW3doNlscDQ64du1NHnnkEQu4whbvApV3x1/FeEeDlTTJuXnzDkZWjCd7lsEvXT7+Ez+BRHH16lXW11coCrvwNpst7t/bY3mlR5LO6PV6OMr6Fdy7e48kmfPdF7/Nxtp5Or0+bq345Kd+hySZcv3qVVzpcHAwoLPUxHE9vvfSd6iqF9ncPMtHnt+k3WxQFQWT6YR+v293LbMpVVWdSGENtiXjGclgeMR4MqYqDZcvP2rbP+J4UdILYy4bQmeVUoaizNCmotaQZeliYTTcvHmDs2fOEkZduzMyNePRmKLIF1LbNnXtIqWgqiwAsWBHnIAea2hnQUmWWc+MY+WJ51iDLL1QRRzzZIDvq8YIcbyo2klfa0sgtV42LMh/bx3HKbbH4Age7pWKH1puNgu777cTZE8cRP+MJczjYLnNzU2MMeR5ThiGJyTc4+Msy5Lh0FYumo0mrueeED6TJKEqqxN10TNPvxchBTs7uzz15DP4gY+UDq1W6+T41tbWuHnz5okC6M7duxwNhgsJsHVitWGEFnz8/u//PqfPnT/5zI8//jiu6zIejzk6OmRpuUdR5ieE2DzPWRGS5+Y5R0owFGCEIPcdKCue14Zbp9ZIPI9NL+QTR68ydAuOFsnDR8I2CZ9Ncr4bezwbl4w8h4nn4jguIwSO8nlqEvPFboBYJPtK4TCbzjk6GnJ66xyuCuj3V7l16zarK2sc6JLGlQukN6/RWbi5rqyscHhwhOu6zKYJrbzkPfOUgauYBbZaMRMKt6x4epry7Y0+O3nGzZs3CQPFeDQ9ITcLaag9h09vdPnjtkdfQxJ6DKU1lnMX7tYW2Asc1ydNU5rNBr7vopTP0eGIqqqRSoDQKEdw+vQpZrMZBgsCR6MxSln/l7IyJw699vpftH8cj7rSCKmJY1v9yLKE8XjMo1ee4saN62RZhec5tNpNPvzhD/G5z36eqjI89ugT7O3tL0iyM8qipK5rVlZW2N27z+mtLfK8Is9r6nqOMZqVlVWUlBRFymg8xvMcut0uBs3u7h6j0ZCjwwllWdHpdNjc3OSFF17k3NlzxHHKmTPnwQiWl5Z4/bXX6Xa7vPLKy/T7y9R1TZImhEHD2u5j3nIP/XnG34bK7Ttp/Lh9H+9osHJwsIN0NTduv0GcjNjf96DyGA9iVlc3FgmrFXv79yjKFN+L2Du4zWR2n6NBm92dezzxxHvodLqkyZQ33niRa9cN5849waXLV5inMw4O73P27Gme/81/yr/9//3PjIYzagXT6YR2u4NEMTg64vzps5TlcyAckiS2E06rjeNIqsownU7sLqjMKcscx/F58XvfZGV5GccJuHf3FsYITp+xuUfHi+N0Oj3xbyiKnEqXDAaDhZRULkrCMxqNAKE04/Fg0RKICEOXN998ldF4xHAw5Dd+439HnmcLW3lzUnmpq4o4iamqGsexQOHataucOrVBnudsb29zeussm6c2SdOMVuvBbuphTstxW+oYMBRFQRzHC7WLt/jWxMmCfzyOlS3HKpxjmeyD1/rh14AFFNmJo/JftL96XFU5Pp5j8HQM6I4BVZIkXLt+DSGPbdz1SbvuWMJ6LKntdjsYY3jxxRdZW1tBlmIBWMTJ9+A4DufOnePu3bu4jsvdu3dZWlrCdV1AnJxfz/PI85zNzS2eeuopXnvtNbTWnDlzhjffeJMrj1xhNBpZD5gwPEk1dl2XTpLRBO5Lm//jeA6VqQjXN7isPD7+nqcYdNr0D0asvXwLf3Od+vCIyWSKEILYVWwUFaeKiqjWHIY+CCjKAiVdnKVV/L1DLvaXSDc2GI1GSOHgeT6PPfYkruvjuiFb65t4boPpdEKWzWjJEN/3yfOcZqO5UDE10RoO9o9YLrQ9bqUsaJC24jWtazaynIvdPvd3t8nznHien2xO0jTDdT3Kxe9kjYADR1nL/ZPr8LhNaigKjecFSFVTVQVBEFBVJUrZJGqtCxqNkL29Hba3t3FdCzhnswnNZos0GTCZTHE9tbiGDLNpSl1XSOUAFWEUUtcVui6ZTmKiyMdRHvfvbeO6PkURk6YZSjVoNTucPn2G27fu8ZGPfJzPfOYzVFVNnhekaWLVRuMRYRCSJiWu69psMqFxHIfNzQ1c12UyGZFmCWka02o1bNXv/EVenqekaUGSpLz66qs8++yzOMphbW2DTqfHUn+N9bVVhK45f/4inU6PUxun2N7ZodVs04haC6bTA8HAn2X8uC2G746/2Hh47v3L+G7f0QTbJB2TFzOGk32KKqG/1KHTbhGGHusbXcp6ws7eVdLigDv3XiLO7hM0EsbT29y5+wr3t6+SpEP29+9wcHSXM+eWiJoGIVOkkxM2BIaUw6Ndmq0GZVmjVEBRGhBW2jqeDJGiZDYfYCgYjQ4JfI84jqnramGiZRiPR4xGQ/YPdrm/fZtvfOOL7Oze4sXvfZPDw/tAyeGhdfI9loimaXrCNTDGkGYxt+9c5/U3XuLV115kMNjn4HAbQ4mhYHv7Djduvsmt29fQJkeomscev8wzzzxBqx1xbIA3m00py5I8y5hMxgyHQ+7fv89kMqIsC8bjEWmasL29za1bNy2HodU6yYQ5Jt4CC3mvtDbrAo53WMeuqTbBVy7yTn5wjHxVVQwGgxPg83Yg86MvfKuSePvz3+6++qcZD5sbHUc8HHucPPx6Qghu3brFcDCk3+u/JVcIoNlsopRtFbRbDTzPJQoD0nTOZDJmPBpSlvmJJ8/D2TutVou8yGm323bxykvqSlMWNUo6lKX1ADo+R77vE0UR165do7/URzmKxx9/nFarxSOPPHLyHKUULPXRjYjOwrKnLCtOLy1xDkmmKwaqJsnmDKUm8xSNsuLRxx+j0jUIQdsIMtdlP4psCGBlFrb69ty1AdnpsF8WvP7Ga1y+fJkgCInjhPksptPucvnSFYxWbG5uWZ+UqqDf77GyurIw4wu5e/cuBwcHDAYDpJTEnksiJc26BsB1bZUpzAsSKbgbT7l48cIC+Nm2Wr5onSklF9VKw+raMr7vIIRZAH0LNC0YBYEiTTKUUviBR1HaEEJ7/iSNRsRoPGD/YI9vv/Atlpb61olWOcxmc3qLa8FzLU/ouD0npUOn3UIbm+bebrdQaqFuEh5B0CJNC5qNDkJYfspslvDVr3yd4WBCWWm+/OWvEoUN0rRgZWV1AYRdJtMxK8vr9HurTCZzPM8nagTWLK7KQFhVYl3bY1lbX13IozXNRod2q2edj7OUvb09Tp3axGhoNTtceeRRoqiFlC5LS6t02l3a7S6XLl6h1eri+xH6RCDw5xvvJtC/O/604x1dWXFcQ3e5T7k7QxtDms7JZwIpHV747g5ZPqPSGXWd4fseV2/uEAQhWhhCv4Uh4cUXv4bRkJczRuNtsnxKEF6g0ZD8x0/+HlrXzKYVX/riF9nfP0Rrg/ElWVYgJVRVwWOPX2EyGfClL/4Rm1sXWd88Qx6nHB7tsbuzy+kzp6l1zuHRjK9+5StceeQ8t29fJStm7O/tgakQCB579Bk++9k/ZKm/xNPPPEOSzJlMRpzaXOfe/Tu02hFpNuPc+dPM5zOC0LHGUXXCq6+9yNmz5xiMDhmNRiALkjim2WrhKEVeTBkMD6grTZImrK2uobVmOp2SJDZbpaob7O0ZdnZ2uHnrFt1uh9XVVfYPttlYX6fV7C4M0saLtpLt5cdxslAblItKwHErBvK8IAh8bty4geMoOp3OgofwYIKr6+rEHv4BmdgOCxQetJyOx7HNt5SSIAgWIW8Coy0HSAr50AT4IPrgLTEIZpFr+eBVT7wjpJQLQ7T6pFpy/HtCCJ544gnOnjtHXuQnfAAp5EmFxXVdms1wwf2x7b319TXu3bvD8soKW6c3qaoSz/NPjnM0Gp2oetrtDkv9JUCehP2BVZoYY78jx/doNCLG4wmj0YinnnyKr37tq1y6dJGDgx16vS7GGJ566ilu375N4fu83Gvx/nmMNIYtpTh7f0BQFAxXOlx58w4vXNgkbTa4d3GTKy/fZJJneNrQqmr6leZz3Ta3XcV3Gj4/nxQYbSiiEDfOkNURX+u1uTEesXX6FJPJhM3NTW7evMtoNKbTGfHLv/x3OTjc5caNNzk6OmAwPODKlUfZ29+j2+0xPBpQlhVpklrysZTEocdLnQYfPRwhypJUQtsYOhq+ttrmUNc8sbJifUCikP39fZRyqCprtNbtdsnznCeffJI3Xn8dKafM47mV9+sHPju+H1AUNhAyCJcB2w6N5wVCOOhC29ZeEDIeD9nZ2SPPc4LAt7lL585z//5doiiinlc4yqHV8knTgeWVOJIiT5lzXIWTFEVNv9/DdUrSNMPzfKq6oK409+5t0+n0UNJhf++ARx55hOeeex9SwlNPPcUXv/THOK5idXWNJ594hvLLX2I2G1vzv9mEKIrodGw6fKvVZjwZoLVmc3MTJRUbpza5desOURTRbreJ44QoiqjqGgwo6XAwPKTTbHJ66+wieNID9Imi8QcVMv9qQcef9F7v8mn+pox3NFjJixnJ/hGRpxB4ZPM5RQqxa6hVjjHVog2jEXWFEVCZCi0kcRGjlWG0t0ccx/R7PYo6ozZw//5VJpN9KFN6zR7jUcI3vvp1ut02w/GE2TQmz3KkEVy6eImlpWUqKm7vXyeuYg6Hu0ilqOuS3Z1t9g+v02pF7OzukDMl7DkczO4ynx1hhGb38BqVTjg8vMNoNEHXp7l3D27ducvO7i5Ru2L3YJ/mLOJwtMM8i4jCBm9ce5M0m9LpNtgdvMLt7RcoiwrX9bh+65usra7SaEa2f14Zvvq1mgvnL9sgtKO7dDodS8asa44Gh5w+c4q8iHjlte8yHo/ww3N858XvEYYBw/ESk0mDfn+Z/f0By0vr9Lp9VBQxHIzo9tqMxyNaC9M0o21VZT6L2d87oN/vMRgMaDYjBoMhzWZ0Qgau6hLXFYzHA/r9ZXSNlWMjqUpNXsxpNSMsd0UunIgNxixIxw7EWWwtzV2fpuNTaUld1dR1ReBbzsCx9TywMBsDFoqt43HsVSOkxAjL4zluCx23po7BTKfdBvvK1IvWz3G0fSOMkEKBelAlWl/f5PTpcwwGA7QWNjPm2NZ+4a4cRRGtVourV68SBE08NwBilKPwPEWaxtYHReRAgRCaxx59jBs3bhHHKc89+37+zk/9JLt7dxmOjtjYWGNnZ5vhcMjP/ezP881K43k+H379GhdqMK0m+6fXiXttnn59myBo8erHLnFndR0fycqLr3G2KJkoyee6EZ9dakJR8IfdkEYj4LGjMRulZiwl/zkQfHm5QeAoAj+i2WjRbvc4PBzxwgsvEAQhzz33HIcHe4zHU1zHR+CwvLzK+XMXiBohv/ep38NVHr4vaXW6ZEdH4Bi+cLpLViY8N69YzQpSR/C1Uz2+dnaFuqy4e/8erVab2XxEq9skCCJu3b5jAXNWkqYFO/cPmU5ykqRC1xIjDWHgU1b2PApZg6ipSs14FNNutwBNo+ESxykCRZJkLC+vcO/efVwPylIviMwhnhcghOTcuXO8/Mr33gK6wyAk89MFj0fTCAOyLMX3BWUZc+nyJfYP9ymKhOnUVoWM0DZWwGjSPKPbX7Z8karmwsWzfPPb32Y2H7KyvkZ3qU+j1abZ6XDnzl36S5vkpWTr9CP0ezFL/TX29u8zi3Oefd/zlFXFG29ep9Fpsr+3x9LKOrdv3eHDH/4Yk9EMYcCRimbYxHdDAj9C19qCOyGt99PDwP+vZbwLVP42jXc0WNF1QdTwcKRVDniRSyYqXK9CypKyLMiLGs91bcnXCUjTlKKsUY5DXswBjee7KKciy+Y4yiXPUsajMVJ4zKczDnaPLFk1r6m0oKwrJAZTasaDEbdv3aC30mF7b8T+/n1WVzYQwGw+waDZH0nW11fZ3d9llmR8/Vtzap1R6xJjavJyzr37b5AurQCGvcM5O4dvECcpynH40lf2FtwPFoofjzAMOTw8oKoz0myK6wIIPNfHmAohMvb2bBZNXVe4jsfAwGBwm7IskEJSVrZNJQSMJxPu3OvS7Xa5efMGg+GQ6fyIXq/H/e1bDIcDnn3vB7hz/1V8r8HB0U2UdFlaWkYKB80q0+mcMDpPmqREURNhXEbjA8bjCZ5/htdee5VezxIfq6qk3W7zwgvf4cknn2QwHtFud0hTe2xB4KI1OI6HLmvgmDdiFmDBtuHAkGUJN27cwBjDmdPnaDUjyrIGBJPJCHepj+MoytJWgn7YHGaMoazKE2CSxznj0Yi1tTXgQZvouOry9ooLPCAKH+/WHyYTnz17nrquyfOS/f1DzpwJTwDQaGSTdhtRgzzPybKMVtPuioUQC/t8+3ld1xKly6q0qqJuhw9+8Hn29w/4mZ/5ab785S/TiJpcvfY6nuczHk845fosDUZ4Zc2311e4ePM2RbNJvrHKHMPZU6fQ4ynnb+0x+aDk9DdfZOv+IW6zQzydc913+KNek0ra3XTtefzHEL7UW+extQ32qpK7Wca5c+cpk5Rms215DY0m3W6Xsiy5ffs2q6vWYOzpp59me+c+/f4Sd+/cI0pywqMx3UoTK43rSrIsRbmSsi4RjsfvrrX5fKNmSQjypsfEA5UmdLt9nn76GW5cv0kQNFha6vPmm1fp9ZZsK0kdf18wmYwX/CbF8VeTZxnOQq2nta2IVGXNcDA+IZp7noM2iiIvuHXrFmma0Gi0aDQaBEGDXrfPeDzhypVHmccxYRBx5sxZxuMZk0mMoxThgldVVQWe56Ic642SpCk7u9ssLffZ3Z0ilSWo+4FDWWesrq3g+x69Xp8L5y9x794OnXafc+cucvNWwZtvvslHP/qTFFXJs+99jiQp6PeX2Nzc4pFHHsfUBYeHe7xx9VX6S10GowGNRpMg8AnCkEazSa/bJ0lS4nnM2uoqSlg2SlkUuK0OUkiUqxa+Tg8qjH/R8ePi4fHu+PEf72iwAgZHSQQaKUqUB21XkZVz6jJFGIHvKDA1pjIox8VTDmhDs9WgKFLqukApzXw2IgpdmlGb4SBmPJxRloYwaGOMJE1z6hq0FuhaW5VBXmCqkipPyOeGZuCg3ILp/N5J5kscz2h3Wty7P7BBaNIwmxZMx3OSOEEKgd9RVHXC/mBKo9VgXoiFWVhFq9liOkop8gJwGI/jBZ8iIEnmJMkMZEUQeERRA10WNhnYc6gqGB0eoLWh3W6TxVMajQYI6Hb7TKYzHAfyIicMK2bxEXfvXWc6neH7IZPJmLIoGY7GxPGM6Xwfz3MtD2E+J2o0OLWxiTGSzdkjHOyPQCQkSUpRWsfXG7feZG31FDs7d7hy5SJvXn2NJLZVkAsXLzCP5ziOw7172zz6aIs8y4mixokKCmA8mqKkg8ESjz3Xw2hbvanqGn9R9r59+xatVoNWO1os8D6g7fNKTVFUqNAuWghb0Xi7S2OaWoVVFEU2cmE6Y2XF8imKongLr+UHlbvzPEdrTavVOlFEHY9j2/y7d+8yHA65ePHiCeDJi/xkIRBC8OQTT/CpT32SLLchhlVdoXWFEJK6NggknU6XTmdGluVsnNuiLAu+++J3uH79Ko4LFy5cpJzMeHZ/zKWd+1waZfxcp8OXdncIariRF6S37pEXBfk0YanZYmUe8+z/839h8+42OC5Op4XTXcI52CdG8LtLriWkanB8D9NrE2+dYqPZ4vUvfInDgyOWOl0ajQYbGxtkWYkQkqeeepo7d+4ynU6RSnH6zCZZmtEQDh9+8y7FZz+Pnk54JvD4diD53EqLZnuJ9Cgj8EO8IGA+L0giQS4l7U6DtiM4dWqDNE2Zz2OUcvGDiI2Ns4yGc7IsXai5AhA1g+E+zZZPFAaUlSLL0gUPy8F1PKR0CYJoAeCV5Q/lh7iujxDC8lMcRbPZtKR35TKfZ2xtnuWRRx5hPk1Z0ktcv/kGFy8+smgjjonCkLIsOHPmDK+++ipg1XF+4BEnMa1WmzTNyLKc8SIcst1usLGxTlVrsrSg025S5jlHB4ek8znpPOEjz3+E2WTA/u4eB3v7nNk8TZZmfOwjH8H3I1aWV3AdB+kITm9tcenCRW7fvYGuKlzXYXm5z2Qy4bnnnmM6mXPh/AX2Dw548rGnmE2sBP8ts+1feyXl3fG3ebyjwUqW5riOwHPsFinPYnzfYzI+ZGmpT5KmBF5AXUGelzgqtBMPmS1lalvCN2UFRuIGIYOjIYcHcwaDCQKH1DcLgBCSJoV1mDUSaQxKKKgqjvZ2aTY20boiCBtUWH8U1xdoA0qVVJUl6lGVlFnGUrdDmeQUec58PgGREvk+87iyNuzzhCov0a5HMp1SVzUYhSlKlOvioGn4HqHbpixzgjDAUS5BGNg06CBkOByy0luhyCsc5SJUjq4L0iShyGKkVHZnryt8X1LUOb2lFkJCntXM5zF7O4dkWYkfglCrhKHLYJQCNVq02D/KqCvD0eEhEo/h6B5RaHkU7XaX8XhM1ICjgwkXL65y794Ou3u73L+/zfbONVzXJc2GHBzcZmOjQ13PyXILxo65IMf+M1VRUlFQFTm1rjk6PKDdaSMdw9ap09y/u81kNCVfyWg2m0ghmIzHtBpdHMdbgJAAa/1sHU3Nwq33uB0DD0h/VVXRaDbfYsN/7Fvyw/ryRVEwn89pNpvfD2qM5UDkec6ZM2eYTqe0Wi2b/RLHJ4tD4NsWwXe/+8LCn8ejyHMc1zrWNhoRnhdweuss167ewlEOjz32KHfv3eVLX/o8jmMBTb+/ztO39njyaMo9ISlPbbARRrzn9avIvGYmDYUUeMYhGc1Ru0d05zErteFQCWLfpVXmtB2Hhob3zFK+vrzMvrAVoyKvSJKMwWCEUj4XL1xmf/+QwEl49ZXXCAPbjgn8EM/zeP75D7GyvGorCHvbnD59hsd2Drhw7XW2Oz1uGUOzyvnY0RzP97j29BqTWUyvt0Kc5mCmWEdow1JvBaGg319lZXkZYwzxPCMMWhSZ5vHHnuLg0GbgNBpNfN8jjqd4viJseHT9FllWsLuztyCfNvB8RZLYZOcsy5FS0u3aymQUNahq6z/UbrfJ8xypFplFGtI058knnyaO5wyHQzbWT3H79k274Wg3iecZynPxwoAkiYmzlLwu0Lpm49QGk2nKZDyh2WxS1TmOq3A9iWMkgR9QVxWrK8usra4zHk1ZW9vA8x3W106xs7NHPI/52Ed/gi99+St89MM/wWg0xmhDu9kmS+x56/f7vPHmK9y7c5eN9Q0whrIoOHv2LC9867tsntrk+o2buJ5Lp9uhrmuWlpdxHesZ9bfFGPJv6/iLfrd/2dfGOxqsHB3MmY1iNk6tUJUVVZkjW5K6MDgqwtQ103GGrgVKeUyyjCzPKeuKbreN7weUWY3n+OhKkxQlu9tHHA1jpPSRwmFwNGE4nC4SYCWOsqZvXuRx6tQpJsNdRFqQTOd4oSKdxbihpKwzuxND4uLiOi5lVlDluX2NNMNUVsK41FujMi5RFDAZFwz2p1ALQr9BHoMnWmR1QRKn+F4D34lwsG6YaZKBcRBegHI9Ir+N7wQkaUK/s0JRaOoCqCXS8fB9F11U7OwO6Ha7ZPkcozXNbgvHcQHJ0tISSVIyHceUpaauLXiJ5xaE6drg+Q7zeUKeWSllM8rodZegzLm1f4fpdEaz0cIPAq5ee5HJeMKnPr3Lzo5Nwz46GjCb73Lx4iX+w2//a6q6RrwyZzAY8PgTj+E6Dt/61jf54PMfotc+TZbZBSzPrcnV8vISg9F9dvdLer0VZvOYXrfH3Tt3efzxJ0jiDN+HRqNBVecMhoccHR3S6zetJb6wRllqETqXpim+7zOZWLKqdZx18F2PJEkYDoc0m80TH5qH1UFaa8rSOraGYXhie++4thpUV9aTwspxNR/8wAdxXIcvf/nL/MRP/AT7+/sEfnDirWOzb1KSJMZ1FXle47oOVVUihKKqNGmasbu7x7mzF7h48TJPP/00V6++iRCG2XwKGC73l7i4s8+RkujuMnGtuTqdok5t4F2/wWpeonyPImjQKCv6lSEQikrWzJWiqmtGGJIspYGg63uEaY7yfTDg+Q5KusTzlN6VJWotGQwmNKImTzx+Fq01H3j/B3HUi7z80qv8w3/4j2m1G7SaTfZRjK/d4szt+wwbDdz1VXQ8Z+pYafj7UkOwcQapQp566r1cv3mb/b0/RtcVnXabRx99nPF4Atrhzp37vP/9zyGlg+c0mcdzTp3a4sqVK1RViW0dwngyZDA85NSpLQZHA4SQrKyskCQpg8GQjY01+v0Vjg4PybKcJLFVF60NSZLSattsKGM0vV6f6WSG47oMBkM6nS6PXnmcMAgRQvHGG6+yvLxGUeTs7G4ThBFhFPHIo1d45ZWXqY1GaEG322X/8JCLFx5F6w1a7YAvfulzKOVw9uwFdra3GY1mXDx3losXLtLvrdFp9XGlx5mtszz1xDO89OIr7O3s84H3Pc/TTzzNcn+ZZtSiLEuMMQReQLvd4PSp05w7ewGBIAqarK/4RH6LMAhZXl4ijEJarRYHBwc8evlRyrLGcz0ED5PV3x3vjr+e8Y4GK+msxm23ONhOqOucTqdBPIUy87l3e7qYhEYYDe1Ol+WlVabjmNFozPAgJYp8qiJDawtYysIuyFo7uKHPZDJfBMe55FWFkpI4SSjqGscR7B8OoK7pd1vUleTunT3a3Yil5S6O6xIETeazmHQYE/g+UaNJs9FGOZJ5nNIIPYSokELSbVo1g4dL6LskScZ8XFKXJa4ToLXHzvY+npeTF/u2LFwWBGFEr9vncC9hlhywvrGM60lqXTIeTxkN50h8jJa4rqbf7xIEITfe3OfUKcu7iOOYTmzIZYIxgjBoEgYtsrRkNovJsxLPdxgczuj3+vg+7O1tgxA0oy5h0GAyGjEdjWg22/hewHw6xncV08mApBHhBy47OzepSluxieczXEdw9c1XmE6nNBoRw6P7KCWZzXYIgwAp4cb171EVb7LUX6fVajAYHHJ0dMD7P/Acw+ERw9GI/cMueV6xvrbBeDLhOy98nUcffcKmYw8OaXUa7O8dsLOzx7nzW9b/RrkLiXhFresTmXWe5wyHQ86dO3eSeguwu7vL6urqScXk4cn74cpMXdcnhFxjDNoYam19Wdqttm1jNT2UsjEOe3t7zGazhXcND9J9EeR5hutaD5csy7CqIpjPEzZObfL4408ShU263T7T6ZTt7XtsbW2S5Qnb97eZ376HmCdkTcuf8H0fjGCYpORRyG6zzanxGCeJiR2PN9odrsxnhHlOKCBRwpKMpaFZ1FS+YKoURV7ieS5ZmlMbzdrGKXa2d3nu/c/zyCNP8MarL/PMM+/l7t37LC+vsLV1mvPtLs6tW2QC1JlNyr09tqIGa40mnDuL8Tze88yzxPGU22+8znKlccYFH/nwJxBCEc9fp9dbYjIc0OstsbpyinNnL7Oxsca1629yeDDgIx/9KF/58jcYDAZ4nsvW1rP85Md/mtdee5Vmq8l4PKTIK1wn5D3veT+3b93ijTdep9Y17XaH2Wy++O4gCptWaVdYnx2Bot/r43ouZ8+d5u7dO+ga6jrGdT3Go4n9XtY3WV3ZoK40eRFzdLSP40jSNOHS5ct4nsv2zn1837NZO56LrjXPPvcck/GY2XxEs2klxWlcYoyH50bcv3sPU1V0W20CJ7KKvGbIlUtX+NAHnscRinSe8PiVx9Blzeb6BsPhyFbrnJBmo8ulC48wmczY3r1PllYsLa+yvnaa/aM9VlZWOTjYxxjDq6++yoWzF/C94MTn6N32z7vjr3u8o8FKMtfcvXmDPMvZPLVB0tXE8YRWp8tkNmU4GmOMpigKhoOK2cQghGIyqjhM53ieQ7MR4vseg9mMIi8py5pKVaysRghs+JgQCiE0dW0wQqJcQWUMBoGSHllec3g0I05qyjyhihWNRgPfk5SloK7h9TvXOHfuPJ1+QG1SRtMjlld7rCz3iacxRjuMBnMaXsjBwQ7j8ZTV1U3qUlAWJWmaM5nkOEoync4QhCRJznC4j+C+JaLKnMF4zqOPXuTuvW2kdNndnZJn4Eif0KsYHExYXl6m21plPslttLyImIxSKq8gjhPaLZg7Fe12jyyr2YsPFjLknOlsThhKfD8AJMPBjMDXeE4NpmYwGLK2uk4jikjihDTLqKqaVX8ZjCGJE6aThGbUxdSSw/0hzVYDEHanjCbNIjxXkWYJe3u71JWg31sGYWzLjJrqG4dIuYgLmCjCsMn93UPysua1N/YYjrbZ3DzLm2++gXIF02nM/v4RHx6/n+l0wvr6FlIohBQkiZVslmXJZDJBCMHe3h7NZhOweT3HMtzjSgpwwjfRWjOZTE7UVa1Wy/qgCIE2mjzPSZKEOInJskWL6iGb/ZWVFaSUJ/lDWmtu3LxBHE9JsxghDUmcEoWWy+O5PlqD5/q8+eY1fuM3foOr165aYmfkcvr0FlffvEq5dopYQqMoGY/HCCFpt9uowYhlpTB5hjIGLQS3opBvLS2xlqV4RcGKrjFCkilo1gYFfKsRYDScTjNiA2NlMBKWlpa5cuUx+v1lPM9ncGCjH8IwJB2M+MSd+3S/+59Rb75JeXCAWl1hudPmI5cu0FpZxVMOdbdPo9Eimke04pwizVi58iSbFx9l/2CfjY1TTKYz8njO008/xcrKGqPRmF5vBd+/R1Fk7O7uEgQea2srrK6uIFCcOX0egcNkOuXJx9+DEjYle3PjLKfWtzg4OGI6G/Pss8/wjW98nYODgxPfoOaiBXjsf1NVNWVVsbe7d9KO6fU8HOUzGI5Q0mEep/S6y4zHEy5cPMf+wX1u3LzK+97/IXpLS/i+xzPvfS9VVfLqq6/Q6nTo9frMZzYV2fcDtjbPoJTiqaeeI55nHBwc8MYrL3M0OOLiBQjDkLKscByPtbVT/NRP/QwvvPACjUbTWuIfHVGWFVlquTee4xJ4Tc6cvojrBfybf/M/sr66iXI8mq0m82RKPI/xPJ8sK8jSjCyz2UbyB7hNvzveHX8d4x0NVopMo2uFI0Jmk4wszqlNQZbPGUwzytIqKZKkwgD3twcURY7CRZc1MQVFZnBUTlXYqHmlXBt5n9fUlaGuS7Q2VGVNVdW0Om3yqqSuStK8wBGG0XSOOAKlIPR9sniKFDF1rel2ehwcHhF4AbvbQ0YjifQqpFMyOBqiTY3jheS5w/7OGE9OkEJRloLxJOZoOMZoxXQ2J41rup0mUgVMpzlxnCKlj+eG1LXA9RRJUvLKq1cpy9I6eRqHWhvKvKLOC8LAZeomhGHAfD7DUZDnGX4jJPJb7Ny/yfBoj163x6VLK0RRwOpqj6q27p15lhLHCUvLSyRxDliAJ7QmT1M8L2A6jmk0QhvOp6EuNHvbhzSbbSQe42FC6gl8PyLLDMOjA+bxhLIq8X0XgSFqhrSaEd2uYjIZM5/N8DyXSpek2Zy8jDm1vkZVF0zmYxpRkzBo4jgBybxgOtnnxo2XrZoj8DAolILR+IAkKZHCUBYVpza3SOIprVbIcDBk+/5tfN9neztnqd9nb3ebV155lccff5Lbt28svEtcbKSApqo0w+GAN998k6eeepp79+6zurrKxsY6tdEMh0NG4xHXrl1ja3OLb3/72/yjf/yPLOAxlnTrByFSSDodG4SIgHt371iyLZYHgzG4jmM/j+uytLSCkh7veea9BH5ozdm0YWd7l0uXLtqcmtUVRk8+zvkXX0HPY2SvT3l/m83DQyLPY19r9lyHVlnxgdGIoir5pqP4mKnRUtCua3qVBSovRx5nspL/0/19KmOYiwnfbYb88VqX+/fuc+HCZR5/9HFu3rpNXRmisM1+MaD7B59h+ZXXYDRGDAYIrRGHR7TKkhXHIWu2aBwOcKSDCXy84Zhmbdj/iY/z4V/6VW7cuskjlx5jNJpx984OTz31DB94//OMx1OMNty5fYcL5y4i1Hk+99nPEIQhly9dpqw06+sb7O7sEscpvhtw7txFtu/vsLO7Q6vZ5Tvf/RYf+MDzvPjSd2i1ezRbLdZWV7h9+zZBEJAkyULJZB1xjRE0mk2WV9bp93q0Wj0+97nPc+rUilV5VQXb29vcvXfPcsfygtFoShg2cByP4WDI6dNbnFo/xTe/9XV63Q66LsHU3Lx1nXPnLtDpdPnoRz+O5/qsrWwydqco4XDj2msnLVAhoNlsoByrHjt39jzzWUIUNgiDkH5vCSkVrXaHuqowtXWYjaIGG2ubvO99z3P/3i5nz57jcP8IYRyee+/7uXnjFqEXcfnCmUVLWPw5vWl/nMZfpHX1zv7kbx/v9DbeOxqsuErhORLlKxxHohyHMqtxvQa1ycmKCikEjaiNMDaVuBkFSGGtuLXWlBoc18XxXVzl2LKnFJS5QdeSIsspixIpIAw8It+jznKKomY0tIQ4P+pTVCVJnlNJSExJrxPZFGRR4TR8hKPIKTC1SzFJaDQdpvGEra1NDg6GpPGEWiuyQuO4LtNJijvRCwJfTW1cSlMxTSa4rstwOuE4Z8d1DG4g0cYSUgO/SZpOMNoBI2m3Q6pSk80EWQFhJYgc29oq4xgpJXt396lwyQsHzw1IZposyWlELkJKktTgeoqiLGm3ezSiNmUxQ4gcYzTDwZiqsNNbp6dI0yFr6ytkWYESEPgOk9EEbRRZWnNwcEBeGPq9JXRlqKqQJKnRlUNdV8TzjEORsrbmkCUpQqZ0l7pM4xlB4LG9fZ/xUUWz0aC71Ofu7V08b4rne8ymM8Iw5NSpDRy3ZDo5xAtaGOPyx1/4NBurG3zv25+nLnM+/NG/wzzJwVzmOy98mxs3brC83Gdvb8/arJczRuMJX/vaPYpS0Wk3iBoN2p02X/nK18jSgtlsxnA45PBwn9ffeJ1HrzzKpUuXWF5d5vd+/9MLB9Gaz37uM/i+z8uvfo/pZMoHn3/eEkeFoqps7lKa51R1xre+801mE0usrsvKErsFSGEBdDNscmbzIo8//hiOq3j6iWf44h9/nt7pHk9ceYbPd77AzvY9yl/7JYbtFufv76NHQ44GA8K6piEU50YjrhyHYgK/WuT8h16XL/d7PFmkNPMSJQS9subZWUZLp5TAtu/yWuDzU9M5XuDypdkcXVv/jfe95/1sLK0zHAw4E7Q5c+c7iEYD9b2XMLMZvhBIIehqjX70MRxHUb3vvaiXX4E7O7heQP1Lv0jyoQ+x2WzwyOVHmMcxW6fO8qu/sszh/jadZo92WhN1HEy7w/LFyxgD3s+GHBzsEUURKysrnN7cwpGKozfe5FQQ0C0KVpeWiYKAc2fPMZ1PuXv/Lr/2D/4xr772KtoI5vGMs+fOMhqOGQ7HPPnkM1y/dgNw6S2t0Gq3mM1ynnjiHHVtePSxIZ1OjzwrOBjs0YiazNMxyu9ics2p0+dpdpZ46vGnGBwNaIUdmmGTRy5c4vBol739bcZSM3OmPPv+51hb2sB1A3rtJdrNJvfEXV556QWilkeczdBUKOEvUro1URgSeAFXLj9OM+rgKJdW00VKSbtpnX6zIkU6YJCEUYNnnn4v165dw1UO2glY6ayzuXqalt8njmMcxyUIGgihbGXlHbvIvQtU/rTjJEsN+JH5Jn+N4x0NVupak2YZnXaHqqqZzSwJM8tyTF1DrRFSohDoqkYJG0RWan0iiy3KCt+xVtSOcphMJtRZjR8Gb3EwtXk7NlHVdRzyPMNxPeI4Ic1zpOPgOC5FWUJZkaVHbG2eQimJkaA8SZamKC0Io5Cqzllf30RrSZmD60Zk05wkLSmrdKEcSZFS4ro+WkuMETYRWdcYDFLa4yurkrIqcT1L+kzTHM/1iPMM11WkaUpV1Qgp0UYTpzFrp5bQWCJnt9uj3WmzfzixPiTYFsdgMGR1vUPgNzBG02haguv+wV3yYkyr1WFtvY3WgiyeIIWHENYfxfUVSZoQhAFJXDA/nDCPM/JCI6XHcUbQ4eEQozWB74OxKhalXFxPkaYJR0dDfMchKzKK2uBHAYPBjKqseeP1Wywv9dC3y4Vnhs0gcl1FVRoOnAGOkhweHbG2bnNsxuMhRZpCXeFg+OPP/2dKrfnSVz69MLVrUZQDsiwjyxLCUCFUwXg6BxHyP/3b/xe9/jJSSHYXfBPf90mShJu3XyIvCsKG5vqt7/H0089y8cIWL730ElJKHKVpRC6723eYTqco8QGm8YyDvX0aUYuoEXJ0NCEIFMPBIUJKHMdZBBkqXNdeY67r8f4PfICLFy9QViVh5LO2topSDt1ej8cefYzNjVPs7m7zyrVrnP2JD/GHX/kGl/pPsvWFrxIMx4yyFKVrmlqDMYylRGrNR+cxf7DS41+uruLHCf/HUcyZOMXVhlgKBIYzeYkWglcaIe8bTrnbOGLT9XEdnygMuXTpMvr8BRob2wR/+BnE/h6MRsgwxLguoixR0yns7FD0utw6e4byyStkO9vM3JDLz3+EbP8ArWvCMEAsWmZBEPDeSxfg3/875Fe/ymrUoPQ8Xl9Zpvr7f48wCjl39jx7e3tgBG5Zc+GLX2X5jz6Pk2WIP/gsTzz+GFff+zRR1EApl6Ko2FjfIE0TDva3eerJx5hMprz++uskaQVCIV0X5dgcoOX+Kr1ej8CPODw64hd+4ZeYjKe89trr7OzscO7cORBYaX8UcfXNa5w7d45G1CSLMvq9Pq+//ipPPvkUo9E6L3y3Igh92p0+m5ubVJklT5873aHdaHIPCMOIdCd7SEpsbQxt5qlEKVheXsZZkMVt0OgDmXEYRkjlLKIxHOrasLKyhl4kU6+urqOUotfrn7QKpVQP4iX+Kif1vwXjR53PHwdY+MOO7+3H9lddqXlHg5XJbE4QNpinKY5y0AiEcqjKCqnBUw5KKlylQEikEAgjQCo8z5Icy0U+S57nVLKy0khtyLIM3/dPVB9CiAXRzgIX1/VwF8qNStcnXAatLUelLEvu3ttm89QaWZ4Tx1MC3yMrNKPJnKrKGI1HRFGTulJ4riLLNVVlwNgwP5BI6aFra+PebLQpq2KRUeQuzoKNjLfkyZKirMFUVHV1QoyzRmUCKa0DbG0qJrMxq+urHBwcojF4vsfm1joHh0dkWYJUPrMZJOkMY0qWVls2AM6RKCXQpmI6G+K6HqBYXl1icBgjURg0BguKfLfB+sYG2TylOU8ZjKbE8xyBtE6sUiCkeigJ2hpzgUEpB2OgKmvQkrIw+KFDllWYGpa6qwR+yDSeECdzlpf7zKYxYejjOoqD/SlJPKPZjMizmqKy4X7j4QBP2SP1QoeiLpFSoBzDdHbI3n5KWRY0Gk2McXB9n2bT587dXe7evYMfhLRaHTAGz1eMxgOUUmR5xWw2o3p9xvLyMt97qSQKW9y7f5der4cQJRcubjGdHiKl4sUXv8VoOCHLcq5ceYyjYcVsNuXs2TOURYEUCr0ApcYI5vNkEeRXs33/HuPJAMdR3L17k1On1vF9z37HC5ATBAH7u3t8/rOfZ215lToveX+cMgPyNKNfa8qF+Vdbaw5clyPX5anpnM+1Q6R0eWwak0hJw2hKKTD2BuFUXpAoyXpZsXRvn63f/TQ9PKpf+wd47TZ1XeGsrYHrInZ2Ea4LUiKUgroG1yW/dZPXDpv8v//Nv2EoDc1mxMbGFo3TZ9HGoHVN4PkUAvIs5dLF83T/4++gvvFNps0WZnOTfGeX/he+SNlscfALP8fFi+fZ2Nggiho4v/07yP/tdymkpPHII4jJhP4Xv8yjQiKfe46zW2d5/bXXWOmtsLG2Tl1pnnvfc8xmU1rtJb74xS/g+gFnzp3l+vXr3Lx+k0cvP8bW1mmuX7+J7/s8/sjjJEnKUneJyWRKK2zhb/ns7u4SeiGbG5s8+fiTSCm5fPkRGlHIk08+TVVnhFFgU7KHR1y4cIkzp89w7Y2bXLp4hXa7jYNkfX2DpaVlLprLBEHDbjqUzYcSAsQi5PIYoBzLix9OHVdSLQjfdsZYXlrGcVx6vR6ua8M2j80NW63W20JExY/HCvru+LEcbwcsf5lE7Hc0WCmKCsdxUNLDDwKUci0LP0vQdU2z0UBJ66ciFsalxhiEkpR1dSJdPXYlFUJY6WihrX364vnHhErP86xiJLVZQ/XbTJK01ugaykpgjMJxQ2oNUimMcU6M3VutDoYW0+mMfJhSltBuBcRJcSKlBQuMirxCSmuGliQpQlpLd1tZsRNTmqbUtQZq6rpESY88qxYAReH7PvP5HCkkUmqUsGFww9GI2XyON8841+lzP01IkjlSgh/45HlFpAKKvGI0SJnNElbX+jiqSZFXRFFAnlUoJfAdB8/3mIxjsiJmeblJu9MgzTLGd+7g4tNud9na7LG/f8TB0Rg/EJRFja41jlILC2/rAFsUlXUNrS0BVAiF0QIpXVwnJMnmJHGB54R02itMJylHBzNcz8F1FKNhYpUzs5gobJAkOVmREcdzdFWhy5JL584zS2eMpiO2NrfY3z8gjhMODg7Z2tri9q0dpDCsrW0gpAPGYToZ4SQ1ZSFwHYVSVvYMnPjCFFnJwd4hd27fJwwjtNYcHe1iDHz9a1Nc1yUIAl588TtgJFHUwHUlt27dYn9/n0ajxeHBIebE+kVQltXCDdfQ6XRZWu5jKGm2mkymQ9Is5j3vfYb9/T2yNOXCufNcOHeWb3zt68STKaK/wvosxp3OOPI8TiUpUV1jgwjsHwWMHUk3z2mXNetFQWAMQ0cRao0DVEJQSkG70pzPS2aOYjvwWKsNzqf+M1Ioin/0j/B8D93rYZ58Er7weUyjgZjPoSxBa0rfJx+N+V4Y8MbhIWVV2KpWrnnj9df51b//9/BcB2NqwsBnZXmJyfXrLH3ta4j1dfxOG1wPdWoDMRnRfvklbjx6haN2h3PnzyFHY7I/+hxzqTCry9SOg7O2jslyNu/coRKS9hNPYozmcP+AZ55+hgtnLxG4DVbPbXBu6zyjoyGu59BsNEhmMa4T0Gx2uHD+ErNpwmw6x2jB6vIaRVbR7y3R7XWRQrKzvcPGxgZhELKxvoGSDmfOnMFxFM1Wk+lsxCc/+Z/YOn0e1w0wRrC7a4nEs9mcjdVNJIJmo8n62imMqHEdn7wo8BvRQ62Zt5oUnqjJHsqxwggQAiHsfBU1mijHxXWPQxwfeAwdV5CP08UX7/BfdN5+d/zljb/Uaocxb8Gt71ZW/gzDCyNrlqUE03lMXdd4nkelbRJwVVvSoTAGJdVJbos2emFZLu0NiwBj7Gs57lts1cGClGPPArFY6IuyRKCpaku8VcdyVpTNlUFSVjX7B0f0ui2UA5tbp8hSa2mdpilKNVCuS57HTKZzmq02SZrY19GLfBpjzcscx0pY67pESIVSi5RfIzG6pixsDpJctGFAnsTJ+75LEHiUlabS1npfOg6eFvzyOOPJcUyk98kch687gj9oh3Q6bcqyIE0zq6YYl7ieQzwfsL7RJ4wUs+mM8XhMp91GRw7a1HiBR14lRI0QP/SpqpxBPENWFWiP7b1bFoA5Po7jYhzQQmJqjan1AlEKHMfFcVzqusLoGqGswsrU9vHAb5BnJYNqQtSI6HVXmM6mSOExnWQoJSiKgjD0qSrwXI+isi6xSZJAZdjfHxA1m6ADXn/tDnEcL0jJITeu7dvvXxvS5Ii1tXWKXDIZZ5RlQrsN7XYL34OykARhSJLOqKqaZD61JXdlaHfrhSQ6I89y4vmcdrvFZDxhHicIIQjDkIODXUajCVHUJEvvkiY5dVXj+z5SKIyuLC9EKTzX5/XXX2M0HfD4Y4+ztrbO7//+7/Haa68TBAFf+eqXOX/uHF//2lfJ05SOcvi1WcaV+zucmkyptCYqS/zFfWSACvC15kqaccdzmPs+9+uaFIGvNXOl6FQ2wsCtLXCpgTuBz0RIZlGIs7RK/dWvk370o+jNU1YR9Gt/n+jTn7atIGvYgwlDKinYCQO+GEWsTmZMlCRVGWmYcOvmdbbv3SfLc9bWN1hdXWNtdRnGI1SSok9v2kpikhJlGUV/iWaaEqQpL3znO2ydPo0/GhHWNeL0FmJBlhUiw+v1EIMBajzD6/d57j3PEsdzXOVweuMUs+mcpVabMAj5e7/8q9y+fYN2p003auO4EZ7yaARNnnr8aV566WUkkiwt0LVh65RNkp5MJlw4d4H5dE4zarK5sYnBXtO+7zEeT1HKxfNC3vue9/H7f/D76Npw/fp1PvSBjzEezlDKRQn7OxcuXObW3VtEUYvpZEorai/msreaDj6ooj5Q8Bj7hJPqiFnMhVbNhw38/AGlk5P083erKn8rxp+nIvJXLWd/R4OVurYlk6woLZfEdShK60BZa01dljYi3oDUNZLFjS0kQtlKSlkU5JXdYRqtybIcJEhH2bh3zzspswph7d3rqrKkV63RdY3jKBCWROl4rm0tCet4ajkTYzxXMhy+QRQEVGWFMSzaNwLfbxAnCePxiE63Q1EUGANlUS4cOyuyLF2AKEld25KunaRsTo7No7Hto7KsF5kyttpjpbMpQjkYBGlWcOPmXX7xaMxPznIOhOKehG5t+LmyIvA9vpBlCKHRumZleZWrV2/TancIg4jRYI7WIXGS0ojauK7PaDxCCQuIXNdhnswpqhTPa9Dr9yimwibXHkcWlDUI+zmkkAhHnWQWaa1xlLOoFi2qLUIuWhziJC9HKddmHJU1zVYLpVwGgyM8z0EpRRg6VFWGMTVRFFDqnHa7zVA5zEZzhoMZRSVAuGRphec0ec/TT7G9vcvdO/eRSFzPgrDxaE7QaLC6ssZ4klDXgvFwRqvhIaUkNxWdZp/9/b3FRK/wA4+qgCKz32eRawQ1w2JKEISY2jAaD2l3WihlLd+n4ylxXFDk5aKtaL/jY3WG1obpdMbXvv41Or0G3/zm1/Fcj6OjAXGc0Ost0e12ONjdZjIcIgz8Q+Xz9O27HCG4HwZcnkxpGIPVuNihAQ/DVlnyuaUOYylxej1eOBrx8WnMVApmUtCtagIDuRBcjSJuRzaQstPuYtptnO0d5GjMuBFZE7xTp6j+q/8K53d+B91qUYUB5WDA9O5d7hrNr+/uEdY1mVK8ttTje/0+r7/2Gv+3/+v/mZ/8Oz/F448/wfLyMr4fUK2sYJoNxHCEPBog7t2DsqBflrC2yuq5s0SdHtPpjPWVVZxulzDPMZ0uUkpmszlqPkM3muhezy7UuqbTbFMUBZPhgFdeeYnXX3FZWVmxKeHNFq5wePTyo1Ra0mg0UUKxtrzGxtoRUdAgTXO2Tm2RJHO63S7ddpfGIw2uX7/O+uo6URChHAekII4TyqIk6rZ533MfoNvp02p2CEOfC+cvksQJy8srFlQoxyZPK8VTTz5LPJ8jhDwJ27TjT7NgPAA2YLHLsSu0TT83f9v4pO+Od+B4R4MVXQukUEiJ5T8YSVEU+L5nK59SULN4TEClrfyTxQ7kuGxPXVNXNY5U1rtDWiJtkiQnvVyxACNlWUJtHuxGFq910icWhtrUYAy61uTG4LkO8yJfWMtrdG39XoyBOs1ptTw83yVJEkajIUEQ0Gq1iIEyTlDKwXHkImDPeUtr6uEetTHCghgNurbH4rrCEjPdklorjAbpSPoG3p8bjpTDwWLjdSjs778vr/hGnLCdzVhZWWM6nQDWbyTLJb2lkNksZ2m5Q56nuJ5EpoKiKDFG0u/3KPWcoihI0xJXtHCdJkKoBVm4pFpUCYSQ5GmO0VYNY0yNEFBVNcZoQKNETU1NksyJWm2qusTUhsDxUcqhrKoFn6MiDCPKMoeqptNt026vkGYD0iwmy6yfis0KAlEJJuMp7X6HRqPBdDrn6tWr5Hm1OOcORZ4iFNRakGUz5vEEsIBRGphNU6Io5PLlJxBCMJ3Y8DtdV8wmBUF0HFYoiYI2RZETeCHCOOgqp9vpWnKw1pRVSZEVCKPs4zp7EDmwuAaPwacnJLPZhMlkBMgFIbsgywqOjg6JfBeFYdMLeF9WcgSMHYc8CDk3jzGL66cUloeiBfjGsOs5vNxtk6YJZ50Ov73UQ+Ql7y0rHANDx+Ga71FLSep76EX1yXVd5GyGaDaJTm8hGhGNRsO2E3791+wu/ctfphwcUYUB1foaW5Mxh2HITEqi2Yyf3j3ASVP+bSti6/Rp1lZW2FhfZz6f2e92eQn9kQ/j/Mt/BaMRtNsY5SDnMWYec/baDQ5+4RfodDoQhlTPfwjnU59CC4nfaaPyHDWdUf3yL0GvjzCGuqq5fuMqX/nKl7l+4zWSeEKv1+epp55haWmZuq4ZjSZs7+xSITl3/gIry6tsbm7R6fTQGtqtDsoRC4dhge/7uK7LuXPncBy7gVCuc3IPbWycYv9gn1OnTtNotPmFn/8lXn79RdbXNqhKYwmxQlkvHc9HOorLl6/w+muv0u12F/eIWsyCfzaUYU6mrYeqMuIdiFUGAxgOod+HpaW/7qN5d/wVjHc0WHEWnAwBCGMoqsqqb4xGLSonVVlR1fVJSd9xnAV3hcWuFYqqRhiDWSwYUSMEY6sonuuiF4uFchSiEmhTW0mxscz7vCyodW1JgVmGg33turZVBl0ZJArP8aE2KCzpzRJorelbVWukVJR1TZrnlFVtyaYLMCUtW5a6rhaEOEFeFGij0UYvqj7HFRsLxDzftQTVSuN7Po7vk+e5zTepDE6Ws6ccG0yH1RjMHcVSbWhXhl3pEwQNJtOJddrMC+tqWSvytGJvZ4gUmsiPCNwmwlQcDHcJQo3rCWoUEocir2n4kKYxzXZEcTTBGENdVovyuGPPXVFYAqaxYFAqhRQuRmc28NHzqIoMoWvQkKUZ2tNkZYY21pJeKkmz3WI8HhMnKUk6p9GSDAYD/EBRFjGOEpRFRZUJWl0f3xGM0gRdlhQio8wrzCIXZf/QvnYjsqqU2HFI4wSjHYRjqy5ZlnL71s2FqZ3A9Tyk1tSmQjkAGs/z7fUmJNpAnhfUZU0zaiKkxHU88rwkjSt0XVv5fKZRjkNVW+m8VICw3hdlBVlR2sRgaYMvpeNipKGsaqq8grLgSkeh0pRYCdAVjqlJlcSpJS6wHwYUQuIYTaMsOYgiTLPJ37t5l+d2BwR1zVxK/n2rxa0g4E4Qctdz+LXZjJ+OEwDmjoPe2wOpqP/ur6JW1+gocdJK0GFE9U/+Cebnfpa9771I6Hss/w//mlvBHW4dDbiYF6wVOU1t+AdpijSrfGr/iH/1//hX9PpLPPvsszz33Pu4/MgjrH/0o6j/7/+ISFKoa4TrYZ55GrO0RPSd79D6Oz9F4Ft7An7jH9jNyte+hrh/H1c51B/8IPKnfwaDYPv+ff7w93+Pq2+8wZ07t+lUY7ZCH991efl7L3Hn3i5+ENJf7jGZTogcl3tX3+T1777IB5//CI89+TSBF5zwQqKoYe8lY6udzWabixcuMZ5MLACRksgP6fV6+K6/iGcIaAQNxuMRodugs9Tl1s079JpdcOz36kiHTqvDytIq8TyhETbsdaTtzuSYy/QDhzmGIW+Vpp6Akx9WyjcP/v7RnaDvf/QvtT2Qpsjf/m3EV74C8zk0m5iPfAT9678OYfiX974/Yvwg7sbD5+DHzt/kbdyTt39bP2ZHezLe0WBFYvBcaSdqqUDavBeMQRl7o5vFY0opaqwM2SBASGqjMdrYnSWQ5jYgTxRWthcEAa7nkSSxtWcXEk2NkAptNEWWw8KpFKxayJESU2lqDL7n43keRWGTkMu8xFcOEokSCoNN0y3zirKqQIqFjsZYAIK9cI7dUBd7IcqysHOQtLkztdE2VBH7O9UxyBGCsqpRjqLVbhNnczQlUinuTSfMgWZVMXCOLwNDo9ZMpOR+khN028RJznye4LgOzWaLPC8oc0mvt4zvwXR6xMHuiFanje+6tNsR/X6b+XxOu9kBYxOSHSERSpAOErxALdpkJa7jUxr7qTUGifW5QViVkJACre3x2cpJQDMKybOSwmhqbajqkvE0Y3l5Cc/3GAyHuK6L43rM5xM8P8CEEqEFyWyGMC51mSNkwHg4YmW5x7mtM1SV5t7dbXxH4bsKTEkUhNSmJpnnKEex3FtF1GPm8wwlFF7kY4xhOJ5ijLCVowWhsdQ5gbIwUChs1a40ZEVGXVeouiKLU8KoSdCIuHfvFmVtbH5VVVNr0GWNEJZ/5SpnocTSSHwwLq6UuNKCcC0E0g2YxhlZmlKncwZRiwRDVJRMXUUubHhhLSWO1sjF9eZqjRSC1zsd3j+JeXKesC8ER0rSrGvem6WMHMkXVIApM36/FSGk4NkkYy0rKLOM6h/+Q8w/+HXkSWdBYLRZeMQITK/P+kc/Rv36a8z39hiWFZeKgnNFQSIkYwU9rfnocMxcKP7DPGU8mrJ9b5tvf/PbvOc97+EDKyt8YmUV9eRT9n4IQ/snyxD37tGt64ViCQhCzG/+JvUnPoH6d/8O8eqrOC+9hLlxg1ubp/i/vPkGt/d2UXnOT+zv81EqukqRfvcaLxvJ110Pc3qLo+mI06fXCKoKqSsme3v8m9/6//DUe9/H3/m5n+WRR68wnc0IwoAoiiwXy2ir2HMtiXwyntLrdlnqLSGlpNPucgIghOD0+jmyPCPyGmysrpMmsY12kFbF6EhFFEbE84S6b5BycU4xC3nxjwII+i3/evi54rhF9NAKZd6+mP1I8PFXu7TJ3/5txCc/CaurcOYMTCaIT34SCeh/9s/+So/lR42/LoDyp4GJJzymk1/604PLPy8M/S8BX9/RYMXU9UmZvK7eWi4/HsdckwfSYo1yXIqqOMl9cRwbOKexpNZaa9QiyK1KU8zCyVEbC2zMojXkuC5lXaMXsg2B5V9oU6MWfBellFW6LI5JSomSklpbyXOtNUVZ4rouWZGjsSBDK43nuCck2+PfNcZQa239NjwJOYhF5UggqerK/qysSV6aplRJhUajHCtNTJKMmefx7dDnp+cJVDCVkrauWdKGzzQ8xkoRlBVFOaWqahylKIqSLMuoKomQFd2OJZWeO7/J8mqXo+GACxfP23MsLd+m0XCtLFg4pNmcdsfHdWuyVDOdJDiuJMkSDGLBz/n+vB0lIC8Kur0O1ULBpRygtLwOx/WIGi7NZovBYEi1IIImSYqua7I4xpNA5XF66xTj4ZiqyFnqt2h1zlGVBt9z6Pcb1HXJcDQiCPyFVNthPontZ4kisjSj2+niOjl5kSOUJC9yysoSG6VStNttjgZHmFqQxKUlfZfZYiFzSJLE7v6BJElx/fAkl2a8f2TJ22LRkmRBilTixE9USonjKGptSc9KGDzXo9S1NQasLflbGMNIwMthwAcHI4zWpI4iVooeFUPPVt66dYUyhpf6fb6+tMw/unePoedyqO3Cn7kuVBXvSVI+FwbMPRfj+fyeH/D1vqBn4Od+5mf5J7/5m/Yard+aYH3cQgUYDof88Re+wMa1a7hJwkZVk0pBKgSRgbmUbAvB0/GcP3AdproiS0uK7fvsD464vbrGpdmMc70eYmPjwaIwmUCzCf3+yf0OdqGVn/884hvfwKyuolstpvfucfhb/wNbjuJrvsc/04aPz+bobhOZpmyNZlwqaz4QBHwpivhPsyNmocMzzz7HZDTj1p0dyqriC1/8In/wuc9y6cojvHn1Kr7v84u/+Iv803/6T2g0GtSL+cnzPMubWygOH3iXPJinGo0GcRxTliX9fv9kg3Oi7lE2YDRN0xMS7XGL2rZLf7Qt/sPr0Z+0kD78+I9VJtBgYCsqq6uwtmb/L7BEYfHVr8Iv/MKPRUvoT6q0vDv+fOMdDVaqqgIlT37WC18RY2yOj+u+Ndr8WJZXlAVpZh1sXceh1rYioZRa7CoE2kCtF9wQY0vrGGOJn2WBkBIhBVI9qJKIxQ6oFJXd2S9e73iSquuaajFpV4u2UZbn9nNIQVGWGMHJROQqx3rDPPRHL/gpZVmicKzU+bhNJZXlUiiFkFBWpTXFUyzaPPpkoZNK8LuBjzHwXJpzqqpIpOCPGiGfCoOT8LxGI6Igt2Ribf1IpIBGI8TzFOfPnwZR4Po1Fy9t0Woucef2NnlWU5YzWm0fqKgqQRAacmmBoJSQF4oo9JnNY4qiPOFkOItKzzEXCClwPZ8kywijkLLITqpHxgg83ycIfOLY8lbsXGFbgIHvoUSB5/iYQjLYnaGU4vKFc+R5zODoPtLxMHQZjvaoqooiT5lNp9Q11JVLXWii0MdVLq7rEM8zTFWjhFy0c6qTNpWuKzzXoRmFjCcVulKUi2tgNp0jBERBiK2Q2cpNnheUhWY2mxMnCWHYIMtypHIAYwnGC2m357k0mw00kJc1UsmFvNveA4YKXWZ0mw1qR1JXFV9dWQJd8+hkxlKu2fd9djwfJaChNVopXu10+OOtTTpxSlhr7rDYgS023lMp2SprekDhuvQ6HeZxzLDWzIOAg4Xk/PhafHhYsKXY2dnhP/yv/54vf+kLPG00v1yWRHXFSEgCrQmM4YbrcOg4bJUlUVVyqARKStIyp9AV10cef5Dn/OatW0RCIDodu7s+OED/3b/7fYuVOTpCfvWrmNVVzOoqs+mUb965w6zIeSqt+Y6/xPNaMwsjOpM53Zn1lckcxYrWfGRnF+/MBi9KhZAOygvwwgbT+S53d3fxo4gvfOELizBMyW/91m/xrW99i1/5lV/mQx/6EBsbG1RVhO+7J3Lg4/no4fPkui5LS0snc9bDxm5KKYzghAMUx/FJ/tRffAf/VqLu21/v7cf51zqGQ9v6OXPmrf/f6cC9e/bxHwOw8u74yxnvaLAiF/Lduq4xPAAbRVEsyLIPQMKxn4DWGiMlSqmTiUFrjQHqkx2LoapsJlBRFAtiq8JxHKS0ZXNT1xakmAcg5Vgm3Ygi2+s19rniGLDAyQ4T+WD3Z9s11YKfUJ3snMyilfNwtejhycR+bnOyqBd1aUm7dY1Uwqa9ZqlVxywAilKSPLdJ07k0/Ico4LOBR89YF9Opay26G55HVZULVY1tuxwPbTRJklLkFYaAjY1llCPY2b2HMEfUlSKO5wShIs9jqjrHUwFS1SBy2t0eiJz9/RmT8QRORLScALGHP6/WoNGEUQhI8qIkjjOq0sq6tXEw+thjQtudpqkojCH0Q9CG0WCEq0KkUezv3Wdza4VLl8/wSNPjcGBN2rJMcHSQIaVLFDSZTBJMbWyOVD0nDHz8KKRwCnQNkR/glJJMCBAaJa2R2f7uXRxHIdAYjW3TeYpGo8VsZvk6YRggao3neijXYzyZMR5NLGG4sNcAogJhO411XVIUtZVL+y4aQ1aA6zlURbXw8DEoKfCUIZ1PWVtaxnclpRJ8bWud7yz1iPKSiXSYSEFHQ6OuiP2Ame+jK42WkkRK2hhSR50sZR1tSJSkbrXoNZrEk5mV8Fcl2qlJ4hi94IEJY9VcD3b+hqOjI37rt36LV155mZ3dXXbaTSKt+cd5TqeuiJXiluPypqPomZqZgJEjMQJqjgntBmc65VuOw+O9Hh+ra8S9e5hmk/pXfgX9a7/2feVmOR5jZjM4c4aiKLh27Rq7u7toYENr3tvtEu0fMKpKLiQZsZBkUuErRVWWTIzh8sGAF5Y7vPDiy+wfDjg4HFMbRX9pmXkSU1YlxmiEUMRxzLe+9S2uXbvGpUu/y3/33/0fePzxJ4jnMb7n/VAug9bWTuFhQ7djB20eqhSFYWhVjKWt2B3Li81bOz3/RcefDIjeWon5Uc9/+PMfz81vB10n1ae3g6R+31bPJpOTigpg/91oYHq9t7Q3/iSQ9ZfVqvmzvu9/yef/2PFj/guOdzRYqasalFkAFYm7cGKsq+rk/mm1WjSbTeZzq07Jsgw/DKlJF9WIB34sVBUIYY3ijLGKFCyfRRis06rWx9IjyqpCALqu0UKC1kjXcluOJ54sK6lKa14nhYDFjglj+TVFtWjRYFUEJjdW7bFoIZ1IS7UGYXNV5KJtJZRc+Lws8l0WE5bRBkco25pw1KJqUxCEIXEypyyt94rrehR5RSok43wByrSGRZUoCALiJEZKS+xVamGgJxSBHzGdHNHptKlKQV0KOp0e00lGo+EThJ4NlBwaet0OjagNkSaKcrQWGAoMBZcfucydOwdQPNiBH5fP5QJUPuD6Caaz2UL5Y9Ba4jruolJjqxBVuYhHWEjP4zjDkQWe69JsuaR5iuO5HB4NSdOER544S7cXIZVkPKpZ2+gzn2oGhwlhEFGXJaGvyPIYRxp63SZFnlBkBWiJJ0G4At8P8XwXpQRlVZClCXUNRVUhheXAxLMpVVEwzVPyLKUZRCwvrzCazJhNbSaL53pMJ3M7gWMWxGrLclRK2iiBqly09AKyvMLxPcq8QJuayAvorq0w2D9EmBrf81DC2JagERwZYZlBdc1ISQbSs63LskILQQp8Nwp5fzyn1jACOnXNioYvtFskjouTF7SiBp7vW6k/MBoOqesKbQyT0YilXt8aLC7A+Wc+8xm++93vMhgcUVQlOIr/qdtCSMnHBkP2HYeh49ApC5YQfKbZYIhALCptvtb84mTG+5KMrlL4WUbxX//XqE98Ak6dOtlRv32yrtptvGYTM5mwm6bs7++T5zlLxtBYWyN49DGmu3t0ywpHGzKlUMrBN5pCCMaOQydJEeOY1+bXGQ6nSOkTNdtoA2lm3Y4RoKTdVFRVxXA45Hvf+x7//X//f+ef//N/zhOPP2aPp6pONlUP+6G8fRE//ixCHDf/Hkh2ju0UTuYFxI9IR/6zLV5/9irKn78S8/a2/fE5OTGje/vrLC1hPvIRxCc/aT//n1BVe3f88PFjUy37M4x3NFgR0vIyjDFUiwrLcRUF/WDhe7i1UJa2QnB8sx/zU2pqokZEmqbkRYHreva14QEIOl7Isfb1lugoQBuMBNdxcJRCCas8qiqr7DDagDZIxxJli7q0x2oWnrbCKo9crU92TmYBZow2iGPeDXa3dfx8XdeAOakaSSlsu2YBpJRjowH0ouVUlAW+57K01KcoKo6ORkjh0Gg0AE4WHqtosBWaoihwXYdaV2ht8P0Az/GYTlLiecnh/pQsyTkj+0jXkOcpQhgefewCo9GIJM4ocsVBMibPY9bXlxFSc+bsKRwVUJUaqChLg+/7JyDlmK+itT7h3xSFVV0Jqah1gaO8EzLjfD4nyzLraLyw7XccZc+/cPBCHzeSdFpNyrzBnVvbpEcZ3q0hZ8+vkWYTpLLZQr2zfVxPkCeavXsxnVabRqQIQxdHaS6eP829e/dJkxxHCxwlkcYudv1el053lTxPub8/Yvdwgh84LC13GQ6HaF3iuSGdTgehDTdv3mI0nuK4Pp7nM5rOMUYsqnnQqSp6xjASgrlv811qXUJZI13LqxJCgwTXczF1SbvXQlY9TKVpRCF5Etvrd9HOFNISX42prXmhrixRecHZ+myvS1zkPDEas6U1qZR8rhnyxU4HWRlrSFhWZFoTeD5ZVXNwcEiSpvY7kFZWW1UVVWWdor/5zW+Spil+PGepyBhLyZHR/Mdum6Ms5YNlzUZVMROSz4QBn44i+lVNzxhmBn5xOuNn5jH7jstuGLISx8jPfx7Z76Ofeuot9/Rb5ojlZfSHP4z85CcZ7+4yHwzoFQWnw4DXNzb41BuvU0rJTxUFyhiC4/lC19yNIqhqJgZuJRmHdYnWgmYjJEkz5nFMUWYsQnpOrt3jnwHu37/Pv/gX/4L/5r/53/NzP/uzBEHwlvyeP9e8J8RbgM6PrXzjB4y3V1CO57qiKJhMJvT7/RMw94OG/vVfR4Llrty/j2k0TqpqvA0o/XVVGf6s7/uX/fy/KeMdDVaOSbMPh20do3WpJK12G4yx0lvHRSor0U3ShKKqcD0XFm2Uqq6gFijXwRWWBAlYEOE4Jzb4YRhQVfmCTCuoigpnwdiXQiCRJ6S46hgYLYCO0IIKTlpLLDxdMAYlrUeMlJJer4cxhjzNqM2D3KETku+CxyGEzYCRi5u7qq06xrZS8gccGWHVUXUNzVaPujakkxlRFFJXZkGarRfAxoK+PM+REtwFGAjDkCIvFrwESNMCKXySuKYqU1rdjEbbsT4nVUKcTFlf32A6ThkcTSiLgjSpODgYoBQkXs7W1ha7OwObBWQEKyvLbG9vU5YLjgYWOAmsEVyW5egFN8DzPKSwfJ2qLBHYvv/xInBcbVKOQDoeRVWwvXsHqTaJZzmOG5CWcOfWlHt3j3C8AuXlXLy0SdhQPPLoMnUp6UYtfDegKFK0KcjTOcv9Lqc2VhiPJkjj2AwpaXA9SVXlmCrFkTVRKOkvtWzgZjrDUZJWo0mtDWmSMjg8oshLW11Lc6oahHQQAgJh+JV5wvuyjAaGRAheiHy+EHpUrkOWJbR6LUuoVhJd1XhCUFUFStqk3DyzmVnVwufFGE1ZWRNDC0Y1QkKlLblcOS5GCErl8nv9Hn/kuay5LlMpkRrW84J5UTNREqkcolYT1/OI5zOmswlFnvPa66/x3qeetgBZa4bDAVobhtvbfPzOHZ6aTAjqkjnwQuDzuSWX32lEfNFAR2tGSpFKxS/OY96X5bR0zVpVcbqsyKSgW2sOpeR+u800CFj66lepf/7nH9pV2zoEPOCvFb/6qxTzOZN/+S9ZjhNmSvHFXp/fnc3IyoI/Xu6jdc0vj8acKUpiIbjjuRwBnSLnM40Gt/MKgcb3Q9IsJ4lTaxBZ6xO+2nFb4+GfJxPb9nvl5Vfo93o8++yz1ixP6x/gb/In7Hbfptg5abkYEOJP26oRP/B/3yJtPnmPxX+cnFJx0mZ5K4n5be/0EGh4eF19+/PsBkuhdU2apifz3PdxcY6PASAIqP/ZP4Wf/3kYDm3r56GKyrHL+A/6rN83Hj4l5odjvrecgpNf+0Ek2rd+3j/NW/8pjvLdsRjvaLBSKyv1dRyJkmJR1bA8AY0gzhI838GoitxUFGkFrsQU1qjN93ziOEYpSVlYRY42hqJOkY4FHT4W0LgLsKKrEl1VICTCQOB5YAyu41pkLxcut8bKlzWGWkJtaqra2B3sgrkoEFZyLMUDvk1WUlczwiCwZFmhqBc3kuKBEkkY6y1jqkUFoq5s+0vYaooSxyV4OwGUZQG4TKczpOOgfMV8nqKkT1bW1AYoq0ULxQP0wkEzsIqdNMMISVFWCGPQ2npL5GkOeNy8vkOr22B9vU+j1UZXDuPJjKIskG6NMIKo1bHJ00JgSs1RdcTZjVOEssm3vv0KYVDT7EIcp7huiKg96tKAtu0FqRZx9cZuZmttHW+VtAvxMVh52HbcVRJdF+AIWlGbVtTCFRHb4yFCS7Kqpiw1AR5lnJO/ch/PaRD4IZEfcGariy4NSVJzcDihzDKGRweEQcT5c2eIkwQjBAdHA+JpzJnzW1SUzOIRhAK3Bp3ZpOyy1kRRg9l0zmg0QEkX4SoklqQr1HEGkOIXk5SfyXMOleS+kvQE/FxWEMUpn17rEhAgtUTWgjzL8V3ftjOLmuk0pjQarSRZVVFridDGBnwKg9YFxiwqANqS0WujkdpQG1BlRSgEbneJWGs+urfHe5KEoKpIpOTFIOCP2x2SPKPZbiEkzKdjXnrxO/zP/8v/TPTf/rc88shlrt28DkIQhQ0+duMGzx4O2FOKHeXQqGt+Jk5RaspvNyL20oz9BTD9jTjmZ+KUI6UIteFUVdHUmlQoNHA2y0AK9vKMzmRMurNN6TkoR1JXJZ5nE5Udx14jTjvkjQ8+z7/6T5+i8vZJw5AB1iFayoBJbfjt9hJfDCJ+ZTbjcpGjtKGqaz7tB/xn355bKQRpllOWFbq2FUxt6pNKh9b1YuUx1hpBlwjhMJ1N+NKXv8qVR59gb++IjQ1F1AgAjREL2bFR/Ohl66GKxPHzjOEBRvlRq+QPeMw89F6i5uGV2xhtww8XKqN6AVTsYmwl7sfVue8DN/zwNpA5mfce/NsgbPirNrTabaSzECtITu5jjDjxkrJ8bwkry7DUt214/QAgam1OKsPYuvcPGQJzAtDEAnDaFvrJ6yw2kg+jrAdAxfD28/oAqIjFMTxQwn1fBAJvf823vNL3HfcxODTwEEj8wZ/ubyr4eWeDlcVNhbBGbWVZAxrf9ynrCkGB5ymE9PDDkKKoGQ0n1LUNBdOLILe6qk8qI2mWL5JuJUKA6/m2qrGw1q/rGiXtc493AoHvs7y0TJ6mNkNoUU43tkOEdF1qXduEYWH78LXWSGNvPaMNQi0eUwK9qCIcp5/am0VQaRtCeKKakdbgTkmJ5EGryFEK9VCekV6ondCGyXhMs91gZXWVOLlP1AqRXk0yTyywqiqUsO4bXuCQlxmtTp84y3BclyKrMIuFT0mBVA55XqI0HOxPyLOSXq8JokA6mmYzYGtrk9FshhKKZD63Si0ctKqoq4y11Q7ra216fZ+gvUZZ15S5ZPv2IWiJG3hkebbY3tie9nHlxLb35ELl5Jz0vKvKmucdA7u6rKkQ7O8NUMLDdVySIkG6Do70SPOMpd4azVAxGuaU+Q7ra0s8snmBLE5xHI0QfYbjCVWZUyhBNswYjWcUlcbxPIrKcPPONpXJSPI5CImuJVL45FmF7zUYjWbM5nOrNqvszryoqsWm1aAca2n//jzlSElGrlV2xWFAw2jenxd8XZeU3RZZXKIrK3HHWNm8E4ZI5WBEgVAS6TiU5FRFYZOcF6ozId2TqpWUtiJoFltDJQTSCKos5+ODAR+fzThyFPcdRbPWfHw+p9aaT6HxGiHtdpvDwRF/8Aef5iMf+RC3b19n/3Cbm3dvo4qSZ194mV+5dcu6CktJy5Fc930E8Mxszmc8j0IpqqpiSWueSzMGjiIWgtWFWsgXhrbWJEKQC8FampHu7DDc2uS7V19FDPdI0pg0TQj8FlHUpNn0SfMp585e4M2rb7CdZRRBiO/5mDynv7REkiSWkO8KDiX86zCgU5Z0tWa/qjhY+DZJrakWVc23OFZjF0etHxBj7Zp9rI7SKCMYj8cnLWapFGVZoRz5ll37D/NK+VEL7p9uZTpeWI8XTPHQz+YEt1hq1IMF1vLxqoWXlMRovZjTDMemmPbKOa5kcbJIH4OGt4CixXs/XNAQQqJNxWw+J88zokZIEFjDPNvWOXbuti19ISUITlSaelEFdl1vQTjmhF/3lkrU950SW6kWx68FlmRfZCfCC8zJ1LsAlJZreEz2fhgrPGwvYYnlb7Wc+MHfyZ9+vB2gHFfwfvBr/80c72iwAizIlQJtBFEYoKQgCD06nYjRaEAQuUhls4LcwGdsZTpUVUWWZW8hqpVlads1yhI75YIs5xyDBgCMJUJ63gkA0HXNfD6zyIRFT1lYZ1aqivohwqqUlnth83/MCbP/ePE9BiLHO4VjDoZ95wf96uPHHi45y5PdEA8kj4v3cLVDrxlRVA5ZkWHqkq2tNeK0YDybgQLXtTeCcjSup8iKGb7vMpkOUM5CImsqBDZIUWBvcLsbUfiuT5oUrPR8knnKbDpCrPaQaxJfOSgl6XQiyiwjT+cYk5NmEe2Ww6nNLsqHRrtFrRWjYQoG2u0OaV4isHk7Nj/ISp/NYjIDTlRExy0gKSV5nlPmmk47ZHVlhSybMRlPcR0fRWB34kZTlRV1UTMZTRF1RBR4HMYzXOGT9lPKqiCMfJBWlZRkKUVZ4LoBURgSIsjLilYUIRyBH7bYH9SUtaHUgiwraYRdhsMZWZqTxIndOWLbMVrb61gpa5TXo6YlDDsOSGl5NK4rmRvDel6z4TncoaauKxxXoQ04jiJLYlzfQzkPOEzH1zYLAO4sqmNa1ws/Q7kgUJuTFmON3Tm3q5r3JAlDx2HkKCpdM1qo2p5JUz4bBQxGI7ZOb+H6Hrdu36bdaQMlnU6bm7eu87Gb99l87RoKw8CRiFpzPrcVvJuuy1pd0wcGi2u4g6BhDLtSEegaRxvm2NDEnjE4eX4SEbCzu8t3HrnAC7evYm5XgP0MrhNitMAPJHkx5403XuO1V26RJDFCWOnvMRFWSonvezY52xgQMFKKieuSLDYnx9fX/5+9P4+1bbvvesHPGGP2q93d6e4551772r72tR3bcXATpyEQ0hJeKnm8V1FVSFWhkioC/iAgIVRIJCCIxD/89Z5KJRDoPRXwHOBB4jiJ45CQOG4St9fNbc+99/S7X+3sR1N/jDnXXufYCQmhnspFTWnrnL332nPNOdecY/zG9/dtNko+ttrN2yvmx3oAfUEzNYbx0RFf/q3f4kMf+vYuw2m6ma9EPyP+Aa0a/+z/4VXJH/Rbt+nlbL9SbhVGParj+jfCOsuDew85Pz9nMh5zsL9PVVcUZclkOiUbDqi7+BCsJ2wL6O5l5w00vWPUY8fy9WflOYWGPF/DIKVd1MCYJEkwxhFFMU3TkHTqn221lNGG1WrFarUijmP29va6gkV1gMij1crjn0/P9euJvWVZMpvN2N3d7QQSduva9YXoN0JUHo3D2Jamb4/ljxzMf3J79BPd7IcLh3Ip5f+/WPlm2YzxH5o1jjBU1HXD7u6Utl4zyEaEwYQwDjk5Oefg0jXCIKNYl2QpHB6dbiZ8rbVX4jg/2Oluwt8G+2TXXgmDgCgKusHLdi0R64MUvbe6X9lb0wEBEjpzMf9gskl77sm+/cTak4B7hODxHq61BuNEV6D5m3SbpNYavfmbfjWy/TppHZN0wDCLMU1JmCbk+TlKWoQTDEcD8rxhNI7Z3ZtQ1wXaaJq6IU0GtKFX2+jmURMq3Rq/gtcWpy0P7p8QKRinu4gmpF5aRCyoqoLL+3tMxgPu371HEqfESYih5tr1XebLJat8QVk5rA6IItVB1H0IokUKbz/voV8/0fcPbn8d+uuntabVLXnuOMFitJdsYw17l6es1w/965WAQOGMo60MJ0dzBmnAeLjPar1iPBoiA0VgJE5oJtMhu3t7NI1GEpCkKWezmZcTtzUOR5ZJisZxdLpGCcvZ2ZzlYt0NogKj/fH7Y/fokHMGKQVFElLmkgmWdRJ7xZrW7BhLpQIWIiCOEoJxzLqoWK5LwjBiNBpi6pKizDu4XtG2LdbYriiRXTvQbVqEUga4DkmzXetDSINBcFlrRlJylMTEShF2iGAOXGladqRk6QxHJ8fs7u5y7+5dHj64z9VLU95x/RqX793jrXeOOEtjZCBRraFQCpzlStMyR1AIwVxKQuWPdWYthVQMjWEtQEvBJWOIcdQCtBDE1iGE4zRUfP4tN2lNDsIgpWPUWNL5nIVSzCqQgaU8ybn16otYp9GNz/dSSlHXlefx9M+h8j42fSFzkbTuv9+eeLbltZt2xWNbbC1/vix5f9MwRFB/5N9T7E3Z+5m/4Vu8UrLh12xaQv0C/tEJTUj1dfvfbF098gfzJdyjKp3H65cO16GbtH/1Vz/GP/l//hO01uyPJwzSBGssRVOh4oi9ywe8813v4sf/4l8kEIE3yBWdX7jw92pZlgRBQBTFj3E5HuWiuM69um1rsmyX5557ju/8jg9x+85t8rxgMpkShhFXrlymqmqCQBFFfqw21hBFIefn5wwGA4qi4HJnFpdlmX+3rffd5tj0fKa2rQEfj5DnK1555SXe/va3M51OqeuK09PTLtTSm1t+fZCk3+q6RghBFEUeNVPekLOua6bT6Tf4wL6OMrP1kfT3FpufbqN2bds+ajC4zR/qTvAbScH/sO//sG0b1fmj7vfx3/2XIAV/UxcrVVXSNDXWaOIoZJilnJ+fkyaK1WKFDAQnp6ek2ZAXnn+BS5eeIJAByyrfDERBx0XR+kJeHAehhzydl44669cJbduSJMkGCXHWEUjpkZdHiGSP3mBwURkr1bV+3IVaqa/soyjqTM3cIwPjRsorxMZbBugGA2/nr5RiXeSbHKS+IOpbIdZayrwkCgaEUUDZFMg4YGeQYQeCsmiYTobs7SUkaYAxNZdvHuAslGVNGI146aXXsbbFWFBCIruWjFTKFzFWEwpJU1laawhdxM2rN3A6ZFWc0zQ5oyxjOV/Sti2T8Q5JkpIXOVVdYC2MByNMs0YomO4MqGuL1Aa3peKS0it92g4h2H4w+pXX9krYGkdZNgjnCX0GWK/XRJHCtBojfP4K1uKc5xEZ7Th8eExbBTRtzZUrV3DCIZVDBI6yyXE49nbGnM0eMpwmtNowDlOsc0ynIa0bkZf3eHj/VRaLHGv6lVZveOfJws7ikStnvYRaWb4wjPj+SrMwMDeakXGMjeETe0NOXcjV4ZSyLFFRhOmQlSQKMbID2oXwgZ7OFxhBPyl2cnALmzwrbf16cWwcWdUyV5JlEHKKY+UscVVRxTFSKZI4ZtTdr5eiiFJKlsbSVjX7O7sUZ2d88KXX+VNffZlLzvHEIufBdMxRGPBE3eKEo0WwYwxXjObfDQfMOoJ6kiSstOazxvjcIaU4l5In2xYclFIQ4CebQgp21zlidgoHI0JT8s7nXufG6w+JG0vjLCdXdvj8268zH4zJ8xXWaLQ2jzxbzjn2gGHTcC4s87bZjAnb7Z7+GX0cjn/83+378M9XFT9YVZ4QrBRTazH/5t8Qv+lp+D//n1itC/I89+hcGpIkMU03tiRJQr7OqeqKyWSKEmHn/6QJgnDTghBCeDSkUw46HK4LllytVjjnCCMv74+j2BcTpu3yxTrEyFoC5aXyWmve9KY38d3f/V189Jc/yq3TU6aDIdlgwGK1xC5mLKzh8Pnn+Qs/+qMEUeBbR93n0rdg792/x9UrV0iSqDPd9DyeXgzhYzR8gVZ18u8sS9G6oWoqHj58QFXVnJ2d8eSTT+Gc5cGD+wwGg654CBA4rNUcHT8kDAKeuH6d27df4/Lly8RxSNBx7/oFwjafxlpPGbhz5w7T6ZQ8zwHvsJzna8bjMWdnZ7z00otorVmv1zz99NMbf67tohXgtdde4/r167zyyiscHFxiZ2eXs7OzjZHf9jyA8AjN9n11cVwWyQUi/vj80e+rvxd7dOiiEH20KOjVsRf3p92g+WEY8Z8qIh4vOrZ/vl0sbXsq/X9KFv1NXaxkWcZ6vcYZzWh/l3Kd88QTV2nqkqPTFVGkSNIhTeuY7Oxzcjbn/HxJWbdY69Ujm+RlvNQyTmLiLCNfr6mrmhZfFIRdCq8QdKF0Fuv8wOeTnQW0Fx9c3/vti56eGGWM/bq+9/bN3PdL+w9ca721T3DKFyL7UrLXapYOZlJsyKX0r92asH3xojAWjIZ0kBAnIQ7NEwcHnJ+fo6KA+dkRTz55jVavGQ4jpK0QQpGGgsE4YzjMqMsWJSRtuw2x+iyjUPk+c+f8QNlYnn/lNdI0xKmSySShrRylKRGE3H9wxDCbMBgOCYIhSRQxny0ZpkN85p9klRcUdY4MWqaTKavVGq39O6jAF36qa61trzL6LyUlUgaeqGxsx+3wq5OdnSl6NidSIUESdOoZyTDLqKolAsdwOCIIIvKiAgRV29I6iwoDhITT1SkygtquQUJtGoQISLMBs6OCV2/doSga6qpFyahzeO1aVcqTlKX0xFqERQmLMTVfessNnsgb3nIyZ6c1rITjk9MxHxtnmNZw7+59VCC4dPUacZJSVTVVUeDREuFdiruVu1ISYR6dZJWSnljeahLn+LPrkndXNUFVk0vJl7IBHxsO+Hyc8D157ifDMOSSgyfKgjyM+L8slpRS8vkk5SN1i9rd5QcXa757vaIYZhwmIfvzNVfOZpwHkltRyNVGs+MsGvj4cMCvjkab+7sfVD+CR03f1zRooEIQCEdmfa7RcaColeRqUfK2zz3HZ//ct/HO517imZfuUQ0zwrLi8mzJ07cf8paX7/DZd72FT3ahn9o0+EBJRyosP7Ba8W1FSWoMuZR8Jgz4SJZhutbr4wPwdlGyjab091s/iO9Yy/vqhmMhOev2daIUrJaMf+HD6He9jRdOjzk9PWYyGZOlCbu7u+R5zu7uDteuPcFv//ZvUxQ5BweXuX7zKeIkYb1eM51Ou/TlTsnXNEgEn/r0p3n66TfSti2LxZK2aRBCkKYpb3rTm5lMpniOHBSlJE1SZvM5uwf7aNN2Dtgtzmm+5V1v5+WXX+CrX/oy67qkrUs+dHzIOxYLBsDV4ZDkX/5L1j/4g6jBsGtxSxCOVle8/votsizC0pDEMXGS4Kwfy4Ts+C/ddSvyNfP5DNXxPebzGcvVguFwzOuvvc673vUulsslh4cPuHTpEnt7Pu36fHbCfD7j/v07jMdj5otznn76aZq2ZDbXpElGVTUcHOxvWn6mM90Mw5Cmabl162WSJOHo6IjLly9z9+5t3vve93B2dsrZ2Qm3b7/Ok08+yfPPf5Xj40Nu3rxJVVWMRmOuX7+xadFnWYYQgpdffpnBYMBkMqUoCvb3979uIrcdmpTnOavVEqUUo9G4a3U5HL38/aII6ZHivojuC5f/FNlWduIDuOD+LBZL710Ux9ivBwQfuae3n4GNLQh83Tk93gJ7pID6L7R9Uxcro9GY3d1dinyNkoq3ve2trFZL8nUFIqA1gtly4TkaSMIwpukg8SCQ3/DDkK3CVRXgfSskYLVFO4+8NE2Ls2bjHxEIvyrsOQI9MoIQCHXxwbnN+2iMubjhVEcs7FGVIAg2lft2e0N2ctQU+MHFknfnBZkxFFLyhSzj49MxcRwTdCjRBWnMH0NTN8RpilAxEDAZjjCmxhnNm68/SVFVzNZL9iYDxpNd5otjqmpNHA+Ik4TVYsZqtUAFAmcArC8ohA+GFFiCwLv86lZjtEErSWsd5bJklCiS/SlV4VttURSzmM1xOuXw4QOEFLzh5k0imXF6dszBlX3CTLFzKWNnf8SD+8co5Whay6XJHkVeU5Q11ggEaiMHf7w/3LfCpFRo540DpXAYoymK3JvvBTGmbbukboejxdEQhlAUFUIIyqoBCUVeMdmdYJzDaoM2Gm0akjjsSH/Kq9GN5atfeYmyaMjXJUr6lbF1Xr0WxzEOg7OSPjVXCAHCMRxnXH7jNV4YKB5oR1ZoHlY1ZwjCszlCW+IwxVrvHFu3mrbR4CxtXaGiEGfZtDh9uOfFZOv5WGxIyd83W/GhsmKZxJwIQVw3fNdqiXGWX59OCHD80GLJlaIk64iNc+e4H4UMreVDJyc0gwG/0xreuVgyjwNmztEWBbGCtzSaJ7Xgt5OYhVBc1i2/EkX8627C3fZBAqiE4BfShI/HMW8xhv/7bM6b2hYD6J71KCVNKLmxXPPS0Sk3XnvIKouZrCv25kuaKKBVMFiXPPvlW3yPFfyLYLDFO5H84GrN967XnErFqZLsW8uPFCXOwf86HgGPIiX989RvjxfG29vEGAbWcq+TiYtuHwsBr375Ob72L/9nTneHWNcihCMKY6zxCrzRaEjbtiyXS5xz3HrtJbLnnwOgKArCMOSJJ65z+fIlgiDg7OQU23o045VbX6OqPBk9jEKUVFy6dJnF8pyD/QOqumExX3D16hMcHBwwmy9QkWC9WnF+fs61K1e5d/82n/v9z3Lv/usYYcibhu+enfNdiyVngeIszbiqJMEv/hLtndv8Zprwru/500ze+EaGwyF1nXPv/msslmeAQamAZ599lps3b3J4eMje3h5NU1PVLWk6YJ2vSJIYbVqGowGf+cynWa9XhGFEURTcvv06d+7c4eDggOVyyWIxRwhBUa742vNf5u69V3nqqac4OzsjjCAvFty//4D93au0reZ7vud7kFLwwgsvYa1ltV6RxAlXrlzh5VdeZrlYcv36dT73uc9xenrKRz/6UfYP9rl/7z5lWdI0Fffv3+P+/Xtcv36dL33pS7ztbW9nZ2eXyWTiUeuy5PT0tMt08r5Pr732Gjdv3nwMbXB+7LSGT37yd3n55Zd9/Meb38wHPvABTxZWMUmSbtqVvVPxhquyxUXcdgDuUav+3gQ2/EitNaenp7z5zW/m/Pycq1ev+MUbf3Chs33f9+/Vt6DAmxMCjxTt/eK/R33+SxYsf6xi5Wd/9mf5uZ/7uUd+9swzz/DCCy8AUFUVf+Nv/A3+1b/6V9R1zfd///fzP/6P/+Omjwhw584dfvqnf5rf/M3fZDgc8lM/9VP8/M///COD1R91K9Y+/E0KyZXLVzg7m3F6ekwcZQgZU5QtxgAi7AzOak9m7CbUbUitH8SqqiLuiHpSCKIwwinbKTd8QGHbaHx0u0J0H6Yx1stPO7a6cw5hHx3s+oG5N6rrCxVPrvQVa9u21HW9sfff/uClgO+dzfmudc4sDHkQhoyM4bvmc5q24aP7ezjcI3+7IeqGAcZC1WqaecXpyRHONOztTBjFQ8bZmL29Xcp6SZUXmNZitSVIFKfHp7hoRBRFrMqyQ1W8WioII6zWSCGJ4oDBcEhdtayLgkpr7zAbhFgDTgecnSwp6yV7+7sIUo6P5yTJgPnZEtPcYzxMybIxQiiUhLJaUVY5l6/sEIUpb376jdSV4e6dB1Rl4Ylx1UUrrZ9ALgoX762D8CiDL/o0YaB4w1PXeXB0xny5JlQBUSiJFExGKTeuTRHCcnoyZzTawaGpyoKj41OMg30RenVWlmAsnJytSJOUuqxIk5CXX3yFhw/PkDIiihLPrWm8sZ6/59RFTpTu3IElDAYJTz99nbJc0jYV4aV9yjTAlYbr6Ygnb15iNpuzmK1ZLDXONh4Od5rpaMzSNIRxTFnUJGlGU1borr3lnC+oA6m6VS3sInjHKmcWKNZJ7N2Os5RVXvKtdcVvNQkNjkIKXogjnqwbpIB9Y1iVJS/HMY2QvKsouRvHDJzjpGtzSiG5PchIVMC1Zc6NVnMkFf9uMODfhQHy6yBru1k5SinJo4A3rNdkzqGAsiuMd7vW3cPLB4QORqczEm3JI8n4dIYGtJJYqRDGhzd+y+mSXx5FHHfPxK5zvLcsOZeSfWO41rYEzqGc478vCj6RxNzbKkK2EdBt2PsbbdZazoFcCsbWcarchkcwspYFlt987os89d53YF2NdS1BEOGMJQhDlutT3/rVhiAMUEFIu2xpmqZ7f0lVr7h3/7XOTkETCInWhrquNq0BbXxr7fj0PuezY155pZfGS1597WWvmtSar73wJeq6ZrVcMhqOWK9zXn7lZRbLOY1uGGrLt1U19XQKgwFKa567d5/J4RF7X/w879nbRf/qr3LnXe+k/d/9CGdFQZ4vePjwLlGkqOqKO3df4cqVK515Y921sRRveOObmc/n7EynvPLKS9y7d4cvfOHztE3D66+/hhCST/zub6O15sHDe1hr+PJXvkCWZdR1zuHDBywW5zz/Qo6Siratcc4joi+//Aq7O7vEiWI0HvF7v/d7vO2tb+Pll1+mqmvSJOXhw3sIKTk9O2K+XDCbnzI8SxlNMk5nR4RBwK9//NdY50uSJOGll5/nfHbKa6/fYjqdkGUpSim+9rWv4pyjbRvOzk559dVXOT4+pq4rP26ulvQii6Ja8+rrt3j51kscHh+RZRn7B/t87YXnefjwIW96w5u5du0JnLUbtKX/8sGt4WYO6R3bRbfw8DEf/fMnePDgAa+8/Aof+tC3c3x8wpNPPunFJWXtC2TnaJvat42jaEOc8bweNoudOI45Pj7m+PiYa9euEYYhWZoSdjSDpml47bXXuHT5Mk3nwD7d2fEdB/642qdvvP2xK4S3v/3tfPzjH7/YwVaR8df/+l/nl3/5l/mFX/gFJpMJf/Wv/lV+7Md+jN/93d8FfNX1wz/8w1y5coVPfvKTPHz4kL/0l/4SYRjyD//hP/xjH7x0klj56m5+tqCpGwKZAL4v66wnZ2IVwvoBD3zWi3BAp6VXUhEF4ddp4Y32fgpKShrT+BaNcwQy3Mj8rLNY4zkH2lqiKPStmC6vyAkvLzRdfohXH8S0bbtR8/SDC/AIktL/DvxNsyME761qzqOIs64ddCa9MuJbq5r/2NSswnBThW9Xxaozj0MqirJCihDdWs4Xmqo+JUsj3vz0TaaDK9x/eI/WDNA6ZDazGJMwPy7QpSCNB4yHEWdn5z5oT0ocPlpAG8N65Vd+SRRTFBVSCJQUpIOEZZFTlAVxnKLUhDRNWa2OOT1boI3j8GTGbL5mNBpwxcUUxZp1kXO+PEcFAdPxDsdmQZZm3Lx6FWU1p2czpAyJorAr6jzfoGl8ASCVQkWeDxJGAVI44jDg6sGUN968wnQy4PDohKosGA0yBJbLl/bZGY9wOI5P1jx8cE5jKoxrUDKjWjvunJ9gnKGNGoajjKopaTJYrQqK/JymdqwLjQpiqs5MT0iHMxZjZVc8+badCgVCaoLAcf3mJcLIm/opAurC4VxNGocoDMpVXDsYsjOMmM9zyqahtZCXDucqhtMBurW0DmoHtXO0HddDKoHwJkRIEeKAoS5JneU+wsP0sfKqsmDIflHxrQc7vP/2fU6SCCMlb9CalZIkFq4Zw10sRai43LQQSlbCkRhNoWJwDoPlOITjzPI/j4fcUSHHTpBl2YY35u9XOk6G2RTnO8bwvrrhpTRhWJSk1iIBI6ByjoUSiDBAD4coY3ny7gnTZYmTAlPUtEpSJhHzKCY1hqlrOO4g9qmxZNYyMIYn25ZCCpYIUud4Srf86HrN/zAc+rHgMV7B9iJge9W8/bzNleLTYcgPdcnuS+nzlfad5dfShK8dzgjunbB/MCGKE2Rg0UITpJGX4zY5CMF6vWA6nhJYME3ZFbkOTEPemV1q3XYp4dZngoWia3cYqqZCl5Y4STqZuqRpPcctNt4xOr9z0plmKg6PzglUQhhFPPWGN3N2NucprTlY5xwlMVmW0jQtT6/XDGYzaizzy7u4csn4N/8Dzz+8zafe/CRCKpyzVE2FsYZGLzib3WM4HHU5Uj6E9OTsXsfdc3zxiyGHR0fopu3Q6AYVhOTFrGtz++c4y9KOLN5yfHxMvi4Ia68CquoK6yAvc4TUnC9zPv3Zh8RxQtM0fPaLZ2htWC3X1LVfKMZBzGJ9hhOW1hSsinOOZyEqNgjpqPUSJyu0sTz/wnOsVivC0LHKb/LKq2vqquErX/0inojtid7n5zPOz2d87vO/x+VLl6mbmulkSl7kaGE5nZ8z2BljTo5Z1xVfe+Vlqqrhc5/9LOLPSsaTAYeHR9x44jqzueHq5Sss5mfgBFpHJEmGUj4X6uTkkDiOCcOQ0XDkuYTWt8oDGeAsOAv5KudLX3yO+XzBznSH9Trn8PCQV165xWq15Md+7MeQqm/x+H+//OUvkRdrnnrqKZ7/2gvsTHdZLxccHx9TliVlWXqOUJLwtRdeZHTnLkEQkCQJH/zgBzsKjefF/Um3P3axEgQBV65c+bqfLxYL/uk//af8i3/xL/gzf+bPAPDP/tk/421vexuf/vSn+cAHPsDHPvYxvva1r/Hxj3+cy5cv8+53v5u///f/Pn/rb/0tfvZnf3YDK/1Rt7pp0PP5ZqWTpinaWCwGofzgIpx/QJVUXTKzb8n07RZrfbpwj0ZEUURVVRc8Eedo7UWQoW71Bt6y1qKbTu+ulPdP6eRurbnIANkmOfVffWH0OIlvG1LeHgS11owExFrzQClM53fgrGUpBJetZdS0LDup88aXQFycv3TO97ERVHVDWzdIFdDmBdlwwNlsjZ05bt8+YlWsGIwG7O7tslznzOdrhApIUi/5vXr1Mqt1SdVY6FaEUjis8b3YftHcti0qDMnLElWLLphNcn6+wLc+FNrU3rhMCBoDq7xGncyZTseU6zm2VTSNpi7OscaRJiuGw4w0jTk42GWRQ9NY1ut8o0KQUhB2HKMgCLDakKYpwzTm2be8kRtPXCIMYDAY8exb387pyRF5vkI3DWWZc9L1pVeLBSfn57TWUrcVSRxTFwanNcY0VFTMTgEpqMYtUTTk+PiMvf2rKLlktVpvoNH+fvMrsBYhJEp5fokzmmuX9nnm6ScpivOub+1Io4CmqWmqilKv0W3LerUkSzOuXnvCq+WV4vDklKq2VJWlspYoSVmXNVVZEUpJVZdEUnYBnwJnPdIyQ1AqxcAaVt392GrNoNGspUSFkkmgOBkNEEZjqorUgU1C0rYlU5JEKWwUsLyyx/MCPnByhmoCdJIRNAW7reHj4zHPxzGtNp2J3wXnY7uFIqU3CQPfSkmN5TAMeT6OeVNd00jvszKyhuR8zirc59s++wJXHp4zzSscjipQSGDQaPIkQWlHLiSn1itIlFIswgAjBDfblkJKcuGNwawQrJE802omWnO+hZ5sIyk71rLjHOfATMqvOw+AX+oKhPe1DdeNIReSX01SfjnNaJuW1159nf3992C1Q8QBcRzS1LpDXX2453AYEycptjUksY9k0NondDd1QzSZUpX1RSyG0QSBJIpCmtbLi4UUtG3tSfcyRAjbEW1rj2ZpS1U5giCiaSxXLu1x6XLI7GxN0zjyfMWsbdDrFceLJRmO8WJJIQQijVgIi0tjrDNce/U12ItYRH4cSpKkk4pLhsMBy+UC53x7wre+LY4A3bacHM8342XTNNS6IklTpBQ+/b31UQ+OAKlCiuWa+fwc56CsCqIw9otFJEW5JkkFy1WBlAFh4LPQynLuRQfSEEWez6VNBd0Yu7s/IQhhvjijaSrfBqoappMdwkBQ1xVVnfPg8C6f/vTv0HvsNE3uY0CM4fNf/H0Ensj+/Atf5vT0IXle8PTTT3N0fMT1J2+g65zd6YgyX6BkwGuvvMR8vsDqms99/vc4OrxLGIZ89StfJMsy3vXOdzKbzXj11quMpzvcfPJJjDEcPnxInuckacpb3/pWXnzxqwjg7t17XLlylThOuPXqC7z1rW+iLNc8fN4XE7fvjDk4OODs/Ii8mHPv/h1u37nFwaUDXnzxRc7Pznjbs2/l7r3bPPfcc4zHQ9J0wMMHDxgOB9R1zb179xgOh9x69WWyQcbZ2ZxLl69weHjIe771Pdx+7RZRFHHtiWtI/uRJm3/sYuXll1/m2rVrm8rp53/+57l58yaf+9znaNuW7/3e79289q1vfSs3b97kU5/6FB/4wAf41Kc+xTvf+c5H2kLf//3fz0//9E/z1a9+lfe85z3f8D3ruqbuVigAy+US8O6bzvqAPyF8yF2glEdQOvnxRhHSQWbAxtCtzy7pXyeEYLVa+bZJhxj1K6eLFGRotfd0cK5jW3eui30vcbsN0fcUgyDwMlL7KMG23+RjA/i2v0rPPTg3mlJKRs4yl6E3tBOCQTfgrjtIbjuRtT9P4UA5MJteokSGEWWXcPzg9Iyi1FR1xfnMu7LaXLOuTnH4TKA8X5FlMTjN/sEecZxw5+4DP/FZiZ9j/MDtCW2+FWWdxVhBqw3GtJRNQ9V0xDfrfCih8/pGa6xHoeyqcyFNCIxDWMdqvfK5IKLl3r17XLo0RQWKdVWjjUcvVIc49SRnYy1NUyMsWBkggZPjY+6+/jLvfMdbGY2mfOFzn6Opa8BSlSV7u7ucnp+RryuyQcywGbBcNVjjWMxrcBVXL0954/UnufbEVZq2RUUJlXa89MpdFouK8/PbtFY+Qjrri1et9YZcGAYBbVMTKsdokDIIFZPdKa0psMISBCGjQQzW0dQtSZzSNg3WGJQzTEYDpJJIN6FuoawtJ6dL0sGIqnFYIX2bzgnqtvHBh0qinENJxSrN+PJwxPvPzxBVzSKQTI1hp9X89u6EW9KyFo6oqpinEaeDlBvrEte2GOe44hyR1vz2lX2KYcJnRzdwSvDW0xl7FnLgk3u7/EboAxBFEFBa5/kFW2Ty7SJeKf98zoRgLWBgDK9EHtW8YgxDYzBC8lAods8XrGNFKTxyNGwtSa0plWQlwCyWRI3ms3HKiRUb9dhMSl6MIt5fFNSdcVvsHJlzvColAd7XpS9E+kIlcY4fKUve17QMnCUXgk+HIR9JU8rH2kIV8AtZymd1yA1juReEvB6EHUwvOD9fcOfOPd70piepax+DEIYp63JNkiacn8+YTCY4G/igQuHRCKVCVsucwWBAXbdUdbMpUoajIWEYsVqvEQJGoynGtdR1iZQhWjcURbHhchljuhy1oFM4BpycnPK7n/gs+dp40i0t14Xke+rGy3IFJE2DE4L7g5iVMYR1A1KwVzbERU7lFGEYIVBIEdI0DWXhx/CmbZHSFwrOCdrGF5kIw2CQ0eoWUVqvMRLelTsvFqRpQr4qMK72/MAWwKIC1aHTGoShKguqumS6k/nWW23YlQF5HONizw3MspSqrjG69oad1hJEYefOa0G0ONEymQwoFDhaqtrL++vGozZ37t4iSVKsdRjXIp3PUDOmxRi/wD05uc/R0V2EEJyePfSE4IevbnyrhKkYZhPaakkSOYYHE1pb8uDh7W5MAmcdDx68TqAUWhtev/sqX/nq56GbX4bDIWVZ8sKLz4Hz79vUDU27YrFYUdctn/3c7/LiSy+B8C3o45N73LhxE2M0L7/8ii+EXnuRs/NjPve5z3F0dMhXvvpF8nzF8fERiMvk+QrTWtZ5TpokBGHI4eGC1XrN3u4exjluvfoiTdPw+c99huXyHGstly9dYrVef8O5/Y+z/bGKlfe///3883/+z3nmmWd4+PAhP/dzP8d3fud38pWvfIXDw0Pfp3pMU3758mUODw8BODw8fKRQ6X/f/+4P2n7+53/+67gy4M2LRI9yOG8ZbrQlCXyQ4fbKzTm3QVOklLRab/WAL9JS+8p+W7K1jVIopTZOtgDOMxWptwqRfhDoV9Q9N2Vb/rhdmPSD9DbK0hdXPYsd/Arui8MB37NcItuWlZTsWMuetfzGcMhcSUR3/L2KaGMgJxVYibOOxrRoZ1BRSGs02rQ0uaWsLAJB2fprZUpDnIY0TUWa+gcxChUqVAyyhAcPDgkDRW00UnZ24d113FZPaGO9Q6rzvijaGNLEOwq3rcGi8ObDjij0kHDTWh48PPYeGEohlCIKBzRNRVU1HJ+UjCcZZVXhnFcxIDy6dpHajO9j1w1pkoBznJ+dU6xmSNfwlS9/mTBMaBpDXdU888xbSKIUISRXrj7B6ekJg0AyHo2wrmWZa5w0JHHKU2++yXgQMz875/R8TmMkrQs4OV3RNoLW+uLAuQvPG2MMSZLgnKOsKh+9oDXOGo8Gdq3KySCjKDXpOKEoSuqyRiAJhSQJInYGExyGslwirSYMAq7uTagaR2MjkmjI0dx0KqoQ6yAKQtog3EhcjbYUZUkUhXxsZ0xlW95dVlxtNFUY8Ft7GR/fHdBKxxfGGd9xMsNguB1KslDxhnWJFRCvLcdJhNANiYKFbvjolV1+c5Qyrg2jGzc4N1Dfe7jheCnpNn3ubQVXb/DonH8ODoXg96OIH6h8FterUcR53XLZCT4zGfGOOOJEWLT16MBhmpCGhnFjWEUKaR2hdXxxPOEXUdA2m+fLGMMvDgd893rNnjEoHA3wchBwJnz7dtGNLc65DZLy3VXNd7XtRo48tpYf6gJAP9wZl/VbCvxIWfKBtmVgHbmU/F4Y85EsoxJemXLv7gOuXLmE7iIlRuMRdeVYLucsFyucCWgyg0R3CxCfo5VkA+rWUDeGpjXotsURUteatjVEUUxRFpRVjaPFWkeeF50BoC+Ci6LoJMCg8HEWZd1QVyvqumG1KhBCgTT8+ziiSRO+tSgZO4cWcBwEfNUJ3PGcQEkOnPe+OTMKKRLKouHh7AHrVU4YhVy6tL9p1xqjfcRJ0DnOhgHWtiANloYo9QoZ2/E28nyNMQ0+brNltcqJwpQ087EiYRh3ZnQG61oGgxSqkm998S5vO1kxRFIFiuf3pzz/zjcjBgnWNeTFyvtnhSFx4sf2qs4ZJClSOoxrGI5S2kYTh6rzenEkSUAYhYSBpK5qkjSgqVuGw4S69nOOR45q6qbyIbn1nGwQsJgfo7VvgY4GEUYXRFFMXqz9AjUQ5OuaQZZ58z4JZ+eHJHFM07TekC8KiaKYsiw5nx12NgWOpqmZjseUVUV7O6eqGybjKS+8+CVm8zlJmm0KnNdef5FWa+aLM7Jhxpe/8nniOOPe3bsEgaKsHK2pUAGs87kfw62gKHOsrQijyPOqAsdydYbDdxjCUFFWS1544TmiMOLBg9d5+ODhHzi//1G3P1ax8oM/+IOb/3/Lt3wL73//+3nyySf58Ic/TJqmf+KD+YO2v/23/zY/8zM/s/l+uVxy48YN4EJCxdbEvC2r2t76SVxutWR6Sdh2H3qbK7Ldm+6Li+3994TZPg25Z/1voyp9YfO4Sdu2Rv5xIm5fHPW8lt6h9mOTEdZa3lMUXLWWQkr+w3jErw78TbhtOb8dRe+cd0NwUnr0wjq/4g4UEm/21uIIA4UTkrrVxHEIxjFIBiipSaMEJRXDdMCD+w+oqxolFWkcsrs7Zb1e0Tama5lFfsVhDMZaSm06L5mYbDjGWs35bNH9XnRqKUmjNUnnwhpHKeezmZcbW9kVbmHHa7BYApAhbVv5Y+3Mnaqy9sqoIEC3NUkUkUQxgzihrQR7kyE3b1zmyqVdjDYcHR1z6dIl9vcPKKqKPM8Jw5i6bkiSkLLOcWisa0gHEWnmKNo1h6/dZhhMuXP3kCAaMl+1rMoWIaONbLS33e4LViG8zDxNfXKvMZpQKaSQnJ3PgAClYi5duoqxNaYyZMMUKQOKdYkwjmKZoyJJHKdINGkYUZQVuICqKDHaF0MOi5ACoRTaOoyQBB1CQeB821IIToqcDycJn7l6iUy3FGnEQvq2VagkX3jLLkEQ8PbzJbuVV12dJBF30ojTMCAG3n+6hOiY33rDE1hXM3AS2a44OzsnuXTFF+OiK/Dt10vML55P25H7fBvv4ztTwtWaby0rLtc1KyT/djDklXTIt1ZrzocDlHRouSa0liIIUULywnhI5hyVtfzazWvU94+wtd0gW1JKDpOEDw+H/HCeswTOlCRyjn1j+ZUo4lwIUuCHi5IP6JYd63jGaA6l4uUgoAVOOrO29zctH49jZlty0h+pKn6o8vlOd5Vk4uAHai+B/9eDDJxktSp48YVXGE1HSCk5OTlj1zg4nyHSmLvrgigKSBP/PoPBiCROkDLk3r2HnRqy6KwUBHHsfCxGa1itcpQKKYoVYMmybIP0rVdzkjQB592odWup6zVF0VCVDikhChVNa7BGsxaCD6cpv6oUl8OQb88LPlSWDCpNI2DfOsZYfmN/ly/fn/vEc2fIu2MLghqcYDgcMhh6pYuzFmFhNpvhpLdUyGxCkiUUZUFVFz4awRl6E8imrTDLhsEg81zCpd//YJDStBVpPGA4SnHO8YEXT3j29hHVZMx5EhMWFe969R5CCr7ybW8FYWh1TRBKysoXLv0cUev4YoxvvaKplAFa+xaVdX58yPOVj7kIItb5ilZ7cqkxLctVThgGTKajjkdXo+KYKJIdshQSqJAwjDvPqIgwimiMQQiHkI4s9W20pq4x2sdlOLS33ogSqtoSJwFNUxIEIUkSgNSowNHoArCcz448MoemqlfeesM1zOYL4jgiCMHamtV6xmIxI0q83Llpc3CWyTTzBop4NeU0Gm44LUEiUSrcRCH4+ASHNQ1t2+BcTd2CDC7cn/9ztz+RdHk6nfKWt7yFV155hT/35/4cTdMwn88fQVeOjo42HJcrV67we7/3e4/s4+joaPO7P2iL45g4jr/u5yoIPcmIrl/c29I7tykatr/6QqbtBqtt5KQvCLaRjW9kZWy7tlMvT0YIb3rUIyP2UW18vz3OXdlGcYBHjIb6v/WQuNoUHc5ZGqX45b0dfns0ZGIMcylZdPbqdPvrj38jowackNi+byglSgYgQAmfPROlvmoPIslAJKwWc9LIq288xCw4PnnIcr4ijlPyVQlG0DaaKE6ZzxdUVYEUPWdGdlLg3vzJr5iN80iUlBHaWrTVCKkIpFcXta1BKsFoPKIqS5IkptaGpm0AgZCKKMoIQ1jOK+q2ZDSe0DQteV7inGUyGWOMoygKrly9gqkrnn3mbdRFSRoqslihhGYyHvPWZ97E/t4eO7t7qCjGSMVyvuDXfuVXeeMbn+LB/XuMhkNmqxXj3Ygwkbzlmac4O72PdoKTZcF47yp5YajNkkaDVAFStggZdCtZs2nP9f34KIoIQ4Wgs8JXAYtVyeefe57rT+wjhWY6iNmdThkMh1RFiWt9DtXr915n52AfUzkGsSJAE4iAxbLiay/dRcYTVqVG205uqL3LMwicDJBBgEETDYZY3RIJaNqGsyBkMcgwukUZ7/5rncPWlo/sTPn1QPEGBz/+4JT7YcAs8hycUgoELW87W/CF3R2+53zJ288XRE3L0lhenC3RV6/x+uGhJ5xzsVB4XPa7rX4LggAdhvzbwYBfk4pBXTMLEuZhzLUgoJCStG1YJiHHScTNvCIxLa0QCKtR2vKly7usIkkQOoTsks87hYNzjl8ZDQHHe6uKfetYC/jVJOaXOpTkR8qKH2hqTqTiTEKg4Zo1rLXmq0EAOOYIbjjD1NpN22jHWt7ftJwoyUn3s5OuS/S+tuY/uJQzFLptODtbsHewR+wsH3pwwrNnc5JWUwWKL02G/NaVXeZNSRzHFKXnaERRQlFWBKuc1WoFAgbOgZSIPMcrR6CoKsIwBWeJowHL5ZI4DsnSeGOGFkdJN1a06FawXi6RCsJY0HTml/24tXCOIgy5PxrS4PhvVjlXjVdHHoUhOq9ZHy+wcYyUgqbRhFFAax1Hh2esBmv293eJ4ohL+5e49/ot1kXOjSev09qGRmvSJKSo/ORrjGG5XG6sIYANrzAMDOPJkKbuEBenUcovZrOi4ubrD1kPUlaRQglHlXkk4i1HZ7xeNZxLvwAMumytqqpZr1YkaUJZVb6gkB6l17qG0Gc8JUlCVZWAX2ANJxNm5wvCMCAMA5bLuY8FkZK29UTjKIooy4K6LhlPhgShN+2sa59FVxYlbasJY/8znEN05oRx7NvAcRwTRxFW+DnGt/NER0xvybKUJPaxJmHoW2xBEHJ2dk7d+ATwIIwZDIYezVKSsszphQlN448hTVNP8FfSk6OrGmM0aZKC8J+1VIpWV7StD/TsEbs4jrs2W8Z6taLp5sU8L/7A+f2Puv2JipX1es2tW7f4yZ/8Sd773vcShiG/8Ru/wY//+I8D8OKLL3Lnzh0++MEPAvDBD36Qf/AP/gHHx34lC/Drv/7rjMdjnn322T/2+8dxtJHo9i2d3n1SbqEjcCE37PN/erKjEIK6rh9BVrYLmW0Dnr74+EZa8r7w2DjNdihPv5Lu2evb3in9zbotZd4m124jLr3BV1+ILALFrHOBpDue7f1s/60Q3ohJW/xDIOgqbUsgJCoKiZMYpOP69SdYLRY8vzoH23D7tVewxrB/cMBqkSMDSVNpyqLG030ChFM4rHcHNbr7TECILSdGoZAywBpDnpeAxRiNNhopvUNwoHxSbdVA09aoQNCalkZr6rrLJJKB78tWrkukNizXhwyHQ/J8RRgm+AA1vAqr9WnUX/jCF2jLiiwOGaYhVy/v8uanb6KEY35+zMMHd3DCo0phlCCE5eHRIZIAFQWkmWRyeQcrWiwVrdHIMKGtJK/evk9Tg3UBDunTWjs30B7h2Ua4Nn4JCIJAUlWNz5IKIx6czMkmE4ZpSCQH5IsaXTkkjisHVzG64akbN5nlJVpIVnnDcl4wGAwYDnd4+ukhtx/OsXpGFCqqViO6QtZnpvhVWa0NzlgGacq6KFBBQFlWxFIgfMseq31hboFAhhTRkEVRorTlWFmc6c7JwloprtQt333nkLcvc2ZxwDJNyFrN+w6PCMKIk8mY9TrHuvaRAmWbuOrvc9+GHI5GWGNo24Zjo3FSglQIHKdYvjjM+J6Fl3jfS73ny5WioogiXKj41MGY33/qCpmTDAZhx3ULNyhXVVXYKOLfDIf8ehwzNZaZEL4N5Bw7xvD+tuFE+oIjxbGSgtjBE8ZwS0lKBBNnWQsfG9BvO9YycI57jy14lkJy3VjG2nCqBCCpqorVas13n8359pM5Z6HiYRwyto7vOvGk049emdA2ZpNLpmRANhiitfHFvvLjw3yx8G7AaYJSgtUqJxDeYTtf16hAUVVroigGt/beTjKmqhqEgPl8jjaGMBQoZQlDCdovGD1i4IvtylpMEFAFiueU5DwMiBx853pBaWp+aWeXMIzwIaOeK2MtVFXDapUTVjVREBAGMXs7CYNsxKpc0TYaV5Ts7u/TVm03VvgsxSCImE52WK9XqEDRal8Q7O3vMp8vqGu/WBFCkBQlcaM5GXrLB/Bk/yZUTMuaYJnTZCFGW+qq9eZ62gCKtrUIZ4gTjw47awmVt9EXSMIONe6Lp/l8QRAEFEVFWdZY60nS22KN3p8kjEJW+RrrDFp7TlCaDljla4yxVLpFG0cWJ5tcurIsNxl0RVGgIsVoNPI/5yIrKIpCojhCt9XG6TgIAnZ3d2jqlizLcKiO8Ozv8eFwuBmn6roiCBRxHGBdi9aeWySld0EOAoFpm25hDVGkkDL0LthaI4Tt7hs/zgyGPqZBBQr+tybY/s2/+Tf5kR/5EZ588kkePHjA3/27fxelFD/xEz/BZDLhL//lv8zP/MzPsLu7y3g85q/9tb/GBz/4QT7wgQ8A8H3f9308++yz/ORP/iT/6B/9Iw4PD/k7f+fv8Ff+yl/5hsjJH2ULQ9+Hb7aLBy4cJrcRkk2LCDbk2seLjv6rvwG2/RW2iwF41ENlY8ClNYYumC4IUB3yYjskZpsAu02y1Vp38Jl5RAXUt1H64bx/f2v9ze6PF/xq1ZPwvGS3a/cY/zqHQaoQ5zoKrPCQXVGWBN0qttYVL7/4EnVTkyQpw9EYJSWz83PyqmI83eX09JTi8ARjnLfdD/xgq01DU+uOl+L8IEfXzkIghLeWl9KnStd11fWaFRbPO/BScY2zlvPZue8H1xWt9sRbnMVKb90tlcJax/7uPueLc+Yzb6BVtiV11ZCmqS/qliukFLR1jbCGomqJVEKWpGRJyle/+lWM8T4FxgkePPSr/9lsxvHpGXUNaZYw3ksZDBJUlFLWNWGYYownWy9XOVGY0ZqmQ/sUg0Hiuy1htFGc9fdVfy+FYYyx/voUlZd4olJeeOV1JsMBd166w1M3ruNMw3iQ0FSGQRqzv7/HfP2A3Z19To/v0VSayTRBO0c2HJJmNcznOAt13Tm2IlFBCDiUgCCQtMZQ1TVxmhLFIXVTY41jmKakmWK9WnufBOc5LnEYUyVQhxFjYznvVnhSSUbaYqTk6aJmFoWcIBDGUQYhTRzxhsNDrrzjWV5rGwKz5Ryx1ant7+U4jplMJl7BB1TNRZI5vpmJ1ppfv7zLYJDytvNz9ozlfhbziWv7fG1/h8PAMZMCV5UY48hGA+TRmff76Z75pmk25Pe5Usy6VSDd76fWklnH3e75r4TgvlS8ReuuXWSIHAyd5d/HyQZVAc8vywWdz8pFMTZ2llwKL7sWvlgx2qCPTvmWvGaWhMxCvxqfS4lUgnevCz4jd1lECiecL5hd65PakQzjDG00BoMIvM+TdpqmsSgpcNL/S2dWiPDW+mfzGU1Vs1qtaOrKGz4KiwoEk+kIqQKMy9FVS5SEDId+tTyMUnZtyncVBcdxwInsfD2kQir4dhyfEBVFJLGtwFqNlDAaTXCu3eRTCSm4fO2Ak9MTal1TVSWXr11GhtLnW2nHMB2hjaaqSipdE4aBN6WUflGhdcvR8QlpmhInCWma+Zy1y/s08X2mCPLOmM/hyFrNWirOhR9vlBQoaQkCj3BOJ5lXWXaLSWfBWD+uPXjwgL29PVqtUUGIkJKmbjYcoTAKqauayXTCqlgSJAGxjFDhBZm5qItuLKMrNC1a55SdvUFVFKRpSlmX3lxzOPTzldWYymcZRXFAVRf04H7T+lZWqxtkK9CNwSEYJAOcc1RVTdN4h2KEb/F1TxxN29K0Da1pOhQnoagqrLHeI0spAuEL5CAMyIsSqSS6bUnSGGM1GIMKBJIQXbeoQOJMSxzHiDDwfMbBnzxw8Y9VrNy7d4+f+Imf4OzsjIODA77jO76DT3/60xwcHADwj//xP0ZKyY//+I8/YgrXb0opPvKRj/DTP/3TfPCDH2QwGPBTP/VT/L2/9/f+sw6+bRoCdZGc2TQXuR4916Nf2V/YLZuN3XNfrKRpuikQ+sDBvlDZLhz64mGbRNtPQBtTt57Y2n1vrIWuYOoLojhJNkWLdd6K3xiD3OLF9IZh/WDaq4O8W243+aiLj8+j612UO8L7Vjj/paTCWeudVZ3dZB0BWAStdSzXeYeO6K54C6m1IIpDjIw5na9RgWCxLjZx7EZrAiEIIoVrHLjWq1t0C85Lt3HghMM5DydLJx+5tsY4lBCILvm198PtV/Rt26KIMMaB9FC+7AY6TEu5ynFG+C8nUdKjKuvlurOTbgmSGMIQhCNLUm7efILJaMSXv/gVGtuyyNfM50uWqzVZNuRNz7yJ1kquP/kUR6dztGkRQcx8XrC7t8vRgxOsFeS5Zn5WEElFGAgfYeBE1yYUWMfGlbhH59pOfeXbfwIpFEr56AY/KDeMRgPOzteEMuTs+Ve5ee0Si7ygbBquX76EfnhKHCQ8uHuPPF+wWp8iggAjAg6eeIoo8vkwtbYY7f1v4ihAdVlO2jVEQUSL8Ssx7Z/NJE58Me9AKEUQe+5NDz8LwKQpX12t+PaTM4xtKMKQKbDTar4yHPBMWbFWIVEQ+BWXMeRRzM5ySXD3DnsWBtmERYe69S3VvnUbdRlHum2wKsDbAHTPLCC7NiwC1sby7yYTfmuUMrWGY9OyiEPQhlCEyK4QKKuqQ658S3H7me5XvN9ISj3vJM1THCfd37wYBIyt5Wlj+GDTYoXggZSEOCJjqLrXnQvBp4OAH2pahICFEEyc48BYfiVJOO/QN48ASuS6INKWoyzGON1JdA0LZbncaNK6YpUNcM4gAv8sBzIkCkO0swjjx4ok8fk/faRDECgC6bxDbGtoW90paUpQjiARiMASSUk68OGqRlucVYRJSDYe0BhLFPoAytF4TBhETB7OGOKYZwnTxNu2u24s260rbkxSbinDaGdKqEKM9UqfNE0wxrBer6nbkpPlGhc5inqFwXB+fgY40iwlkhFlUfhWSYeGl01Fkgx8YdB0ztDAeDChCVvG4zHr9YrKGr62P+RDRyvCoKVOEyZWkGjH557aw4xHqLIgTUKyLELJGqUSBBJEgCMgzTIePjzEGMNkkhGEimyQdJxHj1CrMMB0C1sQBFFIa1u+7QPf5o3ijKNsC68akgbdamKREKqIxrUdsdgLECIVIoRESYG2miSLqZpys4D184xAqICiWm/m1LIu0dqQDmKaVYsUvUXCmrZtWa2WvtgI/DwQhb59GYYRRVWgIsmqWHhLMiH8IsUJlJCEQchoMKFY1pTNmka3pFlC01QY54jjAOd83lGgEhbLFcNRQpx4O/+mbnFW0Xadjz/JJtzjLNRvgm25XDKZTHjjG5/acER6pCTLss0E3w9GcFFstG2L49FcoG1TNp+a2W4kzdvtHLiQQm+TV8EXOev1mqIoiON4kxVhtwqVfn/9wNjbIG+jP/3+H/djiaKIKAwfQYn6Sa8vmhAX5GLgkbDGfutv/O0WWV/s9ZLR7fPuIXNrfX+4yEt6m+a2tRtCr8+iEbjOQ6HvMVvrNiTTbY5Of4z+dYGXKeLJlaESBKEkCiTL5YpAxX7Sdb5gUVKQJQnT4YCqKFnVDdrozqfHJ+f2RYG/XoY4DhG6ZZgk7I2H3Lx2leEgY7Za88rrdzg5nRGGMYPRkNZohLQMBgmjYcZsdk6aRYSRwjg4Pj5nOJ4ySEfMz5eMJxPiJCVNvcHccrWmamrqLoMKvE16T+bu/+3VaXTmglr7Yns6HZOmCYv5HCkMwzTkbW96iiZf8qYnnyIQkjDKqNqWKAJr/fm/8vod1rXj+pNv4ssv3MYJRVVVmyK+5z9pY1BhRKt7hclFEeURwhbVfUbr1QopBDvTKaPBECkk7WrFdxwd8ez5jMQY2ijixf09Pjse83+49ZpX0sQx2ni0cKcsOVgsuKckkZCUMuB3peIX44R11xYFcNaytztG4HllYRQDgpPTs84ZWuKMvyfDUDEcZUwmQxyekNi0DVXdoFSACoJNLlcvFS/XJWXRPIKIAiRJ0kVpNI88DwB/sSw3JNm+4Hh3qwmc46XAowqRgwNr+I9hyH+MYmbSS6Nja/nRpuH9rWbgHLkQfCbyMue6P2fnwwdv2pq/12hcmvAw8TwyHOy2BmHh//Gm69TjBIEBDIFUREHMarUGaRGBIQgVo+EAhyFJIlpdI4WgrTVhGHfPoiBNBp25mM+o8ROOIYolCG+C6VPAJUXZUlYtUoDVLfu7e+jWEM5L/vefew3tLOvBgFYbnINRUSKc43961xtZhh7J6e+7OPZtkT5kVUnIsog4jlitVpRlQZYlBGHg0V4ZUpctfZ7PRYigvzjjQQbOj6NJHHsX1jjyCrqqInHwvttn3LhzRGocdRjw/M6Q33/qCqNLl8nLkjTzvjN5sWI0HHWJyaFHKbOM8/NzjDEblWEQBF3ooSNJkk6RUyGFN+srioLdvSlOaRaLJWVZkmUZURRuLPTbUpNEGav1yp9TFKGt2fBXAiVpm4bR2B9P1e2jqqtORdlRHrpCJwx9i3M4HIIVZOlo8zyHYdh5T3WqWWPwid8BKgwJo5C8WtO0Zdcuk/7eqBvSNMPpLlbCOOpWY63fpwoExtSMu+evLAowEcv5GhXA/sGuXwgIhdaWxXzN/+tffITFYsF4PP7Dpvc/cPumzgay5lGb9d5Urm3bzYcFF4PSttfKRrYnxMZIrG/zPC4zfnxy71+33ULq+TCPc2i2FUfbEuntf3uUZtv+f7tA2kzsvsfydce1XaxtFz59wdWfS8/j2VZNXRAcvW9Mz83pf97727gusTgbDDDdwKSUP/44jtjdnRKEiitXrvDaa69zdnbmfVM68rFuveX7tkR7I9l2nshmLQRKoHVNmgwYDweMsgEnpwtUpLC6xdErskyHoIUkUrFar5G9VwSCJM2oqoosiXji0pRBlnUeLg3j4Yg7Dx/6B2f3gNH0gPHOFWbzJeuiwDpHksUUjWH58AG70ylaW+7df0DdGKY7l1gtNA/v3SOOAy5ducqDBw+pG810d48ky2itwZZ+4qy6rKn+2m4Xvj0SCJ6cppTvKUsFo52E+eyEVamJBm9k/+Aqy3JNUzaMBlOmk13GoxQlLav1mqefejNn8zX3b9/zpV93/fviu39GgjBEa70hdQMb865+MqibijRJfHxCWZIXBUZ76bWIQj52+RK/MxoyMZaFkuQdIfXL0zEfOj5FCMiDkEw3PJ0XtA5KJIcI9qTkR53DFDm/kGaPcKt88d14iLttcXj3Za/i8+2LnuLi/07Qtr6dmMYD4nhIVVZecYRvCUdhgDUWG0NVXvBl+mc6SRImkwmnp6ebxU2/9UTb9zct160PYFwLeC0IOVQeDVTO8bR2/F/Liu9qW2ZS8pkw5N9FER9OUz6e+JbSXEpm/bPd7T9xjj9flryvbbhsDFeKmoMk5OVJxgDBTm34DztDjrXBnMyZTocMBwOK9RoroVgWqAiGk5jAgWkrzzFQAfu7U8CSr1usuRgjdFOQpCk4jcPhpCZJFE2zJh1EJEmItb6oizLJUHuZtXMSJxY0TUU9hFs3Et796jmUmnUYMNSOXWH5zI093EHKwBratqJtK0QQ0rqWdBTjnA/rFDKgLAu0rrCuYTz1YYeIFhXGlPnaLyCGifdqMoYw8Mn1bauBwquAmoKga38Z0xBHEIchASEvvnfKa2++yrC1nGM4xxEBTbWkqirG4z3qRrM73cE6R9O0DIe+MGiqHOE0cSAZDRJ026KNwenaj4VJ7FuVHaKltSEJR2AMZZEziBOEsQxiXwy3VYVEoHAo5Ygi6cNwnW+LITqOm5S0bUldFR3PRWJMg8AjK+t13tEm/PhZFhU4gdEWKSR1U3ZzQ0hRFjjnKEqvThwOhuTFgiSRREqibctqPSeKQ7TVRIG3oIhThbENo/GYxXzmF/ZCEUSpD2AVvhVfVTV54RVGZbEmGw1QStJoizGOMFTeuiII/9C5/I+yfVMXK9sk1G0C6zYHZXu7CDHjkdVtXyz0hcp2EdC/z3bWzuNEyX7S6avvxxUO22jC4/vd3l9fCW//7QVy4f0QtguwxxVLj793rz7qJ8me+Lt9DfqW2bZyqP/d9nHu7EyRCurKW3U3TdOZOnkk5fTsFGs1q9WSuq4ZDHxmxvn5OU5fpH7216APbgSo6ta3zYTDOk86w3jfkUAGHOztox2cz+do1+KM57+kaUpbN9R1QxSGPiMDR12VJOMJ49GQNzxxmVHUTdjWoMKAdbFGO0c6GqHbiiyOuHnzSerG8Mqrt6iqklWxZDTOuHbjOtpY7t8/ZL1umUwPePOb38ELL7xMUc5pdcOdu/d9T1xIbt++g91c+xBjmo6f8qjaq0es+qLar1TMhoy9XC54+s3XgZpBJrjz4BbvfOvbMcJw7/ABoZzRVC/x1re8iSdvXmcxX9NawexsTiAkQtiN23KPqhjjV2/+OsTQFSz9V2++6FzHsZKCKPAkdpxP+LXOD4hKKYo0ZdaR05uV96v4jZ0pEsVbT0/ZqSqsEBRKcitIWAYBQige1g0qCPjTUvIbznG+dV8GYUigPCHcyzktPh3aq+HAF6rGuI7kGBBHnusipcJqSxwlmyLNObe5DlF4wYvbRi0PDg7Y29vbhL31zxd4nsqHEx+qOLWWiXP83/Ji08ZxDp7RmmvGS2vPhJfh/2B3XT6cppwLwfk3GI/Aq41+sK45koJPBYpvE4LrZUts1rySpfxKGvJLCPSiBGGQ4zHCSMp1hW4tZV6RZgln+Yq9/R0CQsbTjFgECB0QRgFEBmMdlS29AlBKhkmGSAXatlhV0+qaNEsZj1PqZo0MLGHgOWarRYE1hizNkFIxHk0wxvKFVBDF8MZ7K6ZNQxkpvvLGXZ574w6j2LFcFdi2xWKQoV8MFpX3fEnTjCyLCYU3fotRDIYhYeTRh0ApRsOYpm6QouXs/IwojJjs7CGATMWU9ZpKatK9kLyak8TJJq4BC6GImC9PmO7ssLIOZ2G347SsVivi1JGNwK5aVKhoq5oocWizJAwVQSAJgpT5bIZuu+ckUsznLcNhSqC8SGAwCBlkWRd62IKAJBkTBAGjQbrJ6qlLn180GqWs8hVC0rW0fc5dNsgQyhEFgiyNPIKh1GYRrLUvWuIk9G3AQFKWlX++A9/6UwGo0AdixnHgfVKkomlbnIXZfObHm0iSDEOatiKMJXXrlUr70xFSBJRFxXA4AWVJhjHGtLS1pW0qitK3hqM4xAm/GFWBZDBOGWSZR25UQBSOWK1zCARh+l95sdJP8D3UC2y+3+abwNfb2rM1ccBFxlE/ccPFSngTGLVV7GxDyf3kW1XVIwjJ9nFsNPuPEX77icJaS13Xm8Klf9/t93P+RB7Zd4/q9AVNf6zbhOH+vNwWotGjShdozMV16/OLfJz4YpMAi7CEYYwUskOOPElX65am9SteYxYIBHEcI4UnwcqOSyPERSHVT9T9+TscxlhvTiW8id31q9fI0ozPfekrXLr2BHGakJe57+l2aaKT8ZjEaubzOXVdI4SibRuK9ZLd3V3iUDEdZQgpKMrKoyAPHzIcDYjimJ3hwIdRupo6X2GqFbuTITeemHL/8AFhFKKrhnVRgYwoyobPf/455oslUjoiGbBYLEEqj+xYzwZqtSGOU4q8eAQ56++z/nvflnQo5UPE6romjr3h0+nxGe/9tndSFmfsTFLqtkQoQZwmnB7PyNIhTgXcvX/EcpnTtJ7sW2uDjAabga4nc/fcJ9MV5dvZPP093LZejRDGEXXTUBpDGicMB95i21iLxRcuQRBQlqVfgUdRp2yz/EKScPn6E1yLQ77tTW8g/thvsNLeq0JIRdO0nBrDG5Xiepoy71DIJPHvY3RD07ZcunSZk9NTqH2BrK1F0cv4wWiLNQ5jOll/U9Irifr7erN4kcoXs+LC38g5D+WHoTdIm0wm3in6/Hzz/D+uWlpJuQkoPJE+S+i6sbTCxwAspdrwVt7faj4WGebfoFBxzhvNebWR8hlfzvKZOOK+kgyV4p/sTnk9CJFSEQpBEAiaUqOrJW1liIKIdDrYEKmXpw3BbkoTBBSLmjw/JkljmqZhvfaxD0EYkKYJupLEcYS2LeFAUjcOpwWzxhc/Vb1GjSKUUuwMd2hqTRjEpNmAdV6QZREuG/HKlSvcXRakZYueDFhEkkHd0rSWaDrk5Czn9PiQ1TJHONlxuiSLWUkcLpBoJpOMOBEkaUi5WNK2lvF44hcldGIHbViWS5RSpJn3UXExNMp/xoWtyYu6U7YkhCqgqJe4wGIS4/2MgoDKFZDBMA6RFpwsSEbQNjkitL5Ql5pBOgYked4y3U1JUh+SKIRgOA5JswApNSqUaN2gXYGKLLHyFgzWWa+migJMJ3TIshBjfOZRmkVEcUzTNp5/Jw1pmmBxSAfDYULbtmRpwsrUONv4Nj0WpXzBLpVjZ3dEWVYop5DK0egcpxSomKppEcpQVAUoGKQZUZgxn88QSqNtQVkvuXx1h7zMmZ01KOVIkoizsxkyiCibimyQMh4Pmc9WXLv+BNZaHwbZCpIkJogCtG0IIkUyEqxWNdo2ntMYS5w0qPh/Y4Lt/7dtPTmuH4Ae50NsFxSPoB3iUR+UC+7AxYp3mzy7PWhdkCMvNt254W4jG49D/v3fbhcu/T576fTj7/P1rRrf+trmt2wXWduZQEmSbGDusiw3Kqht3siFf4snWxlzwVt4nBtT13XnDcDm2mmvhcZah+lSrAU9N6VivfYGZSLoV6EX7923ynxcQYToPCuctT5pFsl6sQJtSZOUF154ARkEyEAwHGQo4UPDQqW4cf0Kp1niDXTx7Y/lcslwMGA6HhDHyvdspWAxP8OYhuFoRFXXvPDSHY4PjwmCmCTJGI5GyEixzNeEccT5+TlVY7tzlRjjWBdLhHQIJRiOxzgLjdaY7l5pWkMUJ/gWRXshp9/iLvXXti9k6qbeuHs655hOdjk/Pebo/jHve9+zWJNTFznj4S5O+3yUojZ87ZVXKJc5SZTwxPUbrIuKZZGjMkeUDjbvsd1+8/elfMRIEei8IEqMNZR1DQ6yJEVIiVQBu3t73L19m+lksrkn4jjeFM1FUdA0LWGQcizARiHHX/4Su0VBhqBOvdW8AAZtw9wFFGlKFgQURcHe7i5hpLhy+YDDoyMQMJlMqDv4fRtZ7J+7oihJkpSyrDvbAI21LTgYty0Ta5gJQW5l1364ME7cNmTsFyhpmjIcDikKbzYWGcOPNo2317eWXHo08YrtxhrnGDpvDf+qlJtCZSEE1613vZ1vjUX9+4HPPsqs454SvguAV/CdBxGpNaAdCOWjLLCEUuG0wEpHIALGwymBChFOsrQrnDXEaoBtJNiIJpeszlcgFHXjj9s5Q5O0CFMQRa1f1R/mZNmA03KBChTDUcz+wS4xQ1xd01YNeW5ZLc+omiNk4FOMpzsjQqEomphmMsHQksgUoTSmLCjWJYN4wsFUcHJyRlFUFHmFEAGDwYgKg9Yti1lFmkqWi4amaTk7XYJ9QJqERJH/TJIkQWvJ8WFJWZ4jpSTbiQkTT8L2XlAh1iru3X7Ikzdubpxz5fnKE3ut7j53g25LBmlE42qsFVS6ZTDI/MJBQisqlJSkY0XTGKyocM5zsAbTEFxLay1SKFwAZdsSxTFINjJmnMJYQdM5J6tIIB3gLJEKqJucuCNEIxOkFBjrcK1BComUjropiSKFkt34tVghhCEbJFjrs920KbvFrGK6NwShadqaOBlgCo0MvNqitTXCKMY7AxCa3f0B5nxNnIEGolQQRYrj4yOE8IuvVrcUVUWQSN7x7rcRyZjf+cQn2N0fMRyONihVECiC2FK7OZUpaRtIkyHT6ZTVqmSr5v/P3r6pi5V+6wf4bfv67Z9vf21zSPrgwkcyW5x7pOXzuCV+v99tHkvbthuybH8c/QD+OP+k//veKbFHF7ZX39vns9066X/2ONdlo2KAzWp9u/DZFFePoUHbuUnexOhRyXYv7UySpOst+5+pzlq/7QoUf2x0Nu546aT2+lRjBca0fiWwNTH2Vuu2U0uhNbg+eVcz3N3j/PyM+dkZ82W+QXNiFbFerwmkpHQWXVc8vH+LGzeu84Y3vMGjQknCrVduIaSgqUrO87YLQmsQEkaDhLrKCZTkyhPX0SJkvlhx9/ScN052qBvD2WzOeDJENy2L5Zqm9Wx/JSAIJTIQSOERpKqq8UWb9UGaDoRUFJ0PQr/K33Ys7u+tDSHaXbgWl2XJ2ck5ygU8vHvEJ8oZ73zn0wyyGCng0qUDZJhy7/CMs+MzirqhbixXELzjXe/i1uuv4sKMVV5tiu9+kvT3kqQ17pHitb+P5vO5V02NBkShl7p3jCZ29/Y4PTmhLD2fxRhDVZWEoW/pRVFIXTcbf4i2qamU4sXdKR+cLVHGsLCwpzX71vGxMGQmJU8+eZP5fM7O7i7r5Tl5nm+eof39fWbzBbVswBgQDoHsjttS1w1x7NV8URhhrCFoa75vseK9RcUQQakkX55O+LXJmHPxKILYPx/bC5PLly/TNA3n5+f88OkZP1A3HAvBXaWYOMdlazgWEolj1zkMgkMpeV0pdqylFIKR84Ta2WPP/fb41KuNJs5xInyhb7RlJCwrB+dOYU2vAoEwiIijBCkcUTYmSwdURcXB/j5VnpMMM0xTMztbXvA6rMISgQ0IpPCqs9oyP6sIQ4N1hiAKKeYlRVGxu7vD+dGS+UlL095mlIbEEiaTPZSdQF1zcnjOqSpp2jtkUUYWp4wmKYdH97h89RJXr13D1QlHtw8p9IzpdA/XBthaMYh3KMuGYqGJopSmkZjGsVqUmNbRNBLX7uJsSG0M68UKa9bAiunODkWRe25VFBIdF6RZQhSF3XjX+YUsQl6a3ScdKKSsqUdQlseMxgMGg4zBIMM4WOoS0LQNzGYL7849zNjdmyBliZDWZ3AFXtlipc/yktJ7sSirvGsrnkcSKe8QXdclIrCIwBfIIvAyacBb1DeWui48wT0JPK/DWt/etIZhNqCpatrGI/WBChhPxsxn3hnYCUmcKJxT5MUS0MSJT9BG+OIkFBCEjjhRaNMwGYzR2qJkQFnmJJkiHQQw9+jLydlDAhGQ52uiMCKOMpwF6wSL5ZJ1OefBg9tMRxO0KWlNQGsCjk8PfbHVONZtyV44JRlEBJGgWM9Y3jsljEKWi8V/7vS+2b65i5VOpgtdy8b1xEXr2fRcDBB0oljndcJIKTfweI+kbOfw9NvjrZxv1Hfe5oX416pu0nd4q2iLcz5RNorCR9o//SSxbRYHbAqQ7cnNAUi1qVIt+JkEgbZeVSCct9VvOoniRkrdTYhqC/FpOr6Enyx9zeCvhyUIpEcuwrA7DzDGUTctzvocpN651183hbaauoufx22rgC4iEXqZtnWus2cW4Cy60f74BDTGcvfokP2dne7hzD05WCqM9vsLYkUQhoxGGaZqWcxOeKFYY5wgihOMdVy7dp3rN59gvTghDBR72Q77e7vgPAHu9u07ZJMJQZhy69Zr7O7ugYTTsxlhGNFUmnxtyNcWayRCKWQYbsLWBsWavbNj5lLxLe97P5/45Kdo6xopQySCuvakNmd9C89/pm4TSIbwro+2+5lxXVEsJOu6QtqWohXkZcRy8SKXDiZcv3YJgWW1WPKOp9/IS87ytdNz4ske2jmiMGJ/usetu/coW0sQxSADrPPqAaMbnLaEKsB1H7rVBqcMhw8eUlUlURxRr0tcZIjjuMsRqlBqyd6lS5ydnGCdJQwVRlsUmp1R5lOy85xrVy/z7ne9iyxLkcDnPvkZPvv7X+LZVcGkblhnKf9BCP69lAS6pqlLrl25zHwxR0Uhx/M5URxRtg12uQC8AkzikQH/bPfGh7BYzDDW99Fxju9brPkzy5wTKbmtBDvW8afnS1pt+Gdb7dW+IOqLubIoCALlr4m1XItCvsM5VmnCzPoQuxNrgQBhLf9DloGDP1vX/Hd1zfc2Te9FSAH889R7rzj79W7YzjnOgM+EAT9UN6AESyEYacMO8OvZgFMB0vkWhnACqw3C+XMeDsZY49O1LQ4nHav10vOeohAlFSrweTG1kTjlvY3aVvvJsXVYAc4Jau1XGNpI5vMCh+DwcEkcheSLmskgYbFYUNUtk8kUq2MW5wschrUtEJRIBQ5Dvj6jWPsiVzcxi9mStspZLw35WhPHAVHoJ0KBIgpUt9gTHD1YolSIFCFJEnUcjXiDXOf5OXGS0DQBbeNYrzRRBM6VJB0RWtBQVrVXL04zRqMhs5MVrW7IMgesGI4GZIOIwSABJ1gu1qyWFXVVoeQ5YXRInAhu3LiGc46qXgKCuqpIs5TpZIqKPck0iRVhFBCqlLaxnM4PieOUOIkoqwKpekcJg5Q+Xy3OBuSzJdo6rPMBrXEckyYheZ5TlTmLxZI8zxkNxzgn0K0jjr3yZjCUXoZfNqwbv+g2bYuUEKkAIR1VvkIh0U1DIDVaLz0KJwRZJpEKjh8ekYUDdK7ZSXe94d0wYbkoKEvtkabGEo2HKGWZ7gxQEuIk5PxsxcnJDElANshoGt++HowykiTi5OQYF1QEKqaqNGE0/KPN6X/I9k1drGxDS9b6hN3NpLj53QVXZBsRSbpKtJeUPs4P2YZr/f63iobtlhIX7RS/OnMEgY9G71OWPTzpB8DeHKwfvB4hwHZf2+oN2EJGRFdwQSflFJuf201RdbFPv3rs/+/bQBetivZR4m9XOHgXx14x4c+naRpM56Mgt65jf12klN6Uynbktg5lCIIARIfm4Afx/vw2aJCUSIRPz8YXYM4v+VmWpTfZM1595P/GkylbbciSCBkoxrtTBmlGnGas1iXroubh4RGv3n5A8oWIydD7hFy9fImjoxlVkeOs5uz0lPPFKyTDCcPhkDiOKeuaLEpYrVcIoagrkCIENE8+9RRFWSDqmu88n/Gu5ZIxjt0bN/n9Lz7Hb69WqDDxcuDuvnOdtXxPsguUh6wFDm0gCELajSGg71cL56WZEoMxCltAvl5zdppz984hz77tKa4e7JEvzrlysEte1Bwez3j99m3m5zOcMSRhSFGXxFFC0WjPpTG2C3trN3491hiEwxdtSiEcmNbb87dNS1mURFFEmqSEMmA4HDJKY8r1gjQKefd73sGzz7yZt73lzVy+tEdTlyxX3lBvNp9xfDbjybc/w0fu3OaTexPUIkccXKYejnhTUTCfz8nXKwIlydKE4/MzGqOJZIyzjtVqxXg8pmlaBI03zuPieoZhQFGskcpSVmv2CfjWvOIIOJPeXbdREgF8y2LFNE0444Kj1iNLfsGgCQOFMV41M6wbxlJwlCSMpDdQrOuaOXCju59vBYoPth2S2/mmuG+ApG6PF9v//8UkYeDgg23LnnOcS8FH45iPDQdoY9C63ngH1U1DXlSEYUBe1hRFyWg04uVXb5Gvl0x3ph7pDL2rqKCL9RDWm8lhSQfphtzunE8730R/WENR6Q3RWxuJ0YLFWtM0BUII1uWxR7CM3NzbUnqEwbmAsnacnuVYq6nqhrYVnFerjd16g8Z18lec54gESmCVR7M9GtugtaDpwv6MMeje4NKWG+GAEApjO6K6ri/a4FZhDJyf1ayWBodHjhfzmtFoSBLHnK4r7uYz0jjGGsMgGxKEA6wxBEZhCsO913IQkK9z2rbpxp8V2WDFZC/zPi9KkA1idncnBKFkfl4Shpowjjy6pX2auxCSQEVo7bC2pCgbrNHoxmGtIVA1O9MxicqoXc1gMGQwnICTzGZLZrNTqqpmMhkxHEckcYQkYTLyQbNJEqFNw3pZEkYRtpGoJMK2nmArrKNpSxbLU65cuYazhra0PmcKQSJCz+OUjiiWXcfBK+lUSJc/VFDrGt04kkixf+MG67zwiHcI+3uX2d894P7D11jnc4wBqx26iamKP7nPyjd1sfKNtseLjO32UI+meIi03bRDtifexwmy/b+Pq3C2i5btwsMTTvVm39uEWc/uvmi19CuG7UKl788KITacjm1yrieqikfO9fHBcNtp9yIKHgSPkoS3iy9jL7KF+muxXeA9fhzb590PKNvtq14C2qM4/XVo27ZrnVSb/fTFCHi+iRAQBKqTWnqfBRmESCHRxuKcASNYl5bBIOTywSXCIKSsNXuXrhHlBXcfHmOFpDUSzYCzoxmHJzmDLGU0HPgWiI3QVpOvapJoSF0VzJcLrIP1okIqRTIYslgseM9738Mzb30rv/2J3+EDrz3gz+Yl8yhilkTsRyHf8vo9fhjL/xokGGvQjUePOrsMBHSkZscwy9BtQ+h81pE2vm3ihIAtN1BM6AdkA9Y4ct1ijePV1+6Tr5a88eYNyrJkOMq4qkLqSnN0coiSoAkwxst+RWsRwl/7II42ZOr+8wE2n0cU+QLQdQVsoBRKSJw2LM9n6KLkYJry3/x3f4EPvP9PkYSdiVtTeWn5ICJOE167d8St125z9/4h7/22D/B//Mm/xP/yv/xrhlee4OaTT3Pv3j0Arl27xsnJCcfHxwRBwP7OlPsP7yGMwbYtuvX8E4tABCGiCzrE0RWwHWldCXCCrK6IteZYSrx5osNawbkQXDOaqTGciEeNHHtivHAXPKqmaTjSmoUxJFVF0cm9nXNM6Vo80qeev7fVfDEMWQtB7Cy1kIwFfKs2/LK1nG89r9tb4hx/oa55h9aEztHi+GoQ8W+VIg4CXJchJQDTNtRNTRAGJGnC6dkZaZqyWC5Yrla0TY3tFhTrvHc37VreFurm4nns25LbSsoeXbbWblAKrTUCaJp245btYwrqzX0TRRE94TlOkq6VYf196y7Gq+0W/PYY21s8aK0fEQVsvK4Emzbl9tjUv67nGm2P3xctV5815lO8BXVrMDqnqnzSubMGk0HbNjS1z90KVIAIPSoimobdvV1y/OeQpimnp6csFyVOejn3Ol8RRYqzkzUHl/bI0l3myyVVtSJOIsbjjPn8nPl8ibOSvb0DpHTk64rBIOO1lx+yv7tLGDrKxTlZlhKmkihOqKoWZxxXLz3F7//+F2hbw/27Z1y5esBk7Fgu1ywWc6Y7Y8bjjDhWIAW3Xr6NVIKnnsqo6wBjNCoQJEnGIA0xjaVpKoZZihKgm5ooEIRxiFE+3NZZL7LQrSGKAoz1XDRrE4/0aM/9CqKQ5XJNVRcMssu8fus2050h8UGEQHF2knO2rmnri8/pP3f7/4li5fFiYhsZ6SfEIAgYDAadfLOibZuNIVavfgE28ufHSbrb3z9OlN1GZbyc8tEHsf97/1r/fRiGjMfjjTdMnuebwK5e5tqTF/uJv3fx7M9xW278OJEXLoqWjX/LFsl1m0TbD1Ry67z669KfszXeRZStAi3cyrwJkmSzn54n0Q9APXm533cYhpvr3L9/78ZrrCVQF0iXQJAOMqrGe29YYf0KVvjcoPlySZqmKKmoqoZl2bJarREypG289fODozM/gEnBIp8RzdeeSJhljJKUJAqw1gDOq1GcpW4qhqMRyWjAW97+Fp55y1v4yC//Ms3Dh7y3yFmlCeHVq+SzGZ+7d4+p1rzPOT6uW1ZSYi0XrTvXKZyCgCSO2N/fo21qyqrB5ykZWq0xzvgYARxBEIETKKG84RYS6TzXpNGSvNScLxbMFyviZMju7pi61oQRHD48Jq8bUMnms/btupamqYmCR7OjnPNmVlrrDWInOuVSoBTSOjAWLBgaiqXmM5/6FC+/8Dy6rcmSGGcMw2HGyckZL9y+S1nXPDw8RlvBi6/cZnfvMqt1gbGSO+I2xvj2a29h7ol6S3Z3JnzH+9/HvXv3uXv/Ic5YFnmBQ3Ured8K6u/vXoHUtyOPtWGNY+Icx9C93jI0jjUwV8pHYGwV315y2hCozgG5uyfPhOD3wpDvr3zWSmstO9Zx4BwfjSNmUvKGzvDtrhQ0AkrhnaULpTgoCnacY/YY2X5qDFNj+e6m4bvblhMpuRUoJg6+q2nIBfzy47w05zboaVGWXYid4nw2w1pN26Fn1niZcqAUDi8Fx8JytSaKog1XbHvf/bMZBMGmcOuvQRAobNcOruuaMIweISdr68dW47zFexB6H5/BKGPdcba2vWu2i5P+/b/RIsf7M104gffH+ziiu/3vtmCg99nxHDjjnbxVRNO0tK1XjSVRiDECISKaxhNrV6s1zjriNMYJizld0jR+DqmbgrqBOE5YLdqO1DtgvSzJVzmmjbl0KWZxZpjNC4QomEy9uWFTJrSNxbYNVhcsF3NvHprG1Lnk7uEDwLG7u0syUUx3ptS15ux0QRLnrJYaKUKcyXhwd8lDkXecxwbT+raY1hXXbzzJcu6Jy4cPvsJwOKCuK+LO6yVNJNPxEKUEOpOMhp7Uvbs3xdiG2Wrlnc+NIF9VKBWxtrDO10ipqEofMDkYDpCyJc0CLl/epW4SBklEqHZpdcE4G6C1ZWciMbUkFP+VFyvbE/E24gEXE2MvyfQk0bJ7WM3m5o+iaFOh94P39o2/HS7YT/Tb77/97yNIxdZg+Oh+Lx62/sEcDAaMRqNN66goCqqqIo7jjTKhrmsknrjZ//12obL9EG8f9/Yxa3NBtOxX0Wnq/VB6Cer2INYXef11NMZghXiE69MXH0EYkqYpWmuuXLlC27bcv39/E9TYr2J7UvG2ad/29ewni/68skHGtWtXeHh07A3b2n5gFYRKslqtWK89yTOOE9zZHGvhyuXLnJ2fU5UlKvDumzIMEaEgGcaURcWqzrGmIC8sSZpy5coVdvZ2Mcby5re+kctXrvLirVscHt7n+ee/wiAbEDcNmbG04zFnpycURY11joUUXG0NOw6WovMxiELqsurgdi9NH4+GnqQsJE/euMnh4RFpmjKbzWi19i0wa7rumV8dmk4JE6cJV68cMBplnJ7coapyDg4usVwt/cpZBORFQTYaUtucVV76ArKz/g+CAN3UiM7g7/GCfCN/x9FqTVHXBEISyYAsSnDGoK0jF4ovfu0Wi8UcpTzRVAgIlOLSwSV2Lu1z+2tfwymfvCtV7A331gVn53PEnXubz1dKydHRkZ8M6hp0y954wA/9wA8wX674nU98kldfv+25aU6g+0Kj8+1puiJWKT+ZngvB70URP1jXIBULBBNrObCWjybxBuXo37/fR39vGnhk8vtIlmKd49uqiieMJReCj8YRv9gtbmZSsBaiywCSG9QqaxpyKTjfOs9MCP58UfCn6poda3lGGw6l5CWlMEJyKgEc72tbfqdtqQaDR56xKIoQUlIUhXcm7dCwtmlBCC9h7Xx1jPWJwnXTEgQXga/9gqJHdrfHtx5N7S0UAJ9o7FxHsu5I/EptWtDOAp3vjsOrWWKl0K2XlXuvoWpzPbfHgn6senws24yZrkdaL5CZbZQcLlCXfn/9a/w90iuFuv07i8Dz3oxtSdMAS5cO76BpLU2jaeqGxhhk5+8TBAFtqwmCmDQd0DQtUZRgTMN6VRKGCbrVHB8tMTqkKjXF2l+iIl+QZQltC84qyqJG4dCNxLSGAMHM5JSFb/AXUQuh5PX5IWXR0LaOJAZszHJdeNSdAIQikAkqtjgX4ExElqaslg1RMKLMF0RxRplbmkZRrBucqwkEnMW157eEEqWgrtbs7EzY3ZuQ7WXeKwbB4cNjdGtRKiJfV4RRRJGXWGtQgSTNQkbjiMk05eq1PUzbYpoSSYtrBbbVhAIipZHpf+XS5W3ocDtpuG9bJEmyMWrL83yDAtR1vXGs3R6ogEcm/cfbLI8XQo9D6T3hr3/otz1a+t/LrWKjH4jyPGc+n+MD8HzhEoahDxhrmo11f6++ebxFs32c2yjPI8XA1kPfv64/9779JLtjqarqkYIkCAKiIMT2LSgHTndQrvDJxn3yZpIkzOdzwjBkMBg8sp/+eJum2RSP3cFtjkN4ytEGjRkOBui2IVKCUCpE5M9pkMXsToYsFnPO5+sNt0ZuOCKWN9y8QVWtmS9OiOIBKvBeOE5APMkIw5C3P/MWDvZ2uX3nNkEYUrY1h0dHFFVJ+fnPUVXeqj5JUpx1NIMBWTRE1w0njcZaz0sZakuhFOskQXakQec6rw7wsHO3ijdGc7C3y97uLnVVe3+TpmW2mPsk5jD2fia65yp1SJo1HJ+fczafgWko85o4GSGcIwxASEdeVFgkk8mUnb2UMBmwWJfo1pv4+fu+AR5NBu+vf59G7KxvYQVRDDiM1kTdM1PUDi0ENSGmtQTSZ1ZJp1gfztjXjtoIqrJGBSHW+eA3YwxS+cyg/hno0b84jjvelOS11+5ireTZd7yD//6//W/55Kc/xenZGV/60nOoMCBvtTfU8omc+JRt4R1rheOX0hSE4P1Nyw37aIHRP5ePE9n9PXOx6OnbloVzfDiJ+bVAMWg1cylZdBM/sHGr/aG6RkrBioCxNewbwy93aifVjU8/WlV8X9tyJARnQqLQXLOWZ7Tma5HPlVoIyXWrmRhD6S7UW0qFTHd2qOoKFQYghY9SaBu0MQyyAWXl5eZSSdqm9e0cAVqvNmZp26rCbSfux8fV7QVaGMYY47AO0GZjrd/3blvjE3+DMESblrKuUWHAYDikaS5QsL5g2R6nt++/b8Tt6bls28fTf26PLxIfR8SF9OZoXlghcLY/N09619r4DLbuXKT07dcwitDGqxR9CSExFqq6IQh8a9ZasMZHjjjnybNt01LkDVo72tYRxyFta1jM+7ZcgFIBdZdADYblumSQpaggYTweoU3D+VlBawxhENFUFc60OCewxnXpyYMu0dqfq5Ighc/hqcqCNB2zWBQUeUsYhEjiDSWgqVuqGsJA0TY+OFZrn92zWq8wd2YbCwiHwGhHUaywWhCGAmM9clgZQ7lqWc1bzo4KipXg5o0xUrU0TQXDTo3mFGkQQRjxJ92+qYuV7UKiJ4X1N3OPGpRlubER76t6pYKN90Vf6GxP9P33xhgfpKcezfXpH/rtFcIFYuEeGQQeL2y2i6L+wep/ZoxhNpshpWQymbC7u0tZlpRl6eXNKvBOhH/Ag94f4/bPN5tgQ0rs33+7laSUIuv4Mo/zTQaDQRdqFWyKmSRJmE6nAMw7Wdpg4I3I1uv1Bjnpj6NHsOI4RghBkiRbNvRb3iNSYnSLMb7lNJ/NqPKA8XjMMBuR1zWr5ZI3XL/BG5+6QRxHPDg+oyi9y+bp8Qmj4ZBxl1Yaq4xxcpnxdMr+wWWKyqcMOwu3Xn2V119/wOe/8DXyIsc4r6hSQUDVxwwYTagU1594kquXL3My3eOlB8d8y+07XFEBD7EMtOaSc3w0jDgVoD0r1xNmubhnoijiiSeu8eyzz/Kp3/0Eezu7XLtyDYvj3e95D3fv3+NLzz0HeG+c1dJHyQvhiW/aaKSWGOlbQlJErJYl+7tTDvYPsELyxJNvQkUJVVnTtJbjsxmsi4tJWMku9PFiYti+j4v/N3l/HmNJlqX3gb97r21v9ed7uMcekXtmVWatXUt3dbdI9salh6SAEaCFojCaGREShemmhqQADSEMJIIzmD96hFGTkCCRWihQLS5iV1cVey8Wu7u6sqoyK/clIjIWD9/d32673Tt/XLvPzT2TVJMFCCjJgEBEuL9nz57ZXc75zvd9J47xlUcr9G39HkFZFOTVmU8P2lDVDTHDIFokAfZZCybzlJX1S8xnMfP5nKzUeGiMsIHbxbHpeFuV1qRZiRKCh48es7KySjyb0g49/i//1r/Bzs4O/+Brv8Krr72Jrs7Pq6rSiw0sFvB3wpBf9X1WgLFSnHCWyTeRQ3dobbt5u6C+OUe01pwAhzXK6K7eve4fRiFCwOe15qrRzIXkq6HP/+yfNbBc0ZpPpxmnyuMECMqSmZAExnBZa+5qQyatjHkuJMd1MOHmDWgqXdlA0lhTPoNFHtrtDgDzeUyn08EYW07N6h5dDm1okordc2/Ktt09aQoOpKzXwkbC1kyIrIy3IMsyiiKvS9dZjcha23hX8rnoO9VMAP9pRzMgubh2XSz1ny8VGRAVDhOSQqAba6b0fLQwpFlCK4rAQFkU1qdJaOsGJGplaV3CyDIbGBptCIVB6xKw6knfl3ieX3c5p3ZdzkFY4rZL8rQW+KFVyAgpKE1FoSta7YiyJiVnRVm3E3DOtbbPkEXHAFEBtoeREFZtNpnM2Nre5PDoiDSdURR2Xc+LEt8LMEYihIfwanKyFHgqsL5dZYiQEl35ZIWgKDKyPCEMfZuMaYmSEXle4HkSJQN8z0Mpa0thSsHugzGDTpflQQdReiRjW0qqKoMUEcqL/pnP+Q9y/EAHK82jGVm3Wi2kFIzHY8ryrF+PC2hshmlNrBxnxJ0DHGnvo4m29SsX77m44DnOStOEq/lHqfNISLMh4gLSFDAcDgmCgE6ng5SSJE0IgpBWq3WOeNs8LiqaFle6EA2Jc3+a9eKqzqh932d7e9u6es7n1uV1eZl21CKezdGV7RxalgXz2QyBoNNuM41tEOO4Ku4Iw3DhQeM8WVwgeevWLU5OTmqExQaN/V7X+pcEtqOs7ylEUdDtdHnm+ecZT6fM5zMm41PeeuNNDIZCSKtQqGvs3Xab4ekJZVEQKEWepkRBlzwtefxgl4OjE6RUzOI5uVZkhaYspV2IECAqhAzQpqIdeggMSZLz3vv3UAh+Z3OTJE65trvHVlkxBb4SBfxyq2VRpMo2mqsfhA0SEAwGA1rtNvfv32cex3zvtdfo95fwQx+lPOZJzPraGmVlG6cZA3E8twZ0QiCkwPetgeHG6hY3Lm9xeWuDQNmxvnd8wv7JIyoUgedRlJqsOFvIy7LEVwLl2Vq+NrUkuKHQiqIIX0g8bdEz6oXfCME8jbncivj8E7eJQ5/ffv01kmROnGYIqWyJUkA5g+PTkV0I3caf53jKJ0mt1404N9+sWiPPC+azhMALKSvNO+++x43rVyiLhG98/TcZLPX5v/37f57vvfEOf/2v/5d4oznLumTseYyxHZ6bCuGhlEwcgf5CcHIxyLd/mwVvq6qqBd/Ivaa52Tc3zkxK/sdWi3/s+6xJyVFZcujQVWG5IOtC0NaaXWX7FaVSsCMlT5clAYZ+PXfXtearYcgJ0KqshX9VlQRhwCyJkZ7HaDRGSlsaardbdNsdjo+OF67Tbt41+XphGJ7j0jV7lTXviUNI3ZhxSkZjbC+Yi4mSqYnONvGpUKqk0obZbGY7DVd2w2/e64vKzI8KYIQQmNok0r5P1Kjr+ZL3xeu312JVRlJZZRcIhPTAWEsGISSVKcnzDFPzfFz5WUlru2CoEXNh6u7pFUpZormS9ry2X1qtqtMlrsFiWdk1wBhLTrUaR0mlbasBL5D4kY+SNtAwyhLr55mlKVSlDQ4QVlma51aKbcnCBSoTGBPhGW25bTW6uLu7T1kWpFlqSbXKIqGeMpSlRdErk1Poyja2RCEFqMC34gojKNKKstS2K3uWY1sjdDFao6QGUyGMQpcGKlv600KgvIg89hiVFQhFp92mFYbsHe9RVRmDpQ7f7/G/iWDFbfp+zZvQumI6nVCWzmTNGu5oXZFlST2h7SR0tuHno3WwbdYVllNq6oEoaimwG6BnE8SiA2bBSWkGKO6wG450GBuuFqxULUM2AukpqyKRkqIqGY3HdpNy0LOu6LSiGsGpG5NVlZ1gDWRIa73IYkUtd6aReTQzFreQ+TVyNDw+sQTWoiAIfIYnpwwGA8qqZDybgoBCV5RpghQST9fXgFu0zmzN3Wc0fWxcJuaupdvtoKSgFUWsrqyw1O9TlSWm0vieR5Kl+H7Anfu2QeLx8THG6IUEs8KqmXzloY1m/2hYe4gAxiIlu+/cpXrz/QX6VZQFAkEYRWRZspB/h2EEQpLleX0OW5rbebS3QJLePN3hXrdL2uvRLnMOq4pZaLNroQ3SCJv8VFaF4ORAaZpycnJCN8+4kmUc3nmXvcGAMAxotdqMxiO63S6z2Yxubwm8AD/SCKMZLPV47pmnuLS+yunJMWHgEwY+B4dHpHnOaDIjLTQqiGp+h0QoiUIjhEZJs/B7MQiMApCgFBpIspy8qFBCLlAcISwXxaBZDkL+r+uXuPzBA7rfeZnlK5f5oy+9wDcvb/LanXt89/W3OR3P0cK2obfCVk3gKcrCNpf0wxYIZXkQVYVfuxaHQUCeZ6RJAnhoo5CZJj8aY1TAl374h3j91Zcpi5R5VvCJ51/gv/mxH2f8la+SnY6YS8nLrYi/p3yyRiLQRDVFHTQ0HYQvJgxCqjqAU05qZLcZKa2qSpx1OW8e7udHNQJjGoGQS1zmQUARBiwbw5FUaF3xjqfoGcOm1qxow1DAV8OIL7dbdNoRly9v88ILz/M7v/MN0BXTeUxWWuO4KPSp8hwhDaeHM7K8XKgIgcWa5vs+lbGoTFmVIIT9v9F2o2TRwQNtaSJoLLHVKZHsWli/BrMIJGwVzgC2LGdqAr+poCw0uayQwkMbuUBohKj9oYRFLgwC6vutK0uErdsuYTdhhe0JJc5QnspxFR2K7AKUxROxz6RsjoWyXqclYINIYRRa2O7TMvDq71cjOHVya/2EfHSlybMC5Sl8zyBlAEKha0VfVQcsrsWFJckrKg1C+vWYEXXQUdgmvErZAKgCY6z0WwiDkAYjNEraYCBJMwpXdpeCeVyQFRAEhlYkazPGgna7hVIB47Eti7tEKY7nlkDu+0ivRpZMhVDgRxESSaVLMFZVp6RX81Ja6KpEC4EfBVYSnhdkhQ2ESl1ZVVClUKVid28EurDfiWOEkAwGffKi4IP7b/+ztvA/0PEDH6y4BaHb7S5KELYNuQ0C7OC2mf7Z/8/gVcenOA99NuHSM0hY1V19hVCL3xvDYmNuIinNnzl40g3+mqJhr0VbIpepr6PJdVkEOpXNIqw3gVpcr+VSWBg+z3N8bKDiOiV7jfowxiyM6KSUtm9OGDKfz7FOpClVWVrPD1fWwjCdz+25AvvevHbH1fU5jQTR4NA49MSVwtx3at4bV15z39PzbIQ/nUw4OTqGOhsUNf/HyLNsypX6hLCT2BhrblUUBYU865YNjoBtW5RLKalq4qgQYrEomTQBNEEQ0u70LCcjTRHCLp5JWpHWHKf08NA27VKKeZZRBT7HXl1SqL+jgjpjMovnLGt0ophM+PzxEZ/OcjpaE6yu8s3pjK+JLqlSlixcc6sMkk6nx6QquX7lMlHoocuK6WRKPJshOm3a7RatdhvhBRTGo5zFVEaQ5QXFNCVPU9ucMLe8m6rmeClPIZRCKevzYhfMnDJP0EKghUEaje97BEFIp93lP9i6zMcfPOa+kogr22RpzpXvvs4fard56k//ST7zuS/wP/3PX+GD+4/ot9p0WxGtQCGx4w7l8/6dD8h1SdRqIer7pKTAU4ow9MmiiOl0RtQKEVIjFZyOhnzr29/GlBkvPPsMuzuPeemNd9ja2Wf51m3eivbg8ICfShJKr+SXWq1zpVBXunWHG3MfhUzCmUdRWbdPEDad/9C5PqqE0ex11Zz3nudxKgTfjiL+yDxGSjiRilZhlWP/MAj4emhLk0oJulLgLS0xGo24e/culy9f4d6du5R5QVkaPN/6bFxaX0HnCfvJ0YIP4gIvV2IFWxJygcS5Q1h/Ixts2YDfzR/XR6rJkWsiKlpbU0hdlQhhm1tWVUmeOwsHUfcEc4EbyBpV1jViJIRcEGjd9cjGff7Q9XIejWsa7QlxRm62aw31687W2mbgungf50URC6sJ93NtyGtzx6IoCAgsn0s66oABqsV73X2xvc5sUmvvby2VFwZhsN2R08wGmL5EV8aSlZHAGb8nTVPSNMPJ8PPcypAREmOseMHLbBCeZjlKykUiXVW2nL9wOTcGYyRVWYEpMMogRUErimw5L8vrIMWOI20MgVRoY5NTo898o2T9jCutUfVYGY0n5HmG59n5LAScjE4tWhudNRH9Fz1+4IOVKIpYXV0lTVPG43HNS5E1knJeHdScBB+GM80CMrUR8NnkhLMsrIlIuJ+78zbPBWeqovPuleYjF45m7fVDE6o+Z1kUGKnPNSF0n+9k2GVZ0u/32dzcZH9/f5FRuuDJIRvD4ZCiKOrArm5M2IC73bncwlQUxSIwai5YFxGk5j115/hfWnjKqqybCVIHLhJPCFrtFnmWUdQQvpN0u2sGq0zwPX+R3QohFi0UwEoRO53uAvG5eG/LqlyUpRzx2lOeNSGTgqosCevAtqqquuOzXbiiVotOq7fo1OueuVUPFDYjxd2rij+epvxklnMoBTtKcqWs+OJoRFoU/PrliK0rl7lx4wZ37twhTZ1pVMbR0QHXrlxGSEEYtej2l2i1IpYGy0xnc4aTQ4bDIbv7h4ynMwyCTrtVBwHhIuOeTibMZlaaaep7HfgemIqqyvFlBVrT77R47uknCcOAg/1DPnHjFi988JhiqcfKlW3GkyFVOySJ5wS//x2KJ26ytbHFn/jpn+bXf+u3Od7ZpS0VqqwIPYXOC9Y317j02c/yxnvvc+Cst43BU5IoClluL7G9dYnVlQGvv/UGygvx/JDJZMr7d+5w+dIGoFiqNJ3vvcHu0hL4HteefpL3leRob48fKkt+wxhGF/gRzXJuk9twcX65+fpRpFM37z9qfjbXAWeP4N7fLL38fd+niiI+naVc0ZqxkHwl9PlHvs9PpDmfKws6GOJE8q35nK9027yyv8fm5iZZZhV0rVYbISSrywMm4xFK2GDEXbfzy3EJizVJ0zZJqq/HzY2zNUecu+aP+rvJ73PrXVWTU40RaFEjxI37VRQZSImhspu29Di7bZbf1Aw6LpalLh4XidH/tDJ48zzNtbn5rBaZBGfr0PkSk8Gck3Db8qttlBgufLrcvXHXU9Uot5Dnx9S58rvWeLVqSwixUH82v6O7v00Uz9TouaxtHlwi6I7RaES7FZ37Tu7zz/icZy1bdI12C2z/JTyP6WyKxiaFQlqEWUhhjea0rtFicw4Zz7LMfk7tWJ4kKe12m6gVkI9yJtNp83b/Cx8/0MHK9vY2nucxHo+Z1wiAHTTng4gmWtHcRIEF4uHgWgdVwvnsyaEX58tFZxv1xa7PzTJIczAjbVHm4mIG5xsLuv+71wiXdYjzmaGbIK5hnud53Lp1i9PTU2az2YcGbZNE15xszevRNSTrFkGEteZ3A/RilnXxj7vfzfvcPNxrzhYci+IIgfUTUR5RZFuNF4XGC86QruXl5cVkqarKQuF5tghk3L1x/BvnVeP4Se5e2v4zFaKevNPpFM+3PhRJmiGVshJNz5IYqxrNEsKWjsqqsrVhc2Zs1xxv9h7bEpAQgmWt+UxWcCjgSFj+yaMyZyMM+OODZUa3bnJnOGQynbJ56ZL1hig0vicZnZ4wnUxoRSHzOEYIxeHxKQ93djk5Oal7EXl0Wh1AkGY5najN2trqYmxWuiJPM6paFYIWtbV8SVmV9Nst+v0Oa6vL9DoRzz31BLrS+MLw6evXyF9+jdnKgGI6I68zsNXNTaZvvsN3fu03mF++Al7IzatXmRwdk5cF6XzC8sA2PXz85hsMhGKj32fe6zGdTJD1XHGL3ebmJn/0Z/4wn/nsi/zDL3+VQgumU4PyPcKozaPdPaKH9+nXZRWTxGRFQRSF7CnFVpkz0JpRQ47bHHNNLlnz58257LLQZkDj7uFFlObi+d18bXqZAIs5lyvFP1jq8w36DLRmN03ZLwr+bJzwM1nGvufxWCq6VclPpXaT+h+CgPF4SqcubwdKoKTg5HAfjOVbeJ6HJ72Fp44rbTsvIyPOr2XNZq12Hp5t/s2NtTmPm+tYM2goSsv3sGRLt45SZ/+2/K6U47VUaC3Pnf9i4uju90cFhc3/NxVbbt1yG/JHrWvuGbnzSHneRdxdj/u91pqyFlc0k8+Lf7t7cU5s4Ur9je8CnK3RvkJXTUPM87Ybxi6JFJU1aPPDYKHcNIuWKOW5e5E0+pCdQ9cbfe7s9ZztJ1VZgWIhhW9FEUpaZVBVVbWvlwEjKNOEqqxQnM2V5nfXWtPptMiKkjAMKKqKfDan1e7gVyHr62t899X3P3Lu/EGPH+hgJU3TRXfU5qav9Rl/Aj5MymoO8uYAPcuwztek3dFEZy5+5kehB82IfTHoay+F5rW5BcH9cQP4YgBQlhVS6HPX0AxwnIonSRJ2dnbOBWEXCb8flSk2EREDKCHqOrJeOEq60lHTuM4FNFJaCbRDHtz3vmhOd3HRMYCoeUAum4mTZMH1aHbXnk6naK1pt9sL6bmo2xu4/7uJbRfuDOdymuf5WU25LhGZqkDU120QZJkNes/GiKnr6GfX666hrEryRusAd5x5yNgvUJZWitoxmkeiDswQFHnBNAwoRyP6ecHG+rp9plKxt7cHlaHbbrE6uAnA/v4BB/v7GGO5NUIIev1lyy8qra/GyurquWDWlQbKsmS9Pn+RW2dMKUBXBf1+h363xWh0gqc85vOYV197nU6nwwsvvoRp9yjCADOa4G9uEg9PCE0E5ZRobQ25sspkPGU4PaBCEvZ6HB7sg4KyzPnD4znPDkeEpSY+PuW7nQ5/V0pSQa3Ksyq4qqrodjoUWYqN6yWDwYA8yzkZjZACnnzxJZa9d+gaw1FdOvM8jyWtSTyPaSNoNcac46i4Z+PmnVtwgXP+H8353ZwXblw0CaLN4EcI24SyieC537uNSinFNAxt92bg35pM+T8lCQrDagX7MuANaX0/PpPn/HoUMaoMQtkxroQN7f3Qp8hzjFSgfLSxpYY8zxeb9gJtrUqyxhxsJht2jNt/u2SnOTcv+kW5dWaRMJQa4flYsikYKioN/X6P4XCIH/qYvLSEVWGDAruOWTm2EWdrbXNdc8/BrYVNxaZbO5sNOpvrbZOX55Kli2uzfdYfRsPP9gH9oXntUI7m5zukqrn+2+7JH+7tdj5Y+nCy6BIro825MuS5MSw+rIYqy7IWjqgFsuuSueY1KGUNJ9016zr5dYn4cn+Jmzdvcvf+B2R5TuXurzgDRi4GkedK/LoiSZLFWiuVpGiFbG9tkf3v3cF2Op1+ZFRszPlyxMUIvfmzZkBR//YjP6s50OC8cdRH/b45eJu/E43zNQeqe03TCvtD2Ua9+TXP25yErpzz7rvvfihj/Gd9F3dobK1W10PTYGyUj+1nk6YW3hsMBguui4vk3fkcTOwmh7tPFwOV8wsECGTtnWHZ+kmW4yuPqiywrHo7GePY9hhxsGwURaBk7V9y5sZZVRWTiW1A5nn+4rObfZeqmuS5PBjgBwHHJ1Y2rrz62Vj6n0VIhCDNssUGtAhI6s9sfj93VNUZP2koBHMhGADF8jLr6+t02h16ccx4OGQ/TSlrGShA5PmU2AmexDFJmjKZTBabqg1EvXMLpaoXIVWjQhdLIUtLSyzVJOEiL0EYsjghSRNGoyGeFJye7lLpklY7QutTdo8m3L79BJN+lx/aPyYejaiSnL708NOcd29c5ckv/Ahb85j9oyEPH+8RlAUqipiNh/zY3gE/PJ6yZ2Bf2a7FPz4ek0URf7f2D4rqgPfOnTt859uv8PD+PcanE7qDVcKwRZrmJPM5gVKwusLouWdY+d3fx2Q5qhVRHp+yrg2/2m4zjyJ8fd6D42Ji4P7txmXZQM3cRtIMsJvBfvN1F593cz43A6FmwuECZaUU/+pkwh/LMnytOVaSUEpulwUF8L7yuKoNSyUMhZUld1shn3rp40SBhxRw5+49bty6zTvv3+Xo+PQjeTh23Tj7vq60cDYuFFKqc2hBc91xY6c5Z13mbnkQgrIyBDXvyPOtYu2FF55jOBpyfHJEms7J8xItNIFvS8yeCqCxnn1UAtW8h81rc9fk3uOeS3P+N4OBi2upnZvWhr9Zpm+OD98PUFItULIm+tEsFzaReWiIGxoBiTvOyvAlge8vZMnnxg2gPPt8dVGitXW1DlpRbQx63vW3GZgBdNotyobpZlavWQt/G0ltGCjxa6WX73kIrIv6k08+yerGOt/73vcYTyYYGo1nhaC5/zQTfvv5AcoLmMUxvV53sW6NRmMr4/4+jx/oYMVG6eW5h3V2487Dlc1B3nz/xQF1Yf8+9177e/Ohc12MwN0gamZtiw3bUAcdfGhBcxGvg2+bi6Uxxu3qH/lZ7nPcuZqoS/NnF0sV7nc1hlAHLPV9qCcPQiCVhZWn0+mif5FDMpp17Ol0SpZli6zIRfwuOHCf18x+ASr3/QwYYRUZRWm7OXt1cNDMss4FqFIsEB23IbjJ2rx/zUzNHU8/9RTLKyu8+r3vYWrmvymtjbtQVolge2QUi8XE1LJW0RgPF4PCxfOqO4GfAN/0PP61Xhezska4tkYnLwgqzd7HP87ypU1Oh0PKNCOez+sRUvOTfI/jExscttttPN+aTQmh6pKiXtynqiwwukIDRV6QFzlFrQRQ8oyA6ZAwoyTdpQGD5VXaYcR0NiMrCkqjSdKER/sj3rzzG/xup80jXfHxncd0Ss3RdM5v93t8fTwm+uWv0O0uMc9yigpUFLHUXeF2p8cXd4+YdzrECEIkqRDEZc6PAw9u3qR3/RrD01OMqZhNJ/zmr/82Rms21y+DCnjwaIer165QljmPHz3g9775TYrnnuFLLzxL53tvok6HHGUpX29F/OrSEv6FTcttzu7ZuGBukf010EBXTv0o92l3vo8i0jdBEiprAAEAAElEQVRfdzH5aG6wLtMPqor/4zzm35hO8Y2hC0htOJQCYQQ3gFgpYjRDYcuilbZcvOeefZq9Rw/51rd+n/XNLd577z20sbL4w8NDq/poQP+LMXthg/mnbejN37n75X5+EfG1GzXW2r5WS6ZpRp6nvPvuu1y+ssWVK9skyZwsK5jNrKQ2CFpQN+sU8sOeN03juIulnItHcz1rXv9FpORi8qprk8Ym+tC0kXCBTBiGi0TIldmaAUZT+dg8vyNbO7l7M9BBnykmL3JUhBRk9cZuy0Hnkfc0Sc/ta+5wiWNVFFy6tLko8zWRxFrahTZ1405z5hpeVZbvdO+DeyCtYGU6m9nn4Vnek+MoXRzX7l4URUlVlUihSJOUoiwAu167hPH7OX7ggxW3eDQRDiEs6evDPBT3u7Nsw3EKLNxfBwOGxrnOw8VnE+cMmrSEXtP47GbGYB+qVR9Rcxx0HXicLSauzOAmT3MQL76nEIvNz12f1i7qN3V9WJ3LNv5pUf6HD4tIWbdUsQio3FHpykqgtSbNskXN1RhrliSlwA+DWl1QB42VRgZ+DQ8L6p70GON4Ki5Qo/65DZeElCAtbFxVJUZb+bEju0oBVVng17B1f6lLmmb4YUSapCyvrxEnc1qtkDROQHjkZYnyPCbTmZUcaguTIyXv37mDMYYsy2smv7N0t3X28fSMXyGQdXBguT1SfBjBWjy3ejDZZ6T5lXaLF2/e5KV5jP7gAUdS8lqnwz/Jc4rHuyilaPkB7eWQSlckRUae58TzeMGZsWZMHmmSYUx5Ltg1znoSbPmusoRZjAGt8T1F5AcUVUmFg8idtX5CkmT4no/n2y7V3d4SYdSm3bW2718bBLx9+xarKPaSlCEaXZYkoynD8Yw4ySkrg/B8EBpV5Ij5nKEfgADl2fE4Bi5lGcnuDu8dH1GUOUJAmiSIUuN7Pu3ulOs3b5OmCWVRsLmxzvD4kKOjE+483mOn3WG6NiDOU3bR5IMBvh8SwZl7sBCY2gzSmXnJWiKfZTmVrPA926fKes/UPCopQIOuNyxHlmzOu4uITXMMXAxW3CBX0kNK+BNxzE+lKdIYjup5uWoMsijZN7BqDJeE5GuraxRBRBXPGSx1CXyf09MT7t29R5zETKYT1je20EKxt7ePlHXzudIFWxYVdAG+XSPPFji3blxc35oohzuasm+3UXW7XdAOifCQ0hBJn7LK8f2Ay5evcOnSGk8/+SRpmgGKe/fu8+67d8nSjMqAlR9LysqWet06aLReKIku3uPmRu0SpWaJZrHe4VRQNVasbbnZaLMosQgEQteqIVHLpoVVfmpdLTxq8qKwXD5d7yWLcvwZsditCWKx79j772wtXBBT1U0AhbA8RD8I8P2AvLCtI4QUICRSa8oa547CwO5VBoRpOLXrEuncnIU10jw8OqTX7RHHMZ46s8KotENk6rYn+sx/yVM5VRhRVBXXb94gS1MODg8xNafPc+OnVjm6hpX2OmygKur7JoTGmHLBGbTcyfPj6V/k+IEOVuC81f1ZpqA+EiGBs3KK9ewSGOGgx7O6nNWan3UsbtZG7ed9mK/SzA6ErRqc+z9YOZ1Usn7Y1si5GZ2W2m6QF70gzj5LLAaqPa9ECGtE54yXmjDuRSa5+32zPNPcZJWw58eYekmrZXb1xDPSyn91WdnBi0JKu2G3Wj69fo/xeIzwFVoJMJpOVHfkFQI/8K1EzwjsWqTqfbRAKVNPUmpnx5J21KLKa8IjmiAMrLW0MYRKsrq8RFnkrPVb6E4IRjETgNFsXlpHyYqjYs7K6jIyjBjOZqR5TF5Y34kiLzk4OGI2tz4ERVEsmkUKIRDuPmgXxFoPBGtGYRaGe82x1xwHToFhsOZrcVXxn52c8vGtbfr9Jca+ZKgEoswJ8K3R3nRGnufM4jmlOCOfep5X+14YdJnjKdtJVwivfs4KrWs0TwikJyxCJRSlLlG+4ujkiMPjw0UJLQiChXJKKQ9NhZEKoyuy1FqEx7NJDSUrCp3zSAvi5WX8wRIrAqvWKkvWVldxDQVV6DNPYrpphplNWBcwjlr1uBP04gTRbrHx9FP0Om2MMQuF13w2YzIeW0dNk7G+tsTJ4R7tQHH18hXu3rnHnXs7XNre5t7RkLjSVFKxHARgNKayCiSprORSeT7WADChyDNMWZLW/Zo8pfCUoipL+zwFqKBmgyLwhIeqN54gCEjjMwVE80/TzbpJtG8igEIolB+wrCt+qKg49H2Wq4qgLo8pY+hUmjUMhZR82VP8PWHo9tq0OwG3rl9la3ODnUePuH7zOt2lHgfHQz64fx+kR1HoRVxqSzuOS2NLuAjOo7S4eW3NzZpIzGKdbAQtTQSh+X5hqsUmbFEERRS2mc9S3nz9XR7ceZ92K2Q8mbK8vMzzzzzB9uYq9+/f59079zEypKKy64gUtvyhnS+WDSrcxt68viby49zIHRpdFIWde9Lg1y7CZyUm6zGjDaBlHYgCSIwWVr1UAdIsWkR4QUipDaasKOvrwbkxSwFSURlb9vU86/ocBGeqnLNgSuD7IVJW5/atvChBSPwgpKwqdJ1oW5fvBLRG1NffCn0w2CC7LClK6xllkHiBj+f5pHFW9x+zCUxe5EhPURqNadAxXUBbVbaFQlFpWlHIo0eP6Pf7CGF5dVJKdFmbJArpNkmUdH5eNnCRxgY1YChLgTD1PTXSlp2/z+MHOli5OHDdRu2atF2sPbv31Bznj4QK3aBqZlLO5fbM9fG8pf3Fif5R5aFmxiWFAnlWxnJIDDT+amQPboGU8iyqvihrc4QxKT98Xc171byei3XcDyEEjUw9CHzKqrR9YKREV7b3kkSQpgmDTp9ACbobq5RlSV5WeH6A8qz+P8IS64yyPSMqYYMVRE3gqhdNq3jCWr1XJS1P0W136Xe6KIktGVQV3U6LeD6h1+8xTxKCoEVVGVAhSZ4Rn06RCrJSoCcx7b7PLKuIC0OlFRqJMYrxZEoynyCVt+hU6/o9u3ty8T591DN3r20uQgvYvB4LEus0+14UWT8EYZ1bq7JEV7YXkNFWAhiEIco/Uzg0a+tS2p+Xla4z57MxsRhj9ZiJomgRcMMZXOyI6c3N1hEs2+0WypOouudPWVgvjVxblYCk8Z3qDfDg8HCBIKrAohW5lNzd3OTTj3fxZMHM07SLnH6a87sba0x9j0hKet0eZcdKyP1rAfP5nDiOGQ9HCCHIspTRqO5S2+lwOjzl5OSE9Y0NdnZ26PX7tKLIOpEaU/vJSJTn15uKIAx9lID20lKtBLOtBdw1u9KP1GfI5gL21gZTnUlQXV+xiyRm93tXXjo/ZjRGl6waQ9cYHivFQRBwI8tACI6VoioKDqTiH7Rb/K1OB8qS2dEh6+trKM/j8PCQ7c11et02b7/zLifDMWlRkeYlgR8u1pzmeLHriw1aLq5HF8dz88/FMe6SnGYwXlWV9VmBBeG+WR45PT3FUwblKSSCR4/2uPv+PdbWVtnYWGe0OuVonCCFdYk2urIBr7Ddm43RiPLsHjtuyEX33YvSYNdXTEpBGIRYryVbohBCWIWfBqM/jNgsgiBPLhATN49caflsnznPCWsmtG5uuDKQGzNa68W9cu9rlhbDIAQpa1RP0opCe6FG193lJYGv6HR6pGnGwdGhRX0qg2ckGDsf8zwj9AKog/JKW/6hkGekWvccF55jlSXIugTp9u3bPHjwYDFffD+o0aUzJWdzbLjv0nw+WZbR6XQWoofv5/jfRLByPnL+6FryxdqlIw5dhDwd8vJRk9cdbiNyC/XFfj9uwDa5HO4B6zpLl9K2oxeejza2ZKJNU8774cNtEG7BbFrbu2DG/RvO15sv3ofmz5rv+Sio1QZYhs3VFaSUZGlKu90i8HzSJCX0BUstn5WVZTq9PrM44dHjXYYnQ4RSeMqnV3sTpHlGp9umApI0o9RWPmeEpNPqooSgzHLaUQtdlHhK0fY9QlPR6/TohT5ZlrKyMiBqRfWzr9BacHQ8ssHRLEMFIWmcgZaYJKMUc5CSMGxRlBqjQGgDVUHoezaLMlb503zc9r5+ODBp3iuHfFwcZ2ccJSsxNQLmacLDnR2kFIBmaalvG25GLXtSbRYW40VdbmouBGcZ8xmi58aimwduzLn3dLu207PLOt17m2RuV+O2Crs5Bl2XuBVZlqOU9R8qioIiz+oSnMK5m1rysTWZE0CSxFSV5u+3Wsx6XT42nbKSGBKl+L21VX5nYwNTVMT5jDxOF8iWxuBH1io+y3Nms9lCcWa0ts83z6lGI1ZWV1nf2KAsC3qdLmHgE08nFIW0FulFYTNBIQk8hajvrSUi2z/tdgspJHmRk+UZxqgaFbVNNOM4sbLvLK/XjA/7gjRJ5W5Tas49691jrdunQhBLQacoeEsItO9zxRh6RUEO/P12i79dq0LcHJ5MpsRxynw65vjwgMBXPNx5jPQChBfQbgf4XrDYZIwxiw2oqqraePKs35kLJppKDreWXVwHLn7Xi2uuWx8dP80hHHaj9hHKq5EMg1SS6TRmqbdE5Ac8cesW5v5jhqMxXuBTlGfrs70+8P3z6svzpf3zScTCjM5ds5G2P01ZYjsv1y80Z2IFjC0WSeF4f2XtQySpqvPcnWbA5NA2Z7JpjFmsAU3RQXOvcP3p3PxtEmWNcaadHsrzyMsCU0vTBZpLm5tcv36NbqfN/t4O7XaH1bVN7t77gPfeu2NLsJlBV/azW2HE1e3LHB0ecjw8tZ+vBLo4Wx+a16KUwvN9Qt9+B9/3SdN0wdmRdQAl5JmYovk8XAmveZ/c+Z1C6Ps9fuCDFbdRNAdtk5TVHNyLcopo1PbrCXs+K/2wh0ozom6ev/nv5mc41KPJvBdC4Ksz7xSAoszOAqJGkHCRjGq/h0LWg6wZkJyVfs6CjouZ/8UMoHm9F8tBZ0iOy+QFUeiDqei026ytDIjCiDSOKbKUzUubDFo+noS9x4/QwnYgLYoCnVuTOqErWkHA6uoy0/kMFQRIqZnO4xqyN1RFRrfbo9ProxBsrK1x5dIWx4d7dNothITjk2Omk1PmM8np8IhLGxvoZI7vB4QmJU0SljsBQatFUUUUaUKoJGmeoU3FaifAGEEymxN4Pn4YMZvPLCwsfbLSZqoW7BJgrGrgXKB74Z42A95zgWBdE5aNoFXUfXksIx/SPKfb6+GHAaKuJaOt3BR9BnO7DdFmTfkia2sufBefvft/q9UiDMNFqaVJAm2WDc+QRWM7z5YVgR/i+0Gd8dr3llVJlmfnPtf9abdamMrWI4IgIK4qvrKyzD/p9ehXFUkUkvd6NhiZzRZzy0nOjRQLx+J4PicMwkWDzDAIODw9xRhDlucMh0PW1tbotNu0W20GSz0G3Q6+51FpzfHpKafDEYEfkOU5S/2e7eAd2IacZZFT5NYIcBVDX0pGUnKCtUVYHizb7sXKBk9JllMU6YfI703Uy83bZvCqlCIKFUpCoTxeTTO+NJqgPI/T1XXmoxGDquJrUcjf6nTs82kEkULAweEhuszpRCFplhBGEdILUEFEEFh/jCbxs3l9mvOom0Prmrw2t5acKws35MIXkYPFGgGUZbEIgpslG8/3wWvZDVeXFEVGoDwOD4/YWFsliFqsrSzhe5LT0yEysGaMZeUCIMt/a66XzX8313xnZOlIsABl7tBnUZfXbSBp3VcBziNHQlieU810qV15q8W5g8AGhM3gtLkHuGftzOOaa6n7vbvfDo1wAgR3v6uqwvM9lIR21CaezygLTTyf83hnhzDwGJ4eopRHkqQcHx4RhhFpUqIrQ1FmeL7gytY2RVEQhiH9Xp/RZAzSBuWO4uDGgtv/lFKLvlGu+7pTr9lDUFZnyY4bFwu/HkGjmeUZH8btod/v8QMdrFwMMtxxEdprLuLWE4QF6chFu+73TXjvo5GZs06j7oE1N3kXsV7MVBYTrD6Xf6Ep2iK4MWeLlPuO7ju5azgj9p5FufUd+dD9aU7Gixn3xY2qeZ+aKgq7iRUksWE0nhCFofV4qKrFBjZTNmtN8xLh+YxnMXleIqRFgJSALEvBU4zGY1rtFqvLA3zPNhQsqlo+qkvi6ZjNtXUOHu+gs5QosuWkIPB5/oXnGez0GU8nTPdjWoHP9sYVsiRm0A7ItWYyj4naPYQMGA+HbKyt0ul1ebj7mCCMCHyf8emQzY1NZlnBo/19Tk5HeFGbeZpTlDHlgsh8dt+aAWJzTDQX9ebCJZV1sDXGoWnKZieetKVAXZEXlp/iWPkWtvVQwkMKb/GcmwtmVVXESbJYBNzzctfhSj1CWJWUUwU0vW+0tr2V3DU3O5YLYQmTGEfOtG3h3TCzyIO9L0W9mbrxkqQpsqb4lGVpA1GtOazvixd7cHLiTrIglwtpy6vaGApXWDfQ6/UI6vbyk+mUUmva3Q5JYtGY8XjMtWvXeOL2be7fvQO6IFOKMIzQZUlRK9PiOCGezzCVpt/vWX4W0BGCPxEnvDAeE5UVcyn4Xq/Lb62tIYWg3W4vnnOWF4t7FIbhYn44VCEMQ6bTKUKIxYIdRRGbm5t84Yc+wXw6IokTfveV7yEEfDorWM5zdvKCX2+FfLnV/hB6Z4zBD0K0gThJWer1yPOMeZyiyUGmBIH1suh0OgsjSPdcgQVB2G1CYRguggqn6GuWj5oZdxOxa66TC2Jrg9Tv7ocLFmzpyXYQDjxpTRYlJFnKZBbzzNWrHA9P2VxfJc9S4jQjLkuk8vGDAKmsss0FQm4dvKj8ac45hxpZ2/nSZvueh/EUQRjY19WEdyHPEkMhrHigqjQohZHn0WmlbI+whRdK/VnOfM2tny4IcRu/MWf+V016QDNYcTQD26C2BOOjgDzPuHL5MhhDEs/pddpkWUIapxRVxVJ/1ZZp85woDJnHCdpUBEGb09NTrmxtA7WXUc1X8TzPovj6TH7trmk+nyHq75ZlGfP5nHa7vdiLgjCkKqtFma25BwJ1I9GzXnNZln1kw8x/0eMHOlhx/ggXTZiake/FjLgsS5Dn5WBuALpBr43BV2d2yE2U5WzBbsr3zuRvTVSiGRQ0J5gb5O51S4MB4/GYvChw6KSTH7oMWkpZ9444gxbhvHSv+e9zcOiFjNv9zNUX3SbXnHBNmFgIQaVrspqBNC+RKIy0PY3itMAEHnmag5AIren2BxSjMVIq27G3LKmKEu1lVgVkBLqoCJXH0toGQejTabfJ0oxkHqPLAm0qHj56gBcGNbogbaPDXhdTaa5fuUyZV6RpSRi12NjaYv/wgJXVZfb3T+zEVZKyKJiMRrSjiHanzfrqGv0oYjIcMYtjqErKIiNJM7LaIRLhgbCEYyE/LPFsZnoXj7MSkCUoOwaQrRmrWllFrSLTTGczlFS0awktWLmgK1W6wwUdbjMsK33md9F4TRPt8H3/XJddN04csdaVhs6ge9tcUNa9VqyyxLqSLr6XMLZxGwY/qEmIUtpM2i1epl7osT6IeZ4vNo4zZEnZrtr1OLIbtaDlB7YPix9YRChJMfV1drpdToanlj+lrFvr7uPHUGni2ZQXP/YMk9mM0WjMUr9vgzttmAZWhim03WhPplMkhp+Zz/lDacZJEDBse/S15ieTlH6W89ubmws1Q/OeOm6PC1SCIKDdbi/uo1LWwM3zPNbX19nYWKfIU6SpePHFF/j4xz/Gd777Kv/NnQ/Qx6e8XxYcy/Pdh5sIXhRGTGZzTFni+T6VtkpC+8x80ixnd3d3sQG5dcs927Kszq1vbt10JQw3/90YaXJ43FrTDMqbQbINNM9Iu4ssW2s8zzbBq6qS0GtbbqYUrKyvs398TPZ6Qcv3MGWOr+TChEwbs3DPlp46l1g1S+tuLTXGLAIuN46DwCcKLHH80qVNWq0W3W6P+XzO3bt3sCR/+73CwCFNrqSsqUpswCTlYlN2UmQ3913wd5FG8FEJb3NNdtUAh1q4OW5t/RVZlmJ0xerqCuPRCE9JpIAoCnj26ad4tTTc++A+9+5+QK/fJwoDwlaLSpfkpWEymRAsKyaTMfE8hlrUoTyFKe2C5MapQ2ctWqvQ5VkA4gJwz/MWLrd+EC7GgbOvcGiTqYOVbrfLpUuXODg4YFy31rgYhP+LHD/QwQqc5w24zf3iYLm4mFsZ5Zmhlo1wvbNJeQHabWYWdiKeff7FcsvFSdvU07smXdKR3SR02m1WV1botNvs7u6RV6VV4YizWudZgFGrlhpQrMtibFZjWfSVriiL8zCk+x4XJ3sTgnSLU5Mo6LIlP7BdRqnZ7whBO2pR5DnzOCVJG72DNIgKwjCyXZPznMLYHib5PMGXkul0ji5Klpf6PHXrFvt7j5menNAfDOisDFheWWE0GpLnOYenI2aZbedeGcPR0TE3rl5heWkJpSQPdvdQQrOSFCCg2+kTtFNMJRFeCy/qkJU5USdiOB4xnj7m8cOH3Lp2HeUX3Lq5wcdefJFv/O7vk41neEpSImobe4EFuw26snLKj3reUn743gonNaxFlBoWTr0WmnakworZfEYQBESBzf6cA+ZFTxkHGxdliaosSfRiENXcUBwPpanmaGbO7mguwvbibdnH910gayWmVgZc1t/mrLHkORSzDtRk7c/gyl++K9dWNlCxsuC6DCEt90UBWZYRBAHdTgcppV0oPY/JeExVZ4mdTgdZuwMnScLp6Smh7/H+++/z5JNPcP+D++RFSdRu8yNf+hK/+Zu/hRCC7e1Nqqpib3eXga742GTCfGWFmZSURcksahGEIS/N53zQ6bIjY6qiZDafLXpQnc23881B3XxvchfstQ3xTMrWxoCTowMOj045PDjg8WxGKuHkI5JOdz4hBMpTxMN4gU70+32msxlVXa4oy5KkOuOuuXHjAiobmErKsqIsi3r8WnTMmDOSp9uUmwGPe8ZuY26uqXbNtbyK5me7+wAGIXWNqtZZelUyTWKuX7/O6PgIAkW73eLatSvoh48p9ZjKSPKyRAqDJ8/zAh2K5ca5C8RdkO7KNsJoFJJuZ8BkMuTwYJdr168zGAz43Oc+Q5bmdLt9Dg+PbPACtNotlLQCgDjNF+VWhxa5++DmYxOdcnPAtWQJgmDBX3LjwSEpbp649dIhGQ7lr0prgmm0YWNzndDz7P5UFty5c4+T4xGD/grT+QwpJBvrywgpWVrqs7O7S1t3wFgn83a7TdCKmMcx8yxBV9bDyl2re24XqxBCWH7afD6n3+/bRrdVheeftZRw5eler2efTVksDOlms5mVttfPbDqdfniQ/3MeP9jBiqksIRGDJ20mqqTA6NLaQEuJrAmAVt5b4qgIVV5S1YGJMQZhDJ60PXGK0lg2uLAbi7Pf93yBEKqenAKoMMbKthzkIur6KLCQwCrPQwGmqtCVwFR6kVnGkwkfTKd02m18JagEVNrUHZPBddt0CqSyzqrO/D2osz0fhG0TH4UhR8dH6EojpPX7sFp4DynP9xtSqtFPiQrPt9bZfuATBIKqtOhLmuUY7VmvjiDg5vWrSAwnx8e015ZotVtsbm4Akv2DQyqt2T84JMsTsqzABC2M9CjKHLRAAZWxHaePj/bZXO1z9aXn7KLkFlyT4QUDnnzqCcbTOScnp2RZxsnxCfd393jrzh2LDuUZg6U+N55+AgTsn5yC8hlNxhyfjIg6PfwgYPjoEScnx3YjKEpa4xErK8u8/tZbJGnO6vo6fhgymk5t63chMNjMw2Dvo9Yaqey4qurA0AW5lYZ2q8NsngA2KBXGusno2nDPerPUvUOEXEDReVExGk9YGQwIPM+Wjer74CmJI7IKrE1+nuWU2tSEN2tfLpF101ZD6chwQhInlihnFyNJWdmeSG6MSiGQyo71LLcmchZODuh2unYMCgGc2YgbDV5dKrIt5e2YhqaLq8DKGGsUUBiUErgOuSBQysfzzrxRijzHyvoF8yQmyzPCdsR4PKY96JIXVuLuK1AKuu1lTk5GTGdTepcucfXyJs88cZPPf/oTVGXB3sEBm1uXqL74eXZ2HnLz+hVKI/nWt19my2iissRfW2VdQJpn9v7kOYPpjCgeE4QB3V6E50u6vR7z92e27l8rRTwpWFvuEIVtDvISgVWg9Lo9K+kPI9pRxGya8CBOuX49IkkqpvOMSsNoPEUqn6LUbkgAwqKopkZYKw2mLrkaw2B5mVJrTk5OKcqy9i+yS5ALcFzZT0qJR4URnlUgKhukCCGoTElVVtjZeJaUObS1WTZ2GbYLHh3CVBQ51ImYqJVXLuBRnh2bVWXDqjQrAEGWxdy9d59+p01ZQV4ajg4eM53OEVhnVakc2lGBVEhVJ4tVhdbZIoh3JU8XgC/QYyHJC808yRau12+8+Ta9Xo8oiui2O6yv5Vy7ss362gpvvfUWURSRBD6TyQShWujab+kiUu/uCbAQOsRxvECsXALh+CBNRNsFt81GiO73WltjNus5I5iMJ+zv7rF96RLXrlzh8uYVjo6O6fS6dk7FM9IsZbu7zWg8ZjabcfPGdaRSVHlOJ2rR73Y5PT2BS6vs7O0xj3PgjKjvgicbOGmMp84l2C5Jj6KINMso8hxLtM9rCwBHXLbdwIMgIE0znnziCVZWltjf3UVXJVob9vYO/oW2eXf8QAcrfg3fORYygKcUpdaUlcYXktJoJFYTb+pN4gxttd4mdtFUBDIky3K0lmCsZ4DlLJzVGcta/w/NspCDuOy0l7jF2P4MZL2Q2NdqjJVUCkFeb3Sj6dQGIb6ymxUSoUB4TnJta6pQ1XXHql4YIC+KOripKApLqLx29ToHhwe1+2KJXxtzObM3m1nX5m/G1nA1Vua4sjzgxRc/xsnxEa0oZGdnh53HR+SZzVbQFVQl3U6LztY6ga+4vLXB2toqk2lMVczxgxZh6PHo8R5aSkojKIqKMPDRRYaUhigK+OSnXuT69jr9ts/m5gZCCMbjCVVZ8uLHnrVSusIQBB3ysuRoeMqjx7t86zvfRQQBha74Y3/sp9i6tLnIVJLtBCFsd2Y/atPtD8DAbD5nb2+PLMtYXVlBKsXe3mNeaEVsbGwsLKWlFKytrTOfx5yMRpycnvLOO+9w6+YtNjc3rfcBgvfuvM+N2zd47733rd25FvieYnlpiVmcoAvrb2HLShLfs11Mpag5L7XqQGCoDMRxSrtV4CuXsdlFoywNRWEVOWVZojxFURbkpVWXSakWfjCe9CwA5gmk56S2isipjQCp9GI8G+1M8NwmZ/sdtVptpFRk+VnGfsZJ8BfZpst4lfLwal8KfcEAKgzOSiatqHWuju0aKypl+5kYbTk7ttRhmOcxZVXSilrkVYkQVtLueYLP/9Cn+OIXPs/LL7/GV3/1tzk6OeaJa5s889QTHO7tcPvaFa5cWmWWFDzzxHU+8bGnmU1G7L53j1vGUAnFFNgsC4LtTfzAetmYgyPyXpvt558gnUwwSOazmNOTCcJUNiEyBqXgqSdv8aUvfJbJZM7f//JvgNF0WhHPPPVkXU4o6HY6KCEpkoyTkylZURFFbaSc1fV/gaGsA446iNQVQkArikiTGDAMdMXqcMhwNMTv91kaLDGbTAiCkDw/K93azc+uPaImPZa6WJTlLJ/KytKFpxCoOoA8c4peNM6rN123ZjRL31WNkLnXUSdmUlqPkDAMFyUGVy6zXCdNnpVMqzmq2yaIUzqdLsPRzCLCpcYLIoyAqN0iSy0JWirriVMUBUWWLYwa3ec3N36EQKOYxflC1aO1ZjieIyYx7XDGfD7nwcOHdDod+kt9sixnOpuSZhlGeovuxq7M4RATR3J35T7nVqu1toGOsJ2UwzBczB1g0VnZqdtc/x4XVEZRRBRFzNO5LdsIgTAwHE7Q+UOU8Tg+PWU4Gdvn5yuG4zFvvf0Oy8vLTCZTlpaX6fW67O3vsTYY0G+FhJ5AVgW3r13m/uMj5nGKFALP95CyJtlWujZNPCPuO2R/8V3MGfUi8ANEcEagzbKMrCjJihJPCb7z3e/QiXyuXdlmpd9nY2ODr/yzNvM/wPEDHax4QeQoHnXdVCP9AIrCwtB+uLBGtxO0JjliMDWZQNTKA23sIhl4JWSawIssWiNsiqOlrcclWUZBgRO/WeKkaAQuVv1hpJNAGioEFdjPpDYkasjl3CEMqMKWWdz1gHNAFFQmB51RFVaKp1TNG6kKkKC1IE8lwyLBEJPnBmMihDAI4VGZgrJI6sXm/IYihLCkXwOT8ZTXX3vDthDPLUHR80OMEChhA7y79x/hK0E7Cti6tEFy9x5vvvUuKI/ZfI7yQ9K8YGlpieL0FFmWfOKl50nmM55+4ja+J9FFxsbKEmurK1y/erlGcwIOjr7H2toaS0tLjIYj4nlBlmvW15Z57vnneffu+4RhwI3bN0niOc899SRrKyuUZcnLL3+bDz64x8/+7M9SVZrNS1sg/TNLdW1dS6vanfLxo4f4noUyq7rmqlTdv8TzMQLeefc9Hn/yJT7+4ouLDCiez1G+R9RpgYFXXn2N+w92ePjoMQ8ePsKXIXkuqWoialnVsnUZWAgega7KOoK1AbcubT+jyPct+qfLBcEzzXI8z8p5g8B2NdV1jFxVFUiJEtKaDtaZk1CWl9Bqtc6VMd3zdpmyyxYdj8rzzpqiudc3NwPgHCTeLI24c7tzLlCn+m+3oTYXfCHEgtDX63URNRm5Kit0aWi3ejWHpUWn5dNtBzz95A02VwbsP3rI+kqPn/2jP8Gv/MrXePOdd/nOK6/x5M1rPHj0iMvbl9lY7xNGERu9Pkvv3GH+rVf42DRmLgRr6+tc93wms5g0UKTTffpJxvGnXmTz6dtci9r0+wOU8vlr/69fWGQ6vu8Rhh5PPHGby5cvs7PzMmk6IwgU2pSEoaIqDaeTCePTQzrtDkv9Hu1Wm57XYnN7jdnvDckLu3F585wkyazkuiwXG7HneZSzKf9yHPNFbdjMK06LnG8qxZdbLSo/sHwipVCuYGlsMqW1k7L6gLbNK5VtIWG0s284Xz53ZT1XznDPzJU8mg0Z3TNvigyaiIzbnC9yfZxM3Q88a84pLPfp0tYGe/sHVMagZAXG4CNA2WBeG4GWCiENprJIufOVulgCvahSa5ZGAXRZMJ1OrFS4odCUsk5staE05eJ7uUDNKescWtNU6bl75Mqurgzk5oeUctEduem/4oIDJxXudLrE8RwpJN12h+eefoadBw+5e+8ep6MhBK4HmMfq2hrdbtcG+sDu7i6ep1hZGtDr9RmORkwmI05OTrjxxG2evH2L9957HyEseXw8mdTJo5UmB2HEYLDMfD5fXKtD6ZaW+pycDM9xM51ZZavVAuURJwnCaHSZk2Y5w9GYeD63MOj3efxABytL7ZBWK6oX4pLV1VXW1tY5OT3l+OTUcgzwFnVzsJB3JQTtXo+8yEniBKnqQa4NQeQjpMETwk4KYxDCUFYVgecjjITCtc5m8beukRYpBL6QKOW4LA1VkTBARSUqEAYlnC10vRFooKqwjq4e0hhA1s3yBJ6oQLmSU4k0Fb708QLrBWKEh1I+RZGT54XlACiF0VAWOZXJ6tJSVaMpZ1mIMBJTlhbtKUsOd/dptyKSeM7SUp+lTotWu8cLzz/L/u5jlpf6xLMprVbEH/2ZnyL0JP2lJU6GIybTGb/85V8hSTPW1jcZnR5hypjPfvxZ2q2QF1/8OLdv3ODB/bscHRzS7nZ48HAHKSUrKyv0l1ZQKuDw8ATP8+n3BnQ6PabzCVWeMRsN+eynXqQ/6LO7u8vuw4fIeqN8/tlnuHxpk/lkwvPPP08QtSg1ZHWH7tXVVbI8s0ZJWcpSt4USEPmS3JRgNKHyKMscpQRxnPDE1ctc3VwDrFW7NoYqmVFkCcPJkF6/zw994uP8kR/7Ekma8+DhI1793mu89e7bPN7fJ8msDX1RaDsekGRpbpE+F+AiMPUClGRWXpum2UK1pjwfWfMKKu2s/LEcIiEsg8RohLbW8hiNqt1tpZSLjaPJ4XKBgttYmgqhZgmgGYQ0s3et9aK81CRjX+TDOMjbdsFOz0lnlbLNKdM0rTtfG6toQRJFbZQM0KXBEz7PPf08X/zcx6mKOTsP7tJrRfhSkJY5H3v2SYos4x/96m/w1V/7DeIvfp6N1QFKHdDrdvE9j9l/+7fpvvIG7+7vsy9gICS3Oh3Y3OBSK6QajciWVzj93G06f+wn2J7PmCcJ7739OltbV87mk7BrSb/f50d/9EeZDo+5tHWpdmA2XL9+hc9/7jPMpmOGJyd4niQKQpI0YXV1Fd/3WVlb43Of/QRvvvUmCMmv/drXWV+/xLe/8x2U9KwRn7SEz5+eTfmpouBIKh55Hh2j+cPTOVUFf6/ro+vkyW3YTQJwWZYUWbLImo0QOEdRXSdysuEJ0iTSXhwn7jV5HTC7DbfJ7XNIgQsK3LUAxHFMFEWL6yzLkjSvaOsOR/s7rKyscWlrvVbPCDbXVtnb2UFEbU6GY4StYVFVNuHzlIdAfyhQARZckWbJxh1CCItcSUmeF0StGlEzhqq0pWkhFXmNfCxQ9RrVcWPezStX9nJj2N2jJkrRRCs6nQ5gERuHYLl55fs+ke/R63TptNt87PkXuLy1TZHnvP/e+yS59Y5yPKNWq8V0Nre+OlIxHI3wfZ/l/oB5nICpiFptgihiMpmwvLzK9StbAFy/ZktGr77yCsPRkGjRdRt8TyHbLdIspSwNeZ6R52c8HDev3b2RSlIVpX2fUGgJga+YzmPC1RXSvOD7PX6gg5Uf/8wL1v8D6tboAYPBgHizx3S2RllYyLwqLWRZlIXtCSIE7W6PoiyZTqfEdQfhoiwxWhMstfCUhzGaSpcIYdDamkgZLTEmWigqmhNUCIGnPCQsovAoihYZqB/4SGnhfRcoOLXGYgBUmrKqyDL7cHUFRVHWvU0s78ROQosKOXgTACnodDvMasdBR3wrqxJjFJoA6ppyUbPnwRHsfDzhc+vWTbIsZf/xY1sO6XcAw+1bV/mZn/wJPE9xfLzNc88+y2w6QUlbQpinGWvL13nlu9/hk5/6NH/pL/wc33v9de4/eEQR30JUKbJKIK+YjY/5zu/vcHJ8RBgEbF+5zGQy58rVq8ymEzbWN1DK4/LlK6yub3D37TtMRkOWVwZ0ex3WBkvEkzGmzMhmE564+QSPHz8miiI++clPIo22pLB4zunJCSurq8wnY05PTwk8SeD75LlmNpuRzGcIo5nN5zz73HPsPHxIt9slTVPyomA8HtfZfr0h95fQRtMOPOZFRjcKULpEVSWz4QmnwxH7j+7z2Zde4Md/7AtMk5i9/UN2Hu/x7rt38MM2Dx7skCQpRZ6QpWkd9No6uzFW6VPkBQYrV221IusRoa2M1fM9KmFVTgKL3lDZQNmhiKbSC9lwM/t1i0tzIzojCZ8FKM0NwG1UzX+7rMvxG5rE7Ga9252jGRi57FMIwaz2WnGvi+MEY6yrptGW56OwGdzJwT6/+41Trl/b5JmnnqTfaeH7PhvrkBQZn3zpBXb3Dvn2t7/Dd179Hv/mv/6v8sTN68SzMf54xJXjIXeBR3mBEYojIXhvMuXJ8ZjXfuSP8PD+PfrXrxK3QrJXXuXWk7d5+sknuXr5MsPxlIP9fXzfJ5vHhI15fenSJVZW14mi/xnQ/ORP/gQbG6ss99u8+PwzrCwv8U9+55/wmc+8RBiGjEY20w3DkC989lNEUZv33nqPn/8P/iL/v//8P+dXf+3XKdGWW5GlfCbPOURyJARBmkB/iWQe85ks5bdaLY4lC9M3YyrKUtccZ1tqNUVOZUxN6NaLtWEhDtAazVnX9qb3hyvjNJEROAsGmsGpI6E6E7BmiaZpbOiQtKIq6nEuCMKQ+XzGpUuXiOczBktLfO7Tn2LpD/0Yz77wcf7u3/8HfPd7b/B4/whEQFlplISqyBfBwEWit1ub3TVAIyDz/EU5rywrpKwWCLsubTLZ5Js0OTLz+XwRiBRFsQjelFILozh3Dxzq4jZ2N/bdvXJIlQvepJTkSVr3ECp56+23efz4MXmeM1hd5nQysmaZvo9Sgvk8rsuxFd1uj+lsRlEUDEcjBlevsbo8oChzCm1Zc6GnuHTrhkWGdIGUhueefhJtNK+9/gaj2YQ8T9nY2GA4HGK07Uwf+JZAG9bmnp7nkdT2CW7MCCUp6yaMeZ5SljYIPDg8Qaj/nTcyHIQVQhcIKRh0AqQ0iGzERqfFWru/iPCltCz6IAgsQUsAtYlSWS4tFvE8t3Xddq9Hqaua4Ggt7LPcZoStMKDth9ZTJE0X5ZgkqWFBqfDVmdJILDYQS0Qqy4aUsLIGRc663o9CcmEbBVaVreNrbUiTlKoy1pyrPDMecqoJ9x29EBCW3+Aablm+hMBTPlXlURSl5TvkhUWZdEVRlLTbHTCy7u3Q4dp632bn0kqQfFHx5su/XddaU3bff50g8FGeIora5AR4EpZ7He6++yZJnPDg0UPKsmKtFyGFz/HeY4ypGJ8eIqUizy1B6+jklJW1TYwuee+dt5nHMZ//whd447X3LTHr8S5ouHRpkziJSbOEVitERT7JeMKjBw84OT5mbW2Nb33zm6ytrvLg5ASlFNPplJXVVdIk5fHjx9x99x1WVlcwxtDpdPDr2nG73eaN732Psiw52N9nZ2eHhw8fUhQFL730cdbW1pBK8fD+Bwup6tLSEkVaUEpFMk/44P4HdLs9Tg72oczZvnaFd95/n16/z+ZyH3PzKjdvPcn4hQl/53/8n+x519f56tf+EWlWWEK08pnPYysZlo7UazP2PM/tOAk8MLZtu+vMqnHlJouauW62jm/QalmuiJM6Z1m2qEUvHGKNWdTPm9l1swTk3muModfrndvUXCADZ9LI+XyO53nWKrzOPpuy+GaX4DRNUXUjRc/z6Hba1ielKKCqqPI5QWDRvueefYZkNiPLcpY6baZxQpIV/Mt/6v/Anffe46233+G/+e//B/6T/+T/iZ7NePN3vkl0/yGHRYnnB+SlRgrJbjznydmct954nd5LH6dzaYvh4x2idovj41MOj4/p9rosL6/xxR/+Ir/8la/h+8Fibr/22mv82A9/ni9/5R9Qahsw7u8d8ImPvcC8LDna32dyeszayjJSQqfTQgq4enmbfq9HPJuTphnXrlyiFSp+/Etf5Ju/97sMx1OkUnTmKR1t2FHSOliXljMz8xWXi4pNz5C3WhT1muI4BGmc2WeeJSjlFCC1v4ZkQfKuKo0RZ0ZlzYDSlfpc4OmevyNlNn2YLj5LVwZx53VeI0VR0G636ff71vdGS5KsYDZPAcN8NmdtZZmVwTJ5mpH5AV/9lS+TxHOeffopVlZWee/9O8RJankb+IugwV07nLexd4FAs6RZ1xopc7sea5MvrOgRgqosFoHORYJtk3h6sUwUxzGtVuucNNkhMc3SlFu7m74tFrksFnNvOp2S5zmnpycYYKm/hBf4ZFlBWWkqnVvrAGNIsgwjBFGnTRLHJGnGLI7JkoTT0ZDeoMf6xjpUFXfv3WMynnD5ymVu37rNO+98izzP+eIXv4jf6rDzeJejoyM7PuOYXt2B2TaBtQmF23fOrruESixoFSAoK0OZZEStiAcPd7/v/f4HOljpRRHtdki3060NqZJ6gBriJKMqC3RZUmpNpTVlEFp/Bs/DD32kMbQ8j5WVZaZ1AzljNDqLierabhD6GFOhsJ4gARpfFxitabVtlBkEAarfJsvzGqa0UlQQC8gviVMwhpYXUtaKCT9qEUUtPE8RxwllkeFJQ0daEm6WzdFFRVf5tHtd2u02w+EQzxN0uxHGWAJXlmU1QcvDDzwbEFUVRb145EWB7wVofOI4RSnrXREEgdXKa818Pkcomzkl8ZzOoGtLS9pQZClFoZFoK6vzrReH1oYyzSkrjZEhb7/+KmVN0CvKAk9rTJkTebrmH2RgIEsS8loK6fseB3t7zKcz9nceMp1O8TyPf/Tlf9iAnm0F7b3R4UJZomvFlDGG8XBEEITs7+4yHo/qzNDD82328sGdO3ieQnke8XTC6fGhLaUJQZpZ7w4pFePJmCzNFhvr1WvXGQ1P+eCDB3zwwQNrlAYL4lwYhCRxzMrqKqfDIWmWsbq+RlmmPHr0gN3dx6RphqpsLRkp2ZOCJM148voWeTLn/gcTotC338tT5KWDoi25G1jwVISQzGZzQFBxZja18KVobCpeY0FtwrVwJot1fJZmwNus7Tdr/Y7T4mBrB+c7uLt53qZ7s/t/kiQLwmVZ2j5Ak8lkkXkurP+1Jk0zlvo9Xnzp49y6epWlXovjw30+9rEXePjwLkeHB7x35x6dMOK9997jmeeeJSlyBsvLSBR/8mf/OP/tf/+3uXv/If/f/+w/58e/9EWe/uEvMf+N3yM/PkGLuu+RklyKWoQrKzz/hS/wYDYH4fPaG+/w/MeeZzSbc/PWDW7eusnXv/4N3nrzLVZWVxlPphjgJ/7IT/DFL36RDz64xzf+8TcockEUBvz2b32dT7zwAqbM2Fxd5ubN6yR5wp0P7vLKd7/L1sYlTo6PuXnzJlliydrT8ZC7773L5a1NvvQjX+TLX/tVgsAnwZDPfVaRHEmrDIsCn43BEqYsGNzYZJ4WpHllO1RLQa/Xp9ft0el0GAwGJGnKbDbj5OSELKs9d6QlN5eVJstt+W8xnhpoivNcuVjac8+tqcZx46GJ1rmjyYtyYyuKWsRxTWAVAmE082nM2vIq9+8/5MEHH3Dl8jbvvfce3V6Pzc0tnn/6CZ64cZU3336bd+7cXwQB7lpcadH9cZ+tlFoEBmfBck00ruePMAItzoJy952bQcvF7wWc4/g4oq0LblyzUJcoNKXPbl46qbgjLcfxfIHkOMm48j2m8ZzKGLw6iKyqCqXtWgy2a7znBUiVkaQpjx49wq9Lx7MkJmy1GHR7KD9kMo9RB8fs7O6zvr5Onud8/Ru/w/rmJpcuXaIqc4anJ6ysLHP58jbz+Zw7dz8AYdcqd89dsCKlxA98hBT1ddSlXgRFZSj0h+/bP+/xAx2sFEYwniVMY0cM9IikJUX2BsvM4xgMbG1dYn9/38qYpVVH2F7gtnGUEZLl1TUC38rWfCSb6+vs7OygdYmmIgw9ojCgKEs8LyBOEobjoZVq1dbySlqXRIBubwk/CMiznOl0Um/MPhoJ0kfrkrISjKdzOp0O7W4fXZUYbTOEJEmRSlAZjfJ8dFqQ6RmUKVUFiTmDFT1P4SsfUdiNWyk3iCRlWqLLkozMRuOVRvkBHaUwVU4VFygpCanqYMtjqRPY6zAGgaYyNjgTUQdhrO24VUYVeF5gi0smoRRp7RfgocsSiSBSgrzQFLV5ULfbYzKbIoSk0galfIwuyJOY4bGtvcaTMbpZmsA2+LOqF8v3EEKBtl4uBQXaTBb9LJrmVlbaXKA8j3a7XaNgiXWK9X3SUjOczGi32wsUIB1P8XyP49Mh8XSyQBJcjficK2xeED3eYzAYcDI85eHODrP51C5qWiKM4L133rOLn9G0O23SPCMvSrQK7XgwNvBK85Iss66/dnF3ZRa58JMoCpt5eaFtKmbJ5XVpryjPSIINCD5JkoWbpluImy7LLvuTUi6QFxeAuOCiGYS43zkjrmbG2fQfcdfQVEtkWbbgrlwsoToCZ9Rqc3x8zNe+8hW2Nlf5iT/8o3RaAQcHD/FDn+eff55knrC0uYJSPvcfPmL7yhZlUbC21OMn//CPM5vH/Hf/w9/hN7/+DTY2NtjptHjy1g2enCeoOGEiPVpZSns247uhx3sPH3B4NOK1t97n0vYVjk6GvPnWm7zx9tv8m3/232Q8nS1KLa70deXqFaKoxeuvv05ZGqT0a2v0hOFwzI98/rP4QqPzkkF/wMryKjeu3iDPMlYHqxRFzsHuAd995RUe7DyiLBJOT1K+9CNf5NuvvIJGMZnBmyvL/NhwTCsImUpBazLhhe0thj/yGf7dn/6XmMcZw9MZo/GYsixpt1r82q//OgBbG6u0uh1uXL9Br99jMplwdHTMcDhECMGjnV2+/d03kMpjaWmJg4MD6zSNsGXwysqxfc9bGN2VZbkYTx/FcQEWHBCtz+zuhTyTuc+mUzw/oCy1lb8ri4R4UqArw/7uvkUQpWKwsoquKm5cu0qv2+bmiy9QZjGeH7F/dLKwaVDSOSqLs/KWtpJwF0w1x3xZVXUmdGZs55BoKeUFs8JaLOHQEc6sKZqeOG6+hEFgqQR14NdUVTl+mAueXEltgVTUPbhE3XBQ1o0gi7JEeh5Vaf2mtNZk9dyKwpBKawLPJwxbzCcTTGC9olAS5fu89fa7tKOIzY0NlBcwixM2Njb44MEjtra26HR7fHDvHg8fPOCllz5BGISMx2NbYTCGfq+H8iOeevopRqMRB/v71tOrLGtSvsQPAtqtFp1uh8lkRrvTxfctWrxz//73td//QAcrrXYPiSEMffIsQwD9Xp+o02GW5WR1V7rT0YSNjS021tdtk7UsIWxbWLzf73N6esp8bhvdLa+us9zroSuNF0YYjO2DYgxJbkjSggKPvDQIPyRotanKEk94rK2tkcRzJtMJaTpDqaAm+co6ePGYTWOM0STpbMGqt9lFTllq67HiKcpS43kBy+u2S2yS2g1WhJHNGJQir8ld2XSGlApPBWRpvoAttS5ptVpIPyBJEnzp44eKvMgpHHmz3hS1gaw0ZEVssy5tS0C+UpiyIow8ymxuvT2UhxaSJMtQRlAKgSwNVakpKk0ynNJqtzBVialy8iwD6SGlT6u7jMqtaZDWFfM4JwgUpZ5T6op4Oq27/Xq2sZw2aFMglcDU7qP2nlq+Rpbk6FqCrYpysZCGYUiZWt8MbUBUhkl8upAellVFMhwTRhFeELJ/cIgGylJb5Y3yLfG5SK3aQghKKtL5jElumfdlUSKNYprNGM5Syqoins/J8txypUxJUUvdi1rBJSeZJe/lBYVOyUvbh0hrYxHASiOE7aYsPQnCLqq+sJ1gpfKpdElLtch0vXgCqvZAMVpj5BkBr5nJAYv+QE3jMgdZn3mxnOeonG8vIBeoissK3TnKslz4tjRRlWbW6Ac+aZ4v5KAlGuXbEm2RFwitMVUO2qqdjk8n/MrXfpt2O2J5eYl2EFhvk8GAIi2JZ3NWQp/9+w9oRxEP52/RWhqwttInDH3iLOP9u/f4Ez/9U/ydb36blzptbk9nbOY5M+BrUYvfvHMXsbODUrY3ze3bNzk6PmZ7e4tuf52v/+Nvsbc/BOmRpZn1tBGCX/nlr5DFKa++/i55JahMRdSKkEpxeLjPd7/7bbqtkCduXSdO5owOT2gpnw/ufcBsPmf/YJ9PfvpTRP0ut/d3EaakzEt+65f+Ps8ieXc0YjiP+W+lz8QP+KE0ZVlXVJ02r924yvjJ2wymMcPhhNffem/Bp3jiids8++xzfOMb3yCOY06HJywt9fnZP/4nuHHzBtPTU1a6HeL5nBvblwjDDvc+uI+UkmeffpLBYECaJPT6fZZ6Xb75e7/HjRvX+cIXvsDx8QlFUbC7+5jDoyM0kjTLOTg4IK8DUWOsrUSaprSjELAIbadr0eGjw0M7LiS1itH2m9JGgudxeGr9lHSsmaQZ7VZEKwi49+Ahkecxn8XsP96z3lVFQjvwrD9QWaGkHdOVMUS+bSBpjCGrDMJYn5bKWI5OpR2PRdYloLMykqqREZc0CQFlWVFU1srfkxYJsrbUoIQVSihheTQVLMpgUvqUuiROY+sFVoseqkIjA0WaZASB1bV6zuHZWPpAqQWqEghhyflaW5Wg53ngWdm7lB5B2CbPM8qiQmhB4EfM4pQwsCXV6Syj1eozj6ccnQwxQpJmBV7Y4tLlqxggaPe4fnuZvd1d3r17n5XlZU6GU8QHD/CUbYhalhlCp3z6E8+RJNeJ4zmVLpnNZgRBRFlaPtStmzfZ3d1jZWWZzfUNqlLznW/9/ve13wvzUbjWP+N4/Pgxf/Ev/kW++tWvEscxTzzxBP/1f/1f8+lPf3qxoP2Vv/JX+C/+i/+C0WjEF7/4RX7xF3+RJ598cnGO09NT/r1/79/jl3/5l5FS8qf/9J/mF37hFxaOd/9Lx2QyYWlpib/y5/41wsCjrK28MZb7EbYiNq5eJUszjg8PEQZaYUSWpkgEQegjPBcFtinLgjiOcY2uOq2I6XTGyvIys9mMee2gubG5yfblbfKq4OTo2D6oomA0GhLHcS0zrZhMxoRhm067x3A4otWKkJ7lvigUg+UB4/GILLNkwna7x/VrtygLwyy2m/iVq9vM5zP29/cYj0dUuuLSpU3SImNnZ2eRJdv250XNY2nZZlZ1XTiKIobDId1ulyAISNKcLMuZzWeMRiO78Kpacoe1lleBT6k1vcGSLSUUFdPRCG1KCm272EqpMFiljB+GYAyhsgGWH7aojIV2iyxFUBEFIb2lVba2txiPxkynU/wgYDIZW5v5bkSWJXS7PQuVFhVGGwQ1QiQNpmb9B35gXTuLEs/3LEJT6VpuLBYQZVlnhJ7ziKg3S6U8WnW35jCKCMKI4XhMluVsb1+htzTg29/9LsrzSeKYQGqKPLObq+9TaJvd5IWVmraDzqIzqed55FlOkiZUZUWuS1RoGwJiJEVRYYzL0DRaSApHAKwzuiapT6pG402svbWU1mnVDwKyrFjY2Jd1cOA8hxxPywYbzqvH8kJswKaoc8OF9N8hG77vUxbFwj1I174bZVUtzMGox58zEXQSWFeay7LMZoLSEpqlst4M2miSxKIqTtCkpMLza/O9yvaRajpIu2vXWiOrAk9Ar9NmZdAn8hW+ErRCn6Vej8gPiauKKoh488493rv7Ad2ozZWNTYTnMZzNacUJS1XJSErGNdERoKybM3o1iuBQItdzp6jqcV2rm7rd7gKNWlvfYDybg6lY6rb50hd+iJ/8Qz/GbDyk24q4vLVly3XGMBqP+Mbv/A6Xr11jb+8xB4cHbK2v8ebL3+aHHh9z+2hEWBn25nO+ISX/oNUiNpJLAlZMSbixwrOf/yy3rl2hE4XM5gnSD9HGMBmPWVoaMJmM+bVf+3XiOEaGPmVV8ZlPf5rL25cZnpxweWub4ekpo8mU3YNj7ty9gxCCTqfLD3/xi0RRxHQ2ZT6dUZYFrVaL2WzG0dERq6uri+aSm9uXefnb36GsKk6OT+ryouWt9Pt9jK5YWVlZSFwHgwFlWTIejZhMpxwdn3D7iSc4PDzEq5HLbrfH22+/xTyJ8QOftZUVnn3qKYQxVHnGeDgk8DwGG+uEUZu9vX0m0xm7+/tMplOCIGRra5ub164yHA5ZXVvjle+9yuHxEZXWSM8jSXPQ0paKvbPSjTEGXXN7MNbp3Bj7b8+3RFajK2St5MRwTm3qeRZN8ZVfBzuaqB3hPL1m8znSCCSyThCiBVrjysxpkVPVZSpnCCqlrLlCdi2UUpFnRZ1ISMIwIghC0BVR6NuO5dNpXUk4UySVua1C9Opmop7nsby8TJ7ndV8rVZcBM37os59BGM1kPLaNZIWh047odNpkWUq322Z5eYmoFbK7u0uvt0TU6lhuVa2ebbVanBwds7e3z//nF/8m4/GYfr//B9rnLx7/XMHKcDjkE5/4BD/+4z/Ov/Pv/Dusr6/z/vvvc/v2bW7fvg3AX/trf42/+lf/Kn/rb/0tbt68yX/0H/1HvP766wuHQICf/umfZm9vj7/xN/4GRVHwZ//sn+Uzn/kMf/tv/+0/0HW4YOWv/7W/wuXtbTY31qmKgkePHjA8OaHd7dBb6tPpdjnY32d9dY1up0uepTy4/4Bu1/apcPXWjY0N6zrZ6xEnMbPJjMlkwsb6OhubmyileP+993n/zvsURcGtmzdYW1+jqip2dnaYTqcLddDGxhrKUygZ2LbuszkIyLIYzxPMJnPS2qgtzWKbSeMRhm0CPyTP5ygl6PW6xHFMXlgSap5bMpVGk9dQve/79Pt9Kq2tBFt6jEdju4FoO+l836cVRfSWBlQyYB6ni3JHHMeLTT1LE8o0Jq8KSuNIuR5oTb/dpdSSSnhEYcRSv0+SxEwnY9svSAh63YB5khJELfYODq35mbGdlKuqwo96dLpd4tgqYMqypKys466tz0IUtvB8nyROkcJOwLKqKI11wHSbV5ZlFpZutWhFEUbXtttFQRhZ86I8z62XirL9Z1ww01RetVpttq5cxiB49PgxKyur3H7iSYqyoNtf4lu///uUaUxZlBydHCOkpNvr2k1hNqXICqgEQeBav1uVlTOC63S7zOY2myrzirwobNZUVZRlRWbMArZuKm2s4aCVwzunV4d8OGWBMeB5/gIpafalcfVyh4C4xdBt+Iu2CAtO0HkL+aY3hKuNw1lH3SaZ1iEr7t/N+r1DcBx/wBELF59XVosSlnutRIM5U1G417ogTgnwpKTTigg8hRKGwFOEvocnodWKKIxgkldM04L7Dx8iDWxvrNNqtZnWSgs4c3HudNqEYUiWptZbqEaKHGHUfr4hyXLS1I5n64fRWRCStTEEUUSWJnRbEc8+dZvQE6wO+kzHI5b6fZZXlhdcqnsP7nN4dMTly5cpy4Lnn3+W4S/8Df5U0CFbGvBoMmH0eJdwMuGrUcgvRRG+Jwk9yfUrW1zZ2mR7c51OK+TNN98lLWypdHV1lS/96I9ydHjIcDhEKsXJPGZnb59Ou82N6zd4/Xvf47Of/SztqMWlS5t88/d+F2MMw9Mh7XabLMt4+pmn2draYj6f02q1+c3f+i067Ta9fp8iz0mShCAMWV5dZzKdLfrmhGHIYLDEeDzmxvUblEWB1taV+ubNWxzs79Nut3j33Xc5HY64evUaYRSRZRnf+Mf/GAOMhkM2NjfpD5bo9bo8eviQz376U+ii4GPPP8/7773Lq9/9LtvXrvHU088QhCFvvf0uS4MB77z7Xl0isUhIVZsoxknK4fERo/HIklErTSts8fRTT5FmGUdHh4BY+KdYpNB1kvZpt1uEYcTe3q5NODjfuLapNjIoZF0CAotQt1oh165dod/rsrd7wAcfPFiU8O0Yc4mCt2hH2yzJujK073v4vk3Q8txKvMEGTVHUAmEIAmvQWBSWO7K+vk4czxFC0O92uXfv3qJM7taUMAzpdbscHRxw6dIm165cxfMEl7e3CQOfdivCU7bVxaOHDzg6PuSTn/wkS0t9oigkzVI63T5pnuFJxXQyBWOYTWfkeUaRl/y7//f/x/96wcpf+kt/id/5nd/hG9/4xkf+3hjD9vY2P//zP89f+At/AYDxeMzm5iZ/82/+Tf6Vf+Vf4e233+a5557j5ZdfXqAxX/va1/iZn/kZdnZ22N7e/l+8Dhes/NLf/C9J04SbN66jBBwfHROFvoVve5bYenJ6Sp5mJHHMdDqlyG2bd1N7jWR5zlptrGOMIU8zTo6O6LQ7RFG06Huwv79fN2UyRLUXxSyOuXzlCnlhJdFra2ssryxTVhWTyZwsLfD9gCgKCEKPwFekyZQ8Tzg5PmQ2n5HECbNZgq4knU6P9bUBxmiiKER5kuOjQ4ajIWmaIIAwCun3+3Q6nYWqyELoOVWl6/KRlZUVeYE2mjzLLRpgrOHRxsY6nU6X0XhEkVtr5+l0wnR0SlpkaCEI2xErg2WqvCD0A9q9dVTQptvpkMxn3HnvXYwuEaY23RMleaWpjKCoKnrdHkWWksyn9JaWWN28ijaC1ZUVjk+OOTk5JQh8ut0eu4/36HY6XL1ylf7SEiB586236Pb6LC0NODo9wQ8CZrPZwiFyMBiQJLEtHZiSNElZW10FIZiMLefF8xRKCJQUJElsVVV1kBfWsnPpe3hBQKU1rU4bqRST6QQhFN1enyJN2Vhf57i26b985Qr9/hJFWTCbzHh4/wFZmhEnNgjzfZ+oZUnTUip2d/co8oL53C6CTdJrhrZloLp+rmuSrG2NYBYOmnmRY7Su5cuu6VlllTFJghCSorDP3xoUnvlbGG3bTqja7AocDK1rx17rPmtL9wLfD85xEJzLZrNxH5wt1I4E6EjHxpgFKrOQido62hlBUZ8t8M5nyKJgHr4USOEcWJ0FvHVYlkotxrySgtBTBL6Pp+r3ABiNkR6V8ik0jCdTMJqtjXWefOIJZrM5k8mEVqsNnDUidFyUWS39dEFYM7iN02xxD6RUeJ4lbZZlWfvheAS+hy5zQl/hCVMHGAo/8ClK67ERhSF5adV4Ua3S6qUJf/zbbxC22szabSptOB4OUacjBIb/d7/N1FMsD3o8+9QTrA6W8CS2AWBlKDUkSYrve3Q6XdbWVjk4OGBpsMzReIzyfTrtDlmaUuQ5L734ki3HlCXtdos0TTjYP2A6m7G/t8cTTz7JJz/xCUpt+L3f/xaHBwdMJhM+8YlPkqQJT9y+zdHREY8e7/HkU0/yyiuvkiQJvV6XlZUVbtyw3JwXP/4xptMpWZqiPI+lfp+9/X263S6vv/4GN27e5PYTT1BVFePxmJOTE/I8Z3PzEmVZcvX6Vb73yqt85+WXuXH9GjdvXEeXFb7v8fa773Lj5i0ePHzIg4ePeOFjH+e1199gOp0yns3JiorBYJlOp0NVVXzik5+ozegCHj64z3K/z40bN7h//wF7+3scHR2xtbXFs888w2Q65eHDh8RxTFVVHB0dsTwYsLa+zsnJKY929yw6WnNiEGcmiBpJZayLuW1AoIkCxcc/9gKXt7dYWV3jK7/yK4wnk0Vw7ymPwWDAxsYG09mUoihYXVmlKAumkylxHDOZTtBas7a2WoOtgm6vx9HRySJZRRqUJ7h16zYrKytMxmMOj47o9/tkWcblrS1Ojk/qoMws+JFRFPLM009zdfsyy4M+mxvrtKOIOJ7ZLvWjEbYPUJ/xZMzy8mBxb7IsI05iKmPwo5CVpQFSSvrdHq1Wi4P9fbQx/Mv/+v/5f71g5bnnnuMnf/In2dnZ4etf/zqXL1/mz/25P8e//W//2wDcu3eP27dv88orr/DSSy8t3vejP/qjvPTSS/zCL/wC/9V/9V/x8z//8wyHw8Xvy9Jm2L/0S7/En/yTf/JDn5tl2aIVN9hg5erVq/zmr3yZMs9qgqmi3+8iBWRZyuHBfh09hpYbMZ/T7nRYXl6m3+3gSRtZDodD3nnnHU5PTymKgiRJmI2HhHXb9WvXr3N4eMhSv8/6xgbz6QRhNJ1OlyS3PBPlB6RZzjyOUZ6i1W7T6y5RaTg9GbK3v0tV5RRFQrctyNIZZZnjeYp2u0MQRORZxfB0ioa6VXnF8vIS/X4PMNx/cJ8kjmum9tmG4Xne4l4GfogQCqUkSW0CVpXlAlXQpiDwvTrQKRfOg57ncXxySlaUpHnONJkjpKTf77PcX8ITkrDTZziLSeI5eZbQCnzb/dhU1oVV+USdLq1un3anR5wkPH70gL2dhyRxytL6tvUJ8XxaNV/IOVkKLYjCFtoY8qLE90PWNzdod7r0+kvWiTUIFmWDqqyYzWw2J4UAXXJwsI/neRwdHrK5ubkwljo5PqLbjtjb21sQbieTiZUe95eYJ/aZtbsdxpOJhX3RjCZjizxltidLt9uxHBUp647E0gZLaYKqN61er8/29na9wVZgrGRceYqjw6PaQErz6NEDZvOYSkK73SZJEsbjsS2f1L15rCuGRZKqqrJBjJR1tmfr2UVRLch7ZVVa181FXw9ly0CIOnBypSDwPFVL263nj21Rb69ZqSZ6ctZmwv7euu26FUM0LN2lVDWH5awjbdMledE5vA5M7LJjFnwBgVVS+AoUZ4067efUKE9VURlqWbcg9H3Lk8cQBgFh4KM8W9eXQYuj41OSLMVX0hLkg5AsPiNgU5PyAz9ASoFQiizLFwFYE10pioI4SdHmDFUKw6jml5U1umQDs8BTbK6v0goD8ixhqd/HYOh02wv30/F4jC6tMZnvedwoSn7q269z0mlRCNsoNC9KKAtW44T/7voWh70OQeCxsbaCL22bhtD3KEuNoVZUVRVBHUBVNYcIoZHKGhp6yqMqCltyTVOkVCRFdU6i7FBiz/PZ3NoiKyoePXpEFFkDTtvCwwZeldacnJ5QldXCn2h5eZlPf+bTBH5A4ClGoxFRFPHOO+9QliXD4alNkpTiZ//Un2I2nTGbz/j0Zz7Ly7//+4xGI9rtNo8f207SRmuuXLlCEHhsbqwznYxrorbPx1/6BI8e7fDKq6/y2utvMJvNOTw6ojQgfMvBCoOQIAi4vL3Ns88+y/7BAZ4UHOzt8sEHH7C0tMSnPvUpRqOR5e089yz37t7j5PSEG9dvWMuDe3e5tHmJF196kYcPH5FXts2JW0ffeOONOnEQaKQ16PSENcrShpVBn+vXrtjGrmlKXDcZLIpicX+MsQnJxtoa6+vr7NdB3fb2Njs7O5RlybVrV2l37JrR7/VJ04yHDx9xeHBIu9Ol1WnhBxY1j1otrl+7zsbmBlmW8e677xLP5gvri6eeesryitptLl++TOD7bK6uUOQ5vqcs/y6NmU0mzOdzuw8uLyOE4OrVayz1BzVXJSCOY6bJnNJoTGWfWTKP2dvbY7C0RFGU/Pt/+a98X8HKPxfB9t69e/ziL/4iP/dzP8d/+B/+h7z88sv8+T//5wmCgD/zZ/4M+/v7AGxubp573+bm5uJ3+/v7bGxsnL8Iz2NlZWXxmovHX/2rf5X/+D/+jz/080G/S6CWmM+mDE9P2H14nySZ28y60qysrBC2Io6OjsjLksHyMqfDIUu9PrPZlPk8ptfr0u4NWL9yDQ+Yjicc7D5gPDxl/2CPvMgsS/5wnw/u37PZs4D+0oBCG/Ky4smnnmVtYxNOT3m8v0uUFwStHlL6hO0OTz/3PAcHu+w8fsjewRFKVni+pNSaeTpi0F9mY2OL6zefpjSSk5PjmkMjORqP8TzF5pXrNkjRgtV+nzC0DaOGw1NavZU6Y2sxHI3o9XrcWluj3+8zn8957bXXkEqyuboJRuP5HtPplDBqWb8ZbZDKx8Oj3+6xff0GUbtNr9tFGoinc1q9DstrqxwdHzGdTNi6tEmWpgxPT6xxWn+FVj9EqggvaBFoydXrt9lc32R9fYP+yganwxFFUdDpdFhbWyPLMu7evYvOq5r8G6INzNME5QfM4pjhZEyaJPieRyuKyPOCILDyuel4zGQ8YbC8xK3bTzIejRiOJhSVYW3jEqPRiMHqGkWWcfupZ+n1egghSLOUeD5nPo9Z31hiMh6TzjO6nR79pT5RK+Dg6BDl2UZ9eWql4fF8jq4quu2OVd/kHWbTMVmeE4UBnjQcHe5Zlr82TKczTk+HlKXlgSRpYsMMAZ2OrWkHoWSpvcTaoMPJyQlaB3UbCIkxtrzgOBJWaWANu/KioNSGsvQXxFbf9y0C43noytgOq56yDsu1csgAfsP/wnEJ3IZrsA7HRltyoTECJS3noSz0on7elIbCWUChlIIapbH1/KouSVqyuxH2Wpzz80K5gUFJ2xhQYg0YXd+Zs3PX6FAULFRSlrsFRVXhC9+OjTSnzFJ6nTbCVAih8aShKrNzUuwgCPCVIgh8W2cX523hnUurK/dohufs0oPAcl1c52FdVaAUhZFMp1OqMrIIUKvFPIltCarSHB0dMx1P6LZtV2lP+RwbwwyDmsUkUVTLUwv6WU4iBdP6mnzPI8sKCgFFnhGFIUVeAmpxLWluuys79ErJEk+BqCxiVBUlWTIHrRHSY57rRYNKz/NQDWXKZDquO2Mb8rRC6woJKA+yZIYxEHkKrQSmzOm0AnSZ8dor36EVRoi6bJdmVgzgeR7PP/0UV69d5bXXXuft11+j2+0ymU54dO8Op0cHPH78mM9+9rNwaYM8KxgMljg5PcaTbY4O9zk+PuTw8JAsyXj7rTdRnk8Qhgz6PbYubbJ1aZP1zU1eef0N0tT2AXvpxRdZGSxz5fIVnrh6hW+9/PtEnmRtuc/Vq1dohx7Hacyzzz3Lt3/vd/n85z/PeH0FXVW0Q48vfeFzrKyscPnyZUIlSUvN6emQg8MDQk/yyY+/sECHVtZW2d7eZjgcorVVaPb7PbqdLu+//z7PPPsMl7Y2mccxRtsO4qenp6yurvLqq68ym47J04SrV69y//590BVHBwdkWcYPf+FzZHnCbDLi5W99k5PjU1ZWVnni9k22ty+zt7/H6toqTz31JG+99Raf++ynabdsX7B/6Ud+hJ3Hu7z33nt895XvkiUJa2vWUfn+vXssDwa88vLLzGczlJJsbW5w5cplKmNodzqUlS1lLy0tsTQY4HkBaxub1mW8rFjVBZ1em3g2pywKxsMRo9EIXWmOjo7+6YHFH/D45wpWtNZ8+tOf5j/9T/9TAD7xiU/wxhtv8Nf/+l/nz/yZP/N9X8w/7fjLf/kv83M/93OL/ztk5ct/73+iFYVEYchSv0u71SIej8jmc3q9Proo6K4u0791CyMMoo4oKw2VFnzy059FSduv4XR4yiRJSJMY5Xssr63QzXOWBgN83+dkdEqpS5ZXBpg8ZefxQ4TyuHrjFqUu2dl9zHg6RUjFpe3LhK02Qii8MKTTadEb9BmsLfPo7h2ODvYJ/AiEod/psr11lSI3xIlAtUO8qI9vFJ1uh+7yai11q1hqd+l3+5RFyXg8RoVdNrb6tiQxneG3e2z3V8AYVNDm4GRMVVVsX7vF1StXWN9YZefxDjs7j+ivXyXNMiaTMUVe4EmPQb/HlevXWFlbY57EPHjwgNHJCaPjIX7goeu68+atLaYzq/kfrF4laK8R+B6t1hKD5TWysmJ5ddNav2cpRZYxm8Z0270FyTOeJTa6f+JppsMRw+NTVgcDwnYb6flE3TalNiRpytH+Lsv9pQUB26qz7FhQnkdWWkfH69ev80Of+xz37t1lMpnyyU9+EtdDw7L5S4rCerskaUq30yGeTK1jZFngBQrlS45Pjiz0Lwz9/hLdTpfD/QOm4zHtVpu87j4qOxHLS+1zEl3nYDkej9lYX+Hpp57k8PAAhGZ/f4/pdIJSAs8LWBkMaEcRp8Mh7dYST924TJZnxPOYNM3IC1MHQuFCBj+bz2zzPylI82JhwlTUChtXmikKTeCHNUnPmRJaYq/n2V5TLiDwlFqgNlVl5arOxdRB3dYcUS0ChCZHRgjnk2J5O1JaRNPW4s3i9WVZYgRUtdrMZc2u5iPAkg+N7bdlXVjB+s0IkKJ2lBa0Ir9uOnpGTC6KAm1ypPSsOqrI8bDP0eJUEl+5YMiWcYQQSGwZrrCA5UKp5CSmzscIofEDb1Gi0saq7ZQnyD2Frk0lwzBkaWmJKLRInlQ+YauF8i3S1ev1bd+xyspXk/mcGMObvS6fPToFA3FhA5VBWfBPVgYk7RZUFbKUpHlJUeTMZlN8pWyHeSMXkmrnjOoUYFra7utRkNuSHFa94jq3F1W1uBeiyG3vrPraFQpd5DaALb1F+4eyLO3z8iSCCl0W1ssqtwhZlafMsT14/LrXlCXLC4bHR3xw932U53F8fGj5QnnG40f3bSlTCL798jcJ/YgwiDjcf7zodO35iqqybTE8JahKy1WbTEZIYcizjH6vSzKfcfP6ZRtwCsV8fMrGUo/Dxw/ZefQQWRU8/eRtnnn6CaS0SpfZZEw8m3Jpc5293UdWQOD7lHnGwd4urTDgznvv0ul0eHDnXSqtiaQgn4/5yZ/4SY6Oj7h69Sp7e4/ptEMGz99mMo2R0uP4ZMT62gYrg2XibM6DBx/w8OFDdnd36Xa7bG1tcfv2TS5vb5EliRWKBAE/9JlPMZlM6Hc7LC31ef+9d/CUZHd3l2uXt1ldXsb3fA4P9nnmqaeIrl1lbXWFt773Kq997zXmoxHPP/8cVVVx9+5dXn7lVa7fuMHVK5epqoLTk2Mmkynj0Yhfe/8O6+sbPPnEEzze2eH9999ne3uL61evsrKywvaVK1y6vEWWZkznM/K8ZG/vkDCI2N7eJs4S1qoV0iTh2y9/i7KwVAu04IMP7n/fccA/V7CytbXFc889d+5nzz77LH/37/5dAC5dugTAwcEBW1tbi9ccHBwsykKXLl3i8PDw3DnKsuT09HTx/ouHc9m8eKyurlBkGbqqGA5H7D1+jO95LC+v0Ov3EdISqyqtKaoCIVUtkRO02l3u3LlLnmW2/XWSEoYBy0srxKri7t33LfR1bPt+DJYHVFqTxTF5luIHPu1u3zaJq0qCIOTmjRuWnCot1N5qd4lCq3RRQrN96RKXVi/hS0VRZByfnKCURxi0ODw4odSSXrvPxsY2yrPs7yDwmM/n5EWGr3yyNCNLM7av3rAZdm3CZLvyKsqqYnh6SqfTYXn9kq2rB1YpUHkKv73M9vU2S4Ml2u1WbRSV0QkCyth6SWgj8b2Im9efRN54imQ2J88L5kmKNoZ2e5XV9evM4wRtBGEU0OtEeL6HEYqi0mR5RjKfk6QZuqxIi5gsGy42t5Pj0wX5MosTxqenaKNZ3digPxjgSUmWp2A0t2/eRArY39un+P+T9yc/t23reR/2G2PMeq7qK/bep7oFS5FiXShxBMlAevY/4IaFNN1zx0XHTffcNmJHYGIgiREgDRmJYxqWFSuyKZsyJZKSScekKPOSl/fcs4uvWtWs5xgjjXeMudY+lBPICgwTdwHnnnP3/r615prFGO/7vE8xTdzc3jD0A13fiSIDTZ4kDG1L3zQYFJ+9eUNV5Ij1uOb5+Zk8eBHM1pKmotLJ0owiSWm7lpf9E2lmyIzhk7t7TCrGgAbL6/sb1lVOkUlxjPV4LJtVxfPzE8fjUbgugLUzr2427G5uSdIMb3umaeSLz36OtmvRCj77/HPe3N/xX/xnf5NdXTKOI2PXkKUp9c2WerWm68egjlgF6fPM4XAkSQzWe5quX7xOvBfnY1F2wfkkHBk8dP2wEGzHSZ6DC+8ioapLjBb/mb4fGAc57857dJosgZtKKxKj0SZhinwOF11RNSb4w7hABjShCBInXRkTeYVYv0dEJSBGKnhd4ECjPnIMjX8vyrcxOK9a8jSR0DtjyAOMPrsJrTRucmSpITFSTBgjRGXvxIvDBNQmekNYa0mtYrJuUTqlScI0jctxFnnObILnTPjesZAp8xyVRd4O7A9HaTC2W47nBp1o0syglaZvWuw0CbF9nLDThDaa/+e6Yp4tP9O0vBpGOmP4zbsb/ttvf862yHFeirYkSTBpAlpSeZ11OOthnES5YhRlXgXuWs8QeGWTMkyAnWbhUoRxpfaeAoXRhmkeUWWCH8XzqSgyjL4Up7EYLssSbZSgOk5yejwzSmsSrTFKkaUpVuvAzSlIkpRhHOjtIIZ00yz8rNlR56UUxjqYGI4zjonBWmYnhXCiUxKdoFUiaI3vUcHXZOyibNoyjyGvR3mMVSQJjG3HP/r9/xajpDm1zvF2vFjiKx1iJM4H8J72FLxOCKZySvHV9/6YsijozhWff/KKt+/eYwIS+Hf+y/+C2c789//wv6MsMpSfyLKcb//Qj2C9Zuobvv/ld/ns08/45re+idbw9u1bNpstDw8f+L3f+z2+/93vcrvZ4NerMJ4p+c2/+xt8+9s/xNi3NFqcdctNxevXd/yFX/4lXr16ze///h9wOp35z//W3+Qnf+InqLOUH/nWt/HTzLk58+t/+2/zy7/0y1R5wReffEqV5Xz7i2/w7t17zs2Rv/jP/DPsX1548+o1Hx6euLu7J01S/sIv/TKPjw/c3N7LPm1nfud3fgdjEjbrDd/78vv8zE//LI+Pz/RDz4d3b2nPR7744gt+6s//FH/0nT+iLAqyNOOf/ct/mf/L/+1X/39UGP/fX/9EnJV/8V/8F/ne9773EcH2X/lX/hV+4zd+g1//9V9fCLb/+r/+r/Ov/Wv/GiAoyOvXr/8UwfY3f/M3+aVf+iUA/sbf+Bv8c//cP/dPTLD9rV/7NbabDeemoTmfFw5HWZYkmcCmQ5CdRui663osApO+PD/TnRuKNKM9nZmHkd1ux2q7Js2SYEs/UFUl4DkcDuxfnkiUSDMTk0pGhUmo6hXjMHI8t9y9ei0M6a7DKHDzTJFlbDdrull4ITc3N7y87MVLZXdLluXkeUmS5UR+gF2iAjTTNOO9ZZoH+q5jDrlG0VI9TVPKuma1Er7Iy/Mz1jlubm6WQm8KKo08yzge9jw8fCDRijzPeHp64Nyc2Gx3JCajLFdU5SqQBnPSMscnQuT0gQfRNh3rzZo8z6lyMQE7ty3aaIqqDNJZsfb2lmXBi+MHCOmj3rF/fsR7x/5woO8Hbm529L3IRJM0p6pFhZOlKev1mnmeaZp28YLpu46madnutjJ+sDO3t7dyDJqFmBldJIkbqoI3r+45n8/sD88MQ78kMm93a/ZPH2ias/iXIGOOqiyZpxnvLHYc0FpiC9r2TJYlQcrc0rQteRHk5LPlfDrjnGQ9vbq/5/XNDX/0ne8wzBMOT5bn/OIv/5L4/rQNT49S1L/55A3v3ooB02F/IMtSpsmCg6Y5Mc1T4I0o/vI/+5cpioK/93d+Ha0V9WrDMEx4pSjrFQ+PT3gUdnYcTic2NxuyQpx4h25kHibWdUmeJ9zs7mjbkaenZ1EtpArnRvppYrIOjxeCc5AnS+Hhmb34ypzOLQ4FWrhZRVny4d0HXBu4H1wyXKJ6KS8Lkc/PUpiBhIgqpGk5n9sFvdAGvBfFV5IZ+n5gVuAdeOe5dYqqHzhqwylLZcNLg1oKkZVGtZNWGmcNiogG2WBo5nA+qD1Mggkqj/lKIZUmCdZ6pkHub+ti3IEjSTOyvMDOHd5N5GHkNkVkIshfjfJLvMdmsmyto8lyTokgJUkhuVHamEBeBm8lt0XIywnez3gkpiHLMrI0J88LyY+6IhIPQ888W3bbLa9fv+F4EtPA9+8/4J0jSVNev34l+VXTwN39LSDhh23Xcz4dmeYRlIRlGmW4vblhGnqO+2dSI+tEnmW42bFarXj15g0vL3tOpzMmpJl3bQfW8dnnnwsaud9jrSPN0mCCOGBSLaKIeSYxF9PNoig4HU/Mk5C7PVK0RYdl8KA9u+2WupYR6ziMi19KkiQhtTya39lLOKjQmYKaJvkoPVpcmD3KpBKxoi4uz9emc1p+IdzbMs5NkzTc55WM1caJ29tb4XsERek0jTgviKNSCpMkCwKWJEKwT4uU0+nEq1ev+ZEf+TE+fHhknmbevn1HmoglQZblEsS4GDwKSpznFXZ2bLdb1us1r9685h/9w3/I+XzmcDrRdSNDyPfZ7XY466iqkk8//ZT9/om2a5imic8//5zz+cz9/Su01jw8PPDq/jVFWfLFF5/z9qu39H3PZrPhH/3BH/AP/pvf4T/463/zfzqC7d/7e3+Pv/gX/yL/5r/5b/Iv/Av/An/37/5d/qV/6V/iV37lV/grf+WvACJd/rf+rX/rI+ny7/zO7/wp6fL79+/5q3/1ry7S5V/+5V/+J5Yu/3v/23+Hb3zxBWVZSk7LJGZjbdvQdS37/Z7EGD759FNWq5XkqqQpeV0x2pkiyyUl03kSbbDzLJsQKsCdQkA8nQ70Q49WiuNxj1FOQufCDVdVNev1hnm2PD8903ctbSOpvGP453g48Mknr0Ebur4P0rqZoigpi5rb2xvafqQsN7x+88kiQT2fz9S1eHmcTntmK6FRt7e3fPXVV3Rdz3q9DhsmVKvV4oCYFwVaKbI8YxxnHvYHvIe6KimzjKY5C0kvRgoYKIsS71WIH5ipq1q6H5Ng8nQZD5xOp8Vbpus6Prz9SjaUsqCqKp4P+8U1NjEJiUk/Kiaj5HOaRr7/5ffYbWq22x37/Z79ywtZCMy6u7vj5vaephuWMUf0kMmyDK0UQ9fw/PTM09MTm81G8orygnq1Cp3RJTk2Oqd2nUQzVHmGnWfGsUdplmt+Pp/RRnF6eQjS1mIhj4p8Vbpzb8WwLs8z9vvnsFj6RV0j92OL93Bzc0tVVRwPJ6ydsb0s6GVd0bQNwzQxzTNFVVIUOefTiyhmgpPluWl4fn5mnmfudresqhXWzgxDL2qnoSfLBN2Y+o4kNaA0P/8Lv8h3/ui7zM6zXm8oioIqz/iTP/5j3nzxKcM0Mk8zQ9fxyd1r/vhPvku5XvPJm8/54R/5cd6//8B//wd/QJYb8iLlfHphDEVfURaURbl4wsyTyFSLsqLrB47nhtV6x+s3b/hzP/3T/M2//jfYPz8zzROff/Y5f/InfyJqur7n1atXfP7Nb/HFt36YP/6jP+IP//APOZ1O3N3f8+7t2zBKsmLYmJqAlshowVo5d5OF1Dr+V1898WMf9pTWMRcZ8//yl/h/aM/ZSvE8W8vr16+Z55k/+qM/QgHjOLPb7ciLHK2h65qAoEhnb20w6ppnyZUK45aqqti/vJAog8dTlBWffv45p1PD4XDiFEZ3VVkGxCblk08/wXvPU8iw6qaO6KXz5vUbmqbleDiQpjl4y6rOgwuw59xI+GNipND3XrHbbpntgPMzRZmx3W7J0pzzqaXIcsZ+4ObmZlGiWWu5v79ns91hPbz/8IG+67i9u+MbX3yDp+cnPrx/jwfysmTsJ7TJRAk3DTTNibxI2ay3JEnGNA68/er7nA4HbncbtFI0zZl26Mnygu3uhrpeUdY1Jkn58PDA+XTCzyKlv7nZsVqtqaqKcRx5eHigO5/ouoa2aWi7DhtUZ7G4HYeBaRjJi5ybnXiFvOxf8M6TZgmb7Zr9fr9IkCNvSpRsijSRwie6KseCQylFUWbUdcXz87MQ0Z0jTcTl93g8yhg8jEEvtvwuNA3RJTeSyKWgiX4+SWJou1YQPJCIFmcXtC/yjxIjvClt9FJQWzszzQPTNJPnBXW1CuvaKJk9eY42Koxw7eLMGxEi5yzOy3gmTdPAR4qqJo13cp5iY2uC7YO1goQlSbpI92MR55zwQ6uqZg7Xs21bmqYJnjvw9PLM/+mv/Uf/0xUrAL/6q7/Kv/Fv/Bv8o3/0j/ihH/oh/tV/9V9d1EBy3sUU7ld+5VfY7/f8pb/0l/h3/91/lx//8R9ffub5+Zl/+V/+lz8yhfu3/+1/+5/YFO5v/up/TB0s1OPmaYyhLAo269VidWyM4Xw6S2Vc5JgswwJv371lGEb6XmSvX3zxhWzOJLx/957j6YhJNEZLwNQ0T7y8PDGN4gPy6vUb1qs1Nze3KGXwTlQJRkGRp5xOR96/+5KXlwe+//3vMU8D4zQxDnMoOO7Z7W4WdKRarSnLGzyK5nymLEvSoNiZxpHH5we6tqGqKykgvOf29haUSI9Xmy3rzQbvoW0bmqahKIoQfpdTrG/QRlQZyjv6vmXse/GGqCuqqmCeJ56engN50i/eCbP1ZEW5VPhxs/dhAcjTJMywxZ312JxJ0gStjXSTk10WhWvrduccL8+P2CXtVR7yuq6pqoq8yMmLmnPbcj7LNdxut0vR4ZyjzFM5r+PA/mVPUZakaSJdpfeLTLnrOna73UXaPU2cT0c04s8xjgN5npKkCcPQczwceHl6T5qKSZgKxytWatLduTBLX61qHh7fcz6fIbgeq/A8nM8nVquKLM8Zx4E0laj7dVnTnM50wXtEGy2y5zSlLHLevf0SZ91VpogsrHmeg1UkSQreXazKEwmnm6cJYxRDL0672hhRlqBouw6toDIIgTrPOJ7Pcg0TiaefrMOUK8pyxbd+6IfR2vAHv/97dF1D37coN7FeS8hg9E0pimK5Ls55QSWnCWfFCC3PS+lklUKnKda7kPwtsuQ8F6XJar3l0298i+PhwLu379gf9qSJFMkx/6trO/q+YxgGijJfTOcEJdH8/O99lx/9g+9yKjP6LKWcJup24L/79uf87p//82JIeDji8axqgdzzIufUnoITqqjm8jwnSWNatBOH5mlmVdeUZcnpLLJO5xxFnpMlhmmeuH/1mp/+mZ/l/YcH3r17z7v3H5hmIJgcpmm6qIKapuH27o6RkR/+0R/lj7/zx1jnWK+3nM9n7OxZrSrqIme9rrHTzJs3rwDN8XTi+199xdiN3O52NO2RLDeUVU7bdig0q9WGMiuX8Dln7ZKWW9c1u5sbdre3vH//jqfnZ252O9I0owwj4pubW7ROefPmU9qm5/Hxmd1uyzB2HE8v3L16w6effYHRiuPxwMvzM7vNRp6ZssAZaZwkONYyjJOgEUqRpxlDGFEuUQ3OUdW1uCCPM3p2PD4+0oWxyHa7C2uH5/DyxPv3MhrerGUkn+WCXKHE6bVrW9qu47A/sFqvOJ/O/PAP/zB1vaJpGt69e7+QW0WdKPvBer3COsvQ9zw8PIhsfprRRgf/FBkLpmkqcvBgQeC8o67qkCItMnmjtZD1kUI3zTTjKMqhqqoArsa5HjtLYbFar1mv12HvYmmEpmEQCwOvgqFiFjyeZnSaYL1duEvAEmeRJIbZDoxjLw1FUQgpPDiA29minCj0YvBukkQO1IwxKfPkSQJC1Pe9yPDHkbwosLOl7wchrCuN8448Ew+Wtuv4P/8H//H/tMXK/xxesVj5tf/0b1CHix1lhm3bcjocSJUQvaL8sSxL1uu1wKF5wjSPNE3D6Xzm5u6WInT7TdNSJBVVWZFlKWmaMFupzIVY5zg3J7quBy8b4Hq1IUkz+q5jHKXTyfOU1bpkmnu8n3h6euAf/P3foms7bm9uUUrRNB1lUfDpZ5/zyZtP8MpgMkFp1us11rlQvXqGfqBpG7773e+w2+341re+zWolXjJt24UOe+Jlf+B8PvPmzRv6vufp6Ymqqnj1+jVJnpMmCXmWMQ49bSPEqq5tePXmDUlRoY1eyKht2/D09Mjd3R1lWZOl4usRjdyiF0VZlhRpxjyJHBGlmOzM7OwShOatW6TD0Xo9judkxn0JGGuaNhAsxXOkWq1JsmyBeOPCJv4HH5jtxGeffYb3nqZpFuMzgDxNOe0PwdyqXI47GpzF8eE4iQT+NrgLn5swYlNSyGw2W5SC8/m8+IpkScLQnXn79itQBDh5Ct+tl8LHGOEFaIKs1gqKozyJlxHAOEnwZFkI7wcPRsucPEkSTiGfKKJDWmu6tmfoZNHSisBnkNGMUop1vVoWURVk+t6HlF1nWRUpSht0WuCQBa0ucuzU0/cDp7ZfiK/XQWub7Zab7ZqbV7e8/+orHj58WBxx42KfJvlimiW8BCmsVquVJMNWlXSwRu7F159+wu/+/b9P0zT0IcoAYLeTRPSu6xekcRyHcD4j3J0FblcmRNPnhp/6D/9zLNCuCpyX+y4/NczDxK/95V+kL3Pm4B0Tu17vPSSGIqAfhM49mu45Z2Xz1JfMpCg1/dmf/VnefPIJ/9Wv/b8wxlCvViRJMEnMctIsF96KSRZFmgTWtSiFhJ1qz5/7iZ/gD/7gv+d0bnh8fAZl2O52VNUaVIJ3sygRN2t++md+hi+/eieZRU7Tnc84N3A+Hzidjnz+xRd8+slndN3APMkzpZTi7u5usTv48OGDGEvebthstmE0IuO55+cnXr95w9BPZFmN0il5Ks9927U4N6OUI6tqsqJaiLtt1zJ0PdvdVprEszxHSZZRlhVpnot6K0irh7ajrlfgxRFY1G+jRG7ohHGYBA1ZIkRckJUbiswwW8lkG8cBFY0EUThECRd/53w+L2aHWQg6jXL701mKAe9lVP69L78nxUJdiZJ0mnj16hVAkPl7xq4J4xlN27a8vLwwzZMoBr3nzevXEukRTObqerUIIbRWPDw+0LUtn372KcMw0LUdRVmyXq94eXmm7/vgPJ4uvklPT0/0XUuRpmHsZdE64fbmjvP5LD47ZY43l0DJPMtBKeqq4ng60nUt2+2GLMs4HI68enWPUnrhXr5/+56mETVPRA4jemStoyzqJTFaKVm7FFJ0KRXRJJhmyZOzzuGso2lb/v2/9h/+4BYr//Ff+2v82I/+2NLhT5PYj9tpwig4HU+8e/+Ox4dHnJdI+OPxgPYT49AsDPg0y1hv1qRpxuPDI5qEMq8oyozNZiOOtwp2NztWK6m4x3HAzjKb7rshdHWeJK9Z715JCKGbKMoMbWCepzBCUnRty9PjEy8ve8ZxYre7wXvPN7/1QySZoCld1/Hy8rIQLI0x1HUdRlwdr17dL26T1orRWZKKX8bhILNhkYh73r//QJ6n5IkH7xj6juNhz9D3Irk1GqdTyps3aCPW51O42e9f3ZFlOefTWXxDrszBjkfZRO/v79EuVP5hM02LDIcP9tSOJFrAB+JkRFm6riNLEso8Fwmok8UqemEkaUofTO1WK0nXjt4iQJjFi4w0OqfO88x+f+Dp+YndZoNyfvFWEft5E4qxlvV6TVXVVFXJal0zDD2n04GmOYtpXSExDfH8y+dPQZJr6c8nBIEy7A8vTNPIJ5++4Xw6s395YruVUU2SGg6HF6ydGKcB7yxuminyHBO6nLquGYdBzJZmK0Re75c08ZharJCu5vByWDqmJDFYN+O92J2vNlv5jk0r3XTbMM/TIsstqoqsqPnsmz9EUa85vrzwve9+h7E94+wUclIcSkOayhjPWslNmgInIxK6o/dMnJX3oyVJM1Z1zWpVsX9+kYiFNEF5j5tnttsd3/z2txl6SSOPm+c49VR1zuvXr/nxn/xJzqcTX335JeMgjsTez2GmL5JlGQcI0TdLM9YfjvzEf/J3OG9X2FRk1NoYEuvJH575rf/1z/C+zhaJdZEXwgkbJ5TJKaqaoij58T/353h8eOR73/te4C0MrKsKrTX7w37hPwHc7G6Yp5Eil2yhvh+wQVJtshyFounPeObl/o/k8hhRUOeSI2Y9pFkJJDg09WqNUymkFXPf0zcHyjyl7XrSoub1Z9/gtD/x/OF9GBNLltVut8Naj8LgSVDaMI1BrZalUiB5aNqG27tbkuBZtFqvhTujYkhfSp6vODc9eL0YDzo3keaG2/s3TI7FZHCaZ47HI/Ns2aw3IeTUY9KE2TqKssQ56PpeOnQv45V5yci6pKrXmy3dOC3jGfNRwTIBM33fLvdBmiQfBaBudzeLFX7btguZVjh+GVmWY8M6s5CHtWaephC6qOj6njE0CrFgH4aexChenl8oChlLPT8/h0JUL/YMVVnS9z1pllEUOU3T8vL8jNYpaSrPcl3Xy3hcSMoO63rGcWAYB16eX+j7PvgUKY6HPc35JAG8wXPImJT1aiNFtwlGl4EYX9UV59M5IHoJ7989MU1i47HdbJjnmQ8fPnBuGozWDG1H10lhu1qtApm6CsW5Zb2pOJ/PPD8/UxRF4K3cs16vOZ2a4O9jlwYgulu/ff+Of+dX/r0f3GLl3/+VX+Fmd7NkTzw/P3M6nWR+37ZkWcaP/tiP8a1vfpMsyxY4/Xh4ou/PvDy/0JxO7J9faJpGVEdZzjhOeCdEtH7o6ftWvDKylDQQnmZrpQM2KdMoLHhnPUlRcf/p58xWLlK9qinrmrv7Oza7DUN75nQ8imtgXsiYJC+5v78XC+Vp4vnpibbrFjLp7d3dYoscjZ+uHTeHYWC1XnM4nPjk008FlsuykAF0EJJqoujPzzw+fKBtzlRVQZFlfPeP/4g8y7h5/YY33/gRfvhHflRk3zYUGrBID4fxYqh1bSL1/v17Ht5+YLvZ8tkXn7PZbNBpQtt3ixX8NPTLQi3GUC+LOdYXn33O5opTdO2DEUm7ZV0tfiDxPaN80jvxhFjVK+EvzDZ0iIIYuEAujNB7NP8DcW99eHggzVLJcNIKEAfhJEmw40gTeEPiRTLx8rIP2SGO7Uo2sOPpgLUzWovfyTD0jEOLQkiaIGhV3w+UZUFR5NippTmfRDmRZbjZillWQAWd8ozDGNQ1gYhphBcxDhNGJaDk71yAxpWGJE2p6jU/+bM/y/f+8A95+PCeaZARV55JtzQ5S1Ft+Imf+UXq23uevvySl+cPpNrx9PCe40Ek1tqAd3If1NWGth2YrOSrgIy5rBMexGaz4cd//Cc4j/D+/QdWdc3P/sLPMzvLd/67/zfHw56qzGleXoTjkSSLaWPfD0GGLeRWcXEuFsj55uaGw3HP+XxYumClTPBq0aRpLo7TduaT/+OvMjrLtNuik4R6tSZ5PtC3Z37nn/9FDtovFuNZmpIHpLAfJoLfIs7DYX/AWS/Qv1ckJiqqRvpODP6yLKcoJD1X46VQcX5J8tVGmgiTOIbhLOtMUbBercK4QkkQasif0SYlyUo22zvq9Y5hsgyTo1rfgh0Yzi/c3+z46t07rM7Y3L7h7uae7nziw8Nb+v6M0oIka22ws6cfnLjczhNlVfHqXgjl5/NZ/HdC8R7deKdpWmT4eV4Bhs8//yareoNSBq3h9WdvaJsT794/sLu5xyQJ1onrcj+M9MNAnhXkzqJUIHzOMoLSSYIJ2Tl5Kmuq90IwPx6PS3PgU0ldBoKLcrrk5whnRwmpdhoXgrcYL0JVV2RVvawXsTCMm2eSCGIR15sYwRDH34IkyM8KmsKC/GotVNxpHDHBMBAvRf0Y5OsqyttddIm+mDSCwTlBRpTSTIFToxCi/un8jAlFlUnMYnMfJwXOyvVJjIxntBFfpSRJGOeZ2c9M4xRM+yxpkgZZe0Lfz7y87DEm4e7uVvgqVtLgx36QZiqM5IVr5y9NZZbwvJcQyuj4Hk3h0jQlTXJAL66+cb+areV8PvFX/jd/5Qe3WPkHf+e/pipLnp+feXh4YL/fczgcuLu7ZbPdcDjsmaaZzz/7jG984xuUlYx6emvBaL78k+9x3B/om4btasOnbz6hriryqsSEWZ0oVF54//4tz8/PHA97bjY78CpUn2sSI8S3LMuxfiYrMlarNWkmXJF2GFHaCPlJWaydmKdZpL9W/DDyopDOahroOukC+r5fMmG22y1VVQsxM3Bc4oMXu4XZOnzo2opCRjaxuJCHeaJtznRdIwz7cRBzuNQwO4dJMopSHHXLoqYsapIkk4LMjjTdcYEnjTGLs7C1ltv1jfBbykK6Eyfppn3IW9HhgXXOLQFwaZoGq2nD2PUfyVijz8U0z8JBybOwEPnF8bgoCtqmwQ6DdP/R0CoQMSOzXnxHpEOLAXULc98YTOhyx3GgbRuKMidNE1FzofDW0gaOC1y6uHmemPqOeRoDl0SSmud5FA7M0DCNTfB2yYKSRGGt43jcs14lDH1D37Yyo59mNIpEa5Q2mCJDGeH8xCwocU1NmMaJsR8DV2OSY5lGZhuyajDcvX7N4fmJ5nik7xrAU5cFRZFSZJpx8nhVYJ0mSxPWqxyjRqZpphtBaUc/NAxDh7OeNC2ZJof2HrBoE+zuowR9HKnrNZ//6E+zvr3ne3/0HdwwYDQ8Pz5QVQU/8sPfZv/ygWGUEeU0zUF1YCnKUrr32dO2HU1zZp4td3cyNhU3WS+psybFe0WWFdzc3DFNMrbspjOf/O3f4fVv/wG8eUX+5g0rlcD7Rz788k/ym58KSrqqV8szNowCw49Tjwlo62q1Ic9KQJHnBUVZCtn+fFxUalop1htB5rIkpW0aTqczaZ5ze3tHWdWgNE3bMo4tx8ML+/1+ySET8qcgRWmiBd0wGUW94dUnXzA7qFc7pmnGTT1Td+T53ZeMXVDUJCXf+rGfZBwtzfnIfv/INA94P4d8MIVSBucN+RXfzBixQ+iDg+3dq0/CCLyhrqvAB5PGYrPZcnN7T55VpGnOOM44J4hG1zUkaUaSFqw3O7RJwhokiqWu72HuBbl1XvyTipK8qmiajvWqRiNZQDo4NFdVFdY4GSHYwDus65qyKIJRoajOEiUqtMgXUVqTRGJpkuC1YYq5VVova2ksaLxXS7ZZ3Fjlz93yjwnrQ0ztmq3kDSktYyQxLAzeRsFIT0E4lmRxbHYuhJgSVUMqNBgsztz4UPxblvMvHJmAFpkQNmrFZ0rGWGLcCIR1Jl0Is0LOFeGIjDblQ2L0RkSaQAqnJE1Q5uMssRiJMQfLA63F7FHG3lEFpYOSTS/XBx/9neRnT8cjf+kv/oUf3GLld3/zt1kHUm4kWwL0fc9sxQdCnDGlOtdGy5+ZhKJekWcZGkXfdjTHUwgYtJy7ls8+/5S7+3usnVFGCEdt1zKPE682O/FICNbcSZIRs1KGoUUrScX0KGYrLrfjOAczs5n9/omqrrm9uaMIrqyHw5HT8cBmXVIUBVPwIDifG7puCEQ3RV2LSubx8THAgPLfeZbz5rPPubt/tYxJIlpU17UszOMgpNUsJYZiCesb2qbBe8s0TGhtSNMCIQRK99KNLSbV9F2H89Kl1fUqoFEZzVlGW/VKMpV8gBGTVLhEzfmE0TLPL0MoFmHTt3bCzuKPMQXkKCIknhA3P/SCLAQp4dPjoyQIpyl5kpKlKWUhBDsZO0kejXUOrzVj7JwCGhT/Pw6yLA1W4lZgZSxaC7rhA6zd952EmaUZ5/MJvKBmKhibpcEF9XAUzoBYUO8Zh4Z5FolpmRekoWBz1jJ7KY7sPDMNIzrcx8MwiFFfnmNC4RlHb2mSUK/EAHFVr1Fa87x/wTvLuKA5IvcdximYZ2naQPzVWpGnCam2zNYxz4okKzFGU+YGpRxdP9J0DmUkgBM8aZajQnK0Rgy5NpuNJNFOMzFoMk1zdq+/IC0r9o+PPLz/CqOUeIloRZoZLBdH3lic3tzcoJSmawfsKOOEiIp5pLuNRmvn8zHwTTQQlRaJoFVqwjYdr37j99n+w+9Roinu7zn9+R/jq1/4CSbX05zPNI00BEmaLIuzswNKy+Jb1ytWqw2Hw1Ek1trQDT0vLy8LCuDiCMwYlBepq+KSg6SNBMo57yAURmmSiMFfeO688yijZcS83tCPE59969tok/NHf/gdlEnZrNaMzYGXpw9oPEWeUdU1h+OZ1WbHu8cPTPOI1oqyyoPZn8QO1NUar1NQEisQfWOOh2MYt1p0UnBzexfWBEEUPULQlntq4P7+NVW9oR9FdSKb2ozRCavVViwXdEKal4zjsAQYducXGcGut6w3ksprklQ4XCbBxxR35+i7jiyQqedpRmmPc/ZiMOhFpelsTCb3yzWIoaxaaYpCnLC1yRbkJCr4VqsV/TBgtMFZRxrQ22sJfeQoxTiIeI+6gH55PHMoFjyEkWk0Y5R1JQ3xElG6PM/C5/POoY08h7HZjPtW/NlpUvT9EEZVKaACRy80p4le8r7iMyDv4yV3y4lySGklhU5QKAEYE7l1l+gME9ZKlHB9omO0JxQkwRVbzj+LcMB7fxFZIAVP9Fy6uE8LT+58PvG/+OWf+8EtVn7tb/4X3NzcEHNGhE/BoiiJSpZhGOj7jjTNmKaJssjlJDq3sPmVUrx//56+H8Babu/uuLm7Ba1ouo7JifFbd27o9ycUArVetPdSoBxPByEajpIAXBblUsEWZYkximkacN5hZyHZffX2LW+/+grwVFVCVdaU5YpptnzxjW9TVmu0TkgShZtb/vAP/5CmadlsNlRVuSARWVEJSTfMdrtOHBhXAXKOUrPoU5LnkfAqC2kWZqvGJAscHP0ZmrZhttPHRLUwlvn000/xDgnrUywjl2j2dz6fubm5WYi5kfUeEZZx7BjHMMPW8mA6a8MYIFn8E2SclYTZcYCrx0ncQJ2jyAsUHmdn8izEqesEqwxJmjKNEyjCDDpk1VjLPI5iyjb1aOVB+bDYi79HEhK6U6NF5jx0oBRZmsjmraWoNEY6w+fn56CS8Vg3YucROw4YHFWegrfYecLqBOtZyMLA4j/Tdz3KyeJZlSVDGO31w8B2u5HFzlqGaUYZI7D00OGnAeUtdu7JAtLT973IOYPCJc8lK6XvO87n5pIuHDgHWidU9S6QnZvQcYq3iFZakJ7AGxHuymVR7roOnSbEjKF5Gqmrgjf3r+h7Gfv54IQbM2ZAOtwYDigL3aXd7ENKd57n+FlUD3KNYL2uyQtJmB2GgTSTBFuFwj0dKfoZc3uD3a5FcbGEPQrnZBzHIP9fUVUFHpFljuO4fK5SCu+gqDdsNzu+8Y1vLMGmQXwqXjwBSYwW/SDdbnQAluLLLbB/URSsVjFd3bEJSj7n7FKslUVJWZZURUk7TFTrLUleCFLQNbSnPaMdmXEkiUD3UVmyWq2k+NcF9WrLOhzT0PfYkEhsrSPNK5IkAy1GlnlR0px7Rmu53axxQ0NW5Jg0Z/Lg0ORFRT+OuGkmTXKqzY7Zy4Y3jx3NYQ92JDOK2TqqzY7N7lYaLDuRKEWaF/isAB/Ioh4SbXCzEFP3pxeGeSBLM5TSZFlJWVQkSS4uzGrGupksTcXs0zqmeeYb3/iC07HBWunwTWIWNHqaZB3cbbdLlMUwDKw3a+aQJzYOAyYR9Y8J92N0Z06TRAz2vFuQDmAJvMyyjLIoGfuB63ytPM/ZrDeL71fcE+JaGAshpSRXKMZfxFy3WBhENWX0BxL0xSx+PfEzYxER15UYjir5XeZPjcWWIg1pGCUEVWIxEmMEMQmVipL/uSBLPsRnuHmRSKNUiPeQguh0OvKLv/izP7jFyn/41/7v7HY7vPdLwRCJlLe3t8sFBdhsNsvo5MP7d2y3m2VTj8oUmbuluFBogEIZRR9MiWZn0SjSQDSNlfTxeKSua0ESkgSTyIYsRm5yg8925rA/4JVsvIfDQYLEjJFjS0R11DXyXvVqgzECC3s0SsvIYOhP7PcvaG2WhNM0zRiGPsCveoHMh2FYJMZCFF59dE6yIOmNqpxhEHg7ssAjocx7mdsnqVlY6ZEUVte1GFBlubhlBuIsiMz25eWFx8fHxeOh73vquma73YZjT2nbM97bRdEVC5P7+/sFJUqCEiY+6NeeEW4KD64PnioKcTd1M2030A7jUijF+XMaSJMmdIjaaJx1tK1s3lVVMduZvmtQXjaOPE0wRjP23VIgR4lf17XUqxXbzZZ3799JsYJnthMKR9ec6E4niiyhzFNQntEhhNUgLYzdWbSOLxJBxcSheFpGWHJue5TWIjNOU5EQ9h1+6sVSXjmyzCwy4hi4VlVVkPqahaw8jZNwrLxwfKbZ0vWycMc5ebymkQMALBuiIIHCA5rmGZ2Io6zysokbDXVRMs+jyGa1WZBIKVal249BeVHeG5/LJEn45JNPSJKEeZDjlGTagSwL47g5kiBNKIjl/nNWlAp5XiwbU/zsWIzEYk24FULSdM4F52fH0PcobUAbbm/vMEZzPB6FL6AVt7e3TNMUTOxkrYnXNG4s0Zvlp37qp/jqq6+W0LwkkEufnvdhBHFJgo4NQZZl4KWbd8owTGJqmGpINfTjwGDnhcCepunSSM2zZfIJzgs3Rnx8ZSycpSllWWHSgrYfKfKSOfDwirKmqlaMfcs8dVjnyfKCqt5g0oxpFim3NhpjUpK8YHYy/mibE3Pfobw4PN/evWb0QBidTKM8G0mWghH5ctd1ZCaBwKFIk4SsSMHI2HQYRoqiQiHoXpalZJmQpCNCF1WASgkqdG7OV+PTfEEox3EMSqd+4WXE/SPGFIgKrVuKhOgvpZSiqmv6caDrL3lHwMJ90Urkzct4PjwzcURzjUbE5/4y0jd4r5cIiXhNr38+PnsRCYqffW1OF983/s7Xfy/+Tvy3tVbIyYEgHIskrfRiKjrb+ePP9Zf3QNIwgqz7UvzE4zgc9vziL/0AIyt/59d+nd1ut9yE11XiYopzddGyLAvduLjSRknbdQdVlSWJF+Mok5jgGOkX2a0H3CwXLC6okTR6PB7ph57NdktVyex3GAYOhwPPz88i/dysKYqCpmk4HA4Lt0Rr8erYrGvyPKOuVyRpwew8/TAJNF0VeCuVedM2tI2QRetVzWa9QRmRtFlreffu3bJBXzJc1PLgxRRca20I3HILUhJ/JhYiEQI9NydiwFksboZhEGv/GyEBHw4H+kCQu5a9AQtx7+bmZkFIQDrJNPhZRBLbddGTZxl2thxPR6Zx4u7+LhChR5rmTFkUeOc4H08UeUZMgnZO7gfrXCAc6uU8ROXY88sj0zyz3W5JkpS6WvH4+ITWCev1ijSFNFG8ffsWo6CuKprzMSxq0uGDJ4b6dV0X+EuBoGYlm0bjmceBeRxEWOkchI4lIivGmMUwr+97EnSw2r9kztzc3Cy+Qh8eHjg1LbvbWymi2hY3Dbipx2ExOliGh5GaCRu5QsyeootrfP+qqkiTFK3NQjQdx3GRa8c0XqXVYhSltciSpdAU+eh6K7LVLBHO1+m4p29a0lR4LXNAPa8LhYj4RKQyKt1Op5N46ZSS1J0lGSYxgYMkKrNh7GnbJtzD6XJf29nhHMEduqAfeiGzJon40YR7eRxH6qrGeUvfd4tCiKAycV42jehsDCwFSHzJ2NYsZMP4jFx3t/GZgwt/aylqvPDf1usVXdcFWbOm7zuRh8aYAp2ErlXh3YzR4lnilV7Q0dhkpGlKVdXk9QZQEuy52ZHnGc+PT6IGLEvKes1qvSNNc1brraB1OmEaZ6Z5ZLQjh/2BoZ+oqhWfvPmMVS12DUmWkGQZs/d0oegwSqGREWKSpJT1mtlrLBqTpOAcaRLUO8ioous6mvMZbx1lWdD3A5vtmjRId7MsX0YL1kZ0IFk2fzvPwnEJRblzomTz4dqVZSnNzTguCrZrrkpcJ0+n09IgxE09IiBRNVSvVkx2XrKjorowjjazLMMoHXgdF7QDhKtyzQeRreny37JGZ+F72uV++XrBEfe068Yh3lfXP3P9c18vXq73Ru/9ZewTjkmFmI14fKDCcyE/E8+hoE4hEuPqOGOxLiDCgV/+C7/wg1us/P7v/h5VdQmSiwVHJH3GrjDeqCA3wDiId0n8/yBGdYeDJPzqMIYYx5G72zvuX92TZ7KQFmVJVkiKZdd1iwNl0zQM48Dd/T1JYHHPQX46zRNd2/H49Ehd1YzTyDhObDZrXt2/oigL2Tispe8bxmEkL0oJP0ukWGi7TrpG5ZYHw1qZD8abcn88cjyexOTu/p6bm5tl4xtHQRe01qzXa4CPOglgMSiKC22EHQHGQN6M5zh2qfHBPp9FBpym6aJQMsYsficRCo3S5/gwRStocQi1y6IRPVW8FzfFLM0Wk6WiKMQwL015fHjAziNffPEFRmn6vhWFgdFChuw61huRHA+9oGdFUfDy8iLfaxbId7e7wZiU5txxOjUMgyBMWQ5ae5JA3lOKQILu6LqGcbr4zkR55KJYGke00mijcPOEIjhDIp2c0nwUBREXPh3m0WPXX5Ct0IWnQSEmhbLj8WVPP46SezT0KDcz9S1Nf2aap7A4XuSySZIsC06W5UuoY9d2V+hLTV0K279t28U5OG6EJk2o6noZlahgWCWbY0USxq12EiO7aew57Q8oJcGMk/0YigZBI3a73SIpj7B6bAZiTIPSwoUqyxIbFshh6Bn6ASkaBTsQ5YVIbefZCgneCycJJMQxPv/DIMZ+eS7mWn3fUxT5YlQ3z1PYhJIlsTpes4juaW2wlgW9i6hIkiS8fvOG7/3JnzDP8yJ3jh15WZYijR4GnPOsN2v5fcRJtDk3DOPIOAv6txQ7IeesaVqGcZIiAC7XOGyweV6g05y27UmC0qXIcrRSwQk4ZXKOJBGXa6VTxtlSFBX1ak29XlNtVlIYWDAqIdEylum7gW4amKzw8cZxZLfdsd1tmWfL8XgANEVZo7OS1faGrhO35TLPSBNDVVbEjDNrBblGCTctotfjOIX73yzrtTFG/Hw8S8Ebv/M0iXLTmMsmLmPPS3Fxve1dI2ARyYgFX9yQ4zWzYTyd5qlEt1w1xHFjvi5Sr9fSWEjE9fDrBcRyTF4v7xeLilg8/em181KYxDUk/k787vBxUXNdrFwXTvHvZR1OwnHF8xNdueNzE2X4cf2Wpu1j3o9bjrfrOn7u53/qB7dY+ev/0X+ycCFi0RK792vSUoSaI1yNj14BbnkgIkrSNi2Jkk3l3bu3qCBva85ntFJ0/cBqI/bh2+12kXDFrgatmNxFWhwX2ohEaH1Z4F5e9pRlwd3d3VJgFUFKmWYZWS6jKA8yglJK+AjWLp3d6XRa3FxFc28WWDPa0l8/JPEmApYHNy6gsbON7xEfPpDZI+ridzCHDjUiSvv9cXn/+JBCcGwMsHRcxCMSdoEs5dii42JcuJb57DRhtGS2XNJ+E8qywNqZse8wRmOUxntLliTkqXgWnM8nhr4LwWCVnNs0YegHnp+fSTPJ+qnqGqMTpmmmKGratuXh4ZF+7sgSGSW4kBWlEf7Hfv+Cxy6jM7mf0uU4nZ0DnyOMT8LCMAXztMQomvMJYDmX14Z1CSwOsfGcLjEDWuORMZAL8+GxbRi7BpxkDVl3WWzjYgYBulWQZwEaRyTSUyA5G2Pw9rJ4xQVcYh00eVnIBhU8Ya47T7ln/ELMS5MEvGUeRsS0DrzSS2EaIfqI4FyPga4X3AV18zaMI6Ptd8kwjCIdzRKmqQ9eIMLF0sqETlGRZXJPXG9Yca14/fo1n3/+BW/fvuWrr94uKrWivJCb7262nE7H5ffj+EpGDGKK9bHsV57B9WbDu7dvPyrG49oTi+fTWdxzv/69kyQhy0v2xzN2nkgTgwmcgMlaxsliHYuZW0Tn1pu1qOlGyXFCa7yzFFlBElDPKWTljNaJ705W0PcTSqconVAUJW8+/xyrNAohTTanFucUt7f3MuLAY1ItaIlRdG3HZC0mzYnkz7peY/ISp4RPpLUiSwxd06BQgah6MX4jFHrzNKODAmUaJ4mPQMj9bdtS5BVZljMM43KPXlBZs5DkdZD2j+MY1tSofhEETCHoq0jHtbClrhACAo8oKmu0NqRZKsZnXgQcC/k2IhRc1rdYAAjXywePILcg9ZFvEknq8dmLe1d8n/iK63JcQ+PeBReuynURUtc1dV1xOBwvCeJXz3Z8qaDwcU5sOVQYAxELqauCRgeenBXpUuCnyHN2fVxRAt00Db/wiz/zT1Ws/BOlLv/P7bXdbiULJmyeAB8+fFgMtuKMP86A4387KzLH6662DM6VxmiGeWQaevpREp232YbNbssff+c7AvealC+//JK2bXnz5s3ioGmMwSqP0cJRKauKx8dH2lYstdfbTUim1YzDsBB+I9djmmZGPWL3Z9bbNbsbqcbHeaIfBuZplCh264RghrCzd4H7oQKMaK0N4wi3SHYjQgKyAcYxVCTSbjYbPv300wX5uGZ7z/MsacFGLec0Qvha6zAuKj6CHCNvpmmaBeXq+34h+cbCxHvPer0OXBIVChQfPBXkZ+w0L51Onl82ChcWGPk5L0olZzmNI6u6Wjp5rwwmzUjyAo9inCyzg93tHd5K9+9mRCnStKzWK1argtMpYVe9xnuBfCNSl6cm+A7InHu92tH1HfM0s93cSOE5N2ij8G4KKcOaYRD+jTYJRZ1hsKxXgvo8Pz8vG5UsTD5ct8umGKX0wyDS0iTJSNKM2QkPynkpojSG2c0ob5ZOO01TvHPs93uGgPjMs6Prh3C9CGnEmtRJHlFcLOPsPnrTqNSgF/8HT5YYiqJckIR5lEJm7AeZY4fjivJyTBz/uWUDjwjcNXk7mvBFb5yqKpndGGbrovQ6N2eytBDJurVkeSE5VEaej6Efw7Ng6fp5yUWJklTnhJT59u1bnp5eOJ5k1ImHzlmGcVzGXH0/8vS0R3wmLhw1GSVNy/e/5p0kScL3v/ySvpcuPI4j4+irrmtZvJXHHsRDJk2ThVtmrTgQf/GNH2LoWg4vj2jgs88/4+7VJ7x/fOZl/0IXisY0TRd+Wtd1NE1D23eyKSKjlyzLeHV/zzQMtF1P2/cM4wjKU1U5SVpw/+oNSZLR9gOz11RVLWPS9YauG/jw/MQ4TszeUlUFr293GBLevLnHeTi1A7Pz+GnGTiNJltP3DdqkGOVR1pAlhnnyzNOMxaOtIytytFZYPNYRuFqiihuGgWkeiHb7s7OMzXk5pyAGgNrIvTRNU1BmJUR1ZiR+qzT7qCGLBbaLa6W/4od4hwoyYR3Gn2NwBPbeozItyqa4bgY1HgiqKd5NSnxotMYrh1JyPFJ0XDxUpCm5FNPX9hTXI6FYxFyv7bGB/bgAkbUkIkfxubtGUy4/Cy7wUq5RomsDw1jgl0m5CB3iaFUrs3BtIj8xjjmvj+l/7OvPNLLy93797y5E2rhBimS0XchW8QRHKFvUQZ1IR+ONYIx0gEhXr4xkPOyfnsOCq3h4955pmlivN6zWOwTx8KxWa4awkW13W1bbTVBDSJfQ9z3H43GZ94t9db5s9LGzizBbnsZxk1ivT/OESRPSLIWArkCQPOpLpsY8iWLDO/8RMhIXT2CpuLuuW8Ys17yUZQQzTSG35cJnMKGriT9zPJ6YplHkq0kaVCNS3RdFwXq9/oivEkdX17EI+/1+kVdvNhvW61VAGCxJYsiyXNAADzb6JVypiaTQGtDBS0XC9SYI6ErXNVT1Ch1QC+vkPEW0Ik0T5mFknqQozYuceZaRAMoJ5wTxElGhGzRaM4390kFHuNRZB0q+e3NuwjhA0bVH2q7FeyVSYqQDGceBKjckQcJorYSEZUFWnuc57fEQIO0sFLepLNp9L9C4yTBpikNGIW4aUXZkHgdGO4NRi+GWUmqRsEeEJt6jkYztnXwHoxO0Z3GoBIiBfau1WOb3U3SUlftMrMEzUQy40Ak6jw5joHEYRAFlLU5dYO6vw+DXhdnd3R13d3copTgcDlJcuwGvpPiz1uOdwlkfNiKNMYJ0lGVJWdZSHHtBHed5RGtEBh/u01iIOecYx4khEJ6HQbw4Iv/EBrVKjA8oqyqMakLBpTUmNcvMXmu9OCZHGDyignFkEXk6aZoyu5m+75aFPhbrSgEqw5sKrIV5oKpyNpstXqf0s6iIilwK3TbkJsVCvyhKlFFM88TQCWenKkvwnq6VImacR/phpKxXVNWKvKjQOiXLC/KsYvaSVu+1oV5vKVdrrIem6+n6lkyrcN/1zNPEzd09g1M4NGNzpioqivUGZXIcCjuN5KnBqITZSqq8D8Vnmgs6rYyB2eMm4Z6IbNcxzSNlKehI3w9yjZKEJE0XfxMdRhXiP3IZTUyh6ZHrI2o6Z23gGyli/pZ4GQnqgb/Ib4nar49UN8lHG/Ey9hhlLCl+N35BL01ilkYgCR4vLiDF0VNFBVTyegwan9X4rFyPmlxAh5JgUBcLm+vx1bVY4vqZi+tpXKc1HoVQDKyzAfm6vJdzNpzPy3de0EK5WZc/j8+01prT6cTP/8JP/+COgX77N36bTbAMjnDsZbZ3gcquZ6LOOYYxpEV68btwXjrJcRqZ54msyOTm957z8URdlEtX0g8DJuRbFHku3inIYn4+namqOlhJSzcbg6zSNKUfBoqiXMYiIHJfG6rZJEnZ7bakaULbtmIip9Xiknja75n6IRjEVXLMV+jFw8sT1aoOOQ+WLBfzKaM18yz27lEhVFUleV4s50cnhrTM0SHOXLIltMiJCfNa55inOahk+sWsKc1SdDgHETlZrcR0K6oessCXGceRNCwYp9MJlBIr+GlcUCClxC5cSJYjbvYLMVAWEVHZnANPKE9lwUiUDlCmEGy1QoyMjCzckfk/BRm3AtZ1SpElKCWEPZHANhxPL4CnzGq84yOORdu2wX3UkyV6+d6xCIiy+XnqmIaOsqzYbHd4BYfjC0/PjyEfKOGzTz9Ha8UxJITP00iaSscyTMJfUlpm+5vNDlC8vOxZ1WJvLXkunq5tGQZRYMzzxORdgKYtys8YpaiLijIEMno/L/C5EJwDGTPPKMqKJM35/Jvf5Hvf/S5vv/pqQQmquma1lgyXIWTnTKM4lp6Ox2UzFmh6wgcTQxVk4NNs8UEVgmIZiV6PPrI0W7x84itLszAqkO5PKU2WF2zWG/He6TratguokyLNUm52NyGZWzaea3RpnEfWGyG0npszD49PGO9DarRnmETtMYwzXT+ivCbxF06EV7IR5XkK0UdDRWTQBbMxvUDns51IjA5kWcV6vSaPyrQ0RSdm8VYSdFSs2O/v7mn7ifeP4pCMs2gsdV2S5UWQjqfMQUIfOTB1VXE6nXh8esJkRcjk8WzXK1brNfv9S3CItgzjQJrmWAvem3ANNWmWoHSGSVeI03a++ByN80Sel4zTTHduaI8veCc5PTf3r7l98zlpWWHHibZt0IkhKyXMsmk67CxmmGM/YT188a1v4RyYNGOcZE02yqO95XA40LZt+G4pq/VKzN+cDaaI0sTJCEkaOUE2dDinUrR571g8SbzDB/+S2c7ii/IRL8SjtQkGamLsFoueeZ6Yx/EyZrrinvgwPulC0Q2XYiE2DVrLtRYPFcI1v4xl4nbvrGO2sxQwogkmklgjGRYfgwfDeyv5s5idFQmzkfbglUKbZIlHiJ/nAeVFCKCVCuZucr6slWdXGyMN8dcaDBCFWWTIXJOB48+cz+d/as7Kn+kxkHRB8wKfz7MlBilFi3NrLU3TLMhGlmXkRYZzNvhGqOUmJcCAXqmgoJiZJ8e7Dx/I04ybzVbY67MVNYGdcc5zOp24v7+XLhDxeImqFqPNUpjIZxdLJXvN9o7VaZJqhrHn4fEDTdMsRVi8YYqiRGcJM06UL24mrXIypbhPNFku8tTIB4BImDVgDEWes92sl05ini2n44FT27LabRcS7GXmKXNc4YxAkmakWU5Z1gvPRmzPBd2IFXzkH8TO0fqLG2LsBHZBFWT0K7wTS+ZYAIkxnYx37Gjx1jNPI+dTx6mV6HGTSJ5GkmQo5bHhnNtZ1BOpMaQmXRbaLJOObJpk5n0+n2mbhq5xgGFVr6lXFUWRUdUFp9OButzw8vQSRhVSDFelGPmN4wA41usN5/OZpjmHjVcQtWloUc5CoWjOrXT0WpRP0yQoWFlvmKeJJM252WyEE6M8/dijUoUyY1joFCpArWVdo5OU7fYmGBMK4XKzWaO1ouka2n7A4ZjHFjt2zOPI09MTfnZopTBGLTLfaZJRw4vey9gozUjynO99+eXCZ+qCwut4OpE9Z38KrXROsrdUQDbyLMOTcjoccc6SJwnOWwwJRb2mKMql2BGo/BJVPwbUKj4XdV0v8skxEFGd9/RzzzxaklBIKi/IpAnFUmpSiqyg63sO+3145gJBHU8TeEllVfHNb36LOitpzme6/sypPZL3OdokbDe3JGnO4fGFt1+9FT4CnnEWmbdJtYzjpgnnoapX4rhaltS1oDun44Fh6ETSraKKwospGYphFDVLlIu2/cg0wzB6kjCyUMZgvZP7axyZ3RSaqAzngiJx/xyIyJo0ScT8cLKk08xqVdM0jRRhfReUYANOQZ6V5HlFVa7J8oyyTGm7E+fjgTxpJPPp5KiDirI/nxmUYr2955MvPuH5UdM1R7RO2NQlys0oO5PohN16i04C6VMpijRHkQRX7Vm8YMYWnWQor/F2xjvQKdi5JUsVus4lR8o5tK+ZxxHrHXW+ErJxGGdfCwOsC+tp33M49ou/kAsbtVKGJDWSEqwv/BQpTi4vo68sHIRPGvh9Yae5QghBSMhlEeS+YT1QKsYvaEDjZoeb3YLcOSv7weTEiVpG/SIfjuiNnS1egYqmf2HktBRSWqG8XxSKsfgR8cLF8RYNUao0T3ahFFgnQaheB58UJao5HVSDc2j2Y+P29YKEK35ZPC8RvbmmIfyPff2ZRlZ+73d+fyG4RhJPrHS7rlmq2T7o6aNN+263Zb2qP+YIqDgXNPSTjEAEup5F1eA8m81m+dnY/Sil8M5xOB7FVXUYWW+3y3tHe+fY9aSBHJmE2XHsKJeLr9wC78VRQ2S7Z6U4w0bOiEcQkDzPxaQuK9Bc5GvX8B2A5jI+iRuN9z4ch6YfJ86n0xLznecFeZGHc5oISc9f2O3xIRWPE8c0CT8gyk2jrDxJEvwCs6rl96KUHO8C6Vkvc/14W3ZdxzQO5GG8JyOCPKAYZfg+Yufft62YRCUJxgR5p/f0V6Qy4EqRZEm1J0uE+DeOQb0y92gtG0e52tCem1AQiEHcy9MTzlpWmw3aZMsoTxuzGJ3ZeWZsj0xDS5LmMrJQcG5PWDeQ5QlGFazXW6ZpoDmfGLsO70VeOc0T9XoTDMTiLN1wc3PDer2iLjckWsIf67rm5eUJa8X0LstSdCpGaU+P7zgfnnl5fmbqB9zkwCvQLN2TCemzNhBii7KgKAtubm4Wk7trP4pICo1xDpHwu16vOZ/PeOUXzkrfNyTGkATiYpKmoMXfI8q0o7eOcJLyBe1br9eLueD5fA62AyPn03lBSeO9vhT7oVCJf1ZV1UK6dM6KFNZ5+mFEGUOSZExWNvcizcV91cr1Z0FnFfPkZFzovPATorEEGp1kUpzZy1hM1pkdt7c3OOc5Hl84HQ8cA/oE0oWP4yD8mkRk3VobEpMyDCNZVoTRXypjrsQIeuZmtPKkqQ6fCUpdEGUVvDHSYISok4I0zYNCcaJpJHxTxp4TMwqlE25v7tlub2iaMw8P7ymrnMRbcg9ZXmC9p+16dJKSlxUmybBkWKco8wSjFGma4U1Kud6RZiUZOd5ZZjehtCYvSkySMw4W62Yme6YbBsqiJi8qTJozDFLAWDcxjB14KMoKow1KiVlb3w+YLF+4ibGojmur0rL5xsbrOiMojt2t+3jr+7jokFGHd7JJWyuNnfd+SVJPruTn0Q9qUYMZvRgDXoss4v83Jg2fJZLgOK6SUaSgg7FZjQ6+VVVh54tRXSQCRyQECE7Ww0fPwPWoZ5xm0iwL7rdKXH+DuZtzbiHJKnVNmpU11tlpSYKPr+v95R/Hg4k/8/8PZOXPdLHyW//1bwd/jIspT5zzdl176Z4DhBg3aGcn7Dgu1fSqrtnudguhT4eOEx/CqsIYZZ5nmqZh/7JfoDsJ05NNcXezwyQp1XotnhVXzP54mm1AVNq25e3btyRJwmazWYzI8uJiPhQJU1FZYL1boLaFBBXSpKd5wk02bNRm6UQvc3JDniVh/npRDUF4mGbHPDpi8nFZFKBUMGgbcUpjwiggnmtjDMejWJ9nqTDl43eO7rlZlgmbHBZFVJRSR9j/ZrfDaHXF6r/MRK2dOZ1eQHnyVNCRsR85HyUfZbPZMju5NkPbCpEzCYVJGCnN9uKPEUcOEXny84yzkpgNUFUF49TLCAOPKXIOB0nLdvNI3xyZ+g43T0yzI603MiZKDGkiCoFVvZJR2WmPdhPOK+nAk4R6VdH2Z4ahYxwm8qxgtarBW8a+p23PdE0jfIFixThNNO0J7y1CzBd2/+3ujt3mfkGfPI5h6GhbKdi9Ugx9x/n8IiZdw8jYj+A1ZVExOglRi0V85DfN80RZFGglQW6RmB7PW7yv4j0a76VoPDiOI/00MoTxkCSb+2Us54Bp8gvSGRVQ8b+BJYI+pr7GjnkcR8qypGs7qqri3DTgvfBHlCCh0zxLdASEEZB0tEopjocXlPKs6g1396959eZT6noDSkzklDb0Q8fx8EBz3jONPX3X4CZH17Y4JY6i1no8hqJcsd3e4JCu19l5KZai+m1RanjxRDmeTpxOp8B9kdRcj2fSMDuHs55xFPL6arUOfJjgGaWVZGwph1YepdwS0XDhABHGExneu8AjE8t28KRZQmLipm2ZnSPJK6p6zfPLnrIQdeLuZsf5dIR5oN+/0I8Ts4NhdpCkVKsN290tkw3NltZsN1u8h83NDQ9PzwyjJZ0VZZ6jDRxPJ0yS8Oln38SY4IrtO8Z5RgwwE/KsEGUOmhmH9ZG0n5AmkpYsm75idnpBIK5VjLFJG0ZJ9NbGLP8OWACztVc8p8vm6wk8ECv8JBu+W/QqarsO7xx5QLCjii/eu5Eg24/9FQ9yomkbiRupK+ZZRjdFkZOF76O0Ik2EKmDnWYjrVyqfPM/xiDv7NM2h8E8WMnBcx+d5ou9aqqpEXzWUcWoAl8JEG7MoFFEhh0glWBul0XEdDkWIk6iNuP9E7kx8v9j4x6LlmhzcNA0/+3N//ge3WPmd3/5dbnY3y6YXSUhS7Q4LsW0YBtbrdeAWdBxfXvjqyz9ZOsT46kLWjBQn86IWyPOc1XrN48MDp9OJ7faGTz/5dEFarqWQx/OZMaADEf66rnKtcxflUOjEol+LkK6EjHQOSb/Rw2W9XrNerdFeZHfTPAtSk4SMnTQlrwr0PwZyk1GNbHTWzhwOR7RWnI4im63qmkQbsKF4Czb48UYXBYfBX810QQq15+dnkQQmJpBis+W8gxQ1dV3jws/HcxKr/b7vZSwRUJVIOozk467rOLcHjsc9WmlWVY2bHQbx20AZTECoxkCWHPo2MNQNRejeI9kZ5EGLs31nHdM4YRJNlgn3YZykcNTa0Ew9T89P4CawliKBeehxViB/nVZCpNRqMfICSIwhSzzNYY/3mqpakaQpwzSA8iSpQeOx8yiFTdsyjwPD0LOuV9ze3TNbwzAO9EPL+XzEe8s4iVmexmAnH5QGUFZ5KOxOOGfJ8owsMYxjhzZCdq2Kis3mhjTJsUH9o5XM5Odp5ng6CldJebwdL2O1UJBcq7wiMhiL5shpOZ3PWA9Ky/W24ygbbKIJeg1mK89rREGjGih2adfJ3nGxjqOgrCh4fHykj8okrcmzXJBRJZB513eB+J0sRdbpeMQ7y6quWK835HlF240kSY7HsN3doAMpu++OeDfSNkf2z4/YyZKlKf3UMs/iXbLe3LLa3FCVK9K8lCT2aeTx8ZHn52chvYbvJMWKoH/xvh7HcRldrNdr6vsbQS+s43g8Y4zh9es3KBQfPjzQNueFB9R3J8a+Q2svo840ch8+PmexK3/96jPSLKNrWw7HPcPQh2ITUaSEDSp6Z1RVFThSGxTS4U+zJStr+smBSemGiX6cSBTkiaGqa/KiJskyNpstSZpK8Xg60rVnrBNrfJOkzFaRJIWsO3lKc5awz8Qk5HkRxuaKCS9RGUmC1klw8lUBKRvRRjKArsUU19J/Se++8EXifSWvQOINa1Q8X8sIwwb9lLoYpV1zIa836fge8c8iQn5tUhr/O01TxmFaxAjXz1V013Yu2gxIvltEj/M8W56XSLy9eMKEGAkX13oh7sY8oshP8T6OeC58mssxCyn6uiTwXBFlvUerCzH+Ag5czsO1t0p8X6015/OZn/nZn/zBLVb+2v/1P+D1q9eUZbkYnsWbNUn0RzdVZOMPQ09uFMeXZw7HIy/PL2RZusB5WmsOLy/i2DiN3Nzc8NkXX7BaizFS1/UkaQ4eTqcTh8MepTX39/es12vef3jg7tUrPvvsM7yX/Jvoxuq952W/X9QKcV4ppmpnifNO5Ub88EE4K1mWLRJj7aFIsoUwnCQJu0C2HcaRY3vGKTHYyvOcrpNF+/b2Vm4uBVOo8h8eHhiGgVWwiMd7XMgqimjP9YPvAIJN+rXJUcwcspOYQsk5OVBVFa9fvyZmuqhQ6EQr/7igOifFgptlw1uvV1IkWkfTnKXiT8DjhPdgZ+ZxZBqEWOsAlcjoYOhEvkmwSNdKFhxUIAqrS55RVJ4YnQZPBhjHlrZrybMsOJ5O+GAdb7QUKd35RNeeaJszeEddi5W7jBccaSp8qL7v6c5H2uZMkVdU9Yb1ZkdZleRVTtOcaM8vvP3qS7quxWhFGtAD72bSNGd2wTHZOZSKJmLi3aHQeOs4nY90XSvoVyB2GqMDt2NecnP6psV7TWIkxr1er0Te2neMw7ioKaZ5IjXi55Pn2XIfRVSyqqrlOkdEJEpmvfe0XYcJvinjMOACKVQWNkEr+35c0JrrQj46K0fIPo4KY9E+jiMoEyD3i5wzqqm0MfRdR5KkC8+r7dplzOX9zGZV4ZygFx6NMgltF4nvGXae6PsToT7HzjNDN4b7R8Zg0ziTZDnVas23vvXDrDZbEpOICmsUkn7X9cszorXGaEhTs4y14mjrdJJMIbJEyOBKCyesqNA6CeOcLCCTCutmxq6ha888Pz/Qns8458lz8ZqKDYHwx8RrZrO95+bmjnmeePvuK15enhmGflHc1asdXdfg/HzV2CQkJsX6BJ1W3Nzds1ptaIeBYZxJ84LVegOzZFEV1ZrDqeHcdpJlVBbM48DUHmnaE20vo9TVZsubTz4nyyrGQZLn26aVa68FnXRWRqHkOUlRipt3kpKF5HJrJcrEJMJPWgpCrjdfUN4uReziKbLwLCTZPP6O9zHlSV7exRHHBSmQX41IxEXR9nWDQ6VEURPJqB45hvtXr9Ba8/DhcRkbLYF/Ic1YAhTVgrbIwQghOOb1yDE7ohGbfKY0b9M4Mo59+DnJ7orqJecd2qTLnujD++r4PeWNiGof+f4XxC6WeRcy8cf+L9cF0PW/QfbKf1qflT/Txcr/4a/+exR5sTiyxjntzc2O9VqklF3XBfJjs8wNq9QwtucFvVhvNkI2smIKN7StyM0UnJszTddxbhuKsqSqV2AkHPD169fLQhoXztPpTJpmizKpaRrevXtHWZZL6KKHZbwTN89hGEjShDyXRT8aS0UexziOYq8/jAwh/XXoJUV5nCZ2uy2393dsd7tFkRA74rIsZYGeRabXNA3zJNH0q9UaBbRdi1ICeeZ5vqTKWufCv0XOFsc3kfiotebp6Qmcpa6rxQU3ZqFcLwQqoDX7/R6l1PKzRhnGQfKSkiRls1kvBYUHRjtJqqzylGnC2Dfsnx+ociHulmvhBSjE5r4PHjrTOJKmOWlaLsVKEtQMMWJhs9mhFPTDGeeFNKtQrFdbymqFC51cvapD5+KCw+qAn3vG5lkWDmvZbre04X4TZj4cDkfKYoUxOdYpdre7SP1mGvY8fHgrIXjOkpmENBUFAkqjTUZZyUz+6emJ9XrL3d29kKCNRiMRCI+Pjzw8fCBJDFUlhbtRcDy+YGdxGt4fj1TlGq0zDvsjCuFvrIMfxxDCHU0iiI9SF7QkFinzPH+EmF2UL+nFDC3PMXnBbGemccQ7i50miZAIdv7b7Zau63j//v2CMMSCJ75isRKJtos5lk4C0Vgv9v7GyCafBNdopVhI3GVVLWmwm1VFez7z8vwiRMIws/dI3kumk+Br04IKUvSywlrPPDkSZbi/u+N0OnNuGyYr6GZRiWpnnsalCI4ZVpHjMI49ErAqHXdixJMicnPs8nz4wMnxpGlGmmaSUpxk4VwNeDuRplrGavPENFnm2QeViBAuo7/OOE5k5QqlJStrGkVynudpWDc9n735nHNzpB8auq7BmISyqJkmz+xSvKmWKICsKMRGQIlDM4jCKylXmLyk6wfJnvIWb6Uos97imSWZ3KSsVlvGUZQmbprpu5ab3Y4iz9EopkFSmwfv8SaGkM4URUmeFXgfQ/f8MuKLVgzAssmej3uUYkG5rwsZlOStXSPFH78uviaXYuWyGTt/cYmN6/9lrRM1YkQ44j/39/dYa9nvD0EMckEh4rMU39M5FxKazUcFQUQwImIRX7HptfO0iB0uqJ6Mebz3aJMu5+z6fF2URVxM8bhywlXCi/JKf3Q+4rm7NppbzsPVP6fT8Qfbbv93f/t3qar6I0g1VsBKCRQ/29jVyfzv+19+yYevvof2juPhECS6kgH0+tUrGUF4CWCrqoqsKNjebFHhvfKiZHBQVTWb7QZrRVq3DmGEdrKL/DeOY2xAHx4fH/HAZ5999pHipu/7Jc24rPKl8o1hZDGM0HpP07Ucj0dWqxVVVdF1HY+Pj9zf3VMEm27xOEmCfFmgxXmyOBLSNFs+dxj6YNePWJFPoaBTYeNQl7BDZy+s8zi6ipb7h8OBtjlR5Dmfff65fDdk8R36filUVqsVWqlFvhnRrMQkZEm+kCavH/DoWVAUOVp5uuaAtwOn/RPN+cBqteKzb/4w1jn2z8/iWNv1JKmoQ7zXrNa3HzkcSyekadoGhQ45USNNe6TtzvTdQJ6VOC9Fiw2qjWhVXxSVRCicnmn3XzJP4vdQlZUE9xlxWEWJrFSpDKUS+mHieD4x24mizBm7Z+apxzlLmee4Wbw2ikIM8mYncsEiL4UgaTJAeCaS6j4v9/p2u6XvRb5s7UxzPJEmepG/P7/s6fuRzeaWm5s7Ht59SROK9Vho2OBcWdUVNrjvXocnyv2oac6nZYOI92ocC83WMsu6R5ZJ9ELXNigligcTpJKxmI4FijYyzrkmzMaYjKUROJ/FlMtBngeTx9gxe78oYGLBEkeQq9Wa29tbXp4eOR32HPZ7+qHH4TBJgkm0vI8Do5WM6ILCx6M5HBu0SrhZ71ivVjw+PjLOI3lRYN2MMprddkvXtIzDAIolEuDx4SEYKPYYLcWJCTbzu91OHICNCeMMHwp5I2PEvODu7p77V28wWcnj0xPPT48oLNPUM40DdprwTlOUK4qAJsV1A+959eoVp27k7v6etmvo+pYiz3l4fBAb/37AoCmLFGUc8yyFVpaWaJ1RlDck+ZqyLIQP0ffM07jw9JKiwpucrFqTlTUog7czVS4dvFMpymjSNMF5R9v1ZHmFVuJAnWrH+XjETrP4Fw1jKIg9WSn+LNvNjiRJkSiEgf3+IL5IVb6M+yJKcREVeIyyuOAVEt1h8Zdi41KQiFT5QngFxcc+JVHNA1IIzz6ygMAEhaNfNmoXuB1+KWQSIwTZpm3EFyjsunEzF48VtyBk0zQxTtKgxsLkequOXEXgagwTj/3iteUJni1Brec8C2ri8dIYRfTIOXAz17YfcJUBhMJpGeGbQMq1zi6+MVLUXBRCl5coZn/2536ACbb/5d/628sISMLzhCB4Oh5x87RwQ+Zpout7iqJgu91ivacfetq2W2DkDx8+sN+/cHt7S5HC0HchtVWyIOpKFvXt7pZqdQNKUa8EStdJgg9kW/kdv7DUdSgGlBLDuaGXwK44Y43wpKAKCfWqJkuzxTgq3qTOOcZ5ghAcFwOxIgEvTVO8dTISGCcOxwPNuVmOQRuD0rlkDqUJdh5x88w0SvJwUZbMiEX7cjOHgivPBYYeh3HZTGIxFTvJvm95fn4O8+6S4+nE8XgMya7CrYneK5EPI7LZKRg+Cfk1ScQKP8ulq8yylNQY3DzjrOV0OpBnGf3QczwcRGmiNEVZkCYpxohTbNd3KC+kRZ3mIXXb0LRnjFacGzG10yjKvFjs0sexF+nq6cg0D2xv7inLiq5v6ZomkN9EKVJkCVkmRNa+7UhNSte2Qiw1hrLasLt7g9Yps5XVzamZ0Q00zZ6nr75Ee8KM3jMEl9PNVsZyp/MJ54RT1PcDeVECYndf5gUP799hErGRHyexs08Sw93dDWPX8/z0RFHkotIK97N0mQn7l70UD6GgNiG4T4oDw+l0Xq4xSjwfoqLNzj1VWSzon6g1isXsL1EyfjBJRj9IcKF1Qg5PjUZ5IR86/7GE3zsvEslZvCWSkHQb5/fjOJGlOev1hqoqsbNlnEaU0sv7jcGdN0lTEp2yu7ljs5ZR6ePjB55fPgTTyOgwernvSBJMkjEG3xxnPSZJ2d3cUpU1XutgppdQlDk//GM/xvu3b6WoqyrWhVyTl6dHPJamObPfv9D3HfM0kBhNXa+Ci7Vhs9myXm14fn4hLQrqesX3v/99zs2ZspB17dw0pFnG7u41s7VUZYVzNtznQxgx56xWG7l/ImrjLMp7irwAazm3J9bbCpMqbm5vePv+Pd0w450mIWEcO2DCukk2Um/wPiGvd9i0WPhFaZoKqihGH6RpQZaJtb61HqUNQz8EtVeF1wnW+3DMhjGc6zTNpXhTjnEc6ENDlgZSdJqmJHmNN/LekrEVFCxzyGOCJUsIpfGItUIk0ioCInAlRI5rzzxbUGbZ8KPhmyLa38/4EDi6FMTxpcAhyFgkp8fmjsApFGfskOsTiygdiNDWLaMhpfWSVBzXVW1kz4gFxEUvtHz8UogEUsny5zIqtKRpUJuiQoCnELTjSEd+9zqsUK6n+qjousQFxOJF68tok3gcwfTOyyRLjtP7Zf+KBNuf+bkfYFO43/qvf3PZ1K21i+/Hu7dfCfScXfwgYtrvt771LSbv6QJsGJ1WT6fTMpfXfqRtG077I6fjke/+8Xe5u73lG198gVea+1efsLu5JckEUjNJwjCNQUZckOfFgvScz+dlkTfGkCeX6j9K0+AyNrl2zIwLeZQ7bnc7sqDfB7mxI5qU57lk4/xjCE7y/z0+dDnzOAaS6Iyz0+LQ2s0z3VUCdV3XC0kx5jzEhx0uZEFBEvRSzccxy7VLbmoMh8MB59yCcsTQw+fnJ+7ubtlsNsvoYZokNNFZi/YOb4VEt1jwB0TrfD4zdG0gWmZB3SKLeZbl4muhE+EF4KnrkiSVNNth6PHWkmiD0SliQOCxbmQcO47nPbODvCipihwxm5Ok4bZpaNt2cWVVOKZ+QGtZrJSHvKzQJqOuN2x3d5RVxewnng8PNM2R/rgn0ZqyKjmH4kB8aBxDP4RrqEU66TxJkmGSJGwOikQbVuuaqir48OEdx+MhOGQqykwko6fTEe892916cUh1zlJXG+7vX3E+n3l8fCRNZbS5Xq3puo7j6bz4p8RrGe+zsW+ws4wgI8J3PB6X4vb4cqDrO5FnoqnXW7yC4/HE6XSgzBWJudh3XztrXsPb8TNF6aFlLJKklEWxfG6WpTgvnVvf9aRZQlkEn6EkY5ot8yQch2kamebuI9g+bjQ3Nzc4BTe3d3gXVXuiztE6EIAzUaMVRU7bdcv4zDnH6bDHTYOohrxls1nRdg1NcxaUV2vqasWrTz4hSzOmYeDp8YlpElO2JM1ZrVZSoFrH6zev2W63/MN/+AeM00xRrwTZzUSCPI1TeJ4st7f3cr4GUUvVlZimrddr1us1fdPw8PiBtjvRDQ3eOzabHXf3b1AqYZ6F6N10J0mvti6Mgtak5Qof0NgirGt5ni+his15CM7LWUALNG3TYoy4LZusJEkzsjwLo4Y4GgGvFCasKSKPnUM3r0LpYEAJcX9B4FTY9J1HB/6Gc57JWpQ2JMEB3Lp5ITTH+ymOFC/IbXSBvUIllgKDBZ1zV8WKDwieIxrEzcHr6+Ja7qzYXsQ1LjZ48ff/cf++NjCNe8LH6MTldT1+ufBkIhJt8VzIvV9Xb16/x2VvuBQnmj/9meqqGPKLUiikL1//oFYLxy7uP/F1Op/4xV/6AR4D/ae/+td58+bNctKbRpCEzXqNc3NgXIt73pdffsnnn31OVVd0/YAPBUSEs5+ensjznJubG8osFUtqAOt4/PCA9555mpitMKvRWrJ+koSyqkjzjGg6FXXr166F8QIrJ/LgeNqvi5LlQfnaDRs71n6QhNP43hKgJmomYwyJ1oxB/gsXKbc8bAZ0Eip2x3H/Qns+cz4fGYeB3e0NWSgirmepsUBYLNnDTR8JjLGoKIpsQXHiK6oe8iwjS1NOp9NSXHovi9PLywtNc+bzzz9bpKrxoRJCmcPPM7iPPRMiSVeQMZEZl0W5kBcj0VBGP8GgzwivYZyGhZSaJwajE7I0J88rxrGnHxqGsaXrGyYr5m3OWYyCxIjkME0SvLWMoyjMtFbgLX3XkiSaLBUljwecVZgkpygkKRjlsW5iHluUdxSlqEDigtf3PUmSsq4kMyn6bHh0UKz0opzIcu7v7zgc9uKC7MXksGnOoUOy7Pd72rYJariB5+cnrLXU1ZpVvQpptB1PT8+UVUldCQdLfD4y7u7uPlJT7Pd72uZAmuqFD1aWZfDUyZmnmZfnA835HIp5Q16UKC2Bgn3foZXYASyeGAEhPJ1Oy2gpjghjAXRZaN0i5U/TlMSIcZUoKGTsGDtnoyXaYZ4sSuslyuC66I3hpqKGkNFo38sGbHSQIReVjPTSnCyX5+2Lb3+Lse0gMbTnhj/5o+/QnOVe9rhQyIvsX75DLsWmNpjkYp5IkMqqKxK4MYIkbIJf08v+gEokMyuO5OxsSdKwEdkZbRR5VgpakKSkac7+cKBebcT9uhE1WZ5lMh5Oc6pyxWq9kRFSmXM8HWgaadryrKSq1jjnhX8UpNbnplmI5G/evCHNK2Z3Ia0mSYadJWDSo/BJShI4fNFLhOCKaj2oJF3WJO+ChFbrwA+LrqwfK1iIha2TlGaPZKRpk8h7hmLIXaHEOqAaUYUDYvB2bQjqAwSur5yH4zp6vS6aRHx25rBmC/8oyvl1gBcuvxNf15v3NZ/jmu9xXRh9nbB6/T7xd7/+3koho8nrgicQaRfVT0AUFWpBeyI6ouPxh3MUTrdwoTxhtO7ihy4jNjtbZjdD2Jvi8xqP83A8/GAXK//Vf/5fst1uiaF6MfPl7u4uPFDpwlWJCoZpmtjd3pEGp9bY1Q3DyNPTI1makacph8OBsiiEFGgS+rYL7w/KqOA2GODIJMGFOeHt7Z24zAboSwfouIg258F2/Ppmig9B7CxigRHRosNBMmLq1QoX5GxRiRGRjTzPSbTk2MSq1lq7eLl8+umnoYjSy4MLXsizVmyrT22LtZb1er0UDbGYApacmEgqHoZhyZgRAqFZ0JYo+V64KQH1ihtDJBYLyiTw5TWjPqJPRmsMnmkYFzO8a3dT7z2zE9Jw04gR4O3t7TJi8s4viat5GC1578RP43gkFYduEpOLqidNcH6i684o7TBpLpsHnvP5GDZDi0EWARUEudZNnE4HssygtUCseZHg3EzXDUyTwzkV4P2SJE3o+yPj2IVzneDchXQ39AMGkXImaUq9XpPlJX0/iMmd0oz9JIjUWh7+pj0zjBKn8OnrNxRFtoS/xVHT+Xzm6fGR40HyqjbrtUhIQxEZeSJZluOdo+t7Hj584Hw+s91u8cDDh7d4NzPPF3fOeN3KUmzgx2G8LFrBr6HIBdXq2hPv378jJnLHAjgicXF8G43irtUWzknGk7X2ioCIFC34xc3Wh9l8nmfs93uMEbfj2LkvydbBW0YIj5PYjWsj4wQl7r59PwhKELxA7CxBhmVZsd1teffuHcfDnskK+TgPjYsU9BIOud295u7uDVorDodnur4JnjjSwitvyFLxZVkIz4nwzoZpAh1tAawgPleSWaMcaarQWgisOsnJColMuLm9Y/SevCx4fHjAoJiHGaMMBHv7YrUmy1Pm2S5oZ5amGJPQdy1T34VnVMZr/SDckjwvqDa37O5ek+c5Ly97+i6Gm64FndKG9Wa9RCjE4lOeZeEcRRQgGgLqwD0ySboQQr37+DsrQHkXkGQICzOENU4Ago+daK+LA+ctSl1QDcJIJaqulEqvMoaufy5YUPgLenAZ18tnei9qorieXzeh14VJfF3zUa7NNv+HEJbrQuUaWbneT64bvpi39dF7yC9/jBgp9dF+dH0M8ZxHZEsh46brz3RBNn0t4Y7vczwe+dmf/wFOXX73/t1ys/R9z7t372TzCgtckRdstuKFMk4zyhj8NHM8nqhqGQ+9vLxgreXNmze8fvUm5IdY6rpe5o3zNLHarFmzkY0tkxtq9m5JgEVHl8LLmCe+h/d+ceDEibtpLJKKorhkzQBVVXFzc/PROCgWEPVqRZKlvHnzZrkZnXPLIj/24uJ4fQNvNpslTHEaB4a+p16tWG82Mk7QmiRI+m5ubj56+K5HVNM0sVqtlk0jEmyjPX58GIdhWIqQhQvgL3HhUUEUv1c0UdJaLb4qscCLD1129XeRdxG/O7DYmg/9gPNu8VNJkoRxGvABGo0zb+8VdbWmyCvyTJMag9YJ3ilmO+J9itKOLLvAps7NUgjOM/NomcYeax1lXlJWFU07he8g2TLeOyY7k+eZoApIkF7fQ55X5GlB2wrBO3b30bzv0j3PnMcjHgnyS9KMsq7JspxpHFFY3r39kndfycYcQ9LGceJP+oGyzEUhYyfevv2KNE1omjYQJD1j39FqRXM+4b3IhMUJVPHq/hXOORlBOgfe8/z4GIi4M0rJJmGDA6Ykx0oxmGYFWVExDB1JmrGqKvI8Z7/fc+4GtBGCel3XjMPANM9LivE8W/b7l+ArUQTl1RgSri1eGdI0oQpNyDSOi3y1yAs2VY3zYuI2zRPH45F5jhbmEhgn94UsptEnYpon3DyJ2ZrXMn5UksxclTL+mKceNUsBlKWaqkxRfmJV5Ri9pht0eI7FPVU2M0NeVORZTt93rFY1N7c3qL0PIyxLXdV0/RA4dzOTs6SF5HqN44jqOqYYVIqnLAvJMZuCxNvOzLNDKYsdJ9DCZZmd4uXlmaQuqVZrsjRnU2/Jkoznh6fFFVWFCI1YmFXVBkF8ZDS9+/QTqrpi7AfWmzXjNHEKyr1+ZhEpvHr9mmkQf6rnpxdMYhjngebkOTlBxDbbDWWeMI6evjmi3My6KPB4+vZFfIKsoyhLdvefoIsEhXCZQGqQ6Lys3SzFilIYnUqaMWINb5T5iK8SOR4qSJnhwtvAuwVIEZQgrp8XzuDXR5Sx4ZS/d2LCjXBAtFI4dXHKhn+Mm/jV+8a1LHKQ4p9H8nB8XRck1+91XfhYGws6+f/jOCFIytfM2+J3jS7sXpDJUAb+qWN0Ts6V40I2XpCjgGYRxmYLB+1r3++f9vVnGln57b/726SB3xA3t67raNqWt+8/kKYp3/zmNwFRJngfZ4OQmDRU8m6BOJXSdF3Lw+MHbm52GDQvz89ivOYll0QpRVFklFVFVuQCBWqFD11aVdVU1cVmOSIcEWq087QUKxFpOBwOS4R8RA/qWhbzN2/eXKpfRG4JLEqK1Wp18S1xfvEUiAXG6XQKTpoJxkts/ZLUO04kWYYJQX/OzctDc43YxMIjpjXHBzcWHtKtiOFRfNjknAeisBLJb5QLxw1CPlNs+tsgN47S2O12K8WK1sxDj0YtbsXxwY8ZRH3fLVLNuIDEUZBSEn43jhN5XpClOc5xkQr6CaNh6Ce8V8GvQ8Y01g4S/Kdlit53HX3biss6MmaZx5Gm65C8GnlYk0zcLdE6mN2NeCuy3ywt2Ky2pFlO173w9t2XC9dKa70YQ+EcU9/T9T1pmqONEcOsuiZNcxKjOJ/2IQjT0TYddb0ObsI51k/M80SWpWER7NEmhKA5iwsKIEHyMozRKCU5IuvVhtV6DYiZ2mq9EvNF7zgcDgz9IJk23geHY7nPiqKQTebuXrrrsuL9+7eBHD3hnWxCKBVGMVI8NU3DOI5st9sF1SvLclHQDcOw+JOMdmK32y0LobXipXE8HCnygiLNmEaR9w9jj0kMWZYsjQHIWDAGdUY5/jzPELhCSiuyNMeYRPxYnBDm0eLfIwnejrqqqFc13ouPxBiDF5UGH3kRZml60lTUZOMwMNuZ+/t79i8v4g9j5+AnkiwEVjvPi8ljnou1fNu2S4cez71RHo0oSW5u7wBN07QMo4wDTJVxajqMzimzmiwtWa/XDEMPCpKsIMsK6nqDc3A8HHjZP5EkijSMueZZEsancQSl+NY3v8nNzQ2jUxTVipu7O9KiZGx7PIrHD49UVUnbnYQ304vrMEr8PaZpBOdZr2pma/nqyy8XjyaQBqTY3LG9fb2gtxFxnudZUtsfPwQi/ExelDgHt3d33N2/xiSiWLl+XY9eZM1SH/35NRLgvF5GT9f/xLVHa5aG89qEb7n+fMxTuS4wrrfcr49zvq7C+TpH8Pq9vl60XN7/wrURdCe+Vzz+i9ldPIY4ilQhc2hB/9XVMXqWVHEZ2cXPEhm6Qvygvl5IeS9xOP+0yMqf6WLlf/+/+xVe3b8iy7JFXRPzd8ZZmPOr9TqMVKRrEJdLgTyjLDdudLJ4O7LcEFMq3TwzTxPHw0EUFLPl7u5OFpEslaIlzxmmMUB4Fwld3FjhcmOnISMl3kxKXUiyzjnO5zNfffUVSqkl7yKiFvWqZnd7KyTH8TIWsdYKadWKHHsYhkWNpJTiw4cPTOPA8eUhZM1oVpsN6+0Nt/ev2GwFyUmDkV6s8ONxRT8UYDHuijd4HL8lyYVEvN/vL8fkfXD3nBY1QYRHIzo0z9NyvuMrGvQBGO8kn0ldCGEQ+AbjeBWJMC1s9WiFrQyMc8/hcAzdhtjNp6ksgLhepq6O4FcxgbKcmwPD0JIYTds2TOOIBopSxoTeCVKSpyIHPDcd1kK92lJvthRFhcMwO493A6kBZyeGbuCwP9F3A963NM3xo9FZVFg5a0mVp20bWfyCwqWsaoZxYujPGOUwiUGaTsmUEdmuxivHNE9kaSKKoHnE2kk4Nl1LqpT4YRi9FKFRsi4jKb9siJFYHRGvPK8oKxmDxOs9DIOYDwIvxxPaGLIk4bjfs13XrFc17z+85enpWb5LKBbbtl3ui68v4hFZ2+125HlOVdeYNOV5f1hUGxGtE0JtyvP7R/b7Pa9e3TFOPePYX9Rm87Asptecpzhy6vthGR/Jv0VVUlVr8jyl7Q4L1H3dfeZ5zmQt57YPZNySebIcj2fmWRAnrWacHRdkB0TVotB4pUirChUk9XGDut60vIcYPBn/XsZBojwxSiS0RmuyJEUrWNViYTC4kW4cKYoNZbWjrjbUqxWn84F+7PHWs6q3aJ2TJCnTOEouUCJE49lKgRezoFarFW3Xst1sccpIthIhk8xruq6naVrKoiBNLN7N2MAVGceRzWZNludU9Ybt/SdkeUHfiey763uqsmS92ZKWNUolQoT3nqqWwrBtJCTVDp0wuYxBaUPXDyHcT9CBxGSBjBvUN/rau0eRmHRJMF4QGEIxoy9Kn2tU5JoP46+Ujd77RTVkTILzX/+d/+HCKP5M/Ptrp91FNnw12v+6Q/l1EeX9JYD2Ggn6H/qs63GN1po5oKixUHPOLiiN9Gj+o9+LL/mcC6k58m+uKQQ/0MXK3/rP/hZ2FhJhXLC8FwJUmhXCGDeyieL9oqLwTshc0sX6ZQExxkgSqRtFVheY3fM04axlfzjw/e9/n9Qky+JZliVZISZYaZYxjxNDP9J1rVjrh2j7YRD1xN3tzZWtsvtoXnmxo08/ulnbtg0FQYpJYm6QjGCa5oyoXGqapmW/l5uhDpu1C/A+3vH08Janx0eKouSLb36Lql7JaAyFs47j6SiGVVfz/IgKpbGgG0ec8+Lf4hGWv/NYPy/waiSe2cAlOp/PpDGPxAqUnSSJbHxZDpplw7wm93rvOez3lHlOEZxUsyylzIvQZYnhkUbSTeOYKBaKeZaR5CkqjVLpNMCZimEcRfUxdxgtadbnU4OdR4xROD9xPh8BSwRNNeLBsaorhmHg6fEDRs0kSUaei5V9WW1IkxylDFm1wmvNODR03QnlZWw0jjN92zONZ07nF/pw3ATuzjAMuHmStORpEkVEmpHlOUUlnejp9IxRYla32+wkFbcfmUZxRrXB9O18Ft6UdLctXdeAd7hpXIqPqqwYp5lxuPIrCpyLJZUcKVSd98yzo+tGjBbDrggFWyscEpNn6EQFpA92mzXHw56ua/Fe8l7idbr2dMgy4Qk4e1HprDdrbm/vWdUrqrrCIwGiEYU8n04cjkdJSLeWrhmYppGiyEJ+jrxvWZaUZUYbzCCj662zjjSTYmycLdpIBs1mK7JipRTHo8jwu75lmqIlfb2Q28dxYJwmVusNVVlhEiMZNyHnR6HIc80wtgy9nPeuG5hGSYnXxqDyPHTkksYs2LpnHHuUksK+LISI3feDyGETuTPFnybBzhPTOKCcQ3koMuGA9DZu4glKZ9zevmJ3cyvZMFpxPnUSmmikkWiahrxM2WxFKZmkRXCMTUJ+Tc84TWRZznq9pahqxmEUjsrsAw/IcT6fwPVkacJsLXWIuJiCUlOlBSMpYxgbgwoxIjI2VF4KsL4fcLBYNiRGspisHRdvnjncQ9okDOMI3ktG13QpLmWcmAl3T0twpA1ju+uiQyS+knCcGDlelPA1IpoQCcBaq2X0shSx4TrGP7xmi8QRFkRk4yNB8oKCyJ+owH3KKMtKzDznOaBT/k8VIPIdLh4uC1HZXwqUCyLzEYeYaPy2JDlf0QHkYAmTARtQ/QStzUdFNcwLIuNcLGhCLtfpxE//zE/84BYrv/1b/80itYzoQ7wY4ziy3++Xv4ubo7WWBMgTQQheXl5o25Ysz3DWkeUZKkkXBOGaoR3tx2P3E9OcnXNLsSQkWk/TnHl5EYShbRvSJOWzzz/jJqgrYsjifr/ncDjwxRdfhEwe6bouqhofuuUwUppmZjtTFDLDB0eShu5wtsz2Y/VE7CQUMHQNh/0BpRVlWQWTp0GkgZkkOse8pBhCGG/yw8sLzenM8ST8hmjzX9c11lnOzWlBYSJJLMpem/OZoZOAvaosJSpdCZu+73umeSbNc+7v7xc+y3WuineOKebFTAMGgd2nUWLfU3Op5GM3lAcCdbVagUnIUglUlJAuxzzNYe7fM44ddp5pzuKvUmYZ8zzinCXPE1YbcUg+HU/idVFVbDZb8BZvB/CesZvwTqGC2YD2mqRMIVFC7E2yQLwWlYn3Hjs19O2J8/lE33ecToeAgMx0bUuRJty/ekVZrWRJU5pT04Zk54nZDjK+8UBQikyDkOlMkaKMoa42GJOFmAjF+XzEzj1ZjIWfHUVRsdvesFqvGYeJD+/fMg7iQxMbgJjubYy4DXvnsbMotDabHZvNBq2luN03RyY7M46DSI2rkqqsqKoSpRXn80EiJOxM37c0Z1HQpEmC8oo8yQO/yASvnYzJWiGDDyNKCQF5mibKsmS7Fcn7uw+PDGixZddeCr5pXBKf0zRhu9ngrOVl/8I8R9QItDbM3geHThmpFMXFwn6cHJubT3jz5nNWqy1aJQzjwMvLI4+PHzif9rhxFIRg6jBGU9UrEpORJjnbmx0mMbSRk5UXS3hdUVVk5QpjCoxJmcIGO9uOp6d3PDy+pTsf8V6xXu9Y1WtO5xP7/SNKeaxTJFmFtRPGwO3NLfe3r1jVG5qm43ASRZgkB09Mk4zCb3av+eKLb9MNlq450TUHhu7MMA0kWcb9axlBT4O4Ops0ZZ4dzkM/DOx2N1TVFmMykiwTbx5tKKuKslxh0gTrbUBcvHgRJSnr9Wohqtvg52ySDOfFJ8UFdEF7TVmumL3CerlGYMkTg7eWyTtUuI8jeh4RKO8sGhsKCRWIoQZj5B7LylyIwtMkis4wdlzkzV7Un4kxCydvDvegB7yK+WZ+2fjjaOkjNdFVQbHIepUlFilfRygAsB9zbXTw1rLLZ18Ulx+NrgJ/Me6FEYG5eKREldFlDHQtMRZej+PiQXQpcLQWhZr3LA3s9R4jh3sRY+ANF66M5nxq+Jmf+7Ef3GLlv/n7/+2SvXOdY3MN211vYrFzz41hCmOOIljL27Bx9sPA7ITcFDfbaxa6FDCycWZZxvF4xDnH/f09q9VqGZE4JwiCd57j6UjXChcjKmpWq9UyvgIWom/kqpRlhXWWIs+JN46MXcTBcRh6uq6jH3rmwC/RSSIZL1fFWdxgsizDTsMyE752dE2ShNV6zWz9UoA1TbNwBVarFXhPe24+4rFEYq/MeOfF0TbC6osUvCxZhQc+EjjrWsZnfdfTtGfO59Ni5x4JxVHt5JxsimmSMI49iQqBbWF8pBXLyCCe/ziCk8VPTM0InZLSakF4lPbM8ygbphcIdB56zueTkPrCHLcocuZ5xI7CBVFePFvG4UxVSHx92w3UZc1sg08KDrTHWc9ms8U5hICLFGvnwwNaWaqqJC9ypmlAJJWK56dH3DRLZPw0k6TZ4iUhAY4ekNHiPE7M0Yk5LHMmzyiqGqNTtttbNpst2oDWHjsPi9Pru3fvePjwEEzcpDPDOdarckEkPYJcZWH8+PjwnqYRybJCCL1pmmFtyKjZ7PBeLQhGvZJ7Gk8YX04UZUEekM2Xlyeenp+E0LlaAYK0CclVUZY5WZoKiuiCPbmRzWKeZ7I8oyxKXr15Q7XdoTx0XUNzPvL08ECWpuRZyul4pm17vBcZuzZK0BytGMaBeXbMzgdXZ7+48pqAPmbFmqpagzcizfUe5ybKMmNVV7h5ZLajJGd3DYfDkabpMVq4KlHeClzcdq0VzlBekOc1VbkmTaOX0sy5OfD48A78LKovL6F+w9gzDC3OTXhlUCrFe4vXiqqsuLm5p653eG9QOPLCMM8DTXMOBHTNOMw4b3DesKoKsgSUn5mdpR16+nEmT3PSJI2MBFmXPJzOZzabLUW55nhqub2T9W+aHU/7FxSau/t7tFG0bUNVliTa4Ky4JKdJQtM2jNOItY6iqkizDGVSxuAhk6c5N7s70qLGZOLqrBW4aWQeO0gzlLl4sHzc5Xu0sotJZ5pG47poE6HlGqSiNkqMEbGEj66vsxBK3SV4tu/7ZTS/2d5xd3fD4XBaGuIL0iGmkSrwIBdoJfI7BH5Y1s6vFxwa81EBEV+CdIjbcXxdj5Wu3+9PFUDhdf1312OahZNjPv7zj49BydjSfyy3Xv5eOWJSs7Phu4eR3PF44hd+8Qc4yPC3/t7fX+z24wmMJzpyHuKfRXJn3/cUWYoK45Gbm5uPMlAAlJaKNKpd4qZ9sR6/SNYiZySOc0ySkBVCiJxGSdTV6pKJ8/jhA9M4LbPu1WolqcTOcTqdeHr6QNO2Szd7cY5MwsKc03U9aZJxd3fPer0Nv+9x3mIScbY8nU5orbm5ubmgFPMUfDfEcrusqoW8VpTlAkGqUAzEKt17SeUdhz4gRS3jOLHZrFmt1oF3IOfuOqMDCEiJQyfB/EkptiGtGiUeBUYr7Dws1y3KtY0xS6BiREwUnnkcUIAN1uDA4r1xrVY6Ho/BOjxDGyGvGm2INtNKKWZvKYpc3neapKPtJRkZpXBKpJDzNKIRg7rj/hnvZuoyR2uH0kJoPJ0bikqku23XkZgUnCgFknAdE5MG62sFrgMfk6tlE52tGEylSYodRilwnEUpwzBNVPWKzz77nKY9M47SnfZth5slsdrNM23T4pWiqtekwUVWKUVZ5JRVgQr8Bu8hz1PyQoqGGIIIiueHZx4eHsLip5fzL0T1kbKWTex4PDFNM1kwTBvHmd3tJ3z++TeX5yX+rjQEQ8i3EgM9lCdJDNM0ohR8+q1vsdruaM5nHt6/FYv8/RNGQ5ZIZIH3H3tSVNUl+bof5T6SPCCLm2fKssBZS1WvKIq1PB/nA13XS3jfLN11keVkaR5cexVd2wrk7pwQgbsO5wAvqI8xhixPuLu7oSxzNOD8zOHwzLk5MQ4jEgiY4RG0JEnMIqFWOgbwqTCO1WidUpVrttstAMMw8Pz0QJYp6nrNOFiKogrjs4lhbCHIwutVjfXQDhN5sQJdsF7dMA4Nx/0TTXvAuZmyygK6pxjHCYchTQx27Bi6BqU129tbsqLEOcU8iJTdeWj7Huchy3IZJaUFSZLR9gNoRZ6Ll1CS5mRZCmE8vFmtJf4kGGXiheQ+W0uSpHTDgHOeJMtD+rSlO7c4p1BJxvb2LowzPENzBjeTr9YUgU93PRKx4dq3zYksz0iSlDwrZKMlKDxnG9K/L+MK8AtSkqYacYQWtNDOlugY64E0LSiKXEZUV82sREBM4GO2mV8camUtBaVjaGeKUgRFTkQ8FLivcVzCMS6BnHxciMT1OjZs115XXy9k/nEcnGVtVTLmVohvkRjMXX1OKDyux0PXxYrnqsC5CoJUSnM6nfj5X/jzP7jFym/8nd/k9evXAIv6Ay6x25HMKemmJ06nE2mastusmcOmGpGC+v9D3p/E2Lat+V3obxSzXFVE7OqckzcLDPKzEzLTmabh7NEAUgh60IVEQjSSQkJJAyHRoQFG0AbRQaaBAL0GcgOEZDBJ8Uzy/Ox8trGNEyPse/PeU+wqYhWzHsVrfGPMtWKfk+Y5UwKsO6U4J3bEirXmXGvMMb7x//7FdotJaEGIVyJnbvPkttCySKoqyE1RVZKCm90+TSkusVnps2Y0pC+T2pQZcnzeK5UdobUmtYSuKpxxHHl8euTS9+x2O774/HtC/CtrtLZppxCAsPqj3EaU58F161NyKwXOaqpMBM55MbfvEbCqNW4HuNaaqjDPqnG4uTmsZXHQ9z3As/aSPNCh8evEsCS78zw0b4uYsjCYdB3j0CVVh6XdSEvKaLPmZozTyDyKzXnuV4tiaSYmk6SiqrClyKuD94xDhwqycFd1g67kfZmHnmXquTx9ZB576lIiC6a5x8coXiJaSNfKiutsZSrCLDvnvu/Sbj3vAj1+uQjvxRhpPyY4ua5rCmuYOsmNUcYiduKKYZrEmbeqxFjNaOZxwqWCpDCCLm6222fo4DhmsjaJTDqm5G6ZlEVOXIv/zPFC9AKZ50I+I3HH45FhOuHDhFYmEdvz5CpRB2hLXTeUVbnmnBgtpGfvA2M/cj4dialAkl69LIjbF59Rbe/YtS0ah5snnj685esf/Q4KCTIsqpppmhjHcUURp5QyHcM1IDSP5U3byq54CUQKIpG6rtjtttzdHVjcRHfpaKsGvzienh4JIXI47NnvD/R9lxBWT5nuN6Mtu90ObRTOTXz4+J7L+cw0D8TosIURFZcSa3lrFcNwWZ/7UxRYNiVCULVFRVNv0NqmoNFIvSnRqmCeHV3X03cddVNRlpIyPg4DtiyIxjBOgXpzx+H+DcZUuGmk7544nx+JOKpaNmNV2bC4hafTmcIaNnXB6emRaZpRRYU2Bc5DoSqUNty/eODlq9dUdU3XDQzjxLh46qZlf3dH3bQ4H3n//j1KSyRBZRXLMjP1A9ZY6qqSoE9gGEa0sXz+xReUVU1EobRNfJiS0lqmcWFcPNvDvSi3pgE3DSzTgK4a7l+8XOe4W66bEFWnlIUmGV06ISsyD4pL76fzVUaalUp2BzcLf57fnHMorm2W2/wq8XBZCMGlcf1c7SPfisgjz6MrL7AoKKw4aee2Um4DPW/jqGdFSD63XIjcXk9+/C2aAjwz58z3yfV59bNzjwkRylyq3424e4vyXI1NhR9zPl/4+V/4/SErf0f7rFgrkGpGQPqESOQPXilFVcnEfmt0RgykRO6V35QXuMoaCFeX1LZteXp64uPHj6v6pe8vKzTYNA2vXr1aB4q2JpkYCaQ4DsM6QEKQsLSsnthsNpTJIv7p6Yndbsd216yqGmuLRDarsdbyM3/XH8AFT2Z9j8PMubtI7oPSGMNaSGW+zTzPq5fHbrdPCIQUc03TSoESPMGH1TI/IyoZiTocDgl5CWvORM6UmaaJeZoozLV6z/ydLK92s5BED/udcEYSsrEmOKe2zPl8Xp8jf375Bri7u5NdaPS4hBzJolSByu6oC0UR1x1809S8fPUKokumX0n9Yc0aIVDWNeM4JbTKEAJUCWUbhwE39BAj59MTBEdwc+I4KNrdjq3ece46eV9iYOx70MkcrShwAY5PH3HO0W5alPI8Pj6y3bbSOgh3OOeTrLnixcMLqqrmcjklS3OEo9S0aGNZkuupUiqpVyLd5YKfFymYi4LgA0PX8/j+PTHJCW0hpmZlUbNtW/a7DU/HR86nI13fYYzifBKSn6QMZwRD0sI3mxalFMPQcxmEJ7EEB2qhrlspwqKgkoGFaTkxLXA+C4F0tVKPUCiT+AWB8zm18rTiiy8+xyoP3vOjH/4Ox8f3qOBZpgGrVWqZKra7LdXLF0kWLwT0p+OTICl+ZtO21I3IZMtkVmbKip98eMVmc8+8THjvOJ1OvP/wMVnMe3rTMaTguO12yzwvvH37dm2pKq2ZpgW3OMrCoI0FFTiezzw+HpnGEaWTX0VQ4tdh4M3rz3l4dc/T8SP2Rz8S+fGNK7BbFqZpQSufYHQYx4C1FcenRdAiDZKEY1Zn7mN/JkaHjh6T57DCUjVbLolsf3//mt1mQ1WVnC/XFhdR0fe98MUKizWK8/kESCvPI0GKu6qlrcQ4EK2Z5oXZCVndlhEThKT64d17bFmy3R3Y7/b4GMUnZrwIlyVE2lZCJeu64fj0hPed+LvMC2VVy7w4i1Chrmt8WRJCTL5KgoIUpcXPopKbLj3jvPD555+vhcaHDx+SMu2e3X6Lc0EK6DgTfKRpJQMsbwKzE/KKLFiDMYplkbaooIVzQhUrnHfYxPXKa8SnyEYkgEoeUYXEDmSn1xCi+PdocV5WCXkgFQUhCLFaqAvC15NkcUGlb4sRuBYut0Zst0cu4G7lz1lddKu4zKgP3KZQX8m4Evp4JQd/t8oot31y8RKQBGrwfvnbWdq/8/g7Gln5n//iX10ly/lDGoaBrpP0YFHQWAnUS94Xh8OBsrTPvD4+rcpLU64f6G0VmyvQYehWk7ciBfVldUJGIUKUYLq3b9/SdR37w4H9brcamsV4TcY8Ho8cDocUtvd8kQ7JhtqkIsjFa5srIxUhBD68/5CyatwzX4a2bdeKP1ueN03zbJCuKEr0a9WcK+9cITvvnvVZc0WeUattU1PcEIf7vqdtW169erUiGjkJekkoWN931HXDZiPuqrfIWG5D5VbY2q6qCkLy9ZDz1bgQeXo6JqRAiF1FUSQCcM089ZzPJ5m0EGm5ICt6TTP2bpFzmCcKa5inMbX5BuZlYhrETG273bDfbXGLqKL62YmHidaUlbjQdn2HJkIAq0zalYjhW1kVCWXxVEWBQiTTwzAyDCMgLrfGKNpaQs2cD4zjzOw8RZWC3bTFLUL2tVrjF0EU7g937Hc7+u7EV1/9kLdvvwJ8kjgrrKlo2w2v3rzAFpbT6UzXSfG93++xSU1xPl+Yppk5KeEygqGUtFpYFz1AiWoutyeimkGFNbDNJjfh4/GI0Yoy3SOC8ozkXVxZWQItpthzf3cnadjOUdeFGLMtC8enR4ahX5GVPBFnjokiYAuL9xFtDe1mR4gwLwtts+X+7iUgWUKZuzJOgyxSKC7n07p4FUVBu2kJXgi3sw9cLj1g2LQ7IdlPHcfjR8ZhpDRWQiWd8G1sUSCBgJqyrTAJfWzbFm00l/OF8+UsxX8ECLgkrxakxRKjxhQlTkWCh7bdcjjcY61J3kQdU38hLNJWM9aklpKkfRdFQ9201E2NTvwsHzwxQHcZQSmabY1kW/UQAmVRY6sWW7bYsqLvBnwQs82IYprFE+aL731PSOVpDM/LgjKW+4cHirIi+MDT6ZF5mthtt5LVltSV55PY+gcCr16/5u7+gdP5IgKBlKfj55nHpye++N5PUdQb+mGgu5wIy0hdlphqw2a3X/lzmVg/pA2iKcwzvoocKhFFA8ssCry6qlfVTEhRKFlIUNc1x+OR9+/f8+LFi5WvV6Y8pHxPPEMYkPcqz2UmuSznQiCGwDJfkWtgnXMla07QV2k5pUJgJbxGiOH5660IjH62lsE1Gyj/O69Rt7L92+MTcJzb2kdeL67ne31MLnBMKspC2jSLGk9pQVZ+4Rd+jKXLv/Xn/oL0O4eB/X6/DqRbnkqGvJVSa3ZQ09QrKnO5XJjnmTbxN6y14PmWv0S2nZfdoJBq67pOE1K8tlCCqBrOZ5EU57aGTH4l7XaTzqFZM3fOlwun45FxnNjvD6kAatNkviSoHMq6fhb8lXfN3i90XUffDShEql2m9FKXzltrTUJAGYYBCR/LqqeU4sw1oyhf98r4hpUgmK8VWNtNReq/L4mAmiHGYRiZ54nSaIqyWPkzEIVbESM+cX0yPyWbXWXeTi4alVKUhUGlls00ijy8TsqDGOMzYzkAaxRKi9OpD57CFthCdjpaayE02pLu3BGixxrF0HV8eP+OEBYKPeO9tJDm2TGMQgAuq4q7+xeMDoa+w+hktuYXYvBs20Zk3k4ci4exYxh76rpivxdSuEZs9kWhpMRBNzm3xrDglxP9IPwTW5T4GMVnZZoBjdFizOadS8RfR3BeZOvaY7TnfHkiImiAUhatRBUVtcDg8j6Y5MAJShlBjqxOi8C0tpByfIILAtcLcbtaofYlEW2NDfRDz+l4omkbvvcT3xPlzMePVGWBmwaGvkdp2ckSIz446ZFHg9EVtpCkb+ekIGu3G4zSzOOZaexXmbVSiv1+B0pxOh1FRWKMSKiLkqKqaTc7Hl68ZL85oCK8f/+er7/+CqVgs21TDEOB0XA8PkkhI2xISZs2mqKsOLz4jMPhBVXZ4JwYIVZ1SQgLKgR0UJK/9PhelFBFQVNvGMeF3s0cHu5pNy0kzk0O/CTCPIxMY0fXH/Fhgiip1TFAQKOKAm0LHu5f8vDwkr7vuFxOLMvE2B2pCo1WkWUZkx26xeoK5yLDIjyHopBi+f7uBXd3L7BGeCjHywceP7zDzZMY+AWFKWruHt6wv7tfWwHnS7eiJ+vnHmDoOvphpGk3GCtS5Gl2UpTZZKKW5orSykYwG+MtbhIkOiUrt1vxgFmWhe58ZhxH7l+8JJoiybNn5qGT+A5VULcb7u/v1/kmH0oppmUWdCK19bMpZIxgTBTFWNr45fk8m6gpbQkxWet7t7aHvA+i+EqoZi5YsoJybZvonDnEjfuukgJZq5VTdauczHwQH1fAH7ha3IsiB2He3BQlt3YP8NxM7raouaUCZBQob9ivPJbnxcvtIajK8yiA/F7LmlFAukaJDnGQUKbz+cwv/uKPcTbQX/5L/wvbzQbvPOMwQEg9M6Mpa0ncBdaCIU9uSl0NgrI6pa6rVX0Sved8klC10+mUBqrI1Oq6xhQWoyXV1BZXeXNGc4SkKPD98UlyfeZl4dXLl7x48Wo9p5z22SVXym2yjJ8Sv0JaAwV1I1kri3OSLlyV1KngisGLimLTUlcNfpHiRGmBFsXzIBmqJR1+tsUP8Uq2kkJjehYmd6sqajctmedxCz0KE1/aOBmlyUd+T86nI/PYoZVa22dVLR4Q0zgyL46QdjFlckaFG4hRXUm/yzytUKTRkrSMuvabJazQrtdI2g1oLS0Mo5D2jF+4nM/ECG3TEmJkSTyiORF4Ywz48YQxUFYynk7nkxRDWuNDZJpFZr3dbjHWMgw98yKtCDeMhEV8P6zVlKVlHAeaRsh5Hz+8YxxEhVLYGjCS+2QVyzygEZfVEKSNszjPdrelbTaEKFC1SwViUQqR8N379yI5VhGtPcQFbUSdEL1inoMw9YkpfyWPj4ixOqEKEZt66t6HpIZKBYOxRGXFdUZrjJIWzuPHD1wuZzRC5LVGlAub7YYqjeVpmhJ6E1deUj/0q0qirEqUsTTNlqZtpdU5O07njmkWw0blBzabWtxdlaZtG6ZBNiXtpiVEx+XS4QNobUUJdffANC28f/uOeZooSzHK22w2VFUhu2zn6MeRaZnxbkmbmYCKgW6Q5PSy2LHZHKiqFu880yTqn8vlyHbTsm22zPOYXJxDmnM8IYAywiNq2jZB/AHSpqMsa3bNRuTy0YGG0/lEiDCME857mu2G/f4BayqUFjNKlKfrTvSXI1N/xvmZurL03YWqrLGmYhwWgjIYW2BSi0lUWy3WSrus3lVUhaHvLvTnM22zoW13bLcHdvsH3n544tJ1iW8lkuxpmjmdT2ybEqJncYKYGlMQgLpuqeparAaMYbPZUpQlWhc4L+/N4h1TmpdJrZmAuFcf9gfEUTniQ0RnBCOIfDh4JwnyXiJOcqtca5MciYUrpvM8ADR1A0h4n1VgEgn26fjE0+OTcKhsIefablHa4G9Uhmu7x3usuSZ353iRjFjECMZatDEQwQcvfi1ONmBVQu4yuivEXvneWlH7ZJlz9grLBa6gFbm4gMxtyc8RkprtUwLstWCRQikmhWwE2cBZswoKiNkjRv6zclGSyinP/Z+2pOSxrOeYrzEiG/xf+qUf42Llv/tv/gfevHqNWxa6S8f5eBIzq7ah2rarHbzWevU0yX067z339/fc3d2tsC8gkuBLxzgOq+297Kpz6jCoG8t3a4Votw5oDYtb1t/BFX2IURYB4tXRtkqw/jSJ66NKC29ue+RCKBvAlWXJ4XBFIB4fHxmGgSZJA00i5VZVxX6/f2a0hlZrPzgPtNveYz5uwwuBVLyIUVK+YbOJ2+poq/QaMyC7VJP674p5mRkvp/U9zddjrX1m2Z+5Rzb5Hlwl0gtzsqTfbKW15RbH49OTgI5BWjEZBs7kyhACm3aDRq+tnXke1pbONA+M47B6suR2YeY5Be85Pz4yjn0qSj27/QalYJrHhCIF6bt7URdEbSTJtm5Y+gsmuDV9dxwHFietpLZtOJ8+0HcDMRqC12zabUIJPEN/JoSF/W4rnI9l4fHxKaloJIPIaM1mu6VuNuK9oDXaWJwPlLYCAsN4ZujPzNOARjMO4qKrgkw8xqjUX4eilMTftt0SneJ0vuB9wIVAUZZierbZYmydAh5FYaaRHdfp6SPH4xPL7NfW5eFwEKv8RSzSQ3QEJ5yRWyg6F8yYgrJp2e33bLd7irLBFpWQN52nO7/n9PRR0LcU6GiNKGyqqqRqCsZJkpYP+3ti1MyzwztpqYp3jNilD8PAMHREZIesyxpTlFJwjB3eTcToUFHCR0vbQLQYXVBVTeLIOSKezbalsOVK9r0d58MwyLyUCkofRAmktEalBU0FMd9r2gZdlJLzlByLrTUsbqFt9jSbA2XRCJkWz9PpI5fTI2PfcT4/AZ5pHLGmIAbFtt3x2fd+is12S2Ut8zjQXXrO/UjT7Ciahsk7nh4f6bsOawz77Q6tDFYbqnrD5GQzYY0gvlobhmFEoRiGE3WdiMDaopTBFJZhmBmnkbDM7Hc7yqpGG4st65QrgyAIKY4gzz7ZyTqGCEZSu1UeI+vCGRL/Y0pRKYqiqNKCLzb7SqXgyygO2s+Rl4iOkSIFGWby/uVyYV5mCltQbfc0m+06r+eWfZ7L87/z5i1zVzISncd2nls/tdS4FV18+phstrYKL265KAqUvqIany7fidL67Pe3rxP8kpSQEg7pvMRJFGUhAaTZVC7mBlRSQEUxeFTp2m6P22sA1nO9vdbz+cQv/dFf+PEtVv7s//Tn2e92Ag9PM33XcXx8YnYLd69erIMjt4jym5zJp5k3csvkjiGgUw89owTe+7UPejqfOV26FXbLSolrayfJP1PFnT1OgHQT3fT4YmRJ3IwYY/JWEdtmuHI3cj8TIvN8dWi9Je7O88zldOZ0PDIMwzMJW7YkV9as3J3c5/2Uc5PbZrmFJs6fDXUtOUr5fToej2uIISByunRdTXKqVCr7qgRwLrUUrr414yhcj7Zt0UpTJr5OPn9jZJK+nE8EL1b4SmmapkZrg7UGrU2Ct0uWZebp6ZhksJq2aURK6QIxGegt80T0C93lxPH0JDv6slzf06waAtYdBBGMAUXALSPT2KOVKEqU1gl9sBRVwzx7+nHG2AI/DYxdh1KRh4d7lmXmcjnjvaMoC4ZR0m21LpknaRPu91umeeR0eiQGR85jEUlzSV0Jya8/PUJIIX7aUjcbXr76jIeHV9TNBmtbylJUKDE6iJ7gPPO0CKHReMBzvhx59/4bzucj8zzS9WeqssLNPpEKawKgjfTTIwptG1CFtPtSLg14ghOnz2GR3ndVVcyJD5XVeoWGAglSzMqDorBJIabRxhDI/AhDiIqqEnLxOI3sNzV1JSo/k0ng/cAyLSLBNpF5kfeqqlrqakPbbilsISZlfk4ZXxCjR2lRX202G4p6S1E3GA3DcOHt11/y9Zc/wmjFy1cvUFGcZxWG168+pywL3r37mh/88PsE7/A+st1ueXx8pCiKtejNstO22YASdYwLHlvaZJ63YFUipJflmp8UlDgrb7c7TucLx+MFsDTtVtRWdQE6ooLwIN6++5rL+QnnFgpbUhiRYu9fvBGU5nKiLMSSv2pbtJHE5ZjagNM0412gH0aGfkRpTVsXBC+quxgi292ew+EOrQ3dpZdNggtstjvu7h7WzDGlLE3bYtOm6sOHD+KObSxlWYjzdVSgLSjFLvH5hHsk9/USIeriW4t2Rse9F+PGrK6R+zVtJG/aGevcfrNwi3bteaFw5V5AuJnPPlXSPD+P57/Lz7fOHTxX6txyEm+v6/q3rHzJ/By3xUaqRq7X8QnpVslk/Ox4ToKNCTV+HjKYPYWGUfLvMjIfQgCVy5+bIMebQuTbr3FNb87H+XzmF/7I3/t/XrHyMz/zM3z/+9//1s//uX/un+Pf/Xf/XcZx5F/+l/9l/tP/9D9lmiZ+5Vd+hX/v3/v3ePPmzfrYH/zgB/zar/0av/Ebv8F2u+VXf/VX+eN//I9/K+/gb3XkYuV/+O9/k7vDQd7IELmczyzTTNO2DG5ei5O6rtdiIkN1wKo+AVbH1rIsKc01MCorWvIRguyec4sjk0Jz/zJ6DzFQplTYKZFEHz9+pOt7tm2dVCBX75e8+yyrGl3KDiQXD3kw5nTijG5kM7lc4YMYn+WbNxcGV/IX9ENPzpZQSkmibjLD01rz+vVrDofD2h7L0mwZmGElZ+XXzPbPl8slFQ9ifpXVQGsoX4xYfd3B5F1nvkmqspRiJRFxM7R6Pp959+6d9OO1GLqBFDld11FWZepzt2u/Obuaei/ZT303EqNYdNe1EGmXaRCeiVXJN0TM8LKzcD4W52i3e0HOdOTt1z9i7M6EZaQ7H1mWiXa/oaoa2nbLdnvH3d1LQPN0PPP2m6+Zx0HQuapgWWZCcElFVuPiQlTCL1jmnPmhuFzOfPz4gfP5iDUi+d3utomkbLhcLnTHb9BOsme0KaiqDWXZYkxJ02yoN3csLjLPgxRqUXbcRDAKtJJziUgb0YeFEDxNU0kRPU/M8yJEQSUkT6USEqAqfLBA4OXDA9PYAZ7SSkqxrlqKomKaZHe9zAs+CG9o7DtYxPI/I4pAKlQ01oiUWmkrBV9Q+AARSUIurXiCWCPjxi0OlxbSoirwyqeMFoNLTq3L4iEq2k2DhBc6sf5HxnPbCoo3O8WlG5J/jUhPSyOp3MZqxDl3YZ4dRhdrarL3YjVvjWwKPnz4ALD6/kj7QuG9eFf4KMnc2lrQIuXftlvmaZYQubSFVVpRlAWbzQ6iGG0ZWyZVmEDsPgYIgrSFKHwpoxWbdkNdbajKhqI9oK1l11bMU0ffnxnnCVPWjNOcyNkNRdHQbHZEJCNmnCam4cTQvZd5tGppmhaSV0luQcyz8FxevnrDZrunqlqUtozjhE/zr1YQ3EJ3OXM5HSFKC3NysmHc7Xfc3d2tHjRaa6IpMWVzXfjSRk84HFJsrvy2IEnXdd1IrEoqRW55GbfFRj5iatPEJCfP6EnmCV4f95wP8y3Eg28v2J+SUG8FBLd/cyvkyO2gW3TimRCEuJKAb7l86+s8w1Wen/96buk/WmlyFlIIYogpERX5j66EY0GpRKTxXQXLtxCeT96j/9OLlXfv3j2DgP7yX/7L/EP/0D/Eb/zGb/AP/AP/AL/2a7/Gf/Ff/Bf8h//hf8jhcOBf+Bf+BbTW/Jk/82cAQSj+yB/5I3z22Wf8O//Ov8NXX33FP/VP/VP8s//sP8u/+W/+m/9/n/SaDfQb/y82TSP9z3lmHifapmV32FNU5erbAVdC223acYxxbUPkRXJZFpZxpCjsWqzktonwPCI6ZfeoxMHIqI2cx8TleFzN05QS+XQ/DBTWMPYd795+Q13XfO973+Ph4WH1HYlKMwfwQQqBvu9XjkeGy3MyciZI5Uo9o0jZtTZ7xORMotwaAdYbMhduGTXKKMttLzZDoPM80ffdGmaWixiBYGUXmZVOWuvVG8Z7T991GC296K7rxJ0y7TpjFB8MvKRfb7dbCmvxISSybM/QnykKcZDMrrhZcVSWpRidIRELi1tWt1qlRD7qvSIER1UVtE0FwTNPIjOVAuLKOeq6blVTTfMMtmS33zEPPcfH9xQKdHQs88g4dlCAdwGtpD2gVIExBU2zQSvFPI+pNywTS9NUaC2Foq0KiqqUv9c2uf1uWBaXrq+jaaTIvr9/WFtcyzIznt/y9Q//V969e888OWLUDN0EUcig0RhczBMQKSF6ShNZoCyswOkxK2lMkmpKSGMMC7YoKGzJEjzjuEiKdFHQNvfsDi9p24YYHO+++YphuCTiHzfcMEWdzBRfvHwpnKxp5nd+5we8ffcWow0+5PlEiHmF0Rgd2ez2vHnzOcaWGFvStFuc88zjQN91fP3114y9OIeWiVdlrcVUlrqpiRFOx/MqS888LGuTEkxDUZikEBTk9dSNPD4eOR2fmKeJZZlYpomqrGjbmqYtUcoQPLjFJ9doGVvLMhODbHvXfKW06HrvUaQ2QvDEXKgoKKqCsijFETcIcmoLQR+EqhAIAaypIILzAVsU2LKS760Up7aoRRk0DbRNw8PdA/f3L6iKlljUDONAfzni3YBSslueU+L0fD5xOY9EVfATP/kz7PYHxnmk3QrJfxqFZD3PVw6O99l7Cln4dEHT7jC2Zr+7R5tCSOFKcsyqwlAVNoV59gQvKKlObr3ZeiKjvWVZEnWBqdp1Y3brBwIRY0X2K3EiJhXzBd4nD5VPFtNn7Yn0davkuUVWor8mKd/+/NMiZH2+T+bVTxGHWw+U25bU7fOtRcMnbZzbc1BKgf7dWy9aqRvl0HcjHjHxSvL/heCfuDDRrXPCbREUQiCg0ab41nPevs53FS1KKc6XM7/0Sz//f10b6F/6l/4l/vP//D/nr//1v87pdOLVq1f8x//xf8w/8U/8EwD8tb/21/jDf/gP85u/+Zv8sT/2x/gv/8v/kn/sH/vH+PLLL1e05d//9/99/pV/5V/h3bt31534/8GRi5W/8Bf/iphOTTNunoWEmhasqmlWy/YY41qM3EJseULJqp9lEVUNXibb4/GItZY3b95Iwm8iykZ1lYjlYiGEwDAMPH78SJHQBVHdiEw6oxzLPFLmosAYLufzWkRoY7BVu/Zo1+dMvBS5Bs9+v2e/36990kyCLauKMikbYoiycAfJwZkm6THfIhcZzcpFz62KKtvmxxiTuiay223Xz+C2f2utxWiBt5u6ISLvbfCSCpxZ8GLzfUWxQFASfGAaxhXhMUbIy1VVMU0jj08fmFPwXVmWqPRZlWUBRMZBEJ5bc76MSoWgMLaSnV3Kohm6M/M0YgtNXVdrsbrb7W4mDFl0j/2FeRZZd3QeTcBN4rXStjXlpmSZZ6IHosI7T9+Nq3Sv6zq0VolcO7LZigpMK8U4S0q0LRLXyHmKoqYqG3G31CIZX9JEro1ht91KK2F8oj+/YxpnpnHE+0hhRBpaFhVOTYzLyDwtKBRtXRGDYxp7nF8wSmIcMkReliXb7ZYQPJfuRHAzMYrzri2qpLCx7O/u+N73/gBlvQMC3eXMD3/wN3n79ms0XlAPJT5Gq2wTJDgyRmKUHXO7ES6Gc35tO8rE53HLnJBsIy2udkvTbFLRLgGZ0ziK02ZK/M1xDFWT2ihRAkrbtuXt23e8f/9unZBD8ITo08R6Y5GuhYh6f/+AT2qUy/mCW0ThNi09xhQYLSiTeBdtKEpZfMpCeHLzPK+WBfkerRvJAnrz5jOO5wsfHx9lEdbyvlpTCAGVQPAOrUGpQGmtqG6SzXk3dEzzjNYWU1Q0zZaHl29oNztO5ycu56MUfVbOs7I1qqzYbBvaqqDvjjw9PTLOMyGZpJkQMaqgKDZs95Jw3Y9i/DfNgc8+/7t5eHhBCKLuO58ustmoa8b5xFff/JAYDChLVW158+Z7KFOwOE9VF6ktLoF8pU3O3ml8oTR9L9lcx+ORORlpNk2DKUrQ0p5oN3JvxxAT+dSjdXZaDcyzx2jDdruTn0UwN3P+LZqR2xxKXYmsK+rCtUX/6SKfv//0/7ftjk/Rm98Ngbl9zPPvvwsXub6O1pqovr1kr+cQr1KiXDxk3k0IEiIaQxRlpzZAvLZ8vCRk55Tqa3EjUsGYfFS+qwX1aRnxaUFzuVz4xV/6/UmXf8+mcPM88x/9R/8Rv/7rv45Sij//5/88y7LwD/6D/+D6mD/0h/4QP/VTP7UWK7/5m7/Jz/3czz1rC/3Kr/wKv/Zrv8Zf+St/hV/8xV/8zteapmlldIMUKwDTMlG6ckUEJE05rAzuvOvPcFluO+R/5wGcB0GW/Ea3rK8ZY+Tt27cURcF+vydNb6vkLfut5MHStOLcWTc12MQ5KS2WkmkU6HW7F2+AxTnqrXxw4iuyMKR2RD7Ptm1XaZ5LWTgZEQkJfcg7Du895/N504wr/wAAet9JREFU3dleU2FnypSg651fW1a3N1yGKDNClBewOWfOpDTgvLvPHIRsruQX+XzGZLRUliVlXSXlw9VV9Hqt8rzb7RYdIG62aKXEfC9dl1Yi7W2aLbas17/3wWOKkiicYbTusUbjlonT8SkRd1tcCBgj5zInAm5RFJSHA5czshtO71ve2Q3DQObdKAXKKrwbZeG1lsIY6nrL/d09dVvxeH5iGS/EZO/dnc9456jKkqoqePHigLFJmVVA35/p+khT14yjwxpDf1nWyaN9UfPZq1cSI+EmpmlOMsCrXNw5hwotvn3AlgtlLRLp4B2PH9/TDRfGpSOqSGEq4aAsjuBnIjLJ591TVbbsdwd2u91aHFtTgjZpIQDQFIUQIx8/Hun7/41ms6csCy6nI8enR4rC0tYb5nnCBclO6hIJMxLXgrUoa4zWTMvClDxcvPf4NFkqBSgDWiDnZQmMpwv95NIGY2ZZJmKQ8ND9dpeUbDPdMMAppAKBVW5qjGFZRFUj0nkS8VxaYM4toJJ8/u5AVSqULZhHzXZT431JjB4fa/p+Yp69XF93oe/PXI1QC9qmXYv4vMvWWuHDgtGKt++/QpuSFy9fUFZbjJU2j1JaNkrRQ/AEL8nbKioxVYwKpQNVqSiKkhg1VbJOeP/uG/of/gAJvBMU7bIErClp65aqqVnmJ47BMXQXkdSjqTd7Xn/2BZu712hdoYJlHCY+fHzL+XxCmQVrSnHnvVzI2Tq73UE8eo5H+vlIXdcc7l7iFvBBM0wT292GTWk5Ht+yzBPaaOpGDAa7aV7nmLatuXuQ1tLh4eVKlC8KCUfMKc0hBJE7p/dV5qvA4XBPWZVUpbQSiaISLEzBnEjct0KHPN+pIJytbL4GUvCapIrJLrKftlo+LTzykX92S7z9rqIEbmXC353D86mz7u3r5lbQ7XHLj1EgBd0nr71KlSO4KK1SoiiNgpPiRZukYOK5/HkNNkz38HehOrfFyaeozHe9B7+X4/dcrPzJP/kneXp64p/+p/9pAL7++mvKsuTu7u7Z4968ecPXX3+9Pua2UMm/z7/73Y4//sf/OP/6v/6vf+vnZVFhtFlNzbTWuBuztFvipFJqXYyy6yrE1INlJcM2TUOVcnjaZNOdF+Wu66ibZm0xbbfbmyoTQAnMi/Rj2+1GbpTUF1WASVr0EALyHRKt7j2N94kcqtYW1q30zDlZaAQdIZmIsSpnSD3uXJhlRnom1MbEsnfOMU3T6vibWzKZm6O1XpU46y4n9frzezVN00pG3e12RL8wTQNDP+C0wlrNNEpxNQ7CtL/d5dR1hfeOy2XBTTPnpxNVXa2krsU5TscjddNgm4aqqhP6o3h8fEo8Gc2mrdM5Lel65bWHBCmPk6PZ9Lx48YAiEe28oqprTHKrhMRrahqRRUZBRKZxRPkZQ6SyJdPkGJynsBUfjmdsN2Crgv3dS8ahJyLOp5fzkXkaKUqDUhHSlzYyDpwTGW5bbVBEpmFEadC24P3br+hOF0JQdP2FoiwkJDAXk04M3qqmZvYpr8nNWK3wi7QQpkTMjRGMLiltyzJPTKOnbipQkRhkjIzDQN8NvH//XgjdXrxo3CKOnyiRD3f9jC1LtDZENXO6fCOEZwImsfpOpyOLc+iipKprttt77u/vOZ+F+K2UwsdA9H5Nuj0kI8RL1/HxwwfGeWKaJ6KPq+dH3W6o64a23dAPF0LwHI9PXPqBaV5EZaMN3eWCCiEFhCaELUgLToIK3ZpBZIygWUormrYGIoubOX58R3d6oipKGUu6wFqFQlKZNxtD22pU1CxuQRsFuFTYCWndJ96BToWSdw6lPEVVil2/rdnvH3h48Rm73R0hiJrk7t7hl4llyi2bmdLqVX1H8KAD3i0obblcTpSFp95sUdagTaROUuyxnxiHma47M81nabsQwEtWlU++Ph91xe7z/we7w2sKW+MWh93dsenuUYw0ZUFbtpzPF95+8xbnPGWlGUbo+4F+OuOjp6q33N+/AlVibMM0OZqq5rPXr/B+YV6cyOydZ7M90I9igOijwSX5f1GXlI3EOoyTyNrrVLAUtiCnHKOksEUpClsSE5KS5y5pswT5fLkWByYrr5KnlIoQtULbBLFET1iSQ7i2BJ85G4iFgA9p7qrZH/b4ECTFPq3DMfFqssBgWWS8ZYuKfMR4s9BrJecRry2hTxf2PP/nTSn62S8lgDEdt2qg23ZTjOJZhBbVoALhRwExEXa9E0QlIynaiOiBlBIv7eHwrEi5TV7+tPWlk+oLvl28/F6O33Ox8h/8B/8B/8g/8o/wxRdf/L5P4v/o+Ff/1X+VX//1X1//fTqd+Mmf/EmWfiDagjopCYiR0hiaul5dUuGah/Px40fmaaIpLThhPesgabK7+we5KcoSlBEHTK1FTZJIqH3fS2hbdW1J5HZN/nAW71ic+J4YpdHkm0Ms+H0qirRW6wdvjUhrAbRu8V4mP5UGUS62BDbPPhma3XYvKcvkfq5Uy0or/OLwwTMtDpQEj/n0nuRBv91uV8XRbTERY1zToc/nM13XcX9/R11Xz+TU+cYtigKj5DNwThx5z+ee7U6SpcuiolAC3YfomeYJY6DdbMShtSzZHx4kfyYhY/M8czyKyZdJLRGlNdMy0mwamlYi3ktraKoXTEOXFuSeZRoxBoxasNphjWMcn1hm6ZMrDbawaODdh/ccj0c+//xzZi/eI9M841ygrBtMMtpa5gsKxaZpKQrF+fzEog0btcMtig9ff4VLsti2qXjY76nbDadLl1CpmWGaUWaPxhOAaYa+O2OMBCXOLqD1TDd0AnOrSAgl02gxCKG0NJFNU2FsQIeFujZo3QqnyAVKa6jLbcqwEv8K70emZcITGWZxbcWL22+zqVEYZifoBtbgtUZVLW1dU5U1IQaRqmrZNY/LmcV5jC2oq0rUOMuAd5MgA93IcflAVRac3r6VQkyBLQyL87igmPqe4XJmHqRgntJuW6lIlbJQfIgoa2Dp6cYLc3fClMJx2DYV1FYWiODQIbLfNISYZaWeiJBkdYK7iQq/JA6Pd4RE5o1KURSiIkErvHdEn9R7KYYjElEhaUwS+bW04rwcY0XAE7VnCQZHibIVznmMjpjo8L5ndJ5xnihKw+zPnLqZovoG7xzLPKTkbEVpJRIkBodfltUQbLPZiFpqGPFeCqDIwOwMmEa4SkpRViUhDkzuwsP9PREvaHSMECJumonBY63GjWd++Nf+Irv71+zvX1E1W/Hl8YHLpaOPC0cdEw/tRFXWtE1J2274wfcvGGXYbPYi5R4GYKGsIm27paw1xlQoZ3j/+CWX/sTd4cB2syNMC2Bpd6+Fl+PjygGalwkfnWR2ac0SICyBohDn44i4QWslbR5bXJPunZPNQHCBwhQYkOLMeTFB1EZ8yhCibiBgMCtxVT7e9DkbRYhSyMQYVuVbU1kKo/DLAmEhpgiR3HpWVcF+u2dxjuOTUBTENVfm5qhkg/otxD/GJDmPwK2CKcuYUxfgky7QszIgFT7y7bWoCCTJt0p8MiS8QRCmeG0faYVJ6I2KnrAE2WgplcIJRVp9K9yQder5mTxHdnJQ5O/v+D0VK9///vf5r//r/5r/7D/7z9afffbZZ8zzzNPT0zN05ZtvvuGzzz5bH/Nn/+yfffZc33zzzfq73+3IO/9Pj7/+2//r6ieS2z3GGLa7nQTKqWtPvqoqXr16JZVr9ExDz/F45PHpaXWwrSpJ0UQZXr16vVbIGT24VofX4iS3OSDBccmULIYEa3N1DJTqODmHqis5NZPwckjisiwrYfB2VyBhgwKpk4iMPkHnkVzYCAx5S1iz1qK21x6qWN1fyb+3EksQ+Dy3Rl68eMHDw0PaIUjR1jQN+5ScHELgeDxyOZ+Zp4k3b96w3e5Wrss4TjRVRVPVtHVDP/a4IPD85XLCBYfRJYaS4/m0toymcSQC2+1GzP3SazVWSLzH4yNyO0bxYZmXpDrJMmVxGG2aiqqyqa2jVh6B0YamaXn54gWfv3mDLQpO6TpyW9A7xTJ3KBWSh0bB0E8McaYoS0IIPH58T2Ht6ixptWHbbqjKAq3gxcODQNrJe2ZZXJKICtTfdxculyPzIhLvOiUEn8/iX6EwTKNHRyPuyEVB118YxyGhNFKMZhfWYGRhbjYbiJFhHKUtVVfE4FFafEm6yxHvHdO0YExAGUuZ5MnzLEXu3PVMi0jntRHUrtm02KJgmhaqquZ0vKSCVdNdfJrMSkwhqEa/pERaPAzSCiFN6uMw0F26m4kvoDXJvCwjE5aisOzv79ludzgvJlqn40fOlyeRTWtFTCaL2siuOt9zwkWb0zwgiGeX7diNEG4nI+qWqmpSezbiEhogRYwY3xmrUgstEqNj8ZKfJEo4yW5589nn/D1/6BfY7O+ZxpEP777k9PSefuqoapFSh2h5fDxzPJ2wIYob8r4R6Xh3EWL2KJk74sCsWGLk1E9obcQzJoDSWtp/rkPbwHxjC2CMYZknUSZpMSkrrBXkpNKoGFBGY4yithHjB6buiXG8sN3u+d4XX/DhneGrH36fUELTSHTGMi98/fVXVFVF15/ZHx74yZ/+GYZp4nzqKCqJL/DBs7iJuqoIfqYqFcEb3HjhOHRYW2JNwXyRseBDJBQl8+IAxXa3R5dWyvoUWSDIdW7Ee0FJlMaHZW0t58+8LqvVjyT6lLujY/KTF26UIgW9otDWoJPc+drCiatyhijqGWmV9gzjQIipwNGy8Edkrp7mibfv3q5rg3iNS0EjPgDyhCohkpn0nfPbPrx/Styq3IK6Ja7GFeH+rkMhieBKJ9VUbhtFiFHjwjWAVubUm5aOkjJN64zPCDpVFjbN/bMUMeoarnj7fslmV1+5auq59f/v9/g9FSt/4k/8CV6/fs0/+o/+o+vP/ugf/aMURcGf/tN/mn/8H//HAfjt3/5tfvCDH/DLv/zLAPzyL/8y/8a/8W/w9u3bNS35v/qv/iv2+z0/+7M/+7d9Hn/gD/wByrLkdDrx4YNk4zRNw6W7UKUk4Vut+zAMqfequL87UNQtDYYqyCKMi7S7g0hpE4kzIyrH41ESm+/uRN4JiTypV+fVuq4pi1J8IhKKka3fczEwz44Y3SoLzioFIbmW3MKF2XgtE6TEU6Rci6RpmhLRNMPealXJZGLvZrNZ+S9VVeGcW30lciGX+/q5uMuW+M/Nf8JNwXW18M/PWxhpx53PZ25VUmVZMvY9nnlVWG02G6ZFyNCRyHZXExzCbtAKoxW73ZbL5cLjh4802ySFriqKQrxXsvKIGNC2YNNUFNbg3Mw4dHTdGbfMWFsyTgvD8Ci97xhEchoCHz6+x88T27ahrmqaquSwbWWcdBfmYWEYpDjDlLhFJlyFhlig8DjX0dQ1drNBb1qUEtn3MieHYGvQSqOVnH9V1tR1Q7PfpoJUE4Kn6y6czkfO5xND31O9bAhRCLd11RA9uGXGO3FMnZwHF9jsdkzTQDcMxF7s/G1hOZ4HIbV6jzWaTWMo65pNW2OMZrupWBZPiFA3jXBGkuX+09MJ7yWQLywLPn3mo5FJdfGyZ9tsdkQPTVWy2ezZbVtCXJgpmF3gdDozzlIYZjfP2Y2YZUIRn5EeQTxFrNGMY8+8LDRG4iDa7RaTze7KmrouAYfzIwSPWzwBIbuHlEtyazlw5S0ECqvY7u4SEiuS35w4rlTiK2gtEnfvGKYBnwiv2oaEgOp0b8hzWGspiwKrLOenC3/jr/2vhAjLPOCXnuAGKCvqcsc0eTbtlu/9xD2HQ8fj00e67sTlKGnNRXLEzrwFk9CewlTEaNDKUtWiKJuXgWkS8rsK0hJESUvTqEhRV4QYcDGiES5LVVrK1FJRSsIwz92Zfl6oFsfu8AK/LIwo9rt76r+rZLOpEvF+4N37d3z8+htilPv+0p34q3/1L1MUJQHNZrOjbjaU7YamaQiL4+OH94xjTwjCN9o28nmO/RNPb/86wzChdY33GlRFWW14Klsm5VG1Zb9/YLc9rLEgkJDKfl53+XmjlYnaRVFgdUQpiykSMRSpFQB01AntliBXl5DpiNQmxpi0eKdpJl5dW11qFd8qdEK88jJ8CGtrJqtvMgoh6LdkfuXHO+dW0UQIAaUlQgVuM3ikxRRldXm+EK7PL+0waXdfC4Z8/nI+et0A5zl+fQzSGpINMTL/WFEpTuPIPD+mdlskJzCHG0KvSK6vr/W3UlD9Xo6/7WIlhMCf+BN/gl/91V995o1yOBz4Z/6Zf4Zf//Vf5+Hhgf1+z7/4L/6L/PIv/zJ/7I/9MQD+4X/4H+Znf/Zn+Sf/yX+Sf/vf/rf5+uuv+df+tX+Nf/6f/+e/Ezn5PzoOh4PkhiQSKqQ+HWCKK5E2+1PIzTmKFXMqSPLP1zc0irth/iAz4bIsy5UbYoxan+vt27c0SXnknKNqaopk25+LjBzud7lcKIqKly9frp4v6wD32TNgWs8nF1tZtaRWuFoxz56qKmkTZ0NuJMXhcCVLwvOBchs5kCfyTwPhclvnVksvxYcgVbdJnfkGqWtBAzIilCXJ2QNhniZMjPggxGNlNCyg0Sx+pu8uWFtSlSWbjbx/4zAIV6ApKIyQCWOM+GUhOMc0j7hlYXEzygeC9xSFEYg/BsqqpShrqkqM4ZRiRaGmaeTSXeQzCI7oBNEauwm92WBUZNtUuMJSN0V6PwzeeaqqSOoNy5QSqB8fH2nqmtIayrJgWSZxyh0HYpSCT8LUcrEKVVmvvKC8A6uqku1uQ9tsaZqWu5cvsEVJYS3BifT4m2++Zlpm1LCId8ymIWqDA8ax4zwMxD5gTE3TbPns9StKq3j39Zf0l46l70T+rg3OJ8MnZfjsiy9o2w2ny4Xdbo+1lvPpzNdff828CAGVCGVZsK03fP7FT7Db3VHairosCGGm7068//CWx8vA61cv+IWf/zn2uzuWRfJZtDacH9/x8eu/wccP71fSfFagnc/nxAGJFFZ4L0prTqcL8dzJnBh1IkYvWKswKqZiSDK6hNMlsmxrNdvtVtRj8yxcomVY7zejC16+fMVnn32Gc566bbl/eKBpW87nM1999aU48i4Lp9OR2Y2o5IE0OZHzyqIAJiVxH4czb9/3WK2RuIOJqtCoMjC5d2y2dxx2r5gnxzw52QljEtoqqJEgDVKERSWclAULyqBNRVlvAcloMlYTwwRxoS6NvEfByWccZekNSjN5j1GCJ4SioKo3RDTbwz0ueIZxYRp7TucLm3bP3eGBu8Md47hwOl847PeU1Ya2HXn1BgprKEppxYQIShvm2RMVdJdzQmIdRb3l1U/8NGVp8G5m6M48fTxxGR0WA6qgakqsrXh4eI3SJWXVUpYN53lgDIEqkevF7l/GSowktFs/M9/M68CyiIdR1Kk8SaRZn9ophVYYbYn6SgF5hgLk/kYm1sYoLvSpnRGVLNSR1C4kFysJxUkBtCJ4urZ0gvdJaXO16y9LwzBMeH/k9etXbLctT09P8hkni/7cBtIK4cGRL0vdvK6sd9fHCDH2euRS7NvkX1knVKbRPlPMPj4+rpSFW3JvtgHJhZzwMa9Hfg6AeZn5/R5/29LlP/Wn/hS/8iu/wm//9m/zB//gH3z2u2wK95/8J//JM1O42xbP97//fX7t136N//a//W/ZbDb86q/+Kv/Wv/Vv/Z5M4f7y//evrHJTuKInIQTKpn7GzL71DpGWCWQi6+3C7J3HqmuuTW6jiBon8z38Wmxk5CVzS0xhcenvAB4fH/He8+LFCw6Hg0B0NxV1JgPnRV6swPUKa2a1za3Fc76hcspw07Rr3zMbq90iShkJyTvMXLzdkrbyoM9FS44XyEWTteK14px4oeT3NHvAFOb5dWVINrPJ+1SsWWvQBoL3wh9qhCRtzHUXHGLAJtlpCIHFS9aIXMOSoHohCLtFCLreixtpP3Ti61BVPDzcUxYV1hRXKN9oJHlWjMH8OKCjXO/5fBK+UdumHnJgXC7rdUvPWPrKRVFSlpXYoRsrUuShY5pGNk1DYQ0xOvrhIqjEMIlPyLwQU/ZGYQzhxu8mW4fv93vqdsupH8T0Kzo2jaAwxEhZlFhbpmybEh8cl8uZb95+vfq6zLMgFw93e6wGNw2URtGdj0zjSD8t+Bik8CoKNtsdMcK8SDtSK+T9TXwvKaxEnVU2LT4qQoDS1mgCSgVRG0WXiuhNQgIrHh5e4JbAsngu5yP95ePa7swt1DxuQgzkgMRXb95grOV86VhcWMMLnZ9wi1jhG51yjKLsJtFhnUSFHN7SpGwtVEAh+SnLLLC2QoscOWe5JEJ6WVZrltHnn32O857LMDIMU9ohQ/AxkRQVVbthu39BVVY8vn/Plz/8AV33RIwzEUcMDkVSkt2/5id/+u8WZUx/4ePTB6bxxPl8ZByHpAKKbLctLx9esITAN48nliWgVYlWUjzH6AnRscwd0Q9J3WHQSon0XYkvS9AWpWCZBgqVWmxFjbI1ylYomxK1g0JrS11t2Gz26Z5UyVVbVFPOzSgdKUuLT/e1NZZNu8EFsLbCR+HIPbz6DGe26MJitdy70TtiVEzDzDyM7GtxrZ3mkc2uxfmZaZ7E6LHZoU0pHjMYvA/0/YWnpw/0w5kYNA8PL57FneR5z3uHS14uKC0KM6XWYtNKn4V5mpMFghBQc4vl2oK/NXZLxYHWgnJ8QirNR0ZQPlUE5fonU2CzIVtMY6goS8m6moYUq/E8RVkKEZ30dem1Yyqa8rnkXB/Us3PO54ASY5xbldBa8Ch17VIR1xwma3QiD2viSqq9kn5Xb5ioV4rD7TqltcTd/Pwv/OEfX7v9/+ZP/QbWWIZh4O7u7przk0rQvJjeFjNSBUr9GIIsHlnqGGOk7weqolxbGbdFVE7g7PuO0+m0tngy6bRtW8q6grSQb7fbG/lX6hMmv4Tsz5JdXed5TgsCKz/lirYsq6S4KK4GcZnLIqiKSeqnazZFXgxyyyZ7rOTJPEsAbxcMYI0PyI8VabME/GU56O0NYIzBJsJxvt78nucU5GkeOT494ZZF/EmMhDAuy0xRFmhjmJPpXlEUFNamtk+BNQ3aloLQGJ0IhLKwu2VmOAsPRVvp1Spj0vNYSltikd24sZqqkjDBjx/f8/bdN7hx5P5woN20jIMgLrnn6v2C8yfhk8QsgycVcG1CC8qVm7TM4q9ilLSyilKM1nK/V9QIGmOtEGJPj8zzSNu0RGSnV5WSOFzVG5agOJ0ecW6kri0hOOqykrDLaaKqhDdji4L9/sDDwwP3d3dy7kEUUkPf8/abr/j47hsu5xPLPDNNIxEprsu6ZrPZ4gKApHRP44hbRnzwzz5LKQAfqDd7tK2YZ8cyLVitcW5k6M8SXKciWsl4Ew5SlRR4cp+F6J9NZLfOoTkx2RaWdrPl/uEFu/0dRVninGfoRS7slolp6pinAbd4VJTPxEe3ErPzRK21WdG/GLwUnFYQs7quefPmDd5H3r57K54fPiSuofh2tO1GXGRNQdcNBC/eFEYXvHjxghcPL6EomIPCGkOhYBwvdN2J0+VIP17wU8/U99zdveTNm59gu71jWhzD2AMBY9P1a7icjpyennh8/ChEYAXBaLQqMLpmnsN6Td4vDMOZolC0mxatlIRyLkviW2hcjKgYiG7EIhlEZXvA2wYXDbYsuNvv2dY15+MT7999AGX4/Iuf5OHFa8oqOfzOU8o66olRkrXPx3dUZYGxhSR3Kxl74zRTVC2Hlz/Jdr+nsKCUxF0EH9lvD5RFC7EQab4KLG7kw+NbKd6mgapo+OLz73F/9wqtbeIHyfOEuDAOC+M4rVYKIcnZMx/KOUG+6qaR3KLko+O9CBH84pjmmbYVX6NboUSMQcwlEf+rT/1EtNErT/B3W0Jvi5V8aK3RmWQIN3ySKyclEnHLsrahVFLvyJiT7+NNgXKr+Pn2Och9nUm1MbXDvvO8UwtU68SYjaL+qasSYmRZHM22vbG9EG+t8/ki62cQvkw+bueO0/nEL/0+fVb+ji5W/rs//d9z2B/o+361S6/rGhQMCWLOC2j+O2M0u7aBlIqajcpyeJ4tCkJQzPOS3C0Pz3p7kvgbV3fWtT+aeCGLF4ne+Xymz2nKqWiRybEhhG/nRlylwjcGc4+PaxCatZbD4UDTVKufSt/3q6NrURQURbUWL3kBuO1Z3lbqGeLLhVCWI8cYV9TolmibUYlcMWdZ9PqFFDn5+cdx5PHxEa01+/0eU4pUVBj0HhUifd/Rd2dRV6hr0ZeRKinaGpSqiDEZwin5ufiFyKRCWnwCUeINnAOtKeuaUhnC7LBWC9dIeZybuHRnTqcn3OKoy3rlLeRspGVZ6IcOQk8IHqM11paEIHL37VaSZ6d5Zuh74SxoxX6/FRO+sWeYRwKBpmnZbLYpEiDgfWDoe8L4xDR1eCdGbnXdYm1BXbc0zZZ2e0+Iju//4K/z7u2PmKdBkD9ToDXYUgocpQxtuxOCKIbLpSciUfTzMmMLQ2HsWjj6ZZE21SQk5qbdYssSpY3seseeKUmE83FL8t49vOLFq8/YbndYU9KdTgyDBCYenz4SgsKaAh+W1H5z1zBM71nSOLttv2avJKW1fIaLE+JsSncuyjr5RARCmLEGIo4icRiCE9WPMqxeHRltlMJTHJKjj2lhE5m79w6lkSIx2bEqlPB5grgTL4uoqjA6EaUlCkChcM5zd7hnuz8QombsLkyjODbXm4b9izsmN3N5+oAfZ5QusLZlsxGX7Y8p/BEF2+2WpqmI3jH2PePYr1b/oxsIAaxpiMFgjHDkitISFQRCIkfPq0cRCRUt64pl7PFjh1WRsqox1Y7d65+EomEYFx4/vMUPHdGNoig0lrrZUjYb2s2BGFXakG24O9ytDs3Bn/nw/ks+vH+kbiSHaVocSll2+zuaekOMjmXueXz8SHc+YU0h3j53L4m2wpbSIj13HSHGFLMgoZNlUdI2O+p6g5A3lSCzQSz2YxRE7u3btwDc398ngv7ANHT0XYdSht3hDpSiabepmI9J3v+ce3KdJzXGPEc28n1AatMJXSPLbz5tt7DKZJTKapg0H4erigZIAotrCvNKcFXPM4JUKnKkWPnktRXf8kHJ8/563pB4L99GhGJqaV2RFVApYsVqJWog4MWrl6v4A2RT/NVXXzGOE0aXgFo3e+vzxsjpdOIX/sjP/vgWK3/1L/0vyXVTKtF5mVHIQJpvHFpzq0baKQvLONBU4gmTuTJXGW5JVOI3sRKXQCrxpCBQKjKvrYfEEO/7FJFuqNsGm/xJMtpx6S4JUSnpun4tUPL5yaK+W5EJuJKrMtSWXRsF2ZDBn6V60zQzJ2luVZXsdvvkjRJu2NmsN8JVEpcHM+v31+cVn4B+GERFYM3qPptN8ZQSX4EiSaD7vr8mMafdsvMObbVAmDES3EJ/uTCNI9M4yHVbzel0YhoFrdJKyIXb7Zai3BJJIZBabkhjUqS896gINgXVmbJkdp4xuXwG59BeUoGLQqN0JAThYEzTQN+NVFVDYQtZyL0TaW8Ub4Vl7OguZ+ZFyHyiDo/JuM9w/3CgKCyX85mh61iWSfw+lGJyI1VT8eLFS+pKCmHvkiLlcqE/fsMy94LyRVlsrSkwxvJw/5K6eaAsNOfzB56e3tFfJNCRCD4ueOScjCrxTtxhC1vR1C0xOk7HR6KCsqwlVM5aIkocb90g8kVr5XNXhqqumWbhZ/hFlCn3Dw+0jbgSD/2AUppme+D+xSvu7x9YZkd/OfPx4zv67oz3C8rUtM2WGD0uEQXl/R4JzmGTFDK3IMtC2jBNXfPi1QtevHrJNDsejxeMKdnv7yXtVymsCXz15Q/44Y++zzQNmDRul1m8ZWyZTNi89OvzJB9CYJmSdwys97HSV6Jv8IHgwtouDgEZQz5x4Yw432qd0sKVZV5mYoCmbsW92TvcPOKDYwkBh2KJAULAKIW1FUaXFEVFu92gjOZyEVK1UoqmbYSXlTZFhbVYq/FxhmioypZliVRlLUhyWWIKi61LHp+OfPjwQdqEhU3FlGNZJoKbMMFBlBRtU22pD6+w1Zaq2aGjZxkvVEbur8V7XICq3rBpD6A0xpYURS3tmE7m0+AuuLlDWwlYDAEW5zns72ibhjAOfPzwju78EVtogpdkdYVijjCbkt3ujvuH1zy8eMPh8BJjSpbFs7iRj08fIGj2h3u8D7z/8J5p6nn1+iW77R113ab7JzDPy7p5EtWZbLZ8CEyzoDDb3Y6qqhPPxq73cuZ9XC0juKEQXOfMldNinqc4f1qsyPNcf3adX2Pmyq6oh/x+pfLi/TUEN4/NdZMZ4sqF4RmHJKMwUlg8OxKqggIf3Ce/um5mM79G5WoIYdwbfS3mTAodvbs70PfDWhx77wleffK+XVtokg30Y1ys/H9/6y+x38mF3+7UbndsubLLRYD3Hr8mGRtA3VTNuSf5bRZ1Jt9KIvK3q9cYxMiMGKmKcp3wbk3pfBABW1bZyEBU0qcOQW6iAN6Jz8o8yyCwRSG8CKMwRq0tHCBxSeYU2a5ThLtOyInkPNR1s7ZAViJVen/ytUek3/1s8N6gL8vsiLeOzvFa8YuB3UzXSRp1RnpWe+gYsKl9pUB2Fj7g5pllXkiqRKL3jMOF7vTEcDmxjANVXRJ1SdBmbWNlZEqnCaYpa7TRzC6gtCGgWLwUhtZqvF9Wd1TJA5LcF6UVTbmhqbciYR3HZLLmaOoKRWQaBxY3M00SCFhVpZiHHZ9omoZN3aaxo1YytbT84PHxHX1/RGnN3eGO7XbHmEmlPqCVoqwK8dlwAq+PY4+1hpcvP2PTvhROVlj48off53x6TPk7kXkZGZaeGEmfTaQua3a7HW1dM40jT09PK2qWw9HkC8pKYHXnPUVRUlQ1TbshEjmdOiH8BUdRGJQWfoYEQgpfIg8GKe4MSkfxt0jyahnflrvDHWVZITRGeczp+Mg4Dqi00zRpKxe8p6hAlxptWl6++Sn+rp/5Q5Jvs3iOHz/w+PQVj8lhdXFSQAt/IzIvM4uXMMC2FbfUeV748OE9SimqsiAuglQuycnZaCnWZP6QghglHBy5xaXVZ40lxJnseRGitGK2mw3GGrqzFPRKR6q6pCgLTFHx4uVn7HYHLpcLb9+9Y5pn8VUq7Gp37pYFowJODJRYnMeWJfOyiMrJlBhVUlcN9w8PeOfXRVhMDB3j0K2KphcPL9kfDmhtmKeZYegYhh7nFmxh8THggcUHptnRNgeausUHj7UFUSmmWRyDN3XL/faeom558eYL0IYfffklX3/1JdYoqrrhzWefixy+6ygKS13VFMlWf+l7vF8wVuHDxMent1wuT4niYAk0vPrsJyjrLSEqCmO52x/YNjUxeMYUdNkPEyFEtrsdSmvqpsVSo5RkAHlkjOYU68JaijLJfsmLtXpmNa+UWaNFsr1F3uDmuVFa6eLrknlAt+uK3Px5DYgJJdZr6XLbCsn0gNyv+bQVk+fT23Xndl27FgL5L/JcffVhWdewzEPheaGzyrlvXvd5UfXcjfZ2zsh/Z62oTMdxWN2F8+t/dysqZwP9GLeB/t+/+efQCfrMyEjmcuQCYVmkkr8tHICVPX7rY3ILB95+aBkhmFO+SdNUzx5/3YlJL7RI8OIwDKuaZ5qmtU1UZJlzyi3KLRVR0VwHTh68z2ztE8E0O8vmFpW0pZ4XEPn8Mu+lLOWcq6pa1Upt26YWUrFys1bS1011rOIVcs074lvnYGuv/KDMgcmFUCa7aa2TaqYgOI930hLwSuDHwmpKozk9fuTj+2/QREJwqzlYXcnnOwwDwzhQWAlQVICxBUVZQZqEbFVRFJWomBI/Kftq+OCEN+E9RC0upcZQFMK9gYjVwtMZpxSc981XyTgvrDdvWRaQiML5M87XLe/hjDFhDYcURC2y2bQ8Pj4lTpRZ+77WyhhuN20qoLbsD3usUXz88I4xhQU6NzNNF6ZFCqPSliuLPwZZ9IP3zPOU7gGXJhmzyj+LsmC73bHd7bi7fyDEKCF+lzPLEjC2ZJpGprmjaSrevPmCz978BG27wS2OH/3wh3z99Vf0fSeTpYQjJeTNrARz4VAJcZMoSgUpFmchByaul9iveGxhmJaZcfFoW1NWTfKvieAdOpkhPjw8COqWiuJvvvmGj48fWYKXomVeONwdZGKMcOkujH1H8AtDPzAMcr/My4JOffaykFDMotBI2B+4JeB9BNLP0tyR20vZI8ktTjYZeHxYRCViS+7vX3N3/xLnHY9Pj6tlQdvUSQ46pWJLELds7qi0lnGarM6Nrlbbgnz/hxgwKe3cWItzKUx1cSsJPBP4lRJE9nB3h7EZJfAsbiEGC2h8jNR1iwuRqKQVPpw7rLYoU7K7f8nrz7/HbrvHKAmCxEpRNg4DIXjhfxgreVZDxzJ3srPXOhFhbTpHzzQNNHVJs9nho6EbRj68f8/j+/e4aUTFQFVW8jke7mg2W4pkARGDElJ1IkejtbSqiGiVQyO/rUC5LuJicnY7h92afH66Wc1rxG276JaLeLsW5PHx6d/frie3Rcxte/5WSJF//4wEy6fOtNfnvH2e29fM368b0/g81PH261MezO37dvv9bTDu9Tyev9e37us/9sXKX/it/3klVME1OO1TPsjtApoX73zc/jwjATmPIhcRWUIcQkgLjP7OAWeMQSN9+dsPPSMCMUrln4MK83Hb9lHJWMh5R/CCBOUsorxjz4M3Z/fkaxqnQVjbsKp+IpEiOcASr4Nzmqb1vLTWYtOf0ScnO7zczjHapDTPK6wXiSmqXZ43xKuMbY1tjzk40rM4Oc86GcTp1I6YhhEHLCFI5otzqOjxy4LWEN2CdxPTJJNuUYqU1xZF+owCAVl8jZUWCkq8EIwp2Gy2lKYSCNS7qwlfcJxOR/qux2hhuxujqauSOsm053lk8hPWFjR1sxaTbnGM08w89pyf3vH0+CgQftNwOBzWDKPj8QN9fwQkAG4Yerz3axutSNdQFgXT7IQ/U7eJmBrBiGeK0bBpG4xWuGXifD7SXR6JQcL8VETOXxs2bSNqoGlinqfr53szBrXWTMtM8CRCuLQLlTEJ0BbuinMzKM/hsGe323N//xKjC6ZppmnEq6eu5Vr+9//9f+Pd+3dM00hhoa4rYoRpksC64IVPpOVmu+mhSy/8ulAoIoJC+ugZ5wFroCrS+SxW8oySJ0V2YAZR1JWN8KbO5xPznEPxaqZp5vHpA9lMrLAl9w8PvHr5WjxcnGeZBr788ne4XM6EKG1eomaz2XN/94KqlpiLpq7ZbDcUtqDrOn7nh7/D5XxGPH8USgs8r3SJ0gVNvUVbQ5dQt5z1JIZ3pZBHlZYiQysinmWebiTIAbROxGD7LGnd2gKUoW62FEWZ5jbJD9pu9zw9HlkWJ/c3EbfMtG2N0dBdTiIE2D+w2WzRxkr6MwptLZeu5+nxkXEYiFiqzYFXb36Cw/6O0haSeK+kzSUEZnGfHoeBeVm4XM68//gl1lraZodSlrv9C6ypUMrQNAWFnojKoGxJxECMjH3H1F0kdqC7ECLcPzyw2e2ZkgFkUZbiXG5EXBCUFB9pJl3nyXW+utmA5WJFULWwzqG3fL81+O9GSZk3alVKEr+d/5/zXa5FRX7t/Lq3+US3c/9tMXJ7vvn41GTtdn35tPC4LUqecW24FjpXpeY1J+93E6R8itKAkO2vSqB8K1+v/baYAlmzf6zVQP/zX/yrtG27kkMzoS7DeXn3nzkr2bgt3+i3raFbNCaEwNPT08q/EEmxFBdiOf8cBswfuveemDw/bn8OrKZyL16/Wr1M4FrxrgjLSqCKqxdMPl8fwsp+yjfOs1ZO9EzzIOml87wOipzhUxZVguSvf5+Likyoze9Lfj9WjxqSS+O64CViYwzSf05J1SGElaibF2bZBabog2WRKIDFUViBfIPW9NOE946qKLBG4d2Cd47+ciG6DsKS/C5OdL20SqqqEoZ/WcvuCvGhcT6sqBoIpyOTpcuqXNG4oR8IYSYER3fpIAb2uy0KhKA3DSxhxoXIdrNns9my39/hFo/3EashLMKEz6Z/X3/9NT/60Y9S0dajVEjtvCCLRGoBaK05HY+Jh1GgtaUsa+GctBuqqqFsN5SVMO8jgRgTKXRZOB3f882X3+f09IQxAp8vy0TfdSmzh2eTEZD4D7ILRyFk39xSswVVVdNsWna7A3XZME6dqJG8Y5klpyfLWauq5uXLFxijkoneia6TmABrPHVVrYoaKSzgdD5LcrUSefbh7o5xmvjq6284X7qV8FhVDSYRgru+Y7/bs9m0dF3HPPRJmmufwfFFUdC0LVXTEnzg0l0YElH79evXvHz5UjiyhaHret5+846+H5K0WeIKhsuZaexY3ITzU1qYGsqiIQb1bHK+lf6XZcnLlw8QPU/HjwxTL4pEZTFJdhti8ppIKIO4GpfSgitrAha3LGw3DVZHzqcntPJoJLzR1rKTzc7T+fWNMbTbA/3oadtWxqUtUEozjSmXRuVcG4VVQHBEvxDcgrYWWzZSqEZoN1tevf6Mpm1xzqO0ouvOFM0GrRuMrYlBcfz4xDJNzCoSrRDhN5stthA/qqKsZMPlZtwsqrTgA/eHOzattE6Hsed8euLj8cybL36Sw90LaVUET6FA47EGQoTz+cLxdBKuScplKwotnCFEoq2NeNHEKB5cWl+VnLeL+jAIsiak9vpb6Do8Dxu8LQyyT9UtH+N2Dr8tWG7n/0/FDbngvPUq+fR183E7J38XOnNbtNz++xYd/7TYuH1fbq/x0zbQ7fuWr+32PL/93OrZ3+bHXi4X/t6/7w/++BYrv/Gn/3seHh7WXWNGG27zeuD5BxSCGHDlXmX+O2Nk8cuQ/aeD5vp8wiXpum71csiVpzEGa6xkK9x88LfBf82mpU2usqtpWsrBIUbevHz5LBHZGJsmphTvnZ7zU/dPOYeIkBqv6p5pEsLn6XzGmmI1TcrwMEgx8/LlS3G7TdJRtyxrdLjzXhCP1E4ZR2l3WGvIroUYs2b65LZS9lpZlgWU7L6ssZDIjtM4YrTYqqMVZSGLqTUGa+UzHLoON53pLk9M85SUGgvOiy1/3WzwGOZlSUoZYdNbY6iqEpC8omVx7HZ7SSBOCdfyuc4olZAwJ6F3Okacd4xDz2Xo0rlV7HYH9rsDZVkDivPpiXdf/5AYwxpL0DRNGmMLl/OJp6ePDENPWVrGqed0OgrStExEHzBKvAmsFSZ9Uch7ZGxJ2Wy4f7hnuxcfiXkRXpBzjtIq2rqgKCxPjx/5nR98n2nsIUr7Z54m3JLydlAYe90xlalQJqp18bSlLJqoREeJEKNjcTKGrLXUVct2e6ButqtZVUyI2jj2dL3kIOEnsoKqaTYYYxnHifv7B3abDY+P79P9pFDWcukHnIfFe6xRbNqapmoJHupyw4uXn9M2B5wPnI4/pO+f0oIzYIxhSmm8t3wZ8cURifGarVVYqkbCBMdxYp4XDoc7Ob9ByLplKcTbp6cPOO+oq5YQFN7JPTfPE13fM0/XfDBB3AIuZdpEhGfWtDvazZ6yEGlsVVWYouR4OnM8X4S0WpSEAMYUhBAJfsYvI9PUU1gtoarLBCnPSu7z6+54WWaULomqWFVPZVmJc/M4Cdq23QppnEhTlKKoIdJ3HdM0U20OgoLNC2jNvLjVybRuKjbbhqrZUlYbqnJD3w24acRPM+3dHts0zItjXjzGltRty/5wR1W3lMEQF4dzPeN44XL6SHd5ROGp6g2mviMoi7YVm90dVVFSWoOJgWUSZVWWv1d1Q9U0+BBZ3MI4jQzTQF03a1DidifXEqN4M33aGoG8+BtxPk5o+u2Ce7t+fIpefLoQP59/nyMjt6jG7eM/5ZR813N+yje5RVXyNdw+xy2PMa9f33V+f6vz+vRcnvFtPnmu/P/nr/Uc+bk9x8vlws/9/B/6fRUrv+cgw/87HE8p12ez2awLfF7Is9HWrbw4Q3xZJnl7fAq7ZYj5thDJH8RmY5/9/S2kN4/TGlaY+5a55WKtxce4LjjzPK9FT13XeOf5m3/zb1AnmDHGmDJ4tFiIh4gqCrbb7TOvkytcl5mqQAwUtkKrgv3unlevllVCGkLg5cuXz2C/oihQqbDqu269tpz+3F3OGKWp64qykJt5HMTWv26aRDyG/X6/ntvVxlzkhgBDPzBOE0sy+FLGorUjese5mwkhJvKboCR13VC3LWVdcjydxC+lrKUoKgsCGm1r2qKlbWqKwnA5HZmmMSEnwgXQWuOWgmUZGYYRoy1N3VAUZpXnKVtACAS3iNFVc8cD9+t7HELAzwNPZwlFDMFTlqIi+vjxI33f07btuoBOYwpUNEK8XdzE/f09dVPSdxf6U3fjDCnchGE4ymfPwvjYczl+wBQl2oikNyrY7fYUZcWXX79nHHqWRcZc8AprLNWmpm49292Wtmnpe5GNns5nQXxCpEqJfLNbBImaZowZqRoploIL2EJRlSWKgLXCr4nBM44DZXJpziQ7H2THucwzKgRplWy2At+3UrA09UaQmmmSeACliWiitvhIQsSSHD3JkoMb6E4fWeYZdImxYgWQoxwyogokZ1uX7gvhfrjZ4xaVPpPIpSO1d8S4bBpE2loUJdu2xblAXQs6cL6cAdhsNrx+9Qal47qxyJ+3UjFJtFX63Dw+ymIvJmqOtkmtm0TmL6uKgy5otnv2h3tm5zg9vufpwwd0DJiyTK810c+LSKVVxHvhSUnhWDEvCzEExmlhmiXJe7fZUNXNSmye54XL5cjj0zvc4jinNG4Qjs5ud0ezu2O3P7DbSfim84EPHz/ydDwxDBc+fHzkcGc53L1ms9lSGM3pcUSYLo67bUNR1Swu4FFSgMXAMo8oSrxbJKhzmXFJoTMNFz4+PrG5D7z+/HtUzYYQPOfLCUPEEol+wOAhej5+POJ8EBPGsoQofkptK+24ut1ibZls859Hhdwi57d8j++iDWR+IFw9gKy1V04i1zb8pwjEd60H+Xm+C9H4FHm5Xdy/qyV0+3X7mrdox+1z/W7HdyEwt+edv//UqPXTa/iU2/NdhU9em5b/Kxxs/+9wZGTlz/3Z32K3P6DVVdmS2xuZqASs6EbuTeYixlqb3CrLb1WJuY10mzIsf+dWlU5GVjInwFq7cghuob3bgscF/8xkyHvP5XLheDwydD24mbuDGNxllCifM8agCzFQW9tON8iQUtLXvu2vKiUx37LLFM5Lvq6YCqfTSXrXZXq/ylIQJufcel1uniCIu2yWdGdItKxrdFHiU9HzaXtN2PLpXFHoGPHOC+QbIxqP9yOn44lhHNFGAv/kM7SUbSleEyn3I5OCrbVM00I/iMLLWgPOE4OTDJTC4vzEOPd0KerAGJGSZ4nwMg9MU/ZSUcmfYSDGQFkUBB/WcaJTbs00DXjv2e/vsEW97uyHYWC727LdbCUn6XRiHAZpK7iZxQ2E4KjqQuzKVcGY5NJNUxOCx4fkANtsqYodd3f3aGsY54VL3zNOE3XdUDY7MJKVRAxcLk88fnzLMk94vzCOfYosMIkMl6SwRYGbJtQyiOdI8ouo25a7+wequqbvenCiMpFrXQhBIgOsLVkc2NRStVYg3q6/4L2TFg1AzAo6gYbLshbZuV9YppOAOrZkd7jni+/9NHcPL4jK0Pcdj+/fcn58i18Ggp9RxrCEyLQErK4kCZbnglFtNME7YuKayIIj6MBt7pUxacECYoiiCgo5gM0SohCxfRD0cLc7ELxiGiex6k9qC33DDyirCqMlJNC5aVUkaVNii5qqbKXdU5SgDdqW1O2OerMDLVyW/vEb5qkXpNJoMXbz4pRdFQXKuxtfqMhms0VkrlJchaTK2h/uUGgKW7A4aRs53+HdTEARgiYERVFlJU1B2d7RNC1lWTMtC4e7O/aHO+qmpTuf+OarH/H23Xv69NqFVpQq0FQGHxQ+Ktrtjv3dA7ZuKKqWoqrRxjJ6SY4ujMVqjZ8WVIg0VU3fn/FxodrsMEVNiJplXnDTSH8+cn56y9AdBR3UBpQmonjx8iX39y9Al0yLYxxnyrrizWdfUFYVbvHMyyL8qdTGv21hZDdqo+06j9zOWZ+iA7dk2ds5/W+FPsDvXqzcohy3c3h+3C1C8ikPJZ/H7e9u20K3f/dd5N98nZ9yWT49freffxcQcH3s87+5fd0fe+ny//hn/ixN06wBdTYt2GUhPVui7ISmWULz5sTJqKqSwhrKoqSsSrzzHE9HTqeTkCmbOrULFsQyPMkb14JF5Gy5OALxGhEhLfTDQN931LVEyNui4LDfc3d3LxPRTQWfB4/WmqHvUyoonE9n+qGnSoUDSooInwzpQAqq3W53QzIWt04hxV6huRAzdyGsRVge3OM40nWdEMoSMTNyrc5zYdOUhYTGeZdUSVKo9V3P4heqtqWqm0QYFF7AsixstzvKskAbkdgV1mK1xaficZpGopsp7dXCGkS+6RbpmSutMYmjAolvk0K2FFocRSPYQtN1He/efcPQ99RNRdvW2EKyjYjw7pu3+MVz2B/Y7/YoBfM8cbmcU6bPnPxkpHVxPp5k8lBQVlVCkTxVVa/J3tYKyTNzUKoyEZfJckgpck6nJ0IQF+IQPMvkJN9omSVgsSqpann/Sluhvdj4l6V4uqAVyhpsWVNULWW9paprkcoHl0zjRvr+TDdIyOZmI8WTLUqMNozTyNc/+pK3P/obnJ6eVpdaYw3tZkNRFmLa5pz442iFtsl2XFnxs7Gy8ArfidTKTJlWKKKyxGjY7w9ENE/HJ+a0s45+RvsZWXsiJiFzTbsVl1ivOB+PRD8SwoAPE7Y0LD4yO08Mleyq80QYovgbaY01GoUUTFVViY9SXXM5nzmnNG1rC9pW7pdhGFOB4dI4t8ld14OSIEetZYzFGEFJwSeW/z55xQRpfSW1HESUSfC6Et+b3f7Aw/1L7u5esNnt2G73FFWNC4HHpxNffvklx6f3wkfy4oPivITSVXXFptkQlpF5mvDBM80TOqEK0npWuHmk77tkzXAlTVqt0cZRltKWskVNWbbYosGYSuznI/TDyMenI+dLx0/99E/zkz/9M9R1S1VYglsIITLO89oePT5+oLtciLqgLGtiDJwvR2nVKmibmv3dgWa7w4dIU7VCrlUlQz+jlEEXmqglK8d7z9iPKKWpyko2Xm5Aq3BNhTca58Qw04dApERbQZpZbfUhBxSezydijLRNm/yZ4oqqSSyBXhf0W6QaWLl3wzCshNrb39vCpseJX1VMdg5wlS5/F9qQF3dxsY1r0Xz1S2FFqT9tBeXvb5EduA07TK/xLHFZxuR6nuGK6Ny2t+S51yVD1IVKrfENKo3x/AD52/RgMkLz3cUKwPn8Y06w/Ut/6X9hs2kF2p6XVe1hlFkd97JhjfiUJNtiFcUZ8kZJEKOk5WbzuEycyzC9D4Hj0xMfPnygKSuatpFU43bDbr8XrofW2LoCc3V2zShHbr/EmGzOk5X/Lfs6q4FQStxeU3EEudVzVQNl5CQ76VZV9ezGyzuCrDoQfolai5X8nPmcbq34c2WeuT3eOawGnxCEnPaciceLWwg6Jmi+FqlnJEmHhVQ6TldH3ywZd85xOV/wbqata6qqTJDhkhKyOxSwbTcYpZnmicUtqVgQZESpyDJ2WGPZ7XeUTZ3ScifxoQheIOhxxADROQqtiV4WuLrdYEyBy5JXv6zI1bk7Y7WiKiVQT9sSW1TUTYspSrH674744AUhWWaCXyjLgrqUIniapBAK3tO29arIWJaZxYmldtO2SYmF2F0bKwZloyMsEyosqChOq0uMRC2EzbKo0bqAoJK0fCH4EWuh3rQYY+m6gWlaEpF0w6bdsswT33z5NzifT6KwIYjjbQoRVFHMyJTS6bpFYbV4hw8h8bJy0Usq3CNN3fD5Fz/Bm5/6e6iaPcZWGFviCfRDx/FylNybyXE6fuB4fEt/OaKi3J9uiUQnKrOqMhSVZnaztIyiWHlra4gqGWOlVouECWqGfsCntpBS0r6JMUnnF/FAMqs7aFpUUiyHNRaln2dkrbvGmO6VeCXC5x155rkBDOMohmfqStLM94hzYHRuRRdrYR6jeIe8/Px7bHd7QNxzl8WxLCkOZJ4Yp4Gnpw8sbsaWlmbTYkxJ140s04iKLs1/lrIo0UolI0OV4hpqYtSMw4xzoJU4XhdlhSks7XYDaBYXBEEraoZxEqM/N2EKUdvVzQZTFuJ6bEuqzYGqqlHR0Z0/cHp6R3f6wPHpHdE7tKopioYXL99wuHuJ0gWLi9iyZnO4I5gCHT1WRQgOrQy23qBsyTyPeDfT9R0qxTcsy5wWZ0VVbyiT87fKhUdSJ8YYMXw3L+RTXsYt+pCPjMZlH5FrrEmycjDPWy23KAmpiL793aetFhWfq5NycYAS7lZ+7C3i8hwZis/XjXydz8qe7+DWyIO+hcas7wHP1UrP3zsln88N0vT8cc+Jurd8ofP5zM//wu+Ps/J3dLHyP/2P/x/2u528eWlSMeLDneyQr+6EeVGe5xm/zJxPJ87n8zrhZAXRyi25gbhuXQSD91SpPTPP8wp3W2ul2tYaF59Xxp/eKJ+Sq7JMOkboe3Fw3e/3z1AX+RJ3yXzz3B63MF8uNG4Tlm9/96l87lNi1i0Umosi/CKtoHiVv03zJO0cIlHLwiVyzKTGCtdAtaK82k3P87y25JRKeSpWFAs5BXqchABdFqXIhkuZvMahx7kZnRZAjfjetO0GtKEoK6Z5IUSwZYH3TiSaRKL3DN0FHSJ1VWK1ZvKRiKFpG6q6YlpmzpcLIUYZQ8FRlQWH3R6TiMNG2yQPXiiKRtxMQRaVlJuy3bTUTUHE8fXXX/Hlj36IW2a0UoImeSeJr1pTp5iH7fbAi9ev2O/vUMDl6cj56QNGRawODOPIueuZvBdr/MDqWFtow+Vy5OnxHcEnyD+CDxFrLDHKZyEkTs8yngle/GbK0orxWxCeR4iREGVXJXJ3TVVXGK3TWHWyuCtNTONhWTzBB4qy4vDqDfcvXtM0QjDdHvYUZZnM+SI2FCgV8K7n8eNbHj9+IPpA22wE0Rp6Pj69Z1oGUU9VFfv9g6iErPCmQgicTifevn27hpdezmfKwlDYIiVyu3Wj4H0KEwyCFt1O/jGyFi/fWlQ++X9VVex2O7z3azbYqj6EdaOT77Xs/TSME85d7+VbrkRRlKiiwnvZATdNy+vXb7i/f0FhJUF6HHt+8Dt/k49P79E2Bcbp6z1mUrbNOmel+3iZZ/r+xLIs7LZSDEG2TlDEqLm7/4KXr96wLB7nI027papaXAgs88AwPnG5dHRdjwuRsq5589nnvHr1mrrd4X3ALzNdfyL4mbJQmGQSuDgrzqZKo7W4g/fDhDaWu3shpE9Dx/HxbTJdrFGmQBfZy0pa1Tk7zafWmBBuJXDytti4nVtzOPHtpuzT+fjTefr2Z3me+5RHEmMkqm9zQ54hFvE56faWsMt3FC/PluFPfnY7Bm+Lqk/PVYqd3HZ/XpTlL6O+zYt5JkFW4dnf3L6uWFd828tlXYOIkr0VVxu+9b66XM78wh/5+358CbZhXpiHiViI86IxBh1EvRKCE0h1nhOioljcwjzNxCB+Ka9fv15zDjKJKsZIIGJ1kciZjj6ZkHknk3ldSjWfORTb7VakdMnKOnDt7cF1MEG2u78eKkHWUlBF6rpZJzt5/HUCzVCecxLWdotu5OfKk+V2u11JuvlGycZ4t3yaXMzlnUQeuBn1EXVFhOCJvllVTdbIbmwYBpGsRk/bbtO1CGoyLXM6X4+exAwtc1puyW3WWoJ3zOOMCyK1LKuaupFARW0KnF+wuqI1QLDMU8c0zhJ+iGNyM+O4YExJ07aApkHyiFSIRCQSYZpmVAxoBcWmpa4qgjKYomRcHM5HDvcvMEVB3/f4RXa2527E6pEYHJpIXZZU1QajC+ZxgRA4HR85Ho8E79BA2RjKWjPP0raLsrKz2WwgBnzwUlwDy7zw9PTE8XRC24KmrvDTyOPjewoVsDohMCkdV2rzgLEQWXB+IUaHtYo5RKZxWgmrXktx4VWkKiuqbcM5DvJ+pclHK0VUAWMKlC1xylIUVrgPSVnknV/D3TLsHTPq4FMhukwM3SPeTwQviEJZSexEVZe0zQar66SoO3E+PxH8gtEKa8+pIJc2jI+KaXIYB5f+HcsSKK2mbSVDaBxHQoBNu5dWggMfFtCWsikxIbBpW9589hnLsvD0/mvcfJFibVm4XC7rPSwLN2vxdXvf3RLRM2/kNgg0y/9v02/z/bssS8ocs6CzmzDiErtIwb74mdCdUcoIYfZy5On9N1RViv+IYVUpWRaJDdAGY8XHRZE9OCSIsq4bseEPgYChVRprVMoSKiTqob8khHJDiIG+H0GJydo0Lyi9gDK02x1vPn+JVophHDmdz0L0Bt5+8w1N3VGVFWVVCtJXt6DETbhoDpQYYlA4L222aXHc15vVadsQ0WXB0mwESfGOZZ7ZFpbdbk+MiBGhc3Rdh9aeZRGOW1k2CcGISdgmWU0xFW3G2pVbdFtY3C7Etz4jtzy7W07KpzLmEMSJfG0r8Vzhk1stt3P8bfGTUX9uCqHI8yLqtrj5lHPy6TXcHr9b62mVZT8rJL7tnxJRqS0llhQrUhViSoAP67XcFtwQpYVKRIXbgkeWD+d/zAm2f/L/+SepSlmsu06SdzebjfQY20qImKkVknvMWmtJdU2tmDwQMtvbOce4SN5B7lPeogxija0pEppjtLRtmrqmrCRx2cdrcRK8QJPXajSsLZnbyl2+l8ny9nhe3QJcgwRvq/5PWzu58MjZDZkfk4/sgJsNzPq+X1tiSl1JrPnajQarWTODVOpXZg8Ra4tnxOFMwpL2lxR5twVRdhz2XlolRMkjGscRW1g2bbv6zARkEXTzyDJ2DJdHPr7/mugXdtsN9V6UDwrLpt0KmVPJLiAGz9CfuHRngnMMQ8c0DmuWkUfR7u548eIlIBHpShtcEDt1MU4VVGnoTmgVOD194OnpIyjD/u4N1hjqqmK32642/afjkePpiagSWuE9IHLeTdvSVCUoQQC7rufSdfTDJOPUyG56nnoIC4VRmLRDcRHQUpjPyTF320o2U99fWFIytdaiQOr7AZNymfLYl4EmbYMYoUgJxFVViuy82TBhMKn3Hb0jLAvLPLLMYjYnZN0cK+HRyjDPCy44ooo07Yam3qFVwTy7ZNInxnlVJfEIWmuikhxYrRVt23B3uEcrLZuEsRen4qKmqjagDG6SZOu+75nneVVfLcvC+XxinAeWZebu7o4vvviCaZp49+6dFBXjGeV6JJTTrPYBmSDtXO7tXxeh28I634+fTtQ53TxbBuTHPCPeW0NZyb3mktU/CuqqEsQrtYmNsez3Yn9/uVwYB+GpFIWEd45ThwsLRVVjdIkyBcbUmKLh/u5B2jnKoG3BMIx0XQ/BU1pD01acz0fO52Pi5IA2Jdvd52xa4dEUZc2yBKSLIUTkpq6x1qT3rMBo+XxcUqD13cDxfOLj0xFTFjy8fEW73dI2DbWKEMQt93Q6rWi3c9JS3O0ObHdbIToju3NljDjy+phUaFebhzy3gHj4yH3+vCVxu5hmVD1v1D5FSLLr9C0fLhef56Se2263q9hhnUO1evY55/ESo3AbtXreIrrO4d9GUj4tGvI5XzPhvm0cd1tI5/+vRQ9/C5VS5Ft8mlWxmdpp+Wfynso8/ylCdPuc16IqEPHPfp+/v1wu/P1//9//49sG+q3/6bfY7fd458QpE9k5hiAqhjyJaK1pmoamqUUNYARQyoPidhFflgWPkPbGcVzls7f29fam1QK3WRHgfWCalnXwZLfc/PeX7kzXnbm/v18X9LIs080g0PqtUugW2pUBdX0fPq2iP4Uz89etAVw+l8xdyc+fb9B8TbeEMyGYlZCSjpfFpd48gMI7j4pyI4cYkh38mNQ84kWxmsvdPCewKn4iXInQaWKqknne4hamaWDshd+iwkJwI3VpxXhKSV7NOIyMwwhREmwPuz1VWSZ+i6hF+r7j7dtv1gweW1ZoYzkc7kEZun4UozQjJO2m3rDfSihf3x+ZhouMrXmUa40550f8+upk/z/0YpAmi1egtJa6qXDzzPH4SEzF8zRPRCJl1bDbH0TN4CL9cGEYO2Jw0oJbFhSasm7RRZ1M7CTYsrSawirmacZqQ9sIgfTp6RGtJX9ISHBhbQ8S87mBNWIIlxejZrun2T1grabrLnTnE5fLkbEX47yytBhjGYaRGMV9trAlSmkWt+CQVkFTbrjb3/Pi4QUxet6+/Zph7DhfTsnEq8A5jy0txiqM1fg54GaHUpGqqTkc7inrDW2zlyKUIOfUXYTTpODFi5fc3d3h/cLp9FGuo5BizSajubdv3zJcnvCzmObdOsHKYubJ0v/cfs1jNRc2uVefeSrTNImKb5C5xqbCJLeWb5PMY+KgAd+6F4zWWB1RkAi7kvDtfcAtjsV52QuriPMzSksAZVnWbHcHdjsxKjydLsLVigpTVoKSTDPBe6w2lJVNyi55f9FCitSmoWl3bLZbtrsD+/0DyliKsiI4OJ0HpmEUUvg8Mw4DWsH9/R2bzYG6bdlsdyhj8FEze48PEe8c/vyey+nI2F+kOF9mqjQefYhoW1NvdmwPDzSbvUjYnVtbW9LuDGgjKGkuDEIUbhPxqti8bXXnRT7nnX0nP+MTZOTT43buVOrq4eW9x6yeN9f2U/6bEMLagrqdn9dzy/O2dGzWgkk8tSRE9facbguCfM553bodR7kNlLlVt6+b35/gQ+Jtqme/uyJBNxErn1zfd53P80MoAcS4Fkw5vLHrOn7pl37xx7hY+XN/gcPhjnlKaopM3Jwnpu7C09OjLKpa2kDeS2uorBuB1JXs9Lbb7dVTRWsEzLp6rWT4dy0cIqtHSgjCVs8fcFU2ibORF4g8MBI642eRsS7L6mSbBx0Iiel28MvPrlWyUt/+uPLAWQfkDdP7Fv7MRVW+AfOAz5N2fq6u6/j48eOaHyRoVYGxz50b89+6xTFcMsJiVk8V7wVVkcdeW21aa9rkYhmjRNb7EFfPjLyryK+hhIHBOIjE2PtZ0BFrsIUhBJ0iASR4UNxvZ6zWovZYPGUlWTcheC7dBRDyrTGSV7TdHWiaLYuPLLPHlhV13VJWG4w2hOiZlzE58UbmZSKmCHWiEDdVIqe6ZDU+DR1D3+HdIryXww4VEXRnGHDzDEqUCSHCy1evefnqNZvtTjgzcUHFwHC58PbrbxjGibLeUDU7gvcM3Znz+ZGhPxG8mL8VpkRFjXMT09ShjXCCyqpIYZJpJ0VuK8pnU1cNL1++JoTIMEwoXXI5n+mGM9PY46MDPHVT0dQNCs2ckmyVEtO8tt2gtGaOiojhs9efsd/uGIeed2+/pLs8Yazi7v4FL16+QeuSYZyY3MK0DPgwE+cFP03My0Q39Hgf8cFgTMX93UvuHg4odSWCQyQbJy7LRH955HI+i1okIaq5YAhuRpOCG28UflPitMhCGL7VFl45adau92y+J7uu43K5rAvo7UbgGTfAFpDk0UUpwaQoaf9prTDKoYigNDHInFEUMq+Iusfi/MI0j1R1SVmL9Fhpg44e7R3jvDAvHkyBMgXtbk/TblnmjCxL605ahqJsioCPBucTZK8MKIPSBZvtDmtrCluz2bRUZQne03cXuu5MXVVsD3eYqhLzv5sCY5xmxqFHuYFlGlAxEMIizrl+pruc04bSUTYHXn3+07z87Keo6h0g6ezGGiF1f4JyxVSg5BZP5g/mjLhblOF2/sxzzy3x87bIuW27fMrLuJ1TlZIMotv2yq0vV+rpEEJOpn+uNgppMf8upESQmbgWFL/bBvR22X62odWamBRr+X26JcRmF/Jb9P32kDDPXIxcaQtSyMuGZ+0afFL8GaOzOOh6PciPzuczP/fzP8ZqoL/4F/8Kh8Pd+uGJS6NCE9BOgsV8uDL4ieJoGVCM08T79++5XC5sNpt18QxRQvOaVoiTipSbkCreECQt93aQ3/Ynxe0ySM6HNakYydVvBCQn59ZxUMLfylSw6G/dJNdBG8nyOPg20zv/7FPo8PbmvL0BIE9XKYmZb7eTrkGQMxFpZ/ggC0Wx3oAKq2sUiqoq8MHR9xfGcWAcJQ/lcLjncDg8I6GtkQEmTbo3hVN+f4L3Mhmr5A2jksxxGtb3buhHuvOJwio2TY3CscwD/eXM0E8QRbFkC1FFjdOIsWK0Z5QXR90U3mdsxXZ34OWrNzTNhkiF1kbIsEYTtfRuh3FAK02R5PJumVFEIa26hb7rmfojwQ0MQ8/x6VEQp2HA+YUyScXze62NQRmb2mmS5WQKibpXyY/m4eEV9y/fYIoaowzBTXTdkb4/skwD0zjRXwa6y8DQn4gsqSBTCLqbF6ggi3IKuzOmQKG5v3/BNM5M44JRVpChsODjgi01RWXFodVHtJJco3maKYqKw+GOz958RlHWTEGkxkRQMTCPHafjB4b+yMPDA4fDK8pyS1m2QpJsKpSFrj/y4evf4endV3S98EmKqqFu9mhVEtEooxIiIN4oeRy5ZWGeRlSYMFqk7kabdbIep5HufMGlduj9/T0vXrwQ+X3fp1bQzOl8SlyYRDRO7dasQBHkMY1FkrpCKSmWjah/2s0GrVQyKBNjx7sXr4hK0J7Hjx8J8epCvSwzihlhDIiTrGSESXFjtEVrKVxsoSnrQgIHfWRZPDpMaNezuEBAUzYbbFnTbHe8ev2Gur1DKbnO9+/f0/Wyyarrmt1uD7qWazAFEU1ZNdhUfILhfOnQKtLWFWFZUCoyjwPd5YyzlqA1hRUn3xCv81lRlIJix8CyDDw9vufp8T395cQ4XFB+ptKRqr1n9/AFzf41dXOgKhusEnWWC4sgEOq62xeDvzFxqQQp3GwkmkLmZlFv2rK4CiHS32aF5KeGZrf/v93k3RY1wzAwjiMxRppNu44Jpb5tz6+/Q4m0rhFEsSFYs3Ru0HEA/9w1N5/TbSsmP++3vm7UQLfXkOdTzXVNyffGLRIFz5Go2wJOiOnP157n16hSZ+BapGeH8+PxyN/3cz/Gdvu/9Wd/i/v7+2fcD+K1Zyf/jrLYyC/E1AxROsR4Dd0LIbkcGouKgb7v1krdWpmYLpcLXddTlKVo+9NAFSJcDsgLKWxrXqv/iEDJRcpludrUy0DNH3Aaqs+gPsitEpMkyUIEzIhFfnw2i8ro0jzPz3YLWsmitywLaEVRllKFK1EYWSs2+DrBIrJzvTo+hpjIZ0DwC8EvECLznPwfMDifpMJOUKjNVtQd3jvmyTEvM2VRrpPZmjTrhD+RzdestSyz2P2XRYEKjnkcWeaFcZpwbqGoyqQ4Cvipwy0Tw3BhHLokS45oFVmWQFXvhE9kTWoVlqgYuZzPLEuP0smpNwTm2QnSUIviYLt5SJO2pm03NJsNPmUPRWBxM84tKC1kbPG9KNGqQEeYxo7L5ZF3737Ex6d3eDez2Yjh3vl8prIF1kqLap4X/OLxLjJNM+iFuqooi1J24mhsQu1kgVvEFyc4ihSiYhJj/zJ1TMuMW0Sx5r2HELGFkA4dwi2JUQLdFFoIm+i0OCaZcAzMzhGVhB4aq5n6E1VpqcoKgqKutrhJiI8RWMLMlJQ/2hQ0zYbPP/+cl69eQoz05wvv33/k+HTGu4XAQggjMU5iAhclyt6agrbdpXEo7duIwSajL60NxhqRiM8T09jTnd8l52D5m2VZiCGu8n2tSEq09e5Z20CSCD3j/IwPHh9Bm4rN5o6iqHBOkLJ5WYgJzl+8l80IgU1VYm2BLQohuNpiXToKW1AVYk52Pp9Tq8Ned+PZf8m7dR7I7Q1rC5x3KGUxukhzhxFZuVbUlaUoVDImHJPHxtUjyVZbfNAobVK78R5TlHgfGccZY4XMCgrnBSGom5ZNuyEoSVw2KuKnnrE78fTxPcs80W42XOaRp/MZYyoe7l+x3ewQRZkTo7nqIPOddng/Y4xkVJ3PF37nb/zvuPHCZnfgzeffo2r3RFWgdUHwkWUWl+u6keBItbY4AtM8MfRSrMzztG4413nYe6Y0B9/yQNb2UCpoY5SAzZB4JiEG3LII+mZvY0+uCIqQSTPxlbTGaLTJEnjJ8pJ2CKjkdZX/nedTUM9Qj5jXppsVOa9jt7wSyee6/v7694nkGm+UR+lYi4f47YDC22uL8fm1XjeX19fP4zN3DjJ+clucPK8q4v+vvW+Nsasq33/WWvt6rjOdoR2KFKoSUEH/XAQLqB9oRCTeYyKppF6iQUsENQhK0A8GIZqYqPGeiB9EiCSASlBDCoIkpVykQEEBA1p+/GhL25lz37e13v+Hd+999pmiP9DS6cB6kklmzllzztprr73Wu973eZ8X3W73vxaFW9bZQH/761/RbDTQaDYxNzeXl5oXiNMMg9GYsV8UMPQ8DyjLh+cFyXIDogg9RKMR4uGAVTxBCGs1pEmab/gKMytm+ORUIVUVWTNCCKhioXUqBhQKF2ZByMvKSQIAxmQTHo3C41CdiNXsGeaNpGUsFeC4ZzeXmZ+ZmSmznArDwHEcKM5py1NT2bonMMmTN9+xrkCWaSily1orTu4hStMEcZRAkIYSgO86qPkeyPGgaVyjoqgPBKBMH42iqCQiUl5cUeUCWgLEon58wQgCzpBKkgQmSaBTFqNLkgTNZrNUpYxGQ5gsBcigFgQIPQUQZ4MlSYxazYfns0S38BwIKZGlKaL+ADAE1/EBJUpXvlIKhlJEEade6iTDsM+ZDsPeQh4yCUAEkMwXT2gMhh0MBv3c3e6i2ZxGLagjGg3R6+9DkiZMKDYSfhBAZQaZAVeojSIIcJXt4WgERzrwPcnqvP0EieNwqqYXMBE5ieD7Dod1hEKvM0A85Aq9Kp8PKQAjBOqNOmSeZZLGHNbzPA9+w4Xru9AZscS8H6LRaEIILhVg8gXZcT00Wy3U600YAPPz8/jf/30ao0EXcZxCZwaku1DCQ+jXeCNJO9AmQ1CrQyqCq7huzvO7drGLWwJTK9psvBiN0bCDTmc3FhYGMJTmqq88xlw5muv4cLkBBddz0W5PQQhREuu5YrYPSAE/DGCMQJZqQPLznhkgjRMowQssgUoJfOTZWyBi4T/l5x4lQMkQzdYUPNdHmrLBEycxICWa7TaCMESSZSy+ZjSSOGHpe4AzA2lcAd333JxHIyGFUxojmdZIEn4Wi+y+8gBmDBrNJpqtBjqdPpJYo91egWazxaTV3gJq9Tr8Wh0wGvN796LXWWBeClgpOs36mJqexYrZlfC8EK5fRxA2QJDIMo0kjctir0IIaCmhhEEiDdygASI2prudPdi76zk8v2c3DBGOOvq18HwXQeJhqj2LMKihUW/ADzxE0QDGZPCUgRQGRCkMxegs9GA00Kg3ceSa18IRAo7rAoIz0jRlfOghQAiDWuCx/pLJgJwAymPqod3mBIjiMJdlGVf2zsNF1ZpxBedESlmGSIqisL4YcxdLDzRx/aDxwZE997wkC7jO2GCo8l8G0ZC1cFxV1pkzhTdcCFDFqyMES2FIKUs9LyGK8Ow46aPQzSr2B8r5ZkV4suqBUUpBibF2VzWEVewti702izOXiqrgVQ9O1RNV/dzid5HzF8eBn0lv1YHwiSxrz8r9W+4riaqLY5WaxmzvxXG+KrO/mGSFQVB6V5C79qREnIs9pWmaF8hz92eH52CuwaQ64ORNVuX3vVCOfjWOWDVOisldpPCNRiMQ0cTpTAgWwErTFJ1OBwsLC3AcrmPkum4eBmFyZJEZ5TouHKVK9+Fi8tY4Zl+UpRcgo5ElMXSWApRv8JJDOcW4FpO5IHQWD1c1C6lYTNIkxqDfK6tFG8MF6VqtFmq1GkRO7Cu4SUWsOsqLNBrNBRFdR4JIQxB7yqQAlFQASRghkJgMSZrCZBpKCASOCwIg80wNKTj7YTgYlDWbBHHIhDMCAgyHI7h50cHMGDiBj3otxGjU5aJrSQwBidAP4boBE06zGIPRAvr9LgCg0Wii0WgjTbkWSppyAcI4HsLJ+SMmy5gjY5hkaTSgHJfDRPm9FtIgjSOkaQxHSbiOyoX1DFJNCGohDpuZhaMUet0e5vdyPRsignCLEx5BCR4H12VSZ5okIEHwfA+1egOeHyIMaxB5yGsw6KLf73FIRXCmUbs5han2FEaDAfbM7+I6PbkwltGGqwxLhTjLAMdHrd5ELaiBtEaSjJDEA+gszrOn+BDByrEAGbCBawgaGZ9gHQWlOKMPxKmySZJCQJWhEw6jKBgDDv9q1swBUHo+tdZ5SnvMac+C4DgCyGs1+W4N9VoDWaZZIM3kJEIhoFwXMg/1Nut1SCFZFVcbQACZYWPP931QxvOu0EkiAFnKm2yj2UBzig9BhZe03+9jNBrlBUcF/KCGdmsKtTzbLU1jRNEAqU6QpIRMKxamFIDJEijFhUE9z829pRK+HyKsN+B5IQDFyrXDiNc1xykF8ooSJGy0AsoL4EgBVwKj0ZCzdIKAw5GOgCME0sRACg+u5yEajdDr96AUQTkCxiSYX9iDTqcDAtBuroDv11ALGwj8OhzXYwKt48L1fEjH5USF0YAPIwTUG034Qa30KBUciiq3pLqVFWtQNaOmWOO5WvvYiKh6oMvwh66GzJmPURXwHLcdpxSXpVFMBsI4zFdNcS8OXJ7nlevyYr6MyjmW1X5X94piLa5mhRaZPFKJ0vFRXH9x3QDyOTipoVIdt2rqdtWjszjsU/y++P/HlIfJENGrXm7//q1/QavdgpvfvCo/Q+cWaVUBtmpBVmNyhRBZkU4rlTuRxlV8bpZxwT0JXd786o2RMq85Iva/qWOjaBwPLCZClehadQsWKPq9OMe++BkTx0RumbORMD8/j6IGkh+wPLzjOHw6jxP0uz2kSYpaGMAPAsBRpf6EEFxCwA88OIrj/2RYGyRJ4jw7BXAku+WF4+Zxb1X+FP2ojvti/RkhmJhKeeZMoWFReIS01kjyej2Ft6i45sKAHI0GGAx6rJ3i8qbkeVwzJxnlRf4AZDCcsaEUklEEBZHH510UT3jh8ZKSOQfxqMdhHiE43TavvySlQmtqCkG9AZ0lmN+zG935vdBphNB34SqFJDNItQHzlDg91vMCeG4Ax/EB4cBxJZQyGI36mF/YgygaIfQ5BIXcNc0E0vGcFEKwFL7gzWPf/B5kaQylBILAg+95yLQZe9/AIlUmJ5FKkXM68s3aGCZ2MjEuP4VRloceeBNRngsiAc/zQULBcT1OxyaNJIqA/P8ynYGkC5N/n5MTj2E0ey+ERKTZMPJcHzpNuX8534egoVwFIuTePYfnHnGmDAldmZtBqZkSxzGSNIMfNjA9PYMV0zOlsVvIFgz6fQx7XSRpyq77/DQ+Go2QZSkSk2A4HCAa9ZEmMc9vIeF7Hkw2ziwxZKBJ57IEyKUJ2CDwvICLNzaa6I9GGA5H5boRlLw0fkZHUQQyPCfDeou9vPl3dDqdksBfrzcxd/hrUAsb0Jrgug6UAvbNPw+IDGEwDddr5zIOAkLyWEYR18QaDpjMmqWGQ0fKYS6e62JubjXqjSYGgwGimEUeozyTjwzriUAQdMbPe1BroN6chhM0EGugHnjwFFelNpogpOKMTDKIoxHSrA9tOBTXaLTgewFcNwCg0On0AMmHJ60Jyh0XmXVcl70pMDCGIJQHx/EhpAORZ/9pzQq9xdpcZDtWvdlVYyWO4zJhQDkKqKzR1fVISgkJObFHVJMtqoZJda0u2gEAiTFp94VIslWOy2LeYyEoV/28MY9mLAZaNWbG/4yc4zNpSBTPehonE9dQvF58x2K+Y9WIqv69GPz6JE+mes29fg8nnXTCqzcMJBXHcHWuysmbCYFjiuO6PYWFWSUoFTeqIF25rlu2KdQdC9eWMQZJzBoYIANhOCV6OByi0+kAAJrNJlqtFrzAL8vSV113Rc6+n4cQqizsxROiIIQlScKS/vX6RKipaggVadk8kTi2WBg+tVpt/BAIASNYEEgAgB9AQqDf7bJ8ulQQeSw0DENQXuo+jocY6gykNRyZC8V5Luq1FhzFsuFaa3Yp62oBxcnJWpwCqimjZdqcZpd1NQQGoFTgFVKi0W6VBpDOMkhHoebVYQzBC334tRA6jUGkAaMxGPZBOkMY+JAIQBKAkhCKja7A9wFtkKQGnV4faZahVquhVqshNQY60exu1RokmCMkpADlVXb7gy7mu/sgPQ+uVDB5unY86qPfieE5EsL14Hh+fioDPC9AENThOgGkcOH5IaQkxOkAWmelUGGj3uIFPOWCeHm4GwDQ7w85lVU4kEohqCmsCkIY4v6mWZLzLjJkZJBpHi8BziSr+2xUjqIERsfFDIQQYpzKSybXihDQJgO0gEkIzVYbc3OHY2rFKjiuDyEIaRJh757n0e3MYzgcAKlALOqQrodmGMB3FNI4J2OmzO9BnpmXkIESYEKrNhAkkOgYEFyHyXW9kuQKkgjDOg5beRgc5ZRehyhKEScaZCSMVuh0+hgOE+zZs6/UEOLrY+NJGJbhbzQacGTh8ubCd9KTOPyIw5l02x9g2OWMrixhcnkUF8rOGgQuaurkXh5tCJAetAFUt8fGnxyvN8YY9IaD0isIIeC5Lmr1OgCuzZP00vIk7ObV1XnhV+gsdDC/rwutDZSSSJIRoniARtOHmA7gB21A5DWFUoNur4u9+/YiiWIQpVCi2CAJShqEYYgoivDMjicRhA3OfPM81EMPjjRIUg6bCBgMR13EpKG1QTTsYhgnaK4AGu1Z6CxBfxiXQmyu60FL9gJmaQZtJFy3iXq9iVZrCkpyOjV7K114oQ8CK/8WkvNxFKGzsA+j0QB+4KHRbMOBzHVX/Ly2FiaInIsPeosPSFLKiYKGUsoJY6W6CWdZBlHZeAvPCrdDflhKy/bFOl96kBXrGS0uUlhdC6sk16Jd8Xk6T1WvetSLw+7YsODDqVKLBOsKBeqJxAxMSFIUKENei/YTYH/ZjGqIbDGqBtMLeYLKgfsvsaw9K3+5/2E0Go1J9nPuHiyIc4u9KYtdXNVY3PimsXZIFI0wGAzR7XYhBFCvN+C5LsJg7LorvrecVAJIc09HlmXodrtl3JGNAFEKzhUp04V3p6hjpLVmD0/OTSmMl2aziTAMJ7wTVatcqcmJPTFhhOCidfmJO8szOdJcFGs4HMIPa2i3W3A9B4DOuTR8kjVJit5CFyQFy8srB0mmc5XIosQB96MaIisMkMXM+urfZDRMnqI4JhKP6yoZkyHMBeSqD0MR33elhDZcht5RAkIQRqMBugvziIZDfk6UgHQdhLUanxrTDAoSSaahzbhkQjFmacocGylYtbHwrhRFBQn54gHDdXxSDo2l8Qhax/x/UJDKhSEeR9/30Wi0kcQZup0hsmQEx+W+d3oLUEqh1Z6G43h8SsU4a6rwqCjp5BWpTRlO83wPKtejieII/cEAWZIyEdlx8ppEGv1+FzqNSzc3E85501V5eFPnhGeTpbnejGJZd4CLISoHUvnw/DAnUycgoyFgEPoepmdm0F55FKZWHJaXUMiwML8Xz/7PDgyHAygh4QiBKBqCTArfU/A9F81GC4500On3sG9+H4QQ8P2AORUx85wCP8TU1Aq+75LDAUEQIAh8UE4610ajn39Gv99DmiTQJstTQQHSHN4ZP0PFJmCghUaqMzbCScKRKi80GQMwIBJslJCBlAQpDBzF2kdaEzLDnpWwVmNvlMthDZbmH45DQPk6VOWUAapMAy8kA1gbKkSaZoiTDGnCnllOvVZQDpCmEdJUQzkBlOMCQnGmlmYl6DCooV534bsKxmSseRL12VMiCEIoJBnKjCYIDtslSQLXc+E6EkLkdbSCOqZWHIZGeyUGCTBMDEIlkSUsKeC4zP0L/DqajTaCoI5IG3Q6Pfh+DfVac8x7SyIoJVBvclkR5qxJwHBpgWg4gjYZ4iwFQaBWbyAIuR5RQTDlTXTsqapumlVDoLrZll4DKctEjKrhUOwTRLo41qHgkhRcjGKu8XcVm/m4sKZ0ilIUk1WZq+tx9YC6OAxVTS9efD2ciMGlQIrXq0ZJnvS8nxFSfA8InNKfe7yre191nBZ7Wap7ZxWTXv7JiEC1bb/fw0knv/nVGwa680/3MKeh8uDzhgMOAVTUKasxviAISm/EJEmIB9cpQhOEiZteTmaMreAila3M3hGijGMXG03hyeENmEqxunq9XhJRq8JGxecVrs1qleXFExBA7sbO0G43y1AJMJl+nGUZfI95AyIPV4mcYJvpDFmS5WqREnE8wmDYRRQNOY4PDYcEPOVCKgfScaAN84KCGseRTabLIm7F2FZPDAWxs+hTYaQJwRwYMmPjqlr0keXQCfV6vTTAHMXXoJTiMMBogDhhoao0iQDSObmQTz/aEDIyGCUxRM7YVxBo1up5ITcXruNCOeO0veKUZFIuhDgY9PPKwpRru2ikaQQSGo504TgBu/aHQ/T6C4jjAXy/hkatiSQZYhQNiruGMKhDSQc6HoBMilSnGA5H8IIQK2ZWQqoa0iSB0UOEQYjp6em8jEGu2EksoEdEHOf3QwRhncOXjgtjgDRJkGYxQBpEGeJoiE5nH4aDHkAE32GDezQaoNOZx2DQh+s58H0PaZwAhnkjjUYLQkr0ByNWv4WAVA5c10MSs2iiAJClKeKI08kL41UoCa2ZD9FsNbD6iCNw2OwqKOlhobMHzz77NDoLzyOJRyysRxIZCWREuTYSc1bSVOcuZgXPYQMDQE4eZTl75htx7ROe8zG0ZqVlbTLeXIyBoLHcQDV0CyLAkWg02zhqzVpMr5iDEAqjKMLze3ZxJe/8OdRpgixlQ9bkukmkCanmOj213JhPDWfXFRmFtRqX0igqQBeeXa55w8RKoBBhDACMvYsEgpQufK+GRqONdrsFrVPs3PkchqMu6+AIB44TQigfvt/E7OwcavUGev196HXnYXQKKQgmi6A1k+QhBFy/DYA9KTMzM/A8D91uF3v27EEUD9gDR1w5OQgb8IIWjAwQZ0AWR1DQkIoQxSNkmUbo17FixUrMzM5Buy4MKUjhAGA5fEMJRqMutCEEfhOBH8BzJLiOJqfXCoCLi+YhYUhZGs8sGOdACRfAOFRfPXwu5llU1/fCs1Ws41WDpVxbMQ7/vBD+leeB9wmH+1rZ+AtUjZAXkuoX2C+VZr9wE8SkAB5QMYRyYcOqcVH01XXd/Bw/STEYUxTGtfSq+2UV+3lMKn3kvowzjKrGzmAwwP878U2vPmOl0+lgamoKmzf/Ge1WG6z8Wp0wfBrmDT8tSVAAb4RpluYlxPvodDpQSqHZbDKpLIqQxaMyjdfzHPh+AN/3OVMGHEtPsywne8oy68V1XfhBAOmo0mjQRpflujmlbDwpuRT9uJx3MUGqD1XV+gYmJ27RvjAGirThKIoQJzFXms5DQr7nwZNMrC0yPbQANJmcjKogIKEzPknG8RBxMkSaxUiSCOlwBM/x0Gy20GxNwQ1CQEhkJhcgMnxSLzwUKk8rTtO0JPA5DqeSRnEEnenSk6KUhMnSCaZ6yR/KXdfFQ1ekOpdx6CgGpSwQJwRhNOzD6BRZGoMl/l1Il3+g2PM1GgyRRjFU7rb2ayE8x4UmTr9l8bm8sJ7joxbU4AUuPNdBZlLMz+9FEkdQjgCQQAoXQriIY+4XCYMkGUGSgud4GEY9RNEAymE1X88NkCYp4u4+RFEfjuvAD2uYnl6JWr0NCA/aGKTRAobDQZmtI6WC0cwLybSG63lMSFQ+wloTYb0Nx2WVZsdTgOBKygImz5BKMRh0Mez1IA1hNGQ12eFoAKMz9Ic9DId9rloOB0Iw+bNebyCKU05lNuyNEUJgNBwiTVIoqdCo1SClQr+7AJUO4UqBjDSSTEMoyZuL68DzanC8NggZkrQL0iMQZbnipwCkB+X6mJk9DNPtaU4PTlOkKcsJxKMuBsNeWZxQa5OHlgCjAVf4oOIAogCpACU5nEVGszdJa+Y6qLEqdeFCF46PwG/AD1pQboiwXkOtUYPvu8xribmmUTwawnNlqefD89ZhgnKWQUgJCAlNXJagXg/hKOZ3Fcq2xhj4HpNL01TDdX00GpxpWBj+RXhXuQIg5mu4TpiToTP0egvo9vfAUIx6vY3pmTkEQRvKqaPRXAEBB53eHswvPA9Aw/ccACmUMIiHAxitYUTImYD5MygrXglIApkUJsngOS5qYRON9iyCsA03rCNwXaTZCNGoj/6wl6eAu2jU2iwsyG5LOMpDq91m9VqRItMxRoMIJgvgeS48paCzGNGgj87CAtIkxtT0LGbnDofne6xinURI0qzUqXIUe2uCkA07gUkvxb8zFoRkg4U36XzzL6T7kdfvoUnuRvE7r7t64rWx14W9VRCT373YgGJDYFw9udpOClmGxKr9LsL8hkwZAitQKJ8bM65RVRo/YMkOYwyv0ULkisCLNFXyQ4BaRJJlY2cyVPWvUNpT+XeY3NgZjkZ4xzvWYWFhAe12+99+xr/CsuSs7N27FwBw1llvX+KeWFhYWFhYWLwY9Hq9V5exsmLFCgDAjh07/uMLt/jv0O12ceSRR+KZZ575j916Fv857PgvLez4Ly3s+C89Xso9IOL05dWrV//H37csjZUiJNJut+1EXWK0Wi17D5YQdvyXFnb8lxZ2/JceL/Ye/LeOhRdmy1hYWFhYWFhYHCKwxoqFhYWFhYXFIY1laaz4vo+vf/3r8H1/qbvyqoW9B0sLO/5LCzv+Sws7/kuPg30PlmXqsoWFhYWFhcWrB8vSs2JhYWFhYWHx6oE1ViwsLCwsLCwOaVhjxcLCwsLCwuKQhjVWLCwsLCwsLA5pWGPFwsLCwsLC4pDGsjRWfvCDH+Doo49GEAQ47bTTcO+99y51l5Y9rrrqKrz1rW9Fs9nEypUr8YEPfACPP/74RJsoirBp0ybMzMyg0Wjgwx/+MHbt2jXRZseOHTj33HNRq9WwcuVKXHLJJciy7GBeyisCV199NYQQuPjii8vX7Pi//Hj22WfxsY99DDMzMwjDECeccALuv//+8n0iwte+9jUcfvjhCMMQ69evx5NPPjnxGfv27cOGDRvQarUwNTWFT33qU+j3+wf7UpYdtNa44oorsHbtWoRhiNe97nX4xje+MVE8z47/gcVdd92F9773vVi9ejWEELj55psn3j9Q4/3www/j7W9/O4IgwJFHHolvfetbL72ztMxw/fXXk+d59POf/5weffRR+vSnP01TU1O0a9eupe7assbZZ59N11xzDW3fvp22bdtG73nPe2jNmjXU7/fLNhdccAEdeeSRtHnzZrr//vvpbW97G51++unl+1mW0fHHH0/r16+nBx98kG699VaanZ2lr3zlK0txScsW9957Lx199NH05je/mS666KLydTv+Ly/27dtHRx11FH384x+nrVu30lNPPUV//OMf6e9//3vZ5uqrr6Z2u00333wzPfTQQ/S+972P1q5dS6PRqGzz7ne/m97ylrfQPffcQ3/+85/p9a9/PZ133nlLcUnLCldeeSXNzMzQLbfcQk8//TTdcMMN1Gg06Lvf/W7Zxo7/gcWtt95Kl19+Od14440EgG666aaJ9w/EeHc6HVq1ahVt2LCBtm/fTtdddx2FYUg/+clPXlJfl52xcuqpp9KmTZvKv7XWtHr1arrqqquWsFevPOzevZsA0J133klERAsLC+S6Lt1www1lm7/+9a8EgLZs2UJEPPGllLRz586yzY9+9CNqtVoUx/HBvYBlil6vR8cccwzddttt9M53vrM0Vuz4v/y49NJL6cwzz/yX7xtjaG5ujr797W+Xry0sLJDv+3TdddcREdFjjz1GAOi+++4r2/z+978nIQQ9++yzL1/nXwE499xz6ZOf/OTEax/60Idow4YNRGTH/+XGYmPlQI33D3/4Q5qenp5Ygy699FI69thjX1L/llUYKEkSPPDAA1i/fn35mpQS69evx5YtW5awZ688dDodAOMK1w888ADSNJ0Y++OOOw5r1qwpx37Lli044YQTsGrVqrLN2WefjW63i0cfffQg9n75YtOmTTj33HMnxhmw438w8Nvf/hannHIKPvKRj2DlypU48cQT8bOf/ax8/+mnn8bOnTsn7kG73cZpp502cQ+mpqZwyimnlG3Wr18PKSW2bt168C5mGeL000/H5s2b8cQTTwAAHnroIdx9990455xzANjxP9g4UOO9ZcsWvOMd74DneWWbs88+G48//jjm5+dfdH+WVdXlPXv2QGs9sRgDwKpVq/C3v/1tiXr1yoMxBhdffDHOOOMMHH/88QCAnTt3wvM8TE1NTbRdtWoVdu7cWbZ5oXtTvGfx73H99dfjL3/5C+6777793rPj//Ljqaeewo9+9CN88YtfxFe/+lXcd999+PznPw/P87Bx48ZyDF9ojKv3YOXKlRPvO46DFStW2Hvwf+Cyyy5Dt9vFcccdB6UUtNa48sorsWHDBgCw43+QcaDGe+fOnVi7du1+n1G8Nz09/aL6s6yMFYuDg02bNmH79u24++67l7orrxo888wzuOiii3DbbbchCIKl7s6rEsYYnHLKKfjmN78JADjxxBOxfft2/PjHP8bGjRuXuHevfPz617/Gtddei1/96ld405vehG3btuHiiy/G6tWr7fhbLK9soNnZWSil9suA2LVrF+bm5paoV68sXHjhhbjllltwxx134DWveU35+tzcHJIkwcLCwkT76tjPzc294L0p3rP413jggQewe/dunHTSSXAcB47j4M4778T3vvc9OI6DVatW2fF/mXH44YfjjW9848Rrb3jDG7Bjxw4A4zH8d+vP3Nwcdu/ePfF+lmXYt2+fvQf/By655BJcdtll+OhHP4oTTjgB559/Pr7whS/gqquuAmDH/2DjQI33gVqXlpWx4nkeTj75ZGzevLl8zRiDzZs3Y926dUvYs+UPIsKFF16Im266Cbfffvt+bruTTz4ZrutOjP3jjz+OHTt2lGO/bt06PPLIIxOT97bbbkOr1dpvE7CYxFlnnYVHHnkE27ZtK39OOeUUbNiwofzdjv/LizPOOGO/dP0nnngCRx11FABg7dq1mJubm7gH3W4XW7dunbgHCwsLeOCBB8o2t99+O4wxOO200w7CVSxfDIdDSDm5JSmlYIwBYMf/YONAjfe6detw1113IU3Tss1tt92GY4899kWHgAAsz9Rl3/fpF7/4BT322GP0mc98hqampiYyICxeOj772c9Su92mP/3pT/Tcc8+VP8PhsGxzwQUX0Jo1a+j222+n+++/n9atW0fr1q0r3y9SZ9/1rnfRtm3b6A9/+AMddthhNnX2P0Q1G4jIjv/LjXvvvZccx6Err7ySnnzySbr22mupVqvRL3/5y7LN1VdfTVNTU/Sb3/yGHn74YXr/+9//gqmcJ554Im3dupXuvvtuOuaYY2zq7IvAxo0b6YgjjihTl2+88UaanZ2lL3/5y2UbO/4HFr1ejx588EF68MEHCQB95zvfoQcffJD++c9/EtGBGe+FhQVatWoVnX/++bR9+3a6/vrrqVarvfJTl4mIvv/979OaNWvI8zw69dRT6Z577lnqLi17AHjBn2uuuaZsMxqN6HOf+xxNT09TrVajD37wg/Tcc89NfM4//vEPOueccygMQ5qdnaUvfelLlKbpQb6aVwYWGyt2/F9+/O53v6Pjjz+efN+n4447jn76059OvG+MoSuuuIJWrVpFvu/TWWedRY8//vhEm71799J5551HjUaDWq0WfeITn6Ber3cwL2NZotvt0kUXXURr1qyhIAjota99LV1++eUTKa92/A8s7rjjjhdc9zdu3EhEB268H3roITrzzDPJ93064ogj6Oqrr37JfRVEFXlACwsLCwsLC4tDDMuKs2JhYWFhYWHx6oM1ViwsLCwsLCwOaVhjxcLCwsLCwuKQhjVWLCwsLCwsLA5pWGPFwsLCwsLC4pCGNVYsLCwsLCwsDmlYY8XCwsLCwsLikIY1ViwsLCwsLCwOaVhjxcLCwsLCwuKQhjVWLCwsLCwsLA5pWGPFwsLCwsLC4pDG/wcYUGnyH7dsjAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image_index = 42\n", "\n", "plt.imshow(stimuli.stimuli[image_index])\n", "\n", "fixation_indices = fixations.n == image_index\n", "plt.scatter(fixations.x[fixation_indices], fixations.y[fixation_indices], 20, 'red', alpha=0.5)\n", "\n", "plt.title(\"Fixations on a given image\");" ] }, { "cell_type": "markdown", "id": "a365fb7b-39e2-42c6-89bb-a75bc536b931", "metadata": {}, "source": [ "Just like `Stimuli`, `Fixations` support `len`, slicing and indexing. For example, the last cell could have\n", "been written more elegantly as:" ] }, { "cell_type": "code", "execution_count": 6, "id": "0c9d8e6c-dccc-4c28-bb76-2cea4b9d4df7", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAGzCAYAAADuc1ebAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9ebRlWV3Y/znzOXce3zxUvZqrq7obGppZWmkGoxEQlZgYRaNhKWgiy4WQtQySn5FEY+LAEKIRNUuXCMaI0aiIiNDQQM/dNQ9vHu6783DmYf/+uK9e1+uu7q6Ghq5u7qfX6Xp3333O2Wfffc7+nu+0JSGEYMSIESNGjBgx4gZFfrYbMGLEiBEjRowY8WSMhJURI0aMGDFixA3NSFgZMWLEiBEjRtzQjISVESNGjBgxYsQNzUhYGTFixIgRI0bc0IyElREjRowYMWLEDc1IWBkxYsSIESNG3NCMhJURI0aMGDFixA3NSFgZMWLEiBEjRtzQjISVEd+SLC0tIUkSv/d7v/esnF+SJH7xF3/xWTn3txo3el//4i/+IpIkPdvNGDHihmYkrIx4XvJ7v/d7SJJ0ze0973nPN6UNf/VXf3VDT5IjRowY8VxBGq0NNOL5yO/93u/xoz/6o/yH//Af2L9//57vTpw4wS233ILv+2iahqIo35A2vPOd7+RDH/oQ17rFPM9DVVVUVf2GnHvEo9zofR1FEVEUYZrms92UESNuWG7Mu3fEiGeI7/zO7+RFL3rRNb97NieH0cT0zeNG7+sbWZAaMeJGYWQGGvEtyWN9Vra3t6lWq9xxxx17NCEXL14knU7z1re+dbfs85//PN///d/P3NwchmEwOzvLz/7sz+K67m6dt73tbXzoQx8C2GOCusK1/Cjuv/9+vvM7v5NcLkcmk+E1r3kNd9999546V8xbd911F+9617uoVquk02ne/OY3U6/X99S95557eP3rX0+lUsGyLPbv38+P/diPXVf/fPjDH+amm27CMAympqZ4xzveQafT2VPnjjvu4MSJE5w+fZpv//ZvJ5VKMT09za/8yq9c1zk+9rGP8R3f8R2MjY1hGAbHjx/nIx/5yHXtC/CJT3yC48ePY5omJ06c4M/+7M9429vexr59+/bUu7qvP/nJTyJJEp/73Oced7yPfvSjSJLEI488slt29uxZvu/7vo9SqYRpmrzoRS/iU5/61J79ns5vci2u5bMiSRLvfOc7d6/Rsixe9rKX8fDDD++29eDBg5imyR133MHS0tKe/a9njD7dfkyShF//9V/npptuwjRNxsfHefvb30673X7Kaxwx4utlJM6PeF7T7XZpNBp7yiqVyuPqjY2N8ZGPfITv//7v57d+67f4mZ/5GZIk4W1vexvZbJYPf/jDu3U/8YlP4DgOP/mTP0m5XOYrX/kKv/Vbv8Xa2hqf+MQnAHj729/OxsYGn/70p/lf/+t/PWU7T506xate9SpyuRzvfve70TSNj370o9xxxx187nOf4yUvecme+j/90z9NsVjkfe97H0tLS/z6r/8673znO/n4xz8ODIWv173udVSrVd7znvdQKBRYWlrif//v//2UbfnFX/xF3v/+93PnnXfykz/5k5w7d46PfOQjfPWrX+Wuu+5C07Tduu12mze84Q187/d+Lz/wAz/AJz/5SX7+53+ekydP8p3f+Z1Pep6PfOQj3HTTTXzP93wPqqryF3/xF/zUT/0USZLwjne840n3/cu//Eve+ta3cvLkST7wgQ/Qbrf5V//qXzE9Pf2k+33Xd30XmUyGP/mTP+HVr371nu8+/vGPc9NNN3HixAlg+Ju84hWvYHp6mve85z2k02n+5E/+hDe96U386Z/+KW9+85v37P9Uv8nT5fOf/zyf+tSndvviAx/4AN/93d/Nu9/9bj784Q/zUz/1U7TbbX7lV36FH/uxH+Pv//7vd/e9njH6dPvx7W9/+6559Wd+5mdYXFzkgx/8IPfff//jxsWIEc84YsSI5yEf+9jHBHDNTQghFhcXBSA+9rGP7dnvB3/wB0UqlRLnz58Xv/qrvyoA8X/+z//ZU8dxnMed7wMf+ICQJEksLy/vlr3jHe8QT3SLAeJ973vf7uc3velNQtd1cenSpd2yjY0Nkc1mxbd927c97rruvPNOkSTJbvnP/uzPCkVRRKfTEUII8Wd/9mcCEF/96lefoqf2sr29LXRdF6973etEHMe75R/84AcFIH73d393t+zVr361AMQf/MEf7Jb5vi8mJibEW97ylqc817X68fWvf71YWFh4yn1PnjwpZmZmRL/f3y37h3/4BwGI+fn5PXUf29c/+IM/KMbGxkQURbtlm5ubQpZl8R/+w3/YLXvNa14jTp48KTzP2y1LkkS8/OUvF4cOHdotu97f5Il43/ve97hxAgjDMMTi4uJu2Uc/+lEBiImJCdHr9XbL3/ve9wpgT93rHaPX24+f//znBSD+8A//cM8x//qv//qa5SNGPNOMzEAjntd86EMf4tOf/vSe7cn44Ac/SD6f5/u+7/v4hV/4Bf7lv/yXvPGNb9xTx7Ks3b9t26bRaPDyl78cIQT333//025jHMf87d/+LW9605tYWFjYLZ+cnOSf//N/zhe+8AV6vd6eff71v/7Xe0wHr3rVq4jjmOXlZQAKhQIA//f//l/CMLzutvzd3/0dQRDwb//tv0WWH308/MRP/AS5XI6//Mu/3FM/k8nwQz/0Q7ufdV3n9ttv5/Lly095rqv78YoG7NWvfjWXL1+m2+0+4X4bGxs8/PDD/PAP/zCZTGa3/NWvfjUnT558yvO+9a1vZXt7m3/4h3/YLfvkJz9JkiS75r5Wq8Xf//3f8wM/8AP0+30ajQaNRoNms8nrX/96Lly4wPr6+p7jPtVv8nR5zWtes8cUc0W79pa3vIVsNvu48qv7/HrG6NPpx0984hPk83le+9rX7vZFo9HgtttuI5PJ8NnPfvZrusYRI66XkbAy4nnN7bffzp133rlnezJKpRK/+Zu/yUMPPUQ+n+c3f/M3H1dnZWWFt73tbZRKJTKZDNVqddek8GST7BNRr9dxHIcjR4487rtjx46RJAmrq6t7yufm5vZ8LhaLALv+A69+9at5y1vewvvf/34qlQpvfOMb+djHPobv+0/alisT62Pbous6CwsLj5t4Z2ZmHudvUSwWr8uP4a677uLOO+8knU5TKBSoVqv8u3/374An78crbTh48ODjvrtW2WN5wxveQD6f32Oe+fjHP86tt97K4cOHgaGvkhCCX/iFX6Bare7Z3ve+9wFDU9vVPNVv8nR57PHy+TwAs7Oz1yy/+jzXM0afTj9euHCBbrfL2NjY4/pjMBg8ri9GjHimGfmsjBjxGP7mb/4GGD7819bWdrUUMNSCvPa1r6XVavHzP//zHD16lHQ6zfr6Om9729tIkuSb0sYnCrcWO87BkiTxyU9+krvvvpu/+Iu/4G/+5m/4sR/7MX7t136Nu+++e8+b9DeyHU/EpUuXeM1rXsPRo0f5r//1vzI7O4uu6/zVX/0V/+2//bdvaD8ahsGb3vQm/uzP/owPf/jD1Go17rrrLn75l395t86V8//cz/0cr3/96695nMdO6F9rXzwRT3S8pzrPN2KMJknC2NgYf/iHf3jN76vV6tM+5ogRT4eRsDJixFX89V//Nb/zO7/Du9/9bv7wD/+QH/mRH+HLX/7ybmjpww8/zPnz5/n93/99fviHf3h3v2uZl643K2m1WiWVSnHu3LnHfXf27FlkWX7c2/T18tKXvpSXvvSl/Mf/+B/5oz/6I/7Fv/gX/PEf/zE//uM/fs368/PzAJw7d26PSSoIAhYXF59SM3W9/MVf/AW+7/OpT31qjwbheswJV9p48eLFx313rbJr8da3vpXf//3f5zOf+QxnzpxBCLEn4uvKtWua9oxd8zeL6x2jT6cfDxw4wN/93d/xile8Yo+JacSIbxYjM9CIETt0Oh1+/Md/nNtvv51f/uVf5nd+53e477779rxxX3mrvfptWQjBb/zGbzzueOl0eve4T4aiKLzuda/jz//8z/eEoNZqNf7oj/6IV77yleRyuad1Le12+3Fv9LfeeivAk5qC7rzzTnRd5zd/8zf37P8//+f/pNvt8l3f9V1Pqx1PxLX6sdvt8rGPfewp952amuLEiRP8wR/8AYPBYLf8c5/73G5o71Nx5513UiqV+PjHP87HP/5xbr/99j3JA8fGxrjjjjv46Ec/yubm5uP2v56Q5GeL6x2jT6cff+AHfoA4jvn//r//73Hni6LoKcf4iBFfLyPNyogRO/ybf/NvaDab/N3f/R2KovCGN7yBH//xH+eXfumXeOMb38gtt9zC0aNHOXDgAD/3cz/H+vo6uVyOP/3TP72mX8Jtt90GwM/8zM/w+te/HkVR+Gf/7J9d89y/9Eu/xKc//Wle+cpX8lM/9VOoqspHP/pRfN+/7rwlV/P7v//7fPjDH+bNb34zBw4coN/v89u//dvkcjn+yT/5J0+4X7Va5b3vfS/vf//7ecMb3sD3fM/3cO7cOT784Q/z4he/eI8z7dfD6173OnRd55/+03/K29/+dgaDAb/927/N2NjYNYWDx/LLv/zLvPGNb+QVr3gFP/qjP0q73eaDH/wgJ06c2DPxPhGapvG93/u9/PEf/zG2bfNf/st/eVydD33oQ7zyla/k5MmT/MRP/AQLCwvUajW+9KUvsba2xoMPPvg1Xfs3mqczRq+3H1/96lfz9re/nQ984AM88MADvO51r0PTNC5cuMAnPvEJfuM3foPv+77v+2Ze5ohvNZ6FCKQRI77hXAknfaLQ3ceGLv/5n/+5AMSv/dqv7anX6/XE/Py8uOWWW0QQBEIIIU6fPi3uvPNOkclkRKVSET/xEz8hHnzwwceFQkdRJH76p39aVKtVIUnSnvBUHhNOK4QQ9913n3j9618vMpmMSKVS4tu//dvFF7/4xeu6rs9+9rMCEJ/97Gd3j/WDP/iDYm5uThiGIcbGxsR3f/d3i3vuuee6+u+DH/ygOHr0qNA0TYyPj4uf/MmfFO12e0+dV7/61eKmm2563L4/8iM/8rjw4WvxqU99Stx8883CNE2xb98+8Z//838Wv/u7v/u4MNwn4o//+I/F0aNHhWEY4sSJE+JTn/qUeMtb3iKOHj26p961+loIIT796U8LQEiSJFZXV695jkuXLokf/uEfFhMTE0LTNDE9PS2++7u/W3zyk5/crXO9v8kT8UShy+94xzv2lF0Zs7/6q796zfN84hOf2C273jEqxPX3oxBC/I//8T/EbbfdJizLEtlsVpw8eVK8+93vFhsbG096jSNGfL2M1gYaMWLE84Zbb72VarX6lCHqI56cUT+OuNEY+ayMGDHiOUcYhkRRtKfsH/7hH3jwwQe54447np1GPQcZ9eOI5wojzcqIESOecywtLXHnnXfyQz/0Q0xNTXH27Fn++3//7+TzeR555BHK5fKz3cTnBKN+HPFcYeRgO2LEiOccxWKR2267jd/5nd+hXq+TTqf5ru/6Lv7Tf/pPown2aTDqxxHPFZ5VzcqHPvQhfvVXf5WtrS1uueUWfuu3fovbb7/92WrOiBEjRowYMeIG5FnzWfn4xz/Ou971Lt73vvdx3333ccstt/D6179+lLZ5xIgRI0aMGLGHZ02z8pKXvIQXv/jFfPCDHwSG6ZxnZ2f56Z/+ad7znvc8G00aMWLEiBEjRtyAPCs+K0EQcO+99/Le9753t0yWZe68806+9KUvPa6+7/t7sm4mSUKr1aJcLl93SvMRI0aMGDFixDcfIQT9fp+pqak9q7k/HZ4VYaXRaBDHMePj43vKx8fHOXv27OPqf+ADH+D973//N6t5I0aMGDFixIhnmNXVVWZmZr6mfZ8T0UDvfe97ede73rX7udvtMjc3x+lLq2SzT2/NlCdipKAZceORwFXj8uloER+7sm4cxwCoqookSQghkCSJOI6JwpAgCEhn0kiSTJIkxHGEbdt4rotpWaRMAy8IUGUFSVaQJJl2u4VpmYSBTxgFBGHA3V/+EpIGnXYNQ00Y9BpYpkar20WWdWzXwTI01tfqzM4exA8Fc3NzbG2v4vp9TFVjrFSl0+3Q7Q8oVycxjAy6mSGfK+P6A/q9PrbtMDM5TaGQ4eGH7kfTJNrdJidO3sLs3H7arRbdXp0oGdDv95iZOciLbr2d5aUlCoUiFy5copAvEYWCQr6ILMtIckJ/0AYiatubjI9PMDtzkF7HpVwep1ZfZ3VtjbnZOS4tnqLT2WB5eZF2vU86m+emW48DMvfeez+vec3rqJbHKRTLrK9uUGuuk81ncJ2QKApJRIBpGkxPHSBlluk0mhw+chhNlTh16gHq26u02jUa9XVkRSKOdYIwwdRTIGQKxTyqJpCVmGazxc0338bpM2fQ9RSzc/uZnJrl1Olz3HLLC5BjiYMHDqFqGrIESPLuWHoqLwCRSChAHAtkFRzXJpESFpcuo2kqQeDj+wFB4GMYBhsbGywsLNDpdshlc0yMTRB4PvVGkzCKGPQH7F/Yz8z0NLKiIaGyZ5A/A1x9Sc/Ec/2b4SjxXJh/vp5+6Pd7nFyYJZvNfs3HeFaElUqlgqIo1Gq1PeW1Wo2JiYnH1TcMA8MwHleezeae9gJvT8RzYbCM+FYjecLn+FMJLkmSXHMiunqRuytCSxRFxHGMosgoylCY6XY7KIqCoqrIskyChOO4pFIppESiWCrQ7/cpFotsbq7T7XXQdZVXvOJlpHIGDz54Lxurl5FlBctMc2Jmls/f9SUc12b//DSFQp5cpsixYy+gUM7zl/9vmTDwyFsGgecgkoTp6Vle/opvx/YC/vELd2Ok8xQKRYIgpNFscejwIY4ePoimy/R7LeRViOKIXK5AIV9ic0ulN9gmDCM0TaM/sKlUxuj1erRabWRJJZ/Pc+TYAeqNBufPn8I0FZaWL4Ik2NoOCcOI/fNH0Aw4dvwgcRLQGzSxLI1GyycWAX4cMFksUCgUaLXaFPJ5LNPi6NFjnDt3EVlRkGSYmp6gttViMOgTRjC/f46L5xd5+UsWOHrgMIoi2K6vc3nxLO12jcmpCkGUp1weY2W5jqYrLOw/wMrKMkHoUG81GBsvMTE5RS5XZW42otPtI8kaiqoxNT2NYRqETohhmhi6DoBuGNcUVq7+e1egRUZKdsaLkhBEPrEIWV69zPh4lVQqTb/VZf/8AtlcDlU1GRuboVAYJwxCTDPP1GSJ2bkQVR0KuA8+dD+TE2Pk83kkyXzGTfkjYeUbwzPRD1/Pb/2sRAPpus5tt93GZz7zmd2yJEn4zGc+w8te9rJno0kjRtzQCCF2t+vhykPhioblyuRzRYi5olXxfR9FUdA0lWazRafTIQgCarUamqZhGga2bdNoNEinUkRRRLlcQlUUFFVBliUy2TTTM5MkIuKhh+/jnnu/jCQJdF1nfn4/mWwOWVIp5AtMTU6Ry+bwXY92u8W5s2dwBn1e9tKXks1kGNgOFy8tsry8jmVmuP+BB1FklXQqxdhYiaPHDpMvZDEtg0azzsXLF3CcAesba1SqJXq9DrXaJtlsjkwmx+ZmjUuXLvLgg/eztr7K+fPniOOIEyeOs1Vbp92ps7a+zMbGMrbdZnnpPIN+kyRysPtNet06G+uL9Lt1up0areYmhUIGVQXHG4AsyOYzTE9Pk8sU6HVswiBBV00a200810OVFRCC9fU1BAlJElNvbCNEgmWaOI6NaRi49oDLl87R77cJY5/tepNCaYLNzTa+H5MkEvVGg3Q2Rc/uUalWiJOYYrGCLKlEkWD//gXuuec+trcbnDhxEkVWaLVbCCGQFRlV055y7IRhSBzHSBJIJMRxiCCi2+uysbnK2XOnGB+v0u12cB2Xm46fZHZuH7puccstt6KqOtXqBJPjk7QaXTRVI5XKoOsW1eo4R4/eRDqTRTzDGpURz2+eNTPQu971Ln7kR36EF73oRdx+++38+q//OrZt86M/+qPPVpNGjHje8URvMo8tlmWZYrGA47iEQUC1WkHXNNY7HTRNY2pqCl3XCcMQWZGJowRN07BMkzhOsba+zOXLF+l0WoTCo9drUykUaTT6HD18mPsffAABKLLCWGWC9VQdWY4Igg533fX33PEd30Y6leXSRh1F0tANlWarx4tf/FJOPXKKdquO3RvjwQfrbGxsUC7n0TSZS5cvEAY2SRIgRMji0gU03UIkUKkWmJqa4tz5U9x88600m5vIKJw7P0yPkMtb9Hptzp8/SzaXwdAlluobaIaE6/qk01kkKUTTwPcHtDvrxJGL6/SQFUEQuNhOH8cBx7NZW1un02mTJCGra5dYXb3EwLbRdZ2B18UNc3RaNi984QtIhMdg0COTTdNqtZCEoFwsIESE7zsoisoLXvASwlCitukwMzuO77tkMhaVSolLl89x7NhBkASmaTI/P08ul2NxeYWbb74Z3/eJo4RCvkgpVyYIAmRZRtc1NE1/ijEDSRIjS5AIQSJCuu0uzXaTWm2TmBhJhlKpTL/nEoYJqqJTyJkEYUyhUMQ0TBBgD3zW17eYnZ1Ekoa/fyadRZV1JJRviEbhRtRS3Ihteq7xrAkrb33rW6nX6/z7f//v2dra4tZbb+Wv//qvH+d0O2LEtzRPpEgRPKWpX5blJ9TIxPFQw6Lr+s5btIQQYFkWQRDguh7NZoswDNna2mJiYnxoFlIUEBKyJhMGIRcuXeTMmYdIpQ16vTaZbIpcqQJiin67R9v1WFpaJpVKUx6rMOj3cQY+42NVeoMeyAqFQhpVAVXWOXDgKIGfkEplSRI4cPAQpmUSxy7dVg1JU0mSgCD0OHX6QU7edJzllW0q1SKyIhifKFOvb9Fqdti/MIfttLAsjaXlC5SLFSbGxnFcj82NLdLpLBOTU/T6bWRF8NCDD2DbTSanqgwGAzKZFEHg4Ps2tjOgXM5z8NB+NjdrfOGuf0TRY4RIOHrsGJVqiUG3QxB0cZwO9cYikiRQVQVFGBimiqapVKoVEpGQJAnV6hieC56d4HkuYTBgbWUZz3ORFAXQaTUHZLJVLi+eplQqoBkS7W6Lg4cPsr6xSS6Xw3VqaOop5ubnOXRogcWlVfq2R78/QFU1qsUySZIgSRJhGCHLw9/xWoKsEII4TvB9D13XkWXodepEccTFi+dYW1+jMlZhfHISz/NoNrtMTYYkiYSsKliWgSJLyLJCEIRMTo3j2C6JEEhCQlZkSoUSAhlJkhFiNJGPuD6eVQfbd77znbzzne985g98PZryx9wgj9Wuj26gEc8+jx2kV4qk4Z/XIbBcMf/AFT+VvSalIAh2fVaSJEFRFHRdYWV1EQmZmZk5DN1keXmNubmZoWlAVjFMjfXaCq3WNn2nz4FD+1lZW6bRbGFYEo3tbXRFo1ytcO7sWaIkIt5KmJudpVQpc9cX72J2fo52p8fMTJav3vNl8rksrY7N2MQUvhcyPT3L0tIqlqWRTmVodxrM7Jul2eyQzRTwpAGLy4tsba1TLORRNQWEwvZ2jXKpyvLyZWQlpte1UWST/fMF+v0eqiphmCpra0tousrhQ0dpNlu4ro0QCacefphjRw+T0nWcQY+23qBe77C6LhHFCZtbNWQVNE2n3elTsR2ESBBSTLvbIJfLEMUuA7vHzOw0jfomQSyzf6HA5uYqlUqJanWcVrNLGCR02g7N3gy9dp0Ll88Ti5hsOsfa+haanqfd6TI5MUUYurRaTXp9iUqlQhBEKJpFFEYE0YD1zWVcNyCMBBIRrtNjamqWfD6H7wWYho6iKESx2BESpEefc8lOGTLdTpe19RUymTTplEGtts7k5BS9bgfHscnlDqDIKp12l9nZObLZHEmSoKoqQoAfROi6TBBG6IqKuSPACCEhCQkkBUmASED6FllK9+vx97gR5qIbYQXB5+1QkZ9kGzHiuYG0u0k7b6ISMgjpugTyq/1THhVYhg8/SZJQlKFZQJalYYRKEqPrGn7gE0U+qXSKwcBm//79jI9NEIYRiYix3QGnzz5Eo73M6uZ5Bk6XtfUt0tkCMzMzLF24TD6dwR70SUSMnjJxfR8/jOj0+rS6XfYtHGFs7AAnbnoZBxZuJmVl2dxaZTBos7a6Qj6fIxEJjWaTRqtDvlTFCRIazQ7V6iSWUaBSmWZ9Y5MkiVlaXgGhMzd7mCiMiZMQw1CxBx5xaKApeRQphe9EbG/V6bRaICIKuTR2v83S5YtomkkUJGioJF5A4gUYikY+kwVJoj3ooxgydtDDCzzW12uYehYRS2iqztLyGpqeRjfSeIFAoAMmxdIUnhfQbGxRrRbpdboU81UKuQrTk7OMjRU5u3Q/y7ULaKZCr9tnc7OGokn4wmb2wARHjxxFJDHHjh0ml0/R6jRRNB3TzKJoCmHs4Hk9XLfH/NwECJ8L507Rbm4RRQG5/NBHRCChaSqKIg8F1wRkAYosoUgyIhLYvT52v8vS4jmazRqWZSHLMqZlceTIMVRVp9PpUipVKOTz9AYd+nafZquNH4QkYjhmNU1HllU0VUWRZRRJQkpAETIKw8/PdCTQiOcvo7l7xIjnMVdU/YHvE4YhkjQ0ASEEgR+SxMOID03V0XWTJBakrAzTU/NMTc4wNTVFHMfEcUiz1aDZbBB4fc6ffYgo6JO2VExd4sypB+i16wSejWHorK+v0+8P2N6uo6oGppnGstLYtk+z2ec7vuM7KRTGUeQ0llVibvYo2cwYYZhQqeQ5e/YRzp8/Ta/f4uChBXr9Lul0ikKhwB13fAcveclLOHjwINl0hjAICH0f13EwDJ1MJo0sw2DQR9c1KpUyc3OzeJ43NJu4LrlcjlKpRBzHLC0tsbW1Sa/bZdAfEMYxnh+wsbnFdqNBs9miVCzS7/WHWqgoImVZhGFAKpWiUMijaSq2bSOEYHx8krHqOLKsEYYx/X6fKByG7ToDh0a9SRJJZNNFFNlgYmyaVnObe+/7Mtvbm9iOTaVSQhBhDzrkMjqFvE7gd9neXGbf7DRx6DNRrVAplhmrTLKyvIHrRaiqQSqVwzAyjI9Ps7a6ycWLF4miCFmWkBAEQbhjBgQkSBJBHA0FlyQRjI+PcezoMcbHx6nXayQiZLu+iaarxHGEaZp4XkjKypJKZ9jc3KK+vc2DDz2IbTt4nkccJ6iqjK7LKKpMnLDjUHuVcHIDvK2PeO7wnMizMmLEiKfHUMUv7Qoruq7T7fXQNA1NUxGJxDCRpIQkqUhSgoRMEERYlkmlMk6SCGRZJooiattbRHFALpfG913yOYMg9PDdiE6nSaU0xubWBnYfCvkCmqqQJIAko5sqvhchKQpHjxxlaXEJ3Upx8dIlXvnKb2dtfYOpqXGqY7M0Wi3q9Q0s0wIRsra6jGPfTMrSaXdizp09S8oocGD/UVRZ5eCBA1xe9DA0jSDw+OIX/pHpmUlUTcb3PZIkoVLOYxoqnXabpcYWiirheR7j42OkUhZnz57Hth10TSabzVKrdUkkhQiIgpD19XVsNySMQkQCpmGSTWeIwwgjqzEzOUmSxJimSb/fJ58voigKruuTJIL19XUMwyCdSjE/N0/KLOK5ATIa0xMzbGyusXTpIoE3wPNtgjDEcW06nRaxEDh2i023Ri6rkMsaaApMjY0TeB5kBJKQ0bQM1fIUt9z8QmRZw+7FTE5OE4QR6XQaVVWI4pAojFFVDXacW5NkqFWJI0EUJXieh5WyyGRNllYusr29Radbx7ZdBraH78ekM3kW9h0gTgSe53P8+HE6nQ4iSYiikDiOkGXQNI1Y0dA0BVWRh6YmaagV3LFmjrgOrmWCuRFMQ99sRpqVJ0CIvduIETcaV4ciP3aDvZFAgmG+Ivkqk5CqDm9/RZGxLAvL0vE8h1arjarKOG6fIPRotev4gUMU+Vw4f57LF87jDnogQpYWL1DIpel2GijEmIaK49gUiyV03QAUAj/C9yNSVg7fD4nikM/8/d+iaAm17RUkOcQPbUxTw7a7BL6NECGmqTE+VuEfP/cPuI7N0uXLuAOX9dV17v7S3Tz80ENsbm4iCdA1lW67SatZw7G7pC2dtKVhaDKaKqGrEo7dZ3JyinK5jBCCra0tarUaSRyTSlkEvk8cx4yNT5IrlhGouH5IOp1BBizdIvRCUrrFoNdnvFrF1DVMQ+PcmdMkScL8/D4KhRK97oBUKoum6UiShOe59Htd4ijEMDQ8x6HbbiMDnmszPlakVMwNfV8QKIpCEgcUsmkatXWiaMBYpcB2bZNOq002nWV9ZZ1sJkO71WXf3GEQOrYdYBpZjh29iX7fYXZmjlKxiMQw3fn5C+dpd9pEcUiSCGA4XhRlKLzKMqiKTBTHHDl8hAMHD6AbKkgJ+XwOVZU5f+EiyysrlMtVJiYmcByXQqHI5OQkzWadVMrA913Onz9Hu9Om2WqTJAkJAiEJhATJ9Vkyn5c8/l79Vu2Jp8dIWBkx4jmIEJCIZFdgAR4juCR7hBVFGUYGBeHQBBBGPpAgyQlRHBDFAWE43LZqGyyvXGRl5QK9Xp1szmBufpJ9++Y4duwYhmGytr7OxQuXCINoqMFBwjQtJiYmSKfTdNodtjZrTE5OUSmPMzuzn5tPvoDZ2f2srK6gqBIDp8vm9jKdfo3eoE6nXydJAjzPod1q4rkunuuiazobG1tMTkyyb98CqqLhOR6GptNrd/Bch8XFS6RTJieOH2VirIJIAhr1TXIZk2I+g67KFPJZyuUSIhG4rsvs7OwwrHgwACSmp6bwfY+Z2TnCRNB3PIIoJpfLYxoGhw4eptfuo0gKlVKJ2ekp5mamWVtZptVq0Ot1cV2PKBSUSmNk0jm6nR75fG5HexEgywmXL59DkkPuve+LnLvwIFtbi9j9DkkUDh1U/YhjR4+jKRoyAl1TaXd6rG/WSKfz1LbbqKqFplt0Oj3mZvejqRa5bInNjW16vQGSrBDHCb1eHyTBdn2bRqOO4wxot1vEUYTrOgyFleF4UlWVdDpFFMd4rkcYDfOt1LZqCCG49dabUVWVdrvBmbOnaLbqw6ixRmuYtFMSbG5tsL6xjmFqmJZGf9BHAH4QkCTDkGchCYb/AdLohRCu+JE99fdPVe8bxY2gyRkJKyNGPAe54iSbJMlOqLGLJDH0S5AkpMeEWVz5PDRPuGxsrNEfdImTkDgJiGIf2+0jKQmqJugNthk4dVbWznH2/IOcPvMwYRSQzmSZmJyhUp0kiiWmZ/ezb+EwuXwFL0hwvYg4Hk5E4+MT+F5It9tHkhTq9RYXzi/xipffycteegegMjk5xebWJpIMtmszNz+HSASBH6ApGuPVCSbGp7D7LoEXc/uLXsIrXv5KZqZnWF9bx3VskiRmcmIcVVHptBvEoU/g2qQtk1Zjm3IxRxwNw6Vd10XVhll5V1dXCYKQYqlEKpUik0mzsLDA4tIyzVaXl7ziVaiaied51La2GHR6kAgKuTxJGBOHEVISkzJ1ZqeneMlLXsLC/gUmJ6Yp5EtsbzdwHHfow5GEhKFHr9dieqrK2vpFcnmVL939t2zVLtFp1PFdnyQS6HqKxZV10ulhuvp8rkAQSszNH8G0SqQzFfxIRkga2XyJdrfH+vomlcoY6XSGWm2YR2ZmehoQnDlzmuXlJZrNOqVSCdM0UBSZRCS7yzBcQQjY2Njkri/eRW1ri1OPnGJmZo7x8UkkSeK1d74GRYEo9glDm3a7iaqqbG9vMzE+zvz8HJ1Oi6Xly6iKjJVOkYiEja1t/CAkitkVUobhbGLPub8VeLYFj+cqI5+VESOegwgBkiwNzQVXUuvvPPuvlT/jSsiyqsh0PRfPd1lZ7TA2Vt1ZCyih0+ngeR6ptIqqx3S62ziOQ7PVpVQYwx54FPJVLpw/ix/2kVWDKIZ6o4OsGszOLSBEQKu1CcDx4ws8cuo8G5t1bnvhizly6Cif+8cvcuKmF1Eu56lW5qhUZqhW2wSBwDIzZEyTTX2NQCQoskoSCzrtHi98wYtYW1tjbXWdcinhyOGjiNjHC7YRwqaQq7C2usb05DiB5zA7N8WFC+dZW1vjwMIBMimL2nYLI5Wm3WqTSqWoVitcvrxIp9Nn0HcY9DIcPrzAvv0LoGq0Oz0M02RpeQlJlnjwgQcplYpsbWwyNlYmSQQH9u9H1026to2ZK9LtOBiGSbe7wcT4JI7bodvzcR2bfDYDIiJfsGDZo9d3iZMBYRRSyGfp9l1yuQKNtoNpWLQabXzHZmH/HKDQt0NkxcAw00SxgmakkTUDVTOYny+j6zr75ua5cOkSte1tomjofzSwB1TKJTRNR9N0LCuFrCikNW24Am6yM54YjpFup8vi4iK9fotcrsD99z9ENpul1erykpe+jH3753j4kVP0Bx1UNcTzYiDhzNkzjI1ViaKQU49cRAg4ftMLSaczGJrO0vIqszMzpCwDSZKJoxhJHgrWQyEbvjUcWb4VrvGZ5zktrEhiRzi/Bsm1i79mnkzqv27peOcYT1ZdPFWFEc8jvp7kC0NzjyxLu4nd2p0Ouj6MhpGu6Ne5MgmBpihIgKGrpDIW3a7HI6cfQCIhDAKmJ6cYdBuEvk6712ZjvYWiaCzsO44z8Bj0B8Rxwr6FKoOB4OyZZRShU1u9BOiYVppYxExM7iOdydDu9vECn2wuw9LKItl8jrX1ZS5eHKdcehG5XI711TUkIXHuzHnyhSy12hqRAD8O6LtdGueajE9MctPJoyRJxIMPPsCbv/cQVkbjZa96CXMLFT73j39LOpOiUHTYd2AfjfrWcJ2hbBp9/zwrK4uYqSyaqtNubBB4fYI45ty583T7Ln4gSBQTzciyvd1FM3Ri4bAwN09NBkmT0bMpfN+jXClQLGapVsoEvosiYrbWFjHSKZxezPkzF1GBfXOz3P2VLxGFIaqikklnMVSVVn2dRTUinxZkswVaTYmtrW1SKR0/9NEMCdOSOHP2AcYrY5jaMHT64MIJbNvm1COnmb9pgXSuwKDnEfgxlpWlWChRLlUAmWNHj9Pr9ag36rRaTYqFErpukUlnh5FdEbi2SzqTwfcjwiBEVzUkeegIK8kCWRYocsz6xjq5fB7LypDPVymXp5ibc9nYbNPu2BRyBqVikUZzm063yezcJIVCnmr1xXzxi1/mzNkz7Nu/n7mZWUzJZGl1EUkkFEs5dE0nny8ShcNsyIqs7vG8lZ7HjrgjjcrT57ktrHDtcXwjT/hPllngW0QLOmIPX9uvPnyIS7tCtKIo5PM5hBiGJsuAfFU0kASoikIURnQ6Hc5cOE2lUqTZ3MaydMqFAmvryyioZDJZ0ukyMjUOLhwZZiP1WggiarUN/ECitrXIwYV5uo0m83P7SGLQzQzNdh8/sUhnchSKFbwgpNvtkssVqVbLVKtFDh+Z46FH7uHEicOsri7T7gZAjCyDZaVwPRvd0un0GzSbbTRD4av33M3k5Cxjk1X+8v/9H4SAhf3zKDLMzOzj3nu+zNTUOPVWE0mRGAwGtFpNJienCKIEWVPZt2+GxUvnGB8v0Gx36PZ9Or02mpEll6+wvlmnUswzPTVOt7XN+vIyvtMnn8+zvV3j2E0HcF2fAwf2s7m+gd3vEIcmKVPC83rUt2sU8ym2NpbI5zP0e21WV5fRdQlVlrAHA1y7g2UK6vU6pcI4mpLG1FK0Oy1kRSIhZP+Bac6evUQiCiiyjuN4mGaeuZkD1Na3WVlaYm5+P6/59jsIooT+wMG2bSRJRtM0kgQymSxI0Gxu02y2yWXz9Ps26XSaTCaLbTsIPPr9Po4z1ORMTY4P/Z2ISZKARmOTMHLIFSbYt+8AxeIkkmwwN3sQzxesrCxRqZik0yk2aiGJiPnil74IKBw7epJbX3Abl5Yu4fsOhXyWxcUl7EGPbDbFXV/6HPv27WNmeh/V6jgSkJAgo3y9N9QzzmMFi2+kuepGFmK+mf1wLZ7TwsqIEc95nqGH0zCduoyiSMRxQhRFIASqOrzFr6TK7/f7nHr4EWyvA7GHpetEQcihw0dpNrtsbNbYf+AonW6PjfVttrZqlMslCoUc6xsrrG+soptTTE7sA5FQqY4TBAGr6xsYZppiaQxdy5LJ5JBkgecGjFcnyBdKdDu9nbwoLvff/1VOnVKRZUEiIkxTwba7mKaOY9tYlolmGBi6Tr/XI44kJBQOHzvMI6ceQVdViqUs+VyWL3zuc8SRwB44ZDLZofNxHFMslrBtm0K5ysDxeOTUQ0xOFLFthzwpImKiJMb1Bd3ONnHkYw9kHn5km7RlsO+OVxH4A/KFDEIESHLMWLVAf9DB9fpUxkpsrC8jKzGOH2AHErqWwvMCvvKVLxKGHoau4Hg2uVQKGYl0KkW71SVlZYjiBM8dkCQKYRShmQay7BOHEdl0BpEIwihm4DjkskWiBHTTxPVsVteWKJQLHDh4CKurc9+9DxCELqqqIEsSuq6hhxrZbJZ8Po/jeGxsbDAxMUkmM9SwbG1tYpoW6VQWVVEIwxBVg36/TUJA4A3odFpkc0V838U0VQb9Lp4fEoU+Y2NV+v0+0zPTzMzOcf68Q6u+xeTkFGvra9x224vI5i02NjY4e/4s2XSGwtQUqgbzs3P0Oh0ebj3EgQMHmZyYwTStZ+ZmGPG8ZORgO2LE8wBZlnfffIahqMPJ57FrAkVRRLFYxFQUxssV0qk0CJlLiytMz+zDMLJsbTfRdZNKZYyFhYNYlkWn0+HgwQNYlsHE+AxJYtLtDteGuXDpEtvbNdbX10EeajXq23XarTaBH7C+vkEmlaZcKiPJEvfc+yVUVdDrNen3W3jeAMNUSKcNkjhkfm4e13FZW13DNAwkJHrdLstLyzTrWxgaTEyU2Nxc5sK50xiGjjNwUCRtGCYbJxQLJRRFxfMCet0eS0vLdLsDTDNNqVJBVTUGgwFRHBMFIZ7r4HsukHDbC17I2FiV5aUlLNNgZfkSKUslCh22aqvEic/4ZAVFl0nlUrR7HarjFSQSNjZXQIqIYhfdkHe0FDFh4O+EjUsEYYKQVPwwxgl8FF0FWWZjcxNn4NBpd1F2onlMy6LTHbC6WcOyMpw4cZJWs4Esxdx7zxc5deo+bKdFIjyWli/SbG3jBQ69fodWu4GuK5RLRSanqszMTJLLpTEMlTgOSKctcrkspVKRdCaFpil0Ok2C0GVlZRFZkdA1hU67hW136XWbZLMGpqmysb6CKss0GnWCIAAk9u3bx9jY2DBU23dotbbJpAwG/Taea4NI8H0Xu99n//w84+Pj5PO5Hf+Y7o6z71VOt6MEtyOuYqRZeQZ4WuqwUYKfEddg7/o90p7yKwsSPtEKylfqDU1Aw0UJFVnCdV02NzcplUpks1kURUHTNG46fpzzckSz0aI6MUYQxqyvbzE3d4gjx27CHgxoterUalssLBzkBS94AX/xF/+H06dPc/z4cdLpIplUkfvv3eLwgWnanQZ+GHFo3yEM3SKfHyOTyXHvfV8hl02zsb7OFz7/jziORy6XpVCycAY98rk02VyazY11Wo06nU6LamUCx/FIoohsOg2JoN1sMjY2jRDQ7/fI5dLUaqv0Oh0OHzrCoQO3srWxied5DNoNHFvn0rlzjI+NYdsuXhBjGCbZXJH19Q5CErRaLvnsGGHQJ3B6ZC0DyQjQNQVVlqmUqmyur2OYEoWched1mZieod936HZbeN5wMcViKYeRMtms1YbrNUmCXr+FblqIWEaWBaoq4/surWbI2HgVLwjwwi5+EGOZGTQrwkxnMB0Hx/YggYyVJp8rsbq2zv6FYyANo6WQBOMTYwwGXVRdodOpk4iYSrVIt9tEUWRsp4ciKywvn0fVZDTNIJvJIYTAdfsEvsNWrUYmk6VYzA0XkVSVnbw6Dn7gYA/6bG7GjE+Ms7S8TrFYZHpqilptFdv2KZZyNFt1wsBleekSMzMzjE1N0diucenyRUzT4K67PsPc3CzZTAYRB6RTBs7AJgxd1ta6lMolCsUyDzzwIPfeew+33Hwr5XIVXR+u2KzK15JWnlsPy0dfIJ7ddlwPT9TGG+UaRsLKN5PnwIAd8ewiyzJxHKOqytN+OCiKsrv/MKV6TLVaQQjwPG/3+yiKsTJ5UBRKpSqGlWJxeZW7vnAX6XSe8bEqYeQQJyGVSolUKs13fMed/PVf/yWqYhGFcGDhAN3ONuOTRer1LVTVRAiFc+cus7CgE4U+lqlhGCny2RSbmzX63Q6BpxAFCmOVMrqhDVcmlmUG/T6moRNHIa7toMoK9mBAebaCphhEQYhlZWg3W6ystPD9AblsilzWYH1jiUMH99EfDFg/fQlFHq6Dk0qlWFtbpzw2jizJNBtdBgObYrGIbbs0m226XQdZ0gnDBFNPmJ+dxR70MXQdRVa4+cRROt0NOt0GK8uLGEaaJBYomk65UsUPPaoTk9TrdQqlIuMTY5w5cwokBVXRMS2VONGI46HAZNsuA9cmXywhEDi+h9eMkGSIY4Ghm5QLZTrtPkEQoigqi8tLpAun8Pw+SWBTKuXx/T5CxDTrNQzDYmxihlOnzgLg+z6KCv1BizgJ2dzY4sCBQyRJwmDg4PsBU1PTNFsbVKpFjIJJvV4bCj6Jy/LyZbK5LN1Om/m5ffTabVy7x4ULpym16vh+QpIotJtdcvksg06DhiqhKyAin2oxy+LSRUqlAoFvk8umsAcOS0uXSFkposBndXWFUrlEJp9HiITx8QpJEtNqtYiihEq5jK6qKIq6swjiTvj9DSCrfC2+G6MX0q+fkbAyYsQNwpWEbkmSEIZDU86V8uthN4QZaLfbGIaBZVlsb9fJZDK7xx/YDrqVJYgFbhATJ3D08GFc2yMME1xvwPlLj5BOZ4njAE1VGatO8qY3fj/1Ro0v3PUlcvkikjQMm04SkGQd2w4RaPzjP34WQUy5XMCyDObn93HkyAIry4scOnSChx95iGw2jUDQbG6TSlk0mg0URSL0E3w/JAxDwiDEcRwMPUXKylDbatC3FSYmy4xXi6hazNlzD6PKGlEg7VzHEeLIp5jLoKkq8/P7OH7yZlrdPhubNdzAo97a3tVAdLsdctkyuqZjGioXL56nVCxz9MgRVCVmbXWFmdkiYdQnnanS6QwdWTPpLPlCiU6vTRQHOF5Ivb7I/PwMmi4DEapuoOsqtuOiasowUR8ySSwhUJFVmZgEL/SHv1sskBOZ5cUlDCNFLl/GTKURsoakJKxtLFPJp1ElWFtb5dZbbyGKE+r1JvsPHOPmm29mbW2VMPIJQwdJjsjl02Syszh2h5XVNXwv4NChwzRbGyiKypmzD3BgIaLbaXPu3COMjRVpd1qARLEwhuv4lMp51taWiZOQdCbF5laDcmmKublZaptrKDIolOg0aigiwlAEUuwS+jKuk8I0pijmimxtbuO7LoVcjpuOH6Pd7VKplFFVjTNnztBstJmf308+X8R2bC6sbXLowAJWKo2yE9o8et/71uX567MinmJ7FnlsM26AJo14lpEkaTehmyzLuK5LEAR7Vk6Gvam6r/58JYvtcCXdhF63R5IkKIpKpVLB9zwWFxfZ3NxAlmU63QGV6jhhBIqss7K8SsqySFsGUeBRLhdQVUiSECEiSIZrykxOznDT8ZOcOvUIYRzSaLZQNA2BRKk8xotf/DJe+9rXcujQATrdNlu1Dc6dP809936ZYinH4SMHCYKAW255AeVShSiK8X2flGXhuT7p9HCFY103SKXSeJ4HO8nuZmanSWKBaVhD/xiRkC9kMAyFQiFNKqWRyaRJkoRCoUipVMIwTWpbWyxeuszGxjrdXpMgdJiZHUMzBELy8fwushwxPT2BqigkcUyr2aTX7eC6DrqhUygUiaOEXC7P1NQ0tm1z/sJFLlxcpFZvEcXDtXDq9Tq6rhGEHumUiaoNH7GyMlzQT1ZlDNMkSRL8IBj+3ooEEliWSRiFlCtl5ubm6PV6aKqKqijUttaolgskcYBhqkyMVXFsh0K+hO+FfPGuuzEMg9mZGTRVHjrDDjpsba3S67dIREC/38IP+mzVVuj1m6yuLbK8eom1tUUS4SNJEV/56t0cOXKIra0aN504gZXK4PkeU1MTHDg4z6WLZ5GIuXTxHLqmcOstJxh022xvrGN3OxSyaRQpxtAgDocmn7NnT2GaGiduOs74+BiJEJimxfTUNJubNaampmi1WnR7neFCmYFHPpdlanKcpeUVoihCIEierrn9692u89jStc73jeIZbPdTXgePKXuWed5qVp4yl8mzxGPPfa22jDSG36pIXBkRYTjULmSz2Z1w1GSYwItHBZQrmhcYmo9keRgJNLAHO2YgsWv60Q2DSrVMv9+j3+8SRQH2YEAcCCRVY2bmIN1+D1WFZqdLKp1lvJxifeUccjxA03VUI4MfJOyfn6Ne26K+vU6pbBFLKpJu4IQeqqEwXVhgfHyGVrtHkgT4QYjnB2SzOaIopFzM02lvIxFQKWWpVCv0en1EnOD6IagGjuOQSmUpV8okccLy+iLFQoFKJYPv97n9xS/C8/u4Xp9ytUB9u45haeSyOcbKaaIoJpsvMSEmuOer91Idm0AkUN/uk8tbKKo6NKd4LoGboOsqYeAzMV6hUq2SSll02gGdXovtuo2q6KSMNJu1LWzHwfYcZFWn2+szcAOmJqap17YQcYyu6egpBRHFyLKCruuQxLiBi54xUSWJMHFIRIQEpFMGQRTiOANKYyUmqpPcduvtPHjfI2xtNSjn0/S7Te7++7/ipuMLLJ3bQFENwjhFmEjYtk8qpbC5vs7kxBj5bJ4v3HU/hqGRyZikVZnVlUUUEdLutYgSh2y2gm7m8MOIi5ceYtDvMTZWodvdZntb48DBGTQrIa9ZVCer+NGAixcfIpdJk4gB+YJBvmhg5dIUKlVOnzmLoWmcPHGUQjEPGqAItpbPAToXiwVueeHLGJ+aYW1tE80qosoyjr3K9naHhYVDbDeaNDsdFMOgPFalVCnhOD79/gDTHIZIP6276et40Isrt+ITPIwfe+yrq32j5/Ynu66n2+7Hfb/nYI8xcUnXqvTN43mpWZGeYnvWuOEaNOJG4erFB4UQZDJpSqUimqY9oXPto9qUoYo8iYepSJvNFocOHaRcHiYJk2WFbDZDksSkUilM08C2e8iyQrFYwnECyuUxFNXAcQOiWCKXLQASigSnTz/IF7/4OR568B58z6bbbvPyl7yc40eOIRJYvLxEIZfD91021lcRiaBYLLJ/336SOOHY0SMghhExly9fJAhcHnn4AZrNLQxTwfMGJDvrE/X7fVRVZ2JqGkXV2NzcotvroBsqrjug2aiRhAGdZoNeu0O5UCSfzaFrMlHkEQQOpXKBbC5DEAYoisLExATtdpNuq4OMjDdwSIKIV7/qVVRLZTRFQZUhl8vy2tfdyezsFEePHmJ2boYTJ29GUXRsJyCXzXPyppNUKxWiMCQKI3zfY31tk0dOnWFjfQvLShN4IaEf0ml38F0Xx3HwfB9NMwijCMdxUFSJfCGNJMdAyES1yOR4hVRKxzC04Zo5MlgpC1lWmJycIo4iVlaWaTbrBKFHp9NicXGR8fEqjtPnwvmzfP7z/8i9936F9bVV6ts1UlYGdxAwVp4kDiEKEjqtPq1Wj0p5kjiW6Hab6LpEq1UHBKtry8RJQL2+ycbWGt1ej4FtAxLpTBpNUxkM+pw6fYrtehPXCzh+4jhWWuOBh+7l8tIlSuUqkqySJBGDfpv777+H++67Dy/wSWeyDGybRMDx4yeYGJ+k1x0wMT6B67okIsEPA4QkMzs7g2WZRFH0Nfl9PNVc8PU8jp9o393P38Bn+zPd7mseTzzmukYOtiNGjIBHI4EkSULT9GFyN1neFWCA3e/jON7jy6LIEqGI6XZ7qIqCqum7KywPzUSCXC7HxsYG7XZ7Z4XhiGwuQ7fbo9+3mZmexfddoiimVMqzub5EImSy2SJHj57g/OVl7vril8iaZdJmmvHJApVCGUXIrC2v4Dg+1XyZ//dXf84Lb3sBlqXS7TZptws7CynGbG6t4/sOgpgw8pEVlVKpQqfTI5Ox6Nod3G6bVNoijiMURaZSKiEhcF0Hb9Aj9FzsXg9dV9hYXePB+7aYmZ3Ctm1U1cKPItzAR5IU+oM+tu9QrJSIkUGSsAdt1peXEYHL8cNHsNQVTCONqau4rs3a2gqDQY8oDhgfH2N7e0Cr1SKTSlMqFslm87zm2+/kzPkLIKnYg8vIIqFcrbCwbx/nzvYI4xhNVdG1oaOv53h4XkQ6lcXUEzRVQ1dVBoMehq7Qb7c4fPgY6+vbRKFPp9thYNsISWKr2eT221+EH/s4dgMrnaPd6VEdy9FpN9A0FdPSaHcajI9X6Ww2QIqJYg/XHbB8cZ2ZmRn6fRddS6PqOtlsEdd26LRaKJJDr+ujaRq5XAZN0+h0OqTTGRIhkc+VaDVrRGEH3Ujh+0M/J8d2qG1so2kmmVyaRApY2bhM12lSHZ9CNyziWBAlEbKqsLaxiqqnue0Ft9Pt9nj4kQc5fOAwcRDh+wGHDh7i81/4AiIBGRlZkpFlyGSGxxk62T5bd+eIZ5vnpWbluc6VVUhHq5E+nmGfXPHb2NtHz6W+GmYK3atRGZY/ulry1Q62j9WsXBFiYKhhiXe0LJcvXyZfyF+1SJrYPdauky0CWZGIk4hGc3u4KF3k0Wo3kCSJcqWClU5Tro5h2z6SpNFs9Zif308qnaZcLROEAefPn6e53SSfLSDChEqhRLtex+63ue/eu7l86TyWqbGyfJna5jokEa16nVK5iCCh3+9RHavQaG6jaQqmqZNKmeSyWaIwxDR0FHmo137DG17P/Owsr/uOb6dSzHFoYR/752aZmZhAERKNzRr5TI56vcH5Cxc4e+4sa+srbG1vUBkro2oSuiqTsXTGyiVUSUKRJFKGzni5hK5AytJZXr5MsZTHMNXhYn9Jgiwr3HLLrXiex/mLF6ltbROGMblMliQKOXHsMDNT40yMD5OkZTM5dNWg1WyTy2YpFfNIyPQ7No16G1VR8RyXRr2BqWuoikylWKRV22bf7Cye4/DQg/eTJDHpXIZ8uUDHdrCDEFkz2ag1mN+/QKfTob69wblzp/B9h3TGpN9v02zVaTRruJ7N+YtnkJQI2+mQiABZjjEMmX6vztryOZJoQBi6NJrbuJ6NosgEQYBtOywuLlGpjNHvD+j1bbp9l/OXlnC8AEmWabWbWKaFYZpcuHgOocSopoztOaTTWRTZoNMb0BnYZHJ5kCTa3Q5nzp1hfKzKi267DRiu9nzziZNkM1lmpmeQBJiGsTPOJeJ4mI15xHUgvnYT1I0+74yElRHPKa6O+Zd2Vm2VrtJPfnOfaVccXQVxHO+aZa5/92G7hxE1w8XghpOjtCeyZ1hV7NmuFkRA4HkeSRwTRRGZdBoYJiC74rirqsOVhiuVCpVyhWajRT6Xpdttks7o9PotarV1otij1+/SbLWIIkEYCfYtHMZK53C9kKnpGY6dOMbxk8eYnZtnYnKafL6AYVhUyiUQMZKUsL62zPLSZRQFAn+Y7bRSruL7Pv1+H8916fV6O74IaYQQHD58mFd/27ehKjLtVoO0ZRL6Hp1Wk63NDWx7QL/fQZEloijg0qWLpCyLyckJ5ufnyWZzhGHExvo6ge9jDwYU8jmKhSytVp1Dh/azsH8Gw1A5eGCB8WqVQi4HSYhlKESRh+c7zM1N43nDbLCnTp2iXq/T6/WYnpllYnKSfL5Ir9Mnl80xNz1Dr9NicryCpio06/UdJ+A0IhEkUYyuqmRSwzT3KSuFoRmEfkgURAR+iITMoOdg6hbdVhvPdcnnswgR0Wxu0W5t02hu4/ku/V4fVVWxBwN6vS6SLIjjkLW1FTY31tnY3MC2+6RSFkIkJEmEooVoRogs+yTCxbbrVKsWlhXS668Thg7ptInnuSiqTKPRYH19nWazyUMPPYQkyQgkOv0BQlbp2zaNZh3LUnHcHum0yYte8mIkVUXRTfwg5MLFS3iuRzpXIF+ooJtpNCNFtTpBp9Pl4oVL5LN5VFmhWW+yf99+cuks++b30e/28V0f6aoxPFKpjBgJKyOeU1yZv4cp5QVxLIjjhCsT9zfzjeDKG4gkQZIMt+tl+PwdPoDjOKbfH+C6/s617CUMQ3zff9y5QSJJYjqdLs1mi0azyczMNK7n75qJrmighkKdhKaqrK2tUS5XWFtfJQgdGs0Nur1NeoMa2401MlmD2vY2mm6wcOAwqmJwYOEIvf4A23EQMqTyGQ4fP4qs6xw6cpR0OkO73cb3XW6++SSZdAbLNHEGDoVcge2tOqZuMejZ2LaL74eUyxV63QHVygRCSHS7fbZr20gIEAm23ceyzGEGWN8nZZqUygVkVVCv19i/f54wDJmYmECSFOrbDeqb24SOT8a0KOfzzM/MMDs1iaHKpCyNTC6NokqMT05w8NAhquNVFg4tMLNvGiutgxSztLyI49hX/TZ9zp+/yFe++lUqlSob65tsbW5z8fwl4jBkYqyC5w5YW13m4sWL9HsDpETi4MJhdE1nfKyKqkjksxkMXd/xQ4I4TMhni8johJFEs9VDVgwQ4Np9SoUUodejlDcgshkv55ifmeDIwYO0my00TaU6VgFJotVq0253aLfa9HoDklgQhkPzSpwEqBoEgUsceiiyoNuu43ltkshGNxSEiElERKfTJopDVFVFVTUy6TSuO2B6ZppytYLju6RzabL5NIYp025vcN/9X0KSFY4dfyGHj9xKlKh4vsvA6VAdnyKTq2BaOWwnoNPpMz01h2GmCPyQQr5Ip9MFASnLolQoMT8/T6/bQ+ZRsydIj9MujvjWYuSzMuI5xqPahCQZahiCICKVMnaFhm/Wi9iV8wwFlQRNezq3k0Qikl2hZZisLRo+nNH2aFWuRAFdjdjVygh83yebzSKEwLZtNE1DlqDf76Mow1WZVVXeCZ9V6Xa7SIrGkSOHCOMB6xuLbGyuDpPJRX2OHruVQ4cP0Wm2Ma0McSKTzRUoFsssr6/w0OlHkJGwDJ1SMc/ihRXSuSz9XoSMzAMPPLgTBZNiYPfpdgcEfszp0+eQZZ3Aj/DcgO1aE1XVaLU6DAYOmxs1hKwSxdJwMpZl0ukUuUyOWm2L9bVVxqpphJSg6ApTs9Osr60ho5DJ5eh0e8xPzwASlqkTez5SFHHhzGmOHjhAr9tC1TVe/0/ewPLSIvlCDkSClbHwWg6qpjA1NUE6naFeb2LbNvl8HtM0CfwI2+mzurLGxMQUg4GDLgTjlSqLS200ReGlL7mdpctLtJtNCvkCm/UtpuammZ6dRkEhcCOWV5bRVJVMKoNIZXDtAN1UCLyIxa0NgkhCUxVWl5eYnZ2hlDNIWzKFQg5FlqhvbeH7PvagT75UpdPrUq6UeejBU1SrFXzfQ5ZV4hgK+TJB4BLFcPHSEt3ugLHqGDIqmVQWiQRTNZENDU3TaDTaaNpw/agoigjDcGdMChzXRlZkJEUiiAOkOMJze2QrKTy/h207mGaBl7zsO7h8eQXPGTrtpjMFpmeP0BsEQxNfrkSpVGV6YhINCTUjc/DAQTbWNkml0mRSacIwIorjkY/K18COXLfnpe350offksKKxDf4Dfx5MjieWa42aQDSo4G60pN12GN+qOE+Q5VGHEWomoph6DtVBdeY16/ZiitjQAIezTYlHm3PbuPE3n2vhAWK4Vu37/ukUxaaJqPIYvfaruwgkJ7gYSGQuNqsI9FqtZiZniKKHzUBXQlRvjpM+UpwpEgSonCYOC2fy+P7LtlMFpFAGCdIskycCKJ4KMFpO4eojo0xcB1MSydxEuLAxXd65PM5Wo0t1lcX0Ywis1OziDDizKlH0DSwDI2J8Sqf/8LnmZmdQVN0wsDDcV10TaPT63DrzSdYWl4iEQnVahnBcNLpdttMT0+jqlkGgz6WmaLT6WKkupRKZRrNLlEM3XaLMIoxDJ2FhX0cOniAarXMxQvnCUKHdncbK60jyzJnzp9jbW2dl7/iZayvryFpCi+79UVsrK9jmjqmadBoNaiWK+iGTqvTRTGHESrtThtNU0mShCDwmJ6awHYGPPzww5w8cZIoishmMyiKSq/TAxHjewEbG5vE0VDAtAddyqUsuVyGKA6xLJVypYDr2Ozbt4/p+VkuLV3CtgcEgc9g4DI3N8/y6iqZdJY4SYjimGwqx5nlCwRBTL/f4+SJYxQLaXzPJQpDCvkModtDMS0OLuzD8QImp2c5f2mRKIjo9bqk0haKKhPZMZ7nDbPHBj6maZCx0nRbAwq5Kv2Bz+RUifW1OiKJ2Lewj1K1SrvTZmbGJAyTYZ4YLSaVStNsNmg2GkxMT5JEEXEYYmgam7Ua/XYHCYmTNx/l/PmzGFaRY8dOYhoZ0pZKpTpBu9MnV5gglytQrU5h6CmiKCJJYiRNJxaCOIlQVBmRxFipFBNGhW5/gOt6pFNXFjd8eg/VrzdyRRJXhQF/rTzZvleZsx/31VNd6lP4pew+fq4Ie4+p/NhzXn2+J03T8izPa9+yZqCvJaTtmQ59+9bi6uxDj/4rSeIpnyzDfk2QSJAlgSRiNFUiDFw0VUZRh2LG8FiPOde1NpGQiBikHc0GV/12MUhCIAtxVbMe3VeQAAlICaoq0eu08D0HVRYkcUwSh0hCoOzIP1f8aq7OvnBFI3MlqkdRhrdhFEVD4WInTPPxjrViN0Q5CgKajTqSEKQME01RKWTz5DJZdNXAdT0SwA1CIiHhRwIviIgliSAJkA3Bw6fv44F7v4IIPPaNT5CSNUrpHIHrsLF+mfNn74OoR86CjeVzuL06/WaN2Kujig6FjIRvd+i0Wgz6fZBhfXudBJ/5+Sm2tzfQdBnDlBmfzHPi5gXSaYlD++aZHB9H1XUSSUXIKulsDtuxifwAXZHJWAZOv4ttd7j77s9x7vyDIHtYqYQEh1whSxAkeF5CbbtNECeksmk6dpfZA/P4IuLs5Qv0XYdmr08gYOA6qLLEmdOPIJIY27bZ2NhgdXWDRx45j+OFVMcn2arVieMYGchYJook2N7aJG2lmZ6codvpIsNwwlVA1VVkBRr1VcBHNWQikbC5VcP3A2obm8NVod0AgUK5VCEMQ0xDQyQRceAxPznN/tkpinmTjY1LhImN7ds02l0eOX2OeqPGmbNnuHD5MlGc4PsRlp5GRILa1gaqAa12gyiJiOMYxxngOj08p4cmK7gDnygG2ws4f/kysm6hGClOn77EynKNXDaH5/fpdpvYA5skkdjYGPq/iCTGtweYioQaxViyBqFMEqrDbMOBi5R4OL0GmhQzNTHFds2mXF0gRubs+bMkiU+7vcV4tcDK2hKbjRp26BHLAj1jkillUXQNWZGRFQVd0ei1OyRRwvAR8eg9eT3b18PVobpP9/jX2lcWw4n2yrbH3ewZRgwfb8NHlBj+u2d7CilHPMl2pc6zxbesZuUbxQ3oRH3jIO3559HJ+Ek6bRgxkwz9GLiiPZF3VxT2PQ/dMneja65oaZ7Ivn0tbc5VipCrlSw7ehZxzXbLsowsSWiauutcOzTXyEjS0F9AkhUSIaEoOxEN8vDAj/q6CFRVIYqG+2ezWTzPBSQMwwCkPRqV4SKFMt1el26nSy6TxbKGb56GoSJEQhA4uK5Pd9DDtNKk01kkIBEJjWYL1+2jKLCytIjn9DFVKGQnOXf2Aq12l8npGWazRdJWim6nw91bd1MuFkilTBrNbZZWltA0iQvnzzI56eG7grXVDQxT49jxg3z1ni+hSyq1rTqO46PrDplsmm53wIULFwmjmCM3HeOmkyf52P/6fQaeTbfTgDhhvFJg7gU3c+niJcYnxjh8+CDjE1U6rQ0mxsvMTE9SKOisr63RqNUIgqEPTm1zi4OH99HutIZajDCg1++RCEGz1cIwDMYnxkkSgePYNJtN2u0O1Wp1GBKbwNZWjVTaQtN2tHRRQhjFbNcbFEtlkGQ2NutcWryE74ckO8sJbGxtIUkxSeKjqQqSpHHo0CEcz8XzXQqFHEHoMz5W4vzZC/jugGKxwHilRCplMVYp0u10adWaZDIWE9US7W6dQjbN0uIyimwhEpkoAUVTCZOYtdoGlco4Qo3p2m0c1yF2XHw/QhIKqqLiewJ34GBZOrMTBrZtY/sOyBKxFBMnMc1GHbtvMzYxxYULFwhCmyiETCaHPQjo97sMek1MQ6GQG0NVZUSk0GqsUsrr9PsOmxurFIsFZMkkCnzu+erdHDt6nHp9myQSlIoV6o0mUZSwunqZseo0QRCyvLxCGEbMTk9RKpc4f/Yc5WKZJNERiSCbTZHECUkcoyryToLDJ9XBPqN8Ped5AkXqo39+nRdxtfL2us79pEe6KpryygFuULvRt6SwMuLZ54qgEscxcZSg68YT1k3iBFnmcRlbk2QY5nt15MzVIb1PdF5gT+6Sqx8kkryTl+Qqe84eweaq/TzPo1gs0O8PkGUZwzDwfA9DN5BkCVkGP4h21tARXFFkXkniJhj6uui6ShRpSJI0dMDUNcIwQlXVPb4rvV6fTCbD5cuLzEzNDhO3CQnLNIjjiO36FkHgoWoyjusRJzG6ppKyLBIBuiZx4fwinj/A7bdQJIGmmlxeXETICulcDtUw0XSDzZUNoiDA2EkPH0Q+miZRLKRIcMhk0jQadVqNHiKRkKME2x7gewGpbBrb9hBCJopg0HeR0AgDQbc34IFHHqLZb2OYKuvr2+SyaU6cPIZr28zNzHFoYY5HHnmIOLQRUYaD++coV0r0ez00JeGmoyfZ3GyRSuU5f+ESIgrpttq47oB0Oss993yVXC6L63p0Oh0MwyCTyWIYBmEwNEslccLFi5d4+cteyalTp5ianMZ1XMYnxojjmEa3QafdJp/N4bkeM9MzpLM5/CCmVqvRaDaQ5IQgdIkij+p4GXfgMDMzh6arPPzI6eGkMpAoFPKEYcBEtcz6+jq+2yefzyNTIJ/PcegFtzA32eShB+/HGXQ5uH8/nV6Po4cOsb6+jSzrREKi2e2jmmlsb4Bi6Fg5g/mD03huzOZmk/p2iziKkCWZXK6Ermr0ex0ajTb5fI5Ov4usquRzecIwGubxkSTqtRqp9HDVZccOsYwS3XaHOA7IpC2yGZ2ZyXGQErJpnVQqw+rKOtm0jqJbWKaF44SkUgaOa7Nd20BVZDqdJsdP3MLf/d1n0FQDVdZ58IEHOX78Jnq9Af/w2c/y8pe9lIMHDjI5NUkQBKStFJIsI4RENpvZvTfl3cUMn+ShcoNyTZeD67d8D6t/A677sefZFVZuUEbCynOA56Oz1BV838f3w6FTqCxdM6ImjqNHQxi5ojURBIFPOpNGluU9JpOr19G5+vMVPM/D8zxyudyOQ+vuOwayDI7jDderSVkoir6zl9j9Ha4ISEEQYO84Iw7bENPtdUmlLAxDx3EDFNUgilSCMCCdTiMhoahDoUuCYbK0ZChkDSMw1F0h7Mo1JUlCHMfkcjlkWWJiYoJqtYwiqTvClUSv12Vx6QKJCAl8j97A4eaTL2B55SL79y2gqgq6LtHpNkhbGlLsMzUzRbPZIghCgigCWaHVaWPUtjB0lTte9W1EoY9IQra2Vmi2Oug6eJ6DbmSYnZ1mY32LbCZLFPqcP38Oy0rR6fbJpLMIZCRZZWZ2lsWllWGK+CRhq7GB7feIo4D5uSlMXeUlL76V+++9l9rWGlNTU5RLWQwd4sjFcwdcOLfN1NQ0KT3F5tommUwRx7GRRMzU5BRJEhEHIfedvZfJqUkajRb5fB5dNzAMkzgSVMpjLC1eYrw6hjNwKZeqrCytcuvNL0CSZNrtOpqs0qw3cWyHqalp0uk0JILK+Di1xjZTM1UuL15GUVU63R6GqRKEEb4foUgaSAqLy0s4no2qqZSKlR3fh4RcNo08PcHG5jrddsj++SlMXeHypfPk0jluvvk4QehS395GkhW6nT6+O4yq6TkRiqow8JaQZAiTgLGJKo12DV2zmJkd48CBOR64/yGcwYCNjUXSVgZZUuh2OpimSdxp0W330UwNyzCxLBNZgO97WFZqaD4QCrbtUcgVyeYt1lcXkWPw7JhEhBh6mkwqjyrXcTwPw9Q49fDDyIqOlcoRBAkT42NEoYtjD1i8dJkjh45gaAZjlSqWlSabTtNtdzANna985SsU8gUKhQKmZjzqR7Zzbzwu5ftzErH3GqShx9rTUcPvmQOesXY9hht8bvmW9VkZ8ewTRREAmqoS7LzxRlGM43hEUYwkDUOUdcPAMIw9WpQkActK4TguXJX59dFF/YZRMs1mE9d1gUe1Iq7rDhdH28lXciWqKEnioQ+JKhMnAZ5nI5KIqyOPro7eSe/kM7GsFJ7vIckQBB5B4LFd30KWJGx7gKyAaRrDXCqSIEligjDYMTOB6zoEQUAYhjiuu3OuR58cVwsykjTMlaKq6u7DJYpCzl84g+MMaLcbrG+uDp1pRYiugjNos7WxQq/TwB10WFq6iGnqNBoNmu02EzMzWJk0tt3HUCRE5LK6cplTpx+k0Wrg+h5nz50lTiIcp0/asnBdh/379/HmN38PpXIRP/CI4xjTNLEdlzCK8DwfPwhwXI9mq0W92aRaLaFqCWHQ58C+aQpZi/FKiTOnTzG/fx4zZbC5tUGxnCOTs2h36sgKqKqGJKmsrG2CrNJstwmiAFmB9fU1BOB6HumMhe+7FIuFHeEQNje2sG2Hrc0aiqzQ7w0oFooc2H+QXrdHo95k0O8z6PUhEXiOR7FYopAvIEkyA9fh9NkzWJZJrb5FImIc10WWFUBGVjSiUKCoJoPB8LeUZIGqKSiKRL3ZIBEC13OpjFW487WvpVgq8vAjj+B4LqZl0Oo00Q0VK5XC80I6nQFCyNx8y60cP36ChQOHAJVOp0en06Xd6dJotojihGazwfkLp/CDHvP7xqiMpUllZNrdTRLhEIQuju2QJAmqqqEoCmEYDv1yZAkRx2RSGfbN7UeRVJy+S7/bx1A1Jsdn0dQC2cwkjW2Xixc2ePihi2hqDtMo0mr2aLc6xFGISALGxorISoxpqrRbTerb26TTGY4dOYpp6iwszON5Dr7v0GzWmZ+f58tf+TK+7+/Rig7fR4aeZFfu7edD6PLQrM3X7i/wDApuV/fr8KXtxva6HGlWRnzTueLfMVTtChR1OAyFGE7Muj58oAqxkzANCVmSd/dlJwmaLMuoskwYRei6tpuUbaihueIPEu0xE0nSMP25ZRpEcUKykx1TiIROp0sma7G2ukJ/0CWXy5ENYyqVKopyJXX9o9otSZKZmBjH8zw6nTa9Xh9JgmarTiplohkKhjC4fPkyExOTu/4lAEEQEAQ+hmEgKyq6InaEEu0qgWsvV65B04Zrx0jSMCGYIMbzHFzPIZVOIYhRVYkgcAiDgK0tmyAIWFldZHKywt1fPs/EWJUzZ86ip1LMIw/DVQMPWfisr1wijBK26+usb65hWhYTU1NEQQ+ZNEGQkMsZnD79CJubNcIwBCmh3+9jmhaFYp5UNkXf6ZKQsLK+QnW8Qipt4bgDNC0kY1mQeFi6Qi6boTvo0+73KVQqeI5DJmvQateIiEniCEnTabZ7+EFCOpvGC3qsba5hWRYD12F1c42VlRXGxssgCTRNJZfLoigaly4t4zg+5WIBmYROp8PLXvZyGvUmx48e2xF8LTacNaqVcZaWVqht1lhaXGZ6doZUKkUQ+viBR6OxjaTIqJpKt99jYmIcVVeYmZ0bmp26bay0Rc/t0u13yJeKWFYKSVUplCuomort+dzywheysbFOqVKl3W6jGArbzQaqaqBbWYLYoV5r4IXLuG6IrKYYq44ThhG9focoSGjWOiQkaJqFY3e4fHmR+flZDh1ewDLTfOkLd+N5Npo0DD2fnpmh0+9RKBRwBvZQG4jE4YMHCUOXZr2FKumYukI+lycKAlRVozJWpVyZ4tzFS7S6NqYREiUy+VyBnCJjmBq2PSCKAizL4NzZDrfc+iJWVjbRNANDV1hZXWR+bhqRhDTqG+SyJuVSDtd1mZmZGZrHHI/xsQkUWdnRsDyZd8ZzE+lqZ9URT4uRsDLim8De14FHVw9mOFnLyq4wAkPflCja0XI8JuX81X4ckjSs67oucRzvaF/YE1asadqOBuTRyd4wDMIo3s3uGkfxMIQ0m8H3Xbq9DoKYIPDwfG+YlC0IUFV1uILulasSAgkJSZLRNI2t2jrptIkkCTY31ymVxjB0HcMwiKIQSbJ20+lrmsrKygqqqpFOpyiXygjY1bBYlnlNk98V517f94gCbyio+A5xHDE2VmWsWubLX72bjt9iamoSz3PJ5Qp4vo3j9tmuu6TTKWr1Oul8ngSZRmuoWZCTCKffpt/vctOtLyBMJPqOg6yqIMnEMcSRwHFc/FhgGAalUoFGo4FjDygUShiGgaYbaJpKNpfBdgbk81kUTUZSwHUGTJcMRBiQTenEoSCbzdAa9LF9n0MHDvPIQw/ghgNK5SKSBGfPXGBm+gCV8iQbaytcuHSJcqVMTEK93UBSFDa3t5icnqK+vUEmkyGOh+a8amWcSqVCGCZ02l1IAsbHJ4ZOvGMTzM7Oc/fdd3PkyBFmpqZ48IEHqG9vUyyVmJmdJYpj6o06qq6yvLaCLMlkMhls2yNOBH17wPhYhaXlVWRJZuD0kHsx5UoRwzQIQn/oO2SYSFmFTrfD8trajmN2SHdgk05ZxKEPyDS3miCpSLJGJlei0WwxGDikrDzVapUD+w6wsblBvz9A1RIM06DRGqBIWdotB89ZZHpqglwuYW5uP912H6IEz3UhkgmDgCAIUBQZy0yjpCUGgwFJEqIqGoqS4HsucSahP3CIEsHEZIVGp8H03BSSFhEnIb1ej4E3QEGmVMrR7XXo9dvYzoDxiVnW11Ypl6qcPbdEf9BnbGyMfP4Ea8tLaJqK43aJkwBJYpjLxjDodLqUSxU0UyWJh5P682Nevyp9wZWQ5d3/XbP6U/Kct4x9DYzMQN8IniJy9mvdHhcF+7Vsz8pF760ikmQnvFcCIVAkmSQWIIZaDgmQJVBlBVWR9zjWXlEFW5a1q4WQBDi2TbfdIgoDgsBDYlieRPEwn0mc7Co54zAiDiOSKCZJYmxngBAxiiJhGCaTk9Nk0nlAJWUNU8GHfkAUhIidVLVJFAHJcL+ddPZjYxPDDKSSgZkqoOkWiqKgqxqe49DrdOm0OwS+j6aq2H2bXCaDqmhEUYyIYwxNw9CNXW+3R1XGQ2dkxDBEOo5ier3+0AwmSUzOTJLOpLCdAb1uh1IhjyJBFPmcPfsIp049jGkYqJqOJCsIGE5ScYhr9zEtHVlTaHa6aKZJ37VZWVvB9z1K5RJ+GFOuThPLJrKqs7a+iusNkJWEQjGLZshEsY+mK6RSFlEUceDgAcbGKzhej7HxPLLisW//JJpiIJDp9mz6tsO9991Hu9Gk3+ly6eJ51tZWWV1ZxTKzLC2uE0dw+vQZHnjgfi4tXsKLfAbOgCiJdxyhBeVKhTiOMAzryq+MJMX4gYNhaIg4RlUkJibGKBSy2IMuZ848wurqIrlsBlmSUDUVRVXI5nMomkIYh2xubSAQFMslOn2bzXoTSVEJopg4EfT6A9Y3NxEkSESIOEDeGXckIAuJXrvDpUuXyGTSmIaBpqqoioLj2MRxhKpq2IOIVrNPpVJlfGIMz3fp9nu0un2iWKK2vUmUhOxbmNvJwqtS227ieglxIiOrGhIqth2yudlClk1WVjZod3pM71vADiJ6vT6aKlPK5iAZLqaYzxdpt7p0OwMQKo1miyAO6TsDthotBo6P4zmUqhWCOCaIY/woRjF0hAI912ar0UTRVKrj49iOTaO5TX/QJZ21yOQsMlmLMPLZ3t5keWWRi5cuUN/eYmFhP/Nz8+SyOXzXY3pqCk1VEbG4wQ0ST49Hzcc7BU/1HH6K779RmpkbXeMzElaeYST2xtQ/k9vXc+xv9Dgctk08yfZoWxRpuKKqSIYChUiS4Sqr7OQlQKCrCoiEOIx4bL4RRVF2/UYsyyKdSqPKCnEU0mpuc/7cGVqNOro61NgMev2hX8sw/AaEQNc0dE1DiARNU9A0BdcdalFSVoaZ6XkOLBzC0DREnJCyTCQEURDS63YJfB9JJPieA8Toho4kq0Qx5AtViqVJPDeg124j4gjPcRn0ekhCEHg+Ik7IpDMgZPLZ3I4wNQyHlpEI/YgkhiQeOtAOhTmJJB46CLdabdqtYSZaWdXYbtTJ5jNs1dbpdRooCFqNOoamcGBhnjgJcNwBqqpSKpfRNA3T0Ah9lyh06fd71Op1YlVnq9llbaPG5lYNSRoKjpOTU1iZHEGsYKayVMplmo06SRww6Lc5dvQQJ08cRVWGbV5ZXqO2uY0zGNBpt9iubTA3P8n4+DhxoqFoGVo9l1bXxvUCdFVHCmOcbhdNkRAxbG82GXRdZDRUWWbQbxOLEMPUCeMQ13VxXZdCvoBlGMPoFs3AtFLDZGflPEgxigwSCUHgs3//HI4zYOD0KRRz6IZGEPrUtreQFIl8qUA6m6bd65DLZxHSMLdOrVbDdV1UVcP1A6anZ4jiGEWScW0H37ExDYmpiSpRGOIOPEQAKjoTY5PIQnD61CNMjo+Ty2SIQn/48E0iNEWmWKhy9PBNzM3Ok8QhlqWTxAJVsWi1+niuTbOxxflzpzB0mX6vgyLJKEJBlgSqMlwHKAwjdN1ibW2LWEious75pRW6rsfUzCxj5TK1tVVMRUFmqGWcmJhECIV2Z4BpmUhqgu0NsFIZXC/A9Ry63S79gU0mW6De6jBwA/woJlHBT0L8OAJZplgqEcUxfWfAA488iKrLDOw+yytDDUsqnWZxcZmNrdrQ+VnX6fd6FHJ5UpaFqqhc8Z54/ogrj7Kb00l8jTlirmhmniInyteyXX38G5GRGWjEs8DQYTWKIqIowjQVJGmnbMd8Y5kmQTg0vfAY23Ucx8CjWhZZHr7Nu25Cr+8QxzGtdptCoYznuTiOTSaT2dn70UghIQRxHA19QBiuwZPP5/F9f1cYMi2NbrdDLpsbmqvkYfr6MAxxPZva1ipjY2Moqs76+sowJwcRhqHQ7TVIGRqaDqlUhnQ6w+ZmjXQ6QxQGzMxMo6rD1X03NxuMj1dpNfukM2l0XQVJIMQwy6nruliWRRD4NJotspk0Qk7wQp9+t83K+gqqniCImJgcoz/oYeg63W6bdDZNGNi0Wn1a7WG69na7jabpjI1VkCSJYCcTLZI0dPTt2+yfHa7Rsr2xialqyDI0ahtoqkp9q42uy8ShYHZ6ltm5eS5euMjEeJXz51YIfY/V5SUy2TSGalEs5Fi5vI3nLSKSkP379tPv9TFNE0SI4/jksgW265uMjY/T7fZYWVnZSY4Xk8tnAAkhCTrdDrquY1lDDU4cx5TLZS4vLhJGMZlqBUVW6PX6iEQin8+TRBKWobG6tka73SGfL5Av5NnY3CBfKNJqtSioOSJ3mGK+UCjQbDbRdZ1GvUG+VCCbSQ//zhbRDJNSPkcSB5hmhmw6xfZWnbGJCTw3ZGNjEdNMEScSiYBWo4llmiwuLlIul4jiYJhUTpLYqm1BrCPJgrXNLrGIMfUUUdDD7tkoQkWVDNRERYmGjunH9i9w6cISbrMBqoKmp5ATQcZMYVkWvd4A0EDW6LR6KCp4roeVtzh85DDNdpN2t4Vh6UxNTdF3enR7ffKFNIap0+/bBJ6DKoNtNzl1+j5a7TaGaQDDhTujKELRBIoqgRhmvlVkFdvp4jgBR+YPsbS4iCyrOLbH9vYWY2MT6LpBfbtNp9PH0AZUKhXa3Q6lYnl4h8o762w9R0OVr4cnuqzrUn4/T/vkqRgJKyO+6VwJwxs62V7JPTK8UYcfExRVRlGGTriPFVau7HNlsT6QkGVlxxdE4+DBAxhGiihMyOfzdDqdXadeSZLwfR9VVXfCpYf5WsIwRFGUnbwnOp7n4XoOA7tBrbbFxMQ0hm5iGGkUWcN1PfK5NKoiOH/+NLph4jguiQiZnJyi0+lj99tkreEbfzZbIElCet0mYeCRzWbQdBNZhiDwKeQz2La92xZVVYmTGMdxME0Tw9DRNIVOZxihFIsESVUwUzoPnb2Xnt2h3TXZWluilMuQMTKEkU86bTLot0mnNLL5Co1mnWzOpD+Q0XWFMPSxbWfHP0jFcTxy2TS6rJCEMWkjRdpM02m2ECIkDj0219pMjM3Q7TVRZJ1yqcLk+DjLS8tsba7T7daQhEPKypJJqYxVplhf3yAKI6yURSqdZmuzTqVapVQoUMjluXjhPFEUY5oWtdo2R44cIREJURSwurqCqsr4vj9Mzx4P+6VUKmHbNqlUiu3tbZI4RiSCwcBGVRWmJmdoNlps1+oosoqsykSJIBaCfCZDbzBACGh1mniBx+JigyPHjhInMX4QIIRg0O+jadpwQcNum5ShYekat548Tr83w0MPPkC5nEeWJZoxtJpdAj/GMjMoqooQMhIS+xcWCHZC5i9cqCNEhGH+/+z9WYyke3rWi/6+eY45I3KorHnNPQ9uPOhgb2zA7I1A4sbIF0gbYQnJF4gLJCTgwkJCQlwgIyRfIiQ4V+fIGwHH+3CYbHC77e5e3WuoWjVmVWZGZszDN8//c/FFZQ+0e3Cvtt3d9UqhtSojMiIzIjK+93vf5/k9BqZpNI2obbFeL+h0OwRhRBjE2KbF/p5JkdW4nkmn45EkTerypz7xSW4f3+L+vQ+oRE2UxNiqRnfQR5FldN0gjDKQNeqyZNDtUZcVd+7c5WC/z8PHH3A5PWe1moBUotky5TZlPV+gKQYHwxGz6ZyaEtOG0X6HtT9jvfEpK2g5XXw/wFAVdEOhLkoCP2B//xpJUoNQaHk9HGfGbDbdIQlqDN1gb2/EycmXuX/vIa3PDTg7O0PTNOI0QVZUVEVt/t5/TA/KL+tb18tm5WV9CCWuwIcvGpEXzYEQ4sqi3Dh8GgtwUbz4mkxVlVdNiaKArqu7RqRGUV4Iar8+R+dr28sXj1OWBabZUFY7ne7OEVRRFAWGYezEtjpC1KRpiud5VxOaomhWCqZpNk1CVV6F//lBRafbwjINNtstdlmDUHdWWhnPtXnnnS/jtdosV2tu375LFG1ZLRZkacjjD875yZ/8Keo6J45SBoMujx49xrFNDLtFXpTUVYN+b7Xb9LrdpqHKU4IoAQRxHDMY9HciXQlZUTAsgyIpOBufMltM8f01Rd7CdXQ0VYAo6HX6nJ4/ZzafgiywFBNZEoTBmrLMODo8wPcDoihku902CHjTxvd9RJljmR5377zBaNhwTIJojaZpOzeVieddY72ekR8OmUwmXDs6IIkCHEtAJfGpT7/K+fkZi/kJpq5jeR100+Lw6Ii8yCmLHEmSSbMUVVUIw4Dp7AKn5RInMfujIbP5lNu3b3Fy8gRFUXnrzTc5Ox8zHo+J4/jqdYuiiKqukaTmQF2WNRcXEyzToigKarlsmkkctn5AGMV0u10ODg6o64r5YoZt2mw2G1RFpVYFrVaL1XqN67qUosK1LepK4LoOX/3KV7h+/TqDXo/VakGv120mX0XJnTt3QYI0y6lrgWnbjT6lKAjDkG63ja6rGGYjvJZlmfV6haKoINUgahRZZjmfkmcQhCnSWsNemqRpiGXp3HjlNus04OjuDcqkQFcUhASma/HOvXtEYYCoa6qi4Ma1Aw7393YAu4Knz04oy4JbN68zW0ypyClFTbvr0u/1yJMC2zTYG/Q5vnlIXIUsVhN6vTZJmuLaDlXZNLeNXd0lLgKCMGT7wQM++ak/Q6szZLFYMhrts1zOSZKIOAkR1KRphmU6PH58wqc/+2cwDYOTpyfcvn2HegdklGSaycrLjuVl7eqlZuVlfd/1wmLc2HpfrGYk8jwjz3OyLCMIgqtG4eLikjhupgjn52OKohmJJ0nCarUmSRI2mzVhGDQriTjewcuyq8f82lSlYbOUZUkURyRJQlEUyLKCpunYto0kSWy3/q5pkojjhoUhRGMPbtYgOWVZkuc5L9gOdV03UDih8Pz0jHarS12DaZoURcF4fMZqueDo4IC6LGi3WmRZwvnZGTduHONYNq7T4tnT5yRRTF2XaJpEr9uiqjKiaEtd5XiezWR6wec//z84PTshyxPW6wWCkigOkXcW5aZZEVi2SRyH6KoKomKv3yONAlazS+Q6hzJFIsc0ZZ4+eUi77eFvt4gKFElBU3WqqiQvUmpR0et3KMuCoiiJooiiLLCdhiQaRilBkCHQ2WwSPLfP8bVr6LqMLFVAxdPHj5Comc9mGLrGrZuHDAYul5dPODzs0uvaWIbE3qCDZRo7EGDGRz/2Ufb3RyRJhKBq9Da+j23ZqKpCLSrm8ymqKnFwOEJQsl6v8H0f13VptVo4jsPDhw9xXZc8yyl2q8WyqDk9PWc+X9Af9PFaDnt7A9brLaP9AzrdLqqmEYQBAoFu6DiOw95wSKfTQVEVNE3j8PCQvb09hBBohkktaDD+6w2Xkwm265IVOfJOMOsHPrZtMplc8olPfBzXdWi1PKqyZLPZIEkSvt8c2MMwRNd1Li7GCCqKImO1WlDXJYpS85lPfZSjgy6WKahEQV4V1DJsooCz2ZTT2ZSTyYSnZ+cUeYmmyqiKoNexcE0FTS6xTYXDvQ55tOb89Cmr9ZKiLMnyglrU3Ll1k16nRcdzOBgNGfR7fOQjH2EynTGdz5kvlxxdu0av2yNNM9rtdsMoAjRVQ5ZVsrRAQiHPCoSAhw8fcHlxgee1SOIcVW0YQ8+fn/Dg4X2Ojg44PDwkzTK+/PaXWW3WHF0/ZrlaISkSTWbY16IxXtbLgpeTlZf1IVQjwpR2kCl5pwWprsirLwBsdS3Ybre765opies6yLJCEkc8PXmGZdk4js1gMCAMw53Dw0DTGgR9XedXkLRmotI8pqIoFGXjysnznLquMQydMIyBRpQ6m812BzgbyzKJohjD0HeZOxK+7ze25qIgz3NUTaXd6tPrDul2UhzbIQpnxCImjmOyNGIxm+MHPoPBHvPFkkF/j9PTM+qiZtAb0fX6KIrMcrlgMrmgP+jSanlcXDznrY99mrPxJfPllLxIUBTB4ycPkRXodft0en0CIwSgKAsADLOB48VJxKNHH6AoNav5lMNhn7pIUQydPIrIooQ4iiiLjGDr0+8NSOKcJCzRDANFkXAciyRJWK9XWJZJmvqUZY0s10yXM4pK5dOf+TluHL/G6dkpB0d36PYcfvu//UcMvabbbeN4MkWR42/X7I/2icKINNow6g/xWiZxErGcbPjIp97kv//X38PxOrzx1lsUZc58PqXl2VxcnnLz+jGeZ7BYrDk7P0c3NJIkRAjBerOiLHPSNOb09BTdsMiyDF3XuXXrFpeXl9y/fx/X9XC8FocHR0wnM3rdAUWRoesqmq2yXG2pavD9ZjoxmV5iWTb9fpd2p0UWpUynU9I0xbBMVqsV0m7l2Gq1kFUZ01GZzJf4YYysrFmuVmRFTQ14LYeiLrj/wXvohsnv/d7/pBaQXY4Z9Pv0pB5JkpCmCYO9LoPBgIcPH9DtdlE0GV3TSZMMUVXcuXXMoweP6PcN6rrD+WVAkcZomkRRVpw9P8OPUvJCcGv/mEdPTqjqmNffuo1Ezpuv3+Dd9x7Q71qM9hwUxeFifsZkcsnxjetIkowiq8RhTCVKZrMFR0c3qGuFJ09PqABFN5jOV2Tv5Bxfv46i6ERhuFsxJSAkNF1FlnTyssBxWpRFSa/XI8sThsMBH//oJ/i//t3/m/V6ja5rPHx4H0O3+ehH3ySKUwRw/cYN3vnqV9E1g6IoG6cRL8Bwf+wfZy/rT2l9z5OV3/7t3+Yv/+W/zOHhIZIk8Zu/+ZvfcL0Qgn/0j/4RBwcNBOvnf/7nefTo0TfcZrVa8cu//Mu0Wi06nQ5/82/+TcIw/L5+kZf13ZiHPwTv84sUvm+69+bLDcwsjpuDuWEYOI5Du92m02mjKDKj0ZDBYHDFK9G0RsD65OSk4TGMhjiO3cDiFOVKhCvLEpv1msVifoWjf4Hd3263BEFAnue0223yPMf3ffK8QFEajcr+/pB2uwWAYZgI0ayl4jhGlhso2ttvv82DBw8oy5J2u41jO9iWhyypVCU70aJ0FTrY6/ZoeW36vQGvv/Ymo+EBZVFxdHSNKIoBFUP36LT32N8/QtU0XNdhNptgmBrj8TlFUbBerwgCnyzPME0TUddkeUYtmuyWIPA5OXlKEARkWcZquUSRZfrdToPWX6/5yJtvYus658+fsVpMWa/nnI9PCcMAWYK6rFEkHV13qKuGrDuZXFAUOWEYkqYpdV2hqRqSLFPUJXldUktwMZ1x55U3UTSbJKsIQ58k3pAkm926qcViPiOJQoJtgG32iIKa8dmKkycXpIngcrxEEgam4fL4yWPmixme51CLitdef5WyyqhFjm6o9Ho99kf7+NuAvb0Bvu9jWSaarpBlKaZhYNs2YRBycXFBEDS3cxwLXVO5nFxy+84drl+/Qbfb5fT0OZPpJePzMVEUkyQxW9+n2+1yfO0QWYaqKrAci3w3BcyyjNVqDcDR0RFJkrJcbUiygvU2xHZbOK0Ohu0hqRqqbjAa7TEYdHFdi6JICcMtnmcDJcvFHFVV6XQ6GIbO5cUlDx8+ZH9/BBLEUYxu6JimjmFohOGWw6MBo2GL/b0WN/f6qHnKwHYYuB6TszFpkKCiEoYxWVHQHw4Igi1lkZDEW/7MZz5Gr2UiiwzH0njjtVfodLzdCUUTruk6bXRFZ9Drs1qsmE5nhFFMUQmEopJmJZtNyGy25vzskuVqQ5GXOyeeiWlaaJpFVUEURg2PSFNQFMiyhPXGR1F0VFWl2+tSlgXb7Zo8TxCUrFYrdMMgzTKSNCUIAq5E8B/qp+OPSH2/eIofJN7iB4zN+J6blSiK+PjHP86//Jf/8lte/0//6T/l13/91/mN3/gNvvCFL+A4Dn/hL/wF0jS9us0v//Iv8/777/Of/tN/4t//+3/Pb//2b/Mrv/Irf/Tf4mUB3/m90tjmxB/tIgSy9IIqKa7uqy4LsiRGiEb/IUTTrLwg0L7QsCiKshPUKliWhWU2EwxN03h+fo7X7dAfDSlFRS3JCFkiSlIs2/laI9Jpc3iwj6apVw2MEM0ERVFUqrKm0x6gKsbO8ts0PIah7sBXzRqq+dlq6lqgKBqKouE4Hj/xE5/j1q1b2LZNXVekaUKSRmy3W/I8I01TTNNsBK+mxmI1R9F0Do+uM5kuWG4C9ob7+GFAEAdIqopuO1zOZzx99gTDULm8GBMGAav5kqoCy3TotPu02z1sy6Pd6iHLJrpmEfor8jSkyhJajo2pyYTbJXG05sH9twmDGf52RZJkxFlNlFes44hMqigViUqWsD0XRVUo8gzTUnA8ndVmTp5mrFYbaqAQNWldNvZTpclnqguZIvV59OjzjC++wmL+kCxZoMo5RZVwfOs6WZnhxz6KLlFR8MHDe8Spj+3IPHz0PpqmcvPGHd5686OoqkarbeO6Jv5mi2VYnJw84/333mc6nbJcbzgbX6AbGv1OizLP6Xf2CNYJRSxYTLYMuweM+n3uvftVTh4/4eGDhyxnS7IkRZIELc9GlQpGow7n4ydswiVpkXJ0dB1DayPLBqZjkiQxuqpg6xrBdkGSbJHVGiFnqCYMhgNELaGrFqO9Q77y5XcY9YcUWUESxewP9/joR9/klbu3GY2GjPYPSJKCKBLkqYQsa/S6XTRVJgq2HB8ecjDqk2c+qlpS1SlRHCIpEn4Q4rU6FGVjiU/TFMdzyasK0/UYXbvORz75cX7hF3+K//Nv/jX+/M9/hmv7Lq4uYcoSVVZRFDlCrSjqAt2ykBSNxXJFEDaPEWc52yjk5q3rVEVKHG0xTYONHxJlNXmpslgm5KXM/v4xpumQxjFlnmI7Nk3muYym2hiaS78zwjNbyLVEmWZUeYpl6aiGiuU4XE7m1Gj4fsKTJw+R5BJdlxmN+uwNe6zWc9IsYbg3ZH+0T5XnaIqMqAtkpVmzFdXLVuUPq+9oe/7DnjrxXX7/n9L6ntdAv/iLv8gv/uIvfsvrhBD883/+z/kH/+Af8Ff+yl8B4F//63/NaDTiN3/zN/mlX/ol7t+/z2/91m/xB3/wB3zmM58B4F/8i3/BX/pLf4l/9s/+GYeHh9/Hr/OyvpMe7Y8qVxM0tNYXIn0hKvI8Z73eNALEssIwdIqisX2+aAxM07x65BdpwwCS3FBfdV3jrbfeoJLA931kRQEBYRhRlSV11dyfpir4QUBeVDvqLVerJVVVUBQTRZFRFBXLsq8mN6qqst2GHB8fkucFVaVQlgVpWl7l+zRZQUUjHLUsFFVGiJrFcoZt2UgIDF3frZ1KEIKqKAmDiCz1GQxGvP/+Q157800EDdq82+9gGR6qYmBEBu1eBxmP02cnZFnFZz/zadr9A5brLbpuEQQxkOM6HaIwoa5qomhFmqYURcX+/ojLC58sjzk7e4ZuSCiRRJY1K4vlcs3aD9BMG820EJKKrmq4ohEQ9/tdFEMnWa2xHBM7sJAlhSwvMCyTtR/wQiiQ5yV1VWOaEqYp2G7GPH5UY1kOp9sx/X4XwzTxWi0WJzPGl5fIkoppW2w2G+7FAa+8chtFUXAch/H6glbLxn71JqvNBsPQWK0WdLsdVss5yIIw9Ll955WGMyNkbNshDGL29vYp8pI4CdkQoKkSb77+OlGUEEQpk8mEw+MDsjRhWxd0Wy163RbT2QTddLEcmypv3ClIGrZt4K99Ls4n5HFjpy1FyZsfeRO3ZSHLAtsy6bT7zOdroijBsmxOT88o0pwbt26yXC754IN7eJ53RRT2g5A8STFNlb3+AMvSee/9OWIHsfAct9EZaRqe28IwLMpK4PsRmmmhq/pVfo9hmJRlxerZKZtugG0aHO0POX1+QrfT4Sd/6tO0B0PuP7hgvs4oqwJFkZEUhSzPGeztkWceyKDqCpswpGf0SbKcxXLBgWmy2iwp6oonJ884Gh2gaSZhGLGYLVBVnddefZXpdEoUBtSShKFZGIZFEqd0Wm0ux+fouopAIo1y4iTA9izKqqCoYjabCc9PFeIwx3FNFFXh5OQZo+GQKCx57733GI2OCf014/PnzGYXSFLF3bt3qaoKVVG/yQP4o1Nfb8f+o4Y0fqvn5bu5K0l8n7bpP8H6UAW2JycnTCYTfv7nf/7qa+12m8997nN8/vOfB+Dzn/88nU7nqlEB+Pmf/3lkWeYLX/jCt7zfLMvwff8bLi/rj7deCGdf6FCKoiCKYkzTwLIaF01ZNl9frVYEQYCuN4RZVflaQNmLyvOC9XqNLEtYloWuaQwHe6iKQlVVbNYrWp5z9YdVlDWe124cE7u5TlUJZFm90snUtbhaEW23W1arFWVZ4botkqTEshx6vS5lVRCEW+aLS7I8uaLebjZLTs9OKIqU7XaFqsoEOzGn5zlIUk1V5ahq82czn885OjpkNpty8+ZNbLvBvOuaxXYTsvF9KlHidT3KumbtByw3IXdefQtZc5AVg7IQdNo99vePODw4Zn9/n1u3bu2mOU3a9NHREXEcU9UFVZXx/PQRm+2C9WbJYjGl3bJJs4gkDgmDgCROqcqS+XyGrmo7x1XFerliMp1cuX9c10HsnrMiL7AsCyGa8EhZVvB2qdSKIhPHIdQVcRTScluIWiIKExy7zXYTUlWCd9+7RyUkvFaPTm8PRdU5vxgTxAHbcI2sCW7cOkSWK6LY53IyxjB1bMthf3SAIutMJjNmizlFmbPZrinLguFoj6OjQxRFIcsyTFOn1WqhaQqHRwcNoZaaTrdDp9vjyZOnSJLCarUky1Ic10aSQZFlTNPk1Vdep9fZIwkLqA167QP8TU6WVmRZcSX6fu21V1gs5nQ6HWRZ5u7du2ia1qDmw+Y5nE6nLJdLer0O/UELQUkYNmLuIq8I/ITlYkOSFFQlhGFKXcuoioFjt5AklcuLGYZhNdoVRQEagWkY+o2gOAj44PETslrw4OkJF9MZrW6L/WtDDo8HKJqELCmURYnntVBVlRs3jrl+4xpIFUmWMZ7MuP/gCaPDY9KioBICpBpFhcvJJavVkiDwGzeUqnDz5g1u3bqFIit4jkfLbTW6tDJHkmo6HRevZTHoDjAUB6lWGXT6qBJ02yZ15ZOnTUM6ncyRJZ3h3gHbbcSrr71Ou93h7Ow577z7NifPHlOWGVEUslzOCEJ/RyZ+WS/ra/WhNiuTyQSA0Wj0DV8fjUZX100mE4bD4Tdcr6rNnvrFbb65/sk/+Se02+2ry/Hx8Yf5Y7+s77JeCGeLonGOGIZ+lQQsy9KVm+bk5ITxeHwVOljVX7Mdfy2fpzngXFmYd8wV07TwHJu6KgiCLfP5lDRJGi2FJKPIyo6zIiEhk+zSb2W5sUBXVUVVlbg7Ee12GyJqhThOqaq6aV4cG0TZ4NizhCDyiZMQxzXx/TVh5LPZrhBUrJZzQn8DdU6a+JyfPeXJ4/v4mxWKJJjPpxRlRqfbwfcD8qykrkHTTHx/S5T4pHnKbDGn0x9w59U3QDW5nK958uSk4cOgUhZw7eg6sqyQpo0bajQcYds2y+WCfr/HbHbB+PI5uiExm11Q5DFRtCEK18wmYzRVoSorqCXmswXUNWkaYxo6SRxR5Bn7oxFlnu2Ad9YOELdBUZSrZGfDNBByI+p9/PQJl9MLLMtks1kh6hLHsnn84AmL2YrlcsVstuD0dMz+/hF5WbPZhCRJTlnVuK7HteMjdFMhCJfM5uMGAihKpLrCsUxkAbpqYKgGuqajagqn52coukacJZxdjJkuZtQIdF1jsVzQ6bYZjob0+30URWF/NCIMQ2zL4eio+XzYbreMx2MkCWzbYG+vg+PoKErNT3z2UxwfH/LqK3f5y//HX+bWjVvUu7Vcq9VCULFczdF1hfl8xt7eHkiC8/NT6rqk220jRLVzaeU8ePAB6/USVZVptV3SNKXd6qAqOv42Jk1KdM2i3ervwjM16gokVEQtk2Q5Wz/AdlwuLicISfDKa68iKxIoEkGcUgoJzbTIynI3UVM4vjHitdfusLc3JAgCzs5Omc0mjMdndLstTMvAabXwo5QgSkFSESjohkEtSlptG0vX6XgehqaBKNFVhdOTE4LNGtvQUSUQVYEkakbDIZZl8Orrd5GUmqqKyNMtg56HZ1u4lkOZFoiqQhISt27eRpI0bt96laqS0VSTw/0GhvjZz34aqAgjn1bLo65LLi4v2PpralHwoYseXtYPdf1QWJf//t//+2y326vL2dnZn/SP9GNZzVn21w5qzb9fbBIbIFsTbtfj1VdfpSwrkqQRbn4t+bihsgrR5Ptomo6oBZqsIiMhU1NXBbIEH9x/n8ViymK5IM0yyjJHiIa/IksCVZPQNIXpdEoQhLiuh2WZzQpIkul0unS7nYYFYZgIIUiShFrUSLKEYZqUZcl26+M49pWGJkliFKVxuUynYzbbOVGyIs02fPWrX+D09BGGKXPn7g2m8zNm8wu6XY9ut4Usw2q5RkIlDHymkzHr1ZwoConjGMux2QYhZRMru9PH1MRRjOO6xHHEdrvCtFTyIkXTFdptj16vg+vayLLAsg2S1EfUGapSsd3Oubh4Thg0tl9F0hgNR+RZ4wjyXJv9/SG6pjHaG/CJT3wc3Wie97IqkWAHoatZrVes1mvKWjSQPkVmNpuxXM7odlr0uy2SMERGoi4rirxomlJZot3tIisKs8WSMEoa+7MiISngB2vaXQ9FhYPRkNdffYXDw32ODg4YDHo8efSI6WSCosh0ex3yIqMoMkAQBE3DF0Q+eZmTZSlPnz5hOrnEdW36/R5IAsdx0FSdZyenCAGDwYBWy+PJ48eNYNo16HVNqiogSZe02zrtts7z5/ep6gjbsilLQZblxHHMarVgs10hy43I+fz8HCGax3khIocGWS/Jjf09TVPiON6liTdTkrquuby8ZLlcc34+3jmuVNbrLbKsYJo2SZKR7RrdVrvNarViONxDViTCKEDIIKnN2iXYTXXyPCHPQ/I8QtMk2m0PWYYsS4mikMvLS6qdXV/TNNI0x99GhH6EpZtItUAB6rpx7KmKjK5pRJGPqimNC6vIkERJ6G8Y9NoM93rcvXsbSRLcuXsLw6o4OLJQtJyjo30+8YlPYxldFKmNY+1RFjV7gyHH167zuc/+JJpmcD4+J0lDgnBNf9AlyzI8r80bb3yEs7MxX/rSl3j85DGI+ht6FPFjMG35USX2fhj1oTYr+/v7AEyn02/4+nQ6vbpuf3+f2Wz2DdeXZaMKf3Gbby7DMGi1Wt9w+VGqbwi5+lNaL9Y/L/gjDea8vqLANh8k9W6EfBPLsjBNE8uykGXl6/D2Nc+ePefi4uLq/kQtKMoSSQj8zZaL8ZgsS2i3XXRNJs8TomgL1BRls7JZb9ZUVUGWNYnL6/V6t2qSrh5XCIGmyVi2jqDJANJ1fZcBVBP4EWmSoWsm7XaPQX/I/v4hdQWq0thIERXr9SW/89v/X37nd/5vNttL2m2Dhw/fQ1VronjNZjthsbxAULBcztgbDtB0hYPREEORefb4Idv1gsnlGc+fPaHlWfS6LbI8RtMV/GC9ew8I0iziK+/8PpajoKgCXZcJwg2Xk3PSLOHmjRsYhoGoKxAFQuQYhoyi1JR5hiQkbMtFU1Rsy6bbaVOVJYau7hwqPtPJhDzLG8uyae2mUhVVWZImKZIkUwlBWddEu4lMmed4joWpyZRFSttzsUwdCYFpGuiGxnw5Jc5isjLldHzKZD5luVny7Ow5iq6SVQV5URKFMXlecO3oiCdPHrNaLpCkiiDYIEmCMA5ZrleEcUSSpdTUiF1opCTDnTt3iKIAx7XJ8xRNVdD1xmkVRjFJku4mac37SlAzvjgjigKePHlEWeZomows19R1w6SxrGYduV5tSdOMMAzYbJYURY7rOqhqo69K03SXmFzuBNwyeZ5j2za9/gBJUqjK5u9jNBpiWSbtjodtW1/H9qmZTC6Zzaes12tc1yEvSoqqpqgFW9+nEjWXswkoErqlU1YZUeizWi4pspLD/QOG3T5lmhL6a27eOsKyteZ30zUUVeP583OiKKWuS1RZxtB0qrxGZIL5eE4RpmgoyAgQzetfFgVpnPDk0cPda25yMOpwuN/D3654/uwp5+dnpGlOGCb09tqgVgyGPVabDU+fnmM5PYpSIQhyLNvi1Vfv4gc+XstDVRWenjzm7is38f2GqRSGEfv7h/S6Q2zLo9Xq8PjRY2bz+RVo8ke1T/n6kMM/7ceAP+n6UDkrt27dYn9/n//8n/8zn/jEJ4BGNPmFL3yBv/23/zYAP/mTP8lms+FLX/oSn/70pwH4L//lv1DXNZ/73Oc+zB/nZX2I9TVxrHTFTnEcB3V39iiEIAgibNu6Ere+yNd5MVV5EUjoOA6e517h9KVapixK4jRq9C6+z3IxRYiSuvK4fffgajT8An2u6wZ+sOHJk0eAyuuvv4aqKl+H35d2zZCMYagURQ6AaRhNFk82RdQKYdjYJUejEbpukmUFdZ0ync6oq5z+oM0HH3yFrAgJwzXXjg94/OR99gZHaHpFnKwpC8FX3vkikmzTavUxbYvFcsag47GZz1kv5sjUZHFAGKeoqszh4QhfrijLhHfffZePf+wTBMGW2fyCskoJwiVnZ08pi4qD/UOqysZxHDrdLi2vg6gF6/WCejdRKIuUOq/ot9sUWYlQm+YsjiLs6yYnT5+SZxlREDCbTnBdp1nn5QWSLLO/v8/leIIsS8iySpEXeK02wXZGZyeE3W5WlEXE3Ts3eHryjNFowNH1EY9PnhImKYKGrgsllm2wDTYUeUa316asmnykLC4Igxh/E/L00RMkGVzbYH/UpywKDFPl7GJMXQvyPCXPDQ6PDqiqAlWFKk8JIx9ZlncZTTkiqSirgrIoMVWPdrvDcr1CURT6vQ6b5ZooCrEdlySqae+1KDL41Cc/i6YqnI6fc++DD8gro4kbaDtsNmt0Q0PXVcYX5xiGTUW5s95ud2GajcstikL294bsDQ44S56R5xXdnst2u8YwFSS5Is0iOh0P3TTQdJOyqtgGW4oiY7GcY9suktSsWSVZoabk4uICqDAtg0G/TbgNyJMUQ9LpuG2oQ8wDm0cnT3h+9phu30PVKtqehyKrqIrOfL5Et2pu37zG+GyOY7ZxHRfLUFGUGyxWU9qtFsaO9FuWzQlIt9vFdV3G4+d02iZ/8Af/E8tu42eN4Hi12ZKXJZJUc/JsiuvkvPrqAW7H5unT59x97Q7n52f4wYb9/X1+//d/v5mQpQm3bh2TZin7B3soqsnP/PTP0W4NqEoJTTXZHx2yWi0IgoD9wQhJgroWVzEbL+vHs77nZiUMQx4/fnz175OTE77yla/Q6/W4fv06f+fv/B3+8T/+x7zyyivcunWLf/gP/yGHh4f81b/6VwF44403+It/8S/yt/7W3+I3fuM3KIqCX/3VX+WXfumXPhQn0IsO/Me2S/1+zkC+zXMmSdLV9CTP850jR97Zf+Xd+DnbrYi0He6++WHKsiSOYxzHQVFkOp02kqyAJDVQN0lCVE0Q2vHxMVHcRjcULEPn3vvvM1+uaXd6lOUeYRg2Y3dJotft4noO61XAZrNlPl9wcHCAvHMaNWJFCd1ozjirukLVDKBmuLePbVvEcUotJLK8RDdrHMcBCdrtDk8eP+LsYoxuanT6A8QkaYL2hkecj58zvniO5coMh/vkeYppWTunjEY8jbEPR+z1G/bFarOk3+sgyxvWi0t+//f+G/3BIecX52iGRlmVFHHKbHZBmvqMxz6WabNar6lFxWa7otfr8fTpCcnOfm3bFqfnp7BeYho6ki6hqSpRHJHXKVVdsL+33zizkgTLtgmiiMFgj7PxmDRNSYsSTdPJsoYO7DgO222AaxtsNht0RWlElp6HbZtois6jR4+wHIflZsHwcNj87EWG2+qiGwav3LzOcr7A81wWi3gH9Msp84p2q8vFeoagQiA4OtxnOBqQZQnz+YwsS3YTMf0qVHG5moOoGY0GTSyB56EoMqvVEsu2UAoJSRb424DNPEQzTdIkxpQswizh4GCfvW6f2XTN9SOLuigpC5mikBmPL1mstuiGC6WMmpd4nothKlRViYRMEM7p9fqkeUyaRBimiaqpmLqBqqqInVfu8cMn1KJkPtuQpk3eT1lV5ElGf9AnikKqtKJGoOkqBwcjgiAkiUN0vUMYRTuXXcLx9SNWmwWOY7LdbiiylI7X4ejoEEtz+OCDB0Rxyt1XXqXX7bANN6iKzBtvvEkUxYR+hB9EO4Be1Lz/q4KyKKjKmtl6Rrfn8rnP/AT3HnzA2fk5hmHx+MlTDNNivdrw1//6X8e0VBbzU1zXQjd06vWW5XzOxz76Mf7rf/ttDg5G2HZAWUuomkOVZ7TaNl5bx1rLPHr0Ae12m5s3b7BarcmLgqKs6ff3yPOKN177LL3eCMduMZ+vcd02ntvaifSjHUVavorp+FGdsPwv9Ud1CQmaBOVv8/3f8a6/3Q3+BI+r33Oz8sUvfpGf+7mfu/r33/27fxeAv/E3/gb/6l/9K/7e3/t7RFHEr/zKr7DZbPiZn/kZfuu3fuvrLKzwb/7Nv+FXf/VX+XN/7s8hyzJ/7a/9NX7913/9Q/h1frzru3sffbtbffu3sSQ1Lh5Jkr7h9Wxw+02WStMgcNWovMjdyfO8STeWDGoU8jRrmhpFoc4KpLJCNRXCKGTtb5B1gyfPLtgGJa+/fpvDwwOiKMK1W2RZSq/XZbsJyNIC13WwLZNZGLBer3CcRvthmo1+RdQN+yJLczTFRFM18qxkMLCRNRMziKikmk244sEH97h96zZ5XlFLoBoWriERxWuW6wDHtnYU0hDT9FClHp59QJLktFt9lssNnY5MXYPdapEWCcOj68xXG86ej9lu5rz55itcTk7YGx2SFgW6aTBfzplentJuGxQtizjeYJg2d+5cI8squp02aRqxWE65d/9dyiwgSVa4jsvFxQWu6zYaIEslTVNUTSUJEoIo4Pl5QVFX1FlOb7DH+HKCkFUqkYOQUZCRaoGpaU2IoiKoy4Qs0jEcm6qs0XWV8/EphqZSyyqT5RIhBOPxDF2zkeUCUdXUZYmmGXhem8uLC65dO8QyDPIkRRYN2G+9XWM7BpJaoZqCJ88+wHFcDNtgMVmg640rqSxy4qpClBUH+/skYUwYJCwWKxzXRdNk0jTk8OiAKArpdlpMzmfcHtwmT8zmeZAU1BqyJMVzTC7H52iajiSXPDh5gut6BGlJVsnIKnR7LkjNOnO72SDJEpJU4XkG1TpFRcU1XdbbLbTaVLXCch0jCwNX0xAobHLBxXhJf29AFMdUtSAv5jiOxbWDQx4/fowsK+wN91BUGc/18Lch14YHxEmKH2xZzXyQJCQUJBTKQpAXgryCtb+i2+0RpAnPxyd47TZWaZMkFZeXG9abBjCoaDKKApUsyESNbBjUmsLJ+DmqUqN68Oj5IyxD5a3XX8O0XSoBJ8/OSMua3/nCFxGiQCHjrY+8xWQywfNM5rMLDN2kjAtkSaUsBb1ujzzLOX12iqbLLCdL9nsjWt0DQMayWnzso7d58vgEXbVouQdYpsu1o+t0uz3Wa5/lfMH+cIioJRyrhaboV860F9ymH/n6Ntbi71RXka/frlH5TmiL7/S9f4J+8u+5WfnZn/3Zbyt0kiSJX/u1X+PXfu3X/tDb9Ho9/u2//bff60N/i8f6cDzrf1rq+50Gfedv/6M3KsDO9dOwM16sdICr/1cUBd/38TwXVdWu3EO6rqNpWiPKBSSxs8qaZgOAyjNsw2C5XCAUCKOYqqxpt7scDI+4fv2IWlSoqsazZ89wXRddN9D1AlXVyYuS5WrF3t4erusihEDXnQbBX5SUZYXvB2iqiut41KIh59YViJ1jKYoikiSg1++TJCmu02JezFhvNoThgqdPH7JZzxju9TF1E8d1kCWFs/NTjq5dQ5IFmq6wWvmkacGdV15jOlswGo1wnDbLxRp/s2B/tEdVpLRcj/HzE/qDawz7+9RVTVUW2Fabbvcu7773JU5PntLvDxjuHWJoIAkZWZQ4pkpYQq/Xx/M8xuMLer0+uq6T5zlhFOK4Dq7noak6nU6X6WRGq6NxdnZGiaCoSizbRkQJaRJj2y51XVJVZRNlIDUHUVWRWc6XHOztoekKXstFzQuSImsYNVnCcrWiKsvGmaXIZFmO57Uwbht0Wy3KImvydiQJ27EZDPrUoiCMEqbTZppimjZC1GzWG4YHRzvmjoTrOmxW+U603Lhouj33KviwqkomlzMURca2XV555VVUTSfPS3S9EXDnRU0ZRNRVhaoqtDttiqJkvdlQ1jWW4+CHIZ1uizBsAIT9fo84vqDX65LEKev1hjTJGQ6HXFxekqQ5QlJAUpAEpEnc2PerEsexidIGlGjbBkEYkOcVpqEBkGdN4z6fzcjSjLbn0W5bqDJ4Xo8kCcnzDN3U0RSdtE6okAjDlCwtieIU1ytpdTyEKCmrjDgOkSSVJM1wvTbBNECTZeIsQ9PkJhJiOERXLdIkpOXZrLcLqjqhpTusN2M++enPMhqNOLu4RFYVagSdTpdwu+TJ02d8+lOfZrvdMp+vmoTojkvLbXAAaZLib33yPGc0OiJPkybawvbo94YEQUi0s20fH90iihI8x2C1WnN4cMg0n6EbGtevX0NRNDabht/UOP2az5+qgh+HTdD3cxj4rj7hvwNo5Q9juFxNbf4oD/4hNDgvs4Fe1ndVL1D6svy/MlO+/jYvUpFfCHF1XacsSyRZRt3h84Wo6LQcZEWiyEuqMqeQwHUcojxB1GBZDlbbpO11miTWWlAUBb1ej6qqmEymRFGEpmtomoEQYmeFbiYted5YH1VVQVU1qspjvd4QJzHz+RzTtAijAFDQVYXNZsN777/HZz77Kfr9AbKk0u50aLkOUbjA0A1u3rhFkoTEcULLa3F2fo6mmSyWF7z55sfRDJfxeILXspnN5uyPRiiKQRT7XFzM+bk/+9MURchXvvwFojCk33exVTh98gGGaeM5Nv3egKfPHhLHOaoM68UMU9MYn55gGDqSDKpccXztkMlswuXlJY7jEoURves9Tk6eoal6k0QcRtSKYDFfUNc1edEEIa6WC1qtDlEUo6gyruaSZRl1XZHnGZZlN0GRtaDTa5NGAevNgtGww/jijMV6i2m7rNdrBM17IklTWq0WeZFz8uwp9k64q2sqURQgCairghvXbzGfvc9kcomsCAyjjRAKYdjY0w8OjpBVgywtsNo2cZySZTmibszqkqJQlzKW0yKOcvKywjBclssFsmQwGT9jbzDENF103STwQ/IspqoqDg8PSdIGJNcbDDAMg/liQVGW2I5DGEYsl43WJc9KZFljudxQVTWtVo8kmiArSpMbpapUeY5pOYiqQpUVbMdmONzj5PkJquYg6pROt01dxyiKxnq9xLJ0et02fuAThRHdbpeiSJlNzrh79y6DQYvh6JOs1z5bf8022FCX0Gp3SNOCLMtxnRb+1sdxTSzLIEoibEcnjjNW6w39vR6S1ASDqloTsGgYJmG4IU0W1KIgz2N0XWG5XFDaBaqm8+jJY6I4pd/voWg6gb/BNg0s02MyuWS99nd/g122G5+5v0CfamRJwnq1xbVbWLbNZrul128jSSp1LVPkNbpm4Xba6JqH53WRJJ0XLqUojnn48AGvv/4Wtm2yWm2xTLMJIZXZ8ZSkH4tG5WX94fWyWXlZ31V9fZpyM5qVv+E6SQJd1+j1ukhSc52mqTvCrEZR1VcTOUmSUJUmA36zWkJdIzSl0QHIBrdv32Wz8amLAlVVURSJsiyoa+j39xpxr51iGCa/+7ufx7ItfuInPoumqTvHRpPq+yLoUAiFqiwIw5DhcEBZFkRRievZVEVKXSSsl3OCbYOBr6qKlb/B9Rz8YMP4/JzlbIGqyuRZhFxL5GlKWWTEacTW73M5GSOEThiFBGHOq6916XT7+H7Ik6enHB5dx3E7vP2lewRBQhhELKbvcf78lOHwkOPjIx49fsKTpzl5WRL4EZaWoqoKeRJgWzJlGaOpGndvHXN+eYmEgiw1NvI0zbm8mCJLMv3+HkWV0+/3yeOUIPBpt9uohoaj2azDDVkak6YJkiRTi2o3Zm84JlmWoCs6lmEiSxL7+3tomkJNTV5lVKJh7WRZRpqnWJZJt9slTVPyMsMxbbbBhna7zfPTU/rdHg8efYBrOWRpyeHh4Q6cVuB5PY6Pj7i4aHgoJ4+eIKQmQ6eqauqqwjJt8iyjyAtM2yPwE3TdIgwSTNMkjnL2BgdMp1OytCArKizbQ5ZkbLdxOhmyymQ6p912EELCcR3efOst/vN//W9IisxwNOTpk8dX1OW8KJElBWQJfxtRFiAkmaPDY9arLWVZYjs2ZVnRcm2Ggz1kUWNaJp7rsFhO6PY8TF2m13HIkpLaNNku1wgEe4MB/f6APM84OzvF80yms3OWywWd7hDb9lgsVqiagqHZxFHOZDLH81r4fkhdl7ieRRhFVHWBrKpIUgnkrDczNF0mzUrCOKLfb1NVJXEcMhodEGwDsiREN1TyIqeoa6qiRGsAMDvbeEpZ1+RFhr8KCIOEr37lPYajPqZp7PADGWkSsFrOUFSD4+NrpFnGu++9g+M5dHsDaiGzWKzJsoKqnPHWWx9DVQxGwxZJEtPv9xCi5sb16ywWU4Z7feq6Ym/Yx7Vs5J3D78dei/iyfjg4K99tvbR//eBLCL7lZKX5MJGQZeWbvvbiNRHUCLIsZXJ5gahriixBV0GWKjbbBRcXY9IkRdf0xpq5t4csSeRZjqqo7A2GGLqJ67jkeYFju/zMT/80kiQxm82uLLhJklDXjashiRMUWaLbbZPnKULUHB0dEIZb5vNLyjLi4vwJTx7f59atYxRJ4mJ8jqbLXE7OmF5ecHF+ThhGjM8vaLlddE3j6ePHbLcbhsMRtu00iDpZxrEdOp0OnudhGCaSrNJp93jzrY8gKyrIKjUQhDGf+OQn+amf+TN0ei3eeffLxImPJAmOjg5ptzuURUVVFhRliu8vUNQK1zUpypSyLDBNC8dxWS6XV2nW7XYTdWBZNmEYE0UxgkYY3+l0d+sSi7qukGUoioyyKjEtg3zHNVEVib1+m7JISNMI3/cRCEohSLKCqq53YMCCosjRdZ0wDK8yoZBBM3WyPMNrt/DDgNu37uJ4LYQkkaZJk5Bse3TafcIgY390DV2zsUwH23KIopiiqNB1E9t26Xb7O1FmQVkKZtMFlungui0W8xVJnHH9+CZIEnGcEIQh6/WG6XTKxeWEs7Mzuv0eG99HUmSKsuRLb79NLQS6YWC5DjdvXafT8dA0FVmGMGxC9fb3D1AUDd+PuHfvPmEYNU20JKMA4dbncnxOEPicnj7HMDVaLQ9d1SnSHE1SGHR7jAZ9Wp5Dr9NmtDfAc2yOrx3hWCauY3P92jVkSWJ8dobntdjr79FpdcnSgiTJALkByskaQshMJwvSJCdNczqdFpquNmh7pbHBu14LQ7eQJY2qrAHYbFbkedxocZBQFJ28qgnTmPlyydbfIisSjmPiOibU5S6uoY8sq8ymC/xt0GjWpIrpZIy/2ZDFCffef48sz7EdD92wmC4W9Pt91ustg/4e164dI8sy/X4f3w8aMF1do6oqR9eOsG0bXddodzw8z8W2rUa38/JD/ap+nG3OP1LNyov60/5C/rB66198aHynDw/p6z5gpN0vqaoyvh8ymUxZr9ZURQZ1xWo+pchj0jRC11Xa7U7zAZoXbP3G5eM4DpqmoygKaZpRVYL9/X329gbcvnOXP/8Lv8Dz56e89957+L7PfD7H8zzqusJrtahrWK+3V+upvMi4cfOYssx496u/TxjM6bZtDE3m87/3O1R1Rp7HXE7OqEXZTBiQ2N8bMRqOmFxc0mp5DVQth8Vsw8nTU87PL7m8nKCpKlmWst6u8VoeN2/f4vT8jN/8d/8Xz549Q9M0jo6PEKrCw6dP+eDJQ1Br4iwAqaTdcuh2Ok2EpJBYrZb0B12qKmezXTbU3KJgu/VRVY0oSjAMiyCIODo6RtdNNhufVqvDaHSALEl0e12m0wnb7RZR1+iGDjsibGMRznbZTgWGrmJZCq5rUJYpYRQwny+Yz1ecX0ybqdfOjm4aJlVVXb3WiqygKBJVVaBoKnmRMVvMmS+XLFYrbt68hRA1262PLGs8fzZmsw4Rtcx2G1DXYJoWaZrh+wGLxZLJ5YR+f7ADoeVIEqRpiiQ3q8Ysa6ZH682awV6foiqYzaecX55jORZJGmPaFmfnpxiWyWqzJoxC/CCgKEuyPOf09JQoDtg/GDAc9hCipNNt02o1wuXx+BxNMwCZmzdvYWg6URCiyhL9bhvT0MiLjDDy0XUdUzdJ45RBZw9/FaJKCoejfQ6GQw6GIyzdQJGgSFKGgwFtt01dCqoCbNNjPd9gaAab1ZZOp43nuVimyWw2Y7VckWclSVIShTl1BUEQMhwOGxE7YBgWy8WGsoCyFNS1RBQnxHFCGIaNZmftk+c1ZV0jJImqrhBS4/jb2+tj6Mqu8alYr1eEYYRpOmRZgSRLfOxjH6PTblNXJY5jsVgueP+993E9D900MUyLqqxot1wkGV5/7RUOD/aRZNjfH2HoOnESYZoarmM3bh9Eo3nTXpz0SP/LZ8qPcn3zseGH9Vjxg6gfyWblZf3pq1p87cNmNNwDIRBVg/Y+PztlOr2k2+1gGiZ5XhAnGXX1tbTmJMmYz5eoqraLpjeRJAVZlrAdh5/6qZ+iLCseP36C7/vMZjOCINzZrPWr/KI0TZEkUBWZWpQsVxPSdMv14xHdroftmFi2QZoFvH//q+RZgqI24K8sK9hutpimxbXDQ1RZRZEt7tx+A0UxURUDWVYJoxBNUzg/PyMvMn7/93+P//Af/gNHRwd88tOfRJIl4jRBc1xm/paHz5/y5PlTTMfg9PSEJ08esL8/ZLR/RFaWyKrCNgjIq5LpYoasqGi6QRiGnJ+fYxgGURRTVTVxnDQH9axgs25SfNvtNrZlcefuHfb2+tSiIkki1B3c7NatW1cCaMPUMU0NVa1peRYHByMsy+S1115HklU0zUIIifV63SRht9tomoqqqGRZhqCh2dquw2a7RtFUhAA/CAiDmP/xP/8nRZEz3NtrCMdxRp6VPHt2hhAScRSzXC52VN+oOasf9NlsNkiShKappGlMnqekaYIQNf1BD0WViaKAOIkIwi1RFNBqeZRVgW6oyAo711CELMtstj61qFFUBUmSWCwW1KJCVgS2a3B4OLp6L3ievUv4FiiqxmQyQdSNILpJJ85peS6mreO6Dkkco8gqw8EBs+mKOzdfw7VbLBYzwsDHMnUcx0KRpWayVRR020M2y4h+d0S71dutTRq4XBD4KIpEt9fm4OAAXTepa4nV0keWDHq9EUVeslgsQUBRlkRRjKYaxFHOehlQleDYLTYbnzQpoFbw3C6tVpeyFkiqTNkIgwjCgDgJuXnrBpou7zQuCVEUstlsybKCxXwForGWt1wHTdWoqhJFVQmCkDCM8LwWRZmDJDg7e4brOliWAVSUVY6gYjqdNPooGrp0sMt9e6lR+QHWN6cYfK9pBn/Y9/+ADS4vNSt/AvXNrqUPr2P+rnI3P6wH++5LgCJDy/MwNJXVYsFsscA2FL709h9w7fo11vMVSRJhOR5JGOL7EZZpNrC4JKEWFWmW0ev3kWT5ip4bxxFlVTEc7qFpOuPxOY7j0O60EHWNQCZJExzXQtMah9L5+Zi6bvD9hmHT8mwm0wllVRMmEZpW8vZX32Z6/hxLkzAMDdMwaXe6tDs9PM+hqAVIGh//6GfQNIubt98iCGOyvMk3OTt9zhtvvMHzpw/YrCb8H//7n6fXaaOpEllWoesuQsD4YszB4QjHNDk9fYapGlRFxY3rx8wXKxbLNfsHPZ6dnaEbKhISg+sHqFFOlmeUZU2WZk1atKIyPr9AUWRuHt9mPB4zmyzodRtBZpImCEUhTwq63R6L+YooiqmrEhDIkoRj2ziOjtdyEVSURYHreoS710RTdVzbQYiKxXKOH2xwPY80TahrQZaWuK7KfNrAxpaLOZqmoBgmEgpIgul8ye3bt3ny7DkH+4dczi7o9XpATbfXp9Pr8+zZc9K6wDRsJCSWyzllldEfjJjO56iaTBj5dLoeVVWyv7/PfP4i1iBsXGuuzXK5pt9vXFPPnj8jL3I8r42gJEl3YZyWRZLEnJ0lfOSt16iqirPZmCTJWCyXvPXmm7S7LlpUICsyVZmjqAqttouqKIz2BlR1TZULdKtZPe6PDvC3WyzLQ1ENwnBNsG1WcRfjKaq2otPrISsyaVFTVjJ7w2sIFAQSe8NRo+NBItxGZFnJtaMbdNuN+2hvMGS1WlIWBY7h0O+5nI9PkSSavCVJsF4vyYuKVquFqZsoMtRVjSSpCKEgyQpxklABRdGkIxdFk8IdRTFJGlGJHCFXlKJCqmvqWkKWNMqixvdDTMvi8JpDVUO9WhJFWypqojTh5s27PH7yiLOzMa+9+gZ+FDIaNs43qoqzyynj0zFvvPoGnXaXQXfAarFir7fXkPZ/zCcJP4j6do6eb2db/q7rB2ht/pFrVn6swEHfqv6wd9xOU/I93dW36aK+p5wOqUnyVWSF1WqLkGT8IOL+B48p5JKzyTme2+HZ86eoqkocxEiVBFVDYG11HARNKF6W5WiaCpIgTmLiJMbzOpRlyWQy5dq1YxaLOQ8fPuD4+AhN10GFLM+QFRnbsvCcFnWVEEYbbLfH7buv8IUv/A66LkjjNePnG2xVcNDt8Mrrt/jKV94DCdIsxfFaJElIFAeoms3J01P+t1/4Rbp7+w1wToLuowe895W3+Xf33iHPMz72sY+xnl1SxAG6ZnC4fwPbtshin+v7I5I0ZD2/II4ThjfvQi2QJQXd0AjjiDRz6fYGTCYX6LpOECbM5yuE1Nili0JG1zVankccRchIsK/gmB5KDXlSIAF5VuG4DqJSqXJBWVSISjQcFVXDc52d/TnG9TxarTbz2RJZlnBdF5CYTmbkWbLL4rFQFFBVmVo0Cdid9h51nqMrOqqsUIuKui6JogTH9oijGEUpuZzPSPKQXCRkdUKQhmzXAVVdI6sGaZJTlYIiK1ksZmiaIC9ifN/cWaTBsnSgRlEUkjinKmlcL1GI43pUdZMBNZ3NKMqSUtQkRUrpQ13JqJqBJKkURdlY7ZH44pfvc/v2LcI4b9Koq5J3779Lt9PDdlzW600D/YtDDF2l024jawplJlDYuZhMj9V6y2q5oNvrUogaUcu8cus1ZosFgR8jFIXpcktS5NiWRVle0h8MyMqMTrfL8/Mz8jTj+tENtu6WrCigFlR1Tsdz6LgWUuly4/g6y80CRzOpsgJVA0USzWskKoo8pyoqJCHQNQlDk0niklpI5HlGkobkhUSv7+6CPVMkdLIi53J6geNaGI6BIzQ2qwRFzrl1a58oWu5OFhKKEq5fv8ZsNUNSFGxLJ80kZDQ22xX7B0O2YcBsuWRvsN+kmFs2tmawmm8xFQtZKIz6I9brNXWxe081Q7qX9YfU93qs+244LH/Y0/1hMFy+33o5aHtZP/gSAkmBWpRoWiPe6/ZalFWBLEts1hsOD6+BkHj77a8wnc7wPBfXdXdprPUukdng8rIZG8dRwr179zg9PeXZs2csFkuCIECS4Pr1Y65du8YX/+CLXFyMWa1mqCoUeaNtePrkEaqqEWxDHMfli1/8A2azGbPZDMMwODo6wt/6aJrK+fk5pmk0ziC/SfFdrdfouoHrtVENi6++9z6PHj+mKgvWixnb5YwP7r3D+Owcy3K4GF/i+wH37r1PkoacnT1FkBOEW9brJVHoo6oylqURJ1sWy3Pu3f8yF5dnWLaOH2zJ0oyqFORZzWK+5oP7DynLiiAIkSSJLMvQdQ1JkgjCgCj2cVs2rbZLLSrCOAYULidzTMshTTLKstzpgFK63S6e5xHHMd1ul/l8znK5pKrKnSZkiyJLxHFIUWTUdYOer6qaoiivCLhhFJFlOUVREEURy+WCOI6RZQnfD5rQvCplsbhkMOzhtRzKsllZzRdL0izj9PQ5tahwPYeiyqjqJt+n022jaM26qSorsixruDK7YL+qbmBuh4eH6LpOVVV02h1UVWW5XBJHCVlaUgtQVK2ZBokKy9axLI08L/C3Ie+//wF377xGkuQURY2oFaARDhdlRpan1EIghMR2G7Jcbjk9G/P87BQBnF+MWW82CEliOptz74MH3H/wmHv3H3Lt+Aam5ZDlGUHkYzs63X6b5XrOeHxGlifEWYisCrI85v1773B5OSZOIsqyaMTJjs18PsU0debzKf1un2uHNxh0h9iGQ5VXKJKErjROpKrIEKJG01QkWUYAURTi+817vKxyNtsVRZkhSRJpmtFu95FlHVlWd6DHBncfRhEf3P8Ay7Q4PT2lrCqKMse0LWzLpshLVssVbc9hu1mRJTEXF+fkWYosw/nZKTKNtqnT6bA/GqFrBghwdtbv5iAsXjYqP4CSvsPlj/q9fxz1sll5WT/4kkBVJJBqwmhDloes13Om00uOj48Zjg4wTQdJblJl2+0Omq6jqCrmLkXZcR00XUY3FDbbDWmWkqTpDgTnYJoGr7/+2s4JYyIEfPzjn+Lpk6c8O3lCkSfYtoaqQL/XwdA0PNfj2dOnKEqD579+fAOQcB2Pu3fvkCQRs9l8Jy4MMAy9yTSSFXTNBElFsx1a3Q7Pnz3hv/z//j+Mnz/my3/wu3z6E29imTqf/tRn+dmf/d8YDPocHR2wWEzQDHj85H3GF893cDOf8cUZtciJkw1xusYPpmi64Pr1IwxDR9cNVFXn+vFtNquYPBckSYZpWhg7/cp2u+HatQMMQ+Ph4/vohoJuqmy2a1zPpa5hudogSSrboMnXMQyDsiwxTZPFYkGSJCAakWuv1+PmzZuYpkmSxFi2ieNa5EVCXZfougaS3DBvwpg4SqjKkk6nTbvdpq5rTMNsRM15DggkGWS1ArkgSQKePHlImqZkeY6Egue2UVSVLM9odzxUXSHJYsqqJMvyhu0iBHmR0+v1WC5XmGaTqJ1lGUmSsFwudynDKVEcXTlOwjBCUXSytAR28RCaRFXlqJq0u4+CIq94//0PuHXzVbKsQpJ0NpuIzXbLNvBZrlbouklVS0RhzmabYJoerucym8+RFZWyalaQumFxeHiMohrEaUmalziu29j3NQXPs8myCE1TCCKfINwynV2SlymWq9MdtFD1JjLi+fMTVqsFtSjp9TpYlkFZ5uRZTrCOuH3jVY5G1+m4Heo8x7V0WpaOpjYJ4kEQ7NxgJnVdoagyh4f7QIGmSViWga6r5FmBqCUkNDTV3Ll0dGRJxjJNPM9D1TRs26YoC8IoZL5YYNn2TtheoqoSnmtw/dohLdehyFLKPCfLc8IouopSAIla7AJNRYM/aPRBf4KfVy/rT2X9SDQrL1I5f5TTOf8k6gXk7Vtdvpf7QAjKsqQoUmxb58nThyxXc/b39ymKGsfxyEvBweE1+ntDkGUMyySMI2oEsiJT5MXugFdzdvYcWQbPddlsNmw2a+I4RFEUVFUhywp0zWC4N+QTH/8kqizzxS9+gen0AkkqcRwDeaehuX37FrqucXhwiGHYIGTeffc9nj0/Ic8zNus1hqnj+2sEDdBOUVQkSWU2WxIlCQ8efkCaBEh1zv/z3/wr9oc93nv3K/zUT/4kvt/YPOM4RNNkiipFkHFx+Zxa5Dw9eUiaxQjqZl0Sb5HkAstWCUOf5XJOnmcEQUC3O6DldfG3MVUl7cLqNOpaYJoWeZZxcTFG1xVMSycvE7b+it6gS6/fQ1YkDo+OWK03AFf5Tmmasl6vKYomKDLNmiiEzXrDs2cnJMnuYBpuqKocw9DwWk0Q4mazpdcdkOclQkjYtn3VMJRliWVZXLt2DcdxKYoC399SlQV5liHJEu12m7IsQUg4jsdmE7DxI9I85/GTR2R51liLbQdkFWlH1nUddxd70LBW6rrG8zzKqiKKop1bqSYMQ9I0pSgKdMPA2F2aSZBAiBpVlahFQbvdwvNa5HlBGMY8evgEx2pRFhCFCZIsY1oWZV2TZjllJUBWyfOavChJ0hTLtjEtE1lRKUqBHyRcXM7o9vfoDva4/+AhUZpS1RWWbZLnKXEcomoKpm0SpzFhHHAxPceydW7duc5HP/Y6w2Ef3dTIi5T1esl0dsnDRx8QRj737t/jwcMnHOxfwzY98rTENix6nRau0zQ0W3+zgyWmSJJEWTYTs8l0gqxItNre1aRK03XiOEYI2Po+Qghsywag1+83a7ckwfM8TNvEtC2iOKEoK/b29rAtkzyNKLIIx9TI4hBZEmRJgmk0NvdK1GRZxt7eYPf39EKA31zkl2OVl/VN9SOnWXlZf7L19TqXF02NoNmvbrdbwijEti3SKMJxXGbTBTdvv0IUpViWR6fbx3MbJP74YoyqquztDUmSBMs2eP/eOxzsH6CozeMURcHJyQNu3bqF6zo7PLdMp9OlrgXD4T6ObXL//ntMLsaUWYRp6nQ7Dk+fPkDIML44R5ElBoPXKYuaMIw4Pz9voG9Jgp076LqymwyFjIYHmIbLX/rF/x2vP+I//od/h6nKZHHAndvHfPWdt7EsC9MySXaai7LMKcqcMNzy6PH93dltTqfjNU2W0sK0NEJ/Qy0qsjxlOBpxcXFBt9sl8EMs0yMMIyzLRgsCqkowny92ehWXoixQchjuDVAME0VVSfMcQ1FIs4Tbd27x3//n72FZGnXaTB7quqaua6IoujqIVWVJVVakVY0sSxRlTl6kKKqJ45p0u81qZb0OEJuI6XS+w9s3kQzHx0csFlPyIieOJYIgotXqU9gVeZ6SJAXtdvO7BP4S123R7Qxpe31Ozy/ZjiMkSWCaOl6rRctzmE0nTTilrDXNa5k1BGNNI45jsqykKMtd8nbzvqiqekdMbsI02+0WaZ7S6w4afH5a7/Q4DrUoCbcNIfnrSc2dTo8sS9lsNhQl5HmNohhkWQmiatYXlkMQRpRlRC1qQKIsa+I4w7EdwjDdPa85iiphtUwOjo7YbJfUNUjItNpdFqs1rXaH5+fP6HQ8pvMJH3njTd57512iqECSaixLR9eVJp6hKsjzFEXTma+WXE5n2G6LVqtLkuWYpolt2cyWzZqx4afYLBcBumaiqjKeayPJKkmcoqi7SI3d+8BAQgKquiaKM5BgtVqhSAJF6bBcXdAZ9NEthyhMSNOCflfB81xkqSYM1ri2g79Z0e70SeIQy7SYjC8Z7Q3RdIu9PQdFUb/u8+NH4vz5j62+kznjR+nk/eU742X9sVRdw3y+pN3u0G53UFUNfxtzdHgD0KmEhB9GmKZNWdWMLy9AlhiORrQ7bfr9HmVZcHb2DD/YUOQZr776CtevH2NaBheX50Rxk8hsWfbuTE0GoWBoDq7T5md++qd499232WzmjM+fsVhccnF5DtR8/GOfwLZsDMPCtptcoaoqkSRYrZa88updfuEX/hxCVKRpThylgMT999+mZWucnTxEFgV7gx7Xrh9zeOMGw4MRR9f2KauM999/F0WRCcMAa3eWOl9MSLOIomiyb27deIWiaNDkUZSxWmzJ8xJJahwi8/mCBw8eEEWNTiVJ0h0cz6aqyh12T7D1fSaTGQ8ePmG12pAXBevNhg8e3cNxDWqpwratK1t4URQNFdhxALBsG1lWrvQvstyIMZXdKg+pZrmaU5YFsAu3REaWZZ6ePOV3f/d3AdDUJvlakmTyPNsxUUryFLJUYFttBv19LMtp8puCAIGCobsUJVS1xHyxwnU7+H7KfL7F90OABpq2t8fx8TF7e3vs7+/viMW7piRNCYIAWVZ27wkL319jmirzxYSqLppVlqZzeTljvQ6YTC5ZrWZARavlNnEQRYYQoGk6201EWcoIoRCECUgySBJhHKJqMkVZUNZlo+MoqmbahMJm61NUJUKpQRZcTi7RdJ033vgIrt1ClXWCIMGyHLZBiBAySZoRpxnT2ZS94d5OJ1TsdEIFrmczHA1AEtRSRVymvP/wPn4UEcQZAoUgjNhsAxzHpq4q0jTBMHSyLEGSpKapUGSGw0OKQiDRZGnVokJRJSS5xms5tFpNeKiiKBwcHGAaJp1uh8HeoHmvKDJ7e0PqSjCbzcjSBE2VmVycMbscYxkapq7z/NkJdV0RJwnj8WVD5zUMQLzkiLys71gvm5WX9QMvIaDIK1RVJ0tz8ryg3e7hOC1ct8v4/JIsK5jOZnitFkVVEsUxb7zxBoZlYugaQeBzcvIY3VDRDYXxxTmTyYQwagLjLi8v2G63uwPu13besqSQxhmqovD2219mb6/HF7/4ef7rf/2/KYqIs9NneJ7H7du3Wa3WzGYzTk5OEKKm1fLo9/s4jsPNmze4vLxgtVqBgG63B3VNnoQ4lk6/2+KTH/8ovr/F8TzavT7v3Xufx08e8PZXvsjdV27zwYP7HBwckCYZutHkuuR5SlVVaJrB/fsfIEsartPG0BrI2/7oEMMwuX79Ou12i/6gh+2Y9Pt9sqygrgW27eC6LnVdU1WN+ybLcvK8xHU9QGI4GhBGAaomURTp1VSlqqorTossy6iqSl3VZFneIOUtuxE7yhKLxRzD0FGUJkqhqiqqqqaqBIZpUtfNitBx7CtoXKM1yXbi2uaAKMsm200KQiHwI9arLRfjS5I4Yb3ymc3XaKpFVUJZCC4upnQ6/Wb1pWrkeU6aNDyZKIoaem4SAy/SvqUmRM+xUWQZ13XRNA3D0BEUVHWOLIOma2y3PnUtWK+2TYijJlHVOfPFFCFK0ixhs11zeHhIllVkuSCMUqq6Ce+U1UYD4rUaC/WL5/SFHmM+W3BwcIjXchCUZEWKoio8f35KHGeslz5RmBEGCWdnl8RRE+qoqjplUZJmOVUtaLfbu6TzRvej6w1vaG844ODaPmmZMl8vOZ9MQFbIy0YzI8tNHEOv3+NiPGaz2TTp3JpGURYslyvm0yXr1ZY4ThE1uyiMkk7HRZbFVU6UpmqURYGiyERRhKHrVKKkrEuCKETfrY90TWua+iREV2VsQyf0t+RZxmR6SRiGvPveu8zmS4qiQJblr63x+WNBdrysH8L6oW5WflQ0Kh+q5kZI3/pypdn+4yH6fL2uRQKqoqAum7PpJ4/PsO0ub330k9Q0uSwSFd12i7LISeOYTqtNkZeYukkpBLrVhBUqkkwaJ8znMz7/+5/nfHxGEG5ot70mDVqCsi6pqcirhDCZ8eDRF3nnnS8wm51hGDW2LZCklPOz52RJwnI+4dnJB3zwwVeIkw03bhxTljXd7pBbN1+j191nMfc52D+i1WoBNReXZzx5+ojj45sEYYLXGeC0eyw3GyRZYrmYUhQhZ+dPuLg84fnpQ8qyEabGcUwcZkwvtyiyg6ZaSJLEdrsmioOdeFXlYEf5TOKYTqdFFIdstk14YBD5VKIGRaWWZJI8R1JU9oYjgjhBNw2KMmfjr1ltV6w2G4SQiOKcohRUdUUtBJbtIkkq3W4f07KxHQckuH79egPB2/qUZYVlObRaXSRJYXw+Q1QasqKTVwUFOZIqqMkxTRUkhSyviJIEbSfEFBVsVjF1pWLZNpquNnoRubGTdzod4iRju91CWVGkKaKqkSWZ5XKJYZhoqgZyje3Z7B/uo2g6UZIyXy6ZL+aUVUFRCaIkQ1F1aiFRVBW9QZ+8zGm1ulSZhoKOKqt4jo2pa8jIaLJGllf4YUyalyDJRElKXhbkZcGjx4842B9QFQmKBHVVEcUh6/WSrIhRdOgNeqiaRpxkzBZLDFPj+OaQtFyiGwqGYTeOl9EQP0h4/OgMy2lWNnmV02q7mLqKaSiIqiJLc2TZYH//Bv29AzTdohICx2vjhzGO2+X4+h3aXgfXNkDUdFodVss1eVrQ7fZRVZVXX71Nt9UiT2s2q5iykMiykijKqWuN+WyNpbepCx3P6aAqKtQ1MjJxlFMVoCgqjmux9hdoloQfL9hGc1Qlo9vW0dUKz7HwtxsQjXg5KzIW6xlR4rNazQj8Df1+j8Foj/6oz9nkgtliw3obkhUlpSip6wKkmko0oLiX9bJe1A91s/Kyvrm+gzFNojHKf8vLh/DouznuNzQqu5BDVVXZG+wBEt3OgCwvabc7dHs9ZFnm2bMTut02uqZS5M0ZvSIr6LqOJMmcnZ+x3qxJ44Qkjtlut8xmM+arBaqqomkadV0h6mbUXomch0/e43f+x39ifPGQLPeZzcfUdUqeh4TRhu12w6DfwdAVnj17jKoJ4rihn6qKQRQV3Lr1GteObpCmJefnl2iawXwxJ0ljbt66xbvv32f/8DpJXnHvwWMOr91gvVqhIKjKjI997C3KKiPNIvxgw9bfcnh4hGW59Hr7RGHBdhuSZimCGkkSFGVKy3PJswRdV+l22o1LB4EkS+RlQVYWlEKQFTmn52dYtkMt4PT8HMtxUDQFRZPI8pS8yBlfXOAHIXUFcdxkC7Va3k7f0+HatWu78EcIQ58gDHei6JI0zXgxrcrSHNNwQaj4QUiYRKimiqxJINeoikQQBbTaLVRNpyhqTMOmqkBCwbYdoiRkMrkg9H1s06TlurTabZI8RVUkNEVClSUMTW3sqwKyLEU3dAT1Fdpdlpsz/DzPkCSBbmhkaYaoBGEQoms6koD5bI4syeiKiW20ccwWmqyiKQp1VVKkGf7GR5Z10qzGD2KSrCAvS+IsRTc1hgd7+NsV148PgRpZavKWHMchL3PW69Uum0oQBDHdTh9VU1B1QM5ZrZcUeYFh6CwWczTNIIxSHMdFVhq0vEAQBD7UFaKq0VWTIoPxeMbz5+ekecneaB9F16gluJhMmS/XnJw8483XX6PXbnM5HpNEMWXRWIg9173K7znaP6YuZapKQlYUJFklTQrqEvK0wtRdVMVAkZv8qOlkTp5W1JXAsmwURcF1Lco64/YrNzHsJjxRkysm4zPi0McyLcIwwjAtdN1gNr9ku12SRD7j8TPuv/9VNps522BFq+0QxgHT+SWVyKnqpmmrRI2sSC9blZf1DfWyWXlZfyy12WyaMbygEctaFrKiMhj0KcuS1WrJfD5HUZoz7saO2vRQZVVSVQ1T5Pr169y8eZOPf+xj/Nk/+7Psjw741Cd/gs0mIAhChChJoi3j86d84fP/nceP77FYzZFliU67S14UuJ7HerOm3fHYG/ZptRzOx8958uQRLa/NvfcfMNw74Prxbe69f48nT56SZxnL5ZrzszGGYdNp97h2dMytm7cwTZM0zXj+/JTPfPrTLJfLnbOmgY598hOfIc+b6cTp6RmWZVNVFaPRCNd1qKoC0zRxXY/rx9fZ3z8kDCOyLMO2bSzbYrVaEQQBSRxj7ALgFEW5+m+W5w1XpNNFlpXGdVM17owsy1kuVrRbbfI8Q9NUOh2PTsdFkmrKMuP+/fdQFIHjGCiyhCxJ6LoOgGVaTKdTfN8nSRJ0XUfTVOI4RlUUdK1BrRu6Tl7kGJaCbihUhSCNa8pCIUtzsjxguRrvHDkmr7zyGt1ul+3WJ/C3ZFmCkCo0TUGWQVFkJElGCMF6vW7WZYpKXZRokoImq+RxilyDbZiUWYZtaHi2haYohNsA27CxNItX77xBkdX0+/2rwMUX1m0hBLZtoykaqqxhaCZVUZIlCaIq8VybKNgihKDX66MqGkXRHMQVRcU0bSRZwWt1QTT2a03TqSpBGMTUlUxdVZiWDlIJUoUs1yyXM2bzCWHkU5QVy8WG7SZivQzQVQtQefr0ObWQSPOcWgjG44bhgiSRpAmnZ2fkZY7jWHS6LVRVQlYkqrokz3Mc18HzPLq9Dpqm0m57ZFlEmjZrv7IqSdOEoiiRJOlKXKzsAgezNGW73hJufahqNEXFczxcy8M2XKIgIYkLFEXfTXKabKLt1idNMzzXRdQFWRqyXU/5gy/8d/7v//j/4t67v89y/pytf8njJ+/y7ntfJEkDzsfPSZKoSXZ/qWF5WV9XL91AL+tb1jfbk7/fEDHbtq/u9+jo6IrDoSgKo9GIGzduNPyMLNtBvTRkWaUoKhRF4fr1m8wml2yWC/7sn72DkBRGyCRZxuxywmc/+zm22zXL5YTtdsnvfeF/sN3MSOKAqjA5ODjmvXfe4ePDV1hvtsiy0hy4dZX33n+XzWbN8fExm02Iomokcc7wjUNOnj1jsVjwxhuvoSgK/jbi2tF1hsMjLi+nTVBiVXLn7h0ux+eNBmA+R9c1/CBivQ4Y7e9x7eiY58+f4XnNgayuKkzTwLIMkiSgLAoqTebw8Ijtds3l5ZTe7sCaFxlVVXF4eIjrdRiPZ+i634TWicbNs1quaLccNO0FV8UhCAImkwnb1QZFVlEUDU3XcRUJVQFJlKhyjaHrlIVEFDZrKahYLBZomrZrKmmmVqImTVNGQ5PJZI6iKMRJhqbImKZJlmb0Om3qOsMwNTTdJNxmBNsZ7ZaHbSskeSO2NU2L7cZnfHZOLQSFiHbuHpsqL7FsA6QaSYIsy2m12qRJyt1bt5lNL/B9H9MwkQXYpkmepFi2xehwQJpm6LrFsLuHqprcuHGLr3zlq+iKTpIkOI6DZZn0Bz3Ozs7Y2xswmczptLtsgwhN1SiKjDgOqcoCISokSRAEEU+fPEMIGUlqwirzvKTV6qKqEovZmihKcd32zhpdo+sWoJEmEZomoesyaRojUOgP2hRlQi2K5sDudYiiiCKHqlKa9ZvpMJsumkDL9QbDaKaNq+WSlttBllRqJKazCaPhIVEYU9cNibbVspnNpiRp2Kz8TIfDoyHLzbJxB8lSg9+Xm//fbrfISqsRQuclXsul2+nx7OSCqhKNHqcs6bT7pHFOkcPleIVtF2RpIyjOs5xWu41p6qxXc9r7XbJ0zmDQoRYym814l5pd8Du/HXD95iuoqsKzL90jybaoioEQcPvmq9Ti5dn0y/pavWxWXtYPvCRJRt6tdMKwsS5rmtrgzIuCa9eOCMItpmkyXyw4Pr6xCy9M0XWDJE0aTLuscufuK814X9VIiwpZkjk4PGJ8fkpRprzzzpc5O39MHG4Z9ttY10bMlhtA4eOf+Ayz2XNqoXJ84xaaYqMoGp1OlyzL2Gy2tLwud+64VIXEO199l9deew3dUBmNDgjDENftMJ0u2B/exLFbXC4uQdQYusF2u+XZ82c4jtOEAuoG262P69qN4FICSap58vQhe4MRYbil3fGIYp88zymrgqcnz5AlkGVlx41ROTw65IMHH5AXBauVz3LlUxQFqqpSFjnAbgXR6GHarTZ5XaAqMnkt6PUGqIpOlucoikSS5Qz3DijLElXtN+LSMqcqCyxTR5ElqqpxB+V5hh9s0HXt6jWzbZsoihAIRF2TJI27xHWcxu7b61JVDWFWURQsx6asUsoqRdfVhnOSZGw2AY7jkeU5SbSlFgVCFJimiqYplFWJojSi3yxroGab9QZE8/zUdc3e3h6j0Yj9/X1msxnL9RLHcSiLmsAPuHVrn8nFhCIryasKUTYsEd9vrOTN980ZjYYsVxGaoiCJmkoCRZYxDZO6rLBMG02Wd0nFFRJgWw5FXrFabYiiEMe0d+ngMooiIxDkWQ5SjawoVHVOltfYtskiDtFUFVXVabc95vMIz23ca1les5hvcBybNA14/do1gjgESUI3TOI0wW25TKcT3nzjI8xmlwhRsd4siZOYqs6xHaOxwGdFEzSpqTRcGcHx8T5xnKHpCkhg6AZCVFRV1TSdWU0UNa6r5XKFrhqkcUiWpERVhYTAtDSiKKCuNOKoREJDQuHOnVeYzWbEUcpw75C6zonjHFmRSaKIds8jTXPKLGB8vkFSG1G053mML05ot/aIwpQbN26hSPLOIfQSEveyfsgb1x8Vu9ufxhjwF5Cmr49m/+avfavb/GHfq2kNmVLXdcqyQtf1q/F+WTYrEcuyaHfau/F/Yz2u6xpdNyirildffQ3X9Viu1oDEZuMDEmWVs1rPkaSSd9/7MmnsI+qcJAppeS2ytETTHebzLScnF+QZmKZHu9NnOp0jSxqaauC5LTabLfujfXq9HlVV8vGPf5w333wLwzB59dXX+Zmf/n8gagVNM1mvfEajEaqqcvPmDXzfpyxKfuInPofneYz293Ech1bLA0mQpCGr9Zy3PvI6WR6hqjLz+QxFkUmSBN/3kSWZMIwAmbPTM+7du8eTx4+Zz+fYdtMEKTs77ouxvaIoO3BciBCC6WzaNC5JTF3VFEWFJMmoioYkS9iO2YD6ioK6aiYreZajKc1911VFFMUNW2Tn1iiK4irB+dmzZ/R3cDBFbX6Wluehqirdbo84ytisAyzTQFEFZd0cQLMsZ73aEEUxgR8yny2IogRVVRsXkWsx2u9ju41LStdV4jhAVRXiOKIWgizPKaqSTrfDjVs36fS6nF+M2QY+F5NLXK9Dq9Pn+dkFQlYYTy55dn7KdDEjy1NWqxWGYaDrOpPJhIuLxuGV5wWaKqFrYJkqhqrSctxGeBsXFGndoPeF3GQqCRlJUqgqwXYbMNwb7VD1CUkaYTsGigK60TR5hm4Q+CGqqlHVTUSBIivN34Uk02q7FGUzlRB148DJ86aBfPDwA6IkZb1u1kVplhHFEe1Oi0ePHyCo2OymJWWZ0+m06PU6vP76K7Q7LqqmUotq53TK0AwZr2VTFBnDvT2iKCKKYoqiiUkQQlCLpgltt9o4lsvBaJ9Oq8Xdm7eJ/JA6F2yWAXWpURYKUZiy3QYcHh6Rphm3bt1l/+CYbVBSSyabIKEQgjRLgZo4Cen2Ok1CduxzPj4ljAJ8f8O9++/y9OkjalG/bFK+z/rmY8uftuPM91I/1M3Ki/phfxF+HErTtGbaYDRQsboWxHGMbVsAdDtdVFUlz3LKsrrC5jchdQnz2ZxOuwkszPOcJMnQNY0kTdn6a2S1Ji9iHEcnjgJ0TSVYb1kvN+wfHJNlglde+Si3br3Bwf5NFMWiLKDV6lMWEjdv3sW2PQaDAdPpJYKSwaDL229/mVarRb8/YLXcUJY1gR+R5xWvvvo6/f6gWccA0+mUbq9Lq+Uxny+Yz6bs7fVYrucEwRrL0qjqjM1mTpL6rNbzHTK9oChzDMMgSVI8t90QQyWa8bzfWLLTJMFrtdANnTRNUZWmUcjzHEmSMHS9GeVLEnVdosgyw+GoEZfqJlVdX6H14zglTQpCP2zC9ywbkFmvt6iqRlmWRFGEaZqoqorrujsNi8RmsyEIAnzfbwIAd1Rj3TCIo5jQL5pcnTKl3TXYP+hwcDDANO3GrrveABJRFOG6HnUtqOsSxzHJ84SybKYRlmWQFzl1XQHNhC7OUyRNZbpc8OTZCWESY7kO4+mEdr/PdLkmyUpMxyVKE/KqaBxLdc7aX5NlGfP5gvX/n70/i5FsPc9zwef/17xWzDlnjbuq9rw3R9EiNdCWrSMdW75wW0BfHNhquN19RTVgCzAMG74wbLR16SvBaBwY9oVbgGHABho6Ph4k26IoUiK5Se551665corMjHHFmof/74s/KknK4kxJHOojgmRWRkVFREau9a3ve9/nnS9QShHHMWAEvINBB9vWQEuWpdi2Q1mYFUdTQZaVuI6HlGZFY1k2juPx8kuvXJzk/cAlCB3CyKFpS/LcuLt832eVZBRFBVqiWmgaRZJkVHUNGB3L1ubGxeeprAo++hMfwnEkaZphrQF4TduyjGPqpsb1HZRuqZuag4NH2I5E69bof2gBTVkYe3xRZCjdoFRFUeaEQbDm0LhoTIPkrv/tMIxwHIcojMjSnMUsNplMqsX3QpaLhDDoUVeKKOwRhj3aVjMej4miDtPp3CAJahvL7TNb5vhhj7JWrLIcpM3dew/oRD3iZUKe59y9c4eiNGnP9+6/j9YtSn2HoalP60e2fiSaFfj6QKUf3Yblm9mOv//W4+8Gr/+NSkoJCLrd7gViXanWBAomK5bxkocPH66bkZKyLNewsppVsiLLMtIkxfMCbMulrmuOj4+RQrOK5yzm53zxi5/jnbffoG0q9nZ22dzYBm3R6w148cVXuXz5Os8//zI7u/tMJjOquqHXGwCSxSImjpM1rTPl/fdvE3VDzs7O19TYCM8L6XX77O7t0rYNB4cH5FnGxz72Mc7Ozuh1u/jrrB3HsdncGvHw4X081+bGzWdo2oqmrVitFvT6HZq6Zmd3F8/zsSxz9b25ucUqMc9DWha+H7C9tX0hhlwuFjRtA2jKqsS2bCzLJgxD9vb3MIFzRmDb73cZn56glCbwfQb9PraUlHlBnpXmBLgWRqoWiqLEsT0c2yDpNzc3qOuaTseESlaVgceFoWGvOI6D7/t4ayFuWzdkWY5qJYFvqLBFsSJJ55xPzowQFZswCHBdB9f1KcuawWDIaDTi1s2brJLVxc8/jld4bkCWFViWjZQWfhAQdiL8MABLkuQZrdY0SvH44Ig4znj3vbskWQ6WxcHRAYcnB0T9EGGZiAHfc9Fa4bounXVOT5Zl1HWBkIq8SAhCD9u2aBt1kQCtlaIoC/wgQCmoqubCWt3v9+h0ImxbonWD41j0+kZDpLSiaVp6vT5lUeM4RmheliVFXqKVMNOjPFmHfdpIKWiaiscHj5CWWYc0bUuznqR5nkfV1GZS2TY0bY1YBy0KCdu7W9y+fZvFckGrWtIkWUdfKCxLEvgmITxNV3i+j+8F9PtD8/NTykyI8hzHMWLqzc0RdVVxdnpqWDZCAha+3+H8fHYh0F0uDYivLEuCsEuvv4XSLnFScj5dUpQNWV4hLAchLN566x20htHaZu15Lp1OxNHRAaskXoMOuTi46/V/ntJYfoDrT+j09COhWfm63uRH/vP7rV7g96dT+14Ftf/z433tY0ocB0aj4XrNAG+/8xa3b99GKU1Vlbiuy3A4wrZtJqfH9KKATqfDV77yOi+9+DyT2QStanxbIXSFqjIW52N2RgP6vQHn5zGbW1e48dxzKCE4Hh8TeCFvvv0V0vyU08ljhFVzen6E43jUdYPjBZyfT9na3uPq1Q6HR2OuXbvGpf1nyNKKKNzEcW2krTk4eRd9rNlfXuXy7g5SQ+B7HB0dcO3aFXr9DvPphM3RgMANePf229Q5aG0zmyXs7nVxLMnZ8TnD3oijgyM+8OpNHj1+bCYfVUHYjbBsydn5mH6vR384omkFj0++jKZla2NIEudorcjTFY4zIvAdBDYCF9+W9LshruUT+Dah61K7HbIsY7KcYbsOgpZOx8K2PVbxhDCo0VpSVQVN61HXGY4bIoSHlA51JShbRdU2NGhsy6IqS7rdHukqQSBxXEldl3Q7A+LlnHgZ09ZQlQmWtImCgGk2IwhCpAvTxYSyqnj84AihJJZlQhody6KuNVLaTBcxm5tDdAtR0GFjNCJNV1RVy2y64PKlZzg7iWmqnF6/R9uC1A2Dfgc/kDgubHh9FpOYvEwZDLpsbW/jez6HB0f0OwG2o9juDQnDgCyrqYoWITWImgaNcKEtWqS0kZZFVTdEnYAsn6O1R+h1qKsaSzhkaY4lLLRlqMJFuWJze0TbNAhp8cJLz3F0aBLEhTAW7TRdsr29jRcIqthMGMcnUza3N6nVKa4r16GSFXVZMez1sIRFKx2jEWprijrHDizmqxl5Y1w6UgikI8jLFtuWjAYbnJyc4UuXNFlQVRauE1BkLb4PUeRgiYDDR8d0XxyirZxpHNPrj/DcDqtVgmprHMfocLRUuL6DG/g0uiHNc7qdLnVTcT6ZkSQJAoe6hrxoaRUs4pi6rbFRzOfnLOMFW5t7LOKE89kMy1qRlClRt4cWNlJIA//XAIaijLb408v8fVrfrAR801OT+D6cl3+oJyvfLJ76W0Vhf69R139mUdl/ijnd30iT8t3UH53QGGqlpmlaDg4ecXT0mPl8avgbSYrWkKYpi0VM4PsMB30zZVnFKK0ZDPtMp+c8eHiX0/Ex45NjpBBkacb4+BRpOQw3ttnc2uHtd95mtVpyenrMZHLGfD7B8x2ybEVVFURRRJ7nnJyMeeHFl2gaxfbOHs899wJ5kaEwY/eNzQ2UUty5+z5llXE+OUapiiRZYDuSeLXg/PyML3zh8xweHjIaDikLMyV4Yne9fOkqAEma0DYtSZLS7XXxfY/Hjx/R6XTRmAThMArNSqBV1GXJ6XjMG2+8Qdu2dHsdtrY2CcOIpqpxbJvxyTFNW4MQbG1u0u10ee6557h8aZ+6LDg+OiKNVwz7Q4IgMATStmY2O6dpjPj2CXnWcR1OT8cICVVVMpvOqMqasqzJ85LlaoXjmJNkXTWota1VoEEbnL/n+RRZjcShyEuaxqTrLhYL2rahaSoOjw44Oz8jSUzGkMknatEKkiSnqhqKokIg2dzY4OMf/yn6/SF11fD48RHLRUyeV5yfTSiLkv2dXV584QU6HUMd/vCHP0i302W5WCAE+L6HFNJMbpZLxuMTbt68RRCEJEnK8ZGhKSerjLY1n1fPMyd8S0rQDU2VYsmK4cCnzFdsb2ygmoa6NhoTz/MIfJ+qKqmqArUWCqMNjE1rwXK5QEiFkBppCZR+IlxesbW5gW1L2rblmes3kNLGdT263Z7hyChDhK5rRdtomkYjhI2wbLa3d7Edl9liQdtqqqqlaRWBHxCGoZm+1BWDQZ/BoIvrSFRjco18z8K2FK4rkLZmuDnEdo0Nva5L8xposR3JaDTEcS0czyYMA6QlGY9NftOlS/vM5jOKssC2Ja7rrP8ua82aSb22HYm0tJkKCYhXMcPhkKIs6HQ6vP3O2yziGMQTWObXcKGe1vdc32/5xPfr3PqN6oe6WXlaPxwlhFivgbhwiRgkuQmeE1KT5TGbW0OuX7vK5cuX0FpQVZq2hd29XYLAJ89Tnr11C9/3WMyN/uDo6JAwDFksYj74wQ/T74/o9vqsVgmWJXn77bfIs5Smrnj44B47O5vYtkXg+/R6Q65cuU68SqiqBsdx2draJk0yVquU//UX/zK2LRCiQVPy4OEdHj++jxRwcnJCVZXcvfcOv/eZ/0Fd57Rtw6PHj8iynDAw2H/QFEWOUi37+3v4gYeU4gITj9AcHR2ilGK1WtEqg6dPkoS2bul1eliWxSpekaWZEbJaNm3T8PjR4wsNg23bdLpdLu3vg9YUeclkMmW5XDKZTtFaAZq6qSjLHCkVabpEqZokMU4c2xHYNihVI4DBYMBisaAoCjzfw/c90Jp4lVBmJapVFHlJFIb0Ot11qnKM67rkecHZ6YSybLBsD9s2Wo/t7R0TSGhZjEaji4YnCAKapmG5XJFlBY7jIS2jv0iSGCHgxZde4rUvfokvfuE1Dg9PsKTDZDLDc30Ojw7N1KHIzc+7aTg9PePO+3cpihIpHS7tX6aqasqyIk2zddr0AjBibq0kxmbcEC8z8rwCbdHvjcz6EQdHCEIPhgOLZ29u8+yNy+SrlI3hJnt7OwyGPYoiZTafgGjxfJtuN6CsapQyq5OyrA3htczJshXdXkCSrIwjrGmo1llN3U4XpRS9Xp/QD1Et+G5AXbdoLVAaGmUaFSld0DaLecpsEpOnNXlWUVfNOoywWvNdoD/osrE5oN+P2N3ZZGsz4uUXr/LSS9d44cXrCLshGgbcfP4GV69fxXFcqrpEWpAXCZalsdafE9aNdV0bVlAcxzzzzA0DanQdlK6p6pQo8ggC/wLgqHVLXZfrlZjGktbFyrdpKuaLGWWZk6YJq2RFq9r1hfuf2mXh0/oBqx/qZuU7wdN/X5H2T+vbKjNJ+eO1L03Trg/YFUq3JGnMfDFjGS9oW6NlUS3469wac3VmMRj22dwYUdUlnU7AaDTk9PyUq9evc//BI2zHRyMJow7L1Yr377zP7s42VZWzvb3BeHxIJ/Q5PDhEaMHBwTFV3XLtmZtsbO3w5dff5OBozHQeMz4/ZzQa8oef/wPqugBqwsjlhRefp9Pp8OKLL3JyeowWLWVdEHYCqqrCclw2Nrc4ODThinmeURRmXbNKYlzX5tatG0RRYJDztsTzXHr9rtFJtDU7O9vrZqDAlg5toxBaIJDUZY0UFvbaZeJ7Rn9gScnjgwM8z6MoSnq9AXlmcoDyIicMA5Qyq43BsI/tWLSqwbIlo9EAMFf5ZVmYNcdauDscDlHKpAkLKbGExWi4RZYU1GVNVVQs5nPyLGNrc5O6rplMZpyfzdDaJlkVpEl+MVlpakW321/TXk14pecFRFEXrQV1ZYTAjm1hWYKoE7C/v8dbb5kr7f5ggzDsUZaKIOgyHG1iOw4bmyP+ws/9eSzbMnBBBXGcUJUNWVqwjBNWSYoWAj8IsGwHP4g4Oj7hxs1naVvBKi7QygYtEdgURU3dtHQ7PVzLwXcsdrb7bG+FbG0GqCbjxeeeI1nEVGVBWRZEnRDPc1kuFzRNTVFmOLbLdDInSVKWy9g4uXSL61nM59O1nd9BCNPIdrtdok7EbDYjiVd0wg55mlGWFa7j0un2cAOD30/SlKqqKfKaKOqjlAXapm2hbTWtamjbCiE0YRiYBmC1JElXPPPMZX7h53+aKBS8//5bSNliOYLBsMv9R/e4c/8uly5d4crlK7RtRa8f0bQVaboiyzOKvLgA7I1GIyzL4u7dO2xsbCAEtG1J01bUTY3WsEoM8M31HPwgMO6opiVJE7rdDnfuvIfSDct4SllmKGXYLWgTdmj0D380QuRp/TjUD3Wz8qSeNh8/WPVHxblPwt0AkmTF6ekpeZ4zn89RquX4+JiiKEhWq7WgM6KqKlzXIcty4njJ/Qf3OD09MRbfVcxsNuUrr3+Fs7MxCHj0eB0Mt1ixtbVL0yp6/R79fo/T8QmT81NOTw+xpDZpykqwmMVsbu3g+SHSsrl6/QbT2ZKNrW0c10dpyc7OZV584RW+8IUvcnp2aoLtFjM6HeOW6HZ79AcDtIYkyfCDiOFwg+Uy4fj4GNW2SEvQtLXhlTgOeZ4xnU2JV0vq2nA/lDbTkStXLtPpRAjAsV3qssaSFkILfNdHtxopJFEUEQYmbblpTRMwGAy4fOkyWptmcDZdsLOza0SkCHZ2ttne3iSOFyRJcnEl/8TK27YtYRhi2w5VWV6IaOM4xnO9dVJvF0s6lHmFbbl0wi6u43J0eIjnuAz6fSO89UIsy8W2fCzpY9s+rmM4O0pp2kahWo1ju1RlbZqqvKRtoG0FYdBhMBwQdUKE0PT6HdrWoPvn85iTkwlR1EcIh4PDI7TW5EWOxjxn13VZrVb4nk8Ydul2BxwfjQmCkDwvyLKCeLXCdhziVcKXv/xl0rSgbTTLRYznBWRZfjEeb5oa2xJ0OxH9Xpcb168z6g9om4rTkyOauuLg4IAkWTGdTmnbBtd12dnZMeubqkRaEsdxcBznQpcVRR1As1wu1yuwhuVySd00WJbNdDoFrel3Owx6Xex1AnZVl5RVieXaCEus124e08mcqmwBy7i62po8N9O3oijXjYUgSRKm0ymvv/EGd+/ep6kVnU6POE6Iog5FURNFXZpa8/7te7SNpq5bHMemaSo8z6Xb6RqOThCsic3Omt1jwhPjOMbzHdz170mvO0C3kniZgBY0tSFT2+u/57g2SRLjOE/AeS2OazE+PaFpm685zgt+RE5dT+s7qB+Zn/iTD/IPg5/8z/L5fTP9yfdLo/LkdbVt89XRdmUsqFJabGxscHZ2RhiGJEnClcvXGA426feHa8dJy3DYJ4x87t67w3I55wtf+DxKa65fv0q5voKdTM557/a7nJ6dsru3h+cHKCV4/849rl27wfj0FMsyQXmqrQl8h/nsnNOTI+O4EDavvvIBHNdHSodup8/P/dxf4saNZ3n55Vc5P58RBEM+8uFP8Df/xv+dL33py2RZSrcXEYYBx8djbLdDVcMHP/wxbDdgY2uH0/NzTifnDIZ9gsBjPD4hjpcMhwO2tjYMvbdt2dnZxvNM4GCv12M+nzKfz3j22VukWYotLXzPZ393Hykk8TJmPpuTZzmz6YwkTYztVZtEXK2NHTzLM0ajDeLligcPHmJZ9tqye8bu7vbabVOhWtjd2SfPy/VJqkuel0hhuC9KaTqdLnmeUzc1bWu0BmhBvzfEczzaRuHaLi+98BKdKCJZrVBKIYRECIckzsmSAinsC0v0k5/7fL7AdV08z18Lq42ttxv1aFtFmq4QEq5evUwcxySrhLPzCZbjE3YGxEmJsDzmi5io1yUIPe7dfZ/VKmY6mXB0dMLJ+IzFPKbfH8I68DEvK9K8oKhq8qJEC4ESgjAM6XS664lfaRoUW7JazUnSBZar2drdRGvBdL6irDSO67NM5vihQ920NI3Cc33KoqapDeOmaYwNH1o8z+WJY0sKi7bRpElB2ypGo9F60mYjgLPTUwCUbsjTGMcS9LoR0tLYjkCLBj9yEVKRpvH6hG84Rm3bICVEkY/n+ZRlY6YvRYnWkiwt1s6kgFb5PDqYcXQ44/x8xbC3Q1sJ7r//iNPjKVq5rFYFbaOI42TN7bGpa4VSet2AKu7fv88LL7zAfD7H9/31hNTkQa3iHK1sfL/LlSs3yPMG1w3w3ICmNs7AxWKO41p0exEbm0Pee+8dxDplum0bvs4W9HSq8n2vH/Tz5o9Ms/Kt6gf9B/GjVFpDVdUkSUocr0jTFCkFtm3j+z5JkrC5ubkOMHyE5wXs7V7h+ede4v79h+R5gWVJlKqRUtM0FUo1aGUsu0m6otOJuHx5H8uyQGhGGyO8IMR2PF555YN0ej0T1iYEWxsbjE+OQBt2x2IxZ7lYkq4S2lbjBx3OJjPmccxktuDWc8+hpeTP/eRPsTm6xGiwxzPXn+Xa1eu89dbb9HpdyrLg9Oyc0XAPS4ScHE/x/Q5XrlwDvhogOJvNEEKvgVuG5Gms2zX9QR/Xc0iShDRd0TQmp2WVrOh2OqRJRppkTCcz2rpFIqmrmrqs8TwfrTRCmPdVaUVVVXS6HbqdDnle4vs+AIv5nCAIGG0MuXPnfXZ2dxBYdDp9Tk7OaGqN54aoVpClFULYbGwY3keemxyd5dJkDc1nM7a3tqmLhiKvkEJy7dp1lFIkcYxYaxiWy5g0zdFaorVFXZsr+yfAOyHMqqaqmgsNiecFOLZL0yiyLCNJVjzzzFVaZcS4VV2xSlLiOKHb7eN7EUmS0+32WS6XlFXJ+3feZzabXiD6PTdguYxJVplphLpder0+GrEWeBvWynKxZDQa0TQ1lm0BCmhBKFzXom0rkDV5mTNbpEynBZ/93FdIi4rd/S26gwjPC/C8EKUEjuPhOB7TyQLBOmHaBiEUdV1QloYVVJYtQri4rrdek5oLhdUqMZbkpkEgcGxBXedYtsbzbYYbfbRoSJIFjiuwHUmnE5ImCbZtoVRL01RIaeIKHMtDK0lZNpRFhW2br7W2OThaMD7NyAub89OEt9+4i2xdbl17jvn5ijxtyLOG5TJlNl2s0QGKtjH27bZtL4TZGxsbNE3Le++9h1KK4WCDa9eu8+EPfZTtrUsUueKlFz7AaLhNlhWcn5+vV4wmPww0YeiTpjHT6YSzyRnbW5scHBySZdn6CPP0AP7jWD8S1uVvVX+0Oflma6Mna9E/sefyLe/xvf7r3+zv6z+RldkTLcrXTmNs26bb7V58bbgRZhVkQglX60A7j5OTMc8//yIPH97n7OyUN9/6Cnt7e2gN3V5EsqrQGmzXZXx2xsnpmKOjA3Z2dyjLjCRJOD875ejogCiIsB3LOGO05vz8lKuX9teo/6+uBeJlyouvvkJeVnQ7HU6Oj6nKkrIoCTyfra1dVGNQ9ZbtIiyXm8++yJ17d7h/7wEf/sgHmJwvGI/HWJbNzu4uQsh10NwUx5F4gWfSl1VDEPq0qmE6PcfzbeSqZW9vm+nkHMQmRZ7h+y5t23B8fEBZFXQCQ/NN8owWzf7eLkpaVEqRJTFht4NE4dgeo2GH09Nzup0u6aqkqRt830UIi+HGAM9xuHvvPpcvXaZVLVtbm5yMT9f024rpbIpSZg3UKtP0jMcGMOfYHp4f8uD+I5pGY8mSptZsbPRwXYuT40OquqBRirKqcW0fS6a0TYEQEt/3KPIWMI6Y07Fx/VRWQxAEbG5uMB6PqaqaqlaoViCkMgGO07khFWuLMOxQpwlFWXLv/j2iKCLLU9q2RKmGoqypVUurFdvbW5RVw3y+xLFskngFlkVdN+zv73N2dkqaZAgcVKtwI4fDw0NQGksClkCs86PCqIfWiiyvyPMlTaW4fPkaw+Em88UprTbNYxgFFHnFfB4TdXwcx2Zvf5/5bL5ewUhjyXY8pLQRwiYMQpq6JUlzM5lpFbblGDCiBtuxSNIYx9V0ugF5mTLaHGE7DkpFJGlOGPh0wwhpuSjVoJSgbitCz6fF/M5Zlm24LI6z1oB5aNXQ6/ZZLGqijjIRBUlG3WScjie89OKrBN6MqnryXGykdNjY2GI+W1JVRs8jbRON4Ic+jx4/wHIk3V6XnZ1d5ssFUdRnGSdcvXqL6wg8PyLwI6SwcAOH6WyG7Xl0opCNjU3Ozs6xhE1Vljx4cI/A79DpDta4Dm0OcesD9dMLzx+f+rGZrHwn9Sdhaf72H+d7AL+JPz1o3B+tPyqgFUKss21Mxo1YWz9XqxVCSHq9Ho5tE0Udtrd32dzcpG0rbt66ysn4MVJqHMel2+nz6OEB3e6Q/nAEtuTh4QFZmbOxMcKWEtG20NZsbw2om5Tj44eURYalBVLZ/N7vfQ7PdVnFS4q8ptvdIuxvcLpY4LgenaDD7vYOQrc4lkQ1LTubO+hW8P7d90iLBOHY9De3uPXCy5yezQi8kE4UcP3qLq6tuP3umwjd0O8EeI4gWU2xLIEWFo8PD2lUS5LFLOMps/mYra0+Dx/fo6Wh3++t06YFSbzCtS0cW5DmKbN4iXYszuMlWVNg+ZKoYxOENkk8x7EV+/ubtE1LPM/pBEPiRUKapliOy8n4jLysScsaL+ry/v2HpGVFowr8QDIYhvQHIWHo0qqa6ewUdIVrK1RTApI0rSnyhl5/iEKTJCk727s0TU28mpGXK8qqIIi6PH58ynKekcQZAoVlK1pdYDmQFwVtIwiciF44YH/vKr4XcXR0RpZXKC1BSKRto7Qky2sePjohjkva1ub8PGYyW6KlJAhdWpXjOprQ9+j4XeoK0qpkvlpSVAXxfAZtw6XdXbY3N8iThDItOLx/iINDx4soVhmdIECoFlvYlIVJpW6bEikUnuugapA6QLQhunHNJMpziVcrgqCD7XQ4Gp/hhAb90Wpp0pgDx9Bii5ymAtfp0tSSTjTEku6a3Grgd3WrmM4W2I6H5XhoYbKFkBolFL1hl7zOQSocx6IsCppSky0rqrTFc3yyJDVuu6Yk7IRUbUUQBUjbIi9Sqioj7Dh4gaSqMhzHJUlzknSB42p6/YDdvW28wKfVinv3D8iKikY1KC2oG6gqRVUrWq0oqhyFNs9VQFnnLJMF/VGHJF/hBh6WtDk9mxCnKfcfP2T/6hVGm5tmBbm9T5bnOL6HsG06/QGb27u8/OIHiLwObd2iULx/9zbLeEFe5iAUWqin9uUfw/qhbla+29XON8tL+EaP+Z00Hd/ycf+Yx/t2bt/ei/teH+C7qz9O52K+Nn+u1lfdq1VCpxMRRRFCmrH85f19XNdnONyi1x1SlQ1B0KHfH1w88Z2dHbTW1E3DaDRkMBigtDbiUNvHtlwuX75KGIbM53O63Q5FnvOhD32QssoNkXVji+UiIVmV3Lr5MuPxnNe+9DpIi2du3ORsMiPq9pCWTd22hp6pBA8ePjYaDCmNS0c3fOUrr9HthhwdHdDpRNR1SVPXHJ8cXaDvHdemrAoGgz5CQBAEJj+oaTg4OGAym14EKNZ1Q9sqPN9je3uHLMtZrVYgzKqs3x+Q5wVJsqLf75sMH6WZL+bYtlnTOLbDbGZeu+t5ZFmG4zhsbGzw/vvvM5lMsWybNM1Ikgzb9mgaA13zvJBO1GNzYwffD1nMY2zHJ01KTo7PqEpNHOcIYZgZSbKgacp1oKIL2iLPagQuqpEEXhdberhOgGogCvtobQHWRdbQwcFjHM9ltLGBWL+eul5bbduGbrdL25qwSsexzXpQGPJp07TG3q0aPNem2wspi4xef2CIvoEJVfQ9n2SVEC+NSLjfMy6bKArZ2tpaU1M9vFXGxjRmx3LxPJebN5/h0qX9izBJx7VZrZbUTYUfeIzHx2hdU7cl0CItyWQyIU1TbMemKEuUNkGdo9GI7e1tkiRBSovT01MjPvU8o09Zr/IQ0Ot1sSwoygxpgbSkgfhNZmsxdEAcr8hzo3OxbRuF5r17d6iaGmkbjZYAfMelE4bs7mzhui6O4zCbzgh8H8c1gYtVWdDvd4hXMQ8fPuB0fEq/P6Db7TGZmnVaVde0SqG1oGk1i4WJZHhC4tVar6MHHJPyvVzStC2Hhwe0reEQhaHP6dkpm1vbuG7A9evP8uD+AXnWgHaIwp6ZNIURTV1z89YNHFcwnY2ZzU45Oz8hSZYUZfXVY83TqcqfaP2gSSd+qJuVp/WDU0921sD/1LSYqYv5/3t7e3Q6HRzHod/vMxoNkZaNbdk8c/0WzzzzLLu7V8jSAktY5HnOcDBgOBxy7/59lssl9+/fJ8tyXn/9de7du4dl2cznS5Mx00JZ1nzmM59BSsl0eoZSFWHoc3BwRJIUbG3u8/KLH+L5515ltLFNEEV86fU3mM0X2K7P0XiMsCTj83O2dnbY37tEvErI0ozRxpDNrRFn58dMZyfE8YK33nqDra1NBoP+OnyuIc1SDh4fkOcZq2TFfLHg+PiE6XRKnhvNwsHjQ4rCZOXUdQMIup0+b7zxJnVV07Rq7ZgCEIxGGwwGQ5bLJVqD6/r4ns90OqMoCjSKMPS4fHlvnehrMxwOuXPnzkXGT9M0uK6HYwckqwytJFXZUuQVo+EWySqnbSTxMseWPkXeEAZdFouEtgHHDhASpKVIswTLcimLFscOEXis4hIhXNK0MI9dtVTVWt9QKhzHxQsc+sMuURSwXC6Yz2cIKUFaaIFpCHwPKSWdTkSr2oscqSCIQFt4XkDbtnQ6EXv72+RFTJIuWS5XWJZDlhXUVYvWoLQiyzMTjui57OxsobVmtYrZ6nT4Swdz/ta7Y37lzhn/2xuP+cl3D5kfn3Lr1k2j+ahy6rqgP+gQhC5FmaJ0RVmlCKEoyoxeL0RKcfE7oJQiz3LG4zGLxcKIlOv6gjFkfg4ueZ4jLTN9jKKIeBWzWsU4joXj2OvfHxgOh+xs7wGS5WJFFHZwXYeyzCnqEunYaEsiLAvbslB1g1CaZL6gKo0GLAxNyvhkMqepG6QlsR1J05b0+x08zyfPCw4Pjnj++Rfo9boIKWlajdJQNS15WVLWDXGSridga9cfep383ZKmKZ7rkqYJRZXgepKrV/f56Ec/wnQyY2tzD9VaOHYE2kVrh92dywwHG6xik/bsuS5CtrRtRn8QkKQLtnc21zBJaE3s0dP6MaqnzcrT+r5U25qIea3VRePy5CaEIdaaqzsbEMj11aTjuGvniCDwwwsI13K5wg8C0sxkxdi2zcsvvsTpeMxP//TPkOe5AYttbFDkJR/4wAdpm5aiyJlMz2nbmng1573bb7O3v41SDZOJEcH+X/7a/5WNjV2Go00uX7lsOBVlzWi0ydbWNlvbWxwcPqI3iNZAMcnO1hZVVTIeH9O2NVm+4uHD+xRlSpIs+YM/+KzRfkynpGmGVlBVFdIShv4prfXBXLFapXheaHQ4lkMQhJRlRVM3jMdj+r0BSkGeFQR+iBCSNE05OTE6C8OoMcAv34/wXN9kxZQlq1XMcjmn1+8xHA5pmobJZEJd12v+yYTT01OOj8fmSrkx06OtzV02NrYpiposLQAbIVzKoqEqFVlaUWQNWVahtaIoMkDQ6w6wpEfbSNK0QrWSyfmcujYW5KaGLC05P59jWx5C2FRNRZwsQWo2NjfwAo92LRBGa7RWVFW5trEasWhZFlR1Sd2YCRRgxMTdiCD0iCIfIaGpW7K0YjZbUJaVYczkxoJsqLwnLJeL9XQv5K8kio8+mmC5DtXeFnWr+NnzjJ8+nJNmKzY2h1RVjuNaXL6yj+MIosgjL1KUbhFCY9uCuqm4eu3aRZNSliW2YyOEaVyOjo+p6xrLsmia5sImDiCFCSqUUmLbAj9w6HRNfhKA63osFjGz2QLfi3AcE0QZRcY1k+UJQRhQ1hVaa1qlEELQ63QRGhazKXVdMZuZiYhSep06fYK0wHElvu+uM4gstDaBlsPhCCmfUI1BSgshJFlemFDMddPVNA1aPRGSmIuVoihomobRqI/n28SrBdKSDIdDgiAkDLrs713jgx/4CVAWtuXzwguvMBptIaXN/fv38QOPsspYrhZMZ+eAxrYt0CCF/oG42n9af3r1YyGw/V7qB+kXQsA3fULfTeDg9ysDyLbtC55KHK/wfd8csG0by7LWfIaWOF7iOC79fh8hFFJKLMtCtRohBRsbW7RtjdaK07Nj3n//HfZ3d9ZXnaaBaduWq1ev8P57b3FyfMJP/uRPEsdLhFA4rkSIlvHpEVvbmwihuXf/fYpVQVkU9LojgiBgY6fPl998g+PjFFtKbl6/hut6NGVBkiQUaUyZrdjZvkxZFhSVw+HRAdeu7rCYDuh3bQ4PD4xNVyratqLTDdm/tE2arlgsFJcu7eO4FllmqJxKt/i+EVQWeYnr+Dx88MgEyKUZnU50MYHKsoy60pRVBespSpbl+L6LJW0saa8bBphMp4RBQFOVWGvA3HS2QMMF/wJgtTKU1Kqq1s6NTeLlitlsTlHUXL58hcFgwNnpITdvXmIZ50hp4zgBVdWgdYtWmuFggBQtXadPU2uqsqIoGhbzmKZR2A5rgq+JTNBaoBQX6xshS1zPxfVcyrpACxBSIIVZBfmOY5KKAxeERlpQFSVKC67dvMzp+SnzeUUQWqySGIliY2ODw+Mx0jYOI1VW2JZNEAQgYJWan7PrOpydnVHVJUMluHWyYOpA7Fo4SpF1Q+y84OVZzmQRE4YejmuhVM3h4SFRFNI0lZkIStOk9/p92kSxWCyQ0sK2JVBfxEkIjJC4LKuv+12Zz+cXExa39SiKnDB06fc7tG2NZXnYVkBTm0iDPM8ocmMfr+uMbrdPFIXsuTukZQFPQg4dj7au6PV7aN2SlRlB4FGUxl0WBBGO7Zl1oWtxMh4jdIRA0DbGGbaKMybnU8qyRgHStgy/p1U4rkcUddeAQw/HdbAdiW1bFEVOkiRry3tBHC8JAn8NOMzpdLp0oi6r1YrhaMT5ZIyUDloLLOkSBh6H5zOeffZFFqtzVtkK23I4OBxTVzWhLy7kKlr9YB2ff9TrW73Xf9K8s6eTlaf1PdeTkELbti8mKKtVzNHR0fp7RpQppUBK6wIelSYGi17XFUo3oA1hczDoY9uCvFjR7fskScx8PufBg/vcuHGDjY0NNjc2DQq+LDg7G2M7oHSNZWt6fR9ERbyaEHU9tG5Zxsu1JRUcx+J8MqZVBUW6JFlMoa25ur9PPwp5/923WC1OuXfnTSbnx5RVxtnZCa5rMx6fkGUZRV7heT79QRfHtbFsc7CeTCZ4vkHFC2kahOvXrxEEAVrrtV7DAL+aVqExV6FaC9I0AyTL5QqBxPMDsjRjOBwxGAzodLpUVUPTKNCCttUXk5+yLFjGC4oiZzqbEKybOvPYes28sOn1eiilqKqC09MTmram042oqpytrQ3qumRjc0SWZqi2IcsypCUM7lybK3bVKsqixfNC2kYwHk/I0mKdNm0RraF+lmUmZlI+0S21KN3Q6UUMRn1c36Vpa8qqNAj2tQ5Ba0PSNSf7msD3UdrM/eumQFMDDbYtUcpk6sRxClqum6ca3zd01CRJyfMCx3XQGEBhVRutTZAV6HgF/e76M2imBXI0YGA7qPMpVVUaNHxTES/j9efYoixqqrJhOBwxmczQSpuV5XCI1trkLjUt3U6H/f19wjBkMBhcwPee3MqyJM+NpkpKSRQZN5bnO7RtTa/Xu0g0Ns1Jh6ZRNI1iMjknzVY0TY0jLIQGrZSZyEjBYhWzylOQEHVCgiBguVyBFmiNWauFPp5vUZQZQghc1yOOE8bjsYENSmmowk1L0zYojUECSLOKG45GZtXWtvi+j+M47O7uUpYGeheGEVLaXL12HSktrl69yt7eLrdu3eDRo4dsb2/h+z5RZCzlUdTn1s2XGPZ3aGqLQX+b3Z0rOHZIkhZ8HWflaaPyY1VPJytP6/tQgnX0D1qzvvqs2d7eptOJKIpina7cMUhuKVBaM5kkDIcOnuetT6AtWZbSNDXT2Zjb77/BcNRHCIv5fE7Y6xJFEZ1OwIP77xOGIXsvvojjSA4OHjJfTAy8y7LY3hmSZnPuP0iQ0oStbW9e4o2vvMM/+/X/N1E/Im8KemHA/GxKnqQcPT7AtmwsS9MNPcpKs1xOwALX9yiKjCJf0en0mJ2n7Ozs0bQtaWpw8oPBgMePH/HRj36YOJ6ztbXFZHLGdDqhbiryPL94x6Kow9nEiGNBkOcFrmuzile4nkWn22WxMJMNKS2SVUpZFSYsr20pq4zAD9HacDi01vR6PYqiYDTscz45x3HNyUMpRRAE9Ho9wxRZLrFsk6xsWRaua5HnJqBxtVoyHHSxLEHdFAxHfRbzBULaRpsR9CmKCseymU2XrFYpruNRVUZnEkURVd0CiixLAXXhBgNDD87yhivXLvPo8RFaSwbDAdPpgk6nQ1NWhJ6L0g1a1xdrDbTGDzzqusD3LaQFRZHjOjaTyYyT41OEtA0Z1wKQdDsRk+kZQgrqolpPACwzyXAdctlS+y7uLCZUDW0rcR0Xa5XS+C5zoZASNjdHJtwwqUiTnCDwGG0MWK2WzKYLyqLC9Q3hN8tSMwWMJziej8YInbXWxPGKTqezpgTbTGdTojBiuVxS1AVR5GPZFnVh1lae53J4cIi0HGzbp2nM5NK2POrGJF3P56cMBgMs6TCbL0FpyrJCS4HlOjSquqDmbm5sUeQ1da2YzyfrxrVhY3OELRWHSWxWk7a9pgw3SCkR2qyBlNJmCqo1WW6ysJ7Yu21HGr3WugHb2d3FczzyPGY42ES1mro2P88oCrh56waeZ/PMM9cYn5/S6XQJfEND9kKX2fmErY1LdIcRntvhxRdeZjFfsr+7j7gATDztVn6c6mmz8sNW6nuctX23v9/f4p/V6wygJ+mqg0HfsCKUcex0u3oNelMoba60h4M+vmdOplVjsNtVXfPG619gMAh5eP8+vvsyL7z4Ep1unzfeep3XvviHbG0N+dJrf8DJ0QHWlSvs7uxz98E9hGg4OR7juDZB4K/D4DrkZckqzrClTbfXZTTa5Bf/yv/CH772Gc5Pxrz88kt4QYcXXniV09MzTsfHPHp8zBe/+AVuPfsyn/iZn0HrltGgz2mRcHBwSL8bgLQ5HZ+znC9pmpa8KJBSU9Um3C/PMhzHpqpKhoMBdVUDUJYFJ8djXMtF2JKN0Sa6NYLPLC8IwiHdbki3J9ZZSTVCwq1nn+PLX36dMOqSLmIsy0wh0iyl34vwvT6rOGYV59iWyZkxOiJNFEVMJpO1fkOgsfD8gKLMDV+mLNnYGJGsEvK8QkiPPK+xpEfTKKLIQ3mgGwiiEK01SZJRljWe51A1DRpFmzUmL0ZauK5PaPvUVYXtWBdNallUnI2nVEWF6/os53Oa2oQnqrYmyQpc10GpFhDUtcJyXPwgQLcQeh3ieI7QirhNyIKS1Sol7HSMFsqxkJbkaDymrAqubFxGWoI4XtK2DZubG/i+R52Z1/7BcUwjoLRzVo5N2wn44qUeM+kwEA5hZBOvDE/Hth18P2KxWLG1tUXTGCJsUxvLsrY0abYiikIEklWcEc8zFIK2XUPcPIut7ibQUJYFQRBRVDmO45pkZySTyTmdaABCI2gRQF2WtC1EUUSRr0C3WJbAkoosXwIttmVRVxn9UQcpWs7HZ9hC0gl71HV78dwFgqbWxgpsN+zs75BlivPTOa4VgIaqqbCkRKgWx5bUTYPt2Ki2QmsJWq3FwA6WhGSV4ns+q+WSeB4zGo4YdIdQwXR8gmt5JPNT8vQaWxvb/PRPfZKd3U06nS7jk2OWN29yafcyy1nM/t5lLjn7HJ4+Js9zOt2IJImRAtDGmSSfNis/VvUdrYF+/dd/nY997GN0u122t7f5a3/tr3H79u2vu09RFHzqU59iY2ODTqfDL//yL3O6xkY/qcePH/NLv/RLhGHI9vY2f+/v/b2L2PCn9d3Xn7Rr+Rs/9hMh7VcFdmbaIs2VmRAEvoclDWulbVqaqkEKiyKvyYqSLC84OByTpilCNJyfH+FaNjevPc/lyzfY3N7l0qVLDAZdDh7fJ10tcD2Ln/nZnyEratpWsrt3mU5nYNKMtSZeLqnKgs1Rn7pJmc5PCaOIn/2Zn6OtYXtzy6xyhhtcuXaDKzdu4IQRy7Tg4aNjPvHxT/Lccy/xB5/7Q37z3/x/OT0+4erlK7z88svsX75CnOQ0CqTj4nguq2TFxuYI3TZMzs+QQlDkGf1ez9hvbRvPdfFcD7m2N9vSosjTi7wVhMCyXfLC6Eq63S5lVdCohrPzMxSCLG/QGJibY9tsbIyYz+ekScqlS1dx3RCNJEkSc9VeFEwmE8qyZDQa4QcBfhARdDqEUUjUCdnc2sBzHWxpU1WaTmeD5TKnKIyg1TQgNo7lMpvOL3g5/f6AoBOSVzlRL8ILXaSjEVLTtjVSmHWDbms81+KZa1cI/C697hCUYLWMSeMVQrW4EhxbokWLkIJWaaTl0CqB6wU0raLOk8wOgwABAABJREFUK3QFsnXQrUVdKIq8MnqjpkZpc7N9l6pt0UKyWCxpmoaNjQ1YT6HKsuIn7p/RyyvGHY/cknhNy1aSk0QhX7x5hUv71xHC5fhkTLfXQ6wBeY7j4DoeZ6cTlsuUVZwynS3BclAo6rZEociziiyp0ErgWDbdbkTTFti25tLlbXZ3N8nzHNtycVwT2FnXgLawpI3n+XS7HfzAZRUv6HRCfN+hyFOiKKAuSzphRKsqwo6LENDtdrCtlkHPRzcloRtgSQ/bNhEAqsX83hW1odmWLbbjUtYpw80IL7RNllCjUK0iDHw810HrBscRaGXQ+FVhUrelEFiWZDAc0tQtoR/QNi29KCCJF5yfntGLAvqRDW3M4cN3efP1L/D48UM812N/b5+f/Mk/x/bOJmkWo3RFmq0IwxC1XmMGoc/52RhpS5p1+rKQ4mkm3I9ZfUeTld/93d/lU5/6FB/72MdomoZ/+A//Ib/wC7/AO++8QxQZQd3f/bt/l//j//g/+Hf/7t/R7/f51V/9Vf76X//r/P7v/z5gBGm/9Eu/xO7uLp/97Gc5OTnhV37lV3Ach3/2z/7Z9/8VPq0/+XoieOOrDcuT5uWJ80EiqOtqHQ8Ptm2C1gAaXeMHHrZlEQaCNN3idJyzt7/HlauX8XyPoirM/r5J+NKXXmM2mbC1tUkcrwjCkH5vwPjkdO2yiCiLkuFwROBHnI4nWNKlqVv29/ZxXY9+r8PqvRTfi3j15Q/x8PCIL3zhNeNcsgST6Rn/t1/539jcusIHP/QhHjy8x3/8j/8/yipnuZwipMZxbLY2tzg5PubWzedJ0xV5mqxdOEYjYlD6KTdu3EAIwePHjy+YF2VZopSmrHKquqDb6VA3DY4rUa2kLGuyLMUPHLI8ZZUk5sS1SgmjCNU01E1Dnuf4vgcIDg8P8b0AKSUbGxvM53MzuleG+fGkeekNRrRNs87xEQz7A+68f4c0ztna2ePll1/m/v0HbG9trXH7MUIKqsoIXvM8ZWO0jUYwm01wXQfXtdG0WJZF6Bl8f39g1kgAlrRIkoThYLgOT/RQSrO1tU1d17iuyyJeYDvGAVWWCUEQIUS5zroRCGnQ9VIKtBLGBVWZRGHHttFCIRBMJ1NGoxFpklxYhfMsJ4o6RnQcr3jhbMUscEg6PnZZE0oXkWSIIkcnGWXHI8tzqrLm7PQc3x9Q1y1to1EK6tpMDutKYbmCqlwHBQqBkALfD8mTFLXW+mgh1u43wdnZOb3eEClnxiFUKHr9PpPzuREya5s0KYnW9FzLBtsRxgauGmgtZrMlYejRH4VsbIzQ7Yo0K3A9B9d1jB1em4sDrSRJkrGxsbHWxywpy4ysSOiMdrEdwcZGl+ODY/AtqrLA8x3KKiXwA7IsW1vowbZMrlbbtNiejeu6FLlZj1V1BWiKIjeBhm7D2eQR+5f2qKqEL73+BxyfHaNai4PHR7x35w1q1XD1mascHTxkPpnw0vMvY7tQLAuevfkc3U6P6dmCoihQTYOwXKynassfu/qOmpX/9J/+09d9/a//9b9me3ub1157jU9+8pMsl0v+5b/8l/zmb/4mf/Ev/kUA/tW/+le8+OKL/MEf/AEf//jH+S//5b/wzjvv8Nu//dvs7OzwoQ99iH/6T/8pf//v/33+8T/+x7iu+/17dU/rT6fWAtsnCctGGGlEgfP5jDTNGfZHDAY9c+d1d5OmCW2rSNIFjVZ4bsBbb75BUS7Y3dvj/fgex+Mxzw62cNepvwcHh0wnM7a3dljFMVpLOlGX7Z1dPv/596nrhijs4LmGKbKzvUc37PHo4BFep0u32+XZZ28RJwsuX7rCM1dvMT6dsL21yzJJODo55PHjR9y4cZUHD+8SRiO2t7ZZxnM2Nrd46aXn8Tyb9957h89/8Q+wLGjqhkcPH/LKyy/x1punfPp3P00nirBtm+3tbZOcC/R6PTY3N9fsFGP/7XQihLAQIgCh6HR8yjJjONxiMctwHZ+yzOlEHfrDHifjM1xXsFzOGfYG9Ho9JpNTbEsQ+AF5VdCNutRFfZGAK4S44HzYtjm5tE1NVSo8xybNcwSQJQlB0KEqCh7cv8/+3i7z2ZwsWyFocRzBcDggWS1RbUVRZDStRquWfreDFBov8FFty+Zwy6xAljF67VJxXIcgDFECkiTBcYxeabFYrEWyMQKBlKbJs21ja3ddh7Y1J+ko8Gkas1ai0VRViWM762bGplZGUPzEuuC6BkGfpilN09KJemyM+liLRwStZumbiUTrO5zlJcLSXKlqwiLnzuOYIHQBaaIidIPn+iglKPKKtmEtXpaoBmha1LpZy9fvad0YuF1VV7RNi+sGSCGZzxMCD+rKBAy2raIsGuraZEp1u11syyPPSrSucV1Brxetm31N22rKUmNZHk2tOTk+RWsHgb6wqbuux7JJydKSo8NjitIkfCM0jiMoq4qr1/axbOj3ItpKsL+3RRT2OTo4MdbvqqJVhUlAtn3Dz7FbbMs1mIKmpa5LOlG4hgRWBIFPNwxJs5TBsMd0es5X3jijVTAYbBJGAU0j2Lu0i1IarRrefut1lssFf/6TP4frCGxLMxwOGAw2aJXixjM3CfyApm2xpEYKk970tH586nvqT5fLJQCj0QiA1157jbqu+fmf//mL+7zwwgtcvXqVz33ucwB87nOf49VXX2VnZ+fiPr/4i79IHMe8/fbbf+y/U5YlcRx/3e1pfWclLv7r+1fGKq3RSn/16jXPL7QrJqBMMJ/PGY9PSRIjylOqZTo947Of+33eu/0Or7/xFR4/fMDx4SFNXfOVr3yFk/GYK9euY9kOcbxiGS949OghZVmyv3+JTtRld/cShwfHLBZLTk9OsW2XTtRhNNpguYwpioqrV5+hqVkLVQXjs2Nm8wmT8zOyPGO1Snj08BHLZcy1a9fZ2NhgYzRiMOjz2mtf5MHDe4zPTjg7O2U6ndLvD8iyAsfxGPSHfPDVD3Dl8mWauub1r7yOQPDC88+ztbHB0dERSZKshZcZ5+fna6iVXpNTXTzPYzAYEAT+ReJuWZZMJudUVY0QZrUadUJOT8dYtkDpBse1qZuGJFlh2/Y6hK9hMBgi17EGnuddOIKatVjyyYmsrmraujGNxcYGvufhux6ObTMcDFjFS65fu0qeZ/S6Hbq9iE7k09Q5UmpeeflFPM+mbWo818FzbYLApalKbt68QVVVxn69FlW6rsmBStOUs7Mz8twQemez2cUUrixLqqomTTPKssJ1XZpmvXaoivWqzEFp40wyQlXTyDxhxriuh2VbBH6wDlRcolSL53q0TcN0OjHv/e42zmiAX1QXn1MpJJu2S+XaLB3Y2d0mCALCsHMRRuj7PlJK4niFbTtI4WBJByktUBqxFqZWVWUSjoWmqiuapkVKiWrBsnwkLo8eHREEEWVZmt9RIel2h3huRFNDXRuir+t5NE1DHC8oywzHkYARHddVy2KRsFqldDs9Q0oO/YtUazAOnzQzKP7pdGrWSYFHXVcMBz2KLAWtaJuKPE+QUnPp0jbP3rrGKy89y8YoQugaS2hCP6DMS1bLFWLNEmL9GsEQdx3HJoyMG+345Iwsa+l1t+l1d9EqYGN0iVde/jCf/OTPMZ1O+dmf/Rl00xC4LtlqwcOHd1Gq5vj4hLpuWS5WbG1ssbezh+e4ZlX4vWr3ntYPXX3XzYpSir/zd/4OP/3TP80rr7wCwHg8xnVdBoPB1913Z2eH8Xh8cZ+vbVSefP/J9/64+vVf/3X6/f7F7cqVK9/t0/6hryeo6e/0hhAI/pg//56ei3EIPLHGPpmqSCmwLBvXdRgOB+zsbBNGPo4jmM4m3H7/XZJ0hePCZz/3P7h9+x3eev1N5tM5cZzgeQFKWyAc0rwhXiXcu3ePg4NDgiDkox/9GP3+kOvXb3Lp0lWeffY5Ll26zKX9SziOx+np2QWK/PN/+HlWSYIQmrLKWK1mfOELn+Xq9UsURcpoYwBCM5mcMRoNuXPnLs8++xxR1KWuG7I84Z1336AoElxHcnxyQK/f4ez8jOHQ8FHqsmRrY5Miy1BNw+ZohOd5BJ7PYrEgSRIWiwVN01xMVfI8R0rLXFGXNUVRMRiMCIIQ3w/x/YD+oEPUCXAci8Uaq48GzzPk2qIoqKoKtXZgbG5uEgQBh0dHuK5LmqZr+2iI4zgEQYBSai2INAf8uqpYzBccHhyytbWF57gkqxVVUTIaDtnd3mR/b4etjQEvvvAsW5sDXnj+FlmasLO1gWMLYJ3wi6apSw4PDqnr2jQTjk0QhrRtw8nJCcvlkt3d3YsQy7Y1J3Ewx5SyrCjyCtt2jeV4PUlwHAvPs7Edk8atdHthXW6aFiHsCyeLEII0S0mShLIqjQ5KSnq9Ln7gUFUZcmfE/UsbDIqGQd7iI+kXDb205P2dHnNLI6WZfLSNSWe2LLEGqDlYtiDLU9R6aoTW+K5LGASEQYDrusYqLyUtmm6vR1k2FzTfJ9oUIYyVPfA96rWluiwrbNtZr0otsjQBbVFVLcPBkKouEEKDeOKwgW5nQFEUhGGIXDd+ySojTc0658qVSwyHfbrdiMVigeu67O5uM53O6fcGdDtd3r/9PqvVColmMZ/y6OF9HEfx6qu3eP65a+ZnrVrkGiEr0QSBa2jDa+2KFBAEJsTx1q2bRJ0Btt1BEPDTP/XzlKXk6GhG3VhoHLqdPsky5dkbt5hPpqA0RZZy7+Fdrl29im5h2BsyGo6IgghLGou2WHuC/izrBwVD/4NS3250zXdb37Ub6FOf+hRvvfUWn/nMZ773Z/Et6h/8g3/Ar/3ar118Hcfxj3XD8oNQT4Bvci10s23zUXoirjUHcSOw3d7eZhUvuXf/DpPJGXVdcHJ6RK8boSn5qY//LM/ffIUsq/hI5yPM44/QG3ZRysGyQvIiZ0tvEscz8ixGYNJfB/0ht9+7w4c+/GEODg/RWrO3t8/b70zYGG2xXC5o3faC9XLjxnXSrOV4/IjT00PSLEbrisViQqfXZbGYkWc5t249y+/85/+ElA62rQnCgDRL2b+8w2DQ5c6d2+zsbHL79rukqqWuKgSCZ65fp2lqqrLk6OCQ/shYrc366gAhBJ1Oh7Zt8TyPMIxI04y2Kc36pWyoq9ZoMGTOcCA4GR8RRh6ua7O7t8/9BwcEQYf5zIhGq0qZ91EryrJEK0G30yHJU6Rt1j/dbpfBYMBqtUJrbci6oiUKfSSCbq9LvFyZFdmNfR4+esz59Iyz0zFlaXgsu3vbdDoew+EzqFZhCcmdO/fZ39thMpvhBx6dTkRepGg0ruexmMU0dUPbNJRFThh6WFKytblJ3TT0+33ee++9Cy2NmaIIQGJbzkVDonVrOChKMp2em+lTUdK2xpWkWhC2QCtNlmVsX9vh5Oh0DSN08X2XqiwJI5eBF7FczfADl/8ysDnf6fHqoqA3S8ltiz+81OPNZ/eILFguF9iWS6fT4+xsRrcn8Dxj0R2NBozHp7RNg+f5CMzKajgcXliVHcfBsiwWyyfZTy2ObYIQZ7MZtm0SkI07q8UPHNrG8ErMCk/guAKwaOqWyfkCz/UMZE0ppBWSZyUoODubEAQRo1EXaQmaRrO/f5l40SCthq3tAYNhh/H4hDheYVkW3W7fONCSnKPymL3dS8zOF9y/d59L+5cQGpqm4Pw85sbNqwR+l7vvH1IW5foo0GBZZlrmeS6ObWjDeZ5RlzmL5RLHiQiDAdeeucHm5jZh2GEZx/zup3+XD37gAwyGmxwfjXnvnffQteZ8fM6HfuKj9Lo9gjDCs7v0uh0sKbGtNUtGa7NMftoo/FjVd9Ws/Oqv/iq/9Vu/xac//WkuX7588ee7u7tUVcVisfi66crp6Sm7u7sX9/n85z//dY/3xC305D5/tJ5chX279cepxL++s/sxHiF+1y/d/MWvfW/Nic8IH580KE9cQIZmq4jjFefnZ9gWvPPOm8TxnDD06PXCNeshYjDok2cFaInrBvR6IwLfQymLyWSF5Rg8/727dwlDjw+++govPv8Cn//8F9jb26ffG3D58hWm02M6kcPlS5fJswTP89e5JYpON+Ly5T2OT6ZsbW7x2pf+EMeRvPvu20xn53zwox/lM7//GUajEZubWyRJRl2XnJ2P6fV7HBwccHD4kCw3a5erVy/zzjs1NC27Ozucn5/hB0OE9kwAXFHg+1vUqsJxjE5ESkkQBFRVtV6ZlbSNRkuFpSVB4DMcDomiiHv37pLlK1zPTKiqqmIVJ6RpTlHElGWDJQRlWSH6JvAvSRKqsiZexghLAl8F0SVJQlXVFIWBddnSTDLatadrMV+gahh2R8ymM/Is4/HDx6SrhDDwuXrlMmenp3ieR1mW3LjxHAcHR5yejo2DSQZMJxN2d3aYTFekSXIBkcuylE4U0utGSAtWSUIQ+CwWS0ajkXltqxWO40Kj0K3RPnmeuw7sa7FtC9d1LuByvX6XxWKBY9tUwqy2eoMutq/JshylFFEYobXRkJRViaLCskw+0WoVE1cF//3aBicfucTq0SF1P6TsunS6IY5QnJ6uGPRHTKYLHNfBsgQIRZIuUQp836NpTBZOpxswPZ9Qr9c/Qgj8IGA6nRoBeH/Acr5YN5gN/f7gQmBtWRLLNiTnLM1RivU6sABhIS2LqmixpGMcUlLS7/fJ0hzbssmKnKYylFxgLV52OTtZIEUXTcPR8WMERnMkhLGVTydLmramP/A5Ojxm0N80eUVZtV7DOSwWM6SnODx6xI3rr+C5Hf7gc180iH0ckmSFsCW2FAS+Q5bkzIoMz3EY9PvMlwlB1PDg4R2uXnsGx5Wk2RJpCaStsWyPzc0RnhPQ1oqjw2P+l1/8y2gkUdSFxiZJUga9rqEjyz86UflmB7Rv1s18OwfCp93Qd1tfpamLP/L1d1/f0RpIa82v/uqv8h/+w3/gv/23/8Yzzzzzdd//6Ec/iuM4/M7v/M7Fn92+fZvHjx/ziU98AoBPfOITvPnmm5ydnV3c57/+1/9Kr9fjpZde+l5ey3dY+gf09gNa+kmjYlY+QrAme5a0qqHIclRrGpRGaaqm4fjslE9/9jOsspi7924zmY4JPMlsMmZzMOTtN9/FdTuAQ6MUYRRQlhl1UVOmFePDI1ypCG3Nvffe5vY77/LKKx/k5Vc+RBj1EBr2trdwpKQXdbAtie97BKGPsCSu79PoBssVzOYzvvz6V5CWIl6d8+DBHc7GZp/+kx//89Rly73377CzOcJzJdKBTj/i4PAYhEVRVrzyygfJ8pIw6vDo0QG2Y+O4FkcnB6ySJQ8e3uXRwQOSde7N4aPHJIsVy+mStmzohB2ktKgbRd0qHFcQdT2kLSiqkryoyIuK4WiEsCRlVeO6AXUDedZwfHSKUBD5ATsbm1y/fJXhYMRyHpOmOds7WwhbcfnaLl5kkzc5ju9gOw55XnH92i2k8KhKtc6oaXG8AD/o0htsUrWKg/EJtuti4VFkJS8+/yK3btzAtW1QmtUypqwq7j64j+P72K6PsDyk8IiXBUWuKKuStq2wbZODE0URe3s7FFVGksYcHj7i3v07tKr+KtxtrU8BcBybKDLk0zCtuJTUdEpoGoluoSprFrMFTV3TNvV67WjRVC26skgWhjNTlBlCauJkQX/QYWt7hzDapCwlcVzguiFCW8yl4p6omYoMPwJ0Caqh3+2SpCu01Gzt7SCFjWo0ZV6RrlJzdd8KfDciXpbUjcV8kaLWVuu6bqmqBiEc4jinbjR+6BF2XRxfY3sWjQJEQJ5K0pVGKWsd1Ffheo4Rw7qSoOPiBh5pWjKdZTx4eMLZZEHdtgRRQKcbgGiwLUldKNpS47s2jl3iuor+IGB3b5NeP8JxDDyvqjRn4zlZXOK5DkFg0euFRFGI7Vq4gUW8SplNGqoS3n33LYpqydbOANtxaGubtgDZQJGk5KsY2zbNpOV4KOEQdUf4foSwzJQoCG0ePHif3R0jNP9zH/84Ub9H0dYUdUm8WvF7n/ksSZyyWq7wA5fpdEqjjR5GCUErJA0CjcbIbNfHTvE1tycHrW90MLv43yc39TW3H+Dj8A9Rff2F7ff+eN9Rs/KpT32Kf/Nv/g2/+Zu/SbfbZTweMx6PL8ic/X6fv/23/za/9mu/xn//7/+d1157jb/1t/4Wn/jEJ/j4xz8OwC/8wi/w0ksv8Tf/5t/k9ddf5z//5//MP/pH/4hPfepT39H05Gl9Z/XNGCzf1k2YfBeltNFK1DV5kfH222+hVEsYBWsnQglKMV8sgJZbz13n4cE93rn9JqNRj8n5Kcv5nDffeJPAC+n3RuR5QRDa3L3/LvfuvUfTlDx++JAo8Dk5eszRwUMWswmvvPwyVy5fJU1zjo9PQMO9u3fpRCGg2N/b4+zsjNPTcwQWg/4IKS38wOXWszdxHIc8T8143ZGs4ngNygp599332N/bp6kbbt++QxhGKKXpdgcUWcXu9j797oBXX36Vy/tXKIqKTtQFAb1eF89zDBI/isjzjOFwQLfbwXNcNkYjfM9nY7TJcmGswxsbG3R7Ea5nm5PHWqBs2ZKHjx4yGAwQQqIU62BBEzJnNB0lTdMwm82oygozzbKYzedsb20xW8wQlqRuapCQFTlFWfDgwf31esHBtm2effZZLl26zHyxpCgrhGWDlDiux+7uHpf2L3Fpf5eqKsjShOnknNl0Sn8wYHx+StnUSMtBSofTsylaS9LU0Irzi4yYEMuSzOdzg94X4iKoMAyDi1BGy1pnz6yt7tUy5i89Puf/+f6Y/8eDGf+vh3N+4WhB1/bQSqMVdKKOmUxIiVZqDUSrzbrFsY2eR5mV2zJecXY+oygayrJBSgeBXE8SMoaDPkHo0usblsliPqdtzGPVTU3dGJbLZDJFqQbLMrZpIQRN3VKWNW0rkNIBJEVRUhQFtu2Clmvkv0uWZ0hLG3CdFOvoCUVVKpJVsc5HUhcr1rZp106uirpuSbMK142Q0sPzQpQSF5EGly7t0ev1EEh6nR5RGBBFLqNhn0F/wHw+I0lMTtVyGbNcrLAtn7pW9Hs9dne2cH0LyxZrkazPqx94FdUKykKxXKacjs+IQhMCqXSF6zioRmEJi7pqaVuF4/iG32J5DAYbVLUBE87mU+7cuU3bVLzz9tsM+gN2dy8RBB00AstxQMB7t9/l/Tu3qcqcqqqIooDHjx/TqJZWa7P+kV97YHvSoOiv6iPEt3N2/NoG52sPdk+blR/E+o6alX/xL/4Fy+WSv/AX/gJ7e3sXt3/7b//txX3++T//5/zVv/pX+eVf/mU++clPsru7y7//9//+4vuWZfFbv/VbWJbFJz7xCf7G3/gb/Mqv/Ar/5J/8k+/bi/qTEPf8uJdxsAjSNF3DyAyUbHt7m6LIWa1mLOIJs+kZ7777Jlm6oK5zfu/3foe7d98hXk05OLzP8ckho40hq9UKy7LpdPp4nkO8Oufg8B537r6DUiVFmWLZkratef2N1ymr0uQCjTZ48623zNVWY0Bp7773Fts7myyXMUqBFA6XL13HtgM6nYFZtWiwHTNaFxJDW21rjo4PODsbo1TLrVu3mM8XRGGHD7z6EV595SNcu3Id3w+5euU6vd6A/b3LOI7L5sY2/d4Az/XI85ymbVks5kSdiFdeeYWyLMmyHNtxmM0XXLv+DKenZxRFiS1t8jQnz0vyvFg7Wsy6om1bej2TVVNVNdvbOwRBSKfTQSvQSlw4eqp1yGEQBOYk3SiEsOh2ejSNYjTcQLWaqioIQg9hKfzAwvUEQRisHXhvAazXMcZ1N1/MyKuEos44OHnMIp6T5Am2Z1M2JatkRdu0hrNh2zTrk3q/3zPNkOWgWm1cPLUJVxQCklWC0hq1xrdnWUZRFPh+sJ5AmBNvXdf84jznz08TbN/jLHQp25ZPnsX87PEcx3ZxXY9er78WeIu16ylD04BoybJk3UT6VFVDsjK8lDRbYSCGhv7atjVRFKBpuXbtGp4bEgTR2qVTGyhZEFCWBW1bAS07u1tEHR+lKsLQBdGC1rRmTELdNCY6oDHMn6Zp6HS669wsDyZLtmYZ9mIFQqN1Q6cT4rj22soO9trWvb29A1pS14bm69gerhMw6G8Q+J31FMbEN0wnM5TSDIdDfD9ke3uXXm+A73c4OjplPD4nXWdN9bomc8h1fVwnAG2Snw3lOFsHksZMp+dcurzH+fmUMpes4pbDw1O2t0dokWLZCqU0UtpUVYtjGwhdXbXUtbpo2tq2JVmtGA6H5HlOmhpxsuv62I5HrzdkMTe6JXTDdHbKnXvvkWYJQehzdnbCZHKOvaY2P3EiPq0fn/qONCvfzt7J931+4zd+g9/4jd/4hve5du0a//E//sfv5J9+Wn/GJQTEcUKaZuugQfPReZKqPJ0ekiQJR8enPHx4wF/+K/8rDx7d5ezskDxfka5illLiSJv5fEonGrC5vc/m5hZptuIrX3mdum44O5vw4OEuBjk+5r3b7+DYFs8+9yy9wSbzxZy6rpjPZuzu7vLlL32Bk90hy7tLzs5OeeHFFxDSpUUy3NhhU25y+/aX6A36+L7k5OSYXj/F9z1mszPmi4rDo8c8/9zz3L59G9t2OD46pdfrc3x0ynO3bvLe7Xcps5xbz97i5OSYpqn5yZ/4c/z2b/+fF2uO0WjI2emYKIoYn465fPkyp6en3Ll7F2nbzBYLer0etrQvLKB5nlI3DU1tTrZKK3zfM/lIbU2v119rTiz29i7xuD4woXJ1Y9KPhwPSzFijfT8gywqEkNi2S+BHHB+PsaSk1+mglBmcS0uidU0YBBfsFaUUaHP1O5lMAI2ULWFnxGwx4fK1Szx8+JCXX36F+/cfooGoExEvcybTGa7jXzQZxrrqIqVFGAZ4jkdVldS1ZjQasVrFRFHI5qaZMnUiw95Jk4y6aimrlG5Z8+o840zAQrcoKZk5Zrp362RO1HVIg4Dx2RlV1dAqhe8HaCEQ0qy4giBai1w9PE+RZ4UZRWuN77mo1kwsPNdocDY3NknTgrLKGHT75HlOWZZEvT7CcRBSEHUCer2QLEvNBBGF0i2ua9NqicBGtSazpm2MNsb3fNJkgS1reo7Nx+6f8eosI1RLUiF4fRDwX4chaWZyk2azCssWlEVJGHgsFkvaVuF5PlLauE6wDv+skZZGKY1jOyglyLOMplZ4jksQGMeYbRsqcifqkyQZV69eBW1Tl3O0huPjMUJv0OnZDIZdbt28yfu3H6JUg+M4LJcLhAwI/IjlrAYc6lJj2fDKqzd4+OAMrc10yAigY1zXp24aTk/P6A8GhFHE+GTMX/y5n+eNN95Ca00YhhwdH5vwxrCD5wYoZVxMA7vPdDrm3ffe5JlnblHXAdefucq7771NFIVEUfcbOhn/qFbiT6v+6Knx6QXy97+ecgCf1rdVSkHbmkC8J+F4AN1ul1Y13Lv7Hp///Gf48mufZzjoURY54/EhTZ1RlSlxPGc6PwdaknSJpmU6OeP8/JTf/+xnWC6XHB0dsbm5QVlmTKenPHp8n9FGHyHNlf/e3h4HBwdrB4Xk8PCQn/3kJ7l7/wFeENHpDfH8Dpvb+yyWBjj3+PAY2w7p9zZQSvLCC69QVi1pVtDrDfjQhz+IUjWalqOjRxwcPCTqBGsh6JI4XnJ+dsrB48fcu3uHvb0dqqrg8OAxSZLwwgsvEEUdFvM5V65cIc9yzk5NeOFisUTaDpblkGUFTdMyHG4QeCGqVqhWX5zowayTtrY2SLMVnudSFAXles2TpQWrOAHkGpb2VdDbk5OZY7ukSU6eGxt0vzfE8wLSNGNra8e4ZoREWiYYslkTbLXSF06lIAiom4rrN69jeTZBN2K2nJNXJbfv3mO4sUGcrNja3OLy5Us8e+tZLMvC81yyLCNNM/KiRFoWnufguBa2Ldf4fcwEBcHp+Jy6VmvrdkWW5Rdi4G3bxW8aMte+yDaybZvElkQaOtWaCKuegMEEVV2vdRhmGuJ5LlrrC9ZLv99fr9bEOtXZpGJXVUFZFaRZwvHxMVXVcnh4ghQ2nhesoXVzhGTNdzJr0CiKTGNmGwF0EJiUZ41AKU2aZmRpTlXVSClp2pYP3z7kZ8YJwrYYezZKwCfPVvylyQqtW6MVsS2iKLp4LDCrQNd1jJi4KijLgqLIkFLQ7XZxHBfVahaLFWVRMZ3OGZ+cMZnMODo84eDxmNkswZIOb731No8fH1DVFa7jY0mXxTzFsX3u3rnHbD7Dtq21rVyxvbOD6zlGRC8kaIltGyT/5Sv7bG+PcF3z8w+CkDDoApLRaBMpLdI0Y3NzEz/wOTh4TBRFWJbFyckJ9+7eJU0Sdnd3mU5ndDs9sszY8V3XZrmc8fbbXyEvVliWptfrGvF6WaCV+hbtyLcxdflmHcXTZuN7rq99e78fzdvTZuUb1HfLM/mmrJMf4pJSrMfYDvY61CxJElzXZRXHPHhwl8V8yvbWFs/deg7XdZnP5pydjZlOT5GWRZ4V3L79Hlq33H9wh14/5O13Xqfb7fDii6+YK+uy4iuvf4WTkyOSdMHx8SMEmvdvv0+SrIiiiDzLyIvcoMN9nytXrrOzvc+VSzdQreTmzefo9XpYtkWeFzStg7Qjev0tqhq2tvcZDLfp9IZUVY3WLffu3aHbC5nNz3n3vdeZTI75yY9/hM/+/u8Sx3OqKsOxBe+89QaPH93n93//04xGA46Pj5nNZiAERVFg2RaLxQIhBNt7u4w2Nxmtn2fTtAgNge/T6/YMCr1VKAWWdNjf32M2mxGGAZZldA9lWSGF5OHDh5gTl15TXiWe79DrdY27x3aoqoY0zclS0xgJKRBC4jgeVdnQ1NBUApRBsQsh1s1JzXw+x7aN22Z3b4+sLBifnxEnCVWrEJZDfzhiOl8RBBHvvvsu/V6fGzduMBwO6XZ7dDpdjBJbolpFmqZk2YosT6jrhlW8QilBkmQopej3B6hWI5AXqb5R1KHuRuS2RadpL35vmqYhqltyS3KOYpVmYFloIbBcl7pp0UKAlkhhs1ql5HmOUi2OYyGkwnEkjiMNjdWx6HRCEIrRaECarmhqxXweUxaVcZ6sLdIG/V+CFiwWK0DQthrVCoqioihqptM5Td3Q1A3qAlLXEscryrImyktemWXMPYdzS1JbFjPXYhV5fCSt6VQltm2hdEtd11hr/U2elWuGUUNeJAShh+ta1I2Z7Gjd0DQ1Wgs8NyBNC6qqwXUDqrJhMV8xmz7Rppj3OkkSTk5OyLKcrc1tHDtgPltSVQ11VeGvQ0Aty8KSFr7nIy2JtBR5YVZXeV5xfDRhe2ebqPOEEWPyrHzPX2txarI8A8zE4+joiCAIKIqCPM+ZL2Z8+fXXODw6IAjCdTiiZBUnTCZTzidjVsmCBw/ucj45pa4Ljo+PaJr64jG/aX1D5ocw/8s3Oc7zJ88N+VGur76XX33Pv9d6mrr8tL6t0mtthFINbWuuHDudLnVdc3R4xN7uLlLA1uY23W4PWqjKyogUmwax1hlIpfF9DyEkd++9x8b2HrYtuXvnAK1sAj8yV8a+y3h8ZJqS0Rar1Yq6NqLS+w8e8HOf/BmOHj3kQx/6AGULWV6wf+U6Z2dj8qKibGq2+kNuPnuLj334E5yfn4BULBaTNePC4+Tk2FhFW42QwthjPZt7998lXp2TlXOSdEFdVdRNjZAtp6djmqbmp37qE7z77jsobU76GxsboDVpmuC4DqenZ7z8gQ8yPp9eCEKX85hslTEaDJG+xLZssAR5XiKlYDab0ekELJbxeg1RkSYpWVYwHG5gWzZ5XuL7Pm1bIUSL67pkWU4n6nJ+NqVtNIWqLqiwUlqURcPR4Sm6BUuGuHaEJmcymWDbDp7nU1cNi8UCraFaViip0cJitlhx5colrlzfoKk182WCY5lJV5ZlnK3mDIcmSLEsTXKwWGszpFSEYYAQ0FQKYRlXlSUt6rpFK02SpARBuJ6gQF3XnGrFOxs9PjlZ0ZQ1uesQ1A2jquHTm12KKMK3DCAvijpGB+Q4gHWhGymKau1MMeTeVtVrfYhGCrFG+LcmniGK0EojbYumbcmrGttqcJVD0OlgKM2KLCsRaKIopG0UWgmKwkyKtAYhLVAN0rKM7sR21u+vTXeRECrNuWTN2TGY/8TSbNctm0Iytwy+YTw+wbYjLMvB8wJWyZymLfF8F8eRNI1CSsVsfs7Vq/tI6VCWLY7j4lg2WinDt2lb+v0Bq1VDllZoBbbnrPVMI/J0wWQyp9/t4LqSqOuxSmLCoE/dGGeMWimqWhGGLlpZFEW+JgaHHDya0Bsk3Lx1lffevUeaNgih8QOXOJ7huJIszrl3795aNOxc2M9B4Lo288UZJ0ePefHFF7j3/m2KIsV2fHq9IUma4jiSxXJG1OkyHGzy3rt32dzc4uaNW8gfoI7hB+ip/MjWj+hk5Zvbg58ItL4q1Hpa36rM2seE94EmDHyCwCdNE5NyDBRFSdM2vPX229y+cwfHdREIgiAELDqdHo7rUjUVnV5Iksx59tY1ZlMj/Lt06Sof+fBP8ou/8FcZjbY4P59y+fIlzs/PiKKQL3/5NY6PDxkO+0wmZ7iujW1bdDsD5vMlvU6XuqpYrRb4nk3bVGxubnDt2jOkaUmS5OzuXiaOU3Z2L3P58lXapiUMA569+QzPPnuDNF2RpCvmiymf/vTv0O0FdLohUeSjVM0rr7zI3v4Oq2SJ69kURUoU+cznU8oyp1WKLC9oGoWQAs9zqMryohmKwoAsS5hOz/EDH8/zcV0P13XIUhN6V+S5mcxsb+MHwTogUrNKY5bxgjRLDMsDyWQyWWcPmc9x07bYtsN0OqOqauq6MbeqRSPXLBvfhBuu8fFt2yLX8LJVsqIoC+bzBU1t6LrnZzPaWmNLhzCImE0X+H5IUZj1SVUVNG1NkprPQVkURryqGlarmKqqsCxJr9/Dtk1MgJSC88k5nu8SdUJ293aNHV4p0iTl9/ZH/N5WF6k0W1kBbcvvbfX477t9NEakW7cNjucSRhFBGKJ5IrZV2JaNkAJpSaq2xHYNI8XxHBzPIV6tyMsCpRSz+ZxOr4cGssxoVaqyotvpsrW5iW1ZWNaT6Y9YBxhClhV4ro9tOWgFaI1tyQvx55OrSSks5pagsCTdRq2ddQaa6BcNKxRzYZqYQX/AoD9AK4Xj2Pi+R91UaBTdXoeyzok6PrYtcR0LKS0TilkY2m+322X/0iUEEpBUVUNe5EhpuC9KaYIgJOp0CIMAIc1Vb1U1gE2WlSRpSlVXSFuS5TmW5ZigRF3jepLNrRFSSMKgx3Q6J81y+v0BrWovjhe9fhffd7EtiyxNaeoa13UuJoUAWZqwXE7IshVJGlNWpZlCur4Ji2xb0izh3r27lEXB3t4eQegTxyZNHfHNpytrwsc3uK3rm37728NNCPH1tz9RtIV+cvvWL+1/qu/HU/gzrB/RZgV+oN/1H8KSEhxHXuz6Hde6cFNo0VI3LbbrMdze4uqt60ySGa+/8yZpWZAVFcs4Zb6M2b92mbRIeO/225RlwqN777M5ikjzGZs7GxydTDg7T5hOcybTFZPpkuViRtMUzKZnOK7koz/xIW7ffpuiiOlEHqP+EFdaLBdjPK+iqubk2YJuGLKczvnya69hC4thd8hkPMV3AhaTKd0gIFnE2Chs2XI+PmJrtMMrL/4Eg942UdS7IO6enY9xPYdnn3sWgPH4lG43ompS8jLGcmC+nDOZzQw7w3Y4n0ywLYHjWhR5TpKlJFmM5YLjg+vbKN2SZTF1XQAK3SiksimSkrxakeQLop5HWq5oqbA8TdVmKNWQrQqyJKcscpRuaHWFtKFWDWWpKLIGKWwcxyHs+LiuxvEUZbOiUhWu75NkBY8eH1FUDcKycXyXRpnoBIlEaosyqzk9OiWez6EpibyIKmupywqo8QLBiy/fpNuLUELh+xAGNjtbG1iWTVlWNG3D+fQUx7eRtqSoS/IqJytTFvGU89kYy4MkiemGEY2U/OftPv/7s9v879eG/H+e3eS/3dikEKBaBUpjS0ldFniujVY1UmqkJRBCUTUljWoQtsByLWrdULYlta5IigQ38qjamlYIlquEvKxAgGVJXMch8AOu7F9hMZmi6xrRNDi2EeYWa4JrEARYltHVWEKg6hLVlDiWcRtJywQZxmlK6nvcu7zJsG4YljV2UxOtMjZrzVd6IYdlSZaVTM6njPpDysx8HqSjkY5Fi02SFezsboHQa7G2SzzLKNISzxHotkSpmtlsQlHlKN3i+Q5aFNRtirsO95zPF6TpiiCycNwGjZnWFYUiiHpIx6JsM0pV0WqF1oosyy6cSqBwXRvHlezsXOHevRNOz1co7dA0Gtfx6HV6WELiuzaX93fpdSIe3LuL0ArHshCtxkLQ1gV7+5t87g8/gxc6JNmKvMjRrTKp0VLSHfTRUlA1NUVZkuYZ83iBWjcqT6zbX7tqMLMb/S1u3wzX8K3+7je+STQC9Q1vfC838c2f97eq7xVh8WdZP8LNyjeuP25/9qehL/luNS0/CLqXJyLBJ5bZujZugeFwZK7oOn1sxyNJUrq9nnFkhIHBvMcxq3gOtHQ6IcvVgqLMqCpDiD04us/B8fu8/e4XmS+P+R+/+3/y1juvMdyIQNQcHD6mbRXL5RJLWNy6cQPP9fD9gOl0jus6KG3C47RS7O/t4tg2777zHkEQMp1MsCxJXdVMJ1M+8OoH6PcG5HmB7/vs7u0hJJydnVHXDUpptrZ3zGSiaUjShLquuX37Np/97GcvwggfPz7Etnw8N8R1zJQkSRI8z6VtTWheVVVorQhCwxvxPJder0unEzKbz81VZ9NccEEmk+l60uJejPKfHIyfXI0/mWC0bYO1ztV5/PgRUgqjXVm/BqUgSVLqyjy+57nUdclg0KNtFa7noTTYjkuaZjhr7cuTRGTLkviej+u69Ho9w6UpKywpDV0VTVFm5PmK6fSMosywbYHt2ORFRhiF1HVFr9elqut1AnKL1hLX8XEcH9VCmpXEcUocp2xsbDEcDI3GJUlYWJKHnk3qe0Zu+jU5Qk9E3k+gcFopHNtGSgvbMryTqm7x/QCETaMUTVMThgFFnq91Py5N21BWBbZl0Q0DRv0+z1y9Rrpa0VQ1tAqpJfEyxrJsqrJa/1y1yQXCCIAtaVOV9dqFY6/DIwV+4FI3Fb+9FfDprQjHstjOG2wh+fyVEb9/ZQPX81EaqrJkMZ+TJDFJsmI+M1lSWZYhBOS5aVSbpqSqSs7Pz4mXS6qqwvcDiiJHCAijECEEtm2ztbVJkZsQUaNtKtFaMRj0GY76DEcdmrUGJgwjqromDLt0u0OaViOEXKdaW3Q7XfK1DuX8/Jyjo2PSLCfNTNaV1iZ403Fco3mxBHG8JAxD2qbh/OwM1porcz/J+eSMIPTRGKv7Kl5wdHTAdDplNptxcjLm8OiQsiq5cvUys/lsbdvXF1OqJwOWrx4nzbHye+ZLfRc3vkUj9D09sfWF9vfSTPwwNirwY6RZ+WEQuP4gP8emaUGbsDJ4krJsckwcxyNbzRkOthgMhkwmE5bxkixNWS5Nqu7mZh8pYRUv+f+z91+xtqbpfR/4e8MXV945nFyxq4qdmx1smRIVaIrwCGNdW7qQZyBBMDAWoAsBhi9sGDJ8YcPA2L7y2AIMw4AvfCHNeGjZVhiYpERRTVZXVw7n1Ek7r/zFN8zF++1VRbqbZOfA8xQWqrrP3mfvvdba3/t8z/P///6ffe013n/vPU5PzijrgsVqjtCS84uHPD15TKRTlusZB0d3aU25saZ+7nOfw3qBt/D5z32RJNKcnV2iooLnn78HGPKsz+uvv0m/N+LG8YReNsA0jqJYMxwOmE6vqOuaw8NDPvroPju7u+R5znR2GsbYUjCeTEBa9vf3uTh50rkzwq/K7/7u75IkCYPBAGPCmmM8HjEaDZnPzhEIBsM+X3j+89x/8CHTqyvKsiTP+kgpmc3mRLFiOMiDUNBLoijg9IfDIXmaIQDnHeUqOIHm8wW9Xh+AsigRyC6QUJEQU7clUilMEyzNWkeY1tK2Afsepu4C78Ohfnp6RpLFwQLtPcZarHFkWWiSlAoBemmads9bn9lsymq5Ik9y+v0e8+WcupFoLSjKFatixWjUZ7ZcokRIk764uGCytcVoOKZpWop1yfnFFW3Zdo2RJk4CpwaviLRGyaD3aduGOE0wre2aQ0AIkiQO4mEEbduAEJs1pNY6aGG8p2lresMeddmwWtVhIoggidOA4TcuMG6kQmpNWzdYAdLDYnmFNLZLEhYIJyhXNY3xQEWaxkghMabhcn5KEmcIFEnSC0LTxpJkKaZYEyd6Q/Rtdc4/urPN614waAx2PKTq92hOzhEEEa8QQ+IkJo4j1us1KVmwgfdikjTg7weDAd5per0MfB3YQV1GT78/YDqdYa0jCJcFOtb0+32kCMGJ43EIPVwuS6xpmJVL7ty5hdIwXy1I4pSrqwXjyQF37jzPe2+/xc7OYQhHXK/QOt40a0maYJzveC/htVJKbUI0hRRIGaIJrLN89rOf5dd//R8CMBjkjMZDZvMlw1GP2fQK5wzZoI+QkGYJH3zwAd4LxqMt3n77bY4ObzLoD0izDCHlRsQJbHRaz+rns/5ETlbgxzNJ+fmq4CzxDrSKSNOs42loplczbt28x1e+/FXu3L5Hv98n72Xs7++xt3eAMS1luaJcL5lPZ7z33gfs7OwTJQm94QDrBFqn9HoDXnv1NV555VUEiiwdsL11yC987gsUZcmTx4/JkghnWkaDIVrHfPzwMW+8+XtISacLiUnjHjvbB+BVAL3Nrnj48GNW6yWTrQnWWk5PzviFX/gsL774IutizXy5ZP/wkO2dHQ6Pj0jSlLqp8QR7dl2H0f8Xv/hFxuMxRVFgjGE+W7FalsxmC0DQ6/e6puiSKNLcuXOHKIoAiKIoCGLXa6bTGXmWU9chaTdJEuqqRinFfLHAO0ccJxRFiVJqk+3jnKDX6+OcZbVa4js2S9lZf/O8j2lt4G9EKd5DpGNWy3XI3vGCpm4py4qyqFBSI4Uiy3LqumExXzIcjhBCsljM6fVzDg9D5le/N6QsG8qyQkpBkmiqqiBJIsDR62dBuOo+sRvjPQ8ePKAsggOoqmuk0NSVoa5a5rOQKJzEOVrFKBUR7rx1x4AJicfeg20d1gYRJ8JibIMxDUI6tA5hgFEcEUURAoWzAVZWV+F5k0KjpWK1DEF+k9GINIlJYklYwQW4m5KCxXyKaSrSJME0jiTOQ36N8d3UypH3cnZ3ttjb28UTpjbWWYqyppcNiDp3VZomjEZDguYrYaokj3opZ12eEyLobCDkKLWmIo41eZ4R6RgQXT5a3KVoB0eNMUGo67vlQm0M8+UKJwQ6SegNhzgpQKjwezqdYY3tnleLtQ29fsbOzrjTYIWsn/W6RMmIt996n7Z1ZL0ezntUFBGnKVVT01hDnKXYLmIjiROsNRhjWK+DE0vrALmLIo33DucMaRqygLI8NFdKS5I45ujokC984fPk+SfZWc6FSc+9e8+RZRnLxZJHjx4xGo1QUtJ0v5M/6uv3j1Lf+Af1k890lN+9fo6blWcNyA+zpJTdQxEOExVG4k1Dvz/k7p0XGA6CRiFONMdHB8zms5DIGickWoJ3FEVFuW6xVjKZ7IJQxEmPSOU0tUerlGLVkGcTzk9X7G7f4rXPfp7X3/g9nj79mPffe4tiOePR44eMxhN6wyHvvf829x98gJSKjz76mOfuvcT+3jFCaIqyQmvBrds3ePXVV+j3B7z91jssl2vmsyVxknLvuedQsUZHGmMNURzx0f37lE2gbz59+rS7cObcvn2bg4MDyjKgwKUCqSCKFKPRkL29HebzGXVdcfv2bVbrNb1ej7qqQvKuswyGQ6IoZPYIITbpvAh48uQJo9GItm1ZLlekaUaSpCipaRu7wdMDG+bG1vYWust7WS7WOEdYZ9kwDWualkjHNI3Be7FpYIK41tE2LVVZkyQZcZyQ5zmj0YgsS5DCMZtdkudpp+2TOAtKacbjCc77zmobtCmRjnAdj6epG9q2DXTcNoQVZmmKda5bJ7YYYzaEU+/D2koISZpm5FkvvOeUDKJLF3KHlBIMBn2EIMDuBFhnsM4gpcP6lv4gQyqJMXZjI7bWggeBwDQNy+UiiLSV5PBgLwiZEcRJxN7BDlIJTk5PiJMUvGY4mjAebwUGivdoHezji8WMwSBHKofWwba9WKzo94fgryc+4TWzxrFalizma6qq7VZ0Cq0jpFRUdcXR4QE6ChPM1Sp8nJKB+5LnPfr9IVpFnaXZI5SibR3GOoqqIstz4jhlXZTUdct6XXBtk/f4kM7tHQcHe3gs48kIa1tms7Bykp3l3XvPo8eP8VIglKSsK6IkDl/PBBhfa0zXiLgNgXm9XmOM7Sz1Gucds1mw5P/mb/3G5uv3ejmRTrm4uGIy3mG1LNjfP8TaAPFbLdcURbFZhS67JjPkWoW1b3gP/Gwd8M9Opu+vfq6ale/sp382Ofnh1afkZ90Foixrjo9vhsC9Jrgkzs8vePe9dynLgjiKiaMY21pM43AGjBEYK3ju+ZdJkj5l2XB2eonWCf3+gLfeepv5bMF0uiRJBpxfnJP1YrIs4vz0MW+99TpZFlHVJYdHh9y7dxutBacnJwz6I4RQWAvnZ1dMr2YopXj8+BHvvvsuxhiMcRweHiGEQkcxr3/7DaIoYjgeUTcNDz7+mM994fO4bjpgraXXC5bqf/pP/yllGYL3ynJNkgqcLxkMM4pqwdXVBYvlnNl8xsOHD1nM52HN07akaci+GvT79Ps9mrrGeddB1ZLNhXi9WmFssKKG3XzIZQKBUrqzrkqkDCTZi4sLIq1DiKQJr4H3bBJ6vQ+Hj7N+k3m0XK46FHqDkIqiKDGtCYdjWQfAW7HCdVj63d1d6qqmqS1axVjrOTu7JEt7lEXNcDDppg5BVDqfzxkMB4xGI1arFcvlcpPGraTA2JY41mzvbHH7zi2sM4CnaeoQG4DcTBsiHXUhh4I0Sbrsnygcdt2/Q/OgyHsJg0GGVAFjDzaIXaXAW49pDbGOEIA1LZGSjIZ9nG+RWqDiiCjRZP0MoSVOQN4b8PwLL+GsZ7FYMugPSdPgrFmtVigFOgIdOaQK+TTh+QRjoCxb6qbFWt8JciHPBrRtyNJZrdfd1CwcuMtVoPymaUqW5TgLHz98wtnZFUXRMJuukCIm0hnWipC9AzStIUlT2taG5GkPaRoCFZfLdcfBCVOaXj8PBN8kYbUqyLMey8Wq00dJlAop4M6FGIjglApale3tbdI0xXVwvSiKQYT3nBACYy3L5YLlchlWVAK8d1RV4N5orcjzlFu3bnF6ckGvN+TevRfY3dlnMBijZIz3Eu9CZENdV3zwwQdd/MBup+lZsS6KsJ7+Kas/Upv47Dj6vurnqll5Vj+6+oNNoJTh8Ov3ehwfH6NUxGA4CkyLbtR8faiMRmO89eRZD2sEzz/3Mrdvv8BwuMNwtIUUAqU909kZi+UVk+0+88VVFzoo+fab3+LBww+5uHxKrx/z+PF95vMrrqaXPD15AjLYqsP42BHpjLKs+cxnXuGb3/wmWZZwcvKU5XLJw4cPuXv3LlpH7Ozs0Ov1mc/nfPZzn2M8mbBcr9je2eHjjx9wfHxMHAcS6nq9DgGOTcOTDhOutCBOHHlfs1hesF4v0JFkNBoxGo1Cng/hIt7v9QA2BE8hJOtijZIKrTW+m1A0TcNoPCbqwgbzvNdNs9g0Gsa0FMUapRXWtl3GSszR8THeQ6/7OLFZ3TmaJkC+wtrJbCBsSilspz3yXoSgxLqhqqpu/RVsyUVREMUpZVEzm60YDibMpguM8TRNmCRonXTi2YStrS3ofvbrNZcQgjzPSdIIrQVKC5xrubg4JY4VHkNVlxjjsM7hve+geWH10tQNHh+0IpfnrFZLtJahEfEWKT1VtcZjiWOFEI4sS5EyiJERPsQMFAXFuiDSmrIqibSETq9khaPxlrKpQMHO/h7z1YrX3/g2Umm2JtvMZrONeBbhKauCpq0YDFMmW0E8bS2sVhXFusEZsbE2l2WNs54kycLP5j2Dfj+sCKMYZw1FsaauQyCklGHqItDdyrHi5OSc2WxJsa6pa4NA4RFEcYKOEpCSxXpNnGY0JriXVqtVWN8JyWQyJs9zzs5Ow5oqyakbQ573iOIkiJe96dxMwcmltKJpW7I8p2kbhuMRUqlg19Y66I2UCgLojkS8XC7w3lHXFf1BH6UUzz9/D9816BcXVxweHBPrFGtgf++YOMrY2dlne2ufKEooy4rFYhG0T6MRcRzz7nvvdYLjT0Icf1br+weI/iS8xz9Zh+3PeLPyzJb8463f/xx7H3QYWZpu0oONDSN+01q+8Y0/RZr1WBU1qIiyMnzm1c/TG2xz794LWGt58OADhAy6i53tCXjLwf4uh4c7fO5zLzMa5VRlQVkUXFxdMl/Msd4SJ5rlasb9++9TVSv+xb/8bVrTcHlxzvnZKd5YellGP8/4nd/55+S9mA8/fJc4TlA65t33PmB7d5veIGM0GvF7v/s6H3zwIYNBj92dMavVkkhFaK1o2godCayrqOoVUSwZT8LKp64r5vMpUjn6gwQpPavVkpOnJzx+9AiJp64KnG1x1mCNCQFxV5dopbHOIjvRstYBurVaLSnLNVuTMQKP9w5rWgSus+mGVOHD4yPibq0yX654enoKStJ2IDRPWH+oKCKKY5I8p6waWhPWB8Y6PCCkxDqHkJIoCowLKQLiv9cbYloYDsfUVUXeS0izhLzXw3kwrUMp3WkoGpJIsb21zXJRsJhVnJ3MWS5qinUTNBixoiwL8l6KdTVNu8a6GrAoFVYoxtrOcWZxXtDagNG33eQhzZKOURNjnWXZaXecdwgNSFgsV5Rl3aUAR6SdE8oB67IizXMQkp2dXaaLGVVTobSkqksghBEKqVgsFrTWIJXg4uKS6XTGNQK/bQ2RTrDWk6U5i8UaKcIqJ4klSayQArx1KKGZzxe0bUt/0Kdtmy6TKXBY8jQOYLZ++F0S0oFvwbfEGoa9PolO0FJTrasgtJYgpSfLIuJIsLM1JotjFDDIU/JE0ssVaaIQQmCNZ7kswlSigxeWZcW6KAMYTwA4PI6mrbGu07YYx/37D2lqw/RqhrOWXi/ixZdukGQyNIaSDaZfCBHeC2mCvE7Tbg2hYwMUOOEZTEasiop7z70QWDyR4mtf/zp5r0fe6xFHKYvpEtNaHtz/iCSNuP/gw8AaWq+p25rGNJvVlRTik8DkH8EA44c5of9ju3D+wNF27Tb6bg/xRzYT3+/5+OnP/y5/9/fLf/lj1s90s/IMf/yTq08LwaRUNNZiseS9nPOzC44ObjK9WhDFOSrOIIpxMkHIlK985V8JxM1YMxrnJLFGoRn2xrz84mcQXlCXFXGk6OUx4+GIPOtjveCjRw+ZF0t+743Xefz4Yx7c/yCIGosV9z/6AIFjvZyxWs7AtQz7GUo6tDJ4X3N2dsZiUTJfrHjvw3ep25rRaIvPfe5LzKczFrNz/td/+A+oVgvefetdELC1MyZOJV626MTjaFmuw6rH2uvpQUPbVqzWc5q2QmnNYjGnrtahUenG2chwOXF4lJY4Z0IzVhasV0uEgNn0irquaKqSfi8njTWDfo5WApwlUor+sM9qvUbpiNZ66tbTGM+6KjEuuHzwvmtKLEKpYGkWgjhL8ULiPMRpRNbPaExDUZW03aqorqqAv89GSJHw/rsfMZmMkNKB8nx0/z5hLaXAC0zTsre7jcBR14aycFycrVjMDbEekKZ9vA/6FyGDq8zahjjRDAYhcbhp6k0qc2vC96ziCKU0ToguByg0b21b47wlyxKECM+l947GGLyQeCGJ4hxrBXiJtw4dxSyLAhnFLIuK2niKqqZqWoy3ZHmKkgLbGiIVk0Rp18AJWhPyaKRUXRPkSeIMrWMinbBYVKyXlrJo6fdypDB4W9FLNbGSSCHD2lErhPB4LEJci3VB+BbXFgH7bxsclqpaI0VLP4sQzuLblvViiZaS8WhIFAuyXBFpQ6w8kfRoPE1RIFwLrqKfCYRs2dvfJU36pEmPtrXBYq4USsVcXF2S9XKKck0Ua4bjAVme4ZyjNS1KSpTQRCqlrgymbRmNM0bjmCSBKBboSKC1AALrxlrLulizXpcIqYKeKYp5+513sDiMcIy2J/yZX/7z7B8ec3zrJnefu8Mbb32LW7dvcXp2ynAwplhVRCqmtS3v33+XJIs7BP+HFHWJwYYT3Tpkp0faHPV/xDrmjzvJ+GHHqEjE5iE83/Gx+XPo8H7hIaATmH/nhxCB8/KdH39UntIfXuF7+e5/96e/z+/0+EHrZ7pZeVY/uZKbd064e3c+uALeevttxl0E/WS8jY4SBv0RL7/0C9y9+zyvvvY5hoMxy0VBlg546cXX0CoJ43olOTk5wTmIopiryzmm9YyGY27fvsPBwQGDwQApJU+ePuaDD99nMBzy8YMn3Lv3Isc3bjKbT0E6Tk4f8Xuv/0uSLGI4GtA0ga67v7fHF77wOXq9nMV8xuX5Jbdv3qGpGoaDIVGkKdYL5vNLhsO8u/AGPYU1lixLN+PtsgopxwLBfL6kqhqKdbnJ9Nnb3cXjw0VTCuIkoT8YhHWDsxgTmoooCoLNvf19er0eCIFSEcZ6rPFoHQeabGUR3ehgvarQUczFxRRnRSecFQipKasm3EnbsM8P2qKyC0YMduaiKDbrlaoKmHzdhfJFcRQamSTh/PyCp09PWBdrFsslSZpQ1SWj0TCE9ImIOM6QMmZ764CyaDk9uWC5WHfIe4kQUHWrksAlkVRljVYJznrq2oRpjtQopbv3l0RJhUDg3DVrJlyur/H2edajquqNI8XYkIAcRUFzoVXA7zsraBsfpiBdw1EUFVmWsVqt8UCcxN06ztGaJrze1qCVYjgYoKQADHkef7Ju8gGAZgxoFZw6189vYLAEga9WirZtESIED4bcp3LzM1lrUSrCOUkcZRRFSy8fk2WD4IAy4b0XrMFB11RWJWUZnGJlUTKfz9E6oqrrYIHPeyipaJq2s6SHMEmlJb1egrENWZ5QVUFXdnp6xny2JIpSer0hEKZtWZqSJJokjZAqfL913fL40SkfvP8A04mtTdtSVgVVHdZrQgTXmZCSfn9A0zTkaU4UhXgIYyxHR8f0e30+88LLmKZlPJ5wfHTE5WVgDV1dXSGFpKprsl6P6SzwZEzT8v5774b1sVKbycqz+vmuZ83Ks/q+KiDHXfffDXXdcH5xzuHBAXu7exzfuMnHHz8kS/tMJjsslyWvvfo5dnf2sRb29o442L/J4f4djo5vI5QiThJ+65//NkJqer0xN27e4d69F1mtS5TUfOYzr3J0dMx7773XOW5KDg8OODy8jRAJSZLTG/T43Od/gS986bMslld885u/w9OnJ4zHE6QQXFyed5bcjDe+/Tqz2ZRbN29y8vQEawxb4zGHB3scHe6zNRlh2pa2bdFaIwQsFkuapmE+m1NVVdcUQJLkNLVFyhhrgr7i8PiI7d0ddKTJez2SNKUoC5z35L1+t0aLKcsKEFxeXrG1tY2UwVWRJjnWenr5kLq2VFVDWdQomYBXLBeBTyJlRNulOEuhwSsEagNQCw1CaACcDzbgOE7Jsqz7Wm7TPIHDuhahwiTDekfdNjgfXCRCSZRW6FhzcHhIVTeslhVN4/lH//v/wXrVcuP4Dv3+kDRNMbZlXSxQCoT0RHGEFBrnJEJo1uuasmgIDUzbNarBJeWC0GMD4bsmjVsbspaMsZjW0sv74AVSBOdQWa5DonJbY4ylaSx17SmK4KrROmJrazu4n7pcpPB8pHg8+/t7QQ/Urdvm8xm9fkaWR+gYlA4ru5A+TicG9kHs2TUq17CywaC/aSriOOby8pK6rjvdUvhYYwxKxwgZ0VrB5eWKk5MpZWmpG89gOCbLMqy1NE1ohgb9/mayaayn3x8znc6YzqYorZBKUTeGsmrRkUJIjzE1USTZ2dmi38+Q0tPvZ7StIc8GaJ1yenLFallRV4bxaIu8l6MjSZpG7O1ts729jdYxq2XN1WWBQAfu0HBAlgd9kCesBuM4JdIRRRESqBfLVbBzd/faJyenPHr4mNV6hXees9MzBKFJbeqGLO9x4/YtJtvbFOuKfm/EoD9itVzx9PFjvvXGtzr7s9pA4b7f+kkF0f44v9bPej1rVp7V91VKhah45zx13XQckJrReMzNm7fQOmJ374A873F8dItBf8RysWZvd5/BYMid2/fo5SM+fnDCalVT1gak5s7d51C6G9frmN/55uus1xX9/pD1qgAEVRUSXtMs4eOHHwMR29t73Lx1i0ePH3H/wYc4DP1Bzmq94vLyisvLS/r9PltbEz786H0ODnZZred4Z5leXVGXBbZtMG1NEsdkScxoOPh9QDjRsR0+PR43xmKMo6kt/f4W63XNZLyNMYaL80CwzXs9dveCNdYDRVlSViVpltO2XQCdjkmTDGscvbwfwiJXBUeHxxRFFQ7pLqRvvV6Hf68KqrLu7LGBT+JsyJ65hnY55zbMCrjeeQvaxtI2luFgFA4P70iSiCSJWRdr4g7qZl0ndhUEp4kxjMdjhBS0pqU/GJD1esRxyuHRDXZ293j06DHb21s43zIaDRgO+6RpjHPXMEG3yUTSKuoO3WCLD6Jgsfner+mk4TDy3c8hupVKhBCSoijJspw0zTqba5hWpGkIC2wag3eStrWUVU3b2s30K8Dz2o3N2pqWi4tz2qam3++RJDHgyXs5xjbUdUnThlDB9XrdWa59N5WLcM5R1/WG1luWZff78klzIqXcNGTGBEpxVbd4JNYpjFUgYxrjESrCWNdNvWKcczRNQ5ZlZFmG6KZwW7t7XM3moASXs0scHi8EpmuiFos5O7vb4flo640dOAhVw7RLq4y6cizmJdYKlssC0wYrctsGy7jWMiQkO0mkMpRMKMumsy9brA3vteEwpHBb69nd3aPXGxDHCVXdbN6FbWuIVIREcfP4Fns7e7z4/As0ZQMuXGOk1mgds7N7wP2PPqYuGl58/gUuzs95+uRJN9G1gbP047n0PaufUD1rVp7V91WfvgNI05ReL8cYQxzHpElK21jyrM9nP/tFtrf3Qnrscsnu7jY7O9vs7+9xdnbBzRt32ds95tbNewiR8OJLryCV5t6953jv/fd4evKEp09PePnlV7i6mvLWW291abkNT58+Jklj0jwI8nr9HmfnJ/yjf/q/89GD+wzHY6wLF+vxeExRFpso+9nsCiE8RbFgOj3j4uIkrHaKgl7ehfXFGVmWMxgMyLIMrRQ6iropS/jV8d53oYKaxXxNlg5YLguyNOf0/IzZfIaxhsurS5arZaCZOktd1ywWS+hcOVEcs7O9w3Q259Gjx+E5lvDw0cfM5lf0+zmTrTFRHDEej1itl1hngshSyaCFkOFzPo2Aj+OYKAoritFohNYh9M77oDU6OzvfIOhb0+B8S7/fQ2lNmme01tCaEDtgfRDknp6dUjcNKopYrJYkaYxQYH3LdHYFwnJ+/hStBf1BwmjcpyhLpldzbAuh6Qj037qpMKYNIZDWbGi1QojQPHSBfsEFFFKSr+2q1ysg2Vmvy6IGr9E6IYmTjsSru+YgQcmoc0G54LZSGq00SkVY61kulwhJCAxsKqqqRGlJlofAzutpidaauq66RGeP1oL5coYxbVgbDYcdh6elLMsgODdm44qSUm6amGvq67pY0bQGrWPq2rBeV3gvwjSuKrpVkfrE3r5ebzD81nmWyzVplofVVaRp2payavBeUpY1TRNWbet1yXKxQmtNVdVIpRmNxrSNZbWsMK1gMa/AhxXSYrHsQhtLVusV62K1WVu13YrqmnocRYo0C9A6ax3LxYr1uqBYVxweHRFHMf3eIBCEPbz2yi9QlRVaaQ4Pjzg6OOLs9IIszTCtZbK1hbGGJyen/Mpf+FW+8fU/xWK+5MXnX8AZwzvvvMvFxUXX2P7krBU/yonMs4nLJ/WsWXlW32d19+hCbPb929vbZGlKUZa8+eZb5PkAZyBLc+7dvYvH8tH9D3HeUFUFBweH7O8fsl43fOMbfxatc66uljTGsX94QJREnF2cEMcxT5+csDXZ7vbdbUd1XTKejIjjiFt3bvPtt94g64X1xjvvvIeOMo5v3EapkB67XC44PTvh6dPH/N7vfZMk0Xzw4VsYs6Yslxwf7SOAsqjYmuwiiVgtVyipQnjdhkGhNvH03nsG/RFF0eCspG0cedbv7mYX9AcD2ralbhqSNCXLcw6PjpBK07YGpTRlWXF1OeX84oobxze4c+cu3gUqa1WFvJ1eP0VIv7H4Hh7tkaQK71s8poOIOaQEY2u2d7Y2NunrkLeLiwuAzg0EWdbrQHVhUuR9cOHoSNG0Da0xVHWFdS5wYLpVmLEO52G+mIe05WKBcTXrcsbW7oDROGdvfxsdBW3HYjGjLCq0yjHG43yLEIFJ0u9n9HopSgsQruO6fLLCirTmmq8SxxFNcw2Tc7StBQRlGbRDTWNoG4FWCVUVwGODYQ+lBN4bPC5IFn1odIqipCiqTtsTLAv9fh+tFeBQWrJadawQABR4SVVWNE1N3VRI5ZHa41yzSWxerUIjkSQBsBd36eNAt2qjm4iEn1MIgcfRG/SC8DqKQuPhHWke07QVq/Vq06j0+33a1pCmKds7OzgHp2dhmlK3LTqOaG0IAXTIjuYLVRWs/ctlSVW1IQJCKExruLqa470iTQcolVDXIam7aSxN7VAyZno1ZblcUpahearr8DM3ddNNaDyj0YBeL9tMy+I44ezsHNuxja5/F+I4ptfrc3x0zPbWNkoo4jjl1o2bbE+2yTpr94svv8zO7h5JkrO3c8BLL7zMndt32BqPWC0XPH78uJtuuR/LVe9Z/eTqWbPyrH6guu72pQxY+msiaxwnVGVFVdXkeY/p7Ip79+6yXM1JkojzizNu3bpJ27Z89rNfZLms+NW/+H+haR1KKy4uL0A63n73TZ5//jlefe21Tn/QbIBVOzs7XF1e4rs79UePH9If9Dk5P+Xo6AZ1ZTg9Pefevef48MMPN2sFrSWvvvYZnDfs7Ix5971vd5ZkxXK54uDgmNWq4uJiShRFtCawTJo6hKe1bdt9zaCjmM3mNHXLzvYeddUwm82pmwatNRdXl5RNTW/QxwuYLRb0B30a06CjMOJ3LjBiAjxt1aUUW05OnyKlp64rynLNajVjMMxp2orz8xOqqgThQu5MG+7041ihZGiitra22N7e3qwa8jz/fcLboJ2QgV/iXcd1yVksFhtCrzG2a0gTDg4OEFKio4i6bri8usJ5g44EjSnpD1OMLXE0NO0aYyusC7ydOE6RImT/SMGnmogWoRx5npCmEVKC61KfrzUZdV13E5JATb7G3QeYXgoI4jghilKq0nB1uUTJqHtOFxgTAHRxFJHnOSA2rJmqqoLduGro9XLiOAT/jUbjzfPUtg3OO5ylE6AOutfe4DFEkUDpYN29fn8OBgOapqWq6k2oXxzHv28VFJxBgiiOQn6TCE6aKI66949ivphtsnXC3XVYIeW9nCxLiXSYoljraY0lzVKss90BHmILnBdkaY/LyylKhigC7yT9/ogkziiKiqbjtVgDbWOQIuicBIrtrf2usQ32ciHA2rYTClfU3QrtWn8zm88pywrTGvK8z3A4ZL0uKMuyayyDxuSD9z/g4OAguJKkoN/r8dzd5/jyl7/M1tYWVV2xLtf84i9+lcVixdZkmxvHN7l35x4vvvACky6H7Orq6lMXpB/Xle9nub5fPstPFgvyrFl5Vn/M+g5v4A4EUJYl3gcolRAKKRTDwYjbt24zHo3Y2dpFkSKJ0TJhvVxRFTXOOJ6/9wKHewfkScbTJ0/p9XtUdUXTFnzh868w7Ee8/dbrPHr4ER988Ba2LXC2QQLP33uBWGfcODrm8OAYJRM+/viUO3deYjDaYv/oCKQAHcSiSZqwLqaU5SWmXYILyO71uqSpHXXluHF8m6IoqKoF0/kJFxdPmV5dUqwLbOdcaa+hZSLAs7Z3x2zvjji/eorUQROAkEipwYN3hun0giyLydOY09MTtrd3u7vMCuMCdCvNYqbzCxwNOlYkcUKkYrRSxFGEVhKBZ3trjG1bEqXZ3trumCpBmCqFRDhPLBOkV0wvL4gjQb8fY9oaZzzKx6RxCg60VMRxhNIaHUd4GVws0gu0UCg0TWkpVy33P3zMclEjfUSe5qRRjHCQRBFpFLFeLDBNS7mugOBsWa8K6tp0zUNLFCtUZCnLNeDRkWR7Z0Cvr0E4kiwiGyXEfY2IPBaD1AJjwxpF4DvdRBKStNsWISSqe66FF0gkWsaY2hHJmCSKEN6hJcRaEKnOtisjPJokHYCIePDxA3Z2JjjhWazXyCgOZk+hgpVUefqDHCklg/6IJMmD4FlplAzuo1jHtLVDyZSmCQj8si6RWnYNmOvWXWBtWLvVdUGkJMI7hHc405BGiqYuiZUkyyKiJFizlVTMp1OSWNK2Fafn54H0Kzxaafq9IctlQa+fE8Uw2co4ONhCiIokNsQJID2j8RAZO5wM71XnNQcHNwMcz1YIV5NIQaRiitYwWxVEWUKUxESJ7oIhwTQ1pmlQQoELIaPCCbwNWUDluiBSCdZ7TBd8GMcRwnvm8yuenj7B47HmOtl6idIRxzePGY3HYSJzcESsNG9869uoSCMiyb/6y7/E3t4ew3wQmDfO4kTgt2ysvD+mw/W75fv8dOb8BNH69//4ydWfmNTlZ/VDqD9419I5NJSSrMsypOSakF/zpS9+scvuEHzw3ntsTfbY399nvSqZThfBttjvB6R4muKdZby9xXx1hZCCpqn56KMP+cbXvsaH79/n9d/7lzx6+BHWNcRxhpKSJEp57t49pBS88ca3iOOEo8Nj7ty+Q1WXfHj/A2bLGVUD1ns+enAfrSTz+Rnf/JePibUmUgrvBXneZ7UsWS6XrNYrklRx9uQpSgnSNGXtHJ6Iug76Dec9SaKRMqJpQ4aQkA6lIe+N8N6zXM64dfsAa4PQ0rQ11racnZ6ytbUNQNO2xHEEnd4kzRKEFGR5ymy6RCBJkgTvgn11vVrhvSeJ4g4Zb5FKB1eKFEihcEIxn4bU6/4wx7mKONGs2zrYfp2grppuleWCpiVJ8N4GXYoxxFJjakOkU/Z3t/Becnk5w7QWJSWr+QLhuveE9ThjiVREpCPoaYp11a1qDBBWZ96HzCBTlJ2bLExN0jTGtCaEOdaGKFU0jQPFBude1wH+5aRDi2AFjqKoC1UM6sosS3DGU5Ultg13/q1pEQ4ipUmTCNMW5HmG9zLYzoWkrBqsNWRZwnwxpd8fsVitONB9kmnJMo44MwbvDUWxpNfLqaqKoqiQ8lqjsgiW3Simqht0HfD5aZ6EQMxumhKsxGmYSGm9YZJkadadsR7bNiipURqiSOOsRSrBarXmaPeYti3AG9bFkqpyxGmKsaYTIosukyc4gopyzrDXI+4gdcYZdKxZrdcMxj16w5TlosJ6w/nlFUJInGvRKqEuW7wWnJxdURYFsY6pm4Z+r0+5rkjihDTNw9fUSRAqV4YkTsLUTkoW80VIAo8jyqrk1u3bqCjinXfeDiujQQ4yJFtLLzg8OGK2mHLv+Xv8g1///5DM5swup9y78xxZlqOUREWCu/fu0rawXpdBaCwlTvjNuu3ntf6gbuV7aoY2n/rT1kD98epZs/Ksvq+6tmde3z30sgwlJJFSpHHCoN/b/Nl0MiFSmqOjIx4+fEiapmxtTTYo+/FoQt7rUTYFdVVzfn7GYn6FxPPqi5/h8PCQq+kFw+GAopwTcN6Cjz++T5zkRGnO5eUFFxfnPP/cTaZXZ7zw4gu8887bCByrVWCK7OzsslrMEVJ2gX0Zy+UCrSOOjve4vLhguVrhqGFhWa4WOBvcBoPBgMFggLEzEqKOsSKo6uAuydI8BPrJiNlshlbB7VFVNaPRsLOxDnj8+DFSBg1NiLgP7grVZas0TdvxNxTb29u0bUtVVZuww2sbsrcO31Fnj46OePTkhKqsUVLjvCfSkldf/Qyvv/Ev2doadnfzAUhmTAj/E5mkqit0GkTDxnqMbbGtJUnjziYc4F5xlAIO7wPcTqkA4zLdikwgGY8nQUhalxvHS1iBRHgX/q7pdEachjgEIQRZHlOWNYvlCmPC92aXRRAyS0UaxzRNS12WHXZedtlIDmvrLowxCK7zLKOy4XUpy8B0aZvQ1NRNhSgMjoa6CvEDw0GC0mqjgUrilCROWZ5e8Kc/mvHa9IK4ttSR5o1Jzv+6k9CaBqnSjtFSAI6saMgXFUWWUmQpWitWXYpx07R4D2UVEpydc4xGI5arBUqJDWvHYVFCdS67CGsNcRKF95EUNG3DaDRhuVyxtzemMWvyLGexXBArSKKESCq0FBjvWa+WTMYjXMdAAYHWEcWqZJj3qCsDyyJYhSOo2gKJI401UsTEaU5ZLrGG0DSiSaKUOI+wxpLE17wUwAu8DzqfNE3w3rC9M+Hq6opeP2d3d5eTs9NOiBsmREmScHZ2SlWVgT6LBAdJktC2htu377C/d8ALL32G9aogTVOG/T5ZGvKKBoMxvbzH7vbep+BpYnMMi5/N8/hZ/SH1bA30rL6v+nRHf+06AfDObxgQ13c5e3v7vPD884yGQw4PD4mi648Ph9PZ+RlVVXUHZst4POLg8JCDgyMePXxK07ZMZ9PAYklzVJQSpxmHN474zCsv88EHb1I3SxbLS95661vMZpdkqWY+v2QyGXHj+Cbew9nZedcE7JIkKbPZohM3epqm4sWX7jIYpPR6KcPRACkFWZZ3h4dnsVgF7YAUZFkCImTXxFHCfL5AqwhjHP1esGxmeU7TtMxmc1argqYJ6cS9Xh8lVbgIJ+nGejyfzSmKgrKsKcuK2Wy20W5cQ8XatiVNU8qq7LJjZEij7VKxrQ2TD+dbfu/13yWOoi6jKaQmbzJtCGnMdFA7KcLBbk1gZFgviOKUqmlwgIwUtWkwWPJ+ipcO6y1plnS25oSyqqnrFg8dmyairmvyPN+ISdM0o208pgFnRUiJtuHrWxP0O1EUBVqskEQ6RgqNQG3Q9Nb4DVgsZCMZmqaiqkuSJMI601nBU6RUnQYm2JWdC7ZuJUMukdYh6iAwVRJW64ovvHPOv3Je45XkLNN4Jfjqkyl/9qJkOBywXHTYfKX5S1PDX3njEf/2xzP+b++f8WcenJE4RxQpXOduEki0ChqaKNKsViuctZRlyP8JDJXg7ErTlCQJgZe9vI9SUWj2CI2NjmJOT86Zz+edoyswS8SnpldRJMmzlKZpmF7Naeqgo7m2eFdVQ9MYyqKlrhv6wwwhg7jZAkontNaRZj3iKEJ5gfRdg+xDeKOnc1VFCU3t8E52k7QWYw3OGUajAUmssa4l8HsMTVvz4ksv4HHUTcXJydMuOduHhkOGsM67d57jxvFtvvD5L/PySy+zXq1Zr9abVd8gH/LlL32JV155ZaOV895/sgF6Vj939axZeVbfV316HBlFUdBNuADECEdhCOeQQjIejRiOhsRJwu7uDmnauQU8WOt44YUXWC1XzGZT6qrsouMj9vYOefHFV5gvVqzLmsWqYjDc4ej4DnefC7qU5XpFkmiePP6YNNWUxRIlPdOrS3a3tzk8OOD4+AZxlCCFIst6Xfx8gbXBUTIaDoOItSr58le+iNKC0XC0aT6Gg0nHQ4m679l0gkvTMVEs4/EEOkLpzZu3O7FhcEyMRmN6vR5FEXJn6roJz4HzRHGCMS5krnRNS1mWHaXVB2tqJ/Tc2tpCd4FxhwcHvPLKK1RlyWKxoGkaxuMJSinSLGUyGbG7u0WWBZ5JuW4wrcfZayhcjBASKRRxknYiySDK1FHIu2mtwwvJZHubummw3uKFo24rhPBEsaZuauq6ZrlaU9cNZVWzXhdoHW2SpMuyDAdtF9gY0qwjnAtNSl23mNYjhEbJGOFDqrdEslqsaesWiSRScdfMxGgVY4yhrmuMaWnbGimD0FtJ2blDAoslTTO0VlgbHC5aRSHqwIdDMtBqLdaBvFrx8nnJVSRZZDGtlszSiNUg4/PLhl5VIVVoeP706YqvPrnCeMcTrfBC8mdmFb98vqQ/yENWlmnZ2tqm1+8jRJhuhIBG2yUShzVQkkQd8C2IVyMdbYCBpnV4B6vlmsV8GdKZfdBEDQZDIt1piNIY7wxpEpOnKWmcEqlAk82yhChSNE0dVkRShjR0azk63qU/SFFKkKUJAkfbVGzvDPG+IdaePI0wbcV6uUJLHYIvEZjW4VxwGlnrNmGJ6/Ua72GyFaamvrsuzGczvv3tN+j3e1hjePL0EWVZdO/LkOO0t3fAeLzFL37layRxiuriBXa2t8L1RQSHk5ZRN01SKCE/wdHzTGf781jPmpVn9X3VdbMiRMCfCwdYR6IjNvd6PuRb6A72Za1lNpszGISoeoQgijTDwYBXX32Fw6NDLq8uMcZwfnbBZLzDaLjDF7/4VbJswHCwzfbWIX/xV/+v9PIxbRsQ7V/58hd57u4tBlmOAmxruDg7J4tDQwBw69ZtBoMhpjUbQFVTtwwHE/Z2D7HWIQS8+eabTK9mPH1yghQRg/44ZP80bZeiHOil3lviJNnc+RrjKIuaqmy4f/9+YIH4cHc8vZoxHIwZDseY1pEmGU1j0DoijlJ6vT7ehcbOWbfRaDRNQ57n5HnOcDjcpDIDrNcFb7zxRrBDZxl1E/gxAN47inJJaypa07JcFhRFiXdhWJ6maecMCsnZbdPS1C1ta4jjoDtqWou1Ad4mpaRpG4SAJImJkgihgk5ERRoVxUFQrGOyvM+gP+gmGRV5nhNF0YbB0zY1eR4hVdD8BN1JGbRAjce2Dmccpja0lcE0BuFA+vAeE92R5Fy4iw8Tm9CkJEmEsQ15L8fasOoJEQSCKArWWLwmy3qkaYbsLM0IR6Q11kFWOTILZRKFMEMhsc5Sxpq4acmr0FAMG8tr04JprJmnKY0QXEaKy0jx2qxgbIP1OY5jVqsu5blr6q0Nrp1Pg+K0lmgtcM7Q7/eBAGwz3esAwdkTxzFSKqbTGUpppNR4C+vFivnVDJxl2MvAWZQQjIcTtBbEiWRnd8xgmCOlxzmLxzGeDEkiydHBLsIZ6mKFNzWxsihRc/fOLjdvbLG9lTIcRHhX47GsVmsQCk/Io6mqkrYJ66amsUgRUVUNi/mC9XrJ4dFBSHA2DVVVdOwcR5olXRZScHcZ4zg4OKTXG3Dz+BbD/ghvPXVVhXRsH94LshM9C9EFGG7+edaw/LzWs2blWf3g1SWcboijv2/CAuARUiAEXaNy3eyEka/zgjTNGY/GHB8d8cYbr7O3v4fWMYPBhNbAnXsvMhztcufey8TJkJ29G6TZkIPDGyRxwr2795hMJozHE6xxfOHzX2Z7axclY3Z2dpnNZtRVTRynnDw9QeuIfn9AXRmm0yU3b97BGsfF+SW7u/tYK/Be8/jxUyCIPKuq6i7IQTNTFGuEYJNLkmUZk8kE79lYbbUOuoW9vb0wLeiotwEn7zvNgiDNwspoMtnGGrtBsocMn8CQsDZYUs/PzwOwTCmWiwVVVYEPDYyzDtOGVY9UwfLadroJ7yHL8o6caje4/9Bs2S6kr8J5kFphnKM2LU9OT/DC46UnzmLSLEFpgfUWRPe6d1OyNM1pO5ZIOHxMp10JDUuvnxElDqlapHJIBc7a7g5d4Ox1Qm9grKRJ2n1vgXAbsoSCHT7Pe3jvwuokTUjXBXcaR7xcEUdpN5EIOUJVXXbaDclquabXyzuaa4VS4TLoHEyloJCCuGxBBEqvB3RRUGnJMhIhXXhdkBnLonvttQ5OrKWCnvPEqzVVXYbVZic6hyAo9t6zXCxDtlLHk7HOBiK0D01YnuedZsiRJCEwsW1byrIiTTPyXo+ma6BDI6OItCaONFpLimJNVVbkWc7R0R7bO0MGw4wkEVhXozQ415ImEYvpJXmsUThSLUgjGPYjhF8jxZrxSLK9lXL79gHDYQa+E057wXA4wLm2czmFn7MoKuraMr1aYKyhqevQ5EaaO3fucPfuXQ4O9hES6qokijTXoY7Xjjbv4faduxzuH3F8dAOtFMbacGB5Nv/+bmnFP856Bm778dQzge2z+uPXd9kFezzr1WqD/96Ibz/1MWEUnZGmvjso/eYXu65rVusVUSb58KOPeOWV15iMttiebDPZmnA1v2K9qsnTAb10SJr0+eyrn+fd99/FtI4HH3/MxfkpxljiOAo8EAS93oBePqCumk0DATAcjlivwzhd4JlOF91qx3Dz5i0OD45oa8/Tp2c0TcvOzg6LxYw0zVgu5yGYkO6OTgRh4Xw+YzzaYr0uEEKQphlZFhNFIYfm5OR0w9zQOqIqa0xrWK2WQaNhDEmSsV6XxHG2Ebdea4GKoqA/6OOmDucdkQrZNdfxA3gRDvEsJ1IRYKjrikinRFGMaX2AprVmc6i4EGyDb0qSLEZ2r5hzXc6Q0hTFupvu9EJEQJp2sbCy+zlz5rMFWR4iBeazOb7L8ynLcFhfZ+PUdU2Sa5JUkmSKqqyJVUySphuiqxASa8IdupciJEaLa6Baixd00wlD24Tcm57U/KuP53x2VpI7TyEU/zyO+N93JyyNoadiqqYkTtKwQpAOrSRJpHFGUtc1URSaIjEa8O3dgq8+miOEZIZn2LaMS8NvHSbMtEA7xTqNKbWi11iuEJsp1dg46khT9TLitiHPM5pmTRRppBRUVXB/ZXmGW4cGRsrAVlEycGiuyjllWeDxZFnCel2wtTNGuoj1fE2v38fL0ExlaQYsAudo2CfPJcW6wHu4uprinWS2WHLz9gHD4TZV4yjr0CQJLdFaslitMFXMzmTEcDBgOJTcvrOLoOVqPuXk8pw06TObzXnu3m2ePp1yejrDOIe1FXEsuwnV9QJYEcc5VWWxttN1de/j6XTKjdu3uX37Lt/85uuhObYBIHd90xNpRdW2pFlCKSriKMI7z+nJKXdv3cFZQEHH9/upq+tr4J+8+sN+5h/8+Xg2WXlWP3B5B5fTK4QWCC1wAnzXrITH9d1GuJhdI9CDaBSiVJP0Y+4/eEC5bhn2trGV586tOyRJzNHhMcP+iH7aZ3e8xc5wxPN37jIZjnj69Am9XkpRLjk9fURZLVksrnjy5BFXV1f0e33quubVV1+jrBsWqxXz9Yo4y+mPRty8fZO9vR1u3ryFs544SlksFzx++oAkERhXMVtcUbc1edYj0ilYxSCf0EvHxFEP0wqUDNk8WR4TJxKlHSpWXFxdsSzWPHr8OFBN4wRrDEkU0cti8C22rTehglVtcE4jZUZdO6TSeAEqkpTVGuPCIdUfDWhcC0Jha4WtNZHP8W2YVKRpim0Fq2VNWQZoGFgcNUIGYam1tkv0hShKQkZRFKGVCssW58ji4DJZL1a0VctyvqQoQpZTUTaBnRIpnKkp13PqagHCdwRVgXGWpm2pmhqkYLkouDhfU1UW5y1FWVBWBiElSaZDno2LsD6iMT6so/BYbxHa411DL01wrcG1Fuk0X/vwim88XdE6wZNEY6Xgl2Yr/ux0gfEtRd2gdIZSCc47TNMyu5hSrUtiFRGpJMDTXJgG/aP9nH92PAZrOawdwnn+yW6P/+84xtmwSrvwjteHKdvWMa4blDFsG8OobvmX/YwHyxJnHXU1ZW83YjyM0BKEtxjTkGYpSZaiYh3AuM7ibUukBQhL61p0pPFSUdaGs9MzVCQZbvWx3tA2nsW8YNQfcu/OEWkK+JZ+r08cBT5PnvYQSPJ0wscfXXFxWlGsBLEeIISirte0puDmjZsMhhl37x5x++YRL9x7mVj0uTiZs5zXHBw8T9bbYmd7jGsLXri3z71bYwZJTT9tyZKWOGrIUvCuwTjLfFkgVEysciSaSEUhayvvcXk6JdVDDrdv04tGCKeQ4jrcxyMRmKpBOEusIFaCyXBEL83DFUQ6xLOT66evhCdwXL7b4werZ5OVZ/XHrO8+3hQCRuMJILi+oQi77E9/zical2tLc3CmSHSsiUVMawzD4YjDgyMOdveDXVcrjHXcvHGLxw8+5jMvvYwzFgG88MILXP72CfP5gp2dbZwzFEXJc88/R9M2KBVsxOtyweMnj5nP52xtTTZ3+kFk6ZnPF0Q6ZJ2cnJwjRKB4jidjFqs1Z2dnOOeYTqdUVY0xhlLUNE3D0VGAuwVrsODmzRucnp4GPU6isc6EILimYTQaIYCz03OObh7Rdnbik6enFBSdJVfjXeDMeC/Ie32KYoHWMdaF9cpwOOxyXSRxnOLdCtlRQZXUgOuSnCVRpPDOdJOuwMMoyjWmbcBD0zYkWRKEvVVNEoX1lQ1ReOAFWsc4B/jg3hlPjvFYFCpk3QiwbctyMQegrpuQgiwDD6UxBgi8mqqq0DLuDh0YDPpIqWiqCtMG2q1AdVwSgdKStjUBQiYlWmm8FzgXVi8HOuGzszVXkWKeRAgFl8JTR5Ivrmv+YSppCDlITRPIw1kSE0cxy+WawWCALRtaY1EquNxarfgHuym/sZORly3toM8FjlQpnHU0bYtpLb8+Tmgay+dXNUeNpYo1/2Srzz8apTjr8Tq4e3SkWK8L8IoszanbmroKjyhNUBKE9HjCWq1pauI4wgsfwiL7I0xbsFwu6OU9prMpbWPQSlGsVwjpGY1GSBUyfJqm4eDggLOzS5q6ZjQ85vTsiifmlLIyRKlGyJa4F9ZqDsl0vmR+dUISpRwdHjAeZazKkvuPHhGdL8Lvi5Rsb024vDxne3vI3Ts3wEs++PAjyrKmKE14XyKwpkInGWWxYjDsIaUnjjVpGjO9uiJNM5577nnSOKMsK0b9ER42qpM0SdFSMez3cc4xGY83oZbhuuI/9fE/ffXHXQf9oBOYP+rr/NgnPN/t2/khvEzPmpVn9UOpay3KtZ3yj/olCSuENPyyeXDGsV6tuH37DsdHx9w4Ou74HRDHEVvbW7z9xhvs7e2GILmmpliv2dne5Z03/zknJ49oTUOkNWVZoFVK05ScnDzF0lIUxUbceHCwTxzHLBZz7n/0gDzNGY62ePPNt0mSmNl8Rr/f5/TkYqO3iKKIy8vLwOzokpeNMVxdXTIejzHGkKZZSGBuQm7L+fklRRmyhaTUPHz4mEGvj/fw+PFT1uuSum7wnXbHmIa2DZj+oHVomc8W3L5zA7CAY3o1Z7Va0+/3kEJTFhVJmtA2YbWW5xlVve6aLxM0MfguKLAN5FcZpjUeHxwhzoUkZ+momwbhIdY5RVF0rBKHaT1SxkTCh1WXDKuowWDIarWgbK+TnQOUzNkQmheCAkPDIYRAyC5fp66QEra3dinWJXOzDqJJaVAqvCeuxZu9Xo7zgaUStg0Oa4NINKsqet7zWAuE8CHmoWoo4phhZdhTEU+E6vQholtRKUChVdyh3wO117nwM0gpcc4y0xHLUS+A2YqS2rhuEuXxXlMJyd/f7vN/TAYMrUNsT3hcF1RVjdYhJ6osG8DR1C1lEfgzcZrgHCRJSpJlrJYLpJbUbYPWReCRRKEpS7KMXq/HYl7hXaDfOmsRMvxeTGeXNHXb6V0asjzCe8NqNSdJFPv7RyRpTttYnk5PSbIcGeU4a9A2Iu+NuP/gKTrOyPqC8/MLLIbZOuHgYAvzSOAbj5QepRwPHjxif2+XPM/Y3hnzwfsfcniwS1HUHB7e5snTcx5+/JQnJ2dooTHekmaKxXLKcNhHqjDhS9OYYT9na9w1+y5ABfF0KzO5uU4EQTsdo+Wa7/Sju5b9OOs7NRt/MldIf3Q9G6Y9qx+4grCMTsPxR3/8tSBVbtggjtPTMy4up12eSsNwOCbpNAZJkhLHMXt7e0HEmaU8fvKYBx8/QGvN5dWM4XAMXtC0LQ8ffszl5TlJHCGV4Pz8nOl02okWPf3+gKOjI7a2tjg8PCZJcrYmO7zyymtkWY8kTrm4uCLPe6RJSpoGZsXNmzcZj8dAyJQJQXUZZbkKF2Ip2NnZQQjJeLQVRLy9PnEc88pnXgHg8vIyWInLirKoaBvL7s5u5/igY7vEFOWa3d09BoMhl5eXfPTRfT7++FF30ArqqsW7ML1QMthdk0RTNyGY71qfYkyLMTVpGpEkcZhSSIXrLojOO6yxgUbsXGejDfqNKIo30xEpA0k3y1La1mySeBeLJVVZo5XuHDgNpmuSlFLESUyWZWxtbQUNThRjaoNwgl7aYzlbMb28QjiH8IZeniKERSiHkIGjYmwT3CsuTHpMGwS5SiqWkaKOFHnbkFhLXjak1jMwjrUUXAm5CYwMycBhXTebzinLhunVgqZuaNqGuq5YrVab9+V1ajOAjmKUinFeBouzilA6xlrJIs44G40598FN5ZwPrBoDq2WJd4q6tgg0xjishaKoGQ4n4MNayXvBZLJDWdZdqrcIr5dpmc1nRDrwcow1gCOOFQiLEI4sS0nTnJ2dPYwJbrL5fIbWoQGr64btrT0Gg5C6PR6P6fcGaJ1weTHDOoHSCWmeM96aULQ10+USdMTxrdt4NG3rcFZRVpbFas3b777D+x+8x7pcgvQUZcG33ngd7yzHR/u8+NxtnCmJItG9/zR5L2Uw6DMYDiiLgpu3bpFlGW3zSbYQ+K5hDBNb31GygY3QHJ4d6H8S61mz8qx+CBUGsh42F5Y/bDwZQuA+EXNaZ1gsVwwHQ05OTjg6Ouou2IHwGgShhpdeeom6rlgulpydnuGc5/def53joxtMxlvs7e2xNdlmtVoynU45PXvauXUkw+GQySQQVh8/fsz+/j5t2zK9uqLf73P37h16vR6taWnahigK5FCECGF3UrLoYGDXk6OAS/e0pmE8GdE0QTS7t7vPkydPiaIET1ijPHjwMatlgUB1d9mhUUmSlChKgp01TYkiRds29HoZx8fHzGcLLi+nmyZCKY1Asl5XGEMAozmLkJ6qXoc7b+fCKmbz6+1o22ZDv73OLMKDsw6p5Mamm+f5xrmVJDHWGqJI07YNTVuzXq+xxuKc76BsPmSzmPAa9Xp9yrLqxMFRh6UvPtUECPI8JYkTri6nXF1ecbC/zyuvvMRo1GM0zun1E46P93EuZAmlabJZGdpu8qG1QkjBqWl5c5DxudLx9fMVnz+b843zBa8sS94a9Lnq1greg7EWpVWYZnmFc4q2MXhCo42gCwAMzcan15VNHRKLw3tbdv+/65xGdefUCswRJSOSOAc0poX1usaawK+RIsI7SZLkFOuK9arEOYG1ntUyUGmTJOmaJI8gBG86H3hGVVninMXYNmQppZrBsM/9+/dD5IOApvt+tNZcXl7y+OEJadLnlc+8hkAGQTaCujL0+2PwhOYm0gyGA3QcMRhPWFeGOO0xmkxYrkuSNCdOeky29phs7XB+eUF/NOT04oLj27dJ8z7rouTBgwfkeQK0COHxuG6SJrlx4wbj0YTlcs3hwRG7u3vESYLs3pt0YuVPH03X14o/eE15Zrz5k1XPmpVn9UOp7+e6cX0Rev+9D1gsloyGY175zGtMr2ZEnU4i8E9Et4a5oj8Y0LYtx8fHTMZj4jjmzp27ZFnOndv3Ojx9Td1UgYviHbPZjK985SsMh6Ng8wU++ugjxpMxUoU7vw8+fI8okkyvLmjbGnDgw6qhLEt6vR5KKdbrNUII+v0+N24e0zQ16/WK5XJJUZR8+NFHndPGMxgM2d7aIYriDUa/bdvu4FY4B94JTp6ednf9irt376CjcBhfXl5RNw1tN22aTmfdgRmgWJHu3C2CLnE5CgeZMeAlrrNKR3GEx3TTL4VAkWU5URyF106IjkYapg+BkbJCSAfCIIRFaUgSzbBbiwSwXLLRypRFhfcBeKeU6pxh8velDUspMW3LYJCTJlGYfAlBVRVU1ZL+IGI0zsh7Cav1nDjuDmzRfY/eEc40i9IBzBZYJKZzZYV3onWdnqGDsIWdl0ApRdVBy/ASazzOCXp5D601g/7gU1ONgMa/Xh8KKajaCieCTNlLgfEGNAgl8SLQgRfzeQe8A61jIp2gZMJoOKZYV+R5jyRJw2StbCirlkgHMKBSitbYDVPHe0veyyjLgrZpcRZ2dnZDCrQ11HWB1gJja5JUM51esF4vKcqCKAq/P6tVxXy+ourSwF959RXa1nB1NSPSKbFOOT44wNQlWsJkPKRcFyxma6ZXa6IoQ8eym4yBVilnp1OKomWxqqiMpTcesSwr8sGY41t3mOzsYJzhznO3GU8mpElGEifgJXk2ADRapQg0/X6fg4OD4Ijz4fULrzeb9+bvYzp96r9/XuuHaYP+ebJUP2tWntWPvcLB1RFYvENpzc52oHx+8YtfZDQabyYiYS1Ud5TYMQJBnMT0+30OD49Ik4SPP/6YR48e886776KUYm9vlxeef56yXGNtAKl9+OGH3L9/nzzP2dqa0DQN/X6fKFbM5ld89NH7vPX2t1FaEMeKKJK0JoSkxXFgrNy+fXtzhz8cDkMujg/7d6Ukk0n4/q6bkSRO6feHWOvw3bhfKo0QoVm4vosM/JMMrTVX0yvW6zVSCu7ff9AdxNcrmOCG0FoH1kbe7xgeLWkaVlLWhlWE9xDpiDiJiWMdBJve07QtECZa11Ay1a08lNLUdQ0iBDKCIctiEJYolugIhAgunpC/U3NxcYEQIc8mQMpUB0Jbbe7uwyQkAM36g96mEUoSTZYn7O9vY12FsQVHRzsIbGB3YENyb1l2awGB0qAj0aUWG0bG81ph+Fam+SejnN8e5/zTccqb/YxXVwVbzmPsNeAuweNo6ibA4pAIoYiimKIomM2mG7rstf36+hANuomQjeQJxFkhPEoJvLc0bRk0VlLhnO+mNwLnJOtVyXod3EFV2WwEyCHoM7zHrXGdbduidUyv1yNJEsoy8FrWxZqqrlivV/R6PeJYo5RgvV6yWEyZTIZUdYHWitFozHA4YTZdkSZ9nBM8eXzCu+++i3OOra0tjLEB4+9henHGqJ+HhkUI9nf3KdcN4+Eus+kCKT3D8YC83yfN+kRxjlQJSdzj/GqKTBJq6+iPJkRpDlqDVmS9HlIqtrZ2aBtHFKVEUcovfuVrnJ1dYdqgcdJKhQZFPjuOfpT1B3kwP2sNzLN3x/dcP0i89s/r43sr5wIt1lmLEJJBf8BsOmNvZ4fxcMjNGze6w0SgVXAQDAYDbt2+TVnX2I4BsrO7zcsvv0yW9bhx6xZJmiGkZmd3nyTr8fY7H9A0ji994SvY1iEIQkopQGvJwf4e21sTimLFfD6jbRuMaYkiTZLEXXptmDZMJpMg6OxYMlVdsVgs0UqzNdmmLEqaugEBZ2dnVB0y/+z0jO3JFtuTrYAod44oVsSxRquQSxPEnZZBf8B4NMKY0KBladbh6GuUDLj4um1w3qO0pG5KnA/W4MFwAEIgVadHkR4vwnQo1glaxtiOQ2NdaHDC1EWhowgpRRdwGKY/UkmqqsRag7HhLr8sC8AjvKCtQ2qz1OHrCSnBK7ztRvgiaGuc85vmtG0awCKkQQjDcBRydlarFV/+0pd59ZVXeeftd0jSFO/FBgcfxRHGGeJEh8C/4AFGKcHIWpKmZQ6YOGIZa2olWSpBH5h4hxDBWdS2LYN+H6TH+RZUaPyU1l2DJ7oMH4N3UBTVBukvpUIoSPOEsi6x3qI7IagXPky6omTjThFC4KzrmsAgyu7e/YBDCMdkPAoXYOdDErOzFOWai4tT6qpASjBNg/AercLqrixrrAXng85E6YjhcIDSYXIUMP41q3XJYrmibhtW6zVREtMfDPjt3/4dTk7OGPRHnJ5coESMJ2Q2zWZz5tMpO1sThv0+d249h5QJvd6Are0doijlhRdfYjAcYp1jNBnhnaCqApm2P8x4cvKUg8MbLNcNFk1rLTpOKKqGqgqU4nt3n+fmzRus10vktVjWC3ywnH2P15Sf9LXvOzy6KJEfxuOPbCc8bIiP11/3D3t6+EPAeT9dx8T/qX6mm5XvRi/8QR/P6vuo7+GJ+4TiCufn5yghUB7aqkIiiLS+5ksFe7E1FOsVewcHDMcT0BFCKZq2YbVa4oVERSllY7h193lUlLMuDP3hDoPBDv/av/pn+PIXv8zB3h572xOqcklbF6RxxIMHDxgOhx1ZFLKsF/KDyhbvJE1V09QVSawxtmE8GYAC6x0IibMQ6YTRYEyaJFRFSaQVYImVpJ/mDHtDtJKMhwOcbxmNehTlgjTTZGmEwPK5X/gF5rMZprEMe2Mkirpq0EoxGgyxzhInMU3bMF/NqW0FytC6muFkwPbuDls721jfHYeiBdGihEA6hbQR0oc7WGRYozjX4lxITw4alBbvHKYxmNqCk+Akic4QTqOIaCqLDk8ADo9xBhVr4jTFOUG1qqlbi5Aa34HSjDF454jjiEhLskyQZcFS7R3s7x7zzX/xLd5750OuLucspgvWy4LZbE0UZ0itsd7RuBovwWKRkSBOJe0gYekdQwfOdOJMJelbQxVJFrFHKgfCIXBIaZHa4bVBxh4rgl5Ky5i6MhjjybM+RVHSywcINHhN21gkEtu2KKGwrcM0wWYtvaaqDGVhsDboXpwL06i6LjeNoVQCqUFgSCKPbQuEbRDOoJUA4RiP+wz6MRJDL42JpaSXpDgbJi5SxlivMU5R1pZ12TBbLEnimCRJsQ6upgukkmSDhKQXI7Tg/OqCdVWho5iLixneacaDXYaDXbYOjml9FAS0Zclifk7ei0jSlPFoBykTbt14jvF4ByE1xzeP6Y1SWipiFdEsV6SRRakSpULGko4HDMcHOAn3Hz6gqGpmixUf3b+Pw3Lv3k3OLp6AF4Tdmkd8inodjPbP6hMz9x9yXv2B5uYPO8/Epz/2j/HxP031rFnh+3yhflSd0s/q43sqjxSC+WKJlJKtyYTnn3+O9957H9M2aP2JRREgzTIm40mwOktJa9oNdv7DDz7g8ePHtG3L1mSbLO3x6NETiqLi6OiY27fvMh5N+MIXvsTW1jZSKfr9Pmma8u6773ZBeIaLiwuWyyWLxSI4bJQiTdOgWRCKxWJJU7ddUnM3uneWqktHXq1WHRrfdmLYhAcPPgo2W1ezLpZU9RqlBYvFnLqpqKuKxWJOFEWbEX1RFN16IEwjrvHrECZSzz33fEgzrlrW6xqlIkAyGo3p9/toLVAKkjjYQPO8F1gndXBcSCE3QlfnAkslaF5C4rEHlNYb0u41hj+KIpzzxHGwOl+7iZIkAe9ZrdYBbKsUe3v7AbOvoyCgtnZjRXUupFTneZ/joxtIKXnjjW8zX6yYTpf0837nqAlaJWMMSspNAyul6PDs0Ovl+O0tfm+YstVato0l9p5d69kxjt/JNfNIdZqhBmMt8/mCOE5AEJw1AlarNa2xNE0TbNfOdbTiJUmSbDJ9ojg8BxCepyhOsMbRNC3OBrieNb7TLFmqKqRA570Ao7t2GKVp0pF912gtUZIumTq8t9u2ZTIZY0zQOWVp1kUZpEwmWzRNQ1VVVFWFx2NMw2q95PLybBPT4JGUZYtAE0UJUgaO0GpVksY5i/kaJSNOTk7Y2z9gvliCEAjpkRp0BGfnj9neHpMmKfv7e0wmYwaDPlVVBx3OYDeEcUaKKBZcXZ3hsTx+/IibN28ynV6yuzth/3CHmzePmM2mZFnG05OnCCnY3z/o3htBz3QtVPn0JeUnfVn76Xh893++U/3BP//DLtPf6e/6qTgivkN9T83Kf/Vf/Vd89rOfZTgcMhwO+frXv87//D//z5s/r6qKv/k3/ybb29v0+33+8l/+ywGQ9an6+OOP+bVf+zXyPGdvb4+//bf/dhAEPqs/QRUw6lprrLW0pqWX97h16xbroqBtA4b8umGJo2gjfJRSIrtD7PGjRxjrODw8plhXlGWNMZYszfFecHx0k17e5+pqytZki/39A7SO6PX61HXDxcUlcRxT1zVpmlLXNUKITjMiWS6X5HkW0PFFzePHJ5RFEyzNacKNG8fEScJwOAzfZ5ywXC4Zj8fs7e2hI8liecVicUVVr6nqguFwgJAhDBHBpiH7hO8RNAUQGCNNU5PlWXdRVyyXqyCcVRFZ2g/kXB+OzzSLSdIIpUFIgdJRwN9bKKsqME7o8pg2rppgiVXyeiWlNv9dlgXGhKbsOhEYPMZ2MD+lwuje+y6YLnydy8vLQNDtnEnGdLwX77vpQIRAcnU1BcJrWlcNVRkw+0pKsiRFCdBSILwniSOUlPR6Ob1+zrW8IY5j/tHukF/PNM5YDmuLQvD/2x3xj/fHQYNiLHGUoFUUtERJGmB6XtA0Buc81oTE36qqNinRUn7iRrPWopQGREcDhqZuuqZNBEePASmiTSbUYNCnP8jxmKB3cYK6aig7R08cx8RxRJxolstF0B+1Lev1mrIsN+//KI7IsgDUu9YZGRN+f9o24Pul9IxGQwSwWCw5PTmnWNcsFyV5PiRNMsqioSxaVquSyWQL70PO0tMnT4miKKz8TI2zNU2zoqrmvPDiXbI07tKYLbPpFceHN9jbuUE/3yVNc8BQFCucM+zu7TAYDji+cQR4rG9p2zJkBznDYrno3n+eNEk3v+fwye/Cs/re6mdVg/K91vfUrNy4cYP/+D/+j/md3/kd/sW/+Bf88i//Mn/pL/0lvv3tbwPw7/67/y5//+//ff7H//F/5J/8k3/CkydP+Df/zX9z8/nWWn7t136Npmn4jd/4Df7e3/t7/Lf/7X/Lv//v//s/3J/qWf1U1bUF9Pqi5L3veB8iCEOzDB1F3Lhxg+Fg+Pt4CkJ0eo7hMAgthWA8HiOl4s033+T87Jy6bjk5OeP8/JL9vUOyvM/p6TmDwYhf+qU/Q6/X55133uXo6MaGcbJcrinLmizLN5bm7e1thBDs7u5u9v9ZljIcDvFe0DYOrWKGgzFZnrNaLXGd1fX27dvcvn17Y2sNwDvwWMpqvQG22Y5au7e3z40bN7DWMplMWK1WG33E5eUlQgisMwwGPcqy4Pj4mH5/wHK5pm0MZVFtrLPb28Gu/fTpE8BhbUhfTuKUum7Z2truUp1VcFdJSZZlQNANWWORnWvHdhC5KFbkvZQkjQOkTTiGoz7WBgqsF8Hu643FGxv8SVqBFCDoQF9uA/67nqyY1jCdzpnPF5RlQV2XHXwuTHOaumV6dQneEmlNpDVaK7SSiM4NVNclCE/btsznS2Z1w/97d8L/88Ye/697+/y9V+/w93d6lELQtq5z2uiOfhoAcWENKVFKo3UQmsZRyK+5nq6ElPDpRujdNoGQbE1YlQWWSjeR8tC2YTWIFxvmTF2XtE2Fd2xyctrWdL8HDimD0PZaL5SmKaPRqEuiroNux3zS2J+cnGzccXmeb1xLSkm2tiZsbe9wzW1RMg46kbYlSVJAomRgvRRFiRCWydaQ9XrN1tYWWiukFAgF460h1jW8/da3iaOYp08eUpYLXnrxHvt7e3zx81/h+OAO21s79Pt5p/NK2NnexjuPs54vfOGLxJ0T7unTEwaDEY8ePaIsK4yxLBbLDeDtGTfl+6ufddHs91LfU7Pyb/wb/wZ/8S/+RV544QVefPFF/qP/6D+i3+/zW7/1W8znc/7r//q/5j/9T/9TfvmXf5kvfelL/Df/zX/Db/zGb/Bbv/VbAPwv/8v/wptvvsl/99/9d3z+85/nV3/1V/kP/8P/kP/iv/gvaJrmR/IDPquffF1bET+xHbKJuldKUzftxi0iOxDXp3/xQkBh2Ml7oGka3n//PT7++GNa03Lv7gvs7R3R7w349rff4mtf/QbeC6ZXc/K8z/b2LstV4IMcHR6T5wHxbq2jWBdYG+zJTRP4KoPBgCRJaJpAlG2aljQNybef/dwXuLi4xBhD2zZEnQj3/v37zGYztra2UEp1uUQ5WZZQVSXj8ZCiKMGLoONoQ8JzlmWcnp5uuC1CiMA6EQRUvncMhwO2t7e6O3IYDSeEO3yDUpLZbMbFxSVXV1PiOEHrGJCdCwkuLq/Y3d3D+5AsXFcN14PZ64PPe4/qxKbeu4241tqWwaBHlqU0TU0Ua5wghNg5hxKSpqhoyoqmbVGR3qwpoigKryOfJHIrrbh18wZCBC6MUp69vS2Goz53bt8OlFlnMG2LswYBNHXVwe2aTgjcYG2Y1Eys4yUZMWosU6l43MtYJnFgdXiJFBJjHFXVdOnFEd4B104gnWymLEka7vS1Dq6oLMtwzrFcLjaHKgQmjXOeJE6RMli3w3qNrnl13ZTCEEUxHrFpFrOsF9Z4XRJx2wba7XU6NUJgbbAvB/Gv2rw3lFIMBgOapumytUzHhFFdcrbHtLZrxkJgpRCB/Lper7uVVoD0KQWr9QznG0bjEcvFsnNFlWxNtlgvC8bjbf71f/3XwHkePrhPvxcjRMvlxQnHB/vcOr7Fndt3OTw66qIUai4urtje3qcsW/Z2j9neOuQbX/vT3L71HN4KqqLhvXff5+joeGOtv4a/Pasffn0vTcxPe9PzfWtWrLX8D//D/8B6vebrX/86v/M7v0Pbtvy5P/fnNh/z8ssvc+vWLX7zN38TgN/8zd/kF37hF9jf3998zK/8yq+wWCw205nvVHVds1gsft/jWf1sVRhpg3N2k0wcx3E3frabO0v4hHALobFJ0rTLygkXtPliEUBtShFHCfP5gvWqoNcb0jSG27fvcevGbT7zmVeZz5dkacbV5RWDwRBjDFmWMxyMkOKaaArHx8cdlC3i0aNHrFYrhsMhSiusMyyXa+IoQ3hFnvfD+kOEJGm6A6PX6zEej9nd3WW9XjEeTzDGksQZq1WBs+CsYDiYIAgrBWNMZ0vOSJKEyWTC4eEhUgYrdK+XIQQ8fPiQ9brAe89sNu+eK0ueB8LufDYPd+8GrBEoqTe4/cV8yXwWUoRNGxgwy+UyBCsmyeZ5lh1vRQhBr5ejtWJ7ewtjWlarJUkSdxwWtzk8bWvC9ENpRJeSfP28lEUZbNxKbdZAUaRZrhYYW3Pr9iHPPX+TR48/4uTkMWfnJ2glGQ2HbE3GJEmM945YayQ+BCzKkL6cAb/ydMG//e4J//eHl/w/Tqb82sWMdrHk9PQMa13QVihNnufEUYx3nrYx1HWLFCqkHCsdHEB1w3y+2FjVsyyjbUMDUZYV3gduTp7nOBsst9cTFaXCVEFIh8d01m66jwmvyXWekXe+e49LrDNBdKsEWR5IrnhP0zQd2C+sJ51zmwalqqrAjsGTJGEStFqVeKfBK9brkkF/QBRp4kShI4FzDa0piSKFkCAlVM0KT8N0esZqtUAIGPQHOCc4Pb1CypTp1RrhI+7evkcUKRbzC1arCx49ep/Ts0csFsFmv5ivkDJia2uX27fu0e+N2N054vbNF1CiT57v8NVf/NcY9Le4ceMOT58+paoqWtN0YaYdyuBZw/Ks/pD6npuVb33rW/T7fZIk4a//9b/O//Q//U+88sornJycEMfxBkd+Xfv7+5ycnABwcnLy+xqV6z+//rPvVn/37/5dRqPR5nHz5s3v9dt+Vj/BunbVOec3WPlwNxiahWukOfCpO9juc7u/oN/vE0XhgJ/P5ty9ezeIEnXQcRRFwXy2YD5fcvL0lCzrcXZ63n1Ny8HBAUdHR0GvUtUbEasxluVySV3XmwNBa705tJI45vDgkNFwTBynPHl6Ql03nYYhQM5W6zVFETQGzjn6/eAmcT5MUAaDYdAyiIiibFivG05OzjeH/rUAtSiKTfMURRHGNBTlmqZtaJrw/Xn3yWosigVSgdLBSm2MpSjqbj0UrKBBOzEgTlJMazqEfLAqX0PbrLObu9zrtc11Y9m2YYpx+/YtjGkpqwKpJap7zZSUjEdj0jRBCdlNxkLAoY4+eR6lkt00oGE6u2QyGbJczTC2YjDKuHfvFucXpywWM1w3McrSFGdMd4g7tJLMpleMRkP+/EXFn5nWeCV5Emm8lPzZRcGfu1gRRTFpknWTEY9pDaPRmBs3boSmQoTVy7XNd7lcUpbVZvpzvQa6fj6uBdnO+dDkqAiP6Jg1giROuwbFboB+aRrWLm3jcDZMeYIt3HSvb9xh+YMmRgq50XEZY1gul0CYel3/rlxrqq7zo5qm3awom9qyXle0rd2s3bIsRQiHiiCKFW1bobUiyxO0lhwe7aAiR1UVHN84Js0ytIqwBvAafMRyUbBYrJBSUJRLnjy9z2CY8tv/4jdwvmUy2aIqW/b3j8jSPv3+mK/+4jeYjHe4cXyHG8fPcfvmC3z+s7/IN77xSxwcHLG7s8+HH3xAL88pyqK7kfmZ9no8qx9Dfc/vkJdeeonf/d3f5Z/9s3/G3/gbf4O/+lf/Km+++eaP4nvb1N/5O3+H+Xy+eTx8+PBH+NU84g99dOpm/yfr8YP65K81C7ITSYYE1SAgTJKwSvlOo2ABSKHpZX3wYGzDZHuEjiUWy/nFGX/6T/8SB/tHDPpjvJW89eY7fPUXv85ka5vf/eY3OzGjZr6YMRjk3Ll7i7v3bjEeDzvtgupWJp5+v8fW1pi9vW28NxRlzXyxIk5iJlsjJpMhEs/x4SGJTrhxfIcs7RNFCbPZgjzv46zDOx8ElbVhPlthWkvbGibjMUWxRilJ0zbhrrqX0R/2WKzmfHj/Qz5++IDLqwukCjwYgacqa/Ikp6kqvDVoKcBLsjRnuVyhtew0EtXmwKvqGmNbWtvQmAohQaiwVkBKHJaqLZAyBNV5d60/kQgkSZzgrMFZw2I+wxpLFqeEGMCA6ldRwqooqVtLkqaBaSIsxpTkWYLSEmtNAKhFntaEdOXZfNY5ZirWqzXnF+fcuHEDIQRFsaZpana2d4DQNHnvMG1LL+8jr1a8crHmXMJVrGi15DyCaaz4SmmZNJ6qqGnrln4vJ0kiTp4+4o03vsV6vcZ5G3Q4eIqi6Bo8kKQURXBZBWAcGxdP21pWizWrxZpYhXyjpgoMFGct3lpElykkhOgmNp3eRmuUBufbTmgb9C3eSawV1HWLQBKpmLaxYeIjw7rq7OSCpgn6rixPkMozGKQoFeB4TVOHIEvX0LqGOI2wPkDtQuCmJYk1vTzGupo4EgRpkSdJIuqyYpAPOD15St2WqESDVLStZb2a88/+2W8ymxc8/8KrRHGPphXcuHmHxjienpzivMIT0TaOs7MTXn/9nzOdPeHmjUO0ivlTX/8ldib7HO4f89pnPoupLS89/xKD3hAtNFoosK5bWXVi8+7xs10/KSjJH/J3/vRtdr6n+p6blTiOef755/nSl77E3/27f5fPfe5z/Of/+X/OwcEBTdMwm81+38efnp5ycHAAwMHBwf/JHXT9v68/5jtV0jkuPv34UdVP3qb20/n4QerTiOyQT/MHd6Pi933cp8t3qHSB7NZDHiFhOB4yHA+ZLhY8fPiQu3fvdZknislkh8nWNnt7eyHBViuUkpycPCbNEo6O9pnNZ1xeXQS3TZZt7KmnpydMp1cMR2EFFEUJi8UKhGd3d5uHD+9TVQVxFFNXwRkkhArQLgTr9YrLq0tGo+FGlLlarbu79IReL+eFF54jisJ6qbVhWlFWJVvbWwgJVVMRpxHGtuEwXxcM+kPapsFbw2jQ42Bvn7o2rNclZVkhkB29Nu5AbgahJR7HqlhQlCta23QQs/A8+y63JY5DGGKkg6OmrVvWyzU4H/4/Gzgppm3BC2KpkYQmtjWWujU0JnxN5wxaeZJYYTttyWq9pGlrnDcY15CmaVjfzdaMx7tAyOtp6prBYIAQkvksWMh1FGNsSFMGQVlWDFtHz8E6ifCAVBDFklUsiZuWfhMmFVop5tMrkliTphFS8Ek+kgo2XecC0E2JmDjq4YzCtB6tI5I03jjRpJBEKsK2jqqsCcBCiXfdTYyQmzPh2mHmCWnWgYhrSbMIRNARtY0FH5Kf0ySnbUzXpGhMY9BK01QNeBUEvSaA+ZTyxGlofvr9PLiDpMP4hoOjPZIsIuslVNU6QPRcWBX2eilaQduWpGnMZDIiTVIirbl96xZNUzGaDEB41sWaui65cWOP09Mn3Lp9j9F4ly984Rv0Bzt8+60PWK4qlus1F5dX3HvuJZ4+PSOKI6QyPD35iF4WkacZd2/d5cbBEeWy4HD3gK99+Wt44xEWZpdT+nnONUXZ++8A/vhZrJ/UhfaP+vrf61/zU6Zh+YFnb9e71S996UtEUcT/9r/9b5s/e+edd/j444/5+te/DsDXv/51vvWtb3F2drb5mH/4D/8hw+GQV1555Qf9Vp7Vz2X58I8HCHesWZYT6YQ865MkKW+99Ra9Xo9er8dXv/pVtra2cM5xfHREmqWsVisGwxH93pAs7XH//kOaOmD4szxmOOxxeLhPFGnyvEeW9dnbPSDSGc7DYDCgqireeuutMLVoW5bLJefnZ3z44XsYUyOkJ0k0xtT0egk3bx1xdnaKUtEmhdd7wWpVcHU1DVwSodAqQuvQ+FwTVA8ODun3g5AySRL29/cZDoNAt98PYtc4CXqfum4YDkbdGkdvkoKF+ASuJwSbO3xr2yBWtYYsy8iyfLOGE+I6tDHQXq9XImElZYijmLZt0VGE7TRGUkpc5/QqigKBQF0Lp+uAk78OYGxbQ5al9AdhPXN4cJvTkwVNHdYkHoOQgjTr47zk6ckpIEIekoiI4oxev8c6jSiUYGgDlTjLM/I8Y1sr1hLmqmvGvCNJAxm23+8zHA7Ispx+f7CxbysVJn1xEhqMsiy7lV7Q/Vxbl5u2CSF/kUIqgda6W2F2kQVKhDVgF4J4/dzpzWsQxL7Xa9DrIeJqtWa9XtM0LavVOuQDtSb8/XHQCEkpiWPN7u72xjEW4g1UF64ZcqGUEpRlmLIpLbC2ZjjsoXWIJwjRFpIkjYmi8PpEkWY2v2Q8GSCVpz/I2d7ZIkk1dbMm78Wcn19yenrJZLzD3u5B0JlIz8eP7ofwTmMZ9MfcvnWP/b0jevmAurO6a60YDgab99hrr73G4eEhZVmytbXFcrn81NrxZ3+e8qx+dKX/6A/5pP7O3/k7/Oqv/iq3bt1iuVzy3//3/z3/+B//Y37913+d0WjEX/trf42/9bf+FltbWwyHQ/6df+ff4etf/zpf+9rXAPgLf+Ev8Morr/Bv/Vv/Fv/Jf/KfcHJywr/37/17/M2/+Tc3Qr9n9aw+XYJPunofbByUVU2xXpHEKavFmp2dXW7duoXWmvfe+4BXXnmFq6srjg6PuHP3DsV6zmK5Jkpynp485KP7D+n1MoajMRfnT7manQecuoQ4SSjKiqvpguFwgsdxdTWl3+9zdTWjbWusc5xfnJNmCfP5lLt37+K95+233+bg8JDZfIpz4a5aqXDgTaczoihiNBoxm83wPkwMrXVY6yg7AWqapjx5/JQ0TZAifG4UxeRZOJC1Vhwc7PPk6QneOZrW0DYGa9g4R5SSaKnB+fD/uSBORQqMdagowstAK83zvAs/DHwPzycTLikl8/kcpSRlWaFkhHOC6FN3WdcfG0URaZpydXWJc8FqnOc9rPF4DUJ6hAzQuzTJyfM+Z6dTvI0wtUTKBLAhydlIpNRUdR1WK0rjUQgZU9cVLlF8ezvnXzlZArAoCvLGMLKC/7WfM9cKb1u8F6RZghBQluFnc4AQGeAwxoamRHfaH6/QWrJerYmiIcY4oijuAhs9xTqs75IkJolDA4cAa2zQyaQp1gUdTBRHnS4qTFoEwUIthQpaIRmylZRStK5FKsXW9pjFcopQkqIMuVTWCoQLtnBj2i7JukSpkFuUJBlnZyfcvHmD6TSkc2e5REeCvJeiNTRNSd5LGQz7VKXBGkNVeYwN0Lqr6RlJEnF6+mQz2ZrNZqzXM7a2b7FcLpjNLXfv3qYqLd7LIHLOrgXPjtu3n+Px40c8//xLRLLPo0ePuHf3ZdIoBinZ3t7uGt6I4+NjhsMhZVXRSkOv19tkSD2rZ/Xd6ntqVs7Ozvgrf+Wv8PTpU0ajEZ/97Gf59V//df78n//zAPxn/9l/hpSSv/yX/zJ1XfMrv/Ir/Jf/5X+5+XylFP/gH/wD/sbf+Bt8/etfp9fr8Vf/6l/lP/gP/oMf7k/1rH5+SsBmh+tBIEmTlLOzc1555Rf4vde/RVVVPHjwgD/1p/4UTWM4OzvnM595Ba01e7t7XEXQ6+ecnj3h0ZPHqCgjSjK8lCAFRbmmKCp2tncD38RaPvroPlIqymodVgo6NBqXl+d475lOp2gleenFlxgMB6xWK6I4Cm6X5XIj1vVedEwZzXK54tGjJx09FuqqJc1SlFLdwenxPkwS1usS4T3j8YjzswuemhOKsmB7e5vFcoZ1BussbdPSNP9/9v4r1tLtPM9EnxH+OPPKtSrtqtqJOzGKEiVbskJLraZPN1qCz8WR7QPDwEEDOmgHwBcGDF/YF/KVfdHmhY8h2MYxDgy44Yu2WlZoBSqSkjZFimmTO1dcOczwp/GPMc7F+Oes2hRJMYjk3uT6iIldrLVqrbnmnGuO7/++931eF0i2XuIdRGlEWZcIPIPBgLpLGq7qGqkVSgla41YJy8PhoMtF6uyzBCjf8ko3joOl1RhDmuYrJ9cSUrZ0qZRlGZKNqwbTWJwV5Hl/9ZjKzoI7GW9QLjyL+TllUTAYTmjqkP3jvcL5QMI1tkXgiTpqbt00RJGnLCt+bT3FOXj+tGBzUVNFil/raX5tFANBKNvaFtPWDAYh2ffo5ISirEJytvRhYmQeAu9EAON0PJZwn5umoGkCVK6qqs6ZUwOCLO11Dp8G54JwNjznjkG/j3UNRTGnqgxSxiueiO3Q+fgwn0/iBE8ImXTekSQxUobvD8Etl0cxTRMEss55FvOC+WxOmqYMBhOKoiZA/TRlEVLC0zQhinTnigrCWtt6qqoMUxQdhwRn52mUwhhDURT0+32EgNOzQ/LeGqPRGof3j6jKhueffy9VXfLyy59lc3stiH1VxPPPvZfWCB7cPyGOKrY2Imazc/rZADwPQyFhNQW1rcW2F9OUi/ra6utqVn7xF3/xq348TVM+8pGP8JGPfOQrfs7169f55V/+5a/n217URYUSIiDdpUZKzfb2Lv08QK2+8IUv8MEPfnBlyX3ppZfY2txiPB6zt3+XxtQMBhM2N3aZTmfcuftawON7KKqG9Y0tTNsyr0riKKZtSjbWN2hMyc72JYqi7OzEE05Oj4K9t58hkPR7A9584zZlUXOwf9jZUl3Iamk9Reup6pLJeI35fN65RTLqpqapDVEEAo2SUJU1vd4A27bMpmchVM8vCbeCmzcfY29vL6QQAxDWC6YJAuEsi3HehWC5fp/F+QzhBF5KnA+oe8fyUPU0jeHs7KzDyDuEFAiCS6m1luGwz2DQZ7EoaE2Hzld0SchyFQUQwGMBTY8LzZltw5TBduwRGYeww/0H5xSLsE7q9wW3bj3GSy99lvncoLTGdjEAeT/Di5Cu7G1ITxZ40jSjcY7/Yz3n99f6ZEXNkXMcWMsg7yHxON8ig5UM8JycHGPqFu8EQopO/6E6+F9obqI4xCv47jUWJk4BoKb6Gikgz7Nu9W2o6wohNFIFjkrbWlxrV1wThO2w+g3eO9zKyRWsyDrSXVPkEUpQVFUIhsSTZVlIizYOL0BIyJKUs7NznLOdJXsQ4giEJk0CsO9gekivnyMI+VvgsdZ0sQgW01Z4UprG0DSC0ahH09QUhel0T3D//h55HizT88Upvf4GCMeLL36CmzdvMRquE8UpdVUz6Oc0teH4cMq7n/sgv/f7v0+cp2xtb4EMvBmt4y9LqdVah0yki7qor6Eu5m4X9Y4qT+BaLImgzz//bt58800mkwlCCJ577jlCdk/DUrCxvrVJWRsGwwlbO7uM1zZQUUJZ10RxihARRVFzenZOkkRIBdY1nM9OWFubsL29zWQSsol6vV5go6QpTdNy//4+r7zyOqen0w4QJgGFs/Dcs89zfh6YQE8+8eQKj15VNXVVd3oGiXOeyWTSMSck89mcOE5QKmI+X+A9qzH5Zz7zacqyoKwKXCfstNbjnECgKKuKYlGEA9SHFNsoCmyUPM8IDtGlbsKvOBfBMr5MSA6HeGsC/XQ2m62amyWiHVhpVuJOWwGBv7QUKy+1OoFH4snzHmVhONxfsFg0pKnG+pJXXn0J5wSxHqJVRpKm7F69hMPQ6yfoGFrXrVwctC0URUPTOKZRyt08ZxoHuFtZFSyKReCzdJqRYAufUzc1+LAKk539u+1ypsLEw6FkIPm2pqUsQ9pyEicdrC9DKsHaWkjgXjZCzlmctyu7s5RihewP9u8AuYPw2IamIKzmgp4nPD+mDesg6xyNMRRFGSzptmWxmKF1SN7uD/rdCtECkmLRMJtWXL1yi8uXH6M1MJ9XFIs6NJguuIkee+w6AKYxKxu1tQ7nQ5pzVRsW84qN9R2kCOyYopxh/YKnnrpJnMQURcVkvMn21uUuTbrh+vUbvPHGHZyTPPH4M8xnBZ/73OeYzc5Ck9i9xpavmdWtywS6WP9c1NdSF6+Si/ra65t1432TN9GN55Mo5srly1hjSZOMW7ee4Pz8HCkkly9f5saNG2xtbVHXFRDC/J54/Cmq2nTIc8+txx+n3x/QWo/SMaZ1DEcTdBTR6/cYDHvM5ucgHDuXdnDe8fjjtzg+PubSpUt45zpei8E5ODk+w7ZQLCpAEkcpt2/fxRjD+voag8GAsixXZNxlRg2ENUtZlSshaxRHzOdzNjc3Vxbvre2t4FqKY4qyDFfGddO5pFTI6fF0FmmDkiG3J8uCjiLv5YEL4kNQ4TJUb7li01qtnuPlWqq1lrKsKIoCY9oQcBjH6CiIS5dNyTJHZ5kB9CjBtmmaFWXVWU+a9YGYSMcoLegPEqztPkfmlFXbIf1b4kSRZhEOC8KR5RmD/qjjgCikCI3QfFYGK7CX9Hq9FSsnZPkQohQ21gOThABmc+6hPXSZoaeUIu2otcvYBIEgTTOU0ii1zEwq2dhYJ+9lpFnSNSmPcoOi1YtWddwZ5x4mWwdYn+2ew/D6kUqho4iqrqmbBgSYtkUouTKyLr/WMsl5aVEPCH/Hq6++TlUaoigh70i5IQagIY411rX0ejmmNezu7qK1CvDDwSDcZy+YjNe7lV+Lc9DLM6pqjmkr1tYmq1DEXj7k6tWbIBS379yl1x9weHhCYyxHRydorfnEiy/yyU998ssytB4Nz/vSOI6Lgr8sy/NbH9tvtW36W1sXzcpFfU31nbZOL2/SB8bHMM/ZnIy5dmmXx67e4NLOZWbTBf18wPbmNloqIqXRUpPqjFjFmKqhNZbt7W2eeOIp1jY2sNYR6YhrV69z68YTVEWLkjFSKPqDPoVZsHd8j/3jPV55/VVmiwV1bdA6aCZcl/1z5dpVsiwPq5W6IssiFtNjdjfXGGQJ0juKxSJwXXRE6zzGOKrKMJ/PaZqKKJa0tqFpKpQSFOWUvBdjXcX9B3cRSmIsWB9jXIT1CpQMSbnKgTfEWpHGMbHSKBG8VA5PWXfAMCS+9VjvULEiShWOBqUlIFBECCK8UEQ6RfoIU3q0SFAyxnmxmpg0jVmF9tmO6iqlIiQB18SJ7LQhqsvKCY1MnHp0Ehw0ea8PQmKdp6gWWGeQSnB8eISSmpOTEMTY1BZTW5rS4q1AKoWTHustUgucsdRF3YUJalrjmU7nXVMiqMqG1hisaVBCoESYgHkUUkc4KQKLRkNtaopyERoYCc7WCG8xTdvZax0IixAOfBscQUAU6c4V1BDFgjjWXcZPgxcSj8ABXnqSLAGlaFqLaaGpW7x1aBWRpTl4iZYRWka41uNbSTGfB0twBsOBZtBPSCKFs6YDvonOHm2J4tDoKQVp2ieKM157/U02NjfZ2Fwj7yf0him7VzfZ3t1gNBkglaSsK2bzOY6QvlyUJQ/u7rO/dx8hG964/XlmizM2t65w5fJzDAZXSLIxi7LCa4uThtoajJXcfPw5tIw4PjzCti0SAc6D8ysp2qProbeDPfai3r510axc1NdU3+km5S03D5FSTIZDnnn6aa5du871a49xcBDEr71eD2MMZVGEdUAUMxwMGQ/HnByfcP36dT7xiU9QlTX9Xo/WtMF1oxM2NrZRKuLKlWuYxlDVFcdnx5jWcH/vPhubGyFtt9NVGNtgXUtVlQD0ejneOxazKdOzE9YnQ67s7vDKy18g0po0y1A6wljHcl20vr5OmoYJg2krnGspqwXWGuJYMxoP6fWDvVbKiLI0tC04BKa1OP9w1K+UJNIRggBTq7s1gjEGrSNMY7CtQ0hFUVYBXueDsDTSMXGUUFchhFBIFTDxNoQiGmNpbVhRLFcnbdtSluUjqdiKkMkT4Z3oMnPkas2C97S2Jk1jirKgaYJ2xXlL05REWnXrm4cNUJrmaBUFofIjTBuPR6jAr0nimDROqDoLuFKa4XDI5uYWSslO9FyDDwJZIYPLSojQ+EglaZ2lbmqyLEVHUXi1eU+kgxC5quvAtBECCKnYAXgH1lmE9HjvyHshxHJtbQ0hugmQJWiupMB5R6/fI00z6HQiwXquSJMUKdTq+QpUXY934THVOiLPsvAam8+7yIiENE1obRs0USrkO50cn1DVDVVV8+DBAcY43njzDtPpgsPDI6QI2prj4+MOzx8Hx04cB+G3VlRVxXQ65/DwgJe+8GecT4/Z2lqnqhqOj6e8//0/yHi8zVPveg4hFTu7uzx28wZRmjAYjvjBH/wh5vM5J8cnOPcI+M257jF862roe77+Mt4gv9Gv/zavi2blot6RtQzpW47ed3Z2GA6HCCHIspQnnniCKI6B8CYohWD38mV2d3c5O5uSJBlCSCaTNa5evc7R4TE6Snjve94HXjDoDxGEdUia5qyvraO1Znp+Tq+XU9cVxoRsk6ZpOj7HAiEEw8EgcF4uX8bawPhYX1/Hect8PqVtQ7OzmBe4bsLiPQyHQ9bWJmxurpNmQQfyzDPPcOnSpRWOfClkhRCeF0cRcfdzLimg3vuV+yKEIkrG46DpKasyRAXICOElrfFEKiGKEoQnpCEXBbLTE4SsmhpEwPcLwUpnoJRasWCiKMIYE7QqVqJUSl0HcaW1IYPIe9th9xWLxbx7rhJ6eU4cR0RxyBeadZqd1li8C+F8tju0W2NWicRCCOjWN8vIgrQLI2zbgOzf3w8riDjW3YEcVm2CkIwc+De+y/IJB6hSwUFjjKVuTEiDbmrqqulExt3ap4tKAMFkMiaONXkvBFc65zg/n6FkBCy/h0dKQaQVZ2enzOdTPA5rzeo+13W9ui0bwiRJiXRCsaipSkNde5raE0UZrXFdY+exXZBh23qm0wLvNcKHgMO9BwdY6ykWNYt5xdHhGU3jOT8r2d87ZjotcI4gpLaW6fmMumrI0gwhHGW14OjokCyLeekLnyOKFBsba2RJxsZkC+E0e/cOmZ0teO5dzzHIe5imRmnFE088sVoNLgXZYTXxbXu7uKjvgnpHNysXe87v3VpeiSmtuHXrFltbm6xNJjgbbLej4bA7uDoRpHcdQXbAyck5j12/ydpkkyhKaa1HqIjr1x8j7w9wCB7sH6DjhDTLuXf3Hm1rybKMtfV1yrIgTWOkEkRxtMoFEkKuwgOjOGIxn5PnKa++9gpt25DEceemkSi9dGsEaJoQgn5/QL+f09qGQb9P09S8/PLLHB4ecn5+TpIkK1jbMlV3mS+0vCo1pqGsylX2kXOeOI7xhCmAc2G6IbzAOwFeU1ctbd3SmhbvWoQI9tm6LnGuJUlizs9OO9BYwO9771gm+goBdV0FlLz3eC8xTZgGeB9yeHq9kCQtZEjNXob1RVFMlqdI6cM6S0KehbDAum6IkxQQZGmOtUGsusxxcs51dFiPdRYPK5Gy98GarbSkbkqcN/R6KVmWdiC7wAdZ6lqkBB2FpmcxL2iNx1lojSOOw30IjYqnrhqaOvBtoniZYixRSiCEJ4ribiok8V7hnQ7W8u458Hi0VisxbhCaihWbpa5roiiIg5dp5CqOUVFC6xTTeY0XMYuiQegY09pVoGKW9YJ7zAoG/RF13SJQZFmOklGg5RpPsWioCktZtOzvnXJ+tsBZz9raGmmShN8dL6mbhiSNkNLR2oqz82PuP7jL2fSY8+kJe/t7rK1tcnx0zlNPPkc/H1KWFVppDg8PaI1hY2O903n5EHfQWeIvBilv73pU7/J2OGff8c3KRX3vlvd+hTeHMM5//Y03KIoSBAGExnLEHPJu8rzHpZ3LrK9v8thjjzNe28R6iNOMe3v7DEYTNjYvIXWCdbBYVIxGa4Dg7PQs2Fd7GUJ64lgTRzGTyXiVHh1yiAINFRF0DIPBgNa2NKZGiHAVr7u03SjSOBfG9/v7+5yfz2hNCCLM8yCGtNYyGPQpy5Isz4Ew3RBSdFA5u8qkaZogopUdNAzCyN22tvt3CiUVMiBD8dYhvcA0hjxLUdITxxKpwgGuVACmSQXeB6GrjhVKC9q2QUdBZyOkJ06iboLRCXe7n997F8TOwiO756JtW/I8Yzo95fT0GCE9xpSdOyespoSQlEW5mmRIGZEkycpdtBTSRjog8ZUMDWBYZSRorYgijbU1SkNtShaLQEytipq6MsFu7Gy3oqDjnVhA4b1CShWyf7zvmCrLtY2jLKtOaKw5OzvFWtfpmTSTyVpY41i/SqgO9zmskKJYMxz2AY/WspuisOLWLDkvS5fQfLGgNi3T2QJjPI1xKB2TdDTj0WjQrf3qkElV1cxmC4wJvJkliRhEF+7pMSYg/1sDrfGAoqpKZvNZ97oJwZx1U7BzaRMhHY0pOTx8wBtvvMIbb77K8fE+Z6eHbKxPGA2H9PNAib529Tp79x8wm88RIjxPaZaRpillGVamYVp40bFc1NdW7+hm5aK+t2u5BhCEiUKWpezs7HSOFBM0DZ2SL4TSecbjCfv7B2RpnzhOuXrlOlnW48qV63ivyNI+V65cYzpdgFB4Bycnp1RVTRzHTKdTlonEUaQpq5L5fM50eo61ltlszt7+HlJKGlNzcnJC3ktp24alQzNJwqRDKodSnsViStsalNIUi5rxaIP5LIzMd7Z36HWpv6tcGynDdEaqoIFwYRICkOd5WP0gwsHVUX+ttd3qzKG1QnXNiPAOY0KqsZKefj8iTYNFdmkpFUKgVfhewRETHu/AK/EkScg8iiLdaWccUlmkbLEu6FMaU9O2QYwqhWQ0miA69ktIKpasrY27r912q6MUIVTnkrJhddMh8ofDIQIR8pIIDp+mCYCysAILTdN4PGQ4GpCkMf1+vpqsWOtRKgoYfBmaMx0FPYxzQdDbmjBFCcRhF/QnUmNMIN9KKUJgZFVgraOqWkzjwCtmswWHBwcIGcIolZJdHlGYLkWRxnSxB1KKju0iSdOsW90FpH+SJCEmwRnSNFByl9A6HSkWiymjUY/W1jjfgHDEiSLPM6qqomlqIq3oD3KyPMG0DeA7cnFoWJxTCBFouG1r0Up3wumYPM/Z3Jwwm52yvj5iNjtlbW3I8ckh9++/ydnZHp/40z/k7OyQPEs7q3fK2mSNH/zBH2J6Pl052pQKadRZlnVTP3txwXlRX3NdNCsX9c6uMFxhOBySJCmTyZg0TTuHjQlri6Vtr6OTJknK66+/yXi0BkKwvrHJeLLGZG2dwXDIcDQiy3tcuXqN0WhCWVRoHREnCR63coosA+7Kslwd2rrD24eDosF5y+c/9zlm02m4Co/1an2iNSSZBGGpmwBZa2rL3oNjmjqISxeLBWVRMJ1OOTk+Bjo9io5Cvk+akqZpl3OjqOuatjUMh8MVv0MI0Wk8AAJ/JYkkWkKSaLI0JooEw1FGHDmktCRJhLUmHOBaIhSA66ZBAf2+nLzEsQZCiq/3Fqlb0gyUdiBamrbqhKiCugksEQiOkKDvaImicJiHdYrCtjZYeqUCAnzOeY9pzCqhWwiBJwQwIlitx8J6qGV/f5+6Ken3c6T01HUJwtOaFhBUVU1rLIPBIKQap2HdIwiE4SROEUKvnlspI5ylY/w48rxH2xoQAts6bCsRaIqiIs/yDl3vUVrgsV1T4hAEq3hVlUgRmk2pJGVRBBDbfN5RjgMHx9oWpGNezjHOopOI1jvmxQwvHdP5KaYNVFrvW6SCLE8QXVBleG2FFVsUKaIu36jXyzv9TkSxaJjPF4Fmu9KXtNRVzfb2JrPZOScnR0zWxjSmQojwdW/feYU3b3+BT37y4zRNQb+XIbyg3xtwefcKW1tbb+GoLJuWRwNML+qivpb6rmhW/vxO7ZsBelzUX359KwEsgPer/BvvCc6QJKEoCsqVnkRRNzUPHjzg1q1bVFUQaWZpj3c9/Rzj0RqPXb9BmuZcuXyNx289gW09/f4QpTWNMcFpE0cIyeqqsNfLVyN87z3OhtdiawxpktLUdXBv4GlbS6RjXMd6SdN0peMYj0YkSYaUGu8ldW3IsgzTNt3h7ch7A3zoYXA2pCPbLlNICEFrW9I0RUcB3mVMILC2rXvIHREESqpv0VoSx1EQ3pYFjalAegbDnLbTxiy1IN4H7YvwkMRRSBz2LrhkqpI0TanrOugRulWRkA6tl9MYAUhaY1GyS1luDMPhiH5vgPdhzeY9ZGnO5tYOrbXhwZFhPaO/hDzrXIC4LRsqqcLKrzV25SSyraVpmpXexbYOT8dUicNEqFjMiXX3NQRkaYIgaGvwAikj6iqs1wJTRtEaSxQl4fWhQphj3TSAxDkoq4okS0jSwJNRKsBcwgrGMZ8X4AVJmoTpktKITrQcRTHehzVQ29ogbia81rIswXtH21aU5Zy1yYAs00hpsTbg+rUOUxvnHOWipCwq5rM5RTFnMMwQyuF8S1UHW7WQAh0rjKlp2hpE0ACZNsQCNI1lPq9Ikj69fMB8FlxcxhjSLGZnZ5Pj0yM+8ckX8T40irPZDAEMBw+b5qUw+yEg7kK38vasr3QWfmfPz++KZuWi3gn1rWtYJCHpFx84LAJBL8uZdAehkhHO+6C90KAU3Lh5gzTN2Fzf4smbT/H4Y0+yu7nLMB/ijGMyHJPohGvXbpJmfVpr8VJg2obJ2hihFcPRiH4vJU1jkjgOQlZvwXuUFPR7PeIoJc/7DAZjvJc0jaVtATSRzikWhtaAc4LFfAEElL21FucNu7uXuqYD6jKg960NjZP0ohPFWto2gMSs9zStxQtFY6B1AqljZJSgohipYmrT4pVDxZrWeRrjsV5yfHJG1u/jnEfLAAkTMnyO8wK8xFsPzhFpQawlsRZIHLOzc/Iko5f2kDKmqGpq09I6R5QkoDTWS6wPmT9BnxEhvKZtYHf7Gt5IXA1V0XByeoYFWhzGGZJYEqmgEwrxAx6PoO2aJalaPCHlWUhF2zqSJAvcFRkR6Yw8H9I0Hq1ipPBo5WnrAqxBWBeoK9IhZct4mCG8xbWOpnK0zXIlJvFOoHXC+dmCXq/PZDLBY7G+xrga41qMdbTWU5ma8/kZxhmUjoiTDNNCVbW0NoRyeiDr5eE1UJRIrSnKGhWF160xFtOAUjGj4ZBISSIJSQSDXOPaBVoGKJ7SOefzEqk0WkbEsoe0fU6PKjY3NohSkIlBpZ7GtkRphkotXlUkfYlOIcrCc6STlCwfc/fNU6Qfc3LYsphq7t87ZzatQjJ46/EqYXv3Cm/evsv5bMpg1OeNN16hbgqWB9jSpfZ2EWte1NdQbzN78zu6Wbnw5V/UitNA9/u0/PMj3AbnwuQhiiLSOGY2nRJHEWVZ8olPfIKjwyPe++73cHp6hkCw92CfK5evcvXqtY7FMuT07JzRcMRgMKIoqpXewbSWLM8QClpbE6caj0HqACxz3nP79h2KogpcDykxjcE5S1mUxHGCs0G8WRT1KoVZ65CKfHR0yPn5GXGiQTikgjiOiOOoQ8Y/vGKNIo0nQNCWVNWwRhGdwDKILIM1NriG6FZS3nlGowlta5nO5kQd5VaIoMuAMGlIkzTYjDvxbtDBCAaDQec86hpDoUK2jQuTgbC6ARBEcYyKwrRqUZYsyqITLwd4XFkGHRBApBUSQZIkxEnU6R+6LJ/OLh1eCLI7EB2yE6cWxQKlNefnU4piEYLzbNBPFEWBbVuctUgkpgluqLqqOiZM+LrOe/Cic26J1feGYO0+Ojzm9PSMfr/fBRAGgWzSrQyjSHe27aAdWpJ/tY6654pO67TA+bCCiaIIpcQqOFHrCKU0i1nB6fEpxjQoJcmzjEG/z2Q8ZjAYoFVEVYWJVdW5s8AH3o1UzBcL0jTGtYYkilkUCxpjiHTMeDzBmJbxaIIxjqaxHB+ecXBwwuHhKc5KsnTI66/dZTFvKAtDpFM2Nrbo9fqMJ2tMZ1P29h6QJimz+YzT07PVz3jxXv0OqC9pTMSX3L7c53w7G5l3dLNyURf1F9WSsRLsuiCFII5iZtMZxWLBpZ1LfO7zn+Xs/Iwnn3ySV155jZOTM1555TXW1zaJ44SNjY0uq0dz8+bjIUVXxBjjieKMvDdACBhN+jSmoDdMyfsJ09k5t+89YDCakOW9IPp1Qew76PfJ8qCtyfMeTd1SFCW7u1dIkgD5CiJIi9aCspoDZmXxNaZeQcjC1atDSLpGQq90B977zjVkQ85MG9YZRVFj26Dx8D6se5RUzOcF3getTHBZERqFLv/HEzQfQXugEWgEiv39o04v064Sg5Mk7pD1YW3lO8uq8x4voLUhOdq2lrKuyPo9fMdN0VIRS02iIyLdNUedoNqLcHXey3LSOCWJU+IkQ8fJ6o01IO4dg0G/sxQLmqZm0O+tGjljWqbTGaYJ67LGtOgoaFSqqgI80Qp2J5DSh8NX+C5bJ+T94MPbqNIKj+3E3Halz8iytHssLNa2RJFeMVdCtlLUIf99l1pddqseAwQHlXdBr2NMS1M1CC+oq4q7d+7RNiZk+1RNcDdZ162MgiW+qisQAolAOtjd3qEuS9rG4L1nbbwOThDrhEjFtI1jc/0STWV5cO+Qsmg5O5vzYz/633H16g2cFWidUpUG72U3UfT88A//CHfv3uPX/6//izTJuX///qrRvaiL+mbrolm5qO/qCsTbsLqwbYuSitOTE0aDARtr61y9cpkkiVksFpgmkGzX1zaYzwqiKOU973kvRVGytbWNc5Dnfcajje6NWtHvj5E6om4bqqZkfXNM3gtXlrOixKE4PZ8TJSFFVyqFVpLp+TlVXXZ015Ca7KygWJSA6HJ2gmZmOBoEgaQi2JxnM5x1nWNEISXESUSaxljbdpbicJ9UpKmbhrppkFqDFKg4gMICAj9oFuI4WK8H/RGRjkPScOc+Wk6lpJTY1mJa000fPG3rMU3IaNra3KJp6pBNpFRghERxt9LyiC4/qGmD3sGL4F4SSjKfz0P2UDd16OU5sdaYqobWUpcVtrXIWIVpkJSU8wJnLFVZBwqvUNguVFAIaK1ZWZWF8CwWc0xriOO3ToWs9dR1i7Mu8EW6n7dpGmwn2g2NmlvlCC1zp7xfBgKGpOQoihACmqZexSasAHuEfytV0BeB75rRwINJ09BseR9CJ8OkJlidtQzC7bZpiaOY0XCMqQ11WVGUNd6xAty5RxgzSosA4hMCJQTXLl8hi2PSJKXX7xNF8crt1tSG6fmcSKUdMTcB/5AH9KlPfZbZdEEcp1Rlwxtv3Obw4JD5vMBZ2N7aYTAYcevm40ynczY3trrJHqvVz8UK6O1bX8pVebut7C6alYv67i4fbK04T11WpEnCoNenl/U6bDlcu3ad23duU1YVk8ka/f6QSztXePzWk/R6/SBC7CBq5aIm0imtgd1L1/jg93+ItnVkWUaW52xsbbEoSloLUdLDWE+c9vAIxpNJd2UdoaSg6Q4807T0+yO2t3dJ4qwj0Up2tq8iRdStkKIVWwXCYedxIYuGsDZaXsm3bYtUQbsxL4oOmBKEqk4ERD8+YPHjJFz1R1FEVYWrc9t6kiTu7KWOxWJBURQsU5alDJMK2zqq0lCWJgh628B7yTueRhQFx1JAxgcYHUIgZAjns87RtCag7n3giZi2DZZo5zFVQyw1p0chW6Z1AQDnujfQqqxoG8vpyZTZrKRqutWNs4AjjiKsaxHC0+vnpFnyMLARupTlmDjK0CrBdoC9LE1XNGDVNRm2ay68t51TJ3xu0zRBwKz0ym2jtOy0NeHnDkJdRxSpLvXad/bxIOrNsqQT6VqkEmgd4gnyPAQlRpFGeIm3DoEMTUpVk6U5edbHWU9Z1uR5b2ULruuKre0N1tbG1E1FFGkW8zknh0coJKqbNkZxRBwplAdvHWcnZ7jWMTudE6uYPAuxAFJIjo6OuklJANnVdcNwOOaLX3yZjY1NpNJsbW5jreOxGzcIazPfMWrcamV7URf1jdRFs3JR39W1BHE5Zzk+PmJna5urV68yn89wraWX53zms5/mpc9/niRJuHLlGtZ6nnnmOSbjNXa2LzEcDpFS8573vI/hcMJotMa1azd48/ZdDo+Omc8XqEgznc24ffs+i0VLXQuMEd1kZcrJ6Rlxkq7SfuM4WuWwKBVRlQ1Z1idJs26Vojk7WTCbVhSLml7eRymNFBFC6I6BkeG87Rw9QXviXGCZOAKUrbVtt55Y2rbjIKDVUaCdWt8xVQJQrjUOJfXK3RSYJA95GEv6aLAJO8qiBq9o6pbTkxOquqIxZoXef/R5QATtTGttcFdpjXUO611Iml4sqJvgfqrKEuk9w7zPeDjCmqAv8QKiODRuCgWtRwpNWdZ4HxxBcRyalCjWxHFEv1v9xHFMVZWYjmmjdac7MpaqCs6f1rSde8XT7/dX9z+KwuRKiDANWYLqhJDgQwNpuwYycFeCVVupsOZBeJSWGNOs+CmyI9eatgmwQEK2FIBp687erlcsoX5/iLcerTTz6Zy6qknihDRJGQ7H9PI+SRz0MWVVcHR0xNHxIVEU0pe9ddSLgmI6QwnZTb1a6mqBMRW9PGEyGjKfTTk42KdYLLrsKIeOFGdnJ3jvuHJllzSJ0Vpy//49tNKcnp6hpKbf67O5sRnWnVKuSMpLR9BFXdQ3WhfNykV9V5eUAmc95+dT8KCVZDDoY63ljTde55Of+iQf+MAHANjfOyDPcm7dfJyd7V3a1rG394CyKHn22Wf54Pd9kO2tHS7vXuXZZ55nPJpw9+49FmWJtYF8WpYtRWGxbURVO4TS6DgBIamqKtiVAdu2xHFEmmYsFgtGozUEKhw+aUpZVFSlZTGviaOctg0Y+3AVnwSuhw3E0rIsV6sG6yxxEiGkJEkT4iTBtEFwuRS7BqCcJooSlFYUxWIlVJVSo3RErzvgjTGhwWjbbt1hVyLQ5YENAq0joji4VZYTnsWiQHaUUqUUqpsMeTwogXWWra0tXKd/UUqR5Vn4nlozGY2oyhJ8EPdKHey94Eh0hG1NsBYLiVQRrXXdz+G7Rslj25ayKjGmIU2TlRjZOdet0oLVWKuw9pkvFoGKG0XMZmFttArak+C860BynYhZKJYhjXVd4VyIZVjyUVprEDLolIxpVg2K1oo4iVA6IP6XTU2YWgXdTmubMI0ByqKkKorQ3EpNa1qssQz7A2wbcpNOTk+o63rVlDV1RRyH7xFHEWmcdEnkEtu2VHVFFEesrY2JO+uyx3LjxnWE8LStwXu7atKUljhneP75Z9na3kDrAHnr9fq89trrfOLFT6CjmH5/yGAwpFgUnJ6cYpqmm6rIt9Va4aLeWaW/03fgm6tHvd3iLb8IF9PGr7e+lW8i37k3qO6CvstvUdS1QQrF1cvX+K3f+E3+xv/9bwRGyJOO87Mz8I5bN2+itaJta8p5w2NXb3Hzxk0SnfPcs+/lpZdeYjKZcO/+A1565c9I05jxeMDhwT44iY0l1giGwwlZlrG3d5/xZIj1lryfUZUFsY47DYvEASenp/y1H/1x2rbi9OyEoiwZjCY888yTvPLaS5ycHlAUs26loHFO4J1FqZjGWLT2eCzOeaqqRjlPU9V45/B0B7P3wZ0SS6SHqgqHX+3CaiaOYoSSWNtgnUGrCGsFUmhq05ImikgrWusxxjPoDzipZx1bJaxltM5QUqNUmGYlaYoQLW0rKMuGpjIgJEkaAY6jo1OKeUMSpR3ptgLhaJ1gXoRAwKoxxHHaHeAWJSXT6ZQ861HXBtuJQ6uqQicRSku0Fjhfh+cfSa835OjwjLYNh2YU6UDF7VYmHotvw6qiKkviWGHTCGddiBNQEVVZdz+bprUWY1uU1hjfoggMnuXEJEy5LKbppmcy0HfTJLB1vLOBwaIkTSMp6xLrFUpnJGmKbQ1aKdqmxjnoDTLA42xokJRMyPtDDg9POTubodOW2nhaG/D9zkZ4oWitoapqvGtx0pL1eohKkEqHV47WW7SKGQxy4lhT1zWjccyNW5t4K+n1+zjrmC8aJuNNzmclt25cpazOOD6+y4P7t3niyWfQyvMnL36MjfU1nnvu3Xgfc3p8gk4iqqaBzq0muoTvt5R4uzYw3z2HyFvPxq/v5/r6Gsyv9Lnf/HP8Dm9WHq0/9xvwHbkX7+x6u75pfOPlu3VImmXdwRmukjfXt7hx/XHeeO0O3//BD/LEzYS8l9NL0zB6BzbG62gSnrr5HI8//nhwTqyvURUhs+V97/0A9/depW5E0MIMBhyWJzSmRakUqSLe977v4/Bwj8OjPfYP7lHXBXkvwXqDUIp5McdawWA45Pl3P88rr3yR3nCAxZP3B+zsXuH+/h73HuwhRIq1FW6JzifQZJ1tmc1LtA4uGB3F5FlOVVYkkaaX9zg5OQbX4NogJM2yAYvWYIxFqoBhT7tAoLJY0M/TcFA3Du8kvWyAtw6vPErGNKbF2WDBllISxYplkGOkY4yp0TKQSo1tcV6CEkgngXCfpQhp09YIZKRpbU2WS4QMLBQhNOW8Yr6oA2beWeJYYgkynEVZBCeOVyghiVSEF4KyahDCkSaKpgk6j7ax4OXKcUOHyw/3X4TG0cswLUgSBB0or63JspwojnBOoFQQsFrvSdI4OJsUCBX0KiEsMli1q6rF2pYkSVeTr9YEKrAQXaAkgcmjIw1CUJYV/X4ewgO9D5ob7/G0tFZ0P0cQ3TpXBft7njEYB0v9YrEgjmK07GOtAKlIkwxjWgb9GGNDs4jzTAY5MpZILzk7P2EyzhGixvs5Ozs9pNSsTzYoy5qmFqGpKhwnxw94/OZlZrPrnJ6XSNFS11M8La++/hIf+L73M5+V9AYDZBQhI43zrjPVP7z55e/n27K+O86PJd/ma/3cR+vrnoJ9tYfsL+HhvFgDXdT3RMmODhpQ38GO+yM/8iNcu3qNLMuYTCaBhqof9u9xHPP8cy9QVRVra2tMxhOkkLzrXe/i/Pyce/cesHv5KnjJ7qWrFIsGISK0jjFNQOFPJmPqpubs7GzFLGkag2nCeiVJYoSENI159ZWXSdOEd7/wbsajMZcvX8YYw9raGr28x2I+DyuPTvi6dNwgws8XaKesQvNs26K7VOHwuRHOQV2bDjUfLMi+Sz9umoaiLHEuuJKkUKtE59YakjQBCHRTE75G0mUWLVcrZVmt3D9Al02jg33Zue7vXRd8qImTiCxPsM6Q91LyXkKaxkRxxPl0ymKxIEmywKFZVG/hPkjp0ZFESAsiWLyX4YZhDSQ67Y2gKEqiKHBQohXzpWPwSNGh8SVlGVD5y/ydXi/oVkxjaE37SGKweCS80q04MtPpdLVGWj4PVRUs48s4AK3UyrnTtp1T6pH/LRObi6JckWI3NibBvi48WZ6Q5ynet3gss9kU0xi8C4yZSEfkWYaSquPJCJTMODstuHv3gJOTGcugyLZt2NpeC4nUeWi4t7a2sS1MzxfM5nNaazg4PODk5JjReMj9Bw+YTNaZTDaIo5gnn3ycOI7Ispg33niV//Yrv4SOBNPpCWmSEHVcHu8fnYRf1EV9fXXRrFzU90wtrzKWkLjNzU2eeuopyrJa2U6ttavP994zGo/Y2NgIYsbusN7b22M6nbK7u8vTTz5DFPXwLmI03OCJx9+FlBFaK6azKV/84kucHB9RFEWn9YjBQ78/CJOatQmjUY8sj/niy5/n6OiIxaJkY2OL09NTPv/5l9jf28M5R5ploSmxdiVYDAdA0DmECABWDcZSczKfzwOQTiiiKAEkdR3sqM65FTukaRoW8xKlEhAaL2RHcGmpm5CrUzcNxoQmyNoW7zxxEncHsEIpGXgrxrC9vR3uh3eBAOw9Sgl6/ZQ8TxDCUtclSkFrG+JY4b2lP+hR1yGbJs/7ZFkPrWOiKMF5EMIxWRuRpjHQcu36ZXYvb+Fcg1/ZdtUKva9VzGJedYd/s9LUBIdVWEuEJlaSZz2m0xAsGUUx49EEgVw1L0tQ3rLxXL6mrLWUZbFqfAPXpVmJk+M4Wb22QjwCK4eM7BxeQgT9j2lMaFuE7HKKHAjPoJ+TZhFKhUynvJeSphGmqWmqirZpaKqapqppm2BrDs2PZDqraRqBtRGHh1OMAdO6Lu37AdaGpsiYlv29I46OTjg7W3Dv3j7T2YzWtRR1wfHJMePxhMnaVlj5Kc1w0CeJNU88cYtnnnmK2eyUw8P7WFdTFIvV795FXdQ3U+/oZuWr+cG/kmf8Sz/37e4tv6hvrh4l2S6fV9FNIvI8RylFnuchyK6zfS7Le48UkmeffZayKlfulqOjI65evUrTGObzkl4+ZHPzEhvrl1jMS2wbLLaz2TkHB/sdlySQSIMAVFJXDW1rOD09prUNZ2dHNKbi8PCQ0WjM0089y9UrVzk+PqYxhjTLgv6kywFa/tda201aPFKGNYQxhrquVnA45zwChbPhv1olCC9RKurEnQJnw3QljhOcEzgXVjqNaWitAeEDF0Y9tEgHKm7UwcoMTdMEfooPwX9nZ2eUZccjcW4lojVtCHg0rUHKAC0TAiaTMW1rmc8WNHWYPjWNoVgU3X0PAY5SauI4YX19He8db775Og8e3CVJ9SNhgbILlGyoK0OeD4l0l2Bs3WrSEYSzQbciusfR+zB9ao1jOp0FAa6OVk0g/uFraWVtdnYVsDibBR1PeF09nOgtGx3ThS0GIB9dYGHLfL7AWkccJZychsdOKU2aZcxm551jKEwnojhMzHQUHE9BZC1Jk4S6qpBCYpoGnF+lVjfGUVaWXm/MdFZSlA3OC8qqWt331rScn0/JswFHhydY52laQxQrEJ6iLGidZzor+dCHfpT1tS0iHfHC8y+ghOT89Iwrly9RlQvKYtGtxdzF++p3uFYi8Xdw0/iOblYu6qK+kQpujvDSl1KSpiHa/mEa7PITg/tjyQxZ8kKSJOHSpUuMx2OOj485ODjgD//gY4xGE/b3D7o9vCNONOfTM8bjIWmaMBj00TrqmgpPlqW0tsF5Q6+f4rH89//9TzEcjhmPJ916IXBBptNp+Krd34kA7Fitg6JIr+5jURTAMtlZde6QCu8F3ksG/VHIy+mAbTrSSNXttr3EtlBWhtZ6hBI4LJP1MTpWWG9X3zeO4/BvO4fNcqpR1zVA535KOuuq7oSnGtO0NJ3Q2TkfNCFRzHA4IklyyqLFO7VqpsJkxuBxNE2L95KyqDg9PVvh86UMSH4heMRho1lf3wzQtypMDR5OROQqoXmJz4fQlKRpRpKEqUHbIfrDCs114Y5u5Z4KryexYuM451Y//8PAvjBl0VqTpmn3fR9GQIRARk1dNwSGiSRJUgaDYfiaNvzcVR2aN+c8VRnge8v7i/eYuiFJkiBAPj+nl+dEWoGwSM2KjXJ2PmexqDFtgOGZxoXAwrolSbMuwTyhl+ekSUKvn5PlGUkWk/VzyqomywY88cSztAZsCzdvPM762gZ5nvPqK6/x+uuvk2UpZVmupnxKytXjfFHfvnqnNynLumhWLup7uh49UL5cLa+GlVKcnZ3Rti15npNnOY9dv05VFXzwgx/k+77vg8xms45Z4ohiQFjyPOXk5BjTNpycnOIcqymIlMFG7VyL0pLJZMxiMefGjZu85z3vY21tnSeeeIKiKKirKogSuyvU5UpiuYJo23YFMgv6gNBULaFkQSMhmE3nLBYVxjhs66Bjr0RRhG09ZdlgjAOCUBcBk7UxyMDNiJMkQN2WU6rOlswjb4hKKYwx3SEebLVtF17YthaBxrQeKSPSJFuJaV9//TZN7XFWE+mMJMkBQZYl9PspUQSLRcFiVjE9L5jPCkCRpjnj8YSiKPA8fGysdRSLEiE0VRVszoH3wipK4GHAXrifi0XB8fEJVVWvbNlRFNO2djU1kt3P+nANx8N1DQ/XcMsJU9CmmBUJd4nvF0JQVWX4t539efk9hZAIIZnN5kihGAzGZGkP71Wg7ToZgIGLmjQJYmitNXVVsbW1xWAwwDsfxNi+RCcOqT1ShVVg3ViqKjipmsZzeHiOEJpIx8RxRN5LufX4Y+g4sGGSNLwWRuMRG5tbNI0jjQe89z0fwFQW6TW7O1eQQrG5vsmtG7dYG00695r/rjgsL+o7W99FbqCLuqivv75WDPhwOKQoCvb399ne2ibLM9oHLUfHh5yenhLHKe973/v5/Eufpm48zhlaUzOdna+mG3EcdwF9kqKsUMqzvjFZEUz39u4hJBwfHTMarnF6dsrm5iZJEocxPT64UZxAa4GQCmtZWXWNqTsQWownrCUC0dYRRzF1HWBztnXdAWwQgoDv9w5BEH4CZHkP54MepWoCr0NIjWsD9RQXiL4hZ0fgvQAZ7LqttaQyRiqFI0wjvOsisRHEcYpUGts2lK6hKhskgvm0CBMUpzu3jOyCFyuiOPBVvAMvJW3riaKss5gb6qpFCBUIuLpbtRB0PEqG/BrXTYXCY2JWYmpnHaLL51HKB45J62isWa28rHXdfzv3k4hWomIhAkVYCLmawhljOvT+Q+rvMidHqcAnqaqKqqqJk4ThYEhRlqG5s4HVUhULpFAsFiV9rTGNRcoIa4IlWinBYlGQ2CiIgrvGzjnHZDwmy3Jefe010iTCeYNE0TYO23ik0gGEpxowjjxLOD46I0kV/X5OXdckccZwOKBqS8q6QmnRCasrGmMxjWPQn7A2vsrl3WscHh7ynhfex+/97u/j2i+in024fOVGcFrxkAZ8URf1jdR31avn69Gc/EWf9/bUsYT1wrfm9nb4+b4z9VWv+lxwyyydFqfHx+GAn0555ZVX8N5z7fp1ptMpb7zx2kqjIIQKOH9rMFXDoD+ibT1NE7Jx8l6PLB9QFDVKK+qmYDTO+eSn/pjPfu5T/O7v/jZ3793BOktZlTg8cZLghSCKQ2JvCM3zJEmE95ZeP2DunbMIKZgvFgitEEpgXIMXligVqMijVHeQq4i6tpRFg1JxcJLkKZEWeGuxxlJXDVVdU1YLrGhw3lPVBmMNKhbI2IGyoEAoDTKiNBbj4HQ6pWlb6CBqgW9SI4SkadpOM+KQyq+mMDoS6EiQpAprGwJfBAS6ixUIP3doLsIkYrEoscajvA6U1842jfB40eJFS5anQLAtG2NxFpqm7aZdQQjsEJS1oXUC00oaIygrg9QRUSRBWIT0q5WGlGGVhVf4zsMcGiVHWZW0zmLall6/h5B0uH1CAxmnKBURRwlKeibDHt4GqF1VVSRpRpRmTOcldWlpWvBIpI5ASRprkLHCeUVReJraIhWcT49xNJycHFGWNb0sY20yIkkUcSrwqg33BYFEY51kUTYcHp+hdMrJ6ZzzacnZeUlRlEBLHGvyvEee9TjYP0GiSdKcq7s3uXHtacb9TXa3r3Pz+tM0lSeN+gx6425tFRo66/78+0zI7vrW3L65t7Tvnvfar/Uce/udd2+t76pm5aIu6putRxuXTpaLkkGsOD0/Z21tjXt37/Lmm2/Sy3PWJmtMp1Pe/e4X2NvbI0kSQDAajRgNh6RJzPraOk8/9TTr65voKMILyHoZSZJy5fJVDg+P8N7TmIbXXnuN4XDAZG3MX/mrf4Wqqdna3uLmzRurYMHGhImI944o1mGiEmmSZDm5AefC5znnQ/ZO25JmcQehswjpECK4VJRUCGSwBEuPtQ2xVsSRBg/Oevq9PrpLC25MS1U3XcPU0toaqYNQVekI6z2t89QmMEbyXg+lVTddMDSmpixKWuOwdpmZ40mzNHBitKTfzxiN+8RJyNgJKy6F0hqxCnW0AejWva9qrQODxAULcGCRhFVNf9AL7Z21IVagDUJk3QmF8Z4ki4hTyXgy6AIAw/dZ/tn5tku9Dq+LkHjNSkBrO6GsdcFSHCeKpgmU2KCTCc6fOI6o67qLJFDdysjifUtrG8qyCEJg56iqmn5/QN00gfXiPUJJWm/J+j2yfg9jHYtZhbWOXp6hlOfk5BDrLFJAL8upq3KVWxTFGtPUYXUkFU1jaIwJ6d+NoTEteW+AB6azGZGOGAx6AZTnarIsvOYWiwU3b95ifW0TITT9fMigP+KH/+pf49KlK8FuXhRopVcTprf8rn0Lbxf11no7Nh9fb33XNitv9y7xot4ZtdQiRFHExsYGOzs7DIdDRqMRw9EQ58PEBcJrLqwBYoxpwyg9Sej1cqbTGdevX6fX69GalqIsKMuS+/fvkSYZAkW/N2TQH5PnfTbWN/FOsLO9y2OP3WJ9fQNY8lQMtlu/hDWMwnvY2NiiacIBmGc9+l2ujkSihCKNU6yxaKkwpsFjUdrjhUXHgjTTmLbE+5a6bjBNuxIgl2WFEJI4TmiaGoFYOY2WuHgpJc6GA1ICrrUoqdFSg/PgLKapqOuaum5WuhHTGhaLBU3T0Ov1AGjblvl8vppUee+DDoaHmUVRFHcC44di43Dwu84mLPAO5vMFVVlTVzXO2ZVQVkix0t8ICd4btnfGXL6ygVCGpl3gqcOfzaLTrDzUptC1a1GkUVoRRYIk0XjfAm3A7EtHVS06N5judCrV6mssV0y+Y7XEcfiZzs/PKcsS7z29Xg9jGuq6WuUWORciCtI0o9/vM56MUEoxmy9oGkPA5VdI5bskbEvTtCAEcZQEgF/TApIkTgM4rzV4PJcubTEa98jykDlljKcxNYOhJkpqTs7v8ebtL/La66+smvulyDqKIt773veys7PD9WvXGQ6GK90XfomFu6iL+vrru7ZZ+dK6aF4u6i+qP7/6A0SwKs/ncw4ODlZOoCzLONg/oK5qvvDFL/Diiy/ykz/5kwz6A7Is6BaWdXh4yHA4ZDabUddNF4qXgXA0xjCdztje3sU7zc/+zN/gf/of/2eGwzG7l67yvve+nyuXrxLHacjakUG4GUUBbrc8+Kz1vPT5LzLojxAovIPWWHCgZYRWEYt5iRRBs7LEwUsFWRajlCeKBf1BijEhb6gsSwSKLO9hre+C70JzpKPoIacEAtK9CyTUWpFlGcPBkDTOUEIHlD/hwAqH/rKx8G/J61km9EopMcZ09l6/EqYuHTzWWqqqDOLd1j7i5PJEcdyJjwkQuCgJguJVU6C65orQ+NkW7x1NY7h79x4vv/wqrXGdCFqhVdRRYxUChei0NOFn74i4OKwzON+CcCRJRNvWxLFG6bCOyvOcpmlWt9B4tqtGRkpJWZZvcZ/JLlpAKUmaJngsUgaRdppmVFXNbL5gtpgznc8xxrK2vsHaxgZIsM6wt78ffgbClKysQtPTNCY8JkKQ5TlSKayzgalSLKhNxfrGJt4p6qrCuorN7SFRbCiqM7Jc431oVL13K5F1HMcMh0Pm8znr62vfNW6Ud2J9N5113zPNyl9GXTQ733u1PEDPTs/o9XqrAyWO426iEK6Mb968SZqm5L0c5xzTjr7qnOeFF17Ae9d9zBInMc5ZtFbkedalLrfk2ZDRaIPDwxNOT6bcuHGLnZ0rBNBb1OlRwmG7PMyWXJWgn4jQOqGqWspFxfRsxsH+Md6Cs1AVNVsbW+AFi8U8AMucJ441UkHTlAFn70xnNQ5pzWtrGysWyBKqFxqTeNUkOGdDJpFUDPo5eZphaoN3YI1DiW6txHLC8NDJtGxWlpOiwWAQcn46EmzvkfRjYHUFH5wzS0JsZ0P2bhUB4H3QpNgWTOOoqgYdKdbWJgwG/dX9DlMVT9t6tMwpC4drNa6NwCcs5i3OapwFEGEVtTp8PUJ2+oYuh6rX6+F9yKOytgU8xWLBdDoNcL8u9sEYs2L7BBtyEPyWZchE6vf73YRo+T1C4rMxBrynKIIjaj5bUBY1prGcnEw5O51TlgF+l/dCplBRlkipyLKcumpWdmzvBQ6BlwrjPBboDyfUrWM6rzg8Oqc1AojwTlEWFcPhgLKc8oUvfoa6axiXz+fyudzb2wtNo1Ir+/3Fu+ZFfTN10axc1EV9lVrC4/qDPtPplJOTE87Pz8nznOlsyv7+Ppubm+R5TpomqxRkpRQf+tCHSJKY2WzOD3z/h8izLHxekjKZTFhfnwAQ6Zg06XF59xq2FWiVkiQZrQHvoN8bMpvO3gKEs9YSdYyT5cETmBwwmy7Yf3BILBOkl8zP59jG4g3cu32Pcl7ivexsqsnK9qy15tKlbaQM6544TiiLkrt37gaui5AdqyUcO86G4MQ4DtAxgKJY0DY1VTHHNoat9U36WY9hr493Ld7ajhMTGsHlpCU0XoFRsmw+lgnP89kMYNUcLnklywnWkhK7tCAb06ww+ErpLkqgXTVZUj20dwf2SwgeDKRaTRLnCBGBV5jGIUSElDHW+g4oZ1ecFe/tiv66THCu62Wzp4milNY46rpZPX/LKYq1trvvrJKal5j+YDcPP2t/0CeOQ6PnvacoSuraUBYVznmKsqFqPI0RCJkxWxiMAesFQinqxpAm6eo5VUp3SH+xSoeuqgqQWCs5PDzDthGmkUgyBv0JebrOaHgFazKcjVjf2GAwSKiaxVt1Xt0UZW1tjbZtkUKG/KQL+/J3tC6gcBd1Ud9F9egv9PJmjKGu6pWW4s0332QwGNC2LYP+gCRNME3D9evXqaqa6XS6Wmks9SSXdi8RxRqtw60oCmazKScnpxTFAik1zgquXb3JF77wClevXucHvv8Hmc3mvPTSy5yeniNEmOAkSYK1LW378EBerlzwgjzvA8EhMxqN8S4csE1lsNbT1EGnENwrEmcFEJKE4zjm4OCoY64EUFnTGIqixNlgP06T0CCEKUBoXIJFWnZNiCDucmC01iRxilIhzVcrTRTp1cEVx8GS3bZt9/V897jZFcdEdmwX6Jg3Swpsl020nHwppej1eqv74JfcEqGRQqO6VZgQgrJY0DT16ntGUXAZ9Qc9hsOMvBeBMESxQEhLHAuUChqTJUBOqShoPHxweEgl8V7SGo93kqoMdGNjHPiQy7RsTpYsnCVMTimJjsLkbDwerz6+bFyqssTa4CgCTxzHHS24JU1zkjhHqxQpU87PK5zVnJ4uqCpDYyy9Xp+qqjk8OOLk5JQkTjqYHqGp0ooojnFeMJ9XnJ9XeB+ztfUY1irms5Kmlly7/ByHe4bd7SeoK0OcCJqmXFmyl8+ZEILhcBgIuhd1UX9J9T3brPxFOH7v6W4P1z7eLzNCxTIytPtYlx+6+vNX/K7f5O2ivt1lOueI1po8z9ne3ibPc6IoYjKZ0Ov12dzaYmtri4ODgy5ILyKOY15+5RXqumHvwR6/+Zu/yfn5OVpr1tbWqMqaQX+AFJr1tU3e//7vYzSa8K6nn2E2mzEcDbCt4/TklFu3blHX9eq1qLWmMUFvIKQkTTNOTk7x3nN4eBQmE8ZyenpOVTYoGRD1QawaATIc4CpiPi9YzEta46kqQ1k0eBcagkUX/uddONQEkta0wSZsPUqHEX+Y8oS1VJzEeO+p65pLl3bJshxjWkxrHgZJQvjlEsGpFMcRdJm8WZaTpFmXtVOGA1tKrLMrbckyB0kIj/OWPM9W3zO4n8JEItK603dY4jjpnDeh4aFzBokOzx+mGQXWVQhliWKPjixStTiqTngcJjVL7YyUEhl2SEgEUgbwXrBgg0BRFg1ChL+vyqqbHjUrIXTQ+gRC8XLq4pwlSWLu379PsSioyhrvBE1t6PUGIV8KhXeBRjxZW6c/GOHROCdZFIaz8zlKJ2R5ztbWDhsbWzz99DO0xuG6JsVDB6Nr8bCa8igZs1gYfviv/gQbGzuY1tPLJowGOzz9xHuJ1IAnbj3N8XF4rRljAkeHruEHBv0wiWyNWUH0cN+B97Fv9i33W3bz39ztW3VOeP8l9/Hr/Lm+hfU926x8tQrXSr67gXXhz3b15InuRudyCKRIIR4SLb9yXTQsb/daNad4jk9OGE/C1W6/32d7e3t1hdy2LQJBlubkWX9FAFVKU1c1cZwhdUrW6/Pu97yb46MDNtYnXNraZmt9i+vXnmJ78zpZOqIsmxXZ1JiGvb09NtbXuHXzBnduv8nrr72KVhJ8cNuAxDiB84raWHavXEEAcSTRKriYtA45P0IohsMhAM614D1pEmObFlO1tJXH1opqJmgWGm8TKmPwQtJagVIpwkcIL/HWYVuoKot1HusMOpYIFZD41jlUnCC0YlpMMa5GaI/QEi8FHoFvLWmsydKIXi8iTlVgtGiJ9Z6iqmk9GGuZFwvm5QKhBSiPVgIpPHEkiVNQyiG0xwmwwHCyBlIQJYooAaEMbVvgfQMebCsoi4a6bnCu7bgtgqoyOBcmI+PxhCSJQHiiWK3WSU44VCTxwmOtQQlF263XgpumQQjXNZYCgUbJBNOAcwqkxnmBVBGNsTTG0lpPYyyzRYnFM1ucsb4VmCjDfh+JpimhKgWt0ZgayqLBWcHZ2YLbb+4zHk3wvkVLSS/rIbwAJ2mbFi0VWZqQxD3Gw0sMh9s01uGkxUmHE5BEOd46pPAo6ej1IrT0KCF49pn3kmbrjCfbPPOu57h54yaP33ica5du0I8mtFXL+ekZSgDWoRAID2mccO3yFVIdI61H+iBPFt9GN9C30hr9zd38N3X7Vp0PS+aNJNyEX97Xt4dd/KJZ+ZIK13eP3Hx4g8R5tAzpto8+MVIKrA3q+ZCW+t1b3wvMg+UKAaA1LVEn8lzSYJernNlsxqc//Wm0juj3+3zu85/n3r17q9H+cDjqbL8xr7/xGtPplLW1ddKkh7OCyWiDH/jgD7G7e5XJZJ0bj90iTQPU7ZVXvsjmxhpSCUbjIX/2Z5/CmAZnbZfxs6SpBmz/lStXaJqG4XBAUzdsbmwihGRjfYMkSWiaZrWeWqHlu3VEmLZoBIqqbFZYegjhht6DFGqlp0jiBIUkjmKUCGRYASip0Ep36cuSONZY23Dn7huU1bwDwYU31OXjCJ7FYs5wOECqIFxW3ZpMSkmSpmzrmMeMpVfVJJ0wVSAoyyLoRGTIbxKCjgMTfqYsT7tEZUcca/zKshzEysvn+qE7yXdaFEtd1QyHI5QS3bRDgQhZS867bm2kVw1r21pwIZ9o+Xlaq2CNFiHkcMnIAVbY/eVryYfrnY5qrCnLko3NDQSCqmjAC+I4wzQt3gviOOk4LZa2tezt7dE0Bc43NE1J3ssZj4fkecbBwSEHBwesr62zt3cAXYK00goVqdV9U0qTpRnb29tkacZkbR0hFJub2wyH6yRJjyTJ2d7aYT5dkCd93vPC+1mbbFCVZad5eaTR957hcIhW6iGkjW/fe8J3viH51ty+1fWormX5u/Z2uW8XuP0vKfGl3algpch3ToAPo9/lG6+1wYHwu7/7u3zf972fydrGivtwUe+8WjoaAJqmYTQeAUEvscx5WTpWojhepff+yZ/8Mb1en42NDZJUM1+c0xrDg717aK35zd/8DeI4413veoZXX3mNfq/Pn/3ZZ+j1Bjx4cICUipOTE7RahHVRFJGmES+99Bke3L8XsmU6/HvgqoiAoPdw585tLm3v0Ov1OT+bMp1OgbBSyLKMoihWrpsVYr4LQFyuHqqqehhIiCfSmsa1YZLSNTZ13ZDnMf1BjmlawpqjwnmD8ILhcETbOqqqQEqw3f21zhBFqnPxKNoIrA2OqTSNqeu602mElGiBQDWGvz5veeFsTu48Cyn49NzwWxsDfKTQIkJF4cBvzhfoOKyYjo+PEHimrkGJwCLp5T3K0iCblnYZRtiJdZ0LjBUpBZEIwLmT0xM21je6N+3l48TKjeXcQ2bKsmkBiOMIqSDPQ1ihbduucYnQkcb5aNW0tG0QtQZXkyTVGXGkSBPJ+vqEpqoRItiuW+sxTctwOFw1Fvv7h2itQzgjFb1+eJ5VpIhjSa+XMpufhWZSSpTWnJyc0LZBUyJVsJfXVdlNlwApybMhw+GELB1x7dpjNI1lY327sy9bnnj8XeTJXSaTdUajAQpNkVYcHBxy5cqV1e/KoxlWy9+rZb2TRZ7f7fXlUurfLnVxqn6ZEnhCCIlbjd5ee/VVfuejH+2aFMvZ2Vn3JkvnXDAcHh5wsa5559ayEVn+Ocsy8ix/y8ettQgEi/mCPM9JkpThcEAURWxvb3NwcMDjjz/OfDbn6OiwE8K2aB2xvraOaSyPP/40mxuXuHb1BlJE9PIB29s7bG/vkOc5g8EgHLrS8Su/+sucnh1jbXCfDOqWa5VhYn1HsbXEccz5+ZQoSrhy5Sr9/miVP7ME2i0nRr1e7y0C4qVANYoiHnvsseDMsZbW2G6y4mmNpSxDjk1V1cSRwtmGNI7ppTlplHJp+xLSC2KpyLIUhGPn0haXr1wiLFUD0wXxMESwqYPItWnq7v5BWVYYY/nw1PDXTma03nM/0jgEP3w054f3zijL4IKBcCGR9zKsDY9zlmWd2DXEE0RxhDEtTV2jdEh+XsLXlFJEccSotezOK8Y2TH2kJAQMCvDeEeiyDy3Ry3o0G2iZD+Tc0qUVYhGUUnhnaY1ZNSrLoMe1tbUVa6VtHVonlGVNsSiZzRbY1oWmVMfM5wvquubs7JyqCrk9cZTgrEdHmrCyCnEAeR5jnaWX9xFCceXyle69SrK2thZ0T13OUZ5ntK6lPxiQxCl13VIsaq5eeQy8JNIJu7tXOT05D1lCaITQxFFGpFPiOIiCl2nSy8fkK9VFo/J2rofPzdvxebpoVr6kvKdLzu1GYYAUIYTr937v99jf38N7x6/92q9w+86bOG85OTnGWsuTTz5xEdb1Dq9H10DLKcPygCrLktu3byOkoKxKrl29yvr6Ordv32Fn5xLHx8dM1tbCwdKBv6IoIY5SxuMJZ+dTlNK88PwLHB8fMxyOeeLxp3jssVtoHZFlQbg7Ho+4f/8OD/busShmWNeiTcOHD875e3dP+H+9ecj/evuQ/+loSto1UP1+n6eefIqmCQJYISRHR0f0ej2cc7RtuwLVJUnSgenCWmV5eL7++uvhQfCsrL5BHBrgaqYxzGeBxtrrJbTGUNeGPO1h2+AUajobsneeg4ND9vb2iOOE4XCEs47WtMFJNejT6zgizoXDM8sylFRMrOPd04IDIThSkhrPSaw51or3zms2vEZJtVrDDIcDrly9gnUtjz/xRLDLOkdrTNCp2BbTweWECLA5pRSp9/zfjuf8r3dP+V/unfC/vLrPT90/pSdEeMy1QirRCXvtyolkrV2lSi+TrYX0eGfx3lKWRbf36OIQ2qYLjQyP9XKtqLWm3+93hFdFEmdolVBWDaenU8rSMByMaVvf2Y0D42Y+L5AysGq0jkmTDCV154qSFGVBFMVsbGwjiNnbO2B/f4/JJKQgi65hdM6F+Adn2dnZ4dq1G6yvbaNVzt6DY6z1DPojsiwny3K0jlgsFhSLkul0xnIBIGXQRC1/Tx5dI1xwqd5ZFXS1b8/n7Js6Wf/Fv/gXCCH4+3//76/+rqoqfv7nf5719XX6/T4/+7M/y/7+/lv+3e3bt/nwhz9MnudsbW3xj/7RP1rZ394OdXZ6yitf/CI4G96AnGX30g7XrlzGtQHyFMUR+/t7CAFlWbC1tQ5i+UQ/fMLfjk/6RX35+lLbMjwcYzdNs6KLOvvwyrGqKi5d2uGFF55nfX2d2XTKZz7zaTbW13n2mefp90b0eiN6eWgmbt26RZrFIMKhM1nb5Mknnu7Wi4qzszNu3LjBtevXODzc54svv4S1hg/PG37keI4TgoMsASX5sdM5/8NZQZqmfOADH2B7e5vzsymRjomimDzPWSwWqyveJe12e3sbYwxpmgKsrviXVmixQtyL4K7pwvrC/4e6LrGtQUpBuSg4OT7l3t0HTM9n1FVFXTUY4zCN5fxsBj6g3XV3v5a245CXE5w1rW27ZGTPutD0PMw6PY3SEXiYKklmPf0ufLBtbZdG7ZhMxkgJs+mUy5cvdxMbQ9MEvkmSJCGQ8pHfyR87mvLDxzO8FGF6IwQ/elLwU2c1aZog5MPcHyEErWlWj9XSVr2MA5BKoaPgjjJtg5SColiQJPHKog2sGkalFEdHR8xmsxWf5ejoBKUiNje2QtNS1Ggd84H3fx/DwZBer8+lS5cxTdtNzQIQ7/T0PNikmxAEubmxTZb2SOI+G2u7XZPjOD8/4z3veTfr6xurbKQkiVcuqzTp0e+PeeyxW9y69STnZ3OyLOP8/Ixr16+Cd2xubiBEyJVaOrOWMRNBf+P/Qp7Hl0MEfK1X8u90Vsi3u74Zvso38vx8K+sbblb++I//mH/zb/4NL7zwwlv+/h/8g3/Af/2v/5X//J//Mx/96Ee5f/8+P/MzP7P6uLWWD3/4wzRNwx/8wR/wH/7Df+Df//t/zz/9p//0G/8pvub6i9XTAs/egz3+6I/+iE984hN89jOfQXV6haeefJIo0ljXcu3aFa5evUJdh8Oqqip+5Vd+maapLxqV75IKT9/DX9SlAFEqyXA4pG5qXnjhed544w0+85nP8uqrrzKfL8KaIYk5P59jGs9kvM6P/MiP8tRTT3Pr5k1OT4+5fu0KTz7xNFevXOPZZ59n0B/gvWd3d5eNjXXG4yE7O1v0ehkbUvDM0TlHSjDv59go4lBLTmLF91UtI2O4eu0q83kBSIwJVuv5fL5C1i/txVmWdYLMhrOzs9XUZXnAh7WE7aYpYUXRNCZMK7ombQlSq+sKEFRVQxzFncBcUpU1cZxQFCVV1bBYlDSNpWksWdbrVlHhMW6aJrBWTNBweOcp0pRCSvqPrBOEEAytYyEEsygEA2qlkUqQ5zn3798jjmNM265CE5WSxEm8aiqWeUrWWvp1w/sXDYdKcKgkVivOs5gjLXj2ZM5WR8eVki5cUa/ynpYNzPLxWU4UQl6PIo4jsiwl72UIAVGkV4d5FAVb+3g8Xq3njDGMx2M2N7dJ4pSybMizHqPRmMW84GMf+ziLouTs7IzDg6BvUkqvHm/vBKbpmkkLDx7s09SW+/cOuHnzKSbjdZQMk6ujo2Mev3Vrteacz+esra1RliXn51Oeefp5qtLwxONP8cwzz7EoCvb27jMeD0EEoN5wNKDXzxEyWMeXouYvd6B9swfe2+3AvKjvXH1Dzcp8Pufnfu7n+Lf/9t8ymUxWf39+fs4v/uIv8i//5b/kx37sx3j/+9/Pv/t3/44/+IM/4GMf+xgAv/Zrv8bnPvc5/uN//I+85z3v4ad/+qf55//8n/ORj3yE5itAhOo6wLYevYXyK53Bw+Yg7L69gxBCx0ObsYe3xnSHILIlH2XJUTEd7CnLM7I8777ewx05XrA2WevSURXn0zM8ngcPHnRrvy9piDpv/JeyWy4amu98PcrJefS5ErByfjnnAm/FBhdHURQopXj81pMIGQWnRtty8+Ytnnv+eXZ2drl27SZCaoqypKwqxpMJ/f6AL3zhC0zGa2xsbLJYzLly5TKDQZ/pdMbBwQHj0VJvknVrlzn6bEbStCwi1WkoHBLBVEBmWj7w2C0WswVVVXJ6dkpVVZRlQWsNzjs8HiGDk6UoC6JEk6QxG8Jz3TSMbMvxyTEIMB1TJkxTlmF7EqkUbccP8R6iOFBQlYqJoxzvFVk+wFqxgqK5FqqiwVmoy4amNrTG0u8PiOO4EwuLlY7Eu8CQqXoZnxrmbBjLWtOSOM9661g3lj/tp9SDPlJFq9/zk+Nj5rNz2rZmd3ebpqnQOqJtPUVRU5QmYPetQyCxrWNoHKm1zFVw+njhEVIwixRZaxl7gVQCLzwqkgF3L0AIyWg06gi4fpWy7bumr6rrrtkLr6PFYkZrG6QS6DgKje5oSJzEgCdNE9I0YW004dLWDu977/cxO190MD3FophzaXcbgWQ2XTCfl6RptppKJUlCFMdkWYAFIqBpQuDi+dmc7c3LXLp0jcUiTGDu3HmTKJIoCYmOkGEcjFaKne1NLu/usHtph9YYykXB2njM5Z1tPv6Hv8/J0QE4g3OGulogBSgpV78jX0qp/XonKF/6kYvm5KvUV3Qv+694C0u7r2x7/tps0X/B53wLqRvfULPy8z//83z4wx/mJ37iJ97y9y+++CLGmLf8/dNPP821a9f4wz/8QwD+8A//kOeff57t7e3V5/zUT/0U0+mUz372s1/2+/3CL/wCo9Fodbt69Srw8KARgg4Q1RFSXItE4lqIpAyR8V52Ytlls/Lo7a2PZpTFbF7aQShFWVdYPNZ5RuM1ikW4muwPhl28fcz62honJ8dsbm6gtUTgQvKsWL44vvR7feP1TrTNfSfv8190U529dXmTkm7E3a0Au+fLurByWCbhhqtpQb+/xk/85E8TJxmHx8dM1jZ417MvMJtXCKWI85jT81P+26/8N5RS9HsDYp1x/cotNtbWOT06om1qklgTa413DuFhdjbjU3/6aUzVsohiCikZtA6c615X0G88563j1vs+wCf+5BN89KMfDa87JfCypbE1Qgm8CC9G6y2NrdC25mcWBf/vewf8/P4xP3/nAR8+OiVyLUJJvBAYE9ayYWjg8c5hW0+kM7KsT123mNaiVITzkvm84fyswrYaJWK0CFyWXtZHOBESnxtLMS+AgJeXUqwiA+IkDlMQ4fCu4nd3hvzueh8tPLumRTrHb096/MowpaoDXC3k/jiwjiTSjAY9Tk72mM7OOmhdimkFrZU0rcC00LYeUJzpiIWU9J1FKIFQIKSg11hKrXjQ1jgJKI9XHq9cZwMPOp22dV0Q4DICQRB1+U3Aqllsmirg+K1BRpK6rTk5O8F6y2g0pFws2N7cYO/ufXpxRjmr2N26TDGvEALWN0a8973PAYos7aNkFPQpDqIo5umnnyaOFToCpQVRFCY7ZVkyHk948cVPoFXGZLyNVpokVpTFGYkW9NKEteGEJEqxTcvx0QG3b7/KtWu77O/dD2sz43nPsy+wMRrz2he/wOnRPjiDNeE9Nvx6eKQQAf72DbxfrP5N9zWWt6/6b75CI/TdPoERPOSgfOWb/yq3wLf+cre/iOMixPKspZuMvvXjf9H9+mbr6/4a/+k//Sc+8YlP8Au/8At/7mNBTBdGnI/W9vY2e3t7q895tFFZfnz5sS9X//gf/2POz89Xtzt37gDhKkcIgZAO0y5ALJgXe1g/x4sSoVqssyuxGxCmLI88ZQKJEN1T7D1HR8c88eRTbG9vk6RJt8cP1uWmrqmqgtY2PHhwl9FoyHw+wxjDdDrl+eef59sJPbqob76WaPdHk3+NCVkvi2JB1aUPLzUrRVGsViebm5vsXrrES59/CYGgl/d5/NbjVGXN+dk5WinW19cIeTUtv/3bH+Xy7hXu3L7D9evXmEwmfPHlL67WMfv7+5ydnVGWJScnx9y9cxeAEwGfXRuwZixrxhJ7z6hqWDMtH48V//6X/mvnFDkLFmUhmUwmjB+ZALAUCnv4iaMpP3I8RUSaB1GEF4IfO5/zU2eL1ecvcfpLkeQSAV9VNWVZPcy26R4/69qQ0iwEVRWaiaXleTlBHBrLtaohnc+7tOMAW0uShKZuSJK4Y5t4Zrbhv27k/G9XJ/x/rk74366M+eWtEW0UBL/hDTMcc1IpBoMB733Pu1lfX+OZZ57Ge9tZjCMQAqkkURx1AZJhKnQ7idiuWzZaSwKMyoqN1vGZ9T7nOpBpIx0Iur28T57nKCUoijnWGoQI9uxl+nJr285hZVcTUyHDa6sxhtn5jDhOGfSHCC9W65zl42yt443XX+f4+ASlNLu7u0SR5uVXXmZtMglTXREmv5PJGCFFEEnL8O8n4wlKacbjCd2Qmfl8xkuff4k7d+6Q5Tmz2Yzj4yPW1tYYDkcIqciyIf3+GIHi05/+DEeHh+hIUhRzolgzGo744Pd9kM3NLZIk5bHHbrC+vs6F6/Givp31dXFW7ty5w9/7e3+PX//1X19dQXw7KkmSbtz61gr2RYdzNdYt+Ngf/z6f+rMXWV/b4Kd/8n9mNNrAO4XWKd4t+3cJXvJo798hBrDWMpvNWduYsLa2xvn5Oc5Zzs/OGI9GlNWc09Mzbty6wWuvvUJZXiLLIu4/uI+1ljRNaZqGJLnA17xTKjSqfiUuPT4+XmkLsizDeYd1jqIouHPnDicnJ/zQD/0QWZaRpClHJ0f00pzxcESWpSgRIFi3btzi9PyYw6MDDvYOMKbhmXc9y507dzquhWcwGLC7u8ve3h6f+9zneN/73kfTNNR1zenpGZtbmzRNWCH8+iTH2pZ3z0q2CsNcSH5z1OO314Y8Nh53mPZgU7527Rpv3nkljPLLcjUF0lpzYzjgPfeOOVKCUzxOwpEInI33FzUfHVrOtSLuGDKhiVMorRDu0SvXpbNFoRSUlWA0mTCfzxESbGs7QW6Lbgw/9PJdXpgW9BzIkeH35BH/Rw+0h00Z4ZuWGXOUliRphMdB4zkyLUXewzlPliSYadv9nEHUmuc5VV1QFAX37z/g2effzWc/+zmU7jKEGsvDC21P4iw/fb7gfYuKvrfkznO9aJgJwUzA7+8M+O2dPvPZOUpFLHOH6rLGGtHpVxTWtpRlgdIBhKe1wncNnVLQ64WYASlCQ5ZlGfPFnNn5FDmEclFgW9vh6CW9POeLX/wCN2/e4PTsFCEEo9GI4+NjHty/z5NPP09RFngfCLlHx4dEkea1119BEJKeq6pAyYijoxPW19ap65Knn77B+fkZ48lNwGJtTV0Fwm6SxCEWwCueffbd/M7vfBQhNPcf3OPJx8csiilNUxErzfb2Dnt7hxweHvL4rSdWAYXfzVOMi3p71dd1qr744oscHBzwvve9b/V31lp+53d+h3/9r/81v/qrv7oS7j06Xdnf32dnZweAnZ0d/uiP/ugtX3fpFlp+ztdcoqUoz/n9P/wtzs73acyU59/9JA/u7/GHH/9Nrl65xTNPvxfrJIIY8RAk3F0BBSCUwK14CVmeIKRgMpnwyiuvIPDMvzDlBz74QfJeAmLA7TuvMxz28VjOp+ccHx+TJAllWdEOWpKEP7e//Wr11XQrF28G39oKQXRBTPryyy8TRRGXLl1apeouE2qHwyG9Xq9jU+QURUGSpoxHE5566l0YU5NlwQp87cp1fuVXf4WqmZNkMVIq7ty5wwc/8P0cHx+zubHFbDpjd/cKW1tbaK352Mc+hpRhIpKmKWuTCXEcdw6ZhoV3/LftIR/bHNCvW469Ys9ZlBB87nOfY2srZBZVZc1sNl9Nf5aOjaXlVpycklnLfhIhRVgROeeYKcVua1lHMO1Enyt+SPf6XNptRUeFjaKIuqmIo4jRMA8HWxxTVUVwNylFkkT8d4czfnRact+13I8jhkXBXytr3nXkUXJG3wtKLfnUKOdXxxGNDN/LWdcxWfzDCWc39YkiRd2ESZfS4f4eHx9x585tTk6OuygEhfMhKNB7wcgYfuZ4ygfmFftZzD0dca4FO8bw+V7E/3llyKmANFIkPummQ+GmZITphMfOi9AMSdjYWFyF74UAAQAASURBVOf09DS4pwSURcn6xpg8zzg7O++cMgmutfTzPmVRMDuf0poWJRUSyd69PawNr8HDwyP2Dx4QxzGvvfZacBJFEdPpCds76zR1iGLI8rgTP5ekWcLu7i7T6Xmw1ovwddI05ehon8uXr/DMM0/zsY//ATs7OxwdH9DU4fWdJClJkvPkE0/zp3/6SYqi5PjohOpKyWPXb+E9zOcLtre3efbZZ7l39x5FUdLvD8Lrx8N34i3qQuv3vVdf1xrox3/8x/n0pz/NJz/5ydXtAx/4AD/3cz+3+nMURfzGb/zG6t984Qtf4Pbt23zoQx8C4EMf+hCf/vSnOTg4WH3Or//6rzMcDnnmmWe+rjv/Z5/+OOezA3r9mOGwz2g04fab96nKGmMqfu/3P8qbt18jwN0eHvyrRNblPt47pAJPQ5YFhf4yjM6Yhlde/iKf//xncc7w4osf54svf57Ts2OqqiBNEj7+8Y+zsbHB1atXyb4EInbxS/V2r4drjo2NDXZ2dkiShDRNAxDNtrgOb3/p0iXe/e53Y4xhMBiwmM/JkgzbtORJzvHhMYO8z2PXHuOFZ59nOBhibYC2SSl59dXXmKxNGI/HHB4dgSckNycJTz/9NKenp6yvr6O1ZnNrk7quieN4pZMxxlD1cu71UuxkRKTD9CdNU9797vdQFAVt23J+fkav1+scGqE5T5IAYptGEaVSDOxDwbj3noG1FFJy0h08j8Lxlnbd5dQHBHVlOD8/RylAtGS9hDSN2dzc6PR8InA6Gsf7C8OxlkzzBBdrpnlMz8FfmRsiBPciiYojfuRkzo8fFXgvESjaNgh6m8aGIL/GIqUKh3THc1kGKbqQHMibt9+kKAqEgMbUeO9IvOPDhyf8ozsH/I3jKVcaw3rd4KTgOJLsJ5rLRUOkJWkS4W1LpBQSgXCg0bgmNEtKSbIspd8PCc+BVh1eJ8tmznaxCFrrlQDXmJZIR7StxVnP7qXLRCo0ZM45BA/5LVJKimLBbDYLq73WcHxyiJQeIR3jyRApPcZUKA3GNJyfnzOfL0iSnCTJaFuLdS1vvPEqe3v3+djHPkaeZVy6dInHH7/FxuYGs9ksTI3qiqqquH7tOleuXOXq1esIER7/9bV1nIOz03M21jcZDIbcvXsP53zH4flmFW0X9Xapr1f38+3WCX1dzcpgMOC55557y63X67G+vs5zzz3HaDTi7/7dv8s//If/kN/6rd/ixRdf5O/8nb/Dhz70IX7gB34AgJ/8yZ/kmWee4W/9rb/Fpz71KX71V3+Vf/JP/gk///M//2VXPV+tXnv9U/z//tMvcnp6xNNPPcNP/vj/yOWdJ/A25f6DPbyHfq+P7NCZS+ql7FKanGtxvgXR0rYVjSnIMo2zFikU165do2ka+oM+OpLUTUlVlYzGAxpTIYSnKBZkWcr169dZLBZf0dH0jdZbXEMXjc9fej2qqZhMJp3wU9I04QBo6oYsyzg9Pe3YHQ15nnfsjIjp2Tmj4YjXX3udtjGYxnBl9zJPPfkU62vrzGYzHn/8FteuXWNza5MlBffy7i7WBadO0zQ8++yzq4NqNpsFN1DHQsmybIXHD3qQtgOz9SiKgizLGA6Dw2Z7e5urV68RR6E5kTLk9zSNIY5jZrHmk4OMiTFMGkPsYdN6NpznU4Occ63egksPGURulWcjpaSuDdZCnMS0bY2nBSytbQLF2Qd2Ch4m3hPVNXpzLTQyzpI6x9C0eK0plMTFMYdKMssS3n1e0ita5rMS7xTeK5SMcU7irEAIubofSzvxkkhrW8tsNu1yg2pc9zj95OmMHz8v0AKMENQCbtQtNxYV/X4P00vpI+jXBiUFtm2DK0tInPUhwLFzKgkBm5sbrK+vIQScnByTJDGym1AhoCgKFosFw+GQ3cu7oalSmqZqEB2Rdnp+jqkNVVlTLkraDnB3cHDAfL5gsVistFNplmJty717d5kvZmgtkUoQx3oldrxz5y7nZ7OQnF3W3RozJcsjRqNhQOs3Daenp2xsbHDjscfQWnN8fMTZ2Ql//CcfZ31jLeQCTdYZj9c5PDxFypi1yYTDw6PuwktSVXV3sacu3pIu6ttWf+m41X/1r/4Vf/2v/3V+9md/lh/+4R9mZ2eH//Jf/svq40opfumXfgmlFB/60If4m3/zb/K3//bf5p/9s3/2dX+vL7z6SY6O7/Mnf/JH/Lt/9//ljTce8Nj15/mxv/bXef7Z9xLrlM9+7iX29vZXFsnlm7BUHo9FCMsnP/knvPHGK7z00md48RN/xIMH9zk5OWE+n6G1pq4rPvnJP2V6fkZ/kHNyfMTR0QGmNXzs4x8DBPP5nNdfe71rjL41dfG+8K0o3wHQ/OqQfjTzRUrB3t4e+/v7ISxwMAxXER7SNGE8nrC5uUWW5TgHa2tr5HnOs888y3g8oa5rdncvo5Ti3r17HB8dcf36dZI0oSyLleUzSRIuX76MMYaT45MAHROSxWKxAooppTvIGauMmrW1NbIsW00zsyxj/2B/JXbt93sA5Hm+4nr8yjjjdzbGYB3XkQjv+K1Rn19fH67YIY82K0qF4MLldAcCO0QrRZxEIUCxqToXlSeOk456mlEkCW2aIs5mlEWBkIK4teQO9GhIPBqvDrwTHD0vEKdTWgtKJZ2+LAjhnQ0TCqUUtg0k3MlkghAiNG15RhxpRLdG8jjGzvL+ec1pEnGYxDRa4aSklIJLTYusanqtYyFhFkuapibryL7ee5RUQTfUPS5CiFVcwLJhCivCnCXgbqkfiuOYs9Oz7rkKU5PRaESkNbPzKd57+r0+WiuKRZiKBYifQcpAul1iGxbFvGOaQNuJe9MsJcszoihMcLSOiXQImwwTNUfTVByfHHN6esrp6Snz+Zy9vX2Ojo67qUqJjiQPHtxnf3+PBw/2+MynP4uzArzGtp4kSTk9PaWqanq9PoeHR5RlCf5iTX1R3776ppWgv/3bv/2W/5+mKR/5yEf4yEc+8hX/zfXr1/nlX/7lb/ZbY13Lu558jnF/C2vh93//d7ly9RbPvOtZ3vfeH2EyusrLL7/Cnbt3GAxGZGkeRpfKB6FZveClL36Wz3zuk4EOubPL9Ws3KIsZbWSIk5Rrjz3GK6++xNHhfZ568qdw9hKf+cznSNIeBw/2wEkeu34D5zxPPPVUYFSIcDUqpAh8lu732XsPSnR22PCXQsguATd8jsCFN5pHLlmWX0cIgetYIGJ5SeWXuouH1FXXJcCuVlDLz3Phz1I8jGsULO8Pb/37RyYOQgj+XMDjX2r9RUb8rzZq/ksy8fNwD962LbPZjHv37jEcDZnP5rSNYTwak6bJ6r4IQsMynU25fu06n/7Mp8nzPkpFxFHK1sYWV3Yus7uzgylrlFQ433J2fsR0dsp4PKEoCgaDAUVRcOnSpXAgdtC1OAk8jLIyaBXRGoeSMbLDrOtIc3h4xI0bNzg5OQPg8OggUF9VTNPUrK+H9Od+v890eo5HUAnBL63n/MGkT140lGmPQxfWHN4ZpADvJQjZXc0IlJAIpalMRWNrsl6AIyaRJkpimrM5zmqE1CgpMabBC0GzNuaVy4IfOy14ZXqOUTDWGo3nwLZsXX+M4o3XaW3DoLZUkcQMM6R0eExYfYiQqOysR3q1WrMoHZ4r72B9fYv1ySbz+StksWbUH/DgwSEjY0mtZS+JKZ3nnpLcMpZaCiJr2SgbUu/57Y2U4c3rmKNDhsMJtiWwB2SYIAkpcUYgVczjjz/N66+/Tp7PaG3N+sYaVV1xdHhEax3j0YijowOkCIRjQQgSnM8WFEUBHpI4obWOre1N7t17gPWCtrakWYz3FtMapvOS1lgGw5wk9sznc3QdMRgMyPMB4/GYV155LTQoSURVtjjrcQ5c12jmvZQ8Szg5mRLHEbYVbKxv88abr1HXczY2x7StZGf7Ms8/9wJ/8IcfQyrPn37y41y98hhn5ztsrW1R10ELNR6PmYwnNHWLGIqVJurbUl/21/yiWfpW1dcfaPjV3ou/cl7U11rv6CCbsmwRLuZHf+TH2N3d5nx6yGtvfoF7e/f41V/5dax1rK2Pca5m/+AOb955laPjfW7feZ0/efHj/M7v/iZ3777G6ekBu5e2WMxn/NEf/zGfePFFmrpha3uHoqqQUcRkbY3//X//T/ze7/wGB/t3uXJ5hySKuf3GbRbzkhdeeC/zRUG9XAOJ0AgIwQrkReglwAsEEolC+C8hE/iHXvcQnCZwnbjQe9ddzYSY99W+WwYcuPMP98jOeawLllm3FAp2lsrADlpC8Hz3bZd/Xn5v170RfW/keyz3rlKG7JiDgwOccyRxgjEmpCnHMY9GpkshaUxDlqVcvXaVF55/AaU0AsVsvuDKpWtEMub8ZEoxL+n3+oyGIz7/+c+trs4FYmWLvn//PlVVMRwMaZqG+fQ8gL48eCuoS4M1HlNbnn/uBfCBVus9jEajLhAv5O7keY6Qgv2DPeqmRGmB88GCb9uWNWsZNS3nSrHfmNXPJaUHb5EC4kgzMi2784KsKNFKE0cxSkmsb3FYotmc9eMZg8YilUYqhdSCKIlobcva+gbJ//P/wf5f/X601GyVlsI0fHwtZyEFa6ZFt5ZhUTGpDZ8eZ0xjyLKIXi8lioLzRgiQCibWsTsvGHdC57IsKcuK+azgwYM9tJLYtmExm+Gdo0xTKq0Z2ABlfDXLeD1N0Naivadxjo9u9PidS2OOjo6ZL+YURcHe/h5VXZHmKTrReAHWeeI45eT0nNFojdZ6WutABNJtEsfgPc5a4jji9OSkY/lItNL4DpsQxQkIgY5jjHM4QUDiexGAbuHFBUIQJwllGaZpUirKquLsfIpHMJlskuf9cAEig1Op1xvgHZimpVhUrK9tEicJSimaxrK3d8hsWoTwwSzCeYNpHHfvPuDxW0+xubEVXosPbnNydp83br+Mx3H16lVmsylRpOkPeuhIYVrzVRuVr5V19LX++y8FmP1lcJa+E7d3Wr1dpmfvaI/txtolJuNNXn/jNmdnp7RtQ12X/Nqv/Z+sjYbEiSVOIm7evMpkssHeg0O8t7z26v+fvf+KkixJ7zvBn5ld7dpDZ0TqzMrS1V3Volqh0YRWBAkQS4mZhz1z5mFn3/mwLzxnxezzkvuwi1nuLGeGhxwC5DSaaACNbrTW1V1dulKr0OHar77XbB/MIzKrWkARQBdQdk6ejEz3cL9+/V6zv33fX9zi8Gifza0N/GCdx648Q13XvPzil/mVX/lVdOnh+SFSKJRUPPbYY7z52osEQcB4eIDB8NLL3+V9z32ED3/0Q6RZxXe/810Mgg+8/wNUdbUIwbOchKouUcqxfe2TRf/YxI6HWPXipJQOnGTRHGeTgPVmOLb8PrH5rm1OiQCSNCUMwxN3T3MCbqzvhW15/CCU+wDoPAxMtK5OPEj+pgMW4ASwTBcZM6urq2RZxsrKyvc91wDj8Zgsy4iiiF6/d8JpaTabXLt2nV/4+V/mD//wD7lw4QKO49LtLC12p8vkeU6j0UIkAkfZW3E8HtNsNjFac3hkeQJFUVKWxx4e+iQ4bjQacfbsWe7du3fS4gmCgEajgeM0GI2HC3VOdkKmLKdTfj0u+EA2x8tL5kLwYrPB73cihO9S5fYaabsOPzWY8fR4RgOYac2LrZDPrXSZC5BZzi/PMp6ZJER6SqocXu5qvnp6k0mRU1XlghMxINGG3Z/9BH843KdVZOwUY3ZHc37T6/HMcMhykjHDWNCw3kaVNVGjgRQS34+YTWNkXvBTR1OemaVEWhOjeSnO+crpFRKtybKcxx57lNu33qTVbJNmJZ5XMkbynYbPJ8YxlZTMpeHQkSjf44W2x39ciZgHilBAMU/wvGAB/trsJQeLe80GJwahh5S2BdbvL/Hqa4K6sDyTxx57jNFgQhhEpGlGmmS4rm9bSY7DaDhBa2X5PKUmCDwazYhmK+K0u8mtm/eQEqQDoK0jbVWBMPieS1Xpk4wmpWyUwptvvkFZlpa7IjXtdpPxOF5IqgVlaVAyZD6LF609WwkcT8YsLXVw3RClFLNJzOHhAa++9iqPPfYYRVGgtebGjZuc2brAeDLE9RRHRwOkPI/nO2hdoBRooxcGbj8eC9q742/ueEeDlSefeIZZnDCajoiaIR/+yPO8efUWw6MhZ8+eQRvNdDLhG9/4BpcvP8rtW/dRyqcsCp58/Gm6vS7tdhshDEVZsL56i0bYxGm20AYQgl6vy/bOLSaTCbs7u7RbIbNkwu7efQ6OdllGMp/lYCTPP/8hPM9dECVtYJnWNVVV4roOYMjTRepp4CEFVhbpONhu0VsLXUVRnChJjisnwpiT0MdjxYbnuezt7VPX9UkmjP1cdnE7VolEjRCE+6DFJMSi2vMgdkAJmzRc1xUsFApKKaT3ZyM/vxPHMfn1eNE/tjPf3NwkDMPve35Vluzv73Pu3DmiMMJgmM1mxHGM1poLFy7RajX52Ec/ztmzZynLioODfc6fu0iSJnQ6S0gp6ff7lGXJ0Z0joiii2+0ihN2NPxzGBywApSEKG5w5e4bbt2+jtSYMQy5evMj29vaiKlRQVTYc8HhnVJYlvzjL+MQ0Y+w5HHqKRlXzk9MZNSWfWm3jeDYH5+O7A35iknAkJfeVomkMn5gk1MbwqZUWP30456OThHHocBB6tLXkJw6nhH6D/9SPCAKfNNVMpzPeeOMqj1x+lI///V/njz77h2RjTaRC/tda8N3NU8RNyW4Zo9Z6JNMp/X4fsIu6rUoafupoxseHM0a+y65yaNYlPz0vCQcx/1svQGtoNlssr6wwGAwx2ppNDgYTPrvSRkrFU+M5m3VN5ig+u9LiM0shhSvQVQFGIIVDUdQEASf3i8BWJBylqKqSVtN6y6yurrC8vMz2dkxRlIyGI0s6VZa3EkUN6tpQlSVGFxS5JklygsBFCLNQZxmSZE4cx4RRQF2X+IFDp9NiNBrSX+4xGc+o6pp+t8twOMRocxLiOJ8NaLVaVHWJA+RFQpLMEBKkUEjhMpslHEu/LfDNwUAcp0Rhk0ceucJw8E2effY5BoMBWVZw/twFlpdX+PznP0+e59zfvsPtW3doNlscDQ64du1NHnnkEQu4whbvApV3x1/FeEeDlTTJuXnzDkZWjCd7lsEvXT7+Ez+BRHH16lXW11coCrvwNpst7t/bY3mlR5LO6PV6OMr6Fdy7e48kmfPdF7/Nxtp5Or0+bq345Kd+hySZcv3qVVzpcHAwoLPUxHE9vvfSd6iqF9ncPMtHnt+k3WxQFQWT6YR+v293LbMpVVWdSGENtiXjGclgeMR4MqYqDZcvP2rbP+J4UdILYy4bQmeVUoaizNCmotaQZeliYTTcvHmDs2fOEkZduzMyNePRmKLIF1LbNnXtIqWgqiwAsWBHnIAea2hnQUmWWc+MY+WJ51iDLL1QRRzzZIDvq8YIcbyo2klfa0sgtV42LMh/bx3HKbbH4Age7pWKH1puNgu777cTZE8cRP+MJczjYLnNzU2MMeR5ThiGJyTc4+Msy5Lh0FYumo0mrueeED6TJKEqqxN10TNPvxchBTs7uzz15DP4gY+UDq1W6+T41tbWuHnz5okC6M7duxwNhgsJsHVitWGEFnz8/u//PqfPnT/5zI8//jiu6zIejzk6OmRpuUdR5ieE2DzPWRGS5+Y5R0owFGCEIPcdKCue14Zbp9ZIPI9NL+QTR68ydAuOFsnDR8I2CZ9Ncr4bezwbl4w8h4nn4jguIwSO8nlqEvPFboBYJPtK4TCbzjk6GnJ66xyuCuj3V7l16zarK2sc6JLGlQukN6/RWbi5rqyscHhwhOu6zKYJrbzkPfOUgauYBbZaMRMKt6x4epry7Y0+O3nGzZs3CQPFeDQ9ITcLaag9h09vdPnjtkdfQxJ6DKU1lnMX7tYW2Asc1ydNU5rNBr7vopTP0eGIqqqRSoDQKEdw+vQpZrMZBgsCR6MxSln/l7IyJw699vpftH8cj7rSCKmJY1v9yLKE8XjMo1ee4saN62RZhec5tNpNPvzhD/G5z36eqjI89ugT7O3tL0iyM8qipK5rVlZW2N27z+mtLfK8Is9r6nqOMZqVlVWUlBRFymg8xvMcut0uBs3u7h6j0ZCjwwllWdHpdNjc3OSFF17k3NlzxHHKmTPnwQiWl5Z4/bXX6Xa7vPLKy/T7y9R1TZImhEHD2u5j3nIP/XnG34bK7Ttp/Lh9H+9osHJwsIN0NTduv0GcjNjf96DyGA9iVlc3FgmrFXv79yjKFN+L2Du4zWR2n6NBm92dezzxxHvodLqkyZQ33niRa9cN5849waXLV5inMw4O73P27Gme/81/yr/9//3PjIYzagXT6YR2u4NEMTg64vzps5TlcyAckiS2E06rjeNIqsownU7sLqjMKcscx/F58XvfZGV5GccJuHf3FsYITp+xuUfHi+N0Oj3xbyiKnEqXDAaDhZRULkrCMxqNAKE04/Fg0RKICEOXN998ldF4xHAw5Dd+439HnmcLW3lzUnmpq4o4iamqGsexQOHataucOrVBnudsb29zeussm6c2SdOMVuvBbuphTstxW+oYMBRFQRzHC7WLt/jWxMmCfzyOlS3HKpxjmeyD1/rh14AFFNmJo/JftL96XFU5Pp5j8HQM6I4BVZIkXLt+DSGPbdz1SbvuWMJ6LKntdjsYY3jxxRdZW1tBlmIBWMTJ9+A4DufOnePu3bu4jsvdu3dZWlrCdV1AnJxfz/PI85zNzS2eeuopXnvtNbTWnDlzhjffeJMrj1xhNBpZD5gwPEk1dl2XTpLRBO5Lm//jeA6VqQjXN7isPD7+nqcYdNr0D0asvXwLf3Od+vCIyWSKEILYVWwUFaeKiqjWHIY+CCjKAiVdnKVV/L1DLvaXSDc2GI1GSOHgeT6PPfYkruvjuiFb65t4boPpdEKWzWjJEN/3yfOcZqO5UDE10RoO9o9YLrQ9bqUsaJC24jWtazaynIvdPvd3t8nznHien2xO0jTDdT3Kxe9kjYADR1nL/ZPr8LhNaigKjecFSFVTVQVBEFBVJUrZJGqtCxqNkL29Hba3t3FdCzhnswnNZos0GTCZTHE9tbiGDLNpSl1XSOUAFWEUUtcVui6ZTmKiyMdRHvfvbeO6PkURk6YZSjVoNTucPn2G27fu8ZGPfJzPfOYzVFVNnhekaWLVRuMRYRCSJiWu69psMqFxHIfNzQ1c12UyGZFmCWka02o1bNXv/EVenqekaUGSpLz66qs8++yzOMphbW2DTqfHUn+N9bVVhK45f/4inU6PUxun2N7ZodVs04haC6bTA8HAn2X8uC2G746/2Hh47v3L+G7f0QTbJB2TFzOGk32KKqG/1KHTbhGGHusbXcp6ws7eVdLigDv3XiLO7hM0EsbT29y5+wr3t6+SpEP29+9wcHSXM+eWiJoGIVOkkxM2BIaUw6Ndmq0GZVmjVEBRGhBW2jqeDJGiZDYfYCgYjQ4JfI84jqnramGiZRiPR4xGQ/YPdrm/fZtvfOOL7Oze4sXvfZPDw/tAyeGhdfI9loimaXrCNTDGkGYxt+9c5/U3XuLV115kMNjn4HAbQ4mhYHv7Djduvsmt29fQJkeomscev8wzzzxBqx1xbIA3m00py5I8y5hMxgyHQ+7fv89kMqIsC8bjEWmasL29za1bNy2HodU6yYQ5Jt4CC3mvtDbrAo53WMeuqTbBVy7yTn5wjHxVVQwGgxPg83Yg86MvfKuSePvz3+6++qcZD5sbHUc8HHucPPx6Qghu3brFcDCk3+u/JVcIoNlsopRtFbRbDTzPJQoD0nTOZDJmPBpSlvmJJ8/D2TutVou8yGm323bxykvqSlMWNUo6lKX1ADo+R77vE0UR165do7/URzmKxx9/nFarxSOPPHLyHKUULPXRjYjOwrKnLCtOLy1xDkmmKwaqJsnmDKUm8xSNsuLRxx+j0jUIQdsIMtdlP4psCGBlFrb69ty1AdnpsF8WvP7Ga1y+fJkgCInjhPksptPucvnSFYxWbG5uWZ+UqqDf77GyurIw4wu5e/cuBwcHDAYDpJTEnksiJc26BsB1bZUpzAsSKbgbT7l48cIC+Nm2Wr5onSklF9VKw+raMr7vIIRZAH0LNC0YBYEiTTKUUviBR1HaEEJ7/iSNRsRoPGD/YI9vv/Atlpb61olWOcxmc3qLa8FzLU/ouD0npUOn3UIbm+bebrdQaqFuEh5B0CJNC5qNDkJYfspslvDVr3yd4WBCWWm+/OWvEoUN0rRgZWV1AYRdJtMxK8vr9HurTCZzPM8nagTWLK7KQFhVYl3bY1lbX13IozXNRod2q2edj7OUvb09Tp3axGhoNTtceeRRoqiFlC5LS6t02l3a7S6XLl6h1eri+xH6RCDw5xvvJtC/O/604x1dWXFcQ3e5T7k7QxtDms7JZwIpHV747g5ZPqPSGXWd4fseV2/uEAQhWhhCv4Uh4cUXv4bRkJczRuNtsnxKEF6g0ZD8x0/+HlrXzKYVX/riF9nfP0Rrg/ElWVYgJVRVwWOPX2EyGfClL/4Rm1sXWd88Qx6nHB7tsbuzy+kzp6l1zuHRjK9+5StceeQ8t29fJStm7O/tgakQCB579Bk++9k/ZKm/xNPPPEOSzJlMRpzaXOfe/Tu02hFpNuPc+dPM5zOC0LHGUXXCq6+9yNmz5xiMDhmNRiALkjim2WrhKEVeTBkMD6grTZImrK2uobVmOp2SJDZbpaob7O0ZdnZ2uHnrFt1uh9XVVfYPttlYX6fV7C4M0saLtpLt5cdxslAblItKwHErBvK8IAh8bty4geMoOp3OgofwYIKr6+rEHv4BmdgOCxQetJyOx7HNt5SSIAgWIW8Coy0HSAr50AT4IPrgLTEIZpFr+eBVT7wjpJQLQ7T6pFpy/HtCCJ544gnOnjtHXuQnfAAp5EmFxXVdms1wwf2x7b319TXu3bvD8soKW6c3qaoSz/NPjnM0Gp2oetrtDkv9JUCehP2BVZoYY78jx/doNCLG4wmj0YinnnyKr37tq1y6dJGDgx16vS7GGJ566ilu375N4fu83Gvx/nmMNIYtpTh7f0BQFAxXOlx58w4vXNgkbTa4d3GTKy/fZJJneNrQqmr6leZz3Ta3XcV3Gj4/nxQYbSiiEDfOkNURX+u1uTEesXX6FJPJhM3NTW7evMtoNKbTGfHLv/x3OTjc5caNNzk6OmAwPODKlUfZ29+j2+0xPBpQlhVpklrysZTEocdLnQYfPRwhypJUQtsYOhq+ttrmUNc8sbJifUCikP39fZRyqCprtNbtdsnznCeffJI3Xn8dKafM47mV9+sHPju+H1AUNhAyCJcB2w6N5wVCOOhC29ZeEDIeD9nZ2SPPc4LAt7lL585z//5doiiinlc4yqHV8knTgeWVOJIiT5lzXIWTFEVNv9/DdUrSNMPzfKq6oK409+5t0+n0UNJhf++ARx55hOeeex9SwlNPPcUXv/THOK5idXWNJ594hvLLX2I2G1vzv9mEKIrodGw6fKvVZjwZoLVmc3MTJRUbpza5desOURTRbreJ44QoiqjqGgwo6XAwPKTTbHJ66+wieNID9Imi8QcVMv9qQcef9F7v8mn+pox3NFjJixnJ/hGRpxB4ZPM5RQqxa6hVjjHVog2jEXWFEVCZCi0kcRGjlWG0t0ccx/R7PYo6ozZw//5VJpN9KFN6zR7jUcI3vvp1ut02w/GE2TQmz3KkEVy6eImlpWUqKm7vXyeuYg6Hu0ilqOuS3Z1t9g+v02pF7OzukDMl7DkczO4ynx1hhGb38BqVTjg8vMNoNEHXp7l3D27ducvO7i5Ru2L3YJ/mLOJwtMM8i4jCBm9ce5M0m9LpNtgdvMLt7RcoiwrX9bh+65usra7SaEa2f14Zvvq1mgvnL9sgtKO7dDodS8asa44Gh5w+c4q8iHjlte8yHo/ww3N858XvEYYBw/ESk0mDfn+Z/f0By0vr9Lp9VBQxHIzo9tqMxyNaC9M0o21VZT6L2d87oN/vMRgMaDYjBoMhzWZ0Qgau6hLXFYzHA/r9ZXSNlWMjqUpNXsxpNSMsd0UunIgNxixIxw7EWWwtzV2fpuNTaUld1dR1ReBbzsCx9TywMBsDFoqt43HsVSOkxAjL4zluCx23po7BTKfdBvvK1IvWz3G0fSOMkEKBelAlWl/f5PTpcwwGA7QWNjPm2NZ+4a4cRRGtVourV68SBE08NwBilKPwPEWaxtYHReRAgRCaxx59jBs3bhHHKc89+37+zk/9JLt7dxmOjtjYWGNnZ5vhcMjP/ezP881K43k+H379GhdqMK0m+6fXiXttnn59myBo8erHLnFndR0fycqLr3G2KJkoyee6EZ9dakJR8IfdkEYj4LGjMRulZiwl/zkQfHm5QeAoAj+i2WjRbvc4PBzxwgsvEAQhzz33HIcHe4zHU1zHR+CwvLzK+XMXiBohv/ep38NVHr4vaXW6ZEdH4Bi+cLpLViY8N69YzQpSR/C1Uz2+dnaFuqy4e/8erVab2XxEq9skCCJu3b5jAXNWkqYFO/cPmU5ykqRC1xIjDWHgU1b2PApZg6ipSs14FNNutwBNo+ESxykCRZJkLC+vcO/efVwPylIviMwhnhcghOTcuXO8/Mr33gK6wyAk89MFj0fTCAOyLMX3BWUZc+nyJfYP9ymKhOnUVoWM0DZWwGjSPKPbX7Z8karmwsWzfPPb32Y2H7KyvkZ3qU+j1abZ6XDnzl36S5vkpWTr9CP0ezFL/TX29u8zi3Oefd/zlFXFG29ep9Fpsr+3x9LKOrdv3eHDH/4Yk9EMYcCRimbYxHdDAj9C19qCOyGt99PDwP+vZbwLVP42jXc0WNF1QdTwcKRVDniRSyYqXK9CypKyLMiLGs91bcnXCUjTlKKsUY5DXswBjee7KKciy+Y4yiXPUsajMVJ4zKczDnaPLFk1r6m0oKwrJAZTasaDEbdv3aC30mF7b8T+/n1WVzYQwGw+waDZH0nW11fZ3d9llmR8/Vtzap1R6xJjavJyzr37b5AurQCGvcM5O4dvECcpynH40lf2FtwPFoofjzAMOTw8oKoz0myK6wIIPNfHmAohMvb2bBZNXVe4jsfAwGBwm7IskEJSVrZNJQSMJxPu3OvS7Xa5efMGg+GQ6fyIXq/H/e1bDIcDnn3vB7hz/1V8r8HB0U2UdFlaWkYKB80q0+mcMDpPmqREURNhXEbjA8bjCZ5/htdee5VezxIfq6qk3W7zwgvf4cknn2QwHtFud0hTe2xB4KI1OI6HLmvgmDdiFmDBtuHAkGUJN27cwBjDmdPnaDUjyrIGBJPJCHepj+MoytJWgn7YHGaMoazKE2CSxznj0Yi1tTXgQZvouOry9ooLPCAKH+/WHyYTnz17nrquyfOS/f1DzpwJTwDQaGSTdhtRgzzPybKMVtPuioUQC/t8+3ld1xKly6q0qqJuhw9+8Hn29w/4mZ/5ab785S/TiJpcvfY6nuczHk845fosDUZ4Zc2311e4ePM2RbNJvrHKHMPZU6fQ4ynnb+0x+aDk9DdfZOv+IW6zQzydc913+KNek0ra3XTtefzHEL7UW+extQ32qpK7Wca5c+cpk5Rms215DY0m3W6Xsiy5ffs2q6vWYOzpp59me+c+/f4Sd+/cI0pywqMx3UoTK43rSrIsRbmSsi4RjsfvrrX5fKNmSQjypsfEA5UmdLt9nn76GW5cv0kQNFha6vPmm1fp9ZZsK0kdf18wmYwX/CbF8VeTZxnOQq2nta2IVGXNcDA+IZp7noM2iiIvuHXrFmma0Gi0aDQaBEGDXrfPeDzhypVHmccxYRBx5sxZxuMZk0mMoxThgldVVQWe56Ic642SpCk7u9ssLffZ3Z0ilSWo+4FDWWesrq3g+x69Xp8L5y9x794OnXafc+cucvNWwZtvvslHP/qTFFXJs+99jiQp6PeX2Nzc4pFHHsfUBYeHe7xx9VX6S10GowGNRpMg8AnCkEazSa/bJ0lS4nnM2uoqSlg2SlkUuK0OUkiUqxa+Tg8qjH/R8ePi4fHu+PEf72iwAgZHSQQaKUqUB21XkZVz6jJFGIHvKDA1pjIox8VTDmhDs9WgKFLqukApzXw2IgpdmlGb4SBmPJxRloYwaGOMJE1z6hq0FuhaW5VBXmCqkipPyOeGZuCg3ILp/N5J5kscz2h3Wty7P7BBaNIwmxZMx3OSOEEKgd9RVHXC/mBKo9VgXoiFWVhFq9liOkop8gJwGI/jBZ8iIEnmJMkMZEUQeERRA10WNhnYc6gqGB0eoLWh3W6TxVMajQYI6Hb7TKYzHAfyIicMK2bxEXfvXWc6neH7IZPJmLIoGY7GxPGM6Xwfz3MtD2E+J2o0OLWxiTGSzdkjHOyPQCQkSUpRWsfXG7feZG31FDs7d7hy5SJvXn2NJLZVkAsXLzCP5ziOw7172zz6aIs8y4mixokKCmA8mqKkg8ESjz3Xw2hbvanqGn9R9r59+xatVoNWO1os8D6g7fNKTVFUqNAuWghb0Xi7S2OaWoVVFEU2cmE6Y2XF8imKongLr+UHlbvzPEdrTavVOlFEHY9j2/y7d+8yHA65ePHiCeDJi/xkIRBC8OQTT/CpT32SLLchhlVdoXWFEJK6NggknU6XTmdGluVsnNuiLAu+++J3uH79Ko4LFy5cpJzMeHZ/zKWd+1waZfxcp8OXdncIariRF6S37pEXBfk0YanZYmUe8+z/839h8+42OC5Op4XTXcI52CdG8LtLriWkanB8D9NrE2+dYqPZ4vUvfInDgyOWOl0ajQYbGxtkWYkQkqeeepo7d+4ynU6RSnH6zCZZmtEQDh9+8y7FZz+Pnk54JvD4diD53EqLZnuJ9Cgj8EO8IGA+L0giQS4l7U6DtiM4dWqDNE2Zz2OUcvGDiI2Ns4yGc7IsXai5AhA1g+E+zZZPFAaUlSLL0gUPy8F1PKR0CYJoAeCV5Q/lh7iujxDC8lMcRbPZtKR35TKfZ2xtnuWRRx5hPk1Z0ktcv/kGFy8+smgjjonCkLIsOHPmDK+++ipg1XF+4BEnMa1WmzTNyLKc8SIcst1usLGxTlVrsrSg025S5jlHB4ek8znpPOEjz3+E2WTA/u4eB3v7nNk8TZZmfOwjH8H3I1aWV3AdB+kITm9tcenCRW7fvYGuKlzXYXm5z2Qy4bnnnmM6mXPh/AX2Dw548rGnmE2sBP8ts+1feyXl3fG3ebyjwUqW5riOwHPsFinPYnzfYzI+ZGmpT5KmBF5AXUGelzgqtBMPmS1lalvCN2UFRuIGIYOjIYcHcwaDCQKH1DcLgBCSJoV1mDUSaQxKKKgqjvZ2aTY20boiCBtUWH8U1xdoA0qVVJUl6lGVlFnGUrdDmeQUec58PgGREvk+87iyNuzzhCov0a5HMp1SVzUYhSlKlOvioGn4HqHbpixzgjDAUS5BGNg06CBkOByy0luhyCsc5SJUjq4L0iShyGKkVHZnryt8X1LUOb2lFkJCntXM5zF7O4dkWYkfglCrhKHLYJQCNVq02D/KqCvD0eEhEo/h6B5RaHkU7XaX8XhM1ICjgwkXL65y794Ou3u73L+/zfbONVzXJc2GHBzcZmOjQ13PyXILxo65IMf+M1VRUlFQFTm1rjk6PKDdaSMdw9ap09y/u81kNCVfyWg2m0ghmIzHtBpdHMdbgJAAa/1sHU3Nwq33uB0DD0h/VVXRaDbfYsN/7Fvyw/ryRVEwn89pNpvfD2qM5UDkec6ZM2eYTqe0Wi2b/RLHJ4tD4NsWwXe/+8LCn8ejyHMc1zrWNhoRnhdweuss167ewlEOjz32KHfv3eVLX/o8jmMBTb+/ztO39njyaMo9ISlPbbARRrzn9avIvGYmDYUUeMYhGc1Ru0d05zErteFQCWLfpVXmtB2Hhob3zFK+vrzMvrAVoyKvSJKMwWCEUj4XL1xmf/+QwEl49ZXXCAPbjgn8EM/zeP75D7GyvGorCHvbnD59hsd2Drhw7XW2Oz1uGUOzyvnY0RzP97j29BqTWUyvt0Kc5mCmWEdow1JvBaGg319lZXkZYwzxPCMMWhSZ5vHHnuLg0GbgNBpNfN8jjqd4viJseHT9FllWsLuztyCfNvB8RZLYZOcsy5FS0u3aymQUNahq6z/UbrfJ8xypFplFGtI058knnyaO5wyHQzbWT3H79k274Wg3iecZynPxwoAkiYmzlLwu0Lpm49QGk2nKZDyh2WxS1TmOq3A9iWMkgR9QVxWrK8usra4zHk1ZW9vA8x3W106xs7NHPI/52Ed/gi99+St89MM/wWg0xmhDu9kmS+x56/f7vPHmK9y7c5eN9Q0whrIoOHv2LC9867tsntrk+o2buJ5Lp9uhrmuWlpdxHesZ9bfFGPJv6/iLfrd/2dfGOxqsHB3MmY1iNk6tUJUVVZkjW5K6MDgqwtQ103GGrgVKeUyyjCzPKeuKbreN7weUWY3n+OhKkxQlu9tHHA1jpPSRwmFwNGE4nC4SYCWOsqZvXuRx6tQpJsNdRFqQTOd4oSKdxbihpKwzuxND4uLiOi5lVlDluX2NNMNUVsK41FujMi5RFDAZFwz2p1ALQr9BHoMnWmR1QRKn+F4D34lwsG6YaZKBcRBegHI9Ir+N7wQkaUK/s0JRaOoCqCXS8fB9F11U7OwO6Ha7ZPkcozXNbgvHcQHJ0tISSVIyHceUpaauLXiJ5xaE6drg+Q7zeUKeWSllM8rodZegzLm1f4fpdEaz0cIPAq5ee5HJeMKnPr3Lzo5Nwz46GjCb73Lx4iX+w2//a6q6RrwyZzAY8PgTj+E6Dt/61jf54PMfotc+TZbZBSzPrcnV8vISg9F9dvdLer0VZvOYXrfH3Tt3efzxJ0jiDN+HRqNBVecMhoccHR3S6zetJb6wRllqETqXpim+7zOZWLKqdZx18F2PJEkYDoc0m80TH5qH1UFaa8rSOraGYXhie++4thpUV9aTwspxNR/8wAdxXIcvf/nL/MRP/AT7+/sEfnDirWOzb1KSJMZ1FXle47oOVVUihKKqNGmasbu7x7mzF7h48TJPP/00V6++iRCG2XwKGC73l7i4s8+RkujuMnGtuTqdok5t4F2/wWpeonyPImjQKCv6lSEQikrWzJWiqmtGGJIspYGg63uEaY7yfTDg+Q5KusTzlN6VJWotGQwmNKImTzx+Fq01H3j/B3HUi7z80qv8w3/4j2m1G7SaTfZRjK/d4szt+wwbDdz1VXQ8Z+pYafj7UkOwcQapQp566r1cv3mb/b0/RtcVnXabRx99nPF4Atrhzp37vP/9zyGlg+c0mcdzTp3a4sqVK1RViW0dwngyZDA85NSpLQZHA4SQrKyskCQpg8GQjY01+v0Vjg4PybKcJLFVF60NSZLSattsKGM0vV6f6WSG47oMBkM6nS6PXnmcMAgRQvHGG6+yvLxGUeTs7G4ThBFhFPHIo1d45ZWXqY1GaEG322X/8JCLFx5F6w1a7YAvfulzKOVw9uwFdra3GY1mXDx3losXLtLvrdFp9XGlx5mtszz1xDO89OIr7O3s84H3Pc/TTzzNcn+ZZtSiLEuMMQReQLvd4PSp05w7ewGBIAqarK/4RH6LMAhZXl4ijEJarRYHBwc8evlRyrLGcz0ED5PV3x3vjr+e8Y4GK+msxm23ONhOqOucTqdBPIUy87l3e7qYhEYYDe1Ol+WlVabjmNFozPAgJYp8qiJDawtYysIuyFo7uKHPZDJfBMe55FWFkpI4SSjqGscR7B8OoK7pd1vUleTunT3a3Yil5S6O6xIETeazmHQYE/g+UaNJs9FGOZJ5nNIIPYSokELSbVo1g4dL6LskScZ8XFKXJa4ToLXHzvY+npeTF/u2LFwWBGFEr9vncC9hlhywvrGM60lqXTIeTxkN50h8jJa4rqbf7xIEITfe3OfUKcu7iOOYTmzIZYIxgjBoEgYtsrRkNovJsxLPdxgczuj3+vg+7O1tgxA0oy5h0GAyGjEdjWg22/hewHw6xncV08mApBHhBy47OzepSluxieczXEdw9c1XmE6nNBoRw6P7KCWZzXYIgwAp4cb171EVb7LUX6fVajAYHHJ0dMD7P/Acw+ERw9GI/cMueV6xvrbBeDLhOy98nUcffcKmYw8OaXUa7O8dsLOzx7nzW9b/RrkLiXhFresTmXWe5wyHQ86dO3eSeguwu7vL6urqScXk4cn74cpMXdcnhFxjDNoYam19Wdqttm1jNT2UsjEOe3t7zGazhXcND9J9EeR5hutaD5csy7CqIpjPEzZObfL4408ShU263T7T6ZTt7XtsbW2S5Qnb97eZ376HmCdkTcuf8H0fjGCYpORRyG6zzanxGCeJiR2PN9odrsxnhHlOKCBRwpKMpaFZ1FS+YKoURV7ieS5ZmlMbzdrGKXa2d3nu/c/zyCNP8MarL/PMM+/l7t37LC+vsLV1mvPtLs6tW2QC1JlNyr09tqIGa40mnDuL8Tze88yzxPGU22+8znKlccYFH/nwJxBCEc9fp9dbYjIc0OstsbpyinNnL7Oxsca1629yeDDgIx/9KF/58jcYDAZ4nsvW1rP85Md/mtdee5Vmq8l4PKTIK1wn5D3veT+3b93ijTdep9Y17XaH2Wy++O4gCptWaVdYnx2Bot/r43ouZ8+d5u7dO+ga6jrGdT3Go4n9XtY3WV3ZoK40eRFzdLSP40jSNOHS5ct4nsv2zn1837NZO56LrjXPPvcck/GY2XxEs2klxWlcYoyH50bcv3sPU1V0W20CJ7KKvGbIlUtX+NAHnscRinSe8PiVx9Blzeb6BsPhyFbrnJBmo8ulC48wmczY3r1PllYsLa+yvnaa/aM9VlZWOTjYxxjDq6++yoWzF/C94MTn6N32z7vjr3u8o8FKMtfcvXmDPMvZPLVB0tXE8YRWp8tkNmU4GmOMpigKhoOK2cQghGIyqjhM53ieQ7MR4vseg9mMIi8py5pKVaysRghs+JgQCiE0dW0wQqJcQWUMBoGSHllec3g0I05qyjyhihWNRgPfk5SloK7h9TvXOHfuPJ1+QG1SRtMjlld7rCz3iacxRjuMBnMaXsjBwQ7j8ZTV1U3qUlAWJWmaM5nkOEoync4QhCRJznC4j+C+JaLKnMF4zqOPXuTuvW2kdNndnZJn4Eif0KsYHExYXl6m21plPslttLyImIxSKq8gjhPaLZg7Fe12jyyr2YsPFjLknOlsThhKfD8AJMPBjMDXeE4NpmYwGLK2uk4jikjihDTLqKqaVX8ZjCGJE6aThGbUxdSSw/0hzVYDEHanjCbNIjxXkWYJe3u71JWg31sGYWzLjJrqG4dIuYgLmCjCsMn93UPysua1N/YYjrbZ3DzLm2++gXIF02nM/v4RHx6/n+l0wvr6FlIohBQkiZVslmXJZDJBCMHe3h7NZhOweT3HMtzjSgpwwjfRWjOZTE7UVa1Wy/qgCIE2mjzPSZKEOInJskWL6iGb/ZWVFaSUJ/lDWmtu3LxBHE9JsxghDUmcEoWWy+O5PlqD5/q8+eY1fuM3foOr165aYmfkcvr0FlffvEq5dopYQqMoGY/HCCFpt9uowYhlpTB5hjIGLQS3opBvLS2xlqV4RcGKrjFCkilo1gYFfKsRYDScTjNiA2NlMBKWlpa5cuUx+v1lPM9ncGCjH8IwJB2M+MSd+3S/+59Rb75JeXCAWl1hudPmI5cu0FpZxVMOdbdPo9Eimke04pwizVi58iSbFx9l/2CfjY1TTKYz8njO008/xcrKGqPRmF5vBd+/R1Fk7O7uEgQea2srrK6uIFCcOX0egcNkOuXJx9+DEjYle3PjLKfWtzg4OGI6G/Pss8/wjW98nYODgxPfoOaiBXjsf1NVNWVVsbe7d9KO6fU8HOUzGI5Q0mEep/S6y4zHEy5cPMf+wX1u3LzK+97/IXpLS/i+xzPvfS9VVfLqq6/Q6nTo9frMZzYV2fcDtjbPoJTiqaeeI55nHBwc8MYrL3M0OOLiBQjDkLKscByPtbVT/NRP/QwvvPACjUbTWuIfHVGWFVlquTee4xJ4Tc6cvojrBfybf/M/sr66iXI8mq0m82RKPI/xPJ8sK8jSjCyz2UbyB7hNvzveHX8d4x0NVopMo2uFI0Jmk4wszqlNQZbPGUwzytIqKZKkwgD3twcURY7CRZc1MQVFZnBUTlXYqHmlXBt5n9fUlaGuS7Q2VGVNVdW0Om3yqqSuStK8wBGG0XSOOAKlIPR9sniKFDF1rel2ehwcHhF4AbvbQ0YjifQqpFMyOBqiTY3jheS5w/7OGE9OkEJRloLxJOZoOMZoxXQ2J41rup0mUgVMpzlxnCKlj+eG1LXA9RRJUvLKq1cpy9I6eRqHWhvKvKLOC8LAZeomhGHAfD7DUZDnGX4jJPJb7Ny/yfBoj163x6VLK0RRwOpqj6q27p15lhLHCUvLSyRxDliAJ7QmT1M8L2A6jmk0QhvOp6EuNHvbhzSbbSQe42FC6gl8PyLLDMOjA+bxhLIq8X0XgSFqhrSaEd2uYjIZM5/N8DyXSpek2Zy8jDm1vkZVF0zmYxpRkzBo4jgBybxgOtnnxo2XrZoj8DAolILR+IAkKZHCUBYVpza3SOIprVbIcDBk+/5tfN9neztnqd9nb3ebV155lccff5Lbt28svEtcbKSApqo0w+GAN998k6eeepp79+6zurrKxsY6tdEMh0NG4xHXrl1ja3OLb3/72/yjf/yPLOAxlnTrByFSSDodG4SIgHt371iyLZYHgzG4jmM/j+uytLSCkh7veea9BH5ozdm0YWd7l0uXLtqcmtUVRk8+zvkXX0HPY2SvT3l/m83DQyLPY19r9lyHVlnxgdGIoir5pqP4mKnRUtCua3qVBSovRx5nspL/0/19KmOYiwnfbYb88VqX+/fuc+HCZR5/9HFu3rpNXRmisM1+MaD7B59h+ZXXYDRGDAYIrRGHR7TKkhXHIWu2aBwOcKSDCXy84Zhmbdj/iY/z4V/6VW7cuskjlx5jNJpx984OTz31DB94//OMx1OMNty5fYcL5y4i1Hk+99nPEIQhly9dpqw06+sb7O7sEscpvhtw7txFtu/vsLO7Q6vZ5Tvf/RYf+MDzvPjSd2i1ezRbLdZWV7h9+zZBEJAkyULJZB1xjRE0mk2WV9bp93q0Wj0+97nPc+rUilV5VQXb29vcvXfPcsfygtFoShg2cByP4WDI6dNbnFo/xTe/9XV63Q66LsHU3Lx1nXPnLtDpdPnoRz+O5/qsrWwydqco4XDj2msnLVAhoNlsoByrHjt39jzzWUIUNgiDkH5vCSkVrXaHuqowtXWYjaIGG2ubvO99z3P/3i5nz57jcP8IYRyee+/7uXnjFqEXcfnCmUVLWPw5vWl/nMZfpHX1zv7kbx/v9DbeOxqsuErhORLlKxxHohyHMqtxvQa1ycmKCikEjaiNMDaVuBkFSGGtuLXWlBoc18XxXVzl2LKnFJS5QdeSIsspixIpIAw8It+jznKKomY0tIQ4P+pTVCVJnlNJSExJrxPZFGRR4TR8hKPIKTC1SzFJaDQdpvGEra1NDg6GpPGEWiuyQuO4LtNJijvRCwJfTW1cSlMxTSa4rstwOuE4Z8d1DG4g0cYSUgO/SZpOMNoBI2m3Q6pSk80EWQFhJYgc29oq4xgpJXt396lwyQsHzw1IZposyWlELkJKktTgeoqiLGm3ezSiNmUxQ4gcYzTDwZiqsNNbp6dI0yFr6ytkWYESEPgOk9EEbRRZWnNwcEBeGPq9JXRlqKqQJKnRlUNdV8TzjEORsrbmkCUpQqZ0l7pM4xlB4LG9fZ/xUUWz0aC71Ofu7V08b4rne8ymM8Iw5NSpDRy3ZDo5xAtaGOPyx1/4NBurG3zv25+nLnM+/NG/wzzJwVzmOy98mxs3brC83Gdvb8/arJczRuMJX/vaPYpS0Wk3iBoN2p02X/nK18jSgtlsxnA45PBwn9ffeJ1HrzzKpUuXWF5d5vd+/9MLB9Gaz37uM/i+z8uvfo/pZMoHn3/eEkeFoqps7lKa51R1xre+801mE0usrsvKErsFSGEBdDNscmbzIo8//hiOq3j6iWf44h9/nt7pHk9ceYbPd77AzvY9yl/7JYbtFufv76NHQ44GA8K6piEU50YjrhyHYgK/WuT8h16XL/d7PFmkNPMSJQS9subZWUZLp5TAtu/yWuDzU9M5XuDypdkcXVv/jfe95/1sLK0zHAw4E7Q5c+c7iEYD9b2XMLMZvhBIIehqjX70MRxHUb3vvaiXX4E7O7heQP1Lv0jyoQ+x2WzwyOVHmMcxW6fO8qu/sszh/jadZo92WhN1HEy7w/LFyxgD3s+GHBzsEUURKysrnN7cwpGKozfe5FQQ0C0KVpeWiYKAc2fPMZ1PuXv/Lr/2D/4xr772KtoI5vGMs+fOMhqOGQ7HPPnkM1y/dgNw6S2t0Gq3mM1ynnjiHHVtePSxIZ1OjzwrOBjs0YiazNMxyu9ics2p0+dpdpZ46vGnGBwNaIUdmmGTRy5c4vBol739bcZSM3OmPPv+51hb2sB1A3rtJdrNJvfEXV556QWilkeczdBUKOEvUro1URgSeAFXLj9OM+rgKJdW00VKSbtpnX6zIkU6YJCEUYNnnn4v165dw1UO2glY6ayzuXqalt8njmMcxyUIGgihbGXlHbvIvQtU/rTjJEsN+JH5Jn+N4x0NVupak2YZnXaHqqqZzSwJM8tyTF1DrRFSohDoqkYJG0RWan0iiy3KCt+xVtSOcphMJtRZjR8Gb3EwtXk7NlHVdRzyPMNxPeI4Ic1zpOPgOC5FWUJZkaVHbG2eQimJkaA8SZamKC0Io5Cqzllf30RrSZmD60Zk05wkLSmrdKEcSZFS4ro+WkuMETYRWdcYDFLa4yurkrIqcT1L+kzTHM/1iPMM11WkaUpV1Qgp0UYTpzFrp5bQWCJnt9uj3WmzfzixPiTYFsdgMGR1vUPgNzBG02haguv+wV3yYkyr1WFtvY3WgiyeIIWHENYfxfUVSZoQhAFJXDA/nDCPM/JCI6XHcUbQ4eEQozWB74OxKhalXFxPkaYJR0dDfMchKzKK2uBHAYPBjKqseeP1Wywv9dC3y4Vnhs0gcl1FVRoOnAGOkhweHbG2bnNsxuMhRZpCXeFg+OPP/2dKrfnSVz69MLVrUZQDsiwjyxLCUCFUwXg6BxHyP/3b/xe9/jJSSHYXfBPf90mShJu3XyIvCsKG5vqt7/H0089y8cIWL730ElJKHKVpRC6723eYTqco8QGm8YyDvX0aUYuoEXJ0NCEIFMPBIUJKHMdZBBkqXNdeY67r8f4PfICLFy9QViVh5LO2topSDt1ej8cefYzNjVPs7m7zyrVrnP2JD/GHX/kGl/pPsvWFrxIMx4yyFKVrmlqDMYylRGrNR+cxf7DS41+uruLHCf/HUcyZOMXVhlgKBIYzeYkWglcaIe8bTrnbOGLT9XEdnygMuXTpMvr8BRob2wR/+BnE/h6MRsgwxLguoixR0yns7FD0utw6e4byyStkO9vM3JDLz3+EbP8ArWvCMEAsWmZBEPDeSxfg3/875Fe/ymrUoPQ8Xl9Zpvr7f48wCjl39jx7e3tgBG5Zc+GLX2X5jz6Pk2WIP/gsTzz+GFff+zRR1EApl6Ko2FjfIE0TDva3eerJx5hMprz++uskaQVCIV0X5dgcoOX+Kr1ej8CPODw64hd+4ZeYjKe89trr7OzscO7cORBYaX8UcfXNa5w7d45G1CSLMvq9Pq+//ipPPvkUo9E6L3y3Igh92p0+m5ubVJklT5873aHdaHIPCMOIdCd7SEpsbQxt5qlEKVheXsZZkMVt0OgDmXEYRkjlLKIxHOrasLKyhl4kU6+urqOUotfrn7QKpVQP4iX+Kif1vwXjR53PHwdY+MOO7+3H9lddqXlHg5XJbE4QNpinKY5y0AiEcqjKCqnBUw5KKlylQEikEAgjQCo8z5Icy0U+S57nVLKy0khtyLIM3/dPVB9CiAXRzgIX1/VwF8qNStcnXAatLUelLEvu3ttm89QaWZ4Tx1MC3yMrNKPJnKrKGI1HRFGTulJ4riLLNVVlwNgwP5BI6aFra+PebLQpq2KRUeQuzoKNjLfkyZKirMFUVHV1QoyzRmUCKa0DbG0qJrMxq+urHBwcojF4vsfm1joHh0dkWYJUPrMZJOkMY0qWVls2AM6RKCXQpmI6G+K6HqBYXl1icBgjURg0BguKfLfB+sYG2TylOU8ZjKbE8xyBtE6sUiCkeigJ2hpzgUEpB2OgKmvQkrIw+KFDllWYGpa6qwR+yDSeECdzlpf7zKYxYejjOoqD/SlJPKPZjMizmqKy4X7j4QBP2SP1QoeiLpFSoBzDdHbI3n5KWRY0Gk2McXB9n2bT587dXe7evYMfhLRaHTAGz1eMxgOUUmR5xWw2o3p9xvLyMt97qSQKW9y7f5der4cQJRcubjGdHiKl4sUXv8VoOCHLcq5ceYyjYcVsNuXs2TOURYEUCr0ApcYI5vNkEeRXs33/HuPJAMdR3L17k1On1vF9z37HC5ATBAH7u3t8/rOfZ215lToveX+cMgPyNKNfa8qF+Vdbaw5clyPX5anpnM+1Q6R0eWwak0hJw2hKKTD2BuFUXpAoyXpZsXRvn63f/TQ9PKpf+wd47TZ1XeGsrYHrInZ2Ea4LUiKUgroG1yW/dZPXDpv8v//Nv2EoDc1mxMbGFo3TZ9HGoHVN4PkUAvIs5dLF83T/4++gvvFNps0WZnOTfGeX/he+SNlscfALP8fFi+fZ2Nggiho4v/07yP/tdymkpPHII4jJhP4Xv8yjQiKfe46zW2d5/bXXWOmtsLG2Tl1pnnvfc8xmU1rtJb74xS/g+gFnzp3l+vXr3Lx+k0cvP8bW1mmuX7+J7/s8/sjjJEnKUneJyWRKK2zhb/ns7u4SeiGbG5s8+fiTSCm5fPkRGlHIk08+TVVnhFFgU7KHR1y4cIkzp89w7Y2bXLp4hXa7jYNkfX2DpaVlLprLBEHDbjqUzYcSAsQi5PIYoBzLix9OHVdSLQjfdsZYXlrGcVx6vR6ua8M2j80NW63W20JExY/HCvru+LEcbwcsf5lE7Hc0WCmKCsdxUNLDDwKUci0LP0vQdU2z0UBJ66ciFsalxhiEkpR1dSJdPXYlFUJY6WihrX364vnHhErP86xiJLVZQ/XbTJK01ugaykpgjMJxQ2oNUimMcU6M3VutDoYW0+mMfJhSltBuBcRJcSKlBQuMirxCSmuGliQpQlpLd1tZsRNTmqbUtQZq6rpESY88qxYAReH7PvP5HCkkUmqUsGFww9GI2XyON8841+lzP01IkjlSgh/45HlFpAKKvGI0SJnNElbX+jiqSZFXRFFAnlUoJfAdB8/3mIxjsiJmeblJu9MgzTLGd+7g4tNud9na7LG/f8TB0Rg/EJRFja41jlILC2/rAFsUlXUNrS0BVAiF0QIpXVwnJMnmJHGB54R02itMJylHBzNcz8F1FKNhYpUzs5gobJAkOVmREcdzdFWhy5JL584zS2eMpiO2NrfY3z8gjhMODg7Z2tri9q0dpDCsrW0gpAPGYToZ4SQ1ZSFwHYVSVvYMnPjCFFnJwd4hd27fJwwjtNYcHe1iDHz9a1Nc1yUIAl588TtgJFHUwHUlt27dYn9/n0ajxeHBIebE+kVQltXCDdfQ6XRZWu5jKGm2mkymQ9Is5j3vfYb9/T2yNOXCufNcOHeWb3zt68STKaK/wvosxp3OOPI8TiUpUV1jgwjsHwWMHUk3z2mXNetFQWAMQ0cRao0DVEJQSkG70pzPS2aOYjvwWKsNzqf+M1Ioin/0j/B8D93rYZ58Er7weUyjgZjPoSxBa0rfJx+N+V4Y8MbhIWVV2KpWrnnj9df51b//9/BcB2NqwsBnZXmJyfXrLH3ta4j1dfxOG1wPdWoDMRnRfvklbjx6haN2h3PnzyFHY7I/+hxzqTCry9SOg7O2jslyNu/coRKS9hNPYozmcP+AZ55+hgtnLxG4DVbPbXBu6zyjoyGu59BsNEhmMa4T0Gx2uHD+ErNpwmw6x2jB6vIaRVbR7y3R7XWRQrKzvcPGxgZhELKxvoGSDmfOnMFxFM1Wk+lsxCc/+Z/YOn0e1w0wRrC7a4nEs9mcjdVNJIJmo8n62imMqHEdn7wo8BvRQ62Zt5oUnqjJHsqxwggQAiHsfBU1mijHxXWPQxwfeAwdV5CP08UX7/BfdN5+d/zljb/Uaocxb8Gt71ZW/gzDCyNrlqUE03lMXdd4nkelbRJwVVvSoTAGJdVJbos2emFZLu0NiwBj7Gs57lts1cGClGPPArFY6IuyRKCpaku8VcdyVpTNlUFSVjX7B0f0ui2UA5tbp8hSa2mdpilKNVCuS57HTKZzmq02SZrY19GLfBpjzcscx0pY67pESIVSi5RfIzG6pixsDpJctGFAnsTJ+75LEHiUlabS1npfOg6eFvzyOOPJcUyk98kch687gj9oh3Q6bcqyIE0zq6YYl7ieQzwfsL7RJ4wUs+mM8XhMp91GRw7a1HiBR14lRI0QP/SpqpxBPENWFWiP7b1bFoA5Po7jYhzQQmJqjan1AlEKHMfFcVzqusLoGqGswsrU9vHAb5BnJYNqQtSI6HVXmM6mSOExnWQoJSiKgjD0qSrwXI+isi6xSZJAZdjfHxA1m6ADXn/tDnEcL0jJITeu7dvvXxvS5Ii1tXWKXDIZZ5RlQrsN7XYL34OykARhSJLOqKqaZD61JXdlaHfrhSQ6I89y4vmcdrvFZDxhHicIIQjDkIODXUajCVHUJEvvkiY5dVXj+z5SKIyuLC9EKTzX5/XXX2M0HfD4Y4+ztrbO7//+7/Haa68TBAFf+eqXOX/uHF//2lfJ05SOcvi1WcaV+zucmkyptCYqS/zFfWSACvC15kqaccdzmPs+9+uaFIGvNXOl6FQ2wsCtLXCpgTuBz0RIZlGIs7RK/dWvk370o+jNU1YR9Gt/n+jTn7atIGvYgwlDKinYCQO+GEWsTmZMlCRVGWmYcOvmdbbv3SfLc9bWN1hdXWNtdRnGI1SSok9v2kpikhJlGUV/iWaaEqQpL3znO2ydPo0/GhHWNeL0FmJBlhUiw+v1EIMBajzD6/d57j3PEsdzXOVweuMUs+mcpVabMAj5e7/8q9y+fYN2p003auO4EZ7yaARNnnr8aV566WUkkiwt0LVh65RNkp5MJlw4d4H5dE4zarK5sYnBXtO+7zEeT1HKxfNC3vue9/H7f/D76Npw/fp1PvSBjzEezlDKRQn7OxcuXObW3VtEUYvpZEorai/msreaDj6ooj5Q8Bj7hJPqiFnMhVbNhw38/AGlk5P083erKn8rxp+nIvJXLWd/R4OVurYlk6woLZfEdShK60BZa01dljYi3oDUNZLFjS0kQtlKSlkU5JXdYRqtybIcJEhH2bh3zzspswph7d3rqrKkV63RdY3jKBCWROl4rm0tCet4ajkTYzxXMhy+QRQEVGWFMSzaNwLfbxAnCePxiE63Q1EUGANlUS4cOyuyLF2AKEld25KunaRsTo7No7Hto7KsF5kyttpjpbMpQjkYBGlWcOPmXX7xaMxPznIOhOKehG5t+LmyIvA9vpBlCKHRumZleZWrV2/TancIg4jRYI7WIXGS0ojauK7PaDxCCQuIXNdhnswpqhTPa9Dr9yimwibXHkcWlDUI+zmkkAhHnWQWaa1xlLOoFi2qLUIuWhziJC9HKddmHJU1zVYLpVwGgyM8z0EpRRg6VFWGMTVRFFDqnHa7zVA5zEZzhoMZRSVAuGRphec0ec/TT7G9vcvdO/eRSFzPgrDxaE7QaLC6ssZ4klDXgvFwRqvhIaUkNxWdZp/9/b3FRK/wA4+qgCKz32eRawQ1w2JKEISY2jAaD2l3WihlLd+n4ylxXFDk5aKtaL/jY3WG1obpdMbXvv41Or0G3/zm1/Fcj6OjAXGc0Ost0e12ONjdZjIcIgz8Q+Xz9O27HCG4HwZcnkxpGIPVuNihAQ/DVlnyuaUOYylxej1eOBrx8WnMVApmUtCtagIDuRBcjSJuRzaQstPuYtptnO0d5GjMuBFZE7xTp6j+q/8K53d+B91qUYUB5WDA9O5d7hrNr+/uEdY1mVK8ttTje/0+r7/2Gv+3/+v/mZ/8Oz/F448/wfLyMr4fUK2sYJoNxHCEPBog7t2DsqBflrC2yuq5s0SdHtPpjPWVVZxulzDPMZ0uUkpmszlqPkM3muhezy7UuqbTbFMUBZPhgFdeeYnXX3FZWVmxKeHNFq5wePTyo1Ra0mg0UUKxtrzGxtoRUdAgTXO2Tm2RJHO63S7ddpfGIw2uX7/O+uo6URChHAekII4TyqIk6rZ533MfoNvp02p2CEOfC+cvksQJy8srFlQoxyZPK8VTTz5LPJ8jhDwJ27TjT7NgPAA2YLHLsSu0TT83f9v4pO+Od+B4R4MVXQukUEiJ5T8YSVEU+L5nK59SULN4TEClrfyTxQ7kuGxPXVNXNY5U1rtDWiJtkiQnvVyxACNlWUJtHuxGFq910icWhtrUYAy61uTG4LkO8yJfWMtrdG39XoyBOs1ptTw83yVJEkajIUEQ0Gq1iIEyTlDKwXHkImDPeUtr6uEetTHCghgNurbH4rrCEjPdklorjAbpSPoG3p8bjpTDwWLjdSjs778vr/hGnLCdzVhZWWM6nQDWbyTLJb2lkNksZ2m5Q56nuJ5EpoKiKDFG0u/3KPWcoihI0xJXtHCdJkKoBVm4pFpUCYSQ5GmO0VYNY0yNEFBVNcZoQKNETU1NksyJWm2qusTUhsDxUcqhrKoFn6MiDCPKMoeqptNt026vkGYD0iwmy6yfis0KAlEJJuMp7X6HRqPBdDrn6tWr5Hm1OOcORZ4iFNRakGUz5vEEsIBRGphNU6Io5PLlJxBCMJ3Y8DtdV8wmBUF0HFYoiYI2RZETeCHCOOgqp9vpWnKw1pRVSZEVCKPs4zp7EDmwuAaPwacnJLPZhMlkBMgFIbsgywqOjg6JfBeFYdMLeF9WcgSMHYc8CDk3jzGL66cUloeiBfjGsOs5vNxtk6YJZ50Ov73UQ+Ql7y0rHANDx+Ga71FLSep76EX1yXVd5GyGaDaJTm8hGhGNRsO2E3791+wu/ctfphwcUYUB1foaW5Mxh2HITEqi2Yyf3j3ASVP+bSti6/Rp1lZW2FhfZz6f2e92eQn9kQ/j/Mt/BaMRtNsY5SDnMWYec/baDQ5+4RfodDoQhlTPfwjnU59CC4nfaaPyHDWdUf3yL0GvjzCGuqq5fuMqX/nKl7l+4zWSeEKv1+epp55haWmZuq4ZjSZs7+xSITl3/gIry6tsbm7R6fTQGtqtDsoRC4dhge/7uK7LuXPncBy7gVCuc3IPbWycYv9gn1OnTtNotPmFn/8lXn79RdbXNqhKYwmxQlkvHc9HOorLl6/w+muv0u12F/eIWsyCfzaUYU6mrYeqMuIdiFUGAxgOod+HpaW/7qN5d/wVjHc0WHEWnAwBCGMoqsqqb4xGLSonVVlR1fVJSd9xnAV3hcWuFYqqRhiDWSwYUSMEY6sonuuiF4uFchSiEmhTW0mxscz7vCyodW1JgVmGg33turZVBl0ZJArP8aE2KCzpzRJorelbVWukVJR1TZrnlFVtyaYLMCUtW5a6rhaEOEFeFGij0UYvqj7HFRsLxDzftQTVSuN7Po7vk+e5zTepDE6Ws6ccG0yH1RjMHcVSbWhXhl3pEwQNJtOJddrMC+tqWSvytGJvZ4gUmsiPCNwmwlQcDHcJQo3rCWoUEocir2n4kKYxzXZEcTTBGENdVovyuGPPXVFYAqaxYFAqhRQuRmc28NHzqIoMoWvQkKUZ2tNkZYY21pJeKkmz3WI8HhMnKUk6p9GSDAYD/EBRFjGOEpRFRZUJWl0f3xGM0gRdlhQio8wrzCIXZf/QvnYjsqqU2HFI4wSjHYRjqy5ZlnL71s2FqZ3A9Tyk1tSmQjkAGs/z7fUmJNpAnhfUZU0zaiKkxHU88rwkjSt0XVv5fKZRjkNVW+m8VICw3hdlBVlR2sRgaYMvpeNipKGsaqq8grLgSkeh0pRYCdAVjqlJlcSpJS6wHwYUQuIYTaMsOYgiTLPJ37t5l+d2BwR1zVxK/n2rxa0g4E4Qctdz+LXZjJ+OEwDmjoPe2wOpqP/ur6JW1+gocdJK0GFE9U/+Cebnfpa9771I6Hss/w//mlvBHW4dDbiYF6wVOU1t+AdpijSrfGr/iH/1//hX9PpLPPvsszz33Pu4/MgjrH/0o6j/7/+ISFKoa4TrYZ55GrO0RPSd79D6Oz9F4Ft7An7jH9jNyte+hrh/H1c51B/8IPKnfwaDYPv+ff7w93+Pq2+8wZ07t+lUY7ZCH991efl7L3Hn3i5+ENJf7jGZTogcl3tX3+T1777IB5//CI89+TSBF5zwQqKoYe8lY6udzWabixcuMZ5MLACRksgP6fV6+K6/iGcIaAQNxuMRodugs9Tl1s079JpdcOz36kiHTqvDytIq8TyhETbsdaTtzuSYy/QDhzmGIW+Vpp6Akx9WyjcP/v7RnaDvf/QvtT2Qpsjf/m3EV74C8zk0m5iPfAT9678OYfiX974/Yvwg7sbD5+DHzt/kbdyTt39bP2ZHezLe0WBFYvBcaSdqqUDavBeMQRl7o5vFY0opaqwM2SBASGqjMdrYnSWQ5jYgTxRWthcEAa7nkSSxtWcXEk2NkAptNEWWw8KpFKxayJESU2lqDL7n43keRWGTkMu8xFcOEokSCoNN0y3zirKqQIqFjsZYAIK9cI7dUBd7IcqysHOQtLkztdE2VBH7O9UxyBGCsqpRjqLVbhNnczQlUinuTSfMgWZVMXCOLwNDo9ZMpOR+khN028RJznye4LgOzWaLPC8oc0mvt4zvwXR6xMHuiFanje+6tNsR/X6b+XxOu9kBYxOSHSERSpAOErxALdpkJa7jUxr7qTUGifW5QViVkJACre3x2cpJQDMKybOSwmhqbajqkvE0Y3l5Cc/3GAyHuK6L43rM5xM8P8CEEqEFyWyGMC51mSNkwHg4YmW5x7mtM1SV5t7dbXxH4bsKTEkUhNSmJpnnKEex3FtF1GPm8wwlFF7kY4xhOJ5ijLCVowWhsdQ5gbIwUChs1a40ZEVGXVeouiKLU8KoSdCIuHfvFmVtbH5VVVNr0GWNEJZ/5SpnocTSSHwwLq6UuNKCcC0E0g2YxhlZmlKncwZRiwRDVJRMXUUubHhhLSWO1sjF9eZqjRSC1zsd3j+JeXKesC8ER0rSrGvem6WMHMkXVIApM36/FSGk4NkkYy0rKLOM6h/+Q8w/+HXkSWdBYLRZeMQITK/P+kc/Rv36a8z39hiWFZeKgnNFQSIkYwU9rfnocMxcKP7DPGU8mrJ9b5tvf/PbvOc97+EDKyt8YmUV9eRT9n4IQ/snyxD37tGt64ViCQhCzG/+JvUnPoH6d/8O8eqrOC+9hLlxg1ubp/i/vPkGt/d2UXnOT+zv81EqukqRfvcaLxvJ110Pc3qLo+mI06fXCKoKqSsme3v8m9/6//DUe9/H3/m5n+WRR68wnc0IwoAoiiwXy2ir2HMtiXwyntLrdlnqLSGlpNPucgIghOD0+jmyPCPyGmysrpMmsY12kFbF6EhFFEbE84S6b5BycU4xC3nxjwII+i3/evi54rhF9NAKZd6+mP1I8PFXu7TJ3/5txCc/CaurcOYMTCaIT34SCeh/9s/+So/lR42/LoDyp4GJJzymk1/604PLPy8M/S8BX9/RYMXU9UmZvK7eWi4/HsdckwfSYo1yXIqqOMl9cRwbOKexpNZaa9QiyK1KU8zCyVEbC2zMojXkuC5lXaMXsg2B5V9oU6MWfBellFW6LI5JSomSklpbyXOtNUVZ4rouWZGjsSBDK43nuCck2+PfNcZQa239NjwJOYhF5UggqerK/qysSV6aplRJhUajHCtNTJKMmefx7dDnp+cJVDCVkrauWdKGzzQ8xkoRlBVFOaWqahylKIqSLMuoKomQFd2OJZWeO7/J8mqXo+GACxfP23MsLd+m0XCtLFg4pNmcdsfHdWuyVDOdJDiuJMkSDGLBz/n+vB0lIC8Kur0O1ULBpRygtLwOx/WIGi7NZovBYEi1IIImSYqua7I4xpNA5XF66xTj4ZiqyFnqt2h1zlGVBt9z6Pcb1HXJcDQiCPyFVNthPontZ4kisjSj2+niOjl5kSOUJC9yysoSG6VStNttjgZHmFqQxKUlfZfZYiFzSJLE7v6BJElx/fAkl2a8f2TJ22LRkmRBilTixE9USonjKGptSc9KGDzXo9S1NQasLflbGMNIwMthwAcHI4zWpI4iVooeFUPPVt66dYUyhpf6fb6+tMw/unePoedyqO3Cn7kuVBXvSVI+FwbMPRfj+fyeH/D1vqBn4Od+5mf5J7/5m/Yard+aYH3cQgUYDof88Re+wMa1a7hJwkZVk0pBKgSRgbmUbAvB0/GcP3AdproiS0uK7fvsD464vbrGpdmMc70eYmPjwaIwmUCzCf3+yf0OdqGVn/884hvfwKyuolstpvfucfhb/wNbjuJrvsc/04aPz+bobhOZpmyNZlwqaz4QBHwpivhPsyNmocMzzz7HZDTj1p0dyqriC1/8In/wuc9y6cojvHn1Kr7v84u/+Iv803/6T2g0GtSL+cnzPMubWygOH3iXPJinGo0GcRxTliX9fv9kg3Oi7lE2YDRN0xMS7XGL2rZLf7Qt/sPr0Z+0kD78+I9VJtBgYCsqq6uwtmb/L7BEYfHVr8Iv/MKPRUvoT6q0vDv+fOMdDVaqqgIlT37WC18RY2yOj+u+Ndr8WJZXlAVpZh1sXceh1rYioZRa7CoE2kCtF9wQY0vrGGOJn2WBkBIhBVI9qJKIxQ6oFJXd2S9e73iSquuaajFpV4u2UZbn9nNIQVGWGMHJROQqx3rDPPRHL/gpZVmicKzU+bhNJZXlUiiFkFBWpTXFUyzaPPpkoZNK8LuBjzHwXJpzqqpIpOCPGiGfCoOT8LxGI6Igt2Ribf1IpIBGI8TzFOfPnwZR4Po1Fy9t0Woucef2NnlWU5YzWm0fqKgqQRAacmmBoJSQF4oo9JnNY4qiPOFkOItKzzEXCClwPZ8kywijkLLITqpHxgg83ycIfOLY8lbsXGFbgIHvoUSB5/iYQjLYnaGU4vKFc+R5zODoPtLxMHQZjvaoqooiT5lNp9Q11JVLXWii0MdVLq7rEM8zTFWjhFy0c6qTNpWuKzzXoRmFjCcVulKUi2tgNp0jBERBiK2Q2cpNnheUhWY2mxMnCWHYIMtypHIAYwnGC2m357k0mw00kJc1UsmFvNveA4YKXWZ0mw1qR1JXFV9dWQJd8+hkxlKu2fd9djwfJaChNVopXu10+OOtTTpxSlhr7rDYgS023lMp2SprekDhuvQ6HeZxzLDWzIOAg4Xk/PhafHhYsKXY2dnhP/yv/54vf+kLPG00v1yWRHXFSEgCrQmM4YbrcOg4bJUlUVVyqARKStIyp9AV10cef5Dn/OatW0RCIDodu7s+OED/3b/7fYuVOTpCfvWrmNVVzOoqs+mUb965w6zIeSqt+Y6/xPNaMwsjOpM53Zn1lckcxYrWfGRnF+/MBi9KhZAOygvwwgbT+S53d3fxo4gvfOELizBMyW/91m/xrW99i1/5lV/mQx/6EBsbG1RVhO+7J3Lg4/no4fPkui5LS0snc9bDxm5KKYzghAMUx/FJ/tRffAf/VqLu21/v7cf51zqGQ9v6OXPmrf/f6cC9e/bxHwOw8u74yxnvaLAiF/Lduq4xPAAbRVEsyLIPQMKxn4DWGiMlSqmTiUFrjQHqkx2LoapsJlBRFAtiq8JxHKS0ZXNT1xakmAcg5Vgm3Ygi2+s19rniGLDAyQ4T+WD3Z9s11YKfUJ3snMyilfNwtejhycR+bnOyqBd1aUm7dY1Uwqa9ZqlVxywAilKSPLdJ07k0/Ico4LOBR89YF9Opay26G55HVZULVY1tuxwPbTRJklLkFYaAjY1llCPY2b2HMEfUlSKO5wShIs9jqjrHUwFS1SBy2t0eiJz9/RmT8QRORLScALGHP6/WoNGEUQhI8qIkjjOq0sq6tXEw+thjQtudpqkojCH0Q9CG0WCEq0KkUezv3Wdza4VLl8/wSNPjcGBN2rJMcHSQIaVLFDSZTBJMbWyOVD0nDHz8KKRwCnQNkR/glJJMCBAaJa2R2f7uXRxHIdAYjW3TeYpGo8VsZvk6YRggao3neijXYzyZMR5NLGG4sNcAogJhO411XVIUtZVL+y4aQ1aA6zlURbXw8DEoKfCUIZ1PWVtaxnclpRJ8bWud7yz1iPKSiXSYSEFHQ6OuiP2Ame+jK42WkkRK2hhSR50sZR1tSJSkbrXoNZrEk5mV8Fcl2qlJ4hi94IEJY9VcD3b+hqOjI37rt36LV155mZ3dXXbaTSKt+cd5TqeuiJXiluPypqPomZqZgJEjMQJqjgntBmc65VuOw+O9Hh+ra8S9e5hmk/pXfgX9a7/2feVmOR5jZjM4c4aiKLh27Rq7u7toYENr3tvtEu0fMKpKLiQZsZBkUuErRVWWTIzh8sGAF5Y7vPDiy+wfDjg4HFMbRX9pmXkSU1YlxmiEUMRxzLe+9S2uXbvGpUu/y3/33/0fePzxJ4jnMb7n/VAug9bWTuFhQ7djB20eqhSFYWhVjKWt2B3Li81bOz3/RcefDIjeWon5Uc9/+PMfz81vB10n1ae3g6R+31bPJpOTigpg/91oYHq9t7Q3/iSQ9ZfVqvmzvu9/yef/2PFj/guOdzRYqasalFkAFYm7cGKsq+rk/mm1WjSbTeZzq07Jsgw/DKlJF9WIB34sVBUIYY3ijLGKFCyfRRis06rWx9IjyqpCALqu0UKC1kjXcluOJ54sK6lKa14nhYDFjglj+TVFtWjRYFUEJjdW7bFoIZ1IS7UGYXNV5KJtJZRc+Lws8l0WE5bRBkco25pw1KJqUxCEIXEypyyt94rrehR5RSok43wByrSGRZUoCALiJEZKS+xVamGgJxSBHzGdHNHptKlKQV0KOp0e00lGo+EThJ4NlBwaet0OjagNkSaKcrQWGAoMBZcfucydOwdQPNiBH5fP5QJUPuD6Caaz2UL5Y9Ba4jruolJjqxBVuYhHWEjP4zjDkQWe69JsuaR5iuO5HB4NSdOER544S7cXIZVkPKpZ2+gzn2oGhwlhEFGXJaGvyPIYRxp63SZFnlBkBWiJJ0G4At8P8XwXpQRlVZClCXUNRVUhheXAxLMpVVEwzVPyLKUZRCwvrzCazJhNbSaL53pMJ3M7gWMWxGrLclRK2iiBqly09AKyvMLxPcq8QJuayAvorq0w2D9EmBrf81DC2JagERwZYZlBdc1ISQbSs63LskILQQp8Nwp5fzyn1jACOnXNioYvtFskjouTF7SiBp7vW6k/MBoOqesKbQyT0YilXt8aLC7A+Wc+8xm++93vMhgcUVQlOIr/qdtCSMnHBkP2HYeh49ApC5YQfKbZYIhALCptvtb84mTG+5KMrlL4WUbxX//XqE98Ak6dOtlRv32yrtptvGYTM5mwm6bs7++T5zlLxtBYWyN49DGmu3t0ywpHGzKlUMrBN5pCCMaOQydJEeOY1+bXGQ6nSOkTNdtoA2lm3Y4RoKTdVFRVxXA45Hvf+x7//X//f+ef//N/zhOPP2aPp6pONlUP+6G8fRE//ixCHDf/Hkh2ju0UTuYFxI9IR/6zLV5/9irKn78S8/a2/fE5OTGje/vrLC1hPvIRxCc/aT//n1BVe3f88PFjUy37M4x3NFgR0vIyjDFUiwrLcRUF/WDhe7i1UJa2QnB8sx/zU2pqokZEmqbkRYHreva14QEIOl7Isfb1lugoQBuMBNdxcJRCCas8qiqr7DDagDZIxxJli7q0x2oWnrbCKo9crU92TmYBZow2iGPeDXa3dfx8XdeAOakaSSlsu2YBpJRjowH0ouVUlAW+57K01KcoKo6ORkjh0Gg0AE4WHqtosBWaoihwXYdaV2ht8P0Az/GYTlLiecnh/pQsyTkj+0jXkOcpQhgefewCo9GIJM4ocsVBMibPY9bXlxFSc+bsKRwVUJUaqChLg+/7JyDlmK+itT7h3xSFVV0Jqah1gaO8EzLjfD4nyzLraLyw7XccZc+/cPBCHzeSdFpNyrzBnVvbpEcZ3q0hZ8+vkWYTpLLZQr2zfVxPkCeavXsxnVabRqQIQxdHaS6eP829e/dJkxxHCxwlkcYudv1el053lTxPub8/Yvdwgh84LC13GQ6HaF3iuSGdTgehDTdv3mI0nuK4Pp7nM5rOMUYsqnnQqSp6xjASgrlv811qXUJZI13LqxJCgwTXczF1SbvXQlY9TKVpRCF5Etvrd9HOFNISX42prXmhrixRecHZ+myvS1zkPDEas6U1qZR8rhnyxU4HWRlrSFhWZFoTeD5ZVXNwcEiSpvY7kFZWW1UVVWWdor/5zW+Spil+PGepyBhLyZHR/Mdum6Ms5YNlzUZVMROSz4QBn44i+lVNzxhmBn5xOuNn5jH7jstuGLISx8jPfx7Z76Ofeuot9/Rb5ojlZfSHP4z85CcZ7+4yHwzoFQWnw4DXNzb41BuvU0rJTxUFyhiC4/lC19yNIqhqJgZuJRmHdYnWgmYjJEkz5nFMUWYsQnpOrt3jnwHu37/Pv/gX/4L/5r/53/NzP/uzBEHwlvyeP9e8J8RbgM6PrXzjB4y3V1CO57qiKJhMJvT7/RMw94OG/vVfR4Llrty/j2k0TqpqvA0o/XVVGf6s7/uX/fy/KeMdDVaOSbMPh20do3WpJK12G4yx0lvHRSor0U3ShKKqcD0XFm2Uqq6gFijXwRWWBAlYEOE4Jzb4YRhQVfmCTCuoigpnwdiXQiCRJ6S46hgYLYCO0IIKTlpLLDxdMAYlrUeMlJJer4cxhjzNqM2D3KETku+CxyGEzYCRi5u7qq06xrZS8gccGWHVUXUNzVaPujakkxlRFFJXZkGarRfAxoK+PM+REtwFGAjDkCIvFrwESNMCKXySuKYqU1rdjEbbsT4nVUKcTFlf32A6ThkcTSiLgjSpODgYoBQkXs7W1ha7OwObBWQEKyvLbG9vU5YLjgYWOAmsEVyW5egFN8DzPKSwfJ2qLBHYvv/xInBcbVKOQDoeRVWwvXsHqTaJZzmOG5CWcOfWlHt3j3C8AuXlXLy0SdhQPPLoMnUp6UYtfDegKFK0KcjTOcv9Lqc2VhiPJkjj2AwpaXA9SVXlmCrFkTVRKOkvtWzgZjrDUZJWo0mtDWmSMjg8oshLW11Lc6oahHQQAgJh+JV5wvuyjAaGRAheiHy+EHpUrkOWJbR6LUuoVhJd1XhCUFUFStqk3DyzmVnVwufFGE1ZWRNDC0Y1QkKlLblcOS5GCErl8nv9Hn/kuay5LlMpkRrW84J5UTNREqkcolYT1/OI5zOmswlFnvPa66/x3qeetgBZa4bDAVobhtvbfPzOHZ6aTAjqkjnwQuDzuSWX32lEfNFAR2tGSpFKxS/OY96X5bR0zVpVcbqsyKSgW2sOpeR+u800CFj66lepf/7nH9pV2zoEPOCvFb/6qxTzOZN/+S9ZjhNmSvHFXp/fnc3IyoI/Xu6jdc0vj8acKUpiIbjjuRwBnSLnM40Gt/MKgcb3Q9IsJ4lTaxBZ6xO+2nFb4+GfJxPb9nvl5Vfo93o8++yz1ixP6x/gb/In7Hbfptg5abkYEOJP26oRP/B/3yJtPnmPxX+cnFJx0mZ5K4n5be/0EGh4eF19+/PsBkuhdU2apifz3PdxcY6PASAIqP/ZP4Wf/3kYDm3r56GKyrHL+A/6rN83Hj4l5odjvrecgpNf+0Ek2rd+3j/NW/8pjvLdsRjvaLBSKyv1dRyJkmJR1bA8AY0gzhI838GoitxUFGkFrsQU1qjN93ziOEYpSVlYRY42hqJOkY4FHT4W0LgLsKKrEl1VICTCQOB5YAyu41pkLxcut8bKlzWGWkJtaqra2B3sgrkoEFZyLMUDvk1WUlczwiCwZFmhqBc3kuKBEkkY6y1jqkUFoq5s+0vYaooSxyV4OwGUZQG4TKczpOOgfMV8nqKkT1bW1AYoq0ULxQP0wkEzsIqdNMMISVFWCGPQ2npL5GkOeNy8vkOr22B9vU+j1UZXDuPJjKIskG6NMIKo1bHJ00JgSs1RdcTZjVOEssm3vv0KYVDT7EIcp7huiKg96tKAtu0FqRZx9cZuZmttHW+VtAvxMVh52HbcVRJdF+AIWlGbVtTCFRHb4yFCS7Kqpiw1AR5lnJO/ch/PaRD4IZEfcGariy4NSVJzcDihzDKGRweEQcT5c2eIkwQjBAdHA+JpzJnzW1SUzOIRhAK3Bp3ZpOyy1kRRg9l0zmg0QEkX4SoklqQr1HEGkOIXk5SfyXMOleS+kvQE/FxWEMUpn17rEhAgtUTWgjzL8V3ftjOLmuk0pjQarSRZVVFridDGBnwKg9YFxiwqANqS0WujkdpQG1BlRSgEbneJWGs+urfHe5KEoKpIpOTFIOCP2x2SPKPZbiEkzKdjXnrxO/zP/8v/TPTf/rc88shlrt28DkIQhQ0+duMGzx4O2FOKHeXQqGt+Jk5RaspvNyL20oz9BTD9jTjmZ+KUI6UIteFUVdHUmlQoNHA2y0AK9vKMzmRMurNN6TkoR1JXJZ5nE5Udx14jTjvkjQ8+z7/6T5+i8vZJw5AB1iFayoBJbfjt9hJfDCJ+ZTbjcpGjtKGqaz7tB/xn355bKQRpllOWFbq2FUxt6pNKh9b1YuUx1hpBlwjhMJ1N+NKXv8qVR59gb++IjQ1F1AgAjREL2bFR/Ohl66GKxPHzjOEBRvlRq+QPeMw89F6i5uGV2xhtww8XKqN6AVTsYmwl7sfVue8DN/zwNpA5mfce/NsgbPirNrTabaSzECtITu5jjDjxkrJ8bwkry7DUt214/QAgam1OKsPYuvcPGQJzAtDEAnDaFvrJ6yw2kg+jrAdAxfD28/oAqIjFMTxQwn1fBAJvf823vNL3HfcxODTwEEj8wZ/ubyr4eWeDlcVNhbBGbWVZAxrf9ynrCkGB5ymE9PDDkKKoGQ0n1LUNBdOLILe6qk8qI2mWL5JuJUKA6/m2qrGw1q/rGiXtc493AoHvs7y0TJ6mNkNoUU43tkOEdF1qXduEYWH78LXWSGNvPaMNQi0eUwK9qCIcp5/am0VQaRtCeKKakdbgTkmJ5EGryFEK9VCekV6ondCGyXhMs91gZXWVOLlP1AqRXk0yTyywqiqUsO4bXuCQlxmtTp84y3BclyKrMIuFT0mBVA55XqI0HOxPyLOSXq8JokA6mmYzYGtrk9FshhKKZD63Si0ctKqoq4y11Q7ra216fZ+gvUZZ15S5ZPv2IWiJG3hkebbY3tie9nHlxLb35ELl5Jz0vKvKmucdA7u6rKkQ7O8NUMLDdVySIkG6Do70SPOMpd4azVAxGuaU+Q7ra0s8snmBLE5xHI0QfYbjCVWZUyhBNswYjWcUlcbxPIrKcPPONpXJSPI5CImuJVL45FmF7zUYjWbM5nOrNqvszryoqsWm1aAca2n//jzlSElGrlV2xWFAw2jenxd8XZeU3RZZXKIrK3HHWNm8E4ZI5WBEgVAS6TiU5FRFYZOcF6ozId2TqpWUtiJoFltDJQTSCKos5+ODAR+fzThyFPcdRbPWfHw+p9aaT6HxGiHtdpvDwRF/8Aef5iMf+RC3b19n/3Cbm3dvo4qSZ194mV+5dcu6CktJy5Fc930E8Mxszmc8j0IpqqpiSWueSzMGjiIWgtWFWsgXhrbWJEKQC8FampHu7DDc2uS7V19FDPdI0pg0TQj8FlHUpNn0SfMp585e4M2rb7CdZRRBiO/5mDynv7REkiSWkO8KDiX86zCgU5Z0tWa/qjhY+DZJrakWVc23OFZjF0etHxBj7Zp9rI7SKCMYj8cnLWapFGVZoRz5ll37D/NK+VEL7p9uZTpeWI8XTPHQz+YEt1hq1IMF1vLxqoWXlMRovZjTDMemmPbKOa5kcbJIH4OGt4CixXs/XNAQQqJNxWw+J88zokZIEFjDPNvWOXbuti19ISUITlSaelEFdl1vQTjmhF/3lkrU950SW6kWx68FlmRfZCfCC8zJ1LsAlJZreEz2fhgrPGwvYYnlb7Wc+MHfyZ9+vB2gHFfwfvBr/80c72iwAizIlQJtBFEYoKQgCD06nYjRaEAQuUhls4LcwGdsZTpUVUWWZW8hqpVlads1yhI75YIs5xyDBgCMJUJ63gkA0HXNfD6zyIRFT1lYZ1aqivohwqqUlnth83/MCbP/ePE9BiLHO4VjDoZ95wf96uPHHi45y5PdEA8kj4v3cLVDrxlRVA5ZkWHqkq2tNeK0YDybgQLXtTeCcjSup8iKGb7vMpkOUM5CImsqBDZIUWBvcLsbUfiuT5oUrPR8knnKbDpCrPaQaxJfOSgl6XQiyiwjT+cYk5NmEe2Ww6nNLsqHRrtFrRWjYQoG2u0OaV4isHk7Nj/ISp/NYjIDTlRExy0gKSV5nlPmmk47ZHVlhSybMRlPcR0fRWB34kZTlRV1UTMZTRF1RBR4HMYzXOGT9lPKqiCMfJBWlZRkKUVZ4LoBURgSIsjLilYUIRyBH7bYH9SUtaHUgiwraYRdhsMZWZqTxIndOWLbMVrb61gpa5TXo6YlDDsOSGl5NK4rmRvDel6z4TncoaauKxxXoQ04jiJLYlzfQzkPOEzH1zYLAO4sqmNa1ws/Q7kgUJuTFmON3Tm3q5r3JAlDx2HkKCpdM1qo2p5JUz4bBQxGI7ZOb+H6Hrdu36bdaQMlnU6bm7eu87Gb99l87RoKw8CRiFpzPrcVvJuuy1pd0wcGi2u4g6BhDLtSEegaRxvm2NDEnjE4eX4SEbCzu8t3HrnAC7evYm5XgP0MrhNitMAPJHkx5403XuO1V26RJDFCWOnvMRFWSonvezY52xgQMFKKieuSLDYnx9fX/5+9P4+1bbvvesHPGGP2q93d6e4551772r72tR3bcXATpyEQ0hJeKnm8V1FVSFWhkioC/iAgIVRIJCCIxD/89Z5KJRDoPRXwHOBB4jiJ45CQOG4St9fNbc+99/S7X+3sR1N/jDnXXufYCQmhnspFTWnrnL332nPNOdecY/zG9/dtNko+ttrN2yvmx3oAfUEzNYbx0RFf/q3f4kMf+vYuw2m6ma9EPyP+Aa0a/+z/4VXJH/Rbt+nlbL9SbhVGParj+jfCOsuDew85Pz9nMh5zsL9PVVcUZclkOiUbDqi7+BCsJ2wL6O5l5w00vWPUY8fy9WflOYWGPF/DIKVd1MCYJEkwxhFFMU3TkHTqn221lNGG1WrFarUijmP29va6gkV1gMij1crjn0/P9euJvWVZMpvN2N3d7QQSduva9YXoN0JUHo3D2Jamb4/ljxzMf3J79BPd7IcLh3Ip5f+/WPlm2YzxH5o1jjBU1HXD7u6Utl4zyEaEwYQwDjk5Oefg0jXCIKNYl2QpHB6dbiZ8rbVX4jg/2Oluwt8G+2TXXgmDgCgKusHLdi0R64MUvbe6X9lb0wEBEjpzMf9gskl77sm+/cTak4B7hODxHq61BuNEV6D5m3SbpNYavfmbfjWy/TppHZN0wDCLMU1JmCbk+TlKWoQTDEcD8rxhNI7Z3ZtQ1wXaaJq6IU0GtKFX2+jmURMq3Rq/gtcWpy0P7p8QKRinu4gmpF5aRCyoqoLL+3tMxgPu371HEqfESYih5tr1XebLJat8QVk5rA6IItVB1H0IokUKbz/voV8/0fcPbn8d+uuntabVLXnuOMFitJdsYw17l6es1w/965WAQOGMo60MJ0dzBmnAeLjPar1iPBoiA0VgJE5oJtMhu3t7NI1GEpCkKWezmZcTtzUOR5ZJisZxdLpGCcvZ2ZzlYt0NogKj/fH7Y/fokHMGKQVFElLmkgmWdRJ7xZrW7BhLpQIWIiCOEoJxzLqoWK5LwjBiNBpi6pKizDu4XtG2LdbYriiRXTvQbVqEUga4DkmzXetDSINBcFlrRlJylMTEShF2iGAOXGladqRk6QxHJ8fs7u5y7+5dHj64z9VLU95x/RqX793jrXeOOEtjZCBRraFQCpzlStMyR1AIwVxKQuWPdWYthVQMjWEtQEvBJWOIcdQCtBDE1iGE4zRUfP4tN2lNDsIgpWPUWNL5nIVSzCqQgaU8ybn16otYp9GNz/dSSlHXlefx9M+h8j42fSFzkbTuv9+eeLbltZt2xWNbbC1/vix5f9MwRFB/5N9T7E3Z+5m/4Vu8UrLh12xaQv0C/tEJTUj1dfvfbF098gfzJdyjKp3H65cO16GbtH/1Vz/GP/l//hO01uyPJwzSBGssRVOh4oi9ywe8813v4sf/4l8kEIE3yBWdX7jw92pZlgRBQBTFj3E5HuWiuM69um1rsmyX5557ju/8jg9x+85t8rxgMpkShhFXrlymqmqCQBFFfqw21hBFIefn5wwGA4qi4HJnFpdlmX+3rffd5tj0fKa2rQEfj5DnK1555SXe/va3M51OqeuK09PTLtTSm1t+fZCk3+q6RghBFEUeNVPekLOua6bT6Tf4wL6OMrP1kfT3FpufbqN2bds+ajC4zR/qTvAbScH/sO//sG0b1fmj7vfx3/2XIAV/UxcrVVXSNDXWaOIoZJilnJ+fkyaK1WKFDAQnp6ek2ZAXnn+BS5eeIJAByyrfDERBx0XR+kJeHAehhzydl44669cJbduSJMkGCXHWEUjpkZdHiGSP3mBwURkr1bV+3IVaqa/soyjqTM3cIwPjRsorxMZbBugGA2/nr5RiXeSbHKS+IOpbIdZayrwkCgaEUUDZFMg4YGeQYQeCsmiYTobs7SUkaYAxNZdvHuAslGVNGI146aXXsbbFWFBCIruWjFTKFzFWEwpJU1laawhdxM2rN3A6ZFWc0zQ5oyxjOV/Sti2T8Q5JkpIXOVVdYC2MByNMs0YomO4MqGuL1Aa3peKS0it92g4h2H4w+pXX9krYGkdZNgjnCX0GWK/XRJHCtBojfP4K1uKc5xEZ7Th8eExbBTRtzZUrV3DCIZVDBI6yyXE49nbGnM0eMpwmtNowDlOsc0ynIa0bkZf3eHj/VRaLHGv6lVZveOfJws7ikStnvYRaWb4wjPj+SrMwMDeakXGMjeETe0NOXcjV4ZSyLFFRhOmQlSQKMbID2oXwgZ7OFxhBPyl2cnALmzwrbf16cWwcWdUyV5JlEHKKY+UscVVRxTFSKZI4ZtTdr5eiiFJKlsbSVjX7O7sUZ2d88KXX+VNffZlLzvHEIufBdMxRGPBE3eKEo0WwYwxXjObfDQfMOoJ6kiSstOazxvjcIaU4l5In2xYclFIQ4CebQgp21zlidgoHI0JT8s7nXufG6w+JG0vjLCdXdvj8268zH4zJ8xXWaLQ2jzxbzjn2gGHTcC4s87bZjAnb7Z7+GX0cjn/83+378M9XFT9YVZ4QrBRTazH/5t8Qv+lp+D//n1itC/I89+hcGpIkMU03tiRJQr7OqeqKyWSKEmHn/6QJgnDTghBCeDSkUw46HK4LllytVjjnCCMv74+j2BcTpu3yxTrEyFoC5aXyWmve9KY38d3f/V189Jc/yq3TU6aDIdlgwGK1xC5mLKzh8Pnn+Qs/+qMEUeBbR93n0rdg792/x9UrV0iSqDPd9DyeXgzhYzR8gVZ18u8sS9G6oWoqHj58QFXVnJ2d8eSTT+Gc5cGD+wwGg654CBA4rNUcHT8kDAKeuH6d27df4/Lly8RxSNBx7/oFwjafxlpPGbhz5w7T6ZQ8zwHvsJzna8bjMWdnZ7z00otorVmv1zz99NMbf67tohXgtdde4/r167zyyiscHFxiZ2eXs7OzjZHf9jyA8AjN9n11cVwWyQUi/vj80e+rvxd7dOiiEH20KOjVsRf3p92g+WEY8Z8qIh4vOrZ/vl0sbXsq/X9KFv1NXaxkWcZ6vcYZzWh/l3Kd88QTV2nqkqPTFVGkSNIhTeuY7Oxzcjbn/HxJWbdY69Ujm+RlvNQyTmLiLCNfr6mrmhZfFIRdCq8QdKF0Fuv8wOeTnQW0Fx9c3/vti56eGGWM/bq+9/bN3PdL+w9ca721T3DKFyL7UrLXapYOZlJsyKX0r92asH3xojAWjIZ0kBAnIQ7NEwcHnJ+fo6KA+dkRTz55jVavGQ4jpK0QQpGGgsE4YzjMqMsWJSRtuw2x+iyjUPk+c+f8QNlYnn/lNdI0xKmSySShrRylKRGE3H9wxDCbMBgOCYIhSRQxny0ZpkN85p9klRcUdY4MWqaTKavVGq39O6jAF36qa61trzL6LyUlUgaeqGxsx+3wq5OdnSl6NidSIUESdOoZyTDLqKolAsdwOCIIIvKiAgRV29I6iwoDhITT1SkygtquQUJtGoQISLMBs6OCV2/doSga6qpFyahzeO1aVcqTlKX0xFqERQmLMTVfessNnsgb3nIyZ6c1rITjk9MxHxtnmNZw7+59VCC4dPUacZJSVTVVUeDREuFdiruVu1ISYR6dZJWSnljeahLn+LPrkndXNUFVk0vJl7IBHxsO+Hyc8D157ifDMOSSgyfKgjyM+L8slpRS8vkk5SN1i9rd5QcXa757vaIYZhwmIfvzNVfOZpwHkltRyNVGs+MsGvj4cMCvjkab+7sfVD+CR03f1zRooEIQCEdmfa7RcaColeRqUfK2zz3HZ//ct/HO517imZfuUQ0zwrLi8mzJ07cf8paX7/DZd72FT3ahn9o0+EBJRyosP7Ba8W1FSWoMuZR8Jgz4SJZhutbr4wPwdlGyjab091s/iO9Yy/vqhmMhOev2daIUrJaMf+HD6He9jRdOjzk9PWYyGZOlCbu7u+R5zu7uDteuPcFv//ZvUxQ5BweXuX7zKeIkYb1eM51Ou/TlTsnXNEgEn/r0p3n66TfSti2LxZK2aRBCkKYpb3rTm5lMpniOHBSlJE1SZvM5uwf7aNN2Dtgtzmm+5V1v5+WXX+CrX/oy67qkrUs+dHzIOxYLBsDV4ZDkX/5L1j/4g6jBsGtxSxCOVle8/votsizC0pDEMXGS4Kwfy4Ts+C/ddSvyNfP5DNXxPebzGcvVguFwzOuvvc673vUulsslh4cPuHTpEnt7Pu36fHbCfD7j/v07jMdj5otznn76aZq2ZDbXpElGVTUcHOxvWn6mM90Mw5Cmabl162WSJOHo6IjLly9z9+5t3vve93B2dsrZ2Qm3b7/Ok08+yfPPf5Xj40Nu3rxJVVWMRmOuX7+xadFnWYYQgpdffpnBYMBkMqUoCvb3979uIrcdmpTnOavVEqUUo9G4a3U5HL38/aII6ZHivojuC5f/FNlWduIDuOD+LBZL710Ux9ivBwQfuae3n4GNLQh83Tk93gJ7pID6L7R9Uxcro9GY3d1dinyNkoq3ve2trFZL8nUFIqA1gtly4TkaSMIwpukg8SCQ3/DDkK3CVRXgfSskYLVFO4+8NE2Ls2bjHxEIvyrsOQI9MoIQCHXxwbnN+2iMubjhVEcs7FGVIAg2lft2e0N2ctQU+MHFknfnBZkxFFLyhSzj49MxcRwTdCjRBWnMH0NTN8RpilAxEDAZjjCmxhnNm68/SVFVzNZL9iYDxpNd5otjqmpNHA+Ik4TVYsZqtUAFAmcArC8ohA+GFFiCwLv86lZjtEErSWsd5bJklCiS/SlV4VttURSzmM1xOuXw4QOEFLzh5k0imXF6dszBlX3CTLFzKWNnf8SD+8co5Whay6XJHkVeU5Q11ggEaiMHf7w/3LfCpFRo540DpXAYoymK3JvvBTGmbbukboejxdEQhlAUFUIIyqoBCUVeMdmdYJzDaoM2Gm0akjjsSH/Kq9GN5atfeYmyaMjXJUr6lbF1Xr0WxzEOg7OSPjVXCAHCMRxnXH7jNV4YKB5oR1ZoHlY1ZwjCszlCW+IwxVrvHFu3mrbR4CxtXaGiEGfZtDh9uOfFZOv5WGxIyd83W/GhsmKZxJwIQVw3fNdqiXGWX59OCHD80GLJlaIk64iNc+e4H4UMreVDJyc0gwG/0xreuVgyjwNmztEWBbGCtzSaJ7Xgt5OYhVBc1i2/EkX8627C3fZBAqiE4BfShI/HMW8xhv/7bM6b2hYD6J71KCVNKLmxXPPS0Sk3XnvIKouZrCv25kuaKKBVMFiXPPvlW3yPFfyLYLDFO5H84GrN967XnErFqZLsW8uPFCXOwf86HgGPIiX989RvjxfG29vEGAbWcq+TiYtuHwsBr375Ob72L/9nTneHWNcihCMKY6zxCrzRaEjbtiyXS5xz3HrtJbLnnwOgKArCMOSJJ65z+fIlgiDg7OQU23o045VbX6OqPBk9jEKUVFy6dJnF8pyD/QOqumExX3D16hMcHBwwmy9QkWC9WnF+fs61K1e5d/82n/v9z3Lv/usYYcibhu+enfNdiyVngeIszbiqJMEv/hLtndv8Zprwru/500ze+EaGwyF1nXPv/msslmeAQamAZ599lps3b3J4eMje3h5NU1PVLWk6YJ2vSJIYbVqGowGf+cynWa9XhGFEURTcvv06d+7c4eDggOVyyWIxRwhBUa742vNf5u69V3nqqac4OzsjjCAvFty//4D93au0reZ7vud7kFLwwgsvYa1ltV6RxAlXrlzh5VdeZrlYcv36dT73uc9xenrKRz/6UfYP9rl/7z5lWdI0Fffv3+P+/Xtcv36dL33pS7ztbW9nZ2eXyWTiUeuy5PT0tMt08r5Pr732Gjdv3nwMbXB+7LSGT37yd3n55Zd9/Meb38wHPvABTxZWMUmSbtqVvVPxhquyxUXcdgDuUav+3gQ2/EitNaenp7z5zW/m/Pycq1ev+MUbf3Chs33f9+/Vt6DAmxMCjxTt/eK/R33+SxYsf6xi5Wd/9mf5uZ/7uUd+9swzz/DCCy8AUFUVf+Nv/A3+1b/6V9R1zfd///fzP/6P/+Omjwhw584dfvqnf5rf/M3fZDgc8lM/9VP8/M///COD1R91K9Y+/E0KyZXLVzg7m3F6ekwcZQgZU5QtxgAi7AzOak9m7CbUbUitH8SqqiLuiHpSCKIwwinbKTd8QGHbaHx0u0J0H6Yx1stPO7a6cw5hHx3s+oG5N6rrCxVPrvQVa9u21HW9sfff/uClgO+dzfmudc4sDHkQhoyM4bvmc5q24aP7ezjcI3+7IeqGAcZC1WqaecXpyRHONOztTBjFQ8bZmL29Xcp6SZUXmNZitSVIFKfHp7hoRBRFrMqyQ1W8WioII6zWSCGJ4oDBcEhdtayLgkpr7zAbhFgDTgecnSwp6yV7+7sIUo6P5yTJgPnZEtPcYzxMybIxQiiUhLJaUVY5l6/sEIUpb376jdSV4e6dB1Rl4Ylx1UUrrZ9ALgoX762D8CiDL/o0YaB4w1PXeXB0xny5JlQBUSiJFExGKTeuTRHCcnoyZzTawaGpyoKj41OMg30RenVWlmAsnJytSJOUuqxIk5CXX3yFhw/PkDIiihLPrWm8sZ6/59RFTpTu3IElDAYJTz99nbJc0jYV4aV9yjTAlYbr6Ygnb15iNpuzmK1ZLDXONh4Od5rpaMzSNIRxTFnUJGlGU1borr3lnC+oA6m6VS3sInjHKmcWKNZJ7N2Os5RVXvKtdcVvNQkNjkIKXogjnqwbpIB9Y1iVJS/HMY2QvKsouRvHDJzjpGtzSiG5PchIVMC1Zc6NVnMkFf9uMODfhQHy6yBru1k5SinJo4A3rNdkzqGAsiuMd7vW3cPLB4QORqczEm3JI8n4dIYGtJJYqRDGhzd+y+mSXx5FHHfPxK5zvLcsOZeSfWO41rYEzqGc478vCj6RxNzbKkK2EdBt2PsbbdZazoFcCsbWcarchkcwspYFlt987os89d53YF2NdS1BEOGMJQhDlutT3/rVhiAMUEFIu2xpmqZ7f0lVr7h3/7XOTkETCInWhrquNq0BbXxr7fj0PuezY155pZfGS1597WWvmtSar73wJeq6ZrVcMhqOWK9zXn7lZRbLOY1uGGrLt1U19XQKgwFKa567d5/J4RF7X/w879nbRf/qr3LnXe+k/d/9CGdFQZ4vePjwLlGkqOqKO3df4cqVK515Y921sRRveOObmc/n7EynvPLKS9y7d4cvfOHztE3D66+/hhCST/zub6O15sHDe1hr+PJXvkCWZdR1zuHDBywW5zz/Qo6Siratcc4joi+//Aq7O7vEiWI0HvF7v/d7vO2tb+Pll1+mqmvSJOXhw3sIKTk9O2K+XDCbnzI8SxlNMk5nR4RBwK9//NdY50uSJOGll5/nfHbKa6/fYjqdkGUpSim+9rWv4pyjbRvOzk559dVXOT4+pq4rP26ulvQii6Ja8+rrt3j51kscHh+RZRn7B/t87YXnefjwIW96w5u5du0JnLUbtKX/8sGt4WYO6R3bRbfw8DEf/fMnePDgAa+8/Aof+tC3c3x8wpNPPunFJWXtC2TnaJvat42jaEOc8bweNoudOI45Pj7m+PiYa9euEYYhWZoSdjSDpml47bXXuHT5Mk3nwD7d2fEdB/642qdvvP2xK4S3v/3tfPzjH7/YwVaR8df/+l/nl3/5l/mFX/gFJpMJf/Wv/lV+7Md+jN/93d8FfNX1wz/8w1y5coVPfvKTPHz4kL/0l/4SYRjyD//hP/xjH7x0klj56m5+tqCpGwKZAL4v66wnZ2IVwvoBD3zWi3BAp6VXUhEF4ddp4Y32fgpKShrT+BaNcwQy3Mj8rLNY4zkH2lqiKPStmC6vyAkvLzRdfohXH8S0bbtR8/SDC/AIktL/DvxNsyME761qzqOIs64ddCa9MuJbq5r/2NSswnBThW9Xxaozj0MqirJCihDdWs4Xmqo+JUsj3vz0TaaDK9x/eI/WDNA6ZDazGJMwPy7QpSCNB4yHEWdn5z5oT0ocPlpAG8N65Vd+SRRTFBVSCJQUpIOEZZFTlAVxnKLUhDRNWa2OOT1boI3j8GTGbL5mNBpwxcUUxZp1kXO+PEcFAdPxDsdmQZZm3Lx6FWU1p2czpAyJorAr6jzfoGl8ASCVQkWeDxJGAVI44jDg6sGUN968wnQy4PDohKosGA0yBJbLl/bZGY9wOI5P1jx8cE5jKoxrUDKjWjvunJ9gnKGNGoajjKopaTJYrQqK/JymdqwLjQpiqs5MT0iHMxZjZVc8+badCgVCaoLAcf3mJcLIm/opAurC4VxNGocoDMpVXDsYsjOMmM9zyqahtZCXDucqhtMBurW0DmoHtXO0HddDKoHwJkRIEeKAoS5JneU+wsP0sfKqsmDIflHxrQc7vP/2fU6SCCMlb9CalZIkFq4Zw10sRai43LQQSlbCkRhNoWJwDoPlOITjzPI/j4fcUSHHTpBl2YY35u9XOk6G2RTnO8bwvrrhpTRhWJSk1iIBI6ByjoUSiDBAD4coY3ny7gnTZYmTAlPUtEpSJhHzKCY1hqlrOO4g9qmxZNYyMIYn25ZCCpYIUud4Srf86HrN/zAc+rHgMV7B9iJge9W8/bzNleLTYcgPdcnuS+nzlfad5dfShK8dzgjunbB/MCGKE2Rg0UITpJGX4zY5CMF6vWA6nhJYME3ZFbkOTEPemV1q3XYp4dZngoWia3cYqqZCl5Y4STqZuqRpPcctNt4xOr9z0plmKg6PzglUQhhFPPWGN3N2NucprTlY5xwlMVmW0jQtT6/XDGYzaizzy7u4csn4N/8Dzz+8zafe/CRCKpyzVE2FsYZGLzib3WM4HHU5Uj6E9OTsXsfdc3zxiyGHR0fopu3Q6AYVhOTFrGtz++c4y9KOLN5yfHxMvi4Ia68CquoK6yAvc4TUnC9zPv3Zh8RxQtM0fPaLZ2htWC3X1LVfKMZBzGJ9hhOW1hSsinOOZyEqNgjpqPUSJyu0sTz/wnOsVivC0LHKb/LKq2vqquErX/0inojtid7n5zPOz2d87vO/x+VLl6mbmulkSl7kaGE5nZ8z2BljTo5Z1xVfe+Vlqqrhc5/9LOLPSsaTAYeHR9x44jqzueHq5Sss5mfgBFpHJEmGUj4X6uTkkDiOCcOQ0XDkuYTWt8oDGeAsOAv5KudLX3yO+XzBznSH9Trn8PCQV165xWq15Md+7MeQqm/x+H+//OUvkRdrnnrqKZ7/2gvsTHdZLxccHx9TliVlWXqOUJLwtRdeZHTnLkEQkCQJH/zgBzsKjefF/Um3P3axEgQBV65c+bqfLxYL/uk//af8i3/xL/gzf+bPAPDP/tk/421vexuf/vSn+cAHPsDHPvYxvva1r/Hxj3+cy5cv8+53v5u///f/Pn/rb/0tfvZnf3YDK/1Rt7pp0PP5ZqWTpinaWCwGofzgIpx/QJVUXTKzb8n07RZrfbpwj0ZEUURVVRc8Eedo7UWQoW71Bt6y1qKbTu+ulPdP6eRurbnIANkmOfVffWH0OIlvG1LeHgS11owExFrzQClM53fgrGUpBJetZdS0LDup88aXQFycv3TO97ERVHVDWzdIFdDmBdlwwNlsjZ05bt8+YlWsGIwG7O7tslznzOdrhApIUi/5vXr1Mqt1SdVY6FaEUjis8b3YftHcti0qDMnLElWLLphNcn6+wLc+FNrU3rhMCBoDq7xGncyZTseU6zm2VTSNpi7OscaRJiuGw4w0jTk42GWRQ9NY1ut8o0KQUhB2HKMgCLDakKYpwzTm2be8kRtPXCIMYDAY8exb387pyRF5vkI3DWWZc9L1pVeLBSfn57TWUrcVSRxTFwanNcY0VFTMTgEpqMYtUTTk+PiMvf2rKLlktVpvoNH+fvMrsBYhJEp5fokzmmuX9nnm6ScpivOub+1Io4CmqWmqilKv0W3LerUkSzOuXnvCq+WV4vDklKq2VJWlspYoSVmXNVVZEUpJVZdEUnYBnwJnPdIyQ1AqxcAaVt392GrNoNGspUSFkkmgOBkNEEZjqorUgU1C0rYlU5JEKWwUsLyyx/MCPnByhmoCdJIRNAW7reHj4zHPxzGtNp2J3wXnY7uFIqU3CQPfSkmN5TAMeT6OeVNd00jvszKyhuR8zirc59s++wJXHp4zzSscjipQSGDQaPIkQWlHLiSn1itIlFIswgAjBDfblkJKcuGNwawQrJE802omWnO+hZ5sIyk71rLjHOfATMqvOw+AX+oKhPe1DdeNIReSX01SfjnNaJuW1159nf3992C1Q8QBcRzS1LpDXX2453AYEycptjUksY9k0NondDd1QzSZUpX1RSyG0QSBJIpCmtbLi4UUtG3tSfcyRAjbEW1rj2ZpS1U5giCiaSxXLu1x6XLI7GxN0zjyfMWsbdDrFceLJRmO8WJJIQQijVgIi0tjrDNce/U12ItYRH4cSpKkk4pLhsMBy+UC53x7wre+LY4A3bacHM8342XTNNS6IklTpBQ+/b31UQ+OAKlCiuWa+fwc56CsCqIw9otFJEW5JkkFy1WBlAFh4LPQynLuRQfSEEWez6VNBd0Yu7s/IQhhvjijaSrfBqoappMdwkBQ1xVVnfPg8C6f/vTv0HvsNE3uY0CM4fNf/H0Ensj+/Atf5vT0IXle8PTTT3N0fMT1J2+g65zd6YgyX6BkwGuvvMR8vsDqms99/vc4OrxLGIZ89StfJMsy3vXOdzKbzXj11quMpzvcfPJJjDEcPnxInuckacpb3/pWXnzxqwjg7t17XLlylThOuPXqC7z1rW+iLNc8fN4XE7fvjDk4OODs/Ii8mHPv/h1u37nFwaUDXnzxRc7Pznjbs2/l7r3bPPfcc4zHQ9J0wMMHDxgOB9R1zb179xgOh9x69WWyQcbZ2ZxLl69weHjIe771Pdx+7RZRFHHtiWtI/uRJm3/sYuXll1/m2rVrm8rp53/+57l58yaf+9znaNuW7/3e79289q1vfSs3b97kU5/6FB/4wAf41Kc+xTvf+c5H2kLf//3fz0//9E/z1a9+lfe85z3f8D3ruqbuVigAy+US8O6bzvqAPyF8yF2glEdQOvnxRhHSQWbAxtCtzy7pXyeEYLVa+bZJhxj1K6eLFGRotfd0cK5jW3eui30vcbsN0fcUgyDwMlL7KMG23+RjA/i2v0rPPTg3mlJKRs4yl6E3tBOCQTfgrjtIbjuRtT9P4UA5MJteokSGEWWXcPzg9Iyi1FR1xfnMu7LaXLOuTnH4TKA8X5FlMTjN/sEecZxw5+4DP/FZiZ9j/MDtCW2+FWWdxVhBqw3GtJRNQ9V0xDfrfCih8/pGa6xHoeyqcyFNCIxDWMdqvfK5IKLl3r17XLo0RQWKdVWjjUcvVIc49SRnYy1NUyMsWBkggZPjY+6+/jLvfMdbGY2mfOFzn6Opa8BSlSV7u7ucnp+RryuyQcywGbBcNVjjWMxrcBVXL0954/UnufbEVZq2RUUJlXa89MpdFouK8/PbtFY+Qjrri1et9YZcGAYBbVMTKsdokDIIFZPdKa0psMISBCGjQQzW0dQtSZzSNg3WGJQzTEYDpJJIN6FuoawtJ6dL0sGIqnFYIX2bzgnqtvHBh0qinENJxSrN+PJwxPvPzxBVzSKQTI1hp9X89u6EW9KyFo6oqpinEaeDlBvrEte2GOe44hyR1vz2lX2KYcJnRzdwSvDW0xl7FnLgk3u7/EboAxBFEFBa5/kFW2Ty7SJeKf98zoRgLWBgDK9EHtW8YgxDYzBC8lAods8XrGNFKTxyNGwtSa0plWQlwCyWRI3ms3HKiRUb9dhMSl6MIt5fFNSdcVvsHJlzvColAd7XpS9E+kIlcY4fKUve17QMnCUXgk+HIR9JU8rH2kIV8AtZymd1yA1juReEvB6EHUwvOD9fcOfOPd70piepax+DEIYp63JNkiacn8+YTCY4G/igQuHRCKVCVsucwWBAXbdUdbMpUoajIWEYsVqvEQJGoynGtdR1iZQhWjcURbHhchljuhy1oFM4BpycnPK7n/gs+dp40i0t14Xke+rGy3IFJE2DE4L7g5iVMYR1A1KwVzbERU7lFGEYIVBIEdI0DWXhx/CmbZHSFwrOCdrGF5kIw2CQ0eoWUVqvMRLelTsvFqRpQr4qMK72/MAWwKIC1aHTGoShKguqumS6k/nWW23YlQF5HONizw3MspSqrjG69oad1hJEYefOa0G0ONEymQwoFDhaqtrL++vGozZ37t4iSVKsdRjXIp3PUDOmxRi/wD05uc/R0V2EEJyePfSE4IevbnyrhKkYZhPaakkSOYYHE1pb8uDh7W5MAmcdDx68TqAUWhtev/sqX/nq56GbX4bDIWVZ8sKLz4Hz79vUDU27YrFYUdctn/3c7/LiSy+B8C3o45N73LhxE2M0L7/8ii+EXnuRs/NjPve5z3F0dMhXvvpF8nzF8fERiMvk+QrTWtZ5TpokBGHI4eGC1XrN3u4exjluvfoiTdPw+c99huXyHGstly9dYrVef8O5/Y+z/bGKlfe///3883/+z3nmmWd4+PAhP/dzP8d3fud38pWvfIXDw0Pfp3pMU3758mUODw8BODw8fKRQ6X/f/+4P2n7+53/+67gy4M2LRI9yOG8ZbrQlCXyQ4fbKzTm3QVOklLRab/WAL9JS+8p+W7K1jVIopTZOtgDOMxWptwqRfhDoV9Q9N2Vb/rhdmPSD9DbK0hdXPYsd/Arui8MB37NcItuWlZTsWMuetfzGcMhcSUR3/L2KaGMgJxVYibOOxrRoZ1BRSGs02rQ0uaWsLAJB2fprZUpDnIY0TUWa+gcxChUqVAyyhAcPDgkDRW00UnZ24d113FZPaGO9Q6rzvijaGNLEOwq3rcGi8ObDjij0kHDTWh48PPYeGEohlCIKBzRNRVU1HJ+UjCcZZVXhnFcxIDy6dpHajO9j1w1pkoBznJ+dU6xmSNfwlS9/mTBMaBpDXdU888xbSKIUISRXrj7B6ekJg0AyHo2wrmWZa5w0JHHKU2++yXgQMz875/R8TmMkrQs4OV3RNoLW+uLAuQvPG2MMSZLgnKOsKh+9oDXOGo8Gdq3KySCjKDXpOKEoSuqyRiAJhSQJInYGExyGslwirSYMAq7uTagaR2MjkmjI0dx0KqoQ6yAKQtog3EhcjbYUZUkUhXxsZ0xlW95dVlxtNFUY8Ft7GR/fHdBKxxfGGd9xMsNguB1KslDxhnWJFRCvLcdJhNANiYKFbvjolV1+c5Qyrg2jGzc4N1Dfe7jheCnpNn3ubQVXb/DonH8ODoXg96OIH6h8FterUcR53XLZCT4zGfGOOOJEWLT16MBhmpCGhnFjWEUKaR2hdXxxPOEXUdA2m+fLGMMvDgd893rNnjEoHA3wchBwJnz7dtGNLc65DZLy3VXNd7XtRo48tpYf6gJAP9wZl/VbCvxIWfKBtmVgHbmU/F4Y85EsoxJemXLv7gOuXLmE7iIlRuMRdeVYLucsFyucCWgyg0R3CxCfo5VkA+rWUDeGpjXotsURUteatjVEUUxRFpRVjaPFWkeeF50BoC+Ci6LoJMCg8HEWZd1QVyvqumG1KhBCgTT8+ziiSRO+tSgZO4cWcBwEfNUJ3PGcQEkOnPe+OTMKKRLKouHh7AHrVU4YhVy6tL9p1xqjfcRJ0DnOhgHWtiANloYo9QoZ2/E28nyNMQ0+brNltcqJwpQ087EiYRh3ZnQG61oGgxSqkm998S5vO1kxRFIFiuf3pzz/zjcjBgnWNeTFyvtnhSFx4sf2qs4ZJClSOoxrGI5S2kYTh6rzenEkSUAYhYSBpK5qkjSgqVuGw4S69nOOR45q6qbyIbn1nGwQsJgfo7VvgY4GEUYXRFFMXqz9AjUQ5OuaQZZ58z4JZ+eHJHFM07TekC8KiaKYsiw5nx12NgWOpqmZjseUVUV7O6eqGybjKS+8+CVm8zlJmm0KnNdef5FWa+aLM7Jhxpe/8nniOOPe3bsEgaKsHK2pUAGs87kfw62gKHOsrQijyPOqAsdydYbDdxjCUFFWS1544TmiMOLBg9d5+ODhHzi//1G3P1ax8oM/+IOb/3/Lt3wL73//+3nyySf58Ic/TJqmf+KD+YO2v/23/zY/8zM/s/l+uVxy48YN4EJCxdbEvC2r2t76SVxutWR6Sdh2H3qbK7Ldm+6Li+3994TZPg25Z/1voyp9YfO4Sdu2Rv5xIm5fHPW8lt6h9mOTEdZa3lMUXLWWQkr+w3jErw78TbhtOb8dRe+cd0NwUnr0wjq/4g4UEm/21uIIA4UTkrrVxHEIxjFIBiipSaMEJRXDdMCD+w+oqxolFWkcsrs7Zb1e0Tama5lFfsVhDMZaSm06L5mYbDjGWs35bNH9XnRqKUmjNUnnwhpHKeezmZcbW9kVbmHHa7BYApAhbVv5Y+3Mnaqy9sqoIEC3NUkUkUQxgzihrQR7kyE3b1zmyqVdjDYcHR1z6dIl9vcPKKqKPM8Jw5i6bkiSkLLOcWisa0gHEWnmKNo1h6/dZhhMuXP3kCAaMl+1rMoWIaONbLS33e4LViG8zDxNfXKvMZpQKaSQnJ3PgAClYi5duoqxNaYyZMMUKQOKdYkwjmKZoyJJHKdINGkYUZQVuICqKDHaF0MOi5ACoRTaOoyQBB1CQeB821IIToqcDycJn7l6iUy3FGnEQvq2VagkX3jLLkEQ8PbzJbuVV12dJBF30ojTMCAG3n+6hOiY33rDE1hXM3AS2a44OzsnuXTFF+OiK/Dt10vML55P25H7fBvv4ztTwtWaby0rLtc1KyT/djDklXTIt1ZrzocDlHRouSa0liIIUULywnhI5hyVtfzazWvU94+wtd0gW1JKDpOEDw+H/HCeswTOlCRyjn1j+ZUo4lwIUuCHi5IP6JYd63jGaA6l4uUgoAVOOrO29zctH49jZlty0h+pKn6o8vlOd5Vk4uAHai+B/9eDDJxktSp48YVXGE1HSCk5OTlj1zg4nyHSmLvrgigKSBP/PoPBiCROkDLk3r2HnRqy6KwUBHHsfCxGa1itcpQKKYoVYMmybIP0rVdzkjQB592odWup6zVF0VCVDikhChVNa7BGsxaCD6cpv6oUl8OQb88LPlSWDCpNI2DfOsZYfmN/ly/fn/vEc2fIu2MLghqcYDgcMhh6pYuzFmFhNpvhpLdUyGxCkiUUZUFVFz4awRl6E8imrTDLhsEg81zCpd//YJDStBVpPGA4SnHO8YEXT3j29hHVZMx5EhMWFe969R5CCr7ybW8FYWh1TRBKysoXLv0cUev4YoxvvaKplAFa+xaVdX58yPOVj7kIItb5ilZ7cqkxLctVThgGTKajjkdXo+KYKJIdshQSqJAwjDvPqIgwimiMQQiHkI4s9W20pq4x2sdlOLS33ogSqtoSJwFNUxIEIUkSgNSowNHoArCcz448MoemqlfeesM1zOYL4jgiCMHamtV6xmIxI0q83Llpc3CWyTTzBop4NeU0Gm44LUEiUSrcRCH4+ASHNQ1t2+BcTd2CDC7cn/9ztz+RdHk6nfKWt7yFV155hT/35/4cTdMwn88fQVeOjo42HJcrV67we7/3e4/s4+joaPO7P2iL45g4jr/u5yoIPcmIrl/c29I7tykatr/6QqbtBqtt5KQvCLaRjW9kZWy7tlMvT0YIb3rUIyP2UW18vz3OXdlGcYBHjIb6v/WQuNoUHc5ZGqX45b0dfns0ZGIMcylZdPbqdPvrj38jowackNi+byglSgYgQAmfPROlvmoPIslAJKwWc9LIq288xCw4PnnIcr4ijlPyVQlG0DaaKE6ZzxdUVYEUPWdGdlLg3vzJr5iN80iUlBHaWrTVCKkIpFcXta1BKsFoPKIqS5IkptaGpm0AgZCKKMoIQ1jOK+q2ZDSe0DQteV7inGUyGWOMoygKrly9gqkrnn3mbdRFSRoqslihhGYyHvPWZ97E/t4eO7t7qCjGSMVyvuDXfuVXeeMbn+LB/XuMhkNmqxXj3Ygwkbzlmac4O72PdoKTZcF47yp5YajNkkaDVAFStggZdCtZs2nP9f34KIoIQ4Wgs8JXAYtVyeefe57rT+wjhWY6iNmdThkMh1RFiWt9DtXr915n52AfUzkGsSJAE4iAxbLiay/dRcYTVqVG205uqL3LMwicDJBBgEETDYZY3RIJaNqGsyBkMcgwukUZ7/5rncPWlo/sTPn1QPEGBz/+4JT7YcAs8hycUgoELW87W/CF3R2+53zJ288XRE3L0lhenC3RV6/x+uGhJ5xzsVB4XPa7rX4LggAdhvzbwYBfk4pBXTMLEuZhzLUgoJCStG1YJiHHScTNvCIxLa0QCKtR2vKly7usIkkQOoTsks87hYNzjl8ZDQHHe6uKfetYC/jVJOaXOpTkR8qKH2hqTqTiTEKg4Zo1rLXmq0EAOOYIbjjD1NpN22jHWt7ftJwoyUn3s5OuS/S+tuY/uJQzFLptODtbsHewR+wsH3pwwrNnc5JWUwWKL02G/NaVXeZNSRzHFKXnaERRQlFWBKuc1WoFAgbOgZSIPMcrR6CoKsIwBWeJowHL5ZI4DsnSeGOGFkdJN1a06FawXi6RCsJY0HTml/24tXCOIgy5PxrS4PhvVjlXjVdHHoUhOq9ZHy+wcYyUgqbRhFFAax1Hh2esBmv293eJ4ohL+5e49/ot1kXOjSev09qGRmvSJKSo/ORrjGG5XG6sIYANrzAMDOPJkKbuEBenUcovZrOi4ubrD1kPUlaRQglHlXkk4i1HZ7xeNZxLvwAMumytqqpZr1YkaUJZVb6gkB6l17qG0Gc8JUlCVZWAX2ANJxNm5wvCMCAMA5bLuY8FkZK29UTjKIooy4K6LhlPhgShN+2sa59FVxYlbasJY/8znEN05oRx7NvAcRwTRxFW+DnGt/NER0xvybKUJPaxJmHoW2xBEHJ2dk7d+ATwIIwZDIYezVKSsszphQlN448hTVNP8FfSk6OrGmM0aZKC8J+1VIpWV7StD/TsEbs4jrs2W8Z6taLp5sU8L/7A+f2Puv2JipX1es2tW7f4yZ/8Sd773vcShiG/8Ru/wY//+I8D8OKLL3Lnzh0++MEPAvDBD36Qf/AP/gHHx34lC/Drv/7rjMdjnn322T/2+8dxtJHo9i2d3n1SbqEjcCE37PN/erKjEIK6rh9BVrYLmW0Dnr74+EZa8r7w2DjNdihPv5Lu2evb3in9zbotZd4m124jLr3BV1+ILALFrHOBpDue7f1s/60Q3ohJW/xDIOgqbUsgJCoKiZMYpOP69SdYLRY8vzoH23D7tVewxrB/cMBqkSMDSVNpyqLG030ChFM4rHcHNbr7TECILSdGoZAywBpDnpeAxRiNNhopvUNwoHxSbdVA09aoQNCalkZr6rrLJJKB78tWrkukNizXhwyHQ/J8RRgm+AA1vAqr9WnUX/jCF2jLiiwOGaYhVy/v8uanb6KEY35+zMMHd3DCo0phlCCE5eHRIZIAFQWkmWRyeQcrWiwVrdHIMKGtJK/evk9Tg3UBDunTWjs30B7h2Ua4Nn4JCIJAUlWNz5IKIx6czMkmE4ZpSCQH5IsaXTkkjisHVzG64akbN5nlJVpIVnnDcl4wGAwYDnd4+ukhtx/OsXpGFCqqViO6QtZnpvhVWa0NzlgGacq6KFBBQFlWxFIgfMseq31hboFAhhTRkEVRorTlWFmc6c7JwloprtQt333nkLcvc2ZxwDJNyFrN+w6PCMKIk8mY9TrHuvaRAmWbuOrvc9+GHI5GWGNo24Zjo3FSglQIHKdYvjjM+J6Fl3jfS73ny5WioogiXKj41MGY33/qCpmTDAZhx3ULNyhXVVXYKOLfDIf8ehwzNZaZEL4N5Bw7xvD+tuFE+oIjxbGSgtjBE8ZwS0lKBBNnWQsfG9BvO9YycI57jy14lkJy3VjG2nCqBCCpqorVas13n8359pM5Z6HiYRwyto7vOvGk049emdA2ZpNLpmRANhiitfHFvvLjw3yx8G7AaYJSgtUqJxDeYTtf16hAUVVroigGt/beTjKmqhqEgPl8jjaGMBQoZQlDCdovGD1i4IvtylpMEFAFiueU5DwMiBx853pBaWp+aWeXMIzwIaOeK2MtVFXDapUTVjVREBAGMXs7CYNsxKpc0TYaV5Ts7u/TVm03VvgsxSCImE52WK9XqEDRal8Q7O3vMp8vqGu/WBFCkBQlcaM5GXrLB/Bk/yZUTMuaYJnTZCFGW+qq9eZ62gCKtrUIZ4gTjw47awmVt9EXSMIONe6Lp/l8QRAEFEVFWdZY60nS22KN3p8kjEJW+RrrDFp7TlCaDljla4yxVLpFG0cWJ5tcurIsNxl0RVGgIsVoNPI/5yIrKIpCojhCt9XG6TgIAnZ3d2jqlizLcKiO8Ozv8eFwuBmn6roiCBRxHGBdi9aeWySld0EOAoFpm25hDVGkkDL0LthaI4Tt7hs/zgyGPqZBBQr+tybY/s2/+Tf5kR/5EZ588kkePHjA3/27fxelFD/xEz/BZDLhL//lv8zP/MzPsLu7y3g85q/9tb/GBz/4QT7wgQ8A8H3f9308++yz/ORP/iT/6B/9Iw4PD/k7f+fv8Ff+yl/5hsjJH2ULQ9+Hb7aLBy4cJrcRkk2LCDbk2seLjv6rvwG2/RW2iwF41ENlY8ClNYYumC4IUB3yYjskZpsAu02y1Vp38Jl5RAXUt1H64bx/f2v9ze6PF/xq1ZPwvGS3a/cY/zqHQaoQ5zoKrPCQXVGWBN0qttYVL7/4EnVTkyQpw9EYJSWz83PyqmI83eX09JTi8ARjnLfdD/xgq01DU+uOl+L8IEfXzkIghLeWl9KnStd11fWaFRbPO/BScY2zlvPZue8H1xWt9sRbnMVKb90tlcJax/7uPueLc+Yzb6BVtiV11ZCmqS/qliukFLR1jbCGomqJVEKWpGRJyle/+lWM8T4FxgkePPSr/9lsxvHpGXUNaZYw3ksZDBJUlFLWNWGYYownWy9XOVGY0ZqmQ/sUg0Hiuy1htFGc9fdVfy+FYYyx/voUlZd4olJeeOV1JsMBd166w1M3ruNMw3iQ0FSGQRqzv7/HfP2A3Z19To/v0VSayTRBO0c2HJJmNcznOAt13Tm2IlFBCDiUgCCQtMZQ1TVxmhLFIXVTY41jmKakmWK9WnufBOc5LnEYUyVQhxFjYznvVnhSSUbaYqTk6aJmFoWcIBDGUQYhTRzxhsNDrrzjWV5rGwKz5Ryx1ant7+U4jplMJl7BB1TNRZI5vpmJ1ppfv7zLYJDytvNz9ozlfhbziWv7fG1/h8PAMZMCV5UY48hGA+TRmff76Z75pmk25Pe5Usy6VSDd76fWklnH3e75r4TgvlS8ReuuXWSIHAyd5d/HyQZVAc8vywWdz8pFMTZ2llwKL7sWvlgx2qCPTvmWvGaWhMxCvxqfS4lUgnevCz4jd1lECiecL5hd65PakQzjDG00BoMIvM+TdpqmsSgpcNL/S2dWiPDW+mfzGU1Vs1qtaOrKGz4KiwoEk+kIqQKMy9FVS5SEDId+tTyMUnZtyncVBcdxwInsfD2kQir4dhyfEBVFJLGtwFqNlDAaTXCu3eRTCSm4fO2Ak9MTal1TVSWXr11GhtLnW2nHMB2hjaaqSipdE4aBN6WUflGhdcvR8QlpmhInCWma+Zy1y/s08X2mCPLOmM/hyFrNWirOhR9vlBQoaQkCj3BOJ5lXWXaLSWfBWD+uPXjwgL29PVqtUUGIkJKmbjYcoTAKqauayXTCqlgSJAGxjFDhBZm5qItuLKMrNC1a55SdvUFVFKRpSlmX3lxzOPTzldWYymcZRXFAVRf04H7T+lZWqxtkK9CNwSEYJAOcc1RVTdN4h2KEb/F1TxxN29K0Da1pOhQnoagqrLHeI0spAuEL5CAMyIsSqSS6bUnSGGM1GIMKBJIQXbeoQOJMSxzHiDDwfMbBnzxw8Y9VrNy7d4+f+Imf4OzsjIODA77jO76DT3/60xwcHADwj//xP0ZKyY//+I8/YgrXb0opPvKRj/DTP/3TfPCDH2QwGPBTP/VT/L2/9/f+sw6+bRoCdZGc2TQXuR4916Nf2V/YLZuN3XNfrKRpuikQ+sDBvlDZLhz64mGbRNtPQBtTt57Y2n1vrIWuYOoLojhJNkWLdd6K3xiD3OLF9IZh/WDaq4O8W243+aiLj8+j612UO8L7Vjj/paTCWeudVZ3dZB0BWAStdSzXeYeO6K54C6m1IIpDjIw5na9RgWCxLjZx7EZrAiEIIoVrHLjWq1t0C85Lt3HghMM5DydLJx+5tsY4lBCILvm198PtV/Rt26KIMMaB9FC+7AY6TEu5ynFG+C8nUdKjKuvlurOTbgmSGMIQhCNLUm7efILJaMSXv/gVGtuyyNfM50uWqzVZNuRNz7yJ1kquP/kUR6dztGkRQcx8XrC7t8vRgxOsFeS5Zn5WEElFGAgfYeBE1yYUWMfGlbhH59pOfeXbfwIpFEr56AY/KDeMRgPOzteEMuTs+Ve5ee0Si7ygbBquX76EfnhKHCQ8uHuPPF+wWp8iggAjAg6eeIoo8vkwtbYY7f1v4ihAdVlO2jVEQUSL8Ssx7Z/NJE58Me9AKEUQe+5NDz8LwKQpX12t+PaTM4xtKMKQKbDTar4yHPBMWbFWIVEQ+BWXMeRRzM5ySXD3DnsWBtmERYe69S3VvnUbdRlHum2wKsDbAHTPLCC7NiwC1sby7yYTfmuUMrWGY9OyiEPQhlCEyK4QKKuqQ658S3H7me5XvN9ISj3vJM1THCfd37wYBIyt5Wlj+GDTYoXggZSEOCJjqLrXnQvBp4OAH2pahICFEEyc48BYfiVJOO/QN48ASuS6INKWoyzGON1JdA0LZbncaNK6YpUNcM4gAv8sBzIkCkO0swjjx4ok8fk/faRDECgC6bxDbGtoW90paUpQjiARiMASSUk68OGqRlucVYRJSDYe0BhLFPoAytF4TBhETB7OGOKYZwnTxNu2u24s260rbkxSbinDaGdKqEKM9UqfNE0wxrBer6nbkpPlGhc5inqFwXB+fgY40iwlkhFlUfhWSYeGl01Fkgx8YdB0ztDAeDChCVvG4zHr9YrKGr62P+RDRyvCoKVOEyZWkGjH557aw4xHqLIgTUKyLELJGqUSBBJEgCMgzTIePjzEGMNkkhGEimyQdJxHj1CrMMB0C1sQBFFIa1u+7QPf5o3ijKNsC68akgbdamKREKqIxrUdsdgLECIVIoRESYG2miSLqZpys4D184xAqICiWm/m1LIu0dqQDmKaVYsUvUXCmrZtWa2WvtgI/DwQhb59GYYRRVWgIsmqWHhLMiH8IsUJlJCEQchoMKFY1pTNmka3pFlC01QY54jjAOd83lGgEhbLFcNRQpx4O/+mbnFW0Xadjz/JJtzjLNRvgm25XDKZTHjjG5/acER6pCTLss0E3w9GcFFstG2L49FcoG1TNp+a2W4kzdvtHLiQQm+TV8EXOev1mqIoiON4kxVhtwqVfn/9wNjbIG+jP/3+H/djiaKIKAwfQYn6Sa8vmhAX5GLgkbDGfutv/O0WWV/s9ZLR7fPuIXNrfX+4yEt6m+a2tRtCr8+iEbjOQ6HvMVvrNiTTbY5Of4z+dYGXKeLJlaESBKEkCiTL5YpAxX7Sdb5gUVKQJQnT4YCqKFnVDdrozqfHJ+f2RYG/XoY4DhG6ZZgk7I2H3Lx2leEgY7Za88rrdzg5nRGGMYPRkNZohLQMBgmjYcZsdk6aRYSRwjg4Pj5nOJ4ySEfMz5eMJxPiJCVNvcHccrWmamrqLoMKvE16T+bu/+3VaXTmglr7Yns6HZOmCYv5HCkMwzTkbW96iiZf8qYnnyIQkjDKqNqWKAJr/fm/8vod1rXj+pNv4ssv3MYJRVVVmyK+5z9pY1BhRKt7hclFEeURwhbVfUbr1QopBDvTKaPBECkk7WrFdxwd8ez5jMQY2ijixf09Pjse83+49ZpX0sQx2ni0cKcsOVgsuKckkZCUMuB3peIX44R11xYFcNaytztG4HllYRQDgpPTs84ZWuKMvyfDUDEcZUwmQxyekNi0DVXdoFSACoJNLlcvFS/XJWXRPIKIAiRJ0kVpNI88DwB/sSw3JNm+4Hh3qwmc46XAowqRgwNr+I9hyH+MYmbSS6Nja/nRpuH9rWbgHLkQfCbyMue6P2fnwwdv2pq/12hcmvAw8TwyHOy2BmHh//Gm69TjBIEBDIFUREHMarUGaRGBIQgVo+EAhyFJIlpdI4WgrTVhGHfPoiBNBp25mM+o8ROOIYolCG+C6VPAJUXZUlYtUoDVLfu7e+jWEM5L/vefew3tLOvBgFYbnINRUSKc43961xtZhh7J6e+7OPZtkT5kVUnIsog4jlitVpRlQZYlBGHg0V4ZUpctfZ7PRYigvzjjQQbOj6NJHHsX1jjyCrqqInHwvttn3LhzRGocdRjw/M6Q33/qCqNLl8nLkjTzvjN5sWI0HHWJyaFHKbOM8/NzjDEblWEQBF3ooSNJkk6RUyGFN+srioLdvSlOaRaLJWVZkmUZURRuLPTbUpNEGav1yp9TFKGt2fBXAiVpm4bR2B9P1e2jqqtORdlRHrpCJwx9i3M4HIIVZOlo8zyHYdh5T3WqWWPwid8BKgwJo5C8WtO0Zdcuk/7eqBvSNMPpLlbCOOpWY63fpwoExtSMu+evLAowEcv5GhXA/sGuXwgIhdaWxXzN/+tffITFYsF4PP7Dpvc/cPumzgay5lGb9d5Urm3bzYcFF4PSttfKRrYnxMZIrG/zPC4zfnxy71+33ULq+TCPc2i2FUfbEuntf3uUZtv+f7tA2kzsvsfydce1XaxtFz59wdWfS8/j2VZNXRAcvW9Mz83pf97727gusTgbDDDdwKSUP/44jtjdnRKEiitXrvDaa69zdnbmfVM68rFuveX7tkR7I9l2nshmLQRKoHVNmgwYDweMsgEnpwtUpLC6xdErskyHoIUkUrFar5G9VwSCJM2oqoosiXji0pRBlnUeLg3j4Yg7Dx/6B2f3gNH0gPHOFWbzJeuiwDpHksUUjWH58AG70ylaW+7df0DdGKY7l1gtNA/v3SOOAy5ducqDBw+pG810d48ky2itwZZ+4qy6rKn+2m4Xvj0SCJ6cppTvKUsFo52E+eyEVamJBm9k/+Aqy3JNUzaMBlOmk13GoxQlLav1mqefejNn8zX3b9/zpV93/fviu39GgjBEa70hdQMb865+MqibijRJfHxCWZIXBUZ76bWIQj52+RK/MxoyMZaFkuQdIfXL0zEfOj5FCMiDkEw3PJ0XtA5KJIcI9qTkR53DFDm/kGaPcKt88d14iLttcXj3Za/i8+2LnuLi/07Qtr6dmMYD4nhIVVZecYRvCUdhgDUWG0NVXvBl+mc6SRImkwmnp6ebxU2/9UTb9zct160PYFwLeC0IOVQeDVTO8bR2/F/Liu9qW2ZS8pkw5N9FER9OUz6e+JbSXEpm/bPd7T9xjj9flryvbbhsDFeKmoMk5OVJxgDBTm34DztDjrXBnMyZTocMBwOK9RoroVgWqAiGk5jAgWkrzzFQAfu7U8CSr1usuRgjdFOQpCk4jcPhpCZJFE2zJh1EJEmItb6oizLJUHuZtXMSJxY0TUU9hFs3Et796jmUmnUYMNSOXWH5zI093EHKwBratqJtK0QQ0rqWdBTjnA/rFDKgLAu0rrCuYTz1YYeIFhXGlPnaLyCGifdqMoYw8Mn1bauBwquAmoKga38Z0xBHEIchASEvvnfKa2++yrC1nGM4xxEBTbWkqirG4z3qRrM73cE6R9O0DIe+MGiqHOE0cSAZDRJ026KNwenaj4VJ7FuVHaKltSEJR2AMZZEziBOEsQxiXwy3VYVEoHAo5Ygi6cNwnW+LITqOm5S0bUldFR3PRWJMg8AjK+t13tEm/PhZFhU4gdEWKSR1U3ZzQ0hRFjjnKEqvThwOhuTFgiSRREqibctqPSeKQ7TVRIG3oIhThbENo/GYxXzmF/ZCEUSpD2AVvhVfVTV54RVGZbEmGw1QStJoizGOMFTeuiII/9C5/I+yfVMXK9sk1G0C6zYHZXu7CDHjkdVtXyz0hcp2EdC/z3bWzuNEyX7S6avvxxUO22jC4/vd3l9fCW//7QVy4f0QtguwxxVLj793rz7qJ8me+Lt9DfqW2bZyqP/d9nHu7EyRCurKW3U3TdOZOnkk5fTsFGs1q9WSuq4ZDHxmxvn5OU5fpH7216APbgSo6ta3zYTDOk86w3jfkUAGHOztox2cz+do1+KM57+kaUpbN9R1QxSGPiMDR12VJOMJ49GQNzxxmVHUTdjWoMKAdbFGO0c6GqHbiiyOuHnzSerG8Mqrt6iqklWxZDTOuHbjOtpY7t8/ZL1umUwPePOb38ELL7xMUc5pdcOdu/d9T1xIbt++g91c+xBjmo6f8qjaq0es+qLar1TMhoy9XC54+s3XgZpBJrjz4BbvfOvbMcJw7/ABoZzRVC/x1re8iSdvXmcxX9NawexsTiAkQtiN23KPqhjjV2/+OsTQFSz9V2++6FzHsZKCKPAkdpxP+LXOD4hKKYo0ZdaR05uV96v4jZ0pEsVbT0/ZqSqsEBRKcitIWAYBQige1g0qCPjTUvIbznG+dV8GYUigPCHcyzktPh3aq+HAF6rGuI7kGBBHnusipcJqSxwlmyLNObe5DlF4wYvbRi0PDg7Y29vbhL31zxd4nsqHEx+qOLWWiXP83/Ji08ZxDp7RmmvGS2vPhJfh/2B3XT6cppwLwfk3GI/Aq41+sK45koJPBYpvE4LrZUts1rySpfxKGvJLCPSiBGGQ4zHCSMp1hW4tZV6RZgln+Yq9/R0CQsbTjFgECB0QRgFEBmMdlS29AlBKhkmGSAXatlhV0+qaNEsZj1PqZo0MLGHgOWarRYE1hizNkFIxHk0wxvKFVBDF8MZ7K6ZNQxkpvvLGXZ574w6j2LFcFdi2xWKQoV8MFpX3fEnTjCyLCYU3fotRDIYhYeTRh0ApRsOYpm6QouXs/IwojJjs7CGATMWU9ZpKatK9kLyak8TJJq4BC6GImC9PmO7ssLIOZ2G347SsVivi1JGNwK5aVKhoq5oocWizJAwVQSAJgpT5bIZuu+ckUsznLcNhSqC8SGAwCBlkWRd62IKAJBkTBAGjQbrJ6qlLn180GqWs8hVC0rW0fc5dNsgQyhEFgiyNPIKh1GYRrLUvWuIk9G3AQFKWlX++A9/6UwGo0AdixnHgfVKkomlbnIXZfObHm0iSDEOatiKMJXXrlUr70xFSBJRFxXA4AWVJhjHGtLS1pW0qitK3hqM4xAm/GFWBZDBOGWSZR25UQBSOWK1zCARh+l95sdJP8D3UC2y+3+abwNfb2rM1ccBFxlE/ccPFSngTGLVV7GxDyf3kW1XVIwjJ9nFsNPuPEX77icJaS13Xm8Klf9/t93P+RB7Zd4/q9AVNf6zbhOH+vNwWotGjShdozMV16/OLfJz4YpMAi7CEYYwUskOOPElX65am9SteYxYIBHEcI4UnwcqOSyPERSHVT9T9+TscxlhvTiW8id31q9fI0ozPfekrXLr2BHGakJe57+l2aaKT8ZjEaubzOXVdI4SibRuK9ZLd3V3iUDEdZQgpKMrKoyAPHzIcDYjimJ3hwIdRupo6X2GqFbuTITeemHL/8AFhFKKrhnVRgYwoyobPf/455oslUjoiGbBYLEEqj+xYzwZqtSGOU4q8eAQ56++z/nvflnQo5UPE6romjr3h0+nxGe/9tndSFmfsTFLqtkQoQZwmnB7PyNIhTgXcvX/EcpnTtJ7sW2uDjAabga4nc/fcJ9MV5dvZPP093LZejRDGEXXTUBpDGicMB95i21iLxRcuQRBQlqVfgUdRp2yz/EKScPn6E1yLQ77tTW8g/thvsNLeq0JIRdO0nBrDG5Xiepoy71DIJPHvY3RD07ZcunSZk9NTqH2BrK1F0cv4wWiLNQ5jOll/U9Irifr7erN4kcoXs+LC38g5D+WHoTdIm0wm3in6/Hzz/D+uWlpJuQkoPJE+S+i6sbTCxwAspdrwVt7faj4WGebfoFBxzhvNebWR8hlfzvKZOOK+kgyV4p/sTnk9CJFSEQpBEAiaUqOrJW1liIKIdDrYEKmXpw3BbkoTBBSLmjw/JkljmqZhvfaxD0EYkKYJupLEcYS2LeFAUjcOpwWzxhc/Vb1GjSKUUuwMd2hqTRjEpNmAdV6QZREuG/HKlSvcXRakZYueDFhEkkHd0rSWaDrk5Czn9PiQ1TJHONlxuiSLWUkcLpBoJpOMOBEkaUi5WNK2lvF44hcldGIHbViWS5RSpJn3UXExNMp/xoWtyYu6U7YkhCqgqJe4wGIS4/2MgoDKFZDBMA6RFpwsSEbQNjkitL5Ql5pBOgYked4y3U1JUh+SKIRgOA5JswApNSqUaN2gXYGKLLHyFgzWWa+migJMJ3TIshBjfOZRmkVEcUzTNp5/Jw1pmmBxSAfDYULbtmRpwsrUONv4Nj0WpXzBLpVjZ3dEWVYop5DK0egcpxSomKppEcpQVAUoGKQZUZgxn88QSqNtQVkvuXx1h7zMmZ01KOVIkoizsxkyiCibimyQMh4Pmc9WXLv+BNZaHwbZCpIkJogCtG0IIkUyEqxWNdo2ntMYS5w0qPh/Y4Lt/7dtPTmuH4Ae50NsFxSPoB3iUR+UC+7AxYp3mzy7PWhdkCMvNt254W4jG49D/v3fbhcu/T576fTj7/P1rRrf+trmt2wXWduZQEmSbGDusiw3Kqht3siFf4snWxlzwVt4nBtT13XnDcDm2mmvhcZah+lSrAU9N6VivfYGZSLoV6EX7923ynxcQYToPCuctT5pFsl6sQJtSZOUF154ARkEyEAwHGQo4UPDQqW4cf0Kp1niDXTx7Y/lcslwMGA6HhDHyvdspWAxP8OYhuFoRFXXvPDSHY4PjwmCmCTJGI5GyEixzNeEccT5+TlVY7tzlRjjWBdLhHQIJRiOxzgLjdaY7l5pWkMUJ/gWRXshp9/iLvXXti9k6qbeuHs655hOdjk/Pebo/jHve9+zWJNTFznj4S5O+3yUojZ87ZVXKJc5SZTwxPUbrIuKZZGjMkeUDjbvsd1+8/elfMRIEei8IEqMNZR1DQ6yJEVIiVQBu3t73L19m+lksrkn4jjeFM1FUdA0LWGQcizARiHHX/4Su0VBhqBOvdW8AAZtw9wFFGlKFgQURcHe7i5hpLhy+YDDoyMQMJlMqDv4fRtZ7J+7oihJkpSyrDvbAI21LTgYty0Ta5gJQW5l1364ME7cNmTsFyhpmjIcDikKbzYWGcOPNo2317eWXHo08YrtxhrnGDpvDf+qlJtCZSEE1613vZ1vjUX9+4HPPsqs454SvguAV/CdBxGpNaAdCOWjLLCEUuG0wEpHIALGwymBChFOsrQrnDXEaoBtJNiIJpeszlcgFHXjj9s5Q5O0CFMQRa1f1R/mZNmA03KBChTDUcz+wS4xQ1xd01YNeW5ZLc+omiNk4FOMpzsjQqEomphmMsHQksgUoTSmLCjWJYN4wsFUcHJyRlFUFHmFEAGDwYgKg9Yti1lFmkqWi4amaTk7XYJ9QJqERJH/TJIkQWvJ8WFJWZ4jpSTbiQkTT8L2XlAh1iru3X7Ikzdubpxz5fnKE3ut7j53g25LBmlE42qsFVS6ZTDI/MJBQisqlJSkY0XTGKyocM5zsAbTEFxLay1SKFwAZdsSxTFINjJmnMJYQdM5J6tIIB3gLJEKqJucuCNEIxOkFBjrcK1BComUjropiSKFkt34tVghhCEbJFjrs920KbvFrGK6NwShadqaOBlgCo0MvNqitTXCKMY7AxCa3f0B5nxNnIEGolQQRYrj4yOE8IuvVrcUVUWQSN7x7rcRyZjf+cQn2N0fMRyONihVECiC2FK7OZUpaRtIkyHT6ZTVqmSr5v/P3r6pi5V+6wf4bfv67Z9vf21zSPrgwkcyW5x7pOXzuCV+v99tHkvbthuybH8c/QD+OP+k//veKbFHF7ZX39vns9066X/2ONdlo2KAzWp9u/DZFFePoUHbuUnexOhRyXYv7UySpOst+5+pzlq/7QoUf2x0Nu546aT2+lRjBca0fiWwNTH2Vuu2U0uhNbg+eVcz3N3j/PyM+dkZ82W+QXNiFbFerwmkpHQWXVc8vH+LGzeu84Y3vMGjQknCrVduIaSgqUrO87YLQmsQEkaDhLrKCZTkyhPX0SJkvlhx9/ScN052qBvD2WzOeDJENy2L5Zqm9Wx/JSAIJTIQSOERpKqq8UWb9UGaDoRUFJ0PQr/K33Ys7u+tDSHaXbgWl2XJ2ck5ygU8vHvEJ8oZ73zn0wyyGCng0qUDZJhy7/CMs+MzirqhbixXELzjXe/i1uuv4sKMVV5tiu9+kvT3kqQ17pHitb+P5vO5V02NBkShl7p3jCZ29/Y4PTmhLD2fxRhDVZWEoW/pRVFIXTcbf4i2qamU4sXdKR+cLVHGsLCwpzX71vGxMGQmJU8+eZP5fM7O7i7r5Tl5nm+eof39fWbzBbVswBgQDoHsjttS1w1x7NV8URhhrCFoa75vseK9RcUQQakkX55O+LXJmHPxKILYPx/bC5PLly/TNA3n5+f88OkZP1A3HAvBXaWYOMdlazgWEolj1zkMgkMpeV0pdqylFIKR84Ta2WPP/fb41KuNJs5xInyhb7RlJCwrB+dOYU2vAoEwiIijBCkcUTYmSwdURcXB/j5VnpMMM0xTMztbXvA6rMISgQ0IpPCqs9oyP6sIQ4N1hiAKKeYlRVGxu7vD+dGS+UlL095mlIbEEiaTPZSdQF1zcnjOqSpp2jtkUUYWp4wmKYdH97h89RJXr13D1QlHtw8p9IzpdA/XBthaMYh3KMuGYqGJopSmkZjGsVqUmNbRNBLX7uJsSG0M68UKa9bAiunODkWRe25VFBIdF6RZQhSF3XjX+YUsQl6a3ScdKKSsqUdQlseMxgMGg4zBIMM4WOoS0LQNzGYL7849zNjdmyBliZDWZ3AFXtlipc/yktJ7sSirvGsrnkcSKe8QXdclIrCIwBfIIvAyacBb1DeWui48wT0JPK/DWt/etIZhNqCpatrGI/WBChhPxsxn3hnYCUmcKJxT5MUS0MSJT9BG+OIkFBCEjjhRaNMwGYzR2qJkQFnmJJkiHQQw9+jLydlDAhGQ52uiMCKOMpwF6wSL5ZJ1OefBg9tMRxO0KWlNQGsCjk8PfbHVONZtyV44JRlEBJGgWM9Y3jsljEKWi8V/7vS+2b65i5VOpgtdy8b1xEXr2fRcDBB0oljndcJIKTfweI+kbOfw9NvjrZxv1Hfe5oX416pu0nd4q2iLcz5RNorCR9o//SSxbRYHbAqQ7cnNAUi1qVIt+JkEgbZeVSCct9VvOoniRkrdTYhqC/FpOr6Enyx9zeCvhyUIpEcuwrA7DzDGUTctzvocpN651183hbaauoufx22rgC4iEXqZtnWus2cW4Cy60f74BDTGcvfokP2dne7hzD05WCqM9vsLYkUQhoxGGaZqWcxOeKFYY5wgihOMdVy7dp3rN59gvTghDBR72Q77e7vgPAHu9u07ZJMJQZhy69Zr7O7ugYTTsxlhGNFUmnxtyNcWayRCKWQYbsLWBsWavbNj5lLxLe97P5/45Kdo6xopQySCuvakNmd9C89/pm4TSIbwro+2+5lxXVEsJOu6QtqWohXkZcRy8SKXDiZcv3YJgWW1WPKOp9/IS87ytdNz4ske2jmiMGJ/usetu/coW0sQxSADrPPqAaMbnLaEKsB1H7rVBqcMhw8eUlUlURxRr0tcZIjjuMsRqlBqyd6lS5ydnGCdJQwVRlsUmp1R5lOy85xrVy/z7ne9iyxLkcDnPvkZPvv7X+LZVcGkblhnKf9BCP69lAS6pqlLrl25zHwxR0Uhx/M5URxRtg12uQC8AkzikQH/bPfGh7BYzDDW99Fxju9brPkzy5wTKbmtBDvW8afnS1pt+Gdb7dW+IOqLubIoCALlr4m1XItCvsM5VmnCzPoQuxNrgQBhLf9DloGDP1vX/Hd1zfc2Te9FSAH889R7rzj79W7YzjnOgM+EAT9UN6AESyEYacMO8OvZgFMB0vkWhnACqw3C+XMeDsZY49O1LQ4nHav10vOeohAlFSrweTG1kTjlvY3aVvvJsXVYAc4Jau1XGNpI5vMCh+DwcEkcheSLmskgYbFYUNUtk8kUq2MW5wschrUtEJRIBQ5Dvj6jWPsiVzcxi9mStspZLw35WhPHAVHoJ0KBIgpUt9gTHD1YolSIFCFJEnUcjXiDXOf5OXGS0DQBbeNYrzRRBM6VJB0RWtBQVrVXL04zRqMhs5MVrW7IMgesGI4GZIOIwSABJ1gu1qyWFXVVoeQ5YXRInAhu3LiGc46qXgKCuqpIs5TpZIqKPck0iRVhFBCqlLaxnM4PieOUOIkoqwKpekcJg5Q+Xy3OBuSzJdo6rPMBrXEckyYheZ5TlTmLxZI8zxkNxzgn0K0jjr3yZjCUXoZfNqwbv+g2bYuUEKkAIR1VvkIh0U1DIDVaLz0KJwRZJpEKjh8ekYUDdK7ZSXe94d0wYbkoKEvtkabGEo2HKGWZ7gxQEuIk5PxsxcnJDElANshoGt++HowykiTi5OQYF1QEKqaqNGE0/KPN6X/I9k1drGxDS9b6hN3NpLj53QVXZBsRSbpKtJeUPs4P2YZr/f63iobtlhIX7RS/OnMEgY9G71OWPTzpB8DeHKwfvB4hwHZf2+oN2EJGRFdwQSflFJuf201RdbFPv3rs/+/bQBetivZR4m9XOHgXx14x4c+naRpM56Mgt65jf12klN6Uynbktg5lCIIARIfm4Afx/vw2aJCUSIRPz8YXYM4v+VmWpTfZM1595P/GkylbbciSCBkoxrtTBmlGnGas1iXroubh4RGv3n5A8oWIydD7hFy9fImjoxlVkeOs5uz0lPPFKyTDCcPhkDiOKeuaLEpYrVcIoagrkCIENE8+9RRFWSDqmu88n/Gu5ZIxjt0bN/n9Lz7Hb69WqDDxcuDuvnOdtXxPsguUh6wFDm0gCELajSGg71cL56WZEoMxCltAvl5zdppz984hz77tKa4e7JEvzrlysEte1Bwez3j99m3m5zOcMSRhSFGXxFFC0WjPpTG2C3trN3491hiEwxdtSiEcmNbb87dNS1mURFFEmqSEMmA4HDJKY8r1gjQKefd73sGzz7yZt73lzVy+tEdTlyxX3lBvNp9xfDbjybc/w0fu3OaTexPUIkccXKYejnhTUTCfz8nXKwIlydKE4/MzGqOJZIyzjtVqxXg8pmlaBI03zuPieoZhQFGskcpSVmv2CfjWvOIIOJPeXbdREgF8y2LFNE0444Kj1iNLfsGgCQOFMV41M6wbxlJwlCSMpDdQrOuaOXCju59vBYoPth2S2/mmuG+ApG6PF9v//8UkYeDgg23LnnOcS8FH45iPDQdoY9C63ngH1U1DXlSEYUBe1hRFyWg04uVXb5Gvl0x3ph7pDL2rqKCL9RDWm8lhSQfphtzunE8730R/WENR6Q3RWxuJ0YLFWtM0BUII1uWxR7CM3NzbUnqEwbmAsnacnuVYq6nqhrYVnFerjd16g8Z18lec54gESmCVR7M9GtugtaDpwv6MMeje4NKWG+GAEApjO6K6ri/a4FZhDJyf1ayWBodHjhfzmtFoSBLHnK4r7uYz0jjGGsMgGxKEA6wxBEZhCsO913IQkK9z2rbpxp8V2WDFZC/zPi9KkA1idncnBKFkfl4Shpowjjy6pX2auxCSQEVo7bC2pCgbrNHoxmGtIVA1O9MxicqoXc1gMGQwnICTzGZLZrNTqqpmMhkxHEckcYQkYTLyQbNJEqFNw3pZEkYRtpGoJMK2nmArrKNpSxbLU65cuYazhra0PmcKQSJCz+OUjiiWXcfBK+lUSJc/VFDrGt04kkixf+MG67zwiHcI+3uX2d894P7D11jnc4wBqx26iamKP7nPyjd1sfKNtseLjO32UI+meIi03bRDtifexwmy/b+Pq3C2i5btwsMTTvVm39uEWc/uvmi19CuG7UKl788KITacjm1yrieqikfO9fHBcNtp9yIKHgSPkoS3iy9jL7KF+muxXeA9fhzb590PKNvtq14C2qM4/XVo27ZrnVSb/fTFCHi+iRAQBKqTWnqfBRmESCHRxuKcASNYl5bBIOTywSXCIKSsNXuXrhHlBXcfHmOFpDUSzYCzoxmHJzmDLGU0HPgWiI3QVpOvapJoSF0VzJcLrIP1okIqRTIYslgseM9738Mzb30rv/2J3+EDrz3gz+Yl8yhilkTsRyHf8vo9fhjL/xokGGvQjUePOrsMBHSkZscwy9BtQ+h81pE2vm3ihIAtN1BM6AdkA9Y4ct1ijePV1+6Tr5a88eYNyrJkOMq4qkLqSnN0coiSoAkwxst+RWsRwl/7II42ZOr+8wE2n0cU+QLQdQVsoBRKSJw2LM9n6KLkYJry3/x3f4EPvP9PkYSdiVtTeWn5ICJOE167d8St125z9/4h7/22D/B//Mm/xP/yv/xrhlee4OaTT3Pv3j0Arl27xsnJCcfHxwRBwP7OlPsP7yGMwbYtuvX8E4tABCGiCzrE0RWwHWldCXCCrK6IteZYSrx5osNawbkQXDOaqTGciEeNHHtivHAXPKqmaTjSmoUxJFVF0cm9nXNM6Vo80qeev7fVfDEMWQtB7Cy1kIwFfKs2/LK1nG89r9tb4hx/oa55h9aEztHi+GoQ8W+VIg4CXJchJQDTNtRNTRAGJGnC6dkZaZqyWC5Yrla0TY3tFhTrvHc37VreFurm4nns25LbSsoeXbbWblAKrTUCaJp245btYwrqzX0TRRE94TlOkq6VYf196y7Gq+0W/PYY21s8aK0fEQVsvK4Emzbl9tjUv67nGm2P3xctV5815lO8BXVrMDqnqnzSubMGk0HbNjS1z90KVIAIPSoimobdvV1y/OeQpimnp6csFyVOejn3Ol8RRYqzkzUHl/bI0l3myyVVtSJOIsbjjPn8nPl8ibOSvb0DpHTk64rBIOO1lx+yv7tLGDrKxTlZlhKmkihOqKoWZxxXLz3F7//+F2hbw/27Z1y5esBk7Fgu1ywWc6Y7Y8bjjDhWIAW3Xr6NVIKnnsqo6wBjNCoQJEnGIA0xjaVpKoZZihKgm5ooEIRxiFE+3NZZL7LQrSGKAoz1XDRrE4/0aM/9CqKQ5XJNVRcMssu8fus2050h8UGEQHF2knO2rmnri8/pP3f7/4li5fFiYhsZ6SfEIAgYDAadfLOibZuNIVavfgE28ufHSbrb3z9OlN1GZbyc8tEHsf97/1r/fRiGjMfjjTdMnuebwK5e5tqTF/uJv3fx7M9xW278OJEXLoqWjX/LFsl1m0TbD1Ry67z669KfszXeRZStAi3cyrwJkmSzn54n0Q9APXm533cYhpvr3L9/78ZrrCVQF0iXQJAOMqrGe29YYf0KVvjcoPlySZqmKKmoqoZl2bJarREypG289fODozM/gEnBIp8RzdeeSJhljJKUJAqw1gDOq1GcpW4qhqMRyWjAW97+Fp55y1v4yC//Ms3Dh7y3yFmlCeHVq+SzGZ+7d4+p1rzPOT6uW1ZSYi0XrTvXKZyCgCSO2N/fo21qyqrB5ykZWq0xzvgYARxBEIETKKG84RYS6TzXpNGSvNScLxbMFyviZMju7pi61oQRHD48Jq8bUMnms/btupamqYmCR7OjnPNmVlrrDWInOuVSoBTSOjAWLBgaiqXmM5/6FC+/8Dy6rcmSGGcMw2HGyckZL9y+S1nXPDw8RlvBi6/cZnfvMqt1gbGSO+I2xvj2a29h7ol6S3Z3JnzH+9/HvXv3uXv/Ic5YFnmBQ3Ured8K6u/vXoHUtyOPtWGNY+Icx9C93jI0jjUwV8pHYGwV315y2hCozgG5uyfPhOD3wpDvr3zWSmstO9Zx4BwfjSNmUvKGzvDtrhQ0AkrhnaULpTgoCnacY/YY2X5qDFNj+e6m4bvblhMpuRUoJg6+q2nIBfzy47w05zboaVGWXYid4nw2w1pN26Fn1niZcqAUDi8Fx8JytSaKog1XbHvf/bMZBMGmcOuvQRAobNcOruuaMIweISdr68dW47zFexB6H5/BKGPdcba2vWu2i5P+/b/RIsf7M104gffH+ziiu/3vtmCg99nxHDjjnbxVRNO0tK1XjSVRiDECISKaxhNrV6s1zjriNMYJizld0jR+DqmbgrqBOE5YLdqO1DtgvSzJVzmmjbl0KWZxZpjNC4QomEy9uWFTJrSNxbYNVhcsF3NvHprG1Lnk7uEDwLG7u0syUUx3ptS15ux0QRLnrJYaKUKcyXhwd8lDkXecxwbT+raY1hXXbzzJcu6Jy4cPvsJwOKCuK+LO6yVNJNPxEKUEOpOMhp7Uvbs3xdiG2Wrlnc+NIF9VKBWxtrDO10ipqEofMDkYDpCyJc0CLl/epW4SBklEqHZpdcE4G6C1ZWciMbUkFP+VFyvbE/E24gEXE2MvyfQk0bJ7WM3m5o+iaFOh94P39o2/HS7YT/Tb77/97yNIxdZg+Oh+Lx62/sEcDAaMRqNN66goCqqqIo7jjTKhrmsknrjZ//12obL9EG8f9/Yxa3NBtOxX0Wnq/VB6Cer2INYXef11NMZghXiE69MXH0EYkqYpWmuuXLlC27bcv39/E9TYr2J7UvG2ad/29ewni/68skHGtWtXeHh07A3b2n5gFYRKslqtWK89yTOOE9zZHGvhyuXLnJ2fU5UlKvDumzIMEaEgGcaURcWqzrGmIC8sSZpy5coVdvZ2Mcby5re+kctXrvLirVscHt7n+ee/wiAbEDcNmbG04zFnpycURY11joUUXG0NOw6WovMxiELqsurgdi9NH4+GnqQsJE/euMnh4RFpmjKbzWi19i0wa7rumV8dmk4JE6cJV68cMBplnJ7coapyDg4usVwt/cpZBORFQTYaUtucVV76ArKz/g+CAN3UiM7g7/GCfCN/x9FqTVHXBEISyYAsSnDGoK0jF4ovfu0Wi8UcpTzRVAgIlOLSwSV2Lu1z+2tfwymfvCtV7A331gVn53PEnXubz1dKydHRkZ8M6hp0y954wA/9wA8wX674nU98kldfv+25aU6g+0Kj8+1puiJWKT+ZngvB70URP1jXIBULBBNrObCWjybxBuXo37/fR39vGnhk8vtIlmKd49uqiieMJReCj8YRv9gtbmZSsBaiywCSG9QqaxpyKTjfOs9MCP58UfCn6poda3lGGw6l5CWlMEJyKgEc72tbfqdtqQaDR56xKIoQUlIUhXcm7dCwtmlBCC9h7Xx1jPWJwnXTEgQXga/9gqJHdrfHtx5N7S0UAJ9o7FxHsu5I/EptWtDOAp3vjsOrWWKl0K2XlXuvoWpzPbfHgn6senws24yZrkdaL5CZbZQcLlCXfn/9a/w90iuFuv07i8Dz3oxtSdMAS5cO76BpLU2jaeqGxhhk5+8TBAFtqwmCmDQd0DQtUZRgTMN6VRKGCbrVHB8tMTqkKjXF2l+iIl+QZQltC84qyqJG4dCNxLSGAMHM5JSFb/AXUQuh5PX5IWXR0LaOJAZszHJdeNSdAIQikAkqtjgX4ExElqaslg1RMKLMF0RxRplbmkZRrBucqwkEnMW157eEEqWgrtbs7EzY3ZuQ7WXeKwbB4cNjdGtRKiJfV4RRRJGXWGtQgSTNQkbjiMk05eq1PUzbYpoSSYtrBbbVhAIipZHpf+XS5W3ocDtpuG9bJEmyMWrL83yDAtR1vXGs3R6ogEcm/cfbLI8XQo9D6T3hr3/otz1a+t/LrWKjH4jyPGc+n+MD8HzhEoahDxhrmo11f6++ebxFs32c2yjPI8XA1kPfv64/9779JLtjqarqkYIkCAKiIMT2LSgHTndQrvDJxn3yZpIkzOdzwjBkMBg8sp/+eJum2RSP3cFtjkN4ytEGjRkOBui2IVKCUCpE5M9pkMXsToYsFnPO5+sNt0ZuOCKWN9y8QVWtmS9OiOIBKvBeOE5APMkIw5C3P/MWDvZ2uX3nNkEYUrY1h0dHFFVJ+fnPUVXeqj5JUpx1NIMBWTRE1w0njcZaz0sZakuhFOskQXakQec6rw7wsHO3ijdGc7C3y97uLnVVe3+TpmW2mPsk5jD2fia65yp1SJo1HJ+fczafgWko85o4GSGcIwxASEdeVFgkk8mUnb2UMBmwWJfo1pv4+fu+AR5NBu+vf59G7KxvYQVRDDiM1kTdM1PUDi0ENSGmtQTSZ1ZJp1gfztjXjtoIqrJGBSHW+eA3YwxS+cyg/hno0b84jjvelOS11+5ireTZd7yD//6//W/55Kc/xenZGV/60nOoMCBvtTfU8omc+JRt4R1rheOX0hSE4P1Nyw37aIHRP5ePE9n9PXOx6OnbloVzfDiJ+bVAMWg1cylZdBM/sHGr/aG6RkrBioCxNewbwy93aifVjU8/WlV8X9tyJARnQqLQXLOWZ7Tma5HPlVoIyXWrmRhD6S7UW0qFTHd2qOoKFQYghY9SaBu0MQyyAWXl5eZSSdqm9e0cAVqvNmZp26rCbSfux8fV7QVaGMYY47AO0GZjrd/3blvjE3+DMESblrKuUWHAYDikaS5QsL5g2R6nt++/b8Tt6bls28fTf26PLxIfR8SF9OZoXlghcLY/N09619r4DLbuXKT07dcwitDGqxR9CSExFqq6IQh8a9ZasMZHjjjnybNt01LkDVo72tYRxyFta1jM+7ZcgFIBdZdADYblumSQpaggYTweoU3D+VlBawxhENFUFc60OCewxnXpyYMu0dqfq5Ighc/hqcqCNB2zWBQUeUsYhEjiDSWgqVuqGsJA0TY+OFZrn92zWq8wd2YbCwiHwGhHUaywWhCGAmM9clgZQ7lqWc1bzo4KipXg5o0xUrU0TQXDTo3mFGkQQRjxJ92+qYuV7UKiJ4X1N3OPGpRlubER76t6pYKN90Vf6GxP9P33xhgfpKcezfXpH/rtFcIFYuEeGQQeL2y2i6L+wep/ZoxhNpshpWQymbC7u0tZlpRl6eXNKvBOhH/Ag94f4/bPN5tgQ0rs33+7laSUIuv4Mo/zTQaDQRdqFWyKmSRJmE6nAMw7Wdpg4I3I1uv1Bjnpj6NHsOI4RghBkiRbNvRb3iNSYnSLMb7lNJ/NqPKA8XjMMBuR1zWr5ZI3XL/BG5+6QRxHPDg+oyi9y+bp8Qmj4ZBxl1Yaq4xxcpnxdMr+wWWKyqcMOwu3Xn2V119/wOe/8DXyIsc4r6hSQUDVxwwYTagU1594kquXL3My3eOlB8d8y+07XFEBD7EMtOaSc3w0jDgVoD0r1xNmubhnoijiiSeu8eyzz/Kp3/0Eezu7XLtyDYvj3e95D3fv3+NLzz0HeG+c1dJHyQvhiW/aaKSWGOlbQlJErJYl+7tTDvYPsELyxJNvQkUJVVnTtJbjsxmsi4tJWMku9PFiYti+j4v/N3l/HmNJlqX3gb97r21v9ed7uMcekXtmVWatXUt3dbdI9salh6SAEaCFojCaGREShemmhqQADSEMJIIzmD96hFGTkCCRWihQLS5iV1cVey8Wu7u6sqoyK/clIjIWD9/d32673Tt/XLvPzT2TVJMFCCjJgEBEuL9nz57ZXc75zvd9J47xlUcr9G39HkFZFOTVmU8P2lDVDTHDIFokAfZZCybzlJX1S8xnMfP5nKzUeGiMsIHbxbHpeFuV1qRZiRKCh48es7KySjyb0g49/i//1r/Bzs4O/+Brv8Krr72Jrs7Pq6rSiw0sFvB3wpBf9X1WgLFSnHCWyTeRQ3dobbt5u6C+OUe01pwAhzXK6K7eve4fRiFCwOe15qrRzIXkq6HP/+yfNbBc0ZpPpxmnyuMECMqSmZAExnBZa+5qQyatjHkuJMd1MOHmDWgqXdlA0lhTPoNFHtrtDgDzeUyn08EYW07N6h5dDm1okordc2/Ktt09aQoOpKzXwkbC1kyIrIy3IMsyiiKvS9dZjcha23hX8rnoO9VMAP9pRzMgubh2XSz1ny8VGRAVDhOSQqAba6b0fLQwpFlCK4rAQFkU1qdJaOsGJGplaV3CyDIbGBptCIVB6xKw6knfl3ieX3c5p3ZdzkFY4rZL8rQW+KFVyAgpKE1FoSta7YiyJiVnRVm3E3DOtbbPkEXHAFEBtoeREFZtNpnM2Nre5PDoiDSdURR2Xc+LEt8LMEYihIfwanKyFHgqsL5dZYiQEl35ZIWgKDKyPCEMfZuMaYmSEXle4HkSJQN8z0Mpa0thSsHugzGDTpflQQdReiRjW0qqKoMUEcqL/pnP+Q9y/EAHK82jGVm3Wi2kFIzHY8ryrF+PC2hshmlNrBxnxJ0DHGnvo4m29SsX77m44DnOStOEq/lHqfNISLMh4gLSFDAcDgmCgE6ng5SSJE0IgpBWq3WOeNs8LiqaFle6EA2Jc3+a9eKqzqh932d7e9u6es7n1uV1eZl21CKezdGV7RxalgXz2QyBoNNuM41tEOO4Ku4Iw3DhQeM8WVwgeevWLU5OTmqExQaN/V7X+pcEtqOs7ylEUdDtdHnm+ecZT6fM5zMm41PeeuNNDIZCSKtQqGvs3Xab4ekJZVEQKEWepkRBlzwtefxgl4OjE6RUzOI5uVZkhaYspV2IECAqhAzQpqIdeggMSZLz3vv3UAh+Z3OTJE65trvHVlkxBb4SBfxyq2VRpMo2mqsfhA0SEAwGA1rtNvfv32cex3zvtdfo95fwQx+lPOZJzPraGmVlG6cZA3E8twZ0QiCkwPetgeHG6hY3Lm9xeWuDQNmxvnd8wv7JIyoUgedRlJqsOFvIy7LEVwLl2Vq+NrUkuKHQiqIIX0g8bdEz6oXfCME8jbncivj8E7eJQ5/ffv01kmROnGYIqWyJUkA5g+PTkV0I3caf53jKJ0mt1404N9+sWiPPC+azhMALKSvNO+++x43rVyiLhG98/TcZLPX5v/37f57vvfEOf/2v/5d4oznLumTseYyxHZ6bCuGhlEwcgf5CcHIxyLd/mwVvq6qqBd/Ivaa52Tc3zkxK/sdWi3/s+6xJyVFZcujQVWG5IOtC0NaaXWX7FaVSsCMlT5clAYZ+PXfXtearYcgJ0KqshX9VlQRhwCyJkZ7HaDRGSlsaardbdNsdjo+OF67Tbt41+XphGJ7j0jV7lTXviUNI3ZhxSkZjbC+Yi4mSqYnONvGpUKqk0obZbGY7DVd2w2/e64vKzI8KYIQQmNok0r5P1Kjr+ZL3xeu312JVRlJZZRcIhPTAWEsGISSVKcnzDFPzfFz5WUlru2CoEXNh6u7pFUpZormS9ry2X1qtqtMlrsFiWdk1wBhLTrUaR0mlbasBL5D4kY+SNtAwyhLr55mlKVSlDQ4QVlma51aKbcnCBSoTGBPhGW25bTW6uLu7T1kWpFlqSbXKIqGeMpSlRdErk1Poyja2RCEFqMC34gojKNKKstS2K3uWY1sjdDFao6QGUyGMQpcGKlv600KgvIg89hiVFQhFp92mFYbsHe9RVRmDpQ7f7/G/iWDFbfp+zZvQumI6nVCWzmTNGu5oXZFlST2h7SR0tuHno3WwbdYVllNq6oEoaimwG6BnE8SiA2bBSWkGKO6wG450GBuuFqxULUM2AukpqyKRkqIqGY3HdpNy0LOu6LSiGsGpG5NVlZ1gDWRIa73IYkUtd6aReTQzFreQ+TVyNDw+sQTWoiAIfIYnpwwGA8qqZDybgoBCV5RpghQST9fXgFu0zmzN3Wc0fWxcJuaupdvtoKSgFUWsrqyw1O9TlSWm0vieR5Kl+H7Anfu2QeLx8THG6IUEs8KqmXzloY1m/2hYe4gAxiIlu+/cpXrz/QX6VZQFAkEYRWRZspB/h2EEQpLleX0OW5rbebS3QJLePN3hXrdL2uvRLnMOq4pZaLNroQ3SCJv8VFaF4ORAaZpycnJCN8+4kmUc3nmXvcGAMAxotdqMxiO63S6z2Yxubwm8AD/SCKMZLPV47pmnuLS+yunJMWHgEwY+B4dHpHnOaDIjLTQqiGp+h0QoiUIjhEZJs/B7MQiMApCgFBpIspy8qFBCLlAcISwXxaBZDkL+r+uXuPzBA7rfeZnlK5f5oy+9wDcvb/LanXt89/W3OR3P0cK2obfCVk3gKcrCNpf0wxYIZXkQVYVfuxaHQUCeZ6RJAnhoo5CZJj8aY1TAl374h3j91Zcpi5R5VvCJ51/gv/mxH2f8la+SnY6YS8nLrYi/p3yyRiLQRDVFHTQ0HYQvJgxCqjqAU05qZLcZKa2qSpx1OW8e7udHNQJjGoGQS1zmQUARBiwbw5FUaF3xjqfoGcOm1qxow1DAV8OIL7dbdNoRly9v88ILz/M7v/MN0BXTeUxWWuO4KPSp8hwhDaeHM7K8XKgIgcWa5vs+lbGoTFmVIIT9v9F2o2TRwQNtaSJoLLHVKZHsWli/BrMIJGwVzgC2LGdqAr+poCw0uayQwkMbuUBohKj9oYRFLgwC6vutK0uErdsuYTdhhe0JJc5QnspxFR2K7AKUxROxz6RsjoWyXqclYINIYRRa2O7TMvDq71cjOHVya/2EfHSlybMC5Sl8zyBlAEKha0VfVQcsrsWFJckrKg1C+vWYEXXQUdgmvErZAKgCY6z0WwiDkAYjNEraYCBJMwpXdpeCeVyQFRAEhlYkazPGgna7hVIB47Eti7tEKY7nlkDu+0ivRpZMhVDgRxESSaVLMFZVp6RX81Ja6KpEC4EfBVYSnhdkhQ2ESl1ZVVClUKVid28EurDfiWOEkAwGffKi4IP7b/+ztvA/0PEDH6y4BaHb7S5KELYNuQ0C7OC2mf7Z/8/gVcenOA99NuHSM0hY1V19hVCL3xvDYmNuIinNnzl40g3+mqJhr0VbIpepr6PJdVkEOpXNIqw3gVpcr+VSWBg+z3N8bKDiOiV7jfowxiyM6KSUtm9OGDKfz7FOpClVWVrPD1fWwjCdz+25AvvevHbH1fU5jQTR4NA49MSVwtx3at4bV15z39PzbIQ/nUw4OTqGOhsUNf/HyLNsypX6hLCT2BhrblUUBYU865YNjoBtW5RLKalq4qgQYrEomTQBNEEQ0u70LCcjTRHCLp5JWpHWHKf08NA27VKKeZZRBT7HXl1SqL+jgjpjMovnLGt0ophM+PzxEZ/OcjpaE6yu8s3pjK+JLqlSlixcc6sMkk6nx6QquX7lMlHoocuK6WRKPJshOm3a7RatdhvhBRTGo5zFVEaQ5QXFNCVPU9ucMLe8m6rmeClPIZRCKevzYhfMnDJP0EKghUEaje97BEFIp93lP9i6zMcfPOa+kogr22RpzpXvvs4fard56k//ST7zuS/wP/3PX+GD+4/ot9p0WxGtQCGx4w7l8/6dD8h1SdRqIer7pKTAU4ow9MmiiOl0RtQKEVIjFZyOhnzr29/GlBkvPPsMuzuPeemNd9ja2Wf51m3eivbg8ICfShJKr+SXWq1zpVBXunWHG3MfhUzCmUdRWbdPEDad/9C5PqqE0ex11Zz3nudxKgTfjiL+yDxGSjiRilZhlWP/MAj4emhLk0oJulLgLS0xGo24e/culy9f4d6du5R5QVkaPN/6bFxaX0HnCfvJ0YIP4gIvV2IFWxJygcS5Q1h/Ixts2YDfzR/XR6rJkWsiKlpbU0hdlQhhm1tWVUmeOwsHUfcEc4EbyBpV1jViJIRcEGjd9cjGff7Q9XIejWsa7QlxRm62aw31687W2mbgungf50URC6sJ93NtyGtzx6IoCAgsn0s66oABqsV73X2xvc5sUmvvby2VFwZhsN2R08wGmL5EV8aSlZHAGb8nTVPSNMPJ8PPcypAREmOseMHLbBCeZjlKykUiXVW2nL9wOTcGYyRVWYEpMMogRUErimw5L8vrIMWOI20MgVRoY5NTo898o2T9jCutUfVYGY0n5HmG59n5LAScjE4tWhudNRH9Fz1+4IOVKIpYXV0lTVPG43HNS5E1knJeHdScBB+GM80CMrUR8NnkhLMsrIlIuJ+78zbPBWeqovPuleYjF45m7fVDE6o+Z1kUGKnPNSF0n+9k2GVZ0u/32dzcZH9/f5FRuuDJIRvD4ZCiKOrArm5M2IC73bncwlQUxSIwai5YFxGk5j115/hfWnjKqqybCVIHLhJPCFrtFnmWUdQQvpN0u2sGq0zwPX+R3QohFi0UwEoRO53uAvG5eG/LqlyUpRzx2lOeNSGTgqosCevAtqqquuOzXbiiVotOq7fo1OueuVUPFDYjxd2rij+epvxklnMoBTtKcqWs+OJoRFoU/PrliK0rl7lx4wZ37twhTZ1pVMbR0QHXrlxGSEEYtej2l2i1IpYGy0xnc4aTQ4bDIbv7h4ynMwyCTrtVBwHhIuOeTibMZlaaaep7HfgemIqqyvFlBVrT77R47uknCcOAg/1DPnHjFi988JhiqcfKlW3GkyFVOySJ5wS//x2KJ26ytbHFn/jpn+bXf+u3Od7ZpS0VqqwIPYXOC9Y317j02c/yxnvvc+Cst43BU5IoClluL7G9dYnVlQGvv/UGygvx/JDJZMr7d+5w+dIGoFiqNJ3vvcHu0hL4HteefpL3leRob48fKkt+wxhGF/gRzXJuk9twcX65+fpRpFM37z9qfjbXAWeP4N7fLL38fd+niiI+naVc0ZqxkHwl9PlHvs9PpDmfKws6GOJE8q35nK9027yyv8fm5iZZZhV0rVYbISSrywMm4xFK2GDEXbfzy3EJizVJ0zZJqq/HzY2zNUecu+aP+rvJ73PrXVWTU40RaFEjxI37VRQZSImhspu29Di7bZbf1Aw6LpalLh4XidH/tDJ48zzNtbn5rBaZBGfr0PkSk8Gck3Db8qttlBgufLrcvXHXU9Uot5Dnx9S58rvWeLVqSwixUH82v6O7v00Uz9TouaxtHlwi6I7RaES7FZ37Tu7zz/icZy1bdI12C2z/JTyP6WyKxiaFQlqEWUhhjea0rtFicw4Zz7LMfk7tWJ4kKe12m6gVkI9yJtNp83b/Cx8/0MHK9vY2nucxHo+Z1wiAHTTng4gmWtHcRIEF4uHgWgdVwvnsyaEX58tFZxv1xa7PzTJIczAjbVHm4mIG5xsLuv+71wiXdYjzmaGbIK5hnud53Lp1i9PTU2az2YcGbZNE15xszevRNSTrFkGEteZ3A/RilnXxj7vfzfvcPNxrzhYci+IIgfUTUR5RZFuNF4XGC86QruXl5cVkqarKQuF5tghk3L1x/BvnVeP4Se5e2v4zFaKevNPpFM+3PhRJmiGVshJNz5IYqxrNEsKWjsqqsrVhc2Zs1xxv9h7bEpAQgmWt+UxWcCjgSFj+yaMyZyMM+OODZUa3bnJnOGQynbJ56ZL1hig0vicZnZ4wnUxoRSHzOEYIxeHxKQ93djk5Oal7EXl0Wh1AkGY5najN2trqYmxWuiJPM6paFYIWtbV8SVmV9Nst+v0Oa6vL9DoRzz31BLrS+MLw6evXyF9+jdnKgGI6I68zsNXNTaZvvsN3fu03mF++Al7IzatXmRwdk5cF6XzC8sA2PXz85hsMhGKj32fe6zGdTJD1XHGL3ebmJn/0Z/4wn/nsi/zDL3+VQgumU4PyPcKozaPdPaKH9+nXZRWTxGRFQRSF7CnFVpkz0JpRQ47bHHNNLlnz58257LLQZkDj7uFFlObi+d18bXqZAIs5lyvFP1jq8w36DLRmN03ZLwr+bJzwM1nGvufxWCq6VclPpXaT+h+CgPF4SqcubwdKoKTg5HAfjOVbeJ6HJ72Fp44rbTsvIyPOr2XNZq12Hp5t/s2NtTmPm+tYM2goSsv3sGRLt45SZ/+2/K6U47VUaC3Pnf9i4uju90cFhc3/NxVbbt1yG/JHrWvuGbnzSHneRdxdj/u91pqyFlc0k8+Lf7t7cU5s4Ur9je8CnK3RvkJXTUPM87Ybxi6JFJU1aPPDYKHcNIuWKOW5e5E0+pCdQ9cbfe7s9ZztJ1VZgWIhhW9FEUpaZVBVVbWvlwEjKNOEqqxQnM2V5nfXWtPptMiKkjAMKKqKfDan1e7gVyHr62t899X3P3Lu/EGPH+hgJU3TRXfU5qav9Rl/Aj5MymoO8uYAPcuwztek3dFEZy5+5kehB82IfTHoay+F5rW5BcH9cQP4YgBQlhVS6HPX0AxwnIonSRJ2dnbOBWEXCb8flSk2EREDKCHqOrJeOEq60lHTuM4FNFJaCbRDHtz3vmhOd3HRMYCoeUAum4mTZMH1aHbXnk6naK1pt9sL6bmo2xu4/7uJbRfuDOdymuf5WU25LhGZqkDU120QZJkNes/GiKnr6GfX666hrEryRusAd5x5yNgvUJZWitoxmkeiDswQFHnBNAwoRyP6ecHG+rp9plKxt7cHlaHbbrE6uAnA/v4BB/v7GGO5NUIIev1lyy8qra/GyurquWDWlQbKsmS9Pn+RW2dMKUBXBf1+h363xWh0gqc85vOYV197nU6nwwsvvoRp9yjCADOa4G9uEg9PCE0E5ZRobQ25sspkPGU4PaBCEvZ6HB7sg4KyzPnD4znPDkeEpSY+PuW7nQ5/V0pSQa3Ksyq4qqrodjoUWYqN6yWDwYA8yzkZjZACnnzxJZa9d+gaw1FdOvM8jyWtSTyPaSNoNcac46i4Z+PmnVtwgXP+H8353ZwXblw0CaLN4EcI24SyieC537uNSinFNAxt92bg35pM+T8lCQrDagX7MuANaX0/PpPn/HoUMaoMQtkxroQN7f3Qp8hzjFSgfLSxpYY8zxeb9gJtrUqyxhxsJht2jNt/u2SnOTcv+kW5dWaRMJQa4flYsikYKioN/X6P4XCIH/qYvLSEVWGDAruOWTm2EWdrbXNdc8/BrYVNxaZbO5sNOpvrbZOX55Kli2uzfdYfRsPP9gH9oXntUI7m5zukqrn+2+7JH+7tdj5Y+nCy6BIro825MuS5MSw+rIYqy7IWjqgFsuuSueY1KGUNJ9016zr5dYn4cn+Jmzdvcvf+B2R5TuXurzgDRi4GkedK/LoiSZLFWiuVpGiFbG9tkf3v3cF2Op1+ZFRszPlyxMUIvfmzZkBR//YjP6s50OC8cdRH/b45eJu/E43zNQeqe03TCvtD2Ua9+TXP25yErpzz7rvvfihj/Gd9F3dobK1W10PTYGyUj+1nk6YW3hsMBguui4vk3fkcTOwmh7tPFwOV8wsECGTtnWHZ+kmW4yuPqiywrHo7GePY9hhxsGwURaBk7V9y5sZZVRWTiW1A5nn+4rObfZeqmuS5PBjgBwHHJ1Y2rrz62Vj6n0VIhCDNssUGtAhI6s9sfj93VNUZP2koBHMhGADF8jLr6+t02h16ccx4OGQ/TSlrGShA5PmU2AmexDFJmjKZTBabqg1EvXMLpaoXIVWjQhdLIUtLSyzVJOEiL0EYsjghSRNGoyGeFJye7lLpklY7QutTdo8m3L79BJN+lx/aPyYejaiSnL708NOcd29c5ckv/Ahb85j9oyEPH+8RlAUqipiNh/zY3gE/PJ6yZ2Bf2a7FPz4ek0URf7f2D4rqgPfOnTt859uv8PD+PcanE7qDVcKwRZrmJPM5gVKwusLouWdY+d3fx2Q5qhVRHp+yrg2/2m4zjyJ8fd6D42Ji4P7txmXZQM3cRtIMsJvBfvN1F593cz43A6FmwuECZaUU/+pkwh/LMnytOVaSUEpulwUF8L7yuKoNSyUMhZUld1shn3rp40SBhxRw5+49bty6zTvv3+Xo+PQjeTh23Tj7vq60cDYuFFKqc2hBc91xY6c5Z13mbnkQgrIyBDXvyPOtYu2FF55jOBpyfHJEms7J8xItNIFvS8yeCqCxnn1UAtW8h81rc9fk3uOeS3P+N4OBi2upnZvWhr9Zpm+OD98PUFItULIm+tEsFzaReWiIGxoBiTvOyvAlge8vZMnnxg2gPPt8dVGitXW1DlpRbQx63vW3GZgBdNotyobpZlavWQt/G0ltGCjxa6WX73kIrIv6k08+yerGOt/73vcYTyYYGo1nhaC5/zQTfvv5AcoLmMUxvV53sW6NRmMr4/4+jx/oYMVG6eW5h3V2487Dlc1B3nz/xQF1Yf8+9177e/Ohc12MwN0gamZtiw3bUAcdfGhBcxGvg2+bi6Uxxu3qH/lZ7nPcuZqoS/NnF0sV7nc1hlAHLPV9qCcPQiCVhZWn0+mif5FDMpp17Ol0SpZli6zIRfwuOHCf18x+ASr3/QwYYRUZRWm7OXt1cNDMss4FqFIsEB23IbjJ2rx/zUzNHU8/9RTLKyu8+r3vYWrmvymtjbtQVolge2QUi8XE1LJW0RgPF4PCxfOqO4GfAN/0PP61Xhezska4tkYnLwgqzd7HP87ypU1Oh0PKNCOez+sRUvOTfI/jExscttttPN+aTQmh6pKiXtynqiwwukIDRV6QFzlFrQRQ8oyA6ZAwoyTdpQGD5VXaYcR0NiMrCkqjSdKER/sj3rzzG/xup80jXfHxncd0Ss3RdM5v93t8fTwm+uWv0O0uMc9yigpUFLHUXeF2p8cXd4+YdzrECEIkqRDEZc6PAw9u3qR3/RrD01OMqZhNJ/zmr/82Rms21y+DCnjwaIer165QljmPHz3g9775TYrnnuFLLzxL53tvok6HHGUpX29F/OrSEv6FTcttzu7ZuGBukf010EBXTv0o92l3vo8i0jdBEiprAAEAAElEQVRfdzH5aG6wLtMPqor/4zzm35hO8Y2hC0htOJQCYQQ3gFgpYjRDYcuilbZcvOeefZq9Rw/51rd+n/XNLd577z20sbL4w8NDq/poQP+LMXthg/mnbejN37n75X5+EfG1GzXW2r5WS6ZpRp6nvPvuu1y+ssWVK9skyZwsK5jNrKQ2CFpQN+sU8sOeN03juIulnItHcz1rXv9FpORi8qprk8Ym+tC0kXCBTBiGi0TIldmaAUZT+dg8vyNbO7l7M9BBnykmL3JUhBRk9cZuy0Hnkfc0Sc/ta+5wiWNVFFy6tLko8zWRxFrahTZ1405z5hpeVZbvdO+DeyCtYGU6m9nn4Vnek+MoXRzX7l4URUlVlUihSJOUoiwAu167hPH7OX7ggxW3eDQRDiEs6evDPBT3u7Nsw3EKLNxfBwOGxrnOw8VnE+cMmrSEXtP47GbGYB+qVR9Rcxx0HXicLSauzOAmT3MQL76nEIvNz12f1i7qN3V9WJ3LNv5pUf6HD4tIWbdUsQio3FHpykqgtSbNskXN1RhrliSlwA+DWl1QB42VRgZ+DQ8L6p70GON4Ki5Qo/65DZeElCAtbFxVJUZb+bEju0oBVVng17B1f6lLmmb4YUSapCyvrxEnc1qtkDROQHjkZYnyPCbTmZUcaguTIyXv37mDMYYsy2smv7N0t3X28fSMXyGQdXBguT1SfBjBWjy3ejDZZ6T5lXaLF2/e5KV5jP7gAUdS8lqnwz/Jc4rHuyilaPkB7eWQSlckRUae58TzeMGZsWZMHmmSYUx5Ltg1znoSbPmusoRZjAGt8T1F5AcUVUmFg8idtX5CkmT4no/n2y7V3d4SYdSm3bW2718bBLx9+xarKPaSlCEaXZYkoynD8Yw4ySkrg/B8EBpV5Ij5nKEfgADl2fE4Bi5lGcnuDu8dH1GUOUJAmiSIUuN7Pu3ulOs3b5OmCWVRsLmxzvD4kKOjE+483mOn3WG6NiDOU3bR5IMBvh8SwZl7sBCY2gzSmXnJWiKfZTmVrPA926fKes/UPCopQIOuNyxHlmzOu4uITXMMXAxW3CBX0kNK+BNxzE+lKdIYjup5uWoMsijZN7BqDJeE5GuraxRBRBXPGSx1CXyf09MT7t29R5zETKYT1je20EKxt7ePlHXzudIFWxYVdAG+XSPPFji3blxc35oohzuasm+3UXW7XdAOifCQ0hBJn7LK8f2Ay5evcOnSGk8/+SRpmgGKe/fu8+67d8nSjMqAlR9LysqWet06aLReKIku3uPmRu0SpWaJZrHe4VRQNVasbbnZaLMosQgEQteqIVHLpoVVfmpdLTxq8qKwXD5d7yWLcvwZsditCWKx79j772wtXBBT1U0AhbA8RD8I8P2AvLCtI4QUICRSa8oa547CwO5VBoRpOLXrEuncnIU10jw8OqTX7RHHMZ46s8KotENk6rYn+sx/yVM5VRhRVBXXb94gS1MODg8xNafPc+OnVjm6hpX2OmygKur7JoTGmHLBGbTcyfPj6V/k+IEOVuC81f1ZpqA+EiGBs3KK9ewSGOGgx7O6nNWan3UsbtZG7ed9mK/SzA6ErRqc+z9YOZ1Usn7Y1si5GZ2W2m6QF70gzj5LLAaqPa9ECGtE54yXmjDuRSa5+32zPNPcZJWw58eYekmrZXb1xDPSyn91WdnBi0JKu2G3Wj69fo/xeIzwFVoJMJpOVHfkFQI/8K1EzwjsWqTqfbRAKVNPUmpnx5J21KLKa8IjmiAMrLW0MYRKsrq8RFnkrPVb6E4IRjETgNFsXlpHyYqjYs7K6jIyjBjOZqR5TF5Y34kiLzk4OGI2tz4ERVEsmkUKIRDuPmgXxFoPBGtGYRaGe82x1xwHToFhsOZrcVXxn52c8vGtbfr9Jca+ZKgEoswJ8K3R3nRGnufM4jmlOCOfep5X+14YdJnjKdtJVwivfs4KrWs0TwikJyxCJRSlLlG+4ujkiMPjw0UJLQiChXJKKQ9NhZEKoyuy1FqEx7NJDSUrCp3zSAvi5WX8wRIrAqvWKkvWVldxDQVV6DNPYrpphplNWBcwjlr1uBP04gTRbrHx9FP0Om2MMQuF13w2YzIeW0dNk7G+tsTJ4R7tQHH18hXu3rnHnXs7XNre5t7RkLjSVFKxHARgNKayCiSprORSeT7WADChyDNMWZLW/Zo8pfCUoipL+zwFqKBmgyLwhIeqN54gCEjjMwVE80/TzbpJtG8igEIolB+wrCt+qKg49H2Wq4qgLo8pY+hUmjUMhZR82VP8PWHo9tq0OwG3rl9la3ODnUePuH7zOt2lHgfHQz64fx+kR1HoRVxqSzuOS2NLuAjOo7S4eW3NzZpIzGKdbAQtTQSh+X5hqsUmbFEERRS2mc9S3nz9XR7ceZ92K2Q8mbK8vMzzzzzB9uYq9+/f59079zEypKKy64gUtvyhnS+WDSrcxt68viby49zIHRpdFIWde9Lg1y7CZyUm6zGjDaBlHYgCSIwWVr1UAdIsWkR4QUipDaasKOvrwbkxSwFSURlb9vU86/ocBGeqnLNgSuD7IVJW5/atvChBSPwgpKwqdJ1oW5fvBLRG1NffCn0w2CC7LClK6xllkHiBj+f5pHFW9x+zCUxe5EhPURqNadAxXUBbVbaFQlFpWlHIo0eP6Pf7CGF5dVJKdFmbJArpNkmUdH5eNnCRxgY1YChLgTD1PTXSlp2/z+MHOli5OHDdRu2atF2sPbv31Bznj4QK3aBqZlLO5fbM9fG8pf3Fif5R5aFmxiWFAnlWxnJIDDT+amQPboGU8iyqvihrc4QxKT98Xc171byei3XcDyEEjUw9CHzKqrR9YKREV7b3kkSQpgmDTp9ACbobq5RlSV5WeH6A8qz+P8IS64yyPSMqYYMVRE3gqhdNq3jCWr1XJS1P0W136Xe6KIktGVQV3U6LeD6h1+8xTxKCoEVVGVAhSZ4Rn06RCrJSoCcx7b7PLKuIC0OlFRqJMYrxZEoynyCVt+hU6/o9u3ty8T591DN3r20uQgvYvB4LEus0+14UWT8EYZ1bq7JEV7YXkNFWAhiEIco/Uzg0a+tS2p+Xla4z57MxsRhj9ZiJomgRcMMZXOyI6c3N1hEs2+0WypOouudPWVgvjVxblYCk8Z3qDfDg8HCBIKrAohW5lNzd3OTTj3fxZMHM07SLnH6a87sba0x9j0hKet0eZcdKyP1rAfP5nDiOGQ9HCCHIspTRqO5S2+lwOjzl5OSE9Y0NdnZ26PX7tKLIOpEaU/vJSJTn15uKIAx9lID20lKtBLOtBdw1u9KP1GfI5gL21gZTnUlQXV+xiyRm93tXXjo/ZjRGl6waQ9cYHivFQRBwI8tACI6VoioKDqTiH7Rb/K1OB8qS2dEh6+trKM/j8PCQ7c11et02b7/zLifDMWlRkeYlgR8u1pzmeLHriw1aLq5HF8dz88/FMe6SnGYwXlWV9VmBBeG+WR45PT3FUwblKSSCR4/2uPv+PdbWVtnYWGe0OuVonCCFdYk2urIBr7Ddm43RiPLsHjtuyEX33YvSYNdXTEpBGIRYryVbohBCWIWfBqM/jNgsgiBPLhATN49caflsnznPCWsmtG5uuDKQGzNa68W9cu9rlhbDIAQpa1RP0opCe6FG193lJYGv6HR6pGnGwdGhRX0qg2ckGDsf8zwj9AKog/JKW/6hkGekWvccF55jlSXIugTp9u3bPHjwYDFffD+o0aUzJWdzbLjv0nw+WZbR6XQWoofv5/jfRLByPnL+6FryxdqlIw5dhDwd8vJRk9cdbiNyC/XFfj9uwDa5HO4B6zpLl9K2oxeejza2ZKJNU8774cNtEG7BbFrbu2DG/RvO15sv3ofmz5rv+Sio1QZYhs3VFaSUZGlKu90i8HzSJCX0BUstn5WVZTq9PrM44dHjXYYnQ4RSeMqnV3sTpHlGp9umApI0o9RWPmeEpNPqooSgzHLaUQtdlHhK0fY9QlPR6/TohT5ZlrKyMiBqRfWzr9BacHQ8ssHRLEMFIWmcgZaYJKMUc5CSMGxRlBqjQGgDVUHoezaLMlb503zc9r5+ODBp3iuHfFwcZ2ccJSsxNQLmacLDnR2kFIBmaalvG25GLXtSbRYW40VdbmouBGcZ8xmi58aimwduzLn3dLu207PLOt17m2RuV+O2Crs5Bl2XuBVZlqOU9R8qioIiz+oSnMK5m1rysTWZE0CSxFSV5u+3Wsx6XT42nbKSGBKl+L21VX5nYwNTVMT5jDxOF8iWxuBH1io+y3Nms9lCcWa0ts83z6lGI1ZWV1nf2KAsC3qdLmHgE08nFIW0FulFYTNBIQk8hajvrSUi2z/tdgspJHmRk+UZxqgaFbVNNOM4sbLvLK/XjA/7gjRJ5W5Tas49691jrdunQhBLQacoeEsItO9zxRh6RUEO/P12i79dq0LcHJ5MpsRxynw65vjwgMBXPNx5jPQChBfQbgf4XrDYZIwxiw2oqqraePKs35kLJppKDreWXVwHLn7Xi2uuWx8dP80hHHaj9hHKq5EMg1SS6TRmqbdE5Ac8cesW5v5jhqMxXuBTlGfrs70+8P3z6svzpf3zScTCjM5ds5G2P01ZYjsv1y80Z2IFjC0WSeF4f2XtQySpqvPcnWbA5NA2Z7JpjFmsAU3RQXOvcP3p3PxtEmWNcaadHsrzyMsCU0vTBZpLm5tcv36NbqfN/t4O7XaH1bVN7t77gPfeu2NLsJlBV/azW2HE1e3LHB0ecjw8tZ+vBLo4Wx+a16KUwvN9Qt9+B9/3SdN0wdmRdQAl5JmYovk8XAmveZ/c+Z1C6Ps9fuCDFbdRNAdtk5TVHNyLcopo1PbrCXs+K/2wh0ozom6ev/nv5mc41KPJvBdC4Ksz7xSAoszOAqJGkHCRjGq/h0LWg6wZkJyVfs6CjouZ/8UMoHm9F8tBZ0iOy+QFUeiDqei026ytDIjCiDSOKbKUzUubDFo+noS9x4/QwnYgLYoCnVuTOqErWkHA6uoy0/kMFQRIqZnO4xqyN1RFRrfbo9ProxBsrK1x5dIWx4d7dNothITjk2Omk1PmM8np8IhLGxvoZI7vB4QmJU0SljsBQatFUUUUaUKoJGmeoU3FaifAGEEymxN4Pn4YMZvPLCwsfbLSZqoW7BJgrGrgXKB74Z42A95zgWBdE5aNoFXUfXksIx/SPKfb6+GHAaKuJaOt3BR9BnO7DdFmTfkia2sufBefvft/q9UiDMNFqaVJAm2WDc+QRWM7z5YVgR/i+0Gd8dr3llVJlmfnPtf9abdamMrWI4IgIK4qvrKyzD/p9ehXFUkUkvd6NhiZzRZzy0nOjRQLx+J4PicMwkWDzDAIODw9xRhDlucMh0PW1tbotNu0W20GSz0G3Q6+51FpzfHpKafDEYEfkOU5S/2e7eAd2IacZZFT5NYIcBVDX0pGUnKCtUVYHizb7sXKBk9JllMU6YfI703Uy83bZvCqlCIKFUpCoTxeTTO+NJqgPI/T1XXmoxGDquJrUcjf6nTs82kEkULAweEhuszpRCFplhBGEdILUEFEEFh/jCbxs3l9mvOom0Prmrw2t5acKws35MIXkYPFGgGUZbEIgpslG8/3wWvZDVeXFEVGoDwOD4/YWFsliFqsrSzhe5LT0yEysGaMZeUCIMt/a66XzX8313xnZOlIsABl7tBnUZfXbSBp3VcBziNHQlieU810qV15q8W5g8AGhM3gtLkHuGftzOOaa6n7vbvfDo1wAgR3v6uqwvM9lIR21CaezygLTTyf83hnhzDwGJ4eopRHkqQcHx4RhhFpUqIrQ1FmeL7gytY2RVEQhiH9Xp/RZAzSBuWO4uDGgtv/lFKLvlGu+7pTr9lDUFZnyY4bFwu/HkGjmeUZH8btod/v8QMdrFwMMtxxEdprLuLWE4QF6chFu+73TXjvo5GZs06j7oE1N3kXsV7MVBYTrD6Xf6Ep2iK4MWeLlPuO7ju5azgj9p5FufUd+dD9aU7Gixn3xY2qeZ+aKgq7iRUksWE0nhCFofV4qKrFBjZTNmtN8xLh+YxnMXleIqRFgJSALEvBU4zGY1rtFqvLA3zPNhQsqlo+qkvi6ZjNtXUOHu+gs5QosuWkIPB5/oXnGez0GU8nTPdjWoHP9sYVsiRm0A7ItWYyj4naPYQMGA+HbKyt0ul1ebj7mCCMCHyf8emQzY1NZlnBo/19Tk5HeFGbeZpTlDHlgsh8dt+aAWJzTDQX9ebCJZV1sDXGoWnKZieetKVAXZEXlp/iWPkWtvVQwkMKb/GcmwtmVVXESbJYBNzzctfhSj1CWJWUUwU0vW+0tr2V3DU3O5YLYQmTGEfOtG3h3TCzyIO9L0W9mbrxkqQpsqb4lGVpA1GtOazvixd7cHLiTrIglwtpy6vaGApXWDfQ6/UI6vbyk+mUUmva3Q5JYtGY8XjMtWvXeOL2be7fvQO6IFOKMIzQZUlRK9PiOCGezzCVpt/vWX4W0BGCPxEnvDAeE5UVcyn4Xq/Lb62tIYWg3W4vnnOWF4t7FIbhYn44VCEMQ6bTKUKIxYIdRRGbm5t84Yc+wXw6IokTfveV7yEEfDorWM5zdvKCX2+FfLnV/hB6Z4zBD0K0gThJWer1yPOMeZyiyUGmBIH1suh0OgsjSPdcgQVB2G1CYRguggqn6GuWj5oZdxOxa66TC2Jrg9Tv7ocLFmzpyXYQDjxpTRYlJFnKZBbzzNWrHA9P2VxfJc9S4jQjLkuk8vGDAKmsss0FQm4dvKj8ac45hxpZ2/nSZvueh/EUQRjY19WEdyHPEkMhrHigqjQohZHn0WmlbI+whRdK/VnOfM2tny4IcRu/MWf+V016QDNYcTQD26C2BOOjgDzPuHL5MhhDEs/pddpkWUIapxRVxVJ/1ZZp85woDJnHCdpUBEGb09NTrmxtA7WXUc1X8TzPovj6TH7trmk+nyHq75ZlGfP5nHa7vdiLgjCkKqtFma25BwJ1I9GzXnNZln1kw8x/0eMHOlhx/ggXTZiake/FjLgsS5Dn5WBuALpBr43BV2d2yE2U5WzBbsr3zuRvTVSiGRQ0J5gb5O51S4MB4/GYvChw6KSTH7oMWkpZ9444gxbhvHSv+e9zcOiFjNv9zNUX3SbXnHBNmFgIQaVrspqBNC+RKIy0PY3itMAEHnmag5AIren2BxSjMVIq27G3LKmKEu1lVgVkBLqoCJXH0toGQejTabfJ0oxkHqPLAm0qHj56gBcGNbogbaPDXhdTaa5fuUyZV6RpSRi12NjaYv/wgJXVZfb3T+zEVZKyKJiMRrSjiHanzfrqGv0oYjIcMYtjqErKIiNJM7LaIRLhgbCEYyE/LPFsZnoXj7MSkCUoOwaQrRmrWllFrSLTTGczlFS0awktWLmgK1W6wwUdbjMsK33md9F4TRPt8H3/XJddN04csdaVhs6ge9tcUNa9VqyyxLqSLr6XMLZxGwY/qEmIUtpM2i1epl7osT6IeZ4vNo4zZEnZrtr1OLIbtaDlB7YPix9YRChJMfV1drpdToanlj+lrFvr7uPHUGni2ZQXP/YMk9mM0WjMUr9vgzttmAZWhim03WhPplMkhp+Zz/lDacZJEDBse/S15ieTlH6W89ubmws1Q/OeOm6PC1SCIKDdbi/uo1LWwM3zPNbX19nYWKfIU6SpePHFF/j4xz/Gd777Kv/NnQ/Qx6e8XxYcy/Pdh5sIXhRGTGZzTFni+T6VtkpC+8x80ixnd3d3sQG5dcs927Kszq1vbt10JQw3/90YaXJ43FrTDMqbQbINNM9Iu4ssW2s8zzbBq6qS0GtbbqYUrKyvs398TPZ6Qcv3MGWOr+TChEwbs3DPlp46l1g1S+tuLTXGLAIuN46DwCcKLHH80qVNWq0W3W6P+XzO3bt3sCR/+73CwCFNrqSsqUpswCTlYlN2UmQ3913wd5FG8FEJb3NNdtUAh1q4OW5t/RVZlmJ0xerqCuPRCE9JpIAoCnj26ad4tTTc++A+9+5+QK/fJwoDwlaLSpfkpWEymRAsKyaTMfE8hlrUoTyFKe2C5MapQ2ctWqvQ5VkA4gJwz/MWLrd+EC7GgbOvcGiTqYOVbrfLpUuXODg4YFy31rgYhP+LHD/QwQqc5w24zf3iYLm4mFsZ5Zmhlo1wvbNJeQHabWYWdiKeff7FcsvFSdvU07smXdKR3SR02m1WV1botNvs7u6RV6VV4YizWudZgFGrlhpQrMtibFZjWfSVriiL8zCk+x4XJ3sTgnSLU5Mo6LIlP7BdRqnZ7whBO2pR5DnzOCVJG72DNIgKwjCyXZPznMLYHib5PMGXkul0ji5Klpf6PHXrFvt7j5menNAfDOisDFheWWE0GpLnOYenI2aZbedeGcPR0TE3rl5heWkJpSQPdvdQQrOSFCCg2+kTtFNMJRFeCy/qkJU5USdiOB4xnj7m8cOH3Lp2HeUX3Lq5wcdefJFv/O7vk41neEpSImobe4EFuw26snLKj3reUn743gonNaxFlBoWTr0WmnakworZfEYQBESBzf6cA+ZFTxkHGxdliaosSfRiENXcUBwPpanmaGbO7mguwvbibdnH910gayWmVgZc1t/mrLHkORSzDtRk7c/gyl++K9dWNlCxsuC6DCEt90UBWZYRBAHdTgcppV0oPY/JeExVZ4mdTgdZuwMnScLp6Smh7/H+++/z5JNPcP+D++RFSdRu8yNf+hK/+Zu/hRCC7e1Nqqpib3eXga742GTCfGWFmZSURcksahGEIS/N53zQ6bIjY6qiZDafLXpQnc23881B3XxvchfstQ3xTMrWxoCTowMOj045PDjg8WxGKuHkI5JOdz4hBMpTxMN4gU70+32msxlVXa4oy5KkOuOuuXHjAiobmErKsqIsi3r8WnTMmDOSp9uUmwGPe8ZuY26uqXbNtbyK5me7+wAGIXWNqtZZelUyTWKuX7/O6PgIAkW73eLatSvoh48p9ZjKSPKyRAqDJ8/zAh2K5ca5C8RdkO7KNsJoFJJuZ8BkMuTwYJdr168zGAz43Oc+Q5bmdLt9Dg+PbPACtNotlLQCgDjNF+VWhxa5++DmYxOdcnPAtWQJgmDBX3LjwSEpbp649dIhGQ7lr0prgmm0YWNzndDz7P5UFty5c4+T4xGD/grT+QwpJBvrywgpWVrqs7O7S1t3wFgn83a7TdCKmMcx8yxBV9bDyl2re24XqxBCWH7afD6n3+/bRrdVheeftZRw5eler2efTVksDOlms5mVttfPbDqdfniQ/3MeP9jBiqksIRGDJ20mqqTA6NLaQEuJrAmAVt5b4qgIVV5S1YGJMQZhDJ60PXGK0lg2uLAbi7Pf93yBEKqenAKoMMbKthzkIur6KLCQwCrPQwGmqtCVwFR6kVnGkwkfTKd02m18JagEVNrUHZPBddt0CqSyzqrO/D2osz0fhG0TH4UhR8dH6EojpPX7sFp4DynP9xtSqtFPiQrPt9bZfuATBIKqtOhLmuUY7VmvjiDg5vWrSAwnx8e015ZotVtsbm4Akv2DQyqt2T84JMsTsqzABC2M9CjKHLRAAZWxHaePj/bZXO1z9aXn7KLkFlyT4QUDnnzqCcbTOScnp2RZxsnxCfd393jrzh2LDuUZg6U+N55+AgTsn5yC8hlNxhyfjIg6PfwgYPjoEScnx3YjKEpa4xErK8u8/tZbJGnO6vo6fhgymk5t63chMNjMw2Dvo9Yaqey4qurA0AW5lYZ2q8NsngA2KBXGusno2nDPerPUvUOEXEDReVExGk9YGQwIPM+Wjer74CmJI7IKrE1+nuWU2tSEN2tfLpF101ZD6chwQhInlihnFyNJWdmeSG6MSiGQyo71LLcmchZODuh2unYMCgGc2YgbDV5dKrIt5e2YhqaLq8DKGGsUUBiUErgOuSBQysfzzrxRijzHyvoF8yQmyzPCdsR4PKY96JIXVuLuK1AKuu1lTk5GTGdTepcucfXyJs88cZPPf/oTVGXB3sEBm1uXqL74eXZ2HnLz+hVKI/nWt19my2iissRfW2VdQJpn9v7kOYPpjCgeE4QB3V6E50u6vR7z92e27l8rRTwpWFvuEIVtDvISgVWg9Lo9K+kPI9pRxGya8CBOuX49IkkqpvOMSsNoPEUqn6LUbkgAwqKopkZYKw2mLrkaw2B5mVJrTk5OKcqy9i+yS5ALcFzZT0qJR4URnlUgKhukCCGoTElVVtjZeJaUObS1WTZ2GbYLHh3CVBQ51ImYqJVXLuBRnh2bVWXDqjQrAEGWxdy9d59+p01ZQV4ajg4eM53OEVhnVakc2lGBVEhVJ4tVhdbZIoh3JU8XgC/QYyHJC808yRau12+8+Ta9Xo8oiui2O6yv5Vy7ss362gpvvfUWURSRBD6TyQShWujab+kiUu/uCbAQOsRxvECsXALh+CBNRNsFt81GiO73WltjNus5I5iMJ+zv7rF96RLXrlzh8uYVjo6O6fS6dk7FM9IsZbu7zWg8ZjabcfPGdaRSVHlOJ2rR73Y5PT2BS6vs7O0xj3PgjKjvgicbOGmMp84l2C5Jj6KINMso8hxLtM9rCwBHXLbdwIMgIE0znnziCVZWltjf3UVXJVob9vYO/oW2eXf8QAcrfg3fORYygKcUpdaUlcYXktJoJFYTb+pN4gxttd4mdtFUBDIky3K0lmCsZ4DlLJzVGcta/w/NspCDuOy0l7jF2P4MZL2Q2NdqjJVUCkFeb3Sj6dQGIb6ymxUSoUB4TnJta6pQ1XXHql4YIC+KOripKApLqLx29ToHhwe1+2KJXxtzObM3m1nX5m/G1nA1Vua4sjzgxRc/xsnxEa0oZGdnh53HR+SZzVbQFVQl3U6LztY6ga+4vLXB2toqk2lMVczxgxZh6PHo8R5aSkojKIqKMPDRRYaUhigK+OSnXuT69jr9ts/m5gZCCMbjCVVZ8uLHnrVSusIQBB3ysuRoeMqjx7t86zvfRQQBha74Y3/sp9i6tLnIVJLtBCFsd2Y/atPtD8DAbD5nb2+PLMtYXVlBKsXe3mNeaEVsbGwsLKWlFKytrTOfx5yMRpycnvLOO+9w6+YtNjc3rfcBgvfuvM+N2zd47733rd25FvieYnlpiVmcoAvrb2HLShLfs11Mpag5L7XqQGCoDMRxSrtV4CuXsdlFoywNRWEVOWVZojxFURbkpVWXSakWfjCe9CwA5gmk56S2isipjQCp9GI8G+1M8NwmZ/sdtVptpFRk+VnGfsZJ8BfZpst4lfLwal8KfcEAKgzOSiatqHWuju0aKypl+5kYbTk7ttRhmOcxZVXSilrkVYkQVtLueYLP/9Cn+OIXPs/LL7/GV3/1tzk6OeaJa5s889QTHO7tcPvaFa5cWmWWFDzzxHU+8bGnmU1G7L53j1vGUAnFFNgsC4LtTfzAetmYgyPyXpvt558gnUwwSOazmNOTCcJUNiEyBqXgqSdv8aUvfJbJZM7f//JvgNF0WhHPPPVkXU4o6HY6KCEpkoyTkylZURFFbaSc1fV/gaGsA446iNQVQkArikiTGDAMdMXqcMhwNMTv91kaLDGbTAiCkDw/K93azc+uPaImPZa6WJTlLJ/KytKFpxCoOoA8c4peNM6rN123ZjRL31WNkLnXUSdmUlqPkDAMFyUGVy6zXCdNnpVMqzmq2yaIUzqdLsPRzCLCpcYLIoyAqN0iSy0JWirriVMUBUWWLYwa3ec3N36EQKOYxflC1aO1ZjieIyYx7XDGfD7nwcOHdDod+kt9sixnOpuSZhlGeovuxq7M4RATR3J35T7nVqu1toGOsJ2UwzBczB1g0VnZqdtc/x4XVEZRRBRFzNO5LdsIgTAwHE7Q+UOU8Tg+PWU4Gdvn5yuG4zFvvf0Oy8vLTCZTlpaX6fW67O3vsTYY0G+FhJ5AVgW3r13m/uMj5nGKFALP95CyJtlWujZNPCPuO2R/8V3MGfUi8ANEcEagzbKMrCjJihJPCb7z3e/QiXyuXdlmpd9nY2ODr/yzNvM/wPEDHax4QeQoHnXdVCP9AIrCwtB+uLBGtxO0JjliMDWZQNTKA23sIhl4JWSawIssWiNsiqOlrcclWUZBgRO/WeKkaAQuVv1hpJNAGioEFdjPpDYkasjl3CEMqMKWWdz1gHNAFFQmB51RFVaKp1TNG6kKkKC1IE8lwyLBEJPnBmMihDAI4VGZgrJI6sXm/IYihLCkXwOT8ZTXX3vDthDPLUHR80OMEChhA7y79x/hK0E7Cti6tEFy9x5vvvUuKI/ZfI7yQ9K8YGlpieL0FFmWfOKl50nmM55+4ja+J9FFxsbKEmurK1y/erlGcwIOjr7H2toaS0tLjIYj4nlBlmvW15Z57vnneffu+4RhwI3bN0niOc899SRrKyuUZcnLL3+bDz64x8/+7M9SVZrNS1sg/TNLdW1dS6vanfLxo4f4noUyq7rmqlTdv8TzMQLeefc9Hn/yJT7+4ouLDCiez1G+R9RpgYFXXn2N+w92ePjoMQ8ePsKXIXkuqWoialnVsnUZWAgega7KOoK1AbcubT+jyPct+qfLBcEzzXI8z8p5g8B2NdV1jFxVFUiJEtKaDtaZk1CWl9Bqtc6VMd3zdpmyyxYdj8rzzpqiudc3NwPgHCTeLI24c7tzLlCn+m+3oTYXfCHEgtDX63URNRm5Kit0aWi3ejWHpUWn5dNtBzz95A02VwbsP3rI+kqPn/2jP8Gv/MrXePOdd/nOK6/x5M1rPHj0iMvbl9lY7xNGERu9Pkvv3GH+rVf42DRmLgRr6+tc93wms5g0UKTTffpJxvGnXmTz6dtci9r0+wOU8vlr/69fWGQ6vu8Rhh5PPHGby5cvs7PzMmk6IwgU2pSEoaIqDaeTCePTQzrtDkv9Hu1Wm57XYnN7jdnvDckLu3F585wkyazkuiwXG7HneZSzKf9yHPNFbdjMK06LnG8qxZdbLSo/sHwipVCuYGlsMqW1k7L6gLbNK5VtIWG0s284Xz53ZT1XznDPzJU8mg0Z3TNvigyaiIzbnC9yfZxM3Q88a84pLPfp0tYGe/sHVMagZAXG4CNA2WBeG4GWCiENprJIufOVulgCvahSa5ZGAXRZMJ1OrFS4odCUsk5staE05eJ7uUDNKescWtNU6bl75Mqurgzk5oeUctEduem/4oIDJxXudLrE8RwpJN12h+eefoadBw+5e+8ep6MhBK4HmMfq2hrdbtcG+sDu7i6ep1hZGtDr9RmORkwmI05OTrjxxG2evH2L9957HyEseXw8mdTJo5UmB2HEYLDMfD5fXKtD6ZaW+pycDM9xM51ZZavVAuURJwnCaHSZk2Y5w9GYeD63MOj3efxABytL7ZBWK6oX4pLV1VXW1tY5OT3l+OTUcgzwFnVzsJB3JQTtXo+8yEniBKnqQa4NQeQjpMETwk4KYxDCUFYVgecjjITCtc5m8beukRYpBL6QKOW4LA1VkTBARSUqEAYlnC10vRFooKqwjq4e0hhA1s3yBJ6oQLmSU4k0Fb708QLrBWKEh1I+RZGT54XlACiF0VAWOZXJ6tJSVaMpZ1mIMBJTlhbtKUsOd/dptyKSeM7SUp+lTotWu8cLzz/L/u5jlpf6xLMprVbEH/2ZnyL0JP2lJU6GIybTGb/85V8hSTPW1jcZnR5hypjPfvxZ2q2QF1/8OLdv3ODB/bscHRzS7nZ48HAHKSUrKyv0l1ZQKuDw8ATP8+n3BnQ6PabzCVWeMRsN+eynXqQ/6LO7u8vuw4fIeqN8/tlnuHxpk/lkwvPPP08QtSg1ZHWH7tXVVbI8s0ZJWcpSt4USEPmS3JRgNKHyKMscpQRxnPDE1ctc3VwDrFW7NoYqmVFkCcPJkF6/zw994uP8kR/7Ekma8+DhI1793mu89e7bPN7fJ8msDX1RaDsekGRpbpE+F+AiMPUClGRWXpum2UK1pjwfWfMKKu2s/LEcIiEsg8RohLbW8hiNqt1tpZSLjaPJ4XKBgttYmgqhZgmgGYQ0s3et9aK81CRjX+TDOMjbdsFOz0lnlbLNKdM0rTtfG6toQRJFbZQM0KXBEz7PPf08X/zcx6mKOTsP7tJrRfhSkJY5H3v2SYos4x/96m/w1V/7DeIvfp6N1QFKHdDrdvE9j9l/+7fpvvIG7+7vsy9gICS3Oh3Y3OBSK6QajciWVzj93G06f+wn2J7PmCcJ7739OltbV87mk7BrSb/f50d/9EeZDo+5tHWpdmA2XL9+hc9/7jPMpmOGJyd4niQKQpI0YXV1Fd/3WVlb43Of/QRvvvUmCMmv/drXWV+/xLe/8x2U9KwRn7SEz5+eTfmpouBIKh55Hh2j+cPTOVUFf6/ro+vkyW3YTQJwWZYUWbLImo0QOEdRXSdysuEJ0iTSXhwn7jV5HTC7DbfJ7XNIgQsK3LUAxHFMFEWL6yzLkjSvaOsOR/s7rKyscWlrvVbPCDbXVtnb2UFEbU6GY4StYVFVNuHzlIdAfyhQARZckWbJxh1CCItcSUmeF0StGlEzhqq0pWkhFXmNfCxQ9RrVcWPezStX9nJj2N2jJkrRRCs6nQ5gERuHYLl55fs+ke/R63TptNt87PkXuLy1TZHnvP/e+yS59Y5yPKNWq8V0Nre+OlIxHI3wfZ/l/oB5nICpiFptgihiMpmwvLzK9StbAFy/ZktGr77yCsPRkGjRdRt8TyHbLdIspSwNeZ6R52c8HDev3b2RSlIVpX2fUGgJga+YzmPC1RXSvOD7PX6gg5Uf/8wL1v8D6tboAYPBgHizx3S2RllYyLwqLWRZlIXtCSIE7W6PoiyZTqfEdQfhoiwxWhMstfCUhzGaSpcIYdDamkgZLTEmWigqmhNUCIGnPCQsovAoihYZqB/4SGnhfRcoOLXGYgBUmrKqyDL7cHUFRVHWvU0s78ROQosKOXgTACnodDvMasdBR3wrqxJjFJoA6ppyUbPnwRHsfDzhc+vWTbIsZf/xY1sO6XcAw+1bV/mZn/wJPE9xfLzNc88+y2w6QUlbQpinGWvL13nlu9/hk5/6NH/pL/wc33v9de4/eEQR30JUKbJKIK+YjY/5zu/vcHJ8RBgEbF+5zGQy58rVq8ymEzbWN1DK4/LlK6yub3D37TtMRkOWVwZ0ex3WBkvEkzGmzMhmE564+QSPHz8miiI++clPIo22pLB4zunJCSurq8wnY05PTwk8SeD75LlmNpuRzGcIo5nN5zz73HPsPHxIt9slTVPyomA8HtfZfr0h95fQRtMOPOZFRjcKULpEVSWz4QmnwxH7j+7z2Zde4Md/7AtMk5i9/UN2Hu/x7rt38MM2Dx7skCQpRZ6QpWkd9No6uzFW6VPkBQYrV221IusRoa2M1fM9KmFVTgKL3lDZQNmhiKbSC9lwM/t1i0tzIzojCZ8FKM0NwG1UzX+7rMvxG5rE7Ga9252jGRi57FMIwaz2WnGvi+MEY6yrptGW56OwGdzJwT6/+41Trl/b5JmnnqTfaeH7PhvrkBQZn3zpBXb3Dvn2t7/Dd179Hv/mv/6v8sTN68SzMf54xJXjIXeBR3mBEYojIXhvMuXJ8ZjXfuSP8PD+PfrXrxK3QrJXXuXWk7d5+sknuXr5MsPxlIP9fXzfJ5vHhI15fenSJVZW14mi/xnQ/ORP/gQbG6ss99u8+PwzrCwv8U9+55/wmc+8RBiGjEY20w3DkC989lNEUZv33nqPn/8P/iL/v//8P+dXf+3XKdGWW5GlfCbPOURyJARBmkB/iWQe85ks5bdaLY4lC9M3YyrKUtccZ1tqNUVOZUxN6NaLtWEhDtAazVnX9qb3hyvjNJEROAsGmsGpI6E6E7BmiaZpbOiQtKIq6nEuCMKQ+XzGpUuXiOczBktLfO7Tn2LpD/0Yz77wcf7u3/8HfPd7b/B4/whEQFlplISqyBfBwEWit1ub3TVAIyDz/EU5rywrpKwWCLsubTLZ5Js0OTLz+XwRiBRFsQjelFILozh3Dxzq4jZ2N/bdvXJIlQvepJTkSVr3ECp56+23efz4MXmeM1hd5nQysmaZvo9Sgvk8rsuxFd1uj+lsRlEUDEcjBlevsbo8oChzCm1Zc6GnuHTrhkWGdIGUhueefhJtNK+9/gaj2YQ8T9nY2GA4HGK07Uwf+JZAG9bmnp7nkdT2CW7MCCUp6yaMeZ5SljYIPDg8Qaj/nTcyHIQVQhcIKRh0AqQ0iGzERqfFWru/iPCltCz6IAgsQUsAtYlSWS4tFvE8t3Xddq9Hqaua4Ggt7LPcZoStMKDth9ZTJE0X5ZgkqWFBqfDVmdJILDYQS0Qqy4aUsLIGRc663o9CcmEbBVaVreNrbUiTlKoy1pyrPDMecqoJ9x29EBCW3+Aablm+hMBTPlXlURSl5TvkhUWZdEVRlLTbHTCy7u3Q4dp632bn0kqQfFHx5su/XddaU3bff50g8FGeIora5AR4EpZ7He6++yZJnPDg0UPKsmKtFyGFz/HeY4ypGJ8eIqUizy1B6+jklJW1TYwuee+dt5nHMZ//whd447X3LTHr8S5ouHRpkziJSbOEVitERT7JeMKjBw84OT5mbW2Nb33zm6ytrvLg5ASlFNPplJXVVdIk5fHjx9x99x1WVlcwxtDpdPDr2nG73eaN732Psiw52N9nZ2eHhw8fUhQFL730cdbW1pBK8fD+Bwup6tLSEkVaUEpFMk/44P4HdLs9Tg72oczZvnaFd95/n16/z+ZyH3PzKjdvPcn4hQl/53/8n+x519f56tf+EWlWWEK08pnPYysZlo7UazP2PM/tOAk8MLZtu+vMqnHlJouauW62jm/QalmuiJM6Z1m2qEUvHGKNWdTPm9l1swTk3muModfrndvUXCADZ9LI+XyO53nWKrzOPpuy+GaX4DRNUXUjRc/z6Hba1ielKKCqqPI5QWDRvueefYZkNiPLcpY6baZxQpIV/Mt/6v/Anffe46233+G/+e//B/6T/+T/iZ7NePN3vkl0/yGHRYnnB+SlRgrJbjznydmct954nd5LH6dzaYvh4x2idovj41MOj4/p9rosL6/xxR/+Ir/8la/h+8Fibr/22mv82A9/ni9/5R9Qahsw7u8d8ImPvcC8LDna32dyeszayjJSQqfTQgq4enmbfq9HPJuTphnXrlyiFSp+/Etf5Ju/97sMx1OkUnTmKR1t2FHSOliXljMz8xWXi4pNz5C3WhT1muI4BGmc2WeeJSjlFCC1v4ZkQfKuKo0RZ0ZlzYDSlfpc4OmevyNlNn2YLj5LVwZx53VeI0VR0G636ff71vdGS5KsYDZPAcN8NmdtZZmVwTJ5mpH5AV/9lS+TxHOeffopVlZWee/9O8RJankb+IugwV07nLexd4FAs6RZ1xopc7sea5MvrOgRgqosFoHORYJtk3h6sUwUxzGtVuucNNkhMc3SlFu7m74tFrksFnNvOp2S5zmnpycYYKm/hBf4ZFlBWWkqnVvrAGNIsgwjBFGnTRLHJGnGLI7JkoTT0ZDeoMf6xjpUFXfv3WMynnD5ymVu37rNO+98izzP+eIXv4jf6rDzeJejoyM7PuOYXt2B2TaBtQmF23fOrruESixoFSAoK0OZZEStiAcPd7/v/f4HOljpRRHtdki3060NqZJ6gBriJKMqC3RZUmpNpTVlEFp/Bs/DD32kMbQ8j5WVZaZ1AzljNDqLierabhD6GFOhsJ4gARpfFxitabVtlBkEAarfJsvzGqa0UlQQC8gviVMwhpYXUtaKCT9qEUUtPE8RxwllkeFJQ0daEm6WzdFFRVf5tHtd2u02w+EQzxN0uxHGWAJXlmU1QcvDDzwbEFUVRb145EWB7wVofOI4RSnrXREEgdXKa818Pkcomzkl8ZzOoGtLS9pQZClFoZFoK6vzrReH1oYyzSkrjZEhb7/+KmVN0CvKAk9rTJkTebrmH2RgIEsS8loK6fseB3t7zKcz9nceMp1O8TyPf/Tlf9iAnm0F7b3R4UJZomvFlDGG8XBEEITs7+4yHo/qzNDD82328sGdO3ieQnke8XTC6fGhLaUJQZpZ7w4pFePJmCzNFhvr1WvXGQ1P+eCDB3zwwQNrlAYL4lwYhCRxzMrqKqfDIWmWsbq+RlmmPHr0gN3dx6RphqpsLRkp2ZOCJM148voWeTLn/gcTotC338tT5KWDoi25G1jwVISQzGZzQFBxZja18KVobCpeY0FtwrVwJot1fJZmwNus7Tdr/Y7T4mBrB+c7uLt53qZ7s/t/kiQLwmVZ2j5Ak8lkkXkurP+1Jk0zlvo9Xnzp49y6epWlXovjw30+9rEXePjwLkeHB7x35x6dMOK9997jmeeeJSlyBsvLSBR/8mf/OP/tf/+3uXv/If/f/+w/58e/9EWe/uEvMf+N3yM/PkGLuu+RklyKWoQrKzz/hS/wYDYH4fPaG+/w/MeeZzSbc/PWDW7eusnXv/4N3nrzLVZWVxlPphjgJ/7IT/DFL36RDz64xzf+8TcockEUBvz2b32dT7zwAqbM2Fxd5ubN6yR5wp0P7vLKd7/L1sYlTo6PuXnzJlliydrT8ZC7773L5a1NvvQjX+TLX/tVgsAnwZDPfVaRHEmrDIsCn43BEqYsGNzYZJ4WpHllO1RLQa/Xp9ft0el0GAwGJGnKbDbj5OSELKs9d6QlN5eVJstt+W8xnhpoivNcuVjac8+tqcZx46GJ1rmjyYtyYyuKWsRxTWAVAmE082nM2vIq9+8/5MEHH3Dl8jbvvfce3V6Pzc0tnn/6CZ64cZU3336bd+7cXwQB7lpcadH9cZ+tlFoEBmfBck00ruePMAItzoJy952bQcvF7wWc4/g4oq0LblyzUJcoNKXPbl46qbgjLcfxfIHkOMm48j2m8ZzKGLw6iKyqCqXtWgy2a7znBUiVkaQpjx49wq9Lx7MkJmy1GHR7KD9kMo9RB8fs7O6zvr5Onud8/Ru/w/rmJpcuXaIqc4anJ6ysLHP58jbz+Zw7dz8AYdcqd89dsCKlxA98hBT1ddSlXgRFZSj0h+/bP+/xAx2sFEYwniVMY0cM9IikJUX2BsvM4xgMbG1dYn9/38qYpVVH2F7gtnGUEZLl1TUC38rWfCSb6+vs7OygdYmmIgw9ojCgKEs8LyBOEobjoZVq1dbySlqXRIBubwk/CMiznOl0Um/MPhoJ0kfrkrISjKdzOp0O7W4fXZUYbTOEJEmRSlAZjfJ8dFqQ6RmUKVUFiTmDFT1P4SsfUdiNWyk3iCRlWqLLkozMRuOVRvkBHaUwVU4VFygpCanqYMtjqRPY6zAGgaYyNjgTUQdhrO24VUYVeF5gi0smoRRp7RfgocsSiSBSgrzQFLV5ULfbYzKbIoSk0galfIwuyJOY4bGtvcaTMbpZmsA2+LOqF8v3EEKBtl4uBQXaTBb9LJrmVlbaXKA8j3a7XaNgiXWK9X3SUjOczGi32wsUIB1P8XyP49Mh8XSyQBJcjficK2xeED3eYzAYcDI85eHODrP51C5qWiKM4L133rOLn9G0O23SPCMvSrQK7XgwNvBK85Iss66/dnF3ZRa58JMoCpt5eaFtKmbJ5XVpryjPSIINCD5JkoWbpluImy7LLvuTUi6QFxeAuOCiGYS43zkjrmbG2fQfcdfQVEtkWbbgrlwsoToCZ9Rqc3x8zNe+8hW2Nlf5iT/8o3RaAQcHD/FDn+eff55knrC0uYJSPvcfPmL7yhZlUbC21OMn//CPM5vH/Hf/w9/hN7/+DTY2NtjptHjy1g2enCeoOGEiPVpZSns247uhx3sPH3B4NOK1t97n0vYVjk6GvPnWm7zx9tv8m3/232Q8nS1KLa70deXqFaKoxeuvv05ZGqT0a2v0hOFwzI98/rP4QqPzkkF/wMryKjeu3iDPMlYHqxRFzsHuAd995RUe7DyiLBJOT1K+9CNf5NuvvIJGMZnBmyvL/NhwTCsImUpBazLhhe0thj/yGf7dn/6XmMcZw9MZo/GYsixpt1r82q//OgBbG6u0uh1uXL9Br99jMplwdHTMcDhECMGjnV2+/d03kMpjaWmJg4MD6zSNsGXwysqxfc9bGN2VZbkYTx/FcQEWHBCtz+zuhTyTuc+mUzw/oCy1lb8ri4R4UqArw/7uvkUQpWKwsoquKm5cu0qv2+bmiy9QZjGeH7F/dLKwaVDSOSqLs/KWtpJwF0w1x3xZVXUmdGZs55BoKeUFs8JaLOHQEc6sKZqeOG6+hEFgqQR14NdUVTl+mAueXEltgVTUPbhE3XBQ1o0gi7JEeh5Vaf2mtNZk9dyKwpBKawLPJwxbzCcTTGC9olAS5fu89fa7tKOIzY0NlBcwixM2Njb44MEjtra26HR7fHDvHg8fPOCllz5BGISMx2NbYTCGfq+H8iOeevopRqMRB/v71tOrLGtSvsQPAtqtFp1uh8lkRrvTxfctWrxz//73td//QAcrrXYPiSEMffIsQwD9Xp+o02GW5WR1V7rT0YSNjS021tdtk7UsIWxbWLzf73N6esp8bhvdLa+us9zroSuNF0YYjO2DYgxJbkjSggKPvDQIPyRotanKEk94rK2tkcRzJtMJaTpDqaAm+co6ePGYTWOM0STpbMGqt9lFTllq67HiKcpS43kBy+u2S2yS2g1WhJHNGJQir8ld2XSGlApPBWRpvoAttS5ptVpIPyBJEnzp44eKvMgpHHmz3hS1gaw0ZEVssy5tS0C+UpiyIow8ymxuvT2UhxaSJMtQRlAKgSwNVakpKk0ynNJqtzBVialy8iwD6SGlT6u7jMqtaZDWFfM4JwgUpZ5T6op4Oq27/Xq2sZw2aFMglcDU7qP2nlq+Rpbk6FqCrYpysZCGYUiZWt8MbUBUhkl8upAellVFMhwTRhFeELJ/cIgGylJb5Y3yLfG5SK3aQghKKtL5jElumfdlUSKNYprNGM5Syqoins/J8txypUxJUUvdi1rBJSeZJe/lBYVOyUvbh0hrYxHASiOE7aYsPQnCLqq+sJ1gpfKpdElLtch0vXgCqvZAMVpj5BkBr5nJAYv+QE3jMgdZn3mxnOeonG8vIBeoissK3TnKslz4tjRRlWbW6Ac+aZ4v5KAlGuXbEm2RFwitMVUO2qqdjk8n/MrXfpt2O2J5eYl2EFhvk8GAIi2JZ3NWQp/9+w9oRxEP52/RWhqwttInDH3iLOP9u/f4Ez/9U/ydb36blzptbk9nbOY5M+BrUYvfvHMXsbODUrY3ze3bNzk6PmZ7e4tuf52v/+Nvsbc/BOmRpZn1tBGCX/nlr5DFKa++/i55JahMRdSKkEpxeLjPd7/7bbqtkCduXSdO5owOT2gpnw/ufcBsPmf/YJ9PfvpTRP0ut/d3EaakzEt+65f+Ps8ieXc0YjiP+W+lz8QP+KE0ZVlXVJ02r924yvjJ2wymMcPhhNffem/Bp3jiids8++xzfOMb3yCOY06HJywt9fnZP/4nuHHzBtPTU1a6HeL5nBvblwjDDvc+uI+UkmeffpLBYECaJPT6fZZ6Xb75e7/HjRvX+cIXvsDx8QlFUbC7+5jDoyM0kjTLOTg4IK8DUWOsrUSaprSjELAIbadr0eGjw0M7LiS1itH2m9JGgudxeGr9lHSsmaQZ7VZEKwi49+Ahkecxn8XsP96z3lVFQjvwrD9QWaGkHdOVMUS+bSBpjCGrDMJYn5bKWI5OpR2PRdYloLMykqqREZc0CQFlWVFU1srfkxYJsrbUoIQVSihheTQVLMpgUvqUuiROY+sFVoseqkIjA0WaZASB1bV6zuHZWPpAqQWqEghhyflaW5Wg53ngWdm7lB5B2CbPM8qiQmhB4EfM4pQwsCXV6Syj1eozj6ccnQwxQpJmBV7Y4tLlqxggaPe4fnuZvd1d3r17n5XlZU6GU8QHD/CUbYhalhlCp3z6E8+RJNeJ4zmVLpnNZgRBRFlaPtStmzfZ3d1jZWWZzfUNqlLznW/9/ve13wvzUbjWP+N4/Pgxf/Ev/kW++tWvEscxTzzxBP/1f/1f8+lPf3qxoP2Vv/JX+C/+i/+C0WjEF7/4RX7xF3+RJ598cnGO09NT/r1/79/jl3/5l5FS8qf/9J/mF37hFxaOd/9Lx2QyYWlpib/y5/41wsCjrK28MZb7EbYiNq5eJUszjg8PEQZaYUSWpkgEQegjPBcFtinLgjiOcY2uOq2I6XTGyvIys9mMee2gubG5yfblbfKq4OTo2D6oomA0GhLHcS0zrZhMxoRhm067x3A4otWKkJ7lvigUg+UB4/GILLNkwna7x/VrtygLwyy2m/iVq9vM5zP29/cYj0dUuuLSpU3SImNnZ2eRJdv250XNY2nZZlZ1XTiKIobDId1ulyAISNKcLMuZzWeMRiO78Kpacoe1lleBT6k1vcGSLSUUFdPRCG1KCm272EqpMFiljB+GYAyhsgGWH7aojIV2iyxFUBEFIb2lVba2txiPxkynU/wgYDIZW5v5bkSWJXS7PQuVFhVGGwQ1QiQNpmb9B35gXTuLEs/3LEJT6VpuLBYQZVlnhJ7ziKg3S6U8WnW35jCKCMKI4XhMluVsb1+htzTg29/9LsrzSeKYQGqKPLObq+9TaJvd5IWVmraDzqIzqed55FlOkiZUZUWuS1RoGwJiJEVRYYzL0DRaSApHAKwzuiapT6pG402svbWU1mnVDwKyrFjY2Jd1cOA8hxxPywYbzqvH8kJswKaoc8OF9N8hG77vUxbFwj1I174bZVUtzMGox58zEXQSWFeay7LMZoLSEpqlst4M2miSxKIqTtCkpMLza/O9yvaRajpIu2vXWiOrAk9Ar9NmZdAn8hW+ErRCn6Vej8gPiauKKoh488493rv7Ad2ozZWNTYTnMZzNacUJS1XJSErGNdERoKybM3o1iuBQItdzp6jqcV2rm7rd7gKNWlvfYDybg6lY6rb50hd+iJ/8Qz/GbDyk24q4vLVly3XGMBqP+Mbv/A6Xr11jb+8xB4cHbK2v8ebL3+aHHh9z+2hEWBn25nO+ISX/oNUiNpJLAlZMSbixwrOf/yy3rl2hE4XM5gnSD9HGMBmPWVoaMJmM+bVf+3XiOEaGPmVV8ZlPf5rL25cZnpxweWub4ekpo8mU3YNj7ty9gxCCTqfLD3/xi0RRxHQ2ZT6dUZYFrVaL2WzG0dERq6uri+aSm9uXefnb36GsKk6OT+ryouWt9Pt9jK5YWVlZSFwHgwFlWTIejZhMpxwdn3D7iSc4PDzEq5HLbrfH22+/xTyJ8QOftZUVnn3qKYQxVHnGeDgk8DwGG+uEUZu9vX0m0xm7+/tMplOCIGRra5ub164yHA5ZXVvjle+9yuHxEZXWSM8jSXPQ0paKvbPSjTEGXXN7MNbp3Bj7b8+3RFajK2St5MRwTm3qeRZN8ZVfBzuaqB3hPL1m8znSCCSyThCiBVrjysxpkVPVZSpnCCqlrLlCdi2UUpFnRZ1ISMIwIghC0BVR6NuO5dNpXUk4UySVua1C9Opmop7nsby8TJ7ndV8rVZcBM37os59BGM1kPLaNZIWh047odNpkWUq322Z5eYmoFbK7u0uvt0TU6lhuVa2ebbVanBwds7e3z//nF/8m4/GYfr//B9rnLx7/XMHKcDjkE5/4BD/+4z/Ov/Pv/Dusr6/z/vvvc/v2bW7fvg3AX/trf42/+lf/Kn/rb/0tbt68yX/0H/1HvP766wuHQICf/umfZm9vj7/xN/4GRVHwZ//sn+Uzn/kMf/tv/+0/0HW4YOWv/7W/wuXtbTY31qmKgkePHjA8OaHd7dBb6tPpdjnY32d9dY1up0uepTy4/4Bu1/apcPXWjY0N6zrZ6xEnMbPJjMlkwsb6OhubmyileP+993n/zvsURcGtmzdYW1+jqip2dnaYTqcLddDGxhrKUygZ2LbuszkIyLIYzxPMJnPS2qgtzWKbSeMRhm0CPyTP5ygl6PW6xHFMXlgSap5bMpVGk9dQve/79Pt9Kq2tBFt6jEdju4FoO+l836cVRfSWBlQyYB6ni3JHHMeLTT1LE8o0Jq8KSuNIuR5oTb/dpdSSSnhEYcRSv0+SxEwnY9svSAh63YB5khJELfYODq35mbGdlKuqwo96dLpd4tgqYMqypKys466tz0IUtvB8nyROkcJOwLKqKI11wHSbV5ZlFpZutWhFEUbXtttFQRhZ86I8z62XirL9Z1ww01RetVpttq5cxiB49PgxKyur3H7iSYqyoNtf4lu///uUaUxZlBydHCOkpNvr2k1hNqXICqgEQeBav1uVlTOC63S7zOY2myrzirwobNZUVZRlRWbMArZuKm2s4aCVwzunV4d8OGWBMeB5/gIpafalcfVyh4C4xdBt+Iu2CAtO0HkL+aY3hKuNw1lH3SaZ1iEr7t/N+r1DcBx/wBELF59XVosSlnutRIM5U1G417ogTgnwpKTTigg8hRKGwFOEvocnodWKKIxgkldM04L7Dx8iDWxvrNNqtZnWSgs4c3HudNqEYUiWptZbqEaKHGHUfr4hyXLS1I5n64fRWRCStTEEUUSWJnRbEc8+dZvQE6wO+kzHI5b6fZZXlhdcqnsP7nN4dMTly5cpy4Lnn3+W4S/8Df5U0CFbGvBoMmH0eJdwMuGrUcgvRRG+Jwk9yfUrW1zZ2mR7c51OK+TNN98lLWypdHV1lS/96I9ydHjIcDhEKsXJPGZnb59Ou82N6zd4/Xvf47Of/SztqMWlS5t88/d+F2MMw9Mh7XabLMt4+pmn2draYj6f02q1+c3f+i067Ta9fp8iz0mShCAMWV5dZzKdLfrmhGHIYLDEeDzmxvUblEWB1taV+ubNWxzs79Nut3j33Xc5HY64evUaYRSRZRnf+Mf/GAOMhkM2NjfpD5bo9bo8eviQz376U+ii4GPPP8/7773Lq9/9LtvXrvHU088QhCFvvf0uS4MB77z7Xl0isUhIVZsoxknK4fERo/HIklErTSts8fRTT5FmGUdHh4BY+KdYpNB1kvZpt1uEYcTe3q5NODjfuLapNjIoZF0CAotQt1oh165dod/rsrd7wAcfPFiU8O0Yc4mCt2hH2yzJujK073v4vk3Q8txKvMEGTVHUAmEIAmvQWBSWO7K+vk4czxFC0O92uXfv3qJM7taUMAzpdbscHRxw6dIm165cxfMEl7e3CQOfdivCU7bVxaOHDzg6PuSTn/wkS0t9oigkzVI63T5pnuFJxXQyBWOYTWfkeUaRl/y7//f/x/96wcpf+kt/id/5nd/hG9/4xkf+3hjD9vY2P//zP89f+At/AYDxeMzm5iZ/82/+Tf6Vf+Vf4e233+a5557j5ZdfXqAxX/va1/iZn/kZdnZ22N7e/l+8Dhes/NLf/C9J04SbN66jBBwfHROFvoVve5bYenJ6Sp5mJHHMdDqlyG2bd1N7jWR5zlptrGOMIU8zTo6O6LQ7RFG06Huwv79fN2UyRLUXxSyOuXzlCnlhJdFra2ssryxTVhWTyZwsLfD9gCgKCEKPwFekyZQ8Tzg5PmQ2n5HECbNZgq4knU6P9bUBxmiiKER5kuOjQ4ajIWmaIIAwCun3+3Q6nYWqyELoOVWl6/KRlZUVeYE2mjzLLRpgrOHRxsY6nU6X0XhEkVtr5+l0wnR0SlpkaCEI2xErg2WqvCD0A9q9dVTQptvpkMxn3HnvXYwuEaY23RMleaWpjKCoKnrdHkWWksyn9JaWWN28ijaC1ZUVjk+OOTk5JQh8ut0eu4/36HY6XL1ylf7SEiB586236Pb6LC0NODo9wQ8CZrPZwiFyMBiQJLEtHZiSNElZW10FIZiMLefF8xRKCJQUJElsVVV1kBfWsnPpe3hBQKU1rU4bqRST6QQhFN1enyJN2Vhf57i26b985Qr9/hJFWTCbzHh4/wFZmhEnNgjzfZ+oZUnTUip2d/co8oL53C6CTdJrhrZloLp+rmuSrG2NYBYOmnmRY7Su5cuu6VlllTFJghCSorDP3xoUnvlbGG3bTqja7AocDK1rx17rPmtL9wLfD85xEJzLZrNxH5wt1I4E6EjHxpgFKrOQido62hlBUZ8t8M5nyKJgHr4USOEcWJ0FvHVYlkotxrySgtBTBL6Pp+r3ABiNkR6V8ik0jCdTMJqtjXWefOIJZrM5k8mEVqsNnDUidFyUWS39dEFYM7iN02xxD6RUeJ4lbZZlWfvheAS+hy5zQl/hCVMHGAo/8ClK67ERhSF5adV4Ua3S6qUJf/zbbxC22szabSptOB4OUacjBIb/d7/N1FMsD3o8+9QTrA6W8CS2AWBlKDUkSYrve3Q6XdbWVjk4OGBpsMzReIzyfTrtDlmaUuQ5L734ki3HlCXtdos0TTjYP2A6m7G/t8cTTz7JJz/xCUpt+L3f/xaHBwdMJhM+8YlPkqQJT9y+zdHREY8e7/HkU0/yyiuvkiQJvV6XlZUVbtyw3JwXP/4xptMpWZqiPI+lfp+9/X263S6vv/4GN27e5PYTT1BVFePxmJOTE/I8Z3PzEmVZcvX6Vb73yqt85+WXuXH9GjdvXEeXFb7v8fa773Lj5i0ePHzIg4ePeOFjH+e1199gOp0yns3JiorBYJlOp0NVVXzik5+ozegCHj64z3K/z40bN7h//wF7+3scHR2xtbXFs888w2Q65eHDh8RxTFVVHB0dsTwYsLa+zsnJKY929yw6WnNiEGcmiBpJZayLuW1AoIkCxcc/9gKXt7dYWV3jK7/yK4wnk0Vw7ymPwWDAxsYG09mUoihYXVmlKAumkylxHDOZTtBas7a2WoOtgm6vx9HRySJZRRqUJ7h16zYrKytMxmMOj47o9/tkWcblrS1Ojk/qoMws+JFRFPLM009zdfsyy4M+mxvrtKOIOJ7ZLvWjEbYPUJ/xZMzy8mBxb7IsI05iKmPwo5CVpQFSSvrdHq1Wi4P9fbQx/Mv/+v/5f71g5bnnnuMnf/In2dnZ4etf/zqXL1/mz/25P8e//W//2wDcu3eP27dv88orr/DSSy8t3vejP/qjvPTSS/zCL/wC/9V/9V/x8z//8wyHw8Xvy9Jm2L/0S7/En/yTf/JDn5tl2aIVN9hg5erVq/zmr3yZMs9qgqmi3+8iBWRZyuHBfh09hpYbMZ/T7nRYXl6m3+3gSRtZDodD3nnnHU5PTymKgiRJmI2HhHXb9WvXr3N4eMhSv8/6xgbz6QRhNJ1OlyS3PBPlB6RZzjyOUZ6i1W7T6y5RaTg9GbK3v0tV5RRFQrctyNIZZZnjeYp2u0MQRORZxfB0ioa6VXnF8vIS/X4PMNx/cJ8kjmum9tmG4Xne4l4GfogQCqUkSW0CVpXlAlXQpiDwvTrQKRfOg57ncXxySlaUpHnONJkjpKTf77PcX8ITkrDTZziLSeI5eZbQCnzb/dhU1oVV+USdLq1un3anR5wkPH70gL2dhyRxytL6tvUJ8XxaNV/IOVkKLYjCFtoY8qLE90PWNzdod7r0+kvWiTUIFmWDqqyYzWw2J4UAXXJwsI/neRwdHrK5ubkwljo5PqLbjtjb21sQbieTiZUe95eYJ/aZtbsdxpOJhX3RjCZjizxltidLt9uxHBUp647E0gZLaYKqN61er8/29na9wVZgrGRceYqjw6PaQErz6NEDZvOYSkK73SZJEsbjsS2f1L15rCuGRZKqqrJBjJR1tmfr2UVRLch7ZVVa181FXw9ly0CIOnBypSDwPFVL263nj21Rb69ZqSZ6ctZmwv7euu26FUM0LN2lVDWH5awjbdMledE5vA5M7LJjFnwBgVVS+AoUZ4067efUKE9VURlqWbcg9H3Lk8cQBgFh4KM8W9eXQYuj41OSLMVX0hLkg5AsPiNgU5PyAz9ASoFQiizLFwFYE10pioI4SdHmDFUKw6jml5U1umQDs8BTbK6v0goD8ixhqd/HYOh02wv30/F4jC6tMZnvedwoSn7q269z0mlRCNsoNC9KKAtW44T/7voWh70OQeCxsbaCL22bhtD3KEuNoVZUVRVBHUBVNYcIoZHKGhp6yqMqCltyTVOkVCRFdU6i7FBiz/PZ3NoiKyoePXpEFFkDTtvCwwZeldacnJ5QldXCn2h5eZlPf+bTBH5A4ClGoxFRFPHOO+9QliXD4alNkpTiZ//Un2I2nTGbz/j0Zz7Ly7//+4xGI9rtNo8f207SRmuuXLlCEHhsbqwznYxrorbPx1/6BI8e7fDKq6/y2utvMJvNOTw6ojQgfMvBCoOQIAi4vL3Ns88+y/7BAZ4UHOzt8sEHH7C0tMSnPvUpRqOR5e089yz37t7j5PSEG9dvWMuDe3e5tHmJF196kYcPH5FXts2JW0ffeOONOnEQaKQ16PSENcrShpVBn+vXrtjGrmlKXDcZLIpicX+MsQnJxtoa6+vr7NdB3fb2Njs7O5RlybVrV2l37JrR7/VJ04yHDx9xeHBIu9Ol1WnhBxY1j1otrl+7zsbmBlmW8e677xLP5gvri6eeesryitptLl++TOD7bK6uUOQ5vqcs/y6NmU0mzOdzuw8uLyOE4OrVayz1BzVXJSCOY6bJnNJoTGWfWTKP2dvbY7C0RFGU/Pt/+a98X8HKPxfB9t69e/ziL/4iP/dzP8d/+B/+h7z88sv8+T//5wmCgD/zZ/4M+/v7AGxubp573+bm5uJ3+/v7bGxsnL8Iz2NlZWXxmovHX/2rf5X/+D/+jz/080G/S6CWmM+mDE9P2H14nySZ28y60qysrBC2Io6OjsjLksHyMqfDIUu9PrPZlPk8ptfr0u4NWL9yDQ+Yjicc7D5gPDxl/2CPvMgsS/5wnw/u37PZs4D+0oBCG/Ky4smnnmVtYxNOT3m8v0uUFwStHlL6hO0OTz/3PAcHu+w8fsjewRFKVni+pNSaeTpi0F9mY2OL6zefpjSSk5PjmkMjORqP8TzF5pXrNkjRgtV+nzC0DaOGw1NavZU6Y2sxHI3o9XrcWluj3+8zn8957bXXkEqyuboJRuP5HtPplDBqWb8ZbZDKx8Oj3+6xff0GUbtNr9tFGoinc1q9DstrqxwdHzGdTNi6tEmWpgxPT6xxWn+FVj9EqggvaBFoydXrt9lc32R9fYP+yganwxFFUdDpdFhbWyPLMu7evYvOq5r8G6INzNME5QfM4pjhZEyaJPieRyuKyPOCILDyuel4zGQ8YbC8xK3bTzIejRiOJhSVYW3jEqPRiMHqGkWWcfupZ+n1egghSLOUeD5nPo9Z31hiMh6TzjO6nR79pT5RK+Dg6BDl2UZ9eWql4fF8jq4quu2OVd/kHWbTMVmeE4UBnjQcHe5Zlr82TKczTk+HlKXlgSRpYsMMAZ2OrWkHoWSpvcTaoMPJyQlaB3UbCIkxtrzgOBJWaWANu/KioNSGsvQXxFbf9y0C43noytgOq56yDsu1csgAfsP/wnEJ3IZrsA7HRltyoTECJS3noSz0on7elIbCWUChlIIapbH1/KouSVqyuxH2Wpzz80K5gUFJ2xhQYg0YXd+Zs3PX6FAULFRSlrsFRVXhC9+OjTSnzFJ6nTbCVAih8aShKrNzUuwgCPCVIgh8W2cX523hnUurK/dohufs0oPAcl1c52FdVaAUhZFMp1OqMrIIUKvFPIltCarSHB0dMx1P6LZtV2lP+RwbwwyDmsUkUVTLUwv6WU4iBdP6mnzPI8sKCgFFnhGFIUVeAmpxLWluuys79ErJEk+BqCxiVBUlWTIHrRHSY57rRYNKz/NQDWXKZDquO2Mb8rRC6woJKA+yZIYxEHkKrQSmzOm0AnSZ8dor36EVRoi6bJdmVgzgeR7PP/0UV69d5bXXXuft11+j2+0ymU54dO8Op0cHPH78mM9+9rNwaYM8KxgMljg5PcaTbY4O9zk+PuTw8JAsyXj7rTdRnk8Qhgz6PbYubbJ1aZP1zU1eef0N0tT2AXvpxRdZGSxz5fIVnrh6hW+9/PtEnmRtuc/Vq1dohx7Hacyzzz3Lt3/vd/n85z/PeH0FXVW0Q48vfeFzrKyscPnyZUIlSUvN6emQg8MDQk/yyY+/sECHVtZW2d7eZjgcorVVaPb7PbqdLu+//z7PPPsMl7Y2mccxRtsO4qenp6yurvLqq68ym47J04SrV69y//590BVHBwdkWcYPf+FzZHnCbDLi5W99k5PjU1ZWVnni9k22ty+zt7/H6toqTz31JG+99Raf++ynabdsX7B/6Ud+hJ3Hu7z33nt895XvkiUJa2vWUfn+vXssDwa88vLLzGczlJJsbW5w5cplKmNodzqUlS1lLy0tsTQY4HkBaxub1mW8rFjVBZ1em3g2pywKxsMRo9EIXWmOjo7+6YHFH/D45wpWtNZ8+tOf5j/9T/9TAD7xiU/wxhtv8Nf/+l/nz/yZP/N9X8w/7fjLf/kv83M/93OL/ztk5ct/73+iFYVEYchSv0u71SIej8jmc3q9Proo6K4u0791CyMMoo4oKw2VFnzy059FSduv4XR4yiRJSJMY5Xssr63QzXOWBgN83+dkdEqpS5ZXBpg8ZefxQ4TyuHrjFqUu2dl9zHg6RUjFpe3LhK02Qii8MKTTadEb9BmsLfPo7h2ODvYJ/AiEod/psr11lSI3xIlAtUO8qI9vFJ1uh+7yai11q1hqd+l3+5RFyXg8RoVdNrb6tiQxneG3e2z3V8AYVNDm4GRMVVVsX7vF1StXWN9YZefxDjs7j+ivXyXNMiaTMUVe4EmPQb/HlevXWFlbY57EPHjwgNHJCaPjIX7goeu68+atLaYzq/kfrF4laK8R+B6t1hKD5TWysmJ5ddNav2cpRZYxm8Z0270FyTOeJTa6f+JppsMRw+NTVgcDwnYb6flE3TalNiRpytH+Lsv9pQUB26qz7FhQnkdWWkfH69ev80Of+xz37t1lMpnyyU9+EtdDw7L5S4rCerskaUq30yGeTK1jZFngBQrlS45Pjiz0Lwz9/hLdTpfD/QOm4zHtVpu87j4qOxHLS+1zEl3nYDkej9lYX+Hpp57k8PAAhGZ/f4/pdIJSAs8LWBkMaEcRp8Mh7dYST924TJZnxPOYNM3IC1MHQuFCBj+bz2zzPylI82JhwlTUChtXmikKTeCHNUnPmRJaYq/n2V5TLiDwlFqgNlVl5arOxdRB3dYcUS0ChCZHRgjnk2J5O1JaRNPW4s3i9WVZYgRUtdrMZc2u5iPAkg+N7bdlXVjB+s0IkKJ2lBa0Ir9uOnpGTC6KAm1ypPSsOqrI8bDP0eJUEl+5YMiWcYQQSGwZrrCA5UKp5CSmzscIofEDb1Gi0saq7ZQnyD2Frk0lwzBkaWmJKLRInlQ+YauF8i3S1ev1bd+xyspXk/mcGMObvS6fPToFA3FhA5VBWfBPVgYk7RZUFbKUpHlJUeTMZlN8pWyHeSMXkmrnjOoUYFra7utRkNuSHFa94jq3F1W1uBeiyG3vrPraFQpd5DaALb1F+4eyLO3z8iSCCl0W1ssqtwhZlafMsT14/LrXlCXLC4bHR3xw932U53F8fGj5QnnG40f3bSlTCL798jcJ/YgwiDjcf7zodO35iqqybTE8JahKy1WbTEZIYcizjH6vSzKfcfP6ZRtwCsV8fMrGUo/Dxw/ZefQQWRU8/eRtnnn6CaS0SpfZZEw8m3Jpc5293UdWQOD7lHnGwd4urTDgznvv0ul0eHDnXSqtiaQgn4/5yZ/4SY6Oj7h69Sp7e4/ptEMGz99mMo2R0uP4ZMT62gYrg2XibM6DBx/w8OFDdnd36Xa7bG1tcfv2TS5vb5EliRWKBAE/9JlPMZlM6Hc7LC31ef+9d/CUZHd3l2uXt1ldXsb3fA4P9nnmqaeIrl1lbXWFt773Kq997zXmoxHPP/8cVVVx9+5dXn7lVa7fuMHVK5epqoLTk2Mmkynj0Yhfe/8O6+sbPPnEEzze2eH9999ne3uL61evsrKywvaVK1y6vEWWZkznM/K8ZG/vkDCI2N7eJs4S1qoV0iTh2y9/i7KwVAu04IMP7n/fccA/V7CytbXFc889d+5nzz77LH/37/5dAC5dugTAwcEBW1tbi9ccHBwsykKXLl3i8PDw3DnKsuT09HTx/ouHc9m8eKyurlBkGbqqGA5H7D1+jO95LC+v0Ov3EdISqyqtKaoCIVUtkRO02l3u3LlLnmW2/XWSEoYBy0srxKri7t33LfR1bPt+DJYHVFqTxTF5luIHPu1u3zaJq0qCIOTmjRuWnCot1N5qd4lCq3RRQrN96RKXVi/hS0VRZByfnKCURxi0ODw4odSSXrvPxsY2yrPs7yDwmM/n5EWGr3yyNCNLM7av3rAZdm3CZLvyKsqqYnh6SqfTYXn9kq2rB1YpUHkKv73M9vU2S4Ml2u1WbRSV0QkCyth6SWgj8b2Im9efRN54imQ2J88L5kmKNoZ2e5XV9evM4wRtBGEU0OtEeL6HEYqi0mR5RjKfk6QZuqxIi5gsGy42t5Pj0wX5MosTxqenaKNZ3digPxjgSUmWp2A0t2/eRArY39un+P+T9yc/t23reR/2G2PMeq7qK/bep7oFS5FiXShxBMlAevY/4IaFNN1zx0XHTffcNmJHYGIgiREgDRmJYxqWFSuyKZsyJZKSScekKPOSl/fcs4uvWtWs5xgjjXeMudY+lBPICgwTdwHnnnP3/r615prFGO/7vE8xTdzc3jD0A13fiSIDTZ4kDG1L3zQYFJ+9eUNV5Ij1uOb5+Zk8eBHM1pKmotLJ0owiSWm7lpf9E2lmyIzhk7t7TCrGgAbL6/sb1lVOkUlxjPV4LJtVxfPzE8fjUbgugLUzr2427G5uSdIMb3umaeSLz36OtmvRCj77/HPe3N/xX/xnf5NdXTKOI2PXkKUp9c2WerWm68egjlgF6fPM4XAkSQzWe5quX7xOvBfnY1F2wfkkHBk8dP2wEGzHSZ6DC+8ioapLjBb/mb4fGAc57857dJosgZtKKxKj0SZhinwOF11RNSb4w7hABjShCBInXRkTeYVYv0dEJSBGKnhd4ECjPnIMjX8vyrcxOK9a8jSR0DtjyAOMPrsJrTRucmSpITFSTBgjRGXvxIvDBNQmekNYa0mtYrJuUTqlScI0jctxFnnObILnTPjesZAp8xyVRd4O7A9HaTC2W47nBp1o0syglaZvWuw0CbF9nLDThDaa/+e6Yp4tP9O0vBpGOmP4zbsb/ttvf862yHFeirYkSTBpAlpSeZ11OOthnES5YhRlXgXuWs8QeGWTMkyAnWbhUoRxpfaeAoXRhmkeUWWCH8XzqSgyjL4Up7EYLssSbZSgOk5yejwzSmsSrTFKkaUpVuvAzSlIkpRhHOjtIIZ00yz8rNlR56UUxjqYGI4zjonBWmYnhXCiUxKdoFUiaI3vUcHXZOyibNoyjyGvR3mMVSQJjG3HP/r9/xajpDm1zvF2vFjiKx1iJM4H8J72FLxOCKZySvHV9/6YsijozhWff/KKt+/eYwIS+Hf+y/+C2c789//wv6MsMpSfyLKcb//Qj2C9Zuobvv/ld/ns08/45re+idbw9u1bNpstDw8f+L3f+z2+/93vcrvZ4NerMJ4p+c2/+xt8+9s/xNi3NFqcdctNxevXd/yFX/4lXr16ze///h9wOp35z//W3+Qnf+InqLOUH/nWt/HTzLk58+t/+2/zy7/0y1R5wReffEqV5Xz7i2/w7t17zs2Rv/jP/DPsX1548+o1Hx6euLu7J01S/sIv/TKPjw/c3N7LPm1nfud3fgdjEjbrDd/78vv8zE//LI+Pz/RDz4d3b2nPR7744gt+6s//FH/0nT+iLAqyNOOf/ct/mf/L/+1X/39UGP/fX/9EnJV/8V/8F/ne9773EcH2X/lX/hV+4zd+g1//9V9fCLb/+r/+r/Ov/Wv/GiAoyOvXr/8UwfY3f/M3+aVf+iUA/sbf+Bv8c//cP/dPTLD9rV/7NbabDeemoTmfFw5HWZYkmcCmQ5CdRui663osApO+PD/TnRuKNKM9nZmHkd1ux2q7Js2SYEs/UFUl4DkcDuxfnkiUSDMTk0pGhUmo6hXjMHI8t9y9ei0M6a7DKHDzTJFlbDdrull4ITc3N7y87MVLZXdLluXkeUmS5UR+gF2iAjTTNOO9ZZoH+q5jDrlG0VI9TVPKuma1Er7Iy/Mz1jlubm6WQm8KKo08yzge9jw8fCDRijzPeHp64Nyc2Gx3JCajLFdU5SqQBnPSMscnQuT0gQfRNh3rzZo8z6lyMQE7ty3aaIqqDNJZsfb2lmXBi+MHCOmj3rF/fsR7x/5woO8Hbm529L3IRJM0p6pFhZOlKev1mnmeaZp28YLpu46madnutjJ+sDO3t7dyDJqFmBldJIkbqoI3r+45n8/sD88MQ78kMm93a/ZPH2ias/iXIGOOqiyZpxnvLHYc0FpiC9r2TJYlQcrc0rQteRHk5LPlfDrjnGQ9vbq/5/XNDX/0ne8wzBMOT5bn/OIv/5L4/rQNT49S1L/55A3v3ooB02F/IMtSpsmCg6Y5Mc1T4I0o/vI/+5cpioK/93d+Ha0V9WrDMEx4pSjrFQ+PT3gUdnYcTic2NxuyQpx4h25kHibWdUmeJ9zs7mjbkaenZ1EtpArnRvppYrIOjxeCc5AnS+Hhmb34ypzOLQ4FWrhZRVny4d0HXBu4H1wyXKJ6KS8Lkc/PUpiBhIgqpGk5n9sFvdAGvBfFV5IZ+n5gVuAdeOe5dYqqHzhqwylLZcNLg1oKkZVGtZNWGmcNiogG2WBo5nA+qD1Mggkqj/lKIZUmCdZ6pkHub+ti3IEjSTOyvMDOHd5N5GHkNkVkIshfjfJLvMdmsmyto8lyTokgJUkhuVHamEBeBm8lt0XIywnez3gkpiHLMrI0J88LyY+6IhIPQ888W3bbLa9fv+F4EtPA9+8/4J0jSVNev34l+VXTwN39LSDhh23Xcz4dmeYRlIRlGmW4vblhGnqO+2dSI+tEnmW42bFarXj15g0vL3tOpzMmpJl3bQfW8dnnnwsaud9jrSPN0mCCOGBSLaKIeSYxF9PNoig4HU/Mk5C7PVK0RYdl8KA9u+2WupYR6ziMi19KkiQhtTya39lLOKjQmYKaJvkoPVpcmD3KpBKxoi4uz9emc1p+IdzbMs5NkzTc55WM1caJ29tb4XsERek0jTgviKNSCpMkCwKWJEKwT4uU0+nEq1ev+ZEf+TE+fHhknmbevn1HmoglQZblEsS4GDwKSpznFXZ2bLdb1us1r9685h/9w3/I+XzmcDrRdSNDyPfZ7XY466iqkk8//ZT9/om2a5imic8//5zz+cz9/Su01jw8PPDq/jVFWfLFF5/z9qu39H3PZrPhH/3BH/AP/pvf4T/463/zfzqC7d/7e3+Pv/gX/yL/5r/5b/Iv/Av/An/37/5d/qV/6V/iV37lV/grf+WvACJd/rf+rX/rI+ny7/zO7/wp6fL79+/5q3/1ry7S5V/+5V/+J5Yu/3v/23+Hb3zxBWVZSk7LJGZjbdvQdS37/Z7EGD759FNWq5XkqqQpeV0x2pkiyyUl03kSbbDzLJsQKsCdQkA8nQ70Q49WiuNxj1FOQufCDVdVNev1hnm2PD8903ctbSOpvGP453g48Mknr0Ebur4P0rqZoigpi5rb2xvafqQsN7x+88kiQT2fz9S1eHmcTntmK6FRt7e3fPXVV3Rdz3q9DhsmVKvV4oCYFwVaKbI8YxxnHvYHvIe6KimzjKY5C0kvRgoYKIsS71WIH5ipq1q6H5Ng8nQZD5xOp8Vbpus6Prz9SjaUsqCqKp4P+8U1NjEJiUk/Kiaj5HOaRr7/5ffYbWq22x37/Z79ywtZCMy6u7vj5vaephuWMUf0kMmyDK0UQ9fw/PTM09MTm81G8orygnq1Cp3RJTk2Oqd2nUQzVHmGnWfGsUdplmt+Pp/RRnF6eQjS1mIhj4p8Vbpzb8WwLs8z9vvnsFj6RV0j92OL93Bzc0tVVRwPJ6ydsb0s6GVd0bQNwzQxzTNFVVIUOefTiyhmgpPluWl4fn5mnmfudresqhXWzgxDL2qnoSfLBN2Y+o4kNaA0P/8Lv8h3/ui7zM6zXm8oioIqz/iTP/5j3nzxKcM0Mk8zQ9fxyd1r/vhPvku5XvPJm8/54R/5cd6//8B//wd/QJYb8iLlfHphDEVfURaURbl4wsyTyFSLsqLrB47nhtV6x+s3b/hzP/3T/M2//jfYPz8zzROff/Y5f/InfyJqur7n1atXfP7Nb/HFt36YP/6jP+IP//APOZ1O3N3f8+7t2zBKsmLYmJqAlshowVo5d5OF1Dr+V1898WMf9pTWMRcZ8//yl/h/aM/ZSvE8W8vr16+Z55k/+qM/QgHjOLPb7ciLHK2h65qAoEhnb20w6ppnyZUK45aqqti/vJAog8dTlBWffv45p1PD4XDiFEZ3VVkGxCblk08/wXvPU8iw6qaO6KXz5vUbmqbleDiQpjl4y6rOgwuw59xI+GNipND3XrHbbpntgPMzRZmx3W7J0pzzqaXIcsZ+4ObmZlGiWWu5v79ns91hPbz/8IG+67i9u+MbX3yDp+cnPrx/jwfysmTsJ7TJRAk3DTTNibxI2ay3JEnGNA68/er7nA4HbncbtFI0zZl26Mnygu3uhrpeUdY1Jkn58PDA+XTCzyKlv7nZsVqtqaqKcRx5eHigO5/ouoa2aWi7DhtUZ7G4HYeBaRjJi5ybnXiFvOxf8M6TZgmb7Zr9fr9IkCNvSpRsijSRwie6KseCQylFUWbUdcXz87MQ0Z0jTcTl93g8yhg8jEEvtvwuNA3RJTeSyKWgiX4+SWJou1YQPJCIFmcXtC/yjxIjvClt9FJQWzszzQPTNJPnBXW1CuvaKJk9eY42Koxw7eLMGxEi5yzOy3gmTdPAR4qqJo13cp5iY2uC7YO1goQlSbpI92MR55zwQ6uqZg7Xs21bmqYJnjvw9PLM/+mv/Uf/0xUrAL/6q7/Kv/Fv/Bv8o3/0j/ihH/oh/tV/9V9d1EBy3sUU7ld+5VfY7/f8pb/0l/h3/91/lx//8R9ffub5+Zl/+V/+lz8yhfu3/+1/+5/YFO5v/up/TB0s1OPmaYyhLAo269VidWyM4Xw6S2Vc5JgswwJv371lGEb6XmSvX3zxhWzOJLx/957j6YhJNEZLwNQ0T7y8PDGN4gPy6vUb1qs1Nze3KGXwTlQJRkGRp5xOR96/+5KXlwe+//3vMU8D4zQxDnMoOO7Z7W4WdKRarSnLGzyK5nymLEvSoNiZxpHH5we6tqGqKykgvOf29haUSI9Xmy3rzQbvoW0bmqahKIoQfpdTrG/QRlQZyjv6vmXse/GGqCuqqmCeJ56engN50i/eCbP1ZEW5VPhxs/dhAcjTJMywxZ312JxJ0gStjXSTk10WhWvrduccL8+P2CXtVR7yuq6pqoq8yMmLmnPbcj7LNdxut0vR4ZyjzFM5r+PA/mVPUZakaSJdpfeLTLnrOna73UXaPU2cT0c04s8xjgN5npKkCcPQczwceHl6T5qKSZgKxytWatLduTBLX61qHh7fcz6fIbgeq/A8nM8nVquKLM8Zx4E0laj7dVnTnM50wXtEGy2y5zSlLHLevf0SZ91VpogsrHmeg1UkSQreXazKEwmnm6cJYxRDL0672hhRlqBouw6toDIIgTrPOJ7Pcg0TiaefrMOUK8pyxbd+6IfR2vAHv/97dF1D37coN7FeS8hg9E0pimK5Ls55QSWnCWfFCC3PS+lklUKnKda7kPwtsuQ8F6XJar3l0298i+PhwLu379gf9qSJFMkx/6trO/q+YxgGijJfTOcEJdH8/O99lx/9g+9yKjP6LKWcJup24L/79uf87p//82JIeDji8axqgdzzIufUnoITqqjm8jwnSWNatBOH5mlmVdeUZcnpLLJO5xxFnpMlhmmeuH/1mp/+mZ/l/YcH3r17z7v3H5hmIJgcpmm6qIKapuH27o6RkR/+0R/lj7/zx1jnWK+3nM9n7OxZrSrqIme9rrHTzJs3rwDN8XTi+199xdiN3O52NO2RLDeUVU7bdig0q9WGMiuX8Dln7ZKWW9c1u5sbdre3vH//jqfnZ252O9I0owwj4pubW7ROefPmU9qm5/Hxmd1uyzB2HE8v3L16w6effYHRiuPxwMvzM7vNRp6ZssAZaZwkONYyjJOgEUqRpxlDGFEuUQ3OUdW1uCCPM3p2PD4+0oWxyHa7C2uH5/DyxPv3MhrerGUkn+WCXKHE6bVrW9qu47A/sFqvOJ/O/PAP/zB1vaJpGt69e7+QW0WdKPvBer3COsvQ9zw8PIhsfprRRgf/FBkLpmkqcvBgQeC8o67qkCItMnmjtZD1kUI3zTTjKMqhqqoArsa5HjtLYbFar1mv12HvYmmEpmEQCwOvgqFiFjyeZnSaYL1duEvAEmeRJIbZDoxjLw1FUQgpPDiA29minCj0YvBukkQO1IwxKfPkSQJC1Pe9yPDHkbwosLOl7wchrCuN8448Ew+Wtuv4P/8H//H/tMXK/xxesVj5tf/0b1CHix1lhm3bcjocSJUQvaL8sSxL1uu1wKF5wjSPNE3D6Xzm5u6WInT7TdNSJBVVWZFlKWmaMFupzIVY5zg3J7quBy8b4Hq1IUkz+q5jHKXTyfOU1bpkmnu8n3h6euAf/P3foms7bm9uUUrRNB1lUfDpZ5/zyZtP8MpgMkFp1us11rlQvXqGfqBpG7773e+w2+341re+zWolXjJt24UOe+Jlf+B8PvPmzRv6vufp6Ymqqnj1+jVJnpMmCXmWMQ49bSPEqq5tePXmDUlRoY1eyKht2/D09Mjd3R1lWZOl4usRjdyiF0VZlhRpxjyJHBGlmOzM7OwShOatW6TD0Xo9judkxn0JGGuaNhAsxXOkWq1JsmyBeOPCJv4HH5jtxGeffYb3nqZpFuMzgDxNOe0PwdyqXI47GpzF8eE4iQT+NrgLn5swYlNSyGw2W5SC8/m8+IpkScLQnXn79itQBDh5Ct+tl8LHGOEFaIKs1gqKozyJlxHAOEnwZFkI7wcPRsucPEkSTiGfKKJDWmu6tmfoZNHSisBnkNGMUop1vVoWURVk+t6HlF1nWRUpSht0WuCQBa0ucuzU0/cDp7ZfiK/XQWub7Zab7ZqbV7e8/+orHj58WBxx42KfJvlimiW8BCmsVquVJMNWlXSwRu7F159+wu/+/b9P0zT0IcoAYLeTRPSu6xekcRyHcD4j3J0FblcmRNPnhp/6D/9zLNCuCpyX+y4/NczDxK/95V+kL3Pm4B0Tu17vPSSGIqAfhM49mu45Z2Xz1JfMpCg1/dmf/VnefPIJ/9Wv/b8wxlCvViRJMEnMctIsF96KSRZFmgTWtSiFhJ1qz5/7iZ/gD/7gv+d0bnh8fAZl2O52VNUaVIJ3sygRN2t++md+hi+/eieZRU7Tnc84N3A+Hzidjnz+xRd8+slndN3APMkzpZTi7u5usTv48OGDGEvebthstmE0IuO55+cnXr95w9BPZFmN0il5Ks9927U4N6OUI6tqsqJaiLtt1zJ0PdvdVprEszxHSZZRlhVpnot6K0irh7ajrlfgxRFY1G+jRG7ohHGYBA1ZIkRckJUbiswwW8lkG8cBFY0EUThECRd/53w+L2aHWQg6jXL701mKAe9lVP69L78nxUJdiZJ0mnj16hVAkPl7xq4J4xlN27a8vLwwzZMoBr3nzevXEukRTObqerUIIbRWPDw+0LUtn372KcMw0LUdRVmyXq94eXmm7/vgPJ4uvklPT0/0XUuRpmHsZdE64fbmjvP5LD47ZY43l0DJPMtBKeqq4ng60nUt2+2GLMs4HI68enWPUnrhXr5/+56mETVPRA4jemStoyzqJTFaKVm7FFJ0KRXRJJhmyZOzzuGso2lb/v2/9h/+4BYr//Ff+2v82I/+2NLhT5PYj9tpwig4HU+8e/+Ox4dHnJdI+OPxgPYT49AsDPg0y1hv1qRpxuPDI5qEMq8oyozNZiOOtwp2NztWK6m4x3HAzjKb7rshdHWeJK9Z715JCKGbKMoMbWCepzBCUnRty9PjEy8ve8ZxYre7wXvPN7/1QySZoCld1/Hy8rIQLI0x1HUdRlwdr17dL26T1orRWZKKX8bhILNhkYh73r//QJ6n5IkH7xj6juNhz9D3Irk1GqdTyps3aCPW51O42e9f3ZFlOefTWXxDrszBjkfZRO/v79EuVP5hM02LDIcP9tSOJFrAB+JkRFm6riNLEso8Fwmok8UqemEkaUofTO1WK0nXjt4iQJjFi4w0OqfO88x+f+Dp+YndZoNyfvFWEft5E4qxlvV6TVXVVFXJal0zDD2n04GmOYtpXSExDfH8y+dPQZJr6c8nBIEy7A8vTNPIJ5++4Xw6s395YruVUU2SGg6HF6ydGKcB7yxuminyHBO6nLquGYdBzJZmK0Re75c08ZharJCu5vByWDqmJDFYN+O92J2vNlv5jk0r3XTbMM/TIsstqoqsqPnsmz9EUa85vrzwve9+h7E94+wUclIcSkOayhjPWslNmgInIxK6o/dMnJX3oyVJM1Z1zWpVsX9+kYiFNEF5j5tnttsd3/z2txl6SSOPm+c49VR1zuvXr/nxn/xJzqcTX335JeMgjsTez2GmL5JlGQcI0TdLM9YfjvzEf/J3OG9X2FRk1NoYEuvJH575rf/1z/C+zhaJdZEXwgkbJ5TJKaqaoij58T/353h8eOR73/te4C0MrKsKrTX7w37hPwHc7G6Yp5Eil2yhvh+wQVJtshyFounPeObl/o/k8hhRUOeSI2Y9pFkJJDg09WqNUymkFXPf0zcHyjyl7XrSoub1Z9/gtD/x/OF9GBNLltVut8Naj8LgSVDaMI1BrZalUiB5aNqG27tbkuBZtFqvhTujYkhfSp6vODc9eL0YDzo3keaG2/s3TI7FZHCaZ47HI/Ns2aw3IeTUY9KE2TqKssQ56PpeOnQv45V5yci6pKrXmy3dOC3jGfNRwTIBM33fLvdBmiQfBaBudzeLFX7btguZVjh+GVmWY8M6s5CHtWaephC6qOj6njE0CrFgH4aexChenl8oChlLPT8/h0JUL/YMVVnS9z1pllEUOU3T8vL8jNYpaSrPcl3Xy3hcSMoO63rGcWAYB16eX+j7PvgUKY6HPc35JAG8wXPImJT1aiNFtwlGl4EYX9UV59M5IHoJ7989MU1i47HdbJjnmQ8fPnBuGozWDG1H10lhu1qtApm6CsW5Zb2pOJ/PPD8/UxRF4K3cs16vOZ2a4O9jlwYgulu/ff+Of+dX/r0f3GLl3/+VX+Fmd7NkTzw/P3M6nWR+37ZkWcaP/tiP8a1vfpMsyxY4/Xh4ou/PvDy/0JxO7J9faJpGVEdZzjhOeCdEtH7o6ftWvDKylDQQnmZrpQM2KdMoLHhnPUlRcf/p58xWLlK9qinrmrv7Oza7DUN75nQ8imtgXsiYJC+5v78XC+Vp4vnpibbrFjLp7d3dYoscjZ+uHTeHYWC1XnM4nPjk008FlsuykAF0EJJqoujPzzw+fKBtzlRVQZFlfPeP/4g8y7h5/YY33/gRfvhHflRk3zYUGrBID4fxYqh1bSL1/v17Ht5+YLvZ8tkXn7PZbNBpQtt3ixX8NPTLQi3GUC+LOdYXn33O5opTdO2DEUm7ZV0tfiDxPaN80jvxhFjVK+EvzDZ0iIIYuEAujNB7NP8DcW99eHggzVLJcNIKEAfhJEmw40gTeEPiRTLx8rIP2SGO7Uo2sOPpgLUzWovfyTD0jEOLQkiaIGhV3w+UZUFR5NippTmfRDmRZbjZillWQAWd8ozDGNQ1gYhphBcxDhNGJaDk71yAxpWGJE2p6jU/+bM/y/f+8A95+PCeaZARV55JtzQ5S1Ft+Imf+UXq23uevvySl+cPpNrx9PCe40Ek1tqAd3If1NWGth2YrOSrgIy5rBMexGaz4cd//Cc4j/D+/QdWdc3P/sLPMzvLd/67/zfHw56qzGleXoTjkSSLaWPfD0GGLeRWcXEuFsj55uaGw3HP+XxYumClTPBq0aRpLo7TduaT/+OvMjrLtNuik4R6tSZ5PtC3Z37nn/9FDtovFuNZmpIHpLAfJoLfIs7DYX/AWS/Qv1ckJiqqRvpODP6yLKcoJD1X46VQcX5J8tVGmgiTOIbhLOtMUbBercK4QkkQasif0SYlyUo22zvq9Y5hsgyTo1rfgh0Yzi/c3+z46t07rM7Y3L7h7uae7nziw8Nb+v6M0oIka22ws6cfnLjczhNlVfHqXgjl5/NZ/HdC8R7deKdpWmT4eV4Bhs8//yareoNSBq3h9WdvaJsT794/sLu5xyQJ1onrcj+M9MNAnhXkzqJUIHzOMoLSSYIJ2Tl5Kmuq90IwPx6PS3PgU0ldBoKLcrrk5whnRwmpdhoXgrcYL0JVV2RVvawXsTCMm2eSCGIR15sYwRDH34IkyM8KmsKC/GotVNxpHDHBMBAvRf0Y5OsqyttddIm+mDSCwTlBRpTSTIFToxCi/un8jAlFlUnMYnMfJwXOyvVJjIxntBFfpSRJGOeZ2c9M4xRM+yxpkgZZe0Lfz7y87DEm4e7uVvgqVtLgx36QZiqM5IVr5y9NZZbwvJcQyuj4Hk3h0jQlTXJAL66+cb+areV8PvFX/jd/5Qe3WPkHf+e/pipLnp+feXh4YL/fczgcuLu7ZbPdcDjsmaaZzz/7jG984xuUlYx6emvBaL78k+9x3B/om4btasOnbz6hriryqsSEWZ0oVF54//4tz8/PHA97bjY78CpUn2sSI8S3LMuxfiYrMlarNWkmXJF2GFHaCPlJWaydmKdZpL9W/DDyopDOahroOukC+r5fMmG22y1VVQsxM3Bc4oMXu4XZOnzo2opCRjaxuJCHeaJtznRdIwz7cRBzuNQwO4dJMopSHHXLoqYsapIkk4LMjjTdcYEnjTGLs7C1ltv1jfBbykK6Eyfppn3IW9HhgXXOLQFwaZoGq2nD2PUfyVijz8U0z8JBybOwEPnF8bgoCtqmwQ6DdP/R0CoQMSOzXnxHpEOLAXULc98YTOhyx3GgbRuKMidNE1FzofDW0gaOC1y6uHmemPqOeRoDl0SSmud5FA7M0DCNTfB2yYKSRGGt43jcs14lDH1D37Yyo59mNIpEa5Q2mCJDGeH8xCwocU1NmMaJsR8DV2OSY5lGZhuyajDcvX7N4fmJ5nik7xrAU5cFRZFSZJpx8nhVYJ0mSxPWqxyjRqZpphtBaUc/NAxDh7OeNC2ZJof2HrBoE+zuowR9HKnrNZ//6E+zvr3ne3/0HdwwYDQ8Pz5QVQU/8sPfZv/ygWGUEeU0zUF1YCnKUrr32dO2HU1zZp4td3cyNhU3WS+psybFe0WWFdzc3DFNMrbspjOf/O3f4fVv/wG8eUX+5g0rlcD7Rz788k/ym58KSrqqV8szNowCw49Tjwlo62q1Ic9KQJHnBUVZCtn+fFxUalop1htB5rIkpW0aTqczaZ5ze3tHWdWgNE3bMo4tx8ML+/1+ySET8qcgRWmiBd0wGUW94dUnXzA7qFc7pmnGTT1Td+T53ZeMXVDUJCXf+rGfZBwtzfnIfv/INA94P4d8MIVSBucN+RXfzBixQ+iDg+3dq0/CCLyhrqvAB5PGYrPZcnN7T55VpGnOOM44J4hG1zUkaUaSFqw3O7RJwhokiqWu72HuBbl1XvyTipK8qmiajvWqRiNZQDo4NFdVFdY4GSHYwDus65qyKIJRoajOEiUqtMgXUVqTRGJpkuC1YYq5VVova2ksaLxXS7ZZ3Fjlz93yjwnrQ0ztmq3kDSktYyQxLAzeRsFIT0E4lmRxbHYuhJgSVUMqNBgsztz4UPxblvMvHJmAFpkQNmrFZ0rGWGLcCIR1Jl0Is0LOFeGIjDblQ2L0RkSaQAqnJE1Q5uMssRiJMQfLA63F7FHG3lEFpYOSTS/XBx/9neRnT8cjf+kv/oUf3GLld3/zt1kHUm4kWwL0fc9sxQdCnDGlOtdGy5+ZhKJekWcZGkXfdjTHUwgYtJy7ls8+/5S7+3usnVFGCEdt1zKPE682O/FICNbcSZIRs1KGoUUrScX0KGYrLrfjOAczs5n9/omqrrm9uaMIrqyHw5HT8cBmXVIUBVPwIDifG7puCEQ3RV2LSubx8THAgPLfeZbz5rPPubt/tYxJIlpU17UszOMgpNUsJYZiCesb2qbBe8s0TGhtSNMCIQRK99KNLSbV9F2H89Kl1fUqoFEZzVlGW/VKMpV8gBGTVLhEzfmE0TLPL0MoFmHTt3bCzuKPMQXkKCIknhA3P/SCLAQp4dPjoyQIpyl5kpKlKWUhBDsZO0kejXUOrzVj7JwCGhT/Pw6yLA1W4lZgZSxaC7rhA6zd952EmaUZ5/MJvKBmKhibpcEF9XAUzoBYUO8Zh4Z5FolpmRekoWBz1jJ7KY7sPDMNIzrcx8MwiFFfnmNC4RlHb2mSUK/EAHFVr1Fa87x/wTvLuKA5IvcdximYZ2naQPzVWpGnCam2zNYxz4okKzFGU+YGpRxdP9J0DmUkgBM8aZajQnK0Rgy5NpuNJNFOMzFoMk1zdq+/IC0r9o+PPLz/CqOUeIloRZoZLBdH3lic3tzcoJSmawfsKOOEiIp5pLuNRmvn8zHwTTQQlRaJoFVqwjYdr37j99n+w+9Roinu7zn9+R/jq1/4CSbX05zPNI00BEmaLIuzswNKy+Jb1ytWqw2Hw1Ek1trQDT0vLy8LCuDiCMwYlBepq+KSg6SNBMo57yAURmmSiMFfeO688yijZcS83tCPE59969tok/NHf/gdlEnZrNaMzYGXpw9oPEWeUdU1h+OZ1WbHu8cPTPOI1oqyyoPZn8QO1NUar1NQEisQfWOOh2MYt1p0UnBzexfWBEEUPULQlntq4P7+NVW9oR9FdSKb2ozRCavVViwXdEKal4zjsAQYducXGcGut6w3ksprklQ4XCbBxxR35+i7jiyQqedpRmmPc/ZiMOhFpelsTCb3yzWIoaxaaYpCnLC1yRbkJCr4VqsV/TBgtMFZRxrQ22sJfeQoxTiIeI+6gH55PHMoFjyEkWk0Y5R1JQ3xElG6PM/C5/POoY08h7HZjPtW/NlpUvT9EEZVKaACRy80p4le8r7iMyDv4yV3y4lySGklhU5QKAEYE7l1l+gME9ZKlHB9omO0JxQkwRVbzj+LcMB7fxFZIAVP9Fy6uE8LT+58PvG/+OWf+8EtVn7tb/4X3NzcEHNGhE/BoiiJSpZhGOj7jjTNmKaJssjlJDq3sPmVUrx//56+H8Babu/uuLm7Ba1ouo7JifFbd27o9ycUArVetPdSoBxPByEajpIAXBblUsEWZYkximkacN5hZyHZffX2LW+/+grwVFVCVdaU5YpptnzxjW9TVmu0TkgShZtb/vAP/5CmadlsNlRVuSARWVEJSTfMdrtOHBhXAXKOUrPoU5LnkfAqC2kWZqvGJAscHP0ZmrZhttPHRLUwlvn000/xDgnrUywjl2j2dz6fubm5WYi5kfUeEZZx7BjHMMPW8mA6a8MYIFn8E2SclYTZcYCrx0ncQJ2jyAsUHmdn8izEqesEqwxJmjKNEyjCDDpk1VjLPI5iyjb1aOVB+bDYi79HEhK6U6NF5jx0oBRZmsjmraWoNEY6w+fn56CS8Vg3YucROw4YHFWegrfYecLqBOtZyMLA4j/Tdz3KyeJZlSVDGO31w8B2u5HFzlqGaUYZI7D00OGnAeUtdu7JAtLT973IOYPCJc8lK6XvO87n5pIuHDgHWidU9S6QnZvQcYq3iFZakJ7AGxHuymVR7roOnSbEjKF5Gqmrgjf3r+h7Gfv54IQbM2ZAOtwYDigL3aXd7ENKd57n+FlUD3KNYL2uyQtJmB2GgTSTBFuFwj0dKfoZc3uD3a5FcbGEPQrnZBzHIP9fUVUFHpFljuO4fK5SCu+gqDdsNzu+8Y1vLMGmQXwqXjwBSYwW/SDdbnQAluLLLbB/URSsVjFd3bEJSj7n7FKslUVJWZZURUk7TFTrLUleCFLQNbSnPaMdmXEkiUD3UVmyWq2k+NcF9WrLOhzT0PfYkEhsrSPNK5IkAy1GlnlR0px7Rmu53axxQ0NW5Jg0Z/Lg0ORFRT+OuGkmTXKqzY7Zy4Y3jx3NYQ92JDOK2TqqzY7N7lYaLDuRKEWaF/isAB/Ioh4SbXCzEFP3pxeGeSBLM5TSZFlJWVQkSS4uzGrGupksTcXs0zqmeeYb3/iC07HBWunwTWIWNHqaZB3cbbdLlMUwDKw3a+aQJzYOAyYR9Y8J92N0Z06TRAz2vFuQDmAJvMyyjLIoGfuB63ytPM/ZrDeL71fcE+JaGAshpSRXKMZfxFy3WBhENWX0BxL0xSx+PfEzYxER15UYjir5XeZPjcWWIg1pGCUEVWIxEmMEMQmVipL/uSBLPsRnuHmRSKNUiPeQguh0OvKLv/izP7jFyn/41/7v7HY7vPdLwRCJlLe3t8sFBdhsNsvo5MP7d2y3m2VTj8oUmbuluFBogEIZRR9MiWZn0SjSQDSNlfTxeKSua0ESkgSTyIYsRm5yg8925rA/4JVsvIfDQYLEjJFjS0R11DXyXvVqgzECC3s0SsvIYOhP7PcvaG2WhNM0zRiGPsCveoHMh2FYJMZCFF59dE6yIOmNqpxhEHg7ssAjocx7mdsnqVlY6ZEUVte1GFBlubhlBuIsiMz25eWFx8fHxeOh73vquma73YZjT2nbM97bRdEVC5P7+/sFJUqCEiY+6NeeEW4KD64PnioKcTd1M2030A7jUijF+XMaSJMmdIjaaJx1tK1s3lVVMduZvmtQXjaOPE0wRjP23VIgR4lf17XUqxXbzZZ3799JsYJnthMKR9ec6E4niiyhzFNQntEhhNUgLYzdWbSOLxJBxcSheFpGWHJue5TWIjNOU5EQ9h1+6sVSXjmyzCwy4hi4VlVVkPqahaw8jZNwrLxwfKbZ0vWycMc5ebymkQMALBuiIIHCA5rmGZ2Io6zysokbDXVRMs+jyGa1WZBIKVal249BeVHeG5/LJEn45JNPSJKEeZDjlGTagSwL47g5kiBNKIjl/nNWlAp5XiwbU/zsWIzEYk24FULSdM4F52fH0PcobUAbbm/vMEZzPB6FL6AVt7e3TNMUTOxkrYnXNG4s0Zvlp37qp/jqq6+W0LwkkEufnvdhBHFJgo4NQZZl4KWbd8owTGJqmGpINfTjwGDnhcCepunSSM2zZfIJzgs3Rnx8ZSycpSllWWHSgrYfKfKSOfDwirKmqlaMfcs8dVjnyfKCqt5g0oxpFim3NhpjUpK8YHYy/mibE3Pfobw4PN/evWb0QBidTKM8G0mWghH5ctd1ZCaBwKFIk4SsSMHI2HQYRoqiQiHoXpalZJmQpCNCF1WASgkqdG7OV+PTfEEox3EMSqd+4WXE/SPGFIgKrVuKhOgvpZSiqmv6caDrL3lHwMJ90Urkzct4PjwzcURzjUbE5/4y0jd4r5cIiXhNr38+PnsRCYqffW1OF983/s7Xfy/+Tvy3tVbIyYEgHIskrfRiKjrb+ePP9Zf3QNIwgqz7UvzE4zgc9vziL/0AIyt/59d+nd1ut9yE11XiYopzddGyLAvduLjSRknbdQdVlSWJF+Mok5jgGOkX2a0H3CwXLC6okTR6PB7ph57NdktVyex3GAYOhwPPz88i/dysKYqCpmk4HA4Lt0Rr8erYrGvyPKOuVyRpwew8/TAJNF0VeCuVedM2tI2QRetVzWa9QRmRtFlreffu3bJBXzJc1PLgxRRca20I3HILUhJ/JhYiEQI9NydiwFksboZhEGv/GyEBHw4H+kCQu5a9AQtx7+bmZkFIQDrJNPhZRBLbddGTZxl2thxPR6Zx4u7+LhChR5rmTFkUeOc4H08UeUZMgnZO7gfrXCAc6uU8ROXY88sj0zyz3W5JkpS6WvH4+ITWCev1ijSFNFG8ffsWo6CuKprzMSxq0uGDJ4b6dV0X+EuBoGYlm0bjmceBeRxEWOkchI4lIivGmMUwr+97EnSw2r9kztzc3Cy+Qh8eHjg1LbvbWymi2hY3Dbipx2ExOliGh5GaCRu5QsyeootrfP+qqkiTFK3NQjQdx3GRa8c0XqXVYhSltciSpdAU+eh6K7LVLBHO1+m4p29a0lR4LXNAPa8LhYj4RKQyKt1Op5N46ZSS1J0lGSYxgYMkKrNh7GnbJtzD6XJf29nhHMEduqAfeiGzJon40YR7eRxH6qrGeUvfd4tCiKAycV42jehsDCwFSHzJ2NYsZMP4jFx3t/GZgwt/aylqvPDf1usVXdcFWbOm7zuRh8aYAp2ErlXh3YzR4lnilV7Q0dhkpGlKVdXk9QZQEuy52ZHnGc+PT6IGLEvKes1qvSNNc1brraB1OmEaZ6Z5ZLQjh/2BoZ+oqhWfvPmMVS12DUmWkGQZs/d0oegwSqGREWKSpJT1mtlrLBqTpOAcaRLUO8ioous6mvMZbx1lWdD3A5vtmjRId7MsX0YL1kZ0IFk2fzvPwnEJRblzomTz4dqVZSnNzTguCrZrrkpcJ0+n09IgxE09IiBRNVSvVkx2XrKjorowjjazLMMoHXgdF7QDhKtyzQeRreny37JGZ+F72uV++XrBEfe068Yh3lfXP3P9c18vXq73Ru/9ZewTjkmFmI14fKDCcyE/E8+hoE4hEuPqOGOxLiDCgV/+C7/wg1us/P7v/h5VdQmSiwVHJH3GrjDeqCA3wDiId0n8/yBGdYeDJPzqMIYYx5G72zvuX92TZ7KQFmVJVkiKZdd1iwNl0zQM48Dd/T1JYHHPQX46zRNd2/H49Ehd1YzTyDhObDZrXt2/oigL2Tispe8bxmEkL0oJP0ukWGi7TrpG5ZYHw1qZD8abcn88cjyexOTu/p6bm5tl4xtHQRe01qzXa4CPOglgMSiKC22EHQHGQN6M5zh2qfHBPp9FBpym6aJQMsYsficRCo3S5/gwRStocQi1y6IRPVW8FzfFLM0Wk6WiKMQwL015fHjAziNffPEFRmn6vhWFgdFChuw61huRHA+9oGdFUfDy8iLfaxbId7e7wZiU5txxOjUMgyBMWQ5ae5JA3lOKQILu6LqGcbr4zkR55KJYGke00mijcPOEIjhDIp2c0nwUBREXPh3m0WPXX5Ct0IWnQSEmhbLj8WVPP46SezT0KDcz9S1Nf2aap7A4XuSySZIsC06W5UuoY9d2V+hLTV0K279t28U5OG6EJk2o6noZlahgWCWbY0USxq12EiO7aew57Q8oJcGMk/0YigZBI3a73SIpj7B6bAZiTIPSwoUqyxIbFshh6Bn6ASkaBTsQ5YVIbefZCgneCycJJMQxPv/DIMZ+eS7mWn3fUxT5YlQ3z1PYhJIlsTpes4juaW2wlgW9i6hIkiS8fvOG7/3JnzDP8yJ3jh15WZYijR4GnPOsN2v5fcRJtDk3DOPIOAv6txQ7IeesaVqGcZIiAC7XOGyweV6g05y27UmC0qXIcrRSwQk4ZXKOJBGXa6VTxtlSFBX1ak29XlNtVlIYWDAqIdEylum7gW4amKzw8cZxZLfdsd1tmWfL8XgANEVZo7OS1faGrhO35TLPSBNDVVbEjDNrBblGCTctotfjOIX73yzrtTFG/Hw8S8Ebv/M0iXLTmMsmLmPPS3Fxve1dI2ARyYgFX9yQ4zWzYTyd5qlEt1w1xHFjvi5Sr9fSWEjE9fDrBcRyTF4v7xeLilg8/em181KYxDUk/k787vBxUXNdrFwXTvHvZR1OwnHF8xNdueNzE2X4cf2Wpu1j3o9bjrfrOn7u53/qB7dY+ev/0X+ycCFi0RK792vSUoSaI1yNj14BbnkgIkrSNi2Jkk3l3bu3qCBva85ntFJ0/cBqI/bh2+12kXDFrgatmNxFWhwX2ohEaH1Z4F5e9pRlwd3d3VJgFUFKmWYZWS6jKA8yglJK+AjWLp3d6XRa3FxFc28WWDPa0l8/JPEmApYHNy6gsbON7xEfPpDZI+ridzCHDjUiSvv9cXn/+JBCcGwMsHRcxCMSdoEs5dii42JcuJb57DRhtGS2XNJ+E8qywNqZse8wRmOUxntLliTkqXgWnM8nhr4LwWCVnNs0YegHnp+fSTPJ+qnqGqMTpmmmKGratuXh4ZF+7sgSGSW4kBWlEf7Hfv+Cxy6jM7mf0uU4nZ0DnyOMT8LCMAXztMQomvMJYDmX14Z1CSwOsfGcLjEDWuORMZAL8+GxbRi7BpxkDVl3WWzjYgYBulWQZwEaRyTSUyA5G2Pw9rJ4xQVcYh00eVnIBhU8Ya47T7ln/ELMS5MEvGUeRsS0DrzSS2EaIfqI4FyPga4X3AV18zaMI6Ptd8kwjCIdzRKmqQ9eIMLF0sqETlGRZXJPXG9Yca14/fo1n3/+BW/fvuWrr94uKrWivJCb7262nE7H5ffj+EpGDGKK9bHsV57B9WbDu7dvPyrG49oTi+fTWdxzv/69kyQhy0v2xzN2nkgTgwmcgMlaxsliHYuZW0Tn1pu1qOlGyXFCa7yzFFlBElDPKWTljNaJ705W0PcTSqconVAUJW8+/xyrNAohTTanFucUt7f3MuLAY1ItaIlRdG3HZC0mzYnkz7peY/ISp4RPpLUiSwxd06BQgah6MX4jFHrzNKODAmUaJ4mPQMj9bdtS5BVZljMM43KPXlBZs5DkdZD2j+MY1tSofhEETCHoq0jHtbClrhACAo8oKmu0NqRZKsZnXgQcC/k2IhRc1rdYAAjXywePILcg9ZFvEknq8dmLe1d8n/iK63JcQ+PeBReuynURUtc1dV1xOBwvCeJXz3Z8qaDwcU5sOVQYAxELqauCRgeenBXpUuCnyHN2fVxRAt00Db/wiz/zT1Ws/BOlLv/P7bXdbiULJmyeAB8+fFgMtuKMP86A4387KzLH6662DM6VxmiGeWQaevpREp232YbNbssff+c7AvealC+//JK2bXnz5s3ioGmMwSqP0cJRKauKx8dH2lYstdfbTUim1YzDsBB+I9djmmZGPWL3Z9bbNbsbqcbHeaIfBuZplCh264RghrCzd4H7oQKMaK0N4wi3SHYjQgKyAcYxVCTSbjYbPv300wX5uGZ7z/MsacFGLec0Qvha6zAuKj6CHCNvpmmaBeXq+34h+cbCxHvPer0OXBIVChQfPBXkZ+w0L51Onl82ChcWGPk5L0olZzmNI6u6Wjp5rwwmzUjyAo9inCyzg93tHd5K9+9mRCnStKzWK1argtMpYVe9xnuBfCNSl6cm+A7InHu92tH1HfM0s93cSOE5N2ij8G4KKcOaYRD+jTYJRZ1hsKxXgvo8Pz8vG5UsTD5ct8umGKX0wyDS0iTJSNKM2QkPynkpojSG2c0ob5ZOO01TvHPs93uGgPjMs6Prh3C9CGnEmtRJHlFcLOPsPnrTqNSgF/8HT5YYiqJckIR5lEJm7AeZY4fjivJyTBz/uWUDjwjcNXk7mvBFb5yqKpndGGbrovQ6N2eytBDJurVkeSE5VEaej6Efw7Ng6fp5yUWJklTnhJT59u1bnp5eOJ5k1ImHzlmGcVzGXH0/8vS0R3wmLhw1GSVNy/e/5p0kScL3v/ySvpcuPI4j4+irrmtZvJXHHsRDJk2ThVtmrTgQf/GNH2LoWg4vj2jgs88/4+7VJ7x/fOZl/0IXisY0TRd+Wtd1NE1D23eyKSKjlyzLeHV/zzQMtF1P2/cM4wjKU1U5SVpw/+oNSZLR9gOz11RVLWPS9YauG/jw/MQ4TszeUlUFr293GBLevLnHeTi1A7Pz+GnGTiNJltP3DdqkGOVR1pAlhnnyzNOMxaOtIytytFZYPNYRuFqiihuGgWkeiHb7s7OMzXk5pyAGgNrIvTRNU1BmJUR1ZiR+qzT7qCGLBbaLa6W/4od4hwoyYR3Gn2NwBPbeozItyqa4bgY1HgiqKd5NSnxotMYrh1JyPFJ0XDxUpCm5FNPX9hTXI6FYxFyv7bGB/bgAkbUkIkfxubtGUy4/Cy7wUq5RomsDw1jgl0m5CB3iaFUrs3BtIj8xjjmvj+l/7OvPNLLy93797y5E2rhBimS0XchW8QRHKFvUQZ1IR+ONYIx0gEhXr4xkPOyfnsOCq3h4955pmlivN6zWOwTx8KxWa4awkW13W1bbTVBDSJfQ9z3H43GZ94t9db5s9LGzizBbnsZxk1ivT/OESRPSLIWArkCQPOpLpsY8iWLDO/8RMhIXT2CpuLuuW8Ys17yUZQQzTSG35cJnMKGriT9zPJ6YplHkq0kaVCNS3RdFwXq9/oivEkdX17EI+/1+kVdvNhvW61VAGCxJYsiyXNAADzb6JVypiaTQGtDBS0XC9SYI6ErXNVT1Ch1QC+vkPEW0Ik0T5mFknqQozYuceZaRAMoJ5wTxElGhGzRaM4390kFHuNRZB0q+e3NuwjhA0bVH2q7FeyVSYqQDGceBKjckQcJorYSEZUFWnuc57fEQIO0sFLepLNp9L9C4yTBpikNGIW4aUXZkHgdGO4NRi+GWUmqRsEeEJt6jkYztnXwHoxO0Z3GoBIiBfau1WOb3U3SUlftMrMEzUQy40Ak6jw5joHEYRAFlLU5dYO6vw+DXhdnd3R13d3copTgcDlJcuwGvpPiz1uOdwlkfNiKNMYJ0lGVJWdZSHHtBHed5RGtEBh/u01iIOecYx4khEJ6HQbw4Iv/EBrVKjA8oqyqMakLBpTUmNcvMXmu9OCZHGDyignFkEXk6aZoyu5m+75aFPhbrSgEqw5sKrIV5oKpyNpstXqf0s6iIilwK3TbkJsVCvyhKlFFM88TQCWenKkvwnq6VImacR/phpKxXVNWKvKjQOiXLC/KsYvaSVu+1oV5vKVdrrIem6+n6lkyrcN/1zNPEzd09g1M4NGNzpioqivUGZXIcCjuN5KnBqITZSqq8D8Vnmgs6rYyB2eMm4Z6IbNcxzSNlKehI3w9yjZKEJE0XfxMdRhXiP3IZTUyh6ZHrI2o6Z23gGyli/pZ4GQnqgb/Ib4nar49UN8lHG/Ey9hhlLCl+N35BL01ilkYgCR4vLiDF0VNFBVTyegwan9X4rFyPmlxAh5JgUBcLm+vx1bVY4vqZi+tpXKc1HoVQDKyzAfm6vJdzNpzPy3de0EK5WZc/j8+01prT6cTP/8JP/+COgX77N36bTbAMjnDsZbZ3gcquZ6LOOYYxpEV68btwXjrJcRqZ54msyOTm957z8URdlEtX0g8DJuRbFHku3inIYn4+namqOlhJSzcbg6zSNKUfBoqiXMYiIHJfG6rZJEnZ7bakaULbtmIip9Xiknja75n6IRjEVXLMV+jFw8sT1aoOOQ+WLBfzKaM18yz27lEhVFUleV4s50cnhrTM0SHOXLIltMiJCfNa55inOahk+sWsKc1SdDgHETlZrcR0K6oessCXGceRNCwYp9MJlBIr+GlcUCClxC5cSJYjbvYLMVAWEVHZnANPKE9lwUiUDlCmEGy1QoyMjCzckfk/BRm3AtZ1SpElKCWEPZHANhxPL4CnzGq84yOORdu2wX3UkyV6+d6xCIiy+XnqmIaOsqzYbHd4BYfjC0/PjyEfKOGzTz9Ha8UxJITP00iaSscyTMJfUlpm+5vNDlC8vOxZ1WJvLXkunq5tGQZRYMzzxORdgKYtys8YpaiLijIEMno/L/C5EJwDGTPPKMqKJM35/Jvf5Hvf/S5vv/pqQQmquma1lgyXIWTnTKM4lp6Ox2UzFmh6wgcTQxVk4NNs8UEVgmIZiV6PPrI0W7x84itLszAqkO5PKU2WF2zWG/He6TratguokyLNUm52NyGZWzaea3RpnEfWGyG0npszD49PGO9DarRnmETtMYwzXT+ivCbxF06EV7IR5XkK0UdDRWTQBbMxvUDns51IjA5kWcV6vSaPyrQ0RSdm8VYSdFSs2O/v7mn7ifeP4pCMs2gsdV2S5UWQjqfMQUIfOTB1VXE6nXh8esJkRcjk8WzXK1brNfv9S3CItgzjQJrmWAvem3ANNWmWoHSGSVeI03a++ByN80Sel4zTTHduaI8veCc5PTf3r7l98zlpWWHHibZt0IkhKyXMsmk67CxmmGM/YT188a1v4RyYNGOcZE02yqO95XA40LZt+G4pq/VKzN+cDaaI0sTJCEkaOUE2dDinUrR571g8SbzDB/+S2c7ii/IRL8SjtQkGamLsFoueeZ6Yx/EyZrrinvgwPulC0Q2XYiE2DVrLtRYPFcI1v4xl4nbvrGO2sxQwogkmklgjGRYfgwfDeyv5s5idFQmzkfbglUKbZIlHiJ/nAeVFCKCVCuZucr6slWdXGyMN8dcaDBCFWWTIXJOB48+cz+d/as7Kn+kxkHRB8wKfz7MlBilFi3NrLU3TLMhGlmXkRYZzNvhGqOUmJcCAXqmgoJiZJ8e7Dx/I04ybzVbY67MVNYGdcc5zOp24v7+XLhDxeImqFqPNUpjIZxdLJXvN9o7VaZJqhrHn4fEDTdMsRVi8YYqiRGcJM06UL24mrXIypbhPNFku8tTIB4BImDVgDEWes92sl05ini2n44FT27LabRcS7GXmKXNc4YxAkmakWU5Z1gvPRmzPBd2IFXzkH8TO0fqLG2LsBHZBFWT0K7wTS+ZYAIkxnYx37Gjx1jNPI+dTx6mV6HGTSJ5GkmQo5bHhnNtZ1BOpMaQmXRbaLJOObJpk5n0+n2mbhq5xgGFVr6lXFUWRUdUFp9OButzw8vQSRhVSDFelGPmN4wA41usN5/OZpjmHjVcQtWloUc5CoWjOrXT0WpRP0yQoWFlvmKeJJM252WyEE6M8/dijUoUyY1joFCpArWVdo5OU7fYmGBMK4XKzWaO1ouka2n7A4ZjHFjt2zOPI09MTfnZopTBGLTLfaZJRw4vey9gozUjynO99+eXCZ+qCwut4OpE9Z38KrXROsrdUQDbyLMOTcjoccc6SJwnOWwwJRb2mKMql2BGo/BJVPwbUKj4XdV0v8skxEFGd9/RzzzxaklBIKi/IpAnFUmpSiqyg63sO+3145gJBHU8TeEllVfHNb36LOitpzme6/sypPZL3OdokbDe3JGnO4fGFt1+9FT4CnnEWmbdJtYzjpgnnoapX4rhaltS1oDun44Fh6ETSraKKwospGYphFDVLlIu2/cg0wzB6kjCyUMZgvZP7axyZ3RSaqAzngiJx/xyIyJo0ScT8cLKk08xqVdM0jRRhfReUYANOQZ6V5HlFVa7J8oyyTGm7E+fjgTxpJPPp5KiDirI/nxmUYr2955MvPuH5UdM1R7RO2NQlys0oO5PohN16i04C6VMpijRHkQRX7Vm8YMYWnWQor/F2xjvQKdi5JUsVus4lR8o5tK+ZxxHrHXW+ErJxGGdfCwOsC+tp33M49ou/kAsbtVKGJDWSEqwv/BQpTi4vo68sHIRPGvh9Yae5QghBSMhlEeS+YT1QKsYvaEDjZoeb3YLcOSv7weTEiVpG/SIfjuiNnS1egYqmf2HktBRSWqG8XxSKsfgR8cLF8RYNUao0T3ahFFgnQaheB58UJao5HVSDc2j2Y+P29YKEK35ZPC8RvbmmIfyPff2ZRlZ+73d+fyG4RhJPrHS7rlmq2T7o6aNN+263Zb2qP+YIqDgXNPSTjEAEup5F1eA8m81m+dnY/Sil8M5xOB7FVXUYWW+3y3tHe+fY9aSBHJmE2XHsKJeLr9wC78VRQ2S7Z6U4w0bOiEcQkDzPxaQuK9Bc5GvX8B2A5jI+iRuN9z4ch6YfJ86n0xLznecFeZGHc5oISc9f2O3xIRWPE8c0CT8gyk2jrDxJEvwCs6rl96KUHO8C6Vkvc/14W3ZdxzQO5GG8JyOCPKAYZfg+Yufft62YRCUJxgR5p/f0V6Qy4EqRZEm1J0uE+DeOQb0y92gtG0e52tCem1AQiEHcy9MTzlpWmw3aZMsoTxuzGJ3ZeWZsj0xDS5LmMrJQcG5PWDeQ5QlGFazXW6ZpoDmfGLsO70VeOc0T9XoTDMTiLN1wc3PDer2iLjckWsIf67rm5eUJa8X0LstSdCpGaU+P7zgfnnl5fmbqB9zkwCvQLN2TCemzNhBii7KgKAtubm4Wk7trP4pICo1xDpHwu16vOZ/PeOUXzkrfNyTGkATiYpKmoMXfI8q0o7eOcJLyBe1br9eLueD5fA62AyPn03lBSeO9vhT7oVCJf1ZV1UK6dM6KFNZ5+mFEGUOSZExWNvcizcV91cr1Z0FnFfPkZFzovPATorEEGp1kUpzZy1hM1pkdt7c3OOc5Hl84HQ8cA/oE0oWP4yD8mkRk3VobEpMyDCNZVoTRXypjrsQIeuZmtPKkqQ6fCUpdEGUVvDHSYISok4I0zYNCcaJpJHxTxp4TMwqlE25v7tlub2iaMw8P7ymrnMRbcg9ZXmC9p+16dJKSlxUmybBkWKco8wSjFGma4U1Kud6RZiUZOd5ZZjehtCYvSkySMw4W62Yme6YbBsqiJi8qTJozDFLAWDcxjB14KMoKow1KiVlb3w+YLF+4ibGojmur0rL5xsbrOiMojt2t+3jr+7jokFGHd7JJWyuNnfd+SVJPruTn0Q9qUYMZvRgDXoss4v83Jg2fJZLgOK6SUaSgg7FZjQ6+VVVh54tRXSQCRyQECE7Ww0fPwPWoZ5xm0iwL7rdKXH+DuZtzbiHJKnVNmpU11tlpSYKPr+v95R/Hg4k/8/8PZOXPdLHyW//1bwd/jIspT5zzdl176Z4DhBg3aGcn7Dgu1fSqrtnudguhT4eOEx/CqsIYZZ5nmqZh/7JfoDsJ05NNcXezwyQp1XotnhVXzP54mm1AVNq25e3btyRJwmazWYzI8uJiPhQJU1FZYL1boLaFBBXSpKd5wk02bNRm6UQvc3JDniVh/npRDUF4mGbHPDpi8nFZFKBUMGgbcUpjwiggnmtjDMejWJ9nqTDl43eO7rlZlgmbHBZFVJRSR9j/ZrfDaHXF6r/MRK2dOZ1eQHnyVNCRsR85HyUfZbPZMju5NkPbCpEzCYVJGCnN9uKPEUcOEXny84yzkpgNUFUF49TLCAOPKXIOB0nLdvNI3xyZ+g43T0yzI603MiZKDGkiCoFVvZJR2WmPdhPOK+nAk4R6VdH2Z4ahYxwm8qxgtarBW8a+p23PdE0jfIFixThNNO0J7y1CzBd2/+3ujt3mfkGfPI5h6GhbKdi9Ugx9x/n8IiZdw8jYj+A1ZVExOglRi0V85DfN80RZFGglQW6RmB7PW7yv4j0a76VoPDiOI/00MoTxkCSb+2Us54Bp8gvSGRVQ8b+BJYI+pr7GjnkcR8qypGs7qqri3DTgvfBHlCCh0zxLdASEEZB0tEopjocXlPKs6g1396959eZT6noDSkzklDb0Q8fx8EBz3jONPX3X4CZH17Y4JY6i1no8hqJcsd3e4JCu19l5KZai+m1RanjxRDmeTpxOp8B9kdRcj2fSMDuHs55xFPL6arUOfJjgGaWVZGwph1YepdwS0XDhABHGExneu8AjE8t28KRZQmLipm2ZnSPJK6p6zfPLnrIQdeLuZsf5dIR5oN+/0I8Ts4NhdpCkVKsN290tkw3NltZsN1u8h83NDQ9PzwyjJZ0VZZ6jDRxPJ0yS8Oln38SY4IrtO8Z5RgwwE/KsEGUOmhmH9ZG0n5AmkpYsm75idnpBIK5VjLFJG0ZJ9NbGLP8OWACztVc8p8vm6wk8ECv8JBu+W/QqarsO7xx5QLCjii/eu5Eg24/9FQ9yomkbiRupK+ZZRjdFkZOF76O0Ik2EKmDnWYjrVyqfPM/xiDv7NM2h8E8WMnBcx+d5ou9aqqpEXzWUcWoAl8JEG7MoFFEhh0glWBul0XEdDkWIk6iNuP9E7kx8v9j4x6LlmhzcNA0/+3N//ge3WPmd3/5dbnY3y6YXSUhS7Q4LsW0YBtbrdeAWdBxfXvjqyz9ZOsT46kLWjBQn86IWyPOc1XrN48MDp9OJ7faGTz/5dEFarqWQx/OZMaADEf66rnKtcxflUOjEol+LkK6EjHQOSb/Rw2W9XrNerdFeZHfTPAtSk4SMnTQlrwr0PwZyk1GNbHTWzhwOR7RWnI4im63qmkQbsKF4Czb48UYXBYfBX810QQq15+dnkQQmJpBis+W8gxQ1dV3jws/HcxKr/b7vZSwRUJVIOozk467rOLcHjsc9WmlWVY2bHQbx20AZTECoxkCWHPo2MNQNRejeI9kZ5EGLs31nHdM4YRJNlgn3YZykcNTa0Ew9T89P4CawliKBeehxViB/nVZCpNRqMfICSIwhSzzNYY/3mqpakaQpwzSA8iSpQeOx8yiFTdsyjwPD0LOuV9ze3TNbwzAO9EPL+XzEe8s4iVmexmAnH5QGUFZ5KOxOOGfJ8owsMYxjhzZCdq2Kis3mhjTJsUH9o5XM5Odp5ng6CldJebwdL2O1UJBcq7wiMhiL5shpOZ3PWA9Ky/W24ygbbKIJeg1mK89rREGjGih2adfJ3nGxjqOgrCh4fHykj8okrcmzXJBRJZB513eB+J0sRdbpeMQ7y6quWK835HlF240kSY7HsN3doAMpu++OeDfSNkf2z4/YyZKlKf3UMs/iXbLe3LLa3FCVK9K8lCT2aeTx8ZHn52chvYbvJMWKoH/xvh7HcRldrNdr6vsbQS+s43g8Y4zh9es3KBQfPjzQNueFB9R3J8a+Q2svo840ch8+PmexK3/96jPSLKNrWw7HPcPQh2ITUaSEDSp6Z1RVFThSGxTS4U+zJStr+smBSemGiX6cSBTkiaGqa/KiJskyNpstSZpK8Xg60rVnrBNrfJOkzFaRJIWsO3lKc5awz8Qk5HkRxuaKCS9RGUmC1klw8lUBKRvRRjKArsUU19J/Se++8EXifSWvQOINa1Q8X8sIwwb9lLoYpV1zIa836fge8c8iQn5tUhr/O01TxmFaxAjXz1V013Yu2gxIvltEj/M8W56XSLy9eMKEGAkX13oh7sY8oshP8T6OeC58mssxCyn6uiTwXBFlvUerCzH+Ag5czsO1t0p8X6015/OZn/nZn/zBLVb+2v/1P+D1q9eUZbkYnsWbNUn0RzdVZOMPQ09uFMeXZw7HIy/PL2RZusB5WmsOLy/i2DiN3Nzc8NkXX7BaizFS1/UkaQ4eTqcTh8MepTX39/es12vef3jg7tUrPvvsM7yX/Jvoxuq952W/X9QKcV4ppmpnifNO5Ub88EE4K1mWLRJj7aFIsoUwnCQJu0C2HcaRY3vGKTHYyvOcrpNF+/b2Vm4uBVOo8h8eHhiGgVWwiMd7XMgqimjP9YPvAIJN+rXJUcwcspOYQsk5OVBVFa9fvyZmuqhQ6EQr/7igOifFgptlw1uvV1IkWkfTnKXiT8DjhPdgZ+ZxZBqEWOsAlcjoYOhEvkmwSNdKFhxUIAqrS55RVJ4YnQZPBhjHlrZrybMsOJ5O+GAdb7QUKd35RNeeaJszeEddi5W7jBccaSp8qL7v6c5H2uZMkVdU9Yb1ZkdZleRVTtOcaM8vvP3qS7quxWhFGtAD72bSNGd2wTHZOZSKJmLi3aHQeOs4nY90XSvoVyB2GqMDt2NecnP6psV7TWIkxr1er0Te2neMw7ioKaZ5IjXi55Pn2XIfRVSyqqrlOkdEJEpmvfe0XYcJvinjMOACKVQWNkEr+35c0JrrQj46K0fIPo4KY9E+jiMoEyD3i5wzqqm0MfRdR5KkC8+r7dplzOX9zGZV4ZygFx6NMgltF4nvGXae6PsToT7HzjNDN4b7R8Zg0ziTZDnVas23vvXDrDZbEpOICmsUkn7X9cszorXGaEhTs4y14mjrdJJMIbJEyOBKCyesqNA6CeOcLCCTCutmxq6ha888Pz/Qns8458lz8ZqKDYHwx8RrZrO95+bmjnmeePvuK15enhmGflHc1asdXdfg/HzV2CQkJsX6BJ1W3Nzds1ptaIeBYZxJ84LVegOzZFEV1ZrDqeHcdpJlVBbM48DUHmnaE20vo9TVZsubTz4nyyrGQZLn26aVa68FnXRWRqHkOUlRipt3kpKF5HJrJcrEJMJPWgpCrjdfUN4uReziKbLwLCTZPP6O9zHlSV7exRHHBSmQX41IxEXR9nWDQ6VEURPJqB45hvtXr9Ba8/DhcRkbLYF/Ic1YAhTVgrbIwQghOOb1yDE7ohGbfKY0b9M4Mo59+DnJ7orqJecd2qTLnujD++r4PeWNiGof+f4XxC6WeRcy8cf+L9cF0PW/QfbKf1qflT/Txcr/4a/+exR5sTiyxjntzc2O9VqklF3XBfJjs8wNq9QwtucFvVhvNkI2smIKN7StyM0UnJszTddxbhuKsqSqV2AkHPD169fLQhoXztPpTJpmizKpaRrevXtHWZZL6KKHZbwTN89hGEjShDyXRT8aS0UexziOYq8/jAwh/XXoJUV5nCZ2uy2393dsd7tFkRA74rIsZYGeRabXNA3zJNH0q9UaBbRdi1ICeeZ5vqTKWufCv0XOFsc3kfiotebp6Qmcpa6rxQU3ZqFcLwQqoDX7/R6l1PKzRhnGQfKSkiRls1kvBYUHRjtJqqzylGnC2Dfsnx+ociHulmvhBSjE5r4PHjrTOJKmOWlaLsVKEtQMMWJhs9mhFPTDGeeFNKtQrFdbymqFC51cvapD5+KCw+qAn3vG5lkWDmvZbre04X4TZj4cDkfKYoUxOdYpdre7SP1mGvY8fHgrIXjOkpmENBUFAkqjTUZZyUz+6emJ9XrL3d29kKCNRiMRCI+Pjzw8fCBJDFUlhbtRcDy+YGdxGt4fj1TlGq0zDvsjCuFvrIMfxxDCHU0iiI9SF7QkFinzPH+EmF2UL+nFDC3PMXnBbGemccQ7i50miZAIdv7b7Zau63j//v2CMMSCJ75isRKJtos5lk4C0Vgv9v7GyCafBNdopVhI3GVVLWmwm1VFez7z8vwiRMIws/dI3kumk+Br04IKUvSywlrPPDkSZbi/u+N0OnNuGyYr6GZRiWpnnsalCI4ZVpHjMI49ErAqHXdixJMicnPs8nz4wMnxpGlGmmaSUpxk4VwNeDuRplrGavPENFnm2QeViBAuo7/OOE5k5QqlJStrGkVynudpWDc9n735nHNzpB8auq7BmISyqJkmz+xSvKmWKICsKMRGQIlDM4jCKylXmLyk6wfJnvIWb6Uos97imSWZ3KSsVlvGUZQmbprpu5ab3Y4iz9EopkFSmwfv8SaGkM4URUmeFXgfQ/f8MuKLVgzAssmej3uUYkG5rwsZlOStXSPFH78uviaXYuWyGTt/cYmN6/9lrRM1YkQ44j/39/dYa9nvD0EMckEh4rMU39M5FxKazUcFQUQwImIRX7HptfO0iB0uqJ6Mebz3aJMu5+z6fF2URVxM8bhywlXCi/JKf3Q+4rm7NppbzsPVP6fT8Qfbbv93f/t3qar6I0g1VsBKCRQ/29jVyfzv+19+yYevvof2juPhECS6kgH0+tUrGUF4CWCrqoqsKNjebFHhvfKiZHBQVTWb7QZrRVq3DmGEdrKL/DeOY2xAHx4fH/HAZ5999pHipu/7Jc24rPKl8o1hZDGM0HpP07Ucj0dWqxVVVdF1HY+Pj9zf3VMEm27xOEmCfFmgxXmyOBLSNFs+dxj6YNePWJFPoaBTYeNQl7BDZy+s8zi6ipb7h8OBtjlR5Dmfff65fDdk8R36filUVqsVWqlFvhnRrMQkZEm+kCavH/DoWVAUOVp5uuaAtwOn/RPN+cBqteKzb/4w1jn2z8/iWNv1JKmoQ7zXrNa3HzkcSyekadoGhQ45USNNe6TtzvTdQJ6VOC9Fiw2qjWhVXxSVRCicnmn3XzJP4vdQlZUE9xlxWEWJrFSpDKUS+mHieD4x24mizBm7Z+apxzlLmee4Wbw2ikIM8mYncsEiL4UgaTJAeCaS6j4v9/p2u6XvRb5s7UxzPJEmepG/P7/s6fuRzeaWm5s7Ht59SROK9Vho2OBcWdUVNrjvXocnyv2oac6nZYOI92ocC83WMsu6R5ZJ9ELXNigligcTpJKxmI4FijYyzrkmzMaYjKUROJ/FlMtBngeTx9gxe78oYGLBEkeQq9Wa29tbXp4eOR32HPZ7+qHH4TBJgkm0vI8Do5WM6ILCx6M5HBu0SrhZ71ivVjw+PjLOI3lRYN2MMprddkvXtIzDAIolEuDx4SEYKPYYLcWJCTbzu91OHICNCeMMHwp5I2PEvODu7p77V28wWcnj0xPPT48oLNPUM40DdprwTlOUK4qAJsV1A+959eoVp27k7v6etmvo+pYiz3l4fBAb/37AoCmLFGUc8yyFVpaWaJ1RlDck+ZqyLIQP0ffM07jw9JKiwpucrFqTlTUog7czVS4dvFMpymjSNMF5R9v1ZHmFVuJAnWrH+XjETrP4Fw1jKIg9WSn+LNvNjiRJkSiEgf3+IL5IVb6M+yJKcREVeIyyuOAVEt1h8Zdi41KQiFT5QngFxcc+JVHNA1IIzz6ygMAEhaNfNmoXuB1+KWQSIwTZpm3EFyjsunEzF48VtyBk0zQxTtKgxsLkequOXEXgagwTj/3iteUJni1Brec8C2ri8dIYRfTIOXAz17YfcJUBhMJpGeGbQMq1zi6+MVLUXBRCl5coZn/2536ACbb/5d/628sISMLzhCB4Oh5x87RwQ+Zpout7iqJgu91ivacfetq2W2DkDx8+sN+/cHt7S5HC0HchtVWyIOpKFvXt7pZqdQNKUa8EStdJgg9kW/kdv7DUdSgGlBLDuaGXwK44Y43wpKAKCfWqJkuzxTgq3qTOOcZ5ghAcFwOxIgEvTVO8dTISGCcOxwPNuVmOQRuD0rlkDqUJdh5x88w0SvJwUZbMiEX7cjOHgivPBYYeh3HZTGIxFTvJvm95fn4O8+6S4+nE8XgMya7CrYneK5EPI7LZKRg+Cfk1ScQKP8ulq8yylNQY3DzjrOV0OpBnGf3QczwcRGmiNEVZkCYpxohTbNd3KC+kRZ3mIXXb0LRnjFacGzG10yjKvFjs0sexF+nq6cg0D2xv7inLiq5v6ZomkN9EKVJkCVkmRNa+7UhNSte2Qiw1hrLasLt7g9Yps5XVzamZ0Q00zZ6nr75Ee8KM3jMEl9PNVsZyp/MJ54RT1PcDeVECYndf5gUP799hErGRHyexs08Sw93dDWPX8/z0RFHkotIK97N0mQn7l70UD6GgNiG4T4oDw+l0Xq4xSjwfoqLNzj1VWSzon6g1isXsL1EyfjBJRj9IcKF1Qg5PjUZ5IR86/7GE3zsvEslZvCWSkHQb5/fjOJGlOev1hqoqsbNlnEaU0sv7jcGdN0lTEp2yu7ljs5ZR6ePjB55fPgTTyOgwernvSBJMkjEG3xxnPSZJ2d3cUpU1XutgppdQlDk//GM/xvu3b6WoqyrWhVyTl6dHPJamObPfv9D3HfM0kBhNXa+Ci7Vhs9myXm14fn4hLQrqesX3v/99zs2ZspB17dw0pFnG7u41s7VUZYVzNtznQxgx56xWG7l/ImrjLMp7irwAazm3J9bbCpMqbm5vePv+Pd0w450mIWEcO2DCukk2Um/wPiGvd9i0WPhFaZoKqihGH6RpQZaJtb61HqUNQz8EtVeF1wnW+3DMhjGc6zTNpXhTjnEc6ENDlgZSdJqmJHmNN/LekrEVFCxzyGOCJUsIpfGItUIk0ioCInAlRI5rzzxbUGbZ8KPhmyLa38/4EDi6FMTxpcAhyFgkp8fmjsApFGfskOsTiygdiNDWLaMhpfWSVBzXVW1kz4gFxEUvtHz8UogEUsny5zIqtKRpUJuiQoCnELTjSEd+9zqsUK6n+qjousQFxOJF68tok3gcwfTOyyRLjtP7Zf+KBNuf+bkfYFO43/qvf3PZ1K21i+/Hu7dfCfScXfwgYtrvt771LSbv6QJsGJ1WT6fTMpfXfqRtG077I6fjke/+8Xe5u73lG198gVea+1efsLu5JckEUjNJwjCNQUZckOfFgvScz+dlkTfGkCeX6j9K0+AyNrl2zIwLeZQ7bnc7sqDfB7mxI5qU57lk4/xjCE7y/z0+dDnzOAaS6Iyz0+LQ2s0z3VUCdV3XC0kx5jzEhx0uZEFBEvRSzccxy7VLbmoMh8MB59yCcsTQw+fnJ+7ubtlsNsvoYZokNNFZi/YOb4VEt1jwB0TrfD4zdG0gWmZB3SKLeZbl4muhE+EF4KnrkiSVNNth6PHWkmiD0SliQOCxbmQcO47nPbODvCipihwxm5Ok4bZpaNt2cWVVOKZ+QGtZrJSHvKzQJqOuN2x3d5RVxewnng8PNM2R/rgn0ZqyKjmH4kB8aBxDP4RrqEU66TxJkmGSJGwOikQbVuuaqir48OEdx+MhOGQqykwko6fTEe892916cUh1zlJXG+7vX3E+n3l8fCRNZbS5Xq3puo7j6bz4p8RrGe+zsW+ws4wgI8J3PB6X4vb4cqDrO5FnoqnXW7yC4/HE6XSgzBWJudh3XztrXsPb8TNF6aFlLJKklEWxfG6WpTgvnVvf9aRZQlkEn6EkY5ot8yQch2kamebuI9g+bjQ3Nzc4BTe3d3gXVXuiztE6EIAzUaMVRU7bdcv4zDnH6bDHTYOohrxls1nRdg1NcxaUV2vqasWrTz4hSzOmYeDp8YlpElO2JM1ZrVZSoFrH6zev2W63/MN/+AeM00xRrwTZzUSCPI1TeJ4st7f3cr4GUUvVlZimrddr1us1fdPw8PiBtjvRDQ3eOzabHXf3b1AqYZ6F6N10J0mvti6Mgtak5Qof0NgirGt5ni+his15CM7LWUALNG3TYoy4LZusJEkzsjwLo4Y4GgGvFCasKSKPnUM3r0LpYEAJcX9B4FTY9J1HB/6Gc57JWpQ2JMEB3Lp5ITTH+ymOFC/IbXSBvUIllgKDBZ1zV8WKDwieIxrEzcHr6+Ja7qzYXsQ1LjZ48ff/cf++NjCNe8LH6MTldT1+ufBkIhJt8VzIvV9Xb16/x2VvuBQnmj/9meqqGPKLUiikL1//oFYLxy7uP/F1Op/4xV/6AR4D/ae/+td58+bNctKbRpCEzXqNc3NgXIt73pdffsnnn31OVVd0/YAPBUSEs5+ensjznJubG8osFUtqAOt4/PCA9555mpitMKvRWrJ+koSyqkjzjGg6FXXr166F8QIrJ/LgeNqvi5LlQfnaDRs71n6QhNP43hKgJmomYwyJ1oxB/gsXKbc8bAZ0Eip2x3H/Qns+cz4fGYeB3e0NWSgirmepsUBYLNnDTR8JjLGoKIpsQXHiK6oe8iwjS1NOp9NSXHovi9PLywtNc+bzzz9bpKrxoRJCmcPPM7iPPRMiSVeQMZEZl0W5kBcj0VBGP8GgzwivYZyGhZSaJwajE7I0J88rxrGnHxqGsaXrGyYr5m3OWYyCxIjkME0SvLWMoyjMtFbgLX3XkiSaLBUljwecVZgkpygkKRjlsW5iHluUdxSlqEDigtf3PUmSsq4kMyn6bHh0UKz0opzIcu7v7zgc9uKC7MXksGnOoUOy7Pd72rYJariB5+cnrLXU1ZpVvQpptB1PT8+UVUldCQdLfD4y7u7uPlJT7Pd72uZAmuqFD1aWZfDUyZmnmZfnA835HIp5Q16UKC2Bgn3foZXYASyeGAEhPJ1Oy2gpjghjAXRZaN0i5U/TlMSIcZUoKGTsGDtnoyXaYZ4sSuslyuC66I3hpqKGkNFo38sGbHSQIReVjPTSnCyX5+2Lb3+Lse0gMbTnhj/5o+/QnOVe9rhQyIvsX75DLsWmNpjkYp5IkMqqKxK4MYIkbIJf08v+gEokMyuO5OxsSdKwEdkZbRR5VgpakKSkac7+cKBebcT9uhE1WZ5lMh5Oc6pyxWq9kRFSmXM8HWgaadryrKSq1jjnhX8UpNbnplmI5G/evCHNK2Z3Ia0mSYadJWDSo/BJShI4fNFLhOCKaj2oJF3WJO+ChFbrwA+LrqwfK1iIha2TlGaPZKRpk8h7hmLIXaHEOqAaUYUDYvB2bQjqAwSur5yH4zp6vS6aRHx25rBmC/8oyvl1gBcuvxNf15v3NZ/jmu9xXRh9nbB6/T7xd7/+3koho8nrgicQaRfVT0AUFWpBeyI6ouPxh3MUTrdwoTxhtO7ihy4jNjtbZjdD2Jvi8xqP83A8/GAXK//Vf/5fst1uiaF6MfPl7u4uPFDpwlWJCoZpmtjd3pEGp9bY1Q3DyNPTI1makacph8OBsiiEFGgS+rYL7w/KqOA2GODIJMGFOeHt7Z24zAboSwfouIg258F2/Ppmig9B7CxigRHRosNBMmLq1QoX5GxRiRGRjTzPSbTk2MSq1lq7eLl8+umnoYjSy4MLXsizVmyrT22LtZb1er0UDbGYApacmEgqHoZhyZgRAqFZ0JYo+V64KQH1ihtDJBYLyiTw5TWjPqJPRmsMnmkYFzO8a3dT7z2zE9Jw04gR4O3t7TJi8s4viat5GC1578RP43gkFYduEpOLqidNcH6i684o7TBpLpsHnvP5GDZDi0EWARUEudZNnE4HssygtUCseZHg3EzXDUyTwzkV4P2SJE3o+yPj2IVzneDchXQ39AMGkXImaUq9XpPlJX0/iMmd0oz9JIjUWh7+pj0zjBKn8OnrNxRFtoS/xVHT+Xzm6fGR40HyqjbrtUhIQxEZeSJZluOdo+t7Hj584Hw+s91u8cDDh7d4NzPPF3fOeN3KUmzgx2G8LFrBr6HIBdXq2hPv378jJnLHAjgicXF8G43irtUWzknGk7X2ioCIFC34xc3Wh9l8nmfs93uMEbfj2LkvydbBW0YIj5PYjWsj4wQl7r59PwhKELxA7CxBhmVZsd1teffuHcfDnskK+TgPjYsU9BIOud295u7uDVorDodnur4JnjjSwitvyFLxZVkIz4nwzoZpAh1tAawgPleSWaMcaarQWgisOsnJColMuLm9Y/SevCx4fHjAoJiHGaMMBHv7YrUmy1Pm2S5oZ5amGJPQdy1T34VnVMZr/SDckjwvqDa37O5ek+c5Ly97+i6Gm64FndKG9Wa9RCjE4lOeZeEcRRQgGgLqwD0ySboQQr37+DsrQHkXkGQICzOENU4Ago+daK+LA+ctSl1QDcJIJaqulEqvMoaufy5YUPgLenAZ18tnei9qorieXzeh14VJfF3zUa7NNv+HEJbrQuUaWbneT64bvpi39dF7yC9/jBgp9dF+dH0M8ZxHZEsh46brz3RBNn0t4Y7vczwe+dmf/wFOXX73/t1ys/R9z7t372TzCgtckRdstuKFMk4zyhj8NHM8nqhqGQ+9vLxgreXNmze8fvUm5IdY6rpe5o3zNLHarFmzkY0tkxtq9m5JgEVHl8LLmCe+h/d+ceDEibtpLJKKorhkzQBVVXFzc/PROCgWEPVqRZKlvHnzZrkZnXPLIj/24uJ4fQNvNpslTHEaB4a+p16tWG82Mk7QmiRI+m5ubj56+K5HVNM0sVqtlk0jEmyjPX58GIdhWIqQhQvgL3HhUUEUv1c0UdJaLb4qscCLD1129XeRdxG/O7DYmg/9gPNu8VNJkoRxGvABGo0zb+8VdbWmyCvyTJMag9YJ3ilmO+J9itKOLLvAps7NUgjOM/NomcYeax1lXlJWFU07he8g2TLeOyY7k+eZoApIkF7fQ55X5GlB2wrBO3b30bzv0j3PnMcjHgnyS9KMsq7JspxpHFFY3r39kndfycYcQ9LGceJP+oGyzEUhYyfevv2KNE1omjYQJD1j39FqRXM+4b3IhMUJVPHq/hXOORlBOgfe8/z4GIi4M0rJJmGDA6Ykx0oxmGYFWVExDB1JmrGqKvI8Z7/fc+4GtBGCel3XjMPANM9LivE8W/b7l+ArUQTl1RgSri1eGdI0oQpNyDSOi3y1yAs2VY3zYuI2zRPH45F5jhbmEhgn94UsptEnYpon3DyJ2ZrXMn5UksxclTL+mKceNUsBlKWaqkxRfmJV5Ri9pht0eI7FPVU2M0NeVORZTt93rFY1N7c3qL0PIyxLXdV0/RA4dzOTs6SF5HqN44jqOqYYVIqnLAvJMZuCxNvOzLNDKYsdJ9DCZZmd4uXlmaQuqVZrsjRnU2/Jkoznh6fFFVWFCI1YmFXVBkF8ZDS9+/QTqrpi7AfWmzXjNHEKyr1+ZhEpvHr9mmkQf6rnpxdMYhjngebkOTlBxDbbDWWeMI6evjmi3My6KPB4+vZFfIKsoyhLdvefoIsEhXCZQGqQ6Lys3SzFilIYnUqaMWINb5T5iK8SOR4qSJnhwtvAuwVIEZQgrp8XzuDXR5Sx4ZS/d2LCjXBAtFI4dXHKhn+Mm/jV+8a1LHKQ4p9H8nB8XRck1+91XfhYGws6+f/jOCFIytfM2+J3jS7sXpDJUAb+qWN0Ts6V40I2XpCjgGYRxmYLB+1r3++f9vVnGln57b/726SB3xA3t67raNqWt+8/kKYp3/zmNwFRJngfZ4OQmDRU8m6BOJXSdF3Lw+MHbm52GDQvz89ivOYll0QpRVFklFVFVuQCBWqFD11aVdVU1cVmOSIcEWq087QUKxFpOBwOS4R8RA/qWhbzN2/eXKpfRG4JLEqK1Wp18S1xfvEUiAXG6XQKTpoJxkts/ZLUO04kWYYJQX/OzctDc43YxMIjpjXHBzcWHtKtiOFRfNjknAeisBLJb5QLxw1CPlNs+tsgN47S2O12K8WK1sxDj0YtbsXxwY8ZRH3fLVLNuIDEUZBSEn43jhN5XpClOc5xkQr6CaNh6Ce8V8GvQ8Y01g4S/Kdlit53HX3biss6MmaZx5Gm65C8GnlYk0zcLdE6mN2NeCuy3ywt2Ky2pFlO173w9t2XC9dKa70YQ+EcU9/T9T1pmqONEcOsuiZNcxKjOJ/2IQjT0TYddb0ObsI51k/M80SWpWER7NEmhKA5iwsKIEHyMozRKCU5IuvVhtV6DYiZ2mq9EvNF7zgcDgz9IJk23geHY7nPiqKQTebuXrrrsuL9+7eBHD3hnWxCKBVGMVI8NU3DOI5st9sF1SvLclHQDcOw+JOMdmK32y0LobXipXE8HCnygiLNmEaR9w9jj0kMWZYsjQHIWDAGdUY5/jzPELhCSiuyNMeYRPxYnBDm0eLfIwnejrqqqFc13ouPxBiDF5UGH3kRZml60lTUZOMwMNuZ+/t79i8v4g9j5+AnkiwEVjvPi8ljnou1fNu2S4cez71RHo0oSW5u7wBN07QMo4wDTJVxajqMzimzmiwtWa/XDEMPCpKsIMsK6nqDc3A8HHjZP5EkijSMueZZEsancQSl+NY3v8nNzQ2jUxTVipu7O9KiZGx7PIrHD49UVUnbnYQ304vrMEr8PaZpBOdZr2pma/nqyy8XjyaQBqTY3LG9fb2gtxFxnudZUtsfPwQi/ExelDgHt3d33N2/xiSiWLl+XY9eZM1SH/35NRLgvF5GT9f/xLVHa5aG89qEb7n+fMxTuS4wrrfcr49zvq7C+TpH8Pq9vl60XN7/wrURdCe+Vzz+i9ldPIY4ilQhc2hB/9XVMXqWVHEZ2cXPEhm6Qvygvl5IeS9xOP+0yMqf6WLlf/+/+xVe3b8iy7JFXRPzd8ZZmPOr9TqMVKRrEJdLgTyjLDdudLJ4O7LcEFMq3TwzTxPHw0EUFLPl7u5OFpEslaIlzxmmMUB4Fwld3FjhcmOnISMl3kxKXUiyzjnO5zNfffUVSqkl7yKiFvWqZnd7KyTH8TIWsdYKadWKHHsYhkWNpJTiw4cPTOPA8eUhZM1oVpsN6+0Nt/ev2GwFyUmDkV6s8ONxRT8UYDHuijd4HL8lyYVEvN/vL8fkfXD3nBY1QYRHIzo0z9NyvuMrGvQBGO8kn0ldCGEQ+AbjeBWJMC1s9WiFrQyMc8/hcAzdhtjNp6ksgLhepq6O4FcxgbKcmwPD0JIYTds2TOOIBopSxoTeCVKSpyIHPDcd1kK92lJvthRFhcMwO493A6kBZyeGbuCwP9F3A963NM3xo9FZVFg5a0mVp20bWfyCwqWsaoZxYujPGOUwiUGaTsmUEdmuxivHNE9kaSKKoHnE2kk4Nl1LqpT4YRi9FKFRsi4jKb9siJFYHRGvPK8oKxmDxOs9DIOYDwIvxxPaGLIk4bjfs13XrFc17z+85enpWb5LKBbbtl3ui68v4hFZ2+125HlOVdeYNOV5f1hUGxGtE0JtyvP7R/b7Pa9e3TFOPePYX9Rm87Asptecpzhy6vthGR/Jv0VVUlVr8jyl7Q4L1H3dfeZ5zmQt57YPZNySebIcj2fmWRAnrWacHRdkB0TVotB4pUirChUk9XGDut60vIcYPBn/XsZBojwxSiS0RmuyJEUrWNViYTC4kW4cKYoNZbWjrjbUqxWn84F+7PHWs6q3aJ2TJCnTOEouUCJE49lKgRezoFarFW3Xst1sccpIthIhk8xruq6naVrKoiBNLN7N2MAVGceRzWZNludU9Ybt/SdkeUHfiey763uqsmS92ZKWNUolQoT3nqqWwrBtJCTVDp0wuYxBaUPXDyHcT9CBxGSBjBvUN/rau0eRmHRJMF4QGEIxoy9Kn2tU5JoP46+Ujd77RTVkTILzX/+d/+HCKP5M/Ptrp91FNnw12v+6Q/l1EeX9JYD2Ggn6H/qs63GN1po5oKixUHPOLiiN9Gj+o9+LL/mcC6k58m+uKQQ/0MXK3/rP/hZ2FhJhXLC8FwJUmhXCGDeyieL9oqLwTshc0sX6ZQExxkgSqRtFVheY3fM04axlfzjw/e9/n9Qky+JZliVZISZYaZYxjxNDP9J1rVjrh2j7YRD1xN3tzZWtsvtoXnmxo08/ulnbtg0FQYpJYm6QjGCa5oyoXGqapmW/l5uhDpu1C/A+3vH08Janx0eKouSLb36Lql7JaAyFs47j6SiGVVfz/IgKpbGgG0ec8+Lf4hGWv/NYPy/waiSe2cAlOp/PpDGPxAqUnSSJbHxZDpplw7wm93rvOez3lHlOEZxUsyylzIvQZYnhkUbSTeOYKBaKeZaR5CkqjVLpNMCZimEcRfUxdxgtadbnU4OdR4xROD9xPh8BSwRNNeLBsaorhmHg6fEDRs0kSUaei5V9WW1IkxylDFm1wmvNODR03QnlZWw0jjN92zONZ07nF/pw3ATuzjAMuHmStORpEkVEmpHlOUUlnejp9IxRYla32+wkFbcfmUZxRrXB9O18Ft6UdLctXdeAd7hpXIqPqqwYp5lxuPIrCpyLJZUcKVSd98yzo+tGjBbDrggFWyscEpNn6EQFpA92mzXHw56ua/Fe8l7idbr2dMgy4Qk4e1HprDdrbm/vWdUrqrrCIwGiEYU8n04cjkdJSLeWrhmYppGiyEJ+jrxvWZaUZUYbzCCj662zjjSTYmycLdpIBs1mK7JipRTHo8jwu75lmqIlfb2Q28dxYJwmVusNVVlhEiMZNyHnR6HIc80wtgy9nPeuG5hGSYnXxqDyPHTkksYs2LpnHHuUksK+LISI3feDyGETuTPFnybBzhPTOKCcQ3koMuGA9DZu4glKZ9zevmJ3cyvZMFpxPnUSmmikkWiahrxM2WxFKZmkRXCMTUJ+Tc84TWRZznq9pahqxmEUjsrsAw/IcT6fwPVkacJsLXWIuJiCUlOlBSMpYxgbgwoxIjI2VF4KsL4fcLBYNiRGspisHRdvnjncQ9okDOMI3ktG13QpLmWcmAl3T0twpA1ju+uiQyS+knCcGDlelPA1IpoQCcBaq2X0shSx4TrGP7xmi8QRFkRk4yNB8oKCyJ+owH3KKMtKzDznOaBT/k8VIPIdLh4uC1HZXwqUCyLzEYeYaPy2JDlf0QHkYAmTARtQ/QStzUdFNcwLIuNcLGhCLtfpxE//zE/84BYrv/1b/80itYzoQ7wY4ziy3++Xv4ubo7WWBMgTQQheXl5o25Ysz3DWkeUZKkkXBOGaoR3tx2P3E9OcnXNLsSQkWk/TnHl5EYShbRvSJOWzzz/jJqgrYsjifr/ncDjwxRdfhEwe6bouqhofuuUwUppmZjtTFDLDB0eShu5wtsz2Y/VE7CQUMHQNh/0BpRVlWQWTp0GkgZkkOse8pBhCGG/yw8sLzenM8ST8hmjzX9c11lnOzWlBYSJJLMpem/OZoZOAvaosJSpdCZu+73umeSbNc+7v7xc+y3WuineOKebFTAMGgd2nUWLfU3Op5GM3lAcCdbVagUnIUglUlJAuxzzNYe7fM44ddp5pzuKvUmYZ8zzinCXPE1YbcUg+HU/idVFVbDZb8BZvB/CesZvwTqGC2YD2mqRMIVFC7E2yQLwWlYn3Hjs19O2J8/lE33ecToeAgMx0bUuRJty/ekVZrWRJU5pT04Zk54nZDjK+8UBQikyDkOlMkaKMoa42GJOFmAjF+XzEzj1ZjIWfHUVRsdvesFqvGYeJD+/fMg7iQxMbgJjubYy4DXvnsbMotDabHZvNBq2luN03RyY7M46DSI2rkqqsqKoSpRXn80EiJOxM37c0Z1HQpEmC8oo8yQO/yASvnYzJWiGDDyNKCQF5mibKsmS7Fcn7uw+PDGixZddeCr5pXBKf0zRhu9ngrOVl/8I8R9QItDbM3geHThmpFMXFwn6cHJubT3jz5nNWqy1aJQzjwMvLI4+PHzif9rhxFIRg6jBGU9UrEpORJjnbmx0mMbSRk5UXS3hdUVVk5QpjCoxJmcIGO9uOp6d3PDy+pTsf8V6xXu9Y1WtO5xP7/SNKeaxTJFmFtRPGwO3NLfe3r1jVG5qm43ASRZgkB09Mk4zCb3av+eKLb9MNlq450TUHhu7MMA0kWcb9axlBT4O4Ops0ZZ4dzkM/DOx2N1TVFmMykiwTbx5tKKuKslxh0gTrbUBcvHgRJSnr9Wohqtvg52ySDOfFJ8UFdEF7TVmumL3CerlGYMkTg7eWyTtUuI8jeh4RKO8sGhsKCRWIoQZj5B7LylyIwtMkis4wdlzkzV7Un4kxCydvDvegB7yK+WZ+2fjjaOkjNdFVQbHIepUlFilfRygAsB9zbXTw1rLLZ18Ulx+NrgJ/Me6FEYG5eKREldFlDHQtMRZej+PiQXQpcLQWhZr3LA3s9R4jh3sRY+ANF66M5nxq+Jmf+7Ef3GLlv/n7/+2SvXOdY3MN211vYrFzz41hCmOOIljL27Bx9sPA7ITcFDfbaxa6FDCycWZZxvF4xDnH/f09q9VqGZE4JwiCd57j6UjXChcjKmpWq9UyvgIWom/kqpRlhXWWIs+JN46MXcTBcRh6uq6jH3rmwC/RSSIZL1fFWdxgsizDTsMyE752dE2ShNV6zWz9UoA1TbNwBVarFXhPe24+4rFEYq/MeOfF0TbC6osUvCxZhQc+EjjrWsZnfdfTtGfO59Ni5x4JxVHt5JxsimmSMI49iQqBbWF8pBXLyCCe/ziCk8VPTM0InZLSakF4lPbM8ygbphcIdB56zueTkPrCHLcocuZ5xI7CBVFePFvG4UxVSHx92w3UZc1sg08KDrTHWc9ms8U5hICLFGvnwwNaWaqqJC9ypmlAJJWK56dH3DRLZPw0k6TZ4iUhAY4ekNHiPE7M0Yk5LHMmzyiqGqNTtttbNpst2oDWHjsPi9Pru3fvePjwEEzcpDPDOdarckEkPYJcZWH8+PjwnqYRybJCCL1pmmFtyKjZ7PBeLQhGvZJ7Gk8YX04UZUEekM2Xlyeenp+E0LlaAYK0CclVUZY5WZoKiuiCPbmRzWKeZ7I8oyxKXr15Q7XdoTx0XUNzPvL08ECWpuRZyul4pm17vBcZuzZK0BytGMaBeXbMzgdXZ7+48pqAPmbFmqpagzcizfUe5ybKMmNVV7h5ZLajJGd3DYfDkabpMVq4KlHeClzcdq0VzlBekOc1VbkmTaOX0sy5OfD48A78LKovL6F+w9gzDC3OTXhlUCrFe4vXiqqsuLm5p653eG9QOPLCMM8DTXMOBHTNOMw4b3DesKoKsgSUn5mdpR16+nEmT3PSJI2MBFmXPJzOZzabLUW55nhqub2T9W+aHU/7FxSau/t7tFG0bUNVliTa4Ky4JKdJQtM2jNOItY6iqkizDGVSxuAhk6c5N7s70qLGZOLqrBW4aWQeO0gzlLl4sHzc5Xu0sotJZ5pG47poE6HlGqSiNkqMEbGEj66vsxBK3SV4tu/7ZTS/2d5xd3fD4XBaGuIL0iGmkSrwIBdoJfI7BH5Y1s6vFxwa81EBEV+CdIjbcXxdj5Wu3+9PFUDhdf1312OahZNjPv7zj49BydjSfyy3Xv5eOWJSs7Phu4eR3PF44hd+8Qc4yPC3/t7fX+z24wmMJzpyHuKfRXJn3/cUWYoK45Gbm5uPMlAAlJaKNKpd4qZ9sR6/SNYiZySOc0ySkBVCiJxGSdTV6pKJ8/jhA9M4LbPu1WolqcTOcTqdeHr6QNO2Szd7cY5MwsKc03U9aZJxd3fPer0Nv+9x3mIScbY8nU5orbm5ubmgFPMUfDfEcrusqoW8VpTlAkGqUAzEKt17SeUdhz4gRS3jOLHZrFmt1oF3IOfuOqMDCEiJQyfB/EkptiGtGiUeBUYr7Dws1y3KtY0xS6BiREwUnnkcUIAN1uDA4r1xrVY6Ho/BOjxDGyGvGm2INtNKKWZvKYpc3neapKPtJRkZpXBKpJDzNKIRg7rj/hnvZuoyR2uH0kJoPJ0bikqku23XkZgUnCgFknAdE5MG62sFrgMfk6tlE52tGEylSYodRilwnEUpwzBNVPWKzz77nKY9M47SnfZth5slsdrNM23T4pWiqtekwUVWKUVZ5JRVgQr8Bu8hz1PyQoqGGIIIiueHZx4eHsLip5fzL0T1kbKWTex4PDFNM1kwTBvHmd3tJ3z++TeX5yX+rjQEQ8i3EgM9lCdJDNM0ohR8+q1vsdruaM5nHt6/FYv8/RNGQ5ZIZIH3H3tSVNUl+bof5T6SPCCLm2fKssBZS1WvKIq1PB/nA13XS3jfLN11keVkaR5cexVd2wrk7pwQgbsO5wAvqI8xhixPuLu7oSxzNOD8zOHwzLk5MQ4jEgiY4RG0JEnMIqFWOgbwqTCO1WidUpVrttstAMMw8Pz0QJYp6nrNOFiKogrjs4lhbCHIwutVjfXQDhN5sQJdsF7dMA4Nx/0TTXvAuZmyygK6pxjHCYchTQx27Bi6BqU129tbsqLEOcU8iJTdeWj7Huchy3IZJaUFSZLR9gNoRZ6Ll1CS5mRZCmE8vFmtJf4kGGXiheQ+W0uSpHTDgHOeJMtD+rSlO7c4p1BJxvb2LowzPENzBjeTr9YUgU93PRKx4dq3zYksz0iSlDwrZKMlKDxnG9K/L+MK8AtSkqYacYQWtNDOlugY64E0LSiKXEZUV82sREBM4GO2mV8camUtBaVjaGeKUgRFTkQ8FLivcVzCMS6BnHxciMT1OjZs115XXy9k/nEcnGVtVTLmVohvkRjMXX1OKDyux0PXxYrnqsC5CoJUSnM6nfj5X/jzP7jFym/8nd/k9evXAIv6Ay6x25HMKemmJ06nE2mastusmcOmGpGC+v9D3p/E2Lat+V3obxSzXFVE7OqckzcLDPKzEzLTmabh7NEAUgh60IVEQjSSQkJJAyHRoQFG0AbRQaaBAL0GcgOEZDBJ8Uzy/Ox8trGNEyPse/PeU+wqYhWzHsVrfGPMtWKfk+Y5UwKsO6U4J3bEirXmXGvMMb7x//7FdotJaEGIVyJnbvPkttCySKoqyE1RVZKCm90+TSkusVnps2Y0pC+T2pQZcnzeK5UdobUmtYSuKpxxHHl8euTS9+x2O774/HtC/CtrtLZppxCAsPqj3EaU58F161NyKwXOaqpMBM55MbfvEbCqNW4HuNaaqjDPqnG4uTmsZXHQ9z3As/aSPNCh8evEsCS78zw0b4uYsjCYdB3j0CVVh6XdSEvKaLPmZozTyDyKzXnuV4tiaSYmk6SiqrClyKuD94xDhwqycFd1g67kfZmHnmXquTx9ZB576lIiC6a5x8coXiJaSNfKiutsZSrCLDvnvu/Sbj3vAj1+uQjvxRhpPyY4ua5rCmuYOsmNUcYiduKKYZrEmbeqxFjNaOZxwqWCpDCCLm6222fo4DhmsjaJTDqm5G6ZlEVOXIv/zPFC9AKZ50I+I3HH45FhOuHDhFYmEdvz5CpRB2hLXTeUVbnmnBgtpGfvA2M/cj4dialAkl69LIjbF59Rbe/YtS0ah5snnj685esf/Q4KCTIsqpppmhjHcUURp5QyHcM1IDSP5U3byq54CUQKIpG6rtjtttzdHVjcRHfpaKsGvzienh4JIXI47NnvD/R9lxBWT5nuN6Mtu90ObRTOTXz4+J7L+cw0D8TosIURFZcSa3lrFcNwWZ/7UxRYNiVCULVFRVNv0NqmoNFIvSnRqmCeHV3X03cddVNRlpIyPg4DtiyIxjBOgXpzx+H+DcZUuGmk7544nx+JOKpaNmNV2bC4hafTmcIaNnXB6emRaZpRRYU2Bc5DoSqUNty/eODlq9dUdU3XDQzjxLh46qZlf3dH3bQ4H3n//j1KSyRBZRXLMjP1A9ZY6qqSoE9gGEa0sXz+xReUVU1EobRNfJiS0lqmcWFcPNvDvSi3pgE3DSzTgK4a7l+8XOe4W66bEFWnlIUmGV06ISsyD4pL76fzVUaalUp2BzcLf57fnHMorm2W2/wq8XBZCMGlcf1c7SPfisgjz6MrL7AoKKw4aee2Um4DPW/jqGdFSD63XIjcXk9+/C2aAjwz58z3yfV59bNzjwkRylyq3424e4vyXI1NhR9zPl/4+V/4/SErf0f7rFgrkGpGQPqESOQPXilFVcnEfmt0RgykRO6V35QXuMoaCFeX1LZteXp64uPHj6v6pe8vKzTYNA2vXr1aB4q2JpkYCaQ4DsM6QEKQsLSsnthsNpTJIv7p6Yndbsd216yqGmuLRDarsdbyM3/XH8AFT2Z9j8PMubtI7oPSGMNaSGW+zTzPq5fHbrdPCIQUc03TSoESPMGH1TI/IyoZiTocDgl5CWvORM6UmaaJeZoozLV6z/ydLK92s5BED/udcEYSsrEmOKe2zPl8Xp8jf375Bri7u5NdaPS4hBzJolSByu6oC0UR1x1809S8fPUKokumX0n9Yc0aIVDWNeM4JbTKEAJUCWUbhwE39BAj59MTBEdwc+I4KNrdjq3ece46eV9iYOx70MkcrShwAY5PH3HO0W5alPI8Pj6y3bbSOgh3OOeTrLnixcMLqqrmcjklS3OEo9S0aGNZkuupUiqpVyLd5YKfFymYi4LgA0PX8/j+PTHJCW0hpmZlUbNtW/a7DU/HR86nI13fYYzifBKSn6QMZwRD0sI3mxalFMPQcxmEJ7EEB2qhrlspwqKgkoGFaTkxLXA+C4F0tVKPUCiT+AWB8zm18rTiiy8+xyoP3vOjH/4Ox8f3qOBZpgGrVWqZKra7LdXLF0kWLwT0p+OTICl+ZtO21I3IZMtkVmbKip98eMVmc8+8THjvOJ1OvP/wMVnMe3rTMaTguO12yzwvvH37dm2pKq2ZpgW3OMrCoI0FFTiezzw+HpnGEaWTX0VQ4tdh4M3rz3l4dc/T8SP2Rz8S+fGNK7BbFqZpQSufYHQYx4C1FcenRdAiDZKEY1Zn7mN/JkaHjh6T57DCUjVbLolsf3//mt1mQ1WVnC/XFhdR0fe98MUKizWK8/kESCvPI0GKu6qlrcQ4EK2Z5oXZCVndlhEThKT64d17bFmy3R3Y7/b4GMUnZrwIlyVE2lZCJeu64fj0hPed+LvMC2VVy7w4i1Chrmt8WRJCTL5KgoIUpcXPopKbLj3jvPD555+vhcaHDx+SMu2e3X6Lc0EK6DgTfKRpJQMsbwKzE/KKLFiDMYplkbaooIVzQhUrnHfYxPXKa8SnyEYkgEoeUYXEDmSn1xCi+PdocV5WCXkgFQUhCLFaqAvC15NkcUGlb4sRuBYut0Zst0cu4G7lz1lddKu4zKgP3KZQX8m4Evp4JQd/t8oot31y8RKQBGrwfvnbWdq/8/g7Gln5n//iX10ly/lDGoaBrpP0YFHQWAnUS94Xh8OBsrTPvD4+rcpLU64f6G0VmyvQYehWk7ciBfVldUJGIUKUYLq3b9/SdR37w4H9brcamsV4TcY8Ho8cDocUtvd8kQ7JhtqkIsjFa5srIxUhBD68/5CyatwzX4a2bdeKP1ueN03zbJCuKEr0a9WcK+9cITvvnvVZc0WeUattU1PcEIf7vqdtW169erUiGjkJekkoWN931HXDZiPuqrfIWG5D5VbY2q6qCkLy9ZDz1bgQeXo6JqRAiF1FUSQCcM089ZzPJ5m0EGm5ICt6TTP2bpFzmCcKa5inMbX5BuZlYhrETG273bDfbXGLqKL62YmHidaUlbjQdn2HJkIAq0zalYjhW1kVCWXxVEWBQiTTwzAyDCMgLrfGKNpaQs2cD4zjzOw8RZWC3bTFLUL2tVrjF0EU7g937Hc7+u7EV1/9kLdvvwJ8kjgrrKlo2w2v3rzAFpbT6UzXSfG93++xSU1xPl+Yppk5KeEygqGUtFpYFz1AiWoutyeimkGFNbDNJjfh4/GI0Yoy3SOC8ozkXVxZWQItpthzf3cnadjOUdeFGLMtC8enR4ahX5GVPBFnjokiYAuL9xFtDe1mR4gwLwtts+X+7iUgWUKZuzJOgyxSKC7n07p4FUVBu2kJXgi3sw9cLj1g2LQ7IdlPHcfjR8ZhpDRWQiWd8G1sUSCBgJqyrTAJfWzbFm00l/OF8+UsxX8ECLgkrxakxRKjxhQlTkWCh7bdcjjcY61J3kQdU38hLNJWM9aklpKkfRdFQ9201E2NTvwsHzwxQHcZQSmabY1kW/UQAmVRY6sWW7bYsqLvBnwQs82IYprFE+aL731PSOVpDM/LgjKW+4cHirIi+MDT6ZF5mthtt5LVltSV55PY+gcCr16/5u7+gdP5IgKBlKfj55nHpye++N5PUdQb+mGgu5wIy0hdlphqw2a3X/lzmVg/pA2iKcwzvoocKhFFA8ssCry6qlfVTEhRKFlIUNc1x+OR9+/f8+LFi5WvV6Y8pHxPPEMYkPcqz2UmuSznQiCGwDJfkWtgnXMla07QV2k5pUJgJbxGiOH5660IjH62lsE1Gyj/O69Rt7L92+MTcJzb2kdeL67ne31MLnBMKspC2jSLGk9pQVZ+4Rd+jKXLv/Xn/oL0O4eB/X6/DqRbnkqGvJVSa3ZQ09QrKnO5XJjnmTbxN6y14PmWv0S2nZfdoJBq67pOE1K8tlCCqBrOZ5EU57aGTH4l7XaTzqFZM3fOlwun45FxnNjvD6kAatNkviSoHMq6fhb8lXfN3i90XUffDShEql2m9FKXzltrTUJAGYYBCR/LqqeU4sw1oyhf98r4hpUgmK8VWNtNReq/L4mAmiHGYRiZ54nSaIqyWPkzEIVbESM+cX0yPyWbXWXeTi4alVKUhUGlls00ijy8TsqDGOMzYzkAaxRKi9OpD57CFthCdjpaayE02pLu3BGixxrF0HV8eP+OEBYKPeO9tJDm2TGMQgAuq4q7+xeMDoa+w+hktuYXYvBs20Zk3k4ci4exYxh76rpivxdSuEZs9kWhpMRBNzm3xrDglxP9IPwTW5T4GMVnZZoBjdFizOadS8RfR3BeZOvaY7TnfHkiImiAUhatRBUVtcDg8j6Y5MAJShlBjqxOi8C0tpByfIILAtcLcbtaofYlEW2NDfRDz+l4omkbvvcT3xPlzMePVGWBmwaGvkdp2ckSIz446ZFHg9EVtpCkb+ekIGu3G4zSzOOZaexXmbVSiv1+B0pxOh1FRWKMSKiLkqKqaTc7Hl68ZL85oCK8f/+er7/+CqVgs21TDEOB0XA8PkkhI2xISZs2mqKsOLz4jMPhBVXZ4JwYIVZ1SQgLKgR0UJK/9PhelFBFQVNvGMeF3s0cHu5pNy0kzk0O/CTCPIxMY0fXH/Fhgiip1TFAQKOKAm0LHu5f8vDwkr7vuFxOLMvE2B2pCo1WkWUZkx26xeoK5yLDIjyHopBi+f7uBXd3L7BGeCjHywceP7zDzZMY+AWFKWruHt6wv7tfWwHnS7eiJ+vnHmDoOvphpGk3GCtS5Gl2UpTZZKKW5orSykYwG+MtbhIkOiUrt1vxgFmWhe58ZhxH7l+8JJoiybNn5qGT+A5VULcb7u/v1/kmH0oppmUWdCK19bMpZIxgTBTFWNr45fk8m6gpbQkxWet7t7aHvA+i+EqoZi5YsoJybZvonDnEjfuukgJZq5VTdauczHwQH1fAH7ha3IsiB2He3BQlt3YP8NxM7raouaUCZBQob9ivPJbnxcvtIajK8yiA/F7LmlFAukaJDnGQUKbz+cwv/uKPcTbQX/5L/wvbzQbvPOMwQEg9M6Mpa0ncBdaCIU9uSl0NgrI6pa6rVX0Sved8klC10+mUBqrI1Oq6xhQWoyXV1BZXeXNGc4SkKPD98UlyfeZl4dXLl7x48Wo9p5z22SVXym2yjJ8Sv0JaAwV1I1kri3OSLlyV1KngisGLimLTUlcNfpHiRGmBFsXzIBmqJR1+tsUP8Uq2kkJjehYmd6sqajctmedxCz0KE1/aOBmlyUd+T86nI/PYoZVa22dVLR4Q0zgyL46QdjFlckaFG4hRXUm/yzytUKTRkrSMuvabJazQrtdI2g1oLS0Mo5D2jF+4nM/ECG3TEmJkSTyiORF4Ywz48YQxUFYynk7nkxRDWuNDZJpFZr3dbjHWMgw98yKtCDeMhEV8P6zVlKVlHAeaRsh5Hz+8YxxEhVLYGjCS+2QVyzygEZfVEKSNszjPdrelbTaEKFC1SwViUQqR8N379yI5VhGtPcQFbUSdEL1inoMw9YkpfyWPj4ixOqEKEZt66t6HpIZKBYOxRGXFdUZrjJIWzuPHD1wuZzRC5LVGlAub7YYqjeVpmhJ6E1deUj/0q0qirEqUsTTNlqZtpdU5O07njmkWw0blBzabWtxdlaZtG6ZBNiXtpiVEx+XS4QNobUUJdffANC28f/uOeZooSzHK22w2VFUhu2zn6MeRaZnxbkmbmYCKgW6Q5PSy2LHZHKiqFu880yTqn8vlyHbTsm22zPOYXJxDmnM8IYAywiNq2jZB/AHSpqMsa3bNRuTy0YGG0/lEiDCME857mu2G/f4BayqUFjNKlKfrTvSXI1N/xvmZurL03YWqrLGmYhwWgjIYW2BSi0lUWy3WSrus3lVUhaHvLvTnM22zoW13bLcHdvsH3n544tJ1iW8lkuxpmjmdT2ybEqJncYKYGlMQgLpuqeparAaMYbPZUpQlWhc4L+/N4h1TmpdJrZmAuFcf9gfEUTniQ0RnBCOIfDh4JwnyXiJOcqtca5MciYUrpvM8ADR1A0h4n1VgEgn26fjE0+OTcKhsIefablHa4G9Uhmu7x3usuSZ353iRjFjECMZatDEQwQcvfi1ONmBVQu4yuivEXvneWlH7ZJlz9grLBa6gFbm4gMxtyc8RkprtUwLstWCRQikmhWwE2cBZswoKiNkjRv6zclGSyinP/Z+2pOSxrOeYrzEiG/xf+qUf42Llv/tv/gfevHqNWxa6S8f5eBIzq7ah2rarHbzWevU0yX067z339/fc3d2tsC8gkuBLxzgOq+297Kpz6jCoG8t3a4Votw5oDYtb1t/BFX2IURYB4tXRtkqw/jSJ66NKC29ue+RCKBvAlWXJ4XBFIB4fHxmGgSZJA00i5VZVxX6/f2a0hlZrPzgPtNveYz5uwwuBVLyIUVK+YbOJ2+poq/QaMyC7VJP674p5mRkvp/U9zddjrX1m2Z+5Rzb5Hlwl0gtzsqTfbKW15RbH49OTgI5BWjEZBs7kyhACm3aDRq+tnXke1pbONA+M47B6suR2YeY5Be85Pz4yjn0qSj27/QalYJrHhCIF6bt7URdEbSTJtm5Y+gsmuDV9dxwHFietpLZtOJ8+0HcDMRqC12zabUIJPEN/JoSF/W4rnI9l4fHxKaloJIPIaM1mu6VuNuK9oDXaWJwPlLYCAsN4ZujPzNOARjMO4qKrgkw8xqjUX4eilMTftt0SneJ0vuB9wIVAUZZierbZYmydAh5FYaaRHdfp6SPH4xPL7NfW5eFwEKv8RSzSQ3QEJ5yRWyg6F8yYgrJp2e33bLd7irLBFpWQN52nO7/n9PRR0LcU6GiNKGyqqqRqCsZJkpYP+3ti1MyzwztpqYp3jNilD8PAMHREZIesyxpTlFJwjB3eTcToUFHCR0vbQLQYXVBVTeLIOSKezbalsOVK9r0d58MwyLyUCkofRAmktEalBU0FMd9r2gZdlJLzlByLrTUsbqFt9jSbA2XRCJkWz9PpI5fTI2PfcT4/AZ5pHLGmIAbFtt3x2fd+is12S2Ut8zjQXXrO/UjT7Ciahsk7nh4f6bsOawz77Q6tDFYbqnrD5GQzYY0gvlobhmFEoRiGE3WdiMDaopTBFJZhmBmnkbDM7Hc7yqpGG4st65QrgyAIKY4gzz7ZyTqGCEZSu1UeI+vCGRL/Y0pRKYqiqNKCLzb7SqXgyygO2s+Rl4iOkSIFGWby/uVyYV5mCltQbfc0m+06r+eWfZ7L87/z5i1zVzISncd2nls/tdS4FV18+phstrYKL265KAqUvqIany7fidL67Pe3rxP8kpSQEg7pvMRJFGUhAaTZVC7mBlRSQEUxeFTp2m6P22sA1nO9vdbz+cQv/dFf+PEtVv7s//Tn2e92Ag9PM33XcXx8YnYLd69erIMjt4jym5zJp5k3csvkjiGgUw89owTe+7UPejqfOV26FXbLSolrayfJP1PFnT1OgHQT3fT4YmRJ3IwYY/JWEdtmuHI3cj8TIvN8dWi9Je7O88zldOZ0PDIMwzMJW7YkV9as3J3c5/2Uc5PbZrmFJs6fDXUtOUr5fToej2uIISByunRdTXKqVCr7qgRwLrUUrr414yhcj7Zt0UpTJr5OPn9jZJK+nE8EL1b4SmmapkZrg7UGrU2Ct0uWZebp6ZhksJq2aURK6QIxGegt80T0C93lxPH0JDv6slzf06waAtYdBBGMAUXALSPT2KOVKEqU1gl9sBRVwzx7+nHG2AI/DYxdh1KRh4d7lmXmcjnjvaMoC4ZR0m21LpknaRPu91umeeR0eiQGR85jEUlzSV0Jya8/PUJIIX7aUjcbXr76jIeHV9TNBmtbylJUKDE6iJ7gPPO0CKHReMBzvhx59/4bzucj8zzS9WeqssLNPpEKawKgjfTTIwptG1CFtPtSLg14ghOnz2GR3ndVVcyJD5XVeoWGAglSzMqDorBJIabRxhDI/AhDiIqqEnLxOI3sNzV1JSo/k0ng/cAyLSLBNpF5kfeqqlrqakPbbilsISZlfk4ZXxCjR2lRX202G4p6S1E3GA3DcOHt11/y9Zc/wmjFy1cvUFGcZxWG168+pywL3r37mh/88PsE7/A+st1ueXx8pCiKtejNstO22YASdYwLHlvaZJ63YFUipJflmp8UlDgrb7c7TucLx+MFsDTtVtRWdQE6ooLwIN6++5rL+QnnFgpbUhiRYu9fvBGU5nKiLMSSv2pbtJHE5ZjagNM0412gH0aGfkRpTVsXBC+quxgi292ew+EOrQ3dpZdNggtstjvu7h7WzDGlLE3bYtOm6sOHD+KObSxlWYjzdVSgLSjFLvH5hHsk9/USIeriW4t2Rse9F+PGrK6R+zVtJG/aGevcfrNwi3bteaFw5V5AuJnPPlXSPD+P57/Lz7fOHTxX6txyEm+v6/q3rHzJ/By3xUaqRq7X8QnpVslk/Ox4ToKNCTV+HjKYPYWGUfLvMjIfQgCVy5+bIMebQuTbr3FNb87H+XzmF/7I3/t/XrHyMz/zM3z/+9//1s//uX/un+Pf/Xf/XcZx5F/+l/9l/tP/9D9lmiZ+5Vd+hX/v3/v3ePPmzfrYH/zgB/zar/0av/Ebv8F2u+VXf/VX+eN//I9/K+/gb3XkYuV/+O9/k7vDQd7IELmczyzTTNO2DG5ei5O6rtdiIkN1wKo+AVbH1rIsKc01MCorWvIRguyec4sjk0Jz/zJ6DzFQplTYKZFEHz9+pOt7tm2dVCBX75e8+yyrGl3KDiQXD3kw5nTijG5kM7lc4YMYn+WbNxcGV/IX9ENPzpZQSkmibjLD01rz+vVrDofD2h7L0mwZmGElZ+XXzPbPl8slFQ9ifpXVQGsoX4xYfd3B5F1nvkmqspRiJRFxM7R6Pp959+6d9OO1GLqBFDld11FWZepzt2u/Obuaei/ZT303EqNYdNe1EGmXaRCeiVXJN0TM8LKzcD4W52i3e0HOdOTt1z9i7M6EZaQ7H1mWiXa/oaoa2nbLdnvH3d1LQPN0PPP2m6+Zx0HQuapgWWZCcElFVuPiQlTCL1jmnPmhuFzOfPz4gfP5iDUi+d3utomkbLhcLnTHb9BOsme0KaiqDWXZYkxJ02yoN3csLjLPgxRqUXbcRDAKtJJziUgb0YeFEDxNU0kRPU/M8yJEQSUkT6USEqAqfLBA4OXDA9PYAZ7SSkqxrlqKomKaZHe9zAs+CG9o7DtYxPI/I4pAKlQ01oiUWmkrBV9Q+AARSUIurXiCWCPjxi0OlxbSoirwyqeMFoNLTq3L4iEq2k2DhBc6sf5HxnPbCoo3O8WlG5J/jUhPSyOp3MZqxDl3YZ4dRhdrarL3YjVvjWwKPnz4ALD6/kj7QuG9eFf4KMnc2lrQIuXftlvmaZYQubSFVVpRlAWbzQ6iGG0ZWyZVmEDsPgYIgrSFKHwpoxWbdkNdbajKhqI9oK1l11bMU0ffnxnnCVPWjNOcyNkNRdHQbHZEJCNmnCam4cTQvZd5tGppmhaSV0luQcyz8FxevnrDZrunqlqUtozjhE/zr1YQ3EJ3OXM5HSFKC3NysmHc7Xfc3d2tHjRaa6IpMWVzXfjSRk84HFJsrvy2IEnXdd1IrEoqRW55GbfFRj5iatPEJCfP6EnmCV4f95wP8y3Eg28v2J+SUG8FBLd/cyvkyO2gW3TimRCEuJKAb7l86+s8w1Wen/96buk/WmlyFlIIYogpERX5j66EY0GpRKTxXQXLtxCeT96j/9OLlXfv3j2DgP7yX/7L/EP/0D/Eb/zGb/AP/AP/AL/2a7/Gf/Ff/Bf8h//hf8jhcOBf+Bf+BbTW/Jk/82cAQSj+yB/5I3z22Wf8O//Ov8NXX33FP/VP/VP8s//sP8u/+W/+m/9/n/SaDfQb/y82TSP9z3lmHifapmV32FNU5erbAVdC223acYxxbUPkRXJZFpZxpCjsWqzktonwPCI6ZfeoxMHIqI2cx8TleFzN05QS+XQ/DBTWMPYd795+Q13XfO973+Ph4WH1HYlKMwfwQQqBvu9XjkeGy3MyciZI5Uo9o0jZtTZ7xORMotwaAdYbMhduGTXKKMttLzZDoPM80ffdGmaWixiBYGUXmZVOWuvVG8Z7T991GC296K7rxJ0y7TpjFB8MvKRfb7dbCmvxISSybM/QnykKcZDMrrhZcVSWpRidIRELi1tWt1qlRD7qvSIER1UVtE0FwTNPIjOVAuLKOeq6blVTTfMMtmS33zEPPcfH9xQKdHQs88g4dlCAdwGtpD2gVIExBU2zQSvFPI+pNywTS9NUaC2Foq0KiqqUv9c2uf1uWBaXrq+jaaTIvr9/WFtcyzIznt/y9Q//V969e888OWLUDN0EUcig0RhczBMQKSF6ShNZoCyswOkxK2lMkmpKSGMMC7YoKGzJEjzjuEiKdFHQNvfsDi9p24YYHO+++YphuCTiHzfcMEWdzBRfvHwpnKxp5nd+5we8ffcWow0+5PlEiHmF0Rgd2ez2vHnzOcaWGFvStFuc88zjQN91fP3114y9OIeWiVdlrcVUlrqpiRFOx/MqS888LGuTEkxDUZikEBTk9dSNPD4eOR2fmKeJZZlYpomqrGjbmqYtUcoQPLjFJ9doGVvLMhODbHvXfKW06HrvUaQ2QvDEXKgoKKqCsijFETcIcmoLQR+EqhAIAaypIILzAVsU2LKS760Up7aoRRk0DbRNw8PdA/f3L6iKlljUDONAfzni3YBSslueU+L0fD5xOY9EVfATP/kz7PYHxnmk3QrJfxqFZD3PVw6O99l7Cln4dEHT7jC2Zr+7R5tCSOFKcsyqwlAVNoV59gQvKKlObr3ZeiKjvWVZEnWBqdp1Y3brBwIRY0X2K3EiJhXzBd4nD5VPFtNn7Yn0davkuUVWor8mKd/+/NMiZH2+T+bVTxGHWw+U25bU7fOtRcMnbZzbc1BKgf7dWy9aqRvl0HcjHjHxSvL/heCfuDDRrXPCbREUQiCg0ab41nPevs53FS1KKc6XM7/0Sz//f10b6F/6l/4l/vP//D/nr//1v87pdOLVq1f8x//xf8w/8U/8EwD8tb/21/jDf/gP85u/+Zv8sT/2x/gv/8v/kn/sH/vH+PLLL1e05d//9/99/pV/5V/h3bt31534/8GRi5W/8Bf/iphOTTNunoWEmhasqmlWy/YY41qM3EJseULJqp9lEVUNXibb4/GItZY3b95Iwm8iykZ1lYjlYiGEwDAMPH78SJHQBVHdiEw6oxzLPFLmosAYLufzWkRoY7BVu/Zo1+dMvBS5Bs9+v2e/36990kyCLauKMikbYoiycAfJwZkm6THfIhcZzcpFz62KKtvmxxiTuiay223Xz+C2f2utxWiBt5u6ISLvbfCSCpxZ8GLzfUWxQFASfGAaxhXhMUbIy1VVMU0jj08fmFPwXVmWqPRZlWUBRMZBEJ5bc76MSoWgMLaSnV3Kohm6M/M0YgtNXVdrsbrb7W4mDFl0j/2FeRZZd3QeTcBN4rXStjXlpmSZZ6IHosI7T9+Nq3Sv6zq0VolcO7LZigpMK8U4S0q0LRLXyHmKoqYqG3G31CIZX9JEro1ht91KK2F8oj+/YxpnpnHE+0hhRBpaFhVOTYzLyDwtKBRtXRGDYxp7nF8wSmIcMkReliXb7ZYQPJfuRHAzMYrzri2qpLCx7O/u+N73/gBlvQMC3eXMD3/wN3n79ms0XlAPJT5Gq2wTJDgyRmKUHXO7ES6Gc35tO8rE53HLnJBsIy2udkvTbFLRLgGZ0ziK02ZK/M1xDFWT2ihRAkrbtuXt23e8f/9unZBD8ITo08R6Y5GuhYh6f/+AT2qUy/mCW0ThNi09xhQYLSiTeBdtKEpZfMpCeHLzPK+WBfkerRvJAnrz5jOO5wsfHx9lEdbyvlpTCAGVQPAOrUGpQGmtqG6SzXk3dEzzjNYWU1Q0zZaHl29oNztO5ycu56MUfVbOs7I1qqzYbBvaqqDvjjw9PTLOMyGZpJkQMaqgKDZs95Jw3Y9i/DfNgc8+/7t5eHhBCKLuO58ustmoa8b5xFff/JAYDChLVW158+Z7KFOwOE9VF6ktLoF8pU3O3ml8oTR9L9lcx+ORORlpNk2DKUrQ0p5oN3JvxxAT+dSjdXZaDcyzx2jDdruTn0UwN3P+LZqR2xxKXYmsK+rCtUX/6SKfv//0/7ftjk/Rm98Ngbl9zPPvvwsXub6O1pqovr1kr+cQr1KiXDxk3k0IEiIaQxRlpzZAvLZ8vCRk55Tqa3EjUsGYfFS+qwX1aRnxaUFzuVz4xV/6/UmXf8+mcPM88x/9R/8Rv/7rv45Sij//5/88y7LwD/6D/+D6mD/0h/4QP/VTP7UWK7/5m7/Jz/3czz1rC/3Kr/wKv/Zrv8Zf+St/hV/8xV/8zteapmlldIMUKwDTMlG6ckUEJE05rAzuvOvPcFluO+R/5wGcB0GW/Ea3rK8ZY+Tt27cURcF+vydNb6vkLfut5MHStOLcWTc12MQ5KS2WkmkU6HW7F2+AxTnqrXxw4iuyMKR2RD7Ptm1XaZ5LWTgZEQkJfcg7Du895/N504wr/wAAet9JREFU3dleU2FnypSg651fW1a3N1yGKDNClBewOWfOpDTgvLvPHIRsruQX+XzGZLRUliVlXSXlw9VV9Hqt8rzb7RYdIG62aKXEfC9dl1Yi7W2aLbas17/3wWOKkiicYbTusUbjlonT8SkRd1tcCBgj5zInAm5RFJSHA5czshtO71ve2Q3DQObdKAXKKrwbZeG1lsIY6nrL/d09dVvxeH5iGS/EZO/dnc9456jKkqoqePHigLFJmVVA35/p+khT14yjwxpDf1nWyaN9UfPZq1cSI+EmpmlOMsCrXNw5hwotvn3AlgtlLRLp4B2PH9/TDRfGpSOqSGEq4aAsjuBnIjLJ591TVbbsdwd2u91aHFtTgjZpIQDQFIUQIx8/Hun7/41ms6csCy6nI8enR4rC0tYb5nnCBclO6hIJMxLXgrUoa4zWTMvClDxcvPf4NFkqBSgDWiDnZQmMpwv95NIGY2ZZJmKQ8ND9dpeUbDPdMMAppAKBVW5qjGFZRFUj0nkS8VxaYM4toJJ8/u5AVSqULZhHzXZT431JjB4fa/p+Yp69XF93oe/PXI1QC9qmXYv4vMvWWuHDgtGKt++/QpuSFy9fUFZbjJU2j1JaNkrRQ/AEL8nbKioxVYwKpQNVqSiKkhg1VbJOeP/uG/of/gAJvBMU7bIErClp65aqqVnmJ47BMXQXkdSjqTd7Xn/2BZu712hdoYJlHCY+fHzL+XxCmQVrSnHnvVzI2Tq73UE8eo5H+vlIXdcc7l7iFvBBM0wT292GTWk5Ht+yzBPaaOpGDAa7aV7nmLatuXuQ1tLh4eVKlC8KCUfMKc0hBJE7p/dV5qvA4XBPWZVUpbQSiaISLEzBnEjct0KHPN+pIJytbL4GUvCapIrJLrKftlo+LTzykX92S7z9rqIEbmXC353D86mz7u3r5lbQ7XHLj1EgBd0nr71KlSO4KK1SoiiNgpPiRZukYOK5/HkNNkz38HehOrfFyaeozHe9B7+X4/dcrPzJP/kneXp64p/+p/9pAL7++mvKsuTu7u7Z4968ecPXX3+9Pua2UMm/z7/73Y4//sf/OP/6v/6vf+vnZVFhtFlNzbTWuBuztFvipFJqXYyy6yrE1INlJcM2TUOVcnjaZNOdF+Wu66ibZm0xbbfbmyoTQAnMi/Rj2+1GbpTUF1WASVr0EALyHRKt7j2N94kcqtYW1q30zDlZaAQdIZmIsSpnSD3uXJhlRnom1MbEsnfOMU3T6vibWzKZm6O1XpU46y4n9frzezVN00pG3e12RL8wTQNDP+C0wlrNNEpxNQ7CtL/d5dR1hfeOy2XBTTPnpxNVXa2krsU5TscjddNgm4aqqhP6o3h8fEo8Gc2mrdM5Lel65bWHBCmPk6PZ9Lx48YAiEe28oqprTHKrhMRrahqRRUZBRKZxRPkZQ6SyJdPkGJynsBUfjmdsN2Crgv3dS8ahJyLOp5fzkXkaKUqDUhHSlzYyDpwTGW5bbVBEpmFEadC24P3br+hOF0JQdP2FoiwkJDAXk04M3qqmZvYpr8nNWK3wi7QQpkTMjRGMLiltyzJPTKOnbipQkRhkjIzDQN8NvH//XgjdXrxo3CKOnyiRD3f9jC1LtDZENXO6fCOEZwImsfpOpyOLc+iipKprttt77u/vOZ+F+K2UwsdA9H5Nuj0kI8RL1/HxwwfGeWKaJ6KPq+dH3W6o64a23dAPF0LwHI9PXPqBaV5EZaMN3eWCCiEFhCaELUgLToIK3ZpBZIygWUormrYGIoubOX58R3d6oipKGUu6wFqFQlKZNxtD22pU1CxuQRsFuFTYCWndJ96BToWSdw6lPEVVil2/rdnvH3h48Rm73R0hiJrk7t7hl4llyi2bmdLqVX1H8KAD3i0obblcTpSFp95sUdagTaROUuyxnxiHma47M81nabsQwEtWlU++Ph91xe7z/we7w2sKW+MWh93dsenuUYw0ZUFbtpzPF95+8xbnPGWlGUbo+4F+OuOjp6q33N+/AlVibMM0OZqq5rPXr/B+YV6cyOydZ7M90I9igOijwSX5f1GXlI3EOoyTyNrrVLAUtiCnHKOksEUpClsSE5KS5y5pswT5fLkWByYrr5KnlIoQtULbBLFET1iSQ7i2BJ85G4iFgA9p7qrZH/b4ECTFPq3DMfFqssBgWWS8ZYuKfMR4s9BrJecRry2hTxf2PP/nTSn62S8lgDEdt2qg23ZTjOJZhBbVoALhRwExEXa9E0QlIynaiOiBlBIv7eHwrEi5TV7+tPWlk+oLvl28/F6O33Ox8h/8B/8B/8g/8o/wxRdf/L5P4v/o+Ff/1X+VX//1X1//fTqd+Mmf/EmWfiDagjopCYiR0hiaul5dUuGah/Px40fmaaIpLThhPesgabK7+we5KcoSlBEHTK1FTZJIqH3fS2hbdW1J5HZN/nAW71ic+J4YpdHkm0Ms+H0qirRW6wdvjUhrAbRu8V4mP5UGUS62BDbPPhma3XYvKcvkfq5Uy0or/OLwwTMtDpQEj/n0nuRBv91uV8XRbTERY1zToc/nM13XcX9/R11Xz+TU+cYtigKj5DNwThx5z+ee7U6SpcuiolAC3YfomeYJY6DdbMShtSzZHx4kfyYhY/M8czyKyZdJLRGlNdMy0mwamlYi3ktraKoXTEOXFuSeZRoxBoxasNphjWMcn1hm6ZMrDbawaODdh/ccj0c+//xzZi/eI9M841ygrBtMMtpa5gsKxaZpKQrF+fzEog0btcMtig9ff4VLsti2qXjY76nbDadLl1CpmWGaUWaPxhOAaYa+O2OMBCXOLqD1TDd0AnOrSAgl02gxCKG0NJFNU2FsQIeFujZo3QqnyAVKa6jLbcqwEv8K70emZcITGWZxbcWL22+zqVEYZifoBtbgtUZVLW1dU5U1IQaRqmrZNY/LmcV5jC2oq0rUOMuAd5MgA93IcflAVRac3r6VQkyBLQyL87igmPqe4XJmHqRgntJuW6lIlbJQfIgoa2Dp6cYLc3fClMJx2DYV1FYWiODQIbLfNISYZaWeiJBkdYK7iQq/JA6Pd4RE5o1KURSiIkErvHdEn9R7KYYjElEhaUwS+bW04rwcY0XAE7VnCQZHibIVznmMjpjo8L5ndJ5xnihKw+zPnLqZovoG7xzLPKTkbEVpJRIkBodfltUQbLPZiFpqGPFeCqDIwOwMmEa4SkpRViUhDkzuwsP9PREvaHSMECJumonBY63GjWd++Nf+Irv71+zvX1E1W/Hl8YHLpaOPC0cdEw/tRFXWtE1J2274wfcvGGXYbPYi5R4GYKGsIm27paw1xlQoZ3j/+CWX/sTd4cB2syNMC2Bpd6+Fl+PjygGalwkfnWR2ac0SICyBohDn44i4QWslbR5bXJPunZPNQHCBwhQYkOLMeTFB1EZ8yhCibiBgMCtxVT7e9DkbRYhSyMQYVuVbU1kKo/DLAmEhpgiR3HpWVcF+u2dxjuOTUBTENVfm5qhkg/otxD/GJDmPwK2CKcuYUxfgky7QszIgFT7y7bWoCCTJt0p8MiS8QRCmeG0faYVJ6I2KnrAE2WgplcIJRVp9K9yQder5mTxHdnJQ5O/v+D0VK9///vf5r//r/5r/7D/7z9afffbZZ8zzzNPT0zN05ZtvvuGzzz5bH/Nn/+yfffZc33zzzfq73+3IO/9Pj7/+2//r6ieS2z3GGLa7nQTKqWtPvqoqXr16JZVr9ExDz/F45PHpaXWwrSpJ0UQZXr16vVbIGT24VofX4iS3OSDBccmULIYEa3N1DJTqODmHqis5NZPwckjisiwrYfB2VyBhgwKpk4iMPkHnkVzYCAx5S1iz1qK21x6qWN1fyb+3EksQ+Dy3Rl68eMHDw0PaIUjR1jQN+5ScHELgeDxyOZ+Zp4k3b96w3e5Wrss4TjRVRVPVtHVDP/a4IPD85XLCBYfRJYaS4/m0toymcSQC2+1GzP3SazVWSLzH4yNyO0bxYZmXpDrJMmVxGG2aiqqyqa2jVh6B0YamaXn54gWfv3mDLQpO6TpyW9A7xTJ3KBWSh0bB0E8McaYoS0IIPH58T2Ht6ixptWHbbqjKAq3gxcODQNrJe2ZZXJKICtTfdxculyPzIhLvOiUEn8/iX6EwTKNHRyPuyEVB118YxyGhNFKMZhfWYGRhbjYbiJFhHKUtVVfE4FFafEm6yxHvHdO0YExAGUuZ5MnzLEXu3PVMi0jntRHUrtm02KJgmhaqquZ0vKSCVdNdfJrMSkwhqEa/pERaPAzSCiFN6uMw0F26m4kvoDXJvCwjE5aisOzv79ludzgvJlqn40fOlyeRTWtFTCaL2siuOt9zwkWb0zwgiGeX7diNEG4nI+qWqmpSezbiEhogRYwY3xmrUgstEqNj8ZKfJEo4yW5589nn/D1/6BfY7O+ZxpEP777k9PSefuqoapFSh2h5fDxzPJ2wIYob8r4R6Xh3EWL2KJk74sCsWGLk1E9obcQzJoDSWtp/rkPbwHxjC2CMYZknUSZpMSkrrBXkpNKoGFBGY4yithHjB6buiXG8sN3u+d4XX/DhneGrH36fUELTSHTGMi98/fVXVFVF15/ZHx74yZ/+GYZp4nzqKCqJL/DBs7iJuqoIfqYqFcEb3HjhOHRYW2JNwXyRseBDJBQl8+IAxXa3R5dWyvoUWSDIdW7Ee0FJlMaHZW0t58+8LqvVjyT6lLujY/KTF26UIgW9otDWoJPc+drCiatyhijqGWmV9gzjQIipwNGy8Edkrp7mibfv3q5rg3iNS0EjPgDyhCohkpn0nfPbPrx/Styq3IK6Ja7GFeH+rkMhieBKJ9VUbhtFiFHjwjWAVubUm5aOkjJN64zPCDpVFjbN/bMUMeoarnj7fslmV1+5auq59f/v9/g9FSt/4k/8CV6/fs0/+o/+o+vP/ugf/aMURcGf/tN/mn/8H//HAfjt3/5tfvCDH/DLv/zLAPzyL/8y/8a/8W/w9u3bNS35v/qv/iv2+z0/+7M/+7d9Hn/gD/wByrLkdDrx4YNk4zRNw6W7UKUk4Vut+zAMqfequL87UNQtDYYqyCKMi7S7g0hpE4kzIyrH41ESm+/uRN4JiTypV+fVuq4pi1J8IhKKka3fczEwz44Y3SoLzioFIbmW3MKF2XgtE6TEU6Rci6RpmhLRNMPealXJZGLvZrNZ+S9VVeGcW30lciGX+/q5uMuW+M/Nf8JNwXW18M/PWxhpx53PZ25VUmVZMvY9nnlVWG02G6ZFyNCRyHZXExzCbtAKoxW73ZbL5cLjh4802ySFriqKQrxXsvKIGNC2YNNUFNbg3Mw4dHTdGbfMWFsyTgvD8Ci97xhEchoCHz6+x88T27ahrmqaquSwbWWcdBfmYWEYpDjDlLhFJlyFhlig8DjX0dQ1drNBb1qUEtn3MieHYGvQSqOVnH9V1tR1Q7PfpoJUE4Kn6y6czkfO5xND31O9bAhRCLd11RA9uGXGO3FMnZwHF9jsdkzTQDcMxF7s/G1hOZ4HIbV6jzWaTWMo65pNW2OMZrupWBZPiFA3jXBGkuX+09MJ7yWQLywLPn3mo5FJdfGyZ9tsdkQPTVWy2ezZbVtCXJgpmF3gdDozzlIYZjfP2Y2YZUIRn5EeQTxFrNGMY8+8LDRG4iDa7RaTze7KmrouAYfzIwSPWzwBIbuHlEtyazlw5S0ECqvY7u4SEiuS35w4rlTiK2gtEnfvGKYBnwiv2oaEgOp0b8hzWGspiwKrLOenC3/jr/2vhAjLPOCXnuAGKCvqcsc0eTbtlu/9xD2HQ8fj00e67sTlKGnNRXLEzrwFk9CewlTEaNDKUtWiKJuXgWkS8rsK0hJESUvTqEhRV4QYcDGiES5LVVrK1FJRSsIwz92Zfl6oFsfu8AK/LIwo9rt76r+rZLOpEvF+4N37d3z8+htilPv+0p34q3/1L1MUJQHNZrOjbjaU7YamaQiL4+OH94xjTwjCN9o28nmO/RNPb/86wzChdY33GlRFWW14Klsm5VG1Zb9/YLc9rLEgkJDKfl53+XmjlYnaRVFgdUQpiykSMRSpFQB01AntliBXl5DpiNQmxpi0eKdpJl5dW11qFd8qdEK88jJ8CGtrJqtvMgoh6LdkfuXHO+dW0UQIAaUlQgVuM3ikxRRldXm+EK7PL+0waXdfC4Z8/nI+et0A5zl+fQzSGpINMTL/WFEpTuPIPD+mdlskJzCHG0KvSK6vr/W3UlD9Xo6/7WIlhMCf+BN/gl/91V995o1yOBz4Z/6Zf4Zf//Vf5+Hhgf1+z7/4L/6L/PIv/zJ/7I/9MQD+4X/4H+Znf/Zn+Sf/yX+Sf/vf/rf5+uuv+df+tX+Nf/6f/+e/Ezn5PzoOh4PkhiQSKqQ+HWCKK5E2+1PIzTmKFXMqSPLP1zc0irth/iAz4bIsy5UbYoxan+vt27c0SXnknKNqaopk25+LjBzud7lcKIqKly9frp4v6wD32TNgWs8nF1tZtaRWuFoxz56qKmkTZ0NuJMXhcCVLwvOBchs5kCfyTwPhclvnVksvxYcgVbdJnfkGqWtBAzIilCXJ2QNhniZMjPggxGNlNCyg0Sx+pu8uWFtSlSWbjbx/4zAIV6ApKIyQCWOM+GUhOMc0j7hlYXEzygeC9xSFEYg/BsqqpShrqkqM4ZRiRaGmaeTSXeQzCI7oBNEauwm92WBUZNtUuMJSN0V6PwzeeaqqSOoNy5QSqB8fH2nqmtIayrJgWSZxyh0HYpSCT8LUcrEKVVmvvKC8A6uqku1uQ9tsaZqWu5cvsEVJYS3BifT4m2++Zlpm1LCId8ymIWqDA8ax4zwMxD5gTE3TbPns9StKq3j39Zf0l46l70T+rg3OJ8MnZfjsiy9o2w2ny4Xdbo+1lvPpzNdff828CAGVCGVZsK03fP7FT7Db3VHairosCGGm7068//CWx8vA61cv+IWf/zn2uzuWRfJZtDacH9/x8eu/wccP71fSfFagnc/nxAGJFFZ4L0prTqcL8dzJnBh1IkYvWKswKqZiSDK6hNMlsmxrNdvtVtRj8yxcomVY7zejC16+fMVnn32Gc566bbl/eKBpW87nM1999aU48i4Lp9OR2Y2o5IE0OZHzyqIAJiVxH4czb9/3WK2RuIOJqtCoMjC5d2y2dxx2r5gnxzw52QljEtoqqJEgDVKERSWclAULyqBNRVlvAcloMlYTwwRxoS6NvEfByWccZekNSjN5j1GCJ4SioKo3RDTbwz0ueIZxYRp7TucLm3bP3eGBu8Md47hwOl847PeU1Ya2HXn1BgprKEppxYQIShvm2RMVdJdzQmIdRb3l1U/8NGVp8G5m6M48fTxxGR0WA6qgakqsrXh4eI3SJWXVUpYN53lgDIEqkevF7l/GSowktFs/M9/M68CyiIdR1Kk8SaRZn9ophVYYbYn6SgF5hgLk/kYm1sYoLvSpnRGVLNSR1C4kFysJxUkBtCJ4urZ0gvdJaXO16y9LwzBMeH/k9etXbLctT09P8hkni/7cBtIK4cGRL0vdvK6sd9fHCDH2euRS7NvkX1knVKbRPlPMPj4+rpSFW3JvtgHJhZzwMa9Hfg6AeZn5/R5/29LlP/Wn/hS/8iu/wm//9m/zB//gH3z2u2wK95/8J//JM1O42xbP97//fX7t136N//a//W/ZbDb86q/+Kv/Wv/Vv/Z5M4f7y//evrHJTuKInIQTKpn7GzL71DpGWCWQi6+3C7J3HqmuuTW6jiBon8z38Wmxk5CVzS0xhcenvAB4fH/He8+LFCw6Hg0B0NxV1JgPnRV6swPUKa2a1za3Fc76hcspw07Rr3zMbq90iShkJyTvMXLzdkrbyoM9FS44XyEWTteK14px4oeT3NHvAFOb5dWVINrPJ+1SsWWvQBoL3wh9qhCRtzHUXHGLAJtlpCIHFS9aIXMOSoHohCLtFCLreixtpP3Ti61BVPDzcUxYV1hRXKN9oJHlWjMH8OKCjXO/5fBK+UdumHnJgXC7rdUvPWPrKRVFSlpXYoRsrUuShY5pGNk1DYQ0xOvrhIqjEMIlPyLwQU/ZGYQzhxu8mW4fv93vqdsupH8T0Kzo2jaAwxEhZlFhbpmybEh8cl8uZb95+vfq6zLMgFw93e6wGNw2URtGdj0zjSD8t+Bik8CoKNtsdMcK8SDtSK+T9TXwvKaxEnVU2LT4qQoDS1mgCSgVRG0WXiuhNQgIrHh5e4JbAsngu5yP95ePa7swt1DxuQgzkgMRXb95grOV86VhcWMMLnZ9wi1jhG51yjKLsJtFhnUSFHN7SpGwtVEAh+SnLLLC2QoscOWe5JEJ6WVZrltHnn32O857LMDIMU9ohQ/AxkRQVVbthu39BVVY8vn/Plz/8AV33RIwzEUcMDkVSkt2/5id/+u8WZUx/4ePTB6bxxPl8ZByHpAKKbLctLx9esITAN48nliWgVYlWUjzH6AnRscwd0Q9J3WHQSon0XYkvS9AWpWCZBgqVWmxFjbI1ylYomxK1g0JrS11t2Gz26Z5UyVVbVFPOzSgdKUuLT/e1NZZNu8EFsLbCR+HIPbz6DGe26MJitdy70TtiVEzDzDyM7GtxrZ3mkc2uxfmZaZ7E6LHZoU0pHjMYvA/0/YWnpw/0w5kYNA8PL57FneR5z3uHS14uKC0KM6XWYtNKn4V5mpMFghBQc4vl2oK/NXZLxYHWgnJ8QirNR0ZQPlUE5fonU2CzIVtMY6goS8m6moYUq/E8RVkKEZ30dem1Yyqa8rnkXB/Us3PO54ASY5xbldBa8Ch17VIR1xwma3QiD2viSqq9kn5Xb5ioV4rD7TqltcTd/Pwv/OEfX7v9/+ZP/QbWWIZh4O7u7przk0rQvJjeFjNSBUr9GIIsHlnqGGOk7weqolxbGbdFVE7g7PuO0+m0tngy6bRtW8q6grSQb7fbG/lX6hMmv4Tsz5JdXed5TgsCKz/lirYsq6S4KK4GcZnLIqiKSeqnazZFXgxyyyZ7rOTJPEsAbxcMYI0PyI8VabME/GU56O0NYIzBJsJxvt78nucU5GkeOT494ZZF/EmMhDAuy0xRFmhjmJPpXlEUFNamtk+BNQ3aloLQGJ0IhLKwu2VmOAsPRVvp1Spj0vNYSltikd24sZqqkjDBjx/f8/bdN7hx5P5woN20jIMgLrnn6v2C8yfhk8QsgycVcG1CC8qVm7TM4q9ilLSyilKM1nK/V9QIGmOtEGJPj8zzSNu0RGSnV5WSOFzVG5agOJ0ecW6kri0hOOqykrDLaaKqhDdji4L9/sDDwwP3d3dy7kEUUkPf8/abr/j47hsu5xPLPDNNIxEprsu6ZrPZ4gKApHRP44hbRnzwzz5LKQAfqDd7tK2YZ8cyLVitcW5k6M8SXKciWsl4Ew5SlRR4cp+F6J9NZLfOoTkx2RaWdrPl/uEFu/0dRVninGfoRS7slolp6pinAbd4VJTPxEe3ErPzRK21WdG/GLwUnFYQs7quefPmDd5H3r57K54fPiSuofh2tO1GXGRNQdcNBC/eFEYXvHjxghcPL6EomIPCGkOhYBwvdN2J0+VIP17wU8/U99zdveTNm59gu71jWhzD2AMBY9P1a7icjpyennh8/ChEYAXBaLQqMLpmnsN6Td4vDMOZolC0mxatlIRyLkviW2hcjKgYiG7EIhlEZXvA2wYXDbYsuNvv2dY15+MT7999AGX4/Iuf5OHFa8oqOfzOU8o66olRkrXPx3dUZYGxhSR3Kxl74zRTVC2Hlz/Jdr+nsKCUxF0EH9lvD5RFC7EQab4KLG7kw+NbKd6mgapo+OLz73F/9wqtbeIHyfOEuDAOC+M4rVYKIcnZMx/KOUG+6qaR3KLko+O9CBH84pjmmbYVX6NboUSMQcwlEf+rT/1EtNErT/B3W0Jvi5V8aK3RmWQIN3ySKyclEnHLsrahVFLvyJiT7+NNgXKr+Pn2Och9nUm1MbXDvvO8UwtU68SYjaL+qasSYmRZHM22vbG9EG+t8/ki62cQvkw+bueO0/nEL/0+fVb+ji5W/rs//d9z2B/o+361S6/rGhQMCWLOC2j+O2M0u7aBlIqajcpyeJ4tCkJQzPOS3C0Pz3p7kvgbV3fWtT+aeCGLF4ne+Xymz2nKqWiRybEhhG/nRlylwjcGc4+PaxCatZbD4UDTVKufSt/3q6NrURQURbUWL3kBuO1Z3lbqGeLLhVCWI8cYV9TolmibUYlcMWdZ9PqFFDn5+cdx5PHxEa01+/0eU4pUVBj0HhUifd/Rd2dRV6hr0ZeRKinaGpSqiDEZwin5ufiFyKRCWnwCUeINnAOtKeuaUhnC7LBWC9dIeZybuHRnTqcn3OKoy3rlLeRspGVZ6IcOQk8IHqM11paEIHL37VaSZ6d5Zuh74SxoxX6/FRO+sWeYRwKBpmnZbLYpEiDgfWDoe8L4xDR1eCdGbnXdYm1BXbc0zZZ2e0+Iju//4K/z7u2PmKdBkD9ToDXYUgocpQxtuxOCKIbLpSciUfTzMmMLQ2HsWjj6ZZE21SQk5qbdYssSpY3seseeKUmE83FL8t49vOLFq8/YbndYU9KdTgyDBCYenz4SgsKaAh+W1H5z1zBM71nSOLttv2avJKW1fIaLE+JsSncuyjr5RARCmLEGIo4icRiCE9WPMqxeHRltlMJTHJKjj2lhE5m79w6lkSIx2bEqlPB5grgTL4uoqjA6EaUlCkChcM5zd7hnuz8QombsLkyjODbXm4b9izsmN3N5+oAfZ5QusLZlsxGX7Y8p/BEF2+2WpqmI3jH2PePYr1b/oxsIAaxpiMFgjHDkitISFQRCIkfPq0cRCRUt64pl7PFjh1WRsqox1Y7d65+EomEYFx4/vMUPHdGNoig0lrrZUjYb2s2BGFXakG24O9ytDs3Bn/nw/ks+vH+kbiSHaVocSll2+zuaekOMjmXueXz8SHc+YU0h3j53L4m2wpbSIj13HSHGFLMgoZNlUdI2O+p6g5A3lSCzQSz2YxRE7u3btwDc398ngv7ANHT0XYdSht3hDpSiabepmI9J3v+ce3KdJzXGPEc28n1AatMJXSPLbz5tt7DKZJTKapg0H4erigZIAotrCvNKcFXPM4JUKnKkWPnktRXf8kHJ8/563pB4L99GhGJqaV2RFVApYsVqJWog4MWrl6v4A2RT/NVXXzGOE0aXgFo3e+vzxsjpdOIX/sjP/vgWK3/1L/0vyXVTKtF5mVHIQJpvHFpzq0baKQvLONBU4gmTuTJXGW5JVOI3sRKXQCrxpCBQKjKvrYfEEO/7FJFuqNsGm/xJMtpx6S4JUSnpun4tUPL5yaK+W5EJuJKrMtSWXRsF2ZDBn6V60zQzJ2luVZXsdvvkjRJu2NmsN8JVEpcHM+v31+cVn4B+GERFYM3qPptN8ZQSX4EiSaD7vr8mMafdsvMObbVAmDES3EJ/uTCNI9M4yHVbzel0YhoFrdJKyIXb7Zai3BJJIZBabkhjUqS896gINgXVmbJkdp4xuXwG59BeUoGLQqN0JAThYEzTQN+NVFVDYQtZyL0TaW8Ub4Vl7OguZ+ZFyHyiDo/JuM9w/3CgKCyX85mh61iWSfw+lGJyI1VT8eLFS+pKCmHvkiLlcqE/fsMy94LyRVlsrSkwxvJw/5K6eaAsNOfzB56e3tFfJNCRCD4ueOScjCrxTtxhC1vR1C0xOk7HR6KCsqwlVM5aIkocb90g8kVr5XNXhqqumWbhZ/hFlCn3Dw+0jbgSD/2AUppme+D+xSvu7x9YZkd/OfPx4zv67oz3C8rUtM2WGD0uEQXl/R4JzmGTFDK3IMtC2jBNXfPi1QtevHrJNDsejxeMKdnv7yXtVymsCXz15Q/44Y++zzQNmDRul1m8ZWyZTNi89OvzJB9CYJmSdwys97HSV6Jv8IHgwtouDgEZQz5x4Yw432qd0sKVZV5mYoCmbsW92TvcPOKDYwkBh2KJAULAKIW1FUaXFEVFu92gjOZyEVK1UoqmbYSXlTZFhbVYq/FxhmioypZliVRlLUhyWWIKi61LHp+OfPjwQdqEhU3FlGNZJoKbMMFBlBRtU22pD6+w1Zaq2aGjZxkvVEbur8V7XICq3rBpD6A0xpYURS3tmE7m0+AuuLlDWwlYDAEW5zns72ibhjAOfPzwju78EVtogpdkdYVijjCbkt3ujvuH1zy8eMPh8BJjSpbFs7iRj08fIGj2h3u8D7z/8J5p6nn1+iW77R113ab7JzDPy7p5EtWZbLZ8CEyzoDDb3Y6qqhPPxq73cuZ9XC0juKEQXOfMldNinqc4f1qsyPNcf3adX2Pmyq6oh/x+pfLi/TUEN4/NdZMZ4sqF4RmHJKMwUlg8OxKqggIf3Ce/um5mM79G5WoIYdwbfS3mTAodvbs70PfDWhx77wleffK+XVtokg30Y1ys/H9/6y+x38mF3+7UbndsubLLRYD3Hr8mGRtA3VTNuSf5bRZ1Jt9KIvK3q9cYxMiMGKmKcp3wbk3pfBABW1bZyEBU0qcOQW6iAN6Jz8o8yyCwRSG8CKMwRq0tHCBxSeYU2a5ThLtOyInkPNR1s7ZAViJVen/ytUek3/1s8N6gL8vsiLeOzvFa8YuB3UzXSRp1RnpWe+gYsKl9pUB2Fj7g5pllXkiqRKL3jMOF7vTEcDmxjANVXRJ1SdBmbWNlZEqnCaYpa7TRzC6gtCGgWLwUhtZqvF9Wd1TJA5LcF6UVTbmhqbciYR3HZLLmaOoKRWQaBxY3M00SCFhVpZiHHZ9omoZN3aaxo1YytbT84PHxHX1/RGnN3eGO7XbHmEmlPqCVoqwK8dlwAq+PY4+1hpcvP2PTvhROVlj48off53x6TPk7kXkZGZaeGEmfTaQua3a7HW1dM40jT09PK2qWw9HkC8pKYHXnPUVRUlQ1TbshEjmdOiH8BUdRGJQWfoYEQgpfIg8GKe4MSkfxt0jyahnflrvDHWVZITRGeczp+Mg4Dqi00zRpKxe8p6hAlxptWl6++Sn+rp/5Q5Jvs3iOHz/w+PQVj8lhdXFSQAt/IzIvM4uXMMC2FbfUeV748OE9SimqsiAuglQuycnZaCnWZP6QghglHBy5xaXVZ40lxJnseRGitGK2mw3GGrqzFPRKR6q6pCgLTFHx4uVn7HYHLpcLb9+9Y5pn8VUq7Gp37pYFowJODJRYnMeWJfOyiMrJlBhVUlcN9w8PeOfXRVhMDB3j0K2KphcPL9kfDmhtmKeZYegYhh7nFmxh8THggcUHptnRNgeausUHj7UFUSmmWRyDN3XL/faeom558eYL0IYfffklX3/1JdYoqrrhzWefixy+6ygKS13VFMlWf+l7vF8wVuHDxMent1wuT4niYAk0vPrsJyjrLSEqCmO52x/YNjUxeMYUdNkPEyFEtrsdSmvqpsVSo5RkAHlkjOYU68JaijLJfsmLtXpmNa+UWaNFsr1F3uDmuVFa6eLrknlAt+uK3Px5DYgJJdZr6XLbCsn0gNyv+bQVk+fT23Xndl27FgL5L/JcffVhWdewzEPheaGzyrlvXvd5UfXcjfZ2zsh/Z62oTMdxWN2F8+t/dysqZwP9GLeB/t+/+efQCfrMyEjmcuQCYVmkkr8tHICVPX7rY3ILB95+aBkhmFO+SdNUzx5/3YlJL7RI8OIwDKuaZ5qmtU1UZJlzyi3KLRVR0VwHTh68z2ztE8E0O8vmFpW0pZ4XEPn8Mu+lLOWcq6pa1Upt26YWUrFys1bS1011rOIVcs074lvnYGuv/KDMgcmFUCa7aa2TaqYgOI930hLwSuDHwmpKozk9fuTj+2/QREJwqzlYXcnnOwwDwzhQWAlQVICxBUVZQZqEbFVRFJWomBI/Kftq+OCEN+E9RC0upcZQFMK9gYjVwtMZpxSc981XyTgvrDdvWRaQiML5M87XLe/hjDFhDYcURC2y2bQ8Pj4lTpRZ+77WyhhuN20qoLbsD3usUXz88I4xhQU6NzNNF6ZFCqPSliuLPwZZ9IP3zPOU7gGXJhmzyj+LsmC73bHd7bi7fyDEKCF+lzPLEjC2ZJpGprmjaSrevPmCz978BG27wS2OH/3wh3z99Vf0fSeTpYQjJeTNrARz4VAJcZMoSgUpFmchByaul9iveGxhmJaZcfFoW1NWTfKvieAdOpkhPjw8COqWiuJvvvmGj48fWYKXomVeONwdZGKMcOkujH1H8AtDPzAMcr/My4JOffaykFDMotBI2B+4JeB9BNLP0tyR20vZI8ktTjYZeHxYRCViS+7vX3N3/xLnHY9Pj6tlQdvUSQ46pWJLELds7qi0lnGarM6Nrlbbgnz/hxgwKe3cWItzKUx1cSsJPBP4lRJE9nB3h7EZJfAsbiEGC2h8jNR1iwuRqKQVPpw7rLYoU7K7f8nrz7/HbrvHKAmCxEpRNg4DIXjhfxgreVZDxzJ3srPXOhFhbTpHzzQNNHVJs9nho6EbRj68f8/j+/e4aUTFQFVW8jke7mg2W4pkARGDElJ1IkejtbSqiGiVQyO/rUC5LuJicnY7h92afH66Wc1rxG276JaLeLsW5PHx6d/frie3Rcxte/5WSJF//4wEy6fOtNfnvH2e29fM368b0/g81PH261MezO37dvv9bTDu9Tyev9e37us/9sXKX/it/3klVME1OO1TPsjtApoX73zc/jwjATmPIhcRWUIcQkgLjP7OAWeMQSN9+dsPPSMCMUrln4MK83Hb9lHJWMh5R/CCBOUsorxjz4M3Z/fkaxqnQVjbsKp+IpEiOcASr4Nzmqb1vLTWYtOf0ScnO7zczjHapDTPK6wXiSmqXZ43xKuMbY1tjzk40rM4Oc86GcTp1I6YhhEHLCFI5otzqOjxy4LWEN2CdxPTJJNuUYqU1xZF+owCAVl8jZUWCkq8EIwp2Gy2lKYSCNS7qwlfcJxOR/qux2hhuxujqauSOsm053lk8hPWFjR1sxaTbnGM08w89pyf3vH0+CgQftNwOBzWDKPj8QN9fwQkAG4Yerz3axutSNdQFgXT7IQ/U7eJmBrBiGeK0bBpG4xWuGXifD7SXR6JQcL8VETOXxs2bSNqoGlinqfr53szBrXWTMtM8CRCuLQLlTEJ0BbuinMzKM/hsGe323N//xKjC6ZppmnEq6eu5Vr+9//9f+Pd+3dM00hhoa4rYoRpksC64IVPpOVmu+mhSy/8ulAoIoJC+ugZ5wFroCrS+SxW8oySJ0V2YAZR1JWN8KbO5xPznEPxaqZp5vHpA9lMrLAl9w8PvHr5WjxcnGeZBr788ne4XM6EKG1eomaz2XN/94KqlpiLpq7ZbDcUtqDrOn7nh7/D5XxGPH8USgs8r3SJ0gVNvUVbQ5dQt5z1JIZ3pZBHlZYiQysinmWebiTIAbROxGD7LGnd2gKUoW62FEWZ5jbJD9pu9zw9HlkWJ/c3EbfMtG2N0dBdTiIE2D+w2WzRxkr6MwptLZeu5+nxkXEYiFiqzYFXb36Cw/6O0haSeK+kzSUEZnGfHoeBeVm4XM68//gl1lraZodSlrv9C6ypUMrQNAWFnojKoGxJxECMjH3H1F0kdqC7ECLcPzyw2e2ZkgFkUZbiXG5EXBCUFB9pJl3nyXW+utmA5WJFULWwzqG3fL81+O9GSZk3alVKEr+d/5/zXa5FRX7t/Lq3+US3c/9tMXJ7vvn41GTtdn35tPC4LUqecW24FjpXpeY1J+93E6R8itKAkO2vSqB8K1+v/baYAlmzf6zVQP/zX/yrtG27kkMzoS7DeXn3nzkr2bgt3+i3raFbNCaEwNPT08q/EEmxFBdiOf8cBswfuveemDw/bn8OrKZyL16/Wr1M4FrxrgjLSqCKqxdMPl8fwsp+yjfOs1ZO9EzzIOml87wOipzhUxZVguSvf5+Likyoze9Lfj9WjxqSS+O64CViYwzSf05J1SGElaibF2bZBabog2WRKIDFUViBfIPW9NOE946qKLBG4d2Cd47+ciG6DsKS/C5OdL20SqqqEoZ/WcvuCvGhcT6sqBoIpyOTpcuqXNG4oR8IYSYER3fpIAb2uy0KhKA3DSxhxoXIdrNns9my39/hFo/3EashLMKEz6Z/X3/9NT/60Y9S0dajVEjtvCCLRGoBaK05HY+Jh1GgtaUsa+GctBuqqqFsN5SVMO8jgRgTKXRZOB3f882X3+f09IQxAp8vy0TfdSmzh2eTEZD4D7ILRyFk39xSswVVVdNsWna7A3XZME6dqJG8Y5klpyfLWauq5uXLFxijkoneia6TmABrPHVVrYoaKSzgdD5LcrUSefbh7o5xmvjq6284X7qV8FhVDSYRgru+Y7/bs9m0dF3HPPRJmmufwfFFUdC0LVXTEnzg0l0YElH79evXvHz5UjiyhaHret5+846+H5K0WeIKhsuZaexY3ITzU1qYGsqiIQb1bHK+lf6XZcnLlw8QPU/HjwxTL4pEZTFJdhti8ppIKIO4GpfSgitrAha3LGw3DVZHzqcntPJoJLzR1rKTzc7T+fWNMbTbA/3oadtWxqUtUEozjSmXRuVcG4VVQHBEvxDcgrYWWzZSqEZoN1tevf6Mpm1xzqO0ouvOFM0GrRuMrYlBcfz4xDJNzCoSrRDhN5stthA/qqKsZMPlZtwsqrTgA/eHOzattE6Hsed8euLj8cybL36Sw90LaVUET6FA47EGQoTz+cLxdBKuScplKwotnCFEoq2NeNHEKB5cWl+VnLeL+jAIsiak9vpb6Do8Dxu8LQyyT9UtH+N2Dr8tWG7n/0/FDbngvPUq+fR183E7J38XOnNbtNz++xYd/7TYuH1fbq/x0zbQ7fuWr+32PL/93OrZ3+bHXi4X/t6/7w/++BYrv/Gn/3seHh7WXWNGG27zeuD5BxSCGHDlXmX+O2Nk8cuQ/aeD5vp8wiXpum71csiVpzEGa6xkK9x88LfBf82mpU2usqtpWsrBIUbevHz5LBHZGJsmphTvnZ7zU/dPOYeIkBqv6p5pEsLn6XzGmmI1TcrwMEgx8/LlS3G7TdJRtyxrdLjzXhCP1E4ZR2l3WGvIroUYs2b65LZS9lpZlgWU7L6ssZDIjtM4YrTYqqMVZSGLqTUGa+UzHLoON53pLk9M85SUGgvOiy1/3WzwGOZlSUoZYdNbY6iqEpC8omVx7HZ7SSBOCdfyuc4olZAwJ6F3Okacd4xDz2Xo0rlV7HYH9rsDZVkDivPpiXdf/5AYwxpL0DRNGmMLl/OJp6ePDENPWVrGqed0OgrStExEHzBKvAmsFSZ9Uch7ZGxJ2Wy4f7hnuxcfiXkRXpBzjtIq2rqgKCxPjx/5nR98n2nsIUr7Z54m3JLydlAYe90xlalQJqp18bSlLJqoREeJEKNjcTKGrLXUVct2e6ButqtZVUyI2jj2dL3kIOEnsoKqaTYYYxnHifv7B3abDY+P79P9pFDWcukHnIfFe6xRbNqapmoJHupyw4uXn9M2B5wPnI4/pO+f0oIzYIxhSmm8t3wZ8cURifGarVVYqkbCBMdxYp4XDoc7Ob9ByLplKcTbp6cPOO+oq5YQFN7JPTfPE13fM0/XfDBB3AIuZdpEhGfWtDvazZ6yEGlsVVWYouR4OnM8X4S0WpSEAMYUhBAJfsYvI9PUU1gtoarLBCnPSu7z6+54WWaULomqWFVPZVmJc/M4Cdq23QppnEhTlKKoIdJ3HdM0U20OgoLNC2jNvLjVybRuKjbbhqrZUlYbqnJD3w24acRPM+3dHts0zItjXjzGltRty/5wR1W3lMEQF4dzPeN44XL6SHd5ROGp6g2mviMoi7YVm90dVVFSWoOJgWUSZVWWv1d1Q9U0+BBZ3MI4jQzTQF03a1DidifXEqN4M33aGoG8+BtxPk5o+u2Ce7t+fIpefLoQP59/nyMjt6jG7eM/5ZR813N+yje5RVXyNdw+xy2PMa9f33V+f6vz+vRcnvFtPnmu/P/nr/Uc+bk9x8vlws/9/B/6fRUrv+cgw/87HE8p12ez2awLfF7Is9HWrbw4Q3xZJnl7fAq7ZYj5thDJH8RmY5/9/S2kN4/TGlaY+5a55WKtxce4LjjzPK9FT13XeOf5m3/zb1AnmDHGmDJ4tFiIh4gqCrbb7TOvkytcl5mqQAwUtkKrgv3unlevllVCGkLg5cuXz2C/oihQqbDqu269tpz+3F3OGKWp64qykJt5HMTWv26aRDyG/X6/ntvVxlzkhgBDPzBOE0sy+FLGorUjese5mwkhJvKboCR13VC3LWVdcjydxC+lrKUoKgsCGm1r2qKlbWqKwnA5HZmmMSEnwgXQWuOWgmUZGYYRoy1N3VAUZpXnKVtACAS3iNFVc8cD9+t7HELAzwNPZwlFDMFTlqIi+vjxI33f07btuoBOYwpUNEK8XdzE/f09dVPSdxf6U3fjDCnchGE4ymfPwvjYczl+wBQl2oikNyrY7fYUZcWXX79nHHqWRcZc8AprLNWmpm49292Wtmnpe5GNns5nQXxCpEqJfLNbBImaZowZqRoploIL2EJRlSWKgLXCr4nBM44DZXJpziQ7H2THucwzKgRplWy2At+3UrA09UaQmmmSeACliWiitvhIQsSSHD3JkoMb6E4fWeYZdImxYgWQoxwyogokZ1uX7gvhfrjZ4xaVPpPIpSO1d8S4bBpE2loUJdu2xblAXQs6cL6cAdhsNrx+9Qal47qxyJ+3UjFJtFX63Dw+ymIvJmqOtkmtm0TmL6uKgy5otnv2h3tm5zg9vufpwwd0DJiyTK810c+LSKVVxHvhSUnhWDEvCzEExmlhmiXJe7fZUNXNSmye54XL5cjj0zvc4jinNG4Qjs5ud0ezu2O3P7DbSfim84EPHz/ydDwxDBc+fHzkcGc53L1ms9lSGM3pcUSYLo67bUNR1Swu4FFSgMXAMo8oSrxbJKhzmXFJoTMNFz4+PrG5D7z+/HtUzYYQPOfLCUPEEol+wOAhej5+POJ8EBPGsoQofkptK+24ut1ibZls859Hhdwi57d8j++iDWR+IFw9gKy1V04i1zb8pwjEd60H+Xm+C9H4FHm5Xdy/qyV0+3X7mrdox+1z/W7HdyEwt+edv//UqPXTa/iU2/NdhU9em5b/Kxxs/+9wZGTlz/3Z32K3P6DVVdmS2xuZqASs6EbuTeYixlqb3CrLb1WJuY10mzIsf+dWlU5GVjInwFq7cghuob3bgscF/8xkyHvP5XLheDwydD24mbuDGNxllCifM8agCzFQW9tON8iQUtLXvu2vKiUx37LLFM5Lvq6YCqfTSXrXZXq/ylIQJufcel1uniCIu2yWdGdItKxrdFHiU9HzaXtN2PLpXFHoGPHOC+QbIxqP9yOn44lhHNFGAv/kM7SUbSleEyn3I5OCrbVM00I/iMLLWgPOE4OTDJTC4vzEOPd0KerAGJGSZ4nwMg9MU/ZSUcmfYSDGQFkUBB/WcaJTbs00DXjv2e/vsEW97uyHYWC727LdbCUn6XRiHAZpK7iZxQ2E4KjqQuzKVcGY5NJNUxOCx4fkANtsqYodd3f3aGsY54VL3zNOE3XdUDY7MJKVRAxcLk88fnzLMk94vzCOfYosMIkMl6SwRYGbJtQyiOdI8ouo25a7+wequqbvenCiMpFrXQhBIgOsLVkc2NRStVYg3q6/4L2TFg1AzAo6gYbLshbZuV9YppOAOrZkd7jni+/9NHcPL4jK0Pcdj+/fcn58i18Ggp9RxrCEyLQErK4kCZbnglFtNME7YuKayIIj6MBt7pUxacECYoiiCgo5gM0SohCxfRD0cLc7ELxiGiex6k9qC33DDyirCqMlJNC5aVUkaVNii5qqbKXdU5SgDdqW1O2OerMDLVyW/vEb5qkXpNJoMXbz4pRdFQXKuxtfqMhms0VkrlJchaTK2h/uUGgKW7A4aRs53+HdTEARgiYERVFlJU1B2d7RNC1lWTMtC4e7O/aHO+qmpTuf+OarH/H23Xv69NqFVpQq0FQGHxQ+Ktrtjv3dA7ZuKKqWoqrRxjJ6SY4ujMVqjZ8WVIg0VU3fn/FxodrsMEVNiJplXnDTSH8+cn56y9AdBR3UBpQmonjx8iX39y9Al0yLYxxnyrrizWdfUFYVbvHMyyL8qdTGv21hZDdqo+06j9zOWZ+iA7dk2ds5/W+FPsDvXqzcohy3c3h+3C1C8ikPJZ/H7e9u20K3f/dd5N98nZ9yWT49freffxcQcH3s87+5fd0fe+ny//hn/ixN06wBdTYt2GUhPVui7ISmWULz5sTJqKqSwhrKoqSsSrzzHE9HTqeTkCmbOrULFsQyPMkb14JF5Gy5OALxGhEhLfTDQN931LVEyNui4LDfc3d3LxPRTQWfB4/WmqHvUyoonE9n+qGnSoUDSooInwzpQAqq3W53QzIWt04hxV6huRAzdyGsRVge3OM40nWdEMoSMTNyrc5zYdOUhYTGeZdUSVKo9V3P4heqtqWqm0QYFF7AsixstzvKskAbkdgV1mK1xaficZpGopsp7dXCGkS+6RbpmSutMYmjAolvk0K2FFocRSPYQtN1He/efcPQ99RNRdvW2EKyjYjw7pu3+MVz2B/Y7/YoBfM8cbmcU6bPnPxkpHVxPp5k8lBQVlVCkTxVVa/J3tYKyTNzUKoyEZfJckgpck6nJ0IQF+IQPMvkJN9omSVgsSqpann/Sluhvdj4l6V4uqAVyhpsWVNULWW9paprkcoHl0zjRvr+TDdIyOZmI8WTLUqMNozTyNc/+pK3P/obnJ6eVpdaYw3tZkNRFmLa5pz442iFtsl2XFnxs7Gy8ArfidTKTJlWKKKyxGjY7w9ENE/HJ+a0s45+RvsZWXsiJiFzTbsVl1ivOB+PRD8SwoAPE7Y0LD4yO08Mleyq80QYovgbaY01GoUUTFVViY9SXXM5nzmnNG1rC9pW7pdhGFOB4dI4t8ld14OSIEetZYzFGEFJwSeW/z55xQRpfSW1HESUSfC6Et+b3f7Aw/1L7u5esNnt2G73FFWNC4HHpxNffvklx6f3wkfy4oPivITSVXXFptkQlpF5mvDBM80TOqEK0npWuHmk77tkzXAlTVqt0cZRltKWskVNWbbYosGYSuznI/TDyMenI+dLx0/99E/zkz/9M9R1S1VYglsIITLO89oePT5+oLtciLqgLGtiDJwvR2nVKmibmv3dgWa7w4dIU7VCrlUlQz+jlEEXmqglK8d7z9iPKKWpyko2Xm5Aq3BNhTca58Qw04dApERbQZpZbfUhBxSezydijLRNm/yZ4oqqSSyBXhf0W6QaWLl3wzCshNrb39vCpseJX1VMdg5wlS5/F9qQF3dxsY1r0Xz1S2FFqT9tBeXvb5EduA07TK/xLHFZxuR6nuGK6Ny2t+S51yVD1IVKrfENKo3x/AD52/RgMkLz3cUKwPn8Y06w/Ut/6X9hs2kF2p6XVe1hlFkd97JhjfiUJNtiFcUZ8kZJEKOk5WbzuEycyzC9D4Hj0xMfPnygKSuatpFU43bDbr8XrofW2LoCc3V2zShHbr/EmGzOk5X/Lfs6q4FQStxeU3EEudVzVQNl5CQ76VZV9ezGyzuCrDoQfolai5X8nPmcbq34c2WeuT3eOawGnxCEnPaciceLWwg6Jmi+FqlnJEmHhVQ6TldH3ywZd85xOV/wbqata6qqTJDhkhKyOxSwbTcYpZnmicUtqVgQZESpyDJ2WGPZ7XeUTZ3ScifxoQheIOhxxADROQqtiV4WuLrdYEyBy5JXv6zI1bk7Y7WiKiVQT9sSW1TUTYspSrH674744AUhWWaCXyjLgrqUIniapBAK3tO29arIWJaZxYmldtO2SYmF2F0bKwZloyMsEyosqChOq0uMRC2EzbKo0bqAoJK0fCH4EWuh3rQYY+m6gWlaEpF0w6bdsswT33z5NzifT6KwIYjjbQoRVFHMyJTS6bpFYbV4hw8h8bJy0Usq3CNN3fD5Fz/Bm5/6e6iaPcZWGFviCfRDx/FylNybyXE6fuB4fEt/OaKi3J9uiUQnKrOqMhSVZnaztIyiWHlra4gqGWOlVouECWqGfsCntpBS0r6JMUnnF/FAMqs7aFpUUiyHNRaln2dkrbvGmO6VeCXC5x155rkBDOMohmfqStLM94hzYHRuRRdrYR6jeIe8/Px7bHd7QNxzl8WxLCkOZJ4Yp4Gnpw8sbsaWlmbTYkxJ140s04iKLs1/lrIo0UolI0OV4hpqYtSMw4xzoJU4XhdlhSks7XYDaBYXBEEraoZxEqM/N2EKUdvVzQZTFuJ6bEuqzYGqqlHR0Z0/cHp6R3f6wPHpHdE7tKopioYXL99wuHuJ0gWLi9iyZnO4I5gCHT1WRQgOrQy23qBsyTyPeDfT9R0qxTcsy5wWZ0VVbyiT87fKhUdSJ8YYMXw3L+RTXsYt+pCPjMZlH5FrrEmycjDPWy23KAmpiL793aetFhWfq5NycYAS7lZ+7C3i8hwZis/XjXydz8qe7+DWyIO+hcas7wHP1UrP3zsln88N0vT8cc+Jurd8ofP5zM//wu+Ps/J3dLHyP/2P/x/2u528eWlSMeLDneyQr+6EeVGe5xm/zJxPJ87n8zrhZAXRyi25gbhuXQSD91SpPTPP8wp3W2ul2tYaF59Xxp/eKJ+Sq7JMOkboe3Fw3e/3z1AX+RJ3yXzz3B63MF8uNG4Tlm9/96l87lNi1i0Umosi/CKtoHiVv03zJO0cIlHLwiVyzKTGCtdAtaK82k3P87y25JRKeSpWFAs5BXqchABdFqXIhkuZvMahx7kZnRZAjfjetO0GtKEoK6Z5IUSwZYH3TiSaRKL3DN0FHSJ1VWK1ZvKRiKFpG6q6YlpmzpcLIUYZQ8FRlQWH3R6TiMNG2yQPXiiKRtxMQRaVlJuy3bTUTUHE8fXXX/Hlj36IW2a0UoImeSeJr1pTp5iH7fbAi9ev2O/vUMDl6cj56QNGRawODOPIueuZvBdr/MDqWFtow+Vy5OnxHcEnyD+CDxFrLDHKZyEkTs8yngle/GbK0orxWxCeR4iREGVXJXJ3TVVXGK3TWHWyuCtNTONhWTzBB4qy4vDqDfcvXtM0QjDdHvYUZZnM+SI2FCgV8K7n8eNbHj9+IPpA22wE0Rp6Pj69Z1oGUU9VFfv9g6iErPCmQgicTifevn27hpdezmfKwlDYIiVyu3Wj4H0KEwyCFt1O/jGyFi/fWlQ++X9VVex2O7z3azbYqj6EdaOT77Xs/TSME85d7+VbrkRRlKiiwnvZATdNy+vXb7i/f0FhJUF6HHt+8Dt/k49P79E2Bcbp6z1mUrbNOmel+3iZZ/r+xLIs7LZSDEG2TlDEqLm7/4KXr96wLB7nI027papaXAgs88AwPnG5dHRdjwuRsq5589nnvHr1mrrd4X3ALzNdfyL4mbJQmGQSuDgrzqZKo7W4g/fDhDaWu3shpE9Dx/HxbTJdrFGmQBfZy0pa1Tk7zafWmBBuJXDytti4nVtzOPHtpuzT+fjTefr2Z3me+5RHEmMkqm9zQ54hFvE56faWsMt3FC/PluFPfnY7Bm+Lqk/PVYqd3HZ/XpTlL6O+zYt5JkFW4dnf3L6uWFd828tlXYOIkr0VVxu+9b66XM78wh/5+358CbZhXpiHiViI86IxBh1EvRKCE0h1nhOioljcwjzNxCB+Ka9fv15zDjKJKsZIIGJ1kciZjj6ZkHknk3ldSjWfORTb7VakdMnKOnDt7cF1MEG2u78eKkHWUlBF6rpZJzt5/HUCzVCecxLWdotu5OfKk+V2u11JuvlGycZ4t3yaXMzlnUQeuBn1EXVFhOCJvllVTdbIbmwYBpGsRk/bbtO1CGoyLXM6X4+exAwtc1puyW3WWoJ3zOOMCyK1LKuaupFARW0KnF+wuqI1QLDMU8c0zhJ+iGNyM+O4YExJ07aApkHyiFSIRCQSYZpmVAxoBcWmpa4qgjKYomRcHM5HDvcvMEVB3/f4RXa2527E6pEYHJpIXZZU1QajC+ZxgRA4HR85Ho8E79BA2RjKWjPP0raLsrKz2WwgBnzwUlwDy7zw9PTE8XRC24KmrvDTyOPjewoVsDohMCkdV2rzgLEQWXB+IUaHtYo5RKZxWgmrXktx4VWkKiuqbcM5DvJ+pclHK0VUAWMKlC1xylIUVrgPSVnknV/D3TLsHTPq4FMhukwM3SPeTwQviEJZSexEVZe0zQar66SoO3E+PxH8gtEKa8+pIJc2jI+KaXIYB5f+HcsSKK2mbSVDaBxHQoBNu5dWggMfFtCWsikxIbBpW9589hnLsvD0/mvcfJFibVm4XC7rPSwLN2vxdXvf3RLRM2/kNgg0y/9v02/z/bssS8ocs6CzmzDiErtIwb74mdCdUcoIYfZy5On9N1RViv+IYVUpWRaJDdAGY8XHRZE9OCSIsq4bseEPgYChVRprVMoSKiTqob8khHJDiIG+H0GJydo0Lyi9gDK02x1vPn+JVophHDmdz0L0Bt5+8w1N3VGVFWVVCtJXt6DETbhoDpQYYlA4L222aXHc15vVadsQ0WXB0mwESfGOZZ7ZFpbdbk+MiBGhc3Rdh9aeZRGOW1k2CcGISdgmWU0xFW3G2pVbdFtY3C7Etz4jtzy7W07KpzLmEMSJfG0r8Vzhk1stt3P8bfGTUX9uCqHI8yLqtrj5lHPy6TXcHr9b62mVZT8rJL7tnxJRqS0llhQrUhViSoAP67XcFtwQpYVKRIXbgkeWD+d/zAm2f/L/+SepSlmsu06SdzebjfQY20qImKkVknvMWmtJdU2tmDwQMtvbOce4SN5B7lPeogxija0pEppjtLRtmrqmrCRx2cdrcRK8QJPXajSsLZnbyl2+l8ny9nhe3QJcgwRvq/5PWzu58MjZDZkfk4/sgJsNzPq+X1tiSl1JrPnajQarWTODVOpXZg8Ra4tnxOFMwpL2lxR5twVRdhz2XlolRMkjGscRW1g2bbv6zARkEXTzyDJ2DJdHPr7/mugXdtsN9V6UDwrLpt0KmVPJLiAGz9CfuHRngnMMQ8c0DmuWkUfR7u548eIlIBHpShtcEDt1MU4VVGnoTmgVOD194OnpIyjD/u4N1hjqqmK32642/afjkePpiagSWuE9IHLeTdvSVCUoQQC7rufSdfTDJOPUyG56nnoIC4VRmLRDcRHQUpjPyTF320o2U99fWFIytdaiQOr7AZNymfLYl4EmbYMYoUgJxFVViuy82TBhMKn3Hb0jLAvLPLLMYjYnZN0cK+HRyjDPCy44ooo07Yam3qFVwTy7ZNInxnlVJfEIWmuikhxYrRVt23B3uEcrLZuEsRen4qKmqjagDG6SZOu+75nneVVfLcvC+XxinAeWZebu7o4vvviCaZp49+6dFBXjGeV6JJTTrPYBmSDtXO7tXxeh28I634+fTtQ53TxbBuTHPCPeW0NZyb3mktU/CuqqEsQrtYmNsez3Yn9/uVwYB+GpFIWEd45ThwsLRVVjdIkyBcbUmKLh/u5B2jnKoG3BMIx0XQ/BU1pD01acz0fO52Pi5IA2Jdvd52xa4dEUZc2yBKSLIUTkpq6x1qT3rMBo+XxcUqD13cDxfOLj0xFTFjy8fEW73dI2DbWKEMQt93Q6rWi3c9JS3O0ObHdbIToju3NljDjy+phUaFebhzy3gHj4yH3+vCVxu5hmVD1v1D5FSLLr9C0fLhef56Se2263q9hhnUO1evY55/ESo3AbtXreIrrO4d9GUj4tGvI5XzPhvm0cd1tI5/+vRQ9/C5VS5Ft8mlWxmdpp+Wfynso8/ylCdPuc16IqEPHPfp+/v1wu/P1//9//49sG+q3/6bfY7fd458QpE9k5hiAqhjyJaK1pmoamqUUNYARQyoPidhFflgWPkPbGcVzls7f29fam1QK3WRHgfWCalnXwZLfc/PeX7kzXnbm/v18X9LIs080g0PqtUugW2pUBdX0fPq2iP4Uz89etAVw+l8xdyc+fb9B8TbeEMyGYlZCSjpfFpd48gMI7j4pyI4cYkh38mNQ84kWxmsvdPCewKn4iXInQaWKqknne4hamaWDshd+iwkJwI3VpxXhKSV7NOIyMwwhREmwPuz1VWSZ+i6hF+r7j7dtv1gweW1ZoYzkc7kEZun4UozQjJO2m3rDfSihf3x+ZhouMrXmUa40550f8+upk/z/0YpAmi1egtJa6qXDzzPH4SEzF8zRPRCJl1bDbH0TN4CL9cGEYO2Jw0oJbFhSasm7RRZ1M7CTYsrSawirmacZqQ9sIgfTp6RGtJX9ISHBhbQ8S87mBNWIIlxejZrun2T1grabrLnTnE5fLkbEX47yytBhjGYaRGMV9trAlSmkWt+CQVkFTbrjb3/Pi4QUxet6+/Zph7DhfTsnEq8A5jy0txiqM1fg54GaHUpGqqTkc7inrDW2zlyKUIOfUXYTTpODFi5fc3d3h/cLp9FGuo5BizSajubdv3zJcnvCzmObdOsHKYubJ0v/cfs1jNRc2uVefeSrTNImKb5C5xqbCJLeWb5PMY+KgAd+6F4zWWB1RkAi7kvDtfcAtjsV52QuriPMzSksAZVnWbHcHdjsxKjydLsLVigpTVoKSTDPBe6w2lJVNyi55f9FCitSmoWl3bLZbtrsD+/0DyliKsiI4OJ0HpmEUUvg8Mw4DWsH9/R2bzYG6bdlsdyhj8FEze48PEe8c/vyey+nI2F+kOF9mqjQefYhoW1NvdmwPDzSbvUjYnVtbW9LuDGgjKGkuDEIUbhPxqti8bXXnRT7nnX0nP+MTZOTT43buVOrq4eW9x6yeN9f2U/6bEMLagrqdn9dzy/O2dGzWgkk8tSRE9facbguCfM553bodR7kNlLlVt6+b35/gQ+Jtqme/uyJBNxErn1zfd53P80MoAcS4Fkw5vLHrOn7pl37xx7hY+XN/gcPhjnlKaopM3Jwnpu7C09OjLKpa2kDeS2uorBuB1JXs9Lbb7dVTRWsEzLp6rWT4dy0cIqtHSgjCVs8fcFU2ibORF4g8MBI642eRsS7L6mSbBx0Iiel28MvPrlWyUt/+uPLAWQfkDdP7Fv7MRVW+AfOAz5N2fq6u6/j48eOaHyRoVYGxz50b89+6xTFcMsJiVk8V7wVVkcdeW21aa9rkYhmjRNb7EFfPjLyryK+hhIHBOIjE2PtZ0BFrsIUhBJ0iASR4UNxvZ6zWovZYPGUlWTcheC7dBRDyrTGSV7TdHWiaLYuPLLPHlhV13VJWG4w2hOiZlzE58UbmZSKmCHWiEDdVIqe6ZDU+DR1D3+HdIryXww4VEXRnGHDzDEqUCSHCy1evefnqNZvtTjgzcUHFwHC58PbrbxjGibLeUDU7gvcM3Znz+ZGhPxG8mL8VpkRFjXMT09ShjXCCyqpIYZJpJ0VuK8pnU1cNL1++JoTIMEwoXXI5n+mGM9PY46MDPHVT0dQNCs2ckmyVEtO8tt2gtGaOiojhs9efsd/uGIeed2+/pLs8Yazi7v4FL16+QeuSYZyY3MK0DPgwE+cFP03My0Q39Hgf8cFgTMX93UvuHg4odSWCQyQbJy7LRH955HI+i1okIaq5YAhuRpOCG28UflPitMhCGL7VFl45adau92y+J7uu43K5rAvo7UbgGTfAFpDk0UUpwaQoaf9prTDKoYigNDHInFEUMq+Iusfi/MI0j1R1SVmL9Fhpg44e7R3jvDAvHkyBMgXtbk/TblnmjCxL605ahqJsioCPBucTZK8MKIPSBZvtDmtrCluz2bRUZQne03cXuu5MXVVsD3eYqhLzv5sCY5xmxqFHuYFlGlAxEMIizrl+pruc04bSUTYHXn3+07z87Keo6h0g6ezGGiF1f4JyxVSg5BZP5g/mjLhblOF2/sxzzy3x87bIuW27fMrLuJ1TlZIMotv2yq0vV+rpEEJOpn+uNgppMf8upESQmbgWFL/bBvR22X62odWamBRr+X26JcRmF/Jb9P32kDDPXIxcaQtSyMuGZ+0afFL8GaOzOOh6PciPzuczP/fzP8ZqoL/4F/8Kh8Pd+uGJS6NCE9BOgsV8uDL4ieJoGVCM08T79++5XC5sNpt18QxRQvOaVoiTipSbkCreECQt93aQ3/Ynxe0ySM6HNakYydVvBCQn59ZxUMLfylSw6G/dJNdBG8nyOPg20zv/7FPo8PbmvL0BIE9XKYmZb7eTrkGQMxFpZ/ggC0Wx3oAKq2sUiqoq8MHR9xfGcWAcJQ/lcLjncDg8I6GtkQEmTbo3hVN+f4L3Mhmr5A2jksxxGtb3buhHuvOJwio2TY3CscwD/eXM0E8QRbFkC1FFjdOIsWK0Z5QXR90U3mdsxXZ34OWrNzTNhkiF1kbIsEYTtfRuh3FAK02R5PJumVFEIa26hb7rmfojwQ0MQ8/x6VEQp2HA+YUyScXze62NQRmb2mmS5WQKibpXyY/m4eEV9y/fYIoaowzBTXTdkb4/skwD0zjRXwa6y8DQn4gsqSBTCLqbF6ggi3IKuzOmQKG5v3/BNM5M44JRVpChsODjgi01RWXFodVHtJJco3maKYqKw+GOz958RlHWTEGkxkRQMTCPHafjB4b+yMPDA4fDK8pyS1m2QpJsKpSFrj/y4evf4endV3S98EmKqqFu9mhVEtEooxIiIN4oeRy5ZWGeRlSYMFqk7kabdbIep5HufMGlduj9/T0vXrwQ+X3fp1bQzOl8SlyYRDRO7dasQBHkMY1FkrpCKSmWjah/2s0GrVQyKBNjx7sXr4hK0J7Hjx8J8epCvSwzihlhDIiTrGSESXFjtEVrKVxsoSnrQgIHfWRZPDpMaNezuEBAUzYbbFnTbHe8ev2Gur1DKbnO9+/f0/Wyyarrmt1uD7qWazAFEU1ZNdhUfILhfOnQKtLWFWFZUCoyjwPd5YyzlqA1hRUn3xCv81lRlIJix8CyDDw9vufp8T395cQ4XFB+ptKRqr1n9/AFzf41dXOgKhusEnWWC4sgEOq62xeDvzFxqQQp3GwkmkLmZlFv2rK4CiHS32aF5KeGZrf/v93k3RY1wzAwjiMxRppNu44Jpb5tz6+/Q4m0rhFEsSFYs3Ru0HEA/9w1N5/TbSsmP++3vm7UQLfXkOdTzXVNyffGLRIFz5Go2wJOiOnP157n16hSZ+BapGeH8+PxyN/3cz/Gdvu/9Wd/i/v7+2fcD+K1Zyf/jrLYyC/E1AxROsR4Dd0LIbkcGouKgb7v1krdWpmYLpcLXddTlKVo+9NAFSJcDsgLKWxrXqv/iEDJRcpludrUy0DNH3Aaqs+gPsitEpMkyUIEzIhFfnw2i8ro0jzPz3YLWsmitywLaEVRllKFK1EYWSs2+DrBIrJzvTo+hpjIZ0DwC8EvECLznPwfMDifpMJOUKjNVtQd3jvmyTEvM2VRrpPZmjTrhD+RzdestSyz2P2XRYEKjnkcWeaFcZpwbqGoyqQ4Cvipwy0Tw3BhHLokS45oFVmWQFXvhE9kTWoVlqgYuZzPLEuP0smpNwTm2QnSUIviYLt5SJO2pm03NJsNPmUPRWBxM84tKC1kbPG9KNGqQEeYxo7L5ZF3737Ex6d3eDez2Yjh3vl8prIF1kqLap4X/OLxLjJNM+iFuqooi1J24mhsQu1kgVvEFyc4ihSiYhJj/zJ1TMuMW0Sx5r2HELGFkA4dwi2JUQLdFFoIm+i0OCaZcAzMzhGVhB4aq5n6E1VpqcoKgqKutrhJiI8RWMLMlJQ/2hQ0zYbPP/+cl69eQoz05wvv33/k+HTGu4XAQggjMU5iAhclyt6agrbdpXEo7duIwSajL60NxhqRiM8T09jTnd8l52D5m2VZiCGu8n2tSEq09e5Z20CSCD3j/IwPHh9Bm4rN5o6iqHBOkLJ5WYgJzl+8l80IgU1VYm2BLQohuNpiXToKW1AVYk52Pp9Tq8Ned+PZf8m7dR7I7Q1rC5x3KGUxukhzhxFZuVbUlaUoVDImHJPHxtUjyVZbfNAobVK78R5TlHgfGccZY4XMCgrnBSGom5ZNuyEoSVw2KuKnnrE78fTxPcs80W42XOaRp/MZYyoe7l+x3ewQRZkTo7nqIPOddng/Y4xkVJ3PF37nb/zvuPHCZnfgzeffo2r3RFWgdUHwkWUWl+u6keBItbY4AtM8MfRSrMzztG4413nYe6Y0B9/yQNb2UCpoY5SAzZB4JiEG3LII+mZvY0+uCIqQSTPxlbTGaLTJEnjJ8pJ2CKjkdZX/nedTUM9Qj5jXppsVOa9jt7wSyee6/v7694nkGm+UR+lYi4f47YDC22uL8fm1XjeX19fP4zN3DjJ+clucPK8q4v+vvW+Nsasq33/WWvt6rjOdoR2KFKoSUEH/XAQLqB9oRCTeYyKppF6iQUsENQhK0A8GIZqYqPGeiB9EiCSASlBDCoIkpVykQEEBA1p+/GhL25lz37e13v+Hd+999pmiP9DS6cB6kklmzllzztprr73Wu973eZ8X3W73vxaFW9bZQH/761/RbDTQaDYxNzeXl5oXiNMMg9GYsV8UMPQ8DyjLh+cFyXIDogg9RKMR4uGAVTxBCGs1pEmab/gKMytm+ORUIVUVWTNCCKhioXUqBhQKF2ZByMvKSQIAxmQTHo3C41CdiNXsGeaNpGUsFeC4ZzeXmZ+ZmSmznArDwHEcKM5py1NT2bonMMmTN9+xrkCWaSily1orTu4hStMEcZRAkIYSgO86qPkeyPGgaVyjoqgPBKBMH42iqCQiUl5cUeUCWgLEon58wQgCzpBKkgQmSaBTFqNLkgTNZrNUpYxGQ5gsBcigFgQIPQUQZ4MlSYxazYfns0S38BwIKZGlKaL+ADAE1/EBJUpXvlIKhlJEEade6iTDsM+ZDsPeQh4yCUAEkMwXT2gMhh0MBv3c3e6i2ZxGLagjGg3R6+9DkiZMKDYSfhBAZQaZAVeojSIIcJXt4WgERzrwPcnqvP0EieNwqqYXMBE5ieD7Dod1hEKvM0A85Aq9Kp8PKQAjBOqNOmSeZZLGHNbzPA9+w4Xru9AZscS8H6LRaEIILhVg8gXZcT00Wy3U600YAPPz8/jf/30ao0EXcZxCZwaku1DCQ+jXeCNJO9AmQ1CrQyqCq7huzvO7drGLWwJTK9psvBiN0bCDTmc3FhYGMJTmqq88xlw5muv4cLkBBddz0W5PQQhREuu5YrYPSAE/DGCMQJZqQPLznhkgjRMowQssgUoJfOTZWyBi4T/l5x4lQMkQzdYUPNdHmrLBEycxICWa7TaCMESSZSy+ZjSSOGHpe4AzA2lcAd333JxHIyGFUxojmdZIEn4Wi+y+8gBmDBrNJpqtBjqdPpJYo91egWazxaTV3gJq9Tr8Wh0wGvN796LXWWBeClgpOs36mJqexYrZlfC8EK5fRxA2QJDIMo0kjctir0IIaCmhhEEiDdygASI2prudPdi76zk8v2c3DBGOOvq18HwXQeJhqj2LMKihUW/ADzxE0QDGZPCUgRQGRCkMxegs9GA00Kg3ceSa18IRAo7rAoIz0jRlfOghQAiDWuCx/pLJgJwAymPqod3mBIjiMJdlGVf2zsNF1ZpxBedESlmGSIqisL4YcxdLDzRx/aDxwZE997wkC7jO2GCo8l8G0ZC1cFxV1pkzhTdcCFDFqyMES2FIKUs9LyGK8Ow46aPQzSr2B8r5ZkV4suqBUUpBibF2VzWEVewti702izOXiqrgVQ9O1RNV/dzid5HzF8eBn0lv1YHwiSxrz8r9W+4riaqLY5WaxmzvxXG+KrO/mGSFQVB6V5C79qREnIs9pWmaF8hz92eH52CuwaQ64ORNVuX3vVCOfjWOWDVOisldpPCNRiMQ0cTpTAgWwErTFJ1OBwsLC3AcrmPkum4eBmFyZJEZ5TouHKVK9+Fi8tY4Zl+UpRcgo5ElMXSWApRv8JJDOcW4FpO5IHQWD1c1C6lYTNIkxqDfK6tFG8MF6VqtFmq1GkRO7Cu4SUWsOsqLNBrNBRFdR4JIQxB7yqQAlFQASRghkJgMSZrCZBpKCASOCwIg80wNKTj7YTgYlDWbBHHIhDMCAgyHI7h50cHMGDiBj3otxGjU5aJrSQwBidAP4boBE06zGIPRAvr9LgCg0Wii0WgjTbkWSppyAcI4HsLJ+SMmy5gjY5hkaTSgHJfDRPm9FtIgjSOkaQxHSbiOyoX1DFJNCGohDpuZhaMUet0e5vdyPRsignCLEx5BCR4H12VSZ5okIEHwfA+1egOeHyIMaxB5yGsw6KLf73FIRXCmUbs5han2FEaDAfbM7+I6PbkwltGGqwxLhTjLAMdHrd5ELaiBtEaSjJDEA+gszrOn+BDByrEAGbCBawgaGZ9gHQWlOKMPxKmySZJCQJWhEw6jKBgDDv9q1swBUHo+tdZ5SnvMac+C4DgCyGs1+W4N9VoDWaZZIM3kJEIhoFwXMg/1Nut1SCFZFVcbQACZYWPP931QxvOu0EkiAFnKm2yj2UBzig9BhZe03+9jNBrlBUcF/KCGdmsKtTzbLU1jRNEAqU6QpIRMKxamFIDJEijFhUE9z829pRK+HyKsN+B5IQDFyrXDiNc1xykF8ooSJGy0AsoL4EgBVwKj0ZCzdIKAw5GOgCME0sRACg+u5yEajdDr96AUQTkCxiSYX9iDTqcDAtBuroDv11ALGwj8OhzXYwKt48L1fEjH5USF0YAPIwTUG034Qa30KBUciiq3pLqVFWtQNaOmWOO5WvvYiKh6oMvwh66GzJmPURXwHLcdpxSXpVFMBsI4zFdNcS8OXJ7nlevyYr6MyjmW1X5X94piLa5mhRaZPFKJ0vFRXH9x3QDyOTipoVIdt2rqdtWjszjsU/y++P/HlIfJENGrXm7//q1/QavdgpvfvCo/Q+cWaVUBtmpBVmNyhRBZkU4rlTuRxlV8bpZxwT0JXd786o2RMq85Iva/qWOjaBwPLCZClehadQsWKPq9OMe++BkTx0RumbORMD8/j6IGkh+wPLzjOHw6jxP0uz2kSYpaGMAPAsBRpf6EEFxCwA88OIrj/2RYGyRJ4jw7BXAku+WF4+Zxb1X+FP2ojvti/RkhmJhKeeZMoWFReIS01kjyej2Ft6i45sKAHI0GGAx6rJ3i8qbkeVwzJxnlRf4AZDCcsaEUklEEBZHH510UT3jh8ZKSOQfxqMdhHiE43TavvySlQmtqCkG9AZ0lmN+zG935vdBphNB34SqFJDNItQHzlDg91vMCeG4Ax/EB4cBxJZQyGI36mF/YgygaIfQ5BIXcNc0E0vGcFEKwFL7gzWPf/B5kaQylBILAg+95yLQZe9/AIlUmJ5FKkXM68s3aGCZ2MjEuP4VRloceeBNRngsiAc/zQULBcT1OxyaNJIqA/P8ynYGkC5N/n5MTj2E0ey+ERKTZMPJcHzpNuX8534egoVwFIuTePYfnHnGmDAldmZtBqZkSxzGSNIMfNjA9PYMV0zOlsVvIFgz6fQx7XSRpyq77/DQ+Go2QZSkSk2A4HCAa9ZEmMc9vIeF7Hkw2ziwxZKBJ57IEyKUJ2CDwvICLNzaa6I9GGA5H5boRlLw0fkZHUQQyPCfDeou9vPl3dDqdksBfrzcxd/hrUAsb0Jrgug6UAvbNPw+IDGEwDddr5zIOAkLyWEYR18QaDpjMmqWGQ0fKYS6e62JubjXqjSYGgwGimEUeozyTjwzriUAQdMbPe1BroN6chhM0EGugHnjwFFelNpogpOKMTDKIoxHSrA9tOBTXaLTgewFcNwCg0On0AMmHJ60Jyh0XmXVcl70pMDCGIJQHx/EhpAORZ/9pzQq9xdpcZDtWvdlVYyWO4zJhQDkKqKzR1fVISgkJObFHVJMtqoZJda0u2gEAiTFp94VIslWOy2LeYyEoV/28MY9mLAZaNWbG/4yc4zNpSBTPehonE9dQvF58x2K+Y9WIqv69GPz6JE+mes29fg8nnXTCqzcMJBXHcHWuysmbCYFjiuO6PYWFWSUoFTeqIF25rlu2KdQdC9eWMQZJzBoYIANhOCV6OByi0+kAAJrNJlqtFrzAL8vSV113Rc6+n4cQqizsxROiIIQlScKS/vX6RKipaggVadk8kTi2WBg+tVpt/BAIASNYEEgAgB9AQqDf7bJ8ulQQeSw0DENQXuo+jocY6gykNRyZC8V5Luq1FhzFsuFaa3Yp62oBxcnJWpwCqimjZdqcZpd1NQQGoFTgFVKi0W6VBpDOMkhHoebVYQzBC334tRA6jUGkAaMxGPZBOkMY+JAIQBKAkhCKja7A9wFtkKQGnV4faZahVquhVqshNQY60exu1RokmCMkpADlVXb7gy7mu/sgPQ+uVDB5unY86qPfieE5EsL14Hh+fioDPC9AENThOgGkcOH5IaQkxOkAWmelUGGj3uIFPOWCeHm4GwDQ7w85lVU4kEohqCmsCkIY4v6mWZLzLjJkZJBpHi8BziSr+2xUjqIERsfFDIQQYpzKSybXihDQJgO0gEkIzVYbc3OHY2rFKjiuDyEIaRJh757n0e3MYzgcAKlALOqQrodmGMB3FNI4J2OmzO9BnpmXkIESYEKrNhAkkOgYEFyHyXW9kuQKkgjDOg5beRgc5ZRehyhKEScaZCSMVuh0+hgOE+zZs6/UEOLrY+NJGJbhbzQacGTh8ubCd9KTOPyIw5l02x9g2OWMrixhcnkUF8rOGgQuaurkXh5tCJAetAFUt8fGnxyvN8YY9IaD0isIIeC5Lmr1OgCuzZP00vIk7ObV1XnhV+gsdDC/rwutDZSSSJIRoniARtOHmA7gB21A5DWFUoNur4u9+/YiiWIQpVCi2CAJShqEYYgoivDMjicRhA3OfPM81EMPjjRIUg6bCBgMR13EpKG1QTTsYhgnaK4AGu1Z6CxBfxiXQmyu60FL9gJmaQZtJFy3iXq9iVZrCkpyOjV7K114oQ8CK/8WkvNxFKGzsA+j0QB+4KHRbMOBzHVX/Ly2FiaInIsPeosPSFLKiYKGUsoJY6W6CWdZBlHZeAvPCrdDflhKy/bFOl96kBXrGS0uUlhdC6sk16Jd8Xk6T1WvetSLw+7YsODDqVKLBOsKBeqJxAxMSFIUKENei/YTYH/ZjGqIbDGqBtMLeYLKgfsvsaw9K3+5/2E0Go1J9nPuHiyIc4u9KYtdXNVY3PimsXZIFI0wGAzR7XYhBFCvN+C5LsJg7LorvrecVAJIc09HlmXodrtl3JGNAFEKzhUp04V3p6hjpLVmD0/OTSmMl2aziTAMJ7wTVatcqcmJPTFhhOCidfmJO8szOdJcFGs4HMIPa2i3W3A9B4DOuTR8kjVJit5CFyQFy8srB0mmc5XIosQB96MaIisMkMXM+urfZDRMnqI4JhKP6yoZkyHMBeSqD0MR33elhDZcht5RAkIQRqMBugvziIZDfk6UgHQdhLUanxrTDAoSSaahzbhkQjFmacocGylYtbHwrhRFBQn54gHDdXxSDo2l8Qhax/x/UJDKhSEeR9/30Wi0kcQZup0hsmQEx+W+d3oLUEqh1Z6G43h8SsU4a6rwqCjp5BWpTRlO83wPKtejieII/cEAWZIyEdlx8ppEGv1+FzqNSzc3E85501V5eFPnhGeTpbnejGJZd4CLISoHUvnw/DAnUycgoyFgEPoepmdm0F55FKZWHJaXUMiwML8Xz/7PDgyHAygh4QiBKBqCTArfU/A9F81GC4500On3sG9+H4QQ8P2AORUx85wCP8TU1Aq+75LDAUEQIAh8UE4610ajn39Gv99DmiTQJstTQQHSHN4ZP0PFJmCghUaqMzbCScKRKi80GQMwIBJslJCBlAQpDBzF2kdaEzLDnpWwVmNvlMthDZbmH45DQPk6VOWUAapMAy8kA1gbKkSaZoiTDGnCnllOvVZQDpCmEdJUQzkBlOMCQnGmlmYl6DCooV534bsKxmSseRL12VMiCEIoJBnKjCYIDtslSQLXc+E6EkLkdbSCOqZWHIZGeyUGCTBMDEIlkSUsKeC4zP0L/DqajTaCoI5IG3Q6Pfh+DfVac8x7SyIoJVBvclkR5qxJwHBpgWg4gjYZ4iwFQaBWbyAIuR5RQTDlTXTsqapumlVDoLrZll4DKctEjKrhUOwTRLo41qHgkhRcjGKu8XcVm/m4sKZ0ilIUk1WZq+tx9YC6OAxVTS9efD2ciMGlQIrXq0ZJnvS8nxFSfA8InNKfe7yre191nBZ7Wap7ZxWTXv7JiEC1bb/fw0knv/nVGwa680/3MKeh8uDzhgMOAVTUKasxviAISm/EJEmIB9cpQhOEiZteTmaMreAila3M3hGijGMXG03hyeENmEqxunq9XhJRq8JGxecVrs1qleXFExBA7sbO0G43y1AJMJl+nGUZfI95AyIPV4mcYJvpDFmS5WqREnE8wmDYRRQNOY4PDYcEPOVCKgfScaAN84KCGseRTabLIm7F2FZPDAWxs+hTYaQJwRwYMmPjqlr0keXQCfV6vTTAHMXXoJTiMMBogDhhoao0iQDSObmQTz/aEDIyGCUxRM7YVxBo1up5ITcXruNCOeO0veKUZFIuhDgY9PPKwpRru2ikaQQSGo504TgBu/aHQ/T6C4jjAXy/hkatiSQZYhQNiruGMKhDSQc6HoBMilSnGA5H8IIQK2ZWQqoa0iSB0UOEQYjp6em8jEGu2EksoEdEHOf3QwRhncOXjgtjgDRJkGYxQBpEGeJoiE5nH4aDHkAE32GDezQaoNOZx2DQh+s58H0PaZwAhnkjjUYLQkr0ByNWv4WAVA5c10MSs2iiAJClKeKI08kL41UoCa2ZD9FsNbD6iCNw2OwqKOlhobMHzz77NDoLzyOJRyysRxIZCWREuTYSc1bSVOcuZgXPYQMDQE4eZTl75htx7ROe8zG0ZqVlbTLeXIyBoLHcQDV0CyLAkWg02zhqzVpMr5iDEAqjKMLze3ZxJe/8OdRpgixlQ9bkukmkCanmOj213JhPDWfXFRmFtRqX0igqQBeeXa55w8RKoBBhDACMvYsEgpQufK+GRqONdrsFrVPs3PkchqMu6+AIB44TQigfvt/E7OwcavUGev196HXnYXQKKQgmi6A1k+QhBFy/DYA9KTMzM/A8D91uF3v27EEUD9gDR1w5OQgb8IIWjAwQZ0AWR1DQkIoQxSNkmUbo17FixUrMzM5Buy4MKUjhAGA5fEMJRqMutCEEfhOBH8BzJLiOJqfXCoCLi+YhYUhZGs8sGOdACRfAOFRfPXwu5llU1/fCs1Ws41WDpVxbMQ7/vBD+leeB9wmH+1rZ+AtUjZAXkuoX2C+VZr9wE8SkAB5QMYRyYcOqcVH01XXd/Bw/STEYUxTGtfSq+2UV+3lMKn3kvowzjKrGzmAwwP878U2vPmOl0+lgamoKmzf/Ge1WG6z8Wp0wfBrmDT8tSVAAb4RpluYlxPvodDpQSqHZbDKpLIqQxaMyjdfzHPh+AN/3OVMGHEtPsywne8oy68V1XfhBAOmo0mjQRpflujmlbDwpuRT9uJx3MUGqD1XV+gYmJ27RvjAGirThKIoQJzFXms5DQr7nwZNMrC0yPbQANJmcjKogIKEzPknG8RBxMkSaxUiSCOlwBM/x0Gy20GxNwQ1CQEhkJhcgMnxSLzwUKk8rTtO0JPA5DqeSRnEEnenSk6KUhMnSCaZ6yR/KXdfFQ1ekOpdx6CgGpSwQJwRhNOzD6BRZGoMl/l1Il3+g2PM1GgyRRjFU7rb2ayE8x4UmTr9l8bm8sJ7joxbU4AUuPNdBZlLMz+9FEkdQjgCQQAoXQriIY+4XCYMkGUGSgud4GEY9RNEAymE1X88NkCYp4u4+RFEfjuvAD2uYnl6JWr0NCA/aGKTRAobDQZmtI6WC0cwLybSG63lMSFQ+wloTYb0Nx2WVZsdTgOBKygImz5BKMRh0Mez1IA1hNGQ12eFoAKMz9Ic9DId9rloOB0Iw+bNebyCKU05lNuyNEUJgNBwiTVIoqdCo1SClQr+7AJUO4UqBjDSSTEMoyZuL68DzanC8NggZkrQL0iMQZbnipwCkB+X6mJk9DNPtaU4PTlOkKcsJxKMuBsNeWZxQa5OHlgCjAVf4oOIAogCpACU5nEVGszdJa+Y6qLEqdeFCF46PwG/AD1pQboiwXkOtUYPvu8xribmmUTwawnNlqefD89ZhgnKWQUgJCAlNXJagXg/hKOZ3Fcq2xhj4HpNL01TDdX00GpxpWBj+RXhXuQIg5mu4TpiToTP0egvo9vfAUIx6vY3pmTkEQRvKqaPRXAEBB53eHswvPA9Aw/ccACmUMIiHAxitYUTImYD5MygrXglIApkUJsngOS5qYRON9iyCsA03rCNwXaTZCNGoj/6wl6eAu2jU2iwsyG5LOMpDq91m9VqRItMxRoMIJgvgeS48paCzGNGgj87CAtIkxtT0LGbnDofne6xinURI0qzUqXIUe2uCkA07gUkvxb8zFoRkg4U36XzzL6T7kdfvoUnuRvE7r7t64rWx14W9VRCT373YgGJDYFw9udpOClmGxKr9LsL8hkwZAitQKJ8bM65RVRo/YMkOYwyv0ULkisCLNFXyQ4BaRJJlY2cyVPWvUNpT+XeY3NgZjkZ4xzvWYWFhAe12+99+xr/CsuSs7N27FwBw1llvX+KeWFhYWFhYWLwY9Hq9V5exsmLFCgDAjh07/uMLt/jv0O12ceSRR+KZZ575j916Fv857PgvLez4Ly3s+C89Xso9IOL05dWrV//H37csjZUiJNJut+1EXWK0Wi17D5YQdvyXFnb8lxZ2/JceL/Ye/LeOhRdmy1hYWFhYWFhYHCKwxoqFhYWFhYXFIY1laaz4vo+vf/3r8H1/qbvyqoW9B0sLO/5LCzv+Sws7/kuPg30PlmXqsoWFhYWFhcWrB8vSs2JhYWFhYWHx6oE1ViwsLCwsLCwOaVhjxcLCwsLCwuKQhjVWLCwsLCwsLA5pWGPFwsLCwsLC4pDGsjRWfvCDH+Doo49GEAQ47bTTcO+99y51l5Y9rrrqKrz1rW9Fs9nEypUr8YEPfACPP/74RJsoirBp0ybMzMyg0Wjgwx/+MHbt2jXRZseOHTj33HNRq9WwcuVKXHLJJciy7GBeyisCV199NYQQuPjii8vX7Pi//Hj22WfxsY99DDMzMwjDECeccALuv//+8n0iwte+9jUcfvjhCMMQ69evx5NPPjnxGfv27cOGDRvQarUwNTWFT33qU+j3+wf7UpYdtNa44oorsHbtWoRhiNe97nX4xje+MVE8z47/gcVdd92F9773vVi9ejWEELj55psn3j9Q4/3www/j7W9/O4IgwJFHHolvfetbL72ztMxw/fXXk+d59POf/5weffRR+vSnP01TU1O0a9eupe7assbZZ59N11xzDW3fvp22bdtG73nPe2jNmjXU7/fLNhdccAEdeeSRtHnzZrr//vvpbW97G51++unl+1mW0fHHH0/r16+nBx98kG699VaanZ2lr3zlK0txScsW9957Lx199NH05je/mS666KLydTv+Ly/27dtHRx11FH384x+nrVu30lNPPUV//OMf6e9//3vZ5uqrr6Z2u00333wzPfTQQ/S+972P1q5dS6PRqGzz7ne/m97ylrfQPffcQ3/+85/p9a9/PZ133nlLcUnLCldeeSXNzMzQLbfcQk8//TTdcMMN1Gg06Lvf/W7Zxo7/gcWtt95Kl19+Od14440EgG666aaJ9w/EeHc6HVq1ahVt2LCBtm/fTtdddx2FYUg/+clPXlJfl52xcuqpp9KmTZvKv7XWtHr1arrqqquWsFevPOzevZsA0J133klERAsLC+S6Lt1www1lm7/+9a8EgLZs2UJEPPGllLRz586yzY9+9CNqtVoUx/HBvYBlil6vR8cccwzddttt9M53vrM0Vuz4v/y49NJL6cwzz/yX7xtjaG5ujr797W+Xry0sLJDv+3TdddcREdFjjz1GAOi+++4r2/z+978nIQQ9++yzL1/nXwE499xz6ZOf/OTEax/60Idow4YNRGTH/+XGYmPlQI33D3/4Q5qenp5Ygy699FI69thjX1L/llUYKEkSPPDAA1i/fn35mpQS69evx5YtW5awZ688dDodAOMK1w888ADSNJ0Y++OOOw5r1qwpx37Lli044YQTsGrVqrLN2WefjW63i0cfffQg9n75YtOmTTj33HMnxhmw438w8Nvf/hannHIKPvKRj2DlypU48cQT8bOf/ax8/+mnn8bOnTsn7kG73cZpp502cQ+mpqZwyimnlG3Wr18PKSW2bt168C5mGeL000/H5s2b8cQTTwAAHnroIdx9990455xzANjxP9g4UOO9ZcsWvOMd74DneWWbs88+G48//jjm5+dfdH+WVdXlPXv2QGs9sRgDwKpVq/C3v/1tiXr1yoMxBhdffDHOOOMMHH/88QCAnTt3wvM8TE1NTbRdtWoVdu7cWbZ5oXtTvGfx73H99dfjL3/5C+6777793rPj//Ljqaeewo9+9CN88YtfxFe/+lXcd999+PznPw/P87Bx48ZyDF9ojKv3YOXKlRPvO46DFStW2Hvwf+Cyyy5Dt9vFcccdB6UUtNa48sorsWHDBgCw43+QcaDGe+fOnVi7du1+n1G8Nz09/aL6s6yMFYuDg02bNmH79u24++67l7orrxo888wzuOiii3DbbbchCIKl7s6rEsYYnHLKKfjmN78JADjxxBOxfft2/PjHP8bGjRuXuHevfPz617/Gtddei1/96ld405vehG3btuHiiy/G6tWr7fhbLK9soNnZWSil9suA2LVrF+bm5paoV68sXHjhhbjllltwxx134DWveU35+tzcHJIkwcLCwkT76tjPzc294L0p3rP413jggQewe/dunHTSSXAcB47j4M4778T3vvc9OI6DVatW2fF/mXH44YfjjW9848Rrb3jDG7Bjxw4A4zH8d+vP3Nwcdu/ePfF+lmXYt2+fvQf/By655BJcdtll+OhHP4oTTjgB559/Pr7whS/gqquuAmDH/2DjQI33gVqXlpWx4nkeTj75ZGzevLl8zRiDzZs3Y926dUvYs+UPIsKFF16Im266Cbfffvt+bruTTz4ZrutOjP3jjz+OHTt2lGO/bt06PPLIIxOT97bbbkOr1dpvE7CYxFlnnYVHHnkE27ZtK39OOeUUbNiwofzdjv/LizPOOGO/dP0nnngCRx11FABg7dq1mJubm7gH3W4XW7dunbgHCwsLeOCBB8o2t99+O4wxOO200w7CVSxfDIdDSDm5JSmlYIwBYMf/YONAjfe6detw1113IU3Tss1tt92GY4899kWHgAAsz9Rl3/fpF7/4BT322GP0mc98hqampiYyICxeOj772c9Su92mP/3pT/Tcc8+VP8PhsGxzwQUX0Jo1a+j222+n+++/n9atW0fr1q0r3y9SZ9/1rnfRtm3b6A9/+AMddthhNnX2P0Q1G4jIjv/LjXvvvZccx6Err7ySnnzySbr22mupVqvRL3/5y7LN1VdfTVNTU/Sb3/yGHn74YXr/+9//gqmcJ554Im3dupXuvvtuOuaYY2zq7IvAxo0b6YgjjihTl2+88UaanZ2lL3/5y2UbO/4HFr1ejx588EF68MEHCQB95zvfoQcffJD++c9/EtGBGe+FhQVatWoVnX/++bR9+3a6/vrrqVarvfJTl4mIvv/979OaNWvI8zw69dRT6Z577lnqLi17AHjBn2uuuaZsMxqN6HOf+xxNT09TrVajD37wg/Tcc89NfM4//vEPOueccygMQ5qdnaUvfelLlKbpQb6aVwYWGyt2/F9+/O53v6Pjjz+efN+n4447jn76059OvG+MoSuuuIJWrVpFvu/TWWedRY8//vhEm71799J5551HjUaDWq0WfeITn6Ber3cwL2NZotvt0kUXXURr1qyhIAjota99LV1++eUTKa92/A8s7rjjjhdc9zdu3EhEB268H3roITrzzDPJ93064ogj6Oqrr37JfRVEFXlACwsLCwsLC4tDDMuKs2JhYWFhYWHx6oM1ViwsLCwsLCwOaVhjxcLCwsLCwuKQhjVWLCwsLCwsLA5pWGPFwsLCwsLC4pCGNVYsLCwsLCwsDmlYY8XCwsLCwsLikIY1ViwsLCwsLCwOaVhjxcLCwsLCwuKQhjVWLCwsLCwsLA5pWGPFwsLCwsLC4pDG/wcYUGnyH7dsjAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image_index = 42\n", "image_fixations = fixations[fixations.n == image_index]\n", "\n", "plt.imshow(stimuli.stimuli[image_index])\n", "plt.scatter(image_fixations.x, image_fixations.y, 20, 'red', alpha=0.5)\n", "\n", "plt.title(\"Fixations on a given image\");" ] }, { "cell_type": "markdown", "id": "4ef4f436-05e7-41c9-a6f8-8951ed3a229c", "metadata": {}, "source": [ "Fixations don't happen independently, they happen in sequences of so called *Scanpaths* and hence can depend on the previous fixations. Because of that,\n", "for each fixation in a dataset, `Fixations` makes the previous fixations available via the attributes `Fixations.x_hist` and `Fixations.y_hist`." ] }, { "cell_type": "code", "execution_count": 7, "id": "26fd7dd6-52e3-4490-a62f-a07beaf1aeed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x, y position: (660.9873417721519, 495.93600000000004)\n", "number of previous fixations: 6\n", "previous x locations: [226.55696203 435.41772152 532.88607595 842. 869.84810127\n", " 858.70886076]\n", "previous y locations: [434.688 521.28 541.344 658.56 470.592 436.8 ]\n" ] } ], "source": [ "fixation_index = 130\n", "\n", "print(f\"x, y position: ({fixations.x[fixation_index]}, {fixations.y[fixation_index]})\")\n", "print(f\"number of previous fixations: {fixations.scanpath_history_length[fixation_index]}\")\n", "print(f\"previous x locations: {fixations.x_hist[fixation_index]}\")\n", "print(f\"previous y locations: {fixations.y_hist[fixation_index]}\")" ] }, { "cell_type": "markdown", "id": "a4a03586-ef62-422c-88f7-e12acecef95c", "metadata": {}, "source": [ "`pysaliency.plotting` contains some functions to make visualizing a fixation with its history easy:" ] }, { "cell_type": "code", "execution_count": 8, "id": "c217590e-8b70-48aa-b543-b271417dea3e", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pysaliency.plotting import plot_scanpath\n", "\n", "plt.imshow(stimuli.stimuli[fixations.n[fixation_index]])\n", "plot_scanpath(stimuli, fixations, fixation_index, visualize_next_saccade=True)" ] }, { "cell_type": "markdown", "id": "619b5455-63a0-4edc-92a0-c5da4a9e6960", "metadata": {}, "source": [ "## ScanpathFixations\n", "\n", "While for modeling, it often makes sense to think about each fixation separately, together with their respective history of previous fixations, when building datasets and doing analysis, it's also convenient to think about whole scanpaths. This is what `ScanpathFixations` is for: it's a `Fixations` subclass for fixations which come from scanpaths. Actually, the `fixations` object we worked with so far is such a case: It has an additional attribute `scanpaths` which holds a `Scanpaths` instance containing the actual scanpaths from which the Fixations instance has been build by concatenating the fixations from all scanpaths." ] }, { "cell_type": "code", "execution_count": 9, "id": "52fa9787-11b0-4564-8954-047efecc5291", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The dataset has 104171 fixations coming from a total of 15045 scanpaths\n" ] } ], "source": [ "scanpaths = fixations.scanpaths\n", "print(f\"The dataset has {len(fixations)} fixations coming from a total of {len(scanpaths)} scanpaths\")" ] }, { "cell_type": "markdown", "id": "9c545624-dfad-4f99-a5ed-4fa1aaf9fcf1", "metadata": {}, "source": [ "`Scanpaths` as attributes `xs` and `ys` with the x and y locations of all fixations in a scanpath, and `length` containing the length of each scanpath." ] }, { "cell_type": "code", "execution_count": 10, "id": "ff7d2df8-9cd5-414c-89df-3693446cdd2f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scanpath no 8787:\n", "0: (513.4, 278.4)\n", "1: (329.6, 457.9)\n", "2: (493.9, 550.8)\n", "3: (496.7, 369.2)\n", "\n", "Scanpath no 13030:\n", "0: (530.1, 272.1)\n", "1: (538.5, 372.4)\n", "2: (254.4, 356.5)\n", "3: (516.2, 234.0)\n", "4: (290.6, 272.1)\n", "5: (493.9, 150.6)\n", "6: (546.8, 139.0)\n", "7: (496.7, 337.5)\n", "8: (410.4, 386.1)\n", "\n", "Scanpath no 9256:\n", "0: (622.0, 413.6)\n", "1: (638.7, 586.8)\n", "2: (148.6, 293.2)\n", "3: (680.5, 428.4)\n", "4: (505.0, 464.3)\n", "5: (679.1, 410.4)\n", "6: (920.0, 413.6)\n", "\n" ] } ], "source": [ "rst = np.random.RandomState(seed=23)\n", "scanpath_indices = rst.randint(len(fixations.scanpaths), size=3)\n", "\n", "for scanpath_index in scanpath_indices:\n", " print(f\"Scanpath no {scanpath_index}:\")\n", " scanpath_length = scanpaths.length[scanpath_index]\n", " xs = scanpaths.xs[scanpath_index]\n", " ys = scanpaths.ys[scanpath_index]\n", " for k, (x, y) in enumerate(zip(xs, ys)):\n", " print(f\"{k}: ({x:.01f}, {y:.01f})\")\n", " print()" ] }, { "cell_type": "markdown", "id": "f04b0db8-f770-47fb-ad23-a1f6dbc88deb", "metadata": {}, "source": [ "In a `ScanpathFixations` instance, where the fixations come from scanpaths, the fixations have an additional attribute `scanpath_index`, which allows to got back from individual fixations to the scanpaths:" ] }, { "cell_type": "code", "execution_count": 11, "id": "b0f5b246-16bb-46a3-b600-c06203b0e813", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[513.39240506 329.59493671 493.89873418 496.6835443 ]\n", "[278.4 457.92 550.848 369.216]\n" ] } ], "source": [ "scanpath_index = scanpath_indices[0]\n", "fixation_indices = fixations.scanpath_index == scanpath_index\n", "\n", "print(fixations.x[fixation_indices])\n", "print(fixations.y[fixation_indices])" ] }, { "cell_type": "markdown", "id": "426d304a-6328-4a61-8cba-82f7583fb7f8", "metadata": {}, "source": [ "### Filtering fixations\n", "\n", "One might ask: why separating between `ScanpathFixations` and `Fixations` in the first place? After all, fixations always come from scanpaths.\n", "The main reason is there are many case where we are only interested in some fixations, but need to be aware of the full scanpath\n", "history for each of those fixations. One very important case is that in most experiments, the first fixation is not a voluntary fixation, but a forced central fixation.\n", "There is no point in modeling and predicting this fixation, or including it in evaluating models. But of course when predicting the later, voluntary fixations, models need to be aware of the initial central fixation. In this case, we can easily filter a `ScanpathFixations` instance (or a `Fixations` instance) accordingly:" ] }, { "cell_type": "code", "execution_count": 12, "id": "d7a92d74-4512-42c7-b21f-e62e211df361", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: 674.9, previous x positions: [502.25316456]\n", "1: 686.1, previous x positions: [502.25316456 674.91139241]\n", "2: 505.0, previous x positions: [502.25316456 674.91139241 686.05063291]\n" ] } ], "source": [ "# filter fixations to exclude initial fixations from each scanpath\n", "voluntary_fixations = fixations[fixations.scanpath_history_length > 0]\n", "\n", "# show first three fixations\n", "for fixation_index in range(3):\n", " x_pos = voluntary_fixations.x[fixation_index]\n", " x_hist = voluntary_fixations.x_hist[fixation_index]\n", " print(f\"{fixation_index}: {x_pos:.01f}, previous x positions: {x_hist}\")" ] }, { "cell_type": "markdown", "id": "5d8378b5-69da-4f82-a4a9-2bc5a3fd84b0", "metadata": {}, "source": [ "We can see that the original initial fixation at x=502.3 is included in the history of all subsequent fixations, but it's not a fixation in the `Fixations` instance on its own." ] }, { "cell_type": "markdown", "id": "983438de-18e0-46c8-a220-852e495ead7b", "metadata": {}, "source": [ "Similarly, we can filter `Fixations` and `ScanpathFixations` instances according to other attribute. One important attribute is `subject`, which encodes\n", "the id of the subject which made a certain fixation:" ] }, { "cell_type": "code", "execution_count": 13, "id": "f2085fb4-6a30-426a-8a47-0da876d92ef7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 0 0 ... 14 14 14]\n" ] } ], "source": [ "print(fixations.subject)" ] }, { "cell_type": "markdown", "id": "b733938e-8d5b-472f-8478-3312522bc059", "metadata": {}, "source": [ "With this attribute, we can easily select all fixations from any given subject:" ] }, { "cell_type": "code", "execution_count": 14, "id": "658a06b0-f339-41d7-9f74-bba4ee9bebb3", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "subject_id = 5\n", "\n", "subject_fixations = fixations[fixations.subject == subject_id]\n", "\n", "image_index = 42\n", "image_fixations = fixations[fixations.n == image_index]\n", "image_subject_fixations = subject_fixations[subject_fixations.n == image_index]\n", "\n", "\n", "f, axs = plt.subplots(1, 2, figsize=(10, 4))\n", "axs[0].imshow(stimuli.stimuli[image_index])\n", "axs[0].scatter(image_fixations.x, image_fixations.y, 20, 'red', alpha=0.5)\n", "axs[0].set_title(\"all fixations\");\n", "\n", "axs[1].imshow(stimuli.stimuli[image_index])\n", "axs[1].scatter(image_subject_fixations.x, image_subject_fixations.y, 20, 'red', alpha=0.5)\n", "axs[1].set_title(f\"fixations from subject {subject_id}\");" ] }, { "cell_type": "markdown", "id": "4ef971db-ed8e-4e49-92e8-2f4d918def01", "metadata": {}, "source": [ "### Attributes\n", "\n", "`Fixations` instances can have more attributes besides `x`, `y`, `t`, `{x,y,t}_hist` and `subject`. We can check `Fixations.__attributes__` to see all available attributes (this will probably change in a future version a bit). In the case of the MIT1003 dataset, we also have information about fixation durations and the duration of previous fixations:" ] }, { "cell_type": "code", "execution_count": 15, "id": "a8068b3c-c1ca-4a64-adae-a7ac769e3a82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all attributes: ['subject', 'scanpath_index', 'duration', 'duration_hist']\n" ] } ], "source": [ "print(\"all attributes:\", fixations.__attributes__)" ] }, { "cell_type": "markdown", "id": "ce329c1e-9b74-45fe-9d94-ee1d37e067ad", "metadata": {}, "source": [ "## Models\n", "\n", "Pysaliency's main modeling framework is that of probabilistic models, where a model predicts fixations via the means of a probability distribtion (see, e.g. Kümmerer & Bethge 2023). For predicting spatial fixation densities, pysaliency uses the class `Model`, which needs to implement a function `_log_density` for computing a predicted log density. This is an example for a simple model, which predicts fixations based on the local contrast of the stimulus." ] }, { "cell_type": "code", "execution_count": 16, "id": "d5946e45", "metadata": {}, "outputs": [], "source": [ "class LocalContrastModel(pysaliency.Model):\n", " def __init__(self, bandwidth=0.05, **kwargs):\n", " super().__init__(**kwargs)\n", " self.bandwidth = bandwidth\n", " \n", " def _log_density(self, stimulus: Union[pysaliency.datasets.Stimulus, np.ndarray]):\n", " \n", " # _log_density can either take pysaliency Stimulus objects, or, for convenience, simply numpy arrays\n", " # `as_stimulus` ensures that we have a Stimulus object\n", " stimulus_object = pysaliency.datasets.as_stimulus(stimulus)\n", "\n", " # grayscale image\n", " gray_stimulus = np.mean(stimulus_object.stimulus_data, axis=2)\n", "\n", " # size contains the height and width of the image, but not potential color channels\n", " height, width = stimulus_object.size\n", "\n", " # define kernel size based on image size\n", " kernel_size = np.round(self.bandwidth * max(width, height)).astype(int)\n", " sigma = (kernel_size - 1) / 6\n", " \n", " # apply Gausian blur and calculate squared difference between blurred and original image\n", " blurred_stimulus = gaussian_filter(gray_stimulus, sigma)\n", "\n", " prediction = gaussian_filter((gray_stimulus - blurred_stimulus)**2, sigma)\n", "\n", " # normalize to [1, 255]\n", " prediction = (254 * (prediction / prediction.max())).astype(int) + 1\n", "\n", " density = prediction / prediction.sum()\n", " \n", " return np.log(density)\n", "\n", "contrast_model = LocalContrastModel(bandwidth=0.05)" ] }, { "cell_type": "code", "execution_count": 17, "id": "83c3fc70", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axs = plt.subplots(1, 2, figsize=(8, 5))\n", "\n", "image_index = 0\n", "\n", "axs[0].imshow(stimuli.stimuli[image_index])\n", "axs[0].set_axis_off()\n", "axs[0].set_title(\"Image\")\n", "\n", "axs[1].imshow(contrast_model.log_density(stimuli[image_index]))\n", "axs[1].set_axis_off()\n", "axs[1].set_title(\"model log density\");" ] }, { "cell_type": "markdown", "id": "e97fb2cd", "metadata": {}, "source": [ "When using the model, we use the function `log_density`, which mainly adds a cache around `_log_density` to avoid recomputing log densities multiple times. This is how the resulting prediction looks like:" ] }, { "cell_type": "markdown", "id": "84d4e1f9-7a0b-476a-9a41-41e97561245b", "metadata": {}, "source": [ "pysaliency comes with a range of fixation models for comparision, for example [DeepGaze I](http://arxiv.org/abs/1411.1045). In this example the prior is just a simple gausian model." ] }, { "cell_type": "code", "execution_count": 18, "id": "a389d2c5", "metadata": {}, "outputs": [], "source": [ "class CenterBiasModel(pysaliency.Model):\n", " def __init__(self, width=0.5, **kwargs):\n", " super().__init__(**kwargs)\n", " self.width = width\n", "\n", " def _log_density(self, stimulus: Union[pysaliency.datasets.Stimulus, np.ndarray]):\n", " \n", " # _log_density can either take pysaliency Stimulus objects, or, for convenience, simply numpy arrays\n", " # `as_stimulus` ensures that we have a Stimulus object\n", " stimulus_object = pysaliency.datasets.as_stimulus(stimulus)\n", "\n", " # size contains the height and width of the image, but not potential color channels\n", " height, width = stimulus_object.size\n", "\n", " xs = np.arange(width, dtype=float)\n", " ys = np.arange(height, dtype=float)\n", " XS, YS = np.meshgrid(xs, ys)\n", "\n", " XS -= 0.5 * width\n", " YS -= 0.5 * height\n", "\n", " max_size = max(width, height)\n", " actual_kernel_size = self.width * max_size\n", "\n", " gaussian = np.exp(-0.5 * (XS ** 2 + YS ** 2) / actual_kernel_size ** 2)\n", " \n", " density = gaussian / gaussian.sum()\n", " return np.log(density)\n", "\n", "center_bias_model = CenterBiasModel(width=0.2)" ] }, { "cell_type": "code", "execution_count": 19, "id": "b1b6dad7-e6a9-4a66-b924-8dbc3312ec94", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using cache found in /Users/janriedelsheimer/.cache/torch/hub/matthias-k_DeepGaze_main\n", "Using cache found in /Users/janriedelsheimer/.cache/torch/hub/pytorch_vision_v0.6.0\n", "/opt/anaconda3/envs/pysaliency/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", " warnings.warn(\n", "/opt/anaconda3/envs/pysaliency/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n", " warnings.warn(msg)\n" ] } ], "source": [ "# deepgaze needs a spatial prior, for which we use our Gaussian model here\n", "deepgaze1_model = pysaliency.external_models.DeepGazeI(centerbias_model=center_bias_model)" ] }, { "cell_type": "markdown", "id": "f8141d94-eb1e-44ca-81b5-43ae30a4c5ab", "metadata": {}, "source": [ "Because visualizing densities is nontrivial, `pysaliency.plotting` contains the function `visualize_distribution`\n", "to get a nice visualization (for details check Figure 5 in the appendix of https://arxiv.org/abs/1704.08615)" ] }, { "cell_type": "code", "execution_count": 20, "id": "b66e64ed-ff99-4932-98f8-35cabf60c26c", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pysaliency.plotting import visualize_distribution\n", "\n", "f, axs = plt.subplots(1, 3, figsize=(12, 8))\n", "\n", "image_index = 0\n", "\n", "axs[0].imshow(stimuli.stimuli[image_index])\n", "axs[0].set_axis_off()\n", "axs[0].set_title(\"Image\")\n", "\n", "axs[1].matshow(deepgaze1_model.log_density(stimuli[image_index]))\n", "axs[1].set_axis_off()\n", "axs[1].set_title(\"model log density\")\n", "\n", "visualize_distribution(deepgaze1_model.log_density(stimuli[image_index]))\n", "axs[2].set_axis_off()\n", "axs[2].set_title(\"model prediction\");" ] }, { "cell_type": "markdown", "id": "6e827eba-9389-4e51-a581-7f509447877d", "metadata": {}, "source": [ "Probabilistic models allow for straight forward sampling of new fixations:" ] }, { "cell_type": "code", "execution_count": 21, "id": "b20ad889-dbeb-41d6-b79d-1adb0b23150a", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# sample 120 new fixations (or, more precisely, scanpaths of length 1):\n", "rst = np.random.RandomState(42) # pysaliency allows to specify the random state for deterministic behaviour\n", "actual_fixations = fixations[fixations.n == 0]\n", "new_fixations_contrast = contrast_model.sample(stimuli[:1], train_counts=120, lengths=1, rst=rst)\n", "new_fixations_deepgaze = deepgaze1_model.sample(stimuli[:1], train_counts=120, lengths=1, rst=rst)\n", "\n", "\n", "\n", "f, axs = plt.subplots(2, 3, figsize=(12, 6))\n", "\n", "axs[0, 0].imshow(stimuli.stimuli[0])\n", "axs[0, 0].scatter(actual_fixations.x, actual_fixations.y, 20, 'red', alpha=0.5)\n", "axs[0, 0].set_title(\"Real fixations\")\n", "\n", "visualize_distribution(deepgaze1_model.log_density(stimuli[0]), ax=axs[0, 1])\n", "axs[0, 1].set_title(\"DeepGaze I prediction\")\n", "axs[0, 2].imshow(stimuli.stimuli[0])\n", "axs[0, 2].scatter(new_fixations_deepgaze.x, new_fixations_deepgaze.y, 20, 'red', alpha=0.5)\n", "axs[0, 2].set_title(\"DeepGaze I fixations\")\n", "\n", "visualize_distribution(contrast_model.log_density(stimuli[0]), ax=axs[1, 1])\n", "axs[1, 2].imshow(stimuli.stimuli[0])\n", "axs[1, 1].set_title(\"Contrast model prediction\")\n", "axs[1, 2].scatter(new_fixations_contrast.x, new_fixations_contrast.y, 20, 'red', alpha=0.5)\n", "axs[1, 2].set_title(\"Contrast model fixations\")\n", "\n", "\n", "for ax in axs.flatten():\n", " ax.set_axis_off()" ] }, { "cell_type": "markdown", "id": "cdb13b89-44c4-42d6-b0bd-331142f11d3b", "metadata": {}, "source": [ "## Scanpath models\n", "\n", "As already mentioned, fixation usually depend on previous fixation locations. Scanpath models aim at incorporating these dependencies. As discussed in [Kümmerer & Bethge: Predicting Visual Fixations, Annual Reviews in Vision Science 2023](https://www.annualreviews.org/doi/10.1146/annurev-vision-120822-072528) in detail, \"next-fixation-prediction\" is a powerful way to unify many different gaze prediction settings including scanpath prediction and spatial density prediction. The key idea is to not model whole scanpaths at once, but instead for each fixation in a scanpath predict a probability distribution of possible next fixation locations given the previous fixations, that is:\n", "\n", "$$\n", " p(x_{i+1}, y_{i+1} \\mid x_0, y_0, \\dots, x_{i}, y_{i}, I)\n", "$$\n", "\n", "In the case of spatial gaze density prediction as above, e.g. using DeepGaze I, the dependency on previous fixations is not used and the model prediction for the next fixation is simply the predicted gaze density:\n", "\n", "$$\n", " p(x_{i+1}, y_{i+1} \\mid x_0, y_0, \\dots, x_{i}, y_{i}, I) \\stackrel{\\text{density prediction}}{=} p(x, y \\mid I)\n", "$$\n", "\n", "Pysaliency provides the class `ScanpathModel` for modeling scanpaths. Instead of implementing `_log_density`, now we need to implement `conditional_log_density(stimulus, x_hist, y_hist)`:" ] }, { "cell_type": "code", "execution_count": 22, "id": "9696744b-6cf7-4c43-8fe8-93a6f1a0fa4d", "metadata": {}, "outputs": [], "source": [ "from pysaliency.utils import remove_trailing_nans\n", "\n", "class MySimpleScanpathModel(pysaliency.ScanpathModel):\n", " def __init__(self, spatial_model_bandwidth: float=0.05, saccade_width: float=0.1):\n", " self.spatial_model_bandwidth = spatial_model_bandwidth\n", " self.saccade_width = saccade_width\n", " self.spatial_model = LocalContrastModel(spatial_model_bandwidth)\n", "\n", "\n", " def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None,):\n", " stimulus_object = pysaliency.datasets.as_stimulus(stimulus)\n", "\n", " # size contains the height and width of the image, but not potential color channels\n", " height, width = stimulus_object.size\n", "\n", " spatial_prior_log_density = self.spatial_model.log_density(stimulus)\n", " spatial_prior_density = np.exp(spatial_prior_log_density)\n", "\n", " # compute saccade bias\n", " last_x = x_hist[-1]\n", " last_y = y_hist[-1]\n", " \n", " xs = np.arange(width, dtype=float)\n", " ys = np.arange(height, dtype=float)\n", " XS, YS = np.meshgrid(xs, ys)\n", "\n", " XS -= last_x\n", " YS -= last_y\n", " \n", " # compute prior\n", " max_size = max(width, height)\n", " actual_kernel_size = self.saccade_width * max_size\n", "\n", " saccade_bias = np.exp(-0.5 * (XS ** 2 + YS ** 2) / actual_kernel_size ** 2)\n", "\n", " prediction = spatial_prior_density * saccade_bias\n", " \n", " density = prediction / prediction.sum()\n", " return np.log(density)\n", "\n", "my_simple_scanpath_model = MySimpleScanpathModel()" ] }, { "cell_type": "code", "execution_count": 23, "id": "3f010b8c-d368-4c5c-b241-1ebf88219b94", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Prediction')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fixation_index = 40\n", "f, axs = plt.subplots(1, 3, figsize=(12, 6))\n", "axs[0].set_axis_off()\n", "axs[1].set_axis_off()\n", "axs[2].set_axis_off()\n", "\n", "axs[0].imshow(stimuli.stimuli[fixations.n[fixation_index]])\n", "plot_scanpath(stimuli, fixations, fixation_index, visualize_next_saccade=True, ax=axs[0])\n", "axs[0].set_title(\"Image\")\n", "\n", "axs[1].imshow(my_simple_scanpath_model.spatial_model.log_density(stimuli.stimuli[fixations.n[fixation_index]]))\n", "\n", "axs[1].set_title(\"Prior\")\n", "\n", "prediction = my_simple_scanpath_model.conditional_log_density(\n", " stimuli.stimuli[fixations.n[fixation_index]],\n", " x_hist=fixations.x_hist[fixation_index],\n", " y_hist=fixations.y_hist[fixation_index],\n", " t_hist=None,\n", ")\n", "\n", "visualize_distribution(prediction, ax=axs[2])\n", "plot_scanpath(stimuli, fixations, fixation_index, visualize_next_saccade=True, ax=axs[2])\n", "axs[2].set_title(\"Prediction\")" ] }, { "cell_type": "markdown", "id": "880840df-56c8-4275-b241-9eaa8f0a8cfa", "metadata": {}, "source": [ "Since computing the conditional log density for a given fixation in a dataset is a very common task, `ScanpathModel` provides\n", "the convenience method `conditional_log_density_for_fixation(stimuli, fixations, fixation_index)` to that end. Using it, we\n", "could also have written:" ] }, { "cell_type": "code", "execution_count": 24, "id": "e74fe712-34fa-4201-8415-8542c0080ffc", "metadata": {}, "outputs": [], "source": [ "prediction = my_simple_scanpath_model.conditional_log_density_for_fixation(\n", " stimuli,\n", " fixations,\n", " fixation_index=fixation_index,\n", ")" ] }, { "cell_type": "markdown", "id": "b3e49392-d81d-47ac-be1b-27db3eebf7ef", "metadata": {}, "source": [ "## Evaluating models\n", "\n", "\n", "Pysaliency incorporates extensive mechanisms for evaluationg model performances. For probabilistic models, i.e., instances of `pysaliency.Model` and `pysaliency.ScanpathModel`, pysaliency makes it simple to compute log-likelihood and information gain scores ([Kümmerer et al, PNAS 2015](http://www.pnas.org/content/112/52/16054), [Kümmerer & Bethge, Ann.Rev.Vis.Sci 2023](https://www.annualreviews.org/doi/10.1146/annurev-vision-120822-072528)). Other popular metrics like AUC, CC etc can also be computed as we'll see below by using the methods from [Kümmerer et al, ECCV 2018](http://openaccess.thecvf.com/content_ECCV_2018/html/Matthias_Kummerer_Saliency_Benchmarking_Made_ECCV_2018_paper.html).\n", "\n", "Information gain is the difference in log-likelihood between a model and a baseline model:\n", "\n", "$$\n", " IG(\\hat p, p_\\text{baseline}) = \\log \\hat p(x_i, y_i) - \\log p_\\text{baseline}(x_i, y_i)\n", "$$\n", "\n", "The model method `information_gains` computes information gain values for each fixation:" ] }, { "cell_type": "code", "execution_count": 25, "id": "38220bad-2512-465d-8d90-f4bfc7426667", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 3.58530229, 2.97403468, -0.44368986, 2.23284311, 3.65884896,\n", " 1.9408317 , 0.17386681, -2.1538991 , -0.77001112, 0.31825519,\n", " 1.34417986, -0.24838774, 0.26198797, -0.6487262 , -0.41097188,\n", " 1.67419118, 3.50254424, 3.13763833, 2.68714147, 1.0400994 ])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we want to exclude the initial fixation from evaluation\n", "eval_fixations = fixations[fixations.scanpath_history_length > 0]\n", "\n", "# we only want to evaluate the first 20 fixations\n", "eval_fixations = eval_fixations[:20]\n", "deepgaze1_model.information_gains(stimuli, eval_fixations, verbose=False)" ] }, { "cell_type": "markdown", "id": "df99e67d-9238-45db-9f71-8603c90edb1b", "metadata": {}, "source": [ "By default, `information_gains` uses a uniform baseline model, but we can hand over any other model. Often, it makes sense\n", "to use a center bias model as prior, in which case the name \"information gain\" is actually justified.\n", "\n", "Often we're only interested in average performance over a full dataset. In this case, we can use the method `information_gain` instead of `information_gains`\n", "which takes care of the averaging. Since we want each image to contribute equally to the score, we'll use `average='image'`. By default each fixation contributes equally (`average='fixations')." ] }, { "cell_type": "code", "execution_count": 26, "id": "9e032691-527e-456e-8639-7f360e4582e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "information gain relative to a uniform baseline model 1.192803964730545\n", "information gain relative to a centerbias baseline model 0.569543965938904\n" ] } ], "source": [ "print(\"information gain relative to a uniform baseline model \", deepgaze1_model.information_gain(stimuli, eval_fixations, average='image'))\n", "print(\"information gain relative to a centerbias baseline model\", deepgaze1_model.information_gain(stimuli, eval_fixations, baseline_model=center_bias_model, average='image'))" ] }, { "cell_type": "markdown", "id": "cb48ff16-f17a-4838-9015-90b0834656d9", "metadata": {}, "source": [ "One advantage of the framework of next-fixation-prediction is that it allows easy comparison of spatial models and scanpath models:" ] }, { "cell_type": "code", "execution_count": 27, "id": "442b8e2e-24a6-44c0-9992-e37336317962", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simple Scanpath Model: IG = -1.081381551939945\n", "DeepGaze I: IG = 1.192803964730545\n" ] } ], "source": [ "print(\"Simple Scanpath Model: IG =\", my_simple_scanpath_model.information_gain(stimuli, eval_fixations, verbose=False, average='image'))\n", "print(\"DeepGaze I: IG =\", deepgaze1_model.information_gain(stimuli, eval_fixations, verbose=False, average='image'))" ] }, { "cell_type": "markdown", "id": "92bfd978-9932-44df-ae87-f3545aa945a2", "metadata": {}, "source": [ "We can see that in this case the better understanding of image-based effects of DeepGaze I outweights the additional dynamics of the simple scanpath model" ] }, { "cell_type": "markdown", "id": "9d13b917-fc3f-4dd9-9a2f-b62835085ff1", "metadata": {}, "source": [ "## Saliency Map Models\n", "\n", "Traditionally, the field of fixation prediction mainly formulated their models as so called *saliency models*. Saliency models predict fixation locations by the means of a *saliency map*, where areas of high saliency are expected to have more fixations. The reason for this somewhat vague definition has historical reasons, see [Kümmerer & Bethge, Ann.Rev.Vis.Sci 2023](https://www.annualreviews.org/doi/10.1146/annurev-vision-120822-072528)." ] }, { "cell_type": "markdown", "id": "f63e00a8-be31-4529-a50a-2a5e928a93fa", "metadata": {}, "source": [ "Pysaliency uses the class `pysaliency.SaliencyMapModel` to implement saliency map models. They behave very similarly to the `Model` class, but instead of methods `log_density` and `_log_density`, they have methods `saliency_map` and `_saliency_map` with identical signature. Pysaliency comes with a range of published saliency models prewrapped, we'll use the [AIM](https://jov.arvojournals.org/article.aspx?articleid=2193531) model here as an example. Most saliency models are implemented in matlab and hence require matlab to run. Pysaliency will automatically download the original source code, potentially apply some patches to make it run in more modern matlab versions and then call matlab as part of the `_saliency_map` method:" ] }, { "cell_type": "code", "execution_count": 28, "id": "3255672f-e3df-4b1d-8cad-cb8e7a39fcd2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/pysaliency/lib/python3.9/site-packages/urllib3/connectionpool.py:1099: InsecureRequestWarning: Unverified HTTPS request is being made to host 'home.cs.umanitoba.ca'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings\n", " warnings.warn(\n", "Downloading file: 31.5MB [00:03, 8.80MB/s] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Checking md5 sum...\n" ] } ], "source": [ "aim_model = pysaliency.AIM(location='pysaliency_models')" ] }, { "cell_type": "code", "execution_count": 29, "id": "f8c137e6-0fa0-4f19-bf92-75e93ed6bbab", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: package sun.awt.X11 not in java.desktop\n", "WARNING: package sun.awt.X11 not in java.desktop\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reading Image.\n", "Loading Basis.\n", "Projecting local neighbourhoods into basis space.\n", "0 25 50 75 100\n", "........................................\n", "Performing Density Estimation.\n", "0 25 50 75 100\n", ".......................................\n", "Transforming likelihoods into information measures.\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axs = plt.subplots(1, 2, figsize=(12, 5))\n", "\n", "image_index = 0\n", "\n", "axs[0].imshow(stimuli.stimuli[image_index])\n", "axs[0].set_axis_off()\n", "axs[0].set_title(\"Image\")\n", "\n", "axs[1].matshow(aim_model.saliency_map(stimuli[image_index]))\n", "axs[1].set_axis_off()\n", "axs[1].set_title(\"AIM saliency map\");" ] }, { "cell_type": "markdown", "id": "66259030-fd15-422c-b6fe-aa1dba6d73ad", "metadata": {}, "source": [ "Saliency map models don't allow information theoretic evaluation with information gain. Instead, a multitude of different saliency metrics has been proposed. Pysaliency implements many of the common metrics, such as AUC, sAUC, NSS, CC, SIM and KL-Div as methods of `pysaliency.SaliencyMapModel`:" ] }, { "cell_type": "code", "execution_count": 30, "id": "c17d068c-96c2-410b-8a62-fc229ad45453", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AUC: 0.7745\n" ] } ], "source": [ "print(f\"AUC: {aim_model.AUC(stimuli, eval_fixations, average='image'):.04f}\")" ] }, { "cell_type": "markdown", "id": "88ceb085-6943-45ec-9b6a-29a8262fb03e", "metadata": {}, "source": [ "### Saliency metric score for probabilistic models\n", "\n", "`Model` and `ScanpathModel` don't support saliency metrics as AUC and CC directly, because it's not clear what to use as saliency map for the metric\n", "and indeed for different metrics different saliency maps are optimal ([Kümmerer et al, ECCV 2018](http://openaccess.thecvf.com/content_ECCV_2018/html/Matthias_Kummerer_Saliency_Benchmarking_Made_ECCV_2018_paper.html)). Before computing saliency metrics, probabilistic models have to be converted to saliency map models. For AUC, we can simply use predicted fixation density as saliency map (but for other metrics, much more complicated maps might be appropriate):" ] }, { "cell_type": "code", "execution_count": 31, "id": "87c10abe-57a2-4bbc-937f-74488a2636e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DeepGaze AUC: 0.8220\n" ] } ], "source": [ "deepgaze1_density_map_model = pysaliency.DensitySaliencyMapModel(deepgaze1_model)\n", "print(f\"DeepGaze AUC: {deepgaze1_density_map_model.AUC(stimuli, eval_fixations, average='image'):.04f}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "61804a56-ed7c-4aa8-a2ef-2684a0abb018", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "pysaliency", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.20" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: pyproject.toml ================================================ [tool.ruff] select = ["B", "E", "F", "FIX", "I", "T20"] line-length = 200 ignore = ["T201"] # ignore print statements [build-system] requires = [ "numpy", "setuptools", "wheel", "Cython" ] build-backend = "setuptools.build_meta" ================================================ FILE: pysaliency/__init__.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals from . import datasets from . import saliency_map_models from . import models from . import external_models from . import external_datasets from . import utils from .hdf5 import read_hdf5 from .datasets import ( Fixations, FixationTrains, ScanpathFixations, Scanpaths, Stimuli, FileStimuli, create_nonfixations, create_subset, remove_out_of_stimulus_fixations, concatenate_datasets, ) from .dataset_config import load_dataset_from_config from .saliency_map_models import ( SaliencyMapModel, GeneralSaliencyMapModel, ScanpathSaliencyMapModel, GaussianSaliencyMapModel, FixationMap, CachedSaliencyMapModel, ExpSaliencyMapModel, DisjointUnionSaliencyMapModel, SubjectDependentSaliencyMapModel, StimulusDependentSaliencyMapModel, ResizingSaliencyMapModel, BluringSaliencyMapModel, DigitizeMapModel, HistogramNormalizedSaliencyMapModel, DensitySaliencyMapModel, LogDensitySaliencyMapModel, EqualizedSaliencyMapModel, WTASamplingMixin, ) from .sampling_models import SamplingModelMixin, ScanpathSamplingModelMixin from .models import ( ScanpathModel, GeneralModel, Model, UniformModel, CachedModel, MixtureModel, DisjointUnionModel, SubjectDependentModel, ShuffledAUCSaliencyMapModel, ResizingModel, ResizingScanpathModel, StimulusDependentModel, ) from .saliency_map_conversion import ( optimize_for_information_gain, ) from .precomputed_models import ( SaliencyMapModelFromFiles, SaliencyMapModelFromDirectory, SaliencyMapModelFromFile, ModelFromDirectory, HDF5SaliencyMapModel, HDF5Model, export_model_to_hdf5, ) from .external_models import ( AIM, SUN, ContextAwareSaliency, BMS, GBVS, GBVSIttiKoch, Judd, IttiKoch, RARE2012, CovSal, ) from .external_datasets import ( get_mit1003, get_mit1003_onesize, get_cat2000_train, get_cat2000_test, get_toronto, get_iSUN_training, get_iSUN_validation, get_iSUN_testing, get_SALICON, get_SALICON_train, get_SALICON_val, get_SALICON_test, get_mit300, get_koehler, get_FIGRIM, get_OSIE, get_NUSEF_public, ) from .metric_optimization import SIMSaliencyMapModel ================================================ FILE: pysaliency/baseline_utils.py ================================================ from collections import OrderedDict from typing import List import numba import numpy as np from boltons.iterutils import chunked from scipy.ndimage import gaussian_filter from scipy.special import logsumexp from sklearn.base import BaseEstimator, DensityMixin from sklearn.model_selection import cross_val_score from sklearn.neighbors import KernelDensity from . import Model, UniformModel from .datasets.utils import decode_string, hdf5_wrapper from .numba_utils import fill_fixation_map from .precomputed_models import get_image_hash from .roc import general_roc from .utils import inter_and_extrapolate @numba.jit(nopython=True) def _normalize_fixations(orig_xs, orig_ys, orig_ns, sizes, new_xs, new_ys, real_widths, real_heights): for i in range(len(orig_xs)): height, width = sizes[orig_ns[i]] new_xs[i] = orig_xs[i] / width new_ys[i] = orig_ys[i] / height real_widths[i] = width real_heights[i] = height def normalize_fixations(stimuli, fixations, keep_aspect=False, add_shape=False, verbose=True): sizes = np.array(stimuli.sizes) xs = np.empty(len(fixations.x)) ys = np.empty(len(fixations.x)) widths = np.empty(len(fixations.x)) heights = np.empty(len(fixations.x)) _normalize_fixations(fixations.x, fixations.y, fixations.n, sizes, xs, ys, widths, heights) real_widths = widths.copy() real_heights = heights.copy() if keep_aspect: max_size = np.max([widths, heights], axis=0) widths = max_size heights = max_size xs = fixations.x / widths ys = fixations.y / heights real_widths /= widths real_heights /= heights if add_shape: return xs, ys, real_widths, real_heights return xs, ys def fixations_to_scikit_learn(fixations, normalize=None, keep_aspect=False, add_shape=False, add_stimulus_number=False, add_fixation_number=False, verbose=True): if normalize is None: xs = fixations.x ys = fixations.y data = [xs, ys] if add_shape: raise NotImplementedError() else: data = normalize_fixations(normalize, fixations, keep_aspect=keep_aspect, add_shape=add_shape, verbose=verbose) if add_stimulus_number: data = list(data) + [fixations.n] if add_fixation_number: data = list(data) + [np.arange(len(fixations.n))] return np.vstack(data).T.copy() # crossvalidation generators class ScikitLearnImageCrossValidationGenerator(object): def __init__(self, stimuli, fixations, within_stimulus_attributes=None, leave_out_size=1, maximal_source_count=None): self.stimuli = stimuli self.fixations = fixations self.within_stimulus_attributes = within_stimulus_attributes or [] self.leave_out_size = leave_out_size self.maximal_source_count = maximal_source_count if self.within_stimulus_attributes and leave_out_size != 1: raise NotImplementedError("cannot yet specify both batchsize and within_stimulus_attributes") for attribute in self.within_stimulus_attributes: if attribute not in self.stimuli.attributes: raise ValueError(f"stimulus attribute '{attribute}' not available in given stimuli") def __iter__(self): if self.leave_out_size == 1: elements = chunked(range(len(self.stimuli)), size=1) else: indices = np.arange(len(self.stimuli)) np.random.RandomState(seed=42).shuffle(indices) elements = chunked(list(indices), size=self.leave_out_size) source_selection_rst = np.random.RandomState(seed=23) for ns in elements: test_inds = np.isin(self.fixations.n, ns) train_inds = ~test_inds for attribute_name in self.within_stimulus_attributes: target_value = self.stimuli.attributes[attribute_name][ns[0]] valid_stimulus_indices = np.nonzero(self.stimuli.attributes[attribute_name] == target_value)[0] valid_fixation_indices = np.isin(self.fixations.n, valid_stimulus_indices) train_inds = train_inds & valid_fixation_indices if test_inds.sum(): if self.maximal_source_count is not None and train_inds.sum() > self.maximal_source_count: train_inds = np.nonzero(train_inds)[0] selected_train_inds = source_selection_rst.choice( train_inds, size=self.maximal_source_count, replace=False ) train_inds = np.zeros_like(test_inds, dtype=bool) train_inds[selected_train_inds] = True yield train_inds, test_inds def __len__(self): return int(np.ceil(len(self.stimuli) / self.leave_out_size)) class ScikitLearnImageSubjectCrossValidationGenerator(object): def __init__(self, stimuli, fixations): self.stimuli = stimuli self.fixations = fixations def __iter__(self): for n in range(len(self.stimuli)): for s in range(self.fixations.subject_count): image_inds = self.fixations.n == n subject_inds = self.fixations.subject == s train_inds, test_inds = image_inds & ~subject_inds, image_inds & subject_inds if test_inds.sum() == 0 or train_inds.sum() == 0: #print("Skipping") continue # scikit at some point loads all indices from all crossvalidation folds into memory # if we use the binary masks, this will use a lot of memory, hence # we convert to indices here train_inds = np.nonzero(train_inds)[0] test_inds = np.nonzero(test_inds)[0] yield train_inds, test_inds def __len__(self): return len(set(zip(self.fixations.n, self.fixations.subject))) class ScikitLearnWithinImageCrossValidationGenerator(object): def __init__(self, stimuli, fixations, chunks_per_image=10, random_seed=42): self.stimuli = stimuli self.fixations = fixations self.chunks_per_image = chunks_per_image self.rng = np.random.RandomState(seed=random_seed) def __iter__(self): for n in range(len(self.stimuli)): image_inds = self.fixations.n == n _image_inds = np.nonzero(image_inds)[0] self.rng.shuffle(_image_inds) chunks = np.array_split(_image_inds, self.chunks_per_image) for chunk in chunks: if not len(chunk): continue test_inds = np.zeros_like(self.fixations.n) test_inds[chunk] = 1 test_inds = test_inds > 0.5 train_inds = image_inds & ~test_inds # scikit at some point loads all indices from all crossvalidation folds into memory # if we use the binary masks, this will use a lot of memory, hence # we convert to indices here train_inds = np.nonzero(train_inds)[0] test_inds = np.nonzero(test_inds)[0] yield train_inds, test_inds def __len__(self): #counts = 0 #for n in range(len(self.stimuli)): return len(self.stimuli)*self.chunks_per_image # Scikit-learn compatible estimators for baseline models class GeneralMixtureKernelDensityEstimator(DensityMixin, BaseEstimator): """ computes the log likelihood of data under a mixture of a kernel density estimator and multiple other regularizations. Other regulariations are given by their log likelihoods for each sample, where X must contain sample indices in the last column. Previous columns will be used for the KDE. bandwidth: bandwidth of the kernel density estimator regularizations: list of regularization weights of the regularizations. The sum of the weights must be <= 1.0, the difference to 1 will the the weight of the KDE. regularizing_log_likelihoods: list of log likelihoods of the regularizations for samples. The second dimension must match the length of regularizations. The first dimension will be indexed by the last dimension of the handed over samples. """ def __init__(self, bandwidth: float, regularizations: List[float], regularizing_log_likelihoods: List[float]): self.bandwidth = bandwidth self.regularizations = np.asarray(regularizations) self.regularizing_log_likelihoods = np.asarray(regularizing_log_likelihoods) if not len(self.regularizations) == self.regularizing_log_likelihoods.shape[1]: raise ValueError("regularizations and regularizing_log_likelihoods don't match") def setup(self): assert np.sum(self.regularizations) <= 1.0 self.kde = KernelDensity(kernel='gaussian', bandwidth=self.bandwidth) self.kde_constant = np.log(1-self.regularizations.sum()) self.regularization_constants = np.log(self.regularizations) def fit(self, X): assert X.shape[1] == 3 self.setup() self.kde.fit(X[:, 0:2]) return self def score_samples(self, X): assert X.shape[1] == 3 kde_logliks = self.kde.score_samples(X[:, :2]) fix_inds = X[:, 2].astype(int) fix_lls = self.regularizing_log_likelihoods[fix_inds] logliks = logsumexp(np.hstack([(self.kde_constant + kde_logliks)[:, np.newaxis], self.regularization_constants + fix_lls ]), axis=-1) return logliks def score(self, X): return np.sum(self.score_samples(X)) class RegularizedKernelDensityEstimator(DensityMixin, BaseEstimator): def __init__(self, bandwidth=1.0, regularization = 1.0e-5): self.bandwidth = bandwidth self.regularization = regularization def setup(self): self.kde = KernelDensity(kernel='gaussian', bandwidth=self.bandwidth) height, width = self.shape self.uniform_density = -np.log(width*height) self.kde_constant = np.log(1-self.regularization) self.uniform_constant = np.log(self.regularization) def fit(self, X): self.shape = X[0, 2:4] self.setup() self.kde.fit(X[:, 0:2]) return self def score_samples(self, X): kde_logliks = self.kde.score_samples(X[:, :2]) logliks = np.logaddexp( self.kde_constant + kde_logliks, self.uniform_constant + self.uniform_density ) return logliks def score(self, X): return np.sum(self.score_samples(X)) class MixtureKernelDensityEstimator(DensityMixin, BaseEstimator): def __init__(self, bandwidth=1.0, regularization = 1.0e-5, regularizing_log_likelihoods=None): self.bandwidth = bandwidth self.regularization = regularization self.regularizing_log_likelihoods = np.asarray(regularizing_log_likelihoods) def setup(self): self.kde = KernelDensity(kernel='gaussian', bandwidth=self.bandwidth) self.kde_constant = np.log(1-self.regularization) self.uniform_constant = np.log(self.regularization) def fit(self, X): assert X.shape[1] == 3 self.setup() self.kde.fit(X[:, 0:2]) return self def score_samples(self, X): assert X.shape[1] == 3 kde_logliks = self.kde.score_samples(X[:, :2]) fix_ns = X[:, 2].astype(int) fix_lls = self.regularizing_log_likelihoods[fix_ns] logliks = np.logaddexp( self.kde_constant + kde_logliks, self.uniform_constant + fix_lls ) return logliks def score(self, X): return np.sum(self.score_samples(X)) class AUCKernelDensityEstimator(DensityMixin, BaseEstimator): def __init__(self, nonfixations, bandwidth=1.0): self.bandwidth = bandwidth self.nonfixations = nonfixations def setup(self): self.kde = KernelDensity(kernel='gaussian', bandwidth=self.bandwidth) def fit(self, X): self.setup() self.kde.fit(X) self.nonfixation_values = self.kde.score_samples(self.nonfixations) return self def score_samples(self, X): pos_logliks = self.kde.score_samples(X) neg_logliks = self.nonfixation_values aucs = [general_roc(np.array([p]), neg_logliks)[0] for p in pos_logliks] return aucs def score(self, X): return np.sum(self.score_samples(X)) # Classes for computing crossvalidation scores of fixations on stimuli on KDE models # with multiple regularization models def _normalize_regularization_factors(args): """ makes sure that sum(10**args) <= 1.0, i.e. they can be used as regularizing weights """ log_regularizations = np.asarray(args) for i, value in enumerate(log_regularizations): if value >= 0: log_regularizations[i] = -1e-10 for i in list(range(len(log_regularizations)))[::-1]: if np.sum([10**value for value in log_regularizations]) <= 1.0: break #else: # print("not normal", np.sum([10**value for value in log_regularizations])) new_value = 1.0 - (10**log_regularizations).sum() if new_value < 0: new_value = -10 else: new_value = np.log10(new_value) log_regularizations[i] = new_value return log_regularizations class CrossvalMultipleRegularizations(object): """Class for computing crossvalidation scores of a fixation KDE with multiple regularization models n_jobs: number of parallel jobs to use in cross_val_score verbose: verbosity level for cross_val_score """ def __init__(self, stimuli, fixations, regularization_models: OrderedDict, crossvalidation, n_jobs=None, verbose=False): self.cv = crossvalidation self.n_jobs = n_jobs self.verbose = verbose X_areas = fixations_to_scikit_learn( fixations, normalize=stimuli, keep_aspect=True, add_shape=True, verbose=False ) self.X = fixations_to_scikit_learn( fixations, normalize=stimuli, keep_aspect=True, add_shape=False, add_fixation_number=True, verbose=False ) stimuli_sizes = np.array(stimuli.sizes) real_areas = stimuli_sizes[fixations.n, 0] * stimuli_sizes[fixations.n, 1] areas_gold = X_areas[:, 2] * X_areas[:, 3] self.mean_area = np.mean(areas_gold) correction = np.log(areas_gold) - np.log(real_areas) self.regularization_log_likelihoods = [] self.regularization_models = [] self.params = ['log_bandwidth'] for model_name, model in regularization_models.items(): model_lls = model.log_likelihoods(stimuli, fixations, verbose=True) self.regularization_log_likelihoods.append(model_lls - correction) self.params.append('log_{}'.format(model_name)) self.regularization_log_likelihoods = np.asarray(self.regularization_log_likelihoods).T def score(self, log_bandwidth, *args, **kwargs): for i, arg in enumerate(args): name = self.params[i+1] if name in kwargs: raise ValueError("double arguments!", args, kwargs) kwargs[name] = arg log_regularizations = np.array([kwargs[k] for k in self.params[1:]]) log_regularizations = _normalize_regularization_factors(log_regularizations) val = cross_val_score(GeneralMixtureKernelDensityEstimator( bandwidth=10**log_bandwidth, regularizations=10**log_regularizations, regularizing_log_likelihoods=self.regularization_log_likelihoods), self.X, cv=self.cv, verbose=self.verbose, n_jobs=self.n_jobs).sum() / len(self.X) / np.log(2) val += np.log2(self.mean_area) return val # baseline models class GoldModel(Model): def __init__(self, stimuli, fixations, bandwidth, eps = 1e-20, keep_aspect=False, verbose=False, **kwargs): super(GoldModel, self).__init__(**kwargs) self.stimuli = stimuli self.fixations = fixations self.bandwidth = bandwidth self.eps = eps self.keep_aspect = keep_aspect self.xs, self.ys = normalize_fixations(stimuli, fixations, keep_aspect=self.keep_aspect, verbose=verbose) self.shape_cache = {} def _log_density(self, stimulus): shape = stimulus.shape[0], stimulus.shape[1] stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) #fixations = self.fixations[self.fixations.n == stimulus_index] inds = self.fixations.n == stimulus_index if not inds.sum(): return UniformModel().log_density(stimulus) ZZ = np.zeros(shape) if self.keep_aspect: height, width = shape max_size = max(width, height) x_factor = max_size y_factor = max_size else: x_factor = shape[1] y_factor = shape[0] _fixations = np.array([self.ys[inds]*y_factor, self.xs[inds]*x_factor]).T fill_fixation_map(ZZ, _fixations) ZZ = gaussian_filter(ZZ, [self.bandwidth*y_factor, self.bandwidth*x_factor]) ZZ *= (1-self.eps) ZZ += self.eps * 1.0/(shape[0]*shape[1]) ZZ = np.log(ZZ) ZZ -= logsumexp(ZZ) #ZZ -= np.log(np.exp(ZZ).sum()) return ZZ class KDEGoldModel(Model): def __init__(self, stimuli, fixations, bandwidth, eps=1e-20, keep_aspect=False, verbose=False, grid_spacing=1, **kwargs): super(KDEGoldModel, self).__init__(**kwargs) self.stimuli = stimuli self.bandwidth = bandwidth self.eps = eps self.keep_aspect = keep_aspect self.grid_spacing = grid_spacing self.X = fixations_to_scikit_learn( fixations, normalize=self.stimuli, keep_aspect=self.keep_aspect, add_shape=False, verbose=False) self.stimulus_indices = fixations.n self.shape_cache = {} def _log_density(self, stimulus): shape = stimulus.shape[0], stimulus.shape[1] stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) inds = self.stimulus_indices == stimulus_index if not inds.sum(): return UniformModel().log_density(stimulus) X = self.X[inds] kde = KernelDensity(bandwidth=self.bandwidth).fit(X) height, width = shape if self.keep_aspect: max_size = max(height, width) rel_height = height / max_size rel_width = width / max_size else: rel_height = 1.0 rel_width = 1.0 # calculate the KDE score at the middle of each pixel: # for a width of 10 pixels, we are going to calculate at # 0.5, 1.5, ..., 9.5, since e.g. fixations with x coordinate between 0.0 and 1.0 # will be evaluated at pixel index 0. xs = np.linspace(0, rel_width, num=width, endpoint=False) + 0.5 * rel_width / width ys = np.linspace(0, rel_height, num=height, endpoint=False) + 0.5 * rel_height / height if self.grid_spacing > 1: xs = xs[::self.grid_spacing] ys = ys[::self.grid_spacing] XX, YY = np.meshgrid(xs, ys) XX_flat = XX.flatten() YY_flat = YY.flatten() scores = kde.score_samples(np.column_stack((XX_flat, YY_flat))) if self.grid_spacing == 1: scores = scores.reshape((height, width)) else: x_coordinates = np.arange(0, width)[::self.grid_spacing] y_coordinates = np.arange(0, height)[::self.grid_spacing] XX_coordinates, YY_coordinates = np.meshgrid(x_coordinates, y_coordinates) score_grid = np.empty((height, width)) * np.nan score_grid[YY_coordinates.flatten(), XX_coordinates.flatten()] = scores score_grid = inter_and_extrapolate(score_grid) scores = score_grid scores -= logsumexp(scores) ZZ = scores if self.eps: ZZ = np.logaddexp( np.log(1 - self.eps) + scores, np.log(self.eps) - np.log(height * width) ) ZZ -= logsumexp(ZZ) return ZZ class CrossvalidatedBaselineModel(Model): def __init__(self, stimuli, fixations, bandwidth, eps=1e-20, **kwargs): super(CrossvalidatedBaselineModel, self).__init__(**kwargs) self.stimuli = stimuli self.bandwidth = bandwidth self.eps = eps self.xs, self.ys = normalize_fixations(stimuli, fixations) self.fixations_n = fixations.n.copy() def _log_density(self, stimulus): shape = stimulus.shape[0], stimulus.shape[1] stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) inds = self.fixations_n != stimulus_index ZZ = np.zeros(shape) _fixations = np.array([self.ys[inds]*shape[0], self.xs[inds]*shape[1]]).T fill_fixation_map(ZZ, _fixations) ZZ = gaussian_filter(ZZ, [self.bandwidth*shape[0], self.bandwidth*shape[1]]) ZZ *= (1-self.eps) ZZ += self.eps * 1.0/(shape[0]*shape[1]) ZZ = np.log(ZZ) ZZ -= logsumexp(ZZ) return ZZ @hdf5_wrapper(mode='w') def to_hdf5(self, target, include_stimuli=True): target.attrs['type'] = np.bytes_('pysaliency.baseline_utils.CrossvalidatedBaselineModel') target.attrs['version'] = np.bytes_('1.0') target.attrs['bandwidth'] = self.bandwidth target.attrs['eps'] = self.eps target.create_dataset('xs', data=self.xs) target.create_dataset('ys', data=self.ys) target.create_dataset('fixations_n', data=self.fixations_n) if include_stimuli: stimuli_group = target.create_group('stimuli') self.stimuli.to_hdf5(stimuli_group) @classmethod @hdf5_wrapper(mode='r') def read_hdf5( cls, source, *, stimuli=None, caching=True, memory_cache_size=None, cache_location=None, ): from .hdf5 import read_hdf5 as _read_hdf5 data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type != 'pysaliency.baseline_utils.CrossvalidatedBaselineModel': raise ValueError("Invalid type! Expected 'pysaliency.baseline_utils.CrossvalidatedBaselineModel', got", data_type) if data_version != '1.0': raise ValueError("Invalid version! Expected '1.0', got", data_version) if stimuli is None: if 'stimuli' not in source: raise ValueError( "No stimuli found in HDF5 file. Pass stimuli= explicitly." ) stimuli = _read_hdf5(source['stimuli']) model = cls.__new__(cls) Model.__init__(model, cache_location=cache_location, caching=caching, memory_cache_size=memory_cache_size) model.bandwidth = source.attrs['bandwidth'] model.eps = source.attrs['eps'] model.xs = source['xs'][...] model.ys = source['ys'][...] model.fixations_n = source['fixations_n'][...] model.stimuli = stimuli return model class BaselineModel(Model): def __init__(self, stimuli, fixations, bandwidth, eps = 1e-20, keep_aspect=False, **kwargs): super(BaselineModel, self).__init__(**kwargs) self.bandwidth = bandwidth self.eps = eps self.keep_aspect = keep_aspect self.xs, self.ys = normalize_fixations(stimuli, fixations, keep_aspect=keep_aspect) self.shape_cache = {} @hdf5_wrapper(mode='w') def to_hdf5(self, target, include_shape_cache=True): target.attrs['type'] = np.bytes_('pysaliency.baseline_utils.BaselineModel') target.attrs['version'] = np.bytes_('1.0') target.attrs['bandwidth'] = self.bandwidth target.attrs['eps'] = self.eps target.attrs['keep_aspect'] = self.keep_aspect target.create_dataset('xs', data=self.xs) target.create_dataset('ys', data=self.ys) if include_shape_cache: shape_cache_group = target.create_group('shape_cache') for shape, value in self.shape_cache.items(): shape_key = f"{shape[0]},{shape[1]}" shape_cache_group.create_dataset(shape_key, data=value) @classmethod @hdf5_wrapper(mode='r') def read_hdf5( cls, source, *, caching=True, memory_cache_size=None, cache_location=None, reset_shape_cache=False, ): data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type != 'pysaliency.baseline_utils.BaselineModel': raise ValueError("Invalid type! Expected 'pysaliency.baseline_utils.BaselineModel', got", data_type) if data_version != '1.0': raise ValueError("Invalid version! Expected '1.0', got", data_version) model = cls.__new__(cls) Model.__init__(model, cache_location=cache_location, caching=caching, memory_cache_size=memory_cache_size) model.bandwidth = source.attrs['bandwidth'] model.eps = source.attrs['eps'] model.keep_aspect = bool(source.attrs['keep_aspect']) model.xs = source['xs'][...] model.ys = source['ys'][...] model.shape_cache = {} if not reset_shape_cache and 'shape_cache' in source: shape_cache_group = source['shape_cache'] for shape_key in shape_cache_group.keys(): shape = tuple(int(value) for value in shape_key.split(',')) model.shape_cache[shape] = shape_cache_group[shape_key][...] return model def _log_density(self, stimulus): shape = stimulus.shape[0], stimulus.shape[1] if shape not in self.shape_cache: ZZ = np.zeros(shape) height, width = shape if self.keep_aspect: max_size = max(height, width) y_factor = max_size x_factor = max_size else: y_factor = height x_factor = width _fixations = np.array([self.ys*y_factor, self.xs*x_factor]).T fill_fixation_map(ZZ, _fixations) ZZ = gaussian_filter(ZZ, [self.bandwidth*y_factor, self.bandwidth*x_factor]) ZZ *= (1-self.eps) ZZ += self.eps * 1.0/(shape[0]*shape[1]) ZZ = np.log(ZZ) ZZ -= logsumexp(ZZ) self.shape_cache[shape] = ZZ return self.shape_cache[shape] ================================================ FILE: pysaliency/dataset_config.py ================================================ from .datasets import read_hdf5, clip_out_of_stimulus_fixations, remove_out_of_stimulus_fixations from .filter_datasets import ( filter_fixations_by_number, filter_stimuli_by_number, filter_stimuli_by_size, train_split, validation_split, test_split, filter_scanpaths_by_attribute, filter_fixations_by_attribute, filter_stimuli_by_attribute, filter_scanpaths_by_length, remove_stimuli_without_fixations ) from schema import Schema, Optional dataset_config_schema = Schema({ 'stimuli': str, 'fixations': str, Optional('filters', default=[]): [{ 'type': str, Optional('parameters', default={}): dict, }], }) def load_dataset_from_config(config): config = dataset_config_schema.validate(config) stimuli = read_hdf5(config['stimuli']) fixations = read_hdf5(config['fixations']) for filter_config in config['filters']: stimuli, fixations = apply_dataset_filter_config(stimuli, fixations, filter_config) return stimuli, fixations def apply_dataset_filter_config(stimuli, fixations, filter_config): filter_dict = { 'filter_fixations_by_number': add_stimuli_argument(filter_fixations_by_number), 'filter_stimuli_by_number': filter_stimuli_by_number, 'filter_stimuli_by_size': filter_stimuli_by_size, 'clip_out_of_stimulus_fixations': _clip_out_of_stimulus_fixations, 'remove_out_of_stimulus_fixations': _remove_out_of_stimulus_fixations, 'train_split': train_split, 'validation_split': validation_split, 'test_split': test_split, 'filter_scanpaths_by_attribute': add_stimuli_argument(filter_scanpaths_by_attribute), 'filter_fixations_by_attribute': add_stimuli_argument(filter_fixations_by_attribute), 'filter_stimuli_by_attribute': filter_stimuli_by_attribute, 'filter_scanpaths_by_length': add_stimuli_argument(filter_scanpaths_by_length), 'remove_stimuli_without_fixations': remove_stimuli_without_fixations } if filter_config['type'] not in filter_dict: raise ValueError("Invalid filter name: {}".format(filter_config['type'])) filter_fn = filter_dict[filter_config['type']] return filter_fn(stimuli, fixations, **filter_config['parameters']) def _clip_out_of_stimulus_fixations(stimuli, fixations): clipped_fixations = clip_out_of_stimulus_fixations(fixations, stimuli=stimuli) return stimuli, clipped_fixations def _remove_out_of_stimulus_fixations(stimuli, fixations): filtered_fixations = remove_out_of_stimulus_fixations(stimuli, fixations) return stimuli, filtered_fixations def add_stimuli_argument(fn): def wrapped(stimuli, fixations, **kwargs): new_fixations = fn(fixations, **kwargs) return stimuli, new_fixations return wrapped ================================================ FILE: pysaliency/datasets/__init__.py ================================================ from typing import Dict, List, Optional, Union import numpy as np from ..hdf5 import read_hdf5 as _read_hdf5 from .fixations import Fixations, FixationTrains, ScanpathFixations, scanpaths_from_fixations from .scanpaths import Scanpaths, concatenate_scanpaths from .stimuli import FileStimuli, ObjectStimuli, Stimuli, StimuliStimulus, Stimulus, as_stimulus, check_prediction_shape, get_image_hash from .utils import concatenate_attributes, get_merged_attribute_list def read_hdf5(source, **kwargs): return _read_hdf5(source, _expected_kind='dataset', **kwargs) def create_subset(stimuli, fixations, stimuli_indices): """Create subset of stimuli and fixations using only stimuli with given indices. """ if isinstance(stimuli_indices, np.ndarray) and stimuli_indices.dtype == bool: if len(stimuli_indices) != len(stimuli): raise ValueError("length of mask doesn't match stimuli") stimuli_indices = np.nonzero(stimuli_indices)[0] new_stimuli = stimuli[stimuli_indices] if isinstance(fixations, FixationTrains): scanpath_inds = np.isin(fixations.scanpaths.n, stimuli_indices) index_list = list(stimuli_indices) new_pos = {i: index_list.index(i) for i in index_list} new_image_indices = [new_pos[i] for i in fixations.scanpaths.n[scanpath_inds]] new_fixations = fixations.filter_scanpaths(scanpath_inds) new_fixations.scanpaths.n = np.array(new_image_indices) new_fixation_ns = [new_pos[i] for i in new_fixations.n] new_fixations.n = np.array(new_fixation_ns) elif isinstance(fixations, ScanpathFixations): scanpath_inds = np.isin(fixations.scanpaths.n, stimuli_indices) index_list = list(stimuli_indices) new_pos = {i: index_list.index(i) for i in index_list} new_image_indices = [new_pos[i] for i in fixations.scanpaths.n[scanpath_inds]] new_scanpaths = fixations.scanpaths[scanpath_inds] new_scanpaths.n = np.array(new_image_indices) new_fixations = ScanpathFixations( scanpaths=new_scanpaths, ) else: scanpath_inds = np.isin(fixations.n, stimuli_indices) new_fixations = fixations[scanpath_inds] index_list = list(stimuli_indices) new_pos = {i: index_list.index(i) for i in index_list} new_fixation_ns = [new_pos[i] for i in new_fixations.n] new_fixations.n = np.array(new_fixation_ns) return new_stimuli, new_fixations def concatenate_stimuli(stimuli): attributes = {} for key in get_merged_attribute_list([list(s.attributes.keys()) for s in stimuli]): attributes[key] = concatenate_attributes(s.attributes[key] for s in stimuli) if all(isinstance(s, FileStimuli) for s in stimuli): return FileStimuli(sum([s.filenames for s in stimuli], []), attributes=attributes) else: return ObjectStimuli(sum([s.stimulus_objects for s in stimuli], []), attributes=attributes) def concatenate_fixations(fixations): if all(isinstance(f, FixationTrains) for f in fixations): return FixationTrains.concatenate(fixations) elif all(isinstance(f, ScanpathFixations) for f in fixations): return ScanpathFixations.concatenate(fixations) else: return Fixations.concatenate(fixations) def concatenate_datasets(stimuli, fixations): """Concatenate multiple Stimuli instances with associated fixations""" stimuli = list(stimuli) fixations = list(fixations) assert len(stimuli) == len(fixations) if len(stimuli) == 1: return stimuli[0], fixations[0] for i in range(len(fixations)): offset = sum(len(s) for s in stimuli[:i]) f = fixations[i].copy() f.n += offset if isinstance(f, ScanpathFixations): f.scanpaths.n += offset fixations[i] = f return concatenate_stimuli(stimuli), concatenate_fixations(fixations) def remove_out_of_stimulus_fixations(stimuli, fixations): """ Return all fixations which do not occour outside the stimulus """ widths = np.array([s[1] for s in stimuli.sizes]) heights = np.array([s[0] for s in stimuli.sizes]) inds = ((fixations.x >= 0) & (fixations.y >= 0) & (fixations.x < widths[fixations.n]) & (fixations.y < heights[fixations.n]) ) return fixations[inds] def clip_out_of_stimulus_fixations(fixations, stimuli=None, width=None, height=None): if stimuli is None and (width is None or height is None): raise ValueError("You have to provide either stimuli or width and height") if stimuli is not None: widths = np.array([s[1] for s in stimuli.sizes]) heights = np.array([s[0] for s in stimuli.sizes]) new_fixations = fixations.copy() x_max = widths[fixations.n] - 0.01 y_max = heights[fixations.n] - 0.01 else: x_max = width - 0.01 y_max = height - 0.01 new_fixations = fixations.copy() new_fixations.x = np.clip(new_fixations.x, a_min=0, a_max=x_max) new_fixations.y = np.clip(new_fixations.y, a_min=0, a_max=y_max) if isinstance(x_max, np.ndarray): x_max = x_max[:, np.newaxis] y_max = y_max[:, np.newaxis] new_fixations.x_hist = np.clip(new_fixations.x_hist, a_min=0, a_max=x_max) new_fixations.y_hist = np.clip(new_fixations.y_hist, a_min=0, a_max=y_max) if isinstance(fixations, FixationTrains): if stimuli is not None: x_max = widths[fixations.train_ns] - 0.01 y_max = heights[fixations.train_ns] - 0.01 x_max = x_max[:, np.newaxis] y_max = y_max[:, np.newaxis] else: x_max = width - 0.01 y_max = height - 0.01 x_max = widths[fixations.train_ns] - 0.01 y_max = heights[fixations.train_ns] - 0.01 new_fixations.train_xs = np.clip(new_fixations.train_xs, a_min=0, a_max=x_max[:, np.newaxis]) new_fixations.train_ys = np.clip(new_fixations.train_ys, a_min=0, a_max=y_max[:, np.newaxis]) return new_fixations def calculate_nonfixation_factors(stimuli, index): widths = np.asarray([s[1] for s in stimuli.sizes]).astype(float) heights = np.asarray([s[0] for s in stimuli.sizes]).astype(float) x_factors = stimuli.sizes[index][1] / widths y_factors = stimuli.sizes[index][0] / heights return x_factors, y_factors def create_nonfixations(stimuli, fixations, index, adjust_n = True, adjust_history=True): """Create nonfixations from fixations for given index stimuli of different sizes will be rescaled to match the target stimulus """ x_factors, y_factors = calculate_nonfixation_factors(stimuli, index) non_fixations = fixations[fixations.n != index] other_ns = non_fixations.n non_fixations.x = non_fixations.x * x_factors[other_ns] non_fixations.y = non_fixations.y * y_factors[other_ns] if adjust_history: non_fixations.x_hist = non_fixations.x_hist * x_factors[other_ns][:, np.newaxis] non_fixations.y_hist = non_fixations.y_hist * y_factors[other_ns][:, np.newaxis] if adjust_n: non_fixations.n = np.ones(len(non_fixations.n), dtype=int)*index return non_fixations ================================================ FILE: pysaliency/datasets/fixations.py ================================================ import json import warnings from typing import Dict, List, Optional, Tuple, Union import deprecation import numpy as np from tqdm import tqdm from ..utils import remove_trailing_nans, deprecated_class from ..utils.variable_length_array import VariableLengthArray from .scanpaths import Scanpaths from .utils import _load_attribute_dict_from_hdf5, concatenate_attributes, decode_string, get_merged_attribute_list, hdf5_wrapper class Fixations(object): """Capsules the fixations of a dataset and provides different methods of accessing them, e.g. in fixation trains, as conditional fixations or just all fixations at once. Fixations consist of: x: the x-position of the fixation y: the y-position of the fixation t: the time of the fixation x_hist: the previous x-positions in the history of this fixation y_hist: the previous y-positions in the history of this fixation t_hist: the previous times in the history of this fixation subject: the subject who made the fixation n: the number of the stimuli (optional, only needed when evaluating not on single images) Fixations support slicing via fixations[indices] as a shortcut for fixations.filter. Although all fixations have a history of previous fixations, these histories do not have to form a set of fixation sequences. For example, if a fixation has a previous fixation, this previous fixation does not have to be as a fixation of its on in the dataset. This is important because otherwise a lot of useful filtering operations would not be possible (e.g. filter for all fixations with at least one previous fixation to calculate saccade lengths). If you need fixation trains, use the subclass `FixationTrains`. """ def __init__(self, x: Union[List, np.ndarray], y: Union[List, np.ndarray], t: Union[List, np.ndarray], x_hist: Union[List, VariableLengthArray], y_hist: Union[List, VariableLengthArray], t_hist: Union[List, VariableLengthArray], n: Union[List, np.ndarray], subject: Optional[Union[List, np.ndarray]] = None, subjects: Optional[Union[List, np.ndarray]] = None, attributes: Optional[Dict[str, Union[np.ndarray, VariableLengthArray]]] = None): self.__attributes__ = [] self.x = np.asarray(x) self.y = np.asarray(y) self.t = np.asarray(t) self.n = np.asarray(n) # would be nice, is not yet supported. But we can simply pass the VariableLengthArray instead # if isinstance(x_hist, list): # x_hist = VariableLengthArray(x_hist) # self.lengths = x_hist.lengths if isinstance(x_hist, (list, np.ndarray)): x_hist = np.array(x_hist) self.scanpath_history_length = (1 - np.isnan(x_hist)).sum(axis=-1) x_hist = VariableLengthArray(x_hist, lengths=self.scanpath_history_length) elif isinstance(x_hist, VariableLengthArray): self.scanpath_history_length = x_hist.lengths y_hist = self._as_variable_length_array(y_hist) t_hist = self._as_variable_length_array(t_hist) if subjects is not None: warnings.warn("subjects is deprecated, use subject instead", DeprecationWarning, stacklevel=2) subject = subjects if subject is not None: self.__attributes__.append('subject') subject = np.asarray(subject) self.x_hist = x_hist self.y_hist = y_hist self.t_hist = t_hist self.subject = subject if not len(self.x) == len(self.y) == len(self.t) == len(self.x_hist) == len(self.y_hist) == len(self.t_hist) == len(self.n): raise ValueError("Lengths of fixations have to match") if self.subject is not None and not len(self.x) == len(self.subject): raise ValueError("Length of subject has to match number of fixations") if attributes is not None: for name, value in attributes.items(): if name not in self.__attributes__: self.__attributes__.append(name) if not len(value) == len(self.x): raise ValueError(f"Length of attribute '{name}' has to match number of fixations") setattr(self, name, value) def _check_lengths(self, other: VariableLengthArray): if not len(self) == len(other): raise ValueError("Length of scanpaths has to match") if not np.all(self.scanpath_history_length == other.lengths): raise ValueError("Lengths of scanpaths have to match") def _as_variable_length_array(self, data: Union[np.ndarray, VariableLengthArray]) -> VariableLengthArray: if not isinstance(data, VariableLengthArray): data = VariableLengthArray(data, self.scanpath_history_length) self._check_lengths(data) return data @property @deprecation.deprecated(deprecated_in="0.3.0", removed_in="1.0.0", details="Use `scanpath_history_length` instead") def lengths(self): return self.scanpath_history_length @property @deprecation.deprecated(deprecated_in="0.3.0", removed_in="1.0.0", details="Use `subject` instead") def subjects(self): return self.subject @classmethod @deprecation.deprecated(deprecated_in="0.3.0", removed_in="1.0.0", details="Use `FixationsWithoutHistory` instead") def create_without_history(cls, x, y, n, subjects=None): """ Create new fixation object from fixation data without time and optionally without subject information """ if subjects is None: subjects = np.ones(len(x)) return cls.FixationsWithoutHistory(x, y, np.zeros(len(x)), n, subjects) @classmethod def from_fixation_matrices(cls, matrices): """ create new Fixation object with fixations from fixation matrices Often, fixations are stored in fixation matrices: For each stimulus, there is a matrix of the same size as the image which ones in each fixated location and zero everywhere else. This method allows to construct a `Fixation` instance from such fixation matrices. >>> matrix1 = np.zeros((10,10)) >>> matrix1[5, 2] = 1 >>> matrix1[3, 3] = 1 >>> matrix2 = np.zeros((20, 30)) >>> matrix2[10, 20] = 1 >>> fixations = pysaliency.Fixation.from_fixation_matrices( [matrix1, matrix2]) """ xs = [] ys = [] ns = [] for _n, matrix in enumerate(matrices): y, x = np.nonzero(matrix) n = [_n] * len(y) xs.append(x) ys.append(y) ns.append(n) x = np.hstack(xs).astype(float) y = np.hstack(ys).astype(float) n = np.hstack(ns) return cls.create_without_history(x, y, n) @classmethod def concatenate(cls, fixations): kwargs = {} for key in ['x', 'y', 't', 'x_hist', 'y_hist', 't_hist', 'n', 'subject']: kwargs[key] = concatenate_attributes(getattr(f, key) for f in fixations) attributes = get_merged_attribute_list([f.__attributes__ for f in fixations]) attribute_dict = {} for key in attributes: if key == 'subject': continue attribute_dict[key] = concatenate_attributes(getattr(f, key) for f in fixations) kwargs['attributes'] = attribute_dict new_fixations = cls(**kwargs) return new_fixations def __getitem__(self, indices): return self.filter(indices) def __len__(self): return len(self.x) def filter(self, inds): """ Create new fixations object which contains only the fixations with indexes in inds .. note:: The fixation trains of the object are left as is. Filtering the fixation trains is not possible as the indices may include only some fixation of a fixation train. The attributes `consistent_fixation_trains` tracks whether a `Fixations` instance still has consistent fixation trains. The return of this function will be marked to have inconsistent fixation trains. If you need to filter with consistent fixation trains, use `Fixations.filter_fixation_trains`. """ kwargs = {} other_attributes = {} def filter_array(name): result = getattr(self, name)[inds] # Fancy/boolean indexing already returns copies; only slices return views if isinstance(inds, slice): result = result.copy() kwargs[name] = result for name in ['x', 'y', 't', 'x_hist', 'y_hist', 't_hist', 'n']: filter_array(name) for name in self.__attributes__: filter_array(name) if name != 'subject': other_attributes[name] = kwargs.pop(name) kwargs['attributes'] = other_attributes return Fixations(**kwargs) def _get_previous_values(self, name, index): """return fixations.name[np.arange(len(fixations.name)),index]""" a = getattr(self, name) inds = np.arange(len(a)) if index >= 0: return a[inds, index] else: indexes = self.scanpath_history_length + index return a[inds, indexes] def get_saccade(self, index = -1): """ Return saccades for all fixations. @type index: integer @param index: index of the saccade to return. `index==-1` returns the the last saccades of all fixations etc. @return dx, dy, dt of the saccade Example: dx, dy, dt = fixations.get_saccade(-1) mean_saccade_length = np.sqrt(dx**2+dy**2).mean() """ if index > 0: raise NotImplemented() if index == -1: x1 = self.x y1 = self.y t1 = self.t else: x1 = self._get_previous_values('x_hist', index+1) y1 = self._get_previous_values('y_hist', index+1) t1 = self._get_previous_values('t_hist', index+1) dx = x1 - self._get_previous_values('x_hist', index) dy = y1 - self._get_previous_values('y_hist', index) dt = t1 - self._get_previous_values('t_hist', index) return dx, dy, dt #return np.vstack((dy,dx)).T @property def x_int(self): """ x coordinates of the fixations, converted to integers """ return np.asarray(self.x, dtype=int) @property def y_int(self): """ y coordinates of the fixations, converted to integers """ return np.asarray(self.y, dtype=int) @property def subject_count(self): return int(self.subject.max())+1 def copy(self): cfix = Fixations(self.x.copy(), self.y.copy(), self.t.copy(), self.x_hist.copy(), self.y_hist.copy(), self.t_hist.copy(), self.n.copy(), self.subject.copy() if self.subject is not None else None) cfix.__attributes__ = list(self.__attributes__) for name in self.__attributes__: setattr(cfix, name, getattr(self, name).copy()) return cfix @classmethod def FixationsWithoutHistory(cls, x, y, t, n, subject=None, subjects=None): if subjects is not None: warnings.warn("subjects is deprecated, use subject instead", DeprecationWarning, stacklevel=2) subject = subjects x_hist = np.empty((len(x), 1)) x_hist[:] = np.nan y_hist = np.empty((len(x), 1)) y_hist[:] = np.nan t_hist = np.empty((len(x), 1)) t_hist[:] = np.nan return cls(x, y, t, x_hist, y_hist, t_hist, n, subject) @hdf5_wrapper(mode='w') def to_hdf5(self, target): """ Write fixations to hdf5 file or hdf5 group """ target.attrs['type'] = np.bytes_('Fixations') target.attrs['version'] = np.bytes_('1.2') variable_length_arrays = [] for attribute in ['x', 'y', 't', 'x_hist', 'y_hist', 't_hist', 'n', 'lengths'] + self.__attributes__: if attribute == 'lengths': data = self.scanpath_history_length else: data = getattr(self, attribute) if isinstance(data, VariableLengthArray): variable_length_arrays.append(attribute) data = data._data target.create_dataset(attribute, data=data) target.attrs['__attributes__'] = np.bytes_(json.dumps(self.__attributes__)) target.attrs['__variable_length_arrays__'] = np.bytes_(json.dumps(sorted(variable_length_arrays))) @classmethod @hdf5_wrapper(mode='r') def read_hdf5(cls, source): """ Read fixations from hdf5 file or hdf5 group """ # TODO: rewrite to use constructor instead of manipulating the object directly data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type != 'Fixations': raise ValueError("Invalid type! Expected 'Fixations', got", data_type) if data_version not in ['1.0', '1.1', '1.2']: raise ValueError("Invalid version! Expected '1.0', '1.1' or '1.2', got", data_version) if data_version < '1.2': data = {key: source[key][...] for key in ['x', 'y', 't', 'x_hist', 'y_hist', 't_hist', 'n', 'subjects']} data['subject'] = data['subjects'] del data['subjects'] else: data = {key: source[key][...] for key in ['x', 'y', 't', 'x_hist', 'y_hist', 't_hist', 'n', 'subject']} fixations = cls(**data) json_attributes = source.attrs['__attributes__'] if not isinstance(json_attributes, str): json_attributes = json_attributes.decode('utf8') __attributes__ = json.loads(json_attributes) fixations.__attributes__ = list(__attributes__) if data_version >= '1.1': lengths = source['lengths'][...] json_variable_length_arrays = source.attrs['__variable_length_arrays__'] if not isinstance(json_variable_length_arrays, str): json_variable_length_arrays = json_variable_length_arrays.decode('utf8') variable_length_arrays = json.loads(json_variable_length_arrays) else: lengths = fixations.scanpath_history_length variable_length_arrays = ['x_hist', 'y_hist', 't_hist'] + [key for key in __attributes__ if key.endswith('_hist')] for key in __attributes__: data = source[key][...] if key in variable_length_arrays: data = VariableLengthArray(data, lengths) if key == 'subjects' and data_version < '1.2': key = 'subject' fixations.__attributes__[fixations.__attributes__.index('subjects')] = 'subject' setattr(fixations, key, data) return fixations class ScanpathFixations(Fixations): """ Contains fixations which come from full scanpaths (as opposed to potentially only some fixations of a scanpath). """ def __init__(self, scanpaths: Scanpaths): N_fixations = scanpaths.length.sum() max_scanpath_length = scanpaths.length.max() if len(scanpaths) else 0 max_history_length = max(max_scanpath_length - 1, 0) if N_fixations == 0: # Edge case: no fixations at all x = np.empty(0) y = np.empty(0) t = np.empty(0) n = np.empty(0, dtype=int) scanpath_index = np.empty(0, dtype=int) x_hist = VariableLengthArray(np.empty((0, 0)), np.empty(0, dtype=int)) y_hist = VariableLengthArray(np.empty((0, 0)), np.empty(0, dtype=int)) t_hist = VariableLengthArray(np.empty((0, 0)), np.empty(0, dtype=int)) hist_lengths = np.empty(0, dtype=int) mask = np.empty((0, 0), dtype=bool) row_src = np.empty(0, dtype=int) col_src = np.empty(0, dtype=int) scanpath_indices = np.empty(0, dtype=int) fix_indices = np.empty(0, dtype=int) else: # Precompute index arrays scanpath_indices = np.repeat(np.arange(len(scanpaths)), scanpaths.length) fix_indices = np.concatenate([np.arange(length) for length in scanpaths.length]) # Direct values via advanced indexing x = scanpaths.xs._data[scanpath_indices, fix_indices] y = scanpaths.ys._data[scanpath_indices, fix_indices] t = scanpaths.ts._data[scanpath_indices, fix_indices] n = scanpaths.n[scanpath_indices] scanpath_index = scanpath_indices.copy() # History arrays hist_lengths = fix_indices col_indices = np.arange(max_history_length) mask = col_indices[None, :] < hist_lengths[:, None] row_src = np.broadcast_to(scanpath_indices[:, None], mask.shape)[mask] col_src = np.broadcast_to(col_indices[None, :], mask.shape)[mask] x_hist_data = np.full((N_fixations, max_history_length), np.nan) x_hist_data[mask] = scanpaths.xs._data[row_src, col_src] x_hist = VariableLengthArray(x_hist_data, hist_lengths) y_hist_data = np.full((N_fixations, max_history_length), np.nan) y_hist_data[mask] = scanpaths.ys._data[row_src, col_src] y_hist = VariableLengthArray(y_hist_data, hist_lengths) t_hist_data = np.full((N_fixations, max_history_length), np.nan) t_hist_data[mask] = scanpaths.ts._data[row_src, col_src] t_hist = VariableLengthArray(t_hist_data, hist_lengths) # Scanpath attributes auto_attributes = [] attributes = { 'scanpath_index': scanpath_index, } for attribute_name, value in scanpaths.scanpath_attributes.items(): new_attribute_name = scanpaths.attribute_mapping.get(attribute_name, attribute_name) if new_attribute_name in attributes: raise ValueError("attribute name clash: {new_attribute_name}".format(new_attribute_name=new_attribute_name)) attributes[new_attribute_name] = np.repeat(value, scanpaths.length, axis=0) auto_attributes.append(new_attribute_name) # Fixation attributes + histories for attribute_name, value in scanpaths.fixation_attributes.items(): if attribute_name == 'ts': continue new_attribute_name = scanpaths.attribute_mapping.get(attribute_name, attribute_name) if new_attribute_name in attributes: raise ValueError("attribute name clash: {new_attribute_name}".format(new_attribute_name=new_attribute_name)) if N_fixations == 0: attributes[new_attribute_name] = np.empty(0) else: attributes[new_attribute_name] = value._data[scanpath_indices, fix_indices] auto_attributes.append(new_attribute_name) hist_attribute_name = new_attribute_name + '_hist' if hist_attribute_name in attributes: raise ValueError("attribute name clash: {hist_attribute_name}".format(hist_attribute_name=hist_attribute_name)) if N_fixations == 0: attributes[hist_attribute_name] = VariableLengthArray(np.empty((0, 0)), np.empty(0, dtype=int)) else: hist_data = np.full((N_fixations, max_history_length), fill_value=np.nan) hist_data[mask] = value._data[row_src, col_src] attributes[hist_attribute_name] = VariableLengthArray(hist_data, hist_lengths) auto_attributes.append(hist_attribute_name) subject = np.empty(N_fixations, dtype=int) super().__init__( x=x, y=y, t=t, x_hist=x_hist, y_hist=y_hist, t_hist=t_hist, n=n, subject=subject, attributes=attributes ) self.scanpaths = scanpaths self.auto_attributes = auto_attributes def copy(self) -> 'ScanpathFixations': copied_scanpaths = self.scanpaths.copy() return ScanpathFixations(scanpaths=copied_scanpaths) @classmethod def concatenate(cls, scanpath_fixations: List['ScanpathFixations']) -> 'ScanpathFixations': concatenated_scanpaths = Scanpaths.concatenate([sf.scanpaths for sf in scanpath_fixations]) return ScanpathFixations(scanpaths=concatenated_scanpaths) def filter_scanpaths(self, indices) -> 'ScanpathFixations': filtered_scanpaths = self.scanpaths[indices] return ScanpathFixations(scanpaths=filtered_scanpaths) @hdf5_wrapper(mode='w') def to_hdf5(self, target): """ Write ScanpathFixations to hdf5 file or hdf5 group """ target.attrs['type'] = np.bytes_('ScanpathFixations') target.attrs['version'] = np.bytes_('1.0') self.scanpaths.to_hdf5(target.create_group('scanpaths')) @classmethod @hdf5_wrapper(mode='r') def read_hdf5(cls, source): """ Read ScanpathFixations from hdf5 file or hdf5 group """ data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type == 'FixationTrains': fixation_trains = FixationTrains.read_hdf5(source) if fixation_trains.non_auto_attributes: print("NA", fixation_trains.non_auto_attributes) non_auto_attributes = ', '.join(fixation_trains.non_auto_attributes) raise ValueError(f"FixationTrains object has non-auto attributes ({non_auto_attributes}), can't convert to ScanpathFixations") return cls(scanpaths=fixation_trains.scanpaths) if data_type != 'ScanpathFixations': raise ValueError("Invalid type! Expected 'ScanpathFixations', got", data_type) if data_version != '1.0': raise ValueError("Invalid version! Expected '1.0', got", data_version) scanpaths = Scanpaths.read_hdf5(source['scanpaths']) return cls(scanpaths=scanpaths) @deprecated_class(deprecated_in="0.3.0", removed_in="1.0.0", details="Use `ScanpathFixations` instead") class FixationTrains(ScanpathFixations): """ Capsules the fixations of a dataset as fixation trains. Additionally to `Fixations`, `FixationTrains`-instances have the attributes train_xs: 2d array (number_of_trains, maximum_length_of_train) train_ys: 2d array (number_of_trains, maximum_length_of_train) train_ts: 2d array (number_of_trains, maximum_length_of_train) train_ns: 1d array (number_of_trains) train_subjects: 1d array (number_of_trains) scanpath_attributes: dictionary of attributes applying to full scanpaths, e.g. task {attribute_name: $NUM_SCANPATHS-length-list} scanpath attributes will automatically also become attributes scanpath_fixation_attribute: dictionary of attributes applying to fixations in the scanpath, e.g. duration {attribute_name: $NUM_SCANPATH x $NUM_FIXATIONS_IN_SCANPATH} scanpath fixation attributes will generate two attributes: the value for each fixation and the history for previous fixations. E.g. a scanpath fixation attribute "durations" will generate an attribute "durations" and an attribute "durations_hist" """ def __init__(self, train_xs, train_ys, train_ts, train_ns, train_subjects, scanpath_attributes=None, scanpath_fixation_attributes=None, attributes=None, scanpath_attribute_mapping=None, scanpaths=None): # raise ValueError("DON'T USE FIXATIONTRAINS ANYMORE, USE SCANPATHFIXATIONS INSTEAD") if isinstance(train_xs, Scanpaths): scanpaths = train_xs elif scanpaths is None: scanpath_attributes = scanpath_attributes or {} scanpath_attribute_mapping = scanpath_attribute_mapping or {} scanpath_fixation_attributes = scanpath_fixation_attributes or {} lengths = [len(remove_trailing_nans(xs)) for xs in train_xs] scanpaths = Scanpaths( xs=train_xs, ys=train_ys, ts=train_ts, n=train_ns, subject=train_subjects, length=lengths, scanpath_attributes=scanpath_attributes, fixation_attributes=scanpath_fixation_attributes, attribute_mapping=scanpath_attribute_mapping, ) super().__init__(scanpaths=scanpaths) if attributes is None: attributes = {} if attributes: warnings.warn("Don't use attributes for FixationTrains, use scanpath_attributes or scanpath_fixation_attributes instead! FixationTrains is deprecated, the successor ScanpathFixations doesn't support attributes anymore", stacklevel=2, category=DeprecationWarning) if attributes: self.__attributes__ = list(self.__attributes__) for name, value in attributes.items(): if name not in self.__attributes__: self.__attributes__.append(name) else: raise ValueError(f"attribute {name} already set!") if not len(value) == len(self): raise ValueError(f"Length of attribute '{name}' has to match number of fixations") setattr(self, name, value) self.full_nonfixations = None @classmethod def from_scanpaths(cls, scanpaths: Scanpaths, attributes: Optional[Dict]=None): return cls( train_xs=None, train_ys=None, train_ts=None, train_ns=None, train_subjects=None, attributes=attributes, scanpaths=scanpaths ) @property def train_xs(self) -> VariableLengthArray: return self.scanpaths.xs @property def train_ys(self) -> VariableLengthArray: return self.scanpaths.ys @property def train_ts(self) -> VariableLengthArray: return self.scanpaths.ts @property def train_ns(self) -> np.ndarray: return self.scanpaths.n @property def train_subjects(self) -> VariableLengthArray: return self.scanpaths.subject @property def train_lengths(self) -> np.ndarray: return self.scanpaths.length @property def scanpath_attributes(self) -> Dict[str, np.ndarray]: return { key: value for key, value in self.scanpaths.scanpath_attributes.items() if key != 'subject' } @property def scanpath_fixation_attributes(self) -> Dict[str, VariableLengthArray]: return { key: value for key, value in self.scanpaths.fixation_attributes.items() if key != 'ts' } @property def scanpath_attribute_mapping(self) -> Dict[str, str]: return { key: value for key, value in self.scanpaths.attribute_mapping.items() if key != 'ts' } @property def non_auto_attributes(self): """lists all attributes of this `FixationTrains` instance which are not auto generated from scanpath attributes""" return [attribute_name for attribute_name in self.__attributes__ if attribute_name not in self.auto_attributes + ['scanpath_index']] @classmethod def concatenate(cls, fixation_trains: List['FixationTrains']) -> 'FixationTrains': concatenated_scanpaths = Scanpaths.concatenate([f.scanpaths for f in fixation_trains]) attributes = get_merged_attribute_list([f.non_auto_attributes for f in fixation_trains]) attribute_dict = {} for key in attributes: attribute_dict[key] = concatenate_attributes(getattr(f, key) for f in fixation_trains) return cls.from_scanpaths(concatenated_scanpaths, attributes=attribute_dict) def set_scanpath_attribute(self, name, data, fixation_attribute_name=None): """Sets a scanpath attribute name: name of scanpath attribute data: data of scanpath attribute, has to be of same length as number of scanpaths fixation_attribute: name of automatically generated fixation attribute if it should be different than scanpath attribute name """ if not len(data) == len(self.train_xs): raise ValueError(f'Length of scanpath attribute data has to match number of scanpaths: {len(data)} != {len(self.train_xs)}') self.scanpath_attributes[name] = data if fixation_attribute_name is not None: self.scanpath_attribute_mapping[name] = fixation_attribute_name new_attribute_name = self.scanpath_attribute_mapping.get(name, name) if new_attribute_name in self.attributes and new_attribute_name not in self.auto_attributes: raise ValueError("attribute name clash: {new_attribute_name}".format(new_attribute_name=new_attribute_name)) attribute_shape = np.asarray(data[0]).shape self.attributes[new_attribute_name] = np.empty([len(self.train_xs)] + list(attribute_shape), dtype=data.dtype) if new_attribute_name not in self.auto_attributes: self.auto_attributes.append(new_attribute_name) out_index = 0 for train_index in range(len(self.train_xs)): fix_length = (1 - np.isnan(self.train_xs[train_index])).sum() for _ in range(fix_length): self.attributes[new_attribute_name][out_index] = self.scanpath_attributes[name][train_index] out_index += 1 def copy(self): copied_attributes = {} for attribute_name in self.__attributes__: if attribute_name in ['scanpath_index'] + self.auto_attributes: continue copied_attributes[attribute_name] = getattr(self, attribute_name).copy() copied_scanpaths = FixationTrains( train_xs=self.train_xs.copy(), train_ys=self.train_ys.copy(), train_ts=self.train_ts.copy(), train_ns=self.train_ns.copy(), train_subjects=self.train_subjects.copy(), scanpath_attributes={ key: value.copy() for key, value in self.scanpath_attributes.items() } if self.scanpath_attributes else None, scanpath_fixation_attributes={ key: value.copy() for key, value in self.scanpath_fixation_attributes.items() } if self.scanpath_fixation_attributes else None, scanpath_attribute_mapping=dict(self.scanpath_attribute_mapping), attributes=copied_attributes if copied_attributes else None, ) return copied_scanpaths def filter_scanpaths(self, indices): """ Create new fixations object which contains only the scanpaths indicated. """ filtered_scanpaths = self.scanpaths[indices] scanpath_indices = np.arange(len(self.scanpaths), dtype=int)[indices] fixation_indices = np.isin(self.scanpath_index, scanpath_indices) attributes = { attribute_name: getattr(self, attribute_name)[fixation_indices] for attribute_name in self.__attributes__ if attribute_name not in ['scanpath_index'] + self.auto_attributes } return type(self).from_scanpaths( scanpaths=filtered_scanpaths, attributes=attributes ) @deprecation.deprecated(deprecated_in="0.3.0", removed_in="1.0.0", details="Use `FixationTrains.filter_scanpaths` instead") def filter_fixation_trains(self, indices): """ Create new fixations object which contains only the scanpaths indicated. """ return self.filter_scanpaths(indices) def fixation_trains(self): """Yield for every fixation train of the dataset: xs, ys, ts, n, subject """ for i in range(len(self.train_xs)): length = (1 - np.isnan(self.train_xs[i])).sum() xs = self.train_xs[i][:length] ys = self.train_ys[i][:length] ts = self.train_ts[i][:length] n = self.train_ns[i] subject = self.train_subjects[i] yield xs, ys, ts, n, subject @classmethod def from_fixation_trains(cls, xs, ys, ts, ns, subject=None, subjects=None, attributes=None, scanpath_attributes=None, scanpath_fixation_attributes=None, scanpath_attribute_mapping=None): """ Create Fixation object from fixation trains. - xs, ys, ts: Lists of array_like of double. Each array has to contain the data from one fixation train. - ns, subjects: lists of int. ns has to contain the image index for each fixation train, subjects the subject index for each fixation train """ if subjects is not None: warnings.warn("subjects is deprecated, use subject instead", DeprecationWarning, stacklevel=2) subject = subjects maxlength = max([len(x_train) for x_train in xs]) train_xs = np.empty((len(xs), maxlength)) train_xs[:] = np.nan train_ys = np.empty((len(xs), maxlength)) train_ys[:] = np.nan train_ts = np.empty((len(xs), maxlength)) train_ts[:] = np.nan train_ns = np.empty(len(xs), dtype=int) train_subjects = np.empty(len(xs), dtype=int) padded_scanpath_fixation_attributes = {} if scanpath_fixation_attributes is not None: for key, value in scanpath_fixation_attributes.items(): assert len(value) == len(xs) if isinstance(value, list): padded_scanpath_fixation_attributes[key] = np.full((len(xs), maxlength), fill_value=np.nan, dtype=float) for i in range(len(train_xs)): length = len(xs[i]) train_xs[i, :length] = xs[i] train_ys[i, :length] = ys[i] train_ts[i, :length] = ts[i] train_ns[i] = ns[i] train_subjects[i] = subject[i] for attribute_name in padded_scanpath_fixation_attributes.keys(): padded_scanpath_fixation_attributes[attribute_name][i, :length] = scanpath_fixation_attributes[attribute_name][i] return cls( train_xs, train_ys, train_ts, train_ns, train_subjects, attributes=attributes, scanpath_attributes=scanpath_attributes, scanpath_fixation_attributes=padded_scanpath_fixation_attributes, scanpath_attribute_mapping=scanpath_attribute_mapping) def generate_crossval(self, splitcount = 10): train_xs_training = [] train_xs_eval = [] train_ys_training = [] train_ys_eval = [] train_ts_training = [] train_ts_eval = [] train_ns_training = [] train_ns_eval = [] train_subjects_training = [] train_subjects_eval = [] # We have to make the crossvalidation data # reproducible. Therefore we use a # RandomState with fixed seed for the shuffling. rs = np.random.RandomState(42) for n in range(self.n.max()+1): inds = np.nonzero(self.train_ns == n)[0] rs.shuffle(inds) parts = np.array_split(inds, splitcount) for eval_index in range(splitcount): for index in range(splitcount): part = parts[index] if len(part) == 0: continue xs = self.train_xs[part] ys = self.train_ys[part] ts = self.train_ts[part] ns = self.train_ns[part] subjects = self.train_subjects[part] if index == eval_index: train_xs_eval.append(xs) train_ys_eval.append(ys) train_ts_eval.append(ts) train_ns_eval.append(ns * splitcount + eval_index) train_subjects_eval.append(subjects) else: train_xs_training.append(xs) train_ys_training.append(ys) train_ts_training.append(ts) train_ns_training.append(ns * splitcount + eval_index) train_subjects_training.append(subjects) train_xs_eval = np.vstack(train_xs_eval) train_ys_eval = np.vstack(train_ys_eval) train_ts_eval = np.vstack(train_ts_eval) train_ns_eval = np.hstack(train_ns_eval) train_subjects_eval = np.hstack(train_subjects_eval) train_xs_training = np.vstack(train_xs_training) train_ys_training = np.vstack(train_ys_training) train_ts_training = np.vstack(train_ts_training) train_ns_training = np.hstack(train_ns_training) train_subjects_training = np.hstack(train_subjects_training) fixations_training = type(self).from_fixation_trains(train_xs_training, train_ys_training, train_ts_training, train_ns_training, train_subjects_training) fixations_evaluation = type(self).from_fixation_trains(train_xs_eval, train_ys_eval, train_ts_eval, train_ns_eval, train_subjects_eval) return fixations_training, fixations_evaluation def shuffle_fixations(self, stimuli=None): new_indices = [] new_ns = [] if stimuli: widths = np.asarray([s[1] for s in stimuli.sizes]).astype(float) heights = np.asarray([s[0] for s in stimuli.sizes]).astype(float) x_factors = [] y_factors = [] for n in range(self.n.max()+1): inds = np.nonzero(~(self.n == n))[0] new_indices.extend(inds) new_ns.extend([n]*len(inds)) if stimuli: other_ns = self.n[inds] x_factors.extend(stimuli.sizes[n][1]/widths[other_ns]) y_factors.extend(stimuli.sizes[n][0]/heights[other_ns]) new_fixations = self[new_indices] new_fixations.n = np.asarray(new_ns) if stimuli: x_factors = np.asarray(x_factors) y_factors = np.asarray(y_factors) new_fixations.x = x_factors*new_fixations.x new_fixations.x_hist = x_factors[:, np.newaxis]*new_fixations.x_hist new_fixations.y = y_factors*new_fixations.y new_fixations.y_hist = y_factors[:, np.newaxis]*new_fixations.y_hist return new_fixations def shuffle_fixation_trains(self, stimuli=None): """ """ if not self.consistent_fixation_trains: raise ValueError('Cannot shuffle fixation trains as fixation trains not consistent!') train_xs = [] train_ys = [] train_ts = [] train_ns = [] train_subjects = [] for n in range(self.n.max()+1): inds = ~(self.train_ns == n) train_xs.append(self.train_xs[inds]) train_ys.append(self.train_ys[inds]) train_ts.append(self.train_ts[inds]) train_ns.append(np.ones(inds.sum(), dtype=int)*n) train_subjects.append(self.train_subjects[inds]) train_xs = np.vstack(train_xs) train_ys = np.vstack(train_ys) train_ts = np.vstack(train_ts) train_ns = np.hstack(train_ns) train_subjects = np.hstack(train_subjects) full_nonfixations = type(self)(train_xs, train_ys, train_ts, train_ns, train_subjects) #self.full_nonfixations = full_nonfixations return full_nonfixations def generate_full_nonfixations(self, stimuli=None): """ Generate nonfixational distribution from this fixation object by using all fixation trains of other images. The individual fixation trains will be left intact. .. warning:: This function operates on the fixation trains. Therefore, for filtered fixation objects it might return wrong results. """ if self.full_nonfixations is not None: print("Reusing nonfixations!") return self.full_nonfixations train_xs = [] train_ys = [] train_ts = [] train_ns = [] train_subjects = [] for n in range(self.n.max()+1): inds = ~(self.train_ns == n) train_xs.append(self.train_xs[inds]) train_ys.append(self.train_ys[inds]) train_ts.append(self.train_ts[inds]) train_ns.append(np.ones(inds.sum(), dtype=int)*n) train_subjects.append(self.train_subjects[inds]) train_xs = np.vstack(train_xs) train_ys = np.vstack(train_ys) train_ts = np.vstack(train_ts) train_ns = np.hstack(train_ns) train_subjects = np.hstack(train_subjects) full_nonfixations = type(self)(train_xs, train_ys, train_ts, train_ns, train_subjects) self.full_nonfixations = full_nonfixations return full_nonfixations def generate_nonfixation_partners(self, seed=42): """Generate nonfixational distribution from this fixation object such that for every fixation in the original fixation object there is a corresponding fixation on the same image but on a different position that comes from some other fixation. This destroys the temporal ordering of the fixation trains.""" train_xs = self.train_xs.copy() train_ys = self.train_ys.copy() train_ts = self.train_ts.copy() train_ns = self.train_ns.copy() train_subjects = self.train_subjects.copy() rs = np.random.RandomState(seed) for train_index in range(len(train_ns)): n = train_ns[train_index] inds = np.nonzero(self.n != n)[0] length = (1 - np.isnan(train_xs[train_index])).sum() for i in range(length): new_fix_index = rs.choice(inds) train_xs[train_index][i] = self.x[new_fix_index] train_ys[train_index][i] = self.y[new_fix_index] train_ts[train_index][i] = self.t[new_fix_index] return type(self)(train_xs, train_ys, train_ts, train_ns, train_subjects) @hdf5_wrapper(mode='w') def to_hdf5(self, target): """ Write fixationtrains to hdf5 file or hdf5 group """ target.attrs['type'] = np.bytes_('FixationTrains') target.attrs['version'] = np.bytes_('1.3') variable_length_arrays = [] for attribute in ['train_xs', 'train_ys', 'train_ts', 'train_ns', 'train_subjects', 'train_lengths'] + self.__attributes__: if attribute in ['subjects', 'scanpath_index'] + self.auto_attributes: continue data = getattr(self, attribute) if isinstance(data, VariableLengthArray): variable_length_arrays.append(attribute) data = data._data target.create_dataset(attribute, data=data) saved_attributes = [attribute_name for attribute_name in self.__attributes__ if attribute_name not in self.auto_attributes] target.attrs['__attributes__'] = np.bytes_(json.dumps(saved_attributes)) target.attrs['scanpath_attribute_mapping'] = np.bytes_(json.dumps(self.scanpath_attribute_mapping)) scanpath_attributes_group = target.create_group('scanpath_attributes') for attribute_name, attribute_value in self.scanpath_attributes.items(): scanpath_attributes_group.create_dataset(attribute_name, data=attribute_value) scanpath_attributes_group.attrs['__attributes__'] = np.bytes_(json.dumps(sorted(self.scanpath_attributes.keys()))) scanpath_fixation_attributes_group = target.create_group('scanpath_fixation_attributes') for attribute_name, attribute_value in self.scanpath_fixation_attributes.items(): scanpath_fixation_attributes_group.create_dataset(attribute_name, data=attribute_value._data) scanpath_fixation_attributes_group.attrs['__attributes__'] = np.bytes_(json.dumps(sorted(self.scanpath_fixation_attributes.keys()))) @classmethod @hdf5_wrapper(mode='r') def read_hdf5(cls, source): """ Read train fixations from hdf5 file or hdf5 group """ data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type != 'FixationTrains': raise ValueError("Invalid type! Expected 'FixationTrains', got", data_type) valid_versions = ['1.0', '1.1', '1.2', '1.3'] if data_version not in valid_versions: raise ValueError("Invalid version! Expected one of {}, got {}".format(', '.join(valid_versions), data_version)) data = {key: source[key][...] for key in ['train_xs', 'train_ys', 'train_ts', 'train_ns', 'train_subjects']} json_attributes = decode_string(source.attrs['__attributes__']) attribute_names = list(json.loads(json_attributes)) attributes = {} for key in attribute_names: if key in ['scanpath_index']: continue if data_version < '1.3' and key == 'subjects': continue attributes[key] = source[key][...] data['attributes'] = attributes if data_version < '1.1': data['scanpath_attributes'] = {} else: data['scanpath_attributes'] = _load_attribute_dict_from_hdf5(source['scanpath_attributes']) if data_version < '1.2': data['scanpath_fixation_attributes'] = {} data['scanpath_attribute_mapping'] = {} else: data['scanpath_fixation_attributes'] = _load_attribute_dict_from_hdf5(source['scanpath_fixation_attributes']) data['scanpath_attribute_mapping'] = json.loads(decode_string(source.attrs['scanpath_attribute_mapping'])) if data_version < '1.3': train_lengths = np.array([len(remove_trailing_nans(data['train_xs'][i])) for i in range(len(data['train_xs']))]) else: train_lengths = source['train_lengths'][...] data['scanpath_fixation_attributes'] = { key: VariableLengthArray(value, train_lengths) for key, value in data['scanpath_fixation_attributes'].items() } fixations = cls(**data) return fixations def _scanpath_from_fixation_index(fixations, fixation_index, scanpath_attribute_names, scanpath_fixation_attribute_names): history_length = fixations.scanpath_history_length[fixation_index] xs = np.hstack(( fixations.x_hist[fixation_index, :history_length], [fixations.x[fixation_index]] )) ys = np.hstack(( fixations.y_hist[fixation_index, :history_length], [fixations.y[fixation_index]] )) ts = np.hstack(( fixations.t_hist[fixation_index, :history_length], [fixations.t[fixation_index]] )) n = fixations.n[fixation_index] subject = fixations.subject[fixation_index] scanpath_attributes = { attribute: getattr(fixations, attribute)[fixation_index] for attribute in scanpath_attribute_names } scanpath_fixation_attributes = {} for attribute in scanpath_fixation_attribute_names: attribute_value = np.hstack(( getattr(fixations, '{attribute}_hist'.format(attribute=attribute))[fixation_index, :history_length], [getattr(fixations, attribute)[fixation_index]] )) scanpath_fixation_attributes[attribute] = attribute_value return xs, ys, ts, n, subject, scanpath_attributes, scanpath_fixation_attributes def scanpaths_from_fixations(fixations: Fixations, verbose=False) -> Tuple[ScanpathFixations, np.ndarray]: """ reconstructs scanpaths as ScanpathFixations from fixations which originally came from scanpaths. when called as in scanpath_fixations, indices = scanpaths_from_fixations(fixations) you will have scanpath_fixations[indices] == fixations. :note only works if the original scanpaths only used scanpath_attributes and scanpath_fixation_attribute, but not attributes (which should not be used for scanpaths anyway). """ if 'scanpath_index' not in fixations.__attributes__: raise NotImplementedError("Fixations with scanpath_index attribute required!") scanpath_xs = [] scanpath_ys = [] scanpath_ts = [] scanpath_ns = [] scanpath_subjects = [] __attributes__ = [ attribute for attribute in fixations.__attributes__ if attribute != 'subject' and attribute != 'scanpath_index' and not attribute.endswith('_hist') ] __scanpath_attributes__ = [ attribute for attribute in __attributes__ if '{attribute}_hist'.format(attribute=attribute) not in fixations.__attributes__ ] __scanpath_fixation_attributes__ = [ attribute for attribute in __attributes__ if attribute not in __scanpath_attributes__ ] scanpath_fixation_attributes = {attribute: [] for attribute in __scanpath_fixation_attributes__} scanpath_attributes = {attribute: [] for attribute in __scanpath_attributes__} indices = np.ones(len(fixations), dtype=int) * -1 fixation_counter = 0 for scanpath_index in tqdm(sorted(np.unique(fixations.scanpath_index)), disable=not verbose): scanpath_indices = fixations.scanpath_index == scanpath_index scanpath_integer_indices = np.nonzero(scanpath_indices)[0] lengths = fixations.scanpath_history_length[scanpath_indices] # build scanpath up to maximum length maximum_length = max(lengths) _index_of_maximum_length = np.argmax(lengths) index_of_maximum_length = scanpath_integer_indices[_index_of_maximum_length] xs, ys, ts, n, subject, this_scanpath_attributes, this_scanpath_fixation_attributes = _scanpath_from_fixation_index( fixations, index_of_maximum_length, __scanpath_attributes__, __scanpath_fixation_attributes__ ) for key, value in this_scanpath_attributes.items(): other_values = getattr(fixations, key)[scanpath_indices] if not np.all(other_values == value): raise ValueError("attribute {key} not consistent in scanpath {scanpath_index}, found {other_values}".format( key=key, scanpath_index=scanpath_index, other_values=other_values, )) scanpath_xs.append(xs) scanpath_ys.append(ys) scanpath_ts.append(ts) scanpath_ns.append(n) scanpath_subjects.append(subject) for attribute, value in this_scanpath_fixation_attributes.items(): scanpath_fixation_attributes[attribute].append(value) for attribute, value in this_scanpath_attributes.items(): scanpath_attributes[attribute].append(value) # build indices for index_in_scanpath in range(maximum_length+1): if index_in_scanpath in lengths: # add index to indices index_in_fixations = scanpath_integer_indices[list(lengths).index(index_in_scanpath)] # there might be one fixation multiple times in fixations. indices_in_fixations = scanpath_integer_indices[lengths == index_in_scanpath] indices[indices_in_fixations] = fixation_counter + index_in_scanpath fixation_counter += len(xs) scanpath_attributes = { attribute: np.array(value) for attribute, value in scanpath_attributes.items() } attribute_mapping = {} scanpaths = Scanpaths( xs=scanpath_xs, ys=scanpath_ys, ts=scanpath_ts, n=scanpath_ns, subject=scanpath_subjects, scanpath_attributes=scanpath_attributes, fixation_attributes=scanpath_fixation_attributes, attribute_mapping=attribute_mapping ) return ScanpathFixations(scanpaths=scanpaths), indices ================================================ FILE: pysaliency/datasets/scanpaths.py ================================================ import json from typing import Dict, List, Optional, Union import numpy as np from ..utils.variable_length_array import VariableLengthArray, concatenate_variable_length_arrays from .utils import _load_attribute_dict_from_hdf5, create_hdf5_dataset, decode_string, get_merged_attribute_list, hdf5_wrapper class Scanpaths(object): """ Represents a collection of scanpaths. Attributes: xs (VariableLengthArray): The x-coordinates of the scanpaths. ys (VariableLengthArray): The y-coordinates of the scanpaths. n (np.ndarray): The image index length (np.ndarray): The length of each scanpath. scanpath_attributes (dict): Additional attributes associated with the scanpaths. fixation_attributes (dict): Additional attributes associated with the fixations in the scanpaths. attribute_mapping (dict): Mapping of attribute names to their corresponding values, will be used when creating `Fixations` instances from the `Scanpaths` instance. for example {'durations': 'duration'} """ xs: VariableLengthArray ys: VariableLengthArray n: np.ndarray def __init__(self, xs: Union[np.ndarray, VariableLengthArray], ys: Union[np.ndarray, VariableLengthArray], n: np.ndarray, length=None, scanpath_attributes: Optional[Dict[str, np.ndarray]] = None, fixation_attributes: Optional[Dict[str, Union[np.ndarray, VariableLengthArray]]]=None, attribute_mapping: Optional[Dict[str, str]] = None, **kwargs): self.n = np.asarray(n) if not isinstance(xs, VariableLengthArray): self.xs = VariableLengthArray(xs, length) else: self.xs = xs if length is not None: if not np.all(self.xs.lengths == length): raise ValueError("Lengths of xs and lengths do not match") self.length = self.xs.lengths.copy() self.ys = self._as_variable_length_array(ys) if not len(self.xs) == len(self.ys) == len(self.n): raise ValueError("Length of xs, ys, ts and n has to match") scanpath_attributes = scanpath_attributes or {} fixation_attributes = fixation_attributes or {} self.attribute_mapping = attribute_mapping or {} self.attribute_mapping = dict(self.attribute_mapping) for key, value in kwargs.items(): if value is None: continue if not len(value) == len(self.xs): raise ValueError(f"Length of attribute {key} has to match number of scanpaths, but got {len(value)} != {len(self.xs)}") if isinstance(value, VariableLengthArray) or isinstance(value[0], (list, np.ndarray)): if key in fixation_attributes: raise ValueError(f"Attribute {key} already exists in fixation_attributes") fixation_attributes[key] = self._as_variable_length_array(value) if key not in self.attribute_mapping and key[-1] == 's': self.attribute_mapping[key] = key[:-1] else: if key in scanpath_attributes: raise ValueError(f"Attribute {key} already exists in scanpath_attributes") scanpath_attributes[key] = np.array(value) # setting scanpath attributes self.scanpath_attributes = {key: np.array(value) for key, value in scanpath_attributes.items()} for key, value in self.scanpath_attributes.items(): if not len(value) == len(self.xs): raise ValueError(f"Length of scanpath attribute {key} has to match number of scanpaths, but got {len(value)} != {len(self.xs)}") # setting fixation attributes self.fixation_attributes = {key: self._as_variable_length_array(value) for key, value in fixation_attributes.items()} def _check_lengths(self, other: VariableLengthArray): if not len(self) == len(other): raise ValueError("Length of scanpaths has to match") if not np.all(self.length == other.lengths): raise ValueError("Lengths of scanpaths have to match") def _as_variable_length_array(self, data: Union[np.ndarray, VariableLengthArray]) -> VariableLengthArray: if not isinstance(data, VariableLengthArray): data = VariableLengthArray(data, self.length) self._check_lengths(data) return data def __len__(self): return len(self.xs) @property def ts(self) -> VariableLengthArray: return self.fixation_attributes['ts'] @property def subject(self) -> VariableLengthArray: return self.scanpath_attributes['subject'] @hdf5_wrapper(mode='w') def to_hdf5(self, target): """ Write scanpaths to hdf5 file or hdf5 group """ target.attrs['type'] = np.bytes_('Scanpaths') target.attrs['version'] = np.bytes_('1.0') target.create_dataset('xs', data=self.xs._data) target.create_dataset('ys', data=self.ys._data) target.create_dataset('n', data=self.n) target.create_dataset('length', data=self.length) scanpath_attributes_group = target.create_group('scanpath_attributes') for attribute_name, attribute_value in self.scanpath_attributes.items(): create_hdf5_dataset(scanpath_attributes_group, attribute_name, attribute_value) scanpath_attributes_group.attrs['__attributes__'] = np.bytes_(json.dumps(sorted(self.scanpath_attributes.keys()))) fixation_attributes_group = target.create_group('fixation_attributes') for attribute_name, attribute_value in self.fixation_attributes.items(): fixation_attributes_group.create_dataset(attribute_name, data=attribute_value._data) fixation_attributes_group.attrs['__attributes__'] = np.bytes_(json.dumps(sorted(self.fixation_attributes.keys()))) target.attrs['attribute_mapping'] = np.bytes_(json.dumps(self.attribute_mapping)) @classmethod @hdf5_wrapper(mode='r') def read_hdf5(cls, source): data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type != 'Scanpaths': raise ValueError("Invalid type! Expected 'Scanpaths', got", data_type) valid_versions = ['1.0'] if data_version not in valid_versions: raise ValueError("Invalid version! Expected one of {}, got {}".format(', '.join(valid_versions), data_version)) length = source['length'][...] xs = VariableLengthArray(source['xs'][...], length) ys = VariableLengthArray(source['ys'][...], length) n = source['n'][...] scanpath_attributes = _load_attribute_dict_from_hdf5(source['scanpath_attributes']) fixation_attributes_group = source['fixation_attributes'] json_attributes = fixation_attributes_group.attrs['__attributes__'] if not isinstance(json_attributes, str): json_attributes = json_attributes.decode('utf8') __attributes__ = json.loads(json_attributes) fixation_attributes = {attribute: VariableLengthArray(fixation_attributes_group[attribute][...], length) for attribute in __attributes__} return cls( xs=xs, ys=ys, n=n, length=length, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=json.loads(decode_string(source.attrs['attribute_mapping'])) ) def __getitem__(self, index): # TODO # - integer to return single scanpath # - 2d index to return single Fixation (for now via index of scanpath and index of fixation in scanpath) # - 2d index array to return Fixations instance (for now via index of scanpath and index of fixation in scanpath) if isinstance(index, tuple): raise NotImplementedError("Not implemented yet") elif isinstance(index, int): raise NotImplementedError("Not implemented yet") else: return type(self)(self.xs[index], self.ys[index], self.n[index], self.length[index], scanpath_attributes={key: value[index] for key, value in self.scanpath_attributes.items()}, fixation_attributes={key: value[index] for key, value in self.fixation_attributes.items()}, attribute_mapping=self.attribute_mapping) def copy(self) -> 'Scanpaths': return type(self)(self.xs.copy(), self.ys.copy(), self.n.copy(), self.length.copy(), scanpath_attributes={key: value.copy() for key, value in self.scanpath_attributes.items()}, fixation_attributes={key: value.copy() for key, value in self.fixation_attributes.items()}, attribute_mapping=self.attribute_mapping.copy()) @classmethod def concatenate(cls, scanpaths_list: List['Scanpaths']) -> 'Scanpaths': return concatenate_scanpaths(scanpaths_list) def concatenate_scanpaths(scanpaths_list: List[Scanpaths]) -> Scanpaths: xs = concatenate_variable_length_arrays([scanpaths.xs for scanpaths in scanpaths_list]) ys = concatenate_variable_length_arrays([scanpaths.ys for scanpaths in scanpaths_list]) n = np.concatenate([scanpaths.n for scanpaths in scanpaths_list]) length = np.concatenate([scanpaths.length for scanpaths in scanpaths_list]) merged_scanpath_attributes = get_merged_attribute_list([scanpaths.scanpath_attributes.keys() for scanpaths in scanpaths_list]) scanpath_attributes = {key: np.concatenate([scanpaths.scanpath_attributes[key] for scanpaths in scanpaths_list]) for key in merged_scanpath_attributes} merged_fixation_attributes = get_merged_attribute_list([scanpaths.fixation_attributes.keys() for scanpaths in scanpaths_list]) fixation_attributes = {key: concatenate_variable_length_arrays([scanpaths.fixation_attributes[key] for scanpaths in scanpaths_list]) for key in merged_fixation_attributes} merged_attribute_mapping = {} for key in merged_fixation_attributes: mappings = {scanpaths.attribute_mapping.get(key) for scanpaths in scanpaths_list} if len(mappings) > 1: raise ValueError(f"Multiple mappings for attribute {key} found: {mappings}") elif len(mappings) == 1 and list(mappings)[0] is not None: merged_attribute_mapping[key] = mappings.pop() return Scanpaths(xs, ys, n, length, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=merged_attribute_mapping) ================================================ FILE: pysaliency/datasets/stimuli.py ================================================ import json import os from collections.abc import Sequence from hashlib import sha1 from typing import List, Union import numpy as np try: from imageio.v3 import imread except ImportError: from imageio import imread from PIL import Image from tqdm import tqdm from ..utils import LazyList from .utils import create_hdf5_dataset, decode_string, hdf5_wrapper def get_image_hash(img): """ Calculate a unique hash for the given image. Can be used to cache results for images, e.g. saliency maps. """ if isinstance(img, Stimulus): return img.stimulus_id return sha1(np.ascontiguousarray(img)).hexdigest() class Stimulus(object): """ Manages a stimulus. In application, this can always be substituted by the numpy array containing the actual stimulus. This class is just there to be able to cache calculation results and retrieve the cache content without having to load the actual stimulus """ def __init__(self, stimulus_data, stimulus_id = None, shape = None, size = None): self.stimulus_data = stimulus_data self._stimulus_id = stimulus_id self._shape = shape self._size = size @property def stimulus_id(self): if self._stimulus_id is None: self._stimulus_id = get_image_hash(self.stimulus_data) return self._stimulus_id @property def shape(self): if self._shape is None: self._shape = self.stimulus_data.shape return self._shape @property def size(self): if self._size is None: self._size = self.stimulus_data.shape[0], self.stimulus_data.shape[1] return self._size def as_stimulus(img_or_stimulus: Union[np.ndarray, Stimulus]) -> Stimulus: if isinstance(img_or_stimulus, Stimulus): return img_or_stimulus return Stimulus(img_or_stimulus) class StimuliStimulus(Stimulus): """ Stimulus bound to a Stimuli object """ def __init__(self, stimuli, index): self.stimuli = stimuli self.index = index @property def stimulus_data(self): return self.stimuli.stimuli[self.index] @property def stimulus_id(self): return self.stimuli.stimulus_ids[self.index] @property def shape(self): return self.stimuli.shapes[self.index] @property def size(self): return self.stimuli.sizes[self.index] class Stimuli(Sequence): """ Manages a list of stimuli (i.e. images). The stimuli can be given as numpy arrays. Using the class `FileStimuli`, the stimuli can also be saved on disk and will be loaded only when needed. Attributes ---------- stimuli : The stimuli as list of numpy arrays shapes : The shapes of the stimuli. For a grayscale stimulus this will be a 2-tuple, for a color stimulus a 3-tuple sizes : The sizes of all stimuli in pixels as pairs (height, width). In difference to `shapes`, the color dimension is ignored here. stimulus_ids: A unique id for each stimulus. Can be used to cache results for stimuli stimulus_objects: A `Stimulus` instance for each stimulus. Mainly for caching. """ __attributes__ = [] def __init__(self, stimuli, attributes=None): self.stimuli = stimuli self.shapes = [s.shape for s in self.stimuli] self.sizes = LazyList(lambda n: (self.shapes[n][0], self.shapes[n][1]), length = len(self.stimuli)) self.stimulus_ids = LazyList(lambda n: get_image_hash(self.stimuli[n]), length=len(self.stimuli), pickle_cache=True) self.stimulus_objects = [StimuliStimulus(self, n) for n in range(len(self.stimuli))] if attributes is not None: assert isinstance(attributes, dict) self.attributes = attributes self.__attributes__ = list(attributes.keys()) else: self.attributes = {} def __len__(self): return len(self.stimuli) def _get_attribute_for_stimulus_subset(self, index): sub_attributes = {} for attribute_name, attribute_value in self.attributes.items(): if isinstance(index, (list, np.ndarray)) and not isinstance(attribute_value, np.ndarray): sub_attributes[attribute_name] = [attribute_value[i] for i in index] else: sub_attributes[attribute_name] = attribute_value[index] return sub_attributes def __getitem__(self, index): if isinstance(index, slice): attributes = self._get_attribute_for_stimulus_subset(index) sub_stimuli = ObjectStimuli([self.stimulus_objects[i] for i in range(len(self))[index]], attributes=attributes) # populate stimulus_id cache with existing entries self._propagate_stimulus_ids(sub_stimuli, range(len(self))[index]) return sub_stimuli elif isinstance(index, (list, np.ndarray)): index = np.asarray(index) if index.dtype == bool: if not len(index) == len(self.stimuli): raise ValueError(f"Boolean index has to have the same length as the stimuli list but got {len(index)} and {len(self.stimuli)}") index = np.nonzero(index)[0] attributes = self._get_attribute_for_stimulus_subset(index) sub_stimuli = ObjectStimuli([self.stimulus_objects[i] for i in index], attributes=attributes) # populate stimulus_id cache with existing entries self._propagate_stimulus_ids(sub_stimuli, index) return sub_stimuli else: return self.stimulus_objects[index] def _propagate_stimulus_ids(self, sub_stimuli: "Stimuli", index: List[int]): for new_index, old_index in enumerate(index): if old_index in self.stimulus_ids._cache: sub_stimuli.stimulus_ids._cache[new_index] = self.stimulus_ids._cache[old_index] @hdf5_wrapper(mode='w') def to_hdf5(self, target, verbose=False, compression='gzip', compression_opts=9): """ Write stimuli to hdf5 file or hdf5 group """ target.attrs['type'] = np.bytes_('Stimuli') target.attrs['version'] = np.bytes_('1.1') for n, stimulus in enumerate(tqdm(self.stimuli, disable=not verbose)): target.create_dataset(str(n), data=stimulus, compression=compression, compression_opts=compression_opts) self._attributes_to_hdf5(target) target.attrs['size'] = len(self) @classmethod @hdf5_wrapper(mode='r') def read_hdf5(cls, source): """ Read stimuli from hdf5 file or hdf5 group """ data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type != 'Stimuli': raise ValueError("Invalid type! Expected 'Stimuli', got", data_type) if data_version not in ['1.0', '1.1']: raise ValueError("Invalid version! Expected '1.0' or '1.1', got", data_version) size = source.attrs['size'] stimuli = [] for n in range(size): stimuli.append(source[str(n)][...]) __attributes__, attributes = cls._get_attributes_from_hdf5(source, data_version, '1.1') stimuli = cls(stimuli=stimuli, attributes=attributes) return stimuli def _attributes_to_hdf5(self, target): for attribute_name, attribute_value in self.attributes.items(): create_hdf5_dataset(target, attribute_name, attribute_value) target.attrs['__attributes__'] = np.bytes_(json.dumps(self.__attributes__)) @classmethod def _get_attributes_from_hdf5(cls, source, data_version, data_version_for_attribute_list): if data_version < data_version_for_attribute_list: __attributes__ = [] else: json_attributes = source.attrs['__attributes__'] if not isinstance(json_attributes, str): json_attributes = json_attributes.decode('utf8') __attributes__ = json.loads(json_attributes) attributes = {} for attribute in __attributes__: attribute_value = source[attribute][...] if isinstance(attribute_value.flatten()[0], bytes): attribute_shape = attribute_value.shape decoded_attribute_value = [decode_string(item) for item in attribute_value.flatten()] attribute_value = np.array(decoded_attribute_value).reshape(attribute_shape) attributes[attribute] = attribute_value return __attributes__, attributes class ObjectStimuli(Stimuli): """ This Stimuli class is mainly used for slicing of other stimuli objects. """ def __init__(self, stimulus_objects, attributes=None): self.stimulus_objects = stimulus_objects self.stimuli = LazyList(lambda n: self.stimulus_objects[n].stimulus_data, length = len(self.stimulus_objects)) self.shapes = LazyList(lambda n: self.stimulus_objects[n].shape, length = len(self.stimulus_objects)) self.sizes = LazyList(lambda n: self.stimulus_objects[n].size, length = len(self.stimulus_objects)) self.stimulus_ids = LazyList(lambda n: self.stimulus_objects[n].stimulus_id, length = len(self.stimulus_objects)) if attributes is not None: assert isinstance(attributes, dict) self.attributes = attributes self.__attributes__ = list(attributes.keys()) else: self.attributes = {} def read_hdf5(self, target): raise NotImplementedError() class FileStimuli(Stimuli): """ Manage a list of stimuli that are saved as files. """ def __init__(self, filenames, cached=True, shapes=None, attributes=None): """ Create a stimuli object that reads it's stimuli from files. The stimuli are loaded lazy: each stimulus will be opened not before it is accessed. At creation time, all files are opened to read their dimensions, however the actual image data won't be read. .. note :: To calculate the stimulus_ids, the stimuli have to be loaded. Therefore it might be a good idea to load all stimuli and pickle the `FileStimuli` afterwarts. Then the ids are pickled but the stimuli will be reloaded when needed again. Parameters ---------- filenames : list of strings filenames of the stimuli cache : bool, defaults to True whether loaded stimuli should be cached. The cache is excluded from pickling. """ self.filenames = filenames self.stimuli = LazyList(self.load_stimulus, len(self.filenames), cache=cached) if shapes is None: self.shapes = [] for f in filenames: img = Image.open(f) size = img.size if len(img.mode) > 1: # PIL uses (width, height), we use (height, width) self.shapes.append((size[1], size[0], len(img.mode))) else: self.shapes.append((size[1], size[0])) del img else: self.shapes = shapes self.stimulus_ids = LazyList(lambda n: get_image_hash(self.stimuli[n]), length=len(self.stimuli), pickle_cache=True) self.stimulus_objects = [StimuliStimulus(self, n) for n in range(len(self.stimuli))] self.sizes = LazyList(lambda n: (self.shapes[n][0], self.shapes[n][1]), length = len(self.stimuli)) if attributes is not None: assert isinstance(attributes, dict) self.attributes = attributes self.__attributes__ = list(attributes.keys()) else: self.attributes = {} @property def cached(self): return self.stimuli.cache @cached.setter def cached(self, value): self.stimuli.cache = value def load_stimulus(self, n): return imread(self.filenames[n]) def __getitem__(self, index): if isinstance(index, slice): index = list(range(len(self)))[index] if isinstance(index, (list, np.ndarray)): index = np.asarray(index) if index.dtype == bool: if not len(index) == len(self.stimuli): raise ValueError(f"Boolean index has to have the same length as the stimuli list but got {len(index)} and {len(self.stimuli)}") index = np.nonzero(index)[0] filenames = [self.filenames[i] for i in index] shapes = [self.shapes[i] for i in index] attributes = self._get_attribute_for_stimulus_subset(index) sub_stimuli = type(self)(filenames=filenames, shapes=shapes, attributes=attributes, cached=self.cached) # populate stimulus_id cache with existing entries self._propagate_stimulus_ids(sub_stimuli, index) return sub_stimuli else: return self.stimulus_objects[index] @hdf5_wrapper(mode='w') def to_hdf5(self, target): """ Write FileStimuli to hdf5 file or hdf5 group """ target.attrs['type'] = np.bytes_('FileStimuli') target.attrs['version'] = np.bytes_('2.1') import h5py # make sure everything is unicode hdf5_filename = target.file.filename hdf5_directory = os.path.dirname(hdf5_filename) relative_filenames = [os.path.relpath(filename, hdf5_directory) for filename in self.filenames] decoded_filenames = [decode_string(filename) for filename in relative_filenames] encoded_filenames = [filename.encode('utf8') for filename in decoded_filenames] target.create_dataset( 'filenames', data=np.array(encoded_filenames), dtype=h5py.special_dtype(vlen=str) ) shape_dataset = target.create_dataset( 'shapes', (len(self), ), dtype=h5py.special_dtype(vlen=np.dtype('int64')) ) for n, shape in enumerate(self.shapes): shape_dataset[n] = np.array(shape) self._attributes_to_hdf5(target) target.attrs['size'] = len(self) @classmethod @hdf5_wrapper(mode='r') def read_hdf5(cls, source, cached=True): """ Read FileStimuli from hdf5 file or hdf5 group """ data_type = decode_string(source.attrs['type']) data_version = decode_string(source.attrs['version']) if data_type != 'FileStimuli': raise ValueError("Invalid type! Expected 'Stimuli', got", data_type) valid_versions = ['1.0', '2.0', '2.1'] if data_version not in valid_versions: raise ValueError("Invalid version! Expected one of {}, got {}".format(', '.join(valid_versions), data_version)) encoded_filenames = source['filenames'][...] filenames = [decode_string(filename) for filename in encoded_filenames] if data_version >= '2.0': hdf5_filename = source.file.filename hdf5_directory = os.path.dirname(hdf5_filename) filenames = [os.path.join(hdf5_directory, filename) for filename in filenames] shapes = [list(shape) for shape in source['shapes'][...]] __attributes__, attributes = cls._get_attributes_from_hdf5(source, data_version, '2.1') stimuli = cls(filenames=filenames, cached=cached, shapes=shapes, attributes=attributes) return stimuli def check_prediction_shape(prediction: np.ndarray, stimulus: Union[np.ndarray, Stimulus]): stimulus = as_stimulus(stimulus) if prediction.shape != stimulus.size: raise ValueError(f"Prediction shape {prediction.shape} does not match stimulus shape {stimulus.size}") ================================================ FILE: pysaliency/datasets/utils.py ================================================ import json import os import pathlib import warnings from collections.abc import Sequence from functools import wraps from hashlib import sha1 from typing import Dict, List, Optional, Union from weakref import WeakValueDictionary import numpy as np from boltons.cacheutils import cached from ..utils.variable_length_array import VariableLengthArray, concatenate_variable_length_arrays try: from imageio.v3 import imread except ImportError: from imageio import imread from PIL import Image from tqdm import tqdm from ..utils import LazyList, remove_trailing_nans def hdf5_wrapper(mode=None): def decorator(f): @wraps(f) def wrapped(self, target, *args, **kwargs): if isinstance(target, (str, pathlib.Path)): import h5py with h5py.File(target, mode) as hdf5_file: return f(self, hdf5_file, *args, **kwargs) else: return f(self, target, *args, **kwargs) return wrapped return decorator def decode_string(data): if not isinstance(data, str): return data.decode('utf8') return data def create_hdf5_dataset(target, name, data): import h5py if isinstance(np.array(data).flatten()[0], str): data = np.array(data) original_shape = data.shape encoded_items = [decode_string(item).encode('utf8') for item in data.flatten()] encoded_data = np.array(encoded_items).reshape(original_shape) target.create_dataset( name, data=encoded_data, dtype=h5py.special_dtype(vlen=str) ) else: target.create_dataset(name, data=data) def load_hdf5_dataset(source, name): import h5py if name not in source: raise KeyError(f"Dataset '{name}' not found in HDF5 file.") dataset = source[name] if isinstance(dataset, h5py.Dataset) and dataset.dtype == h5py.special_dtype(vlen=str): return [decode_string(item) for item in dataset[...]] else: return dataset[...] def get_merged_attribute_list(attributes): all_attributes = set(attributes[0]) common_attributes = set(attributes[0]) for _attributes in attributes[1:]: all_attributes = all_attributes.union(_attributes) common_attributes = common_attributes.intersection(_attributes) if common_attributes != all_attributes: lost_attributes = all_attributes.difference(common_attributes) warnings.warn(f"Discarding attributes which are not present everywhere: {lost_attributes}", stacklevel=4) return sorted(common_attributes) def _load_attribute_dict_from_hdf5(attribute_group): json_attributes = attribute_group.attrs['__attributes__'] if not isinstance(json_attributes, str): json_attributes = json_attributes.decode('utf8') __attributes__ = json.loads(json_attributes) attributes = {attribute: load_hdf5_dataset(attribute_group, attribute) for attribute in __attributes__} return attributes def concatenate_attributes(attributes): attributes = list(attributes) if isinstance(attributes[0], VariableLengthArray): return concatenate_variable_length_arrays(attributes) attributes = [np.array(a) for a in attributes] for a in attributes: assert len(a.shape) == len(attributes[0].shape) if len(attributes[0].shape) == 1: return np.hstack(attributes) else: assert len(attributes[0].shape) == 2 max_cols = max(a.shape[1] for a in attributes) padded_attributes = [] for a in attributes: if a.shape[1] < max_cols: padding = np.empty((a.shape[0], max_cols-a.shape[1]), dtype=a.dtype) padding[:] = np.nan padded_attributes.append(np.hstack((a, padding))) else: padded_attributes.append(a) return np.vstack(padded_attributes) ================================================ FILE: pysaliency/external_datasets/__init__.py ================================================ from __future__ import absolute_import, print_function, division import zipfile import os import glob import numpy as np from scipy.io import loadmat from tqdm import tqdm from ..datasets import FixationTrains from ..utils import ( download_and_check, atomic_directory_setup, ) from .utils import create_stimuli, _load from .toronto import get_toronto, get_toronto_with_subjects from .mit import get_mit1003, get_mit1003_with_initial_fixation, get_mit1003_onesize, get_mit300 from .cat2000 import get_cat2000_test, get_cat2000_train from .isun import get_iSUN, get_iSUN_training, get_iSUN_validation, get_iSUN_testing from .salicon import get_SALICON, get_SALICON_train, get_SALICON_val, get_SALICON_test from .koehler import get_koehler from .figrim import get_FIGRIM from .osie import get_OSIE from .nusef import get_NUSEF_public from .pascal_s import get_PASCAL_S from .dut_omrom import get_DUT_OMRON from .coco_search18 import get_COCO_Search18, get_COCO_Search18_train, get_COCO_Search18_validation from .coco_freeview import get_COCO_Freeview, get_COCO_Freeview_train, get_COCO_Freeview_validation, get_COCO_Freeview_test ================================================ FILE: pysaliency/external_datasets/cat2000.py ================================================ from __future__ import absolute_import, division, print_function import glob import os import warnings import zipfile from tempfile import TemporaryDirectory import numpy as np from natsort import natsorted from importlib.resources import files as _resource_files def resource_string(package, resource): return _resource_files(package).joinpath(resource).read_bytes() from scipy.io import loadmat from tqdm import tqdm from ..datasets import FixationTrains from ..utils import atomic_directory_setup, download_and_check, filter_files, run_matlab_cmd from .utils import _load, create_stimuli def get_cat2000_test(location=None): """ Loads or downloads and caches the CAT2000 test dataset. The dataset consists of 2000 images of sizes: 1080 x 1920. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli .. seealso:: Ali Borji, Laurent Itti. CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research [CVPR 2015 workshop on "Future of Datasets"] http://saliency.mit.edu/datasets.html """ if location: location = os.path.join(location, 'CAT2000_test') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) return stimuli os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://saliency.mit.edu/testSet.zip', os.path.join(temp_dir, 'testSet.zip'), '903ec668df2e5a8470aef9d8654e7985') # Stimuli print('Creating stimuli') with zipfile.ZipFile(os.path.join(temp_dir, 'testSet.zip')) as f: namelist = f.namelist() namelist = filter_files(namelist, ['Output']) f.extractall(temp_dir, namelist) for filename in ['Pattern/134.jpg']: import piexif print("Fixing wrong exif rotation tag in '{}'".format(filename)) full_path = os.path.join(temp_dir, 'testSet', 'Stimuli', filename) exif_dict = piexif.load(full_path) exif_dict['0th'][piexif.ImageIFD.Orientation] = 1 exif_bytes = piexif.dump(exif_dict) piexif.insert(exif_bytes, full_path) stimuli_src_location = os.path.join(temp_dir, 'testSet', 'Stimuli') stimuli_target_location = os.path.join(location, 'stimuli') if location else None images = glob.glob(os.path.join(stimuli_src_location, '**', '*.jpg')) images = [i for i in images if 'output' not in i.lower()] images = [os.path.relpath(img, start=stimuli_src_location) for img in images] stimuli_filenames = natsorted(images) stimulus_category_names = [os.path.dirname(filename) for filename in stimuli_filenames] category_names = sorted(set(stimulus_category_names)) categories = [category_names.index(category_name) for category_name in stimulus_category_names] attributes = {'category': categories} stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location, attributes=attributes) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) return stimuli def get_cat2000_train(location=None, version='1'): """ Loads or downloads and caches the CAT2000 training dataset. The dataset consists of 2000 images of sizes: 1080 x 1920. and the fixations of 18 subjects per image under free viewing conditions with 5 seconds presentation time. All fixations outside of the image are discarded. This includes blinks. in v1.1 a few details have been fixed that change the dataset: - the quite common repeated fixations are deduplicated (happens serveral thousand times) - a values of 1 is substracted from the x and y values to account for the one-based indexing in MATLAB To use v1.1, call this function with version='1.1' @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. note:: This code needs a working matlab or octave installation. .. seealso:: Ali Borji, Laurent Itti. CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research [CVPR 2015 workshop on "Future of Datasets"] http://saliency.mit.edu/datasets.html """ name = 'CAT2000_train' if version == '1': warnings.warn("You are still using version 1.0 of the CAT2000 dataset implementation in pysaliency. Please upgrade to version 1.1 with pysaliency.get_cat2000_train(..., version='1.1'). This will become the default in pysaliency version 1.0. For more details please see the docstring of this function") get_fn = _get_cat2000_train elif version == '1.1': get_fn = _get_cat2000_train_v1_1 name += '_v1.1' else: raise ValueError(version) return get_fn(name=name, location=location) def _get_cat2000_train(name, location): """ Loads or downloads and caches the CAT2000 training dataset. The dataset consists of 2000 images of sizes: 1080 x 1920. and the fixations of 18 subjects per image under free viewing conditions with 5 seconds presentation time. All fixations outside of the image are discarded. This includes blinks. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. note:: This code needs a working matlab or octave installation. .. seealso:: Ali Borji, Laurent Itti. CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research [CVPR 2015 workshop on "Future of Datasets"] http://saliency.mit.edu/datasets.html """ first_fixation = 0 if location: location = os.path.join(location, name) if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://saliency.mit.edu/trainSet.zip', os.path.join(temp_dir, 'trainSet.zip'), '56ad5c77e6c8f72ed9ef2901628d6e48') # Stimuli print('Creating stimuli') with zipfile.ZipFile(os.path.join(temp_dir, 'trainSet.zip')) as f: namelist = f.namelist() namelist = filter_files(namelist, ['Output']) f.extractall(temp_dir, namelist) stimuli_src_location = os.path.join(temp_dir, 'trainSet', 'Stimuli') stimuli_target_location = os.path.join(location, 'Stimuli') if location else None images = glob.glob(os.path.join(stimuli_src_location, '**', '*.jpg')) images = [os.path.relpath(img, start=stimuli_src_location) for img in images] stimuli_filenames = natsorted(images) stimulus_category_names = [os.path.dirname(filename) for filename in stimuli_filenames] category_names = sorted(set(stimulus_category_names)) categories = [category_names.index(category_name) for category_name in stimulus_category_names] attributes = {'category': categories} stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location, attributes=attributes) # FixationTrains print('Creating fixations') with open(os.path.join(temp_dir, 'load_cat2000.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/{}'.format('load_cat2000.m'))) # It is vital _not_ to store the extracted fixations in the main # directory where matlab is running, as matlab will check the timestamp # of all files in this directory very often. This leads to heavy # performance penalties and would make matlab run for more than an # hour. out_path = 'extracted' os.makedirs(os.path.join(temp_dir, out_path)) run_matlab_cmd('load_cat2000;', cwd=temp_dir) print('Extracting fixations. This can take some minutes.') print('Warning: In the IPython Notebook, the output is shown on the console instead of the notebook.') xs = [] ys = [] ts = [] ns = [] train_subjects = [] subject_dict = {} files = natsorted(glob.glob(os.path.join(temp_dir, out_path, '*.mat'))) for f in tqdm(files): mat_data = loadmat(f) fix_data = mat_data['data'] name = mat_data['name'][0] n = int(os.path.basename(f).split('fix', 1)[1].split('_')[0]) - 1 stimulus_size = stimuli.sizes[n] _, _, subject = name.split('.eye')[0].split('-') if subject not in subject_dict: subject_dict[subject] = len(subject_dict) subject_id = subject_dict[subject] x = [] y = [] t = [] for i in range(first_fixation, fix_data.shape[0]): # Skip first fixation like Judd does via first_fixation=1 if fix_data[i, 0] < 0 or fix_data[i, 1] < 0: continue if fix_data[i, 0] >= stimulus_size[1] or fix_data[i, 1] >= stimulus_size[0]: continue if any(np.isnan(fix_data[i])): # skip invalid data continue x.append(fix_data[i, 0]) y.append(fix_data[i, 1]) t.append(len(x)) # Eye Tracker rate = 240Hz xs.append(x) ys.append(y) ts.append(t) ns.append(n) train_subjects.append(subject_id) fixations = FixationTrains.from_fixation_trains(xs, ys, ts, ns, train_subjects) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations def _get_cat2000_train_v1_1(name, location): """ Loads or downloads and caches the CAT2000 training dataset. The dataset consists of 2000 images of sizes: 1080 x 1920. and the fixations of 18 subjects per image under free viewing conditions with 5 seconds presentation time. All fixations outside of the image are discarded. This includes blinks. in v1_1 a few details have been fixed: - the quite common repeated fixations are deduplicated (happens serveral thousand times) - a values of 1 is substracted from the x and y values to account for the one-based indexing in MATLAB @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. note:: This code needs a working matlab or octave installation. .. seealso:: Ali Borji, Laurent Itti. CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research [CVPR 2015 workshop on "Future of Datasets"] http://saliency.mit.edu/datasets.html """ first_fixation = 0 if location: location = os.path.join(location, name) if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://saliency.mit.edu/trainSet.zip', os.path.join(temp_dir, 'trainSet.zip'), '56ad5c77e6c8f72ed9ef2901628d6e48') # Stimuli print('Creating stimuli') with zipfile.ZipFile(os.path.join(temp_dir, 'trainSet.zip')) as f: namelist = f.namelist() namelist = filter_files(namelist, ['Output']) f.extractall(temp_dir, namelist) stimuli_src_location = os.path.join(temp_dir, 'trainSet', 'Stimuli') stimuli_target_location = os.path.join(location, 'Stimuli') if location else None images = glob.glob(os.path.join(stimuli_src_location, '**', '*.jpg')) images = [os.path.relpath(img, start=stimuli_src_location) for img in images] stimuli_filenames = natsorted(images) stimulus_category_names = [os.path.dirname(filename) for filename in stimuli_filenames] category_names = sorted(set(stimulus_category_names)) categories = [category_names.index(category_name) for category_name in stimulus_category_names] attributes = {'category': categories} stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location, attributes=attributes) # FixationTrains print('Creating fixations') with open(os.path.join(temp_dir, 'load_cat2000.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/{}'.format('load_cat2000.m'))) # It is vital _not_ to store the extracted fixations in the main # directory where matlab is running, as matlab will check the timestamp # of all files in this directory very often. This leads to heavy # performance penalties and would make matlab run for more than an # hour. out_path = 'extracted' os.makedirs(os.path.join(temp_dir, out_path)) run_matlab_cmd('load_cat2000;', cwd=temp_dir) print('Extracting fixations. This can take some minutes.') print('Warning: In the IPython Notebook, the output is shown on the console instead of the notebook.') xs = [] ys = [] ts = [] ns = [] train_subjects = [] subject_dict = {} files = natsorted(glob.glob(os.path.join(temp_dir, out_path, '*.mat'))) for f in tqdm(files): mat_data = loadmat(f) fix_data = mat_data['data'] name = mat_data['name'][0] n = int(os.path.basename(f).split('fix', 1)[1].split('_')[0]) - 1 stimulus_size = stimuli.sizes[n] _, _, subject = name.split('.eye')[0].split('-') if subject not in subject_dict: subject_dict[subject] = len(subject_dict) subject_id = subject_dict[subject] x = [] y = [] t = [] for i in range(first_fixation, fix_data.shape[0]): new_x = fix_data[i, 0] - 1 # one-based indexing in MATLAB new_y = fix_data[i, 1] - 1 # one-based indexing in MATLAB if new_x < 0 or new_y < 0: continue if new_x >= stimulus_size[1] or new_y >= stimulus_size[0]: continue if any(np.isnan(fix_data[i])): # skip invalid data continue if x and x[-1] == new_x and y[-1] == new_y: # repeated fixation, skip continue x.append(new_x) y.append(new_y) t.append(len(x)) # Eye Tracker rate = 240Hz xs.append(x) ys.append(y) ts.append(t) ns.append(n) train_subjects.append(subject_id) fixations = FixationTrains.from_fixation_trains(xs, ys, ts, ns, train_subjects) print(subject_dict) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations ================================================ FILE: pysaliency/external_datasets/coco_freeview.py ================================================ import json import os import zipfile from tempfile import TemporaryDirectory import numpy as np from tqdm import tqdm from ..datasets import ScanpathFixations, Scanpaths, create_subset from ..utils import atomic_directory_setup, download_and_check, filter_files from .coco_search18 import _prepare_stimuli from .utils import _load, create_stimuli TEST_STIMULUS_INDICES = [ 1, 5, 10, 11, 12, 17, 24, 31, 35, 41, 62, 65, 69, 71, 73, 77, 79, 83, 86, 102, 103, 104, 105, 106, 110, 137, 140, 157, 164, 165, 173, 181, 188, 201, 203, 206, 214, 216, 217, 226, 231, 235, 236, 240, 241, 256, 262, 263, 267, 270, 277, 279, 280, 283, 288, 289, 301, 302, 303, 308, 322, 325, 329, 332, 337, 338, 339, 341, 343, 355, 356, 364, 368, 373, 380, 382, 388, 398, 404, 409, 413, 414, 415, 422, 426, 433, 435, 438, 441, 442, 446, 451, 455, 469, 470, 482, 483, 486, 493, 495, 498, 501, 505, 506, 508, 509, 518, 524, 525, 529, 535, 537, 541, 543, 551, 553, 600, 601, 616, 618, 621, 622, 623, 626, 629, 631, 634, 637, 640, 652, 653, 655, 658, 662, 666, 667, 674, 680, 681, 693, 701, 703, 707, 708, 716, 721, 725, 740, 753, 771, 786, 789, 794, 806, 808, 812, 820, 826, 840, 841, 842, 857, 904, 906, 907, 909, 910, 919, 923, 930, 958, 960, 965, 977, 979, 989, 990, 997, 999, 1008, 1013, 1037, 1042, 1045, 1046, 1047, 1048, 1050, 1060, 1061, 1065, 1074, 1077, 1091, 1093, 1109, 1115, 1119, 1120, 1126, 1131, 1132, 1137, 1139, 1142, 1156, 1158, 1172, 1174, 1175, 1178, 1182, 1185, 1196, 1200, 1203, 1213, 1229, 1236, 1240, 1246, 1248, 1249, 1253, 1255, 1262, 1273, 1274, 1277, 1285, 1289, 1292, 1295, 1300, 1301, 1306, 1312, 1316, 1320, 1322, 1324, 1328, 1336, 1342, 1351, 1352, 1355, 1364, 1370, 1371, 1373, 1388, 1391, 1394, 1398, 1406, 1412, 1421, 1422, 1426, 1430, 1436, 1443, 1444, 1447, 1449, 1456, 1465, 1466, 1467, 1469, 1473, 1480, 1484, 1490, 1501, 1508, 1513, 1515, 1518, 1524, 1532, 1536, 1540, 1543, 1546, 1552, 1569, 1574, 1577, 1585, 1589, 1590, 1591, 1596, 1601, 1611, 1612, 1624, 1626, 1628, 1646, 1651, 1674, 1676, 1684, 1686, 1691, 1698, 1701, 1704, 1709, 1712, 1713, 1715, 1716, 1743, 1749, 1751, 1753, 1764, 1767, 1768, 1774, 1779, 1782, 1784, 1785, 1790, 1791, 1792, 1803, 1811, 1815, 1816, 1820, 1821, 1829, 1830, 1833, 1851, 1855, 1859, 1869, 1884, 1888, 1893, 1902, 1903, 1905, 1906, 1920, 1922, 1924, 1925, 1932, 1936, 1940, 1942, 1943, 1944, 1954, 1955, 1956, 1959, 1962, 1973, 1975, 1978, 1980, 1985, 1986, 1989, 1995, 1997, 2001, 2004, 2014, 2018, 2019, 2020, 2025, 2029, 2032, 2033, 2040, 2044, 2048, 2053, 2054, 2077, 2083, 2084, 2088, 2090, 2097, 2107, 2108, 2110, 2118, 2119, 2125, 2129, 2133, 2134, 2143, 2176, 2181, 2192, 2193, 2195, 2197, 2209, 2211, 2223, 2226, 2228, 2233, 2244, 2247, 2251, 2254, 2257, 2260, 2269, 2277, 2282, 2284, 2289, 2291, 2292, 2294, 2296, 2304, 2305, 2319, 2321, 2328, 2343, 2344, 2349, 2351, 2353, 2355, 2357, 2366, 2370, 2374, 2376, 2386, 2387, 2397, 2399, 2404, 2410, 2414, 2432, 2440, 2443, 2452, 2454, 2455, 2456, 2457, 2464, 2465, 2480, 2488, 2491, 2499, 2500, 2507, 2515, 2516, 2524, 2527, 2531, 2533, 2534, 2536, 2540, 2549, 2557, 2578, 2580, 2587, 2590, 2591, 2601, 2612, 2619, 2643, 2646, 2647, 2648, 2649, 2655, 2661, 2665, 2667, 2672, 2674, 2676, 2683, 2689, 2696, 2697, 2701, 2702, 2712, 2716, 2738, 2739, 2741, 2747, 2748, 2753, 2754, 2757, 2760, 2764, 2765, 2776, 2781, 2784, 2786, 2789, 2797, 2798, 2810, 2820, 2824, 2825, 2829, 2843, 2846, 2847, 2848, 2855, 2864, 2867, 2869, 2874, 2879, 2883, 2885, 2888, 2891, 2898, 2904, 2909, 2911, 2923, 2928, 2931, 2950, 2955, 2957, 2958, 2962, 2967, 2968, 2973, 2979, 2981, 2990, 2995, 3007, 3043, 3054, 3057, 3065, 3067, 3069, 3071, 3079, 3081, 3084, 3090, 3103, 3105, 3115, 3122, 3126, 3130, 3134, 3138, 3148, 3153, 3169, 3171, 3179, 3183, 3190, 3194, 3196, 3202, 3203, 3204, 3210, 3215, 3220, 3224, 3233, 3235, 3239, 3242, 3244, 3245, 3248, 3268, 3272, 3277, 3286, 3296, 3297, 3301, 3303, 3306, 3318, 3324, 3327, 3329, 3330, 3331, 3336, 3337, 3340, 3345, 3346, 3349, 3352, 3363, 3370, 3375, 3379, 3385, 3386, 3395, 3400, 3406, 3409, 3411, 3423, 3428, 3437, 3440, 3446, 3447, 3452, 3461, 3467, 3468, 3469, 3480, 3487, 3488, 3490, 3501, 3502, 3511, 3518, 3520, 3530, 3554, 3559, 3564, 3573, 3578, 3579, 3583, 3588, 3589, 3602, 3603, 3607, 3614, 3620, 3632, 3646, 3655, 3662, 3664, 3667, 3675, 3683, 3689, 3698, 3712, 3719, 3734, 3735, 3736, 3737, 3738, 3740, 3746, 3752, 3757, 3765, 3769, 3770, 3775, 3779, 3781, 3783, 3784, 3791, 3809, 3810, 3811, 3818, 3827, 3833, 3840, 3851, 3859, 3860, 3862, 3863, 3876, 3890, 3891, 3902, 3903, 3904, 3908, 3911, 3912, 3916, 3926, 3927, 3930, 3935, 3954, 3957, 3964, 3968, 3971, 3973, 3994, 3997, 4001, 4003, 4004, 4009, 4011, 4012, 4013, 4014, 4018, 4020, 4021, 4023, 4031, 4037, 4045, 4051, 4055, 4065, 4066, 4067, 4068, 4071, 4073, 4078, 4080, 4085, 4104, 4108, 4112, 4125, 4128, 4139, 4141, 4145, 4150, 4151, 4152, 4154, 4156, 4174, 4175, 4183, 4189, 4199, 4211, 4231, 4236, 4239, 4248, 4249, 4253, 4256, 4258, 4259, 4261, 4263, 4281, 4285, 4290, 4309, 4318, 4320, 4322, 4325, 4334, 4336, 4338, 4341, 4348, 4351, 4359, 4366, 4370, 4371, 4374, 4376, 4380, 4382, 4390, 4392, 4407, 4412, 4416, 4418, 4424, 4428, 4429, 4445, 4448, 4453, 4455, 4456, 4458, 4465, 4470, 4475, 4478, 4479, 4492, 4497, 4498, 4501, 4502, 4506, 4509, 4511, 4512, 4513, 4518, 4525, 4527, 4535, 4544, 4548, 4553, 4556, 4562, 4566, 4570, 4574, 4579, 4583, 4588, 4605, 4623, 4626, 4628, 4635, 4636, 4643, 4644, 4647, 4651, 4664, 4675, 4683, 4684, 4687, 4689, 4690, 4694, 4695, 4699, 4701, 4702, 4708, 4709, 4717, 4723, 4734, 4736, 4737, 4744, 4761, 4771, 4774, 4778, 4781, 4792, 4799, 4806, 4813, 4819, 4820, 4824, 4828, 4833, 4837, 4847, 4848, 4851, 4859, 4863, 4869, 4871, 4913, 4914, 4920, 4923, 4929, 4931, 4934, 4939, 4940, 4944, 4946, 4956, 4966, 4968, 4970, 4973, 4977, 4981, 5001, 5008, 5011, 5030, 5031, 5041, 5049, 5056, 5060, 5061, 5062, 5063, 5071, 5073, 5087, 5088, 5090, 5092, 5105, 5107, 5110, 5114, 5118, 5120, 5123, 5132, 5152, 5157, 5165, 5170, 5174, 5181, 5188, 5189, 5191, 5201, 5207, 5208, 5211, 5212, 5224, 5233, 5236, 5241, 5246, 5252, 5253, 5255, 5256, 5258, 5259, 5263, 5269, 5271, 5272, 5275, 5276, 5278, 5283, 5284, 5285, 5292, 5294, 5311, 5313, ] def get_COCO_Freeview(location=None, test_data=None): """ Loads or downloads and caches the COCO Freeview dataset. The dataset consists of about 5317 images from MS COCO with scanpath data from 10 observers doing freeviewing. The COCO images have been rescaled and padded to a size of 1680x1050 pixels. The scanpaths come with attributes for - (fixation) duration in seconds @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `COCO-Search18` of location and read from there, if already present. @type test_data: string, defaults to `None` @parm test_data: filename of the test data, if you have access to it. If that's the case, also a test data FixationTrains object will be created and saved, but not returned. @return: Training stimuli, training FixationTrains, validation Stimuli, validation FixationTrains .. seealso:: Chen, Y., Yang, Z., Chakraborty, S., Mondal, S., Ahn, S., Samaras, D., Hoai, M., & Zelinsky, G. (2022). Characterizing Target-Absent Human Attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 5031-5040). Yang, Z., Mondal, S., Ahn, S., Zelinsky, G., Hoai, M., & Samaras, D. (2023). Predicting Human Attention using Computational Attention. arXiv preprint arXiv:2303.09383. """ if location: location = os.path.join(location, 'COCO-Freeview') if os.path.exists(location): stimuli_train = _load(os.path.join(location, 'stimuli_train.hdf5')) fixations_train = _load(os.path.join(location, 'fixations_train.hdf5')) stimuli_validation = _load(os.path.join(location, 'stimuli_validation.hdf5')) fixations_validation = _load(os.path.join(location, 'fixations_validation.hdf5')) stimuli_test = _load(os.path.join(location, 'stimuli_test.hdf5')) return stimuli_train, fixations_train, stimuli_validation, fixations_validation, stimuli_test os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://vision.cs.stonybrook.edu/~cvlab_download/COCOSearch18-images-TP.zip', os.path.join(temp_dir, 'COCOSearch18-images-TP.zip'), '4a815bb591cb463ab77e5ba0c68fedfb') download_and_check('http://vision.cs.stonybrook.edu/~cvlab_download/COCOSearch18-images-TA.zip', os.path.join(temp_dir, 'COCOSearch18-images-TA.zip'), '85af7d74fa57c202320fa5e7d0dcc187') download_and_check('http://vision.cs.stonybrook.edu/~cvlab_download/COCOFreeView_fixations_trainval.json', os.path.join(temp_dir, 'COCOFreeView_fixations_trainval.json'), 'c7f2fbc92afbe55d4dedc445ac2063d3') # Stimuli print('Creating stimuli') with zipfile.ZipFile(os.path.join(temp_dir, 'COCOSearch18-images-TP.zip')) as f: namelist = f.namelist() namelist = filter_files(namelist, ['.svn', '__MACOSX', '.DS_Store']) f.extractall(temp_dir, namelist) with zipfile.ZipFile(os.path.join(temp_dir, 'COCOSearch18-images-TA.zip')) as f: namelist = f.namelist() namelist = filter_files(namelist, ['.svn', '__MACOSX', '.DS_Store']) f.extractall(temp_dir, namelist) # unifying images for different tasks stimulus_directory = os.path.join(temp_dir, 'stimuli') os.makedirs(stimulus_directory) filenames, stimulus_tasks = _prepare_stimuli(temp_dir, stimulus_directory, merge_tasks=True, unique_images=False) stimuli_src_location = os.path.join(temp_dir, 'stimuli') stimuli_target_location = os.path.join(location, 'stimuli') if location else None stimuli_filenames = filenames stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) print('creating fixations') with open(os.path.join(temp_dir, 'COCOFreeView_fixations_trainval.json')) as fixation_file: json_data = json.load(fixation_file) all_scanpaths = _get_COCO_Freeview_fixations(json_data, filenames) scanpaths_train = all_scanpaths.filter_scanpaths(all_scanpaths.scanpaths.scanpath_attributes['split'] == 'train') scanpaths_validation = all_scanpaths.filter_scanpaths(all_scanpaths.scanpaths.scanpath_attributes['split'] == 'valid') del scanpaths_train.scanpaths.scanpath_attributes['split'] del scanpaths_validation.scanpaths.scanpath_attributes['split'] ns_train = sorted(set(scanpaths_train.n)) stimuli_train, fixations_train = create_subset(stimuli, scanpaths_train, ns_train) ns_val = sorted(set(scanpaths_validation.n)) stimuli_val, fixations_val = create_subset(stimuli, scanpaths_validation, ns_val) if test_data: with open(test_data) as f: json_test_data = json.load(f) scanpaths_test = _get_COCO_Freeview_fixations(json_test_data, filenames) ns_test = sorted(set(scanpaths_test.n)) assert len(ns_test) == len(TEST_STIMULUS_INDICES) assert np.all(np.array(ns_test) == TEST_STIMULUS_INDICES) _, fixations_test = create_subset(stimuli, scanpaths_test, ns_test) stimuli_test = stimuli[TEST_STIMULUS_INDICES] if location: stimuli_train.to_hdf5(os.path.join(location, 'stimuli_train.hdf5')) fixations_train.to_hdf5(os.path.join(location, 'fixations_train.hdf5')) stimuli_val.to_hdf5(os.path.join(location, 'stimuli_validation.hdf5')) fixations_val.to_hdf5(os.path.join(location, 'fixations_validation.hdf5')) stimuli_test.to_hdf5(os.path.join(location, 'stimuli_test.hdf5')) if test_data: fixations_test.to_hdf5(os.path.join(location, 'fixations_test.hdf5')) return stimuli_train, fixations_train, stimuli_val, fixations_val, stimuli_test def get_COCO_Freeview_train(location=None): stimuli_train, fixations_train, stimuli_val, fixations_val, stimuli_test = get_COCO_Freeview(location=location) return stimuli_train, fixations_train def get_COCO_Freeview_validation(location=None): stimuli_train, fixations_train, stimuli_val, fixations_val, stimuli_test = get_COCO_Freeview(location=location) return stimuli_val, fixations_val def get_COCO_Freeview_test(location=None): stimuli_train, fixations_train, stimuli_val, fixations_val, stimuli_test = get_COCO_Freeview(location=location) return stimuli_test def _get_COCO_Freeview_fixations(json_data, filenames): train_xs = [] train_ys = [] train_ts = [] train_ns = [] train_subjects = [] train_durations = [] split = [] for item in tqdm(json_data): filename = item['name'] n = filenames.index(filename) train_xs.append(item['X']) train_ys.append(item['Y']) train_ts.append(np.arange(item['length'])) train_ns.append(n) train_subjects.append(item['subject']) train_durations.append(np.array(item['T']) / 1000) split.append(item['split']) scanpath_attributes = { 'split': split, } fixation_attributes = { 'durations': train_durations, } scanpath_attribute_mapping = { 'durations': 'duration' } fixations = ScanpathFixations(Scanpaths( xs=train_xs, ys=train_ys, ts=train_ts, n=train_ns, subject=train_subjects, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=scanpath_attribute_mapping, )) return fixations ================================================ FILE: pysaliency/external_datasets/coco_search18.py ================================================ import glob import json import os import shutil import zipfile from hashlib import md5 from tempfile import TemporaryDirectory import numpy as np from PIL import Image from tqdm import tqdm from ..datasets import ScanpathFixations, Scanpaths, create_subset from ..utils import atomic_directory_setup, download_and_check, filter_files from .utils import _load, create_stimuli condition_mapping = { 'present': 1, 'absent': 0 } TASKS = ['bottle', 'bowl', 'car', 'chair', 'clock', 'cup', 'fork', 'keyboard', 'knife', 'laptop', 'microwave', 'mouse', 'oven', 'potted plant', 'sink', 'stop sign', 'toilet', 'tv'] def get_COCO_Search18(location=None, split=1, merge_tasks=True, unique_images=True): """ Loads or downloads and caches the COCO Search18 dataset. The dataset consists of about 5317 images from MS COCO with scanpath data from 11 observers doing a visual search task for one of 18 different object categories. The COCO images have been rescaled and padded to a size of 1680x1050 pixels. The scanpaths come with attributes for - (fixation) duration in seconds - task, i.e. search target. Check pysaliency.external_datasets.coco_search18.TASKS for label names. - target present (1) or target absent (0) - target_bbox: bounding box of target (x, y, width, height) - correct_response: whether the subject correctly responded whether the target is present or not - reaction time to response in seconds @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Training stimuli, trainint FixationTrains, validation Stimuli, validation FixationTrains .. seealso:: Chen, Y., Yang, Z., Ahn, S., Samaras, D., Hoai, M., & Zelinsky, G. (2021). COCO-Search18 Fixation Dataset for Predicting Goal-directed Attention Control. Scientific Reports, 11 (1), 1-11, 2021. https://www.nature.com/articles/s41598-021-87715-9 Yang, Z., Huang, L., Chen, Y., Wei, Z., Ahn, S., Zelinsky, G., Samaras, D., & Hoai, M. (2020). Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 193-202). https://sites.google.com/view/cocosearch/home """ if split != 1: raise NotImplementedError if merge_tasks: # automatically the case, no need to modify unique_images = False dataset_name = _dataset_name(merge_tasks, unique_images, split) if location: location = os.path.join(location, dataset_name) if os.path.exists(location): stimuli_train = _load(os.path.join(location, 'stimuli_train.hdf5')) fixations_train = _load(os.path.join(location, 'fixations_train.hdf5')) stimuli_validation = _load(os.path.join(location, 'stimuli_validation.hdf5')) fixations_validation = _load(os.path.join(location, 'fixations_validation.hdf5')) return stimuli_train, fixations_train, stimuli_validation, fixations_validation os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://vision.cs.stonybrook.edu/~cvlab_download/COCOSearch18-images-TP.zip', os.path.join(temp_dir, 'COCOSearch18-images-TP.zip'), '4a815bb591cb463ab77e5ba0c68fedfb') download_and_check('http://vision.cs.stonybrook.edu/~cvlab_download/COCOSearch18-images-TA.zip', os.path.join(temp_dir, 'COCOSearch18-images-TA.zip'), '85af7d74fa57c202320fa5e7d0dcc187') download_and_check('http://vision.cs.stonybrook.edu/~cvlab_download/COCOSearch18-fixations-TP.zip', os.path.join(temp_dir, 'COCOSearch18-fixations-TP.zip'), 'bfcf4c005a89c43a1719b28b028c5499') download_and_check('http://vision.cs.stonybrook.edu/~cvlab_download/coco_search18_fixations_TA_trainval.json', os.path.join(temp_dir, 'coco_search18_fixations_TA_trainval.json'), 'bd491cce105ff6470536afdab1184776.') # Stimuli print('Creating stimuli') with zipfile.ZipFile(os.path.join(temp_dir, 'COCOSearch18-images-TP.zip')) as f: namelist = f.namelist() namelist = filter_files(namelist, ['.svn', '__MACOSX', '.DS_Store']) f.extractall(temp_dir, namelist) with zipfile.ZipFile(os.path.join(temp_dir, 'COCOSearch18-images-TA.zip')) as f: namelist = f.namelist() namelist = filter_files(namelist, ['.svn', '__MACOSX', '.DS_Store']) f.extractall(temp_dir, namelist) # unifying images for different tasks stimulus_directory = os.path.join(temp_dir, 'stimuli') os.makedirs(stimulus_directory) filenames, stimulus_tasks = _prepare_stimuli(temp_dir, stimulus_directory, merge_tasks=merge_tasks, unique_images=unique_images) stimuli_src_location = os.path.join(temp_dir, 'stimuli') stimuli_target_location = os.path.join(location, 'stimuli') if location else None stimuli_filenames = filenames if not merge_tasks: attributes = { 'task': stimulus_tasks } else: attributes = None stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location, attributes=attributes) print('creating fixations') with zipfile.ZipFile(os.path.join(temp_dir, 'COCOSearch18-fixations-TP.zip')) as tp_fixations: json_data_tp_train = json.loads(tp_fixations.read('coco_search18_fixations_TP_train_split1.json')) json_data_tp_val = json.loads(tp_fixations.read('coco_search18_fixations_TP_validation_split1.json')) with open(os.path.join(temp_dir, 'coco_search18_fixations_TA_trainval.json')) as ta_fixations: json_data_ta = json.load(ta_fixations) if unique_images: orig_filenames = [os.path.splitext(filename)[0] + '.jpg' for filename in filenames] else: orig_filenames = filenames all_scanpaths = _get_COCO_Search18_fixations(json_data_tp_train + json_data_tp_val + json_data_ta, orig_filenames, task_in_filename=not merge_tasks) scanpaths_train = all_scanpaths.filter_scanpaths(all_scanpaths.scanpaths.scanpath_attributes['split'] == 'train') scanpaths_validation = all_scanpaths.filter_scanpaths(all_scanpaths.scanpaths.scanpath_attributes['split'] == 'valid') del scanpaths_train.scanpaths.scanpath_attributes['split'] del scanpaths_validation.scanpaths.scanpath_attributes['split'] ns_train = sorted(set(scanpaths_train.n)) stimuli_train, fixations_train = create_subset(stimuli, scanpaths_train, ns_train) ns_val = sorted(set(scanpaths_validation.n)) stimuli_val, fixations_val = create_subset(stimuli, scanpaths_validation, ns_val) if location: stimuli_train.to_hdf5(os.path.join(location, 'stimuli_train.hdf5')) fixations_train.to_hdf5(os.path.join(location, 'fixations_train.hdf5')) stimuli_val.to_hdf5(os.path.join(location, 'stimuli_validation.hdf5')) fixations_val.to_hdf5(os.path.join(location, 'fixations_validation.hdf5')) return stimuli_train, fixations_train, stimuli_val, fixations_val def _dataset_name(merge_tasks, unique_images, split): if merge_tasks: if unique_images: raise ValueError("Deduplicate cannot be true when merge_tasks is activated") dataset_name = 'COCO-Search18' else: if unique_images: dataset_name = 'COCO-Search18_no-task-merge_unique-images' else: dataset_name = 'COCO-Search18_no-task-merge_duplicate-images' return dataset_name def _prepare_stimuli(source_directory, stimulus_directory, merge_tasks=True, unique_images=False): filenames = [] rst = np.random.RandomState(seed=42) for filename in tqdm( glob.glob(os.path.join(source_directory, 'images', '*', '*.jpg')) + glob.glob(os.path.join(source_directory, 'coco_search18_images_TA', '*', '*.jpg')) ): basename = os.path.basename(filename) task = os.path.basename(os.path.dirname(filename)) if merge_tasks: target_filename = os.path.join(stimulus_directory, basename) else: target_filename = os.path.join(stimulus_directory, task.replace(' ', '_'), basename) if unique_images: # we need to use PNG, otherwise our tiny modifications well get lost in saving stem, ext = os.path.splitext(target_filename) target_filename = stem + '.png' if os.path.isfile(target_filename): with open(target_filename, 'rb') as old_file: md5_previous = md5(old_file.read()).hexdigest() with open(filename, 'rb') as new_file: md5_new = md5(new_file.read()).hexdigest() if md5_previous != md5_new: raise ValueError("same image with different md5 sums! " + md5_previous + '!=' + md5_new) continue os.makedirs(os.path.dirname(target_filename),exist_ok=True) if not unique_images: shutil.copy(filename, target_filename) else: _modify_image(filename, target_filename, rst) filenames.append(os.path.relpath(target_filename, start=stimulus_directory)) filenames = sorted(filenames) if not merge_tasks: tasks = [TASKS.index(os.path.basename(os.path.dirname(filename)).replace('_', ' ')) for filename in filenames] else: tasks = None return filenames, tasks def _modify_image(source_filename, target_filename, rst: np.random.RandomState): image = Image.open(source_filename) image_data = np.array(image) width, height = image.size x_pos, y_pos = rst.randint(0, width), rst.randint(0, height) if image_data.ndim == 3: channel = rst.randint(0, image_data.shape[-1]) image_pos = (y_pos, x_pos, channel) else: image_pos = (y_pos, x_pos) if image_data[image_pos] > 0: offset = -1 else: offset = 1 image_data[image_pos] += offset new_image = Image.fromarray(image_data) new_image.save(target_filename) def get_COCO_Search18_train(location=None, split=1, merge_tasks=True, unique_images=True): stimuli_train, fixations_train, stimuli_val, fixations_val = get_COCO_Search18(location=location, split=split, merge_tasks=merge_tasks, unique_images=unique_images) return stimuli_train, fixations_train def get_COCO_Search18_validation(location=None, split=1, merge_tasks=True, unique_images=True): stimuli_train, fixations_train, stimuli_val, fixations_val = get_COCO_Search18(location=location, split=split, merge_tasks=merge_tasks, unique_images=unique_images) return stimuli_val, fixations_val def _get_COCO_Search18_fixations(json_data, filenames, task_in_filename): train_xs = [] train_ys = [] train_ts = [] train_ns = [] train_subjects = [] train_tasks = [] train_durations = [] target_present = [] target_bbox = [] #fixOnTarget = [] correct_response = [] reaction_time = [] split = [] for item in tqdm(json_data): filename = item['name'] task = TASKS.index(item['task']) if task_in_filename: filename = f"{TASKS[task].replace(' ', '_')}/{filename}" n = filenames.index(filename) train_xs.append(item['X']) train_ys.append(item['Y']) train_ts.append(np.arange(item['length'])) train_ns.append(n) train_subjects.append(item['subject'] - 1) # subjects are 1 indexed in the source data train_durations.append(np.array(item['T']) / 1000) train_tasks.append(task) if 'bbox' in item: target_bbox.append(item['bbox']) else: target_bbox.append(np.full(4, np.nan)) target_present.append(condition_mapping[item['condition']]) correct_response.append(item['correct']) #reaction_time.append(item['RT'] if 'RT' in item else np.nan) reaction_time.append(item['RT'] / 1000.0 if 'RT' in item else np.nan) split.append(item['split']) scanpath_attributes = { 'task': train_tasks, 'target_present': target_present, 'target_bbox': target_bbox, 'correct_response': correct_response, 'reaction_time': reaction_time, 'split': split, } fixation_attributes = { 'durations': train_durations, } scanpath_attribute_mapping = { 'durations': 'duration' } fixations = ScanpathFixations(Scanpaths( xs=train_xs, ys=train_ys, ts=train_ts, n=train_ns, subject=train_subjects, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=scanpath_attribute_mapping, )) return fixations ================================================ FILE: pysaliency/external_datasets/dut_omrom.py ================================================ from __future__ import absolute_import, division, print_function import glob import os import zipfile from tempfile import TemporaryDirectory from typing import Tuple import numpy as np from scipy.io import loadmat from tqdm import tqdm from ..datasets import ScanpathFixations, Scanpaths, Stimuli from ..utils import ( atomic_directory_setup, download_and_check, ) from .utils import _load, create_stimuli def get_DUT_OMRON(location=None) -> Tuple[Stimuli, ScanpathFixations]: """ Loads or downloads the DUT-OMRON fixation dataset. The dataset consists of 5168 natural images with a maximal size of 400 pixel and eye movement data from 5 subjects in a 2 second free viewing task. The eye movement data has been filtered and preprocessed, see the dataset documentation for more details. Note that the dataset contains subject ids but they might not be consisten across images. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `DUT-OMRON` of location and read from there, if already present. @return: Stimuli, FixationTrains .. seealso:: Chuan Yang, Lihe Zhang, Huchuan Lu, Xiang Ruan, Minghsuan Yang. Saliency Detection Via Graph-Based Manifold Ranking, CVPR2013. http://saliencydetection.net/dut-omron """ if location: location = os.path.join(location, 'DUT-OMRON') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) n_fixations = 0 with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://saliencydetection.net/dut-omron/download/DUT-OMRON-image.zip', os.path.join(temp_dir, 'DUT-OMRON-image.zip'), 'a8951db9297afacf78bc0e5079103cf1') download_and_check('http://saliencydetection.net/dut-omron/download/DUT-OMRON-eye-fixations.zip', os.path.join(temp_dir, 'DUT-OMRON-eye-fixations.zip'), 'd9f4f83fcc78b1e5efb579ae9fb0edc2') # Stimuli print('Creating stimuli') f = zipfile.ZipFile(os.path.join(temp_dir, 'DUT-OMRON-image.zip')) f.extractall(temp_dir) stimuli_src_location = os.path.join(temp_dir, 'DUT-OMRON-image') images = glob.glob(os.path.join(stimuli_src_location, '*.jpg')) images = [os.path.relpath(img, start=stimuli_src_location) for img in images] stimuli_filenames = sorted(images) stimuli_target_location = os.path.join(location, 'Stimuli') if location else None stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) stimuli_basenames = [os.path.basename(f) for f in stimuli_filenames] # FixationTrains print('Creating fixations') f = zipfile.ZipFile(os.path.join(temp_dir, 'DUT-OMRON-eye-fixations.zip')) f.extractall(temp_dir) train_xs = [] train_ys = [] train_ts = [] train_ns = [] train_subjects = [] for n, basename in enumerate(tqdm(stimuli_basenames)): eye_filename = os.path.join(temp_dir, 'DUT-OMRON-eye-fixations', 'mat', basename.replace('.jpg', '.mat')) eye_data = loadmat(eye_filename)['s'] xs, ys, subject_ids = eye_data.T n_fixations += len(xs) - 1 # first entry is image size for subject_index in range(subject_ids.max()): subject_inds = subject_ids == subject_index + 1 # subject==0 is image size if not np.any(subject_inds): continue # since there are coordinates with value 0, we assume they are 0-indexed (although they are matlab) train_xs.append(xs[subject_inds]) train_ys.append(ys[subject_inds]) train_ts.append(np.arange(subject_inds.sum())) train_ns.append(n) train_subjects.append(subject_index) fixations = ScanpathFixations(Scanpaths( xs=train_xs, ys=train_ys, ts=train_ts, n=train_ns, subject=train_subjects, )) assert len(fixations) == n_fixations if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations ================================================ FILE: pysaliency/external_datasets/figrim.py ================================================ from __future__ import absolute_import, division, print_function import glob import os import zipfile from tempfile import TemporaryDirectory import numpy as np from boltons.fileutils import mkdir_p from natsort import natsorted from scipy.io import loadmat from ..datasets import FixationTrains from ..utils import ( atomic_directory_setup, download_and_check, ) from .utils import _load, create_stimuli def _load_FIGRIM_data(filename, stimuli_indices, stimulus_type): data = loadmat(filename)['allImages'].flatten() xs = [] ys = [] ts = [] ns = [] train_subjects = [] which_times = [] which_time_names = ['enc', 'rec', 'rec2'] stimulus_types = [] responses = [] for stimulus_data in data: n = stimuli_indices[stimulus_data['filename'][0]] # category = stimulus_data['category'][0] # TODO: use for subject, subject_data in enumerate(stimulus_data['userdata'].flatten()): if not subject_data['trial']: # No data for this subject and this stimulus continue for which_time in which_time_names: fixations = subject_data['fixations'][0, 0][which_time] if not len(fixations): continue # if len(fixations) and which_time != 'enc': # print("Problem:", n, subject_name, which_time) subject_response = subject_data['SDT'][0][which_time_names.index(which_time)] xs.append(fixations[:, 0]) ys.append(fixations[:, 1]) ts.append(np.arange(len(xs[-1]))) ns.append(n) train_subjects.append(subject) which_times.append(which_time_names.index(which_time)) stimulus_types.append(stimulus_type) responses.append(subject_response) return xs, ys, ts, ns, train_subjects, which_times, stimulus_types, responses def get_FIGRIM(location=None): """ Loads or downloads and caches the FIGRIM dataset. The dataset consists of >2700 scenes of sizes 1000x1000px and the fixations of subjects while doing a repetition recognition task with 3 seconds presentation time. subject responses etc are included. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. note:: This dataset comes with additional annotations: - stimulus_type: 0=filler, 1=target - which_time: 0=encoding, 1=first recognition, 2=second recognition - response: 1=hit, 2=false alarm, 3=miss, 4=correct rejection .. seealso:: Bylinskii, Zoya and Isola, Phillip and Bainbridge, Constance and Torralba, Antonio and Oliva, Aude. Intrinsic and Extrinsic Effects on Image Memorability [Vision research 2015] http://figrim.mit.edu/index_eyetracking.html """ if location: location = os.path.join(location, 'FIGRIM') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://figrim.mit.edu/Fillers.zip', os.path.join(temp_dir, 'Fillers.zip'), 'dc0bc9561b5bc90e158ec32074dd1060') download_and_check('http://figrim.mit.edu/Targets.zip', os.path.join(temp_dir, 'Targets.zip'), '2ad3a42ebc377efe4b39064405568201') download_and_check('https://github.com/cvzoya/figrim/blob/master/targetData/allImages_release.mat?raw=True', os.path.join(temp_dir, 'allImages_release.mat'), 'c72843b05e95ab27594c1d11c849c897') download_and_check('https://github.com/cvzoya/figrim/blob/master/fillerData/allImages_fillers.mat?raw=True', os.path.join(temp_dir, 'allImages_fillers.mat'), 'ce4f8b4961005d62f7a21191a64cab5e') # Stimuli mkdir_p(os.path.join(temp_dir, 'stimuli')) print('Creating stimuli') f = zipfile.ZipFile(os.path.join(temp_dir, 'Fillers.zip')) f.extractall(os.path.join(temp_dir, 'stimuli')) f = zipfile.ZipFile(os.path.join(temp_dir, 'Targets.zip')) f.extractall(os.path.join(temp_dir, 'stimuli')) stimuli_src_location = os.path.join(temp_dir, 'stimuli') stimuli_target_location = os.path.join(location, 'Stimuli') if location else None images = glob.glob(os.path.join(stimuli_src_location, '**', '**', '*.jpg')) images = [os.path.relpath(img, start=stimuli_src_location) for img in images] stimuli_filenames = natsorted(images) stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) stimuli_basenames = [os.path.basename(filename) for filename in stimuli_filenames] stimulus_indices = {s: stimuli_basenames.index(s) for s in stimuli_basenames} # FixationTrains print('Creating fixations') print('Fillers...') (xs_filler, ys_filler, ts_filler, ns_filler, train_subjects_filler, which_times_filler, stimulus_types_filler, responses_filler) = _load_FIGRIM_data(os.path.join(temp_dir, 'allImages_fillers.mat'), stimulus_indices, stimulus_type=0) print("Targets...") (xs_target, ys_target, ts_target, ns_target, train_subjects_target, which_times_target, stimulus_types_target, responses_target) = _load_FIGRIM_data(os.path.join(temp_dir, 'allImages_release.mat'), stimulus_indices, stimulus_type=0) print("Finalizing...") xs = xs_filler + xs_target ys = ys_filler + ys_target ts = ts_filler + ts_target ns = ns_filler + ns_target train_subjects = train_subjects_filler + train_subjects_target which_times = which_times_filler + which_times_target stimulus_types = stimulus_types_filler + stimulus_types_target responses = responses_filler + responses_target fixations = FixationTrains.from_fixation_trains( xs, ys, ts, ns, train_subjects, scanpath_attributes={ 'which_time': which_times, 'stimulus_type': stimulus_types, 'response': responses }) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations ================================================ FILE: pysaliency/external_datasets/isun.py ================================================ from __future__ import absolute_import, division, print_function import os import zipfile from tempfile import TemporaryDirectory from scipy.io import loadmat from ..datasets import FixationTrains from ..utils import atomic_directory_setup, download_and_check, filter_files from .utils import _load, create_stimuli # TODO: Add test def get_iSUN(location=None): """ Loads or downloads and caches the iSUN dataset. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `iSUN` of location and read from there, if already present. @return: Training stimuli, validation stimuli, testing stimuli, training fixation trains, validation fixation trains .. seealso:: P. Xu, K. A. Ehinger, Y. Zhang, A. Finkelstein, S. R. Kulkarni, and J. Xiao.: TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking http://lsun.cs.princeton.edu/ http://vision.princeton.edu/projects/2014/iSUN/ """ if location: location = os.path.join(location, 'iSUN') if os.path.exists(location): stimuli_training = _load(os.path.join(location, 'stimuli_training.hdf5')) stimuli_validation = _load(os.path.join(location, 'stimuli_validation.hdf5')) stimuli_testing = _load(os.path.join(location, 'stimuli_testing.hdf5')) fixations_training = _load(os.path.join(location, 'fixations_training.hdf5')) fixations_validation = _load(os.path.join(location, 'fixations_validation.hdf5')) return stimuli_training, stimuli_validation, stimuli_testing, fixations_training, fixations_validation os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://lsun.cs.princeton.edu/challenge/2015/eyetracking/data/training.mat', os.path.join(temp_dir, 'training.mat'), '5a8b15134b17c7a3f69b087845db1363') download_and_check('http://lsun.cs.princeton.edu/challenge/2015/eyetracking/data/validation.mat', os.path.join(temp_dir, 'validation.mat'), 'f68e9b011576e48d2460b883854fd86c') download_and_check('http://lsun.cs.princeton.edu/challenge/2015/eyetracking/data/testing.mat', os.path.join(temp_dir, 'testing.mat'), 'be008ef0330467dcb9c9cd9cc96a8546') download_and_check('http://lsun.cs.princeton.edu/challenge/2015/eyetracking/data/fixation.zip', os.path.join(temp_dir, 'fixation.zip'), 'aadc15784e1b0023cda4536335b7839c') download_and_check('http://lsun.cs.princeton.edu/challenge/2015/eyetracking/data/image.zip', os.path.join(temp_dir, 'image.zip'), '0a3af01c5307f1d44f5dd309f71ea963') # Stimuli print('Creating stimuli') f = zipfile.ZipFile(os.path.join(temp_dir, 'image.zip')) namelist = f.namelist() namelist = filter_files(namelist, ['.DS_Store']) f.extractall(temp_dir, namelist) def get_stimuli_names(name): data_file = os.path.join(temp_dir, '{}.mat'.format(name)) data = loadmat(data_file)[name] stimuli_names = [d[0] for d in data['image'][:, 0]] stimuli_names = ['{}.jpg'.format(n) for n in stimuli_names] return stimuli_names stimulis = [] stimuli_src_location = os.path.join(temp_dir, 'images') for name in ['training', 'validation', 'testing']: print("Creating {} stimuli".format(name)) stimuli_target_location = os.path.join(location, 'stimuli_{}'.format(name)) if location else None images = get_stimuli_names(name) stimulis.append(create_stimuli(stimuli_src_location, images, stimuli_target_location)) # FixationTrains print('Creating fixations') def get_fixations(name): data_file = os.path.join(temp_dir, '{}.mat'.format(name)) data = loadmat(data_file)[name] gaze = data['gaze'][:, 0] ns = [] train_xs = [] train_ys = [] train_ts = [] train_subjects = [] for n in range(len(gaze)): fixation_trains = gaze[n]['fixation'][0, :] for train in fixation_trains: xs = train[:, 0] ys = train[:, 1] ns.append(n) train_xs.append(xs) train_ys.append(ys) train_ts.append(range(len(xs))) train_subjects.append(0) fixations = FixationTrains.from_fixation_trains(train_xs, train_ys, train_ts, ns, train_subjects) return fixations fixations = [] for name in ['training', 'validation']: print("Creating {} fixations".format(name)) fixations.append(get_fixations(name)) if location: stimulis[0].to_hdf5(os.path.join(location, 'stimuli_training.hdf5')) stimulis[1].to_hdf5(os.path.join(location, 'stimuli_validation.hdf5')) stimulis[2].to_hdf5(os.path.join(location, 'stimuli_test.hdf5')) fixations[0].to_hdf5(os.path.join(location, 'fixations_training.hdf5')) fixations[1].to_hdf5(os.path.join(location, 'fixations_validation.hdf5')) return stimulis + fixations def get_iSUN_training(location=None): """ @return: Training stimuli, training fixation trains See `get_iSUN` for more information""" training_stimuli, _, _, training_fixations, _ = get_iSUN(location=location) return training_stimuli, training_fixations def get_iSUN_validation(location=None): """ @return: validation stimuli, validation fixation trains See `get_iSUN` for more information""" _, validation_stimuli, _, _, validation_fixations = get_iSUN(location=location) return validation_stimuli, validation_fixations def get_iSUN_testing(location=None): """ @return: testing stimuli See `get_iSUN` for more information""" _, _, test_stimuli, _, _ = get_iSUN(location=location) return test_stimuli ================================================ FILE: pysaliency/external_datasets/koehler.py ================================================ from __future__ import absolute_import, division, print_function import itertools import os import zipfile from tempfile import TemporaryDirectory import numpy as np from scipy.io import loadmat from tqdm import tqdm from ..datasets import FixationTrains from ..utils import ( atomic_directory_setup, check_file_hash, ) from .utils import _load, create_stimuli def _get_koehler_fixations(data, task, n_stimuli): tasks = {'freeviewing': 'freeview', 'objectsearch': 'objsearch', 'saliencysearch': 'salview'} task = tasks[task] data_x = data['{}_x'.format(task)] data_y = data['{}_y'.format(task)] # Load Fixation Data xs = [] ys = [] ts = [] ns = [] subjects = [] subject_ids = range(data_x.shape[0]) for n, subject_id in tqdm(list(itertools.product(range(n_stimuli), subject_ids))): x = data_x[subject_id, n, :] - 1 y = data_y[subject_id, n, :] - 1 inds = np.logical_not(np.isnan(x)) x = x[inds] y = y[inds] xs.append(x) ys.append(y) ts.append(range(len(x))) ns.append(n) subjects.append(subject_id) return FixationTrains.from_fixation_trains(xs, ys, ts, ns, subjects) def get_koehler(location=None, datafile=None): """ Loads or or extracts and caches the Koehler dataset. The dataset consists of 800 color images of outdoor and indoor scenes of size 405x405px and the fixations for three different tasks: free viewing, object search and saliency search. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `koehler` of location and read from there, if already present. @return: stimuli, fixations_freeviewing, fixations_objectsearch, fixations_saliencysearch .. note:: As this dataset is only after filling a download form, pysaliency cannot download it for you. Instead you have to download the file `PublicData.zip` and provide it to this function via the `datafile` keyword argument on the first call. .. seealso:: Kathryn Koehler, Fei Guo, Sheng Zhang, Miguel P. Eckstein. What Do Saliency Models Predict? [JoV 2014] http://www.journalofvision.org/content/14/3/14.full https://labs.psych.ucsb.edu/eckstein/miguel/research_pages/saliencydata.html """ if location: location = os.path.join(location, 'Koehler') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations_freeviewing = _load(os.path.join(location, 'fixations_freeviewing.hdf5')) fixations_objectsearch = _load(os.path.join(location, 'fixations_objectsearch.hdf5')) fixations_saliencysearch = _load(os.path.join(location, 'fixations_saliencysearch.hdf5')) return stimuli, fixations_freeviewing, fixations_objectsearch, fixations_saliencysearch #mkdir_p(location) if not datafile: raise ValueError('The Koehler dataset is not freely available! You have to ' 'request the data file from the authors and provide it to ' 'this function via the datafile argument') check_file_hash(datafile, '405f58aaa9b4ddc76f3e8f23c379d315') with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: z = zipfile.ZipFile(datafile) print('Extracting') z.extractall(temp_dir) # Stimuli stimuli_src_location = os.path.join(temp_dir, 'Images') stimuli_target_location = os.path.join(location, 'stimuli') if location else None stimuli_filenames = ['image_r_{}.jpg'.format(i) for i in range(1, 801)] stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) # Fixations data = loadmat(os.path.join(temp_dir, 'ObserverData.mat')) fs = [] for task in ['freeviewing', 'objectsearch', 'saliencysearch']: fs.append(_get_koehler_fixations(data, task, len(stimuli))) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fs[0].to_hdf5(os.path.join(location, 'fixations_freeviewing.hdf5')) fs[1].to_hdf5(os.path.join(location, 'fixations_objectsearch.hdf5')) fs[2].to_hdf5(os.path.join(location, 'fixations_saliencysearch.hdf5')) return [stimuli] + fs ================================================ FILE: pysaliency/external_datasets/mit.py ================================================ from __future__ import absolute_import, division, print_function import glob import os import zipfile from tempfile import TemporaryDirectory import numpy as np from natsort import natsorted from PIL import Image from importlib.resources import files as _resource_files def resource_string(package, resource): return _resource_files(package).joinpath(resource).read_bytes() from scipy.io import loadmat from ..datasets import ScanpathFixations, Scanpaths from ..utils import atomic_directory_setup, download_and_check, filter_files, run_matlab_cmd, get_matlab_or_octave from .utils import _load, create_stimuli def _get_mit1003(dataset_name, location=None, include_initial_fixation=False, only_1024_by_768=False, replace_initial_invalid_fixations=False): """ .. seealso:: Tilke Judd, Krista Ehinger, Fredo Durand, Antonio Torralba. Learning to Predict where Humans Look [ICCV 2009] http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html """ if include_initial_fixation: first_fixation = 0 else: first_fixation = 1 if location: location = os.path.join(location, dataset_name) if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://people.csail.mit.edu/tjudd/WherePeopleLook/ALLSTIMULI.zip', os.path.join(temp_dir, 'ALLSTIMULI.zip'), '0d7df8b954ecba69b6796e77b9afe4b6') download_and_check('http://people.csail.mit.edu/tjudd/WherePeopleLook/DATA.zip', os.path.join(temp_dir, 'DATA.zip'), 'ea19d74ad0a0144428c53e9d75c2d71c') download_and_check('http://people.csail.mit.edu/tjudd/WherePeopleLook/Code/DatabaseCode.zip', os.path.join(temp_dir, 'DatabaseCode.zip'), 'd8e5e2b6ec827f4115ddbff59b0bdf1d') # Stimuli print('Creating stimuli') f = zipfile.ZipFile(os.path.join(temp_dir, 'ALLSTIMULI.zip')) namelist = f.namelist() namelist = filter_files(namelist, ['.svn', '__MACOSX', '.DS_Store']) f.extractall(temp_dir, namelist) stimuli_src_location = os.path.join(temp_dir, 'ALLSTIMULI') stimuli_target_location = os.path.join(location, 'stimuli') if location else None images = glob.glob(os.path.join(stimuli_src_location, '*.jpeg')) images = [os.path.split(img)[1] for img in images] stimuli_filenames = natsorted(images) if only_1024_by_768: def check_size(f): img = Image.open(os.path.join(stimuli_src_location, f)) return img.size == (1024, 768) stimuli_filenames = [s for s in stimuli_filenames if check_size(s)] stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) # FixationTrains print('Creating fixations') f = zipfile.ZipFile(os.path.join(temp_dir, 'DATA.zip')) namelist = f.namelist() namelist = filter_files(namelist, ['.svn', '__MACOSX', '.DS_Store']) f.extractall(temp_dir, namelist) f = zipfile.ZipFile(os.path.join(temp_dir, 'DatabaseCode.zip')) namelist = f.namelist() namelist = filter_files(namelist, ['.svn', '__MACOSX', '.DS_Store']) f.extractall(temp_dir, namelist) print("Patching checkFixations.m to work with octave") check_fixation_file = os.path.join(temp_dir, 'DatabaseCode', 'checkFixations.m') with open(check_fixation_file) as f: check_fixation_code = f.read() # nanmedian is not available in new octave statistics packages and deprecated in matlab # we replace it with median(data, 'omitnan') as suggested in the matlab documentation check_fixation_code = check_fixation_code.replace( 'Fix.medianXY(1,1) = nanmedian(data(:,1)); Fix.medianXY(1,2) = nanmedian(data(:,2));', 'Fix.medianXY(1,1) = median(data(:,1), "omitnan"); Fix.medianXY(1,2) = median(data(:,2), "omitnan");' ) # same for nanvar check_fixation_code = check_fixation_code.replace( 'Fix.driftXY(1,1) = nanvar(data(:,1));', 'Fix.driftXY(1,1) = var(data(:,1), "omitnan");' ) check_fixation_code = check_fixation_code.replace( 'Fix.driftXY(1,2) = nanvar(data(:,2));', 'Fix.driftXY(1,2) = var(data(:,2), "omitnan");' ) with open(check_fixation_file, 'w') as f: f.write(check_fixation_code) subjects = glob.glob(os.path.join(temp_dir, 'DATA', '*')) # Exclude files subjects = [s for s in subjects if not os.path.splitext(s)[1]] subjects = [os.path.basename(s) for s in subjects] subjects = sorted(subjects) with open(os.path.join(temp_dir, 'extract_fixations.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/{}'.format('extract_fixations.m'))) cmds = [] # It is vital _not_ to store the extracted fixations in the main # directory where matlab is running, as matlab will check the timestamp # of all files in this directory very often. This leads to heavy # performance penalties and would make matlab run for more than an # hour. out_path = 'extracted' os.makedirs(os.path.join(temp_dir, out_path)) total_cmd_count = len(stimuli_filenames) * len(subjects) for n, stimulus in enumerate(stimuli_filenames): for subject_id, subject in enumerate(subjects): subject_path = os.path.join('DATA', subject) outfile = '{0}_{1}.mat'.format(stimulus, subject) outfile = os.path.join(out_path, outfile) cmds.append("fprintf('Processing scanpath %d/%d\\r', {}, {});".format(n * len(subjects) + subject_id, total_cmd_count)) cmds.append("extract_fixations('{0}', '{1}', '{2}');".format(stimulus, subject_path, outfile)) print('Running original code to extract fixations. This can take some minutes.') print('Warning: In the IPython Notebook, the output is shown on the console instead of the notebook.') with open(os.path.join(temp_dir, 'extract_all_fixations.m'), 'w') as f: for cmd in cmds: f.write('{}\n'.format(cmd)) command = 'extract_all_fixations;' is_octave = 'octave' in get_matlab_or_octave().lower() if is_octave: # in octave, the statistics package has to be loaded explicitly command = "pkg load statistics; {}".format(command) run_matlab_cmd(command, cwd=temp_dir) xs = [] ys = [] ts = [] ns = [] train_subjects = [] duration_hist = [] train_durations = [] for n, stimulus in enumerate(stimuli_filenames): stimulus_size = stimuli.sizes[n] height, width = stimulus_size for subject_id, subject in enumerate(subjects): subject_name = os.path.split(subject)[-1] outfile = '{0}_{1}.mat'.format(stimulus, subject_name) mat_data = loadmat(os.path.join(temp_dir, out_path, outfile)) fix_data = mat_data['fixations'] starts = mat_data['starts'] _durations = mat_data['durations'] _xs = mat_data['fixations'][:, 0] _ys = mat_data['fixations'][:, 1] _ts = mat_data['starts'].flatten() _durations = mat_data['durations'].flatten() full_durations = _durations.copy() # this would be consistent with how it was handled in matlab # however, originally I ported this slightly differnt # and now I'm staying consistent withing pysaliency # valid_indices = ( # (np.floor(_xs) > 0) & (np.floor(_xs) < width) # & (np.floor(_ys) > 0) & (np.floor(_ys) < height)) valid_indices = ( (_xs > 0) & (_xs < width) & (_ys > 0) & (_ys < height)) _xs = _xs[valid_indices] _ys = _ys[valid_indices] _ts = _ts[valid_indices] _durations = _durations[valid_indices] #_xs = _xs - 1 # matlab uses one-based indexing #_ys = _ys - 1 # matlab uses one-based indexing #_ts = _ts - 1 # data starts with t == 1, we want to use 0 _ts = _ts / 240.0 # Eye Tracker rate = 240Hz _durations = _durations / 1000 # data is in ms, we want seconds if not valid_indices[0] and (replace_initial_invalid_fixations or first_fixation > 0): # if first fixation is invalid, no valid initial fixation is removed in the dataset # for those cases, we add a central fixation with same duration as the invalid fixation _xs = np.hstack(([0.5 * width], _xs)) _ys = np.hstack(([0.5 * height], _ys)) _ts = np.hstack(([0], _ts)) _durations = np.hstack(([full_durations[0] / 1000], _durations)) # if first_fixation == 1 the first fixation is skipped, as done # by Judd. _xs = _xs[first_fixation:] _ys = _ys[first_fixation:] _ts = _ts[first_fixation:] _durations = _durations[first_fixation:] xs.append(_xs) ys.append(_ys) ts.append(_ts) ns.append(n) train_subjects.append(subject_id) train_durations.append(_durations) for i in range(len(_durations)): duration_hist.append(_durations[:i]) # x = [] # y = [] # t = [] # duration = [] # # TODO: This contains a subtle inconsistency: if the first fixation is invalid, # # the next fixation is _not_ skipped. Therefore, the dataset still # # contains a few "real" initial fixations. However, this is consistent # # with how the dataset has been used in the past, so we are # # keeping it. # for i in range(first_fixation, fix_data.shape[0]): # if fix_data[i, 0] < 0 or fix_data[i, 1] < 0: # continue # if fix_data[i, 0] >= stimulus_size[1] or fix_data[i, 1] >= stimulus_size[0]: # continue # x.append(fix_data[i, 0]) # y.append(fix_data[i, 1]) # t.append(starts[0, i] / 240.0) # Eye Tracker rate = 240Hz # duration_hist.append(np.array(duration)) # duration.append(_durations[0, i] / 1000) # data is in ms, we want seconds # xs.append(x) # ys.append(y) # ts.append(t) # ns.append(n) # train_subjects.append(subject_id) # train_durations.append(duration) #attributes = { # # duration_hist contains for each fixation the durations of the previous fixations in the scanpath # 'duration_hist': build_padded_2d_array(duration_hist), #} #scanpath_attributes = { # # train_durations contains the fixation durations for each scanpath # 'train_durations': build_padded_2d_array(train_durations), #} #scanpath_fixation_attributes = { # 'durations': train_durations, #} fixations = ScanpathFixations(Scanpaths( xs=xs, ys=ys, ts=ts, n=ns, subject=train_subjects, durations=train_durations, )) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations def get_mit1003(location=None): """ Loads or downloads and caches the MIT1003 dataset. The dataset consists of 1003 natural indoor and outdoor scenes of sizes: max dim: 1024px, other dim: 405-1024px and the fixations of 15 subjects under free viewing conditions with 3 seconds presentation time. All fixations outside of the image are discarded. This includes blinks. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. note:: This code needs a working matlab installation including the Statistics and Machinelearning Toolbox as the original matlab code by Judd et al. is used to extract the fixation from the eyetracking data. Alternatively, the code should work with octave if the statistics package is in installed. The first fixation of each fixation train is discarded as stated in the paper (Judd et al. 2009). .. seealso:: Tilke Judd, Krista Ehinger, Fredo Durand, Antonio Torralba. Learning to Predict where Humans Look [ICCV 2009] http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html """ return _get_mit1003('MIT1003', location=location, include_initial_fixation=False) def get_mit1003_with_initial_fixation(location=None, replace_initial_invalid_fixations=False): """ Loads or downloads and caches the MIT1003 dataset. The dataset consists of 1003 natural indoor and outdoor scenes of sizes: max dim: 1024px, other dim: 405-1024px and the fixations of 15 subjects under free viewing conditions with 3 seconds presentation time. All fixations outside of the image are discarded. This includes blinks. This version of the dataset include the initial central forced fixation, which is usually discarded. However, for scanpath prediction, it's important. Sometimes, the first recorded fixation is invalid. In this case, if `replace_initial_invalid_fixations` is True, it is replaced with a central fixation of the same length. This makes the dataset consistent with the ones without initial fixation in the sense of `fixations_without_initial_fixations = fixations_with[fixations_with.lengths > 0]. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. note:: This code needs a working matlab or octave installation as the original matlab code by Judd et al. is used to extract the fixation from the eyetracking data. Unlike in the original paper (Judd et al. 2009), in this version of the MIT1003 dataset the first fixation of each scanpath is *not* discarded. See `get_mit1003` if you want to use the dataset as suggested by Judd. .. seealso:: Tilke Judd, Krista Ehinger, Fredo Durand, Antonio Torralba. Learning to Predict where Humans Look [ICCV 2009] http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html """ name = 'MIT1003_initial_fix' if replace_initial_invalid_fixations: name += '_consistent' return _get_mit1003(name, location=location, include_initial_fixation=True, replace_initial_invalid_fixations=replace_initial_invalid_fixations) def get_mit1003_onesize(location=None): """ Loads or downloads and caches the subset of the MIT1003 dataset used in "How close are we to understanding image-based saliency" (http://arxiv.org/abs/1409.7686) and "Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet" (http://arxiv.org/abs/1411.1045). The dataset conists of 463 natural indoor and outdoor scenes of size 1024 x 768 px and the fixations of 15 subjects under free viewing conditions with 3 seconds presentation time. All fixations outside of the image are discarded. This includes blinks. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. note:: This code needs a working matlab or octave installation as the original matlab code by Judd et al. is used to extract the fixation from the eyetracking data. The first fixation of each fixation train is discarded as stated in the paper (Judd et al. 2009). .. seealso:: Tilke Judd, Krista Ehinger, Fredo Durand, Antonio Torralba. Learning to Predict where Humans Look [ICCV 2009] http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html """ return _get_mit1003('MIT1003_onesize', location=location, include_initial_fixation=False, only_1024_by_768=True) def get_mit300(location=None): """ Loads or downloads and caches the MIT300 test stimuli for the MIT saliency benchmark. The dataset consists of 300 stimuli and the fixations of 40 subjects under free viewing conditions with 3 seconds presentation time. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli .. seealso:: Zoya Bylinskii and Tilke Judd and Ali Borji and Laurent Itti and Fr{\'e}do Durand and Aude Oliva and Antonio Torralba. MIT Saliency Benchmark http://saliency.mit.edu """ if location: location = os.path.join(location, 'MIT300') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) return stimuli os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: download_and_check('http://saliency.mit.edu/BenchmarkIMAGES.zip', os.path.join(temp_dir, 'BenchmarkIMAGES.zip'), '03ed32bdf5e4289950cd28df89451260') # Stimuli print('Creating stimuli') f = zipfile.ZipFile(os.path.join(temp_dir, 'BenchmarkIMAGES.zip')) namelist = f.namelist() namelist = filter_files(namelist, ['.svn', '__MACOSX', '.DS_Store']) f.extractall(temp_dir, namelist) stimuli_src_location = os.path.join(temp_dir, 'BenchmarkIMAGES') stimuli_target_location = os.path.join(location, 'stimuli') if location else None images = glob.glob(os.path.join(stimuli_src_location, '*.jpg')) images = [os.path.split(img)[1] for img in images] stimuli_filenames = natsorted(images) stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) return stimuli ================================================ FILE: pysaliency/external_datasets/nusef.py ================================================ from __future__ import absolute_import, division, print_function import glob import os import zipfile from datetime import datetime from tempfile import TemporaryDirectory from tqdm import tqdm from ..datasets import FixationTrains from ..utils import ( atomic_directory_setup, download_and_check, ) from .utils import _load, create_stimuli IMAGE_TYPOS = { '3005_0.jpg': '3005.1.jpg', '3005_2.jpg': '3005.2.jpg', } # for some images, only segmentation masks are included in the dataset, # the actual images seem to be part of the non-public IAPS dataset. IMAGES_WITH_ONLY_PUBLIC_SEGMENTATION_MASKS = [ '1112_0.jpg', '1112_2.jpg', '1303_0.jpg', '1303_2.jpg', '3005.1.jpg', '3005.2.jpg', '7233_0.jpg', '7233_1.jpg', '7233_2.jpg', '9006_0.jpg', '9006_1.jpg', # '9501_0.jpg', # actual image included # '9501_2.jpg', # actual image included # '9502_1.jpg', # actual image included # '9502_2.jpg', # actual image included '9561_0.jpg', '9561_2.jpg', '9635_0.jpg', '9635_2.jpg' ] def get_NUSEF_public(location=None): """ Loads or downloads and caches the part of the NUSEF dataset, for which the stimuli are public. The dataset consists of 758 images of size 1024x700px and the fixations of 25 subjects while doing a freeviewing task with 5 seconds presentation time. Part of the stimuli from NUSEF are from the IAPS dataset and are available only under a special license and only upon request. This function returns only the 444 images which are available public (and the corresponding fixations). For some images only segmentation masks are included in the public data, those images and their fixations are also not included in this pysaliency dataset. Subjects ids used currently might not be the real subject ids and might be inconsistent across images. The data collection experiment didn't enforce a specific fixation at stimulus onset. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. seealso:: Subramanian Ramanathan, Harish Katti, Nicu Sebe, Mohan Kankanhalli, Tat-Seng Chua. An eye fixation database for saliency detection in images [ECCV 2010] http://mmas.comp.nus.edu.sg/NUSEF.html (link seems outdated) https://ncript.comp.nus.edu.sg/site/mmas/NUSEF.html (seems to be the new location of the dataset) """ if location: location = os.path.join(location, 'NUSEF_public') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: source_directory = os.path.join(location, 'src') os.makedirs(source_directory) source_file = os.path.join(source_directory, 'NUSEF_database.zip') download_and_check('https://ncript.comp.nus.edu.sg/site/mmas/NUSEF_database.zip', source_file, '429a78ad92184e8a4b37419988d98953') # Stimuli print('Creating stimuli') with zipfile.ZipFile(source_file) as f: f.extractall(temp_dir) stimuli_src_location = os.path.join(temp_dir, 'NUSEF_database', 'stimuli') images = glob.glob(os.path.join(stimuli_src_location, '*.jpg')) images = [os.path.relpath(img, start=stimuli_src_location) for img in images] images = [filename for filename in images if os.path.basename(filename) not in IMAGES_WITH_ONLY_PUBLIC_SEGMENTATION_MASKS] stimuli_filenames = sorted(images) stimuli_target_location = os.path.join(location, 'Stimuli') if location else None stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) stimuli_basenames = [os.path.basename(f) for f in stimuli_filenames] stimuli_indices = {s: stimuli_basenames.index(s) for s in stimuli_basenames} # FixationTrains print('Creating fixations') xs = [] ys = [] ts = [] ns = [] train_subjects = [] durations = [] date_format = "%H:%M:%S.%f" fix_location = os.path.join(temp_dir, 'NUSEF_database', 'fix_data') for sub_dir in tqdm(os.listdir(fix_location)): stimulus_name = sub_dir + '.jpg' stimulus_name = IMAGE_TYPOS.get(stimulus_name, stimulus_name) if stimulus_name not in stimuli_indices: # one of the non public images continue if stimulus_name in IMAGES_WITH_ONLY_PUBLIC_SEGMENTATION_MASKS: continue n = stimuli_indices[stimulus_name] scale_x = 1024 / 260 scale_y = 768 / 280 size = stimuli.sizes[n] # according to the MATLAB visualiation code, images were scaled to screen size by # 1. scaling the images to have a height of 768 pixels # 2. checking if the resulting width is larger than 1024, in this case # the image is downscaled to have a width of 1024 # (and hence a height of less than 768) # here we recompute the scale factors so that we can compute fixation locations # in image coordinates from the screen coordinates image_resize_factor = 768 / size[0] resized_height = 768 resized_width = size[1] * image_resize_factor if resized_width > 1024: additional_factor = (1024 / resized_width) image_resize_factor *= additional_factor resized_height *= additional_factor resized_width = 1024 # images were shown centered x_offset = (1024 - resized_width) / 2 y_offset = (768 - resized_height) / 2 for subject_data in glob.glob(os.path.join(fix_location, sub_dir, '*.fix')): subject_id = int(subject_data.split('+')[0][-2:]) data = open(subject_data).read().replace('\r\n', '\n') data = data.split('COLS=', 1)[1] data = data.split('[Fix Segment Summary')[0] lines = data.split('\n')[1:] lines = [l for l in lines if l] x = [] y = [] t = [] fixation_durations = [] initial_start_time = None for i in range(len(lines)): (_, seg_no, fix_no, pln_no, start_time, fix_dur, interfix_dur, interfix_deg, hor_pos, ver_pos, pupil_diam, eye_scn_dist, no_of_flags, fix_loss, interfix_loss) = lines[i].split() # transform from eye trackoer to screen pixels this_x = float(hor_pos) * scale_x this_y = float(ver_pos) * scale_y # transform to screen image coordinate this_x -= x_offset this_y -= y_offset # transform to original image coordinates this_x /= image_resize_factor this_y /= image_resize_factor x.append(this_x) y.append(this_y) current_start_time = datetime.strptime(str(start_time), date_format) if i == 0: initial_start_time = current_start_time t.append(float((current_start_time - initial_start_time).total_seconds())) fixation_durations.append(float(fix_dur)) xs.append(x) ys.append(y) ts.append(t) ns.append(n) train_subjects.append(subject_id) durations.append(fixation_durations) fixations = FixationTrains.from_fixation_trains( xs, ys, ts, ns, train_subjects, scanpath_fixation_attributes={ 'durations': durations, }, scanpath_attribute_mapping={'durations': 'duration'} ) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations ================================================ FILE: pysaliency/external_datasets/osie.py ================================================ from __future__ import absolute_import, division, print_function import os import urllib from tempfile import TemporaryDirectory import numpy as np from boltons.fileutils import mkdir_p from scipy.io import loadmat from tqdm import tqdm from ..datasets import FixationTrains from ..utils import ( atomic_directory_setup, download_and_check, ) from .utils import _load, create_stimuli def get_OSIE(location=None): """ Loads or downloads and caches the OSIE dataset. The dataset consists of 700 images of size 800x600px and the fixations of 15 subjects while doing a freeviewing task with 3 seconds presentation time. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. seealso:: Juan Xu, Ming Jiang, Shuo Wang, Mohan Kankanhalli, Qi Zhao. Predicting Human Gaze Beyond Pixels [JoV 2014] http://www-users.cs.umn.edu/~qzhao/predicting.html """ if location: location = os.path.join(location, 'OSIE') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: stimuli_src_location = os.path.join(temp_dir, 'stimuli') mkdir_p(stimuli_src_location) images = [] for i in tqdm(list(range(700))): filename = '{}.jpg'.format(i + 1001) target_name = os.path.join(stimuli_src_location, filename) urllib.request.urlretrieve( 'https://github.com/NUS-VIP/predicting-human-gaze-beyond-pixels/raw/master/data/stimuli/' + filename, target_name) images.append(filename) download_and_check('https://github.com/NUS-VIP/predicting-human-gaze-beyond-pixels/blob/master/data/eye/fixations.mat?raw=true', os.path.join(temp_dir, 'fixations.mat'), '8efdf6fe66f38b6e70f854c7ff45aa70') # Stimuli print('Creating stimuli') stimuli_target_location = os.path.join(location, 'Stimuli') if location else None stimuli = create_stimuli(stimuli_src_location, images, stimuli_target_location) stimulus_indices = {s: images.index(s) for s in images} # FixationTrains print('Creating fixations') data = loadmat(os.path.join(temp_dir, 'fixations.mat'))['fixations'].flatten() xs = [] ys = [] ts = [] ns = [] train_subjects = [] for stimulus_data in data: stimulus_data = stimulus_data[0, 0] n = stimulus_indices[stimulus_data['img'][0]] for subject, subject_data in enumerate(stimulus_data['subjects'].flatten()): fixations = subject_data[0, 0] if not len(fixations['fix_x'].flatten()): continue xs.append(fixations['fix_x'].flatten()) ys.append(fixations['fix_y'].flatten()) ts.append(np.arange(len(xs[-1]))) ns.append(n) train_subjects.append(subject) fixations = FixationTrains.from_fixation_trains(xs, ys, ts, ns, train_subjects) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations ================================================ FILE: pysaliency/external_datasets/pascal_s.py ================================================ from __future__ import absolute_import, division, print_function import os import zipfile from tempfile import TemporaryDirectory import numpy as np import requests from ..datasets import ScanpathFixations, Scanpaths from ..utils import ( atomic_directory_setup, download_and_check, ) from .utils import _load, create_stimuli def get_PASCAL_S(location=None): """ Loads or downloads and caches the PASCAL-S dataset. The dataset consists of 850 images from the PASCAL-VOC 2010 validation set with fixation from 12 subjects during 2s free-viewing. Note that here only the eye movement data from PASCAL-S is included. The original dataset also provides salient object segmentation data. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `PASCAL-S` of location and read from there, if already present. @return: Stimuli, FixationTrains .. seealso:: Yin Li, Xiaodi Hu, Christof Koch, James M. Rehg, Alan L. Yuille: The Secrets of Salient Object Segmentation. CVPR 2014. http://cbs.ic.gatech.edu/salobj/ """ if location: location = os.path.join(location, 'PASCAL-S') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) n_stimuli = 850 with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: try: download_and_check('http://cbs.ic.gatech.edu/salobj/download/salObj.zip', os.path.join(temp_dir, 'salObj.zip'), 'e48b4e5deac08bddcaec55ce56e4d420') except requests.exceptions.SSLError: print("http://cbs.ic.gatech.edu/salobj/download/salObj.zip seems to be using an invalid SSL certificate. Since this is known and since we're checking the MD5 sum in addition, we'll ignore the invalid certificate.") download_and_check('http://cbs.ic.gatech.edu/salobj/download/salObj.zip', os.path.join(temp_dir, 'salObj.zip'), 'e48b4e5deac08bddcaec55ce56e4d420', verify_ssl=False) # Stimuli print('Creating stimuli') with zipfile.ZipFile(os.path.join(temp_dir, 'salObj.zip')) as f: f.extractall(temp_dir) stimuli_src_location = os.path.join(temp_dir, 'datasets', 'imgs', 'pascal') stimuli_filenames = ['{}.jpg'.format(i + 1) for i in range(n_stimuli)] stimuli_target_location = os.path.join(location, 'Stimuli') if location else None stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) print('Creating fixations') train_xs = [] train_ys = [] train_ts = [] train_ns = [] train_subjects = [] import h5py # we don't import globally to avoid depending on h5py with h5py.File(os.path.join(temp_dir, 'datasets', 'fixations', 'pascalFix.mat'), mode='r') as hdf5_file: fixation_data = [hdf5_file[hdf5_file['fixCell'][0, stimulus_index]][:] for stimulus_index in range(n_stimuli)] for n in range(n_stimuli): ys, xs, subject_ids = fixation_data[n] for subject in sorted(set(subject_ids)): subject_inds = subject_ids == subject if not np.any(subject_inds): continue train_xs.append(xs[subject_inds] - 1) # data is 1-indexed in matlab train_ys.append(ys[subject_inds] - 1) train_ts.append(np.arange(subject_inds.sum())) train_ns.append(n) train_subjects.append(subject - 1) # subjects are 1-indexed in matlab fixations = ScanpathFixations(Scanpaths( xs=train_xs, ys=train_ys, ts=train_ts, n=train_ns, subject=train_subjects, )) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations ================================================ FILE: pysaliency/external_datasets/salicon.py ================================================ from __future__ import absolute_import, division, print_function import glob import os import zipfile from tempfile import TemporaryDirectory import numpy as np from scipy.io import loadmat from tqdm import tqdm from ..datasets import Fixations, clip_out_of_stimulus_fixations, read_hdf5 from ..utils import atomic_directory_setup, check_file_hash, download_file_from_google_drive from .utils import _load, create_stimuli def get_SALICON(edition='2015', fixation_type='mouse', location=None): """ Loads or downloads and caches the SALICON dataset as used in the LSUN challenge prior to 2017. For memory reasons no fixation trains are provided. @type edition: string, defaults to '2015' @param edition: whether to provide the original SALICON dataset from 2015 or the update from 2017 @type fixation_type: string, defaults to 'mouse' @param fixation_type: whether to use the mouse gaze postion ('mouse') or the inferred fixations ('fixations'). For more details see the SALICON challenge homepage (below). @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `SALICON` of location and read from there, if already present. @return: Training stimuli, validation stimuli, testing stimuli, training fixation trains, validation fixation trains .. seealso:: Ming Jiang, Shengsheng Huang, Juanyong Duan*, Qi Zhao: SALICON: Saliency in Context, CVPR 2015 http://salicon.net/ .. note: prior to version 0.2.0 of pysaliency the data was stored in a way that allowed accessing the subject ids. This is not possible anymore and each scanpath on each image is just assigned an auto-incrementing id. """ if edition not in ['2015', '2017']: raise ValueError('edition has to be \'2015\' or \'2017\', not \'{}\''.format(edition)) if fixation_type not in ['mouse', 'fixations']: raise ValueError('fixation_type has to be \'mouse\' or \'fixations\', not \'{}\''.format(fixation_type)) name = _get_SALICON_name(edition=edition, fixation_type=fixation_type) stimuli_train, stimuli_val, stimuli_test = _get_SALICON_stimuli( location=location, name='SALICON', edition=edition, fixation_type=fixation_type) fixations_train, fixations_val = _get_SALICON_fixations( location=location, name=name, edition=edition, fixation_type=fixation_type) return stimuli_train, stimuli_val, stimuli_test, fixations_train, fixations_val def _get_SALICON_name(edition='2015', fixation_type='mouse'): if edition not in ['2015', '2017']: raise ValueError('edition has to be \'2015\' or \'2017\', not \'{}\''.format(edition)) if fixation_type not in ['mouse', 'fixations']: raise ValueError('fixation_type has to be \'mouse\' or \'fixations\', not \'{}\''.format(fixation_type)) if edition == '2015': if fixation_type == 'mouse': name = 'SALICON' elif fixation_type == 'fixations': name = 'SALICON2015_fixations' elif edition == '2017': if fixation_type == 'mouse': name = 'SALICON2017_mouse' elif fixation_type == 'fixations': name = 'SALICON2017_fixations' return name def _get_SALICON_stimuli(location, name, edition='2015', fixation_type='mouse'): if edition not in ['2015', '2017']: raise ValueError('edition has to be \'2015\' or \'2017\', not \'{}\''.format(edition)) if fixation_type not in ['mouse', 'fixations']: raise ValueError('fixation_type has to be \'mouse\' or \'fixations\', not \'{}\''.format(fixation_type)) if location: location = os.path.join(location, name) if os.path.exists(location): if all(os.path.exists(os.path.join(location, filename)) for filename in ['stimuli_train.hdf5', 'stimuli_val.hdf5', 'stimuli_test.hdf5']): stimuli_train = read_hdf5(os.path.join(location, 'stimuli_train.hdf5')) stimuli_val = read_hdf5(os.path.join(location, 'stimuli_val.hdf5')) stimuli_test = read_hdf5(os.path.join(location, 'stimuli_test.hdf5')) return stimuli_train, stimuli_val, stimuli_test os.makedirs(location, exist_ok=True) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: stimuli_file = os.path.join(temp_dir, 'stimuli.zip') download_file_from_google_drive('1g8j-hTT-51IG1UFwP0xTGhLdgIUCW5e5', stimuli_file) check_file_hash(stimuli_file, 'eb2a1bb706633d1b31fc2e01422c5757') print("Extracting stimuli") with zipfile.ZipFile(stimuli_file) as f: f.extractall(temp_dir) stimuli_train = create_stimuli( stimuli_location=os.path.join(temp_dir, 'images', 'train'), filenames=[os.path.basename(f) for f in sorted(glob.glob(os.path.join(temp_dir, 'images', 'train', 'COCO_train*')))], location=os.path.join(location, 'stimuli', 'train') if location else None ) stimuli_val = create_stimuli( stimuli_location=os.path.join(temp_dir, 'images', 'val'), filenames=[os.path.basename(f) for f in sorted(glob.glob(os.path.join(temp_dir, 'images', 'val', 'COCO_val*')))], location=os.path.join(location, 'stimuli', 'val') if location else None ) stimuli_test = create_stimuli( stimuli_location=os.path.join(temp_dir, 'images', 'test'), filenames=[os.path.basename(f) for f in sorted(glob.glob(os.path.join(temp_dir, 'images', 'test', 'COCO_test*')))], location=os.path.join(location, 'stimuli', 'test') if location else None ) if location is not None: stimuli_train.to_hdf5(os.path.join(location, 'stimuli_train.hdf5')) stimuli_val.to_hdf5(os.path.join(location, 'stimuli_val.hdf5')) stimuli_test.to_hdf5(os.path.join(location, 'stimuli_test.hdf5')) return stimuli_train, stimuli_val, stimuli_test def _get_SALICON_fixations(location, name, edition='2015', fixation_type='mouse'): if edition not in ['2015', '2017']: raise ValueError('edition has to be \'2015\' or \'2017\', not \'{}\''.format(edition)) if fixation_type not in ['mouse', 'fixations']: raise ValueError('fixation_type has to be \'mouse\' or \'fixations\', not \'{}\''.format(fixation_type)) if location: location = os.path.join(location, name) if os.path.exists(location): if all(os.path.exists(os.path.join(location, filename)) for filename in ['fixations_train.hdf5', 'fixations_val.hdf5']): fixations_train = read_hdf5(os.path.join(location, 'fixations_train.hdf5')) fixations_val = read_hdf5(os.path.join(location, 'fixations_val.hdf5')) return fixations_train, fixations_val os.makedirs(location, exist_ok=True) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: fixations_file = os.path.join(temp_dir, 'fixations.zip') if edition == '2015': download_file_from_google_drive('1WVEiXba-I4GN33f0uUl4KhaN1rK7qzs6', fixations_file) check_file_hash(fixations_file, '9a22db9d718200fb90252e5010c004c4') elif edition == '2017': download_file_from_google_drive('1P-jeZXCsjoKO79OhFUgnj6FGcyvmLDPj', fixations_file) check_file_hash(fixations_file, '462b70f4f9e8ea446ac628e46cea8d3d') with zipfile.ZipFile(fixations_file) as f: f.extractall(os.path.join(temp_dir, 'fixations')) fixations = [] if fixation_type == 'mouse': fixation_attr = 'location' elif fixation_type == 'fixations': fixation_attr = 'fixations' for dataset in ['train', 'val']: ns = [] train_xs = [] train_ys = [] train_ts = [] train_subjects = [] subject_id = 0 data_files = list(sorted(glob.glob(os.path.join(temp_dir, 'fixations', dataset, '*.mat')))) for n, filename in enumerate(tqdm(data_files)): fixation_data = loadmat(filename) for subject_data in fixation_data['gaze'].flatten(): train_xs.append(subject_data[fixation_attr][:, 0] - 1) # matlab: one-based indexing train_ys.append(subject_data[fixation_attr][:, 1] - 1) if fixation_type == 'mouse': train_ts.append(subject_data['timestamp'].flatten()) elif fixation_type == 'fixations': train_ts.append(range(len(train_xs[-1]))) train_subjects.append(np.ones(len(train_xs[-1]), dtype=int) * subject_id) ns.append(np.ones(len(train_xs[-1]), dtype=int) * n) subject_id += 1 xs = np.hstack(train_xs) ys = np.hstack(train_ys) ts = np.hstack(train_ts) subjects = np.hstack(train_subjects) ns = np.hstack(ns) fixations.append(Fixations.FixationsWithoutHistory(xs, ys, ts, ns, subjects)) fixations_train, fixations_val = fixations if edition == '2017' and fixation_type == 'mouse': fixations_train = clip_out_of_stimulus_fixations(fixations_train, width=640, height=480) fixations_val = clip_out_of_stimulus_fixations(fixations_val, width=640, height=480) if location is not None: fixations_train.to_hdf5(os.path.join(location, 'fixations_train.hdf5')) fixations_val.to_hdf5(os.path.join(location, 'fixations_val.hdf5')) return fixations_train, fixations_val def get_SALICON_train(edition='2015', fixation_type='mouse', location=None): """ Loads or downloads and caches the SALICON training dataset. See `get_SALICON` for more details. """ if location: name = _get_SALICON_name(edition=edition, fixation_type=fixation_type) if os.path.exists(os.path.join(location, name)): stimuli = _load(os.path.join(location, 'SALICON', 'stimuli_train.hdf5')) fixations = _load(os.path.join(location, name, 'fixations_train.hdf5')) return stimuli, fixations stimuli_train, _, _, fixations_train, _ = get_SALICON(location=location, edition=edition, fixation_type=fixation_type) return stimuli_train, fixations_train def get_SALICON_val(edition='2015', fixation_type='mouse', location=None): """ Loads or downloads and caches the SALICON validation dataset. See `get_SALICON` for more details. """ if location: name = _get_SALICON_name(edition=edition, fixation_type=fixation_type) if os.path.exists(os.path.join(location, name)): stimuli = _load(os.path.join(location, 'SALICON', 'stimuli_val.hdf5')) fixations = _load(os.path.join(location, name, 'fixations_val.hdf5')) return stimuli, fixations _, stimuli_val, _, _, fixations_val = get_SALICON(location=location, edition=edition, fixation_type=fixation_type) return stimuli_val, fixations_val def get_SALICON_test(edition='2015', fixation_type='mouse', location=None): """ Loads or downloads and caches the SALICON test dataset. See `get_SALICON` for more details. """ if location: name = _get_SALICON_name(edition=edition, fixation_type=fixation_type) if os.path.exists(os.path.join(location, name)): stimuli = _load(os.path.join('SALICON', 'stimuli_test.hdf5')) return stimuli _, _, stimuli_test, _, _ = get_SALICON(location=location, edition=edition, fixation_type=fixation_type) return stimuli_test ================================================ FILE: pysaliency/external_datasets/scripts/extract_fixations.m ================================================ function [ ] = extract_fixations(filename, datafolder, outname) % fprintf('Loading %s %s\n', datafolder, filename); addpath('DatabaseCode') datafile = strcat(filename(1:end-4), 'mat'); load(fullfile(datafolder, datafile)); variable_name = datafile(1:end-4); % matlab's variable name have a maximum length of 63 variable_name = variable_name(1:min(end,63)); stimFile = eval([variable_name]); eyeData = stimFile.DATA(1).eyeData; [eyeData Fix Sac] = checkFixations(eyeData); fixs = find(eyeData(:,3)==0); % these are the indices of the fixations in the eyeData for a given image and user fixations = Fix.medianXY; starts = Fix.start; durations = Fix.duration; save(outname, 'fixations', 'starts', 'durations', '-v6') % version 6 makes octave files compatible with scipy ================================================ FILE: pysaliency/external_datasets/scripts/load_cat2000.m ================================================ load trainSet/allFixData.mat; ind = 0 ks=keys(allData); for i=1:size(ks,2); k=ks(i); tmp=allData(char(k)); for j=1:size(tmp,1); ttmp=cell2mat(tmp(j)); name = ttmp.name; data = ttmp.data; filename = sprintf('extracted/fix%d_%d.mat', i, j); save(filename, 'name', 'data'); ind = ind+1; disp(sprintf('%d/%d', i,j)); end; end ================================================ FILE: pysaliency/external_datasets/toronto.py ================================================ from __future__ import absolute_import, division, print_function import os import warnings import zipfile from tempfile import TemporaryDirectory import numpy as np from scipy.io import loadmat from ..datasets import Fixations, FixationTrains from ..utils import atomic_directory_setup, download_and_check from .utils import _load, create_stimuli def get_toronto(location=None): """ Loads or downloads and caches the Toronto dataset. The dataset consists of 120 color images of outdoor and indoor scenes of size 681x511px and the fixations of 20 subjects under free viewing conditions with 4 seconds presentation time. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, Fixations .. warning:: At the moment, the subjects stated in the FixationTrains object will not be correct, as they are difficult to infer from the published data (the data per subject is not stated in image dimensions) .. seealso:: Neil Bruce, John K. Tsotsos. Attention based on information maximization [JoV 2007] `http://www-sop.inria.fr/members/Neil.Bruce/#SOURCECODE """ if location: location = os.path.join(location, 'toronto') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: src = 'http://www-sop.inria.fr/members/Neil.Bruce/eyetrackingdata.zip' target = os.path.join(temp_dir, 'eyetrackingdata.zip') md5_sum = '38d5c02217060d4d2d1a4649cc632af1' download_and_check(src, target, md5_sum) with zipfile.ZipFile(target) as z: print('Extracting') z.extractall(temp_dir) # Stimuli stimuli_src_location = os.path.join(temp_dir, 'eyetrackingdata', 'fixdens', 'Original Image Set') stimuli_target_location = os.path.join(location, 'stimuli') if location else None stimuli_filenames = ['{}.jpg'.format(i) for i in range(1, 121)] stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) points = loadmat(os.path.join(temp_dir, 'eyetrackingdata', 'fixdens', 'origfixdata.mat'))['white'] xs = [] ys = [] ns = [] subjects = [] for n in range(len(stimuli.stimuli)): _ys, _xs = np.nonzero(points[0, n]) xs.extend(_xs) ys.extend(_ys) ns.extend([n for x in _xs]) subjects.extend([0 for x in _xs]) fixations = Fixations.create_without_history(xs, ys, ns, subjects=subjects) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations def get_toronto_with_subjects(location=None): """ Loads or downloads and caches the Toronto dataset. The dataset consists of 120 color images of outdoor and indoor scenes of size 681x511px and the fixations of 20 subjects under free viewing conditions with 4 seconds presentation time. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. warning:: This function uses the positions as given per subject in the toronto dataset. Unfortunately, these positions do not seem to be pixel positions as the fixations are not located as in the file `origfixdata.mat'. The code is still in the package as a template for fixing this issue. .. seealso:: Neil Bruce, John K. Tsotsos. Attention based on information maximization [JoV 2007] `http://www-sop.inria.fr/members/Neil.Bruce/#SOURCECODE """ warnings.warn('This function reports wrong fixation positions! See docstring for details.') if location: location = os.path.join(location, 'toronto') os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory() as temp_dir: src = 'http://www-sop.inria.fr/members/Neil.Bruce/eyetrackingdata.zip' target = os.path.join(temp_dir, 'eyetrackingdata.zip') md5_sum = '38d5c02217060d4d2d1a4649cc632af1' download_and_check(src, target, md5_sum) with zipfile.ZipFile(target) as z: print('Extracting') z.extractall(temp_dir) # Stimuli stimuli_src_location = os.path.join(temp_dir, 'eyetrackingdata', 'fixdens', 'Original Image Set') stimuli_target_location = os.path.join(location, 'stimuli') if location else None stimuli_filenames = ['{}.jpg'.format(i) for i in range(1, 121)] stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) print("Getting fixations") raw_path = os.path.join(temp_dir, 'eyetrackingdata', 'fixdens', 'Raw') subjects = os.listdir(raw_path) train_xs = [] train_ys = [] train_ts = [] train_ns = [] train_subjects = [] subjects = [s for s in subjects if s != 'processed'] subjects = sorted(subjects) for subject_nr, subject in enumerate(subjects): print("Doing subject", subject) for n, image in enumerate(stimuli_filenames): imagename = os.path.splitext(os.path.split(image)[-1])[0] with open(os.path.join(raw_path, subject, imagename + '.fix')) as f: content = f.read() content = content.replace('\r', '') if 'No fixations' in content: print("No fixations for {}, skipping".format(image)) continue subject_fixations = content.split('Fixation Listing')[1].split('\n\n')[1].split('Average')[0] _xs = [] _ys = [] _ts = [] for line in subject_fixations.split('\n'): if not line: continue parts = line.split() parts = [p.replace(',', '') for p in parts] _xs.append(int(parts[1])) _ys.append(int(parts[2])) _ts.append(float(parts[3])) _xs = np.array(_xs, dtype=float) _ys = np.array(_ys, dtype=float) _ts = np.array(_ts, dtype=float) xs = [] ys = [] ts = [] for i in range(len(_xs)): if _xs[i] > 680: continue if _ys[i] > 510: continue xs.append(_xs[i]) ys.append(_ys[i]) ts.append(_ts[i]) train_xs.append(xs) train_ys.append(ys) train_ts.append(ts) train_ns.append(n) train_subjects.append(subject_nr) fixations = FixationTrains.from_fixation_trains(train_xs, train_ys, train_ts, train_ns, train_subjects) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations ================================================ FILE: pysaliency/external_datasets/utils.py ================================================ from __future__ import absolute_import, print_function, division import os import shutil import warnings from ..datasets import FileStimuli, Stimuli, read_hdf5 def create_memory_stimuli(filenames, attributes=None): """ Create a `Stimuli`-class from a list of filenames by reading the them """ tmp_stimuli = FileStimuli(filenames) stimuli = list(tmp_stimuli.stimuli) # Read all stimuli return Stimuli(stimuli, attributes=attributes) def create_stimuli(stimuli_location, filenames, location=None, attributes=None): """ Create a Stimuli class of stimuli. Parameters ---------- @type stimuli_location: string @param stimuli_location: the base path where the stimuli are located. If `location` is provided, this directory will be copied to `location` (see below). @type filenames: list of strings @param filenames: lists the filenames of the stimuli to include in the dataset. Filenames have to be relative to `stimuli_location`. @type location: string or `None` @param location: If provided, the function will copy the filenames to `location` and return a `FileStimuli`-object for the copied files. Otherwise a `Stimuli`-object is returned. @returns: `Stimuli` or `FileStimuli` object depending on `location`. """ if location is not None: shutil.copytree(stimuli_location, location) filenames = [os.path.join(location, f) for f in filenames] return FileStimuli(filenames, attributes=attributes) else: filenames = [os.path.join(stimuli_location, f) for f in filenames] return create_memory_stimuli(filenames, attributes=attributes) def _load(filename): """attempt to load hdf5 file and fallback to pickle files if present""" if os.path.isfile(filename): return read_hdf5(filename) stem, ext = os.path.splitext(filename) pydat_filename = stem + '.pydat' if os.path.isfile(pydat_filename): import dill # raise deprecation warning warnings.warn("Using pickle files is deprecated. Please convert to hdf5 files instead. Pickle support will be removed in pysaliency 0.4", DeprecationWarning, stacklevel=2) return dill.load(open(pydat_filename, 'rb')) else: raise FileNotFoundError(f"Neither {filename} nor {pydat_filename} exist.") ================================================ FILE: pysaliency/external_models/__init__.py ================================================ from .deepgaze import ( DeepGazeI, DeepGazeIIE, ) from .matlab_models import ( AIM, BMS, GBVS, RARE2007, RARE2012, SUN, ContextAwareSaliency, CovSal, GBVSIttiKoch, IttiKoch, Judd, ) from .utils import ExternalModelMixin ================================================ FILE: pysaliency/external_models/deepgaze.py ================================================ import numpy as np import torch from ..datasets import as_stimulus from ..models import Model from ..utils import as_rgb class StaticDeepGazeModel(Model): def __init__(self, centerbias_model, device=None, *args, **kwargs): super().__init__(*args, **kwargs) self.centerbias_model = centerbias_model self.torch_model = self._load_model() self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu") self.torch_model.to(self.device) def _load_model(self): raise NotImplementedError() def _log_density(self, stimulus): stimulus = as_stimulus(stimulus) stimulus_data = stimulus.stimulus_data stimulus_data = as_rgb(stimulus_data) stimulus_data = stimulus_data.transpose(2, 0, 1) centerbias_data = self.centerbias_model.log_density(stimulus) image_tensor = torch.tensor(np.array([stimulus_data]), dtype=torch.float32).to(self.device) centerbias_tensor = torch.tensor(np.array([centerbias_data]), dtype=torch.float32).to(self.device) log_density_prediction = self.torch_model.forward(image_tensor, centerbias_tensor) return log_density_prediction.detach().cpu().numpy()[0].astype(np.float64) class DeepGazeI(StaticDeepGazeModel): """DeepGaze I model see https://github.com/matthias-k/DeepGaze and DeepGaze I: Kümmerer, M., Theis, L., & Bethge, M. (2015). Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. ICLR Workshop Track (http://arxiv.org/abs/1411.1045) """ def __init__(self, centerbias_model, device=None, *args, **kwargs): super().__init__(centerbias_model=centerbias_model, *args, **kwargs) def _load_model(self): return torch.hub.load('matthias-k/DeepGaze', 'DeepGazeI', pretrained=True) class DeepGazeIIE(StaticDeepGazeModel): """DeepGaze IIE model see https://github.com/matthias-k/DeepGaze and DeepGaze IIE: Linardos, A., Kümmerer, M., Press, O., & Bethge, M. (2021). Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling. ICCV 2021 (http://arxiv.org/abs/2105.12441) """ def __init__(self, centerbias_model, device=None, *args, **kwargs): super().__init__(centerbias_model=centerbias_model, *args, **kwargs) def _load_model(self): return torch.hub.load('matthias-k/DeepGaze', 'DeepGazeIIE', pretrained=True) def _log_density(self, stimulus): return super()._log_density(stimulus)[0] ================================================ FILE: pysaliency/external_models/matlab_models.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import os import zipfile from tempfile import TemporaryDirectory from importlib.resources import files as _resource_files def resource_string(package, resource): return _resource_files(package).joinpath(resource).read_bytes() from ..saliency_map_models import MatlabSaliencyMapModel from ..utils import download_and_check, run_matlab_cmd from .utils import ExternalModelMixin, apply_quilt, download_extract_patch, extract_zipfile class AIM(ExternalModelMixin, MatlabSaliencyMapModel): """ The saliency model "Attention based on Information Maximization" (AIM) by Bruce and Tsotso. The original matlab code is used. .. seealso:: Bruce, Tsotso: Saliency, attention, and visual search: An information theoretic approach. 2009. http://www.cs.umanitoba.ca/~bruce/datacode.html """ __modelname__ = 'AIM' def __init__(self, filters='31jade950', convolve=True, location=None, **kwargs): self.setup(location) super(AIM, self).__init__(os.path.join(self.location, 'AIM_wrapper.m'), only_color_stimuli=True, **kwargs) self.filters = filters self.convolve = convolve def matlab_command(self, stimulus): return "AIM_wrapper('{{stimulus}}', '{{saliency_map}}', {}, '{}');".format(1 if self.convolve else 0, self.filters) def _setup(self): with TemporaryDirectory() as temp_dir: if not os.path.isdir(temp_dir): os.makedirs(self.location) download_and_check('http://www.cs.umanitoba.ca/~bruce/AIM.zip', os.path.join(temp_dir, 'AIM.zip'), '6d52bc2c0cb15bc186d3d6de32751351', verify_ssl=False) with zipfile.ZipFile(os.path.join(temp_dir, 'AIM.zip')) as z: namelist = z.namelist() namelist = [n for n in namelist if n.endswith('.m') or n.endswith('.mat')] z.extractall(self.location, namelist) with open(os.path.join(self.location, 'AIM_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/AIM_wrapper.m')) class SUN(ExternalModelMixin, MatlabSaliencyMapModel): """ The saliency model "Saliency using natural image statistics" (SUN) by Zhang et al. The original matlab code is used. .. note:: Due to the patch based approach, the original code returns a saliency map that is smaller than the original stimulus. Here, in a wrapper script, we enlargen the saliency map to match the original stimulus by setting the boundary area to the minimal saliency value, as is done also by Bruce and Tsotso in AIM. As the original code can handle only color images, we convert grayscale images to color images by setting all color channels to the grayscale value. This model does not work with octave due to incompabilities of octave with matlab. This might change in the future. .. seealso:: Lingyun Zhang, Matthew H. Tong, Tim K. Marks, Honghao Shan, Garrison W. Cottrell. SUN: A Bayesian framework for saliency using natural statistics [JoV 2008] http://cseweb.ucsd.edu/~l6zhang/ """ __modelname__ = 'SUN' def __init__(self, scale=1.0, location=None, **kwargs): self.setup(location) super(SUN, self).__init__(os.path.join(self.location, 'SUN_wrapper.m'), only_color_stimuli=True, **kwargs) self.scale = scale def matlab_command(self, stimulus): return "SUN_wrapper('{{stimulus}}', '{{saliency_map}}', {});".format(self.scale) def _setup(self): with TemporaryDirectory() as temp_dir: if not os.path.isdir(temp_dir): os.makedirs(self.location) download_and_check('http://cseweb.ucsd.edu/~l6zhang/code/imagesaliency.zip', os.path.join(temp_dir, 'SUN.zip'), 'df69e6c34b2e9e5ddd7a051d98e880d0') with zipfile.ZipFile(os.path.join(temp_dir, 'SUN.zip')) as z: namelist = z.namelist() namelist = [n for n in namelist if n.endswith('.m') or n.endswith('.mat')] z.extractall(self.location, namelist) with open(os.path.join(self.location, 'SUN_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/SUN_wrapper.m')) with open(os.path.join(self.location, 'ensure_image_is_color_image.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/ensure_image_is_color_image.m')) class ContextAwareSaliency(ExternalModelMixin, MatlabSaliencyMapModel): """ The saliency model "Context-Aware Saliency" by Goferman et. al. The original matlab code is used. .. seealso:: Stas Goferman, Lihi Zelnik-Manor, Ayellet Tal. Context-Aware Saliency Detection [CVPR 2010] [PAMI 2012] http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html (this URL is down as of 2024-06. Ther doesn't seem to be a new version, but you can check an old version at https://web.archive.org/web/20230106184636/https://cgm.technion.ac.il/Computer-Graphics-Multimedia/Software/Saliency/Saliency.html) """ __modelname__ = 'ContextAwareSaliency' def __init__(self, location=None, **kwargs): self.setup(location) super(ContextAwareSaliency, self).__init__(os.path.join(self.location, 'ContextAwareSaliency_wrapper.m'), **kwargs) def _setup(self): with TemporaryDirectory() as temp_dir: if not os.path.isdir(temp_dir): os.makedirs(self.location) # url = 'http://webee.technion.ac.il/labs/cgm/Computer-Graphics-Multimedia/Software/Saliency/Saliency.zip' # The original URL is not available anymore. We use the webarchive version url = 'https://web.archive.org/web/20230106184636/https://cgm.technion.ac.il/Computer-Graphics-Multimedia/Software/Saliency/Saliency.zip' download_and_check(url, os.path.join(temp_dir, 'Saliency.zip'), 'c3c6768ef26e95def76000f51e8aad7c') with zipfile.ZipFile(os.path.join(temp_dir, 'Saliency.zip')) as z: source_location = os.path.join(self.location, 'source') os.makedirs(source_location) z.extractall(source_location) with open(os.path.join(self.location, 'ContextAwareSaliency_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/ContextAwareSaliency_wrapper.m')) class BMS(ExternalModelMixin, MatlabSaliencyMapModel): """ "Boolean Map based Saliency (BMS)" by Zhang and Sclaroff. The original matlab code is used. .. note:: The original code is slightly patched to work from other directories. Also, their code uses OpenCV for performance. The original compile script had hardcoded Windows paths for the location of OpenCV. The compile script is patched to use the path of OpenCV in Ubuntu. If you want to use the BMS model in other systems, you might have to adapt the compile script and run it yourself. In Debian-based linux systems like Ubuntu you can install all needed opencv-libraries with apt-get install libopencv-core-dev libopencv-highgui-dev libopencv-imgproc-dev libopencv-flann-dev libopencv-photo-dev libopencv-video-dev libopencv-features2d-dev libopencv-objdetect-dev libopencv-calib3d-dev libopencv-ml-dev libopencv-contrib-dev .. seealso:: Jianming Zhang, Stan Sclaroff. Saliency detection: a boolean map approach [ICCV 2013] [http://cs-people.bu.edu/jmzhang/BMS/BMS_iccv13_preprint.pdf] http://cs-people.bu.edu/jmzhang/BMS/BMS.html """ __modelname__ = 'BMS' def __init__(self, location=None, **kwargs): self.setup(location) super(BMS, self).__init__(os.path.join(self.location, 'BMS_wrapper.m'), **kwargs) def _setup(self): with TemporaryDirectory() as temp_dir: if not os.path.isdir(temp_dir): os.makedirs(self.location) download_and_check('http://cs-people.bu.edu/jmzhang/BMS/BMS-mex.zip', os.path.join(temp_dir, 'BMS.zip'), '056fa962993c3083f7977ee18432dd8c') source_location = os.path.join(self.location, 'source') extract_zipfile(os.path.join(temp_dir, 'BMS.zip'), source_location) apply_quilt(source_location, __name__, os.path.join('scripts', 'BMS', 'patches'), os.path.join(self.location, 'patches')) run_matlab_cmd('compile', cwd=os.path.join(source_location, 'mex')) with open(os.path.join(self.location, 'BMS_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/BMS/BMS_wrapper.m')) class GBVS(ExternalModelMixin, MatlabSaliencyMapModel): """ "Graph Based Visual Saliency (GBVS)" by Zhang and Sclaroff. The original matlab code is used. .. note:: The original code is slightly patched to work from other directories. This model does not work with octave due to incompabilities of octave with matlab. This might change in the future. .. seealso:: Jonathan Harel, Christof Koch, Pietro Perona. Graph-Based Visual Saliency [NIPS 2006] http://www.vision.caltech.edu/~harel/share/gbvs.php """ __modelname__ = 'GBVS' def __init__(self, location=None, **kwargs): """ The parameter explanation have been adopted from the original code. Parameters ========== General ------- @type salmapmaxsize: integer, defaults to 32 @param salmapmaxsize: size of calculated saliency maps (maximum dimension). don't set this too high (e.g., >60). @type blurfrac: float, defaults to 0.02 @param blurfrac: final blur to apply to master saliency map (in standard deviations of gaussian kernel, expressed as fraction of image width). Use value 0 to turn off this feature. Features -------- @type channels: string, defaults to `DIO`. @param channels: feature channels to use encoded as a string. these are available: C is for Color I is for Intensity O is for Orientation R is for contRast F is for Flicker M is for Motion D is for DKL Color (Derrington Krauskopf Lennie) == much better than C channel e.g., 'IR' would be only intensity and contrast, or 'CIO' would be only color,int.,ori. (standard) 'CIOR' uses col,int,ori, and contrast @type colorWeight, intensityWeight, orientationWeight, contrastWeight, flickerWeight, motionWeight, dklcolorWeight: float, defaults to 1 @param colorWeight, intensityWeight, orientationWeight, contrastWeight, flickerWeight, motionWeight, dklcolorWeight: weights of feature channels (do not need to sum to 1). @type gaborangles: list, None is the default: [0, 45, 90, 135] @param gaborangles: angles of gabor filters @type contrastwidth: float, defaults to 0.1 @param contrastwidth: fraction of image width = length of square side over which luminance variance is computed for 'contrast' feature map. LARGER values will give SMOOTHER contrast maps. """ self.setup(location) super(GBVS, self).__init__(os.path.join(self.location, 'GBVS_wrapper.m'), **kwargs) def _setup(self): source_location = os.path.join(self.location, 'gbvs') url = "http://www.vision.caltech.edu/~harel/share/gbvs.zip" download_extract_patch(url, 'c5a86b9549c2c0bbd1b7f7e5b663b031', source_location, location_in_archive=True, patches=os.path.join('GBVS', 'patches')) run_matlab_cmd("addpath('compile');gbvs_compile", cwd=source_location) with open(os.path.join(self.location, 'GBVS_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/GBVS/GBVS_wrapper.m')) class GBVSIttiKoch(ExternalModelMixin, MatlabSaliencyMapModel): """ IttiKoch as implemented in "Graph Based Visual Saliency (GBVS)" by Zhang and Sclaroff. The original matlab code is used. .. note:: The original code is slightly patched to work from other directories. This model does not work with octave due to incompabilities of octave with matlab. This might change in the future. .. seealso:: Jonathan Harel, Christof Koch, Pietro Perona. Graph-Based Visual Saliency [NIPS 2006] http://www.vision.caltech.edu/~harel/share/gbvs.php """ __modelname__ = 'GBVSIttiKoch' def __init__(self, location=None, **kwargs): self.setup(location) super(GBVSIttiKoch, self).__init__(os.path.join(self.location, 'GBVSIttiKoch_wrapper.m'), **kwargs) def _setup(self): source_location = os.path.join(self.location, 'gbvs') download_extract_patch('http://www.vision.caltech.edu/~harel/share/gbvs.zip', 'c5a86b9549c2c0bbd1b7f7e5b663b031', source_location, location_in_archive=True, patches=os.path.join('GBVS', 'patches')) run_matlab_cmd("addpath('compile');gbvs_compile", cwd=source_location) with open(os.path.join(self.location, 'GBVSIttiKoch_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/GBVS/GBVSIttiKoch_wrapper.m')) class Judd(ExternalModelMixin, MatlabSaliencyMapModel): """ Judd model by Judd et al. The original matlab code is used. .. note:: The original code is patched to work from other directories. The model makes use of the [SaliencyToolbox](http://www.saliencytoolbox.net/). Due to licence restrictions the Toolbox cannot be downloaded automatically. You have to download it yourself and provide the location of the zipfile via the `saliency_toolbox_archive`-keyword to the constructor. This model does not work with octave due to incompabilities of octave with matlab. This might change in the future. .. seealso:: Tilke Judd, Krista Ehinger, Fredo Durand, Antonio Torralba. Learning to predict where humans look [ICCV 2009] http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html """ __modelname__ = 'Judd' def __init__(self, location=None, saliency_toolbox_archive=None, include_locations=None, library_locations=None, **kwargs): self.setup(location, saliency_toolbox_archive=saliency_toolbox_archive, include_locations=include_locations, library_locations=library_locations) super(Judd, self).__init__(os.path.join(self.location, 'Judd_wrapper.m'), only_color_stimuli=True, **kwargs) def _setup(self, saliency_toolbox_archive, include_locations, library_locations): if not saliency_toolbox_archive: raise Exception('You have to provide the zipfile containing the Itti and Koch Saliency Toolbox!') if include_locations is None: include_locations = ['/usr/include/opencv2'] if library_locations is None: library_locations = ['/usr/lib/x86_64-linux-gnu/'] source_location = os.path.join(self.location, 'source') print('Downloading Judd Model...') download_extract_patch('http://people.csail.mit.edu/tjudd/WherePeopleLook/Code/JuddSaliencyModel.zip', '03e56c6f37c0ef3605c4c476f8a35b6b', os.path.join(source_location, 'JuddSaliencyModel'), location_in_archive=True, patches=os.path.join('Judd', 'JuddSaliencyModel_patches')) print('Downloading matlabPyrTools...') download_extract_patch('http://www.cns.nyu.edu/pub/eero/matlabPyrTools.tar.gz', 'da53c02c843ed636bb35e20dac68a1b2', os.path.join(source_location, 'matlabPyrTools'), location_in_archive=True, patches=None) print('Downloading voc') download_extract_patch('http://cs.brown.edu/~pff/latent-release3/voc-release3.1.tgz', '20502f8a40f1122e00f81dcc0d11a843', os.path.join(source_location, 'voc-release3.1'), location_in_archive=True, patches=os.path.join('Judd', 'voc_patches'), verify_ssl=False, # doesn't seem to support SSL ) run_matlab_cmd("compile;quit;", cwd=os.path.join(source_location, 'voc-release3.1')) print('Extracting Saliency Toolbox') extract_zipfile(saliency_toolbox_archive, source_location) apply_quilt(os.path.join(source_location, 'SaliencyToolbox'), __name__, os.path.join('scripts', 'Judd', 'SaliencyToolbox_patches'), os.path.join(source_location, 'SaliencyToolbox_patches')) print('Downloading Viola Jones Face Detection') download_extract_patch('http://www.mathworks.com/matlabcentral/fileexchange/19912-open-cv-viola-jones-face-detection-in-matlab?download=true', '26d3f6bc6641d959661afedadff8b479', os.path.join(source_location, 'FaceDetect'), location_in_archive=False, patches=os.path.join('Judd', 'FaceDetect_patches')) includes = ' '.join(['-I{}'.format(location) for location in include_locations]) libraries = ' '.join(['-L{}'.format(location) for location in library_locations]) run_matlab_cmd("mex FaceDetect.cpp {includes} {libaries} -lopencv_core -lopencv_objdetect -lopencv_imgproc -lopencv_highgui -outdir .".format( includes=includes, libaries=libraries ), cwd=os.path.join(source_location, 'FaceDetect', 'src')) print('Downloading LabelMe Toolbox') download_extract_patch('http://labelme.csail.mit.edu/LabelMeToolbox/LabelMeToolbox.zip', '66d982aae539149eff09ce26b7278f4d', os.path.join(source_location, 'LabelMeToolbox'), location_in_archive=True, patches=None) with open(os.path.join(self.location, 'Judd_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/Judd/Judd_wrapper.m')) class IttiKoch(ExternalModelMixin, MatlabSaliencyMapModel): """ Model by Itti and Koch. The original matlab code is used. .. note:: The original code is patched to work from other directories. The model makes use of the [SaliencyToolbox](http://www.saliencytoolbox.net/). Due to licence restrictions the Toolbox cannot be downloaded automatically. You have to download it yourself and provide the location of the zipfile via the `saliency_toolbox_archive`-keyword to the constructor. This model does not work with octave due to incompabilities of octave with matlab. This might change in the future. .. seealso:: Saliency Toolbox by: Dirk Walther, Christof Koch. Modeling attention to salient proto-objects [Neural Networks 2006] http://www.saliencytoolbox.net/ """ __modelname__ = 'IttiKoch' def __init__(self, location=None, saliency_toolbox_archive=None, **kwargs): self.setup(location, saliency_toolbox_archive=saliency_toolbox_archive) super(IttiKoch, self).__init__(os.path.join(self.location, 'IttiKoch_wrapper.m'), only_color_stimuli=True, **kwargs) def _setup(self, saliency_toolbox_archive): if not saliency_toolbox_archive: raise Exception('You have to provide the zipfile containing the Itti and Koch Saliency Toolbox!') print('Extracting Saliency Toolbox') extract_zipfile(saliency_toolbox_archive, self.location) apply_quilt(os.path.join(self.location, 'SaliencyToolbox'), __name__, os.path.join('scripts', 'Judd', 'SaliencyToolbox_patches'), os.path.join(self.location, 'SaliencyToolbox_patches')) with open(os.path.join(self.location, 'IttiKoch_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/IttiKoch_wrapper.m')) class RARE2007(ExternalModelMixin, MatlabSaliencyMapModel): """ RARE2007 by Mancas. The original matlab code is used. .. note:: The source code published by the authors is used without modifications. .. seealso:: Matei Mancas. Relative influence of bottom-up and top-down attention. [International Workshop on Attention in Cognitive Systems 2008] https://numediart.github.io/VisualAttention-RareFamily/ https://github.com/numediart/VisualAttention-Rare2007 """ __modelname__ = 'RARE2007' def __init__(self, location=None, **kwargs): self.setup(location) super(RARE2007, self).__init__(os.path.join(self.location, 'RARE2007_wrapper.m'), only_color_stimuli=True, **kwargs) def _setup(self): source_location = os.path.join(self.location, 'source') directory = 'VisualAttention-Rare2007-ee7ef25c9b6cc7e96837091e868d03bd97b4b131' print('Downloading RARE2007 Model...') url = 'https://github.com/numediart/VisualAttention-Rare2007/archive/ee7ef25c9b6cc7e96837091e868d03bd97b4b131.zip' download_extract_patch(url, '7ee05ae059dac10478b5cabc8ee066dc', os.path.join(source_location, directory), location_in_archive=True, patches=None) with open(os.path.join(self.location, 'RARE2007_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/RARE2007_wrapper.m')) class RARE2012(ExternalModelMixin, MatlabSaliencyMapModel): """ RARE2012 by Margolin et al. The original matlab code is used. .. note:: This page is not available anymore, the RARE models are now hosted at https://numediart.github.io/VisualAttention-RareFamily/, the RARE2012 model is published at https://github.com/numediart/VisualAttention-Rare2012. This model is a wrapper around the published (compiled) matlab code. The model used to be hosted at http://www.tcts.fpms.ac.be/attention/?categorie16/what-and-why. This page is not available anymore, the RARE models are now hosted at https://numediart.github.io/VisualAttention-RareFamily/ and GitHub. With this change, the behaviour of RARE2012 changed slightly compared to the earlier version (e.g., saliency maps look a bit smoother). This model does not work with octave due to incompabilities of octave with matlab (RARE2012 comes as precompiled matlab file). This might change in the future. .. seealso:: Nicolas Riche, Matei Mancas, Matthieu Duvinage, Makiese Mibulumukini, Bernard Gosselin, Thierry Dutoit. RARE2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis [Signal Processing: Image Communication, 2013] https://numediart.github.io/VisualAttention-RareFamily/ """ __modelname__ = 'RARE2012' def __init__(self, location=None, **kwargs): self.setup(location) super(RARE2012, self).__init__(os.path.join(self.location, 'RARE2012_wrapper.m'), only_color_stimuli=True, **kwargs) def _setup(self): source_location = os.path.join(self.location, 'source') directory = 'VisualAttention-Rare2012-55ba7414b971429e5e899ddfa574e4235fc806e6' print('Downloading RARE2012 Model...') url = 'https://github.com/numediart/VisualAttention-Rare2012/archive/55ba7414b971429e5e899ddfa574e4235fc806e6.zip' download_extract_patch(url, 'a5bbd8a42c231530834659acfd0d2b64.', os.path.join(source_location, directory), location_in_archive=True, patches=None) with open(os.path.join(self.location, 'RARE2012_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/RARE2012_wrapper.m')) class CovSal(ExternalModelMixin, MatlabSaliencyMapModel): """ CovSal by Erdem and Erdem The original matlab code is used. .. note:: This model does not work with octave due to incompabilities of octave with matlab. This might change in the future. .. seealso:: Erkut Erdem, Aykut Erdem. Visual saliency estimation by nonlinearly integrating features using region covariances [JoV 2013] http://web.cs.hacettepe.edu.tr/~erkut/projects/CovSal/ """ __modelname__ = 'CovSal' def __init__(self, location=None, size=512, quantile=0.1, centerbias=True, modeltype='SigmaPoints', **kwargs): """ Parameters ---------- @type size: int @param size: size of rescaled image @type quantile: float @param quantile: parameter specifying the most similar regions in the neighborhood @type centerbias: bool @param centerbias: True for center bias and False for no center bias @type modeltype: string @param modeltype: 'CovariancesOnly' and 'SigmaPoints' to denote whether first-order statistics will be incorporated or not """ self.setup(location) super(CovSal, self).__init__(os.path.join(self.location, 'CovSal_wrapper.m'), only_color_stimuli=True, **kwargs) self.size = size self.quantile = quantile self.centerbias = centerbias if modeltype not in ['CovariancesOnly', 'SigmaPoints']: raise ValueError('Unkown modeltype', modeltype) self.modeltype = modeltype def matlab_command(self, stimulus): return "CovSal_wrapper('{{stimulus}}', '{{saliency_map}}', {}, {}, {}, '{}');".format(self.size, self.quantile, 1 if self.centerbias else 0, self.modeltype) def _setup(self): download_extract_patch('https://web.cs.hacettepe.edu.tr/~erkut/projects/CovSal/saliency.zip', '5c1bcdce438a08051968b085f72b2fdc', os.path.join(self.location, 'saliency'), location_in_archive=True, patches=None) with open(os.path.join(self.location, 'CovSal_wrapper.m'), 'wb') as f: f.write(resource_string(__name__, 'scripts/CovSal_wrapper.m')) ================================================ FILE: pysaliency/external_models/models.py ================================================ from __future__ import absolute_import, print_function, division, unicode_literals import os import tempfile import zipfile import tarfile from importlib.resources import files as _resource_files def resource_string(package, resource): return _resource_files(package).joinpath(resource).read_bytes() def resource_listdir(package, resource_name): return [r.name for r in _resource_files(package).joinpath(resource_name).iterdir()] from boltons.fileutils import mkdir_p import numpy as np from scipy.ndimage import zoom from ..utils import download_and_check, run_matlab_cmd from ..quilt import QuiltSeries from ..saliency_map_models import MatlabSaliencyMapModel, SaliencyMapModel from .utils import write_file, extract_zipfile, unpack_directory, apply_quilt, download_extract_patch, ExternalModelMixin ================================================ FILE: pysaliency/external_models/scripts/AIM_wrapper.m ================================================ function [ ] = AIM_wrapper(filename, outname, convolve, filters) addpath('AIM'); saliency_map = AIM(filename, 1.0, convolve, filters); save(outname, 'saliency_map', '-v6'); ================================================ FILE: pysaliency/external_models/scripts/BMS/BMS_wrapper.m ================================================ function [ ] = BMS(filename, outname) addpath('source') directory = sprintf('tmp_%s', int2str(randi(1e15))); while exist(directory, 'dir') directory = sprintf('tmp_%s', int2str(randi(1e15))); end mkdir(directory); copyfile(filename, directory) output_directory = fullfile(directory, 'output'); BMS(directory, output_directory, false); [path, name, ext] = fileparts(filename); outfile = fullfile(output_directory, sprintf('%s.png', name)); saliency_map = imread(outfile); save(outname, 'saliency_map'); rmdir(directory, 's'); ================================================ FILE: pysaliency/external_models/scripts/BMS/patches/adapt_opencv_paths.diff ================================================ Index: src/mex/compile.m =================================================================== --- src.orig/mex/compile.m 2013-09-14 14:27:14.000000000 +0200 +++ src/mex/compile.m 2014-07-04 16:12:38.350680006 +0200 @@ -5,8 +5,8 @@ function compile() % set the values -opts.opencv_include_path = 'C:\opencv240\install\include'; % OpenCV include path -opts.opencv_lib_path = 'C:\opencv240\install\lib'; % OpenCV lib path +opts.opencv_include_path = '/usr/include'; % OpenCV include path +opts.opencv_lib_path = '/usr/lib/x86_64-linux-gnu'; % OpenCV lib path opts.clean = false; % clean mode opts.dryrun = false; % dry run mode opts.verbose = 1; % output verbosity @@ -23,7 +23,7 @@ if opts.verbose > 0, disp(cmd); end if ~opts.dryrun, delete(cmd); end - cmd = fullfile('*.obj'); + cmd = fullfile('*.o'); if opts.verbose > 0, disp(cmd); end if ~opts.dryrun, delete(cmd); end @@ -49,7 +49,7 @@ % Compile the mex file src = 'mexBMS.cpp'; -obj = 'BMS.obj MxArray.obj'; +obj = 'BMS.o MxArray.o'; cmd = sprintf('mex %s %s %s', mex_flags, src, obj); if opts.verbose > 0, disp(cmd); end if ~opts.dryrun, eval(cmd); end @@ -83,7 +83,7 @@ function l = lib_names(L_path) %LIB_NAMES return library names - d = dir( fullfile(L_path,'opencv_*.lib') ); - l = regexp({d.name}, '(opencv_core.+)\.lib|(opencv_imgproc.+)\.lib|(opencv_highgui.+)\.lib', 'tokens', 'once'); + d = dir( fullfile(L_path,'libopencv_*.so') ); + l = regexp({d.name}, 'lib(opencv_core.*)\.so|lib(opencv_imgproc.*)\.so|lib(opencv_highgui.*)\.so', 'tokens', 'once'); l = [l{:}]; -end \ No newline at end of file +end ================================================ FILE: pysaliency/external_models/scripts/BMS/patches/correct_add_path.diff ================================================ Index: src/BMS.m =================================================================== --- src.orig/BMS.m 2013-09-14 12:28:06.000000000 +0200 +++ src/BMS.m 2014-07-04 16:44:51.765801441 +0200 @@ -33,7 +33,8 @@ % **sod** is a boolean value indicating whether to use the salient object % detection mode -addpath('mex/'); +[directory name ext] = fileparts(mfilename('fullpath')); +addpath(fullfile(directory, 'mex')); if input_dir(end) ~= '/' && input_dir(end) ~= '\' input_dir = [input_dir,'/']; ================================================ FILE: pysaliency/external_models/scripts/BMS/patches/fix_FileGettor.diff ================================================ Index: src/mex/fileGettor.h =================================================================== --- src.orig/mex/fileGettor.h 2013-09-13 19:44:12.000000000 +0200 +++ src/mex/fileGettor.h 2014-12-05 20:34:55.405538000 +0100 @@ -35,8 +35,13 @@ cout << "Error opening " << directory << endl; } - readdir(dp);//. - readdir(dp);//.. + //commented out by Matthias Kuemmerer: + //readdir does not garantue any order, in + //particular . and .. are not garanteed to + //be the first results. + // + //readdir(dp);//. + //readdir(dp);//.. while ((dirp = readdir(dp)) != NULL) { string filename(dirp->d_name); _name_list.push_back(filename); ================================================ FILE: pysaliency/external_models/scripts/BMS/patches/series ================================================ adapt_opencv_paths.diff correct_add_path.diff fix_FileGettor.diff ================================================ FILE: pysaliency/external_models/scripts/ContextAwareSaliency_wrapper.m ================================================ function [ ] = ContextAwareSaliency(filename, outname) addpath('source') file_names{1} = filename; img = imread(filename); [nrows ncols cc] = size(img); MOV = saliency(file_names); saliency_map = MOV{1}.SaliencyMap; saliency_map = imresize(saliency_map, [nrows, ncols]); save(outname, 'saliency_map'); ================================================ FILE: pysaliency/external_models/scripts/CovSal_wrapper.m ================================================ function [ ] = CovSal_wrapper(filename, outname, size, quantile, centerbias, modeltype) addpath('saliency') % options for saliency estimation options.size = size; options.quantile = quantile; options.centerBias = centerbias; options.modeltype = modeltype; saliency_map = saliencymap(filename, options); save(outname, 'saliency_map'); ================================================ FILE: pysaliency/external_models/scripts/GBVS/GBVSIttiKoch_wrapper.m ================================================ function [ ] = GBVSIttiKoch_wrapper(filename, outname) addpath('gbvs') addpath('gbvs/algsrc') addpath('gbvs/compile') addpath('gbvs/initcache') addpath('gbvs/saltoolbox') addpath(genpath('gbvs/util')) img = imread(filename); map = ittikochmap(img); saliency_map = map.master_map_resized; save(outname, 'saliency_map'); ================================================ FILE: pysaliency/external_models/scripts/GBVS/GBVS_wrapper.m ================================================ function [ ] = GBVS_rwapper(filename, outname) addpath('gbvs') addpath('gbvs/algsrc') addpath('gbvs/compile') addpath('gbvs/initcache') addpath('gbvs/saltoolbox') addpath(genpath('gbvs/util')) img = imread(filename); map = gbvs(img); saliency_map = map.master_map_resized; save(outname, 'saliency_map'); ================================================ FILE: pysaliency/external_models/scripts/GBVS/patches/get_path ================================================ Index: src/algsrc/initGBVS.m =================================================================== --- src.orig/algsrc/initGBVS.m 2013-08-19 15:25:41.513971846 +0200 +++ src/algsrc/initGBVS.m 2013-08-19 15:25:41.505971965 +0200 @@ -27,7 +27,8 @@ % weight matrix if ( ~param.useIttiKochInsteadOfGBVS ) - load mypath; + [directory name ext] = fileparts(mfilename('fullpath')); + [pathroot name ext] = fileparts(directory); ufile = sprintf('%s__m%s__%s.mat',num2str(salmapsize),num2str(param.multilevels),num2str(param.cyclic_type)); ufile(ufile==' ') = '_'; ufile = fullfile( pathroot , 'initcache' , ufile ); Index: src/util/getFeatureMaps.m =================================================================== --- src.orig/util/getFeatureMaps.m 2013-08-19 15:25:41.513971846 +0200 +++ src/util/getFeatureMaps.m 2013-08-19 15:25:41.509971905 +0200 @@ -4,7 +4,8 @@ % this computes feature maps for each cannnel in featureChannels/ % -load mypath; +[directory name ext] = fileparts(mfilename('fullpath')); +[pathroot name ext] = fileparts(directory); %%%% %%%% STEP 1 : form image pyramid and prune levels if pyramid levels get too small. ================================================ FILE: pysaliency/external_models/scripts/GBVS/patches/make_mex_files_octave_compatible ================================================ Index: src/algsrc/mexArrangeLinear.cc =================================================================== --- src.orig/algsrc/mexArrangeLinear.cc 2010-02-19 23:54:34.000000000 +0100 +++ src/algsrc/mexArrangeLinear.cc 2015-01-05 17:48:52.028027000 +0100 @@ -2,7 +2,6 @@ #include #include #include -#include #include // Avalues = mexArrangeLinear( A , dims ) Index: src/algsrc/mexAssignWeights.cc =================================================================== --- src.orig/algsrc/mexAssignWeights.cc 2010-02-19 23:54:34.000000000 +0100 +++ src/algsrc/mexAssignWeights.cc 2015-01-05 17:48:55.823430000 +0100 @@ -2,7 +2,6 @@ #include #include #include -#include #include // mexAssignWeights( AL , D , MM , algtype ) Index: src/algsrc/mexColumnNormalize.cc =================================================================== --- src.orig/algsrc/mexColumnNormalize.cc 2010-02-19 23:54:34.000000000 +0100 +++ src/algsrc/mexColumnNormalize.cc 2015-01-05 17:48:59.834418000 +0100 @@ -2,7 +2,6 @@ #include #include #include -#include #include // Normalizes so that each column sums to one Index: src/algsrc/mexSumOverScales.cc =================================================================== --- src.orig/algsrc/mexSumOverScales.cc 2010-02-19 23:54:34.000000000 +0100 +++ src/algsrc/mexSumOverScales.cc 2015-01-05 17:49:04.403376000 +0100 @@ -3,7 +3,6 @@ #include #include #include -#include #include // Vo = mexSumOverScales( v , lx , N ) Index: src/algsrc/mexVectorToMap.cc =================================================================== --- src.orig/algsrc/mexVectorToMap.cc 2010-02-19 23:54:36.000000000 +0100 +++ src/algsrc/mexVectorToMap.cc 2015-01-05 17:49:15.931328000 +0100 @@ -3,7 +3,6 @@ #include #include #include -#include #include // outmap = mexVectorToMap( v , dim ) Index: src/saltoolbox/mexLocalMaximaGBVS.cc =================================================================== --- src.orig/saltoolbox/mexLocalMaximaGBVS.cc 2010-02-19 23:54:52.000000000 +0100 +++ src/saltoolbox/mexLocalMaximaGBVS.cc 2015-01-05 17:49:31.381773000 +0100 @@ -2,7 +2,6 @@ #include #include #include -#include #include double getVal(double* img, int x, int y, int w, int h); Index: src/saltoolbox/mySubsample.cc =================================================================== --- src.orig/saltoolbox/mySubsample.cc 2010-02-19 23:54:54.000000000 +0100 +++ src/saltoolbox/mySubsample.cc 2015-01-05 17:49:35.284199000 +0100 @@ -2,7 +2,6 @@ #include #include #include -#include #include void lowPass6yDecY(float* sptr, float* rptr, int w, int hs); Index: src/util/myContrast.cc =================================================================== --- src.orig/util/myContrast.cc 2010-02-19 23:54:56.000000000 +0100 +++ src/util/myContrast.cc 2015-01-05 17:49:44.572766000 +0100 @@ -2,7 +2,6 @@ #include #include #include -#include #include void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { ================================================ FILE: pysaliency/external_models/scripts/GBVS/patches/series ================================================ get_path make_mex_files_octave_compatible ================================================ FILE: pysaliency/external_models/scripts/IttiKoch_wrapper.m ================================================ function [ ] = IttiKoch(filename, outname) addpath('SaliencyToolbox') img = initializeImage(filename); params = defaultSaliencyParams; salmap = makeSaliencyMap(img, params); saliency_map = imresize(salmap.data,img.size(1:2)); save(outname, 'saliency_map'); ================================================ FILE: pysaliency/external_models/scripts/Judd/FaceDetect_patches/change_opencv_include ================================================ Index: FaceDetect/src/FaceDetect.cpp =================================================================== --- FaceDetect.orig/src/FaceDetect.cpp 2008-05-15 12:09:24.000000000 +0200 +++ FaceDetect/src/FaceDetect.cpp 2013-08-19 15:00:33.552389407 +0200 @@ -23,7 +23,7 @@ #include "mex.h" // Required for the use of MEX files // Required for OpenCV -#include "cv.h" +#include "opencv.hpp" static CvMemStorage* storage = 0; static CvHaarClassifierCascade* cascade = 0; ================================================ FILE: pysaliency/external_models/scripts/Judd/FaceDetect_patches/series ================================================ change_opencv_include ================================================ FILE: pysaliency/external_models/scripts/Judd/JuddSaliencyModel_patches/find_cascade_file ================================================ Index: JuddSaliencyModel/findObjectFeatures.m =================================================================== --- JuddSaliencyModel.orig/findObjectFeatures.m 2013-08-13 17:43:04.087668365 +0200 +++ JuddSaliencyModel/findObjectFeatures.m 2013-08-13 17:50:50.604698538 +0200 @@ -71,7 +71,7 @@ Img = double(rgb2gray(img)); % Run face detector -cascade='haarcascade_frontalface_alt2.xml'; % a little noisy a few misses +cascade=fullfile(directory,'haarcascade_frontalface_alt2.xml'); % a little noisy a few misses FaceData = FaceDetect(cascade,Img); if find(FaceData==0) ================================================ FILE: pysaliency/external_models/scripts/Judd/JuddSaliencyModel_patches/locate_FelzenszwalbDetector_files ================================================ Index: JuddSaliencyModel/findObjectFeatures.m =================================================================== --- JuddSaliencyModel.orig/findObjectFeatures.m 2010-02-26 21:12:28.000000000 +0100 +++ JuddSaliencyModel/findObjectFeatures.m 2013-08-13 17:51:27.536146740 +0200 @@ -14,6 +14,10 @@ % Contact: Tilke Judd at % ---------------------------------------------------------------------- +[directory name ext] = fileparts(mfilename('fullpath')); +carfilename = fullfile(directory, 'FelzenszwalbDetectors', 'car_final.mat'); +personfilename = fullfile(directory, 'FelzenszwalbDetectors', 'person_final.mat'); + [w h c] = size(img); Cars=zeros(w, h); People=zeros(w, h); @@ -23,7 +27,7 @@ % Find Cars %-----------------% fprintf('Finding cars...'); tic; -load FelzenszwalbDetectors/car_final.mat; % loads a car model +load(carfilename); % loads a car model boxes = detect(img, model, 0); top = nms(boxes, 0.4); @@ -44,7 +48,7 @@ % Find People %-----------------% fprintf('Finding people...'); tic; -load FelzenszwalbDetectors/person_final.mat; % loads a person model +load(personfilename); % loads a person model boxes = detect(img, model, 0); top = nms(boxes, 0.4); ================================================ FILE: pysaliency/external_models/scripts/Judd/JuddSaliencyModel_patches/series ================================================ locate_FelzenszwalbDetector_files find_cascade_file ================================================ FILE: pysaliency/external_models/scripts/Judd/Judd_wrapper.m ================================================ function [ ] = Judd(filename, outname) addpath('source/FaceDetect') addpath('source/FaceDetect/src') addpath('source/LabelMeToolbox/features') addpath('source/LabelMeToolbox/imagemanipulation') addpath('source/matlabPyrTools') addpath('source/SaliencyToolbox') addpath('source/voc-release3.1') addpath('source/JuddSaliencyModel') saliency_map = saliency(filename); save(outname, 'saliency_map'); ================================================ FILE: pysaliency/external_models/scripts/Judd/SaliencyToolbox_patches/enable_unit16 ================================================ Index: SaliencyToolbox/centerSurround.m =================================================================== --- SaliencyToolbox.orig/centerSurround.m 2013-07-03 17:30:40.000000000 +0200 +++ SaliencyToolbox/centerSurround.m 2013-08-13 16:50:58.958392479 +0200 @@ -33,7 +33,7 @@ switch class(params.exclusionMask) case 'struct' exclusionIdx = (imresize(params.exclusionMask.data,siz,'nearest') ~= 0); - case {'double','uint8'} + case {'double','uint8','uint16'} exclusionIdx = (imresize(params.exclusionMask,siz,'nearest') ~= 0); case 'logical' exclusionIdx = imresize(params.exclusionMask,siz,'nearest'); Index: SaliencyToolbox/guiSaliency.m =================================================================== --- SaliencyToolbox.orig/guiSaliency.m 2013-07-03 17:30:40.000000000 +0200 +++ SaliencyToolbox/guiSaliency.m 2013-08-13 16:51:20.074076565 +0200 @@ -67,7 +67,7 @@ newImg = varargin{1}; err = ''; state = 'ImageLoaded'; - case {'char','uint8','double'} + case {'char','uint8','uint16','double'} [newImg,err] = initializeImage(varargin{1}); otherwise err = 1; Index: SaliencyToolbox/initializeImage.m =================================================================== --- SaliencyToolbox.orig/initializeImage.m 2013-07-03 17:30:40.000000000 +0200 +++ SaliencyToolbox/initializeImage.m 2013-08-13 16:56:03.041842803 +0200 @@ -47,7 +47,7 @@ Img.filename = varargin{1}; Img.data = NaN; Img.type = 'unknown'; - case {'uint8','double'} + case {'uint8','uint16','double'} Img.filename = NaN; Img.data = varargin{1}; Img.type = 'unknown'; @@ -62,14 +62,14 @@ case 'char' Img.data = NaN; Img.type = varargin{2}; - case {'uint8','double'} + case {'uint8','uint16','double'} Img.data = varargin{2}; Img.type = 'unknown'; otherwise error('Don''t know how to handle image data of class %s.',class(varargin{2})); end - case {'uint8','double'} + case {'uint8','uint16','double'} Img.filename = NaN; Img.data = varargin{1}; Img.type = varargin{2}; Index: SaliencyToolbox/loadImage.m =================================================================== --- SaliencyToolbox.orig/loadImage.m 2013-07-03 17:30:40.000000000 +0200 +++ SaliencyToolbox/loadImage.m 2013-08-13 16:52:33.556977143 +0200 @@ -19,6 +19,8 @@ if isa(Image.data,'uint8') imgData = im2double(Image.data); +elseif isa(Image.data,'uint16') + imgData = im2double(Image.data); elseif isnan(Image.data) imgData = im2double(imread(Image.filename)); else ================================================ FILE: pysaliency/external_models/scripts/Judd/SaliencyToolbox_patches/series ================================================ enable_unit16 ================================================ FILE: pysaliency/external_models/scripts/Judd/voc_patches/change_fconv ================================================ Index: voc-release3.1/compile.m =================================================================== --- voc-release3.1.orig/compile.m 2009-06-09 04:26:04.000000000 +0200 +++ voc-release3.1/compile.m 2013-08-13 15:37:36.404434669 +0200 @@ -6,8 +6,8 @@ % 1 is fastest, 3 is slowest % 1) multithreaded convolution using blas -mex -O fconvblas.cc -lmwblas -o fconv +% mex -O fconvblas.cc -lmwblas -o fconv % 2) mulththreaded convolution without blas % mex -O fconvMT.cc -o fconv % 3) basic convolution, very compatible -% mex -O fconv.cc -o fconv +mex -O fconv.cc -o fconv ================================================ FILE: pysaliency/external_models/scripts/Judd/voc_patches/matlabR2014a_compatible ================================================ Index: voc-release3.1/compile.m =================================================================== --- voc-release3.1.orig/compile.m 2015-01-07 22:06:26.797893000 +0100 +++ voc-release3.1/compile.m 2015-01-07 22:07:09.316640000 +0100 @@ -6,8 +6,8 @@ % 1 is fastest, 3 is slowest % 1) multithreaded convolution using blas -% mex -O fconvblas.cc -lmwblas -o fconv +% mex -O fconvblas.cc -lmwblas -output fconv % 2) mulththreaded convolution without blas -% mex -O fconvMT.cc -o fconv +% mex -O fconvMT.cc -output fconv % 3) basic convolution, very compatible -mex -O fconv.cc -o fconv +mex -O fconv.cc -output fconv ================================================ FILE: pysaliency/external_models/scripts/Judd/voc_patches/matlabR2021a_compatible ================================================ Index: voc-release3.1/resize.cc =================================================================== --- voc-release3.1.orig/resize.cc 2009-05-19 16:13:23.000000000 +0200 +++ voc-release3.1/resize.cc 2023-06-13 23:11:21.000000000 +0200 @@ -82,7 +82,7 @@ // returns resized image mxArray *resize(const mxArray *mxsrc, const mxArray *mxscale) { double *src = (double *)mxGetPr(mxsrc); - const int *sdims = mxGetDimensions(mxsrc); + const mwSize *sdims = mxGetDimensions(mxsrc); if (mxGetNumberOfDimensions(mxsrc) != 3 || mxGetClassID(mxsrc) != mxDOUBLE_CLASS) mexErrMsgTxt("Invalid input"); @@ -91,7 +91,7 @@ if (scale > 1) mexErrMsgTxt("Invalid scaling factor"); - int ddims[3]; + mwSize ddims[3]; ddims[0] = (int)round(sdims[0]*scale); ddims[1] = (int)round(sdims[1]*scale); ddims[2] = sdims[2]; Index: voc-release3.1/dt.cc =================================================================== --- voc-release3.1.orig/dt.cc 2009-05-19 16:13:23.000000000 +0200 +++ voc-release3.1/dt.cc 2023-06-13 23:16:11.000000000 +0200 @@ -47,7 +47,7 @@ if (mxGetClassID(prhs[0]) != mxDOUBLE_CLASS) mexErrMsgTxt("Invalid input"); - const int *dims = mxGetDimensions(prhs[0]); + const mwSize *dims = mxGetDimensions(prhs[0]); double *vals = (double *)mxGetPr(prhs[0]); double ax = mxGetScalar(prhs[1]); double bx = mxGetScalar(prhs[2]); Index: voc-release3.1/features.cc =================================================================== --- voc-release3.1.orig/features.cc 2009-05-19 16:13:23.000000000 +0200 +++ voc-release3.1/features.cc 2023-06-13 23:18:18.000000000 +0200 @@ -35,7 +35,7 @@ // returns HOG features mxArray *process(const mxArray *mximage, const mxArray *mxsbin) { double *im = (double *)mxGetPr(mximage); - const int *dims = mxGetDimensions(mximage); + const mwSize *dims = mxGetDimensions(mximage); if (mxGetNumberOfDimensions(mximage) != 3 || dims[2] != 3 || mxGetClassID(mximage) != mxDOUBLE_CLASS) @@ -51,7 +51,7 @@ double *norm = (double *)mxCalloc(blocks[0]*blocks[1], sizeof(double)); // memory for HOG features - int out[3]; + mwSize out[3]; out[0] = max(blocks[0]-2, 0); out[1] = max(blocks[1]-2, 0); out[2] = 27+4; ================================================ FILE: pysaliency/external_models/scripts/Judd/voc_patches/series ================================================ change_fconv matlabR2014a_compatible matlabR2021a_compatible ================================================ FILE: pysaliency/external_models/scripts/RARE2012_wrapper.m ================================================ function [ ] = RARE2012(filename, outname) addpath('source/VisualAttention-Rare2012-55ba7414b971429e5e899ddfa574e4235fc806e6') I = im2double(imread(filename)); saliency_map = rare2012(I); save(outname, 'saliency_map'); ================================================ FILE: pysaliency/external_models/scripts/SUN_wrapper.m ================================================ % created by Aykut Erdem % adapted by Matthias Kuemmerer for pysaliency function [ ] = SUN(filename, outname, scale) addpath('saliency') img = imread(filename); salmap = saliencyimage(img,scale); salmap = imresize(salmap,1/scale, 'nearest'); height = size(salmap,1); width = size(salmap,2); ydiff = size(img,1)-size(salmap,1); xdiff = size(img,2)-size(salmap,2); ydiff = round(ydiff/ 2); xdiff = round(xdiff/ 2); saliency_map = ones(size(img,1),size(img,2))*min(salmap(:)); saliency_map(ydiff+1:ydiff+height,xdiff+1:xdiff+width) = salmap; save(outname, 'saliency_map'); ================================================ FILE: pysaliency/external_models/scripts/ensure_image_is_color_image.m ================================================ function [new_img] = ensure_image_is_color_image(img) if length(size(img)) == 2 new_img = ones(size(img,1), size(img, 2), 3, class(img)); new_img(:,:,1) = img; new_img(:,:,2) = img; new_img(:,:,3) = img; else new_img = img; end end ================================================ FILE: pysaliency/external_models/utils.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import os import tarfile import tempfile import zipfile from tempfile import TemporaryDirectory from importlib.resources import files as _resource_files def resource_string(package, resource): return _resource_files(package).joinpath(resource).read_bytes() def resource_listdir(package, resource_name): return [r.name for r in _resource_files(package).joinpath(resource_name).iterdir()] from ..quilt import QuiltSeries from ..utils import download_and_check def write_file(filename, contents): """Write contents to file and close file savely""" with open(filename, 'wb') as f: f.write(contents) def extract_zipfile(filename, extract_to): if zipfile.is_zipfile(filename): with zipfile.ZipFile(filename) as z: #os.makedirs(extract_to) z.extractall(extract_to) elif tarfile.is_tarfile(filename): t = tarfile.open(filename) t.extractall(extract_to) else: raise ValueError('Unkown archive type', filename) def unpack_directory(package, resource_name, location): files = resource_listdir(package, resource_name) for file in files: write_file(os.path.join(location, file), resource_string(package, os.path.join(resource_name, file))) def apply_quilt(source_location, package, resource_name, patch_directory, verbose=True): """Apply quilt series from package data to source code""" os.makedirs(patch_directory) unpack_directory(package, resource_name, patch_directory) series = QuiltSeries(patch_directory) series.apply(source_location, verbose=verbose) def download_extract_patch(url, hash, location, location_in_archive=True, patches=None, verify_ssl=True): """Download, extract and maybe patch code""" with TemporaryDirectory() as temp_dir: if not os.path.isdir(temp_dir): os.makedirs(temp_dir) archive_name = os.path.basename(url) download_and_check(url, os.path.join(temp_dir, archive_name), hash, verify_ssl=verify_ssl) if location_in_archive: target = os.path.dirname(os.path.normpath(location)) else: target = location extract_zipfile(os.path.join(temp_dir, archive_name), target) if patches: parent_directory = os.path.dirname(os.path.normpath(location)) patch_directory = os.path.join(parent_directory, os.path.basename(patches)) apply_quilt(location, __name__, os.path.join('scripts', patches), patch_directory) class ExternalModelMixin(object): """ Download and cache necessary files. If the location is None, a temporary directory will be used. If the location is not None, the data will be stored in a subdirectory of location named after `__modelname`. If this sub directory already exists, the initialization will not be run. After running `setup()`, the actual location will be stored in `self.location`. To make use of this Mixin, overwrite `_setup()` and run `setup(location)`. """ def setup(self, location, *args, **kwargs): if location is None: self.location = tempfile.mkdtemp() self._setup(*args, **kwargs) else: self.location = os.path.join(location, self.__modelname__) if not os.path.exists(self.location): self._setup(*args, **kwargs) def _setup(self, *args, **kwargs): raise NotImplementedError() ================================================ FILE: pysaliency/filter_datasets.py ================================================ from __future__ import division, print_function import numpy as np from boltons.iterutils import chunked from .datasets import create_subset, FixationTrains, Fixations, Stimuli, ScanpathFixations def train_split(stimuli, fixations, crossval_folds, fold_no, val_folds=1, test_folds=1, random=True, stratified_attributes=None): return crossval_split(stimuli, fixations, crossval_folds, fold_no, val_folds=val_folds, test_folds=test_folds, random=random, split='train', stratified_attributes=stratified_attributes) def validation_split(stimuli, fixations, crossval_folds, fold_no, val_folds=1, test_folds=1, random=True, stratified_attributes=None): return crossval_split(stimuli, fixations, crossval_folds, fold_no, val_folds=val_folds, test_folds=test_folds, random=random, split='val', stratified_attributes=stratified_attributes) def test_split(stimuli, fixations, crossval_folds, fold_no, val_folds=1, test_folds=1, random=True, stratified_attributes=None): return crossval_split(stimuli, fixations, crossval_folds, fold_no, val_folds=val_folds, test_folds=test_folds, random=random, split='test', stratified_attributes=stratified_attributes) def crossval_splits(stimuli, fixations, crossval_folds, fold_no, val_folds=1, test_folds=1, random=True, stratified_attributes=None): return ( crossval_split(stimuli, fixations, crossval_folds, fold_no, val_folds=val_folds, test_folds=test_folds, random=random, split='train', stratified_attributes=stratified_attributes), crossval_split(stimuli, fixations, crossval_folds, fold_no, val_folds=val_folds, test_folds=test_folds, random=random, split='val', stratified_attributes=stratified_attributes), crossval_split(stimuli, fixations, crossval_folds, fold_no, val_folds=val_folds, test_folds=test_folds, random=random, split='test', stratified_attributes=stratified_attributes), ) def crossval_split(stimuli, fixations, crossval_folds, fold_no, val_folds=1, test_folds=1, random=True, split='train', stratified_attributes=None): train_folds, val_folds, test_folds = get_crossval_folds(crossval_folds, fold_no, test_folds=test_folds, val_folds=val_folds) if split == 'train': folds = train_folds elif split == 'val': folds = val_folds elif split == 'test': folds = test_folds else: raise ValueError(split) return _get_crossval_split(stimuli, fixations, crossval_folds, included_splits=folds, random=random, stratified_attributes=stratified_attributes) def _get_crossval_split(stimuli, fixations, split_count, included_splits, random=True, stratified_attributes=None): if stratified_attributes is not None: return _get_stratified_crossval_split(stimuli, fixations, split_count, included_splits, random=random, stratified_attributes=stratified_attributes) inds = list(range(len(stimuli))) if random: print("Using random shuffles for crossvalidation") rst = np.random.RandomState(seed=42) rst.shuffle(inds) inds = list(inds) size = int(np.ceil(len(inds) / split_count)) chunks = chunked(inds, size=size) inds = [] for split_nr in included_splits: inds.extend(chunks[split_nr]) stimuli, fixations = create_subset(stimuli, fixations, inds) return stimuli, fixations def _get_stratified_crossval_split(stimuli, fixations, split_count, included_splits, random=True, stratified_attributes=None): from sklearn.model_selection import StratifiedKFold labels = [] for attribute_name in stratified_attributes: attribute_data = np.array(stimuli.attributes[attribute_name]) if attribute_data.ndim == 1: attribute_data = attribute_data[:, np.newaxis] labels.append(attribute_data) labels = np.hstack(labels) if labels.shape[1] > 1: # StratifiedKFold doesn't support multiple labels final_label_dict = {} final_labels = [] for label in labels: label = tuple(label) if label not in final_label_dict: final_label_dict[label] = len(final_label_dict) final_labels.append(final_label_dict[label]) labels = np.array(final_labels) X = np.ones((len(stimuli), 1)) rst = np.random.RandomState(42) inds = [] k_fold = StratifiedKFold(n_splits=split_count, shuffle=random, random_state=rst) for i, (train_index, test_index) in enumerate(k_fold.split(X, labels)): if i in included_splits: inds.extend(test_index) stimuli, fixations = create_subset(stimuli, fixations, inds) return stimuli, fixations def create_train_folds(crossval_folds, val_folds, test_folds): all_folds = list(range(crossval_folds)) if isinstance(val_folds, int): val_folds = [val_folds] if isinstance(test_folds, int): test_folds = [test_folds] train_folds = [f for f in all_folds if not (f in val_folds or f in test_folds)] return train_folds, val_folds, test_folds def get_crossval_folds(crossval_folds, crossval_no, test_folds=1, val_folds=1): assert test_folds <= 1 if test_folds: _test_folds = [crossval_no] _val_folds = [(crossval_no - i - 1) % crossval_folds for i in range(val_folds)] else: assert val_folds == 1 _test_folds = [crossval_no] _val_folds = [crossval_no] _train_folds, _val_folds, _test_folds = create_train_folds(crossval_folds, _val_folds, _test_folds) return _train_folds, _val_folds, _test_folds def iterate_crossvalidation(stimuli, fixations, crossval_folds, val_folds=1, test_folds=1, random=True, stratified_attributes=None): """iterate over crossvalidation folds. Each fold will yield train_stimuli, train_fixations, val_, test_stimuli, test_fixations """ kwargs = { 'crossval_folds': crossval_folds, 'val_folds': val_folds, 'test_folds': test_folds, 'random': random, 'stratified_attributes': stratified_attributes, } for fold_no in range(crossval_folds): train_stimuli, train_fixations = train_split( stimuli, fixations, fold_no=fold_no, **kwargs) val_stimuli, val_fixations = validation_split( stimuli, fixations, fold_no=fold_no, **kwargs) test_stimuli, test_fixations = test_split( stimuli, fixations, fold_no=fold_no, **kwargs) yield train_stimuli, train_fixations, val_stimuli, val_fixations, test_stimuli, test_fixations def parse_list_of_intervals(description): """parses a string as "1.0:3.0,5.0:5.6,7" into [(1.0, 3.0), (5.0, 5.6), (7,)] """ intervals = description.split(',') return [parse_interval(interval) for interval in intervals] def parse_interval(interval): parts = interval.split(':') if len(parts) not in [1, 2]: raise ValueError("Invalid interval", interval) return tuple([float(part.strip()) for part in parts]) def filter_fixations_by_number(fixations, intervals): intervals = _check_intervals(intervals, type=int) inds = np.zeros_like(fixations.x, dtype=bool) for n1, n2 in intervals: _inds = np.logical_and(fixations.scanpath_history_length >= n1, fixations.scanpath_history_length < n2) inds = np.logical_or(inds, _inds) return fixations[inds] def filter_stimuli_by_number(stimuli, fixations, intervals): intervals = _check_intervals(intervals, type=int) mask = np.zeros(len(stimuli), dtype=bool) for n1, n2 in intervals: mask[n1:n2] = True indices = list(np.nonzero(mask)[0]) return create_subset(stimuli, fixations, indices) def _check_intervals(intervals, type=float): if isinstance(intervals, (float, int)): intervals = [intervals] new_intervals = [] for interval in intervals: new_intervals.append(_check_interval(interval, type=type)) return new_intervals def _check_interval(interval, type=float): if isinstance(interval, (float, int)): interval = [interval] if len(interval) == 1: if type != int: raise ValueError("single-value intervals only allowed for integer data!") interval = [interval[0], interval[0] + 1] if len(interval) != 2: raise ValueError("Intervals need two values", interval) new_interval = [] for value in interval: if type(value) != value: raise ValueError("Invalid value for this type", value, type) new_interval.append(type(value)) return tuple(new_interval) def filter_stimuli_by_size(stimuli, fixations, size=None, sizes=None): if size is not None: sizes = [size] sizes = [tuple(size) for size in sizes] indices = [i for i in range(len(stimuli)) if stimuli.sizes[i] in sizes] return create_subset(stimuli, fixations, indices) def filter_scanpaths_by_attribute(scanpaths: ScanpathFixations, attribute_name, attribute_value, invert_match=False): """Filter Scanpaths by values of scanpath attribute (fixation_trains.scanpaths.scanpath_attributes)""" mask = scanpaths.scanpaths.scanpath_attributes[attribute_name] == attribute_value if mask.ndim > 1: mask = np.all(mask, axis=1) if invert_match is True: mask = ~mask return scanpaths.filter_scanpaths(mask) def filter_fixations_by_attribute(fixations: Fixations, attribute_name, attribute_value, invert_match=False): """Filter Fixations by values of attribute (fixations.__attributes__)""" mask = np.asarray(getattr(fixations, attribute_name)) == attribute_value if mask.ndim > 1: mask = np.all(mask, axis=1) if invert_match is True: mask = ~mask return fixations[mask] def filter_stimuli_by_attribute(stimuli: Stimuli, fixations: Fixations, attribute_name, attribute_value=None, attribute_values=None, invert_match=False): """Filter stimuli by values of attribute (stimuli.attributes) use `attribute_value` to filter for a single value, or `attribute_values` to filter for multiple allowed values """ if attribute_values is not None: mask = np.isin(np.asarray(stimuli.attributes[attribute_name]), attribute_values) else: mask = np.asarray(stimuli.attributes[attribute_name]) == attribute_value if mask.ndim > 1: mask = np.all(mask, axis=1) if invert_match is True: mask = ~mask indices = list(np.nonzero(mask)[0]) return create_subset(stimuli, fixations, indices) def filter_scanpaths_by_length(scanpath_fixations: ScanpathFixations, intervals: list): """Filter Scanpaths by number of fixations""" intervals = _check_intervals(intervals, type=int) mask = np.zeros(len(scanpath_fixations.scanpaths), dtype=bool) for start, end in intervals: temp_mask = np.logical_and( scanpath_fixations.scanpaths.length >= start, scanpath_fixations.scanpaths.length < end) mask = np.logical_or(mask, temp_mask) indices = list(np.nonzero(mask)[0]) scanpath_fixations = scanpath_fixations.filter_scanpaths(indices) return scanpath_fixations def remove_stimuli_without_fixations(stimuli: Stimuli, fixations: Fixations): """Remove stimuli with no fixations""" stimuli_indices_with_fixations = list(set(fixations.n)) return create_subset(stimuli, fixations, stimuli_indices_with_fixations) ================================================ FILE: pysaliency/hdf5.py ================================================ import pathlib from weakref import WeakValueDictionary from boltons.cacheutils import cached from .datasets.fixations import Fixations, FixationTrains, ScanpathFixations from .datasets.scanpaths import Scanpaths from .datasets.stimuli import FileStimuli, Stimuli from .datasets.utils import decode_string @cached(WeakValueDictionary()) def _read_hdf5_from_file(source, **kwargs): import h5py with h5py.File(source, 'r') as hdf5_file: return read_hdf5(hdf5_file, **kwargs) def _read_baseline_model(source, **kwargs): from .baseline_utils import BaselineModel return BaselineModel.read_hdf5(source, **kwargs) def _read_crossvalidated_baseline_model(source, **kwargs): from .baseline_utils import CrossvalidatedBaselineModel return CrossvalidatedBaselineModel.read_hdf5(source, **kwargs) _DATASET_READERS = { 'Fixations': Fixations.read_hdf5, 'ScanpathFixations': ScanpathFixations.read_hdf5, 'FixationTrains': FixationTrains.read_hdf5, 'Scanpaths': Scanpaths.read_hdf5, 'Stimuli': Stimuli.read_hdf5, 'FileStimuli': FileStimuli.read_hdf5, } _MODEL_READERS = { 'pysaliency.baseline_utils.BaselineModel': _read_baseline_model, 'pysaliency.baseline_utils.CrossvalidatedBaselineModel': _read_crossvalidated_baseline_model, } def read_hdf5(source, hdf5_kwargs=None, _expected_kind=None, **kwargs): if isinstance(source, (str, pathlib.Path)): try: return _read_hdf5_from_file(source, hdf5_kwargs=hdf5_kwargs, _expected_kind=_expected_kind, **kwargs) except TypeError: import h5py with h5py.File(source, 'r') as hdf5_file: return read_hdf5(hdf5_file, hdf5_kwargs=hdf5_kwargs, _expected_kind=_expected_kind, **kwargs) if hdf5_kwargs: raise NotImplementedError("Nested `hdf5_kwargs` routing is not implemented yet.") data_type = decode_string(source.attrs['type']) if data_type in _DATASET_READERS: if _expected_kind == 'model': raise ValueError("Invalid HDF model type:", data_type) return _DATASET_READERS[data_type](source, **kwargs) if data_type in _MODEL_READERS: if _expected_kind == 'dataset': raise ValueError("Invalid HDF dataset type:", data_type) return _MODEL_READERS[data_type](source, **kwargs) raise ValueError("Invalid HDF content type:", data_type) ================================================ FILE: pysaliency/http_models.py ================================================ from .models import ScanpathModel from .saliency_map_models import ScanpathSaliencyMapModel from PIL import Image from io import BytesIO import requests import json import numpy as np import orjson from .datasets import as_stimulus class HTTPScanpathModel(ScanpathModel): """ A scanpath model that uses a HTTP server to make predictions. The model is provided with an URL where it expects a server with the following API: /conditional_log_density: expects a POST request with a file attachtment `stimulus` containing the stimulus and a json body containing x_hist, y_hist, t_hist and a dictionary with other attributes /type: returns the model type and version """ def __init__(self, url): self.url = url self.check_type() @property def log_density_url(self): return self.url + "/conditional_log_density" @property def type_url(self): return self.url + "/type" def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): # build request stimulus_object = as_stimulus(stimulus) # TODO: check for file stimuli, in this case use original file to save encoding time pil_image = Image.fromarray(stimulus_object.stimulus_data) image_bytes = BytesIO() pil_image.save(image_bytes, format='png') def _convert_attribute(attribute): if isinstance(attribute, np.ndarray): return attribute.tolist() if isinstance(attribute, (np.int64, np.int32)): return int(attribute) if isinstance(attribute, (np.float64, np.float32)): return float(attribute) return attribute json_data = { "x_hist": x_hist.tolist(), "y_hist": y_hist.tolist(), "t_hist": t_hist.tolist(), "attributes": {key: _convert_attribute(value) for key, value in (attributes or {}).items()} } # send request response = requests.post(f"{self.log_density_url}", data={'json_data': orjson.dumps(json_data)}, files={'stimulus': image_bytes.getvalue()}) # parse response if response.status_code != 200: raise ValueError(f"Server returned status code {response.status_code}") json_data = orjson.loads(response.text) prediction = np.array(json_data['log_density']) return prediction def check_type(self): response = requests.get(f"{self.type_url}").json() if not response['type'] == 'ScanpathModel': raise ValueError(f"invalid Model type: {response['type']}. Expected 'ScanpathModel'") if not response['version'] in ['v1.0.0']: raise ValueError(f"invalid Model type: {response['version']}. Expected 'v1.0.0'") class HTTPScanpathSaliencyMapModel(ScanpathSaliencyMapModel): """ A scanpath saliency map model that uses a HTTP server to make predictions. The model is provided with an URL where it expects a server with the following API: /conditional_saliency_map: expects a POST request with a file attachtment `stimulus` containing the stimulus and a json body containing x_hist, y_hist, t_hist and a dictionary with other attributes /type: returns the model type and version """ def __init__(self, url): self.url = url self.check_type() @property def saliency_map_url(self): return self.url + "/conditional_saliency_map" @property def type_url(self): return self.url + "/type" def conditional_saliency_map(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): # build request stimulus_object = as_stimulus(stimulus) # TODO: check for file stimuli, in this case use original file to save encoding time pil_image = Image.fromarray(stimulus_object.stimulus_data) image_bytes = BytesIO() pil_image.save(image_bytes, format='png') def _convert_attribute(attribute): if isinstance(attribute, np.ndarray): return attribute.tolist() if isinstance(attribute, (np.int64, np.int32)): return int(attribute) if isinstance(attribute, (np.float64, np.float32)): return float(attribute) return attribute json_data = { "x_hist": x_hist.tolist(), "y_hist": y_hist.tolist(), "t_hist": t_hist.tolist(), "attributes": {key: _convert_attribute(value) for key, value in (attributes or {}).items()} } # send request response = requests.post(f"{self.saliency_map_url}", data={'json_data': orjson.dumps(json_data)}, files={'stimulus': image_bytes.getvalue()}) # parse response if response.status_code != 200: raise ValueError(f"Server returned status code {response.status_code}") json_data = orjson.loads(response.text) prediction = np.array(json_data['saliency_map']) return prediction def check_type(self): response = requests.get(f"{self.type_url}").json() if not response['type'] == 'ScanpathSaliencyMapModel': raise ValueError(f"invalid Model type: {response['type']}. Expected 'ScanpathSaliencyMapModel'") if not response['version'] in ['v1.0.0']: raise ValueError(f"invalid Model type: {response['version']}. Expected 'v1.0.0'") ================================================ FILE: pysaliency/metric_optimization.py ================================================ from __future__ import print_function, division, absolute_import, unicode_literals from .saliency_map_models import SaliencyMapModel class SIMSaliencyMapModel(SaliencyMapModel): def __init__(self, parent_model, kernel_size, train_samples_per_epoch=1000, val_samples=1000, train_seed=43, val_seed=42, fixation_count=100, batch_size=50, max_batch_size=None, initial_learning_rate=1e-7, backlook=1, min_iter=0, max_iter=1000, truncate_gaussian=3, learning_rate_decay_samples=None, learning_rate_decay_scheme=None, learning_rate_decay_ratio=0.333333333, minimum_learning_rate=1e-11, initial_model=None, verbose=True, session_config=None, library='torch', **kwargs ): super(SIMSaliencyMapModel, self).__init__(**kwargs) self.parent_model = parent_model self.kernel_size = kernel_size self.train_samples_per_epoch = train_samples_per_epoch self.val_samples = val_samples self.train_seed = train_seed self.val_seed = val_seed self.fixation_count = fixation_count self.batch_size = batch_size self.max_batch_size = max_batch_size self.initial_learning_rate = initial_learning_rate self.backlook = backlook self.min_iter = min_iter self.max_iter = max_iter self.truncate_gaussian = truncate_gaussian self.learning_rate_decay_samples = learning_rate_decay_samples self.learning_rate_decay_scheme = learning_rate_decay_scheme self.learning_rate_decay_ratio = learning_rate_decay_ratio self.minimum_learning_rate = minimum_learning_rate self.initial_model = initial_model self.verbose = verbose self.session_config = session_config self.library = library def _saliency_map(self, stimulus): log_density = self.parent_model.log_density(stimulus) if self.initial_model: initial_saliency_map = self.initial_model.saliency_map(stimulus) else: initial_saliency_map = None if self.library.lower() == 'tensorflow': from .metric_optimization_tf import maximize_expected_sim elif self.library.lower() == 'torch': from .metric_optimization_torch import maximize_expected_sim else: raise ValueError(self.library) saliency_map, val_scores = maximize_expected_sim( log_density, kernel_size=self.kernel_size, train_samples_per_epoch=self.train_samples_per_epoch, val_samples=self.val_samples, train_seed=self.train_seed, val_seed=self.val_seed, fixation_count=self.fixation_count, batch_size=self.batch_size, max_batch_size=self.max_batch_size, verbose=self.verbose, session_config=self.session_config, initial_learning_rate=self.initial_learning_rate, backlook=self.backlook, min_iter=self.min_iter, max_iter=self.max_iter, truncate_gaussian=self.truncate_gaussian, learning_rate_decay_samples=self.learning_rate_decay_samples, initial_saliency_map=initial_saliency_map, learning_rate_decay_scheme=self.learning_rate_decay_scheme, learning_rate_decay_ratio=self.learning_rate_decay_ratio, minimum_learning_rate=self.minimum_learning_rate ) return saliency_map ================================================ FILE: pysaliency/metric_optimization_tf.py ================================================ from __future__ import print_function, division, absolute_import, unicode_literals from tqdm import tqdm import numpy as np from scipy.ndimage import gaussian_filter from scipy.special import logsumexp import tensorflow as tf from .models import sample_from_logdensity from .tf_utils import gauss_blur def sample_batch_fixations(log_density, fixations_per_image, batch_size, rst=None): xs, ys = sample_from_logdensity(log_density, fixations_per_image * batch_size, rst=rst) ns = np.repeat(np.arange(batch_size, dtype=int), repeats=fixations_per_image) return xs, ys, ns def _eval_metric(log_density, test_samples, fn, seed=42, fixation_count=120, batch_size=50, verbose=True): values = [] weights = [] count = 0 rst = np.random.RandomState(seed=seed) with tqdm(total=test_samples, leave=False, disable=not verbose) as t: while count < test_samples: this_count = min(batch_size, test_samples - count) xs, ys, ns = sample_batch_fixations(log_density, fixations_per_image=fixation_count, batch_size=this_count, rst=rst) values.append(fn(ns, ys, xs, this_count)) weights.append(this_count) count += this_count t.update(this_count) weights = np.asarray(weights, dtype=np.float64) / np.sum(weights) return np.average(values, weights=weights) def constrained_descent(opt, loss, params, constraint, learning_rate): """ opt: e.g. tf.train.GradientDescent() """ assert len(params) == 1 constraint_grad, = tf.gradients(constraint, params) constraint_grad_norm = tf.reduce_sum(tf.square(constraint_grad)) normed_constraint_grad = constraint_grad / constraint_grad_norm grads_and_vars = opt.compute_gradients(loss, params) grads0 = grads_and_vars[0][0] params0 = params[0] # first step: make sure we are not running into negative values max_allowed_grad = params0 / learning_rate projected_grad1 = tf.reduce_min([grads0, max_allowed_grad], axis=0) # second step: Make sure that the gradient does not walk # out of the constraint projected_grad2 = projected_grad1 - tf.reduce_sum(projected_grad1 * constraint_grad) * normed_constraint_grad # projected_grad = grads0 - tf.reduce_sum(grads0*constraint_grad) * normed_constraint_grad # param = params[0] # active_set = (param <= 0) # active_grad = (projected_grad > 0) # we are _descending_, i.e. grad>0 means we are trying to go down # mask = tf.logical_not(tf.logical_and(active_set, active_grad)) # projected_grad = projected_grad * tf.cast(mask, 'float') grads_and_vars = [(projected_grad2, grads_and_vars[0][1])] tf_train_op = opt.apply_gradients(grads_and_vars) return tf_train_op def build_fixation_maps(Ns, Ys, Xs, batch_size, height, width, dtype=tf.float32): indices = tf.stack((Ns, Ys, Xs), axis=1) fixation_maps = tf.scatter_nd(indices, updates=tf.ones((tf.shape(indices)[0], ), dtype=dtype), shape=(batch_size, height, width)) return fixation_maps def tf_similarity(saliency_map, empirical_saliency_maps): normalized_empirical_saliency_maps = empirical_saliency_maps / tf.reduce_sum(empirical_saliency_maps, reduction_indices=[1, 2], keepdims=True) normalized_saliency_map = saliency_map / tf.reduce_sum(saliency_map) minimums = tf.minimum(normalized_empirical_saliency_maps, tf.expand_dims(normalized_saliency_map, 0)) similarities = tf.reduce_sum(minimums, reduction_indices=[1, 2]) return similarities def build_similarity_graph(saliency_map, ns, ys, xs, batch_size, height, width, kernel_size, truncate_gaussian, dtype=tf.float32): window_radius = int(kernel_size * truncate_gaussian) fixation_maps = build_fixation_maps(ns, ys, xs, batch_size, height, width, dtype=dtype) empirical_saliency_maps = gauss_blur( tf.expand_dims(fixation_maps, axis=3), kernel_size, windowradius=window_radius, mode='ZERO' )[:, :, :, 0] similarities = tf_similarity(saliency_map, empirical_saliency_maps) return similarities def maximize_expected_sim(log_density, kernel_size, train_samples_per_epoch, val_samples, train_seed=43, val_seed=42, fixation_count=100, batch_size=50, max_batch_size=None, verbose=True, session_config=None, initial_learning_rate=1e-7, backlook=1, min_iter=0, max_iter=1000, truncate_gaussian=3, learning_rate_decay_samples=None, initial_saliency_map=None, learning_rate_decay_scheme=None, learning_rate_decay_ratio=0.333333333, minimum_learning_rate=1e-11): """ max_batch_size: maximum possible batch size to be used in validation learning rate decay samples: how often to decay the learning rate (using 1/k) learning_rate_decay_scheme: how to decay the learning rate: - None, "1/k": 1/k scheme - "validation_loss": if validation loss not better for last backlook steps learning_rate_decay_ratio: how much to decay learning rate if `learning_rate_decay_scheme` == 'validation_loss' minimum_learning_rate: stop optimization if learning rate would drop below this rate if using validation loss decay scheme """ if max_batch_size is None: max_batch_size = batch_size if learning_rate_decay_scheme is None: learning_rate_decay_scheme = '1/k' if learning_rate_decay_samples is None: learning_rate_decay_samples = train_samples_per_epoch log_density_sum = logsumexp(log_density) if not -0.001 < log_density_sum < 0.001: raise ValueError("Log density not normalized! LogSumExp={}".format(log_density_sum)) if initial_saliency_map is None: initial_value = gaussian_filter(np.exp(log_density), kernel_size, mode='constant') else: initial_value = initial_saliency_map if initial_value.min() < 0: initial_value -= initial_value.min() initial_value /= initial_value.sum() graph = tf.Graph() dtype = tf.float32 height, width = log_density.shape with graph.as_default(): # setting up saliency_map = tf.get_variable('saliency_map', shape=log_density.shape, dtype=dtype) Ns = tf.placeholder(tf.int32, shape=(None, ), name='ns') Ys = tf.placeholder(tf.int32, shape=(None, ), name='ys') Xs = tf.placeholder(tf.int32, shape=(None, ), name='xs') BatchSize = tf.placeholder(tf.int32, shape=(), name='batch_size') similarities = build_similarity_graph(saliency_map, Ns, Ys, Xs, BatchSize, height, width, kernel_size, truncate_gaussian, dtype=dtype) similarity = tf.reduce_mean(similarities) loss = -similarity # constraints constraint = tf.reduce_sum(saliency_map) - 1.0 # training learning_rate = tf.Variable(1.0, dtype=dtype) opt = tf.train.GradientDescentOptimizer(learning_rate) train_op = constrained_descent(opt, loss, [saliency_map], constraint, learning_rate) intermediate = saliency_map * tf.cast((saliency_map >= 0), 'float') normalized_saliency_map = intermediate / tf.reduce_sum(intermediate) normalize_op = tf.assign(saliency_map, normalized_saliency_map) # print("starting session") with tf.Session(graph=graph, config=session_config) as session: def _val_loss(ns, ys, xs, batch_size): # print("running", end='', flush=True) ret = session.run(loss, {Ns: ns, Ys: ys, Xs: xs, BatchSize: batch_size}) # print("done") return ret def val_loss(): return _eval_metric(log_density, val_samples, _val_loss, seed=val_seed, fixation_count=fixation_count, batch_size=max_batch_size, verbose=False) session.run(tf.global_variables_initializer()) # initial_value = gaussian_filter(density, kernel_size, mode='nearest') session.run(tf.assign(saliency_map, initial_value)) session.run(tf.assign(learning_rate, initial_learning_rate)) total_samples = 0 decay_step = 0 # print('starting val') val_scores = [val_loss()] learning_rate_relevant_scores = list(val_scores) train_rst = np.random.RandomState(seed=train_seed) # print('starting train') with tqdm(disable=not verbose) as outer_t: def general_termination_condition(): return len(val_scores) - 1 >= max_iter def termination_1overk(): return not (np.argmin(val_scores) >= len(val_scores) - backlook) def termination_validation(): return session.run(learning_rate) < minimum_learning_rate def termination_condition(): if len(val_scores) < min_iter: return False cond = general_termination_condition() if learning_rate_decay_scheme == '1/k': cond = cond or termination_1overk() elif learning_rate_decay_scheme == 'validation_loss': cond = cond or termination_validation() return cond while not termination_condition(): count = 0 with tqdm(total=train_samples_per_epoch, leave=False, disable=True) as t: while count < train_samples_per_epoch: this_count = min(batch_size, train_samples_per_epoch - count) xs, ys, ns = sample_batch_fixations(log_density, fixations_per_image=fixation_count, batch_size=this_count, rst=train_rst) session.run(train_op, {Ns: ns, Ys: ys, Xs: xs, BatchSize: this_count}) session.run(normalize_op) count += this_count total_samples += this_count if learning_rate_decay_scheme == '1/k': if total_samples >= (decay_step + 1) * learning_rate_decay_samples: decay_step += 1 session.run(tf.assign(learning_rate, initial_learning_rate / decay_step)) t.update(this_count) val_scores.append(val_loss()) learning_rate_relevant_scores.append(val_scores[-1]) if np.argmin(learning_rate_relevant_scores) < len(learning_rate_relevant_scores) - backlook: old_learning_rate = session.run(learning_rate) # print("Old Learning Rate", old_learning_rate, type(old_learning_rate)) # print("Decay", learning_rate_decay_ratio) new_learning_rate = old_learning_rate * learning_rate_decay_ratio session.run(tf.assign(learning_rate, new_learning_rate)) # print("Decaying learning_rate to", new_learning_rate) learning_rate_relevant_scores = [learning_rate_relevant_scores[-1]] score1, score2 = val_scores[-2:] last_min = len(val_scores) - np.argmin(val_scores) - 1 outer_t.set_description('{:.05f}, diff {:.02e}, best val {} steps ago, lr {:.02e}'.format(score2, score2 - score1, last_min, session.run(learning_rate))) outer_t.update(1) return session.run(saliency_map), val_scores[-1] ================================================ FILE: pysaliency/metric_optimization_torch.py ================================================ import numpy as np from scipy.ndimage import gaussian_filter as sp_gaussian_filter from scipy.special import logsumexp import torch from torch.optim.optimizer import required import torch.nn as nn from tqdm import tqdm from .models import LogDensitySampler from .torch_utils import gaussian_filter class DistributionSGD(torch.optim.Optimizer): """Extension of SGD that constraints the parameters to be nonegative and with fixed sum (e.g., a probability distribution)""" def __init__(self, params, lr=required): if lr is not required and lr < 0.0: raise ValueError("Invalid learning rate: {}".format(lr)) defaults = dict(lr=lr) super(DistributionSGD, self).__init__(params, defaults) @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue d_p = p.grad learning_rate = group['lr'] # constraint_grad = torch.ones_like(d_p) constraint_grad_norm = torch.sum(torch.pow(constraint_grad, 2)) normed_constraint_grad = constraint_grad / constraint_grad_norm # first step: make sure we are not running into negative values max_allowed_grad = p / learning_rate projected_grad1 = torch.min(d_p, max_allowed_grad) # second step: Make sure that the gradient does not walk # out of the constraint projected_grad2 = projected_grad1 - torch.sum(projected_grad1 * constraint_grad) * normed_constraint_grad p.add_(projected_grad2, alpha=-group['lr']) return loss def build_fixation_maps(Ns, Ys, Xs, batch_size, height, width, dtype=torch.float32): indices = torch.stack((Ns, Ys, Xs), axis=1).T src = torch.ones(indices.shape[1], dtype=dtype, device=indices.device) fixation_maps = torch.sparse_coo_tensor(indices, src, size=(batch_size, height, width)).to_dense() return fixation_maps def torch_similarity(saliency_map, empirical_saliency_maps): normalized_empirical_saliency_maps = empirical_saliency_maps / torch.sum(empirical_saliency_maps, dim=[1, 2], keepdim=True) normalized_saliency_map = saliency_map / torch.sum(saliency_map) minimums = torch.min(normalized_empirical_saliency_maps, normalized_saliency_map[None, :, :]) similarities = torch.sum(minimums, dim=[1, 2]) return similarities def compute_similarity(saliency_map, ns, ys, xs, batch_size, kernel_size, truncate_gaussian, dtype=torch.float32): height, width = saliency_map.shape fixation_maps = build_fixation_maps(ns, ys, xs, batch_size, height, width, dtype=dtype) empirical_saliency_maps = gaussian_filter( fixation_maps[:, None, :, :], dim=[2, 3], sigma=kernel_size, truncate=truncate_gaussian, padding_mode='constant', padding_value=0.0, )[:, 0, :, :] similarities = torch_similarity(saliency_map, empirical_saliency_maps) return similarities class Similarities(nn.Module): def __init__(self, initial_saliency_map, kernel_size, truncate_gaussian=3, dtype=torch.float32): super().__init__() self.saliency_map = nn.Parameter(torch.tensor(initial_saliency_map, dtype=dtype), requires_grad=True) self.kernel_size = kernel_size self.truncate_gaussian = truncate_gaussian self.dtype = dtype def forward(self, ns, ys, xs, batch_size): similarities = compute_similarity( self.saliency_map, ns, ys, xs, batch_size, self.kernel_size, self.truncate_gaussian, dtype=self.dtype, ) return similarities def _eval_metric(log_density_sampler, test_samples, fn, seed=42, fixation_count=120, batch_size=50, verbose=True): values = [] weights = [] count = 0 rst = np.random.RandomState(seed=seed) with tqdm(total=test_samples, leave=False, disable=not verbose) as t: while count < test_samples: this_count = min(batch_size, test_samples - count) xs, ys, ns = log_density_sampler.sample_batch_fixations(fixations_per_image=fixation_count, batch_size=this_count, rst=rst) values.append(fn(ns, ys, xs, this_count)) weights.append(this_count) count += this_count t.update(this_count) weights = np.asarray(weights, dtype=np.float64) / np.sum(weights) return np.average(values, weights=weights) def maximize_expected_sim(log_density, kernel_size, train_samples_per_epoch, val_samples, train_seed=43, val_seed=42, fixation_count=100, batch_size=50, max_batch_size=None, verbose=True, session_config=None, initial_learning_rate=1e-7, backlook=1, min_iter=0, max_iter=1000, truncate_gaussian=3, learning_rate_decay_samples=None, initial_saliency_map=None, learning_rate_decay_scheme=None, learning_rate_decay_ratio=0.333333333, minimum_learning_rate=1e-11): """ max_batch_size: maximum possible batch size to be used in validation learning rate decay samples: how often to decay the learning rate (using 1/k) learning_rate_decay_scheme: how to decay the learning rate: - None, "1/k": 1/k scheme - "validation_loss": if validation loss not better for last backlook steps learning_rate_decay_ratio: how much to decay learning rate if `learning_rate_decay_scheme` == 'validation_loss' minimum_learning_rate: stop optimization if learning rate would drop below this rate if using validation loss decay scheme """ if max_batch_size is None: max_batch_size = batch_size if learning_rate_decay_scheme is None: learning_rate_decay_scheme = '1/k' if learning_rate_decay_samples is None: learning_rate_decay_samples = train_samples_per_epoch log_density_sum = logsumexp(log_density) if not -0.001 < log_density_sum < 0.001: raise ValueError("Log density not normalized! LogSumExp={}".format(log_density_sum)) if initial_saliency_map is None: initial_value = sp_gaussian_filter(np.exp(log_density), kernel_size, mode='constant') else: initial_value = initial_saliency_map if initial_value.min() < 0: initial_value -= initial_value.min() initial_value /= initial_value.sum() dtype = torch.float32 sampler = LogDensitySampler(log_density) model = Similarities( initial_saliency_map=initial_value, kernel_size=kernel_size, truncate_gaussian=truncate_gaussian, dtype=dtype ) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print("Using device", device) model.to(device) optimizer = DistributionSGD(model.parameters(), lr=initial_learning_rate) if learning_rate_decay_scheme == '1/k': def lr_lambda(epoch): return 1.0 / (max(epoch, 1)) scheduler = torch.optim.lr_scheduler.LambdaLR( optimizer=optimizer, lr_lambda=lr_lambda, ) elif learning_rate_decay_scheme == 'validation_loss': scheduler = torch.optim.lr_scheduler.ExponentialLR( optimizer=optimizer, gamma=learning_rate_decay_ratio, ) else: raise ValueError(learning_rate_decay_scheme) height, width = log_density.shape def _val_loss(ns, ys, xs, batch_size): model.eval() Ns = torch.tensor(ns).to(device) Ys = torch.tensor(ys).to(device) Xs = torch.tensor(xs).to(device) batch_size = torch.tensor(batch_size).to(device) model_output = model(Ns, Ys, Xs, batch_size) mean_output = -torch.mean(model_output) ret = mean_output.detach().cpu().numpy() return ret def val_loss(): return _eval_metric(sampler, val_samples, _val_loss, seed=val_seed, fixation_count=fixation_count, batch_size=max_batch_size, verbose=False) total_samples = 0 decay_step = 0 val_scores = [val_loss()] learning_rate_relevant_scores = list(val_scores) train_rst = np.random.RandomState(seed=train_seed) with tqdm(disable=not verbose) as outer_t: def general_termination_condition(): return len(val_scores) - 1 >= max_iter def termination_1overk(): return not (np.argmin(val_scores) >= len(val_scores) - backlook) def termination_validation(): return optimizer.state_dict()['param_groups'][0]['lr'] < minimum_learning_rate def termination_condition(): if len(val_scores) < min_iter: return False cond = general_termination_condition() if learning_rate_decay_scheme == '1/k': cond = cond or termination_1overk() elif learning_rate_decay_scheme == 'validation_loss': cond = cond or termination_validation() return cond while not termination_condition(): count = 0 with tqdm(total=train_samples_per_epoch, leave=False, disable=True) as t: while count < train_samples_per_epoch: model.train() optimizer.zero_grad() this_count = min(batch_size, train_samples_per_epoch - count) xs, ys, ns = sampler.sample_batch_fixations(fixations_per_image=fixation_count, batch_size=this_count, rst=train_rst) Ns = torch.tensor(ns).to(device) Ys = torch.tensor(ys).to(device) Xs = torch.tensor(xs).to(device) batch_size = torch.tensor(batch_size).to(device) loss = -torch.mean(model(Ns, Ys, Xs, batch_size)) loss.backward() optimizer.step() with torch.no_grad(): if torch.any(model.saliency_map < 0): model.saliency_map.mul_(model.saliency_map >= 0) model.saliency_map.div_(torch.sum(model.saliency_map)) count += this_count total_samples += this_count if learning_rate_decay_scheme == '1/k': if total_samples >= (decay_step + 1) * learning_rate_decay_samples: decay_step += 1 scheduler.step() t.update(this_count) val_scores.append(val_loss()) learning_rate_relevant_scores.append(val_scores[-1]) if learning_rate_decay_scheme == 'validation_loss' and np.argmin(learning_rate_relevant_scores) < len(learning_rate_relevant_scores) - backlook: scheduler.step() learning_rate_relevant_scores = [learning_rate_relevant_scores[-1]] score1, score2 = val_scores[-2:] last_min = len(val_scores) - np.argmin(val_scores) - 1 outer_t.set_description('{:.05f}->{:.05f}, diff {:.02e}, best val {} steps ago, lr {:.02e}'.format(val_scores[0], score2, score2 - score1, last_min, optimizer.state_dict()['param_groups'][0]['lr'])) outer_t.update(1) return model.saliency_map.detach().cpu().numpy(), val_scores[-1] ================================================ FILE: pysaliency/metrics.py ================================================ from __future__ import absolute_import, print_function, division, unicode_literals import numpy as np def normalize_saliency_map(saliency_map, cdf, cdf_bins): """ Normalize saliency to make saliency values distributed according to a given CDF """ smap = saliency_map.copy() shape = smap.shape smap = smap.flatten() smap = np.argsort(np.argsort(smap)).astype(float) smap /= 1.0 * len(smap) inds = np.searchsorted(cdf, smap, side='right') smap = cdf_bins[inds] smap = smap.reshape(shape) smap = smap.reshape(shape) return smap def convert_saliency_map_to_density(saliency_map, minimum_value=0.0): if saliency_map.min() < 0: saliency_map = saliency_map - saliency_map.min() saliency_map = saliency_map + minimum_value saliency_map_sum = saliency_map.sum() if saliency_map_sum: saliency_map = saliency_map / saliency_map_sum else: saliency_map[:] = 1.0 saliency_map /= saliency_map.sum() return saliency_map def NSS(saliency_map, xs, ys): xs = np.asarray(xs, dtype=int) ys = np.asarray(ys, dtype=int) saliency_map = np.asarray(saliency_map, dtype=float) mean = saliency_map.mean() std = saliency_map.std() value = saliency_map[ys, xs].copy() value -= mean if std: value /= std return value def CC(saliency_map_1, saliency_map_2): def normalize(saliency_map): saliency_map = np.asarray(saliency_map, dtype=float) saliency_map -= saliency_map.mean() std = saliency_map.std() if std: saliency_map /= std return saliency_map, std == 0 smap1, constant1 = normalize(saliency_map_1.copy()) smap2, constant2 = normalize(saliency_map_2.copy()) if constant1 and not constant2: return 0.0 else: return np.corrcoef(smap1.flatten(), smap2.flatten())[0, 1] def probabilistic_image_based_kl_divergence(logp1, logp2, log_regularization=0, quotient_regularization=0): if log_regularization or quotient_regularization: return (np.exp(logp2) * np.log(log_regularization + np.exp(logp2) / (np.exp(logp1) + quotient_regularization))).sum() else: return (np.exp(logp2) * (logp2 - logp1)).sum() def image_based_kl_divergence(saliency_map_1, saliency_map_2, minimum_value=1e-20, log_regularization=0, quotient_regularization=0): """ KLDiv. Function is not symmetric. saliency_map_2 is treated as empirical saliency map. """ log_density_1 = np.log(convert_saliency_map_to_density(saliency_map_1, minimum_value=minimum_value)) log_density_2 = np.log(convert_saliency_map_to_density(saliency_map_2, minimum_value=minimum_value)) return probabilistic_image_based_kl_divergence(log_density_1, log_density_2, log_regularization=log_regularization, quotient_regularization=quotient_regularization) def MIT_KLDiv(saliency_map_1, saliency_map_2): """ compute image-based KL divergence with same hyperparameters as in Tuebingen/MIT Saliency Benchmark """ return image_based_kl_divergence( saliency_map_1, saliency_map_2, minimum_value=0, log_regularization=2.2204e-16, quotient_regularization=2.2204e-16 ) def SIM(saliency_map_1, saliency_map_2): """ Compute similiarity metric. """ density_1 = convert_saliency_map_to_density(saliency_map_1, minimum_value=0) density_2 = convert_saliency_map_to_density(saliency_map_2, minimum_value=0) return np.min([density_1, density_2], axis=0).sum() ================================================ FILE: pysaliency/models.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import warnings from abc import ABCMeta, abstractmethod from itertools import combinations import numpy as np from scipy.ndimage import zoom from scipy.special import logsumexp from tqdm import tqdm from .datasets import ScanpathFixations, Scanpaths, as_stimulus, check_prediction_shape, get_image_hash from .metrics import convert_saliency_map_to_density, probabilistic_image_based_kl_divergence from .saliency_map_models import ( DensitySaliencyMapModel, DisjointUnionMixin, GaussianSaliencyMapModel, SaliencyMapModel, ScanpathSaliencyMapModel, SubjectDependentSaliencyMapModel, handle_stimulus, ) from .sampling_models import SamplingModelMixin from .utils import Cache, average_values, deprecated_class, iterator_chunks, remove_trailing_nans def _prepare_logprobabilities_for_sampling(log_probabilities): log_probabilities = np.asarray(log_probabilities) valid_indices = np.nonzero(np.isfinite(log_probabilities))[0] valid_log_probabilities = log_probabilities[valid_indices] ndxs = valid_log_probabilities.argsort() sorted_log_probabilities = valid_log_probabilities[ndxs] cumsums = np.logaddexp.accumulate(sorted_log_probabilities) cumsums -= cumsums[-1] return cumsums, ndxs, valid_indices def _sample_from_cumsums(cumsums, ndxs, valid_indices, size, rst=None): if rst is None: rst = np.random tmps = -rst.exponential(size=size) js = np.searchsorted(cumsums, tmps) valid_values = ndxs[js] values = valid_indices[valid_values] return values def sample_from_logprobabilities(log_probabilities, size=1, rst=None): """ Sample from log probabilities (robust to many bins and small probabilities). +-np.inf and np.nan will be interpreted as zero probability """ cumsums, ndxs, valid_indices = _prepare_logprobabilities_for_sampling(log_probabilities) values = _sample_from_cumsums(cumsums, ndxs, valid_indices, size, rst=rst) return values def sample_from_logdensity(log_density, count=None, rst=None): if count is None: real_count = 1 else: real_count = count height, width = log_density.shape flat_log_density = log_density.flatten(order='C') samples = sample_from_logprobabilities(flat_log_density, size=real_count, rst=rst) sample_xs = samples % width sample_ys = samples // width if count is None: return sample_xs[0], sample_ys[0] else: return np.asarray(sample_xs), np.asarray(sample_ys) class LogDensitySampler(object): """use this class if you need to sample repeatedly from the same log density. It will do the slow parts (sorting the log density, computing log cum sums etc) only once. """ def __init__(self, log_density): self.height, self.width = log_density.shape flat_log_density = log_density.flatten(order='C') self.cumsums, self.ndxs, self.valid_indices = _prepare_logprobabilities_for_sampling(flat_log_density) def sample(self, size, rst=None): samples = _sample_from_cumsums(self.cumsums, self.ndxs, self.valid_indices, size, rst=rst) sample_xs = samples % self.width sample_ys = samples // self.width return np.asarray(sample_xs), np.asarray(sample_ys) def sample_batch_fixations(self, fixations_per_image, batch_size, rst=None): xs, ys = self.sample(fixations_per_image * batch_size, rst=rst) ns = np.repeat(np.arange(batch_size, dtype=int), repeats=fixations_per_image) return xs, ys, ns def sample_from_image(densities, count=None, rst=None): if rst is None: rst = np.random height, width = densities.shape sorted_densities = densities.flatten(order='C') cumsums = np.cumsum(sorted_densities) if count is None: real_count = 1 else: real_count = count sample_xs = [] sample_ys = [] tmps = rst.rand(real_count) js = np.searchsorted(cumsums, tmps) for j in js: sample_xs.append(j % width) sample_ys.append(j // width) sample_xs = np.asarray(sample_xs) sample_ys = np.asarray(sample_ys) if count is None: return sample_xs[0], sample_ys[0] else: return np.asarray(sample_xs), np.asarray(sample_ys) class ScanpathModel(SamplingModelMixin, object, metaclass=ABCMeta): """ General probabilistic saliency model. Inheriting classes have to implement `conditional_log_density` """ @abstractmethod def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): raise NotImplementedError() def conditional_log_density_for_fixation(self, stimuli, fixations, fixation_index, out=None): return self.conditional_log_density( stimuli.stimulus_objects[fixations.n[fixation_index]], x_hist=remove_trailing_nans(fixations.x_hist[fixation_index]), y_hist=remove_trailing_nans(fixations.y_hist[fixation_index]), t_hist=remove_trailing_nans(fixations.t_hist[fixation_index]), attributes={key: getattr(fixations, key)[fixation_index] for key in fixations.__attributes__}, out=out ) def conditional_log_densities(self, stimuli, fixations, verbose=False, **kwargs): """ returns iterator over conditional log density predictions for each fixation """ if verbose is True: warnings.warn("Verbose mode is deprecated, use the iterator instead.", DeprecationWarning, stacklevel=2) return (self.conditional_log_density_for_fixation(stimuli, fixations, fixation_index) for fixation_index in tqdm(range(len(fixations)), disable=not verbose)) def log_likelihoods(self, stimuli, fixations, verbose=False): log_likelihoods = np.empty(len(fixations.x)) for i, conditional_log_density in tqdm(enumerate(self.conditional_log_densities(stimuli, fixations, verbose=False)), disable=not verbose, total=len(fixations.x)): check_prediction_shape(conditional_log_density, stimuli[fixations.n[i]]) log_likelihoods[i] = conditional_log_density[fixations.y_int[i], fixations.x_int[i]] return log_likelihoods def log_likelihood(self, stimuli, fixations, verbose=False, average='fixation'): return average_values(self.log_likelihoods(stimuli, fixations, verbose=verbose), fixations, average=average) def information_gains(self, stimuli, fixations, baseline_model=None, verbose=False, average='fixation'): if average != 'fixation': raise NotImplementedError() if baseline_model is None: baseline_model = UniformModel() own_log_likelihoods = self.log_likelihoods(stimuli, fixations, verbose=verbose) baseline_log_likelihoods = baseline_model.log_likelihoods(stimuli, fixations, verbose=verbose) return (own_log_likelihoods - baseline_log_likelihoods) / np.log(2) def information_gain(self, stimuli, fixations, baseline_model=None, verbose=False, average='fixation'): return average_values(self.information_gains(stimuli, fixations, baseline_model, verbose=verbose), fixations, average=average) def _expand_sample_arguments(self, stimuli, train_counts, lengths=None, stimulus_indices=None): if isinstance(train_counts, int): train_counts = [train_counts]*len(stimuli) if stimulus_indices is None: if len(train_counts) > len(stimuli): raise ValueError('Number of train counts higher than count of stimuli!') stimulus_indices = range(len(train_counts)) if isinstance(stimulus_indices, int): stimulus_indices = [stimulus_indices] if len(stimulus_indices) != len(train_counts): raise ValueError('Number of train counts must match number of stimulus_indices!') if isinstance(lengths, int): lengths = [lengths for i in range(len(stimuli))] if len(train_counts) != len(lengths): raise ValueError('Number of train counts and number of lengths do not match!') new_lengths = [] for k, l in enumerate(lengths): if isinstance(l, int): ll = [l for i in range(train_counts[k])] else: ll = l new_lengths.append(ll) lengths = new_lengths for k, (c, ls) in enumerate(zip(train_counts, lengths)): if c != len(ls): raise ValueError('{}th train count ({}) does not match given number of lengths ({})!'.format(k, c, len(ls))) return stimuli, train_counts, lengths, stimulus_indices def sample(self, stimuli, train_counts, lengths=1, stimulus_indices=None, rst=None, verbose=False) -> ScanpathFixations: """ Sample fixations for given stimuli Examples -------- >>> model.sample(stimuli, 10) # sample 10 fixations per image >>> # sample 5 fixations from the first image, 10 from the second and >>> # 30 fixations from the third image >>> model.sample(stimuli, [5, 10, 30]) >>> # Sample 10 fixation trains per image, each consiting of 5 fixations >>> model.sample(stimuli, 10, lengths=5) >>> # Sample 10 fixation trains per image, consisting of 2 fixations >>> # for the first image, 4 fixations for the second, ... >>> model.sample(stimuli, 10, lengths=[2, 4, 3]) >>> # Sample >>> model.sample(stimuli, 3, lengths=[[1,2,3], [1,2,3]]) >>> # Sample 3 fixations from the 10th stimulus >>> model.sample(stimuli, 3, stimulus_indices = 10) >>> # Sample 3 fixations from the 20th and the 42th stimuli each >>> model.sample(stimuli, 3, stimulus_indices = [20, 42]) """ stimuli, train_counts, lengths, stimulus_indices = self._expand_sample_arguments(stimuli, train_counts, lengths, stimulus_indices) xs = [] ys = [] ts = [] ns = [] subjects = [] total_count = sum(len(l) for l in lengths) pbar = tqdm(total=total_count, disable=not verbose) for stimulus_index, ls in zip(stimulus_indices, lengths): stimulus = stimuli[stimulus_index] for l in ls: this_xs, this_ys, this_ts = self._sample_fixation_train(stimulus, l, rst=rst) xs.append(this_xs) ys.append(this_ys) ts.append(this_ts) ns.append(stimulus_index) subjects.append(0) pbar.update(1) return ScanpathFixations(Scanpaths(xs=xs, ys=ys, ts=ts, n=ns, subject=subjects)) def _sample_fixation_train(self, stimulus, length, rst=None): """Sample one fixation train of given length from stimulus""" return self.sample_scanpath(stimulus, [], [], [], length, rst=rst) def sample_fixation(self, stimulus, x_hist, y_hist, t_hist, attributes=None, verbose=False, rst=None): log_densities = self.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes=attributes) x, y = sample_from_image(np.exp(log_densities), rst=rst) return x, y, len(t_hist) class Model(ScanpathModel): """ Time independend probabilistic saliency model. Inheriting classes have to implement `_log_density`. """ def __init__(self, cache_location=None, caching=True, memory_cache_size=None): super(Model, self).__init__() self._cache = Cache(cache_location, memory_cache_size=memory_cache_size) self.caching = caching #self._log_density_cache = Cache(cache_location) # This make the property `cache_location` work. #self._saliency_map_cache = self._log_density_cache @property def cache_location(self): return self._cache.cache_location @cache_location.setter def cache_location(self, value): self._cache.cache_location = value def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): return self.log_density(stimulus) def log_density(self, stimulus): """ Get log_density for given stimulus. To overwrite this function, overwrite `_log_density` as otherwise the caching mechanism is disabled. """ stimulus = handle_stimulus(stimulus) if not self.caching: return self._log_density(stimulus.stimulus_data) stimulus_id = stimulus.stimulus_id if not stimulus_id in self._cache: self._cache[stimulus_id] = self._log_density(stimulus.stimulus_data) return self._cache[stimulus_id] @abstractmethod def _log_density(self, stimulus): """ Overwrite this to implement you own SaliencyMapModel. Parameters ---------- @type stimulus: ndarray @param stimulus: stimulus for which the saliency map should be computed. """ raise NotImplementedError() def log_likelihoods(self, stimuli, fixations, verbose=False): log_likelihoods = np.empty(len(fixations.x)) for n in tqdm(range(len(stimuli)), disable=not verbose): inds = fixations.n == n if not inds.sum(): continue log_density = self.log_density(stimuli[n]) check_prediction_shape(log_density, stimuli[n]) this_log_likelihoods = log_density[fixations.y_int[inds], fixations.x_int[inds]] log_likelihoods[inds] = this_log_likelihoods return log_likelihoods def _sample_fixation_train(self, stimulus, length, rst=None): """Sample one fixation train of given length from stimulus""" # We could reuse the implementation from `ScanpathModel` # but this implementation is much faster for long trains. log_densities = self.log_density(stimulus) xs, ys = sample_from_image(np.exp(log_densities), count=length, rst=rst) ts = np.arange(len(xs)) return xs, ys, ts def pixel_space_information_gain(self, baseline, gold_standard, stimulus, eps=1e-20): log_p_gold = gold_standard.log_density(stimulus) log_p_baseline = baseline.log_density(stimulus) log_p_model = self.log_density(stimulus) p_gold = np.exp(log_p_gold) p_gold[p_gold == 0] = p_gold[p_gold > 0].min() ig = (p_gold)*(np.logaddexp(log_p_model, np.log(eps))-np.logaddexp(log_p_baseline, np.log(eps))) return ig def kl_divergences(self, stimuli, gold_standard, log_regularization=0, quotient_regularization=0, verbose=False): """Calculate KL Divergence between model and gold standard for each stimulus. This metric works only for probabilistic models. For the existing saliency metrics known as KL Divergence, see `image_based_kl_divergence` and `fixation_based_kl_divergence`. log_regularization and quotient_regularization are regularization constants that are used as in kldiv(p1, p2) = sum(p1*log(log_regularization + p1 / (p2 + quotient_regularization))). """ assert isinstance(self, Model) assert isinstance(gold_standard, Model) kl_divs = [] for s in tqdm(stimuli, disable=not verbose): logp_model = self.log_density(s) logp_gold = gold_standard.log_density(s) check_prediction_shape(logp_model, s) check_prediction_shape(logp_gold, s) kl_divs.append( probabilistic_image_based_kl_divergence(logp_model, logp_gold, log_regularization=log_regularization, quotient_regularization=quotient_regularization) ) return kl_divs def set_params(self, **kwargs): """ Set model parameters, if the model has parameters This method has to reset caches etc., if the depend on the parameters """ if kwargs: raise ValueError('Unkown parameters!', kwargs) class CachedModel(Model): """Density model which uses only precached densities """ def __init__(self, cache_location, **kwargs): if cache_location is None: raise ValueError("CachedModel needs a cache location!") super(CachedModel, self).__init__(cache_location=cache_location, **kwargs) def _log_density(self, stimulus): raise NotImplementedError() class UniformModel(Model): """Saliency model assuming uniform fixation distribution over space """ def _log_density(self, stimulus): return np.zeros((stimulus.shape[0], stimulus.shape[1])) - np.log(stimulus.shape[0]) - np.log(stimulus.shape[1]) def log_likelihoods(self, stimuli, fixations, verbose=False): stimulus_shapes = np.zeros((len(stimuli), 2), dtype=int) stimulus_indices = sorted(np.unique(fixations.n)) for stimulus_index in stimulus_indices: stimulus_shapes[stimulus_index] = stimuli.stimulus_objects[stimulus_index].size with np.errstate(divide='ignore'): # ignore log(0) warnings, we won't use them anyway stimulus_log_likelihoods = -np.log(stimulus_shapes).sum(axis=1) return stimulus_log_likelihoods[fixations.n] class MixtureModel(Model): """ A saliency model being a weighted mixture of a number of other models """ def __init__(self, models, weights=None, check_norm=True, **kwargs): """Create a mixture model from a list of models and a list of weights :param models: list of `Model` instances :param weights: list of weights for the different models. Do not have to sum up to one, they will be normalized. If `None`, will be set to a uniform mixture. """ super(MixtureModel, self).__init__(**kwargs) self.models = models if weights is None: weights = np.ones(len(self.models)) weights = np.asarray(weights, dtype=float) weights /= weights.sum() if not len(weights) == len(models): raise ValueError('models and weights must have same length!') self.weights = weights self.check_norm = check_norm def _log_density(self, stimulus): log_densities = [] for i, model in enumerate(self.models): log_density = model.log_density(stimulus).copy() log_density += np.log(self.weights[i]) log_densities.append(log_density) log_density = logsumexp(log_densities, axis=0) if self.check_norm: np.testing.assert_allclose(np.exp(log_density).sum(), 1.0, rtol=1e-7) if not log_density.shape == (stimulus.shape[0], stimulus.shape[1]): raise ValueError('wrong density shape in mixture model! stimulus shape: ({}, {}), density shape: {}'.format(stimulus.shape[0], stimulus.shape[1], log_density.shape)) return log_density class MixtureScanpathModel(ScanpathModel): """ A scanpath model being a weighted mixture of a number of other models """ def __init__(self, models, weights=None, check_norm=True, **kwargs): """Create a mixture scanpath model from a list of models and a list of weights :param models: list of `ScanpathModel` instances :param weights: list of weights for the different models. Do not have to sum up to one, they will be normalized. If `None`, will be set to a uniform mixture. """ super(MixtureScanpathModel, self).__init__(**kwargs) self.models = models if weights is None: weights = np.ones(len(self.models)) weights = np.asarray(weights, dtype=float) weights /= weights.sum() if not len(weights) == len(models): raise ValueError('models and weights must have same length!') self.weights = weights self.check_norm = check_norm def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): log_densities = [] for i, model in enumerate(self.models): log_density = model.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes=attributes).copy() log_density += np.log(self.weights[i]) log_densities.append(log_density) log_density = logsumexp(log_densities, axis=0) if self.check_norm: np.testing.assert_allclose(np.exp(log_density).sum(), 1.0, rtol=1e-7) if not log_density.shape == (stimulus.shape[0], stimulus.shape[1]): raise ValueError('wrong density shape in mixture model! stimulus shape: ({}, {}), density shape: {}'.format(stimulus.shape[0], stimulus.shape[1], log_density.shape)) return log_density class ResizingModel(Model): def __init__(self, parent_model, verbose=True, **kwargs): if 'caching' not in kwargs: kwargs['caching'] = False self.verbose = verbose super(ResizingModel, self).__init__(**kwargs) self.parent_model = parent_model def _log_density(self, stimulus): smap = self.parent_model.log_density(stimulus) target_shape = (stimulus.shape[0], stimulus.shape[1]) if smap.shape != target_shape: if self.verbose: print("Resizing saliency map", smap.shape, target_shape) x_factor = target_shape[1] / smap.shape[1] y_factor = target_shape[0] / smap.shape[0] smap = zoom(smap, [y_factor, x_factor], order=1, mode='nearest') smap -= logsumexp(smap) assert smap.shape == target_shape return smap class ResizingScanpathModel(ScanpathModel): def __init__(self, parent_model, verbose=True, **kwargs): self.verbose = verbose super(ResizingScanpathModel, self).__init__(**kwargs) self.parent_model = parent_model def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): smap = self.parent_model.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes=attributes, out=out) target_shape = (stimulus.shape[0], stimulus.shape[1]) if smap.shape != target_shape: if self.verbose: print("Resizing saliency map", smap.shape, target_shape) x_factor = target_shape[1] / smap.shape[1] y_factor = target_shape[0] / smap.shape[0] smap = zoom(smap, [y_factor, x_factor], order=1, mode='nearest') smap -= logsumexp(smap) assert smap.shape == target_shape return smap class DisjointUnionModel(DisjointUnionMixin, ScanpathModel): def conditional_log_density(self, stimulus, *args, **kwargs): raise def log_likelihoods(self, stimuli, fixations, **kwargs): return self.eval_metric('log_likelihoods', stimuli, fixations, **kwargs) class SubjectDependentModel(DisjointUnionModel): def __init__(self, subject_models, **kwargs): super(SubjectDependentModel, self).__init__(**kwargs) self.subject_models = subject_models def _split_fixations(self, stimuli, fixations): for s in self.subject_models: yield fixations.subject == s, self.subject_models[s] def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None, **kwargs): if 'subject' not in attributes: raise ValueError("SubjectDependentModel can't compute conditional log densities without subject indication!") return self.subject_models[attributes['subject']].conditional_log_density( stimulus, x_hist, y_hist, t_hist, attributes=attributes, **kwargs) def get_saliency_map_model_for_sAUC(self, baseline_model): return SubjectDependentSaliencyMapModel({ s: ShuffledAUCSaliencyMapModel(self.subject_models[s], baseline_model) for s in self.subject_models}) def get_saliency_map_model_for_NSS(self): return SubjectDependentSaliencyMapModel({ s: DensitySaliencyMapModel(self.subject_models[s]) for s in self.subject_models}) class StimulusDependentModel(Model): def __init__(self, stimuli_models, check_stimuli=True, fallback_model=None, **kwargs): super(StimulusDependentModel, self).__init__(**kwargs) self.stimuli_models = stimuli_models self.fallback_model = fallback_model if check_stimuli: self.check_stimuli() def check_stimuli(self): for s1, s2 in tqdm(list(combinations(self.stimuli_models, 2))): if not set(s1.stimulus_ids).isdisjoint(s2.stimulus_ids): raise ValueError('Stimuli not disjoint') def _log_density(self, stimulus): stimulus_hash = get_image_hash(stimulus) for stimuli, model in self.stimuli_models.items(): if stimulus_hash in stimuli.stimulus_ids: return model.log_density(stimulus) else: if self.fallback_model is not None: return self.fallback_model.log_density(stimulus) else: raise ValueError('stimulus not provided by these models') class StimulusDependentScanpathModel(ScanpathModel): def __init__(self, stimuli_models, check_stimuli=True, fallback_model=None, **kwargs): super(StimulusDependentScanpathModel, self).__init__(**kwargs) self.stimuli_models = stimuli_models self.fallback_model = fallback_model if check_stimuli: self.check_stimuli() def check_stimuli(self): for s1, s2 in tqdm(list(combinations(self.stimuli_models, 2))): if not set(s1.stimulus_ids).isdisjoint(s2.stimulus_ids): raise ValueError('Stimuli not disjoint') def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): stimulus_hash = get_image_hash(as_stimulus(stimulus).stimulus_data) for stimuli, model in self.stimuli_models.items(): if stimulus_hash in stimuli.stimulus_ids: return model.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes=attributes, out=out) else: if self.fallback_model is not None: return self.fallback_model.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes=attributes, out=out) else: raise ValueError('stimulus not provided by these models') class FixationIndexDependentModel(ScanpathModel): """ a scanpath that uses different models depending of the index of a fixation within a scanpath. """ def __init__(self, models, *args, **kwargs): super().__init__(*args, **kwargs) self.models = models def _get_model_for_index(self, fixation_index): for (start, end), model in self.models.items(): if start <= fixation_index < end: return model raise KeyError(fixation_index) def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): fixation_index = len(remove_trailing_nans(x_hist)) return self._get_model_for_index(fixation_index).conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes=attributes, out=out) class ShuffledAUCSaliencyMapModel(SaliencyMapModel): def __init__(self, probabilistic_model, baseline_model): super(ShuffledAUCSaliencyMapModel, self).__init__(caching=False) self.probabilistic_model = probabilistic_model self.baseline_model = baseline_model def _saliency_map(self, stimulus): return self.probabilistic_model.log_density(stimulus) - self.baseline_model.log_density(stimulus) class ShuffledAUCScanpathSaliencyMapModel(ScanpathSaliencyMapModel): def __init__(self, probabilistic_model: ScanpathModel, baseline_model: Model): super(ShuffledAUCScanpathSaliencyMapModel, self).__init__() self.probabilistic_model = probabilistic_model self.baseline_model = baseline_model def conditional_saliency_map(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): return ( self.probabilistic_model.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes=attributes) - self.baseline_model.log_density(stimulus) ) def average_predictions(log_densities, log_density_count=None, maximal_chunk_size=None, verbose=False, library='torch'): """ compute average log density given multiple log densities. specifying log_density_count allows to process generator arrays to avoid keeping all predictions in memory. specifying maximal_chunk_size allows to process the log densities such that not too many log densities are kept in memory at all (which only makes sense if log_densities is a generator), see logsumexp_iterator for more details """ if maximal_chunk_size is not None and log_density_count is None: print("Warning: specifying maximal_chunk_size without log_density_count doesn't make sense because then the log densities have to be converted into a list and hence put in memory anyway.") if log_density_count is None: log_densities = np.array(list(log_densities)) log_density_count = len(log_densities) normalization_constant = np.log(log_density_count) def weighted_log_densities(log_densities, normalization_constant): for log_density in log_densities: yield log_density - normalization_constant result = logsumexp_iterator(weighted_log_densities(log_densities, normalization_constant), log_density_count, maximal_chunk_size=maximal_chunk_size, verbose=verbose, library=library) result_norm = logsumexp(result) if not (-0.0001 < result_norm < 0.0001): print(f"Warning: result of averaging not well normalized (logsum={result_norm}). This could either be a problem with the averaged predictions or indicate numerical issues in averaging.") result -= result_norm return result def logsumexp_iterator(log_density_iterator, iterator_length, maximal_chunk_size=10, verbose=False, library='torch'): """computes logsumexp of iterator such not too many values are in memory. Works by splitting the sequence into shorter chunks, processing them and then adding the results up. This is done in a recursive manner: If the chunks are still too long, they are again split up. This guarantees that per recursion level never more than `maximal_chunk_size` items are kept in memory. """ if iterator_length is None or maximal_chunk_size is None or (maximal_chunk_size is not None and iterator_length <= maximal_chunk_size): if verbose: print(f"directly handling {iterator_length} predictions") predictions = np.array(list(log_density_iterator)) else: predictions = [] chunk_size = iterator_length // (maximal_chunk_size // 2) if verbose: print(f"splitting {iterator_length} predictions up into chunks of length {chunk_size}") for i, iterator_chunk in enumerate(iterator_chunks(log_density_iterator, chunk_size=chunk_size)): if verbose: print(f"handling {i}th chunk of length {chunk_size}") predictions.append(logsumexp_iterator(iterator_chunk, chunk_size, maximal_chunk_size=maximal_chunk_size, verbose=verbose)) predictions = np.array(predictions) if verbose: print(f"Done handling {iterator_length} predictions in chunks of {chunk_size}") if library == 'tensorflow': from .tf_utils import tf_logsumexp prediction = tf_logsumexp(predictions, axis=0) elif library == 'torch': import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") prediction = torch.logsumexp(torch.Tensor(predictions).to(device), dim=0).detach().cpu().numpy() elif library == 'numpy': prediction = logsumexp(predictions, axis=0) else: raise ValueError(library) return prediction class ShuffledBaselineModel(Model): """Predicts a mixture of all predictions for other images. This model will usually be used as baseline model for computing sAUC saliency maps. To avoid computing many averages over many images, this model will once compute an an average over all predictions, and then only remove the prediction for a given image when computing model predictions. use the library parameter to define whether the logsumexp should be computed with torch (default), tensorflow or numpy. predict_overall_average: If False (default), for each image, the average over all other images in `stimuli` will be predicted. If set to True, simply the average over all images in stimuli will be predicted. """ def __init__(self, parent_model, stimuli, compute_size=(500, 500), library='torch', prepopulate_cache=False, maximal_chunk_size=20, predict_overall_average=False, **kwargs): super(ShuffledBaselineModel, self).__init__(**kwargs) self.parent_model = parent_model self.stimuli = stimuli self.compute_size = tuple(compute_size) self.maximal_chunk_size = maximal_chunk_size self.predict_overall_average = predict_overall_average self.prediction = None if library not in ['torch', 'tensorflow', 'numpy']: raise ValueError(library) self.library = library if prepopulate_cache: self.get_average_prediction(verbose=True) def get_average_prediction(self, verbose=False): if self.prediction is not None: return self.prediction def log_density_iterable(): for stimulus in tqdm(self.stimuli, disable=not verbose): yield self._resize_prediction(self.parent_model.log_density(stimulus), self.compute_size) prediction = average_predictions( log_density_iterable(), log_density_count=len(self.stimuli), maximal_chunk_size=self.maximal_chunk_size, library=self.library ) self.prediction = prediction return self.prediction def _resize_prediction(self, prediction, target_shape): if prediction.shape != target_shape: orig_shape = prediction.shape x_factor = target_shape[1] / prediction.shape[1] y_factor = target_shape[0] / prediction.shape[0] prediction = zoom(prediction, [y_factor, x_factor], order=1, mode='nearest') prediction -= logsumexp(prediction) if prediction.shape != target_shape: print("compute size", self.compute_size) print("prediction shape", orig_shape) print("target shape", target_shape) print("x factor", x_factor) print("y factor", y_factor) raise ValueError(prediction.shape) assert prediction.shape == target_shape return prediction def _log_density(self, stimulus): average_log_density = self.get_average_prediction() if self.predict_overall_average: prediction = average_log_density else: # here we're effectively computing the average prediction of all predictions except for the # one for this stimulus, by substracting the current prection from the average prediction # with correct weights. This allows us to only once iterate over all predictions at model start. this_log_density = self._resize_prediction(self.parent_model.log_density(stimulus), self.compute_size) N = len(self.stimuli) prediction =np.log( np.exp(average_log_density + np.log(N) - np.log(N - 1)) -np.exp(this_log_density - np.log(N - 1)) ) target_shape = (stimulus.shape[0], stimulus.shape[1]) prediction = self._resize_prediction(prediction, target_shape) return prediction ShuffledSimpleBaselineModel = deprecated_class(deprecated_in='0.2.22', removed_in='1.0.0', details="Use ShuffledBaselineModel instead, which is now as effective as the old ShuffledSimpleBaselineModel")(ShuffledBaselineModel) class GaussianModel(Model): def __init__(self, width=0.5, center_x=0.5, center_y=0.5, **kwargs): super(GaussianModel, self).__init__(**kwargs) self.parent_model = GaussianSaliencyMapModel(width=width, center_x=center_x, center_y=center_y) def _log_density(self, stimulus): saliency_map = self.parent_model.saliency_map(stimulus) density = saliency_map / saliency_map.sum() return np.log(density) class SaliencyMapNormalizingModel(Model): """ Probabilistic model that converts saliency maps into fixation densities by dividing by their sum """ def __init__(self, model, minimum_value=0.0): self.model = model self.minimum_value = minimum_value super(SaliencyMapNormalizingModel, self).__init__(caching=False) def _log_density(self, stimulus): smap = convert_saliency_map_to_density(self.model.saliency_map(stimulus), minimum_value=self.minimum_value) return np.log(smap) class FixedStimulusSizeModel(Model): """ model which scales images to have a fixed size before handing them to anothet model""" def __init__(self, size, parent_model, verbose=False, **kwargs): super(FixedStimulusSizeModel, self).__init__(**kwargs) self.size = size self.parent_model = parent_model self.verbose = verbose def _log_density(self, stimulus): stimulus = self.ensure_color(stimulus) stimulus_size = stimulus.shape[0], stimulus.shape[1] max_size = max(stimulus_size) factor = self.size / max_size if factor != 1.0: if self.verbose: print("Resizing with factor", factor) stimulus_for_parent_model = zoom(stimulus, [factor, factor, 1.0], order=1, mode='nearest') else: stimulus_for_parent_model = stimulus log_density = self.parent_model.log_density(stimulus_for_parent_model) factor_y = stimulus.shape[0] / log_density.shape[0] factor_x = stimulus.shape[1] / log_density.shape[1] if factor_y != 1.0 or factor_x != 1.0: if self.verbose: print("Wrong shape, resizing log densities", stimulus.shape, log_density.shape) log_density = zoom(log_density, [factor_y, factor_x], order=1, mode='nearest') log_density -= logsumexp(log_density) assert log_density.shape[0] == stimulus.shape[0] assert log_density.shape[1] == stimulus.shape[1] return log_density def ensure_color(self, image): if image.ndim == 2: return np.dstack((image, image, image)) return image class DVAAwareModel(Model): """ A model which adapts another model to a new image resolution by rescaling images before computing predictions - dva: expected image resolution in pixel per dva for this model - parent_model_dva: image resolution expected by parent_model """ def __init__(self, dva, parent_model, parent_model_dva, verbose=False, **kwargs): super(DVAAwareModel, self).__init__(**kwargs) self.dva = dva self.parent_model = parent_model self.parent_model_dva = parent_model_dva self.verbose = verbose self.factor = self.parent_model_dva / self.dva def _log_density(self, stimulus): stimulus_data = as_stimulus(stimulus).stimulus_data if self.factor != 1.0: if self.verbose: print("Resizing with factor", self.factor) if stimulus_data.ndim == 2: factors = [self.factor, self.factor] else: factors = [self.factor, self.factor, 1.0] stimulus_for_parent_model = zoom(stimulus_data, factors, order=1, mode='nearest') else: stimulus_for_parent_model = stimulus log_density = self.parent_model.log_density(stimulus_for_parent_model) factor_y = stimulus.shape[0] / log_density.shape[0] factor_x = stimulus.shape[1] / log_density.shape[1] if factor_y != 1.0 or factor_x != 1.0: if self.verbose: print("Wrong shape, resizing log densities", stimulus.shape, log_density.shape) log_density = zoom(log_density, [factor_y, factor_x], order=1, mode='nearest') log_density -= logsumexp(log_density) assert log_density.shape[0] == stimulus.shape[0] assert log_density.shape[1] == stimulus.shape[1] return log_density def ensure_color(self, image): if image.ndim == 2: return np.dstack((image, image, image)) return image class DVAAwareScanpathModel(ScanpathModel): """ A scanpath model which adapts another model to a new image resolution by rescaling images before computing predictions - dva: expected image resolution in pixel per dva for this model - parent_model_dva: image resolution expected by parent_model """ def __init__(self, dva: float, parent_model: ScanpathModel, parent_model_dva: float, verbose=False, **kwargs): super(DVAAwareScanpathModel, self).__init__(**kwargs) self.dva = dva self.parent_model = parent_model self.parent_model_dva = parent_model_dva self.verbose = verbose self.factor = self.parent_model_dva / self.dva def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): stimulus_data = as_stimulus(stimulus).stimulus_data if out is not None: raise NotImplementedError() if self.factor != 1.0: if self.verbose: print("Resizing with factor", self.factor) if stimulus_data.ndim == 2: factors = [self.factor, self.factor] else: factors = [self.factor, self.factor, 1.0] stimulus_for_parent_model = zoom(stimulus_data, factors, order=1, mode='nearest') outer_shape = ( stimulus_data.shape[0], stimulus_data.shape[1] ) inner_shape = ( stimulus_for_parent_model.shape[0], stimulus_for_parent_model.shape[1] ) x_factor = outer_shape[1] / inner_shape[1] y_factor = outer_shape[0] / inner_shape[0] if x_factor != 1: x_hist_for_parent_model = np.array(x_hist) / x_factor if y_factor != 1: y_hist_for_parent_model = np.array(y_hist) / y_factor else: stimulus_for_parent_model = stimulus x_hist_for_parent_model = x_hist y_hist_for_parent_model = y_hist log_density = self.parent_model.conditional_log_density( stimulus=stimulus_for_parent_model, x_hist = x_hist_for_parent_model, y_hist=y_hist_for_parent_model, t_hist=t_hist, attributes=attributes ) factor_y = stimulus.shape[0] / log_density.shape[0] factor_x = stimulus.shape[1] / log_density.shape[1] if factor_y != 1.0 or factor_x != 1.0: if self.verbose: print("Wrong shape, resizing log densities", stimulus.shape, log_density.shape) log_density = zoom(log_density, [factor_y, factor_x], order=1, mode='nearest') log_density -= logsumexp(log_density) assert log_density.shape[0] == stimulus.shape[0] assert log_density.shape[1] == stimulus.shape[1] return log_density def ensure_color(self, image): if image.ndim == 2: return np.dstack((image, image, image)) return image GeneralModel = deprecated_class(deprecated_in='0.2.16', removed_in='1.0.0', details="Use ScanpathModel instead")(ScanpathModel) StimulusDependentGeneralModel = deprecated_class(deprecated_in='0.2.16', removed_in='1.0.0', details="Use StimulusDependentScanpathModel instead")(StimulusDependentScanpathModel) ================================================ FILE: pysaliency/numba_utils.py ================================================ from __future__ import print_function, unicode_literals, division, absolute_import import numba import numpy as np # np.trapz was removed in NumPy 2.0, replaced by np.trapezoid. # This shim supports both; remove once NumPy <2.0 compatibility is dropped. _trapz = getattr(np, 'trapezoid', getattr(np, 'trapz', None)) def fill_fixation_map(fixation_map, fixations, check_bounds=True): if check_bounds: if np.any(fixations < 0): raise ValueError("Negative fixation positions!") if np.any(fixations[:, 0] >= fixation_map.shape[0]): raise ValueError("Fixations y positions out of bound!") if np.any(fixations[:, 1] >= fixation_map.shape[1]): raise ValueError("Fixations x positions out of bound!") return _fill_fixation_map(fixation_map, fixations) @numba.jit(nopython=True) def _fill_fixation_map(fixation_map, fixations): """fixationmap: 2d array. fixations: Nx2 array of y, x positions""" for i in range(len(fixations)): fixation_y, fixation_x = fixations[i] fixation_map[int(fixation_y), int(fixation_x)] += 1 def auc_for_one_positive(positive, negatives): """ Computes the AUC score of one single positive sample agains many negatives. The result is equal to general_roc([positive], negatives)[0], but computes much faster because one can save sorting the negatives. """ return _auc_for_one_positive(positive, np.asarray(negatives)) @numba.jit(nopython=True) def _auc_for_one_positive(positive, negatives): """ Computes the AUC score of one single positive sample agains many negatives. The result is equal to general_roc([positive], negatives)[0], but computes much faster because one can save sorting the negatives. """ count = 0 for negative in negatives: if negative < positive: count += 1 elif negative == positive: count += 0.5 return count / len(negatives) def general_roc_numba(positives, negatives, judd=0): sorted_positives = np.sort(positives)[::-1] sorted_negatives = np.sort(negatives)[::-1] if judd == 0: all_values = np.hstack([positives, negatives]) all_values = np.sort(all_values)[::-1] else: min_val = min(sorted_positives[len(positives)-1], sorted_negatives[len(negatives)-1]) max_val = max(sorted_positives[0], sorted_negatives[0]) + 1 all_values = np.hstack((max_val, positives, min_val)) all_values = np.sort(all_values)[::-1] false_positive_rates = np.zeros(len(all_values) + 1) hit_rates = np.zeros(len(all_values) + 1) hit_rates, false_positive_rates = _general_roc_numba(all_values, sorted_positives, sorted_negatives, false_positive_rates, hit_rates) auc = _trapz(hit_rates, false_positive_rates) return auc, hit_rates, false_positive_rates @numba.jit(nopython=True) def _general_roc_numba(all_values, sorted_positives, sorted_negatives, false_positive_rates, hit_rates): """calculate ROC score for given values of positive and negative distribution""" positive_count = len(sorted_positives) negative_count = len(sorted_negatives) true_positive_count = 0 false_positive_count = 0 for i in range(len(all_values)): theta = all_values[i] while true_positive_count < positive_count and sorted_positives[true_positive_count] >= theta: true_positive_count += 1 while false_positive_count < negative_count and sorted_negatives[false_positive_count] >= theta: false_positive_count += 1 false_positive_rates[i+1] = float(false_positive_count) / negative_count hit_rates[i+1] = float(true_positive_count) / positive_count return hit_rates, false_positive_rates def general_rocs_per_positive_numba(positives, negatives): sorted_positives = np.sort(positives) sorted_negatives = np.sort(negatives) sorted_inds = np.argsort(positives) results = np.empty(len(positives)) results = _general_rocs_per_positive_numba(sorted_positives, sorted_negatives, sorted_inds, results) return results @numba.jit(nopython=True) def _general_rocs_per_positive_numba(sorted_positives, sorted_negatives, sorted_inds, results): """calculate ROC scores for each positive against a list of negatives distribution. The mean over the result will equal the return value of `general_roc`.""" true_negatives_count = 0 equal_count = 0 last_theta = -np.inf negative_count = len(sorted_negatives) for i, theta in enumerate(sorted_positives): if theta == last_theta: results[sorted_inds[i]] = (1.0 * true_negatives_count + 0.5 * equal_count) / negative_count continue true_negatives_count = true_negatives_count + equal_count while true_negatives_count < negative_count and sorted_negatives[true_negatives_count] < theta: true_negatives_count += 1 equal_count = 0 while true_negatives_count + equal_count < negative_count and sorted_negatives[true_negatives_count + equal_count] <= theta: equal_count += 1 results[sorted_inds[i]] = (1.0 * true_negatives_count + 0.5 * equal_count) / negative_count last_theta = theta return results ================================================ FILE: pysaliency/optpy/README.md ================================================ Copied from https://github.com/matthias-k/optpy since that name is already taken on pypi and I'm not sure whether it's work packaging it anyway. ================================================ FILE: pysaliency/optpy/__init__.py ================================================ from __future__ import absolute_import from .optimization import ParameterManager, minimize, LinearConstraint from .jacobian import FunctionWithApproxJacobian, FunctionWithApproxJacobianCentral ================================================ FILE: pysaliency/optpy/jacobian.py ================================================ """ Author: Matthias Kuemmerer, 2014 """ from __future__ import print_function, division, unicode_literals, absolute_import import sys import numpy as np class FunctionWithApproxJacobian(object): def __init__(self, func, epsilon, verbose=True): self._func = func self.epsilon = epsilon self.value_cache = {} self.verbose = verbose def __call__(self, x, *args, **kwargs): key = tuple(x) if not key in self.value_cache: self.log('.') value = self._func(x, *args, **kwargs) if np.any(np.isnan(value)): print("Warning! nan function value encountered at {0}".format(x)) self.value_cache[key] = value return self.value_cache[key] def func(self, x, *args, **kwargs): if self.verbose: print(x) return self(x, *args, **kwargs) def log(self, msg): if self.verbose: sys.stdout.write(msg) sys.stdout.flush() def jac(self, x, *args, **kwargs): self.log('G[') x0 = np.asfarray(x) #print x0 dxs = np.zeros((len(x0), len(x0) + 1)) for i in range(len(x0)): dxs[i, i + 1] = self.epsilon results = [self(*(x0 + dxs[:, i], ) + args, **kwargs) for i in range(len(x0) + 1)] jac = np.zeros([len(x0), len(np.atleast_1d(results[0]))]) for i in range(len(x0)): jac[i] = (results[i + 1] - results[0]) / self.epsilon self.log(']') return jac.transpose() class FunctionWithApproxJacobianCentral(FunctionWithApproxJacobian): def jac(self, x, *args, **kwargs): self.log('G[') x0 = np.asfarray(x) #print x0 dxs = np.zeros((len(x0), 2*len(x0))) for i in range(len(x0)): dxs[i, i] = -self.epsilon dxs[i, len(x0)+i] = self.epsilon results = [self(*(x0 + dxs[:, i], ) + args, **kwargs) for i in range(2*len(x0))] jac = np.zeros([len(x0), len(np.atleast_1d(results[0]))]) for i in range(len(x0)): jac[i] = (results[len(x0)+i] - results[i]) / (2*self.epsilon) self.log(']') return jac.transpose() ================================================ FILE: pysaliency/optpy/optimization.py ================================================ """ Author: Matthias Kuemmerer, 2014 Some wrappers around scipy.optimize.minimize to make optimization of functions with multiple parameters easier """ from __future__ import absolute_import, print_function, unicode_literals, division import numpy as np import scipy.optimize from .jacobian import FunctionWithApproxJacobian class LinearConstraint(object): """Allows to build a linear constraint over the parameters by specifying one matrix for each parameter which will be concatenated as needed""" def __init__(self, A_dict, lb=-np.inf, ub=np.inf, keep_feasible=False): self.A_dict = A_dict self.lb = lb self.ub = ub self.keep_feasible = keep_feasible class ParameterManager(object): def __init__(self, parameters, optimize, **kwargs): """ Create a parameter manager :param parameters: The parameters to manage :type parameters: list of strings :param optimize: The parameters that should be optimized. Has to be a subset of parameters :type optimize: list of strings :param **kwargs: Initial values of the parameters """ self.parameters = parameters self.optimize = optimize self.param_values = kwargs def extract_parameters(self, x, return_list=False): """Return dictionary of optimization parameters from vector x. The non-optimization parameters will be taken from the initial values. if return_list==True, return a list instead of an dictionary""" params = self.param_values.copy() index = 0 for param_name in self.optimize: if not isinstance(self.param_values[param_name], np.ndarray) or len(self.param_values[param_name].shape) == 0: # Only scalar value params[param_name] = x[index] index += 1 else: shape = self.param_values[param_name].shape if len(shape) > 1: raise ValueError('Arrays with more than one dimension are not yet supported!') params[param_name] = x[index:index+shape[0]] index += shape[0] if return_list: return [params[key] for key in self.parameters] else: return params def build_vector(self, **kwargs): """Build a vector of the optimization parameters. The initial values will be taken unless you overwrite them using the keyword arguments""" params = self.param_values.copy() params.update(kwargs) vector_values = [params[key] for key in self.optimize] return np.hstack(vector_values) def get_length(self, param_name): """Return the length of parameter param_name when it is used in the optimization vector""" if not isinstance(self.param_values[param_name], np.ndarray): # Only scalar value return 1 else: shape = self.param_values[param_name].shape if len(shape) > 1: raise ValueError('Arrays with more than one dimension are not yet supported!') if len(shape) == 0: return 1 return shape[0] class KeywordParameterManager(ParameterManager): def __init__(self, initial_dict, optimize): """ Create a parameter manager :param initial_dict: Dictionary of initial values :type initial_dict: dict :param optimize: The parameters that should be optimized. Has to be a subset of inital_dict.keys() :type optimize: list of strings """ parameters = sorted(initial_dict.keys()) super(KeywordParameterManager, self).__init__(parameters, optimize, **initial_dict) def wrap_parameter_manager(f, parameter_manager, additional_kwargs=None): if isinstance(parameter_manager, KeywordParameterManager): def new_f(x, *args, **kwargs): if args: raise ValueError('KeywordParameterManager can only be used with keyword! Try giving all arguments as keywords.') params = parameter_manager.extract_parameters(x, return_list = False) kwargs.update(params) if additional_kwargs: kwargs.update(additional_kwargs) return f(**kwargs) return new_f else: def new_f(x, *args, **kwargs): params = parameter_manager.extract_parameters(x, return_list = True) params.extend(args) if additional_kwargs: kwargs.update(additional_kwargs) return f(*params, **kwargs) return new_f def wrap_linear_constraint(constraint: LinearConstraint, parameter_manager: 'ParameterManager') -> scipy.optimize.LinearConstraint: """Wraps a LinearConstraint object into a scipy.optimize.LinearConstraint object""" # TODO: hande case that all relevant parameters are not being optimized # get output dimension output_dims = [] for A in constraint.A_dict.values(): if A is not None: output_dims.append(A.shape[0]) if len(set(output_dims)) > 1: raise ValueError(f'All A matrices must have the same number of rows! Got {output_dims}') output_dim = output_dims[0] A_dict = dict(constraint.A_dict) lb = constraint.lb ub = constraint.ub for param_name in parameter_manager.parameters: A = constraint.A_dict.get(param_name, None) if param_name in parameter_manager.optimize: if A is None: length = parameter_manager.get_length(param_name) A = np.zeros((output_dim, length)) A_dict[param_name] = A else: if A is not None: param_value = np.atleast_1d(parameter_manager.param_values[param_name]) constraint_value = np.dot(A, param_value) if np.isfinite(lb): lb = lb - constraint_value if np.isfinite(ub): ub = ub - constraint_value A_list = [A_dict[param_name] for param_name in parameter_manager.optimize] A = np.concatenate(A_list, axis=1) return scipy.optimize.LinearConstraint(A=A, lb=lb, ub=ub, keep_feasible=constraint.keep_feasible) def minimize(f, parameter_manager_or_x0, optimize=None, args=(), kwargs=None, method='BFGS', jac=None, bounds=None, constraints=(), tol=None, options=None, jac_approx = FunctionWithApproxJacobian, callback=None): """Minimize function f with scipy.optimize.minimze, using the parameters and initial values from the parameter_manager. Remark: Notice that at least SLSQP does not support None values in the bounds""" if isinstance(parameter_manager_or_x0, ParameterManager): parameter_manager = parameter_manager_or_x0 if optimize is not None: parameter_manager.optimize = optimize else: parameter_manager = KeywordParameterManager(parameter_manager_or_x0, optimize) if args: raise ValueError('Keyword based parameters can only be used with kwargs, not with args! Try giving all additional arguments as keywords.') wrapped_f = wrap_parameter_manager(f, parameter_manager, kwargs) x0 = parameter_manager.build_vector() if callable(jac): def jac_with_keyword(*args, **kwargs): kwargs['optimize'] = parameter_manager.optimize ret = jac(*args, **kwargs) param_values = parameter_manager.param_values.copy() for i, param_name in enumerate(parameter_manager.optimize): param_values[param_name] = ret[i] return parameter_manager.build_vector(**param_values) fun_ = wrapped_f jac_ = wrap_parameter_manager(jac_with_keyword, parameter_manager, kwargs) elif bool(jac): def func_with_keyword(*args, **kwargs): kwargs['optimize'] = parameter_manager.optimize func_value, jacs = f(*args, **kwargs) param_values = parameter_manager.param_values.copy() for i, param_name in enumerate(parameter_manager.optimize): param_values[param_name] = jacs[i] return func_value, parameter_manager.build_vector(**param_values) fun_ = wrap_parameter_manager(func_with_keyword, parameter_manager, kwargs) jac_ = True else: fun = jac_approx(wrapped_f, 1e-8) jac_ = fun.jac fun_ = fun.func # Adapt constraints if isinstance(constraints, dict): constraints = [constraints] new_constraints = [] for constraint in constraints: if isinstance(constraint, dict): new_constraint = constraint.copy() new_constraint['fun'] = wrap_parameter_manager(constraint['fun'], parameter_manager) new_constraints.append(new_constraint) elif isinstance(constraint, LinearConstraint): new_constraints.append(wrap_linear_constraint(constraint, parameter_manager)) else: raise NotImplementedError('Only dictionaries and LinearConstraints are supported as constraints') #Adapt bounds: if bounds is not None: new_bounds = [] for param_name in parameter_manager.optimize: if param_name in bounds: new_bounds.extend(bounds[param_name]) else: length = parameter_manager.get_length(param_name) for i in range(length): new_bounds.append((-np.inf, np.inf)) lb, ub = np.array(new_bounds).T new_bounds = scipy.optimize.Bounds(lb, ub, keep_feasible=True) else: new_bounds = None if callback is not None: callback = wrap_parameter_manager(callback, parameter_manager) res = scipy.optimize.minimize(fun_, x0, args=args, jac=jac_, method=method, constraints=new_constraints, bounds=new_bounds, tol=tol, callback=callback, options=options) params = parameter_manager.extract_parameters(res.x) for key in parameter_manager.parameters: setattr(res, key, params[key]) return res if __name__ == '__main__': def testfunc(x): return np.sum(x ** 4) testFun = FunctionWithApproxJacobian(testfunc, 1e-8) x0 = np.zeros(3) #val = testFun(x0) #print #print val g = testFun.jac(x0) print() print(g) ================================================ FILE: pysaliency/plotting.py ================================================ from __future__ import absolute_import, print_function, division, unicode_literals from typing import List, Union try: import matplotlib.pyplot as plt import matplotlib as mpl except ImportError: # If matplotlib is not there, just ignore it pass from boltons.iterutils import windowed import numpy as np from scipy.ndimage import zoom from .datasets.fixations import Fixations from .datasets.scanpaths import Scanpaths from .utils import remove_trailing_nans def plot_information_gain(information_gain, ax=None, color_range = None, image=None, frame=False, thickness = 1.0, zoom_factor=1.0, threshold=0.05, rel_levels=None, alpha=0.5, color_offset = 0.25, plot_color_bar=True): """ Create pixel space information gain plots as in the paper. Parameters: ----------- information gain: the information gain to plot. ax: the matplotlib axes object to use. If none, use current axes. color_range: Full range of colorbar """ if ax is None: ax = plt.gca() ig = information_gain if zoom_factor != 1.0: ig = zoom(ig, zoom_factor, order=0) if color_range is None: color_range = (ig.min(), ig.max()) if not isinstance(color_range, (tuple, list)): color_range = (-color_range, color_range) color_total_max = max(np.abs(color_range[0]), np.abs(color_range[1])) if image is not None: if image.ndim == 3: image = image.sum(axis=-1) ax.imshow(image, alpha=0.3) if rel_levels is None: rel_levels = [0.1, 0.4, 0.7] # from https://stackoverflow.com/questions/8580631/transparent-colormap cm = plt.cm.get_cmap('RdBu') cm._init() alphas = (np.abs(np.linspace(-1.0, 1.0, cm.N))) alphas = np.ones_like(alphas)*alpha cm._lut[:-3, -1] = alphas levels = [] colors = [] min_val = np.abs(ig.min()) max_val = np.abs(ig.max()) total_max = max(min_val, max_val) def get_color(val): # value relative -1 .. 1 rel_val = val / color_total_max # shift around 0 rel_val = (rel_val + np.sign(rel_val) * color_offset) / (1+color_offset) # transform to 0 .. 1 rel_val = (0.5 + rel_val / 2) return cm(rel_val) if min_val / total_max > threshold: for l in [1.0]+rel_levels[::-1]: val = -l*min_val levels.append(val) colors.append(get_color(val)) else: levels.append(-total_max) colors.append('white') # We want to use the color from the value nearer to zero colors = colors[1:] colors.append((1.0, 1.0, 1.0, 0.0)) if max_val / total_max > threshold: for l in rel_levels+[1.0]: val = l*max_val levels.append(val) colors.append(get_color(val)) else: levels.append(total_max) #print rel_vals ax.contourf(ig, levels=levels, colors=colors, vmin=-color_total_max, vmax=color_total_max ) ax.contour(ig, levels=levels, # colors=colors, # vmin=-color_range, vmax=color_range colors = 'gray', linestyles='solid', linewidths=0.6*thickness ) if plot_color_bar: ## Draw color range bar h = 100 w = 10 t = np.empty((100, 10, 4)) for y in range(h): for x in range(w): val = (y/h) * (color_range[1] - color_range[0]) + color_range[0] color = np.asarray(get_color(val)) if not -min_val <= val <= max_val: color[-1] *= 0.4 else: color[-1] = 1 t[y, x, :] = color ax.imshow(t, extent=(0.95*ig.shape[1], 0.98*ig.shape[1], 0.1*ig.shape[0], 0.9*ig.shape[0])) ax.set_xlim(0, ig.shape[1]) ax.set_ylim(ig.shape[0], 0) if frame: # Just a frame ax.set_xticks([]) ax.set_yticks([]) [i.set_linewidth(i.get_linewidth()*thickness) for i in ax.spines.itervalues()] else: ax.set_axis_off() def normalize_log_density(log_density): """ convertes a log density into a map of the cummulative distribution function. """ density = np.exp(log_density) flat_density = density.flatten() inds = flat_density.argsort()[::-1] sorted_density = flat_density[inds] cummulative = np.cumsum(sorted_density) unsorted_cummulative = cummulative[np.argsort(inds)] return unsorted_cummulative.reshape(log_density.shape) def visualize_distribution(log_densities, ax=None, levels=None, level_colors='black', cmap=plt.cm.viridis): if ax is None: ax = plt.gca() t = normalize_log_density(log_densities) img = ax.imshow(t, cmap=cmap) if levels is None: levels = [0, 0.25, 0.5, 0.75, 1.0] cs = ax.contour(t, levels=levels, colors=level_colors) #plt.clabel(cs) return img, cs def advanced_arrow(x, y, dx, dy, linewidth=1.0, headwidth=3.0, headlength=None, linestyle='-', ax=None, color=None, zorder=None, alpha=1.0, arrow_style='-|>'): """careful: this uses axes data and figure inches coordinates. They can change if the axes limits are changed, which makes the arrow look strange""" if ax is None: ax = plt.gca() if headlength is None: headlength = 1.5 * headwidth trans_data_to_inches = mpl.transforms.composite_transform_factory(ax.transData, ax.get_figure().dpi_scale_trans.inverted()) start = (x, y) end = (x + dx, y + dy) #ax.scatter([x, x+dx], [y, y+dy], 1, color='black', zorder=100) start_inches = trans_data_to_inches.transform(start) end_inches = trans_data_to_inches.transform(end) distance_inches = end_inches - start_inches distance_inches_length = np.sqrt(np.sum(np.square(distance_inches))) # make sure head is not longer than total length headlength = min(headlength, distance_inches_length * 72) new_distance_inches = (distance_inches_length - headlength / 72) new_end_inches = start_inches + distance_inches * (new_distance_inches / distance_inches_length) new_end_data = trans_data_to_inches.inverted().transform(new_end_inches) line = ax.plot( [x, new_end_data[0]], [y, new_end_data[1]], linewidth=linewidth, linestyle=linestyle, color=color, solid_capstyle="butt", # otherwise line is slightly too long zorder=zorder, alpha=alpha, ) color = line[0].get_color() arrow = mpl.patches.FancyArrowPatch( (x, y), (x+dx,y+dy), arrowstyle=mpl.patches.ArrowStyle( arrow_style, head_width=headwidth, head_length=headlength, ), mutation_scale=1, shrinkA=0, shrinkB=0, linewidth=0, color=color, alpha=alpha, zorder=zorder ) ax.add_patch(arrow) def plot_fixation( fixations: Fixations, index: int, ax=None, show_history=True, show_current_fixation=True, visualize_next_saccade=True, history_color='red', next_saccade_color='cyan', current_fixation_size=3, fixation_color='blue', current_fixation_color=None, history_alpha=1.0, history_alpha_decay=1.0, history_alpha_minimum=0.0, history_linestyle='-', saccade_width=2, fixation_size=10, next_saccade_linestyle=None ): x_hist = list(fixations.x_hist[index]) y_hist = list(fixations.y_hist[index]) x_next = fixations.x[index] y_next = fixations.y[index] _plot_fixation( x_hist=x_hist, y_hist=y_hist, x_next=x_next, y_next=y_next, ax=ax, show_history=show_history, show_current_fixation=show_current_fixation, visualize_next_saccade=visualize_next_saccade, include_next_saccade=False, history_color=history_color, next_saccade_color=next_saccade_color, current_fixation_size=current_fixation_size, fixation_color=fixation_color, current_fixation_color=current_fixation_color, history_alpha=history_alpha, history_alpha_decay=history_alpha_decay, history_alpha_minimum=history_alpha_minimum, history_linestyle=history_linestyle, saccade_width=saccade_width, fixation_size=fixation_size, next_saccade_linestyle=next_saccade_linestyle ) def plot_scanpath( scanpaths: Scanpaths, index: int, ax=None, show_history=True, history_color='red', fixation_color='blue', history_alpha=1.0, history_alpha_decay=1.0, history_alpha_minimum=0.0, history_linestyle='-', saccade_width=2, fixation_size=10, next_saccade_linestyle=None ) -> None: x_hist = list(scanpaths.xs[index]) y_hist = list(scanpaths.ys[index]) x_next = None y_next = None _plot_fixation( x_hist=x_hist, y_hist=y_hist, x_next=x_next, y_next=y_next, ax=ax, show_history=show_history, show_current_fixation=False, visualize_next_saccade=False, include_next_saccade=False, history_color=history_color, fixation_color=fixation_color, history_alpha=history_alpha, history_alpha_decay=history_alpha_decay, history_alpha_minimum=history_alpha_minimum, history_linestyle=history_linestyle, saccade_width=saccade_width, fixation_size=fixation_size, next_saccade_linestyle=next_saccade_linestyle ) def _plot_fixation( x_hist: List[Union[float, int]], y_hist: List[Union[float, int]], x_next: Union[float, int, None], y_next: Union[float, int, None], ax=None, show_history=True, show_current_fixation=True, visualize_next_saccade=False, include_next_saccade=False, history_color='red', next_saccade_color='cyan', current_fixation_size=3, fixation_color='blue', current_fixation_color=None, history_alpha=1.0, history_alpha_decay=1.0, history_alpha_minimum=0.0, history_linestyle='-', saccade_width=2, fixation_size=10, next_saccade_linestyle=None): if ax is None: ax = plt.gca() x_hist = list(x_hist) y_hist = list(y_hist) if include_next_saccade or visualize_next_saccade: if x_next is None or y_next is None: raise ValueError("x_next and y_next must be provided if include_next_saccade or visualize_next_saccade is True") if include_next_saccade: assert visualize_next_saccade is False assert x_next is not None and y_next is not None x_hist.append(x_next) y_hist.append(y_next) headwidth = 1.5 * saccade_width headlength = 3 * saccade_width if show_history: for saccade_index, ((x1, x2), (y1, y2)) in enumerate(zip(windowed(x_hist, 2), windowed(y_hist, 2))): inverse_saccade_index = len(x_hist) - saccade_index - 1 advanced_arrow(x1, y1, x2-x1, y2-y1, linewidth=saccade_width, headwidth=headwidth, headlength=headlength, color=history_color, linestyle=history_linestyle, zorder=10, alpha=(history_alpha - history_alpha_minimum) * (history_alpha_decay ** inverse_saccade_index) + history_alpha_minimum, ax=ax, ) ax.scatter([x1], [y1], fixation_size, color=fixation_color, alpha=(history_alpha - history_alpha_minimum) * (history_alpha_decay ** inverse_saccade_index) + history_alpha_minimum, zorder=40) if show_current_fixation: x1 = x_hist[-1] y1 = y_hist[-1] ax.scatter([x1], [y1], current_fixation_size, color=current_fixation_color or 'red', zorder=70,) if visualize_next_saccade: x1 = x_hist[-1] y1 = y_hist[-1] assert x_next is not None and y_next is not None # just for type checking x2 = x_next y2 = y_next advanced_arrow( x1, y1, x2-x1, y2-y1, linewidth=saccade_width, headwidth=headwidth, headlength=headlength, color=next_saccade_color, linestyle=next_saccade_linestyle or (0, (2,1)), zorder=60, ax=ax, ) ax.scatter([x2], [y2], fixation_size, color=next_saccade_color or 'red', zorder=70) ================================================ FILE: pysaliency/precomputed_models.py ================================================ from __future__ import absolute_import, division, print_function import glob import logging import os.path import tarfile import warnings import zipfile import numpy as np from imageio import imread import scipy.ndimage from scipy.io import loadmat from scipy.special import logsumexp from tqdm import tqdm from .datasets import FileStimuli, get_image_hash from .models import Model from .saliency_map_models import SaliencyMapModel from .utils import full_split, get_minimal_unique_filenames def get_stimuli_filenames(stimuli): if not isinstance(stimuli, FileStimuli) and 'filenames' not in stimuli.__attributes__: raise ValueError("Need FileStimuli or Stimuli with filenames attribute") if 'filenames' in stimuli.attributes: return stimuli.attributes['filenames'] else: return stimuli.filenames class NonUniqueKeysError(ValueError): pass class NoCommonPrefixError(ValueError): pass def get_keys_from_filenames(filenames, keys, verbose_error=True): """checks how much filenames have to be shorted to get the correct hdf5 or other keys""" if not filenames: return [] first_filename_parts = full_split(filenames[0]) for part_index in range(len(first_filename_parts)): remaining_filename = os.path.join(*first_filename_parts[part_index:]) logging.debug(f"Checking for {remaining_filename}") if remaining_filename in keys: break else: if verbose_error: print("No common prefix found!") print(f" filename: {filenames[0]}") print(" keys:") for key in keys[:5]: print(f" {key}") for key in keys[-5:]: print(f" {key}") raise NoCommonPrefixError('No common prefix found!') filename_keys = [] for filename in filenames: filename_parts = full_split(filename) remaining_filename = os.path.join(*filename_parts[part_index:]) filename_keys.append(remaining_filename) return filename_keys def get_keys_from_filenames_with_prefix(filenames, keys): """checks how much filenames have to be shorted to get the correct hdf5 or other keys, where the entries might correspond to the keys with a prefix that is shared across all filenames (but not necessarily all keys). """ key_part_index = 0 while True: logging.debug(f"Checking with removing {key_part_index} initial key parts") remaining_keys = remove_initial_key_parts(keys, key_part_index) if not any(remaining_keys): print("No common prefix found!") print(f" filename: {filenames[0]}") print(" keys:") for key in keys[:5]: print(f" {key}") for key in keys[-5:]: print(f" {key}") raise NoCommonPrefixError('No common prefix found from {} and {}'.format(filenames[0], keys[0])) try: filename_keys = get_keys_from_filenames(filenames, remaining_keys, verbose_error=False) except NoCommonPrefixError: key_part_index += 1 continue else: logging.debug(f"Found common prefix with removing {key_part_index} initial key parts") matching_keys = [key for key in remaining_keys if key in filename_keys] if len(matching_keys) > len(filename_keys): for key in filename_keys: full_keys = [full_key for full_key, partial_key in zip(keys, remaining_keys) if partial_key == key] if len(full_keys) > 1: raise NonUniqueKeysError(f"Multiple keys for {key}: {full_keys}") full_filename_keys = [] for filename_key in filename_keys: key = keys[remaining_keys.index(filename_key)] full_filename_keys.append(os.path.join(*full_split(key)[:key_part_index], filename_key)) return full_filename_keys def remove_initial_key_parts(keys, key_part_index): remaining_keys = [] for key in keys: key_parts = full_split(key) selected_key_parts = key_parts[key_part_index:] if not selected_key_parts: remaining_keys.append(None) else: remaining_keys.append(os.path.join(*selected_key_parts)) return remaining_keys def _effective_dtype(smap, dtype, downscale_factor): """Determine the dtype that will actually be stored in the HDF5 file.""" if dtype is not None: return np.dtype(dtype) if downscale_factor > 1: # numpy .mean() returns float64 for integer inputs, preserves float types if np.issubdtype(smap.dtype, np.integer): return np.dtype(np.float64) else: return smap.dtype # float32 stays float32, float64 stays float64 return smap.dtype def _downsample_smap(smap, k): """Downsample a 2D map by integer factor k using area averaging.""" if k == 1: return smap H, W = smap.shape H_pad = int(np.ceil(H / k)) * k W_pad = int(np.ceil(W / k)) * k smap = np.pad(np.ascontiguousarray(smap), ((0, H_pad - H), (0, W_pad - W)), mode='edge') return smap.reshape(H_pad // k, k, W_pad // k, k).mean(axis=(1, 3)) def _check_size_guard(smap, effective_stored_dtype, downscale_factor, dtype): """Warn if compact settings will produce a larger-per-element file than the native dtype.""" native_dtype = np.dtype(smap.dtype) if np.dtype(effective_stored_dtype).itemsize > native_dtype.itemsize: msg = ( f"Model produces {native_dtype} predictions " f"({native_dtype.itemsize} byte/element) but will be stored as " f"{effective_stored_dtype} ({np.dtype(effective_stored_dtype).itemsize} byte/element). " f"Compact export may result in a larger file than the original." ) if np.issubdtype(native_dtype, np.integer): msg += " Consider passing dtype=np.uint8 to preserve the original dtype." if np.issubdtype(native_dtype, np.integer) and downscale_factor > 1 and dtype is None: msg += " Note: casting area-averaging float results back to integer is lossy." warnings.warn(msg) def _validate_append_consistency(f, downscale_factor): """Check that an existing compact HDF5 file is compatible with the current export settings.""" existing = int(f.attrs['downscale_factor']) if existing != downscale_factor: raise ValueError( f"Cannot append to HDF5 file: existing downscale_factor={existing} " f"does not match requested downscale_factor={downscale_factor}." ) def export_model_to_hdf5(model, stimuli, filename, compression=9, overwrite=True, flush=False, dtype=None, downscale_factor=1): """Export pysaliency model predictions for stimuli into hdf5 file model: Model or SaliencyMapModel stimuli: instance of FileStimuli or Stimuli with filenames attribute filename: where to save hdf5 file to compression: how much to compress the data overwrite: if False, an existing file will be appended to (for resuming interrupted exports). Stimuli already present in the file are skipped. flush: whether the hdf5 file should be flushed after each stimulus dtype: numpy dtype for stored predictions (e.g. np.float32, np.float16). None (default) preserves the model's native output dtype. downscale_factor: integer >= 1. Spatially downsample predictions by this factor before storing. 1 = no downsampling (default). """ filenames = get_stimuli_filenames(stimuli) names = get_minimal_unique_filenames(filenames) import h5py mode = 'w' if overwrite else 'a' # Record whether the file existed before opening, to decide whether to write root attrs file_existed = os.path.isfile(filename) with h5py.File(filename, mode=mode) as f: # Determine which stimuli to process if overwrite: indices = list(range(len(stimuli))) else: # append mode is for resuming an interrupted export of the same job if 'type' in f.attrs: _validate_append_consistency(f, downscale_factor) indices = [i for i in range(len(stimuli)) if names[i] not in f] logging.debug(f"Skipping {len(stimuli) - len(indices)} already existing entries") if not indices: return # Compute first smap to determine effective dtype (needed for root attrs) first_stimulus = stimuli[indices[0]] if isinstance(model, SaliencyMapModel): first_smap = model.saliency_map(first_stimulus) elif isinstance(model, Model): first_smap = model.log_density(first_stimulus) else: raise TypeError(type(model)) effective_stored_dtype = _effective_dtype(first_smap, dtype, downscale_factor) _check_size_guard(first_smap, effective_stored_dtype, downscale_factor, dtype) # Write root attrs for new files only (overwrite=True always creates fresh; # overwrite=False writes attrs only if the file is brand new, not for legacy files) if overwrite or not file_existed: f.attrs['type'] = 'pysaliency.precomputed_models.predictions' f.attrs['version'] = '1.0' f.attrs['downscale_factor'] = downscale_factor f.attrs['dtype'] = str(effective_stored_dtype) for i, k in tqdm(list(enumerate(indices))): if i == 0: smap = first_smap else: stimulus = stimuli[k] if isinstance(model, SaliencyMapModel): smap = model.saliency_map(stimulus) elif isinstance(model, Model): smap = model.log_density(stimulus) H, W = smap.shape[0], smap.shape[1] smap = _downsample_smap(smap, downscale_factor) if dtype is not None: smap = smap.astype(dtype) ds = f.create_dataset(names[k], data=smap, compression=compression) if downscale_factor > 1: ds.attrs['original_shape'] = np.array([H, W], dtype=np.int64) if flush: f.flush() class SaliencyMapModelFromFiles(SaliencyMapModel): def __init__(self, stimuli, files, **kwargs): super(SaliencyMapModelFromFiles, self).__init__(**kwargs) self.stimuli = stimuli self.stimulus_ids = list(stimuli.stimulus_ids) self.files = files assert(len(files) == len(stimuli)) def _saliency_map(self, stimulus): filename = self._file_for_stimulus(stimulus) return self._load_file(filename) def _file_for_stimulus(self, stimulus): stimulus_id = get_image_hash(stimulus) try: stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) except IndexError as exc: raise IndexError("Stimulus id '{}' not found in stimuli!".format(stimulus_id)) from exc return self.files[stimulus_index] def _load_file(self, filename): _, ext = os.path.splitext(filename) if ext.lower() in ['.png', '.jpg', '.jpeg', '.tiff']: return imread(filename).astype(float) elif ext.lower() == '.npy': return np.load(filename).astype(float) elif ext.lower() == '.mat': data = loadmat(filename) variables = [v for v in data if not v.startswith('__')] if len(variables) > 1: raise ValueError('{} contains more than one variable: {}'.format(filename, variables)) elif len(variables) == 0: raise ValueError('{} contains no data'.format(filename)) return data[variables[0]] else: raise ValueError('Unkown file type: {}'.format(ext)) class SaliencyMapModelFromDirectory(SaliencyMapModelFromFiles): def __init__(self, stimuli, directory, **kwargs): stimulus_filenames = get_stimuli_filenames(stimuli) self.directory = directory files = [os.path.relpath(filename, start=directory) for filename in glob.glob(os.path.join(directory, '**', '*'), recursive=True)] stems = [os.path.splitext(f)[0] for f in files] stimuli_stems = [os.path.splitext(f)[0] for f in stimulus_filenames] stimuli_stems = get_keys_from_filenames(stimuli_stems, stems) if not set(stimuli_stems).issubset(stems): missing_predictions = set(stimuli_stems).difference(stems) raise ValueError("missing predictions for {}".format(missing_predictions)) indices = [stems.index(f) for f in stimuli_stems] files = [os.path.join(directory, f) for f in files] files = [files[i] for i in indices] super(SaliencyMapModelFromDirectory, self).__init__(stimuli, files, **kwargs) class SaliencyMapModelFromFile(SaliencyMapModel): """ This class exposes a list of saliency maps stored in a .mat file as a pysaliency SaliencyMapModel. Especially, it can be used to import LSUN submissions into pysaliency. """ def __init__(self, stimuli, filename, key='results', **kwargs): super(SaliencyMapModelFromFile, self).__init__(**kwargs) self.stimuli = stimuli self.filename = filename _, ext = os.path.splitext(filename) if ext.lower() == '.mat': self.load_matlab(filename, key=key) else: raise ValueError('Unkown filetype', filename) def load_matlab(self, filename, key='results'): import hdf5storage data = hdf5storage.loadmat(filename)[key] if len(data.shape) == 2 and len(self.stimuli) > 1: if data.shape[0] == 1: data = data[0] elif data.shape[1] == 1: data = data[:, 0] else: raise ValueError('Data has wrong shape: {} (need 1xN, Nx1 or N)'.format(data.shape)) if len(data.shape) > 2: raise ValueError('Data has wrong shape: {} (need 1xN, Nx1 or N)'.format(data.shape)) expected_shape = (len(self.stimuli),) if not data.shape == expected_shape: raise ValueError('Wrong number of saliency maps! Expected {}, got {}'.format(expected_shape, data.shape)) self._saliency_maps = [data[i] for i in range(len(self.stimuli))] def _saliency_map(self, stimulus): stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) smap = self._saliency_maps[stimulus_index] if smap.shape != (stimulus.shape[0], stimulus.shape[1]): raise ValueError('Wrong shape!') return smap class ModelFromDirectory(Model): def __init__(self, stimuli, directory, **kwargs): super(ModelFromDirectory, self).__init__(**kwargs) self.internal_model = SaliencyMapModelFromDirectory(stimuli, directory, caching=False) def _log_density(self, stimulus): smap = self.internal_model.saliency_map(stimulus) if not -0.01 <= logsumexp(smap) <= 0.01: raise ValueError('Not a correct log density!') return smap def get_keys_recursive(group, prefix=''): import h5py keys = [] for subgroup_name, subgroup in group.items(): if isinstance(subgroup, h5py.Group): subprefix = f"{prefix}{subgroup_name}/" keys.extend(get_keys_recursive(subgroup, prefix=subprefix)) else: keys.append(f"{prefix}{subgroup_name}") return keys class HDF5SaliencyMapModel(SaliencyMapModel): """ exposes a HDF5 file with saliency maps as pysaliency model The stimuli have to be of type `FileStimuli`. For each stimulus file, the model expects a dataset with the same name in the dataset. If the file was created with downscale_factor > 1, predictions are transparently upsampled to their original resolution on load. """ def __init__(self, stimuli, filename, check_shape=True, **kwargs): super(HDF5SaliencyMapModel, self).__init__(**kwargs) self.stimuli = stimuli self.filename = filename self.check_shape = check_shape if not os.path.isfile(self.filename): raise ValueError(f'File {self.filename} does not exist') import h5py self.hdf5_file = h5py.File(self.filename, 'r') self.version = self.hdf5_file.attrs.get('version') self.all_keys = get_keys_recursive(self.hdf5_file) self.names = get_keys_from_filenames_with_prefix(get_stimuli_filenames(stimuli), self.all_keys) def _key_for_stimulus(self, stimulus): stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) return self.names[stimulus_index] def _saliency_map(self, stimulus): stimulus_key = self._key_for_stimulus(stimulus) dataset = self.hdf5_file[stimulus_key] smap = dataset[:] if 'original_shape' in dataset.attrs: # Compact downsampled file: upsample to original resolution target_shape = tuple(dataset.attrs['original_shape']) zoom_factors = (target_shape[0] / smap.shape[0], target_shape[1] / smap.shape[1]) smap = scipy.ndimage.zoom(smap.astype(np.float64), zoom_factors, order=1, mode='nearest') if not smap.shape == (stimulus.shape[0], stimulus.shape[1]): if self.check_shape: warnings.warn('Wrong shape for stimulus {}'.format(stimulus_key), stacklevel=4) return smap class HDF5Model(Model): """ exposes a HDF5 file with log densities as pysaliency model. For more detail see HDF5SaliencyMapModel. Files created with downscale_factor > 1 or dtype=np.float16 use a relaxed normalization check and automatic renormalization on load. All other files use the original strict ±0.01 check. """ def __init__(self, stimuli, filename, check_shape=True, max_normalization_error=np.log(1.1), **kwargs): super(HDF5Model, self).__init__(**kwargs) self.parent_model = HDF5SaliencyMapModel( stimuli=stimuli, filename=filename, caching=False, check_shape=check_shape ) self.max_normalization_error = max_normalization_error def _log_density(self, stimulus): key = self.parent_model._key_for_stimulus(stimulus) dataset = self.parent_model.hdf5_file[key] use_relaxed_path = ( 'original_shape' in dataset.attrs or dataset.dtype.itemsize < np.dtype(np.float32).itemsize ) smap = self.parent_model.saliency_map(stimulus).astype(np.float64) if use_relaxed_path: lse = logsumexp(smap) if self.max_normalization_error is not None: if abs(lse) >= self.max_normalization_error: raise ValueError( f'Log density normalization error {abs(lse):.4f} exceeds ' f'threshold {self.max_normalization_error:.4f}' ) smap = smap - lse else: if not -0.01 <= logsumexp(smap) <= 0.01: raise ValueError('Not a correct log density!') return smap class TarFileLikeZipFile(object): """ Wrapper that makes TarFile behave more like ZipFile """ def __init__(self, filename, *args, **kwargs): self.tarfile = tarfile.open(filename, *args, **kwargs) def namelist(self): filenames = [] for tar_info in self.tarfile.getmembers(): filenames.append(tar_info.name) return filenames def open(self, name, mode='r'): return self.tarfile.extractfile(name) class PredictionsFromArchiveMixin(object): def __init__(self, stimuli, archive_file, *args, **kwargs): super(PredictionsFromArchiveMixin, self).__init__(*args, **kwargs) self.stimuli = stimuli self.stimulus_ids = list(stimuli.stimulus_ids) self.archive_file = archive_file _, archive_ext = os.path.splitext(self.archive_file) if archive_ext.lower() == '.zip': self.archive = zipfile.ZipFile(self.archive_file) elif archive_ext.lower() == '.tar': self.archive = TarFileLikeZipFile(self.archive_file) elif archive_ext.lower() == '.gz': self.archive = TarFileLikeZipFile(self.archive_file) elif archive_ext.lower() == '.rar': import rarfile self.archive = rarfile.RarFile(self.archive_file) else: raise ValueError(archive_file) files = self.archive.namelist() files = [f for f in files if '.ds_store' not in f.lower()] files = [f for f in files if '__macosx' not in f.lower()] stems = [os.path.splitext(f)[0] for f in files] stimuli_stems = [os.path.splitext(f)[0] for f in get_stimuli_filenames(stimuli)] stimuli_stems = get_keys_from_filenames_with_prefix(stimuli_stems, stems) prediction_filenames = [] for stimuli_stem in stimuli_stems: candidates = [stem for stem in stems if stem.endswith(stimuli_stem)] if not candidates: raise ValueError("Can't find file for {}".format(stimuli_stem)) if len(candidates) > 1: raise ValueError("Multiple candidates for {}: {}", stimuli_stem, candidates) target_stem, = candidates target_index = stems.index(target_stem) target_filename = files[target_index] prediction_filenames.append(target_filename) self.files = prediction_filenames def _prediction(self, stimulus): stimulus_id = get_image_hash(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus_id) filename = self.files[stimulus_index] return self._load_file(filename) def _load_file(self, filename): _, ext = os.path.splitext(filename) content = self.archive.open(filename) if ext.lower() in ['.png', '.jpg', '.jpeg', '.tiff']: return imread(content).astype(float) elif ext.lower() == '.npy': return np.load(content).astype(float) elif ext.lower() == '.mat': data = loadmat(content) variables = [v for v in data if not v.startswith('__')] if len(variables) > 1: raise ValueError('{} contains more than one variable: {}'.format(filename, variables)) elif len(variables) == 0: raise ValueError('{} contains no data'.format(filename)) return data[variables[0]] else: raise ValueError('Unkown file type: {}'.format(ext)) @staticmethod def can_handle(filename): try: import rarfile return zipfile.is_zipfile(filename) or tarfile.is_tarfile(filename) or rarfile.is_rarfile(filename) except ImportError: warnings.warn("Can't test for rarfiles since rarfile is not installed!") return zipfile.is_zipfile(filename) or tarfile.is_tarfile(filename) class SaliencyMapModelFromArchive(PredictionsFromArchiveMixin, SaliencyMapModel): def __init__(self, stimuli, archive_file, **kwargs): super(SaliencyMapModelFromArchive, self).__init__(stimuli, archive_file, **kwargs) def _saliency_map(self, stimulus): return self._prediction(stimulus) class ModelFromArchive(PredictionsFromArchiveMixin, Model): def __init__(self, stimuli, archive_file, **kwargs): super(ModelFromArchive, self).__init__(stimuli, archive_file, **kwargs) def _log_density(self, stimulus): logdensity = self._prediction(stimulus) density_sum = logsumexp(logdensity) if np.abs(density_sum) > 0.01: logdensity_sum = np.sum(logdensity) raise ValueError("predictions not normalized: log of sum is {}, sum is {}".format(density_sum, logdensity_sum)) return logdensity ================================================ FILE: pysaliency/quilt.py ================================================ """ Code to apply quilt patches to files This module enables pysaliency to use quilt patches to patch code from external saliency models. While in Linux, quilt itself could be used to apply the patches, in Windows and Mac quilt might not be available and nontrivial to install for users. It does not support all possible patch files but only the subset of functionality needed by pysaliency. """ from __future__ import absolute_import, print_function, division, unicode_literals import os.path from .utils import full_split class Hunk(object): def __init__(self, lines): meta_data = lines.pop(0) a, src_data, target_data, b = meta_data.split() assert a == '@@' assert b == '@@' start, length = self.parse_position(src_data) assert start < 0 self.source_start = -start self.source_length = length start, length = self.parse_position(target_data) assert start > 0 self.target_start = start self.target_length = length self.lines = lines def parse_position(self, position): start, length = position.split(',') start = int(start) length = int(length) return start, length def apply(self, source, target): src_pos = self.source_start - 1 assert len(target) == self.target_start - 1 for line in self.lines: type, data = line[0], line[1:] if type == ' ': assert source[src_pos] == data target.append(data) src_pos += 1 elif type == '-': assert source[src_pos] == data src_pos += 1 elif type == '+': target.append(data) elif type == '\\': # Newline stuff, ignore pass else: raise ValueError(line) assert src_pos == self.source_start + self.source_length - 1 assert len(target) == self.target_start + self.target_length - 1 return src_pos class Diff(object): def __init__(self, lines): source = lines.pop(0) assert source.startswith('--- ') _, source = source.split('--- ', 1) source, _ = source.split('\t', 1) source = os.path.join(*full_split(source)[1:]) target = lines.pop(0) assert target.startswith('+++ ') _, target = target.split('+++ ', 1) target, _ = target.split('\t', 1) target = os.path.join(*full_split(target)[1:]) self.source_filename = source self.target_filename = target self.hunks = [] while lines: assert lines[0].startswith('@@ ') hunk_lines = [lines.pop(0)] while lines and not lines[0].startswith('@@ '): line = lines.pop(0) if line: hunk_lines.append(line) self.hunks.append(Hunk(hunk_lines)) def apply(self, location): hunks = list(self.hunks) source = open(os.path.join(location, self.source_filename)).read() source = source.split('\n') target = [] src_pos = 0 while src_pos < len(source): if hunks: if hunks[0].source_start == src_pos+1: hunk = hunks.pop(0) src_pos = hunk.apply(source, target) continue target.append(source[src_pos]) src_pos += 1 open(os.path.join(location, self.target_filename), 'w').write('\n'.join(target)) class PatchFile(object): def __init__(self, patch): self.diffs = [] lines = patch.split('\n') while lines: index1 = lines.pop(0) assert index1.startswith('Index: ') index2 = lines.pop(0) assert index2.startswith('==============') diff = [] diff.append(lines.pop(0)) while lines and not lines[0].startswith('Index: '): diff.append(lines.pop(0)) diff_obj = Diff(diff) self.diffs.append(diff_obj) def apply(self, location, verbose=True): for diff in self.diffs: if verbose: print("Patching {}".format(diff.source_filename)) diff.apply(location) class QuiltSeries(object): def __init__(self, patches_location): self.patches_location = patches_location series = open(os.path.join(self.patches_location, 'series')).read() self.patches = [] self.patch_names = [] for line in series.split('\n'): if not line: continue patch_content = open(os.path.join(self.patches_location, line)).read() self.patches.append(PatchFile(patch_content)) self.patch_names.append(line) def apply(self, location, verbose=True): for patch, name in zip(self.patches, self.patch_names): if verbose: print("Applying {}".format(name)) patch.apply(location, verbose=verbose) ================================================ FILE: pysaliency/roc.py ================================================ from .numba_utils import general_roc_numba as general_roc, general_rocs_per_positive_numba as general_rocs_per_positive ================================================ FILE: pysaliency/roc_cython.pyx ================================================ #%%cython # Circumvents a bug(?) in cython: # http://stackoverflow.com/a/13976504 STUFF = "Hi" import numpy as np cimport numpy as np cimport cython # np.trapz was removed in NumPy 2.0, replaced by np.trapezoid. # This shim supports both; remove once NumPy <2.0 compatibility is dropped. _trapz = getattr(np, 'trapezoid', getattr(np, 'trapz', None)) #Do not check for index errors @cython.boundscheck(False) #Do not enable negativ indices @cython.wraparound(False) #Use native c division @cython.cdivision(True) def real_ROC(image, fixation_data, int judd=0): fixations_orig = np.zeros_like(image) fixations_orig[fixation_data] = 1.0 image_1d = image.flatten() cdef np.ndarray[double, ndim=1] fixations = fixations_orig.flatten() inds = image_1d.argsort() #image_1d = image_1d[inds] fixations = fixations[inds] cdef np.ndarray[double, ndim=1] image_sorted = image_1d[inds] cdef np.ndarray[double, ndim=1] fixation_values_sorted = image[fixation_data] fixation_values_sorted.sort() cdef int i cdef int N = image_1d.shape[0] cdef int fix_count = fixations.sum() cdef int false_count = N-fix_count cdef int correct_count = 0 cdef int false_positive_count = 0 cdef int length if judd: length = fix_count+2 assert len(fixation_values_sorted) == fix_count else: length = N+1 cdef np.ndarray[double, ndim=1] precs = np.zeros(length) cdef np.ndarray[double, ndim=1] false_positives = np.zeros(length) for i in range(N): #print fixations[N-i-1], #print image_1d[N-i-1] # Every pixel is a nonfixation false_positive_count += 1 if fixations[N-i-1]: correct_count += 1 if judd: if i == N - 1 or fixation_values_sorted[N-i-1] != fixation_values_sorted[N-i-2]: precs[correct_count] = float(correct_count)/fix_count false_positives[correct_count] = float(false_positive_count)/false_count else: precs[correct_count] = precs[correct_count - 1] false_positives[correct_count] = false_positives[correct_count - 1] if not judd: if i == N-1 or image_sorted[N-i-1] != image_sorted[N-i-2]: precs[i+1] = float(correct_count)/fix_count false_positives[i+1] = float(false_positive_count)/false_count else: precs[i+1] = precs[i] false_positives[i+1] = false_positives[i] #print false_positives[i+1] precs[length-1] = 1.0 false_positives[length-1] = 1.0 aoc = _trapz(precs, false_positives) return aoc, precs, false_positives #Do not check for index errors @cython.boundscheck(False) #Do not enable negativ indices @cython.wraparound(False) #Use native c division @cython.cdivision(True) def general_roc(np.ndarray[double, ndim=1] positives, np.ndarray[double, ndim=1] negatives, int judd=0): """calculate ROC score for given values of positive and negative distribution""" cdef np.ndarray[double, ndim=1] sorted_positives = np.sort(positives)[::-1] cdef np.ndarray[double, ndim=1] sorted_negatives = np.sort(negatives)[::-1] cdef np.ndarray[double, ndim=1] all_values if judd == 0: all_values = np.hstack([positives, negatives]) all_values = np.sort(all_values)[::-1] else: min_val = np.min([sorted_positives[len(positives)-1], sorted_negatives[len(negatives)-1]]) max_val = np.max([sorted_positives[0], sorted_negatives[0]])+1 all_values = np.hstack((max_val, positives, min_val)) all_values = np.sort(all_values)[::-1] cdef np.ndarray[double, ndim=1] false_positive_rates = np.zeros(len(all_values)+1) cdef np.ndarray[double, ndim=1] hit_rates = np.zeros(len(all_values)+1) cdef int true_positive_count = 0 cdef int false_positive_count = 0 cdef int positive_count = len(positives) cdef int negative_count = len(negatives) cdef int i cdef double theta for i in range(len(all_values)): theta = all_values[i] while true_positive_count < positive_count and sorted_positives[true_positive_count] >= theta: true_positive_count += 1 while false_positive_count < negative_count and sorted_negatives[false_positive_count] >= theta: false_positive_count += 1 false_positive_rates[i+1] = float(false_positive_count) / negative_count hit_rates[i+1] = float(true_positive_count) / positive_count auc = _trapz(hit_rates, false_positive_rates) return auc, hit_rates, false_positive_rates #Do not check for index errors @cython.boundscheck(False) #Do not enable negativ indices @cython.wraparound(False) #Use native c division @cython.cdivision(True) def general_rocs_per_positive(np.ndarray[double, ndim=1] positives, np.ndarray[double, ndim=1] negatives): """calculate ROC scores for each positive against a list of negatives distribution. The mean over the result will equal the return value of `general_roc`.""" cdef np.ndarray[double, ndim=1] sorted_positives = np.sort(positives) cdef np.ndarray[double, ndim=1] sorted_negatives = np.sort(negatives) cdef np.ndarray[long, ndim=1] sorted_inds = np.argsort(positives) cdef np.ndarray[double, ndim=1] results = np.empty(len(positives)) cdef int true_positive_count = 0 cdef int false_positive_count = 0 cdef int true_negative_count = 0 cdef int positive_count = len(positives) cdef int negative_count = len(negatives) cdef int i cdef double last_theta = -np.inf cdef double theta cdef int true_negatives_count = 0 cdef int equal_count = 0 for i in range(len(sorted_positives)): theta = sorted_positives[i] #print('theta', theta) if theta == last_theta: #print('same') results[sorted_inds[i]] = (1.0*true_negatives_count + 0.5*equal_count) / negative_count continue true_negatives_count = true_negatives_count + equal_count while true_negatives_count < negative_count and sorted_negatives[true_negatives_count] < theta: true_negatives_count += 1 #print('.') equal_count = 0 while true_negatives_count + equal_count < negative_count and sorted_negatives[true_negatives_count+equal_count] <= theta: equal_count += 1 #print('=') results[sorted_inds[i]] = (1.0*true_negatives_count + 0.5*equal_count) / negative_count last_theta = theta return results ================================================ FILE: pysaliency/saliency_map_conversion.py ================================================ # -*- coding: UTF-8 -*- from __future__ import absolute_import, division, print_function # , unicode_literals import numpy as np from tqdm import tqdm def optimize_for_information_gain( model, fit_stimuli, fit_fixations, nonlinearity_target='density', nonlinearity_values='logdensity', num_nonlinearity=20, num_centerbias=12, blur_radius=0, optimize=None, average='image', saliency_min=None, saliency_max=None, batch_size=1, verbose=0, return_optimization_result=False, maxiter=1000, method='trust-constr', minimize_options=None, cache_directory=None, framework='torch'): """ convert saliency map model into probabilistic model as described in Kümmerer et al, PNAS 2015. """ if saliency_min is None or saliency_max is None: smax = -np.inf smin = np.inf for s in tqdm(fit_stimuli, disable=verbose < 2): smap = model.saliency_map(s) smax = np.max([smax, smap.max()]) smin = np.min([smin, smap.min()]) if saliency_min is None: saliency_min = smin if saliency_max is None: saliency_max = smax if framework == 'theano': assert nonlinearity_target == 'density' assert nonlinearity_values == 'logdensity' assert average == 'fixations' assert batch_size == 1 assert minimize_options is None assert cache_directory is None from .saliency_map_conversion_theano import optimize_for_information_gain return optimize_for_information_gain( model, fit_stimuli, fit_fixations, num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=blur_radius, optimize=optimize, saliency_min=saliency_min, saliency_max=saliency_max, verbose=verbose, return_optimization_result=return_optimization_result, maxiter=maxiter, method=method ) elif framework == 'torch': from .saliency_map_conversion_torch import optimize_saliency_map_conversion return optimize_saliency_map_conversion( model, fit_stimuli, fit_fixations, nonlinearity_target=nonlinearity_target, nonlinearity_values=nonlinearity_values, saliency_min=saliency_min, saliency_max=saliency_max, optimize=optimize, average=average, batch_size=batch_size, num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=blur_radius, verbose=verbose, return_optimization_result=return_optimization_result, maxiter=maxiter, minimize_options=minimize_options, cache_directory=cache_directory, method=method ) ================================================ FILE: pysaliency/saliency_map_conversion_theano.py ================================================ # -*- coding: UTF-8 -*- from __future__ import absolute_import, division, print_function # , unicode_literals import sys import numpy as np from tqdm import tqdm from .models import Model, UniformModel from .optpy import minimize from .datasets import Fixations def optimize_for_information_gain( model, fit_stimuli, fit_fixations, num_nonlinearity=20, num_centerbias=12, blur_radius=0, optimize=None, saliency_min=None, saliency_max=None, verbose=0, return_optimization_result=False, method='SLSQP', maxiter=1000): """ convert saliency map model into probabilistic model as described in Kümmerer et al, PNAS 2015. """ if saliency_min is None or saliency_max is None: smax = -np.inf smin = np.inf for s in tqdm(fit_stimuli): smap = model.saliency_map(s) smax = np.max([smax, smap.max()]) smin = np.min([smin, smap.min()]) if saliency_min is None: saliency_min = smin if saliency_max is None: saliency_max = smax from .theano_utils import SaliencyMapProcessingLogNonlinearity processing_class = SaliencyMapProcessingLogNonlinearity nonlinearity_ys = np.linspace(-8, 0, num=num_nonlinearity) smc = SaliencyMapConvertor(model, nonlinearity=nonlinearity_ys, centerbias=np.linspace(1.1, 0.9, num=num_centerbias), blur_radius=blur_radius, processing_class=processing_class, saliency_max=saliency_max, saliency_min=saliency_min) res = smc.fit(fit_stimuli, fit_fixations, optimize=optimize, verbose=verbose, maxiter=maxiter, method=method) if return_optimization_result: return smc, res else: return smc def optimize_saliency_map_conversion(saliency_map_processing, saliency_maps, x_inds, y_inds, baseline_model_loglikelihood, optimize=None, verbose=0, method='SLSQP', nonlinearity_min=1e-8, view=None, tol=None, maxiter=1000): """ Fit the parameters of the model Parameters ---------- @type verbose: int @param verbose: controls the verbosity of the output. Possible values: 0: No output at all 1: Output optimization progress (given in bits/fix relative to baseline) 2: Additionally output current parameters 3: Additionally give feedback on evaluation @type baseline_model: GenericModel @param baseline_model: Output optimization progress relative to this model. The default is a uniform model. @param view: a ipython cluster view to parallelize the gradients over multiple stimuli """ import theano import theano.tensor as T from .theano_utils import SaliencyMapProcessing, SaliencyMapProcessingLogNonlinearity, SaliencyMapProcessingLogarithmic if optimize is None: optimize = ['blur_radius', 'nonlinearity', 'centerbias', 'alpha'] weights = np.array([len(inds) for inds in x_inds], dtype=theano.config.floatX) weights /= weights.sum() x_inds = [x.copy() for x in x_inds] y_inds = [y.copy() for y in y_inds] if True: smp = saliency_map_processing negative_log_likelihood = -smp.average_log_likelihood / np.log(2) param_dict = {'blur_radius': smp.blur_radius, 'nonlinearity': smp.nonlinearity_ys, 'centerbias': smp.centerbias_ys, 'alpha': smp.alpha} params = [param_dict[name] for name in optimize] grads = T.grad(negative_log_likelihood, params) print('Compiling theano function') sys.stdout.flush() full_params = smp.params baseline = baseline_model_loglikelihood / np.log(2) if view is not None: dv = view.client[:] dv.execute('import numpy as np; import theano; import theano.tensor as T', block=True) def build_function(processing_class, blur_radius, nonlinearity, centerbias, alpha, nonlinearity_xs, optimize): global cost_with_grads global full_params theano_input = T.matrix('saliency_map', dtype=theano.config.floatX) smp = processing_class(theano_input, sigma=blur_radius, nonlinearity_ys=nonlinearity, centerbias=centerbias, alpha=alpha ) smp.theano_objects.nonlinearity.nonlinearity_xs.set_value(np.array(nonlinearity_xs, dtype=theano.config.floatX)) negative_log_likelihood = -smp.average_log_likelihood / np.log(2) param_dict = {'blur_radius': smp.blur_radius, 'nonlinearity': smp.nonlinearity_ys, 'centerbias': smp.centerbias_ys, 'alpha': smp.alpha} params = [param_dict[name] for name in optimize] grads = T.grad(negative_log_likelihood, params) print('Compiling theano function') sys.stdout.flush() f_ll_with_grad = theano.function([smp.saliency_map, smp.x_inds, smp.y_inds], [negative_log_likelihood] + grads) cost_with_grads = f_ll_with_grad full_params = smp.params def set_param_clients(blur_radius, nonlinearity, centerbias, alpha): global full_params for p, v in zip(full_params, [blur_radius, nonlinearity, centerbias, alpha]): p.set_value(v.astype(p.dtype)) print("Building theano functions in clients") dv.apply(build_function, type(saliency_map_processing), saliency_map_processing.blur_radius.get_value(), saliency_map_processing.nonlinearity_ys.get_value(), saliency_map_processing.centerbias_ys.get_value(), saliency_map_processing.alpha.get_value(), saliency_map_processing.theano_objects.nonlinearity.nonlinearity_xs.get_value(), optimize).wait_interactive() else: f_ll_with_grad = theano.function([smp.saliency_map, smp.x_inds, smp.y_inds], [negative_log_likelihood] + grads) def func(blur_radius, nonlinearity, centerbias, alpha, optimize=None): if verbose > 1: print('blur_radius: ', blur_radius) print('nonlinearity:', nonlinearity) print('centerbias: ', centerbias) print('alpha: ', alpha) for p, v in zip(full_params, [blur_radius, nonlinearity, centerbias, alpha]): p.set_value(v.astype(p.dtype)) values = [] grads = [[] for p in params] all_rets = [] if view is None: for n in tqdm(range(len(saliency_maps)), disable=verbose <= 2): rets = f_ll_with_grad(saliency_maps[n], x_inds[n], y_inds[n]) all_rets.append(rets) else: def f(smap, x, y): global cost_with_grads return cost_with_grads(smap, x.copy(), y.copy()) dv.apply(set_param_clients, blur_radius, nonlinearity, centerbias, alpha).wait() async_rets = view.map_async(f, saliency_maps, x_inds, y_inds) async_rets.wait_interactive() all_rets = list(async_rets) for n in tqdm(range(len(saliency_maps)), disable=verbose <= 10): if len(x_inds[n]): rets = all_rets[n] values.append(rets[0]) assert len(rets) == len(params) + 1 for l, g in zip(grads, rets[1:]): l.append(np.array(g)) else: # No fixations for this image. The theano functions will return # NaN which would screw up the weighted average values.append(0.0) for l, p in zip(grads, params): l.append(np.zeros_like(p.get_value())) value = np.average(values, axis=0, weights=weights) value += baseline av_grads = [] for grad in grads: av_grads.append(np.average(grad, axis=0, weights=weights)) return value, tuple(av_grads) nonlinearity_value = smp.nonlinearity_ys.get_value() num_nonlinearity = len(nonlinearity_value) centerbias_value = smp.centerbias_ys.get_value() # TODO: move to LinearConstraints in the torch implementation if isinstance(saliency_map_processing, SaliencyMapProcessing): print("Using density constraints") bounds = {'nonlinearity': [(nonlinearity_min, 1e7) for i in range(num_nonlinearity)], 'centerbias': [(1e-7, 1000) for i in range(len(centerbias_value))], 'alpha': [(1e-4, 1e4)], 'blur_radius': [(0.0, 1e3)]} constraints = [] if 'nonlineariy' in optimize: for i in range(1, num_nonlinearity): constraints.append({'type': 'ineq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha, i=i: nonlinearity[i] - nonlinearity[i - 1]}) constraints.append({'type': 'eq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha: nonlinearity.sum() - nonlinearity_value.sum()}) if 'centerbias' in optimize: constraints.append({'type': 'eq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha: centerbias.sum() - centerbias_value.sum()}) if isinstance(saliency_map_processing, SaliencyMapProcessingLogNonlinearity): print("Using density constraints on log scale") bounds = {'nonlinearity': [(-100, 100) for i in range(num_nonlinearity)], 'centerbias': [(1e-7, 1000) for i in range(len(centerbias_value))], 'alpha': [(1e-4, 1e4)], 'blur_radius': [(0.0, 1e3)]} constraints = [] if 'nonlinearity' in optimize: for i in range(1, num_nonlinearity): constraints.append({'type': 'ineq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha, i=i: nonlinearity[i] - nonlinearity[i - 1]}) constraints.append({'type': 'eq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha: np.exp(nonlinearity).sum() - np.exp(nonlinearity_value).sum()}) if 'centerbias' in optimize: constraints.append({'type': 'eq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha: centerbias.sum() - centerbias_value.sum()}) elif isinstance(saliency_map_processing, SaliencyMapProcessingLogarithmic): print("Using log density constraints") bounds = {'nonlinearity': [(None, None) for i in range(num_nonlinearity)], 'centerbias': [(None, None) for i in range(len(centerbias_value))], 'alpha': [(1e-4, 1e4)], 'blur_radius': [(0.0, 1e3)]} constraints = [] if 'nonlinearity' in optimize: constraints.append({'type': 'eq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha: nonlinearity[0]}) for i in range(1, num_nonlinearity): constraints.append({'type': 'ineq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha, i=i: nonlinearity[i] - nonlinearity[i - 1]}) if 'centerbias' in optimize: constraints.append({'type': 'eq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha: centerbias[0]}) else: raise TypeError('Unknown processing class', saliency_map_processing) if method == 'SLSQP': options = {'iprint': 2, 'disp': 2, 'maxiter': maxiter, 'eps': 1e-9 } tol = tol or 1e-9 elif method == 'IPOPT': tol = tol or 1e-7 options = {'disp': 5, 'maxiter': maxiter, 'tol': tol } else: options = {'maxiter': maxiter} x0 = {'blur_radius': full_params[0].get_value(), 'nonlinearity': full_params[1].get_value(), 'centerbias': full_params[2].get_value(), 'alpha': full_params[3].get_value()} res = minimize(func, x0, jac=True, constraints=constraints, bounds=bounds, method=method, tol=tol, options=options, optimize=optimize) return res class SaliencyMapConvertor(Model): """ Convert saliency map models to probabilistic models. """ def __init__(self, saliency_map_model, nonlinearity=None, centerbias=None, alpha=1.0, blur_radius=0, saliency_min=None, saliency_max=None, cache_location=None, processing_class=None): """ Parameters ---------- @type saliency_map_model : SaliencyMapModel @param saliency_map_model : The saliency map model to convert to a probabilistic model @type nonlinearity : ndarray @param nonlinearity : The nonlinearity to apply. By default the identity TODO @type saliency_min, saliency_max: float @param saliency_min, saliency_max: The saliency values that are interpreted as 0 and 1 before applying the nonlinearity. If `None`, the minimum rsp. maximum of each saliency map will be used. """ super(SaliencyMapConvertor, self).__init__(cache_location=cache_location) from .theano_utils import SaliencyMapProcessing, SaliencyMapProcessingLogNonlinearity if processing_class is None: processing_class = SaliencyMapProcessing self.saliency_map_model = saliency_map_model if nonlinearity is None: nonlinearity = np.linspace(0, 1.0, num=20) if centerbias is None: if processing_class == SaliencyMapProcessing or processing_class == SaliencyMapProcessingLogNonlinearity: centerbias = np.ones(12) else: centerbias = np.zeros(12) nonlinearity = np.asarray(nonlinearity) centerbias = np.asarray(centerbias) self._blur_radius = blur_radius self._nonlinearity = nonlinearity self._centerbias = centerbias self._alpha = alpha self.saliency_min = saliency_min self.saliency_max = saliency_max if processing_class is None: processing_class = SaliencyMapProcessingLogNonlinearity self.processing_class = processing_class self._build() def set_params(self, **kwargs): import theano no_of_kwargs = len(kwargs) if 'nonlinearity' in kwargs: self.saliency_map_processing.nonlinearity_ys.set_value(kwargs.pop('nonlinearity').astype(theano.config.floatX)) if 'centerbias' in kwargs: self.saliency_map_processing.centerbias_ys.set_value(kwargs.pop('centerbias').astype(theano.config.floatX)) if 'alpha' in kwargs: self.saliency_map_processing.alpha.set_value(np.array(kwargs.pop('alpha'), dtype=theano.config.floatX)) if 'blur_radius' in kwargs: self.saliency_map_processing.blur_radius.set_value(np.array(kwargs.pop('blur_radius'), theano.config.floatX)) if 'saliency_min' in kwargs: self.saliency_min = kwargs.pop('saliency_min') if 'saliency_max' in kwargs: self.saliency_max = kwargs.pop('saliency_max') if no_of_kwargs != len(kwargs): # We used some keywords, thus we have to clear the cache self._cache.clear() super(SaliencyMapConvertor, self).set_params(**kwargs) def _build(self): import theano import theano.tensor as T self.theano_input = T.matrix('saliency_map', dtype=theano.config.floatX) self.saliency_map_processing = self.processing_class(self.theano_input, sigma=self._blur_radius, nonlinearity_ys=self._nonlinearity, centerbias=self._centerbias, alpha=self._alpha ) self._f_log_density = theano.function([self.theano_input], self.saliency_map_processing.log_density, allow_input_downcast=True) def _prepare_saliency_map(self, saliency_map): import theano smin, smax = self.saliency_min, self.saliency_max if smin is None: smin = saliency_map.min() if smax is None: smax = saliency_map.max() saliency_map = (saliency_map - smin) / (smax - smin) saliency_map = saliency_map.astype(theano.config.floatX) return saliency_map def _log_density(self, stimulus): saliency_map = self.saliency_map_model.saliency_map(stimulus) saliency_map = self._prepare_saliency_map(saliency_map) log_density = self._f_log_density(saliency_map) return log_density def fit(self, stimuli, fixations, optimize=None, verbose=0, baseline_model=None, method='SLSQP', nonlinearity_min=1e-8, view=None, tol=None, maxiter=1000): """ Fit the parameters of the model Parameters ---------- @type verbose: int @param verbose: controls the verbosity of the output. Possible values: 0: No output at all 1: Output optimization progress (given in bits/fix relative to baseline) 2: Additionally output current parameters 3: Additionally give feedback on evaluation @type baseline_model: GenericModel @param baseline_model: Output optimization progress relative to this model. The default is a uniform model. """ print('Caching saliency maps') sys.stdout.flush() saliency_maps = [] for n, s in enumerate(tqdm(stimuli, disable=verbose <= 1)): smap = self._prepare_saliency_map(self.saliency_map_model.saliency_map(s)) saliency_maps.append(smap) self.saliency_map_model._cache.clear() x_inds = [] y_inds = [] for n in tqdm(list(range(len(stimuli))), desc='Preparing fixations', disable=verbose < 2): inds = fixations.n == n x_inds.append(fixations.x_int[inds].copy()) y_inds.append(fixations.y_int[inds].copy()) if baseline_model is None: baseline_model = UniformModel() baseline = baseline_model.log_likelihood(stimuli, fixations) res = optimize_saliency_map_conversion(self.saliency_map_processing, saliency_maps, x_inds, y_inds, baseline, optimize=optimize, verbose=verbose, method=method, nonlinearity_min=nonlinearity_min, view=view, tol=tol, maxiter=maxiter) self.set_params(nonlinearity=res.nonlinearity, centerbias=res.centerbias, alpha=res.alpha, blur_radius=res.blur_radius) return res def __getstate__(self): self._nonlinearity = self.saliency_map_processing.nonlinearity_ys.get_value() self._centerbias = self.saliency_map_processing.centerbias_ys.get_value() self._alpha = self.saliency_map_processing.alpha.get_value() self._blur_radius = self.saliency_map_processing.blur_radius.get_value() state = dict(self.__dict__) del state['saliency_map_processing'] del state['theano_input'] del state['_f_log_density'] return state def __setstate__(self, state): self.__dict__ = dict(state) self._build() class JointSaliencyMapConvertor(object): def __init__(self, saliency_map_models, nonlinearity=None, centerbias=None, alpha=1.0, blur_radius=0, saliency_min=None, saliency_max=None, cache_location=None, processing_class=None): """ Parameters ---------- @type saliency_map_model : SaliencyMapModel @param saliency_map_model : The saliency map model to convert to a probabilistic model @type nonlinearity : ndarray @param nonlinearity : The nonlinearity to apply. By default the identity TODO @type saliency_min, saliency_max: float @param saliency_min, saliency_max: The saliency values that are interpreted as 0 and 1 before applying the nonlinearity. If `None`, the minimum rsp. maximum of each saliency map will be used. """ self.saliency_map_models = saliency_map_models if nonlinearity is None: nonlinearity = np.linspace(0, 1.0, num=20) if centerbias is None: centerbias = np.ones(12) self._blur_radius = blur_radius self._nonlinearity = nonlinearity self._centerbias = centerbias self._alpha = alpha self.saliency_min = saliency_min self.saliency_max = saliency_max from .theano_utils import SaliencyMapProcessingLogNonlinearity if processing_class is None: processing_class = SaliencyMapProcessingLogNonlinearity self.processing_class = processing_class self._build() def set_params(self, **kwargs): if 'nonlinearity' in kwargs: self.saliency_map_processing.nonlinearity_ys.set_value(kwargs.pop('nonlinearity')) if 'centerbias' in kwargs: self.saliency_map_processing.centerbias_ys.set_value(kwargs.pop('centerbias')) if 'alpha' in kwargs: self.saliency_map_processing.alpha.set_value(kwargs.pop('alpha')) if 'blur_radius' in kwargs: self.saliency_map_processing.blur_radius.set_value(kwargs.pop('blur_radius')) if 'saliency_min' in kwargs: self.saliency_min = kwargs.pop('saliency_min') if 'saliency_max' in kwargs: self.saliency_max = kwargs.pop('saliency_max') def _build(self): import theano import theano.tensor as T self.theano_input = T.matrix('saliency_map', dtype=theano.config.floatX) self.saliency_map_processing = self.processing_class(self.theano_input, sigma=self._blur_radius, nonlinearity_ys=self._nonlinearity, centerbias=self._centerbias, alpha=self._alpha ) self._f_log_density = theano.function([self.theano_input], self.saliency_map_processing.log_density) def _prepare_saliency_map(self, saliency_map): import theano smin, smax = self.saliency_min, self.saliency_max if smin is None: smin = saliency_map.min() if smax is None: smax = saliency_map.max() saliency_map = (saliency_map - smin) / (smax - smin) saliency_map = saliency_map.astype(theano.config.floatX) return saliency_map def fit(self, stimuli, fixations, optimize=None, verbose=0, baseline_model=None, method='SLSQP', nonlinearity_min=1e-8, view=None, tol=None, maxiter=1000): """ Fit the parameters of the model Parameters ---------- @type verbose: int @param verbose: controls the verbosity of the output. Possible values: 0: No output at all 1: Output optimization progress (given in bits/fix relative to baseline) 2: Additionally output current parameters 3: Additionally give feedback on evaluation @type baseline_model: GenericModel @param baseline_model: Output optimization progress relative to this model. The default is a uniform model. """ if isinstance(fixations, Fixations): fixations = [fixations for m in self.saliency_map_models] assert len(fixations) == len(self.saliency_map_models) print('Caching saliency maps') sys.stdout.flush() saliency_maps = [] x_inds = [] y_inds = [] for n, s in enumerate(tqdm(stimuli, disable=verbose <= 1)): for saliency_map_model, f in zip(self.saliency_map_models, fixations): smap = self._prepare_saliency_map(saliency_map_model.saliency_map(s)) saliency_maps.append(smap) ff = f[f.n == n] x_inds.append(ff.x_int) y_inds.append(ff.y_int) saliency_map_model._cache.clear() if baseline_model is None: baseline_model = UniformModel() lls = [] for f in fixations: lls.append(baseline_model.log_likelihood(stimuli, f)) lls = np.hstack(lls) baseline = np.mean(lls) res = optimize_saliency_map_conversion(self.saliency_map_processing, saliency_maps, x_inds, y_inds, baseline, optimize=optimize, verbose=verbose, method=method, nonlinearity_min=nonlinearity_min, view=view, tol=tol, maxiter=maxiter) self.set_params(nonlinearity=res.nonlinearity, centerbias=res.centerbias, alpha=res.alpha, blur_radius=res.blur_radius) return res def __getstate__(self): self._nonlinearity = self.saliency_map_processing.nonlinearity_ys.get_value() self._centerbias = self.saliency_map_processing.centerbias_ys.get_value() self._alpha = self.saliency_map_processing.alpha.get_value() self._blur_radius = self.saliency_map_processing.blur_radius.get_value() state = dict(self.__dict__) del state['saliency_map_processing'] del state['theano_input'] del state['_f_log_density'] return state def __setstate__(self, state): self.__dict__ = dict(state) self._build() ================================================ FILE: pysaliency/saliency_map_conversion_torch.py ================================================ import numpy as np import torch import torch.nn as nn from tqdm import tqdm from .models import Model from .optpy import minimize, LinearConstraint from .saliency_map_models import SaliencyMapModel from .torch_utils import GaussianFilterNd, Nonlinearity, zero_grad, log_likelihood from .torch_datasets import ImageDataset, ImageDatasetSampler, FixationMaskTransform, collate_fn class CenterBias(nn.Module): def __init__(self, ys=None, num_values=12): super().__init__() if ys is None: ys = np.linspace(1.1, 0.9, num=num_values, dtype=np.float32) self.nonlinearity = Nonlinearity(ys=ys) self.alpha = nn.Parameter(torch.tensor(0.5 * np.sqrt(2), dtype=torch.float32), requires_grad=True) def forward(self, tensor): _, _, height, width = tensor.shape x_coords = torch.linspace(-1.0, 1.0, steps=width, device=tensor.device) + 0.000001 y_coords = torch.linspace(-1.0, 1.0, steps=height, device=tensor.device) + 0.000001 # We cannot have zeros in there because of grad x_coords = x_coords[None, None, None, :] y_coords = y_coords[None, None, :, None] beta_squared = 1.0 - self.alpha**2 dists = torch.sqrt( x_coords**2 / self.alpha**2 + y_coords**2 / beta_squared ) max_dist = torch.sqrt(1.0 / self.alpha**2 + 1.0 / beta_squared) dists = dists / max_dist centerbias = self.nonlinearity(dists) return centerbias class SaliencyMapProcessing(nn.Module): def __init__(self, num_nonlinearity=20, num_centerbias=12, blur_radius=1.0, nonlinearity_target='density', nonlinearity_values='density'): super().__init__() if nonlinearity_target not in ['density', 'logdensity']: raise ValueError("nonlinearity target '{}' not allowed".format(nonlinearity_target)) if nonlinearity_values not in ['density', 'logdensity']: raise ValueError("nonlinearity values '{}' not allowed".format(nonlinearity_values)) self.blur = GaussianFilterNd(dims=(2, 3), sigma=blur_radius, trainable=True) self.nonlinearity_target = nonlinearity_target if nonlinearity_target == 'density' and nonlinearity_values == 'logdensity': self.nonlinearity = Nonlinearity(num_values=num_nonlinearity, value_scale='log') with torch.no_grad(): self.nonlinearity.ys.mul_(8.0) elif nonlinearity_target == 'logdensity' and nonlinearity_values == 'density': raise ValueError("Invalid combination of nonlinearity target and values") elif nonlinearity_target == nonlinearity_values: self.nonlinearity = Nonlinearity(num_values=num_nonlinearity, value_scale='linear') self.centerbias = CenterBias(num_values=num_centerbias) def forward(self, tensor): if self.blur.sigma > 0: tensor = self.blur(tensor) if len(self.nonlinearity.ys) > 0: tensor = self.nonlinearity(tensor) if len(self.centerbias.nonlinearity.ys) > 0: centerbias = self.centerbias(tensor) if self.nonlinearity_target == 'density': tensor *= centerbias elif self.nonlinearity_target == 'logdensity': tensor += centerbias else: raise ValueError(self.nonlinearity_target) if self.nonlinearity_target == 'density': sums = torch.sum(tensor, dim=(2, 3), keepdim=True) tensor = tensor / sums tensor = torch.log(tensor) elif self.nonlinearity_target == 'logdensity': logsums = torch.logsumexp(tensor, dim=(2, 3), keepdim=True) tensor = tensor - logsums else: raise ValueError(self.nonlinearity_target) return tensor class NormalizedSaliencyMapModel(SaliencyMapModel): def __init__(self, parent_model, saliency_min=None, saliency_max=None, caching=False, **kwargs): super().__init__(caching=caching, **kwargs) self.parent_model = parent_model self.saliency_min = saliency_min self.saliency_max = saliency_max def _saliency_map(self, stimulus): saliency_map = self.parent_model.saliency_map(stimulus) if self.saliency_min is not None: minimum = self.saliency_min else: minimum = saliency_map.min() if self.saliency_max is not None: maximum = self.saliency_max else: maximum = saliency_map.max() normalized_saliency_map = (saliency_map - minimum) / (maximum - minimum) return np.minimum(1, np.maximum(0, normalized_saliency_map)) def run_dataset(model, dataset, device, verbose=True): """ processes dataset and accumulate gradients """ model.train() losses = [] batch_weights = [] pbar = tqdm(dataset, disable=not verbose) for batch in pbar: fixation_mask = batch['fixation_mask'].to(device) weights = batch['weight'].to(device) saliency_maps = [] while 'prediction_{:04d}'.format(len(saliency_maps)) in batch: saliency_maps.append(batch['prediction_{:04d}'.format(len(saliency_maps))].to(device)) this_loss = 0 for saliency_map in saliency_maps: log_density = model(saliency_map[:, None, :, :])[:, 0, :, :] loss = -log_likelihood(log_density, fixation_mask, weights=weights) this_loss += loss / len(saliency_maps) losses.append(this_loss.detach().cpu().numpy()) batch_weights.append(weights.detach().cpu().numpy().sum()) pbar.set_description('{:.05f}'.format(np.average(losses, weights=batch_weights))) (weights.detach().sum().detach() * loss).backward() total_weight = np.sum(batch_weights) for param in model.parameters(): if param.grad is not None: param.grad.detach_() param.grad.div_(total_weight) return np.average(losses, weights=batch_weights) def optimize_saliency_map_conversion( model, stimuli, fixations, nonlinearity_target='density', nonlinearity_values='logdensity', saliency_min=None, saliency_max=None, optimize=None, average='image', verbose=0, method='SLSQP', num_nonlinearity=20, num_centerbias=12, blur_radius=20.0, batch_size=8, tol=None, maxiter=1000, minimize_options=None, return_optimization_result=False, cache_directory=None): targets = [([model], stimuli, fixations)] if saliency_min is None or saliency_max is None: smax = -np.inf smin = np.inf for s in tqdm(stimuli): smap = model.saliency_map(s) smax = np.max([smax, smap.max()]) smin = np.min([smin, smap.min()]) if saliency_min is None: saliency_min = smin if saliency_max is None: saliency_max = smax saliency_map_processing, optimization_result = _optimize_saliency_map_conversion_over_multiple_models_and_datasets( targets, nonlinearity_target=nonlinearity_target, nonlinearity_values=nonlinearity_values, saliency_min=saliency_min, saliency_max=saliency_max, optimize=optimize, average=average, verbose=verbose, method=method, num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=blur_radius, batch_size=batch_size, tol=tol, maxiter=maxiter, minimize_options=minimize_options, cache_directory=cache_directory) return_model = SaliencyMapProcessingModel( model, nonlinearity_target=nonlinearity_target, nonlinearity_values=nonlinearity_values, saliency_min=saliency_min, saliency_max=saliency_max, num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=blur_radius, saliency_map_processing=saliency_map_processing ) if return_optimization_result: return return_model, optimization_result return return_model def _optimize_saliency_map_conversion_over_multiple_models_and_datasets( list_of_targets, nonlinearity_target='density', nonlinearity_values='density', saliency_min=None, saliency_max=None, optimize=None, average='image', verbose=0, method='SLSQP', num_nonlinearity=20, num_centerbias=12, blur_radius=20.0, batch_size=8, tol=None, maxiter=1000, minimize_options=None, cache_directory=None): if len(list_of_targets) != 1: raise NotImplementedError() models, stimuli, fixations = list_of_targets[0] models_dict = { 'prediction_{:04d}'.format(i): NormalizedSaliencyMapModel( model, saliency_min=saliency_min, saliency_max=saliency_max, ) for i, model in enumerate(models) } dataset = ImageDataset( stimuli, fixations, models=models_dict, transform=FixationMaskTransform(), average=average, ) loader = torch.utils.data.DataLoader( dataset, batch_sampler=ImageDatasetSampler(dataset, batch_size=batch_size, shuffle=False), pin_memory=False, num_workers=0, # doesn't work for sparse tensors yet. Might work soon. collate_fn=collate_fn, ) saliency_map_processing = SaliencyMapProcessing( nonlinearity_values=nonlinearity_values, nonlinearity_target=nonlinearity_target, num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=blur_radius, ) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if verbose: print("Using device", device) saliency_map_processing.to(device) optimization_result = _optimize_saliency_map_processing( saliency_map_processing, loader, verbose=verbose, optimize=optimize, method=method, tol=tol, maxiter=maxiter, minimize_options=minimize_options, cache_directory=cache_directory, ) return saliency_map_processing, optimization_result def _optimize_saliency_map_processing( saliency_map_processing, data_loader, optimize=None, verbose=0, method='SLSQP', tol=None, maxiter=1000, minimize_options=None, cache_directory=None): if optimize is None: optimize = ['blur_radius', 'nonlinearity', 'centerbias', 'alpha'] full_param_dict = { 'blur_radius': saliency_map_processing.blur.sigma, 'nonlinearity': saliency_map_processing.nonlinearity.ys, 'centerbias': saliency_map_processing.centerbias.nonlinearity.ys, 'alpha': saliency_map_processing.centerbias.alpha, } nonlinearity_target = saliency_map_processing.nonlinearity_target nonlinearity_values = saliency_map_processing.nonlinearity.value_scale num_nonlinearity = len(saliency_map_processing.nonlinearity.ys) initial_params = {key: value.detach().cpu().numpy() for key, value in full_param_dict.items()} def func(blur_radius, nonlinearity, centerbias, alpha, optimize=None): if verbose > 5: print('blur_radius: ', blur_radius) print('nonlinearity:', nonlinearity) print('centerbias: ', centerbias) print('alpha: ', alpha) def set_param(param, value): param.copy_(torch.tensor(value)) with torch.no_grad(): set_param(saliency_map_processing.blur.sigma, blur_radius) set_param(saliency_map_processing.nonlinearity.ys, nonlinearity) set_param(saliency_map_processing.centerbias.nonlinearity.ys, centerbias) set_param(saliency_map_processing.centerbias.alpha, alpha) zero_grad(saliency_map_processing) loss = run_dataset( saliency_map_processing, data_loader, saliency_map_processing.nonlinearity.ys.device, verbose=verbose > 4 ) gradients = [ full_param_dict[param_name].grad.detach().cpu().numpy() for param_name in optimize ] return loss, tuple(gradients) if cache_directory is not None: import diskcache cache = diskcache.Cache(directory=cache_directory) func = cache.memoize()(func) bounds = { 'alpha': [(1e-4, 1.0 - 1e-4)], 'blur_radius': [(0.0, 1e3)] } constraints = [] if 'nonlinearity' in optimize: # mononotic nonlinearity rows = [] for i in range(1, num_nonlinearity): row = np.zeros(num_nonlinearity) row[i] = 1 row[i - 1] = -1 rows.append(row) A = np.array(rows) constraints.append(LinearConstraint( A_dict={ 'nonlinearity': A, }, lb=np.zeros(num_nonlinearity - 1), ub=np.inf, keep_feasible=True, )) if nonlinearity_target == 'density': bounds['centerbias'] = [(1e-6, 1000) for i in range(len(saliency_map_processing.centerbias.nonlinearity.ys))] if 'centerbias' in optimize: # make the center bias not overparametrized constraints.append(LinearConstraint( A_dict={ 'centerbias': np.ones((1, len(initial_params['centerbias']))) }, lb=initial_params['centerbias'].sum(), ub=initial_params['centerbias'].sum(), keep_feasible=True, )) elif nonlinearity_target == 'logdensity': bounds['centerbias'] = [(None, None) for i in range(len(saliency_map_processing.centerbias.nonlinearity.ys))] if 'centerbias' in optimize: # make the center bias not overparametrized row = np.zeros(len(initial_params['centerbias'])) row[0] = 1 constraints.append(LinearConstraint( A_dict={ 'centerbias': np.array([row]) }, lb=0, ub=0, )) else: raise ValueError(nonlinearity_target) if (nonlinearity_target == 'density') and (nonlinearity_values == 'linear'): bounds['nonlinearity'] = [(1e-6, 1e7) for i in range(num_nonlinearity)] if 'nonlinearity' in optimize: constraints.append(LinearConstraint( A_dict={ 'nonlinearity': np.ones((1, len(initial_params['nonlinearity']))) }, lb=initial_params['nonlinearity'].sum(), ub=initial_params['nonlinearity'].sum() )) elif (nonlinearity_target == 'density') and (nonlinearity_values == 'log'): bounds['nonlinearity'] = [(-100, 100) for i in range(num_nonlinearity)] if 'nonlinearity' in optimize: # this is a nonlinear constraint, so we have to specify it as a function constraints.append({ 'type': 'eq', 'fun': lambda blur_radius, nonlinearity, centerbias, alpha: np.exp(nonlinearity).sum() - np.exp(initial_params['nonlinearity']).sum() }) elif (nonlinearity_target == 'logdensity') and (nonlinearity_values == 'linear'): bounds['nonlinearity'] = [(None, None) for i in range(num_nonlinearity)] if 'nonlinearity' in optimize: row = np.zeros(len(initial_params['nonlinearity'])) row[0] = 1 constraints.append(LinearConstraint( A_dict={ 'nonlinearity': np.array([row]) }, lb=0, ub=0, )) else: raise ValueError(nonlinearity_target, nonlinearity_values) if method == 'SLSQP': options = {'iprint': 2, 'disp': 2, 'maxiter': maxiter, 'eps': 1e-9 } tol = tol or 1e-9 elif method == 'IPOPT': tol = tol or 1e-7 options = {'disp': 5, 'maxiter': maxiter, 'tol': tol } else: options = {'maxiter': maxiter} if minimize_options: options.update(minimize_options) x0 = initial_params res = minimize(func, x0, jac=True, constraints=constraints, bounds=bounds, method=method, tol=tol, options=options, optimize=optimize) return res class SaliencyMapProcessingModel(Model): def __init__( self, saliency_map_model, nonlinearity_values='logdensity', nonlinearity_target='density', saliency_min=None, saliency_max=None, num_nonlinearity=20, num_centerbias=12, blur_radius=20.0, saliency_map_processing=None, device=None, **kwargs): super().__init__(**kwargs) self.saliency_map_model = saliency_map_model self.normalized_saliency_map_model = NormalizedSaliencyMapModel( saliency_map_model, saliency_min=saliency_min, saliency_max=saliency_max, ) if saliency_map_processing is None: saliency_map_processing = SaliencyMapProcessing( nonlinearity_values=nonlinearity_values, nonlinearity_target=nonlinearity_target, num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=blur_radius, ) if device is None: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print("Using device", device) self.device = device saliency_map_processing.to(self.device) self.saliency_map_processing = saliency_map_processing def _log_density(self, stimulus): saliency_map = self.normalized_saliency_map_model.saliency_map(stimulus) saliency_map_tensor = torch.tensor(saliency_map[np.newaxis, np.newaxis, :, :]).to(self.device) return self.saliency_map_processing.forward(saliency_map_tensor).detach().cpu().numpy()[0, 0, :, :] def state_dict(self): """returns a state dict for use with torch.load""" nonlinearity_target = self.saliency_map_processing.nonlinearity_target if nonlinearity_target == 'density' and self.saliency_map_processing.nonlinearity.value_scale == 'log': nonlinearity_values = "logdensity" elif nonlinearity_target == 'density' and self.saliency_map_processing.nonlinearity.value_scale == 'linear': nonlinearity_values = "density" elif nonlinearity_target == 'logdensity' and self.saliency_map_processing.nonlinearity.value_scale == 'linear': nonlinearity_values = "logdensity" else: raise ValueError() state_dict = { "version": "1.0", "saliency_min": self.normalized_saliency_map_model.saliency_min, "saliency_max": self.normalized_saliency_map_model.saliency_max, "nonlinearity_target": nonlinearity_target, "nonlinearity_values": nonlinearity_values, "saliency_map_processing": self.saliency_map_processing.state_dict(), } return state_dict @classmethod def build_from_state_dict(cls, saliency_map_model, state_dict, device=None, **kwargs): assert state_dict['version'] == "1.0" saliency_map_processing = SaliencyMapProcessing( nonlinearity_values=state_dict['nonlinearity_values'], nonlinearity_target=state_dict['nonlinearity_target'], num_nonlinearity=len(state_dict['saliency_map_processing']['nonlinearity.ys']), num_centerbias=len(state_dict['saliency_map_processing']['centerbias.nonlinearity.ys']), blur_radius=state_dict['saliency_map_processing']['blur.sigma'], ) saliency_map_processing.load_state_dict(state_dict['saliency_map_processing']) return cls( saliency_map_model=saliency_map_model, nonlinearity_values=state_dict['nonlinearity_values'], nonlinearity_target=state_dict['nonlinearity_target'], saliency_min=state_dict['saliency_min'], saliency_max=state_dict['saliency_max'], saliency_map_processing=saliency_map_processing, device=device, **kwargs ) ================================================ FILE: pysaliency/saliency_map_models.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import os import warnings from abc import ABCMeta, abstractmethod from itertools import combinations from tempfile import TemporaryDirectory import numpy as np from boltons.cacheutils import LRU, cached from imageio import imsave from scipy.io import loadmat from scipy.ndimage import gaussian_filter, zoom from tqdm import tqdm from .datasets import Fixations, Stimulus, check_prediction_shape, get_image_hash from .metrics import CC, NSS, SIM from .numba_utils import auc_for_one_positive, fill_fixation_map from .roc import general_roc, general_rocs_per_positive from .sampling_models import SamplingModelMixin from .utils import Cache, average_values, deprecated_class, remove_trailing_nans, run_matlab_cmd def handle_stimulus(stimulus): """ Make sure that a stimulus is a `Stimulus`-object """ if not isinstance(stimulus, Stimulus): stimulus = Stimulus(stimulus) return stimulus def normalize_saliency_map(saliency_map, cdf, cdf_bins): """ Normalize saliency to make saliency values distributed according to a given CDF """ smap = saliency_map.copy() shape = smap.shape smap = smap.flatten() smap = np.argsort(np.argsort(smap)).astype(float) smap /= 1.0*len(smap) inds = np.searchsorted(cdf, smap, side='right') smap = cdf_bins[inds] smap = smap.reshape(shape) smap = smap.reshape(shape) return smap class FullShuffledNonfixationProvider(object): def __init__(self, stimuli, fixations, max_fixations_in_cache=500*1000*1000): self.stimuli = stimuli self.fixations = fixations cache_size = int(max_fixations_in_cache / len(self.fixations.x)) self.cache = LRU(cache_size) self.nonfixations_for_image = cached(self.cache)(self._nonfixations_for_image) self.widths = np.asarray([s[1] for s in stimuli.sizes]).astype(float) self.heights = np.asarray([s[0] for s in stimuli.sizes]).astype(float) def _nonfixations_for_image(self, n): inds = ~(self.fixations.n == n) xs = (self.fixations.x[inds].copy()).astype(float) ys = (self.fixations.y[inds].copy()).astype(float) other_ns = self.fixations.n[inds] xs *= self.stimuli.sizes[n][1]/self.widths[other_ns] ys *= self.stimuli.sizes[n][0]/self.heights[other_ns] return xs.astype(int), ys.astype(int) def __call__(self, stimuli, fixations, i): assert stimuli is self.stimuli n = fixations.n[i] return self.nonfixations_for_image(n) def _get_unfixated_values(saliency_map, ys, xs): """Return all saliency values that have not been fixated at leat once.""" fixation_map = np.zeros(saliency_map.shape) fill_fixation_map( fixation_map, np.array([ys, xs]).T ) return saliency_map[fixation_map == 0].flatten() class ScanpathSaliencyMapModel(object, metaclass=ABCMeta): """ Most general saliency model class. The model is neither assumed to be time-independet nor to be a probabilistic model. """ @abstractmethod def conditional_saliency_map(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None): """ Return the models saliency map prediction depending on a fixation history for the n-th image. """ raise NotImplementedError() def conditional_saliency_map_for_fixation(self, stimuli, fixations, fixation_index, out=None): return self.conditional_saliency_map( stimuli.stimulus_objects[fixations.n[fixation_index]], x_hist=remove_trailing_nans(fixations.x_hist[fixation_index]), y_hist=remove_trailing_nans(fixations.y_hist[fixation_index]), t_hist=remove_trailing_nans(fixations.t_hist[fixation_index]), attributes={key: getattr(fixations, key)[fixation_index] for key in fixations.__attributes__}, out=out ) def conditional_saliency_maps(self, stimuli, fixations, verbose=False, **kwargs): """ returns iterator over conditional log density predictions for each fixation """ if verbose: warnings.warn("Verbose mode is deprecated, use the iterator instead.", DeprecationWarning, stacklevel=2) return (self.conditional_saliency_map_for_fixation(stimuli, fixations, fixation_index) for fixation_index in tqdm(range(len(fixations)), disable=not verbose)) def AUCs(self, stimuli, fixations, nonfixations='uniform', verbose=False): """ Calulate AUC scores for fixations :type fixations : Fixations :param fixations : Fixation object to calculate the AUC scores for. :type nonfixations : string or Fixations :param nonfixations : Nonfixations to use for calculating AUC scores. Possible values are: 'uniform': Use uniform nonfixation distribution (Judd-AUC), i.e. all pixels from the saliency map. 'unfixated': Use all pixels from the saliency map except the fixated ones. 'shuffled': Use all fixations from other images as nonfixations. fixations-object: For each image, use the fixations in this fixation object as nonfixations :rtype : ndarray :return : list of AUC scores for each fixation, ordered as in `fixations.x` (average=='fixation' or None) or by image numbers (average=='image') """ rocs_per_fixation = [] rocs = {} out = None nonfix_ys = None nonfix_xs = None if isinstance(nonfixations, Fixations): nonfix_xs = [] nonfix_ys = [] for n in range(fixations.n.max() + 1): inds = nonfixations.n == n nonfix_xs.append(nonfixations.x_int[inds].copy()) nonfix_ys.append(nonfixations.y_int[inds].copy()) if nonfixations == 'shuffled': nonfixations = FullShuffledNonfixationProvider(stimuli, fixations) for i, conditional_saliency_map in tqdm(enumerate(self.conditional_saliency_maps(stimuli, fixations, verbose=False)), total=len(fixations.x), disable=not verbose): check_prediction_shape(conditional_saliency_map, stimuli[fixations.n[i]]) positive = conditional_saliency_map[fixations.y_int[i], fixations.x_int[i]] if nonfixations == 'uniform': negatives = conditional_saliency_map.flatten() elif nonfixations == 'unfixated': negatives = _get_unfixated_values( conditional_saliency_map, [fixations.y_int[i]], [fixations.x_int[i]] ) elif nonfix_xs is not None: n = fixations.n[i] negatives = conditional_saliency_map[nonfix_ys[n], nonfix_xs[n]] elif callable(nonfixations): _nonfix_xs, _nonfix_ys = nonfixations(stimuli, fixations, i) negatives = conditional_saliency_map[_nonfix_ys.astype(int), _nonfix_xs.astype(int)] else: raise ValueError("Don't know how to handle nonfixations {}".format(nonfixations)) this_roc = auc_for_one_positive(positive, negatives) rocs.setdefault(fixations.n[i], []).append(this_roc) rocs_per_fixation.append(this_roc) return np.asarray(rocs_per_fixation) def AUC(self, stimuli, fixations, nonfixations='uniform', average='fixation', verbose=False): """ Calulate AUC scores for fixations :type fixations : Fixations :param fixations : Fixation object to calculate the AUC scores for. :type nonfixations : string or Fixations :param nonfixations : Nonfixations to use for calculating AUC scores. Possible values are: 'uniform': Use uniform nonfixation distribution (Judd-AUC), i.e. all pixels from the saliency map. 'unfixated': Use all pixels from the saliency map except the fixated ones. 'shuffled': Use all fixations from other images as nonfixations. fixations-object: For each image, use the fixations in this fixation object as nonfixations :type average : string :param average : How to average the AUC scores for each fixation. Possible values are: 'image': average over images 'fixation' or None: Return AUC score for each fixation separately :rtype : ndarray :return : list of AUC scores for each fixation, ordered as in `fixations.x` (average=='fixation' or None) or by image numbers (average=='image') """ aucs = self.AUCs(stimuli, fixations, nonfixations=nonfixations, verbose=verbose) return average_values(aucs, fixations, average=average) def sAUCs(self, stimuli, fixations, verbose=False): return self.AUCs(stimuli, fixations, nonfixations='shuffled', verbose=verbose) def sAUC(self, stimuli, fixations, average='fixation', verbose=False): return self.AUC(stimuli, fixations, nonfixations='shuffled', average=average, verbose=verbose) def NSSs(self, stimuli, fixations, verbose=False): values = np.empty(len(fixations.x)) for i, conditional_saliency_map in tqdm(enumerate(self.conditional_saliency_maps(stimuli, fixations, verbose=False)), total=len(fixations.x), disable=not verbose): check_prediction_shape(conditional_saliency_map, stimuli[fixations.n[i]]) values[i] = NSS(conditional_saliency_map, fixations.x_int[i], fixations.y_int[i]) return values def NSS(self, stimuli, fixations, average='fixation', verbose=False): nsss = self.NSSs(stimuli, fixations, verbose=verbose) return average_values(nsss, fixations, average=average) def set_params(self, **kwargs): """ Set model parameters, if the model has parameters This method has to reset caches etc., if the depend on the parameters """ if kwargs: raise ValueError('Unkown parameters!', kwargs) class SaliencyMapModel(ScanpathSaliencyMapModel): """ Most model class for saliency maps. The model is assumed to be stationary in time (i.e. all fixations are independent) but the model is not explicitly a probabilistic model. """ def __init__(self, cache_location = None, caching=True, memory_cache_size=None): self._cache = Cache(cache_location, memory_cache_size=memory_cache_size) self.caching = caching @property def cache_location(self): return self._cache.cache_location @cache_location.setter def cache_location(self, value): self._cache.cache_location = value def saliency_map(self, stimulus): """ Get saliency map for given stimulus. To overwrite this function, overwrite `_saliency_map` as otherwise the caching mechanism is disabled. """ stimulus = handle_stimulus(stimulus) if not self.caching: return self._saliency_map(stimulus.stimulus_data) stimulus_id = stimulus.stimulus_id if not stimulus_id in self._cache: self._cache[stimulus_id] = self._saliency_map(stimulus.stimulus_data) return self._cache[stimulus_id] @abstractmethod def _saliency_map(self, stimulus): """ Overwrite this to implement you own SaliencyMapModel. Parameters ---------- @type stimulus: ndarray @param stimulus: stimulus for which the saliency map should be computed. """ raise NotImplementedError() def conditional_saliency_map(self, stimulus, *args, **kwargs): return self.saliency_map(stimulus) def AUCs(self, stimuli, fixations, nonfixations='uniform', verbose=False): """ Calulate AUC scores for fixations :type fixations : Fixations :param fixations : Fixation object to calculate the AUC scores for. :type nonfixations : string or Fixations :param nonfixations : Nonfixations to use for calculating AUC scores. Possible values are: 'uniform': Use uniform nonfixation distribution (Judd-AUC), i.e. all pixels from the saliency map. 'unfixated': Use all pixels from the saliency map except the fixated ones. 'shuffled': Use all fixations from other images as nonfixations. fixations-object: For each image, use the fixations in this fixation object as nonfixations :rtype : ndarray :return : list of AUC scores for each fixation, ordered as in `fixations.x` (average=='fixation' or None) or by image numbers (average=='image') """ rocs_per_fixation = np.empty(len(fixations.x)) nonfix_ys = None nonfix_xs = None if isinstance(nonfixations, Fixations): nonfix_xs = [] nonfix_ys = [] for n in range(fixations.n.max()+1): inds = nonfixations.n == n nonfix_xs.append(nonfixations.x_int[inds].copy()) nonfix_ys.append(nonfixations.y_int[inds].copy()) if nonfixations == 'shuffled': nonfixations = FullShuffledNonfixationProvider(stimuli, fixations) for n in tqdm(range(len(stimuli)), total=len(stimuli), disable=not verbose): inds = fixations.n == n if not inds.sum(): continue out = self.saliency_map(stimuli.stimulus_objects[n]) check_prediction_shape(out, stimuli[n]) positives = np.asarray(out[fixations.y_int[inds], fixations.x_int[inds]]) if nonfixations == 'uniform': negatives = out.flatten() elif nonfixations == 'unfixated': negatives = _get_unfixated_values( out, fixations.y_int[inds], fixations.x_int[inds] ) elif nonfix_xs is not None: negatives = out[nonfix_ys[n], nonfix_xs[n]] elif callable(nonfixations): _nonfix_xs, _nonfix_ys = nonfixations(stimuli, fixations, np.nonzero(inds)[0][0]) negatives = out[_nonfix_ys.astype(int), _nonfix_xs.astype(int)] else: raise TypeError("Cannot handle nonfixations {}".format(nonfixations)) positives = positives.astype(float) negatives = negatives.astype(float) rocs = general_rocs_per_positive(positives, negatives) rocs_per_fixation[inds] = rocs return rocs_per_fixation def AUC_per_image(self, stimuli, fixations, nonfixations='uniform', thresholds='all', verbose=False): """ Calulate AUC scores per image for fixations :type fixations : Fixations :param fixations : Fixation object to calculate the AUC scores for. :type nonfixations : string or Fixations :param nonfixations : Nonfixations to use for calculating AUC scores. Possible values are: 'uniform': Use uniform nonfixation distribution (Judd-AUC), i.e. all pixels from the saliency map. 'unfixated': Use all pixels from the saliency map except the fixated ones. 'shuffled': Use all fixations from other images as nonfixations. fixations-object: For each image, use the fixations in this fixation object as nonfixations :type thresholds: string, either of 'all' or 'fixations' 'all' uses all saliency values as threshold, computing the true performance of the saliency map as a binary classifier on the given fixations and nonfixations 'fixations' uses only the fixated values as done in AUC_Judd. :rtype : ndarray :return : list of AUC scores for each image, or by image numbers (average=='image') """ rocs_per_image = [] out = None nonfix_xs = None nonfix_ys = None if thresholds == 'all': judd = 0 elif thresholds == 'fixations': judd = 1 else: raise ValueError("Unknown value of `thresholds`: {}".format(thresholds)) if isinstance(nonfixations, Fixations): nonfix_xs = [] nonfix_ys = [] for n in range(fixations.n.max() + 1): inds = nonfixations.n == n nonfix_xs.append(nonfixations.x_int[inds].copy()) nonfix_ys.append(nonfixations.y_int[inds].copy()) if nonfixations == 'shuffled': nonfixations = FullShuffledNonfixationProvider(stimuli, fixations) for n in tqdm(range(len(stimuli)), disable=not verbose): inds = fixations.n == n if not inds.sum(): rocs_per_image.append(np.nan) continue out = self.saliency_map(stimuli.stimulus_objects[n]) check_prediction_shape(out, stimuli[n]) positives = np.asarray(out[fixations.y_int[inds], fixations.x_int[inds]]) if nonfixations == 'uniform': negatives = out.flatten() elif nonfixations == 'unfixated': negatives = _get_unfixated_values( out, fixations.y_int[inds], fixations.x_int[inds] ) elif nonfix_xs is not None: negatives = out[nonfix_ys[n], nonfix_xs[n]] elif callable(nonfixations): _nonfix_xs, _nonfix_ys = nonfixations(stimuli, fixations, np.nonzero(inds)[0][0]) negatives = out[_nonfix_ys.astype(int), _nonfix_xs.astype(int)] else: raise TypeError("Cannot handle nonfixations {}".format(nonfixations)) positives = positives.astype(float) negatives = negatives.astype(float) this_roc, _, _ = general_roc(positives, negatives, judd=judd) rocs_per_image.append(this_roc) return rocs_per_image def AUC(self, stimuli, fixations, nonfixations='uniform', average='fixation', thresholds='all', verbose=False): """ Calulate AUC scores for fixations :type fixations : Fixations :param fixations : Fixation object to calculate the AUC scores for. :type nonfixations : string or Fixations :param nonfixations : Nonfixations to use for calculating AUC scores. Possible values are: 'uniform': Use uniform nonfixation distribution (Judd-AUC), i.e. all pixels from the saliency map. 'unfixated': Use all pixels from the saliency map except the fixated ones. 'shuffled': Use all fixations from other images as nonfixations. fixations-object: For each image, use the fixations in this fixation object as nonfixations :type average : string :param average : How to average the AUC scores for each fixation. Possible values are: 'image': average over images 'fixation' or None: Return AUC score for each fixation separately :type thresholds: string, either of 'all' or 'fixations' 'all' uses all saliency values as threshold, computing the true performance of the saliency map as a binary classifier on the given fixations and nonfixations 'fixations' uses only the fixated values as done in AUC_Judd. :rtype : ndarray :return : list of AUC scores for each fixation, ordered as in `fixations.x` (average=='fixation' or None) or by image numbers (average=='image') """ if average not in ['fixation', 'image']: raise NotImplementedError() aucs = np.asarray(self.AUC_per_image(stimuli, fixations, nonfixations=nonfixations, thresholds=thresholds, verbose=verbose)) if average == 'fixation': weights = np.zeros_like(aucs) for n in set(fixations.n): weights[n] = (fixations.n == n).mean() weights /= weights.sum() # take care of nans due to no fixations aucs[weights == 0] = 0 return np.average(aucs, weights=weights) elif average == 'image': stimulus_indices = set(fixations.n) nan_value_indices = np.nonzero(np.isnan(aucs))[0] if stimulus_indices.intersection(nan_value_indices): raise ValueError("Some images with fixations returned AUC of nan, which should not happen") aucs = aucs[~np.isnan(aucs)] return np.mean(aucs) else: raise ValueError(average) def AUC_Judd(self, stimuli, fixations, jitter=True, noise_size=1.0/10000000, random_seed=42, verbose=False): if jitter: model = RandomNoiseSaliencyMapModel( self, noise_size=noise_size, random_seed=random_seed ) else: model = self return model.AUC( stimuli, fixations, average='image', nonfixations='unfixated', thresholds='fixations', verbose=verbose ) def fixation_based_KL_divergence(self, stimuli, fixations, nonfixations='shuffled', bins=10, eps=1e-20): """ Calulate fixation-based KL-divergences for fixations :type fixations : Fixations :param fixations : Fixation object to calculate the AUC scores for. :type nonfixations : string or Fixations :param nonfixations : Nonfixations to use for calculating AUC scores. Possible values are: 'uniform': Use uniform nonfixation distribution (Judd-AUC), i.e. all pixels from the saliency map. 'shuffled': Use all fixations from other images as nonfixations. fixations-object: For each image, use the fixations in this fixation object as nonfixations :type bins : int :param bins : Number of bins to use in estimating the fixation based KL divergence :type eps : float :param eps : regularization constant for the KL divergence to avoid logarithms of zero. :rtype : float :return : fixation based KL divergence """ fixation_values = [] nonfixation_values = [] saliency_min = np.inf saliency_max = -np.inf for n in range(len(stimuli.stimuli)): saliency_map = self.saliency_map(stimuli[n]) check_prediction_shape(saliency_map, stimuli[n]) saliency_min = min(saliency_min, saliency_map.min()) saliency_max = max(saliency_max, saliency_map.max()) f = fixations[fixations.n == n] fixation_values.append(saliency_map[f.y_int, f.x_int]) if nonfixations == 'uniform': nonfixation_values.append(saliency_map.flatten()) elif nonfixations == 'shuffled': f = fixations[fixations.n != n] widths = np.asarray([s[1] for s in stimuli.sizes]).astype(float) heights = np.asarray([s[0] for s in stimuli.sizes]).astype(float) xs = (f.x.copy()) ys = (f.y.copy()) other_ns = f.n xs *= stimuli.sizes[n][1]/widths[other_ns] ys *= stimuli.sizes[n][0]/heights[other_ns] nonfixation_values.append(saliency_map[ys.astype(int), xs.astype(int)]) else: nonfix = nonfixations[nonfixations.n == n] nonfixation_values.append(saliency_map[nonfix.y_int, nonfix.x_int]) fixation_values = np.hstack(fixation_values) nonfixation_values = np.hstack(nonfixation_values) hist_range = saliency_min, saliency_max p_fix, _ = np.histogram(fixation_values, bins=bins, range=hist_range, density=True) p_fix += eps p_fix /= p_fix.sum() p_nonfix, _ = np.histogram(nonfixation_values, bins=bins, range=hist_range, density=True) p_nonfix += eps p_nonfix /= p_nonfix.sum() return (p_fix * (np.log(p_fix) - np.log(p_nonfix))).sum() def image_based_kl_divergences(self, stimuli, gold_standard, minimum_value=1e-20, log_regularization=0, quotient_regularization=0, convert_gold_standard=True, verbose=False): """Calculate image-based KL-Divergences between model and gold standard for each stimulus This metric computes the KL-Divergence between model predictions and a gold standard when interpreting these as fixation densities. As in the MIT saliency benchmark, saliency maps are interpreted as densities by dividing them by their summed value. To avoid problems with zeros, the minimum value is added to all saliency maps. Alternatively the kl divergence itself can be regularized (see Model.kl_divergences for details). If the gold standard is already a probabilistic model that should not be converted in a new (different!) probabilistic model, set `convert_gold_standard` to False. """ def convert_model(model, minimum_value): from .models import SaliencyMapNormalizingModel return SaliencyMapNormalizingModel(model, minimum_value=minimum_value) prob_model = convert_model(self, minimum_value) if convert_gold_standard: prob_gold_standard = convert_model(gold_standard, minimum_value) else: prob_gold_standard = gold_standard return prob_model.kl_divergences( stimuli, prob_gold_standard, log_regularization=log_regularization, quotient_regularization=quotient_regularization, verbose=verbose ) def image_based_kl_divergence(self, stimuli, gold_standard, minimum_value=1e-20, log_regularization=0, quotient_regularization=0, convert_gold_standard=True, verbose=False): """Calculate image-based KL-Divergences between model and gold standard averaged over stimuli for more details, see `image_based_kl_divergences`. """ return np.mean(self.image_based_kl_divergences(stimuli, gold_standard, minimum_value=minimum_value, convert_gold_standard=convert_gold_standard, log_regularization=log_regularization, quotient_regularization=quotient_regularization, verbose=verbose)) def KLDivs(self, *args, **kwargs): """Alias for image_based_kl_divergence""" return self.image_based_kl_divergences(*args, **kwargs) def KLDiv(self, *args, **kwargs): """Alias for image_based_kl_divergence""" return self.image_based_kl_divergence(*args, **kwargs) def CCs(self, stimuli, other, verbose=False): """ Calculate Correlation Coefficient Metric against some other model Returns performances for each stimulus. For performance over dataset, see `CC` """ coeffs = [] for s in tqdm(stimuli, disable=not verbose): saliency_map_self = self.saliency_map(s) saliency_map_other = other.saliency_map(s) check_prediction_shape(saliency_map_self, s) check_prediction_shape(saliency_map_other, s) coeffs.append(CC(saliency_map_self, saliency_map_other)) return np.asarray(coeffs) def CC(self, stimuli, other, verbose=False): return self.CCs(stimuli, other, verbose=verbose).mean() def NSSs(self, stimuli, fixations, verbose=False): values = np.empty(len(fixations.x)) for n, s in enumerate(tqdm(stimuli, disable=not verbose)): inds = fixations.n == n if not inds.sum(): continue smap = self.saliency_map(s).copy() check_prediction_shape(smap, s) values[inds] = NSS(smap, fixations.x_int[inds], fixations.y_int[inds]) return values def SIMs(self, stimuli, other, verbose=False): """ Calculate Similarity Metric against some other model Returns performances for each stimulus. For performance over dataset, see `SIM` """ values = [] for s in tqdm(stimuli, disable=not verbose): smap1 = self.saliency_map(s) smap2 = other.saliency_map(s) check_prediction_shape(smap1, s) check_prediction_shape(smap2, s) values.append(SIM(smap1, smap2)) return np.asarray(values) def SIM(self, stimuli, other, verbose=False): return self.SIMs(stimuli, other, verbose=verbose).mean() def __add__(self, other): if not isinstance(other, SaliencyMapModel): return NotImplemented return LambdaSaliencyMapModel([self, other], fn=lambda smaps: np.sum(smaps, axis=0, keepdims=False), caching=False) def __sub__(self, other): if not isinstance(other, SaliencyMapModel): return NotImplemented return LambdaSaliencyMapModel([self, other], fn=lambda smaps: smaps[0] - smaps[1], caching=False) def __mul__(self, other): if not isinstance(other, SaliencyMapModel): return NotImplemented return LambdaSaliencyMapModel([self, other], fn=lambda smaps: np.prod(smaps, axis=0, keepdims=False), caching=False) def __truediv__(self, other): if not isinstance(other, SaliencyMapModel): return NotImplemented return LambdaSaliencyMapModel([self, other], fn=lambda smaps: smaps[0] / smaps[1], caching=False) class CachedSaliencyMapModel(SaliencyMapModel): """Saliency map model which uses only precached saliency maps """ def __init__(self, cache_location, **kwargs): if cache_location is None: raise ValueError("CachedSaliencyMapModel needs a cache location!") super(CachedSaliencyMapModel, self).__init__(cache_location=cache_location, **kwargs) def _saliency_map(self, stimulus): raise NotImplementedError() class MatlabSaliencyMapModel(SaliencyMapModel): """ A model that creates it's saliency maps from a matlab script. The script has to take at least two arguments: The first argument will contain the filename which contains the stimulus (by default as png), the second argument contains the filename where the saliency map should be saved to (by default a .mat file). For more complicated scripts, you can overwrite the method `matlab_command`. It has to be a format string which takes the fields `stimulus` and `saliency_map` for the stimulus file and the saliency map file. """ def __init__(self, script_file, stimulus_ext = '.png', saliency_map_ext='.mat', only_color_stimuli=False, **kwargs): """ Initialize MatlabSaliencyModel Parameters ---------- @type script_file: string @param script_file: location of script file for Matlab/octave. Matlab/octave will be run from this directory. @type stimulus_ext: string, defaults to '.png' @param stimulus_ext: In which format the stimulus should be handed to the matlab script. @type saliency_map_ext: string, defaults to '.png' @param saliency_map_ext: In which format the script will return the saliency map @type only_color_stimuli: bool, defaults to `False` @param only_color_stimuli: If True, indicates that the script can handle only color stimuli. Grayscale stimuli will be converted to color stimuli by setting all RGB channels to the same value. """ super(MatlabSaliencyMapModel, self).__init__(**kwargs) self.script_file = script_file self.stimulus_ext = stimulus_ext self.saliency_map_ext = saliency_map_ext self.only_color_stimuli = only_color_stimuli self.script_directory = os.path.dirname(script_file) script_name = os.path.basename(script_file) self.command, ext = os.path.splitext(script_name) def matlab_command(self, stimulus): """ Construct the command to pass to matlab. Parameters ---------- @type stimulus: ndarray @param stimulus: The stimulus for which the saliency map should be generated. In most cases, this argument should not be needed. @returns: string, the command to pass to matlab. The returned string has to be a format string with placeholders for `stimulus` and `saliency_map` where the files containing stimulus and saliency map will be inserted. To change the type of these files, see the constructor. """ return "{command}('{{stimulus}}', '{{saliency_map}}');".format(command=self.command) def _saliency_map(self, stimulus): with TemporaryDirectory() as temp_dir: stimulus_file = os.path.join(temp_dir, 'stimulus'+self.stimulus_ext) if self.only_color_stimuli: if stimulus.ndim == 2: new_stimulus = np.empty((stimulus.shape[0], stimulus.shape[1], 3), dtype=stimulus.dtype) for i in range(3): new_stimulus[:, :, i] = stimulus stimulus = new_stimulus if self.stimulus_ext == '.png': imsave(stimulus_file, stimulus) else: raise ValueError(self.stimulus_ext) saliency_map_file = os.path.join(temp_dir, 'saliency_map'+self.saliency_map_ext) command = self.matlab_command(stimulus).format(stimulus=stimulus_file, saliency_map=saliency_map_file) run_matlab_cmd(command, cwd = self.script_directory) if self.saliency_map_ext == '.mat': saliency_map = loadmat(saliency_map_file)['saliency_map'] else: raise ValueError(self.saliency_map_ext) return saliency_map class GaussianSaliencyMapModel(SaliencyMapModel): """Gaussian saliency map model with given width""" def __init__(self, width=0.5, center_x=0.5, center_y=0.5, **kwargs): super(GaussianSaliencyMapModel, self).__init__(**kwargs) self.width = width self.center_x = center_x self.center_y = center_y def _saliency_map(self, stimulus): height = stimulus.shape[0] width = stimulus.shape[1] YS, XS = np.mgrid[:height, :width].astype(float) XS /= width YS /= height XS -= self.center_x YS -= self.center_y r_squared = XS**2 + YS**2 return np.ones((stimulus.shape[0], stimulus.shape[1]))*np.exp(-0.5*r_squared/(self.width)**2) class FixationMap(SaliencyMapModel): """ Fixation maps for given stimuli and fixations. With the keyword `kernel_size`, you can control whether the fixation map should be blured or just contain the actual fixations. If ignore_doublicates is True, multiple fixations in the same location will be counted as only one fixation (the fixation map won't have entries larger than 1). """ def __init__(self, stimuli, fixations, kernel_size=None, convolution_mode='reflect', ignore_doublicates=False, *args, **kwargs): super(FixationMap, self).__init__(*args, **kwargs) self.xs = {} self.ys = {} for n in range(len(stimuli)): f = fixations[fixations.n == n] self.xs[stimuli.stimulus_ids[n]] = f.x.copy() self.ys[stimuli.stimulus_ids[n]] = f.y.copy() self.kernel_size = kernel_size self.convolution_mode = convolution_mode self.ignore_doublicates = ignore_doublicates def _saliency_map(self, stimulus): stimulus = Stimulus(stimulus) stimulus_id = stimulus.stimulus_id if stimulus.stimulus_id not in self.xs: raise ValueError('No Fixations known for this stimulus!') saliency_map = np.zeros(stimulus.size) ff = np.vstack([self.ys[stimulus_id].astype(int), self.xs[stimulus_id].astype(int)]).T fill_fixation_map(saliency_map, ff) if self.ignore_doublicates: saliency_map[saliency_map >= 1] = 1 if self.kernel_size: saliency_map = gaussian_filter(saliency_map, self.kernel_size, mode=self.convolution_mode) return saliency_map class ResizingSaliencyMapModel(SaliencyMapModel): def __init__(self, parent_model, verbose=True, **kwargs): if 'caching' not in kwargs: kwargs['caching'] = False super(ResizingSaliencyMapModel, self).__init__(**kwargs) self.parent_model = parent_model self.verbose = verbose def _saliency_map(self, stimulus): smap = self.parent_model.saliency_map(stimulus) target_shape = (stimulus.shape[0], stimulus.shape[1]) if smap.shape != target_shape: if self.verbose: print("Resizing saliency map", smap.shape, target_shape) x_factor = target_shape[1] / smap.shape[1] y_factor = target_shape[0] / smap.shape[0] smap = zoom(smap, [y_factor, x_factor], order=1, mode='nearest') assert smap.shape == target_shape return smap class DisjointUnionMixin(object): def _split_fixations(self, stimuli, fixations): """ return list of [(inds, model)] """ raise NotImplementedError() def eval_metric(self, metric_name, stimuli, fixations, **kwargs): result = np.empty(len(fixations.x)) done = np.zeros_like(result).astype(bool) verbose = kwargs.get('verbose') for inds, model in tqdm(self._split_fixations(stimuli, fixations), disable = not verbose): assert done[inds].sum() == 0 _f = fixations[inds] this_metric = getattr(model, metric_name) this_result = this_metric(stimuli, _f, **kwargs) result[inds] = this_result done[inds] = True assert all(done) return result class DisjointUnionSaliencyMapModel(DisjointUnionMixin, ScanpathSaliencyMapModel): def AUCs(self, stimuli, fixations, **kwargs): return self.eval_metric('AUCs', stimuli, fixations, **kwargs) def AUC(self, stimuli, fixations, **kwargs): if kwargs.get('nonfixations', 'uniform') == 'shuffled': kwargs = dict(kwargs) kwargs['nonfixations'] = FullShuffledNonfixationProvider(stimuli, fixations) return super(DisjointUnionSaliencyMapModel, self).AUC(stimuli, fixations, **kwargs) def NSSs(self, stimuli, fixations, **kwargs): return self.eval_metric('NSSs', stimuli, fixations, **kwargs) class SubjectDependentSaliencyMapModel(DisjointUnionSaliencyMapModel): def __init__(self, subject_models, **kwargs): super(SubjectDependentSaliencyMapModel, self).__init__(**kwargs) self.subject_models = subject_models def _split_fixations(self, stimuli, fixations): for s in self.subject_models: yield fixations.subject == s, self.subject_models[s] def conditional_saliency_map(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None, **kwargs): if 'subject' not in attributes: raise ValueError("SubjectDependentSaliencyModel can't compute conditional saliency maps without subject indication!") return self.subject_models[attributes['subject']].conditional_saliency_map( stimulus, x_hist, y_hist, t_hist, attributes=attributes, **kwargs) class StimulusDependentSaliencyMapModel(SaliencyMapModel): def __init__(self, stimuli_models, check_stimuli=True, fallback_model=None, **kwargs): super(StimulusDependentSaliencyMapModel, self).__init__(**kwargs) self.stimuli_models = stimuli_models self.fallback_model = fallback_model if check_stimuli: self.check_stimuli() def check_stimuli(self): for s1, s2 in tqdm(list(combinations(self.stimuli_models, 2))): if not set(s1.stimulus_ids).isdisjoint(s2.stimulus_ids): raise ValueError('Stimuli not disjoint') def _saliency_map(self, stimulus): stimulus_hash = get_image_hash(stimulus) for stimuli, model in self.stimuli_models.items(): if stimulus_hash in stimuli.stimulus_ids: return model.saliency_map(stimulus) else: if self.fallback_model is not None: return self.fallback_model.saliency_map(stimulus) else: raise ValueError('stimulus not provided by these models') class ExpSaliencyMapModel(SaliencyMapModel): def __init__(self, parent_model): super(ExpSaliencyMapModel, self).__init__(caching=False) self.parent_model = parent_model def _saliency_map(self, stimulus): return np.exp(self.parent_model.saliency_map(stimulus)) class BluringSaliencyMapModel(SaliencyMapModel): def __init__(self, parent_model, kernel_size, mode='nearest', **kwargs): super(BluringSaliencyMapModel, self).__init__(**kwargs) self.parent_model = parent_model self.kernel_size = kernel_size self.mode = mode def _saliency_map(self, stimulus): smap = self.parent_model.saliency_map(stimulus) smap = gaussian_filter(smap, self.kernel_size, mode=self.mode) return smap class DigitizeMapModel(SaliencyMapModel): def __init__(self, parent_model, bins=256, return_ints=True): super(DigitizeMapModel, self).__init__(caching=False) self.parent_model = parent_model self.bins = bins self.return_ints = return_ints def _saliency_map(self, stimulus): smap = self.parent_model.saliency_map(stimulus) min = smap.min() max = smap.max() bins = np.linspace(min, max, num=self.bins+1) smap = np.digitize(smap, bins) - 1 if self.return_ints: return smap else: return smap.astype(float) class HistogramNormalizedSaliencyMapModel(SaliencyMapModel): def __init__(self, parent_model, histogram=None, **kwargs): super(HistogramNormalizedSaliencyMapModel, self).__init__(**kwargs) self.parent_model = parent_model if histogram is None: histogram = np.ones(256) / 256 self.histogram = histogram self.histogram /= self.histogram.sum() self.bins = np.linspace(0, 1, len(self.histogram)) self.cdf = np.cumsum(self.histogram) def _saliency_map(self, stimulus): smap = self.parent_model.saliency_map(stimulus) return normalize_saliency_map(smap, self.cdf, self.bins) class LambdaSaliencyMapModel(SaliencyMapModel): """Applies a function to a list of saliency maps from other models""" def __init__(self, parent_models, fn, **kwargs): super(LambdaSaliencyMapModel, self).__init__(**kwargs) self.parent_models = parent_models self.fn = fn def _saliency_map(self, stimulus): saliency_maps = [model.saliency_map(stimulus) for model in self.parent_models] return self.fn(saliency_maps) class RandomNoiseSaliencyMapModel(LambdaSaliencyMapModel): def __init__(self, parent_model, noise_size=1.0/10000000, random_seed=42, **kwargs): super(RandomNoiseSaliencyMapModel, self).__init__( [parent_model], self.add_jitter, **kwargs ) self.rst = np.random.RandomState(seed=random_seed) self.noise_size = noise_size def add_jitter(self, saliency_maps): saliency_map = saliency_maps[0] return saliency_map + self.rst.randn(*saliency_map.shape)*self.noise_size class DensitySaliencyMapModel(SaliencyMapModel): """Uses fixation density as predicted by a probabilistic model as saliency maps""" def __init__(self, parent_model, **kwargs): super(DensitySaliencyMapModel, self).__init__(caching=False, **kwargs) self.parent_model = parent_model def _saliency_map(self, stimulus): return np.exp(self.parent_model.log_density(stimulus)) class LogDensitySaliencyMapModel(SaliencyMapModel): """Uses fixation log density as predicted by a probabilistic model as saliency maps""" def __init__(self, parent_model, **kwargs): super(LogDensitySaliencyMapModel, self).__init__(caching=False, **kwargs) self.parent_model = parent_model def _saliency_map(self, stimulus): return self.parent_model.log_density(stimulus).copy() class EqualizedSaliencyMapModel(SaliencyMapModel): """Equalizes saliency maps to have uniform histogram""" def __init__(self, parent_model, **kwargs): super(EqualizedSaliencyMapModel, self).__init__(caching=False, **kwargs) self.parent_model = parent_model def _saliency_map(self, stimulus): smap = self.parent_model.saliency_map(stimulus) smap = np.argsort(np.argsort(smap.flatten())).reshape(smap.shape) smap = smap.astype(float) smap /= np.prod(smap.shape) return smap def nd_argmax(array): return np.unravel_index(np.argmax(array.flatten()), array.shape) class WTASamplingMixin(SamplingModelMixin): def sample_fixation(self, stimulus, x_hist, y_hist, t_hist, attributes=None, verbose=False, rst=None): conditional_saliency_map = self.conditional_saliency_map(stimulus, x_hist, y_hist, t_hist, attributes=attributes) y, x = nd_argmax(conditional_saliency_map) if not t_hist: t = 0 elif len(t_hist) == 1: t = t_hist[0] * 2 else: t = t_hist[-1] + np.mean(np.diff(t_hist)) return x, y, t GeneralSaliencyMapModel = deprecated_class(deprecated_in='0.2.16', removed_in='1.0.0', details="Use ScanpathSaliencyMapModel instead")(ScanpathSaliencyMapModel) ================================================ FILE: pysaliency/sampling_models.py ================================================ from __future__ import print_function, absolute_import, division, unicode_literals from abc import ABCMeta, abstractmethod from .utils import remove_trailing_nans class SamplingModelMixin(object, metaclass=ABCMeta): """A sampling model is supports sampling fixations and whole scanpaths.""" def sample_scanpath( self, stimulus, x_hist, y_hist, t_hist, samples, attributes=None, verbose=False, rst=None ): """return xs, ys, ts""" xs = list(remove_trailing_nans(x_hist)) ys = list(remove_trailing_nans(y_hist)) ts = list(remove_trailing_nans(t_hist)) if not len(xs) == len(ys) == len(ts): raise ValueError("Histories for x, y and t have to be the same length") for i in range(samples): x, y, t = self.sample_fixation(stimulus, xs, ys, ts, attributes=attributes, verbose=verbose, rst=rst) xs.append(x) ys.append(y) ts.append(t) return xs, ys, ts @abstractmethod def sample_fixation(self, stimulus, x_hist, y_hist, t_hist, attributes=None, verbose=False, rst=None): """return x, y, t""" raise NotImplementedError() class ScanpathSamplingModelMixin(SamplingModelMixin): """A sampling model which only has to implement sample_scanpath instead of sample_fixation""" @abstractmethod def sample_scanpath( self, stimulus, x_hist, y_hist, t_hist, samples, attributes=None, verbose=False, rst=None ): raise NotImplementedError() def sample_fixation(self, stimulus, x_hist, y_hist, t_hist, attributes=None, verbose=False, rst=None): samples = 1 xs, ys, ts = self.sample_scanpath(stimulus, x_hist, y_hist, t_hist, samples, attributes=attributes, verbose=verbose, rst=rst) return xs[-1], ys[-1], ts[-1] ================================================ FILE: pysaliency/tf_utils.py ================================================ from __future__ import print_function, division, absolute_import, unicode_literals import tensorflow as tf slim = tf.contrib.slim def normalize_axis(input_tensor, axis): """ convert negative indices into positive indices since tensorflow can't handle them """ if axis < 0: ndims = len(input_tensor.get_shape()) axis = ndims + axis return axis def replication_padding(input_tensor, axis=0, size=1): """ add replication padding to a tensor along a given axis """ with tf.name_scope('replication_padding'): if not isinstance(size, (tuple, list)): size = (size, size) ndims = len(input_tensor.get_shape()) axis = normalize_axis(input_tensor, axis) start_slice_obj = [slice(None)] * axis + [slice(0, 1)] start_slice = input_tensor[start_slice_obj] repeats = [1] * axis + [size[0]] + [1] * (ndims-axis-1) start_part = tf.tile(start_slice, repeats) end_slice_obj = [slice(None)] * axis + [slice(-1, None)] end_slice = input_tensor[end_slice_obj] repeats = [1] * axis + [size[1]] + [1] * (ndims-axis-1) end_part = tf.tile(end_slice, repeats) return tf.concat((start_part, input_tensor, end_part), axis=axis) def get_gaussian_kernel(sigma, windowradius=5): with tf.name_scope('gaussian_kernel'): kernel = tf.cast(tf.range(0, 2*windowradius+1), 'float') - windowradius kernel = tf.exp(-(kernel**2)/(2*sigma**2)) kernel /= tf.reduce_sum(kernel) return kernel def blowup_1d_kernel(kernel, axis=-1): #with tf.name_scope("blowup_1d_kernel") assert isinstance(axis, int) shape = [1 for i in range(4)] shape[axis] = -1 return tf.reshape(kernel, shape) @slim.add_arg_scope def gaussian_convolution_along_axis(inputs, axis, sigma, windowradius=5, mode='NEAREST', scope=None, outputs_collections=None): with tf.name_scope(scope, 'gauss_1d', [inputs, sigma, windowradius]): if mode == 'NEAREST': inputs = replication_padding(inputs, axis=axis+1, size=windowradius) elif mode == 'ZERO': paddings = [[0, 0], [0, 0], [0, 0], [0, 0]] paddings[axis+1] = [windowradius, windowradius] inputs = tf.pad(inputs, paddings) elif mode == 'VALID': pass else: raise ValueError(mode) kernel_1d = get_gaussian_kernel(sigma, windowradius=windowradius) kernel = blowup_1d_kernel(kernel_1d, axis) #print(windowradius) output = tf.nn.conv2d(inputs, kernel, strides=[1, 1, 1, 1], padding="VALID", name='gaussian_convolution') return output #return slim.utils.collect_named_outputs(outputs_collections, sc, output) @slim.add_arg_scope def gauss_blur(inputs, sigma, windowradius=5, mode='NEAREST', scope=None, outputs_collections=None): with tf.name_scope(scope, 'gauss_blur', [inputs, sigma, windowradius]) as sc: outputs = inputs for axis in [0, 1]: outputs = gaussian_convolution_along_axis(outputs, axis=axis, sigma=sigma, windowradius=windowradius, mode=mode) return outputs return slim.utils.collect_named_outputs(outputs_collections, sc, outputs) def tf_logsumexp(data, axis=0): """computes logsumexp along axis in its own graph and session""" with tf.Graph().as_default() as g: input_tensor = tf.placeholder(tf.float32, name='input_tensor') output_tensor = tf.reduce_logsumexp(input_tensor, axis=axis) with tf.Session(graph=g) as sess: return sess.run(output_tensor, {input_tensor: data}) ================================================ FILE: pysaliency/theano_utils.py ================================================ from __future__ import absolute_import, print_function, division#, unicode_literals import numpy as np import theano import theano.tensor as T from theano.ifelse import ifelse def nonlinearity(input, x, y, length): """ Apply a pointwise nonlinearity to input The nonlinearity is a picewise linear function. The graph of the function is given by the vectors x and y. """ parts = [] for i in range(length-1): x1 = x[i] x2 = x[i+1] y1 = y[i] y2 = y[i+1] #print x1.tag part = (y2-y1)/(x2-x1)*(theano.tensor.clip(input, x1, x2)-x1) parts.append(part) output = y[0] for part in parts: output = output + part return output def gaussian_filter(input, sigma, window_radius = 40): """ Filter input with a Gaussian using mode `nearest`. input is expected to be three dimensional of type n times x times y """ # Construction of 1d kernel #filter_1d = T.arange(-window_radius, window_radius+1) # Work around some strange theano bug filter_1d = T.arange(2*window_radius + 1) - window_radius filter_1d = T.exp(-0.5*filter_1d**2/sigma**2) filter_1d = filter_1d / filter_1d.sum() filter_1d = filter_1d.astype(input.dtype) W = filter_1d.dimshuffle([0, 'x']) W2 = filter_1d.dimshuffle(['x', 0]) blur_input = input.dimshuffle(['x', 0, 1, 2]) filter_W = W.dimshuffle(['x', 'x', 0, 1]) filter_W2 = W2.dimshuffle(['x', 'x', 0, 1]) # Construction of filter pipeline blur_input_start = blur_input[:, :, :1, :] blur_input_end = blur_input[:, :, -1:, :] padded_input = T.concatenate([blur_input_start]*window_radius+[blur_input]+[blur_input_end]*window_radius, axis=2) blur_op = T.nnet.conv2d(padded_input, filter_W, border_mode='valid', filter_shape=[1, 1, None, None]) #x_min = (W.shape[1]-1)//2 #x_max = input.shape[2]+(W.shape[1]-1)//2 #y_min = (W.shape[0]-1)//2+window_radius #y_max = input.shape[1]+(W.shape[0]-1)//2+window_radius #cropped_output1 = blur_op[:, :, y_min:y_max, x_min:x_max] #cropped_output1_start = blur_op[:, :, y_min:y_max, x_min:x_min+1] #cropped_output1_end = blur_op[:, :, y_min:y_max, x_max-1:x_max] cropped_output1 = blur_op cropped_output1_start = blur_op[:, :, :, :1] cropped_output1_end = blur_op[:, :, :, -1:] padded_cropped_input = T.concatenate([cropped_output1_start]*window_radius + [cropped_output1] + [cropped_output1_end] * window_radius, axis=3) blur_op2 = T.nnet.conv2d(padded_cropped_input, filter_W2, border_mode='valid', filter_shape=[1, 1, None, None]) #x_min2 = (W2.shape[1]-1)//2+window_radius #x_max2 = input.shape[2]+(W2.shape[1]-1)//2+window_radius #y_min2 = (W2.shape[0]-1)//2 #y_max2 = input.shape[1]+(W2.shape[0]-1)//2 cropped_output2 = blur_op2[0, :, :, :] # [0, :, y_min2:y_max2, x_min2:x_max2] return cropped_output2 class Blur(object): def __init__(self, input, sigma=20.0, window_radius=60): self.input = input self.sigma = theano.shared(value=np.array(sigma, dtype=theano.config.floatX), name='sigma') apply_blur = T.gt(self.sigma, 0.0) no_blur = T.le(self.sigma, 0.0) self.output = ifelse(no_blur, input, gaussian_filter(input.dimshuffle('x', 0, 1), self.sigma, window_radius)[0, :, :]) self.params = [self.sigma] class Nonlinearity(object): def __init__(self, input, nonlinearity_ys = None): self.input = input #self.num_nonlinearity = num_nonlinearity if nonlinearity_ys is None: nonlinearity_ys = np.linspace(0, 1, num=20) nonlinearity_ys = nonlinearity_ys.astype(theano.config.floatX) self.nonlinearity_xs = theano.shared(value=np.linspace(0, 1, len(nonlinearity_ys)).astype(theano.config.floatX), name='nonlinearity_xs') self.nonlinearity_ys = theano.shared(value=nonlinearity_ys, name='nonlinearity_ys') self.output = nonlinearity(input, self.nonlinearity_xs, self.nonlinearity_ys, len(nonlinearity_ys)) self.params = [self.nonlinearity_ys] class LogNonlinearity(object): def __init__(self, input, nonlinearity_ys = None): self.input = input #self.num_nonlinearity = num_nonlinearity if nonlinearity_ys is None: nonlinearity_ys = np.linspace(0, 1, num=20) nonlinearity_ys = nonlinearity_ys.astype(theano.config.floatX) self.nonlinearity_xs = theano.shared(value=np.linspace(0, 1, len(nonlinearity_ys)).astype(theano.config.floatX), name='nonlinearity_xs') self.nonlinearity_ys = theano.shared(value=nonlinearity_ys, name='nonlinearity_ys') self.output = nonlinearity(input, self.nonlinearity_xs, T.exp(self.nonlinearity_ys), len(nonlinearity_ys)) self.params = [self.nonlinearity_ys] class CenterBias(object): def __init__(self, input, centerbias = None, alpha=1.0): self.input = input if centerbias is None: centerbias = np.ones(12) self.alpha = theano.shared(value = np.array(alpha).astype(theano.config.floatX), name='alpha') self.centerbias_ys = theano.shared(value=np.array(centerbias, dtype=theano.config.floatX), name='centerbias_ys') self.centerbias_xs = theano.shared(value=np.linspace(0, 1, len(centerbias), dtype=theano.config.floatX), name='centerbias_xs') height = T.cast(input.shape[0], theano.config.floatX) width = T.cast(input.shape[1], theano.config.floatX) x_coords = (T.arange(width) - 0.5*width) / (0.5*width) y_coords = (T.arange(height) - 0.5*height) / (0.5*height) + 0.0001 # We cannot have zeros in there because of grad x_coords = x_coords.dimshuffle('x', 0) y_coords = y_coords.dimshuffle(0, 'x') dists = T.sqrt(T.square(x_coords) + self.alpha*T.square(y_coords)) self.max_dist = T.sqrt(1 + self.alpha) self.dists = dists/self.max_dist self.factors = nonlinearity(self.dists, self.centerbias_xs, self.centerbias_ys, len(centerbias)) apply_centerbias = T.gt(self.centerbias_ys.shape[0], 2) self.output = ifelse(apply_centerbias, self.input*self.factors, self.input) self.params = [self.centerbias_ys, self.alpha] class AdditiveCenterBias(object): def __init__(self, input, centerbias = None, alpha=1.0): self.input = input if centerbias is None: centerbias = np.ones(12) self.alpha = theano.shared(value = np.array(alpha).astype(theano.config.floatX), name='alpha') self.centerbias_ys = theano.shared(value=np.array(centerbias, dtype=theano.config.floatX), name='centerbias_ys') self.centerbias_xs = theano.shared(value=np.linspace(0, 1, len(centerbias), dtype=theano.config.floatX), name='centerbias_xs') height = T.cast(input.shape[0], theano.config.floatX) width = T.cast(input.shape[1], theano.config.floatX) x_coords = (T.arange(width) - 0.5*width) / (0.5*width) y_coords = (T.arange(height) - 0.5*height) / (0.5*height) + 0.0001 # We cannot have zeros in there because of grad x_coords = x_coords.dimshuffle('x', 0) y_coords = y_coords.dimshuffle(0, 'x') dists = T.sqrt(T.square(x_coords) + self.alpha*T.square(y_coords)) self.max_dist = T.sqrt(1 + self.alpha) self.dists = dists/self.max_dist self.factors = nonlinearity(self.dists, self.centerbias_xs, self.centerbias_ys, len(centerbias)) apply_centerbias = T.gt(self.centerbias_ys.shape[0], 2) self.output = ifelse(apply_centerbias, self.input+self.factors, self.input) self.params = [self.centerbias_ys, self.alpha] class LogDensity(object): def __init__(self, input): self.input = input self.output = T.log(input / input.sum()) class LogDensityFromLogarithmicScale(object): def __init__(self, input): self.input = input self.output = input - T.log(T.exp(input).sum()) class AverageLogLikelihood(object): def __init__(self, log_densities, x_inds, y_inds): self.log_densities = log_densities self.log_likelihoods = log_densities[y_inds, x_inds] self.average_log_likelihood = self.log_likelihoods.mean() class SaliencyMapProcessing(object): def __init__(self, saliency_map, x_inds = None, y_inds = None, sigma = 0.0, window_radius = 80, nonlinearity_ys = None, centerbias = None, alpha = 1.0): self.saliency_map = saliency_map if x_inds is None: x_inds = T.lvector('x_inds') if y_inds is None: y_inds = T.lvector('y_inds') class TheanoObjects(object): pass self.theano_objects = TheanoObjects() self.x_inds = x_inds self.y_inds = y_inds self.theano_objects.blur = Blur(saliency_map, sigma=sigma, window_radius=window_radius) self.blur = self.theano_objects.blur.output self.theano_objects.nonlinearity = Nonlinearity(self.blur, nonlinearity_ys=nonlinearity_ys) self.nonlinearity = self.theano_objects.nonlinearity.output self.theano_objects.centerbias = CenterBias(self.nonlinearity, centerbias=centerbias, alpha=alpha) self.centerbias = self.theano_objects.centerbias.output self.theano_objects.log_density = LogDensity(self.centerbias) self.log_density = self.theano_objects.log_density.output self.theano_objects.average_log_likelihood = AverageLogLikelihood(self.log_density, self.x_inds, self.y_inds) self.average_log_likelihood = self.theano_objects.average_log_likelihood.average_log_likelihood self.params = self.theano_objects.blur.params + self.theano_objects.nonlinearity.params + self.theano_objects.centerbias.params self.blur_radius = self.theano_objects.blur.sigma self.nonlinearity_ys = self.theano_objects.nonlinearity.nonlinearity_ys self.centerbias_ys = self.theano_objects.centerbias.centerbias_ys self.alpha = self.theano_objects.centerbias.alpha class SaliencyMapProcessingLogNonlinearity(object): def __init__(self, saliency_map, x_inds = None, y_inds = None, sigma = 0.0, window_radius = 80, nonlinearity_ys = None, centerbias = None, alpha = 1.0): self.saliency_map = saliency_map if x_inds is None: x_inds = T.lvector('x_inds') if y_inds is None: y_inds = T.lvector('y_inds') class TheanoObjects(object): pass self.theano_objects = TheanoObjects() self.x_inds = x_inds self.y_inds = y_inds self.theano_objects.blur = Blur(saliency_map, sigma=sigma, window_radius=window_radius) self.blur = self.theano_objects.blur.output self.theano_objects.nonlinearity = LogNonlinearity(self.blur, nonlinearity_ys=nonlinearity_ys) self.nonlinearity = self.theano_objects.nonlinearity.output self.theano_objects.centerbias = CenterBias(self.nonlinearity, centerbias=centerbias, alpha=alpha) self.centerbias = self.theano_objects.centerbias.output self.theano_objects.log_density = LogDensity(self.centerbias) self.log_density = self.theano_objects.log_density.output self.theano_objects.average_log_likelihood = AverageLogLikelihood(self.log_density, self.x_inds, self.y_inds) self.average_log_likelihood = self.theano_objects.average_log_likelihood.average_log_likelihood self.params = self.theano_objects.blur.params + self.theano_objects.nonlinearity.params + self.theano_objects.centerbias.params self.blur_radius = self.theano_objects.blur.sigma self.nonlinearity_ys = self.theano_objects.nonlinearity.nonlinearity_ys self.centerbias_ys = self.theano_objects.centerbias.centerbias_ys self.alpha = self.theano_objects.centerbias.alpha class SaliencyMapProcessingLogarithmic(object): def __init__(self, saliency_map, x_inds = None, y_inds = None, sigma = 0.0, window_radius = 80, nonlinearity_ys = None, centerbias = None, alpha = 1.0): self.saliency_map = saliency_map if x_inds is None: x_inds = T.lvector('x_inds') if y_inds is None: y_inds = T.lvector('y_inds') class TheanoObjects(object): pass self.theano_objects = TheanoObjects() self.x_inds = x_inds self.y_inds = y_inds self.theano_objects.blur = Blur(saliency_map, sigma=sigma, window_radius=window_radius) self.blur = self.theano_objects.blur.output self.theano_objects.nonlinearity = Nonlinearity(self.blur, nonlinearity_ys=nonlinearity_ys) self.nonlinearity = self.theano_objects.nonlinearity.output self.theano_objects.centerbias = AdditiveCenterBias(self.nonlinearity, centerbias=centerbias, alpha=alpha) self.centerbias = self.theano_objects.centerbias.output self.theano_objects.log_density = LogDensityFromLogarithmicScale(self.centerbias) self.log_density = self.theano_objects.log_density.output self.theano_objects.average_log_likelihood = AverageLogLikelihood(self.log_density, self.x_inds, self.y_inds) self.average_log_likelihood = self.theano_objects.average_log_likelihood.average_log_likelihood self.params = self.theano_objects.blur.params + self.theano_objects.nonlinearity.params + self.theano_objects.centerbias.params self.blur_radius = self.theano_objects.blur.sigma self.nonlinearity_ys = self.theano_objects.nonlinearity.nonlinearity_ys self.centerbias_ys = self.theano_objects.centerbias.centerbias_ys self.alpha = self.theano_objects.centerbias.alpha ================================================ FILE: pysaliency/torch_datasets.py ================================================ import random import numpy as np import torch from boltons.iterutils import chunked from torch.utils.data.dataloader import default_collate from tqdm import tqdm from .models import Model from .saliency_map_models import SaliencyMapModel def ensure_color_image(image): if len(image.shape) == 2: return np.dstack([image, image, image]) return image def x_y_to_sparse_indices(xs, ys): # Converts list of x and y coordinates into indices and values for sparse mask x_inds = [] y_inds = [] values = [] pair_inds = {} for x, y in zip(xs, ys): key = (x, y) if key not in pair_inds: x_inds.append(x) y_inds.append(y) pair_inds[key] = len(x_inds) - 1 values.append(1) else: values[pair_inds[key]] += 1 return np.array([y_inds, x_inds]), values class ImageDataset(torch.utils.data.Dataset): def __init__(self, stimuli, fixations, models=None, transform=None, cached=True, average='fixation'): self.stimuli = stimuli self.fixations = fixations if models is None: models = {} self.models = models self.transform = transform self.average = average self.cached = cached if cached: self._cache = {} print("Populating fixations cache") self._xs_cache = {} self._ys_cache = {} for x, y, n in zip(self.fixations.x_int, self.fixations.y_int, tqdm(self.fixations.n)): self._xs_cache.setdefault(n, []).append(x) self._ys_cache.setdefault(n, []).append(y) for key in list(self._xs_cache): self._xs_cache[key] = np.array(self._xs_cache[key], dtype=int) for key in list(self._ys_cache): self._ys_cache[key] = np.array(self._ys_cache[key], dtype=int) def get_shapes(self): return list(self.stimuli.sizes) def __getitem__(self, key): if not self.cached or key not in self._cache: image = np.array(self.stimuli.stimuli[key]) predictions = {} for model_name, model in self.models.items(): if isinstance(model, Model): prediction = np.asarray(model.log_density(image)) elif isinstance(model, SaliencyMapModel): prediction = np.asarray(model.saliency_map(image)) predictions[model_name] = prediction image = ensure_color_image(image).astype(np.float32) image = image.transpose(2, 0, 1) if self.cached: xs = self._xs_cache.pop(key) ys = self._ys_cache.pop(key) else: inds = self.fixations.n == key xs = np.array(self.fixations.x_int[inds], dtype=int) ys = np.array(self.fixations.y_int[inds], dtype=int) data = { "image": image, "x": xs, "y": ys, } for prediction_name, prediction in predictions.items(): data[prediction_name] = prediction if self.average == 'image': data['weight'] = 1.0 else: data['weight'] = float(len(xs)) if self.cached: self._cache[key] = data else: data = self._cache[key] if self.transform is not None: return self.transform(dict(data)) return data def __len__(self): return len(self.stimuli) class FixationMaskTransform(object): def __call__(self, item): shape = torch.Size([item['image'].shape[1], item['image'].shape[2]]) x = item.pop('x') y = item.pop('y') # inds, values = x_y_to_sparse_indices(x, y) inds = np.array([y, x]) values = np.ones(len(y), dtype=int) # mask = torch.sparse.IntTensor(torch.tensor(inds), torch.tensor(values), shape) mask = torch.sparse_coo_tensor(torch.tensor(inds), torch.tensor(values), shape, dtype=torch.int) mask = mask.coalesce() item['fixation_mask'] = mask return item class ImageDatasetSampler(torch.utils.data.Sampler): def __init__(self, data_source, batch_size=1, ratio_used=1.0, shuffle=True): self.ratio_used = ratio_used self.shuffle = shuffle shapes = data_source.get_shapes() unique_shapes = sorted(set(shapes)) shape_indices = [[] for shape in unique_shapes] for k, shape in enumerate(shapes): shape_indices[unique_shapes.index(shape)].append(k) if self.shuffle: for indices in shape_indices: random.shuffle(indices) self.batches = sum([chunked(indices, size=batch_size) for indices in shape_indices], []) def __iter__(self): if self.shuffle: indices = torch.randperm(len(self.batches)) else: indices = range(len(self.batches)) if self.ratio_used < 1.0: indices = indices[:int(self.ratio_used * len(indices))] return iter(self.batches[i] for i in indices) def __len__(self): return int(self.ratio_used * len(self.batches)) # we need to extend the defaut collate fn to handle sparse coo tensors def collate_fn(batch): result = {} for key in batch[0]: if isinstance(batch[0][key], torch.sparse.Tensor): result[key] = torch.stack([item[key] for item in batch], 0) else: result[key] = default_collate([item[key] for item in batch]) return result ================================================ FILE: pysaliency/torch_utils.py ================================================ import math from boltons.iterutils import windowed import numpy as np from packaging import version import torch import torch.nn as nn import torch.nn.functional as F def gaussian_filter_1d_old_torch(tensor, dim, sigma, truncate=4, kernel_size=None, padding_mode='replicate', padding_value=0.0): """for torch version < 1.6.0 TODO: Remove soon. """ sigma = torch.as_tensor(sigma, device=tensor.device, dtype=tensor.dtype) if kernel_size is not None: kernel_size = torch.as_tensor(kernel_size, device=tensor.device, dtype=torch.int64) else: kernel_size = torch.as_tensor(2 * torch.ceil(truncate * sigma) + 1, device=tensor.device, dtype=torch.int64) kernel_size = kernel_size.detach() kernel_size_int = kernel_size.detach().cpu().numpy() mean = (torch.as_tensor(kernel_size, dtype=tensor.dtype) - 1) / 2 grid = torch.arange(kernel_size, device=tensor.device) - mean # reshape the grid so that it can be used as a kernel for F.conv1d kernel_shape = [1] * len(tensor.shape) kernel_shape[dim] = kernel_size_int grid = grid.view(kernel_shape) grid = grid.detach() padding = [0] * (2 * len(tensor.shape)) padding[dim * 2 + 1] = math.ceil((kernel_size_int - 1) / 2) padding[dim * 2] = math.ceil((kernel_size_int - 1) / 2) padding = tuple(reversed(padding)) if padding_mode in ['replicate']: # replication padding has some strange constraints... assert len(tensor.shape) - dim <= 2 padding = padding[:(len(tensor.shape) - 2) * 2] tensor_ = F.pad(tensor, padding, padding_mode, padding_value) # create gaussian kernel from grid using current sigma kernel = torch.exp(-0.5 * (grid / sigma) ** 2) kernel = kernel / kernel.sum() # convolve input with gaussian kernel return F.conv1d(tensor_, kernel) def gaussian_filter_1d_new_torch(tensor, dim, sigma, truncate=4, kernel_size=None, padding_mode='replicate', padding_value=0.0): sigma = torch.as_tensor(sigma, device=tensor.device, dtype=tensor.dtype) if kernel_size is not None: kernel_size = torch.as_tensor(kernel_size, device=tensor.device, dtype=torch.int64) else: kernel_size = torch.as_tensor(2 * torch.ceil(truncate * sigma) + 1, device=tensor.device, dtype=torch.int64) kernel_size = kernel_size.detach() kernel_size_int = kernel_size.detach().cpu().numpy() mean = (torch.as_tensor(kernel_size, dtype=tensor.dtype) - 1) / 2 grid = torch.arange(kernel_size, device=tensor.device) - mean # reshape the grid so that it can be used as a kernel for F.conv1d #kernel_shape = [1] * len(tensor.shape) #kernel_shape[dim] = kernel_size_int kernel_shape = (1, 1, kernel_size) grid = grid.view(kernel_shape) grid = grid.detach() #padding = [0] * (2 * len(tensor.shape)) #padding[dim * 2 + 1] = math.ceil((kernel_size_int - 1) / 2) #padding[dim * 2] = math.ceil((kernel_size_int - 1) / 2) #padding = tuple(reversed(padding)) #if padding_mode in ['replicate']: # # replication padding has some strange constraints... ## assert len(tensor.shape) - dim <= 2 # padding = padding[:(len(tensor.shape) - 2) * 2] source_shape = tensor.shape tensor = torch.movedim(tensor, dim, len(source_shape)-1) dim_last_shape = tensor.shape assert tensor.shape[-1] == source_shape[dim] # we need reshape instead of view for batches like B x C x H x W tensor = tensor.reshape(-1, 1, source_shape[dim]) padding = (math.ceil((kernel_size_int - 1) / 2), math.ceil((kernel_size_int - 1) / 2)) tensor_ = F.pad(tensor, padding, padding_mode, padding_value) # create gaussian kernel from grid using current sigma kernel = torch.exp(-0.5 * (grid / sigma) ** 2) kernel = kernel / kernel.sum() # convolve input with gaussian kernel tensor_ = F.conv1d(tensor_, kernel) tensor_ = tensor_.view(dim_last_shape) tensor_ = torch.movedim(tensor_, len(source_shape)-1, dim) assert tensor_.shape == source_shape return tensor_ def gaussian_filter_1d(tensor, dim, sigma, truncate=4, kernel_size=None, padding_mode='replicate', padding_value=0.0): if version.parse(torch.__version__) < version.parse('1.6.0'): return gaussian_filter_1d_old_torch(tensor, dim, sigma, truncate=truncate, kernel_size=kernel_size, padding_mode=padding_mode, padding_value=padding_value) else: return gaussian_filter_1d_new_torch(tensor, dim, sigma, truncate=truncate, kernel_size=kernel_size, padding_mode=padding_mode, padding_value=padding_value) def gaussian_filter(tensor, dim, sigma, truncate=4, kernel_size=None, padding_mode='replicate', padding_value=0.0): if isinstance(dim, int): dim = [dim] for k in dim: tensor = gaussian_filter_1d( tensor, dim=k, sigma=sigma, truncate=truncate, kernel_size=kernel_size, padding_mode=padding_mode, padding_value=padding_value, ) return tensor class GaussianFilterNd(nn.Module): """A differentiable gaussian filter""" def __init__(self, dims, sigma, truncate=4, kernel_size=None, padding_mode='replicate', padding_value=0.0, trainable=False): """Creates a 1d gaussian filter Args: dims ([int]): the dimensions to which the gaussian filter is applied. Negative values won't work sigma (float): standard deviation of the gaussian filter (blur size) truncate (float, optional): truncate the filter at this many standard deviations (default: 4.0). This has no effect if the `kernel_size` is explicitely set kernel_size (int): size of the gaussian kernel convolved with the input padding_mode (string, optional): Padding mode implemented by `torch.nn.functional.pad`. padding_value (string, optional): Value used for constant padding. """ # IDEA determine input_dims dynamically for every input super(GaussianFilterNd, self).__init__() self.dims = dims self.sigma = nn.Parameter(torch.tensor(sigma, dtype=torch.float32), requires_grad=trainable) # default: no optimization self.truncate = truncate self.kernel_size = kernel_size # setup padding self.padding_mode = padding_mode self.padding_value = padding_value def forward(self, tensor): """Applies the gaussian filter to the given tensor""" for dim in self.dims: tensor = gaussian_filter_1d( tensor, dim=dim, sigma=self.sigma, truncate=self.truncate, kernel_size=self.kernel_size, padding_mode=self.padding_mode, padding_value=self.padding_value, ) return tensor def nonlinearity(tensor, xs, ys): # TODO: This could probably be sped up quite a bit with torch.search_sorted assert len(xs) == len(ys) output = torch.ones_like(tensor, device=tensor.device) * ys[0] for (x1, x2), (y1, y2) in zip(windowed(xs, 2), windowed(ys, 2)): output += (y2 - y1) / (x2 - x1) * (torch.clamp(tensor, x1, x2) - x1) return output class Nonlinearity(nn.Module): def __init__(self, xs=None, ys=None, num_values=20, value_scale='linear'): super().__init__() if value_scale not in ['linear', 'log']: raise ValueError(value_scale) self.value_scale = value_scale if ys is None: if self.value_scale == 'linear': ys = np.linspace(0, 1, num_values) elif self.value_scale == 'log': ys = np.linspace(-1, 0, num_values) else: raise ValueError(self.value_scale) self.ys = nn.Parameter(data=torch.tensor(ys), requires_grad=True) if xs is None: xs = np.linspace(0, 1, len(ys)) self.xs = nn.Parameter(torch.tensor(xs), requires_grad=False) def forward(self, tensor): ys = self.ys if self.value_scale == 'log': ys = torch.exp(ys) return nonlinearity(tensor, self.xs, ys) def zero_grad(model): """ set gradient of model to zero without having to use an optimizer object """ for param in model.parameters(): if param.grad is not None: param.grad.detach_() param.grad.zero_() def log_likelihood(log_density, fixation_mask, weights=None): weights = len(weights) * weights.view(-1, 1, 1) / weights.sum() dense_mask = fixation_mask.to_dense() fixation_count = dense_mask.sum(dim=(-1, -2), keepdim=True) ll = torch.mean( weights * torch.sum(log_density * dense_mask, dim=(-1, -2), keepdim=True) / fixation_count ) return (ll + np.log(log_density.shape[-1] * log_density.shape[-2])) / np.log(2) ================================================ FILE: pysaliency/utils/__init__.py ================================================ from __future__ import absolute_import, division, print_function import hashlib import os import os as _os import shutil import subprocess as sp import sys as _sys import warnings import warnings as _warnings from collections.abc import MutableMapping, Sequence from contextlib import ExitStack, contextmanager from functools import partial from glob import iglob from itertools import chain, count, filterfalse, groupby from tempfile import mkdtemp import deprecation import numpy as np import requests from boltons.cacheutils import LRU from PIL import Image from scipy.interpolate import griddata from tqdm import tqdm def build_padded_2d_array(arrays, max_length=None, padding_value=np.nan): if max_length is None: max_length = np.max([len(a) for a in arrays]) #output = np.ones((len(arrays), max_length), dtype=np.asarray(arrays[0]).dtype) dtype = np.asarray(arrays[0]).dtype if arrays else np.float64 if np.issubdtype(dtype, np.integer) and padding_value is np.nan: dtype = np.float64 output = np.full((len(arrays), max_length), fill_value=padding_value, dtype=dtype) for i, array in enumerate(arrays): output[i, :len(array)] = array return output def full_split(filename): """ split filename into all parts """ path, basename = os.path.split(filename) components = [path, basename] while components[0] and components[0] != '/': first_part = components.pop(0) path, basename = os.path.split(first_part) components = [path, basename] + components if components[0] == '': components = components[1:] return components def get_minimal_unique_filenames(filenames): if len(filenames) <= 1: return [os.path.basename(item) for item in filenames] components = [full_split(filename) for filename in filenames] while len(set(item[0] for item in components)) <= 1: components = [item[1:] for item in components] return [os.path.join(*item) for item in components] def remove_trailing_nans(data): """Filters a scanpath arrays to remove the ending part of nans.""" for i in range(len(data)): if np.all(np.isnan(data[i:])): return data[:i] return data def lazy_property(fn): """Lazy property: Is only calculated when first used. Code from http://stackoverflow.com/questions/3012421/python-lazy-property-decorator""" attr_name = '_lazy_' + fn.__name__ @property def _lazyprop(self): if not hasattr(self, attr_name): setattr(self, attr_name, fn(self)) return getattr(self, attr_name) return _lazyprop class LazyList(Sequence): """ A list-like class that is able to generate it's entries only when needed. Entries can be cached. LayList implements `collections.Sequence` and therefore `__contains__`. However, to use it, in the worst case all elements of the list have to be generated. .. note:: As `LazyList` stores the generator function, pickling it will usually fail. To pickle a `LazyList`, use `dill`. """ def __init__(self, generator, length, cache=True, cache_size=None, pickle_cache=False): """ Parameters ---------- @type generator: callable @param generator: A function that takes an integer `n` and returns the `n`-th element of the list. @type length: int @param length: The length of the list @type cache: bool, defaults to `True` @param cache: Wether to cache the list items. @type pickle_cache: bool, defaults to `False` @param pickle_cache: Whether the cache should be saved when pickling the object. """ self.generator = generator self.length = length self._do_cache = cache self.pickle_cache = pickle_cache if cache_size is None: if not cache: cache_size = 1 else: cache_size = 1000000 self.cache_size = cache_size self._cache = LRU(max_size=cache_size, on_miss=self.generator) def __len__(self): return self.length def __getitem__(self, index): if isinstance(index, slice): return [self[i] for i in range(len(self))[index]] elif isinstance(index, list): return [self[i] for i in index] else: return self._getitem(index) def _getitem(self, index): if not 0 <= index < self.length: raise IndexError(index) return self._cache[index] def __getstate__(self): # we don't want to save the cache state = dict(self.__dict__) if not self.pickle_cache: state.pop('_cache') else: # pickle only the cached valueas state['_cache'] = dict(state['_cache']) return state def __setstate__(self, state): if state['_do_cache']: actual_cache_size = state['cache_size'] else: actual_cache_size = 1 if not '_cache' in state: state['_cache'] = LRU(max_size=actual_cache_size, on_miss=state['generator']) else: state['_cache'] = LRU(max_size=actual_cache_size, values=state['_cache'], on_miss=state['generator']) self.__dict__ = dict(state) @property def cache(self): return self._do_cache @cache.setter def cache(self, value): self._do_cache = value if value: self._cache.max_size = self.cache_size else: self._cache.max_size = 1 self._cache.clear() @contextmanager def atomic_directory_setup(directory): """ context manager that makes sure that directory is deleted in case of exceptions. """ with ExitStack() as stack: if directory is not None: stack.callback(lambda: shutil.rmtree(directory)) yield stack.pop_all() def which(program): """ Check whether a program is present on the system. from https://stackoverflow.com/a/377028 """ def is_exe(fpath): return os.path.isfile(fpath) and os.access(fpath, os.X_OK) fpath, fname = os.path.split(program) if fpath: if is_exe(program): return program else: for path in os.environ["PATH"].split(os.pathsep): path = path.strip('"') exe_file = os.path.join(path, program) if is_exe(exe_file): return exe_file return None def filter_files(filenames, ignores): """ Filter a list of files, excluding all filenames which contain an element of `ignores` as part of their path """ parts = map(full_split, filenames) inds = [i for i, ps in enumerate(parts) if not any([ignore in ps for ignore in ignores])] return [filenames[i] for i in inds] class MatlabOptions(object): matlab_names = ['matlab', 'matlab.exe'] octave_names = ['octave', 'octave.exe'] def get_matlab_or_octave(): for name in MatlabOptions.matlab_names + MatlabOptions.octave_names: if which(name): return which(name) raise Exception('No version of matlab or octave was found on this system!') def run_matlab_cmd(cmd, cwd=None): matlab = get_matlab_or_octave() args = [] if os.path.basename(matlab).startswith('matlab'): # args += ['-nodesktop', '-nosplash', '-r'] args += ['-batch'] args.append("try;{};catch exc;disp(getReport(exc));disp('__ERROR__');exit(1);end;quit".format(cmd)) else: args += ['--traditional', '--eval'] args.append("try;{};catch exc;struct_levels_to_print(10);print_struct_array_contents(true);disp(lasterror);for i=1:size(lasterror.stack);disp(lasterror.stack(i));end;disp('__ERROR__');exit(1);end;quit".format(cmd)) sp.check_call([matlab] + args, cwd=cwd) def check_file_hash(filename, md5_hash): """ Check a file's hash and issue a warning it is has not the expected value. """ print('Checking md5 sum...') hasher = hashlib.md5() with open(filename, 'rb') as f: # read file in chunks to avoid "invalid argument" error # see https://stackoverflow.com/a/48123430 for block in iter(partial(f.read, 64 * (1 << 20)), b''): hasher.update(block) file_hash = hasher.hexdigest() if file_hash != md5_hash: warnings.warn("MD5 sum of {} has changed. Expected {} but got {}. This might lead to" " this code producing wrong data.".format(filename, md5_hash, file_hash)) def download_file(url, target, verify_ssl=True): r = requests.get(url, stream=True, verify=verify_ssl) total_size = int(r.headers.get('content-length', 0)) with open(target, 'wb') as f: with tqdm(total=total_size, unit='B', unit_scale=True, desc='Downloading file') as progress_bar: for data in r.iter_content(32*1024): f.write(data) progress_bar.update(32*1024) if r.status_code in [403, 404, 500, 503]: raise ValueError("Error downloading file from {}. Status code: {}".format(url, r.status_code)) def download_and_check(url, target, md5_hash, verify_ssl=True): """Download url to target and check for correct md5_hash. Prints warning if hash is not correct.""" download_file(url, target, verify_ssl=verify_ssl) check_file_hash(target, md5_hash) def download_file_from_google_drive(id, destination): import gdown gdown.download(id=id, output=destination, quiet=False) class Cache(MutableMapping): """Cache that supports saving the items to files Set `cache_location` to save all newly set items to .npy files in cache_location. .. warning :: Items that have been set before setting `cache_location` won't be saved to files! """ def __init__(self, cache_location=None, pickle_cache=False, memory_cache_size=None): if memory_cache_size: self._cache = LRU(max_size=memory_cache_size) else: self._cache = {} self.cache_location = cache_location self.pickle_cache = pickle_cache def clear(self): """ Clear memory cache""" self._cache = {} def filename(self, key): return os.path.join(self.cache_location, '{}.npy'.format(key)) def __getitem__(self, key): if not key in self._cache: if self.cache_location is not None: filename = self.filename(key) if os.path.exists(filename): value = np.load(filename) self._cache[key] = value else: raise KeyError('Key {} neither in cache nor on disk'.format(key)) return self._cache[key] def __setitem__(self, key, value): if not isinstance(key, str): raise TypeError('Only string keys are supported right now!') if self.cache_location is not None: if not os.path.exists(self.cache_location): os.makedirs(self.cache_location) filename = self.filename(key) np.save(filename, value) self._cache[key] = value def __delitem__(self, key): if self.cache_location is not None: filename = self.filename(key) if os.path.exists(filename): os.remove(filename) del self._cache[key] def __iter__(self): if self.cache_location is not None: filenames = iglob(self.filename('*')) keys = map(lambda f: os.path.splitext(os.path.basename(f))[0], filenames) new_keys = filterfalse(lambda key: key in self._cache.keys(), keys) return chain(iter(self._cache.keys()), new_keys) else: return iter(self._cache.keys()) def __len__(self): i = iter(self) return len(list(i)) def __getstate__(self): # we don't want to save the cache state = dict(self.__dict__) if not self.pickle_cache: state.pop('_cache') return state def __setstate__(self, state): if not '_cache' in state: if state.get('memory_cache_size'): state['_cache'] = LRU(max_size=memory_cache_size) else: state['_cache'] = {} self.__dict__ = dict(state) def average_values(values, fixations, average='fixation'): if average == 'fixation': return np.mean(values) elif average == 'image': import pandas as pd df = pd.DataFrame({'n': fixations.n, 'value': values}) return df.groupby('n')['value'].mean().mean() else: raise ValueError(average) def deprecated_class(deprecated_in=None, removed_in=None, current_version=None, details=''): def wrap(cls): class DeprecatedClass(cls): @deprecation.deprecated(deprecated_in=deprecated_in, removed_in=removed_in, current_version=current_version, details=details) def __init__(self, *args, **kwargs): super(DeprecatedClass, self).__init__(*args, **kwargs) return DeprecatedClass return wrap def inter_and_extrapolate(data, interpolation_method='linear', extrapolation_method='nearest'): """intrapolate data with one method and then extrapolate outside values with another method By default, linear interpolation and nearest extrapolation is used. """ def get_values(data): xs, ys = np.nonzero(~np.isnan(data)) zs = data[xs, ys] points = np.vstack([xs, ys]).T return points, zs grid_x, grid_y = np.mgrid[:data.shape[0], :data.shape[1]] points, values = get_values(data) interpolated = griddata(points, values, (grid_x, grid_y), method=interpolation_method) points, values = get_values(interpolated) extrapolated = griddata(points, values, (grid_x, grid_y), method=extrapolation_method) return extrapolated def iterator_chunks(iterable, chunk_size=10): """return iterarable in chunks which are themselves iterables.""" counter = count() for _, g in groupby(iterable, lambda _: next(counter) // chunk_size): yield g def as_rgb(image: np.ndarray): """makes sure that image data is in 8 bit RGB format""" pil_image = Image.fromarray(image) if pil_image.mode == 'I;16': # convert 16 bit images to 8 bit array = np.uint8(np.array(pil_image) / 256) pil_image = Image.fromarray(array) rgb_image = pil_image.convert('RGB') return np.array(rgb_image) ================================================ FILE: pysaliency/utils/variable_length_array.py ================================================ from typing import Optional, Union, List import numpy as np from . import build_padded_2d_array class VariableLengthArray: """ Represents a variable length array. The following indexing operations are supported: - Accessing rows: array[i] - Accessing elements: array[i, j] where j can also be negative to get elements from the end of the row - Slicing: array[i:j, k] where k can also be negative to get elements from the end of each row Args: data (Union[np.ndarray, list[list]]): The data for the array. Can be either a numpy array or a list of lists. lengths (np.ndarray): The lengths of each row in the data array. Attributes: _data (np.ndarray): The internal data array with padded rows. lengths (np.ndarray): The lengths of each row in the data array. Methods: __len__(): Returns the number of rows in the array. __getitem__(index): Returns the value(s) at the specified index(es) in the array. """ def __init__(self, data: Union[np.ndarray, List[list]], lengths: Optional[np.ndarray] = None): """List Initialize the VariableLengthArray object. Args: data (Union[np.ndarray, list[list]]): The input data, which can be either a numpy array or a list of lists. lengths (np.ndarray): An array containing the lengths of each row in the data. Raises: ValueError: If the input data shape doesn't match the provided lengths. """ if lengths is not None: if len(data) != len(lengths): raise ValueError(f"The number of rows in the data array has to match the number of elements in lengths ({len(data)} != {len(lengths)})") if not isinstance(data, np.ndarray): for row, length in zip(data, lengths): if len(row) != length: raise ValueError(f"The length of row {row} does not match the specified length {length}") else: if not data.ndim >= 2: raise ValueError("If data is a numpy array, it has to be at least 2-dimensional") if len(lengths) and np.max(lengths) > data.shape[1]: raise ValueError("The specified lengths are larger than the number of columns in the data array") lengths = np.array(lengths, dtype=int) else: if isinstance(data, np.ndarray): raise ValueError("If data is a numpy array, lengths must be provided") lengths = np.array([len(row) for row in data]) if isinstance(data, np.ndarray): self._data = data else: self._data = build_padded_2d_array(data, max_length=np.max(lengths) if len(lengths) else 0) # max_len = np.max(lengths) # self._data = np.full((len(data), max_len), np.nan) # for i, row in enumerate(data): # if len(row) < lengths[i]: # raise ValueError(f"Row {i} has fewer elements than specified in lengths ({len(row)} < {lengths[i]}") # self._data[i, :lengths[i]] = row[:lengths[i]] self.lengths = lengths def __len__(self): return len(self._data) def __getitem__(self, index): if isinstance(index, tuple): row_idx, col_idx = index if isinstance(row_idx, slice): if isinstance(col_idx, int): return np.array([self._data[i, :self.lengths[i]][col_idx] for i in range(*row_idx.indices(len(self._data)))]) elif isinstance(col_idx, slice): # does this work? return self._data[row_idx, :self.lengths[row_idx]][col_idx] else: return self._data[row_idx, :self.lengths[row_idx]][col_idx] elif isinstance(index, (int, np.integer)): return self._data[index, :self.lengths[index]] else: return VariableLengthArray(self._data[index], self.lengths[index]) # new_lengths = self.lengths[index] # max_length = np.max(new_lengths) # new_data = self._data[index, :max_length] # return VariableLengthArray(new_data, new_lengths) def copy(self) -> 'VariableLengthArray': return VariableLengthArray(self._data.copy(), self.lengths.copy()) def __repr__(self): representation = "VariableLengthArray(\n" if len(self) < 10: for i in range(len(self)): representation += f" {self[i]}\n" else: for i in range(5): representation += f" {self[i]}\n" representation += " ...\n" for i in range(len(self)-5, len(self)): representation += f" {self[i]}\n" representation += ")" return representation def concatenate_variable_length_arrays(arrays: List[VariableLengthArray]) -> VariableLengthArray: """ Concatenate a list of VariableLengthArray objects along the first axis. Args: arrays (List[VariableLengthArray]): List of VariableLengthArray objects to concatenate. Returns: VariableLengthArray: The concatenated VariableLengthArray object. """ lengths = np.concatenate([array.lengths for array in arrays]) datas = [array._data for array in arrays] max_cols = max(a.shape[1] for a in datas) padded_datas = [] for a in datas: if a.shape[1] < max_cols: padding = np.empty((a.shape[0], max_cols-a.shape[1]), dtype=a.dtype) padding[:] = np.nan padded_datas.append(np.hstack((a, padding))) else: padded_datas.append(a) data = np.vstack(padded_datas) return VariableLengthArray(data, lengths) ================================================ FILE: pytest.ini ================================================ [pytest] markers = slow: marks tests as being slow (skipped by default, select with '--runslow') nonfree: marks tests as requiring nonpublic data(skipeed by default, select with '--run-nonfree') theano: marks tests as using theano (deselect with '-m "not theano"') matlab: marks tests as using matlab (deselect with '-m "not matlab"') download: marks tests as using downloads (deselect with '-m "not download"') skip_octave: marks tests to be skipped when using octave instead of matlab serial ================================================ FILE: requirements.txt ================================================ Cython dill h5py imageio natsort numpy scipy ================================================ FILE: setup.py ================================================ # -*- coding: utf-8 -*- from os import path from setuptools import setup, find_packages from setuptools.extension import Extension from Cython.Build import cythonize import numpy as np import io PACKAGE_NAME = 'pysaliency' VERSION = '0.2.23' DESCRIPTION = 'A Python Framework for Saliency Modeling and Evaluation' AUTHOR = 'Matthias Kümmerer' EMAIL = 'matthias.kuemmerer@bethgelab.org' URL = "https://github.com/matthiask/pysaliency" try: this_directory = path.abspath(path.dirname(__file__)) with io.open(path.join(this_directory, 'README.md'), encoding='utf-8') as f: long_description = f.read() except IOError: long_description = '' extensions = [ Extension("pysaliency.roc_cython", ['pysaliency/*.pyx'], include_dirs = [np.get_include()], extra_compile_args = ['-O3'], #extra_compile_args = ['-fopenmp', '-O3'], #extra_link_args=["-fopenmp"] ), ] setup( name = PACKAGE_NAME, version = VERSION, description = 'python library to develop, evaluate and benchmark saliency models', long_description = long_description, long_description_content_type='text/markdown', classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Topic :: Scientific/Engineering", ], packages = find_packages(), author = AUTHOR, author_email = EMAIL, url = URL, license = 'MIT', python_requires='>=3.9', install_requires=[ 'boltons', 'deprecation', 'imageio', 'natsort', 'numba', 'numpy', 'piexif', 'requests', 'schema', 'scipy', 'tqdm', ], include_package_data = True, package_data={'pysaliency': ['external_models/scripts/*.m', 'external_models/scripts/*/*.m', 'external_models/scripts/*/*/*', 'external_models/scripts/BMS/patches/*', 'external_models/scripts/GBVS/patches/*', 'external_models/scripts/Judd/patches/*', 'external_datasets/scripts/*.m' ]}, ext_modules = cythonize(extensions), ) ================================================ FILE: tests/conftest.py ================================================ import os import sys from pathlib import Path from unittest.mock import MagicMock import pytest import requests from tqdm import tqdm # make sure that package can be found when running `pytest` instead of `python -m pytest` sys.path.insert(0, os.getcwd()) def pytest_addoption(parser): parser.addoption("--runslow", action="store_true", default=False, help="run slow tests") parser.addoption("--run-nonfree", action="store_true", default=False, help="run tests requiring nonpublic data") parser.addoption("--nomatlab", action="store_true", default=False, help="don't run matlab tests") parser.addoption("--nooctave", action="store_true", default=False, help="don't run octave tests") parser.addoption("--notheano", action="store_true", default=False, help="don't run slow theano tests") parser.addoption("--nodownload", action="store_true", default=False, help="don't download external data") def pytest_collection_modifyitems(config, items): run_slow = config.getoption("--runslow") run_nonfree = config.getoption('--run-nonfree') no_matlab = config.getoption("--nomatlab") no_theano = config.getoption("--notheano") no_download = config.getoption("--nodownload") skip_slow = pytest.mark.skip(reason="need --runslow option to run") skip_nonfree = pytest.mark.skip(reason="need --run-nonfree option to run") skip_matlab = pytest.mark.skip(reason="skipped because of --nomatlab") skip_theano = pytest.mark.skip(reason="skipped because of --notheano") skip_download = pytest.mark.skip(reason="skipped because of --nodownload") for item in items: if "slow" in item.keywords and not run_slow: item.add_marker(skip_slow) if 'nonfree' in item.keywords and not run_nonfree: item.add_marker(skip_nonfree) if "matlab" in item.keywords and no_matlab: item.add_marker(skip_matlab) if "theano" in item.keywords and no_theano: item.add_marker(skip_theano) if "download" in item.keywords and no_download: item.add_marker(skip_download) @pytest.fixture(params=["matlab", "octave"]) def matlab(request, pytestconfig): import pysaliency.utils if request.param == "matlab": pysaliency.utils.MatlabOptions.matlab_names = ['matlab', 'matlab.exe'] pysaliency.utils.MatlabOptions.octave_names = [] elif request.param == 'octave': if pytestconfig.getoption("--nooctave"): pytest.skip("skipped octave due to command line option") elif any([marker.name == 'skip_octave' for marker in request.node.own_markers]): pytest.skip("skipped octave due to test marker") pysaliency.utils.MatlabOptions.matlab_names = [] pysaliency.utils.MatlabOptions.octave_names = ['octave', 'octave.exe'] return request.param #@pytest.fixture(params=["no_location", "with_location"]) #def location(tmpdir, request): # if request.param == 'no_location': # return None # elif request.param == 'with_location': # return tmpdir # else: # raise ValueError(request.param) # we don't test in memory external datasets anymore # we'll probably get rid of them anyway # TODO: remove this fixture, replace with tmpdir @pytest.fixture() def location(tmpdir): return tmpdir @pytest.fixture(autouse=True) def cache_requests(monkeypatch): """This fixture caches requests to avoid downloading the same file multiple times. TODO: There should be an option to disable this fixture, e.g. when we want to test downloading. """ original_get = requests.get def mock_get(url, *args, **kwargs): cache_dir = Path("download_cache") cache_dir.mkdir(exist_ok=True) cache_filename = ( url.replace("http://", "") .replace("https://", "") .replace("/", "_") .replace("?", "_") .replace("=", "_") .replace("&", "_") .replace(":", "_") .replace(".", "_") ) cache_file = cache_dir / cache_filename print("caching", url, "to", cache_file) if not cache_file.exists(): response = original_get(url, *args, **kwargs) total_size = int(response.headers.get('content-length', 0)) with open(cache_file, 'wb') as f: with tqdm(total=total_size, unit='B', unit_scale=True, desc='Downloading file') as progress_bar: for chunk in response.iter_content(32*1024): f.write(chunk) progress_bar.update(len(chunk)) with open(cache_file, 'rb') as f: content = f.read() mock_response = MagicMock() mock_response.iter_content = lambda chunk_size: [content[i:i+chunk_size] for i in range(0, len(content), chunk_size)] mock_response.headers = {'content-length': str(len(content))} mock_response.status_code = 200 return mock_response monkeypatch.setattr(requests, "get", mock_get) ================================================ FILE: tests/datasets/test_datasets.py ================================================ import numpy as np import pytest from imageio import imwrite import pysaliency from tests.datasets.utils import assert_fixation_trains_equal, assert_fixations_equal, assert_scanpath_fixations_equal @pytest.fixture def file_stimuli_with_attributes(tmpdir): filenames = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) attributes = { 'dva': list(range(len(filenames))), 'other_stuff': np.random.randn(len(filenames)), 'some_strings': list('abcdefghijklmnopqr'), } return pysaliency.FileStimuli(filenames=filenames, attributes=attributes) @pytest.fixture def scanpath_fixations() -> pysaliency.ScanpathFixations: xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subject = [0, 1, 1] tasks = [0, 1, 0] multi_dim_attribute = [[0.0, 1],[2, 3], [4, 5.5]] durations_train = [ [42, 25, 100], [99, 98], [200, 150, 120] ] scanpaths = pysaliency.Scanpaths( xs=xs_trains, ys=ys_trains, n=ns, scanpath_attributes={ 'task': tasks, 'multi_dim_attribute': multi_dim_attribute, 'subject': subject, }, fixation_attributes={'durations': durations_train, 'ts': ts_trains}, attribute_mapping={'durations': 'duration', 'ts': 't'}, ) return pysaliency.ScanpathFixations(scanpaths=scanpaths) @pytest.fixture def fixation_trains(): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subjects = [0, 1, 1] tasks = [0, 1, 0] multi_dim_attribute = [[0.0, 1],[2, 3], [4, 5.5]] durations_train = [ [42, 25, 100], [99, 98], [200, 150, 120] ] some_attribute = np.arange(len(sum(xs_trains, []))) return pysaliency.FixationTrains.from_fixation_trains( xs_trains, ys_trains, ts_trains, ns, subjects, attributes={'some_attribute': some_attribute}, scanpath_attributes={ 'task': tasks, 'multi_dim_attribute': multi_dim_attribute }, scanpath_fixation_attributes={'durations': durations_train}, scanpath_attribute_mapping={'durations': 'duration'}, ) @pytest.mark.parametrize('stimulus_indices,scanpath_indices,fixation_indices', [ ([0, 1], [0, 1, 2], [0, 1, 2, 3, 4, 5, 6, 7]), ([0], [0, 1], [0, 1, 2, 3, 4]), ([1], [2], [5, 6, 7]), ]) def test_create_subset_scanpath_fixations(file_stimuli_with_attributes, scanpath_fixations, stimulus_indices, scanpath_indices, fixation_indices): sub_stimuli, sub_fixations = pysaliency.datasets.create_subset(file_stimuli_with_attributes, scanpath_fixations, stimulus_indices) expected_sub_fixations = scanpath_fixations.filter_scanpaths(scanpath_indices).copy() expected_scanpath_n = np.array([stimulus_indices.index(n) for n in expected_sub_fixations.scanpaths.n]) expected_sub_fixations.scanpaths.n = expected_scanpath_n expected_fixation_n = np.array([stimulus_indices.index(n) for n in expected_sub_fixations.n]) expected_sub_fixations.n = expected_fixation_n assert_scanpath_fixations_equal(sub_fixations, expected_sub_fixations) @pytest.mark.parametrize('stimulus_indices,scanpath_indices,fixation_indices', [ ([0, 1], [0, 1, 2], [0, 1, 2, 3, 4, 5, 6, 7]), ([0], [0, 1], [0, 1, 2, 3, 4]), ([1], [2], [5, 6, 7]), ]) def test_create_subset_fixation_trains(file_stimuli_with_attributes, fixation_trains, stimulus_indices, scanpath_indices, fixation_indices): sub_stimuli, sub_fixations = pysaliency.datasets.create_subset(file_stimuli_with_attributes, fixation_trains, stimulus_indices) expected_sub_fixations= fixation_trains.filter_scanpaths(scanpath_indices).copy() expected_scanpath_n = np.array([stimulus_indices.index(n) for n in expected_sub_fixations.scanpaths.n]) expected_sub_fixations.scanpaths.n = expected_scanpath_n expected_fixation_n = np.array([stimulus_indices.index(n) for n in expected_sub_fixations.n]) expected_sub_fixations.n = expected_fixation_n assert_fixation_trains_equal(sub_fixations, expected_sub_fixations) @pytest.mark.parametrize('stimulus_indices,scanpath_indices,fixation_indices', [ ([0, 1], [0, 1, 2], [0, 1, 2, 3, 4, 5, 6, 7]), ([0], [0, 1], [0, 1, 2, 3, 4]), ([1], [2], [5, 6, 7]), ]) def test_create_subset_fixations(file_stimuli_with_attributes, fixation_trains, stimulus_indices, scanpath_indices, fixation_indices): fixations = fixation_trains[:] sub_stimuli, sub_fixations = pysaliency.datasets.create_subset(file_stimuli_with_attributes, fixations, stimulus_indices) expected_sub_fixations= fixations[fixation_indices].copy() expected_fixation_n = np.array([stimulus_indices.index(n) for n in expected_sub_fixations.n]) expected_sub_fixations.n = expected_fixation_n assert not isinstance(sub_fixations, pysaliency.FixationTrains) assert_fixations_equal(sub_fixations, expected_sub_fixations) def test_create_subset_numpy_indices(file_stimuli_with_attributes, fixation_trains): stimulus_indices = np.array([0, 3]) sub_stimuli, sub_fixations = pysaliency.datasets.create_subset(file_stimuli_with_attributes, fixation_trains, stimulus_indices) assert isinstance(sub_fixations, pysaliency.FixationTrains) assert len(sub_stimuli) == 2 np.testing.assert_array_equal(sub_fixations.x, fixation_trains.x[np.isin(fixation_trains.n, stimulus_indices)]) def test_create_subset_numpy_mask(file_stimuli_with_attributes, fixation_trains): print(len(file_stimuli_with_attributes)) stimulus_indices = np.zeros(len(file_stimuli_with_attributes), dtype=bool) stimulus_indices[0] = True stimulus_indices[2] = True sub_stimuli, sub_fixations = pysaliency.datasets.create_subset(file_stimuli_with_attributes, fixation_trains, stimulus_indices) assert isinstance(sub_fixations, pysaliency.FixationTrains) assert len(sub_stimuli) == 2 np.testing.assert_array_equal(sub_fixations.x, fixation_trains.x[np.isin(fixation_trains.n, [0, 2])]) def test_concatenate_datasets_with_scanpath_fixations(file_stimuli_with_attributes, scanpath_fixations): stimuli1 = file_stimuli_with_attributes stimuli2 = file_stimuli_with_attributes fixations1 = scanpath_fixations fixations2 = scanpath_fixations stimuli_list = [stimuli1, stimuli2] fixations_list = [fixations1, fixations2] concatenated_stimuli, concatenated_fixations = pysaliency.datasets.concatenate_datasets(stimuli_list, fixations_list) modified_fixations2 = fixations2.copy() modified_fixations2.n += len(stimuli1) modified_fixations2.scanpaths.n += len(stimuli1) expected_fixations = pysaliency.datasets.concatenate_fixations([fixations1, modified_fixations2]) assert len(concatenated_stimuli) == len(stimuli1) + len(stimuli2) assert_scanpath_fixations_equal(concatenated_fixations, expected_fixations) def test_concatenate_datasets_with_fixation_trains(file_stimuli_with_attributes, fixation_trains): stimuli1 = file_stimuli_with_attributes stimuli2 = file_stimuli_with_attributes fixations1 = fixation_trains fixations2 = fixation_trains stimuli_list = [stimuli1, stimuli2] fixations_list = [fixations1, fixations2] concatenated_stimuli, concatenated_fixations = pysaliency.datasets.concatenate_datasets(stimuli_list, fixations_list) modified_fixations2 = fixations2.copy() modified_fixations2.n += len(stimuli1) modified_fixations2.scanpaths.n += len(stimuli1) expected_fixations = pysaliency.datasets.concatenate_fixations([fixations1, modified_fixations2]) assert len(concatenated_stimuli) == len(stimuli1) + len(stimuli2) assert_fixation_trains_equal(concatenated_fixations, expected_fixations) ================================================ FILE: tests/datasets/test_fixations.py ================================================ import os.path import pickle import numpy as np import pytest from hypothesis import given from hypothesis import strategies as st from imageio import imwrite from test_helpers import TestWithData import pysaliency from pysaliency.datasets import Fixations, FixationTrains, ScanpathFixations, scanpaths_from_fixations from pysaliency.datasets.scanpaths import Scanpaths from pysaliency.utils.variable_length_array import VariableLengthArray from tests.datasets.utils import assert_fixation_trains_equal, assert_fixations_equal, assert_scanpath_fixations_equal, assert_scanpaths_equal, assert_variable_length_array_equal, compare_fixations_subset @pytest.fixture def file_stimuli_with_attributes(tmpdir): filenames = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) attributes = { 'dva': list(range(len(filenames))), 'other_stuff': np.random.randn(len(filenames)), 'some_strings': list('abcdefghijklmnopqr'), } return pysaliency.FileStimuli(filenames=filenames, attributes=attributes) class TestFixations(TestWithData): def test_from_fixations(self): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subject = [0, 1, 1] tasks = [0, 1, 0] string_attribute = ['train', 'train', 'test'] some_attribute = np.arange(len(sum(xs_trains, []))) # Create Fixations f = pysaliency.FixationTrains.from_fixation_trains( xs_trains, ys_trains, ts_trains, ns, subject, attributes={'some_attribute': some_attribute}, scanpath_attributes={'task': tasks, 'string_attribute': string_attribute}, ) # Test fixation trains assert_variable_length_array_equal(f.train_xs, VariableLengthArray(xs_trains)) assert_variable_length_array_equal(f.train_ys, VariableLengthArray(ys_trains)) assert_variable_length_array_equal(f.train_ts, VariableLengthArray(ts_trains)) np.testing.assert_allclose(f.train_ns, ns) np.testing.assert_allclose(f.train_subjects, subject) # Test conditional fixations np.testing.assert_allclose(f.x, [0, 1, 2, 2, 2, 1, 5, 3]) np.testing.assert_allclose(f.y, [10, 11, 12, 12, 12, 21, 25, 33]) np.testing.assert_allclose(f.t, [0, 200, 600, 100, 400, 50, 500, 900]) np.testing.assert_allclose(f.n, [0, 0, 0, 0, 0, 1, 1, 1]) np.testing.assert_allclose(f.subject, [0, 0, 0, 1, 1, 1, 1, 1]) np.testing.assert_allclose(f.scanpath_history_length, [0, 1, 2, 0, 1, 0, 1, 2]) with pytest.deprecated_call(): np.testing.assert_array_equal(f.lengths, f.scanpath_history_length) with pytest.deprecated_call(): np.testing.assert_array_equal(f.subjects, f.subject) assert_variable_length_array_equal( f.x_hist, VariableLengthArray([ [], [0], [0, 1], [], [2], [], [1], [1, 5] ]) ) def test_filter(self): xs_trains = [] ys_trains = [] ts_trains = [] ns = [] subjects = [] for n in range(1000): size = np.random.randint(10) xs_trains.append(np.random.randn(size)) ys_trains.append(np.random.randn(size)) ts_trains.append(np.cumsum(np.square(np.random.randn(size)))) ns.append(np.random.randint(20)) subjects.append(np.random.randint(20)) f = pysaliency.FixationTrains.from_fixation_trains(xs_trains, ys_trains, ts_trains, ns, subjects) # First order filtering inds = f.n == 10 _f = f.filter(inds) self.assertNotIsInstance(_f, pysaliency.FixationTrains) compare_fixations_subset(_f, f, inds) # second order filtering inds = np.nonzero(f.n == 10)[0] _f = f.filter(inds) inds2 = np.nonzero(_f.subject == 0)[0] __f = _f.filter(inds2) cum_inds = inds[inds2] compare_fixations_subset(__f, f, cum_inds) def test_scanpaths(self): xs_trains = [] ys_trains = [] ts_trains = [] ns = [] subjects = [] for n in range(1000): size = np.random.randint(10) xs_trains.append(np.random.randn(size)) ys_trains.append(np.random.randn(size)) ts_trains.append(np.cumsum(np.square(np.random.randn(size)))) ns.append(np.random.randint(20)) subjects.append(np.random.randint(20)) f = pysaliency.FixationTrains.from_fixation_trains(xs_trains, ys_trains, ts_trains, ns, subjects) # First order filtering inds = f.train_ns == 10 _f = f.filter_scanpaths(inds) self.assertIsInstance(_f, pysaliency.FixationTrains) equivalent_indices = f.n == 10 compare_fixations_subset(_f, f, equivalent_indices) ## second order filtering # inds = np.nonzero(f.n == 10)[0] # _f = f.filter(inds) # inds2 = np.nonzero(_f.subjects == 0)[0] # __f = _f.filter(inds2) # cum_inds = inds[inds2] # compare_fixations_subset(__f, f, cum_inds) @pytest.fixture def scanpath_fixations() -> ScanpathFixations: xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subject = [0, 1, 1] tasks = [0, 1, 0] string_attribute = ['train', 'train', 'test'] multi_dim_attribute = [[0.0, 1],[2, 3], [4, 5.5]] durations_train = [ [42, 25, 100], [99, 98], [200, 150, 120] ] scanpaths = Scanpaths( xs=xs_trains, ys=ys_trains, n=ns, scanpath_attributes={ 'task': tasks, 'multi_dim_attribute': multi_dim_attribute, 'subject': subject, 'string_attribute': string_attribute, }, fixation_attributes={'durations': durations_train, 'ts': ts_trains}, attribute_mapping={'durations': 'duration', 'ts': 't'}, ) return ScanpathFixations(scanpaths=scanpaths) @pytest.fixture def fixation_trains(): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subjects = [0, 1, 1] tasks = [0, 1, 0] multi_dim_attribute = [[0.0, 1],[2, 3], [4, 5.5]] durations_train = [ [42, 25, 100], [99, 98], [200, 150, 120] ] some_attribute = np.arange(len(sum(xs_trains, []))) return pysaliency.FixationTrains.from_fixation_trains( xs_trains, ys_trains, ts_trains, ns, subjects, attributes={'some_attribute': some_attribute}, scanpath_attributes={ 'task': tasks, 'multi_dim_attribute': multi_dim_attribute }, scanpath_fixation_attributes={'durations': durations_train}, scanpath_attribute_mapping={'durations': 'duration'}, ) def test_copy_scanpath_fixations(scanpath_fixations): copied_scanpath_fixations = scanpath_fixations.copy() assert_scanpath_fixations_equal(copied_scanpath_fixations, scanpath_fixations) assert copied_scanpath_fixations is not scanpath_fixations def test_copy_fixation_trains(fixation_trains): copied_fixation_trains = fixation_trains.copy() assert_fixation_trains_equal(copied_fixation_trains, fixation_trains) def test_copy_fixations(fixation_trains): fixations = fixation_trains[:] copied_fixations = fixations.copy() assert_fixations_equal(copied_fixations, fixations) def test_write_read_scanpath_fixations_pickle(tmp_path, scanpath_fixations): filename = tmp_path / 'scanpath_fixations.pydat' with open(filename, 'wb') as out_file: pickle.dump(scanpath_fixations, out_file) with open(filename, 'rb') as in_file: new_scanpath_fixations = pickle.load(in_file) # make sure there is no sophisticated caching... assert scanpath_fixations is not new_scanpath_fixations assert_scanpath_fixations_equal(scanpath_fixations, new_scanpath_fixations) def test_write_read_scanpath_fixations_pathlib(tmp_path, scanpath_fixations): filename = tmp_path / 'scanpath_fixations.hdf5' scanpath_fixations.to_hdf5(filename) new_scanpath_fixations = pysaliency.read_hdf5(filename) # make sure there is no sophisticated caching... assert scanpath_fixations is not new_scanpath_fixations assert_scanpath_fixations_equal(scanpath_fixations, new_scanpath_fixations) def test_write_read_fixation_trains_pathlib(tmp_path, fixation_trains): filename = tmp_path / 'fixation_trains.hdf5' fixation_trains.to_hdf5(filename) new_fixation_trains = pysaliency.read_hdf5(filename) # make sure there is no sophisticated caching... assert fixation_trains is not new_fixation_trains assert_fixation_trains_equal(fixation_trains, new_fixation_trains) def test_write_read_fixation_trains(tmp_path, fixation_trains): filename = tmp_path / 'fixation_trains.hdf5' fixation_trains.to_hdf5(str(filename)) new_fixation_trains = pysaliency.read_hdf5(str(filename)) # make sure there is no sophisticated caching... assert fixation_trains is not new_fixation_trains assert_fixation_trains_equal(fixation_trains, new_fixation_trains) def test_scanpath_lengths(fixation_trains): np.testing.assert_array_equal(fixation_trains.train_lengths, [3, 2, 3]) def test_scanpath_fixations_scanpath_attributes(scanpath_fixations): assert "task" in scanpath_fixations.scanpaths.scanpath_attributes assert "task" in scanpath_fixations.__attributes__ np.testing.assert_array_equal(scanpath_fixations.scanpaths.scanpath_attributes['multi_dim_attribute'][0], [0, 1]) np.testing.assert_array_equal(scanpath_fixations.multi_dim_attribute[2], [0, 1]) def test_scanpath_fixations_scanpath_attributes_zero_values(scanpath_fixations): scanpaths = scanpath_fixations.scanpaths scanpaths.scanpath_attributes['task'] = np.zeros((len(scanpaths), 2)) scanpath_fixations = ScanpathFixations(scanpaths=scanpaths) assert "task" in scanpath_fixations.scanpaths.scanpath_attributes assert "task" in scanpath_fixations.__attributes__ np.testing.assert_array_equal(scanpath_fixations.scanpaths.scanpath_attributes['multi_dim_attribute'][0], [0, 1]) np.testing.assert_array_equal(scanpath_fixations.multi_dim_attribute[2], [0, 1]) def test_fixation_trains_scanpath_attributes(fixation_trains): assert "task" in fixation_trains.scanpath_attributes assert "task" in fixation_trains.__attributes__ np.testing.assert_array_equal(fixation_trains.scanpath_attributes['multi_dim_attribute'][0], [0, 1]) np.testing.assert_array_equal(fixation_trains.multi_dim_attribute[2], [0, 1]) def test_scanpath_fixations_scanpath_fixation_attributes(scanpath_fixations): # test attribute itself assert "durations" in scanpath_fixations.scanpaths.fixation_attributes assert isinstance(scanpath_fixations.scanpaths.fixation_attributes['durations'], VariableLengthArray) np.testing.assert_array_equal(scanpath_fixations.scanpaths.fixation_attributes['durations'][0], [42, 25, 100]) np.testing.assert_array_equal(scanpath_fixations.scanpaths.fixation_attributes['durations'][1], [99, 98]) np.testing.assert_array_equal(scanpath_fixations.scanpaths.fixation_attributes['durations'][2], [200, 150, 120]) # test derived fixation attribute assert "duration" in scanpath_fixations.__attributes__ np.testing.assert_array_equal(scanpath_fixations.duration, np.array([ 42, 25, 100, 99, 98, 200, 150, 120 ])) # test derived history attribute assert "duration_hist" in scanpath_fixations.__attributes__ assert isinstance(scanpath_fixations.duration_hist, VariableLengthArray) np.testing.assert_array_equal(scanpath_fixations.duration_hist[0], []) np.testing.assert_array_equal(scanpath_fixations.duration_hist[1], [42]) np.testing.assert_array_equal(scanpath_fixations.duration_hist[2], [42, 25]) np.testing.assert_array_equal(scanpath_fixations.duration_hist[3], []) np.testing.assert_array_equal(scanpath_fixations.duration_hist[4], [99]) np.testing.assert_array_equal(scanpath_fixations.duration_hist[5], []) np.testing.assert_array_equal(scanpath_fixations.duration_hist[6], [200]) np.testing.assert_array_equal(scanpath_fixations.duration_hist[7], [200, 150]) def test_scanpath_fixations_values(scanpath_fixations): """Regression test: exact values produced by ScanpathFixations.__init__""" # x, y, t from the 3 scanpaths: [0,1,2], [2,2], [1,5,3] np.testing.assert_array_equal(scanpath_fixations.x, [0, 1, 2, 2, 2, 1, 5, 3]) np.testing.assert_array_equal(scanpath_fixations.y, [10, 11, 12, 12, 12, 21, 25, 33]) np.testing.assert_array_equal(scanpath_fixations.t, [0, 200, 600, 100, 400, 50, 500, 900]) np.testing.assert_array_equal(scanpath_fixations.n, [0, 0, 0, 0, 0, 1, 1, 1]) np.testing.assert_array_equal(scanpath_fixations.scanpath_index, [0, 0, 0, 1, 1, 2, 2, 2]) # x_hist np.testing.assert_array_equal(scanpath_fixations.x_hist[0], []) np.testing.assert_array_equal(scanpath_fixations.x_hist[1], [0]) np.testing.assert_array_equal(scanpath_fixations.x_hist[2], [0, 1]) np.testing.assert_array_equal(scanpath_fixations.x_hist[3], []) np.testing.assert_array_equal(scanpath_fixations.x_hist[4], [2]) np.testing.assert_array_equal(scanpath_fixations.x_hist[5], []) np.testing.assert_array_equal(scanpath_fixations.x_hist[6], [1]) np.testing.assert_array_equal(scanpath_fixations.x_hist[7], [1, 5]) # y_hist np.testing.assert_array_equal(scanpath_fixations.y_hist[0], []) np.testing.assert_array_equal(scanpath_fixations.y_hist[1], [10]) np.testing.assert_array_equal(scanpath_fixations.y_hist[2], [10, 11]) np.testing.assert_array_equal(scanpath_fixations.y_hist[5], []) np.testing.assert_array_equal(scanpath_fixations.y_hist[7], [21, 25]) # t_hist np.testing.assert_array_equal(scanpath_fixations.t_hist[0], []) np.testing.assert_array_equal(scanpath_fixations.t_hist[2], [0, 200]) np.testing.assert_array_equal(scanpath_fixations.t_hist[7], [50, 500]) # scanpath_history_length np.testing.assert_array_equal(scanpath_fixations.scanpath_history_length, [0, 1, 2, 0, 1, 0, 1, 2]) # task (scanpath attribute) np.testing.assert_array_equal(scanpath_fixations.task, [0, 0, 0, 1, 1, 0, 0, 0]) # multi_dim_attribute (scanpath attribute with shape) np.testing.assert_array_equal(scanpath_fixations.multi_dim_attribute[0], [0, 1]) np.testing.assert_array_equal(scanpath_fixations.multi_dim_attribute[3], [2, 3]) np.testing.assert_array_equal(scanpath_fixations.multi_dim_attribute[5], [4, 5.5]) # duration (fixation attribute) and duration_hist np.testing.assert_array_equal(scanpath_fixations.duration, [42, 25, 100, 99, 98, 200, 150, 120]) np.testing.assert_array_equal(scanpath_fixations.duration_hist[0], []) np.testing.assert_array_equal(scanpath_fixations.duration_hist[1], [42]) np.testing.assert_array_equal(scanpath_fixations.duration_hist[2], [42, 25]) np.testing.assert_array_equal(scanpath_fixations.duration_hist[4], [99]) np.testing.assert_array_equal(scanpath_fixations.duration_hist[7], [200, 150]) def test_scanpath_fixations_empty(): """ScanpathFixations should handle empty Scanpaths without errors.""" scanpaths = Scanpaths( xs=[], ys=[], n=[], length=[], scanpath_attributes={}, fixation_attributes={}, ) sf = ScanpathFixations(scanpaths=scanpaths) assert len(sf.x) == 0 assert len(sf.y) == 0 assert len(sf.t) == 0 assert len(sf.n) == 0 assert len(sf.x_hist) == 0 def test_fixation_trains_scanpath_fixation_attributes(fixation_trains): # test attribute itself assert "durations" in fixation_trains.scanpath_fixation_attributes assert isinstance(fixation_trains.scanpath_fixation_attributes['durations'], VariableLengthArray) np.testing.assert_array_equal(fixation_trains.scanpath_fixation_attributes['durations'][0], [42, 25, 100]) np.testing.assert_array_equal(fixation_trains.scanpath_fixation_attributes['durations'][1], [99, 98]) np.testing.assert_array_equal(fixation_trains.scanpath_fixation_attributes['durations'][2], [200, 150, 120]) # test derived fixation attribute assert "duration" in fixation_trains.__attributes__ np.testing.assert_array_equal(fixation_trains.duration, np.array([ 42, 25, 100, 99, 98, 200, 150, 120 ])) # test derived history attribute assert "duration_hist" in fixation_trains.__attributes__ assert isinstance(fixation_trains.duration_hist, VariableLengthArray) np.testing.assert_array_equal(fixation_trains.duration_hist[0], []) np.testing.assert_array_equal(fixation_trains.duration_hist[1], [42]) np.testing.assert_array_equal(fixation_trains.duration_hist[2], [42, 25]) np.testing.assert_array_equal(fixation_trains.duration_hist[3], []) np.testing.assert_array_equal(fixation_trains.duration_hist[4], [99]) np.testing.assert_array_equal(fixation_trains.duration_hist[5], []) np.testing.assert_array_equal(fixation_trains.duration_hist[6], [200]) np.testing.assert_array_equal(fixation_trains.duration_hist[7], [200, 150]) @pytest.mark.parametrize('scanpath_indices,fixation_indices', [ ([0, 2], [0, 1, 2, 5, 6, 7]), ([1, 2], [3, 4, 5, 6, 7]), ([2], [5, 6, 7]), ]) def test_scanpath_fixations_filter_scanpaths(scanpath_fixations, scanpath_indices, fixation_indices): sub_fixations = scanpath_fixations.filter_scanpaths(scanpath_indices) assert_scanpaths_equal(sub_fixations.scanpaths, scanpath_fixations.scanpaths[scanpath_indices]) assert_fixations_equal(sub_fixations, scanpath_fixations[fixation_indices]) @pytest.mark.parametrize('scanpath_indices,fixation_indices', [ ([0, 2], [0, 1, 2, 5, 6, 7]), ([1, 2], [3, 4, 5, 6, 7]), ([2], [5, 6, 7]), ]) def test_filter_fixation_trains(fixation_trains, scanpath_indices, fixation_indices): sub_fixations = fixation_trains.filter_scanpaths(scanpath_indices) assert_variable_length_array_equal( sub_fixations.train_xs, fixation_trains.train_xs[scanpath_indices] ) assert_variable_length_array_equal( sub_fixations.train_ys, fixation_trains.train_ys[scanpath_indices] ) assert_variable_length_array_equal( sub_fixations.train_ts, fixation_trains.train_ts[scanpath_indices] ) np.testing.assert_array_equal( sub_fixations.train_ns, fixation_trains.train_ns[scanpath_indices] ) np.testing.assert_array_equal( sub_fixations.some_attribute, fixation_trains.some_attribute[fixation_indices] ) np.testing.assert_array_equal( sub_fixations.scanpath_attributes['task'], fixation_trains.scanpath_attributes['task'][scanpath_indices] ) assert_variable_length_array_equal( sub_fixations.scanpath_fixation_attributes['durations'], fixation_trains.scanpath_fixation_attributes['durations'][scanpath_indices] ) assert_fixations_equal(sub_fixations, fixation_trains[fixation_indices]) def test_read_hdf5_caching(fixation_trains, tmp_path): filename = tmp_path / 'fixations.hdf5' fixation_trains.to_hdf5(str(filename)) new_fixations = pysaliency.read_hdf5(str(filename)) assert new_fixations is not fixation_trains new_fixations2 = pysaliency.read_hdf5(str(filename)) assert new_fixations2 is new_fixations, "objects should not be read into memory multiple times" def test_fixations_save_load(tmp_path, fixation_trains): fixations = fixation_trains[:-1] assert isinstance(fixations, Fixations) filename = tmp_path / 'fixations.hdf5' fixations.to_hdf5(filename) new_fixations = pysaliency.read_hdf5(filename) assert_fixations_equal(fixations, new_fixations) def test_concatenate_fixations(fixation_trains): fixations = fixation_trains[:] new_fixations = pysaliency.Fixations.concatenate((fixations, fixations)) assert isinstance(new_fixations, pysaliency.Fixations) np.testing.assert_allclose( new_fixations.x, np.concatenate((fixation_trains.x, fixation_trains.x)) ) np.testing.assert_allclose( new_fixations.n, np.concatenate((fixation_trains.n, fixation_trains.n)) ) assert new_fixations.__attributes__ == ['subject', 'duration', 'duration_hist', 'multi_dim_attribute', 'scanpath_index', 'some_attribute', 'task'] np.testing.assert_allclose( new_fixations.some_attribute, np.concatenate((fixation_trains.some_attribute, fixation_trains.some_attribute)) ) def test_concatenate_scanpath_fixations(scanpath_fixations): new_scanpath_fixations = pysaliency.ScanpathFixations.concatenate((scanpath_fixations, scanpath_fixations)) assert isinstance(new_scanpath_fixations, pysaliency.ScanpathFixations) np.testing.assert_allclose( new_scanpath_fixations.x, np.concatenate((scanpath_fixations.x, scanpath_fixations.x)) ) np.testing.assert_allclose( new_scanpath_fixations.n, np.concatenate((scanpath_fixations.n, scanpath_fixations.n)) ) assert set(new_scanpath_fixations.__attributes__) == {'subject', 'duration', 'duration_hist', 'multi_dim_attribute', 'scanpath_index', 'task', 'string_attribute'} def test_concatenate_scanpath_fixations_partial_attributes(scanpath_fixations): scanpath_fixations2 = scanpath_fixations.copy() del scanpath_fixations2.scanpaths.scanpath_attributes['task'] delattr(scanpath_fixations2, 'task') scanpath_fixations2.auto_attributes.remove('task') scanpath_fixations2.__attributes__.remove('task') new_fixation_trains = pysaliency.ScanpathFixations.concatenate((scanpath_fixations, scanpath_fixations2)) assert isinstance(new_fixation_trains, pysaliency.ScanpathFixations) np.testing.assert_allclose( new_fixation_trains.x, np.concatenate((scanpath_fixations.x, scanpath_fixations2.x)) ) np.testing.assert_allclose( new_fixation_trains.n, np.concatenate((scanpath_fixations.n, scanpath_fixations2.n)) ) assert set(new_fixation_trains.__attributes__) == {'subject', 'duration', 'duration_hist', 'multi_dim_attribute', 'scanpath_index', 'string_attribute'} def test_concatenate_fixation_trains_partial_attributes(fixation_trains): fixation_trains2 = fixation_trains.copy() del fixation_trains2.scanpaths.scanpath_attributes['task'] delattr(fixation_trains2, 'task') fixation_trains2.auto_attributes.remove('task') fixation_trains2.__attributes__.remove('task') new_fixation_trains = pysaliency.FixationTrains.concatenate((fixation_trains, fixation_trains2)) assert isinstance(new_fixation_trains, pysaliency.Fixations) np.testing.assert_allclose( new_fixation_trains.x, np.concatenate((fixation_trains.x, fixation_trains2.x)) ) np.testing.assert_allclose( new_fixation_trains.n, np.concatenate((fixation_trains.n, fixation_trains2.n)) ) assert set(new_fixation_trains.__attributes__) == {'subject', 'duration', 'duration_hist', 'multi_dim_attribute', 'scanpath_index', 'some_attribute'} np.testing.assert_allclose( new_fixation_trains.some_attribute, np.concatenate((fixation_trains.some_attribute, fixation_trains2.some_attribute)) ) def test_load_scanpath_fixations_from_fixation_trains(fixation_trains, scanpath_fixations, tmp_path): filename = tmp_path / 'fixations.hdf5' fixation_trains.to_hdf5(filename) with pytest.raises(ValueError): scanpath_fixations = ScanpathFixations.read_hdf5(filename) del fixation_trains.some_attribute fixation_trains.__attributes__.remove('some_attribute') fixation_trains.to_hdf5(filename) scanpath_fixations = ScanpathFixations.read_hdf5(filename) assert_scanpaths_equal(fixation_trains.scanpaths, scanpath_fixations.scanpaths) assert_fixations_equal(fixation_trains, scanpath_fixations) @given(st.lists(elements=st.integers(min_value=0, max_value=7), min_size=1)) def test_scanpaths_from_fixations(fixation_indices): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subjects = [0, 1, 1] tasks = [0, 1, 0] some_attribute = [ [0, 1, 3], [6, 10], [15, 21, 28] ] scanpaths = Scanpaths( xs=xs_trains, ys=ys_trains, n=ns, scanpath_attributes={ 'task': tasks, 'subject': subjects, }, fixation_attributes={'some_attribute': some_attribute, 'ts': ts_trains}, attribute_mapping={'ts': 't'}, ) scanpath_fixations = ScanpathFixations(scanpaths=scanpaths) sub_fixations = scanpath_fixations[fixation_indices] new_scanpath_fixations, new_indices = scanpaths_from_fixations(sub_fixations) new_sub_fixations = new_scanpath_fixations[new_indices] assert_fixations_equal(sub_fixations, new_sub_fixations, crop_length=True) ================================================ FILE: tests/datasets/test_scanpaths.py ================================================ from copy import deepcopy import numpy as np import pytest import pysaliency from pysaliency.datasets import Scanpaths, concatenate_scanpaths from pysaliency.utils.variable_length_array import VariableLengthArray def assert_variable_length_array_equal(array1, array2): assert len(array1) == len(array2) for i in range(len(array1)): np.testing.assert_array_equal(array1[i], array2[i], err_msg=f'arrays not equal at index {i}') def assert_scanpaths_equal(scanpaths1: Scanpaths, scanpaths2: Scanpaths, scanpaths2_inds=None): if scanpaths2_inds is None: scanpaths2_inds = slice(None) assert isinstance(scanpaths1, Scanpaths) assert isinstance(scanpaths2, Scanpaths) assert_variable_length_array_equal(scanpaths1.xs, scanpaths2.xs[scanpaths2_inds]) assert_variable_length_array_equal(scanpaths1.ys, scanpaths2.ys[scanpaths2_inds]) assert scanpaths1.scanpath_attributes.keys() == scanpaths2.scanpath_attributes.keys() for attribute_name in scanpaths1.scanpath_attributes.keys(): np.testing.assert_array_equal(scanpaths1.scanpath_attributes[attribute_name], scanpaths2.scanpath_attributes[attribute_name][scanpaths2_inds]) assert scanpaths1.fixation_attributes.keys() == scanpaths2.fixation_attributes.keys() for attribute_name in scanpaths1.fixation_attributes.keys(): assert_variable_length_array_equal(scanpaths1.fixation_attributes[attribute_name], scanpaths2.fixation_attributes[attribute_name][scanpaths2_inds]) assert scanpaths1.attribute_mapping == scanpaths2.attribute_mapping def test_scanpaths(): xs = np.array([[0, 1, 2], [2, 2, np.nan], [1, 5, 3]]) ys = np.array([[10, 11, 12], [12, 12, np.nan], [21, 25, 33]]) n = np.array([0, 0, 1]) lengths = np.array([3, 2, 3]) scanpath_attributes = {'task': np.array([0, 1, 0])} fixation_attributes = {'attribute1': np.array([[1, 1, 2], [2, 2, np.nan], [0, 1, 3]]), 'attribute2': np.array([[3, 1.3, 5], [1, 42, np.nan], [0, -1, -3]])} attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths = Scanpaths(xs, ys, n, lengths, scanpath_attributes, fixation_attributes, attribute_mapping) assert isinstance(scanpaths.xs, VariableLengthArray) assert isinstance(scanpaths.ys, VariableLengthArray) assert isinstance(scanpaths.n, np.ndarray) assert isinstance(scanpaths.length, np.ndarray) assert isinstance(scanpaths.scanpath_attributes, dict) assert isinstance(scanpaths.scanpath_attributes['task'], np.ndarray) assert isinstance(scanpaths.fixation_attributes, dict) assert isinstance(scanpaths.fixation_attributes['attribute1'], VariableLengthArray) assert isinstance(scanpaths.fixation_attributes['attribute2'], VariableLengthArray) assert isinstance(scanpaths.attribute_mapping, dict) np.testing.assert_array_equal(scanpaths.xs._data, xs) np.testing.assert_array_equal(scanpaths.ys._data, ys) np.testing.assert_array_equal(scanpaths.n, n) np.testing.assert_array_equal(scanpaths.length, lengths) np.testing.assert_array_equal(scanpaths.scanpath_attributes['task'], np.array([0, 1, 0])) np.testing.assert_array_equal(scanpaths.fixation_attributes['attribute1']._data, np.array([[1, 1, 2], [2, 2, np.nan], [0, 1, 3]])) np.testing.assert_array_equal(scanpaths.fixation_attributes['attribute2']._data, np.array([[3, 1.3, 5], [1, 42, np.nan], [0, -1, -3]])) assert scanpaths.attribute_mapping == {'attribute1': 'attr1', 'attribute2': 'attr2'} def test_scanpaths_from_lists(): xs = [[0, 1, 2], [2, 2], [1, 5, 3]] ys = [[10, 11, 12], [12, 12], [21, 25, 33]] n = [0, 0, 1] expected_lengths = np.array([3, 2, 3]) scanpath_attributes = {'task': [0, 1, 0]} fixation_attributes = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths = Scanpaths(xs, ys, n, length=None, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=attribute_mapping) np.testing.assert_array_equal(scanpaths.xs._data, np.array([[0, 1, 2], [2, 2, np.nan], [1, 5, 3]])) np.testing.assert_array_equal(scanpaths.ys._data, np.array([[10, 11, 12], [12, 12, np.nan], [21, 25, 33]])) np.testing.assert_array_equal(scanpaths.n, n) np.testing.assert_array_equal(scanpaths.length, expected_lengths) np.testing.assert_array_equal(scanpaths.scanpath_attributes['task'], np.array([0, 1, 0])) np.testing.assert_array_equal(scanpaths.fixation_attributes['attribute1']._data, np.array([[1, 1, 2], [2, 2, np.nan], [0, 1, 3]])) np.testing.assert_array_equal(scanpaths.fixation_attributes['attribute2']._data, np.array([[3, 1.3, 5], [1, 42, np.nan], [0, -1, -3]])) assert scanpaths.attribute_mapping == {'attribute1': 'attr1', 'attribute2': 'attr2'} def test_scanpaths_from_lists_with_keyword_arguments(): xs = [[0, 1, 2], [2, 2], [1, 5, 3]] ys = [[10, 11, 12], [12, 12], [21, 25, 33]] ts = [[1, 2.5, 4], [2, 3], [3, 4, 6]] subject = [0, 1, 2] n = [0, 0, 1] expected_lengths = np.array([3, 2, 3]) scanpath_attributes = {'task': [0, 1, 0]} fixation_attributes = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths = Scanpaths( xs, ys, n, length=None, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=attribute_mapping, ts=ts, subject=subject, ) np.testing.assert_array_equal(scanpaths.xs._data, np.array([[0, 1, 2], [2, 2, np.nan], [1, 5, 3]])) np.testing.assert_array_equal(scanpaths.ys._data, np.array([[10, 11, 12], [12, 12, np.nan], [21, 25, 33]])) np.testing.assert_array_equal(scanpaths.n, n) np.testing.assert_array_equal(scanpaths.length, expected_lengths) np.testing.assert_array_equal(scanpaths.scanpath_attributes['task'], np.array([0, 1, 0])) np.testing.assert_array_equal(scanpaths.scanpath_attributes['subject'], np.array([0, 1, 2])) np.testing.assert_array_equal(scanpaths.fixation_attributes['attribute1']._data, np.array([[1, 1, 2], [2, 2, np.nan], [0, 1, 3]])) np.testing.assert_array_equal(scanpaths.fixation_attributes['attribute2']._data, np.array([[3, 1.3, 5], [1, 42, np.nan], [0, -1, -3]])) np.testing.assert_array_equal(scanpaths.fixation_attributes['ts']._data, np.array([[1, 2.5, 4], [2, 3, np.nan], [3, 4, 6]])) assert scanpaths.attribute_mapping == {'attribute1': 'attr1', 'attribute2': 'attr2', 'ts': 't'} def test_scanpaths_init_inconsistent_lengths(): xs = np.array([[0, 1, 2], [2, 2, np.nan], [1, 5, 3]]) ys = np.array([[10, 11, 12], [12, 12, np.nan]]) # too short, should fail n = np.array([0, 0, 1]) lengths = np.array([3, 2, 3]) scanpath_attributes = {'task': np.array([0, 1, 0])} fixation_attributes = {'attribute1': np.array([[1, 1, 2], [2, 2, np.nan], [0, 1, 3]]), 'attribute2': np.array([[3, 1.3, 5], [1, 42, np.nan], [0, -1, -3]])} attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} with pytest.raises(ValueError): Scanpaths(xs, ys, n, lengths, scanpath_attributes, fixation_attributes, attribute_mapping) def test_scanpaths_init_invalid_scanpath_attributes(): xs = np.array([[0, 1, 2], [2, 2, np.nan], [1, 5, 3]]) ys = np.array([[10, 11, 12], [12, 12, np.nan], [21, 25, 33]]) n = np.array([0, 0, 1]) lengths = np.array([3, 2, 3]) scanpath_attributes = {'invalid_attribute': np.array([1, 2]), 'attribute2': np.array([4, 5, 6])} # Invalid attribute length scanpath_fixation_attributes = {'fixation_attribute1': np.array([[1, 2], [3, 4], [5, 6]]), 'fixation_attribute2': np.array([[7, 8], [9, 10], [11, 12]])} scanpath_attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} with pytest.raises(ValueError): Scanpaths(xs, ys, n, lengths, scanpath_attributes, scanpath_fixation_attributes, scanpath_attribute_mapping) def test_scanpaths_init_invalid_scanpath_fixation_attributes(): xs = np.array([[0, 1, 2], [2, 2, np.nan], [1, 5, 3]]) ys = np.array([[10, 11, 12], [12, 12, np.nan], [21, 25, 33]]) n = np.array([0, 0, 1]) lengths = np.array([3, 2, 3]) scanpath_attributes = {'attribute1': np.array([1, 2, 3]), 'attribute2': np.array([4, 5, 6])} scanpath_fixation_attributes = {'valid_fixation_attribute': np.array([[1, 2], [3, 4], [5, 6]]), 'invalid_fixation_attribute': np.array([[7, 8], [9, 10]])} # Invalid fixation attribute length scanpath_attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} with pytest.raises(ValueError): Scanpaths(xs, ys, n, lengths, scanpath_attributes, scanpath_fixation_attributes, scanpath_attribute_mapping) def test_scanpaths_init_invalid_scanpath_fixation_attributes_dimensions(): xs = np.array([[0, 1, 2], [2, 2, np.nan], [1, 5, 3]]) ys = np.array([[10, 11, 12], [12, 12, np.nan], [21, 25, 33]]) n = np.array([0, 0, 1]) lengths = np.array([3, 2, 3]) scanpath_attributes = {'attribute1': np.array([1, 2, 3]), 'attribute2': np.array([4, 5, 6])} scanpath_fixation_attributes = {'fixation_attribute1': np.array([1, 2, 3]), 'fixation_attribute2': np.array([[7, 8], [9, 10], [11, 12]])} # Invalid fixation attribute dimensions scanpath_attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} with pytest.raises(ValueError): Scanpaths(xs, ys, n, lengths, scanpath_attributes, scanpath_fixation_attributes, scanpath_attribute_mapping) def test_scanpaths_init_invalid_scanpath_lengths(): data = { 'xs': [[0, 1, 2], [2, 2], [1, 5, 3]], 'ys': [[10, 11, 12], [12, 12], [21, 25, 33]], 'n': [0, 0, 1], 'scanpath_attributes': {'task': [0, 1, 0]}, 'fixation_attributes': {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]}, 'attribute_mapping': {'attribute1': 'attr1', 'attribute2': 'attr2'}, } # make sure original data works Scanpaths(**data) for scanpath_attribute in ['xs', 'ys']: data_copy = deepcopy(data) data_copy[scanpath_attribute][-1].append(4) with pytest.raises(ValueError): Scanpaths(**data_copy) for scanpath_attribute in data['fixation_attributes'].keys(): data_copy = deepcopy(data) data_copy['fixation_attributes'][scanpath_attribute][-1].append(4) with pytest.raises(ValueError): Scanpaths(**data_copy) @pytest.mark.parametrize('inds', [ slice(None, 2), slice(1, None), [0, 1], [1, 2], [0, 2], [2, 1], [False, True, True], ]) def test_scanpaths_slicing(inds): xs = [[0, 1, 2], [2, 2], [1, 5, 3]] ys = [[10, 11, 12], [12, 12], [21, 25, 33]] n = [0, 0, 1] scanpath_attributes = {'task': [0, 1, 0]} fixation_attributes = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths = Scanpaths(xs, ys, n, length=None, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=attribute_mapping) sliced_scanpaths = scanpaths[inds] assert_scanpaths_equal(sliced_scanpaths, scanpaths, inds) def test_write_read_scanpaths_pathlib(tmp_path): filename = tmp_path / 'scanpaths.hdf5' xs = [[0, 1, 2], [2, 2], [1, 5, 3]] ys = [[10, 11, 12], [12, 12], [21, 25, 33]] n = [0, 0, 1] scanpath_attributes = {'task': [0, 1, 0]} fixation_attributes = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths = Scanpaths(xs, ys, n, length=None, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=attribute_mapping) scanpaths.to_hdf5(filename) # test loading via class method new_scanpaths = Scanpaths.read_hdf5(filename) assert scanpaths is not new_scanpaths # make sure there is no sophisticated caching... assert_scanpaths_equal(scanpaths, new_scanpaths) # test loading via pysaliency new_scanpaths = pysaliency.read_hdf5(filename) assert scanpaths is not new_scanpaths # make sure there is no sophisticated caching... assert_scanpaths_equal(scanpaths, new_scanpaths) def test_scanpaths_copy(): xs = [[0, 1, 2], [2, 2], [1, 5, 3]] ys = [[10, 11, 12], [12, 12], [21, 25, 33]] n = [0, 0, 1] scanpath_attributes = {'task': [0, 1, 0]} fixation_attributes = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths = Scanpaths(xs, ys, n, length=None, scanpath_attributes=scanpath_attributes, fixation_attributes=fixation_attributes, attribute_mapping=attribute_mapping) new_scanpaths = scanpaths.copy() assert_scanpaths_equal(scanpaths, new_scanpaths) assert scanpaths is not new_scanpaths def test_concatenate_scanpaths(): xs1 = [[0, 1, 2], [2, 2], [1, 5, 3]] ys1 = [[10, 11, 12], [12, 11], [21, 25, 33]] n1 = [0, 0, 1] scanpath_attributes1 = {'task': [0, 1, 0]} fixation_attributes1 = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping1 = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths1 = Scanpaths(xs1, ys1, n1, length=None, scanpath_attributes=scanpath_attributes1, fixation_attributes=fixation_attributes1, attribute_mapping=attribute_mapping1) xs2 = [[0, 1, 2], [2, 2], [1, 5, 4]] ys2 = [[10, 11, 12], [12, 12], [21, 25, 33]] n2 = [0, 1, 1] scanpath_attributes2 = {'task': [0, 1, 0]} fixation_attributes2 = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping2 = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths2 = Scanpaths(xs2, ys2, n2, length=None, scanpath_attributes=scanpath_attributes2, fixation_attributes=fixation_attributes2, attribute_mapping=attribute_mapping2) concatenated_scanpaths = concatenate_scanpaths([scanpaths1, scanpaths2]) assert_variable_length_array_equal(concatenated_scanpaths.xs, VariableLengthArray(xs1 + xs2)) assert_variable_length_array_equal(concatenated_scanpaths.ys, VariableLengthArray(ys1 + ys2)) np.testing.assert_array_equal(concatenated_scanpaths.n, np.array(n1 + n2)) assert concatenated_scanpaths.scanpath_attributes.keys() == {'task'} np.testing.assert_array_equal(concatenated_scanpaths.scanpath_attributes['task'], np.array([0, 1, 0, 0, 1, 0])) assert concatenated_scanpaths.fixation_attributes.keys() == {'attribute1', 'attribute2'} assert_variable_length_array_equal(concatenated_scanpaths.fixation_attributes['attribute1'], VariableLengthArray([[1, 1, 2], [2, 2], [0, 1, 3], [1, 1, 2], [2, 2], [0, 1, 3]])) assert_variable_length_array_equal(concatenated_scanpaths.fixation_attributes['attribute2'], VariableLengthArray([[3, 1.3, 5], [1, 42], [0, -1, -3], [3, 1.3, 5], [1, 42], [0, -1, -3]])) assert concatenated_scanpaths.attribute_mapping == {'attribute1': 'attr1', 'attribute2': 'attr2'} def test_concatenate_scanpaths_no_mapping(): xs1 = [[0, 1, 2], [2, 2], [1, 5, 3]] ys1 = [[10, 11, 12], [12, 11], [21, 25, 33]] n1 = [0, 0, 1] scanpath_attributes1 = {'task': [0, 1, 0]} fixation_attributes1 = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping1 = {} scanpaths1 = Scanpaths(xs1, ys1, n1, length=None, scanpath_attributes=scanpath_attributes1, fixation_attributes=fixation_attributes1, attribute_mapping=attribute_mapping1) xs2 = [[0, 1, 2], [2, 2], [1, 5, 4]] ys2 = [[10, 11, 12], [12, 12], [21, 25, 33]] n2 = [0, 1, 1] scanpath_attributes2 = {'task': [0, 1, 0]} fixation_attributes2 = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping2 = {} scanpaths2 = Scanpaths(xs2, ys2, n2, length=None, scanpath_attributes=scanpath_attributes2, fixation_attributes=fixation_attributes2, attribute_mapping=attribute_mapping2) concatenated_scanpaths = concatenate_scanpaths([scanpaths1, scanpaths2]) assert_variable_length_array_equal(concatenated_scanpaths.xs, VariableLengthArray(xs1 + xs2)) assert_variable_length_array_equal(concatenated_scanpaths.ys, VariableLengthArray(ys1 + ys2)) np.testing.assert_array_equal(concatenated_scanpaths.n, np.array(n1 + n2)) assert concatenated_scanpaths.scanpath_attributes.keys() == {'task'} np.testing.assert_array_equal(concatenated_scanpaths.scanpath_attributes['task'], np.array([0, 1, 0, 0, 1, 0])) assert concatenated_scanpaths.fixation_attributes.keys() == {'attribute1', 'attribute2'} assert_variable_length_array_equal(concatenated_scanpaths.fixation_attributes['attribute1'], VariableLengthArray([[1, 1, 2], [2, 2], [0, 1, 3], [1, 1, 2], [2, 2], [0, 1, 3]])) assert_variable_length_array_equal(concatenated_scanpaths.fixation_attributes['attribute2'], VariableLengthArray([[3, 1.3, 5], [1, 42], [0, -1, -3], [3, 1.3, 5], [1, 42], [0, -1, -3]])) assert concatenated_scanpaths.attribute_mapping == {} def test_concatenate_scanpaths_missing_fixation_attribute(): xs1 = [[0, 1, 2], [2, 2], [1, 5, 3]] ys1 = [[10, 11, 12], [12, 11], [21, 25, 33]] n1 = [0, 0, 1] scanpath_attributes1 = {'task': [0, 1, 0]} fixation_attributes1 = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping1 = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths1 = Scanpaths(xs1, ys1, n1, length=None, scanpath_attributes=scanpath_attributes1, fixation_attributes=fixation_attributes1, attribute_mapping=attribute_mapping1) xs2 = [[0, 1, 2], [2, 2], [1, 5, 4]] ys2 = [[10, 11, 12], [12, 12], [21, 25, 33]] n2 = [0, 1, 1] scanpath_attributes2 = {'task': [0, 1, 0]} fixation_attributes2 = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]]} attribute_mapping2 = {'attribute1': 'attr1'} scanpaths2 = Scanpaths(xs2, ys2, n2, length=None, scanpath_attributes=scanpath_attributes2, fixation_attributes=fixation_attributes2, attribute_mapping=attribute_mapping2) concatenated_scanpaths = concatenate_scanpaths([scanpaths1, scanpaths2]) assert_variable_length_array_equal(concatenated_scanpaths.xs, VariableLengthArray(xs1 + xs2)) assert_variable_length_array_equal(concatenated_scanpaths.ys, VariableLengthArray(ys1 + ys2)) np.testing.assert_array_equal(concatenated_scanpaths.n, np.array(n1 + n2)) assert concatenated_scanpaths.scanpath_attributes.keys() == {'task'} np.testing.assert_array_equal(concatenated_scanpaths.scanpath_attributes['task'], np.array([0, 1, 0, 0, 1, 0])) assert concatenated_scanpaths.fixation_attributes.keys() == {'attribute1'} assert_variable_length_array_equal(concatenated_scanpaths.fixation_attributes['attribute1'], VariableLengthArray([[1, 1, 2], [2, 2], [0, 1, 3], [1, 1, 2], [2, 2], [0, 1, 3]])) assert concatenated_scanpaths.attribute_mapping == {'attribute1': 'attr1'} def test_concatenate_scanpaths_inconsistent_attribute_mappings(): xs1 = [[0, 1, 2], [2, 2], [1, 5, 3]] ys1 = [[10, 11, 12], [12, 11], [21, 25, 33]] n1 = [0, 0, 1] scanpath_attributes1 = {'task': [0, 1, 0]} fixation_attributes1 = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping1 = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths1 = Scanpaths(xs1, ys1, n1, length=None, scanpath_attributes=scanpath_attributes1, fixation_attributes=fixation_attributes1, attribute_mapping=attribute_mapping1) xs2 = [[0, 1, 2], [2, 2], [1, 5, 4]] ys2 = [[10, 11, 12], [12, 12], [21, 25, 33]] n2 = [0, 1, 1] scanpath_attributes2 = {'task': [0, 1, 0]} fixation_attributes2 = {'attribute1': [[1, 1, 2], [2, 2], [0, 1, 3]], 'attribute2': [[3, 1.3, 5], [1, 42], [0, -1, -3]]} attribute_mapping2 = {'attribute1': 'attr1', 'attribute2': 'attr2'} scanpaths2_clean = Scanpaths(xs2, ys2, n2, length=None, scanpath_attributes=scanpath_attributes2, fixation_attributes=fixation_attributes2, attribute_mapping=attribute_mapping2) # make sure that everyuthing else wearks concatenate_scanpaths([scanpaths1, scanpaths2_clean]) attribute_mapping2 = {'attribute1': 'attr1', 'attribute2': 'attr3'} scanpaths2_inconsistent = Scanpaths(xs2, ys2, n2, length=None, scanpath_attributes=scanpath_attributes2, fixation_attributes=fixation_attributes2, attribute_mapping=attribute_mapping2) with pytest.raises(ValueError): concatenate_scanpaths([scanpaths1, scanpaths2_inconsistent]) ================================================ FILE: tests/datasets/test_stimuli.py ================================================ from __future__ import absolute_import, division, print_function import os.path import pickle import unittest from copy import deepcopy import dill import numpy as np import pytest from hypothesis import given from hypothesis import strategies as st from imageio import imwrite from test_helpers import TestWithData import pysaliency from pysaliency.datasets import Fixations, FixationTrains, Scanpaths, Stimulus, check_prediction_shape, scanpaths_from_fixations from pysaliency.utils.variable_length_array import VariableLengthArray class TestStimuli(TestWithData): def test_stimuli(self): img1 = np.random.randn(100, 200, 3) img2 = np.random.randn(50, 150) stimuli = pysaliency.Stimuli([img1, img2]) self.assertEqual(stimuli.stimuli, [img1, img2]) self.assertEqual(stimuli.shapes, [(100, 200, 3), (50, 150)]) self.assertEqual(list(stimuli.sizes), [(100, 200), (50, 150)]) self.assertEqual(stimuli.stimulus_ids[1], pysaliency.datasets.get_image_hash(img2)) np.testing.assert_allclose(stimuli.stimulus_objects[1].stimulus_data, img2) self.assertEqual(stimuli.stimulus_objects[1].stimulus_id, stimuli.stimulus_ids[1]) new_stimuli = self.pickle_and_reload(stimuli, pickler=dill) self.assertEqual(len(new_stimuli.stimuli), 2) for s1, s2 in zip(new_stimuli.stimuli, [img1, img2]): np.testing.assert_allclose(s1, s2) self.assertEqual(new_stimuli.shapes, [(100, 200, 3), (50, 150)]) self.assertEqual(list(new_stimuli.sizes), [(100, 200), (50, 150)]) self.assertEqual(new_stimuli.stimulus_ids[1], pysaliency.datasets.get_image_hash(img2)) self.assertEqual(new_stimuli.stimulus_objects[1].stimulus_id, stimuli.stimulus_ids[1]) def test_slicing(self): count = 10 widths = np.random.randint(20, 200, size=count) heights = np.random.randint(20, 200, size=count) images = [np.random.randn(h, w, 3) for h, w in zip(heights, widths)] stimuli = pysaliency.Stimuli(images) for i in range(count): s = stimuli[i] np.testing.assert_allclose(s.stimulus_data, stimuli.stimuli[i]) self.assertEqual(s.stimulus_id, stimuli.stimulus_ids[i]) self.assertEqual(s.shape, stimuli.shapes[i]) self.assertEqual(s.size, stimuli.sizes[i]) indices = [2, 4, 7] ss = stimuli[indices] for k, i in enumerate(indices): np.testing.assert_allclose(ss.stimuli[k], stimuli.stimuli[i]) self.assertEqual(ss.stimulus_ids[k], stimuli.stimulus_ids[i]) self.assertEqual(ss.shapes[k], stimuli.shapes[i]) self.assertEqual(ss.sizes[k], stimuli.sizes[i]) slc = slice(2, 8, 3) ss = stimuli[slc] indices = range(len(stimuli))[slc] for k, i in enumerate(indices): np.testing.assert_allclose(ss.stimuli[k], stimuli.stimuli[i]) self.assertEqual(ss.stimulus_ids[k], stimuli.stimulus_ids[i]) self.assertEqual(ss.shapes[k], stimuli.shapes[i]) self.assertEqual(ss.sizes[k], stimuli.sizes[i]) class TestFileStimuli(TestWithData): def test_file_stimuli(self): img1 = np.random.randint(255, size=(100, 200, 3)).astype('uint8') filename1 = os.path.join(self.data_path, 'img1.png') imwrite(filename1, img1) img2 = np.random.randint(255, size=(50, 150)).astype('uint8') filename2 = os.path.join(self.data_path, 'img2.png') imwrite(filename2, img2) stimuli = pysaliency.FileStimuli([filename1, filename2]) self.assertEqual(len(stimuli.stimuli), 2) for s1, s2 in zip(stimuli.stimuli, [img1, img2]): np.testing.assert_allclose(s1, s2) self.assertEqual(stimuli.shapes, [(100, 200, 3), (50, 150)]) self.assertEqual(list(stimuli.sizes), [(100, 200), (50, 150)]) self.assertEqual(stimuli.stimulus_ids[1], pysaliency.datasets.get_image_hash(img2)) self.assertEqual(stimuli.stimulus_objects[1].stimulus_id, stimuli.stimulus_ids[1]) new_stimuli = self.pickle_and_reload(stimuli, pickler=dill) self.assertEqual(len(new_stimuli.stimuli), 2) for s1, s2 in zip(new_stimuli.stimuli, [img1, img2]): np.testing.assert_allclose(s1, s2) self.assertEqual(new_stimuli.shapes, [(100, 200, 3), (50, 150)]) self.assertEqual(list(new_stimuli.sizes), [(100, 200), (50, 150)]) self.assertEqual(new_stimuli.stimulus_ids[1], pysaliency.datasets.get_image_hash(img2)) self.assertEqual(new_stimuli.stimulus_objects[1].stimulus_id, stimuli.stimulus_ids[1]) def test_slicing(self): count = 10 widths = np.random.randint(20, 200, size=count) heights = np.random.randint(20, 200, size=count) images = [np.random.randint(255, size=(h, w, 3)).astype(np.uint8) for h, w in zip(heights, widths)] filenames = [] for i, img in enumerate(images): filename = os.path.join(self.data_path, 'img{}.png'.format(i)) print(filename) print(img.shape) print(img.dtype) imwrite(filename, img) filenames.append(filename) stimuli = pysaliency.FileStimuli(filenames) for i in range(count): s = stimuli[i] np.testing.assert_allclose(s.stimulus_data, stimuli.stimuli[i]) self.assertEqual(s.stimulus_id, stimuli.stimulus_ids[i]) self.assertEqual(s.shape, stimuli.shapes[i]) self.assertEqual(s.size, stimuli.sizes[i]) indices = [2, 4, 7] ss = stimuli[indices] for k, i in enumerate(indices): np.testing.assert_allclose(ss.stimuli[k], stimuli.stimuli[i]) self.assertEqual(ss.stimulus_ids[k], stimuli.stimulus_ids[i]) self.assertEqual(ss.shapes[k], stimuli.shapes[i]) self.assertEqual(list(ss.sizes[k]), list(stimuli.sizes[i])) slc = slice(2, 8, 3) ss = stimuli[slc] indices = range(len(stimuli))[slc] for k, i in enumerate(indices): np.testing.assert_allclose(ss.stimuli[k], stimuli.stimuli[i]) self.assertEqual(ss.stimulus_ids[k], stimuli.stimulus_ids[i]) self.assertEqual(ss.shapes[k], stimuli.shapes[i]) self.assertEqual(list(ss.sizes[k]), list(stimuli.sizes[i])) @pytest.fixture def stimuli_with_attributes(): stimuli_data = [np.random.randint(0, 255, size=(25, 30, 3)) for i in range(10)] attributes = { 'dva': list(range(10)), 'other_stuff': np.random.randn(10), 'some_strings': list('abcdefghij'), } return pysaliency.Stimuli(stimuli_data, attributes=attributes) def test_stimuli_attributes(stimuli_with_attributes, tmp_path): filename = tmp_path / 'stimuli.hdf5' stimuli_with_attributes.to_hdf5(str(filename)) new_stimuli = pysaliency.read_hdf5(str(filename)) assert stimuli_with_attributes.attributes.keys() == new_stimuli.attributes.keys() np.testing.assert_array_equal(stimuli_with_attributes.attributes['dva'], new_stimuli.attributes['dva']) np.testing.assert_array_equal(stimuli_with_attributes.attributes['other_stuff'], new_stimuli.attributes['other_stuff']) np.testing.assert_array_equal(stimuli_with_attributes.attributes['some_strings'], new_stimuli.attributes['some_strings']) partial_stimuli = stimuli_with_attributes[:5] assert stimuli_with_attributes.attributes.keys() == partial_stimuli.attributes.keys() assert stimuli_with_attributes.attributes['dva'][:5] == partial_stimuli.attributes['dva'] assert stimuli_with_attributes.attributes['some_strings'][:5] == partial_stimuli.attributes['some_strings'] partial_stimuli = stimuli_with_attributes[[1, 2, 6]] assert stimuli_with_attributes.attributes.keys() == partial_stimuli.attributes.keys() assert list(np.array(stimuli_with_attributes.attributes['dva'])[[1, 2, 6]]) == partial_stimuli.attributes['dva'] assert list(np.array(stimuli_with_attributes.attributes['some_strings'])[[1, 2, 6]]) == partial_stimuli.attributes['some_strings'] mask = np.array([True, False, True, False, True, False, True, False, True, False, True, False]) with pytest.raises(ValueError): partial_stimuli = stimuli_with_attributes[mask] mask = np.array([True, False, True, False, True, False, True, False, True, False]) partial_stimuli = stimuli_with_attributes[mask] assert stimuli_with_attributes.attributes.keys() == partial_stimuli.attributes.keys() assert list(np.array(stimuli_with_attributes.attributes['dva'])[mask]) == partial_stimuli.attributes['dva'] assert list(np.array(stimuli_with_attributes.attributes['some_strings'])[mask]) == partial_stimuli.attributes['some_strings'] @pytest.fixture def file_stimuli_with_attributes(tmpdir): filenames = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) attributes = { 'dva': list(range(len(filenames))), 'other_stuff': np.random.randn(len(filenames)), 'some_strings': list('abcdefghijklmnopqr'), } return pysaliency.FileStimuli(filenames=filenames, attributes=attributes) def test_file_stimuli_attributes(file_stimuli_with_attributes, tmp_path): filename = tmp_path / 'stimuli.hdf5' file_stimuli_with_attributes.to_hdf5(str(filename)) new_stimuli = pysaliency.read_hdf5(str(filename)) assert file_stimuli_with_attributes.attributes.keys() == new_stimuli.attributes.keys() np.testing.assert_array_equal(file_stimuli_with_attributes.attributes['dva'], new_stimuli.attributes['dva']) np.testing.assert_array_equal(file_stimuli_with_attributes.attributes['other_stuff'], new_stimuli.attributes['other_stuff']) np.testing.assert_array_equal(file_stimuli_with_attributes.attributes['some_strings'], new_stimuli.attributes['some_strings']) partial_stimuli = file_stimuli_with_attributes[:5] assert file_stimuli_with_attributes.attributes.keys() == partial_stimuli.attributes.keys() assert file_stimuli_with_attributes.attributes['dva'][:5] == partial_stimuli.attributes['dva'] assert file_stimuli_with_attributes.attributes['some_strings'][:5] == partial_stimuli.attributes['some_strings'] partial_stimuli = file_stimuli_with_attributes[[1, 2, 6]] assert file_stimuli_with_attributes.attributes.keys() == partial_stimuli.attributes.keys() assert list(np.array(file_stimuli_with_attributes.attributes['dva'])[[1, 2, 6]]) == partial_stimuli.attributes['dva'] assert list(np.array(file_stimuli_with_attributes.attributes['some_strings'])[[1, 2, 6]]) == partial_stimuli.attributes['some_strings'] mask = np.array([True, False, True, False, True, False, True, False, True, False]) with pytest.raises(ValueError): partial_stimuli = file_stimuli_with_attributes[mask] mask = np.array([True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False]) partial_stimuli = file_stimuli_with_attributes[mask] assert file_stimuli_with_attributes.attributes.keys() == partial_stimuli.attributes.keys() assert list(np.array(file_stimuli_with_attributes.attributes['dva'])[mask]) == partial_stimuli.attributes['dva'] assert list(np.array(file_stimuli_with_attributes.attributes['some_strings'])[mask]) == partial_stimuli.attributes['some_strings'] def test_file_stimuli_readhdf5_cached(file_stimuli_with_attributes, tmp_path): filename = tmp_path / 'stimuli.hdf5' file_stimuli_with_attributes.to_hdf5(str(filename)) new_stimuli = pysaliency.read_hdf5(str(filename)) assert new_stimuli.cached new_stimuli2 = pysaliency.read_hdf5(str(filename), cached=False) assert not new_stimuli2.cached def test_concatenate_stimuli_with_attributes(stimuli_with_attributes, file_stimuli_with_attributes): concatenated_stimuli = pysaliency.datasets.concatenate_stimuli([stimuli_with_attributes, file_stimuli_with_attributes]) assert file_stimuli_with_attributes.attributes.keys() == concatenated_stimuli.attributes.keys() np.testing.assert_allclose(stimuli_with_attributes.attributes['dva'], concatenated_stimuli.attributes['dva'][:len(stimuli_with_attributes)]) np.testing.assert_allclose(file_stimuli_with_attributes.attributes['dva'], concatenated_stimuli.attributes['dva'][len(stimuli_with_attributes):]) def test_concatenate_file_stimuli(file_stimuli_with_attributes): concatenated_stimuli = pysaliency.datasets.concatenate_stimuli([file_stimuli_with_attributes, file_stimuli_with_attributes]) assert isinstance(concatenated_stimuli, pysaliency.FileStimuli) assert concatenated_stimuli.filenames == file_stimuli_with_attributes.filenames + file_stimuli_with_attributes.filenames def test_check_prediction_shape(): # Test with matching shapes prediction = np.random.rand(10, 10) stimulus = np.random.rand(10, 10) check_prediction_shape(prediction, stimulus) # Should not raise any exception # Test with matching shapes, colorimage prediction = np.random.rand(10, 10) stimulus = np.random.rand(10, 10, 3) check_prediction_shape(prediction, stimulus) # Should not raise any exception # Test with mismatching shapes prediction = np.random.rand(10, 10) stimulus = np.random.rand(10, 11) with pytest.raises(ValueError) as excinfo: check_prediction_shape(prediction, stimulus) assert str(excinfo.value) == "Prediction shape (10, 10) does not match stimulus shape (10, 11)" # Test with Stimulus object prediction = np.random.rand(10, 10) stimulus = Stimulus(np.random.rand(10, 10)) check_prediction_shape(prediction, stimulus) # Should not raise any exception # Test with Stimulus object prediction = np.random.rand(10, 10) stimulus = Stimulus(np.random.rand(10, 10, 3)) check_prediction_shape(prediction, stimulus) # Should not raise any exception # Test with mismatching shapes and Stimulus object prediction = np.random.rand(10, 10) stimulus = Stimulus(np.random.rand(10, 11)) with pytest.raises(ValueError) as excinfo: check_prediction_shape(prediction, stimulus) assert str(excinfo.value) == "Prediction shape (10, 10) does not match stimulus shape (10, 11)" @pytest.mark.parametrize( 'stimuli', ['stimuli_with_attributes', 'file_stimuli_with_attributes'] ) def test_substimuli_inherit_cachedstimulus_ids(stimuli, request): _stimuli = request.getfixturevalue(stimuli) # load some stimulus ids cache_stimulus_indices = [1, 2, 5] # make sure the ids are cached for i in cache_stimulus_indices: _stimuli.stimulus_ids[i] assert len(_stimuli.stimulus_ids._cache) == len(cache_stimulus_indices) sub_stimuli = _stimuli[1:5] assert set(sub_stimuli.stimulus_ids._cache.keys()) == {0, 1} ================================================ FILE: tests/datasets/utils.py ================================================ from pysaliency.datasets import ScanpathFixations from pysaliency.datasets.scanpaths import Scanpaths import numpy as np from pysaliency.utils.variable_length_array import VariableLengthArray def assert_variable_length_array_equal(array1, array2): assert isinstance(array1, VariableLengthArray) assert isinstance(array2, VariableLengthArray) assert len(array1) == len(array2) for i in range(len(array1)): np.testing.assert_array_equal(array1[i], array2[i], err_msg=f'arrays not equal at index {i}') def assert_scanpaths_equal(scanpaths1: Scanpaths, scanpaths2: Scanpaths, scanpaths2_inds=None): if scanpaths2_inds is None: scanpaths2_inds = slice(None) assert isinstance(scanpaths1, Scanpaths) assert isinstance(scanpaths2, Scanpaths) assert_variable_length_array_equal(scanpaths1.xs, scanpaths2.xs[scanpaths2_inds]) assert_variable_length_array_equal(scanpaths1.ys, scanpaths2.ys[scanpaths2_inds]) assert scanpaths1.scanpath_attributes.keys() == scanpaths2.scanpath_attributes.keys() for attribute_name in scanpaths1.scanpath_attributes.keys(): np.testing.assert_array_equal(scanpaths1.scanpath_attributes[attribute_name], scanpaths2.scanpath_attributes[attribute_name][scanpaths2_inds]) assert scanpaths1.fixation_attributes.keys() == scanpaths2.fixation_attributes.keys() for attribute_name in scanpaths1.fixation_attributes.keys(): assert_variable_length_array_equal(scanpaths1.fixation_attributes[attribute_name], scanpaths2.fixation_attributes[attribute_name][scanpaths2_inds]) assert scanpaths1.attribute_mapping == scanpaths2.attribute_mapping def compare_fixations_subset(f1, f2, f2_inds): np.testing.assert_allclose(f1.x, f2.x[f2_inds]) np.testing.assert_allclose(f1.y, f2.y[f2_inds]) np.testing.assert_allclose(f1.t, f2.t[f2_inds]) np.testing.assert_allclose(f1.n, f2.n[f2_inds]) np.testing.assert_allclose(f1.subject, f2.subject[f2_inds]) assert f1.__attributes__ == f2.__attributes__ for attribute in f1.__attributes__: if attribute == 'scanpath_index': continue np.testing.assert_array_equal(getattr(f1, attribute), getattr(f2, attribute)[f2_inds]) def assert_fixations_equal(f1, f2, crop_length=False): if crop_length: maximum_length = np.max(f2.scanpath_history_length) else: maximum_length = max(np.max(f1.scanpath_history_length), np.max(f2.scanpath_history_length)) np.testing.assert_array_equal(f1.x, f2.x) np.testing.assert_array_equal(f1.y, f2.y) np.testing.assert_array_equal(f1.t, f2.t) np.testing.assert_array_equal(f1.n, f2.n) assert_variable_length_array_equal(f1.x_hist, f2.x_hist) assert_variable_length_array_equal(f1.y_hist, f2.y_hist) assert_variable_length_array_equal(f1.t_hist, f2.t_hist) f1_attributes = set(f1.__attributes__) f2_attributes = set(f2.__attributes__) assert set(f1_attributes) == set(f2_attributes) for attribute in f1.__attributes__: if attribute == 'scanpath_index': continue attribute1 = getattr(f1, attribute) attribute2 = getattr(f2, attribute) if isinstance(attribute1, VariableLengthArray): assert_variable_length_array_equal(attribute1, attribute2) continue elif attribute.endswith('_hist'): attribute1 = attribute1[:, :maximum_length] attribute2 = attribute2[:, :maximum_length] np.testing.assert_array_equal(attribute1, attribute2, err_msg=f'attributes not equal: {attribute}') def assert_fixation_trains_equal(fixation_trains1, fixation_trains2): assert_variable_length_array_equal(fixation_trains1.train_xs, fixation_trains2.train_xs) assert_variable_length_array_equal(fixation_trains1.train_ys, fixation_trains2.train_ys) assert_variable_length_array_equal(fixation_trains1.train_ts, fixation_trains2.train_ts) np.testing.assert_array_equal(fixation_trains1.train_ns, fixation_trains2.train_ns) np.testing.assert_array_equal(fixation_trains1.train_subjects, fixation_trains2.train_subjects) np.testing.assert_array_equal(fixation_trains1.train_lengths, fixation_trains2.train_lengths) assert fixation_trains1.scanpath_attribute_mapping == fixation_trains2.scanpath_attribute_mapping assert fixation_trains1.scanpath_attributes.keys() == fixation_trains2.scanpath_attributes.keys() for attribute_name in fixation_trains1.scanpath_attributes.keys(): np.testing.assert_array_equal(fixation_trains1.scanpath_attributes[attribute_name], fixation_trains2.scanpath_attributes[attribute_name]) assert fixation_trains1.scanpath_fixation_attributes.keys() == fixation_trains2.scanpath_fixation_attributes.keys() for attribute_name in fixation_trains1.scanpath_fixation_attributes.keys(): assert_variable_length_array_equal(fixation_trains1.scanpath_fixation_attributes[attribute_name], fixation_trains2.scanpath_fixation_attributes[attribute_name]) assert_fixations_equal(fixation_trains1, fixation_trains2) def assert_scanpath_fixations_equal(scanpath_fixations1: ScanpathFixations, scanpath_fixations2: ScanpathFixations): assert isinstance(scanpath_fixations1, ScanpathFixations) assert isinstance(scanpath_fixations2, ScanpathFixations) assert_scanpaths_equal(scanpath_fixations1.scanpaths, scanpath_fixations2.scanpaths) assert_fixations_equal(scanpath_fixations1, scanpath_fixations2) ================================================ FILE: tests/external_datasets/test_COCO_Search18.py ================================================ import numpy as np import pytest from pytest import approx import pysaliency from scipy.stats import kurtosis, skew from tests.test_external_datasets import _location, entropy @pytest.mark.slow @pytest.mark.download def test_COCO_Search18_task_merge(location): real_location = _location(location) stimuli_train, fixations_train, stimuli_val, fixations_val = pysaliency.external_datasets.get_COCO_Search18(location=real_location) if location is None: assert isinstance(stimuli_train, pysaliency.Stimuli) assert not isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.Stimuli) assert not isinstance(stimuli_val, pysaliency.FileStimuli) else: assert isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.FileStimuli) assert location.join('COCO-Search18/stimuli_train.hdf5').check() assert location.join('COCO-Search18/stimuli_validation.hdf5').check() assert location.join('COCO-Search18/fixations_train.hdf5').check() assert location.join('COCO-Search18/fixations_validation.hdf5').check() assert len(stimuli_train) == 3714 assert len(stimuli_val) == 623 assert set(stimuli_train.sizes) == {(1050, 1680)} assert set(stimuli_val.sizes) == {(1050, 1680)} assert len(fixations_train.x) == 207970 assert np.mean(fixations_train.x) == approx(835.8440337548686) assert np.mean(fixations_train.y) == approx(509.6030908304083) assert np.mean(fixations_train.t) == approx(3.0987979035437805) assert np.mean(fixations_train.scanpath_history_length) == approx(3.0987979035437805) assert np.std(fixations_train.x) == approx(336.5760343388881) assert np.std(fixations_train.y) == approx(193.04654731407436) assert np.std(fixations_train.t) == approx(3.8411822348178664) assert np.std(fixations_train.scanpath_history_length) == approx(3.8411822348178664) assert kurtosis(fixations_train.x) == approx(-0.6283401149747818) assert kurtosis(fixations_train.y) == approx(0.15947671647330974) assert kurtosis(fixations_train.t) == approx(12.038491881119654) assert kurtosis(fixations_train.scanpath_history_length) == approx(12.038491881119654) assert skew(fixations_train.x) == approx(0.1706207784149093) assert skew(fixations_train.y) == approx(-0.07268825958515616) assert skew(fixations_train.t) == approx(2.804671690266736) assert skew(fixations_train.scanpath_history_length) == approx(2.804671690266736) assert entropy(fixations_train.n) == approx(11.654309812153487) assert (fixations_train.n == 0).sum() == 48 assert len(fixations_val.x) == 31761 assert np.mean(fixations_val.x) == approx(841.0752652624287) assert np.mean(fixations_val.y) == approx(498.3305594911999) assert np.mean(fixations_val.t) == approx(3.107994080790907) assert np.mean(fixations_val.scanpath_history_length) == approx(3.107994080790907) assert np.std(fixations_val.x) == approx(331.6328528765362) assert np.std(fixations_val.y) == approx(195.86110035077112) assert np.std(fixations_val.t) == approx(3.7502120687824454) assert np.std(fixations_val.scanpath_history_length) == approx(3.7502120687824454) assert kurtosis(fixations_val.x) == approx(-0.5973130907561486) assert kurtosis(fixations_val.y) == approx(-0.2797786304225598) assert kurtosis(fixations_val.t) == approx(11.250011182161305) assert kurtosis(fixations_val.scanpath_history_length) == approx(11.250011182161305) assert skew(fixations_val.x) == approx(0.14886675209256964) assert skew(fixations_val.y) == approx(-0.04086275403802345) assert skew(fixations_val.t) == approx(2.671653646130074) assert skew(fixations_val.scanpath_history_length) == approx(2.671653646130074) assert entropy(fixations_val.n) == approx(9.159600084079305) assert (fixations_val.n == 0).sum() == 52 #assert len(fixations_train) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_train, fixations_train)) #assert len(fixations_val) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_val, fixations_val)) @pytest.mark.slow @pytest.mark.download def test_COCO_Search18_no_task_merge_redundant_images(location): real_location = _location(location) stimuli_train, fixations_train, stimuli_val, fixations_val = pysaliency.external_datasets.get_COCO_Search18(location=real_location, merge_tasks=False, unique_images=False) if location is None: assert isinstance(stimuli_train, pysaliency.Stimuli) assert not isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.Stimuli) assert not isinstance(stimuli_val, pysaliency.FileStimuli) else: assert isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.FileStimuli) assert location.join('COCO-Search18_no-task-merge_duplicate-images/stimuli_train.hdf5').check() assert location.join('COCO-Search18_no-task-merge_duplicate-images/stimuli_validation.hdf5').check() assert location.join('COCO-Search18_no-task-merge_duplicate-images/fixations_train.hdf5').check() assert location.join('COCO-Search18_no-task-merge_duplicate-images/fixations_validation.hdf5').check() #assert len(stimuli_train) == 3714 #assert len(stimuli_val) == 623 assert len(stimuli_train) == 4326 assert len(stimuli_val) == 652 assert set(stimuli_train.sizes) == {(1050, 1680)} assert set(stimuli_val.sizes) == {(1050, 1680)} #assert len(set(stimuli_train.stimulus_ids)) == 4326 #assert len(set(stimuli_val.stimulus_ids)) == 652 assert len(set(stimuli_train.stimulus_ids)) == 3714 assert len(set(stimuli_val.stimulus_ids)) == 623 assert 'task' in stimuli_train.__attributes__ assert 'task' in stimuli_val.__attributes__ assert len(np.unique(stimuli_train.attributes['task'])) == 18 assert len(np.unique(stimuli_val.attributes['task'])) == 18 assert set(stimuli_train.attributes['task']) == set(range(18)) assert set(stimuli_val.attributes['task']) == set(range(18)) assert len(fixations_train.x) == 207970 assert np.mean(fixations_train.x) == approx(835.8440337548686) assert np.mean(fixations_train.y) == approx(509.6030908304083) assert np.mean(fixations_train.t) == approx(3.0987979035437805) assert np.mean(fixations_train.scanpath_history_length) == approx(3.0987979035437805) assert np.std(fixations_train.x) == approx(336.5760343388881) assert np.std(fixations_train.y) == approx(193.04654731407436) assert np.std(fixations_train.t) == approx(3.8411822348178664) assert np.std(fixations_train.scanpath_history_length) == approx(3.8411822348178664) assert kurtosis(fixations_train.x) == approx(-0.6283401149747818) assert kurtosis(fixations_train.y) == approx(0.15947671647330974) assert kurtosis(fixations_train.t) == approx(12.038491881119654) assert kurtosis(fixations_train.scanpath_history_length) == approx(12.038491881119654) assert skew(fixations_train.x) == approx(0.1706207784149093) assert skew(fixations_train.y) == approx(-0.07268825958515616) assert skew(fixations_train.t) == approx(2.804671690266736) assert skew(fixations_train.scanpath_history_length) == approx(2.804671690266736) assert entropy(fixations_train.n) == approx(11.967951796529752) assert (fixations_train.n == 0).sum() == 71 assert len(fixations_val.x) == 31761 assert np.mean(fixations_val.x) == approx(841.0752652624287) assert np.mean(fixations_val.y) == approx(498.3305594911999) assert np.mean(fixations_val.t) == approx(3.107994080790907) assert np.mean(fixations_val.scanpath_history_length) == approx(3.107994080790907) assert np.std(fixations_val.x) == approx(331.6328528765362) assert np.std(fixations_val.y) == approx(195.86110035077112) assert np.std(fixations_val.t) == approx(3.7502120687824454) assert np.std(fixations_val.scanpath_history_length) == approx(3.7502120687824454) assert kurtosis(fixations_val.x) == approx(-0.5973130907561486) assert kurtosis(fixations_val.y) == approx(-0.2797786304225598) assert kurtosis(fixations_val.t) == approx(11.250011182161305) assert kurtosis(fixations_val.scanpath_history_length) == approx(11.250011182161305) assert skew(fixations_val.x) == approx(0.14886675209256964) assert skew(fixations_val.y) == approx(-0.04086275403802345) assert skew(fixations_val.t) == approx(2.671653646130074) assert skew(fixations_val.scanpath_history_length) == approx(2.671653646130074) assert entropy(fixations_val.n) == approx(9.243197427307365) assert (fixations_val.n == 0).sum() == 42 #assert len(fixations_train) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_train, fixations_train)) #assert len(fixations_val) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_val, fixations_val)) @pytest.mark.slow @pytest.mark.download def test_COCO_Search18_no_task_merge_unique_images(location): real_location = _location(location) stimuli_train, fixations_train, stimuli_val, fixations_val = pysaliency.external_datasets.get_COCO_Search18(location=real_location, merge_tasks=False, unique_images=True) if location is None: assert isinstance(stimuli_train, pysaliency.Stimuli) assert not isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.Stimuli) assert not isinstance(stimuli_val, pysaliency.FileStimuli) else: assert isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.FileStimuli) assert location.join('COCO-Search18_no-task-merge_unique-images/stimuli_train.hdf5').check() assert location.join('COCO-Search18_no-task-merge_unique-images/stimuli_validation.hdf5').check() assert location.join('COCO-Search18_no-task-merge_unique-images/fixations_train.hdf5').check() assert location.join('COCO-Search18_no-task-merge_unique-images/fixations_validation.hdf5').check() assert len(stimuli_train) == 4326 assert len(stimuli_val) == 652 assert set(stimuli_train.sizes) == {(1050, 1680)} assert set(stimuli_val.sizes) == {(1050, 1680)} assert len(set(stimuli_train.stimulus_ids)) == 4326 assert len(set(stimuli_val.stimulus_ids)) == 652 assert 'task' in stimuli_train.__attributes__ assert 'task' in stimuli_val.__attributes__ assert len(np.unique(stimuli_train.attributes['task'])) == 18 assert len(np.unique(stimuli_val.attributes['task'])) == 18 assert set(stimuli_train.attributes['task']) == set(range(18)) assert set(stimuli_val.attributes['task']) == set(range(18)) assert len(fixations_train.x) == 207970 assert np.mean(fixations_train.x) == approx(835.8440337548686) assert np.mean(fixations_train.y) == approx(509.6030908304083) assert np.mean(fixations_train.t) == approx(3.0987979035437805) assert np.mean(fixations_train.scanpath_history_length) == approx(3.0987979035437805) assert np.std(fixations_train.x) == approx(336.5760343388881) assert np.std(fixations_train.y) == approx(193.04654731407436) assert np.std(fixations_train.t) == approx(3.8411822348178664) assert np.std(fixations_train.scanpath_history_length) == approx(3.8411822348178664) assert kurtosis(fixations_train.x) == approx(-0.6283401149747818) assert kurtosis(fixations_train.y) == approx(0.15947671647330974) assert kurtosis(fixations_train.t) == approx(12.038491881119654) assert kurtosis(fixations_train.scanpath_history_length) == approx(12.038491881119654) assert skew(fixations_train.x) == approx(0.1706207784149093) assert skew(fixations_train.y) == approx(-0.07268825958515616) assert skew(fixations_train.t) == approx(2.804671690266736) assert skew(fixations_train.scanpath_history_length) == approx(2.804671690266736) assert entropy(fixations_train.n) == approx(11.967951796529752) assert (fixations_train.n == 0).sum() == 71 assert len(fixations_val.x) == 31761 assert np.mean(fixations_val.x) == approx(841.0752652624287) assert np.mean(fixations_val.y) == approx(498.3305594911999) assert np.mean(fixations_val.t) == approx(3.107994080790907) assert np.mean(fixations_val.scanpath_history_length) == approx(3.107994080790907) assert np.std(fixations_val.x) == approx(331.6328528765362) assert np.std(fixations_val.y) == approx(195.86110035077112) assert np.std(fixations_val.t) == approx(3.7502120687824454) assert np.std(fixations_val.scanpath_history_length) == approx(3.7502120687824454) assert kurtosis(fixations_val.x) == approx(-0.5973130907561486) assert kurtosis(fixations_val.y) == approx(-0.2797786304225598) assert kurtosis(fixations_val.t) == approx(11.250011182161305) assert kurtosis(fixations_val.scanpath_history_length) == approx(11.250011182161305) assert skew(fixations_val.x) == approx(0.14886675209256964) assert skew(fixations_val.y) == approx(-0.04086275403802345) assert skew(fixations_val.t) == approx(2.671653646130074) assert skew(fixations_val.scanpath_history_length) == approx(2.671653646130074) assert entropy(fixations_val.n) == approx(9.243197427307365) assert (fixations_val.n == 0).sum() == 42 #assert len(fixations_train) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_train, fixations_train)) #assert len(fixations_val) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_val, fixations_val)) ================================================ FILE: tests/external_datasets/test_NUSEF.py ================================================ import numpy as np import pytest from pytest import approx from scipy.stats import kurtosis, skew import pysaliency from tests.test_external_datasets import _location, entropy @pytest.mark.slow @pytest.mark.download def test_NUSEF(location): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_NUSEF_public(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('NUSEF_public/stimuli.hdf5').check() assert location.join('NUSEF_public/fixations.hdf5').check() assert location.join('NUSEF_public/src/NUSEF_database.zip').check() assert len(stimuli.stimuli) == 429 assert len(fixations.x) == 66133 assert np.mean(fixations.x) == approx(461.73823151304873) assert np.mean(fixations.y) == approx(336.54399742934976) assert np.mean(fixations.t) == approx(2.0420471776571456) assert np.mean(fixations.scanpath_history_length) == approx(4.085887529675049) assert np.std(fixations.x) == approx(191.71434262715272) assert np.std(fixations.y) == approx(144.60874197688884) assert np.std(fixations.t) == approx(1.82140623534086) assert np.std(fixations.scanpath_history_length) == approx(3.4339653884944963) assert kurtosis(fixations.x) == approx(0.29833124844005354) assert kurtosis(fixations.y) == approx(1.9158192030098018) assert kurtosis(fixations.t) == approx(5285.812604733467) assert kurtosis(fixations.scanpath_history_length) == approx(0.8320210638515699) assert skew(fixations.x) == approx(0.3994141751115464) assert skew(fixations.y) == approx(0.7246047287335385) assert skew(fixations.t) == approx(39.25751334379433) assert skew(fixations.scanpath_history_length) == approx(0.9874139139443956) assert entropy(fixations.n) == approx(8.603204478724775) assert (fixations.n == 0).sum() == 132 # not testing this, there are many out-of-stimulus fixations in the dataset # assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations)) ================================================ FILE: tests/external_datasets/test_PASCAL_S.py ================================================ import numpy as np import pytest from pytest import approx from scipy.stats import kurtosis, skew import pysaliency import pysaliency.external_datasets from tests.test_external_datasets import _location, entropy @pytest.mark.slow @pytest.mark.download def test_PASCAL_S(location): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_PASCAL_S(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('PASCAL-S/stimuli.hdf5').check() assert location.join('PASCAL-S/fixations.hdf5').check() assert len(stimuli.stimuli) == 850 assert len(fixations.x) == 40314 assert np.mean(fixations.x) == approx(240.72756362553952) assert np.mean(fixations.y) == approx(194.85756809048965) assert np.mean(fixations.t) == approx(2.7856823932132757) assert np.mean(fixations.scanpath_history_length) == approx(2.7856823932132757) assert np.std(fixations.x) == approx(79.57401169717699) assert np.std(fixations.y) == approx(65.21296890260112) assert np.std(fixations.t) == approx(2.1191752645988675) assert np.std(fixations.scanpath_history_length) == approx(2.1191752645988675) assert kurtosis(fixations.x) == approx(0.0009226786675387011) assert kurtosis(fixations.y) == approx(1.1907544566979986) assert kurtosis(fixations.t) == approx(-0.540943536495714) assert kurtosis(fixations.scanpath_history_length) == approx(-0.540943536495714) assert skew(fixations.x) == approx(0.2112334873314548) assert skew(fixations.y) == approx(0.7208733522533084) assert skew(fixations.t) == approx(0.4800678710338635) assert skew(fixations.scanpath_history_length) == approx(0.4800678710338635) assert entropy(fixations.n) == approx(9.711222735065062) assert (fixations.n == 0).sum() == 35 assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations)) ================================================ FILE: tests/external_datasets/test_SALICON.py ================================================ import numpy as np import pytest from pytest import approx from scipy.stats import kurtosis, skew import pysaliency import pysaliency.external_datasets from tests.test_external_datasets import entropy @pytest.mark.slow @pytest.mark.download def test_SALICON_stimuli(tmpdir): real_location = str(tmpdir) location = tmpdir stimuli_train, stimuli_val, stimuli_test = pysaliency.external_datasets.salicon._get_SALICON_stimuli(location=real_location, name='SALICONfoobar') assert isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.FileStimuli) assert isinstance(stimuli_test, pysaliency.FileStimuli) assert location.join('SALICONfoobar/stimuli_train.hdf5').check() assert location.join('SALICONfoobar/stimuli_val.hdf5').check() assert location.join('SALICONfoobar/stimuli_test.hdf5').check() assert len(stimuli_train) == 10000 assert len(stimuli_val) == 5000 assert len(stimuli_test) == 5000 assert set(stimuli_train.sizes) == set([(480, 640)]) assert set(stimuli_val.sizes) == set([(480, 640)]) assert set(stimuli_test.sizes) == set([(480, 640)]) @pytest.mark.slow @pytest.mark.download def test_SALICON_fixations_2015_mouse(tmpdir): real_location = str(tmpdir) location = tmpdir fixations_train, fixations_val = pysaliency.external_datasets.salicon._get_SALICON_fixations( location=real_location, name='SALICONbar', edition='2015', fixation_type='mouse') assert location.join('SALICONbar/fixations_train.hdf5').check() assert location.join('SALICONbar/fixations_val.hdf5').check() assert isinstance(fixations_train, pysaliency.Fixations) assert not isinstance(fixations_train, pysaliency.FixationTrains) assert isinstance(fixations_val, pysaliency.Fixations) assert not isinstance(fixations_val, pysaliency.FixationTrains) assert len(fixations_train.x) == 68992355 assert np.mean(fixations_train.x) == approx(313.0925573565361) assert np.mean(fixations_train.y) == approx(229.669921428251) assert np.mean(fixations_train.t) == approx(2453.3845915246698) assert np.mean(fixations_train.scanpath_history_length) == approx(0.0) assert np.max(fixations_train.scanpath_history_length) == approx(0.0) assert np.std(fixations_train.x) == approx(147.69997888974905) assert np.std(fixations_train.y) == approx(96.52066518492143) assert np.std(fixations_train.t) == approx(1538.7280458609941) assert kurtosis(fixations_train.x) == approx(-0.8543758617424033) assert kurtosis(fixations_train.y) == approx(-0.6277250557240337) assert kurtosis(fixations_train.t) == approx(19515.32829536525) assert skew(fixations_train.x) == approx(0.08274147964197842) assert skew(fixations_train.y) == approx(0.10465863071610296) assert skew(fixations_train.t) == approx(55.69180106087239) assert entropy(fixations_train.n) == approx(13.278169650429593) assert (fixations_train.n == 0).sum() == 6928 assert len(fixations_val.x) == 38846998 assert np.mean(fixations_val.x) == approx(311.44141923141655) assert np.mean(fixations_val.y) == approx(229.10522205602607) assert np.mean(fixations_val.t) == approx(2463.950701930687) assert np.mean(fixations_val.scanpath_history_length) == approx(0.0) assert np.max(fixations_val.scanpath_history_length) == approx(0.0) assert np.std(fixations_val.x) == approx(149.34417260369818) assert np.std(fixations_val.y) == approx(97.93170200208576) assert np.std(fixations_val.t) == approx(1408.3339394913962) assert kurtosis(fixations_val.x) == approx(-0.8449322083004356) assert kurtosis(fixations_val.y) == approx(-0.6136372253463405) assert kurtosis(fixations_val.t) == approx(-1.1157482867740718) assert skew(fixations_val.x) == approx(0.08926920530231194) assert skew(fixations_val.y) == approx(0.10168032060729842) assert skew(fixations_val.t) == approx(0.05444269756551158) assert entropy(fixations_val.n) == approx(12.279414832007888) assert (fixations_val.n == 0).sum() == 8244 assert np.all(fixations_train.x >= 0) assert np.all(fixations_train.y >= 0) assert np.all(fixations_val.x >= 0) assert np.all(fixations_val.y >= 0) assert np.all(fixations_train.x < 640) assert np.all(fixations_train.y < 480) assert np.all(fixations_val.x < 640) assert np.all(fixations_val.y < 480) @pytest.mark.slow @pytest.mark.download def test_SALICON_fixations_2015_fixations(tmpdir): real_location = str(tmpdir) location = tmpdir fixations_train, fixations_val = pysaliency.external_datasets.salicon._get_SALICON_fixations( location=real_location, name='SALICONbar', edition='2015', fixation_type='fixations') assert location.join('SALICONbar/fixations_train.hdf5').check() assert location.join('SALICONbar/fixations_val.hdf5').check() assert isinstance(fixations_train, pysaliency.Fixations) assert not isinstance(fixations_train, pysaliency.FixationTrains) assert isinstance(fixations_val, pysaliency.Fixations) assert not isinstance(fixations_val, pysaliency.FixationTrains) assert len(fixations_train.x) == 3171533 assert np.mean(fixations_train.x) == approx(310.93839540689) assert np.mean(fixations_train.y) == approx(217.7589979356986) assert np.mean(fixations_train.t) == approx(5.020693147446361) assert np.mean(fixations_train.scanpath_history_length) == approx(0.0) assert np.max(fixations_train.scanpath_history_length) == approx(0.0) assert np.std(fixations_train.x) == approx(131.0672366442846) assert np.std(fixations_train.y) == approx(86.33526319309237) assert np.std(fixations_train.t) == approx(5.2387518223254474) assert kurtosis(fixations_train.x) == approx(-0.6327397503173677) assert kurtosis(fixations_train.y) == approx(-0.3662318210834883) assert kurtosis(fixations_train.t) == approx(5.6123414320267795) assert skew(fixations_train.x) == approx(0.10139095797827476) assert skew(fixations_train.y) == approx(0.13853441448148346) assert skew(fixations_train.t) == approx(1.8891615714930796) assert entropy(fixations_train.n) == approx(13.22601241838667) assert (fixations_train.n == 0).sum() == 170 assert len(fixations_val.x) == 1662655 assert np.mean(fixations_val.x) == approx(308.64650213062845) assert np.mean(fixations_val.y) == approx(217.97772358065865) assert np.mean(fixations_val.t) == approx(4.808886389539622) assert np.mean(fixations_val.scanpath_history_length) == approx(0.0) assert np.max(fixations_val.scanpath_history_length) == approx(0.0) assert np.std(fixations_val.x) == approx(130.34460214133043) assert np.std(fixations_val.y) == approx(85.80831530782285) assert np.std(fixations_val.t) == approx(4.999870176048051) assert kurtosis(fixations_val.x) == approx(-0.5958648294721907) assert kurtosis(fixations_val.y) == approx(-0.31300073559578934) assert kurtosis(fixations_val.t) == approx(4.9489750451359225) assert skew(fixations_val.x) == approx(0.11714467225615313) assert skew(fixations_val.y) == approx(0.12631245881037118) assert skew(fixations_val.t) == approx(1.8301317514860862) assert entropy(fixations_val.n) == approx(12.234936723301066) assert (fixations_val.n == 0).sum() == 259 assert np.all(fixations_train.x >= 0) assert np.all(fixations_train.y >= 0) assert np.all(fixations_val.x >= 0) assert np.all(fixations_val.y >= 0) assert np.all(fixations_train.x < 640) assert np.all(fixations_train.y < 480) assert np.all(fixations_val.x < 640) assert np.all(fixations_val.y < 480) @pytest.mark.slow @pytest.mark.download def test_SALICON_fixations_2017_mouse(tmpdir): real_location = str(tmpdir) location = tmpdir fixations_train, fixations_val = pysaliency.external_datasets.salicon._get_SALICON_fixations( location=real_location, name='SALICONbar', edition='2017', fixation_type='mouse') assert location.join('SALICONbar/fixations_train.hdf5').check() assert location.join('SALICONbar/fixations_val.hdf5').check() assert isinstance(fixations_train, pysaliency.Fixations) assert not isinstance(fixations_train, pysaliency.FixationTrains) assert isinstance(fixations_val, pysaliency.Fixations) assert not isinstance(fixations_val, pysaliency.FixationTrains) assert len(fixations_train.x) == 215286274 assert np.mean(fixations_train.x) == approx(314.91750797871686) assert np.mean(fixations_train.y) == approx(232.38085973332957) assert np.mean(fixations_train.t) == approx(2541.6537073654777) assert np.mean(fixations_train.scanpath_history_length) == approx(0.0) assert np.max(fixations_train.scanpath_history_length) == approx(0.0) assert np.std(fixations_train.x) == approx(138.09403491170718) assert np.std(fixations_train.y) == approx(93.55417139372516) assert np.std(fixations_train.t) == approx(1432.604664553447) assert kurtosis(fixations_train.x) == approx(-0.8009690077811422) assert kurtosis(fixations_train.y) == approx(-0.638316482844866) assert kurtosis(fixations_train.t) == approx(6854.681620924244) assert skew(fixations_train.x) == approx(0.06734542626655958) assert skew(fixations_train.y) == approx(0.07252065918701057) assert skew(fixations_train.t) == approx(17.770454294178407) assert entropy(fixations_train.n) == approx(13.274472019581758) assert (fixations_train.n == 0).sum() == 24496 assert len(fixations_val.x) == 121898426 assert np.mean(fixations_val.x) == approx(313.3112383249313) assert np.mean(fixations_val.y) == approx(231.8708303160281) assert np.mean(fixations_val.t) == approx(2538.2123597970003) assert np.mean(fixations_val.scanpath_history_length) == approx(0.0) assert np.max(fixations_val.scanpath_history_length) == approx(0.0) assert np.std(fixations_val.x) == approx(139.30115624028937) assert np.std(fixations_val.y) == approx(95.24435516821612) assert np.std(fixations_val.t) == approx(1395.986706164002) assert kurtosis(fixations_val.x) == approx(-0.7932049483979013) assert kurtosis(fixations_val.y) == approx(-0.6316552996345393) assert kurtosis(fixations_val.t) == approx(-1.1483055562729023) assert skew(fixations_val.x) == approx(0.08023882420460927) assert skew(fixations_val.y) == approx(0.07703227629250083) assert skew(fixations_val.t) == approx(-0.0027158508337847653) assert entropy(fixations_val.n) == approx(12.278275960422771) assert (fixations_val.n == 0).sum() == 23961 assert np.all(fixations_train.x >= 0) assert np.all(fixations_train.y >= 0) assert np.all(fixations_val.x >= 0) assert np.all(fixations_val.y >= 0) assert np.all(fixations_train.x < 640) assert np.all(fixations_train.y < 480) assert np.all(fixations_val.x < 640) assert np.all(fixations_val.y < 480) @pytest.mark.slow @pytest.mark.download def test_SALICON_fixations_2017_fixations(tmpdir): real_location = str(tmpdir) location = tmpdir fixations_train, fixations_val = pysaliency.external_datasets.salicon._get_SALICON_fixations( location=real_location, name='SALICONbar', edition='2017', fixation_type='fixations') assert location.join('SALICONbar/fixations_train.hdf5').check() assert location.join('SALICONbar/fixations_val.hdf5').check() assert isinstance(fixations_train, pysaliency.Fixations) assert not isinstance(fixations_train, pysaliency.FixationTrains) assert isinstance(fixations_val, pysaliency.Fixations) assert not isinstance(fixations_val, pysaliency.FixationTrains) assert len(fixations_train.x) == 4598112 assert np.mean(fixations_train.x) == approx(314.62724265959594) assert np.mean(fixations_train.y) == approx(228.43566163677613) assert np.mean(fixations_train.t) == approx(4.692611228260643) assert np.mean(fixations_train.scanpath_history_length) == approx(0.0) assert np.max(fixations_train.scanpath_history_length) == approx(0.0) assert np.std(fixations_train.x) == approx(134.1455759990284) assert np.std(fixations_train.y) == approx(87.13212105359052) assert np.std(fixations_train.t) == approx(3.7300713016372375) assert kurtosis(fixations_train.x) == approx(-0.8163385970402013) assert kurtosis(fixations_train.y) == approx(-0.615440115290188) assert kurtosis(fixations_train.t) == approx(0.7328902767227148) assert skew(fixations_train.x) == approx(0.07523280051849487) assert skew(fixations_train.y) == approx(0.0854479359829959) assert skew(fixations_train.t) == approx(0.8951438604006022) assert entropy(fixations_train.n) == approx(13.26103635730998) assert (fixations_train.n == 0).sum() == 532 assert len(fixations_val.x) == 2576914 assert np.mean(fixations_val.x) == approx(312.8488630198757) assert np.mean(fixations_val.y) == approx(227.6883237081253) assert np.mean(fixations_val.t) == approx(4.889936955598829) assert np.mean(fixations_val.scanpath_history_length) == approx(0.0) assert np.max(fixations_val.scanpath_history_length) == approx(0.0) assert np.std(fixations_val.x) == approx(133.22242352479964) assert np.std(fixations_val.y) == approx(86.71553440419093) assert np.std(fixations_val.t) == approx(3.9029124873868466) assert kurtosis(fixations_val.x) == approx(-0.7961636859307624) assert kurtosis(fixations_val.y) == approx(-0.5897615692354612) assert kurtosis(fixations_val.t) == approx(0.7766482713546012) assert skew(fixations_val.x) == approx(0.08676607299583787) assert skew(fixations_val.y) == approx(0.08801482949432776) assert skew(fixations_val.t) == approx(0.9082922185416067) assert entropy(fixations_val.n) == approx(12.259608288646687) assert (fixations_val.n == 0).sum() == 593 assert np.all(fixations_train.x >= 0) assert np.all(fixations_train.y >= 0) assert np.all(fixations_val.x >= 0) assert np.all(fixations_val.y >= 0) assert np.all(fixations_train.x < 640) assert np.all(fixations_train.y < 480) assert np.all(fixations_val.x < 640) assert np.all(fixations_val.y < 480) ================================================ FILE: tests/external_datasets/test_coco_freeview.py ================================================ import numpy as np import pytest from pytest import approx import pysaliency from scipy.stats import kurtosis, skew from tests.test_external_datasets import _location, entropy @pytest.mark.slow @pytest.mark.download def test_COCO_Freeview(location): real_location = _location(location) stimuli_train, fixations_train, stimuli_val, fixations_val, stimuli_test = pysaliency.external_datasets.get_COCO_Freeview(location=real_location) if location is None: assert isinstance(stimuli_train, pysaliency.Stimuli) assert not isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.Stimuli) assert not isinstance(stimuli_val, pysaliency.FileStimuli) assert isinstance(stimuli_test, pysaliency.Stimuli) assert not isinstance(stimuli_test, pysaliency.FileStimuli) else: assert isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.FileStimuli) assert isinstance(stimuli_test, pysaliency.FileStimuli) assert location.join('COCO-Freeview/stimuli_train.hdf5').check() assert location.join('COCO-Freeview/stimuli_validation.hdf5').check() assert location.join('COCO-Freeview/stimuli_test.hdf5').check() assert location.join('COCO-Freeview/fixations_train.hdf5').check() assert location.join('COCO-Freeview/fixations_validation.hdf5').check() assert len(stimuli_train) == 3714 assert len(stimuli_val) == 603 assert len(stimuli_test) == 1000 assert set(stimuli_train.sizes) == {(1050, 1680)} assert set(stimuli_val.sizes) == {(1050, 1680)} assert set(stimuli_test.sizes) == {(1050, 1680)} assert len(fixations_train.x) == 572184 assert np.mean(fixations_train.x) == approx(854.6399011506788) assert np.mean(fixations_train.y) == approx(520.0318222809445) assert np.mean(fixations_train.t) == approx(7.568133677278638) assert np.mean(fixations_train.scanpath_history_length) == approx(7.568133677278638) assert np.std(fixations_train.x) == approx(296.0191172854278) assert np.std(fixations_train.y) == approx(181.3128347366162) assert np.std(fixations_train.t) == approx(4.9536161050175025) assert np.std(fixations_train.scanpath_history_length) == approx(4.9536161050175025) assert kurtosis(fixations_train.x) == approx(-0.4658856837827998) assert kurtosis(fixations_train.y) == approx(-0.17242182386194793) assert kurtosis(fixations_train.t) == approx(-0.7932601698667865) assert kurtosis(fixations_train.scanpath_history_length) == approx(-0.7932601698667865) assert skew(fixations_train.x) == approx(0.04888106495259364) assert skew(fixations_train.y) == approx(0.1217343831850603) assert skew(fixations_train.t) == approx(0.2791201142040311) assert skew(fixations_train.scanpath_history_length) == approx(0.2791201142040311) assert entropy(fixations_train.n) == approx(11.853219537063737) assert (fixations_train.n == 0).sum() == 165 # Validation assert len(fixations_val.x) == 92821 assert np.mean(fixations_val.x) == approx(858.7499983839865) assert np.mean(fixations_val.y) == approx(519.7572176554874) assert np.mean(fixations_val.t) == approx(7.561747880328805) assert np.mean(fixations_val.scanpath_history_length) == approx(7.561747880328805) assert np.std(fixations_val.x) == approx(298.68282356632267) assert np.std(fixations_val.y) == approx(184.22406748940242) assert np.std(fixations_val.t) == approx(4.950144502725075) assert np.std(fixations_val.scanpath_history_length) == approx(4.950144502725075) assert kurtosis(fixations_val.x) == approx(-0.48168521133038) assert kurtosis(fixations_val.y) == approx(-0.25828026864894804) assert kurtosis(fixations_val.t) == approx(-0.7630800100767541) assert kurtosis(fixations_val.scanpath_history_length) == approx(-0.7630800100767541) assert skew(fixations_val.x) == approx(0.03072935717644178) assert skew(fixations_val.y) == approx(0.1086910594402604) assert skew(fixations_val.t) == approx(0.28569302638044036) assert skew(fixations_val.scanpath_history_length) == approx(0.28569302638044036) assert entropy(fixations_val.n) == approx(9.230606964850315) assert (fixations_val.n == 0).sum() == 155 #assert len(fixations_train) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_train, fixations_train)) #assert len(fixations_val) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_val, fixations_val)) @pytest.mark.slow @pytest.mark.download @pytest.mark.nonfree def test_COCO_Freeview_with_test(location): real_location = _location(location) stimuli_train, fixations_train, stimuli_val, fixations_val, stimuli_test = pysaliency.external_datasets.get_COCO_Freeview(location=real_location, test_data='ThirdParty/COCO/COCOFreeView_fixations_test_new.json') if location is None: assert isinstance(stimuli_train, pysaliency.Stimuli) assert not isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.Stimuli) assert not isinstance(stimuli_val, pysaliency.FileStimuli) assert isinstance(stimuli_test, pysaliency.Stimuli) assert not isinstance(stimuli_test, pysaliency.FileStimuli) else: assert isinstance(stimuli_train, pysaliency.FileStimuli) assert isinstance(stimuli_val, pysaliency.FileStimuli) assert isinstance(stimuli_test, pysaliency.FileStimuli) assert location.join('COCO-Freeview/stimuli_train.hdf5').check() assert location.join('COCO-Freeview/stimuli_validation.hdf5').check() assert location.join('COCO-Freeview/stimuli_test.hdf5').check() assert location.join('COCO-Freeview/fixations_train.hdf5').check() assert location.join('COCO-Freeview/fixations_validation.hdf5').check() assert location.join('COCO-Freeview/fixations_test.hdf5').check() fixations_test = pysaliency.read_hdf5(str(location.join('COCO-Freeview/fixations_test.hdf5'))) assert len(stimuli_train) == 3714 assert len(stimuli_val) == 603 assert len(stimuli_test) == 1000 assert set(stimuli_train.sizes) == {(1050, 1680)} assert set(stimuli_val.sizes) == {(1050, 1680)} assert set(stimuli_test.sizes) == {(1050, 1680)} assert len(fixations_train.x) == 572184 assert np.mean(fixations_train.x) == approx(854.6399011506788) assert np.mean(fixations_train.y) == approx(520.0318222809445) assert np.mean(fixations_train.t) == approx(7.568133677278638) assert np.mean(fixations_train.scanpath_history_length) == approx(7.568133677278638) assert np.std(fixations_train.x) == approx(296.0191172854278) assert np.std(fixations_train.y) == approx(181.3128347366162) assert np.std(fixations_train.t) == approx(4.9536161050175025) assert np.std(fixations_train.scanpath_history_length) == approx(4.9536161050175025) assert kurtosis(fixations_train.x) == approx(-0.4658856837827998) assert kurtosis(fixations_train.y) == approx(-0.17242182386194793) assert kurtosis(fixations_train.t) == approx(-0.7932601698667865) assert kurtosis(fixations_train.scanpath_history_length) == approx(-0.7932601698667865) assert skew(fixations_train.x) == approx(0.04888106495259364) assert skew(fixations_train.y) == approx(0.1217343831850603) assert skew(fixations_train.t) == approx(0.2791201142040311) assert skew(fixations_train.scanpath_history_length) == approx(0.2791201142040311) assert entropy(fixations_train.n) == approx(11.853219537063737) assert (fixations_train.n == 0).sum() == 165 # Validation assert len(fixations_val.x) == 92821 assert np.mean(fixations_val.x) == approx(858.7499983839865) assert np.mean(fixations_val.y) == approx(519.7572176554874) assert np.mean(fixations_val.t) == approx(7.561747880328805) assert np.mean(fixations_val.scanpath_history_length) == approx(7.561747880328805) assert np.std(fixations_val.x) == approx(298.68282356632267) assert np.std(fixations_val.y) == approx(184.22406748940242) assert np.std(fixations_val.t) == approx(4.950144502725075) assert np.std(fixations_val.scanpath_history_length) == approx(4.950144502725075) assert kurtosis(fixations_val.x) == approx(-0.48168521133038) assert kurtosis(fixations_val.y) == approx(-0.25828026864894804) assert kurtosis(fixations_val.t) == approx(-0.7630800100767541) assert kurtosis(fixations_val.scanpath_history_length) == approx(-0.7630800100767541) assert skew(fixations_val.x) == approx(0.03072935717644178) assert skew(fixations_val.y) == approx(0.1086910594402604) assert skew(fixations_val.t) == approx(0.28569302638044036) assert skew(fixations_val.scanpath_history_length) == approx(0.28569302638044036) assert entropy(fixations_val.n) == approx(9.230606964850315) assert (fixations_val.n == 0).sum() == 155 #assert len(fixations_train) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_train, fixations_train)) #assert len(fixations_val) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli_val, fixations_val)) # test assert len(fixations_test.x) == 154154 assert np.mean(fixations_test.x) == approx(853.7380314490705) assert np.mean(fixations_test.y) == approx(519.4896415273038) assert np.mean(fixations_test.t) == approx(7.594574256911919) assert np.mean(fixations_test.scanpath_history_length) == approx(7.594574256911919) assert np.mean(fixations_test.duration) == approx(0.2615992449109333) assert np.std(fixations_test.x) == approx(293.0185179686817) assert np.std(fixations_test.y) == approx(183.48201719100905) assert np.std(fixations_test.t) == approx(4.983095899305675) assert np.std(fixations_test.scanpath_history_length) == approx(4.983095899305675) assert np.std(fixations_test.duration) == approx(0.1788878832915657) assert kurtosis(fixations_test.x) == approx(-0.43932134352939967) assert kurtosis(fixations_test.y) == approx(-0.16783224862720658) assert kurtosis(fixations_test.t) == approx(-0.7842602163619645) assert kurtosis(fixations_test.scanpath_history_length) == approx(-0.7842602163619645) assert kurtosis(fixations_test.duration) == approx(122.74787622396316) assert skew(fixations_test.x) == approx(0.041065458982844354) assert skew(fixations_test.y) == approx(0.13852645233252112) assert skew(fixations_test.t) == approx(0.28877843053685415) assert skew(fixations_test.scanpath_history_length) == approx(0.28877843053685415) assert skew(fixations_test.duration) == approx(6.980496743557728) assert entropy(fixations_test.n) == approx(9.959947067374634) assert (fixations_test.n == 0).sum() == 154 ================================================ FILE: tests/external_models/test_deepgaze.py ================================================ import os import numpy as np import pytest import pysaliency from pysaliency.external_models.deepgaze import DeepGazeI, DeepGazeIIE @pytest.fixture(scope='module') def color_stimulus(): return np.load(os.path.join('tests', 'external_models', 'color_stimulus.npy')) @pytest.fixture(scope='module') def grayscale_stimulus(): return np.load(os.path.join('tests', 'external_models', 'grayscale_stimulus.npy')) @pytest.fixture def stimuli(color_stimulus, grayscale_stimulus): return pysaliency.Stimuli([color_stimulus, grayscale_stimulus]) @pytest.fixture def fixations(): return pysaliency.FixationTrains.from_fixation_trains( [[700, 730], [430, 450]], [[300, 300], [500, 500]], [[0, 1], [0, 1]], ns=[0, 1], subjects=[0, 0], ) @pytest.mark.download def test_deepgaze1(stimuli, fixations): model = DeepGazeI(centerbias_model=pysaliency.UniformModel(), device='cpu') ig = model.information_gain(stimuli, fixations) np.testing.assert_allclose(ig, 0.9455161648442227, rtol=5e-6) @pytest.mark.download def test_deepgaze2e(stimuli, fixations): model = DeepGazeIIE(centerbias_model=pysaliency.UniformModel(), device='cpu') ig = model.information_gain(stimuli, fixations) np.testing.assert_allclose(ig, 3.918556860669079) ================================================ FILE: tests/skippedtest_theano_utils.py ================================================ from __future__ import absolute_import, print_function, division, unicode_literals import unittest import pytest import numpy as np from scipy.ndimage import gaussian_filter as scipy_filter import theano import theano.tensor as T from pysaliency.theano_utils import nonlinearity, gaussian_filter, CenterBias, Blur # mark whole module as theano pytestmark = pytest.mark.theano @pytest.fixture(params=['float64', 'float32']) def dtype(request): return request.param @pytest.fixture(params=['pixel', 'random']) def input(request): return request.param @pytest.fixture(params=[20.0]) def sigma(request): return request.param class TestNonlinearity(unittest.TestCase): def setUp(self): self.x = T.dvector('x') self.x.tag.test_value = np.linspace(0, 1, 20) self.y = T.dvector('y') self.y.tag.test_value = np.linspace(0, 1, 20) self.input = T.dvector('input') self.input.tag.test_value = np.linspace(0, 1, 20) self.length = 20 self.nonlin = nonlinearity(self.input, self.x, self.y, self.length) self.f = theano.function([self.input, self.x, self.y], self.nonlin) def test_id(self): x = np.linspace(0, 1, self.length) y = np.linspace(0, 1, self.length) inp = np.linspace(0, 1, self.length) out = self.f(inp, x, y) np.testing.assert_allclose(out, inp) def test_mult_id(self): x = np.linspace(0, 1, self.length) y = np.linspace(0, 2, self.length) inp = np.linspace(0, 1, self.length) out = self.f(inp, x, y) np.testing.assert_allclose(out, y) def test_shifted_id(self): x = np.linspace(0, 1, self.length) y = np.linspace(0, 1, self.length)+1 inp = np.linspace(0, 1, self.length) out = self.f(inp, x, y) np.testing.assert_allclose(out, y) def test_random(self): x = np.linspace(0, 1, self.length) y = np.random.randn(self.length) inp = np.linspace(0, 1, self.length) out = self.f(inp, x, y) np.testing.assert_allclose(out, y) def test_constant(self): x = np.linspace(0, 1, self.length) y = np.ones(self.length) inp = np.linspace(0, 1, self.length) out = self.f(inp, x, y) np.testing.assert_allclose(out, y) class TestBlur(object): def setUp(self): theano.config.compute_test_value = 'ignore' def test_blur_zeros(self): sigma = theano.shared(20.0) window_radius = 20*4 data = T.tensor3('data', dtype='float64') data.tag.test_value = np.zeros((1, 10, 10)) blur = gaussian_filter(data, sigma, window_radius) f = theano.function([data], blur) test_data = np.zeros((1000, 1000)) out = f(test_data[np.newaxis, :, :])[0, :, :] np.testing.assert_allclose(out, 0) def test_blur_ones(self): sigma = theano.shared(20.0) window_radius = 20*4 data = T.tensor3('data', dtype='float64') data.tag.test_value = np.zeros((1, 10, 10)) blur = gaussian_filter(data, sigma, window_radius) f = theano.function([data], blur) test_data = np.ones((1000, 1000)) out = f(test_data[np.newaxis, :, :])[0, :, :] np.testing.assert_allclose(out, 1) @pytest.mark.skip("Doesn't seem to work with theano right now") def test_other(self, dtype, input, sigma): theano.config.compute_test_value = 'ignore' sigma_theano = theano.shared(sigma) window_radius = int(sigma*4) if input == 'pixel': test_data = np.ones((100, 100)) test_data[50, 50] = 2 elif input == 'random': test_data = 10*np.ones((100, 100)) test_data += np.random.randn(100, 100) else: raise ValueError(input) test_data = test_data.astype(dtype) data = T.tensor3('data', dtype=dtype) data.tag.test_value = test_data[np.newaxis, :, :] print(data.dtype) blur = gaussian_filter(data, sigma_theano, window_radius) f = theano.function([data], blur) out = f(test_data[np.newaxis, :, :])[0, :, :] scipy_out = scipy_filter(test_data, sigma, mode='nearest') if dtype == 'float32': rtol = 5e-6 else: rtol = 1e-7 np.testing.assert_allclose(out, scipy_out, rtol=rtol) class TestBlurObject(object): def test_blur(self): theano.config.floatX = 'float64' data = T.matrix('data') data.tag.test_value = np.random.randn(10, 10) blur = Blur(data, sigma=20.0, window_radius = 80) tmp = np.random.randn(1000, 2000) tmp += 10.0 out = blur.output.eval({data: tmp}) scipy_out = scipy_filter(tmp, 20.0, mode='nearest') np.testing.assert_allclose(out, scipy_out) def test_no_blur(self): theano.config.floatX = 'float64' data = T.matrix('data') data.tag.test_value = np.random.randn(10, 10) blur = Blur(data, sigma=0.0, window_radius = 80) tmp = np.random.randn(1000, 2000) tmp += 10.0 out = blur.output.eval({data: tmp}) # scipy_out = scipy_filter(tmp, 20.0, mode='nearest') np.testing.assert_allclose(out, tmp) class TestCenterBias(object): def test_centerbias_ones(self): theano.config.floatX = 'float64' data = T.matrix('data') data.tag.test_value = np.random.randn(10, 10) center_bias = CenterBias(data) tmp = np.ones((1000, 2000)) out = center_bias.output.eval({data: tmp}) np.testing.assert_allclose(out, tmp) def test_centerbias_ones_times_two(self): theano.config.floatX = 'float64' data = T.matrix('data') data.tag.test_value = np.random.randn(10, 10) center_bias = CenterBias(data, centerbias=np.array([2.0, 2.0, 2.0])) tmp = np.ones((1000, 2000)) out = center_bias.output.eval({data: tmp}) np.testing.assert_allclose(out, 2*tmp) def test_centerbias_random(self): theano.config.floatX = 'float64' data = T.matrix('data') data.tag.test_value = np.random.randn(10, 10) center_bias = CenterBias(data) tmp = np.random.randn(1000, 2000) out = center_bias.output.eval({data: tmp}) np.testing.assert_allclose(out, tmp) def test_centerbias_ones_nontrivial(self): theano.config.floatX = 'float64' data = T.matrix('data') data.tag.test_value = np.random.randn(10, 10) center_bias = CenterBias(data, centerbias=np.array([0.0, 0.0, 1.0, 1.0])) tmp = np.ones((1000, 2000)) out = center_bias.output.eval({data: tmp}) np.testing.assert_allclose(out.min(), 0.0) np.testing.assert_allclose(out.max(), 1.0) def test_centerbias_empty(self): theano.config.floatX = 'float64' data = T.matrix('data') data.tag.test_value = np.random.randn(10, 10) center_bias = CenterBias(data, centerbias = np.array([1.0]), alpha=3.0) tmp = np.random.randn(1000, 2000) out = center_bias.output.eval({data: tmp}) np.testing.assert_allclose(out, tmp) ================================================ FILE: tests/test_baseline_utils.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals from collections import OrderedDict import numpy as np import pytest import pysaliency from pysaliency.baseline_utils import ( BaselineModel, CrossvalidatedBaselineModel, CrossvalMultipleRegularizations, GeneralMixtureKernelDensityEstimator, KDEGoldModel, MixtureKernelDensityEstimator, ScikitLearnImageCrossValidationGenerator, fill_fixation_map, ) @pytest.fixture def scanpath_fixations(): xs_trains = [ [15, 20, 25], [10, 30], [30, 20, 10]] ys_trains = [ [13, 21, 10], [15, 35], [22, 5, 18]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subjects = [0, 1, 1] return pysaliency.ScanpathFixations(pysaliency.Scanpaths(xs=xs_trains, ys=ys_trains, ts=ts_trains, n=ns, subject=subjects)) @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randn(40, 40, 3), np.random.randn(40, 40, 3)]) def test_fixation_map(): fixations = np.array([ [0, 0], [1, 1], [1, 1], [1, 2], [1, 2], [2, 1]]) fixation_map = np.zeros((3, 3)) fill_fixation_map(fixation_map, fixations) np.testing.assert_allclose(fixation_map, np.array([ [1, 0, 0], [0, 2, 2], [0, 1, 0]])) def test_kde_gold_model(stimuli, scanpath_fixations): bandwidth = 0.1 kde_gold_model = KDEGoldModel(stimuli, scanpath_fixations, bandwidth=bandwidth) spaced_kde_gold_model = KDEGoldModel(stimuli, scanpath_fixations, bandwidth=bandwidth, grid_spacing=2) full_log_density = kde_gold_model.log_density(stimuli[0]) spaced_log_density = spaced_kde_gold_model.log_density(stimuli[0]) kl_div1 = np.sum(np.exp(full_log_density) * (full_log_density - spaced_log_density)) / np.log(2) kl_div2 = np.sum(np.exp(spaced_log_density) * (spaced_log_density - full_log_density)) / np.log(2) assert kl_div1 < 0.002 assert kl_div2 < 0.002 full_ll = kde_gold_model.information_gain(stimuli, scanpath_fixations, average='image') spaced_ll = spaced_kde_gold_model.information_gain(stimuli, scanpath_fixations, average='image') print(full_ll, spaced_ll) np.testing.assert_allclose(full_ll, 2.1912009255501252) np.testing.assert_allclose(spaced_ll, 2.191055750664578) def test_general_mixture_kernel_density_estimator(): # Test initialization estimator = GeneralMixtureKernelDensityEstimator(bandwidth=1.0, regularizations=[0.2, 0.1], regularizing_log_likelihoods=[[-1, 0.0], [-0.1, -10.0], [-10, -0.1]]) assert estimator.bandwidth == 1.0 assert np.allclose(estimator.regularizations, [0.2, 0.1]) assert np.allclose(estimator.regularizing_log_likelihoods, [[-1, 0.0], [-0.1, -10.0], [-10, -0.1]]) # Test setup estimator.setup() assert estimator.kde is not None assert estimator.kde_constant is not None assert estimator.regularization_constants is not None # Test fit X = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]) estimator.fit(X) assert estimator.kde is not None # Test score_samples X = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]) logliks = estimator.score_samples(X) assert logliks.shape == (3,) np.testing.assert_allclose(logliks, [-1.49141561, -1.40473767, -1.95213405]) # Test score X = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]) score = estimator.score(X) assert isinstance(score, float) def test_mixture_kernel_density_estimator(): # Test initialization estimator = MixtureKernelDensityEstimator(bandwidth=1.0, regularization=1.0e-5, regularizing_log_likelihoods=[-0.3, -0.2, -0.1]) assert estimator.bandwidth == 1.0 assert estimator.regularization == 1.0e-5 # Test setup estimator.setup() assert estimator.kde is not None assert estimator.kde_constant is not None assert estimator.uniform_constant is not None # Test fit X = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]) estimator.fit(X) assert estimator.kde is not None # Test score_samples X = np.array([[0, 0.2, 0], [0.3, 1, 1], [1, 1, 2]]) logliks = estimator.score_samples(X) assert logliks.shape == (3,) np.testing.assert_allclose(logliks, [-2.56662505, -2.5272495, -2.38495638]) # Test score X = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]) score = estimator.score(X) assert isinstance(score, float) def test_crossval_multiple_regularizations(stimuli, scanpath_fixations): # Test initialization regularization_models = OrderedDict([('model1', pysaliency.UniformModel()), ('model2', pysaliency.models.GaussianModel())]) crossvalidation = ScikitLearnImageCrossValidationGenerator(stimuli, scanpath_fixations) estimator = CrossvalMultipleRegularizations(stimuli, scanpath_fixations, regularization_models, crossvalidation) assert estimator.cv is crossvalidation assert estimator.mean_area is not None assert estimator.X is not None assert estimator.regularization_log_likelihoods is not None # Test score log_bandwidth = 0.1 log_regularizations = [0.1, 0.2] score = estimator.score(log_bandwidth, *log_regularizations) assert isinstance(score, float) np.testing.assert_allclose(score, -1.4673831679692528e-10) def test_baseline_model_does_not_store_stimuli_and_fixations(stimuli, scanpath_fixations): model = BaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) assert not hasattr(model, 'stimuli') assert not hasattr(model, 'fixations') def test_baseline_model_hdf5_roundtrip_with_shape_cache(tmp_path, stimuli, scanpath_fixations): model = BaselineModel(stimuli, scanpath_fixations, bandwidth=0.1, keep_aspect=True) first_prediction = model.log_density(stimuli[0]).copy() assert stimuli[0].shape[:2] in model.shape_cache path = tmp_path / 'baseline_model.hdf5' model.to_hdf5(path, include_shape_cache=True) reloaded = BaselineModel.read_hdf5(path) np.testing.assert_allclose(reloaded.log_density(stimuli[0]), first_prediction) assert stimuli[0].shape[:2] in reloaded.shape_cache def test_baseline_model_hdf5_roundtrip_without_shape_cache(tmp_path, stimuli, scanpath_fixations): model = BaselineModel(stimuli, scanpath_fixations, bandwidth=0.2) first_prediction = model.log_density(stimuli[1]).copy() path = tmp_path / 'baseline_model_no_cache.hdf5' model.to_hdf5(path, include_shape_cache=False) reloaded = BaselineModel.read_hdf5(path) assert reloaded.shape_cache == {} np.testing.assert_allclose(reloaded.log_density(stimuli[1]), first_prediction) def test_baseline_model_hdf5_type(tmp_path, stimuli, scanpath_fixations): model = BaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) path = tmp_path / 'baseline_type.hdf5' model.to_hdf5(path) import h5py with h5py.File(path, 'r') as hdf5_file: value = hdf5_file.attrs['type'] if not isinstance(value, str): value = value.decode('utf8') assert value == 'pysaliency.baseline_utils.BaselineModel' def test_crossvalidated_baseline_model_stores_fixations_n(stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) assert hasattr(model, 'fixations_n') assert not hasattr(model, 'fixations') assert not hasattr(model, 'shape_cache') np.testing.assert_array_equal(model.fixations_n, scanpath_fixations.n) def test_crossvalidated_baseline_model_hdf5_type(tmp_path, stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_type.hdf5' model.to_hdf5(path) import h5py with h5py.File(path, 'r') as f: value = f.attrs['type'] if not isinstance(value, str): value = value.decode('utf8') assert value == 'pysaliency.baseline_utils.CrossvalidatedBaselineModel' def test_crossvalidated_baseline_model_hdf5_roundtrip_with_stimuli(tmp_path, stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) first_prediction = model.log_density(stimuli[0]).copy() path = tmp_path / 'cv_baseline.hdf5' model.to_hdf5(path) reloaded = CrossvalidatedBaselineModel.read_hdf5(path) np.testing.assert_allclose(reloaded.log_density(stimuli[0]), first_prediction) def test_crossvalidated_baseline_model_hdf5_roundtrip_without_stimuli(tmp_path, stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) first_prediction = model.log_density(stimuli[1]).copy() path = tmp_path / 'cv_baseline_no_stimuli.hdf5' model.to_hdf5(path, include_stimuli=False) reloaded = CrossvalidatedBaselineModel.read_hdf5(path, stimuli=stimuli) np.testing.assert_allclose(reloaded.log_density(stimuli[1]), first_prediction) def test_crossvalidated_baseline_model_hdf5_error_without_stimuli(tmp_path, stimuli, scanpath_fixations): model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_no_stimuli.hdf5' model.to_hdf5(path, include_stimuli=False) with pytest.raises(ValueError, match='stimuli'): CrossvalidatedBaselineModel.read_hdf5(path) def test_crossvalidated_baseline_model_hdf5_stimuli_kwarg_overrides_embedded(tmp_path, stimuli, scanpath_fixations): """stimuli= kwarg takes precedence over the embedded stimuli group.""" model = CrossvalidatedBaselineModel(stimuli, scanpath_fixations, bandwidth=0.1) first_prediction = model.log_density(stimuli[0]).copy() path = tmp_path / 'cv_baseline_with_stimuli.hdf5' model.to_hdf5(path, include_stimuli=True) # Pass the same stimuli explicitly — should still work (kwarg wins) reloaded = CrossvalidatedBaselineModel.read_hdf5(path, stimuli=stimuli) assert reloaded.stimuli is stimuli np.testing.assert_allclose(reloaded.log_density(stimuli[0]), first_prediction) ================================================ FILE: tests/test_crossvalidation.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import numpy as np import pytest from sklearn.model_selection import cross_val_score import pysaliency from pysaliency.baseline_utils import RegularizedKernelDensityEstimator, ScikitLearnImageCrossValidationGenerator, ScikitLearnImageSubjectCrossValidationGenerator, fixations_to_scikit_learn class ConstantSaliencyModel(pysaliency.Model): def _log_density(self, stimulus): return np.zeros((stimulus.shape[0], stimulus.shape[1])) - np.log(stimulus.shape[0]) - np.log(stimulus.shape[1]) class GaussianSaliencyModel(pysaliency.Model): def _log_density(self, stimulus): height = stimulus.shape[0] width = stimulus.shape[1] YS, XS = np.mgrid[:height, :width] r_squared = (XS-0.5*width)**2 + (YS-0.5*height)**2 size = np.sqrt(width**2+height**2) values = np.ones((stimulus.shape[0], stimulus.shape[1]))*np.exp(-0.5*(r_squared/size)) density = values / values.sum() return np.log(density) @pytest.fixture def scanpath_fixations(): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3], [10]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33], [11]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900], [100]] ns = [0, 0, 1, 2] subjects = [0, 1, 1, 0] return pysaliency.ScanpathFixations(pysaliency.Scanpaths(xs=xs_trains, ys=ys_trains, ts=ts_trains, n=ns, subject=subjects)) @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randn(40, 40, 3), np.random.randn(40, 40, 3), np.random.randn(40, 40, 3)]) def _unpack_crossval(cv): for train_inds, test_inds in cv: yield list(train_inds), list(test_inds) def unpack_crossval(cv): return list(_unpack_crossval(cv)) def test_image_crossvalidation(stimuli, scanpath_fixations): gsmm = GaussianSaliencyModel() cv = ScikitLearnImageCrossValidationGenerator(stimuli, scanpath_fixations) assert unpack_crossval(cv) == [ ([False, False, False, False, False, True, True, True, True], [True, True, True, True, True, False, False, False, False]), ([True, True, True, True, True, False, False, False, True], [False, False, False, False, False, True, True, True, False]), ([True, True, True, True, True, True, True, True, False], [False, False, False, False, False, False, False, False, True]) ] X = fixations_to_scikit_learn(scanpath_fixations, normalize=stimuli, add_shape=True) assert cross_val_score( RegularizedKernelDensityEstimator(bandwidth=0.1, regularization=0.1), X, cv=cv, verbose=0).sum() def test_image_subject_crossvalidation(stimuli, scanpath_fixations): gsmm = GaussianSaliencyModel() cv = ScikitLearnImageSubjectCrossValidationGenerator(stimuli, scanpath_fixations) assert unpack_crossval(cv) == [ ([3, 4], [0, 1, 2]), ([0, 1, 2], [3, 4]) ] X = fixations_to_scikit_learn(scanpath_fixations, normalize=stimuli, add_shape=True) assert cross_val_score( RegularizedKernelDensityEstimator(bandwidth=0.1, regularization=0.1), X, cv=cv, verbose=0).sum() ================================================ FILE: tests/test_dataset_config.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import os import numpy as np import pytest from imageio import imwrite from test_filter_datasets import assert_stimuli_equal import pysaliency import pysaliency.dataset_config as dc from pysaliency.filter_datasets import create_subset from tests.datasets.utils import assert_scanpath_fixations_equal from tests.datasets.utils import assert_fixations_equal @pytest.fixture def scanpath_fixations(): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subjects = [0, 1, 1] tasks = [0, 1, 0] multi_dim_attribute = [[0.0, 1],[0, 3], [4, 5.5]] durations_train = [ [42, 25, 100], [99, 98], [200, 150, 120] ] return pysaliency.ScanpathFixations(pysaliency.Scanpaths( xs=xs_trains, ys=ys_trains, ts=ts_trains, n=ns, subject=subjects, scanpath_attributes={ 'task': tasks, 'multi_dim_attribute': multi_dim_attribute, }, fixation_attributes={'durations': durations_train}, attribute_mapping={'durations': 'duration'}, )) @pytest.fixture def file_stimuli_with_attributes(tmpdir): filenames = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) attributes = { 'dva': list(range(len(filenames))), 'other_stuff': np.random.randn(len(filenames)), 'some_strings': list('abcdefghijklmnopqr'), } return pysaliency.FileStimuli(filenames=filenames, attributes=attributes) @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randn(40, 40, 3), np.random.randn(40, 40, 3)]) @pytest.fixture def hdf5_dataset(tmpdir, scanpath_fixations, stimuli): stimuli.to_hdf5(os.path.join(str(tmpdir), 'stimuli.hdf5')) scanpath_fixations.to_hdf5(os.path.join(str(tmpdir), 'fixations.hdf5')) return str(tmpdir) def test_load_dataset(hdf5_dataset, stimuli, scanpath_fixations): loaded_stimuli, loaded_fixations = dc.load_dataset_from_config({ 'stimuli': os.path.join(hdf5_dataset, 'stimuli.hdf5'), 'fixations': os.path.join(hdf5_dataset, 'fixations.hdf5'), }) assert len(loaded_stimuli) == len(stimuli) np.testing.assert_allclose(loaded_fixations.x, scanpath_fixations.x) def test_load_dataset_with_filter(hdf5_dataset, stimuli, scanpath_fixations): loaded_stimuli, loaded_fixations = dc.load_dataset_from_config({ 'stimuli': os.path.join(hdf5_dataset, 'stimuli.hdf5'), 'fixations': os.path.join(hdf5_dataset, 'fixations.hdf5'), 'filters': [{ 'type': 'filter_fixations_by_number', 'parameters': { 'intervals': [[0, 2]], }, }], }) assert len(loaded_stimuli) == len(stimuli) assert len(loaded_fixations.x) == 6 assert np.all(loaded_fixations.scanpath_history_length < 2) def test_apply_dataset_filter_config_filter_scanpaths_by_attribute_task(stimuli, scanpath_fixations): scanpaths = scanpath_fixations filter_config = { 'type': 'filter_scanpaths_by_attribute', 'parameters': { 'attribute_name': 'task', 'attribute_value': 0, 'invert_match': False, } } filtered_stimuli, filtered_scanpaths = dc.apply_dataset_filter_config(stimuli, scanpaths, filter_config) inds = [0, 2] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) assert_stimuli_equal(filtered_stimuli, stimuli) def test_apply_dataset_filter_config_filter_scanpaths_by_attribute_multi_dim_attribute_invert_match(stimuli, scanpath_fixations): scanpaths = scanpath_fixations filter_config = { 'type': 'filter_scanpaths_by_attribute', 'parameters': { 'attribute_name': 'multi_dim_attribute', 'attribute_value': [0, 1], 'invert_match': True, } } filtered_stimuli, filtered_scanpaths = dc.apply_dataset_filter_config(stimuli, scanpaths, filter_config) inds = [1, 2] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) assert_stimuli_equal(filtered_stimuli, stimuli) def test_apply_dataset_filter_config_filter_fixations_by_attribute_subject_invert_match(stimuli, scanpath_fixations): fixations = scanpath_fixations[:] filter_config = { 'type': 'filter_fixations_by_attribute', 'parameters': { 'attribute_name': 'subject', 'attribute_value': 0, 'invert_match': True, } } filtered_stimuli, filtered_fixations = dc.apply_dataset_filter_config(stimuli, fixations, filter_config) inds = [3, 4, 5, 6, 7] expected_fixations = fixations[inds] assert_fixations_equal(filtered_fixations, expected_fixations) assert_stimuli_equal(filtered_stimuli, stimuli) def test_apply_dataset_filter_config_filter_stimuli_by_attribute_dva(file_stimuli_with_attributes, scanpath_fixations): fixations = scanpath_fixations[:] filter_config = { 'type': 'filter_stimuli_by_attribute', 'parameters': { 'attribute_name': 'dva', 'attribute_value': 1, 'invert_match': False, } } filtered_stimuli, filtered_fixations = dc.apply_dataset_filter_config(file_stimuli_with_attributes, fixations, filter_config) inds = [1] expected_stimuli, expected_fixations = create_subset(file_stimuli_with_attributes, fixations, inds) assert_fixations_equal(filtered_fixations, expected_fixations) assert_stimuli_equal(filtered_stimuli, expected_stimuli) def test_apply_dataset_filter_config_filter_scanpaths_by_length_multiple_inputs(stimuli, scanpath_fixations): scanpaths = scanpath_fixations filter_config = { 'type': 'filter_scanpaths_by_length', 'parameters': { 'intervals': [(1, 2), (2, 3)] } } filtered_stimuli, filtered_scanpaths = dc.apply_dataset_filter_config(stimuli, scanpaths, filter_config) inds = [1] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) assert_stimuli_equal(filtered_stimuli, stimuli) def test_apply_dataset_filter_config_filter_scanpaths_by_length_single_input(stimuli, scanpath_fixations): scanpaths = scanpath_fixations filter_config = { 'type': 'filter_scanpaths_by_length', 'parameters': { 'intervals': [(3)] } } filtered_stimuli, filtered_scanpaths = dc.apply_dataset_filter_config(stimuli, scanpaths, filter_config) inds = [0, 2] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) assert_stimuli_equal(filtered_stimuli, stimuli) ================================================ FILE: tests/test_external_datasets.py ================================================ import numpy as np import pytest from pathlib import Path from pytest import approx from scipy.stats import kurtosis, skew import pysaliency import pysaliency.external_datasets from pysaliency.utils import remove_trailing_nans def _location(location): if location is not None: return str(location) return location def entropy(labels): counts = np.bincount(labels) weights = counts / np.sum(counts) return -np.sum(weights*np.log(weights)) / np.log(2) @pytest.mark.slow @pytest.mark.download def test_toronto(location): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_toronto(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('toronto/stimuli.hdf5').check() assert location.join('toronto/fixations.hdf5').check() assert len(stimuli.stimuli) == 120 for n in range(len(stimuli.stimuli)): assert stimuli.shapes[n] == (511, 681, 3) assert stimuli.sizes[n] == (511, 681) assert len(fixations.x) == 11199 assert np.mean(fixations.x) == approx(345.7466738101616) assert np.mean(fixations.y) == approx(244.11393874453077) assert np.mean(fixations.t) == approx(0.0) assert np.mean(fixations.scanpath_history_length) == approx(0.0) assert np.std(fixations.x) == approx(132.7479359296397) assert np.std(fixations.y) == approx(82.89667109045186) assert np.std(fixations.t) == approx(0.0) assert np.std(fixations.scanpath_history_length) == approx(0.0) assert kurtosis(fixations.x) == approx(-0.40985986581959066) assert kurtosis(fixations.y) == approx(0.2748036777667475) assert fixations.t.std() == approx(0) assert fixations.scanpath_history_length.std() == approx(0) assert skew(fixations.x) == approx(-0.09509166105451604) assert skew(fixations.y) == approx(-0.08674038899319877) assert entropy(fixations.n) == approx(6.8939709237615405) assert (np.array(fixations.n) == 0).sum() == 130 assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations)) @pytest.mark.slow @pytest.mark.download @pytest.mark.matlab @pytest.mark.skip_octave def test_cat2000_train_v1_0(location, matlab): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_cat2000_train(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('CAT2000_train/stimuli.hdf5').check() assert location.join('CAT2000_train/fixations.hdf5').check() assert not list ((Path(location) / 'CAT2000_train' / 'Stimuli').glob('**/Output')) assert not list ((Path(location) / 'CAT2000_train' / 'Stimuli').glob('**/*_SaliencyMap.jpg')) assert len(stimuli.stimuli) == 2000 assert set(stimuli.sizes) == {(1080, 1920)} assert set(stimuli.attributes.keys()) == {'category'} assert np.all(np.array(stimuli.attributes['category'][0:100]) == 0) assert np.all(np.array(stimuli.attributes['category'][100:200]) == 1) assert len(fixations.x) == 672053 assert np.mean(fixations.x) == approx(977.9570036886972) assert np.mean(fixations.y) == approx(536.8014098590438) assert np.mean(fixations.t) == approx(10.95831429961625) assert np.mean(fixations.scanpath_history_length) == approx(9.95831429961625) assert np.std(fixations.x) == approx(265.521305397389) assert np.std(fixations.y) == approx(200.3874894751514) assert np.std(fixations.t) == approx(6.881491455270027) assert np.std(fixations.scanpath_history_length) == approx(6.881491455270027) assert kurtosis(fixations.x) == approx(0.8377433175079028) assert kurtosis(fixations.y) == approx(0.15890436764279947) assert kurtosis(fixations.t) == approx(0.08351046096368542) assert kurtosis(fixations.scanpath_history_length) == approx(0.08351046096368542) assert skew(fixations.x) == approx(0.07428576098144545) assert skew(fixations.y) == approx(0.27425191693049106) assert skew(fixations.t) == approx(0.5874222148956657) assert skew(fixations.scanpath_history_length) == approx(0.5874222148956657) assert entropy(fixations.n) == approx(10.955266908462857) assert (fixations.n == 0).sum() == 307 assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations)) @pytest.mark.slow @pytest.mark.download @pytest.mark.matlab @pytest.mark.skip_octave def test_cat2000_train_v1_1(location, matlab): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_cat2000_train(location=real_location, version='1.1') if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('CAT2000_train_v1.1/stimuli.hdf5').check() assert location.join('CAT2000_train_v1.1/fixations.hdf5').check() assert not list ((Path(location) / 'CAT2000_train_v1.1' / 'Stimuli').glob('**/Output')) assert not list ((Path(location) / 'CAT2000_train_v1.1' / 'Stimuli').glob('**/*_SaliencyMap.jpg')) assert len(stimuli.stimuli) == 2000 assert set(stimuli.sizes) == {(1080, 1920)} assert set(stimuli.attributes.keys()) == {'category'} assert np.all(np.array(stimuli.attributes['category'][0:100]) == 0) assert np.all(np.array(stimuli.attributes['category'][100:200]) == 1) assert len(fixations.x) == 667804 assert np.mean(fixations.x) == approx(977.048229720098) assert np.mean(fixations.y) == approx(535.7335899455527) assert np.mean(fixations.t) == approx(10.888694886523592) assert np.mean(fixations.scanpath_history_length) == approx(9.888694886523592) assert np.std(fixations.x) == approx(265.7561897117776) assert np.std(fixations.y) == approx(200.47021508760227) assert np.std(fixations.t) == approx(6.8276447542371805) assert np.std(fixations.scanpath_history_length) == approx(6.8276447542371805) assert kurtosis(fixations.x) == approx(0.8314129075001575) assert kurtosis(fixations.y) == approx(0.16001475266665466) assert kurtosis(fixations.t) == approx(0.07131517526032427) assert kurtosis(fixations.scanpath_history_length) == approx(0.07131517526032427) assert skew(fixations.x) == approx(0.07615972876511597) assert skew(fixations.y) == approx(0.2770231691322164) assert skew(fixations.t) == approx(0.5813051491385639) assert skew(fixations.scanpath_history_length) == approx(0.5813051491385639) assert entropy(fixations.n) == approx(10.955097604631638) assert (fixations.n == 0).sum() == 304 assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations)) @pytest.mark.slow @pytest.mark.download @pytest.mark.skip_octave def test_cat2000_test(location): real_location = _location(location) stimuli = pysaliency.external_datasets.get_cat2000_test(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('CAT2000_test/stimuli.hdf5').check() assert not list ((Path(location) / 'CAT2000_test' / 'Stimuli').glob('**/Output')) assert not list ((Path(location) / 'CAT2000_test' / 'Stimuli').glob('**/*_SaliencyMap.jpg')) assert len(stimuli.stimuli) == 2000 assert set(stimuli.sizes) == {(1080, 1920)} assert set(stimuli.attributes.keys()) == {'category'} assert np.all(np.array(stimuli.attributes['category'][0:100]) == 0) assert np.all(np.array(stimuli.attributes['category'][100:200]) == 1) @pytest.mark.slow @pytest.mark.download @pytest.mark.matlab def test_mit1003(location, matlab): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_mit1003(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('MIT1003/stimuli.hdf5').check() assert location.join('MIT1003/fixations.hdf5').check() assert len(stimuli.stimuli) == 1003 for n in range(len(stimuli.stimuli)): assert max(stimuli.sizes[n]) == 1024 assert len(fixations.x) == 104171 assert np.mean(fixations.x) == approx(487.13683496521253) assert np.mean(fixations.y) == approx(392.72728829760155) assert np.mean(fixations.t) == approx(1.5039892740461995) assert np.mean(fixations.scanpath_history_length) == approx(3.3973754691804823) assert np.std(fixations.x) == approx(190.0203102093757) assert np.std(fixations.y) == approx(159.99210430350126) assert np.std(fixations.t) == approx(0.816414737693668) assert np.std(fixations.scanpath_history_length) == approx(2.5433689996843354) assert kurtosis(fixations.x) == approx(-0.39272472247196033) assert kurtosis(fixations.y) == approx(0.6983793465837596) assert kurtosis(fixations.t) == approx(-1.2178525798721818) assert kurtosis(fixations.scanpath_history_length) == approx(-0.45897225172578704) assert skew(fixations.x) == approx(0.2204976032609953) assert skew(fixations.y) == approx(0.6445191904777621) assert skew(fixations.t) == approx(0.08125182887100482) assert skew(fixations.scanpath_history_length) == approx(0.5047182860999948) assert entropy(fixations.n) == approx(9.954348058662386) assert (fixations.n == 0).sum() == 121 assert 'duration_hist' in fixations.__attributes__ assert 'duration' in fixations.__attributes__ assert len(fixations.duration_hist) == len(fixations.x) assert len(fixations.duration) == len(fixations.x) for i in range(len(fixations.x)): assert len(remove_trailing_nans(fixations.duration_hist[i])) == len(remove_trailing_nans(fixations.x_hist[i])) assert 'durations' in fixations.scanpaths.fixation_attributes assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations)) @pytest.mark.slow @pytest.mark.download @pytest.mark.matlab def test_mit1003_onesize(location, matlab): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_mit1003_onesize(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('MIT1003_onesize/stimuli.hdf5').check() assert location.join('MIT1003_onesize/fixations.hdf5').check() assert len(stimuli.stimuli) == 463 for n in range(len(stimuli.stimuli)): assert stimuli.sizes[n] == (768, 1024) assert len(fixations.x) == 48771 assert (fixations.n == 0).sum() == 121 @pytest.mark.slow @pytest.mark.nonfree @pytest.mark.skip_octave def test_koehler(location): real_location = _location(location) stimuli, fixations_freeviewing, fixations_objectsearch, fixations_saliencysearch \ = pysaliency.external_datasets.get_koehler(location=real_location, datafile='ThirdParty/Koehler_PublicData.zip') if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('Koehler/stimuli.hdf5').check() assert location.join('Koehler/fixations_freeviewing.hdf5').check() assert location.join('Koehler/fixations_objectsearch.hdf5').check() assert location.join('Koehler/fixations_saliencysearch.hdf5').check() assert len(stimuli.stimuli) == 800 assert set(stimuli.sizes) == {(405, 405)} assert len(fixations_freeviewing.x) == 94600 assert np.mean(fixations_freeviewing.x) == approx(205.99696617336153) assert np.mean(fixations_freeviewing.y) == approx(190.69461945031713) assert np.mean(fixations_freeviewing.t) == approx(2.6399788583509514) assert np.mean(fixations_freeviewing.scanpath_history_length) == approx(2.6399788583509514) assert np.std(fixations_freeviewing.x) == approx(86.7891146642233) assert np.std(fixations_freeviewing.y) == approx(67.11495414894833) assert np.std(fixations_freeviewing.t) == approx(2.1333811216291982) assert np.std(fixations_freeviewing.scanpath_history_length) == approx(2.1333811216291982) assert kurtosis(fixations_freeviewing.x) == approx(-0.6927977738542421) assert kurtosis(fixations_freeviewing.y) == approx(0.26434562598200007) assert kurtosis(fixations_freeviewing.t) == approx(1.0000780305443921) assert kurtosis(fixations_freeviewing.scanpath_history_length) == approx(1.0000780305443921) assert skew(fixations_freeviewing.x) == approx(0.04283261395632401) assert skew(fixations_freeviewing.y) == approx(0.15277972804817913) assert skew(fixations_freeviewing.t) == approx(0.8569222723327634) assert skew(fixations_freeviewing.scanpath_history_length) == approx(0.8569222723327634) assert entropy(fixations_freeviewing.n) == approx(9.638058218898772) assert (fixations_freeviewing.n == 0).sum() == 128 assert len(fixations_objectsearch.x) == 125293 assert np.mean(fixations_objectsearch.x) == approx(199.05052955871437) assert np.mean(fixations_objectsearch.y) == approx(202.8867534499134) assert np.mean(fixations_objectsearch.t) == approx(3.9734302794250276) assert np.mean(fixations_objectsearch.scanpath_history_length) == approx(3.9734302794250276) assert np.std(fixations_objectsearch.x) == approx(88.10778886056328) assert np.std(fixations_objectsearch.y) == approx(65.29208873896408) assert np.std(fixations_objectsearch.t) == approx(2.902206977368411) assert np.std(fixations_objectsearch.scanpath_history_length) == approx(2.902206977368411) assert kurtosis(fixations_objectsearch.x) == approx(-0.49120093084140537) assert kurtosis(fixations_objectsearch.y) == approx(0.625841808353278) assert kurtosis(fixations_objectsearch.t) == approx(-0.33967380087822274) assert kurtosis(fixations_objectsearch.scanpath_history_length) == approx(-0.33967380087822274) assert skew(fixations_objectsearch.x) == approx(0.12557741560793217) assert skew(fixations_objectsearch.y) == approx(-0.005003252610602025) assert skew(fixations_objectsearch.t) == approx(0.5297789219605314) assert skew(fixations_objectsearch.scanpath_history_length) == approx(0.5297789219605314) assert entropy(fixations_objectsearch.n) == approx(9.637156128022387) assert (fixations_objectsearch.n == 0).sum() == 140 assert len(fixations_saliencysearch.x) == 94528 assert np.mean(fixations_saliencysearch.x) == approx(203.7605894549763) assert np.mean(fixations_saliencysearch.y) == approx(193.67308099187542) assert np.mean(fixations_saliencysearch.t) == approx(2.7536814488828707) assert np.mean(fixations_saliencysearch.scanpath_history_length) == approx(2.7536814488828707) assert np.std(fixations_saliencysearch.x) == approx(94.18304559956722) assert np.std(fixations_saliencysearch.y) == approx(65.3335501279418) assert np.std(fixations_saliencysearch.t) == approx(2.114709138575087) assert np.std(fixations_saliencysearch.scanpath_history_length) == approx(2.114709138575087) assert kurtosis(fixations_saliencysearch.x) == approx(-0.9085078389136778) assert kurtosis(fixations_saliencysearch.y) == approx(0.319385892621745) assert kurtosis(fixations_saliencysearch.t) == approx(-0.06720050297739633) assert kurtosis(fixations_saliencysearch.scanpath_history_length) == approx(-0.06720050297739633) assert skew(fixations_saliencysearch.x) == approx(0.0019227173784863957) assert skew(fixations_saliencysearch.y) == approx(0.05728474858602427) assert skew(fixations_saliencysearch.t) == approx(0.5866228411986677) assert skew(fixations_saliencysearch.scanpath_history_length) == approx(0.5866228411986677) assert entropy(fixations_saliencysearch.n) == approx(9.639365034197382) assert (fixations_saliencysearch.n == 0).sum() == 103 assert len(fixations_freeviewing) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations_freeviewing)) assert len(fixations_objectsearch) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations_objectsearch)) assert len(fixations_saliencysearch) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations_saliencysearch)) @pytest.mark.slow @pytest.mark.download def test_FIGRIM(location): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_FIGRIM(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('FIGRIM/stimuli.hdf5').check() assert location.join('FIGRIM/fixations.hdf5').check() assert len(stimuli.stimuli) == 2793 assert set(stimuli.sizes) == {(1000, 1000)} assert len(fixations.x) == 424712 assert np.mean(fixations.x) == approx(504.64437312814323) assert np.mean(fixations.y) == approx(512.8821982896645) assert np.mean(fixations.t) == approx(2.9365758443368684) assert np.mean(fixations.scanpath_history_length) == approx(2.9365758443368684) assert np.std(fixations.x) == approx(158.86601835411133) assert np.std(fixations.y) == approx(145.67212772412645) assert np.std(fixations.t) == approx(2.1599063813289363) assert np.std(fixations.scanpath_history_length) == approx(2.1599063813289363) assert kurtosis(fixations.x) == approx(0.5791564709307742) assert kurtosis(fixations.y) == approx(0.709663215799134) assert kurtosis(fixations.t) == approx(-0.7245566668044039) assert kurtosis(fixations.scanpath_history_length) == approx(-0.7245566668044039) assert skew(fixations.x) == approx(0.09245444798073615) assert skew(fixations.y) == approx(-0.008328881229649684) assert skew(fixations.t) == approx(0.37950203945703337) assert skew(fixations.scanpath_history_length) == approx(0.37950203945703337) assert (fixations.n == 0).sum() == 107 assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations)) @pytest.mark.slow @pytest.mark.download def test_OSIE(location): real_location = _location(location) stimuli, fixations = pysaliency.external_datasets.get_OSIE(location=real_location) if location is None: assert isinstance(stimuli, pysaliency.Stimuli) assert not isinstance(stimuli, pysaliency.FileStimuli) else: assert isinstance(stimuli, pysaliency.FileStimuli) assert location.join('OSIE/stimuli.hdf5').check() assert location.join('OSIE/fixations.hdf5').check() assert len(stimuli.stimuli) == 700 assert set(stimuli.sizes) == {(600, 800)} assert len(fixations.x) == 98321 assert np.mean(fixations.x) == approx(401.466024552232) assert np.mean(fixations.y) == approx(283.58293548682377) assert np.mean(fixations.t) == approx(4.369971826974909) assert np.mean(fixations.scanpath_history_length) == approx(4.369971826974909) assert np.std(fixations.x) == approx(171.2760014573171) assert np.std(fixations.y) == approx(117.70943331958269) assert np.std(fixations.t) == approx(2.9882005810119465) assert np.std(fixations.scanpath_history_length) == approx(2.9882005810119465) assert kurtosis(fixations.x) == approx(-0.8437559648105699) assert kurtosis(fixations.y) == approx(-0.6146702485058717) assert kurtosis(fixations.t) == approx(-0.7170874454173752) assert kurtosis(fixations.scanpath_history_length) == approx(-0.7170874454173752) assert skew(fixations.x) == approx(0.029428873077089763) assert skew(fixations.y) == approx(0.170931952813165) assert skew(fixations.t) == approx(0.31008498461792156) assert skew(fixations.scanpath_history_length) == approx(0.31008498461792156) assert entropy(fixations.n) == approx(9.445962418853439) assert (fixations.n == 0).sum() == 141 assert len(fixations) == len(pysaliency.datasets.remove_out_of_stimulus_fixations(stimuli, fixations)) ================================================ FILE: tests/test_external_models.py ================================================ from __future__ import absolute_import, print_function, division import os import pytest import numpy as np import pysaliency import pysaliency.external_datasets @pytest.fixture(scope='module') def color_stimulus(): return np.load(os.path.join('tests', 'external_models', 'color_stimulus.npy')) @pytest.fixture(scope='module') def grayscale_stimulus(): return np.load(os.path.join('tests', 'external_models', 'grayscale_stimulus.npy')) @pytest.mark.skip_octave @pytest.mark.matlab def test_AIM(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.AIM(location=str(tmpdir)) print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__))), rtol=1e-5) @pytest.mark.skip_octave @pytest.mark.matlab def test_SUN(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.SUN(location=str(tmpdir)) print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__))), rtol=1e-5) @pytest.mark.skip_octave @pytest.mark.matlab def test_ContextAwareSaliency(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.ContextAwareSaliency(location=str(tmpdir)) print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__)))) @pytest.mark.skip_octave @pytest.mark.matlab def test_GBVS(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.GBVS(location=str(tmpdir)) print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__)))) @pytest.mark.skip_octave @pytest.mark.matlab def test_GBVSIttiKoch(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.GBVSIttiKoch(location=str(tmpdir)) print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__)))) @pytest.mark.skip_octave @pytest.mark.matlab def test_Judd(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.Judd(location=str(tmpdir), saliency_toolbox_archive='SaliencyToolbox2.3.zip') print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__)))) @pytest.mark.skip_octave @pytest.mark.matlab def test_IttiKoch(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.IttiKoch(location=str(tmpdir), saliency_toolbox_archive='SaliencyToolbox2.3.zip') print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__)))) @pytest.mark.skip_octave @pytest.mark.matlab def test_RARE2007(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.external_models.RARE2007(location=str(tmpdir)) print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__)))) @pytest.mark.skip_octave @pytest.mark.matlab def test_RARE2012(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.RARE2012(location=str(tmpdir)) print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__)))) @pytest.mark.skip_octave @pytest.mark.matlab def test_CovSal(tmpdir, matlab, color_stimulus, grayscale_stimulus): model = pysaliency.CovSal(location=str(tmpdir)) print('Testing color') saliency_map = model.saliency_map(color_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_color_stimulus.npy'.format(model.__modelname__)))) print('Testing Grayscale') saliency_map = model.saliency_map(grayscale_stimulus) np.testing.assert_allclose(saliency_map, np.load(os.path.join('tests', 'external_models', '{}_grayscale_stimulus.npy'.format(model.__modelname__)))) ================================================ FILE: tests/test_filter_datasets.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import numpy as np import pytest from imageio import imwrite import pysaliency import pysaliency.filter_datasets as filter_datasets from pysaliency.filter_datasets import ( create_subset, filter_fixations_by_attribute, filter_scanpaths_by_attribute, filter_scanpaths_by_length, filter_stimuli_by_attribute, remove_stimuli_without_fixations, ) from tests.datasets.utils import assert_fixations_equal, assert_scanpath_fixations_equal @pytest.fixture def fixation_trains(): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subjects = [0, 1, 1] tasks = [0, 1, 0] multi_dim_attribute = [[0.0, 1],[0, 3], [4, 5.5]] durations_train = [ [42, 25, 100], [99, 98], [200, 150, 120] ] some_attribute = [[0, 1, 2], [3, 4], [5, 6,7]] return pysaliency.ScanpathFixations(pysaliency.Scanpaths( xs=xs_trains, ys=ys_trains, ts=ts_trains, n=ns, subject=subjects, scanpath_attributes={ 'task': tasks, 'multi_dim_attribute': multi_dim_attribute, }, fixation_attributes={ 'durations': durations_train, 'some_attribute': some_attribute, }, attribute_mapping={'durations': 'duration'}, )) @pytest.fixture def file_stimuli_with_attributes(tmpdir): filenames = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) imwrite(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) attributes = { 'dva': list(range(len(filenames))), 'other_stuff': np.random.randn(len(filenames)), 'some_strings': list('abcdefghijklmnopqr'), } return pysaliency.FileStimuli(filenames=filenames, attributes=attributes) @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randn(40, 40, 3), np.random.randn(40, 40, 3)]) def test_filter_fixations_by_number(fixation_trains): fixations = filter_datasets.filter_fixations_by_number(fixation_trains, 0) assert len(fixations.x) == 3 np.testing.assert_allclose(fixations.scanpath_history_length, 0) fixations = filter_datasets.filter_fixations_by_number(fixation_trains, 1) assert len(fixations.x) == 3 np.testing.assert_allclose(fixations.scanpath_history_length, 1) fixations = filter_datasets.filter_fixations_by_number(fixation_trains, [[0, 2]]) assert len(fixations.x) == 6 assert np.all(fixations.scanpath_history_length < 2) fixations = filter_datasets.filter_fixations_by_number(fixation_trains, [[0, 2], 2]) assert len(fixations.x) == 8 np.testing.assert_allclose(fixations.x, fixation_trains.x) @pytest.fixture def stimuli_with_different_sizes(): return pysaliency.Stimuli([ np.random.randn(40, 40, 3), np.random.randn(40, 40, 3), np.random.randn(20, 40, 3), np.random.randn(20, 40, 3), np.random.randn(40, 20, 3), np.random.randn(40, 20, 3), np.random.randn(40, 20), np.random.randn(20, 20, 3), np.random.randn(20, 20, 3), ]) def assert_stimuli_equal(actual, expected): assert list(actual.stimulus_ids) == list(expected.stimulus_ids) @pytest.mark.parametrize('size,indices', [ ((40, 40), [0, 1]), ((20, 40), [2, 3]), ((40, 20), [4, 5, 6]), ((20, 20), [7, 8]), ]) def test_filter_stimuli_by_size_tuple(stimuli_with_different_sizes, fixation_trains, size, indices): filtered_stimuli, _ = filter_datasets.filter_stimuli_by_size( stimuli_with_different_sizes, fixation_trains, size=size ) assert_stimuli_equal( filtered_stimuli, stimuli_with_different_sizes[indices] ) def test_filter_stimuli_by_size_array(stimuli_with_different_sizes, fixation_trains): filtered_stimuli, _ = filter_datasets.filter_stimuli_by_size( stimuli_with_different_sizes, fixation_trains, size=[40, 40] ) assert_stimuli_equal( filtered_stimuli, stimuli_with_different_sizes[[0, 1]] ) @pytest.mark.parametrize('sizes,indices', [ ([(40, 40)], [0, 1]), ([(20, 40), (40, 40)], [0, 1, 2, 3]), ]) def test_filter_stimuli_by_size_multiple(stimuli_with_different_sizes, fixation_trains, sizes, indices): filtered_stimuli, _ = filter_datasets.filter_stimuli_by_size( stimuli_with_different_sizes, fixation_trains, sizes=sizes ) assert_stimuli_equal( filtered_stimuli, stimuli_with_different_sizes[indices] ) @pytest.fixture def many_stimuli(): stimuli = [np.random.randn(40, 40, 3) for i in range(1003)] category = np.array([i % 10 for i in range(len(stimuli))]) category2 = np.array([i // 500 for i in range(len(stimuli))]) return pysaliency.Stimuli(stimuli, attributes={'category': category, 'category2': category2}) @pytest.mark.parametrize('crossval_folds', [10, 11, 12]) @pytest.mark.parametrize('val_folds', [1, 2, 3]) @pytest.mark.parametrize('test_folds', [1]) def test_crossval_splits(many_stimuli, crossval_folds, val_folds, test_folds): if not test_folds and val_folds != 1: return # this case is raises an implementation error right now tmp_model = pysaliency.UniformModel() fixations = tmp_model.sample(many_stimuli, 100) train_stimuli = [] train_fixations = [] val_stimuli = [] val_fixations = [] test_stimuli = [] test_fixations = [] for _train_stimuli, _train_fixations, _val_stimuli, _val_fixations, _test_stimuli, _test_fixations in \ filter_datasets.iterate_crossvalidation(many_stimuli, fixations, crossval_folds=crossval_folds, val_folds=val_folds, test_folds=test_folds, random=True): assert not set(_train_stimuli.stimulus_ids).intersection(_val_stimuli.stimulus_ids) assert not set(_train_stimuli.stimulus_ids).intersection(_test_stimuli.stimulus_ids) if not test_folds: # otherwise test is validation assert not set(_val_stimuli.stimulus_ids).intersection(_test_stimuli.stimulus_ids) train_stimuli.append(_train_stimuli) train_fixations.append(_train_fixations) val_stimuli.append(_val_stimuli) val_fixations.append(_val_fixations) test_stimuli.append(_test_stimuli) test_fixations.append(_test_fixations) assert sum(len(s) for s in val_stimuli) == val_folds * len(many_stimuli) assert sum(len(s) for s in test_stimuli) == test_folds * len(many_stimuli) assert sum(len(s) for s in train_stimuli) == (crossval_folds - val_folds - test_folds) * len(many_stimuli) assert sum(len(f.x) for f in val_fixations) == val_folds * len(fixations.x) assert sum(len(f.x) for f in test_fixations) == test_folds * len(fixations.x) assert sum(len(f.x) for f in train_fixations) == (crossval_folds - val_folds - test_folds) * len(fixations.x) assert len(train_stimuli) == crossval_folds @pytest.mark.parametrize('crossval_folds', [10]) @pytest.mark.parametrize('val_folds', [1, 2, 3]) @pytest.mark.parametrize('test_folds', [1]) def test_stratified_crossval_splits(many_stimuli, crossval_folds, val_folds, test_folds): if not test_folds and val_folds != 1: return # this case is raises an implementation error right now tmp_model = pysaliency.UniformModel() fixations = tmp_model.sample(many_stimuli, 100) train_stimuli = [] train_fixations = [] val_stimuli = [] val_fixations = [] test_stimuli = [] test_fixations = [] for _train_stimuli, _train_fixations, _val_stimuli, _val_fixations, _test_stimuli, _test_fixations in \ filter_datasets.iterate_crossvalidation(many_stimuli, fixations, crossval_folds=crossval_folds, val_folds=val_folds, test_folds=test_folds, random=True, stratified_attributes=['category']): assert not set(_train_stimuli.stimulus_ids).intersection(_val_stimuli.stimulus_ids) assert not set(_train_stimuli.stimulus_ids).intersection(_test_stimuli.stimulus_ids) if not test_folds: # otherwise test is validation assert not set(_val_stimuli.stimulus_ids).intersection(_test_stimuli.stimulus_ids) np.testing.assert_allclose( np.sum(_train_stimuli.attributes['category'] == 0), len(many_stimuli) / crossval_folds * (crossval_folds - val_folds - test_folds) * 0.1, atol=1 ) np.testing.assert_allclose( np.sum(_val_stimuli.attributes['category'] == 0), len(many_stimuli) / crossval_folds * val_folds * 0.1, atol=1 ) np.testing.assert_allclose( np.sum(_test_stimuli.attributes['category'] == 0), len(many_stimuli) / crossval_folds * test_folds * 0.1, atol=1 ) train_stimuli.append(_train_stimuli) train_fixations.append(_train_fixations) val_stimuli.append(_val_stimuli) val_fixations.append(_val_fixations) test_stimuli.append(_test_stimuli) test_fixations.append(_test_fixations) assert sum(len(s) for s in val_stimuli) == val_folds * len(many_stimuli) assert sum(len(s) for s in test_stimuli) == test_folds * len(many_stimuli) assert sum(len(s) for s in train_stimuli) == (crossval_folds - val_folds - test_folds) * len(many_stimuli) assert sum(len(f.x) for f in val_fixations) == val_folds * len(fixations.x) assert sum(len(f.x) for f in test_fixations) == test_folds * len(fixations.x) assert sum(len(f.x) for f in train_fixations) == (crossval_folds - val_folds - test_folds) * len(fixations.x) assert len(train_stimuli) == crossval_folds @pytest.mark.parametrize('crossval_folds', [10]) @pytest.mark.parametrize('val_folds', [1, 2, 3]) @pytest.mark.parametrize('test_folds', [1]) def test_stratified_crossval_splits_multiple_attributes(many_stimuli, crossval_folds, val_folds, test_folds): if not test_folds and val_folds != 1: return # this case is raises an implementation error right now tmp_model = pysaliency.UniformModel() fixations = tmp_model.sample(many_stimuli, 100) train_stimuli = [] train_fixations = [] val_stimuli = [] val_fixations = [] test_stimuli = [] test_fixations = [] for _train_stimuli, _train_fixations, _val_stimuli, _val_fixations, _test_stimuli, _test_fixations in \ filter_datasets.iterate_crossvalidation(many_stimuli, fixations, crossval_folds=crossval_folds, val_folds=val_folds, test_folds=test_folds, random=True, stratified_attributes=['category', 'category2']): assert not set(_train_stimuli.stimulus_ids).intersection(_val_stimuli.stimulus_ids) assert not set(_train_stimuli.stimulus_ids).intersection(_test_stimuli.stimulus_ids) if not test_folds: # otherwise test is validation assert not set(_val_stimuli.stimulus_ids).intersection(_test_stimuli.stimulus_ids) np.testing.assert_allclose( np.sum(_train_stimuli.attributes['category'] == 0), len(many_stimuli) / crossval_folds * (crossval_folds - val_folds - test_folds) * 0.1, atol=1 ) np.testing.assert_allclose( np.sum(_val_stimuli.attributes['category'] == 0), len(many_stimuli) / crossval_folds * val_folds * 0.1, atol=1 ) np.testing.assert_allclose( np.sum(_test_stimuli.attributes['category'] == 0), len(many_stimuli) / crossval_folds * test_folds * 0.1, atol=1 ) train_stimuli.append(_train_stimuli) train_fixations.append(_train_fixations) val_stimuli.append(_val_stimuli) val_fixations.append(_val_fixations) test_stimuli.append(_test_stimuli) test_fixations.append(_test_fixations) assert sum(len(s) for s in val_stimuli) == val_folds * len(many_stimuli) assert sum(len(s) for s in test_stimuli) == test_folds * len(many_stimuli) assert sum(len(s) for s in train_stimuli) == (crossval_folds - val_folds - test_folds) * len(many_stimuli) assert sum(len(f.x) for f in val_fixations) == val_folds * len(fixations.x) assert sum(len(f.x) for f in test_fixations) == test_folds * len(fixations.x) assert sum(len(f.x) for f in train_fixations) == (crossval_folds - val_folds - test_folds) * len(fixations.x) assert len(train_stimuli) == crossval_folds def test_filter_stimuli_by_attribute_dva(file_stimuli_with_attributes, fixation_trains): fixations = fixation_trains[:] attribute_name = 'dva' attribute_value = 1 filtered_stimuli, filtered_fixations = filter_stimuli_by_attribute(file_stimuli_with_attributes, fixations, attribute_name, attribute_value) inds = [1] expected_stimuli, expected_fixations = create_subset(file_stimuli_with_attributes, fixations, inds) assert_fixations_equal(filtered_fixations, expected_fixations) assert_stimuli_equal(filtered_stimuli, expected_stimuli) def test_filter_stimuli_by_attribute_multiple_values(file_stimuli_with_attributes, fixation_trains): fixations = fixation_trains[:] attribute_name = 'dva' attribute_values = [1, 2] filtered_stimuli, filtered_fixations = filter_stimuli_by_attribute(file_stimuli_with_attributes, fixations, attribute_name, attribute_values=attribute_values) inds = [1, 2] expected_stimuli, expected_fixations = create_subset(file_stimuli_with_attributes, fixations, inds) assert_fixations_equal(filtered_fixations, expected_fixations) assert_stimuli_equal(filtered_stimuli, expected_stimuli) def test_filter_stimuli_by_attribute_some_strings_invert_match(file_stimuli_with_attributes, fixation_trains): fixations = fixation_trains[:] attribute_name = 'some_strings' attribute_value = 'n' invert_match = True filtered_stimuli, filtered_fixations = filter_stimuli_by_attribute(file_stimuli_with_attributes, fixations, attribute_name, attribute_value, invert_match=invert_match) inds = list(range(0, 13)) + list(range(14, 18)) expected_stimuli, expected_fixations = create_subset(file_stimuli_with_attributes, fixations, inds) assert_fixations_equal(filtered_fixations, expected_fixations) assert_stimuli_equal(filtered_stimuli, expected_stimuli) def test_filter_fixations_by_attribute_subject_invert_match(fixation_trains): fixations = fixation_trains[:] attribute_name = 'subject' attribute_value = 0 invert_match = True filtered_fixations = filter_fixations_by_attribute(fixations, attribute_name, attribute_value, invert_match) inds = [3, 4, 5, 6, 7] expected_fixations = fixations[inds] assert_fixations_equal(filtered_fixations, expected_fixations) def test_filter_fixations_by_attribute_some_attribute(fixation_trains): fixations = fixation_trains[:] attribute_name = 'some_attribute' attribute_value = 2 invert_match = False filtered_fixations = filter_fixations_by_attribute(fixations, attribute_name, attribute_value, invert_match) inds = [2] expected_fixations = fixations[inds] assert_fixations_equal(filtered_fixations, expected_fixations) def test_filter_fixations_by_attribute_some_attribute_invert_match(fixation_trains): fixations = fixation_trains[:] attribute_name = 'some_attribute' attribute_value = 3 invert_match = True filtered_fixations = filter_fixations_by_attribute(fixations, attribute_name, attribute_value, invert_match) inds = list(range(0, 3)) + list(range(4, 8)) expected_fixations = fixations[inds] assert_fixations_equal(filtered_fixations, expected_fixations) def test_filter_scanpaths_by_attribute_task(fixation_trains): scanpaths = fixation_trains attribute_name = 'task' attribute_value = 0 invert_match = False filtered_scanpaths = filter_scanpaths_by_attribute(scanpaths, attribute_name, attribute_value, invert_match) inds = [0, 2] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) def test_filter_scanpaths_by_attribute_multi_dim_attribute(fixation_trains): scanpaths = fixation_trains attribute_name = 'multi_dim_attribute' attribute_value = [0, 3] invert_match = False filtered_scanpaths = filter_scanpaths_by_attribute(scanpaths, attribute_name, attribute_value, invert_match) inds = [1] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) def test_filter_scanpaths_by_attribute_multi_dim_attribute_invert_match(fixation_trains): scanpaths = fixation_trains attribute_name = 'multi_dim_attribute' attribute_value = [0, 1] invert_match = True filtered_scanpaths = filter_scanpaths_by_attribute(scanpaths, attribute_name, attribute_value, invert_match) inds = [1, 2] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) @pytest.mark.parametrize('intervals', [([(1, 2), (2, 3)]), ([(2, 3), (3, 4)]), ([(2)]), ([(3)])]) def test_filter_scanpaths_by_length(fixation_trains, intervals): scanpaths = fixation_trains filtered_scanpaths = filter_scanpaths_by_length(scanpaths, intervals) if intervals == [(1, 2), (2, 3)]: inds = [1] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) if intervals == [(2, 3), (3, 4)]: inds = [0, 1, 2] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) if intervals == [(2)]: inds = [1] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) if intervals == [(3)]: inds = [0, 2] expected_scanpaths = scanpaths.filter_scanpaths(inds) assert_scanpath_fixations_equal(filtered_scanpaths, expected_scanpaths) def test_remove_stimuli_without_fixations(file_stimuli_with_attributes, fixation_trains): fixations = fixation_trains[:] filtered_stimuli, filtered_fixations = remove_stimuli_without_fixations(file_stimuli_with_attributes, fixations) inds = [0, 1] expected_stimuli, expected_fixations = create_subset(file_stimuli_with_attributes, fixations, inds) assert_fixations_equal(filtered_fixations, expected_fixations) assert_stimuli_equal(filtered_stimuli, expected_stimuli) ================================================ FILE: tests/test_hdf5_io.py ================================================ import numpy as np import pysaliency from pysaliency.baseline_utils import BaselineModel, CrossvalidatedBaselineModel def test_unified_read_hdf5_reads_dataset_files(tmp_path): stimuli = pysaliency.Stimuli([np.random.randn(12, 10, 3), np.random.randn(8, 9, 3)]) path = tmp_path / 'stimuli.hdf5' stimuli.to_hdf5(path) loaded = pysaliency.read_hdf5(path) assert isinstance(loaded, pysaliency.Stimuli) assert len(loaded) == len(stimuli) np.testing.assert_array_equal(loaded[0].stimulus_data, stimuli[0].stimulus_data) def test_unified_read_hdf5_reads_baseline_model_files(tmp_path): stimuli = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) fixations = pysaliency.FixationTrains.from_fixation_trains( [[1, 2, 3], [4, 8]], [[5, 6, 7], [9, 2]], [[0, 1, 2], [0, 1]], [0, 1], [0, 1], ) model = BaselineModel(stimuli, fixations, bandwidth=0.1) path = tmp_path / 'baseline_model.hdf5' model.to_hdf5(path) loaded = pysaliency.read_hdf5(path) assert isinstance(loaded, BaselineModel) np.testing.assert_allclose(loaded.log_density(stimuli[0]), model.log_density(stimuli[0])) def test_legacy_datasets_read_hdf5_wrapper_still_works(tmp_path): stimuli = pysaliency.Stimuli([np.random.randn(15, 10, 3)]) path = tmp_path / 'stimuli_legacy.hdf5' stimuli.to_hdf5(path) loaded = pysaliency.datasets.read_hdf5(path) assert isinstance(loaded, pysaliency.Stimuli) np.testing.assert_array_equal(loaded[0].stimulus_data, stimuli[0].stimulus_data) def test_unified_read_hdf5_reads_crossvalidated_baseline_model(tmp_path): stimuli = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) fixations = pysaliency.FixationTrains.from_fixation_trains( [[1, 2, 3], [4, 8]], [[5, 6, 7], [9, 2]], [[0, 1, 2], [0, 1]], [0, 1], [0, 1], ) model = CrossvalidatedBaselineModel(stimuli, fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_model.hdf5' model.to_hdf5(path) loaded = pysaliency.read_hdf5(path) assert isinstance(loaded, CrossvalidatedBaselineModel) np.testing.assert_allclose(loaded.log_density(stimuli[0]), model.log_density(stimuli[0])) def test_unified_read_hdf5_crossvalidated_baseline_model_stimuli_kwarg(tmp_path): """stimuli= kwarg is forwarded correctly through the dispatcher.""" stimuli = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) fixations = pysaliency.FixationTrains.from_fixation_trains( [[1, 2, 3], [4, 8]], [[5, 6, 7], [9, 2]], [[0, 1, 2], [0, 1]], [0, 1], [0, 1], ) model = CrossvalidatedBaselineModel(stimuli, fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_no_stimuli.hdf5' model.to_hdf5(path, include_stimuli=False) loaded = pysaliency.read_hdf5(str(path), stimuli=stimuli) assert isinstance(loaded, CrossvalidatedBaselineModel) assert loaded.stimuli is stimuli np.testing.assert_allclose(loaded.log_density(stimuli[0]), model.log_density(stimuli[0])) def test_unified_read_hdf5_crossvalidated_baseline_model_stimuli_kwarg_bypasses_cache(tmp_path): """Regression: stimuli= kwarg must bypass WeakValueDictionary cache in _read_hdf5_from_file.""" stimuli = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) fixations = pysaliency.FixationTrains.from_fixation_trains( [[1, 2, 3], [4, 8]], [[5, 6, 7], [9, 2]], [[0, 1, 2], [0, 1]], [0, 1], [0, 1], ) model = CrossvalidatedBaselineModel(stimuli, fixations, bandwidth=0.1) path = tmp_path / 'cv_baseline_no_stimuli.hdf5' model.to_hdf5(path, include_stimuli=False) loaded = pysaliency.read_hdf5(str(path), stimuli=stimuli) assert loaded.stimuli is stimuli # Second call with a different stimuli object — must not return the cached first result stimuli2 = pysaliency.Stimuli([np.random.randn(20, 20, 3), np.random.randn(20, 20, 3)]) loaded2 = pysaliency.read_hdf5(str(path), stimuli=stimuli2) assert loaded2.stimuli is stimuli2 np.testing.assert_allclose(loaded2.log_density(stimuli2[0]), CrossvalidatedBaselineModel(stimuli2, fixations, bandwidth=0.1).log_density(stimuli2[0])) ================================================ FILE: tests/test_helpers.py ================================================ from __future__ import absolute_import, print_function, division import unittest import pickle import os.path import shutil import filecmp def assert_equal(a, b): assert a == b class TestWithData(unittest.TestCase): data_path = 'test_data' def setUp(self): if os.path.isdir(self.data_path): shutil.rmtree(self.data_path) if not os.path.exists(self.data_path): os.makedirs(self.data_path) def tearDown(self): shutil.rmtree(self.data_path) def pickle_and_reload(self, data, pickler = pickle): filename = os.path.join(self.data_path, 'object.pydat') with open(filename, 'wb') as f: pickler.dump(data, f) with open(filename, 'rb') as f: new_data = pickler.load(f) return new_data def check_dircmp(dircmp): assert_equal(dircmp.left_only, []) assert_equal(dircmp.right_only, []) assert_equal(dircmp.diff_files, []) assert_equal(dircmp.funny_files, []) for sub_dcmp in dircmp.subdirs.values(): check_dircmp(dircmp) def assertDirsEqual(dir1, dir2, ignore=[]): dircmp = filecmp.dircmp(dir1, dir2, ignore=ignore) check_dircmp(dircmp) ================================================ FILE: tests/test_http_models.py ================================================ import unittest import numpy as np from unittest.mock import patch, MagicMock, Mock import json import pysaliency from pysaliency.http_models import HTTPScanpathModel, HTTPScanpathSaliencyMapModel class TestHTTPScanpathModel(unittest.TestCase): """Test the existing HTTPScanpathModel""" def test_init(self): """Test that HTTPScanpathModel initializes correctly""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathModel', 'version': 'v1.0.0'} mock_get.return_value = mock_response model = HTTPScanpathModel('http://example.com') self.assertEqual(model.url, 'http://example.com') self.assertEqual(model.log_density_url, 'http://example.com/conditional_log_density') self.assertEqual(model.type_url, 'http://example.com/type') mock_get.assert_called_once_with('http://example.com/type') def test_init_invalid_type(self): """Test that HTTPScanpathModel raises error for invalid type""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'WrongType', 'version': 'v1.0.0'} mock_get.return_value = mock_response with self.assertRaises(ValueError): HTTPScanpathModel('http://example.com') def test_conditional_log_density(self): """Test conditional_log_density method""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathModel', 'version': 'v1.0.0'} mock_get.return_value = mock_response model = HTTPScanpathModel('http://example.com') # Mock the POST request for log density with patch('pysaliency.http_models.requests.post') as mock_post: mock_post_response = MagicMock() mock_post_response.status_code = 200 expected_log_density = [-2.1, -1.8, -2.5, -1.2] mock_post_response.text = json.dumps({'log_density': expected_log_density}) mock_post.return_value = mock_post_response # Create test stimulus stimulus = np.random.randint(0, 255, size=(10, 10, 3), dtype=np.uint8) x_hist = np.array([1, 2, 3, 4]) y_hist = np.array([4, 5, 6, 7]) t_hist = np.array([0.1, 0.2, 0.3, 0.4]) result = model.conditional_log_density(stimulus, x_hist, y_hist, t_hist) self.assertIsInstance(result, np.ndarray) np.testing.assert_array_equal(result, expected_log_density) # Verify the POST request was called correctly mock_post.assert_called_once() call_args = mock_post.call_args self.assertEqual(call_args[0][0], 'http://example.com/conditional_log_density') self.assertIn('data', call_args[1]) self.assertIn('files', call_args[1]) class TestHTTPScanpathSaliencyMapModel(unittest.TestCase): """Test the new HTTPScanpathSaliencyMapModel""" def test_init(self): """Test that HTTPScanpathSaliencyMapModel initializes correctly""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathSaliencyMapModel', 'version': 'v1.0.0'} mock_get.return_value = mock_response model = HTTPScanpathSaliencyMapModel('http://example.com') self.assertEqual(model.url, 'http://example.com') self.assertEqual(model.saliency_map_url, 'http://example.com/conditional_saliency_map') self.assertEqual(model.type_url, 'http://example.com/type') mock_get.assert_called_once_with('http://example.com/type') def test_init_invalid_type(self): """Test that HTTPScanpathSaliencyMapModel raises error for invalid type""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathModel', 'version': 'v1.0.0'} mock_get.return_value = mock_response with self.assertRaises(ValueError): HTTPScanpathSaliencyMapModel('http://example.com') def test_init_invalid_version(self): """Test that HTTPScanpathSaliencyMapModel raises error for invalid version""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathSaliencyMapModel', 'version': 'v2.0.0'} mock_get.return_value = mock_response with self.assertRaises(ValueError): HTTPScanpathSaliencyMapModel('http://example.com') def test_conditional_saliency_map(self): """Test conditional_saliency_map method""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathSaliencyMapModel', 'version': 'v1.0.0'} mock_get.return_value = mock_response model = HTTPScanpathSaliencyMapModel('http://example.com') # Mock the POST request for saliency map with patch('pysaliency.http_models.requests.post') as mock_post: mock_post_response = MagicMock() mock_post_response.status_code = 200 expected_saliency_map = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]] mock_post_response.text = json.dumps({'saliency_map': expected_saliency_map}) mock_post.return_value = mock_post_response # Create test stimulus stimulus = np.random.randint(0, 255, size=(10, 10, 3), dtype=np.uint8) x_hist = np.array([1, 2, 3]) y_hist = np.array([4, 5, 6]) t_hist = np.array([0.1, 0.2, 0.3]) result = model.conditional_saliency_map(stimulus, x_hist, y_hist, t_hist) self.assertIsInstance(result, np.ndarray) np.testing.assert_array_equal(result, expected_saliency_map) # Verify the POST request was called correctly mock_post.assert_called_once() call_args = mock_post.call_args self.assertEqual(call_args[0][0], 'http://example.com/conditional_saliency_map') self.assertIn('data', call_args[1]) self.assertIn('files', call_args[1]) def test_conditional_saliency_map_with_attributes(self): """Test conditional_saliency_map method with attributes""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathSaliencyMapModel', 'version': 'v1.0.0'} mock_get.return_value = mock_response model = HTTPScanpathSaliencyMapModel('http://example.com') # Mock the POST request for saliency map with patch('pysaliency.http_models.requests.post') as mock_post: mock_post_response = MagicMock() mock_post_response.status_code = 200 expected_saliency_map = [[0.1, 0.2], [0.3, 0.4]] mock_post_response.text = json.dumps({'saliency_map': expected_saliency_map}) mock_post.return_value = mock_post_response # Create test stimulus stimulus = np.random.randint(0, 255, size=(10, 10, 3), dtype=np.uint8) x_hist = np.array([1, 2]) y_hist = np.array([4, 5]) t_hist = np.array([0.1, 0.2]) attributes = {'subject': 1, 'task': 'search'} result = model.conditional_saliency_map(stimulus, x_hist, y_hist, t_hist, attributes=attributes) self.assertIsInstance(result, np.ndarray) np.testing.assert_array_equal(result, expected_saliency_map) def test_conditional_saliency_map_server_error(self): """Test conditional_saliency_map method handles server errors""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathSaliencyMapModel', 'version': 'v1.0.0'} mock_get.return_value = mock_response model = HTTPScanpathSaliencyMapModel('http://example.com') # Mock the POST request to return error with patch('pysaliency.http_models.requests.post') as mock_post: mock_post_response = MagicMock() mock_post_response.status_code = 500 mock_post.return_value = mock_post_response # Create test stimulus stimulus = np.random.randint(0, 255, size=(10, 10, 3), dtype=np.uint8) x_hist = np.array([1, 2]) y_hist = np.array([4, 5]) t_hist = np.array([0.1, 0.2]) with self.assertRaises(ValueError): model.conditional_saliency_map(stimulus, x_hist, y_hist, t_hist) def test_inheritance(self): """Test that HTTPScanpathSaliencyMapModel inherits from ScanpathSaliencyMapModel""" with patch('pysaliency.http_models.requests.get') as mock_get: mock_response = MagicMock() mock_response.json.return_value = {'type': 'ScanpathSaliencyMapModel', 'version': 'v1.0.0'} mock_get.return_value = mock_response model = HTTPScanpathSaliencyMapModel('http://example.com') self.assertIsInstance(model, pysaliency.ScanpathSaliencyMapModel) self.assertTrue(hasattr(model, 'conditional_saliency_map')) if __name__ == '__main__': unittest.main() ================================================ FILE: tests/test_metric_optimization.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import pytest import numpy as np import pysaliency class GaussianSaliencyModel(pysaliency.Model): def _log_density(self, stimulus): height = stimulus.shape[0] width = stimulus.shape[1] YS, XS = np.mgrid[:height, :width] r_squared = (XS-0.5*width)**2 + (YS-0.5*height)**2 size = np.sqrt(width**2+height**2) values = np.ones((stimulus.shape[0], stimulus.shape[1]))*np.exp(-0.5*(r_squared/size)) density = values / values.sum() return np.log(density) @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randn(40, 40, 3), np.random.randn(40, 40, 3)]) def test_sim_saliency_map(stimuli): gsmm = GaussianSaliencyModel() sim_model = pysaliency.SIMSaliencyMapModel(gsmm, kernel_size=2, max_iter=100, initial_learning_rate=1e-6, learning_rate_decay_scheme='validation_loss') smap = sim_model.saliency_map(stimuli[0]) assert smap.shape == (40, 40) ================================================ FILE: tests/test_metric_optimization_tf.py ================================================ import numpy as np import pytest # from pysaliency.metric_optimization_tf import maximize_expected_sim @pytest.mark.skip("tensorflow <2.0 not available for new python versions, need to upgrade to tensorflow 2 in pysaliency") def test_maximize_expected_sim_decay_1overk(): density = np.ones((20, 20)) density[6:17, 8:12] = 20 density[2:4, 18:18] = 30 density /= density.sum() log_density = np.log(density) saliency_map, score = maximize_expected_sim( log_density=log_density, kernel_size=1, train_samples_per_epoch=1000, val_samples=1000, max_iter=100 ) np.testing.assert_allclose(score, -0.8202789932489393, rtol=5e-7) # need bigger tolerance to handle differences between CPU and GPU @pytest.mark.skip("tensorflow <2.0 not available for new python versions, need to upgrade to tensorflow 2 in pysaliency") def test_maximize_expected_sim_decay_on_plateau(): density = np.ones((20, 20)) density[6:17, 8:12] = 20 density[2:4, 18:18] = 30 density /= density.sum() log_density = np.log(density) saliency_map, score = maximize_expected_sim( log_density=log_density, kernel_size=1, train_samples_per_epoch=1000, val_samples=1000, max_iter=100, backlook=1, min_iter=10, learning_rate_decay_scheme='validation_loss', ) np.testing.assert_allclose(score, -0.8203513294458387, rtol=5e-7) # need bigger tolerance to handle differences between CPU and GPU ================================================ FILE: tests/test_metric_optimization_torch.py ================================================ import numpy as np from pysaliency.metric_optimization_torch import maximize_expected_sim def test_maximize_expected_sim_decay_1overk(): density = np.ones((20, 20)) density[6:17, 8:12] = 20 density[2:4, 18:18] = 30 density /= density.sum() log_density = np.log(density) saliency_map, score = maximize_expected_sim( log_density=log_density, kernel_size=1, train_samples_per_epoch=1000, val_samples=1000, max_iter=100 ) print(score) # We need a quite big tolerance in this test. Apparently there are # substantial differences between different systems, I'm not sure why. np.testing.assert_allclose(score, -0.8204902112483976, rtol=5e-4) def test_maximize_expected_sim_decay_on_plateau(): density = np.ones((20, 20)) density[6:17, 8:12] = 20 density[2:4, 18:18] = 30 density /= density.sum() log_density = np.log(density) saliency_map, score = maximize_expected_sim( log_density=log_density, kernel_size=1, train_samples_per_epoch=1000, val_samples=1000, max_iter=100, backlook=1, min_iter=10, learning_rate_decay_scheme='validation_loss', ) print(score) np.testing.assert_allclose(score, -0.8205618500709532, rtol=5e-4) # need bigger tolerance to handle differences between CPU and GPU ================================================ FILE: tests/test_models.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import pytest import numpy as np import pysaliency class ConstantSaliencyModel(pysaliency.Model): def _log_density(self, stimulus): return np.zeros((stimulus.shape[0], stimulus.shape[1])) - np.log(stimulus.shape[0]) - np.log(stimulus.shape[1]) class GaussianSaliencyModel(pysaliency.Model): def _log_density(self, stimulus): height = stimulus.shape[0] width = stimulus.shape[1] YS, XS = np.mgrid[:height, :width] r_squared = (XS-0.5*width)**2 + (YS-0.5*height)**2 size = np.sqrt(width**2+height**2) values = np.ones((stimulus.shape[0], stimulus.shape[1]))*np.exp(-0.5*(r_squared/size)) density = values / values.sum() return np.log(density) @pytest.fixture def scanpath_fixations(): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subjects = [0, 1, 1] return pysaliency.ScanpathFixations(pysaliency.Scanpaths(xs=xs_trains, ys=ys_trains, ts=ts_trains, n=ns, subject=subjects)) @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randn(40, 40, 3), np.random.randn(40, 40, 3)]) def test_log_likelihood_constant(stimuli, scanpath_fixations): csmm = ConstantSaliencyModel() log_likelihoods = csmm.log_likelihoods(stimuli, scanpath_fixations) np.testing.assert_allclose(log_likelihoods, -np.log(40*40)) def test_log_likelihood_gauss(stimuli, scanpath_fixations): gsmm = GaussianSaliencyModel() log_likelihoods = gsmm.log_likelihoods(stimuli, scanpath_fixations) np.testing.assert_allclose(log_likelihoods, np.array([-10.276835, -9.764182, -9.286885, -9.286885, -9.286885, -9.057075, -8.067126, -9.905604])) log_likelihoods = pysaliency.ScanpathModel.log_likelihoods(gsmm, stimuli, scanpath_fixations) np.testing.assert_allclose(log_likelihoods, np.array([-10.276835, -9.764182, -9.286885, -9.286885, -9.286885, -9.057075, -8.067126, -9.905604])) def test_sampling(stimuli): model = GaussianSaliencyModel() fixations = model.sample(stimuli, train_counts=10, lengths=3) assert len(fixations.scanpaths) == len(stimuli) * 10 assert len(fixations.x) == len(stimuli) * 10 * 3 @pytest.fixture def long_stimuli(): return pysaliency.Stimuli([np.random.randn(40, 60, 3) for index in range(1000)]) @pytest.fixture def test_model(long_stimuli): class TestModel(pysaliency.Model): def __init__(self, stimuli, *args, **kwargs): super().__init__(*args, **kwargs) self.stimuli = stimuli def _log_density(self, stimulus): stimulus = pysaliency.datasets.as_stimulus(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus.stimulus_id) relative_index = stimulus_index / len(self.stimuli) this_model = pysaliency.models.GaussianModel(center_x=relative_index, center_y=relative_index) return this_model.log_density(stimulus) return TestModel(long_stimuli) @pytest.fixture def pixel_model(long_stimuli): class TestModel(pysaliency.Model): def __init__(self, stimuli, *args, **kwargs): super().__init__(*args, **kwargs) self.stimuli = stimuli def _log_density(self, stimulus): stimulus = pysaliency.datasets.as_stimulus(stimulus) stimulus_index = self.stimuli.stimulus_ids.index(stimulus.stimulus_id) density = np.zeros(stimulus.size) density[stimulus_index, stimulus_index] = 1 return np.log(density) return TestModel(long_stimuli[:40]) def test_average_predictions(long_stimuli, pixel_model): def log_density_iter(model, stimuli): return (model.log_density(s) for s in stimuli[:40]) average_log_density = pysaliency.models.average_predictions(list(log_density_iter(pixel_model, long_stimuli)), library='torch') average_density = np.exp(average_log_density) np.testing.assert_allclose(np.diag(average_density), 1/40) def test_average_predictions_iter(long_stimuli, test_model): def log_density_iter(model, stimuli): return (model.log_density(s) for s in stimuli) average_log_density_iter = pysaliency.models.average_predictions(log_density_iter(test_model, long_stimuli), library='torch') average_log_density_list = pysaliency.models.average_predictions(list(log_density_iter(test_model, long_stimuli)), library='torch') np.testing.assert_allclose(average_log_density_iter, average_log_density_list) def test_average_predictions_iter(long_stimuli, test_model): def log_density_iter(model, stimuli): return (model.log_density(s) for s in stimuli) average_log_density_iter = pysaliency.models.average_predictions( log_density_iter(test_model, long_stimuli), library='numpy', log_density_count=len(long_stimuli), maximal_chunk_size=10, verbose=True, ) average_log_density_list = pysaliency.models.average_predictions(list(log_density_iter(test_model, long_stimuli)), library='numpy') np.testing.assert_allclose(average_log_density_iter, average_log_density_list, rtol=1e-6) def test_average_predictions_torch(long_stimuli, test_model): log_densities = [test_model.log_density(s) for s in long_stimuli[:20]] average_log_density_torch = pysaliency.models.average_predictions(log_densities, library='torch') average_log_density_numpy = pysaliency.models.average_predictions(log_densities, library='numpy') np.testing.assert_allclose(average_log_density_torch, average_log_density_numpy) @pytest.mark.skip("need to fix tensorflow, convert to tf2") def test_average_predictions_tensorflow(long_stimuli, test_model): log_densities = [test_model.log_density(s) for s in long_stimuli[:20]] average_log_density_tf = pysaliency.models.average_predictions(log_densities, library='tensorflow') average_log_density_numpy = pysaliency.models.average_predictions(log_densities, library='numpy') np.testing.assert_allclose(average_log_density_tf, average_log_density_numpy) # @pytest.mark.parametrize("library", ['tensorflow', 'torch', 'numpy']) @pytest.mark.parametrize("library", ['torch', 'numpy']) def test_shuffled_baseline_model(long_stimuli, test_model, library): shuffled_model = pysaliency.models.ShuffledBaselineModel(test_model, long_stimuli, library=library, compute_size=long_stimuli.sizes[0]) log_densities = [test_model.log_density(s) for s in long_stimuli[1:]] average_log_density = pysaliency.models.average_predictions(log_densities, library=library) np.testing.assert_allclose(shuffled_model.log_density(long_stimuli[0]), average_log_density, rtol=1e-6) def test_conditional_log_densities(long_stimuli, test_model, scanpath_fixations): log_densities_1 = list(test_model.conditional_log_densities(long_stimuli, scanpath_fixations)) log_densities_2 = [test_model.conditional_log_density_for_fixation(long_stimuli, scanpath_fixations, i) for i in range(len(scanpath_fixations))] np.testing.assert_allclose(log_densities_1, log_densities_2) ================================================ FILE: tests/test_numba_utils.py ================================================ from hypothesis import given, strategies as st, assume, settings import numpy as np from pysaliency.numba_utils import auc_for_one_positive, general_roc_numba, general_rocs_per_positive_numba from pysaliency.roc_cython import general_roc, general_rocs_per_positive def test_auc_for_one_positive(): assert auc_for_one_positive(1, [0, 2]) == 0.5 assert auc_for_one_positive(1, [1]) == 0.5 assert auc_for_one_positive(3, [0]) == 1.0 assert auc_for_one_positive(0, [3]) == 0.0 @given(st.lists(st.floats(allow_nan=False, allow_infinity=False), min_size=1), st.floats(allow_nan=False, allow_infinity=False)) def test_simple_auc_hypothesis(negatives, positive): old_auc, _, _ = general_roc(np.array([positive]), np.array(negatives)) new_auc = auc_for_one_positive(positive, np.array(negatives)) np.testing.assert_allclose(old_auc, new_auc) @settings(deadline=None) #to remove time limit from a test @given(st.lists(st.floats(allow_infinity=False,allow_nan=False),min_size=1), st.lists(st.floats(allow_infinity=False,allow_nan=False),min_size=1)) def test_numba_auc_test1(positives,negatives): positives = np.array(positives) negatives = np.array(negatives) numba_output = general_roc_numba(positives,negatives) cython_output = general_roc(positives,negatives) assert np.isclose(numba_output[0],cython_output[0]) assert (numba_output[1] == cython_output[1]).all() assert (numba_output[2] == cython_output[2]).all() @settings(deadline=None) @given(st.lists(st.floats(allow_infinity=False,allow_nan=False),min_size=1), st.floats(allow_infinity=False,allow_nan=False)) def test_numba_auc_test2(positives,temp_variable): positives = np.array(positives) negatives = positives+temp_variable numba_output = general_roc_numba(positives,negatives) cython_output = general_roc(positives,negatives) assert np.isclose(numba_output[0],cython_output[0]) assert (numba_output[1] == cython_output[1]).all() assert (numba_output[2] == cython_output[2]).all() @settings(deadline=None) @given(st.lists(st.floats(allow_infinity=False,allow_nan=False),min_size=1), st.lists(st.floats(allow_infinity=False,allow_nan=False),min_size=1)) def test_numba_rocs_per_positive(positives,negatives): positives = np.array(positives) negatives = np.array(negatives) numba_output = general_rocs_per_positive_numba(positives,negatives) cython_output = general_rocs_per_positive(positives,negatives) assert (numba_output == cython_output).all() ================================================ FILE: tests/test_precomputed_models.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import os import pathlib import warnings import zipfile import h5py import numpy as np import pytest from imageio import imsave import pysaliency from pysaliency import export_model_to_hdf5 class TestSaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): stimulus_data = pysaliency.datasets.as_stimulus(stimulus).stimulus_data if stimulus_data.ndim == 3: return stimulus_data.mean(axis=-1).astype(float) else: return np.array(stimulus_data, dtype=float) @pytest.fixture def file_stimuli(tmpdir): filenames = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) imsave(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) imsave(str(filename), np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) return pysaliency.FileStimuli(filenames=filenames) @pytest.fixture def stimuli_with_filenames(tmpdir): filenames = [] stimuli = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) stimuli.append(np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) stimuli.append(np.random.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) return pysaliency.Stimuli(stimuli=stimuli, attributes={'filenames': filenames}) @pytest.fixture(params=['FileStimuli', 'attributes']) def stimuli(file_stimuli, stimuli_with_filenames, request): if request.param == 'FileStimuli': return file_stimuli elif request.param == 'attributes': return stimuli_with_filenames else: raise ValueError(request.param) @pytest.fixture def sub_stimuli(stimuli): unique_filenames = pysaliency.utils.get_minimal_unique_filenames( pysaliency.precomputed_models.get_stimuli_filenames(stimuli) ) return stimuli[[i for i, f in enumerate(unique_filenames) if f.startswith('sub_directory_0001')]] @pytest.fixture def saliency_maps_in_directory(file_stimuli, tmpdir): stimuli_files = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) prediction_dir = tmpdir.join('predictions') prediction_dir.mkdir() predictions = [] rst = np.random.RandomState(seed=42) for filename in stimuli_files: prediction = rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8) target_name = prediction_dir.join(filename) pathlib.Path(target_name).resolve().parent.mkdir(exist_ok=True) imsave(str(target_name), prediction) predictions.append(prediction) return prediction_dir, predictions def test_export_model_to_hdf5(stimuli, tmpdir): model = pysaliency.models.SaliencyMapNormalizingModel(TestSaliencyMapModel()) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, stimuli, filename) model2 = pysaliency.HDF5Model(stimuli, filename) for s in stimuli: np.testing.assert_allclose(model.log_density(s), model2.log_density(s)) def test_hdf5_model_sub_stimuli(stimuli, sub_stimuli, tmpdir): model = pysaliency.models.SaliencyMapNormalizingModel(TestSaliencyMapModel()) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, stimuli, filename) model2 = pysaliency.HDF5Model(sub_stimuli, filename) for s in sub_stimuli: np.testing.assert_allclose(model.log_density(s), model2.log_density(s)) def test_hdf5_model_empty_stimuli(stimuli, tmpdir): model = pysaliency.models.SaliencyMapNormalizingModel(TestSaliencyMapModel()) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, stimuli, filename) sub_stimuli = stimuli[[]] pysaliency.HDF5Model(sub_stimuli, filename) def test_export_model_overwrite(file_stimuli, tmpdir): model1 = pysaliency.GaussianSaliencyMapModel(width=0.1) model2 = pysaliency.GaussianSaliencyMapModel(width=0.8) filename = str(tmpdir.join('model.hdf5')) partial_stimuli = pysaliency.FileStimuli(filenames=file_stimuli.filenames[:10]) export_model_to_hdf5(model1, partial_stimuli, filename) export_model_to_hdf5(model2, file_stimuli, filename) model3 = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) for s in file_stimuli: np.testing.assert_allclose(model2.saliency_map(s), model3.saliency_map(s)) def test_export_model_no_overwrite(file_stimuli, tmpdir): model1 = pysaliency.GaussianSaliencyMapModel(width=0.1) model2 = pysaliency.GaussianSaliencyMapModel(width=0.8) filename = str(tmpdir.join('model.hdf5')) partial_stimuli = pysaliency.FileStimuli(filenames=file_stimuli.filenames[:5]) export_model_to_hdf5(model1, partial_stimuli, filename) export_model_to_hdf5(model2, file_stimuli, filename, overwrite=False) model3 = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) for k, s in enumerate(file_stimuli): if k < 5: np.testing.assert_allclose(model1.saliency_map(s), model3.saliency_map(s)) else: np.testing.assert_allclose(model2.saliency_map(s), model3.saliency_map(s)) def test_hdf5_model_sub_stimuli_different_prefix(tmpdir): rst = np.random.RandomState(seed=42) filenames = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) imsave(str(filename), rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{}_{:04d}.png'.format(sub_directory_index, i)) imsave(str(filename), rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) stimuli = pysaliency.FileStimuli(filenames=filenames) rst = np.random.RandomState(seed=42) filenames = [] for i in range(3): filename = tmpdir.join('stimulus_{:04d}.png'.format(i)) imsave(str(filename), rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_prefix_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{}_{:04d}.png'.format(sub_directory_index, i)) imsave(str(filename), rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) stimuli_different_prefix = pysaliency.FileStimuli(filenames=filenames) sub_stimuli = stimuli_different_prefix[[i for i, f in enumerate(stimuli_different_prefix.filenames) if 'sub_prefix_directory_0001' in f]] model = pysaliency.models.SaliencyMapNormalizingModel(TestSaliencyMapModel()) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, stimuli, filename) model2 = pysaliency.HDF5Model(sub_stimuli, filename) for s in sub_stimuli: np.testing.assert_allclose(model.log_density(s), model2.log_density(s)) def test_hdf5_model_wrong_keys(tmpdir): rst = np.random.RandomState(seed=42) filenames = [] for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{}_{:04d}.png'.format(sub_directory_index, i)) imsave(str(filename), rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) stimuli = pysaliency.FileStimuli(filenames=filenames) rst = np.random.RandomState(seed=42) filenames = [] for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_prefix_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('other_stimulus_{}_{:04d}.png'.format(sub_directory_index, i)) imsave(str(filename), rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) stimuli_different_names = pysaliency.FileStimuli(filenames=filenames) model = pysaliency.models.SaliencyMapNormalizingModel(TestSaliencyMapModel()) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, stimuli, filename) with pytest.raises(pysaliency.precomputed_models.NoCommonPrefixError): pysaliency.HDF5Model(stimuli_different_names, filename) def test_hdf5_model_sub_stimuli_different_prefix_nonunique(tmpdir): rst = np.random.RandomState(seed=42) filenames = [] for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) imsave(str(filename), rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) stimuli = pysaliency.FileStimuli(filenames=filenames) rst = np.random.RandomState(seed=42) filenames = [] for sub_directory_index in range(3): sub_directory = tmpdir.join('sub_prefix_directory_{:04d}'.format(sub_directory_index)) sub_directory.mkdir() for i in range(5): filename = sub_directory.join('stimulus_{:04d}.png'.format(i)) imsave(str(filename), rst.randint(low=0, high=255, size=(100, 100, 3), dtype=np.uint8)) filenames.append(str(filename)) stimuli_different_prefix = pysaliency.FileStimuli(filenames=filenames) sub_stimuli = stimuli_different_prefix[[i for i, f in enumerate(stimuli_different_prefix.filenames) if 'sub_prefix_directory_0001' in f]] model = pysaliency.models.SaliencyMapNormalizingModel(TestSaliencyMapModel()) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, stimuli, filename) with pytest.raises(pysaliency.precomputed_models.NonUniqueKeysError): pysaliency.HDF5Model(sub_stimuli, filename) def test_saliency_map_model_from_directory(stimuli, saliency_maps_in_directory): directory, predictions = saliency_maps_in_directory model = pysaliency.SaliencyMapModelFromDirectory(stimuli, directory) for stimulus_index, stimulus in enumerate(stimuli): expected = predictions[stimulus_index] actual = model.saliency_map(stimulus) np.testing.assert_equal(actual, expected) def test_saliency_map_model_from_directory_sub_stimuli(stimuli, sub_stimuli, saliency_maps_in_directory): directory, predictions = saliency_maps_in_directory full_model = pysaliency.SaliencyMapModelFromDirectory(stimuli, directory) sub_model = pysaliency.SaliencyMapModelFromDirectory(sub_stimuli, directory) for stimulus in sub_stimuli: expected = full_model.saliency_map(stimulus) actual = sub_model.saliency_map(stimulus) np.testing.assert_equal(actual, expected) def test_saliency_map_model_from_archive(stimuli, saliency_maps_in_directory, tmpdir): directory, predictions = saliency_maps_in_directory archive = tmpdir / 'predictions.zip' # from https://stackoverflow.com/a/1855118 def zipdir(path, ziph): for root, _, files in os.walk(path): for file in files: ziph.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), os.path.join(path, '..'))) with zipfile.ZipFile(str(archive), 'w', zipfile.ZIP_DEFLATED) as zipf: zipdir(str(directory), zipf) model = pysaliency.precomputed_models.SaliencyMapModelFromArchive(stimuli, str(archive)) for stimulus_index, stimulus in enumerate(stimuli): expected = predictions[stimulus_index] actual = model.saliency_map(stimulus) np.testing.assert_equal(actual, expected) def test_saliency_map_model_from_archive_sub_stimuli(stimuli, sub_stimuli, saliency_maps_in_directory, tmpdir): directory, predictions = saliency_maps_in_directory archive = tmpdir / 'predictions.zip' # from https://stackoverflow.com/a/1855118 def zipdir(path, ziph): for root, _, files in os.walk(path): for file in files: ziph.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), os.path.join(path, '..'))) with zipfile.ZipFile(str(archive), 'w', zipfile.ZIP_DEFLATED) as zipf: zipdir(str(directory), zipf) full_model = pysaliency.precomputed_models.SaliencyMapModelFromArchive(stimuli, str(archive)) sub_model = pysaliency.precomputed_models.SaliencyMapModelFromArchive(sub_stimuli, str(archive)) for stimulus in sub_stimuli: expected = full_model.saliency_map(stimulus) actual = sub_model.saliency_map(stimulus) np.testing.assert_equal(actual, expected) def test_export_root_attrs_written(file_stimuli, tmpdir): """New-format files always have type/version/downscale_factor/dtype root attrs.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename) with h5py.File(filename, 'r') as f: assert f.attrs['type'] == 'pysaliency.precomputed_models.predictions' assert f.attrs['version'] == '1.0' assert int(f.attrs['downscale_factor']) == 1 assert f.attrs['dtype'] == 'float64' def test_export_dtype_float32(file_stimuli, tmpdir): """dtype=np.float32 stores float32 datasets.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=np.float32) with h5py.File(filename, 'r') as f: assert f.attrs['dtype'] == 'float32' keys = list(f.keys()) assert f[keys[0]].dtype == np.float32 def test_export_dtype_float16(file_stimuli, tmpdir): """dtype=np.float16 stores float16 datasets.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=np.float16) with h5py.File(filename, 'r') as f: assert f.attrs['dtype'] == 'float16' keys = list(f.keys()) assert f[keys[0]].dtype == np.float16 def test_export_dtype_none_preserves_native(file_stimuli, tmpdir): """dtype=None (default) preserves the model's native output dtype.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=None) with h5py.File(filename, 'r') as f: keys = list(f.keys()) # GaussianSaliencyMapModel returns float64 assert f[keys[0]].dtype == np.float64 def test_export_downscale_stored_shape(file_stimuli, tmpdir): """Stored shape is ceil(H/k) x ceil(W/k) for divisible dimensions.""" # file_stimuli uses 100x100 images, which is divisible by 2 and 4 model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert f[keys[0]].shape == (50, 50) def test_export_downscale_original_shape_attr(file_stimuli, tmpdir): """original_shape attr is present and correct when downscale_factor > 1.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) ds = f[keys[0]] assert 'original_shape' in ds.attrs np.testing.assert_array_equal(ds.attrs['original_shape'], [100, 100]) assert ds.attrs['original_shape'].dtype == np.int64 def test_export_no_downscale_no_original_shape_attr(file_stimuli, tmpdir): """original_shape attr is absent when downscale_factor == 1.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert 'original_shape' not in f[keys[0]].attrs @pytest.fixture def file_stimuli_nondivisible(tmpdir): """Stimuli with 101x97 images — not divisible by 2 or 4.""" filenames = [] for i in range(3): filename = tmpdir.join(f'stim_{i:04d}.png') imsave(str(filename), np.random.randint(0, 255, (101, 97, 3), dtype=np.uint8)) filenames.append(str(filename)) return pysaliency.FileStimuli(filenames=filenames) def test_export_downscale_nondivisible_stored_shape(file_stimuli_nondivisible, tmpdir): """Non-divisible shapes are padded: stored shape is ceil(H/k) x ceil(W/k).""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli_nondivisible, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) # ceil(101/2)=51, ceil(97/2)=49 assert f[keys[0]].shape == (51, 49) def test_export_downscale_nondivisible_original_shape_attr(file_stimuli_nondivisible, tmpdir): """original_shape stores the pre-padding shape, not the padded or stored shape.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli_nondivisible, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) np.testing.assert_array_equal(f[keys[0]].attrs['original_shape'], [101, 97]) def test_export_float32_downscale_dtype_is_float32(file_stimuli, tmpdir): """float32 model output + downscale_factor > 1 stores float32, not float64.""" # GaussianSaliencyMapModel returns float64, so we need a float32-producing model class Float32SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.float32) model = Float32SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert f[keys[0]].dtype == np.float32 assert f.attrs['dtype'] == 'float32' assert int(f.attrs['downscale_factor']) == 2 # NOTE: The spec test table lists "Append mode — mismatch (dtype only)" but the spec design # section intentionally excludes dtype from the consistency check (it is purely informational # and cannot be determined before the first stimulus is computed). There is therefore no # ValueError raised on dtype mismatch — this is correct behavior, not a gap. def test_export_append_consistency_mismatch_downscale(file_stimuli, tmpdir): """Appending with a different downscale_factor raises ValueError.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) partial = pysaliency.FileStimuli(filenames=file_stimuli.filenames[:3]) export_model_to_hdf5(model, partial, filename, downscale_factor=2) with pytest.raises(ValueError, match='downscale_factor'): export_model_to_hdf5(model, file_stimuli, filename, overwrite=False, downscale_factor=1) def test_export_append_new_file_root_attrs(file_stimuli, tmpdir): """overwrite=False on a new file still writes root attrs.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, overwrite=False) with h5py.File(filename, 'r') as f: assert 'type' in f.attrs assert f.attrs['version'] == '1.0' assert int(f.attrs['downscale_factor']) == 1 assert 'dtype' in f.attrs def test_export_append_legacy_file_no_root_attrs(file_stimuli, tmpdir): """Appending to a legacy file (no root attrs) succeeds without adding root attrs.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) partial = pysaliency.FileStimuli(filenames=file_stimuli.filenames[:3]) remaining = pysaliency.FileStimuli(filenames=file_stimuli.filenames[3:]) # Write a legacy-format file manually (no root attrs) names = pysaliency.utils.get_minimal_unique_filenames(partial.filenames) with h5py.File(filename, 'w') as f: for k, s in enumerate(partial): f.create_dataset(names[k], data=model.saliency_map(s)) export_model_to_hdf5(model, remaining, filename, overwrite=False) with h5py.File(filename, 'r') as f: assert 'type' not in f.attrs # no root attrs written into legacy file all_keys = pysaliency.precomputed_models.get_keys_recursive(f) assert len(all_keys) == len(file_stimuli) # all stimuli present def test_export_uint8_model_downscale_warns(tmpdir): """Uint8 model output + downscale_factor > 1 emits a UserWarning (stored as float64 > uint8).""" class Uint8SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.uint8) filenames = [] for i in range(2): fname = str(tmpdir.join(f'stim_{i}.png')) imsave(fname, np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)) filenames.append(fname) stimuli = pysaliency.FileStimuli(filenames=filenames) model = Uint8SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) with pytest.warns(UserWarning, match='larger'): export_model_to_hdf5(model, stimuli, filename, downscale_factor=2) def test_export_uint8_model_downscale_stored_as_float64(tmpdir): """Uint8 model output + downscale_factor > 1 is stored as float64 (area avg result).""" class Uint8SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.uint8) filenames = [] for i in range(2): fname = str(tmpdir.join(f'stim_{i}.png')) imsave(fname, np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)) filenames.append(fname) stimuli = pysaliency.FileStimuli(filenames=filenames) model = Uint8SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) with warnings.catch_warnings(): warnings.simplefilter('ignore') export_model_to_hdf5(model, stimuli, filename, downscale_factor=2) with h5py.File(filename, 'r') as f: keys = list(f.keys()) assert f[keys[0]].dtype == np.float64 def test_export_uint8_model_float32_dtype_warns(tmpdir): """Uint8 model + dtype=np.float32 warns (float32 > uint8).""" class Uint8SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.uint8) filenames = [str(tmpdir.join(f'stim_{i}.png')) for i in range(2)] for fname in filenames: imsave(fname, np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)) stimuli = pysaliency.FileStimuli(filenames=filenames) model = Uint8SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) with pytest.warns(UserWarning, match='larger'): export_model_to_hdf5(model, stimuli, filename, dtype=np.float32) def test_export_uint8_model_uint8_dtype_no_warn(tmpdir): """Uint8 model + dtype=np.uint8 does not warn.""" class Uint8SaliencyMapModel(pysaliency.SaliencyMapModel): def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]), dtype=np.uint8) filenames = [str(tmpdir.join(f'stim_{i}.png')) for i in range(2)] for fname in filenames: imsave(fname, np.random.randint(0, 255, (20, 20, 3), dtype=np.uint8)) stimuli = pysaliency.FileStimuli(filenames=filenames) model = Uint8SaliencyMapModel() filename = str(tmpdir.join('model.hdf5')) with warnings.catch_warnings(): warnings.simplefilter('error') # any warning becomes an error export_model_to_hdf5(model, stimuli, filename, dtype=np.uint8) @pytest.mark.parametrize('dtype,downscale_factor', [ (None, 1), (np.float32, 1), (np.float16, 1), (np.float32, 2), (np.float16, 4), ]) def test_hdf5_saliency_map_model_roundtrip(file_stimuli, tmpdir, dtype, downscale_factor): """HDF5SaliencyMapModel returns correct shape and values after compact export.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=dtype, downscale_factor=downscale_factor) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) for s in file_stimuli: result = loaded.saliency_map(s) assert result.shape == (s.shape[0], s.shape[1]), \ f"Shape mismatch: {result.shape} != {(s.shape[0], s.shape[1])}" def test_hdf5_saliency_map_model_dtype_reduced_returns_native(file_stimuli, tmpdir): """dtype-reduced-only files return the stored native dtype (not upcast).""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, dtype=np.float32) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) result = loaded.saliency_map(file_stimuli[0]) assert result.dtype == np.float32 def test_hdf5_saliency_map_model_downsampled_returns_float64(file_stimuli, tmpdir): """Downsampled files upsample and return float64.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli, filename, downscale_factor=2) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) result = loaded.saliency_map(file_stimuli[0]) assert result.dtype == np.float64 def test_hdf5_saliency_map_model_nondivisible_loaded_shape(file_stimuli_nondivisible, tmpdir): """Upsampled output shape matches original_shape (not padded shape).""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, file_stimuli_nondivisible, filename, downscale_factor=2) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli_nondivisible, filename) result = loaded.saliency_map(file_stimuli_nondivisible[0]) assert result.shape == (101, 97) # original shape, not (51, 49) or (102, 98) def test_hdf5_saliency_map_model_resized_stimuli(tmpdir): """With check_shape=False and resized stimuli, upsampling targets original_shape.""" from imageio import imsave as _imsave filenames = [] for i in range(2): fname = str(tmpdir.join(f'stim_{i}.png')) _imsave(fname, np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)) filenames.append(fname) full_stimuli = pysaliency.FileStimuli(filenames=filenames) model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(model, full_stimuli, filename, downscale_factor=2) # Load with the same filenames but we'll check that the loaded shape is 100x100 # (original_shape), not 50x50 (stored) nor whatever the stimulus reports loaded = pysaliency.HDF5SaliencyMapModel(full_stimuli, filename, check_shape=False) result = loaded.saliency_map(full_stimuli[0]) assert result.shape == (100, 100) def test_hdf5_saliency_map_model_legacy_file_unchanged(file_stimuli, tmpdir): """Legacy files (no root attrs) still work exactly as before.""" model = pysaliency.GaussianSaliencyMapModel(width=0.1) filename = str(tmpdir.join('model.hdf5')) # Write a legacy file manually names = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) with h5py.File(filename, 'w') as f: for k, s in enumerate(file_stimuli): f.create_dataset(names[k], data=model.saliency_map(s)) loaded = pysaliency.HDF5SaliencyMapModel(file_stimuli, filename) for s in file_stimuli: expected = model.saliency_map(s) np.testing.assert_array_equal(loaded.saliency_map(s), expected) @pytest.mark.parametrize('dtype,downscale_factor', [ (None, 1), (np.float32, 1), (np.float16, 1), (np.float32, 2), (np.float16, 4), ]) def test_hdf5_model_roundtrip(file_stimuli, tmpdir, dtype, downscale_factor): """HDF5Model returns valid log densities after compact export.""" base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) with warnings.catch_warnings(): warnings.simplefilter('ignore') export_model_to_hdf5(base, file_stimuli, filename, dtype=dtype, downscale_factor=downscale_factor) loaded = pysaliency.HDF5Model(file_stimuli, filename) from scipy.special import logsumexp for s in file_stimuli: result = loaded.log_density(s) assert result.dtype == np.float64 assert result.shape == (s.shape[0], s.shape[1]) assert abs(logsumexp(result)) < 0.001, f"logsumexp={logsumexp(result):.6f}, not close to 0" def test_hdf5_model_strict_path_no_renorm(file_stimuli, tmpdir): """Non-compact float64/float32 files use strict path: output not renormalized.""" from scipy.special import logsumexp base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(base, file_stimuli, filename) loaded = pysaliency.HDF5Model(file_stimuli, filename) for s in file_stimuli: result = loaded.log_density(s) # Result should be bit-identical to stored (cast to float64), not renormalized expected = base.log_density(s) np.testing.assert_array_equal(result, expected) def test_hdf5_model_strict_path_raises_on_bad_density(file_stimuli, tmpdir): """Non-compact file with bad log density raises ValueError (strict ±0.01 check).""" filename = str(tmpdir.join('model.hdf5')) names = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) with h5py.File(filename, 'w') as f: for k, s in enumerate(file_stimuli): # deliberately unnormalized: constant map, logsumexp >> 0 bad_map = np.zeros((s.shape[0], s.shape[1]), dtype=np.float64) f.create_dataset(names[k], data=bad_map) loaded = pysaliency.HDF5Model(file_stimuli, filename) with pytest.raises(ValueError, match='correct log density'): loaded.log_density(file_stimuli[0]) def test_hdf5_model_relaxed_path_threshold_exceeded_raises(file_stimuli, tmpdir): """Corrupted downsampled file raises ValueError when logsumexp > max_normalization_error.""" filename = str(tmpdir.join('model.hdf5')) names = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) with h5py.File(filename, 'w') as f: f.attrs['type'] = 'pysaliency.precomputed_models.predictions' f.attrs['version'] = '1.0' f.attrs['downscale_factor'] = 2 f.attrs['dtype'] = 'float64' for k, s in enumerate(file_stimuli): stored_shape = (s.shape[0] // 2, s.shape[1] // 2) bad_map = np.zeros(stored_shape, dtype=np.float64) # heavily unnormalized ds = f.create_dataset(names[k], data=bad_map) ds.attrs['original_shape'] = np.array([s.shape[0], s.shape[1]], dtype=np.int64) loaded = pysaliency.HDF5Model(file_stimuli, filename) with pytest.raises(ValueError, match='normalization error'): loaded.log_density(file_stimuli[0]) def test_hdf5_model_custom_max_normalization_error(file_stimuli, tmpdir): """Custom max_normalization_error: tighter raises, looser allows.""" base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(base, file_stimuli, filename, downscale_factor=2) # Very tight threshold should raise (there is some error after round-trip) loaded_tight = pysaliency.HDF5Model(file_stimuli, filename, max_normalization_error=1e-10) with pytest.raises(ValueError): loaded_tight.log_density(file_stimuli[0]) # Default threshold should pass loaded_default = pysaliency.HDF5Model(file_stimuli, filename) loaded_default.log_density(file_stimuli[0]) # should not raise def test_hdf5_model_threshold_within_bounds_for_float16_4x(file_stimuli, tmpdir): """Explicit check: logsumexp before renorm is within log(1.1) for float16+4x export.""" import scipy.ndimage from scipy.special import logsumexp as _logsumexp base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) with warnings.catch_warnings(): warnings.simplefilter('ignore') export_model_to_hdf5(base, file_stimuli, filename, dtype=np.float16, downscale_factor=4) # Manually load the raw stored data and upsample to measure pre-renorm logsumexp names = pysaliency.utils.get_minimal_unique_filenames(file_stimuli.filenames) with h5py.File(filename, 'r') as f: for k, s in enumerate(file_stimuli): ds = f[names[k]] smap = ds[:].astype(np.float64) target_shape = tuple(ds.attrs['original_shape']) zoom_factors = (target_shape[0] / smap.shape[0], target_shape[1] / smap.shape[1]) smap = scipy.ndimage.zoom(smap, zoom_factors, order=1, mode='nearest') err = abs(_logsumexp(smap)) assert err < np.log(1.1), f"logsumexp error {err:.4f} exceeds log(1.1)={np.log(1.1):.4f}" def test_hdf5_model_max_normalization_error_none(file_stimuli, tmpdir): """max_normalization_error=None skips the guard; renorm is still applied.""" from scipy.special import logsumexp base = pysaliency.models.SaliencyMapNormalizingModel( pysaliency.GaussianSaliencyMapModel(width=0.1)) filename = str(tmpdir.join('model.hdf5')) export_model_to_hdf5(base, file_stimuli, filename, downscale_factor=2) loaded = pysaliency.HDF5Model(file_stimuli, filename, max_normalization_error=None) for s in file_stimuli: result = loaded.log_density(s) assert abs(logsumexp(result)) < 0.001 # renorm still runs ================================================ FILE: tests/test_quilt/.pc/.quilt_patches ================================================ patches ================================================ FILE: tests/test_quilt/.pc/.quilt_series ================================================ series ================================================ FILE: tests/test_quilt/.pc/.version ================================================ 2 ================================================ FILE: tests/test_quilt/.pc/add_numbers.diff/.timestamp ================================================ ================================================ FILE: tests/test_quilt/.pc/add_numbers.diff/source.txt ================================================ foo bar baz abcefg blub 3.14156 2.718281828459045 0.7071 1.4142135623730951 ================================================ FILE: tests/test_quilt/.pc/applied-patches ================================================ add_numbers.diff ================================================ FILE: tests/test_quilt/patches/add_numbers.diff ================================================ Index: test_quilt/source.txt =================================================================== --- test_quilt.orig/source.txt 2014-12-04 23:15:40.372473852 +0100 +++ test_quilt/source.txt 2014-12-04 23:16:04.820103862 +0100 @@ -3,6 +3,8 @@ baz abcefg blub +42 +23 3.14156 2.718281828459045 0.7071 ================================================ FILE: tests/test_quilt/patches/series ================================================ add_numbers.diff ================================================ FILE: tests/test_quilt/source/.pc/.quilt_patches ================================================ patches ================================================ FILE: tests/test_quilt/source/.pc/.quilt_series ================================================ series ================================================ FILE: tests/test_quilt/source/.pc/.version ================================================ 2 ================================================ FILE: tests/test_quilt/source/source.txt ================================================ foo bar baz abcefg blub 3.14156 2.718281828459045 0.7071 1.4142135623730951 ================================================ FILE: tests/test_quilt/source.txt ================================================ foo bar baz abcefg blub 42 23 3.14156 2.718281828459045 0.7071 1.4142135623730951 ================================================ FILE: tests/test_quilt/target/source.txt ================================================ foo bar baz abcefg blub 42 23 3.14156 2.718281828459045 0.7071 1.4142135623730951 ================================================ FILE: tests/test_quilt.py ================================================ from __future__ import print_function, division, unicode_literals, absolute_import import os import shutil import filecmp import unittest from pysaliency.quilt import PatchFile, QuiltSeries from test_helpers import TestWithData, assertDirsEqual class TestPatchFile(TestWithData): def test_parsing(self): p = open('tests/test_quilt/patches/add_numbers.diff').read() patch = PatchFile(p) self.assertEqual(len(patch.diffs), 1) diff = patch.diffs[0] self.assertEqual(len(diff.hunks), 1) self.assertEqual(diff.source_filename, 'source.txt') self.assertEqual(diff.target_filename, 'source.txt') hunk = diff.hunks[0] self.assertEqual(hunk.source_start, 3) self.assertEqual(hunk.source_length, 6) self.assertEqual(hunk.target_start, 3) self.assertEqual(hunk.target_length, 8) def test_apply(self): location = os.path.join(self.data_path, 'patching') shutil.copytree('tests/test_quilt/source', location) p = open('tests/test_quilt/patches/add_numbers.diff').read() patch = PatchFile(p) patch.apply(location) self.assertTrue(filecmp.cmp(os.path.join(location, 'source.txt'), 'tests/test_quilt/target/source.txt', shallow=False)) class TestSeries(TestWithData): def test_parsing(self): series = QuiltSeries(os.path.join('tests', 'test_quilt', 'patches')) self.assertEqual(len(series.patches), 1) def test_apply(self): location = os.path.join(self.data_path, 'patching') shutil.copytree('tests/test_quilt/source', location) series = QuiltSeries(os.path.join('tests', 'test_quilt', 'patches')) series.apply(location) assertDirsEqual(location, os.path.join('tests', 'test_quilt', 'target'), ignore=['patches', '.pc']) if __name__ == '__main__': unittest.main() ================================================ FILE: tests/test_saliency_map_conversion.py ================================================ import numpy as np import pytest import pysaliency from pysaliency import optimize_for_information_gain from pysaliency.models import SaliencyMapNormalizingModel @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randint(0, 255, size=(25, 30, 3)) for i in range(50)]) @pytest.fixture def saliency_model(): return pysaliency.GaussianSaliencyMapModel(center_x=0.15, center_y=0.85, width=0.2) @pytest.fixture def transformed_saliency_model(saliency_model): return pysaliency.saliency_map_models.LambdaSaliencyMapModel( [saliency_model], fn=lambda smaps: np.sqrt(smaps[0]), ) @pytest.fixture def probabilistic_model(saliency_model): blurred_model = pysaliency.BluringSaliencyMapModel(saliency_model, kernel_size=5.0) centerbias_model = pysaliency.saliency_map_models.LambdaSaliencyMapModel( [pysaliency.GaussianSaliencyMapModel(width=0.5)], fn=lambda smaps: 1.0 * smaps[0], ) model_with_centerbias = blurred_model * centerbias_model probabilistic_model = SaliencyMapNormalizingModel(model_with_centerbias) return probabilistic_model @pytest.fixture def fixations(stimuli, probabilistic_model): return probabilistic_model.sample(stimuli, 1000, rst=np.random.RandomState(seed=42)) @pytest.fixture(params=["torch", "theano"]) def framework(request): if request.param == 'theano': import theano old_optimizer = theano.config.optimizer theano.config.optimizer = 'fast_compile' yield request.param if request.param == 'theano': theano.config.optimize = old_optimizer def test_optimize_for_information_gain(stimuli, fixations, transformed_saliency_model, probabilistic_model, framework): expected_information_gain = probabilistic_model.information_gain(stimuli, fixations, average='image') model1, ret1 = optimize_for_information_gain( transformed_saliency_model, stimuli, fixations, average='fixations', verbose=2, batch_size=1 if framework == 'theano' else 10, minimize_options={'verbose': 10} if framework == 'torch' else None, maxiter=50, blur_radius=2.0, return_optimization_result=True, framework=framework, ) reached_information_gain = model1.information_gain(stimuli, fixations, average='image') print(expected_information_gain, reached_information_gain) assert reached_information_gain >= expected_information_gain - 0.01 ================================================ FILE: tests/test_saliency_map_conversion_theano.py ================================================ import numpy as np import dill import pytest from pysaliency import GaussianSaliencyMapModel, Stimuli, Fixations from pysaliency.saliency_map_conversion_theano import SaliencyMapConvertor, optimize_for_information_gain @pytest.mark.theano @pytest.mark.parametrize("optimize", [ None, ['nonlinearity'], ['nonlinearity', 'centerbias'], ['nonlinearity', 'alpha', 'centerbias'], ['centerbias'], ['blur_radius'], ['blur_radius', 'nonlinearity'] ]) def test_optimize_for_IG(optimize): # To speed up testing, we disable some optimizations import theano old_optimizer = theano.config.optimizer theano.config.optimizer = 'fast_compile' model = GaussianSaliencyMapModel() stimulus = np.random.randn(100, 100, 3) stimuli = Stimuli([stimulus]) rst = np.random.RandomState(seed=42) N = 100000 fixations = Fixations.create_without_history( x=rst.rand(N) * 100, y=rst.rand(N) * 100, n=np.zeros(N, dtype=int) ) smc, res = optimize_for_information_gain( model, stimuli, fixations, optimize=optimize, blur_radius=3, verbose=2, maxiter=10, return_optimization_result=True) theano.config.optimizer = old_optimizer assert res.status in [ 0, # success 9, # max iter reached ] assert smc @pytest.mark.theano def test_saliency_map_converter(tmpdir): import theano theano.config.floatX = 'float64' old_optimizer = theano.config.optimizer theano.config.optimizer = 'fast_compile' model = GaussianSaliencyMapModel() smc = SaliencyMapConvertor(model) smc.set_params(nonlinearity=np.ones(20), centerbias=np.ones(12) * 2, alpha=3, blur_radius=4, saliency_min=5, saliency_max=6) theano.config.optimizer = old_optimizer pickle_file = tmpdir.join('object.pydat') with pickle_file.open(mode='wb') as f: dill.dump(smc, f) with pickle_file.open(mode='rb') as f: smc2 = dill.load(f) np.testing.assert_allclose(smc2.saliency_map_processing.nonlinearity_ys.get_value(), np.ones(20)) np.testing.assert_allclose(smc2.saliency_map_processing.centerbias_ys.get_value(), np.ones(12) * 2) np.testing.assert_allclose(smc2.saliency_map_processing.alpha.get_value(), 3) np.testing.assert_allclose(smc2.saliency_map_processing.blur_radius.get_value(), 4) np.testing.assert_allclose(smc2.saliency_min, 5) np.testing.assert_allclose(smc2.saliency_max, 6) ================================================ FILE: tests/test_saliency_map_conversion_torch.py ================================================ import numpy as np import pytest from pysaliency.saliency_map_conversion import optimize_for_information_gain from pysaliency import Stimuli, Fixations, GaussianSaliencyMapModel @pytest.mark.parametrize("optimize", [ None, ['nonlinearity'], ['nonlinearity', 'centerbias'], ['nonlinearity', 'alpha', 'centerbias'], ['centerbias'], ['blur_radius'], ['blur_radius', 'nonlinearity'] ]) def test_optimize_for_IG(optimize): model = GaussianSaliencyMapModel() stimulus = np.random.randn(100, 100, 3) stimuli = Stimuli([stimulus]) rst = np.random.RandomState(seed=42) N = 100000 fixations = Fixations.create_without_history( x=rst.rand(N) * 100, y=rst.rand(N) * 100, n=np.zeros(N, dtype=int) ) smc, res = optimize_for_information_gain( model, stimuli, fixations, optimize=optimize, blur_radius=3, verbose=2, maxiter=10, return_optimization_result=True, framework='torch' ) assert res.status in [ 0, # success 9, # max iter reached ] assert smc ================================================ FILE: tests/test_saliency_map_conversion_torch_extended.py ================================================ import time import numpy as np import pytest import pysaliency from pysaliency.saliency_map_conversion import optimize_for_information_gain from pysaliency.saliency_map_conversion_torch import SaliencyMapProcessing, SaliencyMapProcessingModel, optimize_saliency_map_conversion from pysaliency import Stimuli, Fixations, GaussianSaliencyMapModel import torch @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randint(0, 255, size=(25, 30, 3)) for i in range(50)]) @pytest.fixture def saliency_model(): return pysaliency.GaussianSaliencyMapModel(center_x=0.15, center_y=0.85, width=0.5) #@pytest.fixture(params=[0, 1, 2, 3, 4, 12]) @pytest.fixture(params=[ 0, #1, 2, 18, ]) def num_nonlinearity(request): return request.param @pytest.fixture(params=[ False, True ]) def is_blurring(request): return request.param @pytest.fixture(params=[ 0, #1, 2, 14, ]) def num_centerbias(request): return request.param @pytest.fixture(params=[ False, True ]) def has_alpha(request): return request.param @pytest.fixture def probabilistic_model(saliency_model, is_blurring, num_nonlinearity, has_alpha, num_centerbias): saliency_map_processing = SaliencyMapProcessing( nonlinearity_values='logdensity', num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=3 if is_blurring else 0, ) with torch.no_grad(): if num_nonlinearity > 0: # set nonlinearity #print("OLD", saliency_map_processing.nonlinearity.ys) old_exp_sum = torch.exp(saliency_map_processing.nonlinearity.ys).sum().detach().cpu().numpy() new_ys = 7 * np.linspace(0, 1, num_nonlinearity)**2 new_ys -= np.log(old_exp_sum) saliency_map_processing.nonlinearity.ys.copy_(torch.tensor(new_ys)) #print("NEW", saliency_map_processing.nonlinearity.ys) # set center bias if num_centerbias > 0: #print("OLD CB", saliency_map_processing.centerbias.nonlinearity.ys) new_centerbias = np.linspace(1, 0.5, num_centerbias) saliency_map_processing.centerbias.nonlinearity.ys.copy_(torch.tensor(new_centerbias)) #print("NEW CB", saliency_map_processing.centerbias.nonlinearity.ys) if has_alpha: saliency_map_processing.centerbias.alpha.copy_(torch.tensor(0.83)) if is_blurring: saliency_map_processing.blur.sigma.copy_(torch.tensor(4.0)) return SaliencyMapProcessingModel( saliency_map_model=saliency_model, saliency_map_processing=saliency_map_processing, saliency_min=0, saliency_max=1, ) @pytest.fixture def fixations(stimuli, probabilistic_model): return probabilistic_model.sample(stimuli, 1000, rst=np.random.RandomState(seed=42)) def test_optimize_for_information_gain(stimuli, fixations, saliency_model, probabilistic_model, is_blurring, num_nonlinearity, has_alpha, num_centerbias): if num_centerbias == 0 and has_alpha: pytest.skip("parameter combination doesn't make sense") expected_information_gain = probabilistic_model.information_gain(stimuli, fixations, average='image') optimize = [] if num_nonlinearity > 0: optimize.append('nonlinearity') if num_centerbias > 0: optimize.append('centerbias') if has_alpha: optimize.append('alpha') if is_blurring: blur_radius = 1.0 optimize.append('blur_radius') else: blur_radius = 0.0 if not optimize: return model1, ret1 = optimize_for_information_gain( saliency_model, stimuli, fixations, average='fixations', saliency_min=0, saliency_max=1, verbose=2, batch_size=10, minimize_options={'verbose': 10}, maxiter=100, num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=blur_radius, optimize=optimize, return_optimization_result=True, framework='torch', ) # assert ret1.status in [ # 0, # success # 9, # max iter reached # ] reached_information_gain = model1.information_gain(stimuli, fixations, average='image') #print(expected_information_gain, reached_information_gain) assert reached_information_gain >= expected_information_gain - 0.0015 assert reached_information_gain <= expected_information_gain + 0.001 def test_saliency_map_processing_model_save_and_load(stimuli, saliency_model, probabilistic_model): state_dict = probabilistic_model.state_dict() new_model = SaliencyMapProcessingModel.build_from_state_dict( saliency_map_model=saliency_model, state_dict=state_dict, ) for stimulus in stimuli: old_prediction = probabilistic_model.log_density(stimulus) new_prediction = new_model.log_density(stimulus) np.testing.assert_allclose(old_prediction, new_prediction) @pytest.mark.skip("Some strange behaviour of the diskcache, that I didn't hat time to understand yet makes this test fail") def test_optimize_saliency_map_processing_disk_caching(tmp_path, stimuli, saliency_model): num_nonlinearity = 20 num_centerbias = 12 cache_directory = tmp_path / 'optimize_cache' saliency_map_processing = SaliencyMapProcessing( nonlinearity_values='logdensity', num_nonlinearity=num_nonlinearity, num_centerbias=num_centerbias, blur_radius=3 ) with torch.no_grad(): old_exp_sum = torch.exp(saliency_map_processing.nonlinearity.ys).sum().detach().cpu().numpy() new_ys = 7 * np.linspace(0, 1, num_nonlinearity)**2 new_ys -= np.log(old_exp_sum) saliency_map_processing.nonlinearity.ys.copy_(torch.tensor(new_ys)) new_centerbias = np.linspace(1, 0.5, num_centerbias) saliency_map_processing.centerbias.nonlinearity.ys.copy_(torch.tensor(new_centerbias)) saliency_map_processing.centerbias.alpha.copy_(torch.tensor(0.83)) saliency_map_processing.blur.sigma.copy_(torch.tensor(4.0)) probabilistic_model = SaliencyMapProcessingModel( saliency_map_model=saliency_model, saliency_map_processing=saliency_map_processing, saliency_min=0, saliency_max=1, ) fixations = probabilistic_model.sample(stimuli, 1000, rst=np.random.RandomState(seed=42)) start_time = time.time() optimize_saliency_map_conversion( model=saliency_model, stimuli=stimuli, fixations=fixations, saliency_min=0, saliency_max=1, verbose=3, maxiter=100, method='trust-constr', minimize_options={'verbose': 10}, cache_directory=str(cache_directory), ) optimize_time = time.time() - start_time start_time_2 = time.time() optimize_saliency_map_conversion( model=saliency_model, stimuli=stimuli, fixations=fixations, saliency_min=0, saliency_max=1, verbose=3, maxiter=100, method='trust-constr', minimize_options={'verbose': 10}, cache_directory=str(cache_directory), ) optimize_time_2 = time.time() - start_time_2 assert optimize_time_2 <= 0.3 * optimize_time ================================================ FILE: tests/test_saliency_map_models.py ================================================ from __future__ import absolute_import, division, print_function, unicode_literals import pytest import numpy as np import pytest import pysaliency import pysaliency.saliency_map_models class ConstantSaliencyMapModel(pysaliency.SaliencyMapModel): def __init__(self, value=1.0, *args, **kwargs): super(ConstantSaliencyMapModel, self).__init__(*args, **kwargs) self.value = value def _saliency_map(self, stimulus): return np.ones((stimulus.shape[0], stimulus.shape[1]))*self.value class GaussianSaliencyMapModel(pysaliency.SaliencyMapModel, pysaliency.saliency_map_models.WTASamplingMixin): def _saliency_map(self, stimulus): height = stimulus.shape[0] width = stimulus.shape[1] YS, XS = np.mgrid[:height, :width] r_squared = (XS-0.5*width)**2 + (YS-0.5*height)**2 size = np.sqrt(width**2+height**2) return np.ones((stimulus.shape[0], stimulus.shape[1]))*np.exp(-0.5*(r_squared/size)) class MixedSaliencyMapModel(pysaliency.SaliencyMapModel): def __init__(self, *args, **kwargs): super(MixedSaliencyMapModel, self).__init__(*args, **kwargs) self.count = 0 self.constant_model = ConstantSaliencyMapModel() self.gaussian_model = GaussianSaliencyMapModel() def _saliency_map(self, stimulus): self.count += 1 if self.count % 2 == 1: return self.constant_model.saliency_map(stimulus) else: return self.gaussian_model.saliency_map(stimulus) class GaussianDensityModel(pysaliency.Model): def _log_density(self, stimulus): height = stimulus.shape[0] width = stimulus.shape[1] YS, XS = np.mgrid[:height, :width] r_squared = (XS-0.5*width)**2 + (YS-0.5*height)**2 size = np.sqrt(width**2+height**2) values = np.ones((stimulus.shape[0], stimulus.shape[1]))*np.exp(-0.5*(r_squared/size)) density = values / values.sum() return np.log(density) @pytest.fixture def scanpath_fixations(): xs_trains = [ [0, 1, 2], [2, 2], [1, 5, 3]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900]] ns = [0, 0, 1] subjects = [0, 1, 1] return pysaliency.ScanpathFixations(pysaliency.Scanpaths(xs_trains, ys_trains, ts=ts_trains, n=ns, subject=subjects)) @pytest.fixture def stimuli(): return pysaliency.Stimuli([np.random.randn(40, 40, 3), np.random.randn(40, 40, 3)]) @pytest.fixture def more_scanpath_fixations(): xs_trains = [ [0, 1, 2], [2, 2], [1, 10, 3], [4, 5, 33, 7]] ys_trains = [ [10, 11, 12], [12, 12], [21, 25, 33], [41, 42, 43, 44]] ts_trains = [ [0, 200, 600], [100, 400], [50, 500, 900], [0, 1, 2, 3]] ns = [0, 0, 1, 2] subjects = [0, 1, 1, 0] return pysaliency.ScanpathFixations(pysaliency.Scanpaths(xs_trains, ys_trains, ts=ts_trains, n=ns, subject=subjects)) @pytest.fixture def more_stimuli(): return pysaliency.Stimuli([np.random.randn(50, 50, 3), np.random.randn(50, 50, 3), np.random.randn(100, 200, 3)]) def test_auc_constant(stimuli, scanpath_fixations): csmm = ConstantSaliencyMapModel() aucs = csmm.AUCs(stimuli, scanpath_fixations, nonfixations='uniform') np.testing.assert_allclose(aucs, np.ones(len(scanpath_fixations.x))*0.5) aucs = csmm.AUCs(stimuli, scanpath_fixations, nonfixations='shuffled') np.testing.assert_allclose(aucs, np.ones(len(scanpath_fixations.x))*0.5) aucs = csmm.sAUCs(stimuli, scanpath_fixations) np.testing.assert_allclose(aucs, np.ones(len(scanpath_fixations.x))*0.5) auc = csmm.AUC(stimuli, scanpath_fixations, nonfixations=scanpath_fixations) np.testing.assert_allclose(auc, 0.5) def test_auc_gauss(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() aucs = gsmm.AUCs(stimuli, scanpath_fixations, nonfixations='uniform') np.testing.assert_allclose(aucs, [0.099375, 0.158125, 0.241875, 0.241875, 0.241875, 0.291875, 0.509375, 0.138125], rtol=1e-6) aucs = gsmm.AUCs(stimuli, scanpath_fixations, nonfixations='unfixated') np.testing.assert_allclose(aucs, [0.099248591108, 0.157482780213, 0.240763932373, 0.240763932373, 0.240763932373, 0.291484032561, 0.50876643707, 0.138071383845], rtol=1e-6) aucs = gsmm.AUCs(stimuli, scanpath_fixations, nonfixations='shuffled') np.testing.assert_allclose(aucs, [0.0, 0.33333333, 0.33333333, 0.33333333, 0.33333333, 1., 1., 0.2], rtol=1e-6) aucs = gsmm.sAUCs(stimuli, scanpath_fixations) np.testing.assert_allclose(aucs, [0.0, 0.33333333, 0.33333333, 0.33333333, 0.33333333, 1., 1., 0.2], rtol=1e-6) auc = gsmm.AUC(stimuli, scanpath_fixations, nonfixations=scanpath_fixations) np.testing.assert_allclose(auc, 0.5) auc = gsmm.AUC(stimuli, scanpath_fixations) np.testing.assert_allclose(auc, 0.2403125) auc = gsmm.AUC(stimuli, scanpath_fixations, average='image') np.testing.assert_allclose(auc, 0.254875) auc = pysaliency.saliency_map_models.ScanpathSaliencyMapModel.AUC(gsmm, stimuli, scanpath_fixations) np.testing.assert_allclose(auc, 0.2403125) auc = pysaliency.saliency_map_models.ScanpathSaliencyMapModel.AUC(gsmm, stimuli, scanpath_fixations, average='image') np.testing.assert_allclose(auc, 0.254875) auc = gsmm.AUC(stimuli, scanpath_fixations, nonfixations='unfixated') np.testing.assert_allclose(auc, 0.2396681277395116) auc = gsmm.AUC(stimuli, scanpath_fixations, nonfixations='unfixated', thresholds='fixations') np.testing.assert_allclose(auc, 0.44286161552911707) auc = gsmm.AUC(stimuli, scanpath_fixations, nonfixations='unfixated', thresholds='fixations', average='image') np.testing.assert_allclose(auc, 0.44504278856188684) auc = gsmm.AUC_Judd(stimuli, scanpath_fixations, jitter=False) np.testing.assert_allclose(auc, 0.44504278856188684) auc = gsmm.AUC_Judd(stimuli, scanpath_fixations) np.testing.assert_allclose(auc, 0.44674389480275517) aucs_single = pysaliency.ScanpathSaliencyMapModel.AUCs(gsmm, stimuli, scanpath_fixations) aucs_combined = gsmm.AUCs(stimuli, scanpath_fixations, nonfixations='uniform') np.testing.assert_allclose(aucs_single, aucs_combined) def test_auc_per_image(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() aucs = gsmm.AUC_per_image(stimuli, scanpath_fixations) np.testing.assert_allclose(aucs, [0.196625, 0.313125], rtol=1e-6) def test_auc_per_image_images_without_fixations(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() aucs = gsmm.AUC_per_image(stimuli, scanpath_fixations[:5],) np.testing.assert_allclose(aucs, [0.196625, np.nan], rtol=1e-6) def test_auc_image_average_with_images_without_fixations(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() auc = gsmm.AUC(stimuli, scanpath_fixations[:5], average='image') np.testing.assert_allclose(auc, 0.196625, rtol=1e-6) def test_nss_gauss(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() nsss = gsmm.NSSs(stimuli, scanpath_fixations) np.testing.assert_allclose( nsss, [-0.821596006113, -0.789526121554, -0.740616174533, -0.740616174533, -0.740616174533, -0.707322347863, -0.433094498355, -0.800070419267], rtol=1e-6) nsss = pysaliency.ScanpathSaliencyMapModel.NSSs(gsmm, stimuli, scanpath_fixations) np.testing.assert_allclose( nsss, [-0.821596006113, -0.789526121554, -0.740616174533, -0.740616174533, -0.740616174533, -0.707322347863, -0.433094498355, -0.800070419267], rtol=1e-6) nss = gsmm.NSS(stimuli, scanpath_fixations) np.testing.assert_allclose(nss, -0.721682239593952) nss = gsmm.NSS(stimuli, scanpath_fixations, average='image') np.testing.assert_allclose(nss, -0.706711609374163) def test_nss_uniform(stimuli, scanpath_fixations): gsmm = ConstantSaliencyMapModel() nsss = gsmm.NSSs(stimuli, scanpath_fixations) np.testing.assert_allclose( nsss, [0, 0, 0, 0, 0, 0, 0, 0], rtol=1e-6) nss = gsmm.NSS(stimuli, scanpath_fixations) np.testing.assert_allclose(nss, 0) nss = gsmm.NSS(stimuli, scanpath_fixations, average='image') np.testing.assert_allclose(nss, 0) def test_cc_constant(stimuli, scanpath_fixations): model = ConstantSaliencyMapModel() gold = pysaliency.FixationMap(stimuli, scanpath_fixations, kernel_size=10, ignore_doublicates=True) cc = model.CC(stimuli, gold) np.testing.assert_allclose(cc, 0) def test_cc_gauss(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() gold = pysaliency.FixationMap(stimuli, scanpath_fixations, kernel_size=10, ignore_doublicates=True) cc = gsmm.CC(stimuli, gold) np.testing.assert_allclose(cc, -0.1542654) def test_SIM_gauss(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() constant_gold = ConstantSaliencyMapModel() sim = gsmm.SIM(stimuli, constant_gold) np.testing.assert_allclose(sim, 0.54392, rtol=1e-6) constant_gold = ConstantSaliencyMapModel(value=0.0) sim = gsmm.SIM(stimuli, constant_gold) np.testing.assert_allclose(sim, 0.54392, rtol=1e-6) gold = pysaliency.FixationMap(stimuli, scanpath_fixations, kernel_size = 10, ignore_doublicates=True) sim = gsmm.SIM(stimuli, gold) np.testing.assert_allclose(sim, 0.315899, rtol=1e-6) def test_fixation_based_kldiv_constant(stimuli, scanpath_fixations): csmm = ConstantSaliencyMapModel() fb_kl = csmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations='uniform') np.testing.assert_allclose(fb_kl, 0.0) fb_kl = csmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations='shuffled') np.testing.assert_allclose(fb_kl, 0.0) fb_kl = csmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations=scanpath_fixations) np.testing.assert_allclose(fb_kl, 0.0) def test_fixation_based_kldiv_gauss(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() fb_kl = gsmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations='uniform') np.testing.assert_allclose(fb_kl, 0.4787711930295902) fb_kl = gsmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations='shuffled') np.testing.assert_allclose(fb_kl, 0.0) fb_kl = gsmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations=scanpath_fixations) np.testing.assert_allclose(fb_kl, 0.0) def test_fixation_based_kldiv_mixed(stimuli, scanpath_fixations): gsmm = MixedSaliencyMapModel() fb_kl = gsmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations='uniform') np.testing.assert_allclose(fb_kl, 0.19700844437943388) fb_kl = gsmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations='shuffled') np.testing.assert_allclose(fb_kl, 5.874655219107867) fb_kl = gsmm.fixation_based_KL_divergence(stimuli, scanpath_fixations, nonfixations=scanpath_fixations) np.testing.assert_allclose(fb_kl, 0.0) def test_image_based_kldiv_gauss(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() gold = pysaliency.FixationMap(stimuli, scanpath_fixations, kernel_size = 10, ignore_doublicates=True) ib_kl = gsmm.image_based_kl_divergence(stimuli, gsmm) np.testing.assert_allclose(ib_kl, 0.0) ib_kl = gsmm.KLDiv(stimuli, gsmm) np.testing.assert_allclose(ib_kl, 0.0) constant_gold = ConstantSaliencyMapModel() ib_kl = gsmm.image_based_kl_divergence(stimuli, constant_gold) np.testing.assert_allclose(ib_kl, 0.8396272788909165) ib_kl = gsmm.KLDiv(stimuli, constant_gold) np.testing.assert_allclose(ib_kl, 0.8396272788909165) constant_gold = ConstantSaliencyMapModel(value=0.0) ib_kl = gsmm.image_based_kl_divergence(stimuli, constant_gold) np.testing.assert_allclose(ib_kl, 0.8396272788909165) ib_kl = gsmm.KLDiv(stimuli, constant_gold) np.testing.assert_allclose(ib_kl, 0.8396272788909165) # test MIT Benchmarking settings # (minimum_value=0 can be problematic for constant models) ib_kl = gsmm.image_based_kl_divergence( stimuli, constant_gold, minimum_value=0, log_regularization=2.2204e-16, quotient_regularization=2.2204e-16 ) np.testing.assert_allclose(ib_kl, 0.8396272788909165) ib_kl = gsmm.image_based_kl_divergence(stimuli, gold) np.testing.assert_allclose(ib_kl, 2.029283206727416) ib_kl = gold.image_based_kl_divergence(stimuli, gold) np.testing.assert_allclose(ib_kl, 0.0) def test_image_based_kldiv_gauss_functions(stimuli, scanpath_fixations): gsmm = GaussianSaliencyMapModel() gold = pysaliency.FixationMap(stimuli, scanpath_fixations, kernel_size=10, ignore_doublicates=True) stimuli, scanpath_fixations = pysaliency.create_subset(stimuli, scanpath_fixations, [0]) saliency_map_1 = gsmm.saliency_map(stimuli[0]) saliency_map_2 = gold.saliency_map(stimuli[0]) value1 = gsmm.image_based_kl_divergence(stimuli, gsmm) value2 = pysaliency.metrics.image_based_kl_divergence(saliency_map_1, saliency_map_1) np.testing.assert_allclose(value1, value2) constant_gold = ConstantSaliencyMapModel() saliency_map_1 = gsmm.saliency_map(stimuli[0]) saliency_map_2 = constant_gold.saliency_map(stimuli[0]) value1 = gsmm.image_based_kl_divergence(stimuli, constant_gold) value2 = pysaliency.metrics.image_based_kl_divergence(saliency_map_1, saliency_map_2) np.testing.assert_allclose(value1, value2) constant_gold = ConstantSaliencyMapModel(value=0.0) saliency_map_2 = constant_gold.saliency_map(stimuli[0]) value1 = gsmm.image_based_kl_divergence(stimuli, constant_gold) value2 = pysaliency.metrics.image_based_kl_divergence(saliency_map_1, saliency_map_2) np.testing.assert_allclose(value1, value2) # test MIT Benchmarking settings # (minimum_value=0 can be problematic for constant models) value1 = gsmm.image_based_kl_divergence( stimuli, constant_gold, minimum_value=0, log_regularization=2.2204e-16, quotient_regularization=2.2204e-16 ) value2 = pysaliency.metrics.MIT_KLDiv(saliency_map_1, saliency_map_2) np.testing.assert_allclose(value1, value2) saliency_map_2 = gold.saliency_map(stimuli[0]) value1 = gsmm.image_based_kl_divergence(stimuli, gold) value2 = pysaliency.metrics.image_based_kl_divergence(saliency_map_1, saliency_map_2) np.testing.assert_allclose(value1, value2) def test_shuffled_nonfixation_provider(more_stimuli, more_scanpath_fixations): from pysaliency.saliency_map_models import FullShuffledNonfixationProvider prov = FullShuffledNonfixationProvider(more_stimuli, more_scanpath_fixations) xs, ys = prov(more_stimuli, more_scanpath_fixations, 0) assert len(xs) == (more_scanpath_fixations.n != 0).sum() np.testing.assert_allclose(xs, [1, 10, 3, 1, 1, 8, 1]) np.testing.assert_allclose(ys, [21, 25, 33, 20, 21, 21, 22]) def test_lambda_saliency_map_model(): stimuli = pysaliency.Stimuli([np.random.randn(50, 50, 3), np.random.randn(50, 50, 3), np.random.randn(100, 200, 3)]) m1 = ConstantSaliencyMapModel() m2 = GaussianSaliencyMapModel() fn1 = lambda smaps: np.exp(smaps[0]) fn2 = lambda smaps: np.sum(smaps, axis=0) lambda_model_1 = pysaliency.saliency_map_models.LambdaSaliencyMapModel([m1, m2], fn1) lambda_model_2 = pysaliency.saliency_map_models.LambdaSaliencyMapModel([m1, m2], fn2) for s in stimuli: smap1 = m1.saliency_map(s) smap2 = m2.saliency_map(s) np.testing.assert_allclose(lambda_model_1.saliency_map(s), fn1([smap1, smap2])) np.testing.assert_allclose(lambda_model_2.saliency_map(s), fn2([smap1, smap2])) def test_saliency_map_model_operators(): stimuli = pysaliency.Stimuli([np.random.randn(50, 50, 3), np.random.randn(50, 50, 3), np.random.randn(100, 200, 3)]) m1 = ConstantSaliencyMapModel() m2 = GaussianSaliencyMapModel() for s in stimuli: smap1 = m1.saliency_map(s) smap2 = m2.saliency_map(s) np.testing.assert_allclose((m1+m2).saliency_map(s), smap1 + smap2) np.testing.assert_allclose((m1-m2).saliency_map(s), smap1 - smap2) np.testing.assert_allclose((m1*m2).saliency_map(s), smap1 * smap2) np.testing.assert_allclose((m1/m2).saliency_map(s), smap1 / smap2) def test_fixation_map_model(stimuli, scanpath_fixations): fixation_map = pysaliency.FixationMap(stimuli, scanpath_fixations) smap1 = fixation_map.saliency_map(stimuli[0]) assert smap1.min() == 0 assert smap1.max() == 3 assert smap1.sum() == (scanpath_fixations.n == 0).sum() def test_fixation_map_model_ignore_doublicates(stimuli, scanpath_fixations): fixation_map = pysaliency.FixationMap(stimuli, scanpath_fixations, ignore_doublicates=True) smap1 = fixation_map.saliency_map(stimuli[0]) assert smap1.min() == 0 assert smap1.max() == 1 assert smap1.sum() == (scanpath_fixations.n == 0).sum() - 2 def test_get_unfixated_values(): smap = np.array([[1, 2], [3, 4], [5, 6]]) ys = [0, 1, 1, 2] xs = [1, 1, 1, 0] assert set(pysaliency.saliency_map_models._get_unfixated_values(smap, ys, xs)) == set([1, 3, 6]) def test_density_map_model(stimuli): model = GaussianDensityModel() smap_model = pysaliency.saliency_map_models.DensitySaliencyMapModel(model) density = np.exp(model.log_density(stimuli[0])) smap = smap_model.saliency_map(stimuli[0]) np.testing.assert_allclose(density, smap) def test_log_density_map_model(stimuli): model = GaussianDensityModel() smap_model = pysaliency.saliency_map_models.LogDensitySaliencyMapModel(model) log_density = model.log_density(stimuli[0]) smap = smap_model.saliency_map(stimuli[0]) np.testing.assert_allclose(log_density, smap) def test_wta_sampling(stimuli): model = GaussianSaliencyMapModel() stimulus = stimuli[0] xs, ys, ts = model.sample_scanpath(stimulus, x_hist=[], y_hist=[], t_hist=[], samples=10) assert len(xs) == 10 assert len(ys) == 10 assert len(ts) == 10 np.testing.assert_allclose(xs, 0.5 * stimulus.shape[1], atol=0.6) np.testing.assert_allclose(ys, 0.5 * stimulus.shape[0], atol=0.6) def test_subject_specific_saliency_map_model(stimuli, scanpath_fixations): one_model = ConstantSaliencyMapModel(value=1.0) zero_model = ConstantSaliencyMapModel(value=0.0) model = pysaliency.saliency_map_models.SubjectDependentSaliencyMapModel(subject_models={ 0: zero_model, 1: one_model }) for i in range(len(scanpath_fixations.x)): np.testing.assert_allclose(model.conditional_saliency_map_for_fixation(stimuli, scanpath_fixations, i), scanpath_fixations.subject[i]) np.testing.assert_allclose(model.AUC(stimuli, scanpath_fixations), 0.5) def test_conditional_saliency_maps(stimuli, scanpath_fixations): model = pysaliency.FixationMap(stimuli, scanpath_fixations, kernel_size=10) saliency_maps_1 = list(model.conditional_saliency_maps(stimuli, scanpath_fixations)) saliency_maps_2 = [model.conditional_saliency_map_for_fixation(stimuli, scanpath_fixations, i) for i in range(len(scanpath_fixations))] np.testing.assert_allclose(saliency_maps_1, saliency_maps_2) def test_stimulus_dependent_saliency_map_model(stimuli, scanpath_fixations): # Create stimulus models stimulus_model_1 = ConstantSaliencyMapModel(value=0.5) stimulus_model_2 = GaussianSaliencyMapModel() # Create the stimulus-dependent saliency map model stimuli_models = {stimuli[[0]]: stimulus_model_1, stimuli[[1]]: stimulus_model_2} fallback_model = ConstantSaliencyMapModel(value=0.2) sdsmm = pysaliency.saliency_map_models.StimulusDependentSaliencyMapModel(stimuli_models, fallback_model=fallback_model) # Test saliency map for stimulus 1 saliency_map_1 = sdsmm.saliency_map(stimuli[0]) np.testing.assert_allclose(saliency_map_1, np.ones((40, 40)) * 0.5) # Test saliency map for stimulus 2 saliency_map_2 = sdsmm.saliency_map(stimuli[1]) height = stimuli[1].shape[0] width = stimuli[1].shape[1] expected_saliency_map_2 = np.exp(-0.5 * ((np.mgrid[:height, :width][1] - 0.5 * width) ** 2 + (np.mgrid[:height, :width][0] - 0.5 * height) ** 2) / np.sqrt(width ** 2 + height ** 2)) np.testing.assert_allclose(saliency_map_2, expected_saliency_map_2) # Test fallback model fallback_saliency_map = fallback_model.saliency_map(np.random.randn(50, 50, 3)) np.testing.assert_allclose(fallback_saliency_map, np.ones((50, 50)) * 0.2) # Test saliency map for stimulus not provided by the models if there is no fallback model sdsmm.fallback_model = None with pytest.raises(ValueError): sdsmm.saliency_map(np.random.randn(50, 50, 3)) ================================================ FILE: tests/test_sampling.py ================================================ import numpy as np from pysaliency.saliency_map_models import GaussianSaliencyMapModel from pysaliency.sampling_models import SamplingModelMixin, ScanpathSamplingModelMixin def test_fixation_sampling(): class SamplingModel(SamplingModelMixin, GaussianSaliencyMapModel): def sample_fixation(self, stimulus, x_hist, y_hist, t_hist, attributes=None, verbose=False, rst=None): return x_hist[-1] + 1, y_hist[-1] + 1, t_hist[-1] + 1 model = SamplingModel() xs, ys, ts = model.sample_scanpath(np.zeros((40, 40, 3)), [0], [1], [2], 4) assert xs == [0, 1, 2, 3, 4] assert ys == [1, 2, 3, 4, 5] assert ts == [2, 3, 4, 5, 6] def test_scanpath_sampling(): class SamplingModel(ScanpathSamplingModelMixin, GaussianSaliencyMapModel): def sample_scanpath(self, stimulus, x_hist, y_hist, t_hist, samples, attributes=None, verbose=False, rst=None): return ( list(x_hist) + [x_hist[-1]] * samples, list(y_hist) + [y_hist[-1]] * samples, list(t_hist) + [t_hist[-1]] * samples ) model = SamplingModel() x, y, t = model.sample_fixation(np.zeros((40, 40, 3)), [0], [1], [2]) assert x == 0 assert y == 1 assert t == 2 ================================================ FILE: tests/test_torch_datasets.py ================================================ from pathlib import Path from PIL import Image import numpy as np import pytest from pysaliency import ( FileStimuli, GaussianSaliencyMapModel, DigitizeMapModel, SaliencyMapModelFromDirectory, UniformModel ) from pysaliency.torch_datasets import ImageDataset, ImageDatasetSampler, FixationMaskTransform, collate_fn import torch @pytest.fixture def stimuli(tmp_path): filenames = [] stimuli_directory = tmp_path / 'stimuli' stimuli_directory.mkdir() for i in range(50): image = Image.fromarray(np.random.randint(0, 255, size=(25, 30, 3), dtype=np.uint8)) filename = stimuli_directory / 'stimulus_{:04d}.png'.format(i) image.save(filename) filenames.append(filename) return FileStimuli(filenames) @pytest.fixture def fixations(stimuli): return UniformModel().sample(stimuli, 1000, rst=np.random.RandomState(seed=42)) @pytest.fixture def saliency_model(): return GaussianSaliencyMapModel(center_x=0.15, center_y=0.85, width=0.2) @pytest.fixture def png_saliency_map_model(tmp_path, stimuli, saliency_model): digitized_model = DigitizeMapModel(saliency_model) output_path = tmp_path / 'saliency_maps' output_path.mkdir() for filename, stimulus in zip(stimuli.filenames, stimuli): stimulus_name = Path(filename) output_filename = output_path / f"{stimulus_name.stem}.png" image = Image.fromarray(digitized_model.saliency_map(stimulus).astype(np.uint8)) image.save(output_filename) return SaliencyMapModelFromDirectory(stimuli, str(output_path)) def test_dataset(stimuli, fixations, png_saliency_map_model): models_dict = { 'saliency_map': png_saliency_map_model, } dataset = ImageDataset( stimuli, fixations, models=models_dict, transform=FixationMaskTransform(), average='image', ) loader = torch.utils.data.DataLoader( dataset, batch_sampler=ImageDatasetSampler(dataset, batch_size=4, shuffle=False), pin_memory=False, num_workers=0, # doesn't work for sparse tensors yet. Might work soon. collate_fn=collate_fn, ) count = 0 for batch in loader: count += len(batch['saliency_map']) assert count == len(stimuli) ================================================ FILE: tests/test_torch_utils.py ================================================ import numpy as np from packaging import version from scipy.ndimage import gaussian_filter as scipy_filter import torch import hypothesis from hypothesis import given, strategies as st from hypothesis.extra import numpy as hypothesis_np import pytest from pysaliency.torch_utils import gaussian_filter, gaussian_filter_1d_new_torch, gaussian_filter_1d_old_torch @pytest.fixture(params=[20.0]) def sigma(request): return request.param @pytest.fixture(params=[torch.float64, torch.float32]) def dtype(request): return request.param def test_gaussian_filter(sigma, dtype): #window_radius = int(sigma*4) test_data = 10*np.ones((4, 1, 100, 100)) test_data += np.random.randn(4, 1, 100, 100) test_tensor = torch.tensor(test_data, dtype=dtype) output = gaussian_filter( tensor=test_tensor, sigma=torch.tensor(sigma), truncate=4, dim=[2, 3], ).detach().cpu().numpy()[0, 0, :, :] scipy_out = scipy_filter(test_data[0, 0], sigma, mode='nearest') if dtype == torch.float32: rtol = 5e-6 else: rtol = 1e-7 np.testing.assert_allclose(output, scipy_out, rtol=rtol) @pytest.mark.skipif( version.parse(torch.__version__) < version.parse('1.7') # new code doesn't work because no `torch.movedim` or version.parse(torch.__version__) >= version.parse('1.11'), # old code doesn't work because torch's conv1d got stricter about input shape reason="torch either too new for old implementation or too old for new implementation" ) @given(hypothesis_np.arrays( dtype=hypothesis_np.floating_dtypes(sizes=(32, 64), endianness='='), shape=st.tuples( st.integers(min_value=1, max_value=100), st.just(1), st.integers(min_value=1, max_value=100), st.integers(min_value=1, max_value=100) )), st.floats(allow_nan=False, allow_infinity=False, min_value=0.01, max_value=50), st.integers(min_value=2, max_value=3), ) #@hypothesis.settings(verbosity=hypothesis.Verbosity.verbose) @hypothesis.settings(deadline=5000) def test_compare_gaussian_1d_implementations(data, sigma, dim): data_tensor = torch.tensor(data) old_data = gaussian_filter_1d_old_torch(data_tensor, sigma=sigma, dim=dim).detach().cpu().numpy() new_data = gaussian_filter_1d_new_torch(data_tensor, sigma=sigma, dim=dim).detach().cpu().numpy() np.testing.assert_allclose(old_data, new_data) ================================================ FILE: tests/test_utils.py ================================================ from __future__ import absolute_import, print_function, division import unittest import dill import glob import os import numpy as np from pysaliency.utils import LazyList, Cache, get_minimal_unique_filenames, atomic_directory_setup, build_padded_2d_array from test_helpers import TestWithData def test_minimal_unique_filenames(): assert get_minimal_unique_filenames(['a/b/c.d']) == ['c.d'] filenames = [ 'a/b/c/d.e', 'a/b/c/f.g', 'a/b/c/h.i', ] assert get_minimal_unique_filenames(filenames) == ['d.e', 'f.g', 'h.i'] filenames.append('a/b/C/j.k') assert get_minimal_unique_filenames(filenames) == ['c/d.e', 'c/f.g', 'c/h.i', 'C/j.k'] class TestLazyList(TestWithData): def test_lazy_list(self): calls = [] def gen(i): calls.append(i) print('calling with {} yielding {}'.format(i, i**2)) return i**2 length = 20 lazy_list = LazyList(gen, length) self.assertEqual(len(lazy_list), length) for i in range(length): self.assertEqual(lazy_list[i], i**2) self.assertEqual(calls, list(range(length))) def test_pickle_no_cache(self): def gen(i): print('calling with {} yielding {}'.format(i, i**2)) return i**2 length = 20 lazy_list = LazyList(gen, length) lazy_list = self.pickle_and_reload(lazy_list, pickler=dill) self.assertEqual(len(lazy_list._cache), 0) self.assertEqual(list(lazy_list), [i**2 for i in range(length)]) def test_pickle_with_cache(self): def gen(i): print('calling with {} yielding {}'.format(i, i**2)) return i**2 length = 20 lazy_list = LazyList(gen, length, pickle_cache=True) list(lazy_list) # make sure all list items are generated lazy_list = self.pickle_and_reload(lazy_list, pickler=dill) self.assertEqual(dict(lazy_list._cache), {i: i**2 for i in range(length)}) self.assertEqual(list(lazy_list), [i**2 for i in range(length)]) def test_atomic_directory_setup_success(tmp_path): directory = tmp_path / 'testdirectory' assert not directory.exists() with atomic_directory_setup(str(directory)): directory.mkdir() assert directory.exists() assert directory.exists() def test_atomic_directory_setup_failure(tmp_path): directory = tmp_path / 'testdirectory' assert not directory.exists() try: with atomic_directory_setup(str(directory)): directory.mkdir() assert directory.exists() raise ValueError() except ValueError: pass else: assert False assert not directory.exists() def test_atomic_directory_setup_success_no_location(): with atomic_directory_setup(None): assert True assert True def test_atomic_directory_setup_failure_no_location(): try: with atomic_directory_setup(None): assert True raise ValueError() except ValueError: pass else: assert False assert True class TestCache(TestWithData): def test_basics(self): cache = Cache() self.assertEqual(len(cache), 0) data = np.random.randn(10, 10, 3) cache['foo'] = data self.assertEqual(list(cache.keys()), ['foo']) np.testing.assert_allclose(cache['foo'], data) del cache['foo'] self.assertEqual(len(cache), 0) def test_cache_to_disk(self): cache = Cache(cache_location=self.data_path) self.assertEqual(len(cache), 0) data = np.random.randn(10, 10, 3) cache['foo'] = data self.assertEqual(glob.glob(os.path.join(self.data_path, '*.*')), [os.path.join(self.data_path, 'foo.npy')]) self.assertEqual(list(cache.keys()), ['foo']) np.testing.assert_allclose(cache['foo'], data) cache = Cache(cache_location=self.data_path) self.assertEqual(cache._cache, {}) self.assertEqual(list(cache.keys()), ['foo']) np.testing.assert_allclose(cache['foo'], data) del cache['foo'] self.assertEqual(len(cache), 0) self.assertEqual(glob.glob(os.path.join(self.data_path, '*.*')), []) def test_cache_to_disk_nonexisting_location(self): cache_location = os.path.join(self.data_path, 'cache') cache = Cache(cache_location=cache_location) self.assertEqual(len(cache), 0) data = np.random.randn(10, 10, 3) cache['foo'] = data self.assertEqual(glob.glob(os.path.join(cache_location, '*.*')), [os.path.join(cache_location, 'foo.npy')]) self.assertEqual(list(cache.keys()), ['foo']) np.testing.assert_allclose(cache['foo'], data) cache = Cache(cache_location=cache_location) self.assertEqual(cache._cache, {}) self.assertEqual(list(cache.keys()), ['foo']) np.testing.assert_allclose(cache['foo'], data) del cache['foo'] self.assertEqual(len(cache), 0) self.assertEqual(glob.glob(os.path.join(cache_location, '*.*')), []) def test_pickle_cache(self): cache = Cache() self.assertEqual(len(cache), 0) data = np.random.randn(10, 10, 3) cache['foo'] = data self.assertEqual(list(cache.keys()), ['foo']) np.testing.assert_allclose(cache['foo'], data) cache2 = self.pickle_and_reload(cache) self.assertEqual(cache2._cache, {}) self.assertEqual(len(cache2), 0) def test_pickle_cache_with_location(self): cache = Cache(cache_location=self.data_path) self.assertEqual(len(cache), 0) data = np.random.randn(10, 10, 3) cache['foo'] = data self.assertEqual(glob.glob(os.path.join(self.data_path, '*.*')), [os.path.join(self.data_path, 'foo.npy')]) self.assertEqual(list(cache.keys()), ['foo']) np.testing.assert_allclose(cache['foo'], data) cache2 = self.pickle_and_reload(cache) self.assertEqual(cache2._cache, {}) self.assertEqual(len(cache2), 1) np.testing.assert_allclose(cache2['foo'], data) def test_build_padded_2d_array(): arrays = [ [0.1, 1, 2], [0, 1], [0, 1, 2, 4], [0, 3] ] expected = np.array([ [0.1, 1, 2, np.nan], [0, 1, np.nan, np.nan], [0, 1, 2, 4], [0, 3, np.nan, np.nan] ]) actual = build_padded_2d_array(arrays) np.testing.assert_allclose(actual, expected) expected = np.hstack((actual, np.ones((4, 1)) * np.nan)) actual = build_padded_2d_array(arrays, max_length=5) np.testing.assert_allclose(actual, expected) if __name__ == '__main__': unittest.main() ================================================ FILE: tests/utils/test_variable_length_array.py ================================================ import numpy as np import pytest from pysaliency.utils import build_padded_2d_array from pysaliency.utils.variable_length_array import VariableLengthArray, concatenate_variable_length_arrays def test_variable_length_array_from_padded_array_basics(): # Test case 1 data = build_padded_2d_array([[1.0, 2, 3], [4, 5]]) lengths = np.array([3, 2]) array = VariableLengthArray(data, lengths) assert len(array) == 2 rows = list(array) np.testing.assert_array_equal(rows[0], np.array([1, 2, 3])) np.testing.assert_array_equal(rows[1], np.array([4, 5])) def test_variable_length_array_from_padded_array(): # Test case 1 data = build_padded_2d_array([[1.0, 2, 3], [4, 5]]) lengths = np.array([3, 2]) array = VariableLengthArray(data, lengths) # test accessing rows np.testing.assert_array_equal(array[0], np.array([1, 2, 3])) np.testing.assert_array_equal(array[1], np.array([4, 5])) # test accessing elements np.testing.assert_array_equal(array[0, 1], 2) # acessing elements outside the length of the row should raise an IndexError with pytest.raises(IndexError): array[1, 2] # test slicing np.testing.assert_array_equal(array[:, 0], [1, 4]) # test slicing with negative indices np.testing.assert_array_equal(array[:, -1], [3, 5]) # Test case 2 data = build_padded_2d_array([[1.0, 2], [3, 4, 5]]) lengths = np.array([2, 3]) array = VariableLengthArray(data, lengths) # test accessing rows np.testing.assert_array_equal(array[0], np.array([1, 2])) np.testing.assert_array_equal(array[1], np.array([3, 4, 5])) # test accessing rows with np ints for int_type in [np.int16, np.int32, np.int64, np.int8]: np.testing.assert_array_equal(array[int_type(0)], np.array([1, 2])) np.testing.assert_array_equal(array[int_type(1)], np.array([3, 4, 5])) # test accessing elements np.testing.assert_array_equal(array[0, 1], 2) np.testing.assert_array_equal(array[1, 2], 5) # acessing elements outside the length of the row should raise an IndexError with pytest.raises(IndexError): array[1, 3] # test slicing np.testing.assert_array_equal(array[:, 0], [1, 3]) # test slicing with negative indices np.testing.assert_array_equal(array[:, -1], [2, 5]) def test_variable_length_array_slicing_with_slices(): data = build_padded_2d_array([[1.0, 2, 3], [4, 5], [6, 7, 8, 9]]) lengths = np.array([3, 2, 4]) array = VariableLengthArray(data, lengths) sub_array = array[1:] assert isinstance(sub_array, VariableLengthArray) assert len(sub_array) == 2 np.testing.assert_array_equal(sub_array._data, data[1:]) np.testing.assert_array_equal(sub_array[0], np.array([4, 5])) np.testing.assert_array_equal(sub_array[1], np.array([6, 7, 8, 9])) sub_array = array[:2] assert isinstance(sub_array, VariableLengthArray) assert len(sub_array) == 2 np.testing.assert_array_equal(sub_array._data, data[:2]) # one length item is cut off np.testing.assert_array_equal(sub_array[0], np.array([1, 2, 3])) np.testing.assert_array_equal(sub_array[1], np.array([4, 5])) def test_variable_length_array_slicing_with_indices(): data = build_padded_2d_array([[1.0, 2, 3], [4, 5], [6, 7, 8, 9]]) lengths = np.array([3, 2, 4]) array = VariableLengthArray(data, lengths) sub_array = array[[0, 2]] assert isinstance(sub_array, VariableLengthArray) assert len(sub_array) == 2 np.testing.assert_array_equal(sub_array._data, data[[0, 2]]) np.testing.assert_array_equal(sub_array[0], np.array([1, 2, 3])) np.testing.assert_array_equal(sub_array[1], np.array([6, 7, 8, 9])) def test_variable_length_array_slicing_with_mask(): data = build_padded_2d_array([[1.0, 2, 3], [4, 5], [6, 7, 8, 9]]) lengths = np.array([3, 2, 4]) array = VariableLengthArray(data, lengths) sub_array = array[[True, False, True]] assert isinstance(sub_array, VariableLengthArray) assert len(sub_array) == 2 np.testing.assert_array_equal(sub_array._data, data[[0, 2]]) np.testing.assert_array_equal(sub_array[0], np.array([1, 2, 3])) np.testing.assert_array_equal(sub_array[1], np.array([6, 7, 8, 9])) def test_variable_length_array_from_list_of_arrays(): # Test case 1 data = [[1, 2, 3], [4, 5]] lengths = np.array([3, 2]) array = VariableLengthArray(data, lengths) np.testing.assert_array_equal(array._data, np.array([[1, 2, 3], [4, 5, np.nan]])) def test_variable_length_array_from_list_of_arrays_without_specified_lengths(): data = [[1, 2, 3], [4, 5]] lengths = np.array([3, 2]) array = VariableLengthArray(data) np.testing.assert_array_equal(array._data, np.array([[1, 2, 3], [4, 5, np.nan]])) np.testing.assert_array_equal(array.lengths, lengths) def test_variable_length_array_inconsistent_lengths(): # consistent case data = [[1, 2, 3], [4]] lengths = np.array([3, 1]) VariableLengthArray(data, lengths) # inconsistent case data = [[1, 2, 3], [4]] lengths = np.array([3, 2]) with pytest.raises(ValueError): VariableLengthArray(data, lengths) def test_variable_length_array_copy(): data = build_padded_2d_array([[1.0, 2, 3], [4, 5]]) lengths = np.array([3, 2]) array = VariableLengthArray(data, lengths) copy = array.copy() np.testing.assert_array_equal(array._data, copy._data) np.testing.assert_array_equal(array.lengths, copy.lengths) copy._data[0, 0] = 0 assert not np.array_equal(array._data, copy._data, equal_nan=True) copy.lengths[0] = 0 assert not np.array_equal(array.lengths, copy.lengths, equal_nan=True) def test_variable_length_array_concatenate(): data1 = [[1, 2, 3], [4, 5]] data2 = [[6, 7], [8, 9, 10], [11, 12, 13, 14]] array1 = VariableLengthArray(data1) array2 = VariableLengthArray(data2) concatenated_array = concatenate_variable_length_arrays([array1, array2]) concatenated_data = data1 + data2 expected = VariableLengthArray(concatenated_data) np.testing.assert_array_equal(concatenated_array._data, expected._data) np.testing.assert_array_equal(concatenated_array.lengths, expected.lengths) def test_variable_length_array_zero_length_from_2d_array(): data = np.empty((0, 0)) lengths = np.array([]) array = VariableLengthArray(data, lengths) assert len(array) == 0 assert np.array_equal(array._data, np.empty((0, 0))) assert np.array_equal(array.lengths, np.array([])) def test_variable_length_array_zero_length_from_list(): data = [] lengths = np.array([]) array = VariableLengthArray(data, lengths) assert len(array) == 0 assert np.array_equal(array._data, np.empty((0, 0))) assert np.array_equal(array.lengths, np.array([]))