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\n\nRISE is included in the provided environments. To enable the visualization of the slide type, open the classic notebook interface and select `View -> Cell Toolbar -> Slideshow`.\n\nBy pressing the `Enter\\Exit RISE Slideshow` button at the top you can enter the slideshow presentation.\n\n![](\"https://raw.githubusercontent.com/FilippoMB/python-time-series-handbook/main/notebooks/00/media/slides_rise.png\")
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\n\nSee the [RISE documentation](https://rise.readthedocs.io/en/latest/) for more info.\n\n## 📝 Citation\n\nIf you are using this material in your courses or in your research, please consider citing it as follows:\n\n```bibtex\n@misc{bianchi2024tsbook,\n author = {Filippo Maria Bianchi},\n title = {Time Series Analysis with Python},\n year = {2024},\n howpublished = {Online},\n url = {https://github.com/FilippoMB/python-time-series-handbook}\n}\n```\n"
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"content": "# Book settings\ntitle: Time series analysis with Python\nauthor: Filippo Maria Bianchi\nlogo: logo.png\n\n# # Define the name of the latex output file for PDF builds\n# latex:\n# latex_documents:\n# targetname: book.tex\n\n# # Add a bibtex file so that we can create citations\n# bibtex_bibfiles:\n# - references.bib\n\n# Information about where the book exists on the web\nrepository:\n url: https://github.com/FilippoMB/python-time-series-handbook # Online location of the book\n # path_to_book: docs # Optional path to the book, relative to the repository root\n branch: main # Which branch of the repository should be used when creating links (optional)\n\n# Add GitHub buttons to your book\n# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository\nhtml:\n use_issues_button: true\n use_repository_button: true\n favicon : \"logo.png\"\n\nexclude_patterns:\n - _build\n - README.md\n - CONTRIBUTING.md\n - .venv\n - .venv/**\n - .conda-check\n - .conda-check/**\n - build\n - build/**\n - dist\n - dist/**\n - .DS_Store\n - Thumbs.db\n\nparse:\n myst_enable_extensions:\n # Defaults here: https://jupyterbook.org/en/stable/customize/config.html\n - html_image\n - amsmath\n - dollarmath\n\nexecute:\n execute_notebooks: 'auto' # off, force, auto, cache\n timeout: -1 # Disable timeouts for executing notebooks\n"
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"content": "# Table of contents\n# Learn more at https://jupyterbook.org/customize/toc.html\n\nformat: jb-book\nroot: notebooks/00/intro\nchapters:\n- file: notebooks/01/introduction_to_time_series\n- file: notebooks/02/stationarity\n- file: notebooks/03/smoothing\n- file: notebooks/04/ar-ma\n- file: notebooks/05/arma_arima_sarima\n- file: notebooks/06/unit-root-hurst\n- file: notebooks/07/kalman-filter\n- file: notebooks/08/signal-transforms-filters\n- file: notebooks/09/prophet\n- file: notebooks/10/nn-reservoir-computing\n- file: notebooks/11/nonlinear-ts\n- file: notebooks/12/classification-clustering\n- file: notebooks/00/knowledge_test\n- file: notebooks/00/resources"
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"path": "environment.yml",
"content": "# User notebook environment for the course.\nname: tsa-course\nchannels:\n - conda-forge\n - nodefaults\ndependencies:\n - python=3.12\n - pip\n - numpy=2.4.3\n - matplotlib=3.10.8\n - scipy=1.17.1\n - tqdm=4.67.3\n - statsmodels=0.14.6\n - pandas=3.0.1\n - pandas-datareader=0.10.0\n - seaborn=0.13.2\n - scikit-learn=1.8.0\n - setuptools=80.10.2\n - notebook=6.5.7\n - rise=5.7.1\n - dtaidistance=2.4.0\n - plotly=6.6.0\n - ipywidgets=8.1.8\n - pip:\n - opencv-python-headless==4.13.0.92\n - pmdarima==2.1.1\n - prophet==1.3.0\n - reservoir-computing==1.0.2\n - tck==1.0.0\n - yfinance==1.2.0\n - -e .\n"
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"path": "notebooks/00/intro.md",
"content": "# Time series analysis with Python\n\n\n\nWelcome to a journey through the world of time series analysis using Python! This collection of Jupyter notebooks serves as both a comprehensive course and a practical guide for students, data scientists, and researchers interested in exploring the interplay between statistical theories and practical applications in time series analysis.\n\nTime series analysis is a central discipline in data science, offering insights into patterns over time that are useful for forecasting, anomaly detection, and understanding temporal dynamics. The aim of this course is to introduce fundamental concepts of time series analysis from multiple perspectives: statistical, dynamical systems, machine learning, and signal processing. This interdisciplinary approach aims to give the reader a broad view on the world of time series.\n\n```{image} media/topics.png\n:alt: topics\n:width: 500px\n:align: center\n```\n\nThe course is designed to combine high-level theoretical knowledge with practical programming skills. Each chapter introduces key concepts of time series analysis together with hands-on coding sections. This structure allows you to immediately apply the theoretical concepts you learn, seeing first-hand how these translate into functional tools in data analytics. Through this process, you will gain both the knowledge to understand complex time series data and the skills to analyze and predict it effectively.\nTo reinforce learning and encourage active engagement, each chapter concludes with exercises. These are designed to test your understanding and help you apply the lessons in practical contexts.\n\nWhether you are new to time series analysis or looking to refine your expertise, this course offers a broad exploration of the field, with Python as your toolkit. I hope that you will find this material both educational and entertaining, brining you a step closer to mastering time series analysis.\n\n## 📖 Chapters\n\nThe course is organized into the following chapters.\n\n```{tableofcontents}\n```\n\n```{note}\nThe notebooks are presented in class as slides using RISE (see [here](https://github.com/FilippoMB/python-time-series-handbook?tab=readme-ov-file#-notebook-format-and-slides) for more details). \nFor this reason, the text in the notebooks is organized with bullet points.\n```\n\n\n## 🎓 University courses\n\nThese notebooks are currently adopted in [STA-2003 Tidsrekker](https://sa.uit.no/utdanning/emner/emne?p_document_id=822793) at UiT the Arctic University of Tromsø and [062785 - Time Series Analysis](https://www.polimi.it/) at Polytechnic of Milan.\n\n## 🚀 Getting started with coding\n\nYou can read from here all the content of the course. However, to get your hands-on experience with coding you want to run the code snippets or the whole notebooks that you can download on \n ![\"GitHub\"](\"https://img.shields.io/badge/-GitHub-8A2BE2?style=round-square-small&logo=github\"){kind=icon}
\n.\n\nTo run the code and the notebooks locally, the recommended path is a Conda environment, for example with Miniconda.\n\n1. Install [Miniconda](https://www.anaconda.com/docs/getting-started/miniconda/main) or another Conda-compatible distribution.\n\n2. Clone the repository:\n\n ```bash\n git clone https://github.com/FilippoMB/python-time-series-handbook.git\n cd python-time-series-handbook\n ```\n\n3. Create the course environment:\n\n ```bash\n conda env create -f environment.yml\n conda activate tsa-course\n ```\n\n4. Launch Jupyter Notebook:\n\n ```bash\n jupyter notebook\n ```\n\nIf you are contributing to the repository and prefer the maintainer workflow based on `uv`, the repository [README](https://github.com/FilippoMB/python-time-series-handbook/blob/main/README.md) contains the development instructions.\n\n## ⚒ Roadmap\n\n```{warning}\nThis is an early version of the course. There might be imprecisions and errors. Also, some chapters might undergo significant revisions and changes.\n```\n\nI am planning to add more material over time to cover additional topics in new chapters and to extend the existing ones with new content.\nIf there is a specific topic you feel is missing or passages that you feel are not clear enough, open an Issue on the repository on Github.\n\n### A note on deep learning\n\nFor the moment, I intentionally decided to leave more advanced deep learning techniques aside. There are a couple of reasons for this choice. Firstly, advanced deep learning methods rely heavily on specific knowledge and tools that are generally covered in specialized deep learning courses. This focus does not align with the introductory nature of this course, which is aimed at covering the fundamentals of time series analysis.\n\nSecondly, while deep learning opens up exciting avenues for new applications—such as NLP and analysis of video and spatio-temporal data—it primarily enhances capabilities in handling diverse data types and scaling to large datasets. However, for the core objectives of this course, which are to understand and manipulate time series data effectively, the advantages of moving from the basic neural networks introduced here to more complex deep learning models do not significantly alter the fundamental approach.\n\n## 🤝 Contributing\n\nTime series analysis with Python is designed with accessibility in mind. The material is completely open-sourced and uses only free software, based in Python.\nYou can contribute both by adding new material, fixing typos, and suggesting edits. To do that,\nFork the Github repository and submit a pull request.\n\nIf you want to build the Jupyter Book itself or package the `tsa-course` library, the repository README contains the additional contributor commands.\n\nFinally, if you liked this content, please share it with others who might find it useful and give it a\nStar on GitHub.\n\n## 📝 Citation\n\nIf you are using this material in your courses or in your research, please consider citing it as follows:\n\n```bibtex\n@misc{bianchi2024tsbook,\n author = {Filippo Maria Bianchi},\n title = {Time Series Analysis with Python},\n year = {2024},\n howpublished = {Online},\n url = {https://github.com/FilippoMB/python-time-series-handbook}\n}\n```\n"
},
{
"path": "notebooks/00/knowledge_test.md",
"content": "# Knowledge test\n\n## Chapter 1\n\n**What is the primary purpose of time series analysis?**\n- A) To categorize different types of data as time series data.\n- B) To use statistical methods to describe and interpret data patterns.\n- C) To understand and forecast the behavior of a process that generates time series data.\n- D) To analyze the behavior of non-time series data over fixed intervals.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What type of data is typically analyzed using time series analysis?**\n- A) Textual data\n- B) Image data\n- C) Numerical data collected over time\n- D) Categorical data\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**In the context of time series analysis, which of the following best describes the concept of 'trend'?**\n- A) The irregular and unpredictable movement in data over a short period.\n- B) The consistent, long-term direction of a time series data set.\n- C) A repeating pattern or cycle observed within a given year.\n- D) Variations caused by specific one-time events.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How is time series analysis applied in the business sector?**\n- A) Primarily for historical data archiving\n- B) For designing new products\n- C) In demand forecasting and sales analysis\n- D) Only in employee performance tracking\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Which method is commonly used to decompose time series data?**\n- A) Linear regression analysis\n- B) Fourier transform\n- C) Principal component analysis\n- D) Additive or multiplicative models\n \n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**What is a crucial aspect to consider when dealing with time series data for accurate analysis?**\n- A) The frequency of data collection\n- B) The color coding of data points\n- C) The alphabetical ordering of data entries\n- D) The digital format of the data files\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**Which component of time series data adjusts for variations that recur with fixed periods throughout the data?**\n- A) Trend\n- B) Seasonality\n- C) Cyclical\n- D) Residual\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n\n\n## Chapter 2\n\n**Which of the following tests is used to determine the stationarity of a time series?**\n- A) Pearson correlation test\n- B) Chi-square test\n- C) Augmented Dickey-Fuller test\n- D) T-test\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the significance of stationarity in time series analysis?**\n- A) Stationarity is not significant; most modern time series models do not require it.\n- B) Stationarity is crucial because many time series forecasting models assume it, and nonstationary data can lead to unreliable models.\n- C) Stationarity only applies to financial time series and is irrelevant in other fields.\n- D) Stationarity ensures that the time series data does not require transformations before analysis.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why is stationarity important for applying statistical models to time series data?**\n- A) Stationary data allows for easier identification of outliers.\n- B) Non-stationary data can lead to biases in model parameters.\n- C) Stationarity assures that the mean and variance are consistent over time, which is a common assumption in many time series models.\n- D) Stationary series ensure high performance across all types of data, regardless of the underlying trends.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What impact does non-stationarity have on the predictive modeling of time series data?**\n- A) It improves the accuracy of predictions by introducing variability.\n- B) It has no impact on the predictions as modern models adjust for it automatically.\n- C) It can lead to misleading results and poor forecasts because the statistical properties change over time.\n- D) It simplifies the model selection process by reducing the number of parameters.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What type of transformation is commonly applied to time series data to achieve stationarity?**\n- A) Logarithmic transformation\n- B) Polynomial transformation\n- C) Fourier transformation\n- D) Binary transformation\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**In time series analysis, why do we want to apply differencing?**\n- A) To increase the mean of the series over time.\n- B) To identify and remove trends and cycles, helping achieve stationarity.\n- C) To amplify the seasonal patterns in the data.\n- D) To convert non-numeric data into a usable format.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the primary goal of testing for stationarity in a time series dataset?**\n- A) To detect the presence of outliers in the dataset.\n- B) To ensure the dataset is suitable for seasonal adjustments.\n- C) To confirm the data’s statistical properties do not vary over time.\n- D) To increase the complexity of the statistical model.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What characteristic defines a random walk model?**\n- A) The values of the series are based on a deterministic trend.\n- B) Each value in the series is the sum of the previous value and a random error term.\n- C) The series values change according to a fixed seasonal pattern.\n- D) The series strictly follows a linear path without deviation.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why is a random walk typically considered non-stationary in the context of time series analysis?**\n- A) Because its variance remains constant over time.\n- B) Because its variance depend on the time at which the series is observed.\n- C) Because it consistently follows a predictable trend.\n- D) Because its mean and variance change over time.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does the periodicity of a signal affect its stationarity in time series analysis?**\n - A) Periodic signals are always considered stationary because they exhibit regular cycles.\n - B) Periodic signals are non-stationary because their mean and variance are not constant over time.\n - C) Periodic signals become stationary only when their frequency matches the sampling rate.\n - D) Periodic signals do not affect the stationarity of a time series as they are considered noise.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the characteristic of white noise that generally qualifies it as a stationary process in time series analysis?**\n - A) Its mean and variance change over time.\n - B) Its mean and variance remain constant over time.\n - C) It exhibits a clear trend and seasonality.\n - D) Its frequency components vary with each observation.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How do autocorrelation and autocovariance relate to the concept of stationarity in time series data?**\n - A) Autocorrelation and autocovariance are only defined for non-stationary processes.\n - B) Stationary processes have constant autocorrelation and autocovariance that do not depend on time.\n - C) Autocorrelation and autocovariance decrease as a time series becomes more stationary.\n - D) Stationary processes exhibit zero autocorrelation and autocovariance at all times.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does constant autocorrelation over time imply about the stationarity of a time series?**\n - A) It suggests that the time series is non-stationary, as autocorrelation should vary with time.\n - B) It indicates potential stationarity, as autocorrelation does not change over time.\n - C) It implies the need for further seasonal adjustment, irrespective of stationarity.\n - D) It demonstrates that the series is under-differenced and needs more transformations.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does it indicate about a time series if the autocorrelations for several lags are very close to zero?**\n - A) The series is likely non-stationary with a strong trend.\n - B) The series is highly predictable at each time step.\n - C) The series likely exhibits white noise characteristics, suggesting it could be stationary.\n - D) The series shows a clear seasonal pattern.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What implication does a significant autocorrelation at lag 1 indicate about the stationarity of a time series?**\n - A) The series is stationary with no dependence between time steps.\n - B) The series exhibits long-term cyclic patterns, suggesting non-stationarity.\n - C) The series is likely non-stationary, indicating dependence between consecutive observations.\n - D) The series is perfectly predictable from one time step to the next.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How can summary statistics and histogram plots be used to assess the stationarity of a time series?**\n - A) By showing a consistent mean and variance in histograms across different time segments.\n - B) By demonstrating a decrease in variance over time in summary statistics.\n - C) By identifying a fixed mode in histogram plots regardless of time period.\n - D) By showing increasing skewness in summary statistics over different intervals.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What do consistent histogram shapes across different time segments suggest about the stationarity of a time series?**\n - A) The series likely exhibits non-stationary behavior due to changing distributions.\n - B) The series displays stationarity with similar distributions over time.\n - C) The histograms are irrelevant to stationarity and should not be used.\n - D) The series shows non-stationarity due to the presence of outliers.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n\n## Chapter 3\n\n**Why is smoothing applied to time series data?**\n - A) To increase the frequency of data points\n - B) To highlight underlying trends in the data\n - C) To create more data points\n - D) To prepare data for real-time analysis \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the formula for calculating the Mean Squared Error in time series analysis?**\n - A) MSE = Sum((Observed - Predicted)^2) / Number of Observations\n - B) MSE = Sum((Observed - Predicted) / Number of Observations)^2\n - C) MSE = (Sum(Observed - Predicted)^2) * Number of Observations\n - D) MSE = Square Root of [Sum((Observed - Predicted)^2) / Number of Observations] \n \n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**Which of the following is a limitation of the simple average smoothing technique?**\n - A) It is computationally intensive\n - B) It gives equal weight to all past observations\n - C) It focuses primarily on recent data\n - D) It automatically adjusts to seasonal variations \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is an advantage of using the moving average technique over the simple average technique?**\n - A) It can handle large datasets more efficiently\n - B) It reduces the lag effect by focusing on more recent data\n - C) It gives equal weight to all observations in the series\n - D) It automatically detects and adjusts for seasonality \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does the window size $P$ in a moving average smoothing technique affect the delay in forecasting?**\n- A) Delay decreases as $P$ increases\n- B) Delay increases as $P$ increases\n- C) Delay remains constant regardless of $P$\n- D) Delay is inversely proportional to $P$ \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the trade-off between responsiveness and robustness to noise when using a moving average smoothing technique?**\n- A) Increasing responsiveness also increases robustness to noise\n- B) Decreasing responsiveness decreases robustness to noise\n- C) Increasing responsiveness decreases robustness to noise\n- D) Responsiveness and robustness to noise are not related in moving average smoothing \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**In the weighted moving average formula $\\text{Forecast} = \\frac{\\sum_{i=1}^P w_i \\times X_{n-i+1}}{\\sum_{i=1}^P w_i}$, what does $X_{n-i+1}$ represent?**\n- A) The weight of the i-th data point\n- B) The value of the i-th data point from the end of the data set\n- C) The total number of observations in the data set\n- D) The average value of the data set \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does triple exponential smoothing add to the forecasting model compared to double exponential smoothing?**\n- A) An additional smoothing constant for cyclicality\n- B) A smoothing component for seasonality\n- C) A component that adjusts for random fluctuations\n- D) An enhanced trend adjustment component \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What are the core components of the triple exponential smoothing model?**\n- A) Level, trend, and random error\n- B) Level, cyclicality, and trend\n- C) Level, trend, and seasonality\n- D) Trend, seasonality, and cyclicality \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Can the triple exponential smoothing model account for different types of seasonality?**\n- A) Yes, it can model both additive and multiplicative seasonality\n- B) No, it only models additive seasonality\n- C) No, it only models multiplicative seasonality\n- D) Yes, but it requires additional parameters beyond the standard model \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**For which scenario would triple exponential smoothing likely provide more accurate forecasts than double exponential smoothing?**\n- A) Data where seasonal patterns vary depending on the level of the time series\n- B) Stable data sets with very little change over time\n- C) Time series with rapidly changing trends but no seasonality\n- D) Short time series data with limited historical records \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n\n## Chapter 4\n\n**In the context of AR and MA models, what role does the correlation play?**\n- A) It helps in determining the optimal parameters of the MA model\n- B) It identifies the stationary nature of the time series\n- C) It measures the strength and direction of a linear relationship between time series observations at different times\n- D) It specifies the number of differences needed to make the series stationary \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How does the autocorrelation function help in analyzing time series data?**\n- A) By identifying the underlying patterns of cyclical fluctuations\n- B) By determining the strength and sign of a relationship between a time series and its lags\n- C) By calculating the average of the time series\n- D) By differentiating between seasonal and non-seasonal patterns \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How can cross-correlation help in understanding relationships in time series data?**\n- A) It identifies the internal structure of a single series\n- B) It detects the point at which two series are most aligned or have the strongest relationship\n- C) It determines the overall trend of a single time series\n- D) It measures the variance within one time series \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does partial autocorrelation differ from autocorrelation in analyzing time series data?**\n- A) PACF isolates the correlation between specific lags ignoring the effects of intermediate lags, while ACF considers all intermediate lags cumulatively.\n- B) PACF is used for linear relationships, whereas ACF is used for non-linear relationships.\n- C) PACF can only be applied in stationary time series, while ACF can be applied in both stationary and non-stationary series.\n- D) There is no difference; PACF is just another term for ACF. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**How does an autoregressive model differ from a moving average model?**\n- A) AR models use past values of the variable itself for prediction, while MA models use past forecast errors.\n- B) AR models use past forecast errors for prediction, while MA models use past values of the variable itself.\n- C) AR models consider the trend and seasonality in data, while MA models only focus on the recent past.\n- D) There is no difference; AR and MA models are identical. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What is the primary assumption of an AR model regarding the relationship between past and future values?**\n- A) Future values are completely independent of past values\n- B) Future values are determined by a weighted sum of past values\n- C) Past values have a diminishing linear effect on future values\n- D) Future values are predicted by the mean of past values \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**When selecting the optimal order $p$ for an AR model, what are we looking for in the Partial Autocorrelation Function (PACF)?**\n- A) The point where the PACF cuts off after a significant spike\n- B) The highest peak in the PACF\n- C) The lag where the PACF crosses the zero line\n- D) The lag with the maximum PACF value \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**In the context of AR models, what is meant by 'stationarity'?**\n- A) The mean of the series should not be a function of time.\n- B) The series must exhibit clear trends and seasonality.\n- C) The variance of the series should increase over time.\n- D) The autocorrelations must be close to zero for all time lags. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**Why would you apply differencing to a time series before fitting an AR model?**\n- A) To introduce seasonality into the data\n- B) To convert a non-stationary series into a stationary one\n- C) To increase the mean of the time series\n- D) To reduce the variance of the time series \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is a potential consequence of overdifferencing a time series?**\n- A) It can introduce a trend into a previously trendless series.\n- B) It can create a spurious seasonality in the data.\n- C) It can lead to an increase in the variance of the series.\n- D) It can produce a series with artificial autocorrelation. \n\n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**What is the primary characteristic of a Moving Average (MA) model in time series analysis?**\n- A) It uses past forecast errors in a regression-like model.\n- B) It predicts future values based solely on past observed values.\n- C) It smooths the time series using a window of observations.\n- D) It captures the trend and seasonality of the time series. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**When analyzing the ACF plot to identify the order $q$ of an MA model, what are you looking for?**\n- A) A gradual decline in the lag values\n- B) A sharp cut-off after a certain number of lags\n- C) A constant value across all lags\n- D) Increasing values with increasing lags \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n\n\n## Chapter 5\n\n**What are the parameters of an ARMA model that need to be specified?**\n- A) $p$ (order of the autoregressive part) and $q$ (order of the moving average part)\n- B) $d$ (degree of differencing)\n- C) $s$ (seasonal period)\n- D) A and B are correct \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**Under what condition is an ARMA model particularly useful compared to exponential smoothing?**\n- A) When the time series data is non-stationary\n- B) When the time series has a clear seasonal pattern\n- C) When the time series exhibits autocorrelations\n- D) When the time series is highly erratic and without patterns \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the first step in building an ARMA model?**\n- A) Determine whether the time series is stationary\n- B) Select the orders p and q for the model\n- C) Estimate the parameters of the model\n- D) Check the model diagnostics \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**Which criterion is often used to compare different ARIMA models to find the optimal one?**\n- A) The least squares criterion\n- B) The Akaike Information Criterion (AIC)\n- C) The Pearson correlation coefficient\n- D) The Durbin-Watson statistic \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What type of differencing might be necessary when preparing a time series for ARIMA modeling?**\n- A) Seasonal differencing\n- B) Non-linear differencing\n- C) Progressive differencing\n- D) Inverse differencing \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What characteristic of a sinusoidal signal might lead the ADF test to conclude it is stationary?**\n- A) Its mean and variance are not constant.\n- B) It exhibits clear seasonality.\n- C) Its mean and autocovariance are time-invariant.\n- D) It has increasing amplitude over time. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How does a month plot assist in the preparation for ARIMA modeling?**\n- A) By confirming the stationarity of the time series\n- B) By revealing seasonal effects that might require seasonal differencing\n- C) By estimating the parameters for the model\n- D) By determining the appropriate lags for the ARIMA model \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does it mean to perform out-of-sample validation on an ARIMA model?**\n- A) To re-estimate the model parameters using the same data set\n- B) To test the model on data that was not used during the model fitting process\n- C) To use cross-validation techniques on randomly selected subsamples\n- D) To apply in-sample predictions to check consistency \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How should the residuals of a properly fitted ARIMA model be distributed?**\n- A) Normally distributed around zero\n- B) Uniformly distributed across the range of data\n- C) Log-normally distributed\n- D) Exponentially distributed \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**When evaluating the residuals of an ARIMA model, what plot is used to assess the standardization and distribution of residuals?**\n- A) Scatter plot\n- B) Box plot\n- C) Q-Q plot\n- D) Pie chart \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What aspect of residuals does the Shapiro-Wilk test specifically evaluate?**\n- A) The autocorrelation structure\n- B) The distribution's adherence to normality\n- C) The heteroscedasticity of residuals\n- D) The variance stability over time \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why is the Ljung-Box test important when validating ARIMA models?**\n- A) It confirms the seasonal patterns are significant\n- B) It verifies that the residuals do not exhibit significant autocorrelation, suggesting a good model fit\n- C) It checks the variance of residuals to ensure homoscedasticity\n- D) It evaluates the power of the model's parameters \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does the provision of a confidence interval in ARIMA model predictions signify?**\n- A) The precision of the estimated parameters\n- B) The accuracy of the model’s forecasts\n- C) The range within which future forecasts are likely to fall, with a certain probability\n- D) The model’s ability to predict exact future values \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What key feature distinguishes ARIMA models from ARMA models?**\n- A) ARIMA models require the data to be stationary.\n- B) ARIMA models include an integrated component for differencing non-stationary data.\n- C) ARIMA models use only moving average components.\n- D) ARIMA models cannot handle seasonal data. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why might an analyst choose an ARIMA model for a financial time series dataset?**\n- A) If the dataset is stationary with no underlying trends\n- B) If the dataset shows fluctuations that revert to a mean\n- C) If the dataset contains underlying trends and requires differencing to become stationary\n- D) If the dataset is periodic and predictable \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the primary method for determining the difference order $d$ in an ARIMA model?**\n- A) Calculating the AIC for different values of $d$\n- B) Observing the stationarity of the time series after successive differencings\n- C) Using a fixed $d$ based on the frequency of the data\n- D) Applying the highest $d$ to ensure model simplicity \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What additional parameters are specified in a SARIMA model compared to an ARIMA model?**\n- A) Seasonal orders: $P, D, Q$ and the length of the season $s$\n- B) Higher non-seasonal orders: $p, d, q$\n- C) A constant term to account for trends\n- D) Parameters to manage increased data frequency \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**How does the seasonal differencing order $D$ in SARIMA models differ from the regular differencing $d$ in ARIMA models?**\n- A) $D$ is used to stabilize the variance, while $d$ stabilizes the mean.\n- B) $D$ specifically targets removing seasonal patterns, while $d$ focuses on achieving overall stationarity.\n- C) $D$ adjusts for autocorrelation, while $d$ corrects for heteroscedasticity.\n- D) $D$ is used for linear trends, and $d$ is used for exponential trends. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What methodology is generally used to select the seasonal autoregressive order $P$ and the seasonal moving average order $Q$ in a SARIMA model?**\n- A) Examining the ACF and PACF plots specifically for the identified seasonal lags\n- B) Applying cross-validation techniques across multiple seasonal cycles\n- C) Testing various combinations of $P$ and $Q$ until the model no longer improves\n- D) Reducing $P$ and $Q$ iteratively based on the simplest model criterion \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**In what scenario might the AutoARIMA model be particularly beneficial?**\n- A) When the user has extensive statistical knowledge and prefers to control every aspect of model building.\n- B) When quick deployment and model testing are necessary without detailed prior analysis.\n- C) When the data shows no signs of seasonality or non-stationarity.\n- D) When only qualitative data is available for analysis. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does the MAPE differ from the Mean Squared Error (MSE) in its interpretation of forecasting accuracy?**\n- A) MAPE provides a measure of error in absolute terms, while MSE measures error in squared terms.\n- B) MAPE is more sensitive to outliers than MSE.\n- C) MAPE gives a relative error which makes it easier to interpret across different data scales, unlike MSE which provides an absolute error.\n- D) MAPE is used only for linear models, while MSE is used for non-linear models. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Why is it important to consider both MSE and MAPE when conducting a grid search for the best SARIMA model?**\n- A) Because some models may perform well in terms of low MSE but might show high percentage errors as indicated by MAPE, especially on smaller data scales.\n- B) Because higher values of both MSE and MAPE indicate a more complex and desirable model.\n- C) Because lower MSE and higher MAPE together are indicative of a model that is overfitting.\n- D) Because the regulatory standards in time series forecasting mandate the use of both metrics for compliance. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**How is the complexity of an ARIMA model typically quantified?**\n- A) By the sum of the parameters $p$, $d$, and $q$.\n- B) By the computational time required to fit the model.\n- C) By the number of data points used in the model.\n- D) By the variance of the residuals produced by the model. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n\n\n## Chapter 6\n\n**What is the main purpose of conducting a unit root test on a time series dataset?**\n- A) To determine the optimal parameters for an ARIMA model.\n- B) To identify whether the time series is stationary or non-stationary.\n- C) To confirm if the time series has a constant mean and variance.\n- D) To evaluate the predictive accuracy of a time series model. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How is the concept of a \"unit root\" integral to understanding the behavior of a time series?**\n- A) It helps in identifying the periodic components of the series.\n- B) It indicates whether the time series will return to a trend path or persist in deviation.\n- C) It determines the cyclical amplitude of the time series.\n- D) It specifies the frequency of the time series data. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does the ADF test enhance the basic Dickey-Fuller test for more accurate unit root testing?**\n- A) By incorporating higher-order regression terms to account for autocorrelation in the residuals.\n- B) By increasing the number of lags used in the regression to capture seasonality.\n- C) By applying a transformation to the time series data to ensure normal distribution.\n- D) By reducing the dataset size to focus on more recent data points. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**When evaluating a time series for non-stationarity using the ADF test, why might one choose to include both $\\alpha$ and $\\beta t$ in the regression model?**\n- A) To ensure that the test accounts for both constant and linear trend components, thus avoiding spurious rejection of the unit root null hypothesis in the presence of a trend.\n- B) To increase the regression model’s fit to the data, thereby reducing residual errors.\n- C) To differentiate between seasonal and non-seasonal components effectively.\n- D) To comply with regulatory requirements for financial time series analysis.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**Why is mean reversion considered an important concept in trading and investment strategies?**\n- A) It provides a basis for predicting long-term trends in asset prices.\n- B) It suggests that price extremes may be temporary, offering potential opportunities for arbitrage.\n- C) It indicates constant returns regardless of market conditions.\n- D) It guarantees a fixed rate of return on all investments. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does the rejection of the null hypothesis in a mean reversion test like the ADF suggest about the time series?**\n- A) The series is likely non-stationary with no mean reversion.\n- B) The series does not contain a unit root, suggesting mean reversion.\n- C) There is no correlation between sequential data points in the series.\n- D) The series exhibits clear seasonal patterns and trends. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**In which scenario might a time series be mean-reverting but not stationary?**\n- A) If the mean to which the series reverts itself changes over time.\n- B) If the series displays constant mean and variance.\n- C) If the autocorrelation function shows dependency at only very short lags.\n- D) If the series exhibits no significant peaks or troughs. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What implication does a Hurst exponent greater than 0.5 indicate about a time series?**\n- A) The series exhibits mean-reverting behavior.\n- B) The series is likely to be stationary.\n- C) The series shows persistent behavior, trending in one direction.\n- D) The series has no clear long-term trends. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What Hurst exponent value is typically associated with an antipersistent time series?**\n- A) Around 0.5\n- B) Less than 0.5\n- C) Exactly 0.0\n- D) Greater than 0.7 \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How can the Hurst exponent be utilized by traders or financial analysts when evaluating the behavior of stock prices?**\n- A) A Hurst exponent of 0.5 or higher suggests a good opportunity for trend-following strategies.\n- B) A Hurst exponent below 0 can suggest opportunities for strategies based on price reversals.\n- C) A Hurst exponent above 0.5 may encourage a profitable investment in stable, low-volatility stocks.\n- D) A Hurst exponent above 0 indicates high risk and high potential returns, suitable for aggressive investment strategies.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What fundamental properties does Geometric Brownian Motion (GBM) assume about the behavior of stock prices?**\n- A) Stock prices change in accordance with a Poisson distribution.\n- B) Stock prices follow a path determined by both a constant drift and a random shock component.\n- C) Stock prices are stable and do not show volatility.\n- D) Stock prices are inversely proportional to the market volatility. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does the treatment of volatility differ between Brownian Motion and Geometric Brownian Motion?**\n- A) In Brownian Motion, volatility is constant, whereas in GBM, volatility impacts the rate of exponential growth.\n- B) Volatility is not a factor in Brownian Motion but is crucial in GBM.\n- C) In GBM, volatility decreases as stock prices increase, unlike in Brownian Motion where it remains stable.\n- D) Volatility in Brownian Motion leads to negative stock prices, which GBM corrects by allowing only positive values. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n\n\n## Chapter 7\n\n**How could the Kalman Filter benefit aerospace applications?**\n- A) It is used to maintain and organize flight schedules.\n- B) It aids in the calibration of on-board clocks on satellites.\n- C) It provides precise real-time filtering and prediction of spacecraft and satellite trajectories.\n- D) It is only used for communication between spacecraft. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Why is it important to accurately estimate the noise parameters in the Kalman Filter?**\n- A) Incorrect noise parameters can lead to overfitting of the model to noisy data.\n- B) Accurate noise parameter estimation is crucial for the filter's ability to adapt its estimates to the level of uncertainty in both the process dynamics and observations.\n- C) High noise estimates increase the filter’s processing speed.\n- D) Lower noise estimates simplify the mathematical calculations in the filter.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why is the assumption of Gaussian noise important in the operation of the Kalman Filter?**\n- A) It allows the use of binary noise models.\n- B) It simplifies the mathematical representation of the noise.\n- C) Gaussian noise implies that all errors are uniformly distributed.\n- D) It ensures that the state estimation errors are normally distributed, facilitating analytical tractability and optimal estimation.\n\n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**What is the primary concept of the Kalman Filter regarding the use of multiple sources of information?**\n- A) To disregard noisy measurements in favor of a more precise model.\n- B) To combine information from an imprecise model and noisy measurements to optimally estimate the true state of a system.\n- C) To enhance the accuracy of measurements by filtering out model predictions.\n- D) To use only the most reliable source of information while ignoring others.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**In the Kalman Filter, what does the 'predict' step specifically calculate?**\n- A) The certainty of the measurement data.\n- B) The prior estimate of the state before the next measurement is taken into account.\n- C) The exact value of external influences on the system.\n- D) The posterior state estimate.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**In the Kalman Filter, what role does the prior error estimate play during the update phase?**\n- A) It is used to directly correct the system’s model dynamics.\n- B) It determines how much weight to give the new measurement versus the predicted state.\n- C) It serves as a constant factor to maintain stability in the filter's performance.\n- D) It is adjusted to match the measurement noise for consistency.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does the Kalman Filter algorithm utilize these two sources of error during its operation?**\n- A) It ignores these errors to simplify the computations.\n- B) It adjusts the error estimates based solely on the measurement error.\n- C) It combines both errors to calculate the state estimate and update the error covariance.\n- D) It sequentially addresses each error, first correcting for process error, then measurement error.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How does the Kalman Gain affect the outcome of the 'correct' step in the Kalman Filter?**\n- A) A higher Kalman Gain indicates a greater reliance on the model prediction over the actual measurement.\n- B) The Kalman Gain optimizes the balance between the predicted state and the new measurement, updating the state estimate accordingly.\n- C) The Kalman Gain decreases the measurement noise automatically.\n- D) A lower Kalman Gain speeds up the computation by reducing data processing.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What role does measurement innovation play in the Kalman Filter's 'correct' step?**\n- A) It is used to adjust the Kalman Gain to minimize the impact of new measurements.\n- B) It determines how much the estimates should be adjusted, based on the new data received.\n- C) It recalculates the system's baseline parameters without influencing the current state estimate.\n- D) It provides a direct measurement of the system's performance efficiency.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why is the Kalman Filter particularly effective at dealing with partial observations of a system's state?**\n- A) It can operate without any data, relying solely on system models.\n- B) It integrates available partial data with the system's dynamic model to estimate unobserved components.\n- C) It filters out incomplete data to prevent errors.\n- D) It requires full data observation at each step to function correctly.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What theoretical implication does an error covariance matrix $R$ approaching zero have on the measurement process in the Kalman Filter?**\n- A) It implies that the measurements are becoming less reliable and should be disregarded.\n- B) It indicates an increase in measurement noise, requiring more conservative updates.\n- C) It signals that the measurements are becoming non-linear.\n- D) It suggests that the measurements are believed to be almost perfect, with negligible noise.\n\n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**In what scenario would the predicted error covariance $P_t^{-}$ approaching zero be considered ideal in the Kalman Filter application?**\n- A) In highly dynamic systems where measurements are less reliable than the model predictions.\n- B) When the system model and the process noise are perfectly known and constant.\n- C) In systems where no prior knowledge of the state dynamics exists.\n- D) In applications where the measurement noise $R$ is extremely high, making $P_t^{-}$ the primary source of information.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What role does the process noise covariance matrix $Q$ play in the Kalman Filter?**\n- A) It affects how much the state prediction is trusted over the actual measurements.\n- B) It adjusts the measurement model to fit better with observed data.\n- C) It is negligible and typically set to zero to simplify calculations.\n- D) It defines the expected noise or uncertainty in the dynamics of the system being modeled.\n\n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**How does the Kalman Gain influence the Kalman Filter's operation?**\n- A) It determines the rate at which the state estimate converges to the true state.\n- B) It ensures that the filter always trusts the model's predictions over measurements.\n- C) It optimally combines information from the predicted state and the measurement to generate an updated state estimate with minimum error variance.\n- D) It is used to adjust the process noise covariance matrix $Q$ to account for environmental changes.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n\n\n## Chapter 8\n\n**What information does the magnitude of the Fourier transform provide about the time series?**\n- A) The overall trend of the time series\n- B) The strength of different frequencies present in the time series\n- C) The exact timestamps of specific events in the time series\n- D) The predictive accuracy of the time series model \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does the inverse Fourier transform achieve in the context of signal processing?**\n- A) It generates the amplitude spectrum of the signal\n- B) It compresses the signal data for better storage\n- C) It reconstructs the original time-domain signal from its frequency-domain representation\n- D) It converts the phase spectrum into a usable format \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Is the Fourier transform directly computed in practical applications?**\n- A) Yes, it is directly computed as defined mathematically\n- B) No, it is considered too complex for real-time computations\n- C) Yes, but only for very small datasets\n- D) No, it is approximated using other techniques due to efficiency concerns \n\n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**Why is the Fast Fourier Transform preferred over the traditional Fourier Transform in most applications?**\n- A) It operates in real-time\n- B) It requires less computational power and is faster due to reduced complexity\n- C) It provides more detailed frequency analysis\n- D) It is easier to implement in software \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**In the Fourier Transform of a pure sinusoidal function, what indicates the frequency of the sinusoid?**\n- A) The width of the spikes in the frequency domain\n- B) The height of the spikes in the frequency domain\n- C) The position of the spikes along the frequency axis\n- D) The area under the curve in the frequency spectrum \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the Fourier Transform of a Dirac delta function?**\n- A) A single spike at the origin\n- B) A flat line across all frequencies at zero amplitude\n- C) A continuous spectrum across all frequencies\n- D) Symmetric spikes at specific frequencies \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How does the Fourier Transform of a unit step function typically appear in the frequency domain?**\n- A) As a constant amplitude across all frequencies\n- B) As a spike at zero frequency with a symmetric component inversely proportional to frequency\n- C) As increasing amplitudes with increasing frequency\n- D) As decreasing amplitudes with increasing frequency \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**When does spectral leakage typically occur in the Fourier transform process?**\n- A) When the signal is perfectly periodic within the observed time window\n- B) When the length of the data window does not exactly contain an integer number of cycles of the signal\n- C) When the signal has a very low frequency\n- D) When the signal amplitude is very high \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What mathematical expression best describes the linearity property of the Fourier transform?**\n- A) $ F(ax + by) = aF(x) + bF(y) $\n- B) $ F(x + y) = F(x) * F(y) $\n- C) $ F(x + y) = F(x) / F(y) $\n- D) $ F(ax + by) = aF(x) * bF(y) $ \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**If a signal $x(t)$ is shifted in time by $t_0$, what is the effect on its Fourier transform $X(f)$?**\n- A) $X(f)$ is multiplied by $e^{-i2\\pi ft_0}$\n- B) $X(f)$ remains unchanged\n- C) $X(f)$ is multiplied by $e^{i2\\pi ft_0}$\n- D) $X(f)$ is divided by $e^{i2\\pi ft_0}$ \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What is the effect of multiplying two Fourier transforms in the frequency domain?**\n- A) The corresponding time-domain signals are added.\n- B) The corresponding time-domain signals are multiplied.\n- C) The corresponding time-domain signals are convolved.\n- D) The corresponding time-domain signals are subtracted.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What happens to the Fourier transform of a signal when it is integrated in the time domain?**\n- A) The Fourier transform is multiplied by $-j2\\pi f$.\n- B) The Fourier transform is divided by $j2\\pi f$.\n- C) The Fourier transform is differentiated.\n- D) The Fourier transform is multiplied by $f$.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What mathematical relationship does Parseval's theorem establish between a time-domain function and its Fourier transform?**\n- A) The integral of the square of the time-domain function equals the integral of the square of the frequency-domain function multiplied by $2\\pi$.\n- B) The integral of the square of the time-domain function equals the integral of the square of the frequency-domain function divided by $2\\pi$.\n- C) The sum of the squares of a discrete time-domain signal equals the sum of the squares of its discrete Fourier transform divided by the number of samples.\n- D) The integral of the square of the time-domain function equals the integral of the square of the frequency-domain function.\n\n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**How do filters affect a signal in terms of its frequency components?**\n- A) Filters randomly alter the frequencies present in a signal.\n- B) Filters uniformly amplify all frequencies of a signal.\n- C) Filters remove all frequencies from a signal to simplify it.\n- D) Filters allow certain frequencies to pass while blocking others, based on the filter design.\n\n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**How is the transfer function of a filter related to its frequency response?**\n- A) The transfer function, when evaluated on the imaginary axis, gives the frequency response.\n- B) The frequency response is the integral of the transfer function over all frequencies.\n- C) The frequency response is the derivative of the transfer function with respect to frequency.\n- D) The transfer function is a simplified version of the frequency response that omits phase information.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**How does a Bode plot assist in filter design and analysis?**\n- A) It provides a method to directly measure the filter’s resistance and capacitance.\n- B) It allows designers to visually assess how a filter modifies signal amplitude and phase at various frequencies.\n- C) It calculates the exact dimensions needed for filter components.\n- D) It identifies the specific materials required for constructing the filter.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does a low-pass filter benefit digital communications?**\n- A) It encrypts the communication signals.\n- B) It enhances the clarity of digital signals by filtering out high-frequency noise and interference.\n- C) It converts analog signals to digital signals.\n- D) It increases the bandwidth of the communication channel.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the characteristic of a Butterworth filter?**\n- A) It has a rectangular frequency response, making it ideal for time-domain operations.\n- B) It is known for its maximally flat magnitude response in the passband, providing a smooth transition with no ripples.\n- C) It emphasizes certain frequency components using a tapered cosine function.\n- D) It combines characteristics of both rectangular and triangular filters for versatile applications.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the primary function of a high-pass filter?**\n- A) To allow only low-frequency signals to pass and attenuates high-frequency signals.\n- B) To allow only high-frequency signals to pass and attenuates low-frequency signals.\n- C) To amplify all frequencies of a signal equally.\n- D) To stabilize voltage fluctuations within a circuit.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**In audio engineering, what is a typical use of a band-stop filter?**\n- A) To enhance the overall loudness of the audio track.\n- B) To eliminate specific unwanted frequencies, like electrical hum or feedback.\n- C) To synchronize audio tracks by adjusting their frequency content.\n- D) To convert stereo audio tracks into mono.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What role does the Fourier transform play in identifying the main seasonalities in a dataset?**\n- A) It decomposes the dataset into its constituent frequencies, highlighting predominant cycles.\n- B) It directly filters out non-seasonal components, leaving only the main seasonal patterns.\n- C) It amplifies the seasonal fluctuations to make them more detectable by standard algorithms.\n- D) It compresses the data to reduce computational requirements for forecasting.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n\n\n## Chapter 9\n\n**What role do holiday effects play in the Prophet model?**\n- A) They are considered as outliers and are removed from the dataset.\n- B) They are modeled as part of the trend component.\n- C) They are ignored unless specifically included in the model.\n- D) They provide adjustments for predictable events that cause unusual observations on specific days.\n\n```{admonition} Answer\n:class: note, dropdown\nD\n```\n\n**What feature of the Prophet model allows it to adapt to changes in the direction of time-series data trends?**\n- A) The inclusion of a stationary component to stabilize variance\n- B) The use of change points to allow for shifts in the trend\n- C) A constant growth rate applied throughout the model\n- D) Periodic adjustments based on previous forecast errors\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is a key characteristic of using a piecewise linear function for modeling trends in the Prophet model compared to a standard linear function?**\n- A) Piecewise linear functions model trends as constant over time.\n- B) Piecewise linear functions can adapt to abrupt changes in the trend at specific points in time.\n- C) Standard linear functions allow for automatic detection of change points.\n- D) Standard linear functions are more flexible and adapt to non-linear trends.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How are change points typically determined in the Prophet model?**\n- A) Through manual specification by the user.\n- B) By a random selection process to ensure model variability.\n- C) Automatically during model fitting, based on the data's historical fluctuations.\n- D) Using a fixed interval that divides the data series into equal segments.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How does the Logistic growth model handle forecasts for data with inherent upper limits?**\n- A) By using a predefined upper limit known as the carrying capacity.\n- B) By randomly assigning an upper limit based on data variability.\n- C) By continuously adjusting the upper limit as new data becomes available.\n- D) By ignoring any potential upper limits and forecasting based on past growth rates.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**How does saturating growth occur in the Logistic growth model within Prophet?**\n- A) It happens when the growth rate exceeds the carrying capacity.\n- B) It occurs as the time series approaches the carrying capacity, causing the growth rate to slow down.\n- C) It is when the growth rate remains constant regardless of the carrying capacity.\n- D) It is defined as the exponential increase in growth without bounds.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is required to model holidays in the Prophet framework?**\n- A) A list of dates for the holidays must be manually specified.\n- B) Holidays are automatically detected based on the country's standard holiday calendar.\n- C) The user must input the exact dates and duration of each holiday, along with their potential impact on the forecast.\n- D) A statistical test to determine which holidays significantly affect the data.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n\n## Chapter 10\n\n**Which of the following is a characteristic of using window-based methods for time series prediction?**\n- A) They can only use linear models for forecasting\n- B) Utilize a fixed window of data points to make predictions\n- C) Predictions are independent of the forecasting horizon\n- D) Do not require sliding the window to generate new predictions \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is a common limitation of linear models when predicting time series data involving trends and seasonal patterns?**\n- A) They are highly sensitive to outliers\n- B) They require no assumptions about data distribution\n- C) They struggle to model the seasonal variations effectively\n- D) They automatically handle missing data \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What fundamental concept allows neural networks to model non-linear relationships in data?**\n- A) The use of linear activation functions only\n- B) The application of a fixed number of layers\n- C) The integration of non-linear activation functions\n- D) The reduction of dimensions in the input data \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the primary function of the hidden layers in a Multi-Layer Perceptron?**\n- A) To directly interact with the input data\n- B) To apply non-linear transformations to the inputs\n- C) To reduce the dimensionality of the input data\n- D) To categorize input data into predefined classes \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How is the input data typically structured for training an MLP in time series forecasting?**\n- A) As a sequence of random data points\n- B) In chronological order without modification\n- C) Divided into overlapping or non-overlapping windows\n- D) Categorized by the frequency of the data points \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Why is the Multi-Layer Perceptron considered a part of the windowed approaches in time series forecasting?**\n- A) It uses the entire dataset at once for predictions\n- B) It processes individual data points separately\n- C) It analyzes data within specific time frames or windows\n- D) It predicts without regard to temporal sequence \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is one limitation of the windowed approach to time series forecasting related to the use of historical data?**\n- A) It can only use the most recent data points.\n- B) It requires a constant update of historical data.\n- C) It restricts the model to only use data within the window.\n- D) It mandates the inclusion of all historical data. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How do RNNs benefit time series forecasting compared to MLPs?**\n- A) By handling larger datasets more efficiently\n- B) By processing each data point independently\n- C) By capturing temporal dependencies within sequences\n- D) By using fewer parameters and simpler training processes \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**During the training of an RNN, what method is commonly used to update the model's weights?**\n- A) Backpropagation through time\n- B) Forward-only propagation\n- C) Perceptron learning rule\n- D) Unsupervised learning techniques \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**Why might RNNs encounter difficulties in long sequence time series forecasting?**\n- A) They process data too quickly.\n- B) They favor shorter dependencies due to gradient issues.\n- C) They are unable to handle multiple data types.\n- D) They reduce the complexity of the model unnecessarily. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How do Echo State Networks simplify the training process compared to standard Recurrent Neural Networks?**\n- A) By training only the input weights\n- B) By eliminating the need for hidden layers\n- C) By only adapting the output weights\n- D) By using simpler activation functions \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the function of the Readout in a Reservoir Computing model?**\n- A) It serves as the primary memory component.\n- B) It actively modifies the reservoir's weights.\n- C) It is responsible for making final predictions from the reservoir states.\n- D) It generates random weights for the reservoir. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the primary function of the reservoir in Reservoir Computing models?**\n- A) To reduce the dimensionality of the time series data\n- B) To generate a high-dimensional representation of input features\n- C) To directly predict future values of the time series\n- D) To simplify the computational requirements of the network \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why are the dynamical features generated by the reservoir considered general-purpose in Reservoir Computing?**\n- A) They are specifically tailored to one type of time series data.\n- B) They only predict one forecast horizon accurately.\n- C) They adapt to different tasks without retraining the reservoir.\n- D) They require constant updates to remain effective. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the spectral radius of a reservoir in Reservoir Computing?**\n- A) The maximum eigenvalue of the reservoir's weight matrix\n- B) The total number of neurons in the reservoir\n- C) The minimum value required for computational stability\n- D) The learning rate for training the reservoir \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**In a Reservoir with chaotic dynamics, what happens to two different initial states as time progresses?**\n- A) They converge to the same final state quickly.\n- B) They eventually diverge from each other.\n- C) They stabilize at a midpoint between the two states.\n- D) The evolution of one state is completely independent from the evolution of the other.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the purpose of input scaling $\\omega_{\\text{in}}$ in the context of a Reservoir's input weights?**\n- A) To decrease the stability of the reservoir\n- B) To control the impact of input data on the reservoir's state\n- C) To simplify the network architecture\n- D) To enhance the linear behavior of the reservoir \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the impact of hyperparameter settings on the dynamics of a Reservoir?**\n- A) They are irrelevant to how the Reservoir processes inputs.\n- B) They primarily affect the speed of computations rather than accuracy.\n- C) They dictate the internal dynamics and stability of the model.\n- D) They only affect the output layer and not the Reservoir itself. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is Principal Component Analysis (PCA) primarily used for in data analysis?**\n- A) To increase the dimensionality of the dataset\n- B) To classify data into predefined categories\n- C) To reduce the dimensionality of the dataset\n- D) To predict future trends based on past data \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How are principal components selected in PCA?**\n- A) By choosing components with the lowest eigenvalues\n- B) By selecting components that explain the most variance\n- C) Based on the components with the smallest eigenvectors\n- D) Through random selection of the eigenvectors \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**In what way can Principal Component Analysis (PCA) be applied within Reservoir Computing?**\n- A) To increase the size of the reservoir states\n- B) To decrease computational efficiency\n- C) To reduce the dimensionality of the reservoir states\n- D) To introduce more redundancy into the features \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**In what scenario is a Gradient Boost Regression Tree particularly beneficial as a readout for Echo State Networks?**\n- A) When data is predominantly linear and simple\n- B) When minimal computational resources are available\n- C) When dealing with highly non-linear and variable data\n- D) When the model must be trained quickly with few data points \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n\n## Chapter 11\n\n**How is a dynamical system typically defined in the context of time series analysis?**\n- A) A system where output values are independent of previous states.\n- B) A system described by a deterministic process where the state evolves over time in a predictable manner.\n- C) A random process with inputs that are not related to the time variable.\n- D) A static system where the state does not change over time. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**In terms of mathematical modeling, how are continuous dynamical systems typically represented compared to discrete systems?**\n- A) Using difference equations for continuous and differential equations for discrete.\n- B) Using differential equations for continuous and difference equations for discrete.\n- C) Both use differential equations but apply them differently.\n- D) Both use difference equations but under different conditions. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What distinguishes the outcomes of stochastic systems from those of deterministic systems in dynamical modeling?**\n- A) Stochastic systems provide identical outcomes under identical conditions.\n- B) Deterministic systems yield different outcomes under the same initial conditions.\n- C) Stochastic systems may produce different outcomes even under identical initial conditions.\n- D) Both systems are completely predictable and yield the same results every time. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**In terms of system behavior, how do linear and nonlinear dynamical systems differ?**\n- A) Linear systems show exponential growth or decay, nonlinear systems do not.\n- B) Linear systems' outputs are directly proportional to their inputs; nonlinear systems' outputs are not.\n- C) Nonlinear systems are less predictable over time than linear systems.\n- D) Nonlinear systems are always unstable, while linear systems are stable. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What role does the parameter $r$ play in the logistic map related to population growth?**\n- A) It represents the death rate of the population.\n- B) It signifies the population’s initial size.\n- C) It controls the growth rate of the population.\n- D) It is irrelevant to changes in population size. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Why is the logistic map classified as a nonlinear system?**\n- A) It depends solely on linear equations to predict future states.\n- B) It features a quadratic term that determines the rate of change.\n- C) It behaves linearly regardless of parameter values.\n- D) It simplifies all interactions to direct proportional relationships. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What happens when the growth rate $r$ in the logistic map is increased beyond a critical threshold?**\n- A) The system remains in a steady state.\n- B) The system transitions from contractive to chaotic dynamics.\n- C) Population growth becomes linear and predictable.\n- D) The logistic map becomes a linear system. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the characteristic of a two-points attractor in a dynamical system?**\n- A) The system settles into one of two possible stable states.\n- B) The system’s states alternate randomly between two points.\n- C) The system never reaches any of the two points but orbits around them.\n- D) The system remains unstable and does not converge to any point. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What can period-doubling bifurcations indicate about a system’s dynamics?**\n- A) They signal a transition towards simpler, more predictable behavior.\n- B) They show a system is becoming less sensitive to initial conditions.\n- C) They indicate a system’s route to chaotic behavior as parameters vary.\n- D) They reflect a system’s shift to a lower energy state. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**In a system with multiple points attractors, how do different initial conditions affect the outcome?**\n- A) All initial conditions lead to the same attractor point.\n- B) Initial conditions determine which of the several attractor points the system converges to.\n- C) Multiple attractors lead to a chaotic system where outcomes are unpredictable.\n- D) Initial conditions have no influence on the system’s behavior. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What are Lyapunov exponents used for in the analysis of dynamical systems?**\n- A) To measure the rate of separation of infinitesimally close trajectories.\n- B) To calculate the exact future state of chaotic systems.\n- C) To reduce the complexity of modeling chaotic systems.\n- D) To determine the initial conditions of a system. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What is a return map in the context of dynamical systems?**\n- A) A graphical representation of linear system trajectories.\n- B) A tool for measuring the periodicity of a system.\n- C) A plot showing the relationship between sequential points in a time series.\n- D) A method for calculating Lyapunov exponents. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is a difference equation in the context of dynamical systems?**\n- A) An equation that describes changes in continuous systems over infinitesimal time increments.\n- B) An equation that models the discrete steps in which systems evolve over time.\n- C) A method for determining the equilibrium state of a continuous system.\n- D) A technique used exclusively in physical systems to measure differences in state. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What are the Lotka-Volterra equations commonly used to model?**\n- A) The interaction between predator and prey populations in an ecological system.\n- B) The growth patterns of a single species in isolation.\n- C) The economic dynamics between competing businesses.\n- D) Chemical reaction rates in closed systems. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What makes the Lotka-Volterra equations a continuous-time dynamical system?**\n- A) They model population changes at discrete intervals only.\n- B) They are based on continuous changes over time, not just at specific points.\n- C) They predict exact population sizes at fixed times.\n- D) The equations are used for systems that do not evolve over time. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How does the Rössler system exemplify a chaotic dynamical system?**\n- A) By exhibiting low sensitivity to initial conditions.\n- B) Through its linear interaction of variables.\n- C) By showing chaotic behavior when parameters reach certain values.\n- D) It is inherently predictable regardless of parameter settings. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the implication of a zero Lyapunov exponent in a dynamical system?**\n- A) It signals exponential divergence of system trajectories.\n- B) It indicates neutral stability where trajectories neither converge nor diverge.\n- C) It suggests the system will always return to a stable equilibrium.\n- D) It denotes a complete lack of sensitivity to initial conditions. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the phase space of a dynamical system?**\n- A) A graphical representation of all possible system states.\n- B) A specific region where the system's energy is minimized.\n- C) The timeline over which a system's behavior is observed.\n- D) A mathematical model that predicts system failures. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**Why are fractal dimensions important in the analysis of chaotic systems?**\n- A) They help in designing the system’s mechanical structure.\n- B) They are crucial for understanding the complexity and scale properties of chaotic attractors.\n- C) Fractal dimensions are used to simplify the mathematical model of the system.\n- D) They determine the thermal properties of chaotic systems. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the formula relating the ratio $r$, the number of parts $N$, and the dimensionality $D$ in fractal geometry?**\n- A) $N = D^r$\n- B) $D = \\frac{\\log N}{\\log r}$\n- C) $r = D \\times N$\n- D) $D = N \\div r$\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does a non-integer fractal dimension signify about the structure of a fractal?**\n- A) It represents simple, predictable patterns within the fractal.\n- B) It indicates a higher degree of complexity and fine structure at infinitesimal scales.\n- C) Non-integer dimensions are errors in mathematical calculations.\n- D) It shows that fractals are typically three-dimensional. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What does the dimensionality of an attractor reveal about a dynamical system?**\n- A) The precision of measurements in the system.\n- B) The potential energy levels throughout the system’s operation.\n- C) The complexity and predictability of the system’s dynamics.\n- D) The geographical spread of the system. \n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What does it imply when we say a dynamical system is observed partially?**\n- A) It implies complete observation of all variables and interactions within the system.\n- B) Observations are limited to a subset of the system's variables, not capturing the entire state.\n- C) It means observations are made continuously without any interruption.\n- D) The system is only observed at its initial and final states, not during its evolution. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the primary statement of Takens' Embedding Theorem?**\n- A) It states that a fully observable dynamical system can always be understood from partial observations.\n- B) It suggests that a single observed variable is sufficient to reconstruct a dynamical system’s full state under certain conditions.\n- C) It asserts that dynamical systems cannot be understood without complete data.\n- D) It requires continuous observation of all variables in a system for accurate modeling. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What hyperparameters must be specified to construct time-delay embedding vectors?**\n- A) The embedding dimension and the delay time.\n- B) The system's total energy and mass.\n- C) The variables' initial and final values.\n- D) The linear coefficients of the system equations. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What is a potential consequence of setting $\\tau$ too short or too long in Takens' Embedding?**\n- A) Too short or too long $\\tau$ may cause overlap or excessive separation between data points in the embedding, obscuring the system's true dynamics.\n- B) It can change the fundamental properties of the dynamical system.\n- C) The dimensionality of the attractor will decrease.\n- D) It will automatically adjust to the optimal length over time. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What method is used to ascertain the appropriate $m$ for Takens' Embedding?**\n- A) The method of false nearest neighbors is employed to find the smallest $m$ where points that appear close in the embedding are close in the original space.\n- B) Using a complex algorithm that integrates all known variables of the system.\n- C) Setting $m$ based on the total number of observations available.\n- D) By selecting $m$ randomly to ensure a diverse range of behaviors is captured. \n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**How can Takens' Embedding be used in time series forecasting?**\n- A) By predicting the exact future states of a dynamical system.\n- B) Through constructing a phase space that helps infer future states based on past behavior.\n- C) By ensuring all predictions are absolutely deterministic.\n- D) It is used to reduce the dimensionality of the data for easier visualization only. \n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n\n## Chapter 12\n\n**What distinguishes a supervised task like classification from an unsupervised task such as clustering in time series analysis?**\n- A) Supervised tasks use unlabelled data while unsupervised tasks use labelled data.\n- B) Both supervised and unsupervised tasks use labels to guide the learning process.\n- C) Supervised tasks use labels to guide the learning process, while unsupervised tasks do not use any labels.\n- D) Unsupervised tasks require a set decision boundary predefined by the model.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Under which circumstances is it preferable to use F1 score rather than accuracy?**\n- A) When the data set is balanced and model performance is consistent across classes.\n- B) When the data set is imbalanced and there is a need to balance the importance of precision and recall.\n- C) When the classes in the data set are perfectly balanced.\n- D) F1 score should be used only when accuracy is above a certain threshold.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is Normalized Mutual Information (NMI) used for in data analysis?**\n- A) To measure the dependency between variables in regression tasks.\n- B) To evaluate the performance of clustering by comparing the clusters to ground truth classes.\n- C) To assess the accuracy of classification models.\n- D) To determine the linearity of relationships in data.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Which statement best describes the relationship between similarity and dissimilarity measures in clustering algorithms?**\n- A) Similarity measures are recalculated into dissimilarity measures before use.\n- B) They are often used interchangeably with an inverse relationship; high similarity implies low dissimilarity.\n- C) Dissimilarity measures are derived from similarity measures through complex transformations.\n- D) Only dissimilarity measures are valid in statistical analysis.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why do different (dis)similarity measures affect classification outcomes?**\n- A) All (dis)similarity measures produce the same results.\n- B) Different measures may interpret the relationships between data points differently, impacting the classification boundaries.\n- C) Only linear measures affect classification; nonlinear measures do not.\n- D) (Dis)similarity measures are unrelated to classification results.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**In what scenarios is hierarchical clustering particularly useful?**\n- A) When data is linear and simple.\n- B) When the dataset is extremely large and computational resources are limited.\n- C) When exploring data to find inherent structures and relationships at multiple scales.\n- D) It is only useful for numeric data types.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**Why are standard distances like Euclidean distance often unsuitable for time series data?**\n- A) They ignore the temporal dynamics and patterns specific to time series data.\n- B) They calculate distances too quickly, leading to underfitting.\n- C) They are more computationally intensive than specialized time series distances.\n- D) They only work with categorical data.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What defines a multi-variate time series in data analysis?**\n- A) A series that consists of multiple sequences of categorical data points.\n- B) A series that tracks multiple variables or series over time.\n- C) A time series that is derived from a single variable observed at different intervals.\n- D) A series analyzed only through linear regression models.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is Dynamic Time Warping (DTW) and how does it differ from Euclidean distance in analyzing time series?**\n- A) DTW is a method for measuring similarity between two sequences which may vary in speed, aligning them optimally to minimize their distance; Euclidean distance measures static point-to-point similarity.\n- B) DTW uses a complex algorithm that requires more data than Euclidean distance.\n- C) DTW can only be used with linear data, whereas Euclidean distance works with any data type.\n- D) There is no difference; DTW and Euclidean distance are the same.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What is an \"alignment path\" in the context of Dynamic Time Warping (DTW)?**\n- A) A sequence of steps required to set up the DTW algorithm.\n- B) The optimal route through a matrix that minimizes the cumulative distance between two time series.\n- C) The maximum difference measured between two time series.\n- D) A statistical method for estimating the time delay between sequences.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**How is the optimal alignment path determined in Dynamic Time Warping?**\n- A) By randomly selecting paths until a satisfactory alignment is found.\n- B) Through a greedy algorithm that chooses the shortest immediate path.\n- C) Using dynamic programming to efficiently compute the minimal distance.\n- D) By manual adjustment until the sequences are visually aligned.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What are the key properties of Dynamic Time Warping (DTW)?**\n- A) It is sensitive to outliers and noise in the data.\n- B) It is invariant to scaling and rotation of the time series.\n- C) It adjusts for shifts and distortions in the time dimension.\n- D) It requires the time series to be of the same length.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**How can Dynamic Time Warping (DTW) be combined with classifiers like SVC or k-NN for time series analysis?**\n- A) By using the DTW distance matrix as a feature vector directly in classifiers.\n- B) First computing the DTW distance matrix, then using this matrix to measure similarities in the classifier’s training and testing phases.\n- C) Applying DTW after classification to improve the accuracy of SVC or k-NN.\n- D) DTW cannot be combined with these types of classifiers.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What role does kernel-PCA play when combined with DTW in visualizing time series data?**\n- A) It enhances the computational speed of the DTW calculations.\n- B) It simplifies the time series data into a single variable.\n- C) It projects the DTW (dis)similarity matrix into a lower-dimensional space for easier visualization.\n- D) It directly classifies time series data into predefined categories.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is the fundamental concept behind a Gaussian Mixture Model (GMM) in clustering?**\n- A) A model that uses a single Gaussian distribution to represent all data.\n- B) A non-probabilistic model that assigns each data point to a cluster.\n- C) A probabilistic model that assumes each cluster follows a different Gaussian distribution.\n- D) A model that clusters data based on fixed thresholds of similarity.\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is a primary advantage of using an ensemble approach in TCK?**\n- A) It simplifies the model by reducing the number of parameters.\n- B) It improves clustering robustness and accuracy by integrating diverse model perspectives.\n- C) It reduces computational requirements by using a single model.\n- D) It focuses only on the largest cluster, ignoring smaller ones.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What advantage does embedding a time series into a real-valued vector provide?**\n- A) It allows the time series to be processed by traditional data analysis tools that require fixed-length inputs.\n- B) It enhances the temporal resolution of the time series data.\n- C) It preserves the raw format of time series data without any loss.\n- D) It increases the storage requirements for time series data.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What is the primary purpose of the Reservoir module in the Reservoir Computing framework for time series analysis?**\n- A) To directly predict future values in a time series\n- B) To preprocess data by normalizing and cleaning\n- C) To extract and expand dynamic features from the input time series for use in classification and clustering\n- D) To reduce the dimensionality of the input data\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What advantage does a bidirectional Reservoir offer over a standard Reservoir?**\n- A) It captures temporal dependencies more effectively by integrating past and future context.\n- B) It reduces the computational requirements for processing.\n- C) It operates with fewer parameters and simpler configuration.\n- D) It is easier to implement and maintain.\n\n```{admonition} Answer\n:class: note, dropdown\nA\n```\n\n**What characteristic does the Dimensionality Reduction module bring to the Reservoir Computing framework?**\n- A) It decreases the processing speed of the system\n- B) It compresses the high-dimensional data into a more manageable form without significant loss of information\n- C) It increases the number of features for better classification accuracy\n- D) It requires additional external data to function effectively\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the main difference between using Tensor-PCA and traditional PCA for dimensionality reduction in Reservoir Computing?**\n- A) Tensor-PCA does not support multivariate data.\n- B) Tensor-PCA is better suited for handling the multidimensional data structures typical of reservoir states, unlike traditional PCA which is limited to flat data structures.\n- C) Traditional PCA is faster and less complex computationally than Tensor-PCA.\n- D) Traditional PCA can handle larger datasets more efficiently than Tensor-PCA.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**Why does representing time series using the Reservoir model space typically perform better than using just the output model space?**\n- A) Because it includes only the most recent data points, ignoring earlier dynamics.\n- B) It captures a richer and more comprehensive set of dynamic behaviors from the entire reservoir processing.\n- C) The output model space is more computationally intensive, leading to slower performance.\n- D) It uses simpler mathematical models, making it easier to implement.\n\n```{admonition} Answer\n:class: note, dropdown\nB\n```\n\n**What is the purpose of the readout module in the Reservoir Computing framework for multivariate time series?**\n- A) To store the incoming multivariate time series data for processing\n- B) To filter noise from the input data before it enters the reservoir\n- C) To map the time series representation to the desired output\n- D) To increase the computational speed of the reservoir processing\n\n```{admonition} Answer\n:class: note, dropdown\nC\n```\n\n**What is a disadvantage of using Time Series Cluster Kernel (TCK)?**\n- A) It requires large amounts of memory.\n- B) It is limited to linear time series data.\n- C) It cannot handle multivariate data.\n- D) It's computationally intensive.\n\n```{admonition} Answer\n:class: note, dropdown\nD\n```"
},
{
"path": "notebooks/00/resources.md",
"content": "# Resources and acknowledgments\n\nThe following resources served as inspiration and provided very useful content to write some of the sections of the book. \n\n- Intel course on time series [[Link](https://www.intel.com/content/www/us/en/developer/topic-technology/artificial-intelligence/training/course-time-series-analysis.html)].\n- Collection of links for books, articles, frameworks, etc... on time series [[Link](https://github.com/ElizaLo/Time-Series)].\n- A “semi-auto” way to determine parameters for SARIMA model [[Link](https://tsanggeorge.medium.com/a-semi-auto-way-to-determine-parameters-for-sarima-model-74cdee853080)].\n- Book on Time Series analysis (University of California) [[Link](https://stats.libretexts.org/Bookshelves/Advanced_Statistics/Time_Series_Analysis_(Aue))].\n- Lecture on stats models, ESN, and state-space reconstruct [[Link](https://github.com/FilippoMB/lecture_RNN_phase_space)].\n- Time series classification and clustering with Reservoir Computing [Link](https://github.com/FilippoMB/Time-series-classification-and-clustering-with-Reservoir-Computing)\n- Medium article on Fourier transform [[Link](https://medium.com/the-modern-scientist/the-fourier-transform-and-its-application-in-machine-learning-edecfac4133c)]\n- Neptune blog, inspired by the Intel course [[Link](https://neptune.ai/blog/time-series-forecasting#:~:text=Pseudo%2Dadditive%20models%20combine%20the,related%20to%20the%20multiplicative%20model)].\n- Cheat-sheet TS models in Python [[Link](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/)].\n- Time series analysis with Python [[Link](https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython/tree/master)].\n- An Introduction to Kalman Filter [[Link](https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf)].\n- Python library to extract static features from time series data [[Link](https://github.com/fraunhoferportugal/tsfel)].\n- Basic Concepts in Nonlinear Dynamics and Chaos [[Link](https://www.vanderbilt.edu/AnS/psychology/cogsci/chaos/workshop/Workshop.html)].\n- IPython Cookbook, Second Edition (2018) [[Link](https://ipython-books.github.io/121-plotting-the-bifurcation-diagram-of-a-chaotic-dynamical-system/)].\n- Introduction to Taken's Embedding [[link](https://www.kaggle.com/code/tigurius/introduction-to-taken-s-embedding/notebook)].\n- An introduction to Dynamic Time Warping [[link](https://rtavenar.github.io/blog/dtw.html)].\n- An intuitive approach to DTW — Dynamic Time Warping [[link](https://towardsdatascience.com/an-intuitive-approach-to-dtw-dynamic-time-warping-f660ccb77ff4)].\n\n## Acknowledgments\n\nThanks to:\n\n- [Simone Scardapane](https://www.sscardapane.it/) for spotting many typos, giving feedback, and suggestions.\n- Jonas Berg Hansen for giving feedback on the exercises."
},
{
"path": "notebooks/01/introduction_to_time_series.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Introduction to time series analysis\\n\",\n \"\\n\",\n \"![](\\\"media/cover.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"In this lecture we will cover the following topics:\\n\",\n \"\\n\",\n \"- Definition of time series data.\\n\",\n \"- Introduction to time series analysis and application examples.\\n\",\n \"- The main components of a time series.\\n\",\n \"- Time series decomposition.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"#Imports\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import statsmodels.api as sm\\n\",\n \"from statsmodels.tsa.seasonal import seasonal_decompose, STL\\n\",\n \"from scipy.fft import fft\\n\",\n \"np.random.seed(0) # for reproducibility\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Basics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### What is a time series?\\n\",\n \"\\n\",\n \"- A time series is a sequence of data points organized in time order.\\n\",\n \"- Usually, the time signal is sampled at equally spaced points in time.\\n\",\n \"- These can be represented as the sequence of the sampled values.\\n\",\n \"- We refer to this setting as *discrete* time.\\n\",\n \"\\n\",\n \"![](\\\"media/ts_equal.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Irregularly sampled time signals can still be represented as a time series.\\n\",\n \"- It is necessary to encode this additional information into an additional data structure.\\n\",\n \"- We refer to this setting as *continuous* time.\\n\",\n \"\\n\",\n \"![](\\\"media/ts_unequal.png\\\")
\\n\",\n \"\\n\",\n \"- This setting is less common. We focus on discrete time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### What data are represented as time series?\\n\",\n \"\\n\",\n \"- Time series are found in a myriad of natural phenomena, industrial and engineering applications, business, human activities, and so on.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"![](\\\"media/passengers.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"![](\\\"media/co2.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"![](\\\"media/sunspots.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"![](\\\"media/electricity.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"![](\\\"media/water_temps.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Other examples include data from:\\n\",\n \" - Finance: stock prices, asset prices, macroeconomic factors.\\n\",\n \" - E-Commerce: page views, new users, searches.\\n\",\n \" - Business: transactions, revenue, inventory levels.\\n\",\n \" - Natural language: machine translation, chatbots.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Time series analysis\\n\",\n \"\\n\",\n \"The main pruposes of time series analysis are:\\n\",\n \"1. To **understand** and characterize the underlying process that generates the observed data.\\n\",\n \"2. To **forecast** the evolution of the process, i.e., predict the next observed values.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- There are two main different perspectives to look at a time series.\\n\",\n \"- Each perspective leads to different time series analysis approaches\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Statistics perspective\\n\",\n \"\\n\",\n \"- A time series is a sequence of *random variables* that have some correlation or other distributional relationship between them. \\n\",\n \"\\n\",\n \"![](\\\"media/random_var.png\\\")
\\n\",\n \"\\n\",\n \"- The sequence is a realization (observed values) of a stochastic process.\\n\",\n \"- Statistical time series approaches focus on finding the parameters of the stochastic process that most likely produced the observed time series.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Dynamical system perspective\\n\",\n \"\\n\",\n \"- This perspective assumes that there is a system governed by unknown variables $\\\\{x_1, x_2, x_3, \\\\dots ,x_𝑁\\\\}$.\\n\",\n \"- Generally, we only observe one time series $y$ generated by the system.\\n\",\n \"- What can $y$ be?\\n\",\n \" - One of the system variables.\\n\",\n \" - A function $f$ of system variables.\\n\",\n \"- The objective of the analysis is to reconstruct the dynamics of the entire system from $y$.\\n\",\n \"\\n\",\n \"![](\\\"media/partial.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Applications\\n\",\n \"\\n\",\n \"Time series analysis is applied in many real world applications, including\\n\",\n \"- Economic forecasting\\n\",\n \"- Stock market analysis\\n\",\n \"- Demand planning and forecasting\\n\",\n \"- Anomaly detection\\n\",\n \"- … And much more\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Economic Forecasting**\\n\",\n \"\\n\",\n \"- Time series analysis is used in macroeconomic predictions.\\n\",\n \"- World Trade Organization does time series forecasting to predict levels of international trade [[source](https://www.econ-jobs.com/research/36056-Forecasting-international-trade-A-time-series-approach.pdf)].\\n\",\n \"- Federal Reserve uses time series forecasts of the economy to set interest rates [[source](https://www.federalreserve.gov/pubs/feds/2009/200910/200910pap.pdf)].\\n\",\n \"\\n\",\n \"![](\\\"media/economic.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Demand forecasting**\\n\",\n \"\\n\",\n \"- Time series analysis is used to predict demand at different levels of granularity.\\n\",\n \"- Amazon and other e commerce companies use time series modeling to predict demand at a product geography level [[source](https://www.theverge.com/2014/1/18/5320636/amazon-plans-to-ship-your-packages-before-you-even-buy-them)].\\n\",\n \"- Helps meet customer needs (fast shipping) and reduce inventory waste\\n\",\n \"\\n\",\n \"![](\\\"media/ecommerce.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Anomaly detection**\\n\",\n \"\\n\",\n \"- Used to detect anomalous behaviors in the underlying system by looking at unusual patterns in the time series.\\n\",\n \"- Widely used in manufacturing to detect defects and target preventive maintenance [[source](https://papers.phmsociety.org/index.php/phme/article/view/1256/phmec_20_1256)].\\n\",\n \"- With new IoT devices, anomaly detection is being used in machinery heavy industries, such as petroleum and gas [[source](https://arxiv.org/abs/1607.02480)].\\n\",\n \"\\n\",\n \"![](\\\"media/anomaly.png\\\")
\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Time series components\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- A time series is often assumed to be composed of three components:\\n\",\n \" - *Trend*: the long-term direction.\\n\",\n \" - *Seasonality*: the periodic behavior.\\n\",\n \" - *Residuals*: the irregular fluctuations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Trend\\n\",\n \"\\n\",\n \"- Trend captures the general direction of the time series.\\n\",\n \"- For example, increasing number of passengers over the years despite seasonal fluctuations.\\n\",\n \"- Trend can be increasing, decreasing, or constant.\\n\",\n \"- It can increase/decrease in different ways over time (linearly, exponentially, etc…).\\n\",\n \"\\n\",\n \"![](\\\"media/passengers_trend.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's create a trend from scratch to understand how it looks like.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"time = np.arange(144)\\n\",\n \"trend = time * 2.65 +100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"- Previously, we learned that time series is data that is measured at equally-spaced intervals. \\n\",\n \"- We also learned that time series data can be decomposed into three parts: trend, seasonality, and residuals. \\n\",\n \"- Now we'll dig deeper by discussing an important concept called **stationarity**. \\n\",\n \"- Having a firm grasp of this will lay the foundation necessary to understand more advanced modeling strategies that we'll introduce later.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"In this lecture we will cover\\n\",\n \"- Definition of stationarity and examples of common stationary and nonstationary time series.\\n\",\n \"- How to detect stationarity.\\n\",\n \"- The most common ways to transform nonstationary time series into stationary ones.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"from io import BytesIO\\n\",\n \"import requests\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"from scipy.integrate import quad\\n\",\n \"from statsmodels.tsa.arima_process import ArmaProcess\\n\",\n \"from statsmodels.tsa.stattools import adfuller\\n\",\n \"from statsmodels.tsa.seasonal import seasonal_decompose\\n\",\n \"np.random.seed(0) # Reproducibility\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"## Stationary Time Series\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Why is stationarity important?\\n\",\n \"\\n\",\n \"- Stationarity is a fundamental assumption in many time series forecasting models.\\n\",\n \"- Without it many basic time series models would break.\\n\",\n \"- Transformations can be applied to convert a nonstationary time series to a stationary one.\\n\",\n \"- There are advanced time series models that can handle nonstationary data (beyond the scope of this lecture).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"### What is stationarity?\\n\",\n \"\\n\",\n \"- 💡 Intuition: a time series is stationary when there are no changes in the underlying system that generates it.\\n\",\n \"- In the following, we define:\\n\",\n \" - Strict stationarity.\\n\",\n \" - Weak stationarity.\\n\",\n \" - Things to check in practice.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Strict stationarity\\n\",\n \"\\n\",\n \"- A stochastic process $X(t): t \\\\in \\\\mathcal{T}$ is called strictly stationary if, for all $t_1, t_2, \\\\dots, t_n \\\\in \\\\mathcal{T}$ and $h$ such that $t_1+h, t_2+h, \\\\dots, t_n+h \\\\in \\\\mathcal{T}$, it holds that\\n\",\n \"\\n\",\n \"$$\\\\big(X(t_1),X(t_2), \\\\dots, X(t_n)\\\\big) \\\\stackrel{\\\\text{D}}{=} \\\\big(X(t_1+h),X(t_2+h), \\\\dots, X(t_n+h)\\\\big)$$\\n\",\n \"\\n\",\n \"- where $\\\\stackrel{\\\\text{D}}{=}$ indicates that the distributions are equal.\\n\",\n \"- In plain words, the joint distribution of any set of observations in the series remains the same regardless of the time at which the observations are taken.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- Strict stationarity is very difficult to verify, especially from a limited amount of data.\\n\",\n \"- For this reason, one checks the *Weak stationarity*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Weak stationarity\\n\",\n \"\\n\",\n \"- A time series $X$ is weakly stationary if:\\n\",\n \"\\n\",\n \" 1. The mean is constant over time $\\\\mathbb{E}[X(t)] = m, \\\\;\\\\; \\\\forall t \\\\in \\\\mathcal{T}$\\n\",\n \" 2. The variance is finite $\\\\mathbb{E}[X(t)^2] < \\\\infty, \\\\;\\\\; \\\\forall t \\\\in \\\\mathcal{T}$\\n\",\n \" 3. The covariance of $X(t)$ and $X(t+h)$ depends only on $h$, i.e., is shift-invariance in time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- Strict stationarity + condition 2 $\\\\implies$ weak stationarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Things to check in practice.\\n\",\n \"\\n\",\n \"1. Constant mean (no trend).\\n\",\n \"2. Constant variance (no heteroskedasticity).\\n\",\n \"3. Constant autocorrelation structure.\\n\",\n \"4. No periodic component (✋ careful here)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Mean, variance, and periodic component (aka seasonality) should be familiar from the previous lecture. \\n\",\n \"- Autocorrelation may not be. \\n\",\n \"- Let's start with a simple example of a stationary time series.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's create an utility function to make plots.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def run_sequence_plot(x, y, title, xlabel=\\\"Time\\\", ylabel=\\\"Values\\\", ax=None):\\n\",\n \" if ax is None:\\n\",\n \" _, ax = plt.subplots(1,1, figsize=(10, 3.5))\\n\",\n \" ax.plot(x, y, 'k-')\\n\",\n \" ax.set_title(title)\\n\",\n \" ax.set_xlabel(xlabel)\\n\",\n \" ax.set_ylabel(ylabel)\\n\",\n \" ax.grid(alpha=0.3)\\n\",\n \" return ax\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- First, we create a variable `time` that defines equally-spaced time intervals.\\n\",\n \"- Then we'll sample values from a Normal distribution with a mean of 0 and a standard deviation of 1.\\n\",\n \"- Essentially we're creating white noise, which is a stationary time series as it exhibits all the properties required of a stationary time series.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"T = 200\\n\",\n \"time = np.arange(T)\\n\",\n \"stationary = np.random.normal(loc=0, scale=1.0, size=(T))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/periodic_mean.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"#### Time series with trend and seasonality\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"trend_seasonality = trend + seasonality + stationary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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2WY2rQoAHmzZuH69evK53e5+vrq3JfxYoVw9ChQ3HixAmcO3cOgGqfG5lMlu6Iy+HDh/Hq1Sv89NNPX1zna5+lGjVqYO/evfjjjz8U77dcLsfWrVtRpEgR/Pzzzypt25dyDwCrVq1SqZ/PMjqSWqhQIXTq1AnLly9HcnIyWrVqhWLFimVpLCLSHBZSREQA8uXLh6VLl8LNzQ0fPnxAx44dYWVlhbdv3+L69et4+/YtVqxY8dX1W7VqBXt7e1SrVg2WlpZ4/vw5Fi9ejOLFi6N06dIAgCVLlqBOnTqoW7cuBg0ahBIlSiAmJgaPHj3CoUOHVJoZ7N9WrVqF5s2bo2nTpujduzcKFy6MDx8+4O7du7hy5Qp27doFAKhRowZcXFxQqVIlFCxYEHfv3sWWLVtQq1atbxZRwKfrpNLS0nDixAnF1OHApwJr6tSpkMlkaNiwYYaxVqxYEadOncKhQ4dQqFAh5M+fP1Oz2WWmXwCYO3cumjdvDm1t7QwLJHW/HwULFsTo0aMxc+ZM9OvXD506dcLLly/h5eWVqVPpGjduDD09PXTr1g1jx45FYmIiVqxYgcjISJXi+DcPDw+sX78eLVu2xMyZMxWz9n3rCORnUVFRaNCgAbp3746yZcsif/78CAkJwdGjRxVHP1X53Li4uGDjxo0oW7YsKlWqhMuXL2P+/PkoUqSI0riZ+SzNnj0bjRs3RoMGDTB69Gjo6elh+fLluHXrFrZv356po7n/5ujoiIIFC2LgwIGYOnUqdHV1sW3bNly/fl2lfj7Lnz8/ihcvjgMHDsDZ2RlmZmawsLBQKqZHjBiBGjVqAAA2bNiQpXGISMM0O9cFEdHXZWXWviFDhigte/r0qQAg5s+fr7T880xsu3btUloeFBQkWrZsKczMzISurq4oXLiwaNmyZbp2/7VgwQLh6OgoLCwshJ6enihWrJhwd3cXz549SxdP3759ReHChYWurq6wtLQUjo6OYubMmRnG9u/X/jvb2vXr10Xnzp2FlZWV0NXVFTY2NqJhw4Zi5cqVijbjx48X1apVEwULFhT6+vqiZMmS4vfffxfv3r375rYJ8WkWMwsLCwFAvHr1SrH884x0VatWTbfOl96ja9euidq1awsjIyOlmew+z2QWEhKSqe39r6SkJNGvXz9haWkpZDKZACCePn0qhPjyfvFZdt6Pz/vWhg0blPI0e/ZsUbRoUaGnpycqVaokDh06lG5Wt685dOiQqFy5sjAwMBCFCxcWY8aMEUeOHEmXg/r164sKFSqkW/9LOb9z545o3LixMDAwEGZmZsLd3V0cOHAgw7wmJiaKgQMHikqVKgkTExNhaGgoypQpI6ZOnSri4uKU2mbmcxMZGSnc3d2FlZWVMDIyEnXq1BFnzpxJl5vMfpbOnDkjGjZsKIyNjYWhoaGoWbOmOHTokFIbVfar4OBgUatWLWFkZCQsLS1Fv379xJUrV9K9x5mZtU8IIQICAkSVKlWEvr6+APDF2SpLlCghypUrl245EeUOMiH+/86JRERERPRd3LhxA5UrV8ayZcsUE8MQUe7CQoqIiIjoO3n8+DGeP3+OCRMm4MWLF3j06FGGp9YSUc7E6c+JiIiIvpMZM2agcePGiI2Nxa5du1hEEeViPCJFRERERESkIh6RIiIiIiIiUhELKSIiIiIiIhVpvJB69eoVevToAXNzcxgZGeGXX37B5cuXFa8LIeDl5QVbW1sYGhrCyckJt2/fVuojKSkJw4YNg4WFBYyNjdG6dWuEhoZ+700hIiIiIqIfhEZvyBsZGYnatWujQYMGOHLkCKysrPD48WMUKFBA0WbevHlYuHAhNm7ciJ9//hkzZ85E48aNcf/+feTPnx/ApxsOHjp0CH5+fjA3N8eoUaPg4uKCy5cvQ1tbO8M45HI5Xr9+jfz586t8Ez8iIiIiIso7hBCIiYmBra0ttLS+cdxJc7ewEmLcuHGiTp06X31dLpcLGxsbMWfOHMWyxMREYWpqqrjJ5MePH4Wurq7w8/NTtHn16pXQ0tISR48ezVQcL1++FAD44IMPPvjggw8++OCDDz4EAPHy5ctv1hAaPSJ18OBBNG3aFJ06dUJQUBAKFy6MwYMHo3///gCAp0+fIjw8HE2aNFGso6+vj/r16yM4OBgDBgzA5cuXkZKSotTG1tYW9vb2CA4ORtOmTTOM4/ORrZcvX8LExETNW6kauVyOt2/fwtLS8tsVMGUZcywt5ld6zLG0mF/pMcfSYn6lxxxLS9P5jY6ORtGiRRU1wtdotJB68uQJVqxYgZEjR2LChAm4dOkShg8fDn19ffTq1Qvh4eEAAGtra6X1rK2t8fz5cwBAeHg49PT0ULBgwXRtPq//X0lJSUhKSlI8j4mJAQDky5cP+fLlU9v2ZYVcLkdCQgLy5cvHD6ZEmGNpMb/SY46lxfxKjzmWFvMrPeZYWprOr1wuB4AML/nRaCEll8tRrVo1eHt7AwCqVKmC27dvY8WKFejVq5ei3X83QgiR4YZ9q83s2bMxbdq0dMvfvn2LxMREVTdDreRyOaKioiCE4AdTIsyxtJhf6THH0mJ+pcccS4v5lR5zLC1N5/fzQZaMaLSQKlSoEMqXL6+0rFy5ctizZw8AwMbGBsCno06FChVStImIiFAcpbKxsUFycjIiIyOVjkpFRETA0dHxi+N6enpi5MiRiuefD99ZWlrmiFP7ZDIZDxVLiDmWFvMrPeZYWsyv9JhjaTG/0mOOpaXp/BoYGGSqnUYLqdq1a+P+/ftKyx48eIDixYsDAOzs7GBjYwN/f39UqVIFAJCcnIygoCDMnTsXAODg4ABdXV34+/ujc+fOAICwsDDcunUL8+bN++K4+vr60NfXT7dcS0srR3wYZDJZjoklr2KOpcX8So85lhbzKz3mWFrMr/SYY2lpMr+ZHVOjhdTvv/8OR0dHeHt7o3Pnzrh06RJWr16N1atXA/iUQA8PD3h7e6N06dIoXbo0vL29YWRkhO7duwMATE1N4e7ujlGjRsHc3BxmZmYYPXo0KlasiEaNGmly84iIiIiIKI/SaCH166+/Yt++ffD09MT06dNhZ2eHxYsXw9XVVdFm7NixSEhIwODBgxEZGYkaNWrg+PHjSrNoLFq0CDo6OujcuTMSEhLg7OyMjRs3ZuoeUkRERERERKqSCSGEpoPQtOjoaJiamiIqKipHXCMVEREBKysrHiqWCHMsLeZXesyxtJhf6THH0mJ+pcccS0vT+c1sbcB3noiIiIiISEUspIiIiIiIiFSk0WukiIiIiIgob0lOTsbly5dx5swZmJiYwM3NDYaGhpoOS+14RIqIiIiIiLJFLpdjz549aNy4MQoUKABHR0eMGzcOgwYNQrly5eDn54e8NjUDCykiIiIiIsoSIQT27duHKlWqoGPHjggICEBCQgLMzc3Rtm1bFClSBM+fP0e3bt3g6OiI8+fPazpktWEhRUREREREKktKSkKDBg3Qvn173LhxA/nz58ekSZNw69YtREREYN++fbh//z5mzJgBY2NjXLhwAY6OjujatSuePXum6fCzjYUUERERERGpbPHixQgKCoKRkREmTJiAZ8+eYcaMGahQoYJi2nIjIyNMmjQJDx8+hLu7O2QyGXbs2IGyZcti8uTJkMvlGt6KrGMhRUREREREKgkNDcWMGTMAACtWrMCsWbNgZmb21faFChXC2rVrceXKFTRo0ABJSUmYOXMmPDw8cu21UyykiIiIiIhIJaNHj0ZcXBxq166Nnj17Znq9X375BSdOnMD69esBAEuXLsW8efOkClNSLKSIiIiIiCjTAgMDsWPHDmhpacHHxwcymUyl9WUyGfr06YNFixYBAMaPH49NmzZJEaqkWEgREREREVGmpKSkYOjQoQCAQYMG4ZdffslyXx4eHhgzZgwAwN3dHUeOHFFHiN8NCykiIiIiIsqUhQsX4s6dO7CwsFBcI5Udc+bMQc+ePZGWlobBgwcjOTlZDVF+HzqaDoCIiIiIiHK+c+fOYdKkSQCAefPmoWDBgtnuU0tLC+vWrYOBgQHGjBkDPT29XDOTHwspIiIiIiL6prdv36JLly5ITU1Ft27d0Lt3b7X1rauri9WrV6utv++Fp/YREREREdFXpaWlwdXVFa9evULZsmWxevVqlSeYyItYSBERERER0VfNmDED/v7+MDIywu7du5EvXz5Nh5QjsJAiIiIiIqIvOnfuHKZPnw4AWLVqFSpUqKDhiHIOFlJERERERLnErl27UKdOHezdu1fysRISEtC3b18IIdC7d2/06NFD8jFzExZSREREREQ5XFxcHNzd3dG5c2ecO3cOXbt2xYkTJyQdc8qUKXjw4AFsbW0VN8+l/2EhRURERESUQ8nlcpw5cwZVq1bF+vXrIZPJULlyZaSkpKBdu3a4fv26JONevHgRCxcuBPDplL4CBQpIMk5uxkKKiIiIiCgHSUtLw44dO9CrVy8UKlQI9erVw4MHD1C4cGGcPHkSFy9ehJOTE2JiYtCiRQu8ePFCreMnJiaiT58+kMvl6NGjB1xcXNTaf17BQoqIiIiIKAcZMGAAunbtii1btiAiIgL58uWDm5sbbty4AScnJ+jr62Pfvn2wt7fH69ev0bRpUzx58kRt48+cORN3796FtbU1lixZorZ+8xoWUkREREREOcS+ffuwbt06yGQyjBo1CoGBgXj//j02btwIMzMzRbsCBQrgyJEjKFKkCO7du4dq1arh77//zvb49+7dw7x58wAAy5cvVxqTlLGQIiIiIiLKAcLDw/Hbb78BAMaMGYM//vgDTk5O0NPT+2L7IkWK4Pz586hRowYiIyPh4uKCqVOnIi0tLUvjCyEwZMgQpKSkwMXFBe3atcvytvwIWEgREREREWmYEAL9+/fHu3fvUKlSJcW9mzJSpEgRBAUFYfDgwRBCYPr06XBycsLt27dVjmHHjh04efIkDAwM8Oeff0Imk6ncx4+EhRQRERERkYatXbsWf/31F/T09LB161bo6+tnel19fX0sW7YMmzZtgpGREc6ePYtffvkF48ePR1xcXKb6iI6OxsiRIwEAEyZMgJ2dXZa240fCQoqIiIiISIPu3buH33//HQDg7e2NihUrZqmfXr164c6dO2jTpg1SU1Mxd+5c2Nvb49KlS+naxsbG4uzZs7h48SKuX7+OcePGISwsDKVLl8aYMWOytT0/ChZSREREREQakpCQgM6dOyMuLg4NGzZUFFRZVbx4cezfvx8HDhxAsWLF8OzZM9SpUwfLli2DEAJpaWlYu3YtSpYsibp166JmzZr45ZdfsHLlSgCAj48PDAwM1LFpeZ6OpgMgIiIiIvpRjRgxAjdv3oSVlRW2bdsGLS31HOdo3bo16tevj759+2Lv3r0YOnQoTp48iWfPnuHKlSsAACsrKxgZGSExMRHJyclwdXVFkyZN1DL+j4CFFBERERGRBvj6+mLNmjWQyWTYtm0bbGxs1Nq/qakpdu/ejcWLF2Ps2LHYu3evYvnUqVMxdOhQ6OrqqnXMHwkLKSIiIiKi7+zBgwcYMGAAAGDSpElo1KiRJOPIZDL8/vvvqF69OkaOHImqVati2rRpsLKykmS8HwkLKSIiIiL6IcnlcoSGhuLhw4d49OgR7OzsvsupbampqXB1dUVsbCycnJwwdepUycesXbs2Ll68KPk4PxIWUkRERET0Q3n79i28vb2xZs2adNODjx8/Ht7e3pLeQ+mPP/7AP//8A1NTU2zduhXa2tqSjUXS0eisfV5eXpDJZEqPf58bKoSAl5cXbG1tYWho+MWbiyUlJWHYsGGwsLCAsbExWrdujdDQ0O+9KURERESUw0VHR2Pq1KkoWbIkFi9ejLi4OOjq6qJMmTJo0KABAGDOnDkYNmwY5HK5JDHcvn1bcQRqyZIlKFy4sCTjkPQ0Pv15hQoVEBYWpnjcvHlT8dq8efOwcOFC+Pj4ICQkBDY2NmjcuDFiYmIUbTw8PLBv3z74+fnh7NmziI2NhYuLC9LS0jSxOURERESUA71//x6VK1fG9OnTERsbCwcHBxw9ehTx8fG4d+8eTp48iVWrVkEmk2HZsmXo06cPUlNT1RpDamoqevfujeTkZLRs2RK9evVSa//0fWn81D4dHZ0vzlAihMDixYsxceJEtG/fHgCwadMmWFtbw9fXFwMGDEBUVBTWrVuHLVu2KC7Q27p1K4oWLYqAgAA0bdr0u24LEREREeU8Qgj069cPz549Q9GiRbFw4UJ06NAh3el7v/32G4yNjeHm5obNmzcjISEB27ZtU9vMdv8+pe9z0Ua5l8aPSD18+BC2traws7ND165d8eTJEwDA06dPER4ernTBn76+PurXr4/g4GAAwOXLl5GSkqLUxtbWFvb29oo2RERERPRjW716Nfbv3w9dXV0cOHAAHTt2/GoR4+rqit27d0NXVxe7du2Cq6urWo5MXb9+naf05TEaPSJVo0YNbN68GT///DPevHmDmTNnwtHREbdv30Z4eDgAwNraWmkda2trPH/+HAAQHh4OPT09FCxYMF2bz+t/SVJSEpKSkhTPo6OjAXyauUWq82EzSy6XQwih8TjyMuZYWsyv9JhjaTG/0mOOpcX8Krtz5w5+//13AMDs2bNRuXLlDHPTunVr7N69Gx07dsSuXbsAfDrrSUfn009nVXMcFxeHrl27Ijk5GS4uLujRowffn2/Q9D6c2XE1Wkg1b95c8e+KFSuiVq1aKFWqFDZt2oSaNWsCQLq/FgghMjwMmlGb2bNnY9q0aemWv337FomJiapsgtrJ5XJERUVBCKG2O1uTMuZYWsyv9JhjaTG/0mOOpcX8/k9iYiK6du2KhIQE1K9fH926dUNERESm1q1evTrWrVsHd3d37Nq1C/Hx8Zg9ezYsLS1VzvGoUaNw7949WFtbY86cOXj79m12Ny1P0/Q+/O/5GL5F49dI/ZuxsTEqVqyIhw8fom3btgA+HXUqVKiQok1ERITiKJWNjQ2Sk5MRGRmpdFQqIiICjo6OXx3H09MTI0eOVDyPjo5G0aJFYWlpCRMTEzVvlWrkcjlkMhksLS1/+C8/qTDH0mJ+pcccS4v5lR5zLC3m939GjhyJ27dvw9LSEr6+vl+8Lv9bXF1dYWpqio4dO+Lw4cM4ceIE+vbti5EjR6JAgQKZyvHOnTvh6+sLmUyGrVu3oly5ctnZpB+CpvdhAwODTLXLUYVUUlIS7t69i7p168LOzg42Njbw9/dHlSpVAADJyckICgrC3LlzAQAODg7Q1dWFv78/OnfuDAAICwvDrVu3MG/evK+Oo6+vD319/XTLtbS0csQXjkwmyzGx5FXMsbSYX+kxx9JifqXHHEuL+QWOHDmCJUuWAAA2bNgAW1vbLPXTunVrBAQEYNy4cbhw4QKWL1+O1atXY9iwYZg/f/43c/zs2TMMHDgQwKc/5H+eHI0ypsl9OLNjavTTNXr0aAQFBeHp06e4ePEiOnbsiOjoaLi5uUEmk8HDwwPe3t7Yt28fbt26hd69e8PIyAjdu3cHAJiamsLd3R2jRo3CiRMncPXqVfTo0QMVK1bkjkpERET0g3rz5g169+4NABg2bBhatmyZrf7q1auH4OBgBAYGonHjxkhNTcWiRYswZMiQr15PExwcjPr16yMqKgo1a9aEl5dXtmKgnEejR6RCQ0PRrVs3vHv3DpaWlqhZsyYuXLiA4sWLAwDGjh2LhIQEDB48GJGRkahRowaOHz+O/PnzK/pYtGgRdHR00LlzZyQkJMDZ2RkbN27kHaKJiIiIfkByuRy9e/dGREQE7O3tv3mWkipkMhmcnJzg5OSEVatWYdCgQVi1ahViYmKwceNGxRTpcrkcc+fOxeTJk5GWloZSpUrBz89PbVOoU84hE0IITQehadHR0TA1NUVUVFSOuEYqIiICVlZWP/TheCkxx9JifqXHHEuL+ZUecyytHz2/S5YsgYeHBwwMDPDPP/+gQoUKah9DLpcrTu9LTU1FvXr1ULp0aSQkJODhw4cICQkBAHTv3h0rVqzQ+O/L3EbT+3Bma4McdY0UEREREVFWhYaGYty4cQCAhQsXSlJEfda2bVsULlwYnTt3xunTp3H69GnFa4aGhvDx8UGfPn140908jIUUEREREeUJPj4+SEpKQp06dRSTPEipZcuWOH/+PPbv3w89PT0YGRnByMgIjRo1QsmSJSUfnzSLhRQRERER5XpxcXFYvXo1gE8Tmn2vI0G//PILfvnll+8yFuUsLKSIiIiISC3S0tIwd+5cnDlzRmnq6qSkJCQkJCAhIQFlypTBsmXLUKBAAbWOvXnzZkRGRqJUqVJwcXFRa99EX8JCioiIiIiyLSYmBt26dcPhw4e/2e7y5ct4/fo1jh49+sX7emaFXC5X3DNq+PDhnL2ZvgsWUkRERESULaGhoXBxccH169dhYGCA6dOnw9LSEnK5HEII6Ovrw9DQEMnJyRgwYABOnTqFPn36YOvWrWqZle3o0aO4f/8+TExM0KdPHzVsEVHGWEgRERERUZbdvn0bjRs3RlhYGKysrHDw4EHUqFHjq+0tLCzQokULbN++HcWKFcOcOXOyHcPixYsBAP369VO63yiRlH68mwsQERERkVq8f/8eLi4uCAsLQ4UKFXDx4sVvFlEA0LhxY6xbtw4AMHfuXCxbtixbMdy6dQv+/v7Q0tLCsGHDstUXkSpYSBERERGRylJTU9GlSxc8e/YMJUuWRFBQEEqUKJGpdXv16oUZM2YA+HRN04EDB7Icw9ixYwEA7du3z/T4ROrAQoqIiIiIVDZu3DicOHECxsbG2L9/P8zNzVVaf+LEiejfvz/kcjm6deuGixcvqrS+EAKDBw/GkSNHYGBggIkTJ6q0PlF2sZAiIiIiIpVs3boVCxcuBABs2rQJFStWVLkPmUyG5cuXo0WLFkhISICLiwsePXqU6fVnzJiBNWvWQEtLC9u3b+e9nOi7YyFFRERERJkWEBCAfv36AQAmTZqEDh06ZLkvHR0d7NixAw4ODnj37h2aNWuG0NDQb66TmpqKFStWYOrUqQCAZcuWoW3btlmOgSirWEgRERERUaacOXMGbdq0QVJSEtq3b49p06Zlu898+fLhr7/+QokSJfD48WM4Ojrizp07Sm1u3LiB8ePHw8nJCQUKFMDgwYMBfCrkBg4cmO0YiLKC058TERERUYYuXbqEli1bIj4+Hs2bN4evr69a7gEFADY2Njh16hSaNm2K+/fvo06dOjh06BCsrKwwZcoU+Pn5KbXPnz8/Bg0ahOnTp6tlfKKsYCFFRERERN908+ZNNG3aFDExMWjQoAH27NkDfX19tY5RvHhxnDt3Di4uLrhw4QKcnZ2RmpqKtLQ0AECHDh3QvHlz1KxZE2XLloW2trZaxydSFQspIiIiIvoqIQQGDRqEjx8/wtHREQcPHoShoaEkY5mbm+PEiRPo0qUL/vrrLwBAy5YtMXPmTE4mQTkOCykiIiKiHC46Ohrbtm2Ds7Mzfv755+869qlTp3Du3Dno6+tj586dyJcvn6TjGRkZYd++fdi2bRt+/vln1KpVS9LxiLKKhRQRERFRDpaSkoK2bdsiMDAQurq6GD16NCZOnAhjY+PvMv7nG+e6u7ujcOHC32VMHR0duLm5fZexiLKKs/YRERER5VCfbzobGBgIbW1tpKSkYPbs2ShXrhx8fX2RlJQk6fhnz55VFHDjxo2TdCyi3IaFFBEREVEOtXDhQqxduxZaWlo4cOAA9u/fjxIlSuDly5dwdXVFoUKFMGDAAJw+fRpCCLWP//loVO/evVGsWDG190+Um7GQIiIiIsqBDh48iDFjxgD4VFC1bNkSbdq0wZ07dzBt2jTY2toiMjISq1evRv369dG8eXN8+PBBbeNfuHABx48fh7a2Njw9PdXWL1FewUKKiIiIKIdZt24dOnXqBCEEBg4ciOHDhyteMzQ0xJQpU/DixQucOHECffv2haGhIY4dO4bq1avj1q1baonh89GoXr16wc7OTi19EuUlLKSIiIiIcoikpCQMHDgQ/fr1Q3JyMjp06IA///wTMpksXVttbW00bNgQ69atw/nz51GiRAk8fvwYjo6OiqnDs2rDhg34+++/oaWlhQkTJmSrL6K8ioUUERERUQ7w5s0bNGjQAKtWrYJMJsPMmTOxc+dO6OrqZrhu5cqVERISgoYNGyIuLg79+/dH3bp1ceDAAcjlcpXi+Pvvv9G/f38AwPjx4/HTTz9laXuI8joWUkREREQalpiYiFatWuH8+fMoUKAADh8+jIkTJ0JLK/M/1SwsLHDs2DGMGTMGenp6CA4ORtu2bVG+fHkcP348U31cunQJnTp1QlpaGnr27ImZM2dmdZOI8jwWUkREREQaJITAgAEDEBISAjMzM1y4cAHNmzfPUl86OjqYM2cOLl26hHHjxsHU1BT3799HmzZtcOnSpW+u++DBA7Rs2RLx8fFo2rQp1q1b98VTConoExZSRERERBq0ZMkSbN68Gdra2ti5cyfKlCmT7T6tra3h7e2Nly9fomXLlkhMTETr1q3x4sWLL7a/cOEC6tSpg3fv3sHBwQG7d+/O1CmFRD8yFlJEREREGhIQEIBRo0YBABYsWABnZ2e19p8/f35s374dlSpVwps3b9CqVSvExMQotdm9ezcaNGiAt2/fokqVKvj777+RL18+tcZBlBfpaDoAIiIioh/Rhw8f0KVLF8jlcvTu3VtpinN1yp8/Pw4dOoTq1avjxo0baNeuHVq3bg1tbW08e/YMf/zxBwDAxcUF27dvZxFFlEkspIiIiIg0YMmSJfjw4QMqVKiAFStWSHo9UrFixXDgwAE4OTnhxIkTOHHihNLrw4YNw6JFi6CtrS1ZDER5DQspIiIiou/s48ePWLJkCQBg2rRpMDAwkHzMGjVq4Pjx41izZg2Sk5ORlpYGIQRatWoFNzc3yccnymtYSBEREdEP6+TJkwgICICOjg50dHRgaGiITp06oUSJEpKO++effyIqKgoVKlRAu3btJB3r3+rWrYu6det+t/GI8jIWUkRERPRDWr9+Pfr16wchhNLyWbNmYcOGDZIVONHR0Vi0aBEAYPLkySrdK4qIcg4WUkRERPTD2bBhg6KIatWqFYoXL47U1FT8888/+Oeff9C+fXv8/vvvmDNnDvT09NQ6to+PDz5+/IiyZcuiY8eOau2biL6fHPMnkNmzZ0Mmk8HDw0OxTAgBLy8v2NrawtDQEE5OTrh9+7bSeklJSRg2bBgsLCxgbGyM1q1bIzQ09DtHT0RERLnFpk2b4O7uDiEEhgwZggMHDmDp0qVYsWIFgoODMXr0aADAokWL4OTkhJcvX6pt7JiYGCxYsAAAMGnSJE7uQJSL5YhCKiQkBKtXr0alSpWUls+bNw8LFy6Ej48PQkJCYGNjg8aNGyvd/8DDwwP79u2Dn58fzp49i9jYWLi4uCAtLe17bwYRERHlcL6+vujTpw+EEBg0aBCWLl2qNFuerq4u5s+fj/3796NAgQI4f/48qlSpgiNHjqhl/EWLFuHDhw8oXbo0unTpopY+iUgzNF5IxcbGwtXVFWvWrEHBggUVy4UQWLx4MSZOnIj27dvD3t4emzZtQnx8PHx9fQEAUVFRWLduHRYsWIBGjRqhSpUq2Lp1K27evImAgABNbRIRERHlQEeOHIGbmxuEEBg4cCB8fHy+OuV4mzZtcOXKFTg4OOD9+/do0aIFJk2alK0/1IaEhGDGjBkAAC8vL+jo8AoLotxM45/gIUOGoGXLlmjUqBFmzpypWP706VOEh4ejSZMmimX6+vqoX78+goODMWDAAFy+fBkpKSlKbWxtbWFvb4/g4GA0bdr0i2MmJSUhKSlJ8Tw6OhoAIJfLIZfL1b2JKpHL5RBCaDyOvIw5lhbzKz3mWFrMr/Q0kePz58+jQ4cOSE1NRbdu3bB06VJFLF9TvHhxnD59GqNHj8aKFSswa9YsPHjwANu3b1f5nk/R0dHo1q0bUlNT0aFDB8WNeKXAfVh6zLG0NJ3fzI6r0ULKz88PV65cQUhISLrXwsPDAQDW1tZKy62trfH8+XNFGz09PaUjWZ/bfF7/S2bPno1p06alW/727VskJiaqvB3qJJfLERUVBSEEZ/GRCHMsLeZXesyxtJhf6X3vHN+/fx9t27ZFQkICGjRogDlz5uDdu3eZXn/KlCmoWLEiRowYgV27dqFMmTIYMmSISjEMHToUjx8/RuHChTFz5ky8fftW1c3INO7D0mOOpaXp/P77MqJv0Vgh9fLlS4wYMQLHjx//5k3o/vsXHyFEhn8FyqiNp6cnRo4cqXgeHR2NokWLwtLSEiYmJpncAmnI5XLIZDJYWlrygykR5lhazK/0mGNpMb/S+545jo2NRc+ePfHx40fUrFkTBw4cgLGxscr9DBgwQDE5hbe3N+rXr4+GDRtmat0tW7Zgz5490NLSwvbt2/Hzzz+rPL4quA9LjzmWlqbzm9kbZGuskLp8+TIiIiLg4OCgWJaWlobTp0/Dx8cH9+/fB/DpqFOhQoUUbSIiIhRHqWxsbJCcnIzIyEilo1IRERFwdHT86tj6+vrQ19dPt1xLSytHfBhkMlmOiSWvYo6lxfxKjzmWFvMrve+V4/nz5+Ply5ews7PD4cOHkT9//iz3NWjQIISEhGDjxo3o1q0brly5gqJFi361fWpqKnx8fDBhwgQAn66L+l43w+U+LD3mWFqazG9mx9TYO+/s7IybN2/i2rVrike1atXg6uqKa9euoWTJkrCxsYG/v79ineTkZAQFBSmKJAcHB+jq6iq1CQsLw61bt75ZSBEREVHe9+LFC/zxxx8AgAULFsDMzCxb/clkMixfvhxVqlTBu3fv0LFjR0RFRX2xbXBwMBwcHPD7778jISEBzZs3VxRURJQ3aOyIVP78+WFvb6+0zNjYGObm5orlHh4e8Pb2RunSpVG6dGl4e3vDyMgI3bt3BwCYmprC3d0do0aNgrm5OczMzDB69GhUrFgRjRo1+u7bRERERDmHp6cnEhMTUb9+fbRt21YtfRoaGmLPnj1wcHDApUuXYG9vj7Vr1yomuLp9+zZmzZqF7du3AwDMzMwwZ84cuLu788gFUR6j8Vn7vmXs2LFISEjA4MGDERkZiRo1auD48eNKh+UXLVoEHR0ddO7cGQkJCXB2dsbGjRt5gzsiIqIf2MWLF+Hr6wuZTIaFCxeqPMvet9jZ2eHo0aNwdXXFo0eP0KxZM/Tt2xfR0dHYs2cPhBAAAHd3d8yZMwcWFhZqG5uIcg6Z+Pxp/4FFR0fD1NQUUVFROWKyiYiICFhZWfEvVxJhjqXF/EqPOZYW8ys9qXMshEDt2rVx/vx59O7dGxs2bFD7GAAQHx+PCRMmYMmSJUrL27dvj0mTJqFKlSqSjJsR7sPSY46lpen8ZrY24DtPREREavXx40csXrwYly9f1sj4vr6+OH/+PIyMjDBr1izJxjEyMsLixYsRFBSE6tWro1u3brhx4wb27NmjsSKKiL6fHH1qHxEREeUub968QZMmTXDjxg0AQMuWLTF16lT8+uuv32X8+/fvY9CgQQCA8ePHw9bWVvIx69Wrh4sXL0o+DhHlLDwiRURERGrx/Plz1KlTBzdu3ICpqSm0tLRw+PBhVK9eHc2bN4e/vz+kvKIgLi4OHTp0QExMDOrVqwdPT0/JxiIiYiFFRERE2Xbv3j3UqVMHjx49QvHixfHPP//g7t276NWrF7S1tXH06FE0adIE9vb2WL16NRITE9U6vhAC/fv3x+3bt2FjYwM/Pz/o6PDEGyKSDgspIiIiypaoqCg4OzsjNDQU5cqVw7lz5/DTTz/h559/xqZNm3D//n0MGzYM+fLlw507dzBo0CC0a9cOkZGRaoth2bJl2L59O7S1tbFz504UKlRIbX0TEX0JCykiIiLKlgkTJuD169f46aefcPr0aRQuXFjp9VKlSuHPP/9EaGgoFi1aBDMzM1y7dg1NmjTB+/fvsz3+rVu3MHLkSADAvHnzULdu3Wz3SUSUERZSRERElGUXL17EihUrAACrV6/+5j2TTE1N4eHhgZMnT8Lc3BxXrlxBw4YN8fbt2yyPL5fLMWDAAKSkpMDFxQW///57lvsiIlIFCykiIiLKkpSUFPz2228QQqBXr15o0KBBptarWLEi9uzZAxsbG9y4cQN16tTB6tWrs1RQrV69GsHBwciXLx9WrFih1hvvEhF9CwspIiIiypIlS5bgxo0bMDMzwx9//KHSumXKlEFgYCAKFy6MBw8eYMCAAShUqBAaN26MgICATPXx+vVrjBs3DgDg7e2NIkWKqLwNRERZxUKKiIiIVPb8+XNMnToVADB//nxYWlqq3MfPP/+MK1euYM6cOahatSrS0tIQEBCApk2bYtmyZRmuP3z4cERHR6N69eoYPHiwyuMTEWUHCykiIiJSSWJiIjp37oz4+HjUrVsXffr0yXJfVlZWGDduHC5fvoxHjx7Bzc0NcrkcQ4cOxciRI5GWlpZuncjISMyePRt79uyBjo4O1qxZA21t7exsEhGRyniDBSIiIso0IQR+++03XLp0CQULFsSGDRvUdl1SqVKlsGHDBpQpUwYTJkzAokWLcPfuXTRs2BAmJiYwNDTE0aNHsXfvXiQlJQEARo8ejUqVKqllfCIiVbCQIiIiokxbuHAhtmzZAm1tbezatQulSpVSa/8ymQyenp6ws7ODm5sbjh49iqNHj6ZrV7FiRfz2228YNGiQWscnIsosFlJERESUKceOHcPYsWMBfCqonJ2dJRura9euKF26NLZs2YLIyEhERUUhOjoa5cqVQ58+feDg4MAZ+ohIo1hIERERUYZCQ0PRtWtXyOVyuLu7Y9iwYZKP6eDgAAcHB8nHISLKChZSREREuUBCQgJ27dqFyMhIdO3aFdbW1t9tbLlcjj59+uDjx4/49ddfsWzZMh4NIqIfHgspIiKiHOz169dYvnw5Vq1ahXfv3gEAxo4di65du2Lo0KEoUKAAnj9/jufPnyNfvnzo3Lmz2oucFStWICAgAIaGhtiyZQv09fXV2j8RUW6kciH18uVLyGQyxU3vLl26BF9fX5QvXx6//fab2gMkIiL6Ue3fvx9dunRBcnIyAKB48eKwsrJCSEgINm/ejM2bN6dbJzIyEgMHDlRbDA8ePMCYMWMAAPPmzUOZMmXU1jcRUW6m8n2kunfvjsDAQABAeHg4GjdujEuXLmHChAmYPn262gMkIiL6ET158gRubm5ITk5GzZo1sXv3bjx69AiXLl3ChQsX0L17d+jq6sLIyAjlypVDjRo1AACjRo3Cw4cP1RJDamoqevXqhYSEBDRq1Ig3vSUi+heVC6lbt26hevXqAICdO3fC3t4ewcHB8PX1xcaNG9UdHxER0Q8nOTkZXbt2RXR0NBwdHXHmzBl06NABOjqfTiSpUaMGtm3bhvj4eMTGxuLOnTsIDg5Gw4YNER8fj169eiE1NTXbcXh7e+PixYswNTXF+vXroaWl8s8GIqI8S+VvxJSUFMW50QEBAWjdujUAoGzZsggLC1NvdERERD+gCRMmICQkBAULFsT27dsVBdR/6ejoKK6H0tLSwoYNG2BiYoILFy5g3rx52YohMDAQ06ZNAwD4+PigaNGi2eqPiCivUbmQqlChAlauXIkzZ87A398fzZo1A/DpYlhzc3O1B0hERPQj+fvvv7FgwQIAwPr161GsWLFMr1usWDH4+PgAAKZOnYorV65kKYY3b96ge/fuitn6evTokaV+iIjyMpULqblz52LVqlVwcnJCt27dULlyZQDAwYMHFaf8ERERkeoiIiLg5uYGABg+fDjatm2rch89evRAhw4dkJqaigYNGmDVqlWQy+WZXj8tLQ09evRAeHg4KlSooCjMiIhImcqz9jk5OeHdu3eIjo5GwYIFFct/++03GBkZqTU4IiKiH4UQAgMHDsS7d+9QqVKlLJ+aJ5PJsGrVKoSGhuLixYsYOHAgtm3bhtWrV6Ns2bIZrj979mwEBATAyMgIO3fu5P/biYi+IktXjQohcPnyZaxatQoxMTEAAD09PX7ZEhERZZGvry/27dsHXV1dbN68OVv3ajI3N8e5c+ewePFiGBsb48yZM6hcuTI8PDzw9u3bL64jhMCiRYswZcoUAJ/uHVW+fPksx0BElNepXEg9f/4cFStWRJs2bTBkyBDFF/K8efMwevRotQdIRESU171+/RpDhw4FAEyZMkVx2nx2aGtrY8SIEbh9+zZatmyJ5ORkLFmyBKVKlcL06dMRFRWlaJuWloYRI0Zg5MiREELAw8MDvXr1ynYMRER5mcqF1IgRI1CtWjVERkbC0NBQsbxdu3Y4ceKEWoMjIiLK64QQ6N+/Pz5+/Ihq1aph/Pjxau2/ePHi+Ouvv3D8+HFUrVoVMTExmDp1KszNzVG7dm1MnToV7dq1w9KlSwEA8+fPx8KFC9UaAxFRXqTyNVJnz57FuXPnoKenp7S8ePHiePXqldoCIyIi+hFs27YNf//9N/T09LBp06avTnWeXY0bN4azszN27dqFGTNm4Pbt2wgODkZwcDAAQF9fH1u2bEGnTp0kGZ+IKK9R+dtaLpcjLS0t3fLQ0FDkz59fLUERERH9CIQQ8Pb2BvDplD6pr0nS0tJCly5d0KVLFzx//hwBAQEICAjAs2fPsGDBAjg6Oko6PhFRXqLyqX2NGzfG4sWLFc9lMhliY2MxdepUtGjRQp2xERERfTfJycnYt28f2rVrhy5duuDdu3eSj3ns2DHcvXsX+fPnx7BhwyQf79+KFy8Od3d3bN++HefPn2cRRUSkIpWPSC1atAgNGjRA+fLlkZiYiO7du+Phw4ewsLDA9u3bpYiRiIhIMh8+fMCsWbOwefNmpeKpb9++OHToEGQymWRjL1q0CADg7u4OExMTycYhIiL1U7mQsrW1xbVr17B9+3ZcuXIFcrkc7u7ucHV1VZp8goiIKKc7f/48unbtihcvXgAAbGxs0KFDB6xZswaHDx+Gj4+PZEeKbt26hePHj0NLSwvDhw+XZAwiIpJOlq5oNTQ0RN++fdG3b191x0NERCQ5uVyOBQsWYMKECUhNTcVPP/2EhQsXonnz5tDS0kKhQoUwadIkjBkzBvXq1VPLdOT/9fk0+Xbt2sHOzk7t/RMRkbRUvkZq8+bN33yoYsWKFahUqRJMTExgYmKCWrVq4ciRI4rXhRDw8vKCra0tDA0N4eTkhNu3byv1kZSUhGHDhsHCwgLGxsZo3bo1QkNDVd0sIiL6QcTExKBt27YYO3YsUlNT0bVrV1y+fBmtWrVSzJjXt29ftGzZEklJSejatSvi4uLUGkNERAS2bt0KAPj999/V2jcREX0fKh+RGjFihNLzlJQUxMfHQ09PD0ZGRirdwK9IkSKYM2cOfvrpJwDApk2b0KZNG1y9ehUVKlTAvHnzsHDhQmzcuBE///wzZs6cicaNG+P+/fuKGQI9PDxw6NAh+Pn5wdzcHKNGjYKLiwsuX74MbW1tVTePiIjysOfPn6NVq1a4efMm9PX18eeff6J///7proOSyWRYv349fvnlF9y7dw+9e/fG1q1boa+vr5Y4Vq5ciaSkJPz666+c5IGIKLcSavDgwQPh7Owsjh49mu2+ChYsKNauXSvkcrmwsbERc+bMUbyWmJgoTE1NxcqVK4UQQnz8+FHo6uoKPz8/RZtXr14JLS0tlWKJiooSAERUVFS248+utLQ0ERYWJtLS0jQdSp7FHEuL+ZUec5w158+fF1ZWVgKAsLGxEZcuXfpiu3/n9+TJk0JHR0cAEA0aNBAfP37Mdhxv375VxLF9+/Zs95cbcR+WFvMrPeZYWprOb2ZrA7Xc9a906dKYM2cOevTogXv37mWpj7S0NOzatQtxcXGoVasWnj59ivDwcDRp0kTRRl9fH/Xr10dwcDAGDBiAy5cvIyUlRamNra0t7O3tERwcjKZNm35xrKSkJCQlJSmeR0dHA/h0zrxcLs9S/Ooil8shhNB4HHkZcywt5ld6zLHqTp8+jWbNmiEpKQmVK1fGgQMHULRo0S/m8N/5rV+/Pg4dOoROnTohMDAQdevWxV9//YUiRYpkKY6kpCS0a9cOERER+Omnn9CuXbsf8n3kPiwt5ld6zLG0NJ3fzI6rttuna2tr4/Xr1yqvd/PmTdSqVQuJiYnIly8f9u3bh/LlyyvutG5tba3U3traGs+fPwcAhIeHQ09PDwULFkzXJjw8/Ktjzp49G9OmTUu3/O3bt0hMTFR5G9RJLpcjKioKQghoaal8CRtlAnMsLeZXesyxaoQQ+P3335GUlARnZ2esWrUK+vr6iIiI+GL7/+b3l19+wb59++Dq6oqbN2+ievXq6NWrF9q2bYuSJUuqFMewYcNw9uxZmJiYYO3atYiMjFTXZuYq3IelxfxKjzmWlqbzGxMTk6l2KhdSBw8eVHouhEBYWBh8fHxQu3ZtVbtDmTJlcO3aNXz8+BF79uyBm5sbgoKCFK//97x1IUSG9/TIqI2npydGjhypeB4dHY2iRYvC0tJS4/fxkMvlkMlksLS05AdTIsyxtJhf6THHqjl69CiuXbsGQ0NDbN26FVZWVt9s/6X8NmzYEBcuXECLFi1w7949zJ8/H/Pnz0fVqlUxadIktGnTJsM4ZsyYgT179kBbWxu7du1C3bp11bJ9uRH3YWkxv9JjjqWl6fwaGBhkqp3KhVTbtm2Vnn/eyIYNG2LBggWqdgc9PT3FZBPVqlVDSEgIlixZgnHjxgH4dNSpUKFCivYRERGKo1Q2NjZITk5GZGSk0lGpiIiIb168q6+v/8ULhrW0tHLEh0Emk+WYWPIq5lhazK/0mOPMEUJg5syZAIBBgwbBxsYmU+t9Kb92dnb4559/sHv3bvj5+cHf3x9XrlxBp06dsG/fPrRq1UrRVi6XY/Pmzbhy5QrevHmD8PBwnD59GsCnGWv/fUr6j4r7sLSYX+kxx9LSZH4zO6bKkX2+jujzIy0tDeHh4fD19VUqeLJKCIGkpCTY2dnBxsYG/v7+iteSk5MRFBSkKJIcHBygq6ur1CYsLAy3bt3iLEhERISTJ0/i/PnzMDAwwOjRo7Pdn7GxMdzc3HDkyBGEhYWhR48eSEtLQ+fOnRWnpEdGRqJVq1bo06cPli5dip07dyqKqDFjxqB///7ZjoOIiDRPbddIZcWECRPQvHlzFC1aFDExMfDz88OpU6dw9OhRyGQyeHh4wNvbG6VLl0bp0qXh7e0NIyMjdO/eHQBgamoKd3d3jBo1Cubm5jAzM8Po0aNRsWJFNGrUSJObRkREOcD06dMBAL/99pta/tj3b5aWlli/fj0+fPiAv//+Gy4uLli5ciU8PT3x5MkTGBgYYPDgwShWrBisra3x008/oVq1amqNgYiINCdThdS/ryfKyMKFCzPd9s2bN+jZsyfCwsJgamqKSpUq4ejRo2jcuDEAYOzYsUhISMDgwYMRGRmJGjVq4Pjx44p7SAHAokWLoKOjg86dOyMhIQHOzs7YuHEj7yFFRPSDCwoKwunTp6Gnp4exY8dKMoauri527tyJRo0a4cKFC+jSpQsAoESJEti7dy+qVKkiybhERKR5mSqkrl69mqnOMpoE4r/WrVuXYX9eXl7w8vL6ahsDAwMsXboUS5cuVWlsIiLKu4QQitlZ+/Xrh8KFC0s2lrGxMf766y/UqVMH9+7dQ9OmTbFt2zaYm5tLNiYREWlepgqpwMBAqeMgIiJSm40bNyIwMBC6urqKyYukZG5ujgsXLuCff/6Bk5MTz4ogIvoBaPQaKSIiyptSU1Nx6tQpvH79GlWrVkW5cuW+W3Hx+PFjDB8+HMCna6SKFSv2XcY1NTWFs7PzdxmLiIg0L0uFVEhICHbt2oUXL14gOTlZ6bW9e/eqJTAiIspdhBA4ffo0fH19sWfPHrx//17xWv78+eHg4IBChQrByMgIxsbGKF++PNzd3aGjo76/6aWmpqJnz56IjY1FvXr1MGbMGLX1TURE9G8q/9/Lz88PvXr1QpMmTeDv748mTZrg4cOHCA8PR7t27aSIkYiIcoEJEyZgzpw5iueWlpYoU6YMrl69ipiYGJw6dSrdOg8fPsQff/yhthi8vb1x/vx5mJiYYPPmzTzFjoiIJKNyIeXt7Y1FixZhyJAhyJ8/P5YsWQI7OzsMGDBA7VPLEhFR7jB//nxFEdW7d2+4urrCyckJOjo6SEtLw507d3D58mV8/PgR8fHxCAsLg4+PDxYsWIBff/1VMdtddly8eFEx3fny5ctRvHjxbPdJRET0NSoXUo8fP0bLli0BAPr6+oiLi4NMJsPvv/+Ohg0bKmZJIiKiH8PatWsV04vPmzcv3el02traqFixIipWrKi03NjYGHPnzoW7uzsqVKgAe3v7LMfw8eNHdO3aFWlpaejatStcXV2z3BcREVFmaKm6gpmZGWJiYgAAhQsXxq1btwBA8VdGIiL6cezZswcDBgwAAIwbN06la5JmzpyJRo0aIS4uDu3bt8fHjx+zFIMQAu7u7nj27Bns7OywcuXKLPVDRESkikwXUteuXQMA1K1bF/7+/gCAzp07Y8SIEejfvz+6devG2YqIiH4gL168QK9evSCXy9G/f3/Mnj1bpfV1dHSwfft2FCtWDA8fPkT9+vVx6NAhCCFU6mf58uXYu3ev4ua4pqamKq1PRESUFZkupKpWrQoHBweUK1cO3bp1AwB4enpi9OjRePPmDdq3b5/hDXaJiCjvGD16NOLj41G3bl2sWLFC5ZuyA4CFhQX27t0LU1NT3LhxA61bt0a1atUyXVBduXIFI0eOBPDpOq1q1aqpHAMREVFWZLqQOnfuHKpWrYo//vgDpUqVQo8ePRAUFISxY8fi4MGDWLhwIQoWLChlrERElEOcPHkSu3btgpaWFpYuXZqt2fEcHBzw8OFDjBs3DsbGxrhy5Qpat26NAQMGpLvFxmdCCBw4cADt2rVDcnIy2rRpo7h3FBER0feQ6UKqVq1aWLNmDcLDw7FixQqEhoaiUaNGKFWqFGbNmoXQ0FAp4yQiohwiJSVFUbQMGjQIlStXznaflpaWmDNnDp49e4bRo0dDJpNhzZo1aNSoEd6+fatoJ4TAiRMnULNmTbRt2xYvXryAnZ0d1q9fn6UjYkRERFml8mQThoaGcHNzw6lTp/DgwQN069YNq1atgp2dHVq0aCFFjERElIMsW7YMt2/fhrm5uWK6cXWxsLDA/Pnz8ddff8HExARnzpzBr7/+iokTJ6JFixawtrZGo0aNcOnSJRgZGcHT0xOXL1+GmZmZWuMgIiLKSLZuJ1+qVCmMHz8eRYsWxYQJE3Ds2DF1xUVERDnQmzdvMHXqVACf7isoVQHTokULXLhwAa1bt8ajR4/g7e2teE1fXx8DBgyAp6cnbGxsJBmfiIgoI1kupIKCgrB+/Xrs2bMH2tra6Ny5M9zd3dUZGxER5TBz5sxBdHQ0qlatKvl3frly5XDx4kXMmDEDUVFRqFatGhwcHFCpUiUYGhpKOjYREVFGVCqkXr58iY0bN2Ljxo14+vQpHB0dsXTpUnTu3BnGxsZSxUhERDlAbGws1q9fD+DTPaCyM8FEZpmZmWHRokWSj0NERKSqTBdSjRs3RmBgICwtLdGrVy/07dsXZcqUkTI2IiLKQTZv3ozo6GiULl0aTZs21XQ4REREGpXpQsrQ0BB79uyBi4vLd/krJBERpRcVFYW//voL79+/h7W1NYyNjVG0aFG1zJz3LUII+Pj4AACGDBkCLS2V5yoiIiLKUzJdSB08eFDKOIiI6Bv++ecfrFy5Etu3b0d8fHy6152cnDB16lQ4OTlJMv6JEydw9+5d5MuXD71795ZkDCIiotwkW7P2ERGRtIQQ6N69O/z8/BTLypUrBxsbG6SlpSE+Ph7Xr1/HqVOncOrUKdSvXx9//PEHqlWrptY4li5dCgBwc3ODqampWvsmIiLKjXhuBhFRDubn5wc/Pz/o6OjA1dUVZ86cwc2bN+Hr64vAwECEhITg0aNHGDRoEPT09BAUFIQ6derA19dXbTE8ffoUhw4dAgAMHTpUbf0SERHlZiykiIhyqOjoaIwaNQoAMHXqVGzduhV16tSBTCZTalesWDEsX74cjx49QuvWrZGUlARXV1dMmTIFcrk823EsX74cQgg0btwYZcuWzXZ/REREeQELKSKiHGr69OkICwvDTz/9hNGjR2fYvmjRoti3bx/Gjh0LAJgxYwa6deuGhISELMcQEBCAVatWAQCGDRuW5X6IiIjyGhZSREQ50K1bt7B48WIAn65PMjAwyNR6WlpamDt3LtatWwcdHR3s3LkTTk5OCAsLU2n8tLQ0eHl5oUmTJoiJiUGtWrXQokULVTeDiIgoz2IhRUSUwwghMHToUKSlpaFdu3Zo1qyZyn307dsX/v7+MDMzw6VLl1C9enVcu3YtU+u+fv0aTZs2xbRp0yCEQP/+/XHixAne+oKIiOhfWEgREeUwS5YsQVBQEAwNDbFo0aIs9+Pk5ISLFy+iTJkyCA0NRZ06db55K4uEhATMnDkTP//8M06cOAEjIyNs3rwZq1evhqGhYZbjICIiyotYSBER5SA+Pj74/fffAXy6Rqp48eLZ6u+nn37C+fPn4ezsjLi4OLRt2xYLFiyAEELRRi6XY/v27ShTpgwmT56MuLg41KxZEyEhIejZs2e2xiciIsqrWEgREeUQPj4+igkdxo8fr5ixL7sKFiyII0eOYMCAARBCYPTo0ejfvz+Sk5Nx+PBhVK1aFd27d8fLly9RtGhRbN++HcHBwShfvrxaxiciIsqLeENeIqIcYNmyZUpFlLe3d7ppzrNDV1cXK1asQNmyZTFq1CisW7cOBw4cwLt37wAAJiYmGDNmDEaNGsXT+IiIiDKBhRQRkYbdu3cPI0aMACBNEfWZTCaDh4cHSpcuja5du+Ldu3cwMDDAsGHDMG7cOJibm6t9TCIioryKhRQRkYZNmDABaWlpaNWqlWRF1L+1bNkSISEhOHz4MLp16wZbW1tJxyMiIsqLWEgREf3H3bt3sWnTJjRo0ABNmzaVdKzg4GDs27cPWlpamDNnjuRF1Gdly5ZF2bJlv8tYREREeREnmyAi+n8XLlxAu3btUL58ecydOxfNmjXD0KFDkZCQIMl4QgiMHTsWwKf7PnFyByIiotyDhRQR/dCEEDhy5AicnJxQq1Yt7N+/HzKZDLVq1QLwaRKIX3/9FTdv3lT72AcPHsS5c+dgaGgILy8vtfdPRERE0tFoITV79mz8+uuvyJ8/P6ysrNC2bVvcv39fqY0QAl5eXrC1tYWhoSGcnJxw+/ZtpTZJSUkYNmwYLCwsYGxsjNatWyM0NPR7bgoR5TJCCPj5+aFKlSpo0aIFgoKCoKuri759++LOnTsIDg7GkSNHYG1tjdu3b+PXX3/FqVOn1DZ+amoqxo8fDwDw8PBA4cKF1dY3ERERSU+jhVRQUBCGDBmCCxcuwN/fH6mpqWjSpAni4uIUbebNm4eFCxfCx8cHISEhsLGxQePGjRETE6No4+HhgX379sHPzw9nz55FbGwsXFxckJaWponNIqJcYNy4cejWrRuuX78OY2NjjBo1Ck+ePMG6desU1w41a9YMN27cQJMmTZCUlISOHTviyZMnahl/yZIluHfvHszNzTFu3Di19ElERETfkchBIiIiBAARFBQkhBBCLpcLGxsbMWfOHEWbxMREYWpqKlauXCmEEOLjx49CV1dX+Pn5Kdq8evVKaGlpiaNHj2Zq3KioKAFAREVFqXFrsiYtLU2EhYWJtLQ0TYeSZzHH0soN+fXx8REABAAxadIk8f79+2+2j4+PF9WqVRMAhL29vYiOjs7W+Dt37hQymUwAEMuWLVN5/dyQ49yM+ZUecywt5ld6zLG0NJ3fzNYGOWrWvqioKACAmZkZAODp06cIDw9HkyZNFG309fVRv359BAcHY8CAAbh8+TJSUlKU2tja2sLe3h7BwcFfnHErKSkJSUlJiufR0dEAALlcDrlcLsm2ZZZcLocQQuNx5GXMsbRyen4PHTqE4cOHAwBmzJiBCRMmAMA349XX18fevXtRo0YN3Lp1C927d8fevXuhra2t8viBgYHo0aMHhBAYMGAABgwYoHKucnqOczvmV3rMsbSYX+kxx9LSdH4zO26OKaSEEBg5ciTq1KkDe3t7AEB4eDgAwNraWqmttbU1nj9/rmijp6eHggULpmvzef3/mj17NqZNm5Zu+du3b5GYmJjtbckOuVyOqKgoCCGgpcW5QKTAHEsrJ+f32rVr6NatG+RyObp37w53d3dERERkal1dXV2sW7cO7dq1w19//YUOHTpg0KBB+OWXXzI9Zfnt27fRrl07JCcno0WLFpg8eTLevn2r8nbk5BznBcyv9JhjaTG/0mOOpaXp/P77EqJvyTGF1NChQ3Hjxg2cPXs23Wv//ZEihMjwh8u32nh6emLkyJGK59HR0ShatCgsLS1hYmKShejVRy6XQyaTwdLSkh9MiTDH0sqp+Y2JiUG/fv2QkJCApk2bYv369dDV1VWpj6ZNm2Lt2rXo2bMnDh06hEOHDqFChQpwd3fHoEGDoKen99V1Q0JC4OrqipiYGNSvXx+7du2CgYFBlrYlp+Y4r2B+pcccS4v5lR5zLC1N5zez/3/OEYXUsGHDcPDgQZw+fRpFihRRLLexsQHw6ahToUKFFMsjIiIUR6lsbGyQnJyMyMhIpaNSERERcHR0/OJ4+vr60NfXT7dcS0srR3wYZDJZjoklr2KOpZUT8ztjxgy8evUKpUqVwq5du774HZAZPXr0gJ2dHVauXIndu3fj9u3bGDlyJI4dO4bdu3cjX7586dbZvXs3evbsicTERPzyyy/Yv38/jIyMsrU9OTHHeQnzKz3mWFrMr/SYY2lpMr+ZHVOj77wQAkOHDsXevXtx8uRJ2NnZKb1uZ2cHGxsb+Pv7K5YlJycjKChIUSQ5ODhAV1dXqU1YWBhu3br11UKKiH4sd+7cwZIlSwAAS5cuRf78+bPVX+3atbFlyxaEhYXBx8cHRkZGOHbsGJydnfHu3TtFOyEEvL290alTJyQmJqJly5Y4ffo0ChQokK3xiYiISPM0ekRqyJAh8PX1xYEDB5A/f37FNU2mpqYwNDSETCaDh4cHvL29Ubp0aZQuXRre3t4wMjJC9+7dFW3d3d0xatQomJubw8zMDKNHj0bFihXRqFEjTW4eEeUAn/9gk5qaijZt2qB58+Zq67tAgQIYMmQIqlWrhhYtWuDSpUuoW7cuRowYgXPnzuHUqVOKe9qNGDECCxYsyNIEFURERJTzaLSQWrFiBQDAyclJafmGDRvQu3dvAMDYsWORkJCAwYMHIzIyEjVq1MDx48eV/qK8aNEi6OjooHPnzkhISICzszM2btzIHyxEhB07diAwMBAGBgZYvHixJGPUqFEDZ8+eRdOmTXHv3j0MGjRI8ZqBgQEWLFiAwYMHSzI2ERERaYZGCykhRIZtZDIZvLy84OXl9dU2BgYGWLp0KZYuXarG6Igot4uJicGoUaMAABMmTECJEiUkG6tcuXI4d+4c3NzckJqaivr168PJyQm1atXK9vVQRERElPPkiMkmiCjvevXqFUxNTb84CYPUJk+ejNevX6NUqVIYM2aM5OMVLVoUJ0+elHwcIiIi0jxOM0JEklm6dCmKFi0KU1NTVK5cGb/99hv8/Py+yw32zp07hz///BMAsGzZsixPNU5ERET0JSykiEgSS5cuxfDhwxV3Jr9x4wbWrFmDbt26oVmzZnjz5o1kYyckJKBv374QQqBv375o2rSpZGMRERHRj4mFFBGp3dq1a+Hh4QHg0w2wX716hb1792LUqFEwNDSEv78/KleujICAAEnGnzJlCh48eABbW1ssWLBAkjGIiIjox8ZCiojUysfHB5MnTwbwaYKHWbNmwdbWFu3atcMff/yBf/75B/b29njz5g2aNGmC0aNHIy4uTm3jX7hwAQsXLgQArFq1ivdsIiIiIkmwkCIitdm8eTNGjBgB4NORqJkzZ0Imkym1KV++PC5duoTffvsNQggsWLAA5cuXx6FDh7I9flxcHPr06QO5XI4ePXrAxcUl230SERERfQkLKSJSi0OHDqFv374AgAEDBmDGjBnpiqjPDA0NsWrVKvz1118oXrw4Xrx4gdatW6Ndu3aIiIjIcgxDhgzBvXv3YGNjgyVLlmS5HyIiIqKMsJAiomw7ffo0OnfujLS0NPTq1QtTpkz5ahH1by1btsTt27cxfvx46OjoYP/+/XBwcMClS5dUjmHDhg3YtGkTtLS0sH37dpiZmWVlU4iIiIgyhYUUEWXLvXv30KpVKyQmJqJVq1ZYs2YNtLQy/9VibGyM2bNn48qVKyhTpgxCQ0NRt25drFu3LtN93Lx5E0OGDAEATJ8+HU5OTqpuBhEREZFKWEgRUbaMHTsW0dHRqFOnDnbs2AEdnazd57tixYq4ePEiWrdujeTkZPTr1w/u7u6Ijo7+5nrR0dHo1KkTEhIS0LRpU3h6emZpfCIiIiJVsJAioiy7ePEiDh06BC0tLaxbtw6GhobZ6s/U1BT79u1TXF+1fv16VKhQAUePHk3XNiwsDJMmTULJkiVx//59FC5cGFu3blXpaBgRERFRVmXtT8dERIBimvNevXrh559/VkufWlpamDRpEurWrYu+ffviyZMnaN68Obp06YJChQohMjISb968wYkTJ5CSkgIAKFGiBHbu3AkLCwu1xEBERESUERZSRJQlQUFB8Pf3h46ODqZMmaL2/uvXr48bN25g4sSJ+PPPP7Fjx450bRwdHTFy5Ei0adMmy6cUEhEREWUFf3kQkcqEEIqjUf369YOdnZ0k4xgbG2Px4sXo3LkzduzYAQMDA5iZmaFgwYKoWrUqqlWrJsm4RERERBlhIUVEKgsICMCZM2egr6+PiRMnSj6eo6MjHB0dJR+HiIiIKLN4VTYRqSQuLg5jx44FAAwaNAhFihTRcERERERE3x8LKaJc5vXr12jbti0sLCzg6OiIAQMGYNmyZXj16pXkY8fFxaFly5a4du0aTE1NMX78eMnHJCIiIsqJWEgR5SK7d+9GxYoVceDAAbx//x7nz5/H6tWrMXToUJQtWxarVq2CEEKSsWNjY9GiRQsEBQXBxMQEx44dg7W1tSRjEREREeV0LKSIcoG4uDj07t0bnTp1wocPH1ClShWcOHECvr6+8PT0hIODA2JjYzFw4EA0btwYz549U+v4n4uo06dPw8TEBMePH0eNGjXUOgYRERFRbsJCiiiHe/36NerVq4dNmzZBS0sLEyZMwIULF9CwYUN069YN3t7euHjxIhYtWgRDQ0OcOHECFStWhL+/v9piGDNmDM6cOQNTU1P4+/uziCIiIqIfHgspohzsxo0bqFmzJq5cuQILCwsEBgZi1qxZ0NPTU2qnra0NDw8P3LhxA3Xq1EFsbCxatWqFv//+O9sx3Lp1C6tXrwYA7NmzB9WrV892n0RERES5HQspohzK398fderUwcuXL1G2bFlcvHgR9erV++Y6P/30E06cOIG2bdsiKSkJbdu2xYEDB7IcgxACI0eOhFwuR4cOHeDs7JzlvoiIiIjyEt5HiigHevnyJdq3b4/Y2Fg4OTlh7969KFiwYKbW1dPTw86dO+Hq6opdu3ahY8eO6NevH/T09CCXy2FtbY1hw4bB1NQ0w77+/vtv+Pv7Q09PD/PmzcvuZhERERHlGSykiHIYIQSGDh2K2NhYODo64tixY+lO5cuIrq4ufH19Ff9duXKl0usnT57EsWPHoKur+9U+UlJSMGrUKADAiBEjULJkSdU3hoiIiCiPYiFFlMPs27cPBw8ehK6uLlavXq1yEfWZjo4ONm/ejAYNGuDx48fQ0vp0Ju+ff/6JwMBADB48GKtXr4ZMJvvi+itXrsT9+/dhaWmJiRMnZnl7iIiIiPIiFlJEOUhUVBSGDh0KABg3bhwqVKiQrf60tbXRr18/pWWOjo5o3bo11q5di7JlyyqOOn328uVLLFiwAKtWrQIAzJgxI1OnARIRERH9SFhIEeUgEyZMQFhYGEqXLi3ZUaCWLVti4cKF8PDwwJgxYxAfHw9TU1MkJSXh9u3b8PX1RUpKCgCgadOmcHd3lyQOIiIiotyMhRRRDnHhwgWsWLECALBq1SoYGBhINtbw4cNx7949rFy5ElOmTEn3eoMGDeDp6YlGjRp99dQ/IiIioh8ZCymib0hLS4O2trbk4wghMGrUKAgh4ObmhgYNGkg6nkwmw59//glra2tcv34d+vr60NPTg6mpKVxdXVGzZk1JxyciIiLK7VhIEf1HYmIitm/fjiVLluDhw4cYO3Ysxo4dC0NDQ8nGPHToEIKDg2FoaAhvb2/Jxvk3XV1deHl5fZexiIiIiPIa3pCX6P/FxsZi2rRpKFasGPr27Yvr168jPj4eXl5eKFeuHPbu3QshhNrHTU1NhaenJwDAw8MDtra2ah+DiIiIiNSLhRQRgP3796NcuXLw8vLC27dvUbRoUcybNw9bt25F0aJF8fz5c3To0AE9evSAXC5X69ibN2/GnTt3YGZmhrFjx6q1byIiIiKSBgsp+qGFhoaiTZs2aNeuHUJDQ2FnZwc/Pz88efIEY8aMgaurK+7evYvJkycrbm47Y8YMtY2fkJCAqVOnAgAmTpyIAgUKqK1vIiIiIpKORgup06dPo1WrVrC1tYVMJsP+/fuVXhdCwMvLC7a2tjA0NISTkxNu376t1CYpKQnDhg2DhYUFjI2N0bp1a4SGhn7HraDcKiEhAU5OTjh48CB0dHTg6emJW7duoUuXLtDR+d/lg8bGxpg+fbrivkpeXl7p9tWs8vHxQWhoKIoWLYrBgwerpU8iIiIikp5GC6m4uDhUrlwZPj4+X3x93rx5WLhwIXx8fBASEgIbGxs0btwYMTExijYeHh7Yt28f/Pz8cPbsWcTGxsLFxQVpaWnfazMol5o9ezYeP36MwoUL49q1a/D29oaRkdFX2/fp0wfDhw8HAPTs2TNdUa+qFy9eYNasWQA+3fRWyunOiYiIiEi9NFpINW/eHDNnzkT79u3TvSaEwOLFizFx4kS0b98e9vb22LRpE+Lj4+Hr6wsAiIqKwrp167BgwQI0atQIVapUwdatW3Hz5k0EBAR8782hXOTRo0eYN28eAGDJkiWoUKFCptb7448/0KBBA8TGxqJNmzaIiIjI0vhpaWno0aMHoqKiUL16dfTo0SNL/RARERGRZuTYa6SePn2K8PBwNGnSRLFMX18f9evXR3BwMADg8uXLSElJUWpja2sLe3t7RRui/xJCwNPTEykpKWjRosUXC/mv0dXVxc6dO1GiRAk8fvwYtWrVwoMHD1SOYdasWThz5gzy588PX1/f73KvKiIiIiJSnxx7H6nw8HAAgLW1tdJya2trPH/+XNFGT08PBQsWTNfm8/pfkpSUhKSkJMXz6OhoAIBcLlf7jGyqksvlEEJoPI68zNfXF2fPnoWBgQGWLFkCIYRK05qbmZnhyJEjaNGiBZ48eYJatWph7969qFu3bqbWP3v2LKZNmwbg0zVSdnZ2eer95j4sPeZYWsyv9JhjaTG/0mOOpaXp/GZ23BxbSH0mk8mUngsh0i37r4zazJ49W/FD9t/evn2LxMTErAWqJnK5HFFRURBCQEsrxx4wzLXevXuHUaNGAQCGDx+OfPnyZen0vAIFCuDgwYNwc3PDlStX0KRJEyxZsgRt27b95nofP35E9+7dIZfL0aFDBzRp0iTLpwfmVNyHpcccS4v5lR5zLC3mV3rMsbQ0nd9/z8fwLTm2kLKxsQHw6ahToUKFFMsjIiIUR6lsbGyQnJyMyMhIpaNSERERcHR0/Grfnp6eGDlypOJ5dHQ0ihYtCktLS5iYmKh7U1Qil8shk8lgaWnJD6aa7dmzB0OHDsXbt29RqlQpTJkyBYaGhlnuz8rKCkFBQejZsyf279+PQYMGITIyEmPHjv1iIZ+UlIQePXrg1atXKFmyJNauXavx/U0K3IelxxxLi/mVHnMsLeZXesyxtDSd38xOAJZjCyk7OzvY2NjA398fVapUAQAkJycjKCgIc+fOBQA4ODhAV1cX/v7+6Ny5MwAgLCwMt27dUkwk8CX6+vrQ19dPt1xLSytHfBhkMlmOiSUvePv2LYYOHYqdO3cCACpUqIBly5bB0NAw2znOly8fdu/ejbFjx2LhwoWYMGECnj59imXLlkFXV1fRLi0tDT179sSJEyeQL18+7Nq1K0/fM4r7sPSYY2kxv9JjjqXF/EqPOZaWJvOb2TE1WkjFxsbi0aNHiudPnz7FtWvXYGZmhmLFisHDwwPe3t4oXbo0SpcurZieunv37gAAU1NTuLu7Y9SoUTA3N4eZmRlGjx6NihUrolGjRpraLMpB0tLS4OTkhDt37kBbWxuenp6YMGECoqKi1DaGtrY2FixYADs7O4wYMQJr1qzBixcv4OXlhWrVqkFbWxuDBg3Cnj17oKenh/3796Nq1apqG5+IiIiIvj+NFlL//PMPGjRooHj++XQ7Nzc3bNy4EWPHjkVCQgIGDx6MyMhI1KhRA8ePH0f+/PkV6yxatAg6Ojro3LkzEhIS4OzsjI0bN3IWNAIAHDp0CHfu3EHBggUREBCAqlWrSnbh4tChQ1G8eHF07doVx44dw7Fjx2BiYoKyZcvi0qVL0NLSgq+vL5ydnSUZn4iIiIi+H5lQZbqyPCo6OhqmpqaIiorS+DUrcrkcERERsLKyypOHil+9eoWTJ0+ievXqKFOmjOTjOTs74+TJkxg3bhzmzJkDQPocX716FbNmzcLJkycRGRmpWL569Wr0799f7ePlNHl9H84JmGNpMb/SY46lxfxKjzmWlqbzm9naIMdeI0V5x9OnT7Fx40b89ddfuHLlCoBPp2UeP34c1atXl2zc27dv4+TJk9DS0sKgQYMkG+e/qlSpgt27dyMtLQ1Xr17FqVOnUKJECXTs2PG7xUBERERE0mIhRZJJTU3F4sWLMXnyZMW08p9nYImIiEDjxo1x/Phx1KhRQ5LxfXx8AABt2rRB8eLFJRnjW7S1tVGtWjVUq1btu49NRERERNLisUiSxM2bN+Ho6IgxY8YgMTER9erVw4YNGxAeHo7Hjx+jbt26iI6ORpMmTXDhwgW1j//x40ds3rwZADBs2DC1909EREREPzYWUqR2J06cgIODA0JCQmBqaoq1a9fi1KlT6N27N6ysrJAvXz78/fffqFevnqKYOnr0qFpjWL9+PeLj42Fvbw8nJye19k1ERERExEKK1CosLAzdu3dHSkoKmjVrhjt37sDd3T3dDWrz5cuHw4cPo379+oiJiUGLFi0wbdo0tcyol5aWhmXLlgH4dDTqSzfHJSIiIiLKDhZSpDZpaWlwdXVFREQEKlWqhL1798LW1var7fPly4djx45hwIABEELAy8sLLVu2xIcPH7IVx6ZNm/DkyRMUKFAArq6u2eqLiIiIiOhLWEiR2syYMQOBgYEwNjbGzp07YWhomOE6+vr6WLlyJTZu3AgDAwMcPXoUderUwcePH1UeXwiB+fPnw93dHQAwZMgQGBsbq9wPEREREVFGWEiRWpw8eRLTp08HAKxatUrle0S5ubnhwoULKFy4MO7evYsuXbogNTU10+unpKRgwIABGDt2LIBPN8f18vJSKQYiIiIiosxiIUXZ9u7dO7i6ukIIAXd39yyfTle5cmUcOnQIRkZGOH78OEaOHJmp9cLDw9GsWTOsWbMGMpkMS5YswdKlS6Gjw9n9iYiIiEgaLKQoW4QQGDBgAMLDw1GuXDn8+eef2eqvSpUq2LJlCwBg6dKlWLFixTfbHz9+HJUrV8bJkydhbGyMAwcOYPjw4dmKgYiIiIgoIyykKFs2btyIvXv3QldXF1u3boWRkVG2+2zfvj1mzZoF4NOse4cPH07XJjk5GWPHjkXTpk0RERGBihUr4tKlS2jVqlW2xyciIiIiyggLKcqyJ0+eKI7+zJgxA1WrVlVb356enujZsyfS0tLQvn17pWLqzZs3cHZ2xvz58wEAgwcPxsWLF1G+fHm1jU9ERERE9C28iISyJDU1FT179kRsbCzq1q2L0aNHq7V/mUyGdevWISEhAbt370a7du2wd+9eFCpUCG3btkVoaChMTEywceNGtGvXTq1jExERERFlhIVULpWamopdu3bh/fv3sLKygqWlJcqUKfPN+zap05w5cxAcHAwTExNs3rwZ2traah9DV1cXvr6+kMlk2LVrF9q3bw9tbW0kJiaiTJkyOHDggMqzAxIRERERqQMLqVwoNTUVPXr0wI4dO5SWfy48OnbsKOn4ISEhiqnFly1bhhIlSkg2lq6uLrZt2wYA2LVrF1JSUtCyZUts27YNpqamko1LRERERPQtLKRymdTUVLi6umLnzp3Q0dFBy5Yt8eHDB4SGhuLp06fo3bs3KlSogHLlykkyflxcHHr06IG0tDR07tw5y1Odq+JzgWhvbw8TExMMGzZMkiNgRERERESZxUIqF0lJSYGrqyt27doFXV1d7Nq1C23atAHwqcBq0qQJAgMD0a5dO1y6dAkmJiZqj2H06NF48OABChcujBUrVkAmk6l9jC/R0dHBlClTvstYREREREQZ4ax9ucTnm91+LqL27NmjKKKAT4WGn58fChcujPv376Nv374QQqg1hr/++gsrV64EAGzatAlmZmZq7Z+IiIiIKLdgIZVLbN68GVu2bIGOjg727t37xfslWVlZYffu3YpCa9SoUfjw4YNaxo+MjES/fv0AAL///jucnZ3V0i8RERERUW7EQioXePz4MYYOHQoAmDZtGlxcXL7atmbNmliyZAkAYNGiRShSpAj69++PGzduZCuGsWPH4s2bNyhbtiy8vb2z1RcRERERUW7HQiqH+3xdVGxsLOrVq4dx48ZluM7AgQOxZcsWVKpUCQkJCVi7di0qV66MNWvWZCmG06dPY+3atQCANWvWwMDAIEv9EBERERHlFSykcrjp06fj4sWLKFCgALZs2ZKp2epkMhl69OiBa9eu4fTp04prqQYNGoSAgACVxk9KSsJvv/0GAPjtt99Qp04d1TeCiIiIiCiPYSGVg506dUpxGt2qVatQrFgxldaXyWSoW7cu9u3bp5iyvGPHjrh7926m+5gzZw7u378PGxsbzJ07V6XxiYiIiIjyKhZSOdSLFy/QqVMnyOVy9OnTB507d85yXzKZDGvXrkXt2rURFRUFFxcXvH37NsP1Lly4oCjklixZggIFCmQ5BiIiIiKivISFVA6UkJCADh064N27d6hSpQqWLVuW7T719fWxb98+lCxZEk+ePIGzszPOnj37xbYfP37EsGHDULt2bSQnJ6Nly5bo1KlTtmMgIiIiIsoreEPeHEYIgXHjxuHKlSuwsLDAvn37YGhoqJa+LS0t8ddff6FOnTq4efMm6tatiw4dOmDmzJnQ1tbG48ePcfv2bcyfPx9v3rwBAHTv3h1Lly79bjfeJSIiIiLKDVhI5TA+Pj7YtWsXtLW1sXPnThQvXlyt/ZcrVw63b9/GlClTsG7dOuzZswd79uxJ165MmTJYvnw5GjZsqNbxiYiIiIjyAp7al4O8ePECY8aMAQDMmzcPDRo0kGQcGxsbrF69GtevX0fTpk0BAIaGhrC3t0ebNm2wcOFCXL9+nUUUEREREdFX8IhUDlKsWDH4+fnhwIEDGDFihOTj2dvb4+jRo4iLi4ORkRFP3yMiIiIiyiQWUjlM27Zt4ejo+F2LGmNj4+82FhERERFRXsBT+4iIiIiIiFTEQoqIiIiIiEhFLKSIiIiIiIhUlGcKqeXLl8POzg4GBgZwcHDAmTNnNB0SERERERHlUXmikNqxYwc8PDwwceJEXL16FXXr1kXz5s3x4sULTYdGRERERER5UJ4opBYuXAh3d3f069cP5cqVw+LFi1G0aFGsWLFC06EREREREVEelOsLqeTkZFy+fBlNmjRRWt6kSRMEBwdrKCoiIiIiIsrLcv19pN69e4e0tDRYW1srLbe2tkZ4ePgX10lKSkJSUpLieXR0NABALpdDLpdLF2wmyOVyCCE0HkdexhxLi/mVHnMsLeZXesyxtJhf6THH0tJ0fjM7bq4vpD777w1shRBfvant7NmzMW3atHTL3759i8TEREniyyy5XI6oqCgIIaCllesPGOZIzLG0mF/pMcfSYn6lxxxLi/mVHnMsLU3nNyYmJlPtcn0hZWFhAW1t7XRHnyIiItIdpfrM09MTI0eOVDyPiopCsWLFoK+vDwMDA0njzYhcLkdsbCwMDAz4wZQIcywt5ld6zLG0mF/pMcfSYn6lxxxLS9P5TU5OBvDpwMy35PpCSk9PDw4ODvD390e7du0Uy/39/dGmTZsvrqOvrw99fX3F88+n9hUvXlzaYImIiIiIKFeIiYmBqanpV1/P9YUUAIwcORI9e/ZEtWrVUKtWLaxevRovXrzAwIEDM7W+ra0tXr58ifz583/1dMDvJTo6GkWLFsXLly9hYmKi0VjyKuZYWsyv9JhjaTG/0mOOpcX8So85lpam8yuEQExMDGxtbb/ZLk8UUl26dMH79+8xffp0hIWFwd7eHn///XemjzBpaWmhSJEiEkepGhMTE34wJcYcS4v5lR5zLC3mV3rMsbSYX+kxx9LSZH6/dSTqszxRSAHA4MGDMXjwYE2HQUREREREPwBeHUdERERERKQiFlI5jL6+PqZOnao0GQapF3MsLeZXesyxtJhf6THH0mJ+pcccSyu35FcmMprXj4iIiIiIiJTwiBQREREREZGKWEgRERERERGpiIUUERERERGRilhIERERERERqYiFVA6zfPly2NnZwcDAAA4ODjhz5oymQ8qVZs+ejV9//RX58+eHlZUV2rZti/v37yu16d27N2QymdKjZs2aGoo4d/Hy8kqXOxsbG8XrQgh4eXnB1tYWhoaGcHJywu3btzUYce5TokSJdDmWyWQYMmQIAO6/qjp9+jRatWoFW1tbyGQy7N+/X+n1zOyzSUlJGDZsGCwsLGBsbIzWrVsjNDT0O25FzvatHKekpGDcuHGoWLEijI2NYWtri169euH169dKfTg5OaXbr7t27fqdtyRnymgfzsx3Avfhb8sox1/6TpbJZJg/f76iDffhr8vMb7Pc9l3MQioH2bFjBzw8PDBx4kRcvXoVdevWRfPmzfHixQtNh5brBAUFYciQIbhw4QL8/f2RmpqKJk2aIC4uTqlds2bNEBYWpnj8/fffGoo496lQoYJS7m7evKl4bd68eVi4cCF8fHwQEhICGxsbNG7cGDExMRqMOHcJCQlRyq+/vz8AoFOnToo23H8zLy4uDpUrV4aPj88XX8/MPuvh4YF9+/bBz88PZ8+eRWxsLFxcXJCWlva9NiNH+1aO4+PjceXKFUyePBlXrlzB3r178eDBA7Ru3Tpd2/79+yvt16tWrfoe4ed4Ge3DQMbfCdyHvy2jHP87t2FhYVi/fj1kMhk6dOig1I778Jdl5rdZrvsuFpRjVK9eXQwcOFBpWdmyZcX48eM1FFHeERERIQCIoKAgxTI3NzfRpk0bzQWVi02dOlVUrlz5i6/J5XJhY2Mj5syZo1iWmJgoTE1NxcqVK79ThHnPiBEjRKlSpYRcLhdCcP/NDgBi3759iueZ2Wc/fvwodHV1hZ+fn6LNq1evhJaWljh69Oh3iz23+G+Ov+TSpUsCgHj+/LliWf369cWIESOkDS4P+FJ+M/pO4D6smszsw23atBENGzZUWsZ9OPP++9ssN34X84hUDpGcnIzLly+jSZMmSsubNGmC4OBgDUWVd0RFRQEAzMzMlJafOnUKVlZW+Pnnn9G/f39ERERoIrxc6eHDh7C1tYWdnR26du2KJ0+eAACePn2K8PBwpX1ZX18f9evX576cRcnJydi6dSv69u0LmUymWM79Vz0ys89evnwZKSkpSm1sbW1hb2/P/TqLoqKiIJPJUKBAAaXl27Ztg4WFBSpUqIDRo0fzSLYKvvWdwH1Yvd68eYPDhw/D3d093WvchzPnv7/NcuN3sc53H5G+6N27d0hLS4O1tbXScmtra4SHh2soqrxBCIGRI0eiTp06sLe3Vyxv3rw5OnXqhOLFi+Pp06eYPHkyGjZsiMuXL+f4O2lrWo0aNbB582b8/PPPePPmDWbOnAlHR0fcvn1bsb9+aV9+/vy5JsLN9fbv34+PHz+id+/eimXcf9UnM/tseHg49PT0ULBgwXRt+B2tusTERIwfPx7du3eHiYmJYrmrqyvs7OxgY2ODW7duwdPTE9evX1ec2kpfl9F3Avdh9dq0aRPy58+P9u3bKy3nPpw5X/ptlhu/i1lI5TD//msz8GlH++8yUs3QoUNx48YNnD17Vml5ly5dFP+2t7dHtWrVULx4cRw+fDjdFyMpa968ueLfFStWRK1atVCqVCls2rRJcXEz92X1WbduHZo3bw5bW1vFMu6/6peVfZb7tepSUlLQtWtXyOVyLF++XOm1/v37K/5tb2+P0qVLo1q1arhy5QqqVq36vUPNVbL6ncB9OGvWr18PV1dXGBgYKC3nPpw5X/ttBuSu72Ke2pdDWFhYQFtbO101HRERka4yp8wbNmwYDh48iMDAQBQpUuSbbQsVKoTixYvj4cOH3ym6vMPY2BgVK1bEw4cPFbP3cV9Wj+fPnyMgIAD9+vX7Zjvuv1mXmX3WxsYGycnJiIyM/GobylhKSgo6d+6Mp0+fwt/fX+lo1JdUrVoVurq63K+z4L/fCdyH1efMmTO4f/9+ht/LAPfhL/nab7Pc+F3MQiqH0NPTg4ODQ7pDv/7+/nB0dNRQVLmXEAJDhw7F3r17cfLkSdjZ2WW4zvv37/Hy5UsUKlToO0SYtyQlJeHu3bsoVKiQ4pSGf+/LycnJCAoK4r6cBRs2bICVlRVatmz5zXbcf7MuM/usg4MDdHV1ldqEhYXh1q1b3K8z6XMR9fDhQwQEBMDc3DzDdW7fvo2UlBTu11nw3+8E7sPqs27dOjg4OKBy5coZtuU+/D8Z/TbLld/F3316C/oqPz8/oaurK9atWyfu3LkjPDw8hLGxsXj27JmmQ8t1Bg0aJExNTcWpU6dEWFiY4hEfHy+EECImJkaMGjVKBAcHi6dPn4rAwEBRq1YtUbhwYREdHa3h6HO+UaNGiVOnToknT56ICxcuCBcXF5E/f37Fvjpnzhxhamoq9u7dK27evCm6desmChUqxNyqKC0tTRQrVkyMGzdOaTn3X9XFxMSIq1eviqtXrwoAYuHCheLq1auKGeMys88OHDhQFClSRAQEBIgrV66Ihg0bisqVK4vU1FRNbVaO8q0cp6SkiNatW4siRYqIa9euKX0vJyUlCSGEePTokZg2bZoICQkRT58+FYcPHxZly5YVVapUYY7Ft/Ob2e8E7sPfltH3hBBCREVFCSMjI7FixYp063Mf/raMfpsJkfu+i1lI5TDLli0TxYsXF3p6eqJq1apK03VT5gH44mPDhg1CCCHi4+NFkyZNhKWlpdDV1RXFihUTbm5u4sWLF5oNPJfo0qWLKFSokNDV1RW2traiffv24vbt24rX5XK5mDp1qrCxsRH6+vqiXr164ubNmxqMOHc6duyYACDu37+vtJz7r+oCAwO/+J3g5uYmhMjcPpuQkCCGDh0qzMzMhKGhoXBxcWHO/+VbOX769OlXv5cDAwOFEEK8ePFC1KtXT5iZmQk9PT1RqlQpMXz4cPH+/XvNblgO8a38ZvY7gfvwt2X0PSGEEKtWrRKGhobi48eP6dbnPvxtGf02EyL3fRfLhBBCooNdREREREREeRKvkSIiIiIiIlIRCykiIiIiIiIVsZAiIiIiIiJSEQspIiIiIiIiFbGQIiIiIiIiUhELKSIiIiIiIhWxkCIiIiIiIlIRCykiIvpheHl54ZdfftF0GERElAfwhrxERJQnyGSyb77u5uYGHx8fJCUlwdzc/DtFRUREeRULKSIiyhPCw8MV/96xYwemTJmC+/fvK5YZGhrC1NRUE6EREVEexFP7iIgoT7CxsVE8TE1NIZPJ0i3776l9vXv3Rtu2beHt7Q1ra2sUKFAA06ZNQ2pqKsaMGQMzMzMUKVIE69evVxrr1atX6NKlCwoWLAhzc3O0adMGz549+74bTEREGsVCioiIfmgnT57E69evcfr0aSxcuBBeXl5wcXFBwYIFcfHiRQwcOBADBw7Ey5cvAQDx8fFo0KAB8uXLh9OnT+Ps2bPIly8fmjVrhuTkZA1vDRERfS8spIiI6IdmZmaGP//8E2XKlEHfvn1RpkwZxMfHY8KECShdujQ8PT2hp6eHc+fOAQD8/PygpaWFtWvXomLFiihXrhw2bNiAFy9e4NSpU5rdGCIi+m50NB0AERGRJlWoUAFaWv/7u6K1tTXs7e0Vz7W1tWFubo6IiAgAwOXLl/Ho0SPkz59fqZ/ExEQ8fvz4+wRNREQax0KKiIh+aLq6ukrPZTLZF5fJ5XIAgFwuh4ODA7Zt25auL0tLS+kCJSKiHIWFFBERkQqqVq2KHTt2wMrKCiYmJpoOh4iINITXSBEREanA1dUVFhYWaNOmDc6cOYOnT58iKCgII0aMQGhoqKbDIyKi74SFFBERkQqMjIxw+vRpFCtWDO3bt0e5cuXQt29fJCQk8AgVEdEPhDfkJSIiIiIiUhGPSBEREREREamIhRQREREREZGKWEgRERERERGpiIUUERERERGRilhIERERERERqYiFFBERERERkYpYSBEREREREamIhRQREREREZGKWEgRERERERGpiIUUERERERGRilhIERERERERqYiFFBERERERkYr+DyZm4aCl6i3BAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"![](\\\"media/int.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- This, gives us:\\n\",\n \"\\n\",\n \"$$\\\\text{Var}(\\\\sin(\\\\lambda t + \\\\psi)) = \\\\mathbb{E}[\\\\sin^2(\\\\lambda t + \\\\psi)] = \\\\frac{1}{2\\\\pi}\\\\pi = \\\\frac{1}{2}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- It is easy to see that $\\\\text{Var}[\\\\mu + R\\\\sin(\\\\lambda t + \\\\psi)]$ is $\\\\frac{R^2}{2}$.\\n\",\n \"- We conclude that the variance is finite and constant.\\n\",\n \"- Also condition #2 is satisfied ✌\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4.499999999999999\\n\"\n ]\n }\n ],\n \"source\": [\n \"R = 3\\n\",\n \"lamb = 6.5\\n\",\n \"t = 4\\n\",\n \"\\n\",\n \"def func_sin2(psi):\\n\",\n \" return ((R*np.sin(lamb*t + psi))**2) / (2*np.pi)\\n\",\n \"\\n\",\n \"# Perform the numerical integration\\n\",\n \"variance, _ = quad(func_sin2, -np.pi, np.pi)\\n\",\n \"print(variance)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- The autocovariance function for a lag $h$ is defined as: \\n\",\n \"\\n\",\n \"$$\\\\text{Cov}(X(t), X(t+h)) = \\\\mathbb{E}[(X(t) - \\\\mu)(X(t+h) - \\\\mu)]$$\\n\",\n \"\\n\",\n \"- Since $X(t) = \\\\mu + R \\\\sin(\\\\lambda t + \\\\psi)$, the autocovariance becomes: \\n\",\n \"\\n\",\n \"$$\\\\text{Cov}(X(t), X(t+h)) = \\\\mathbb{E}\\\\left[\\\\left(R \\\\sin(\\\\lambda t + \\\\psi)\\\\right)\\\\left(R \\\\sin(\\\\lambda (t + h) + \\\\psi)\\\\right)\\\\right]$$\\n\",\n \"\\n\",\n \"$$= R^2 \\\\mathbb{E}\\\\left[\\\\sin(\\\\lambda t + \\\\psi) \\\\sin(\\\\lambda (t + h) + \\\\psi)\\\\right]$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- Now, we use the trigonometric identity:\\n\",\n \"\\n\",\n \"$$\\\\sin(A) \\\\sin(B) = \\\\frac{1}{2}[\\\\cos(A - B) - \\\\cos(A + B)]$$\\n\",\n \"\\n\",\n \"- Applying this identity, we get:\\n\",\n \"\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\text{Cov}(x_t, x_{t+h}) & = R^2 \\\\mathbb{E}\\\\left[\\\\frac{1}{2}[\\\\cos(\\\\lambda t + \\\\psi - \\\\lambda (t + h) - \\\\psi) - \\\\cos(\\\\lambda t + \\\\psi + \\\\lambda (t + h) + \\\\psi)]\\\\right]\\\\\\\\\\n\",\n \"& = \\\\frac{R^2}{2} \\\\mathbb{E}\\\\left[\\\\cos(\\\\lambda h) - \\\\cos(2\\\\lambda t + 2\\\\psi + \\\\lambda h)\\\\right]\\\\\\\\\\n\",\n \"& = \\\\frac{R^2}{2} \\\\mathbb{E}\\\\left[\\\\cos(\\\\lambda h)\\\\right] - \\\\frac{R^2}{2} \\\\mathbb{E}\\\\left[\\\\cos(2\\\\lambda t + 2\\\\psi + \\\\lambda h)\\\\right] \\\\end{align*} \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- The expectation is taken over the random variable $\\\\psi$. \\n\",\n \"- Following the same reasoning we did for the mean, $\\\\cos(2\\\\lambda t + 2\\\\psi + \\\\lambda \\\\tau)$ averages out to zero because $\\\\psi$ is uniformly distributed over $[- \\\\pi, \\\\pi]$.\\n\",\n \"- Thus:\\n\",\n \"\\n\",\n \"$$\\\\text{Cov}(x_t, x_{t+h}) = \\\\frac{R^2}{2} \\\\mathbb{E}\\\\left[\\\\cos(\\\\lambda h)\\\\right]$$\\n\",\n \"\\n\",\n \"- So, the autocovariance function depends only on the lag $h$ and is *independent* of time $t$. \\n\",\n \"- Also condition #3 is satisfied ✌.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"In conclusion, the time series $X(t) = \\\\mu + R \\\\sin(\\\\lambda t + \\\\psi)$ is weakly stationary: \\n\",\n \"\\n\",\n \"- its mean is constant,\\n\",\n \"- its variance is constant, \\n\",\n \"- its autocovariance depends only on the lag $h$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"How does $X(t) = \\\\mu + R \\\\sin(\\\\lambda t + \\\\psi)$ looks like?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/stationary_chunk.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"This is how a non-stationary time series looks like.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"![](\\\"media/nonstationary_chunk.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's look at an example based on the `trend` time series that we created before.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# split data into 10 chunks\\n\",\n \"chunks = np.split(trend, indices_or_sections=10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"Summary statistics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Chunk\\t | Mean\\t\\t | Variance\\n\",\n \"-----------------------------------\\n\",\n \" 1\\t | 26.694\\t | 241.23\\n\",\n \" 2\\t | 81.181\\t | 255.62\\n\",\n \" 3\\t | 135.73\\t | 258.47\\n\",\n \" 4\\t | 190.77\\t | 257.08\\n\",\n \" 5\\t | 246.55\\t | 257.82\\n\",\n \" 6\\t | 301.62\\t | 259.15\\n\",\n \" 7\\t | 356.19\\t | 249.28\\n\",\n \" 8\\t | 411.09\\t | 244.11\\n\",\n \" 9\\t | 466.14\\t | 245.37\\n\",\n \"10\\t | 520.99\\t | 275.11\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"{}\\\\t | {}\\\\t\\\\t | {}\\\".format(\\\"Chunk\\\", \\\"Mean\\\", \\\"Variance\\\"))\\n\",\n \"print(\\\"-\\\" * 35)\\n\",\n \"for i, chunk in enumerate(chunks, 1):\\n\",\n \" print(\\\"{:2}\\\\t | {:.5}\\\\t | {:.5}\\\".format(i, np.mean(chunk), np.var(chunk)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- While the variance is relatively constant over time, the mean is clearly changing, which means this data is not stationary. \\n\",\n \"- We do expect some fluctuation in values. \\n\",\n \"- It's unlikely that mean and variance will be exactly the same in all chunk, but they should be close. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- To see this better, it's a good idea to plot the means and variances.\\n\",\n \"- If you want to be more precise, you could run a statistical test to determine if the difference in means or the difference in variances is statistically significant.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"### Augmented Dickey-Fuller Test\\n\",\n \"\\n\",\n \"- This is a statistical procedure to determine whether a time series is stationary or not. \\n\",\n \"- We will discuss more details about the test in the next lectures.\\n\",\n \"- For now, that's what we need to know:\\n\",\n \" 1. **Null hypothesis:** $H_0$: the series is nonstationary.\\n\",\n \" 2. **Alternative hypothesis:** $H_A$: the series is stationary.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- Like any statistical test you should set a significance level or threshold that determines whether you should accept or reject the null. \\n\",\n \"- The values $\\\\alpha=0.05$ or $\\\\alpha=0.01$, which yield a confidence of $95\\\\%$ and $99\\\\%$, respectively, are common choices.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"Example: $p = 0.0001 \\\\rightarrow$ reject $H_0 \\\\rightarrow$ the time series is stationary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"Example: $p = 0.43 \\\\rightarrow$ fail to reject $H_0 \\\\rightarrow$ the time series is non-stationary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- The ADF test might be inaccurate with small datasets or when heteroskedasticity is present.\\n\",\n \"- It is best to pair ADF with other techniques (run-sequence plots, summary statistics, histograms, etc..).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"#### Stationary Data & ADF\\n\",\n \"\\n\",\n \"- Let's see how to run this test in Python.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"adf, pvalue, usedlag, nobs, critical_values, icbest = adfuller(stationary)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- Let's see and comment the main outputs from the ADF test. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ADF: -13.56\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"ADF: {adf:.2f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- First, `adf` is the value of the test statistic. \\n\",\n \"- The more negative the value, the more confident we can be that the series is stationary. \\n\",\n \"- Here we see a negative value. \\n\",\n \"- That may not mean anything to you just yet but `pvalue` should.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"p-value: 2.300172138070688e-25\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"p-value: {pvalue}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- The `pvalue` is interpreted like any p-value. \\n\",\n \"- The `pvalue` should be compared with the confidence levels (e.g., $\\\\alpha=0.05$ or $\\\\alpha=0.01$).\\n\",\n \"- Based on the comparison, we reject or fail to reject $H_0$. \\n\",\n \"- Here ``pvalue`` is very close to zero so we **reject** the $H_0$ in favor of $H_A$ and conclude that the time series is stationary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nobs: 199\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"nobs: {nobs}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- The variable `nobs` is simply the number of observations in the time series.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"critical values: {'1%': -3.4636447617687436, '5%': -2.8761761179270766, '10%': -2.57457158581854}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"critical values: {critical_values}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- Finally, ``critical_values`` are the test statistic thresholds for common significant levels. \\n\",\n \"- Here we see a test statistic lower than -2.87 is sufficient to reject $H_0$ at a significance level of 5%.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- You may have noticed two outputs we didn't discuss. \\n\",\n \"- See [statsmodels docs](http://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.adfuller.html) for a description of ``usedlag`` and ``icbest``.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"#### ADF on time series with trend\\n\",\n \"- Next, we run the ADF test on the `trend` time series. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ADF: 0.82\\n\",\n \"p-value: 0.992\\n\"\n ]\n }\n ],\n \"source\": [\n \"adf, pvalue, usedlag, nobs, critical_values, icbest = adfuller(trend, regression='c')\\n\",\n \"print(f\\\"ADF: {adf:.2f}\\\")\\n\",\n \"print(f\\\"p-value: {pvalue:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- As expected, we fail to reject $H_0$ that the `trend` time series is nonstationary.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"### ADF on random walk\\n\",\n \"\\n\",\n \"- Finally, we run the ADF test on the `rand_walk` time series.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ADF: -1.03\\n\",\n \"p-value: 0.741\\n\"\n ]\n }\n ],\n \"source\": [\n \"adf, pvalue, usedlag, nobs, critical_values, icbest = adfuller(rand_walk, regression='c')\\n\",\n \"print(f\\\"ADF: {adf:.2f}\\\")\\n\",\n \"print(f\\\"p-value: {pvalue:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- As we discussed before, the random walk is nonstationary because its variance $\\\\sigma^2t$ is not constant over time.\\n\",\n \"- In the next section, we'll learn how to make this time series stationary.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- Good news 😎. At this point, we have several tools at our disposal to figure out if a time series is stationary or not.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"## Common transformations to achieve stationarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- There are several ways to transform nonstationary time series data:\\n\",\n \"\\n\",\n \"Transformation | Effect\\n\",\n \":---|:---\\n\",\n \"Subtract trend | Constant mean\\n\",\n \"Apply log | Constant variance\\n\",\n \"Differencing | Remove autocorrelation \\n\",\n \"Seasonal differencing | Remove periodic component\\n\",\n \"\\n\",\n \"- Sometimes you'll have to do multiple transformations on the same dataset.\\n\",\n \"- Note that this is just a small subset of the total possible ways to transform nonstationary time series. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"### Subtract estimated trend & seasonality\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- Let's run the ADF test before transforming.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ADF: 1.01\\n\",\n \"p-value: 0.994\\n\"\n ]\n }\n ],\n \"source\": [\n \"adf_b4, pvalue_b4, _, _, _, _ = adfuller(trend_seasonality)\\n\",\n \"print(f\\\"ADF: {adf_b4:.2f}\\\")\\n\",\n \"print(f\\\"p-value: {pvalue_b4:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- Nonstationary as expected. \\n\",\n \"- Next, we apply the decomposition.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"ss_decomposition = seasonal_decompose(x=trend_seasonality, model='additive', period=12)\\n\",\n \"est_trend = ss_decomposition.trend\\n\",\n \"est_seasonal = ss_decomposition.seasonal\\n\",\n \"est_residual = ss_decomposition.resid\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"In the previous lectures, we learned that:\\n\",\n \"- A time series consists of measurements characterized by temporal dependencies.\\n\",\n \"- The measurements are (usually) collected at equally-spaced intervals.\\n\",\n \"- A time series can be decomposed into trend, seasonality, and residuals.\\n\",\n \"- Many time series models require the data to be stationary in order to make forecasts. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- In this lecture, we will build upon that knowledge and explore another important concept called **smoothing**.\\n\",\n \"- In particular, we will cover:\\n\",\n \" 1. An introduction to smoothing and why it is necessary.\\n\",\n \" 2. Common smoothing techniques.\\n\",\n \" 3. How to smooth time series data with Python and generate forecasts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"# Install dependencies if the notebook is running in Colab\\n\",\n \"if 'google.colab' in sys.modules:\\n\",\n \" !pip install -U -qq tsa-course\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import requests\\n\",\n \"from io import BytesIO\\n\",\n \"from tsa_course.lecture2 import run_sequence_plot\\n\",\n \"np.random.seed(0) # reproducibility\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Smoothing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- A data collection process is often affected by noise. \\n\",\n \"- If too strong, the noise can conceal useful patterns in the data. \\n\",\n \"- Smoothing is a well-known and often-used technique to recover those patterns by *filtering out noise*.\\n\",\n \"- It can also be used to *make forecasts* by projecting the recovered patterns into the future.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- We will explore two important techniques to do smoothing:\\n\",\n \" 1. Simple smoothing.\\n\",\n \" 2. Exponential smoothing.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We start by generating some stationary data.\\n\",\n \"- We discussed the importance of visually inspecting the time series with a run-sequence plot. \\n\",\n \"- So, we will also define the `run_sequence_plot` function to visualize our data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Generate stationary data\\n\",\n \"time = np.arange(100)\\n\",\n \"stationary = np.random.normal(loc=0, scale=1.0, size=len(time))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/values.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- The first step is to select a window size. \\n\",\n \"- We'll arbitrarily choose a size of 3. \\n\",\n \"- Then, we start computing the average for the first three values and store the result. \\n\",\n \"- We then slide the window by one and calculate the average of the next three values. \\n\",\n \"- We repeat this process until we reach the final observed value. \\n\",\n \"\\n\",\n \"![](\\\"media/EqWMA.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- Now, let's define a function to perform smoothing with the MA.\\n\",\n \"- Then, we compare the MSE obtained from applying simple and moving average on the data with trend. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"def moving_average(observations, window=3, forecast=False):\\n\",\n \" cumulative_sum = np.cumsum(observations, dtype=float)\\n\",\n \" cumulative_sum[window:] = cumulative_sum[window:] - cumulative_sum[:-window]\\n\",\n \" ma = cumulative_sum[window - 1:] / window\\n\",\n \" if forecast:\\n\",\n \" observations = np.append(observations, np.nan)\\n\",\n \" ma_forecast = np.insert(ma, 0, np.nan*np.ones(window))\\n\",\n \" return observations, ma_forecast\\n\",\n \" else:\\n\",\n \" return ma\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MSE:\\n\",\n \"--------\\n\",\n \"simple average: 3324.01\\n\",\n \"moving_average: 4.57\\n\"\n ]\n }\n ],\n \"source\": [\n \"MA_trend = moving_average(trend, window=3)\\n\",\n \"\\n\",\n \"print(f\\\"MSE:\\\\n--------\\\\nsimple average: {mse(trend, avg_trend):.2f}\\\\nmoving_average: {mse(trend[2:], MA_trend):.2f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- Clearly, the MA manages to pick up much better the trend data.\\n\",\n \"- We can also plot the result against the actual time series.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/ExpWMA.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": []\n },\n \"source\": [\n \"```{note}\\n\",\n \"\\n\",\n \"- There are numerous methods to find the optimal weighting scheme based on the data and the task at hand. \\n\",\n \"- A full discussion is beyond the scope of the lecture. \\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Forecasting with MA\\n\",\n \"\\n\",\n \"- Instead of pulling out the inherent pattern within a series, the smoothing functions can be used to create *forecasts*. \\n\",\n \"- The forecast for the next time step is computed as follows:\\n\",\n \"\\n\",\n \"$$\\\\hat{X}(t+1) = \\\\frac{X(t) + X(t-1) + \\\\dots + X(t-P+1)}{P}$$ \\n\",\n \"\\n\",\n \"- where $P$ is the window size of the MA.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"- Let's consider the following time series $X= [1, 2, 4, 8, 16, 32, 64]$. \\n\",\n \"- In particular, we apply the smoothing process and use the resulting value as forecast for the next time step.\\n\",\n \"- With the MA technique and a window size $P=3$ we get the following forecast.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"x = np.array([1, 2, 4, 8, 16, 32, 64])\\n\",\n \"ma_x, ma_forecast = moving_average(x, window=3, forecast=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"scrolled\": true,\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"## Overview \\n\",\n \"\\n\",\n \"In this lecture we will cover:\\n\",\n \"- The autocorrelation function (ACF).\\n\",\n \"- The partial autocorrelation function (PACF).\\n\",\n \"- Explanation of how autoregressive and moving average models work.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"# Install dependencies if the notebook is running in Colab\\n\",\n \"if 'google.colab' in sys.modules:\\n\",\n \" !pip install -U -qq tsa-course\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"from statsmodels.tools.sm_exceptions import ConvergenceWarning\\n\",\n \"warnings.simplefilter('ignore', ConvergenceWarning)\\n\",\n \"import requests\\n\",\n \"from io import BytesIO\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"from statsmodels.tsa.stattools import ccf, adfuller\\n\",\n \"from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\\n\",\n \"from statsmodels.tsa.arima.model import ARIMA\\n\",\n \"from statsmodels.tsa.arima_process import arma_generate_sample\\n\",\n \"from statsmodels.tsa.holtwinters import ExponentialSmoothing\\n\",\n \"from tsa_course.lecture2 import run_sequence_plot\\n\",\n \"np.random.seed(0) # For reproducibility\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"## Correlations in time series\\n\",\n \" - The correlation function measures the degree of association between two random variables.\\n\",\n \" - In time series data it measures the association btween two different points in time.\\n\",\n \" - Correlation is essential for understanding the linear relationship and dependency in time series data.\\n\",\n \" - There are two types of correlation functions: \\n\",\n \" - Autocorrelation.\\n\",\n \" - Cross-Correlation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"source\": [\n \"### Autocorrelation Function (ACF)\\n\",\n \" - The ACF measures the correlation of a time series with its own lagged values\\n\",\n \" \\n\",\n \" $$ ACF(k) = \\\\frac{\\\\mathbb{E}[(X(t) - \\\\mu)(X(t-k) - \\\\mu)]}{\\\\sigma^2} $$ \\n\",\n \" \\n\",\n \" - where:\\n\",\n \" - $k$ is the lag number.\\n\",\n \" - $X(t)$ is the value at time $t$.\\n\",\n \" - $\\\\mu$ is the mean of the series.\\n\",\n \" - $\\\\sigma^2$ is the variance of the series.\\n\",\n \" - Values close to 1 or -1 indicate strong correlation, while values near 0 indicate weak correlation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n },\n \"scrolled\": true,\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/timoelliot.png\\\")
\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b1359e9e\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Overview\\n\",\n \"\\n\",\n \"- In the previous chapters, we covered stationarity, smoothing, trend, seasonality, and autocorrelation, and built two different models: \\n\",\n \"\\n\",\n \"> **MA models**: The current value of the series depends linearly on the series' mean and a set of prior (observed) white noise error terms.\\n\",\n \"\\n\",\n \"> **AR models**: The current value of the series depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term).\\n\",\n \"\\n\",\n \"- In this chapter we will review these concepts and combine the AR and MA models into three more complicated ones.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"29644aac\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"In particular, we will cover:\\n\",\n \"\\n\",\n \"1. Autoregressive Moving Average (ARMA) models.\\n\",\n \"2. Autoregressive Integrated Moving Average (ARIMA) models.\\n\",\n \"3. SARIMA models (ARIMA model for data with seasonality).\\n\",\n \"4. Selecting the best model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c765c6ff\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"# Install dependencies if the notebook is running in Colab\\n\",\n \"if 'google.colab' in sys.modules:\\n\",\n \" !pip install -U -qq \\\"numpy==1.26.4\\\" \\\"pmdarima\\\" \\\"tsa-course\\\"\\n\",\n \" import os, sys\\n\",\n \" os.kill(os.getpid(), 9) # force runtime restart\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"876fd91c\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"from statsmodels.tools.sm_exceptions import ConvergenceWarning, ValueWarning\\n\",\n \"warnings.simplefilter('ignore', ConvergenceWarning)\\n\",\n \"warnings.simplefilter('ignore', ValueWarning)\\n\",\n \"warnings.simplefilter('ignore', UserWarning)\\n\",\n \"import time\\n\",\n \"from itertools import product\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import statsmodels as ss\\n\",\n \"import seaborn as sns\\n\",\n \"from tqdm.notebook import tqdm\\n\",\n \"from scipy import stats\\n\",\n \"from statsmodels.tsa.arima.model import ARIMA\\n\",\n \"from statsmodels.graphics.tsaplots import month_plot, plot_acf, plot_pacf\\n\",\n \"from statsmodels.graphics.gofplots import qqplot\\n\",\n \"from statsmodels.tsa.stattools import adfuller, acf, pacf\\n\",\n \"from statsmodels.tsa.seasonal import STL, seasonal_decompose\\n\",\n \"from statsmodels.tsa.statespace.sarimax import SARIMAX\\n\",\n \"from statsmodels.tools.eval_measures import mse\\n\",\n \"from statsmodels.tsa.statespace.tools import diff\\n\",\n \"import pmdarima as pm\\n\",\n \"from tsa_course.lecture1 import fft_analysis\\n\",\n \"np.random.seed(0) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"891afa5a\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"161403b8\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## ARMA\\n\",\n \"\\n\",\n \"The ARMA model (also known as the *Box-Jenkins* approach) combines two models:\\n\",\n \"\\n\",\n \"- An autoregressive (AR) model of order $p$.\\n\",\n \"- A moving average (MA) model of order $q$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f1bd5a33\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- When we have autocorrelation between outcomes and their ancestors, there will be a pattern in the time series. \\n\",\n \"- This relationship can be modeled using an ARMA model. \\n\",\n \"- It allows us to predict the future with a confidence level proportional to the strength of the relationship and the proximity to known values (prediction weakens the further out we go).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e2f1bf76\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"```{note}\\n\",\n \"- ARMA models assume the time series is stationary.\\n\",\n \"- A good rule of thumb is to have at least 100 observations when fitting an ARMA model.\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dd8fdaab\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Example data\\n\",\n \"\\n\",\n \"- In the following, we'll look at the monthly average temperatures between 1907-1972.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"fb49cacb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# load data and convert to datetime\\n\",\n \"monthly_temp = pd.read_csv('https://zenodo.org/records/10951538/files/arima_temp.csv?download=1', \\n\",\n \" skipfooter=2, \\n\",\n \" header=0, \\n\",\n \" index_col=0, \\n\",\n \" names=['month', 'temp'],\\n\",\n \" engine='python')\\n\",\n \"monthly_temp.index = pd.to_datetime(monthly_temp.index)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6a82c7bb\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- This is how the data looks like.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"3c4bb8d6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | temp | \\n\",\n \"
|---|---|
| month | \\n\",\n \"\\n\",\n \" |
| 1907-01-01 | \\n\",\n \"33.3 | \\n\",\n \"
| 1907-02-01 | \\n\",\n \"46.0 | \\n\",\n \"
| 1907-03-01 | \\n\",\n \"43.0 | \\n\",\n \"
| 1907-04-01 | \\n\",\n \"55.0 | \\n\",\n \"
| 1907-05-01 | \\n\",\n \"51.8 | \\n\",\n \"
| \\n\",\n \" | temp | \\n\",\n \"
|---|---|
| count | \\n\",\n \"792.000000 | \\n\",\n \"
| mean | \\n\",\n \"53.553662 | \\n\",\n \"
| std | \\n\",\n \"15.815452 | \\n\",\n \"
| min | \\n\",\n \"11.200000 | \\n\",\n \"
| 25% | \\n\",\n \"39.675000 | \\n\",\n \"
| 50% | \\n\",\n \"52.150000 | \\n\",\n \"
| 75% | \\n\",\n \"67.200000 | \\n\",\n \"
| max | \\n\",\n \"82.400000 | \\n\",\n \"
| \\n\",\n \" | temp | \\n\",\n \"Seasonally_Differenced | \\n\",\n \"
|---|---|---|
| month | \\n\",\n \"\\n\",\n \" | \\n\",\n \" |
| 1908-01-01 | \\n\",\n \"35.6 | \\n\",\n \"2.3 | \\n\",\n \"
| 1908-02-01 | \\n\",\n \"35.2 | \\n\",\n \"-10.8 | \\n\",\n \"
| 1908-03-01 | \\n\",\n \"48.1 | \\n\",\n \"5.1 | \\n\",\n \"
| 1908-04-01 | \\n\",\n \"50.0 | \\n\",\n \"-5.0 | \\n\",\n \"
| 1908-05-01 | \\n\",\n \"46.8 | \\n\",\n \"-5.0 | \\n\",\n \"
| ... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"
| 1972-08-01 | \\n\",\n \"75.6 | \\n\",\n \"-4.9 | \\n\",\n \"
| 1972-09-01 | \\n\",\n \"64.1 | \\n\",\n \"-1.7 | \\n\",\n \"
| 1972-10-01 | \\n\",\n \"51.7 | \\n\",\n \"0.6 | \\n\",\n \"
| 1972-11-01 | \\n\",\n \"40.3 | \\n\",\n \"-1.5 | \\n\",\n \"
| 1972-12-01 | \\n\",\n \"30.3 | \\n\",\n \"-0.3 | \\n\",\n \"
780 rows × 2 columns
\\n\",\n \"| Dep. Variable: | temp | No. Observations: | 750 | \\n\",\n \"
|---|---|---|---|
| Model: | SARIMAX(2, 1, 2)x(0, 1, [1], 12) | Log Likelihood | -2026.066 | \\n\",\n \"
| Date: | Thu, 03 Apr 2025 | AIC | 4066.132 | \\n\",\n \"
| Time: | 16:20:27 | BIC | 4098.350 | \\n\",\n \"
| Sample: | 01-01-1907 | HQIC | 4078.557 | \\n\",\n \"
| - 06-01-1969 | \\n\",\n \" | ||
| Covariance Type: | opg | \\n\",\n \" |
| coef | std err | z | P>|z| | [0.025 | 0.975] | \\n\",\n \"|
|---|---|---|---|---|---|---|
| intercept | -6.589e-05 | 0.000 | -0.370 | 0.712 | -0.000 | 0.000 | \\n\",\n \"
| ar.L1 | -0.7653 | 0.169 | -4.525 | 0.000 | -1.097 | -0.434 | \\n\",\n \"
| ar.L2 | 0.1790 | 0.042 | 4.273 | 0.000 | 0.097 | 0.261 | \\n\",\n \"
| ma.L1 | -0.0549 | 0.180 | -0.305 | 0.760 | -0.408 | 0.298 | \\n\",\n \"
| ma.L2 | -0.9427 | 0.172 | -5.492 | 0.000 | -1.279 | -0.606 | \\n\",\n \"
| ma.S.L12 | -0.9813 | 0.039 | -25.144 | 0.000 | -1.058 | -0.905 | \\n\",\n \"
| sigma2 | 13.3636 | 0.935 | 14.293 | 0.000 | 11.531 | 15.196 | \\n\",\n \"
| Ljung-Box (L1) (Q): | 0.03 | Jarque-Bera (JB): | 201.91 | \\n\",\n \"
|---|---|---|---|
| Prob(Q): | 0.86 | Prob(JB): | 0.00 | \\n\",\n \"
| Heteroskedasticity (H): | 0.77 | Skew: | -0.55 | \\n\",\n \"
| Prob(H) (two-sided): | 0.04 | Kurtosis: | 5.32 | \\n\",\n \"
| \\n\",\n \" | d0_D0_s12 | \\n\",\n \"d0_D1_s12 | \\n\",\n \"d0_D2_s12 | \\n\",\n \"d1_D0_s12 | \\n\",\n \"d2_D0_s12 | \\n\",\n \"d1_D1_s12 | \\n\",\n \"d2_D1_s12 | \\n\",\n \"d1_D2_s12 | \\n\",\n \"d2_D2_s12 | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|
| month | \\n\",\n \"\\n\",\n \" | \\n\",\n \" | \\n\",\n \" | \\n\",\n \" | \\n\",\n \" | \\n\",\n \" | \\n\",\n \" | \\n\",\n \" | \\n\",\n \" |
| 1907-01-01 | \\n\",\n \"33.3 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 1907-02-01 | \\n\",\n \"46.0 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"12.7 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 1907-03-01 | \\n\",\n \"43.0 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"-3.0 | \\n\",\n \"-15.7 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 1907-04-01 | \\n\",\n \"55.0 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"12.0 | \\n\",\n \"15.0 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 1907-05-01 | \\n\",\n \"51.8 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"-3.2 | \\n\",\n \"-15.2 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| ... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"
| 1972-08-01 | \\n\",\n \"75.6 | \\n\",\n \"-4.9 | \\n\",\n \"-4.9 | \\n\",\n \"-3.4 | \\n\",\n \"-10.3 | \\n\",\n \"-2.3 | \\n\",\n \"1.7 | \\n\",\n \"1.1 | \\n\",\n \"8.4 | \\n\",\n \"
| 1972-09-01 | \\n\",\n \"64.1 | \\n\",\n \"-1.7 | \\n\",\n \"-2.5 | \\n\",\n \"-11.5 | \\n\",\n \"-8.1 | \\n\",\n \"3.2 | \\n\",\n \"5.5 | \\n\",\n \"2.4 | \\n\",\n \"1.3 | \\n\",\n \"
| 1972-10-01 | \\n\",\n \"51.7 | \\n\",\n \"0.6 | \\n\",\n \"2.7 | \\n\",\n \"-12.4 | \\n\",\n \"-0.9 | \\n\",\n \"2.3 | \\n\",\n \"-0.9 | \\n\",\n \"5.2 | \\n\",\n \"2.8 | \\n\",\n \"
| 1972-11-01 | \\n\",\n \"40.3 | \\n\",\n \"-1.5 | \\n\",\n \"3.4 | \\n\",\n \"-11.4 | \\n\",\n \"1.0 | \\n\",\n \"-2.1 | \\n\",\n \"-4.4 | \\n\",\n \"0.7 | \\n\",\n \"-4.5 | \\n\",\n \"
| 1972-12-01 | \\n\",\n \"30.3 | \\n\",\n \"-0.3 | \\n\",\n \"3.2 | \\n\",\n \"-10.0 | \\n\",\n \"1.4 | \\n\",\n \"1.2 | \\n\",\n \"3.3 | \\n\",\n \"-0.2 | \\n\",\n \"-0.9 | \\n\",\n \"
792 rows × 9 columns
\\n\",\n \"| \\n\",\n \" | Test Statistic | \\n\",\n \"p-value | \\n\",\n \"#Lags Used | \\n\",\n \"No. of Obs. Used | \\n\",\n \"Critical Value (1%) | \\n\",\n \"Critical Value (5%) | \\n\",\n \"Critical Value (10%) | \\n\",\n \"
|---|---|---|---|---|---|---|---|
| d0_D0_s12 | \\n\",\n \"-6.481466 | \\n\",\n \"1.291867e-08 | \\n\",\n \"21 | \\n\",\n \"770 | \\n\",\n \"-3.438871 | \\n\",\n \"-2.865301 | \\n\",\n \"-2.568773 | \\n\",\n \"
| d0_D1_s12 | \\n\",\n \"-12.658082 | \\n\",\n \"1.323220e-23 | \\n\",\n \"12 | \\n\",\n \"767 | \\n\",\n \"-3.438905 | \\n\",\n \"-2.865316 | \\n\",\n \"-2.568781 | \\n\",\n \"
| d0_D2_s12 | \\n\",\n \"-10.416254 | \\n\",\n \"1.751310e-18 | \\n\",\n \"14 | \\n\",\n \"753 | \\n\",\n \"-3.439064 | \\n\",\n \"-2.865386 | \\n\",\n \"-2.568818 | \\n\",\n \"
| d1_D0_s12 | \\n\",\n \"-12.302613 | \\n\",\n \"7.391771e-23 | \\n\",\n \"21 | \\n\",\n \"769 | \\n\",\n \"-3.438882 | \\n\",\n \"-2.865306 | \\n\",\n \"-2.568775 | \\n\",\n \"
| d2_D0_s12 | \\n\",\n \"-15.935084 | \\n\",\n \"7.651998e-29 | \\n\",\n \"17 | \\n\",\n \"772 | \\n\",\n \"-3.438849 | \\n\",\n \"-2.865291 | \\n\",\n \"-2.568767 | \\n\",\n \"
| d1_D1_s12 | \\n\",\n \"-11.846173 | \\n\",\n \"7.390517e-22 | \\n\",\n \"20 | \\n\",\n \"758 | \\n\",\n \"-3.439006 | \\n\",\n \"-2.865361 | \\n\",\n \"-2.568804 | \\n\",\n \"
| d2_D1_s12 | \\n\",\n \"-18.352698 | \\n\",\n \"2.234953e-30 | \\n\",\n \"21 | \\n\",\n \"756 | \\n\",\n \"-3.439029 | \\n\",\n \"-2.865371 | \\n\",\n \"-2.568810 | \\n\",\n \"
| d1_D2_s12 | \\n\",\n \"-12.221559 | \\n\",\n \"1.104309e-22 | \\n\",\n \"20 | \\n\",\n \"746 | \\n\",\n \"-3.439146 | \\n\",\n \"-2.865422 | \\n\",\n \"-2.568837 | \\n\",\n \"
| d2_D2_s12 | \\n\",\n \"-15.080476 | \\n\",\n \"8.468854e-28 | \\n\",\n \"20 | \\n\",\n \"745 | \\n\",\n \"-3.439158 | \\n\",\n \"-2.865427 | \\n\",\n \"-2.568840 | \\n\",\n \"
| \\n\",\n \" | order | \\n\",\n \"seasonal_order | \\n\",\n \"MAPE | \\n\",\n \"MSE | \\n\",\n \"AIC | \\n\",\n \"BIC | \\n\",\n \"
|---|---|---|---|---|---|---|
| 140 | \\n\",\n \"(3, 1, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069031 | \\n\",\n \"16.776397 | \\n\",\n \"3860.352438 | \\n\",\n \"3896.749644 | \\n\",\n \"
| 45 | \\n\",\n \"(1, 1, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069051 | \\n\",\n \"16.774613 | \\n\",\n \"3855.644633 | \\n\",\n \"3882.942537 | \\n\",\n \"
| 129 | \\n\",\n \"(3, 1, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069079 | \\n\",\n \"16.741025 | \\n\",\n \"3856.094704 | \\n\",\n \"3887.942260 | \\n\",\n \"
| 135 | \\n\",\n \"(3, 1, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069124 | \\n\",\n \"16.792337 | \\n\",\n \"3859.551130 | \\n\",\n \"3891.398685 | \\n\",\n \"
| 81 | \\n\",\n \"(2, 1, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069124 | \\n\",\n \"16.802775 | \\n\",\n \"3857.201338 | \\n\",\n \"3884.499242 | \\n\",\n \"
| 39 | \\n\",\n \"(1, 1, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069130 | \\n\",\n \"16.781163 | \\n\",\n \"3854.503774 | \\n\",\n \"3877.252028 | \\n\",\n \"
| 123 | \\n\",\n \"(3, 1, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069167 | \\n\",\n \"16.746320 | \\n\",\n \"3854.845307 | \\n\",\n \"3882.143212 | \\n\",\n \"
| 93 | \\n\",\n \"(2, 1, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069172 | \\n\",\n \"16.805176 | \\n\",\n \"3860.077582 | \\n\",\n \"3891.925137 | \\n\",\n \"
| 79 | \\n\",\n \"(2, 1, 1) | \\n\",\n \"(1, 0, 1, 12) | \\n\",\n \"0.069201 | \\n\",\n \"17.185380 | \\n\",\n \"3965.090220 | \\n\",\n \"3992.490254 | \\n\",\n \"
| 75 | \\n\",\n \"(2, 1, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069203 | \\n\",\n \"16.811203 | \\n\",\n \"3856.176397 | \\n\",\n \"3878.924651 | \\n\",\n \"
| \\n\",\n \" | order | \\n\",\n \"seasonal_order | \\n\",\n \"MAPE | \\n\",\n \"MSE | \\n\",\n \"AIC | \\n\",\n \"BIC | \\n\",\n \"
|---|---|---|---|---|---|---|
| 129 | \\n\",\n \"(3, 1, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069079 | \\n\",\n \"16.741025 | \\n\",\n \"3856.094704 | \\n\",\n \"3887.942260 | \\n\",\n \"
| 123 | \\n\",\n \"(3, 1, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069167 | \\n\",\n \"16.746320 | \\n\",\n \"3854.845307 | \\n\",\n \"3882.143212 | \\n\",\n \"
| 45 | \\n\",\n \"(1, 1, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069051 | \\n\",\n \"16.774613 | \\n\",\n \"3855.644633 | \\n\",\n \"3882.942537 | \\n\",\n \"
| 140 | \\n\",\n \"(3, 1, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069031 | \\n\",\n \"16.776397 | \\n\",\n \"3860.352438 | \\n\",\n \"3896.749644 | \\n\",\n \"
| 39 | \\n\",\n \"(1, 1, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069130 | \\n\",\n \"16.781163 | \\n\",\n \"3854.503774 | \\n\",\n \"3877.252028 | \\n\",\n \"
| 135 | \\n\",\n \"(3, 1, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069124 | \\n\",\n \"16.792337 | \\n\",\n \"3859.551130 | \\n\",\n \"3891.398685 | \\n\",\n \"
| 81 | \\n\",\n \"(2, 1, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069124 | \\n\",\n \"16.802775 | \\n\",\n \"3857.201338 | \\n\",\n \"3884.499242 | \\n\",\n \"
| 93 | \\n\",\n \"(2, 1, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069172 | \\n\",\n \"16.805176 | \\n\",\n \"3860.077582 | \\n\",\n \"3891.925137 | \\n\",\n \"
| 75 | \\n\",\n \"(2, 1, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069203 | \\n\",\n \"16.811203 | \\n\",\n \"3856.176397 | \\n\",\n \"3878.924651 | \\n\",\n \"
| 87 | \\n\",\n \"(2, 1, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069289 | \\n\",\n \"16.825118 | \\n\",\n \"3859.565045 | \\n\",\n \"3886.862949 | \\n\",\n \"
| \\n\",\n \" | order | \\n\",\n \"seasonal_order | \\n\",\n \"MAPE | \\n\",\n \"MSE | \\n\",\n \"AIC | \\n\",\n \"BIC | \\n\",\n \"
|---|---|---|---|---|---|---|
| 3 | \\n\",\n \"(1, 0, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080283 | \\n\",\n \"61.491561 | \\n\",\n \"3846.181890 | \\n\",\n \"3864.386212 | \\n\",\n \"
| 9 | \\n\",\n \"(1, 0, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.080153 | \\n\",\n \"61.471329 | \\n\",\n \"3846.879649 | \\n\",\n \"3869.635051 | \\n\",\n \"
| 15 | \\n\",\n \"(1, 0, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080286 | \\n\",\n \"61.482998 | \\n\",\n \"3847.914835 | \\n\",\n \"3870.670236 | \\n\",\n \"
| 51 | \\n\",\n \"(2, 0, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080283 | \\n\",\n \"61.485310 | \\n\",\n \"3847.985625 | \\n\",\n \"3870.741026 | \\n\",\n \"
| 99 | \\n\",\n \"(3, 0, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080295 | \\n\",\n \"61.449767 | \\n\",\n \"3848.292676 | \\n\",\n \"3875.599158 | \\n\",\n \"
| 21 | \\n\",\n \"(1, 0, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.080146 | \\n\",\n \"61.461761 | \\n\",\n \"3848.568559 | \\n\",\n \"3875.875041 | \\n\",\n \"
| 57 | \\n\",\n \"(2, 0, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.080145 | \\n\",\n \"61.464316 | \\n\",\n \"3848.650344 | \\n\",\n \"3875.956826 | \\n\",\n \"
| 105 | \\n\",\n \"(3, 0, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.080096 | \\n\",\n \"61.430629 | \\n\",\n \"3849.069448 | \\n\",\n \"3880.927010 | \\n\",\n \"
| 117 | \\n\",\n \"(3, 0, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.080078 | \\n\",\n \"61.396978 | \\n\",\n \"3849.127920 | \\n\",\n \"3885.536562 | \\n\",\n \"
| 63 | \\n\",\n \"(2, 0, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080282 | \\n\",\n \"61.469826 | \\n\",\n \"3849.361800 | \\n\",\n \"3876.668282 | \\n\",\n \"
| \\n\",\n \" | order | \\n\",\n \"seasonal_order | \\n\",\n \"MAPE | \\n\",\n \"MSE | \\n\",\n \"AIC | \\n\",\n \"BIC | \\n\",\n \"
|---|---|---|---|---|---|---|
| 3 | \\n\",\n \"(1, 0, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080283 | \\n\",\n \"61.491561 | \\n\",\n \"3846.181890 | \\n\",\n \"3864.386212 | \\n\",\n \"
| 9 | \\n\",\n \"(1, 0, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.080153 | \\n\",\n \"61.471329 | \\n\",\n \"3846.879649 | \\n\",\n \"3869.635051 | \\n\",\n \"
| 15 | \\n\",\n \"(1, 0, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080286 | \\n\",\n \"61.482998 | \\n\",\n \"3847.914835 | \\n\",\n \"3870.670236 | \\n\",\n \"
| 51 | \\n\",\n \"(2, 0, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080283 | \\n\",\n \"61.485310 | \\n\",\n \"3847.985625 | \\n\",\n \"3870.741026 | \\n\",\n \"
| 27 | \\n\",\n \"(1, 1, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069317 | \\n\",\n \"16.836785 | \\n\",\n \"3855.816702 | \\n\",\n \"3874.015305 | \\n\",\n \"
| 99 | \\n\",\n \"(3, 0, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080295 | \\n\",\n \"61.449767 | \\n\",\n \"3848.292676 | \\n\",\n \"3875.599158 | \\n\",\n \"
| 21 | \\n\",\n \"(1, 0, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.080146 | \\n\",\n \"61.461761 | \\n\",\n \"3848.568559 | \\n\",\n \"3875.875041 | \\n\",\n \"
| 57 | \\n\",\n \"(2, 0, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.080145 | \\n\",\n \"61.464316 | \\n\",\n \"3848.650344 | \\n\",\n \"3875.956826 | \\n\",\n \"
| 63 | \\n\",\n \"(2, 0, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080282 | \\n\",\n \"61.469826 | \\n\",\n \"3849.361800 | \\n\",\n \"3876.668282 | \\n\",\n \"
| 39 | \\n\",\n \"(1, 1, 2) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.069130 | \\n\",\n \"16.781163 | \\n\",\n \"3854.503774 | \\n\",\n \"3877.252028 | \\n\",\n \"
| \\n\",\n \" | order | \\n\",\n \"seasonal_order | \\n\",\n \"MAPE | \\n\",\n \"MSE | \\n\",\n \"AIC | \\n\",\n \"BIC | \\n\",\n \"
|---|---|---|---|---|---|---|
| 140 | \\n\",\n \"(3, 1, 2) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069031 | \\n\",\n \"16.776397 | \\n\",\n \"3860.352438 | \\n\",\n \"3896.749644 | \\n\",\n \"
| 129 | \\n\",\n \"(3, 1, 1) | \\n\",\n \"(1, 1, 1, 12) | \\n\",\n \"0.069079 | \\n\",\n \"16.741025 | \\n\",\n \"3856.094704 | \\n\",\n \"3887.942260 | \\n\",\n \"
| 3 | \\n\",\n \"(1, 0, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080283 | \\n\",\n \"61.491561 | \\n\",\n \"3846.181890 | \\n\",\n \"3864.386212 | \\n\",\n \"
| 3 | \\n\",\n \"(1, 0, 1) | \\n\",\n \"(0, 1, 1, 12) | \\n\",\n \"0.080283 | \\n\",\n \"61.491561 | \\n\",\n \"3846.181890 | \\n\",\n \"3864.386212 | \\n\",\n \"
| \\n\",\n \" | co2 | \\n\",\n \"
|---|---|
| 1965-01-01 | \\n\",\n \"319.32 | \\n\",\n \"
| 1965-02-01 | \\n\",\n \"320.36 | \\n\",\n \"
| 1965-03-01 | \\n\",\n \"320.82 | \\n\",\n \"
| 1965-04-01 | \\n\",\n \"322.06 | \\n\",\n \"
| 1965-05-01 | \\n\",\n \"322.17 | \\n\",\n \"
| ... | \\n\",\n \"... | \\n\",\n \"
| 1980-08-01 | \\n\",\n \"337.19 | \\n\",\n \"
| 1980-09-01 | \\n\",\n \"335.49 | \\n\",\n \"
| 1980-10-01 | \\n\",\n \"336.63 | \\n\",\n \"
| 1980-11-01 | \\n\",\n \"337.74 | \\n\",\n \"
| 1980-12-01 | \\n\",\n \"338.36 | \\n\",\n \"
192 rows × 1 columns
\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b1359e9e\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Overview\\n\",\n \"\\n\",\n \"- We will first recap and provide more details about some concepts we saw in the previous lectures.\\n\",\n \"- Then, we will introduce new analytics tools and some of their application in financial applications.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"29644aac\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The content of the lecture is organized as follows:\\n\",\n \" 1. Small recap.\\n\",\n \" 2. Unit root test.\\n\",\n \" 3. Mean reversion.\\n\",\n \" 4. Hurst Exponent.\\n\",\n \" 5. Geometric Brownian motion.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"876fd91c\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"warnings.simplefilter(action=\\\"ignore\\\", category=(FutureWarning, DeprecationWarning))\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from statsmodels.tsa.stattools import adfuller\\n\",\n \"import yfinance as yf\\n\",\n \"np.random.seed(0) # For reproducible results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0dc2b59e\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1bab131c\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Small recap\\n\",\n \"\\n\",\n \"- We've covered lots of material in the previous lessons. \\n\",\n \"- Now is a good time to step back and rehash what we've covered.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"64b73418\",\n \"metadata\": {\n \"cell_style\": \"split\",\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Decomposition\\n\",\n \"\\n\",\n \"- Time series data can be decomposed into trend, seasonal, and random fluctuation components.\\n\",\n \"\\n\",\n \"**Trends**\\n\",\n \"- Increasing.\\n\",\n \"- Decreasing.\\n\",\n \"- Flat.\\n\",\n \"- Larger trends can be made up of smaller trends.\\n\",\n \"- There is no defined timeframe for what constitutes a trend: it depends on your data and task at hand.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6da354bd\",\n \"metadata\": {\n \"cell_style\": \"split\",\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"**Seasonal effects**\\n\",\n \"- Weekend retail sales spikes.\\n\",\n \"- Holiday shopping.\\n\",\n \"- Energy requirement changes with annual weather patterns.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dcfd6356\",\n \"metadata\": {\n \"cell_style\": \"split\",\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"**Random Fluctuations**\\n\",\n \"- The human element.\\n\",\n \"- Aggregations of small influencers.\\n\",\n \"- Observation errors.\\n\",\n \"- The smaller this is in relation to trend and seasonal components, the better we can predict the future.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7d0cd99b\",\n \"metadata\": {\n \"cell_style\": \"split\",\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Additive vs Multiplicative\\n\",\n \"\\n\",\n \"- Two simple categories of time series models:\\n\",\n \"\\n\",\n \"**Additive**\\n\",\n \"- Data = Trend + Seasonal + Random.\\n\",\n \"- If our seasonality and fluctuations are stable, we likely have an additive model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7105c7ce\",\n \"metadata\": {\n \"cell_style\": \"split\",\n \"run_control\": {\n \"marked\": true\n },\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"**Multiplicative**\\n\",\n \"- Data = Trend $\\\\times$ Seasonal $\\\\times$ Random.\\n\",\n \"- As easy to fit as Additive if we take the log: ``log(Data) = log(Trend) + log(Seasonal) + log(Random)``.\\n\",\n \"- Use multiplicative models if:\\n\",\n \" - the amplitude in seasonal and random fluctuations grows with the trend,\\n\",\n \" - the percentage change of our data is more important than the absolute value change (e.g. stocks, commodities).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"83d046de\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Stationarity\\n\",\n \"\\n\",\n \"A time series is stationary if:\\n\",\n \"\\n\",\n \"- The mean of the series is constant.\\n\",\n \"- The variance does not change over time (homoscedasticity).\\n\",\n \"- The covariance is not a function of time.\\n\",\n \"\\n\",\n \"![](\\\"media/non-stationarity.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f84c01af\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Detect non-stationarity\\n\",\n \"\\n\",\n \"- Make a run-sequence plot.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"879a4919\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- Rolling statistics:\\n\",\n \" - Compute and plot rolling statistics such as moving average/variance on chunks of data.\\n\",\n \" - Check if the statistics change over time. \\n\",\n \" - This technique can be done on different windows (small windows are noisy, large windows too conservative).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b4b7617b\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- Augmented Dickey-Fuller (ADF) test:\\n\",\n \" - Statistical tests for checking stationarity. \\n\",\n \" - The null hypothesis $H_0$ is that the time series is non-stationary. \\n\",\n \" - If the test statistic is small enough and the $p$-value below the target $\\\\alpha$, we _can_ reject $H_0$ and say that the series is stationary.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"94467bb8\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Achieve stationarity\\n\",\n \"\\n\",\n \"- Take the log of the data.\\n\",\n \"- Difference (multiple times if needed) to remove trends and seasonality.\\n\",\n \"- Subtract estimated trend and seasonal components.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"608a476b\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Unit root test\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4b624fcd\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The ADF is one of the most popular unit root tests.\\n\",\n \"- The presence of a unit root suggests that the time series is generated by a stochastic process with some level of persistence.\\n\",\n \"- This means that shocks to the system will have permanent effects. \\n\",\n \"- This is opposed to stationary processes where shocks have only temporary effects.\\n\",\n \"\\n\",\n \"> Where does the term *unit root* comes from?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"59914424\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Consider a simple autoregressive process of order 1, denoted as AR(1). \\n\",\n \"- This is represented as: \\n\",\n \"\\n\",\n \"$$Y(t) = \\\\phi Y(t-1) + \\\\epsilon_t,$$\\n\",\n \"\\n\",\n \"- where \\n\",\n \" - $Y(t)$ is the value of the series at time $t$, \\n\",\n \" - $\\\\phi$ is a coefficient, \\n\",\n \" - $\\\\epsilon_t$ is a white noise error term.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"13e7d08f\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- To analyze the properties of the AR(1), we can rewrite its equation in terms of **lag operator** $L$ where $LY(t) = Y(t-1)$. \\n\",\n \"- The equation becomes: \\n\",\n \"\\n\",\n \"$$(1 - \\\\phi L)Y(t) = \\\\epsilon_t$$\\n\",\n \"\\n\",\n \"- The term $(1 - \\\\phi L)$ is known as the **characteristic equation** of the AR(1) process.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f06d1804\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- The roots of this equation are found by setting $1 - \\\\phi L = 0$ and solving for $L$. \\n\",\n \"- The solution gives $L = 1/\\\\phi$, which is the **root of the characteristic equation**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"264af46c\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- If $\\\\phi = 1$, then $L = 1/\\\\phi = 1$, meaning that the root of the characteristic equation has a \\\"unit\\\" value. \\n\",\n \"- This is what is known as a \\\"unit root\\\". \\n\",\n \"- When an AR(1) process has a unit root (i.e., $\\\\phi = 1$), it implies that the series is non-stationary as it becomes a random walk: $Y(t) = Y(t-1) + \\\\epsilon_t$ \\n\",\n \"- The value of the series will be highly dependent on its previous values (the shocks to the series have a permanent effect).\\n\",\n \"\\n\",\n \"```{note}\\n\",\n \"The term \\\"unit root\\\" describes a specific condition in the autoregressive representation of a time series, indicating that the series is **non-stationary**.\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0a069fdb\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### More on the ADF test \\n\",\n \"\\n\",\n \"**Formulation of the test**\\n\",\n \"- The basic idea of the ADF test is to assess whether lagged values of the time series are useful in predicting current values. \\n\",\n \"- The test starts with a model that includes the time series lagged by one period (lag-1).\\n\",\n \"- Then, other lagged terms are added to control for higher-order correlation (this is the \\\"augmented\\\" part of the ADF test).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eca181a4\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**ADF test equation**\\n\",\n \"\\n\",\n \"The ADF test model the time series with the following equation:\\n\",\n \"\\n\",\n \"$$\\\\Delta Y(t) = \\\\alpha + \\\\beta t + \\\\gamma Y(t-1) + \\\\sum_{i=1}^{p} \\\\delta_i \\\\Delta_{t-i} + \\\\epsilon_t$$\\n\",\n \"\\n\",\n \"- where\\n\",\n \" - $\\\\Delta_t = Y(t) - Y(t-1)$ is the difference of the series at time $t$,\\n\",\n \" - $\\\\alpha$ is a constant, \\n\",\n \" - $\\\\beta$ represents the trend, \\n\",\n \" - $\\\\gamma$ is the coefficient on the lagged value of the series,\\n\",\n \" - $\\\\delta_i$ terms are coefficients for the lagged differences (account for higher-order correlations),\\n\",\n \" - $\\\\epsilon_t$ error term.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7107110e\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**ADF test for AR(1)**\\n\",\n \"\\n\",\n \"- Where does the model used in the ADF test come from?\\n\",\n \"- Consider the simple case of an AR(1) model:\\n\",\n \"\\n\",\n \"$$\\n\",\n \" Y(t) = \\\\phi Y(t-1) + \\\\epsilon_t\\n\",\n \"$$\\n\",\n \"\\n\",\n \"- Subtract $Y(t-1)$ from both sides:\\n\",\n \"\\n\",\n \"\\\\begin{align*}\\n\",\n \" Y(t) - Y(t-1) & = \\\\phi Y(t-1) - Y(t-1) + \\\\epsilon_t \\\\\\\\\\n\",\n \" \\\\Delta Y(t) &= (\\\\phi - 1) Y(t-1) + \\\\epsilon_t\\n\",\n \"\\\\end{align*}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3feecdc5\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"This is exactly the ADF model above when:\\n\",\n \"- $p=1$\\n\",\n \"- $\\\\alpha=\\\\beta=0$ (zero-mean and no trend)\\n\",\n \"- $\\\\gamma = \\\\phi - 1$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"afbf6a1e\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Connection to unit root**\\n\",\n \"\\n\",\n \"- If $\\\\gamma = 0$, then $\\\\phi = 1$ (unit root).\\n\",\n \"- This means that \\n\",\n \"\\n\",\n \"$$\\\\Delta Y(t) = \\\\epsilon_t \\\\implies Y(t) = Y(t-1) + \\\\epsilon_t,$$ \\n\",\n \"\\n\",\n \"- which is the non-stationary random walk.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae6a8441\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Null and alternative hypotheses (revised)**\\n\",\n \"- $H_0$: $\\\\gamma = 0$ the time series has unit root, i.e., it is not stationary.\\n\",\n \"- $H_1$: $\\\\gamma < 0$ the series does not have a unit root.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b431d34b\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"**Test statistics**:\\n\",\n \"- The ADF test statistic is the coefficient $\\\\hat{\\\\gamma}$, estimated from data.\\n\",\n \"- The estimated statistic $\\\\hat{\\\\gamma}$ is then compared to critical values for the ADF distribution.\\n\",\n \"- If the test statistic is more negative than the critical value, $H_0$ is rejected.\\n\",\n \"- If the test statistic is less negative than the critical value, $H_0$ cannot be rejected.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f6687948\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- For example, consider a confidence level $\\\\alpha=0.05$.\\n\",\n \"- According to the distribution of $\\\\gamma$, it correspond to a critical value of $\\\\gamma_\\\\alpha=-2.89$.\\n\",\n \"- If the test statistic $\\\\hat\\\\gamma$ is more negative than the critical value $\\\\gamma_\\\\alpha$, $H_0$ is rejected.\\n\",\n \"- If the test statistic $\\\\hat\\\\gamma$ is less negative than the critical value $\\\\gamma_\\\\alpha$, $H_0$ cannot be rejected.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b5931879\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Choosing lag length**:\\n\",\n \"- The number of lags ($p$) included in the test equation is important. \\n\",\n \"- Too few lags might leave out necessary corrections for autocorrelation.\\n\",\n \"- Too many lags can reduce the power of the test.\\n\",\n \"- The appropriate lag length is often chosen based on information criteria such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"2d215098\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ADF Statistic: -1.13\\n\",\n \"p-value: 0.70\\n\",\n \"Used Lag: 0\\n\",\n \"Number of Observations: 99\\n\",\n \"Critical Values: ['1%: -3.50', '5%: -2.89', '10%: -2.58']\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Generating a random walk time series\\n\",\n \"data = pd.Series(100 + np.random.normal(0, 1, 100).cumsum())\\n\",\n \"\\n\",\n \"# Perform Augmented Dickey-Fuller test. The lag can be set manually \\n\",\n \"# with 'maxlag' or inferred automatically with autolag\\n\",\n \"result = adfuller(data, autolag='AIC') # You can change to 'BIC'\\n\",\n \"\\n\",\n \"adf_statistic, p_value, usedlag, nobs, critical_values, icbest = result\\n\",\n \"print(f'ADF Statistic: {adf_statistic :.2f}')\\n\",\n \"print(f'p-value: {p_value :.2f}')\\n\",\n \"print(f'Used Lag: {usedlag}')\\n\",\n \"print(f'Number of Observations: {nobs}')\\n\",\n \"print(f\\\"Critical Values: {[f'{k}: {r:.2f}' for r,k in zip(critical_values.values(), critical_values.keys())]}\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6b319bc9\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Types of ADF tests**\\n\",\n \"- There are three versions of the ADF test depending on whether the equation includes the constant term $\\\\alpha$ and the trend $\\\\beta t$:\\n\",\n \" - No constant or trend (`'n'`).\\n\",\n \" - Constant, but no trend (`'c'`).\\n\",\n \" - Both constant and trend (`'ct'`).\\n\",\n \"- The choice among these depends on the nature of the underlying time series and on what we want to test.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"2d78d669\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Function to perform ADF test\\n\",\n \"def perform_adf_test(series, title, regression_type):\\n\",\n \" out = adfuller(series, regression=regression_type)\\n\",\n \" print(f\\\"Results for {title}:\\\")\\n\",\n \" print(f'ADF Statistic: {out[0]:.2f}')\\n\",\n \" print(f'p-value: {out[1]:.3f}')\\n\",\n \" print(f\\\"Critical Values: {[f'{k}: {r:.2f}' for r,k in zip(out[4].values(), out[4].keys())]}\\\\n\\\")\\n\",\n \"\\n\",\n \"# 1. No Constant, no Trend\\n\",\n \"series_no_const_no_trend = pd.Series(np.random.normal(0, 1, 200))\\n\",\n \"\\n\",\n \"# 2. Constant, but no Trend\\n\",\n \"series_const_no_trend = pd.Series(50 + np.random.normal(0, 1, 200))\\n\",\n \"\\n\",\n \"# 3. Both Constant and Trend\\n\",\n \"series_const_trend = pd.Series(50 + np.linspace(0, 20, 200) + np.random.normal(0, 1, 200))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"3a7b66b0\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Overview\\n\",\n \"\\n\",\n \"This lecture will cover the following topics:\\n\",\n \"- Introduction to the Kalman Filter.\\n\",\n \"- Model components and assumptions.\\n\",\n \"- The Kalman Filter algorithm.\\n\",\n \"- Application to static and dynamic one-dimensional data.\\n\",\n \"- Application to higher-dimensional data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import cv2\\n\",\n \"np.random.seed(0) # for reproducibility\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## What is a Kalman Filter?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The Kalman Filter (KF) is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies.\\n\",\n \"- It produces estimates of unknown variables that tend to be more accurate than those based only on measurements.\\n\",\n \"- Developed by Rudolf E. Kálmán in the late 1950s.\\n\",\n \"- Applications:\\n\",\n \" - tracking objects (Apollo project, GPS, self driving cars).\\n\",\n \" - image processing.\\n\",\n \" - economic and financial modeling.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Theoretical foundations\\n\",\n \"\\n\",\n \"- Dynamic systems are systems that change over time.\\n\",\n \"- They are described by a set of equations that predict the future state of the system based on its current state.\\n\",\n \"- The KF assumes the system is a *linear* dynamic system with *Gaussian* noise. \\n\",\n \"- Gaussian distributions are used because of their nice properties when dealing with averages and variances.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### What are the ingredients?\\n\",\n \"\\n\",\n \"**State** \\n\",\n \"- The true value of the variables we want to estimate. \\n\",\n \"- The state vector represents all the information needed to describe the current state of the system.\\n\",\n \"\\n\",\n \"**Observation model** \\n\",\n \"- Relates the current state to the measurements or observations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Noisy measurements** \\n\",\n \"- The process noise and measurement noise (assumed to be Gaussian) represent the uncertainty in our models and measurements. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"Example:\\n\",\n \"- Car moving at a constant velocity.\\n\",\n \"- Model: Car position = velocity $\\\\times$ time $+$ system noise.\\n\",\n \"- Measurements: position and velocity (which are also noisy).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### The role of noise\\n\",\n \"\\n\",\n \"The tradeoff between the influence of the model and the measurements is determined by noise.\\n\",\n \"\\n\",\n \"- If the model has relatively large errors, more importance is given to the latest measurements in computing the current estimate.\\n\",\n \"- If the measurement have larger errors, more importance is given to the model in making the current estimate.\\n\",\n \"- Therefore, you need to estimate not only your state but also the errors (the covariance) for both the model and the measurements.\\n\",\n \"- These must be updated at each time step, too.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Update strategy\\n\",\n \"\\n\",\n \"KF is a recursive algorithm:\\n\",\n \"\\n\",\n \"- Uses information from previous time step to update the estimates.\\n\",\n \"- Does not keep in memory all the data acquired so far.\\n\",\n \"- Has predictor-corrector structure:\\n\",\n \" - Make a prediction based on the model.\\n\",\n \" - Update the prediction with the measurements.\\n\",\n \" - Repeat.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Fundamental assumptions\\n\",\n \"\\n\",\n \"The Kalman filter approach is based on three fundamental assumptions:\\n\",\n \"\\n\",\n \"1. The system can be described or approximated by a linear model.\\n\",\n \"2. All noise (from both the system and the measurements) is white, i.e., the values are not correlated.\\n\",\n \"3. All noise is Gaussian.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Assumption 1: linearity\\n\",\n \"\\n\",\n \"Each variable at the current time is a linear function of the variables at previous times.\\n\",\n \"\\n\",\n \"- Many systems can be approximated this way.\\n\",\n \"- Linear systems are easy to analyze.\\n\",\n \"- Nonlinear systems can often be approximated by linear models around a current estimate (extended KF).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Assumption 2: Whiteness\\n\",\n \"\\n\",\n \"The noise values are not correlated in time.\\n\",\n \"\\n\",\n \"- If you know the noise at time $t$, it doesn't help you to predict the noise at future times $t+\\\\tau$.\\n\",\n \"- White noise is a reasonable approximation of the real noise.\\n\",\n \"- The assumption makes the mathematics tractable.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"```{note}\\n\",\n \"White noise contains a mix of all the different frequencies at the same intensity blended together. \\n\",\n \"\\n\",\n \"This is similar to how white light contains all the colors of the rainbow combined.\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Assumption 3: Gaussian noise\\n\",\n \"\\n\",\n \"At any point in time, the probability density of the noise is a Gaussian.\\n\",\n \"\\n\",\n \"- System and measurement noise are often a combination of many small sources of noise. \\n\",\n \"- The combination effect is approximately Gaussian.\\n\",\n \"- If only mean and variance are known (typical case in engineering systems), Gaussian distribution is a good choice as these two quantities completely determine the Gaussian distribution.\\n\",\n \"- Gaussian distribution have nice properties and are easy to treat mathematically.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Technical details\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Combining two sources\\n\",\n \"\\n\",\n \"Assume a car has a certain initial position $x_0=0$ and initial velocity $\\\\dot{x}_0=60 km/h$.\\n\",\n \"\\n\",\n \"If the speed is constant, we have:\\n\",\n \"\\n\",\n \"$$x_{t} = 1\\\\cdot x_{t-1} + \\\\delta t \\\\cdot \\\\dot{x}_{t-1}$$\\n\",\n \"$$\\\\dot{x}_{t} = 0 \\\\cdot x_{t-1} + 1 \\\\cdot \\\\dot{x}_{t-1}$$\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"In matrix form:\\n\",\n \"\\n\",\n \"$$ \\\\boldsymbol{x}_{t} = \\n\",\n \"\\\\begin{bmatrix}\\n\",\n \"x_{t} \\\\\\\\\\n\",\n \"\\\\dot{x}_{t}\\n\",\n \"\\\\end{bmatrix}\\n\",\n \"=\\n\",\n \"\\\\begin{bmatrix}\\n\",\n \"1 & \\\\delta t \\\\\\\\\\n\",\n \"0 & 1 \\n\",\n \"\\\\end{bmatrix}\\n\",\n \"\\\\begin{bmatrix}\\n\",\n \"x_{t-1} \\\\\\\\\\n\",\n \"\\\\dot{x}_{t-1}\\n\",\n \"\\\\end{bmatrix} = \\\\mathbf{A} \\\\boldsymbol{x}_{t-1}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"![](\\\"media/system.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The current position and speed will evolve according to this simple dynamical system \\n\",\n \"\\n\",\n \"$$\\\\boldsymbol{x}_{t} = \\\\mathbf{A} \\\\boldsymbol{x}_{t-1} + \\\\boldsymbol{w}_{t-1}$$ \\n\",\n \"\\n\",\n \"- where $\\\\boldsymbol{w}_t \\\\sim \\\\mathcal{N}(0,\\\\mathbf{Q})$ represents perturbations in the underlying dynamical systems (e.g., holes on the road).\\n\",\n \"- $\\\\boldsymbol{x}_{t} \\\\in \\\\mathbb{R}^{N}$ represents the state of the system.\\n\",\n \"- $\\\\mathbf{Q} \\\\in \\\\mathbb{R}^{N \\\\times N}$ is the *process noise covariance*, where $N$ represents the number of state variables (2 in our case, position and speed).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"Where the car will be after 1 minute?\\n\",\n \"\\n\",\n \"If we rely only on our simple sistem $\\\\boldsymbol{x}_{t+1} = \\\\mathbf{A} \\\\boldsymbol{x}_{t}$ without even consider the noise, the car will keep moving at $60 km/h$ and will be at $1 km$ distance.\\n\",\n \"\\n\",\n \"![](\\\"media/model_pred.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"Let say our car has a GPS.\\n\",\n \"- As all instruments, our GPS will be affected by errors and gives us measurements with uncertainties in it.\\n\",\n \"- In addition, our GPS can only measure the position but not the speed.\\n\",\n \"- Based on our GPS, after 1 minute we are at $0.8 km$ from where we started.\\n\",\n \"\\n\",\n \"![](\\\"media/gps.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let the measure of our GPS be \\n\",\n \"\\n\",\n \"$$\\\\boldsymbol{z}_t = \\\\mathbf{H} \\\\boldsymbol{x}_t + \\\\boldsymbol{v}_t$$ \\n\",\n \"\\n\",\n \"- where $\\\\boldsymbol{v}_t \\\\sim \\\\mathcal{N}(0,\\\\mathbf{R})$ represents the uncertainty in the measures.\\n\",\n \"- $\\\\boldsymbol{z}_{t} \\\\in \\\\mathbb{R}^{M}$ is the measurements vector.\\n\",\n \"- In our case, since the GPS measures only the position, we have $\\\\mathbf{H} = \\\\begin{bmatrix} 1 & 0 \\\\end{bmatrix}$ and $\\\\mathbf{R} = \\\\begin{bmatrix} r_{xx} \\\\end{bmatrix}$.\\n\",\n \"- In general, $\\\\mathbf{H} \\\\in \\\\mathbb{R}^{M,N}$ is a matrix that maps the $N$ state variables into the $M$ measurements and $\\\\mathbf{R} \\\\in \\\\mathbb{R}^{M,M}$ is the measurement noise covariance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"In summary, we have two different sources of information:\\n\",\n \"- a linear stochastic difference equation, representing our imprecise knowledge of a discrete-time controlled process;\\n\",\n \"- a source of measurements, that are noisy.\\n\",\n \"\\n\",\n \"![](\\\"media/inputs.png\\\")
\\n\",\n \"\\n\",\n \"How do we combine them to get the best possible estimate of our system variables?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### The KF algorithm\\n\",\n \"\\n\",\n \"The KF is an interative procedure that consists of two steps: \\n\",\n \"\\n\",\n \"- **Predict** (Time update)\\n\",\n \"- **Correct** (Measurement update)\\n\",\n \"\\n\",\n \"These two steps are used to update two quantities:\\n\",\n \"- The **state estimate**.\\n\",\n \"- The uncertainty about our state estimate, called **covariance estimate**.\\n\",\n \"\\n\",\n \"Before looking at the iterative procedure let's introduce this second quantity.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Covariance estimate**\\n\",\n \"\\n\",\n \"There are two sources of error in the estimate of the state of our system.\\n\",\n \"\\n\",\n \"- The *prior* estimate error $\\\\boldsymbol{e}^{-}_t = \\\\boldsymbol{x}_t - \\\\hat{\\\\boldsymbol{x}}^{-}_t$.\\n\",\n \"- The *posterior* estimate error $\\\\boldsymbol{e}_t = \\\\boldsymbol{x}_t - \\\\hat{\\\\boldsymbol{x}}_t$.\\n\",\n \"\\n\",\n \"Where:\\n\",\n \"- $\\\\hat{\\\\boldsymbol{x}}^{-}_t$ is the estimate of the state based only on the knowledge of the system, e.g., the dynamics equations.\\n\",\n \"- $\\\\hat{\\\\boldsymbol{x}}_t$ is the estimate based also on the measurement $\\\\boldsymbol{z}_t$, e.g., the GPS. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"Each type of error is associated with a covariance matrix, which reflects the amount of uncertainty in the state estimates.\\n\",\n \"\\n\",\n \"- $\\\\mathbf{P}_t^{-} = \\\\mathbb{E}[\\\\boldsymbol{e}^{-}_t \\\\boldsymbol{e}^{-T}_t] \\\\in \\\\mathbb{R}^{N\\\\times N}$ (*prior* covariance estimate).\\n\",\n \"- $\\\\mathbf{P}_t = \\\\mathbb{E}[\\\\boldsymbol{e}_t \\\\boldsymbol{e}^{T}_t] \\\\in \\\\mathbb{R}^{N\\\\times N}$ (*posterior* covariance estimate).\\n\",\n \"- In our case, we have $\\\\mathbf{P}_t = \\\\begin{bmatrix} p_{xx} & p_{\\\\dot x x} \\\\\\\\ p_{\\\\dot x x} & p_{\\\\dot x \\\\dot x} \\\\end{bmatrix}$ (for readability, the index $t$ on the matrix elements is omitted):\\n\",\n \" - $p_{xx}$ is the uncertainty about the position;\\n\",\n \" - $p_{\\\\dot x \\\\dot x}$ is the uncertainty about the velocity;\\n\",\n \" - $p_{x \\\\dot x}$ and $p_{\\\\dot x x}$ represent the correlation between the noise in the measurements of the position and the speed.\\n\",\n \"- $\\\\mathbf{P}_t^{-}$ has the same form.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"#### Predict\\n\",\n \"\\n\",\n \"- The dynamics equations are responsible for projecting forward (in time) the current state and error covariance estimates to obtain the *prior* estimate for the next time step.\\n\",\n \"- Such time update equations can also be thought of as *predictor equations*.\\n\",\n \"\\n\",\n \"State estimate update: \\n\",\n \"\\n\",\n \"$$\\\\hat{\\\\boldsymbol{x}}_{t}^{-} = \\\\mathbf{A} \\\\hat{\\\\boldsymbol{x}}_{t-1}$$\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"Covariance estimate update: \\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{aligned} \\n\",\n \"\\\\mathbf{P}_{t}^{-} &= \\\\mathbb{E}[\\\\boldsymbol{e}^{-}_t \\\\boldsymbol{e}^{-T}_t] \\\\\\\\\\n\",\n \" &= \\\\mathbb{E}[(\\\\boldsymbol{x}_{t} - \\\\hat{\\\\boldsymbol{x}}^{-}_{t})(\\\\boldsymbol{x}_{t} - \\\\hat{\\\\boldsymbol{x}}^{-}_{t})^T] \\\\\\\\\\n\",\n \" &= \\\\mathbb{E}[(\\\\mathbf{A} \\\\boldsymbol{x}_{t-1} + \\\\boldsymbol{w} - \\\\mathbf{A} \\\\hat{\\\\boldsymbol{x}}_{t-1})(\\\\mathbf{A} \\\\boldsymbol{x}_{t-1} + \\\\boldsymbol{w} - \\\\mathbf{A} \\\\hat{\\\\boldsymbol{x}}_{t-1})^T] \\\\\\\\\\n\",\n \" &= \\\\mathbb{E}[(\\\\mathbf{A}\\\\boldsymbol{e}_{t-1})(\\\\boldsymbol{e}_{t-1}^T \\\\mathbf{A}^T)] + \\\\mathbb{E}[\\\\boldsymbol{w}\\\\boldsymbol{w}^T]\\\\\\\\\\n\",\n \" &= \\\\mathbf{A}\\\\mathbb{E}[\\\\boldsymbol{e}_{t-1}\\\\boldsymbol{e}_{t-1}^T ]\\\\mathbf{A}^T + \\\\mathbf{Q} \\\\\\\\\\n\",\n \" &= \\\\mathbf{A}\\\\mathbf{P}_{t-1}\\\\mathbf{A}^T + \\\\mathbf{Q}\\n\",\n \"\\\\end{aligned}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"```{important}\\n\",\n \"- Both the state $\\\\boldsymbol{x}$ and the measurements $\\\\boldsymbol{z}$ are modeled as random variables (with associated process and measurement noise). \\n\",\n \"- As a result, the errors $\\\\boldsymbol{e^{-}}$ and $\\\\boldsymbol{e}$ are random variables themselves.\\n\",\n \"- The expectation $\\\\mathbb{E}[\\\\cdot]$ is taken over the probability distribution governing these random variables.\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"In summary, the Predict step consists of two equations to update:\\n\",\n \"- State estimate.\\n\",\n \"- Covariance estimate.\\n\",\n \"\\n\",\n \"![](\\\"media/update.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"#### Correct\\n\",\n \"\\n\",\n \"The measurement update equations are responsible for the feedback, i.e. for incorporating a new measurement into the prior estimate to obtain an improved posterior estimate.\\n\",\n \"\\n\",\n \"The posterior estimate $\\\\hat{\\\\boldsymbol{x}}_{t}$ is a linear combination of:\\n\",\n \"- The prior estimate $\\\\hat{\\\\boldsymbol{x}}^{-}_{t}$.\\n\",\n \"- A weighted difference between the actual measurement $\\\\boldsymbol{z}_{t}$ and a measurement prediction $\\\\mathbf{H} \\\\hat{\\\\boldsymbol{x}}^{-}_{t}$.\\n\",\n \"\\n\",\n \"$$\\\\hat{\\\\boldsymbol{x}}_{t} = \\\\hat{\\\\boldsymbol{x}}^{-}_{t} + \\\\mathbf{K}_t(\\\\boldsymbol{z}_{t} - \\\\mathbf{H} \\\\hat{\\\\boldsymbol{x}}^{-}_{t})$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The difference $\\\\Delta_t = \\\\boldsymbol{z}_{t} - \\\\mathbf{H} \\\\hat{\\\\boldsymbol{x}}^{-}_{t}$ is called measurement *innovation* or *residual*.\\n\",\n \"- $\\\\Delta_t$ reflects the difference between our imprecise prediction ($\\\\mathbf{H} \\\\hat{\\\\boldsymbol{x}}^{-}_{t}$) and the actual noisy measurement ($\\\\boldsymbol{z}_{t}$).\\n\",\n \"- A residual of zero means that the two are in complete agreement.\\n\",\n \"\\n\",\n \"![](\\\"media/innovation.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"The matrix $\\\\mathbf{K} \\\\in \\\\mathbb{R}^{N \\\\times M}$ is chosen to be the **gain** that minimizes the posterior error covariance.\\n\",\n \"\\n\",\n \"Derivation (sketch):\\n\",\n \"1. Define the posterior error $\\\\boldsymbol{e}_t = \\\\boldsymbol{x}_t - \\\\hat{\\\\boldsymbol{x}}_t$.\\n\",\n \"2. Define the posterior error covariance $P_t = \\\\mathbb{E}[\\\\boldsymbol{e}_t \\\\boldsymbol{e}^{T}_t]$.\\n\",\n \"3. Substitute $\\\\hat{\\\\boldsymbol{x}}_{t} = \\\\hat{\\\\boldsymbol{x}}^{-}_{t} + \\\\mathbf{K}\\\\Delta_t$ in 1).\\n\",\n \"4. Substitute the $e_t$ obtained from 3) in 2) and take the expectation.\\n\",\n \"5. Take the derivative of the trace of the result with respect to $\\\\mathbf{K}$.\\n\",\n \"6. Set the result equal to zero.\\n\",\n \"7. Solve for $\\\\mathbf{K}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"The previous derivation give us the following result:\\n\",\n \"\\n\",\n \"$$\\\\mathbf{K}_t = \\\\mathbf{P}^{-}_t \\\\mathbf{H}^T (\\\\mathbf{H} \\\\mathbf{P}^{-}_t \\\\mathbf{H}^T + \\\\mathbf{R})^{-1}$$\\n\",\n \"\\n\",\n \"Back to our car example, we have:\\n\",\n \"\\n\",\n \"$$\\\\mathbf{K}_t = \\\\frac{\\\\begin{bmatrix} p^{-}_{xx} & p^{-}_{\\\\dot x x} \\\\\\\\ p^{-}_{\\\\dot x x} & p^{-}_{\\\\dot x \\\\dot x} \\\\end{bmatrix} \\\\begin{bmatrix} 1 \\\\\\\\ 0 \\\\end{bmatrix}}{\\\\begin{bmatrix} 1 & 0 \\\\end{bmatrix} \\\\begin{bmatrix} p^{-}_{xx} & p^{-}_{\\\\dot x x} \\\\\\\\ p^{-}_{\\\\dot x x} & p^{-}_{\\\\dot x \\\\dot x} \\\\end{bmatrix} \\\\begin{bmatrix} 1 \\\\\\\\ 0 \\\\end{bmatrix} + \\\\begin{bmatrix} r_{xx} \\\\end{bmatrix}} = \\\\frac{\\\\begin{bmatrix} p^{-}_{xx} \\\\\\\\ p^{-}_{x \\\\dot x} \\\\end{bmatrix}}{p^{-}_{xx} + r_{xx}}$$\\n\",\n \"\\n\",\n \"Once again, the index $t$ has been dropped from the elements of $\\\\mathbf{P}^{-}_t$ for readability.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"The update equations for the position and speed become:\\n\",\n \"\\n\",\n \"- Position: $\\\\hat{x}_t = \\\\hat{x}_t^{-} + \\\\frac{p^{-}_{xx}}{p^{-}_{xx} + r_{xx}} \\\\Delta_t$\\n\",\n \"- Speed: $\\\\hat{\\\\dot x}_t = \\\\hat{\\\\dot x}_t^{-} + \\\\frac{p^{-}_{x \\\\dot x}}{p^{-}_{xx} + r_{xx}} \\\\Delta_t$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- One key observation is that the speed is updated even if we do not have a direct measure for it in our measurement $\\\\boldsymbol{z}_t$.\\n\",\n \"- The update is possible thanks to the term $p^{-}_{x \\\\dot x}$ in $\\\\mathbf{K}_t$ that relates the position (and its measurement) to the velocity.\\n\",\n \"- This showcases one of the main strengths of KF: it can handle *partial observations*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"Let's consider the two extreme cases for the values of $\\\\mathbf{K}_t$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"1. The measurement error covariance $\\\\mathbf{R}$ approaches zero, i.e., there is no noise in the measurements.\\n\",\n \" - The measurements are completely reliable.\\n\",\n \" - We have $\\\\lim \\\\limits_{\\\\mathbf{R} \\\\rightarrow 0} \\\\mathbf{K}_t = \\\\mathbf{P}^{-}_t \\\\mathbf{H}^T(\\\\mathbf{H} \\\\mathbf{P}^{-}_t \\\\mathbf{H}^T + \\\\mathbf{R})^{-1} = \\\\mathbf{H}^{-1}$\\n\",\n \" - $\\\\mathbf{K}_t$ weights the residuals more heavily: \\n\",\n \" \\n\",\n \" $$\\n\",\n \" \\\\begin{aligned} \\n\",\n \" \\\\hat{\\\\boldsymbol{x}}_{t} &= \\\\hat{\\\\boldsymbol{x}}^{-}_{t} + \\\\mathbf{H}^{-1}\\\\Delta_t = \\\\hat{\\\\boldsymbol{x}}^{-}_{t} + \\\\mathbf{H}^{-1}(\\\\boldsymbol{z}_{t} - \\\\mathbf{H} \\\\hat{\\\\boldsymbol{x}}^{-}_{t})\\\\\\\\ \\n\",\n \" &= \\\\hat{\\\\boldsymbol{x}}^{-}_{t} + \\\\mathbf{H}^{-1}\\\\boldsymbol{z}_{t} - \\\\mathbf{H}^{-1}\\\\mathbf{H} \\\\hat{\\\\boldsymbol{x}}^{-}_{t} = \\\\hat{\\\\boldsymbol{x}}^{-}_{t} + \\\\mathbf{H}^{-1}\\\\boldsymbol{z}_{t} - \\\\hat{\\\\boldsymbol{x}}^{-}_{t}\\\\\\\\ \\n\",\n \" &= \\\\mathbf{H}^{-1}\\\\boldsymbol{z}_{t} \\n\",\n \" \\\\end{aligned}\\n\",\n \" $$\\n\",\n \"\\n\",\n \"![](\\\"media/extreme1.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"2. The covariance estimate $\\\\mathbf{P}^{-}_t$ approaches zero.\\n\",\n \" - The model is completely reliable and the measurements are not accounted for.\\n\",\n \" - We have $\\\\lim \\\\limits_{\\\\mathbf{P}^{-}_t \\\\rightarrow 0} \\\\mathbf{K}_t = \\\\mathbf{P}^{-}_t \\\\mathbf{H}^T(\\\\mathbf{H} \\\\mathbf{P}^{-}_t \\\\mathbf{H}^T + \\\\mathbf{R})^{-1} = 0$.\\n\",\n \" - $\\\\mathbf{K}_t$ does not give importance to the residuals: $\\\\hat{\\\\boldsymbol{x}}_{t} = \\\\hat{\\\\boldsymbol{x}}^{-}_{t} + 0\\\\Delta_t = \\\\hat{\\\\boldsymbol{x}}^{-}_{t}$.\\n\",\n \"\\n\",\n \"![](\\\"media/extreme2.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The last thing that remains to update is the posterior covariance estimate $\\\\mathbf{P}_t$ to be used in the next predict step.\\n\",\n \"- The formula is:\\n\",\n \"\\n\",\n \"$$\\\\mathbf{P}_t = (\\\\mathbf{I} - \\\\mathbf{K}_t \\\\mathbf{H})\\\\mathbf{P}^{-}_t$$\\n\",\n \"\\n\",\n \"- Let's look again at the two extreme cases.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"1. $\\\\mathbf{K}_t = \\\\mathbf{H}^{-1} \\\\rightarrow \\\\mathbf{P}_t = 0$\\n\",\n \" - This is the case where the measurements are completely reliable.\\n\",\n \" - The only source of uncertainty at the next Predict step will be the one of the model: $\\\\mathbf{P}_t^{-} = \\\\mathbf{A}\\\\mathbf{P}_{t-1}\\\\mathbf{A}^T + \\\\mathbf{Q} = \\\\mathbf{Q}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"2. $\\\\mathbf{K}_t = 0 \\\\rightarrow \\\\mathbf{P}_t = \\\\mathbf{P}^{-}_t$\\n\",\n \" - This is the case where the measurements are unreliable and not accounted for.\\n\",\n \" - The posterior covariance estimate is exactly the same as the prior covariance estimate $\\\\mathbf{P}^{-}_t$ obtained in the Predict step using the model and not modified with the new measurements in the Correct step.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"Summary of the Correct step:\\n\",\n \"\\n\",\n \"![](\\\"media/correct.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"The whole process is summarized below.\\n\",\n \"\\n\",\n \"![](\\\"media/kalman.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Parameters estimation\\n\",\n \"\\n\",\n \"In general, both the measurement noise covariance $\\\\mathbf{R}$ and the process noise covariance $\\\\mathbf{Q}$ are **unknown**.\\n\",\n \"\\n\",\n \"- Estimating $\\\\mathbf{R}$ is usually done before starting to apply the filter. \\n\",\n \"- Measuring $\\\\mathbf{R}$ is usually possible because we measure the process anyway (while operating the filter).\\n\",\n \"- We can take some offline sample measurements to determine the variance of the measurement noise.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Determining the process noise covariance $\\\\mathbf{Q}$ is generally more difficult.\\n\",\n \"- We typically do not have the ability to directly observe the process we are estimating. \\n\",\n \"- Sometimes a relatively simple process model can produce acceptable results if one \\\"injects\\\" enough uncertainty into the process. \\n\",\n \"- This works well if the process measurements are reliable.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"\\n\",\n \"### Extensions and Variants\\n\",\n \" - The Extended Kalman Filter (EKF) is used for nonlinear systems by linearizing about the current estimate.\\n\",\n \" - The Unscented Kalman Filter (UKF) uses a deterministic sampling technique to capture the mean and variance of the state distribution.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Practical Considerations\\n\",\n \"- Designing an accurate model and having reliable measurements are crucial for effective filtering.\\n\",\n \"- Dealing with nonlinearities can be challenging and may require the EKF or UKF.\\n\",\n \"- Tuning process and measurement noise covariances $\\\\mathbf{Q}$ and $\\\\mathbf{R}$ is essential for optimal performance.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Python implementation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Next, we will implement the Kalman Filter in Python and use it to estimate the value of a signal from noisy data. \\n\",\n \"- Initially, we will construct the algorithm by hand so we understand all the steps involved. \\n\",\n \"- Also, we will generate datasets from scratch so we know what our true result should be and therefore gain insight into how well the Kalman Filter works. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### The algorithm \\n\",\n \"\\n\",\n \"This is the workflow of the algorithm we are going to implement:\\n\",\n \"\\n\",\n \"1. Make an initial estimate of your state vector and covariance matrix.\\n\",\n \"2. Predict the state and covariance for the next time step.\\n\",\n \"3. Compute the Kalman gain.\\n\",\n \"4. Make a measurement.\\n\",\n \"5. Update estimates of state and covariance.\\n\",\n \"6. Repeat from step 2.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"For one-dimensional examples, there are a couple of simplifications: \\n\",\n \"- the state vector is a single number (a scalar).\\n\",\n \"- the covariance matrix reduces to the variance (also a scalar). \\n\",\n \"\\n\",\n \"Therefore, only normal multiplication (as opposed to matrix multiplication) is involved.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Example 1: Static one-dimensional data\\n\",\n \"\\n\",\n \"- Let's start by considering a simple example: determine the position of a stationary car. \\n\",\n \"- We'll assume that all we are interested in is a one-dimensional value, such as the distance along a road from a landmark.\\n\",\n \"- We are going to try to predict the true position based on some noisy measurements of the distance. \\n\",\n \"- Since we are going to generate the noisy measurements by hand, we will know the true position and we can see how well our algorithm performs. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We assume the measurements to follow this distribution:\\n\",\n \"\\n\",\n \"$$z_t = \\\\mu + v_t \\\\;\\\\; \\\\text{with}\\\\;\\\\; v_t \\\\sim \\\\mathcal{N}(0, R)$$ \\n\",\n \"\\n\",\n \"- where $\\\\mu$ is the actual position and $R$ is the actual measurement noise covariance.\\n\",\n \"- Note that both $\\\\mu$ and $R$ are unknown to the filter.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"- In this lecture, we will get a basic understanding of the Fourier Transform (FT), Discrete Fourier Transform (DFT), and learn how a function can be approximated by a series of sines and cosines. \\n\",\n \"- In particular, we will learn the FT of common signals, the main properties of FT, and the practical skills needed to apply the FT.\\n\",\n \"- In addition, we will see some common filters and learn why they are useful to process time series data. \\n\",\n \"- Finally, we will see how FT can be useful in forecasting tasks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"# Install dependencies if the notebook is running in Colab\\n\",\n \"if 'google.colab' in sys.modules:\\n\",\n \" !pip install -U -qq tsa-course\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from IPython.display import YouTubeVideo\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from scipy import signal\\n\",\n \"from scipy.integrate import cumulative_trapezoid\\n\",\n \"import statsmodels.api as sm\\n\",\n \"from statsmodels.graphics.tsaplots import plot_pacf\\n\",\n \"from statsmodels.tsa.arima.model import ARIMA\\n\",\n \"from statsmodels.tsa.arima_process import arma_generate_sample\\n\",\n \"from tsa_course.lecture2 import run_sequence_plot\\n\",\n \"from tsa_course.lecture8 import annotated_sin_plot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Fourier Transform\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The idea behind the Fourier Transform is that any time signal can be decomposed into a sum of sinusoids that have different amplitude, frequencies, and phases.\\n\",\n \"- Let's start from the definition of a **sine wave**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- A sine wave is mathematical curve that describes smooth periodic oscillations.\\n\",\n \"- It is continuous and is described by: \\n\",\n \"\\n\",\n \"$$y(t) = A \\\\sin (2 \\\\pi f t + \\\\psi) = A \\\\sin (\\\\omega t + \\\\psi)$$ \\n\",\n \"\\n\",\n \"- where $A$ is the amplitude, $f$ is the frequency, and $\\\\psi$ is the phase in radians.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/ft.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"The Fourier Transform mathematical formulation is given by:\\n\",\n \"\\n\",\n \"\\\\begin{equation}\\\\mathcal{F}(\\\\omega) = \\\\int_{-\\\\infty}^{\\\\infty} f(t) e^{-i \\\\omega t} dt \\\\end{equation}\\n\",\n \"\\n\",\n \"where:\\n\",\n \"- $t$ is time,\\n\",\n \"- $f(t)$ is a continuous time signal,\\n\",\n \"- $\\\\mathcal{F}(\\\\omega)$ is the Fourier Transform of $f(t)$,\\n\",\n \"- $\\\\omega = 2\\\\pi f$ is the angular frequency,\\n\",\n \"- $i$ is the imaginary unit, satisfying $i^2 = -1$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Explanation**\\n\",\n \"\\n\",\n \"- The term $e^{-i \\\\omega t}$ is a complex exponential.\\n\",\n \"- It can be expressed as $e^{-i \\\\omega t} = \\\\cos(\\\\omega t) -i \\\\sin(\\\\omega t)$.\\n\",\n \"- $e^{-i \\\\omega t}$ can be thought of as a rotating vector (phasor) in the complex plane, where $\\\\omega$ dictates the speed of rotation and $t$ represents time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The integral over time indicates that the transformation considers all points in time from $-\\\\infty$ to $+\\\\infty$, giving a complete representation of the signal in the frequency domain. \\n\",\n \"- Since we cannot deal with infinity, various approximations are used in practice.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The result of the Fourier Transform is a complex function of frequencies.\\n\",\n \"- It shows how much of each frequency is present in the original signal. \\n\",\n \"- The *magnitude* of $\\\\mathcal{F}(\\\\omega) = \\\\mathcal{F}(2\\\\pi f)$ indicates the amplitude of a particular frequency component $f$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The intuitive interpretation of Eq.1 is that the effect of multiplying $f(t)$ by $e^{-i\\\\omega t}$ is to subtract $\\\\omega$ from every frequency component of $f(t)$.\\n\",\n \"- Only the components of $f(t)$ that have frequency $\\\\omega$ can produce a non-zero value of the infinite integral.\\n\",\n \"- All the other shifted components are oscillatory and integrate to zero.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Inverse Fourier Transform\\n\",\n \"\\n\",\n \"$$ f(t) = \\\\int_{-\\\\infty}^{\\\\infty} \\\\mathcal{F}(\\\\omega) e^{i \\\\omega t} d \\\\omega $$\\n\",\n \"\\n\",\n \"- The inverse Fourier Transform expresses a signal $f(t)$ as a weighted summation of complex exponential functions.\\n\",\n \"- The formula is very similar to FT.\\n\",\n \"- It shows us that $f(t)$ can be expressed as a combination of sinusoids.\\n\",\n \"- The sinusoids represent, thus, a *basis*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"![](\\\"https://upload.wikimedia.org/wikipedia/commons/b/bd/Fourier_series_square_wave_circles_animation.svg\\\")
\\n\",\n \"\\n\",\n \"![](\\\"https://upload.wikimedia.org/wikipedia/commons/archive/6/6e/20100707184430%21Convolution_of_box_signal_with_itself.gif\\\")
\\n\",\n \"\\n\",\n \"![](\\\"https://upload.wikimedia.org/wikipedia/commons/archive/6/67/20100707184106%21Convolution_of_spiky_function_with_box.gif\\\")
\\n\",\n \"\\n\",\n \"![](\\\"media/butterworth.png\\\")
\\n\",\n \"\\n\",\n \"![](\\\"media/hpf.png\\\")
\\n\",\n \"\\n\",\n \"![](\\\"media/bpf.png\\\")
\\n\",\n \"\\n\",\n \"![](\\\"media/bpf_composition.png\\\")
\\n\",\n \"\\n\",\n \"- In particular, we can implement a BPF using a LPF Butterworth and HPF Butterworth filters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/bsf.png\\\")
\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b57fa041\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"In this lecture we will learn about [Prophet](https://facebook.github.io/prophet/), a framework for forecasting time series developed by Meta (former Facebook) in 2017.\\n\",\n \"\\n\",\n \"- Prophet is based on an *additive model* where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. \\n\",\n \"- It works best with time series that have strong seasonal effects and several seasons of historical data. \\n\",\n \"- Prophet is robust to missing data, shifts in the trend, and typically handles outliers well.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"61266110\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"In the following, we will cover:\\n\",\n \"\\n\",\n \"- The main components of the Prophet model: trend, seasonality, and holidays.\\n\",\n \"- How to use the prophet library in Python to perform time series forecasting.\\n\",\n \"- Try more advanced options and configurations available within the Python library.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"6f6f9e21\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2026-04-09T15:31:46.033025Z\",\n \"iopub.status.busy\": \"2026-04-09T15:31:46.032922Z\",\n \"iopub.status.idle\": \"2026-04-09T15:31:48.127519Z\",\n \"shell.execute_reply\": \"2026-04-09T15:31:48.127291Z\"\n },\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"import pandas as pd\\n\",\n \"from prophet import Prophet\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import statsmodels.api as sm\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88dcb5f6\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5b8a3a3f\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Model components\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0992b033\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Prophet models a time series $y(t)$ as a combination of three components: \\n\",\n \"\\n\",\n \" 1. trend $g(t)$, \\n\",\n \" 2. seasonality $s(t)$, \\n\",\n \" 3. holidays $h(t)$. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b870ccb7\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The model equation is:\\n\",\n \"\\n\",\n \"$$y(t) = g(t) + s(t) + h(t) + \\\\epsilon_t$$\\n\",\n \"\\n\",\n \"- where $\\\\epsilon_t$ is the error term assumed to be normally distributed.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cee2de7f\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Trend component $g(t)$\\n\",\n \"\\n\",\n \"- The trend component models non-periodic changes in the value of the time series. \\n\",\n \"- Prophet provides two options for modeling the trend:\\n\",\n \" - Piece-wise linear growth model.\\n\",\n \" - Logistic growth model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"832249f7\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Piece-wise linear growth model\\n\",\n \"\\n\",\n \"- A piece-wise linear function can accommodate changes in the trend's direction over time.\\n\",\n \"- This is particularly useful for time series data exhibiting shifts in growth rates due to external factors or internal changes. \\n\",\n \"- This allows to capture and forecast time series trends that do not follow a specific form (linear or logistic).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"55621403\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- In a piece-wise linear trend model, the time series is divided into *segments*.\\n\",\n \"- In each segment, the trend is modeled as a linear function. \\n\",\n \"- The points where the trend changes direction are called *change points*. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"344b51e1\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Prophet allows users to specify the *maximum number* of potential change points or to let the algorithm automatically estimate it based on the data.\\n\",\n \"- Prophet automatically detects the change points, allowing the trend to adjust its slope at these points, hence capturing shifts in the trend's direction.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2f0f8d01\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Users can adjust the flexibility of the model in capturing trend changes by tuning the change point *prior scale* parameter. \\n\",\n \" - A higher prior scale makes the model more sensitive to changes (allowing more flexibility).\\n\",\n \" - A lower prior scale makes the model less sensitive to fluctuations (resulting in a smoother trend).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"101edfa3\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"The piece-wise linear trend model in Prophet is defined as:\\n\",\n \"\\n\",\n \"$$g(t) = (k + a(t)^\\\\top \\\\delta) \\\\cdot t + (g_0 + a(t)^\\\\top \\\\gamma)$$\\n\",\n \"\\n\",\n \"where:\\n\",\n \"- $g(t)$ is the trend component at time $t$,\\n\",\n \"- $k$ is the initial growth rate,\\n\",\n \"- $a(t)$ is time since the corresponding change point if $t$ is after a change point, or 0 otherwise,\\n\",\n \"- $\\\\delta$ represents adjustments to the growth rate at each change point,\\n\",\n \"- $g_0$ is the offset (intercept), i.e., the value at $t=0$,\\n\",\n \"- $\\\\gamma$ smooths the trend at each change point.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"83d835dc\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2026-04-09T15:31:48.129417Z\",\n \"iopub.status.busy\": \"2026-04-09T15:31:48.129275Z\",\n \"iopub.status.idle\": \"2026-04-09T15:31:48.131214Z\",\n \"shell.execute_reply\": \"2026-04-09T15:31:48.131036Z\"\n },\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def compute_a(t, change_points):\\n\",\n \" #return np.array([max(0, t - cp) for cp in change_points])\\n\",\n \" return np.array([np.heaviside(t - cp, 1) for cp in change_points])\\n\",\n \"\\n\",\n \"def compute_g(t, k, delta, g0, gamma, change_points):\\n\",\n \" a_t = compute_a(t, change_points)\\n\",\n \" trend = (k + np.dot(a_t, delta)) * t + (g0 + np.dot(a_t, gamma))\\n\",\n \" return trend\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"f2e602dc\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2026-04-09T15:31:48.132471Z\",\n \"iopub.status.busy\": \"2026-04-09T15:31:48.132377Z\",\n \"iopub.status.idle\": \"2026-04-09T15:31:48.135275Z\",\n \"shell.execute_reply\": \"2026-04-09T15:31:48.135092Z\"\n },\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"change_points = [3, 7, 11] # Times at which changes occur\\n\",\n \"k = 0.3 # Initial growth rate\\n\",\n \"delta = np.array([0.1, -0.2, -0.1]) # Adjustments to growth rate\\n\",\n \"g0 = 4.0 # Initial offset\\n\",\n \"gamma = -delta*np.array(change_points) # Compensations for discontinuities\\n\",\n \"\\n\",\n \"time_points = np.linspace(0, 15, 200) \\n\",\n \"g_values = [compute_g(t, k, delta, g0, gamma, change_points) for t in time_points]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"8ad1e078\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2026-04-09T15:31:48.136486Z\",\n \"iopub.status.busy\": \"2026-04-09T15:31:48.136408Z\",\n \"iopub.status.idle\": \"2026-04-09T15:31:48.205685Z\",\n \"shell.execute_reply\": \"2026-04-09T15:31:48.205460Z\"\n },\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"| \\n\",\n \" | ds | \\n\",\n \"y | \\n\",\n \"
|---|---|---|
| 0 | \\n\",\n \"2007-12-10 | \\n\",\n \"14629 | \\n\",\n \"
| 1 | \\n\",\n \"2007-12-11 | \\n\",\n \"5012 | \\n\",\n \"
| 2 | \\n\",\n \"2007-12-12 | \\n\",\n \"3582 | \\n\",\n \"
| 3 | \\n\",\n \"2007-12-13 | \\n\",\n \"3205 | \\n\",\n \"
| 4 | \\n\",\n \"2007-12-14 | \\n\",\n \"2680 | \\n\",\n \"
| \\n\",\n \" | ds | \\n\",\n \"y | \\n\",\n \"
|---|---|---|
| 0 | \\n\",\n \"2007-12-10 | \\n\",\n \"9.590761 | \\n\",\n \"
| 1 | \\n\",\n \"2007-12-11 | \\n\",\n \"8.519590 | \\n\",\n \"
| 2 | \\n\",\n \"2007-12-12 | \\n\",\n \"8.183677 | \\n\",\n \"
| 3 | \\n\",\n \"2007-12-13 | \\n\",\n \"8.072467 | \\n\",\n \"
| 4 | \\n\",\n \"2007-12-14 | \\n\",\n \"7.893572 | \\n\",\n \"
| \\n\",\n \" | ds | \\n\",\n \"
|---|---|
| 3265 | \\n\",\n \"2017-01-15 | \\n\",\n \"
| 3266 | \\n\",\n \"2017-01-16 | \\n\",\n \"
| 3267 | \\n\",\n \"2017-01-17 | \\n\",\n \"
| 3268 | \\n\",\n \"2017-01-18 | \\n\",\n \"
| 3269 | \\n\",\n \"2017-01-19 | \\n\",\n \"
| \\n\",\n \" | ds | \\n\",\n \"trend | \\n\",\n \"yhat_lower | \\n\",\n \"yhat_upper | \\n\",\n \"trend_lower | \\n\",\n \"trend_upper | \\n\",\n \"additive_terms | \\n\",\n \"additive_terms_lower | \\n\",\n \"additive_terms_upper | \\n\",\n \"weekly | \\n\",\n \"weekly_lower | \\n\",\n \"weekly_upper | \\n\",\n \"yearly | \\n\",\n \"yearly_lower | \\n\",\n \"yearly_upper | \\n\",\n \"multiplicative_terms | \\n\",\n \"multiplicative_terms_lower | \\n\",\n \"multiplicative_terms_upper | \\n\",\n \"yhat | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3265 | \\n\",\n \"2017-01-15 | \\n\",\n \"7.194104 | \\n\",\n \"7.259700 | \\n\",\n \"9.099478 | \\n\",\n \"6.696311 | \\n\",\n \"7.687318 | \\n\",\n \"1.017807 | \\n\",\n \"1.017807 | \\n\",\n \"1.017807 | \\n\",\n \"0.048299 | \\n\",\n \"0.048299 | \\n\",\n \"0.048299 | \\n\",\n \"0.969508 | \\n\",\n \"0.969508 | \\n\",\n \"0.969508 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"8.211911 | \\n\",\n \"
| 3266 | \\n\",\n \"2017-01-16 | \\n\",\n \"7.193085 | \\n\",\n \"7.611644 | \\n\",\n \"9.556202 | \\n\",\n \"6.692419 | \\n\",\n \"7.688378 | \\n\",\n \"1.343849 | \\n\",\n \"1.343849 | \\n\",\n \"1.343849 | \\n\",\n \"0.352294 | \\n\",\n \"0.352294 | \\n\",\n \"0.352294 | \\n\",\n \"0.991555 | \\n\",\n \"0.991555 | \\n\",\n \"0.991555 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"8.536934 | \\n\",\n \"
| 3267 | \\n\",\n \"2017-01-17 | \\n\",\n \"7.192065 | \\n\",\n \"7.405227 | \\n\",\n \"9.242724 | \\n\",\n \"6.688527 | \\n\",\n \"7.689437 | \\n\",\n \"1.132263 | \\n\",\n \"1.132263 | \\n\",\n \"1.132263 | \\n\",\n \"0.119603 | \\n\",\n \"0.119603 | \\n\",\n \"0.119603 | \\n\",\n \"1.012660 | \\n\",\n \"1.012660 | \\n\",\n \"1.012660 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"8.324329 | \\n\",\n \"
| 3268 | \\n\",\n \"2017-01-18 | \\n\",\n \"7.191046 | \\n\",\n \"7.139555 | \\n\",\n \"9.004732 | \\n\",\n \"6.684636 | \\n\",\n \"7.690496 | \\n\",\n \"0.965945 | \\n\",\n \"0.965945 | \\n\",\n \"0.965945 | \\n\",\n \"-0.066674 | \\n\",\n \"-0.066674 | \\n\",\n \"-0.066674 | \\n\",\n \"1.032619 | \\n\",\n \"1.032619 | \\n\",\n \"1.032619 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"8.156990 | \\n\",\n \"
| 3269 | \\n\",\n \"2017-01-19 | \\n\",\n \"7.190026 | \\n\",\n \"7.253944 | \\n\",\n \"9.063056 | \\n\",\n \"6.680744 | \\n\",\n \"7.691556 | \\n\",\n \"0.978926 | \\n\",\n \"0.978926 | \\n\",\n \"0.978926 | \\n\",\n \"-0.072261 | \\n\",\n \"-0.072261 | \\n\",\n \"-0.072261 | \\n\",\n \"1.051186 | \\n\",\n \"1.051186 | \\n\",\n \"1.051186 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"0.0 | \\n\",\n \"8.168952 | \\n\",\n \"
| \\n\",\n \" | ds | \\n\",\n \"yhat | \\n\",\n \"playoff | \\n\",\n \"superbowl | \\n\",\n \"
|---|---|---|---|---|
| 2190 | \\n\",\n \"2014-02-02 | \\n\",\n \"11.633695 | \\n\",\n \"1.224426 | \\n\",\n \"1.199734 | \\n\",\n \"
| 2191 | \\n\",\n \"2014-02-03 | \\n\",\n \"12.833715 | \\n\",\n \"1.900145 | \\n\",\n \"1.447990 | \\n\",\n \"
| 2532 | \\n\",\n \"2015-01-11 | \\n\",\n \"9.887262 | \\n\",\n \"1.224426 | \\n\",\n \"0.000000 | \\n\",\n \"
| 2533 | \\n\",\n \"2015-01-12 | \\n\",\n \"10.895078 | \\n\",\n \"1.900145 | \\n\",\n \"0.000000 | \\n\",\n \"
| 2901 | \\n\",\n \"2016-01-17 | \\n\",\n \"9.644750 | \\n\",\n \"1.224426 | \\n\",\n \"0.000000 | \\n\",\n \"
| 2902 | \\n\",\n \"2016-01-18 | \\n\",\n \"10.655336 | \\n\",\n \"1.900145 | \\n\",\n \"0.000000 | \\n\",\n \"
| 2908 | \\n\",\n \"2016-01-24 | \\n\",\n \"9.781168 | \\n\",\n \"1.224426 | \\n\",\n \"0.000000 | \\n\",\n \"
| 2909 | \\n\",\n \"2016-01-25 | \\n\",\n \"10.782345 | \\n\",\n \"1.900145 | \\n\",\n \"0.000000 | \\n\",\n \"
| 2922 | \\n\",\n \"2016-02-07 | \\n\",\n \"10.698693 | \\n\",\n \"1.224426 | \\n\",\n \"1.199734 | \\n\",\n \"
| 2923 | \\n\",\n \"2016-02-08 | \\n\",\n \"11.872211 | \\n\",\n \"1.900145 | \\n\",\n \"1.447990 | \\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"In this lecture we will cover the following topics:\\n\",\n \"\\n\",\n \"- Windowed approaches and nonlinear models for time series forecasting.\\n\",\n \"- Neural networks and the Multi-Layer Perceptron.\\n\",\n \"- A brief overview of Recurrent Neural Networks.\\n\",\n \"- The ESN, a randomized RNN from the family of Reservoir Computing.\\n\",\n \"- An introduction to dimensionality reduction with Principal Component Analysis.\\n\",\n \"- Examples of forecasting with MLP and ESN on real-world electricity data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"# Install dependencies if the notebook is running in Colab\\n\",\n \"if 'google.colab' in sys.modules:\\n\",\n \" !pip install -U -qq reservoir_computing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import time\\n\",\n \"import plotly.graph_objects as go\\n\",\n \"import statsmodels.api as sm\\n\",\n \"from sklearn.datasets import make_circles\\n\",\n \"from sklearn.neural_network import MLPRegressor\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"from sklearn.datasets import make_blobs\\n\",\n \"from sklearn.decomposition import PCA\\n\",\n \"from sklearn.linear_model import Ridge\\n\",\n \"from sklearn.ensemble import HistGradientBoostingRegressor\\n\",\n \"from reservoir_computing.reservoir import Reservoir\\n\",\n \"from reservoir_computing.utils import make_forecasting_dataset\\n\",\n \"from reservoir_computing.datasets import PredLoader\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Windowed approaches for prediction\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- These methods consider a fixed window of size $P$.\\n\",\n \"- Use the elements of the time series within the window $x(t-P), \\\\dots, x(t-1), x(t)$ to compute the prediction $x(t+\\\\tau)$, where $\\\\tau$ is the forecasting horizon.\\n\",\n \"- Different algorithms combine the elements of the window in different ways to make predictions.\\n\",\n \"\\n\",\n \"![](\\\"media/windowed.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- As the window slides along the time series, new predictions are made.\\n\",\n \"- An example of windowed approach is the linear AR model of order $P$: \\n\",\n \"\\n\",\n \"$$\\\\hat y(t+1) = \\\\beta_0 + \\\\beta_1 x(t-1) + \\\\beta_2 x(t-2) + \\\\dots + \\\\beta_P x(t-P)+\\\\epsilon_t$$\\n\",\n \"\\n\",\n \"![](\\\"media/sin.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Limitation of linear models\\n\",\n \"\\n\",\n \"- Linear models are widely used for their simplicity and interpretiveness. \\n\",\n \"- However, they assume a constant relationship between input and target variables.\\n\",\n \"- As such, they cannot capture these nonlinearities and interactions effectively. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Consider the following examples:\\n\",\n \" 1. Compute the *position* $y$ of a robotic arm given the positions $x_1, x_2, \\\\dots$ of the joints.\\n\",\n \" 2. Compute a trajectory $\\\\dot{y}$ given the angular velocities $\\\\omega_1, \\\\omega_2, \\\\dots$ of the joints. \\n\",\n \"\\n\",\n \"![](\\\"media/robot.png\\\")
\\n\",\n \"\\n\",\n \"- A linear model will fail in the second task.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Example: forecasting electricity demand\\n\",\n \"\\n\",\n \"- This is a classic example where linear models fail.\\n\",\n \"- Electricity demand is often governed by complex and nonlinear interactions with other factors.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Temperature and Demand**\\n\",\n \"- The relationship between temperature and electricity demand is typically nonlinear and can exhibit a U-shaped curve. \\n\",\n \"- Demand is low at moderate temperatures but increases sharply at high or low temperatures due to heating and cooling needs.\\n\",\n \"\\n\",\n \"![](\\\"media/load_vs_temp.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Time and Demand**\\n\",\n \"- Demand patterns vary significantly throughout:\\n\",\n \" - the day (peak hours in the morning and evening),\\n\",\n \" - week (workdays vs. weekends),\\n\",\n \" - year (summer vs. winter).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Advantages of Nonlinear Models\\n\",\n \"\\n\",\n \"- Nonlinear models, such as neural networks, SVM, tree-based methods, etc..., can:\\n\",\n \" - model the nonlinear relationships between demand and factors like temperature, capturing the U-shaped curve accurately,\\n\",\n \" - take into account the interactions between different variables, such as the combined effect of time, holidays, and temperature on demand,\\n\",\n \" - adapt to various patterns and changes in trends over time, making them more robust in dynamic environments.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Neural networks\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Neural Nets are powerful nonlinear models that are *universal approximators*, i.e., they can learn to approximate any function.\\n\",\n \"- Basic idea:\\n\",\n \" 1. Map the data into a high dimensional space (bless of dimensionality).\\n\",\n \" 2. Apply a nonlinear transformation.\\n\",\n \" 3. Data become linearly separable.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_3D(elev=20, azim=30):\\n\",\n \" X, y = make_circles(n_samples=200, factor=.01, noise=.15)\\n\",\n \" fig = plt.figure(figsize=(10, 5))\\n\",\n \" r = np.exp(-(X ** 2).sum(1))\\n\",\n \" ax = fig.add_subplot(1, 2, 2, projection='3d')\\n\",\n \" ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='bwr', alpha=0.5)\\n\",\n \" ax.view_init(elev=elev, azim=azim)\\n\",\n \" xx, yy = np.meshgrid(np.linspace(-1.5, 1.5, 50), np.linspace(-1.5, 1.5, 50))\\n\",\n \" zz = np.full(xx.shape, 0.6)\\n\",\n \" ax.plot_surface(xx, yy, zz, color='grey', alpha=0.7)\\n\",\n \" ax.set_xlabel(\\\"$z_1$\\\")\\n\",\n \" ax.set_ylabel(\\\"$z_2$\\\")\\n\",\n \" ax.set_zlabel(\\\"$z_3$\\\")\\n\",\n \" ax.set_xticks(())\\n\",\n \" ax.set_yticks(())\\n\",\n \" ax.set_zticks(())\\n\",\n \" ax2 = fig.add_subplot(1, 2, 1)\\n\",\n \" ax2.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='bwr', alpha=0.5)\\n\",\n \" ax2.set_xlabel(\\\"$x_1$\\\")\\n\",\n \" ax2.set_ylabel(\\\"$x_2$\\\")\\n\",\n \" ax2.set_xticks(())\\n\",\n \" ax2.set_yticks(())\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- This figure illustrates how mapping the data from a lower dimension (2D) to a higher dimension (3D) and applying a nonlinear function makes data linearly separable by an hyperplane.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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X7Yx8Dliy5vue52t+lkkkjCIIw/ciMaeFaGU9sA1eetS1iW2g3xIMjCFeJ6wL/+I/AyZPAli2jIVrhMLB8OYVlvfEG8K//OtsrnV2+8Q2yZ69ZA6xdSxZttm9v3kwbD5//PB3PyWBZ9NhDQxPfb3CQbPnjWcQFQRCE68c/Y7pcLsuMaWFKuNKsbR7/xeF31WpVZm0Ls4qIakG4ClwX+Pa3ge9/H8hmgf37gYsXqa+XCQaBZcuoEtvbO3trnU2GhoAXX6TqdDQ69uuBAAni/fvJwj1ZHnyQ/i4UWn+9UqE/Dz1EIlwQBEGYWlzXRalU0mKae6ZFTAvTyZXENoAGsc2CW8S2MN2I/VsQrkB/P/CZz9CYp1OnqB/4/HkShT09lDCdSNB9OzuBffuAY8eoSrrQOHyYgsk2bRr/PvE49VwfPDjx/Sbi1ltJWD/zDDkGuroojEwpqlCfOwfcfTfNqxYEQRCmhtmeMS0I43E1s7ZZRDffR2ZtC1OBiGpBmIBCAfj4x4HXX6ewq1SKBBxAo54uXKCE6/vuIxu4YZC4G5kasuCoVukYXKk6bBh038li28BTT5FA/9nPqPJtGOQcSKeBt7yF5lm3qpYLgiAI10arGdNSlRbmAlcjtq80a1vEtnA1iKgWhAnYvp36pDdupOo0QNVQwyBh19VFldmzZympulolUb1Q06YTCXr9tRrZ4VuhFP3h6v5kiUSAD30IeNvbgL17KSAtHKa+7WXLrj7ITBAEQWhN84xpFtMSBiXMda4ktvnfE4nt5pC0Vo8nLBxEVAvCOChFI6GCQQokW7yYKp+FwqggtCz6+qlTFMp1/jywYgUJu4XIpk0U2tbbS8norRgcpE2HW26ZmudctIj+XC2c1l6tUqV7oW6ACIIgjIfMmBYWKtcy/qvV9zZXtflnRsT2/EdEtSCMQ60GXLo0Krqi0dGQLcuiSqlhUHW0VAJOnybb95NP0m0LkUiEZnV/9rPA5cvUc+4nn6eNh7e9jcT3TKIUsGMHbZQcPEjvVSQC3H478OijrWdiC4IgLCQ8z9NiWmZMC8Iok5m17a9etxr7JY6P+YWIakEYB8siK7PjjN62cSMlgR8/TgLRtillmnuo3/9+EmgLmbe8hRLSv/tdsmWnUnQcs1lK/37Tm6jfeSZ/jygF/Pu/A1//Or1XixaRw6BUAn7wA7L5/9ZvAXfdNXNrEgRBaBdkxrQgTI7JiG2+r8zanl+IqBaEcQgEgK1bgR//GFiyhG6zLLpt+XKquA4PU0X2vvuAv/xLsn4vdEwTeNe7yN79yiuUhu55lNh93310/AIz/MmzcycJ6kSi0Soej9P4r+PHgX/4B3r/+L0WBEGY7/iTvF3XlSRvQZgixhPbwNiQNBHb8wMR1YIwAffdR3OX+/tHU78Ng8ZqdXTQXObLl4GPflQEtR/DADZsoD+zDffG12qte68Ng/rh9+wBXn0V+MVfnPElCoIgzCiu6+qZvTIWSxBmlqtNJG8W2wBaBqSJ2G4PRFQLwgRs3Qr8/M+TdTibpdnT4TAJtN5esg8/+ST15U7EwAD18547R/9ftYq+J5OZ/tew0BkcpB7qicLMDANIJoHXXhNRLQjC/IQtqJzkLWJaENqLa5m13Xw/Eduzj4hqQZgAwyArc1cX8PTTo2FkgQBVph97DHjzm8efy6wU8KMfkfX48uXR+3keibxf/mXqwZbPvOmjWqW++PFGfDHBII3lEgRBmE+MNxZLxLQgzA2uRWw330dmbc8cIqoF4QqYJonnBx8Ejh0j4RWJUFp0KDTx9z7zDPCFL1B1e8sWeiyAws7OnQM+9zkKO3vwwel/HQuVeJyOf7E48WzsYpEcBIIgCPMBHotVq9VkxrQgzEOu1kY+0axtEdtTh4hqQbhKgkHgppuu/v7FIvCtb5Fobu63tiwScMeO0X3uuuvKAl2YHMkkHd/vfIfcAa1+V9TrVNG+//6ZX58gCMJU0jxjGoC+eBYEYf5zJbHN/55IbDcHpbV6PKER+YQVhGli925KCF+2bPz7rFgBnDlDo6eE6eORR2hm9tGjZL33U68Dhw7RuLQ77piV5QmCIFw3nuehWq0in8+jWCzCcRxt8xZBLQhCc6p4q0o1i+16va435yqVig42rNfrcBxHjwjzC/WFjlSqBWGaGBigv217/PuEQiTy+vtnZk0LlTVrgA9/mOz2+/aRJZx7qOt1EtS//dsT28MFQRDaERbTPBZLZkwLgnAtTGbWtr963SocbSG2mYioFoRpYrzwMj9Kkai+mvsK18dtt9F88e3baXRWsUijtO69l5LYRVALgjCXkBnTgiBMJ5MR23zfhThrW0S1IEwTq1ePVkNjsdb3yeXoa6tXz+TKFi49PcDb305/BEEQ5hp8ActiWsZiXTtiVxWE62M8sQ1cedb2fBbbIqoFYZrYuJH+7NlDAWfNLW2eR/3Ud94JrFt37Y8/PAy88QbNzw4EKI183ToZzyUIgjDfkBnTgiDMBa42kbxZbANzf9a2iGpBmCYsC/jAB6i3et8+sh5nMvS1gQGgt5cq1O9//7UJYcehJOsf/YhmXwMk0ONxYPNm4IMfBJYsmfKXIwiCIMwwzTOmAYiYFgRhznEts7ab7zdXxLaIakGYRtauBf7wD4FvfpOqyr29dHsqRbOv3/EOEttXi1LA174G/Nu/0WNs2kTiXSkgn6de4aEh4I//GOjunp7XJAiCIEwvzWOxeMa0pHgLgjCfuBax3XyfVrO2Z/MzUkS1IEwzq1cDv//7NF7rwgW6bdkyYOnSa3+skyeBH/yABHNPz+jthkHzmG+6Cdi/H3j6aaqAC4IgCHMHpRRqtRqq1aqeHyszpgVBWGhcrY3cP2vbsiwEg8GZXagPEdWCMMUMDAA7dlAvdaVCVuy77iLBey1V6Va89hqFm61a1frrgQAJ7hdfpDCuVOr6nm++UquRc2D7drLQR6OUDn777aMW/bmCUsC5c/RaTpygDZa1a+mcm8zGjSAIM4/neQ1iWsZiCYIgjGU8se153mwspwER1YIwhbz2GvCFL5DNOxwmkbtnD/CTn5DIeeopqihPluPHKS18ouuszk7g9Gng0iUR1a3o7wc++Ul6X5QCIhES2a++CqxYAfzGbwA33zzbq7w6XBf4+teB732PgusiEXpNL75IfffveAdtrkiRSxDak1YzpqVfWhAEYe4holoQpogDB4BPfYqq01u2NAqZQgF4/nkSw7/3e5OfS30111kcqCjXZGOpVEhQ79gBrF9PIpRxXeDYMeATnwD+5E+AG26YvXVeLd//PvDVr1J1fevW0fdcKWo1+NKX6DW++c2zu05BEBqRGdOCIAhTRzuMypP6hSBMAUpRGvfwMFlvmyuD8TiwZg1Vsg8enPzzrFtHAn2iz46BAaCjA1i8ePLPM1/ZvZts3xs2NApqgDY61q8HLl4EnnlmVpZ3TWSzJKrjcXqv/dfihkHW72AQ+O53gVJp9tYpCALBSd6lUgn5fB7lchlKKQQCAbF6C4IgXCez/RkqoloQpoALF0isLV06foU4kSCb8fbtk3+eu++mquSlS62/Xq+TvfmBB+j55hq1GrBzJ/DxjwP/5/8J/H//H1nns9mpefyXX6b3Jxxu/XXDABYtos2P4eGpec7p4o036DyYaHzasmUUkLd378ytSxCERjjJm8V0pVIBABHTgiAI8wixfwvCFDA8TNXAKwVDRSLjC+KrYeVK6pH9yleAapWez7apcj00RIFVW7YAb3nL5J9jtujvBz79aaomex4dq3odeOkl6nV+6imyOF8PFy9ST/pEJBK0SZLNAun09T3fdDI0RH8HJvgUDwZHzw1BEGaW8WZMt+N8VUEQBOH6EFEtCFOAbZO4cRz693jU6+NXSa8GwwB+6ZcorfoHPwCOHCHRpBQFoD30EI3S6uyc/HPMBlfqdT56dLTXefXqyT8PC/WJqNfpvZzofWwHAoGJ2wAA+rrntf9rEYT5hMyYFgRBmFnaYbNSRPUCx3WBfJ7EWiIhKcGTZeVKqhpfvDh+wJXrkmC73mqraQJvexsJ6D17qEpu28CNN9I65mIBxN/r3LzpYFl0+9691Ov8G78x+ee59VZ6HqXGP06XLgGbNrV/T/q6dbRJkM+Pb/UfHqavrV07o0sThAVJqxnTYu8WBEFYGIioXqAUi9Rf+tOfkhA0DBJkDz0E3HMPEArN9grnFqEQ8OijwGc/S0Fi8Xjj15WicVjLltEs5KkgFgPuvXdqHmu2udpe51dfBd75zsnbsu++m8K9Tp6kzY/ma92hIdr8ePjh9t9gSqdpw+EnP6Fjk8nQxk4iQa/LcYCzZ4EHHxx/rrkgCNePzJgWBEGYXdoh/VtE9QJkeJiCoHbuJBHT0UGi78ABqgbu2gV8+MNkMZ7rZLMUDPbSS5SKnUjQpsFddwE9PVP7XI89Bpw4QRsV0ShVOgMBIJejHt3ubuDXf729+3Rni6vpdY7H6X7X0+u8eDHwwQ/S5sf+/RTyFY9TQNrFi+QkeNvbgPvvn9zjzwRKkZD+6lepql6p0GsJBGgu+dq1ZP/v66OK+/vfPzfdC4LQ7vjHYnmeBwAyFksQBGGBIqJ6gaEU8M//TOnGzVbbzk6qYD//PAntD35w9tY5FZw4QX24x4/T64xGqRJ54ADwwx9S8NW2bVP3fKEQ8Fu/RaLmpz+l1GXXJbH4pjdReNj69VP3fPOJSIQqqxPhOFSZvd7+4PvuI/H54x+TfX5ggATp2rXkNnjwwcnPEZ8JXnwR+Id/oBCyu+4CNm+m+drnztGG2fbtdNv7308bBN3ds71iQZhfuK6rw8d4xrRUpgVBEBY2IqoXGGfOUBjUihWtrbaxGFVwf/YzSpmea4FXzNAQCeoTJ6ha509I9jwKvvrUp4A/+7OptcaGQiRkHnuMrLeOQ1XVRYum7jnmI7feSgJ3ol7nixenrtd582bgppuo0pvL0fu2bNnESdrtQLUKfOc79O+VK+nvRIKO34YNtCl28SJtGvz8z5MlXBCE60cpNaYybZqmVKYFQRDagHYIKmvzrkFhqtm/n0TERBfbPT1UvTtwYObWNdW89hpV75oFNUC9suvXk/h4/vnpef5gkCqfGzaIoL4a2I5/6lTrROvhYdoMeeSRqet1NgwS6OvX08ZKuwtqgH4mT58Gli8f+7VolKrSN91EFvldu2Z+fYIw32g1Y9owDJkxLQiCIDQgonqBUamQKJnoOoBFS7k8M2uaDl54garu49l4DQPo6gJeeYXmSwuzy5IlwK/+Kr1fBw4Ag4PU51wo0OZIby/w1reSdXshMzhILQUTjWWzLDq/Bwdnbl2CMN9gMV0sFlEoFFCtVnVlmmdNC4IgCLOPUkqCyoSZJxajit9ENlvXpa9dKTiqXXFdsn9fKWgtFqOKXrE4P0LZ5jr330+25aefpsC8vj6qHq9eTb3ODz88N6rJ00kgcOWfX4Dus9CPlSBMBpkxPf9ph4tvQRDmH3LZtcDYupWs3wMDVKltxcWLZCPdsmVm1zZVmCYFXw0MTHy/apVCr2R8WPuwZQv1O1+4QPOXg0Hq/xeBSKxZQxsPQ0MUJtiKUonO6TVrZnZtgjCX8TxPi2mZMS0IgiBcK7L1usBYupRmG/f2UoW2mWyWbKOPPkoX73MRw6B5xMPDrftzmcuXgZtvBpLJGVuacBUYBp2nGzbQLGkR1KMsX06J9efOtU5L9zwK51u/nnqrBUGYGM/zUKlUkM/nUSwW4bouLMuSADJBEIQ5RDsElcnl6gLkfe+jXtWXX6b/ZzIkPgcHScA88QTwjnfM7hqvl3vvpZFJJ05Qxa7556y3lyzfDz44O+sThMlgGMB73kNukv37KQSvq2u0h7q3l0LXPvhB2YwQhInwJ3nzWCwR0oIgCMJkMdQsNJfkcjmkUilks1kkpUw4K9RqwOuvU/r1qVN0Ub5+PfW1bts2Py7IX34Z+NznSGz09JCIrlZpjFI4DLz3vcCTT07cmyoI7UhfH/Dd79I5PjREm2KpFHDHHXRO87gtYX4jv0uvHZ4x7R+LJcFjCwvDMGDb9mwvQxCEKYKDykKh0KzmX4ioFuA4JCzHS8qeyxw5Ajz3HM3mrlSoR3fLFgq9uuUWEdTC3GZwEDh/nkT1okUyvm2hIb9Lrw6eMV2tVlGv10VML3BEVAvC/KJdRPU8qEcKAAnjAweAnTuB/n6qyt58M3DrrUA8PvH3zoeq9HisX09/3v1u6iEPh8nuLtdR8x/PA06epJwA26Y2gLmaaD8eHR3jB5YJwkJHKQXHcbSY5iRvsXkLgiAIU808llMLh6Eh4DOfITt3rUbJ17Ua8OyzFPT01FPAxo2zvcrZJZWau8FrwrWzcyfwgx8Ahw5RGnYgACxeTA6Ft72NfkYEQZif8FisWq3WIKbbIchGEARBmJ+IqJ7jVKvApz4FvPIKsG5dYyWuXgeOHgX+/u+BP/kTGk0kCPOd554DPv95oFymtOx4nH4WLl0C/vmfgdOngd/+bRHWgjDfaJ4xDUDbvAVhzqIUUCrBOH0aOH4cGByEkUhAuS6waBFUJDIaHGNZNNJkPlsQBWEcZnvTVH7q5jhvvEFVufXrx4oE26YK9b59VLX+4AdnZ42CMFNcugR86Uv0702bRm/nededncCLL9LPy9vfPjtrFARhapEZ08K8I5eD+X/9XzC/+lUYly8DrgsDAEwTyjAAz4M+u00TKhSii8BFi+DddBO8Bx+Eeughmk0pm0qCMCOIqJ7j8Fis8apupgl0d9P9fvEXZSazML/Zvp3mj2/d2vrr0ShVrp97Dnjzm4FQaGbXJwjC1OF5nk7ydl0XhmGImBbmNhcvwvz852H91/8Ko1od+3W/mGZcF0apBJRKUAMDsA4cgPn1rwOZDNRDD8H9lV+BeuIJEdeCMM2IqJ7jnD8PJBIT3yeZpAre8LCIamF+s2cPtUBMdO3Q00PznHt7KXNgPqMUcOYMOVqKRdpE2LRJihfC3EZmTAvzDqVgPPMMrP/7/4bx0kswPG9SD8M/AYbrQg0MwPje9xB49VV4730v3I98hCxb8nMizDNmYZBVS0RUz3FCIUr+ngjHoQtoabER5jv1+pVHwwUClAx+pZ+buU6hQD3kr7wC5HJ0HaUUVeu3bAF+/ddlBJcwd+CxWCymeSyWiGlhztPXB/O//TdY3/gGcO4cjCkSCIZSNEv0wgWYf/M3MJ5+Gt4v/zLUk09CbdkyJc8hCMIoIrPmONu2Uc+0UuNvPl66RD2kcgEtzHeWLQP27p34PtksWcDn8yiqWo0mAjz/PIW1rVo1+vmQzwOvvUaV6z/+YyCdntWlCsKEjDdjWsS0MOdRCsbzz8P6q7+CsX8/MDw8ZYK6Ac+D4Xkw9u+nsLMvfxnu7/4uvA9+kMJ3BGEe0A7THcQAOMe5+27qmT5zpvXXs1mqyD3yyJUreIIw17n7bgoly+Vaf10p4OJF4K67KLRsvrJ7N+UorF1Lmwf+3zOJBFnA9+8HXnhh1pYoCBPCSd7FYhH5fB7VahWGYSAQCEjftDC3GRG41n/6Twg89RTMnTthFIswRkL2pg3Xpec5fhzWX/wFrP/1fwVOnZre5xSEBYSI6jnO8uXA+95HdtYDB0ZFdLkMnDgBnD0LPPYYcP/9s71SQZh+broJuPde4ORJyhDwb/rXajS3eulSCimbz7z0Ev3tH7Hnx7YpX+G55+i4CEK7oJRCrVZDoVBAoVBArVaDaZqwbRumaYqYFuY2jgPzX/4F1l/+JcxvfIN2gB2HbNozgWEA4TBQLsN49llYn/gE7TQLgnDdiP17HvDoo0AqBfzoR8DBgxReFghQHsWjj5KoFoePsBCwLOA3foMyBF55hRwcoRD1WhsGsHo1fX316tle6fShFG2opVIT3y+TAQYGaPOhp2dGliYI48Ji2j8WS2ZMC/MN83vfg/md79BupmHAqNVmdmezXgeyWRi2DQwNAceOwXzuOXjvec/MrUEQpoF22HAVUT1PuO024NZbSUTk82SBXb2a/haEhUQsBnz0o8Djj9MM974+Gjl3002UQRCNzvYKpx/TJPfKRHAOg2gWYbb5yU9+gmeffRZ/9Ed/JGOxhPnLxYswvvhFoL8fRl8f/XKqVhstVTNFvQ709sJQCmZvL1Q4DPXgg/M7bEQQphkR1fMIw6BAIkFY6BgGcOON9GehYRiU7v2DH1B7yHj09dHxyWTGv49S1FLiumQXF8eLMB2cPXsWzzzzDP70T/9UxLQwLzF274b5//6/MLdvJ/v15cswisXZXRMAdfkyDNeF9ZnPQL30Erzf+i2odetmdV2CMFcRUS0IgjDNeB6JWMehtO3xep2ninvvpX7p/n6gq2vs14tFchw+9FDrAMN6nRLCn3+erOSeRwWMRx6hfAZJDBemkkgkooPIBGG+YezcCeuv/go4dgxGqUS/CKrV2V4W4bpALge1Zg2Ms2dhfupTcP/sz+Z3kqcwL2mH3x8iqoW2pFQCBgfJmtrdLRUyYW5Sr1No2HPPUXia51H69v33k0BdsmR6nnfTJuDJJ4Gvf53mVS9dSq0grksj9vr7SVC3CjCs1YDPfx545hmqend308/h5cvAZz9Lveq/+7vA4sXTs3Zh4REKhVBtF5EhzHvUTNmty2WYn/gErE9+Erh0ifqnXZc+jGfD8t0CA6BfVP39UOvXwzh4EOb27fCeeGK2lyYIcw4R1UJb0dcHPPssjfrJZulifulS4OGHSQREIrO9QkG4Oup1Eqc//jFVgxctogDBbBb4yleoEvyxj9HYq6nGNIF3v5sqyk8/DRw/TtdyAIWSvfvdwDveQSFuzXz/+xR6uGoVWb6ZTIZe0/79JK7/03+SMX3C1MCiWinVFtUGQbhuqlVYf/ZnML/1LRj5PH0o8wdmvd42ohoAlOfBOHYM6sYbadzXt75FPUTLljXOYxQEYUJEVAttw7lzwN/+LXDkCDmPenqosnf2LPDpT9PF/G//9sIImhLmPj/+MYnTFSsak7jjcapQHzwIfOYzwH/5L9OzWWRZwNveRhXxgwepYh0KARs3jp8MXixShTqVahTUjG3TJsD+/fSYW7ZM/bqFhUckEkFlpkYKCcIMYPzLv8D8/veBQADKsmBUq/QBapqTs35b1miqpONMuSg3zp6F8dOfUkXddWEMDsK7806oJ56A2rx5Sp9LEKaaGXOfXAER1UJb4DjA5z4HHDsGbN5MFT0mmSQ7+IsvUtX6ve+dmTVxZU+qccK1Uq2SOI3FWgtY06SQsGPHgDfeAO65Z/rWEonQdICr4cgR4MKFiQPeYjEaqbpvn4hqYWoIh8OoycB0YT6gFLB/PwL/x/9BAhUYHcXgeaPimC8wrgYW04ZBjxGLAeVy42MEAvS1K419aMYwaI35PNTJkzBqNSjDgLF3L6w9e4Af/xjOn/wJ1JvedG2PKwgzTDu4nERUC23BgQNU+Vq7tlFQM9EoBS49/zxV3640g3ey1OvArl0k4I8fp9vWrQMeeIBGlrVamyA0c+IEOS8mSuPntro9e6ZXVF8LlQpdk10pwyAQoKq2IEwF4XBYKtXC3MfzYH7pS7D+/M9hXLhAt/lnFtbrVEHgWYZXK4A9j77HMEZFeSAwOhcxHqfqg+MAAwPXNvfaMHTV2ygWoUaEv3HxIon28+cR+J3fQf3LX6aLIEEQxkUkgnBdDAxQtY1TjTdsmJzwPHyYqnsTWbsXLSLhfeQIcOedV37Mvj6qAuZyo7bXNWvGbxEql6la/sIL9DuMRw29/DLw6qvU1/3rv07TMARhIqpV+pm40px4224vcRqL0TVbtdq63xqg6y/Hmb6NLWHhIUFlwnzA+PGPKeWbU1Ytiy44HGdUQCs1KoavRChE3+u69HjRKAnmYnFUYAP0i8Zx6GvxOF30uO7VWcR5XSOWcsNfTefK+NmzsN/9btSffhpYvXpSx0YQFgIiqoVJkc1SsvDLL5OwBkb7Ld/+dqq8XYsTo1xu3NBtBf/+uNK1V7UKfO1rwE9/Sr/b+PdCPA7cfDPwwQ9Sv3YzX/sa8JOfADfcQAnNzJIl9Dvq6adpM/gDH7j61yUsTBIJ2nwpFhvPpWaq1faaXLJhA7ByJdDbSz8HrcjnSXzffPPMrk2Yv4TDYQkqE+Y2lQrML30JGBqCEYmMWrFZoDZXpf2Cl8/5ZhFsWTTLsFIBqlWoUIhuUwpwXRiBAFUxRi6gVCQC1Gpk5/b3rTnO+OvmSjXvlhoGiXm2qFsWWcIvXEDgYx+D85WvSLCN0HYYhtEWvzuuIGMEYSy5HAWKffOb9Pl7003UW7liBdle//7vqZ/0Wkil6HfORBurlQr9/mgVoMS4LvBP/wT8+7/TfTdvprVt3Uri5aWXgL/7O2BoqPH7+vqoQr1oUWsRlEySEH/++dFNBEEYjxtuANavJ3E6HhwcdrX9zjNBKAQ8/jhdo/X3j/16uUyjwW67jdoiBGEqiIwk9YkFXJirGPv3wzxwgC5kIhH6MFVqVFyPd8Hv75e2rNE/AGBZUEuWQG3YANXRAUQiJB5ME0YkApVMQqXTUIsWwVu/fvTiqKsLCIehMhmojRuh0mmqZnPlHKAqSDw+ehvfbpp0sVUu065vtUoC3vNg7NgB4+mnp+0YCsJcR0S1cM388IfAzp1kp168ePR3QjRKQsK2aWTQpUtX/5jbttHvg2ax66e3l6poGzeOf5/9+6navGIFCWT/76tUiub37t1Lc4P97N1LYrm7e/zH7umh++zde9UvS1igmCaJU8ui3urmzaJymXr2b7uNzsl24k1vAt75TmB4mMLIzp8HLl4EDh2iTbN77qE2iCs5SwThagmP9NSIBVyYswwPA5UKVcu4z9k0G5O6W31o8tdCIaoEsLjlnmm+kBqpdKtly6BWrIC3YQP13I3MajQMAyqRAJYvh1q+nP4djVIfd2cn/TsaBYJBqGgU6o47aISWbY+Kau7dY7u6aY7+AYBSCdbf/d21h6EJwgJBLouEa6JQoIpuR8f4PZfLl1Pld/v2q3/clSuBe++l8Vn5fOPXlKKL+loNeOKJsX2qSgFHjwJf+ALwR39Ez7tvH3DmDP0+8WPb9HvouedI2DDFYuPvjlbw19qpB1ZoX+66C/jVX6Xzc+9e4NQpEtgHD1K19+67gaeear90ecsC3vMemkP9+OP0c25ZwC23AH/wB8Dv/z79DAnCVCGiWpjz2DYJVhbStk0fnn57dfPuKotYrhZzVZtt3bYNo1IBLl+GUSqRUA+HgVAIautWqO5uumCybUoav3CBLlCKRaitW+Ft2waVStH9e3qo2h0IwDBNsiKVy1Tt9v8Saq6q85ptm6zgx4/DkMqC0GbISK15Si5H1VbLog3EK6XozjXOnyfBPFGqsWnS5/6hQ8DP/dzVPa5hAL/yKyScX3qJxDBbwrNZqmK/5z00c9ePUsC3v01273yeNnUti0R4by/1Q99xR+Mc4M5Oeg0DA7QBANDX2X4+nkuLfydOx0xhYf5hGMBb30p9yq+8AuzeTef3pk3A/feTO6NdQ+8Mg9omtmwZ/bloN/EvzB9s24ZhGGL/FmaEKb0Av3AB5o4d9AFfr9MHZrU6+oEZiVBfmuuOfpiaJtTKlTDyeZoXyl8Lh+mXBI/HGqlWG319JNAzGWBgAGrpUmDJEhLJ588Dp0/DqNWAXI6E9pIlwMAAzEKBXzBZyTs7qZoQDkNt3ky2cMeB+Y1vkBj3j+jybwRwFXuksmC89BLULbdM3TEUhHmCiOoporeXbMcvv0zizjSBZctIBD788PhV3bnG1V5gW9bE2RitiMWA3/kd4MEHSVifOkWP8+Y3k+W0VXL3z34GfPnL1Ae9ejWJ6VKJBLnj0Pvy+utUBedKM/8+9T/WTTdR9W1ggNqRWtHfT7/Tbrrp2l6XsLBZvZr+zNR8dYDO8cFB+hlIJq9vI0hs3sJ0YxiGDisThOnGMIzrD8VTCub3vw/z29+mC4dwGCocJnHruiSOlaLKClekR2YWqq6u0bFa0eio1ZuDzTi5lWcXKkUV52yWqs5r19L3h8P07xtugCqVYD7/POB5MM6eHQ0544u1eh1Gfz9QKkGtWgW1efPoS7n5ZhivvDJapeaLJBbUlkVVc8+Dd8MNMA8ehFerXXm8hSDMEO0QUgaIqJ4Sjh+n8KtTp6jvdskS+kw9exb41KeoYvvhD7dvVWo8+DVUqyRalyyhnuNEgqrxrRK0AfocLhbJ0n2tBAJUwdu27cr3dRzgRz+ify9ZQn93ddHIrVSKHiuTIaHd3z+63v5+chH4+6eXLiU77ve/T+K+WYSUyyTQn3yS+sgFoR1xXRr/9vzzNOrOdUlU338/be4tWjTbKxSEsRiGIWO1hDmF8eyzML/8ZahYjCw9hgEsXw7DsqAOH6b+ahbKgB6LpVatgvfAAzBefx0YHBzthU6lgL4+EuUjqEKBvh4Ok1U7lYLaunXsxZdpktjOZGCcOEEieOnSxvvYtp6ZaAwPU7V+RIioBx+EOn8exunTo4LaH5oWCkGNzFFUq1bR2C1/VVsQ2oB2ENYiqq+TSgX47GepV3Lr1saqTiJBPcjPPUfBWb/0S7O3zmvBdalv+plnaKOgXqcNgS1bqGp8553AD35AArZVFWtoiFp/7rpretd54gRtaCxbNnrbihXUr1oq6UwOOA5w4QL9HqpUyEnw7neP3WR93/toI/jVV8lZwKOOBgZoY+G++2a22igI14LjUPL9D39I/+/poY2lbBb4l3+h8/p3f5ccH4LQboTDYbF/C3ODUgnm979PQtUvXiMReI8+CqOnB8brr0MNDpKwtm2ozk7qg77tNrJiB4MwcjmoS5fow7tSAeJxqGQS6OqCisdhFItQfX1QK1aQtfuee+jiqhVcLV+0CEahQD1usRj9EnBduhgNBKC2bqVK9vHjoyMcAgF473wnzM9/HshmSZzYtg4xU/U6EI/Du/9+GJ5HFvP5Yr8U5jzt0k8NiKi+bvbsoYrQunWtBWY8Trbin/6UQn9isZle4bXhusCXvgR85zv0Wbx4MX12Fot0Ub53L1VrV6ygwKW1a0cr8ErR5/ilS9RLvXbt9K61UKDfQ/6RiZ2dwI030tpGfg9oN1VfH1Wb77yTqnbNJBIkOm6/nd4vHoe0bh3d/957pZ9aaF+efRb43vfoGi+TGb09maTbDh4EPvMZ4M//XM5jof0IBoMiqoU5gbFvH4zz58l6DVA1emSmM8JhqNtvh9q0CcZPfgKlFNStt1KAi7+S1t0NtX49DKVINPf0ANEo1OLFdFHlusDhw1B33AH3qacQ+O//nXZIW4lqx4Fx+TLUkiUwgkGonh7g1CkYly7p/m61fDmwejUJ4meegTE4CBw4QFb0YJDCzW68Ecbx4yT4q1WynfPjLV6sQ9O8J54YP3xGEGaBdhHWIqqvEx5LONGm3eLFlE59/Dhw880zt7bJ8OqrdGG+aBG14zDBIF2onz4NPP008MEPklX6xAlqHTJNOg6ZDFXk3/Wu6f/MDYVoI7VaHRX2hkGzqYNBWtvAAIXHhUL0O+3JJ6lKPd5mbzRKI4Uefng0hTyRkJAmob2p1SjTIRJpFNSMZdG4u2PHKE/n3ntnfImCMC7SUy3MKYaHqYpQqcA4dgw4dw6G40AFAnqkFZJJCtPo7IQxNAScOUPiNBAACgVK6l62DO6TT8LctQvIZqFsG8jnYVy8CAwNQcXj8G6+GcbwMNwnnoD1b/8GnDxJQWWhEF10HT0K8/BhqmyPjOBSK1YAd94JVS7TLwfbHq0+KAUsXgzv538eyOdh7tsHFApQsRi8D38YxhtvwDh5Et7IBaBx9iwJcLYAJpOwfvADeNksvPe9b3Q2tiAIIqqvl3J5NINiPNh942uVaUuUIqu6Uo2C2s/KlVSdv3gR+N//d6pcHztGwrazE7j11rGtPNPF2rUklC9epN9djGlS4vINN5At//Rp4P3vB97xjqvvh7YsGRskzB1OnqQRcpxm3wpud9izR0S10H5IT7UwZ7AsYGgIxqlTNOoqHKZRVbUasH8/cPo01LZtgGlSVde2YTz/PIzz5+liMBKBt24dsHUr1Nq1cO+8E8bhwzB27IBRLlMPnevCKBRgfetbZMFevBjeTTfBGBigvumhIRhHj8IYHqaLzI4OqmT398OoVKA2bYJat452WpUCBgbo+U+fpir0xo1QTz4J513vooumWIyE+uOPw/r852Hs3Qvj0CFaSyhEr3HFCnjbtgHVKsyf/ARGoQD3ox8V65MgjCCi+jrp6BgNeRyvMsufue2+odffTwJ5ojAjw6A8jZ07qRp9++30ZzYIh4HHHqOe9myW1uXHssi2/uCDwIc+JC1AwvylWqV2hyud4yOFEEFoO6RSLcwVVDhMAhWgCyb/xV8iQZXd7duBNWtI3K5fD7zpTVT13bcPxmuvwTx3jiyMhgF0dMC7+264//E/wvzqV2EODkItWkSPbVkklE+dgnHgALy3vQ1q2TJY//iPlOS9ZMnouJNCgS5Gh4Zg7NtHc7NXriTBfuQI/aKoVKAWLYJ57Bjw138N74474D311Ogvj0WLaB1f+hLMU6eAjRvJrrdo0WhqeSwGFYnQJsBrr0G16qcThBnEMAwJKpsP3Hor9R/ncmNFHXPuHFVOp7vH+Hqp1+lz+UqVd9umXuZ24M1vpjGNTz89GkbG4UyDg9Rf/Vu/JYJamN/E47TJVCrR9c94VKvju1DmKsUivS4utAhzk1AoJD3VwpzAPHuWLoTq9bEVFcMA0mkYJ09CbdxI1WKAPqAHB2H+4Adkt162jD6sPQ/o64P5ne/AeOEFSuZeswaIx0mcHzkC4/Jlqt7UarD++q/p++p1YNWqxl62eJwu4vJ5WtfhwzBcF8aBAyTODYPE8d130wVruQzzlVcoqOxjHxsNBrIssqevWUMbAq2IRADbhvnCC3AffFBmLwoCRFRfN+vW0Simn/yERLP/800pEnyWBTzxRPv35SaTtP58fuIL83yexGo7YNtUhd64kcYIHT9Ov6NSKeqffuihxtFZgjAfWb2aeqb376efhVaUSrThNFvOkqlEKcqzePFFYNcuuo6MRGh02AMPTGyDF9oTEdXCnKBeh/Haa/C2bCFxzSnbkQiJ1nKZKsaZDFQ0ShckpglkszC//GWgWoXasGFUiJsmCd10Gua3v03zqG++GUZfH4wdO2jXMJmkixqlKHFyeBhGOEyVYz8jY71w5gwQCsHo7aXkWM+jfralS+HddNNoBSgSgVq1CuauXVDHj0PxhV2lAuPsWahWAR0+VCZD4rtUGj+oRhAWECKqrxPTBH7t1+ii7pVXqF0mkaC/cznqM/7gB0l4tzvxOPVa/vu/U+9xq43HapVe6333zfz6xiMQoAvp++7TrUhIJufeXHBBmCymCbzlLcChQ5Rav2RJY/GkWiWn4d13U5DfXEYpGhv25S/TtWt3N13TFovAV75CQvsjH6ERh8LcIRwOo9buwSOCUKlQz3JHB7ylS2EcPw7j3Dm6+ABopvONN5LgDATowzcQgPH66zAuXIDauLF1r6DnAZ4Ho1CAKhZh7N1LAr27e/T+jgMDoA/8apWSWJtDbAwDSCZpNBeP6rrxRqCnh0Ry83Mnk9Rn/cYbo6LaMOjP1SYqt4HtVhDaARHVUwCPYnr4YeDllwF2Bt18M81qXrlytld49TzyCPDaa8Dhw/Q57LeCVyrAkSPAtm3AbbfN1grHxzRHZ0sLwkLj7rup5eFrX6MAwXSafn5zObq2uuMO4Dd/88rtHe3O3r00dzsYBLZsGb09k6GZ9YcPA5/+NI0OE5fK3EHmVAtzgnAYKhQiwdvZCXXzzTQaaySsQsVilLR97hxUOKx7Uoxjx+giZSLLYjgMFIsUEHb5Mglev73c8+j/pgkYBgnwanVs34tlkT08GiWRP559ibHt0U0BAAiF4K1ZA3PHDkosB4BSCcbhw42Vb9uGeuCBxrmmgrCAmeOXV+2DbVN/9a23zvZKro/ly4Hf+R2aZ3voEF2A8+8Pw6DX95GPyGeoILQbhgG87W2U3/DKK8Drr9N11Q03UFjfbbfN/Z9bnlBQKtHrasYwyAa/bx/lBD355MyvUZgckv4tzCYXL17EK6+8gmKxiM7OTnR1daGrqwudnZ1I+PvhbBvq7rthfv3rNNrKMEho+61xSsEYHob35jeP7mK67sR9x6EQVCAAY3CQxloVCkAuBxUMkoBNpwHThBqZ4WooRX3WrUS149BFaTpN/67X6f/j4TiN9m3DgLr/fkqkzeWAs2dhvvoqiXi+z4itXS1eTI/P4yUEYZaQoDKhLdm0CfiLv6DP09dfJ1tlVxdw551UfZcwIEFoTwyDsh3WrgU+8IGJpxLMRYaGaCTYRBMKeDrMyy+LqJ5LSPq3MJPwBfjg4CBef/119Pf3w3EclMtlZLNZDA0NYc+ePahUKgiFQujs7ERPTw86OjrQsXEjejo7EThxgkLF/B+ySsE4cQKqpweev+9v8eKJR8X091MwWbVKu5+BABAMwqjXoS5epMpGVxdVs4NBqFoNRi5HwT2Dg/Q9ySSJ51oNSCbhPfooVcgvXgRWrGh9IMplerxNmxpuVrfeCu+hh2B+8YtUOfc8SjbnKni1CmVZMJ95Bviv/xXun//5/PplIwiTQES10JJkEnj0UfojCMLcZL5d41Qq+npxQsJhKrAIcwepVAszSaFQwO7du7F//364rqtvT6fTcF0XSilEo1Hd61+pVLBv3z44jgMAsFaswNKzZ5E4dgyZaBRdoRC6q1VEBwdJUP/arzX0/nm33Qbzu9+l2aXNfSm1Gow33qAPN7/4Nk0gHIbhOFCDg0C5TCOyzp2jSrZSNNfadakPe2CAKsnJJNSDD8J773thvvACzC9+EapVAq3j0AbA5s1QzWEbtg3vAx+A9alPUThbKESCf+RrqqeHRP6lSzC//nW4730v2YQEYQEjoloQBEGYE/DYrHJ54gkF5bIkgM81IpEIcrITIkwzlUoFu3fvxqFDh+A4DkzTRCwWQywW01+vVCqo1+swTRPpdBqO4yCXyyESicC2bRiGAbO7G73hMLyBAUrjrteRCgRgbN2K+Jo16HIcdB49iq6uLqRSKZgrVsB705tgfvObUEo1BpCdPUtzrxctgtq2DThxgoLKcjltHzcAKNsmm3YgoC05KpGAUSrBqNXIYh4MwnvySbh//MdAKgXv8ceBCxdg/vSngGWRGB5JIzeGh6FuvBHuhz7U0h5uvPIKjXtZswbKNEeTzKPR0ft3dwOnT8P65jfh/smfTPv7JwjNqKsN1JsBRFQLgiAIc4JUikaCPf104zWpH9clUX3vvTO/PmHyBINBqVQL04bjONi7dy/279+PQCCg+6Rd14VlWbh8+XLD/bu6unQFOxqNolqtolKpwLYsDOVy8DwPRjiMyLp1iEcigOOg6rqo1OsYzGZxbvdupNNpFAoFeJ6Hjo4OdPb0YPHDD6Nz92507d+PoGUBrgvj9GmoSAS4804SvUuXwotGSVgHAiRiIxESwmfPUg/3smVQixfDKJUorMw0qcfbssiqwwEaoRC8X/91qI0bYb7wAowzZwDPg0qn4T35JLwHHqCKcwuMEyfI6p1Oj2974p7xkyev+z0ShLmOiGpBEARhzsATCk6dovnc/ms916WAxRtuoAwIYe4QiUQk/VuYcjzPw6FDh3D8+HGUy2V4nod8Po9QKIRYLIZ8Pg/DMBCLxRAMBhEYEYnDw8Oo1+sAANPzkK5UUOrthVOtImMYQHc3nO5umKEQLg8MNDxnV1eXtonHYjFUq1UMDQ3BdV0cMU24GzfCzOWQ8Dwsi8eR7OlBVzaLzs5OJAEKCrvtNiAWg3H8OO0SlkrUO21ZwKpVUFu2QC1fTnZvriADQLkM4/RpGEeOjFq6bRvqoYfgPvAAjeHyPNqhvNLcUX7MqwnnmOtjJYQ5TTuElAEiqgVBEIQ5xIYNwK//OvCFL9B4rUyGgmeLRZpbvWYN8Nu/TbcLc4dQKCRzqoUpQymFU6dOYefOnRjyjYuyLAsdHR1wXReGYSCdTqNWq8EwDLiu23DfcDiMVCAA74034A4NwbQs5G0bIc9D7NQpFC5cgLd4MSKrViEcDmtBnsvltOuCLeSu66JarSKVSgGpFNyeHliWhYNDQ0AyCaNchspm0W1ZCJsmMpaF7htuQNfSpei4fBmBy5fJ4t3ZCe/hhxsTY/2p4pEIVZdbtVKY5jXNGVS33EKPNzQ0/rzSkZ/ZMT3ZgjDDtIOwFlEtCIIgzCkeeIDCbF9+mcaH1WrA0qXAQw/RvG6ZVz/3iEQiYv8WpoRz585h+/bt6O/vB0DiOBQKwbZtOI6DfD6vK8mRSAThcBjZbBaRSASZTEZfnNcrFVzetQtGPg+VSMAyTXRZFqqeB8RiSJdKqJ8/Dy8eh7dkCfr6+vQagsEgMpkMXNeF4ziwLAvFYhHVahWJRAL5fB6e5yEcDiO8bBlCly4BroucYaDPdXG2XoehFDKBAMqdnYgGAuhauhRdtRq6DQOdnodIqxFdjkNV5SkYcaVuuw3q5pupt5qTxRvuoIALFyiY7Z3vvO7nE4S5johqQRAEYc6xahX9efe7R8eytsFGtTBJgsGg2L+F66Kvrw/bt2/H8PAwbNvWVehgMIhSqYRsNqvvy+FknPQdDodRKpVgjgjVfD4PI5tFOJ9HJBaDbdtwARQ8DxWlANdFKBxGrFbDcG8vQp2dyGQyME0TSim4rotLly7p5zNNE5lMRlvKOQCtXq/DSKdxubOT0rsjEdiBADKWBU8peErBKhQwbNsY6ulBf28vXs3l4JgmYiMif3EggJRpoisQQObyZaCnB2rduus/oKYJ9z/+R1i///swTp+m3cpUij5oi0Xg8mUgGoX3sY+NVsCLRRrBVSoBkQjUhg0Tp0oKwjxCRLUgCIIwZzHNKSnKCLOMzKkWJsvw8DD27NmDixcvolKp6PMomUxCKYXh4WFEIhGkUilYlgXTNFEsFhsqy9FoFOl0GvV6HbZto6OjA05vLyzPQ8WyMOQbuxUCkDJNuIYBFY0iWiigODQEo7MTgUBAi/dgMKir5ABQLBZRKpUAAIFAAMlkEsViEUoppDZtgnX0KNTFizCVQp9tUyXY86CCQaTXrYPT1QWvWERycBBuJoOq6yJvGDhbLsMDANdFoFbDstWrEdu9Gx0dHejq6kJnZyeCk/yQVLffDvev/xrW//P/wNi3j/q66QVArVsH7zd/E9673gXU6zB/+EMYzz4L48IF3eetFi2CeugheG9/u3xQC9NGO1i/ARHVgiAIgiDMMmL/Fq6VYrGIXbt24dChQw1jdVKpFACqDpumCcMwUKlUEAqFkM/nqTpsGAiFQojH4zBNE47joFKpoFwuAxgR5LUasraNMICMZcFQChaAslK47HkYeRKEPA/pcBjVkefs6OiA4zi6T9sv3m3bRiaT0fbzaDSKUqkEFQggunkzhpNJYHgYdrWKcCCAaHc3vJ4eFAGUSyV4S5fCqtWQGRpCKRxG0fOQABCoVKDqdZiLF+N0LAa1f3/D8QgGg0gkElpkd3Z2IplMXtVxVnfcAeeLX4SxfTvN0y6XyR6UyQDZLMxvfQs4cQLmK69AJZNUJbdtus+lSzD/9V+BgQF4H/qQBJoJ8xo5uwVBEARBmFVCoZCIauGqqFareOONN7Bv3z64rqurwYFAAIZhIJvNapu1UgqZTAae56FWqyEajWqxGw6HdSo3k0gkEAwGSaTbNmKOg5LnIWwYKCuF8oh4tw0DMcOAXa/DMwyUXRelUgnFYhHRaBS2bSObzSIUCukKuWVZqNVqDeO7AoEAUqkUKpUKFIDMqlXwVqygIDXTxMVslqzWIMHevXIl3I4OqHPnEBmpzBcNA6loFMOdnXAzGVi2rXvFTdNEuVzG4OAg+vr6cOLECaTTaVSrVSildDWb/2QyGViWNfagBwJQ994LBIMw/+VfYJw7RzZw0wT6+mCcOQN1443Axo2UUD7yPVi2DCqRgPnTn0Jt3Qp1993Tc1IICxqpVAuCIAgLFs8Djh2j0VieR6NSt2y58pQXYX7Colop1TYXSEJ74TgO9u/fjyNHjsAwDMTjcdTrdXgjVeMB32gr27aRSqWglILjOFBKoVgswrIspFIpVKtVFItFhEIhJBIJBAIBWJaFbDaLfD4PADCiUSSVQgxAFUDYMBAxTTieB9s0kfc81CsVqFgMyjQRHhnTxVXzRCKBcrkMpRRqtRoKhQIAEtKRSASRSASe56FSqaBSqaBUKiEYDCIejyObzWqLOG8WAEB/fz89fmcnjHQamXAYVcdBPR5HyrbheZ62sA8ODupjYxgGOjo6YBgGlFI6w6C3txeVSgWHDx9GtVrVvd89PT3o6OjQojsSicDYvx/WZz4DFIvUKz1SdTZG7O7G2bNALDY2CTyZBHp7Ybz4ItRdd0n4hTBvEVEtCIIwj8hmgYsX6bpl8WK6nmk3Tp8GvvQl4MAByrPhgseqVcAv/AKle8t118JC5lQL4+F5Ho4ePYpDhw5hcHBQW6dt20Y8Hkcul9OjqwBoUcvp33wbj9JSSmlBXqvVYFlWQ8WaBTkyGXh9fajm8yjH4yiZJtKmiTqAnOsiVKsh6XkIrFwJo6cHpVKpQdhHIhHE43FUq1WEQqGGUV7NFWvLstDd3a1fGwvyer0O0zQxPDwMgKrVHLLGgrxaLqOYz+tjUKvVUK1WERsJWDMMA5ZlYWBgoKEqn0wmEQwG4TgOYrEYotEoarUalFI4duyYrvYDQDqVQs/+/UiWSuhcuRLdhoG0UjA8D8bgIFnBDQPGyZNQK1eOCSdTXV0wT5yAVyoBsdh1nhGC0J6IqBaEhU69DuzfDxw8SAonnQZuuQVYu1aUzRzi8mXghz+kMVM8arWjgwTq44+3z5ips2eB//E/gDNnSESz6K9W6Wuf+hS14j366KwuU5hhwuGwzKkWxnDy5Ens2LFDi0qAxCqLStd1EQgEUCqV4HkeQqGQDgrzj9JSSiGfz+tzzLIsLUArlQqSyaS2hQcCAQwMDJA4X7ECxunTSOVyMINBGMEgIp4Hu1JB2bJgrV6NXCKB2kjfdCAQ0JVv13W1wC2Xy0gkEtqebts2ksmktoXX6/XRKjTGBpl1dHTo12uaZkOyuGEYDYI8Fovp/nDbtjE8PKzFPB870zRRrVb1H4ASyf3BbvF4XAvyfG8vjg0MQCWTQLGIsGEgbpowlUJPZye66nV0myY6e3thX7wI1Zz4bRg6eE0Q5isiqgVhIXPuHPDZz5KgrtepF8pxgG9+E7jrLuDXfk3GYcwBenuBv/kb4PBhoKcHWL2abu/vB77yFWDfPuAP/oC+NpsoBXzjG2T53rqVqtNMKASsW0df+9rXgFtvpf0dYWEQDoelUi1oLly4gNdeew0DAwMIh8PIZDJQSunkbn8VmsO/uKrLYhmgKnfzfbmi3Tw/OplMolAowHVdhEIhhMNhBLu7YXZ3I3fqFCp9fTBG5kCnenoQ6OpCKZNBNBxGwrLgOI5O/2aBC9C5zZsAhmEglUqhVCrpinXR1zMdj8cRjUbhui6q1Spc10U2m0UwGEQsFkOhUIBpmkgkErBtW48AGxwcbKhCd3R0oFwuw3EcbYOv1+sIBoMNtnCAgswCgQCUUjqpvFQqwbZtlMtlVCoVGKUSwo6DcDyOIAAHQNHzUPY89HV0IDJiXc8lk0iaJjoLBXRZFrotCx2WhdTwMNTq1VKlFqYcf0jhbCOiWhAWKn19wN/+LTW2rl0LRKN0u1LkIf7JT4BaDfjYx2QURhvjecA//RNw5AiweXNjuOqyZSSkDx4ku/Uf/MHsmg96e4Fdu4DlyxsFtZ8VK8gWvnMn8NhjM7s+YfaQoDIBoL7o3bt3azu2aZooFAo6XKtSqcAwDESjUR3EVa/XkcvltKjkCnU+n0c4HEY6nda9xI7joK+vT/com6aJzs5ObXVOpVKo1+twHAemaY5Wj7u6YPf0IBWLAYYB1zRRL9Zx8XwZ+XwVgUAGtl1FIpFFOh1EMpnUopeDwvjiPxqNIhqNolKpIBwO6yo0p5T7q9CWZaGrq0tXmjnUrF6vw7IsDI6MuOJjEo/HdQ+367ool8swDAOZTAbVahWlUgmRSATBYBCmacKyLAwPDze4ROLxOBKJBBzH0WutV6sIeh6Knodh3/sVMgykQiG42SxgWYjX68hHo6jV6+h3HLzqeYDnIWxZ6MhksHj7dqRSKXR2dqKjo6N1KJogXCPtksMhorrN6e8HBgaogLh8uYT4zFuUAo4fp5JiuUwCd8sWYM2a6VNBzz/fWokZBpUIbRt49VXgwQeBO++cnjUI1w2fNqtXt55WYtskVHfvHrVczxbnzwO5HK1nPCyLTsEzZ2ZuXcLsw3OqJahsYZLL5fDGG2/g0KFDDbcnEgmEQiEopRCJRBAKhVCpVBAMBvV4LCYWiyEcDsPzPHiep23hAIlTDiALh8NUhR5J+c7lclpUsu2axSgLcs/zYFkW+kd6prNZA+fPA8VieiQTwoDrxmHbIXR1lbF5cwC5XE6vz7IsXV12XVcL92w2i8SIGyyfzzfYx62R6re/suy3hXueh46ODiil4LouLMtq6NMGgM7OTv293N/N48X8FXX/eLF6va7D0/g9UPE4hiIRhKtVpCMRmABMUIDb5WgURjoNlMsIGAbSto3yyNc7HAdeoQC3owOFVAo7d+7UazNNE8uXL0ckEtFjvjgUTRDmIiKq25TTp6k/cvt2oFCgqs7ixcBDDwFvectoUVGYBwwNAf/4j8Drr9Obzb1H8Thw223Ahz409T7YcplEdUfH+HMjYzFax0sviahuY44epVb4iVz66TQ5/Y8enV1RfbXwj4CwcOAL6Uqlgqj8glswlMtlvP766zh06JAWruFwWJ8PpVIJlUoFnudBKaVFbj6f132/Simd3M1WaoCEYjqd1n3SqVRKB5OxoPXfN5VKwfM8OI4D27ZRKpXgOI4OQ/M8D+FwGKVSGKdPh+A4BtLpHJQq671vx0nj4sUATLOMm26i3mUW+MPDw2Ns4fycpmkilUppMV+v1zE0Eo5hGEZDOBmHieVyOdi2jUQioZPF4/E4bNuGZVkwDANDQ0MNz8nW91qtpudU12o1BIPBMbb1aDSKSCSix4sZXV0onT8POxSCY5oojIh12zAQSaUQGTlG5VoNFddFGbSZkFy8GNklSxBwHJ1mzq/r7NmzDfZdfn+bZ2pnMhnZbBPaHhHVbcihQ8D//J9U1Vm8mCpQnkdBRP/4j1Rc/OhHpTVlXlAsAh//OO2erF4N3HDDqKLIZoHnngMqFeD3f39qd1KGh+nxr5RelUzSDo/Qtoy0+E1oaOCv+4o6s8LixST+h4ZoP6cVrkufdxNVs4X5R3jEhlWtVkVULwBqtRr27NmDo0ePasHrOI4WoP4+aMMwGiqutm2jXq9rW3ixWES9Xh9jC69UKsjn89oWzvOjC4UCotEoOkY+hNh27a/ysi2cK9h+W/jZsyYcpw+JhBr5fhuWlR7pSXahlIW+vhJ6e8tYtiyDcrmMarWqw8mCwSAMw0C5XMbQ0JAWlZFIBNFoFOVyucEWzsereX1sCweomsyhaMFgcDRoDfSzxdVwHkHGFfxMJgPHcVAsFhEOhxGPx3V4Wj6fb0gzDy9ZgmSxiPrQEILhMDoiEbhKAdUq3EoFlxctglq0CFAKpuuiK5GAk05DpVKIO44OT0skEhgeHtabCdFoFLGRC9pyuayPy5kzZ7Tt33EcdHR06Go2i+2gtKYteAzDaJsNFxHVbUa5DHz+8ySgt25tvFBetYq+/vLLwMqVwHveM3vrFKaIV1+lCvXGjZTUxLAFOxwGduwAXnsNeOSRqXte06Tn8AWbtMTzyI8rtC2pFP3tuuO/VfX66Ck1m6xYQcHyzz9Pa2nVV33+PPWB3377jC9PmEX8olqYv7iui0OHDuHo0aPI5/O6Mmvbtg7i8idjczuAX9wBaBDh8XhcB3uxrdkf2hWPxxEKhXQVmsPJOOQrn89rQR4KhfTz+tPCWfgPDFQwPFxDJJKBZQGep0ZSw/v089k22cKrVRK1sVhMz4XmirrfFh6PxxEMBsfYwmOxmA4+89vHufrtt4XzfOlCoYB6va6D3Tg8zZ8szseP+5kjkQgsy0KpVEI4HEaxWNTWb35f2LZe3bAB5VOnUBoeRmh4GBGlMGzbsDs6kFi9GpZvo/7y4CD9Yhrp++7o6EClUoHrujppnN+z/v7+hvC0dDqt3wcOT7t06RKq1SpOnjypz5tkMomuri709PQgnU6jq6tLbyAIwkwjorrNeOMN4ORJSsFttfESiVBx8YUXgCeflGDmOY3nUSU6FGoU1H7CYQoJe+454OGHp66/uquLdmaOHRtfaSlFFW2ZbdTWbNtGIvTCBcpdaEVvL7B0KXDzzTO6tDEYBvCLv0ifcQcOkDkjHqev1etkUXcc4L3vHb+SLcxPeJ6uJIDPT3j28Y4dO7RVGSDbdTKZ1H3QkUhEJ2NHIhE9SisQCOieagAoFosol8v6cVikcRWa06x5PJb/Obkiy1/PZDKoVCoIBAINadx8X14fifgAXLeEQKAOIA6lslDKg2GER/7YAAxYVg65XAVDQ0qvz7ZtVCoVPdKKbem5XG6MFZ1t635bOIvkXC4HYGw4WbVahed5yPtmVpfLZX1cQ6GQDicbGhpq6EePx+OIx+Oo1Wq6Ys628GKx2DDSzL7hBqTDYbjDw4BSiAUCKAcCqAYCCHuevq9lWfqxAOiKvb9KzrPCWbjze+2fRw6Q8OdjEolEEA6HUa/XoZTChQsXcPz48Yb3bPny5YhGo7qqLaFo8xdJ/xbG5dAh0loTOVoWLaLROceP0wW1MEcplYCLF4FMZuL7ZTKkmDjAbCqwLBLpBw5QHzcrGz+XLpH9+557puY5hWkhlQLe+lbgn/+ZPje6u0f3XpSit7FUImdLO7SMrF5N3Qxf/CJ9jlUqo/b0pUuBd7xD9nEWIoZh6LAyYX5x5swZbN++HUNDQ1ocGYahk71bjcfiCnE6ndbiyTCMhvtydVYpBc/zdB90sVhEKpVCoVDQlWl/4nW5XNY90gC0oONkbL8tvFwuN9iuXdeE53UCqIHEcwqeV4NSzohQ7gegRuY72+jqSo58n6sfr1wu6zRu/8zq8dLCQ6EQYrEYSqUSQqGQtoXz624OJ+vq6tKCNJFI6NCxcDisZ1YDo6O+TNOE47NnF4tFXe0dHBzUCeqmaer51n35vLZGWZaFVDKpN8R4ffV6HYFAoGF9hmGgo6NDp7HHYjGUy2Xk83lkMhkUi0XUajW9qRIOh2HbNmq1mhblvPZwOIxsNotIJNLQc+26Lo4dO9ZwTDo7O/Vzs9CWUDRhqhFR3WZUKld221oWXSzPdn+kcJ2wBdtneWqJ52EkXnRqn/+BB0hUP/MMCffFi8m3Vi5TadNxgHe/m2wTQlvzcz9Hnx0/+AGwd+9oxlyxSG/te98LPP74bK9ylLVrgf/tf6NNxFOnyCHY1UXW8HYQ/sLMYxiGjNWaZ1y8eBFvvPGG7msOBAIoFot6/rFfILEFmXuluYrK86JzuZyu4LIg8zyvwRbOQWYsOHksVKVSgW3bDbZwruT607gBsoL707hN09T2bbJgezh9ehiVijMyjcWCZaXheTkoVYNlZeC6gON46OoyGnqbAbJds/jjHu9SqaQt6SxMLctCLBbTtnXuK2a7dDQaRTabHXdmtd9KnRnZuK/X6w3hZM0im9ekw8kALXoDgYAOiANo84NFr+d5ep51qVTSx7ZSqehANA4nsywLAwMDDetje77rug0jwfjY+D8TuD/c72wol8twXRfRaFSHuwWDQb2RYhgGCoUCyuUy+vv7ceTIEWQyGZRKJQQCAS2wWWzzOSYI14qI6jajq4tGAys1vtO3WCRX8Gz3RwrXSSRCgnX7dvLvjsfAAHDvveNbxK+W06eBZ5+l/qaODuCuu4Df+A1gyRKylx85MmqTWL2ayp8PPTS7g42Fq8KyaP/j7rup/f74cXrb1q2jt3nFivZ7Gy2Lprlt3jzbKxHahXA4LPbvecDQ0BB27949plqYTCYRDoehlNKBYhzK5RfSQKO44zRuPjeCwaC2QIdCIYRCIT0ey98HDYymSZdKJT0yynVdBIPBloKSBTWP0yqXy1rss2Dr6DBw/nwEoVASgYAHpWoATHheAUAA+XwK8Xgetu3qmdk8Imt4eLhBJCaTSUQiEVSrVV055gpvsVjUrxMgW3R3dzccx9EJ2eVyWSeac+iZ3xYOUE4Bz6wGSGS7rovBwcExtvBcLtewUcEbGRy01tHRod8npdSY8LTu7m59TFmQFwoFZDIZnS7OGxW8kVIul3XSOn+faZrI5XKIRCI6xIxfS19fX8NzdnR06Op2R0cHHMfRx8TfS25Zlj4f+Bwol8s4c+YMstksDhw4oDdgOBStu7tbV7fZni60FxJUJozLbbcB3/72xOm4584BN91E1R5hDmMYVC3esYOSuDlxyk82S+rjgQcmr4ouXQL+23+jGW0jPVAIBKg5/8EHgf/lfyEBffQoUK2SFXzDBqpaC3MGw6C9kNWrZ3slgjA5OMxJmJsUCgXs2LEDR48eBTDaB822a64WAiTIUqmUvr3VeCzuvQVGbeFs5+bxWPxYzRbyVCql5zezpbpWqyGdTqNUKiGfz+sQLv/6WDgDaBD+bAunYDQDlUodQ0OXYdtqpF3PgOt2oVh0EIkA69cnEI9XUK/XEYlEGqrHwWAQyWRSz4Su1+taWCYSCSilMDg4iGAwqAPFTNNErVZrEJR8HEqlEpRSyGQy8Dxv3JnVbOEGSCzzZkMoFGqokodCIf2+8drYFh4MBhGLxTA8PKzHkPH4LqVUw/r4OR3HgeM4SKVScF1Xz8luHjHGQtu/kVIsFnXvO28wsPWbRW4+n9drNwwDmUxGH08+xzhVvrmKH4lE9DHn95sr7oVCAQcOHNCPm0wmsXz5csRiMV3dllA0wY+I6jbjhhuA++8nG6dlNeoszwPOnqWC5ZNPTr0bWJgF7rgDeNObSPCyBTsQIG//xYskqp94Arj11sk9/uXLJJpfeIF8tatX04lVrVLF+tvfpkr4X/3V5J9DEAThOpGe6rlLpVLBrl27cPr0aV0N5FnKzcndzX3QlmWhVqshn89rwVur1XQ1k6uolUoF2WxWCyIWfmzR5sd0XXeMyPYLToDsxmylZls4V18DgQDi8TgCgcC4adyFwjBuuCGAVCqOixeDyOcNAArB4CC6uz10d9Ov8UiE1lSv1/V86Hq9Dtu2x4g7fq3+/3PImOu6OmiNNyoikQg8z9MWa04WT6fTOs2cbeHcvz44ODgmFT2ZTMJxHESjUR1OxvZ8fzhZMBjUQWEsQHl98Xhc94CzLZxFOwfK8WtlkZvP5xsC5XiEl/99Y2s2V+65154fyy/gbdvWYXX+KnSlUtEim99v/yzvcrnccF7xpgFv9HR0dOhzVSmF/fv3N5z7PT09CAaDuprd1dWFTCYjoWgzhFJKgsrmEkoBZ86M9v11dlKVeLqKeIYB/Mqv0HO9+CI9dyxG/y+XySX83vfKuJl5QyAAfOhD5Pt/9llKbgLoRFi8mJpl3/52ut9k+MIXgFdeoRPXH4gWDpPtu7+f/ML/9m/AH/5h+3mEBUFYMEhP9dyiXq/jwIEDOHr06Bhhwj2/bKM2DAOe5407Hsvfw2vbtp63nM/nteBlocj9t3x7sVjUQso/Hosr0IZhNFi9lVK66lypVBCPx2EYBur1essKKtuyeY1su16xwsXy5TVcvpyHUkAoZCCTIds1Pzb3dPv7jIvFoq4EcxV6aGioIXU8EonoajyLNp7F3RxOZhgGenp64DhOg+AtFotIp9P6tftt17xR0Wy7ZpdAc/hX85xstlpXq1W9acGvlRPX/eO+/P3rvNGQy+WQTCbhuq4eccbOAd7UyOVy+n3mx8nlcnojha3cgUBgTJWcxTO/NnbCsNjn6jY/bywWg+d52gGRy+UQCASQTCaRy+VgGEZDfz2fV7VaDefOndPnBv8M+OdpSyjawkBE9QT09gJf+QqwezeQz5PesG0q9r3jHdS/OB0aJBoFPvIRGkv86qtUnbZtYMsW4M47Kf1bmEcEg8A730lJUgcPUlRzNAps2tQ6lftquXAB+PGP6SRtZS03DBLaFy+SoH/3u8efySQIgjDNSKV6buB5Hg4dOoTXX3+9YaxVLBbTwVVs0y6VSnBdVyc1A2joM2ZhwsKOw8a4BzgajWqbNPdSc1gWMGr1ZtGYTCb1+CnPN96Jn5f7jOv1un6OYrGo+3ILhYK+n2VZuoLa3GfMqdqhkI1Vqzoa1n/p0iV9X8Mw0NXV1ZA0ztZknqnNYjkcDuvqLY/24r/9adfc58xW7ubeZmB0XJXneVe0XcfjcV355s0Jtl1zJZ+PH9uueXyZv12DA9FKpZK2t7PtemhoqKFK7k8xt21b98wbhgHHcRps+OFwGKlUSieKsy28VCrpxHDHccb0r+fz+YZRajzeyx/2xu9LvV5vmaLOa+YNkXK5jFAo1PB6OJ3dsiy9scDnDL+fuVyuwTbOvdoSija/EFE9DhcvAv/jf9AY3xUrgFWrSIOUy5T39PGPU6DYQw9Nz/ObJmmqTZum5/GFNiQep12TqeLUKaCvj/oFxusVCARox+by5YkHHQuCIEwzoVBIeqrbGKUUTpw4gR07dmhhEg6HdQ9vPp9vqLZy4BOHUyV9Y5eUUg1ClfuMAWiLL49RYttvoVDQ92NBzuOn/M/J46e4D9sv8tlezEKG7b0AiS6elc7habzJwyIsEAg0jJ9ikRWNRjE8PKz7oNnW3DwGDKDKPFdtOcm6UqloO7s/sI17zTntmnu8Aej0bn7dftt1oVBo6Eln8dbKdp3NZhvEp23b2nZtWdY12a45sbuV7Zrt85ZloVKpYHh4WD+mbdtIJBLI5/MNtmveLPFvVPDrYZLJJKrVqu5994fVBQIB3Svtui6q1aq2y7MVfHh4eCTZPYFAIKDdDc0Wfbau1+t1pEYKFewkaH7f+Liw8I7H4yiXy7h06RJyuRxef/11fb+Ojg4sXrwYqVRKi24JRbt6JKhsDvDNb1Ju05YtjSOuKIACOHmSqtg33ywp3EKbwrvCV/qwMQzqcxAEQZhFRFS3L+fOncP+/fsxPDys+3ir1WrD6CSet8yCt1QqNQhetoWz2PMHa5mmOUZ8snDiKi4HjkUiERQKhQbhxLZxFrwsnvg5i8UiAoGAFk40R9qaUPDGYrGGymbzeKdQKKTty2y75tfGIptDtqLR6FX1GfuDs0zTRLFYHBNO1tnZqfvOuSLt3zRoTrvmxHOe98y2a8dx9DiveDyubc31er3BLs+BaPl8Xotstsvbtj3Gds3HGBi1XZfLZf3+8aaAaZqIx+O6YsxW8lwup5+Txb4/UI6Tzv0iNhaLIR6Po1ar6X/zZk6lUmlIUTcMQ6eU+1PUOdhsaGhItypwCr1lWahWq6jX6/q847YBnr/OqeV6lrfvuHCAGp8/nZ2d2j1Qr9exZ88e/b6xu6GzsxPJZFL/W0LR2h8R1S24dImmHC1dOv7M6JUracTvzp3AY4/N7Ppa4TiUN6UUpYZTGqWwoOnsJNt3Lkcpd62q1UpRaNmKFXTCC4IgzBLhcLhhHJIw+/T19eG1115Db29vw+3+4C8WvJVKBbFYrKFq5xe8bLduFryFQkELO67QcaXQTzKZhGVZDYKXH6dUKmmbMjBq0eaAKe4zrlarerQVr4+Dv1jwlkqlBsFrGMaY8U5832bBy33GfIxqtRocx0EgEGgQvDwKii3SvM58Po9UKgXHccbtM+bRVPw4PPKK0839fcYcIMawuOfXHggEtOCtVqvaaWAYRoPA59dUKBS0mOdxWVwh5/7wZos+V9t5c4Kt5sBY27Vf8ALQNnt2BPhfO883580Cfn/9PeutUso9zxuzEcDvleu6uvLOjgX/c/pfT/OsbHYS8HkbDAa1ZZ6PHbcmAKObRjwb3e8eGB4eblgjh7Y1z9WWULT2QkR1C86do8lDN900/n0sizTKqVMztarWlEoUaPbTn5J7F6DMq4cfpmlJrVpphXmI61I/9vbtFD7GPdk330xpd9lsY1AZk8/T9z76qIhqQRBmFZlT3T4MDw9jx44dOHnyZIM1lkcnDQ4OasulfzyW4zg61KtWqyEcDiOfzzeIEr/g9TxPC162G7M4Nk1TC16AenX9ooQDzljwRqNRAKOC128v96eSA9DBWrVaTYdcjSd42cZdKBQmLXgrlYoWvI7jIBgMYmBgoEHwRiIRLcDC4bBOp26ek+0XvNxnzFXtcrmMdDrd0GccDof1e9c8NoxD2FhAcp8zv+7mPuPOzk6duMxCmWeN+0PFOEXbtm3U6/WWdvmhoaEGOz8fM//mA0CbKexCSCaTWuTzKDD/Rhzb79k9wD3svF7eZODNFN4QanYPcE94LpdDOBxGcKRSxQ6MVrOyq9WqDnDjinarcyudTsOyLN1P7jgOcrmc7vcfGhrSVXzbtvW5NTg4iHq9jvPnz+vKd6FQQCKR0NVs/ltC0WYHEdUt4J/lq3HN+totZpx8HvjEJyjcORolMW0Y1Eb7uc/R+OOPfYxuF+Yx2Szw2c/SG16tUo9CrUbhY5EIpYifO0cnazpNO0JK0TD0wUEapfX+90vytyAIs4qkf88+xWIRu3btwqVLl+C6rq7COY6jK7YAGkQJQIKXrc8cNuZ5nk6SZsHA86D9gjcQCGjBy2nhbGnmCi/DooWrsVylKxaLOi27XC5r6y6HYHHaNFdAA4EAUqmUrm5eTYWXq7YsUtk67Re8AAlVtuqyTbu5z5jFGotntn+3ErwsWq8keP394f4KbyQSaRgbxv3N7B6oVCr6TzAYRDweRzab1cnXLMg5ud1/XBKJBBKJhO4P580Udg/4f565z5hfQzwe1+dNMpnE0NCQrtT6BS+LcX9/sj+lPO4LdK1UKmOq0BxC15xSbtv2mJTyZDKp0+X956s/cIw3NTgUDaCkcL+45yp0uVweMyu7ObTNtm10dXVpC38gENDJ8c2p5txyAEBvQg0MDGBgYEC3YrBjRELRZh4R1S3o6QESCdIcHR2t7+N5ZLmezVynr30NeOkl6vH2b0olk6Sp3niDJirJpKR5TK0GfPrTwM9+Bqxd25gW7jiUtLd4Md1+4gSFAQB0AkejlLT3l39J/QyC0Cb09dEeEZ+uK1fSSPfFi2d3XcL0Iunfs0e1WsW+ffuwZ8+ehqpyMBjU1liusrF4aA7hGm8GdaFQaAit8o+84qRr/0gu7n1mW7K/Cm5Z1hhbOAdgGYahhU65XG54bMafAM4bBSz+xqvwcsXSL3jZCs6Ct7OzUx8313XHCDvuoQVIFLL4iUajDRXeVoK3Wq02JFbn8/mrErxsl3ccR4t8HlVWKpUaktEDgQA6OjoaKrzNgpff02bBywnv/F6YpqkFbzQa1UKuXq9fUfDW63W9Rr/45N7n5nR5HqfG1nC/4OVWglwu1/C5wn3m/pRyf9iev//asix0dXVpAcvClc8R//nPVXd2I3D6fblc1q6C4eHhMaFttVqt4b3jKnSxWITneXqzpFarIRQKjank83HmNgdOR+/v78fQ0NCYULSlS5cikUhIKNo0IKK6BStWkGv2hRfIMdtKkF64QBXg2ZoX3ddHgnrJkkZBzQSDlFj+xht0YbpmzcyvUZgB9u4l9bFuHQ009xMIABs20H1+4RdIdL/4IlW2u7qAN70JuO++8ZPBBWGGUQp4+mkam97XR8H0hkF7R9/8Jp3Gb3+7nLLzFalUzzyO42D//v3YvXu3Fs6xWEz3yJbLZR24BUAnfufzeYTDYV2RY0E80QxqvuDnZO5SqdRQ3YtGo4hGo3oGNad71+t1xONxFAoFXSn29/Bms9mG1HGexdycUA6QsOvv72+o2HGVEBgVvLVaTYeN8dfY0uxPAGfByyKcRZNf8I53XHijwN+3HA6HUSwWGwRvc4WXBS+LNa6qs+Dl/nC+j1/Mjyd4m+dQA42CN51Ow3EclMsVGEYQly8PwTSvLHg9z0MwGLwmwcvfG4/H9bG2LAuFQqGhV9s0zQbBm8lkxghePvf4PeFjzinvPKccALLZbMN7xxsSzSngHR0der43jxHjVofm+/Imjr8aXiqV9PnNG0Smaerzn897ntPNx8V1XQwPD+sNKcMwWjo//DPROdjOcRzdd79r1y59X+5hT6fTSKfTczYUrV0q8CKqW2AYdPF2/Di1qd5ww6hwdV2aX10skmO2p2d21njwIFXSJ+r7TqWonfbAARHV85ZXXqGTsllQM4ZBg83feAN43/uAt751ZtcnCNfAc88B//iPQDhMkxdYPHsebWR+6UsktJ94YnbXKUwPkUikoUokTB+e5+HIkSPYv38/lFKIRCIIBAK6SpjNZhuqhNzXyqOulFK6Qsnvm180sS2bK5xMMpnUjgQWEWxzLZVKLa3ezRXeer2OSCSi7cIAtG3ZNE1tAeeqZzQa1VVIribatq3t3s0J4Bz+5O/h9Y/Z8qdONyeAs+DlQDReY6tANH8PbyKRaBC8/kC0crncMkm6ucLLtmu/pdkwDKRSKQSDQbiue02CN5vN6k0PzwMqlTQuXAigVCoDSCIWs7BokYMlS0yUy/kJBS/3lrcSvMFgUL9+Fry8WTGe4OXqcnN1vlnw+iu8/kRzfu+4gszvAW/0mKbZIHg5GI/fD95UAaBnaA8ODjaMNWMB3crNwS0KPHJuvCp0MplEMBjUIWgA9AYStwzwa/H3fnMoGrdk8KZMsVjUx5A3YgYHBxs2VLg6L6Fo146I6nFYuxb4vd8D/umfyEFbr49OHlq0CPgP/2F2L+x4Y2+iio1h0Ndl438ec+FCo+W7FckkcP48pe+1CisThDagUgG+/W1q+V+xovFrpgksWwacPg1897vA/fdTi44wvwgGg1KpngFOnTqFvXv34uLFiw23czWsWq3qSlW9XkcwGGzoRwZGK388loj7YznEyl8542odC5xqtaqtzX5bLKc085zgarXaYPVmKzhX8VgoVKtVRCKRhrnHAPR8YrajsxOCU6B5A8c/4xmATgBneCZx88grFuqtEsC5P9xvaebxU/7QKhYqHDgGkCDicU35fF7b5dna7nleg2UcGE2vLpfLDbbvQCCAbDbbYIH39/D6q5ocyOZ/PZTQnsS+fSYuXnRgmgaCwTJMs4JsNomhIQ+XLmWxdm2gQfA6jjPG0pxOp8cVvM2bL82Cl88vPq7NFV7ehBhP8A4PD2uHw0SCl48FAL2hw46HiQRvKBTSG00AdP81v5YrCV6llA4d4/c4EAhgeHi4YaPRNE09QzwQCOj3ncPqmhPD+fFc19WjzVqForE7hXu/BwYG4DgOzp8/r4+FhKJdGRHVE7BhA/AXfwHs308Watcl1+ytt85+qjbrKMchl28rPI/+yMXnPCYYpJNgIhyHVMl4J4ogtAF79wJnz1Inw3gsWwYcOkTGiwcemLm1CTNDJBKR9O9ppLe3F6+99pq+8PanayulUC6XG6rQ3D9dKpX06CKew5vL5RqEs23bSKVS+v1jMckJx63mQbOY5O/nKnm9XteCg8cS2bathUetVkOlUtFVWMdxUCgUtDXbHwjlF8e2bet1sSDhTZxmQcJ2YgB6jrbnecjn8w0znv0J4Dzuy18R9Qu15gRw27Zbikne3LAsC8lkUqdXN4+CCofDupLOFWUO9eLQtmw2qxOx2S5frVZbiknuT+ZANLY07907iMuXgXhcwbIAw4jCMKIIhxUcR2FwMI5AoIiNG+lahEVgKzs6bzAAjZZmHoHFgjefz7dcI/fk+y3NoVCoYbOCnRUcOMZ/l0qlhsfnY9h8flUqlSsKXg5KuxrBy5sXflHOPfG8+cHtELlcDrFYDIZhYHh4uGHUXKuEef5Z42q3f4Y4jxTzb4ZxFZrXxD+z7HLwH3PeDOPHM01Th6LxJlmxWEQsFtMCu6enBx0dHXoU3XTj3+RoB+Qq+wrYNrBtG/1pJ7ZsodCeCxfGVnWY/n4KWrv55pldmzCD3HILsGsXWSjG+wC7dAlYvZoa8AWhTRkcpE3Akc38lvC+UFN7ojBPCIVCMqd6Gujv78f27dv1CCMWy6ZpQik1xnLd2dmpE6ZDoZAWkOl0usH+zIFVHLbE1exyuQzbthGPx/VFvb+HmAWvH7aC83Ny2Bjblv3ixd9jzHbmarWKer2uq6FsO2YhxP3h/LzFYhG2bSORSCCfz+sKs1JKi5xmazGLG8MwYFmWDqJqNfKK08K5ks4bFaVSCR0dHahUKrov3d8f3ioZPZlMakHIgWdc4fWLSWB0/BQfRwB6jJXruigUCvoY8vvHa2MXATAq1C5fzmJwMI5QyAa5wi143jCU6h95rUAkEsDwcCfK5TIymYAeKVWv13V13n9+pdNpLU75/S0Wi1qEFQqFMXZ0nuHtd7LwrHS2NPMmTasKL4tJHnPFGypsN/evsbn/ms/FXC6nXQoseBOJBGzb1uK4eZOEjyOfmxMJXrZb85p4Q8XvcuBjyC0ChmHoz8xisah70QHq42dhzO4Mx3FatjtUKhW9odLsIPCvkT8/uN2Bz6/e3l7kcrkxoWg33HADbr311rbpeZ5uRFTPURIJ4M1vBv75n6mdtjmlPJcDLl4E3vEOScyd19x1F/C975EvdvXqsV/P58n//8gj7VGpdhxaTyjUHusR2gae9DbR/hBAX5dTZ37C4VLC1JDNZrFv3z6cOnVKV96KxaLuMS4Wi4hEIg1hY83WbWDUQguQsKzX61qoNc8J5ot9tjOz4OX+ZH8vL6drW5aFXC7XUFXmCiena2cyGX0hr5QakyKd8tkHo9GorrDz6+Q1+vtj2YLuuq4W1qlUStvYORDKNE3de8qilNeYSCR0lY/7Yz3Pg2mauHTpUsMaecYzfy8A/fqKxaKu8vvtwlxh5Uq3P4G9Xq/rTQPu2/b3U/Njscj1J7kDJM6ae2m5Os/v0aVLZdTrBSSTKShVhVJlACYMIwbDCMIwbITDdQwPD6Ovz4FShZbVee6Btm17TJiXbdt6s6DZgt38XnPVHRgdKcXCkx0ErcLieM5zs+DlPvOOkYvo8ezoLBL9/dfs7PAn0vOGCgveSqUCz/P0ecMVXha83EvOYrtVOjo/BgveSqWiMwX8gpcfr3lGt+u6ekY3MDoOj+3enAFQLpcBoKFS7m+LYLHdHGjHGwe8Xt5YW7RoEbZu3TrtgprT79sBuTSZw/zcz5F4/tGPKDxt5Pcislm66HzLW4B3v3tWlyhMN0uWAL/yKzSY/MABYOlS6g2o12lXpVgEHnuMRmfNJufOUVz9z35GzbPhMHDPPdQcK+O8BFAgZDxOn1/8WdZMoUCnzg03zOjShBkiGAyK/XsKKJVK2LVrFw4dOjRmBi9blGu1mq7y+YO8/OOuzJHQFn8gGAAdfMUX97FYDLVaTVtYm227/nFTsVhMC1nLshr6oKl/l+YBs3DniidXdHO5XEOvqFJK21L9F9aJREJvAHAVnKuUzcLANE309PToNbKYZ8HLdls+NrwZUK1WUSgUdCWfw9KGhoa0jZtt4zxP2E88HkckEoHnefpxebPCcZwxfdBcmeX+WO5P9jsIAoGAHsllWRYqlUrDMebKNweisZjkqnJzdR6IwnWjMAwPhhGFUjY8rzxyrB247mh13jDC6OmhSi7bhfk4cjU0m83qTQOuzlcqlTHp1ZlMRqe9s5jkdPSJRkpxTze7FYBRO7ppmvqYA2jYUOHzmsU329G5wjteiB4LUX//dTgcHrO5wUKbBa/nedrWz3kA/D77w86a7egcxpfL5fQamfFmdPNx4H+zk8SfbG8YBmKxmF4jn0vFYlFvWAwNDcEwjJbj5vyf25lMBm9/+9uxZAG6I0VUz2ECAdJTt90GvPwyJYIrRT3f990HbN0qFZ0FwYMPknXhhz+kk6C3l974ZcuARx8lS8NEntrpZtcumqV94QJZKiIREvtf/SrFPf/Gb1DFXVjQrFlDbS2vvEKnc3PIqOdRtsW2bZR3Icw/ZE719VGr1bBnzx4cPHgQgUBAV5i5SpnNZhvESDgc1gKDhR2LBA53Akarb9FoFAB0ZdDzPFQqFSSTyYYwKO6nbCWY2P7LgobDzqrVKmKx2JhANBZMPE6KhSdXj3mNXH0Lh8M6Xdtf+eb5yYVCQY8C889PHq/65nkeksmkFiOcAN5cnWerN4tTHu2UTCaRy+V05dffm57L5cZUviORiHYQsJhkB0HzGvk4c1AcB5WxLZ2FDlfn2UHAP2OFwmhVuVwuo1AoNFQwyUGQhWUNwnXViIMoCMtKwPNKMIwwLKsTnufCdR2Ew4GWa+TE6EgkooU2V+H59QcCgYZgueb+a34/stms3jTgc6y5v9/ff822fg4v477h5v7rYDCob/M8T7sVbNseEzjG718+n28IHOP3I5/P63FgHP5WLBZbzi7nNgzu9a9UKmMcB6ZpIpFIIBKJaCs5v3/cZsGClwPteDNgeHi4IdAuFoshHA6jVCppgc7nV6ufA/98dbbMl0olRKNRZLPZhvnq0WgUGzduxLZt2xZsSrhIrjmOadKF6JYts70SYVbZto36q8+eJfuCbZMdfMS2M2ucPw985jOUPH7zzY2+3mXLaG7dZz9Ls+la2deFBYNh0JjCvj5g3z4yYXR20u0DA7Qns3o18Ku/OlZwC/MDsX9PDtd1cfDgQRw7dqxh7i8HXfF4IhYj/lE6/goTW2u5ysg2Y64AturFNAxDCz+ujNq2rW3LwKgY4T5arqqygGf7OYeN8YU+J1fz/fg1cRgUAB3I1arSytV5nnXsD2EzDAO2bSObzY4JGwMwxlqbSqUQCoVQLpcRjUYRj8d12FOlUmkQI4ZhNMy+TiQSuuebNwL4a7Zt6/eF7ej+fuR4PI5cLqeFlV9MNldDY7GYrs7zZgTPQ/Y8r+UaPc/TYowFYjAYRKFQGElVB4LBCGq1MKLRIAAHnleCUhUoVQFgolbLIBzOIxIxxoxraq58s42aN3o6Ojr0Bk2zHd00TXR0dOjzIRQKoVQq6dAwdlr4xSSPfcvlcqjVanr8FNvaS6WS7u+fKHCsu7tbW7u5yu4P6/IHjvkTttnm7q8kZ7PZhsAxtns3n2OJRAKhUAiVSkULdH7McrncMLKM3z9O3+fAsXK5PMYazg4QDgFkWz4L+EQigWw2q10MgZFqHL9//nMsEokgmUyiVqs1hJhFIhE88sgj6OrqwkzTLtZvQET1lFAsUsiOZZE2kOqwMCsYRvtZqV9+mSrnzYIaoP+vXUuxzz/7mYhqAcuWAX/4hzQ269VXKelbKbKDP/448Pa3jx/MKMx9eOSRcHUopXD06FHs2LFDiwcAehYzW4XZqlwqlZBMJpHP57X92l85Zauw/3G4IsUVZgD6e1uFjbEdlXulS6USbNseY2fmNXKFDICetcxVxmarNydX+63evC62rKZSKf2YoVAI2Wx2TG8sh02xHZj7TgFowdTcG1utVrV4L5fLeuSYfxQYC/JWs6/5661mXzf3pbPVm63CfsGbSqV0wBVvBrCVulwu6wTvYrGIUCiEaDSqe2PZjs6vr3mN3GuulNI26UqlgkWLbJw+XYdlDevrW8OwYZoZ1GoeSiUPy5cHYZol5HJ1ZDIZ/ZrYQcB29Gq12mBH580b3jhoTq9u7m32B2UFg0E937xVNZ97jDnMi8/xSqWiw7ya+685nKzZYs6inMeBAdDJ4/l8vqEabFlWQzsB919zhZ5fEweO+WfEc1hcqVRCKBTSAtl/jvG52/z+xeNxLZz554BfNx93JhAI6B5xz/N0/3Uul0NHR4d+Tf4NJ26d8FvrA4EA7rjjDmzbtk23iyxkRP5dB5cuAc8+C7z4IhUHeZbqI4+QI3dkTrsgLEw8j8RyJjN+8pRhUDny5ZeBX/7l2a+sC7NGtQocPQqUStRu/8QTNMEAoLBFCVyc/7Co5ot+YXxOnz6N7du3o1gs6iAvTsKuVCpjLrjZAgvQxTdXr3mern8EFAs1Hlnkt+PGYjFt2WURzWnYg4ODDZZrTh5m8eG3VLcSBSzaDcPQc39ZbHIVDmi0evNYHxYk/DhckWNBB0AHog0ODurzi23yfpsxi/tWvbH+2cSO4+jZ6qZp6qokAG315uPDVmEmkUjoPvZIJKIDrMYLg/K/f+wmYOHln1nNmwHxeFyPxWLhWa1WkUql9EZDq95Y/+YMi/JFiwqo16O4cKETgEIo5AAwUK+Tnbm7W6GnR+lKOgAtxNgVUK1W9WP77ei8uQCMpldzmBpb9dmOzr3Srfqvi8Wi7r/mDZZW758/SI/DvPjYmKapw7x4wykcDuvnZXs9vz7TNDE0NKSP90TtBOl0Wp/7vCnAmyrN7QQsoF3Xhed5use/XqcNi2w2qzfL/L3fpVJJBwHyuc398+FwWPfP8+dE8xr59XLLg+M4+vX60+15jcuWLcM999yjf24FEdWT5swZ4O/+ji4COzupQu15dPsnP0mzrT/yEWofvRbKZWpBfe01uqCMxYDbbwfuuGNswrcgtDXVKp3QV/ohiETofpWKiOoFiOsCP/4x/Tl7ljL2bJsq0m9+M/0Ru/fCQOZUX5mLFy9i37596O/vR6lU0qFjnPqbz+d1FZj7Kmu1mhYLDPcUc18z99py5dRvNQ0EAg1jdPgin+dK+6vBXHnjqmA+n9e912wDz+fzWjiwXXa8yjcLZ+5l5fFCzaPAeEwSV9447KxYLOqeYZ5x7A8bq9frunJaLBYb0qfL5bIercTJ1UNDQw1W4UAggK6uLh3sxN/L1mJ/xbN5VJM/KCsajWo7OluFWZAbhjEmuTqZTOqfFxZ0bEevVqtjBBNbhQES9JVKBdVqtWVvLAtiFlWeV8eSJVnE4wFksylkswUAJjo64li6NICuLoVgcGzyOLcTcDo6B7DxsWq2o6fTaV3t5/ede8QB6M0cPo9444XDvPizg8et5XI53dvMz1EoFFoGjvG5wdZ07r/226gNw0AikdDCk0evcVWeR8jxODOuQLcK82IBy33svKnC5+94/fNKKb2pwtZ3f3J884YFbxb4NyzK5TIcxxnTP18oFBp+Bv3zy1mU88i5bdu2YcuWLW2x+dkOa2BEVE+Cep3aQE+coF5m/wVfMkl28Oefp6r1u9519Y974cKoILcs0hrVKrBjB01NeuopCh8ThDlBMEgnsc/y15JymcS0WDsWHJ4HfOlLwLe+BUSjlOodCtHnXm8vteNfvgx84APkBBLmN+FwWOZUj8Pg4CC2b9+OM2fONNzut/QC0DOjeeY3Vwd5hBX35HLPKUA2cra0ssDjSieHezULEa4GA9BBXlx540oaE4vFdMo196lyH208Hkc+n4dSasyYrWw2O6byzX3NXJ1nizuAMZVv/7giv0Wan98/V5r7kXkDgAVdc3K1fwwSVweb0865cspWYX7d4XB4zGziUCik+4P5WPGopmAwiMHBQW335ePIr4PD0Dg4ihOdOYiN5zY3b0Dwe8ZhYWyH542P8azC3d3eSIU1PFLBzOmU7my2ru30XDmt1+vaBeHfsOBz8mpHbXFlldsCuI/YLzz9/cDcu86z0zlRni3mvEb+uWl+T3jWd6VS0Y4C7k2vVqsNG04AWvY2c4ZB84YFi2muVDuOo9eVTqd1An/zTPTBwcGW86L5Z45HxHEvfitRzr3u0WhUbxrxz0fz/HIeo9U8v3zFihV45JFHdPuD0IiI6kmwbx9w+DC1g7aqoPDc6OeeIwvj1Zx7xSLwiU9Qe+mGDY0FO9elivgnPgH86Z8Cq1ZN3WsR2pi+PlIUpgksX351J1I7YVkUQ/8v/0Jlx1a7iUpRCtUv/ZJUqRcIfF1gWeTK+cEPKJTM78QJhUhgDw4C3/8+cNNN5NgR5jfhcFgq1U3k83ns3LkTx48f12IJGO1r9osfYGxfM1tXOcDJb53lijZA9k4WqPl8XlcYWYSyuOAqq99CDYzaW7lqDozO/M3n8w22YtM0dXUXIDs6V92bx2xx5bTVmC3u+S4UCnqNzSFZfjilm/txuXLKotU/V5qrllwdDIVCWnBz3yq/Jv9caaUU8vk8qtWqPpe5PzqXy+nXwmvM5XItRTnb7tkKzS6C5sop9z67rqvFKVfKmyunvKnCGxbNlVMOKItGo4hEIlBK6edqFmlccQdo84Lt6M1W72Y7un+2dLlc1sKwedQWtzE0J8dnMhndQ82VUz7Pmjdz/POveZ2cih4MBsf0z0ejUV3p53PM39vc3D8/Xm9zLBZDIpGA4zh6w4Irxq7rjglj4/nlfjt6oVBAOp3W1vTmMDbHcbRtnDeeOjo6dE89r5FdG80bFvy87Brg84zbBfwOCz4nb731VmyQ0RsTIqJ6Ehw6BNRqE7taFy8m4X30KI28uhI7d1KFeuPGsdOPLIuE9t69wE9/Cvzar13X8oV25+xZUhLbt1OzvmEAXV3AAw/QLs1c6l+5914KHjh6FLjxxkZhrRTZPfi1CfOWeh14/XXKnzhxgm5bv55O9Wp1/NaWjg5y8Lz4oojqhYAElY1SqVSwZ88enDlzRgdp8VxiFpLA1fU18xxpf18zC9hWgVoslriSyRVtv5D09zUD0BZUFto8zotDstjeyiK/VT81z85OpVK6n7vVmC1/dddxHH3ecBAbj7DivliuSpZKJW3dZrHE9udIJKJDsjgErNkyz+KQRy1xrzKLZP/IJNu2kU6ntTBlsZbL5fQYsWKxqDcDOCzO87wx4Vz8nrEdnYO8bNtGoVBoGMvlT4Xm94E3ImKx2ITJ42wL5oC4ZDKJQqGgxTufZ62Sx0OhkE6aD4VCesOCe38nqpxGIhEYhqHTqD3P08feX93198/zhgAHcvF7eKW5zdwzzxsW/HPFDgt/4BiPh+PX4e9tTqfTuu2B18jvIY8nu5re5lap+vy8HCDGY+T4XGrubeYwNrajc6uDP4zNP798vPOMK/3ValW3h9TrdfT09ODBBx/UI/XaicaZ6rOPiOpJUCpdOeE7ECBr49U62V56ifoIxxsnbBhAdzfNcH3nO+de0VK4So4fB/72b4HTp6l8t24dic++PuBf/5V2dP7gDyj8ay6wfDnwm78J/P3fk7i2LPrhCAbppF66lOZU33DDbK9UmCZKJWqXefFF+n8mQ6f0888Db7xBp4DjjP+Z2tVFp32lIh0C8x2eU72Qg8rq9Tr27t2LPXv2NFzgs2jhnstgMKjH/ASDQV2RZCsz90ryKB6/vTcSiSCfz+uL94n6miORiBYubOvlsVnNCdLcU8y9oVzlLZVKsCxLCxiuunGisOu6Y1K6eV2lUgmxWEwnZ7eyXAOjQV5KKT3Civuv/RVM0zQbKrH+kKxqtarT0dkm6+85bd6w4JFX5XJZW6TZghsKhcYIOhZIXKXmY9Ms4tnqHY1GYRiGtuDy/GSufg4NDTVYvbnftdWGBQtn/1xwFol+twOPLGsOySoWiwgGgzoUjdfIM5FrtRry+by2o/ut3rzB4B+V1lw55d50rpSy5Z7f876+voYWB96I4HVybzW3CHBivX9uMweLjTe3mW3inB3gum6De4GPpf8c5Upxq97meDyuZ6y3CmMbr7e5WCw2bOj4K/TsoOAMhEgk0tI2z6Kck9xLpRLK5XLD2Lnm9Hg+3rx5Fo1G8eCDD2Lt2rVoZ9rpd4WI6kmQydBFoFLjhxqXy6Qbrlb8XroEjGwqjUs8TuFl+byI6nmBUpRst3s3vbHBIFkRhoaoed7fRLpsGe2qvPEG8LWvAb/1W7O16munWqXXWqvR63Rdem033EAx+XffPdsrFKaRr3yF9lPWrKHPsGqVKtexGBkYzp0j0bxlS+vvtyy6v69QJcxTIiP2r0ql0pZVkenEdV0cOnQIu3bt0lUxnoVs23ZLmzDbN/29nH6RzRfaHLDktzBzZdB1XcRiMS0kWZSwBXdoaKghtZpFealU0tVdto1zBdMPB0VxJR2AFqau6zYIOk5mBugcYCFZrVbHDWIDoO3ofvyBYLwZwf2nLBL9x7Knp0cLNNuOoVAownGod5uFZPMIJE6o5n5ytskWCgU4jtPSgut/3kAggM7OzoZ5yDy2KxQKNVhwDcNoSB7326j5feNj2RySNTw83JBVwBsaHJLFY7TGC8niY8kbFiwkWYjx+8nHp9nyzCFibPVm+zWvkS3jrTZK2J3RPMYql8vpirv/PeTbksmktnqHw+EG4RkOh3VlmVscuPrNVeWhoaGWc5ubR21xb3PzbPBWCdv8c+rvbWYbvN9pwo/rr7w3h7FxddrfUsDP0Sr93x806Hc+2LaNfD7fcH6Ew2Fs2LABt99+u3aiCFeHiOpJsG0b8M1vAsPD4xcMz58nzbB+/dU9ZiRC/YMTUa9TNWdkYoEwlymXqdf4hRfoRLJt6i0+epQE9KJFo1YH26ZyXTBI1evXXgPe8Q6KnG93tm8HPvUpOnkfe4zEtOPQbtSFC9RQu3QpfU2Yd1y8SBXqxYvpVH79dQogcxw6FYpF+vfJk5RR0aqlZniYRphf6yQFYe7BF3CcSrwQUErh+PHjOHz4sE4IZkHCwUwsJLl6axgGyuXymGoWV4lZ0PkTvfP5fEPAEou45kRvFgd+Uc4ixDRN1Ot1FAqFhlCkeDw+ZqY0C8lmke0XIVzZZvFs2/a4852555SroixM+DVNFMQGjIp7FpIs/Him9eXLl1EoGBgYAPJ5A46TRiDgoa8PWLo0gURitL+WhVCtRvvFsVgYHR3JhrnA3J/KFUmumvM6JuoZ5pFeLCQ5tCyXyzUkj1uWhe7ubjiOowUbi1CufPN7GAwG9XM396bz7O5cLtdQBZ7I6s1jyLgPmnvyrxSSxZV6f4o7v6ZAIKAry1z59Vu9/bPB/dkBfIxaPS9vFPiFJG+s+F0ZfF7yBhFvHPEYK9488c92540Vv8uC30MWyv6WAk6P97cy+PMF/GFs1Wp1jIhnC3coFNIWds/zkM/n9bk1ODjYMFvaNE29odPsfGGXRXMw4X333YeVK1dCuHZEVE+CG24A7rqLRsDYdmOFWSm6kPQ8an+9WgF8xx1UrZmo+n3pEvVnd3Vd/2sQZhHPA77wBeCHPyQBvXIlvekHDlDl+vx5UhnJ5KgnNh4nZXHjjXSiHDs2daL60iVKjBocpBN27VqqlF/v7k29DnzjG+Tb9YdbcLrfqlXAqVO0Q3X33Ve2alwNvIPcRnaghczevbRXpBT9u1ajtzmZpB8DxyHR7Hl0Gq5e3fj9tRrtPz38sKR/LwT8onohcPbsWWzfvn2MsOKeSxYpLLJY+LLI4N5Vrl5y9c4fcsUX3WxtvZpEb6UUkslkQ0AWW3sZrqSzuGbBzSFJLLxt224Qu6VSSVdUuaeY+6BZ6LPQ9dvaGR5LRbcZCIUSKJdLUApjKuUsFnlt3JvOa+PqbiAQQD6fwIkTQdRqJiIRA8FgPzxPobfXQF8fsGZNBEuWxFCt1pDNRnDhQgzZbB2ep2DbQDrdh85OpVtUWEzxsQKg7br+IC8Wkv6eYX6/Aeh5xdybzhVK3lgZr2fYPw+5XC7r+eL+jQbbtrVtPp93UalEUKuVUK0W0N2daWn15p5b7nP3W71ZXF4p1dtv9WZrOIeDNYdk+ceL+fv7C4WCFpJDQ0Mtrd7NFXpOyWZhzdbs8eY28/Pyvzm4jDeu/OnxLEwBaCdIqVRCqVTSffgcxuYf8+W67ri5Blyh580aPvZZ30QV0zTR3d3dEMbWyq7Pr58D9dimz9VvwzCwefNm3HvvvXrW+FyAfxbaBRHVk8AwKCzMcYCXXyZbYjJJf+dyQCoFvPe95Gy9Wu6+mzTWqVN0Ydl8jvT3kxZ5+GHRC3MCzyNRyb3Dfg4fpgr1ypWNoWOOQ6W7Wo2+N5EYHYBeKJAqKZXoMX0XN5OmXgf+/d9pd2hggNapFD3+unV0kt9447U9ZrVKiXsDA3Qy79kDbNo0/v2XLQOOHCFRfy0/MH48jyL5X3oJOHiQblu3jpLHb7nlygEIwrSxfz+dAkND9PkYDNIpUihQN8OKFXS6DwyQDdwvqotFihi4+Wbgnntm7SUIMwgLr/meAH7p0iVs374dly9fRjQa1eKXK0/NVubm0VDcK80Xx/7qpb/3mivAbJtmoVypVBrSsjmQyF+584tynsELjFZNS6XSGEHH448AshhzpY/t5Xxx769I8sU9B3eVy2VtZecqGl/k87zfgYEhDA8bGBwEyuUAgAzC4TKWLgUWL84AcPT8Zn/FHRgNbuMqdTgcRn9/FSdP1mAYIaTTgwA8ABZMM4poNIhs1sLJkyWYZgGDgwp9fRV4XhDhcBSGMQzXjaC3N4Ns1sCmTS66usYmj/tHgHGCM480Mk2zQdBxxdIwDL0JAkALcU4Nb+5NNwwDQ0NDDZsfLAyLxaLeCOFzzXVdnDx5GZcvA7mcAdcFPK8D4bBCLmdg+fI4gsGa7hlvtnr7g9s4oZqr+FzJ9qd6T2T15qo8V4b5MXlDh4UkdZIZSKd7YNt13drAz8sbJXwM/EKSRTFXgtmuz2Os2OqtlNLVXX8IHG8esMDlDQlep7/vG4Dui28O1GtOZuegM64sF4tFLd4B6JR7Hg/WHHTW/HMYDof1ZwC3TbBwbrb327aNnp4e3HXXXViyZAmE60OuNidJPA589KOkA15+mXKlAgHgrW8lgXzDDdcmfpctAz74QeBzn6ML0SVLaG5rrUaVbwD4hV+gCrnQxpw/T2lyP/sZldhiMeD++ykFe/Fius+rr9LXmlO8KxVSG4kEqY9CgYSvbdOuTShEFeoVK64/qEwpCj775jeBzk5qaOVSYLlMwv/v/g74oz+6uhluStFr/ta36IfBdan0eOYMqaitW1sHAdj2aBBbKy5epOO1ezc9zpIlpLBuuYWOUb0O/PM/A08/TT8sfFxeeIHW8/DDwIc+JN7hWWDPHtoo5JbJdJo+Ez2PTvXeXnL+r15Nn3lnz9L32Da9reEwcOedwFNPSYbEQoFF43ytVA8PD+ONN97AhQsXtJDM5/PaTuq333K/MAslPyxmeDQUCwa2R7eqXnJoGFe+2fbtn2XNid6c8syjoQDogCwAyGazegQRU6vVxghJvrDnsCS238ZiMV3tZDicigUIj4bi0UI8u9nzDJw7F0dfXwhAAOFwDcAQSiWFI0fKuHBB4eabUwCAcrmsNyRYgBQKBQwMDOjqFomYTtRqzsivjySUqsJ1K7CsIDwvi3jcQT5v4Nw5A/l8HOFwBKGQC6AK1zUQCJQQDFaQz2dw6FABN97oIB4P6w2QQCCAbDbb8D5alqUr+jweijcwQqHQmGRm7iPnecgcPlWv1xEOh3X4FDC6ccDzuPl+vCHC85NLJRsnTiRRLgcQiShEowE4Th/qdeDkSQMDAwrr19Pzsihk67HjOAgGgxP2DIdCIR2Kxj3Z/J775yHzuebv3ef1s9U7HI7j4kUDvb0eSNf3IxhUyGSAzk6Fjo6EDkLj48Sj5Or1ekPvfnMyezAYRLlc1sns7GrwV8k5aK95bBdvfvBGAlfox3MScAgcO0P4/YnH4w3p42wz5/nTtVqtYZ50R0fHuEFnhUJhzMYc96fzv3nTYuPGjbjjjjt05Vy4PuQoXge2TXbsqxmZdTXcfz9deD7zDOVRsRt382bgkUeo8CYWyDbm9deBz3yGhGAqRaqgvx/4p3+iN/XDH6aBuwcOjPYW+z/IqlW6XSm6vVIZFdUAiepSib7vemcFnj5NFeqeHioZ+olEqLq8dy/1PH/kI1d+vGefBT7/eVr7mjW01miU+qbPnKGy4733trZ4k29v7O0/+xkdu8uXaXPCtmlT4fnnqV/iwx+m5/3Odyhl3L/RsGwZJfo9/TStQ+bQzSiOA3z1q3Qa8ynNm4ymSW9JqUR7KT09JK7Xrwfe/nb63kSCKtSbNo12CwjzH8Mw5uVYrWKxiJ07d+LIkSMNlaxEIjEmeIovoLmvGWgUu1yt5QtkHgfE86AjkUjDLGtOPPbDF+GcAOyfZW0YRoM4DgQCSKVSuppdq9W0TTkajcKyLF3p45E9bMkcGhpqeL3xeFxvmnCYFlcUPc8bI0BSKRLHpmnqnu7jxyu4dCmARKIGy2LhYCIcjsF1oxgcBA4fLmHlSkcHiPHM4Hw+3zBnmJKWa+jrG0Q4rMAa1jRTCAQsKFWHaaaglAfLquL8+SBSqSps+7K+r2GYsKxuKOUhlVIYHo4ilysgEKggEonovmBgNECMU6vZjs0WZa58sjOAxalpmsjn82Ps+h0dHQ1WfxaDXNVsHg/FYpN6rm2cOlVHve6ioyMDz8tBKdp4sO0IXDeIgQELly6VoFS2YWYxB45xVZnPjWAwOGYzwLKsMT3DLAa5594/esrfM8yvLZst4MwZGwMDKdj28IgBMIpaLYQzZ0wUCiZMcxCWNfp62RrOPx/NmxatqrtcxeeAN97I4ao1H3f/TGsO++Njf6WZ1q0cDDzjnfv82b7f7GBgIc6Pd7VBZ7xp4Q866+rqwpNPPomeuZDNMwEyUkuYkM2bSXdxyncoRMU5EdNtzpkzwKc/DWSzVJX12xQ8j2zJ/+W/kIXhuefID3vuHFnAV60iAZ7LkTAsFMb2BitFX49ESHFUq6Ne2osX6etdXVfXl9zXB/z3/04V4KVL6fuXLaPn9iufpUspaOyXfmni/u3BwVEF5a9qp9MkhkMh8vcePjx22HC5TGK5uRq+dy/NYXJdOp61GrkALl+mY7xrF60tFKJ1t6rcJxLkDnjxRQo4WLToysdGmBIOHqT9jw0bqPqczdKPgf9zLBwmYd3fT4K6q4vcOM17PMLCIhwOzxv7d7Vaxe7du3Ho0CE95oZv57m4/vAwvk9zNdIwDH1hzxeRLBq4Clsul+F5nh5hxD20LHaDwaCuZA8ODjZUBYPBIBKJhLaIci8ui/tmIeDvAWY7c7Va1UKJNwP4ucPhsO4x5bRqHu/Fx4EFCFcbDcNoqC7S44UxONiBSMQdeT0ZKMXnigXDuIx4HBgaMtDVBSxZktYbFTxujKumHBpVrwNKRRAIhGFZNgADnjcMpWq+543D8wJQqoBwOArLioy8D2yjHhVolmUgn49hzRraDeRjXyyWUKkEUSi4qNWyiEYVDGM09IvFFM8bzuVyWmQVCgUdAsebFkqphvMBIGdAKBTS1nd+LBa9foE2NGSgWOxEKkXrsKw4PK8CpSowDBOmWUIsVsbly0Bnp4lkMqKtzP5zrVgsIpVK6QA73uBhy3orqzdbqB3H0eFgvNFSLpfH9AwPD3ejv18hmfQQCMTgeSUARYTDQdh2DtlsHadOAZs3RxAOh3RbBG9a8KYGbzzU63Vt9WYRnc1mW6aPc4W4o6ND28ebLeZAo+Wa09ur1aoOnONMAq7Qc1gdv15/yntHR4ceCda8aTE8PNwy6Iz7wnnDgxPlW21a3HTTTbj55pu1G0aYOkRUtyE8k1ouLucQL75IdudmQQ2QMMznR1Obli4lT2y5TIqjt5fEplJARwcJRf7lVyyOVqejUSrddXXR4z33HI3gunSJvjedJrvDY4+1FsHlMiVxf+EL1Kxar9PzhMMkfnt6qMqcSJAlPJOhNPLLlycW1Tt20Bo2b268PZOhk/j8eRL7588DGzfScwG05lOnKBjNP09JKeBHP6LXeNNN9Pf27aS+AgE6PpEIvf5gEHj00fHX1tU12tj7lreMfz9hSrlwgU6veJxO92qVTmMeUw7Q12s16mZYvpzeUj41hIULz1+eyziOg4MHD+LIkSM6PIxToXm2ba1W0/3CXMXKZrMNtk2+P/dhcpAYiyYOi2L81TZOdeaRQRxaxBUw7sXlFGoWsNxvnUwmUSwWtchmgWbbNoaHhxvmNVuWpauspmkimUxqkd1cvfSP9/LbWvn+HFrG9mG2UFuWhRMncnCcYcRiamTEng3TTECpPAAbltUB01QoFuuo1wPIZhtDznjzgAVIIpFAPl+Cabqo100EAv0AWNyEYBhxGIYFpWpw3TIsy4Vp5uB5QZhmHK6bA/VeJ2AYHO5koVrtx9mzCvU6bSS6bhBDQ2kUChU4TgiW1YlYrI5Fi+pYtSo0phc3Ho8jEonoEV+80cRCid8rtgjznGHuSed51lw1zWazDfOsHcfB5csBmOYgAD6WtGlhmhkALgwjMrLhUUW16o4J8uJ+ZP/IMK7W8rFmccx9zVwBzmazDW4UrqgWCgVEo1Hd807nh4ELF/oQidCxpPahGPL5ICoVA0olYFkOqtUiFi0ysHRpY89wKBTS4W5cLecqOzstOKzOv2lhGMaYgDXukWb3A6d6889gq1FkbKuOx+OoVqv6Z9HzPG3l9lu9AeiNJ9604L71wcHBhpF3nGzevBkQj8dh27betOA2j0QigUceeUT3ZM8H2imkDBBRLQjXT61GjfWdna0b6Y8cIbt1Ok1K4sYbSUh7HonOvj7y+ycS9O+ODhLRPT2kMkyTHnvpUhKvqRTwD/9AYjaZpGqsYVD1+ytfof7j3/s9UipMqQT8xV/QjGuaAUK3GQYJ/MFBWtOJE/RcnZ0ksA3jyjaJkydJKTXvehoGieVikdbmOFSyjEap6n72LL3GD3ygMWm8t5eCx5Yupe/ZsYMEdVfX6HPE4/RYxSJtVsTjoz3rfkyT1uGrBgkzy5o1dHqFQvSWcRhrNEpC+sYb6fY3vYluExYuc72n2vM8HD58GK+//nqD6GwWuwC0/TedTqNUKmnBxOFhpmmiWq2OsW5zsnJzNTIQCKBYLLastnGllkfocNWLxwQBaEh45mokBxxVq1UdHpbP5xuqkTz6qNV4L7b5cvW9Uqno0DJ/hZ5nbvM6OeSMNx64ylerGVAqAssKgy5fHXheHkAdStWhlAnLSsOyaAzTsmXxhsTzfD6vq+48LqqjI42enhpOnQJisQyA+kg/dRyel4XnOVAKUMpAMBiG6yZh2y6qVQelUgj1ehVAEZaVQTSaRbXqolYzUShE4Xk28nkbtVoZicQw0mkP0WgJrmuiWMzg+PEyKhVg/foULIt6cblq2hyQxRZqtl9z2FYoFGqompqmiXg8rntx/QFiPKaNZh57AMIwzTAAE4AFpQrwPH9YnQmlMnDdnO779geINfcXN4/44gAxfh/9IpUFvz9AjCv0LNipD91ApZJAOk3XCENDAZRKQwBKsCy6nHFdE319aezfX0Yyaepznvv3m8Uxh/k1n2/FYhHpdFpvAjWnnpfLZZ2FwKnnXIXmkVzstGBnSbMVnueS27at55JXq1UEg8GGzRV2qvDmCv+M8/FMpVI66Mw/85uT/f1OFNu2cffdd2Pr1q1tJ0KngnZ6TSKqBeF6KZep/7lVGFa1SoI6EiFBWK/TvzdvJvE7NESCsL+fEptOnSLVsXo1pdL55jCiVhsVwtu3k7eW53cApEgWL6ae7c9/HvjP/3lUhH7zm8B3v0uPwQOCS6VRm7lpkjB2HBK4ly+TpX3TJuo/mAjPGz+VL52mYLEDB2gU2PHjpLCiUUqh+sVfHNsfns3SMV26lEqezYKasW1ad71OmwGLFo1dh1K0Pv9xEq6a4WHa03j99dHE7jvvBLZtm/iQLl1Kp26xSHs7J0/SY61cOXq6KEW35XLUFTHZ8HdhfjFXe6pPnDiBnTt36kAnrhLz381i1x9YxHZVf8ARV+v9/dQA/n/23jxakvOuErxf7EvuL99Wr3ZVaSntu6zN8obwgg3GYGNs7IGGNmBDc6CBaZjTp3uY02fOzJxD08DgtqExbjBgFo/xviB5kSVrtVSl0lpS7W/LfYk9vm/++OUXmfneq1LJKkmvSnnPeade5YvMiIyIzIj7+93fvWOxSL7vZyZS0t3ZHZF7RFG0zrBISoTTNM3Mw4KA5n+jKBoju9I8TEYIyVxeKd0e3U5JtCWJlfFK8u+FQiG74ZcEQBKyOI43jBUaNTkjsykfnFvgPAAwJA3UYS0CSAfkmoGxGN1uN+v0yw687OJJ46xGo4F8HnCcPrpdhlyuCEXRIYQPxgoAgE4ngesqMM0Q7fYKwpC+2zgHoqgCxgQ4Z6jV8gACVKsBcjkVcezD95uDS7+CdjuPatWGaQK63kcYplhcTGBZPrZvL2SZx7III4sWG50/ksBJKbOcF5bn29pZ3Onp6Wx/knzfBxACMCGEDyH8wbImGLMAmIgiAU3rQlEidLvDiC8ZDzWqtABI6r3RXPxoVrRUWsji2VoDMWlWl6bpwB3eh6L0wFgB/X4E3w8gBIPvu0gSE2mqgjEOw2ih10vxyCMeLrtMoFIpZt17ee5JgrvRdpZGjGNl0UKalcm5ZbkvHcfJXLVlDJicV5ZKlFarNZZLLotfa0cp5P4ZzSWXYwBRFG1odCbHI4rFYlYssSwL/X5/3ffG3NwcbrzxRhQKBUzw8mNCqieY4KXCsohBbCRZrNfp6js1Rd1SwyByuGvX0Hir0aAf2yZimCQ0ZDrave12iThedBF1q2dnN2Y1qkqtwSeeIBJ76aVEUv/lX4goc06v67pEXNN0aMmcJPQe5HLtNhUF/q//i2ztb7554+zq7duJ2K4dmpUoFqkdWSoB738/EeS5uWEnfC0Mgwh+HFPXmga+1i+n69T+VFXq8Pf762fKWy3q5u/bt/75E5wWBw7QWPvRo7SbDWOYBnfZZeQTt5E4AKBazN69dBpecgn5yknBga7T4W026fedO+m11mZUT/DaxLnWqT558iT279+PEydOjHXtpOu0NPSybTuTtKZpuu7mejR/2LZtqKqazW9yztflL4/O4cqOm+yeAcjmqaXMWz6+kXmYLGRIubWUHTPG1pG5QqEAVVUzVQGALBpq7XZKki8LBTLia1R62+12szxpacx1Ksfz2VkXJ07ECAIbjmNDiAicCyiKijRdBkBxS4YBbNlSQC6nZPtLEiBZuOh0euj1GFotDUHggHMbnqcgCDqwbW8gNfYRxzZyOR3bt7egaTaWlyk6yzRT6DpDLkdFizSlS1AcOwCKUFWBZtNBEFhwXR+cq+j1VNj2KlxXRiUp8P0yul0VipJk+cpytn1UQi07u7IbuTaDWRZeZHFFdpWlWd16cmZjcdFBGApYlgMh9MGsMgPNla8gCBgKBYFiUc+OO5loAb6fIgi6yOUs2DbNGMvOrjzfVFVFrVZb1yWWEmrLsmDb9thM9VoJtes64NwB5wKdjg3fV6AoPhQlhqYZyOXofSUJkKY2TpxwsWULQ7lMKglpeCZdz5vNZpZ1rus6OOfZdo5CfsbiOIZt27BtOzPqM00Ty8vLY8vLrrL83Mj3JH0FZHFrtEgmSb8c75DrlXFb0nRwNPN77XZqmpZF2cnvmTAMkaYprrvuOlxyukjTcxwyFm8zYUKqJ5jgpcI0qX33+c9TV3eUKCbJ0Po4CGh+WBLPbdvIIKxep07uHXdQ/NPnPgc89BB1aVWVyKrjEKm97DJiOtu2nXp7XJfI8FNPEal+5hmaKw4CIraaRncAUhottzeK6HHZrS6V6L19//v0GgcPUr7RaPccIMb0uc/R9i4srN8ezokcv/GNwLve9cL7c/t2am+ePDk0MtvoNRkjg7OVFVpm5AYDAO2Do0dpv55JLNgEGY4eBf7sz6hWsW/feE0jDElk8Wd/Bvz2b28s2VZV4L3vBf7oj4hY79hBneiTJ0mssLpKtY4Pfxj4mZ85NTmf4LUHabS02VGr1XD//ffjxIkT2WOywyglsnK+tdvtjpHIUZk3gGx+cxS2bWfxPlJaKp23N7q5lvOgkkjJKCHZnZSvL+epbdvOpNtSqj5KrEedsqUZmq7r67bTMAyUy+Wsky1l4tLAbKPtHHU8l91WKceWJFLX9SxfeBjv5WFqiuHkSYYw1GGaLhjrQAgbilJBkgh4XoJt23TEcR2jvFx2QalgoOLkyRJWV/1B2IYOYBWGQZcWzjVoWg6OY2BuLkaxGAwujR5MkxzBVbUDQCAMbcSxiSDQEMcM5XITUeQhjgWShMGyVKRpEarag6pqaLcryOVSCBFBUWxYVhOtlkCtxqGqtJ1SxQAgM6uTc+BrJdSO40DTtCzvW7qcj0qopRRbnh+apqHZbGJqqoGVFQZAQNNIOi8EdYJ7vSmoaoItW3wUCkTywjBFo0H54FFkQogy8vkE8/MpSiUHYehlDtjtdjuTgUtjO03TEIYhut3uWHay7GBzzjO3bEl2m802XNdHt8vAOV1bfL8CXY8BKAiCCoBoEGtmQ1VbOHw4wfQ0zz6To67nhmEgiqLMuVyOQEiyq+t6FkUm5dUS5XI5k91LsiuPS6/XG5PsA8Pc9tH4LJn53e12s6KIlOy7rjsWQeZ5HjzPy0YppIpBztAzxhAEwboC3a5du3Dbbbdl2fITvHL4oUi17/toNBpYWHMD/fjjj+PStWZFE0zwWsCtt5JZ2eHD1HKTRJWyH6hF57rrybCiUOe2VKLs5R07gF/7NepK799PmlvHIXK8dy+1CeXzXgiSZLbb1BaU+UYAEWeyHpUlXrqbEII64LOzpPVtNofb/Y1vEGl+5zvH1zM7C/zYjwF/8zfExrZsGbpR9fuk/d22jZY5ExgGEfBPfGLYQR+FENTZL5UoFP6BB2i9x48PCwX1Ou27a64BPvjBFxcaPwHuvpsI8Ea+e6ZJfnMHDlDt51Sy7X37aLT/7/6OBBnydNy+nbzlfuqnSEY+wQSj2Oykut1u46GHHsLKykqWNSy3dyMSWSgUYBjGmCmXvHEeNZ0aJZGSXEqC6XkeDMOA4zgZSZJy1VFX4FECoOt6Zh4mSVUQBNkM5miusaIoGelKkiSLMpLmYfl8Hv1+PzPNkvPUMt5rVMIrzdV6vR7SNB0zStrI8XzUvVh29SQ5lE7ikmDquo49e2woioVjxziCoA/D4AD6iGMPnJcxM+NjZqa7Lt6r2+0OuvTA0aMM9boC1y1D0wIwxgGU4TjpYA48h61buygWiZVT+qWGJKHu77ZtKXzfRhR5EMKDplkwjDYYi6HrgOfZ8H0TYaiDsQSW1QFjCWw7QpIAcVyCrqfgvANFySFJdOTzKSyLcog3yhlPkmTMrE6qGEYl+ACyAozM+5bGf1IKL43rFEXB3r0uDMPEiRMqer0UhtGGEBxC+DBNgT17CiiXqZuqaXk8/bSKZpMP/EIjCLGMToeh1aKs6EsvLWWX/nw+jyiK4HleRlLXxlLJUQXZ0ZWEVbqeSwn11q15HDhgoN0m1/N8fljYEQLw/RwAF7oeIo4L6HQAziMUCtQ1Xtv9lsQdIPMxKaGWKga5ndJATBag5Hkp35ck4c1mM8sGl4oN6bw9itH4LFl8ky7omqaNdb+lvFt206XSRRasHMcZ++6QqpDLL78ce/bswQSvDl40qf6Hf/gH/MZv/EYmLfnEJz6BG2+8EQDwwQ9+EA8//PBZ38gJJtj02LOH2m5/+ZdEhqeniZxKi2OAOs2DSJUxSInylVfS/xmjjvYFF6xftlCgUm0YErvZCOSqQssCGFxhaPk4Hn+eptFPFCErA2/fTkRfbot8rUKBcqHf/Ob17cl3vIO6xV/8IsnO5XaYJhUEfu7nXly3+A1voALFX/81bb/r0nbKYbZcDrj66qFR2wUXEOFfXKTnz89Th/qWW4b7YYIzQrcL3HcfncKnqkVIhf4995x+FvrSSylJ7sknyfxdCDo0+/ZtLECYYALLssZkrZsFnufhwIEDOHDgwJjMW8o1e71eNjc6GkclZ4slNE3LZmABZFmz0txo1ClbGhDJrnO/389k3kEQoFAoIAiCLJt4NDar1WptaB4mc3Xlzb00PJNmYKPva9jZ5TAMIyNwlmWNmZxJoi23U7qSx3GcZSinaYpul8juaCdyrbmaXD5JkgGBymck0nEchGGIarUD05SxUAaAMqamUszMRFAUhiAQSJI+qtVCNgMuCbvvG2g0DNh2AkVpDUgkdeoVpQjH0dHpeFhddbFjRw6cJ9k87NLSKhSFQdfFwNKjCEABY8CJE3kkSQBd9yCEBiESOE4LcSwnrGxEUR5RhMEMMwcQD2bwXTAGNBrd7PyQKgZFUdYVaiQhldJkx3Eyabuu6+tIpJT4S0d1QDpMJ9i7V0MuV0e7zRCGCjTNwdSUjdlZBiF89PtE4g4dCtFsmiiVHAjRHMxeF2FZKsKQY3lZg2HUMD8/XtiRRQDTNMei12QBaqMc89GCAM0aC+zcqaPfbyJNBZJEQxzbSBIDnKtw3T5yud6gT9AHoCCOC/C8VkZkpYR67WdM7uOpqamsYy070kEQwDAMNBqN7DMvJe6yqyxzrKWhnzQuS9M0+/zKTHTP89bFxMmoriRJsvisKIpgGAa63e5Y1jiAdfFZsiCwa9cu3HrrrdlIxmsFsmi2WfCiSfUf/MEf4OGHH8b09DQefPBBfOhDH8Lv/d7v4f3vf/+m07ZPMMEriltvJYL3ne9Q97Tfpznpd7+b5NcbsYh+n7qsb37zmQ2V7ttHpPfECZpJ3gi1GsVZyTbg1q3DGWrPo+1QVerqCjH8FyC25DjD/6cpuZIDxIYOHaK24xVXjK9TUYC3vpVI7A9+QO/p+edpnfk8cP/99JoXXHBmXWNdB/6X/4X253/5L0SW83navgsvpJn0Umko/f6lXyJjt2aT1lMuT1jbD4lOh07LF4r0y+VovP+FoGk0tTCamvZyQQgSedx/P9VkFIVsCG64gQQUE2x+bLac6iiK8Oijj+LAgQNjclZpyiXjqOR8s4yUknLWXC6XxRjpuo5Op7Mh2ZU3zzKOKggCOI6Dfr8/dmMtI4JklwtAFs9TLpcz12x58y/lrHEco9FoZPPNwLBjJ12SpZxVOo9v1DGVM8+S4AZBkJljjeYLSyk8QDFdstPa7XazLl2n08mijOQ8NYB1xMNxnMysTMpf8/kY8/MCjKnodpfQbjMcPw54HkOSlAezphxbtjAYxnCeulbjEGIVmgZQHnMOgDWIzuojTX3YNkevF2JlxcDMDM0LW5YFxykiTTUwloIxFZzXR7aRodMxEMckRwYM2HYZ/X4IRUlAWdgrg8uvvFfOIQgc7NqVQtPirCjheR4KhUI2/65pWmYKJmXBnU4HALJOcLFYzGT8kpxJYtpoNE7pJq6qChYWypiaIqOtSsVEu91Ep5Nk52eS5NDp5JDLpWAshBAqhAjBeQRVLUPT+rDtGI2Ggd27Ldi2mp1Xa2fjZcFEyq/lZ0y60W8koeacY98+4OjRAhqNALoeIklUuG4PlhUOxpMUJIkD33fgOBxp6mX7x/O8rEs/KqGWRnBxHG/ocSCLFdK/QCpNhBDrjODkPh8Wb/zMOI5znn3mZVdZKlJkcUp+lke737ZtZw7/aZpuOPudy+Xw5je/GTsmI26bAi+aVMdxjOnB3dZ1112Hb3/723j3u9+NZ599dlNVCyaY4FXBnj308773DR3BDQP4x38EvvAF6mKXSnS3324Twbz9durknsnnxzSBH/1RkkYvLa13vG63iYC+851DFjE9TV3i558fuqlItygqm1MHWOLoUWShkKZJ7EkIek6aji/LOZHsen1okuY4xGpOnKB1HD4MfO97wJe/TIWHD35wY6f0tdB1eh979gD/9/9N8u7ZWTJ9i2Oa8TYM4Cd+Anjd64YB7xO8JIz6xJ0O0sh+syCOgc98BvjmN6nb7rp0en7/+ySg+MmfJL+9yWVqc2OzuH+naYrHH38czzzzTCa3lHE6G8VRyTllGUcl3bzl/KWcJTVNMzPvkqZUa4lHcaBokjJRaXok44fW3liPkl0pNZcGVVKqCiAjZ7JbKaN35GymJMGSeMh5atnV24jsFgqFrAMqpcZyOzZy9Jbu4JKgjGZ4bzRPLSXm8j14ngdd1+G6LjqdDkzTQqNRxtGjKoAItq3DNJtIEqq5NhrA9u0Kdu2qDNalQogSGPMAcAAGOG8MfieirWkFxLEFxhIkSZgdJ02LYJoVhGEXhpEOurU2AAWmqQHogbEGDAOwLAFFYTDNCqKoB1WNEQQVlMsCihJCCBPdbg/FYg+WJSDrEdPT09mMeaFQyMiZnNuVnw05Azx6zsl86lESuTYCDdjYbX3UiEweUzmicPgwhxArUFUBOhUYVHUqe66i2DBNgV4vRrOpQ9PWk0jTNEdm48PsfYySSNmBlyRSUZQxsrt1K0OnY4PzMvL5FIALIWxwHgJI4fsacrkV5HKAonAANIogPxOapmU51dL9Xm6n9BKQy3Q6nbHinixctNvtMSM4OdawUfd7WLgYdpVlN3x0Nl5V1ez7Q34epNzc8zxUKhX0+30kSTI2U71lyxbceOONWTLABK8+XjSpnpmZwWOPPYYrBp2qqakpfP3rX8eHPvQhPPbYY2d9AyeY4JyEbY8zjve8h7q7995Lw6hxTNrYW24h2feL+VJ8wxuINfx//x+R9HyeCHC3SyT4zjuJ1Evs2EHP6fdpmXqdiDHlgQxNv1SVnt/pULdbCCLU995LZFbmJMnO9SOPUKHgqafGnc+lO/mow5XMT/rKV+j/v/iLZ85u9u0D/uAPSAFwzz20fZpGcvrbbqO56QlTOmuoVml8/wc/oIb/RhCCDsOZ+M69UvinfyKvwLm5cVsDIai+81d/RfWe229/VTdzghfAq+3+LYTAs88+iwceeAD9fn/s8VFZsuycyi7v2tij0TgqSRAAZORYZt0CGOucAUReR2/QZSyPdBKXsUPS/XYtKZckBkDmKC3J6FpH71G5sZRKA8hmN6WEffR1GWPrTLMAZGRXytTXSuHXzn1LY7O189SyK7e42EKzyRGGgKoaKJVszM/rUJQkm/E+edLD8eMhLKsEwwggRB+K4kLTdJgm0O0qOHasD9NswLIAztmgoFGGECEAPjDo4oMYLQec+9C0LnxfQNcFFEXJIqnqdYETJ2yoqgdVDcGYNZhB9pHLMTSbJkzTgmkaYEygWu2g1YrgedFg8iqPTodBUdooFh1ccomLXE5k58ladYDrutk8reu6sCxr7FiOdkwl8ZPKCDk3TFJvAcuyssLIKDmT4wKjYxfSLbvb7Q7ctytQVQ7OYzBmIE0bAIbHkjENnFcBxDAMJeucy7GGer2enSuyuGNZVhaxBQD9fh++72dZ2rKYJc+5clnF0lIXnU4zCxthDPC8ClS1D9vmUJQKqtUU5fJwTGFUQSHjxaScu1AowPM8hGEI27bHChejBQEAmU+A7KbLOKxGozH22QCwYQxaPp/P4uyk14Ccb1/b/aYM9UrWGbdtOxv7ME0Tt9566zpfqwlefZwxqe52u8jn8/j0pz+dzXpIGIaBz3zmM/joRz961jdwggnOCzBGOtS1mcwvBtK0S9eJzVx5JbXhDh6kv+3cSR3biy8et2tmjKTZTz1FTGjPHupyt1r0b5IQuU4S6nRLt3JFIbK8vEwk/Nln6fX37CGi/YlPDIOI83nqYn/jG9TpVhSSesvCAmPE0IQgQ7c3vWnjmfFTYcsWspN+17vIgEzXaVZ6QqbPOhgDXv964NFHqf4yNTX+dyFIfDAzQ7LqzYCVFTr1qtWhHYAEY3SKPvssdaxvuunF1bAmeGXxanaqDx8+jAcffBDNZnOdQzfnfF2nVsbojMqSJeneaBZ29PUURclmRmUEz+jrj2ZOB0GQEZRutztmWjYqnx4lMFLiDSCLEJLrknLzKIqy+J5Rsisl5nK2VcqS5Ty1nJkelcKrqoooitDpdDJnbwDZ7Hav10M+n4eqqhlp7vV6Y51IIcRgeY7nnmM4fryEKIqhqh44z2FpKcCRIx3Mz4vBhI+OdruMNJXGWQqAGJz3oCgFMJYil+uj21XheS7m5nSkqYrFRQVx3ICqipF56hwYMyCEB9+3YZou5uYSKEoyFklVrWIQxZWDYRiwLIAxG2GogDEfpRKDpjG020NybBgGcrkcikXqpitKgFwuRT7fAaADyGUGdLlcLus8ymM5OsMviyAyPq1SqWQdanks1xY6pHEZ53xMmizPITlLL2O7ZG56r9cbnM/dwZQYSds59wf7Sx8cNwVJ0oWq1hBFAvW6GDv20mtAOpQ7jpOdKxKqqmZycJlR7ft+9j6FEOj3+7jwQoZnn7XQbtuIIm1wDrZgmjGECJHPC2zbRt3vbrcL27azcQk52rC2cEHjBPl1hQupJhldXtO0LOtcvh6AbOxCyvblZ1h+NjYqXCiKglKplG2n7H7L3Om1x1JVVVx33XW45ppr1vGwCTYHzvio3HbbbfjKV76CrVu3nnKZW2655axs1IsBOTnSHF2aApUKNQBP5eE0wQTnHI4cIRJ7331EbgsF6tDedNN4R/p0WFigD8bf/z0R63yeur2WRd3oTofIahAQYTUMIsZRRMSZMWrztdvUHf+bv6Fu+8UXD4ltr0cz1Fu2EBF/+umh+ZpEuUwy7gcffHGkWsKyNs7nnuCs4sYbgbe/nYQI9Tp1fw2DToXFRToFP/ABIqubAY88Qtt5urntrVupGPDEE+tPywk2D2zbHrvZfiWwuLiIxx57DJ1OJ7vplRJqAJlbtexcyS5hu93OyKPv+5nMttPpZBmzAMacrEch46gkyZRyX4AIzqhDt3Twlp0uWXhIEuraFovFQVeRZ9spu9T9fn9s3ZJYR1GEMAwzubmcbd3IPGw05kkSszimOeBRB2rZcZbdVd/3sxztOI6Rz+eRpik6nQ4Mw8hkx3Kft9ttLC4yLC1R3nSp5AJwAUQQwkavZ+PYsQi2nULXNXS7Ndi2lCUDilIAoA88NhUwZkDTYtRqKXbv1sFYHfk8Q7+vIp93oSgWAAVCeOC8PygkRJid1WCaLlqtPgzDQLFYQr+votFIYZo6bLuBKOrD8+hSqaoKLrigjOnpAKoK1Gol+H4EzgPMzblQlBY4HxrBjUY9yZGCMAzR6/VQqVQykyoZtyRzjeM4zsiW3OdSpt/v97P9KY/VRrPxxWIx66jKYovsfidJMnaumKaJbdsKOHlSGcwIMwApOO+CMWfQoe4gDBXYtoutW3XoOstctNdmVLuum3VbbdvOlAyWZa0rQlExooBGg6PfB5KEgTEfe/fGqNcdtFo1yHqDrhuYmrKxd68OXY/WxYuVSiV4npepTGThgjLLO+vUHpVKJevwVyqVLALMcZyx0Q+AyK78fEhi7nkULyY/T/JYyeKbjDeTx1m+npyhbrfbmfmg3Be33norZmZmMME4NtPo8RmT6uuuuw433ngjvvrVr+Liiy/OHn/kkUfwe7/3e/jSl770smzg6bC0BPzt31JHpdOhLzZFIbXrj/0YjW9uon09wQQvHvfcA3zqU0PzMcOgE/8v/gL4138FfvmXSat7OjzwALmSLy7Sh0N2qE+eJHa0sDCcn5aybxmxBdDvQUAE2jSBz36Wnrtv3/gHzPeJhBeL9Nxjx8hUbFQGzxiR4qWls72nJjiLUBTKj96+nQzfn3tuOEN9663kq/dKGI+dKVqtobjiVLAsEmOsuXeaYJNBkotXAq1WC/fddx+OHTs29riUpkqJtYwukp2r0e6qJArS3KvX62UO3VEUZXFUkmzKjGbpQjx6My/jqOR87EYO3WvjqGSnUuZCy/VKkzNZJJBOxLKjPerQ7Xle1qmTBQHZJZcdvrX51NKpWUpTJbmWc9CjBEm+L5mnLAmf7JzLqC0A4NzE6qozKAwoA5mxJDB95HI6Op08jh/vYM8ehjStwDAEgAiMmeC8CTkfPVg7gOpAtgwUi3ns3Bng0CGOZtOEbbeg6wmEAKJIQRznMT9v46KLEkRRMDB2C3HoUIR6vYIkCaCqHXBuIE1tGIaKXbsEZmZCBEENSULfM/k8sG1bCVFkQFFCGAalUMg55TRN17l0S5dqeQ5KR2lZkBjNGs/lcnAGKRzymMvZe0ncWq1WljUuj/va8xcYdrPlaEKlUskKLq7rotmsoVIBajUGxgQ0zYKi5AFwCJEgikykaYSpqT50vZzFoI0WBGRGtVQySJIpO9iyMCSd4RVFweJiH48/Xke/z7LbESFKyOVS7N4tcMEFJXS7MeK4j0rFgK4H6PeHnycqhlDBSBaAgiBAr9fLZrd7vV5m6ifPXcYYms0m0jTNilxyn8oRDHkMZaTd2mNpmmbmhyCzxKXXgvx8yIKZVJ6YppmNYEiZuaqquOaaa3DNNddkxacJNi/OmFR/8pOfxH/6T/8Jt956Kz73uc9hZmYGv//7v49//Md/xDvX5ta+AlheBv7wD6kZtm0b3fwxRvf+x48DH/843d+/6U2v+Ka9IDodkiLGMfGPvXvH1boTTACA5Nr/43/Qibw2MJhzarl9/OPAf/gPJNE41Wv89/9Ort+XXTZkHXFMWthWi05G2x5KvyWplgNLpknmau02ad/uvZf+XXvSMkZ3E60WvYbn0e9r3ayS5OXT30oJ4+i+EoK+FOp16s5v3z6J2ToDqCrNH99yC9VQwpBuFGdmNl+x8kyUcPKmbGIKv7lh2/Yr5v5dLBZx4403Yvfu3VhdXUWtVkOapmg0Gtm8M0CSz9HMZxlllaZpJqEdleg6jgPHcTJDLgCZRNcwDHielxkYSUm0zPKVJFtmSpfL5cxJWRqJyRvzjQynSqVS5kLsOA4URcnIruwES8gbf2lUJolZv9/PjMI6nQ5UVc3muqUbda1WG+sQyWgx3/czkia7extJzKUkXRYu5MxorRaj29VgGC10uwkYAzTNGphy6RAiguN00e+naLf70HUPcVyBZUUQwoeiuCBJtQCgDLrPNRiGQLNJ669WC3AcgZWVFEtLBQRBCsZ8OI6FmZkE+fwyZLOWSOg0FhcZXDeF62rgPMpcsPt9DUeOdKEoAlNTZla4UBRlndxXdmc7nQ4cx0FlcN2WCgHp9j163o2aXclMdOnqXavVsk7wKNGWSgZZ6KFYqnIWm7Y26sn3/YywSx8BSXZlTJTrAk88EWNlRQNjIQxjFUIAccwGSZwlXHABshi0IAgy07ooisYKAq7rwnGczImecz42e6/rOk6ebOP553UEQRG5nAZNI+OxJKnD9xmeeALYtk1gZkZDPi8LAlZm9uX7ISzLxepqDaOz36ZpZuddmqawbRu+78P3fZimmRmOjY42SHd7KYeXx7RcLmddZulLIEc/ut3uuuKFnMGWn+vRkYowDMcUArquY2FhATfeeCOm1s5gTbBp8aJE+f/xP/5HGIaBt7zlLUjTFHfeeSceeOABXHPNNS/X9p0SX/wi8YVLLx2/obIsGvk8epQaaldeuX7G7uVAGBJZVlVqKG5009nvA//yL+S3tLJCN3mWRSrYt72NJJeb7WZ1glcR3/oWtdUuu2zcdandpupRpQI88wx1ou+8c+PX+MY3yP509DUA+tC4Lp24x4/TyRsE9LiqDom1zC3tdocScSl7krPXAG3TM8/QupJkaB/90EM0eCslS0lCJ/4ll5y9/eR5tJ7vfpccqQyDzMtuuom24UtfIsl6v0+Fgulpare+7W0Tcn0GUFUqXG5m7N1Lh93z1keoS0ixx549r+y2TfDiILs1rwRkFE65XMaFF14IgCTIzWYTq6urWFlZge/7WFpaGusoS/m0lDZLGa80LSNyOE52q9Vq1lGWMls5lxyGYdYRG43cATAmSZfyU+lOPCoxl681SpoB2p9SYi7ncWXRwrbtdTf+sgOdJElWRJDvrVAoZJ1GaTalaVpmoiZJgVQaSKM2+Vzp9q1pGjzP2yDGqIJHHwXabQbDyEPXQ6iqh17PRq8Xo1hsIZcT0DTA86ibPjvL8PzzEUyTYyhLzg3m1LvgXEWa5rCwoKFcJof0er0+yCamejFgwbJsMBbAcSyo6lRGlqLIwuJiHY5D2dR02TOhKHkwxlEoCLRaDo4d89Dvp9A0FUnSRD7P4ThibE45iqLMYK3X62XnXxiG8Dwv65ZKYtbr9TZ0eo+iCGmaZsdVjh5EUbTO7EoarMnChVQvSMWEJLuSaBuGkUmiRw3ELMvCvn0mCoUWWi0H/T5FSFWrCebnVQANjE43UKTZVGaYVi6XB513UhQsL9cHDt3IZsllQSCKIiwuagjDBPl8B5pWRpp2ASRQFB35vI1OR8fSkoL5+fGCQLfL0O2W0GgwACEsq4wtWximpmKYJrm4r5XDy88QgKxwIQsCcRxnn33GWKbikI7wsmggs7ilE7j0OtAHVVxN07C6urquaCJHPkzTzCK84jjG1VdfjSuuuCJTLkywHpsxxvmMSfXi4iL+y3/5L/jkJz+Jffv24cknn8T73ve+V4VQ12o0Xjo3d+oOxdatZLL84IOUQPRyYXWVSPJ3vkO8QlHopu3224lLyO3zPOBP/5SShapV8qvSNLrPf+YZ4E/+hPjKm9/88m3rBOcQOh0iiqNtwZUVOllWVogsys7wn/85kcSBXC/D6ioNm87Pr6/WMEbGZouLRKw1jViJEOOkWnZfpGxbkuVnnqETed8+2tb77qOudKlEhQDDoOe022Smdv315Aj+3HMkNz9b3xu1GvBnf0ZW1ZpGJNnzyAr6s5+l9+a69IWwfTu9n5UV4O/+jjK3P/axCbE+D7BvH00aPP44/b72PiSK6FR/61vpNJxg88K27VfV/VtVVVSrVVSrVVwyKP4lSYJarYbV1VU0Go2MdAPIiIfsVEmC47puZgDFOd8wX1ZVVTDGMndhz/MyA6LR5Q3DQD6fzzqPsosu5aTSpVlKzOXMt6IoaDabY/tT5miPSsxlJFMul0On0xnLCibiVM3mRaUUXnbLpXMyMIx5kgUB2RkdlbBLQzbbtsciuzjnOHiwBc9jcF0GyxJIEgdClGDbHHFsodEwwJgP1xUQwkKvtwrHARxHQb8PuG4BimICSEGGZRr6/RSFgo+pqWG3XBYE5Jyy7/vo9VoAgCgKxmZwl5aAKCqhUlEy52sh2uCcjk+aUrf28OEKWq0Eus4gRAm6HqBY9HDJJeu7kHKeeqOscalGkLJkKZ+W6oO1c8qWZWVdftu2x4ipaZrrCKSu62Pz8blcbsxxfdSlW7q5yyg33/dRLAoUi30AfZTLFfR6NLts20OXbhkHNVo0SVPA90tYXFQRBByMVZDPc1SrPmZnNXBOcvg4BpaXGU6eZIiiCno9hnxeDFQXHoAYQujI5ej8WlwUmJ7WYNsOjh418PzzGjjvwbIiMCbQ7/t44gkdpZKLbduaKBTMTGYvu/aj3gXyMyIl8FKlMpodv9GsuFRmJEmSmS12u12Uy+VsBntUZr52Pl4W1RYWFnDHHXdksvUJzi2cManevXs3Lr74Ynz2s5/F29/+dnz1q1/FT//0T+P48eP4nd/5nZdzG9fhxAm6bx8Z7V4HRSGZ33PPvXzbcfQokeGnn6YOSLFIXxw/+AH9vOUtwIc+RPf6//qvxDvWjpi6LhHsY8fIQ2rfvmG88ASvYfT7RAil7Of4ceDhh6mbXCgQeeWcCOJDD9GJ+NGPjrfpOh2acz6VVGPrVmSldyFI5u15Q0Idx0SwpWFZtUpE+frrib08+iht33PPEaGemaFtSlMiu4UCPdZqUTd9+3bqEn/oQ2eHyCYJOZA/9NBw3ltidpZkId0uVapkNpSiEKmvVqni9sUv0vDwBOc0NA348IeB//bfSJQwNzc0m19dJeX/1VcDP/VTr/aWTvBCkFnHmwmapmFubg5zc3PZY9IAanV1Fd1uF8eOHctujGU8j67r6HQ6sCwrk2XLzt1a0zJd1zOJuTQtC8MwI81rc3DXSswlOZUZxZLQyht5XdfHJOayQw4gm2Xt9/twXTeLZAKGBYVRSIIvO3eqqmbSdgBjZErX9Wx5OacLIOvyS/l8EDA0m7mBsZMCQIVpNkB50vQZjyIV7XYJmtaFaXLMzZUHedEhDh+mgoCmdcGYrAczlEpVXHJJDF3HWGSX7K6PFgRGs8al1NfzUmhaCCGoM815DYzZUNUc0pRhdRXwfQHHaSCfF9llKEls1GolPPFEiiuvtDOJb5qmsCxr3QyuNHeTsmTpuC6l+/L4r51T9n0/Uw/IGXUpLU6SJJunljPO/X5/w06tPLekgZ0k6VEUrZMlS6MzqTqQc/lCiOycH5VPx7GCAwc01OsNaFoKXRcQAmg0DNRqOTQaXVx8sQXGKjh0CKjVQnS7NgqF5sDMi25lCgUGx5mGEMngfC8gijykaYyVFR2HD7dhmslgukwFYw5s2wHnCZrNEEIwXHABdaGLxWI24y3VBKM53utVFPms4CffvxyXUFUVy8vL65aXqgNpziZn5GVONTCUw7uui3379uGSSy7ZVMZbmx2bbV+dMan+H//jf+B9I07Dd955J+666y684x3vwJEjR/Cnf/qnL8sGboSNxiY3AmPDObqzjTCk+/lDh0hZOzpeWqkQj/jyl+n+/Y47gLvvpnnEteOlElu3Ao89Btx/P/DjP/7ybPME5xBsm7q9UpL92GNEIqenhye+qlL32HGoYrN3L/ATPzF8DV2nZeJ4Y8dsGaUF0L+yKz3iagnTROa8EsekA77gAiLsP/gBSa4ZG4+3siwi065LneokIVZz223ARz5y+mrYi4Ek9nv3rrf7X1qiD38uR6R/69bxLwzTpH353e+SDHxSFT7nsWsX8Ju/CXz1qySOeP55OuTVKvAjP0ITEpPDvPnxaudUnylM08TWrVvHElEkYWm1WlhcXBzrZidJglwul3WBZadUZj6vNS0blZhLciRJs+M48H1/HdmVkTyyoyylxpLQyg63JJCyQ97tdrN9LudaNU1Dt9vNjK4URcmI2UYFAUnkZPa0JNC5XG7drPDayC5N09DppEgSD8WigSjqIE0TAAxpmgPnOjjXAMRgrIF+X2DLlhBBIGCaRZTLDJbVQa/noF7XkCQchiFQrcYwzRWEId2zAUReyTQL6HRc+L6BNO2jXNbhumyMHOm6DtMsIEl0kPs4XRuF8JGmCfr9PIRoIpcDwtAGYEBRVAAqNK2FQqGFZpPh8GGBapVlc81pmmbHSs7U9nq9sVx0YDgyIJ3fR2fx5RwwsLFxWZqmGdGWhZJWq5WZd6mqmsnM1xJIwzAy4zIpaY7jODueazu78jU3mlO2LAsHDrTRaHDkcoCqWgBMKIoBy4oRhh2srqYAugPyXEY+nyJN22AsBzkf3+8LpGkITVvN7rcVhUFVXeRyFp56SoCxIiwrAeceGNMHowt0PKmzrYDzMubmtLGouDAMwTnPzlX5nmTBiM7PzlixT6oZJCkvl8sZ0ZZeBKOmggAyObx8rhz7mJ6exu23356pFCZ4cdhMxPqMSfX7Nojuueaaa/C9730Pb3vb287qRr0QZmfpHr7ROHUTTgj6Et2+/eXZhsceow71nj0bm4xJFew3v0mGy0tLp+9AM0Yc5PHHJ6R60yFNaYD/6aeH7nJXXTWcEz7b63ryyeG6Dh6ktlu3O06oAQzsSslYwLKAb3+bmIPsVi8skMT70CGq6IyiXqcKjpRqWxaVgpNk2LmWhJxzWmbLFtqWe++lDx9AhJUxqiS57tBU4Mor6V9pWnbkCDlenS1CDVCHOkk2HqKVNwmFAv3eag271RIzM7SfDx+eZCydJ1hYAH7+5+k7dGWFhAlbttAN1QTnBl5t+fdLgew4AcBVV10FAGi326jVaqjValhZWRnpgFJnd1QaKs3AZKe40WhkJmIAshnNjSTmQoh1Dt2O42RScOkcLCXmax265QyolKWOdt2TJEGhUECv1wPnPHNClhLzdru9LpJIdj+TJMl+l87UGxUEVHUKacqgKALFoo1Gw0McpxCCQVUTGEZzEESholCwcPHFJhyH3JtpthXI59uYmjJhWWYW2WVZhawgoOs66vUGlpcZ6nWGKGIA1IGDeIhqVcXevRUoCjkwFwoFmGYbisKRpmJw+dWhqmUIwdHtpkhTE6oawjB8GIYJIXoQIgTAoKoOABPNpo49e+IsE1pKvuUIwNo8ZSnNXruPLMtCLpeDECIrysjYJ13Xx2Z2FUVBPp+HZVmZHFwWV+QxkQ7wa43LPM/b0LhMFkqk6kIS/FPFdtG5wdBqFWDbARjzATAwliJNqVOv6wxpauH4cReMKahWA4Qhh6KkYKwLxnRwnkM+30IQqPC8PEolA2kqwLkGVV3F4qKA59HIQJoq0LQyOO8DUKCqFQjBAfhI0xyWl1tQ1WG02dq5cymHl8Ulz/Oyz5+u62MxV/KzLN3Lpd9Bs9nMjqf0JNioeGHbNt7ylrdg7wsluExwzuAlp4fv3LkT99xzz9nYljPG/DyNZH7jG3Qvv9Ec/9ISKVOvv/7l2Yb9+4n/nC4yd36euMThw8RLXshvQHKYCTYRTpygOKqDB6lrLFGpAG94A/Ce95w9J+vjx4frCkPq8h49SoRedlYlhCCyKCOxkoTmnL/7XSKvkuC+4Q1E0uW8M0Dv46GHiKirKnXCL7yQHm+1iGjLv8mOeaVC1ayDB+k1ikX6W61GZgDyw3D11cRipMVytUqvGcc01JqmZ8/qvtE4dSC9dC/X9fGO/ChUdXxufILzBpXKqQ3xJ9jckPOI5wuKxSKKxSIuuOACAMhmiuv1OpaXl7M5bSm/lXOWwAt3lEcdukcl5jI+aC3RlYZRksTLjrKcd5YkS0LmSHPOMwIl45BM04Tv+wjDMJP6yllR+R6FENn2yoJBr9cbm/2VnfSlpQY0jYFzMbjkWGi1CghDgShiYKwAw+jDMBRccIGOfr8G2djlXIHn5bG6asLzUihKgEIBKJcjuG6czUcnSYrlZQsnTtgwDAWFggLAhxD1bJbX8wSuuKKYjSFs3ZrH0pKCfj9ELqdAUQTSdGVwT8eg60C/X0SppEHTBMLQRhAwcB4CEGBMwPdXUatxqCrLJOaqqiIIAvT7faRpim63mxlXyXnqUUdpKRleOxpRqVQyp/hKpZI5Skup+9oItuqgEyWLI77vZ91VGeMkj/2ocVm73UYYhgjDEP1+P5NMyzxl2akdPfeEEFheZuCcQVUZNG0KQsRgTIeilCFEiDT1YVk2arUW8nma2zcMQNMctNt5OI6AooQQQoemxQiCHoQoot8P4DgBSiUFcewiSQzougLGOJKkCcY4hJDZ6Q4UxYCq9iBEDlNTajb/rKrquoKA9DCQcVi6rsPzvEzqPRqHJYm2aZoD13E/i9TzPA/lcjk7xrZtwzTNzLfh5ptvhn0q+eoE5yReMqkGqIL1SuMd76Am08GDwO7dw2ZVmlL8S68HvPe9RGxfDvR6LxzNYprDbNd8nrjFyEjWOvT7L19nfYIfAisrwB/9EUVO7do1bHfJWeZ/+icioh/+8AtXTF4Iy8u0rkOH6IR2XSLOc3OkZ63XiQTOzhJJ9H06qS65hLrFR47Q8Ogf/RHwhS+Qcdlb3kKS6+efp9dYWaHXO3mS1qdpw0xpXSfyLDOTnnqK/r97NxHn+XnaNtMc7odud9ihNs1h0UHOaR85QuuWnWKAiPCb30zE/6VKdvL5U1ehXHfoXq6qGxP5Tofey6vw/TXBBBNsDEmqhRCbStZ3tiBNkCqVStahkjFe0nFcSshl53C0YynNozRNQxRFWUdZRmLJnGtJXiUhlx3RUaIthBhzD5edUkmaTxfZBRD5kC7m0mxMFgQURcnIhqqq6PV62bZ2u91sblySyZmZPBYXjUHnVINhNDEzU0eSAGnKADAkSRm6HmF6msOySgNnZg/HjpXQavlQ1S40jb72l5Z01GoV7N2bIp8notdup1hejmBZBiwrhRBEIBmjzjuRQYYjRxqYmRlmY+/YYeK551x0u23YtgNdr8DzgGYzQhSZKBSaiGOR1Y05VxBFVTAWg3MVpllEmnpQVSqEdDodxINrlyxISBM3WaRIkgSdTicrfPi+n5FuaWY3qk4gwzig33fRatlIkgCu62B2NodiMQbn5E699niOFk4AUlvIkQNpXOZ5HJ7HoCgGXNfG3JwJIZJsjrrf76Pf72dO5qPnXqOhDBQIIdJ0vFOrKHloWg5pmiAISsjlOBgLIARQKGjgfCWrw9OcfB6+b6HdTqEoJrZuTcFYhDT1oSgm4rgOVRVgTIGi5AAY4FyDED447w+IcIhGA5kbuSwujeZj93q9dV1laUQ2GoclXbuTJBlz3ZcjEKNmaFIOrygKXve612HXrl0bfDtM8GKx2a4RZ4VUvxrYsQP4tV8D/uqvqEEXhsN79JkZGi1961tfvvWXyxs3v0bhecQ1tmyhdJ/Pf562bSP+1W7Tsjfe+PJs7wQ/BL7xDarcrB2aVxQip4YB3HUXEcSLLnpp6/rmN+lEHl0XYzRfsLREs8PdLpHFfJ7IbqVCVaVajbapUKAPRr8PfOYzNEvwsY8BP/dztPxdd9HcwqOP0jI7dtDjzz1HxHdqaphLnc8TCT94kMh3ENDdgqz6SCOzyy+nbr6s9B4+THPXBw9Sh1x+KOfnSfr99NNE2JeXgXe/e72cfXmZ3qdlUQf+dMWKK66g/RZF69UCW7bQuup16vCvJc5CkDvg9deTRH6CCSbYFHglc6o3C1RVxfT0NKanp7Fv3z4A1Imu1+sZ0a7Vauj3+2fUUZaQmdGSAIy6gyuKAiHEum627E5Ko6pyuZzN5hqGsW6eWkYnrXWTltsl3Y2BIdGWkmTpQB7HMRiLUSqV0WgEUJQAimKCsTwMg64BJJtuoFwWCAKBTgdIUxeHDhXQ78eoVGwoigMhIgAcjqPD81bx7LNAHHMUi0C36yKKXORyKRjjACwIEUCIBIACoA7L4qjXga1bbbiulWUUAx0sL3N0On00Gn202xXoegTT9KHrObRaOqKILpelko9CgchrGNJl7Pnn87jsMiMrXsj3Lw3kRo3L5Dy9pmlZfJkkaNKlfa2T+eqqgWee0RAEXeh6B4wB/X6IlRXqZm/b5mVFFCmHl8WOjQonpEgAjh8voVaLkSQehLDAGMehQ6uYmQEqFZGde7JLLc+roaO4A1Vtg3Nkcnja1yo4b4LcvAHbplECRSmB8w4sSx2cD4DvB0hTG4rSgWF0YdsC8/NAoSBQrVYRRSmOHQM8r4hczkOaRuj3FXS7AdLUB+eSfLm48EIb09PIOs9Sji9VH61WK8tQ13U9M7VbP66gZp4HhmFkJoMyPm6tsaCmabjssstw7bXXZoWuCc4/nLOkGiC/pP/tfwOeeIJ4QZoSz7jqqqHS9eXC1VeTEVmvd+p5vRMniBPJTvoPfkDbumfPULVKDoi07FvecnZHTid4Ceh2gXvuIUJ2KrlypUIH7t57Xxqp7nROv66LLyaC2+8TAbzmGlrunnuGpLFWIzMu6dA9O0sE+m//FvjVX6W56/vvp6u7dPrudongSifx5eXh4+22LLnTa9XrRKyPHaPKUBDQerdtIwLe6dD21evUoX76aXotKa+++GIi7VNTtJ7PfY722WWX0Xvcv5+KGPv302vrOn1Q7riDihYbkeurrqJlnn6aOvaj+65YpH3x5JO07tHnJwl10Esl4O1vn4TDTzDBJoLM232twzAMzM/PY35EbiczdkeJtnR23ogYSYIjM6yFEOvcx0c7yozRjLJ0b+52u1knTzpXFwqFzGlcdr9Hu3qSuMmuXqFQyDqtuVxuXfdb07SsW24YCR5/XKDXY9D1CLquADAQhh1wDlQqJqpVEwcPalha0iFEHZwHMAyg1RJwXRWmWQDnHQAKHKeMVguo133s2GHi6ad7ME0fnAsAbLD+6oBUU+fUMChLOU1NBEGQFR1cl2HfPgvHj+fx5JMK5uY8qGqKTocD6EJRHFiWDl1vI4pUMJZHkuiIY4GZGRXd7iqOHgXm5kT2vguFAvr9fhbbNBo91m63xwonjDHMzMwMlAFAkrjwfQ+KkiIMFTz1VB9ChCgWBQAdimKDMQtJIrCyQvLjXbuSLBZKysnl2ICc45cxbHEMPP88Q6/HYJoqXLcMISJwbqDfr+DIkRCcB7jwwty6iClpXCdJ6+HDJpIkAGMeFMWAED3EcYgkYQAcRJEJTdORJBxp2gKQQIgYhgHMzTkIQxVJ0kKn42LrVg1XXcXBeTym4qhUGPp9Bt/X0GpV0eulCEMDimJC1z0ACYLAxGOP1dHpcMzPC6jq0Hlbzp1LT4E4jjPptpS0SwM/OSctz3tZRMrlcjBNE/1+P/ucyHnq2267bczUcIKzg0mn+ixD06hZdvnlr+x6L7mEvI3uu4+4wdrZ6qUl+vdHfoS2cetWahr+xV+QilaqUpOEGow/+qPABz5w9sZNJ3iJWF0lyfILfQnmctSdfSmo1U6/rqkp6hA/9hiReMsiCffhw7T+lRX6d88eWr7dJoJrGBSM/sY3Ui7zY48RKW+1iNhOTZGcYmmJOsm7dlHVp9UiUjs3NyTQMl6r26X/X3IJdXkti5a77jqa067XKaqq1SJi67r04dy9e/h+Zmdp/95zD5Hqb32LZsl7PdqOmRnqPh88SN3248dplmMtsbZt4N/8GwqAP3CAihzFIn2olpfp/z/yI7Tt+/cPST7n1Mn+wAeGpH6CCSbYFLAs6zXXqT5TWJaFbdu2Ydu2bdljvV4vI9qrq6tot9vQdX3DjrJ0aJZGV5IMyOUliRud5U7TNHMRj6IIURQhn89ns7vSMG00b3pUCiu3W3ZnLcvKJP4yUmuUlO/cCXQ6FlZWCoM8Yw7bNlAuhxAixD33uEjTDnQ9RZoCUWTD9y00mxqKxRgzM20YRjowDAMcp4hul2F5mdykhTCgKByAAJAiTceLEYANIcoAkrGuPWMMhmFhdbWGXA5wHAEhGMIwh5MnncHyMRhTIESKJOkhisqoVDqw7QRhqKHZtHHhhSoMQ0UURZl0e220med5mWlckiQQQoBzjuXlFdTrDLUaEAQMaWqDMRthKMC5M8h79sAYwJiKNF0FYwL5PNBu60hTF/PzWkbeAWQxT5JAJkkCy7JQr9tot1UUiwyK4oFzOkaM0e1GEBSxuGhhdjbKiiJSug5grPNeKjGsrORRLBqIY4FWy0EQkCQ8SRKEoY3Z2RUwxuD7ZLDHmAXGNAgRwjT74DxFudzF3JyAppXR6QSZsRi5lQsACh5/vI84rkPThiOaQVCApnEUiyk6nQqefTaBrvvYuZNy4teqBIrFInRdB+c8izmTCpBcLpcdN/k5keaC/X4/m6km8zyGK664AjfccAP0F5oXneC8wDlPql8tqCrdzycJ8Mgjw/HUJCE+kc8TD7jlluFz9uwB/uN/JG7z5JPDGOKrryYl7iYruLy2QVelYX7bqSDES5+nfqF1MUbk74knyLisVqMTrdslImrbRIgBqvIsLxMpFYII8O/9Hv3/ssvo5Oz3aZl+n66OuRyRdV2n5Uxz2Cm++WYi7bIrXq0SSd2xY9xRfMsWOqF7PTI627KFfubnN3bzm5qiD8KhQ8D//J+0rZdeOvy7ZVG1qVajvOnduzeejdi9G/j3/56cz7/7XVq3qtKH6vbbgRtuoO76Aw9Q8UDXqQp27bWTfKUJJtiEON+Myl5u5HI55HK5bEZTCIF2uz1GtNM0RafT2XBOVHYYc7lcFvEjTcTWznJLmW8URRkBD4IgIx+STMt5Wuk6Pmqw1e/3s+43OZ8n0LQy4lggSQLougnL8rFly0qWCknGVWXcdZcK32coFFyoqo8wjKEoGjQthqo2wTldFqtVB4y5IAduD0KEiGMO2+6i0zEBOOC8DUXRoCiFAXnjAHT4fg2m6SOOBRoNZLnPQRCg3RbwvApcNwHgQVVzKJc9+H4vs/fgnKHTqcK2gbm5FPm8BSE8aFqCMBTo9xN0OtT9lqRMGrx1Op2xGXmZ8d1sNmEYJlZXyzh6VIWmJbBtDYrSQhh66HQYGAN6PYF8fgpANHjv1F0WwgPnLpaXfWhaMHZMS6VSpmDQNCLcvV6ApSUVlqUhTTvwPIYksSGEBdPU4DgMltVEt5vg5EmBNO1lnfd2uw3btlEZOEWGYYhLLzWRph3U6z14HgZScCAIpqAoCQoFIEnKUBQfQoRotQzYtg9FIel2GJKT+kUXudi6dWh+J43VLMuCECY4b0HXVfh+HmGoQ1EYGFNQKNSg6+TebhhAmipYXCxhZqaLQsFCpVLJ4urkeIV01QfoHJiens7O+UKhMDC+o4LHaLyZqqqZcdv111+P2dnZH/KTPcG5iAmpfgmYmgJ+4zeAhx8GvvMd4iW2Td5QN99M8blribJpUoPv5XIln+AsYW5uKKs+lXucEEQiX+o89dzcsHu7Y8fGyzz7LJmTXXYZdXKfe47ctGdn6SR79lmSNMvZakkYjx6l0N5ymUh0rTY092o0SLYtBD1WrxNZpwEkIqGHD1PH9+KLqds7PU3PWV6mE1wiTenxd7+bPgymSR+QU0HT6Dn33kvrPZXUpFql/fLtbxNB3qjyNDsL/NRPkXthp0OvXakMl921a1h0mGCCCTY1ZE71+WpU9nJDzuOWSqXMCI1znhmh1Wo1NJtNeJ63rpstpcCcc5imCU3TMmdp13U3jARyXXfMHVzOCpumiW63iyRJxtzBNU0bGIw1sbLCcPJkCs8DPK8AwIBhBFBVB5ZVgOummJlJBjXYFoRQUCoJqCrAuYkgqCBNScotRAGK0ofnUWa0YdQBcKQpdW1dN4dSycbqKhFWw2AQIoEQnYETtY80bSGOdWzbZqNaVTPDMNmZ7HQoEkpRHCiKDSF8qKoDxnJQlASKkoBzE1NTNTgOkM+LwTHRAVTAGIemcViWm82Ta5qGZrOZETZd12FZFmzbzlQC5KId4uTJCK5bhK5HEKI96OYWEMfkSl6vc+h6A4YxLNAzZkFVC1DVFFHkoly2EUUR4jhGLpcbc7IGMDjuRQQBR5py+L4FxkIAAcLQhq630O8TEWbMQhiamJ7WssLNqIN4flB497wW9u1z8OCDJlotBk1LwDlDuVyHbYvMEqXbNWCaZZRKCZpNB2FoQdN8VKsxduywAKxidVXuUzZwzi7g8OEUKysJOh0Nvp9CVbuw7TJctwtVjQEoSFMXQuhIUw1hmMD3m6jV+CD+jAza5Dkrc73leS3jykah63rmJaDrehZ1xznHRRddhGuvvTaLsZvgtYMJqX6JcBwyWr711iEXOV/uAxYXaQz3oYeo4Tk3B7zudTTKet6nANg2dTr/6q+ItG1kLLG8THO5L9VdTlZi/uqvqNu7dl2eRzLofJ46rKWS1F8NyfPJk0RS9+0bzhCkKZVlFYW2tVYjIwLDoOcfPUpFgSgadtsZoy6xlH1/7Wu0XjlrLWUYozOPQUCkfu9eMgbYv38oLz8VOp2hpL1YPP2HRrqRNxqnf03bfg2cmBNMcH5DRszI2c8JXjpklJKMUwLI+EvmZ6+ursL3fSwuLo5J7yVBl2ShXC5npNlxnA3zpiuVSkbSpHxc/j9NU7RaLSQJ8OSTGo4dc9HvWxBCgWH0YJoehAAMowtARRjmcfiwh3ZbQ61WQhBoUNUEaapD11vI5RoIw+Elr9utIElihCG5gwMRwtBHPp+DbYfw/VXMzDCsrDAIocC2pwaFmwRRpCIIgEIhxtSUAs6trOgwzHHWAWhIkhY0rT94TwFcV0G9XkY+3x+4YZdAeczxoAveRxQRgQwC6pjKDjGAbO5cZpcD4/Jpci4vIklUFApRpi6gyKgEjBVh281BMIgFyzIBqAMJeAtCtCAEAyDQbIpsfjuKoiy9JwzDbJ64Xl9FGDIkCdWoOS8CUGHbAOc5BIEPzsMBeRcZ4ZREV84eS+UD5xz1eh9p6mPr1jJUlWK+VNUFYAAQEEKF47QRhk0UiwIzMwKcA6USuWirajzIn+aZwVunAzz55AqCgMEwBEyTodMpIgh0KIqAEA5yuT5UNYGiBEhTA6a5OhAGMgA5VCo6NE2D7/vZmINUXFQqFYRhiCiKxuLNGGPwPG9dkWlmZgZveMMbMHW6+5QJzhrECylJXwVMSPVZxEtVAW8mfO97xPHkuK6uEwe7916aJf+3/3Y8Nvm8xJveRGT2oYdIxlyt0kGOIpIlhCHwvvedurv8w6zrwQeHXXJFoXU88giR35tvHpLouTmaV5ZBnfLLxfOGsmzpFt7rUQdaUajyU68Pu9JUxqd/g4DeY7VK77Hdpg6279P6HIek1EtLtL4nn6S/axrNWP/iL9Jyr3898Od/juyKvBZxTNt9++3AF784HHwSgsh2q4WBHSjtB12nbWm3qeN+Pn3QJphggjFYg3GRMAwnpPplhKZpmJubw9xIzmcYhhnJbjabaDQaWZdWkm05wxoEQdaplrFa0vxqFFLCLImgZTl46KEQx48zeJ6JXK4JIAVjAOcaej0HaWohCFIsLHRhGBz1eowk6UBVi1DVaOAmbUGI3MDECjCMFK7bGORHC3AuEEU2OC9gdjaEaRqwLBO2HUHXU6yu5tDpDEmRpgFTUzouu6wIw+CZpF3mONPceAf5fIx+n0HXTTBmA9Bh24BltcF5CkXxYVn+YC5YAectpKmNOM5h716BYpGM4ta6rmualkU1SaOvMAwHXfUCOp0WbJvmyAGAMROKUoJlpTDNBHFsQFUjRFEAwBjESHkgQzYHaWpifl5FtSqyzriUUcvM6W63O8gQL6HXU6CqIRTFgq43QTPoEgzt9jQ0LcKePSTll7PZlmWh1+tlZoOSaPt+AVEE5HIBOFcAJOC8B8YcMKZCiCY0TUG/nwOgY3ZWZJFeoyZoqqqiWCyi3e7h0CEHvj+FUokjTT2EYQ6G0Yaup9ntDtmoVMFYCiEY4rgAIXwwFkNR6LyW3gKKoiCXy8GyLCiKkjmEy461YRhwHAfNZjMbh5DGZRdeeCGuvPJKKJP7k1cUm03NNCHVE6zDwYPEiWRi0ug5GwTEMf/7fwd+67c2buCeNygUgI9+FPjsZ6llf/DgUIqwbRtltr3xjWdHmpDP07r+8R9pLvrgQXpcVYcz0xdfPFyXbVPXef/+4fy0ohCRFYJIaxxTBeSBB+jqIgRJqbtdWtYwiLTLyCxFoZ9GYxhTZdtEqtttIui7dhGxLpfp5Ni6lSTpl18+jLV63etovvngQZLGj54kQUBu3fv2Uc7cQw/Re+j1qKiwtETLMEbbYlm07VEE/B//B3Wqb7uNnnveV3UmmOC1h1FSPcErC9M0sbCwgIWFhewxz/NQq9WwsrKCXq+HY8eOZQRbdjcLhQJ6vR5M08xyrGUEV6fTGSPay8sMzzxTHTiSC/T7BahqCNP0EAQOdD2F46xAUQDfFyiXdQhRRLerQlUpLgsAFCWAqgo4jgvOmxACCEMLSWIhjhlaLQW63sbcXBOuKyC900qlEhYW+lhYCNDrlRDHAOchKhUduh6g2x2X+cq8YZKw25ibU3DoUIQgYIPO7Sp0HcjnGZpNE77vIpfTYFnBgOgJxLGPSsXHzEwZvV4fQnC4rpt1paWL+tqCRLFYHOzLGEIUoSgcjIUQgoExDWm6DAAoFBjabSAMcwOinw66sTqAGL1eCNe1oet11Go8k+NLiX8UReh2u5l0e3WVQdMqYCyFqraQprnBawkIIaAoMRxndVCT52i3SWEiRwdk5rbsUhPRXoGqssx5nTEXiuICSCFEDKIjCRSlC87L6Ha7WUSXLORIM7xWq4VmU6DX6yCXExDCharqsKwOhHDheTo0jYOUCjoqlVp260T9BRWqWkWpFMOyhp3qJElgmuaYHF+uXzqEy2xr6RC+ZcsW3HHHHZlZ2wSvbUxI9QRjEAL4+teJQ1166Xq+aFnAhReSavexx14Ds+GlEnVg3/lOIoNxTN3iSy8dN+BK02H+MmOUsbxnz4sj3MUi8PM/D/zYj9G6ej3S4D/yCEkHDh2ijrnr0vIXXUTb8+CDwzDMXo9+tyzaxr17h8WANKUusGEQ0R3tcus6Pc4YHXxVpdfwfXrNOKZlkoTk8FNT1J1+97vXv49KBfiVX6HKy1NPDSO85DZecQXwS79E7/eWW8gE7fBhMhkrFqmYAZBc/dAhItkXX0zbs7JCFvp33w388i8PHc8nmGCC8wK6roMxNnEA3yRwHAfbt2/H9hFvkW63i9XVVdTrddRqNSwtLWWOx0EQoFgsZlJv6aANAEHA8fTTCiyrkV0aOQfi2EK/X4GiJAhDE3FsQNd99PsxSqU8XJdk3jK8AbCQpgUAHJoWgzEdnMdI0wBCWDCMHgqFEOUyMDdHcmhVVbNcbpKNxrAsD+UybV+v14WuO3AcB0KIrGMs86AlikUFu3dP4fnnEzQaCkyzBEXxwVgIXXdgWV2YZoxOh5ZXFBuWlYfvC9x3XwhNU1CpcJRK/YHwiiKsFEXJiDblNStZl5ZzQNcZ4liFaZaQpl0AGhSlAoDDtn0EQQ5R1IFp9hBFJDFPEsDzqnAcjksuESgW81nnXRJDIpCAojC4rgPXdbG4yJDPB4PZawFdp/WlaQG63kQcq4iiPFRVRz7PUa3Se5BmXRLS4I1zjkqlgqNHOTj3oWkmhEiQpisjSzMAJXCuwbLSgfmYGLiDh/A8HceO1eH7tM+iyEUQmCgUNAjhIU37MAyOQoF2vOdV4Dgh0jRCGBYGkV0pwpAhl/NRLJIzuqxjuK6bzbGXSqUsc1qa7S0vL2dbqigKSqUSrrrqKlxyySWbrls6wauHCameYAwrK0SW5+dPzQdtm3jS/fe/Bki1xOws/WyE/fupw/zMM0QcAZJK79tHBloXXPDi1jU9TTPSn/sckc1Wizq4rRYRzr17qbKhKNQh5pxIt6KQ/Hp+nkh9qUQHsVql1+v1qCvtunQApTYKIMIqLew7HXotKRGnITf617LosZMngbvu2phUA9TJ//3fBx59lIoCnQ699jXXEKmWXe1rr6V1HTlCWSry8V6PSLUQtL2cUzd/epq2+cknibT/3u+td/FOU+p633cfva6qUoHhxhsnNvsTTLDJwRjLzMom2JzI5/PI5/PYPYhKFEKg1WphdXUVrVYLJ0+eRK/XgxACnuchjmO4rotjx7oIQw1BQBnOAMC5CtetI5cLsikmxoB+vwLTDCBEClUtQdMEFMVHv2/DMCJY1srYNrVaFXCu4OqrU2zfriFNY3CegnO6xsn5V9mltSwrM2LrdDoQQqDb7WZu31J6nc/noWkaOOdZsadQWMWePQzNpvT6zKNYtHDRRSmqVRedDke/76HbVbC6aiEIVjP36TBkOHo0j0bDxqWXJnCcKOvAep4H0zTRbrezPHHZpe12VTz9dALTbAzk7SSvVhQit6VSB3HsIJfTEIYcQiTQdRXz8zVUq7Rj220qWlWrVfh+gtVVFSdPaggCD0CKctlGodBAHHNoGoPrMrRaLjodF1TA8JGmCjSN4q04zyNNY9RqXub+LeXQQgCHD9fRajH4fjCQ9xvodHIol/tgzIaiWBCCI019aFoO3W4TrsuhqgLdLh2rSqWKJ5/kOHFCQZoWoGkeGEvRaKhIEh+O48NxBBhToCg5FIsOfJ9c36UagrEYcawjilzYdhOmqSGXK8D3NZTLHLmcui5rG6CZakmsp6amkCQJPM/D/Pw8Xv/612dGbBO8OmCMbbqCxoRUTzCGToeakzMzp1/OtomAv+bx6KPAn/wJEd4dO4aEtdsl2fWJE8C/+3cvjlgfPEiv2e0SgZZO2SdPErncv5+Wk3LwnTuBH/yAiPRb3rL+9ebnKY6rUKCD2+lQ5zmOx2eqbZu2X35JjcaF5fNEzvN5+vvRo2R7n6anDlc3TXLsvuGGU79Xz6PtWlhAps/TNDq5PI8I8+ws/X7yJEVoqSp16Z94grr0b3rT+Ov95V+SA3kc0/bKffbVrwI//uOkBNhkX8QvF9KUdtNjj9HuLRSoDrNv38bj7ucr0pTEHysrdEpv304/r5HT4JwCY2wSq3WOQRJRaXoFkClZo9HIZrRXVlbQbDIwxmGaJPE1DIp4SlMdvp9DmuoDw1eOfL4xqLFyxDFDPm9hasrE88/76HZtBIENVU3AWIQwtJHLNbF1K8fcnEAcUzdRujMzxpDL5TInbV3XB3FelIQhY5Bs2x4YjfWzv3U6nazb3mq1slnaSkXDwgKHqmoDwk7Etd8HdF1BsVjG4cPBwJSsAs5jCOFBUfLg3EOv18OBAwJ79ggYBkO1WoUQJK+2LAueR6RQ/th2A/k8Q7+vIZezB91eBUL0EMcefJ9hbq6N3bsZbLuETqcP01RRLBYyJ2tFUdDr9XDyZA1HjjB0uwyqCmhaHkIwLC0JrKyU4LoJOPeg6zpmZhQUi8uIIvqyVBQGyyogjg0AKVw3RZoqgyiuHkqlEmq1Jo4cEWg2daSpM8h8VhAEIdKUZsmLxXAw5VUAYyp6vR44L2DnTgVTU2nmJn/gQB1LSwyWJQb2KzoUhVzfl5c11Go6pqf7sCw+mDOvYW6Ow/MY2m0FvZ6LIHBg2xyGEUDTyPV9cbGLEyfysKwEU1MeFhY0FAo5aIMLI2MsK8TIDrxlWbj99ttx0UtNfJngvMVr6LZqgjMB5UEO44pPhTgeqpBfswhD4DOfIZJ6ySXDO3TGiL1cdhl1TP/+74Hf+Z0zM9gKAuBP/5SI9fbtJImenibL9TCk7jFj1KmdniYS2WjQjHEQkFx8bm64LWlKB7NQIDJar9NymkYkOgzp775P8u5Oh/4ugyQ5HzqIq+qQQFsWrfvJJ8fzpddCCOoW799Py9s2Lb97N23j8eO0DW9+M817nzhBBQopoa9WhzPfo/EvmkYn6Pe/PyTVQgB//dfAN75BhQYpI5d/W1oC/vZviai//vVndozPYayukjfCY4/R7jMMOtRf+AKdmr/wC3SqnO947DHgn/+ZSHUY0qmQz5PdwE/9FNkCTLC5YFnWRP59jkNVVUxPT2N6ehqXXHIJ0hT47ncVPPFEA0lSR7m8iGJxBZoWQFVjqKoCzlUUCjQfnSQGNM2Coqjo9zXs2NHE7GwLhsGwuhqg3VbgeWUYRoK5OR8XXJDH/DzL5nCjKFrnzixjvQCS+8ZxDM+jLquqqmOO25qmZXJs6XgOIJulrVQqGfk2TTPrfMtlDhxoIEkY8nlvULt2oSgOhAigKA7yeRetVoJeL8Tu3fY6F3VN01CtVrMs5HLZxc6dHo4cSdDt6mCsA0WJB0UIDZWKg8suM2DbGMy3pwBStNsRHMeBpmlotVowDBOLiyW0WhoKhQS6riNNad2mSfd2nY4Kzkvo9wPk8ypsuwLLisG5B0UpIk17iKIOZmcF4lhkBQwASFOOEycc1Os+HCeBrseDLnUHuRzQbOpotRz4vgHbVqAoXTDmwzCALVtCuC7la9NMNbm+G4YK00wghAKgD87rsG2aY09ToN3Ow7YZhOBQ1TIUJUIu50EIHbOzCmZmlrG4CPg+dd8NowDGSIIeBByLiwrSNMG2bV1UKhW0Wq0sKks6mU9PT+N1r3vdxDxxgtNiQqonGMPWrcRHDh0amkivRZrSzek117yim7b5sH8/ZUPv2rVxy4sxIsYHD9IOHc113gj33w988pMUY6XrRJYVheaXL7uMDMAOHaIu8coKcOAAtR3vvJOI5d13A1/+MrEIwyBCzDl1qn/7t4lhHTpEdu5Swy8EMS1VpYN65AhtixDDeC0Z33XyJLBlCz1X5mE//fSpSXW7DXz609RN7nQwcE6h17vmGuBDHxoaqBkGnXxbt9Lz+n06AUfbqWvjE2ybig4Sx46RDH5hYZxQy2MxP0/E/stfpn0ppebnCJJkeColCXnFXX31xiljvR7w//6/pLzfs2e8AOZ55A8XhsBv/uZ69fz5hIceov0gE9xyOTqNWi0SM0ghyYRYby5Id+kJzh+QG7OOMNyCen0BJ09eCQAwjACOs4x8fgWl0jI0LYJh9KHrEQoFG50OzUeXSgKWZWLXLgt79mjo94F2uwVFSWAYpGrwfSszR5PGWQAy4ttoNDKnZ4CIv3TcVhQlk317nod8nuaPZbwSQNLp0deUTs9hGGZSVBkZ1unYME0qCgAqOG9BCPIxEcKHEEQe63UfCwshyuVyZvAmZekra+SA09N5zMzoaDQE6nUXYRhCUTxMTSmYm1PQbq9iEBENXdezokAcx1lRoNGI0GxGKBQqA4l0PMi7tgCoME2BKEoA1MEYQ6fjDbrEDhhz4fsBfD+HSgXYtSsCY+SM3mg0BnJ/hnqdwbYVmOYMgBQAB2MOOPdRLsdgTIFhtFGpRGAMcBwN8/M2CgXq5PR6PaRpinqdI0niweMmhGhmrueGocIwUjQaOmy7NkgjFYP9y+D7ZShKiD17FHS7FbTbCVy3jyTJQ1V9KAodV9MkVcPKShXbtwtwTvPcvu9nc/U333xzNuowwebBJFJrgk0PVSVD6yefJL4youYCQDekzz5LI7PXXvvqbOOmwbFjxG5GDcvWIp8nEnzs2OlJ9X33AR//ON3lm+aw2xxFNFt8//00E3zllSR9fvRR6rZ+7GNDxvSTP0mxWw89RB1gVaWO8HXX0XY88ADNfccxMSuyPaX1cU7EefT9aBqRU00jku/7RKw1jZap1YB//Vci1Xv2jHfifZ9mnr/3PWIzcpZZCCLN3/oWEef3vIfW0WySwRlArxPH1FW3LCK/abq+yjOa0w2QBL7dpvWdCgsLNKf+1FNUkDhH8NxzwKc+RTWM0YjwahX4kR8B3vWu8frD979Pp8gll6yvHTgOPX7gAJ12d975yryHVxq+D/zN39CpvlZIUi7Taff448A//APw678+kYJvFkxmqs9PMAbs28fx9a9rKJWARkOAc4YoshBFO9Bo7MDhwwAgYJp9LCysoFw+iampZWzfvgxdD7Ob6EaDOtCWRaoGx3GgKAqCIEC3SxnIvu/D87wxouy6LnQZ4Qgixmsdtx3HyYzWpCRculi7rotarTZ2M29ZVhYZliTU+Q3DGGkawjRtCNGDEOFgH9gD8qoBENC0JqIohedRRrVlWbAsKysKjBJtwzAykzVNG1q8TE1NDaTSlCkus6Edx0EQBFnWNkDFqigqIooU5PPRIKuZ8q6FSAfS9AZsG+j3bezYYaLdVtFuq/C8VtZRnpvzMDcn4LpleF6EMAyzYkOtliCOGVyXrzEiAxTFAWM2CgUBz8thyxYfmuZDVQVcVxvbt3ScHKSpDcZIOk/bGkKIEIpSQrkcQFXbaDRMtFoWcjkVnAukKYdtNzE3J1Cv9/HsswxBYMDzilDVEIy5yOVyKJUiCOHDsgoIggaOHuXYupXWr6oqrrjiClx33XVZIsEEE7wQJqR6gnW47TbiHV/5CjVEZ2eJU/V6ZG49N0cm1WsJ92sOspt7OozOJ58Kvk8S8Sgi8nn8+PBvhkHMaXWV2pS33TYkmrpOxFP+DlA39h3vWL+OY8eIxL7hDcS2VlZo21x3KK+WsnDHoR/Oh/JwclihbXVd+ul0qBX6v//vwK23Aj/3c9Q9BojA338/GarJx+T+KJXoPTz88LBQ8J3v0Akl32erRdtrmkSoLYu2SULmat900/CxVosI+emOiTRak9as5wCOHQP+23+jf3fvHu4GzoeK9iQBfvqnh3WLb31reJpsBF2nQ3j33aS8P9VY/LmMH/yA6ll79258SqgqdagffZT27elqMRO8spjMVJ+fuO22BJ/8pI4koUtNo0H1U4qIomVIBOXgqqu24d/+2zlcfjmHYQh0Op0s2suyLNTrdSRJsmEusiTDjDF4nockSQAA/X4fpmlmZmAyQ5tmfjl0XUetVhvrZgNDwyrOOcrlchYXRcWfCIcP1waRVrT9xWIJcaxDCAHHMcFYCiAZEFc2kFtTUcG2HVQq5phpGucc/X4f/X4/66TLooBhGBBCgHM+6ObWkaZ0DWAMyOVMlMvldUWBOI4Hc+UN6LoYRFsBgAFVLWMYbaVDUWIAAXI5HVu3hqjV/MFIoI1y2UA+T2ZkzWYzc+fu9/vQNA1xXISqtgZGZJXBnHgMxgxw3h7MldMx7/UEdu4sZQZhsijged7ABZ1DVVeybWVMh6LkQdSFutz5vADnIYrFCIZRhhAtuC7H9LSJet3G4cMqOh0FuVwfhkHKtjT14PtAmuYwNWVAiACqWoLnAfk8Gcfdfvvt2LZt28v1UZjgLGBiVDbBOQFVBT74QfLWuvtu6pIlCd3Mv+MdxMt27Xq1t3ITYHaWrmIybmoj9PvEbE7lHA7Q3b/sZEfRcF5ZdqAZo45srUby7EZjyAT+7u+o5fa61wEf+ACR2I2+ZHyftnPXLuqGr64ODdDkj2kOB+kl+dV1MkwLgqFMe35+SLxf9zra5i9/mbrEP/uzpEf+zneGc9sbgVoMwLe/TSfbs88C995LVZskIae8pSXaZoDuVPbvp3WWStRp3rmTuvCjr7nGvXMdpDHbqY7XJsSXv0xFrssvHxcDKAqp8TWNCmCvex0RwyCgmskLybpLJarJ0I3Ey/kOXh0cPTq0BDgVymUSX0xI9ebCpFN9fuLSSwV+7McS/O3f6sjlBEoljk6HwffJXMy2gYsv5iiVgI99LMa118rvc4ZisYhisYgLBqafnHM0m82MaK+urqLZbEJVVRiGMTZPLeOqLMvKCKqMa+p0OiiVSlnUlKZp2QyyXI/sZo8aVtm2jV7Px7FjDpaXnYFjtg/Pc3HkSAtxPDQuM03AdadhGCkABkXJIU09pCnHzAyRaUnkZS6yLArIuW3OOXq9Xuay3el0EAQKWq0CajUDccyhKCpct4mpqRZKpWEhv1QqQVVVJEkC1y1heZmDMR+ABsaULO9agvMchLBhmil0XaBU0gZy9wCua2U5zowx2LadzZMnSYI0bQ1Ifx9AH4qSG8xUt6AoDgBjMPWVIJUAGAUAAKbSSURBVJ+nKK5RKIqCarWKKIpQqeg4ckQH5yEY86CquUGcWJwtH0UGbLuEa68VUNUInqeh0wlx8mSI48cZVNVEPt+BpgGcW+DcghAqOFcRBE14XjxwD/cgBMOuXZfhxhtvHFM0TDDBmWJCqifYEKpKTdFbbiGeE8fE3Sb59iO46iqSE8sW4loIQXf2F11ETt2nwsmTQ8JKbh1EMuXsMkBX5dVV0us2GkQ8dZ0Y1eoqWTx/9auUD/2zP7ueNOZyw9fo94dVkSAg0txuDx3BZV61jOOS79E0MXCQGWpqdX3ISj7+cdq+bduI9C4snH7/VSpkrDY1RVngH/kIycBll9wwhtWcapWKCt/9LrGfXbso63r0hLzoInqO5413tUexskIGbxdeePpt2yRYXaWm//z8qX3upqep3nD//bRrVFXOL57+tSmb9PzsUgMvTkjyQvtqglcWpmlOZqrPU/ziL8Y4dozhwAEVQjDk81QArFQEpqc5goDhuutSXH756T+U0hxramoqc2NOkgSNRgOrq6vZT6vVQj6fRxAEp5yPlp1smU/teR5yuVwmnTYMA7ZtQ1VVaJqGdruNVquFo0cZarUYliWQz5fQajFEUYAkKSJJFMRxgjgWCAKBMKyhWBRZ3brXM2FZOczO0gyvoigZsTZNM8uoBkiKnM/ns+xmz/PQ6QgcPcoRhj0YRg6WJcB5E52Ohk6niB07VOzezaGqyliBwTAYNE1FkhShaR4ADapagRDpwETNhef14Lo9cC4yUVdFjmeBTN6kAkB2zVdXVwGQedjysgshqPsuhD8g2Byc9yAEEAQV2DZFY8nIMIAKGFEUjZm25fMMnQ5J8ilerQAhOIQIkCQRfD+H+fkVdLsCYQgsLTG02xZWVkoIghSWlYBzA6YZwTQDcG5C0/rQ9QBxDPT7FnI5C76fxzXXXIdbbz1NA2SCTQPplL/ZMCHVE5wWikI39BNsANelnOZPfILa+du2DclsGFJ7MZ+nWecXk190ySVEcldXiQw7DjGEen0Yc1UsEoHs94eE/MQJIraOQ1rgUczPU47Sl75ERLpapXUsL5MUWnarAWJZvk//djokq5bSatLqUVd9924a3j15kt63nAFvNmn4t1YjwrwRwe33aZ+dOAH87u/S+oChnFsIKioYBhHhxUV6Hd+n2LD3vGd99//SS4lYP/447cO1bNH36bXe855zpjokD8/pxvGln9zRo/R/w6Bdcffdp3f3rtWou30qMcG5jpkZOo1Ol/rW7dK+e6EIwQleWUxI9fmLLVsE/sN/iPCpT+l44gklq+NSMZDhlltSfPCD8Q/lI6lpGmZmZjAz8oGOoggrKytYXl5GrVZDvV5HEAQoFotj89RCCBiGgWKxCM454jgeyJljhGGYjSTI8zKOLTQaDixLg2Ux9HoNeF4KwwBs2wfnGlqtAnS9CcBGr1dGEACVSoQkMeE4bWzfXofvi+zyVyqRFFoIMSaFNk0TjLGMuMYxcOyYjjDMoVTSB4TYB+eAricIww4OH65AVbsoFpMxF+tymWF1NUaz2YTrCigKvR/GHDBmIgj6iGMHe/bkUC6TtNu27WyeW0JRFExPTyNJEjDGkM/nBwZvKQoFHZ2Oh3w+GBQuFSiKC8ZcxDEQRT5mZ1MwRt13XdezIsZoNrcQAvv2qXj00RbabR+mKbJbrCAoIY4VVKsJLr64gjjmeOqpAO22CkVRYBgr0HW6NQlDoNNxYFkOLIsjTc2BtDxCmkbo9/dBVW/AHXcwyGi0CSb4YTAh1RNM8FJw22307z/+I3VnAbqTV1WSJ7/vfTQzfDps2TKcWU4SYgFXX02k8+RJItfdLv29WKRlfZ8eU9VhznSSEJH//OfJxGyUdDJGDuFf+xo9t9UiQut5Q1Y2ehPLORUCDIMkCrJcPTdHQ7jT0zQTfeLEMPZKuo3v3k3b8fjjlGW0sEDbLSOyGg1yBD9+fCihP3GCpO1JQu99y5bhtmzdSo8nCZH1iy/eWE6vaTTs/8d/TC5c1SrpezmnfdjrkZHbu9714o7xq4gzGcmXfx/tyt5yC3nE1esbu4M3m0M1yiYbSTpruOYaOk1OnNhY2i2FJFde+cLG/BO8srAsK5uRneD8gix0XXABx8mTDKurDKYJXHFFire+NcUll/AzSp88E8hO78zMDObm5rLoLN/3s+xs+a+maeCcj3VJhRDI5XKwLCsju1JGvboaIY5zcJw6koTD9xmSxIGum4giHYxFKBZbSFMBy/IQxx56vTySBNi2rYn5eRulUg6MsYzAbySFrlQqmSy9XC4jCAKsrvro9wsolXpI0+HnhDEdqlqCbQNRlKBe11EsJplTuaIoaLVaWFggKXSzSd13XVcBtBHH/iAoIxwQ7uH8eLFYhKIoWVd/tDstYZomSqUSdJ3j8ccdtNs6bLs/uE2xEIY1pKlAtSowM0PZ4Y7jZOtgjCEIUrRafRiGnEZrYu9eDY2Gi9VVE/0+ACgwzVXMzgLVqkCvBxw+rODkyQpUtYcostDvTwHgsCwfjFkwDA9AbUyd5Xlb8Pzzt2P79ire+16OvXsnkqUJXhompPocQKtF46UAcZpzpMn22gBjwO23kxX6I49Qa5ExIoJXXHF6Z3CJSy+l53zpS0PdrmFQ5/u66+gO5J57aBlNI4IrnaZGGVEcU/f5gQfIAfxtbxtfz9VXAz/+48D/+X8SYY8iIqrA0GGFhp/oJ00xCMGkdmalAtxwA7X1ul0ixaOxVzIT+9vfJvv4bnfYgV5ZoXXOzND2t9tE1q+6irro8iQPAiLrcnZaQtPohxxOTr0vd+wAfuu3yJX8u9+lDreM03r964E77ji1NHwTYutWIsWrq+N1hlFIP7lRRfsVVwBvfzvwuc/Rrt6yhU7FMKQ6jefR36+++hV5G68KSiWqn3zqU0SeFxaGN1NSSFIu00fibN3ET3B2MMmpPj+RpsDnP6/hK1/RBqmJAgsLAp0Ow+OPqygWgd27+UtWz3DOsx/GGFRVhaqqWQSWbdvYvn07to9U27rdbiYZX1lZQb1eh+M4aLfb6BOTy1CtVnH0KIeqCiiKgyjqI45ljTvJDLGEYAhDF45jwTRVWFYfhUIfc3McQvTQajGUy2X4vg8hRGaaJqWtYRhumLXd65WgaQkUJQeAD9y7EyhKPjNBsyyg12NIUwPz86Ws+05S6wg7dwaYnTWxuBgiDOmzNjVlYMsWC3NzGhSF5p3DMEavx5AkIVRVxfx8AUHQgmVZY+7kuq6j1+tl27tzJ1CvMzSbBQQBA2MCrlvCzEyEQqEP2zbGssHjGFhdVdFo5OD75Pit6wFsm6FaTTE93cWuXRoajR44T+C6KvJ5mn3v9RiOHBEAGlBVIJeLBv6rBpLEGcjcbQRBbpBlHaHVugLHjl2LUonhgx/keOc7+XlbYD5fsdlMyoAJqd7UWFmhMdnvfY84B0A3ijffDPzoj1KzcIJNAtclB+wXiyShTJ9Gg0iorlNXN03JBdt1iYhOTxNhPXSIGIAczBqFJJ61GknS77+fSO/111PbLpcDfuZngD/5EyKwaTokqJJVyJlpxqizq+sYaNZojlkyu1qNHpcyO+moTeGh9HzDoGWWl4nsui6R7SCg9zP6ernc0JCtXqeu9drqkefR9pzO9A2gv//Mz5CrXr1Odzqzs+dcLjVAu+OWW+gUmZ7e2F/txAn62/XXDx9TFOC976XHv/EN2p1RRM/fto3EBm960/k9T334MJ2WF15I9a7V1aEtgKrSKfn+91ME/ASbCxP37/MTd92l4p//WYOuUzHw6acVcE5f/1NTAt/8pgrLEvjgB5Mfeh1pmmYmWnIGWjmDqlk+n0c+n8/yiIUQaLVaY0S73+9DVVXU6/WBOohBCEAIHb5fhGEQGU5TF4oi54gNAG0A8eB9kxGZ67qZy7fs/na7XZimCdu20Wq1MlMymk0W0DQN9XodUeRDVYdeEIpSBhACSKGqZQjBoSg+0lSHooh1edey+y6EwPy8QK+nIk1jaFqEctnNjMjqdYZazUG/byFNNQAcx461US4LzM560DQPjuNAVVW0Wi3Yto1cLpcdhy1bNKysNBHHAozR9y9jyLrvMie8243w9NM+2u08bDtCPt9BEDD0ekCtpuG554qYmVGwe3eCSkUH5zGESNDv91EsFvHssw2kKYNpagAcJImGNFXBuQfbbgEAOI8G1jJzMM23w7bLYAz4yEc43v3uSYf6XMVmI9YTUr1JsbgI/Nf/SorimZmhr1S9TkrjgweBX/u1ybzzOY+77iJ758suo9LuE09QBUXqeSUTeMc7SEodx6e2ao4i+pGEvFCglty991LL8/rryUis1RpmL6nqUEIudXmSEJsmnXjlMlVxpHTctofLyecdOUJk2TSHXXSAigUAtQrLZSLGaUrv9eqrh6xubo62t9ulTvLx4zQDLlmk1Oru3Uvz0meCfP68sLX+0R+lWsSBA9RtnZqi3R4EtJuEAD70ofVzwapK4+evfz3Fk3seHbq9ezeuyZwvqNWAT3+aTPV7veEpCtD+u+kmmlC48sozE5JM8Mpj4v59/sH3gW9+U0WjwdBsMoQhfQ8pCjUQlpYY8nmBu+/W8KY3pdiy5cXNtsqcaIBk01Lq/cOCMeoil8tlXDiQAaVpimaziZWVFahqDffdVwNjKVTVg23XwdjwkpYkOYShDk1jMAwXjIVIEh+FAm3b8vLQcVtV1cydPEmSzLBMupMXi8VsnlvX9UG3WofrUic+Tek6OxRxaeC8AE3zYFkmXLeCNE0zA7Zer4derzf2fqvVyuA1BBzHxfPPezh+nAPgyOUEGFuFEEAUMSwuOohjC1ddpSBNA/T7/cxAzfM8lEolBEGAJElQLDowDGOwnemYm7rEiRMG2u0KSqUIgIOVFQdhGEDTPKgqRXT1+xyHDgErKwK7d2uYny8PHNxTdDo2gACqmoBzcgZ3Xepah6EOzh2EoYlG42Ls2HE5tmyhoutttwm8+c0TQj3B2cOEVG9CCAH8z/9J46OXXjrucTU/T92ngweBv/5r4Dd/8/ydiTzvEcckU7Ys6sqWSkQuV1aGbMBxqJKycycRRCE2tirmnDrEABFiWYkRgk6kf/kX+pEO3/I1pLx7VFKtqkSKZcZJtUp50seP04lXLg+f0+tRhzoI6HHp/CT/bpr0noJgSNTDkN7X6Imt6zQr/dBDxP4MYxhVFkV0BZTGcC/G9O08wNQU8Ou/TulpDz9M5FpOAuzYQfWW04kkpHHZawGtFmV6799Pc9Q7dw4nGpaX6RT2PKovTSTfmxeTTvX5hyefVLB/v4LlZQbLAqanx0lzkpBceP9+hkcfVbBlS3pGr/tCUu+zCVVVUa1WUa1WsbAAPPGEjlotwY4dq1haWkG3u4pSaRUAg6Y1oOskcWdM2qVMYXaWzutCoYAgCBCGYRb11W63s87baIZ2GIaZSVgcx8jne1heLiNJ2lAUDsZMMGYDUAEwCOEhCJqYmhIIQx9hiIy0+74P13Whqmpmwua67pgRme8Di4sKNG0atp0MLud5cN6HaXIYhoZaLcDTT3uYmRFgjMF1XTiOM5YNLjvxvu+jVCqh2+0CwJjjdxAwNJt92DbJxjsdhjQFdN0FY3moaow0LSEMBQqFAN1ughMnctA0mudOEiCOFTCmwfOmoOsJGEvBuQXLCsBYjHq9iKeeeiP6/TxyOYFCAbjhBoGf+7l0Mk45wVnFa+vu9BzBoUN0U7hjx8b8QdPohnH/fuD55zdOc5rgHMDhw8NhTwlNWz88224Tsb7+emJVQTCcLwaIvHa71AG2LPpbuUwtu4cfJlIN0NXHNOlv7TaRZ2kAJrvOskIj3cCfeopI8B/+ITETyyISLQSR3k6HigFpSlpl+brAkBQXi7RMs0nrj2NiNmsh59sefJDez9NPDzvp27eT6dv5PAR8GlSrwK/+Ks1DHzpEh6xcpmb+Oahqf9lw993AY4+RmGF0v8gUA8uiOtZNN712Cg3nImzbRkeaI05wXqDTYVheVqAoQC63vgutaRSrdeKEgqeeUvDWt74wqf5hpd5nA8Ui8IEPJPjkJ3U888wWOM48Dh9WsbQkYBgRFGUZCwvLKBSWwHkHvV6EUqkOwxDZOJ80H0vTFLquQ9M0+L6POI7hui7a7TbSdLgfTNNEsVhEucxRrydotVTkchyKEgJgYMwE5214HoOmWdixw8TU1NCgLI6piyv9Csrlcvb/UqmUmabVakAYMhSLK2tq+AYUJQ/GBHRdQ6OhYHq6D8YETNNEs9kcUwtIIzIhRNbNFkKg1+vBsiyYponFxQ6iSEWhkAfnOtptktO77iqkE7e8pYiiEnI5gWYzwa5dUygUUnS7ITRNha4r4Hx5bEQqTXPo929GFF0EXQdKJYYPfzjFzTcL7NkjJoXVCc46JqR6E+LZZ8ejhDdCsUh87JlnJqT6nIU0CnshViSzl//dvxuagDWbwxySJCHiq2nUAXYcWv6uu4iFyWEm36fH5ZVEXqxl21N2l5Nk2Cnv9YiNzM4SGT5xgkj9NdeQdfQ3vkGv0+0Ojc6kjDyKxgeBhSAmKOec14IxqiQ1mzQIe8MNQ9O3iVYXANVbTmVY9lpHEADf+hbVb071kSqX6RS+994Jqd7MMAxj0qk+z1Cr0X3N3NypZd26TpeOkydPL78721LvHxZXXy3w0Y/G+NrXVBw8qKBc5jh6VEEQmFhY2IZCYSuWlwWShOGii7p4+9sXwTnNZwdBgFarlWVhS7iuC9u2wTlHoVBAHMeZM7ZlWdl89MICkKYM3a4DIA9dJ+lzmqowTY6FBR/Foo5+30cQBGCMwXEcmKaZOYGPZmF7ngdVVVEsFtFsdqDr1iC/WgCIIYQOoA/OqaNMojMGxhwUi0QlCoVCFgNGruL62Dy3qqqwbRuu6yJJEvi+D84FyGytiyAoAEjguk0IoYJzF5xrAASCQIXr1qBpAr4fYHFRAGAoFsswzS4Mw0G/X0Gvx2HbPuJ4Ae32HUgSN/Nhvf12jg99iCNJgP37WeYru7AAXHSROG89RiZ45TAh1ZsQo43DU0HO7iQ/vJ/HBK82cjkiiv0+VUlOhSAgDfD8POmA/+RPSOdarxPRlW7WuRwGIZlEptN0SLSlg3eSDLva0klbSsolIZZdaMMgG+mLLqIO9NGjpJ/t90kG/mM/Ri5aX/kKdawNg54rf5+aGrrpSXl5tUqvEUXrc6AAWkelAnzwg7StjzxC89r79xOxvuKK83sgeIIfGrUajfC/kIFjoUDFyAk2L2zbnrh/n2eYnSXSEsen/gqXZoqncv/mnCNNUwhBkmPZmX6lutMb4ZJLBC6+OMGJEwzNJl2al5cVHDyowPdJ5n7TTSmuusqE4+wEsDN7bqfTyWK9VldXB13i2tjrCyFQLpezIlOpVBp0nPu4/HIHtZrAysoSwpBBVamouLCQR6lkgHOe7SPOOYIggGVZaLVaSNM06yYbhgFFUbKZcbo0e0jTPhizwZgBIdpQFGfgOC6QJBE4t+D7DQDjI2mVSiVTEMgYMN/3YVkWGGNj8+S2rYGxAjjXB/PQdFNLs+odCFGGqnZhWQlUVYOi2EhTHYYhkM8naDbrqFQYer0OqlWBTsfBsWNvRLu9J1uHYQhs2wb8wi+keOQRhn/+ZwXPP88yr1bTBPbsEXjvezkuvHCSU30uYWJUNsELolRCNoNzqvHRgYpnMg9yLmP7dtKpPvLIqUm159FJcN119P93vpNc7O65hx6Xdsbf//6QfMfxePSU/NLRtPEOsWwLSNMxSagVhX5cF7jgAnIKf+gh6lrbNs12RxFZ019/PcVUyeisqSm6q5ibG2ZqA/R3OSd900203fv3E8l2HOq0r6wQ45FZUI8/PuzkxzHw9a8Twf+lX6Lu9QQTjOBMM71Hl51gc8I0zUlO9XmG6WlgZkag0WAwTbEuyYBsQRhcV2DnziFJazRkpKjAzEyKYhGvuNT7hUCCKjFyWUoHP6dHoVBAoVDAnj1EADnnaLVaGdFuNBrwPC/rZvuD66yqqiiVSoiiCNu2Gdi6VUcURfA8b+Cm3cXq6nA+W1EUTE1NgTEGzjksy4LneeCcZ3FYksyrqopCwUGnYyKXIwftNG0PvCl6g9crIkk4dL2NctmF41AMWBzHUFV1nRGZXL/M45YxYkEQYHY2h+PHPXS7URZgwrmGNC1BCAZFiRHHKhQlgWnGSFMOoIg0baDdFjAMA9u32+j1dCwuzqFafR3yeQudjshSQeMYeMMbKDLr4x9X0e8D27eLrHjT6wFPPMHwx3+s4KMfHRJrIei2xPfpNmV6enLtmOD0mJDqTYgrryS17cmTwzHTtVhcpMbllVe+sts2wVkEY2TPfPAgcOwYEcXRb+wgoJbajTcOtaqOA3zkI0TG776bThIhSBPc65Fs+uGHhzLrUYaRpkSIO52he5NlEUF2nKH0gSwzh7Lthx+mq8rMzHD7pqdpfVFE+dPXXktO5Xv30vJHjxK5zuVoG2o1WvfCAg0Huy7wne9QMUBGid15J8nK/+VfiHDv3TvesggCItp/8ifAv//31NGeYIIBqlU6LeWpdiq02zS5MMHmhW3bE/n3eYadOzmuvTbFvfeqaLXYoCMtsiSDIGAolQSqVYHLL+dYWmL4+tdV3H+/knlwVioGbroJuPNOjmr11X0/LwcURUGlUkGlUsHFF18MAINoq3oW69XtdlGv1zOiLZ3CHcdBPp9HkiTI5/MZYY6iCIVCYRADJsaI9szMTGYoJrPh0zRFtQosLYUIgh50HVAUDYw5YMyEEAo47yCKAszPCwRBd5C6Wclk5jIGTErLoyhal7dtmibK5TI459i928H+/TaSxIeqxvD9AlyXOuBySs22dZhmCb4vYJoxKhUdAL0/0zTxcz93E+66ayd+8AMlm14DqPF0/fUcP/ETHP/P/6PC90nqPYpcjjrV99/P8Lu/q+LaawW6XQHfZ+h2KTaNDD857rhD4PLLxYRcT7AhJqR6EyKfB972NuBTn6IK7ezseBdmeZkUuO95D30ZTHAO45prKA/pM58hh6VikUq13S6R3uuuA/7NvxkPKLZtyll64xvpZEhTItB//MdEbKOIni9ntpOEXktRiNSG4XBuIAjGDc9yuWFXuVym1+90xk9CAFlIZrVKpdw9e4igP/UUVYJKJTJiW14e5lL/7M+Se7c0Adi1i05iz6Pn5nI0o33gADmBrx2MtSwqJjz+OA3Fvv3tL9dRmeAchGmSaOIv/gJZXM9a1OtUP7rppld88yZ4ETAMYyL/Ps9gmsBb3pLi2DEFAEe9rqDXI8Ji2wK7d3OEIRGXUkngj/5Iw9NPK6hWOXbsEFBVBY2Gis9/nuGZZzh+5VeSdTGC5yNUVcXMzAxmZmZw6aC4HsdxJhlfXV1FEAQ4efJk5t4tURwo4NI0RalUAuccvu9nnf4lkgBkRNuyLORyOXAuMDfHcfy4BdsOYBgJGNPAeQtJEsPzGFxXw86dNopFuk53Op1szl12pAuFAprNJnRdR7FYhKqq4JxDVVV0Oh00m00AdAuyfTvDyZM59HoWoogjjktQ1RSm6cF1BYpFF2G4gjBkmJ0VUBQBy7Kwb98+XHPNNTAMA5deyvH88xxPPEHSe9cFLruMY+tW4IEHGI4eZdi9e72UqdsFHn5YwdIS3a6srgJLS5ShPjMDXH01h2kC992n4NFHgfe/P8Wb3jSRib+aWHuubxZMSPUmxZ13Erf54heJa8nYX88jvvLTP028aoJzHIwBb3oTdWXvu4+6vFFEMudbbiG361MNmBkGsG0b/b5jB/AjPwL85/9M3WsqL9MVQgYUb91KFZupqWF32veHA/q53NDJu9mkZWVO9tqybBQN5eczM+T+9Ou/TrL0Bx6gZbZvJ7J95ZXAu95F72ktpLEaQKT+298m8nwqpylNI4n4t75FH5LXWLzWBKfH618PPPooGciPZnonCRUoW61Tn4oTbB5McqrPT7zhDSkWFxn+9V81zM0J5HIpFIWyj7tdhgsv5Hjve0P81/9q4Pvf16DrRG4ch2HHDoGtWwVmZgSeeELBZz+r4Vd+JXlNdgx1XceWLVuwZcS1MgiCsfls3/czkzBZoGKMoVQqod/vZ/POaZrC9304joMgCDIZ+PQ0GaHV63k0m8ZgosyBpvnI5yPs2JEgn9fGzM5M04Rt29B1HZxzdDodCCGyjrn8W6vVgmVZY9FeU1M68vkGWi1SYna7bPCaJWhajFYLUJQpzM1FWFjwUCzmcMcdd4ztA8aoZr979/rY0cVFBs7XF1vDEHjoIQW1Gr3npSWgVmOYmqJbk0YDOHBAwc03c+zZI3DsGPCZz6jYsSPFnj2bk9hN8Ophcke6SaGq1NS7/nriKM8+S4/v2UOmyGuVwhOcg+CcOrsPP0zk1XFoZvqqq4ZE80xB3/zUOW42aYhIVYlMB4EMyRwalsks6gsuIOIuZ61dl57/5S8PtbQbEddud0jSASLuU1M07/wTP4ExW80zHfyPItoPpzNtA+jvzeYLG7xN8JpDPk/TBZ/5DBFrmekN0Jj/Bz5Aud6bZBRzglNgIv8+P6FpwPvfn+DCCwW++10Fzz+vIE0ZSiWOt789xTXXRPj0pzV885s6TFPAcdgghILhBz9gOHFC4NprObZuFXjsMQXHjzNs2zYhNgAVorZv347tIzODvV4vk403m83sB0DmWWAYRkaoJdGVjuOXXVbA8nIbrRZHkjAoioDrAtu2VQYkWyCXy2W51BKrq6tj22XbNjRNQxRF6Pf7AIjoB0GQOZx3Oh3k8zaqVRMXXqig0UhRq6mo15tZcEi5DBQKDFdddSWuv/56aC+iqH6qxubJkwy1Gt2+qCrdylC+OP29UqGewd13K5n1TK8HfPzjCn73d1NMTZ3xJkxwlrHZTMqACane1GCMmn2nmque4BxGp0M61QceINIr85u//nUqtf7CL9B89JlACOCf/okMz265hTrHjz5KVwXTJLK6uEhEN0noqlEoUGf6yiuJbUhEERH0HTvodTWNtk/aZDJGhNowiJAzRt1u0xzKKaanX9iCeSOoKrEd6cJ3KiQJLTfpUk+wAUol4Jd/mQQbTz5Jp3Q+D1x+OZ32E2x+mKY5IdXnEOp1hsceU9Bu06z0BRdw7N3LN4wo0jTgpptS3HBDimYT4JzBdVPoeoovf1nDN79pQNeB+fnhDbNtk+nUygrDo48quPFGjpMngUOHJqT6dMjlcsjlctg1yGcVQqDdbmdEu9frYXFxMcuEl0TbdV2Ypgnf9zE9ncfMDOVXh2EI13XRbNJ89CipmZ6ezjK18/k8+v1+5j4ex3FG5mW0lyTa/X4/+6z7vg/f91GpVKDrHqanE2zfbsM0zSxS7Oabb8bMD6H7n5sTA1XEuBDu6FGWebj6PqkmKpWhUVmjQQZ6nY7Ajh10/goBfOELCjQN+MhHUuzY8aI3Z4KzhM1GrCd3pRNM8EojjoFPfAL47ndprnj0Tj+KSJYgzbhO53Idx2T09bWvAf/wD0R8223qDu/YQUQ6TanrvX07mYcZBvC61wEf/jAR74ceIlMwwxhmue3ZA/z2bxPB/+d/JhO1EyeG7uDlMkkoZmeH9phvexuVd5tNkm+fSrJ+Omgakfyvfe30Ycy1GnDzzS++mz/BawqTTO9zFxNSfW4gjoEvfEHDXXepaDSGN7emCVx0UYr3vz/B1q0bk15FAcplDs7px/MY7rnHhG0zmCYDMP48iosSWFlhaDSGYx0TnDmk/LtUKmHv3r0AyHG80WhksnHP83Ds2LFM0i2l48ViEXEcI45jlMvlTNYthICmaZnUXJIcVVVRrVaz13FdF57nQQhy7O71etlnXEZ72bYNxhj6/T6SJIEQAp7nIQxDXHvttbj66qt/6DzyK6+k8YFjxxguuEAM3jv1GEbTQEkhQc/pdMiHg6bpGCyLXOs5p1urZ59l+PM/V/G//q/pD3XLM8H5hwmpnmCCVxqPPUYd6j17ht1dCcMgk679+4HPfx7Yt4+0RqZJj0vdfxRRp/tf/5Vk0HFMUuhebxhVdemlQ6MxgP5+7bXAH/4haZ3uuAN4+mmSn9dqRFIvu4zk55ZFrb677yayK2ewLYvuho4coXV0OnTFaTSA3/ot6mobBpHuW2+lWfEXg5tvJlfwlRVs6EIjr3C33TaZf5hggvMUMqd61K14gs0FEkhp+MIXNBSLAnv2cOg6fS33+8Bjj6nodBg+9rEYs7PriXWapllnU1VVHDliYGlJxdwcsLwsMm/NUcja79ISdcTL5VfinZ4/CEOKjup2GQwD2LOHY2pKQbVaRbVaxSWXXAIASJIEtVot62j7vo8TJ04AQBbdJQl6p9OBqqqZk3cURVBVFUmSZDJw+Rk2TRO5XA5CCDiOA1VVs2gvwzDQaDTGzgnHcTA9PY0bbrgBlZeY9mHbwLvfzfHJT6p49llSOBgGnWO+DwSBGBR0BOKYDPRaLUAIloni5PkYx3TrdtFFAs8+y/Dooww33TRRTLzS2IzXhgmpnmCCVxr33UdlzrWEWkIIklj/8R8T8TYMKo2WSkSK3/9+Irtf/zp1pKOISK5tk6Q7TYkkGwZw++1EdIUgknzZZciGgFSV3LQHF9IxPPwwScp376a/P/ggXWEMg0j28jLlVF90EZHce+8lku26tL4vfIFMyz70oReXX3TppcCP/zh13tttajXKufCTJ+m9vutdNAc+wQQTnJewLGuSU73J8dxzDJ/7nIZej+HYMQbOGXRdIJ8HFIUIxne/q2JmRuBjH4uzGijnPJu/ZYxB13WoqoowJLflrVuJqPT7G0fjKQrJwG+4gWPfvvWGVBOsB+fAt7+t4GtfUwfHim4JKhWBm27ieNe70rF9rWka5ubmMDcyGhaGIWq1GlZWVlCv19FutzNTMzknraoq8vk8er0ebNuGbdvgnCMIAti2jV6vlz1HEqJ8Pp/NRhcKhSy/WlEUXHHFFbjiiivOGnm68UYBxlL88z8reP552g+ck8Bu505ynm80GB59lAoQrRbLJuDKZZGFnkQRsG+fyDxcH3lkQqonIExI9QQTvFgIQaX4NCUS+2LlSMePnzpIl3OSZR89SuvZsYNIsBzu+cY3iNCurBDJLhSozGpZ9K/cnnKZlmk0qOMrBF0lBrNVL/j+7rqLlr/gAnrstttIAn7kCD0+PU3l2jCk7dizZ7xzPDdHy37qU/T7mXasGSOjs+lpeq/PPUfrMAy66r3pTWTxPHGammCC8xYyM3eCzQkhgL/6Kx2PPqrCcQQsSyCOBY4dUxCGVGetVATiGPjLv9ShKAIf/nAEyyKpN2MMqqpCVVUog+9yxyHSQg7OAgcOMCgKCaRGI0VlAsqP/uhEcnsmEAL40pdU/P3fqzAMYNcuIoOcky/oF76gYnmZ4SMfSU5Z5weoy7ywsICFhYXsMc/zMtl4s9lErVbL8rO73W72PNM04Xle1p1O0xT9vo8gyOPgwTaShGTVxaJALgfs3LkDt956axYJdrbAGBHrK69MsX8/w8oKw+Ii8OUvK6hWBaanh+K+Xm84EQcMb7F6PTr/FhaIRBsG0Olsvo7pBK8OJqR6ggnOFGlKM8jf+Q7JpmVO8+23k2z5TC8AUsO2EVZXgeefp44v58N8asaIXOdyNIsdRUQugWFc1pNP0h2IotA64pheb2Zm6Kp9ww0vvH0rK5QFPWpg5jhEjPfsGWZef/Ob1D2+5Zb1UmzGqCBw4ABt74uRgSsK7dNbbiFSLSPBdu0az+ueYIIJzktMZqo3Nx57TME3vqFB1wWmpwWCgLrHANV5wxAIAob5eY5mk+HrX9egqinuuIOj0TCgaSq2bh03GbvwQoGFBYGlJYY9e8iY7NAhBaurgKbRckFAncOf+ZkEr3/95upSC0HGaQ88oODxxxWkKbBjB8eNN3Jcdpl41S5dR44wfOELKopFMXZJVxSyRSkWBR58UMG3v63grW99cfvUcRzs2LEDO0acurrdbhbt1el0sLi4mBHseGBCKkQOzzxjodsNIEQRjCngPMHKSoytW2/Ae95zIXI5ls1jy0712epYWxZw/fUCgIAQdPv0d3+n4K67gNVVhmKRbnNaLbqVMU3qoxw6RLch117Ls4IO9RUmXepXAxP59wSvCMKQOJQQyLL2JniJSBLg058mE600pU6qolD39hOfIPnzr/zKOBE9Fa68kuaqpZv2KI4dG0ZflUpEokdhmvRz/Ph4t3bPHjroq6v0PNOkv4chbWO7DfzkT+KMbCp9nwi5Za3/m8y0FoI0U5o2/h5kR1wI2oZqlebH3/e+F29epqovfiZ7ggkmOOchc6onM9WbD0IA3/qWiiQZfqW3WgxRxLKusmWRZLbfB3RdwLY5Pv1pC3ffbcIwaF6VHPlJerx9u4BlAW98Y4pPfUpDqwVcfLHAli2Ua91qsUFwhcCb35zi538+3VSWGpwDn/+8ii9+UUWvR4UFVSX5+/e+p+Kmmzg+9KHTd4JfLjz4oIJWiya/NoJl0W3Gd76j4o1v5OtynF8s8vk88vk8Lhio3IQQaLVaGdFeWenj298+Ad9PkMsBikKKFF3fBSFux+OP5/Dnf87x0Y/GY7cM8ntAKhvOFtFmDLjzTg5A4D//Z22wLXQbFcdAHAuYJsuk3lNTIpvll3W/q6+ekOoJCBNSfR6h1wO+9S0at11ZGRo133478IY3UN7eBD8kvv514ItfJGftUXeUqSn65j1wAPjzPwd+53deOOrphhsoB/rYsfV5aY0GkeEkIbnzRjLnqSnqSrdaw/noXA648UaSjq+uEuHtdOj3rVtpDvsd7zgzcy/XpU6375+6IpOmdEWpVun/SUJE/8gRIvDydaamqEMeBD+cI/gEE0zwmoM9+K4IggDOpCq8qbC8zPDkkyq2beN4/nkFcYzM+EpeXuRlq9lkKJc5jh9XUa8rmJnhuOoqIiCtFvC97yk4dozhV381wY4dAm96E0e9nuJrX1OxvExzrJUKycKjCHjjGzl+4ReSTTf9861vKfinf1JRLlPsktwPCwt0X/btbyswTfVVKQY88YSCfP70l/5qVWB5mWF5mWH79rNLEBljKJfLKJfLuOiii/7/9u47vur6+h/46/O5I3uQvQgJJCEkEGaAhF2VIYoo7laR4ShqtThabW1rv/3W2n5/WqvWvVCptaLFjYuRAQQyWBlAGIGEDEL2vp/P+/fH8Wbee7PvSM7z8cgDSC7JOzc3937O+5z3OfjvfzUoLQUmTqyEEOUwGMqg042FXk8b6NHR1OTu0CGB5GQVQhjHWwkIIbpkr4cq0KZO8hLCwgSCgwXy82WUlwOengLV1YDBICDLEvR6gQsXJDQ0UAn9yZMSJk0SSEjgoJoRDqqHiKIAZ850NGoeP77rLLzhVltLU5gOHKAdYD8/eqKoqgLee4/6Tv3iF1Tuw/qpuZlKnd3dTbcbpcGcFFjn5dFAXEvCwihz+847FByHhlIA2tbWcZgnLs58VtnHh4LdsrKOoBqg7fH586lD9rlzFNzeeSewciVtu/aVnx+QkEBl250/f2dC0JWTlxetOzOTvqYkdRyCq6mh9wUF0X3IGGN94PxjlUxLSwsH1XamsZH2U8eOFSgtpRm+nU8qAWivMGhulqAoMoSQ4ONDgYskUQAyZgyVHuflSfjoIw02bzZAowFuuklBXJyKtDQZ6ek0+9rDg0pur7vOYHfJgZYW4Ntv6byyry8lNKqqJNTVUbba318gIEBg/34NrrhCHfKgtTemuqh3J8sdEzOHU3MzkJYmY8wYCXq9PwB/ODnFd7mNszOV+6eny0hOVk0Gyn0NtPsTZJ89S4/hM2cklJZ25AQ8PKgk3NhCpqEBOHJEgrs7HVnYsEExWdTHRicOqgdJCGrm/M03NF64uZl+MceOpZ5KP/lJ74nLofDhh0BGBjVj7vwL7u5OMU1eHvD22zT62N52ee3eiROUhbXU5MvNjbbSDx/uPagG6IHh5QV8/TVQUEBZYa2WfliNjcCMGeYboNXUUNCtqtTl25gtBiiYdXOjH/Lq1ZSh7u/WuCRRaUN2NgXFxjFeRopCa548mb7n3Fx6RRozputOkrEG0GAA/v1v4IEHeAwWY6xXnYNqZl9cXOhpXqcTiIlRkZ0to7UVP47TomDHYJDQ3GzsxynBy0ugrk7q8ZImyxSc5+XJOHWK5gfLMl23VFRIAKT2YqkDB2QUFuqxaJGCa69VrJq0sKSgQML58/Q9pqbKKC6WUF8PtLRQmbtWK+DnJ+DpSaXY4eGKVdcXEaGioMByM9Xqalq/r+/wRtU1NdTUq7czyB4ewIULkskTcoDpjLSpQLv77XoLtGtqgJISOlNtLIP39QVcXARqa2kDqb6ezmFv3KggMVH0K1/Bho4Y7h2gAeKgehCoqyKwdSvFNyEhFM+0tAAXLtBR2/PnaarQAOfV90lZGbB/PxAcbPoYrE5HlcRHj1LgHxMzfGsZkZqaKDDs7bCRVktZ5r6QJGDWLAqez5yhDLVeTwH1M890tJjsrqWFxm2tW0e3+eILoLSUAlqNhmrqFAVISgLuuGPgQWxCAnDbbfTgPnKEXll0OtqmraujM9xr1gCvv06zsr29uwbUxm7lbm70gDtwgL7PvnQfZ4yNajqd7sdMJ1e42JugIIHoaAWHDmkQE6NCkgT27NGgoUGCVkuvNxoNPfVHRgpUVlJzMYOByoy78/Cgvdtz5yioPndOwssva3HhgoSICNF+akgIOs20fbsGigLccot9nKuur5dQWwucOSOjokJCYyO938NDQJKokKu8XEJlJfDppzJWrLBu1/JZs1Ts2qVBTY3pXqrGCZzXX6/2aOEy1LRaemwovewrKAqNZ+uP7sFy9yDbVKDd+S0ykkrgVbXnpZ6rK725u1NwHRUlsHChGPT5czY49thvg4PqQSgspHG6bm4UUBu5uFD5d3U1ZbAnTqRGxsPl+HEq846LM38bDw+KaQoKOKjuN1dXtA8rtFTnYzCYH5VljizTg8VIUYDLL6fdmsZGqtc3Nga7dIl2aWbNAhYvpgfe5MlAejoFvkJQE7SFC2me9WBrki67jErQ9+6loLitjXZubrqJzm/7+tKDb+dOCvAVhQJvg4Ee/KpK911eHm0BP/88nUGIiBjcuhhjI5okSe3Nyph9kSRg4UIFx47RueeICANaWwWys7U/vlRKaGqiANrPj4Lq2loJrq50XtXU55OkjkDr22/pnHV8vOhSVSdJNMhCoxHYuZOaf40fb/tslV4PlJZKqK+X0NLS0ajNyNjgqqFBwvHjMnbvlrF8ufU6l8fGCsyfr+D77zVobRXtRwMB2iM/fVpCdLTAokXDn0EfMwaIjFRx9Khk8iQdQJcx1dXAwoXqoDZN+ls2PnWqBEXRwGCQTDZIVFW6v8aNE2hqklBV1be+tGx04aB6AGpr6Zf+66/pz4QE07fz9qYk4u7dlDgcrrJr4xO5pc9v/DhfowxATAwFlyUlXQPgzurr6ZV06tTBfS2NBvjZzyg4/+EHCkgBekb39gauuIJKuo3B+5Qp9Kaq9DbUZw2ioujt1lspqDZeIRgFBwOxsVSvV1RE5eDNzfTKqNdT4K/V0r/T0mij4M47KehnjDETJEnisVp2bPp0A665RsGnnzohP18Lb29KLBQX02tDQIDAtGkqKisl1NUJeHtLSEgQJjOhxpNPfn4CFRVAZqYGQUHC7PWMjw9VAmZmyhg/3rql1KZ4eAi0tEhobaWXSFMtAIyVilotsGePBkuWDL7Ldl9pNMBPf6rAyQlIS9Pg2DF6CVdVumRJSFBx220KAgKGfy2yDMyfr+LIES1qaoTJzHlZGbWHSUwc+o0HS4F2QIDA+PEqjhyhbukeHqI9n9HaSqXfPj4C48aJH3sDDPnyWD/YY5Ya4KC6X86do6Tcvn1UAZuVRXGDlxedoTZV4h0QQBntqirzPZ8Gy9OT/mxrMz/G1xhzGW/L+kGvp+zxa6/1PMMM0E5FYSEwdy4FmEPx9W64gb7m4cP0YHN2BiZNoisXU08msjy8h+U1GtMPcDc3WltUFJVklJbSL4izc0etmRD07wkT6Ht54w0KxjuXd/SFwUBrsNMnU8bY0HF2dubybzujqmr72/LlEiIjJWRk6HD0qAYTJgh4eYkfzxFT9larBcLCRHswYsr581TmHRcncPIkNfgyt3cN0NO/iwuVi9uDpibqEl1ZKbVn3TsTguZrOzsLeHjQHO6SEio3thZnZ+BnP1Nw2WUqDh2iygG9HoiOVhEbK4b1eGJ3iYkqjh+nDu9VVUBgIJVRNzbSOWpJAm680YAJE/p//xgMwLFjEg4elFFSQmOwpkxRMWOGarZJb+dAe8UKFZcuSWhro0y0sXmbXg+EhqqIj1dRUSFh7FgVY8aoUNWhn6HN+s4e73MOqvsoPx/45z+p+tbfn6pydTqKEQ4coIbL06b1jDu0WgpmDYbhW1t8PMUnJSXmG0ZXVFBQP9hE6qj1k59QQP355xQ4+vp2nGFuaaGz0Rs2DO3h+TFjgEWLhu7z9YcQtItUV0fZ6XHjTO/YxMdTDVRpKTU0Ky6m3Z3O295NTfSqHhREGfbDh6kJwLXX9r6O+nr6BUtJoS1sjYbu66QkqiCwwydVxtjg6fV6DqrthKrSaCNFUSBJEjQaDbRaLaZNkzFtmorGRrU9S9vcTCXFBgM1vyoqkrBlixZFRRJCQzsCuLY2CoydnIBVq5T2DtTGLKolxuET9kCSgOBggYsXJVy6JP3YrLajYZvBALi6ih+7WtPaeztTPFyCg02X4FuTVkuZ85AQgd27NTh/XmovgouKUnH55SrmzOl/6XdtLfDOO1ocPCjDYKD9foMByM6W8dVXAjffrCA52fIDa/ZsFXv2aODhocJgoGMMkkSj3by9BZqa6PE9b54BgAKDYXAdx9nIw0F1H9TXU3KttJSOsBqfzP386JfW3Z0SlV5elLDrrKaGssPDmSF2cwOWLaPu3hUV6HJmBqC4r7ycmkHzSK0BolkfdHA9LQ04doxe+WNiKPBNTMSwd/noj5YWCl5LSuhVPDiYdlT6cs46O5uaAeTlddTmhYdTR/Du7ew9Pel9W7fSdu6FC13r31pb6dUuOhrtwzK9vOgc+OrVloPi8nLayTp6lD63pyd9X599RsPY16zp++xtxpjD4DPV9kNVVRh+zArIsgydTtc+F9io81O+Tocf5/ZS8DZ+vIAQBnz6qQZ5eR1ls5JETc+mTVORkSHjvfc0aG2lvdyGBglTpgiTp5lUlV6WJk603rlkS0JD6ex4aKgKg4Hul9bWjs7fPj7GeceUuXdzoyBtNNNqgSuuULFwoYrTp+ksurs7EBExsKy5ogBbtmiRmipj/PiuxwxUFSgqkvDOO1q4u7dZnCkdGyuwdKmCzz7TwMMDCA8X0Onoc1RU0BzvOXNUzJ8PaDQak83QgK4BNQfaw4O7fzuw7Gxq8hUT03V3NDycYhatll5ITp+m5sbGJwVFod5Sy5dj2Ls9LltGScUvv6Tg39OTXrSMTaWXLwduvHF41zDiyTIFplOn0ja7ovQ8Y2wPsrKADz6gMVe0lUpv48YB119PZerm7NpFuzNNTRSIh4VRYFxcTOXvRUXUVbzz1c7VV9P5hi++oJIN43nvpib6MyKCzn0b7ydnZ6r1UlXzmf22NuDVV2ljIDa2a2fxsDAK3j/4gHaQkpIGdj8xxuwWn6m2LVVVoShKe9MmrVYLWZZ7BNS9kSTgJz+hEtycHBllZfQ6EBQkkJ8v4fvvNTAYKNA0nmHNyZHR2CiQmKj2GJ1VXCzBz09gxgz7CKr9/KikubBQA3d36hAty3TBr9XSZUNNDTVqEwKYNUsZtqOAjsbJiQLZwTp+XEJmpozIyJ7n9mWZmosVFEj49lsNpkwxmL1kk2Xg+usVeHoK/PCDBidOGDuJAz4+AlddpWD1aqV9E8lSx3Hjn5Y6jnf+v2xk4KC6Dw4fpheG7tWvwcFU0XrhAsURxgZmvr4Uhxw/TsdIFy4c/jVqNBQ0T59OR1rz8uiJYO5cIDmZjuNa89zMiKfTmT/AbkvZ2ZTdbWigg2nGbiitrRRkv/IK/dtUYH3+PGWcJYkeMEZaLX2umhrg228p6xwXRwG2otB5iLVrKfj99a9pJ0mS6EzCuHH0S9I5CG9sBEJDLT8gjxyhDHV0NEwOJA0Oplb2331HncjtpRaQMTYkOFNtG53PTXcu9e5vMN2dtzeweHFHILx9uwa7d2sQFta1YZWXl0B6OgXcBoOE+fMp2G5spBnCsgzceKNiV1V3K1YoOHlSxjffUBm4ry+tubkZaGykNXt7C0RECCxZYh+bASNJTo7caS56T8YS/fx86iwfHm4+kNdqgSuvpCx6Xp6M+nq6jIqJUXu00+n5dfrXcdxcgM2BtuPioLoPGhpMX9frdDTdKCuLAuuaGuDUKcoUSxJltu+803ol18avySOzBqitjXYjKiooSAsPp2DSUZ7g2tpoxlt9PTUN67xu6koCnDgBbNtGDQC6l4Lv30/fu7l29l5eFJg/8wwF0tXVtHPj7k5nq6++ms6Vb99O5yRMBc2qSutbsMDy95KdTVl2SyUeISG0c3XmjOXONowxh+Pk5MRnqq2sL6XeQ6GuDti1S4a3d8/Zyc7OQHKyCr1eQkmJjJwcFS4uFOxERgosX65gzhz7CkwDAoB7722Dt7cWH3ygwcmTElpbpR+TMeLH2d4Cd91lMNuwjQ1cebnU68k2d3eqLK2p6dvndHcfmg7kfQm0u9+eA+3edb6P7AkH1X3g59dRydqdqytlgk+dovLv6dMpFjNWCQ92VDCzkoMHgU8+ocPxBkNHsDh5MpUAmOsAZ09yc2n948aZ3wgID6fbHD4MzJ7d9WNZWR3nBkxpaKBsdlkZnSOPi+s4Y5CRQZ931SoKuE+fpjKNzp9LVanjX0REz6/d3aVL6HXmiKsrnbFuaOj6fkWhLHZREX3NgAAqP7fWDBPG2KBxUG09pkq9tUM0nlEIyjDX1dHe7tixAnl5MsrLJcTEmA4wnZ2BefMEsrJULF6sYOZMahQVEyPsskAMoJeZ8HCByEgBNzdAUeh7c3ISkGUKsO30GKhdam4GDh+WcfAgPVZcXOj8/cyZPTPGzs6i/f42p62NNmZMJcisrbdAu3M2u/Ofxg0uDrTtFwfVfTBrFlW9NjRQk4nuZJmqa1etAh591HESm+xH+/bR+d3GRgo6XV3p1a+2lj524QLw4IP0MXtWWtp7dtfJiQLN0tKeHzMOPFcUelB3fyDn59PZaU9POuNgzGB4edH7CgpotvZtt1EZ+ZEj9DHjvIz6egqo776751iy7jw96VXQEmqz2nXn6sQJ4F//orUYS0c1GvrZXXstnb/mX1BmZUJQhsRgoIe2PVzY2TtnZ2e0trbaehkjWvdSb61WC41GM2TZ6cOHJfzwgwZ5eVSeS0/FXUdvmWMcnRUWBsybZ1+ZaVP275fxxRcahIYKTJ3aNcBTVYETJ4C339biscfa4O1tmzU6ivJy4M03tTh6VIYkCbi60pirnBwZO3YI3HabAdOnd9zH8fECu3dLMBhMN7cDgLIyCUFB5ke72VpvgTaA9iqS0d5x3FSG315wUN0HcXE0xSc9nUqrO8csQlBCzMUFuOIKvl53OA0N1PCqtbXrjGljl+r4eDrb+/HHwAMP2PcPuD9r63zb1lYgM5OC5rw8wMeHxnmFh9PZZY2GAuLi4o4gtnsJhiRRCfaJE3T73/6WNiTS06nMIzQUmD+fzj/3FlADVObx/fe0NnMRyIULFKRHRtK/CwuBf/yj4/3GjiUtLdRS9pVXaMOgt9JzxoaIolARzJ49dFJBVSmoXriQfh38/W29QvvFc6qHl6IoUH6c7TRU56Y7S02V8e67WjQ0GEc54cfu3hL275dQVydZzDwLQb8vLi72efHcmaoCe/bIEEKYfHmTZZoMk58vITtbtttz1c3NwJEjMrKyJFRXS/D0FJg2TWDqVLVLh/fhXsObb2qRkyMjOlr8eKlBjwFVBU6dkvDGG1o8+KABUVH0/mnTVISHCxQWmp60WV9Pb6tXKw5VPdqX0nHjW+ff3dEYaNsLDqr7QKsFNm6kX+jMTHqfhwdlHWprKUa49VYKvJmDycqikuaJE01/XJZpq/zQIQrM7DlbHRpKAai5kgqgY0RWaCj9u7EReP11IDWVXs0AemAXF3cMPp8+ndJsTU10pRMWZrqUWq+nX5LTp+lMxJo1NG7rwAEgJ4f+PHuWxo8lJFgux546la5Cjh833WWvspIy2Zdd1jH8c9s2WvPkyV1fVWkAJp3R+M9/6Dy5uY4mjA0RgwF47z1gxw76tfD3p4dqTQ2wZQuwdy9w772OcbLEFrj79/DoXuqt0+mgGeIupiUlEj74QAMhgEmTOoJinY5GbLm6Ajt3Sjh2TMK0aaaD5poa2hcdiu7Qw+3CBQmFhTKCgsyvVaOhl8isLPsMqktKJLzxhgYFBRScOTsDLS0CqakSoqIE1q+3znnww4dlHD3aOaDuIMvAhAkCubkSdu+WERVFm0Lu7sDttxvw2mtaHD1KGWkPD9rULC+nedMLFqi47DL7u9/7y1THcUmSuOO4neCguo/GjKEK4EOH6GKouJiu1adNo+RbWJitV8gG5Nw5CsgsHdQaM4aCtfPn7Tuojo2lbdpjxzrOO3cmREdTrylT6H0ffEBjtCZMoP8rBD24fX2N28L06urhQd1lQkMpC2yOLFM0AVD59xtv0H2s19Nbbi6wezcF1XfdZT5V5+JCHzfOqR4zhlJ8BgPVhmm11Bht0SK6/enT9H2PHWv5PHl+PjVBs0ZLfjaq7dxJU+aCg6n4w8jbm36NcnNpSt1vf8u9N0zh7t9Da7hLvTs7cEBGZaWE+HjTQVhQEODvL1BYSAFb9zFIVFwkYf58FRER9h9Ut7TQHm9vxzr0eqC+3v4Cmupq4NVXtSgokBAdLbrsd7e2Cpw8KeHVV7X45S/b+lRoNhj791PJt7nnREkCAgIEsrM1uHRJaX9ujYsTeOABA3bulJGZqcH583Q5EhoqsGCBgoUL1RHZVmW0dhznRmUjgJMT9VfqrccScyD9OZdhp2c42mk0wM03Uwl0bi6lwIxXKw0NlCX29qbb6HRUJp2eTlf9xsz2zJn0qlVaSkG1wUBZ5nHjKLCeNKlnu1YjVaWt4YAACt5ffpkyynFxXTPNTU1UEysE8NBD5s+AR0QAjzxCWfQ9e6gsRKOhXawFC6jZgfGCsKSEvkdLAb8xo11S0ue7lLGBaG2l9gLOzl0DaiONhvawjh833TOQcaZ6KA13qXd3hw7JcHe3fCJp+nSBzEwJJ09KcHMT8PWl21dVUWZx+nQVt95qfqawPXFzoyCwsdHyBllTk4Cvr/1dRxw4IKOgQEJsbM8zyXo9MHEiZYf379dg5UplWNdSUSHBzU2CseTbFDc3unypq5Pg49Nxu4gIgXXrFKxapaC6WoJWS0cPRlsPC+44bjscVLPRLTiY/lQU83OT6+qoeZk9DcY0JzYW+MUvgA8/7NqsS6+ngPj66zuy1EePUuOxyZM7/r+rKzXzqqig4LO+njpxX3MNcPEifU4hTF8tlZVRhnvmTODzz+lVb8qUnrd1caFy+0OHKGBPSjL//fj7U4OxlSvp56DVWu5Q3hf2vjnCHN7p07SHZamCycmJHoo5ORxUm+Li4oLa2lpbL8OhdR6RNVyl3qY0N1tuQgZQ8Dl+vMCqVQqOH5dQVCRDCOoOPn++grlz1R4ZbHsVEADEx6vYu1fuEuQZGQz0clpWJkOnM6C4WEJIiLCLDQMhgLQ0DVxdzf/MNBraS09NlbFsmdLrz3YwXFwE2tos3zEGA61Vp6MNzFOnaCPGzY0eU76+sMvNC1vijuPWwUE1G91mzKBg+fx504cbjZ3oZs2iEmlHEBsL/OY3lAYrLqb3BQX1PJvc2EjBafcnSY2Gbh8URP8+epTSakuWUBb85ElqDmZ8ZRWCAuqqKho/5uREI7YCAswHv87O9HXS0y0H1UZ6PQXs5oSE0IZAba35TLqxLD0kpPevx9ggNDdTOWhvZd1UDmqdNTkavV7PmeoB6l7qrdFohq3U25SgIIHz5y3fpr4e8PQUuOwyBddcQ3umQlBBlBXi/iElScDixQqOHJFx9qyE8HDRPkLr3DkJBQUSzp+nxl8//KBBdrYG06YpuO46BQEBtl17Swvtm3t4WA5C3d0FamslNDSYf4kdCtOnqzh8WIaqdhSidVdWJiE6WsXRoxL27NHi/HkJra106RERQWenk5NVs//fHEWhn6WVfk1srr8dx41/dm6E1v3/j3YcVLPRzdubMqFvv00ly2FhHcFiUxO9z98fWL3avjt/d6fRUBA9aZL527i40Ku+ucwzQCXdxtsmJNA4rPfeo7PJQMcZah8fCqhXr6Yt+cbG3jP7bm6mR3sNRGQkZdz37+/ZqMyoqIgCau4oyIaZm1vHJDlL2baWFvB4HTNcXFy4+/cAGEu9jcH0cJd6mzJnjoqMDC2am02fjRWCGkitWqXA05PeN5yBmjVMnkyjnj74gJpleXnR95iXJ7Vn4OfMoex7VRWwa5cGpaUS7rvPMOznlLurrASKiiQIIcHLS/z4Mm655NpgAGS5o1t7WxuQmyvh3DkKgP38BBISBl9dMGOGih07BE6fljB+fM9sflUVBb8tLcCWLVo4OwNhYR3l92fPynjtNRkXLyq45hql18u21lYgJ0dGejptiABAbKyKuXNVTJ4sHG6DZ7D6ktHmjuPmcVDN2OWXU3D43/92BIvG5mUTJgA//WnXcVsjRXw8XclcumQ+C1xRQQFzfDz9OzGR7ovsbBphpShUQj9zZkdmW6+noL63OdNtbZZnaveHJAHXXUeBc24una02nhM3jtSSZeCGG7jzNxt2kZEdzetjYkzfprGR9u94j8c0JycnnlPdD51LvWVZbm9EZgtTp6pISFCRmSkjKkp0GcdkMNBLR0iIwIIFjt+NubP581WMG9eGAwdkpKTIOHeOzvxOniwQFNTRAMzHh7L0ubkyvvlGg1tv7XpOuaREQlaWjHPn6KV0/HiBGTNUk/0Z+qOyEvjySw3275dx6RIFPm5udNa7pqbjNJzp/yth/nwFrq40Guzf/9agsFBGW1tHZjc4WOCqqxQsWqQOOAcREAD87GcK3nxTi9xcCYGBAm5udLlQViZBVYGYGBUnTlC39TFjOv6vmxt1By8vBz79VAMnJ4GQEOoEHhEhemSg6+tpdrixOZq3N+URUlI02LtXg8suU3DzzYrFPrajgblA29Ydx+0xeOegmjFJotFMc+ZQsFhRQa8S4eF0JnikPqOGhND3/PXXVDrdPcBtaKBM8urVXbt0e3hQ92xzHbQDAqjDeG6u+TScEFTvN5QRRVQUnSffupWiGeMFufFnee21NOqLsWGm0QBXXEFj20tLO/abjFpb6RTFzJkd+1WsKxcXFy7/7gNbl3qb4uwMbNhggCxr20t5nZ3pcW8wAOHhAmvXGjB27PCce1UUCvwOHpRRXCxBpwPi4wVmzlSGvTXK2LECY8cqcHUVuHCBOqCb+lFotdTFev9+GStWKBgzhtb96acafPONBtXVtD8tBE0S+OwzgTVrFCxYMLCA9eJF4MUXtcjNpWB00iTKAtfUAKWlEoqKqDw9Lq7n/62ooLUkJak4cULCyy9rUVkpYdw40X7Z0NZGmwFbtmigKBjU+KqZM1V4erZh924Z2dka1NQYmzuqWLBAxYEDNBO8c0BtpCjUwOzgQRmFhbr2LHZUFJWFJyaq7aX5//63BqmpMsaPF12mkAYHC1RXA199pYG3N3D11cPbnM0Rde++PRo6jvcFB9WMGbm7U1fp0UKSgFtuoXPIGRm0eeDrS682lZX06rRoEWV3+0OWgcWLOxqhdX/lM472CgykoH4oxcQATzxBFQdFRR0DghMSeG4Rs6rkZPo1+vhjmi43ZkzHnOq2NhrHeNddI3fPbrD0ej2Xf/fCHkq9zfH1BR54wIBjxyRkZtKILVdXICFBxbRpanvZ91BraADefVeDffs0aG2l7KWiAJmZwFdfybjxRsUqGfLCQhlOTpbP5/r6AoWFEkpKJIwZI/DFFxps26aBj49AfHzHKSZFAc6fl34sdzZg9uz+r//TTzXIzZUxaZLo8pzj7Q3Mni3Q3Azk5sqQJBWhobTH3txMAbcQwOrVCqZMEfj737UoL5cQF9e1NFunA8aNEygqkvDZZxrMnKkO6mhLdLRAdLSCixcV1NVJ0OvprH5DA/Dvf2vh59dzQ0ZVgSNHaGa4EPQ8O3GiQFMTkJ8v48QJGVVVBixdqqK4WEJGhgahoV0D6s73S309sHOnjCVLFIdpmmcr3HGccFDN2Gjm6Qncdx+wbx/NjzaOm4qPp0z0nDkY0HDH5GSq8fvyS9oiDwqire6GBuoK7uEB3Hab5XqzgdJoaP2cAmQ2JEk0Sn3iROrHl51NF8dxcfSrNWsWTF7MMcJzqs2zp1JvS3Q6YNo0gWnTrJPpU1Xg/fc12LVLg4gI0eWkj6pS07B339XC3b0N06f3niUvLwfOnJFhMADe3gIxMT1HTllaS2/xgfHjxn3sb7+V4eXVsx2JRkMB68mTEr74QoPp09V+bcaVlwOZmRoEBwuT/0+WgaQkgYMHKZi8dElCSwu9ZMfEqFi8mM4Ynz0rIS9PRliY+c7loaECeXkSsrNlLFky+M0LPz90CaANBrpvTf0czp+XcOqUDE9PAYOh4/51dwdiYgRKSiR88okWMTFtKCiQUFsLjB1r/msHBQmcOEHfc2LiyDqqYA19OZ9tquGZI3cc56CasdHOxYU6ey9aRFuzkkRX+4PJeGg0FDSPG0e1a2fO0KuhszMwbx6dY+88youxEUiSKKieOBG44w66GLTD2Mcucfl3T/ZY6m1PTp2iWcpjx4oerTNkmQLTggLgm280mDrVYPYl7tIlYPt2DQ4e1KCqin6PdTpg/HgVK1aomDWr9xLs8HCB9HTLfUCrqmhfOyBA4NAhGRcvUgbYnNBQgTNnJOTnS5gype+l82fPyqiqsty31MmJWqzMm6di3jwVjY10aRAW1tGsq6yMun+bGpRipNHQfV1aOjyBkIcH4OUlUFkpwdu74z6gQS0SZJnOrjc0UEDe+fk2OFjg6FEJGRkytFpap6Wfo1ZLn7ehYVi+lVGpvx3HOwfaxr93z3rbEw6qGWNEljGkNXkaDQXrCxbQyLLWVnpFDApyrE7qjA0BSeKAuj+cnJw4qO7Enku97UVOjozGRmoU2FlzM3DhApVZ19VRP4Nx4wSuvFLp8ZJXVQW89JIWR47ICAwUmDSJXhobG4FTp2S8+qqM5mZDryXkM2eq+OorDSoqYHJslqrSmpYupbFaVVVSr+OcXFxob7qqynKn7u6UHwsFenu4yDLdNjzc9Oc2nkXujRDDN5ZKp6OGcO++q2mfVw3Qz6e6WoKrK2WpFQU9zuxLEm0cHDokY/58FYpiedPD+PG+nhyrrKTz3E5OQGCg6bP0rKe+lI4b3yRJwrFjx/Dxxx/jqaeesrssNgfVjLHhpdVSN27GGOsjDqqJo5R62wNjQ63O19mVlUB2tozqanop0miovHnLFg2OHpWxYYMBkZEdwdc332hw+LCM2FgBvb7j87i60jnfs2cl/Oc/GkyapFochRUaKrB0qYKPP6bgLzCwI+Pb0ACcPSshIkJg2TIKzjWa3gNW4wTM/v74fX2pWVd9vfkRf6pKQaS/v/lFhIQIeHoC1dU9W6UY/fhQRUjI8GUT585VsH+/jPx8CVFR9L2pKt03igLU1koIChIIDu65Bq2W1hgbq8LdXYPaWvPj3Coq6P6YONHyBsrx4xJ275aRk6NBUxN9jQkTVCxcqGLOnP7Py2amA21FUfDmm2/i8ccfx/3332+rpVnEQTVjjDHG7IpxTrUxOzHaqKoKRVHav39jZpqz0+YZM7lG9fVAVpaMujoJfn6iPRPb1gZERtIs5Nde0+Khh9rg60tNBPfuleHvjy4BdWdjxwrk5tLIq6VLzQdbkgRcc40CrZbOSufndzyG9Xpg0iQVP/2pgrAwCvwiIlTodBSUmZs0eekSBYCdNwH6YsIEgZgYFceOSZg40XRmtrycGqdNm2b+ewoLE5gyRUVamtw+39qosZHuv+JiqX1m9XDx9QXuvtuAN9/U4PhxGYpCmfH6ekAI2qyYOlU1+TOsqxOIihKIjKTbpKXJiIkRPVrH1NcDFy9KWL1aMbuBAAAHD8p46y0tqqpo48Tbu2OGd26uFmfPKrjxRoUD60GqqanB/fffj5SUFGzbtg1Lly61y9cFDqpNMBhoGtDevXQUlMYxUM+mceO4cpUxxlhPQlCvv4wM6tMH0HS5xEQgLIxfO/rD2dl5VM6pVlUVQggu9R6A2FiBb7+lk0Z6PTWuqqmR4O/f0ViroUGCu7uAnx8FQXl5Eg4ckLF8OXWErqyUMGGC+aBVlulznzwpYelSy+vRaIBVqxTMn6/g8GEZVVUStFogMlJFbGzXpmfx8QITJtD85YkTe5YOGww0smrJEqXfWWBZBlasUHD2rA6nTlHpu/FrC0EZ2UuXJFx/veWRY8aNgnPnJOTlSRg7lj7PiRMSzp6VUFUlQZap4/arr2qxYoWCyZOHJ2MdGirwq19Rd/kjR2TU11NWuaBAQmKi6YZszc2AqkpISlIgScCttxrQ0KDFoUMy3NyoGR01jZNgMAALFqhYtcp8k72yMuC99zRobkaPbuheXkBlpcBXX1HTvLlzudHZQGVmZmLt2rWIjIxETk4Ogoejwe0Q4aC6m8ZG4K23gNRU2m3y8KCykqNHgR07aNTtVVfxxRFjjLEOqgp89hm9VVZSuShAnb8//xxYuZJGvnPlbt84OzuPupFa3Uu9dTodB9P9kJCgIiJC4NQpICqKgmonp45gp62NAquoqI7Sbnd3IDVVg6VLO87Y9u3scd8vAn18gMWLLQdVOh1w660KXn5ZQm6uhOBgmsMsBA3QKCuTMGmSimuvHVgn9alTBdavN+Df/9agoEBq7/HQ1kYdv9esUfo0j3nsWIFNmwz46CMNDh2ScfSohNpaCc7O9LGYGAEvL2q8dvo0ldfPnDk8AaVeD0yfLjB9Oq27tFTB3/+uw4kTUpefMUAl96dOSZg5U23Pxvv6Avfea8C+fTJSUjS4eJF+phMnqpg/X8Xs2arF4SeZmRqUldEsclMxga8vcPGiQEqKjNmzuQy8v1RVxT//+U88+eST+PWvf43HH3/c7o++cFDdiRDA++8D339PR0A7N7AQAigtBf71L3r/okU2WyZjjDkcISh7e+AAZXN1OupGO3MmXXQ6um+/pdcHLy8ai955XE5ZGfDhh9TwZuVK267TUYymM9WmSr21fZ3dxNq5ugK33WbAq69qcfgwZaldXATa2ihD3dZGgV/nTLSbm0BNDdDSQplODw86M+zra/prCAE0NaG9bHsoRUcL3H+/AV9/LePQIQ3Kyuh5ZMwYgauvVrBsmQJ//4F//sREFRMnqsjJkXH6NGVjg4OB6dNVk+ePzRk3TmDzZgP+/nctios1mDJFhaengL9/x6ZhbKxAYaGEf/1Lg5gYtUc39uEQFERl4W+9pUVhIT0B63RoHw82Z46KtWsNXRqPeXgAV1yhYskSFbW1Hc3M+hIAZ2ZKcHOznGQLDKSZ5eXltD7WN5WVldi0aROys7PxxRdfYNGiRXZZ7t0dP2t3UlREWYXQ0J5NkCWJnnwaGoCvvwaSksyfuWGMMdahuRl47z1gzx46q+biQmcbd+0CQkKAn/0MmDvX1qscuIYGGsnu7Nxz9Lok0cVUWxvw1Vc0o9oaF5iOzjineiSfqTY1IotLvQcnNlbgwQcpMH35ZRm1tRJcXAB3d4Fx4+itc2lwWxtls3U6+j2dNk3Brl0a+PiYzj5evEhB13BlXyMiBO65R8GFCyrKy+n5IyxMDNnGo6cnsHChioULB/d5qquB06clxMWpJoNFSaLvpaCAZlYvXGid8ufISIHHHmvDkSOURW9okODrS2e8u5fcd6bV9n9zt6lJ6nVeuE5HpfsGQ/86to9WQgjs27cP69atw+TJk5GdnQ3/wewkWRkH1Z3k5NATRXi4+duEhgKnTwMFBcCUKdZaGWOMOSYhgK1bKegMC6NxN8aLVUWhvhWvvUaB9tSpNl3qgB0+TNn3mBjztwkOBvLz6XVmwQKrLc1hufzYram5uRmuxlr6EcQ4IgvgUu+hFh4ucNddCpqagB9+0CAmRsDdHSYDqkuXgKuvVts/tmyZioICaiwWGSnas5qqSmePL16UsGqV0mNc01ALDhY9NujsydmzdP584kTz94NWS8/1p05Jgw7izWlrA44dk3DypIzmZupKnpCgIjFRxezZw/M1jfz9Bc6ft3ybhgbA2VnA3Z0D6t4oioJnn30WTz/9NJ588kls3rzZ4Z4TOajupLqaSlcsbYobu0vW1lptWYwx5rDOnqUMdWhoz0yARkONvHJzKejuXDbtSKqr6U9LWQvjRXtV1bAvZ0Rw/jGaaWlpGVFBdfdSb51OZ/fnBB3VFVeoOHqUOmp7e/f8+PnzEry8gNmzO7KolCk24L33tO0l0sb5zD4+wHXXKbjmGsUhn6eGkqpKfTp/bjy3PRzOnpXwzjsaFBbKaG2ltagq4OmpQVKSgptuUsx2Uh8Kc+aoOHhQi9ZWYbJylY7+SFixQjH5+GMdysrKcOedd6KwsBDfffcdkpKSbL2kAeGguhMXF/qFtOTHjWUu/WaMsT7IzKRNyHHjTH9ckoCxY4G8PKoCGj/euusbClptx5xUcxfbxhmzvZULMtI5qB4Jupd6G+dNO1omxpFMmiSwZo0B//mPFvn51O3byYmOo5SVUVn4LbcYEBXVNYsYEyPw29+24ehROnvc1kbnmqdNUy12xx5NfH0F3Nxgcc6zEBRQD8dZ4gsXgJdf1qKoiCoKjMGzEFR9sGMHzQdft04ZtuaQ06apiItTcfSojOjormO5VBU4dQoICBBYsIA7f5sjhMCePXuwfv16JCcnIysrC2MszTCzcxxUdzJxIgXLjY0dnVu7KysDAgIsl/kxxhgj5eX0vGops+PhQRltR83iRkVRF+GaGtMZMQCoq6PXlagoqy7NYel0OkiSNCI6gHcu9eZz09YjSVTOHRjYhj17NMjLk3HpEuDkBMydq2LhQgVTppguy9XrgRkzVMyYYeVFO4jwcIG4OBUHD8rw9DR9/ryiggLuGTOGPqjcvVuDU6eo83bnoFmSqMmcTieQlqZBUpKK+PjhKb12dQU2bDDgjTe0yM2VodEIuLpKaG2lZnbBwQK3327A+PFc+m2KwWDAX/7yFzz33HN4+umnsWnTJod/XuSgupP4eAqsjx2jrrTdd7eamqhJxQ03mN+ZY4wx1kGv76jwMUdRqHTPURseR0RQ6XpaGkye3VQU2jRITAQmTLDJEh2OJEntzcocVecRWVzqbRuSRGOXpk0zoLwcaG6mjuD+/o551MReSBLNvi4slHDyJD0HGqtwjLOvKyokXHNN/+dq96a2Fti7V4a/vzCbhfb0BM6dAw4ckBEfP7AxZH0RFARs3mzAoUMyMjJkXLwIuLgIzJihYuZMFX5+w/alHVpJSQnWr1+P0tJS7NmzBzNnzrT1koaEg17CDA+tFtiwAXj+eZpL7edHTQ9UlbItDQ1AcjKwapWtV8oYY7ZRWgpkZdFFk05H5dpTp8Ls2bWJE6nrdVub+dJnYwVQZOTwrXs4SRLw05/SpuuxY/S9+PrS+ysr6fuLjgZuu61vo1oYBaGOOlbLVFdvLvW2LUnCj6XbnDUcKrGxAnfeacC//qXFiRO0QyHL+LFcHli9WsG11w79+fPKSgl1dVKvwbq7O3DunAxg+IJqgF775s5VMXcul3n3RgiBHTt24O6778ayZcvw+eefw7P7uCUHxkF1N+HhwMMPAz/8QFmHCxc6xmktXkxvw9n4gDHG7JHBAHz8MfDNN3RmTaOhDUeNhs5L33676YkI06fTxwsLKcDufoHV3EzB6E039Rxl6EgoY0EjF9PT6TwdQFVN11wDrFgBPo/ZT87Ozg5X/s2l3mw0mTpVICqqDYcOyTh1ihq7BQTQeeOhzlAbGRsK99YDSVUBWeZNFHvR2tqKJ598Eq+//jr+/ve/Y/369SNuXCIH1SYEBgK33AJcdRVdPMoyvY+bkzHG7FFNDTUEO3yYjqkEBgKzZgFxcUNTUi0E8NFHwLZtlIGdPLkj49raSgHkP/8J/PKXPftNuLkBa9cCL71EFUAhIXTuWFEog3vpEjB7NrBy5eDXaWv+/pSNvvpq2pAVgjZkHbjvik3p9XqHCao7l3rLstzeiIyxkc7NDUhOVpGcbJ2vFxgoEBgocPGiBDc300GzEFRdGhfHQbU9OHPmDNatW4eGhgbs3bsXkydPtvWShgUH1RZ4eNAbY4zZq0OHgLfeAoqKKNDV6SjQ/fZbCqw3bhx8D4jiYvp8fn4d2daGBgrgZZnOCefnA599Rtna7pvPCQn0/q++ojnNZWX0/wICgOXL6W0kPdd6e5tvWMb6xlHOVHOpN2PW5eQELFyoYMsWLZqaTFePXrggwdsbmDWLS7JtSQiBTz/9FJs2bcL111+Pv//973Bzc7P1soYNB9U2YDBQxmbvXmqkoNNR5mfOHCo/Z4yxvigsBF54gf5sa6PJBcbup35+QEoK7dg/8MDgRjllZlJGOSGBOnQXFgIlJWifDeruTl8vJ4ee00w9j0VHU+frCxfonLFWSwF6eTlw8iR1Up0wwXGblbGhZ+9nqo2l3sZgmku92XBoa6PnTCHouZ2rJoGFC1Xk5anIyJDh6ysQEEBl4U1NQEmJBFUFbr7ZgLFjOVNtK83NzfjNb36DrVu34p///CduvfXWEVfu3R1fvlhZfT3w5pt05k5R6GJUValsc8cO6iy+dCl3pWSM9W77dur9IAQFo87O9Pfz5ym7HBoKZGTQJt706QP/OiUllB2oqAAOHKDnMXd3ysYKQf+uqqLA/fx585uDkkTl34GBwM6dwDvvUIa9tZU+f2QkcPnlwMKF3NCLwW4z1VzqzayhoQFITZWRlqZBWZkEIQA/P4EFCxTMm6c6dA+KwXJ1Be6804DgYA327pVRUED3j04HREQILF2qIDmZs9S2cuLECaxduxayLOPAgQOIGSVziDmotiIh6CJy1y7qmOvu3vVjxcXAu+9Ss56kJJstkzHmAMrLgX//m3bmQ0O7Znjd3akBWFER4OMD7N8/uKBao6HANzubvl5AQNeNvzFj6OudO0dfy9LZOlUFtm6lUnFnZ2DsWPqzqQk4cwZ4+WUK3q+/njcXRzsnJye7OlPNpd7MWmpqgNde0yIrS4arK+DrS7OgL16U8M47WmRnq7j7bgN8fW29UttxdwduvlnB8uUKCgtltLYCnp4CMTFiUJVZbOCEEPjwww/x4IMP4vbbb8ff/vY3ODs723pZVsNBtRUVFtIFZ3h414AaoIvHsDDg+HE6dzh7ds852YwxZnToEJ1N9vc3XTLt7Ay0tFDZ9tmzg/taUVFUflhVBbPzXQ0Gel4rKKAydFdX058rK4ue44KCKOA3cnWlr1NeDnz6KTBpEh2LYaOXPQXVXOrNrOmjjzQ4eFBGdLRA55jEw0OgtRU4fFjGBx9osWmTYdRvPnp7AzNnclba1hoaGvDoo49i+/bteOutt3DttdeO+HLv7vgVwYpycqhM0lLToNBQCr5PnLDashhjDqiwkI6QWDqD7O5OJYS1tYP7WjNnUiBtbEzWnaLQc1tUFFBdTRlnU4Sgc94GQ9eAurOAAPo6aWmDWzNzfM7OzmhtbbXpGlRVRWtrKxRFgSzL0Ol00Ov1HFCzYXPhApCZqUFwcNeA2kivB8aOFTh0SEZR0egKWph9ys3NxeLFi5Gfn4/MzExcd911oy6gBjiotqrKSjrvYelx5upK2aXBXgQzxkY2vZ5GmTQ0mL+NLFPAGxExuK/l5QVMnUrVMxcvUik40DG2pKKCguFJk+jrtbWZ/jwNDdQl3M/P8tfz8aE+Ez8eW2WjlC3nVBvPTRsMBkiSBK1WC51Ox2en2bA7flxGdTUslnZ7eQF1dUB+/ugLXJj9UFUVW7ZswZIlS7By5Urs3r0bkZGRtl6WzXD5txW5uNAFpyUGAwXd3N2RMWaJtzdddFVV0Uack1PXjwtBG3nOzsC0aYP/enPmUMMzVaXPawx4nZ1pNnVsLAXTrq7mx0kpCv3/7mvtTqul2/WWiWcjmy26f6uqCiEEl3ozm2lpoetASwkYSaJN09ZWDqqZbdTW1uLBBx/E999/jw8++ABXXnnlqMxOd8aXK1Y0aRLw+eemL4CNysqA4GAqo2SMMXMmTwbGjaOg9sIFqoJxdzdeaFEWo62NSrctNQ7rq5kzgS+/pCyzwdBRCu7tTRlzIajse+ZM892/3dyoqVlZmfnyb4Ca9EyYwJuLo521u3937+qt0+k4mB6kujrg0CEZpaV0sR0cLDB1qtqjrwzr4OFBfxoM5jcVVZWec93deWQUs76cnBysXbsWoaGhyMnJQWhoqK2XZBc4qLaihAQKlo8fB+Liep5NbGigpkLLl/dsZMYYY51FRVEAu2cPBdjFxXRsxDhey8+PgtKf/YyC2cGKjgbmzgV++IFGX3Uu4VZVCqjd3en5y9xmtVYLLFoEvPGG+QvG1lbqJL5oEXf/Hu2slalWVRWKokAI0V7qLcsyB9SDIASQlibjk080uHCh4xeZxuoJXHedgrlzVf4dNyEuToW/v0B5uYSQENNBc0UFbUxOnswNupj1qKqK1157Db/97W/x8MMP44knnoCWy8na8T1hRU5OwIYNwIsvAkeOUBddb28qcSwvp465CxcCK1faeqWMMXsny8C6dRSAZmfTaCqNhoLVxkYKpK+4AlixYui+3h130MXy3r3UUdzVlZ6/mppo9vTPfkZnry1JTgbS04HcXCob79yIp7GRmjROnUoTENjo5uLigtphbDBiakQWl3oPjb17Zbz9Nl1iTpwo2jfQ2tqA8+clvPWWFjU1BoSF0aio0FBh9tjIaOPlBSxZouLf/9bAxYWqezqrqaHRWqtWKfD3t80a2ehTVVWFTZs24cCBA/jss8+wZMmSUV/u3R0H1VY2YQLw8MOU7UlPp7JN4zitxYspOzOKRroxxgbBxwd48EEa1bd7N1BaSs8nc+cC8+cDM2YM7Wg+d3dg0yZgyRL6miUllA2fMgVITESfLvDGjKHP8frrQF4ebQLodJSh1umAWbOAO+/sKIFko5derx+2TDWXeg+f5mbg0081EAIYP75rplWnA3x8BPbulfG73+kRFaVCo6H3zZ2rYulSZVTPXja68koF9fXA999rUFJCgbYkUUCt0wFXXKHg2mt7adLD2BDJyMjA2rVrERsbi+zsbAQGBtp6SXZJEkJY/UBGbW0tvLy8UFNTA09PT2t/ebtRW0vl3hoNzWzlYfUjU2srleYaDBRQ9Nb5mLGBUNWOc87OzvZfOt3aSo3PcnLo3KW3NzVUi4vj58K+GumvpX/84x9x9OhRvPHGG0P2OU2VenP54tA6cEDGc89pMWGC6NEX4dIl4OBBGVVVElQVWLBAQWAgNT8sL5cQF6di0yYDZ2BBz+l5eRIyMmQcPy5DCCAqSsXs2Sri48WQbpgyZoqqqnj++efxpz/9CU888QQeeeQRnoBgAb+S2JCnJ72xkamlBdi5E9i1i4JqRaGS3FmzgMsvp6oFxoaKLA/N2Wlr0espkz5jhq1XwuyVk5PTkM2p7l7qrdVqodFoODs9DCoqKGDuHlArCnDkiIy6OglBQQIXL0pobpag0wkEBQG+vgJ5eXQO+667OAsry0B8vEB8vAKA7w9mXRUVFbj77ruRl5eHHTt2YP78+bZekt3jVxPGhkFLC/Daa1TiWlJClQjjxtFFxo4dwP/9H83hZYwxZpqLi8uQlH8rigKDwQBVVaHRaKDX67ncexjJsukCyIsXgUuXJHh7i/ZKms4VNToddQfPzu7a3IwxZj1CCKSkpCA5ORlOTk7IysrigLqPOFNtYzU1QGYmcPAgUF9PpcGJiVQG6eJi69WxgfruO8pSR0Z27eTu708/44IC4K23gCef5GoFxhgzRa/Xo7m5ecD/v3upt06n49JFKwgLo7Lvhoau1TNVVRIUpaOHgkYDeHp2DcB9fYFjx4DTpyUEB/O4KMasyWAw4P/+7//w//7f/8Of//xn3H///bz52A8cVNtQXh5lM8+epQymkxMFWykpdK7wnntoZjVzLMaybw8P06PRJInGIeXnA1lZ1KCOMcZYVwOdU82l3rYVGyswYYKKggIZsbEdWWlV7chM19RICAgQPXqMSBKVPf/YQ44xZiWlpaXYsGEDzp07h127diExMdHWS3I4/ApjI+fPAy+9RGdt4+KAiROBiAhg0iQKuI4coY/X19t6pay/zpyhkm9LzRG1WtqlP3LEastijDGHMpDyby71tj2tFrjpJgV+fgK5uRJqa2kUn7MzZajLyyW4uwvEx/ecU93URP/f25uz1Mz+VFQA+/fLSEuTceyYNCI2f4QQ+P777zF37lwEBgYiMzOTA+oB4ky1jezZQ4H15Mm0K9uZkxMF17m5VBbOmUzH0tbWMSbIEp2OLiAYY4z15OTk1OeguvOILC71tr2YGIH77jNg+3YN8vJknD9Po7ZkmUq+ExPVHvOXAaCkRMK4cQKTJnFQzexHVRWNicvI0KCqiioqtFpg/HgVy5dTR3Z7n7hhSltbG/70pz/hpZdewjPPPIONGzfyBuQgcFBtA42NQFoanR0y99jV6agkPDWVg2pH4+UFuLpSlYG3t/nbNTcDAQFWWxZjjDmUvgTV3Uu9NRoNl3rbiagogc2bDTh9WkJpKUUcR49K2LNHg7Y2yl53Lg0vLpYgScCKFQqP1WN2o7oaeOklLQ4flhEYKDBpEl27NzYCZ87IePVVGU1NBixerNp6qf1y7tw5rFu3DtXV1UhPT0dCQoKtl+Tw+FXHBurq6JfRw8Py7dzdgbIyeuFhjiMsjCoQiovN/+zq6mjThCtsGGPMNBcXFzQ3N0OYeSI1lnoLIbjU205JEjB+vEBysorkZBXr1ytYvVpBS4uEY8cknDgh4fhxCbm5NFrrZz8zYO5cxwpO2Mj23XcaHDokY+JEgYCAjmSYqyttHOl0wLZtGpSX23adfSWEwOeff46kpCRMnDgRGRkZHFAPEc5U24BOR+dp29os366tjbKejlhSMppJErBsGTWiO32azsp3vsarrwdOnQIWLQJiY222TMZsQlWBkyeB/fuBc+eohG7SJNpgCgqy9eqYPXF2djY5p7pzqbcsy+2NyJj902qBNWsUzJ2rIjNTRlGRBI1GYMIEgRkzVPj723qFjHWoqwPS0mT4+fWcu24UGkq9A7KyZCxfbt8bQi0tLfjd736Hd955By+88AJuu+02SBxkDBkOqm1gzBgKpg4coBJwU4SgcVvLl1t3bWxoTJkCbNwIbNkCHD1K49G0WhoxotUCCxcC69fT5gpjo0VLC/Dee8CuXVSt4+pKQfa+fcDnnwM33gj85Ce8kciIs7Nzl5FaXOo9MkgSjd0KC1NsvRTGLCoulnDpkoSICPMlo7JMvZCOH7fvoLqwsBB33HEHDAYDMjIyEMtZnSHHQbUNSBIFVVlZwMWL6DFSAgCKiijgnjvX+utjQyMpiTq5HzgAHDpEAUVICDBnDhAfT8E1Y6OFEMC//gV8+SUdkZgwoeNjqkqNG99+m+ba8vMeA7qeqVYUBYqitAfTWq2Wg2nG2LBS1a5n/82RZUCx0z0iIQQ+/vhj3H///bjlllvwzDPPwMXFxdbLGpH4st5GZs0CVq0Ctm8HKitpHrWTE2UyL1ygC8uf/hQYN87WK2WD4e8PXHklvTE2mhUX09SDoCD06Pory0B4OFBQQEH3rFm86cQ65lS3trZCkiQu9WaMWZWfn4CHBzUrM3c0QQiqvAoPt78GSE1NTfjVr36Fbdu24dVXX8UNN9zA5d7DiC9bbESWqdQxLAzYuZPOGLa10RzH5GTgsssA7hvAGBspsrNpLMnYseZvM3YsPRcePw7ExVlvbcw+qSqVUm7evBkLFy7EvHnzEB4ebuNVMcZGi4AAYMYMBd9+q4GvrzA5saeykhoPz5hhX6Xf+fn5uOOOO+Ds7IyDBw9iQufyMDYsOKi2IVkG5s+nILq4mEYsubtTJoc3khhjI0llJWWfLT23uboCra2UFWAsICAADz/8MBobG/HSSy/hnnvuQUhICObOnYukpCQkJycjLi6OM9eMsWFzxRUq8vJk5OdLiIwUMFZOqyod4ayokHD11YrFc9fWJITA+++/j4ceegh33nkn/vKXv0BvrssaG1IcVNsBWbacvXFUZWUdY6WCgug8MW8WMOaYqqspMJZl+l12curf/9frez9zZvw4z6hlAODh4YG//e1vAOhCsa6uDvv27UNKSgo+++wzPPHEE9BqtZgzZ057kD1z5kw4OztziSNjbEiEhwvcc48B77+vQWGhjLY2eh1UVTrKdO21Cq65RrGL69v6+nps3rwZX3/9NbZu3YqrrrqKnwutiINqNuRKSoDPPqMGXdXVFFR7eQHTpwNXX00jphhjjuH8eeC774C9e2kcnCzTJtnixcCSJZRd7ouYGAqWm5vpmIsp5eV0bo2r1Fh3kiTB09MTS5cuxdKlSwHQeJjs7GykpqYiLS0NL730EqqrqzFjxoz2bHZSUhJ8fHz4wpIxNmBRUQKPPWbAsWMSTp2SYTAA3t4CU6eqdjMK8siRI1i7di38/f2RlZXFR2VsQBJCWL1eoba2Fl5eXqipqYGnp6e1vzwbRsXFwHPPASdOUDbL15ey05cu0cfGjgXuv5+6YjPG7FthIfDCC8CZM0BgIODtTdnkigqa35mUBPz859RYsTctLcCTT9Jzw6RJ6HE2raUFyM8Hrr0WuP324fhuRh5+Le1KVVWcOHECqamp7YH2yZMnERsbi7lz5yI5ORnJyckIDw/nzuGMsRFBVVW89dZbeOyxx/DAAw/gD3/4A3Rc7mUTHFSzISME8P/+H5CeTiOjuh9zU1UgL48+9sQTPKOZMXvWWxDc2Egfu/lm4Kab+vY5T5ygIL2oiCYeeHvT80J5OTUxS0wE7rsP4JeFvuHXUsuEECgtLW0PstPT05GTk4PAwMAu57Lj4+Oh5XbzjDEHU1NTg/vvvx+pqanYsmULrrjiCq7KsSEOqtmQOXWKLsLHjKGLZVMaGihj/dhjwJQpVl0eY6wfDhwA/u//gPHjzZ+fLi6mj/35z30PhIuKgG++ATIyKNstSdRhdeFC4IorqIsq6xt+Le0fIQTq6+uxf/9+pKSkIC0tDfv374csy5g9e3Z7kD1r1iy4uLjwxSljzG5lZmZi7dq1iIyMxHvvvYfg4GBbL2nU461ZNmROn6ag2dKZaTc3yoCdPs1BNWP2LD8fMBgsNyQLDKTZ0idPAjNm9O3zhocDGzcCq1dT51RZptGCfT2bzdhASZIEDw8PXH755bj88ssBAK2trcjJyWkvF3/11Vdx6dIlTJ8+vb1kPCkpCb6+vhxkM8ZsTlVV/POf/8STTz6Jxx57DI899hhPQLATHFSzIWPs3NvbdYck9d4FmDFmW83NvR/R0Grp2EdbW/8/v58fvTFmS3q9HrNnz8bs2bOxefNmqKqKwsLC9kz273//exw/fhwxMTHtJePz5s1DREQEn8tmjFlVZWUlfv7zn+PQoUP48ssvsXDhQt7ssyMcVLMh4+tLF+EtLeazWwYD/amqQGYm/T00FHbTPZGx4aaqlNnNzqYGfi4u1GcgIaH/Y6qGk58fBctCmN8oa2igNXt5WXdtjA0XWZYRHR2N6OhorF+/HkIIlJWVIS0tDampqXjzzTdx//33w8/Pr8u57ClTpvC5bMbYsBBCYN++fVi3bh2mTJmCrKws+Pv723pZrBs+U82GTEsLNSArKTHf3fvYMZpfPW4cNTqSJDp/PXMmsGoVBdiMjVR1dcDbb9N54oYGmt1sMNBm1IQJVBY9frytV0lOn6YeCZ6egI+P6dsUFADR0cDvf8+NB22BX0utTwiBhoYGZGRktDdA279/P4QQSExMbA+yExMT4erqylkkxtigKIqCZ599Fk8//TSefPJJbN68matk7BQH1WxIpaYCr7xCZaFhYR0X2qpKF+A5OZTRnjmTLtSFoGxdSQkFE7/4BZ25ZGykaWujztd79lDfAU/PjgxwSwt1xg4PBx55hDpj25oQwGuvAV9/3bHezh87f57Wfe+9NFqLWR+/ltoHg8GAQ4cOtZeMp6WloaKiAtOmTWs/lz137lwEBARwkM0Y67OysjLceeedOHXqFLZu3Yq5c+faeknMAg6q2ZASAti5E/joI8pIazQUOLS2AufOUWbusst6ZrUUhbLYc+YAjz7a+7lsxhxNVhbw17/SrHZTc51VFThyBLjhBuDWW62/PlMaG4F33gFSUuh32N2dflcbGmhz7MYbgcsv599XW+HXUvukqipOnz7dJcjOz89HVFQUkpKS2t8mTJjAGSfGWA9CCOzZswfr16/HvHnz8Nprr2HMmDG2XhbrBQfVbFhcukRnpk+fpn+rKvDtt0BkJF2Ym1JTA1RWAr/7nfnyccYc1YsvArt20flpc0pKAJ0O+Mtf7Ge0lKLQfPn9+2kcllYLTJ4MzJ7NxzVsjV9LHYMQAhcvXkRaWhpSUlKQnp6OzMxM+Pj4dDmXnZCQAJ1OZ+vlMsZsyGAw4KmnnsLzzz+Pv/zlL9i0aRNvvjkI7qrBhoWPD82cNfriC/rTXEANUHlpUREF4hxUs5Hm3LneA2UvL6rwqKqyn6Bao6EgevJkW6+EMcckSRL8/f2xevVqrF69GkIINDY24sCBA0hNTcXOnTvx1FNPwWAw9DiX7e7uziXjjI0SJSUlWL9+PcrKyrBnzx7M6OusSmYXOKhmVqEoNI/WEkmi2/C4LTYSabW9P7YVhX4PuOkXYyOXJElwc3PD4sWLsXjxYgDUjOjw4cPtzc+2bNmC0tJSJCQktM/KTkpKQmBgIAfZjI0wQgjs2LEDd911F1asWIHPP/+cq48cEAfVzCp8fTvm2ZqrbmtpoYDC19e6a2PMGhISqG+ApRFV5eXUFCww0KpLY4zZmEajwfTp0zF9+nTcf//9EELgzJkz7eeyn3rqKeTm5mL8+PFdzmVHR0dzaShjDqy1tRV/+MMf8MYbb+C5557DunXreOPMQXFQzaxi6lQgJAQoLqagwZTz56n78ZQpPT8mBGXxjI3PGHM0c+YA33xDvwNhYT0/Xl8PNDcDixZRVpsxNnpJkoTIyEhERkbi9ttvhxACly5dap+X/f777+OXv/wlvLy8upzLnjp1KvR6va2XzxjrgzNnzuCOO+5AU1MT9u3bh3hLTVeY3eNGZcxqvvsOePNNwMWFGhwZN9cVhQLqtjbgrruAhQs7/s+FC8C+fUB6OnUi9vAA5s+nAIXn3jNH8/XXwHvv0WM+LAxwdaU51aWldI56yRL6HXBysvVKmSPg19LRSwiBpqYmHDhwoD3Q3rdvH1paWpCYmNg+ymv27Nnw8PDgzBdjdkQIgU8//RSbNm3CDTfcgGeffRZupsaCMIfCQTWzGiGAr74Ctm8HKio6snEGA5W7rllD47aMr/1ZWcAbb1BHZC8vwNkZaGoC6uooo3333cCkSbb7fhjrLyFok2jHDppL3dxM1RfBwcDixcCKFfQ4Z6wv+LWUdaYoCo4dO9ZllFdxcTGmTJnSpWQ8ODiYg2zGbKS5uRmPP/44PvjgA7z00ku4+eab+fdxhOCgmlldRQWN2yoqon9HRACzZlHHcKOzZ2msUE0NEB3dteRbVYGCAgpEHn8cCAiw6vLZEKqupo0SV1faOBlOpaVAdjZ9TZ2OHlfx8bYptVYU4ORJ2iDS64EJE0zPrmbMEn4tZZYIIXD27Nn25mfp6ek4evQoIiIiugTZEydO5HPZjFnBiRMnsHbtWmg0GnzwwQeIjo629ZLYEOKgmtml998Htm2j89WmNvBUFTh6FFi3Drj6auuvjw3OsWPAnj0U5La2UmA5YwadJx7q6oPWVuA//wF27qT56ZJEGWO9Hpg4EbjjDpqfzpij4ddS1h9CCFRXV7eXi6elpeHgwYNwc3NrLxdPSkrCtGnToNfrOXvG2BARQuDDDz/Egw8+iLVr1+Jvf/sbnPic14jDQTWzO83NwMMPUzfw0FDztztzhrLUTz89/M3LhKCsoqrSrG1uJDVwP/wAvPsu3Z+BgR1l/eXlNKt87VoKroeCEMDbbwOff06PlYCAjsdKYyNw6hQdJXjoIcuPNcbsEb+WssEQQqC5uRkHDx5sD7LT09PR3NyMWbNmtQfac+bMgaenJwfZjA1AQ0MDHnnkEXz66ad44403sHr1av5dGqE4NGBDoqqKAhSDgcq4J0zofS61OU1NFFj3Vg7r4kKBWWvr8DV2MhiAjAwgJQUoLKQgzc+Pgr7kZAoCWd8dP06NuiQJmDy54/0eHtR4rqiIAu6wMHoMDdbJk5ShDg3terwAoJLzuDjgyBHqyr1u3eC/HmOMOQpJkuDi4oIFCxZgwYIFAOhcdl5eXvu57F/+8pcoKipCfHx8l3nZoaGhHBgw1ovc3Fzcfvvt8Pb2RlZWFiLMjb9hIwIH1cyihgZ6c3Y2HUDW1FDjsb176aw0QLedOBG46ipg+vT+f00nJzrz2tJi+XYtLXQO19zc68FqbQXeeosyqwAFfbJMI5FefZUaTm3axGe6+yMtjR4zpsamSRJljY8coW7vQxFU799Po6rMlXfLMmXL9+0DrrmmZ+DNGGOjiUajweTJkzF58mT8/Oc/hxAC586daz+X/eyzz2L9+vUIDw/vMspr4sSJ0Gg0tl4+Y3ZBVVW8++67eOSRR3DvvffiT3/6E3TDdbHK7AYH1cykwkI685qRQcGrTkcB8oIF1NwJoODoH/+gLt0BAXQWVqOh7PHRo5S5vvNOyuj2h6srNS778ksgKMj0bYSg7Pjy5QPPiPfmyy8pgxke3rWJlo8PBdyHD9OIsEcfHb41jCQtLcDBg4Cvr/nbSBJ9PCMDuOWWwZfZnz1LFQ+WEipjxtBRgooKDqoZY6wzSZIQHh6OW2+9FbfeeiuEEKipqUF6ejpSU1Oxbds2/PrXv4azs3OXIHv69OlwcnLibDYbdWpra/HAAw9g586d+PDDD7FixQr+PRglOKhmPWRkAK+/DlRWUnbWy4sCou++o4zez34GXH45jcfKygJiY7uWX3t4UIB96hSwdSv9fcyY/q1h/nzKahYVUVDbmRAU9AcGAklJg/9+TWlooLJhb2/TXan1emD8eMqqFhTwaK++aG2lWeS9lerr9XTb1tbBB9UaDT1eLBGCgm7eGGGMMcskSYK3tzeuvPJKXHnllRBCoKWlBZmZme3nsp977jk0NDRg5syZ7eXic+bMgbe3NwcXbETLycnB7bffjrCwMOTk5CAkJMTWS2JWxEG1A2tqou7J+/cDFy9SRm7mTMryWsoGWnL+PJU8NzbSmdfOr38BAcC5c9SZ29MTSE2l88XmgqRx46jL88GDwBVX9G8dEycCt91G52uPHqXg3smJvueKCvq669YBY8cO7PvsTUEBzceOiTF/G3d3Ws+xYxxU94WzM1Uh1NZafnw2NlLGeCjOycfG0u+HMXA2paKCHl/82scYY/0jSRKcnZ0xb948zJs3DwCVvubn57efy3700Udx6tQpxMfHt2ez582bh7CwMA6y2YigqipeffVVPPHEE3jkkUfw29/+FlruaDvq8E/cQZWWAi+/TAGdRkNNu1pbKYD94gtgwwZg6tT+f969e+lzJySYDkLGjqUg94svKBiJijL/uTQaKhsvKOh/UA0AS5bQLOo9eygjXltLgdmqVVSGPhRnbs1paqJArLcjMBoNndllvdPp6Of2/vvUiMxUZlhVaY70NdfQfTtYs2fTY7W4mL5mdy0tNGbryit5TjRjjA0FWZYRFxeHuLg43H333RBCoLi4uP1c9gsvvIA777wTYWFhXUrGJ02axOeymcOpqqrCpk2bcODAAXz++edYvHgxbxaNUhxUO6DGRgqoDx+mjG7njJ6qAidO0McffbR/83eFoPJub2/LZ1D9/YHcXEBRei+Z1Wio5HegYmPprb6eOoK7utLbcHNzo7UbZyibIgTdB9wBvO+Sk4Hdu6kLeExM18ePqgL5+VThMFRl/SEhwJo1wJYt1Ak8LIw2ZlSVNoVKS2k+9kA2feyZEB0bQ66uwz9yjjHGzJEkCWFhYbj55ptx8803QwiB2tpa7Nu3DykpKdi+fTt+85vfQKfTtQfZSUlJmDVrFp/LZnZt//79uOOOOzBp0iTk5OQggDvXjmocVDugzEzKFsfG9gz4ZJmClSNHgF27+hdUKwpdiPdWduvk1JGFrqkx39xJCAqEh2L+r7s7vVlLbCwFYCUlgLkJCLW1FHyb6mTNTAsOBu6+G3jtNXoMu7lRkNvcTOfYIyKAu+6i8/J9pSgUjOfm0uPX05MqLSIjKZhctowCyy+/pMZlxk0eHx/qUL9mzcjZGGltBQ4coBFwp0/T+8LCgIULgTlz6L5mjDFbkiQJXl5eWLZsGZYtWwYAaGlpQVZWVvu57BdffBG1tbWYMWNG+7zsuXPnYsyYMRxkM5tTFAXPP/88/vd//xe/+93v8PDDD3OVBYMkRG9tfIZebW0tvLy8UFNTA8+RcjVrRX/9a0eDMHNKS+nPp5823WjLFCGAxx6jQHL8ePO3KymhgDoigjLb3c9eG1VWUifw3/++f8G9vfjySzpfHhzcc+OguZnK2hcuBB58kDOB/XXxIgV/6em0MePtTVnsxMT+9QO4cIE6sB87Rj8TWe6oHpg9G7j9dmqcB1DAmZdHX0+rBaKjqepipGhqogaDKSl0P/j60uOyspLmrc+ZQxsaxvuDOT5+LWUjlaqqOH78eHvJeHp6Ok6ePIlJkyZ1OZcdHh7OQTazqoqKCtx9993Iz8/H+++/395LgDHOVDugsrLes7bu7lTeWlfX96BakujM6xtvUHmsqdJuISgguu46as6VkgJ8+mlHl+yQELqYv3SJ1nnVVeYzvfZu2TIKSL7+mjYSfH3pPqmu7ghS1q3jgHog/PyAFSvobaAuXQJeeIEC5QkTOs5EC0E/o+++o0D63nupokOvH1ifAUfx8cfUsX78+K7PD35+FHCnpVFAfddd/JhljNk3WZYRGxuL2NhYbNy4EUIIXLhwoT3Ifvnll3HPPfcgODi4y7nsuLg4bhDFhoUQAikpKdiwYQMSExORlZUFH57DyTrhZx4H5OJCga0lbW0dJdr9MWcO8P33VE47cWLXZlFC0HntwEAKot95h853NzVR8KkodHtvbwperrkGuOkmx72A12iAn/6UMvGffkrlxZJEFQI/+QllVV1cbL3K0WvPHgqoJ03q+jiXJBrhptdTJcW8eZS1HskqK+n+CAgwveHm4kJl4Pv300bXUBzJYIwxa5EkCSEhIbjxxhtx4403QgiB+vr69nPZX3zxBX7/+99Do9Fg9uzZXc5lu7i4cDabDYrBYMDf/vY3PPPMM3jqqadw3333QeY5nKwbDqod0KxZVO5qaUxQWRkwbRpdZPeHnx9wzz0dncXd3emCvKWFymZDQiig3L6dAuqkJMpql5ZS5rC+nt5iYoBbbhn8nGFbEoJGlu3YQXOxDQYKtGtrKRPKz6e209JCVRJeXuY3jtzc6GeYmjryg+rcXAqs4+LM38bHh7qgHz3KQTVjzLFJkgQPDw9cccUVuOLHTpOtra3Izs5uP5f9yiuvoKqqCtOnT+9yLtvX15eDbNZnpaWlWL9+PYqLi7Fr1y4kJibaeknMTjlwyDN6zZlDJcmnT3c0Y+qsspLet2jRwLLEMTHAb34DZGRQQFJdTcHL6tUUnHz4IVBVBcTH0+fXaGjUlnFmdG0tZbqPH7d8kW/vdu+mM9XGZmsREVQBUFpKWfqiIuDOO4dmnjLrn+pq2sTx9rZ8O29v4MwZyxtQI0FjI/1paaNHkujjxtsyxthIotfrMWfOHMyZMwcPPfQQVFXFyZMn2+dlP/HEEzhx4gQmTpzYpWQ8IiKCs46sByEEvv/+e2zcuBGXXXYZtm/fDq++nqdkoxIH1Q4oOBhYu5bOPh89Sv92c6Pzo6WlFEBcfTUF3wPl69tx5rXz+eqSEhrlFRJiPkjx9KSA88ABxw2qS0qArVvp+540qeP9Tk408qm+nrqrx8QAS5fabJmjlkZDjz9VtXw7Ve193vVICLiNY+aMRzBMEaJjxBZjjI10siwjJiYGMTEx2LBhA4QQKCsrQ1paGlJSUvD666/j3nvvRUBAQJcge/LkyXwue5Rra2vD//zP/+Dll1/Gs88+iw0bNvDGC+sVP2s4qKQkysL98AOVKFdXU6l1fDyweDF1Uh6q3//On6eqikYfhYRY/j/Ozh0dyB1RRgY1ektIMP1xY1n8rl3AkiX9P7vOBmfMGCA8nKohxowxf7uqKvpd6B40nz1L560zM6kSISiIzl7PmtXR8MyRxMfTRlh5OW2ymVJZSc8ZkydbdWmMMWYXJElCUFAQ1qxZgzVr1kAIgYaGBuzfvx+pqanYsWMH/vjHPwJA+7ns5ORkzJo1C66urlwyPkoUFRVh/fr1qK6uRnp6OhLMXQgy1g0H1Q5s0iR6M3b5dnKiC+rh3EzT6SgTZjBYPi9tMDh2WfShQxQ4W3oNDQgAzp+nzQNj6TuzDo2GjjccO0ZVA6aac1VUUICclNT1/Tt3UhVCZWXHmewjRyjAnjKFegoEBVnn+xgqPj50f3z0Ed0X3cdmNTbSeeoVK/g8NWOMARRku7u747LLLsNll10GgBpS5eTktJeMv/7666isrMS0adPaz2UnJSXBz8+Pg+wRRgiBL774Avfccw9Wr16Nf/zjH3DvbdQOY51wUD0C+Ptbb95ueDgF7mVlVAZtiqLQ2eMpU6yzpuHQ1tZ7kzWtlsqLDQbrrIl1lZxMxx927qQsbWAg/UxaWqh8v6kJWLOm6zz3Q4foPLwk0eOz8zVRaysdbXj1VeDRR6nawpFcdx1l5vfsoX8b51RfukSP0QULgFtvte0aGWPMnmm1WsyaNQuzZs3CL3/5S6iqilOnTrUH2U8++SQKCgoQHR3dpWR8/PjxXB7swFpaWvDEE09gy5YteOGFF3DbbbfxpgnrNw6qWb84O1NG7J13qAy8e6msEMCpU5QNmznTNmscCqGhQEGB5dvU1ND3b6n8mA0fJydqFBcURE3lCgro8WdsnLdsGXDZZR2BsxA0u7qhgcqlu9Pr6Yz80aMUfA+mJ4EtODvTDOoZM6gz+qlT9P7Jk4GFC2kEnKNtFDDGmC3JsoyoqChERUVh3bp1EEKgoqICaWlpSE1Nxdtvv41f/OIX8PX17RJkT5kyBTo+F+YQCgsLcccdd0BRFGRkZCC2804867O2trYuj3lVVUfdRpMkhBDW/qK1tbXw8vJCTU0NPD09rf3l2SA1N9PIrZQUKjM1Zgjr6qjE1NcXuPtuxw6qs7OBv/6VZvuaOmOrqlQyvGoVsG6d9dfHuqqtpfPVzc1U/hwb2zOALCmhrvaenpY3QnJzaePo3nuHd83DSQi6LwC6H3jDfWTi11LGbEsIgcbGRmRkZCA1NRWpqanYt28fVFVFYmJie5CdmJgINzc3zn7aESEEtm3bhl/84he49dZb8cwzz8CZd5777eTJk4iMjIRGo8GFCxewb98+XHvttbZelk1wppr1m7MznTuNiqLS23PnqOTbzY0yYsuWdS25dUSTJ1NmLyWFxpZ1vl5tawNOnKBs6I/HsIZdaSmwfz+9NTTQPPHkZFojX0vTfTBrlvmPqypVFhibklni4kLnsR2ZJNH3wRhjbPhIkgQ3NzcsWbIES5YsAUDnsg8fPtweZL/99tsoKyvD1KlT289kJyUlISAggINsG2lqasKvfvUrbNu2Da+99hquv/56/lkMwD333IOcnBzs2rUL3333HW677TZERERAURRcf/31EEKMqvuVM9VsUJqbaXxWWxtl/4KDR05WrLYWePNN4OBBOp/r5ETfJ0AzqzdssM7mwcGDtI7SUqoM0OspsG5poXLle+4xf759NFNVOiOdlgbk5dHP8+hRYOJEYMIE82fm8/Nps+Khh6y7Xsb6i19LGbN/qqrizJkz7eey09LSkJeXhwkTJrQH2ElJSYiKihp15bK2kJ+fj7Vr18LV1RVbt27FhAkTbL0kh5Obm4uVK1fiwoUL8PT0xJkzZ/Df//4XFy5cQEJCAl555RX86U9/Qmxs7KgqA+eg2sHV11OpcnExBRHBwVR2zXfr0FBVOqublUXZSxcXymJPn26deb+FhcDTT1MQHRXVdcPCYKBgMSYGeOwx/pl3ZjAA770HfPNNx4YPAKSnU3A9aRKdPe7eod54n953H41KY8ye8WspY45HCIHKysr2c9np6ek4ePAgvL29u5zLnjp1Kp/LHkJCCLz//vt46KGHcNddd+Gpp56CXq+39bIczt///nds3rwZTzzxBKZOnYoXXngBP/zwAwCqAGhsbMTrr7+OEydO4OWXX4ZWqx01GWsOqh1Yejrw4Yc01qnzTzE4mDoBL1kycrLGo9UbbwBffNGzU7WRMQi8917gJz+x/vrs1fbtFFSHhHQ9P332LLB3L3X6njSJSsaN96txAyUsDPj973mTgtk/fi1lzPEJIdDU1ISMjIz2QHvfvn1oa2tDYmJi+yiv2bNnw93dfVQEJ0Otvr4emzdvxtdff4233noLV111Fd+PA5CRkYFHHnkE9913H2644Qb84x//wIEDB/Duu+92CZxPnjyJZ599FsHBwfjtb38LAKMisOYz1Q4qI4NG/ygKlbMaNzMNBgqy33yT5lUvXmzTZbJBqK0FDhygedjmnoe0Wsq2pqdzUG3U0AB8/z2VyndvSBYeTmerjx6lhmT+/jTjua6O5laHhQEbN3JAzRhjzDokSYKrqysWL16MxT9etCmKgiNHjiA1NRVpaWl4//33UVJSgilTprRnspOSkhAUFDTiA5XBOnLkCG6//XYEBgYiKysL4eHhtl6Sw5o2bRq+/PJLuP3YwffIkSPw8/Prcbvw8HDccsstePvtt/HKK6+gsLAQmzdvRlBvTW0c3Ogoch9hWluBTz6hPydM6AioAQqyIiJorNAnn1CAMRpVVwPffgs8+STwwAPAb38LfP45UF5u65X1XX09nVk31X28Mzc3x2+sNZRyc+n8eXBwz48Z51MvWED3W0kJzXHW64Hrr6f51JMmWX/NjDHGmJFGo8G0adNw33334V//+hfOnj2LEydOYPPmzVBVFU8//TSio6ORkJCAu+66C2+99Rby8/Ohqqqtl243VFXF66+/jssuuww33HADfvjhBw6oB0mv17cH1ABw/PhxJCQkAKDNIYPB0H67+fPnQ6PR4P7774eTk9OID6gBzlQ7pGPHgNOnKXg2JyyMRgwdOkRdokeTU6do5FdhIXUqd3UFqqoo2NqxgxqMTZtm61X2zsmJNklaWy3frrWVMq6MNDRQKbe5o2iSRJ3bq6qAFSuAq6+mrHZvmxfDwWCgjR5Foay6u7v118AYY8y+SZKEyMhIREZG4rbbboMQAlVVVe3l4h988AEeeugheHh4tJeLJyUlYdq0adDpdKMum11TU4P77rsPaWlp+OSTT3D55ZePuvtguBnPTyclJQEAvvnmG7zyyit48cUXERQUhC1btmD37t3YvXt3+21GOg6qHVBFBV2MWxqnp9PROWtHyswOhaoqCqhPnaKMY+cOz6oKnDwJvPIK8Otf23/HbB8fID6ezgD7+pq+jRBUJn7dddZd23CrqaGu5wUF1GgsOJg6ckdE9N4nwPh7oShUsWGKsQdBQEDvI7aGQ0sLjWvbtYuOa6gqBfbz5lEvBFNZdsYYYwygINvHxwdXX301rr76aggh0NzcjAMHDrSP8vrb3/6G5ubmHueyPT09R3SAmZmZibVr12L8+PHIyckZFRlSWygvL4eiKPD19cUTTzyBP//5z/jVr37Vfn9fddVVuPnmm6HX66GqKiRJGtGPO4CDaodk7EwvhOUAQ4iO244WGRkUOMfH9wyoZBmIjgaOHAH27AFuu802a+wrSaK535mZVM7c/XVBCPpeQ0KAOXNss8bhkJEBbNlClRaqShsjOh01bFu8GLj11p5duzuLjaVguayM7htTamspKxwXNyzfgkXNzcDrrwO7d9P3ERBA7zt5ks6CP/MMcMUVwFVXWa/LPGOMMcclSRJcXFywcOFCLFy4EACdy87NzW0f5fXggw+iqKgIkydPbh/jlZycjJCQkBER7KiqihdffBF//OMf8fjjj+PXv/41NOZ21tmgVVRU4Ny5c7jyyitx+vRppKWlYe7cuQDoZ+Hj4wOAHoej5efAQbUDCg+n0U51deYbKjU2UiAy2o6PpKRQGa+5319JAvz8gH37gDVr7D9gmTGD1rltG5WvBwZ2zKmuqKAM9rp1tsm2DofDh4E//YmON6gqvUkS/ZxaW4H//pf+vXat+Q0lb29g0SLg3/+m34/uJdWtrdQFfN48GlNmbV9/DezcCURG0trOn6djGvX19LO9eJG+z4ICYOpUmkMeGGj9dTLGGHNcGo0GU6ZMwZQpU7Bp0yYIIVBUVNSeyX7mmWewfv16hIeHd5mXHRsb63BzhSsrK3HPPffg8OHD+Oqrr7BgwYIRsVFgz4qLi1FZWYmrrroK+/fvhyzL7Rnpzo+f0RJQAxxUO6SoKMqwHTxIGdnuz31CAGfOUFfwyZNtskSbUBQq/+4tUHZzo/Lihgb7D6olCVi9mjZH9uyh8/R1dVTivHIlBY8TJth6lUNDCOC55yjAdHcHvLwoyFRVCjjPnqWS+O+/pxJpS+X711xDRx/27KFMt58f/Z5UVdHPfdo0y4H5cGlspJJvLy/6Hi9epBnoBkNHl3dPT1qnhwfdF6+8Qg3ULB33YIwxxiyRJAnjxo3DuHHj8NOf/hRCCFRXVyM9PR2pqan46KOP8Oijj8LV1bXLvOzp06dDr9fbZZAqhMDevXuxbt06TJ06FdnZ2Sa7UbOhd8011+Djjz/G6tWrAQAGgwFa7egOK0f3d++gZBm4+WYqbz16lJoueXvTx2prgXPn6AL91lu7nike6WSZMviVlZZv19JCWXxLJcT2RJKAmTMpa33pEq3f3X3kjX3at4+CYHf3ro3XZJm+V+PPVggqibcUVDs7A3ffTZnePXsoIBcCCA2ljYh58yhotbaTJ4ELFzo2Qk6dApqauo5N02pprTU11Bfg6FEgJwf4saqKMcYYGzRJkjBmzBisXLkSK1euhBACLS0tyMzMbC8Zf/bZZ9HY2IhZs2a1n8ueM2cOvLy8bB5kK4qCZ555Bn/961/xP//zP3jwwQcdLsPu6IwBtaIooz6gBjiodliRkcCDD1JZ8JEjQHExvd/VlRo6XXstEBNj0yVanSTR2eKtWymza+75vqKCzuY6WlAqSeYblo0EaWmURTbXpEuno2C5qopKpnuj19OZ9PnzabNJVelnbsvn/dZWykrrdJS1Li2l4L77Y1WWqUGbkxN9bN8+DqoZY4wNH0mS4OzsjHnz5mHevHkA6GxsXl5ee5D98MMP48yZM4iPj++SzQ4LC7NqkF1WVoaNGzfi9OnT+P7779vP8jLbGE0l3pZwUO3AIiKAzZuBoqKOICMoCBg/3vplrfYiKYnKg0+dMn0/lJRQxnPBguFfy6VLNNbLYKBKgpgY82e9GR1Z6C3gdXOj+7Wxse+fV5Y7KjlszdOTNgYaGzsCbBeXnrdTlI73u7ryHHLGGGPWJ8sy4uPjER8fj3vuuQdCCBQXFyMlJQWpqan4xz/+gY0bN2Ls2LFdguzY2NhhCbSEENi9ezfWr1+PBQsW4KOPPsKYMWOG/OswNhAcVDs4SaIyWHsfD2UtYWHAHXcAb7xBZbMBARSUtLRQubyLC3DTTcCUKcO3htpaajSVnk5nZgHKmkZF0Tno2bNH76aHJR4e9PNpaDBfmq2qVBrtqA34Jkygx8Hx45SRl2UKoDvP1G5upn8bM/atrfZ/9p8xxtjIJ0kSwsLCcMstt+CWW26BEAK1tbXYu3cvUlJS8Mknn+Dxxx+HXq9vD7KTkpIwc+ZMODk5DSqbbTAY8Oc//xkvvPAC/vrXv+Kee+7hcm9mVzioZiPO3LlUJr17N3DgAJ3D1emoDHjRIjpnO1xBbV0d8Pzz1EQuMJDOxGo0FCieOAG8+CJlKZcsGZ6v7yiMc6I7/xyio6mBV20tBdfds9ZCUMbW25vKuh2RRgMsX05nq2trgTFj6HsyNiFrbQWqq+l4h48PbSI0NdGZesYYY8yeSJIELy8vLF++HMuXLwcAtLS0ICsrq73L+PPPP4+6ujrMnDmz/Vz23Llz4e3t3ecgu7i4GOvXr0d5eTlSUlIwffr04fy2GBsQSQjj5a311NbWwsvLCzU1NfB0tIOtzKHU1lLnaGdnCmCGO0O8bRvw3ns0K9lUI7SzZ+n9f/wjZdFHE1Wl6oG9e2k8mKpS5jY5mbpxnzgBPPkkHWW4dImys25ulM1tbqafZXMzVSL8/ve2/m4GTghgxw7gP/+h7/ncuY5Sb1mmZmozZlB1w4kTtEH0hz+M7PP0bGD4tZQxZu9UVUVBQUF7kJ2eno7CwkLExcV1KRkPDw/vEWQLIfD111/j7rvvxooVK/DPf/4THrboMspYH3BQzdgQaWgAHn+cgvixY03fxhhYbthApeCjhcFADeR27KBsrLc3bXDU1NDH582jYPn994Evv6TbGM9OC0EZXlmmKoP//V/z968jOXuWjgh88AGVg7u60rn74GDKTpeVUbXDXXcBvCnPTOHXUsaYoxFC4MKFC0hNTUVKSgrS09Nx6NAhBAcHt5eLJycnIzo6Gk8++STefPNN/OMf/8Add9xh847jjFnCQTVjQ6SggDKK48ZZnil8/DhlIh9+2GpLs7nt24F33wVCQqisubP6emroduWVNAbu/fcp2Kys7Gjm5epKM9k3bKAy8ZFEVSl7v3s3ZaaNTcpmz6ZjAuPH23qFzF7xayljzNEJIVBXV4d9+/a1dxnPyMhAS0sLQkND8eWXXyIuLs7Wy2SsV3ymmrEhYmyi1VvDS42GAsXRor6eOrJ7efUMqAGaSx0aSoHllVcCd94JLF0KZGVRYO3sTOX0CQmOM1u8P2SZMvVJSUB5OW0keHraT8dyxhhjbLhIkgRPT08sXboUS5cuBQC0trZi69atWLx4MSIiImy7QMb6iINqxoaInx91ra6uBvz9Td9GCCppdtTu1QNx7Bhw4YLluem+vjTu7NAhCrAjIuhtNJFlGonHGGOMjWZ6vR533HGHrZfBWL9wL3rGhoi/PzBrFgWQqmr6NlVV1HwrMdG6a7OlhgbaTOg8Nqo7SaK3hgbrrYsxxhhjjLGhwEE1Y0No+XKalZ2fT7OxjYzjoIqLaaxXVJTt1mhtxpJtRTF/GyHobSSWdzPGGGOMsZGNy78ZG0LjxgH33Qe89RZw6hSdnZZlCii9vYFVq4Cbbx7+0V72ZNIkKu+uqDBf3lxbS2eruRcJY4wxxhhzNBxUMzbEYmKoC/jhw9TNuaWFgsoZM+i88GgKqAFqTjZ/PvDJJ3Tm3M2t68dbW4EzZ+g2EybYZImMMcYYY4wNGAfVjA0DJyc6Nz2azk5bsmYNcPEijcrS66mpmyTRGfO6OprDvHbt6NtwYIwxxhhjjo+DasbYsHN1BX7+c2DqVGDPHuDcOTpDHRQE3HQTZal5zC5jjDHGGHNEHFQzxqzC2Rn4yU+oUVt1NQXVXl6Wu4IzxhizvdbWVuj1elsvgzHG7BZ3/2aMWZVGQ2fM/fw4oGaMMUfwzDPPwNfXF0uXLsUzzzyDwsJCWy+JMcbsCgfVjDHGGGPMrAceeABbt27FrFmzsG3bNkRHRyMsLAyffPIJhBC2Xh5jjNkcl38zxhhjjDGThBBwdnbGsmXLsGzZMnz11Vf485//DI1Gg8WLF0PiDpOMMcaZasYYY4wx1pOqqpAkCZIkoaCgADfeeCMeeeQR3HLLLfj++++xb98+vPnmmzhx4oStl8oYYzbFQTVjjDHGGOtBlmU0NjbixRdfxLJly+Dt7Y1vv/0WmzZtQkNDA06ePIlvv/0Wa9aswV//+lcYDAZbL5kxxmxCEjY4DFNbWwsvLy/U1NTAk+foMMYYY/3Gr6VsOCmKgv379+PZZ59Fbm4unn32WSxduhSqqkKWZaiqCiEENBoNACA2Nha7du1CUFCQjVfOGGPWx5lqxhhjjDHWxbvvvov58+fj0qVLSE1NxdKlSwFQ9tr4pzGgPnPmDJydnXlzhzE2anFQzRhjjDHGuvD398eKFStw6NAhhISEYOrUqdiwYQMOHz4MIUR7qXdxcTFeeuklxMXFwdXV1carZowx2+CgmjHGGGOMdbFy5Up88cUXuHjxIvLy8nDffffh9OnTyM7OhqIo0Gq12LlzJzZu3Ai9Xo/nn38eAHjEFmNsVOIz1YwxxpgD4tdSNpwURYEQAlqt6emrb775Jr7++mvcddddWLRoEXQ6nZVXyBhj9oMz1YyNIooCnDsHnDwJlJUBnFBgjDFmikaj6RJQK4rS3pxsy5Yt2LhxI7y8vBAZGckBNWNs1DO9/cgYG1EUBUhLA3buBE6dAgwGwNkZSEgALr8ciI+39QoZY4zZM2NTMgBYvXo13n//feTm5uLGG29EbGwsXnrpJa6YYIyNWlz+zdgIpyjA1q3AF18AsgwEBQF6PdDQAFy4AHh5AevXA/Pm2XqljLH+4NdSZi9aW1uh0+kgSZKtl8IYYzbBmWrGRrh9+yigDggAfHw63u/sTP8+fRrYsgWIjARCQmy3TsYYY47DWAouyzL0er2tl8MYYzbFZ6oZG4GEoDdVBXbvpvd1DqiNJImC6YoKYP9+666RMcaY4zLOqebsNGOMcaaasRGjpQXIzqaz02fPAhoNEB4OHDwIhIWZ/3+SBHh40O2uvdZ662WMMcYYY2wk4KCasRGgthZ49VUgI4OCZC8vylJ/8w1QUAC0tQFjxtDHTNHpKChnjDHGGGOM9Q8H1Yw5OCGAt9+mDHV0NODq2vExf3+gqAjIzaWgevx405+jvh6YONEqy2WMMcYYY2xE4TPVjDm4wkIq3Y6I6BpQA4CTExAVRZnqkyepE3h3LS30/uRkqyyXMcYYY4yxEYWDasYc3KFDlGk2N1EnMhLw8wPOnwfKyrp+rLmZysOnTAGmTx/+tTLGGGOMMTbScPk3Yw6upoaakpk7L+3tDcyZA3z7LXD8OAXgej3Q2EgfnzYNuPvunlluxhhjjDHGWO84qGbMwbm6mi7r7szHB4iPB5YtA+rqgIYGOm89ezYwdSrNrGaMMcYYY4z1HwfVjDm4uDg6O93YaD7bXFoKjBsH3H474O5u3fUxxhhjjDE2kvGZasYcXFwcvRUWAgZDz4/X1QHV1cCiRRxQM8YYY4wxNtQ4U82Yg9NqgQ0bgBdfBI4dozPU3t40p7q8nErDf/IT4KqrbL1SxhhjjDHGRh4OqhkbAUJCgIceAlJSgD17gKoqalw2aRJlqJOSqDkZY4wxxhhjbGhxUM3YCOHjA1xzDbB8OXUEl2V6n8yHPBhjjDHGGBs2HFQzNsI4OQEBAbZeBWOMMcYYY6MD57AYY4wxxhhjjLEB4qCaMcYYY4wxxhgbIA6qGWOMMcYYY4yxAeKgmjHGGGOMMcYYGyAOqhljjDHGGGOMsQHioJoxxhhjjDHGGBsgDqoZY4wxxhhjjLEB4qCaMcYYY4wxxhgbIA6qGWOMMcYYY4yxAeKgmjHGGGOMMcYYGyAOqhljjDHGGGOMsQHioJoxxhhjjDHGGBsgrS2+qBACAFBbW2uLL88YY4w5PONrqPE1lTHGGGO2YZOguq6uDgAwduxYW3x5xhhjbMSoq6uDl5eXrZfBGGOMjVqSsMEWt6qqKCkpgYeHByRJsvaXZ4wxxhyeEAJ1dXUICQmBLPNpLsYYY8xWbBJUM8YYY4wxxhhjIwFvbTPGGGOMMcYYYwPEQTVjjDHGGGOMMTZAHFQzxhhjjDHGGGMDxEE1Y4wxxhhjjDE2QBxUM8bwr3/9C87OziguLm5/38aNG5GQkICamhobrowxxhhjjDH7xt2/GWMQQmDatGlYsGABXnjhBTz55JN4/fXXsW/fPoSGhtp6eYwxxhhjjNktra0XwBizPUmS8L//+7+4/vrrERISgueeew4pKSntAfW1116LXbt24bLLLsNHH31k49UyxhhjjDFmPzhTzRhrN2PGDBw7dgzffPMNFi1a1P7+nTt3or6+Hu+88w4H1YwxxhhjjHXCZ6oZYwCAHTt2ID8/H4qiIDAwsMvHlixZAg8PDxutjDHGGGOMMfvFQTVjDFlZWbjhhhvwyiuvYNmyZXjiiSdsvSTGGGOMMcYcAp+pZmyUO3PmDFauXIlf//rXuO222xAXF4fExERkZmZi5syZtl4eY4wxxhhjdo0z1YyNYpcuXcKKFSuwatUqPP744wCAmTNn4uqrr8ZvfvMbG6+OMcYYY4wx+8eZasZGMR8fH+Tl5fV4//bt222wGsYYY4wxxhwPd/9mjPVq2bJlyMrKQkNDA3x8fPDJJ58gMTHR1stijDHGGGPM5jioZowxxhhjjDHGBojPVDPGGGOMMcYYYwPEQTVjjDHGGGOMMTZAHFQzxhhjjDHGGGMDxEE1Y4wxxhhjjDE2QBxUM8YYY4wxxhhjA8RBNWOMMcYYY4wxNkAcVDPGGGOMMcYYYwPEQTVjjDHGGGOMMTZAHFQzxhhjjDHGGGMDxEE1Y4wxxhhjjDE2QBxUM8YYY4wxxhhjA8RBNWOMMcYYY4wxNkD/H9qkXEiHD+1sAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"![](\\\"media/mlp.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Input layer**\\n\",\n \"- The size of the input layer corresponds to the size of the input vectors, $d_\\\\text{in}$. \\n\",\n \"- This layer simply passes the input to the first hidden layer without applying any transformation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**First hidden layer**\\n\",\n \"- This layer has $h_1$ nodes. \\n\",\n \"- Each node connects to all the $d_{\\\\text{in}}$ inputs.\\n\",\n \"- It applies a weight matrix $W_\\\\text{in} \\\\in \\\\mathbb{R}^{d_\\\\text{in} \\\\times h_1}$ that transforms the input from vectors in $\\\\mathbb{R}^{d_\\\\text{in}}$ to vectors in $\\\\mathbb{R}^{h_1}$.\\n\",\n \"- Then, it applies a nonlinear activation function $\\\\sigma$, and outputs $h_1$ intermediate results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Second hidden layer**\\n\",\n \"\\n\",\n \"- This layer has $h_2$ nodes. \\n\",\n \"- It takes the $h_1$ outputs from the first hidden layer. \\n\",\n \"- It applies another set of weights and a nonlinear activation function to produce $h_2$ intermediate results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Output Layer**\\n\",\n \"- The output layer has $d_\\\\text{out}$ units. \\n\",\n \"- It takes the $h_2$ outputs from the second hidden layer, applies weights and possibly a different activation function that depends on the task (usually, softmax for classification or no activation for regression).\\n\",\n \"- The final output vector is of size $d_\\\\text{out}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### MLP for time series forecasting\\n\",\n \"\\n\",\n \"- The MLP can be used as a windowed technique for time series forecasting.\\n\",\n \"- The sequence of past values $x(t-P), \\\\dots, x(t-1), x(t)$ represents the input ($d_\\\\text{in} = P$).\\n\",\n \"- $x(t+\\\\tau)$ is be the output that the model learns to predict ($d_\\\\text{out} = 1$).\\n\",\n \"- We can also train the MLP to predict a sequence of $d_\\\\text{out} = H$ future predictions, e.g., $x(t+\\\\tau), x(t+\\\\tau+1), \\\\dots, x(t+\\\\tau+H)$.\\n\",\n \"\\n\",\n \"![](\\\"media/mlp_windowed.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Key for the training of the MLP is the creation of input-output pairs $\\\\{\\\\mathbf{x}_i, \\\\mathbf{y}_i\\\\}$ used for training.\\n\",\n \"- Given the input $\\\\mathbf{x}_i$ the model must learn to predict the output $\\\\mathbf{y}_i$.\\n\",\n \"- This is done by adapting the model weights to minimize the discrepancy between the prediction $\\\\mathbf{\\\\hat y}_i$ and the desired output $\\\\mathbf{y}_i$.\\n\",\n \"- The weights are modified according to the gradient taken with respect to a loss function $\\\\mathcal{L}(\\\\mathbf{\\\\hat y}_i, \\\\mathbf{y}_i)$ such as the MSE, e.g., $W \\\\leftarrow W - \\\\delta \\\\frac{\\\\partial \\\\mathcal{L}}{\\\\partial W}$.\\n\",\n \"- $\\\\delta$ is a small constant defining the gradient step (learning rate).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Given the chunk of time series used for training, the training samples are generated according to the scheme in the picture.\\n\",\n \"- Each input-output pair consists of:\\n\",\n \" - a window of past time series values $x(t-P), \\\\dots, x(t-1), x(t)$.\\n\",\n \" - a window of future values $x(t+\\\\tau), x(t+\\\\tau+1) \\\\dots, x(t+\\\\tau+H)$.\\n\",\n \" \\n\",\n \"![](\\\"media/data_split.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Python example\\n\",\n \"\\n\",\n \"- In this example we will consider a time series of electricity consumption registered on a backbone of the energy distribution network of Rome.\\n\",\n \"- The original time series has 10min resolution.\\n\",\n \"- For this example, we will resample it to 1h resolution as it will become more smooth (and easier to predict).\\n\",\n \"- To train our models faster, we will consider only the first `3000` samples.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded ElecRome dataset.\\n\",\n \"Data shape:\\n\",\n \" X: (137376, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"ts_full = PredLoader().get_data('ElecRome')\\n\",\n \"\\n\",\n \"# Resample the time series to hourly frequency\\n\",\n \"ts_hourly = np.mean(ts_full.reshape(-1, 6), axis=1)\\n\",\n \"\\n\",\n \"# Use only the first 3000 time steps\\n\",\n \"time_series = ts_hourly[0:3000]\\n\",\n \"time_steps = np.arange(0, len(time_series))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Next we split the data in train and test set.\\n\",\n \"- We simply use the first 90% as training and the rest as test.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Split the time series into training and test sets\\n\",\n \"train_size = int(0.9*len(time_series))\\n\",\n \"tr = time_series[:train_size]\\n\",\n \"te = time_series[train_size:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Next, we define the function to create input-output pairs.\\n\",\n \"- For example, we create as input windows of size $P=12$ by specifying `window_size=12`.\\n\",\n \"- Similarly, we create as ouput windows of size `forecast_horizon=12`.\\n\",\n \"- If we are interested in predicting only one sample, e.g., $\\\\tau=12$ and $H=1$, we simply take the last element of the output window.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def create_windows(data, window_size, forecast_horizon):\\n\",\n \" X, y = [], []\\n\",\n \" for i in range(len(data) - window_size - forecast_horizon + 1):\\n\",\n \" X.append(data[i:(i + window_size)])\\n\",\n \" y.append(data[i + window_size:i + window_size + forecast_horizon])\\n\",\n \" return np.array(X), np.array(y)\\n\",\n \"\\n\",\n \"# Define window size and forecast horizon\\n\",\n \"window_size = 12\\n\",\n \"forecast_horizon = 12\\n\",\n \"\\n\",\n \"# Create input-output pairs\\n\",\n \"X_train, y_train = create_windows(tr, window_size, forecast_horizon)\\n\",\n \"X_test, y_test = create_windows(te, window_size, forecast_horizon)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We can see how the input-output pairs look like in the following plot.\\n\",\n \"- The blue dots will represent the input $x_{t-P}, \\\\dots x_{t-1}, x_t$ with $P=12$.\\n\",\n \"- The orange dot is the output $x_{t+\\\\tau}$ with $\\\\tau=12$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"MLPRegressor(hidden_layer_sizes=(16, 8), max_iter=1000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
MLPRegressor(hidden_layer_sizes=(16, 8), max_iter=1000)
![](\\\"media/word_pred.gif\\\")
\\n\",\n \"\\n\",\n \"- In the first case, we need to go back 4 time steps to retrieve the information we need.\\n\",\n \"- In the second case, 9 steps.\\n\",\n \"- How to set $P$? What is the maximum memory we will ever need? \\n\",\n \"- As discussed previously, setting $P$ too high will smooth our data too much.\\n\",\n \"- Finding a good tradeoff is difficult...\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Recurrent Neural Networks\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- RNNs are a class of neural networks designed to recognize patterns in sequences of data, such as time series data, natural language, or sequences of images. \\n\",\n \"- Unlike traditional neural networks, RNNs have a memory that captures information about what has been calculated so far, essentially allowing them to make predictions based on past inputs.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- As an RNN processes a sequence $\\\\mathbf{x}$ it maintains information in a *hidden state* $\\\\mathbf{h}$. \\n\",\n \"- This process allows the RNN to use previous computations as a context for making decisions about new data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"![](\\\"media/RNN.gif\\\")
\\n\",\n \"\\n\",\n \"- $x(t)$ is the input time series, $\\\\mathbf{h}(t)$ the hidden state vector, $y(t)$ the output.\\n\",\n \"- The matrices $W_i$, $W_h$, and $W_o$ represent the input, hidden (recurrent), and output weights respectively.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Back Propagation Through Time (BPTT)**\\n\",\n \"\\n\",\n \"- BPTT is the algorithm used for training RNNs. \\n\",\n \"- It involves unfolding the RNN through time to obtain a standard feed-forward neural network (like an MLP). \\n\",\n \"- After unfolding, one can apply the standard backpropagation algorithm. \\n\",\n \"- BPTT computes gradients for each parameter across *all* time steps of the input sequence. \\n\",\n \"\\n\",\n \"![](\\\"media/bptt.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Challenges and shortcomings in RNNs\\n\",\n \"\\n\",\n \"- In BPTT the gradient has to go through all time steps and, due to the presence of nonlinearities, it can become too small and not reach distant time steps.\\n\",\n \"- This creates the problem called *vanishing gradient*. \\n\",\n \"- Opposed, yet similar, is the problem of *exploding gradient* occurring when the gradients grow across the sequence. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The vanishing gradient problem makes it hard for RNNs to learn long-range dependencies because updates to the weights become insignificantly small, causing the learning to stall. \\n\",\n \"- Conversely, exploding gradients can cause weights to oscillate or diverge.\\n\",\n \"- Techniques such as gradient clipping and gated units (e.g., LSTM, GRU) have been developed to mitigate these issues.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- Another important limitation of the RNNs is that they fail to exploit hardware acceleration like other neural nets.\\n\",\n \"- This is due to their recurrent nature that requires computations to be done sequentially rather than in parallel.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Reservoir Computing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- RC is a familily of randomized RNNs, popularized in machine learning by Echo State Networks (ESNs).\\n\",\n \"- RC and ESNs are terms ofen used interchangeably.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- There are two main differences that separate an ESN from an RNN:\\n\",\n \"\\n\",\n \"![](\\\"media/Reservoir.gif\\\")
\\n\",\n \"\\n\",\n \"- The output weights $W_o$ is the only part of the ESN that is trained.\\n\",\n \"- Optimizing $W_o$ can be done with a simple linear regression algorithm.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"The workflow is the following.\\n\",\n \"\\n\",\n \"1. Generate a sequence of reservoir states $\\\\mathbf{H} = \\\\{\\\\mathbf{h}(0), \\\\mathbf{h}(1), \\\\dots, \\\\mathbf{h}(T)\\\\}$.\\n\",\n \"\\n\",\n \"- This is done by applying the state update equation \\n\",\n \" \\n\",\n \" $$\\\\mathbf{h}(t) = \\\\sigma \\\\left(\\\\mathbf{W}_i x(t)+\\\\mathbf{W}_h \\\\mathbf{h}(t-1)\\\\right)$$ \\n\",\n \" \\n\",\n \" for each time step $t=1,2, \\\\dots, T$ of the input sequence.\\n\",\n \" \\n\",\n \"- The nonlinearity $\\\\sigma$ is usually an hyperbolic tangent ($\\\\texttt{tanh}$).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"2. Apply a linear regression algorithm to compute a linear mapping $g(\\\\cdot)$ between the reservoir states and the desired output sequence $\\\\mathbf{y} = \\\\{y(1), y(2), \\\\dots, y(T) \\\\}$: \\n\",\n \"\\n\",\n \"$$\\\\mathbf{y} = g(\\\\mathbf{H})$$\\n\",\n \"\\n\",\n \"- The function $g(\\\\cdot)$ is called *readout*.\\n\",\n \"- In a forecasting setting, $y(t)$ correspond to a future value of the input, e.g., $y(t) = x(t+\\\\tau)$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Readout \\n\",\n \"\\n\",\n \"- The readout $g(\\\\cdot)$ is usually implemented through a linear regression model, e.g., Ridge Regression.\\n\",\n \"- In this case $g(\\\\cdot)$ corresponds to a weight matrix $\\\\mathbf{W}_o$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Besides the linear readout, any other regression model can be used, including an MLP.\\n\",\n \"\\n\",\n \"![](\\\"media/Readout.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Note that fitting an MLP to the readout states is different from the window approach we saw before.\\n\",\n \"- Window approach:\\n\",\n \" - The model predicts a future value from the fixed amount of temporal information contained in the window \\n\",\n \" \\n\",\n \" $$x(t+\\\\tau) = g\\\\big([x(t-P), \\\\dots, x(t-1), x(t)]\\\\big)$$\\n\",\n \"\\n\",\n \"- Reservoir approach:\\n\",\n \" - The model predicts a future value from a single Reservoir state \\n\",\n \" \\n\",\n \" $$x(t+\\\\tau) = g(\\\\mathbf{h}(t))$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Why the Reservoir approach works\\n\",\n \"\\n\",\n \"- The Reservoir extracts a rich pool dynamical features from the time series.\\n\",\n \"- These are embedded into the high-dimensional Reservoir state $\\\\mathbf{h}(t)$.\\n\",\n \"- Contrary to a fixed window, $\\\\mathbf{h}(t)$ maintains a memory of all the previous inputs, back to the origin of the series $x(0)$.\\n\",\n \"- Some of the features are relevant for the task at hand, while others are not.\\n\",\n \"- The task of the readout is to select those features relevant for the task.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Say, we want make a forecast $\\\\tau_1$ steps ahead.\\n\",\n \"- The readout will select a certain combination of dynamical features from the Reservoir.\\n\",\n \"\\n\",\n \"![](\\\"media/Reservoir_pred1.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- To predict at a different horizon $\\\\tau_2$ the readout will select a different group of features.\\n\",\n \"- Note that in both cases the Reservoir produces always the same pool of features!\\n\",\n \"\\n\",\n \"![](\\\"media/Reservoir_pred2.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The Reservoir is untrained and its states are generated without *supervision*, i.e., without an external guidance.\\n\",\n \"- Since it does not know what task it will have to solve, the Reservoir is configured to produce a pool of dynamic features that is most rich and varied as possible.\\n\",\n \"- In other words, the Reservoir trades the lack of training with a redudancy of generated features.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Reservoir configuration\\n\",\n \"\\n\",\n \"- There are several hyperparameters that can be configured in the Reservoir.\\n\",\n \"- We review the main ones in the following.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Spectral radius\\n\",\n \"\\n\",\n \"- Cleary, the contribution of more recent inputs must have a stronger influence on the current state.\\n\",\n \"- This means that the Reservoir should gradually forget its past states.\\n\",\n \"- This property, called *echo state property*, gives the name to the ESN.\\n\",\n \"- It ensures to not model noise, to forget sporadic shocks and the initial state $\\\\mathbf{h}(0)$ that is uninformative.\\n\",\n \"- In control theory, this translate in having a dynamic that is *contractive*.\\n\",\n \"- A dynamic is contractive if two initially different states converge eventually.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- On the other hand, we want the Reservoir to produce a rich pool of features.\\n\",\n \"- If the dynamics of the Reservoir is too *conctractive* this does not happen.\\n\",\n \"- We have to find a sweet spot by tuning a parameter $\\\\rho$ called *spectral radius*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"![](\\\"media/dynamics.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Recall the state update equation \\n\",\n \"\\n\",\n \"$$\\\\mathbf{h}(t) = \\\\sigma \\\\big(\\\\mathbf{W}_i x(t)+\\\\mathbf{W}_h \\\\mathbf{h}(t-1)\\\\big)$$\\n\",\n \"\\n\",\n \"- The spectral radius is the largest eigenvalue of the state transtion matrix $W_h$.\\n\",\n \"- We can *set* the spectral radius by computing the largest eigenvalue $\\\\lambda_\\\\text{max}$ of $W_h$ and then letting $W_h = \\\\rho \\\\frac{W_h}{\\\\lambda_\\\\text{max}}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- A rule of thumb is to set $\\\\rho$ just below 1.\\n\",\n \"- However, to achieve good performance it is often necessary to fine-tune $\\\\rho$ to values that can be lower or even higher than 1.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Another way of determining a good value of $\\\\rho$ is to look at the *transient* phase of the Reservoir.\\n\",\n \"- This is how much time it takes to forget the initialization.\\n\",\n \"- Assume two different inizializations of the Reservoir state: $\\\\mathbf{h}_1(0)$ and $\\\\mathbf{h}_2(0)$.\\n\",\n \"- If the Reservoir dynamics is contractive, the effect of the different initalizations will enventually fade.\\n\",\n \"- If it is chaotic, it will persist.\\n\",\n \"- Ideally, we want to set the Reservoir to operate at the *edge of chaos*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's check this with a practical example.\\n\",\n \"- We consider two intializations, $\\\\mathbf{h}_1(0) = [0,0,\\\\dots,0]$ and $\\\\mathbf{h}_2(0) = [1,1,\\\\dots, 1]$.\\n\",\n \"- For this example, we set the input $x$ to be always zero to not let it affect the evolution of the Reservoir state.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Initial states\\n\",\n \"initial_state_0 = np.zeros((1, 100), dtype=float) \\n\",\n \"initial_state_1 = np.ones((1, 100), dtype=float)\\n\",\n \"\\n\",\n \"x = np.zeros((1, 100, 1)) # Zero input, it does not contribute to the state\\n\",\n \"rhos = [.3, 0.99, 1.3] # We will use three different spectral radii\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_states_evolution(x, rhos, h0, h1):\\n\",\n \" plt.figure(figsize=(5, 3))\\n\",\n \" for rho in rhos:\\n\",\n \" res = Reservoir(spectral_radius=rho) # initialize the reservoir with a given spectral radius\\n\",\n \" states_0 = res.get_states(x, bidir=False, initial_state=h0) # states using initial state 0\\n\",\n \" states_1 = res.get_states(x, bidir=False, initial_state=h1) # states using initial state 1\\n\",\n \" plt.plot(np.linalg.norm(states_0 - states_1, axis=(0,2)), label=f'rho={rho}') # L2 norm of the difference\\n\",\n \" plt.legend(loc='upper right', ncol=3)\\n\",\n \" plt.xlabel('$t$')\\n\",\n \" plt.ylabel('$\\\\|\\\\mathbf{h}_1(t) - \\\\mathbf{h}_2(t)\\\\|_2$')\\n\",\n \" plt.grid()\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.title('Transient phase of the Reservoir')\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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zwQSUsLEzR6/VKRESE0q1bN+XLL7+0rGPu2bpt27Z8zy1sm6dPn1Z69uyp+Pr6KoBSpUqVosKmKMq13s4zZsxQatSooej1eqVu3brKTz/9lG89W8pgTXzj4uKU4cOHK3Xr1lW8vb0VHx8fpXHjxsrHH3+s5Obm5tvH0qVLla5duyp+fn6KwWBQqlSpotx///3KihUrin1tiqIo+/btU/r166f4+/sr7u7uSpMmTfK9xzfGwRorVqxQmjVrphgMBgVQHn30UUVRrn0uLl26lG/9wnomK4qifPfdd0qbNm0Ub29vxdPTU6lRo4byyCOPKNu3by92/0W9F4qiKJmZmUrlypWVWrVqWeJozWfs1VdfVVq2bKkEBgYqBoNBqV69ujJ27FglISEh3/ZL83m93r///qsACqAcPXq00HWWLl2qtGnTRvHw8FC8vb2V7t27Kxs2bCg0Fjf2+i7quy/KlkZRFKWcjw2EuOVpNBpGjx7N559/7uyiCCFuctLrWwghhHBhkqiFEEIIFyadyYQoA3JGSQjhKFKjFkIIIVyYJGohhBDChUmiFkIIIVzYLXWO2mQyERsbi6+vr0x1J4QQwmUpikJqaiqRkZH5JqYpzC2VqGNjYwtc0UcIIYRwVWfPni1xSuFbKlH7+voC6gv38/Mr9faMRiP//vsvPXv2RK/Xl3p7twuJm/0kdvaRuNlPYmef0sYtJSWFSpUqWfJWcW6pRG1u7vbz83NYovby8sLPz08+wDaQuNlPYmcfiZv9JHb2cVTcrDlNK53JhBBCCBcmiVoIIYRwYZKohRBCCBd2S52jvl3l5eVhNBqdXQwLo9GIm5sbWVlZ5OXlObs4NxWJnX0kbvaT2NmnpLjp9Xp0Op1D9iWJ+iamKAoXL14kKSnJ2UXJR1EUIiIiOHv2rIxnt5HEzj4SN/tJ7OxjTdwCAgKIiIgodVxdKlFXrVqVM2fOFFg+atQovvjiCyeUyLWZk3RYWBheXl4u8yUzmUykpaXh4+NT4kB+kZ/Ezj4SN/tJ7OxTXNwURSEjI4P4+HgAKlSoUKp9uVSi3rZtW74mhP3799OjRw8eeOCBci/L36f/ZuHhhQRkBdCHPuW+/5Lk5eVZknRwcLCzi5OPyWQiJycHDw8P+eLbSGJnH4mb/SR29ikpbp6engDEx8cTFhZWqmZwl0rUoaGh+f6fMmUKNWrUoHPnzuVelsuZl9kat5UG+gblvm9rmM9Je3l5ObkkQgghCmP+fTYajbdOor5eTk4OP/74I+PGjSuySTc7O5vs7GzL/ykpKYAalNJ2rgr3CAfgiumKS3XUMjMajSiKgqIomEwmZxcnH/O1mF2xbK5OYmcfiZv9JHb2sSZu5t/owhK1LXnFZRP10qVLSUpKYvjw4UWu89577zFp0qQCy//9999S1zTj8uIANVFHR0eXaltlwc3NjYiICNLS0sjJyXF2cQqVmprq7CLctCR29pG42U9iZ5/i4paTk0NmZiZr164lNzc332MZGRlW70OjmA8LXEyvXr1wd3fnjz/+KHKdwmrUlSpVIiEhodRTiGYYM+i4sCMAK+9ZSaBXYKm252hZWVmcPXuWqlWr4uHh4ezi5GO+Koy1VzFbvXo13bt35/LlywQEBJR9AV2YrbG72ZTVe32rx60sOTN2N/N335q4ZWVlcfr0aSpVqlTgdzolJYWQkBCSk5NLzFcu2XPgzJkzrFixgscff7zY9QwGg2Ve7+vn99br9aW++Xv5E2AIACA+O94h23T0TaPRoNVqXe5m/tDaUj6g3Mt57tw5BgwYgK+vL2FhYYwZM4bc3Nxin/P0009Tq1YtvL29CQ8P59577+Xo0aNOjd3NdiuL97qkuNnzXp86dYr77ruP8PBwAgICePjhh7l06VK+dXbv3k2vXr0ICgoiNDSUp556ioyMDKfH2JGxuxk/DyXdxo4dS6tWrfD09KR58+ZWPaeo735JcdNoNEX+hlvLJRP17NmzCQsLo2/fvk4tR5R3FACxabFOLcetzhlN93l5efTt25f09HTWr1/P/PnzWbRoES+88EKxz2vRogWzZ8/m0KFD/PPPPyiKQs+ePWWiCCvdLO91eno6PXv2RKPR8N9//7FhwwZycnLo16+f5XxkbGwsd955JzVr1mTLli38/fffHDhwoNjTdSI/Z522UxSFkSNH8tBDD1n9nMK++wMHDiyf777iYvLy8pTKlSsrr7zyis3PTU5OVgAlOTnZIWUZ+99YpeGchsp3e79zyPYcKTMzUzl48KCSmZnp7KIUkJeXp1y5ckXJy8sr9PHOnTsro0ePVsaOHasEBwcrnTp1UgBlxYoVSosWLRRPT0+lXbt2yuHDh/M9b8aMGUr16tUVvV6v1K5dW/nhhx/sLuOyZcsUrVarnD9/3rLs559/VgwGg02fnz179iiAcvz4cbvLcr2SYnezKa/3uri42fNe//PPP4pWq833eGJiogIo0dHRiqIoyldffaWEhYXl2+euXbsUQDl27FjJwXER5fmZc4Xv/vUmTJigNGnSxK7nmt/ro0ePFrlOcb/TtuQrl6tRr1ixgpiYGEaOHOnsohDlc3PVqBVFISMnt9xvih3dHL7//nvc3NzYsGEDgwYNAuCNN97gww8/ZPv27bi5ueX7DCxZsoTnn3+eF154gf379/Pkk08yYsQIVq1aZVmnd+/e+Pj4FHsz27RpEw0bNiQyMtKyrFevXmRnZ7Njxw6rXkN6ejqzZ8+mWrVqVKpUyeYYlIqiQE66c242vt/l8V77+flRsWJF/Pz8HPJeZ2dno9FoMBgMlmXm8bLr16+3rOPu7m5pvoVrY2fN65QXRVHIMGbYfcvMzbT7ubZ+/5393XeE9PR05syZQ5UqVcrlu+9yvb579uxp1w9/WYj0Vr/Y59POO7kk1sk05lF//D/lvt+Db/XCy922j1LNmjWZOnUqABcuXADgnXfesYyZf/XVV+nbty9ZWVl4eHjwwQcfMHz4cEaNGgXAuHHj2Lx5Mx988AFdu3YF4JtvviEzM9Oq/V+8eJHw8PB8ywIDA3F3d+fixYvFPnfGjBm8/PLLpKenU7duXaKjo3F3d7f+xTuCMQPejSx5vbLweiy4e1u9enm818XNEmXPe922bVu8vb155ZVXePfdd1EUhVdeeQWTyWR5Dd26dWPcuHFMmzaN559/nvT0dF5//fV8r7O8ZOZm0mZem3Ldp9mWwVvw0ls/ysbZ3/3SuPG7v2TJknL57rtcjdqVmBP1hfTy/dLdDlq2bFlgWePGjS33zVPumafgO3ToEB06dMi3focOHTh06JDl/6ioKGrWrFns7XqF9dRUFKXEnq9Dhgxh165drFmzhlq1avHggw+SlZVVwiu+fZXXe129enWHvdehoaEsXLiQP/74Ax8fH/z9/UlOTqZ58+aW8bANGjTg+++/58MPP8TLy4uIiAiqV69OeHi4wy7GcCtyhe++va7/7tesWZMRI0aUy3ff5WrUrsTc9H0+/bxVP+DO5qnXcfCtXk7Zr628vQvWyK7vBWmO9fUTCdwY/xvfk969e7Nu3bpi95uWlgZAREQEW7ZsyffYlSvq5DY31r5u5O/vj7+/P7Vq1aJt27YEBgayZMkSSzNeudB7qTVbZ7Ch9gQ373vds2dPTpw4QUJCAm5ubpYLLFSrVs2yzuDBgxk8eDBxcXF4e3uj0Wj46KOP8q1THjzdPNkyeEvJKxbCZDJZhhnd2Bph7b5t4ezPQ2lc/91v3bo1wcHBLFmyhCFDhpR628WRRF2MCt7qkV1mbiZXsq8Q5BHk5BIVT6PR2NwEfbOoV68e69ev55FHHrEs27hxI/Xq1bP8b0vzV7t27XjnnXe4cOGC5Qj+33//xWAw0KJFC5vKpihKvvH85UKjsan5+WZiz3tdXNN3ad/rkJAQAP777z/i4+Pp379/gXXMCf+7777Dw8ODHj162PCKS0+j0djU/Hw9k8lErlsuXnovuxJ1WXP0d9+Ryuu7f2v+qjuIu84dX40vqUoq51PPu3yivpW99NJLPPjggzRv3pzu3bvzxx9/sHjxYlasWGFZJyoqyurt9ezZk/r16zNs2DCmTZtGYmIiL774Ik888YRlPP758+fp3r07P/zwA61bt+bkyZMsWLCAnj17Ehoayvnz53n//ffx9PSkTx/Xu3DLzcqe99pkMpGSkoKfn1+BZGPPew3qMNF69eoRGhrKpk2beP755xk7dix16tSxbPvzzz+nffv2+Pj4EB0dzUsvvcSUKVNuusk7XJmjv/sAx48fJy0tjYsXL5KZmcnu3bsBqF+/Pu7u7lZ996dMmYKHh0e5fPclUZcgUBtIal4q59PP0yi0kbOLc9u65557+OSTT5g2bRrPPfcc1apVY/bs2XTp0sWu7el0Ov766y9GjRpFhw4d8PT0ZPDgwXzwwQeWdYxGI0eOHLFM9efh4cG6deuYPn06V65cITw8nE6dOrFx40bCwsIc8TIFrvFeAxw5coTXXnuNxMREqlatyhtvvMHYsWPzbXvr1q1MmDCBtLQ06taty1dffcWwYcPsKqconKM/DwCPP/44a9assfzfrFkzAE6dOkXVqlWt+u7fcccd/PPPP+Xy3XfZKUTtkZKSYun0UdopREH98o5YOII9xj2MaT6Gxxo95oBSOkZWVhanTp2iWrVqLjeFaHG1G1E8iZ19JG72k9jZx5q4Ffc7bUu+knelBIFadY7vm2WIlhBCiFuLJOoSSKIWQgjhTJKoSyCJWgghhDNJoi6BOVHHpsViUuSi6kIIIcqXJOoS+Gn90Gl0GE1GLmVccnZxCriF+gIKIcQtxVG/z5KoS6DT6IjwigBcq/nbPJPP9cNJhBBCuA7z77Mt154ujIyjtkKkTyTn089zPu08zcObO7s4gDo2NCAgwDIfrpeXl8tMcWoymcjJySErK0uGe9hIYmcfiZv9JHb2KS5uiqKQkZFBfHw8AQEBpZ77XRJ1cUx56EzZLnsVrYgItaZvTtauQlEUMjMz8fT0dJmDh5uFxM4+Ejf7SezsY03czPPDl5Yk6qJs+AS3FRNpGNSZs1XVC124WqLWaDRUqFCBsLAwjEajs4tjYTQaWbt2LZ06dSp1k8/tRmJnH4mb/SR29ikpbnq93mFXUZNEXRR3HzSKCUNussvWqM10Op1LXVZPp9ORm5uLh4eHfPFtJLGzj8TNfhI7+5Rn3OSERFF81PlbDbkp1y53meqaiVoIIcStSxJ1UbxDATAYU4j0UWvUFzMuYjS5ThOzEEKIW5/LJerz588zdOhQgoOD8fLyomnTpuzYsaP8C2JO1LkpBHsE4651x6SYiEuPK/+yCCGEuG25VKK+cuUKHTp0QK/Xs3z5cg4ePMiHH37onGu7Xk3UbqYstLlZllq1q56nFkIIcWtyqc5k77//PpUqVWL27NmWZVWrVnVOYQy+KG4eaHKzIP0SUb5RnE45LYlaCCFEuXKpRP3777/Tq1cvHnjgAdasWUNUVBSjRo3iiSeeKHT97OxssrOzLf+npKQAard5RwxX0nkFo0k5T17yRSK91Bp1THKMSw2FckXm+EicbCexs4/EzX4SO/uUNm62PE+juNBk0eYLa48bN44HHniArVu3MmbMGL766iseeeSRAutPnDiRSZMmFVg+b948vLy8Sl2eTkcmEphxki3Vx7DIkMnyrOU00DdgkPegUm9bCCHE7SsjI4PBgweTnJyMn59fseu6VKJ2d3enZcuWbNy40bLsueeeY9u2bWzatKnA+oXVqCtVqkRCQkKJL9wa2p8fRndyBdm9PmBLVB1GrxpNVb+qLL57cam3fSszGo1ER0fTo0cPGZdpI4mdfSRu9pPY2ae0cUtJSSEkJMSqRO1STd8VKlSgfv36+ZbVq1ePRYsWFbq+wWDAYDAUWK7X6x3ygTP5hgPglpVI3ZC6AJxNPYtJa8KgK7hfkZ+j3ofbkcTOPhI3+0ns7GNv3Gx5jkv1+u7QoQNHjhzJt+zo0aNUqVLFKeVRrvb8JiOBUM9Q/Nz9yFPyOJ182inlEUIIcftxqUQ9duxYNm/ezLvvvsvx48eZN28eX3/9NaNHj3ZOgbxDANCkx6PRaKgZUBOA40nHnVMeIYQQtx2XStStWrViyZIl/PzzzzRs2JC3336b6dOnM2TIEKeUx1KjTr8EIIlaCCFEuXOpc9QAd999N3fffbezi6HyUhO1Jj0BgJqBVxP1FUnUQgghyodL1ahdTVE16mNJx5xVJCGEELcZSdTFMZ+jzkyEvFxLoj6fdp4MY4YzSyaEEOI2IYm6OJ5BKGjU+xkJBHoEEuwRDMDJ5JNOLJgQQojbhSTq4mh1ZLtdHYhubv6+ep762BVp/hZCCFH2JFGXwJKo0+IBqBVQC5Ce30IIIcqHJOoSZOvz16hrBNQAJFELIYQoH5KoS1Cg6VvGUgshhChHkqhLcGPTtzlRx2fEk5yd7KxiCSGEuE1Ioi5BjqVGrU564uPuQwXvCgCcSDrhrGIJIYS4TUiiLkG23l+9kx5vWSbnqYUQQpQXSdQluPEcNUjPbyGEEOVHEnUJLL2+064lasuc35KohRBClDFJ1CXIcjM3fV8CRQGuNX0fu3IM5eoyIYQQoixIoi5BjpuvesdkhKwkAKr7V0eDhqTsJC5nXXZe4YQQQtzyJFGXwKR1RzHkb/72dPOkkm8lQHp+CyGEKFuSqK1x9Spa13coM4+nPpx42BklEkIIcZtwSKLOzMzk/PnzBZYfOHDAEZt3OsU7TL1z3RCtlhEtAVh8bDEmxeSMYgkhhLgNlDpR//rrr9SuXZs+ffrQuHFjtmzZYnls2LBhpd28a/Ay16gTLIvurXkvPnofTiafZO25tU4qmBBCiFtdqRP15MmT2blzJ3v27OG7775j5MiRzJs3D8DmHtETJ05Eo9Hku0VERJS2iKWmeIeqd9Ku1ah93H14oM4DAMzeP9sZxRJCCHEbcCvtBoxGI6GhaiJr2bIla9euZeDAgRw/fhyNRmPz9ho0aMCKFSss/+t0utIWsfTMifq6pm+AofWGMvfgXHbG72TPpT00CW3ihMIJIYS4lZW6Rh0WFsbevXst/wcHBxMdHc2hQ4fyLbeWm5sbERERlpv5IMCpvAs2fQOEeYVxd/W7AZizf045F0qI20ueKa/c9mVSTEXuL8+Ux/zD89kZt7PcyiNsl52XzeP/PM5La14iJy+nwONJWUlM3jyZ6DPRTiidbUpdo547dy5ubvk34+7uzs8//8wzzzxj8/aOHTtGZGQkBoOBNm3a8O6771K9evVC183OziY7O9vyf0pKCqDW8o1Go837vpF5G7keQegAU2oceTdsd2idoSw9vpSVMSs5fvk4VfyqlHq/Nztz3BzxHpRWak4qv5/8nbuq3EWwZ3Ch6yiKYlfrz43yTHm8tP4l4jLiGFZ3GD0q90Cn1VkeW3l2Jb8e+5UGwQ14vtnzhW7DlWLnSv48+SeTt07mpZYvcV/N+wo87si4ZedlM2j5IBRF4aNOH1HNv5rlsVxTLuM3jefvM3/jo/fhj/5/4G/wL/U+nelW/cxtOL+BLRfVPlMmk4l3O7yLVqPWTdOMaTy98mkOJB5g4dGFfNH1C9pEtLFp+6WNmy3P0yguNLXW8uXLycjIoHbt2sTFxTF58mQOHz7MgQMHCA4u+CM7ceJEJk2aVGD5vHnz8PLycli5gtKOcMexd0hzD2Nlgw8KPD43bS5Hco/Qyr0VA7wGOGy/ovSWZy5nQ/YGIrQR/M/3f7hr3C2PKYpCdFY0m7I3McR7CDX1NUu1r63ZW/k983fL/6HaULp4dCGPPNZmrSXBdK1F5gXfFwjUBZZqf7eTL1K/4ELeBbRoGe49nOr6/AfvJ40nOZ57nI6GjnhpS/fd35ezjwUZCwDw1Hgy2Hsw1dyqkafk8WvGr+wz7rOs28nQiZ6ePUu1P1E2/sj4gy051zo3t3NvRx/PPhgx8kPaD5zOO40GDQoKXhovnvZ5uly/kxkZGQwePJjk5GT8/PyKXdemRB0TE2NXgQICAkosSGHS09OpUaMGL7/8MuPGjSvweGE16kqVKpGQkGDX/m5kNBqJjo6mZ4saeH7TAcXdm9yXzhRYb2f8Th5f8TjuWnfebv82dQLrUNGnouXo7XZjjluPHj3Q6/VOK4dJMdF7aW8uZarj33tU7sGUDlMstefvD37PJ7s/AdTZ5ub3no+b1r5GptScVAb8MYCk7CTuiLyDPQl7SMlJybeOn7sf3npvLqRfYHST0TzW4LEC23GV2LmSMylnuPfPey3/BxgC+PGuH4n0jgRg8fHFvLftPfKUPFqFteKLbl/Y/T4CPLf6OdbHrsfTzZPM3Ez0Wj1vtnmTdefXER0TjZvWjftq3seCowvw0Hnw54A/CfIIKvXrdJZb8TOnKAr9fu9HbHos/ar1449TfwAwqvEo9lzaw4YLG/DR+/BZl8+YtmMaBxMPUiewDt/1+A5PN0+r9lHauKWkpBASEmJVorbp01y1alWbC6PRaJgwYQLjx4+3+bne3t40atSIY8eOFfq4wWDAYDAUWK7X6x36gXPzV68/rclJR68YwT3/EXvryNY0DmnM3oS9vLL+FUCdvax+cH1GNx1Nq4hWDivLzcTR74Ottl/czqXMS3i6eWI0GYmOiaZ+SH0eb/Q4f5z4w5Kk9Vo9J5NPsjxmOQNrDbRrX9/t/o6k7CSq+Vfjk+6fkJ2bzbzD8/jx4I/otDqG1R/GQ3Ue4t/T/zJ+43j+Pv03TzZ5ssgmd2fHzpWsPLcSgFYRrUg3pnPw8kFeXPci39/1Pd/s+4ZZ+2YBoEHDtvhtfLH3C15s9aJd+0rITGDThU0AzO09l6/2fkX0mWjGb1J/v/RaPR91+YjOFTuz//J+Dlw+wPeHv+flVi874JU61630mTuZfJLY9Fj0Wj3/1+7/qBtcl2nbpzFj7wwAPHQefNH9C5qHN+cT/0946M+HOHLlCJO3Teb9O9636VSYvXGz5Tk2VflMJpPNt7y8PLuSNKg15kOHDlGhQgW7nu8wBl/QXT0guG52MjONRsPbHd7mnpr3UC+oHu5adzJzM9kRt4OR/4zkzQ1vknR1nnBRfv4+/TcAPar04LXWrwHw6c5P+XTnp4zfoH4mh9UfxvPN1fPFX+z+gszcTJv3cyblDD8d/gmAl1q+hF6rx8fdh/81/h9rH17LqgdXMbLhSLz13txZ5U7cte6cSD7BkStHHPEyb3nm97Ff9X580vUTgjyCOJx4mH5L+1mS9BMNn+Ahr4cAtaVk2clldu3rr5N/kafk0Ti0MXWC6vBB5w8Y3mA4oCbp6V2n06VSFzQaDc82exaABYcXEJceV8pXeXM4mXSSB/54gJ8O/eTsohRr3bl1ALQMb4mX3otHGjyS7338pOsnNA9vDkCEdwQfdv4QN40by08tZ/6R+c4qdpFsqlFXq1bNrk43Y8aM4bnnnitxvRdffJF+/fpRuXJl4uPjmTx5MikpKTz66KM279OhNBp1iFbKOTVRBxbsMFY9oDpvd3gbUDucxKTE8NOhn/jl6C8sPb6UNWfX8GiDR8nIzeBc6jnOpZ0jwBDAh50/xMPNo7xfkUtbcWYFacY0moQ2oapfVbs+c7mmXEtvzt7VetMxqiOHEg/x69FfLT/ud1W9ixdbvkiuKZd5h+YRmx7LvEPzeKxRwSbp4nyw/QNyTbl0iOrAHRXvKHZdX3dfOlfqTPSZaP46+Rd1g+ra/NpuRibFxLJTy2gQ3CBf56ySnEg6wfGk47hp3ehWuRv+Bn8+6vIRj//zOPEZ8eg0Oia0m8DdVe9m2blljKg6gtkHZzNh4wRqBNSgTlAdq/elKAq/nfgNgAE11L4mWo2WF1q+QMeojgR6BFI7sLZl/faR7Wke1pyd8TuZtW8W/9f2/6zely3WnlvLb8d/4/U2rxfZIbI85JpyeWP9GxxOPMyH2z+kU8VOlmseuJr159cD5Ps+jm0xljpBdajqV5WGIQ3zrd8yoiXPN3+eD3d8yJJjSxhUd1C5lrckNiXqOXPm2LUTa5vMz507x6BBg0hISCA0NJS2bduyefNmqlRxgZ7UPtcl6hK4ad2oHlCdN9u9Sb8a/Zi0aRLHk44zfef0Autuit1E18pdy6DAN6eVMSsZu3qs5f8AQwBNQpswqO4gOkR1sHo72+O2k5iVSIAhgDYV1N6cr7d+nRNJJ9gVv4vWEa15p+M7aDVa3HXuPNPsGV5f/zrf7vuW+2vfb3VP3k2xm1h9djU6jY6XW1rX/Nm3Wl+iz0Sz7NQyxjQfY+kZ7mjrzq1j8bHFvNzqZSr4OLdV6tejv/L25reJ9I7kz3v/RK+zrtnPXJvuENnB8p60CG/BOx3fYd7heTzd5Gk6RHWw9KAd1XgUR5OOsiF2A8+vep6F/Rbi6+5r1b4OJx7m2JVjuGvd6VW1V77HzJ+h65lr1SP+GcGiY4sY3mA4FX0rWrUva51NPcuLa14kMzeTcO/wMm1iNykmNl/YzLKTy2gY0pAH6zyY7/G5B+ey//J+AIwmIx/v+JiPunxk0z5WnlnJrH2zeKnVS7QIb1HsuoqikGZMQ6/V21SZyTBmsD1uOwB3RF1L1FqN1jKctjDdK3fnwx0fciLpBLmm3FL1c3A0m0rSuXPnsioHAPPnu16Tg4V5vu+0+OLXu0HTsKb8cvcv/HjoR3bE7SDcK5yKvhXZFLuJTRc2sTdh722VqBVFYdzqcaTkpDC96/R8P6JpOWm8u+VdACr7ViYuI46k7CTWnFvDvoR9rHpwldUd9P4+pf7Ad6/cHb1WTQp6nZ4v7/ySjbEb6RDVAXfdtR7gfar1Yc6BORy9cpRZe2dZdY5TURQ+2qH+UD1U5yGqBxQ+jPBGd1S8A193X+Iz4tkRt4PWFVpb9TxbrD23ludXPU+uKZcgjyDebPemw/dh7odaUotHhjGDGbvVc4Ox6bEsPbGUB2o/YNX2/zn9D0CBxNmneh/6VO9T4Dk6rY73O73PQ38+xPm080Sfiba638HvJ9Qe+10qdbH6QK1lREvaVWjHpgubmLVvFpPaFxyFYi+TYuLNDW9aTscsPbaUZ5o+g5fecSNaABKzElmbtZYv//iSc2nnAFhyfAnxGfGMbjoajUbD6eTTfLH7CwAerf8ocw/NJfpMNDvidpSYcM1yTblM3TaV2PRYnln5DN/1+o56wfUsjyuKwrzD8/j9xO9czrxMYlYiRpMRb703i/ovIsonyqr9bL6wmVxTLpV8K9k0XDbKN8rSgTAmJcbq73N5KFW3ZKPRyNmzZzly5AiJiYmOKpNrssxOVnKN+kZ6nZ4RDUfweffPebPdm4xoOMLyw7P3ku2TwtzMtlzcwoqYFWy9uJUX16jNzmaf7vqU+Ix4KvlWYlH/RWwatIl5febho/chMSuRAwnWXeTFaDKyIkad3e6uanfle8xL78WdVe4s0LNTp9UxtoVak593eB4X0i6UuJ+jV45yOPEw7lp3nm7ytFVlA3DXudOzijqk569Tf1n9PGttit3E2FVjLbFdfmo52XnZJTzLNhnGDPov7c8dC+5g8ubJ7Lm0p8gpg78/8D2Xsy7jplHrBbP2zsKYV/IY0qNXjnIq+RTuWne6VrL+YNbf4E+/Gv0A2By72arnGE1Glp1Sz2sPqGnbEEvzqZK159baPG1ycX48qB7ce7p5EuEdQaoxlT9P/umw7QNcybrCw8sf5t+sfzmXdg4fvQ+dK6oVsq/2fsVHOz4iz5THhI0TyM7Lpl2FdrzQ8gXuq6WOZZ+6barVFyVaEbOC2PRYQB3H/NSKpziToo6iyczN5JV1rzBl6xQOXj5IXEYcRpP6GUk3pvPHiT+sfk3rzqvnpztGdbTptJlWo6VWYC1A/ey5EpsTdVpaGl999RVdunTB39+fqlWrUq9ePUJDQ6lSpQpPPPEE27ZtK4uyOpdPwfm+S6NRaCMA9ifsL9cZl5ztlyO/WO5vjN3I+1vfB2DPpT3MP6y2qIxvNx4PNw/0Oj2NQhvRLrIdAGvPW3fxk82xm0nOTibII4iW4S2tLluHyA40D2uO0WTk3zP/lri+ubbXMaojAR4BVu8HoG/1vgBEn452aBLdfnE7z/33HDmmHLpV6kYF7wqkGlP5L+Y/h+0DYOHRhZxOOU1ydjILjixg6LKh9Fvaj4VHF+ZLVgmZCcw+oM6FP6nDJEI9Q7mQfoElx5eUuI/r4+vj7mNT+dpWaAuoB4bWJJIN5zeQmJVIsEcw7SPb27SvxqGN0Wl0JGQmEJfhmE5lJ5NO8slOdVTCS61e4pH6jwDw8+GfHXow8OfJP0nITMBf48+ENhNY+cBKPu/+Oa+2fhWAOQfmMHTZUHbG78TLzYuJ7dXrMYxuOhofvQ8HLx+0KokqisIPB34A1A6c9YLqkZiVyJPRT7I7fjePLH+E5aeW46ZxY1yLcczvO59/7/uXN9uqLUHmz4I1+7Gcn44qvr9IYcx9EG7qRP3xxx9TtWpVZs2aRbdu3Vi8eDG7d+/m6NGjbNq0iQkTJpCbm0uPHj246667ihxWdVPyu9rsklLwcp72qOFfAy83LzJyMziRfMIh23R1celxloTxbLNn0aBh/pH5/HDgByZunIiCQv8a/S0/smbmL5y5J2dJzOc1e1bpadN5Jo1GY6m5mc9xFaW4ZllrtAhvQZhXGKnGVKtfV0n2XdrH6JWjycrL4o6oO5jWeRr9a/QH4LfjvxVYPycvh9PJp0nNSbVpP1m5Wcw5MAeAR+o/wt3V78bTzZMzKWd4a9NbvL35bUtt6Ms9X5KZm0nD4Ib0q97PUvuctW9WodM6ml0f3xtbRazROKQxnm6eJGYlcuxKyb9D5mbvvtX72nxu0tPNkxoBNQA4cLn0l/bNNeXy+vrXyTHl0CGqA/fXup8BNQfg6ebJ8aTjJX42M3MzuZx52ap9mT8XnTw6MaDGAEuz+pB6QxjfbjwaNJbz0mNajCHSRx27HuwZzP8a/w9QR1JkGDOK3c+u+F3sS9iHu9adxxo+xow7Z1DZtzLn084zbPkwDiceJsgjiFk9ZzGi4QgahDSggk8F7qp2F3qtnuNJx616H48nHedi+kUMOoNdw2LNidqafZUnmxL1xo0bWbVqFdu3b2f8+PHcddddNGrUiJo1a9K6dWtGjhzJ7NmziYuLo3///qxZs6asyl3+zIk6+ZxDNqfT6mgUotaqb5fm78XHFpOn5NE8rDn/a/w/xrQYA8C07dM4nnScQEMgL7YseG7Y3HPzwOUDJGQmFHj8ejl5OayKWQXY9wNvvs74zridxdbEDiUeIiY1BoPOQJdKXWzej1ajpU819RzrXydL3/wdlx7Hc6ueIyM3gzYV2vBx149x17lbei9vurAp3xAik2Ji1MpR9Fvaj/Y/t6ftvLbcs/QeXlv3GolZxZ/GWnJ8CQmZCVTwrsCY5mN47473WPXgKp5v/jwaNCw8upBnVj7DgYQD/Hr0VwDGtRyHRqPh/tr3E+YZxsX0iyw5VnSt2hxfD52HpSnWFnqd3tKasvlC8c3fydnJrD67GsByYGOrBsENAKw+PVMYo8nIijMr+F/0/zhw+QC+7r5MajcJjUaDn7ufpSPUz4d/LnIbJ5JO0HdxX3r+2pMtF7YUuR6oneeOXDmCXqunkb5RgccfqP0AkztOxk3rRoeoDjxU56F8jw+pN4SKPhWJz4zn+wPfF7uvHw6qtel+NfoR7BlMiGcIX/f8mlBPtaWyXlA9Fty9wPL9M/Nz96NDpNqJ1HwAXhxzs3friNZ2jaapFXALNH0vXLiQRo0KvqE3MhgMjBo1iscff9zugrkcf8fWqEFtMgPnJOoTSSdYdHQRb216i4f+fIgOP3cotNblKEaT0fKjbf7Cj2gwgntq3mNZ5+XWLxPoUXAKvxDPEOoH1weuDbsoyobzG0g1phLmFUazsGY2l7NuUF283LxIyUkp9qja/KPRqWInuzv3mH9415xbQ3J2slXPuZRxqcABRHZeNmNXjyUhM4GaATX5pOsnGK6O+6/kV4kW4S0wKSb+OHmtiXLeoXn5fsjTjemcSD7Bnyf/ZNiyYcSkFD4LoTHPyHf7vwNgZMORlt7b3npvHm/0OJ90/QRPN082xm5kyLIh5Cl5dK7Y2VK7MegMVtWqzfG9o+IddsfX3DJjnsCkKCtjVmI0GakZUNOm4VzXMyfqg5cP2vQ8RVE4ePkg03dMp8fCHoxdPZZtF7eh1WgZ32484d7hlnUfrvswAP/F/MfF9IsFtnUk8Qgj/h7BpcxL5JhyGLNqTLGfYfP3vUvFLkVOu9q/Rn/WPrSWGd1nFOjI6a5z57nm6rDbBUcWWFpRbhSTEmNpSTM34QNE+UTxU5+feLvD2/zQ+wcivAu/pHGvamqL1T+n/ymx2d/cOlXSMMmimM9Rx6bH2tzSVJbs7kzWrl07y0Uwbgt+V4ddpF+CXMecUzTXqPcl7CthTcdRFIWPd3zMPb/dw8RNE1l4dCEHLx8kJSeFtze/zfErx8tkv2vOriE+M54gjyDurHInoDY1j287nvtr38/wBsPpW61vkc/vVLETUHLz9/LTywG12dueKVzdtG6WBF9UE6OiKPx7Wj2HbU+zt1mdoDrUDqyN0WQs8RxcYlYiL615iW4Lu/HQnw9ZrtykKApvbXqLfQn78Df482m3T/HWe+d7rrlW/dvx31AUhTMpZyznP99s+yZbBm/h93t+54vuXxDlE0VMagxDlw0t9ADy9xO/czH9IqGeodxb694Cj3et3JU5d80hzDOMPCUPrUbLmOZj8q1zX+37CPMKIy4jjkXHFhXYhqIo/HPqarN3VdtbRczaRqqJemfczmKb2c2dyMwtHPZoEHK1Rn35QInJxKSYWBWzigkbJ9B9YXce+vMhvt3/LZezLhPsEcxjDR/jz3v+LPDaawfWplVEK/KUvHx9PUCtyY/8ZyRXsq9QL6gezcOaqxeeWPF0oZOxGPOMlpacftX6FVteX3ffIr9Ld1a5k2CPYC5nXWbt2cL7kMw9OBcFhTui7ijQk7qCTwXuqXlPsbXfrpW6YtAZOJNyhsOJh4tcLyUnhd3xuwG1X4M9/A3+lgMGV2r+tjtRb9myhaysrALLU1JSeOmll0pVKJfkFQTmD5ODatXmDmUnkk6Uy9GbSTExefNkS42oVUQrRjQcwQedP6BDZAey87J5ed3LDu8hDOoRN8C9Ne/NNyxKr9Mzod0EXmj5QrE9NM3nqTfFbiryyD0zN9PShNm7Wm+7y2puftsRt6PQx/cn7Od82nk83TwtBxD2Mje1FtUhR1EUlp1cxoClAyy1zMOJh3n070d5dd2rzNwzk99P/I5Wo2Vap2mFTkDRq2ovPN08OZ1yml3xu3hzw5tk5WXRtkJbHqj9AF56L6r5V6NTxU782OdH6gXV40r2FR7757F8tZhcUy7f7PsGgOENhltq7TeqH1yfeX3n0bd6X15r/Ro1A/Nf7MSgM/B4I7W1be7BuQVaCPYm7CU2PRYvNy+7a0agNmMGeQSRmZvJnkt7Cl0nITOBbRfVzq/2nCoxqx1YGzetG0nZSZxPK/734d0t7/LcqudYfGyxZYrbrpW68nGXj4l+IJoxLcZQya/wiUTME3H8evRXvt33LXP2z+Hbfd/y+L+Pk5KTQuPQxnzT6xs+7fYp1fyrEZcRx+iVo0nLScu3nbXn13Il+wohniEF+oTYQq/VW1rFFh5bWODx5OxkyyQyjzR4pMDj1vDWe1u+Z8U1f685u4ZcJZca/jVKNRGLK3YoszlRDxw4kClT1AsbxMcX7AGdnp7ORx/ZNgj+pqDRgP/VWnWyYxJ1iGcIUT5RKCjsT9jvkG0WxWgy8tq61/jl6C9o0DCx3US+6/Ud41qMo1fVXkzuOJkgjyCOXTnG9B3THbrv08mn2XxhMxo0PFCn5PGzhWkQ3IBAQyCpxlTLUfON1p1bR2ZuJlE+UZbWCnuYz23uiNtRaO3IXPvtXLGz1RP4F6VPtT5oNVp2X9pdoLk53ZjOc6ue45V1r5CUnUTtwNp83eNr7qt1Hxo0/HXyL2bumQnAiy1ftPSOv5GX3oseVXoA8NLal9gVvwtvvTeT2k8qcHAU4hnCnLvm0DGqI1l5Wby45kV6LerFtG3T+Hrv15xLO0egIZD7a99f7OsK9w5nyh1TLM21NxpQYwA+eh/Opp4tcC7VPAa+S6UupYqvRqOxJKGizlP/c/ofTIqJxiGNS/Xj7q5zt/zAF9ehbPGxxSw4sgANGgbVHcRXPb5i/cPr+bTbp9xZ5U7LmP+idK3UlXCvcK5kX2H6zul8uONDpu+cTpoxjRbhLfi6x9f4ufvhb/BnRvcZBHsEc+TKEcasHpMvWZubvftV71fqiT3MQ7U2nt9Y4CDlx0M/kpmbSZ3AOjZfRvJ65par4pq/zc3r5hY7e90SibpKlSr8+eefKIpCkyZNCAsLo0ePHrz44ov88MMPfPjhh86fm7usOLhDGai9UyF/87eiKMzYPYNJmyZxLrX0+8rJy2Hc6nEsO7UMN40bUztN5b7a+a/pG+IZYpkC9cdDP7L2XNFDoUyKqcRentf75ajaTHdHxTusnrTgRjqtztKcZe4wciNLb++qPUt1fekGwQ3w0HmQmJXIyeST+R4zKSb+OVP6ZlmzUK9QS4K9/hwywMc7Pmb12dW4ad0Y3XQ08/vOp11kOya2n8jPfX+29HG4t+a9DK03tNj9mGs98RnqwfWLLV+09OC9kZfei8+6fcYj9R/By82LC+kX+OHgD5aDgkcaPFLqSTe89F6Wc/QLj16riZkUk+W0giPiW1KiNjd7l6YFxszSoayIRL330l4mb54MwOimo3m9zeu0j2yfr4WpJG7aq9/fWvcxoMYA+lXvR59qfXii0RPMvHNmvtMeFX0r8kX3L/B082TLhS0M+msQJ5JOcDnzsuUUkr2d565Xya8SbSq0QUHJ10Hw+JXjlhaYJxo/UarvZKeKnfB08+R82vlCTxVm5mZa+q9IokYdorV+/Xr0ej2bN29m1qxZdOjQgePHjzNp0iQWLVrE1KlTy6KszmeuUac4MFEX0qFs3fl1zNwzk1+P/kr/pf35aMdHpWoa/3Tnp6w+uxqDzsAn3T4psomvU8VODK47GIA3N7zJypiV+XpZn0k5w2e7PuOuRXfRcX5HNpzfUOK+L6ZfZPGxxQAFeo3aytwMWth56nRjuuXgonfV0v3o6nV6moQ2AdRxydfbe2kvF9Mv4uXmZdOUpsXpX/1a87e5Gfhg4kHLecgvun/BU02eyjftZoOQBsztPZe/7v2r0JrxjVqEt7AcJLWPbG+pBRXFTevGS61eYs1Da5jeZTq9q/XG082TKJ8oHq5TeC3ZVubWlVUxqyyfs51xO4nPjMdX7+uQ+JoT9f6E/QW+Q+dSz7H30l60Gm2p+hqYWTqUJRTsUJaQmcDYVWMxmox0r9ydJxo/Yfd+moc3Z2L7iUzuOJl373iX9zu9z3PNnyu09aFBSAO+7fkt4V7hnE45zaC/BvHWprfIVXJpGNywwGkJe5lbWJYcW0KuKZc8Ux7jN44n15RLl4pdLBP82MvTzZMuFbsAhTd/bzy/kay8LKJ8oqgTaF+HQLPrh2hZO5lLWbO7zSM9PR03N/XpAwbYNpPPTctSoy6bnt+KopCr5PLB9g8ACPMKIz4jntn7Z/Pb8d94rc1rNtcydsbttAyNeL/T+yWeUx3XchxbL27leNJxxqwaA0CkdyQBHgEFerSO3ziepQOWFjmXsrmjU7oxncYhjS3DLOzVPrI9Wo2W40nHuZB2Id/81avOriI7L5sqflUccqGLFhEt2HJxCzvidvBQ3WsHGOZm766VuzrsYipdK3fFW+/N+bTz7L60G5NiYsq2KSgo9K7Wu8gJOLQaLZX9Klu1D61Gy/+1/T/+OPFHif0Brufh5kH3Kt3pXqW7pW9ASc2z1qodWJumoU3ZfWk3S44t4YnGT1h+hLtV7mZTTbMoFXwqUNWvKqdTTrP14la6V+5uecy8r1bhrQj1Ci31vswXejh4+SAmxWTpgGXMMzJu9TjiM+Op7l/dMsd8eWkU2ohf+v3Cy2teZsvFLfx3Vm0itnUGtuJ0q9SNQEMg8ZnxrDu3jpjUGPYl7MNH78P/tf2/UtWmzXpV68Xy08v55/Q/vNjyxXwxNM9E2L1y91Lvq4pfFfRaPRm5GZxPO+8SFx6x6dMSE3PtHJo5SRfn/HnHJTSXYKlRO+511Q2qi16r50r2Fc6lnmPhkYWcSj5FkEcQSwcs5fNun1PVryqJWYm8tva1EscRXy/DmMH/bfg/FBQG1BiQ70eqKAadgZl3zuT+2vdTM6AmGjTEpsdy8PJBtBotHaM6MuWOKVTyrUR8Rjwfbv+wyG39deov1p1fh16r560Ob5X64hP+Bn+ahjYFCjZ/X99L2BE/Cubz1NvjtlvOiV1Mv2g5t+eIZlkzTzfPfFOK7sjZwf7L+/HWe/NSS8d1zOwY1ZH3O71PmFeYXc/Xa/UOS9Jm5lr1r0d/xZhnzHfFM0cxX1DjxulEl59a7tB9VQ+ojkFnINWYmq+/wQ8Hf2BX/C589b580vWTAr3yy0OQRxBf9viSkQ1HAupnzpExdte5WxL/13u/5vNdnwPqKZbrh5iVRseojvjq1Tnyr597wJhnZM1Zdc6O0jZ7g9qaVDNAbWm4vvnbpJjYFLvJoTPDWcumRN2qVSueeOIJtm7dWuQ6ycnJzJo1i4YNG7J48eJSF9Cl+Dv+HLW7zp16QerE9Otj11vOA45uOtpyScTFAxbTMLghuUquTZNjfLzjY86mniXCO4JXWr9i9fMivCOY0G4CSwYsYeOgjczqOYvJHSYTfX80M++cSd/qfS0XH1h0bBGbYguOU001pTJtxzQARjUdZZm5qbTMzd+Lji0i3ZgOqD1L18eq56cclUAbhTRCr9VzKfMSMakxKIrChI0TSDWm0iikkd3DP4pinps6Oiaaf7PUc7TPNH3GITU9V9azSk/83P2ITY/lk52fWK545sgLlbSroPYBuP489YmkExy9chQ3rZtDftxBPZAxj8M2n6dOy0mzTKH6SutXqOpf1SH7soeb1o2xLcYyt/dcvr/re6svPGIt8+mU/Zf3k5WXRZsKbay+IIo1DDoDIxqOAOCjHR9ZOsdtvbiVVGMqIZ4hllNWpVXYnN+/Hv2V/0X/j9fWv+aQfdjCpkR96NAh/P39ueuuuwgPD6dv37488cQTPPvsswwdOpTmzZsTFhbGnDlzmDZtGs8++2xZlds5/Bzb69vM3Pz98Y6PScpOooZ/jXwfcL1WbxmzuvT4UquO6DZf2Gy5APqk9pOsvtTfjXzcfWhboS0Dag7IVxNrFdHKcs550qZJ+TqXKYrC75m/k2pMpX5wfcsF2x2hd7XeeLl5cfDyQR775zESsxL5L+Y/ck251Ayo6bBzbh5uHpae4zvidvDLkV/YGLsRg85gma3JkVqEtyDSO5I0YxqZSia1A2oX2WP6VuLh5mHp0PT9QXV2K2t6P9uiVYVWaDVaTqec5snoJ/l056d8tecrADpGdnRowrqxQ9ncQ3NJzk6mmn+1Yi+xWJ6ahjXNd9UqR6nqX9UysY2nmycT2010SOvW9R5t8ChV/KqQkJnAjD3qFdnMzd7dKnVz2CmFG6cSjc+I5+MdHwPQMLhhkc8rKza9qqCgID744ANiY2OZOXMmtWvXJiEhwTKn95AhQ9ixYwcbNmygd2/HNau4DHONOjsZsh037tmcEMyXs3ux1YsFEsFd1e7CXevO8aTjHEo8lO+xSxmXuGfpPXRe0Jl+S/oxdNlQXl6jXrf2oToP2XyRAWuNbTGWSO9IzqedZ+q2qeyI28HKmJV8ue9LDhkP4aZ14632bzk0qUX5RPFdr+8INARy4PIBHl3+qGXGM0c2RwOWy/f9fuJ3PtyhNvGPaT6G6v6Ov/ydVqPl7hrXfshfa/WaS10PtyzdeMnL0nYGvJGfux+dotS+GRtjNzJr3yzLxDiObP6Fa+epDyQcIDk7mbkH5gIwqsmoMrvuuCt5qvFThHqG8n9t/8/h1+YGtQXytdZqjXbeoXkcSTxiGZbVvUrJp/asdWOifm/Le6QZ02gY3NAylr082fVL4OHhwcCBAxk40HHNGjcFgy94+ENWslqrDit9pyW4VqMG9QpOhTWr+rn70b1yd5afXs5vx3+zTKkJ8MnOTywX9rh+nuaKPhUZ12KcQ8pYGG+9NxPaT+DJ6CdZdGxRgVmmHmvwmN1TMhanQUgD5vSew5PRT3I65bRleWkmrChMy4iWzNo3yzLxSeuI1gyuN9ih+7jeg7UfZOWZlVTMquiwJrybQfWA6rQIb8GOuB2EeIZYfX1jW3zc9WMOJx7m4OWDHLh8gAMJBwjwCHD4teDNNepDiYeYc2AOqcZUagXWomfV0vV6vlm0rtCa/x507JXabtQhqgN3Vr6TFTEreOa/Z0jMSsTX3deui3AUxZyoz6Sc4a+Tf7EiZgU6jY6J7Sc65YDr9jhkdyS/ilcT9TmHJeoonyiq+lUlNi2WF1q+UOR6A2oOYPnp5fx16i9eaPkC7jp3Dlw+YJn556MuHxFgCCAlO4VUYyptIto4/CLzN2of2Z7HGj7G4mOL8XX3JcAjAD+9H4ZEAyPrjyyz/Vb3r87c3nN5MvpJTiafpF5QPZsuEm+NpqFNcdO4kavk4q335u0Ob5dpb91w73AW9l3IsmXLymwfrurxRo+zK34XD9d5uEx+CN20bjQMaWip8ZaVqn5V8XTzJDM3k9n71XPTo5uMLtde3reDl1u9zPrz6y1znnet1NWhp0uCPYMJ8ggiMSuRiRsnAupsfGVR8bCGQxJ1ZmYmiYmJREXln8ziwIEDNGjQwBG7cB3+URB/wKFjqTUaDXPumkNmbmaxzUVtK7QlzDOM+Mx41p5bS/fK3Zm6VR2z3rd6X8vsU+VtTIsxlithARiNRpYtW5Zv3G9ZiPCO4Pu7vueHgz84tNnLzEvvpQ7TurCFV1q9UuQEIaL0OkZ1ZNOgTaWe6c3ZdFod9YPrsyNuB3lKHvWC6tGtcjdnF+uWU8GnAk80foLPdn0GYNWIFlvVDqzN5gubycrLopJvJZ5q8pTD92GtUh/m/frrr9SuXZs+ffrQuHFjtmy5Nh3gsGHD7N7ue++9h0ajYcyYMaUtomOVwVhqUI/gSjqno9PqLOcxfzv+G9FnotkZvxMPnUeBCx/cLgI8Aniu+XOWJkdHm9ppKj/2+bHQC1AIx/LSezm885EzXP9ZHN109C3xmlzR8AbDaRzSmBr+NcqkH465+RtgfLvxDps3wR6lTtSTJ09m586d7Nmzh++++46RI0cyb948ALvHm23bto2vv/6axo0bl7xyeSuDsdS2MF8Jad35dUzbrg5/GtFwRJGXiBOlE+QRdFudLxalZz5X2iS0Sakv2iKK5q5z58c+P7L0nqVlkkTN791DdR4q1YVLHKHUTd9Go5HQUHWsZ8uWLVm7di0DBw7k+PHjdh1JpqWlMWTIEGbNmsXkyZNLWzzHs1yYw3FN37aoHlCdxiGN2ZugTmUZ5hnm0OFPQojS6VyxM1/e+SUNQxpKbbqMlWV821Row/qH1+Pn7ldm+7BWqWvUYWFh7N17bZ7q4OBgoqOjOXToUL7l1ho9ejR9+/blzjsdMwmBw5XBhTlsdf3Uf2NajCnzDmNCCOtpNBo6RHVw+IQiovz5G/xd4mCr1DXquXPnFphO1N3dnZ9//plnnnnGpm3Nnz+fnTt3sm3bNqvWz87OJjv72rWTU1JSALWWbzQWfs1iW5i3kW9b3uHoASXlPLk5OerlL8vZnRXv5Cf/n4j0jqRnpZ4Oea2OVGjchFUkdvaRuNlPYmef0sbNludpFGdMXFqIs2fP0rJlS/7991+aNFHPCXbp0oWmTZsyffr0Qp8zceJEJk2aVGD5vHnz8PIqm1qm1mSk357HAFje6Aty3Oyb8UsIIcTtKyMjg8GDB5OcnIyfX/HN63YnapPJxOzZs1m5ciVxcXH5Oo5pNBpWrlxp0/aWLl3Kvffei053bQxlXl4eGo0GrVZLdnZ2vseg8Bp1pUqVSEhIKPGFW8NoNBIdHU2PHj3Q668NNXKbXh9NejzGx/6DCBfs8OZkRcVNlExiZx+Jm/0kdvYpbdxSUlIICQmxKlHb3fQ9btw4PvtMHcNmTtIajQZFUexq0+/evTv79uW/IPiIESOoW7cur7zySoEkDWAwGDAYDAWW6/V6h37gCmzPPwrS49GnXwS942dRulU4+n24nUjs7CNxs5/Ezj72xs2W59idqH/++WcURSEyMpJq1apZddnL4vj6+tKwYf5Zg7y9vQkODi6w3On8oiB2l8PHUgshhBA3sju7mkwmKlasyLFjxwqt1d7SLGOpndfzWwghxO3B7kQ9bNgw5s6di9FoLLNEvXr16jLZbqn5l83lLoUQQogb2ZSo33rrLct9Ly8vMjIyaNq0Kf379ycgICDfuuPHj3dIAV2SC4ylFkIIcXuwKVFPnFjwQuAnT57kk08+KbDuLZ2onTyNqBBCiNuHTYm6cuXKLjFLi9OZa9QpsWDKg9vggvBCCCGcw6ZEffr06TIqxk3GNwI0OlDyIC0O/OTyh0IIIcqGXM3cHlrdteQsHcqEEEKUIUnU9rJ0KDvr3HIIIYS4pUmitpf/1USdFOPccgghhLilOSRR79y5k5ycHEds6uZRoan69/BfTi2GEEKIW5tDEnWrVq1uv45mjR9SO5Sd2wrxh5xdGiGEELcohyRqF7lSZvnyDYc6vdX7O+c6tyxCCCFuWXKOujSaP6L+3fMz5GYXv64QQghhB0nUpVGjO/hGQmainKsWQghRJiRRl4bODZoNUe/v/MG5ZRFCCHFLkkRdWs2Gqn9ProIrp51aFCGEELceSdSlFVgVqndR7+/6yZklEUIIcQuSRO0I5k5lu35UL9IhhBBCOIhDEvWECRMICQlxxKZuTnXvBs9ASI2FE/85uzRCCCFuIQ5L1EFBQY7Y1M3JzQD1B6j3T61xblmEEELcUqTp21GiWqp/z+9ybjmEEELcUlwqUc+cOZPGjRvj5+eHn58f7dq1Y/ny5c4ulnWimqt/L+yW89RCCCEcxqUSdcWKFZkyZQrbt29n+/btdOvWjQEDBnDgwAFnF61kIXVA7wU5aZBwzNmlEUIIcYsoVaI2Go2cPXuWI0eOkJiYWOrC9OvXjz59+lC7dm1q167NO++8g4+PD5s3by71tsuczg0qNFHvx+50blmEEELcMtxsfUJaWho//fQTP//8M1u3biU7+9oc1xUrVqRnz57873//o1WrVqUqWF5eHgsXLiQ9PZ127doVuk52dna+/aekpADqAYTRaCzV/s3buf5vSbQRTdDFbCLv7HZMDR4o9f5vVrbGTVwjsbOPxM1+Ejv7lDZutjxPo9hw6auPP/6Yd955h6pVq9K/f39at25NVFQUnp6eJCYmsn//ftatW8eSJUto27Ytn332GbVq1bKp8Pv27aNdu3ZkZWXh4+PDvHnz6NOnT6HrTpw4kUmTJhVYPm/ePLy8vGza7402xWmIPq+lUZDCvVVNVj0nKnETLc/M5IpXddbWmViq/QshhLh1ZWRkMHjwYJKTk/Hz8yt2XZsS9QMPPMD48eNp1KhRsetlZ2fz7bff4u7uzuOPP27t5gHIyckhJiaGpKQkFi1axDfffMOaNWuoX79+ofu5sUZdqVIlEhISSnzhJZm7OYa3/jpM02AT80Z3R6/Xl/ykxJPoZ7ZG0RnIfekU6NxLVYabldFoJDo6mh49elgXN2EhsbOPxM1+Ejv7lDZuKSkphISEWJWobWr6XrhwoVXrGQwGRo0aZcumLdzd3alZsyYALVu2ZNu2bXzyySd89dVXhe7HYDAUWK7X60v9gQv0UbebmWvD9sJqg0cAmqwk9InHILJpqcpws3PE+3C7ktjZR+JmP4mdfeyNmy3PsbszWbt27SznhMuSoij5as3lxc9DDWJGrsb6J2k0ENlMvS8dyoQQQjiA3Yl6y5YtZGVlFViekpLCSy+9ZNc2X3/9ddatW8fp06fZt28fb7zxBqtXr2bIkCH2FtNu/p5qos60dUi0OVGfl0QthBCi9Gzu9T1w4EBat26NRqMhPj6esLCwfI+np6fz0UcfMW3aNJsLExcXx7Bhw7hw4QL+/v40btyYv//+mx49eti8rdLyMyfqXBufaJ74JFZmKBNCCFF6NifqKlWq8Oeff6IoCk2aNCE4OJgmTZrQpEkTGjduzN69e6lQoYJdhfn222/tel5ZMDd9Z+aqze9Wi7yaqOMPQU4GuJeu97kQQojbm82J+uOPPwbUjlzr168nNjaWXbt2sXv3bpYsWYLJZGLq1KkOL2h5Mzd9m9CQkZOHu7UduP0iwScc0uLg4l6o3LbsCimEEOKWZ3OiNktPT8fNTX36gAEDHFYgV+Gh16LXaTDmKaRk5RLgY+UTNRq1Vn10uXqeWhK1EEKIUrCpM1lMTIzlvjlJF+f8+fO2l8hFaDQafD3U15iSaePMM5bz1NKhTAghROnYlKhbtWrFE088wdatW4tcJzk5mVmzZtGwYUMWL15c6gI6k//V89QpWTb2KDOfp5ae30IIIUrJpqbvQ4cO8e6773LXXXeh1+tp2bIlkZGReHh4cOXKFQ4ePMiBAwdo2bIl06ZNo3fv3mVV7nLh62lnjdo8RCvxBGQmgWeAQ8slhBDi9mFTjTooKIgPPviA2NhYZs6cSe3atUlISODYMfWyjkOGDGHHjh1s2LDhpk/ScK3nt801au9gCKii3pfmbyGEEKVgV2cyDw8PBg4cyMCBAx1dHpdyrenbjqujVOkASWfg4G9Qo5uDSyaEEOJ2UarrUd/qrjV92zrrCdB0sPp33yLISXdgqYQQQtxOyjRR79ixoyw3X+b8zL2+7a1RB1aFnFQ4+LtjCyaEEOK2UaaJ+t577y3LzZc5u89RA2i10Gyoen/XXAeWSgghxO3E7glPzB588MFClyuKQmJiYmk371R+9vb6NmsyGFa9C2c2wOUTEFzDgaUTQghxOyh1ol6xYgVz587Fxyf/1F2KorB27drSbt6p7B5HbdlAFNToDsejYdePcOcEB5ZOCCHE7aDUibpLly74+PjQuXPnAo81a9astJt3KrvHUV+v+TA1Ue+eB13fAF2pQy6EEOI2UuqsUdzsY3///XdpN+9UpTpHbVa7N3gFQ9pFOLESavdyUOmEEELcDmR4VjH8zTXq0iRqN3do/LB6f+cPDiiVEEKI24ndNWqTycTs2bNZuXIlcXFx+a7ZrNFoWLlypUMK6Ey+V2vUadm55JkUdFqNfRtqNhQ2fwFH/4aL+yCikQNLKYQQ4lZmd6IeN24cn332GYAlSWs0GhRFQaOxM6G5GPM4aoDULCMBXtZelPoG4fWhcnuI2Qhfd4XOL0PHsaDTO6ikQgghblV2J+qff/4ZRVGIjIykWrVqVl328maj12lx1yrkmDSkZOban6gBHpgDf46BI8tg1Ttw6HcYMAMqNHZUcYUQQtyCStX0XbFiRY4dO4bBYHBIYd577z0WL17M4cOH8fT0pH379rz//vvUqVPHIdu3h6cb5ORAcml6fgP4hsPD82Dfr7D8JbUJ/Js74bF/rl1tSwghhLiB3Z3Jhg0bRmZmJkZjKRPYddasWcPo0aPZvHkz0dHR5Obm0rNnT9LTnTdXtqdO/WvXNKI30mig8QMweitU7wJ52bDkKTBmlX7bQgghbkk21ajfeusty30vLy8yMjJo2rQp/fv3JyAgIN+648ePt7kwNw7nmj17NmFhYezYsYNOnTrZvD1H8LoaoVKNpb6RTxjcPxtmtIVLh+G/t6HXO47bvhBCiFuGTYl64sSJBTqKnTx5kk8++aTAuvYk6hslJycD6nWwC5OdnU12drbl/5SUFACMRqNDavpGoxFPNwXQkJiW5dDWA/S+aPp8jNsvg1E2fUFejR4oVTo4bvtOZI6TQ+N1m5DY2UfiZj+JnX1KGzdbnqdRrh9XVYKqVata3aP71KlTVheiMIqiMGDAAK5cucK6desKXWfixIlMmjSpwPJ58+bh5eVVqv2bzT2mZXuClgFV8ugWaXWorNY05luqXF5DunsIq+u+Q67O0+H7EEII4VoyMjIYPHgwycnJ+Pn5FbuuTYm6PI0ePZq//vqL9evXU7FixULXKaxGXalSJRISEkp84dYwGo089fVK1l7U8nTnaoy7s1apt1lAdipuszqhST6Lqekw8vp+7Ph9lDOj0Uh0dDQ9evRAr5chaLaQ2NlH4mY/iZ19Shu3lJQUQkJCrErUNjV9x8TE2FwYgICAAJsS57PPPsvvv//O2rVri0zSAAaDodAe53q93mEfOHNnsrRsU9l8iPVBcM9M+P5utLvnoq3eWe1wdgtw5Ptwu5HY2UfiZj+JnX3sjZstz7EpUVetWtXWsqDRaJgwYYJV56wVReHZZ59lyZIlrF69mmrVqtm8P0dTz1E7qNd3UardAXe8AOs+hN+fgZBaENm07PYnhBDipmFTojaZTGVVDkBt7p43bx6//fYbvr6+XLx4EQB/f388PZ1z7vbqdN+lH0ddkq5vqGOrj/0LC4bCE6vAJ7Rs9ymEEMLl2ZSoq1WrZtf0oGPGjOG5554rcb2ZM2cC6qUzrzd79myGDx9u834dwTKOuqwTtVYHA2fBrG6QeAIWPgqP/CbTjAohxG3OpkQ9Z84cu3ZibZO5K/Zrs4yjLs0VtKzlGQCDfoZZ3eHMBvj7VejzgTpRihBCiNuSTYm6c+fOZVUOl2U+R13mTd9moXVg4NcwfxBs+wZ8IqDzS+WzbyGEEC5HrkddgnJr+r5e3T7Q6131/qrJsOmL8tu3EEIIlyKJugTmpu/sXBNZxrzy23G70dDldfX+P6/Djjnlt28hhBAuQxJ1CQy6a6eIy3SIVmE6vwztr3bC+2MM7F1YvvsXQgjhdJKoS6DVgK9BrVanZJZDh7LraTTQ4y1o+RigwG+jIbF0U7MKIYS4uUiitoKfpzpEqtxr1KAm6z4fQLXO6mUxo98s/zIIIYRwGknUVvDzUGvU5dbz+0ZaLdw1BTQ6OPQHnFzjnHIIIYQod5KorWBO1OXa8/tG4fWh1WPq/b9fg7xyboYXQgjhFJKorWBp+nZmogbo8hp4BkL8Adg5x7llEUIIUS4kUVvBz8N8jtrJtVivIHVOcID/3oHMK84tjxBCiDInidoKLtH0bdZiBITWg8xEWD3F2aURQghRxiRRW8Hc9O20zmTX07nBXe+p97d8Bac3OLc8QgghypQkaitYatTOGJ5VmBpdodlQQIHF/5MmcCGEuIVJorbCtc5kLtTT+q73Iag6pJyDP8eCC155TAghROlJoraC08dRF8bgA/d9A1o3OLAEds9zdomEEEKUAUnUVnC5pm+zqBbQ9eqFO5a9BJdPOLc8QgghHE4StRVcZhx1YTqMgSodwZgOS5+WJnAhhLjFSKK2wrUadS6KqyVCrQ4GfgV6bzi7BQ797uwSCSGEcCCXStRr166lX79+REZGotFoWLp0qbOLBFyb8CTPpJCeU47XpLaWf0Vo/4x6f8UkyHPBmr8QQgi7uFSiTk9Pp0mTJnz++efOLko+Hnot7jo1VC7Z/A3Q/lnwDoXEE7BjjrNLI4QQwkFcKlH37t2byZMnM3DgQGcXJR+NRoOfpwv2/L6ewRc6v6LeX/M+ZKc6tzxCCCEcws3ZBSiN7OxssrOzLf+npKQAYDQaMRpLn1DN2zAajfga3EhIyyExLROj0bPU2y4TjYfgtnkGmsST5K2bjqnzq04pxvVxE7aR2NlH4mY/iZ19Shs3W56nUVyud5RKo9GwZMkS7rnnniLXmThxIpMmTSqwfN68eXh5eTm0PB/t03EmTcPjdfJoFOSSIQOgQtI2Wp/6jFytgRX1p5GtD3B2kYQQQtwgIyODwYMHk5ycjJ+fX7Hr3tSJurAadaVKlUhISCjxhVvDaDQSHR1Njx49eHLeXtYdv8z7AxswsFlUqbddZhQF3fe90Z7fjqlmT/IGzAQP/3ItwvVx0+v15brvm53Ezj4SN/tJ7OxT2rilpKQQEhJiVaK+qZu+DQYDBoOhwHK9Xu/QD5xer8ffyx2A9BzF9T/Mvd6F2b3RHv8X7dd3QP9PoVaPci+Go9+H24nEzj4SN/tJ7Oxjb9xseY5LdSZzZf6udAWtklRuAyOWQ1ANSI2Fn+6H356BrBRnl0wIIYSNXCpRp6WlsXv3bnbv3g3AqVOn2L17NzExMc4tGNcS9eX07BLWdBGV28BT66HtaEADu+bCN3fClTPOLpkQQggbuFSi3r59O82aNaNZs2YAjBs3jmbNmjF+/HgnlwzqRPgCsO9cspNLYgN3L7jrXRixDHwjIeGImqxjdzm7ZEIIIazkUom6S5cuKIpS4DZnzhxnF43mlQMBOBCbQpbRBWcnK06V9vD4CghvCOnxMLsPHP1HfcxkgoxESImVecKFEMIF3dSdycpTxUBPwnwNxKdms/dcMq2rBTm7SLbxj1LPW//yCJxcBT8/DJ6BkHkFFJO6ToWm0OF5qNcfdPLREEIIV+BSNWpXptFoLLXqnTFXnFwaO3n4wZCF0HSompwzLl9L0mjgwm74dQR83gK2zoLcHGeWVgghBFKjtknzKgH8feAiO8/cpIkaQKeHAZ9DxzHqxTu8gtSadXYabJsFW76CK6dh2YvqnOH3fgkRjZxcaCGEuH1JjdoG19eoXXSeGOtoNBBSC8Lrg28EuBnAOxi6vApjD0DvaeAVAnH74euusGYa5OU6u9RCCHFbkkRtg4ZR/uh1GhLScjibmOns4pQNdy9o8z8YtRnq3g0mI6yaDN/eCWc2Obt0Qghx25Gmbxt46HU0iPRn99kkdsZcoXKwY+cTdyk+ofDQj7BvodoMHrsLZt8F1btCl9fUcdpmWcmQeAouHYb4Q+jiD9H2YizaDUehVneo0AS0Oue9FiGEuIlJorZR88qB7D6bxI4zV7jHlef8dgSNBho/CFXvgNXvwe6f1B7jJ1epPcRzsyD5POTkv6SmFggHWL0PVk9Wz4FXaguBVSGgMgRWgZA6EFxD3YcQQogiSaK2UYsqgXy34dTN2/PbHn4V1PnC7xgH6z6E3fPUHuLX8wqG0LoQWpe84NocOLCfhl4JaE+vV4eAHV1ecLteIVC5rXqr2Eod523wKZeXJIQQNwtJ1DZqXiUAgMMXU0nPzsXbcBuFMLAq9P8M7ngBzm4F7xDwrwR+keDubVnNZDRyKn4Z9fr0QasFzu+Ai/sgKebq7QzEH4KMBDj8p3oDQAPBNaFCYwisdrVHetANfwPVq4FpdepkLaZcQFE7xAkhxC3oNsoyjlHB35MK/h5cSM5iz7kk2tcIcXaRyl9gVfVmDZ3+Wq35erk5cGEPxGyCmM3qOfDUWLh8TL0Vy9xcfl3P+8jm0OpxaDgQ9J7WlU0IIW4Ckqjt0LxyIH/tu8CumNs0UTuCmztUaqXeOjynLku7BBf3wIW9kHpBndo04zJkJkLGFbUJPSeVfAnaLHYn/DYK/n0Dmg6B6l0gtA74VUSt1lshLxdSzkN2CgTXAr2Ho16tEELYTRK1HZpXURP1TT3xiSvyCYWad6q3ouTmQFaSOi+51k2d6jQnHfbMh+2zITkGNn2u3gD0Xmpzuk/4tSZ0gy8YMyA7Vb1lJqpXFUs+e7UpHdDoIKye2mkusilUagPhDaT3uhCi3EmitkPzygHAtYlPNNJzufy4uYNPWP5lHv5qR7cOz8Oxf2HvLxB/EC6fUBPyxb3Wb1/nrib3rCR1wpe4/bD7R/Uxd1+o2FJN3N5h6jl6r2Aw+KkJXOt29dx5nrpfYwbkZIBGq45Pd/dRz+Ub/MAzQN2PfHaEECWQRG2HBpH+uLtpuZJh5FRCOtVDpaeyS9DqoE5v9QbqFKlXTsPl42oTekaiWnvOSlETp8FPrV17+F8dNlYVfCuoiTXlPMTuVnu3n9uu3nJSrw1Pc0h59WrnOL2neoCgc0endaNdeh66P5apve19K4B/RbV8AZXVsoLakc6YobYmZF49LZCZCJlJaitBTpp6y81RX6NngPpcrxD1lEBAZTlIEOImIYnaDu5uWhpH+bP9zBW2nkqURO2qdHp1qtSQWrY/17+ieqt3t/q/KQ/iDsDZLZBwFNIT1F7rGYnqOW2TCZQ8telco1Vry+7eahJWFDWh5qSryTM7RV3PZFQvO3odLRAGsHd/4eVy91X3Y8yw/TXduJ3w+uq5eA8/tbZv8FGb/LOS1IRf4G8yGDPVsit5akzcfcA3XJ2K1idCvUqbubNhQBX1dIPeS30vQI2FMUM9WMpKVg+g0i+pt4zL6sGVknftYjFeIerBim+E+tevQr4RBiVSFDXeGYlq2c2tHjq9Wi7PIOv7MAjhJJKo7dSxVgjbz1zhg3+P0q1eGGG+0vHolqbVqcPGKjQu/bYURU3Y5iRozIK8HMjLITc7g72bV9Gkeji6jEtqp7rkc+qwtoyEApPLgEatKXsGXhu+Zk667r7qqYLs1GuJNvUCJBxTt3N2i3orjexk9ZZwtPj1NDpw81AnyVFKeT13g//VA4MwdViezh2dRkerixfRzftWTcxZyeotM6n4/Wm06sGAz9VTGd6h6v/eIWpcde5qud3c1QMT8wGXMf1aHwfzTTEBGnWbGu3VloxAtTXDM1A9TeITpu7DO1Q9UHDzuHagoCjq58B8QJeVcnXbKeqy62ndrr3v5vfelgMYcVORRG2nJzvVYPm+ixyJS+X5n3fz4+Nt0GmlKVFYQaNRf8QNvkClfA8pRiNnj+XSqEMfdHp9/uflpENKrPoj7e6t3tw8ba8R5hnVZB1/EK6cUq+clpOmJgVT3rXk4hFww19/NblodWri1erU56ZegLQ4tWzJ59Rx8ldOqwcXuVlXX1iemtwsMdCpNXmv4KuJ6+r5fp3h6va1auJKv3qwknpR/ZuTdt3BwRHL5rRAJEBSEa9Z76W2bpiutgSYciE3U02u6fEFWjbKlU492CA381pnRnvovdRY+oSpLQVuhms3BTV25tMl5tMj2am4ZafSLzcHzW5N/m15BoKnv/r+mw9ivEPV98nd23K6Bp1efZ8trUbp1z5P2alXDzLMIzU06vtr7qfhEXDtIMZ88/BXt6nTq9uHqwdHV/t8GNOvfWZz0tWDGzRXT+Vo1NEaHv7X3QKuzb1gLUVRW2CMGep9zdUDMFAPrtzLd/poSdR28nTX8cWQ5vT/fD2bTl7mkxVHGdezjrOLJW5l7t72NePfSKdXm73D65d+WwChtQtfriiQm63+2OVmqT98ek/1R9rd275z5FkpVxP3BXU4X14OmIzk5WRz4MA+6jdvh5t38LUfaa8g9Ye6sKF2eUa1uT3taqJOT7h6u6T+zUlVz/HnXm3x0OpA763+SOuv9nHwuNrPwd1HPYBSTFdveddaMsz9B8zbTotXW1Is5chWb9dz87h6MGfevjfX5g9ALU9WsrrtrCT1f2OGepCUdMamkGrMW75+1GNOqnpLtmlTLuy6lie9lzpaRHv1YCAv5+oBQMa15Jzv4OIGLUZAv+nlWXjXS9QzZsxg2rRpXLhwgQYNGjB9+nTuuOMOZxerUDXDfHhvYCOen7+bz1Ydp2XVIDrVDnV2sYRwDZqrtRtHjkf3uJocQ/MfFFtmw2vUB25siSiKTn/13HeE48pnrbyrNXpjlvo3z6gexLh7qwcDOht+ms2nUtLi8x8I5GarN/NBgLvv1dEH3ldPj6itOkatByvXrKd7t27o9fpr/Qgyr1w70MhIuNaXIP2yWubcHMspG7WW6X3d7dr21YOy61p9TLnXDjKuP5DJuKzesoo4OjCPyDBv39KqZFDLjKIeJOVm5z/1YbyadLOS8h8g2csJnTBdKlEvWLCAMWPGMGPGDDp06MBXX31F7969OXjwIJUrV3Z28Qo1oGkUW04lMm9LDGMW7GbKwEZ0rRuGXicdVIQQRdC5gc58+qOUrj+VElzD9ucbjWTrA9QDFmsPcsqSoqjJPM+odrhUlKtN7XaWzTz3QuYVtVNhbqZ6oGQyqvtwM6gHSXpv9aDSfLBkbjkxn4ZBudYMXs5cKlF/9NFHPPbYYzz++OMATJ8+nX/++YeZM2fy3nvvObl0RRt/d312xyRx8EIK/5u7gxAfd+5pGkX/ppFUCfbGz8NNxloLIYQ1NJpr56gdwTz3wo3zL9haJidymUSdk5PDjh07ePXVV/Mt79mzJxs3biz0OdnZ2WRnXzu3k5KSAoDRaMRoNJa6TOZtlLQtHfDdo82Zte4Uv++5wKW0HL5Zf4pv1p8CwOCmJczXQJC3OwY3LQY3Le5uWvQ6LVoNaDQa9TyRBjRorv5V5ft83CTJXjGZiI3VsurXvWhk6ItNJHb2kbjZT2Jnm7bVAhnYLMrq/FAUW57nMok6ISGBvLw8wsPD8y0PDw/n4sWLhT7nvffeY9KkSQWW//vvv3h5Oa5XXnR0tFXrNQYaNIRDSRq2xGs4lqwhM09Ddq6Js1cyOXsl02Flcn1auFT4+yZKIrGzj8TNfhI7a8XFnsPjwh7L/9bmhxtlZFg/F4LLJGqzG5uIi5ui87XXXmPcuHGW/1NSUqhUqRI9e/bEz8+v1GUxGo1ER0fTo0cPtZOFlfpddz/LmEd8ajaXUrO5kmEkJ9dETp6J7FwTxjyTejpGUTAp6muFa30Nles6HSpF9UB0QXl5Jo4dPUqt2rXRybl6m0js7CNxs5/EzjZ1I3y5o2aI3fnBzNwCbA2XSdQhISHodLoCtef4+PgCtWwzg8GAwVDwOsR6vd6uwBWlNNvT6/X4enlQo/CXcEsyGo0sSz9Cn841HPo+3A4kdvaRuNlPYlc69uYHW57jModP7u7utGjRokAzQnR0NO3bt3dSqYQQQgjncpkaNcC4ceMYNmwYLVu2pF27dnz99dfExMTw1FNPObtoQgghhFO4VKJ+6KGHuHz5Mm+99RYXLlygYcOGLFu2jCpVqji7aEIIIYRTuFSiBhg1ahSjRo1ydjGEEEIIl+Ay56iFEEIIUZAkaiGEEMKFSaIWQgghXJjLnaMuDfOEIbYMJC+O0WgkIyODlJQUGV9oA4mb/SR29pG42U9iZ5/Sxs2cpxSl5MmsbqlEnZqaCkClSpWcXBIhhBCiZKmpqfj7+xe7jkaxJp3fJEwmE7Gxsfj6+jrkalXmKUnPnj3rkClJbxcSN/tJ7OwjcbOfxM4+pY2boiikpqYSGRmJtoSLodxSNWqtVkvFihUdvl0/Pz/5ANtB4mY/iZ19JG72k9jZpzRxK6kmbSadyYQQQggXJolaCCGEcGGSqIthMBiYMGFCoVfoEkWTuNlPYmcfiZv9JHb2Kc+43VKdyYQQQohbjdSohRBCCBcmiVoIIYRwYZKohRBCCBcmiVoIIYRwYZKoizBjxgyqVauGh4cHLVq0YN26dc4ukkt57733aNWqFb6+voSFhXHPPfdw5MiRfOsoisLEiROJjIzE09OTLl26cODAASeV2HW99957aDQaxowZY1kmsSvc+fPnGTp0KMHBwXh5edG0aVN27NhheVziVrjc3Fz+7//+j2rVquHp6Un16tV56623MJlMlnUkdqq1a9fSr18/IiMj0Wg0LF26NN/j1sQpOzubZ599lpCQELy9venfvz/nzp2zv1CKKGD+/PmKXq9XZs2apRw8eFB5/vnnFW9vb+XMmTPOLprL6NWrlzJ79mxl//79yu7du5W+ffsqlStXVtLS0izrTJkyRfH19VUWLVqk7Nu3T3nooYeUChUqKCkpKU4suWvZunWrUrVqVaVx48bK888/b1kusSsoMTFRqVKlijJ8+HBly5YtyqlTp5QVK1Yox48ft6wjcSvc5MmTleDgYOXPP/9UTp06pSxcuFDx8fFRpk+fbllHYqdatmyZ8sYbbyiLFi1SAGXJkiX5HrcmTk899ZQSFRWlREdHKzt37lS6du2qNGnSRMnNzbWrTJKoC9G6dWvlqaeeyresbt26yquvvuqkErm++Ph4BVDWrFmjKIqimEwmJSIiQpkyZYplnaysLMXf31/58ssvnVVMl5KamqrUqlVLiY6OVjp37mxJ1BK7wr3yyitKx44di3xc4la0vn37KiNHjsy3bODAgcrQoUMVRZHYFeXGRG1NnJKSkhS9Xq/Mnz/fss758+cVrVar/P3333aVQ5q+b5CTk8OOHTvo2bNnvuU9e/Zk48aNTiqV60tOTgYgKCgIgFOnTnHx4sV8cTQYDHTu3FnieNXo0aPp27cvd955Z77lErvC/f7777Rs2ZIHHniAsLAwmjVrxqxZsyyPS9yK1rFjR1auXMnRo0cB2LNnD+vXr6dPnz6AxM5a1sRpx44dGI3GfOtERkbSsGFDu2N5S12UwxESEhLIy8sjPDw83/Lw8HAuXrzopFK5NkVRGDduHB07dqRhw4YAllgVFsczZ86Uexldzfz589m5cyfbtm0r8JjErnAnT55k5syZjBs3jtdff52tW7fy3HPPYTAYeOSRRyRuxXjllVdITk6mbt266HQ68vLyeOeddxg0aBAgnzlrWROnixcv4u7uTmBgYIF17M0hkqiLcONlMhVFccilM29FzzzzDHv37mX9+vUFHpM4FnT27Fmef/55/v33Xzw8PIpcT2KXn8lkomXLlrz77rsANGvWjAMHDjBz5kweeeQRy3oSt4IWLFjAjz/+yLx582jQoAG7d+9mzJgxREZG8uijj1rWk9hZx544lSaW0vR9g5CQEHQ6XYEjn/j4+AJHUQKeffZZfv/9d1atWpXvEqMREREAEsdC7Nixg/j4eFq0aIGbmxtubm6sWbOGTz/9FDc3N0t8JHb5VahQgfr16+dbVq9ePWJiYgD5zBXnpZde4tVXX+Xhhx+mUaNGDBs2jLFjx/Lee+8BEjtrWROniIgIcnJyuHLlSpHr2EoS9Q3c3d1p0aIF0dHR+ZZHR0fTvn17J5XK9SiKwjPPPMPixYv577//qFatWr7Hq1WrRkRERL445uTksGbNmts+jt27d2ffvn3s3r3bcmvZsiVDhgxh9+7dVK9eXWJXiA4dOhQYAnj06FGqVKkCyGeuOBkZGWi1+X/udTqdZXiWxM461sSpRYsW6PX6fOtcuHCB/fv32x9Lu7qg3eLMw7O+/fZb5eDBg8qYMWMUb29v5fTp084umst4+umnFX9/f2X16tXKhQsXLLeMjAzLOlOmTFH8/f2VxYsXK/v27VMGDRp0Ww73sMb1vb4VRWJXmK1btypubm7KO++8oxw7dkz56aefFC8vL+XHH3+0rCNxK9yjjz6qREVFWYZnLV68WAkJCVFefvllyzoSO1Vqaqqya9cuZdeuXQqgfPTRR8quXbssw3OtidNTTz2lVKxYUVmxYoWyc+dOpVu3bjI8qyx88cUXSpUqVRR3d3elefPmlmFHQgUUeps9e7ZlHZPJpEyYMEGJiIhQDAaD0qlTJ2Xfvn3OK7QLuzFRS+wK98cffygNGzZUDAaDUrduXeXrr7/O97jErXApKSnK888/r1SuXFnx8PBQqlevrrzxxhtKdna2ZR2JnWrVqlWF/rY9+uijiqJYF6fMzEzlmWeeUYKCghRPT0/l7rvvVmJiYuwuk1zmUgghhHBhco5aCCGEcGGSqIUQQggXJolaCCGEcGGSqIUQQggXJolaCCGEcGGSqIUQQggXJolaCCGEcGGSqIUQQggXJolaCFGsMWPGcM899zi7GELctiRRCyGKtW3bNlq3bu3sYghx25IpRIUQhTIajXh7e2M0Gi3LWrduzZYtW5xYKiFuP27OLoAQwjXpdDrWr19PmzZt2L17N+Hh4Xh4eDi7WELcdiRRCyEKpdVqiY2NJTg4mCZNmji7OELctuQctRCiSLt27ZIkLYSTSaIWQhRp9+7dkqiFcDJJ1EKIIu3bt4/GjRs7uxhC3NYkUQshimQymdi7dy+xsbEkJyc7uzhC3JYkUQshijR58mQWLFhAVFQUb731lrOLI8RtScZRCyGEEC5MatRCCCGEC5NELYQQQrgwSdRCCCGEC5NELYQQQrgwSdRCCCGEC5NELYQQQrgwSdRCCCGEC5NELYQQQrgwSdRCCCGEC5NELYQQQrgwSdRCCCGEC5NELYQQQriw/wd248++no/iCwAAAABJRU5ErkJggg==\",\n \"text/plain\": [\n \"![](\\\"media/tanh.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Like for the spectral radius, $\\\\omega_\\\\text{in}$ should be tuned carefully.\\n\",\n \"- A good starting value is usually around 0.1.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- A less critical, still important hypeparameter is the number of Reservoir units $N_h$. \\n\",\n \"- A larger value can give better performance at the cost of higher computation time.\\n\",\n \"- A good starting point is usually $N_h=300$, to be increased until there is no more gain in performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- There are also other hyperparameters in the ESN, such as the sparsity of the Reservoir and an optional noise to inject in the state update equation.\\n\",\n \"- These are usually less critical than $\\\\rho$ and $\\\\omega_\\\\text{in}$ and can be left to their default value in most cases.\\n\",\n \"- Tuning hyperparameters in randomized architectures such as the ESN is much more important than in trainable neural networks, since there is no training that can compensate for poorly initialized models.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Python example: electricity load forecasting with ESN\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"\\n\",\n \"- In the following, we will use the [Python library](https://github.com/FilippoMB/Time-series-classification-and-clustering-with-Reservoir-Computing) `reservoir-computing`. \\n\",\n \"- The library allows us to perform time series classification, clustering, and forecasting with Reservoir Computing.\\n\",\n \"- In this lecture, we will focus on *forecasting*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Load the data\\n\",\n \"\\n\",\n \"We reload the same time series of energy load that we used before.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded ElecRome dataset.\\n\",\n \"Data shape:\\n\",\n \" X: (137376, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"ts_full = PredLoader().get_data('ElecRome')\\n\",\n \"\\n\",\n \"# Resample the time series to hourly frequency\\n\",\n \"ts_hourly = np.mean(ts_full.reshape(-1, 6), axis=1)[:, None]\\n\",\n \"\\n\",\n \"# Use only the first 3000 time steps\\n\",\n \"time_series = ts_hourly[0:3000, :]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- To train the ESN we need input and target data `Xtr` and `Ytr`. \\n\",\n \"- We also need test data `Xte` and `Yte` to test the model and validation data `Xval` and `Yval` if we need to do hyperparameters tuning.\\n\",\n \"- We will use the function `forecasting_datasets` that given a time series `X` does the following computations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"1. Splits the dataset in consecutive chunks: `train`, `val` and `test`. \\n\",\n \" - The size of the chunks is given by the values `val_percent` and `test_percent`. \\n\",\n \" - If we do not need validation data, set `val_percent=0` (default) and the validation data will not be created.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"2. Create input data `X` and target data `Y` by shifting the data `horizon` time steps, where `horizon` is how far we want to predict. \\n\",\n \"- For example:\\n\",\n \" - `Xtr = train[:-horizon,:]`\\n\",\n \" - `Ytr = train[horizon:,:]`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"3. Normalizes the data using a scaler from `sklearn.preprocessing`. \\n\",\n \" - If no scalers are passed, a `StandardScaler` is created. \\n\",\n \" - The scaler is fit on `Xtr` and then used to transform `Ytr`, `Xval`, and `Xte`. \\n\",\n \" - Note that `Yval` and `Yte` are **not** transformed.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The code below exemplifies the use of the function.\\n\",\n \"- Note that we loose some data due to splitting and shifting.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Xtr: [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.]\\n\",\n \"Ytr: [[ 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.]]\\n\",\n \"Xval: [17. 18. 19. 20. 21. 22.]\\n\",\n \"Yval: [[22 23 24 25 26 27]]\\n\",\n \"Xte: [28. 29. 30.]\\n\",\n \"Yte: [[33 34 35]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"X = np.arange(36)[:, None]\\n\",\n \"Xtr, Ytr, Xte, Yte, Xval, Yval, scaler = make_forecasting_dataset(X, horizon=5,\\n\",\n \" test_percent=0.2, \\n\",\n \" val_percent=0.3)\\n\",\n \"print(\\\"Xtr: \\\", scaler.inverse_transform(Xtr.T)[0])\\n\",\n \"print(\\\"Ytr: \\\", scaler.inverse_transform(Ytr.T))\\n\",\n \"print(\\\"Xval: \\\", scaler.inverse_transform(Xval.T)[0])\\n\",\n \"print(\\\"Yval: \\\", Yval.T)\\n\",\n \"print(\\\"Xte: \\\", scaler.inverse_transform(Xte.T)[0])\\n\",\n \"print(\\\"Yte: \\\", Yte.T)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Back to our real data: we want to make forecast 24h ahead. \\n\",\n \"- In practice, we must optimize the hyperparameters on a valiation set.\\n\",\n \"- However, since this is just a demonstration, we will use the default hyperparameters.\\n\",\n \"- Therefore, we leave the default `val_percent=0` and the validation set is not created.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Xtr shape: (2676, 2)\\n\",\n \"Ytr shape: (2676, 1)\\n\",\n \"Xte shape: (276, 2)\\n\",\n \"Yte shape: (276, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Generate training and test datasets\\n\",\n \"Xtr, Ytr, Xte, Yte, scaler = make_forecasting_dataset(time_series,\\n\",\n \" horizon=24, # forecast horizon of 24h ahead\\n\",\n \" test_percent = 0.1)\\n\",\n \"print(f\\\"Xtr shape: {Xtr.shape}\\\\nYtr shape: {Ytr.shape}\\\\nXte shape: {Xte.shape}\\\\nYte shape: {Yte.shape}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Define the Reservoir and compute the states\\n\",\n \"\\n\",\n \"- First, we specify the Reservoir hyperparameters and we initialize it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"res= Reservoir(n_internal_units=900,\\n\",\n \" spectral_radius=0.99,\\n\",\n \" input_scaling=0.1,\\n\",\n \" connectivity=0.25)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Then, we compute the sequence of the Reservoir states `states_tr` and `states_te` associated with the training and test data, respecitvely.\\n\",\n \"- Since the initial states of the Reservoir mostly depend on the initialization, e.g., $\\\\mathbf{h}(0) = [0,0,\\\\dots,0]$, we want to get rid of the initial transient phase.\\n\",\n \"- This can be done by setting `n_drop` that specifies how many of the initial states we drop.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"states_tr shape: (1, 2666, 900)\\n\",\n \"states_te shape: (1, 266, 900)\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_drop=10\\n\",\n \"states_tr = res.get_states(Xtr[None,:,:], n_drop=n_drop, bidir=False)\\n\",\n \"states_te = res.get_states(Xte[None,:,:], n_drop=n_drop, bidir=False)\\n\",\n \"print(f\\\"states_tr shape: {states_tr.shape}\\\\nstates_te shape: {states_te.shape}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Fit a linear readout\\n\",\n \"\\n\",\n \"- We are now ready to train the readout to predict the desired output given the sequence of Reservoir states.\\n\",\n \"- We start by using a linear readout implemented by a Ridge regressor.\\n\",\n \"- After the readout is trained, we use it to compute the predictions $\\\\hat{Y}_\\\\text{te}$ of the test data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training time: 0.0344s\\n\",\n \"Test time: 0.0003s\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Fit the ridge regression model\\n\",\n \"ridge = Ridge(alpha=1.0) \\n\",\n \"time_start = time.time()\\n\",\n \"ridge.fit(states_tr[0], Ytr[n_drop:,:])\\n\",\n \"print(f\\\"Training time: {time.time()-time_start:.4f}s\\\")\\n\",\n \"\\n\",\n \"# Compute the predictions\\n\",\n \"time_start = time.time()\\n\",\n \"Yhat = ridge.predict(states_te[0])\\n\",\n \"print(f\\\"Test time: {time.time()-time_start:.4f}s\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Finally, we plot the results and compute the MSE.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"> \\\"*Something as small as the flutter of a butterfly’s wing can ultimately cause a typhoon halfway around the world.*\\\"\\n\",\n \"\\n\",\n \"![](\\\"media/lokta_volt.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's define the system of differential equations.\\n\",\n \"- To \\\"run\\\" the system, we use a numerical solver `scipy.integrate.solve_ivp`.\\n\",\n \"- Since the solver takes a single state variable, we define `z = [x, y]`.\\n\",\n \"- Note that the variable `t` is not used in the function we define, but is necessary to the solver.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Lotka-Volterra equations\\n\",\n \"def lotka_volterra(t, z, alpha, beta, delta, gamma):\\n\",\n \" x, y = z\\n\",\n \" dxdt = alpha * x - beta * x * y\\n\",\n \" dydt = delta * x * y - gamma * y\\n\",\n \" return [dxdt, dydt]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n }\n },\n \"source\": [\n \"- For the next plots we will use a function `lokta_volterra_attractor` that:\\n\",\n \" - Runs the solver computing the sequence of states over time.\\n\",\n \" - Plots the time series of the state variables.\\n\",\n \" - Plots the attractor.\\n\",\n \"- Note the syntax of `solve_ivp`: all the parameters of the systems are passed through `args` argument. More info [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"def lokta_volterra_attractor(z0, t, \\n\",\n \" alpha=2, beta=1.25, gamma=1.5, delta=0.75,\\n\",\n \" ax1=None, ax2=None):\\n\",\n \"\\n\",\n \" # Solve the differential equations\\n\",\n \" sol = solve_ivp(lotka_volterra, [t.min(), t.max()], z0, \\n\",\n \" args=(alpha, beta, delta, gamma), t_eval=t)\\n\",\n \"\\n\",\n \" if ax1 is not None:\\n\",\n \" # Plot the time series\\n\",\n \" ax1.plot(sol.t, sol.y[0], label='Prey $x(t)$')\\n\",\n \" ax1.plot(sol.t, sol.y[1], label='Predator $y(t)$')\\n\",\n \" ax1.set_title('System variables')\\n\",\n \" ax1.set_xlabel('Time')\\n\",\n \" ax1.set_ylabel('Population')\\n\",\n \" ax1.legend()\\n\",\n \"\\n\",\n \" if ax2 is not None:\\n\",\n \" # Plot attractor\\n\",\n \" ax2.plot(sol.y[0], sol.y[1], label=f'$z(0) = {z0}$', alpha=0.5, linewidth=0.3)\\n\",\n \" ax2.set_title('Attractor')\\n\",\n \" ax2.set_xlabel('Prey Population')\\n\",\n \" ax2.set_ylabel('Predator Population')\\n\",\n \" ax2.legend()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- For the given set of parameters, the system shows a chaotic behavior.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/lorenz.gif\\\")
\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The Lorenz system was one of the first examples to demonstrate the phenomenon of deterministic chaos.\\n\",\n \"- Like the other chaotic systems, this one exhibits sensitive dependence on initial conditions.\\n\",\n \"- Also, it generates apparently random and unpredictable patterns, despite being governed by deterministic laws. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The study of the Lorenz system had profound implications across numerous fields, from meteorology and climate science to engineering, and economics. \\n\",\n \"- It challenged previous notions of predictability in physical systems and contributed to the development of chaos theory by:\\n\",\n \" - deepening the understanding of complex systems,\\n\",\n \" - highlighting the limitations of current prediction models in systems with chaotic dynamics,\\n\",\n \" - inspiring new approaches in the analysis and control of such systems.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"The Lorenz system is defined by the following set of non-linear ODEs:\\n\",\n \"\\n\",\n \"\\\\begin{align*}\\n\",\n \"\\\\frac{dx}{dt} &= \\\\sigma(y - x), \\\\\\\\\\n\",\n \"\\\\frac{dy}{dt} &= x(\\\\rho - z) - y, \\\\\\\\\\n\",\n \"\\\\frac{dz}{dt} &= xy - \\\\beta z,\\n\",\n \"\\\\end{align*}\\n\",\n \"\\n\",\n \"Typical values at which the Lorenz system shows a chaotic behavior are $\\\\sigma = 10$, $\\\\rho = 28$, and $\\\\beta = \\\\frac{8}{3}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Lyapunov exponents in higher dimensional continuous systems\\n\",\n \"\\n\",\n \"- Previously, we introduced the largest [Lyapunov exponent](#lyapunov-exponent) $\\\\lambda$ as a tool for detecting chaos.\\n\",\n \"- For the Logistic map, we showed that when $\\\\lambda > 0$ the dyanmic of the system is chaotic.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- In higher dimensional continuous systems, the analysis of Lyapunov exponents remains a crucial method for understanding the system's dynamics.\\n\",\n \"- In particular, they can still be used to determine stability and the onset of chaos.\\n\",\n \"- However, in this setting things become more complicated.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- In one-dimensional discrete systems like the Logistic map, there is one Lyapunov exponent.\\n\",\n \"- Instead, in a continuous system of dimension $n$, there are $n$ Lyapunov exponents.\\n\",\n \"- Calculating these exponents involves complex numerical methods, which are computationally expensive and sensitive to numerical precision.\\n\",\n \"- Additionally, the initial conditions and parameter values have a stronger influence on the Lyapunov spectrum.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- *Positive exponents* indicate directions in which the system exhibits sensitive dependence on initial conditions, a characteristic of chaotic dynamics.\\n\",\n \"- *Zero exponents* suggest neutral stability along certain directions, often associated with conserved quantities or symmetries in the system.\\n\",\n \"- *Negative exponents* reflect directions of convergence, indicating stability in those dimensions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The largest Lyapunov exponent is still the most important in predicting the overall system behavior, particularly chaos.\\n\",\n \"- However, the entire spectrum can provide insights into more complex dynamics like:\\n\",\n \" - mixed modes (simultaneous stable and chaotic behaviors),\\n\",\n \" - hyperchaotic systems (systems with more than one positive Lyapunov exponent).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- To compute the Lyapunov spectrum we use the function `computeLE(func, func_jac, x0, t, p)`, which is part of the companion code to this course.\\n\",\n \"- The arguments of the function are:\\n\",\n \" - `func`, which specifies the differential equations of the dynamical system,\\n\",\n \" - `func_jac`, which is the Jacobian of the system, i.e., the partial derivatives,\\n\",\n \" - `x0`, which represents the initial conditions,\\n\",\n \" - `t`, the time vector specifying the time steps along which the trajectory of the system is computed,\\n\",\n \" - `p`, the parameters of the system.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- For the Lorenz system, `func` is defined as follows\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def lorenz(t, x, sigma, rho, beta):\\n\",\n \" res = np.zeros_like(x)\\n\",\n \" res[0] = sigma*(x[1] - x[0])\\n\",\n \" res[1] = x[0]*(rho - x[2]) - x[1]\\n\",\n \" res[2] = x[0]*x[1] - beta*x[2]\\n\",\n \" return res\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The Jacobian of the system is the matrix of the partial derivatives.\\n\",\n \"- Consider a general dynamical system with 3 variables $x$, $y$, and $z$, represented by the equations:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\begin{cases}\\n\",\n \"\\\\dot{x} = f(x, y, z) \\\\\\\\\\n\",\n \"\\\\dot{y} = g(x, y, z) \\\\\\\\\\n\",\n \"\\\\dot{z} = h(x, y, z)\\n\",\n \"\\\\end{cases}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"- The Jacobian is:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"J = \\\\begin{bmatrix}\\n\",\n \"\\\\frac{\\\\partial f}{\\\\partial x} & \\\\frac{\\\\partial f}{\\\\partial y} & \\\\frac{\\\\partial f}{\\\\partial z} \\\\\\\\\\n\",\n \"\\\\frac{\\\\partial g}{\\\\partial x} & \\\\frac{\\\\partial g}{\\\\partial y} & \\\\frac{\\\\partial g}{\\\\partial z} \\\\\\\\\\n\",\n \"\\\\frac{\\\\partial h}{\\\\partial x} & \\\\frac{\\\\partial h}{\\\\partial y} & \\\\frac{\\\\partial h}{\\\\partial z}\\n\",\n \"\\\\end{bmatrix}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- So, for the Lorenz system we have:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"J_{\\\\text{Lorenz}} = \\\\begin{bmatrix}\\n\",\n \"-\\\\sigma & \\\\sigma & 0 \\\\\\\\\\n\",\n \"\\\\rho - z & -1 & -x \\\\\\\\\\n\",\n \"y & x & -\\\\beta\\n\",\n \"\\\\end{bmatrix}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def lorenz_jac(t, x, sigma, rho, beta):\\n\",\n \" res = np.zeros((x.shape[0], x.shape[0]))\\n\",\n \" res[0,0], res[0,1] = -sigma, sigma\\n\",\n \" res[1,0], res[1,1], res[1,2] = rho - x[2], -1., -x[0]\\n\",\n \" res[2,0], res[2,1], res[2,2] = x[1], x[0], -beta\\n\",\n \" return res\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Finally, we specify the initial conditions `x0`, the time vector `t`, and the system's parameters `p`.\\n\",\n \"- The latter, in the case of Lorenz systems are $\\\\sigma$, $\\\\rho$, and $\\\\beta$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Lyapunov Exponents: [ 9.06320944e-01 -2.38039941e-03 -1.45705049e+01]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x0 = np.array([1.5, -1.5, 20.]) \\n\",\n \"t = np.arange(0, 1000, 1e-2)\\n\",\n \"sigma, rho, beta = 10., 28., 8/3\\n\",\n \"p = (sigma, rho, beta)\\n\",\n \"\\n\",\n \"LEs, hist = computeLE(lorenz, lorenz_jac, x0, t, p=p)\\n\",\n \"print(f\\\"Lyapunov Exponents: {LEs}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- One of the Lyapunov exponents is positive.\\n\",\n \"- This suggests that the Lorenz system configured with the specific values of $\\\\sigma$, $\\\\rho$, and $\\\\beta$ exibits a chaotic behavior along one direction.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Bifurcation diagrams in higher dimensional continuous systems\\n\",\n \"\\n\",\n \"- When studying the Logistic map we introduced the [bifurcation diagram](#bifurcation-diagrams), which is the succession of period-doubling produced as the control parameter changes.\\n\",\n \"- We can compute bifurcation diagrams also for higher dimensional continuous systems.\\n\",\n \"- Compared to unidimensional discrete systems there are two important differences.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"1. Since we have more system's parameters, we should change one at the time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"2. Since the system is continuous, the system variables assume values in a continuous interval.\\n\",\n \" - In the continuum we cannot plot all the values assumed by the time series.\\n\",\n \" - To detect period-doubling we can plot the local **maxima** and **minima** of the time series.\\n\",\n \" - In a contractive regime, the time series will oscillate between a limited number of local minima/maxima.\\n\",\n \" - At the onset of chaos, the number of local minima/maxima will start to grow\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- As for the Lyapunov exponent, we will use a function from the companion code of this course, `plot_bifurcation_diagram`.\\n\",\n \"- The function takes the following arguments: \\n\",\n \" - `func`, `func_jac`, `x0`, `time_vector` are the same as before. \\n\",\n \" - `parameters` is the set of parameters we want to change. Only one parameter should vary, while the others remain constant.\\n\",\n \" - `p_idx` is the index of the parameter that changes in `parameters`.\\n\",\n \" - `max_time` is the number of time steps from `time_vector` used to estimate the Lyapunov exponents. If is `None` all time steps will be used. Since this function takes a lot of time, it's good to set a limit.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- In the following, we compute the bifurcation diagram for Lorenz system as we vary the paramter $\\\\rho \\\\in [20,100]$.\\n\",\n \"- The other parameters are kept fixed: $\\\\sigma=10$ and $\\\\beta = 8/3$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Specify the parameters to try\\n\",\n \"sigma, beta = 10, 8/3 \\n\",\n \"rho_values = np.arange(20, 100, 0.05) \\n\",\n \"params = np.array([np.tile(sigma, len(rho_values)), \\n\",\n \" rho_values, \\n\",\n \" np.tile(beta, len(rho_values))]).T\\n\",\n \"\\n\",\n \"x0 = [1.5, -1.5, 20.] # Initial conditions\\n\",\n \"t = np.arange(0, 10, 0.002) # Time vector\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"fc795cc242d2426486b7e93a73da1c0b\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \" 0%| | 0/1600 [00:00\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_bifurcation_diagram(func=lorenz, func_jac=lorenz_jac, \\n\",\n \" x0=x0, time_vector=t, \\n\",\n \" parameters=params, p_idx=1, max_time=500)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The function generated 4 plots.\\n\",\n \"- The first 3 are bifurcation diagrams for the system's variables $x$, $y$, and $z$.\\n\",\n \"- The last plot shows the largest value among the 3 Lyapunov exponents.\\n\",\n \"- We see a relationship between the Lyapunov exponents and the onset of chaos in the system.\\n\",\n \"- When the largest Lyapunov exponent is positive, the system shows a chaotic behavior along at least one direction.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n }\n },\n \"source\": [\n \"```{hint}\\n\",\n \"- If running `plot_bifurcation_diagram` takes too long, try reducing the number of configuration explored.\\n\",\n \"- For example, try `rho_values = np.arange(20, 100, 0.5)`.\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Phase space\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The current state of a system is treated as a point in the *phase space* or *state space*.\\n\",\n \"- The phase space represents all the possible states of a system.\\n\",\n \"- It describes how system variables $x, y, z, \\\\dots$ interact and evolve.\\n\",\n \"\\n\",\n \"![](\\\"media/phase_space.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The phase space gives us complete knowledge about the current state of system.\\n\",\n \"- We are particularly interested its *trajectory* (or orbit) over time.\\n\",\n \"- Knowing it, allows us to make predictions about future states of the system.\\n\",\n \"- Clearly, this is of key importance in time series forecasting.\\n\",\n \"\\n\",\n \"![](\\\"media/predict.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- To identify the trajectory of the system in the phase space, we have to identify properties of its attractor.\\n\",\n \"- One of these, is its *dimensionality*.\\n\",\n \"- To determine the dimensionality of an attractor, especially strange attractors of chaotics systems, we have to revise our concept of dimensionality.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Fractal dimensions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We commonly understand dimensions from a geometric perspective: length, width, depth.\\n\",\n \"- Common geometrical objects such as a line, a square, or a cube require an increasing number of dimensions to contain them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The concept of dimensions is also tied to common measures on these objects such as their perimeter, area, and volume, which require an increasing number of dimensions to express them.\\n\",\n \"\\n\",\n \"![](\\\"media/dimensions.png\\\")
\\n\",\n \"\\n\",\n \"- In this perspective, dimensions are integers and the idea of a fractional dimension, e.g., $d=1.26$, makes little sense.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's now take a different approach.\\n\",\n \"- Let's think at dimensions as *how much they scale up* some quantity.\\n\",\n \"- For example, let's double the edge of a square.\\n\",\n \"- The perimeter will grow by a factor of 2, while the area by a factor of 4. \\n\",\n \"- If we double the edge of a cube its volume gets 8 times bigger.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Similarly, we can divide an edge into $r$ pieces and counting how many parts we obtain.\\n\",\n \"- Let $r$ be the *scaling factor* and $N$ the number of pieces we obtain.\\n\",\n \"- For the line is straightforward:\\n\",\n \"\\n\",\n \"![](\\\"media/line.png\\\")
\\n\",\n \"\\n\",\n \"\\n\",\n \"| $r$ | $N$ |\\n\",\n \"|:-----:|:-----:|\\n\",\n \"| 1 | 1 |\\n\",\n \"| 2 | 2 |\\n\",\n \"| 4 | 4 |\\n\",\n \"\\n\",\n \"- We can derive the following rule: $N=r^D$ with $D=1$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's move to the square and repeat the same computations.\\n\",\n \"\\n\",\n \"![](\\\"media/square.png\\\")
\\n\",\n \"\\n\",\n \"| $r$ | $N$ |\\n\",\n \"|:-----:|:-----:|\\n\",\n \"| 1 | 1 |\\n\",\n \"| 2 | 4 |\\n\",\n \"| 4 | 16 |\\n\",\n \"\\n\",\n \"- The same rule as before applies, $N=r^D$, but now $D=2$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Finally, for the cube we have:\\n\",\n \"\\n\",\n \"![](\\\"media/cube.png\\\")
\\n\",\n \"\\n\",\n \"| $r$ | $N$ |\\n\",\n \"|:-----:|:-----:|\\n\",\n \"| 1 | 1 |\\n\",\n \"| 2 | 8 |\\n\",\n \"| 4 | 64 |\\n\",\n \"\\n\",\n \"- The number of pieces is still $N=r^D$ when $D=3$.\\n\",\n \"- So far, the value of $D$ aligns with how we are used to perceive the dimensionality of these objects.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Next, we move to a *fractal* object, called Koch curve (or Koch snowflake).\\n\",\n \"- We try to approximate the length of the curve with segments of decreasing length.\\n\",\n \"- Let's start with a segment of length $r=1$.\\n\",\n \"\\n\",\n \"![](\\\"media/koch1.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- If we split the segment in $r=3$, we can fit $N=4$ pieces.\\n\",\n \"\\n\",\n \"![](\\\"media/koch2.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- If we split it in $r=9$, we can fit $N=16$ pieces, and so on.\\n\",\n \"\\n\",\n \"![](\\\"media/koch3.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The relationship that we defined before, $N=r^D$, is the key to find the dimensionality of this fractal object.\\n\",\n \"- In particular, we have \\n\",\n \"\\n\",\n \"$$4 = 3^D \\\\rightarrow \\\\log(4) = \\\\log(3)\\\\cdot D \\\\rightarrow = D = \\\\frac{\\\\log(4)}{\\\\log(3)} \\\\approx 1.26 $$\\n\",\n \"\\n\",\n \"- Intuitively, think that we need more \\\"dimensionality\\\" to contain this object than what we need to contain a line, but less than what we need to contain an area.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Another example is the Sierpinski triangle.\\n\",\n \"\\n\",\n \"![](\\\"media/triangle.png\\\")
\\n\",\n \"\\n\",\n \"- Following the same procedure, we have that $D = \\\\frac{\\\\log(3)}{\\\\log(2)} \\\\approx 1.58$.\\n\",\n \"- Clearly, we need more \\\"dimensionality\\\" to contain this object than the Koch curve, but, due to all the \\\"holes\\\", still less than an area.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Chaos and Fractals\\n\",\n \"\\n\",\n \"- There is a deep and ubiquitous connection between chaos and fractals.\\n\",\n \"- For example, some bifurcation diagrams are self-similar.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- If we zoom-in on the value $r \\\\approx 3.83$ of the bifurcation diagram of the Logistic map, the situation nearby looks like a shrunk and slightly distorted version of the whole diagram. \\n\",\n \"- The same is true for all other non-chaotic points. \\n\",\n \"\\n\",\n \"![](\\\"media/logistic-zoom.gif\\\")
\\n\",\n \"\\n\",\n \"![](\\\"media/Logistic_Map_Bifurcations_Underneath_Mandelbrot_Set.gif\\\")
\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Examples**\\n\",\n \"- In the Lorenz system, we might observe only the variable $y(t)$.\\n\",\n \"- In weather forecasting, meteorologists often have access only to partial observations of a subset of variables such as temperature, pressure, humidity, wind patterns, and geographic features. However, there are many other variables that are inaccessible such as detailed atmospheric conditions, microclimates, oceans' temperatures, currents, the presence of pollutants, variations in solar radiation, etc..\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Can we reconstruct the trajectory of the system in the state space from partial observations?\\n\",\n \"- If we are able to do so, we can predict the future states of the systems, including the observed time series.\\n\",\n \"\\n\",\n \"![](\\\"media/reconstruct.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Takens' Embedding Theorem\\n\",\n \"\\n\",\n \"- Assume a dynamical system with an unknown or partially known state space.\\n\",\n \"- Takens' Embedding Theorem says that it is possible to reconstruct the dynamics of the whole system using a series of observations from a *single* variable.\\n\",\n \"- This means that even if we cannot observe the entire state of a system directly, we can still understand its dynamics through proper analysis of a single observable variable.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"run_control\": {\n \"marked\": false\n },\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Time-delay embedding vector**\\n\",\n \"- Let $x(t)$ be the observed time series.\\n\",\n \"- The embedding vector $\\\\mathbf{e}(t) = [e_1, e_2, \\\\dots, e_m]$ is defined as:\\n\",\n \"\\n\",\n \"$$\\\\begin{align} e_1(t) &= x(t) \\\\\\\\ e_2(t) &= x(t + \\\\tau) \\\\\\\\ e_3(t) &= x(t + 2\\\\tau) \\\\\\\\ \\\\dots \\\\\\\\ e_m(t) &= x(t + (m-1)\\\\tau) \\\\end{align}$$\\n\",\n \"\\n\",\n \"- Here, $\\\\tau$ is a chosen *time delay* and $m$ is the *embedding dimension*.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def takens_embedding(data, delay, dimension):\\n\",\n \" embedding = np.array([data[0:len(data)-delay*dimension]])\\n\",\n \" for i in range(1, dimension):\\n\",\n \" embedding = np.append(embedding, [data[i*delay:len(data) - delay*(dimension - i)]], axis=0)\\n\",\n \" return embedding.transpose()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/takens.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Takens' theorem does not specify how to choose $m$ and $\\\\tau$.\\n\",\n \"- It only asserts that for a system with an attractor of (fractional) dimension $D$, an embedding dimension $m > 2D$ is sufficient to ensure a *diffeomorphic* (i.e., topologically equivalent) embedding under generic conditions.\\n\",\n \"- Intuitively, the embedding dimension must be large enough to unfold the attractor fully in the reconstructed phase space, capturing its dynamics without self-intersections. \\n\",\n \"- How do we estimate $m$ and $\\\\tau$ from a single time series?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Estimating time delay $\\\\tau$\\n\",\n \"\\n\",\n \"- We can rely on *mutual information* (MI) to compute $\\\\tau$.\\n\",\n \"- MI quantifies the amount of information between two random variables.\\n\",\n \"- We want to compute the MI w.r.t. the time series and its lagged version. \\n\",\n \"- Think of MI as a more powerful autocorrelation that captures also non-linear relationships.\\n\",\n \"- When MI reaches its minumum for a certain lag $\\\\tau$, it means that the the observations become sufficiently independent while still retaining meaningful information about the dynamics of the system.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"1. Compute the minimum $x_\\\\text{min}$ and maximum $x_\\\\text{max}$ of the time series.\\n\",\n \"2. Split the interval $[x_\\\\text{min}, x_\\\\text{max}]$ into $n_\\\\text{bins}$ bins.\\n\",\n \"3. Denote $p_t(h)$ the probability that an element of $x(t)$ is in the $h$-th bin.\\n\",\n \"4. Denote $p_{t+\\\\tau}(k)$ the probability that an element of $x(t+\\\\tau)$ is in the $k$-th bin.\\n\",\n \"5. Denote $p_{t, t+\\\\tau}(h, k)$ the probability that $x(t)$ is in the $h$-th bin, while $x(t+\\\\tau)$ is in the $k$-th bin.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"6. Define the MI as \\n\",\n \"\\n\",\n \"$$I(\\\\tau) = - \\\\sum \\\\limits_{h=1}^{n_\\\\text{bins}} \\\\sum \\\\limits_{k=1}^{n_\\\\text{bins}} p_{t, t+\\\\tau}(h, k) \\\\log \\\\frac{p_{t, t+\\\\tau}(h, k)}{p_t(h) \\\\cdot p_{t+\\\\tau}(k)}$$\\n\",\n \"\\n\",\n \"7. Compute $\\\\tau^* = \\\\text{argmin}_\\\\tau I(\\\\tau)$. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's write a Python code to compute the MI for a time series and lagged versions of itself.\\n\",\n \"- As an example, we consider the time series $x(t)$ from the Rössler system.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Parameters\\n\",\n \"a, b, c = 0.2, 0.2, 5.7\\n\",\n \"y0 = [0.0, 2.0, 0.0] # Initial conditions\\n\",\n \"T = 1500 # Final time\\n\",\n \"t_span = [0, T] # Time span for the integration\\n\",\n \"\\n\",\n \"# Solve the differential equations\\n\",\n \"solution = solve_ivp(rossler_system, t_span, y0, args=(a, b, c), dense_output=True)\\n\",\n \"t = np.linspace(0, T, int(5e3))\\n\",\n \"ross_sol = solution.sol(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/rossler_attractor.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"c_values = np.linspace(2, 15, 300) # value of c to explore\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Exercise 2\\n\",\n \"\\n\",\n \"- Compute the (fractal) dimension of the object in the figure below.\\n\",\n \"\\n\",\n \"![](\\\"media/menger-sr.jpeg\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Exercise 3\\n\",\n \"\\n\",\n \"- Plot the Lorenz attractor for $\\\\sigma = 10$, $\\\\rho = 28$, and $\\\\beta = \\\\frac{8}{3}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Exercise 4\\n\",\n \"\\n\",\n \"- Use the Taken's embedding to reconstruct the Lorenz attractor from the time series of its first state variable $x(t)$.\\n\",\n \"- Compute the optimal time delay $\\\\tau$ and the optimal embedding dimension $m$ using the Mutual Information and the false Nearest-Neighbors algorithms.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Exercise 5\\n\",\n \"\\n\",\n \"- Compute the predictions of the state variable $x(t)$ of the Lorenz system using the Taken's delay embeddings and a regression algorithm of your choice (Ridge Regression, Random Forest, etc...).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Exercise 6\\n\",\n \"\\n\",\n \"- Compute the predictions of the state variable $x(t)$ of the Lorenz system with an ESN using a forecast horizon of your choice.\\n\",\n \"- Make a 3D plot of the Reservoir states reduced to 3 dimensions with PCA and compare it to the actual Lorenz attractor.\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Slideshow\",\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.13\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"title_cell\": \"Table of Contents\",\n \"title_sidebar\": \"Contents\",\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": true,\n \"toc_window_display\": false\n },\n \"varInspector\": {\n \"cols\": {\n \"lenName\": 16,\n \"lenType\": 16,\n \"lenVar\": 40\n },\n \"kernels_config\": {\n \"python\": {\n \"delete_cmd_postfix\": \"\",\n \"delete_cmd_prefix\": \"del \",\n \"library\": \"var_list.py\",\n \"varRefreshCmd\": \"print(var_dic_list())\"\n },\n \"r\": {\n \"delete_cmd_postfix\": \") \",\n \"delete_cmd_prefix\": \"rm(\",\n \"library\": \"var_list.r\",\n \"varRefreshCmd\": \"cat(var_dic_list()) \"\n }\n },\n \"types_to_exclude\": [\n \"module\",\n \"function\",\n \"builtin_function_or_method\",\n \"instance\",\n \"_Feature\"\n ],\n \"window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "notebooks/12/classification-clustering.ipynb",
"content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Time series classification and clustering\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Overview\\n\",\n \"\\n\",\n \"In this lecture we will cover the following topics:\\n\",\n \"- Introduction to classification and clustering.\\n\",\n \"- Similarity and dissimilarity measures and their impact in classification and clustering.\\n\",\n \"- Similarity measures for time series.\\n\",\n \"- Classification and clustering of time series.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"# Install dependencies if the notebook is running in Colab\\n\",\n \"if 'google.colab' in sys.modules:\\n\",\n \" !pip install -U -qq reservoir_computing tsa_course tck dtaidistance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Imports\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib.colors import ListedColormap\\n\",\n \"from sklearn import datasets\\n\",\n \"from sklearn.metrics.pairwise import cosine_similarity, cosine_distances\\n\",\n \"from sklearn.preprocessing import StandardScaler, OneHotEncoder\\n\",\n \"from sklearn.cluster import KMeans\\n\",\n \"from sklearn import svm\\n\",\n \"from sklearn.linear_model import Ridge\\n\",\n \"from sklearn.metrics import accuracy_score, f1_score, v_measure_score, pairwise_kernels, pairwise_distances\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.inspection import DecisionBoundaryDisplay\\n\",\n \"from sklearn.decomposition import KernelPCA\\n\",\n \"from sklearn.mixture import GaussianMixture\\n\",\n \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n \"from scipy.cluster.hierarchy import linkage, dendrogram, fcluster\\n\",\n \"import scipy.spatial.distance as ssd\\n\",\n \"import statsmodels.api as sm\\n\",\n \"from dtaidistance import dtw, dtw_ndim\\n\",\n \"from dtaidistance import dtw_visualisation as dtwvis\\n\",\n \"from tck.TCK import TCK\\n\",\n \"from tck.datasets import DataLoader\\n\",\n \"from reservoir_computing.reservoir import Reservoir\\n\",\n \"from reservoir_computing.tensorPCA import tensorPCA\\n\",\n \"from reservoir_computing.modules import RC_model\\n\",\n \"from reservoir_computing.utils import compute_test_scores\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Classification and clustering\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Classification**\\n\",\n \"\\n\",\n \"- Is a *supervised* task: a classifier uses external supervision to learn a task.\\n\",\n \"- The supervision usually consists of class information (labels).\\n\",\n \"- A classifier fits its parameters to predict the correct label. \\n\",\n \"- This can be seen as learning where to put a decision boundary that separates the classes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Generate some toy data\\n\",\n \"data = datasets.make_blobs(n_samples=800, centers=[[-5,-7], [5,5], [-3,4]], cluster_std=[1.7, 2.5, 1.5], random_state=8)\\n\",\n \"X, y = data\\n\",\n \"X = StandardScaler().fit_transform(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_class_example(X, y, gamma=1):\\n\",\n \" _, ax = plt.subplots(figsize=(5, 5))\\n\",\n \" ax.scatter(X[:, 0], X[:, 1], c=plt.cm.tab10(y), s=15, alpha=0.5)\\n\",\n \" ax.set_xticks(())\\n\",\n \" ax.set_yticks(())\\n\",\n \" ax.spines[['right', 'left', 'top', 'bottom']].set_visible(False)\\n\",\n \"\\n\",\n \" clf = svm.SVC(kernel='rbf', gamma=gamma, C=1).fit(X, y)\\n\",\n \"\\n\",\n \" h = .01 # step size in the mesh\\n\",\n \" x_min, x_max = X[:, 0].min(), X[:, 0].max() \\n\",\n \" y_min, y_max = X[:, 1].min(), X[:, 1].max()\\n\",\n \" xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\\n\",\n \"\\n\",\n \" # Plot the decision boundary. For that, we will assign a color to each point in the mesh [x_min, m_max]x[y_min, y_max].\\n\",\n \" Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n\",\n \" Z = Z.reshape(xx.shape)\\n\",\n \" plt.contour(xx, yy, Z, colors='k', linewidths=1.5, linestyles='dashed')\\n\",\n \" plt.xlim(-3, 3)\\n\",\n \" plt.ylim(-3, 3)\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Most classifiers trade an accurate fit of the training data with generalization capabilities on out-of-sample data.\\n\",\n \"- The behavior is controlled by hyperparameters that are set through a validation procedure (and usually some experience).\\n\",\n \"- In this lecture we use [SVC](https://scikit-learn.org/stable/modules/svm.html), which is a [Support Vector Machine (SVM)](https://en.wikipedia.org/wiki/Support_vector_machine) for classification.\\n\",\n \"- Try setting `gamma=20` in the example below to fit the training data better.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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2dfOMb37A7Eyog5Y3fd388v4lY+wra0IjmUY4H187LzznVtrmIDSqsPctgMCh+8IMftFGAG3oB0tG0NeFWRvvTaENCpmuX8uM3aRmjzxr1PSptw6DC7rDBYFDcdNNNcvToUdm2bVubGdWGblB2TmT7L13vsdpfThRZ+oG20edAApJEGU0auycpZ1zHKgtE4jOcWrwvYEDHyXUi5/c5ERszsqlPz36Xpb2HAGHN2BAZDAaDofdARY0RCLXj1OmunsxsanzJr/t692YoRKgouEkr92RK1tkdIkdecSlnatazbxLJmtfF1LEnRY6udeYlnIO6NlanEX2I0ZgIBlmzIaHHeerqwe9zZqJXU8PFrWRjDEbUBoOhBVVVVWohGhsbKxERQU5RhqCbVOwGWDCXmh7lY2udaYcnZYgFMlv9eZHJl3V854iPUHHjGqZe2imOdKdf03mEyizujT9wfdAxKS71HJ8qsuZLrr2pPU6uF9n0Y7cuTGJ4Pufys65ZA/asCNP6QtyDCSL2g8+LnNzo2sDITsy/UyQphKxthxAh9u4YDKGN6oZqOVp6VOoa62Ry0mTJxC1qFCE1NVXq6urkmmuukbVr1w73ckKTpNd/z6mdiYDpbYawGRzREj1DtIH0dGfAs3vbQy7qRU1dckrk1W+IpM1yE7joT86a3/Y1uIBxPvqiAVF7/l6RU+90TNRntrU+T79nOfcvzquRcJgbhEEGgFR9KCm3d/9O5NAf3QYjPNJZpyJeu+ov+qZaH+EwojYYeoizFWflsYOPyeny09LU3CQpMSly07SbZPXE1aPmHjY0NOj3zZs3D/dSQhP7nnbWnWERTulMJE1kiu3mzACpYLUJOXaVFs7d7EgXS07S0uVnnaUnx8JFjI3Aqj9tS9a4kEUFKbchVmZjY2DSEXSjEES+nI9jqDXn3Nb6NRsAIvmUSRIym6HT77h7qCI2cZsgHdKxf0z2Wpvq22DoASDm548/LyfLTsqUpCkya9wsaWxqlBeOvyB5lXnDeg+J7qvqXcq6vwgPzE8mBb5ly5YBWN0oAoS65wmXQoaQSXEXHW6tL+Or7eu3l3ywazOSunKXNgdYgdaUOXEWxEvETN2alHp7l7HaC62DKpiDDanj4tURSGuT4ob4+NtA9MZrgqNv1sp1YWQSKqBPm9p08KYkItptPHQC2NiDRdQGQw9QXFOsJD0+frxEBXpGxyeMl8Olh+X4heP681CjpqFG1uaulW3526ShqUE3DzdOvVGy4rP6fMz7779ffvWrX+nPX/3qV+XVV3swoWmsgBox9WGETbhzEdGyN6Lue+lH3UxqxGEMmOhuhjKRMilrjufJh9cSRXJcyP/CmbavmXmdG4RBVIniG+JiGhbjJjvClFVu80CtuuKcywIwhxpHMQ82HXh4Q9jtwbpoN2NDwrCMobL0TBzvvtjAcN6wMLeR4J4wr3oMwojaYOgBosKjJCIsQhqJUAJokiYJkzCJHKY+0hdPvCiv574uKdEpuoZ3zr0jhdWF8qkln5I4Zgv3Af/8z//cQtS1tbUDvOIRDkiVminRKSljouj6Sqf0nnxF79qxIFFanVCLMwOaKBKCT53qCLimVCSnnRANIRXCMV7D+fk3M7k72xRgF7rsAVfv5vm4ip1Y52q/qMa5HoCJSnsCpCd8688d0XO9vHbJ/W5a12CDdS+6R2TrQ64GL2FuY4TtKRuNMQgjaoOhB6AePT99vqw/s17Cw8KVuM9VntPodU7qnCG/hxdqL8j2/O2SHpsu6XEuGkqOTtao/0jJEVmcuVjGJLwRB1mPrqZK9faYEB2pWIiaIRmQK5E0ITWq6c6i2s5AVL7yM07cxcQoZkuzbsZX4uONkQgRdHsQ1dK73FMQjUJunuAWZosUHhI5/IpLg3M8auPUrr2imqEYOx9zoybTZ7uNCD/v/I2LcBM6iL4HGtShGSLC/Wmsc+elJBBKgrchhBG1wdBD3Dr9Vk0xHyg+IA2NDTIleYrcNv02JfHhUJ9Tm06NbR2KQFRNLb2qIRApjRRAEhABURN1WCKqvoBU6d4/iBQccuRCtLnwrv4RNvXdXb8Vydvt6r2QKMRBOxN+1wjGLvtk6/jH3oDrROHNF9OsaNfSHuzxLnJNnigDjqJDLkqefLlLd3MtTKw68JzIZZ9wzyk7I1J6wo3V9NagRPoFB0SKjgwNUQPEbaEicBtmGFEbDD0EEesH531Q08t1TXUaTRNZDweIojn/mYozMi15moSFhWkdPT4qXiYm9v0DPj09XVJSUuTChQuSmBgYqThYIFLFvIPJTl6ghRoZwqCVqDdg4MTmn7poMWmi670lxQsReQLqy/p2/Frk1HqRpEmOjGsrXCoWoxNqytiFcg2MgexPDZdNClE2oinWTh/xYICUNgI0P+eZ9L2PXLkWWp94H6hnB5V5WpzQBrLMg2COiV7UwsdNdhFzXzY8YwBG1AZDLwAhdtQ7TSSLuCsmIkYigj5syurKXOQbk9rm8f6CDcLN026WJw49IYdKD0m4hOu5r5l8jeQk5vT5uPHx8VJaWipDgpLjInt+7wiAD+mGOpHzu1xUfPmne5fmRGBFCpmIHLIDkMrZbSIVd/TNOpM6Lm1SyZNF4sYFHgxzGwAiTCJhNgK5G905V/5J92tmQ8ExUX1TF06b6cRabDIwUeFe8O/jb7kNxkC3Ivl7wybEr5UNB1kH//dJJI/YjfX4+0ivOOn4zEBbV39BVL/pR66VrTmwYZhxrVPLG1lfBCNqg6Gf2Fu0V97MfVPyq/IlLTZNrsy5UuaMmyMvnXxJdhXskvqmepmUOElumX6LTE/pwJiij6Bm/idL/0RT8ZxjatJUmTFuhm4mBgJnzpyRSZMGMfVIKxO1XpTLrJlojrQvIitahrpTTgeDaJAIMDjDwYc/x2d2cl8ACbefDMW4R6JdonaiYL6aJrmIlDR5V2nh8jxHTkS1RORcL+TE5uLMFpFx09yEK0iUtq8Dz7s50ANJXOMXO89vUtj0gCOGI7Kdc3PrdfJeQJisgw0Q7WDUqpfe56Z49Rccd/+zbmOVOd9tBKpLnanJhCWurczQBkbUBkM/cOzCMXnswGPax0y9+HTFaXn84OOSk5QjB4sPSkZchiRFJ2nUe+HgBfns0s8OaE2b9Hd/2rE6wokTJ+QjH/mInD59Wvbt26d2ooMC0qvBhhxtUqy9JKfUaU7kRRQMARGmUWvFfxvy7wuSJ7mULDVvNg1EntSQiUoRlbVcR6BVClLvCtSBqfOS3ucYCMe2PexEXOXn3TXo8cKc0QfEXhvorx4ocD20ku17yh2fzcL8dzuiDgap8TVfcPeQ6+JeDNQoS9TmlCh4X3wqnYwF5yLCNqK+CEbUBkM/sDN/p5TXlWsPM5EsZA1Brzu7Tuamzm1RZFM7PlZ6TA6VHJLLxnfi/9wfqKHFGWcUwYdqP2wWn3vuOXnrrbf05x/+8Ify5S9/WQYF2QtcBFp8VCQ5x0W+CMvm3tb7IQyka2nfwTkMe074HyJY9J6+i9MgJnqjj73pIl7v9EXfMXaWRL+QN/edNHhHdXU1LnnDpeBJZ1MPJuongiw84ARw/Ew7FsdGPU4mgNdB1lGDMIyCtWYvcr3J3OfONgJsGEh3DzTYpJCJ4Bo99N42tzU5MbTAiNpg6AeoQVMvDk43Y4hSU1/TRmhGDblZmqUW1fBAAyHW9kcCrSz1Tti0+H0iEy+RkAbkuuwjInt+J1J22hEU053o6+0tuP+QKjVUUstEapBRX8c6ehQdc5E6pAwhc3+JctkMkDZmzaSul7z/4vo0myZS3We3O8KFjCsKXPqY9ww3MsxHiLBxtys55jZYROsca9YNfd9kdAeOOxgk3NMN0LSrRHY+6u4p5Ex0z98DqX7DRTCiNhj6AWrO9DNDwIi5qBXXNdRprZiadWJUovZdoxRPiEpQ+9EBB4KsUxucOCkiRqSMlOovXWTdX6IabORc6iJrenmJsiApruXcTveBzhxkjD96kgqH3DAN4WugshTnINlYkZJzzt6T+m4FlrHNIhOXi8y5yRFtRxkMNk64maXNdr8nhXx6s1M6M5mMGjjR7LhJTpxG/zLXwP2Yusb1Z4caqFdTo4dcA3azfQK94BzrxJsuFT5+0ZiejtUdjKgNhn5gedZyFXPtL9rf8tjstNly/eTr5Y8n/ijHy46rBzep7+smX6cTtwYURGaQGmleP70JJTG1UKK+xGsG9ny9XVtlkWtb6qp1CYLGrxpi3PIz53Gt2YhmkdNbXDq8L1F2fxEWSHNjbEJ07FPbrI0NBcMwiNo720RUlbhr8iTO+4JIDrV7c7iLxsl60I9N6jd5gsjS+0Xm39H5mhCh0fdMLZfXE4H6VqvBBNeBCvzQS+660QHMvaXvqnQ2YQvuEJl9oyN+shb9If5RDiNqw6gDxIjV51BYeyZGJ8qHF3xYibqopkjGxYyTBekLNHqemjxVa9I1jTVK0LRNDZQiu80HtxdgtQAP6qa2fbBDCdbELGHcthgiAdlhYzn/rq7nHqMChgxQVPt2KMRhR191M5T706esU6lqHUH0Rqg2bY2rMfNa2sfo+fWRNa1W2HlCtDhntb82shmci9dQC+ZnImgi/hnXiez7g6vTcr9Qk5OKbm8b2v4aKBMcfinQZ93ssiaXPThwbVOd4dwOkS0/d1kBroHNAmnr1V8QyeyHM59Xzhu6hBG1YdSAXuat57fKxnMb1WKTtPQ1OdeoArunBF9aW6okT5sVKeuOzrG7cLfsK9qn07Pmps2VS7IukeXZyzsk8Y4eH1BAXnxI42pFHRWhDgIlIj5qp/0Es6l7DYRTe3/v6rK4W1GbReSFOIoadGcgcoX8aFPy4Dro4aX1qa9ETc2ajYPWm1NEZl4rMu3qnkVwPG/hAZEtP3FkijhwXI67x6x3/f848uc9WPGJttEtIyxJ3bP58NO2IOyFdzuvb9K8ZA8QphFZU5NGX9DpdRwROfKy2xhA9mzGGJqx90mRq/+/wY1IGeyBUYqfhc37QsaGa+sPURt6BCNqw6gBJP34ocd1UEZ8ZLxsztssZ8rPyIOLH2xRX3clCnv+2PMaGUPU3h60Pcm/fup1HYbBczgP9WnmU9896+6Bj5Z7As6JspnIjA9y6n5YQy68p89ioczM1rr2j370I/nLv/zL3l3bmW1uHd4Ck4lQCKeoz3ZF1KybzQZROKlQADkivuorSZM2R9DFJCo2CtTCaYki60CU3x2Ikld91pEqZA9ZEc0iKuP6iKSpNRNx0vJEdNvy2ij3b2rO+QccSeescATO/fT2od2B2jUCNt7fumrXcgbYSJKCxjyETcNg6hHYKAUPemH9bApVrd5DkBFgU8K6eS/GqG93X2BEbRgVINJdf3a9qqshWQA5Hyk9ooYkV+dc3WUk/czRZ2TTuU3ak4yDGBFzRV2FfGbpZ7S+DIjS3z7ztv7b9y4TgW85v0Uun3C5mpoMCyAMIip6U0nR8kHeDz/me++9VxYvXqwuZQ8++KDU1NRIXFwv0pNEju0/hEn7Qm5dgXUjoCLKZAiGtuwwSOKevhM1imuiXyXH8NYUO+dAedyTKJRrWfwekelXuWMdfNEdgwgd4me+c0K2SN4uV4MO9han9Yp+cYw8WENvr4P173/OlQCYsFV5XqRxdmtPMxsHatUdpY/ZUPB6+rXJJHTWQtYTMBkM8xPeQ87NeelgQEjXE7BRwjMdIxc2STifLbkv9MWOIQIjasOoAMMyymrLWkgV+NR1JW00XQBFNoIwZkp7MxLGRDKJ6mjp0ZZJVNSgK+or2lh0MmISdXdRddHwETVQYdHATMyKjIyUXbt29f0ArIOUKGIyP2kK0iAN3BUgTcYyQnxndwRU3yvcAIm+AuKEGILLGES2WhtuEAnvRfsTJAdRkdrn9Qj4uMaT51ytmAEdwec58bYjJ56jr892ZiM9fZ/YDCCuw1+c/mvIkcgW73FEbGzKOPaCuy52DON+b/pxa/830SzOX9ic9kUVP/0al+om1a6en+EuXT91VfevpbbPyMz8/a5fntdzDaz/yi+biKwHMKI2jArQs4x4a1v+tpb6MhOm+J4d33XLBwM2IPrgvufIsEiNtPmdB0IxRGIX6i6o4xgory9XUud3hgCIiiEZ6ppEgoivSHkjCOvJhoNJUnwNBGhZCxZ0QVqVBW4tfelRRjyGSA/i5DiQJ1+aok9yBM55ULvvfsKRETaZkBOpa4hbJ4TF9OBc2x0xe4tVBHbU8DGGIZrlGIvudQYx7Tcn9NWjC6DtixGX1MghykMvilzx2d5fNxmaNV90GyiulevHQYxSRXcgikYnQJmAvwXAehCk4fTGxiwYbIaYVoZYjXs6YenAurONQBhRG0YFqKFeO/lanSZ1uPSwpsB5bGnmUlmYsbDT10HG9Y31EhEWIUcvHJX5afP1Z2ZNE11D/h5sAC4ff7m8eupVjaw5B/3TpL370nZFKn1nwU7Jq8yTjNgMWZK1pGUDMNx4//vfL0888YRkZWXJ8ePHe5f6hgCXf8SRM2RG7Zm09nC03xD1sXEgwqduDmFCXJijdAWdjgUZxrZN43vCT411/epEqkT+vn7LJDCiVlqwqBu3zFDG5WuyS5WTPqcdrTvweu7fybdd+pyRjxBWzGSR6/+61eGrzWuKRTb+rxOdlZ919X7qwmQzeB/wV/fp694C0iT931fP9GAfds5P7Z3ftY++ySIw6MRbsqZOd/d0uAxaQgBG1IZRA2rTn1ryKdlTuEfryxMSJ8iijEVqRNIRmGr19NGnZdv5bVJQXSDnK89rCpsUONOumE7VnjhvmnaT1r4ZtkEUzvGxBO2J2IpNAcIz/MCpqb984mXZV7xPj8MaGbLx6SWf1vMPN/Lz83W958+f169p04KU2J4QPAl31Mer5iPTL46WhhKQKKTEgAlEXKjHUUzT+9tZ73FzoHebFiiuj5Q2/cI+XU2EyrU1NjsFOKStfcUT3c86GavSkaivy/vo2c+x9hOsugKbCjQHCMVwL+NYGK1EJYpc/qnWnvn2OP6mM1phM6KDTfA/P+82B2wW8BMfgrbFNlC/9WwXPXs/c+rm9I2zzmBQU8fwhhQ56XzuQ8E+kYMviFz+SRmrMKI2jDgQAaO2RvAVHh4ui9IXaeSMCAxiJbLuqUocAVpmXKbWl/MT8uVU+Sk1Mbl1+q2SjUCoHejNXjlhpX71BhDzSydekrdOvyVVDVWSW56rpI03OKlzInTEbDPHzZQPzf+Qtn4dv3Bc8qryNN2Ob3hw/X3YQKTJcAlmMFN7pkeayBnBV1c90kMNLClRYVNXhZhyVorMv717D3HMY7b81EV2pJohvQunRNZ8yUXBuJFNvkJk/9OOCLkHkEl1ofPOpr2KSV6kt73xDKRDupwIl9f2JDIk8iWiTpslUlXgXl9PhB/XtWMZ6W0iX1TV6iNe4iJTasu0kM28rndqa64R5TyE35FAkY0NBMxaOSdZg/bH5z5iaYtlKPcDQNzYrrbPCHAs/sZ8zZ0NSnymuy4i8FD6GxtCjM2rNoxYeIU2Qy+8WGx3wW4VdDFGsjPwe17DYAxNYU+4XPYV7lPi9QIyiBkSxZO7I5LuDyDdN3LfULKdmDhRDhUf0kia60mITJDSulKN5n9/+Pe68Thcclg2n9+sUT/ROin4D877YIezsAcb1dXVbWu0+54MpH+nOVXzgWed0Kon7U5DAYhy80/cuEyIk6h2/5OutouYqyucXOeEWL5fGGOT/L2uTx2ixmXssk+6a3/571q9qnVeB5F2g8iJt1yv9IqPi+x+3JEuqWt6sjvyBO8I1IGb6kWmXuEiewRlamTT2HUJAVU5ZMj7Q7q78IgbekIbF33eU1f37B5CwEdfEzn4R0f21KKxNV14b2ttn/r7jl+7e0MWgQ0Cx8ddrX1qfcpKJ2KjLu1V3x0p4HUjFRC/+ftUX+3S/mN4TrURtWFE4WzlWdmav1XTz17AVVBVoCYnkC8k3B6lNaXyy32/VBU3pHyu4pwcKTmihiQQZXtQox5onCo7JdWN1TI5ebJmBPAE94K3k+UndUxmQ3ODXsv3d3xf+7Rnp86W5OhkfS4bjLW5a+V9c98nQ4Hp06fLG2+8oT/ffvvtcuzYsdbUJBGajpIMKKGJKkkX94Wouf/YYZJKT+wkIustaCNCuBUs2qI+yhqZsNWV5SYisKj2/cIxrcptAFFBTPyOvxU2AtVFztwlY5bbzEDURM5E4hAtz4OYenptiLWInoloUZcTYJK6Z1PQ1foxUuE9YnNAPTsu2RHsms87cgRl59zmg0wASnVS08Hr4px7nxLZ9RuXMeE5bBTIpLBxwfbTb2ogc/4Wxk11mwsyLWxiOqpl8/52145FWeLIa26zoX9bFSIIOvnbGsN910bUhhGFkpoSbbeamDCxVY0dO05yy3L1dx0RNU5ipJpnj5ut6XEf4UKOEDXtWbRZ0RPNsalV/8eW/9B0+KqJq9oIynoCjkm718GSg/ozhEvkzs/HS49rjbq2qdYN8KitcyMZ2S+EuZYyfs+/cTwDqNG5Lo7Ha4LV6YOF733ve/LQQw/pmplP3Xpxje3sSgPGG93NYu4s8t3xiEjuJjeYAVJAjU3U2Z/ZxyijNU0aVAuGfOvKHcF2RXRZ89z4SRU/RbqoUfuFZ7c+hx5vhklApmw0SAtD1kTAkByE7QG5dNW7zPFJr5Nept5PLZy1En0SnXrnMi9ao8QQ3KfdHryeyPnwy+6YZAZm3+S+c+2oqXf+xl0D4Fgck+EinsQxiIGAOS/XBqFPXOpeT/3YEzWqdDYxXpHN+kmBUz7oi+jMewJQgydLQ32edPvM61zP+xiGEbVhREFbpCIT1EnMp6xRT+NERh/1W2fekryKPJ0RjYUotWR+DzxJgyQEOs0iKyevVMOS3AoUvKJECFEmxyRrDZkU9McWfaxXqu43Tr8hLxx/oWWk5YazG1Rw1iRNsr1gu9ac+WKN6nDWHCbREdF6XdTY2XCwFqZwxQWiO9rE0qLTBjzah4hRnuPixkZlTuocWTNpjWQkZkhiYqKUl9OH3I4ISHXygYyNJO1IpJR74rDVHvQZ09ubNKk1IkPERUSGx3ZH0S6RLUTrvcA7AgKl2GRHNKS+/axuovWuLDoBHtzUQ6npagAX5tqDiFQ9uHYiPYRyrIfrj4wXqSl2j5Ou7ggIqCBeTUVPdP3h+IgzoUsV6WFuihREBfld8iHX0gSRQ4iTlolM6GZ0KRsDjkv/ua4rzl37O//rhG6QKNauRNn8/4AK/cAz7hpxXzv0grt2NiGknElFUzfmdyqOC1Jpk4L3pjQtaO5/iprNEtkQDF74+48Y/I1pqMOI2jCiQJS7LHuZisBKakuUaPyIyZ/s/onsL96vkTLERz14acZSFYZh90m9l8d5DWS4JHOJ3D7jdlk9abX+m2MiMCPypi7cHN+srV4QeXdEjfgLkq9pqNFaNOfxDmlE7ET1CREJkhSVpAI41kukfuKCi1bZaNDvHRsVK8U1xUrkpPlxQCPKJ0UO2XfkP94fcG1PHHpCxW6xkbHy2qnX5ETZCXlw0YOycOFC2bhxo663bY/0CdcypCrkeJHp1/Yt4sE4BCLxpOsjMhy+goma6Bg/a6JYPrxRPBMlzr6543otKec5t7qoDHICkPuie7rvXyY1e+WXnA0qGwdET6Rjg0VoiKZQj+tErYCamVo9Efy825w5SHsQqW74fqvynL5ieprJUDABC0LiePQpQ96kzkmxE5n2JTrlvqixS5nI5h+789Fqxr+JjOltJhVOBEupgGvg/rMp4F5BwIjoWB/iLvrh2SzMubn1HGwGWC+ROxsL3js2G32dqNV+/Wy2DAojasOIAgR618y7lLCxBiXChGRJZUOIiMHobwakiCGiWamzZF7aPCVxHoNQ02PTdWAHxyOtzNfLJ19WBbZvteI7/6Zu3BkguE15mzRqJkKGcOmLptXKg3MRmZP+pp2LaJ32McibKJ/1Ur+GmCFLznnfvPs0wiUNT1SNBSpp+L6Amvih0kN6n1CZzxk3R6IiolTMhiUq5O/T+yjguZco6t988005f/qEhB9fK/Ly3zuCoc2J1DTkoe1ZaRfXOHsKsgPtIzKdBNYuImNTQNQHGZBWph2KKVKklDtyO2Mt89/tVM7UqonIcPKiHagn0MEd13X+e64ZktrwvYDzWUD8RFbh0o93HAGShYCkvZUpz+eaeK6vibOJIBXN5gKi7qqmTyTPJgGi7ereQ7zFx13qmwiZ45Omh4R1tGiTS/GzBiJhyg8o5slKoFovOdlaMph7jRsc4kGWgefSElZyzP19MI60qwlghj7BiNow4gDJQFp8EU3/26Z/U6JlpCSRMyIxIlBIHLJ959w7LSRHNH1p1qVy/dTrL7L8nJw4uWUoB6/lO8QfbBlaXluuVqITEiZITGSMbMnbIr879DslYUga0RemK4jdUHcDWq8g21njZmn/Nent81XndfwlETbX09TUJBUNFZIYnijvmfMeeWDBA0qkmtaPitcInI0ARN5ZX3hHIBr/zYHfKPGyqWBjsDB9odw39z5Vt3P8RHpzA9BaujRraSGqplhyNn3DRU1EOHwQM1iDvtyl73c9xf1BzqUuetaIbJyLyCCL9hFZ7mYnBoOYAL3NpKdZV0dETerWp+dJobK56MrZikiW62JwBoTF+Yk2uyJAIk0ImxS2vzc8hpAr2FbTjyEl+8D75jMiLbOui9seVwd+1LprYAMU7J7G73AcY60txiszRLJRUKe79HV7K1Gep+noSPc7UtgowZsb3PtYW+o2MXh2syZa7RhaQrqc+wyp8z6v/HTAGz3CXVNpYLNAHR2hF9dBpoEhHVt/4TYH4ya7UkIoTdeqqxQ5/pbLmHBvKRNMWR3ybV+hvTqDoQfwEbAXbAHIRudSNzWqkCypMEkV1BAQ9WgfdQcD1fj+kv2qCCd1TaoclTaPUy/+2d6faWoY8mNj8J7Z71ElOaTuU+PU0POr8zWlDTGyNp5P2vrmqTcr8UPmrAlyZkwmETfkT3sWz/vogo+2XA+Ef+zCMXn15KtaM4eocUe7evLVPRKVsZEgcp+SNEVfywaGmjT92ldOulLNVbhe1s1aSd1zPdlNYbLvhx+V8hPbZfbEVElLz3BEQd33+Bsu4uzvQIWpV4qU57t2JqJEjcjuuZh8EWm1T/mHtxOwkR6H9DkWqWMlpmSnwEZAtfrzF5OYHrvRERP1cgDZ8jOe4x3Vyb0hyvaHRWrKXUSrQ1Bo49rnrFMhatZDPVrT9ZXudWglvEiNf9PqBdkT9aOmJuImRY2Knh5o6vasg8gX8dee34scftFFumwQyDRwvZyf40C2V3ymrViOujzlBT8mNGuR+1kj6fDAGM97WjcEM6515Ix2gHWTJZh/h6ude6He9l+6+4oAMCYwNpS2Lda88QcuykaERjqdFrnVf9ZWjDdcaGoU2fqQU6tTp+e95m+DDR33IIRhRG0Y0SC6XJKxRG09qfFCdnhxR4VFaTqZlqfM6EyNZiEinkMaGDey2xNvb3MsepQ/tvBjsiN/h0a8PHdZ1jIly1/u/aX8/tDvNSL16W1q4tOSp7Xpbeb3RNIQI0QLWV8/+XqN6ImMqf2i3n7u6HNaCya65TVKmKVHZMX4FW1Eb6TziYhxTiMtDdE+d/w5TVdfN6WL9GwAnCs2IlZJGpBW557Rx006/brJ1+m1kI3w6708abrM3/k7yfz7l6W0pkmiI/Kl4p+bJSo52304Q9Z9GatIlHXkVVcfpaUHsicy57v6R2d23FsLcUOC1FepA5ccdcIyomQIT8n2IRcp8TyIDiIjEoye7B6jHt7RiE3cv4i+IVyI3a+TkZacFzINBmIserQRjUFkpJZpdcLPmzS2r2UjyqKHmvtOShvRFsK787ud05if7YzKHfU095Som+vPXuKiW0ibTQQ1bMoPp95x0T9ETzSuyu3AwBFEZzh4odamNOFBRgHyVRX3OdddAPkufr8jZ0RwwdkG/vZwYuM1vNeUAYLFYRA4qW42AMwNpwxBrzU/s15I2nuT896wJt6XUCDqoqPu74ANkFfOs96ja931ts+66FjOfBGG+vD31P5vYQhhRG0ICVAHRhxGpNpbv+vrp1yv5Ly3YK8UxhVKcW2xRIdHa5QIuTFTOrjuDFEV1gTaU9qBc984NdB+EgAp6LWn1zpRbsDeE9U4LmZE6BEREZIW5xTZCNkgvBum3qAk3x6QNY9znfR24y8OkULA01KmXfQa2rwwa2GjoUKyGJGzFWdVpX1lzpUtUTW1bCZ9kUkgWvbrZFNBTb799SQECIWInnndmMaU15XJ1PA4WXZ2v0TmH5SJKVFSWlMrdY0id/30pDz/uQQXEZIehlTx5Wio1oicND4bFNrmOrRThaiItviOWlx7fQ+JrPyMGwEJuRLJcnw+1Pmw90IxUq6QJ5E8KWEIhOt++79du9DMG9xrWRNrgxypqxYcckYbgHp6R+CDGiWzJ2lARMpGhM1DVGsboAKSgmT5UCdiJPrkA716o1OUI64j6mStEDJGHfpGBCJu6rcQN8+dEnApm3u7i1A3/FAkbWrr4ApU5cy5pmZNGhoSryhwhANhk2YmMiYiZ/2Q6rldbYmae4gBCfeYe8j9IdplI0EWA2jk/qG2/uOQUkfERDaBTYK3MOWes2FgjXQrEL3795/vlFVwZAsFVBe79471e3AvSPOTZQgmarQHOx9zf6f8fVAyIGvQl+6GAYARtWFYAYk8f+x5VVuTIqZeesXEK+SmqTe1iSy7AjXpB+Y/IEfGH1HvbOrWpLlnjJshuwp3yVu5b2mkCEGRqiZ6bGxslPXj1stlEy7rNoVMrZpaLmlhjEsgYoia9DnnodYNSRKtsOZLMi+RBekL3GubGpXMiGSDrwfxFtE7103UTOr80uxLVewVDM4JgtXePoVdWVeprWSktxHNsUlgs4DH+V2z7tLjQfx4n5M2Z+OCQI1NDIp3D849OS7b9deefN2lkGvK5K3PTZX0vzukz9l+ps5FFwi45r1b+1vZHPzm4G/0nnB/UaoTpb9r6rsuJmt6pSHplrnQqI0PuPQwken2XzniAXzYo54mHam13HjXskRquKLQpS0ha9qPUE6TXuU16bMDdd/Ac6ih+hotNdyOQG2c9dDT7YmJ1xG973jUKY/5cMY6lLXonOxGR5iI3iIDKXiiXC6Z6JwWNnXqCiJ/om2+IMzZ72q7BuqjnCe8/QYnTKT6gvsdmy02BUR3pNU5PiRM3Ztr1izFeEe2ZBioYxP9Q+TUrnMud+vCGnXtv7jnkzIHED3p7Gu/1r0qnr/hDkyC9B5yPM7L+fV5TSJ1ZS5VHwpIYCNHG11pKynzt8KmI7DxbAHDVY5h5hLYIDFPm+wGmaCeihIHEEbUhmEFRPXm6TeVoIh+iapJYxMRdhSRdgai2yePPqlTr6hNQ6QQx5UTr5RjJcc01YuqmtQ4BinNYc3yxOEn9Hy0aHUFatV81rORoNeZzQXjLRmFuXLiSnn3jHerAh3R2Pj48UrSkD8pdHqqUVsTqUPgfPZCqESfRLMQaldgEwCx0iOOWhzih9hJmT9+6HElYR1CUl8hMeExSsbUu3keRius5Z7Z9+g9ZrPBfUbtviDNbSRawHSpo686wRYCpfw9ktbeFhuyW/1FJZvmXb+VI7lvSkNDsUyddJlERERLUXWhbD/0tCyqKJGJKYicFrSmgiE4iKrNXOgkR74QOCTkU6ZsCHC4IvpEkARIG0OefKiWonjOdMeixg2xhmOx2exIgeei1CetS82Y4yIQox6JMA2yQyyGiIjfQWCIizg2RFaMUjzapbUhG+4NXtW0JqEkZ7KT9ijHuOiXTQMRM+vhfIAPdH6GzCvPu/YtCJ5sAlkBjgux+c0BP3OtRL0+smUCFxsJ7geRv6b5mxzRcNxw0uqxgc1JgTsHqXQsXvc/22rDScq85JTI0vvcmhCC8R5HBUXuqLuJ1HnPugLteayf95P7RaaCFPqExc7TnAiUdi/uH6I4UuIdlRyGA2mYyKwJlAHy3d8L60SpHtwKxiaIDUd8UCmG0gIZEc1uGFEbxhhIuRKhekcxCA1S2lu4t8dEDSkRlZMSnpEyQ6NPasy0W0FWn1j8CTUgwSOcevbUlKkalUJ4pJDXTFyj5N0ZUExDcKTXSXUDIsi0+DS5bdpt+juEWcFAPf74wce1Ro4xC9fDGrk+0uRc84rsFarw7iqix4DkiglXqEUq1wRI5TPd6+2zb2san6ibmjyGKmwEaMcis0AUDVFj+oLLGapxMgAI5S4CUTREQ6QBCdA3y5cHpHTvj1xkt+7/SWNNqaSWHJKbwyMlLyxODuUskxWl+ZJ2cqNEnjksQuodAljxoCMg6sXUN3WCVLQjKlKRqctcFOr/TcSeOEGk6KATJ5GGJmrk/BA9KXKiNXVDwyUtMOABckYJzutJJ/N4xjxnGQpRQNLbfukEcUS/kC8qb8Yn4t2d9rpLM0O6JRGtNUyiVOrRGLHQjkR7EmI1LDpJPxNc8lxIj3/zMx/6fPi//V2Rg8+6aB3C5G/s9W8G0sZh7r5gagL58/plH3aRLQTNcfl93CWuJs5r+IKU6SXnO2pr0rakaSHsyZc5wkcZznO9Sh4yRXQ2A8JsDhB48JvvrfE6iJTbg3uJWpySQ0mROw8pYYxYuMbVX3C/472D0BhCQto4FBAWJrL0A24jBOHy/x1jUNt3GfB3pVmB9v9fNvfNgW8AYBG1YVgB4fUXGISQAiYK9+llhGCYlZDqhqiYSU307kVlgNYoplNBvl0RNQRHep2xlyiwiYhpqcqOcwYlHWFb/jat21IvLq4udn3eNYVKqrR1kW4mXU1fNV+dgesh6mb9ZAuoOROJP7r/UU2nkwInNU/2QC1JG+skKSZJ28j4nQeEHhPXRVpTlciB9wKiIZqFzCQw7QjFNJHnq99wz8taIIUNpRJTWyk5BUelIjZFpp/bKyVh4VIDQWPxSg167x+c4po2GOqbRKmciw88enU1bXvEkQVkjHsXRAuZEBnuesxFobOudxOwaMnSqVWVLq0aHu2OMetdIvHj3LHSZ7iBGNSuSStDoIxJ5Jje75qo6cwWkfyrXGqYdDTrojeadZO2pp5LRgDRF9GhTrOaLnLj37uImg2G3rsoF82ySeA4vAYBGNfH9fA4GyAiMjYHkB0bAGrEpFOv+2uX3qc2T/qZyBZAKAideD33izQt16uOaBisRLkIl2ibDcai97rnsJkhAgzOhFBm8JaiHUXuPEbECYERgRO1c69ovwoeqcl7tPwBZyPqe7mDBYBYftLDHqqIjHZZB746A5stSjQo9vm7Z6PIppXHh0kUZ0RtGFYszlisgilqp/htq9uYNKtBSU9BREoamhq0h2+N8tEqkSzERlTsh3mQIibaRdVNVE4kzjFoZ4JYEXktyFigqm5+xxekSmodwqZ1i2N2BNLMvsWLtDibAb9GzE8gb1zHyAJ0RdSAqJz7Qi2Y6yGVTnTNV2Zspp6HujzXzBdmKqT+2/eJdwmiCq0jn3MfvBBTcJ80JObV3knjNSPAxuhY/VFJqqmQccXHpb66RBqSJ0g6YkBIhPQvkScf6Bxz1Z+61DPpbiLltDkim//PkRbH5TWkwNUdK10khgEUWe75tNXQqrTgbpH1/+2iSqJINmYaiaeLrPysIywido5Fqrz8QkDsVdy2Tg2Z8vdCrZma75afuZQo5+L56j2e4DIAeXtEJgf1YiMQu+3fXeR65GUXzdMrvOQDricZcmazA5myCVBxWrGLrFkzGwe+k6bHTpR6Mc/TP+a41lYowH1DYQ1Z896wHo6jfdtNLpXtjUbYDPEesW61Wg3UXVkHmxmun43lJQ+49jLImiiRjdH8O0VOrhfZ/UTrPG1tbdsjsupzbiMRDN6z7uxYRyrCwpzhDO9Z0SH3d4J2hL89NjPDACNqw7CCvmFqx8yXRvmNmIx50n4gRU9ANEzk+vzx55VwIS6IBHcwUt8A0xKEY+tOr9PIFGMUolBS4U8ffVp7m4maaxtqNcrWLJc0yca8jXLHzDt0Q0H/sZJ5c4OkxaTpOjuz9MSG9GDxQb0matusyafNIyRC09JE6vyuK0C8jL4kRY94jR5wpoFxnzheYVWhprPZ6ACum7Q+ayOr0GPgtLWQ2vDLbhgCkcSCO+Xqq47LO5s2ydy5c11kBZEQ0UbFq5c6hFZbe0yKpVEmRcbJxPT5el/d4usdkfpUu1p/3hiwz3zB2YISwVEX5UOfdKkOwqh2YrBMhGdhjnAgfCJ87DmPL3VZWjYTRDmQXuHRgIAr0aUt9z0T6AUOWI7qFKbG1lqkT4FrH/NhJ7zS60twUSvEznxpyJkP6gnL2vqLU5MmbVpV4kidqVmkzg8977ID+oEeIPwWBFLLwSYm3YHjcA+4V3o/I90XQzJIlbMm7g9EfnKjc2wjaue8ELAODKkVmXVj6xxsNhVE8WQfIHSyFGyEuAf8PZNSR3SFkJF2MsoiKNTHEpLGi1z9561dBijjh6E27WFEbRhWkELGOIQ6sW/PgmA6bPHpBNRkIT312q6vUaEXqV6cxnT4RqAta1nmMtlTsEeFZdGR0ZoGR4gFcauzWfEhjXaJoqkNA1Lnb59+W9PPHB/XMSJVVOnUujsDJimk3iFYhF4NRG2B6B9ybwx39dWu7EkBvdY783fqPSFqriut02vjvrHJwKCFf7MmvpM5YO3UvntzD1VBTMqSVKcfuJGQLm+8+Z6LTUqwvqyvlqjwSJnTFCHVc++SrAV3SsaWX0gE/c0RAd9qaqPUiIONRhCOMSCC6JDID8KkjxhzkZnXu03A6U0uAvTrh4AhXKI7UtmsD9JJxOgj3aV6+VIDjkTXqgVhQeJE5KzDDw+BtCBiNkgouUlx0jNN+pjIF6KC+CICZM0aEUktuKPtfdjy09aaN1Hq4RcC07+ucmljhHmQpPZ9n3SbCaJh1sBGhXo6oMQQnKYOBn8zpP6pCbPJ0NJEQDBHOpYI+Ko/d+/d7t+JvPGv7j5wTjYX1K85Nul4Ur3Bfw9suLj2t//TETX3UaP1Ktfrrv3TlEPIOjhtRMt7AaEjJuN+UZtuH22PFkTGOJV+CMCI2jDsgFBoKeKrL8Ao5HjZcSV70uZE1ZAjj5EGJlIm+kQlTbp7fsZ8ffz1U69rfZeWIvqbqSkTRdN25J28SIuTBv/Znp/pMYnEqVFnxGdo6ruziJqUOu1XRPXUk+mFZjPB+qgdIwZDuY2RCevtrBUNlToEzYYjvzRf14v6mzWQkudxNhBfXP5FZ/QSHqXpeKxKEctxT5dmLm2ZNNYtiDDbmY7Qyobv93XXXedSgkRxuDtBgBlzJG7SZRKHB/dlDzqCpJUFsmBwBinVYFCn1jatgMIbYji91amuta2pwkWvkAMkyDl0DOR5d0xUxUR6kCE1XlLNkG/aLLe5ANSOIRlMTABkC8Eg2mLtbAaoIRMl7vqtc/viWNQhfZrc3wtqt/Pvauv2RZS15RdurUTckCNpdl57BlV5jYt+uc4pa9w5WTNrxXCDGjhE6mdds2mh7szmBgL1hMrzeL5umjKc0p21c8/iZrvMh7qdNTnBGetBZY2okEgfcueag/25g0Hq21uVcl4/WIMoW9Pa4e6Y9KP7FPo7/+cMXnTTgLHAYpHLP931NDNDv2FEbRjxgIQhO+++BSBQSNkLwbQ9q+KszEyZqY9TY+Y7qeeXTrykpNvc5DyuL9Rc0BozfdjUzSFTjkekyqaC3208u1EjWurM2IXye1LBEL4HP+P8xTF+vOvHemzSwtR2F6Uv0gwCGwFeS1aA37PWYC9vCB3yZZ2ktQFZA1LpXC+PQ9ypcamSlZClLmMP7XtIzpSfcQNImuu1TewjCz7SpWCu03tbViZXXnml7N27V95++21ZtWqVi9CoJTOdivQpddbjr4us+KQTQ0Gq3IeOXMZ8a5Eno/TAOEMiXmq/uIEhGmNONephUuO0NREZQ3YQHJGyF3DRKkUUu/CuVr9mrVO3+2hjIwRxs5nwwDFLW9ImiGQ3uHokkTakRZYakkOV3b7v+eQGR5C8Lgr/7kD7GT3PXBc1Ze3nK3RtUys+5nq81/6zi7J5AZE/7yc18Re+5tL/3Bui97m3ujo1pMl9jksOCLObA2Yule7aScVyn9nk8Fw2Y2oSE5g8xSZH69CdQDMM9S5y1ExChutJb/bK8GaXXfDTwVCOkwbnWngNGwuiazIYoSwgGwUwojaMeNB/DblBdNRrvRIckvMGIkTLgIiU2jG1aKJY/g1hlhWWqZ93ozhRGdEoRI46m2iUIRw+lcy/iZAha/q3IXIibQj47tl3ay81Ua0n1uXZy+UvLvsL+d8d/6vKb9y7ODZEirUotW88xFkzETKZAUZvevJHbIaKnM9+NV+pu6DKb+rSRPlsBrxobsO5DUrS1Oa9OA4zFixTGUTSHYjUaUfT+xebKlXHqmT37t36u//+7/92RE0kd/A5R5akT4muiP6Ipq/+i9ZaaEdACa2120onuiItjFI5e7EzOCG6gwAwOIHMUCqj4oY4IAlqw74PGgcwolnEUUR2wcK43b91hK0GF24euSqeg0HLltbA011EyMaCzUFA1a5RP7Ox25uAsFlQURjHp3UsMHkK4guPaY1KSYVDhAi+EOaxgWGzQbuSr9sjWKL2zmaHtUbucZufa7/qFNWc27uJqbsaOwimb9U79bgnWTYv2qtNix6/p8Wowd27Tt+Lqa62zfvAdUbRZkYbXEygTW68+8618KVDS+Jb74euLd5F5UbUgwojasOIB9abuHBBnEdLjirxQsI6hzpArojJiEAZhsFgjLiIOKmur1ZlN4QKQTVIg/YjU+dFMEa0CgGTBsc3PFncJgDy44v2KghyevJ0FX2hFv/G+m/ov4lw6a2mV5o1IHb78oova/S+7sw6nZQFYW/L26YbBwxYqJOTAXjq6FMajUPwXMd757xXDVJ43RyU0hhtRcbrumkZuyqndV7xyQsnnWtaICXPc2gHQ4TWHdjoPLLvEXVuA6TpY8+1az/jwxqFNIYQ+EgTuWkdd6JLH0MmXamBqckefd21bXnTCQiNdDORJa+FBCERItpxOW60JfaUXBN1VL5DIhAxRNJ+OhPe4ZAjRKykGuNIrf34RT8cQ3+OcKSK4QkpZs4PYXKt1IG9FSigrUmV6iVONc41kCXgPqjmoMmRHY/T+sR1UgPWGdb0VAfKJWxI2GjwnTQ1z4dsSe+fWC9yyQdd+xWRLOlvrpfNCdOyln/YtZ8BFZSlu41AWZ5zOCPq5v2Z20Wky3u36D3OgU090unPTnJlgazApoaIX527MlxqnDJDMEjzQ+LrUOJXutciRvN96IYBgRG1YcQDUrp71t3ao4zyG3IimiaKpMZLDZrI+KpJV+nvsd6sCnN2m4iw6prq5HTDaYlojmghOFq4iNIhKzYBuKWRciYyJ/VNjZrWq+TIZNlZuFPbrWibgtg5JhE34y91aEjArpOaMvVmSJgUN8I0ZlmzObhm8jVKsHxRE6fnG6IGRM0YqECktIYRraOKZyPCcYLBORDA8Tw2CHxnnRmx3funY0VKqxxRPKl10vEbDwZ6hQEksuH7LvqDMBBEQcyYWqiRSVT3FpTaUkX0Hxjz6FXVvheYiJBaLtGzCryynCrbD80gAifShPwgDWrg7VPsEAu926zP11tJ13rv8DYtaRtdjRiy4/m8/0Tr/JvzQWCkd4OJGsJH+EbKl3Q194JUdUK2yLGAiIw0tNqcVrmMARE0HtzUnIlgITKiXm8Awxr9PGgyFrh7sd7lH3GESwmAe5uNy9qlIpEBMw7d9PzeEaYq5mtFeK8heDYYid2879Tp2ZRwPu49XuGo1wtxT8O5K8plOlgvvum0bLGB0Xp2sctW8FqEa+rmtte9X1d8rlXdziYEAR2ZEDZdbCoMvYIRtWFUgAgY4qOuDFEiFqPlizQuqmki0lum3SJ/tuzP5Ge7f6ZkCFFDnBAvhExETaSste36co2SIS2iVkjyD4f/oI9TZ46vj3dGK+Wn3dzquip9Di1UbA5QlNPmBfl5oqYFDCU4tWK1/ayr0HOU1ZdpHRlipmbNY6z93zb/m4yLHieldaWazsZ1jU0I52UTwvPbE/XK8Su1Hs+wD23hqi1T8vak3xVYH1kH71yGsrxNn3jFeWmszpC67EUS01Ar4XxIo0j2lpGIx7qa+wyod/KlEWbATEInR+U7olGb0GRHkoUHnK845EyEzHPjsPmsFrlwyhl8IG7rCN5XuytQD8dVi5Y0LDab6lyqWPu/DwW8xQtcmxdqeC+YYt1XfFZk/FKR3Y850ZiatZxwqWDOzVAQ7kXCTCeKYzPChCbaojb/1KXH2bRQ16ae3qLIpj7c1HoujkuEyld7cF5GX0KEbJZQYLPBYaNx4zdaybw7UOv2rUeMvWTzAuFyvyctc8cFbKAwksFfnQ2aGtM0upY2b/XKtbKxYYOj7WvFIlt+7jZBbKzYVLG5YqPTm66EMQ4jakPIgmgQ8kUlTY04mJSIWN85946mshF0MW4SoiR9zOuoyxLhEmXzXNLhuJf97RV/K19Z8RWNdp888qS+HqJV+80AUFFDlqS1PcliLpKTnCNTEt34Sl5H9Mm5WBttVv44Owp26Bevh8gZUjEpaZLWolkXYjXqyzo/mw1Cc4P+m35yyJHjElmTgj9XcU5T4xA/vwOQOZsJWrdIqQdjVuos+dCCD8mGsxtUWIbqnJo39WZEbxAxrV5sSFDLE61z/0jvs4FQX/Mg1Da1pjqrq4pkV0WulNSdl7SoCJklSZIIkeESdsmdPZvpq6nmwJAMIrOGIvfhrrXXgI0o0RdRJA9BdvhskyqnBUl7g5NaJzYRyVFf7QuCW9I4Hr3dKMbz9rqUtfIItfG9LgWOPaYXqZHmhqRYO+levpeedhsOH42TDkf9TTSuQzvCnDEJpEtdl3u9OVBGoIfcp8shMwRywYCMEY1BxCyMTQgiPDIGOuiESVVxrg5P5oMovi99v0TBlAk68ufmHJiqsMEhzU3a/rVvtlV8swbW6pXzDLc4uzXgLR7nNheYqvCeBU/rMnQJI2pDSIJaLREspAV54I8N4WGQQsT68L6HJbcsV6Nbep+JmiFKHTsZHqECL2rMEBKpZkgT1fdTR56Sv7z8LzV1TNTKsdWIxJcqVYgjGlVCaKR/IWVIjzo39V6AShzi5VxsAiB3jExYA1G6F3Kx0fjO1u/I7dNvl7fPvC2l1aVSVFukBM2xSZUT2bNWroe6OQR+adalOooSsub6zlSe0fo0x2VzwH+deYSjTvd94ABntMe2P6abCcgeUua6T5af1DUSNdP3jakLbV1kGxDMkWmghu9R3FwrUXXlEh0zXvKlWsoiI2RF6lSJpz1nQdfDRdqIyUi11ux2KfCa8wGFcbiLRqmRqrAqwfXn8oFOSpjaOJGsd0tDJEibED3AfSXq9i1pHJ8oGrtRJb54R3ykzWkRI6XrLUgBtWQGf/AaNh2k6omcIU/tnz7uaugzbmgbPZKah/DoVyYdDOHXMeSjPtCzfaerwTNZjONAxKjTDzwX6KduFjn5tlsbZE06WgeDcA2B/ueO/Nx9b7Z6pfcjmuX1ZD04h2/p8jVp6ty8r5QsKAnw/vCzH87CuE6ibTYqRtQ9hhG1YciAkpoP//TYdDfBqrlZo0gepyZMHdnXiKkJU6clZU0USG8zQzWIXokwITWiR0iO4xAdQtqYl2C1SXqYejLnIjqFECFfUsLPHn1WJ2dxDCJuyDayOVJJlufQ6kVETZT7Tt47GvHybx73aGpq0mtAlOYjYyJ/zsuauA4sQ7H4PFDkIm9S0ZyL57aHjsfMukTHX5IGJ91NBoBrR9XOfVLntuhEzRBwXT2xWeXekj1g44N4jQiZe8u1MlmM60VFvv7Mem1du2/ufTppi3OwlitmXSE/k5/psSrjEiUsLk0mVJVKQ3ik1NYUytmJy2UWbmE9BVkBRFLbHnIf8FUxrqZL1Ek6lXoxZMcHPmYpRGIQE9FbUPudEg1/KzxvoEAdHLcvSIQ1EfVr/3SYE5mRlm9zLdFuXRC1rilQYwasGTEcpQMyAO1BBE2WABX7nFtc73n5GZHsec6N7K3vtKrViViJknkuxA/YFNB/zrn9SE2yC5Do8o9eXLfnvpIxYMPBGqdfJTL92ovb2HoDjF5oJWNEKaltzs17x/ASNhFkE3if6PNug8BUL0OPYURtGHQQbf7x+B+1XovimmEYzJwm7UqtFcU1kduizEVy7+x7lVSZqkUa2zuLUXcmsoSQIREIhnow0TBkCymTZobIIDnv9c3rISXqwUSOROMIyvIr85WIiSg9cfK9qaHJqZ0jY3VTgBUoPdeorImOeQwShjgXpy/Wn4lYiVDDmsOUnH1dl0ieqLiisqJlg8L5iLz1XNLkXMqaG5XsEcRxr5479pwKwvymhceoT3NfSOdzL6ibk7LuDuorXlOs18H9iG6O1jVoihuuCwvTTRL3lEgaBzbS5dxT7gFruOOOO2R/3n5Z/ZmbZfukLJlQdELiaivlYEq6ZM69QWb11uyCSOrar4sc+qNz9yLK4kMeFTXE4ZXciKqIHFFxk17GQQyiJ31K7RNSoGY9kIAkiZqrSgN2pPWtM6G9gYoH06qIdNks+OeyPiJI3jscxCDyjgbPEIEjRAu2Sa2Z7kxX9v3BEXzmfPdaRGvcC87HcyFsNhA6ijHdjWPkPOrmRv/g5LZEiEnKph+5DQjngeS576jcIdr+gBQ9mxqMbIjq6QeHqHkf+SJNTisfaW+f+iYKJ0vQH9RVuj54xHlkuRDPcd4ezrAfaTCiNgw6sNFcm7tWldjYepIqfuLQE0pWtDIR6ZH65Xmkl0nD8rv2Fpi0MBGZZidkKzEjFEOwBel4n/DVE1criZMyJzXNZsBPl+KYpKUhW6JuFNkaPQfVp+mjhmwhNiZWkQImCuXY1IP3F+/XlDPRLHOoITMsRp899qwSP8eHCEkxQ77UttmEcI1sUoB3IfN1cNaPKQppZ9LxbCYAx2Azwn2gb/rjiz6uUTuE7+vVfZlORsTP49zLYPhUOuQcbNzyyCOPyKt5r8rrua9LUVyKlE1doe8X2YsPZS7sxV9C8Mli3cSrI6+59iO1AGV+8VRnJ0pEiFnH8TedCAvTEWqdpItpceL6SbeeWCeSc/nAjVKEcLEW3fA/IqfedlEyxItRSvv+cOZZ46FNdoCIO3gcJ/cWQqTNiWtqD01NhwX6ngPkAnGSLlaSn+XIFktW/h6o+ZLi5prZALAmng8ps7mAjNVA5ZRLhwcDJTcbAyJ8zUREuuib1PqM61rnUncEr+5X1zPGlU5vuwkIC9TLOxPuoQFgQ0KrHKJBNhgMtwge+NJbNDU6gRo975RI2LBxfDZvlA1GIYyoDYMO6sfR4dEqggJEgqieIQRvSEIkHFcbp4/TqoRz1xun39BIlrowqW8icciSKJl0rgq2wqKUeCCz8PBwrU0zr3lu6lwlPK9ihpQhTNK8Oi8+LELJl5owZKhRblizPgenL4xI/EYB5TT9yZAk0T/kzfqoYUOYmJzQwgWRkVbHupTj8lyukZQ+s7W3F2zXa/FWopq253t4hG4ofn3g11q3JvrncYjee4O/lvuablCo0/eUpCFTNjAI0sggUOPGEIY1aD2/tkwJG2Ea95ZIuiNwH2aFzZIDYQdUwAb8PG3q2n0Gzmakv1ETQ8rlqKejWr2jIRAICDKDiPGtZlwj9VtSvZA56mwEYDiTDQT4sEe0pVErbmi1AVGZph/aPpeI8Yo/dUSE0jlyllOnay/5RJGMmc7ZrKMhHESUZBJYPxsAMhwVAWJH5U7dGmMTFZ3Vu5o85M561B1spmvVYoPA2qh7Q1iQWPvIX8eClrmxnqTv2YT5yVpqCtMJUbPR2PRjR/K8B9SZsSNd+J6LW906A+viHvkUPdfa3x7rwsNOH4BdqveRJ7WPOh8R3Ci0MzWiNgw6fBo6GF4U1f55Ptq8YeoNUlxbrKlbIk4I/cYpNyrpkWomVatOXWGubgzBQtoQE0QN0UFs9BpDrBA1pP+Lvb/QVLKOoGyqU9Lid6R/IeqchBwVWRFx09ZEREwUDenpGMmmJsmvzpfv7fierD29Vj679LMaDRN1M2VredZy+ffN/6614cqGSiVjb2Ly4QUflgnxE+SVU68oiUdGRCopQ8asg+9sLmgTY+NAbRxbU+4V50bJTRr7U4s/1dJCxb1R9XZdhTqjcX8g4cPFh7UOT51be7bLz6hhCiK4y8dfrpuMgyUHlaQpMdww5QY3DasD/NM//ZP827/9m0RERMhv3/itZEzN0M0WSnTvvtYV2OCw/papWp48sOIkCuU9p4+YtiuIBOU0ZIMSm5Yvn84kIiOiU4vOANQr+4wMGKjhUismZevPq/XgTS46DB4wAiC5VZ93aXkiV4gIwdzUVW4DQrq3I0AmKz4hsvtxl9Zm80XmAKJ5/VsiR15xKWI2L/x/Qko5PlWk9IxIdaEbjjLnJqcaR1RHqpz7R9Scs+Li82FGo9aiKW4zQuRNi1TwrOn2QLFNNM0xybagJKc1i00Gm4Segv/31ZFugFBVFJicltj2flI2QMhnRG0w9B6LMxdr/zD1ZCI3nT0dGBKBmto7ckGgDJAA/P7jCz+uFp180JM6RrzFc145+YoSMUQKgVNPJeImKiUlzneOy0AKImNf52ZjgGp83dl1uiGgbuytRemPnpUySyPwSzIvUXKDxCBGasNE3USytc21uobGhkbZW7hX/nH9PyppEYVD9pBlSmyKkmxUfZReB+uDuBHH/fmKP5ebp9+spIuTGRsClN2+Dc2fi+8Qrkb/ERGSHJWsREodmZo5qXfq1Y8deEzvLc9lI8L94z5TRqAVjGtHFMbGh39zrk8u+qTER8fr8yB4Mh3B/uLtwVAO/z2yKFKuXtNB604HYHPz8smXVW8AURPRkxFgDWrQQfQJCbp3JzBdqjZgqFLhImjI6fVvu6hV246KnTJaRUoYhtS4yLQ7eP/q7iJBzq8unUG1Tq2Ln3GkShRPvZzokvYw1sCIS4xJiHx5XUd1UtTWXF/w0A3q8Nd81UXSumGJdJPF2MSQ3vcjFlNnOotU0uoYhhDhMg2Lx9Z8XuT4265Wq+eOdErrqWtaI/kWO9MGF9UCnsdjndV0SXlzfZjLeAU5UTibJdrnekPUvQXnhoipfXckOkvEWjXO/U14/3o2Ed5BbRTCImrDoIMIDiKiBguBknKmvsv39WfX6+8gmesnXy8rxrdGA5Cpj/KYwYwymecSCRI5Qlq4jPE8IvaEyAQlJp6Lgvm2Gbe1kDTgOZwXcn8z900lqfDacCVefL6JPumb/uD8D6pxCkRIip0oVydnNYdpDdo1RzUryRPZEymyGYF0yQBwPFLfbCw0GpYmJWzIGpEYa5+1YJb6d/96/681Hc11ER2rgrypWSNqyA3SpmwAufGdmjqEDigNEO1jocrvSOs/c+wZjcyJ8lkXGw/OySaC50GeRP/xEq/n9d7onYE1sFnxoL0sGGQGqL1zfcHtYmyKaIWj152NGPcBi1fuKRmBKARYfNDygUyamA9klM+QB33TXtC17/cutcsHM6ROrTZvp2tDgsyZOkX02hkgZ2qZx95wkRj1Yu+MRWo759K2Ji1E7JAwUT3EBBEj2kIoRpQLdj4WGCwC+ZN2TnI95O2HdwBqzPufdqlx1gKpo2bXedWB9LmvfROtEtFjMDLpMpeupn7PqEnWDmFC6mxgPFFyHNTfkDFrgkQpDxCls3ngvkL8vMb7gWsNvSngENcJfIsXpBl8L0F37nN9BZskWtAY38nPXBsljbR2fuX8G0e142+4jYM6qEWLLLx51FqXGlEbBh2Qxf1z79d2IKJpSIfIE+K8bILri+aDng/0jkCN9ZEDjzgXsKZGrRdDPESyR0qOKFFoxJY9R76y/CuSGJOo52ifluU4KL6JSPcV7lMiwwyE1DEkS4RJZA8ZqwAscaKSGypuiAe/b8RmRLuAx3gu66JHGhEZ18fvIV3S02HhYToAZEryFCXY3x3+nf6O9VPf9RE3gzwgMSJxrZnTxtXUrNOvOCfnQbzFxoPsAvcBMR2bAR8NQ85YmXJfiOrDysKUPKuaq1TAR6qf+8IGqSfg+l44/oLqBjxeOPGCXFZ0mfZps1HYeG6jG/sZlyHXT7m+xSCGDQzr0wxDIHvCebn3fM29cM4plnUKVpGLYiEAfLYhMmrEr/yDa+1BeERrFmpw7C1JxbIBS5vmVMe+ZakjMI5z6y8C0XSEcwWD4FFxcz5sRG/8R3deQO13zs2ONOn3ra9wUS3kCUHo1LBn3PHm3+HWT7QNGRPhep9zfs8xGD/p7T+J/NkAsCFheAnRH4YxDOWAcEidcx98BEuESPqcNi1S7NSjiZQRTPm0L8dDcIcXuL8PZB3YnHCfIG42GqS+EclB8JQXCvaJpF7b+X0jGscIhiwCmwnq2mrpmhkYLdrgNhGQKvcHFzKEdd0503WnWdjzB3ctvN9kCaj3X/P/tT0uWZFlD4hkzHYzyv37yNcohRG1YUgAKVMf5b9g9CSqo683uG8aUKeOqolSARmEi6IcYl5/br3WgtvXxCHJ3xz8jbpzEQ0T3UJcpLchuqSYJIlsiNSIkyiaYxL58juIBtL1qm1VpEtYi1ocAscUhawAX9R/6dcuqCmQrLgsJWGiUgiNvmt8wsks8LUse5n2hbM+3MuosRMdk4qHtGPCnQiO9DabiAcXP6gpeDIRGLiwQcHHW4k94O8NeIz+a52DXV/eEsnfNfOuHovRuCY2IMGRMtfKYBHuG21k3CfeP9rVUPJzr0i1k20gsxBsmMJ52WBouUH7i5NdvZMoWYVQzSIxCS49jC0pJEtER4qcmjVRNtEurTiQeXeA5FGVs7GCgIlWIT2ieE1BRzjlM65jTKsC3D+iY9LbkBue17QBEWkDSB4y5bWk72OjHQGzXp7viRoVMiRHvzTpWaZSVea7tC3CKtLT1J3xTud13GMsSGvKXZbAG4SQAifNfdknXVSvLVhBf9tE2kTMwVEnpFZyTGTj/wZ6mSNcxH3wj25kJvdUx2NuEUmb6pTfHaWY6e/GcQ2xHudhw4SxDT3v+54R2fOE21Tw9wTBIghc88W+Rdyk5CF9SNo7qrFRwUY2b/fFTml+0EpHDmqjEEbUhmEFUSjk6N3D2oMPdiJe0tWepKl1QlKQBdEjxAg5cCwmP0FOCMna+1gT+UIqRHrA9yWrW1fiFCVjIl9S4kSKjJ6EIAHnIRIm+vVETaQL1CUsLErJlugb9XRclJvOxZpIqftaOP9mDVwPqXtS6l9Y9gWtxXN+WsNQeL984mXtl6YOz3UiBuP41LX5PfeM6J0oG/U2qXc2A7yGtfE7au5kK1CScx/5Tssa9e2ezKaGfDlnMNmydu4vLWm8X0TMQB3iSg9r3Z72MTIdbDpY98L0hVrD53UQOWuU5BiXhlb3rgmufkudMQfSiBDZ+ZvAkI6AyYzajUKMKc6LuyeApJi65U1ISAezIYBciFB11nKNUwszwMKbf0BapKj5oiZNRAq5E+W1pI0jWmvAfqJXsCEL5ALJKWkFXM6IiOkjJpKG8EnHc/30UnM8Hj/8ksip9c4NjQ0Kx8Y8BMJnPe1B5kFT2sVuEwAgZUoL3Dvq26jEuWbIlvMS+dNORWRNGp/XdVRz5h5d8gGRubc4cidi5/rZGLDBQXnvFeb0cpM5UI/vPkS2pLr5CmoL1HvCpqjelXrGMoyoDcMCoj/aq97IfUMJhlQwU65ocwqOhqlt8iEPybY8JuGaAialvnLCSiVwCI8vnvd/u/5PiX9JxhJZM2mNRqiQBBEeKm4iVQiGn0lL10u9tnWhYiaFS5oZNzEiaD9JisfeOPWGRskIu1CkQ35e9EV0zlq5DkjLR+SkeTPCM5RMSa0fLz+uJihsCIjWCysLlWz5mc0G18TzMIKhFMC5qbmrUK6+RqNYyJ7rog6/r3ifEj5fZBzeN+d9SrC8noidtZFiX5C+QNdIJgLVOaMzuwPZAX0vgvRXG/+wUT6w6gN6LK88B9oeFzCg+dX+X2kfu1q5VhRoipxsCpsdlPsqJgNEiUx+Ij1LmYIBHKR1ITmIiUET1HapyUO6EBeuW6ReewLWR6QJqRBJQ15qShL42ENVzrF5HPJBuNYeRPwZc92aIEUGd0BgfoBIeKVTG0OmwfOuqW1DNES3pLq9iEx7wCNdT/KOX7uaMTVlzk3bGSYnbCZ4jLUlT3YEyDmwamVKVzBI2c+81im0vZMZGwnWSkmE+4dynM0CGQbWoKK2eBfpoxgn5d6VOIxrCE49c90QPudouddRLg2/7VfueKTCyUr01IGMtWAFy3rZAKgBTpG7b6n9tIgdBTCiNgwLiHxJlUI63hmL+i0EC6kEE8AVE66Q35b9VlOxECBRIhHz2fKzqipWa89mcfXh8DA9BiQHqaF+hgRRhauXdkN1C5GqpWdYhD6Gl/iijEW6HkiZ1/AcyNeTPBsGCJPHfWqX9DJpdP4NKbLp4JxEzKS6iaaJkvk9UTDPQThGZEm0SVvWj3f/uGU8Jopw0trUzElXE7USmXMfOF9tfa0eg1arG6feqCTMNVBrv3fOvS31akZ6kqJ+68xbLfczLjxOSZKMgo/+u8Ls1Nk67KToXUUiD7vHjm49KkvTl0pkVKSq7/1mgiwEa+T+kcrndTxOVL+neI9mPT65+JO6GWkBqdQ1X2r1xvZ1V8iMGi+Rs9ZGzznhGeRIXbgjp6+OAElAfviE+/Q6NWadC5Lf2nccXeEmaCFia08skPLln3L+39THo5JFrvxKwMRjl0gDlplXuLnOwf3I4xeKHF/r6vAQG5E010UWgTozNV4IjSg/8ow7LyYnRODUltmUMJjEm6eQAj78siPU9mvEQIR7RW8x1wNJMkpz56Murc51EvGzSeEeelEe94ZdGL/vDby1KsTMe8b6mLjFtUCs1JVxUyMapwWsJ6DuTMmBzRPXCvj7xBQnfbaMdRhRG4YFpGAhIKJAAHGR4iYSDCZqQJTt+4iJ2HDp0j7qMKcGJ/KFUCGs+Ih4TYtDwjz21um3NFIenzheU9yQJMKqtJg0Tf8uyVqis55RjGPmAQnOT5uvhIcqm+drFN7UIA2NDZIal6rHmpoyVdPppKX5HVG7F6/xGtrQthVv09Q6ojTGVSIOIxJnk4KwjcgecuZ8gP7tH2z/gZI2kTzHYVPC9UF6RLgqaGNDUFusQi+MVCAe6tvBLVZsFNjMsEkIrl17NzRvT9oVIPL7592v6feMJzMkeVyyrJq6Sg1p2ISwcaLlyx8TRzlq6pQCfLRN7R+XN6Jt1theO+AsMduJCKkHU7+GUKj5ktolquS5G3/ovpNepa+5K/ESdU8mNenYyDQXUSJcYyIY3EQdmJ5boljmOlP77sgxC1Ja/oBI4/2O8HyLFwSsNqapF5Mn4rO5t7nj8nrORRSNzzlrf+PfAnXosICYrtkRHz3QpJhJiXstAcdm/dS2IeL2/txEnwi/+PIgit/3tBu3qSl5Js0kuI0K2QNa7iBFjhU8aKQnYENC9mP7L11Ezn2mPk32YtIKt15Im80N99TX27sDOoJr/rK1bEAkzWYlvIfmKqMYRtSGYYEafbQTNUE0CKbaA1IhxY0TFilX+nIfPfCoEjrPT21OVTJTU5KmJk0lqxK8qUkjWTYBiMioL0M+RMw1Tc7LmtT3TdNu0giQViKIFRLNSshyUXh5rpKLt+KMb4hX4RrrwFgF0xI2HGweeBwS3VW4S46WHdWaNtE+xyOSJeVNxE3anvORrmbDwYaAVDxkvrFoo14XESjRKudX29KwKGcIwwztZtcihkiMWjo+4Fr37SAi5tqJ6jke18/56WXuqm86GKSrPzDvA+pDDun794zXf2LRJ3TTwXvA81jHhnMbXEt0U2OLeQ2/11naWFf2BJhjQAS7futqntSHiUAhdOrKRMQIo6hpM3qys17gwqMiRYccGXtCrw64malz2CzXGkUKl/OQbg8masiO+rRv62LiFRsIj+Cf24M1EWVDnmQENLU73ZEO0TUqb8D9DA+4ikFODfUuYuXvjcfY/EHipMhJe/fUyxrBFxFq3g5HzijvIUzOTeqbdDi16Vk39a2mTEsc7weiOIiV+jKk7Dcs3FNaydgM9JSo9XVpHc/eHuMwojYMC4ikITSIkvQv5EmUFjyesT344K+pq1ElMmlh0rcQI+QFCXAMyBkyIaKl7gxZQk64ifleX5TX4RHh6vl9z+x7lNB+ufeXGq1jkgLJHCw6qKl0QLRMhEiUznMg8Ttn3qlR4pbzW1SVjrUp6yHtCzFzDm9tyrqoGxMRq2AtaUpLOhyBFylt1sjv2QBA4ET3RNaIt0jlc2/CmsL0+nz2AMJi4xEdGa2RbfvZ1JA3QzZIgROdc27KCLRR6XCQwPp6gpeff1l27Nghq1atkne9y/ULs9Hx7VgepOI3nduk2ZFsiZTMkly5tKlZVoxbKmEqFurCV9qDNZHixUpUo6nowAd+vCNdIkRSrkTZmiLuLDUaGAnpvbQBAjCiyOTxrQSFepvHiNz5mbQtnuKooiFGCA3SxGt71Z/2vFeX60Bs1d7SU41Got1GgNo0/yZSpq0qAlFatIskcQXTNVW77/Rp92bqFKpyInsVkVW6cxCtE/le8Rm3+WBT1NdJVqTo+WID4VP7npSpl7PmUegSNhwwojYMC0hnE42RdvYWo0SSpE87A2nuh/Y+pMYpRGkQIxGw1qhVK9MktVKrx4IEIRIIief4yZIQIb3RECDET+qYdUCMvq4KIEO8uXke0a5PFUPUhVWFGqFyfNLobByw7OQ4pMk5B1E2lqQ60U/CNAImyuc7NWI/IISNAxsBomwI3ntvU/uGSFGD0x9NtE5KnzXHSZx+R43NGiB4VNZkBtpjefZyVXkzvETFYRImzx97XtfKBmDl+JVOkNdNpPaXf/mXcvDgQW2Hq6iokLi4juvbbIYeWPCA7Dj0tKTvfFwyaqs0DZ904CWRimJX7+3MVjMYpIyJIhmhqS1IZe7fkBvRMYpw/t3VmEuEYBAR3tC8fxASr1HHq3CXSiZiZRMwYakjR9K5GGlQ4yX1zu8haQZhED0SYfe07toZiMSJSKlh1zYFlM3Mk850/eFYreKPTX800T9ZBAaV9DZFzfO5Lu4lmxvq32QSln+4721NED7iPDYtdGlMWSkyeZWri1OnZv2+9r/o3q4tSg09hhG1YViAIIt+Z9TBmHTwYQ6hQDzBIJ1NdAYZo+imLozamUgaYvNqa02rhgdaZmPTNdVKBMzxiVgRgQFStKSsIT2eo6nkRifQCm4PUwKMilfHMojei7lIH7O5+KcN/6T11/qGeh14ceTCEd04UA/mP/y61f5TcLASPT5fStphzfozz1U/78Asa84HUVNbxt6UdWFlykxrVNo/2fMTTYVTKydrgJc4ayKaZr1sXoKd2Dw4LtdKuQHhGmUA0uq0TyHg4xjUnbuC92VnM8RXV+Ae31TbKBKTLjL5akeKRIWQHB7akE53gKCoy/JaVMBEnWoKsqO1RUhVzdtdqxEbDURpuZscyaKGJhV75ZdF3vhXF/HpGztOZP6dLgrP3RAY9XibG/eIcQmvT5rkaq60N9VWiRSUBQRvMe7xtomLvmHxe93wD9zOSHOzEaCHnL9/rEVZ/+L39O8cRORlWLQ2u40A4F71Zn54MHjftz7k+tt5P1g3/eMVhc63nEidzQxZE+59cM3c0C8YURuGDdQ5vbd3R6C1CWU4bUYQBcSMTSZpXIgOwxE/UINeXTURqcpT0qIdiH8ToRMxYmdJixTpbkgacte5y401OlqTqJVomEgZ4uS1pOHpC+bxpsYmJV8ImyiaejYRMi5hrEHr0ZB9WJSEN4c7og6MzeTffuY1GwrcxrTmHSYSHRatmQTvssaGgsEekK6OywyP0mvOTMiUZZnLlMBRmfN7CBhiJhrm36+fel3unNX5mD9q3ajJSZF74Rv3groy88F7WrfuFtSTIVoiRC9a81E0UWxPwLQsP2yDSJBol6lS2kaV7mqupJRJU49fKpI+Q2T99x1xQOQQMEM/SFXf+T1HwPWVrrULwRPpZcRovA9eK4HHuIqYUlzNVVPOya6mSy85NXHS0wMBznnd19x1Qqicg3NhKOLNVfoDrn/vH5yYbMb1LptAhoD3pex074ZkqO/3HqeeJ5pmU+Hr82yeTr7lpmpxP/kyDDiMqA0hCaI8RkZCmMys9ilj0sIzkme4fuPaMiVH+pXx5kZVjJ80z71Qc6HF7xsFNcpqUtREn5A0kTG1W+ZkQ95E9BAldWl+T+qZGm9RlRvMATkzlCKi2aWcy+rLNMr387BZL+DcwfOt/WPBINKn5kw07XuwuS5S7JA45AlpshEg20ANHcU75yLy3ZW/SzcxbAoQqZHKB5ih3DL9ljb9ze2zEyDYWpXMBmvhfvSZqPkgpyWHD2/S0kS3jGXUiU3+JpDibXJk1BMQ9UEMOIPhXEffLilgUqkIpRArcS6IGaLj/PxMz7M3JUEgRtr30o92XMfWdH9Qyt+rs0mP+wEXpHr5GVc6WsWCDTl6C6+4pnbLGhFOEfHTR07EC3m2V8D3FZAyGx3vow6otauD2smeE3X5eZFNP3KGM5QIUKOzwyQyZxPG+4EIj4yD91A3DDiMqA0hCRTXRNBErESMkDJEAjlBpvQ0q5d2Q5gSDelpolH6dG+bfpvMS5/Xxu8bkdj75r5PxV4P731Yj8nvtudvl7fr3tZ0OBEqdp9Mz4JA2SioqVREVItanAgZYRjn5jn0NZPehtz5N+TbHsFEDRFDipC8zpyOiNR+8JUTV2p6m/r393d8X0mZLALXzHQr0tRsSFhjTnKOXivnJKWtv68u0M0C0TbReUfjJ8kW8DgpcFLqHJ/n077WU//vthfW7NKgDFLgQxwSw3ubQQozr3PWnJAQwi9+TwQ8aXnPjk1tdeWnRY6udarp8de4IRo6TSrYAAMRQLjbFHg7S6AkMs7Vp3sKItkpq0SOvRbw3OZYYW5zQRTZXO82CcHXzyYBcxLIUM1R5rQVZ2kP9BGRfX9wGwfqMyjN8RMnNcwmgFT3QIEWLwgaO1YEawi8fH+62omG926zceiF1lGX9F1zfK6X9wFDFjZp3JMeON0Z+g4jakNIAkJBuKSjHvmMEad2ZtgEZErqWL21o+K1p9mbj+DYxbSujtTMRKE/3f1TrTFD6JAdZAVJcT4ibyJuImjSztSDiaT5vR/EAfgddVpImxo36/IRdVcIC/xHut0rujEtIdIlJQ4Re5c2onyyBqjXseaE3CFo4KdREYUzOpN7wLXgK/6DHT9QQn7XtHdpzT8YROa0uG3K26SbDa7dD9PoTkwWjF27dqn6W9Oo2x9x0Ssf4qRZ9z8lEpfiBjTAdnhtI+bDFIQ6sPfCvmgEZMA+0r9vvleaL1Vuhzlb0W0PueiaiJA+ZkwxJix2hEz62D+X76ynu3GMRIwMxaCGrQR6k/PaZsgGNXUidzYGpL3jJ7XdaNAitu/J1tGRRMpL3t8qNiMyX///RHb/zvl8Q5JkBTgWrUuo2HkML21U0hD99Kv6JsCifsyGCQtSzFgoNRDBs8nBQ1yFEvVu/T0VpfG+YOrCmtkExUa5XueTb7v3VLMjCPQuiBx9TWTp+3u/bkOPYERtCElANkR6O/J3KGnRCgVBQmjUjiEvatXaYtQcpvajuHF1NnCClidasBiWQaqZ9i4/kMNHwhyXejSpbQgfMqVPWUdcBoiadUCSfClhNzdJTbPz8e4O3hOcQ3F9qyeuVke0lMYUvRYyCIjliN5J5XtgOIIADCczNhjeV5zz850MA9dE9oGaNdf2o50/0o0HmwKibs5FVuHe2fdqzzfCPPzIqe17v242G5xH538nTmy1+hSRu+66S/793/9df77jjjuksLDQ2T1ChlkBMoR4qOsyXIFWIoZJTFkd8NfmusMuJpcTbzoyr73g2oUYtkH0FgxeRy2aucuQImIxojzSr0s/6OrOCM+YGJW/R4QpbKjEScV31pNLrZl6KyRKSYCIkM3W6U0iq/7MZQaYDkWbFiSKHSaiM58yJrI89EdXu84O1JRZ1/5n3ZQqyBYf7X1PuXQ30S2kzOaC7AJ48z8DfuZsVBoCPt8bRK7/m54p44NxPjAEhD8uNhxsVBCrkeZGSMm/2XSwgWon2OwUGn3HidQVBjm9ZbtrYcPFvfD+4pD37BsHLnVvaAMjakNIgvqsJxCU20SZqyau0sgSj3BStkSWCLEgy9dzX9eIkai0I6iZSW2JumRR6/Z1aj8DGnIiDU5aeELqBP09NW4IHsMQXMU01R7w9uY7KW8/mKMr8FxasHSEZVi4OpsR9UPSgDUgGqOfnOjdK8J9VoA0uYruwlxWALcyIuBrJ1+rpM4YStaNaxk15+qoall3dp2mwyHrN8+8qZmCTyz+hGTEpssl0WlyCTxAT228+6CldPDbg79Vhb0fHEKkzQaIdfzrv/6rtmW9+uqrMmdOIFVLhBaUaVCQcvcWoOoTjpNXxzVzHT6x7WHX5oOKGMUw7VdXfaVtihliw2EMIp19iyNhiBrBF9EoSnDMSPDCpqWJdHvWPJEZ13YcUVP7Ji2PwAzjDwiMmnrGPGdfeWKdyKJ7HMlj90mKt/3UKtLLVSVto1PIi6lVRLG0J9GuRApdR1QGhnqw0UCARVSt9y9AiETj9HsTyTM7esXHpcfgXrMpUIvTONdbznvLdzYhDOYg60EmghGdeIMzmKM78N6xYdnxiMs4cA84Jt953PdIsznj90TyvSFqZnVz/4nM+9PPPQZgRG0IOVBzfmT/I0qoEBgpYL6o4ULO39r4LY0eiS4hN0iNCJtotD1RU1emvkufNCRI/RZSQgGtvdfSpMemTxnhGseLDXcRBwQFYUGuTeFNTqmtrdHNek4eh4Tb16V937T/mSwAkXtkc6Sen8gf8uf1PI/1XZJ1iWYOFmUuUhMV1kubGe5jrBMPcDIMEC4q7T0Fe/T61QEsLMINDmmo1TWerjyt10UkzmaHcx4uOSy7zm2R64vzHIFAGHxAItpa/B559dSrer85JsdiTYjtGB4C2XMvfvCDH7R9o4h8aS8iOkXYBUGTEoUIuvvQhVyIaKnZ+pozam4sKenRDSZqyIFeaoZecFzWTUmEiN5PpsL8ZN4dIpc92PV5iVz3/M6RJaQWx0zsSJGiwyJJWe5YKpgKACOUiA4MTiArjTa5j4HfQ4Sk4tXApL51BjYEDZl5QV1doG4cyUStqtaUPecvPenmWC+5z2UoegKcwUjTszkgM8CmANEYGxruVSybgDCRiAh33WxyekLUgDQ+a2fUJ0SMKI/r9lPNAGl9dY3rYi54MPysbkoH/L1wz8lCLPtQ71zMxhCMqA0hByY8UUf2BiSQIilrosT75tzXEolCqsFoX5fGavTFky+qQIu0clVjlQqvFmYs1BotJMe/qfGqZ3h9lUa36vFdeUZr4KSkcRKD5FlTcJqblDskTDo8WOkdLB6DqIl6vR82Tl4fXvhhHSaCypw1k5ZGre3FYUzAYpQlkTOZBNLwCNx4LoQOIF4yAmwy2LwcKDmgc65Zi47uDItqmfOtfebhkVJyZrPIWewzJ4ikTHFR3aE/SnVytpI098L3sdPLjSkKETxE3SFwvSKVShRLzRXCm3ipG6TQHbwtZjAZ8f4RXUJ4wdC0eWB4BLVSBkoQtUKIWH5CRhAQafRZ13cd1UEqtIghbIOAVK0+TqSa1qsSl4ruaIwmz0VEhTCMjUNFgUtjo65mDVwPpMNsZ596JtomQq8NjGnketnUETkzgAPiu3DBERXn1VGaca0RakdjLTsjap2GFZg1DdnpoJrG1qlden8j3OM98Hlvc+8X3u3eZzYlbMiYQ82AENL/bDw437zbWkVr3YFNFZsl/tbS57jSACYzpNERIhoughG1IeRAWjt4sAMERcRLjZYa9KL0RTo6kSiVejIKafqgiTg9IGbMPEihUw/mGDvzd2oKnOdCZkTCEChpY+1bbm7QoR6IuyBCzgW5UquFyFFsQ4QAgoacvdgtGDxHU+MIzcLDW87Dse+aeZdGuQ/Mf0BdznzUHgxmS9PjDXmS1t6St0Vrx6wDtTavX5y+WHYV7WpxXENIx1oxXlGSjknWiBzoPOrmRhkPufDh6H2vVdhUJOF5e3WtwYI4b3DiZ4CDm266SY4dOyaf//zn5Ytf/KKLNhkygVBM/axTXNTbiU6g7U0Kd0R29FUXiRF5QjJEyNSqg0HLFVE3gjFIEILAkYxz+WjWtwkR3XdF1DpTGjV0rSNrBHFEnzpjek/HRh0MmCBVnrfHPd+vSX24m9yxOCdkhhhNNxxhItOvFDnwrCM4SJhrQ3THfceZrICe5jOOpNWwpdS9N/RW9zSaBhpJJ7v2MtqoaJXieDicATY+GAKxmeKaUbb3FpCwJ+LF72+1OOVeEp13JFAjtc+1s6EKHiSC+p1rxiAFkEKHrE+/48acWgr8IhhRG0IOnrjaD3ZQIVV4lNw47UYVeFG7Lmgs0Aj7lmm3qADK42CJ8+qmJg0ZkhLneKiqIWLSzJAXVp/4gEOkRKbebxuCIrpkw0AkrSCTKS5dzTEhM8ia+jOPczwiVzYYECXnpsYNIeP9TQsYCnXA67Hb7AyIvZ488qRuKDjfCyde0PPzegiYKJvMgE7qik5SExPMULRXullk7em1Wm9mLRD1nLQ5sqSkWKQyqLc5gJiIaFmSvkTHVurmJzJW2+PY0AR7r69du1bq6+vVTlSJ2l2IUwLz1VvQokRbFZEq4L2m1ox7WTAgLWq2ux530az+kUwICLvCWuudkDWOXl0BQs25zKVdiaYhWbIhnlxJ5wcP5kDwxnAQtSGl5h7lyJ4Invo15iEcj5R7sHJeRz9uccSI2I7zQFyaCo9215E8yfU5Q9ZsOCBo0uFcf0dRfWegDo8dKdc/7WqRyvOurp86w4nMqANzHazl6v+vbVmhL4B0mfLFV6dte+udOI6NExsTFP+TL2t9zkVT1AKvM3QII2pDyIG50LQQQbYQKARE+phBGD4CxX6U9DVEi5oZEZUHwjCIPViQBYhGUUHzXIaCVNZVamoX5y9S3ukx6VJUW6QRaUN4g5IuGwUsQlUdHhCOISJDXOYBsZGihkDZTDARizVQZybt7OviqMy1jUq6r+X5SV4I2kjF+wieMZJsVphH/SdL/6RTb/SZqTNVMc/GhA0CKfOkU5tF8g+5D3GiGL5DHBOWyPXjF+racGLjvGx+bp52c5vNz4CD6BjhGHOUSSNDXCi525U0FETUV/25I0hSt4dfdIrsAqat4S4W41yxetLahAc12QV8vXk+dVciQgZyEF0TkfqsA2QOqbJWH0ESvVIzJwVPypkSQvv2No5BdEuJwUf42ne9z21GSJmTAWCgBb/nGOX5bpOAmKw3USU+23NuFTn2ukvt6xznG0WWfdhlOjRjwC5vTfcbmYEA7ydtdJyT+0jdfevPXYqcQSHca1LnpPdJd9M5QBlgdiAbYbgIRtSGkAORJnXa7279rtZeVREdESPrz67XyBFihoDbj3akZvvaqddUvU19FyKHRKkxq/tXdZESKUIuQNRLexJEzHOpYdMfjXqaKBRiHB/nlOc85kViELYnbQiU9DsR+5xxc+R89XklRqJZNhik7L1nN2MuDxQd0Dp0dyCdz4aCjQUROa+H/Enlz46breI5+rw7U7mTHqfu3QYodcvOuRYgSITa7vw7NIJLCI+Q++feL/mT81vKBdTuewquEVc47j33aGnGUlk1aZVusLoEH+Q9HXJBupxIGiz9gCM00tFaG7+k5+MaIQxSz7RipQfEUSq2qgmouQPiOD1nZKA+3uhawCAXLXcENmqkbFlHe0DovBahWAsCr2FjcOnH3aYD4Rz1WghaNwAl7t9sDNq3qXV6XyKcdzjXRF94XNBITYRkA2mo0hOgxCeDQGkCcC/ZoCB4g6hpp2MEJ2YqlBK4/2QCaOkzdAgjakNIgpnSEOX1k69XsiWtjPiKVqP75t7X4WteOvmSEjXRLWlsjnGo+JASD0RP//Ct02+VR/c/qp7XeHxrXZaMZli4pnshdM4LKTOQ42T5ST23TriKSVLSrG0iXSq6gUiITNDHSMNTx4b8IS3q7GwmqE+jUIf4iFj96MzuMDlxsqbNsTb1Pdt8keb208ZYUzByy3JVEU46nHPi383GpAWkXFHW4stMNEhtOCjC4piIyHoLMhfPHH1GLVp1QldYmDx97GndtED+PRqlSc1Z+6AZ7Zjs0qQYgHT2Wurg1JL7OviB9iWIESL19WCiWiJsUrUeRLvUYI+94WrA/J4IkQ0I9xAy6mjIBfVcrgETEq/8ZhPARgOiIn1MCnrrLxyp+7o8bWIo32lV6ylRA+4T5M7XYIIoGbEbWY/OTHKIjtv3anONPO7XiviMzAIbH94Hrt+i6U5hRG0ISeB3DQF4FTI1Y+/HDcmiDKceS7SMkhrCJdULQXujDmYvE5GjqkY1TTROzfbu2XcrsRCRkvYG9Cl7wuW8EA6pb0gXQNYQvtahJVInYJGGJvpvrGnU552rOqdpYyJ+Il2MR6iLF9YU6qaB6Jh2J9TlRKtkALgOhG8Q/LzUeboert23jHGtrIPnoMrGqYx0PREzx2C9OLUxSeuhfQ/pBoHn8DpU2x9b+DEdUNJ2RvKE1sh0AIBJDPeeFD+bJIDlK6p7WtHIMHQJoq/NP3ERF+83tVzcwi79mBujGAxv5EGLGeRO6phMgR8S0Su70NUuXUxkq6POop2YKfhY3C/cxlCcE70rGU52JEoET326M49rBmxwLcy1rq5y0TtpdwjfA1EVmw6O31DnCJAInla3UAPit4PPOV0B+gpsYtmk6MzwdjVz7pX2jse4kgLXk9lOcMZ9CL4Xhk5hRG0ISdAOReQYXGcm9RseGS4/2/0zKakr0bTzuuZ12hP9rqnv0jpz8JhHCI60NFElKmoPTFJo/SIF/psDv9HXkZbWGc+RsRpNk2qGhEmF07usKe+Awtv3UZP+hphZlxqhIH6LjtC6sA76iElVsmQTwQaBqH7DmQ1Ksqi/Hzv4mOws2NkinCO9znpZB8eD9C4bf5ken2gfMiZC53HI8T+3/KeuF8U3Gwtq43NT57YI3UiPb87b3JaoBwB1dXVSXFwsaWmu9soGp6a+SuY0RUp8Va5UxSRJU2ySrpHfdQpIjF5oerGpNyO6QlAFMC1B8IXlpVeRo/be/FOXMkXUheAqb6dIwUGRVZ/ruLbdGSAXFOu0QNHeRI27s/Q5qVvmQ1PvVbX4JJed6EmKnYEglBeIQqnHBqufvaf5pp+InN/veq+JUlFJQ+oDCcRkZCxwWyOyzV7sUs1kFnoCSiYbfuAmjLGRwTaUGj/pfWxPg8Gsa0xlaMPyXuwI5EjNG/oEI2pDyArK3sl7R8kGkRbRM8QNUfEz9WAICRLbVbBLo2rIkX5rarukiyFMCIwUd3sQjULkiKcQeUG4ECrpZT/fms0CU7g4DySrxifNTVLXXOdasJrDJCzc+XdTiyVtzucSP3uPcD+Kk3o6rVoVDRXy1pm3NOrfVbhLI2/IFoORN0+/qdEnYzkhbq6FzcEnF39S10DN+4XjL2g2QOvvEbEqrGOteyr2aETrNzV859ohywF7TxYtku3bt+vPH/3oR+WZZ57RnzMjE+X6vKMyqSxPEiVC6qJiZX9KthRmz9I1dQhqvqR9MT2hrkpvMyQIUeLwhfkJqWgda5nmnrPuvxxJNza2kjLWnvhRQwo9HfjhAdlic8pXd9BMRB+FdV21i0GUtDBBnhpZNzry682mIxgIBEmb05JF5E/anuvkPu/4lZNXk7YnMmYzdNWXW1vcugL3nT511tZiFpPqjsv94/6wGSHLQY189efd+4LojhIL68ChzdAnGFEbQhLUSj8w7wM6hpIUNVHo8uzl8urJVzW97QkJUoTMEIohniJKRcjlf4cFZkeCKyJOjkX0yvM0hR1o34KwfZ0ZAodwl2Uta0lfk7LmfGwamhudQxnHSYpM0uNC2JB4Xl2ekjj16XGB6UJE7xiwcE08l15tjsW/va0pmwGOB5nTP03Knc0H9W3EWkTWRNWsG3W7juYMdxsT7gVlAo7BhqbbtHMvsGHDBvnRj34klZWVcsklzngFpJ7ZLitqquVgRJScCw+XxPoKmXG+RCZNXtPGL7wFkBGRMeMTiSCJZjVFWuXERZAXqmm+E10CIkFapCITROICvdCQEq1IRGwQwkgDBH1mu5ul7Z3dIE0yB9iB5rRrU+sOxcdF3vm/wMxvxHGRLr1P5oB+dTQNvvWMe4sbGxmN7jYqROP4sdP6N25awLP8grN7pYRCyp4SAl9srMgczHu36683DAiMqA0hCxyxSFFDfAi3IB/MP/Cwpl4NiIAhQ99L/Jmln9G6LaptCNrbX7YHwi5EV/Q607NMHdnXpolSvZtYTkaOkh/tXIzOBES/pKz5PVEt60FUxvOoNUPwrBlLT45J3Rni5RrIAJAhQOxFWnr7+e1KsKyHiJnjebBZ4LheNIbhC8RMjVsnboVHunGWlP/iMzVLQA2fCJ7XMp8bT/GBQkxMjJqdXITTWyST+xyboNcOJlaVSVJ4J6pxPtD3/t7VLhGz8eGOqAsCp+eXVDSEhXmITzEzXAIhF21cEBzETgRKm1XKxIGtdbKec7ucBSfpdWquPTFx6SkguX1PO8U3rUwQH2Iq3zuN8Qlr6A2o3SNco+VLRXjh7hj4qWNOwn0Ljpz99bCW7oBRCpsoerzVGCbCCf6wWmUDil5g12+cwYo+TjvWL1xk3Zf+esNFMKI2hDR82hkQcWL08fvDv9dIk5Qx5AYZU3cGRNuIx7oDhAgpU0Olx5j0NwQIaSLAQiEO8RJN/3j3j1XARS2YjQFR9k3TblLRF65m1LcRb0HGmIRoDVualWRRnSOs4ovf+2EfEDNfKLXpWcYQpTK8UtfDsbg2jn1tzrUtE8GYdoU5i/f0Js1OLzgRN65sDyx4QNdP5E8UTwaiw4h2oEFNvblZ7w9fioZ9zg2ro+gMBTUf+j7VS9sT14OQDALGHWzG1c7/2YN0+Nltzs0KIoegmQqlfeDLWid49Rf0NxOV0r6m10ZUusoJ23pSl+4JoTIalGlTpIRJ8Z/fI5K7WWTGVS46JbKGbHsDXsOaUayrW1qNU7MjUqsuCriwHXSROxtXNjuQeUdjR9sDdzqOS0RN6xrX4K1DiZqZbIZmwCvOIWuU6+gO2PCQ7aAPHpV3b0V/BoURtWFEgQiRyBRDFEh6WfYyVRYHi8h6AsiPWvDTR5/WnmWIGzEXftkQKAM6IGtEYLQYvXjiRTUwIYK9YeoNOkcaQmUMJopsyB71NZE46XoiXF4LaRJd64hODFTCIvR1DMEg5Q2xQ7Y6Tzs2U9dAVEoEf1XOVZq692DeNOl5jsv6vQo9NixWU/N89agVKgB1Vguk2XuC0tJSSU9P19d985vflK9//evuFxArgi5S0XwQYxLC5grzkvZQ4iBiLHMRnxp0xASsQ1OcqQnGJe1Beha1N9Fd6hSR0tPuXBAoSuqBIFFw6CVHaEzSYiPBGk+uc9cyEKlcIl5qtwj8iDiJSIlWsSlF9Q6R5lze+7YzSgGUCSBGjk+9nz540t8QJ+NDN59zBArx8mcy+QonKusOpMmZ+U0dHWInE0IkjvId1TcitYDdr8LXq5kERhYEEieLQgZh1Z/2rCZuaAMjasOIAkREpMhXf0F0TgSLQKumvkYiIyJV5Q0pE/nOS5undXI2ATh06azqyFiNUtksQOR8BYOWLMRi/J56M9kAVOeQL4SYHZetgjLS32QFLtRf0Ijcj7dsbGhUodyXLv1SS2uaB0TPsYiaif5ZF8pyiJt19JSkIfvN5zer+xlROzahbAh60kPN/QG//vWvW4maD2vqqnxgkw5lQhWRImputdpc3frhDKGinoeYUHjj703Ex3EZaDGnnUmLB7XV1Z8TObpWpPSEyMwbXXvQQKZWWQMkx0Qtnxpm3USP1H8HgqghMNL8XljFJoWInch91k1OGU2fdW83Hmy2dLTlUbd2SJpata6/2UW7a77kUu0Izbhvk1b07Dzaona/y35gXALx4i7GhC82G+OXuvGZRN1cD5sw/h44N5kOXs81+ylfPTW4MbTAiNowJkAUSM8ySnIi2VkpszTK/f2R3+u/iWQhIXqviWZJL/N83LZyK3K1T5iWLdLeTxx6QlPZjJ4MBtEpryMlzVAPhFwQPvViSJefOQ/iLwRvEDfRO+l60tqQLxE5NW1NbbcDGwQ/6hKVO2pvUvagM4eyjoDD21NHn2pxfMOohFY11OW9zUy0NVK50YmWGGFI9JVf6YiBD3fam/gQh8QBRM6HNxEfc8chF5TB7duXgkHa2w9yGGiU5oqc3OAyA6yRSJfUsU/zonIeCGByQsobIZa/FjYzaTNFlt7Xv1o76WxSzNxXvvgZwnz7v5x7GrV2HMz6cg56xVFyY9rCsZNzWkke6082MurZjiF+lCNprs9vHom4SbVz3YZew4jaMCZAK9RjBx5T1TWiK6JmvMR9lEvtt7S+VJ8HwWpdvFl03CSvwaiE9DhtV9SrUZcjaLs0+1I9BtEztXNaxSBPom9EZmwGIGpax0ido/gmbY2oTNXagZnWEDZ16mkp01TBjVisvYUn57l1xq3y+MHHVQBHHRxivXHKjaqK7wkQ2W07+ZosuFAg46OSpCIuQs4nT5NjZadUhEffdp9B2vb8Ppf2TpveGkGiLKYOq8M2ml20RyqZD3E2NqSwqTkPlzMVJLPh+65PGpQedxsNhk7gQw2x9bb1qzNQ1130HteDzAYGcP0YrfRXEEekjCiNzYCaxmxydWXuK5sk2qsQlc24NuBMl+kMY3raCubdzzoi8Su/5FrkND0+QeTA885j3EM1FY1mcNJHGFEbxkQ0vf7Meq0Dzxjnopjy2nJN+0JwzLWGmEkhQ5Ckr6n/8h33MVLKuIIReUPupMmpOz+892FNaWNLuuX8FlVxQ8iQOspwUtuXTr5UyZqfIXxImLQ3xyR6pmYNOSMoQ3xGfzS/9w5f7YFPOMp2Inauh1Q45+xp2rum6JCsOLRW0moqJDo8ShrDI+RMxgw5kTJOFeX9AilPCCC4b5hIlJQp060AkTOkt/9ZF3FB2gi4sNzsjWXmQOLo646kVZDWLBKfIXJuu7MKJS1NzVwndQ0QsBZF3AVRQ2Cosol++wvMU/Y+6VLgvv6PEpu1k8HAanz/U26cpPqbI8Rb6ny3cRtDP+CzFr3dNJF9CJ6ORdRdEoiyydLUFLtNBOl2Q69hRG0YdkA4qJ/5Tgq3N8MgegL6nSHZ4LQuhOnrtYAaMREuESce2xA1RDo1aaqS89a8rS2iq9ioWEkOT1bhGWlkhGTMjoboSY8DxGREyRz3Iws/om1lbAKIoMvqyzQ6X5C2QC1OidKJjvOb89X+8/op17covTsCPd8rxvfhA6+5WRKOviET6mrlSEyc1rcTa8ok59xemRyxoE8+322AaAkFN+lWP8WKiJqUZ7DN5tzbXLo3b6+b00yaecHdrUMchgLUxmldIgo88nprapu/B8RqRH+QGJFiD8V2vQJRb3/HTbYHpQdMTPBLpw+dTRN/86TzEX+R0keBPeESFxnz3rBJ4YtonBo97XLLcVN7d/8yHBjXrPy0yPE3nVkNJE5teiimd41CGFEbhhWoq6n5ooRGTIWTFeMsgy0/+wtqudhoUmf20Sz1XiJpyFRNRpqaJLaxUZKamiWjNFcSopJkwri5kpw2T9dFvZooWqdlRcaqcxqtSNSaSXPT69y+rsy/EYBBzoi9SKlDyEvilugaqHNTc74m55qWwSHMmO5suhaR+cHig1pPZhMxP22+bgh6jIYaCS88JGkZcyWt5Iik5e2V2MZ6iW2olzvi02RKbA/tJEXkyJEj0tjYKBERQSRGzZIZ06R1iaSoS6JoJuWNQMpDnau+4EiFKJ56p7cOHQqQFt7yMzflibQvDmi0HUFujTXO+YyIEGvMYJLm97yG6JM0NWr3gYy0+wsyGWu+6MoMB19wwjI2HFwvPt2kpXVmd1br+4Woj8f9IJLCEpF1/8+pvPsT5UPykHVPJ5oZuoQRtWHYAJHRHgXZMVyDiBVTjyePPKlmHRiPDAQgRVqdcP8iZQx5EmVfl3OdCrMwPmmor5aUpgZZJrGyuF5kSW2NNDUelu0JE5Xk8RPXuQ3hEZquzqvI0/Q0giyi4MWZi2VP0R7126blytefedxHwajMqY2TcteJWM0NKl4jdd5d6prIn/tCOxiEDeHTK80kMYxeegREPtEJMq7mgixpaJbaiFipi0mRhNoqGVffKOGkTS//VKcvT0lJkYSEBHUmq62tlUcffVQeeOCBi9uoSGNTHyUVDkHzWPs6KAQ41OMXPUg50yoEEVEzZuOw7ynnG5443pEbWQCeR01Xva1LXR1bZ1IjiqsVObnRtRuFkqkHKWgi5n1PuiEYtMo1kNUIc9kC3hvfSlVT7hT3XA9ErW1VtSJlp10kPBDpeMOAwIjaMGxg0AS1W1y6fCsSqW/EWsdKj2lKeaBAlPrgogfVUYz+a6Jbjv967usqMkuqKJTMmiopi42XjeFlEl0fLkuryyS76IS8EuFU20TF9ExDzrRI4ROObSk1ZgRhKMI3nN2gfdmkvCHgxRmtfarXTb5Oa917Cvdomp+IGDFaMEl721Kehxrcg80M7VRE8aTwqbvjBf7KyVfU8AXiDwYjN9k48J2om3VoSYFWqnXfleiqUokmKiTipTeWOqmaU7SrMQeBdZ48eVLeeecdufTSSyU5OTk0IilStpALGQ2i866U4wDyoh4LSQPSw6TtUXjHpzoLTHyyqVsTndJednqLI+mMua7OznMh8sMvDy5R1weMTFgv5+lJDzICMkiY51NOIGMAyASwQUI8h/CPNjeug2tvUWdz75rd6w0hAyNqw5gB4yf58kDctadgjxLd+OoaBkxLZmSS7G+ql2JplrLqCqkqPSGNaVka8S9KX6TRMiYm9c312lv9/rnvdzaeImqCcvmEy5UcUYS3b3UiGvfGJO0Bcf/x+B+1lk2bFrXuVRNW6TF5Hef086iBT9/jjoYIjIjdg6j+V/t+JafKT7nnSphsT9suD8x/QBKpE+IjvfPX7sMfUkLEBdHT3hOo2XcGDE9uu+02CRkw1YlhE4VHHFEzvvKS+1tbnyAitRzFHzzwcYewCsKiRqtit4YAyU8QmX5tKxFzbDYyep4zrtfZawcgNsiu+Jh77mAo1iHUbQ8FhF6B9S39gJvy1RUgXu9Elj7HRdHcA9zK2Kgh8mJDRksY86AR+nmnMmrZbGDokzaEDIyoDcMGxEtEowixJodP1vov9Vfq1F6dPZiAqBmyoa1YfHhXF0t4WJKqoaekTJSZTfEybu4t0pCSpb3GkCPrhbSZ6kVtuT0Zk67vS8p+6/mt8kbuG0rwEDBRNWlyomAsUbE7BTpKM1A3pcULpXh7YxQEbijWqX/zXK6T1isGelACkEs+JDUFB6Syuljqx+VIakSUxFCr1NnKXbcIMd5y165dmgK/7LJ+tHINBNhUUA+n/QtFMWRJS9DWh0Su/ZpTbeN/rT7hyYH5ydc6dTdTt5gTHZXo0tgQNgppX5MmCiWNzBAKwH3xhO6zF6T2iVoHg6QRfu141NXxIVQfCe/4tWt982K9jsBzg53I/DWhYF8WKFWgCuee7H9OZP1/uzo110VanFIFdp+GkIERtWHYQLoW4RjRpIrJmhqVvO+YeceA1ae7AhOpaG/amb9TksdNlojKArlAaluaZVpDiaRMXikpiz8oCU11Old6d+Fujahrm2p1M0H03BlwPKP+zTVB7t15bu8u2K2ROeluaveI3FCb/3DnD7U3e9XEVZquJwXOsejNRoB2/eTrW7zQPSgbsIHwhK6Wo+FRLRH2lupzsjc2TGYXnpWEC8ekJDJexk9eLamYYXRDOi+//LLcf//9Le5kH/jAB2TYABEzUQsyJWoEkBoRI7afB593/dnUX1Gib/ula0macY2rxR+f69yyUOrjinboj86whTGQ9ALPu7U1rY2t58mASlxtPytcynywXLZI5XMdiNV8fZ8sAdPFuGZ6vLsCfepXfcXZrtZ6J7JLW01KIgMq/AV3iMSlOBc5omk2a7jD+ftpCAkYURuGFfQxf3rJp1XoRWqXGjWK5qEAETKpZerCxyrOSnNKtsTExsmqmPEya+pNGlU0x6bI0bMb1HGMlLQO8ci8RK1FOxshyQSr3x3+ndaqKfchOnv3zHdrPbozBCvGMTNhY8D9QE1OuxgmKnfNvEvrzmwASHXfNPUmWT3pYk9oyP5MZasDFPVsNkPjosdpbf35489LTUq21GfMkeSKIsmtzpPYrEny6aRs6c2MqH37AoYdAwVNN591KdrkSd3PLyYd7Kc5efAzj1E/pj/bW1gSEZOmZiDI9KtdrZeoky8ibgZxEKHroJAG17409/bWjQttRbirMX+ZlHLiInccDEMGBWEuwuWetIAUeyDt3hMgButOEMb9ItOACQrnCrQtGnoBhIYMjNHRrOOdPoNszADCiNow7CDa66wlabDBxoCNAm1PpJKpO7MWH43uK9yrynSi3aWZS1WIRk82hIexSXvQh/3ssWfV6WxG8gzdDLAJ+eW+X0pxdbHMTJ2pozvbi79o9yI9TX0bFboO24iM1c0AhiaHSg/pKM4Pzf+QRtMoz31tvD2I9EnNQ/BE1vRp05a2NGupZi5Iq6MUrwsLl8LYJGmuz5LT1YXaKtcbK9J+f7gRHXrDD+4HaV0iXFqjELgR4XclShs32X2REiaS5hj8TDRMlAyhBZMaYrraCwFCDrp3B//ohFWopXnfqdcSzVLLD/b3pu8Zq9ShAGI2omDS+tTdyQSwRjYwCNoGGu3vlaFnoEd84w+c4E/vYbgrq6z8zIBmJYyoDWMeRKedpbGxFKV/2pMyNWNSy6jHO1KlQ3Z8oWSH7CF/1O2QNX3TmjIff7ncOevONkSLdSeCMWrVWI9Sr5+aMlUzDpA9/1UjfkIr1C7V3R6oyZn4RV2dTcWSjCU6dAPSZ6PBJoGNgN8s0Cbm52UPCfL2iGx72E3CIoqjrxcBFMYcEBQ1dwZKbH1YJGli5z3WpIQRVyG4KkZMRuSbLbL0fkf2RL+QLuIoyJm6MynuYJKmx5gNA/3oPjL3z2fS1UCotqkTsw6idCXdHhAikS31ZO4PJEDLGINJln7Q0tKhBEZ5UopAfMf/z2SD2OAxoxsDmgGCEbXB0AlIGePNjSEKgixqvfo/TXhkC2m2B45ikB5pa4CoC+KF4KmHQ7Lrz63XwR8M1vDg2O+Z/R6tRf/u0O/UYIW51ZApfddE0PRz9wQQO/3bROmQMKTvW8AQ6U1KnKRtcUTqEDYbC6aR0Zc96IC4dv3GtUihSAYQ5Yl1ru5KTzNgiATpa766MkNBsX7t1106GlLjdRwDD3Gi4dx3AiIwagLTRea2m85FpIowK3hYhJYhmlvbt/oK6uKbfhJYG4M94kVm3uAyBT1JMaPcvvovAqrvAFH31JfbMDTg75O/N7/pZpPJz0XHRGYN3GmMqA1jHtSoN53bpKSKLeiK7BVKYs8ce6alJoz7GOMgSSGjFO/MOQ2yQ22NCxo/Y+ACaWclZKmiG5Kndg1RBhM1gEwh0fvn3S+PHnhU18NmgUiXiH9B+oJevVccLyqsbZRM/f99c98nzx19TqeCsRHAjvSOGXf0apZ1vwRgF846kZSPYMfluClbiJ7ao5t2MQUpRh34EQQIbcWDTgRGaxU1adqaaKkKBoRJfda3QUHObCIgyf72gqM4pw+b1D6bPOw7D78okjWv41ndHYF7NBh92txXTGmI/tjgYJeK01p3PeiGtsAaF+GfB/eSLI3fcA4Q7F0xjGlQr2W4xsnyk9rqhIgLIxNquPQ0Q5wotxF4MXiD8ZgrJ6zsdMoUZHf3rLuVkA8VH1KRGHXihWkL9TGIl6+u0tekxxk5Sc0af3AIn/V0VpPuLahDf2rJp1yKPTyyxVZ1SMA1QD6BjIN7LMpFtRAZqUNIjQiXxzJm9/1cKJxzIPDORXyKaVe5CBrPa698xo+cOnlfQURP6h2BEa1vbEwQtFWed9aeHRE1rVi5m1vbvsgI9Deq7wx7/+A2EppqCHOkTap/yfutVt0bTLvaGeFQnuDvtbIwYC87sO1tRtSGMQ1anxBY+Z5jSPTIhSPq+AWh0QqFipq0NQItCPqBBQ90SZpEzpiLoNRmnvWrp17VKDyiPkKjd2ri3XmZ0zrWlUq8v+BaezoaMxjz5s3TgSZ4o7fx+e4pUCFDvoikqEezQYDIpq1xfcwlx1y0x3CPhXcNjZe2j6ohbGrbpC/7s3GhB/r1bzvi15nbe9z1kPYm4vIWnsHAFW7zj50RCcpzLDzzdoms/Gz36ve+CKDYRNBm5n2/ySJwTvzNySYYegYmwa34hKtVcw8nLhWZc+uA268aURvGNFBnq393IA1LZIl3N9O8vNiK7zrmsqZIya0nka13Drt5+s2aut6Ut0mjd6LlG6bc0KFifCRg6dKlcvbsWamqqpKMjD7MT+Y+L/+IyM5H3aAICaRdEYVBGtSrqWPzQdfVpCUMO/DrhnQwq+EDsxPr016tLbyXrYGcH1EcpMemAoI/9IKLWGkz9Ol9Rk4eecmJ2ca32sq2tJmhPMe207eTcQ/ObHcbmikrZUABoWjUHuS1zj1kkIpP+7cHmydKA2w8uM7BivRHGsLCXOaDEgvGOf3d5HUCI+ohxrhx4+TChQv6c1RUlDzzzDOyZMkSmTCh1drSMHQgrYygyjt+EVFjyTl73GxVSJO2xhWMSJh0dY8HYAQASUPWDOQgjU207UVpweC8Q5Z+7ieys/spOiOlvOZLzksbkRXKbi+uak9inbV2bfyhU1Nrm3GzyIm3XJ/zUE3hglz3PxMwCil1QjHEcCi1j7/hPrQRr6E0Z7BHWJ1L68+77eIsAd7c1NFJmfq/AY2im9wmYKABKWPWQlreO9HxMy1t/K49SOfi9gaRe9vZJe+7WBcwlhEe3vtNXi9gRD3EKCsra/m5vr5ebrnFqVAh6i984QvyV3/1V0O9pDENWpdoicLxC6EVKWrmNN87+16tSVMnpm8ZpzQi4dmpfauZ4t3tZ1UHA/tUBnnQwkU71pWTrtQ0eyjjt7/9rTzyyCOyaNEi+da3vtW3g0BIfU2xntrQVqQFeVAnhCCpsQ4Fzm0X2f+0IzcGX0DGpJO5Js240FPLG5/l0t6Yr1DD7IjcIHmeA1l7okSQxCaGyBrPcTY3A7WR41gMGjn8kttk+PPRTkSfdjBI1e9+3Bl64ADnR2Nu+5VIyhSbLz1ECGtmK28YMkyZMkVyc3M7/b1/Oz73uc/JzJkz5Stf+Yq9O4MMeo0hZWrVpKupDZOaJspGZFZZV6nWpqStBxJsAh7Z94j2WkPijMbkHJ9Y9AmNvKmfI2KjXs30q+CBIsOJu+++W5566in9+cCBAzJ37iAYcHSF9f/jVOK0YgWryYnMb/y7oVkDPd5E08HDK3A4gwTxyn7hq65WHZ/hxkwilJv3bpHbv9Mx4dKetvUXbtNByvzCSUfS1PF1dCXlgfu79vjuDSBmbFYpH/CRM2mZyFTGkbbL9jCw45W/d2vypQU2EHiqX/5p585mGHRYRD3EOHXK+S0zLvDqq6+WM2fOSCOD6tvhRz/6kTQ0NMif//mf679zcnL0QzE2NrZvIh5Dp6D+fPO0my96nFT4YDqmMbaysqFShWyAEZbMy0ZtjvPZjoIdunFDOc5zadvqa0Q/WKirqxv6kxJ1kkYOnlpFX3s3A0V6BT/GEm9vfoaQma3tU/SqX2gf4wTWgzBt+UdFdv3Wpa510MUakWv/svOomAiX55182yne6e8mxYzzGgpysgXUPy/96MBcH4I11slXlwhcY5t1h7mHLcYbMhhRDxOmTp2qZA2ee+45+elPfyqXX+4k/bW1tUrSwTh9+rQkJrrUKenxb3/728OwasNAgro30XKbvueIKNlfvF9NSGgNIx2vs6dLj8ra3LVK6iOllj1oQFyF8xM1U6I8P5oRxfJAgTnTe54Qqatq/feCO90XGL9Q5PhaV2dn6Aepb9zHfC/ymi+ILLzXpeRJf1N778rkhPd08mXuC7X4ljyRzPluQ0BqnAiY9PPCe9zUq6HcFGEqc2pjYFRolEhprusf5nHDkMAc2EMAt99+u/z+979vqU/HxMTImjVrOn3+d77znZafP/ShDynBb9q0aUjWahg4TE+erraifiAHPuF8ITZD4OaHk0DMRP14gDMxa8yDKVJ4KaP0hsiyFohc9smeCdF6AoRVTNKivzt7oftiIwBZo/IG45eKLLjb/UwPbV2Fq/EGR6gYucx5l2vZ6c2wCxTWzTp9o/Ux7T1vatt/PhRgA7H4fSLZi10NHVU+GwVU+v3pMzf0ChZRhyjefvvtlp+fffZZuffee1V8BqKjW+tIjBoEK1eulMhIZxUJ8f/hD38YhlUbegMmX9GzTX82Np84mM1Jm6Ne3TiXBc+eRjFO/bw7n+8xA1LRiLggLsRbA5llQLxVVeLGSnqQhqYuW37WKcsh3vnvdjOeiapxokKINRDryJglEpvkiJFj0hLFOThXe2e1oQCEfPWfO0MW1Oy0zg1UrdzQIxhRjwC8+93vbqkFfv7zn1dBWkfw6fInn3xSCXvy5MktNXFD6IGebIRjiMbosebf2Io2S7MO/YDAEZXhNU7UDbEPlDvZqACkSCp2oEHEGBXnomRPjHXl7jGGhwSDFDBfA4lxU13afP9Trk5OKp1NyaJ7h881jPuM9alhWGCq7xGOhx9+WD7zmc9IdfXFQyJwkPJCte9973vyn//5n3LbbbdpfRyRmonSQhe0azH9CktTWsOwLWVwRvvxmMOBYNX3rl27ZPHiAUo5hwoQSW35mcjR1xwxQ47UwRF84RQ2VDObSbNjMkJPNURtAznGLIyoRwkwUUGE9o1vfEO+//3v62PTp0+XY8eOya9+9Sv58Ic/fNFrsrKyJC4uTntiu6qJG4YPoWiE8sorr8idd96pNqI7d+4c+vasoQAqctqvEFGhIcB5ava7bMSkYVhgRD0G8I//+I/yD//wD10+x9rpDYYO/8dw30Nss2QYWzCiHiN44okn5L/+67+kpqZGtm3bdpGtaUlJif4cHL2hLl+wYIF+kS43hAZwSsMIhTR4TlKO2pQaDIbRCyPqMQiEafRu8x3hGcpxT9CdpVlJk//ud7+TK6+8cohXawjGkZIj8tTRp7TPGqKekjRF3jPnPX2ahNVXPP300/q3gEe9N+QxGAyDByNqw0UCtM7S4HPmzJGDBw+2eJNTE6cN7JprrrG7OASgRet/tv+P5Ffl6whOlOCnyk/JvNR5Ol/at3INpZhs69atsnz58iE5r8HQox54Jo5hPpOS46aRoZof4Rh+CakhpIBAiEh79erVkp6err3ZwSTukZeXp+nya6+9VqNwvm688UZ9zOrdgwN6q89XndehHZiixEbGqnsZfuTnKs/JUGHWrFaPbboIDIaQQOkpkTe/I7L5JyI7fyPy9ndFtj3kXN1GOIyoDReB8Zvr1q2TwsJCNVl56aWX5GMf+5g89thjXd6tV199VdLS0tqQu2HgQH+12kkHOVbpz/rw0M3W+ad/+qeWTdv58wGnLoNhONHcLHLgOdfOljHPudUljnfjT/N2jfj3xoja0C3e9a53yc9//nOtSXocOnRIUlI6didicIgHkTliNaKw9773vRd5mBt6jmnJ0yQrIUvT3fVN9Soqw1Y0JzlHJiQM3WQtWvrYkBkMIYOGWpGCQyIJWc5u1RvXEE0TaY9wGFEb+oTZs2dLaWmpprn5+upXv6oGKnyIl5eX63Potd2wYYP2eB89elQFSETrRNzx8fHy1ltv2d3vBRiFec+seyQzPlNHcp6tOCvTUqbJ3bPuNscyw9hGeKQbB8pIUY+Ah74ONRnhMDGZYdBw1113qUK4K/h69vHjx6W4uFgSEhJk3jyzKuwKVfVVklueKxFhEVqvZuLWUCMzM1NLI8A0CYaQwKGXRHY8IsJEOki7It/5sl/1F+77CIYRtWFQsWXLFh0wglr8f//3fy8ictrDqIMzMcx/4FP/fN/73qffaQv753/+Z43ADaGDiRMnyrlzTsBmRG0ICTQ2ODc5Znfj087wkHl3iGTOkZEOI2rDkCrKKysrtU796KOPyp/+6Z/q4xkZGVJUVNTp61CU81pD6ACx4de//nXVH/hWLYMhJNBQ51Lg3qd9FMCI2jDs+OY3vyn/8i//omTc0XARatp+xOeiRYtk7969+vPChQvVZQ0ip/ZtMBgMoxFG1IaQAunUL3zhCxp5Q867d++W3NxcJeIPfehDLfO32wOyZpITRG4wGAyjCUbUhhGD66+/Xl5//fVOf79s2bIWH/Nbb721JZ3+4IMPyp/8yZ8M2TrHAjA62bhxo5rc/Pa3vx3u5RgMoxpG1IYRiR/96Efy2c9+tk3tGh9y3/LV3rMcYRrtYzk5OZo6p43M0HeY6ttgGDoYURtGPMrKyuSmm27ScZ4333yzPtbVDOdJkybJ6dOn9eeCggK1TAWYeBiB9wxG1AbD0MG8Hg0jHsnJyZqGDQYtXaS+n3322ZZBIh2B9q9grFq1StasWSNLly7VmnhXhG8wGAxDAYuoDaMeCNNwUUNsdvvtt+v0p9///vf6u66ImBncJ06cGMKVjhxYRG0wDB3MQtQw6oHbGeluBGbUtD1Jg66GiOTn57f8/Dd/8zdK6tS6MWd54YUXhmTtBoPBYERtGNMgPU4bGO5a3/jGN2TFihUyffp0/V1SUpJ+f+211+Rb3/qW/szzqGlD+n68J1+mfDYYDIMFq1EbDAH87d/+rX4BfMcRqYEJE7qfTOXHPiJOu/zyy2X+/Pn678WLFyvJj7bRnyjoPZhBnpqaOqzrMRhGM6xGbTD0AETSWJ82NjbKzJkz5ezZsy2/+9rXvqbiNWrhiYmJnabf8St/7rnn5LLLLhvx9/y73/2ulgO4bgxqDAbD4MGI2mAYIOTl5XUbfd9zzz1aI//oRz8qv/zlL+U73/mOfOpTn2qZ4z2SrFDZtARH1gaDYXBgRG0wDCAwU/n2t7+tJIYYjfq2BzVvZnNT00ZRfurUxQPtr7jiCnVR+9jHPhby7wsZBEDvuU/9GwyGgYcRtcEwiEB4hl85avPZs2frtClvd7pjx45OX+dHR9If/olPfKLFQvV73/teyPR2E/1TDsBs5sUXXxzu5RgMoxZG1AbDMAAixlxl06ZNLf9u/3tw3XXXydq1a9v8zqebUafv379/2IRqRNGsMyUlRfvUDQbD4MDyVQbDMIComGiZSJsvCI9xnwjVgsVmHVmaklbn68iRI2qb6vHqq6/K+vXrZd++fUrgNsPbYBgdsIjaYAhxgdrPfvYzJfVnnnnmot9D2ES2zz//vLqutce9996rES/pc4aWDCQsojYYhgZG1AbDCAH14PLycv2ZKWHvfe97WwaK4Hfuf9cR0tPTpbCwUH9evny5tpehOH/ggQf6vB4jaoNhaGBEbTCMAjANbOXKlWrUAmpqatr8HqtUP5+7IzEaj0HaDz/8cI/PaURtMAwNjKgNhlGI2tpa+dznPifHjh3Tf3/xi1+Uu+66S3/uSjU+fvx4OXfunP68YcOGlnGg3mVt3rx5Lf82ojYYhgZG1AbDGANKc9Lmubm5F/0uOjpaSb4zQqe9bOHChTJ58mR56qmnpLq6Wh/DRtRgMAwOjKgNBoN8/OMfl1/84hdy9dVXyxtvvNEpUQdjz549sm7dOsnJyZHbbrvN7qLBMEgwojYYDB0CgxXIGDCgxEfaHu17vw0Gw+BgdI30MRgGCAXltXKiqFKiI8JlTnaSxEWPPU/rYPtTX7PGnxwQRRsMhqGBRdSGIUd9Y5OEh4VJRHhoWGG2x/ojhfLMzrNSUlUvYeEiU9Li5YGVU2VyWvxwLy1kQP82ETfmKvRo/+u//utwL8lgGLWwiNowZCiprJNX9p+XXadLJSoiXC6blibXzcuS2KjQiVbzLtTIUzvPSkNjk8zOTpTGpmY5VlAhT+44I5+7dpaEh+jmYqiBEO3NN9/UnzFjMRgMgwcjasOQRdG/2nhSdp+5IGkJ0VJT3yBP7jgr5TUN8v7LJofMu3CssEIuVNXLnOxEFVOFR4TJhJQ4OVlYKYUVtZKVHNvj1Pm6I4VyJL9C0hOjZeX0dFkwMXnQ128wGEYfjKgNQ4LD5yvk4PlymZ6R0BJBx1TUytaTxXLD/CxJT4zp9zkqaxtk47Ei2X+uXBKiI2T51FRZkpPSq2lTkYFxjU3NIhGBlxFVE0n3NFUP0f983XE5nF8uSTFRGpHvPVsmH1k1VZbkuOlZoy0NbjAYBg82lGOUAAVuWU291NSH5odmZV2DNDQ2S0xk659cfEyk1DQ0SUVtQ7+PX9fQJL/ceFJ+uyVXCXLzyRL52brjsv6oc+PqKeZmJ8mEcbFyrLBSib+0qk7yympk4cQUzQT0BDtOl8rRggoVoVHXnp2dpO/L2oMFo0YpHTwshBatL33pSxepwg0Gw8DAiHoUILe4Sn705jH55+f2y7ef3y/P7zontQ2hRdgTUmIlMTZSiiqdNzWElV9WI+kJ0ZKV1LN0clc4mFcuu09fkKlpCTItPUFmZyVKRFiYvHbgfK82LynxUfLBy6fI9Ix4Kaqslaq6RrliZrrcfcmkHkfmJZW1baJzkBwbpddb3zg6iDo7O7uNS9l///d/y6xZs4Z1TQYs6SpEjrwisvF/RXb8RqTYOdMZRjYs9T3CcaG6Xh7ecEJOFVcp4dXUN8nTO89KU3OzvHvpRAkVTBoXJ9fMyZRX9p3XWi+B5bj4KLlt8YQBaX0qrqqTxubmNsfi+KShyTT0RrBGBPylG+eosCw6MlyykmJ6lT7PTIqVsEBdHtEcm5KSqjpZnJMiUT6fPgqwd+9eWbBggRw8eFD/PWfOnOFe0thGfbXIO/8ncm67SHiUSGOdyKn1Ipd/WmT8ouFenaEfMKIe4dh3tkxyi6tldlZSaw01TOSd48Vy/fwsiY8OjbcYort98QSZmZmogq3I8DCZPyFZpqYn6O+r6xple26JnCqqkuS4SFk6OVXJvacgMueYVXUNLddMexWPE832FhBsX9uxlk4eJ5uOJ8v+vDJN9ZOWT4uPluvnZfWK8EMdeH0fOHBAduzYof3V//Ef/zHcSxrbOLtD5NxOkbSZIpGxpK1ECg+KHPyjSPZC/icc7hUa+ojQ+BQ39Bk+rRssdIqNDJfq+kaprW+S+J6VVYcECLJQPrdXP3MNv9x4QrafKm0Rb60/UiQfXT1No9uegHrwokkpegzIkXp4dGSYkuNQt38lxkTKJ66cLpuPF+umhNr2iqlpMi3DbUpGGy655BL98vibv/kb+a//+i/5+te/Ln/91389rGsbU6jIQwbpSBpAzPEZImWnXbQdbT4AIxVG1CMcOalxEhsVrqnV1PhoaWpqlvzyWlkwIVlS4nofSQ4H9p69IDtOlWoEGxcVoaniw/kV8vK+8zIry7VJdQdS1B++YqqKwfafK5OEmEhZPiVVFk0a+Jao4so62Xy8SE4VV0tmUrSsmJYmOaltPwS59zcuyKaa2+3xeM8oXZCiz06O1a+Ripdfflm+9a1vtRA27mavvvrqcC9rbCAuVQQJRFODSHjgo73mgkjq1FbyNoxIGFGPcJBKvnpOprxxqEDFSoC+39uWTBgx5hx5F2q1pg5JA4iZlPXpkirNDPQ0fQ85Y6DC12ABFfhP3jqm/dFE6oj2tp4slQevnN6niJlU/W8258ru06VSXdekaf+rZ2dq7X6kvH/BuPTSSyU5OVm9wQFEfcMNNxhZDwUmLhPJmC2Sv18kdpxIQ5VIRLTIrBtJZw3JEgyDAyPqEQ4+zFEk0z50sqhSxVRE0wPRlzxUgJworJPy9il8WrZok4qJDB3XMrD1ZImSNKryyIBQjP7wtw4X9Imo3zxUIO8cK9J6/OTUSCmsqJOX9p2XKenxI7LnOi0tTUpLS+XFF1+UW2+9VR8zsh4ixKaIXPFZkSOvieTvE0lIF5l6pcik5UO1AsMgwYh6lJD13PFJ+jXcQBSGiKqsul4je1LX3RmFLJk0Tt5KL1TCQ3RVVd8gYRImV8/O6pHJCASPExiKaurBgynYOltao0IzSBpwLtLcJ4qq+nS8badKtaadFBC8ZSbFqIL9QF75iCRqf09uueUWWb9+vaxevbqFrG+88UZ55ZVXhnt5oxuJWSKX3D/cqzAMMIyoDQOGoopaeWjDCTlyvkJT2dSNV85Il/evmKzk5kkVM5DzZTVKcGwu6F3+xJrpsvZgvhIUkfSaWRmyYmpqt+c8XlgpT+84ozVeVN8Iyu5aNqlPSu+eICspWtuuqCuzQSKixgaV2nhfwG25yAOlWWQEZr0vwqpVq9QMZc2aNfrv7du3D/eSDIYRCSNqw4DhtQP5cuBcudbNIWnEUai3541Plkunpmqb0mObT8mWEyVS29ikhiTzJiTJR66YJuNTYuX+y6co8fU0Ii6vqZdHNp6UM6XVaqiCmchbhwv1dw9cMXVQIuvlU9Nk04kSjf6TYiOlqrZRkuOj5Ko5mX063qVTUuWJbad1YAmGMPSYx0SFa+vaaAARNWT9gQ98QA4dOtTyeEFBgWRm9u2eGQxjDaYwMAwIIFj8rDEZgaQBUW1jU5N6XYMduaWy4ViRpCVGawQ6OTVO3cSo03pArhwL5fYTW3PlN5tOaV2YaVbtceh8uZL0jMwETR2T9h6fHKuDP1BmDwZITX/yquly08Js3VzgWvbJK6dre1hfcOXsTDWCQTR3sshlBe5cOlF1BqMFkPWJEyckJsbpJr785S9LVlaWfPvb3x7upRkMIwIWURv6BaLaLSdLdLoUKm1SttSmAYRLVte7hR0+X67ffVo6JipCf959tlTuuKTVRY2o+A/bT2u9G+J++0iBKqHfe2mOmmx41DW44xOZt/xBR4RJY13zoFp1cn3vvXRgJn6hHL//sily3dxszUDggjYulJrfBwg+u1FfXy/f/e539Wf6rHn8r/7qr4Z5dQZDaMMiakOfgTL7Z28f16gXssYJjBrzQcRkNfVaP4Z0Fk9K0efT793ULjCm3hvcfsUxmVkdERYuc8cna4R+urhafvjGUfnXFw6qE5vHtIx4SYmNlHMXanRTUN/YKPvOlWkf+eNbc+X1A/lK9qEOyIronKh8NJJ0MKKiotpE0l/72tcssjYYuoERtaHP2JlbqinqGRmJWpdeNSNN0hNitM5aXl2vBiYMuPA2oaiYqcPSRgYhn7tQLQ1NzXL5tLSWY6Leplc5IzFah2Jwjoraeo2Qd50udWK1/IrWfnH6jcNIg1fIO8eKJb+sVsdTImh7bHOuPPLOyQ7T5obhAxF0MFkTWd9+++1y5swZe1sMhg5gqW9Dn3H+AgYrYS016YjwcBWHoWL+4o2zJSMxpkXtDWjVuu+yyfLS3jydokVb0o3zs1UZ7oESnAi7rMYROQK0xJgoCQ9v1M0Ar9t8okiPBa6ZmyVzxjs3st9uztXNgd8YMKYSoj9SUKGCNkPowKe7iajB888/L5MnT5bCwkLtxTYYDK0wojb0GeMSooQqcbBRCa1KpHARdbVXXfPvy6alyZKcFCmprFfVNG5iwUAQdvn0NLUPJaVd19ikpD1xXJykJkRLeW2DFFfWt3kNkXVTs6uFsznw4NhE4sUVgyMsM/SfrINr1ImJiZKS4sokBoOhFZb6NvQZl0xOlSlp8aq+PltarSlpBmJgadpVaxRuY9Rk25O0x7uXTNTIe2p6vB4HFzDq3GwIqDnPzLzYASw1PkqFaWwAPIioGcwxklzaxhq++tWvysmTJ+XTn/60FBUVSUREaDnRGQyhgLBmVDgGQx9BehqVNopuWpdWzcjQQRgD0cNMi9VP38ZXu9K5gASmZOGr3V50hSiN1Df2m2wWUFPXNTZq/fsjq6a1OIkNFPjfhvGiRwMjOzFuYR64of/YsmWLXH311eoPjmmKwTDWYURtCGmgHmeyVnFlraTGx0hYWLP2G4eHORcyIu2Cilr59TuntF8br+yGpibt037XwvGycnraoIy5fP3AeXl+d562p1Gnz0iK1jYr1mToO5qamlQZzneAWYq3ITUYxiqMqA0jAkSwf9h+Rt3PsCclwCZKvn3xeDleVCXbTpXI9PQE7aM+XVItCdGR8uV3zdEoPxikzzFeQXxGzM9s7KU543o1qYopZf/x8iFdE7V4Yn1a0aij//lNc0JukMhIA57gwaMx8Qy3yNowlmE1akOXwNMaI5Pc4qpu25wgrpr6Rn1NT0c8QrDrjhRqy1ZXVZizF2pk/ZFCnbk9OytJZmcn6VjMP+7Nk/1ny2RiCnO5IyQyPFzr5qTNfRtXMJ7deVZ+se64npMv+sBf2JPXq78CfMUvVNW3COaI7jk//uV5qoQ39AcM7mA0pgcR9d///d9LdXW13VjDmISpvkcRqNNuPlEsu05f0H+jrkZlHdwi1RtAOk9sPa0pZdTdECCOXLRAtceBvDJ5dV++WnqmJ0bLtXMzZfmU1E5r1dS2f7nhpJwoqlQTlISYCLlublanc5jpr66oc+pvD85zOL9cncnakDwBd3OzNDW33VhApOuOFkpyXFSLOpzo+M3DBbKSHvAg0RkbDu4j60Skxr30v2dDgModRTrtY7SMVdQ0aG3cz9Q29J+sgyPrb3zjG/Lv//7vcu7cOVOGG8YcjKhHCSCmp3eelVfV1csRHQYhqLHfszyn1+IuUsQM0MC/e3Iq6muRg3kV8utNp+SLN8xuU/c9VVQlD60/IaVV9dpeBfn+amO1RrdLJ7cd1QgB7jt7QX639YwcL6qUpTkpupGA7HAkQ5RFtNwe4+KilARp/4JowYXqen18Umq8Opb5DQlRemVtozy946waoNywIFvJFrKn1QsVuQfrZR3UuT0RoyzHWIUebJIDbFLePhIvH18zTXJS47WHe3pGgmw6XqSRe02DI2xmShPFZyWbqGwwyJqI2teuDYaxBEt9jxJgm/kOAy/io2VGZqJ+4RKGW9f5stpeH49UN3VXomjcxGilgpxOFVXq48HYeqpEe5shQCJVnMrqGhpl47Gii8Zg/mDtEfnfN47JG4cLdBPBFCpiYV7HYIrO5jqzDtrB6K0m9cxmAJK8YmaGTsrC9Qyy5ZycZ3IakXeY/HFPnlqcko53ZioROivbA+JOiI6UcXGtKnJq2JA055zHnO+sJE3/Y0kK2KTcs2ySGrvQp42N6eJJyTpk5Lnd59RZzQ8WeXL7GfnDttO6kbAGi76R9dq1a2X69OmydetWSU3tfvSpwTDaYBH1KEFRRZ1Gm7h3eRBtHqmoVStO+pZ7g0ZNHbcamQB+JMIk2g7Gheo6iYoIaxO1x0VFXjTByo/BpD86r6xGqusatMUJkqbeCzhORyAdzlzrnNQ4JVLWRWp91cx0jaQ/e+1M2XqyWH785jGZPz5JB37gmJYQE6dZAYidjQavYcgH0TioqW+S6+ZlSXZya9obUoaEIWRU3WyCyqobZMPRIrlvxWSJjorQ+5OVHKMpcf4dHRGu9wUXNMRsTPx6ZudZ3XxwRWsPFcjtiyfITQvH9+p9MIhcc801cuzYsZZbwWCP66+/Xp566ilzMTOMCRhRjxJQr8Xpi2jOp3BLqpxNJ5F1bwEhThgXp0Q6PWA8QiSbnRwr0zLaGo5MS0+QDUeLpbahURXPtEfRVrVqZqsVJBEthIkYDIvQiSmxcvh8hdQ2NmotHHMS+pBRYXcGnMdumJ+tX+0BcRMxO+vRGqltaNL+Zsg0KjJcvcW5BiZwkaLenlsiYRImy6aMk9UzM9psMpJjI3WTQv2akZlV9Y0qfCuqjNLZ0fddNkViI9kIRGg2AJIGXH90RJiemzQ+98LbmbIxYaPCXO6+GrBwD3kPKBOkJ0S3GMKMNTAis7S0VCZNmqT+4GY5ahjtMKIeJYBAV81Il1f250tpIFqEbK5vFy32FJDMe5fnyG825WqqG0Ki1Yl6N+QfjBXT0mTvmTLZc/aCRqI8lxnRV83JbHkOfEKN2UeyRP4M5DhwrkxqGhplVnKSXDEzrU+tTUcLKmTj0SLZdeaCCt+IsBGdIa6jZjw1Lb5l9CZRMhE0X53hEo26C2X90SJNbcdEhklKbJSumdT6pVPTNM1PWpzIOTOpSaNmIu/FOSn6fK6TCN4jKzFGo202EX0hajYBj285rVkDauhsWljH+1bkjKl2MMoH9FmDmpoamThxopw9e9bI2jCqYUQ9inDH0okaBSMiA4snjZMV0zpXXrcHxEZqGnV0SnyULJiQLF++aY4cza9Q0qfuTdTaHhD3J66crueFrBhNSc04+Lms4YqZ6SpQI4JG5Q1xXzotTaalx8u50hp5ZsdZeeNggVw3N1Oun5fdo97mI/nl8tO3jmv2gK+6RrzHG1U4Rk81kXZsdITUNzVqvRhSJ9VOyhyXMw8mfmGswjEgwbT4KJe2bm6WxIQ4tS2dlBqngrqzF6pV9IbBCVkMom52J9xrNksQJ9eGoC0lzkXbRPQ85mdx9xabjhdrOxkbssmpbmgJ/yaqvmp264ZotIO/o9zcXB3gUVBQILW1tUbWhlEPMzwZwSAVeq6sRr8TQQbXk3sL1NgouredLJGGxmaNgGdmJcrHVk9rM+iiP6APm5QwkaqmupOpTcfJO8eLVMyVHBepXt0QJDah7RXjHeFn644ric3JSpTD+RWy+3SpqrBJX9NGBjmyYWDjcaKwSjcVHB+C/ejqaRr1YoGK4IsaP9E4KnDolYifr0njYmX51DRNpZNdYFNC25sH5YZXD+TrveO60AagTaa1KyU2Gk2bjv1kc8A5+5Ku/uHao7Lv3AUV6nkcK6yQBRNStD7fVQRKdI+sAD/00ZIqr6urk5ycHCVrEBsba2lww6iFRdRDBAw93j5cqDVGSHXNrIyWUY19AfXTx7ee1miXVLPrcc7psMe5J0Cgtfl4sdZvUXij2t50rEhOFVbKnPHJsnBSslwxPV2jzb6CjQStVxAihEbU/daRQm3j8g5i41MiZFduqfZvE+Vyj7i2zgjmTAl9zpH6e9LDRJq0SkFQkaXVkhQXre1l2I7Ozk7Uc/E70tDP7z6nxyCK10xAXKS6nYUJG5VwGZ8So4K4MyU1Ehd1QWKjIzXlTaYhGPvOlclr+/NVHU+dHfEea7hsapoel3IAWYJr52b1mSiZJNreD4Z/dya+8xuIp3ac1fVxzRjF3HnJRI3KRzqio6PbRNakwa+66irZu3fvcC/NYBhwGFEPAVAR//Tt49rTy4c5pIFByKeumqHp5N7C9Tjnyu6zFyRnXJw6Yx3MK1O/a+ZA98Xbmtou8BOtMC6hnkoKGXLZc+aCbgqIsPs64ILIl80FSmqEXNR3EV7RMuXBeY8UlGumAJKDhO+6ZJJcOTuj5TkllXWy43SplFbWaZReXAHJRul8bCfmEqmrb5KSqnpJiImSjOQYOV3q+roB10MKesuJEj0+G4iMxGiNptlIEYHzGJuSRSkpWnuvrGuUmxdNkHctyL5o6hfXwXsSHxWhxEmvNRPFkuIi5VNXz2g5Z39A3RwDFsoSlBboWeeInWUdmgJ/I6wNYub0m08WS3ltvfzZdbNbZoiPZMTExLQha74bDKMRRtRDACJVPmAZFMEHNtENH+S0+/SFqCF+aq1TUuNbSGN6RqKcKq6UYwWVXSqnOwMpYT7cWRvpXt/PnBVQeRMB01t8tKBS67O9BQKoF/fmKaHNG+/WR7TKdZB2x3gE9TQbApxKF05IUiOTs6U18vzus7JwYrLOo+Y+MlGLzU5tfZMSO5HjgfPlSl7aVqVtWZGaqsadLCHaqbghdTYZXCO1aGryvn87r6xWkmIj1CyGa2Xzw/PL4+slMSZCsxUfXDn1outi7dtPlcrR/HJNTZPeQCeAS1ldQ/OApZppKyssr1UXNfrPuT5c3Hi8I1BHP5hXrur9pEBdnI0Efx+kzP17MFrIeunSpXLllVe2PP7www/LAw88IOGBzZnBMJJhRD0EOF9eq+08/kOb77Qo0bLTF0CkF/U4h3fc49wRYZJSpmYbLGwiMqN2DBHHRYdLSWWtREVE6Ac9gBhQQJPWFek9UROZ01YUnHalZoqoLGec8+YuLKcXvF7mjE+UieqGFiYTaOMK9CZD1G8eKtBaM+lwHMjqG5oCpNostfWIt6JkSnqCRscQOOSNWO18WZISF9kGUuS0iSWR4m9s0giYPnTWwC2tqGt0qfPwMBW+sY4zJVW6kWkvcMMEhclerg7M++KEadwvbFS7A+fccLRQ7zstV5dPT+vQmY33+tbFE7TOzWvY2LQf9RkMMhVsRHzrGGCTwt8HG5zRBMj6wIEDLf++9dZb5YUXXpB//Md/lMOHDxtZG0Y8jKiHADhWITTiQ5IPXD7wUQFDIH2B73E+XVytx/A9zoizpmV0fEyIhwj+5X3nNZqESFbPTJebF47XD3D6fT+0cqq8tC9P675EYZAZNWvWnVtSJaVVtUq4RJHt0+s4mW05USyFlXU6xQqxFZsBD8RbCLvwxPZ+2Ii+UEjfs3ySRq+bjpdIZV2DbmogGX5H6xbpbDYPgEwEFqK4kEGOmckxSsaksLmnbGIgYmrEfMWp61iUmo2Q7iUqnTs+TTUCtG5RC0+KDdNaOMeOj4nU8gRpbwgNQxQ2QS/sPa/Cs/dcOrllw9UQ8FbPSIjWjQRrbsL/GzFeXYOqzNkA0UbF74j2g1PO9JojhjuUV67p//1nm2Tn6VKdn93ZuEzIuSuC9uB9w+SGjAMZEVZMJA7Bs8kZzXj99df1OyYpc+bMkUOHDhlZG0Y0jKiHACtnpGt9ESIgJUq0g8Xl6lmtdde+9Dg/uulUmx7ne5fltKQ52wOrTkhJAs9F0IXdZUpcdEv9lx5g0uZEtaTrn919Tg6er5DTmGxU1SnhMWmKTQFk4tuvIDwmUkHiEBGv3XWmVD551YyWqD01EC2+tO+8Ej3+2cUVdUooTLTChrS0sl4j3NMlleokNnd8smYAEJ1hquLXDvkQQQMICJcwyJWI1DmB4ZJGCjpWU9v788pk84kS3aBwDsRVHOPKWRlK4O8cL9bN040LsjWt/+KePM140G7GcUmlUy9/Ztc5mZmVJMsC6WbOy3pZSkpcpDQ2imYcosPCNFtABoHWMYR6+KGz4ksmj9PzcF925V6QI+crVKDmU/KUAtYezNdUf3/S5myk7lw6SR7bckrV8Jyc2va7l0zU92I04+DBgzJz5kxpbGyUo0ePGlkbRjyMqIcAkMWnr56hEeeZ0ho1ICHi7M/wBtKjX7lprpIkEe8sepyDItj2oPaLuxbkByAiSG3TieI2Qi0ifiI2yCQzOVZ+tw3Dkwq5JCdF27UaG5tl9+kLsu5Igdy2eKK+BmIhkobkIBeiR/qNqd1eE2R6cvuSCRIbFa7GHRBlTIAAIUPS3mRp+X12cqLkXajWKP09yyfJrYsntgjYVs3I0L5lhG2FAWtPosRpafH6bzYxKN+1FNAkWovdeKxYr6uGtH+lywiwUTlZWCl3XjJJ/urWeRrJ45NOVI55Cj7d3Fcc37TlKj5KosLDZdup0hai5lyLJqaowxrn4v6SGo8ODwv0UocrSXKtWhMOE93okBr/5JUz1JOcDYu/Nu4dGyfS7ZQZoiP7V99m4zVh3ByN2EnJz8xKaDF+Gc2YOnWqEnQwWc+ePdvS4IYRCyPqIQJuVKiGBxIIwIjQ2oPIDJUyNVafoiaN276+yu9JE3cESINjbz9Zor3N3kOczC3n3Xu2XG5b7NTFCLuItn0ESH2YU9FCFgzIq7q+SaNuRFC0rCFuYsPA8zOTYrX+StqZ1DTkQqo5OM1Oaj8xmvYxF1FDjhhz4UoGGZO6huhI7959GSn1Zk0nZyZGq1aAfu3G2GbdcECQiLNWTE3VjYvfpGBRShQMaXMWHmcjxLqrkJQHARU4inKi8PKACA3RFreCtDwbFurs3mude8eAjpPFVfo3wR1jY8M9cz3PdTJ/QnKXbVe93SRmzBqYPviRTNakwY2sDSMVRtSjDPnlNfLH3Xna/kU70vIp43QQxJzxSWrKQWQN8RG5MkUKi9GuAGm2F6hBLAmBlirIHyKihckLxXi+j0aDQUp964li9R4nhU3aHoKibh0V6WdKN8uFqnqdSJWdEqebiWAQzdN2dfPCbE0955VWy4HzFVqnpkZPWpcUNLVtasgQMKnwqrpGXRNkS4QMkZJ+Pni+TP7ljweUjKdmJMgtC8frPUFZTytZeny09r1DsKdKqi5S1EO2n79+lqyZmS4/evOYHMor0/swNSNRz0/EDGF7QOJnGpp0DUsnp8g7x53IjRo1kT7lBOxNR4sxyXCTNQQ9Y8YMJetTp04N95IMhj7BiHoEA2LzAjVv+PGrjSfVBhQihFD/uDdPSWrBxCSpqKlXURjtTxDgpdNS5aqgtHdHIM2LYIqaMSnm8uoGNe6m3uxx9ZxMJV0VY0W7mdFEvoylvFiJ3KxKb4RNeWXVqphW/+bGCNeq1dAksRHh+vjNKbEa0bbfiEB+TMcCM7KSpLyuUQk8NjJMCitQbodpRMoQjM9dO0vXjWpbAhFrfUOzTM9M0PPRKz4+JU6So6LUQpT2rz+7bpa8b8VkjdqJxqltEy1zL66YkX7RPSJTsGomBjZJOseannQmg7GOqWkJ0hwksj5fXqP1a8gfPcEn1kxXZzZS5F71zXEMA4MpU6YoWa9cuVI2bdpkojLDiIRZiI5QUEN9/WC+Et6E5Di5JtAKhNUkQjU/qAFzEOqvqLypiUJaEDfRG9Hll26c0zLhqSNAoltOlsjL+/J05jTqaoj56tmZbVLpRPCoyqk5z8pMkCtnZ17kgEWa/H9eP6L92OfLqrVmDAGSpifqZaMB0U9MiZOMpBj9md5lzudBPRwRHSptb2CCLSlpdNdX7jy2se8ktc1mpLmpWS7UNLh7UdugUTdrO1ZYKROSY2TFtPSW9R3Kr5APrpyitXU2Omw+aN3ivuEN3p1RCOI3xGqkzZkQxkbg6R1n9DrZdZBBmJWVIOmJsZKZFC3Lp6aqm5lhcMHfsc9SlJWVycc//nF5/PHHjbgNIwIWUY9AEAn+Yv0JTSVTc9177oKcLK7UaI90cHDvbExUuOSVNWh6GZEVhJ2W4D64Dp2v0FasroiaDzeEb0tzxmk7FG1WHTmfIZbqzkQDYn/3kgnqolZQXqcispT4aJmZGS3FVfUqBrt+fqbWqiFhyFfbnxJj9GeuZdK4eFWAH8gr14lWtE9hUgKB8lVR26hRN+Tf1NwgO0+V6jCNcQnROvP68mlpUlHn6sxEvFkB61KujXQ3PdpsBpiMxXl5HHcvCH7hpBQlcEi7M7ipVq2ZBDYUtNNB+LwH9H5D5FERlboRwK3twatmqPrdMHhoaalraJDMzEz1CrfWLcNIgRH1CMSm40VaX6athw8g7C9RHuNGRcRLVEvECBkjrJqQgvtWk0bS3smMFDQBKeKrYBT5/uSkmDatXpCg9+PuD3Biu++yyUrKEGFMdITERIRLXWOFbjySYqNbImUC9n1nL2hrEzVnQI+1i5obpKi8VuZPTJHlUyf+/+2dd3Sd9ZnnH90mXZWr3quL5F7BBptiwCaUMIQQZpIMCQmTnWQyASa7s3uGzLBn5+wwJNk/9oQNmxAyO5kQCAmZBEgjZCgGg3HBuOMiW7Itq/dernS15/O8eoUs29gG2bqSnk+Oo2Lp3ve+Mvr+nvZ95Fc7qjRqpaOaRjFEmK5yolU6w4mGiYzTk2Ll6pxMGYpEdOYaX3F+h9M8xuumlHCgpkP+ZVOFNnUxd07NHHHHl5yO8P/xJwtPu29ng58Pr5k/f9xPx/eAem4T7RPBH2rolE2HG+Uzq4s+8r01zk1PT4/Wq8FGt4ypggn1FIQUNCI01umMVY50OG9YkK0jQESt1Hepe9I5TQ0UoRiMRFQIETSWVBA5AuYgv9lToxEe9VXtfp6fpWnn/TUdulKR+i2CQ1T5YZd/AJuoEEFGw4gqqRkjhIgw7mI0qpG+5sDB3+cRRSckaMf1ywfrNYU8PzdJ+vxePVisKkmVJYUpWkfnMEJ2moYzjFG4L6T+Nd0/GNEtWS/6vXpvyA7QJLbjeJvj1hb0S0FaUBZkh+RwQ6eONbHkg5o7hwZEnC7xn2w5Ll9Zd/aNVWcDUxR+bu44FhmGUKxft3UZl4ZQKHTa6JZF1ka0Y0I9BWE3MnVe17uat0SYjBAxLsS8M2NP7GNekBPS0SAiyqDPK9uONWs0TRMZrmRuGvuto03y8nv1mtal0Ym55ud31WiUynYpUsl4Xm8qb9TZbVYrftgtTIgjzVyvlzeq6JLSTgoSrTuWnUSdfI6UvNt8BbiRIeSILocHZ6TJsfH84lWz5De7q3XxCZkEOtE5uLAFTP3VZVjnmdMS/LI0P0WjaISTjmxGuRBpXN6I8hFQInIsXrlXZCnc5jXS41sqmrWOfTZzmbPBY2OQ4tZLnTG6QVkUmh6+21MFm7M2phom1FOQK2aly76aDnW1AkSI6BgfaASAmWd37tkFQb51aa4ameBJPb7OjBkLn3N3T1PXJSr/w95aiUiMptkBcSbSxBIV7+kPAwKFSK4oTNWol2iV6PZgbYesmpWu3dUYjDBT/cQbRzVSZkKsprVHRdydFqPZjI53ar6k/x+8ZYEuPqHbm69hSQkGM7iGMQuOw9ii3OTR9D+1br6Ggw9rL0ntc/+InPkfAl3V2quP7WYdiPo5HHBtFyrUl5ekyo7jLeoSx8GASJ3XvXbOh3OoMyZ2znrRokVy4MABu61G1GFCPQWhaxmnM6Jqar3M8uIp7Vp6fhBju5apkRLhkY4lxTxuZFlFq603rBG2C5EmUTz2mEDjlkbvI7umz+caALHjscZ+Pc9HRO82Y0UinRohux3crV0D6qPt92B0MqTNWUTGOLwxfkUE/ulVRZqeZ4VlcXq8ijmbxXiNL+6rPeX183rRfO5de19YBRQfch47Ld7pOmdGm/p/KOjTCB73t7yUuPPy2x4P9/Evrp7lLBZp7tEsB65wH2YbmTHxYp2QcPamSsOYTEyopyiYdVxTeu7tTGeLaOk+3nioURus6DjOTIxV8SCFjkBR40V8scikhkokjbAi6NS5ibhJAb+ws1paewd0vIp082dXF51x+9NYEGTE6Vfv4j3upKkbNd3tVfFyrxFBI+0dH/DIkQanSxpY/kQy2/lCZ7kF5iRkGXhuutTxVx8L0fDOE61q+IKAc0AgWqYTHM91OtaZZybd7biHteuoVX5yUJv0iH45CGQnx+kyEzZzca+49gsxJ+ExPrM6qJ3q453ijMkT6wcffFCeeeaZM45zGcZkY3PUMxCWTfzorWMquAg+hh44ZuHsVd3ap/PMpIex8VxRlCJPbj6u9V0iSr4Hkb1zZYE88fpRHY9i/IhUM5H1rMx4nc3ma88EvwAZf/r93lqtMyOAPNfsjAS5uixD/uyyQo2emfP+n795TwWR1DaNbDyXa4CCDTa/R3ESjRkWSU30S2lmkqaSSZ0TuZLKR9z5dcss+E+3npC9J9vUeIXRMGxMP7+mWBblnbqpioazP+yv0zQ6neHUpQ/UdsrszAT52MIcKW/o1PloDjLci7suK/zAkS1eM7V+Gt82lTfpqJbe3zkZeo/Hris1Jp/HHntMfvCDH8ju3bttztqICiyinoFgTEKTleuARWc4W7LWZCSqADOmxFgTAkxUQeMYEbhG36lBNekor+/U2Wd3RAxr6vxUUtC92hA2viscsaIZDNe0F3bWSMDvkevnZWn9mYMD6e13Klu1ie3Olfka6bKUgs1X6v/td5rm3FXK+mbYSaGrzI3sgaYJDTGme72xs092n2zXbnH8uJmZpoGOqBmhLctOPE2kgUOBuz/cG4P7m6hpSi2e3vtrJSbGI0VpQW1WI8VOdMzB4EwRGItFSLljE4rYk3mfnxuS7v5++dn2Kn1NNyzIvjg/aOOC2bhxo9x///36vtfrlS9/+csq2oYxmZhQz0CIoIP+93/0MSOWmx10jp/BvpIa8M3jFoq4izdovPKMLJCgQ5ooc3w0zXz0czurNe1M1M0qSLZgkfql1k00ztIQ0tH7a9o1on9gfak2e/26t0Zr6T6fV1PvTv+2g/sxESmHjTi/M0OOID+/86San1DPR3iPjtTRMYPhOnkuTFNwECPVPZaC1HjdkkW6fPPRJrUE5X0ifdzMWLsZ508UTyBGG/N4HJrcxs+Z87p/suWYLi2JjNiX4hjHXDZizaHmzSNNclVpxqiTnDG54AuOQLuz1k888YTOXv/kJz+xH40xaXywH6IxLaEOTPMU0RwgjIjXrIzzb6bBtIOoGRFDkEhRY2eKALHGcyy/3VMjb5Y3aSqapiyatVjigYBi68nctN/n0bT1rAwes1sbuPC9xk4U/xMc13AXY0SMcwBnAw4KiDWPy3pIoune/iHtKGcVJn+fGvRLanxA/DGihwD2UmN3igUrf/A/d+HQwU5t1kPiVb61wvHg5vM8NwcAUuGVjV0a5QOHDUR4/OISIIKuaunVsTkidK6T+j9lBIQ/Ptaj18rO6v/9x0O6l9t9XGNyvcHnzp07+rmnnnpKPve5z9mPxJg0LKKegdCExiw0USaQsV2SnyyXl7y/aONcYJX5uSuKNVLG3pOo9so56fLJ5fmnpIBp9NpT1a7RJoKJqBFJUq9t7gqoYKFxszODWq8mpc4KyB9uOqqbrNbOTZfu9wbVACXe75GhiBNZEyljzOJEyBFNoXPg6BmIqHMZ0fNQxFm6gWC39oa1Nk3Xd1YwToWV535m6wltYuvoHVA/cD7Ha8MI5kRTj45tUX9OjXcOHy0jCzq6+wY1KicdvjAvedSKdCys7yQbwFtdnMJhQDeEkX0YkvL6bmnrHdDUOc9JZE7nOWYqvC5m2xmb4/Vh4bpuXpb2EvSEh7Q7/Vy1bV4La0AZI7M6+IWJdXl5ubzwwgtyxx136Oeefvppufnmm02wjUnBmslmKNSh2TiFUNDxTToXsRhfVybCc/c0nwmEiHQzX8O88fg6Ld//rRcPqLA4jzGsUfjOqjatgxON+71edRdzatXMPfu1CxvhTUsMyLvH21QQiWb5u7tXF8r6hdnyg9crZEtli3T3hzVKj0REa9+kwWk8S03wa0qaaBrDFkeoYyQx1q8mJ0TPGI54JEad0RD3RXkhCQZ8elhg7VVLz6BkJAR0BA5OtPRoNzhfh095cVq8mp+Mr8mTKfjRm5Xy9LYTejihaY9MAIcRon8yB9TKWQW6rMAZR0OQSa1/YU2J1uyxLyUKR8g58NAzQNaBkTUOKjjEkXUYf8/5mWBMQ+Ma30fHPk1wZAqMC+Pxxx+Xr371q/r+Aw88II8++qjdQuOSY0JtnBGE4te7anQBCBEswnT7snydIz5fqP/uOtEqP99epene5YUpWg9GjIn2PrEiT8e6Xj/cIO/VEEl3q3iz85lmMkakiGhpXksI+DRNzQHjkysKpCQjQR5//aim7zWF3T+kDXJExziMVTaxznNYuvrDTi2bLnGJ0W5r0ueMpDHORW2eSF89wiPO+kzS086ct3Og4YCA+PMIeI5fPSdDPr26UHddMz6G4GLLiukMn2Ns63sbj8jv99RqSaB/pMQQ8Dqd9Z9cmS+FqfG6kSwUFzilYxyTGfZR7zzRNnL4cQ4IzIkTYecmB/V5OIggzziycV/HQiPdU1uO6TUh7PQB8Lq/um6O3jfj/HHnq5ctWyZbt261W2dMCpb6Nk7D3WtNehzBJDVNdEZE+qWzdDePh0iOiJI6LelXGtiY22aGmea0T67I19WcPBarKBmdwgoU0XFd00gZI+iZCbEqoAiqp613VMSoq5dlJekhgPSupqX7whp9M94VI05anUiZeWeuoTc8qBFuuKlbMwYpQceNjD9YrtIhTgSLoCLWi/NCsv14mz5OStAnt5TmyFevm6uRPTVsrpvnRdQRX6Lr+o5+NaOhfk3dPxxxMhMBb4yOod26OFe/lvvLIYPDAtkMDgrq266mK6zJDI7eh51VrerxTlMJ3eZ0rPN5FrSMF2rm20mwu1E+10pXP4tHTKgvDBrLWlpaJD7+/YzJli1bZPXq1Ta6ZVwyTKijGKJFRIhIkLGoS1VnREBY94jI0I2MgLR098sLu6q1IYuomNT0dfOzzrqekdoq88+zMxK1LrwwN6TpbiLzz68pOcUnnLQw+62JoNWm0+9VMUXsEbBTdkAzjhXj1Mj1w2EnHc5IGO5j7RGRBmxG6UbHeUxHuIb174hGaXjj9eDx7emO0SY2hJI7i/A7wu6kygtSgjInK0kGIsNq28rWL9c2lLT587uqNbItzUzU2nNlY7c8v7Nam/WGRsbGvF6P+H3OQYDX5qa4EVLuI/eaEgSvh6id/gEsRVmtSdqenzt1e8bWOHCQgsdN7kBdp6bvOQCMN+fgsELzmos7Pkekb1w4rkhzn7/97W/LN77xDXUzO3z4sIm1cUkwoY5SmL/9xY4qXfHI72BGle66vPCS7C0mckYoGbNCXPZUt0llU7e0dA2oIOaEgtqtTD2VGWvSszSH8TkEh6gR0aIhyhVZImKiSATsTMs8MA5BoNjShRMYlKQlaOMUH3MPWrrCmk6/tqxIm99eT26UI43dkpYQKzVtffp34Qgd7M5jjvRtqQDXdWC1GtDnZknJ3VcWy7+9ValNaNTPEXXnNZPeHpb0RK+aovBzQFV5vrHe3twP/MNJQ5Nap86dmxKntW02g+ljemI09Z0S9Gukzotg9Is/uKDtOtGm3fNu1zz3+5qyDG28Y7nKr951DkaIecDnldgYZ/yNgwz3ZF91WA8zj75SrktWMHBBlHl+NqjxODwmAs2twFrV+Gg88sgj+hY3s9LSUm0684ysZTWMi4UJdZSmnp/ZdkLHlGggAlLIP99+Qu6/ofSsrl8TBQ1SLMUg+iT6q23r01QsootIVLWyJCOif0cNGhHB1APbUb8nRiNKxAlf7rHRHg1fTq33dMgW3HVZgSzOT1ZxHI4RmZ+dpNHkE5sqpaatRzMLNFHRHU3kec/aEn3e403deoCpaByU8EjQyPmAKJIgFsGO9SFgSVrLJlXMwg263DmIkLomHU/0inASzVa1dKu4I+BF6Ql6P8Zfb8yIuCPUoDPlI3PqCC8HG2rajZ2MoHlkeVGqGspwD6kjI+TzRpadAKYoRxu6ZHlhqlw91xHsHcdadPc4teae/iFp7O7XTAvmM2QSaATkezg48Dp5TTSZ8TECz/33ejzarIfLnPHh4d/x3r17T1nkYSsyjUuBCXUUQtqZ7mJmeV0jDNKcx0c+P34z1kRDevVPluZpqntvtVNjdl1GGE3STVbtvbqr+Z2RFC2QAiYaZ4Z5cX5I0hP8OodMbZnID89uNnyNh8c/2oD4D2vn+N7qAU0HbzrUONLc5ZHVJekqmpiOIGivH26UO1bka4PUS/vrNOWMciKapKG5XOeAwEFB/18dxrAFZVXmk28fV2Ejyl6QF5I7V+TrmBVd2TTSuRu9EmL9GpH+fm+dmsG45QdqxNS9yRywbAORpkmOz71ZPlKLT4rVnxcR/e3L8uSeNSWjzXh68Bl34CIwI5UuI9dOeYE/XDu+5/Ny4iW3L072Vnfo7Pn6BVmSkehkJyqaurSPgIUm/Py+uLZEXjvUIJ29g9rtTVf/KSUEY8JWZJpYGxcbE+oohF/6YyM195c4n6P2OpHPwww0KWPqt2OdtVhUQePRE29UyHs12Hj2S017n6TEeqUvPCzBgEdF2ekK98i8bMdKlNQ3j4NofWZVkbxd0axe2UTZRHrUqsdC1PezbSdGFl+E1SAEAeWQQpqaFDGRJH84GHAPiII5RJDuJarE3pQOZ4SPyJlIntuEYHNNSXFeWVeWIV9cO0u7u5/ZXqVZAyJbIl8OG3VtfXLjohwVQMSSyNS9/2zpIsrHoMXdJEYa/M91jvykzoUj4CwD6ewPa5d1UZrTXT0vJ6Te3mQCxnbML8lLkd/sqZGMMEYtXn3tMHdMhO3y8SW5+vccUMhiZDOTnhAYFWlIDPj0Z8TPlPtNpz2HBI4obrOdbemaGEysjUuNCXUUgkjlhoL6ixZBAd6ntjl+XvfDQoRLxzJdxwguqWoitA0LskdT1YgSDVQ/3FShIkAquaOXWqtIUDxam/bF0Fs9fMYVltRvlxYk63gTtdXx3eII7i93nNQUO9Ere66pgVMnJnDFVjQt3q91ep4jFOuXxq4+ae8d1Mj3vz+/T9PHbMVyDEBoMvPJUH9YIkPDuqzD640Z2VhVJIvyk/VQQFyJSCN+u0+2aaq6b2BIXj1Yr9eIkcrYgxJjX3wt9WJwN2ZRi7/v+rnOrLbXo45s33mlXF+r4/YW0XEt0s/c59/srhmZm05RI5ctlc2y7ViLvlaiYBZ0LD9DehpR/sq1c5wZ7vCQdrU/u+OkZgYQea6V9D0bw/hJ0NvAxrNZ6QmaTeAw9ot3quS/fKxMr8e4OGLd1dUlodCpB1HDmAjsv9oohF+mn7qsQH+50rCFMOaFglrDdUeXPiovv1cv7x5v1ZQ6UTCmJWy0IhIcG3nh0MXqSuqkpL0RRGaH+T7qvcw443CGiNCkhbgStSL4riggvGeiqrVHqtt6pDAtKHtPdmg3M49P4prXyRIP0sdExggvIo3ZCAs6iJw3HWmS8GBEO7ep2TpmJj7xev2a8iXVi0j/t5vma6MVYGHKwQHxJNJE8HH7IgtAWhwxJkVOfwB1bwR6+/FWifN59FDxx/31ctXcdO0if+d4m9aLOYxsWJCj4l2WlSivHKiXg7UdmornpeN4lpMcq4KOcJZlNavpiY5sjewCD/q82kx3Ns9vDiTuaBWNhTiY7a5yDg4INYeq9fOzNPLnDz8fN9XNPTjW1K2viR4AY2LFetGiRepcZiJtXCxMqKMUTD+IgPjlyi936tKYVlwo1EJJJzvjSex9dhqkmKmlduw+JvPSeGCTih4r1IgPKd38lDj5/sajmtJGgInsiO5vWZKrKeHf7q7VFDDicOXsNLl5Sc45r013MjO2NBTRlHFSrE/TzlyzM1qFYcmQRvtcJ8YiCDgf8zoQ6ThfjGQkxUpbr9MB7fey3CMgRakJctvSXLlpcY6m9V1o1Np8tFkO13dJfTurM4dU3HktvFa+FwtS3uKURjMbnedzMhK0fh4ZjpF/23xc6+Z5yfGa8n5pf73UtffLl9fN1scbCEekbzCiPzcOOJQrrivLdEarBiPaSMbhA9GkJMDfU8vHAGVO5pxzzqlzsCKNz2w19xzHt2UFydoQRxOZfv/4CsnIWlBj4sWaSNqFf7P/8A//IA8//LB1gxsThgl1FENdcdk4M4sLAdMNarkYcNB9TFNUWnxAf9ETdeWOH/UaceM6E3kp8XL/+lLZVtGikSjRM/aViCApeZqeaMIi9ZyXHKcNVMxSE2kj3nR7E/XxtW5DFuLIrDbz1tSY2/pJIcfooYI5ZrrE+Z6SjDQVPHZhc+1E7dSgnU1azEF7dPSL2iyie+9VJSqYRKyMexGpurVl3tJo9fKBBtl0OKzRLilxDjF6MBgY1IY3rutAbbsKKtdU1dorbX2Dsqo4VddnMh9Olzg4m7g6dIUnjXRE2HTCc5g5XN+h6Xzq1AUDQxq585hs0GJ+3I2W+TxbtugXGDsGdja4D7yu8TAlQDTNAYNudY9bNkmL12s2Lr5P+MmTJ+XZZ5+1OWtjwjChnqaQfn72nSpN7eYmx8qWihZNbw9niMxLxIJySMec3GiVqJi54bIx0fR4sPe8bVmevk8N9nBdl0bgfJ4mKHdFJqlcxsu2VrZIZ++A7oLWxRsZCToa9WerClXgEWzef3Z7lfSF6R7vF7/HI9npTgqdxrOv3TBXG7xojsLjmvQx3tukqxt1PphUO6NKPo1W+7xD8p2XD0tvOKLfpyNOx1vl6xvKRue3uU4yFLcszpEfbz6m89+IJMKaG4pTocY+lXGzgUE2Y0W07kwEzjgar49TDWlvutDj/M7qTA4TfD11Y+r3pKT3V7er+DJfTZMZ4szjIf40frmHFlL6pOU/6rpLDi1/dnnhSDOZM7JVlB4vd11WeJqXuzGxUKuuq6vT960b3JhITKinGQgwFps0RlFvJSKnW5rP0+xEzRUhIYp09yjzB7/pmxfnqJieC/yvn3z7mHYhU+/FGpP0OKKL0BBZbj/WIlmJAalj1IlvionR7+Oant0uct8NpSpSCDbvn2juHhH3Zo02WcaxtDBZBZV093++cZ7cMD9L/vhevS71cBeBxMQMa6qcXc+Ou1hE+nFVEbrDaega0uawt4rT5M7LCkZfA+lhardfvnaOPidiTVRNQxa1aRrcaDLDF5zNkwjrYGRQI2siYCJWavaIPhkADjklmU59nyieKLulq99Jx3uoqTuCTnd7ZmJAv668oVPnoN10/82LsydkhIpMxd9sKNXDB/eBj23f9aWxG0Wg2Wlto1vGRGJCPY0gyv3J28d1XAmnMMSGqA8x1LniMV8bHFle8YW1JbrwIjs5Tt9irEKXMLudF+Yma4p7PJsON+qcM+lht3uaMSzqrQgdKXD8qFlGwSEhNSHWEdBwRKNa6qjUVon0AMGlxkz9e1FeiqbJETicuzZnN8kN8x0BWzUrXcemfrS5Uv2zhyPDsr+2Q6NRwKubyNYbw+HBo2Na/YNOpLv9eIvOExNdI5YFKfHaeU0q/hPL8097jUTimnoPxYm/L6yP4ViMxsic9HiNhomwsRJFdFkUQsqfw05DZ5/OVzuOajGyuCBZ69A8Xk4oVn8ef7Vurs6CM//M2NYtS3J0fG2iILI+06iXcWn2WZtYGxOJCfU0Yl91h/ppU68lKqTO29zdryJHoEbkPCszQd3DiAwX5CTJkvwUFUqEBO/qjYcadFwLMhMbdFaYxrax7Klu1xSu24FOTZVGLxqiEGo+ZmtVRWOn1LT2Smu3R6PQWK9Po0gEm5Q53d5u4xTjS0Tn7iw3DVKIPCsuEWoXvj7W69V6OjXoUCzpYqfrm8iUFLWzKitGZ891g9YAyyta9LHi/DFSkp6o2QSie6J0OqixIOXggFgSxfOcdME36TINtlD5NY1MZI3g0wXO/SQSp1bPrDPXxkHkvutLtWZN09qWiiYdx+IeD46IO+sx+Tkwvka6HVG92G5zxqUV6/GjW/PmzVO7UcP4MJhQTyMaOvpUcN1aJDXePSfb1cIyLyVO/N4hFZ1jzT1SmBrUSBIhI/WLm9ZbR5pV4BlTAsxMECtcuKjFurAGsm5kdaMLNWhSu9DW069Rfe+AM2HdM+K2FfCyptLZf/3rPdV6nWdyKnNx7EdP/3xpdpL8dNsJaewa0JQxaX12UWvleGhYI2vq1bw2np+PEVatOQ95pL6zT8e+mKGmYQzjkKSgXxvlKhq65Cvr5qizGrPXv91TO5KJcBaT4BK2p6pNMpPiZH5ukr7u7KQ4PYi48LpWFKVqtM7PhPE1DiBE/nSQ3zInfVSYba55ZsxZNzc3T/YlGVMYE+ppRCjodAvTHU0XNR3OCBmR719eM1tTrIxqERWSpib9TV2YPcdNnf0aFbtR6azMRI0OSWMTuTL640IEeai2U442dupCDNLCiQGvRo6RSEQee+2opomJ4tVr2/1GNlN5PbIgP1l9uxlHorkMYcO+820a3jr6dIyprXdAl15cV5Z42nYod6/10YZOTYGrK5nPsd9EOGniAncHdT7z3cxbe2N0vrq5u0PqOwLqxR3we+TyEkc4cfxiHST19cK0Al1ZyewyAs5hJjU+1mks88Zo5ztNYoytzTlDipnDw/bKZi1BYFDC68Fi9caF+XLtBKa4jegX6zvuuEO2bds22ZdjTGFMqKcRzOXialZe7/hr94QHdSHDp1YWyPwR686xM8U0ZWnTV1KcCh+CDJXNPZKXishjLuI9pVsY0WROmEYrDEtQw9Q4n0aXL+6t1eUVWuP2epzRJh/dsNSrh8Xv9zg7pWNEZ5Lp8iY1XxCIl6X5yXLr4hyt2+492abNYkTob5Q3SkvvgHpxkw4HXZuZF5J91e1aj46P9Tkp72GnjkyDmWOcEjMq2jSyUffmy+IDfu12J10+OBjRjne+Hncvoubyuk79Hhqwrp+XJalBv94rZpUP1nXpdSPYTd398vGlubIo71QTEfoC/s8r5fK7PbXaNzA8MjO+KDdZ1i/IPuu60vEHEmN6iPXOnTtHP+b9+++/XzZt2mQ/a+O8MaGeRtBpfO9Vs+S1gw1ag2XtIrO2lxc7rlzjocbKfDXfRzobIaQ5imiQjmE0jiansXO9b5Y3yTdfPOCsThzG0GNAuvsGdR55U3mzRpvUoNEbolSE0tUe7EbRKARQHcECvtEsAKnjq0szJD81Tn705jH9eHZmgjalbT3qpA3vXVsy+suNtD7fTyNb18i1DA1HVASZ+SaSdrZ8iTR0Oq5l4UFnSXScf1jaesKaYejuH5JDdR0q8KTL+Zjr3lrRrJ3sZBv+fcdJbVpD7Lkmmug6+wZ0BIwaNyl2mtSONHRq9oJ7Sdc9HqbMNHPNzF7zuT9ZlnfKbDylCrzGNx9t0mvCCe66eVlnbOIzpjaHDh2SlStX6vvPPfec3HnnnZN9ScYUwYR6mkG6mtrq+URnaQl+bcxC6IioERB8s/Hz5u9YJXn9/KzRr+cxn9p6XDu0C1KCOhdMtIiw83ZOZrymq3H2ohFMZMhJRUccIxUiXFLfjEvx9bctydOmNJ6f7VcYpGj3d1uvXDE7TRJj3z8gHKjpGLEUdQSMLVFFaUHZXxPWCJbHJ83u8zme4ridOZ7bw3p94SG3uUzUAY0DQjcLNMKDuh6TQwqZg6L0oFqkvrivTroHBuW5d6v1EMDcNsYhGKJcXpSm3d+sxCRDwbw6Qgu8VurwROsYyrg/g1AwoGl9jE/GCvWWimb52fYqfZ+o/tWDDdoV/9fXz9UDlDF9eOONN0bf/+xnPyu1tbWSlpY2qddkTA1MqKcp55NCnZWRKMsKk2VrJUstPBoZ5iUH5Z412ZrSHd+JjFDStYygILhEoDRptYfDEqsOYV4J+IbUAYulECyNQKh9/hiZm5moqXkui3Gt1SVpajVKTft/vXhQNh5u0Oi5NzwoPQMRTRmvKk6XcCSindFYjRL1jnXgygrh5uXTBi3Gv3wxeIR79HlJsTd39mthHEMXrpX5cf6OxyZ1jwC7BwUi7gW5IT2AEHWfbOmRVw80OB7b6QkSH+tV8xJMV3afbNVmsU+uzFc3tO2VLXpAcp3G8FCnJu74kjtlg77woN4zlnK4cMAgtc8hg5luyEgK6MpP0vos6TCmD/fee6889NBD0tDQIAMDA5Kfny/V1dUm1sY5MaGewZAm/uzqYm2YojucjnCWbJDyPVMdlXQyUR4ihxAxa80YFmDqQcTNCBJig2EJizAW5YVUoGkQOxOvHaxXkeaQoJHviBPYydY+ae2u00iXz9M1zVw4XelA/ZxUMWcJHMi8Hq+uowwPOQLo93H92k2mIsmBYXiQGWtR/3DEOy0xVh3EMC/h70NxPhVzXh8pbJ7XFVoiZ+rau0+26/3B1xth3lTeqPPirkgDzWU02mH2om5rXo92jnMocpeDAPeQdPrY0gKNcUCDnjG98Pl8UlVVJQUFBdLY2Ch9fX2Sl5cnNTU1JtbGB2JCPcMh3cvM8NhZ5bNBAxjjVPWd/SMGIBFNN7O9KsYjOotM3RfRQwCpx55rLSe1XUdgeSy3CQxzkIhGysHBIXUM4/F+t6dGa9OILHVlnNGIthFVdmIjeKTOByM4gPVLRiLz3U4nOB3fdHXzmDSQET17R6J0npOGMjzRuQac0a4py1Qv8dcPN2kUjIDyOLy/bl7mqDBzcOFx3JWTQD2a+jmpd2r3CD5Nfn+zvmy0IQ6oZXPw2F/Toel2Z8XmkB6SMKAxph+BQEDFurCwUMW6v79fI2usR5OTbbOZcWZMqI3zBiG56/JCHYnaebxVo9qFuT5tsDrW1COtvWGNOovT4+WWxbmniHTfSPp6vEXm0YYurVlTSwYEm+iTpjQWiPAYuJHxORrcMBjh8X/1bo1eDyJNJE9TGY9A7fnmRdk6Y834VUvPoFqY8nmv1yP5Cdia9qnNKt9LKp5rwzSFXDijbZiXbFiUrdHu8ZZeTUXTGOaN8WhjHks3XHAlowFs18k2FWua8ZjXxnr0tmX5+pp5fDrkMU8Zfz9vXJijjmw0/7lZDDcTYUxPYmNjTxFrImtmrQ3jbJhQGxcEke1fXzdXa7PUfRFS16EMYaMbnKjT9ZZm5OuP++u05kq99/LiNNmwMEsjTtLCDV39MqwDz+yIdhrCICnWo9E+EScpeaJu6sjUoGnqYi4Zz2xmp4mi3flp6tw8z0MfXyBHGrrlR29V6uO5e597+gc1CtfauZdIm2jcMUjh8wlDPjnW0jOyzCQoX7t+rl47B4Gc5KBG9GMPG7zOz68pluoXe3TxCYJPeh3RpnHMzQBwmHj9UINGzGQIFow8DkJP49juqlZ9TvaBryhKMW/uGSLWdIH/+Mc/ttS38YGYUM9gEKd9Ne3qu00aFmtMt6v6gyDyc5ufxkIaeGynMsL49Jbjsqe6TQU+HB7W9DWih5kIIs44FB3VjG4xKoZYO1adHo12EUJMQ5irRsCwRz3W3K2Hg/zUeF2RScMZ9XJEuiAtTqNu7FQ/tihH69qvHWrQMahjTd363EATHB3gZLCpSSOg3QNhHaei9s1zf+06p/P6XE1dWLWSQsfIhBT2xkONui2Lnd8cHhBm9l/jAU55gGwBvQB/vrrIeR0pwdHauzGzxHr//v2jH4fDYbn55pvlt7/9rQSD9u/BeB8T6hmKu4pyW2Wz1mXJG79xuFHuWVuiXtfjQSxJ6WL/SR36fKho7FYfbbrL3aYsBBUBu3Fhti7fAMSQqJNonJEoouSVxSmysihNo2eu75rSDLltaZ5Gy4gps8zUdhkr62MpRwy7puPkylkZWm+mfo1Q37EiX5LifPKvb1Xq4yzJT9axsrqOfo3EeV19btp9EDHvlfaeQU3h4z52rho7jWubjzRp6pqFG7VtMdLeg5HKkPRFRJeX8IfoeXlhqiQHA/o636lskcV5yeryZhjssF6wYIF0dXXJI488Iv/0T/9kN8UYxYR6hvJeTYcuqiCdi9gR+eFoRpr6r9bNGR3vQtB/v6dWNlc0a9oY0cOQY8OC7FP8rc8EY0+II9GrC9FzW8+A/t3szER1+8KNjCid8SUePzPJJ396WaEKLTPdiPDYSJ2v+/SqQjUiaezok+5YrxSlxsuCvJBGqOyETgo6/7SJzK+ak6GrJzmAkM5mAQiHiAgmJyMjX7wUriE+1uPMcnf0amT/QZDOfm5ntby0r05fE01oWLaSss9KDkpXX1jr7HWdfVKalqQiDRxKyPDj4GZCbbhCjUjDww8/LLfeequsWbPGbo6hmFDPUPDwxskLkQbqtIxAEcHSJIaYAGL+0nt1khIMqHc4Zhw/3FSh0ebty/LOOnblzjoz8kTamjljhA0/cb7Hdd5iAQbCSDMao1bxAWesSxvIYmLOGr1r/fi6uTr69fNtJzRiRVjdFZ3Mbf/wjaOytbJFDwscMnS7VVKshOIcW1QdgRrG3hORdhaLcCqgoSzO79MI/YPAN/3to81qt+r1xuhGsJaeiJqtZPu9cllxmpRmJepiD9LzLtwHVnS6994wrrzySrnhhhvk1Vdf1Zuxdu1a2bx5s4m1odhvihmK0+DkrIF0u43dRjA+xNuaKJvUOKNJfJ4onO5lIscn3z6m0fdlRamytjRDncLcBjIXxHn9/Gz5w/46XfsIpJTpqna3RhFV//fbFqppCGNfbPViVebYAwDPR30X0R5bQyeiv64sU/doY89JzZuxJnzDibY5ZPAadI0kayb19QzrwYDGN70BY5ICeJPTgc7X0zlOqj8tIXBWb262cNGAxoGEiJ9DCcJNUxhLT9gHjWjT9EYWoKqlR8UZj/CMpFhZOsahzDBeeeUV2bBhg751xfqtt97St8bMJmaY470x42jtHpD/+9oRFQ9EEcGhuWz9giyNSolwETmasRDInFBQLT6JRBlBCg8Nicfj0Uh7Vka8NlzdfUXxaelw/nkRabIyk6idbudz1X3Hfu8rB+rVVhNLUhre6Br/xIq80w4FpOhJP/Ov+Q/76rT+zutBEImQaRDjcEJXdlNHn45K0XxGVmGkxC2xvhj9Gurf87JDamRCk9etS3K1O3s8dHU/9mq5Hh7cDMR7te1aQihJi5fEoF+vi1o0S0SwC0XEecwbF2XL/BwbwTJO58Ybb5SXX3559OO3335bI25j5mJCPYNBpP/jQL3OMlMbXjM7Xd490aaNWDRsYZK141irjlAhPHRTI9RE1Qg0USRR6PKCFPXAvv+GUo0iJwrGokizI67UnhF8GsU4FHxxbYlG42ONUxB1Rr5oMiMyJorFShRoGkuM8+lBIuCJkfr2fn19dJ139g/qIaIgNU6WFaWq9SiHFzVC6eyT9IRYuX/93NM64slG/L83K3SpBv7hfIzHN6Nm7OImnX51aabcc2WxJMcHVLSZqU4IONdxLqh30y1OwxrucXTOGzODsZH1d7/7Xbnvvvsm+5KMScRS3zMYItu/uGqWel+T3mZM6/ld1ZrKdVdb0n395pFmnVtG0HANQ8BY2oEFJrXhlAS/dlITaU+0UPMc1M63VTRrrZuO8Jf21Wrq2j0YENm6ETSpajIBpLe5TgSU1DWrNonQOWDwvdSUEeOUeNZVDkhWUkDKskPqJpaaENC0PSDuPD5p/6x5pwo1j/vnVxTryNjuk20qwtS9/T6vlKTHacPc4bpO7Qgnna8p+HH+6WeDrMbTW0+o5zi189QEv9y+LF+d4YzpDxH1TTfdJPPnzzeRNkyoDWdWGRDh8YUQBBxBv3F+lmw83OgIckef1m8RNRZwsNYSi07E5INge9SRxi613KSGiyB+ELoiU0RONPfIiRa6sElTs47S2TqFQP7Xm+bpEgw6rOflhFSM6QCvbeuRjt6Iprd5SRilrJ6Vrs1dP99epaJe39GrXeVEuviLMw7GzmoMVlyItIEDwpmg5nzLklz98/u9NTonPS87SRvXODTQfLf5aLMK9flCZM42sarmHnV9I/rmEPXrXdUyJyvhvGbdjanPSy+9NPo+/6537dolK1asmNRrMiYHi6hnEBh+MKbkzAcH5eq5mafUi6mdFqXHy+G6LvWmRlAx7sgJxcnHFufIqlnp8of9tbLpcJM6k2HuQWRKKpxmMjqtzwaNXc/tPKlrMFFfuq9Zx0nNmno5TWvHmqmXB2R1Sbpex/zckDZnseeZxRr4edPBjZASuf5ub42mpGmCcyNVXmNlU5f4PBw+nL3YjGXxXDcuyNZUfWPngDy19Zg0d4aF3R3BgE/tQfHoJp2+t7pd0/qkzrm2OCLkjHPX1Vn2gbCP3VxGVO/Uzs+9dtSFxjnuO/vEuQb92aQGpbyhUyobu02oZxgbN26UW265Ra1GmbH+xje+MdmXZFxiTKhnCKRS/2VTha6qJAqkuetgbad8ed2cUVcsROGulYXyzPYTWr9WV61QUD51WYEuvODPX14zWz69qkj2nmzTTVKkzWmWom7sisp4ELsXdlVrVFqWnaipXNzFmEFGmH+8+Zg2YOnGqsEh9eG+96pZsqwgRee199e0q/OXJyai6zhp3CLyJYXN1q+52Umy/ViLOoxVNHVrkxve3jwXbxHPtXPSR9P51Ldp+sJjm8di+xaHBw4KCDX1eR4HceV7rpuXKWVZpzeTjYfvQ4upLdPVThNbS8+ArJ+fdd4i7abUOSQRWZ+aXXBmzY2Zt8gDkYa///u/17cm1jMLE+oZApEpIu2mZREhXMO2VjTLnSsLRr+OSPbrG0o1ZYtQ0AHtjlKBzjYH/dokxZ/zgeiQ0Skibr6fuWUOB8xys/OZJrH81Dipa++XTsxG2vpU2P/2Y/Pk9uV5mqL+zsvlPLkKFYLv9XjUQIRFHbcuyVETk7fKm7ROnhDr1aiTPdJE2uWNXbqIw23GYkwrMc4vixNi1c4TpzREn/Q57my3LM5Rce3HkjQ1KLPSnfTzuVhemCL7ilNl54lWjdzJuc/JTFCDmAsB9zfMW7Ycbdb7RYmBLAi7wjl8GDMLxrO++c1vjoozYs2/iwcffHCyL824RJhQzxA0egz4RiM73pKWRcDOVLOeyNEh6tdEierJrSlp0fli6t9EwTR67a5q1xEqvpYFHH/cXy+fWpmvnuIYqzy/q0bFyo0o0xDjUKxGwxwk7r2qROZkJKhVKLVvxJWvJfLGDW3svmjmnTmAIKikzRFp7gWCSIp5+/FWXVlJMxhNZNiO4iA2fvPXeMgIsKCDMgDd6TznovzkU1zVzgd+NneuyFdTlAN1nVo/Z8f1HcuxQ72wxzKmB64ou2LtvjWxnhnYeNYM4d93VKn4MQ9MKphomcy1hcoAAA1rSURBVG5mRPC2ZXkX9bnphv7uq+W6LpIFFUSbHBBYTDE3M0Eee/WIiiUzzxwjiL65PpaEsL/ZGbEa1nlqlmcgfHEBOqsTdPOUm7onS/DUluPy5pEmSYr1azqbJRw0ct27tuSU9DOpfUT95ffqtYGO7m7WZ5Jx2Hi4QQ8ROLHxLRwkrinLkM+sKrqgFPZHhddDpzuNb9w3S3sb3/rWt05Jex84cEA7w43pjUXUMwRqyESHzEhrLTg8pMJ0KbymeT7GmF7YWa2OZxwUrpyTrhEiBGO9GvFjBkKXtkbfQxGtPyOypOERag4UTZ39o6YheIGP3Tql+7IvK9RxrneOtaoAXz8vU1PP4wWWJroHbpirDWQ0uNFwlhLvV/tUZqEZ+3KXk9AMtv1Y62nNdxcbtTwdGRMzDDeCfvfdd+UXv/iFflxdXW1CPQOwiHoGQToWq86adqfeuWpWqooe252YV0aclhWmnPes74VAGvnVA/Wy+WiTdlhfXZou183L1matZ7aekKe3HldBZ+4Zw5D9NZ0yPzdJVhSm6vfTfEa9+IEbSjVtnnAO0xDXcO9cETDXhN1ofKxPnc9Y1kG0jcBjUuI+1uGGTvnqurl6fwxjMmlqapKcnBz5u7/7O/nnf/5n+2HMACyinkEQnY1NczMy9ew7VVoTRs82lTfJVXPTNcV7Ps1T5wu7oH+mKzVbNG09OBzRmnNzd1juvqJI1i/M0vlq1wv7aFOXBHwxmtp24fPUsJHf86nTnm+Kmh3SdFS/dbRZo+klBSF1aXO3agHmKQkBn85FG8Zkk5GRIb29veL3v//fwYsvvqgjXMb0xIR6hkLqm6Ua2FO6PtbUiRFTjEEm0mGMrm9cxoiIXZFt6/HKrhOt6i3OJizGvoi2GSPLSAzIwZFxLTeixWhlRWGKvk8TGOJJdzTX/lEyANR9NyzMkWvKMrWbnIj+93trdZEIjWjIPTX1a0sz9foNIxoYK9LXX3+9zlqvX7/+FI9wY/pgQj0DwSHsd3trtekqLd6ZjyZaJNplDSUp8okUahq61ON6zFpH3seXm9R7brLo2kt3TIyvZTsX89RksIl4+fsrZ6fLv7xZqaNYfJ5aNiNRn7uyeFTUPyx0ursObbiMZYXiZM/JNqepLT95dO2mYUQTQ0ND8vrrr+v7eIPjXrZ8+fLJvixjgjGhnmHQUY3YHanvVLOSE62D0t43qIJHehmjjQsdJzoXiCyPyYyz2xzF+3R0Z5/BDhPRvWdNiZRlN6uRiWtYcqKlW+vpszMSdHyK0SoWYjBKdnVpxoRdLwcAmuwuRaOdYXwUvF6vfO1rX5PHHntMP2andW1trcTG2gKX6YTZHM0wSBuTXl6Yl6z2nax2xDmMDmu6q2ngwod7IsF85Pr5WbpK81Bdh+6mJlLF0pMVmmfCTcPT3FVe3yk/214lv9xxUtdr4vbFdiyicmaf+XvDmKlghhIX5xx4W1tbpbCwUPr7+yf7sowJxCLqGQZpbaJmokbGj1ghycgWwnnb0lxZV5Z1TmOPD8PHFuZIUVqCzm6TQMZ5iwUZZ+M/3qtXxzJS8NSg2Yu9t7pDm95Sgr0adXOgoMZOx7ZhzFQSExN1TCsvL08FurGxUcW6qqrKIutpgkXUMwwat2iOQuBIKbOBCgG967ICuX15/lkj3I8KyzQw7fj40lz5xIp8Fdmz1Xwx+DhY26GWn06j2LDajeLQxSGDKJo69jsjaXHS9oYxk0lLS5OamppRYUasv/3tb0/2ZRkThIUiM4wVRSla9y1v6FLbTJZgIIiMKV0M6NLeWtkirx6s1/EqmtbWL8iWy4tTzyrURPtEzHw99IYj0tzVr01vHDQGhoZ0tSZZANzNzP/aMN4X69mzZ0tBQYF2gRvTAxPqGQYNXP/pmtm6VpKaNC5eq0rS1FP7YsDKSGao3edm4cYz207oXPLCvNBZhfrK2Wnyy3er1WqUCJoVkoj34vzQ6AGjqrlXFuaGrBvbMMaIdUtLi26Qc+np6ZH4+EvnqGdMPCbUMxCi2psX516S58LKE/MQtx5N9zfNXzS1nU2oYd28LBkYHJa3K5q1CY0tVH2DEV2RGY4MS31Hv2Qnx43OgBuG4TBWpNesWSPvvPOOHDlyRIqLi+0WTVFMqI2LCjPUAfZajv1H5/Xo5z8IatO30tw2L1MdwzBKe35ntbxX26F+4CzMYKFI+sjqSsMwTuW5556TLVu26PulpaXS3t4uwaCZ9kxFzOvbuKi8tK9WU9izMxPUUIQmMHy7P72qUG6Yn33B9W4MWXAQy0uJGzUoMQzjdCKRiOTm5kpDQ4N+zNatRx55xG7VFMSE2rioYMP5483H5EBNh6atycotzk9WQxMMVgzDuHhgLYrF6Ngo+4477rBbPsUwoTYuOkTRpKxbugZ05/SC3CSLhg3jEvH5z39ennrqqVPEe926dXb/pxAm1IZhGNOcu+++W37605/q+7NmzZKKiorJviTjAjChNqYNjHJtqWiWk6092mx2xex0yU+x5hnDoF7t8/m0z+PRRx+VBx54wG7KFMKE2pg21qhPvHFUqlp6JT7g1ZGunFCc/OW1s6Ug1WZIDePJJ5+Ujo4Oue++++xmTDFMqI0pC9uzcFnDq/xoY5eK9OXFKTpHymrMQ/WduvjjTy8vnOxLNYyoY/Xq1XL77bfLQw89NNmXYpwDE2pjSoIf+L++VSm7TrSJ3xcjh2o7pSc8pDur3XR3VUuPFKYF5W/Wl+k2rmDA+5H3VhvGdGDZsmWyZ88eTYcPDAyYu1+UY/MxxpSEVZnspi5Kj1dLUYR798l2qWjs1OUfWKx09Q+Kzxsjj712ROvWfB1CvmFh9siyD8OYmRw/flzfDg4Oqmjv3r3bxDqKsd9WxpSkqatf09uILxBFp8b7pa6jX3dYk/bGrrSysVsO1Hbo11G3/vXuGnntoGMAYRgzlW3bto0K8969e2XVqlWTfUnGB2BCbUxJUuMD+oumPzykH7NZqygtXkozE2V2RoK6nq2alSbdA0O605qFIGzeSoj1aWc4EbhhzFTKysrk4MGDox/v2LHDRraiGBNqY0qyIDekm7MqmrrleHO3HGno0vrzV9bNkb+9aZ5alIbiqOzE6M5ql1ifRyPrwYgJtTGzQay/8IUvjH68dOlS6erqmtRrMs6MCbUxJUGU71lbLJ9amS/FGQm6Z/veq2bJmjnpo19TmBavC0FoJAPEmZT53MzE0ZS5Ycxkvv/970so5Gyx6+7ulqqqqsm+JOMMWNe3MW0ZigzLv+84KZuPNEn/ICnyGO0C/8LaEputNowRent7Zfbs2fLwww/Ll770JbsvUYgJtTHtxZq0OF3f8QGfLMoPSSjOP9mXZRhRS319vTQ2NsrixYsn+1KMEUyojQuivSesqeSMpIAKn2EY00uk8/Pz1XJ0165dWrc2Jh/7TWucF+GhiLy4t1bermiWnoEh7bq+cWG2rJ2TbvOXhjFNYF/10JAzScF6zObm5sm+JMOayYzzhZGmP+yv0/ezQ3HS2ReWX+44qfPKhmFMD77zne+I1+s0Wra0tMjjjz8+2ZdkmFAb58u2yhYJeD2SlRSnHdMsuugZGJR91e12Ew1jmoA3wT/+4z+Ofvz1r399NMI2Jg9LfRvnxcBQRLye9+eR3f+ozTjEMKYXLOno7OyU733ve2qE4kbYxuRhzWTGefG7PTXywq4aKR7x1qahrLlrQO69ukQuK06zu2gYhnGRsIjaOC+uLcuUqtZe2V/dLuGhYd1EdU1ZpiwtSLE7aBiGcRGxiNo4bwaHInKksUtHtGgoI7p2jf0NwzCMi4MJtWEYhmFEMeb1bRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRhRjAm1YRiGYUQxJtSGYRiGEcWYUBuGYRhGFGNCbRiGYRgSvfx/FT0BjIqwE9UAAAAASUVORK5CYII=\",\n \"text/plain\": [\n \"![](\\\"media/precision-recall.png\\\")
\\n\",\n \"\\n\",\n \"![](\\\"media/mts_data.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Example:** \\n\",\n \"- Consider the [uWave](https://ieeexplore.ieee.org/document/4912759) MTS dataset.\\n\",\n \"- Each MTS represents the $(x,y,z)$ measurements of an accelerometer wore when doing one of the following gestures.\\n\",\n \"- The dot is the starting point, the arrow the end point.\\n\",\n \"\\n\",\n \"![](\\\"media/UWaveGestureLibrary.jpg\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_uwave():\\n\",\n \" X, Y, _, _ = DataLoader().get_data('UWAVE')\\n\",\n \" _, axes = plt.subplots(3, 8, figsize=(15, 5), subplot_kw={'projection': '3d'})\\n\",\n \" for i in range(len(np.unique(Y))):\\n\",\n \" idx = np.where(Y == i+1)[0][:3]\\n\",\n \" for j, id in enumerate(idx):\\n\",\n \" axes[j,i].plot(X[id, :, 0], X[id, :, 1], X[id, :, 2], color=plt.cm.tab10(i))\\n\",\n \" axes[j,i].set_xticks(())\\n\",\n \" axes[j,i].set_yticks(())\\n\",\n \" axes[j,i].set_zticks(())\\n\",\n \" axes[j,i].spines[['right', 'left', 'top', 'bottom']].set_visible(False)\\n\",\n \" if j == 0:\\n\",\n \" axes[j,i].set_title(f\\\"Class {i+1}\\\")\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded UWAVE dataset.\\n\",\n \"Number of classes: 8\\n\",\n \"Data shapes:\\n\",\n \" Xtr: (200, 315, 3)\\n\",\n \" Ytr: (200, 1)\\n\",\n \" Xte: (428, 315, 3)\\n\",\n \" Yte: (428, 1)\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/DTW_idea_1.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- DTW disregards the exact timestamps at which the observations occur.\\n\",\n \"- DTW seeks for the *temporal alignment* (a matching between time indexes of the two time series) that minimizes Euclidean distance between the aligned series.\\n\",\n \"\\n\",\n \"![](\\\"media/DTW_idea_2.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### DTW algorithm\\n\",\n \"\\n\",\n \"- DTW solves the following optimization problem:\\n\",\n \"\\n\",\n \"$$\\\\text{DTW}(\\\\boldsymbol{x}, \\\\boldsymbol{y}) = \\\\min \\\\limits_{\\\\pi \\\\in \\\\mathcal{P}(\\\\boldsymbol{x}, \\\\boldsymbol{y})} \\\\left( \\\\sum \\\\limits_{(i,j) \\\\in \\\\pi} d(x(i), y(j)) \\\\right)$$\\n\",\n \"\\n\",\n \"- $\\\\pi$ is an *alignment path* of length $K$, i.e., a sequence of index pairs.\\n\",\n \"- $\\\\mathcal{P}(\\\\boldsymbol{x}, \\\\boldsymbol{y})$ is the set of all admissible paths.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"An admissible path should satisfy the following conditions:\\n\",\n \"\\n\",\n \"1. The beginning and the end of the two time series are matched together.\\n\",\n \" - $\\\\pi_{1} = (1,1)$\\n\",\n \" - $\\\\pi_{K} = (T_x, T_y)$\\n\",\n \" \\n\",\n \"![](\\\"media/start_end.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"2. The sequence is monotonically increasing in both $i$ and $j$ and all time series indexes should appear at least once.\\n\",\n \" - $i_{k-1} \\\\leq i_k \\\\leq i_{k-1}+1$\\n\",\n \" - $j_{k-1} \\\\leq j_k \\\\leq j_{k-1}+1$\\n\",\n \" \\n\",\n \"![](\\\"media/conditions.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The DTW path can be represented by a binary matrix $\\\\mathbf{P}_\\\\pi$ whose non-zero entries are those corresponding to a matching between time series elements: \\n\",\n \"\\n\",\n \"$$(\\\\mathbf{P}_\\\\pi)_{i,j} = \\\\begin{cases}1 &= \\\\text{if}\\\\; (i,j)\\\\in \\\\pi \\\\\\\\ 0 &= \\\\text{otherwise}\\\\end{cases}$$\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Using the matrix notation, DTW can be rewritten as \\n\",\n \"\\n\",\n \"$$\\\\text{DTW}(\\\\boldsymbol{x}, \\\\boldsymbol{y}) = \\\\min \\\\limits_{\\\\pi \\\\in \\\\mathcal{P}(\\\\boldsymbol{x}, \\\\boldsymbol{y})} \\\\langle \\\\mathbf{P}_\\\\pi, \\\\mathbf{D}_{\\\\boldsymbol{x}, \\\\boldsymbol{y}} \\\\rangle$$\\n\",\n \"\\n\",\n \"- where the $(i,j)^{th}$ element of $\\\\mathbf{D}_{\\\\boldsymbol{x}, \\\\boldsymbol{y}} \\\\in \\\\mathbb{R}^{T_x \\\\times T_y}$ stores the distance $d(x(i), y(j))$.\\n\",\n \"\\n\",\n \"![](\\\"media/matrix.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Finding the optimal path\\n\",\n \"\\n\",\n \"- First, we fill-up the cost matrix $\\\\mathbf{D}_{\\\\boldsymbol{x}, \\\\boldsymbol{y}}$.\\n\",\n \"\\n\",\n \"![](\\\"media/cost.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Then, we start traversing the matrix from the top-left corner $(1,1)$ to the bottom-right one $(T_x, T_y)$.\\n\",\n \"- Excluding the borders, at each step we have three options to decide where to go.\\n\",\n \"- Each path results in a different cost.\\n\",\n \"- The optimal path is the one with minimum cost... 🤔 But how to find it?\\n\",\n \"\\n\",\n \"![](\\\"media/path.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We need to compute the cost for each possible path.\\n\",\n \"- When $T_x=T_y$, the total number of paths is $\\\\mathcal{O}\\\\left(\\\\frac{(3+2\\\\sqrt{2})^{T_x}}{\\\\sqrt{T_x}}\\\\right)$.\\n\",\n \"- That is a big number: looking at all of them is intractable.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- There are many paths that share the same sections.\\n\",\n \"- To make the problem tractable, we must avoid recomputing the same paths over and over.\\n\",\n \"\\n\",\n \"![](\\\"media/redundant.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We can use *recursion*, a dynamic programming technique that breaks down a complex problem into simpler sub-problems.\\n\",\n \"- Each sub-problem is solved just once and the solution is stored in memory. \\n\",\n \"- When a sub-problem is encountered again, its solution is retrieved instead of being recomputed.\\n\",\n \"- This significantly reduces the number of computations cutting the redundancies.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The recursive algorithm has complexity $\\\\mathcal{O}(T_x T_y)$ and is formulated as follow:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"```python\\n\",\n \"def DTWDistance(x, y):\\n\",\n \" for i in range(len(x)):\\n\",\n \" for j in range(len(y)):\\n\",\n \" DTW[i, j] = d(x[i], y[j])\\n\",\n \" if i > 0 or j > 0:\\n\",\n \" DTW[i, j] += min(\\n\",\n \" DTW[i-1, j ] if i > 0 else inf,\\n\",\n \" DTW[i , j-1] if j > 0 else inf,\\n\",\n \" DTW[i-1, j-1] if (i > 0 and j > 0) else inf\\n\",\n \" )\\n\",\n \" return DTW[-1, -1]\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The idea is that each block $(i,j)$ recursively ask to its predecessors $(i-1, j)$, $(i, j-1)$, and $(i-1, j-1)$ the cost to reach them.\\n\",\n \"- The request is propagated back to the origin $(1,1)$ wich returns the first answer.\\n\",\n \"- The answer is then propagated forward to all the requesters, which update the answer with their own cost.\\n\",\n \"\\n\",\n \"![](\\\"media/recursion.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### DTW properties\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"- The example below show how the DTW changes when two curves are translated and stretched/squeezed at the same time.\\n\",\n \"\\n\",\n \"\\n\",\n \"![](\\\"media/warping.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Note that the distance influenced only by the \\\"stretching/squeezing\\\" part.\\n\",\n \"- In fact, the DTW is invariant to translation.\\n\",\n \"\\n\",\n \"![](\\\"media/warping_fix.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Note that is possible to add constraints that generate a cost if translation exceeds a certain limit.\\n\",\n \"\\n\",\n \"![](\\\"media/warping_constrained.gif\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### DTW example\\n\",\n \"\\n\",\n \"- Let's see DTW in action starting with a simple example.\\n\",\n \"- We generate two groups of time series using two different AR(1) processes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"T = 100 # Length of the time series\\n\",\n \"N = 30 # Time series per set\\n\",\n \"\\n\",\n \"# Generate the first set of time series\\n\",\n \"Y1 = np.zeros((T, N))\\n\",\n \"for i in range(N):\\n\",\n \" Y1[:,i] = sm.tsa.arma_generate_sample(ar=[1, -.9], ma=[1], nsample=T, scale=1) \\n\",\n \"\\n\",\n \"# Generate the second set of time series\\n\",\n \"Y2 = np.zeros((T, N))\\n\",\n \"for i in range(N):\\n\",\n \" Y2[:,i] = sm.tsa.arma_generate_sample(ar=[1, .9], ma=[1], nsample=T, scale=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Let's see how the mean and std of the two groups look like.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/dist_matrix.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Unfortunately, `dtw_ndim.distance_matrix_fast` does not compute distances across two different sets, meaning that it cannot compute **te-tr** explicitly.\\n\",\n \"- In our case, since the dataset is small, we can compute the whole distance matrix and keep only the parts we need.\\n\",\n \"- Note that some computations (**tr-te** and **te-te**) are *wasted*.\\n\",\n \"- If the computing resources are limited or the dataset is too large, it's better to iterate through the elements of the training and test set and compute only the distances that we actually need.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The kernel is a similarity matrix with elements in [0,1].\\n\",\n \"- We can obtain it with the following transformation. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/tck_scheme.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### GMM\\n\",\n \"\\n\",\n \"- A GMM assumes that all the data points are generated from a mixture of $C$ Gaussian distributions (components) with unknown parameters $\\\\{ \\\\mu_c, \\\\Sigma_c \\\\}_{c=1}^C$. \\n\",\n \"- GMMs are used for clustering.\\n\",\n \"- Each cluster is modeled by a Gaussian distribution. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"skip\"\n },\n \"tags\": [\n \"hide-input\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_gmm(X, n_components, ax=None):\\n\",\n \"\\n\",\n \" gmm = GaussianMixture(n_components=n_components, covariance_type=\\\"full\\\", random_state=0)\\n\",\n \" gmm.fit(X)\\n\",\n \"\\n\",\n \" if ax is None:\\n\",\n \" _, ax = plt.subplots(1,1,figsize=(6, 6))\\n\",\n \"\\n\",\n \" # Plot data\\n\",\n \" ax.scatter(X[:, 0], X[:, 1], c='gray', s=10, alpha=0.5, edgecolors='gray')\\n\",\n \" \\n\",\n \" # Plot ellipses\\n\",\n \" for n in range(gmm.n_components):\\n\",\n \" color = plt.cm.tab10(n)\\n\",\n \" covariances = gmm.covariances_[n][:2, :2]\\n\",\n \" v, w = np.linalg.eigh(covariances)\\n\",\n \" u = w[0] / np.linalg.norm(w[0])\\n\",\n \" angle = np.arctan2(u[1], u[0])\\n\",\n \" angle = 180 * angle / np.pi # convert to degrees\\n\",\n \" v = 2.0 * np.sqrt(2.0) * np.sqrt(v)\\n\",\n \" ell = mpl.patches.Ellipse(\\n\",\n \" gmm.means_[n, :2], v[0], v[1], angle=180 + angle, color=color\\n\",\n \" )\\n\",\n \" ell.set_clip_box(ax.bbox)\\n\",\n \" ell.set_alpha(0.5)\\n\",\n \" ax.add_artist(ell)\\n\",\n \" ax.spines[['right', 'left', 'top', 'bottom']].set_visible(False)\\n\",\n \" ax.set_xticks(())\\n\",\n \" ax.set_yticks(())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Create toy data\\n\",\n \"X, y = datasets.make_classification(n_samples=950, n_features=2, n_informative=2, \\n\",\n \" n_redundant=0, n_clusters_per_class=1, random_state=4, \\n\",\n \" n_classes=3, class_sep=1.5, flip_y=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"![](\\\"media/soft_clust.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### GMM for time series with missing data\\n\",\n \"\\n\",\n \"TCK modifies the standard GMM model in two ways.\\n\",\n \"\\n\",\n \"1. To handle *time series data*, the means of the GMM components become multi-variate time series.\\n\",\n \" - Standard GMM: $\\\\boldsymbol{\\\\mu}_c \\\\in \\\\mathbb{R}^V$.\\n\",\n \" - TCK GMM: $\\\\boldsymbol{\\\\mu}_c \\\\in \\\\mathbb{R}^{T \\\\times V}$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"2. To handle missing values, TCK puts priors on the GMM parameters and replaces the EM algorithm with Maximum a-posteriori (MAP) estimation.\\n\",\n \" - EM: $\\\\boldsymbol{\\\\hat \\\\mu}, \\\\boldsymbol{\\\\hat \\\\Sigma} = \\\\text{argmax}_{\\\\boldsymbol{\\\\mu}, \\\\boldsymbol{\\\\Sigma}} p(\\\\mathbf{X} | \\\\boldsymbol{\\\\mu}, \\\\boldsymbol{\\\\Sigma})$.\\n\",\n \" - MAP with priors: $\\\\boldsymbol{\\\\hat \\\\mu}, \\\\boldsymbol{\\\\hat \\\\Sigma} = \\\\text{argmax}_{\\\\boldsymbol{\\\\mu}, \\\\boldsymbol{\\\\Sigma}} p(\\\\mathbf{X} | \\\\boldsymbol{\\\\mu}, \\\\boldsymbol{\\\\Sigma}) p(\\\\boldsymbol{\\\\mu}, \\\\boldsymbol{\\\\Sigma})$\\n\",\n \"- The resulting means are smoother and the parameters are similar to the overall mean and covariance in clusters with few samples.\\n\",\n \"\\n\",\n \"![](\\\"media/map.png\\\")
\\n\",\n \"\\n\",\n \"![](\\\"media/ensemble.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The kernel matrix is obtained by combining the clustering results from the $G$ GMMs in the ensemble.\\n\",\n \"- Specifically, $k(\\\\boldsymbol{x}, \\\\boldsymbol{y})$ is proportional to how many times $\\\\boldsymbol{x}$ and $\\\\boldsymbol{y}$ are assigned to the same GMM:\\n\",\n \"\\n\",\n \"$$k(\\\\boldsymbol{x}, \\\\boldsymbol{y}) = \\\\sum \\\\limits_{g \\\\in G} \\\\Pi^{(\\\\boldsymbol{x})}(g)^\\\\top \\\\Pi^{(\\\\boldsymbol{y})}(g)$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Classification with TCK\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"- We load the same data as before.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded Japanese_Vowels dataset.\\n\",\n \"Number of classes: 9\\n\",\n \"Data shapes:\\n\",\n \" Xtr: (270, 29, 12)\\n\",\n \" Ytr: (270, 1)\\n\",\n \" Xte: (370, 29, 12)\\n\",\n \" Yte: (370, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"Xtr, Ytr, Xte, Yte = DataLoader().get_data('Japanese_Vowels')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- One of the main advantages of TCK is the ability in handling missing values.\\n\",\n \"- From the previous data, we remove 40% of the data by setting the values to `np.nan`.\\n\",\n \"- We fix the random seed so the example is reproducible.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"np.random.seed(1)\\n\",\n \"\\n\",\n \"mask_tr = np.random.choice([0, 1], size=Xtr.shape, p=[0.6, 0.4])\\n\",\n \"Xtr[mask_tr == 1] = np.nan\\n\",\n \"mask_te = np.random.choice([0, 1], size=Xte.shape, p=[0.6, 0.4])\\n\",\n \"Xte[mask_te == 1] = np.nan\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We create a TCK kernel with `G=30` GMMs, each with `C=10` components.\\n\",\n \" - In general, the higher the values of `G` the better the performance but the higher the computation time.\\n\",\n \" - `C` controls the model complexity. Too low $\\\\rightarrow$ underfit, too high $\\\\rightarrow$ overfit.\\n\",\n \"- Then, we fit TCK on the training data.\\n\",\n \"- With the current `tck` implementation and missing-data setup, `C=10` is more stable than the original `C=15` on the full dataset.\\n\",\n \"- As in the DTW case, we compute the two kernels $K_\\\\text{tr-tr}$ and $K_\\\\text{tr-te}$ to train and test our classifier.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The dataset contains missing data\\n\",\n \"\\n\",\n \"Training the TCK using the following parameters:\\n\",\n \"\\tC = 15, G = 30\\n\",\n \"\\tNumber of MTS for each GMM: 216 - 270 (80 - 100 percent)\\n\",\n \"\\tNumber of attributes sampled from [2, 11]\\n\",\n \"\\tLength of time segments sampled from [6, 23]\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"80f535d7d4b64a70b00e4985ea15fc86\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"Fitting GMMs: 0%| | 0/420 [00:00\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The RC framework consists of 4 modules:\\n\",\n \" 1. Reservoir module.\\n\",\n \" 2. Dimensionality reduction module.\\n\",\n \" 3. MTS representation module.\\n\",\n \" 4. Readout module.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Reservoir module\\n\",\n \"\\n\",\n \"- This module generates a sequence of Reservoir states from a given MTS $\\\\boldsymbol{x}$ as \\n\",\n \"\\n\",\n \"$$\\\\mathbf{h}(t) = \\\\sigma \\\\left(\\\\mathbf{W}_i \\\\boldsymbol{x}(t)+\\\\mathbf{W}_h \\\\mathbf{h}(t-1)\\\\right)$$\\n\",\n \"\\n\",\n \"- If we have $N$ MTS they can be processed in parallel by the Reservoir and generate the sequence of states \\n\",\n \"\\n\",\n \"$$\\\\{\\\\mathbf{H}(1), \\\\mathbf{H}(2), \\\\dots, \\\\mathbf{H}(T)\\\\}$$ \\n\",\n \"\\n\",\n \"- with $\\\\mathbf{H}(t) \\\\in \\\\mathbb{R}^{N \\\\times H}$. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- The Reservoir can operate in two modalities:\\n\",\n \" 1. Unidirectional.\\n\",\n \" 2. Bidirectional.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Unidirectional Reservoir**\\n\",\n \"- This is the same Reservoir we saw in the previous lectures.\\n\",\n \" - Input: MTS data `X` of shape `[N, T, V]`.\\n\",\n \" - Output: a sequence of states `H` of shape `[N, T, H]`.\\n\",\n \" \\n\",\n \"![](\\\"media/unidir.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Bidirectional Reservoir**\\n\",\n \"- This Reservoir processes the time series also backwards.\\n\",\n \"- This allows to retrieve context from both past and future data points capture more complex and longer temporal dependencies.\\n\",\n \" - Input: MTS data `X` of shape `[N, T, V]`.\\n\",\n \" - Output: a sequence of states `H` of shape `[N, T, 2*H]`.\\n\",\n \"\\n\",\n \"![](\\\"media/bidir.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"```{attention}\\n\",\n \"- A Bidirectional Reservoir is not *causal*. \\n\",\n \"- It cannot be used for forecasting but is suitable for classification and clustering.\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded Japanese_Vowels dataset.\\n\",\n \"Number of classes: 9\\n\",\n \"Data shapes:\\n\",\n \" Xtr: (270, 29, 12)\\n\",\n \" Ytr: (270, 1)\\n\",\n \" Xte: (370, 29, 12)\\n\",\n \" Yte: (370, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"Xtr, Ytr, Xte, Yte = DataLoader().get_data('Japanese_Vowels')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Unidir\\n\",\n \" H: (270, 29, 300)\\n\",\n \"Bidir\\n\",\n \" H: (270, 29, 600)\\n\"\n ]\n }\n ],\n \"source\": [\n \"H_uni = Reservoir(n_internal_units=300).get_states(Xtr, bidir=False)\\n\",\n \"print(f\\\"Unidir\\\\n H: {H_uni.shape}\\\")\\n\",\n \"\\n\",\n \"H_bi = Reservoir(n_internal_units=300).get_states(Xtr, bidir=True)\\n\",\n \"print(f\\\"Bidir\\\\n H: {H_bi.shape}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Dimensionality reduction module (optional)\\n\",\n \"\\n\",\n \"- This module reduces the dimensionality of the Reservor states from `[N,T,H]` (or `[N,T,2*H]`) to `[N,T,R]`.\\n\",\n \"- This module is optional, i.e., dimensionality reduction can be skipped.\\n\",\n \"- However, it drammatically speed up computations especially when using more advanced representations (more details soon).\\n\",\n \"- Dimensionality reduction can be implemented by \\n\",\n \" 1. Standard PCA\\n\",\n \" 2. Tensor PCA\\n\",\n \"\\n\",\n \"![](\\\"media/dim_red.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Standard PCA**\\n\",\n \"- Standard PCA only works on uni-dimensional data.\\n\",\n \"- Reshape `H` from `[N,T,H]` to `[N*T,H]`.\\n\",\n \"- Apply PCA and keep the first `R` components.\\n\",\n \"- Reshape `[N*T,R]` back to `[N,T,R]`.\\n\",\n \"\\n\",\n \"```{warning}\\n\",\n \"The sample and the time steps are mixed up and modes of variation in time within individual samples are ingored.\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Tensor PCA**\\n\",\n \"\\n\",\n \"- Compute the following covariance matrix \\n\",\n \"\\n\",\n \"$$\\\\mathbf{S} = \\\\frac{1}{N-1} \\\\sum \\\\limits_{n=1}^N (\\\\mathbf{H}_n - \\\\mathbf{\\\\bar H})^\\\\top (\\\\mathbf{H}_n - \\\\mathbf{\\\\bar H}) \\\\in \\\\mathbb{R}^{H \\\\times H}$$\\n\",\n \"\\n\",\n \"- where $\\\\mathbf{H}_n \\\\in \\\\mathbb{R}^{T \\\\times H}$ is obtained as `H[n,:,:]` and $\\\\mathbf{\\\\bar H} = \\\\frac{1}{N} \\\\sum \\\\limits_{n=1}^N \\\\mathbf{H}_n \\\\in \\\\mathbb{R}^{T \\\\times H}$.\\n\",\n \"- This allows to compute the variations across the Reservoir dimension by keeping sample- and time-dimension separated.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Then, we take the first $R$ eigenvectors of $\\\\mathbf{S}$: $\\\\mathbf{D} = [\\\\mathbf{u}_1, \\\\mathbf{u}_2, \\\\dots, \\\\mathbf{u}_R]\\\\in \\\\mathbb{R}^{H \\\\times R}$. \\n\",\n \"- We obtain the reduced states with the following matrix multiplication: $\\\\mathbf{\\\\hat H}_n = \\\\mathbf{H}_n \\\\mathbf{D}$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"H_red: (270, 29, 75)\\n\"\n ]\n }\n ],\n \"source\": [\n \"H_red = tensorPCA(n_components=75).fit_transform(H_bi)\\n\",\n \"print(f\\\"H_red: {H_red.shape}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Representation module\\n\",\n \"\\n\",\n \"- This module is responsible of transforming the sequence of Reservoir states into a vectorial representation $\\\\mathbf{r}_\\\\mathbf{X}$.\\n\",\n \"- The Reservoir extracts and separates the dynamical features.\\n\",\n \"- In addition, it keeps a memory of all the past input.\\n\",\n \"- Therefore, in some cases, is sufficient to keep only the *last Reservoir state* to represent the whole MTS.\\n\",\n \"\\n\",\n \"![](\\\"media/last_state.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"rx_last: (270, 75)\\n\"\n ]\n }\n ],\n \"source\": [\n \"rx_last = H_red[:,-1,:]\\n\",\n \"print(f\\\"rx_last: {rx_last.shape}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- In practice, even when using a bidirectional Reservoir, this simple representation might fail to capture important information in the middle of the time series.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Output model space**\\n\",\n \"\\n\",\n \"- A more effective representation is obtained as follows.\\n\",\n \" 1. Train a linear readout to predict the MTS one step-ahead \\n\",\n \"\\n\",\n \" $$\\\\boldsymbol{x}(t+1) = \\\\mathbf{h}(t)\\\\mathbf{W}_o + \\\\mathbf{w}_o$$\\n\",\n \"\\n\",\n \" 2. The parameters of the linear model $\\\\boldsymbol{\\\\theta}_o = [\\\\text{vec}(\\\\mathbf{W}_o); \\\\mathbf{w}_o] \\\\in \\\\mathbb{R}^{V(R+1)}$ become the $\\\\mathbf{r}_\\\\mathbf{X}$.\\n\",\n \"\\n\",\n \"![](\\\"media/output_ms.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"rx_out: (270, 1824)\\n\"\n ]\n }\n ],\n \"source\": [\n \"out_pred = Ridge(alpha=1.0)\\n\",\n \"\\n\",\n \"# If we use a bidirectional Reservoir we also need to predict the time series backwards\\n\",\n \"X = np.concatenate((Xtr, Xtr[:, ::-1, :]), axis=2) \\n\",\n \"\\n\",\n \"coeff, biases = [], []\\n\",\n \"for i in range(X.shape[0]):\\n\",\n \" out_pred.fit(H_red[i, 0:-1, :], X[i, 1:, :])\\n\",\n \" coeff.append(out_pred.coef_.ravel())\\n\",\n \" biases.append(out_pred.intercept_.ravel())\\n\",\n \"rx_out = np.concatenate((np.vstack(coeff), np.vstack(biases)), axis=1)\\n\",\n \"\\n\",\n \"print(f\\\"rx_out: {rx_out.shape}\\\") # [N, 2*V*(R+1)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**Reservoir model space**\\n\",\n \"\\n\",\n \"- A similar approach is to use the coefficients of a linear model that predicts the next state of the Reservoir \\n\",\n \"\\n\",\n \"$$\\\\mathbf{h}(t+1) = \\\\mathbf{h}(t)\\\\mathbf{W}_h + \\\\mathbf{w}_h$$\\n\",\n \"\\n\",\n \"- The MTS representation $\\\\mathbf{r}_\\\\mathbf{X}$ becomes $\\\\boldsymbol{\\\\theta}_h = [\\\\text{vec}(\\\\mathbf{W}_h); \\\\mathbf{w}_h] \\\\in \\\\mathbb{R}^{R(R+1)}$.\\n\",\n \"\\n\",\n \"![](\\\"media/reservoir_ms.png\\\")
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"rx_res: (270, 5700)\\n\"\n ]\n }\n ],\n \"source\": [\n \"res_pred = Ridge(alpha=1.0)\\n\",\n \"\\n\",\n \"coeff, biases = [], []\\n\",\n \"for i in range(H_red.shape[0]):\\n\",\n \" res_pred.fit(H_red[i, 0:-1, :], H_red[i, 1:, :])\\n\",\n \" coeff.append(res_pred.coef_.ravel())\\n\",\n \" biases.append(res_pred.intercept_.ravel())\\n\",\n \"rx_res = np.concatenate((np.vstack(coeff), np.vstack(biases)), axis=1)\\n\",\n \"\\n\",\n \"print(f\\\"rx_res: {rx_res.shape}\\\") # [N, R*(R+1)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- In principle, the Reservoir model space $\\\\boldsymbol{\\\\theta}_h$ provides a better representation than the Output model space $\\\\boldsymbol{\\\\theta}_o$. Why 🤔?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- The Reservoir generates a large pool of dynamics but only few are needed to predict the input at a specific forecast horizon, i.e., 1 if we predict $\\\\boldsymbol{x}(t+1)$.\\n\",\n \"- Not being useful for the task, the other dynamics are discarded even if they are still useful to characterize the MTS.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- On the other hand, to predict the next Reservoir state $\\\\mathbf{h}(t+1)$ is necessary to consider *all* Reservoir dynamics.\\n\",\n \"- This makes $\\\\boldsymbol{\\\\theta}_h$ a more powerful representation as it fully characterizes the MTS.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"#### Readout module\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"- The readout module is responsible to classify or cluster the MTS representations.\\n\",\n \"- Being each representation $\\\\mathbf{r}_\\\\mathbf{X}$ a vector, any standard classifier for vectorial data can be used.\\n\",\n \"- Similarly, we can use standard (dis)similarity measures for vectorial data.\\n\",\n \"- For example, clustering can be done using the Linkage aglorithm on the Euclidean distances between MTS representations.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Classification with RC embeddings\\n\",\n \"\\n\",\n \"- We can use the high-level function `RC_model` to perform the classification.\\n\",\n \"- The function takes as input the hyperparameters to configure:\\n\",\n \" 1. the Reservoir module,\\n\",\n \" 2. the dimensionality reduction module,\\n\",\n \" 3. the representation module,\\n\",\n \" 4. the readout module.\\n\",\n \"- To keep things clean, let's store the hyperparameters in a Python dictionary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"config = {}\\n\",\n \"\\n\",\n \"# Hyperarameters of the reservoir\\n\",\n \"config['n_internal_units'] = 450 # size of the reservoir\\n\",\n \"config['spectral_radius'] = 0.9 # largest eigenvalue of the reservoir\\n\",\n \"config['leak'] = None # amount of leakage in the reservoir state update (None or 1.0 --> no leakage)\\n\",\n \"config['connectivity'] = 0.25 # percentage of nonzero connections in the reservoir\\n\",\n \"config['input_scaling'] = 0.1 # scaling of the input weights\\n\",\n \"config['noise_level'] = 0.01 # noise in the reservoir state update\\n\",\n \"config['n_drop'] = 5 # transient states to be dropped\\n\",\n \"config['bidir'] = True # if True, use bidirectional reservoir\\n\",\n \"config['circle'] = False # use reservoir with circle topology\\n\",\n \"\\n\",\n \"# Dimensionality reduction hyperparameters\\n\",\n \"config['dimred_method'] = 'tenpca' # options: {None (no dimensionality reduction), 'pca', 'tenpca'}\\n\",\n \"config['n_dim'] = 75 # number of resulting dimensions after the dimensionality reduction procedure\\n\",\n \"\\n\",\n \"# Type of MTS representation\\n\",\n \"config['mts_rep'] = 'reservoir' # MTS representation: {'last', 'mean', 'output', 'reservoir'}\\n\",\n \"config['w_ridge_embedding'] = 10.0 # regularization parameter of the ridge regression\\n\",\n \"\\n\",\n \"# Type of readout\\n\",\n \"config['readout_type'] = 'lin' # readout used for classification: {'lin', 'mlp', 'svm'}\\n\",\n \"config['w_ridge'] = 5.0 # regularization of the ridge regression readout\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We create a RC classifier by passing the configuration parameters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"classifier = RC_model(**config)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We load again the data (good practice to be sure of getting clean data).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded Japanese_Vowels dataset.\\n\",\n \"Number of classes: 9\\n\",\n \"Data shapes:\\n\",\n \" Xtr: (270, 29, 12)\\n\",\n \" Ytr: (270, 1)\\n\",\n \" Xte: (370, 29, 12)\\n\",\n \" Yte: (370, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"Xtr, Ytr, Xte, Yte = DataLoader().get_data('Japanese_Vowels')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- The RC model expects class labels to be one-hot-encoded, e.g.\\n\",\n \"- $1 \\\\rightarrow [1, 0, 0, 0 \\\\dots, 0]$, $2 \\\\rightarrow [0, 1, 0, 0 \\\\dots, 0]$, $3 \\\\rightarrow [0, 0, 1, 0 \\\\dots, 0]$, etc..\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# One-hot encoding for labels\\n\",\n \"onehot_encoder = OneHotEncoder(sparse_output=False)\\n\",\n \"Ytr = onehot_encoder.fit_transform(Ytr)\\n\",\n \"Yte = onehot_encoder.transform(Yte)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training completed in 0.01 min\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Train the model\\n\",\n \"tr_time = classifier.fit(Xtr, Ytr) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy = 0.981, F1-score = 0.981\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Compute predictions on test data\\n\",\n \"pred_class = classifier.predict(Xte) \\n\",\n \"accuracy, f1 = compute_test_scores(pred_class, Yte)\\n\",\n \"print(f\\\"Accuracy = {accuracy:.3f}, F1-score = {f1:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"run_control\": {\n \"marked\": false\n },\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"- Note how this approach is significantly faster than the other we have seen so far.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Clustering with RC embeddings\\n\",\n \"\\n\",\n \"- We will perform the following steps:\\n\",\n \"\\n\",\n \" 1. Generate the vectorial representations for all MTS.\\n\",\n \" 2. Compute a dissimilarity matrix.\\n\",\n \" 3. Perform clustering with the distance-based HC algorithm Linkage.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Since we are doing clustering, we do not need the train/test split.\\n\",\n \"- We concatenate the data from both sets together.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded Japanese_Vowels dataset.\\n\",\n \"Number of classes: 9\\n\",\n \"Data shapes:\\n\",\n \" Xtr: (270, 29, 12)\\n\",\n \" Ytr: (270, 1)\\n\",\n \" Xte: (370, 29, 12)\\n\",\n \" Yte: (370, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"Xtr, Ytr, Xte, Yte = DataLoader().get_data('Japanese_Vowels')\\n\",\n \"\\n\",\n \"X = np.concatenate((Xtr, Xte), axis=0)\\n\",\n \"Y = np.concatenate((Ytr, Yte), axis=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- We can re-use the same `RC_model` as before.\\n\",\n \"- The only difference is that we do not want to apply the readout module.\\n\",\n \"- Instead, it should return the MTS representations.\\n\",\n \"- This is achieved by setting `'readout_type'`to `None`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"config['readout_type'] = None # We update this entry from the previous config dict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {\n \"run_control\": {\n \"marked\": false\n },\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training completed in 0.02 min\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Instantiate the RC model\\n\",\n \"rcm = RC_model(**config)\\n\",\n \"\\n\",\n \"# Generate representations of the input MTS\\n\",\n \"rcm.fit(X)\\n\",\n \"mts_representations = rcm.input_repr\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- At this point, we compute the distance matrix using standard metrics for vectorial data.\\n\",\n \"- For example, we can use the Euclidean or a Cosine distance.\\n\",\n \"- The latter is often preferred in high-dimensional spaces because Euclidean distance can become inflated and less meaningful (\\\"curse of dimensionality\\\").\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Found 8 clusters\\n\",\n \"Normalized Mutual Information (v-score): 0.835\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Compute Dissimilarity matrix\\n\",\n \"Dist = cosine_distances(mts_representations)\\n\",\n \"distArray = ssd.squareform(Dist)\\n\",\n \"\\n\",\n \"# Hierarchical clustering\\n\",\n \"Z = linkage(distArray, 'ward')\\n\",\n \"clust = fcluster(Z, t=4.0, criterion=\\\"distance\\\")\\n\",\n \"print(f\\\"Found {len(np.unique(clust))} clusters\\\")\\n\",\n \"\\n\",\n \"# Evaluate the agreement between class and cluster labels\\n\",\n \"nmi = v_measure_score(Y[:,0], clust)\\n\",\n \"print(f\\\"Normalized Mutual Information (v-score): {nmi:.3f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Summary\\n\",\n \"\\n\",\n \"This is what we covered in this lecture.\\n\",\n \"- We first introduced the problem of classification and clustering for vectorial data and the importance of choosing a proper measure for computing (dis)similarities among samples.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Then, we introduced three approaches to compute (dis)similarities across multivariate time series.\\n\",\n \" 1. DTW, an alignment based-metric.\\n\",\n \" 2. TCK, a kernel similarity.\\n\",\n \" 3. RC embeddings, an approach to embed time series into vectors.\\n\",\n \"- These (dis)similarity measures are the cornerstone in time series classification and clustering.\\n\",\n \"- Once computed, we saw how they can be easily plugged into the same classification and clustering method we saw for vectorial data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"We conclude by highlighting the main pros and cons of the three approaches to compute MTS (dis)similarity.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"cell_style\": \"center\",\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"**DTW**\\n\",\n \"\\n\",\n \"- ✅ In most cases, works well with default hyperparameters.\\n\",\n \"- ✅ Invariant to translations in time.\\n\",\n \"- ❌ Slow.\\n\",\n \"- ❌ Does not account for complex dynamical features.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"cell_style\": \"center\",\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"**TCK**\\n\",\n \"\\n\",\n \"- ✅ Hyperparameters are easy to set.\\n\",\n \"- ✅ Handles missing data.\\n\",\n \"- ❌ Very slow.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"fragment\"\n }\n },\n \"source\": [\n \"**RC-embedding**\\n\",\n \"\\n\",\n \"- ✅ Fast.\\n\",\n \"- ✅ Captures complex dynamical features.\\n\",\n \"- ❌ Many hyperparameters to set.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"- Each approach can achieve better or worse performance depending on the data and the problem at hand.\\n\",\n \"- Selecting the optimal (dis)similarity measure, classification/clustering algorithm, and hyperparameters is often a difficult procedure.\\n\",\n \"- It requires experience and should be performed with systematic approaches such as cross-validation.\\n\",\n \"\\n\",\n \"---\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Exercises\\n\",\n \"\\n\",\n \"We will consider the [Libras](https://www.timeseriesclassification.com/description.php?Dataset=Libras) dataset. The dataset contains 15 classes of 24 instances each. Each class references to a hand movement type in LIBRAS (Portuguese name 'LÍngua BRAsileira de Sinais', oficial brazilian signal language).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded Libras dataset.\\n\",\n \"Number of classes: 15\\n\",\n \"Data shapes:\\n\",\n \" Xtr: (180, 45, 2)\\n\",\n \" Ytr: (180, 1)\\n\",\n \" Xte: (180, 45, 2)\\n\",\n \" Yte: (180, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"Xtr, Ytr, Xte, Yte = DataLoader().get_data('Libras')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Exercise 1\\n\",\n \"\\n\",\n \"1. Compute the DTW distance matrix.\\n\",\n \"2. Obtain a similarity matrix from it.\\n\",\n \"3. Plot the two matrices. Comment about the structure you see (remember to sort the elements class-wise).\\n\",\n \"4. Perform classification with SVC and $k$-NN classifiers and report:\\n\",\n \" - the training and test times,\\n\",\n \" - the accuracy and the F1 score on the test set.\\n\",\n \"5. Perform hierarchical clustering using the Linkage Ward algorithm.\\n\",\n \"6. Plot a dendrogram and inspect it to select the optimal threshold to generate the clustering partition. Report the NMI for the partition you found.\\n\",\n \"7. Perform dimensionality reduction with KernelPCA. Plot the results in a 2-dimensional plot.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Exercise 2\\n\",\n \"\\n\",\n \"1. Add 40% of missing values to the training and test data.\\n\",\n \"2. Compute the TCK kernel.\\n\",\n \"3. Compute a dissimilarity matrix from the kernel (try to do the complementary of what you did to obtain a similarity from DTW). \\n\",\n \"\\n\",\n \"Repeat points 4-7 from the previous exercise.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"subslide\"\n }\n },\n \"source\": [\n \"### Exercise 3\\n\",\n \"\\n\",\n \"1. Compute the RC embeddings.\\n\",\n \"2. Obtain a similarity and a dissimilarity matrix from the MTS representations.\\n\",\n \"\\n\",\n \"Repeat points 4-7 from the previous exercises.\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Slideshow\",\n \"kernelspec\": {\n \"display_name\": \"tsa-course (3.12.13)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.13\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"title_cell\": \"Table of Contents\",\n \"title_sidebar\": \"Contents\",\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": true,\n \"toc_window_display\": false\n },\n \"varInspector\": {\n \"cols\": {\n \"lenName\": 16,\n \"lenType\": 16,\n \"lenVar\": 40\n },\n \"kernels_config\": {\n \"python\": {\n \"delete_cmd_postfix\": \"\",\n \"delete_cmd_prefix\": \"del \",\n \"library\": \"var_list.py\",\n \"varRefreshCmd\": \"print(var_dic_list())\"\n },\n \"r\": {\n \"delete_cmd_postfix\": \") \",\n \"delete_cmd_prefix\": \"rm(\",\n \"library\": \"var_list.r\",\n \"varRefreshCmd\": \"cat(var_dic_list()) \"\n }\n },\n \"types_to_exclude\": [\n \"module\",\n \"function\",\n \"builtin_function_or_method\",\n \"instance\",\n \"_Feature\"\n ],\n \"window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
},
{
"path": "pyproject.toml",
"content": "[build-system]\nrequires = [\"hatchling\"]\nbuild-backend = \"hatchling.build\"\n\n[project]\nname = \"tsa-course\"\nversion = \"0.3.1\"\ndescription = \"A collection of scripts and functions used in the course 'Time Series Analysis with Python'\"\nreadme = \"README.md\"\nlicense = { text = \"MIT\" }\nauthors = [{ name = \"Filippo Maria Bianchi\", email = \"filippombianchi@gmail.com\" }]\nrequires-python = \">=3.10\"\nclassifiers = [\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3 :: Only\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n]\ndependencies = [\n \"numpy>=1.19.5\",\n \"matplotlib\",\n \"scipy\",\n \"tqdm\",\n]\n\n[project.optional-dependencies]\nnotebooks = [\n \"statsmodels\",\n \"pandas\",\n \"pandas-datareader\",\n \"seaborn\",\n \"scikit-learn\",\n # RISE still imports pkg_resources, which is no longer shipped by setuptools 81+.\n \"setuptools<81\",\n \"notebook<7\",\n \"rise\",\n \"pmdarima\",\n \"opencv-python-headless\",\n \"prophet\",\n \"dtaidistance\",\n \"plotly\",\n \"ipywidgets\",\n \"yfinance\",\n \"reservoir-computing\",\n \"tck\",\n]\n\n[dependency-groups]\ndocs = [\n \"jupyter-book<2\",\n]\ndeploy = [\n \"ghp-import\",\n]\n\n[project.urls]\nDocumentation = \"https://filippomb.github.io/python-time-series-handbook\"\n\"Source Code\" = \"https://github.com/FilippoMB/python-time-series-handbook\"\n\n[tool.hatch.build.targets.sdist]\nonly-include = [\"tsa_course\"]\n\n[tool.hatch.build.targets.wheel]\npackages = [\"tsa_course\"]\n"
},
{
"path": "scripts/sync_user_environment.py",
"content": "#!/usr/bin/env python3\n\nfrom __future__ import annotations\n\nimport argparse\nimport re\nimport sys\nfrom pathlib import Path\n\ntry:\n import tomllib\nexcept ModuleNotFoundError: # pragma: no cover - Python < 3.11 fallback\n import tomli as tomllib # type: ignore[no-redef]\n\n\nROOT = Path(__file__).resolve().parents[1]\nLOCK_PATH = ROOT / \"uv.lock\"\nENV_PATH = ROOT / \"environment.yml\"\nTARGET_PYTHON = \"3.12\"\n\nCONDA_PACKAGES = [\n \"numpy\",\n \"matplotlib\",\n \"scipy\",\n \"tqdm\",\n \"statsmodels\",\n \"pandas\",\n \"pandas-datareader\",\n \"seaborn\",\n \"scikit-learn\",\n # Keep pkg_resources available for RISE in the classic notebook stack.\n \"setuptools\",\n \"notebook\",\n \"rise\",\n \"dtaidistance\",\n \"plotly\",\n \"ipywidgets\",\n]\n\nPIP_PACKAGES = [\n \"opencv-python-headless\",\n \"pmdarima\",\n \"prophet\",\n \"reservoir-computing\",\n \"tck\",\n \"yfinance\",\n]\n\nMARKER_RE = re.compile(r\"python_full_version\\s*([<>=!]+)\\s*'([^']+)'\")\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser(\n description=\"Generate or validate the user-facing Conda environment.\"\n )\n parser.add_argument(\n \"--check\",\n action=\"store_true\",\n help=\"Fail if environment.yml does not match the generated content.\",\n )\n return parser.parse_args()\n\n\ndef parse_version(version: str) -> tuple[int, ...]:\n return tuple(int(part) for part in version.split(\".\"))\n\n\ndef marker_matches(marker: str | None, target_python: str) -> bool:\n if not marker:\n return True\n\n match = MARKER_RE.fullmatch(marker.strip())\n if not match:\n raise ValueError(f\"Unsupported marker in uv.lock: {marker}\")\n\n operator, raw_version = match.groups()\n current = parse_version(target_python)\n target = parse_version(raw_version)\n\n if operator == \"<\":\n return current < target\n if operator == \"<=\":\n return current <= target\n if operator == \">\":\n return current > target\n if operator == \">=\":\n return current >= target\n if operator == \"==\":\n return current == target\n if operator == \"!=\":\n return current != target\n\n raise ValueError(f\"Unsupported operator in marker: {marker}\")\n\n\ndef load_lock_data() -> dict:\n with LOCK_PATH.open(\"rb\") as lock_file:\n return tomllib.load(lock_file)\n\n\ndef get_package_record(lock_data: dict, name: str) -> dict:\n matches = [package for package in lock_data[\"package\"] if package[\"name\"] == name]\n if not matches:\n raise KeyError(f\"Package {name!r} not found in {LOCK_PATH.name}\")\n if len(matches) > 1:\n versions = sorted({package[\"version\"] for package in matches})\n raise ValueError(\n f\"Package {name!r} has multiple versions in {LOCK_PATH.name}: {versions}. \"\n \"Add explicit handling in scripts/sync_user_environment.py.\"\n )\n return matches[0]\n\n\ndef get_tsa_course_record(lock_data: dict) -> dict:\n for package in lock_data[\"package\"]:\n if package[\"name\"] == \"tsa-course\":\n return package\n raise KeyError(\"Package 'tsa-course' not found in uv.lock\")\n\n\ndef select_dependency_entries(entries: list[dict], target_python: str) -> list[dict]:\n selected: dict[str, dict] = {}\n for entry in entries:\n if not marker_matches(entry.get(\"marker\"), target_python):\n continue\n selected[entry[\"name\"]] = entry\n return list(selected.values())\n\n\ndef resolve_locked_version(lock_data: dict, dependency: dict, target_python: str) -> str:\n if \"version\" in dependency and marker_matches(dependency.get(\"marker\"), target_python):\n return dependency[\"version\"]\n return get_package_record(lock_data, dependency[\"name\"])[\"version\"]\n\n\ndef collect_expected_versions(lock_data: dict, target_python: str) -> dict[str, str]:\n tsa_course = get_tsa_course_record(lock_data)\n base_dependencies = select_dependency_entries(tsa_course[\"dependencies\"], target_python)\n notebook_dependencies = select_dependency_entries(\n tsa_course[\"optional-dependencies\"][\"notebooks\"], target_python\n )\n\n direct_dependencies = {entry[\"name\"]: entry for entry in base_dependencies + notebook_dependencies}\n expected_names = set(direct_dependencies)\n configured_names = set(CONDA_PACKAGES) | set(PIP_PACKAGES)\n if expected_names != configured_names:\n missing = sorted(expected_names - configured_names)\n extra = sorted(configured_names - expected_names)\n raise ValueError(\n \"The configured user environment package split is out of date.\\n\"\n f\"Missing from script: {missing}\\n\"\n f\"Unexpected in script: {extra}\"\n )\n\n return {\n name: resolve_locked_version(lock_data, dependency, target_python)\n for name, dependency in direct_dependencies.items()\n }\n\n\ndef render_environment(expected_versions: dict[str, str]) -> str:\n lines = [\n \"# User notebook environment for the course.\",\n \"name: tsa-course\",\n \"channels:\",\n \" - conda-forge\",\n \" - nodefaults\",\n \"dependencies:\",\n f\" - python={TARGET_PYTHON}\",\n \" - pip\",\n ]\n\n for package_name in CONDA_PACKAGES:\n lines.append(f\" - {package_name}={expected_versions[package_name]}\")\n\n lines.extend(\n [\n \" - pip:\",\n ]\n )\n for package_name in PIP_PACKAGES:\n lines.append(f\" - {package_name}=={expected_versions[package_name]}\")\n lines.append(\" - -e .\")\n lines.append(\"\")\n\n return \"\\n\".join(lines)\n\n\ndef main() -> int:\n args = parse_args()\n expected_versions = collect_expected_versions(load_lock_data(), TARGET_PYTHON)\n rendered = render_environment(expected_versions)\n\n if args.check:\n current = ENV_PATH.read_text() if ENV_PATH.exists() else \"\"\n if current != rendered:\n sys.stderr.write(\n \"environment.yml is out of sync with uv.lock.\\n\"\n \"Run `uv run --no-sync python scripts/sync_user_environment.py`.\\n\"\n )\n return 1\n return 0\n\n ENV_PATH.write_text(rendered)\n print(f\"Updated {ENV_PATH.relative_to(ROOT)}\")\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\n"
},
{
"path": "tsa_course/__init__.py",
"content": ""
},
{
"path": "tsa_course/lecture1.py",
"content": "import numpy as np\nfrom scipy.fft import fft\n\ndef fft_analysis(signal):\n \"\"\"\n Perform a Fourier analysis on a time series.\n\n Parameters\n ----------\n signal : array-like\n The time series to analyze.\n\n Returns\n -------\n dominant_period : float\n The dominant period of the time series.\n positive_frequencies : array-like\n The positive frequencies.\n magnitudes : array-like\n The magnitudes of the positive frequencies.\n \"\"\"\n \n # Linear detrending\n slope, intercept = np.polyfit(np.arange(len(signal)), signal, 1)\n trend = np.arange(len(signal)) * slope + intercept \n detrended = signal - trend \n \n fft_values = fft(detrended)\n frequencies = np.fft.fftfreq(len(fft_values))\n\n # Remove negative frequencies and sort\n positive_frequencies = frequencies[frequencies > 0]\n magnitudes = np.abs(fft_values)[frequencies > 0]\n\n # Identify dominant frequency\n dominant_frequency = positive_frequencies[np.argmax(magnitudes)]\n\n # Convert frequency to period (e.g., days, weeks, months, etc.)\n dominant_period = 1 / dominant_frequency\n \n return dominant_period, positive_frequencies, magnitudes"
},
{
"path": "tsa_course/lecture11.py",
"content": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import solve_ivp\nfrom scipy.signal import find_peaks\nfrom tqdm.notebook import tqdm\n\ndef _runge_kutta_4th_order(func, initial_value, start_time, end_time, params, stimulus=None):\n \"\"\"\n Compute the integral of an ODE using the fourth-order, 4-step Runge-Kutta method.\n\n Parameters:\n ----------\n func : function\n ODE function. Must take arguments like func(t, x, p) where x and t are\n the state and time *now*, and p is a tuple of parameters. If there are\n no model parameters, p should be set to the empty tuple.\n initial_value : array_like\n Initial value for calculation.\n start_time : float\n Initial time for calculation.\n end_time : float\n Final time for calculation.\n params : tuple, optional\n Tuple of model parameters for func.\n stimulus : function or array_like, optional\n Stimulus to be applied to the system. If stimulus is a function, it will be evaluated at\n start_time, (start_time+end_time)/2, and end_time. If stimulus is an array, it should contain the\n stimulus values at start_time, (start_time+end_time)/2, and end_time.\n\n Returns:\n -------\n step : array_like\n Approximation to the integral.\n\n Notes:\n ------\n This function uses the fourth-order, 4-step Runge-Kutta method to numerically compute the integral of an ODE.\n\n Example usage:\n --------------\n >>> def f(t, x, p):\n ... return x * p[0] + p[1]\n >>> initial_value = 1.0\n >>> start_time = 0.0\n >>> end_time = 1.0\n >>> params = (2.0, 1.0)\n >>> step = RK4(f, initial_value, start_time, end_time, params)\n \"\"\"\n midpoint_time = (start_time + end_time) / 2.0\n time_interval = end_time - start_time\n\n if stimulus is None:\n params_at_start = params_at_mid = params_at_end = params\n else:\n try:\n # test if stimulus is a function\n stimulus_at_start = stimulus(start_time)\n stimulus_at_start, stimulus_at_mid, stimulus_at_end = (stimulus, stimulus, stimulus)\n except TypeError:\n # otherwise assume stimulus is an array\n stimulus_at_start, stimulus_at_mid, stimulus_at_end = stimulus\n params_at_start = (params, stimulus_at_start)\n params_at_mid = (params, stimulus_at_mid)\n params_at_end = (params, stimulus_at_end)\n\n K1 = func(start_time, initial_value, params_at_start)\n K2 = func(midpoint_time, initial_value + time_interval * K1 / 2.0, params_at_mid)\n K3 = func(midpoint_time, initial_value + time_interval * K2 / 2.0, params_at_mid)\n K4 = func(end_time, initial_value + time_interval * K3, params_at_end)\n\n step = time_interval * (K1 / 2.0 + K2 + K3 + K4 / 2.0) / 3.0\n\n return step\n\n\ndef _variational_equation(t, Phi, x, func_jac, p=()):\n \"\"\" \n Compute the time derivative of the variational matrix for a set of differential equations.\n \n Parameters:\n ----------\n t : array_like\n Array of times at which to evaluate the variational equation.\n Phi : array_like\n Array representing the variational matrix.\n x : array_like\n Array representing the system state.\n func_jac : function\n Jacobian of the ODE function.\n p : tuple, optional\n Tuple of model parameters for the ODE function.\n \n Returns:\n -------\n dPhi_dt_flat : array_like\n Array representing the time derivative of the variational matrix.\n \n Notes:\n ------\n The variational equation calculates the time derivative of the variational matrix using the Jacobian of the ODE function.\n \n The variational matrix represents the sensitivity of the system state to initial conditions.\n \n The output is a flattened array representing the time derivative of the variational matrix, which can be used for numerical integration.\n \n Example usage:\n --------------\n >>> t = np.linspace(0, 10, 100)\n >>> Phi = np.eye(num_dimensions, dtype=np.float64).flatten()\n >>> x = np.array([1.0, 2.0, 3.0])\n >>> dPhi_dt = _variational_equation(t, Phi, x, fjac, p)\n \"\"\"\n num_dimensions = len(x)\n Phi_matrix = np.reshape(Phi, (num_dimensions, num_dimensions))\n dPhi_dt = np.dot(func_jac(t, x, p), Phi_matrix)\n dPhi_dt_flat = dPhi_dt.flatten()\n return dPhi_dt_flat\n\n\ndef _combined_state_equations(t, S, num_dimensions, func, func_jac, p=()):\n \"\"\"\n Propagates the combined state and variational matrix for a set of differential equations.\n\n Parameters:\n ----------\n t : array_like\n Array of times over which to propagate the combined state.\n S : array_like\n Array representing the combined state, consisting of the system state and the variational matrix.\n num_dimensions : int\n Number of dimensions of the system state.\n func : function\n ODE function. Must take arguments like f(t, x, p) where x and t are the state and time *now*, and p is a tuple of parameters.\n func_jac : function\n Jacobian of f.\n p : tuple, optional\n Tuple of model parameters for f.\n\n Returns:\n -------\n S_dot : array_like\n Array representing the time derivative of the combined state.\n\n Notes:\n ------\n The combined state is represented as a flattened array, where the first num_dimensions elements represent the system state, and the remaining elements represent the variational matrix.\n\n The variational equation is used to calculate the time derivative of the variational matrix.\n\n The combined state and variational matrix are propagated using the provided ODE function and Jacobian.\n\n The output is the time derivative of the combined state, which can be used for numerical integration.\n\n Example usage:\n --------------\n \n >>> t = np.linspace(0, 10, 100)\n >>> S = np.zeros(num_dimensions + num_dimensions**2)\n >>> S_dot = _combined_state_equations(t, S, num_dimensions, f, fjac, p)\n \"\"\"\n x = S[:num_dimensions]\n Phi = S[num_dimensions:]\n S_dot = np.append(func(t, x, p), _variational_equation(t, Phi, x, func_jac, p))\n return S_dot\n\n\ndef computeLE(func, func_jac, x0, t, p=(), ttrans=None):\n \"\"\"\n Computes the global Lyapunov exponents for a set of ODEs using the method described \n in Sandri (1996), through the use of the variational matrix.\n \n Parameters:\n ----------\n func : function\n ODE function. Must take arguments like func(t, x, p) where x and t are \n the state and time *now*, and p is a tuple of parameters. If there are \n no model parameters, p should be set to the empty tuple.\n func_jac : function\n Jacobian of func.\n x0 : array_like\n Initial position for calculation. Integration of transients will begin \n from this point.\n t : array_like\n Array of times over which to calculate LE.\n p : tuple, optional\n Tuple of model parameters for f.\n ttrans : array_like, optional\n Times over which to integrate transient behavior.\n If not specified, assumes trajectory is on the attractor.\n \n Returns:\n -------\n LEs : array_like\n Array of global Lyapunov exponents.\n LE_history : array_like\n Array of Lyapunov exponents over time.\n \"\"\"\n\n # Change the function signature to match the required format\n func_ = lambda t, x, p: func(t, x, *p)\n func_jac_ = lambda t, x, p: func_jac(t, x, *p)\n\n # Initialize variables\n num_dimensions = len(x0)\n num_time_steps = len(t)\n if ttrans is not None:\n num_time_steps += len(ttrans) - 1\n\n # integrate transient behavior\n Phi0 = np.eye(num_dimensions, dtype=np.float64).flatten()\n if ttrans is not None:\n xi = x0\n for i, (t1, t2) in enumerate(zip(ttrans[:-1], ttrans[1:])):\n xip1 = xi + _runge_kutta_4th_order(func_, xi, t1, t2, p)\n xi = xip1\n x0 = xi\n\n # start LE calculation\n LE = np.zeros((num_time_steps - 1, num_dimensions), dtype=np.float64)\n combined_state_solution = np.zeros((num_time_steps, num_dimensions*(num_dimensions+1)), dtype=np.float64)\n combined_state_solution[0] = np.append(x0, Phi0)\n\n for i, (t1, t2) in enumerate(zip(t[:-1], t[1:])):\n combined_state_temp = combined_state_solution[i] + _runge_kutta_4th_order(\n lambda t, S, p: _combined_state_equations(t, S, num_dimensions, func_, func_jac_, p),\n combined_state_solution[i], t1, t2, p)\n # perform QR decomposition on Phi\n Phi_matrix = np.reshape(combined_state_temp[num_dimensions:], (num_dimensions, num_dimensions))\n Q, R = np.linalg.qr(Phi_matrix)\n combined_state_solution[i+1] = np.append(combined_state_temp[:num_dimensions], Q.flatten())\n LE[i] = np.abs(np.diag(R))\n\n # compute LEs\n LE_history = np.cumsum(np.log(LE + 1e-10), axis=0) / np.tile(t[1:], (num_dimensions, 1)).T\n\n LEs = LE_history[-1, :]\n return LEs, LE_history\n\n\ndef plot_bifurcation_diagram(func, func_jac, x0, time_vector, parameters, p_idx, max_time=None):\n \"\"\"\n Computes and plots the bifurcation diagram for a set of ordinary differential equations (ODEs).\n\n Parameters:\n ----------\n func : function\n ODE function. Must take arguments like func(t, x, p) where x and t are \n the state and time *now*, and p is a tuple of parameters. If there are \n no model parameters, p should be set to the empty tuple.\n func_jac : function\n Jacobian of func.\n x0 : array_like\n Initial conditions.\n time_vector : array_like\n Time vector for the integration.\n parameters : array_like\n Range of parameter values to explore.\n p_idx : int\n Index of the parameter to vary in the bifurcation diagram.\n\n Notes:\n ------\n The ODE function should be defined as a callable that takes arguments for the current state and time, and returns the derivative of the state with respect to time.\n\n The Jacobian function should be defined as a callable that takes arguments for the current state and time, and returns the Jacobian matrix of the ODE function.\n\n The initial conditions should be specified as an array-like object.\n\n The time vector should be an array-like object representing the time points at which to evaluate the ODEs.\n\n The range of parameter values should be specified as an array-like object.\n\n The index of the parameter to vary in the bifurcation diagram should be an integer.\n\n The function will compute the solution of the ODEs for each parameter value in the range, and plot the bifurcation diagram showing the local maxima and minima of the state variables, as well as the maximum Lyapunov exponents as a function of the parameter value.\n \"\"\"\n maxima_x = []\n minima_x = []\n px_max = []\n px_min = []\n maxima_y = []\n minima_y = []\n py_max = []\n py_min = []\n maxima_z = []\n minima_z = []\n pz_max = []\n pz_min = []\n le_list = []\n\n for _p in tqdm(parameters):\n\n solution = solve_ivp(func, [time_vector[0], time_vector[-1]], x0, args=_p, t_eval=time_vector)\n\n local_max_x, _ = find_peaks(solution.y[0])\n local_min_x, _ = find_peaks(-1*solution.y[0])\n local_max_y, _ = find_peaks(solution.y[1])\n local_min_y, _ = find_peaks(-1*solution.y[1])\n local_max_z, _ = find_peaks(solution.y[2])\n local_min_z, _ = find_peaks(-1*solution.y[2])\n\n maxima_x.extend(solution.y[0, local_max_x])\n minima_x.extend(solution.y[0, local_min_x])\n px_max.extend([_p[p_idx]] * len(local_max_x))\n px_min.extend([_p[p_idx]] * len(local_min_x))\n maxima_y.extend(solution.y[1, local_max_y])\n minima_y.extend(solution.y[1, local_min_y])\n py_max.extend([_p[p_idx]] * len(local_max_y))\n py_min.extend([_p[p_idx]] * len(local_min_y))\n maxima_z.extend(solution.y[2, local_max_z])\n minima_z.extend(solution.y[2, local_min_z])\n pz_max.extend([_p[p_idx]] * len(local_max_z))\n pz_min.extend([_p[p_idx]] * len(local_min_z))\n\n LE_time = time_vector if max_time is None else time_vector[:max_time]\n LEs, _ = computeLE(func, func_jac, x0, LE_time, p=_p)\n le_list.append(LEs.max())\n\n x0 = solution.y[:,-1]\n\n mle = np.array(le_list)\n pos_idx = np.where(mle > 0)[0]\n neg_idx = np.where(mle < 0)[0]\n _, axes = plt.subplots(4, 1, figsize=(15, 15))\n axes[0].plot(px_max, maxima_x, 'ko', markersize=0.2, alpha=0.3, label=\"Local maxima\")\n axes[0].plot(px_min, minima_x, 'o', color='tab:blue', markersize=0.2, alpha=0.3, label=\"Local minima\")\n axes[0].legend(loc='upper left', markerscale=15)\n axes[0].set_ylabel(\"x-values\")\n axes[1].plot(py_max, maxima_y, 'ko', markersize=0.2, alpha=0.3, label=\"Local maxima\")\n axes[1].plot(py_min, minima_y, 'o', color='tab:blue', markersize=0.2, alpha=0.3, label=\"Local minima\")\n axes[1].legend(loc='upper left', markerscale=15)\n axes[1].set_ylabel(\"y-values\")\n axes[2].plot(pz_max, maxima_z, 'ko', markersize=0.2, alpha=0.3, label=\"Local maxima\")\n axes[2].plot(pz_min, minima_z, 'o', color='tab:blue', markersize=0.2, alpha=0.3, label=\"Local minima\")\n axes[2].legend(loc='upper left', markerscale=15)\n axes[2].set_ylabel(\"z-values\")\n axes[3].plot(parameters[:,p_idx][pos_idx], mle[pos_idx], 'o', color='tab:red', markersize=2.5, alpha=0.5)\n axes[3].plot(parameters[:,p_idx][neg_idx], mle[neg_idx], 'ko', markersize=2.5, alpha=0.5)\n axes[3].set_ylabel(\"Maximum Lyapunov Exponent\")\n axes[3].set_xlabel(\"Parameter Value\")\n axes[3].axhline(0, color='k', lw=.5, alpha=.5)"
},
{
"path": "tsa_course/lecture2.py",
"content": "import matplotlib.pyplot as plt\n\ndef run_sequence_plot(x, y, title, xlabel=\"Time\", ylabel=\"Values\", ax=None):\n \"\"\"\n Plot the time series data\n\n Parameters\n ----------\n x : array-like\n The time values.\n y : array-like\n The values of the time series.\n title : str\n The title of the plot.\n xlabel : str\n The label for the x-axis.\n ylabel : str\n The label for the y-axis.\n ax : matplotlib axes\n The axes to plot on.\n\n Returns\n -------\n ax : matplotlib axes\n The axes object with the plot.\n \"\"\"\n if ax is None:\n _, ax = plt.subplots(1,1, figsize=(10, 3.5))\n ax.plot(x, y, 'k-')\n ax.set_title(title)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n ax.grid(alpha=0.3)\n return ax"
},
{
"path": "tsa_course/lecture8.py",
"content": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef fourierPrediction(y, n_predict, n_harm = 5):\n \"\"\"\n Predict the future values of a time series using the Fourier series.\n\n Parameters\n ----------\n y : array-like\n The time series to predict.\n n_predict : int\n The number of future values to predict.\n n_harm : int\n The number of harmonics to use in the Fourier series.\n\n Returns\n -------\n out : array-like\n The predicted values of the time series.\n \"\"\"\n n = y.size # length of the time series\n t = np.arange(0, n) # time vector\n p = np.polyfit(t, y, 1) # find linear trend in x\n y_notrend = y - p[0] * t - p[1] # detrended x\n y_freqdom = np.fft.fft(y_notrend) # detrended x in frequency domain\n f = np.fft.fftfreq(n) # frequencies\n \n # Sort indexes by largest frequency components\n indexes = np.argsort(np.absolute(y_freqdom))[::-1]\n\n t = np.arange(0, n + n_predict)\n restored_sig = np.zeros(t.size)\n for i in indexes[:1 + n_harm * 2]:\n amp = np.absolute(y_freqdom[i]) / n # amplitude\n phase = np.angle(y_freqdom[i]) # phase\n restored_sig += amp * np.cos(2 * np.pi * f[i] * t + phase)\n\n out = restored_sig + p[0] * t + p[1] # add back the trend\n return out\n\n\ndef annotated_sin_plot():\n \"\"\"\n Plot a sine wave with a phase shift and annotate it.\n \"\"\"\n A = 1\n f = 1 \n T = 1 / f\n omega = 2 * np.pi * f \n phi = 0.5\n t = np.linspace(0, T, 1000)\n y = A * np.sin(omega * t)\n y_phi = A * np.sin(omega * t + phi)\n plt.figure(figsize=(8, 5))\n plt.plot(t, y)\n arrow_idx = len(t) // 2 - 20\n t_arrow = t[arrow_idx]\n y_arrow = y[arrow_idx]\n plt.plot(t, y_phi, color='tab:red', linestyle='--')\n plt.annotate('', xy=(t_arrow-phi/(2*np.pi), y_arrow), xytext=(t_arrow, y_arrow),\n arrowprops=dict(arrowstyle=\"<->\", color=\"k\", lw=1.5))\n plt.text(t_arrow-phi/(3*np.pi), y_arrow+0.1, r'$\\psi$', va='center', color=\"k\")\n plt.xlim(-0.1, T+0.1)\n plt.ylim(-A-0.2, A+0.2)\n xticks = [0, 1/4, 1/2, 3/4, 1]\n xtick_labels = ['0', r'$\\pi/2$', r'$\\pi$', r'$3\\pi/2$', r'$2\\pi$']\n plt.xticks(xticks, xtick_labels)\n plt.xlabel('Radians')\n ax2 = plt.gca().twiny() # Create a twin Axes sharing the yaxis\n ax2.set_xlim(plt.xlim()) # Ensure the limits are the same\n ax2.set_xticks(xticks) # Use the same x-ticks as ax1\n ax2.set_xticklabels(['0', '90', '180', '275', '360']) # But with degree labels\n ax2.set_yticks([]) # Hide the y-axis ticks\n plt.xlim(-0.1, T+0.1)\n plt.xlabel('Degrees')\n plt.text(0.11, -0.1, 'time ($t$)', ha='right')\n plt.text(-0.03, A+0.02, 'A', ha='right')\n plt.text(-0.03, 0+0.02, '0', ha='right')\n plt.text(-0.03, -A+0.02, '-A', ha='right')\n plt.text(T+0.05, 0, r'$T = 1/f$', va='bottom', ha='right')\n plt.text(T / 2 - 0.38, -A + 0.5, 'f = frequency\\nT = period\\nA = amplitude', ha='center', va='top')\n plt.ylabel('Amplitude')\n plt.axhline(A, color='gray', linestyle='--', linewidth=1)\n plt.axhline(-A, color='gray', linestyle='--', linewidth=1)\n plt.axhline(0, color='gray', linestyle='--', linewidth=1)\n plt.grid(True)\n plt.tight_layout()\n plt.show()"
}
]![Wikimedia](\\\"https://commons.wikimedia.org/wiki/File:Fourier_series_square_wave_circles_animation.svg#filelinks\\\"){kind=link}