From b61e859d82fbacd7e127a58e8dfdf4f03b744d19 Mon Sep 17 00:00:00 2001 From: Jeremy Bejarano Date: Fri, 26 Jul 2024 18:11:47 -0400 Subject: [PATCH] Reorganize all content into new SphinxBook format --- README.md | 31 +- day_01/README.md | 19 - day_01/requirements.txt | 53 - day_02/README.md | 36 - day_02/functions.ipynb | 1573 -- day_02/python_by_example.ipynb | 1671 --- day_03/README.md | 23 - day_03/comparing_plotting_libraries.ipynb | 1029 -- day_03/factor_analysis_demo.ipynb | 2540 ---- day_04/README.md | 26 - day_04/config.py | 61 - day_04/mysine.png | Bin 33563 -> 0 bytes day_04/pca.png | Bin 32193 -> 0 bytes docs_src/01_setting_up_environment.md | 150 + docs_src/WRDS_intro_and_web_queries.md | 123 + docs_src/assets/Compustat_Logo.png | Bin 0 -> 25290 bytes docs_src/assets/compustat_on_WRDS_p1.png | Bin 0 -> 125353 bytes docs_src/assets/compustat_on_WRDS_p2.png | Bin 0 -> 130593 bytes docs_src/assets/crsp-llc-logo-web-01_3.png | Bin 0 -> 9764 bytes docs_src/assets/crsp_in_wrds_thumbnail.png | Bin 0 -> 147996 bytes docs_src/assets/wrds_database_usage.png | Bin 0 -> 79219 bytes docs_src/assets/wrds_logo.png | Bin 0 -> 80412 bytes docs_src/assets/wrds_subscriptions.png | Bin 0 -> 151706 bytes docs_src/assets/wrds_top_10_databases.png | Bin 0 -> 58611 bytes docs_src/assets/wrds_web_queries.png | Bin 0 -> 152224 bytes docs_src/conf.py | 6 +- docs_src/discussion_01.md | 33 + docs_src/discussion_02.md | 51 + docs_src/discussion_03.md | 20 + docs_src/discussion_04.md | 22 + docs_src/index.md | 117 +- docs_src/notebooks.md | 34 - dodo.py | 48 +- requirements.txt | 1 + src/01_example_notebook.ipynb | 63 - src/01_python_by_example.ipynb | 1268 ++ src/01_python_jupyter_demo.ipynb | 23 +- .../02_00-Introduction-to-Pandas.ipynb | 0 .../02_01-Introducing-Pandas-Objects.ipynb | 0 .../02_02-Data-Indexing-and-Selection.ipynb | 0 .../02_03-Operations-in-Pandas.ipynb | 0 .../02_04-Missing-Values.ipynb | 0 .../02_Using_Interact.ipynb | 421 +- src/02_functions.ipynb | 1447 ++ src/02_interactive_plot_example.ipynb | 42 - .../02_occupations--with-solutions.ipynb | 0 .../02_occupations.ipynb | 55 +- src/03_comparing_plotting_libraries.ipynb | 274 + src/03_example_regressions.Rmd | 108 - src/03_factor_analysis_demo.ipynb | 715 + .../03_matplotlib.ipynb | 0 day_03/numpy.ipynb => src/03_numpy.ipynb | 0 day_03/scipy.ipynb => src/03_scipy.ipynb | 0 .../03_simple_example.py | 0 .../04_create_pseudo_data.py | 0 .../example_plot.py => src/04_example_plot.py | 0 day_04/hello.py => src/04_hello.py | 0 day_04/hello2.py => src/04_hello2.py | 0 .../04_my_plotting_module.py | 0 day_04/mymodule.py => src/04_mymodule.py | 0 .../04_occupations--with-solutions.ipynb | 0 .../04_occupations.ipynb | 0 .../occupations.py => src/04_occupations.py | 0 .../04_occupations_no_cells.py | 0 .../pca_example.py => src/04_pca_example.py | 0 src/example_r_plot.r | 38 - src/fred_panel.csv | 11873 ++++++++++++++++ src/fred_panel.parquet | Bin 0 -> 259610 bytes src/install_packages.r | 19 - src/scratch.py | 17 + 70 files changed, 16220 insertions(+), 7810 deletions(-) delete mode 100644 day_01/README.md delete mode 100644 day_01/requirements.txt delete mode 100644 day_02/README.md delete mode 100644 day_02/functions.ipynb delete mode 100644 day_02/python_by_example.ipynb delete mode 100644 day_03/README.md delete mode 100644 day_03/comparing_plotting_libraries.ipynb delete mode 100644 day_03/factor_analysis_demo.ipynb delete mode 100644 day_04/README.md delete mode 100644 day_04/config.py delete mode 100644 day_04/mysine.png delete mode 100644 day_04/pca.png create mode 100644 docs_src/01_setting_up_environment.md create mode 100644 docs_src/WRDS_intro_and_web_queries.md create mode 100644 docs_src/assets/Compustat_Logo.png create mode 100644 docs_src/assets/compustat_on_WRDS_p1.png create mode 100644 docs_src/assets/compustat_on_WRDS_p2.png create mode 100644 docs_src/assets/crsp-llc-logo-web-01_3.png create mode 100644 docs_src/assets/crsp_in_wrds_thumbnail.png create mode 100644 docs_src/assets/wrds_database_usage.png create mode 100644 docs_src/assets/wrds_logo.png create mode 100644 docs_src/assets/wrds_subscriptions.png create mode 100644 docs_src/assets/wrds_top_10_databases.png create mode 100644 docs_src/assets/wrds_web_queries.png create mode 100644 docs_src/discussion_01.md create mode 100644 docs_src/discussion_02.md create mode 100644 docs_src/discussion_03.md create mode 100644 docs_src/discussion_04.md delete mode 100644 docs_src/notebooks.md delete mode 100644 src/01_example_notebook.ipynb create mode 100644 src/01_python_by_example.ipynb rename day_02/03.00-Introduction-to-Pandas.ipynb => src/02_00-Introduction-to-Pandas.ipynb (100%) rename day_02/03.01-Introducing-Pandas-Objects.ipynb => src/02_01-Introducing-Pandas-Objects.ipynb (100%) rename day_02/03.02-Data-Indexing-and-Selection.ipynb => src/02_02-Data-Indexing-and-Selection.ipynb (100%) rename day_02/03.03-Operations-in-Pandas.ipynb => src/02_03-Operations-in-Pandas.ipynb (100%) rename day_02/03.04-Missing-Values.ipynb => src/02_04-Missing-Values.ipynb (100%) rename day_02/Using_Interact.ipynb => src/02_Using_Interact.ipynb (57%) create mode 100644 src/02_functions.ipynb delete mode 100644 src/02_interactive_plot_example.ipynb rename day_02/occupations--with-solutions.ipynb => src/02_occupations--with-solutions.ipynb (100%) rename day_04/occupations.ipynb => src/02_occupations.ipynb (83%) create mode 100644 src/03_comparing_plotting_libraries.ipynb delete mode 100644 src/03_example_regressions.Rmd create mode 100644 src/03_factor_analysis_demo.ipynb rename day_03/matplotlib.ipynb => src/03_matplotlib.ipynb (100%) rename day_03/numpy.ipynb => src/03_numpy.ipynb (100%) rename day_03/scipy.ipynb => src/03_scipy.ipynb (100%) rename day_03/simple_example.py => src/03_simple_example.py (100%) rename day_04/create_pseudo_data.py => src/04_create_pseudo_data.py (100%) rename day_04/example_plot.py => src/04_example_plot.py (100%) rename day_04/hello.py => src/04_hello.py (100%) rename day_04/hello2.py => src/04_hello2.py (100%) rename day_04/my_plotting_module.py => src/04_my_plotting_module.py (100%) rename day_04/mymodule.py => src/04_mymodule.py (100%) rename day_04/occupations--with-solutions.ipynb => src/04_occupations--with-solutions.ipynb (100%) rename day_02/occupations.ipynb => src/04_occupations.ipynb (100%) rename day_04/occupations.py => src/04_occupations.py (100%) rename day_04/occupations_no_cells.py => src/04_occupations_no_cells.py (100%) rename day_04/pca_example.py => src/04_pca_example.py (100%) delete mode 100644 src/example_r_plot.r create mode 100644 src/fred_panel.csv create mode 100644 src/fred_panel.parquet delete mode 100644 src/install_packages.r create mode 100644 src/scratch.py diff --git a/README.md b/README.md index 7237e96..b561fb0 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,26 @@ -FINM Python Introduction and Review -=================================== +# FINM August Review: Python + -# Summary +## Summary The FINM August Review is a series of lectures designed for incoming students to prepare for starting with the Financial Mathematics program. The Python Introduction and Review portion is designed to be a refresher or short introduction to the Python programming language. No prior experience is necessary. Even though some incoming students may have extensive prior experience with Python, this review is designed for those with little experience. The aim is to introduce you to what you need to know for the upcoming FINM program. The academic lectures of September Launch and autumn quarter will assume students have mastered the concepts covered throughout August Review, and so it’s critical that all students enter the year with a solid grasp of this material. +## Course Info * **Class:** - - Tuesday, July 30: 6-9pm CT on Zoom - - Friday, August 2: 6-9pm CT on Zoom - - Tuesday, August 6: 6-9pm CT on Zoom - - Friday, August 9: 6-9pm CT on Zoom + - Discussion 1: Tuesday, July 30: 6-9pm CT on Zoom + - Discussion 2: Friday, August 2: 6-9pm CT on Zoom + - Discussion 3: Tuesday, August 6: 6-9pm CT on Zoom + - Discussion 4: Friday, August 9: 6-9pm CT on Zoom * **Lecturer:** Jeremy Bejarano, jeremiah.bejarano@gmail.com * **Website:** - Canvas: https://canvas.uchicago.edu/courses/57668 will be used for grades. - - Lecture notes will be hosted here: + - Lecture notes will be hosted here: https://jeremybejarano.com/finm-python-crash-course/ - Code for the course will be hosted on GitHub: https://github.com/jmbejara/finm-python-crash-course **Required Software** -However, the first class will use [Google Colaboratory](https://colab.research.google.com/), a free online Python notebook platform that doesn't require any installation. However, each lecture after this will use the following software. Please make sure to install these before then. If you need help installing this software, please ask for help in the discussion section on Canvas. +Each lecture after this will use the following software. Please make sure to install these before then. If you need help installing this software, please ask for help in the discussion section on Canvas. - Python 3.11 or greater, Anaconda Distribution - For this class, please download the [Anaconda distribution of Python](https://www.anaconda.com/products/distribution). Be sure to download current version, with Python version 3.9. or greater. When you install Anaconda, be sure to install the full Anaconda distribution. @@ -34,8 +35,18 @@ However, the first class will use [Google Colaboratory](https://colab.research.g and GitHub Desktop (link here: https://github.com/apps/desktop). - Some classes will use GitHub. GitHub is a website that allows you to store, interact with, and share your Git repositories online. [Please register an account with GitHub](https://github.com/) if you don't already have one. +*NOTE:* It's also important that you have a quality laptop. I recommend a laptop with at least 16GB of RAM and at least 500 GB of storage (at a minimum). +So much of your schooling and of your job will revolve around your laptop. +It's important to invest in a good one. If you have any questions about your laptop, please ask in the discussion section on Canvas. -**Helpful References** +**WRDS Account** + +This course requires that you create a WRDS account. WRDS is a comprehensive data research platform that provides access to a wide range of financial, economic, and marketing data. +Follow the instructions [here](./01_setting_up_environment.md#wrds-how-do-i-sign-up) to sign up. + + + +## Helpful References A lot of my lecture material will use content from the following helpful books: diff --git a/day_01/README.md b/day_01/README.md deleted file mode 100644 index 3772b10..0000000 --- a/day_01/README.md +++ /dev/null @@ -1,19 +0,0 @@ -FINM August Python Introduction and Review: Week 1 -================================================== - -Agenda - - - Introduction: Who am I? What's the goal of this review? - - Review course page on GitHub: https://github.com/jmbejara/finm-python-crash-course - - Today we will use Jupyter Notebooks on Google's Colab platform. Next week we will use Jupyter Notebooks locally to do all of our coding. In week 3 we will use notebooks within VS Code. In week 4 we will move away from notebooks and write `.py` files directly. - - But, first, let me give you a sneak peak of VS Code, standard Jupyter notebook, JupyterLab, Spyder, IPython in the command line, and running a `.py` file from the command line. (Nothing deep. Just showing that these options exist.) - - Discuss assignment for next week (installing software). (Assignments are not graded. This is an optional review.) - - List of software to install is on the main README: https://github.com/jmbejara/finm-python-crash-course/blob/main/README.md - - Helpful text to understand the process of setting up your environment: https://datascience.quantecon.org/introduction/local_install.html - - [Run Python Demo Notebook in Google Colab](https://colab.research.google.com/github/jmbejara/finm-python-crash-course/blob/main/week_1/Part_1_Python_Jupyter_demo.ipynb) - - Start HW1 as a group. Discuss how the Jupyter notebook can be used for HW. Formatting is important! [Work through problems together here.](https://colab.research.google.com/github/jmbejara/finm-python-crash-course/blob/main/HW/HW-1-numpy-scipy/HW1.ipynb) - - With the remaining time, we'll take a step back and go over some of the more basic aspects of Python. We'll go through some simpler examples in the following notebooks (which can be accessed in Google Colab). - - [Python Fundamentals](https://datascience.quantecon.org/python_fundamentals/index.html) - - [Basics](https://datascience.quantecon.org/python_fundamentals/basics.html) - - [Collections](https://datascience.quantecon.org/python_fundamentals/collections.html) - diff --git a/day_01/requirements.txt b/day_01/requirements.txt deleted file mode 100644 index 2575f69..0000000 --- a/day_01/requirements.txt +++ /dev/null @@ -1,53 +0,0 @@ -# Specific package versions are specified here to allow more consistent caching -# in GitHub Actions. -# -# I derived this file from the output of the following command and then edited it -# to match the appropriate syntax: -# conda env export > environment.yml -# -# Dependencies from this file can be installed with the following command: -# pip install -r requirements.txt -# -# This file may be used to create an environment using: -# $ conda create --name --file -# platform: win-64 -altair==5.2.0 -beautifulsoup4==4.12.3 -black==24.3.0 -colorama -doit==0.36.0 -ipython==8.22.2 -jupyter==1.0.0 -jupyter-book==1.0.0 -jupyterlab==4.1.5 -linearmodels==5.4 -matplotlib==3.8.3 -myst-nb -myst-parser==2.0.0 -notebook==7.1.2 -numpy==1.26.4 -numpydoc==1.6.0 -openpyxl==3.1.2 -pandas==2.2.1 -pandas-datareader==0.10.0 -pandas-market-calendars==4.4.0 -plotly==5.20.0 -plotnine==0.13.2 -polars==0.20.16 -pytest==8.1.1 -python-decouple==3.8 -python-dotenv==1.0.1 -pyxlsb==1.0.10 -requests==2.31.0 -scikit-learn==1.4.1.post1 -scipy==1.12.0 -seaborn==0.13.2 -sphinx-book-theme==1.1.2 -sphinx-autodoc2 -statsmodels==0.14.1 -streamlit==1.32.2 -vega_datasets==0.9.0 -wrds==3.2.0 -xbbg==0.7.7 -xlrd==2.0.1 -zstandard==0.22.0 \ No newline at end of file diff --git a/day_02/README.md b/day_02/README.md deleted file mode 100644 index 57deec9..0000000 --- a/day_02/README.md +++ /dev/null @@ -1,36 +0,0 @@ -FINM August Python Introduction and Review: Week 2 -================================================== - -Agenda - - - Questions about HW1? Did anyone attempt? - - Follow-up on previous assignment, HW 0: Installation of software on the main [README](https://github.com/jmbejara/finm-python-crash-course/blob/main/README.md) - - Today we will use Jupyter locally to do all of our coding. We will use Jupyter notebooks. Next week we will use notebooks within VS Code. The week after than we will move away from notebooks and write `.py` files directly. - - Did anyone have any trouble installing Anaconda and VS Code? Share screen if there are issues. - - Review HW 2 from last time. - - Who tried the HW? Any questions? - - Show location of solutions notebook. - - What are some gotcha's when using Jupyter notebooks? - - What is the terminal/command prompt? What is bash? - - Spin up Jupyter notebook. Show how it can't go above the root folder. - - Discuss the importance of maintaining a reasonable folder structure. Folder for all course work, separate folder for each course, for each project, etc. - - Google Colab vs locally-running Jupyter server, Jupyter Notebooks vs VS Code - - Difference between Python and Anaconda? - - Difference between Anaconda and Conda. - - Demo the use of conda for installing packages and using conda environments. - - What is the purpose of a conda environment? - - Skim over the [./Using_Interact.ipynb](./Using_Interact.ipynb) - - We're not going to cover it, but those that are interested can learn more about how to use it here. - - Continue with introductory Python topics: - - To learn about "Control Flow" in the context of generating pseudo-random time series, let's use the ["Introductory Example" or "Python by Example"](https://python-programming.quantecon.org/python_by_example.html) notebook found here: [./python_by_example.ipynb](./python_by_example.ipynb) - - Start with discussion of Pandas. Start going over the Pandas chapter from ["Python Data Science Handbook"](https://jakevdp.github.io/PythonDataScienceHandbook) - - `03.00-Introduction-to-Pandas.ipynb` - - `03.01-Introducing-Pandas-Objects.ipynb` - - `03.02-Data-Indexing-and-Selection.ipynb` - - Break for an set of in-class exercises: [./occupations.ipynb](./occupations.ipynb) - - `03.03-Operations-in-Pandas.ipynb` - - `03.04-Missing-Values.ipynb` - - Homework for next time: See HW 2 folder. These are a series of short exercises to practice using Pandas. - - - diff --git a/day_02/functions.ipynb b/day_02/functions.ipynb deleted file mode 100644 index bdb8c62..0000000 --- a/day_02/functions.ipynb +++ /dev/null @@ -1,1573 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d01b84d5", - "metadata": { - "id": "d01b84d5" - }, - "source": [ - "# Functions\n", - "\n", - "**Prerequisites**\n", - "\n", - "- [Getting Started](https://datascience.quantecon.org/../introduction/getting_started.html) \n", - "- [Basics](https://datascience.quantecon.org/basics.html) \n", - "- [Collections](https://datascience.quantecon.org/collections.html) \n", - "- [Control Flow](https://datascience.quantecon.org/control_flow.html) \n", - "\n", - "\n", - "**Outcomes**\n", - "\n", - "- Economic Production Functions \n", - " - Understand the basics of production functions in economics \n", - "- Functions \n", - " - Know how to define your own function \n", - " - Know how to find and write your own function documentation \n", - " - Know why we use functions \n", - " - Understand scoping rules and blocks \n", - "\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "ddecc742", - "metadata": { - "id": "ddecc742" - }, - "source": [ - "## Application: Production Functions\n", - "\n", - "Production functions are useful when modeling the economics of firms producing\n", - "goods or the aggregate output in an economy.\n", - "\n", - "Though the term “function” is used in a mathematical sense here, we will be making\n", - "tight connections between the programming of mathematical functions and Python\n", - "functions." - ] - }, - { - "cell_type": "markdown", - "id": "33438a42", - "metadata": { - "id": "33438a42" - }, - "source": [ - "### Factors of Production\n", - "\n", - "The [factors of production](https://en.wikipedia.org/wiki/Factors_of_production)\n", - "are the inputs used in the production of some sort of output.\n", - "\n", - "Some example factors of production include\n", - "\n", - "- [Physical capital](https://en.wikipedia.org/wiki/Physical_capital), e.g.\n", - " machines, buildings, computers, and power stations. \n", - "- Labor, e.g. all of the hours of work from different types of employees of a\n", - " firm. \n", - "- [Human Capital](https://en.wikipedia.org/wiki/Human_capital), e.g. the\n", - " knowledge of employees within a firm. \n", - "\n", - "\n", - "A [production function](https://en.wikipedia.org/wiki/Production_function)\n", - "maps a set of inputs to the output, e.g. the amount of wheat produced by a\n", - "farm, or widgets produced in a factory.\n", - "\n", - "As an example of the notation, we denote the total units of labor and\n", - "physical capital used in a factory as $ L $ and $ K $ respectively.\n", - "\n", - "If we denote the physical output of the factory as $ Y $, then a production\n", - "function $ F $ that transforms labor and capital into output might have the\n", - "form:\n", - "\n", - "$$\n", - "Y = F(K, L)\n", - "$$\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "4774ddc2", - "metadata": { - "id": "4774ddc2" - }, - "source": [ - "### An Example Production Function\n", - "\n", - "Throughout this lecture, we will use the\n", - "[Cobb-Douglas](https://en.wikipedia.org/wiki/Cobb%E2%80%93Douglas_production_function)\n", - "production function to help us understand how to create Python\n", - "functions and why they are useful.\n", - "\n", - "The Cobb-Douglas production function has appealing statistical properties when brought to data.\n", - "\n", - "This function is displayed below.\n", - "\n", - "$$\n", - "Y = z K^{\\alpha} L^{1-\\alpha}\n", - "$$\n", - "\n", - "The function is parameterized by:\n", - "\n", - "- A parameter $ \\alpha \\in [0,1] $, called the “output elasticity of\n", - " capital”. \n", - "- A value $ z $ called the [Total Factor Productivity](https://en.wikipedia.org/wiki/Total_factor_productivity) (TFP). " - ] - }, - { - "cell_type": "markdown", - "id": "bcd32baf", - "metadata": { - "id": "bcd32baf" - }, - "source": [ - "## What are (Python) Functions?\n", - "\n", - "In this class, we will often talk about `function`s.\n", - "\n", - "So what is a function?\n", - "\n", - "We like to think of a function as a production line in a\n", - "manufacturing plant: we pass zero or more things to it, operations take place in a\n", - "set linear sequence, and zero or more things come out.\n", - "\n", - "We use functions for the following purposes:\n", - "\n", - "- **Re-usability**: Writing code to do a specific task just once, and\n", - " reuse the code by calling the function. \n", - "- **Organization**: Keep the code for distinct operations separated and\n", - " organized. \n", - "- **Sharing/collaboration**: Sharing code across multiple projects or\n", - " sharing pieces of code with collaborators. " - ] - }, - { - "cell_type": "markdown", - "id": "fed78915", - "metadata": { - "id": "fed78915" - }, - "source": [ - "## How to Define (Python) Functions?\n", - "\n", - "The basic syntax to create our own function is as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e1bdbe8", - "metadata": { - "hide-output": false, - "id": "9e1bdbe8" - }, - "outputs": [], - "source": [ - "def function_name(inputs):\n", - " # step 1\n", - " # step 2\n", - " # ...\n", - " return outputs" - ] - }, - { - "cell_type": "markdown", - "id": "57878b06", - "metadata": { - "id": "57878b06" - }, - "source": [ - "Here we see two new *keywords*: `def` and `return`.\n", - "\n", - "- `def` is used to tell Python we would like to define a new function. \n", - "- `return` is used to tell Python what we would like to **return** from a\n", - " function. \n", - "\n", - "\n", - "Let’s look at an example and then discuss each part:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a3fb19f", - "metadata": { - "hide-output": false, - "id": "8a3fb19f" - }, - "outputs": [], - "source": [ - "def mean(numbers):\n", - " total = sum(numbers)\n", - " N = len(numbers)\n", - " answer = total / N\n", - "\n", - " return answer" - ] - }, - { - "cell_type": "markdown", - "id": "e87d1e82", - "metadata": { - "id": "e87d1e82" - }, - "source": [ - "Here we defined a function `mean` that has one input (`numbers`),\n", - "does three steps, and has one output (`answer`).\n", - "\n", - "Let’s see what happens when we call this function on the list of numbers\n", - "`[1, 2, 3, 4]`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8115113e", - "metadata": { - "hide-output": false, - "id": "8115113e" - }, - "outputs": [], - "source": [ - "x = [1, 2, 3, 4]\n", - "the_mean = mean(x)\n", - "the_mean" - ] - }, - { - "cell_type": "markdown", - "id": "556b1f1a", - "metadata": { - "id": "556b1f1a" - }, - "source": [ - "Additionally, as we saw in the [control flow](https://datascience.quantecon.org/control_flow.html) lecture, indentation\n", - "controls blocks of code (along with the [scope](#scope) rules).\n", - "\n", - "To see this, compare a function with no inputs or return values." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "304fc73d", - "metadata": { - "hide-output": false, - "id": "304fc73d" - }, - "outputs": [], - "source": [ - "def f():\n", - " print(\"1\")\n", - " print(\"2\")\n", - "f()" - ] - }, - { - "cell_type": "markdown", - "id": "c7c7283d", - "metadata": { - "id": "c7c7283d" - }, - "source": [ - "With the following change of indentation…" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63b91a05", - "metadata": { - "hide-output": false, - "id": "63b91a05" - }, - "outputs": [], - "source": [ - "def f():\n", - " print(\"1\")\n", - "print(\"2\")\n", - "f()" - ] - }, - { - "cell_type": "markdown", - "id": "370040f1", - "metadata": { - "id": "370040f1" - }, - "source": [ - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "71eb966e", - "metadata": { - "id": "71eb966e" - }, - "source": [ - "### Scope\n", - "\n", - "Notice that we named the input to the function `x` and we called the output\n", - "`the_mean`.\n", - "\n", - "When we defined the function, the input was called `numbers` and the output\n", - "`answer`… what gives?\n", - "\n", - "This is an example of a programming concept called\n", - "[variable scope](http://python-textbok.readthedocs.io/en/1.0/Variables_and_Scope.html).\n", - "\n", - "In Python, functions define their own scope for variables.\n", - "\n", - "In English, this means that regardless of what name we give an input variable (`x` in this example),\n", - "the input will always be referred to as `numbers` *inside* the body of the `mean`\n", - "function.\n", - "\n", - "It also means that although we called the output `answer` inside of the\n", - "function `mean`, that this variable name was only valid inside of our\n", - "function.\n", - "\n", - "To use the output of the function, we had to give it our own name (`the_mean`\n", - "in this example).\n", - "\n", - "Another point to make here is that the intermediate variables we defined inside\n", - "`mean` (`total` and `N`) are only defined inside of the `mean` function\n", - "– we can’t access them from outside. We can verify this by trying to see what\n", - "the value of `total` is:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11844389", - "metadata": { - "hide-output": false, - "id": "11844389" - }, - "outputs": [], - "source": [ - "def mean(numbers):\n", - " total = sum(numbers)\n", - " N = len(numbers)\n", - " answer = total / N\n", - " return answer # or directly return total / N\n", - "\n", - "# uncomment the line below and execute to see the error\n", - "# total" - ] - }, - { - "cell_type": "markdown", - "id": "5bd0615d", - "metadata": { - "id": "5bd0615d" - }, - "source": [ - "This point can be taken even further: the same name can be bound\n", - "to variables inside of blocks of code and in the outer “scope”." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "388a4029", - "metadata": { - "hide-output": false, - "id": "388a4029" - }, - "outputs": [], - "source": [ - "x = 4\n", - "print(f\"x = {x}\")\n", - "def f():\n", - " x = 5 # a different \"x\"\n", - " print(f\"x = {x}\")\n", - "f() # calls function\n", - "print(f\"x = {x}\")" - ] - }, - { - "cell_type": "markdown", - "id": "de1ca784", - "metadata": { - "id": "de1ca784" - }, - "source": [ - "The final point we want to make about scope is that function inputs and output\n", - "don’t have to be given a name outside the function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5dae5c00", - "metadata": { - "hide-output": false, - "id": "5dae5c00" - }, - "outputs": [], - "source": [ - "mean([10, 20, 30])" - ] - }, - { - "cell_type": "markdown", - "id": "a52ba1d5", - "metadata": { - "id": "a52ba1d5" - }, - "source": [ - "Notice that we didn’t name the input or the output, but the function was\n", - "called successfully.\n", - "\n", - "Now, we’ll use our new knowledge to define a function which computes the output\n", - "from a Cobb-Douglas production function with parameters $ z = 1 $ and\n", - "$ \\alpha = 0.33 $ and takes inputs $ K $ and $ L $." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f01d0518", - "metadata": { - "hide-output": false, - "id": "f01d0518" - }, - "outputs": [], - "source": [ - "def cobb_douglas(K, L):\n", - "\n", - " # Create alpha and z\n", - " z = 1\n", - " alpha = 0.33\n", - "\n", - " return z * K**alpha * L**(1 - alpha)" - ] - }, - { - "cell_type": "markdown", - "id": "ac6912bc", - "metadata": { - "id": "ac6912bc" - }, - "source": [ - "We can use this function as we did the mean function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a533ffc0", - "metadata": { - "hide-output": false, - "id": "a533ffc0" - }, - "outputs": [], - "source": [ - "cobb_douglas(1.0, 0.5)" - ] - }, - { - "cell_type": "markdown", - "id": "46005624", - "metadata": { - "id": "46005624" - }, - "source": [ - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "56e2a4de", - "metadata": { - "id": "56e2a4de" - }, - "source": [ - "### Re-using Functions\n", - "\n", - "Economists are often interested in this question: how much does output\n", - "change if we modify our inputs?\n", - "\n", - "For example, take a production function $ Y_1 = F(K_1,L_1) $ which produces\n", - "$ Y_1 $ units of the goods.\n", - "\n", - "If we then multiply the inputs each by $ \\gamma $, so that\n", - "$ K_2 = \\gamma K_1 $ and $ L_2 = \\gamma L_1 $, then the output is\n", - "\n", - "$$\n", - "Y_2 = F(K_2, L_2) = F(\\gamma K_1, \\gamma L_1)\n", - "$$\n", - "\n", - "How does $ Y_1 $ compare to $ Y_2 $?\n", - "\n", - "Answering this question involves something called *returns to scale*.\n", - "\n", - "Returns to scale tells us whether our inputs are more or less productive as we\n", - "have more of them.\n", - "\n", - "For example, imagine that you run a restaurant. How would you expect the amount\n", - "of food you could produce would change if you could build an exact replica of\n", - "your restaurant and kitchen and hire the same number of cooks and waiters? You\n", - "would probably expect it to double.\n", - "\n", - "If, for any $ K, L $, we multiply $ K, L $ by a value $ \\gamma $\n", - "then\n", - "\n", - "- If $ \\frac{Y_2}{Y_1} < \\gamma $ then we say the production function has\n", - " decreasing returns to scale. \n", - "- If $ \\frac{Y_2}{Y_1} = \\gamma $ then we say the production function has\n", - " constant returns to scale. \n", - "- If $ \\frac{Y_2}{Y_1} > \\gamma $ then we say the production function has\n", - " increasing returns to scale. \n", - "\n", - "\n", - "Let’s try it and see what our function is!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba4280b8", - "metadata": { - "hide-output": false, - "id": "ba4280b8" - }, - "outputs": [], - "source": [ - "y1 = cobb_douglas(1.0, 0.5)\n", - "print(y1)\n", - "y2 = cobb_douglas(2*1.0, 2*0.5)\n", - "print(y2)" - ] - }, - { - "cell_type": "markdown", - "id": "dce2a0e7", - "metadata": { - "id": "dce2a0e7" - }, - "source": [ - "How did $ Y_1 $ and $ Y_2 $ relate?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92f17f1b", - "metadata": { - "hide-output": false, - "id": "92f17f1b" - }, - "outputs": [], - "source": [ - "y2 / y1" - ] - }, - { - "cell_type": "markdown", - "id": "0bc96ca2", - "metadata": { - "id": "0bc96ca2" - }, - "source": [ - "$ Y_2 $ was exactly double $ Y_1 $!\n", - "\n", - "Let’s write a function that will compute the returns to scale for different\n", - "values of $ K $ and $ L $.\n", - "\n", - "This is an example of how writing functions can allow us to re-use code\n", - "in ways we might not originally anticipate. (You didn’t know we’d be\n", - "writing a `returns_to_scale` function when we wrote `cobb_douglas`.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b1315cd", - "metadata": { - "hide-output": false, - "id": "8b1315cd" - }, - "outputs": [], - "source": [ - "def returns_to_scale(K, L, gamma):\n", - " y1 = cobb_douglas(K, L)\n", - " y2 = cobb_douglas(gamma*K, gamma*L)\n", - " y_ratio = y2 / y1\n", - " return y_ratio / gamma" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13d23ac8", - "metadata": { - "hide-output": false, - "id": "13d23ac8" - }, - "outputs": [], - "source": [ - "returns_to_scale(1.0, 0.5, 2.0)" - ] - }, - { - "cell_type": "markdown", - "id": "63637c4c", - "metadata": { - "id": "63637c4c" - }, - "source": [ - "### Exercise\n", - "\n", - "See exercise 1 in the [exercise list](#ex2-4).\n", - "\n", - "It turns out that with a little bit of algebra, we can check that this will\n", - "always hold for our [Cobb-Douglas example](#cobb-douglas-example) above.\n", - "\n", - "To show this, take an arbitrary $ K, L $ and multiply the inputs by an\n", - "arbitrary $ \\gamma $.\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - " F(\\gamma K, \\gamma L) &= z (\\gamma K)^{\\alpha} (\\gamma L)^{1-\\alpha}\\\\\n", - " &= z \\gamma^{\\alpha}\\gamma^{1-\\alpha} K^{\\alpha} L^{1-\\alpha}\\\\\n", - " &= \\gamma z K^{\\alpha} L^{1-\\alpha} = \\gamma F(K, L)\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "For an example of a production function that is not CRS, look at a\n", - "generalization of the Cobb-Douglas production function that has different\n", - "“output elasticities” for the 2 inputs.\n", - "\n", - "$$\n", - "Y = z K^{\\alpha_1} L^{\\alpha_2}\n", - "$$\n", - "\n", - "Note that if $ \\alpha_2 = 1 - \\alpha_1 $, this is our Cobb-Douglas\n", - "production function." - ] - }, - { - "cell_type": "markdown", - "id": "be199e69", - "metadata": { - "id": "be199e69" - }, - "source": [ - "### Exercise\n", - "\n", - "See exercise 2 in the [exercise list](#ex2-4).\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "6abfcb1f", - "metadata": { - "id": "6abfcb1f" - }, - "source": [ - "### Multiple Returns\n", - "\n", - "Another valuable element to analyze on production functions is how\n", - "output changes as we change only one of the inputs. We will call this the\n", - "marginal product.\n", - "\n", - "For example, compare the output using $ K, L $ units of inputs to that with\n", - "an $ \\epsilon $ units of labor.\n", - "\n", - "Then the marginal product of labor (MPL) is defined as\n", - "\n", - "$$\n", - "\\frac{F(K, L + \\varepsilon) - F(K, L)}{\\varepsilon}\n", - "$$\n", - "\n", - "This tells us how much additional output is created relative to the additional\n", - "input. (Spoiler alert: This should look like the definition for a partial\n", - "derivative!)\n", - "\n", - "If the input can be divided into small units, then we can use calculus to take\n", - "this limit, using the partial derivative of the production function relative to\n", - "that input.\n", - "\n", - "In this case, we define the marginal product of labor (MPL) and marginal product\n", - "of capital (MPK) as\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "MPL(K, L) &= \\frac{\\partial F(K, L)}{\\partial L}\\\\\n", - "MPK(K, L) &= \\frac{\\partial F(K, L)}{\\partial K}\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "In the [Cobb-Douglas](#cobb-douglas-example) example above, this becomes\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - "MPK(K, L) &= z \\alpha \\left(\\frac{K}{L} \\right)^{\\alpha - 1}\\\\\n", - "MPL(K, L) &= (1-\\alpha) z \\left(\\frac{K}{L} \\right)^{\\alpha}\\\\\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "Let’s test it out with Python! We’ll also see that we can actually return\n", - "multiple things in a Python function.\n", - "\n", - "The syntax for a return statement with multiple items is return item1, item2, ….\n", - "\n", - "In this case, we’ll compute both the MPL and the MPK and then return both." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46654832", - "metadata": { - "hide-output": false, - "id": "46654832" - }, - "outputs": [], - "source": [ - "def marginal_products(K, L, epsilon):\n", - "\n", - " mpl = (cobb_douglas(K, L + epsilon) - cobb_douglas(K, L)) / epsilon\n", - " mpk = (cobb_douglas(K + epsilon, L) - cobb_douglas(K, L)) / epsilon\n", - "\n", - " return mpl, mpk" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44284da4", - "metadata": { - "hide-output": false, - "id": "44284da4" - }, - "outputs": [], - "source": [ - "tup = marginal_products(1.0, 0.5, 1e-4)\n", - "print(tup)" - ] - }, - { - "cell_type": "markdown", - "id": "eca892b8", - "metadata": { - "id": "eca892b8" - }, - "source": [ - "Instead of using the tuple, these can be directly unpacked to variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b2e4f7e", - "metadata": { - "hide-output": false, - "id": "9b2e4f7e" - }, - "outputs": [], - "source": [ - "mpl, mpk = marginal_products(1.0, 0.5, 1e-4)\n", - "print(f\"mpl = {mpl}, mpk = {mpk}\")" - ] - }, - { - "cell_type": "markdown", - "id": "0c65f1f6", - "metadata": { - "id": "0c65f1f6" - }, - "source": [ - "We can use this to calculate the marginal products for different `K`, fixing `L`\n", - "using a comprehension." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91124022", - "metadata": { - "hide-output": false, - "id": "91124022" - }, - "outputs": [], - "source": [ - "Ks = [1.0, 2.0, 3.0]\n", - "[marginal_products(K, 0.5, 1e-4) for K in Ks] # create a tuple for each K" - ] - }, - { - "cell_type": "markdown", - "id": "490312cd", - "metadata": { - "id": "490312cd" - }, - "source": [ - "### Documentation\n", - "\n", - "In a previous exercise, we asked you to find help for the `cobb_douglas` and\n", - "`returns_to_scale` functions using `?`.\n", - "\n", - "It didn’t provide any useful information.\n", - "\n", - "To provide this type of help information, we need to\n", - "add what Python programmers call a “docstring” to our functions.\n", - "\n", - "This is done by putting a string (not assigned to any variable name) as\n", - "the first line of the *body* of the function (after the line with\n", - "`def`).\n", - "\n", - "Below is a new version of the template we used to define functions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bcfe31d5", - "metadata": { - "hide-output": false, - "id": "bcfe31d5" - }, - "outputs": [], - "source": [ - "def function_name(inputs):\n", - " \"\"\"\n", - " Docstring\n", - " \"\"\"\n", - " # step 1\n", - " # step 2\n", - " # ...\n", - " return outputs" - ] - }, - { - "cell_type": "markdown", - "id": "f210bf45", - "metadata": { - "id": "f210bf45" - }, - "source": [ - "Let’s re-define our `cobb_douglas` function to include a docstring." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5078fd27", - "metadata": { - "hide-output": false, - "id": "5078fd27" - }, - "outputs": [], - "source": [ - "def cobb_douglas(K, L):\n", - " \"\"\"\n", - " Computes the production F(K, L) for a Cobb-Douglas production function\n", - "\n", - " Takes the form F(K, L) = z K^{\\alpha} L^{1 - \\alpha}\n", - "\n", - " We restrict z = 1 and alpha = 0.33\n", - " \"\"\"\n", - " return 1.0 * K**(0.33) * L**(1.0 - 0.33)" - ] - }, - { - "cell_type": "markdown", - "id": "aa4f9b57", - "metadata": { - "id": "aa4f9b57" - }, - "source": [ - "Now when we have Jupyter evaluate `cobb_douglas?`, our message is\n", - "displayed (or use the Contextual Help window with Jupyterlab and `Ctrl-I` or `Cmd-I`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c30fe52", - "metadata": { - "hide-output": false, - "id": "6c30fe52" - }, - "outputs": [], - "source": [ - "cobb_douglas?" - ] - }, - { - "cell_type": "markdown", - "id": "c0326dc6", - "metadata": { - "id": "c0326dc6" - }, - "source": [ - "We recommend that you always include at least a very simple docstring for\n", - "nontrivial functions.\n", - "\n", - "This is in the same spirit as adding comments to your code — it makes it easier\n", - "for future readers/users (including yourself) to understand what the code does." - ] - }, - { - "cell_type": "markdown", - "id": "cfc8949e", - "metadata": { - "id": "cfc8949e" - }, - "source": [ - "### Exercise\n", - "\n", - "See exercise 3 in the [exercise list](#ex2-4)." - ] - }, - { - "cell_type": "markdown", - "id": "05110f75", - "metadata": { - "id": "05110f75" - }, - "source": [ - "### Default and Keyword Arguments\n", - "\n", - "Functions can have optional arguments.\n", - "\n", - "To accomplish this, we must these arguments a *default value* by saying\n", - "`name=default_value` instead of just `name` as we list the arguments.\n", - "\n", - "To demonstrate this functionality, let’s now make $ z $ and $ \\alpha $\n", - "arguments to our cobb_douglas function!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ec51f0d", - "metadata": { - "hide-output": false, - "id": "1ec51f0d" - }, - "outputs": [], - "source": [ - "def cobb_douglas(K, L, alpha=0.33, z=1):\n", - " \"\"\"\n", - " Computes the production F(K, L) for a Cobb-Douglas production function\n", - "\n", - " Takes the form F(K, L) = z K^{\\alpha} L^{1 - \\alpha}\n", - " \"\"\"\n", - " return z * K**(alpha) * L**(1.0 - alpha)" - ] - }, - { - "cell_type": "markdown", - "id": "be2590fd", - "metadata": { - "id": "be2590fd" - }, - "source": [ - "We can now call this function by passing in just K and L. Notice that it will\n", - "produce same result as earlier because `alpha` and `z` are the same as earlier." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9546cb37", - "metadata": { - "hide-output": false, - "id": "9546cb37" - }, - "outputs": [], - "source": [ - "cobb_douglas(1.0, 0.5)" - ] - }, - { - "cell_type": "markdown", - "id": "e4dfe474", - "metadata": { - "id": "e4dfe474" - }, - "source": [ - "However, we can also set the other arguments of the function by passing\n", - "more than just K/L." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "780070a8", - "metadata": { - "hide-output": false, - "id": "780070a8" - }, - "outputs": [], - "source": [ - "cobb_douglas(1.0, 0.5, 0.35, 1.6)" - ] - }, - { - "cell_type": "markdown", - "id": "d421b4f4", - "metadata": { - "id": "d421b4f4" - }, - "source": [ - "In the example above, we used `alpha = 0.35`, `z = 1.6`.\n", - "\n", - "We can also refer to function arguments by their name, instead of only their\n", - "position (order).\n", - "\n", - "To do this, we would write `func_name(arg=value)` for as many of the arguments\n", - "as we want.\n", - "\n", - "Here’s how to do that with our `cobb_douglas` example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "05eb1bbc", - "metadata": { - "hide-output": false, - "id": "05eb1bbc" - }, - "outputs": [], - "source": [ - "cobb_douglas(1.0, 0.5, z = 1.5)" - ] - }, - { - "cell_type": "markdown", - "id": "6a4f28fe", - "metadata": { - "id": "6a4f28fe" - }, - "source": [ - "### Exercise\n", - "\n", - "See exercise 4 in the [exercise list](#ex2-4).\n", - "\n", - "In terms of variable scope, the `z` name within the function is\n", - "different from any other `z` in the outer scope.\n", - "\n", - "To be clear," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "557cf5df", - "metadata": { - "hide-output": false, - "id": "557cf5df" - }, - "outputs": [], - "source": [ - "x = 5\n", - "def f(x):\n", - " return x\n", - "f(x) # \"coincidence\" that it has the same name" - ] - }, - { - "cell_type": "markdown", - "id": "f1b2022f", - "metadata": { - "id": "f1b2022f" - }, - "source": [ - "This is also true with named function arguments, above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5d13655", - "metadata": { - "hide-output": false, - "id": "b5d13655" - }, - "outputs": [], - "source": [ - "z = 1.5\n", - "cobb_douglas(1.0, 0.5, z = z) # no problem!" - ] - }, - { - "cell_type": "markdown", - "id": "d868edfb", - "metadata": { - "id": "d868edfb" - }, - "source": [ - "In that example, the `z` on the left hand side of `z = z` refers\n", - "to the local variable name in the function whereas the `z` on the\n", - "right hand side refers to the `z` in the outer scope." - ] - }, - { - "cell_type": "markdown", - "id": "94841288", - "metadata": { - "id": "94841288" - }, - "source": [ - "### Aside: Methods\n", - "\n", - "As we learned earlier, all variables in Python have a type associated\n", - "with them.\n", - "\n", - "Different types of variables have different functions or operations\n", - "defined for them.\n", - "\n", - "For example, I can divide one number by another or make a string uppercase.\n", - "\n", - "It wouldn’t make sense to divide one string by another or make a number\n", - "uppercase.\n", - "\n", - "When certain functionality is closely tied to the type of an object, it\n", - "is often implemented as a special kind of function known as a **method**.\n", - "\n", - "For now, you only need to know two things about methods:\n", - "\n", - "1. We call them by doing `variable.method_name(other_arguments)`\n", - " instead of `function_name(variable, other_arguments)`. \n", - "1. A method is a function, even though we call it using a different\n", - " notation. \n", - "\n", - "\n", - "When we introduced the core data types, we saw many methods defined on\n", - "these types.\n", - "\n", - "Let’s revisit them for the `str`, or string type.\n", - "\n", - "Notice that we call each of these functions using the `dot` syntax\n", - "described above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f794c03", - "metadata": { - "hide-output": false, - "id": "8f794c03" - }, - "outputs": [], - "source": [ - "s = \"This is my handy string!\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a2fefb42", - "metadata": { - "hide-output": false, - "id": "a2fefb42" - }, - "outputs": [], - "source": [ - "s.upper()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5fa57a97", - "metadata": { - "hide-output": false, - "id": "5fa57a97" - }, - "outputs": [], - "source": [ - "s.title()" - ] - }, - { - "cell_type": "markdown", - "id": "6f69a383", - "metadata": { - "id": "6f69a383" - }, - "source": [ - "## More on Scope (Optional)\n", - "\n", - "Keep in mind that with mathematical functions, the arguments are just dummy names\n", - "that can be interchanged.\n", - "\n", - "That is, the following are identical.\n", - "\n", - "$$\n", - "\\begin{eqnarray}\n", - " f(K, L) &= z\\, K^{\\alpha} L^{1-\\alpha}\\\\\n", - " f(K_2, L_2) &= z\\, K_2^{\\alpha} L_2^{1-\\alpha}\n", - "\\end{eqnarray}\n", - "$$\n", - "\n", - "The same concept applies to Python functions, where the arguments are just\n", - "placeholder names, and our `cobb_douglas` function is identical to" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2688518b", - "metadata": { - "hide-output": false, - "id": "2688518b" - }, - "outputs": [], - "source": [ - "def cobb_douglas2(K2, L2): # changed dummy variable names\n", - "\n", - " # Create alpha and z\n", - " z = 1\n", - " alpha = 0.33\n", - "\n", - " return z * K2**alpha * L2**(1 - alpha)\n", - "\n", - "cobb_douglas2(1.0, 0.5)" - ] - }, - { - "cell_type": "markdown", - "id": "ac370bdb", - "metadata": { - "id": "ac370bdb" - }, - "source": [ - "This is an appealing feature of functions for avoiding coding errors: names of variables\n", - "within the function are localized and won’t clash with those on the outside (with\n", - "more examples in [scope](#scope)).\n", - "\n", - "Importantly, when Python looks for variables\n", - "matching a particular name, it begins in the most local scope.\n", - "\n", - "That is, note that having an `alpha` in the outer scope does not impact the local one." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0a3f795", - "metadata": { - "hide-output": false, - "id": "f0a3f795" - }, - "outputs": [], - "source": [ - "def cobb_douglas3(K, L, alpha): # added new argument\n", - "\n", - " # Create alpha and z\n", - " z = 1\n", - "\n", - " return z * K**alpha * L**(1 - alpha) # sees local argument alpha\n", - "\n", - "print(cobb_douglas3(1.0, 0.5, 0.2))\n", - "print(\"Setting alpha, does the result change?\")\n", - "alpha = 0.5 # in the outer scope\n", - "print(cobb_douglas3(1.0, 0.5, 0.2))" - ] - }, - { - "cell_type": "markdown", - "id": "b670be91", - "metadata": { - "id": "b670be91" - }, - "source": [ - "A crucial element of the above function is that the `alpha` variable\n", - "was available in the local scope of the function.\n", - "\n", - "Consider the alternative where it is not. We have removed the `alpha`\n", - "function parameter as well as the local definition of `alpha`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9f6e8ae7", - "metadata": { - "hide-output": false, - "id": "9f6e8ae7" - }, - "outputs": [], - "source": [ - "def cobb_douglas4(K, L): # added new argument\n", - "\n", - " # Create alpha and z\n", - " z = 1\n", - "\n", - " # there are no local alpha in scope!\n", - " return z * K**alpha * L**(1 - alpha)\n", - "\n", - "alpha = 0.2 # in the outer scope\n", - "print(f\"alpha = {alpha} gives {cobb_douglas4(1.0, 0.5)}\")\n", - "alpha = 0.3\n", - "print(f\"alpha = {alpha} gives {cobb_douglas4(1.0, 0.5)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "3ca2c57c", - "metadata": { - "id": "3ca2c57c" - }, - "source": [ - "The intuition of scoping does not apply only for the “global” vs. “function”\n", - "naming of variables, but also for nesting.\n", - "\n", - "For example, we can define a version of `cobb_douglas` which\n", - "is also missing a `z` in its inner-most scope, then put the function\n", - "inside of another function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46ae03fa", - "metadata": { - "hide-output": false, - "id": "46ae03fa" - }, - "outputs": [], - "source": [ - "z = 1\n", - "def output_given_alpha(alpha):\n", - " # Scoping logic:\n", - " # 1. local function name doesn't clash with global one\n", - " # 2. alpha comes from the function parameter\n", - " # 3. z comes from the outer global scope\n", - " def cobb_douglas(K, L):\n", - " return z * K**alpha * L**(1 - alpha)\n", - "\n", - " # using this function\n", - " return cobb_douglas(1.0, 0.5)\n", - "\n", - "alpha = 100 # ignored\n", - "alphas = [0.2, 0.3, 0.5]\n", - "# comprehension variables also have local scope\n", - "# and don't clash with the alpha = 100\n", - "[output_given_alpha(alpha) for alpha in alphas]" - ] - }, - { - "cell_type": "markdown", - "id": "31407dd3", - "metadata": { - "id": "31407dd3" - }, - "source": [ - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "a2fb7cae", - "metadata": { - "id": "a2fb7cae" - }, - "source": [ - "## Exercises" - ] - }, - { - "cell_type": "markdown", - "id": "e26e52d6", - "metadata": { - "id": "e26e52d6" - }, - "source": [ - "### Exercise 1\n", - "\n", - "What happens if we try different inputs in our Cobb-Douglas production\n", - "function?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a30f1f7c", - "metadata": { - "hide-output": false, - "id": "a30f1f7c" - }, - "outputs": [], - "source": [ - "# Compute returns to scale with different values of `K` and `L` and `gamma`" - ] - }, - { - "cell_type": "markdown", - "id": "d9e2b3ce", - "metadata": { - "id": "d9e2b3ce" - }, - "source": [ - "([back to text](#dir2-4-1))" - ] - }, - { - "cell_type": "markdown", - "id": "d5a4a39b", - "metadata": { - "id": "d5a4a39b" - }, - "source": [ - "### Exercise 2\n", - "\n", - "Define a function named `var` that takes a list (call it `x`) and\n", - "computes the variance. This function should use the mean function that we\n", - "defined earlier.\n", - "\n", - "$ \\text{variance} = \\frac{1}{N-1} \\sum_i (x_i - \\text{mean}(x))^2 $" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d25d314a", - "metadata": { - "hide-output": false, - "id": "d25d314a" - }, - "outputs": [], - "source": [ - "# Your code here." - ] - }, - { - "cell_type": "markdown", - "id": "42b55c73", - "metadata": { - "id": "42b55c73" - }, - "source": [ - "([back to text](#dir2-4-2))" - ] - }, - { - "cell_type": "markdown", - "id": "746199cb", - "metadata": { - "id": "746199cb" - }, - "source": [ - "### Exercise 3\n", - "\n", - "Redefine the `returns_to_scale` function and add a docstring.\n", - "\n", - "Confirm that it works by running the cell containing `returns_to_scale?` below.\n", - "\n", - "*Note*: You do not need to change the actual code in the function — just\n", - "copy/paste and add a docstring in the correct line." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d575966", - "metadata": { - "hide-output": false, - "id": "4d575966" - }, - "outputs": [], - "source": [ - "# re-define the `returns_to_scale` function here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63efd956", - "metadata": { - "hide-output": false, - "id": "63efd956" - }, - "outputs": [], - "source": [ - "# test it here\n", - "\n", - "returns_to_scale?" - ] - }, - { - "cell_type": "markdown", - "id": "d070816f", - "metadata": { - "id": "d070816f" - }, - "source": [ - "([back to text](#dir2-4-3))" - ] - }, - { - "cell_type": "markdown", - "id": "fe3b042a", - "metadata": { - "id": "fe3b042a" - }, - "source": [ - "### Exercise 4\n", - "\n", - "Experiment with the `sep` and `end` arguments to the `print` function.\n", - "\n", - "These can *only* be set by name." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94454380", - "metadata": { - "hide-output": false, - "id": "94454380" - }, - "outputs": [], - "source": [ - "# Your code here." - ] - }, - { - "cell_type": "markdown", - "id": "b8d7ac0e", - "metadata": { - "id": "b8d7ac0e" - }, - "source": [ - "([back to text](#dir2-4-4))" - ] - } - ], - "metadata": { - "date": 1633586295.4326208, - "filename": "functions.md", - "kernelspec": { - "display_name": "Python", - "language": "python3", - "name": "python3" - }, - "title": "Functions", - "colab": { - "name": "functions.ipynb", - "provenance": [] - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/day_02/python_by_example.ipynb b/day_02/python_by_example.ipynb deleted file mode 100644 index 945dea4..0000000 --- a/day_02/python_by_example.ipynb +++ /dev/null @@ -1,1671 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "56ff7eda", - "metadata": {}, - "source": [ - "\n", - "\n", - "
\n", - " \n", - " \"QuantEcon\"\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "84b7ac3c", - "metadata": {}, - "source": [ - "# An Introductory Example\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "adf85dfd", - "metadata": {}, - "source": [ - "## Contents\n", - "\n", - "- [An Introductory Example](#An-Introductory-Example) \n", - " - [Overview](#Overview) \n", - " - [The Task: Plotting a White Noise Process](#The-Task:-Plotting-a-White-Noise-Process) \n", - " - [Version 1](#Version-1) \n", - " - [Alternative Implementations](#Alternative-Implementations) \n", - " - [Another Application](#Another-Application) \n", - " - [Exercises](#Exercises) " - ] - }, - { - "cell_type": "markdown", - "id": "123a514b", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "We’re now ready to start learning the Python language itself.\n", - "\n", - "In this lecture, we will write and then pick apart small Python programs.\n", - "\n", - "The objective is to introduce you to basic Python syntax and data structures.\n", - "\n", - "Deeper concepts will be covered in later lectures.\n", - "\n", - "You should have read the [lecture](https://python-programming.quantecon.org/getting_started.html) on getting started with Python before beginning this one." - ] - }, - { - "cell_type": "markdown", - "id": "dd04aadf", - "metadata": {}, - "source": [ - "## The Task: Plotting a White Noise Process\n", - "\n", - "Suppose we want to simulate and plot the white noise\n", - "process $ \\epsilon_0, \\epsilon_1, \\ldots, \\epsilon_T $, where each draw $ \\epsilon_t $ is independent standard normal.\n", - "\n", - "In other words, we want to generate figures that look something like this:\n", - "\n", - "![https://python-programming.quantecon.org/_static/lecture_specific/python_by_example/test_program_1_updated.png](https://python-programming.quantecon.org/_static/lecture_specific/python_by_example/test_program_1_updated.png)\n", - "\n", - " \n", - "(Here $ t $ is on the horizontal axis and $ \\epsilon_t $ is on the\n", - "vertical axis.)\n", - "\n", - "We’ll do this in several different ways, each time learning something more\n", - "about Python.\n", - "\n", - "We run the following command first, which helps ensure that plots appear in the\n", - "notebook if you run it on your own machine." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0cc7e562", - "metadata": { - "hide-output": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "1671a1d3", - "metadata": {}, - "source": [ - "## Version 1\n", - "\n", - "\n", - "\n", - "Here are a few lines of code that perform the task we set" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f9ef3835", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "plt.rcParams['figure.figsize'] = (5,3)\n", - "\n", - "ϵ_values = np.random.randn(100)\n", - "plt.plot(ϵ_values)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "948de567", - "metadata": {}, - "source": [ - "Let’s break this program down and see how it works.\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "f801fb81", - "metadata": {}, - "source": [ - "### Imports\n", - "\n", - "The first two lines of the program import functionality from external code\n", - "libraries.\n", - "\n", - "The first line imports [NumPy](https://python-programming.quantecon.org/numpy.html), a favorite Python package for tasks like\n", - "\n", - "- working with arrays (vectors and matrices) \n", - "- common mathematical functions like `cos` and `sqrt` \n", - "- generating random numbers \n", - "- linear algebra, etc. \n", - "\n", - "\n", - "After `import numpy as np` we have access to these attributes via the syntax `np.attribute`.\n", - "\n", - "Here’s two more examples" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a783ea17", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sqrt(4)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6adab364", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1.3862943611198906" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.log(4)" - ] - }, - { - "cell_type": "markdown", - "id": "ed236510", - "metadata": {}, - "source": [ - "We could also use the following syntax:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "80d2daad", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy\n", - "\n", - "numpy.sqrt(4)" - ] - }, - { - "cell_type": "markdown", - "id": "71c05b11", - "metadata": {}, - "source": [ - "But the former method (using the short name `np`) is convenient and more standard." - ] - }, - { - "cell_type": "markdown", - "id": "b581441f", - "metadata": {}, - "source": [ - "#### Why So Many Imports?\n", - "\n", - "Python programs typically require several import statements.\n", - "\n", - "The reason is that the core language is deliberately kept small, so that it’s easy to learn and maintain.\n", - "\n", - "When you want to do something interesting with Python, you almost always need\n", - "to import additional functionality." - ] - }, - { - "cell_type": "markdown", - "id": "a4540370", - "metadata": {}, - "source": [ - "#### Packages\n", - "\n", - "\n", - "\n", - "As stated above, NumPy is a Python *package*.\n", - "\n", - "Packages are used by developers to organize code they wish to share.\n", - "\n", - "In fact, a package is just a directory containing\n", - "\n", - "1. files with Python code — called **modules** in Python speak \n", - "1. possibly some compiled code that can be accessed by Python (e.g., functions compiled from C or FORTRAN code) \n", - "1. a file called `__init__.py` that specifies what will be executed when we type `import package_name` \n", - "\n", - "\n", - "You can check the location of your `__init__.py` for NumPy in python by running the code:" - ] - }, - { - "cell_type": "markdown", - "id": "7c7e5c25", - "metadata": { - "hide-output": false - }, - "source": [ - "```ipython\n", - "import numpy as np\n", - "\n", - "print(np.__file__)\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "id": "368a8338", - "metadata": {}, - "source": [ - "#### Subpackages\n", - "\n", - "\n", - "\n", - "Consider the line `ϵ_values = np.random.randn(100)`.\n", - "\n", - "Here `np` refers to the package NumPy, while `random` is a **subpackage** of NumPy.\n", - "\n", - "Subpackages are just packages that are subdirectories of another package.\n", - "\n", - "For instance, you can find folder `random` under the directory of NumPy." - ] - }, - { - "cell_type": "markdown", - "id": "48753714", - "metadata": {}, - "source": [ - "### Importing Names Directly\n", - "\n", - "Recall this code that we saw above" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d501633b", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "np.sqrt(4)" - ] - }, - { - "cell_type": "markdown", - "id": "a6648e84", - "metadata": {}, - "source": [ - "Here’s another way to access NumPy’s square root function" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fafee420", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numpy import sqrt\n", - "\n", - "sqrt(4)" - ] - }, - { - "cell_type": "markdown", - "id": "63fb5694", - "metadata": {}, - "source": [ - "This is also fine.\n", - "\n", - "The advantage is less typing if we use `sqrt` often in our code.\n", - "\n", - "The disadvantage is that, in a long program, these two lines might be\n", - "separated by many other lines.\n", - "\n", - "Then it’s harder for readers to know where `sqrt` came from, should they wish to." - ] - }, - { - "cell_type": "markdown", - "id": "a668dae2", - "metadata": {}, - "source": [ - "### Random Draws\n", - "\n", - "Returning to our program that plots white noise, the remaining three lines\n", - "after the import statements are" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dcc4d204", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ϵ_values = np.random.randn(100)\n", - "plt.plot(ϵ_values)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c2a56d62", - "metadata": {}, - "source": [ - "The first line generates 100 (quasi) independent standard normals and stores\n", - "them in `ϵ_values`.\n", - "\n", - "The next two lines genererate the plot.\n", - "\n", - "We can and will look at various ways to configure and improve this plot below." - ] - }, - { - "cell_type": "markdown", - "id": "c9889d3d", - "metadata": {}, - "source": [ - "### Note: What is a Random Seeds" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "11e485ed", - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4d8223a1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ϵ_values = np.random.randn(100)\n", - "plt.plot(ϵ_values)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4010760d", - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3f12df22", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ϵ_values = np.random.randn(100)\n", - "plt.plot(ϵ_values)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4ed227fb", - "metadata": {}, - "source": [ - "## Alternative Implementations\n", - "\n", - "Let’s try writing some alternative versions of [our first program](#ourfirstprog), which plotted IID draws from the standard normal distribution.\n", - "\n", - "The programs below are less efficient than the original one, and hence\n", - "somewhat artificial.\n", - "\n", - "But they do help us illustrate some important Python syntax and semantics in a familiar setting." - ] - }, - { - "cell_type": "markdown", - "id": "a864e098", - "metadata": {}, - "source": [ - "### A Version with a For Loop\n", - "\n", - "Here’s a version that illustrates `for` loops and Python lists.\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1676245d", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ts_length = 100\n", - "ϵ_values = [] # empty list\n", - "\n", - "for i in range(ts_length):\n", - " e = np.random.randn()\n", - " ϵ_values.append(e)\n", - "\n", - "plt.plot(ϵ_values)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "093877de", - "metadata": {}, - "source": [ - "In brief,\n", - "\n", - "- The first line sets the desired length of the time series. \n", - "- The next line creates an empty *list* called `ϵ_values` that will store the $ \\epsilon_t $ values as we generate them. \n", - "- The statement `# empty list` is a *comment*, and is ignored by Python’s interpreter. \n", - "- The next three lines are the `for` loop, which repeatedly draws a new random number $ \\epsilon_t $ and appends it to the end of the list `ϵ_values`. \n", - "- The last two lines generate the plot and display it to the user. \n", - "\n", - "\n", - "Let’s study some parts of this program in more detail.\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "85eba02a", - "metadata": {}, - "source": [ - "### Lists\n", - "\n", - "\n", - "\n", - "Consider the statement `ϵ_values = []`, which creates an empty list.\n", - "\n", - "Lists are a *native Python data structure* used to group a collection of objects.\n", - "\n", - "Items in lists are ordered, and duplicates are allowed in lists.\n", - "\n", - "For example, try" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ee1f60d5", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "list" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = [10, 'foo', False]\n", - "type(x)" - ] - }, - { - "cell_type": "markdown", - "id": "abb82775", - "metadata": {}, - "source": [ - "The first element of `x` is an [integer](https://en.wikipedia.org/wiki/Integer_%28computer_science%29), the next is a [string](https://en.wikipedia.org/wiki/String_%28computer_science%29), and the third is a [Boolean value](https://en.wikipedia.org/wiki/Boolean_data_type).\n", - "\n", - "When adding a value to a list, we can use the syntax `list_name.append(some_value)`" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "dc1b46c9", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[10, 'foo', False]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2ce1d842", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[10, 'foo', False, 2.5]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.append(2.5)\n", - "x" - ] - }, - { - "cell_type": "markdown", - "id": "2621e155", - "metadata": {}, - "source": [ - "Here `append()` is what’s called a *method*, which is a function “attached to” an object—in this case, the list `x`.\n", - "\n", - "We’ll learn all about methods [later on](https://python-programming.quantecon.org/oop_intro.html), but just to give you some idea,\n", - "\n", - "- Python objects such as lists, strings, etc. all have methods that are used to manipulate the data contained in the object. \n", - "- String objects have [string methods](https://docs.python.org/3/library/stdtypes.html#string-methods), list objects have [list methods](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists), etc. \n", - "\n", - "\n", - "Another useful list method is `pop()`" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "20b510ae", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[10, 'foo', False, 2.5]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e0f4985c", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.5" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.pop()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7840fbbc", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[10, 'foo', False]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x" - ] - }, - { - "cell_type": "markdown", - "id": "5bc40e53", - "metadata": {}, - "source": [ - "Lists in Python are zero-based (as in C, Java or Go), so the first element is referenced by `x[0]`" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "537085ff", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x[0] # first element of x" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7137175e", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'foo'" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x[1] # second element of x" - ] - }, - { - "cell_type": "markdown", - "id": "8e4c3bb9", - "metadata": {}, - "source": [ - "### The For Loop\n", - "\n", - "\n", - "\n", - "Now let’s consider the `for` loop from [the program above](#firstloopprog), which was" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "4c9df0ed", - "metadata": { - "hide-output": false - }, - "outputs": [], - "source": [ - "for i in range(ts_length):\n", - " e = np.random.randn()\n", - " ϵ_values.append(e)" - ] - }, - { - "cell_type": "markdown", - "id": "e9a328dd", - "metadata": {}, - "source": [ - "Python executes the two indented lines `ts_length` times before moving on.\n", - "\n", - "These two lines are called a `code block`, since they comprise the “block” of code that we are looping over.\n", - "\n", - "Unlike most other languages, Python knows the extent of the code block *only from indentation*.\n", - "\n", - "In our program, indentation decreases after line `ϵ_values.append(e)`, telling Python that this line marks the lower limit of the code block.\n", - "\n", - "More on indentation below—for now, let’s look at another example of a `for` loop" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "3989d19d", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The plural of dog is dogs\n", - "The plural of cat is cats\n", - "The plural of bird is birds\n" - ] - } - ], - "source": [ - "animals = ['dog', 'cat', 'bird']\n", - "for animal in animals:\n", - " print(\"The plural of \" + animal + \" is \" + animal + \"s\")" - ] - }, - { - "cell_type": "markdown", - "id": "6f6c25df", - "metadata": {}, - "source": [ - "This example helps to clarify how the `for` loop works: When we execute a\n", - "loop of the form" - ] - }, - { - "cell_type": "markdown", - "id": "dbbce3d9", - "metadata": { - "hide-output": false - }, - "source": [ - "```python3\n", - "for variable_name in sequence:\n", - " \n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "id": "24e8c514", - "metadata": {}, - "source": [ - "The Python interpreter performs the following:\n", - "\n", - "- For each element of the `sequence`, it “binds” the name `variable_name` to that element and then executes the code block. \n", - "\n", - "\n", - "The `sequence` object can in fact be a very general object, as we’ll see\n", - "soon enough." - ] - }, - { - "cell_type": "markdown", - "id": "cd045b26", - "metadata": {}, - "source": [ - "### A Comment on Indentation\n", - "\n", - "\n", - "\n", - "In discussing the `for` loop, we explained that the code blocks being looped over are delimited by indentation.\n", - "\n", - "In fact, in Python, **all** code blocks (i.e., those occurring inside loops, if clauses, function definitions, etc.) are delimited by indentation.\n", - "\n", - "Thus, unlike most other languages, whitespace in Python code affects the output of the program.\n", - "\n", - "Once you get used to it, this is a good thing: It\n", - "\n", - "- forces clean, consistent indentation, improving readability \n", - "- removes clutter, such as the brackets or end statements used in other languages \n", - "\n", - "\n", - "On the other hand, it takes a bit of care to get right, so please remember:\n", - "\n", - "- The line before the start of a code block always ends in a colon \n", - " - `for i in range(10):` \n", - " - `if x > y:` \n", - " - `while x < 100:` \n", - " - etc., etc. \n", - "- All lines in a code block **must have the same amount of indentation**. \n", - "- The Python standard is 4 spaces, and that’s what you should use. " - ] - }, - { - "cell_type": "markdown", - "id": "e210f170", - "metadata": {}, - "source": [ - "### While Loops\n", - "\n", - "\n", - "\n", - "The `for` loop is the most common technique for iteration in Python.\n", - "\n", - "But, for the purpose of illustration, let’s modify [the program above](#firstloopprog) to use a `while` loop instead.\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "2c9effd1", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ts_length = 100\n", - "ϵ_values = []\n", - "i = 0\n", - "while i < ts_length:\n", - " e = np.random.randn()\n", - " ϵ_values.append(e)\n", - " i = i + 1\n", - "plt.plot(ϵ_values)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "bae1c9a2", - "metadata": {}, - "source": [ - "A while loop will keep executing the code block delimited by indentation until the condition (`i < ts_length`) is satisfied.\n", - "\n", - "In this case, the program will keep adding values to the list `ϵ_values` until `i` equals `ts_length`:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "7f9acae6", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "i == ts_length #the ending condition for the while loop" - ] - }, - { - "cell_type": "markdown", - "id": "f6a4977e", - "metadata": {}, - "source": [ - "Note that\n", - "\n", - "- the code block for the `while` loop is again delimited only by indentation. \n", - "- the statement `i = i + 1` can be replaced by `i += 1`. " - ] - }, - { - "cell_type": "markdown", - "id": "9fe1230f", - "metadata": {}, - "source": [ - "## Another Application\n", - "\n", - "Let’s do one more application before we turn to exercises.\n", - "\n", - "In this application, we plot the balance of a bank account over time.\n", - "\n", - "There are no withdraws over the time period, the last date of which is denoted\n", - "by $ T $.\n", - "\n", - "The initial balance is $ b_0 $ and the interest rate is $ r $.\n", - "\n", - "The balance updates from period $ t $ to $ t+1 $ according to $ b_{t+1} = (1 + r) b_t $.\n", - "\n", - "In the code below, we generate and plot the sequence $ b_0, b_1, \\ldots, b_T $.\n", - "\n", - "Instead of using a Python list to store this sequence, we will use a NumPy\n", - "array." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "f3463484", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "r = 0.025 # interest rate\n", - "T = 50 # end date\n", - "b = np.empty(T+1) # an empty NumPy array, to store all b_t\n", - "b[0] = 10 # initial balance\n", - "\n", - "for t in range(T):\n", - " b[t+1] = (1 + r) * b[t]\n", - "\n", - "plt.plot(b, label='bank balance')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9278f1b9", - "metadata": {}, - "source": [ - "The statement `b = np.empty(T+1)` allocates storage in memory for `T+1`\n", - "(floating point) numbers.\n", - "\n", - "These numbers are filled in by the `for` loop.\n", - "\n", - "Allocating memory at the start is more efficient than using a Python list and\n", - "`append`, since the latter must repeatedly ask for storage space from the\n", - "operating system.\n", - "\n", - "Notice that we added a legend to the plot — a feature you will be asked to\n", - "use in the exercises." - ] - }, - { - "cell_type": "markdown", - "id": "0e323dec", - "metadata": {}, - "source": [ - "## Exercises\n", - "\n", - "Now we turn to exercises. It is important that you complete them before\n", - "continuing, since they present new concepts we will need." - ] - }, - { - "cell_type": "markdown", - "id": "bf406a06", - "metadata": {}, - "source": [ - "## Exercise 3.1\n", - "\n", - "Your first task is to simulate and plot the correlated time series\n", - "\n", - "$$\n", - "x_{t+1} = \\alpha \\, x_t + \\epsilon_{t+1}\n", - "\\quad \\text{where} \\quad\n", - "x_0 = 0\n", - "\\quad \\text{and} \\quad t = 0,\\ldots,T\n", - "$$\n", - "\n", - "The sequence of shocks $ \\{\\epsilon_t\\} $ is assumed to be IID and standard normal.\n", - "\n", - "In your solution, restrict your import statements to" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "4da50a38", - "metadata": { - "hide-output": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "53e448a3", - "metadata": {}, - "source": [ - "Set $ T=200 $ and $ \\alpha = 0.9 $." - ] - }, - { - "cell_type": "markdown", - "id": "8eeda2bc", - "metadata": {}, - "source": [ - "## Solution to[ Exercise 3.1](https://python-programming.quantecon.org/#pbe_ex1)\n", - "\n", - "Here’s one solution." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "4578e185", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "α = 0.9\n", - "T = 200\n", - "x = np.empty(T+1)\n", - "x[0] = 0\n", - "\n", - "for t in range(T):\n", - " x[t+1] = α * x[t] + np.random.randn()\n", - "\n", - "plt.plot(x)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b1ee91d3", - "metadata": {}, - "source": [ - "## Exercise 3.2\n", - "\n", - "Starting with your solution to exercise 1, plot three simulated time series,\n", - "one for each of the cases $ \\alpha=0 $, $ \\alpha=0.8 $ and $ \\alpha=0.98 $.\n", - "\n", - "Use a `for` loop to step through the $ \\alpha $ values.\n", - "\n", - "If you can, add a legend, to help distinguish between the three time series.\n", - "\n", - "Hints:\n", - "\n", - "- If you call the `plot()` function multiple times before calling `show()`, all of the lines you produce will end up on the same figure. \n", - "- For the legend, noted that the expression `'foo' + str(42)` evaluates to `'foo42'`. " - ] - }, - { - "cell_type": "markdown", - "id": "682f5011", - "metadata": {}, - "source": [ - "## Solution to[ Exercise 3.2](https://python-programming.quantecon.org/#pbe_ex2)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "c74097b2", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family ['DejaVu Sans Display'] not found. Falling back to DejaVu Sans.\n" - ] - }, - { - "data": { - "image/png": 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A0mqa/riIKDSbT5sGmw7puJJoq4oKk6JuPp1BpdgdFSo5lo7xm62/yXnPTIqbujexrXfbYR3bEQepFAN1IKMhrejTacHegb283vW6brmZKCm67C5cNhdQuCh4OBnmk098knt33nvog7UwpbBIcYZgJi8wCCDffLqrfxdPNj85/uPmqT6vw4vf6S+4bYmrpOA+hxNm0/EfdvyBB/c+qCc898ZEYro0/77eNXJz6n8JDGpKMdBgIkVLhUwHrn3iWq549ArdWjPR6FMwgm0K+RWfOPAEz7U+xy2v3KIviC0cGbBIcYaQT0SSHMxKsS/ex4cf/jCfffqzvNnz5pjHzGQzhFO5yfhVvqoRe1AeCYE28nuXukuJpWPc9PxNvPuv7yaejtMbF6S4umo1gO4j/ZdFSMtnNZtPLaU45UhlU7QPtZNRM/QnBGFNNE8RRk/LkAu8eCbOPTvvOYTRWphqWKQ4Q8hXitKMmL+qjKaFcny+9fkxj1loRTqSPxFm3nyqqqqukH9xzi+49phrcdvddEW72NS9Sc+zXF6+HDDSTf5lkWM+9YrnVr++KUehReJEzacwegK/2epxz457cko9WphZWKQ43UjFoXfvsJfHMp82FDfkvL++ff2YH5V/TBiDFF0zqxQHEgN6V/P5JfP55OpPsjAoGlPLAJxSdymz/KKxdEFS3PdP+MMHh+fvvRUxqCnFQIOIQAVLKU4DCpkzJ2M+HSktoz/er8cLVHor6Yv38fd9f5/4QC1MCyxSnG78/fPw4zVw4MWclwuZT9PZNPGMWDFeOP9CPHYP168RnUI2d2/O8TcWgiRFh2IUKhqNFPWUjBkiRWk6DbqDOO1OAJ0AJSmWe8upLRLpBx2RAubTjb+EnQ/D1n+BBH9z9KlUilZKxpRjIDEw7LXJmE/1nop5an5z92YA5pXM49JFlwLo3WwszDwsUpxu7H9WPHbvzHlZEpjHLnob9sR6dFMpwMdXfpyXPvQSH1/xceqK6khn07za+eqoHyWJtjHQiNvuBkbOUQTDfFpIYR4OyEo+Fd4K/bUGv1DIm7o36e/VFNUAgkSHJfEnNB9q+C3ub0wOQXxAPA/UgUsqRYsUpxqFlOJkzKeSSPOVolzwra5azZzAHCC3/rGFmYVFitOJeAhCzeJ5njKURDS3ZC4gfIpSCTpsDlx2Fw6bA0VRWFu3FoAN7RsAaI+0c8srt/DFZ7/IN9Z/Q99P5igG3UEaA40A1PhqRhye2XxaqK/ldKMrNrwMnVSKMjihwltBmacMl82Fiqrvo0Ouwt/qpCj9iS4/eEpMPkWLFKca+UrRY/fgsE28TLSuFPMCbaQ/cXXlan0R2BL+FzD/HyWwCoJPJzqNvLrvdPyTjQ8+z8m1J3Ppokt1VTc/OJ/tfdvpjnWPWD1jbe1a7t99Py+1vwTA7W/czv2779ff74v38f0zv68TbcAV4OMrP85TzU9xav2pIw5PKsWMmmEoNTTh7uKHCulHNStFSYoSFd4KFEWhtriWA4MHaI+0U19cb2zwr0KK0nQaqBOPVp7itEEqxUZ/I83hZt3NMFEUuYZHnyYyCb0Dzuqq1XoBja5oF8lMEpfddShDtzAFsEhxOtEp0igywO8iu1GBHX07eLblWUo9pYAgRRAEoZOiI5cUT6g5AYBd/bsYTA7q6RkXzr+Qh/c/zOMHHufOLXfqHTX8Lj9nNJzBGQ1njDo8j8ODy+YimU0ymBw87KQoI25lYXIYTorlnnJAKN4Dgwf0YJtkJsmjTY9ySipMBUAhf+NbCVIplmgLAitPcdogleK62etw2pwsLF04qeMUUorPtTxHIpOg2letm069Di+xdIy2SBtzSuYcytAtTAEs8+l0QiPFsM2G2Ti5N7RXL4ItzaeJTELvf5jv1K/wVujq6LXO19g7IKJZrzv2Ov77pP8G4Gebf6aTzERWtjOZqygDbSp9BilW+ip1fyiIQBtA9yvKXMW/7/s7//38f/O/Ls3HGO48HEOeOeiRpxopjpWnmByC20+Hh2+Y/rG9xSBJsdxTznXHXsd5c86b1HEKRZ8+vP9hAN45950oioKiKPq9bfkVjwxYpDid6BRmkgGb+JmLnEWUecoAaA4LX2OVt0qvONMUatK3y8eqilUA3LfrPjJqhjJPGdW+ai5deCnFzmJS2ZReBk2aZMaDmcxVLGQ+tSm2nHQU+V5tsYhAlUrxwOAB8WjTlhvJMCTewh0Jolpd2CLtt3KOUfu0bRN0vAEv3/HWNy1PMWTCvrw3JgupFNe3r+cLz36Bzd2bebZFBN69c+479e3k9T5eUtzdvzunBOJUIpFJzEh8wZEEixSnC9ms7lMcsIufOegOsiC4IGezgDugB5rIklKFSHFl5UoAnm0VN9XS8qX6SlM2CN7etx0Q5tPxQhLoTChFqZbN5lPINaHqpFiUS4pSVXfaTNV6Im9htShNcDLqVDefjkCKMlIVFbb9bTpH9pbDgPbblbpLD+k48l4PJUI8sv8RPvLIR0hkEswJzGFp2VJ9u4kE27RH2nnfg+/jI498ZMrbqe0P7ee0P5zGNzd8c0qPe7TBIsXpwkCTPpGFNKVY4i7RfYgSfpdfJ8V9oX3ACKRYIUhRFvleVrZMf09GmspWVJNSioeZFM3VbMzmUzAmCTBIMd98Kgm1y2EnKzd+KysiGVAkr42x8hRjprSCf4UczimENJ8GPcFDOs7yiuX8/eK/c+vZt7K6crV+70rTqYQ0n7ZExibFvaG9pNU0B8MHeWjfQ4c0vnw82fwk8Uyclztezn0jGX1rW2HyYJHidEEznWJzELKLAJgSV8kwpWgmxdGU4tLypTlh4UvLjZWmVIoSkyHFw52rGE6FSWQSwMhK0aE49PFJpdgWaUNVVV0pphWFfm3R8ZYOtpFmUqkUzXmKhcxdsQHjefNLRqCOhTEho08PVSmCWLCua1zHL875BefNOY8qXxUXL7g4Z5uJKEUZNwBw55Y7p7RX4ysdrwBGIX5AXFu3nwY/OQHSiSn7rCMZFilOFzq0At51x+o+xaA7mKMUfQ4fTpuT6iKRYC+T9wuRotvuZnHpYv3/ZeWGUhxGihMJtJmhUm8ycd/v9ONxeHLek6RY5i3DpojfTirFaDpKOBXWlSJAp0MsOt7SwTZa5xDDfOo13isUbGNWiqiw7a/TNrS3EhKZhH4fHqpSNMPr8HLLmbfwxHue0P3jEhMJtDF3lWkON/NY02NTMr5UNqXnTw4mBw3TbHwA+vZCuK1gucppR38TPPbf8PrvDttHTispfv/73+ess86itrYWt9vN7Nmz+ehHP8q+fftG3e/yyy/X/WXmv4aGhlH3O6KgRZ7SeLLuUyxx5ypFSV75VWd8jsIlpaQJNeAKUFdUp78+259LihPxKc6U+VQqvQpfxbD3jq85nrW1a/nw0g/rr3kdXj1IaWvPVt1UDNBl1xT0FCnF5t4oN97/Bvu6jyCTUSpPKZojlAuRovQpyol971PTNbK3FKQ/0a7YR2y5digo1LFGkuJgcnBMi41UinKOeKL5iUmN42D4IN995bu6CyP/ntIbfkdNjb9790zqsw4JHW/CSz+BV+48bB85raT44x//mH/+85+4XC7q6+tpbm7mrrvu4tRTT2VwcGxzXX19PSeddJL+t2bNmukc7tRCmk9nn6r7FIMOHyXuEt1PJs2c+fVJRyopdWLtiQCsqVqTc3NJn6LE0eBT3DMgbjCZq2WG1+HljnPu4PIVl+e8Pq9kHjC8OLqhFKeGFO/deJA/vHyQ321onpLjTQl0n6JGhjY7yNSVQrmK0nw6/23isfW1wmbWtwBufWIXdz43+kJ7vJD+xBJ3yYgt16YaPqdPX/C1hkdXi1IpHlt9rNh+kmkcd229i99u+y0/fPWHAGzs3JjzvmzbNuOk2N8kHktnj7rZVGJaSfGqq67iwIEDHDhwgH379vHZz34WgI6ODp58cuzGuVdeeSXr16/X//72t9Gj6BKJBIODgzl/M4JEBPqFf5CG4xnQlExQ68QtTaiSvPKV4kik+PbGt3PrWbfy/07+fzmvl7hLcvwfEyFFqVZbI6081vSYXipuurGjbwcAS8qWjHsf+bu91PZSzutTTYqDcWE6CsWmNrrvkKD7FE0FFkbLVZTm07mng80J0R4YODC9Y5wB9EYS3PrEbr758HZiyUP3r8l0jKnwJ04E0q8oU7VGgiTFYyqPAYSPfTKQAWv/aPoH4WRY9ydK6H7FmJkUD7/5VJWkGHyLkOJ///d/09hoqJjTTz9df+52uwvtkoNbb70Vt9vNrFmz+MAHPsDevaOflJtvvpmSkhL9b9asWaNuP23oEqkRFNdAUQUhh+gAUaKIR2lCnahSVBSFdbPXFex8IdWiXXHgdXiHvV8Q3TspGRS+uZ39O/nPf/4nt7566/j2PUTs7BMF0idCilIpytQTCcN8OjU+xag2uQ4l0lNyvClBvk8RTLmKBZSiNJ8W10DNCvG8dfSC8kcjBrSFi6pC68Ch14GdqsjTiUJe2/sGRle80ny6unI1IMZbqInxqFBVnVzjmTj/9+b/6f5EuUA3lKIp6GYGlOLG/u2c0tjAdeFNh+0zD1ugTTqd5ic/+QkA8+bNY926daNu7/F4qK+vp6GhgZaWFv74xz9ywgkn0No6srngxhtvJBQK6X8HDx6c0u8wbnRuEY/VojluSJu0S7Sf+5S6UwAj97DUU5oTWTqZNjUBh3De21Tv+Mw+2Sz8+nwWPngDXrsR6CLrq04nUpkUe0NigTMZpSjh1RYZnR6NKKZIKcZSghQjRxIp6j5F07UxWv1TaT71BqH+OPG89a3XnigcN87Rwb5DrwM7VTmKE4VcKEu3QiGoJjKbE5hD0B0EJmhC7d0L31tE98B+/aU7t9xJLB1jUekiTqwRLhpdKc6w+bRjqIOw3UZMK2F5OHBYSHFoaIhLLrmEp59+mpqaGh588MFRleINN9xAT08PW7duZe/evdx+++0A9Pf386tf/WrE/dxuN4FAIOdvRiD9idoKfUBLMA9qLp0zGs7g+Q88z8dXfBwQVVzMJtTJtKnJJoSfMp3yjLGlhkgnDHVTkYrz9Jk/4fH3PI6CwsHwwZyw7+nAnoE9pLNpAq6AnmoxHsjVtMQyj0jl6HIKciQ+MCUFsmNHmlLMpEF2ZjebT0fLVZTmU2+piRQnoBSHeuD3H4AdR3bz23DcMHEf7D90pSjNp4dbKcoF396BvSTSGa77w+vcuzF3UW9uyl3hraCuWATbTciEuu9pskNd9GkRtgpibqopquG2dbfp8Q5GoI1JKUZ78qKapxmqSqc2jmr/4QuynHZS7Ojo4Mwzz+TBBx9k0aJFvPDCCyxbtmzUfZYvX05RkUEMl112mf68ufkICn4YCZIUqwUphjThFszqaebDHPlmk+hkunyn44IgUqmi8U3m0lYPFMXD1BTVsKh0ETD9DU/N/sSJBDNUeCtyImtXuURwQidZI+hkjykar3kD/OkKGJiYxSCaFL/fUGLqcsAOCWbzmNmKMFJPxWw2N/q0/njxvG2TINjx4M37YNcj8ML/TmLAhw+RHKU4BeZT7XeTKuxwQSrFA4MHeKWpmwc3t3Hb07nKTKrEUncpTrtzcjVT+w8wYLORVgQhXr3qapaXL+cX7/gFNUU1eq1h3Xxq9ikC9E5NQNNoGEoNiUImkS46tDKO1XkL4unEtJLi1q1bWbt2La+++iqnn346L730EvPm5X65JUuWsGTJEt20CvDlL3+Znh6jtt8999yjP58zZ850DvnQoaomUlxOIpMgpogTW5IeOXDDrBQnYz6NDS4m0XMWya7z2N8zDh+DiRSlL25NtYjulf6F6cLO/on7E0H4VOeXGCbUFXZBkENkGJp7mnjxj/8Of/20+H73fJBtux/k9ieu54437tAriowFqRSPGPOpDLJR7OAwWVjyeyqqqvAvJsMgv6s3COULwB2AdAy6c/2xI6L9DfHYt3/07WYYU20+1ZXiYSbFal81xc5i0mpar+vbF0nmbKN3ldEqQMm0rAkpxYFmurViIqWeUj597Ke551336I0JZBSsYT7tzd3/MJhQP/fM57joLxexr/UlOrWx1vjrxthr6jCtpHjJJZdw4IA4weFwmH/7t39j7dq1rF27ljvvFHknO3fuZOfOnTkk+LWvfY3q6moWLlzIggULuOqqqwCoqanhyiuvnM4hHxL++Eozf3ryRdFQ2OaE8oV6qoNdVfGnRq4IYVaK+a2jxoMDPQmS3eeRic1h73jy6wqRYolokfPam7+D7p0THsN4MZnIUwmzX3EOdvwZMfl3nvtVOOkaQIHXfws/OZG7HEneX1/LbUO7+dHrP9KbNI8FGWgjFeOMQwbSuIrArKzz658+/iX4diPsF/VxcXgEcdpsULdavNa+eXyfKbcb6iocyHOEYHCKzafSbChbux0uKIrCvKAQDAfCwt8eTqRJZYyFnN5VRqsANSnz6UAzvVq0drlGgGbIVm1GoI1mLvVoxdEPAynu6d+DisqW9o10OkSshSzecTgwraSYSBgksGnTJjZs2KD/tbSMXNLom9/8JieffDKhUIiWlhYWLFjANddcw8aNG6mqGh55eSQglcny3w+8yeNPPS5eqFwMDpcezRbIZlESI6c75JDiBH2K8VSG1gFjlbyve4JKcagbevdy7INfAGCnTSUyTb4kVVX1yNPFZYvH2Ho4zH7F6nSGKs0c2JmKwDu/Ax9+QNzAmQSbvII0FC0/b0vPlnF9hgy0OeLMp6686yKfFPc9Ddm0UQBc84tFk2konSNeG0+5t3QiV1Gar5UjDOEpNp9KNVYownu6IU2oLUNN+mv9UUMt5ivFSZlPBw7oSrHSNbwLiG4+zVeKDSIAZ7pJUVVVfc48ENpHh0bg+Wlr04lpbTLc1NQ05jaF2pTcdNNN3HTTTdMwoulDNJEhnVVZ6dDMTdrKXCrFkkxWKMgRIEu9wcTNp819UT0vu0HpZvW2++GEL0OwceSd8pXi5nuoHuqjPuWh1elgc+8bnDqhUYwPvfFeIqkICopuspkI5GraY/cQSCWoymTYi1Ehh/lnw5VPwnPfZ4BOGNzDikSSLR633px5LEjzaTKTJZnO4nLMcDXE/MR9ifw8xQHN3y4DarxBnt/dw0d/9TJ/WuRmDUC4fezP69ouyFWiv0mPpD7SYDZxD8bThGIpSrzOSR9PJ0XvGKSYzcJ9H4OKRXD21MxVcsHXEWsCRB5i/1CKKr8InsvvKiOV4rhJMRGBaC/dJSIAsaKARUqS4kBigEw2g12S4qwTYc/j006KsXSMZFYsBHYPtdEvzadvFaX4r4QhzdS2UpGkKCpO6HlP2SzERyFFbSXkdXj1ep/jhVkZ/rv9Cc7u/zO8+uvRdzIncke6dJJcrIp1UssYlTUmi84hQV6V3kqctolPXsdUHkNdUR1va3wbSipKY0r87jn5XRUL4eLb6dd+xtNiInJzvKQYNSWBT1UEajyV4ZfP76dpPP7efOQXA5cw5ynGBiCuVSXq0/J5vaW83txPJquyLaz5H8dTHzbfxHoE+xXN0adwaGoxmhJ1dWF455Zh6Nkluo889wNBkFMAqRS74zKYMMNzLc/xt71/458H/6kv/HSfokaKg8nB8RXdCImAsx6NaCpswzMAgu4gCgpZNctAvN8ItJklleLeaa2MJH26AK8mhUvNa3NOqCDJoWJaleK/BAbbwV+j+Z9UVtq0yTmfFDMZY9LKg6qqzA0soNJbOSk/mwysWVEfoLRT3BxqbIAR4zpTsVzFEOnUK+AXF9dAsoNYftTZFEFW0pjsys/v8vPIpY+IhcOv/o1lCbGqlA2WzZDdDk6OxfhFMEB3rJvOoc4cVZ6PbFbVzacglEhpkWtSYzXj0Tc7+PpD23ituZ/bPjTBcoWFEvchN08xVCDC1hPUfW7NKW1SGU992A4RZJNRFeyKalRnOgJhNp+CIMUV9ZNrDizrgHod3rEjwIe0tKVsSpgYi8cg0XFA+stD6XZQUjhL1/O/W4e7MaRSLHIWEXQHGUgM0BZpK+iOUFXViPDuFwvhHq0WcyXDc/8cNgdBd5D+RD+9g82US4tB3RoR6JUaEvnAgfGnUk0EA6Z5J6ylslV7yg9byT2wlOKh4fkfwg+WwKu/JprMUE8P5UqYrOIw0jGk+TQ7svn0qrs2cu4PNvDABX/ntnW3TXgY+3vEpHnmokqKFWFKiw+NsnKUZjaJSKeuFL0BkQ8UTQwIBfLzM+Ghz014TCOhIyom5dGIaSzoSjoZYVlSI8W+bTmmeLNvotZZwvykIIc3e0dXi/F0rh9xaIqCbToHhVrtDMUnvnN+MXAJPU9xaPg5BfAGGYyJ8e+Pa6ksE1CK67Na6lR/E+x6DB7/Mkxhq6KpgDSf2rUJ9FCCbaR5sspXNfYkbI7KDE9NW65qXzW1RbWoZHH4t+IMitJry8uX41AM/WJWsaMF29y7817W3L2GDfd9GH64ElpEn8RuzU9XMYLg0/2KIc2a5PSBJ2DUH51GE+pA9/DFbfUEcpmnAhYpThYDzfDMt8Xzfc8wlMiw0iZW1P3+hXrofA4pjmA+fWFPL52DCQ72JSe1IpJKcVG1n0qXIIno0Ch1X6U/sUTzOcZDIsoQSHvFa1E1LRK32zfBG/cWPk4qPuFJ8lCVYg6SQ8xPpnApDsLJcE4/unAqTEYVYyv117FCI8+xTKj59TOnynwqFc2k6qkmRwi00fMUYyOQYqmuFHdGtW0jnaObv7IZPaXokaxmMuvZBfdfDS/cCnufnvj4pxGD2u+6oFIou0NJy8iP7hwVUSNafqoqKSmKwkULLgLAXfUodncXdsXFnefcyffO+h4OxYFdsdNQbCSyj9ag+OH9D5POpvlz18sQaoYNPwdM5tN04Wtbj0DV7qdeX5nwtZZrHX6mkRT7C5BiTeDwluu0SHGy+Mf/M6qM9OwimkyzSjOdtvsME6hhPi2sFDMmc91gfBITJgYpzqsopswhzKDx6DhIsXYV2A3TYEj18UaHIOWoohg9+JJhyOSNLRmFH62GX71zQmPVSdE3NaToBBb5BZFv7d3K081Ps7l7s56E7XP4cHtKWJ4YHylG80gxcigRqOt/Bg9eD9msfm5lrc49XWH+sXWck6keaDOCUkxGC5OiyXzantbMp9lUbumufIQOQipKEifPZEWwBwPNRjGA8eY5TjMSmqKXPsVldeL7TYVSHNOfCDBkUopT2MBZNCBWsDkHAKh3nkCxq5h1jev4w7v+wB3n3KErOTB6jx4M55rPs2pWT316we0gDboZvlsrO1mZLJwiVubVchWHOkgo8P6gnfc8+B6iMoJ5Gkkx1D+8vnX1YQyyAYsUJ45MGp76Jmz7i/Fa7x5i8QQrFUGKBzyGbV9vRTOCUjSb5/L9I+NBOJ6iR0vynVPhw68Ios7GRwnokKRYNheKDTNms1pFLCFumKjNllsdJt8f2rtb+CUPbhBRbeNE+5DwZU6VUgRYplXi+e223/KZpz/DZ576TG6+maeElVp60JaeLbqvsRDM/kQ4BKUYaoXHbhIBT51bGIwZnTdUVeW6P2zi6t++yp6ucfx2IylFSZIpEymag7RM5tMUDjIyL220CFRtsu9VA7SpFSTVPL/TNOavjhe/39DM8i89xtM7u/R7ZrlGis2HEGijm0/HijyFPPPp1ChFgNriWkqVFfr/5ZyiP19StoQTak7I2b5RWxDmd9c4GD6oFwoP221scYvFb1RRiGmm5opE4TlCKsW2SBsbPR46bSp98T7e8GqLsGnsltE/OFzxHs7IU7BIcWJQVfjjZfDsd8X/p38eHF7IJFEGmnTz6R77Qn0XaT4NZjJCKXZuy6kfaJ50J0OK0lcV8Djwe5z4VK2mYXqUyUHvUTYHio0JoFmtIp7USFFRDCUMw2semieCQiplBEyZ+VRV9ZXv0rKlALzRIwJE+uJ9+iQRdAfBU8KiZIr5ziBDqSG+9OKXCqYCQSGlOElS3PQ7o6rMUI9u5kums8RTWZp7xYTUHR65oIMOnRTzUjLMtU9lNLEW4AXkmE8B4lqt2FGDbTSzYI/qJ4uNFjVPNfXsGnu804yX9/eSzqq8uKdHL/O2vE4E1xzsi5LOTC4aND8PcFTkmE8LKMXnfgCv/HJS4yjPngFANhXAFl846rayO87BwVyluL03V9E/5xPXisxR9GazFEX7hbk8794+rlrUyn1yYDtP+4yOO5sULWdyOn2K2sIkYGq0PiVWpQnAIsWJoG8f7HpUVKu59Jew7ktQIezs1S3/IKgMEVXd7FINm79UJaXZLKDCz06G379ffz+XFCduPu0cFJNqVUDkMrkyYgJ1ZEbxrciJrWxejlI8qFbnKkUzRiXF8fXpS2fTut/mkEkxHddJZ1nlqmFvb+0RfrFSTym4S7AD3w4ei9Pm5JmDz/CnXX8qeNj8KjaTUorZLLz2W+P/WL+uFEEsZIY08s0P7CkIPXk/LyJSkmQ8ZCxMZFNhEOZT0+cOuUSxZz3YJhWDJ7+eq/40BdSvisCcZrU697O7dwpryfO3zljXDblQOdgX05X9oupiXA4bqYxK+2SCmYCumJiQx5UoPppS7N4FT34VHvnCcLfDOOBOHkOs7T3EWj7CQHR0gpfm07ZIm14sHETgGUCZTcwLzxeJ8yeDbCozGWFG/9Pl8L1FOWk3pzecjt/ppzM9xAPFxjW3KaZdN/37x19DdwQ89EYb33hoG9msaXEa6aY/I87dStM9fShBeZOBRYoTgRaqTs1KWPke8bxCmO4Wt4hJdmN2EX2me7JHK5MUtJm6V/QZOXVmn9WhKMXqgBtUFWdGKETnSKSYThifX7l0mFIciolLIpYf8DMaKY6z4klPrIesmsWhOHQTzaRhKj22sGLFsJxHmaJR6i7VS1QtyShcv+Z6AL77ynfZOzDcDBSfCvPpvqdEYINEtDdHsZlr0yZS4yDFkZL3q5aJMPmeXYZ5e95Z+tuqN6grVICQXTOfSqX4+t3w3PdErViJIaGAehHmyM2qVkHo1OsBRfgWX/oxPPFlePS/xh77NEB+p91dRoR1wOuksUz8Pk29kytLNyGlmONTzDNHH3hePGbT+u+Zg12PwY6HRzx0LJUlHTqebLwhp6JNIVT5qnDb3aTVtO6aANjUIRaFl7lE5OZ2p53uxpNonb0WgIpMRlwHux+HTNKY2wB3PMLbvSKAJ2kz5oE3+neSdXjF9zrEhtU3P7yDO5/fz5ttJrdM5xZCmpI9ptpIW7LMp0cyOrQyYTUrjdcqhP/QnxATzUvZ5XqEYSabIZISfsSm5TfBaf8h9jFN6NFDVIpdmvmtyu+BVAybFnHpzo6wWu7dIxSWuwT8NXlKsZKorhTHIEWzCa5/fDeINJ1W+aqwa/3RDvZFuemBLeOr12qG/A0dXpxODx9b8TFOrT+Vk2tPBoyi40FP0KjbGB/kw8s+zCl1p5DIJPjis18kmcmddKYk0ObN+3P/j/bpvj0g57sm0uMw9Y2UvO+vgaUXGP/7KnKuzZjdT8a0Eu9VtHqeUil2aZF+LS8bnUQ0BdSnClL8WfpC7l95O5z+n0apuPU/E4/5Sf27n4Cdj4z9fQ4R0mTa1Ct+F6/TjtNuY055Uc7rE4G5V+H4fIpm82k+Kb5oGmyeikxE4J7L4J4PwqY/FDx03HQN9g0NJ8VwPKWfV5tiM4JtNBPq87u7ebVDBJSdGo+zKi7miCdP+RjP1YlF/Jp4QuRaprXFc6TL+ICXf86/7fyn/u/Jvll4HV7CqTD7KuaIFw/Br6iqKt0RMaacaOyON+nXLFQrK1Zy9qyzede8d+F3+gsdZtpgkeJEIDsH1JrMdZWLcjZ5MbtMP9GDyUHQOmS0lL4dTr5ObJSK6qkMkSnyKVYF3GCqrerBIMV4KsM1v32VO5/bh9q1Qxv3YlFcOk8pqlnpkB/LfGrKdxvnqrGQP/HXLzbx+w3N/PqFpnEdQ0de8El56kLKwp9iQVD4YGLazV7mKTORYgibYuMbp36DUncpO/t3DjOjClLMYC/aCUpqckpRRiOWaKHksb4RlWK+Mi2IkZL3AU76hPE82Ci+67yzoXwBIXduflenGhRP5ETds9t4c+sD4lGb7PtUPw6bQhw3mx0rRVHxSi2ATCsgz1CXXvSBVAzu+RD88cMTCryaEFIxeO4HlMfEhCyJodgjFnJzyjWlaPp9N3ZsZGPHxhEPGUqE+OBDH+SrL31Vv2YqfBWjj0NVh/cZlL+DqkLTC8Z7ZrIBGGwVEcAAf7sud1t5uFTunGAuCt43lOTkm5/iil+/or/WoPUalH7055v2YHNEQbWzsL+dc4bEIuGhfQ/xfKtQsWdH8yxJ5nGGWjghnqBSS9l4W/lKVlaIxdamgGZtOAS/YiyVIaktBs2tv+jZxYBWWKDUU8qP3vYjbj795sOauA8WKU4MulI0kWKFQYph1cub6lzdj9Ojhb6rGQ/RBLmTmpaQfajRp1IpVvs9OaToIIOq3agv7u3h0a0dfOPv23lhvXYTyglOU4oZbLSpFZAV+ZW6UizTzGfDSNG0Oh6n+VSSotlHsKVVmE8m7AcykWIyneWrD27l9xuaIZ3b3UAE2mjpCJqJsdJXyYeWfggYXgknlszgrnoMX+OvcJX/c3KkKFNvNGWVHerJUaDmsnzx1MhKMZtVhc9lhOR9VVW5r3sW8XItyT6okfCHH4BPvcxgKjdytEVWtZELGrMvcaumbrVrtg8/S2vF9j1SrVTkLgABYwEQaoFMQkz401QNiZ0Pw5Nf5ar4b3Je9mukOLtC/D4HNPNpIpPg2ieu5ROPf4JIsjBRP7TvId7sfZP7dt8njuXy43V4C26rIxEWJkcQ5mswFgoDB3IDbyKdufua75tsCp7+5rDDx5K518RA1FhQ7emKEEmkee2AcT/mR6Du7BdBNu5sLa5QC++Iiutnc/dmhlJDVHorWZHJiyo2j3OoBzvwlbiT98YyvHvVlRxTKdJzNjk1yjC5gCaKftP3MYsCNdzJgGZBKnUf3i4lZlikOF5EurQVtpJbHLl8gR4G/3J2CRnshBNpMlmV/f1i9aVmigThOdzGTaRN6mbz3GTyFLvMSjGv/uFQRPxvJpxQs0bslVoupaZmWtQqUjgMpWh3QOlcWHiu2G6Y+dR0E/UfGFc9RFnNRirFbFZlW5sgkO7w+EnxmZ1dtHRpZbZcxWxtC+nkYs/m3kxmn6I5T3ROYA5ATsI/QH88hDO4XhzL12TctPFB+NX5IsBkLMjUG60CSCaSSxL7eowJeiSlmMmqnP/j57nkZy+ijuBT3HRwgM//+Q1uSb1XREEvPl+8oShgsw+7ng4ktd8h3C7Op1awAcUGba+LiW7IUIrHNgYB6JERsnIhZcagViPXXGZuhHKGhwxNzdRkc9WX3yP8ybpS1MynfbE+4pk4yWxSz9kD2NUZ5pfP7yeVyfLo/kdzjmUOskmms9zwp8385sWm3HFI06nTByXC96b7Fc2mUxhOinI7mR+c/z4Qywv26hyMs1sr3ygX3JFEWr92JClK82lrVKi4omQ5qFnqcLKqwljInzXrLGy+PJ++WSlq3++MdTfzpU9sxVuxkNVVqwF4KdEtch5HaW4wFvpNJmHzojM21Kn7MA93P0szLFIcL6TptHxB7ord4dYVwYuyLBbi4tVJMV1EJJESk5WM4tMmukNPydCUYiBXKQIMhvpztgFYqGiTmCTF2mPInPcdPp+8WoxVI8UkKqlPbYASLZLWTIrZbO7NnBoqHFCQh/zE/ea+qE465jGOhoN9US7/1Sv8+JFN4gVXERubjLFlU8Gc7XN9isZkPUurkpGf37V54DEUu7hpbe42Ilp5OHb/QwRQvHxHzvZ3vdTEzQ9vz03xkOehVHQByeY1ajV/15F8ir2RBNvbB9l0cABVyye769Ue2kwtwlr6xfMHY6vgv9vhmPfnHENOoA5totkTM1W1kaZTfx3MOV083/UPVJNPUZJir64UTaRo14pJh7TracBMipOfMEeFdtwqJXeBFtDNp+L7NfdGyWRV+hLGYmR7n5Gi8OW/buXrD23jr1u2sql7EwqKXjrQXM3mn7u6+dOrLXz7kR05vlm9+IGvXPx+YCjAA8ISk9LyOzODIyhF+Vvm3bOqahTzKHaL7/Wp37/GO374LC/v78vxwfVofjl5LcsE/r6U8PWWxzXFG5zNuXPO1fc7e9bZ4MvrpRjpZDCe4vJfvcxQvzZmX4Xev3Nt7VrKPGV0ZaIiTeMQTOQDIyjFfi3QyWVzjq3WpxEWKY4XHQX8iRInXs1e50L+ljESbUOxFAdD4iSrmSLDdi4JVTPnHEpKhqqqdGkKK998ChAJi0lEqsk6v525iuZPkqt+RWFw5cfYqGokmTUq3MSyCfBqystMitFerbWQAkWaT7KAXzGRSfCdl7/Dy+0vo6qqHu1Zq9UyNEeedUcSueHZI0Cq3kRU+66uIl5uMia/RDy3mr5Iycg1n4IRyt4T6yGqmSdTmRRbhx7St7E5ooSSvbQOxBjc9Zx40VQmLZbM8LUHt/HzZ/fllhfLM58qo5gTR1KKZpWnatfK3a/38puXmvTXZRDGQDRFoV9OHmO2pqB2yVJv6Ti0aD6pykVQL/LS6N1NJiKu2aijxDCfapMvVUtEgJa3DBafp32IprRDJsU9XUpR+12DyhBuDLUhzad1QS9Ou0Iyk6U9FNOrGgHDlCLA8+1PArCmeg3vmvcucYxio8P70zvFojaWyuQWBZALQF+5CHYCQXaqqjd4fjErrEmZwbxAG50UtfzDvHs2kc4ib4O6oIhYP6Ap3+3tgznXRa9WtENXiuGDZNUsCZtY6DXEtAOVzuGcOefgtrsp85RxUu1JUJTnN4108eyubp7Z2Y0iF3GmbVx2F5cuvBSAPwT8hp97EjBH1EpLWTaTZkDrkBF0lRx2P6IZFimOF+Z0jHysvZbPBm6lG8N0F4qlaA+Li0vN+AwVqJOiNJ9OXikOxtO62VAE2uReqEMRMYnIYJzTy8M4lQwxxWsoQPLNtg5sWvOUaCpamBTljV1UYdRDLOBXfGT/I9y9/W5uePYG1revp2mwCY/dw3E1YhLe2mYoikxWNRTJKJALB1n4XHX52Ggixf6wE58p8TfHfJqO6wERAVdAN9HIFfajTY8Sy/aTTftxZYUZbSDdxLt/8gItbzwjjpFN6b/FtvYQaW0G6x3SiCOTMnyAmlK0j1JBZySfolkRKNrxorjpCRu/Ua9GVslMdlglHkCPeJ1bIawTkYyTrOwoLyNkKxaJykYAPbtwJMU5OWbxfCqLhRociKZEsIfbD1c9CVc+YfgXQ4fRfGo6bqUyoD+XispuU5ilpWUc6I3qVY3A8B2Hoin9OtsyIAjsvDnn8cUTv8inVn+Kq1ZdBYgF5zM7DJPi9naT+pXm06IKCJiUYvtmGGgmZXPzYFZEQasj+RTl75eK5uT8mRdJdcFctTQQTeVcF/KaqymqwWFzkMwmWd/6OjgGQYUPxbU80tI51BTV8Ifz/8Dd77wbl90lCB2gdrV4HOqiMxTHTRIf2gIvz8T6vsXvw46NV7wedqcGmCwGTN8hFhtib98u1v7hZL5VKhZhpd6ykXY9LLBIcbzQQpwLkiLmgJksrvKn2di5kZ6YDLQpIpwoTIqHYj6VfriAx4HHaR9m5x/SioJ3aOa643ziJm+2NehmkUKf61DECjWaNkixq6uDF/dok4G80f01RuX8AqS4qWsTICrMfPHZLwJw3tzz9N5ob7bmTp5dY/kVdz1G2c57AJiniMkl5KrNcdx3hRP6al9BEZ/lDoBspGUy7ZkDFFRV5TdbRQBHqu8USu2ijU93ch+xyACLMSlhLUfzjRZj/ANa+badB0xBFppStKejOcrGjJGS9yWh2chi06Iio6qHUMw4To9pEWE2SRnHEK9VFLsI+oTfLTxXU3itWkRmxSI9mErVkvEzqsL5Jy6j1OdCxlvpqQEVC6F8PgQ0X1qogFI8BH/TqDAdtxpjoSF9ioApLWMop5zf/tB+4um4yZ+boSspTMhnzTqLgCvANcdcoxfY3tUZoc3ki9+RQ4qakjIrxVCrXit4s+dEDmiLqmHRpzK/VypFyIkFkIsbl92mL0okBmLJnNQeWd7RYXPoVZ1+8brosjMvlWKtoqVNaHPWwtKFuqmVKs3Vs/pD2s+RJNTfTRla6zmb01hMaqgpquFtmm/yL+o4+jeOgAHtWvIS57Pb3s8zf/kosUycNzzi+wZnMMgGLFIcH8wJ71WFO5BHNTOAL9CMu+oxfrfn+3rdUzVtNp9Kn6I0nxqTYiyVyQm/Hgv51WyGmWK09lHSfLrUIVb1e0wVd4CcqicANtzaeGI6KTqTIT5zz+uEIxFjtVtcY+SuFTCfyrJrYDQPfe+i9wJiJS6DbFxaGHbXaH7F+CD88cMcu+lLLFaaWW5rAuDVpCA2l0Mco3Mwrptng+6gyIe02YTKgYJ+xYPhg6xvX8/O/p3YcJHsP4lqjyCKBs+bHGfbJfoKSmiLAjMphqIpHn2zgyvvECY5nD7ht9ECq4JEdN+eGYkxlKIX4zeJ4s4hP6kUYQRS1FR1wOukQptg983/SO5GFYt0Rato1+Sg4ufkBVXYbAplRWK/nkjeuZGWBhloYy71dxiUYrViJkWjrZIkxb1dQzkNazNqht39u410GJvxfcrTGSOlQoM0ncq147Z2072lm08rjPlgx99hs1iw/T1zIt0IQnFofjIdMtCmdI7hlzVZeGSUssdpG9bHM5SvFCPGokj6DF/tE2bxBYkM/5u+mMGLf2sQnxmnfhY+uQFOuEonv3h/O2WKuCdVb1nOwlni7GrROeVNZXLNC8CIPl2l7Kc03c2uWK6aDnqCkz72VMAixfGgZzeoGXHxyJVhHmR5sJKAuHF7Em1E0uLmyWZ8hBPaRTSK+RTy8nZAFOV+7gegqrQOxDjv1mf54ytiAsqpZgPD7PyJaJhkOqubi+pTYr8d6dz8tcG8z1RULS3DpBRLGOIk2x1c+7s17NmkhcT7a/QJNb9Q9FBqiD39IgpOmikXly5mZcVKfr+hmev+8Dq9Q0nsNoXj55TmfJ+C2P0PEfIPrLVtZ7nSBMAP3xQmprMXV2rHMJRizs2l3fhqfIAX9vTQG0kYSnGwmd9sE9+pPHs6ZH3M9ouVvMfTzHecucE1BikO6C8NRJPs6AgTkKYnd0BMKlpAQ6kSob50ePDAiEpRIzSfRopZFOK4ckxP5klxIDZciUpVEfA4dNXR7JgN89cZG1UuBn+tMUEDWW85Nlk0ulhMzD2RvOOblWI2Y5AjTCMpGmqtzj6gPzcrxSU1YvGzs3NwWOH37X3b9XQYxS7Ok9fuxvm/x8AfPpiz7TMaKb5zhbjfd3QUUIpF5bBgnQhUSscg3IZqd/OnwWV0azmh9vSQQXrZrJEj6q8xFmqmxaxsXeZzOfQi5/WaGbU/mszzKRpEXqmchGJqK/5SdB0/TL+X/oZ1YBveTBibTfiIbTY9LSs92EG5RoopT2ETplSkO+yiE8dkMKD5FOXCdpcrtxrVTEaegkWK40O3THhfWnD1pKqqvsLz+bSVFlkSdqGeCgfaDDefQoEC1H/5lKij2PY6z+3qZkdHmPtfExNQTo4iDFOKqVhErxzhtCsEIkLtbk3X5SjS/NB9PS0jFQVvEACbohIu3cpmj5vvJjVV6K+BhuPF87bXRX9FDW/2vImKSl1RHV844Qu4bC6uOeYaoskM//OXLTz0hlgxL631M6vUl/N9CmL73/Snl9qfpViJE1edbE/X0Fjm4/PniMChUCxFpVdMZDm5Thopbt13kMvu3MB/3b9FD7Z5se1FXmh9AZtiI5AUtUOXlAileNDpxG8Tk6tqykkLx1PsMyWJD8RS9A0l8SuaP1HmRnolKYb172nGSGXeQtpq2qd1PYnjBpRcpWgyn4bGUIqSkPd1D8HJnxIb+MrFhGizES1u1PcrLjUKOkiF2ZN/bmQqQnxAVLYx1d2c7kAbgIVeYwHodxtKcUmtIJrt7WGdFEvc4txv79uuK0XFJn5Xv80t/MRtr+vHGIyn9Ijma88UPvOW/phxn5jNp4oC77pVT7EIN5xJRPUyhIeotrikd7eIWh44YASoFVeDW7MaJYabT70uOxesquPRz57O/3uXIKKBWCrHqmNW7195oAVf1DhvnXFhHh1XQXstWE6JdOnm07izsAlzTukiPNksUZtC8+DkSr3JQJtltgOkgCanIMVZKfHdzK2xZgIWKY4HshxW1ZKCbyczWT3gwuEa0F9XNBONmikikkiLsP286NP8izaHoPTcSGCgWTc7SP+lVFaVUinmkWI6HqFD84vUFjux9wnltlutz7m5DN+TOE42o5FiOgoON3HNx9jpFET6ktfLJrdLkKIsKp5JQuur+jHf6Bam02Mqj+GC+Rfw6odf5e2z307fUJKsKkymn3/HIr7/3tW60i2oFFNxUeZs9+P6S6u0biTtngV88d+W8/jnzmBhVTEeLbG4wSvO09LypcZxtAjUji6hADYfHNBJUdaMXNe4jnRSTAZL1W6qtYoeG7zi+/dXnSSOFe5kS2soJzVzIKqRItGcz5PBCqWEmVU2XCmOlJKRrxSHVDGGUCypp3+YJ8X+aIq7Xmribd97hn1aGTmdFD1OlmmRpFvbBkXR8Ituh/f+BhQFVVXZGjOUgbvETIpSKeaRoqcEXJrSObg+973DoBRnu8P6+tRsPl1U7cemCB9ol1afdG2tqPe5o3eHvpBR7JIUNZUS64O0mKxf2N1DOqsyr7KIlQ0l1JaI335nh3Z/mc2nIJoCvOPr4PSxpUGmxSh0q5pP7oFr4eH/hIe0Mo9FlWB3jqoUPU47NpvCkpoApT5xDoaZT7VF0VAiTVc4QcOgcd4y8TrtvXFUTNKqWjli3bpSHHIEC27q8IqOMwDbu7eMfewCkPPYcqWJfU4naUXBn83yk85u3uObzSULLpnUcacKFimOB10mpVgA5m7tGfvw8Hs1XUQqo4oJMD9PMS9RNyfopdPUEDfcrq+w5IU+llLMJCK6P3FlUT9KJkFMddGqVubcXPIzZeh+Oi0mCpmqEFb8xBSFNqcx+fwsWCJ8iooCs7VUFFPi8ubuzQCsyutgIQM2KopdXLduIYtr/LpPdJhS3P0EfKsOfn6GiNLz15HBMAXNXXkyV58xH7fDjqIoIlcTqHQu48n3PskXTviCcSxNKUZDvfpnBZ1G+D3AR5d/VFf8FaE3OVMrhfXFyip+5lhDe+WpYsNIB1tacif+gWiS3qGEQYpSKZrMpw0FlOJIKRny/Pi0cn1DmupIZYRVIpHO5FwrA7Ek973Wyr6eIX6tJZvr5lOvYYrb3j4oztnqD8JckZ/45PYuNkdN5jJT1KH0KfYVKkwt/YrNL+V9qRDJdJbnd/eMr4zdeGEi2wq1TzcJm82nHqeduVplmy5N0Z1UKxYzewb2sL9HTPrSfFpsngK1YgbSn3j2YkEW0iSrB9uYo081HFz0EX580j/5y4ARQNNNUHui5Ujue1o8BjT3hVw4mRSwbnFyGde59C32R5M551yatNtD4rssjRTTmEqxJOKBrFiAjasik2Y+DWb7dZ/ioK2k8LbOIpYmxedu7x69YfdIGIgmcZFigdKqm04XJpPMS6X5cv25h70rRj4sUhwP5EU9glJsHuzEXrQTl0Mhmu0evkFWTIbheLqA+VQzlzjtxjYSHaaLbrBVJxSpLnOq2YBOiilnQP8Mqb5WuITibLY1kMWWQ4p6PpsWzp5MaaSo9WQcUIs4oBGiJ6tiV1Ve9HnZpwW30KiRYrMgRVVVdaU4jBS1ydUcRFDld+d8Hx3NLwlfbq+WaL78Yg64TVF7tcfkbC5JsWMwTpWvSk/IFgPXgglM1WV6Qg7IaguK+BzU+Gx9gRPs28wNfQPURv0kbCo/qY3Ta9dMSpEuPchmXqU4nwOxFL2RJH7F5FME3SdbSpjyIhdFrlz/Tn5KhkwSl4TmU8RCIYrRZUWaas0IRVN0aJPjQ2+0k8pk9fSVgMepd6ZvHYjlVBQR27dxQDVNRKban+WaUpT7dIcTxmdLE2rzBvEofbiJQX6/4QD//ssN3P7PiRWOTmWyPLKlPcdfBohAmIzxWjDdy3uOa2BBVTErG3In8CWaKg4lxTlaVbEKt91NPBMnoWj3pzSf5rQu6hSpGDvFNpIUZb7mtvawyEWUZfJMdYO/94+dfP+J3fzpVRGFa7eZlGI+/JIUNaVoigWQiwg5HwAEveJ+DMVSuj8ODJ+iLOJQq8Z4qKWdd7bP0wPPxmU+1b5HpRLSzad9BApumsbGopT4zcwFESaCgViKhUoLTiXDbpe4vqT6NDcomClMOyn+/ve/Z82aNXi9XsrKynjPe97D7t27x9zvRz/6EcuWLcPtdlNVVcUVV1xBR0fHmPtNOVIxoxvACErx+69+HV/jr/AG9hBJ5ytFG8UOLU8skR7RfCpNNDkJ/J1bjeeD7foNIVd/MtBCmj0lKaZ92s2aitKpqa+FNuGHbHUK31EOKWoTsMzx0s2nmlLszRbpdv/yhI+iuAhquW1XO3/d1AqzRU4WB1+GTJqeWA/9iX5sio0lZbkLCTm5lvmc8Ma90L1LJ7NhSlFbkYc8DbypzmPXnA+w1WFUDRqJFIeRK+ikmI4O6C89s7ObdEyoxWj3GVz5m1f0RUlR9yY8qkp1yzmoGQ82R4S9aAQW7uCNVnGcMxaK3yLffKrKCU+aT5UIAa+ToGYKE6Y/lU8P/gCe/BoA97/WwvIvP8pTOzr181OnCLXToxqT1EA0mRNkA0I1yIbFfUNJnt9tNDYOeJ34PU7dErDNnF6A+N1zSNGkgKTprm8oRTyV4Zwf/pPzf/ScIG8ZbCMXLTJdKR5ih2ZqbJ5gx4qHt7Rz7e9e41sP78h9I69Kjj/VwxfOW8ITnzuTEm9uoMbSGj+QIZEVY6jwVrAgKHyDdncHXqfd8Cma+wKGO9nWPkhXOIHPZeeEuWJBs7Ba3L/7eyLCdJqOAQoEhFJWVZX1+3KrFi2t9evBNsMgg/UKmE+lUvSaFk8lWjpNVkXvwwniPGezKm0D4ruUM4gC9KoBFlSKMY9PKWqkyIBuPu3OFu5M8eOn9lAfE3br7aE9IzbrBhGc9PxuU6UrVSWTVQnFUnqQzVaXULSLNPVpXmjMFKaVFH/xi19w2WWX8frrr1NbW0smk+G+++7j1FNPpa2tQLdqDTfddBPXX38927dvZ/bs2UQiEX79619z5plnMjQ0uV5pk0bPLkAVK/4RTtiBsAhgsQVeQyV35e+1+Sn2aE74eCrHfJrKZPVq8TU6KZrNpyZSDLfrK/RoMkM2qxqTnjQfaUSrao5zWypKp+ZTnJURkadd7jkAeeZT8bwq4BZ+OS3QZig9RDSZpi9bxH5NKTpS5fTEhFp7aOdr/OefNhMNLhKkk4xAxxv0xMSNUOYpE4nCJsjvcIJtB9x/FfztOp3MusN5VW0089f97nfzrsQ3eLHXzyYEyWYVh5FrpaHaP4pvUjNnqqbJ9YHXW4m3vZ+K8PUUZ46hJ5IkkkhTQgRXSCyEtmSWkE0KktivirFnI516BZvT55XgIUF/NEl/1Ai0Sct2N7r5NEzA46TE60QhS7XfQ4PSw7mpJ+G570MqxtM7u4mnsjy3u0dX70sULWJYNQJhzAnoEnu6wph/ugdeb9V9xfL6kH7FbW2DOd3peyPJPKVoNp+KffujSTpCcfqjKdpDcQ72RWHFpUYtXzBqAsdDeo7fROv5ygl+symyF9BNjBltynJnIkZLrTwsqQmg2MV7Cgol7hIWlYpkeZu7g5X1JYb5NGVaiEU6dZV4yvwK3FpD3sYyo3ycnnrirwWHuLYP9sXoHEzgtCt84bzFfPDERt6xtCZHKW7ImhaHsjScnAsKBdqYlKLbYc8xp0qksyqD8ZRe+q/WKe7/fiXAXM2Cka8UVVXlkS3teqyB+BHEua9UQpQpYiyd6eKcfTJZ8ffHVw5SnXDiUFVCqQivtOxja1thH/Infvsq//7LDbze3A97n4JvN5L4562oKixTRJDONpe49xcWUIr7e4bEdXaYMW2kmEgkuOmmmwC49NJL2bdvH9u3b8fv99Pd3c3NN99ccL+Ojg5uueUWAD7/+c+za9cu1q9fj6Io7Nq1i9tvv326hlwQ2c5tPOf1kJKtlvKQyWboT4gbKePRzJ2pclC10lP+Cj0QIJJnPjWv4moCeUoxnTSiXgEG23KS1IeSaX1bPdBAu7lsAbEStWdielm0qngTAP1FIqpysID51O9xUupzocpOGakovZEkA2oR+zWl6HbMI5sQx7e5O0jberjkwfdxX71WNm7/szopFmokLElxvqpNLv37qSh2oSjiJs/xXWk1Jnuy4gbtHUryXHYFr2YX0rP030XdWRPkwqKjUL6jphR9WcNU1ToQQ02XcFrDWlbPCuqvr7YJk1+vu4GovYRyt5jE2rTGzbbEIG6SnFXWx+mPvZPn3NfT399PVgW/lpKRsGuTiow+JUzA6+Ci7BO84b6Kt/t2E8TkAx5opl2b3DpCcX3RsswmJo8d2VnGprHUMPPiTq10mcyF/NvmNj34K+AV14f0Kz6+rZPTv/s0V921UftdE7SqFUZ0rYkUpVLsH0rmBNvs6YrAvDPh8oeEbxn4ZYtQjmp8kI4BMZnlp/uMBXlN7+8ZImFOV9H8iQP2ciOqM79XoYaldQYpBlwBHDaHQYqedlY1lBiBNklTeb5IF69o1ZHOWGSoZVlovH0wTrKvSbwYnMXOjjAt/VE27BeLt5X1JXzyrAXcfMlKgj6n7lOMKx6+lvqw8Tl5SnH99iZ98pfFwPNJMGhSwz6XXa/32hNJ6KS4oEh8p4Vz5+oLofz+oI9t7eDa373GF+4zcojlYr9CCVGukWJrUsxT2azKxT99kXf88J/8bXMrHYNxUniYr5HYp/78IO/+yQt66TyJbFbVv9Ofn39TBBslBnFo6Vyr7AcI2WxEHGJ8+aQYT2W48MfPc/FPX8itO3sYMG2kuHHjRnp7xcVy6aWiZl5dXR1r14pIsMcee6zgfk8++SRpLepP7rdq1SoWLFgw6n4giHhwcDDn71BxxcY7+GRNFT9WnQXf7451k9XUoar5f06atZAFWlJ7qadUL0M1mEeKchXnstsoK5JqUptEendrfdc0Ig630z9kTEoD0ZQeuagrRY0UnUExiXtJsKc7gkJWT8eI+EWllkKBNgGPg6DPpadkxNIxeiIJQhTrpFhSsppMXPhEbJ52nMFXaI3u408uTXm8+Wd64+K8FwqtlsFCtVnZk68bh6JSXlRA5WlKsUNbtfZEknQlnFya/CqhM78x7NgyYKelP8rrzf25piONFAMMX3muaijhGBMpHmsT5sCyRaew+cvn6FGq3ekecIjPOL74YcqLv8HQUAuVyiBzVVHmTCrFmE07z2bzqcfJielX8CsxTrZvp1Qx5ZX2H9AXMO06Kaq6UtyuztY3HYimhpnOpW9yVUMJK+tzfVlSdSyvE6+/3NRHeyjO0zu6SGey9A0lSeMgXbkcUIx2YaBfl735pCgbJc8+BT79Mj3//hQ/2KtVEsqm6A+Je2+iVZrkPZHJquzvGWJb2yA/fHwXdz6xCYAhfEZfyHBhUqwr8VDkE0RR5BDfeWGpsG7Y3Z3MryrG5RTfxW8ujRjp0AtiNJYZQVFlRS6K3Q5UFQbbxX005K3jgh8/z4U/eYEntotr+cS5xvVe7HawIbuUpOLiQce5bFXn0KZoKkhWgdL8zvtbO/ROHFIpepy5pFjiMywuAY9RjKEnkqRFI8WS7AAAV5xzIsVusX+++fTFveKeenl/r5GWpRFROSGqbeIYB7Ri4tu0ovT7uof4/L0ieC6Ch9la+sRAspN0VuUnT+3J+ZxIMq1bLk7c8W19AeMK7WeRcpAVyl667WKMJaqNIlXVqugEte+VIJxI0xNJ5tRKPRyYNlI8eNCohVhVZZgdq6vFCWhubh62z6HsB3DzzTdTUlKi/82aNWvEbceL4+ziwv2t0sKGA8PzcmTnBzMa/PXM0/LcSj2lenSc8CkaFW1sW//C31z/zRJXl76NvrKWQTZ1q8VjOo5iSkbu0MjDSZoit10kBmvmU7umFH3E6Q4nmKV0Y8/Ewe4iHRQ3ZKFAm4DXSanPafRUTEV5cN/9bPOmaNLMp0url5JNVKOqCjZHGGdgEwD702GyNid0bKG3W6SwjKYUK1Ka+VzNQrRXD7YpRIqdKUEwPZGEPsmaIw4lpPn09eYBLv7pi3z9IVOvRG0CCihDenSixDGzghxrIsVTbGI/ZfYpeF12yl1isg9lOoyVbNVz/KPIxbM+MXk0KEIdS6UYtWmRvNpNLpSikzq7IIuFxQmCGBNytm+/fk7bBmJEEmnq6KVEiZJS7exVjUjZgViSHm2BtKAq97vUlnj56WVrcl6TxZWlUpRIZ1X29Qzpk5fyoXtEbVNZCxUjICoUS+WY3PZ0mcjEU8J2tZEhPGRUbRGXkKQ4MfOpmUR3tIf5+G9e4X+f3M3GneLeG1S9dMkaw/kd703ft6FcayWmCjUmSdHm6qW2VMGhkWIgbZpwI126X7bS7845niTJWLcwq+9KBElqC4rHtkpSNHL7ij0O9qu1fKjiPr6duQxQ+Ezq06jv+DrMPVNspCnFYiWmn/tC0aeAuC81BLwOPQCqN5LUlaInaXTv8Lk061QeKb6q9WGMp7JsaxvkK3/byqk/3kKnrRq7ouJTxbH2R8V1/YIs7Qj6dRJVPdRkxDhtzgFABGvJVCAw8mbr6ebdtue1fq3CavL/HL/FRZqddjG/+9ACiWylopgA5EXZjq+DzlRh2khxJAesfH2kKuiT3Q/gxhtvJBQK6X9mgp0sPvmRP1OkziZtz3DdY1/TfYASskegGbVFtSwuE+bEal+13hk8Ek8ZSjERoXjb71ll28+F9hd1E6g+ich0jPrjdBOcud5jeyjOdx0/5xX3J3H0bM+tZqOZs3xKgiBhfuz6iXi9ZiV+r8x3Gx5oE/A4KC9260pxU/cm/nTgVp5p2EbcZsOBwnENC0B1o6bEmGxaXmY0HePerAi06G19GRhBKQ6Jzw0mTBVQIp3MqdA6OXSaqn9o5tO2lHivtT+mm1LMuWkScyqKckqpvbzfFPSkKUU/MY5pKNEnGZ/LzvzKYl0peolzrKIFjswTk1eVTwRUDGU6obiakM3Gbm3SCVcLv2aDFtUoleKQIs5z1CEmyjIljN/joEorZN3oTVCiGP7xWNc+/bt1hROoKizRuh3sUetIYkyKIZNSnF9p+H5AmJBnlfm45+q1eJ12zlpstEKq9Lt1U6AsrSf9QaU+J45gvdExQ4PZbLfHNOnlkCKwuzMCKIQRxw9ov4N5cosm07z7thf4/j9yqx+ZYSbRv21uoz0Ux+u0U+EQpNGV8tAllWL/yMnjtWXiPk0mxeRe5Cghm9L8vK4O7NJ8mjXu52y4k76h4aQI6NenqrXIWt+XuxhRFDhutpHWIgsKDCRU+rX7a2N6Pv2rrzHcMJIUienns1D0KaDXrgUo8Tp1y0pXOE5HKI6HBHYtWpyiSt06ZVaKQ4l0TmHzJ3d0cff6A7QOJvm/xNv01zOqQnPcLdJqNFJcO098tznlPiJ4qNFM24pTHC+rwm1PG5HGssiEvN7b1TL+nDkDgNPtYm7b6BZ+ViUj5qSDqWLdZJ5DiuG3iFJsbDQCAzo7jdp2XVry9EgqbrL7AbjdbgKBQM7focJhd3DL2V8DVSHmfpk/b3kl5/2OAn6NuuI6PrDkA/zn8f/JFSuu0G+Q/JQMu6YyV7APv8dBPd2oWsKxnghfs0qP8qtRjEm+MxTnbfbXCSoRePCzRg6XzQk+MREXEecu9y0co+wVxHr+9/VIPUmKqqrmhO5X+d2gagnbMVPkGDC7ZC7LaoIAugnVjAccIjq3V4tGrPBWDNtG+AxVioZMC5ZIJyvrxXH10mmJkEjHAFq1ia1J66huU4avpEFEn/724yfx/fceo2+v+6Wk+VQZoqHUx8IqMSGtqCvBblMoK3Ixu9zHibadOJUMlDTqJexqfOL3j9PLoLucjR43qjaxRYoF6eikqJlnw6qYRLsVcS78SgxnKqyXiHPE+3KUYrJneCfzFXbxG5mDbECaT8XknU+KMop57bxy1t+0jjs/crz+nqIo3H3lSTx03WksqhH7yfqzZXl1NiUcdps+IesLFmBvVyRnAbu7S2uCq33vEsS5CsdT+nZvtg6y+eAAf3h5ZGuPeTJ8SutScfL8chaUCPIK4+O1rJaW89pvRFeSAigLiIl0cEh8r+beKNmEuGa7402GT9FEiulQO1lVXF+SdCRksI0rIhZz63uLUBRYo/WcXFITyImCLdLu+baBWE4AVOdgnKd2dPLUjk69ok2xEtM7XsQKRJ8ClHhzzadSKe5oD5POqlTaNJ+e3Q1uv/755ibmmw8O5Izlzuf26X7nezNnktBcRAP4UbHRHorpC8uvv3sFD113Gn+65hRiePTCFjbHgL5AfXxbh36uZdnBRdp5w1PC3mLjWgTY4RGunEiqhKyqsDGzkFf2i4X/SJV7DgemjRRPOOEEysuFUrjvvvsAaG1t5aWXRKLveeeJav1LlixhyZIl/OQnQs2sW7cOh0P8yH/+858B2LRpE3v27MnZ73Di9NlrCNpEZN36to0570mlmE0aqqiuuI6AK8BHl3+UKl+VEWiTyCVFV1RMkIuye6jM9vC4+wt8sePzkIqROSjI9zXbMj3Z11wEuaevjwotfJqWl2H9z8Rztx+c4jPm2ztZpewBmwOueATqjiWQR4pDyYx+o/g1UpSBNhJqVuyzuGwJ1QE3JV6nHmxjxmZnkCFbgF4tSrOsQP3EvqEk5Qwaq1qASBfHaLlmmw9q5K6pRNVVrN+s0rRU7HaMaDE4eX45l6ypx+92kFWhqUcm0xs+xYZSr14OTDbSBVg9K8gpNk2hzztDX9FXestRMy5AZYvqYYPHyBmMaNFzhlIU5qeQVoHm0V0RQhpR0LlNVP5BtJMKmnyKttBwoljpFKS4PStIUQZjDcSSevRpY5kvRx3LSF4QisJhz73FG0p9rKgv0Qs+yPZd5XkdGcwo0/xZu03BFGGtiorEbo0wBynK+R3MaQSyUEXfUHLE4IlCPsjTF1Ywu0i8Hla93JM5m6y3QnRmeePegsfxeQXpDUTchGKiJJ+8ZveG9qLaDFJsUcXizR7tAlTKitzY84q3i3QWlUBCmGxb1AqOayzl1vcfy6kLyrnubQtytpfWofxAl12dYa6+61Wu+e1rJOzityomlhNZDsNJsTRfKWrnS0bpLirWTNtFlaAowqVCrlKUplNZS1V+lt/toJ8Af8uI1KqQIsTEP7Z2kkhnqfK7WVBVzIr6Eir9btKOIt18qjhDnDS3HLtNYTCe1n2yUinWecX3aqip4dhTziGmLbj7XbX0a0q5N1nGsYmf89X0R/TCCXqtaN5CpOhyufjWt74FwP3338+8efNYtmwZkUiEiooK/uu//guAnTt3snPnTnp6hCqpqanhhhtuAOCHP/whixYt4pRTTkFVVRYuXMgnPvGJ6RryqJjlE6vTvaFdOa9Ln2Jq0GgpVVeUWyml2K217cnxKYZxprRVeraPBQf+iE9J0Jg5ABv/D3s2Sbdawk83q3qyb61i5EKlZRScxHrRMgZ3Mbg0E5mqXUzlC/TCA4ZS1CYZTSU67Qoep41Kv2E+lYh3vJt3VH6Gzx33ORRF4ZsXr+DcBYbfKhPT8rVcvbzgOZNebSLON59msioD0SSNyvB2Ois0UmwdiAkVpJXSyhborVbIn2iGoijMrxK/s1QwMjWiWIkzOwCfPnsBn1m3kE+eZUxmxzQEOdWmpcHMPUt/vcjtJJsS32Wz6uYVr0EgES0sf1aeUhzIeFFVlftfa6FN1X4HUxk8JdZHqcl86o0cBK1VcJAwp9jeZLkq1KNUio2a6dMcaFNuagsFhlIcC9XadjJnUZZzKwSjokquKpMmVFVV9ehDqZADGN9NXmOyk0xWNfWfzEOhZPMzFlVS5xHfN4yPGB6yJ39avPnsLTn9CCViGa0GcbpIDxTJJsU10BppJaOdJ382qy867NkkAYaGmU5BkGKAIXyq2K9VreC8FTU0lvv43ZVr+beVuZYTcz1WM/65s5t0VhWNkOPivBUT1xcKhVIyINd8GvA6maf5xWU+6AItsIgica3p5lNTxaxXmwUpfvjk2TkLqdsuW4PHaeNe18WoniCb3MKE/tfNQhWftqAiZxGquooM86ljkLqgS/fTyyhoWbi+3KGdZ3eAi4+fx0ZVWJP2VZyNTTOJqxkfIYoBxSBF0+Koe7SayNOAac1TvPrqq7n77rtZvXo1bW1tKIrCJZdcwosvvkhdXd2I+33zm9/k1ltvZcmSJTQ1NVFUVMRHP/pRnn32WYqKikbcbzqxtFyQSlci18wlSTETa2Sp73wuXnAxNUW5KqrYU8B8mofaXXcZ//zzu4DIbfrn7h7iXhHcYfYpSmXR7JwL88429k1EDOKVqDRypCQpSvOE4U90oigKVX6PHmgjkYnN5vTad+rll961qo7/ecc5OGwOgu5S1lYK9W5zdfNXTqdPiyort+eWNRuMpciq0KjkNV6NdBHwOPXqMG+0hPQgm5S7ECkWnnDMWKiRoj5xu0t0095sWw9VriSfq9tKicOY6N8xx6HnTzH3DP11n8uu5yo+4w6yx2UQSESbXBqUbuxkKNYKePdmvGxuCbG3e4hORTMjm0iRaC/lNkMpujJD1NDH5xz38oL7M/ze9S3qVHFtyUlbVhwaiKb01XNFsTvHbFczXlLUlKJc0eebC80o9eUSpjS1yt+2O5zQA8QG83yKYExw5gl6JD9RfmBOfdDLvIoiKrXAmLDqw2W34TjpKuES6N8P+/+Zs080FeXAoFGM/9UD/ezviZBNCVN2a6SVtIkUW9RK/dqoVEIjkGKRHkzVowZIKm7euXK4C0GiaCRS3GVUvNreJxZBxYowsQ5Ek6YuGfkpGSbzqdfJv62s1f3DALM9hj/R/PlykZHNqrymKcVT51ewXItQXlob4IxFlTz8mdO55ZPvR/nCPh6o+hQgzN0Apy7Ic4O4iinPZLCpoCgqRb4oi6u17iRaN5GQFjFapuWD4glQ4nOyZeln+UP6bEJrPqWnzagZ43vs6x7iQO9Qjvm0+62iFCVk8n48HmdgYID77ruPhQuNUl2qqqKqKl/5ylf01xRF0ZP3k8kk3d3d/PrXv6amZrjJ7nDhpHqhBOO0kjZ1BJDFpNVUCWdVXsnXTv3aMNOe3xxo4xxe/xLAnjIVJYgPALAhu5RURmXLoNjH7FP0DolyUn2uBrjsT7BS9ClkzqnDP6MAKUrzqXyUY6wK5CpFRXWjJsuHTZrVRdX86txf8atz/49PnyZqaNpc3TwbbaBfa1VT3pzrf5U5iAtd0lep/U6an+2YhiCgmYQ0UkxOlhSrpVIUE/eOzgjNqla5I90Oz/8Q/nQ5vHKnvs+s0KvYFBW1cin4jSRir8uum8f3Jl7I+ZyIqpJFwaOkmKMY/uXelIf7XxPnyFmq1Qhte83YMRWlxmQOB/ip63/5jOMvFCkJDmYraXM28oj3Arq1aEtZkWZfT4REOisa0frdepUcQCxqxoGaktzzWT6KUpQJ/BInzRXnRJKi/I1nl/uIaAFGflPqi5zgoiYV2B1J5PizQcwFchKXE/4ZiypRFIUSm5hcB/GJ8+8uhkWihyAHjHOyqWsT77z/nXoJsmyqjNeb+9nfM4SaCgLQFGpCqnJ/VqVbDerBO1XKwLDmviBM17O1usatagUXra7XzZCF4HPZC6U15xRd2Nwt/G3FxACV3qHkiCkZOUrR48DlsHHjvxkVtupc2vyhkWJ+oM3LTX0MxtMUuewsqfXzjqXiXnj/8eLanFdZzJyKIrDZKTf5l5fVBjh/VS752z1+7EAgLcbodA2yWLbs6hDXglxsyfMmo7+vft9FHPPJuzj7uOVkbZIUxe8o/bPP7uouWOP1cMGqfTpOrG1cLPxKthRbuoR/M5lJ0hfXfF/pIEWuwpO137xqs9l0n18+utRSUqpxM7ysVcF4uk3sb55EpW8j7K0TFfcvuQOuehre/VPdfKqjajgpRhJp0pmsXjpOTqxVfrde0UZ8yTrAVnDSXF21mgWlC5hbIgJSbK4BIpleVAVsqkrpNtHu6cU9PXzqd6/xiua0n+/QVstV2k2tdSdfVR/gB86fcuarn9GJMu4MDvvc4hFW4WYs0JTiXm3C/sWz+3RSdA42G62C+vcbO2mKQ9GiTiWKXA5UjRSTCJ+nbEI8lI4ScYmJaKmWUxhXnYSSIlEaoG7WfO2zmnKO24i2oNKS5tfYxHV1Q+pqTk/eyi0Lfsufqq83ttca6KYyYkI/YW4pHqddjxCtKHbrNS/HQlUglzxH8ynmN7tdO0/8Fi/sFQW/pel0UbUfmx7QNFwpRk3FwbvDCb73j52s/trjvHpAXBeJdFb/bh8/fR6NZT4+vFakENmTWqFq1WcsimafKh6bDFJ8YM8D9MX7aChu4DMrvko2NpvXmwfY3WUoxVRWTNh2FdyqSjclekm2SgYKKkW7TWFF0QAAbWoFn8rzIeZDUZSc67RAf2leadNcF0oGN0L9m/spmmFe+Mi4gHOWVetRoQ06KQpVV6STojje7zaIa/PC1fU47TauOXM+f//MaXz0lDnDxiWvjUq/m19efvwwgnZ6BQGWabylOgZYpCnFXXnm04BiKEUQgVvL6gIoikJWM7GrGR/VATdrGsX5ae6L5hR9GNa2bJphkeI4EfC4sKdFFOKLzZtRVZXOITFxK6oTNeMrGBEJhg8sHE/TG0kwaCKd/VlDkWz0nqKXgxpQi+jyCLJ5ul1c4LVKLzJuogFBLFEtMhJFgfo14uLLJ12TUgyYTG2D8bS+opMr0RKvE6dirICTUbFKrBht0vSU6o1B7b4mcbxsFnvT8/z6uV18+P9e5u9b2rnlMRGKr/sUZ2mtmDQCXOtp4hL78xwbewm16XkAYo7hRZXH8ikCenTpvu4hmnqG+NvmNp0U6W8ymiLLNkAA+zQz3Lyzco7lc9nJJLTzpCokes7mwllXiqGnIgx5hStgqVZ9JoyXrnBCDzqonlV4ApUdMKLBRfpr7UVL+VPmTEAh4HHolUvAMJ9KnK7VXZW1McfrTwQjaEeiYoToUzACbQDcDhvnr6qlrMjFvu4h/ucvb7L54AAAi6qLcRQFAeFTrNPGM5jnUwRBis/v6SWTVYm/8HP4+38SNpnMLjuxkWe/cLZeyFzWPg3j090RzNFIsfVVveSbUIFw3bHXccXqi5hV5iWS0K5z1ZXTwLYoK2wV3WqQbsR1VqkUJkWAhW6xKHVXzhkW9VsIZr/inPLhC+HNXcb39WtpGePxKcqFraIo3PGR47njI8czz6ctQnySFLVAm2Sa7nCCR98UC7DLThKmeIfdxvK6koIBax86sZH3Hz+Luz52IrUlw9WwyyfOSYXGW2mlX1eKu7vCWuyA+G7F+a3UTEhp0ddqxkdDqY8ymXs5lNtM+S0TaPNWRNAxB4DHmh/inPvO4cbnbwTAoZYCyrDVnYS8iff3DPHO/32OvpQxyfwzewwZRbzfWnkGf8+Kij/PZ1eyurGMVQ0l7M9WE1edBJUhTg8I9SGjHRPFBVJU7A696Sk2B5TN199y2m16p4ZQLKWHTpfqhaoVKoqLULUkbFkwO9+vlI85AfHbOHzC51qRyYKa5fa/r9cjDaXpSK9m0yi+q1SKC9uMRsKq1oYqYi9EimMrxfqgF4/TRjKT5cb7t5DJqrgrtUotHW9CWCseIBvGDhyEvr2ilqdUIBq8LjvZ+Cxibe/F3/9Zkt3nUlMs1FIkFSFRLExQSxQRLRpWfXo+mN/jwFeRm1KRj4Hgcv151/IrkWblEq8zZxFTX+rNiYqUxcjluRmvPxFyo1Rh/EqxothNRbGbH3/wWGwK/PnVFv6ySfyWC6v8eP1GCspCTT0U8il2hxN6GbA1e2+DV+4g3iaCnIrdDmz50kpLOQqrXvxa4Bqlc0Ud0WwKWoSpXvoSZwdm47Db+OVHT9AXFtUBN3XFRixDQIug7FfKdPPpSD5FgBOCYhI//phjCr6fj6ICzY/NSGcVwqqWR6mIADMj+jR3as41nxrP/R4n71hWjU1ex3nmU1WF377URCqjcsysICvqh99P+ZhTUcR33rNK7w6SD0+ROEa1FmwTzfbSWObD7bART2Vp7osS0uYVX1ZTsJ7hx0pkJSl6aSj16mbbvqHcFlm9WuHzwwWLFCeAxmLhC90/tImOoQ69Z2AsFsCmYKxq8yAv0EQ6S1c4QdphrL4OqlW8tuh6OPbDZOaeyT2Zs7km+Vm+lLqcxjIf65ZUE8fNs1nRgulCp0gJmaWprXRghLxNGdBTvkAvXCxh9ivKiEJzsEaV36M7v7PxBkq8zjHNcvODgnjtRSI6txzxnSuVEOuWVOHW9neTpCyjqbNZJ4rHRAhi/Ti23q8fz6a1CQrbhv+mxeMgRZtN0VfzL2kdDNYepyWmm5viSqUogzWk2jZBmMUV0qHj6OnV0mOKxcQQSUZIa2XgDKXo09MVZpX6jG4SI2C/R5iR29Uyyk54r/56wOvMmQBLvE79PFUUu/U+f7LaijQZjwelPqeewA+j+xTNPqYKjTBOXVDBTZpPq8Tr5KzFlbxtaRX+ErFYKLfHdOtCIaXY1DukpSGouDJi4kwOigWf3+MQKTnP3gJ3vgN+eY7oWg/YfUHevkxT7YpiqMUDLxBJRvQSg40BsRBZVO3n/y4/gYpiF+evrKO+2DgXgazWO7Omnh5VKsVQQZ8iQGlCkH+gdmHB9/Nhvk6X1hjXlGyGDRBBzAXFxEw+RZWyfQ+KFB4N5vszvysIAEOaS0IjRa/Trpts79ZMp1IlHiqK/OK3qtGifkOpbuw2Rffj7+wIMxBNoThCtKoDYifPcDI2SNGnkaLWu3MomeNrzmRV3Rx7OGCR4gSwvNwwQ1Z6jRJ0aqqED6+dPax8mEQgbxKvKDOCRyrqZtN4/hfg3T9hXnUpKjYezZ5IHwEay4tYpznEH8kIAjkl+RIBhiiRPpvgCBe6NKFWLh4+npz+bOJiK80J1nCTaL+EisR7yCZq9EjO0XBa/WkA2JzCp1BuFxd4ldLPmYsrOVELzqjUKlzg8IqVvlZLlI2/EuSYB5kzlTP+cZhPAd2kU+Sy8/33HsPSpVrajClQSm8YK02nc3P9iSAmMWll0ruaaIpoKDWEGhSkWKcFQsnVP8CsMi8E8iKtvUY5sLjq5AnnWdyZfiff8n2B6lJDUQRMJFjksuO023T/4RkLK3Q19YETZ3HnR47nU2eP7ucyQ1EUow8nUDFa9KmJFCtN5Hnl6fPY8fXz2PSld/DrK04k4HFSqwXDVbnipipNw5Xia1p6gJMMdq12cHqwC5unmUjt5/jVQ1fAU98QObgHN4hygMA9153Dx08zytCZ/YrNYTH5l3nK8LuM3/H4OWW8fNPb+dIFy6gtMoJGAlpxiIVzZjGAuMZLiBRWiqpq+J9L5w5/vwDMPsX5VcU47eJ8nbbAqDIU0a4VvxJjX88QyXSW5UoTZY9eC/dfrW/ndtipCXhw2JTCFoGIJEWxKFEURY9x6BtKYlPg3OVTE6hY5A8C0JAWC78uLd96cbW4V3d1hhmIpfA2/pKPe3rosNvBnUuKqWyKRFYLtMl6c82nkWRO9CkcXhOqRYoTwAn1y8imgqipEva/cQWJrnegqgru9GI+947h5CNhXjHOKvNSEjRI8dp3naabsvJX+o1lPpbXBagOuHkyeywp1U5NYj9n2ESF+x41gK94BHOIDLYp0ANSmmIGoklToI1BNJV+N+nIcpr2iwoUq7So0NFwav2p2DEmkzKn+C6VSohVDUHd1KenlfhrxEpftuNa/1PxuORdOcft15qdmv214zGfAly/biHXvW0Bj1x/Bpce1wAls0DJu+SjfZDNwP5nxf/zhpOioijDfDx1gSAAKipFdbkLE1nqDDSl6PLppfoAqDLMpQMUs6cvwzfSH6an9FhcDpueMyjMpw79ORgm0jNN5dvcDjtvX1Y9rgAkM6Rf0WFT9M8pBLNPMd+37HHac/xSjXMEMS/M7qfOpi0SpFI0JbLLxZgHI7IwE+nBGdgCSpZHhrRiBisuzfk8JV9xzBGRzxzcwAGtvODswGzyIRcQZvOpP6uSUB0saahiyKaRolI4T5GhHq2UojLyQjQP5vNRXuTSI4PPXFShE+SQ1jy6mJjee3C1X1scdu8Q16aG33zsRH535UnDqw/FBgx3gImwfW7jml1RX1JYYU4CgZIgAI1p4ROXsRWLawylGIrGsLu7SCuwyeMeZn0ZTJiaNYxhPoXDG2xjkeIEsKSmjKG9nyey9/OQKcUZPofU3q/ztXUf0YMdCsE8oV552jwUc66i31i5Npb5ckxas8t9KIrC2YurGKSYF7NiMr3K8XdAVNXIV6E6ZBf0quGkKCe27nBiWKANGGH9sorXMbPG9kN4HV5m+4y6mX7NF1hrG2BprZ/TtVY8egSt/N6yf9pQtyhRd87XydqNSalXzTURwviiT0Hkln3+nMV60jsOl94Y1oAqgm4iHYIwG04seCyzv7jY7aDY5cVhE6/ZZi0nXbEM1eEh4ynl75m1+raycbPepR5yzsmAWsyBPmE+lIS3TOtmMa+iSFfFUt1/6YJlfP3dy7lg1ch5vuOFXIyVFblGrSmc71McFXVroPEUlHScM1vvAExKsUBivgfTZDfUjc0jEsZ3KylBl6d/HmafZmyTn4NbPl/k6WZTHHhNpNcUIkV9eKbCGoFslkGE6c6uFXcIKtHC95SMHA7UgXN8vlvzdVpW5OKEOaW47DZOW1ipB7BIq0IxMaNdWFAjwmwKQkY5xMU1fk6aN7yeMB1bxGNJo16kAnJ9mmsL7TdJBIPC0jEnKyJLu2PdpLIpPQJ108EBUlq3DYDtLuewQJtQUhC/mvEANqEUtesslsroFX5k2svhzFW0SHECqCvx8KET53Phqjk8ev0ZbP3aeez42oW8e/XoPiNFUbjh3MV86KRGYdc339h+w6ThsNv0wsOgqQzgbUuEmno4K6I1j7GJYJYWtWrkSMy3/Q+s/RQsfuewtyTpdYcTeluWnFy3QO7ENx6lCLC6zEh4t2md4hcWRXE77Cyu9lPpdxul6uT3NjUVZc1HoGwemQqDNGSHDHP03niiT0dEaYEJs32TduDaESc8s1IN+kShg2JNDQ8pKo5Pv4TyP52EP7OLh7In69vOKtNMqdKv6PTljCFEkd5YV3YK+fEHj+Wxz57Bwmo/J8wtY3G1n0vXaME8NQE+fPKc4YEok4AkxdGCbECY/2WAz2iVbwCh/s/5OgCL2v/GYqXZSMnIK3kG4FUMpahEu7FrpJhWFPa4nEL5XPJzcZ3MOknvopDzeRfcCk4fB4aEWhqVFE1KsTibJaL6aCz34SqWzaCHCi8QJmg6hVwLUWmRix+8bzWv/M/bmVtRRF1Q/Pa6T1GmLgDzi015eX3Da+IOQ4fWG7F2Ve7nm0jx5CkkRb9mPi3LZFGwo6LSHe3W3RWtAzFsDsMVss3tGlEpytiFuqCHYrdDFwWyJqss6HE4cxUtUpwAFEXhWxev5EcfPFa/AEZbYZvxqbMX8K2LV4palFIpeoLgzA15lqkElX63Xv/w9IWVrKwvIbLkfYSWvF/ftkWtHDnoZN6ZcN63hjXiBYP0usMJfXVqrq1oDjQIeBw5lTNGwym1p+l1UmPJIACz3cKZrigKX3/3Cs6o1dSCrhQ186ndLVQBYK8X0X1ZVWFnSFyis01jGK/5tCC0PpeA4fyXOYsl+SrSgJkUpf+1SPPbhpNGTdB8FSsXNjopFlflNPEdUIv16FyZH1biderXV0Wxm8f+4wyuOsPocThVqNaug7GITlEU/TtXjBCZmYOG42HZRSiofNP5f0SigvTNPkUJr0kpdic7UezG/9sCVcL0XNIAn3ld1O8thNI5cNZ/0azVTB6NFGuLDcuMP5ulpKyCKr8Hd0CckwARw0RiRp9GimVzRjx2PswpGUGvE5tN0U2Y9UFxXURMSlFilttUF7jX6DwxItpFwB+1uVGx0qdotykcP6c0f69Jw+byksaGDShWgoCo7FUT8Oj3puI0kaLLherKjb4NafEDfleAy0+Zg9shzPD5pmG5GLZ8im91SFLMD8AAvWanOSfN67Lz4HWncduHTyRy3v/ymeSneSqzmj9lzpgUQUjS6zIrRW9hpbiqIThu4p8VLCXRdR626ApigyL4pFoxbo7zVtRwVq2mFqRSrDtWPK69Rjcx2rQV7wBF7O0VE2pj+cTNpwUhSdFTYvhb2zaJx3GSojQ1y2COIVM1IofdlmMubyjNM58W1wwjRYmq8RDOFOKMRZVU+t2cM44ADLkwGimYbBjO+QZpRzHH23bx9kERVWyOPpUw+xSbs7057203+8tdotrKiFh8Pge0np+NhdKUNARcAfxOcd78mSxlZcI36w+KRxcpSMWG7yjNp+ZF1RiQ5suAxzGsMHt9qSDDmNZ306wUK+0mUpRkPBraNaVYk6sU5eevqC85NOtKPhSFuKZwSx1BADqjnSiKokdEm5XioN1Oa3Ig5xDSfLq6rp6vXGj42M2k6HPZdZeC5VN8q0OSon/4ZHTW4kpcDhtnL6ka9h5AscvB37Kn8LHUF9ir1k+KFCXpHeyP6h3bg0XDfYogOrlP5Lip/lMZbP53NveIm6Yk05e7kWwMK5Xi6svgk+vh7V81tmkU5scmtUY3m5T5XNSWeLApw3PsJoQKLZy+eoVe/cPwyYw8mZp9isOUYiqcs608JxXFhtqn4QTxaOqPCdCPQYqH9L0mgaW1AV6+aZ1eNebZlmf52aafkcwMN1Xd+oHV/OZjJ7K8bpzXQ3AW7Wv/B4CPxn8Lfft1pWhe1NQVGaqsSSuQHtA6KWx3jN9EPOALEtJq7jYO594cSLXoV7O6WW/RrFrSqjYdamUWc3AI5tNCbbnqNfNp1CWuwUU2YTaeV1GEy0wgY5lPk1Ho0QpR5JlPpSqVVW+mEkmtrnGNFlUqa0BLv6KiNR+W2N6/I+d/qRQDeb5Gc2qQ3+PQrRiWUnyrQ95Y1SuGvbWmsZQ3v3LuiOH1RaaIMpfDhtsxyup5BEjSa9aSp+02JcfUU17s0lMQjjF1pB8LZT4XDpuCqsLBtFbWKdqVa44Ka/VBtXZY2Owi8MSsRquXc3PND7k2+Vn9pWKPg/+7/AR++/GTRkyuHhcWvRPe/hV453cMxZbWVukTVYqa4hhKDuVsK0mxodRkGp97BnxuO5zzjZxgiNAMKkXINf9/c/03+enmn/KlF780rNl3Q6mPMxdV5u8+KlKr/p3Xswtwk0Ld90/dpzjb1Oh4Sblx3e11CNJ8R1qro5mN6iXZxsKBIUEq1ek03v7R1dW75r2LOnsRa+IJ3YT+7mPrwRsUG8T6h++km0/HT4pSneWXyQP0xUVXlUgpOc32Ji5SrGwoMQpKgCgoMRq6tol0laLKnKA9gMtPmcMlx9ZzxSnjH/N44deir5cFBalLUtTdSpr51KW5Brb1bsvZX5JiiSt3kWVeQPg9Tj2wy/IpvtWx/BK46ik4+78Lvj1aorzDbtMT4UdqTzMW5OQr572g15kzOTrtNpbWBChy2Tl+9vh9ETabol/ECbe4WZRMwmiADMOV4ggYqDieTnIj6ZbWBoZX7J8oHC447T+gZqWhFCVGVYpmUtSUoqb4I6lIzrZyMpyVV5aNQJ0IFDGR4gCGOTI/wOlwIqtm6YqKghB/3/d3frr5p4d8zIDPxZvZOQCk+w7ovlPpJ2oo81LpEa+pwE7tNz4vGqc4myWpZtg3MI5AE2BXvygaMTeVgq7to257xYoreKzq7dSnM3pUpKIoOIq08xIbyN0hGRXRyTAhpXj6ggrOXlyZm1epYUV9CY9cfzr/8eH3QHENPuKcZNvOyvoSiJmsK/1NOWkZOVBVwx9esyp3YQmsbCjhB+9fPaFKR+OFrH9a5xDXeKfsDVudaz5dGxfujxFJMS9/0dx4wO9xsLKhhP/9wGq+fMGyqf4KI8IixZmAzSbMaOMM7c6HND9NNuAkmFfNpFA6yZ+uOZmnbzhrzMjEfFy8pp75lUX8+urTjUCWgQOw7a8iJzCpEYg56rQA8gnikPyII8GXT4ojK0VvjvlU64OnRZ8OJ0Wx7azS3CAqHe4SUU4Ow6fodztGLBN4OBBKhEirRiDMr9/89TC1OFH4PQ5aVKEuEz1N+utysdBY5qPMLSb8FoedsN0GWTurwq0sTQhlkD+ZjoRNXZsAOCaeFPl9Y0Eu1GTqkvl5vvlUq6aDuySn8MJYKC1y8asrTuRdI6TPLK0N4Pe6YNE5ALzT/QbvWFatN9gGRFPqwdbhO7e+Bt+ZAw//p/g/z3Q67XCL67ZGq1ylK8XqXKV4dlRYo7Z0b8kxy0uf4jBSNJlPAx4nVX4P715dz/Fzpt4EPBIsUjwKUaST4uSc54qi5JggC9U1LXI7xt2GyIwvnreEJz9/ljAPSeL766fh3o8YN7A7oN9UIyHflDhSf7pDQr5SDI6sFIsKRJ/qpJjMJUVpHhyxzqTNpk+uspJK5QyqRICemEgc99jFOY9n4sTSBQJOJgC3w06nTZBidkAk43ucNk6eLzq1n76wkjKnIOImp7iWPSk/vtQgKxPCh/Ra12sFjjwcsuTi6kRinKSoJY+bUwVGMp+aI0/HGXQ2ISwULbA+GNzG7FKPQcqSpAtFoO56zNjOVQxLLpj6cY0GrclAdUiQoSTF0iIXlX47Noe4J84ailGNg3AqzFPNT+m7y5SMfFLMNZ/OzCLRIsWjEEWHqBQhN7Q+OEWVLoZBkqLMo9r2V/FYIMAoH/l+w+LpUFGmKFDcgYL1GfVNC/gUi12FleKN71zKPVev5bzRojrnv424q0xvIFw9iQXIVEKSYn1xPS6bmJhCBcruTRT9LmEmd4ZbAJEmcOaiSt78yrl8/LS5BDU/YrMWOVqtiYnjtQozGzs2jvkZvbFevRD4qkQCundBNjv6TrpSNJ1zqQLzzacykGUCptMJYd5ZYHeh9DeJwuZaSTvq14jHQsE2Mqn/zC/CF5ug4bjh20wntA43NR2iiHtvvFdXgnNrxDl1YqM8m+XdLnEf3L/bqG08Xp/iTMAixaMQxVqwzaGQolmJBcfogDFp5JtIZc3RcZFiLkmYA4ymDGalOIrpFHLNp8G86FNzSgaIRcvaeeWjJ9hf8gue+rd/MoAwN82kPxHQC2lXeCv0iEBp4joURNyCFN2xLpyk9dJjMirXL0nRISbARWlhbjvWPwebYqMl0qKrkJEgVeKCkvkEFJcInBpoGn1gssyYmRSlMstXis0bxKOMIJ5quIuNSkq7HtNeCxgt33r3DN9HU96UzRf9VA83NFIsbX8Tt1aBSvqkLz5B3Bc1di8KcHGxyLFd376etkgbrZFWDoTFIma4T9HcN9JSihbGCakUi92TvxlySXGalWI+/GOXKDOPz+2wDcvzmhL4xk+KZlIe5lPMM5+OC4qC2218x8OdjpGP3pggxXJvub56nwqlmPZWEFed2MhSo/QOa8RdbBfRpTLH8KS0CNgorj2WJWWCFF7tfHXUz9jUvQmAY6pWQ4XWm7JrDBOqVIrmlACpFM0+xWzW6KrSaFQqmnLIwv0HXzbGInN49z41fHtJiuOswzrlKGkAfx2KmqHaJX5DuXgJFIuFTY1mcWjw1XBS7UmoqHzx2S9y5WNXEk6GmV8yn+Xly3MOa1aK4y38P9WwSPEoxFSYT83+wmkzn/pNpGheZU/QfDptvoUcpTiyPxFy69cG832KqUmQIuSk08xEOoYZ0nxa4a3QV++DycHRdhkXgkVuWlXxOzcoPcMacfu0Mm8HNVKcl9bspwvP4fhqUZB+Y+foJtTNXZo/sWo1VGnqqnv0CNTC5tOgeDQrxZ5d4n+nb3qDWWT+bKu2APCVw8JzRD3g7h3CJCyRzRjBN6P4wacViqK3fqtWxTntiOb6F2u01/EE+OCSDwJiAdMSaaG+uJ6fv+PnOPNUbn706UzAIsWjEDIVI3AIZGY21wVH6bp+SJBK0VsqcgMlxkjHANF9QRZmnpYgGxAl8GT5qTGUos9UMkuOaySf4nhh7qtXNQNKcVf/Ltbdu44/7vhjDinq5tMpUIp1Qa+JFLuHnUt7OkYKaNVKtM1KpVFdxdB4skGKo/gVU5kUb/a8CcDqytWifyiMXQmmYKBNAZ9is2h2TcPx02umLNdIUQY3+coEScuuLduNBtyE24UrwuYY1700bZB+xYQY8ysdr3DtE9dy3+77AKiWbl1PCesa1/GnC/7E9Wuu56IFF/HLc39JddFwS1LA68ChuR1mihRnLgbcwqTx3uMbaB2IccGqyd8QVYcj0GbhOdB4Cqx6H8xaK1bl8VCughxtjAEPg/HIMJPblKKoHJLhMZWi9IWZczoPyXyKIH6JmVCKz7c+T1esiwf3PYhXa3w91ebT+qCHFo0U65UeQnktuEjHaXc4yCgKnmyWqkwGZeFZ4HCxpnoNCgpNg030xHqo8A7PUe2IdpDMJnHb3aLmaVCreyrTKAohFQetifWIPsVnvi0UWkrk2U2r6RSgIq9Yh6x6tPRC2POEIMUztOjtAS3IJlA/eum76YYkxXAXFLtzAmkAZqW1/EptkbWkbIluEh8JiqJQWuSiO5ywzKcWxo/jZpfx24+fxMJq/9gbjwCz+bRQSsaUwFcGH3sEjr8C7A5RrGD2qTD/beMcoyCKaclRlJhzmjCNzSrcMkpC1outDRq/26GaT81KcSZ8itLMtWdgj6EUPYb5dCoCbepLvXquYoPSM1z1p6K6P7EhnRYT0sJ3ACIIY1Gp8BGOZEKVvtAKb4VYrMgOJP2jkKLey08xLAVgmE8HW+Gf34GtD8AurQh541qmFSWzRFF8CRkZveR80dKsfbNRf1VGns6UP1GidhW4/FTHjUAzj93DdcdexydXf5J3xjVSzOuQMRZk/dRx19mdYlik+C+KysMRaJOPkz4BVzw8auqDGXKM0xJ5KnHhT+AL+wq3lDJheV2A77/3GG55j9GJQJpPY+kYmZGqjoyCmfYpSlIcSg3RFGoCNKUofYqJQ/cp1gd9uvm0nuE+RVIxPR1jdkqLTl7wDv3t42uECfXVjsLBNpLMy70aiUilGGqBzPCuHEBukI25FZU0n0Y6jbQI0PpsTlPkqYTNDmWmTiiy6lFRhVhIAux8VDxKFTyGdWPaYXfCu39MTdYo8nD1qqu5etXVXHvMtXjl9eMe3/0ucdtla3j8P85gXuXouczTBYsU/0VRUezSe+QVKlh8JKBKJ8VpVIqKMqx9V+HNFC49roGltcaqVypFmJxarAq4qQ64WV4XGPM7ZrIZNrRvKFise7IwpzrIajYV3oqpNZ+alGJ9AZ8iqZiejtHoKBLmQlND5rGCbXSl6NFMq/5asLtAzRSuBAMmf2LeZG2ubgOCCMvmwzEfAvfkrTLjhtmEaq6coyln9j4pHgeOEKUIsPxiFq/7Jk5VZUEqzUeXfcR4T/pmx7kIlgh4nIdkBTtUTBsptrS0cM0117By5UpKS0spLi5mxYoVfO973yOVGrvIr6IoBf/+53/+Z7qG/C8Fh93Gf523hKvPmEddcGxSmAksqREENFNmlLHgtDv1HC1zT8Xxwu2w88x/ns1fPnXqmNv+8s1fcuU/ruQPO/4w4c8ZCfn5f3bFTtAdnFLzabXfTYciSLFW6aMo3yhhUoqNZ30J3ndXztvHVYuk9D0De+iPDy/U3RM3AoQAofykghrJr6hXjMkz60nzqcTyS+Azr8FFtxU+zlRDBttAbmGJ+evEY9PzkE6YzKczrBQ11Kz6EA+3dHJ3azsuWcw8OWSYqccRbX4kYdqW4Hv27OHnP/85LpeLhQsX0tLSwtatW7nhhhvYt28fP/3p+AoOr169Oiefa9asI+NCeCtgOhrXTiUuPraehdXFOjkeiSj1lNIx1EF/vJ8G/+gRrIXgzTcnjoDHDzwOCHKYCsTTcfoTuSRT6inFbrNPafSpw27D5q8hEXfgVtJUZ7uBpcYGqRjNWiBVo79xWBm1Uk8pC4IL2DOwh9c6X2Pd7HU575ujZo2dZovuEv0HoFARmkLpGCAsBg4PpLXgmuk2meaj3KQUTUXjqV4u+nBGOqD5JSNHcabNpxIONzWBBlF5p2e3KHw/qBX+dxVP2Kc405g2pVhWVsYdd9zB4OAgb775Jk1NTcydK67Q3/3ud+M+zgMPPMD69ev1v0984hMjbptIJBgcHMz5s3D0wmZTWNUQHLVryExDmu3k5Dwd6In1sKNPJKMXUkuTgexqYIYkFl0pTgEpAtSWFrNbFQuGuvjunPcyqSHaZDqGv/AkL9ViIRPqMJ8ijB2Bqvu6CkzW0mxpdx3+ItsVJqVo6rmJohjBaXueEP5SODLMpxKyaEKPlk+pd8M5ulQiTCMprlq1iiuvvFJXecFgkBUrRP9As/IbC8cffzw+n4/ly5dz8803k0iM3Gzy5ptvpqSkRP+zVKWF6YacjGWZtOnAS20v6c+nihTbh8SkJVMxwPgu0qc4Fcn7IPyKW7UWUtVDu3Le684mSCsKDsVOla9wY20ZbFOIFPu0Nks5pDhWBOpIShEMv2LtMSKP9XBiJKUIsEBTyG/cqylZRaRkHCmQY5cl6cbZIu5IxGHLU9yyZQtPPikcxVddddW49qmoqKC+vp4DBw6wbds2brrpJjZt2sQf//jHgtvfeOONfO5zn9P/HxwcHBcxZjKZcfk5LRwZcDqd2O0zmJ9lglRX06kUX2x7UX/eF+8bZcvxQ/oTV1WsYlP3JhKZhK56pVKMpWMkMgndbzpZ1Ae9bFUFUZWFc8uvtWUTgJNqTwX2EXLuVlUIxbanfw9ZNYtNMdbyBc2nYynFQon7ElIpHm7TKQgiXPMRkSeZT3jzzgYUERkLUDpH9AY9UjCSUgyMXdLxSMOESfErX/kKX/3qV0fd5pVXXuH444/P+f+CCy4gGo1yySWXjLk/wIYNGzjxRJE7Fo1GueCCC3jqqae49957+d73vleQ7Nxu94RUqKqqdHR0MDAwMO59LBwZCAaD1NTU5DRHngmUecSKXkZBTjWyajaHFPP9gJOFJMW64joGk4Ns79uuE0uxsxi7YiejZhhMDFLpqzykz6oLenlJU4qBgdzya22IqNe6AtVNJCq94vPTaprBxCBBTc2pqjqyTxEmpxSrlogqNgvePso3mkZc+OPCrxeVw7r/B/ufg6qloiDGkQSdFDWlOHj0mk8nTIpr1qzh4x//+KjbVFYaN9Ff//pXPvShDxGNRrn66qv56U9/Oq5VviREAJ/Px8UXX8xTT4nCuAcPHpwS06gkxKqqKnw+34xPsBbGhqqqRKNRurpERf7a2pk1z8jJeLrMpzv6dtAX78Npc5LKphhKDZHMJHHZD00lSFKsLarFZXexvW+7qAiDiPwOuAL0J/oJJUKHTIr1pV62q7PJqgruWCdEuqG4ErIZ2jTRV1s0sqJw2p0EXAEGk4P0xft0UgynwiSzIkWl3GP2Kc4Rj5EOSMWGp9yM5lN853fhpGuMAt1HEk7/vPg7EiH9oaFmSEYh3Cb+H0fx/yMNEybFCy+8kAsvvHBc2/7oRz/iP/7jP1BVlW9/+9t88YtfHLbNAw88wI033gjAk08+SX19Pc8++yxdXV1ccskl2Gw24vE4f/3rX/V9Zs8ePdF6PMhkMjohlpeXj72DhSMGXq+Y5Lq6uqiqqppRU6ruU5wmpfjg3gcBOLPhTJ45+AxpNU1fvI+aokNbgetFm4tq+NDSD3FK3SmcXn+6/n6Ju0SQ4lRUtQl6ieJhv1rDfKUdOjYLJZaK6UE29WNE7pZ7yxlMDtIb72UeImpaqsRiZzEeh6kikK9MRD0mIyKnr3JR7sFGU4p255FJiEc6fOXC9BzrF5G/YS3dJ3D0+RSnLdBm/fr1XH/99WSzWYqLi3nggQdYu3at/tfeLuR1KBRi586d7Ny5U/fr7du3j/e+970EAgFWrVpFXV0dTzzxBABXXHEF9fWH7mCWn+Xz+Q75WBYOP+R5m2lfsFQoU+FTfLH1RS76y0Vs6toEQCKT4G97RSHoSxddqiukQwm26RzqZGffTp0Uq4uqKXGX8LbGt+V0LJjKtIx6LQ92m+ZXpF1rOp2K0a5V9akdIfJUopCZ2lziLQeKAmVaLkbziwzDJJPKLYwCRTHyLHt2mcynRx8pTlugTTwe15+Hw2E2bNiQ8/5oUaSnnXYa11xzDc888wz79+/Hbrdz3HHHcdVVV41pup0oLJPp0Ykj5byZzadZNcuzLc+yqHQRdcUTNxs9duAx9ob2cu/Oe1ldtZrHDzzOYHKQ2qJaTq49mVJPKT2xnkmToqqqfOyxj9EcbsauCDIaSXEGXFNHil6XnQ+e2Ei8eTkMrIcOSYrR8StFz/AoX3MPyGFY+T7o2ALPfV9UpDEHpQwJ0zvFhaNdLUwSFYug5WXR5sqKPh2Os846C1VVx9zu8ssv5/LLL895bcGCBfzsZz+bppFZsDB1kBPyUGqIP+78I9/a8C0cNgeXLryUL57wxWH94kaDrDW6oWMDqqry511/BuCShZdgt9kpcwu11JeYXARqS7iF5rBI/M6oolZrja8wKU5lT0WAmy9ZCXvOh7t/SXPnFv7nkY9wzezzDaVYPPrkWchMXTDIRuKEK+Gln4hE9013w/EfM96LdIvHIosUpxTSTL3vachqFpyjMNDmyM2KtmDhKECxs1hPWXis6TEA0tk0f9z5R+7dde+EjiVLxXVFu3jm4DO82vkqNsXGRQsuAkR1F5i8+fS1rtcAcGkd0au8Vfichd0HE6l/urNvJ7/f/vuxF8GaSfPhbD+vd73O17bcTsJmQ1HVEclZ31Uzn5pTUvTEfU8BpejyGUEpz34fslqB72RUtAoDEexjYeowV+v9eFCzChZVTm8PymmCRYoWLBwCFEXRJ2W9A3zlakD0K5wIzKrs6+u/DsAZ9WfoJs5DJcXXu14H4LKll3HLGbfww7N/OOK2I1W1ea7lOTa0G66QdDbNdU9dx80v3zz299VMaX0IgmrV6pZWqcqYirpQkQT5vKBSBFjzUdG5frDFiIaUplOHp3D0qYXJo/aY3GjTo9B0ChYpWhgDv//971mzZg1er5eysjLe8573sHv37rF3PMR9jybISVl2mrhqlShO8WrnqxPqamEmxe6YMPFduuhS/TVJipNN4JekeGzVsZw39zxWVY5cxqxQUfD+eD+feeozfPrJT5PKCPPYU81P6dVxdg+McW6dXvCW0W/PnXbq1LGjh+XCQ1awgTHMpwBOj5E8LjtLmE2nR4hf+i0DRYHF5xn/H4WJ+2CRooVR8Itf/ILLLruM119/ndraWjKZDPfddx+nnnoqbW1t07bv0YYyU53KEncJp9WfRpmnjFg6xubuzeM+Tn6njSpfFafVn2Z8juZTnIxS7I/3sy+0D4DVVavH3D7oDgLQHe3WX9vWu420miaeietEePf2u/X3ZU/GURGooz8vhaZOGdvEpkefxof7FAsG2kjI+qCys4SsCGOZTqcHi//NeH4U+hPBIkUdqqoSTaZn5G88AUlmHDhwgPe+972UlZVRXFzMKaecwhVXXIGiKFx88cVT8nskEgluuukmAC699FL27dvH9u3b8fv9dHd3c/PNN0/LvkcjzEplSdkSbIqNtbWiU7u5buloyKpZnRRlZOjFCy7GYTNi4XTz6SSq2sg0j3kl8/TjjIYFQVHLclf/LrJaw93tfUY1mtZIK1t7turqE6BpsGnsgfhr6bflKUXb2FWoJPFJlZzOpnUSHrU7id5GSussoUeejlxBx8IhYM7p4NRavR2FiftwGGufHumIpTIs+9JjM/LZ2752Lj7X+E5FOp1m3bp17N27F7fbTV1dHS+99BIvvSQm30KkOJ7SfE8//TRnnXWW/v/GjRvp7RWr8ksvFSa8uro61q5dy+OPP85jj438Wx3KvkcjzIEeS8tEW6ST607m4f0P5/jfRsNQaggVsTj62IqP8ULbC3xgyQdytjkUn6LZdDoezAvOw2lzEklFaA23MiswS+/UAdAWaeP5kPAhytZO41aKia0AlDt89KajzLWP3WFd/saxdIxoKkpLpIV4Jk6Rs4g5gTkj7yh7DsrOErr51FKK0wKnB5acD1vuhZoVMz2aScEixaMM69evZ+/evQDceeedXHbZZaxevZo33ngDh8PBu971rmH7NDQ0cNJJJ4163EAgN+jg4MGD+vOqKiN0vbparLCbm5tHPNah7Hs0Il8pArpSfLP3TUKJkO6jGwnSn+i2u/nMms/wmTWfGbZNoQjM8UL2YVxesXxc2zttThaWLmRb7za2920fRoqtkVb9mJcsvITvvvJdvSzcaN9V9dfS3yeU8K1157HjtTt556zjxhyPz+nD6/ASS8fojfWytUcQ69KypTkFwodBKsVh5lNLKU4b3vUDWPNhoRqPQlikqMHrtLPta+fO2GePF2ZCueCCC1AUhdNPP5033niDM888k7KysmH7XHnllVx55ZUTGtNIJl35+mjJ84ey79EIs09LKsWaohrqi+tpjbSys28nJ9aeONLugOFPlEnzhSCV4mBykFQ2hdM2/nB3qS5lce3xYGnZUp0UT6k7hQODRoHttqE29of2A7C8fDlVviq6ol00DTZxTOUxIx4zXFRGWjv/S+3FrA5HRPrEOFDmKaM10kpvvJdtvdv0zx4VUinKQBsrcX/64fbD3DNmehSThkWKGhRFGbcJcyYh634CeDyi3mNraysgih4Uwp133smdd9456nF/+tOfsmbNGv3/xkajgWlnp9GQVhbiHq0g+6HsezRCKkWvw6sX1QaYH5xPa6SVfaF9Y5KiTNz3u/wjblPiKkFBQUUllAiNHHVZANIPKQNoxgNJ8Nv7trOzf2fOe3v69+jBNvNK5jE3MJeuaBcvt7/MV1/6Ku9ofAfXrr52+Di0dk0+FdxaBOuwgt0joNxbPpwUx1K+ZqWoqpb51MKYsAJtjjLMmTNHf75lyxbC4TD/+Mc/ANi8uXCkY0tLCxs2bBj1b3Awt3LJCSecoBdKv++++wBBvtJ3ed55Ruj1jTfeyJIlS1i3bt2E930rYHn5ck6vP50rV16Z0xNwXokoXC0VlRnffvnbXPDABbrZdDxK0W6z66TWFmkjk82Me4zS5CpNsOPB0nKNFHu366bTKq9QWJIkyzxlBD1B5pTMAeAXb/yC3f279Zqt+eh3iaCa0kwWUlHx4jhJUY69c6hTH8+YSlH2JUxFIdpnmU8tjAmLFI8yHHvssSxdKiarD3zgA3qfShA9KD/72c8O2+crX/kKqqqO+mcOsgFwuVx861vfAuD+++9n3rx5LFu2jEgkQkVFBf/1X/+lb9ve3s7OnTt1X+dE9n0rwGV38dO3/5SrV12d87okRZkKIaGqKn/Z8xeaBpv0hH9JjqMpRTBMqJc9fBkX/fUiPV9wNMTTcWLpGIBeVHw8WFi6EJtioy/ex7MtzwJwduPZOdvMLRFVaqRCjmdEzePeeG9BM3qfXVhjSjMp0VEBwDFOpagF27zS8QrJbBK/08+sMQqJ4/QYBBhqhiFNKVrmUwsjwCLFoxC///3vOfnkk2lubua5556jsbGR7373u8yZM4e77rpryj7n6quv5u6772b16tW0tbWhKAqXXHIJL774InV1o4dbH8q+bxVIwsgnxd54L0OpIUAErIBBioExqqysqDAi+poGm4aZNQthIDEAgMPmwO8cnXTN8Dq8OrHLRsen1J2C10Ri8jvmR4DG0jH9O5rRr4piBqWZLPRpCnoC5lOAF9peAGBZ+bLx+aelCbV7l2gnBZb51MKIsEjxKMTq1at58cUXSSaTZDIZDhw4wA033MC+ffvo65tctZORIBPw4/E4AwMD3HfffSxcuDBnm1//+teoqkpTU9OE930rQxJGV7SLiJyMyU1yzyfFsUjrq6d8lb+++6+cVCOiid/seTPnfVVV+dFrP+LrL31dV2rSdFrqLp1wkJPZPPn2xrdzRsMZ1JkaAkvSlOZTMwq10+rXCLo0k4FeYVlghPqr+ZDmU6l6l1UsG9d+erBN66vi0eEVwSAWLBTAkR9ZYsHCUYoSdwnlnnJ64708vP9hfrb5Z3x8xcdzlJYkRd2nOIZSdNgczAvO45iqY9jQsYGtvVtz3n+q+Snu2HIHAB9a+iHmB+czEB8AJmY6lfjU6k9R4a3gzFln6jmO9f569oYEoUlSrCuqY27JXJKZJKlsiq5oFz2xnmFkKaNgyzJZiGiNaN1j5ykCHFd9HHbFjsvu4vjq43n/4veP70uU5JFicaVV4s3CiLBI0YKFacS84Dx6O3r53sbvEUvH+POuP3NGgxGu3hIWSeW6+XSUQBszpIIzK8VoKsq3X/m2/v/B8EHmB+frraZkmbiJoLa4ls8e99mc1wopRbvNzn0X3Ecqm+LaJ64VpBgvoBQ1UiyVQULeMlg4vlSoJWVLeOlDL+GwOSaUjqKXemsTXUKsIBsLo8EiRQsWphHzSubxSscruslvb2hvTpm1loggRakUxwq0kZC+xX2hfbza+SrffeW7dAx15CT2Nw+KnFadiMZR3m08qC8WEZ1ehzenSbHT7sRpdxqNl029DyUkQZdmtFZOF/5oQnVIveMMyslBqTBjo5Wrs/ooWhgNlk/RgoVphPQrmrGxc6P+PJwMM5gcHFdKhhlVvioqvZVk1Sz/8fR/sK13G33xPhQUVlasBIRSBIMUJ5KjOBoaA0J5LQguKOijlKRY0Keom08zcPzHYekFUzKmUTH/bFj2buN/K/LUwiiwlKIFC9MIaV4EqC2q1RPeARyKg/T/b+/eo6Ku8waOv4fbiNyUixdAGCBUVHyI1TAV9ORqbqlnBbV90NbHze67m12s1S7Snl3d7b5tkha7GZrPU2lkdtoUTVMTLCpvaDkKIhdFvITcFfk+fwzzk+swFAwz9nmdM+fMzO8yH76/H/OZ7+/3vah6iiuKreq839Jw/+HsLNzJxbqLuLu4kzoplUFeg/ii5AsOnTt0LSk2dtzvTB9FS+KD43kg5gHGBY5rc7k1SbHvb96FkAldEk+HnJxh9ttw9CPY/7/wi/m2+VzhkKSmKEQ3Gu4/HN9evkT7R/M/w/9He9/NyU0bJ7W4srjTNUWAEX7XumfMi5rHqAGj6O/RX+u7Z06K5oY2XXX51NXJlfv/6/5252O0Kin6Rti2sYtOZ6otJv8fBFo3KLr4eZKaohDdyNvNm62zTCMO5f1wrb9iiHcIg7wGcfj8YYori63uvN/Uf/UzjTHqo/dhwYgF2vvmpFhSWUJ9Q712n/HHtD79Mcz9CVveU6y+Uq117u+qWqsQXU2SohDdTO9sGtossm+kNtODwdtAkJepwUp+eb6WLDrqktFU3IA4nop7imF+w5ol0369+6F31lN3tY7TVaevXT79Ea1Pf4z2aormONyc3OjtYl3fRCFsTS6fCmEjLk4uWiMYg49Ba8VpHsdThw5PV+v67IFpEPs7ht5BdEB0s/eddE4Ee5om3i28VPiT+in+GOakeKH2QrPxWS/UXKuxXm8zpYjrhyRFIWzozmF3Etk3kmnh07SkaJ7R3tPN0/LcgJ0wyNt0CfXkpZPaMG+2umTZt1dfdOi4qq5qnw1QUGGaesqcsIWwR92aFA0GAzqdrtVj3rx5HW575swZFixYQL9+/dDr9QwbNoxXX321O8MVbVi/fj2xsbG4u7vj6+vLrFmzMBqNHW5XVVXF448/zuDBg/Hw8MDb25vo6GiWL1/O1avWz+5wvZk4aCIfzPiAiD4RWteGhsb+c51pZNMR833Fw+cOozAN99bRRMddxdXJVWvU0/QSqvmeanif8Da3E8Ie2OSeYlRUVLOZ3dub98+ssrKShIQEjEYj7u7uhIaGcvToUR566CFKS0v561//2t0hC+CNN97g3nvvBSAsLIzz58+zceNGdu3axf79+y0O7P3ggw/y9ttvA2gzZBw+fJgnn3wSV1dXFi9ebJO/wZ4FeQYxJXQKWwtMDXGadrz/qUK8TAn34LmDgKkBT6dGgfmJ/Nz9uFB7oVljG/MUWk27qQhhb2xy+TQ1NZXs7GztkZKSYnH91atXYzQa0el0ZGdnc+zYMR555BEAnnvuOc6cOdP1QSoFl6t65tHOTPXtKSgoYPbs2fj6+uLp6cnYsWNZsGABOp2OmTNndklx1NXVsXTpUgCSkpLIy8vj6NGjeHl5UVZWxooVKyxuv2fPHgCmTJlCbm4uRqMRLy8vLX5hsiJ+hTbDxKj+o7psv+aaYsElU1nburWnf6/GxjZNhnozzxYiSVHYM5vUFJOSkqiqqiIkJIRf//rXPPXUU81qji19+umnAERGRjJy5EhtHy+99BL19fV89tlnJCcnt9qurq6Ouro67XXLiXMtulINy3toSqOlJeDmYdWq9fX1TJo0iRMnTqDX6wkMDCQrK0ubwLetpJiSksKzzz5rcb87duxoNqdiTk4O58+bfuUnJSUBEBgYyJgxY8jMzGTLli0W9xcfH8+JEyfYunUrw4cPp7KykoqKCsaOHcsTTzxh1d/6c+Dm7Ma7095lo3EjYwPHdtl+RwaMxLeX77XuGF00mo21WrZAvdJwRRt2TpKisGfdnhR9fHwIDg7mzJkzGI1Gnn/+eXbv3s0XX3yBk1PbFdXCQlOn4379rg3H1L//tUF8T5061eZ2K1as6PDL39FlZ2drk/mmpaUxd+5cYmJiOHjwIC4uLkybNq3VNsHBwcTFxVncb8sfKeZjAG0fh/aOgdmqVatoaGggPT2dI0eOAKbJh2NiYggIkLnsmurt2ps7h93Zpfv0cvPixQkvsmCLqf+ieexVW2mZFAsrCqlX9a3GSxXC3nQ6KVpT6/jqq68YNWoUGzZs4MYbb8TZ2Zn6+np+97vfsXbtWrKzs9m7dy/jx49vc/u2Zuxu+l57zbmXLFmiXWYFU01x0KAOZuY2c+1tqrH1BCvnk4PmyWj69OnodDri4+M5ePAgEyZMwNe39WWyhQsXsnDhwk6F1NYxaPp+R03qX375ZdauXcu4cePIyMigrKyMhIQEUlNTcXV15ZVXXulUPKLzRg0YxTM3P8Py7OXMiJhh088297csrysHIP8H0/3EMJ8w6Y4h7Fqnk2JsbCx33XWXxXXMNYFRo67dI3FxcWHOnDmsXbsWsFzTCAkJ4dixY5SWlmrvnT17VnveXqLT6/Xo9fqO/4i26HRWX8LsSe7u12YJ6NWrFwDFxaY5+dprwJSWlkZaWprF/aamphIbG6u9DgkJ0Z63dRws/diorq7m6aefRilFUlISAQEBBAQEMG7cOD766CO2bdtmMRbRdWYPns3tYbfTuxM/vLqCebJk8+TKcj9ROIpOJ8UZM2YwY0bHvzpzc3PJzs5m3rx56PV6rl69yoYNG7TlBoMBgIyMDJYsWQLA9u3bCQoKYurUqWzbto3jx4+zf/9+YmJieP/9900Bu7gwadKkzoZ93TCXG8ChQ4cYMmQIW7eaWi8eOHCgzW2KiorYt2+fxf22vP86evRo/Pz8tBanycnJFBcXa/cup06dqq27ZMkSMjIyCAoKYvv27VRXV1NfXw/A11+bJnatra0lN9c0Ia6Hh/3/+Lie2DohgqnPJUDlFUmKwsGobrJjxw4FKL1er4YPH6769++vAAWoW265RTU0NCillHrrrbe09/Pz85VSSlVUVKjIyEgFKHd3d+05oJYuXWp1DOXl5QpQ5eXlrZbV1NSoI0eOqJqami75e20pKipKASoiIkJNmDBBKxudTqceeuihLvuc1atXa/sOCwtT3t7eClD+/v6quLhYW2/+/PkKUKGhodp7CQkJ2rY33HBDs+O/cuXKnxybIx+/n4PPCz9XI9aMULM/mq2UUmrO5jlqxJoRatvJbT0cmfg5spQLWuq2LhlRUVE8/PDDDBkyhKKiIqqqqoiOjmbFihV8/PHHFu8reHp68vnnnzN//nw8PDw4efIkQ4cO5ZVXXpE+ipg61N98882cOnWK3bt3ExISwnPPPYfBYCA9Pb3LPueee+5h3bp1xMTEUFJSgk6nIzExkb1791rsowjw4Ycfap33S0pKuHz5MnFxcaxbt44HHnigy2IU9sk8XF3llUqUUpwsPwlAWJ/W80sKYU90SnWyk5wDuXTpEj4+PpSXl7dqXVlbW0t+fj5hYWHavTnhOOT42TfjRSOJHyXSV9+XLbO2cNM7NwGQnZyNh6tcPhe2ZSkXtCRjnwohupx51o6KKxVaC1QXnYvMjiHsniRFIUSXM18+rW+o52y1qcWyt95bumMIuydJUQjR5Xq79kaHKQEWV5q6DNlqQHIhfgpJikKILuekc9Jqi1pSdJOkKOyfJEUhRLcw91UsqigCpKYoHIMkRSFEtzAnRbl8KhyJJEUhRLcwD/VmTopdOYmyEN1FkqIQoluYa4qnq04DUlMUjkGSohCiWzTtlgGSFIVjkKQohOgW5g78ZtL6VDgCSYrCovXr1xMbG4u7uzu+vr7MmjULo9HY4XaVlZUsW7aMoUOH4u7uTmBgIPfffz8XL15stl5VVZU2RqqHhwfe3t5ER0ezfPlyrl692l1/lrABc03RTGqKwhF0euoo8fPxxhtvcO+99wIQFhamTSO1a9cu9u/fb3FQ8OnTp7Nz506cnJwYMWIE+fn5rFq1ipycHLKysnBxMZ16Dz74IG+//TYAw4YNo7KyksOHD/Pkk0/i6urK4sWLu/8PFd3CfE/RTJKicARSU2yklKL6SnWPPDo7JntBQQGzZ8/G19cXT09Pxo4dy4IFC9DpdMycObNLyqOuro6lS5cCkJSURF5eHkePHsXLy4uysjJWrFjR7rZHjhxh586dAPzjH//gwIEDfPPNNwDk5OTw3nvvaevu2bMHgClTppCbm4vRaMTLy0v7O4XjMrc+NZPLp8IRSE2xUU19DXHr43rks/cl77N6Itj6+nomTZrEiRMn0Ov1BAYGkpWVpU3+21ZSTElJ4dlnn7W43x07djBx4kTtdU5ODufPnwdMSREgMDCQMWPGkJmZyZYtW9rdV0NDg/bcPNZl0zEvt23bRnJyMgDx8fGcOHGCrVu3Mnz4cCorK6moqGDs2LE88cQTFmMW9q1lTdFbL10yhP2TpOhgsrOzOXHiBABpaWnMnTuXmJgYDh48iIuLC9OmTWu1TXBwMHFxlhN+y+lUCgsLtef9+vXTnvfv3x+AU6dOtbuvqKgoRo4cycGDB3nooYd48803yc/P15YXFxdrz1etWkVDQwPp6ekcOXIEADc3N2JiYggICLAYs7BvTRva6NC1angjhD2SpNjI3cWdfcn7euyzrdU0GU2fPh2dTkd8fDwHDx5kwoQJ+Pr6ttpm4cKFLFy4sFMxtXdJ1/y+pdkOnJ2d+eSTT1iyZAmZmZnk5eWRkJDAd999x4kTJ3B1ddXWffnll1m7di3jxo0jIyODsrIyEhISSE1NxdXVlVdeeaVTcQv70bShjbfeGyed3K0R9k+SYiOdTmf1Jcye5O5+LYGaJ9c117xuuOGGNrdJS0sjLS3N4n5TU1OJjY3VXoeEhGjPS0tLtednz5qmARo0aJDF/QUFBZGenq69rq2tZcCAAQAMGTIEgOrqap5++mmUUiQlJREQEEBAQADjxo3jo48+Ytu2bRY/Q9i3pjVDuZ8oHIUkRQdjMBi054cOHWLIkCFs3boVgAMHDrS5TVFREfv2Wa4FX7p0qdnr0aNH4+fnp7U4TU5Opri4WLt3OXXqVG3dJUuWkJGRQVBQENu3bwfgm2++ITIyEi8vL65evcrixYspLzdNNnvHHXcApqRYX2/q2P31118DpuSZm5sLgIeHzNDuyJolRWl5KhyEXM9wMDfeeCNRUVEA/OY3v2H69OlUV1cDsG/fPhYtWtRqm5SUFJRSFh9NG9mA6b7e8uXLAfjggw8IDw/Xukz4+/vzpz/9SVv39OnTfP/999q9ToB///vf9OvXj+joaAYMGMBrr70GwKJFi7jpppsA8Pf3JyEhAYB33nmHyMhIDAaDtp/58+d3QYmJntLy8qkQjkCSogNav349N998M6dOnWL37t2EhITw3HPPYTAYml2y/Knuuece1q1bR0xMDCUlJeh0OhITE9m7d6/FPooAN910E+Hh4eTl5VFVVUVsbCxpaWm8/PLLzdb78MMPtc77JSUlXL58mbi4ONatW8cDDzzQZX+LsL2mEw3L5VPhKHSqs53kHMilS5fw8fGhvLy8VevK2tpa8vPzCQsL0+7NCcchx88xjF0/loorFfz30P9madzSng5H/ExZygUtSU1RCNFtzH0V5Z6icBSSFIUQ3UZLinL5VDgISYpCiG5jnlhYaorCUXRbUkxJSUGn07X7OHnypMXt29vuqaee6q6QhRBdbG7UXOKD4hkfNL6nQxHCKt3WT7GtocWMRiMXLlxAr9fTt29fq/YTExODXq/XXnfUabyzmo7TKRyHHDfHMDl0MpNDJ/d0GEJYrduSYsuhxWpra7VRUn7729/i42Pd5ZSMjIxmHda7ipubG05OTpSUlBAQEICbm5vFocuEfVBKcfnyZcrKynBycsLNza2nQxJCXEdsNqLNmjVrKCsrQ6fT8eijj1q93ahRo6iuriYsLIx58+bxyCOPNKs5NlVXV0ddXZ32uuUoLU05OTkRFhbG6dOnKSkpsf4PEXahd+/ehISE4OQkt8WFEF3HJv0UGxoaGDp0KEajkRkzZrBp06aOA9Pp8Pf3JygoiIKCAn744QcA5syZw7vvvtvmNu1NkWSpb4pSivr6epnl3YE4Ozvj4uIiNXshhFU600+x00nRmrn5vvrqK0aNGqW9zsjIIDExEYDdu3czfnzHN92//PJLbTiw6upqpk+fzmeffQaYZopo695iWzXFQYMGWVUQQgghrk+dSYqdvnwaGxvLXXfdZXGdlvPgvfDCCwCMGTPGqoQIaAkRTJfKZs6cqSXFwsLCNpOiXq9v99KqEEII0ZFOJ8UZM2YwY8YMq9fPyspi7969ADz22GOtlmdkZLBkyRIAtm/fTlBQELt27eLs2bMkJibi5OREbW1ts0uuoaGhnQ1bCCGE6FC3N7R5/vnnAYiIiGDmzJmtlpeXl/P9998DcOXKFQDy8vJYsGABHh4ehIeHU1RUxMWLFwFYsGABQUFB3R22EEKIn6FuTYrHjx/XaniPPPKI1S0Fx48fz3333cfOnTvJz8/H2dmZX/ziF9x9990dXrptyny71FIrVCGEENc3cw6wpgnNdT1LRlFRUZd39hdCCOGYCgsLCQ4OtrjOdZ0UGxoaKCkpwcvL60c33ze3YC0sLHSIFqyOFi84XswSb/eSeLvXzzFepRQVFRUEBgZ2eMXSZp33e4KTk1OHvwqs5e3t7RAnkJmjxQuOF7PE270k3u71c4vX2lHUZDgQIYQQopEkRSGEEKKRJMUO6PV6li1b5jCDAjhavOB4MUu83Uvi7V4Sr2XXdUMbIYQQojOkpiiEEEI0kqQohBBCNJKkKIQQQjSSpCiEEEI0kqQohBBCNJKkaMH69euJjY3F3d0dX19fZs2ahdFo7OmwePHFF5k4cSIDBw5Er9cTGhrK/PnzycvL09YxGAzodLpWj3nz5tk83pSUlDZj0el01NfXA1BRUcGiRYsIDg7Gzc2NiIgIli1bps2cYksnT55sN16dTkdKSgrQc2W8a9cubrvtNgICArTPXLVqVbN1rC3PnJwcbr31Vry9venduzfjxo0jMzPTpvEWFRVx3333ER0dTd++ffH09GTEiBG88MILzeLduXNnu8dk27ZtNosXrD/29lC+lv7/dDodJ0+eBGxTvtZ8d/X0uXtdD/P2U7zxxhvce++9AISFhXH+/Hk2btzIrl272L9/P4GBgT0W2z//+U8KCgoICQkhKCiI/Px80tPT2bp1K99//32zoZCioqKavb7hhht6ImQA/P39iYiIaPaeTqfj6tWr3HbbbezZswdXV1fCw8MxGo38+c9/5vjx47zzzjs2jVOv1xMXF9fsvR9++EGb4mzgwIHNltm6jL/55hsyMzMJDw/n3LlzrZZbW5779+8nISGBmpoa/P398fb2Zu/evfzqV7/i448/ZurUqTaJ9/jx46xevRo3NzciIyMpKioiNzeXxYsXk5eXR2pqarP13dzcuPHGG5u9Z+0QXl0Rb1OWjr29lG9wcHCr89loNHLhwgX0ej19+/Zttqw7y7ej7y4PD4+eP3eVaKW2tlb5+fkpQCUlJSmllCouLlZeXl4KUL///e97NL6//OUvqqCgQHu9aNEiBShAffDBB0oppUJDQxWgduzY0UNRXrNs2TIFqPnz57e5fMOGDVr8mzdvVkop9eqrr2rv5eTk2DDatj344IMKUH379lUVFRVKqZ4r43Pnzqnq6mqVn5+vldHrr7+uLbe2PKdNm6YAZTAY1KVLl9SVK1dUXFycAtSIESNsFu+BAwfUm2++qWpra5VSSl28eFGFhYUpQHl7e2vr7dixQwEqNDS0y2L7MfEqZd2xt5fybammpkYFBAQoQN19993a+7Yo346+u+zh3JXLp23Iycnh/PnzACQlJQEQGBjImDFjANiyZUuPxQbw5JNPEhISor2Oj4/Xnrcc9SEpKYlevXoxePBgHn/88R6dW3Ljxo24u7szcOBAbr/9dr799lsAPv30UwDc3d257bbbtLjNerq8L1y4wFtvvQXA/fffj6enZ7Plti5jPz8/3N3d211uTXnW19ezfft2AKZMmYKXlxcuLi7MmDEDgMOHD1NSUmKTeEeOHMnChQu1c7dPnz6MGDECaH0+A5SUlNCnTx/69OlDXFwcGzZs6JI4rY23qfaOvT2Vb0tr1qyhrKwMnU7Ho48+2mp5d5ZvR99d9nDuSlJsQ2Fhofa8X79+2vP+/fsDcOrUKZvH1J76+npee+01AMLDw5k0aZK2zMfHh+DgYHx8fDAajTz//PPceuutNDQ02DxOV1dXBg4ciMFg4MyZM3zyySfcfPPNfPvtt1p5+/n5adO6mMsaer68V65cSXV1NXq9nj/84Q/NltlTGZtZU57nzp2jpqYGaPscN6/XEw4dOqR96d19992tlg8cOJDQ0FBqa2v58ssvmT17Nq+//rqtw7R47O21fBsaGnjppZcAmD59OkOGDGm1jq3Kt63vLns4dyUptkG1M/Kd+f0fOzdjV6uqqiIxMZEdO3YwYMAANm/erP2y3rBhA+fPn+fAgQMUFxdz5513ApCdnc3evXttGufcuXMpLS3l2LFjHD16VPs1WFdXx8qVK9ss76bv9WR5m2MEmDdvHgMGDNCW2VMZN2VNeXZ0jpvXs7WvvvqKyZMnU11dTWJiIs8++6y2bPjw4eTl5VFQUMCBAwc4duyY9kX44osv2jTOjo69vZbvpk2btMaCixcvbrbMluXb3neXPZy7khTb0LR6X1paqj0/e/YsAIMGDbJ5TC2dOXOGCRMmsHnzZgYPHswXX3zBsGHDtOWjRo3C2dkZABcXF+bMmaMts/Uv1MjIyGY382+99Vb8/Py0WMzlfe7cOa2GZS5r6NnyTk9Pp7S0tM1LTfZUxk1ZU54BAQHaJbe2znHzera0adMmJk6cSGlpKffccw/vvfceLi7X2gIGBAQQFhamvQ4JCWH8+PGA7cu7o2Nvj+UL8MILLwAwZswYrezMbFW+lr677OHclaTYhtGjR2tf2hs3bgSguLiYrKwsgC5rNfZj5ebmMmbMGL7++mvi4+PJysoiPDy82fJ//etf1NXVAabWiE3vCxgMBpvG+/e//73ZP1VmZqZ2z9ZgMGjlWVtby8cffwzA+++/r63fU+WtlNIuNd1+++1ERUVpy+ytjJuypjxdXFy0S+1bt26loqKCK1eusGnTJgCio6Nt2sL61VdfJTExkZqaGv72t7+xevVqLemYpaens2/fPu11UVERe/bsAWxb3tYce3srX4CsrCztCsZjjz3Warktyrej7y67OHd/UjOd69jq1au1Fk9hYWHK29tbAcrf318VFxf3aGyDBw/WYouJiVFxcXHa480339Raken1ejV8+HDVv39/bf1bbrlFNTQ02DTe0NBQpdPpVGhoqIqKilI6nU4BysPDQ+Xm5qr6+no1fvx4BShXV1c1ZMgQ5eTkpACVnJxs01ib2rRpk1Zun3/+ebNlPVnGGzduVBEREVoLSEAFBASoiIgIlZycbHV57t+/X7m7u2vndWBgoAKUs7Oz+s9//mOzeLOysrT3vby8mp3PcXFxqqSkRCml1Pz587VYR44cqXr16qVtt2bNGpvFa+2xt5fyNZs5c6YCVEREhLp69Wqr/diifDv67rKHc1eSogXr1q1TMTExSq/XKx8fH5WYmKiOHTvW02E1O/lbPpYtW6bOnDmjHn74YTVy5Ejl4+OjPD09VXR0tFqxYoWqrq62ebyrV69WkyZNUgMHDlR6vV4ZDAY1d+5c9d1332nrlJeXqz/+8Y8qMDBQubq6KoPBoJ555hl1+fJlm8drFh8frwA1evToVst6sozfeuutdo//hAkTlFLWl+eXX36pJk+erDw9PVWvXr3U2LFj1ZYtW2warznJtPfIz89XSim1bds2NXv2bGUwGFSvXr1U//791S9/+UuVmZlp03g7c+ztoXyVUspoNGrJZeXKlW3uxxbl29F3l1I9f+7KfIpCCCFEI7mnKIQQQjSSpCiEEEI0kqQohBBCNJKkKIQQQjSSpCiEEEI0kqQohBBCNJKkKIQQQjSSpCiEEEI0kqQohBBCNJKkKIQQQjSSpCiEEEI0+n+erHKCDnk8OwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "α_values = [0.0, 0.8, 0.98]\n", - "T = 200\n", - "x = np.empty(T+1)\n", - "\n", - "for α in α_values:\n", - " x[0] = 0\n", - " for t in range(T):\n", - " x[t+1] = α * x[t] + np.random.randn()\n", - " plt.plot(x, label=f'$\\\\alpha = {α}$')\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6cd8434a", - "metadata": {}, - "source": [ - "Note: `f'\\$\\\\alpha = {α}\\$'` in the solution is an application of [f-String](https://docs.python.org/3/tutorial/inputoutput.html#tut-f-strings), which allows you to use `{}` to contain an expression.\n", - "\n", - "The contained expression will be evaluated, and the result will be placed into the string." - ] - }, - { - "cell_type": "markdown", - "id": "710b5d7c", - "metadata": {}, - "source": [ - "## Exercise 3.3\n", - "\n", - "Similar to the previous exercises, plot the time series\n", - "\n", - "$$\n", - "x_{t+1} = \\alpha \\, |x_t| + \\epsilon_{t+1}\n", - "\\quad \\text{where} \\quad\n", - "x_0 = 0\n", - "\\quad \\text{and} \\quad t = 0,\\ldots,T\n", - "$$\n", - "\n", - "Use $ T=200 $, $ \\alpha = 0.9 $ and $ \\{\\epsilon_t\\} $ as before.\n", - "\n", - "Search online for a function that can be used to compute the absolute value $ |x_t| $." - ] - }, - { - "cell_type": "markdown", - "id": "1cd2da19", - "metadata": {}, - "source": [ - "## Solution to[ Exercise 3.3](https://python-programming.quantecon.org/#pbe_ex3)\n", - "\n", - "Here’s one solution:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "8996551f", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "α = 0.9\n", - "T = 200\n", - "x = np.empty(T+1)\n", - "x[0] = 0\n", - "\n", - "for t in range(T):\n", - " x[t+1] = α * np.abs(x[t]) + np.random.randn()\n", - "\n", - "plt.plot(x)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "97ff965f", - "metadata": {}, - "source": [ - "## Exercise 3.4\n", - "\n", - "One important aspect of essentially all programming languages is branching and\n", - "conditions.\n", - "\n", - "In Python, conditions are usually implemented with if–else syntax.\n", - "\n", - "Here’s an example, that prints -1 for each negative number in an array and 1\n", - "for each nonnegative number" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "6080e122", - "metadata": { - "hide-output": false - }, - "outputs": [], - "source": [ - "numbers = [-9, 2.3, -11, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "165fb094", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1\n", - "1\n", - "-1\n", - "1\n" - ] - } - ], - "source": [ - "for x in numbers:\n", - " if x < 0:\n", - " print(-1)\n", - " else:\n", - " print(1)" - ] - }, - { - "cell_type": "markdown", - "id": "e5a8f5fd", - "metadata": {}, - "source": [ - "Now, write a new solution to Exercise 3 that does not use an existing function\n", - "to compute the absolute value.\n", - "\n", - "Replace this existing function with an if–else condition." - ] - }, - { - "cell_type": "markdown", - "id": "4cadf82a", - "metadata": {}, - "source": [ - "## Solution to[ Exercise 3.4](https://python-programming.quantecon.org/#pbe_ex4)\n", - "\n", - "Here’s one way:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "da4679e0", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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WZd3XkiVLAAAf+9jHIMsyWltbnTq8LczEIw0BH9vB066LvK1YMo3h6QTeOj6FFw+OZhU4WkF/7/VIOm+BZoQNTIg8mxOQtwYYQ5Ha953NQfZzvXSBMS6oQjxSOkZvx1ikTZuJtkY/85CMkn+S7S9qC+P85Z340cffiZ99ai38Xg/+5arTse3rl+B9K+azv6XXPGV+MwDlfc0UMJUkpnpstMGOpzIVdzbKRV13HjHLsQFcnk2tZaPWW/FURrer2XNiytZxSDhC07OJnhYSkAiPzQn4NkNWBdpdTUGE/fWliiRvglqJTcdTOm9DUDjGdlRWXnFbQ4B5SMYNRr+aX1vcoXTCuXzVAvR1Kt/7vB60NgSYUZyYTTKV5VL1ObJsXfhthjHHJsv6zeBcxlXDdvDgQciyjP/8z/9087CWaPPYDIatUV/LRgNJ48mMbmdm37Bl5/IAThkpPDZH0Bk2n0WOrVkTj9RS54VckIe2pL2RCRnGZ0U4shRihuJmY5E2/dzGGSdjjo2EI4vbw5bH4b09yqnNaw6iUb2HC3kf4+omvTUcsPx/zFXq1mNLpTUjZTQ4HYZ+kVSDFk+li/LYjMXZBAtFCo/NEcgL8UhKGJgIqoZNkoD2Bs2w1YvHRgtie6O2yIpwZGkYPTZjKJKub1ujHxF1HTCqIg8xw9ZgeRydx6a+j60NAbQ2BHTHsQOtZfy6VCuNkOvWsJEXBpiEIrkO/4lUhgkQYkl9KHL3iWlbxzK20yJ6hMfmKMaRNQR5bO0NAfi8Hib/rxfxCC1+rQ1+tKkLolBGloaxHZXxevIeW95QZC7D1qD9LTOWDVrerhAhUEzd+IX8XgTViEatCEjq1rBNq7vzgNejC1MBmsc2PB3XtbyKp9LsZgCAfSembCVrp/J4bKKWzRmMQ0YJMmRU2kGqyLjafLbWGWOGLcCEA6KWrTSMYexsjy3bsBlDkbTBpRpXM3iPbYzzvFsbCp8jSUYs5PcwpXCtSP7r1rDNMOFIdpuvTq5f5LTOsOk9tmgibTlUkIfl2NQhpkQPC0XO1owaqZpImkj9Ac0j71VzGXzOrR76RVIehvfYCq2BEujJ57GRFL+1wW86zDiZzmi9G1usDRv97dhMkv09v0EpJBQZFx5b7UE7rLA/27CxRsjROFNEKn+TznrjjXm2Fw+M4oX9I7rfWYUi57coHkM8lRE7Zgcwa4AMAOtPnYd/+sgq/P0HVwBQdqwkoqiHPBsfwhIeW3mg9YSiMqPRhG6zStc8EjaX+w9NxSHLykBc2niZQd5e/+gMq41tbfCjJVxYji2Z1qITIZ8XQb/yGREe2xyHdlhBM8PWSOKRhCEUmclKru7h8mzReAp/+u8v4Nofv4ADw9r4CeP0bCLo87Jw2IDoGVl2WI6N6xMJKB7cxrVLsESVVUuSVl8Yq4N+keSdtYQDFfPYXjwwiku+/yT+8FZhk6KrFVpPKAqTSGd00R7mXYX9iIRV8Qgnzad0xPxICB6P/n7lIaPIC9L8Xo/msdlURfIb9KDfg5CPQpHCY5vT0A4r6Mu+BJrHlmDdSQDVsKWsPba3h6YRSyo7obufepv9ftqkdRdBAhJh2MqPWZ9IK5gysg76RfIeW7vqHbjRfWTPiSm8dmQcqXQGH71rK/YNTuP/PrrX8ePaZd/gNP7s3hdtD/jkYTVhDX62SRrjuvmTd9bCeWwTs0nm1R1XDRt1JbGC/pagjUkb5dhsemx8TjDo82gem1BFzm2Yx2Zi2MiLGo0mdKMl4sk0uyGobmT/kOax7RvUvt/08lGcUCfUUo7N6LEBmgJq76A9haXAPlbiETPqSRk5zuVmNDWds6HI2UQaV/3wOfzRHc/is/e/wn5P+exq4IGXj2DL7iH810uFN17n1xPaLPBz2chja2nwo7MpiIDPg0Qqg61q2oKEIwty5NcAM8Om/Ey1aHbbapHHFvR5IEmS8NhqBYolB33ZoUjaBaUzMo6OaZ5UjBOPUEeA49x4dd6wJdIZ/OSZAwCsC7QB4IzeVgDA9v7xYv8rAgsSaeW9MopHzGhQu4/UQ1utKDdpghZKs9lg5eS5t4cxFUtBloFH39TCj3beG7cgIVihAzsBvRCDNrCUW5dlmRmclrAfIb8XHztbGcf1b48pHqtdjy3g8+h0AW2qEW1pKEzuTwaM1JDksdVKk4LquatcRsuxZV+CgM/DPvCHuPlIiVSGqeb61PzM0FScDRUkw7bu5E4AwC+39SOWTOMtdbTEvEj2TXvm4lYAwKuHx4UysswkUtmd/a2oF49NlmVuU+fRpOcljGSyw+NvDQIAFhqk7NUkVjg6pnzWi5m2wXtAtIGl3Ho0kWZCDfKsPrt+OQJeD144MIqtb4/gmLpBXmCyRhihHB2gbcJbw8WFIkPq+hcUHlttEM+RYwO0PNvhkRnd7+mmX9gWhtcjISNrs9v2qWHJ/+/CpehqDmJ8JonvPrIbJybjiIR8OHdZe9ZxVva0wOeRMDwdt1U6ILBPkhVoWyfjCW10TW3n2BJpfW6FumAU46XYRZZlbFEN2zc/sgqb/+pCfOOPVgKorpxOOTy2oM/LJniQOIReL+D1MEPS0xrGx85RvLa7n3rbtscG6MORJBphnUdsnrvRYwsJj602yBWKBIBOVRl5aDSq+/2EurMN+72Y16w85/hkDIlUhnXnPnV+Mz50eg8A4MdqOPKK1d2mxwr5vVihjp149fB4Kf8lgQGtjs38PebR+kXWxo7VCt5DCvAem4OhyF3HpnBsIoaw34vzlnVg9aIWzFc9k2rxEBIprY6sGO+VL3ZuVmvNKBRJitNI2K9rgv6J85RpJ1v3j7A+kQsKNGzt5LGxOraEraYRzGNTPxvCY6sRcolHALAEsLGPI+2+Qn4vuwmPT8RweDSKdEZGY8CL7pYQPnxmj+7vrjxD/zOPyLM5A9WxBex4bHUSiuS7+Ae8HlbwOx1PFTTypBBI0n/BSZ1aTsdXXXVTxyZmWV1YMUZe57GxUKTeYyPjQ5w8rwmdTUHEkhkM2SjOJnQeG+XY1N9lZK2rUi54Q8z/W00edCnUr2GjUKRJjg3QQpHGFkuaYfOwePjxiVmWX1s+rwmSJGH1whY2TmJ+JIi1Szssz0XLsxUuMxZYk7ToFWlGQ50YNj6/JkkSy9fIstb6rdw8u09R/r3nHV3sd5qHUB0LKZ8GmCxwrhlg9NiUa0qCHF7qzyNJEs5brq0LXo+ELjUKlIsI9zqkigz5vSycPh7Nb5hZuZNf77GJ7v5znHyhSOoXaWSS89gonHJ8Ms4M20ldTQCUm/ZP3qXE0D92dq+uu7yRM3vbAABvDEyKuVhlJJG2Lx6hfpG1roqMJ/WRiqDPy3brxsGX5YKUw/TZAMB1uqiO682rnzMydB2H7MCvJ80WHpvRsAHA+Zxhm9cczLlOEGahSEArJ7LjccaM90GNeWzZ+vM6IZcqErCur6GbNOjzsFDkickYjqtxjOXztA/vpy9chnct7cDqhS05z2VJRwOagz5MxVM4OBJlE3EFpWHVK9KMeglFsgWYk4xHQn7EknFMzCbR68AxKczGeyMsFFklC6lRuDUZS7FcmR34CBD936ZYjs2eYbOTXzO+TqvBsA1OxXUdT6yIGeX+wmOrDfJ6bI3mHpsuxxbRcmw0wuYkzrB5PBLO6G3NuwuTJIkNHT0uOv2XDatekWZQGGe2xjuP8KFIwkkByWwizRbaTp1hq7JQ5JjBsBXovTJDoZP75/fYFrc3oEc1aHYUkYDWCBlQ5rsR1GTdzhRtTe5vyHlWyUajVOrWsBldcSMdFh4baxzq97BQ5L6haVarRvmyQtHCmsKwlQvmsdkQj1COrd5CkQAsB1+Wg2F1WG/Q52EhOv741RKKHDDMRCxU8h/nclZaji2/YVPybErdq7HGz4oWXY5NW6fofaT6uZznyxliQDNwsSrZaJRKHYcic9exGUORXo+kE5KEfF62w6JQS19HA+Y129t1GSHv74Tw2MqG1aBRM+ouFMlFKloc9NgGuTAkL3Xnu8nLsqx7rBKQx+aRlBxboR6bVheWLffPZdgA4MvvOwUNAS/+7IKlto5FrxP2e5lBArTORqV5bLVx/9etxxZPZucaeIyhSH5nRH9njIm/a2l2AbZdWOmA8NjKRlLtPGKrpRZ5bDXywbZCy7GZhCIdEI+Y5dcAzbDKstbTs1JkMjIr6yEl86QN48CjNXywL/cnelrD+McPr8o5YJSH2mgZo0pGTzH3+Rrl/tUVGi6V+jVseerYWsJ+XW7M6MHR1Fl+F3ZOX/GGjUKRJ4RhKxvUK9Jejk1ZFGrfYzMLRZbPsH3zoTfxrYd3sZ8pFNnZZDRs2vErHY4cno4jkc7AI4EJt+xeC2qDx3tsTQZ1Yj6PrVDO6G3FJ8/vw99e/g7d75uC+pE2udDKE/QeW6kNCvYPTeOaHz2HZ/YOl/Q6pVLHhi13KNLjkViRNpC9O6Ibgu/tVorH1p3HY4sl0/ize1/ENx96s+hj1BvFeGxuGraxaELXONsNeM+CMJsPVgwTs0n8+JkDuPup/azbhrXHxhu2ynoJR1RF5IJIiH3m7eTYYsk0Lv7+k/jCf77KjcHimiDHU5BlueyGzeuRcPOVK/GhNfqmD02G+rnc565f/8rlsf3P9gG8dHAMt2zeWdHet8Kw5Wi3RJNsPVL2TUk3xHzVIM2PBNkImmJg4pGJuOnjv3/zBLbsHsK9zx3MKhoXmFNIr0hNPOKeKvKTP30Jl//rU672CDVVRZbJY+M9hROTyn08pHpsXQaPTZIktuGouGFT82uL2hoKyjfuG5zG/qEoHn79GFNFKiIZ5TVkWdko5ZL7l5OIYapALrLl/uXx2EbU93vv4DRePlS5hhN1bNhy17EBWvikMehjPdUIzWNTnnNOX3tJCXDKsY1E42xB5vmfV48CUFSZdPMIcpNgqsj8t3nI777H9vbgNFIZGW8OTLp2TLPJ8eWS+/PT5inyMGzhsQHVI1joV/s09rY36KYdROMpjEatx8CQIU+mZVamE/IrBe+UxpiMJdl1bbHIsZWLwsQj+vuA3otSG0SMTGvX6+cvHC7ptUqhfg1bnu7+gBZ+bAr6skQmtBB++IyF6OtowHXn95V0Pu0NAfi9EmRZU5IRI9NxPLlniP1Mu2FBblgdmwvikel4Ch/90VY2XysfyXSGLYyHR2fyPLt8mHls/ETnUojqPDZloR+yyLEp51AdggWa4LG4vYF5rxOzSVx5+zNY/50tlhMfeM/I2KqMwpED4zHWg9Jpj43UmHZao2lNkMsbihyJamvTQ68fYyFpt6lfw5bSy13NIGVkY9CXZQDphjj/pE488ZX3lCQcAZScHpUKGIu0N+8Y0IUfhXLSHoX1iiytpdaTu4fw4sFR/OyFQ7aez4f9+t00bCYbOi0UWVoYNhrXrh2VrVjl2PhzqLhhU6//4o4wyzfuG5zC20NRTMZSWY3QCTORBq0n5D1RmDnk9+RMe5SDpkJUkZaDRksNRSqGLOBVJoQ/sXsoz184Qx0bttyqSEDz2BqDPl3I0ueR4LOxWBYK36KLeOPoBH7yrDL6hkSawrDZg2TkdkKRjUHlA57KyEV9uLcdGgUAjEbtjQ3hvaNiPbafPnsA/66ORbKLdt+biUfKl2M7PhmDLMtMFTnPzLD5qyMUyQxbu5ZjO8jNYbS6LmaeEa0nZNiOqMNLacCokzQXUKBtrGMLlcl7pvf75PlNup/dpo4NW37xCCW8IyF9ji2Xl1cKfIsuAHj0zRP4ozueRf/oLLqag3j/6m4AwKAwbLZIFNArsinoY3mRYkJylChPZ2Rbf1+qYZuKJXHLQ2/iH//3zYIMEt33AQfEI1GDeGQ6nmILaLWGIhOpDI6pXUd6uVAkj5UHZCbSoP8TvQ4JU5wOQwJgnV0KyrEZmiCX4rElUhmmrF2uNrx2coBtLurWsOVrqQUAl6yYjytWL8CnLlyq89hCOQQnpWCsZfuvlw4jnZFx0SldePjz63DaAqXGRvSTtEchvSIlSSo61zSTSGEnJwCxs0sdN4QiCx2TcmJSyd3Isj5hnw/TOjb1/x1NpE2FS3bhO+KfmIyxMGRT0Mc6u/CUS7BQCgPjs8jIyme6qymoGwlDWMnnzTwjWicoLOimYWviVJFGqf3Drx/DP//uLa7uTu+xkSFOZeSivSwS2ng9EpZ0KApxUoS6TV0aNlmWTTswGGlvDODOje/E+lPn6Tw7p2LlC1q0idwAMKwuWBvXLkZXc1D0kyyQQuT+ANAapinEhX0Yt/eP63KgwzYMDe8dxVMZJrKwCy8gKkQlazaHkGTigL3dvhXTcXPDZjVjrBpybHwYUpIk3bUgrHKP5h6bPhT5xtEJAEBPa3Gt9gqhmTNOMUMz41t/uws/fOJttgEzDhptDPpY15WdRap0ySC2NwbY1IFx4bG5RzItM6WSXSPF73BzGcNSmG8IRZLCiGbD0eODQhVpi2QBcn9Ak2MXquR6+aC+XodXhllh9AoLDUfyXrsdQ0qYheB9Xg8aVY+qlHDkDCceGZ6Osw2YsYaNCDIlXuVybLxhA1CQx2bMsZEiEtC8J/JiTl0QKc8J56DB7wVVHE0ZvMnZREZ3PsbOIwCwSh2vRca4UEbU1+5oDHCbRKGKdA3+g5QrFMnD3wDGmrZyscAQiqQQE7XzEv0kC4OJR2y+x+zDWODivs1QiDo8ZSMUafAKD48UZthOTGn3gB1DSliJpspRy8Z7bBkZ2HVsCgDQ2WwunKgGj42vYQOUsHWDIWxqKR6JZRs2otng+b1jgfMzFj0eSetTaTi3jLqTpw2VJvfnDFuPYnyLNmxcaQf1xXRqeG0+6tSwaR8ku4aNf55TOTbecM0kUqxY2OixTcwmS5bllovByZit3nSVoJAcG6ANbZwoIhQJaCOLRnIU9RKlemyDulBkMR6bwbCVQfIfNdwH2w4qSlFLj60KCrSNHhuALAGJXfEIX+vKj+gBgFNdMGz8cY3nnFKjFxOzSTUVow9FAmADkd8YKNawqR5bU4DlFEUo0kV4ZZjdbiF6w+aMx0aGK5bMYP9QlB2XwkSRkI/diNXQLHliNomLvvMEPvqjrZU+FVMKGVsDgPsw2jcUvApyzaJWAPZCg/Q3lNMptJaNf/+Ly7Hp72GS/JeiYosaCpnJk33X0g6zp1eFKtLUsIX1RsnSsBkMOW8kmjjD1hzy2R4iWipsZI7h3CgHPDGbRDItg1LC/H2wskcxbP2jswVv7gBgmFInjZrHJsQjLmI2bDEfulCkQ4Yt5PeyG4LaLHU2aXOsJEnKKgmoJEfHZjGbTGP3iamKNjy1guXYfPY2Ly1FiEf4TiWL2pSxI7ZUkeoxKK9RqMfGG7ZhGx4iYRWKLEf3kel4tucV8Hmw/tQu0+fzM9kqgSzLuq4jBF2L5V3qCBuLa2I0Hnzespnz+k6d3+zavDmrRshp9fM5OZtkfSIBQweaBj+7DsV4bXqPTYl+TMaSBSt+y0F9GjYbNWxG3AhFAlqebad6YxmnCrCSABt5HKeZTSof7HRGRrQKx70kyTP32nufaVNRyOLOdyqhCch2PChaLCn8c6hgw1akKtIiFElh8P6x4rugUChyfkQLPa47qRONwWylIX8OlRKPzCbTTADSzc1Cu3TFfHQ2BfGRMxcCsPbY6PdhKnLmPTYux+ZWGBKw7hfJe2yUxpCk7Ptg1cLi82xajk0LRcpyaUrbYqlrw1aIgQq6IB4BNMNFktuORr1hY91JSvTYntozhBt/9XpWXqQQ+IbBlSrEzAULRdr02EoxbGG/l8naCwlF0qI3Mh23vbOVZRmDnHgkV6NeI1abOppDtvfElO3XMkL3EhXnAsBlKxdYPp+FIpOV8djIa/Z7JRbuB4Ab3r0cL33tYqzpbQVgLR6hOrZlqmen89g4Y+6GcIQd16KtFm/Y+LZqRk+SwpFvFCH511SRQQR8mginkNB+uahPw1ZUKNJ5uT+geWy7jqmGzZB4N9ay/eGtE/jOI28V7O7/40Nv4hcvHsbTJQwE1Bk2G+G7QhbgUpFlmakibYtH1PBJMaHIhoCXvVcj03Hs6B/H9fdtw7v/ZQv++O6tWYXP9GGn0E9Gtte8FlCuIz91emQ6gaGpOL7/6B4M5BmBYzXVggzb7lIMm5pjo4XeIylNDqyotCpSGycTyFrglUbGalNhE48jmc4wZeFJ8xRDzq8RulCkC1J/7bj66d2A8lmg5YH32MxSKqtLkPzzoUig+LrQclCfhq2oUKTzBdqANt8tyhSR5qFIMmzf/N9duGPL29hxZNz2McaiCexVB1yWIu/mw3D5dmX3bT2Is/7xUWzeMVD08QqBX/hti0caChePzHKLBL1X0UQa//DgG3j0zRM4PDqD5/ePYo/BYJDHNq85xBZ4u9JoCkP61BZgozMJ/Pjp/bjt8b24+6n9Of/WaqoFGbb+0VnLbvb5oCbIF57UCZ9HwuWrFuiG9RrRcmyVCUXSe9BqMU6mOWTdQ5OPdCzrVAwbvy7oQpHz3fTYyBhr58w3D5iMJU2l/gTlfA8MR20NLCX4vqDUPq2lgkXa9W3YCglFuqCKBPQTuQGgs1HvsVFN25jq/ZAXVEgcm6+7KlcoMt+iTDvAYqXEhcJ7SHY982J2mGQEwgEvmoM+VjO344jy/6SQFO+txpJptri0hP0FCzeohm1ZVyMkScljPKV63geGozn/1mpT194YYAvS3hPFTfUmL2FlTwte+OrF+P5Hz8j5/EqrIifUDYxVu6sIpzA0RkTo8xb0eXD+SR3wSMDpi1rY44vawli1MILLVy5wfA4bD6tj4z7XKe7cJ2aTplJ/or0xwHLFhcwJjCbS7H00emyVSFPUpWGz0yfSiGvikRa9ITN6bHwhrSzLzKAUMkeM6ouAUg2b9rf5bl4ygnam+5YDvv9goXL/qViK1f3kI8aFIiVJQifnofS0hNgOmK81o/dMkhSvoFDDRk2we1rDbPGg0HU+8UeuqRanqB3Zjd6lHZLpDLvmTUEfOpqCeTeAWh1bZUORrRaGjTw2WQamDV4sGY7mkB/n9LXjtZsvwxcvOYU97vd68NDn1uFHn3inE6duiVmOjffYJmY0j80q8rRSLdR+vYBwJAlHwn4vGwHF7usKdB9x1LB973vfw/r169Hd3Y1gMIglS5bguuuuw/79ucMlTlNMKNINuT+ghRoJY46NL6SNJtIsdl5IwfZLvGErQc04W4B4hBk2l4q5yWPzSGBd+/PB79wnbRpgalVE90Qn1xfx3OUdaFc3JnzRtlbD5ofHU3jzZQpFzm8OZd0fR8Zms7yLg8NRfOqnL2HbwdGc0QoKRxZj2Ph2WlYqSCOVVkVSiMzKowr5vcwDN0YkNMOm/F+bbP6fncZMFZnmSnGm4im2IbXaoFOerZCekXR/86HnStayOWrYfvCDH+DJJ59EIBDAwoULcfjwYdx333244IILMDlZXKPNcmBnFpsRncdWwN8VijEUaVRFtnCzs/gPm13DFkumdTuxkjy2pH3DRsdxzWMrsDgbUHomUujQbo87WiRIAca/X+cv72Q/j3Jtr+hakUEr3LApHtv8llDW/ZEwaaj84PYBPP7WIO597mDOHqmaYSs8FEkeTcDrsd3CLGiY2jwxk8S19zyP//vonoKPXwyax2adB4xYqAzpPq4Wg0aYFWinuXyzLGuqXWORPlFMz8g4m++mr4sDajDHdv311+PQoUM4dOgQ9u/fjy9+8YsAgOPHj+Pxxx938tA5seq+kAuf18N2/k56bO2NAV3TXuMcK352Fp/Utjv5eXv/uE5YUYoHVd0eW2F9IomWAiX/tKGgWibegzpveQfbwY6aeGwlG7ZI0HTO2RFDOJJ6SfJF/WabulMXFB+KpI0LDWy1g1EVefPmnXju7RH82+N7Cz5+MVCOzUo8AljPqiMFa/UZtuwC7bShecLRceX+MPbEJFaqtWxvD03bFhKlMtkbSdow1FyO7Wtf+xoWL17Mfl63bh37Phg07x8Xj8cxOTmp+yo3VkWq+SBPzUnDJkkS5nEFrkZVGeXYMjJwjFuoZm3mKV45PKYeR/m5fDm23K9DUnC3DFuCFWcX9h63FrjLnEnoDRsZmiUdDVjYGuZKADTDxjwF9ViFGlM2DqYpmJWDBRRlIw8dmzdsZtflpHmKx3ZsIlawWnaaGTb7Cz0fipyOp/DrV48WdEwj6YyM3+wYyFvyQORTRQLWdWHMYzMZc1NJTEORhtD060eVNbW3rQFmzGsOYX4kqDaytrf+ptSNpI8bEVVMJ59y4Zp4JJVK4fbbbwcALFu2DBdffLHp82699Va0tLSwr97e3rKfSzGhSEDz8Ar9u0KhcGQk5MvyOII+D1uUaIghYD8UOTylLHLzm5VjzJSQYyukQJtyMG7n2AoJRQLcLtPmh5FEOzRIk2qaLn6HUr/VwXlsh0ai+OAPnsZPnzsIQNukFOqx0cYsHPDqNj5UIG7sO0keG3l6Aa8HHpO8Y0vYj3nqaxzMo640wjy2QCGGTSvQvv/5Q7rHjIuxHZ7aO4TP/+JV3LJ5p63na3VsuQybKiiKG3Nsys/GZseVhjYW/IY1y7CppUE0DNSMVWqh9utH7IUj6fPm83AeG9uw1Zh4hIhGo9iwYQO2bNmCBQsWYPPmzZYe24033oiJiQn21d/fX/bzKUY8ojzfeY8N0GrZzMJMkiSxBfEIt4DZNWzUBosWwbKFIvPkpJjH5naOzWbXEaLQmWyzhlDkR85ciE2fPQ9/c/mpAKALRf7v68fwxtFJluMsNhTJG20+9HmpWgxtVEZSGJRk37k2ZhQtKHSKclGhSK5X5P9TjT17vSJq6chTG7TZbs6OYaOGyMapB9XqsVF4kVdJGw3bmPr/7utotHwd2qD1j9nzfune4of68uUzu45NFrVZKRbHDdvx48dx0UUXYfPmzTjllFPw7LPPYsWKFZbPDwaDiEQiuq9yY5botIPWE85Zw0Yem1mYCdA+bLzHZlfuT14WGTY3WmrJssyea7e7RqkkiwxFFjpug7XUUhcUr0fCO5e0s3uEPLaRaCLLk6IwFx3TboE2v4hQeUFrgx9nL2kDkB2KNHZ8yVW/SZsp8uztQsXZxYQix6IJDBhaxJndl/c+ewCP7zph+XoUfjNOj7ZCC0Vai0eag9kFz0D15tjIsCXTMtsApSwMSl+ntccWCZv/v60w89hok7h3cBpX//A5XHn7MyyM7jSOGradO3fi3HPPxcsvv4x169Zh69atWLZsmZOHtIUWiizMQP3ZhUtxyWnz2Nwtp6ARF13N5l4tJbSPcLkEu+IRZtiayLCVEIq0qYpMpDNst5ZIZXQ1Zk5RjCoSKLxI22jYjJDHNjGbxNtD+vAe3X+Femwsn+HxYE1vKyIhHz54ejcblsl7bJmMnG3Yctz3ZNiMysp8kIdVyEJP50FGojmo1fQZDdv+oWncsvlNfOr/bbMsD6BF2G70grxyqzo2gPPY5kiOjb8P6bNu5ilJErDIIscGcPWyNufz5cqxAUpZkd/ryVLxOoWj78qGDRtw6JASO5+amsIVV1zBHvv0pz+NT3/6004e3pJiOo8AwCfOXYJPnLvEiVPSceUZPdh1bAobz11s+jjddEe5BSxm01jQQkxTjYsJ+bBj8p1HYinIsmw6nmPGYDyj8RQCPmdvcG1kTXHiEbtGxhiKzH69AOsOslMNQX7mouXYOTCBa965CEBuw5ZKZ5CWZZ0xIqPt80roaQ3jlb+/FD6vh4lDjk3EkEpn4PN6MD6bhHFdyxWKZB5bgYatFPEIsaAlhJlEGhOzyawROHx3kh39E3jX0vas16NF2I5hS6YzrIYzt3jE3HOZ5oxxNRFQ1dvpjIzZRBotYb+pYeuOhHJGnqjMwfZmy0wVafCE//GPVpnmdp3A0XclHtc+HNu3b9c9dvnllzt56JwUq4p0i3nNIXzvo2ssH6ebju8ib99jU3NsTWUIRSb1CerpeErX/JUwGs/peAptDu/cEilV7l+keMR2ji2R27B5PRLaGgIYjSbYQvrJ8/vYlAbA2rDFkmlc8v0nEfZ78eBfXcA6OqQM3qhP/Xdes9JVPZHK4NhEDL3tDbr6OSKXsaeWbYWGjKJFhOaMG8vu1jCOT8zqXo9IcSUqW98eMTVsZHzshOXpWivdX/KrIo0eG4U9c/1tJZAkCQ1+r64Q28ywLcmRXwP0HY7skGRRBM1w8RMT3rdiPlZzLcecxtGV/eDBg5Bl2fTr5ptvdvLQOdFaajmbK3OKiEnoxL54RFkUu1RVZDItF935wWhMrXZ3RuWlG/OZilVFFiq9N6oizeCViwGfhykP2TG5HBvfNeSVw2M4MjaLvYPT+NETb7Pfp0wWEQDweCQsUvv8UT5vxGSETq76TW30TqGGjXJsxTUWBxQvwkzVB2heKgBs3W8+kULLseW/nynU3Bz05exMM9fq2ADtXqR708yw5cqvAdw9adOwGTdbgGJkN5y1ECt7IviXq0+39TrlojpdFoepdo8tHxGTXaJd8cisuovjhSnGUKFdjAbLyhgYFyk7SsyJ2STe/29PF92FgvJ4/kJDkYWKR/KEIgG9YettC2eFY/jaRL4n4YsHtNZndz21nxmrpEnYh+hupUG0SlhyxGRUUK77vouFIgsVj1AHluJDkd2tIWYojF4+39T6lcPjpsZrkuXYMnknumvF2bkjB9Z1bMqxqi3HBnCGjXJsJtcir8cWKjDHlsnOsQHA9z96Bv738+vyXudyMzdX9hJh89gcbGbsJJTQ5rHrsZExagr62MJSjORflmW2qLeRl2MhuDAawOl4fqPxyqEx7Do2iV+8eLjgcwO4HJu3OLm/7Tq2BDVBtl7g+IQ5zV/jCfm97L3gj0s9PUN+D+KpDG5TO3KkcsyZazEk/Qs1bJ3FemxFiUcMhq0lxOrgjDk23rAlUhnWaICHNz75JgYYi+StYKU1Y7M4NKKJf6aqtKUWoG2yNPFI9rXoy1HDBmhrzFQsaWvWY5ITNFUD1XEWLlNsHVu1UJrHpnWjt9od20HZFSvfL2hRwl92PTY7oUhakAen4kXlATWBUGHvMcuxzSbz7voBPhRp/VHivWMzwwZk59mS6QxeOTQOAPjsRScBUFocybJsuTs2e51Rs1CkDVXk+EwyazhqLqaLkPtLkqTL93W3hNGghjKN77nxXJ5/eyTr9fj7Kl/O2U4NG6DUeoX8HgxPx3Hp95/Cw68fw77BaQxOxeH1SFjUFs7595WAJP9k2Pj8JGHXY8vI9tYHLRTpjjgkH3Vu2Obmf9/swxizIR6RZZlJ9BsCPi6fYc8onpiMscWeb6c1Xy3qtZtjs3M8XvRwaCT3KJZcx8wVIjSDdvAkhrF7nFwKs3Zupl6vTcP2xtEJzCbTaG3wY/UipZYzmZZ1NUl+k90xLUjMsKnXkfcsckUqWsN+lnMyy89ZoYlHimt6ACgeW5NVji1lbAuV3RGDz4Pl2uhlMrKtGjZAUWo+9LkLccFJHUikM/jG5jfxy21K04j1p3S5HmKzA0UPqBkDhSJ5wbLVBovQTTawsRFN5thsVYK5ubKXSLEttaoFM/GIHY+NrycLB7xsZ2fHI9ry1iDWfutx/N/HlHCYtqB70NaQu9lpMaFIPoR2kAsB2cVO7ssMPixop5YtVoZQJJBt2Ci/dk5fO/OwkumMznMxW0QihmLvYfU6Lu/Sdui57nuPR2LnW0g4spgcm3Iu2vvT3Rpmmy3jpsLosRlDrJmMrMtPmoXmH9x+FBvufBbLvvowfvLMAQC5a9iIk+Y1498/eQ7mNQdxfDKGHz+tjN3acNaivH9bCcIBYyhS+cz3dTRiYWsY5y5rt+VZa/PUkhiNJtjMNTPMxCOVpDrOwmWK6e5fTURMEtZ2ui3w4RldKNKGYdvePw5Am6rLG458BcbGDuF22mrxIbR8U6HNiNlQK1pRSC1bweKRAg3bu/ramfpRMWya51JIKJIaHAP5Q/DFFGmTWKfQrjxkZJuDPjSpX4B1KJIeNxadTydS4CPHxo3eH946gS/853a8cngcAHBUbW6QLxSpnacXN7xbaS6RkRVRycWnzbP1t27TYBSPqIatMejFE19Zj19cf66t16F1Zmwmgcv+9Slc9q9PW+bytW441WFSquMsXCbOPoRz879v5bHlywnRDs7vleD3eix3x2bQ7p1qhWY4TyWfYTOGHu201eIXrkPFeGxFhiIB++M2EqkM+0DnOk5HgYYtmc7gBdWwrV3WzpSdybSsm+xtForMMmzqdTxZnY4N5I9UMAFJAbVsxYb36fmk5mxkUQRz8QiFvY2GzZi3NW70/nvbEQDAB1Z367zmfOIRnj9512L2/A+e3uN4a71iMebYyLB5JeVzb9ZEwQxaZ/aemMLQVBzD03HLbv9aSy0RiqwYFIostHi3WuDFI3yyNp8SzJh3ot2vnQ7/ZNgo3k5eWDigeWyPvnkC33p4V5ZBKMZj04Uih0vIsRXhsbXYnPzLewW5jrNQFRj0cDkkIxHOIG07OIbpeAodjQGs6mlh92kyrRlSr0cy7eIQMRg2uo4nddk3bF1FeGx07xXa6YWeTwKkRgtBU0L1VKmwfSaR1nkPxjoz3WOxJB5/axAA8BfvWY7PX3xy1vHt0Bj04aYPrcCqhRHmvVUjYb/+c53m7plCoHVmNzd41iy3CfAttapjTa2Os3CZRJEfwmqhmQtFdnHd3clLSaQyeGrPkG53zz9OeRDa2dnz2JQFkhYQXl25elELvB4Jg1Nx3P3UfvzqlSO6v6VFikJydo7H78gPFOGxGQeAFoLWCDm3eIKO4fVIOdVgSzoacfu1Z+KHH3+n5XP48OcTe5RF+N2ndMHjkVh4h8+xWe2MjcXeYzMUiizEY1NzbAU0Qi51FFSParCsCrTpXm5rCLBrzW9+sj02zbD97o3jSKQyOGleE1Z0R/DhM3rYY6fOb0YhfOTMRXjoc+uwtDO3qrCSaKFIfeeRgg2bei/xg2d39FsYNqqtFB5bZZBlmXUxmKuGjRc4tDVqH/SYurj82+N78Kf//iJ+rCbICfKc6Ma3WkTMsApFhv1enNPXjqf/5j24bKUyNsU4NoQKwOcVMCqHN2xDRUj+ixWPAPYbIfPXIF9454On92BNb6vl43wI8cndQwCA9ad2AdByaYlUJmcNG/86k7EUJmaTbFHraQ2za5Evt9xVRL/IYssrgsxj0xs2qzq2gM+jjQKa5g2b/r3ivekHtysDTD98Rg8kSYLP68ELX70Yd33inaatueY6RvFIqkjD1qLWsu05rhm214+Omz43KTy2ypLKyCzJHPRWZ4zcDrSbioT8LNZPXtTv3jgOQAkN8swYBBUFhSJVYzUVTyGjNlgFNCPZ0xrGim6lF5zRIJDHNl8dx5PPsNFEZUBb+ApVRtL5hRwUj+Tr7F8IPWorrD/sGsRbx6cgScC6kxXDRqHIVEYbRWIlqybDNh1PsQ1GszqwlgxCXo+tQMMmyzKLghTqsZFAgaY5N1nWsWm9P6l8YoQrCTG2fqIcWzSewla15u3KNQvZ4/MjIVy2coHtfNNcguXY1M97Ri4tFMnnxPcNTptuMkUdW4XhZcOFDqGsJmhBiIR9mmFLpnF0fJaNR9neP67byRqNERXD5jM0s4k0a+ArqwWbmvenhUWtJuaS4aTEf74cG3lrPo+EFT1KDVehtWysXq8Yj63BXiPkUrxCI+99xzysO7mTveaaRa3MEOlDkbk9Nl4xe2BYyY2QoWprVN6fchs2PrdbqGH73HtPxp+/exnev3oBAOsoAmuRxo0+Gc0RiqTrODQVR0ZW7vnFebpt1Ar0+aZSFPLyvQV2BTETqWVkpcbSCKtjE6HIysDPApur4hFA25k3h/xsYY0l03hqzxB7Tjoj6/oNaoIKZfGwK/c3LnCTsVSW9wdYezp03HnN9jw2KgxuawxgqdohoVDJf6wU8YjNUKRxo1AKfq8Hd248C+9YoOR8LuGk5LQL5odHWuUyfF4PUxbuOqaEkGhwLYUY89UwsRybzQJtvkFxod181vS24sYrTmMbJK2llrnc3+/16KaSE0bDRm3zyKuzGtpbi9Dnm4lH5OKMjrHDEW1azAQkKRZJqI41tTrOwkXIsHmk6nkTioEPRWqGLcMMG+UPn9mndUKnZDJ5MVaLiBGjOm4qljRd1K0MAhnOQj22jsYAFqnS7KPjs7n+JAvasRcjySYDna8RcinHMKM55Mcvrj8X37tmDT69TlPd8Y2c6Zi57l16H3aruRGS0n/+4pPxyfP7cMmK+TnPo0MN9Y3NJEw7wxuhulBJKj0URZuteCqjEz8xw+aTmGHjxSNGVSTdn2ScOxr1ExVqGdYr0tDd31Ng2NXYk5ZC4zuOmBk2bap7NTB3V/YiKVaWXG3QbioS9rE80lQshWdVQ/bJ8/sAgP0MaIuiUTySL8dmrGeanE2ZyumtDBvz2CjHlkjlrLkjw9beGGDnGs9RgP78/hF8/hev6jzL0sQjysJpXCyNsJKHMtYztTUGcNU7F+mMJV+vRgt2rtZFtOnZrarZelQp/ZmL23DzlStNe43qzkE17LIMpqrMBV8+U2rOqoFryRXl7ks+x9ZhIh4xtn0iIRW/SaoXrFSRpXps732HEkXYNzid9VwtFFkd62p1nIWLFDunq9p47zvmob0xgAtO6kRYLTR/8cAoJmMptIT9uOHdyyBJwJ4T0xicVEaYGI1Ro0Wi3ogxJDU5m9QKtP18js28sJkMAKkiZTm3MaWdeFtjgIU/cs2M+8kzB/CbHQP47evH2O9KCRPaDUXGkuULReaC3wWTwTYrzibo/ElwQx6bXXxeD/NajYXQZpSz92rQ52X/X/6+TPChyKZsj41yydR0YTahPJ/aQNVXKNKiQLtgVaRm2LweCcvUtmxm3UdSeURNbjO3V/cioA/IXO0TSXz4zIV4+euX4Jy+dra7f+3IOADgjN5WdDYFsapHUSk+vVfx2oyLvd3OI8Yc21Q8qYU1+Rwbp8jjRTrURaKjMcg+XLmOSY17OxoDLGeTq/icDOmQaoD5kTqltNTKV8dWivKyELweiTWwnSnAYyOnuLulMMMGaDWHdhohl7tFnZmAJGkqHuFVkbR5Uv6v5LGxUGRT/YQijS21ipX78+KRec1Blrogw/bsvmF89devIxpP5S1DcZvqOAsXYcXZVfIGlAKFfSgURjv0HnWHftEpSkz8STXvVmjnkV+/egQ/evLtbPGIRSiS/yCQsUlnNCPTGNT6U+YaXcOHIjWPzdqw0WvR7jyZltkutZj8F3UeiSUzOefczZRRFZkLSdKKtMn7zbWAGPsfdrcUPlrFTHloRbmbipvlfpOcnJzk/qMmHhtFBUg8NFKPoUhD55FM0Z1HtGjMgpYQG81En+fb/7APP3/hMJ7eO8SG3wpVZIWY611HzKCFlXanVC92kVrg+9TeIaQzcpYqMl/nkS/91w58+7dv4Tc7BnS/n4ols/J1gPLBoQ8DGTa+ULaRa3Kby2MbmdYWI3qf4jkMDE0LoL/Ttboqwug0B31sEchVyxYroyoyH6SC5Pt9WmE0bD1FGLZ2E6/IinKPgaJ75Mk9Q/jyL3dgaCqu5di4ejyzziNdZNhUY0ubnc468tjYBO1kGpmMXLTH1szl2LpbQix6Qpu9KfVzF42nq85jq77xrw6TqJEcG48xFEby7jN7WxEJ+TA+k8SOI+NsPhMtxLSAJFJKqyb+mvBqOMo19bSEMDARU+T+Fot6a0MAk7EU+xvyMDySsvAxw2bLYwuyBTyRtuGxqYswE1h4pKI2MJIkoSXsx2g0gfGZJNsoGClnHVs+/D4PkEhroUgbOTZAeX/MJq7nQyuC1ozHL1/qx3NvD+Nfrl6ju66JMg/uJQHJv6ojktob/ez993m0UORULIVEKoOAz8OEPuSx0T3Ae//1Av+ZjKXSrEC7UG8q4PMg7PdiNpnG/EiIGUxlyLDWpCHBt3oTObbKUMseGzFfzan4vB4m0X1y95CJeERb8IwCEmPjYgBYpjbSnYols7w/QmsNpSwo1E6rMeCDJEloCuX32HShSH9uVaQsy8xIGj22UgyO1lbLOhRnZ8houaBNR4zJ/XPk2LgQUndLqCilYmdTdijyB1v24n+2D+D5/SO655ZbaWxsFD2TSOtCkS3cMFRSbdLmhpS31HlEy7HVj2Hj7/uZhOZNmTXNzgd9nrtbQrr7PJ7KsGvMT7kQqsgKUYuGzTh+hxcLUDjyiT1DWV6W3+th1yFqyLOZ5d1IFTU5mzIVjwCc8EL12KidFu3CbYUio9pilE8VGUtqHyrKBZZD1NFio5bNLBzrFIFCcmzcKBZq1VUoZuKRCfU9NRbLO5VjIxZEQrpekR6PxEoSRqYTSKYz7L2gUCSF4SiUWk+hSI9H4tSh6aILtAGtlm1BSxghvp4ykWbXPMHVHIo6tgrB18PUCkbPZAEXOiMByWtHxtnCzy/E2vwro8dmYtjUjuaTvMdmOLZRKs/PbQOgeWxqsn94Oq4LeybTGZbXsiMe4VuGTaqhqXJ4bPlmzAHl7RWZD1owZrgwqxV8KLIYRSTAGTbVMGQyMusZmGXYHFJFstdPZZBM6XM4fPcRPqzNcmzJNMZnk6Bbi6a81wsNXPeRdIaaUhRudC5fuQDdLSGcu7QdPq9H13Cd7v94Ki2aIFeaRFotJq0pj01bUII+j25hmx8JobMpAFnWFqQwV3tmJfk3GrqA14OFaqNaGjoIAPMi+p2wsWsHvQ4Z02buePsGp/Cuf3oMf/3fO9jfHxuPsf+HHbm/cWjpaDRRlvoyCkVO5KhlczPHRgsGLSb+HPev3rAV57F1GJSH/ITqbI+t3OIR/fWMJdNZuXEyVCPROLvXGgNedn/FkmkmHGkJ+2vq824Huidnk2lQeroYj+3/vO9UPPd372Uh3pBPKyXQeWxCFVlZaknuT/AewwKTnMqyTiU3RgsTv+AzyX88dyhySUcDWzD3nJhCRlYWjC5DiMfYtcMY/iRDOhVPYdcx5XV2qPV3AHBkTGl2vLAtDEnSxB8JS49Nb9iGp+NlyX1RsXmuzhulDDMtFFrQKWSca+4V3zGip8DibMLYj5HvwmKctFD2UKSZx2YIdXVwOUB6j9oaA+w9jyUzXDut+vLWAH6Kdop5bIWqIgl+PaHwPh+ij6fzj1Nym/pTRdZijo1Toy0wUfAt62rEiwe1Zsi8YbOS/FNubNXCCL586anobg0xw0jhnZPmNWUZUS3HpiwqFMojo8irImnUCC9QODKm9ISkMSb5cmxGdeVINFEWT4oW9lyGjbzRfE2Fy0FAXdApt2mnVyRQgsfWRP9/ZWDp5Kx2nftHZ5gaESi/KtJ4PWNJTTwSMAlFjlGnmoaAbtLFaLT+hCNEmCvSLlbubwbl7nhRVTwpVJEVJ8HVw9QKRo/NCIk+zJ5vNSaEn7b9nnfMwzsWRLLGWJzMTWUm6Dm0oyPDQB4QTf+OxlNssRyfSbIPRr/qsS1qUxbkoF/LsZn1lzQOmByZjpfU2Z+gxXAoxxRpumZGFZ8T0E6YtdSy0XkEKN5jo1BfOiNjYjapm3eWkYHDo9oYIW3IaHk+UzTtm8JaisemegQ+MmxaOcKYGi5ubfCzhTeWTGud/euoATLBGiEn0qxAuxxhQnrd0ah2PyTSmoArV6s3N6mOs3ARfq5TrcB7JqYeW6feAPEz1NjomoR5jq2RMw7NIf0CfpKJYTNOnx4z1BHxqkh+sSQDSB7bIuaxKceXZU34w2PMsY1MJ0pqp0V0mAyzNDLNrpF7hs1OHVvI78XZS9qwtLMRi9sbLZ+Xi4DPw97vkWgiqyH0QS7PxjdBLgeXrVyAP3z5Ivz9B1cAUHNshs8t3wiZvIf2xgD7LMRTGQxN1V+fSKLBxGMrRu5vhDziMS7KkkhVn8cmQpE1AJ9LMismNnps+lAkeVDmOTadEQz4IElars7UsBkaIfO7aUBTRU7FUrrFcmQ6gXnNIZZjYx4b9z7FU+ms9y0rxxaNszxfKaFIquPK1SuRDJvR4DuBj4Ui89exAcAv//w8ZGS5JJVaR2MAU7EURqOJrO75vIBEU0WW7zO1rKsJrxweV17fJMfGQpEzCRZy5EORAHBU3STVU59IQlNFpoou0DaDru8oH4rkPOpqMWy1s7rbhKkia8lj4wyVmby7t72B3dSSpDcWTRYd/qMmdWoej8RUZ4CVYdPn2JjH1mDmsWnHpMVJ89gUw8a/T2YCkqwc23R5cmy0GI5YTJFOpbUCVXdybHqPLd/96/FIJUuv+bZaRo/twEgUQ1NKqUa8zDk2gg8rktcRMHpsWaFI7RyOqDP86lE8wjr8J0sr0DZC15fPsc1yQjMRiqwQtLOY6939efgC7fkmhs3v9WBxhxLaa/B7dYIPK7k/3azGRZv6xzUEvKY9CFu5+q9MRs7KsekMG7dYDk/HkUhlcFwdsUOhSI9HYouZ0u0grQthUo5NKyiOM7l/OXJs0YRSr7P7+BTeHJhkj/MF7Y3B8i7oZhibILuxM6Y81vB0gl1zCk3/ZvsAzvmnx/D1/3m97KpIgi/1SBpCke2cKpIPRXq5+0Xz2OrPsPGhyFIKtI3QiCxe8MV3KRIeW4WoxxwboOXZjC2wtGGjxhybuXEgYcLyribTHSA9npGV2ifaTbMcWyhbFQkontaxiVnIsrJAdnKLEV+k/ZE7n8NF/7KFnS+FIpeohnskmtAKp0vw2JqDPhb2PD4Zw9U/eg5X/+g5dlzycP1eqeyeihkkmmAhHxd2xrxXREKfVQuVUUi0EXpqzzCniizvOfEeG6tjY+IRTbVK4WLaPNHfHZtQDJtVr89ahp/JVuwEbTNYjo2r7+SjPdWyrlbHWbhIrUzQ5iEvyu+VWOcFI8vVPJuxaFnrPGLMsWWLR5RjKYbJTBEJKDc+LSwTM0mtxqjBIPc3eGyj0QT6R7UwJO9VBrn2QLuOTWJsJokBNcxEC+zSDuX/NzKdKEt9mSRJ6FQXz9eOjGNKbfy8f0jJLbkp9Qey69bcaF3Ee0W0CXnnkjbdPRT0eThVZHkNPG0YEiY5NlJtyrISFlV+p9xjtPhSWcop85vLel5zAX50TaqMqkgqLeLFI3z0QhRoV4haFI+0NwZw84dW4F+uPt1yx0QCEqMXY9l5xEQ8Amg1UsstDBugFWkPT8eZiKRNNRLNQeXv46mMrnP8SDTOCUcadK9HCxzf3oq+pwV3iWrYhqfjbIpBqR1BKM/2qipiALTiZDcVkUD2TtiN1kUd3HgY2oT0tIbxv59fhx//6dkAFFVquTuPELRBiiZSzEhRmNHv1TrskPqRjB2/oVnUFs4a41MPaKHIVNHz2Myga8t7bDPc2lGOY5SDulNFJmtwbA0AfPKCpTkfP395JxoDXqxd1q77fZNlHRt5JHrjcO3axYgl0/jwmQstjzU/EsTxyRh2H59iCkrKvfGvNz6jD0Wy4ux2fe6ONiF8wpoMGxmYvk7FGMZTWseJ0g2bslC+eniM/Y5k7uThulHDBgB+n37BcGNnTN7/4GSMhbGaQz4s7Wxkx5+KJR3PsfECIf5z29EY0G12aPPENyw4rTtS1nOaK1CHkNlkmm0KvGUIXwdNCrSnubB8MZMknKDuDFstemx26G1vwKv/8L6sEFYDq2PThyK1HJv+FnnPqfPwnlPn5TzWorYG7DgygdePTgBQxqiQh+HzajOeeEaiiRwem/K3/C6Rcj78gMmGgBcziTT61eLhUltdUS3bm8c00ciBYeW1mcfmgnAEyN6IuXH/LlQnAxwdn2VeD+VQKSQdS2YwHXfGsGkeG6e6465De2MA+7myAxaKDAjDFvRqOVn6xJezQDvFNS63U1vpNtVzJi5BSehgjXlsdgj4PFk7Kiu5v1WOzQ4k1X9DNWxtBrm1WV5qNJrAoVF9DRuhGTYTj001bJGQH4vbFYN4bEJRVpbqsZGAhS8MN4Yim0LuhLmM8n43FpGF6vtwfCLGvGvqQ8l7qlQSUW4Rjdnr8RszfnhoUB2KCWjKPQBY0V1/+TVA8/CTXFeQcsr9eVjXkSpRRAJ1aNj4uU4C65ZaVjk2O9CCuOvYFIDskSFmBc2DkzEmpzfusoMmCWsybCT3bwr6cOoC/SJWssdmIhPXQpHUTssdj80oo3ZDVj2vOQS/V0IqI+OoKtZpUedzkecNaEXsTnlshM+jD3Xx709bQ4A9xi++K7pbynpOc4WAVyuVKGeBdq7NYjWld6rnTFwiXoNy/1Ig8YOxpdasoSt/IZDHRd5xW4Peq+F3+01cKDSeyqAl7GcKR4Li+nwocmI2iXRGZga4OeTLUr+VOtnabDjliKoQrLR4xI3dsdcjZTVR5icH0AaFwsrl7DwCZHtsxmvAe2yt3D1Gi29T0Jfl/dcLdH8kuc775fHYrN/jaqlhA+rQsNVrjs2KxqCWK6EpuIBm6IrJIRlzZMZQJG/YFrWFdUqqNb2tWR/AoIV4hFdyNoV8eIfRYyuTKpKghfzgcNR9uX8FQpFAdliYb7DcZPC8yx+KzG3M27nmxryRow3NOxY0l2Uxn4vwkxfS5ZT75/hMiRxbBRGGTQ9vuKbjKbx+ZAKJVIbNZysqFNmqXwyNoUh+QWwJ+3WL0pm9rVmvFzDJsU1yhi3g8yDo82Z5bKUMGgX0rZg6GgPMcB7gDJtbqkhjjs2tfAb/Xga8Hp2xaTbkF8v9meK7zpi9Pv/+8PcYLb71KhwBtPslmc6wziPlLNA2Q+TYKohxrlO9E/R52Q35/547hA/d/gz+7fE9LIxYjHFoDPp04cd2g8fG95uMhP26BeqMxa2m5whkhyIpvxZRDeXC1rBO7FJqjo0PRfa2N6BPDZEeHJ5hSkC3PDZjmMetUPpCzmOLhH26HFdz0Oixlf+c+PCm3VDklWt6sKa3FR89u7fs5zNX4D22chZo54qCuFFbaRfHz+TnP/85zjrrLITDYbS3t+Pqq6/G3r17nT6sJQkmHqme3UWlIa/s+f0jAIAXD4xmPVYofDiy1ZBj441BJOTXiQDOWNSa9VpWqkiS+pPX5PFIOJnz2krNsfELZ297A/o6VcM2EnVdPFKJAm1A/z5GDB6aUQTkRGsx/jVzGTb++/OWd+DBv7wAqxfVp3AE0K5VMi1rBdpl8KhyhyKrZ0119NNx9913Y+PGjXj11VfR3d2NdDqNTZs24YILLsDAwICTh7aEhSK97ixIcwEyDHsHFRXjvsFpAEpoodjwEp+bac8RioyEfSxXsrSzMSsfB2iGjS/GnZxNMqk/HxI7lTNspebYAj4P8wYXt4exVDVsB4ajXA6yQqFIlxYRPhTZHLYWAQHOeGwhncem/z/zG6LWhuz7pp6hz22c89i8ZQlFWr/H1STIc+xM4vE4vvrVrwIArrrqKuzfvx+7du1Cc3MzhoaGcOutt1r+3eTkpO6rVB7cfhR/fPdW3PnEPpFjM4HybNSxg0J+xXprgH5BNC46TUaPTTVmZ5jk1wCtByE/QJuf6sy/Hkn+fZ7ijTIPhSN72xpYndyRsdksb9FpsjqPuOaxcaFIg4dmzLGVWxUJ6I1lLo/NqLytd/xcjs2JCdpm1IUqctu2bRgZUUJbV111FQCgp6cH5557LgDgkUceMf27W2+9FS0tLeyrt7f0OPnQVBzP7x/FnuNTXChSGDbCyoCVIr7QeWzGHFtIn2PbcNZCnLusHX963hLT1zLLh0YTaebB8a9Hhq1Ub404o7cVXo+Edy5pY8Z6eDrOxna4ZtiyQpHuLCILWkKg9TBi9NgMhs6JvDUf+jJ+ZoM+L7v+Zp5+PRPU5diUNc+pAm2iWmaxAQ4atv7+fvb9vHlaC6b58+cDAA4fPmz6dzfeeCMmJibYF/86xUIL32QsxY2tqZ7dRaWxWpxLM2xabiZXHVsk5MPpi1rxnzechzMXt5m+llWIi6Y4801uz+htxdLORlx0alfR587znWvW4KWvXYKT5zejtcHPDGa/2v7LNfGIxxiKdGcR8Xs9bBSSMcfGe3C+Mgw2NSOXxwZo44p6DSUm9Q7vsbkm96+iNdWxT6XMx41Mfm/VLDMYDCIYLO8od/pATs4mhcdmglWtWimL9qJ2m6FIG53XrUJcrx1RWnbR4gYo5/yHL19U0LnmwuuRmMcpSRIWtoWxb3CahUVdy7EZQpHG0KSTLGprwMBEDJGw/v/Kv49OfZ74hdRsM3rHtWfh4EjUdJp7PUPvRyojs5ZwzhdoV8+a6tiZLF68mH1/4sQJ9v3g4CAAlCXEaBfKBYzNJNiCFBTiEYbV4lxKOG9pZyOWdzXi/OUdWYueTjxio9eildpu54Bi2EitSEiSc13Geww1ehULRboY9qHp6x1ZIWXtvXNqIn0+j62vsxHr8zTlrkf4TQBNlHfCY+PDz24Jmuzg2KfynHPOQUdHB0ZGRrBp0yZce+21OHr0KLZu3QoAuPzyy506dBa00+TnfwmPTcOqLVQp3kjQ58WjX7oIZvZF77HlP4bVohlLKt53n6EFl5MYi88r1d3fzVD6X6xfjo7GADactUj3ez636dQUcV2OrYo8gmqHX9+o5Vk5ZqX5vR74PBJTWrY0+Nk8vGoKRTp2pwQCAXzrW98CAPzqV7/CsmXLsGLFCkxPT6OzsxN/93d/59Shs6CdJT//S+TYNHgDxu/qSu3c4fGYe05GVWQ+8m1C+FCk0yxsDel+ruVBo8SyribceMVpWb0zec/bCUUkkN9jE5jD52DLadgAfSSHz2/XRSgSAG644Qb87Gc/wxlnnIGBgQFIkoQNGzbgueeeQ09Pj5OH1mGUKXuk6noTKg1fZLxyoVbU6tSi3RQqMMdm8AZ4MUpnUyBLdu4kfCeOxoDXtV6Exo1YNYR9IjqPzYUcm4iy2Mbjkdg9Q+mXchVQU/mN1yPpNsXVcE8Sjm83N27ciI0bNzp9mJw43dNursPL/d/V14Yd/eMASm9JZUV7QwALW8MI+DxZbZnMMC6a3S1hVmu3xMUwJAD0cN3u3RKOAGahyMrfw01BPsfmzL2i99iqZ+GcC/i9HiTT2pDWcvSKBIBwQHlPwn6vbq5lNTkL1XMmDhLweXRqHhGr18OHBs/pa2ffO5U/8nk9eOz/XITffmGdLY/HGObqbtHCgW6GIQG9x+aWcASoXB1bLppd9tjE57YwjBv4cgmOQuomJuT3Gnp5Vv6eJOrmTuFzOcJj08N7Hu9c0sYKckvpPJKPcMBru5ej0RtYwBk24+w2p5kf0QqW3fTYsrv7V/4ebgh42bVw6jPFG8xqMOZzCeM9Ui4hLUVywgGP7r4UY2sqAL+7FDs/PeSZNQV9aG8MYF6zYjhKFY+UC6M3wEvul3S6a9j4gmW3FJGAyQTtKshnSJLEvFbH5P66OjbxuS0E4zpXbo8t7PfqNjTVtPGomzuFFykIj00P9UBc0ROBJEnoaa0uw2Z8v+ZHNI+tz+VQJKAZ1kqGIsulcCsVyl+7kWMTG9LCMH5uynXPUPgx7PdWrWrVvU9mhWkWoUhLlnU14aHPXcgW7GvO7sV0PIXzl3dW+MwUdIubz4P2Ru29dFs8Aih5tm2HxioWivR7nStALxSKhDgl9w8Jj61ojBuBcsv9Q0aPrUo2W0AdGTZemiw+INms4mT+f/KuxfiTdy3O8Wx34cNRjQEvm5bc3hjQ1dG4BfUlbHXx2HwLrWq6fx0PRVapRzAXMLZdK5dho81GOGAMRVbP+1M3hk14bHMXfnFrCPiwemELPnZ2L965xLxpstP8ydrFGJ1J4BPn9bl2TL+3OnfGzGNzofOIm/0xawGnPTYlFMm9P1V0X9aNYeNbN4lY/dyCN2zhgBc+rwf/fPXpFTufha1hfOsjq109Jm/MqslzaVI3jG6oIsXntjCy+4uWy2NTc2xV7LFVz5k4jJD7z13496uxSgQtbiNJWieJalKfaR6byLFVG8Z1rlwF2qEAp4o05H6rhbq5UyJC7j9n4d8vp7qhzAVoYa+meqEPru7Giu4ILl0x35HXF4ateLLl/uUxPGcvaUfA58E5fe1CPFJphNx/7iJJEoI+D+KpjGtNh6sRZWFPV9XO+PyTOvHwF9Y59vqipVbx8OucJJVnHhsAXLpiPnbechn8Xg9+/PR+9nsRiqwAugJtYdjmHLTA1bfHJqn/1s/9q2upJT63BcHfJ94yl4fQa1frxqNu7hRdjq2OFoZagST/wmOrrp2x0wi5f/HwGwGnCvr1ocjqeX+q50wchpf7i/EXcw/ajNS3x+ZR/62enbHTiBxb8eg8NocMGy/3ryZRU93cKULuP7ehzhbV0uarEjBVZBUl6Z2mWkNdc4Ggyx5bNa2r1XMmDsN7bE5JkwXOQTtDN9tYVRv1GIoUY2uKh98IOGbYxDy2ytLowogNgXMw8YjNUTe1SD2GInUem/jcFoQbUnzR3b/CSJLEvDYRq5970ALn5qiYaqMeVZEej8S8gnr6f5cD/nqVqzjbiG7jIcQjlYHybMJjm3ss62oCACxX/61HqrFA2w1o8awnT7Uc1LPHVlcJC0XyPyti9XOQb/zRSnz2ouVYXIH5a9VCoE4X+IVtYewdnEZXc7DSpzKn4Nc5r0P3TKBKxT11ZdioSFt4bHMPv9dT10YNqE/xCAD89M/ehZFonE12F9hDV8fmWCiSk/tXUSShes7EBZZ2KmGshepATYFgLkHhpGoaD+IGC1pCWNnTkv+JAh3u1LGJUGTF+foHTsM1Zy/Cmb2tlT4VgaBgSBVYTQuIoHpxw7AFqrQzTF0ZtsagD2ctrsxwSoGgVIQ6UFAI+pZaztwzujq2KookiE+IQDBHqEe5v6B4AroCbWeOQR2BgOq6L6vnTAQCQU58TO5fPTtjQfXiusdWRSFyYdgEgjkCC0UKVa/ABn4XwoQ+rwfzI0GE/V60cDMvK01d5dgEgrnM+1ctwMuHxvA+h6ZVC2oLXR2bQ3J/AHjgM+cjlkyjoYpGSlXPmQgEgpysXdaBzZ+7sNKnIZgj+F3o7g8Ave3VV18qYhoCgUBQgwRckPtXK8KwCQQCQQ3ixgTtakUYNoFAIKhBhMcmEAgEgprCrRxbNSIMm0AgENQg1doVxA2EYRMIBIIahDdsHmHYBAKBQDDXcWPQaLUiDJtAIBDUIPzgTycLtKsRYdgEAoGgBvF5PSBHTYhHBAKBQFATUL9IYdgEAoFAUBNQnk0YNoFAIBDUBIE6HXXkmGE7cuQIPvOZz2D16tVoa2tDU1MTVq1ahe9+97tIJpNOHVYgEAgEKuSxCbl/mdi3bx/uuusu7NmzBwsXLoTP58POnTvxla98BV/4whecOqxAIBAIVPzCYysv7e3tuOeeezA5OYk33ngDBw8exNKlSwEA999/v1OHFQgEAoFKvXpsjs1jO/3003H66aezn1tbW7Fq1SocOHAAwWDQ8u/i8Tji8Tj7eXJy0qlTFAgEgppGeGwO8/rrr+Pxxx8HAFx//fWWz7v11lvR0tLCvnp7e906RYFAIKgpmCpSFGjn5uabb4YkSTm/tm3bpvubl156CZdeeilmZmawYcMG3HLLLZavf+ONN2JiYoJ99ff3F/6/EggEAgECavcRr6e+BPAFhyLPOussfOpTn8r5nK6uLvb9gw8+iGuvvRYzMzO44YYbcOedd8Lr9Vr+bTAYzBmqFAgEAoE9yGPzeevLYyvYsF155ZW48sorbT33tttuw5e+9CXIsoxvf/vb+Nu//duCT1AgEAgExUE5Nk+dhSIdE488//zzTNbf3NyMX//61/j1r3/NHv/1r3+N7u5upw4vEAgEdU+9Fmg7ZthisRj7fmpqCi+88ILucV75KBAIBILyE/QraR8RiiwT69evhyzLTr28QCAQCPLwx+f0YnI2iUtOm1/pU3EVxwybQCAQCCrLBSd14oKTOit9Gq5TXxpQgUAgENQ8wrAJBAKBoKYQhk0gEAgENYUwbAKBQCCoKYRhEwgEAkFNIQybQCAQCGoKYdgEAoFAUFNUfR0bFXmLuWwCgUBQv5ANsNP4o+oN29TUFACIuWwCgUAgwNTUFFpaWnI+R5KrvO9VJpPBwMAAmpubIZXQoXpychK9vb3o7+9HJBIp4xk6gzhfZxHn6yzifJ2lHs9XlmVMTU2hp6cHnjzz5areY/N4PFi0aFHZXi8SicyJG4EQ5+ss4nydRZyvs9Tb+ebz1AghHhEIBAJBTSEMm0AgEAhqiroxbMFgEDfddBOCwWClT8UW4nydRZyvs4jzdRZxvrmpevGIQCAQCASFUDcem0AgEAjqA2HYBAKBQFBTCMMmEAgEgppCGDaBQCAQ1BTCsAkEAoGgpqh5w/bzn/8cZ511FsLhMNrb23H11Vdj7969lT4tfO9738P69evR3d2NYDCIJUuW4LrrrsP+/fvZc/r6+iBJUtbXxz/+cdfP9+abbzY9F0mSkEqlACg93L74xS9i0aJFCAQCWL58OW666SYkk0nXz/fgwYOW5ytJEm6++WYAlbvGTz31FK644gp0dXWxY/7oRz/SPcfu9dy2bRsuu+wyRCIRNDQ04IILLsCjjz7q6vkeOXIEn/nMZ7B69Wq0tbWhqakJq1atwne/+13d+T7xxBOW78ljjz3m2vkC9t/7ari+uT5/kiTh4MGDANy7vnbWr0rev1XfUqsU7r77bvz5n/85AGDp0qUYGRnBpk2b8NRTT2H79u3o6emp2Ln94Ac/wKFDh7B48WIsXLgQBw4cwH333Yff//732L17t67tzGmnnab7+aSTTqrEKQMAOjs7sXz5ct3vJElCOp3GFVdcgWeeeQZ+vx/Lli3D3r178Y1vfAP79u3D/fff7+p5BoNBrF27Vve78fFx7N69GwDQ3d2te8zta/zKK6/g0UcfxbJlyzA8PJz1uN3ruX37drz73e/G7OwsOjs7EYlE8Nxzz+H9738/HnroIVx++eWunO++fftw1113IRAI4OSTT8aRI0ewc+dOfOUrX8H+/ftx55136p4fCARw5pln6n5nt11SOc6XJ9d7Xy3Xd9GiRVn38969ezE6OopgMIi2tjbdY05f33zrV2NjY2XvX7lGicVickdHhwxAvuqqq2RZluWjR4/Kzc3NMgD5r/7qryp6ft/85jflQ4cOsZ+/+MUvygBkAPKvfvUrWZZlecmSJTIAecuWLRU6S42bbrpJBiBfd911po8/8MAD7Pw3b94sy7Is33bbbex327Ztc/FszfnLv/xLGYDc1tYmT01NybJcuWs8PDwsz8zMyAcOHGDX6Ic//CF73O71/OAHPygDkPv6+uTJyUk5mUzKa9eulQHIq1atcu18d+zYId9zzz1yLBaTZVmWx8bG5KVLl8oA5Egkwp63ZcsWGYC8ZMmSsp1bMecry/be+2q5vkZmZ2flrq4uGYB8/fXXs9+7dX3zrV+Vvn9rNhS5bds2jIyMAACuuuoqAEBPTw/OPfdcAMAjjzxSsXMDgK997WtYvHgx+3ndunXse2N1/lVXXYVQKIRTTjkFf/M3f1PR2XSbNm1COBxGd3c3PvCBD+DVV18FAPzud78DAITDYVxxxRXsvIlKX+/R0VHce++9AIDPfvazaGpq0j3u9jXu6OhAOBy2fNzO9UylUnj88ccBAO973/vQ3NwMn8+HK6+8EgDwxhtvYGBgwJXzPf300/HpT3+a3butra1YtWoVgOz7GQAGBgbQ2tqK1tZWrF27Fg888EBZztPu+fJYvffVdH2N/PSnP8XQ0BAkScKXv/zlrMedvr751q9K3781a9j6+/vZ9/PmzWPfz58/HwBw+PBh18/JilQqhdtvvx0AsGzZMlx88cXssZaWFixatAgtLS3Yu3cvvvOd7+Cyyy5DJpNx/Tz9fj+6u7vR19eH48eP4+GHH8Z5552HV199lV3vjo4ONlKCrjVQ+et9xx13YGZmBsFgEJ/73Od0j1XTNSbsXM/h4WHMzs4CML/H6XmV4PXXX2eL1vXXX5/1eHd3N5YsWYJYLIYXX3wR11xzDX74wx+6fZo53/tqvb6ZTAbf//73AQAf+tCHcOqpp2Y9x83ra7Z+Vfr+rVnDJlt0CqPflzLbrZxEo1Fs2LABW7ZswYIFC7B582a2w33ggQcwMjKCHTt24OjRo/jEJz4BAHj++efx3HPPuXqeGzduxIkTJ7Bnzx7s2rWL7cji8TjuuOMO0+vN/66S15vOEQA+/vGPY8GCBeyxarrGPHauZ757nJ7nNi+99BIuvfRSzMzMYMOGDbjlllvYYytXrsT+/ftx6NAh7NixA3v27GEL2fe+9z1XzzPfe1+t1/fBBx9kArivfOUrusfcvr5W61el79+aNWy8m3zixAn2/eDgIIDqmMh9/PhxXHTRRdi8eTNOOeUUPPvss1ixYgV7/Oyzz4bX6wUA+Hw+fPSjH2WPub1TPPnkk3UJ6ssuuwwdHR3sXOh6Dw8PM0+HrjVQ2et933334cSJE6Zhm2q6xjx2rmdXVxcLX5nd4/Q8N3nwwQexfv16nDhxAjfccAN++ctfwufTNGpdXV1YunQp+3nx4sW48MILAbh/vfO999V4fQHgu9/9LgDg3HPPZdeOcPP65lq/Kn3/1qxhO+ecc9jCu2nTJgDA0aNHsXXrVgAom5qpWHbu3Ilzzz0XL7/8MtatW4etW7di2bJlusd/8pOfIB6PA1BUcnycvK+vz9Xz/ed//mfdB+PRRx9lOcy+vj52PWOxGB566CEAwH//93+z51fqesuyzMI2H/jAB3Daaaexx6rtGvPYuZ4+n4+FrX//+99jamoKyWQSDz74IABg9erVrip/b7vtNmzYsAGzs7P49re/jbvuuosZDuK+++7DCy+8wH4+cuQInnnmGQDuXm877321XV8A2Lp1K4sk/PVf/3XW425d33zrV8Xv36JlJ3OAu+66i6lwli5dKkciERmA3NnZKR89erSi53bKKaewczvjjDPktWvXsq977rmHqZuCwaC8cuVKef78+ez5733ve+VMJuPq+S5ZskSWJElesmSJfNppp8mSJMkA5MbGRnnnzp1yKpWSL7zwQhmA7Pf75VNPPVX2eDwyAPnaa6919Vx5HnzwQXbdnnzySd1jlbzGmzZtkpcvX86UeQDkrq4uefny5fK1115r+3pu375dDofD7L7u6emRAcher1f+7W9/69r5bt26lf2+ublZdz+vXbtWHhgYkGVZlq+77jp2rqeffrocCoXY3/30pz917XztvvfVcn2Jj3zkIzIAefny5XI6nc56Hbeub771q9L3b00bNlmW5Z/97GfyGWecIQeDQbmlpUXesGGDvGfPnkqflu4GNn7ddNNN8vHjx+UvfelL8umnny63tLTITU1N8urVq+Vbb71VnpmZcf1877rrLvniiy+Wu7u75WAwKPf19ckbN26U33rrLfaciYkJ+fOf/7zc09Mj+/1+ua+vT/6Hf/gHOZFIuH6+xLp162QA8jnnnJP1WCWv8b333mv5/l900UWyLNu/ni+++KJ86aWXyk1NTXIoFJLPP/98+ZFHHnH1fMlQWH0dOHBAlmVZfuyxx+RrrrlG7uvrk0OhkDx//nz5kksukR999FFXz7eQ974arq8sy/LevXuZcbjjjjtMX8et65tv/ZLlyt6/Yh6bQCAQCGqKms2xCQQCgaA+EYZNIBAIBDWFMGwCgUAgqCmEYRMIBAJBTSEMm0AgEAhqCmHYBAKBQFBTCMMmEAgEgppCGDaBQCAQ1BTCsAkEAoGgphCGTSAQCAQ1hTBsAoFAIKgp/n9i/Y0Jop62FwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "α = 0.9\n", - "T = 200\n", - "x = np.empty(T+1)\n", - "x[0] = 0\n", - "\n", - "for t in range(T):\n", - " if x[t] < 0:\n", - " abs_x = - x[t]\n", - " else:\n", - " abs_x = x[t]\n", - " x[t+1] = α * abs_x + np.random.randn()\n", - "\n", - "plt.plot(x)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cd087f1b", - "metadata": {}, - "source": [ - "Here’s a shorter way to write the same thing:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "84d67d27", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "α = 0.9\n", - "T = 200\n", - "x = np.empty(T+1)\n", - "x[0] = 0\n", - "\n", - "for t in range(T):\n", - " abs_x = - x[t] if x[t] < 0 else x[t]\n", - " x[t+1] = α * abs_x + np.random.randn()\n", - "\n", - "plt.plot(x)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "1c7fc965", - "metadata": {}, - "source": [ - "## Exercise 3.5\n", - "\n", - "Here’s a harder exercise, that takes some thought and planning.\n", - "\n", - "The task is to compute an approximation to $ \\pi $ using [Monte Carlo](https://en.wikipedia.org/wiki/Monte_Carlo_method).\n", - "\n", - "Use no imports besides" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "3c65f875", - "metadata": { - "hide-output": false - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "133925e8", - "metadata": {}, - "source": [ - "Your hints are as follows:\n", - "\n", - "- If $ U $ is a bivariate uniform random variable on the unit square $ (0, 1)^2 $, then the probability that $ U $ lies in a subset $ B $ of $ (0,1)^2 $ is equal to the area of $ B $. \n", - "- If $ U_1,\\ldots,U_n $ are IID copies of $ U $, then, as $ n $ gets large, the fraction that falls in $ B $, converges to the probability of landing in $ B $. \n", - "- For a circle, $ area = \\pi * radius^2 $. " - ] - }, - { - "cell_type": "markdown", - "id": "e25a7b53", - "metadata": {}, - "source": [ - "## Solution to[ Exercise 3.5](https://python-programming.quantecon.org/#pbe_ex5)\n", - "\n", - "Consider the circle of diameter 1 embedded in the unit square.\n", - "\n", - "Let $ A $ be its area and let $ r=1/2 $ be its radius.\n", - "\n", - "If we know $ \\pi $ then we can compute $ A $ via\n", - "$ A = \\pi r^2 $.\n", - "\n", - "But here the point is to compute $ \\pi $, which we can do by\n", - "$ \\pi = A / r^2 $.\n", - "\n", - "Summary: If we can estimate the area of a circle with diameter 1, then dividing\n", - "by $ r^2 = (1/2)^2 = 1/4 $ gives an estimate of $ \\pi $.\n", - "\n", - "We estimate the area by sampling bivariate uniforms and looking at the\n", - "fraction that falls into the circle." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "e59ff3dc", - "metadata": { - "hide-output": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.143048\n" - ] - } - ], - "source": [ - "n = 1000000 # sample size for Monte Carlo simulation\n", - "\n", - "count = 0\n", - "for i in range(n):\n", - "\n", - " # drawing random positions on the square\n", - " u, v = np.random.uniform(), np.random.uniform()\n", - "\n", - " # check whether the point falls within the boundary\n", - " # of the unit circle centred at (0.5,0.5)\n", - " d = np.sqrt((u - 0.5)**2 + (v - 0.5)**2)\n", - "\n", - " # if it falls within the inscribed circle, \n", - " # add it to the count\n", - " if d < 0.5:\n", - " count += 1\n", - "\n", - "area_estimate = count / n\n", - "\n", - "print(area_estimate * 4) # dividing by radius**2" - ] - } - ], - "metadata": { - "date": 1660181781.2761576, - "filename": "python_by_example.md", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "title": "An Introductory Example" - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/day_03/README.md b/day_03/README.md deleted file mode 100644 index edf3b12..0000000 --- a/day_03/README.md +++ /dev/null @@ -1,23 +0,0 @@ -FINM August Python Introduction and Review: Week 3 -================================================== - -Agenda - - - Today we will use notebooks within VS Code. We'll also begin the discussion of writing `.py` files directly. The week after that we will move away from notebooks entirely. - - Discuss the features of using the Python and Jupyter extensions within VS Code. - - Overview: https://code.visualstudio.com/docs/datascience/overview - - Variable explorer and data viewer: https://code.visualstudio.com/docs/datascience/jupyter-notebooks#_variable-explorer-and-data-viewer - - Custom notebook diffing: https://code.visualstudio.com/docs/datascience/jupyter-notebooks#_custom-notebook-diffing - - Demonstration of Git and GitHub - - VS Code especially makes Git diffs of Jupyter notebooks easy. Demonstrate why they are otherwise difficult. - - Finish discussion of Pandas from previous lecture: - - Set of in-class exercises: [./occupations.ipynb](./occupations.ipynb) - - `03.03-Operations-in-Pandas.ipynb` - - `03.04-Missing-Values.ipynb` - - Demonstrate Pandas in the context of factor analysis/principal components analysis of a panel (Note from 2023. Ran out of time at the beginning of discussing this notebook.) - of economic and financial time series. [./factor_analysis_demo.ipynb](./factor_analysis_demo.ipynb) - - Very quick review of Numpy, Matplotlib, and Scipy, with emphasis on plotting - - Introduction to [NumPy](https://python-programming.quantecon.org/numpy.html) - - Introduction to [Matplotlib](https://python-programming.quantecon.org/matplotlib.html) - - Compare Matplotlib to other plotting libraries: [./comparing_plotting_libraries.ipynb](./comparing_plotting_libraries.ipynb) - - Introduction to [SciPy](https://python-programming.quantecon.org/scipy.html) diff --git a/day_03/comparing_plotting_libraries.ipynb b/day_03/comparing_plotting_libraries.ipynb deleted file mode 100644 index 9e2f838..0000000 --- a/day_03/comparing_plotting_libraries.ipynb +++ /dev/null @@ -1,1029 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Comparing Plotting Libraries and Declarative Visualizations" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from plotnine import *\n", - "from matplotlib import pyplot as plt\n", - "from plotnine import data\n", - "import chart_studio.plotly as py\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio\n", - "# chart_studio.tools.set_credentials_file(username='...', api_key='...')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "mpg = data.mpg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bar Chart" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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manufacturermodeldisplyearcyltransdrvctyhwyflclass
0audia41.819994auto(l5)f1829pcompact
1audia41.819994manual(m5)f2129pcompact
2audia42.020084manual(m6)f2031pcompact
3audia42.020084auto(av)f2130pcompact
4audia42.819996auto(l5)f1626pcompact
....................................
229volkswagenpassat2.020084auto(s6)f1928pmidsize
230volkswagenpassat2.020084manual(m6)f2129pmidsize
231volkswagenpassat2.819996auto(l5)f1626pmidsize
232volkswagenpassat2.819996manual(m5)f1826pmidsize
233volkswagenpassat3.620086auto(s6)f1726pmidsize
\n", - "

234 rows × 11 columns

\n", - "
" - ], - "text/plain": [ - " manufacturer model displ year cyl trans drv cty hwy fl \\\n", - "0 audi a4 1.8 1999 4 auto(l5) f 18 29 p \n", - "1 audi a4 1.8 1999 4 manual(m5) f 21 29 p \n", - "2 audi a4 2.0 2008 4 manual(m6) f 20 31 p \n", - "3 audi a4 2.0 2008 4 auto(av) f 21 30 p \n", - "4 audi a4 2.8 1999 6 auto(l5) f 16 26 p \n", - ".. ... ... ... ... ... ... .. ... ... .. \n", - "229 volkswagen passat 2.0 2008 4 auto(s6) f 19 28 p \n", - "230 volkswagen passat 2.0 2008 4 manual(m6) f 21 29 p \n", - "231 volkswagen passat 2.8 1999 6 auto(l5) f 16 26 p \n", - "232 volkswagen passat 2.8 1999 6 manual(m5) f 18 26 p \n", - "233 volkswagen passat 3.6 2008 6 auto(s6) f 17 26 p \n", - "\n", - " class \n", - "0 compact \n", - "1 compact \n", - "2 compact \n", - "3 compact \n", - "4 compact \n", - ".. ... \n", - "229 midsize \n", - "230 midsize \n", - "231 midsize \n", - "232 midsize \n", - "233 midsize \n", - "\n", - "[234 rows x 11 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mpg" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Number of Cars by Make')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Pandas\n", - "(mpg['manufacturer']\n", - " .value_counts(sort=False)\n", - " .plot.barh()\n", - " .set_title('Number of Cars by Make')\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Plotnine (ggplot2 clone)\n", - "(ggplot(mpg) + \n", - " aes(x='manufacturer') +\n", - " geom_bar() + \n", - " coord_flip() +\n", - " ggtitle('Number of Cars by Make')\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mpgGrouped = mpg.groupby('manufacturer').size()\n", - "fig = {\n", - " 'data' : [{\n", - " 'type' : 'bar',\n", - " 'x' : mpgGrouped.values.tolist(),\n", - " 'y' : mpgGrouped.index.tolist(),\n", - " 'orientation' : 'h'\n", - " \n", - " }],\n", - " 'layout' : {\n", - " 'title' : 'Number of Cars by Make'\n", - " }\n", - "}\n", - "\n", - "py.image.ishow(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scatter Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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manufacturermodeldisplyearcyltransdrvctyhwyflclass
0audia41.819994auto(l5)f1829pcompact
1audia41.819994manual(m5)f2129pcompact
2audia42.020084manual(m6)f2031pcompact
3audia42.020084auto(av)f2130pcompact
4audia42.819996auto(l5)f1626pcompact
....................................
229volkswagenpassat2.020084auto(s6)f1928pmidsize
230volkswagenpassat2.020084manual(m6)f2129pmidsize
231volkswagenpassat2.819996auto(l5)f1626pmidsize
232volkswagenpassat2.819996manual(m5)f1826pmidsize
233volkswagenpassat3.620086auto(s6)f1726pmidsize
\n", - "

234 rows × 11 columns

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" - ], - "text/plain": [ - " manufacturer model displ year cyl trans drv cty hwy fl \\\n", - "0 audi a4 1.8 1999 4 auto(l5) f 18 29 p \n", - "1 audi a4 1.8 1999 4 manual(m5) f 21 29 p \n", - "2 audi a4 2.0 2008 4 manual(m6) f 20 31 p \n", - "3 audi a4 2.0 2008 4 auto(av) f 21 30 p \n", - "4 audi a4 2.8 1999 6 auto(l5) f 16 26 p \n", - ".. ... ... ... ... ... ... .. ... ... .. \n", - "229 volkswagen passat 2.0 2008 4 auto(s6) f 19 28 p \n", - "230 volkswagen passat 2.0 2008 4 manual(m6) f 21 29 p \n", - "231 volkswagen passat 2.8 1999 6 auto(l5) f 16 26 p \n", - "232 volkswagen passat 2.8 1999 6 manual(m5) f 18 26 p \n", - "233 volkswagen passat 3.6 2008 6 auto(s6) f 17 26 p \n", - "\n", - " class \n", - "0 compact \n", - "1 compact \n", - "2 compact \n", - "3 compact \n", - "4 compact \n", - ".. ... \n", - "229 midsize \n", - "230 midsize \n", - "231 midsize \n", - "232 midsize \n", - "233 midsize \n", - "\n", - "[234 rows x 11 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mpg" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(mpg\n", - " .plot\n", - " .scatter(x='displ', y='hwy')\n", - " .set(title='Engine Displacement in Liters vs Highway MPG',\n", - " xlabel='Engine Displacement in Liters',\n", - " ylabel='Highway MPG'));" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(ggplot(mpg) +\n", - " aes(x = 'displ', y = 'hwy') +\n", - " geom_point() + \n", - " ggtitle('Engine Displacement in Liters vs Highway MPG') +\n", - " xlab('Engine Displacement in Liters') +\n", - " ylab('Highway MPG')\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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PXF9s2GzVT+bSBkas5W/ywT15xrtY/f5kK1RGwWXKhYxy5b7kZ9mtv+3Em1NHWP8sLGQKg3xw575ktG/dpi2W4MrlVHhlBHHle3l3GVmyYj16D51oyUbwHMvAs0RSeO1wlxHG3K8NeR6VvhXc4PLFQabOvPrMg9afhOltfR7FvOmP4Oyqp2qVGXiNhFvAKveYNedja2eL/IQ3IGsyWl+n5lk5jyxfRmVXgUC9A18mg7/kyK8mjdrci8BrUKbvDB49Favfn2KNhkv/PPfsKtbUB5kK8/m7k6wvDvIeJKPE8v6R3yVf3D6RBar/7cohfaTPsEn4aPZTqFS+rPWFUr68y4Ky2zu1CltMbuGVX0Da3PoAGtY7L+e+dw18yvriJV9EZHQ3nPAKI/k1Rr4kinBLOX3uuN6aRpH79RBgEPz6DPx37i8e+VaafyABEiCBKBJwrfAea9FauJ8uAwzlw0YETH4el0tWFq9c9w2effQ+nHn6Kfh68y/WaIrMsZVRRbnyG+HNPRcwXPkyP7huizstCU9IODpqKLGy6rlVs4shZYS7ChJeGZ3pO/yZnJHGcAtrZCRZ5l6+/9FK66dsEd/bbmiZI74iedXPrIwR92fPMQ1c67/+EbJyO/ABH0543/94Fd5d9Dl+2f4HUtLS4PN6ISN5N7RpagnvZ198DflAlZFWWbEdfKmyya8tA8K78LWxkFHIwCXCm3tUs/M9o/DN5l+sn5xzX+npmdbP4PIlRq7g0dhArAivzFeeM22U9U9OhTfc9m4B4V31/uQ8P/nnft5ICa9d7o8Pvdua2xx8FdS3guM/X/uN9bpaNOtx62d7+VIp02neeO7oT+x2yyzoNVLQHF6RuLZdhljPM2pgV5QuVRLzFq3AmImvWV+kZMpJ7tf/sneexkknHp13L1NrZC7uxFG9rak2IpiNr+tt7WBw/rlnWEI6Y8IQnF31FDS+rhcmPtIbtWqcYX3Jll8MAlujbfruZ7zy33x4kWj5hUF+VTi54kk5witrC67o2M+ag9vj1mvx0afrrF84PnlrfL5bpQULr8yllykuUo+/9/6LKzvdb73GZTpNfsIb2KWhSGKiVffixYrmadrAPQbe29maghG4ZMqHfKGUK/iLR9g3PP4jCZAACUSZQNwLr/zUKHtsfvX9L0gqkmj97C0jiTLCE7h0hVfyZfGczPvLXV6gXJl/mt++ngV9mN837Bms//oHLHtngjUCc6yV5CKN8xauwJPPzbbq1u+uG3MkT6aAyKKh3NeHy76AjMjlN8IrWxM9NmkWhve/DVdcVi9H0uSn/rrnnW0Jr3yIy4iv/CR7Qa3w+3+qsHEqvCLdUobM8w2+fD6fNYfyeBJe1T55rC+NuTnm17eCWYtctujUH+2vbmLN9RQZ7H/XjSGLqCRPtcyCXiMFCa/cS36mly93IpgyX/esqqfg3i7t8kxxyO+1FSy8Ut4N3UfgotrVrHJkmziRSxkZFdmXUU6ZAz14zPNY9d5ka0GafIG68a4RuKndFdZ0CfmVQeIfe2YWVq77Ns+CyGenz8V7H62yvjTILwIyJ37S6D75fkQEC+8/+w7g8g734bEHu1tfVD9d/VXOnN78hDd4l4ZwN5PFevJeJiPl4a7gdojyZxpvTwIkQAJhCcS98Mo8V5nTKAvIZCFGuMuJ8MriFBntmDnxAVvb9Rzrw1x+FpefSOWnSNkZQS6VrZNk5EpGO2VOX0DyZB6j/OSbe/W3yO6mb3/Kdw6vTHf4+5/9OeVIWbJbQtMOfXBdy8aW8MqiKRntkoVvMloe7lJh41R4X3pjAV549X0seG3sMXdBUB3hDWzjJD/Ni9wf6wo3h7cwR3hl/uiYSa9hw+IX8vQ9J9zzq29w3woXJ4vUPlq+Dj27trd++pfR9dy7aQTnFFRmJIRX+seylRut3RVyz1PO/Sx2hFemIsgvRrInuIyIBrb0kraQqTayeFV2ZQhs6SUjuyK36xe/kOdXCNnma9+/h/IIr0yzklHe5x+/Hz0eGG9NRWp2SV1l4ZVA+aK8/8BB/Pb7HnS/pW3OnGonwiu/9sgUoeA5zoEHo/DSLkiABGKBgGuFVxZvBH9YymidjJjamdIgq8PlQ0o2ZS9dqgS8Hg/KJJdCxZOyR1rCyaSd8mUERxaLyMjOjW2bWs+379+D2VsBNaprjQSFuwIf5oG5yrLbwPYduyCjr/IBLKIrwhu4gj+URYplBwiZ3yo7HPy07Q+MHPeydWBF/7uPjvBu/20Xatc805oLWKpkCciHl3z4yQKxwJSO4CkNMtIkW0g9PrQHqp95Gn79Yw+emzkfIoNXXlYv50NeFmjJgR3dOl9tbYUmC1lkykibKxtZc4NV2DgVXvmyISNoMqVE6ijboslonoxwiZDI/OeA/OfeQizANXhKg0wJkMV4ctKUjJTLT82y3V24K9LC++nqTdboXPBV/azTrZ+ag+8nc23l32T+eGBagowyOuEu91bpW+F4BHYbqFW9Kk4/rSLGPnh0QalOmZEQXlmgKqeC9ezazhJemU4gX3xzbxFnR3hlbvId/R+3RjzHP9wT9evWsFDIlmeyi4PsaCKvh8Dit8DUHJkrLXP6ZRHp7PmfQHZyOKFkiZAt7+TL+fYdu633kE/eHp9ngWUw8+AR3uz3suwdImR0efmcidZcY7mcCK/ky0K8GW99aG2xJ/WQHS3k/fOfvQcwa97H1jMH5lLHwgcfn5EESOD4I+Ba4Q3XFIGjhe0IqSyuurP/E9ZWV7kvmb8nC5hk/qmTEV4pU7aBemb6XGtF9YFDRyyxvqh2dQy8p1POzgHB9QlegS4jsCLLsrjmxjaX5ywayU94JV/m64lkyFZoIvCyY0KP266z5hQHJO/CWudYXxze/mA5/vxrn/U8Be3DK6O3ss/rJ59tsPatPaNyJfTu1gHrvtqClJS0PBvVz1nwqTU/UWRYhEJOJxOuAaEoiI1T4ZV6ygf/pJfmYunn6y3xkJ+uZXGdzIUMHJGqOsIbkIaxz76OHX/sQY1zqoQsiAu0SaSFV6aShLsCc63D1UFERrYnE0GSL0mBRZK63OX+Kn0rv7fKrn3HYs2G7/HS+EE5MqhbZiSE950PPsWwJ14KeVw5Reyp4fdaYmhHeGWrL1nIJnPGZQQ797aD8sVXflF6d/roPF90RRRlu7o9f+7FSeWS0bHt5TjnjNPw1NQ3Q4Q38CyyUO3+uzse8xMpnPDK1BI5OEX2Cs6964lT4ZUHkXnab8xbYk1nknvLlCE5il12hZC9x4P36D7+Pk5ZYxIgATcTcJ3wRhKWfHjI9j+yubzMVROplO13ZE/KfiOexckVyoXMb43k/aNdliVIZ5+O4f1ui/aj8P4kUOgEJk57BzLF5Klh9+CM00+25sLLCL58GejW/3FrN4LAlKFCf7h8bhgYEZatyOSLJi8SIAESIIHIEIhr4ZWV4jLiF9hjNTcy2VNT5szJCV/xeuU3qhmv9WW9SCA3AdmDu9N1zXL25M79N9nBoEPry3D3rW1dBU0OKfn34CHrFEleJEACJEACkSMQ18I7+91PMO75tzBm8J248Pxz4PV58edfe/Hh8nWYOnO+NV9SpgHE60XhjdeWZb1UCMi2XjKXW47llalLMh1BFpS99s5HWLpyo7Vll8xFjfYlX7xT09KsUxCffXmutbd1fjufRPtZeX8SIAESiFUCcS280igyd07mmf72x5/Wh4rMLa1V/Qzrp0yZ5xbPF4U3nluXdSuIgCxolLn1ctDKnr/3WYsqZZs+2adbTlnLfRpeQWWZ/LscRS6ntskhMb27tcdVTS82eTuWTQIkQALHJYG4F97jslVZaRIgARIgARIgARIggRwCFF52BhIgARIgARIgARIggbgmQOGN6+Zl5UiABEiABEiABEiABCi87AMkQAIkQAIkQAIkQAJxTYDCG9fNy8qRAAmQAAmQAAmQAAlQeNkHSIAESIAESIAESIAE4poAhTeum5eVIwESIAESIAESIAESoPCyD5AACZAACZAACZAACcQ1AQpvXDcvK0cCJEACJEACJEACJEDhZR8gARIgARIgARIgARKIawIU3rhuXlaOBEiABEiABEiABEiAwss+QAIkQAIkQAIkQAIkENcEKLxx3bysHAmQAAmQAAmQAAmQAIWXfYAESIAESIAESIAESCCuCVB447p5WTkSIAESIAESIAESIAEKL/sACZAACZAACZAACZBAXBOg8MZ187JyJEACJEACJEACJEACFF72ARIgARIgARIgARIggbgmQOGN6+Zl5UiABEiABEiABEiABCi87AMkQAIkQAIkQAIkQAJxTYDCG9fNy8qRAAmQAAmQAAmQAAlQeNkHSIAESIAESIAESIAE4poAhTeum5eVIwESIAESIAESIAESoPCyD5AACZAACZAACZAACcQ1AQpvXDcvK0cCJEACJEACJEACJEDhZR8gARIgARIgARIgARKIawIU3rhuXlaOBEiABEiABEiABEiAwss+QAIkQAIkQAIkQAIkENcEKLxx3bysHAmQAAmQAAmQAAmQAIWXfYAESIAESIAESIAESCCuCVB447p5WTkSIAESIAESIAESIAEKL/sACZAACZAACZAACZBAXBOg8MZ187JyJEACJEACJEACJEACFF72ARIgARIgARIgARIggbgmQOGN6+Zl5UiABEiABEiABEiABCi87AMkQAIkQAIkQAIkQAJxTYDCG9fNy8qRAAmQAAmQAAmQAAlQeNkHSIAESIAESIAESIAE4poAhTeum5eVIwESIAESIAESIAESoPCyD5AACZAACZAACZAACcQ1AQpvXDcvK0cCJEACJEACJEACJEDhZR8gARIgARIgARIgARKIawIU3rhuXlaOBEiABEiABEiABEiAwss+QAIkQAIkQAIkQAIkENcEKLxx3bysHAmQAAmQAAmQAAmQAIWXfYAESIAESIAESIAESCCuCVB4HTbvH38fcVhC7KcnFfGheFEf/jmQFvuVcWkNyp5QBIdTMpCSnuXSJ4ztx/J6gPJlimHXP3w9m2rJksUS4PF4cOBwuqlbHPflViiThL/2pyIzy3/cszABoEiCF6VKJFqMo3GdfGKxaNw2bu5J4XXYlBRegMLrsBMppFN4FSA5CKHwOoCnmErhVQTlIIzC6wCeQiqFVwGSi0MovA4bh8JL4XXYhZTSKbxKmLSDKLza6JQTKbzKqLQDKbza6JQSKbxKmFwbROF12DQUXgqvwy6klE7hVcKkHUTh1UannEjhVUalHUjh1UanlEjhVcLk2iAKr8OmofBSeB12IaV0Cq8SJu0gCq82OuVECq8yKu1ACq82OqVECq8SJtcGUXgdNg2Fl8LrsAsppVN4lTBpB1F4tdEpJ1J4lVFpB1J4tdEpJVJ4lTC5NojCG9Q0WVl+3NZnDM6qcgqG9+9i/fW5mfPx3CvzkeDzWv99zhmnYdbkodb/pvBSeAvj1U3hNUuZwmuWr5RO4TXPmMJrljGF1yxf06VTeIMIv/zmIqxY8xUqn1w+R3jHTHwVF55/Dq5qenFIe1B4KbymX6RSPoXXLGUKr1m+FF7zfOUOFF6znCm8ZvmaLp3Cm4vwtt92YfDoqejSsRXWrP8uR3j7j5yMm9pdYUlv8EXhpfCafpFSeM0TpvCaZ8wRXvOMKbxmGVN4zfI1XTqF9z/CMpXh9r6PYXDPm/Dr73uw+stvc4S3x+Dx2PbbTqRnZKLiSWXRq1t71K9bw8qk8FJ4Tb9IKbzmCVN4zTOm8JpnTOE1y5jCa5av6dIpvP8Rnv7GQhw+koJ7b2+HD5etzSO8Kalp8Pl81hzetRu3oN+IZ/HWCyNRqXxZHDjCU4MSvF4kJnhwJC3TdH89bssvVsSH9IwsZPAEJSN9wAOgRFIiDqbw9WwEMACRBcCDtAy+T5hiXCIpAUdSM5Hl50lrJhj7vB4UTfThcGqGieILLPOEYokFxjAgfwIUXgC//LoTDz32IuyLNzwAACAASURBVGZMHILEBF+I8AbjGzBqCpo0qINrrmyIA4ej0/Hd1KkTEjxI9HmtN1peZggUK/qf8Gbyg8wEYY8HKFEsAQf5ejaB1yqzSKIXwjk1jcdjm4Iso+iHUjJA3zVD2OcT4fXicEp0PutOKJ5gpmLHSakUXgBTX3kPL856H15v9i4MGRmZyMzMRJXTKmHe9EdCuoKM8LZu3gBXNL6QUxrAKQ2F8V7BRWtmKXNKg1m+UjqnNJhnzCkNZhlzSoNZvqZLp/CGIRw8pWHZyo24tH4tJPh8WLtxMwaNnoq50x5B6VIlKLwUXtOvUat8Cq9ZzBRes3wpvOb5yh0ovGY5U3jN8jVdOoVXQXh7D52IDV//iMTEBJxSsRwG3tMZtWqcYWVy0RpHeE2/SCm85glTeM0z5givecYUXrOMKbxm+ZouncLrkDCFl8LrsAsppXOEVwmTdhCFVxudciKFVxmVdiCFVxudUiKFVwmTa4MovA6bJt6Fd9uvXvzxB1CjehbKJIeHlVTEh+JFffjnQJpDmkzPjwCF12zfoPCa5SulU3jNM6bwmmVM4TXL13TpFF6HhONVePfuA6ZMTUBK6lFA1av5cVPH0NWpFF6HnUghncKrAMlBCIXXATzFVAqvIigHYRReB/AUUim8CpBcHELhddg48Sq8s2b7sHmL7E6a9+raJQtVKufdVojC67ATKaRTeBUgOQih8DqAp5hK4VUE5SCMwusAnkIqhVcBkotDKLwOGydehffpSQn4Z28onFYtstCwAYXXYbexnU7htY3MVgKF1xYurWAKrxY2W0kUXlu4bAdTeG0jc1UChddhc8Sr8E6b4cP27aEjvBRehx1GM53CqwlOMY3CqwjKQRiF1wE8xVQKryIozTAKryY4l6RReB02RLwK76rVXixcnH0QR+ASKRgxNPRkOU5pcNiJFNIpvAqQHIRQeB3AU0yl8CqCchBG4XUATyGVwqsAycUhFF6HjROvwitYRHrXrPXi0GGgbBk/Ot2YGXanBgqvw06kkE7hVYDkIITC6wCeYiqFVxGUgzAKrwN4CqkUXgVILg6h8DpsnHgWXlU0FF5VUvpxFF59diqZFF4VSs5iKLzO+KlkU3hVKOnHUHj12bkhk8LrsBUovDx4wmEXUkqn8Cph0g6i8GqjU06k8Cqj0g6k8GqjU0qk8Cphcm0Qhddh01B4KbwOu5BSOoVXCZN2EIVXG51yIoVXGZV2IIVXG51SIoVXCZNrgyi8DpuGwkvhddiFlNIpvEqYtIMovNrolBMpvMqotAMpvNrolBIpvEqYXBtE4XXYNBReCq/DLqSUTuFVwqQdROHVRqecSOFVRqUdSOHVRqeUSOFVwuTaIAqvw6ah8FJ4HXYhpXQKrxIm7SAKrzY65UQKrzIq7UAKrzY6pUQKrxIm1wZReB02DYWXwuuwCymlU3iVMGkHUXi10SknUniVUWkHUni10SklUniVMLk2iMLrsGkovBReh11IKZ3Cq4RJO4jCq41OOZHCq4xKO5DCq41OKZHCq4TJtUEUXodNQ+Gl8DrsQkrpFF4lTNpBFF5tdMqJFF5lVNqBFF5tdEqJFF4lTK4NovA6bBoKL4XXYRdSSqfwKmHSDqLwaqNTTqTwKqPSDqTwaqNTSqTwKmFybRCF12HTUHgpvA67kFI6hVcJk3YQhVcbnXIihVcZlXYghVcbnVIihVcJk2uDKLwOm4bCS+F12IWU0im8Spi0gyi82uiUEym8yqi0Aym82uiUEim8SphcG0Thddg0FF4Kr8MupJRO4VXCpB1E4dVGp5xI4VVGpR1I4dVGp5RI4VXC5NogCq/DpqHwUngddiGldAqvEibtIAqvNjrlRAqvMirtQAqvNjqlRAqvEibXBlF4HTYNhZfC67ALKaVTeJUwaQdReLXRKSdSeJVRaQdSeLXRKSVSeJUwuTaIwuuwaSi8FF6HXUgpncKrhEk7iMKrjU45kcKrjEo7kMKrjU4pkcKrhMm1QRReh01D4aXwOuxCSukUXiVM2kEUXm10yokUXmVU2oEUXm10SokUXiVMrg2i8DpsGgovhddhF1JKp/AqYdIOovBqo1NOpPAqo9IOpPBqo1NKpPAqYXJtEIXXYdNQeCm8DruQUjqFVwmTdhCFVxudciKFVxmVdiCFVxudUiKFVwmTa4MovA6bhsJL4XXYhZTSKbxKmLSDKLza6JQTKbzKqLQDKbza6JQSKbxKmFwbROF12DTxLLyfLPdi8xYvdu0CalTLQtMmWahUMRRYUhEfihf14Z8DaQ5pMj0/AhRes32DwmuWr5RO4TXPmMJrljGF1yxf06VTeB0Sjlfh/X6zF6+/6c1DJykJ6Ns7A8WS8kKj8DrsRArpFF4FSA5CKLwO4CmmUngVQTkIo/A6gKeQSuFVgOTiEAqvw8aJV+Fd+KEXq9bkFV5BdfutmahaxZ9XhDnC67AXFZxO4S2YkZMICq8Temq5FF41Tk6iKLxO6BWcS+EtmJGbIyi8DlsnXoV3zrs+bNzkCaFD4XXYYTTTKbya4BTTKLyKoByEUXgdwFNMpfAqgtIMo/BqgnNJGoXXYUPEq/Bu2OjB3Pm+vCO5RYEed2WiTDJHeB12G9vpFF7byGwlUHht4dIKpvBqYbOVROG1hct2MIXXNjJXJVB4HTZHvAqvYJFpDRs2epGSCiSX9qPVVX7UqJ4VQoxzeB12IoV0Cq8CJAchFF4H8BRTKbyKoByEUXgdwFNIpfAqQHJxCIXXYePEs/CqoqHwqpLSj6Pw6rNTyaTwqlByFkPhdcZPJZvCq0JJP4bCq8/ODZkU3qBWyMry47Y+Y3BWlVMwvH8X66979x/AA2Oex1ff/4KyyaXw8ICuuKDW2dbfKLzch7cwXsgUXrOUKbxm+UrpFF7zjCm8ZhlTeM3yNV06hTeI8MtvLsKKNV+h8snlc4R30OipOKViOfS8vT2+2fwL+o2cjPdnPoqkokUovKDwmn6RSvkUXrOUKbxm+VJ4zfOVO1B4zXKm8Jrla7p0Cm8uwtt+24XBo6eiS8dWWLP+O0t4ZcS3Udt7sfTtp1EsqYgV3euhiejQ+jI0bVSHwkvhNf0atcqn8JrFTOE1y5fCa54vhdc8YwqvecYm70Dh/Y+uiO3tfR/D4J434dff92D1l99awrv7z734X6/R+OiNJ3PaYdzUN5FcuiS6dmpN4aXwmnx95pRN4TWLmcJrli+F1zxfCq95xhRe84xN3oHC+x/d6W8sxOEjKbj39nb4cNnaHOHdvmM3eg55Gu/NfDSnHSa/PM8a+e3ZtR1S0jJNtk9MlO31euDzepCeEbqDQ0xUIAYeMjHBi8zMLGTl3REuBp48Nh5RdpwukuhDajpfz6ZaLMGXva93RiY7sSnGRRO9SMvIgp+IjSCWL8YJvmzG0bhkgTgvfQIUXgC//LoTDz32ImZMHILEBF8e4d3z1z50vHuENaUhcI199nWUK1sa3Tq3xt//purTj5NM+dYrb7QHjmTESY3cV40TiiUgNT0ram+07iMS2SfyeIDkkkWx9wBfz5Ele7Q0+bD2eDw4ksr3CVOMk0sWwb+H0pFF4zWCWGS3eJLPYhyN68RSRaNx27i5J4UXwNRX3sOLs96H15t9lG5GRiYyMzNR5bRKmPvSKFzSticWvf4ESpUsbv397kHjcMM1TdG88QWc0sApDYXyZsApDWYxc0qDWb5SOndpMM+Yi9bMMuaUBrN8TZdO4Q1DOPeUBvnzsCdewollSqNX1+xdGno+OAELX3scJYonUXgpvKZfo1b5FF6zmCm8ZvlSeM3zlTtQeM1ypvCa5Wu6dAqvgvD+e/Awhox5Aeu/+QGlSpbA0L634pKLzrMyuQ8vtyUz/SKl8JonTOE1z5gjvOYZU3jNMqbwmuVrunQKr0PCFF4Kr8MupJTOEV4lTNpBFF5tdMqJFF5lVNqBFF5tdEqJFF4lTK4NovA6bBoKL4XXYRdSSqfwKmHSDqLwaqNTTqTwKqPSDqTwaqNTSqTwKmFybRCF12HTUHgpvA67kFI6hVcJk3YQhVcbnXIihVcZlXYghVcbnVIihVcJk2uDKLwOm4bCS+F12IWU0im8Spi0gyi82uiUEym8yqi0Aym82uiUEim8SphcG0Thddg0FF4Kr8MupJRO4VXCpB1E4dVGp5xI4VVGpR1I4dVGp5RI4VXC5NogCq/DpqHwUngddiGldAqvEibtIAqvNjrlRAqvMirtQAqvNjqlRAqvEibXBlF4HTYNhZfC67ALKaVTeJUwaQdReLXRKSdSeJVRaQdSeLXRKSVSeJUwuTaIwuuwaSi8FF6HXUgpncKrhEk7iMKrjU45kcKrjEo7kMKrjU4pkcKrhMm1QRReh01D4aXwOuxCSukUXiVM2kEUXm10yokUXmVU2oEUXm10SokUXiVMrg2i8DpsGgovhddhF1JKp/AqYdIOovBqo1NOpPAqo9IOpPBqo1NKpPAqYXJtEIXXYdNQeCm8DruQUjqFVwmTdhCFVxudciKFVxmVdiCFVxudUiKFVwmTa4MovA6bhsJL4XXYhZTSKbxKmLSDKLza6JQTKbzKqLQDKbza6JQSKbxKmFwbROF12DQUXgqvwy6klE7hVcKkHUTh1UannEjhVUalHUjh1UanlEjhVcLk2iAKr8OmofBSeB12IaV0Cq8SJu0gCq82OuVECq8yKu1ACq82OqVECq8SJtcGUXgdNg2Fl8LrsAsppVN4lTBpB1F4tdEpJ1J4lVFpB1J4tdEpJVJ4lTC5NojC67BpKLwUXoddSCmdwquESTuIwquNTjmRwquMSjuQwquNTimRwquEybVBFF6HTUPhpfA67EJK6RReJUzaQRRebXTKiRReZVTagRRebXRKiRReJUyuDaLwOmwaCi+F12EXUkqn8Cph0g6i8GqjU06k8Cqj0g6k8GqjU0qk8Cphcm0Qhddh01B4KbwOu5BSOoVXCZN2EIVXG51yIoVXGZV2IIVXG51SIoVXCZNrgyi8DpsmVoX3+81erF7jwZFUD4oV9aNpkyxUreLXopFUxIfiRX3450CaVj6TCiZA4S2YkZMICq8Temq5FF41Tk6iKLxO6BWcS+EtmJGbIyi8DlsnFoV37z4Pxk/0hdS8b+9MlEm2L70UXoedSCGdwqsAyUEIhdcBPMVUCq8iKAdhFF4H8BRSKbwKkFwcQuF12DixKLyfLPdi2XJvSM3btc1E3ToUXoddwkg6hdcI1pxCKbxm+UrpFF7zjCm8ZhlTeM3yNV06hdch4XgS3pYtstCoQZZtIhzhtY3MdgKF1zYyWwkUXlu4tIIpvFrYbCVReG3hsh1M4bWNzFUJFF6HzRGLwrt1mwfTZ4ZOaejRPQOVKtoHQuG1z8xuBoXXLjF78RRee7x0oim8OtTs5VB47fGyG03htUvMXfEUXoftEYvCK1XOPa0hqSisRWs6o7tSFoXXYSdSSKfwKkByEELhdQBPMZXCqwjKQRiF1wE8hVQKrwIkF4dQeB02TqwKr8Nq50mn8EaSZviyKLxmGVN4zfKV0im85hlTeM0ypvCa5Wu6dAqvQ8IUXo7wOuxCSukUXiVM2kEUXm10yokUXmVU2oEUXm10SokUXiVMrg2i8DpsGgovhddhF1JKp/AqYdIOovBqo1NOpPAqo9IOpPBqo1NKpPAqYXJtEIXXYdPEqvBOm+HD9u2enNpXr+bHTR0ztWhwSoMWNltJFF5buGwHU3htI7OdQOG1jcx2AoXXNjJbCRReW7hcF0zhddgksSi8q1Z7sXBx6D6813fIwPk17QOh8NpnZjeDwmuXmL14Cq89XjrRFF4davZyKLz2eNmNpvDaJeaueAqvw/aIReENHt0NINAd5aXwOuxECukUXgVIDkIovA7gKaZSeBVBOQij8DqAp5BK4VWA5OIQCq/Dxokn4b2gThaua8uDJxx2CSPpFF4jWHMKpfCa5SulU3jNM6bwmmVM4TXL13TpFF6HhGNReLf96sVLL4dOaejaJQtVKlN4HXYJI+kUXiNYKbxmseYpncJrHjaF1yxjCq9ZvqZLp/A6JByLwitVlnm8cvhE4GrWJAsNNY4VlnxOaXDYiRTSKbwKkByEcITXATzFVAqvIigHYRReB/AUUim8CpBcHELhddg4sSq8DqudJ53CG0ma4cui8JplTOE1y1dKp/CaZ0zhNcuYwmuWr+nSKbwAMjOz8OZ7S/HK24tx6HAKSpcqiQE9OqJx/fMt/s/NnI/nXpmPBF/2iOg5Z5yGWZOHWv+bwssRXtMvUimfwmuWMoXXLF8Kr3m+cgcKr1nOFF6zfE2XTuEFkJXlx6y5H6NVs/o4sUwpfLtlG+7o/zg+nTcJiQk+jJn4Ki48/xxc1fTikPaIZeGVaQ1+GXk5IUtrO7IADI7wmn6ZUnhNE6bwmibMEV7zhCm8phlTeE0TNls+hTcfvpde2wvzpj+CcmVLo//Iybip3RWW9AZfsSq8o8cmIDX1aG1OKOnHgH48eMLsy02/dI7w6rNTyaTwqlByFsMpDc74qWRzhFeFkn4MhVefnRsyKbxBrZCWlo5X53yEz9d+g2lPDbT+2mPweGz7bSfSMzJR8aSy6NWtPerXrWH9LRaFd958L9ZvDN2loVULvYVrHOE1/1Km8JplTOE1y1dKp/CaZ0zhNcuYwmuWr+nSKby5CHe4Yxh+3LoDp1Y6CU8M7YGa1apYf01JTYPP57Pm8K7duAX9RjyLt14YiUrlyyIzSyYFxNb15DOZ+PHn0Oc++0wP7u/ps10ZOaDY4/Egyx97LGxXNkoJXo8Hfr/fmoLCywwBn9cTk69nMzQiX6rnv5PM+TYRebaBEtmHzbENlCzvxdH6rJP25aVPgMIbxE4WsG367mcMeHgKpo0biCqnVQyhO2DUFDRpUAfXXNkQu/el6NOPUuarr3vx/ebQF85ll/px1ZX29+EtmuhDsSI+7DuUFqUaxf9tk0sUwZHUDKRm2G+f+KfjvIbye8eJpZPw5/7Yez07r33hlFCiaIL1xfhgSnrh3PA4vMtJpYrinwNpyOS3CiOtX8TnRcniCRbjaFwVkpOicdu4uSeFN5+mHDjqOVzWoLYltcGXjPC2bt4AVzS+MCanNOzdB4yfmBBSr769M1Am2X7f5pQG+8zsZnBKg11i9uI5pcEeL51oTmnQoWYvh1Ma7PGyG80pDXaJuSuewgtg7/4D2PPXPlQ78zSrdX7e9jvuuP8JvPjUQJx5+slYtnIjLq1fCwk+H9Zu3IxBo6di7rRHULpUiZgUXqmjnLa2ZOnRUd7212Zqya6UReE1/6Km8JplTOE1y1dKp/CaZ0zhNcuYwmuWr+nSKbwAdv+5FwMfeQ7bfttlbUNWJrkU7rqljTWCK1fvoROx4esfkZiYgFMqlsPAezqjVo0zrL/F4qK1SHcqCm+kiYaWR+E1y5jCa5Yvhdc8X7kDhdcsZwqvWb6mS6fwOiRM4eUIr8MupJRO4VXCpB1E4dVGp5zIEV5lVNqBFF5tdEqJFF4lTK4NovA6bJpYFd4jKcDqNdlbkyUlAXVqZ6GY5nx4jvA67EQK6RReBUgOQii8DuApplJ4FUE5CKPwOoCnkErhVYDk4hAKr8PGiUXhFdmVRWspuRakJyf70aN7ppb0UngddiKFdAqvAiQHIRReB/AUUym8iqAchFF4HcBTSKXwKkBycQiF12HjxKLwrlztxaLFoQdPdL4xCzWq29/2isLrsBMppFN4FSA5CKHwOoCnmErhVQTlIIzC6wCeQiqFVwGSi0MovA4bJxaF95PlXixbHiq8TZtkoVkTCq/DLmEkncJrBGtOoRRes3yldAqvecYUXrOMKbxm+ZouncLrkHAsCu+GjR7MnR96otrtt2aiahX7Z3lxhNdhJ1JIp/AqQHIQQuF1AE8xlcKrCMpBGIXXATyFVAqvAiQXh1B4HTZOLAqvVPnZqQnYvfto5auc7kfX2zK1aFB4tbDZSqLw2sJlO5jCaxuZ7QQKr21kthMovLaR2Uqg8NrC5bpgCq/DJolV4ZVqb93mwb59QHIytEZ2A+govA47kUI6hVcBkoMQCq8DeIqpFF5FUA7CKLwO4CmkUngVILk4hMLrsHFiWXgdVj0nncIbKZL5l0PhNcuYwmuWr5RO4TXPmMJrljGF1yxf06VTeB0SpvDy4AmHXUgpncKrhEk7iMKrjU45kcKrjEo7kMKrjU4pkcKrhMm1QRReh01T2MK7d58HS5d7sX9f9oPXrZOFOrXtLzST6QyBnRqSkvxoUN+vPa2BI7wOO5FCOoVXAZKDEAqvA3iKqRReRVAOwii8DuAppFJ4FSC5OITC67BxClt4gxebyePb3T9XpHn8xLy7NMhpa3LwRJlk+/JM4XXYiRTSKbwKkByEUHgdwFNMpfAqgnIQRuF1AE8hlcKrAMnFIVET3n37D6J0qRLweDwWnjffW4b09Iw8qGpVr4rzzz3TxfiAwhTenbuAKc8nhPCoXi0LN3VU3z83v31427XNRN06FF43djgKr9lWofCa5SulU3jNM6bwmmVM4TXL13TpURPeIY++YMlsp2ubWXWs17I7zqpyCooWLWL99979B3H6KeUxaXQf0wwclV+YwivTEKbPDN0/1+6WYvkJb8sWWWjUQF2cA+A4wuuoCyklU3iVMGkHUXi10SknUniVUWkHUni10SklUniVMLk2KGrCe2Wn+/HE0LtRp+ZZOcI7Z9ooVD6lgvXfm3/6FT0Gj8PSt592LTx5sMIUXrnfmLEJSEnNi8SuqOYnzj26Z6BSRfu4Kbz2mdnNoPDaJWYvnsJrj5dONIVXh5q9HAqvPV52oym8dom5Kz5qwlvnyjuw4JXHcHLFchaR+lf3wFvPj0TlU8pb//333n/R7Pq+2LRkmruIBT1NYQuvnJK28ENfjvTK6G7njpkolmQP08IPvVi15ujxwrrHCstdKbz22OtEU3h1qKnnUHjVWelGUnh1yannUXjVWelEUnh1qLknJ2rC26jtvXj1mYdwRuVKYWn8uHUHbrxrJDYsfsE9tMI8SWELb+ARZOGZzgKz4CpEohwKr/kuSuE1y5jCa5avlE7hNc+YwmuWMYXXLF/TpUdNeLv2HYsWTS/KmcMbXNE5Cz7FjLc+xLvTR5tm4Kj8aAmvo4eOcDKFN8JAwxRH4TXLmMJrli+F1zxfuQOF1yxnCq9ZvqZLj5rwLliyBmOfnYVXn3kQp52cPY0hcO35ax9uvGsEbm5/Be68+RrTDByVT+HllAZHHUgxmcKrCEozjMKrCc5GGkd4bcDSDKXwaoJTTKPwKoJyaVjUhFd4jBw3Ax98vArtWjVGzWpVkJKajp+3/Y45C1bg/HPPwHOP9UNiYug2XG5iWdjC+8ECH1av8yB7MzcgsQgwdHDe7dwKm08sjfCuXO3Fsk+9SEkBZO/hppfp7UxR2IwpvGaJU3jN8pXSKbzmGVN4zTKm8Jrla7r0qAqvVG7pyg2Yu3AFtm7fiYzMTGu0t0WTiywJ9vmOLqoyDUK3/MIW3mEjE5Bju/89dPmTgJ49oie9sSK8+e1jrLs7hW6f0cmj8OpQU8+h8Kqz0o2k8OqSU8+j8Kqz0omk8OpQc09O1IXXPSj0nqQwhVdGd9esC4ztHn1eObtj5FAKb0EtmN/+w052qCjonpH6O4U3UiTDl0PhNcuXI7zm+codKLxmOVN4zfI1XXpUhXfHzj+xZv33yMzKQtOGdVC+XLLp+ka8/MIU3uWfA0uWhE7x8CX4MXxIZsTrplpgrIzwynSGRYtDfzWwu4+xKpdIxlF4I0kztCwKr1m+FF7zfCm85hlTeM0zNnmHqAnv+q9/QPcBT6JS+RPh8Xqxc/ffePnpwdZc3li6ClN4hUu4KQ2nn+ZHt9spvAX1G9mCbfzE0JPq+vbOjMgWbwXd38nfKbxO6BWcS+EtmJHTCE5pcEqw4HyO8BbMyEkEhdcJvejnRk14u/V/HNXPqowBPTpZFF547X2sXv8dpj01MPpUbDxBYQuvjPIuW+5DZrrHmstburQf/ftET3YFVayM8Mqzfr9ZDtzwYNt2D+TQjob1/ahR3f5xyja6SERCKbwRwZhvIRRes3yldAqvecYUXrOMKbxm+ZouPWrCe8m1PTHr2aE4/dTso4QPHU7BJW3vxYaPXoRHJqXGyFXYwutGLLEkvG7kp/JMFF4VSvoxFF59dqqZFF5VUvpxFF59diqZFF4VSu6NiZrw1mzaBWs+mIKSJYrl0Lm49d1Y8OpYlCtb2r3Egp6MwhtbI7wx07GCHpTCa7blKLxm+XKE1zxfuQOF1yxnCq9ZvqZLj6rwThzVG0WKJObUsf/IZzH0vluRXPqEnH9rXL+WaQaOyqfwAr/v8CGpiBfeIhlRnwv7yac+/PyzHyeUzkKn9uGbVrYnS0nxICnJj0oVHTV/oSVTeM2ipvCa5UvhNc+XwmueMYXXPGOTd4ia8F7Rsb9SvT6e/ZRSXLSCjmfhlUVg02d6sW/f0Sko7dpmom4df1SaY+RoHzIzc02H8QM335yBamcdfZxZs73YvOXoTg11avvR/trozoFWgUXhVaGkH0Ph1WenmskpDaqk9OM4wqvPTiWTwqtCyb0xURNe9yKx92THs/DOedeHjZvyzreW08uGDCz8PYHfmAN8903olm1FigAP/XcSnSxYe/3N0G3JOt+Y5fqFaxRee69Lu9EUXrvE7MdTeO0zs5tB4bVLzF48hdceL7dFU3gdtsjxLLwvzfBZux0EX9E4uWzaSz5s3xH6LD6fH8MfzB7B5cETDjt7HKdTeM03LoXXPGMKr1nGFF6zfE2XHjXhXbXuW6W6NaxXUykuWkEU3lDJfHhY4Y/wqgjvho0ezJ0fug9vNKdhqPZbjvCqktKLo/DqcbOTReG1Q0svlsKrx001i8KrSsqdcVETXtml4eSK5ZDg8yEtLR1Z/vB7oS59+2l3kvvvM6lkSgAAIABJREFUqY5n4Q03RUD2tu16W3TmxIY7lOPc8zJyFq8dSQHGT0hASurRLpVUFOjbJwPFklzdzUDhNds+FF6zfKV0Cq95xhRes4wpvGb5mi49asLbb8RkrFz3DZo2qoMOrS9DvdrVYmr/3UDDHM/CKwy2bvPgxx+9+PdfD04+JQt162RFTR63/AS8MduHjAwPfF4/qtfMDNmpQRbabdjkwbZtHlSp4kfd2v6o7yyh8iKn8KpQ0o+h8OqzU82k8KqS0o+j8OqzU8mk8KpQcm9M1IRXkOzbfxDvfbQS73zwKVLT0tC+9WW49qpLUb5csnuJBT3Z8S68goMHT5jvrhRes4wpvGb5coTXPF+5A4XXLGcKr1m+pkuPqvDmrtzXm7dizgfL8eGytahz3lno0LoJmjSqbU15MH1lZmbhzfeW4pW3F1snvpUuVRIDenRE4/rnW7feu/8AHhjzPL76/heUTS6Fhwd0xQW1zrb+RuGl8Jrun1I+hdcsZQqvWb4UXvN8KbzmGVN4zTM2eQfXCG+gkkdS0jB34Qo8OeUN6xS2T+dONFl/q+ysLD9mzf0YrZrVx4llSuHbLdtwR//H8em8SUhM8GHQ6Kk4pWI59Ly9Pb7Z/Av6jZyM92c+iqSiRWJWeGU+65YtHshP/GWS/ahWza81FUHyv/nGh8QEDypUzEDVKtHZgzfQSebN9+KffR6rLi1bZKJM7PxYcMx+TuE1+zZA4TXLl8Jrni+F1zxjCq95xibv4Crh/fKrHzBnwaf4cNkXOPuM09C+dWPccE1Tk/XPt+xLr+2FedMfsUZ0G7W9F7J4rlhSESu+10MTrXnHMv84Fkd4RXanPO/Lc2BExYrAPd3t7a4g83enz8w7At+wfhZaXRV+AaLphhw9NgGpuRakyf26dslClcrReZ5I1pfCG0maoWVReM3ypfCa50vhNc+Ywmuesck7RF14//x7H+Yt+swa1T146AjatGhkzeU98/STTdY737Jlx4hX53yEz9d+g2lPDcTuP/fif71G46M3nszJGTf1TSSXLomunVpj5z9HovKcTm66Xrbnejd0qshNcgBDDXU5fE1OLdscepDDqOH2xNlJXQK5K1d5sXBx6LPIrhHdukRn14hI1CtQRpmSRXAkJQMpGertE8n7x3tZIrwnJRfD7r2x93qOlbYpkZRgLUw+eCQ9Vh455p6zfHIS/t6fikx/dH9pizlwig9cxOfFCSUS8fe/QSMrivlOwyqVLea0iOM6P2rC+/GKL63R3FVffodL6p1njeZe1rBw5uzm1+Id7hiGH7fuwKmVTsITQ3ugZrUq2L5jN3oOeRrvzXw0J23yy/OsaRA9u7ZDLL6vzF+UifcWhYpTm5ZetG2pPmf6iUkZ+OHn0DfW++9NQLWzQ/fnNflK+3hpFma/Gyq255zlwYCeoSewmXwWE2V7PIBFmp9jJvBaZVqMydcoX6sLk7FRxnyfMIYX8Fj/F7U+LO9RvPQJRE14ZR/eqpUr4YrGFyK5VEn483kXvL1TK/3aaWTKArZN3/2MAQ9PwbRxA1G8WBI63j3CmtIQuMY++zrKlS2Nbp1bx+SUhvwOYLj91kxbc3BnyQjvltBR1WgcPPHVt8Db74SK7ekywhulfYE1ul++KZzSEEmaoWVxSoNZvlI6tyUzz5i7NJhlzCkNZvmaLj1qwjvk0ReU6jbmgTuV4iIdNHDUc7isQW1cfUUDXNK2Jxa9/gRKlSxu3ebuQeOsucXNG18Qk8Irc3hfmpGA3buPUqtQAbj3LntTEXbuAqbPyHuQQzTn8D4+zoeDB/N+Be7bOyMuFq5ReCP9Cs9bHoXXLF8Kr3m+cgcKr1nOFF6zfE2XHjXhNV0xO+XLtmN7/tqHameeZqX9vO133HH/E3jxqYHWXOJhT7yEE8uURq+u2bs09HxwAha+9jhKFE+KSeENsJGT0nbuBipVAGpU15sbKrs0/L7Di4wMLypUTEelinbIRz521WovvtviQeVTgHr1uEtD5AnHZ4kUXvPtyhFe84wpvGYZU3jN8jVdOoUXsBamDXzkOWz7bZe1DVmZ5FK465Y21nQLuf49eBhDxryA9d/8gFIlS2Bo31txyUXnWX+LxV0aIt2pePBEpImGlscRXrOMKbxm+XKE1zxfjvCaZ0zhNc/Y5B0ovA7pUnh58ITDLqSUTuFVwqQdROHVRqecyBFeZVTagRzh1UanlEjhVcLk2iAKr8OmKWzhlfm3y5Z7sWuXB0lJftSprTcd4b1FwLq1CdZqU1n5We+iDLRpaR/G0JE+a9WqrF31e4CmTTLQvIm9cvbuAxYt9mHr1uySap6bhWvb2J9iIdMrli73Yv8+WGwa1PeHLMJb/jnw6YoEZKYDvkTgssYZaHKJveeNRnQsCa8sity4KXsxY5Uq0g5ZWoeaFCZnCq952hRe84wpvGYZU3jN8jVdetSFV/bflakDJ/y3IMx0hSNdfmEL77NT8y42k/p0lv1zbczBXb3Whw8Wev4T1WwispVNty4ZqFJZnZAlu8H7pPiBh23uw/v0pAT8szfvfe3urhDuMA0psUf3jJx5xV9/58Obb4fW+8br/ah1rrv36o0V4f1kudf6Qpb7qlEtC5072v8Co94TnUdSeJ0zLKgECm9BhJz/ncLrnOGxSqDwmuVruvSoC2+bWx/Ab3/swaUXn4+WzS5Gs0vqWluBxcpVmMIruyJMeT50663q1bJwkw2hePypBBw8FEr4xDJ+9OmlLn5DR8pG8qHlNLE5yjvs4dA6FS0KPDhIfdeIcKe+yZM1bZKFZk2yZeuZKQnY82fo85Y/CejZQ/1e0eibsSK8L83wYdv20E4Rja3q7LQThdcOLb1YCq8eNztZFF47tOzHUnjtM3NTRtSFV2D8tPV3LP50HT7+dJ21cKxJw9poeXl96/8nFc0+ztetV2EKb35SJ6eJdbWx1+zosT6kpoZKSckSwMD+6uKXn/CWKJ2BQX3UWyyc8IqAjBiq/iwqwvvUBB/27w+td+nSfvTvoy766jWLXGSsCG+4XyCEAoU3cn0hVkui8JpvOQqvWcYUXrN8TZfuCuHNXclff9+DJSu+xLJVG/HdD9txeaM6uObKhmhc//zQn89N01EovzCFVx5nzNi8+97Kv7VskYVGDdR/Mn7+xQTs+CO0chddbG8ebzhRlbkRdqc0jBiVgKyg05dKlvRjYD91CZX5u+Mnhp4Sl5uNzN9dsiR0NLl5c/fP440V4Q13GElyaT/6ufwLBUd4Fd7sHIZQeB0CVEin8CpAchBC4XUAzwWprhLe9IxMrFr3DT769EusWPMVKpYvi9rnnomln29AUlJRPP1wT5xRuZILsB19hMIWXlkQtPBDH1L+O8pbRnc7d8y0vSho1Bgf0jOOjnYmJvgxdIi6YAqBJcuBZctyTWvwy1xgP0YNt1eO7J27cPHReZ8iH+3bZ+D8mvaaeuVqLxblKifcyPfYJ304dPhovUsU92PQ/fae195TRSY6VoRXvnhMn+HFvv9G0pOKwuqfVau4+zxZCm9k+umxSqHwmmdM4TXLmMJrlq/p0qMuvFlZfqzbtAULlqzG4uVrUeqEEmhzZUO0adEIlU+pYNVfYqa+Oh/LPt+I2VOHm2Ziq/zCFt7Aw8lP+MnJQJlkfZHY9iuwaqUPDRtl2lqsFgzopVd82PmbDw8OSbPFLjhYjgeWy67oBpcjbCpW9Of7JUAO21j/pQ8XXJhpHboRC1esCG+ApYhvSoo/6geRqLYthVeVlH4chVefnWomhVeVlF4chVePm1uyoi68TTvch7T0dLS6vL4luXVqnhWWTUpqGhq1uRfrF6sdSVxYgKMlvIVVP5X78OAJFUrOYmJNeJ3VtvCzKbzmmVN4zTOm8JplTOE1y9d06VEX3k8+32DNz5UTzo51paWl46MVX+Lq5g1MM7FVPoWXB0/Y6jCawRReTXCKaRReRVAOwii8DuApplJ4FUFphlF4NcG5JC3qwiscvtmyFT/+sgMitcFXx2ubuQRV+Mc4noVXpgbMmOnDoSPZe9sm+Pxo0jTT1Qc5fL/Zi6WfysEdQHKyH61a+G3tYRytzlgYwit76G7c5MG+fTIlBLj8Mnv7Owsb2Q9Z5phv3iJTGgDZg1e2hqtUMfLkZOrKwsW+nLasW9uPy//bgs7u3Si8donZj6fw2mdmN4PCa5eYvXgKrz1ebouOuvA+N3M+pr2+AOefewa++u4XnFe9Crbv2I2Dh46g07XN0O+uG93GLM/zHM/CG26bL5lRPGqY+nZihdm4+e1j3Ld3pqO50IVRB9PCK18EXn8z74ERSUlycIc9Ngs/9GLVmrzliDzf0z2yfULEevzEBEuqc1/t2maibh3789opvOZ7MYXXPGMKr1nGFF6zfE2XHnXhbdK+D55/4n5UO/M0tO82FHOmjbIWqT0x5Q2UKV0S3f/XxjQDR+Ufz8I7fFT20cTB1w0uPbks3Clg8ux2t3Vz1GE0k00Lb35sbr/V3g4LhXXwRH77Ltep7Uf7a+3vukHh1eyYNtIovDZgaYZSeDXBKaZReBVBuTQs6sJ74VXdsfr9yUhMTEC7rg9h7kuPWKj2HzgEOYXt07kTXYou+7EovBTewuigFN68lCm8hdHrInsPCm9keYYrjcJrljGF1yxf06VHXXg73DEMowZ2xbnnVEG3/o+j/103Wv/7r3/2o9XNg7B24XOmGTgq/3gW3rAnl2kcPOGoAWwkc0pD/rDCTmkoCvS4y/mUhgoVgHvvMjClYULoISyc0mDjBVHIoRRe88ApvGYZU3jN8jVdetSF9/2PV6FS+RNx4fnnYNHSLzB6wivWrg1fff8Lqp9VGU8O62GagaPyj2fhlUVrM1/x4dAhD2TVmi/Bj6ZN3L1oTQ7uWLnGh927ATkBrNVVXLQWeAFYi9Y2eqxDI0RSmzVxsGhts8c6HKV6tSxrIZmpRWsLPjzalnXq+K1n1rk4pUGHmr0cCq89XjrRFF4dauo5FF51Vm6MjLrwBkORbcq+2PA9Tq10Em5o0xRFiyS6kVvOMx3PwhuAwH14zXdR01MazNfA3Xeg8JpvHwqvecYUXrOMKbxm+ZouPerCKyO8DS44F+XKljZdVyPlU3i5D6+RjhVUKIXXLGUKr1m+UjqF1zxjCq9ZxhRes3xNlx514ZU5vFt+/g1VT6uI+heci/oX1MDFdWqgdKkSpusekfIpvMAff/jw5x4vatcJ3UfZDuRIHS38wQIfKlcBap0bfrW+TMVY96UX9S7MiqujhSNx3LRs97Vr17GPZlZpU5kvLZeJqQzB949EvSm8Kq3qLIbC64yfSjaFV4WSfgyFV5+dGzKjLrwC4d+Dh7H+qx+w7qst+PKrH/D9j9tx5uknWyO/A+7p5AZO+T7D8Sy82371YuarXmTkWo90QZ0sXNfW3jzKVau9WLj46N6tHi/QoV0Gzq9pr+mfmZKAPX8ezRGJGTE072KpsU/6cOiwHJORfRUv5sfgAfa3sbL3ZM6jjzXCu3K1F4ty8at6uh+dOmaiWJK9+86a7cXmLUfbQQ6MsDsndu8+D6bP9FqHV8gle/l2vtHe1maqTx2pesv9KLyq1PXjKLz67FQzKbyqpPTiKLx63NyS5QrhzQ3j1993Y8mK9Xh93hL8sftvfLN0ultYhX2O41l4n56UgH/2hmJ52ObBEyNGJSAraD/fkiX9GNhPXUSXfw4sWZIQ8jDlTwJ69siWXhn5XbPuqOwGgps3z3D16XDynPkJrwjm+Imhx3Lb3Vs4WB4DbHp0z7A1ShsszVKOnGjXr7d6W6q84CNV78C9KLwq1J3FUHid8VPJpvCqUNKPofDqs3NDZtSFNz09wxrVXb56E5av2oi9+w7gkotroUmD2ri0fi2UKX2CGzjl+wzHs/COHpuA1NRQNNd3sDc6O+zhUFGVUd6RD6lvZZWfzJYu7Uf/PtmyFXYbNQC5Y9za2fIT3kjtRxupgyeenZpg7YARfNn9ElRQO+RXb9kV4qaO9n5hkHtReAsi7vzvFF7nDAsqgcJbECFnf6fwOuMX7eyoC+9Fre7CiWVKo3Xz+rjkolqoU/Ms+Hx5jyaNNqRj3Z/CG0qnb+8MlElWb7Vwwlu0KPDgoMgKb/CUh8AT5h4FVn/qwo20K7x2pyNESnijfdJaw/pZaHUVhbdwe6fa3Si8apycRFF4ndArOJfCWzAjN0dEXXjHPvs6lq3cgNS0dDSqdx4ub1QXDevVRPFiRd3MLefZjmfhnTffi/Ub8345sSuqAjLc1IjTT/ej223qP4PLQrQpzyVY+wHnvnJPV/j6Ox/eeit7z+Ccyw/ccIM/3wVubumEx5rDO26Cz9o7N/dl90jgcIdyyD7FcvCEnbnA4cRZd9S1IPaRqHfgHhzhLYi2879TeJ0zLKgECm9BhJz9ncLrjF+0s6MuvAEAv/y6E8tXbsTSlRusRWtyEEXTRnXR6dpm0WZ0zPsfz8IrYER6f/zZg/R0OazAj/bXyslc9pps7z5g4Yc+yM/UctWsYX/hm+SJ0C5YABw+4oHX50e9OsDVrfNKs8z1XbEiAenpQGIi0Lix++fvSt2OJbwyn3X1Gg927vIgKcmPhvX9qFolaFK0QpMI/w2bZMGZzLuFdWBEmWT75cjhHlKOXNWr+VG3TpYtaVZ4VCskUvWWsii8qtT14yi8+uxUMym8qqT04ii8etzckuUa4RUgfr8fP279HSvWfIXZ736C33f9hW+XvewWVmGf43gXXoHCgyfMd1Huw2uWMYXXLF8pncJrnjGF1yxjCq9ZvqZLj7rwfvXdz9Z2ZOs2bcGGr3+06luvdjVcXLeGtS/vOWecapqBo/IpvBReRx1IMZnCqwhKM4zCqwnORhqF1wYszVAKryY4xTQKryIol4ZFXXgvanU3Ljz/7GzBrXsuapx9Orzy6RMjVywL7/ebvdi1G6hYEahRzf5CH2mi1Wt9WLsWSE3zolr1dLRpqddwcujEt9/6rH1xa9cGqlTWex75WX7bdjk4Aahyevif0idMSsC+A7I7A3DfveoL4+zWTNh88YUfqaleVD83Q5vNG3OAX35KRGJCJi6s50Gzy0LnNstext//4MGppwIXXWB/Wonduh0rXqYabN8OHEnxWG0Q7vAJ2cN51ers13nDBn7t9o5EveWwje3bvTiwPxHlyqdpTQeJJL94Let4FF6V10Ik29up8MprYdt2L3btkvdPvalRkayP28qi8LqtRew9T9SFNyMzEwm+0H1E7VUjetGxKLzypjZ9ZoL1pha4RBDv6W5P/t5bBHzxRUKeNWCJCX4MHaK+2EzuP2u2D5u35P2S06pFFho2sCe9wXvAyqEHsmNE7kVXQ0cmwJPrVjJDdZTNfYNVetvzLyZgxx95I0uWAAb2t8d4xCgfsvxHH1iet+Z5GejU/mjZj4/z4eDBvPzs7pShUieVmHDbhQXvCRxusWPjRn5ceYW9fhNusWPXLlm25FleC1Oe9+UclCF1lIM7brexYFKFC2OOvykNMqDw+pt5F/W2a5uJunXsz4tX7T9OhFdeC+MnJiAl5ejdZCCks8Y2f6rPG2txFN5Ya7G8zxt14T2SkobX5nyEb7dsxaHDuV5p/z3n80/c72rCsSi8sqho7vzQLxl2V/YPG+UDcslYoKHs7rkaiW3Jwu0yIM+Te3suGdn9O8xBGSeWAfr0sieiBXXKYSNDd4zQketwbPzwY9SwbDmUkfG33wndx9juLhcF1Uf17+G2JZMvHkMGHuUb7qARu7t7RKre+R24Yfe1oMrneI473kZ4VV4Lke4PToQ3v60J7R4+E+k6uak8Cq+bWsP+s0RdeAePeR7f/bAdba5siOJh9j+6uf0V9mtViBmxKLz5vbHZ3bs1nNQJ+hrn+NG5k/poXTipC3cs8LGaNb+DCHLXaeRoHzIzQ6fL+Hx+DH9Q/XlVuld+bOx8GZj2kg/bd4Q+r98PjBqeLZDBxzIHnq1sGeC+CEu8Sr1VDp4I195Sth02+dXbrjjn91owPRKnwjLeYo434Y1EP7fbB5wI78IPvVi1JnQPfH75O9oKFF67PdJd8VEX3kZt7sU700ahUvmy7iKj+DSxKLz5jWp1vjELNaqrTyNw0wivivCOfdKHQ4dDBbJEcT8G3V8IwusHHv5PVBW7F+JhhDdYZsPVSWY1DX9QfZSdI7yqPcg9cceb8IYd4S0KDLFxoI7d1nMivJE6fMbuM8dSPIU3llor9FmjLrwXt74by96ZEDMHTQQjjEXhlblaL83IewRshQrAvXepC4dweP0Nn7VQKvelM4d32gwftm/PW47OnM7gD5ikorAOTgjsJbvlJ+DV14Lm8PqB/92cgWpnRfaFHG4Ob9Gifjw4yJ5YB8/hlac8/VQ/unU9Wk64I57tzmWNVO3DzVsM/uUgUnN4I1Fva97ihASk5DoiW+e1ECl+8VzO8Sa8Kq+FSLe3E+GVBXZTpvryvBZk4VpXzmfPaSYKb6R7bOGWF3XhfWjsNNSqXhUdXX7ARH7NEovCK3WRD/rNmz3Yu9+DMqX9qF7dr3U4gOxEsGSpHF7mQdXK9qYy5GYqP1F/99/CteaX66/al/nJUieR3XAHHoj0zpuX/aYuMdddlxlx2Q3Uy2LzSfZ/nXE6bE3zyM1Gpjbs2CVfCPxofCnC7tIgEvn3Xo/Vhq2uiv4uDRs2ZX+BkQVg4Q7BkBHateuy55FfVC8T59fUe+OLRL3ltbBxkxc+TwISi6QbXVSkV8v4yDrehFdaTSSyoNdCJFvXifAGPhc2bPRa74/yuWBygV0k611YZVF4C4u0mftERXhHPnX0MImMzCy8//EqVDvzNFQ9rRKSiibmqenw/l3M1DxCpcaq8Eao+lYxPHgikjTDl8V9eM0y5j68ZvlK6cej8JqnmvcOToW3sJ831u5H4Y21Fsv7vFER3icmv6FMbcA9nZRjnQQuWLIGU1+dj337D6Js8gkY0vt/uKhOdavI52bOx3OvzEeCL3tC/zlnnIZZk4da/5vCS+F10u9Ucym8qqT04ii8etzsZFF47dDSi6Xw6nFTzaLwqpJyZ1xUhNeNKERq27ZohJMrlsPajZvRf+RkLJ8zAR6PB2MmvooLzz8HVzW9OOTRY1V45ae2jZs82LbNgypV/KhT258z19Vu+8TSCG/2FAwPUlOAokmATJ9ocJG9ebWqfOTn9q3bvchI9+Ocs/24to36gsDAPQI/t/+xw4sTSvlx9tlZIVMEdu4GXp+dgH/3A0lJftSqCVzd2kydVOoucxe/3yKM/ahSBWhY3369Ve4TYLNtG5CcDFSvpr9RPoVXhbizmMIQXukTq9d4rfe1ihXlfS38wSfOauLebKfCK9s7yvSeXbuyPxca1A9/eI97CZh9MgqvWb6mS4+68Ha8ayRmTx0etp6//bEHz7/6Pg4fSUGLJvXCCqcpQPWv7oHFbzyJ0ieUsOT3pnZXWNIbfMWi8FqLE5735dlgPNwhDapsY0V4t/0KTHs570EZsjduty4ZqFJZtbZqceEO06hU0Y8e3e2JaLiV3sG7aYTbAq158ww0uUTtWSMZFW4HEPky1f5ae/VWeaZwW6DpbqFE4VUh7izGtPCGO0REnrhv76MLV53VwP3ZToQ33F7mycl+9Osd+deu+0mGf0IKb6y2XPZzR114azbtglbN6uOr736Gz+dDs0vrolfX9kgqWgQd7hhmze2tWa0KZrz5Ie678wa0bl7fOPGft/+BnkOexsLXHrfu1WPweGz7bSfSMzJR8aSy6NWtPerXrWH97c99oYdlGH9Ahzf4bJUHHywK3W/xf52yULOG/VOAiiT6kFTEi38PpTt8MrPpT07w4u9/QrclO7GsH/f3iewo5MgxeVc7B2r26Ej1Dw/5AH/40dADQmQhWPeu2c87/z0vVq0LrZN8UA3qG1SnQjix+/mXvJAt4oKvRx9Wr7dKL/hlqwcvTA/tw5c09OOaVvbbUkoqUyoJf/8be69nFV5uiCleNPuAlMOp9naDUX32/PpE88uzcMXl9t/XVO/rpriyJxTFvoNpyJLNum1eHy/1YMnS0NfUnV2ycEZV++XZvH1MhCcmeFEiKcFiHI3rpOSkaNw2bu7pCuFt16oxrr3qEmv6wFvvL0PZ5FLo3a0D6rXsjjUfTEHJEsXw9fe/4NFJr+XMnTXVAnLU8Z33P4H/tW+B5o0vsG6TkppmybjM4V27cQv6jXgWb70w0to7OC3D/oerqWdXLff9D7OwYHHoG1jrFh5cc1XoG15B5cromNfjQUaWu98U+z2YmWdUO1AvGd0eNzqyx1vf0z+84E1+Sv0+v/3ux6PjQvvX2Wd40Pfe7Haa/Y4fy1eGxiQV9WDcmKC2LITmGf1UFn7fGXqjyU+q17ug/iZ//+EnP55+LrTe55/nwd1d7PdhOR870edFegy+nlV4uSHG64X1Hp+ZaaYjbvraj6kzQvuE9b7WQqNPuAGazWdITPAgI9MPDd/FW+9mYemK0La5r4cX55xVCN+WbdY1GuFCwefLZhyNS0aYeekTiLrwNmp7Lz5/9xnrjVAuEU4Z2X356cG49Npe+HbZ0R0dmna4D8veeVq/tgVkZmX5MXj0VFQ+pQJ6dm2Xb/SAUVPQpEEdXHNlw5hctJbf0cK6R0jGypSGcHvjSiOfejLQ/Y7Ijjo9Ps6HgwdDPyTsnCYmzzZmbN49YuXfck8RWP45sGRJ6NHC5U8CevaIbJ1UXnizZnuxeUveN2XZ/i3Sm+3LtJzxE0MlumWLLDRqYP9LKKc0qLSusxjTUxryO15ct084q210sp1MacjvQCLdz4XoEDB7V05pMMvXdOlRF95WNw/CG1OGoXSpElZd//x7H9p1HWrN623R6X5sWjINCXIME4Am7ftYC8lMXH6/H8OfnG4dbzy4503HvIWM8LZu3gBXNL4wJoVXKhc8N9TJPMtYEV6p94hHEpCVy4cfkhgzAAAgAElEQVRk1GnEQ5EXw3AngekcphH85SS5tB+335aVZ4HhUxN82L//qFzLd8e7u2egUgUTr5RjlynS8fpsH/b99zwiu+2utXeCn+pTB58MJQdGdL0tQ2s/aQqvKnX9ONPCK08WfDzu8XZwghPhlSlU8trdlusQILvHzev3jtjIpPDGRjvl95RRF94XXnsfcxZ8iisvqwev14tFS7/A2Wecas3plVFfkc+Wl1+Mz9d+g0nT3sEbz4Vf4Oa0GcZMfM3a2F+2Iwu+lq3ciEvr17LEW3ZwGDR6KuZOe8SS9FhctBaon8hJSorHWtlfqaI+wVgSXqnle4uAg/t8KJmciTYt9eutkikHashVvbpIqkpGaIyMZmalJiIlLROnnBZ+qsTX3/nw6zagZOnMqCxWC37qwDxeWSkvh2GYuoTNvn3ZpYc74EL1vhReVVL6cYUhvPJ0gT7h9H1Nv6bRy3QivMGfC7LzSeCUyujVyF13pvC6qz3sPk3UhVce+NPVm7By3bdITU1DgwvPtXZj+GPXXyhSJBFd7nsMBw4exuEjqZg4qhca1tM8kukYZLb9tgtX3zIYXvnUy3XJIrlunVuj99CJ2PD1j0hMTMApFcth4D2dUavGGVZkLAuv3c6SX3ysCW+k6l2Y5XAfXrO0Kbxm+UrphSW85mvi3jtEQnjdW7voPxmFN/pt4OQJXCG8x6pAenoGZNcE2R+3VMniTupqJJfCy4MnjHSsoEIpvGYpU3jN8qXwmucrd6DwmuVM4TXL13TpURPeTz5bj7OqnopvNm89Zh0LYxsyJ5ApvO4RXvkZfdlyL3bu9qBSBT+qV/eHHHoghyKsXuPB1u0eyPZeDer7UaO6/UVOgXmzslY30edHk6Z5pxLIYRAzZvpw6Ej2rwZFigB33G5/Xq1MO5E6bfvVa02JqHZOFi5vYv95Vfq47B0sG/anpAIy91bu01BjAZjKvdwUY/WJLzzWdmrSJ2TeopPpEW6qm5uehSO85luDwmuOsawZ2LLFi337gSqVs6z3CSdTAXWe9OQTi+mkMec/AlET3m79Hrd2OZg1d8kxG+Ot50e4urEovO4QXllwMX5iQsi2Y7kPachvZb/djemnTfdh+295p7+I+I4adnTx29gnfTh0OG+MjCKOGGpvgdy4CUcXgAVeCCYWkshc44WLQ7e86doly3pzj9dLJHf6zLy7Pcg2dXJACOcvRrbVKbyR5RmuNAqvGcbhdjaKxqEcFF5n7Rs14XX22O7JpvC6Q3jDiYv0ktxymN+2O3a3LRo+KiHsPpc3XO9HrXOzF5UNezh0qzD5dzvbkuUn6CZWnk+b4cP2XKuzA6+wVi3ie5Q3eKeHQL2DT7NzzztO7D4Jhdd821F4zTAOt92i3Kmwt2yj8DprXwqvM35ctAZ3C2/D+llodVX2CGV+cmN3xDQ/4c19nG+4435jUXh1tlJz+JIq1HQKb+HhpvCaZ03hNcM43BHvcifdo8x1n5LCq0suOy9qwivbkalcd958jUpY1GI4wusO4c1vNLRd20zUrZN9Kk5+G9Pb/ZYevO+tVbgfeHj40ekKjzyWgLQwp0/aGeGVYgtrSsO8+V6s33j8TWmQ+buvvxlab7vTXKL2BhRDN6bwmm8sCq8ZxuF+HTRxoE5BT0/hLYjQsf8eNeHtMXh8nif77IuvUK92NSQVLZrn36c81tdZDQ1nU3jdIbzSzCIvc9/1Wouu5Ao3citvXLIILLAwS2LsnswlC9KmTc8ltH6g+RUZefa/lZjnnj869UEOg2jWLG+MStcMPshBDghpdVWmkb1tg6c1xPvoboC/jPKuXn20TwjfwJcklTZijBoBCq8aJydRFF4n9I6dO+ddHzZuyl6XIQcAyYE6hb24lcLrrH2jJrzBj12vZXfMmTbKOtY3li4Kr3uEN5b6jd1n5bZkdonZi+e2ZPZ46URTeHWo2cuh8NrjZTea25LZJeaueAqvw/ag8FJ4HXYhpXQKrxIm7SAKrzY65UQKrzIq7UAKrzY6pUQKrxKm/7d3JnA2Vv8f/9xlFgwzpIw2tKmkaBMSRUQiSqJSKJpsUZbIHqXsZS0RFaWNFiGlX6t/Kq2ofkXLz5IYJWbMzL3/13nGzNx15jzPec5d5n6e1+v3ev2a+/2e55z3Ofd633PPc07MBlF4FbuGwhufwivW/EZi2ymxXZo4vln1XhTe8G9UsexDdT9MIbw5h1ORXCFH6RPBrv5WqoRPsh1s7KpLogpvJMcEhdeu0Rq6HAqvXr66S6fwKhKm8MaX8K5Z68Qnm0oeUvLdxUFxKPilC6Fe/qILu3cX/lns2djtpgLLYkbhDe4dsTfmmnWu4r2Xz6nrQbeu5vcMDhwTZrepEzUT/f3aKqdxoIm4xF6+or8jvcaviFLgQza6xrmZ90yiCa/dnwEyrCm8MpSsx1B4rbOLhcyoCW/u0Ty/9jft0A8vzB+Lk088we/vKclJscApbB0ovPEjvHbt0iAzIEPt26iyUTmF1596uF05zMpqqA3lxZ3M7tzh+0BLUU1V+ltmjIWLCTfOfXcsUSnfam6iCa/dnwEy3Cm8MpSsx1B4rbOLhcyoCW+9FndItf+7jUuk4qIVROGNH+G1ax9embEWajsxkWd2W7Kie1F4/amHO2hE7GLRuWPh4R8yV7gxYVYOw+3T+cCwfC07apTWtnASb3a/aRl+ZmISTXgnT3EX7xjjy8nqZ4AMawqvDCXrMRRe6+xiITNqwvvH7n1S7T8ps7pUXLSCKLzxI7x2nbQmM9bmLHBjz57gSKv/2FF45YTX7E/34YTX7Iby4YTXan/LjLFwMeH2FqbwqlA1n2v3Z4BMDSi8MpSsx1B4rbOLhcyoCW8sNN6OOlB440d4Q/0MLjYPz+pboPxQWeBYCiVSZ9f1oLuFNaaibAqvP2HxINC8BS5kHyxcM1t0mRXVUD//izExeJC5mdlQ/a3jCGiZzyzBZsas4NlFs8s0ZO5lJibRZnhDjQmzv0CY4StiKbxmiZmLp/Ca4xVr0RRexR6h8MaP8IquFj+Ff7LJYeyckJrqReNGXm0PFol/8HbsKBSymple4yCMCqnWBhyFN5ib+AKzZm1hX4pL9OU5Z5t/aE2Miffed0L8Y+Z0F+DK5h5LDxeKXxC2bT+2MX0GtB0QIjOChMiLNhWxEW2K1gN0RfVNNOEV7bbzM0Cm3ym8MpSsx1B4rbOLhUwKr2IvUHjjS3gVuztq6RRevei5D69evqL0RBRe/VT970Dh1UucwquXr+7SKbyKhCm8FF7FISSVTuGVwmQ5iMJrGZ10IoVXGpXlQAqvZXRSiRReKUwxG0ThVewaCm9iCq9YJ7l7twMHDwI1angt/QQuO/TEz9OHDiahYqUCVKtufVmEzP0++dSJvX8CdU7z4Px6MhnRjRFs9uxxID0dyMz0Wl4yUh6Ft6gvjz8eaHKZ+aUedvcshdduosHlUXj1Mqbw6uWru3QKryJhCm/iCa9YOzpvYcmBB2II6XoCPnBnCXGgQc8e+bYL9oFsYOYTbnh9vKhymhdDh8hv8aX4VjKdHrj3rdj3NqtPgSXpLW/C++h0Fw4dKnmgz+0GBtyTj6oZpjHblkDhtQ1l2IIovHoZU3j18tVdOoVXkTCFN/GEN9QhA2IY6diCaswEd9AIVdntIdxwf/4FV/EDV74xN96QH5MzveEOV7D6xaM8Ca+Y2V2zruQ0waL+PLuuF927Ru8LDIVX8R8biXQKrwQkhRAKrwK8GEil8Cp2AoU38YQ33J6rZrfEKmvohTtcQcd2V4uecWHnsWNxfevVrIkXV7eKniSFYxSOjdUvA+VJeMN9ealVy4vet0evLym8Zb3j1V+n8KozLK0ECq9evrpLp/AqEqbwJp7whjoyVAwjHadqRWqGN5zwtm3tQeMYWP8Z+DYNJ7yc4QXCzfBSeBU/7OMgncKrt5MovHr56i6dwqtImMKbeMIbSrasziyWNfxCLZ+weyZZ1GHHr048vcT/Z3CXCxg7Kr+sKkbt9VAz7YMHWjtEpDzN8IoOGT/JjYKAydzBA7mGN2qDNUI3pvDqBU3h1ctXd+kUXkXCFN7EE14xZMQa0q3bCwWxaroXDRt4FUdS+PQvtziQc8SNvHwPzjyzwPYH1oruLKR3w3uFDzqJAzLathHyqK1ZthQsHurLyS0squEFXssn5pU34RUPIa5Z64LYTURcnTtGvy+5pMGWIV9qIRRevYwpvHr56i6dwqtImMKbmMKrOGxMp3MfXtPITCWUN+E11fgIBVN49YOm8OplTOHVy1d36RReRcIUXgqv4hCSSqfwSmGyHEThtYxOOpHCK43KciCF1zI6qUQKrxSmmA2i8Cp2DYWXwqs4hKTSKbzBmMTP9Vu+ciI7GxD7E4t11DUzpXAGBZU34Q1k00BhuYc1osFZFF67SIYvh8KrlzGFVy9f3aVTeBUJU3gpvIpDSCqdwhuMKdRDa1l9rB3KUZ6EV8iuOBglO7vk4AnxhUAcylE1Q99a87IGMoW3LELqr1N41RmWVgKFVy9f3aVTeBUJU3gpvIpDSCqdwuuPiduShR82drORGqASQRReCUiKIRReRYBlpFN49fLVXTqFV5EwhZfCqziEpNIpvHLCa3V7uPI0wxt4HHUROat7FEsNUIkgCq8EJMUQCq8iQAqvXoBRLp3Cq9gBFF4Kr+IQkkqn8Ppj4tHC4YdNODbXtPagSRQPEaHwSr3VlYIovEr4ykzmDG+ZiGI6gMKr2D0UXgqv4hCSSqfwBmN6930nNr5fclhGRroXWX0LjD2EzV7laYZXtD3wNMAaNYB+faN7iAiF1+yoNB9P4TXPzEwGhdcMrdiLpfAq9gmFl8KrOISk0im8oTGJ2cycnMKHs+rUtv5AVnkTXsHDLjZSA1QiiMIrAUkxhMKrCLCMdAqvXr66S6fwHiP81oZNWPDsamQfPIRqGZUxcuCtuKTB2carBw7+gwcmL8TXW39GtYwqmDC0Fy6sf6bxGoWXwqv7TSrKp/DqpVwehVcvMfOlU3jNMzObQeE1S8xcPIXXHK9Yi6bwHuuR+UtXo0PrJjgxszo+27IN942fi/dfmQWHw4HhkxbgpMzq6N+zM77d9jOGjJ+LN5Y+jNSUZAovKLyReFNTePVSpvDq5StKp/DqZ0zh1cuYwquXr+7SKbxhCDe6NgvrVkxF5UoV0aRDP7z30kxUSE02ogc8OBs3tLsCLZo0oPAmqPA+Os2NQ/8WDh4HvGjbFrjskgJt71cKrza0WPiUG7//r6Qvzz4L6Hazvr7U15LYLjkSwvvJp058+LEDeXmFy1wuuciLq1v596XYts137bfYvUJlOUwsUafw6u0NCq9evrpLp/CGIPzfnf9D/5Ezsea5R7HnzwO4dcAkrF8xtThy+oIXkZGehl43t6PwJqDwznrchb8OlGzqLwaGWD06cYy+h4IovHo+CpevcGHrD8F92fuOfNQ+Vc89E7VU3cJ7IBuYMdsdhLdtaw8aH9ud4kC2AzNmu/xiYuFQDrvGBIXXLpKhy6Hw6uWru3QKbwDh/IIC3HX/Y7i1c2u0bHYhdv6+x5Df15c+XBw5d8lr8Hi86N+rE7IP5enuo5gvP8ntRHKSA/8eSYxZsftHGtO6Qdf11wGXX6anuypVcOFongd5+dYfzNJTs/guddREIDcnuA21TgYGZMV322Kt9qnJTsDhQE6uns+JDz4EVq0JbvVpdYB77iz8+9oNwPp3g2O6dhazwbFGzHx9qlRKwqEjefB4zOcyo2wCbpcDFVJc+OewvsmN0mqRkZZUdiUZEZYAhdcHjZDYEZMW4NSTahgyK669+7LR9e5xxpKGomvKnOWoXi0dvbu1w+Hc6Az8WBrTLqcDbqcTufl6/iGLpbaKugwc6g0pvM0uB7p0CGHCNjQgxe1CfoEHBV4Krw04i4sYNsaLnBDCe/xxwOjhevrSzvrHU1lul9P4nphXoMfGVr8FvLMx+P1x+mnAoLsL+/Kt9V68vT6YWqfrHLiyWTzRDF3XCsku5Bz1wGv85sTLbgJOhwNigic3Lzr/1lVMCf4Fw+42lufyKLzHetfr9WLs1MWoWCEVI/p3L+5z8femHfrj7eWPoUpaRePvdw+fji7tWxgzwNylIfEeWps0xYXc3AAZ8gITxur78sMlDXo+hkMtTxF3atfWq3VNtp7WxHapupc07PjViaeXlOzLXETjwgYeXN+hULLDHbuc1ScfNTNjm59M7bikQYaS9RguabDOLhYyKbzHemHy7OeMlZhiO7LAa8xjT+O4qukY0Ktwl4b+o2YZ63srVUyl8CbgGt4dvwJPP+MCvMek1wucU9er9UEnCq++j8uJk13Iyy/py7Q0YNh9+r686GtJbJesW3hF659/wYVt20u+jNbM9CKrj/9sXOCBJdE+gc7OXqPw2kkzuCwKr16+ukun8ALY8dtuXHvbCDjF3kQ+1713dTGWLfx96DBGTn4SX3z7A6qkVcLowT3Q9JLzjEjO8CbeDK/uN2Wo8im8eqlzWzK9fEXpkRDeolaIB9iqZuhvU6zdgcKrt0covHr56i6dwqtImMJL4VUcQlLpFF4pTJaDKLyW0UknRlJ4pStVzgIpvHo7lMKrl6/u0im8ioQpvBRexSEklU7hlcJkOYjCaxmddCKFVxqV5UAKr2V0UokUXilMMRtE4VXsGgovhVdxCEmlx4Lwip+Jn3rahX8OlSz9adYkeGN/qQaVESQOEHj7HSe8xx7oT00Bsvrma/mZespUF/49XNgm8Wx7jeOB/lnxvYY3cC1rZg0v7unrv5Y10u2OhPB+ucWBNetcxTtvNG7kQds2enaFsGOc210Ghdduov7lUXj18tVdOoVXkTCFl8KrOISk0mNBeAMlqqjiEzQcuDFuohuegJ2VQj2AJAWvlKBFi13Y+Vvw9mNdbvSi/rnR2XpItU3hdivw/XKy4gU3vt8efKeWLfPRvKlqDULn6xbeUIdKiJp06lCAhg0SY5suCq+esVtUKoVXL1/dpVN4FQlTeCm8ikNIKj0WhHfSFDdyc4Or63uSlVRjyggKJ2wpKcCo4fbOvE6b5cLBg8HCW+sUL3r3jE/hfW21E19sCd6eq1YtL3rfXtimaLRbt/Bu3ebE8heD2y2ODr6qeWLM8lJ47fgECl8GhVcvX92lU3gVCVN4KbyKQ0gqPRaE99HpLhzyWc5QVPEbb8jH+fWkmiEVFO6I2EgK77l1gZu72ivXUo23IWj9Oy588HEIiZcQXp3t1i284fbYpfDaMKhYhEGAwhvfA4HCq9h/FF4Kr+IQkkqPBeENNXMoHvYaN9p+MRw/yY2CgAlW3xlKKWgSQW++5cKmzcGHiHTpUv6WNPjOxL//EbBhQ8CpTV5AZ7t1C++RHGDGLDdyAn6F6NmjAHVqc0mDxNuBIWUQoPDG9xCh8Cr2H4WXwqs4hKTSY0F4RUXFOl4xkyaupCQvbrrRi9qn2v9zsVjW8NoqJw7+DbjdQGZmyc/xUsBMBIn1rFt/ALxeB1xuDy5uAFzbLj6XMxQ1Wzz0J2Z5jxxxGPwuvSj44cKSdgMut1d7u3ULr2i7GJufbHLgQLYTFVK9aNzIi3POtn98mhheEQ3lkga9uCm8evnqLp3Cq0iYwkvhVRxCUumxIrxSlY3DIG5Lpr/TIiG8+lsR23eg8OrtHwqvXr66S6fwKhKm8FJ4FYeQVDqFVwqT5SAKr2V00okUXmlUlgMpvJbRSSVSeKUwxWwQhVexayi8FF7FISSVnojCK7aZys4uxCOWNFRIlUJlKWjnDgcyqqTg8NEc1My0VATEGtLduwuXe2RkiKNtra0bjWS7rbXUWlYsCa9Y8iF6J62yx/IDlzL9vWsPsPnzwp0jatd2aN/qjsJrbWzKZlF4ZUnFZhyFV7FfKLwUXsUhJJWeaMIrDhB4dbWrmE1Ghhc9e3gsS2RpkJ9+xoUdO0seXGtwgRedO5pbw7trN7B4qbv4wANxPyv7vwa2OzUV6Nkj37KESw2uCAXFivAGbq9XOc2LoUPM9bf4UrJ4qRPZ2SXj5prWHjS5rGS9sHgg8tPNDhRFCMGup3n3Dwqv3sFM4dXLV3fpFF5FwhReCq/iEJJKTzThnTwl+Gn7s+t60L2rvQ8gfSxOdFsXvHdrVh9zkvn8C05s2+5fjpDVkcPM7WARqXZLDTqbg2JBeMPtUWx2L2mZ/h4/2YWCfP8dQIT0TtRwUEtRV1F4bR60AcVRePXy1V06hVeRMIWXwqs4hKTSE0l4xWzpvIUBW2aJn4RredHr2MEJUtAkgt5934mN7wcLb+BsXVlFBc4SF8U/MCxfeimG+In84Ucj0+6y2qPj9VgQ3kXPuLDTZza/qJ1mt7wL19++X5TGjHejeHrXB6iOkwkpvDpGbHCZFN7IcNZ1FwqvIlkKL4VXcQhJpSeS8AogYyZERvzCCa/ZvVvDCZBZuYlUu6UGnc1BsSC84Y7H9j12WabZMv0dqi9F2WbHhEx9KLxmKFmPpfBaZxcLmRRexV6g8FJ4FYeQVHqiCW+on4y73eSxfU9VsRZz3gKX32EFGeleZPUtkJ6ZFR0YuPZW/M3KWuBXVrmw5Sv/n8GtrAWWGlQRDooF4Q13it/ggfmomiEPJNQxxoFLbp6Y58beP/3LrFTRi+H3m1svLF8rgEsazNAyH0vhNc8sljIovIq9QeGl8CoOIan0RBNeAUWsr929xwHxwFqdWl5tp2UJ6RWSmXPEjZQK+WjcyGNKdos6UBx68MtOB3JzgMwaXjRsYG2XBiHPot0pqdDabqmBZ2NQLAivaI441GTDew7887cDlasUPqBoRnYD+1s8uCb62/eBtaIYcbjHzt8Kx0H144DePfXJrrgHhdfGARuiKAqvXr66S6fwKhKm8FJ4FYeQVHoiCq8UGJuCuA+vTSBLKSZWhFd/S6N3BwqvXvYUXr18dZdO4VUkTOGl8CoOIal0Cq8UJstBFF7L6KQTKbzSqCwHUngto5NKpPBKYYrZIAqvYtdQeCm8ikNIKj0SwvvN9y6sf6dw/WhKMtC5Uz5q1vCvnvg5+N2NhTHHVfWi43X2bhMmBcNEkNj4/5VX3cg9WpjUrWtwm0TM8hfccDkdcLu9Idstc0txmMHWH2KDjVir+vY6l3EYhrjEOuDAn+2/+R546ZXChwNdTqDT9fmof65MS83HCDY//CR2w3DgrDMK0Nhnv1ozpYnlHtkHCxmLNdJWDvco6u/SxrmZOpUW6zsmTjkRuLqVniUNop+3fOWEy+HG4ZwCXHC+nj2r7eIiyhHroHfvKSxR7MBSp7a1JUB21qmssii8ZRGK7dcpvIr9Q+Gl8CoOIal03cIrZPfFl0o2yS+qlO8T5V9/B7z0sv/uCVY27ZdqsE1BgU/Ki39S7+lbIr1CfuYucAftHmX2Sfr177jwwcf+D5vVzPQiq48ewSkLz/hJbhQE3Nr3wSwhuy++5N9uXXvEhtoZwew2YKK9oR5kNLtfcqj+DhwTZbGVfT3UmLDSbpn7zVngxp5j8ijixR7QYuxZ+UIgcz/VmFC7o+h4KFW1noH5FF67iUa2PAqvIm8KL4VXcQhJpesW3mmzXDh4bObMt0KNLvbi2naF5jTzcTf2Hwiubq87PKh9auzN9IqTrjZt9pdQUfsTjgf6ZxUeCCHTbpkOCjy9K9QXBply7IgRs4prQhym4StbYye64PUGs6lR3Yt+99gr6aHkW7TTzJcK8WDhjNklJ+8VcTK7E0aonRNEWedqOAEtUmNCPCy5eGkwmxbNPbiqeey9LwXveN1+j8JrxydU9Mqg8Cqyp/BSeBWHkFR6tITXVwTC/QNu9pQqqQbbEBROZtPTvbhvUKHUhYupdYrX1BP14diY3e7KhmYj3F6zMsLrcgFjR5k7Ha6sOtuxH204qTN7GInMmCirPbKv29FumXuFYyN2G2nbJn6ENzUFGDnc3rEnw89MDIXXDK3Yi6XwKvYJhZfCqziEpNJ1C2+4ma+WLfPRvGlhFectdGHX7uBZwWhInQw0mRnecO32ndmWudej0104dCiYjZlZTJn7yMSEm+E9u64X3bsWin64WVcdM7yhvgyIhwTHjZaXm3Cn0JmVukWLXdj5W3A/me1vmX6I1AxvuJMJzZ4WKNMmu2JCHaFt9suLXXUxUw6F1wyt2Iul8Cr2CYWXwqs4hKTSdQuvWNs4b77/UagutxdjR5b8vC0eWHt6if8xvNFcpyoDbvxkFwry/QUnK2AN77wF/uuSA9stc59QkukrmDJl2BkTKOBi5nZgv5LDFYw1vCvdcPig8cKLiWPsXc4g2hRqLavZk81EOWvWOvHJppLxJ2YExQEhZtepBs68CgbjTci3bD+FGhMXNvDg+g72z7oGrm+2cniKbLvsiAs8qEX0ZbeuBTH/4BqF147ej14ZFF5F9hReCq/iEJJK1y28RZUQs6Li8IT655fM7PpWUDz9v22bE3v2Aqed7sH59aSqH9Ug0SbxNHhmDRSvRw6skIj57XcnTjnZEzamrEYINps3u/DvYS8aNEDU1zUL4RJ9KQ7tCLcrwpy5Lhz4G6haBbav3fXlJb4s7fjZCYfDgVp1CiyzEbOZu3eLw0iAzEyvpQNCRL1kxkRZ/S3zumj3j8d27jjzLK/ldsvcS7DZtzcZ6en5OLWW/VItUwczMWJdtuhLcYm+NPvFxcy97Iql8NpFMjrlUHgVuVN4KbyKQ0gqPVLCK1WZchjEfXj1dyr34dXPmPvw6mVM4dXLV3fpFF5FwhReCq/iEJJKp/BKYbIcROG1jE46kcIrjcpyIIXXMjqpRAqvFKaYDaLwKnYNhZfCqziEpNIpvFKYLAeVN+EVeyavXetCXl7hT8ZXNvcELWsQSx4+/NhhxCQledGmTYHWJSqqwiuWB7y6yokjhwu7ud65HksHn4hdDT7d5EBOjgOpqV6I7btqZloeOjGVSOHV2x0UXr0AnbQAACAASURBVL18dZdO4VUkTOGl8CoOIal0Cq8UJstB5Ul4xVriGbP9H8QTYG68Ib9YaEMdIiJidO64oSq8oXaWMPvwW6j9fGP9kAYzg5rCa4aW+VgKr3lmsZRB4VXsDQovhVdxCEmlU3ilMFkOKk/CG2pXBAHGdx/eRc+4sHNn8PZcZgXSDHAV4Q0n6GlpXgwbIr+zRKgTvkQbxNHLDRvE/tG2ZfGm8JZFSO11Cq8av2hnU3gVe4DCS+FVHEJS6RReKUyWg8qT8L622okvtvhvHycrvLq2zRL3VxHecHsLp6QAo0wcVhBOeGN5z1ozg5rCa4aW+VgKr3lmsZRB4VXsDQovhVdxCEmlU3ilMFkOKk/CG2421Fdmw0mx77IHyzDDJKoIrygy1MllZvc6DncqWVaf/HKxjpfCa/eo9S+PwquXr+7SKbyKhCm8FF7FISSVTuGVwmQ5qDwJr4AQeLxwqANCAk/O813yYBlkKYmqwhso6WJ2956+JYdpyNY58AALs6e1yd4nGnEUXr3UKbx6+eouncKrSJjCS+FVHEJS6RReKUyWg8qb8BaBELO9ZR0OIpYLhDuYwjLQEImqwuvbplNOAqpmqNVOzPbWqR3/63Z9KVB41cZEWdkU3rIIxfbrFF6f/tn5+x70GzkTN13XAj26tCl+Zf7S1Zi/bDXcrsJ1cWeddgqenzva+P8UXgpvJN7iFF69lMur8OqlZq50u4TX3F0TK5rCq7e/Kbx6+eouncJ7jPCmL7di0sxlOKPOyWhQ73Q/4Z08+1lcdP5ZaNPi0qD+oPBSeHW/SUX5FF69lCm8evmK0im8+hlTePUypvDq5au7dArvMcI//fIH0tIqYOXrG5FeuZKf8N43fi66d2plSG/gReGl8Op+k1J49RIWT+5/usmJnBxA7MnatnX8b1H15RYH1qxzFbdJrFMVh0/4XpFut6rwiqUXb7/jhPdYMzJreHFPX/8tybZuc2LNOgeys8WhEkA0230kB1jxggu/HNv+rU4tL65pU2D64TixLOXlV93F7a5aFbjjttBrlym8ej8rKLx6+eouncIbQHjWUy+janqan/BmjZiBHb/tQl5+ATKPr4YBvTujUcNzjEwKL4VX95uUwquPsBCk5S8Gb+E1eGABqmbE5/rOUIcrCILdbvLgnLMLbTHcbgU6260qvGXt0hBr7X5llQtbvvLf61hIb8/b5fcNFn1VVrt93x0UXn2fFaJkCq9evrpLp/BKCG9O7lG4XC5jDe9nW7ZjyLg5WPnkeNQ8oRoOHcnX3UcxX77b5YD4X85R/xmkmK94HFUwNdmJ/Hwv8j3xKWGxinrNO16sfSe4dr1vc6B+vVitden12rTZi+UvBce0aQW0bVUoYOHa3e1GoNHFwQdS2EEiye2Aw+HA0TzznxPvfwC8+mbw2E9NAR4ZX1jfb74DFi0LjpFp9/XtHWhxuR2tLCljwiNe7M8OLnPmI/J8P//Ki2XLg8vwbbfvqxVTXcjJLQA/Juzty6LSXE4HkpOcOJJr7kuLXbURXxp5WSdA4ZUQ3kC8QyfOQ/PLGqD91Y3x9+E86/TLSab4IiC++R7Opfzr6tKKKW4czfcgv8C8LOiqU3ko9+13gHUbglvS81bErfAK8Vv8bHCbWrcErmlV+PdotDslyWXcOzfPvCx88TXwbAjxq1wZGD+ysE0//ReY+5S1dt98I3DpRfaO6IemIKTwTn9Y/j7h2i2Ed/K44HIqV0jCvzn58Hj5xViesnyky+mEmHwQjKNxVamYFI3blpt7UngtCK+Y4W3X8jK0anYRlzSASxoi8WnAh9b0UA71076Qiay+8b2kYd4CMdPnz6xnj4LibbjicUnDuInuoJlL38M0xJKGstq9azcwb2HwLJmOpRyhljTUruVFL5NLGspqt28vc0mDns+JolK5pEEvX92lU3glhHfjx1tweaP6cLtc+GzLNgyftACvLnoI6VUqUXgpvLrfo0b5FF59mD/+1Ilt2x3YsdMBISSNG3mL17rqu6veksXa5E82ld4mmRg7a6m6hlc8vPX2WhcOHXLA5QJOPtmL3gHyKET+vfedRl/WqAE0vMCDJpf5/yoSqXaLh9bWrHVhx45CirVrA23bFKBCqjmqMu0uKpHCa46t2WgKr1lisRVP4ZUQ3oGjZ+PLb35EUpIbJ2VWx7B7uqH+OacZmXxojTO8kXhLU3j1Uua2ZHr5itJVhVd/DeP/DhRevX1I4dXLV3fpFF5FwhReCq/iEJJKp/BKYbIcROG1jE46kcIrjcpyIIXXMjqpRAqvFKaYDaLwKnYNhZfCqziEpNIpvFKYLAdReC2jk06MlPCKtbzZ2WJPZa/pPW+lGxOjgWUJbxGbjAxxNDMfbDPbjRRes8RiK57Cq9gfFF4Kr+IQkkqn8EphshxE4bWMTjoxEsK7Zq1Yu1yyr7KVfW+lGxSDgaUJb+BDdA0u8KJzR/M7ZsRgsyNWJQpvxFBruRGFVxErhZfCqziEpNIpvFKYLAdReC2jk07ULbzhdmC4pnXwg2vSlY6zwHDCG+6AFd/DSOKsqVGpLoU3KthtuymFVxElhZfCqziEpNIpvFKYLAdReC2jk07ULbzhpK5Fcw+uCjhWWbrScRYYTnjFMdIb3w8+UTCR2NjRlRReOyhGrwwKryJ7Ci+FV3EISaVTeKUwWQ6i8FpGJ52oW3jD7S2cSFIXTni/3OLAq6sLD/7wvTp1KEDDBlzLKzuIKbyypGIzjsKr2C8UXgqv4hCSSqfwSmGyHEThtYxOOlG38Ip9b8XBE9kH/Y/u1XGohHSjIxwYTngFmxmz3H6HkYgDVgYPyje9L3CEmxRTt6PwxlR3mK4Mhdc0Mv8ECi+FV3EISaVTeKUwWQ6i8FpGJ52oW3hFRcQuBF9+5cCOHQ7UzPSiwQWehNqpobSH1gSbTzc5sGt3IZvLGnm5U4P06C0MpPCaBBZj4RRexQ6h8FJ4FYeQVDqFVwqT5SAKr2V00omREF7pypTTwLK2JSunzY5Ysyi8EUOt5UYUXkWsFF4Kr+IQkkqn8EphshxE4bWMTjqRwiuNynIghdcyOqlECq8UppgNovAqdg2Fl8KrOISk0im8UpgsB1F4LaOTTqTwSqOyHEjhtYxOKpHCK4UpZoMovIpdQ+Gl8CoOIal0Cq8UJstBFF7L6KQTKbzSqCwHUngto5NKpPBKYYrZIAqvYtdQeCm8ikNIKp3CK4XJchCF1zI66UQKrzQqy4EUXsvopBIpvFKYYjaIwqvYNRReCq/iEJJKp/BKYbIcROG1jE46kcIrjcpyIIXXMjqpRAqvFKaYDaLwKnYNhZfCqziEpNIpvFKYLAdReC2jk06k8EqjshxI4bWMTiqRwiuFKWaDKLyKXUPhpfAqDiGpdAqvFCbLQRRey+ikEym80qgsB1J4LaOTSqTwSmGK2SAKr2LXUHgpvIpDSCqdwiuFyXIQhdcyOulECq80KsuBFF7L6KQSKbxSmGI2iMKr2DUUXgqv4hCSSqfwSmGyHEThtYxOOpHCK43KciCF1zI6qUQKrxSmmA2i8Cp2DYWXwqs4hKTSKbxSmCwHUXgto5NOpPBKo7IcSOG1jE4qkcIrhSlmgyi8il1D4aXwKg4hqXQKrxQmy0GJKLwrXgG++9YNhxdwOLxo3gK46ooCywzLSqTwlkVI/XUKrzrD0kqg8Orlq7t0Cq8iYQovhVdxCEmlU3ilMFkOSjThffc/Lmzc6PDj5QVwa/d81D3DMsZSEym8erj6lkrh1cuYwquXr+7SKbyKhCm8FF7FISSVTuGVwmQ5KNGEd/wkFwoK/IVXwDuuKjBoQL5ljqUlUni1YPUrlMKrlzGFVy9f3aVTeBUJU3gpvIpDSCqdwiuFyXIQhbcQXXIy8OAICq/lgRTlRAqv3g6g8Orlq7t0Cq8iYQovhVdxCEmlU3ilMFkOSjThnTLVhX8PB8/wnntePm7ubBljqYmc4dXD1bdUCq9exhRevXx1l07hVSRM4aXwKg4hqXQKrxQmy0GJJrwC1OgJLjhQIr1OhxfjRvOhNcuDKAYSKbx6O4HCq5ev7tIpvIqEKbwUXsUhJJVO4ZXCZDkoEYVXwBIPr/3yE3D5FQXaHlYr6hTO8FoentKJFF5pVJYCKbyWsMVMEoVXsSsovBRexSEklU7hlcJkOShRhdcyMAuJFF4L0EymUHhNAjMZTuE1CSzGwim8ih1C4aXwKg4hqXQKrxQmy0EUXsvopBMpvNKoLAdSeC2jk0qk8EphitkgCq9i11B4KbyKQ0gqncIrhclyEIXXMjrpRAqvNCrLgRRey+ikEim8UphiNojCq9g1FF4Kr+IQkkqn8EphshxE4bWMTjqRwiuNynIghdcyOqlECq8UppgNovAqdg2Fl8KrOISk0im8UpgsB1F4LaOTTqTwSqOyHEjhtYxOKpHCK4UpZoMovIpdQ+Gl8CoOIal0Cq8UJstBFF7L6KQTKbzSqCwHUngto5NKpPBKYYrZIAqvYtdQeCm8ikNIKp3CK4XJchCF1zI66UQKrzQqy4EUXsvopBIpvFKYYjaIwqvYNRReCq/iEJJKp/BKYbIcROG1jE46kcIrjcpyIIXXMjqpRAqvFKaYDaLwKnYNhZfCqziEpNIpvFKYLAdReC2jk06k8EqjshxI4bWMTiqRwiuFKWaDKLw+XbPz9z3oN3ImbrquBXp0aVP8yoGD/+CByQvx9dafUS2jCiYM7YUL659pvE7hpfBG4t1N4dVLmcKrl68oncKrnzGFVy9jCq9evrpLp/AeI7zpy62YNHMZzqhzMhrUO91PeIdPWoCTMqujf8/O+Hbbzxgyfi7eWPowUlOSKbyg8Op+k4ryKbx6KVN49fKl8OrnK+5A4dXLmcKrl6/u0im8xwj/9MsfSEurgJWvb0R65UrFwuvxeNGkQz+899JMVEhNNqIHPDgbN7S7Ai2aNKDwUnh1v0eN8im8ejFTePXypfDq50vh1c+Ywqufsc47UHgD6M566mVUTU8rFt49fx7ArQMmYf2KqcWR0xe8iIz0NPS6uR2Fl8Kr8/1ZXDaFVy9mCq9evhRe/XwpvPoZU3j1M9Z5BwpvGcIr1vX2HzkTry99uDhy7pLXIGZ++/fqhNy8Ap39ExdlOx0OOJ0O5Bd44qK+8VhJt8tpjDmP1xuP1Y+LOie7XTiaz/ezrs5yiW8VcKDAw88JXYyFkOXle8BPCT2EHQ4H3E4H8qL0b11KkktPwxKkVApvGcK7d182ut49zljSUHRNmbMc1aulo3e3dth3MDdBhkr4ZiYnOZGa5MTfh/MTnoUuAFUqupFztABH8/lPmQ7GwsUyKqdg/998P+vgK8qskOKCEIbDOfyc0MW4auVkHPw3z/hyzMt+AkkuBypWcOPgoTz7C5cosXp6ikQUQ8IRoPCWIbxerxdNO/TH28sfQ5W0ikb03cOno0v7FmjZ7EIuaeCShoh8unBJg17MXNKgl68onbs06GfMh9b0MuaSBr18dZdO4S1DeMXLYx57GsdVTceAXoW7NPQfNQtrnnsUlSqm6u4flk8CJEACJEACJEACJKBIgMIrIbx/HzqMkZOfxBff/oAqaZUwenAPNL3kPEX0TCcBEiABEiABEiABEogEAQpvJCjzHiRAAiRAAiRAAiRAAlEjQOGNGnremARIgARIgARIgARIIBIEKLyRoFxO77Fv/0FMm/8iPvrsG7jdLjRrdD4evLcHktzcOsWuLv9487eYs/g1/PrHHqSmpuDmjlcZu4Pwsp+AGM8dbh9pjOF2LRvZf4MELfGLb37AHfc+guQkdzEBsetN5WMPAScoFtub/cnm7zBr0cv4c182TsysjmWPj7T9Hola4OCxT+CDTV/7Nf9IzlF8t3FJoiKJy3ZTeOOy22Kj0lu++wk7ftuN9lc3hqfAY5xAJ06f63Z9y9ioYDmoxevrPsY5Z9bCGXVOQvbBQ+h2zwQ8MqovLjj39HLQuthqwoBRs/Dv4Rzc2L4FhdfGrln/n8349PPvjWcfeOkh8PX3/8WDjz6NqWOycNZpJ+u5CUstJvDRZ99i2UtrMX/KfaQSRwQovHHUWbFe1aUr1+L3XfswcuAtsV7VuK2fmGlo3fwStL2KM5B2duLqdR/hi69/REpKEi449wwKr41wX1j1LvYf/AdZPTraWCqL8iUwcPRsY6tM8SsbL/0E+g6bhltvaI1mjerrvxnvYBsBCq9tKFnQkHFzcVXThsaMLy97CYiN5D/5/Ds8NHMpnp87GlXTK9t7gwQu7c+/so29tZfOHokZC1fiwvpnUXhtHA+LV6zBMyvXIiU5CRVSU3BThyvRvRN/BbIRMZp06IdBvW/AS2/+Bx6PB107XGlw5mU/gZ9/3QXxa9AbSx82DlLhFT8EKLzx01cxXVOx1nT2Uy/j2TkPwu3iGl47O+uhmcvw6poPjHXSDw66Dde1bmJn8QlfVr+RM9GjSxs0angOBGsKr71DoqDAg9yjeahYIcVYiy6WPvXv2QlXX3GxvTdK0NIE24va9MGd3a9F1u0dcfhIDnoMmIxJI+7E+Vz6ZPuoGD9tibHE7JbOV9teNgvUS4DCq5dvQpT+zbZfMHLyQiycOhQ1T6iWEG2ORiN//WMvRj3yJG64tjmuv+byaFSh3N1TfJH4/ocdGDXoNqNtFF79Xfzi6xuNA3wmDO2l/2YJcIe8vHxc0rYv/u+t+UhOTjJaPG/pKmPi4a5b2icAgcg1UTxH0eGOkTx4KnLIbb0ThddWnIlX2Pb//oahE+Zh5sQBOO3UmokHIMItXvnGRnyzlbJgF/a77p+Kr77/qfinydzco3C5XOh4zeUYw4es7MLsV87y1zbgl193c62/jXSv6jIYL8wfi+OPyzBKnfXUy8hIT8PtXdrYeBcWtfDZ17Fv/98cu3E6FCi8cdpxsVBtsZZpyNg5mDbuHpxe68RYqFK5q8Pmr7aj4XlnwuVyGrs0DB73BK5t2Rg3tm9e7toaCw3iDK/9vSB2aKh/zmnGUez/270PfYZNw9ght+OSBmfbf7MELXH2opexa89+PDS8Nw4c/Ae39p+E2Q8N5I4NNo6HvPwCtOl2P56ePhy1T8m0sWQWFSkCFN5IkS6H9xk6cR7e2rAJTmfJwn3xYMrmtxeWw9ZGp0nDJy0wtnQSwpuakoyObS5Hn1vb82EJTd1B4bUf7JPPvYHnXnnHKLhK5Uq4s3s7dGjd1P4bJXCJOblHMW7aEoi9eMWDgX1vuw6d2jZLYCL2N11sEfnmhk+4FZn9aCNWIoU3Yqh5IxIgARIgARIgARIggWgQoPBGgzrvSQIkQAIkQAIkQAIkEDECFN6IoeaNSIAESIAESIAESIAEokGAwhsN6rwnCZAACZAACZAACZBAxAhQeCOGmjciARIgARIgARIgARKIBgEKbzSo854kQAIkQAIkQAIkQAIRI0DhjRhq3ogESIAESIAESIAESCAaBCi80aDOe5IACZAACZAACZAACUSMAIU3Yqh5IxIgARIgARIgARIggWgQoPBGgzrvSQIkQAIkQAIkQAIkEDECFN6IoeaNSIAESIAESIAESIAEokGAwhsN6rwnCZAACZAACZAACZBAxAhQeCOGmjciARIgARIgARIgARKIBgEKbzSo854kQAIkQAIkQAIkQAIRI0DhjRhq3ogESIAESIAESIAESCAaBCi80aDOe5IACZAACZAACZAACUSMAIU3Yqh5IxIgARIgARIgARIggWgQoPBGgzrvSQIkQAIkQAIkQAIkEDECFN6IoeaNSIAE4pnAxo+34LF5K/DmskfiuRmsOwmQAAkkJAEKb0J2OxtNAkCrrvdh156//FBccdkFmPfIYFvw9B7yKG654Wpc1bShLeX5FvLH7n1offP9xX+qkJqM2qfURMc2TdG9Uyu4XE7jtX8P56B9jxFYuXA8qldLV6pHeRfeB6cswu03tcGZdU4OyenTz7/HzKdewop5YyxxfHXNB3hh9Xsh8wPLXrX2Ixz69whu6dzK0r2YRAIkQAKBBCi8HBMkkKAEhPDef3dXNLrwnGICSW430ipVsIWIEJyLzq+LU086wZbyQgnvR6ueQFKSG7v/3I+vvvsJsxe9gvPq1sHshwbC6XTA4/Fi4bOvo3e3dkacylWehTcvLx/XdB+G+Y8OCSu8gvH7H29B145XWcJYmvAGln3f+Lm4sP5ZFF5LpJlEAiQQigCFl+OCBBKUgBDesUPuQLNG9YMICLlbsWoDGtQ7E2K2LS8vD6ecdALG39+rWGAPH8nB5NnPYfNX27F33wHkHs0zyul5c1tDpLv0GYeeXduiXctGkClPSNdj817AG+s/BhxAi8YN8MCAW1A5rWJQ/YpmeL9Y9yRSkpOKXxcz1u17PICx992ODq2bQtTxkrZ34/1XZhkzvF988wMenbMcP/z8OypUSMFF55+FaWP7IcntMuorZhRXr/0Iu/b+hbz8Atxze0d0bneFUX6g8H63fQdmPvkSvvvhF0OsL7+0PiYO6w0x2yyuXXv3Y/LsZ/Hp59/B4XCgeeML8NjoLOO1L7750Xjtvzv/Z/C8964bcWWTwpnwx+auQKWKqdj5xx58+c2PBtcu7VvgisvOx+THn8NfB/5GanISRg26DZdddG5x28sqMyUlCYLblm9/QkFBAS5teA7GDLkdbrcLXfuOx7affjVYiy8KE4b2QqtmF/lxD2x/Ea/X3v7QKNcBoNv1LY3+D3WVJry+ZY+bugQiNinJheTkJHS6phmG3nMzShsfop9b3XQfVswfixGTFuD7H3caM8nVMqpgwvRn8NlX24w+qnv6KZg4rBfqnFozQd/1bDYJJC4BCm/i9j1bnuAEyhLefiNnGvIypM9NhgQ9/Phz2Pn7bsyfcp9Bbtr8F/Hb//Zi2th7cCQnF7f2n4TunVripg5XGq8HCm9Z5c1YuBI/7fgDYwbfjuRktyEqQsaKJNG3u8IJr4gZO3Ux9u0/iDmT7w0S3uadBxny1PqKi7H/4D/Y9uOvaNGkQXF9s/8+hGdmjsCJmdWx9ceduH3Qw1gycwTOPat2kPD++sde/PrHHlxY/0wcyTmKrBHT0fbKRsXCJySy1sk10L9XJ4iZ871/ZeOCc0/HgYP/4LoeIzFhWC80vqgetnz7IwaOfhzL547GGXVOMoT32ZfXY/HM4cYs50+//IEb7hxjlLVw6v3IPL4aVq/7CNMXrMR7L80wZFqmzGdWrjVmvsUSE1HfXkOmGFIrZr/F1eDqO7Fy4biwM7yhhHff/mwsnjECtU/JNKS3c+/ReGrqUNQ/57Sgd5es8IrEPkOnonnjBn4zvKWNj6IvNg3PO9NYllHvrNo4vnpVPDB5Iaqmp2Fwny7GF5jNW7bj8kb1/b4kJfjHAJtPAglDgMKbMF3NhpKAPwEhvNkH/4HL5Sp+4f6srsZsopCbe8c8jk/fnIfUlMIZy2+2/oysETPw4arHjf8WEnbvXV2KZ4jnLnkN/9vzFx4a3juk8JZWnpCRy67NwhvLHkHNE6oZ+UL0Ot85Gp+vfdKYgZUVXiF2r7z1H6xaPClIeJtdP8CY1bz6iouDhoMQ9CubNjRmdYuuiTOWwuPxYOx9dwQJb2ABC5a9bnwhmPzAXcasd/9Rs/DeSzOLZ3yL4kXc77v+NGYai67BY5/AGbVPQr+enQzhFbL99Izhxa936vUg2rS4FHf36GD8LSf3KC5q06d45lqmzK+3/oxlj48sLnPR8rfw3fZfMH1cP+NvVoRXzDoP6NW5uMy7h08zRFXM9AZeKsJb1vgQv0CImfzx9/fEje2bF996yLg5qF4tAyMH3sK3PwmQQIIToPAm+ABg8xOXgBDe/j074ZIGZxdDyKiSZvycLoR30uxnsX7F1OLXfvzld2PWdsv6p4y/XXvbCIwceCuaXnKe8d9zn1mFPX/uN6RDXIEzvKWVt/P3PWh36/Cgdbbip/d1K6YVS3BRZUqb4V3+2gasfH0jXlk0MUh4P9j0DUY+vBBnn1ELt95wtbFMQMyQFtX3thuvNpZCFF3Pv7oBGz74HIumDwsS3m+3/4LFK9Zgx2+74fV6jaUGF19Q15jxfvH1jXhtzQd4fu7ooAE2bOJ8vL3x/+B0Fj5YJy6vx4uO1zQ1lhII4RUzzZNG3Fn8+s1ZE3Bzx6tw/TWXF/+tXos7sOa5KTj1pBqwUuZzr7yD9z/ZgoWPFT78Z0V4xRIQ3zrdO+YJ1KtbG3fd0j6o3SrCW9b4SK9c0RDeNc896rdmXHyxGDBqFjxeL7pf3xIdr7m8+Atc4r7z2XISSEwCFN7E7He2mgSMXRpKW8MbuAVXoPCK2c8/92dj6ugsHD6Six6DHsaQPl38lgj4ruEtrbyff92F63o8gM/WLEDFCill9k5ZSxr+OXTYmLkMXMMrChazo2+s/wSLlr+JWidnGksfxK4OQtBv6tDCmOEuupauXIv3P/0Ki6b5C+/efdnG7g8P3nsbrm3Z2MgXwv/fHX8YwvvCqnexet3HeG7Og0FtEQ9k1axxnLHOOdQlhFfUW8wqF11CeG/p1ArXtW4SUnitlGmH8Bb1b1GldAlvWeMjVD8Xf5nwevHh/32Lp1e8iV179mPJrBHGshBeJEACiUWAwptY/c3WkkAxAVXh/fvQYWNZg9fjQXqVNOOnZN+fsgNneEsTXvFA0qXXZuGJSYOKZ4xL66pwwivW1Xbu/SAeHtnHWLZQmgiJdaxtut1vrGttUO8MQ3gvuaAuhvXrVnzr4Q8tQMWKqRg75Ha/Gd53P/zCeIDsnRemFceKWdYCj8cQ3k+/+B6DxzyBja/MClovKsT4w01fh5z9FYVZEV4rZQYKr1giIQT97DNODYk+1BpeXcJ79/Dpxji47cbWRl3KGh+l9bNvY+55YAbEOt9QM9D8aCABEijfBCi85bt/2ToSCEtACK94mKdRw5JtycRT8VXSKoZcrxo4wytmLC6TfwAABe1JREFUScXDU0IuXU4n0tIqwO2zHtiM8IpKih0P1m78zFgDfOZpJxvLI8RDZb6zmkWNKRJesS2ZeLBtf/Y/+GTzt5iz5DU0vrgepozqa4QGitDKNzYauyFUTa8MsSThrvsfw6olk40lE6K+4p6PjOpjCPB/Pv0aIyYvNB4mO+fMWn5MxI4GN989HsseH2XU9Z0PPsfkWc8a9xbCK3YEEA9wnXd2HWNdrliDLHZkEKxFXdveMszYL1gsUxAPBIr10bVPrYnTTq1pSXitlBkovB3uGIX2rS5D727XIr+gIEjU7RBe8TDenIfv9RuTx1VNx0f/943foR4PzVxmPAQ3c0J/g6XY+aK08RFOeNdu/D9j+cpJNavjz33ZEMLbu/u1aN+qMT8ZSIAEEowAhTfBOpzNJYEiAqUdPBFqz9lA4d305VZDGAsKPEaRQtzErOqUB+8u3uZLdkmDMYuXX4A5i1+F2ObqQPY/OK5aFXRue4Wxy0HgFXjwhNg7+KzTTkHnds2MNaVF63IDRUis5/z86x9wJPcoTjnxBGT16IC2VzUyihfCKw6uWP+fzcaevmIbMyGrndo2M14PZDJ/6Wose3kdcnOPokWThmjd/BIIwRLCKy6xg8WkWc/isy3bjPW6YkeEh0feZbwmBFfMeIsHyYQMi7pPGNoTp9cu3KXB7JIGK2UGCu9Hn31r7HCx/8DfxvrhIi5F7O0QXnG4ReD1woKx2PfXQT/h/d/ufRAPnG3/+Xd0ad/cWCte2vgIJ7ziYT6xvZ4YT9WqVkHHNpdjYO/OxeODnwYkQAKJQ4DCmzh9zZaSgG0ExJrKvsOm4fGHBhp7m4pLrGvtM2wq7ux2bchZWdturqkgIby9bm4bJHqabsdiSYAESIAEIkiAwhtB2LwVCZQXAuJhLjE7OXVM4UEKRdeg0Y+jycX1LJ/GFU0+vkswolkP3psESIAESMB+AhRe+5myRBIo9wTEz99jHn0az8x+ACfXPN5Y8ynW306atQwr5o3VcpywbqgUXt2EWT4JkAAJRI8AhTd67HlnEohrAuKAhxWvbTC2JBPbctU9/VTjYARxmlg8XhTeeOw11pkESIAE5AhQeOU4MYoESIAESIAESIAESCBOCVB447TjWG0SIAESIAESIAESIAE5AhReOU6MIgESIAESIAESIAESiFMCFN447ThWmwRIgARIgARIgARIQI4AhVeOE6NIgARIgARIgARIgATilACFN047jtUmARIgARIgARIgARKQI0DhlePEKBIgARIgARIgARIggTglQOGN045jtUmABEiABEiABEiABOQIUHjlODGKBEiABEiABEiABEggTglQeOO041htEiABEiABEiABEiABOQIUXjlOjCIBEiABEiABEiABEohTAhTeOO04VpsESIAESIAESIAESECOAIVXjhOjSIAESIAESIAESIAE4pQAhTdOO47VJgESIAESIAESIAESkCNA4ZXjxCgSIAESIAESIAESIIE4JUDhjdOOY7VJgARIgARIgARIgATkCFB45TgxigRIgARIgARIgARIIE4JUHjjtONYbRIgARIgARIgARIgATkCFF45TowiARIgARIgARIgARKIUwIU3jjtOFabBEiABEiABEiABEhAjgCFV44To0iABEiABEiABEiABOKUAIU3TjuO1SYBEiABEiABEiABEpAjQOGV48QoEiABEiABEiABEiCBOCVA4Y3TjmO1SYAESIAESIAESIAE5AhQeOU4MYoESIAESIAESIAESCBOCVB447TjWG0SIAESIAESIAESIAE5AhReOU6MIgESIAESIAESIAESiFMCFN447ThWmwRIgARIgARIgARIQI4AhVeOE6NIgARIgARIgARIgATilACFN047jtUmARIgARIgARIgARKQI0DhlePEKBIgARIgARIgARIggTglQOGN045jtUmABEiABEiABEiABOQIUHjlODGKBEiABEiABEiABEggTglQeOO041htEiABEiABEiABEiABOQL/D+ldy03n7zN9AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = {\n", - " 'data' : [{\n", - " 'type' : 'scatter',\n", - " 'mode' : 'markers',\n", - " 'x' : mpg.displ,\n", - " 'y' : mpg.hwy \n", - " }],\n", - " 'layout' : {\n", - " 'title' : 'Engine Displacement in Liters vs Highway MPG',\n", - " 'xaxis' : {\n", - " 'title' : 'Engine Displacement in Liters'\n", - " },\n", - " 'yaxis' : {\n", - " 'title' : 'Highway MPG'\n", - " }\n", - " }\n", - "}\n", - "py.image.ishow(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scatter Plot, Faceted with Color" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for c, df in mpg.groupby('class'):\n", - " plt.scatter(df['displ'], df['hwy'], label=c)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots()\n", - "for c, df in mpg.groupby('class'):\n", - " plt.scatter(df['displ'], df['hwy'], label=c)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Highway MPG')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "for c, df in mpg.groupby('class'):\n", - " ax.scatter(df['displ'], df['hwy'], label=c)\n", - "ax.legend()\n", - "ax.set_title('Engine Displacement in Liters vs Highway MPG')\n", - "ax.set_xlabel('Engine Displacement in Liters')\n", - "ax.set_ylabel('Highway MPG')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "(sns\n", - " .FacetGrid(mpg, hue='class', height=5)\n", - " .map(plt.scatter, 'displ', 'hwy')\n", - " .add_legend()\n", - " .set(\n", - " title='Engine Displacement in Liters vs Highway MPG',\n", - " xlabel='Engine Displacement in Liters',\n", - " ylabel='Highway MPG'\n", - "))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(ggplot(mpg) + \n", - " aes(x = 'displ', y = 'hwy', color = 'class') +\n", - " geom_point() + \n", - " ggtitle('Engine Displacement in Liters vs Highway MPG') +\n", - " xlab('Engine Displacement in Liters') +\n", - " ylab('Highway MPG'))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "traces = []\n", - "for cls in mpg['class'].unique():\n", - " traces.append({\n", - " 'type' : 'scatter',\n", - " 'mode' : 'markers',\n", - " 'x' : mpg.displ[mpg['class'] == cls],\n", - " 'y' : mpg.hwy[mpg['class'] == cls],\n", - " 'name' : cls\n", - " })\n", - " \n", - "fig = {\n", - " 'data' : traces,\n", - " 'layout' : {\n", - " 'title' : 'Engine Displacement in Liters vs Highway MPG',\n", - " 'xaxis' : {\n", - " 'title' : 'Engine Displacement in Liters',\n", - " },\n", - " 'yaxis' : {\n", - " 'title' : 'Highway MPG'\n", - " }\n", - " }\n", - "}\n", - "py.image.ishow(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - }, - "vscode": { - "interpreter": { - "hash": "92ddb4577615dc6bc87e2eacf3d4e43431cdfc0b825490da08155240a4e9716a" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/day_03/factor_analysis_demo.ipynb b/day_03/factor_analysis_demo.ipynb deleted file mode 100644 index 3076134..0000000 --- a/day_03/factor_analysis_demo.ipynb +++ /dev/null @@ -1,2540 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Factor Analysis and Principal Component Analysis on Financial and Economic Time Series" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# If you're running this on Colab, make sure to install the following packages using pip.\n", - "# On you're own computer, I recommend using conda or mamba.\n", - "\n", - "# !pip install pandas-datareader\n", - "# !pip install yfinance\n", - "\n", - "# !conda install pandas-datareader\n", - "# !conda install yfinance" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import yfinance as yf\n", - "import pandas_datareader as pdr\n", - "import sklearn.decomposition\n", - "import statsmodels.multivariate.pca" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Downloading macroeconomic and financial data from FRED" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "fred_series_long_names = {\n", - " 'BAMLH0A0HYM2': 'ICE BofA US High Yield Index Option-Adjusted Spread',\n", - " 'NASDAQCOM': 'NASDAQ Composite Index',\n", - " 'RIFSPPFAAD90NB': '90-Day AA Financial Commercial Paper Interest Rate',\n", - " 'TB3MS': '3-Month Treasury Bill Secondary Market Rate',\n", - " 'DGS10': 'Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity',\n", - " 'VIXCLS': 'CBOE Volatility Index: VIX',\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "fred_series_short_names = {\n", - " 'BAMLH0A0HYM2': 'High Yield Index OAS',\n", - " 'NASDAQCOM': 'NASDAQ',\n", - " 'RIFSPPFAAD90NB': '90-Day AA Fin CP',\n", - " 'TB3MS': '3-Month T-Bill',\n", - " 'DGS10': '10-Year Treasury',\n", - " 'VIXCLS': 'VIX',\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "start_date = pd.to_datetime('1980-01-01') \n", - "end_date = pd.to_datetime('today') " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "df = pdr.get_data_fred(fred_series_short_names.keys(), start=start_date, end=end_date)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, an aside about reading and writing data to disk." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "df.to_csv('fred_panel.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "dff = pd.read_csv('fred_panel.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 11609 entries, 0 to 11608\n", - "Data columns (total 7 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 DATE 11609 non-null object \n", - " 1 BAMLH0A0HYM2 6939 non-null float64\n", - " 2 NASDAQCOM 10989 non-null float64\n", - " 3 RIFSPPFAAD90NB 6344 non-null float64\n", - " 4 TB3MS 523 non-null float64\n", - " 5 DGS10 10896 non-null float64\n", - " 6 VIXCLS 8468 non-null float64\n", - "dtypes: float64(6), object(1)\n", - "memory usage: 635.0+ KB\n" - ] - } - ], - "source": [ - "dff.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "dff = pd.read_csv('fred_panel.csv', parse_dates=['DATE'])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 11609 entries, 0 to 11608\n", - "Data columns (total 7 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 DATE 11609 non-null datetime64[ns]\n", - " 1 BAMLH0A0HYM2 6939 non-null float64 \n", - " 2 NASDAQCOM 10989 non-null float64 \n", - " 3 RIFSPPFAAD90NB 6344 non-null float64 \n", - " 4 TB3MS 523 non-null float64 \n", - " 5 DGS10 10896 non-null float64 \n", - " 6 VIXCLS 8468 non-null float64 \n", - "dtypes: datetime64[ns](1), float64(6)\n", - "memory usage: 635.0 KB\n" - ] - } - ], - "source": [ - "dff.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "dff = dff.set_index('DATE')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df.to_parquet('fred_panel.parquet')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_parquet('fred_panel.parquet')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "DatetimeIndex: 11609 entries, 1980-01-01 to 2023-08-01\n", - "Data columns (total 6 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 BAMLH0A0HYM2 6939 non-null float64\n", - " 1 NASDAQCOM 10989 non-null float64\n", - " 2 RIFSPPFAAD90NB 6344 non-null float64\n", - " 3 TB3MS 523 non-null float64\n", - " 4 DGS10 10896 non-null float64\n", - " 5 VIXCLS 8468 non-null float64\n", - "dtypes: float64(6)\n", - "memory usage: 634.9 KB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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BAMLH0A0HYM2NASDAQCOMRIFSPPFAAD90NBTB3MSDGS10VIXCLS
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1980-01-04NaN148.02NaNNaN10.66NaN
1980-01-07NaN148.62NaNNaN10.63NaN
.....................
2023-07-263.9114127.28NaNNaN3.8613.19
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11609 rows × 6 columns

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" - ], - "text/plain": [ - " BAMLH0A0HYM2 NASDAQCOM RIFSPPFAAD90NB TB3MS DGS10 VIXCLS\n", - "DATE \n", - "1980-01-01 NaN NaN NaN 12.0 NaN NaN\n", - "1980-01-02 NaN 148.17 NaN NaN 10.50 NaN\n", - "1980-01-03 NaN 145.97 NaN NaN 10.60 NaN\n", - "1980-01-04 NaN 148.02 NaN NaN 10.66 NaN\n", - "1980-01-07 NaN 148.62 NaN NaN 10.63 NaN\n", - "... ... ... ... ... ... ...\n", - "2023-07-26 3.91 14127.28 NaN NaN 3.86 13.19\n", - "2023-07-27 3.78 14050.11 NaN NaN 4.01 14.41\n", - "2023-07-28 3.82 14316.66 5.53 NaN 3.96 13.33\n", - "2023-07-31 3.79 14346.02 5.47 NaN 3.97 13.63\n", - "2023-08-01 3.82 14283.91 5.44 NaN NaN 13.93\n", - "\n", - "[11609 rows x 6 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cleaning Data\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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High Yield Index OASNASDAQ90-Day AA Fin CP3-Month T-Bill10-Year TreasuryVIX
DATE
1980-01-01NaNNaNNaN12.0NaNNaN
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1980-01-03NaN145.97NaNNaN10.60NaN
1980-01-04NaN148.02NaNNaN10.66NaN
1980-01-07NaN148.62NaNNaN10.63NaN
.....................
2023-07-263.9114127.28NaNNaN3.8613.19
2023-07-273.7814050.11NaNNaN4.0114.41
2023-07-283.8214316.665.53NaN3.9613.33
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High Yield Index OASNASDAQ90-Day AA Fin CP3-Month T-Bill10-Year TreasuryVIX
DATE
1980-01-01NaNNaNNaNNaNNaNNaN
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.....................
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High Yield Index OASNASDAQ90-Day AA Fin CP3-Month T-Bill10-Year TreasuryVIX
DATE
1997-01-023.061280.705.355.056.5421.14
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1997-01-073.101327.735.335.026.5719.35
1997-01-083.071320.355.315.026.6020.24
.....................
2023-07-193.8914358.025.545.263.7513.76
2023-07-203.9014063.315.475.263.8513.99
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2023-07-313.7914346.025.475.283.9713.63
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6324 rows × 6 columns

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" - ], - "text/plain": [ - " High Yield Index OAS NASDAQ 90-Day AA Fin CP 3-Month T-Bill \\\n", - "DATE \n", - "1997-01-02 3.06 1280.70 5.35 5.05 \n", - "1997-01-03 3.09 1310.68 5.35 5.04 \n", - "1997-01-06 3.10 1316.40 5.34 5.05 \n", - "1997-01-07 3.10 1327.73 5.33 5.02 \n", - "1997-01-08 3.07 1320.35 5.31 5.02 \n", - "... ... ... ... ... \n", - "2023-07-19 3.89 14358.02 5.54 5.26 \n", - "2023-07-20 3.90 14063.31 5.47 5.26 \n", - "2023-07-21 3.89 14032.81 5.54 5.27 \n", - "2023-07-28 3.82 14316.66 5.53 5.28 \n", - "2023-07-31 3.79 14346.02 5.47 5.28 \n", - "\n", - " 10-Year Treasury VIX \n", - "DATE \n", - "1997-01-02 6.54 21.14 \n", - "1997-01-03 6.52 19.13 \n", - "1997-01-06 6.54 19.89 \n", - "1997-01-07 6.57 19.35 \n", - "1997-01-08 6.60 20.24 \n", - "... ... ... \n", - "2023-07-19 3.75 13.76 \n", - "2023-07-20 3.85 13.99 \n", - "2023-07-21 3.84 13.60 \n", - "2023-07-28 3.96 13.33 \n", - "2023-07-31 3.97 13.63 \n", - "\n", - "[6324 rows x 6 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dropna()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Transforming and Normalizing the data\n", - "\n", - "What is transformation and normalization? Are these different things?\n", - "\n", - " - Why would one transform data? What is feature engineering?\n", - " - What is normalization?\n", - "\n", - "What does stationarity mean? See the the following plots. Some of these variable are stationary. Other are not? Why is this a problem?" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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- "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df.drop(columns=['NASDAQ']).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's try some transformations like those used in the OFR Financial Stress Index: https://www.financialresearch.gov/financial-stress-index/files/indicators/index.html" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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High Yield Index OASNASDAQ90-Day AA Fin CP3-Month T-Bill10-Year TreasuryVIX
DATE
1980-01-01NaNNaNNaNNaNNaNNaN
1980-01-02NaNNaNNaNNaNNaNNaN
1980-01-03NaNNaNNaNNaNNaNNaN
1980-01-04NaNNaNNaNNaNNaNNaN
1980-01-07NaNNaNNaNNaNNaNNaN
.....................
2023-07-26NaNNaNNaNNaNNaNNaN
2023-07-27NaNNaNNaNNaNNaNNaN
2023-07-28NaNNaNNaNNaNNaNNaN
2023-07-31NaNNaNNaNNaNNaNNaN
2023-08-01NaNNaNNaNNaNNaNNaN
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11460 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " High Yield Index OAS NASDAQ 90-Day AA Fin CP 3-Month T-Bill \\\n", - "DATE \n", - "1980-01-01 NaN NaN NaN NaN \n", - "1980-01-02 NaN NaN NaN NaN \n", - "1980-01-03 NaN NaN NaN NaN \n", - "1980-01-04 NaN NaN NaN NaN \n", - "1980-01-07 NaN NaN NaN NaN \n", - "... ... ... ... ... \n", - "2023-07-26 NaN NaN NaN NaN \n", - "2023-07-27 NaN NaN NaN NaN \n", - "2023-07-28 NaN NaN NaN NaN \n", - "2023-07-31 NaN NaN NaN NaN \n", - "2023-08-01 NaN NaN NaN NaN \n", - "\n", - " 10-Year Treasury VIX \n", - "DATE \n", - "1980-01-01 NaN NaN \n", - "1980-01-02 NaN NaN \n", - "1980-01-03 NaN NaN \n", - "1980-01-04 NaN NaN \n", - "1980-01-07 NaN NaN \n", - "... ... ... \n", - "2023-07-26 NaN NaN \n", - "2023-07-27 NaN NaN \n", - "2023-07-28 NaN NaN \n", - "2023-07-31 NaN NaN \n", - "2023-08-01 NaN NaN \n", - "\n", - "[11460 rows x 6 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dfn = pd.DataFrame().reindex_like(df)\n", - "dfn" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DATE\n", - "1980-01-01 NaN\n", - "1980-01-02 NaN\n", - "1980-01-03 NaN\n", - "1980-01-04 NaN\n", - "1980-01-07 NaN\n", - " ..\n", - "2023-07-26 NaN\n", - "2023-07-27 NaN\n", - "2023-07-28 NaN\n", - "2023-07-31 NaN\n", - "2023-08-01 NaN\n", - "Name: NASDAQ, Length: 11460, dtype: float64" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['NASDAQ'].rolling(250).mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DATE\n", - "1997-01-02 NaN\n", - "1997-01-03 NaN\n", - "1997-01-06 NaN\n", - "1997-01-07 NaN\n", - "1997-01-08 NaN\n", - " ... \n", - "2023-07-19 12101.52228\n", - "2023-07-20 12105.62556\n", - "2023-07-21 12107.86248\n", - "2023-07-28 12110.35064\n", - "2023-07-31 12112.72912\n", - "Name: NASDAQ, Length: 6324, dtype: float64" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['NASDAQ'].rolling(250).mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# 'High Yield Index OAS': Leave as is\n", - "dfn['High Yield Index OAS'] = df['High Yield Index OAS']\n", - "dfn['CP - Treasury Spread, 3m'] = df['90-Day AA Fin CP'] - df['10-Year Treasury']\n", - "# 'NASDAQ': # We're using something different, but still apply rolling mean transformation\n", - "dfn['NASDAQ'] = df['NASDAQ'] - df['NASDAQ'].rolling(250).mean()\n", - "dfn['10-Year Treasury'] = df['10-Year Treasury'] - df['10-Year Treasury'].rolling(250).mean()\n", - "# 'VIX': Leave as is\n", - "dfn['VIX'] = df['VIX']" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "dfn = dfn.drop(columns=['90-Day AA Fin CP', '3-Month T-Bill'])\n", - "dfn = dfn.dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "DatetimeIndex: 6075 entries, 1998-01-05 to 2023-07-31\n", - "Data columns (total 5 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 High Yield Index OAS 6075 non-null float64\n", - " 1 NASDAQ 6075 non-null float64\n", - " 2 10-Year Treasury 6075 non-null float64\n", - " 3 VIX 6075 non-null float64\n", - " 4 CP - Treasury Spread, 3m 6075 non-null float64\n", - "dtypes: float64(5)\n", - "memory usage: 284.8 KB\n" - ] - } - ], - "source": [ - "dfn.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We finished with our transformations. Now, let's normalize. First, why is it important?" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dfn.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, normalize each column,\n", - "$$\n", - "z = \\frac{x - \\bar x}{\\text{std}(x)}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "dfn = (dfn - dfn.mean()) / dfn.std()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dfn.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "def pca(dfn, module='scikitlearn'):\n", - " if module == 'statsmodels':\n", - " _pc1, _loadings, projection, rsquare, _, _, _ = statsmodels.multivariate.pca.pca(dfn,\n", - " ncomp=1, standardize=True, demean=True, normalize=True, gls=False,\n", - " weights=None, method='svd')\n", - " _loadings = _loadings['comp_0']\n", - " loadings = np.std(_pc1) * _loadings\n", - " pc1 = _pc1 / np.std(_pc1)\n", - " pc1 = pc1.rename(columns={'comp_0':'PC1'})['PC1']\n", - "\n", - " elif module == 'scikitlearn':\n", - " pca = sklearn.decomposition.PCA(n_components=1)\n", - " _pc1 = pd.Series(pca.fit_transform(dfn)[:,0], index=dfn.index, name='PC1')\n", - " _loadings = pca.components_.T * np.sqrt(pca.explained_variance_)\n", - " _loadings = pd.Series(_loadings[:,0], index=dfn.columns)\n", - "\n", - " loadings = np.std(_pc1) * _loadings\n", - " pc1 = _pc1 / np.std(_pc1)\n", - " pc1.name = 'PC1'\n", - " else:\n", - " raise ValueError\n", - "\n", - "\n", - "\n", - " loadings.name = \"loadings\"\n", - "\n", - " return pc1, loadings\n", - "\n", - "def stacked_plot(df, filename=None):\n", - " \"\"\"\n", - " df=category_contributions\n", - " # category_contributions.sum(axis=1).plot()\n", - " \"\"\"\n", - "\n", - " df_pos = df[df >= 0]\n", - " df_neg = df[df < 0]\n", - "\n", - " alpha = .3\n", - " linewidth = .5\n", - "\n", - " ax = df_pos.plot.area(alpha=alpha, linewidth=linewidth, legend=False)\n", - " pc1 = df.sum(axis=1)\n", - " pc1.name = 'pc1'\n", - " pc1.plot(color=\"Black\", label='pc1', linewidth=1)\n", - "\n", - "\n", - " plt.legend()\n", - " ax.set_prop_cycle(None)\n", - " df_neg.plot.area(alpha=alpha, ax=ax, linewidth=linewidth, legend=False, ylim=(-3,3))\n", - " # recompute the ax.dataLim\n", - " ax.relim()\n", - " # update ax.viewLim using the new dataLim\n", - " ax.autoscale()\n", - " # ax.set_ylabel('Standard Deviations')\n", - " # ax.set_ylim(-3,4)\n", - " # ax.set_ylim(-30,30)\n", - "\n", - " if not (filename is None):\n", - " filename = Path(filename)\n", - " figure = plt.gcf() # get current figure\n", - " figure.set_size_inches(8, 6)\n", - " plt.savefig(filename, dpi=300)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "pc1, loadings = pca(dfn, module='scikitlearn')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "stacked_plot(dfn)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's compare solutions from two different packages" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6.169615255499479e-16" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def root_mean_squared_error(sa, sb):\n", - " return np.sqrt(np.mean((sa - sb)**2))\n", - "\n", - "pc1_sk, loadings_sk = pca(dfn, module='scikitlearn')\n", - "pc1_sm, loadings_sm = pca(dfn, module='statsmodels')\n", - "root_mean_squared_error(pc1_sm, pc1_sk)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Factor Analysis of a Panel of Stock Returns?" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[*********************100%***********************] 3 of 3 completed\n" - ] - } - ], - "source": [ - "sample = yf.download(\"SPY AAPL MSFT\", start=\"2017-01-01\", end=\"2017-04-30\")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AAPLMSFTSPYAAPLMSFTSPYAAPLMSFTSPYAAPLMSFTSPYAAPLMSFTSPYAAPLMSFTSPY
Date
2017-01-0327.09584857.375919201.33741829.03750062.580002225.24000529.08250062.840000225.83000228.69000162.130001223.88000528.95000162.790001225.0399931151276002069410091366500
2017-01-0427.06552557.119198202.53520229.00499962.299999226.58000229.12750162.750000226.75000028.93750062.119999225.61000128.96250062.480000225.619995844724002134000078744400
2017-01-0527.20315957.119198202.37426829.15250062.299999226.39999429.21500062.660000226.58000228.95249962.029999225.47999628.98000062.189999226.270004887744002487600078379000
2017-01-0627.50643257.614292203.09837329.47750162.840000227.21000729.54000163.150002227.75000029.11750062.040001225.89999429.19500062.299999226.5299991270076001992290071559900
2017-01-0927.75837357.430935202.42793329.74749962.639999226.46000729.85750063.080002227.07000729.48500162.540001226.41999829.48749962.759998226.9100041342476002038270046939700
.........................................................
2017-04-2433.65408362.289642212.92363035.91000067.529999237.16999835.98749967.660004237.41000435.79499867.099998234.55999835.87500067.480003237.1799936853720029770000119209900
2017-04-2533.86260662.649364214.16259836.13250067.919998238.55000336.22499868.040001238.94999735.96749967.599998237.80999835.97750167.900002237.910004754860003024270076698300
2017-04-2633.66345262.566360214.02787835.91999867.830002238.39999436.15000268.309998239.52999935.84500167.620003238.35000636.11750068.080002238.509995801648002619080084702500
2017-04-2733.68922462.972202214.20748935.94749868.269997238.60000636.04000168.379997238.94999735.82749967.580002237.97999635.98000068.150002238.770004569852003497100057410300
2017-04-2833.65642563.147472213.74060135.91249868.459999238.08000236.07500169.139999238.92999335.81750167.690002237.92999336.02249968.910004238.899994834416003954880063532800
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81 rows × 18 columns

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" - ], - "text/plain": [ - " Adj Close Close \\\n", - " AAPL MSFT SPY AAPL MSFT \n", - "Date \n", - "2017-01-03 27.095848 57.375919 201.337418 29.037500 62.580002 \n", - "2017-01-04 27.065525 57.119198 202.535202 29.004999 62.299999 \n", - "2017-01-05 27.203159 57.119198 202.374268 29.152500 62.299999 \n", - "2017-01-06 27.506432 57.614292 203.098373 29.477501 62.840000 \n", - "2017-01-09 27.758373 57.430935 202.427933 29.747499 62.639999 \n", - "... ... ... ... ... ... \n", - "2017-04-24 33.654083 62.289642 212.923630 35.910000 67.529999 \n", - "2017-04-25 33.862606 62.649364 214.162598 36.132500 67.919998 \n", - "2017-04-26 33.663452 62.566360 214.027878 35.919998 67.830002 \n", - "2017-04-27 33.689224 62.972202 214.207489 35.947498 68.269997 \n", - "2017-04-28 33.656425 63.147472 213.740601 35.912498 68.459999 \n", - "\n", - " High Low \\\n", - " SPY AAPL MSFT SPY AAPL \n", - "Date \n", - "2017-01-03 225.240005 29.082500 62.840000 225.830002 28.690001 \n", - "2017-01-04 226.580002 29.127501 62.750000 226.750000 28.937500 \n", - "2017-01-05 226.399994 29.215000 62.660000 226.580002 28.952499 \n", - "2017-01-06 227.210007 29.540001 63.150002 227.750000 29.117500 \n", - "2017-01-09 226.460007 29.857500 63.080002 227.070007 29.485001 \n", - "... ... ... ... ... ... \n", - "2017-04-24 237.169998 35.987499 67.660004 237.410004 35.794998 \n", - "2017-04-25 238.550003 36.224998 68.040001 238.949997 35.967499 \n", - "2017-04-26 238.399994 36.150002 68.309998 239.529999 35.845001 \n", - "2017-04-27 238.600006 36.040001 68.379997 238.949997 35.827499 \n", - "2017-04-28 238.080002 36.075001 69.139999 238.929993 35.817501 \n", - "\n", - " Open \\\n", - " MSFT SPY AAPL MSFT SPY \n", - "Date \n", - "2017-01-03 62.130001 223.880005 28.950001 62.790001 225.039993 \n", - "2017-01-04 62.119999 225.610001 28.962500 62.480000 225.619995 \n", - "2017-01-05 62.029999 225.479996 28.980000 62.189999 226.270004 \n", - "2017-01-06 62.040001 225.899994 29.195000 62.299999 226.529999 \n", - "2017-01-09 62.540001 226.419998 29.487499 62.759998 226.910004 \n", - "... ... ... ... ... ... \n", - "2017-04-24 67.099998 234.559998 35.875000 67.480003 237.179993 \n", - "2017-04-25 67.599998 237.809998 35.977501 67.900002 237.910004 \n", - "2017-04-26 67.620003 238.350006 36.117500 68.080002 238.509995 \n", - "2017-04-27 67.580002 237.979996 35.980000 68.150002 238.770004 \n", - "2017-04-28 67.690002 237.929993 36.022499 68.910004 238.899994 \n", - "\n", - " Volume \n", - " AAPL MSFT SPY \n", - "Date \n", - "2017-01-03 115127600 20694100 91366500 \n", - "2017-01-04 84472400 21340000 78744400 \n", - "2017-01-05 88774400 24876000 78379000 \n", - "2017-01-06 127007600 19922900 71559900 \n", - "2017-01-09 134247600 20382700 46939700 \n", - "... ... ... ... \n", - "2017-04-24 68537200 29770000 119209900 \n", - "2017-04-25 75486000 30242700 76698300 \n", - "2017-04-26 80164800 26190800 84702500 \n", - "2017-04-27 56985200 34971000 57410300 \n", - "2017-04-28 83441600 39548800 63532800 \n", - "\n", - "[81 rows x 18 columns]" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AAPLMSFTSPY
Date
2017-01-0327.09584857.375919201.337418
2017-01-0427.06552557.119198202.535202
2017-01-0527.20315957.119198202.374268
2017-01-0627.50643257.614292203.098373
2017-01-0927.75837357.430935202.427933
............
2017-04-2433.65408362.289642212.923630
2017-04-2533.86260662.649364214.162598
2017-04-2633.66345262.566360214.027878
2017-04-2733.68922462.972202214.207489
2017-04-2833.65642563.147472213.740601
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81 rows × 3 columns

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" - ], - "text/plain": [ - " AAPL MSFT SPY\n", - "Date \n", - "2017-01-03 27.095848 57.375919 201.337418\n", - "2017-01-04 27.065525 57.119198 202.535202\n", - "2017-01-05 27.203159 57.119198 202.374268\n", - "2017-01-06 27.506432 57.614292 203.098373\n", - "2017-01-09 27.758373 57.430935 202.427933\n", - "... ... ... ...\n", - "2017-04-24 33.654083 62.289642 212.923630\n", - "2017-04-25 33.862606 62.649364 214.162598\n", - "2017-04-26 33.663452 62.566360 214.027878\n", - "2017-04-27 33.689224 62.972202 214.207489\n", - "2017-04-28 33.656425 63.147472 213.740601\n", - "\n", - "[81 rows x 3 columns]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample['Adj Close']" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "tickers = [\n", - " 'AAPL','ABBV','ABT','ACN','ADP','ADSK','AES','AET','AFL','AMAT','AMGN','AMZN','APA',\n", - " 'APHA','APD','APTV','ARE','ASML','ATVI','AXP','BA','BAC','BAX','BDX','BIIB','BK',\n", - " 'BKNG','BMY','BRKB','BRK.A','COG','COST','CPB','CRM','CSCO','CVS','DAL','DD','DHR',\n", - " 'DIS','DOW','DUK','EMR','EPD','EQT','ESRT','EXPD','FFIV','FLS','FLT','FRT','GE',\n", - " 'GILD','GOOGL','GOOG','GS','HAL','HD','HON','IBM','INTC','IP','JNJ','JPM','KEY',\n", - " 'KHC','KIM','KO','LLY','LMT','LOW','MCD','MCHP','MDT','MMM','MO','MRK','MSFT',\n", - " 'MTD','NEE','NFLX','NKE','NOV','ORCL','OXY','PEP','PFE','PG','RTN','RTX','SBUX',\n", - " 'SHW','SLB','SO','SPG','STT','T','TGT','TXN','UNH','UPS','USB','UTX','V','VZ',\n", - " 'WMT','XOM',\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'AAPL ABBV ABT ACN ADP ADSK AES AET AFL AMAT AMGN AMZN APA APHA APD APTV ARE ASML ATVI AXP BA BAC BAX BDX BIIB BK BKNG BMY BRKB BRK.A COG COST CPB CRM CSCO CVS DAL DD DHR DIS DOW DUK EMR EPD EQT ESRT EXPD FFIV FLS FLT FRT GE GILD GOOGL GOOG GS HAL HD HON IBM INTC IP JNJ JPM KEY KHC KIM KO LLY LMT LOW MCD MCHP MDT MMM MO MRK MSFT MTD NEE NFLX NKE NOV ORCL OXY PEP PFE PG RTN RTX SBUX SHW SLB SO SPG STT T TGT TXN UNH UPS USB UTX V VZ WMT XOM'" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\" \".join(tickers)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[*********************100%***********************] 107 of 107 completed" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "6 Failed downloads:\n", - "['APHA', 'COG', 'BRKB', 'UTX', 'BRK.A', 'RTN']: Exception('%ticker%: No timezone found, symbol may be delisted')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "data = yf.download(\" \".join(tickers), start=\"1980-01-01\", end=\"2023-08-01\")" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data['Adj Close']['AAPL'].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "df = data['Adj Close']" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.pct_change().drop(columns=['BRK.A', 'APHA', 'UTX', 'RTN', 'COG', 'BRKB']).dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pc1.cumsum().plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " - Why does this plot only go back to 2019? What happened? \n", - " - What are methods that we might use to deal with missing data?" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/day_04/README.md b/day_04/README.md deleted file mode 100644 index 8b7734d..0000000 --- a/day_04/README.md +++ /dev/null @@ -1,26 +0,0 @@ -FINM August Python Introduction and Review: Week 4 -================================================== - -Agenda - - - Today we move away from Jupyter notebooks entirely and focus on writing `.py` files directly. We'll focus on writing our own modules, discuss automating tasks by using the command line, we'll discuss task management software (Python's `doit` package and Makefiles), and discuss the importance of conda environments (and hint at Docker containers). - - Give an overview of GitKraken and GitHub. - - Create a new repository on GitHub and clone it in GitKraken. - - Create a commit and push to GitHub - - Make edits to code and view the diffs. - - Discuss pull requests and the open source model (delegating oversight) - - Now, let's briefly move away from notebooks and write `.py` files directly. We'll discuss the pros and cons of working with Notebooks vs `.py` files. - - To do this, complete again the `Occupations` exercises the following in-class Pandas exercises within a `.py` file. Complete using the %% cells. - - Once the assignment is complete, remove the %% cells for comparison. - - Show how to use the debugger. - - Show how to run the script from the command line. - - Use the script to print to the command line. - - Use the script to save a figure. - - Write a shell script to run several Python scripts. - - Discussion of writing our own modules - - Start with a review of functions in Python: review the ["Functions"](https://datascience.quantecon.org/python_fundamentals/functions.html) chapter found here: [./functions.ipynb](./functions.ipynb) - - Demonstrate my own, very simple module that I use, called `config` - - Write an end-to-end automatically-run program using a conda environment, the command line, and Python's `doit`. This should download data on it's own, store it somewhere as a cached data set, run the analysis, generate the charts, and insert the charts into a PDF document (do this using a Jupyter notebook). - - Do this by looking at the structure of my `blank-project` repository. - - \ No newline at end of file diff --git a/day_04/config.py b/day_04/config.py deleted file mode 100644 index 79f8e09..0000000 --- a/day_04/config.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Provides easy access to paths and credentials used in the project. -Meant to be used as an imported module. - -Example -------- - -import config -path = config.output_dir -path - -## The config YAML should look something like this: -# config.yml - -default: - data_dir: "C:/My Documents/data/misc_project" - private_data_dir: "D:/My Documents/private_data/misc_project" - output_dir: "C:/Users/jdoe/GitRepositories/misc_project/output" - wrds_username: "jdoe" - -AWS: - data_dir: "/data/awshomes/jdoe/data/misc_project" - private_data_dir: "/data/awshomes/jdoe/private_data/misc_project" - output_dir: "/data/awshomes/jdoe/GitRepositories/INT_misc_project/output" - -""" -import yaml -from pathlib import Path - -with open("../config.yml") as f: - config = yaml.safe_load(f) - -def _read_config_entry(upper_key, lower_key): - entry = config[upper_key][lower_key] - if entry is None: - p = None - else: - p = Path(entry) - return p - -def switch_to(pathset_name='default'): - global data_dir - global private_data_dir - global output_dir - global pathset - - data_dir = _read_config_entry(pathset_name, "data_dir") - private_data_dir = _read_config_entry(pathset_name, "private_data_dir") - output_dir = _read_config_entry(pathset_name, "output_dir") - pathset = pathset_name - -def read(key): - upper_key = pathset - value = config[upper_key][key] - return value - -switch_to(pathset_name='default') - -if __name__ == "__main__": - pass - - diff --git a/day_04/mysine.png b/day_04/mysine.png deleted file mode 100644 index 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z4^*xK4`M%PqlLO{13VsjadABtbI33Nb09k+1-Mj7hNr9fAr41Vb&5!m}(F*LwW!>N7I0R2Jsmb3iyS?gT}zpisskEe$$mC zO2(;=07doQ;nVyEx*d0#@ao8q9}>JB&?#Nu;70R*{`b@UUz1n=AAk4)pS$Bh V3yCj^5a`qqvQmnV^Ck7Y{~s&g9 + +Now, let's take a look at GitHub. + + + +You can find another nice video about GitHub [here.](https://www.youtube.com/watch?v=pBy1zgt0XPc) +The code for this course is available on GitHub. You can find the repository [here](https://github.com/jmbejara/finm-python-crash-course). + + +```{note} +Let's pause here and make sure that everyone has Git installed and is able to download the +``` + + + + + + +## WRDS: How do I sign up? + +[![WRDS Logo](./assets/wrds_logo.png)](https://wrds-www.wharton.upenn.edu/) + +This course requires that you create a WRDS account. WRDS is a comprehensive data research platform that provides access to a wide range of financial, economic, and marketing data. +Follow the instructions below to create an account. + +```{important} +If you have not requested an account already, please do so ASAP. You will need this for the next session. +``` + + + +**New to WRDS?** + +Use the following link to access the WRDS Registration form at https://wrds-www.wharton.upenn.edu/register/?user_type=class-student + +- Follow the directions on the Registration form to enter your identifying information. + - For the Subscriber, select your school's name from the drop-down list. + - Your User type, Class - Students with Code, has been selected by default. + - You will need to enter the course code. This can be found on Canvas here: TODO + +- Click the Register for WRDS button. + - WRDS accounts require two-factor authentication. We recommend you use a smartphone for the verification process. First, install the Duo Mobile app on your phone. This free app can be downloaded through your device’s app store. Follow the directions at How to Log into WRDS to register your smartphone and use Duo two-factor authentication to set up your WRDS account. + +**Already Have a WRDS Account?** + +After you have logged into WRDS, use the following steps to enroll in our class account. You will need the Class Code above to enroll. + + - In the top right corner of the screen, select Your Account > Your Account Info. + - Scroll down until you see the Your Classes table and click the Enroll in a Class button. + - Enter the Class Code and click Submit. You can find the code here: TODO + + +```{important} +If you have any difficulty setting up your account please contact WRDS Support at: https://wrds-www.wharton.upenn.edu/contact-support/. When opening your support ticket you must use the email associated with your existing WRDS account, or the email you intend to use to set up your new WRDS account. +``` + + + + + + diff --git a/docs_src/WRDS_intro_and_web_queries.md b/docs_src/WRDS_intro_and_web_queries.md new file mode 100644 index 0000000..c56b73c --- /dev/null +++ b/docs_src/WRDS_intro_and_web_queries.md @@ -0,0 +1,123 @@ +# 2.1 Introduction to WRDS + +## A Platform For Financial Data + +Wharton Research Data Services (WRDS) is a data research platform and business intelligence tool widely used in academic, government, and corporate sectors. It provides access to a vast repository of financial, economic, and marketing data, which is pivotal for conducting rigorous research in various fields, especially in finance and economics. The platform is known for its comprehensive and high-quality datasets. + +[![WRDS Logo](./assets/wrds_logo.png)](https://wrds-www.wharton.upenn.edu/) + +WRDS offers a variety of datasets from numerous sources, including leading data providers like Compustat, CRSP, IBES, and Bloomberg. It covers a wide range of data types, including: + +- Stock prices and trading volumes +- Financial statement data +- Analyst forecasts +- Corporate governance data +- Mutual fund and bond data +- Macroeconomic data + +![WRDS Datasets](./assets/wrds_subscriptions.png) + +One of the key strengths of WRDS is its user-friendly interface, which allows for easy data extraction and manipulation. It provides powerful tools for data analysis, including the ability to execute custom queries and perform complex statistical analyses. In academic settings, WRDS is particularly valued for its role in facilitating empirical research in finance and economics. It allows researchers, professors, and students to access a wealth of data necessary for testing financial theories, exploring economic trends, and developing new insights in the field of quantitative finance. + +## The Core Data Sets + +WRDS provides some usage statistics on their website in an introduction presentation [here](https://wrds-www.wharton.upenn.edu/documents/1400/wrds_research_data_overview.pdf). This chart shows +the percentage of usage across all WRDS data sets. + +![WRDS Database Usage](./assets/wrds_database_usage.png) + +The two most popular data sets are CRSP and Compustat. + +WRDS did an analysis finance papers published in the top 3 finance journals---the Journal of Finance, the Journal of Financial Economics, and the Review of Financial Studies---from the years 2004-2016. Out of all of these papers, the following chart shows how many times each data set was cited. + +![Top 10 Databases](./assets/wrds_top_10_databases.png) + + +All of the listed data sets, except for those colored in red, are available in WRDS. + +## Compustat + +![Compustat Logo](./assets/Compustat_Logo.png) + +[Compustat Financials, S&P Global Market Intelligence](https://www.marketplace.spglobal.com/en/datasets/compustat-financials-(8)) + +Compustat is a comprehensive database of financial, statistical, and market information, primarily focused on publicly traded companies. It is widely used in academic research, particularly in the fields of finance and economics, for conducting in-depth analysis of company performance and market trends. The dataset includes information from various countries and markets, making it a valuable resource for both domestic and international financial research. + +Key features of the Compustat dataset include: + +1. **Financial Statements:** Detailed income statements, balance sheets, and cash flow statements for a wide range of companies. + +2. **Historical Data:** Longitudinal data that allows for historical trend analysis and time-series studies. + +3. **Global Coverage:** Data on companies from various global markets, including North America, Europe, Asia, and more. + +4. **Segment Data:** Information on business segments and geographical segments of companies. + +5. **Market Data:** Includes stock prices, trading volume, and other market-related information. + +6. **Corporate Actions:** Data on dividends, stock splits, mergers and acquisitions, and other corporate events. + +7. **Ratios and Metrics:** Key financial ratios and metrics that are pre-calculated for ease of analysis, such as ROE, ROA, and EBITDA. + +Compustat is highly regarded for its accuracy, depth, and consistency, making it a fundamental resource for both theoretical and empirical research in finance. It's extensively used for tasks like asset pricing models, risk management, portfolio construction, and corporate finance studies. For students and researchers in quantitative finance, Compustat provides a rich dataset for modeling, back-testing theories, and conducting robust financial analyses. + +The following two videos provide a short introduction to Compustat on WRDS. + +[![Compustat on WRDS Part 1](./assets/compustat_on_WRDS_p1.png)](https://wrds-www.wharton.upenn.edu/pages/grid-items/introduction-compustat-part-1/) +[![Compustat on WRDS Part 2](./assets/compustat_on_WRDS_p2.png)](https://wrds-www.wharton.upenn.edu/pages/grid-items/introduction-compustat-part-2/) + + +## CRSP + +![CRSP Logo](./assets/crsp-llc-logo-web-01_3.png) + +[Center for Research in Security Prices](https://www.crsp.org/) + +The Center for Research in Security Prices (CRSP) is a renowned financial research database, primarily recognized for its comprehensive historical data on securities traded in the United States. Established at the University of Chicago's Booth School of Business, CRSP is a crucial resource for academic, commercial, and governmental research in finance. + +Key characteristics of the CRSP database include: + +1. **Extensive Historical Data:** CRSP is particularly noted for its long historical time series, which in some cases go back as far as 1925. This historical depth is invaluable for long-term financial studies and analyses. + +2. **Stock Data:** The database provides detailed information on stocks listed on NYSE, AMEX, and NASDAQ, including prices, returns, trading volumes, and other market indicators. + +3. **Indices:** CRSP develops and maintains a series of stock indices that serve as benchmarks for the investment industry, including value- and equal-weighted indices. + +4. **Corporate Actions:** Information on dividends, stock splits, and other corporate events that impact stock valuation is extensively covered. + +5. **Treasury and Mutual Fund Data:** Beyond stocks, CRSP also includes data on US Treasury bills, bonds, and mutual funds, expanding its utility for various types of financial research. + +6. **Survivorship Bias-Free Data:** CRSP’s dataset is known for being free of survivorship bias, as it includes data on companies that have ceased to exist, which is crucial for accurate historical analysis. + +7. **Research Quality:** The accuracy, completeness, and cleanliness of the data make CRSP a gold standard for financial research, particularly in academic settings. + +For students and researchers in quantitative finance, CRSP provides essential data for analyzing stock performance, conducting empirical tests of asset pricing models, and studying market anomalies and behaviors. Its extensive historical data and robustness make it a fundamental tool for both historical analysis and contemporary market studies. + +The following [video](https://wrds-www.wharton.upenn.edu/pages/grid-items/crsp-basics/) provides a nice introduction to the basics of CRSP. + +[![CRSP in WRDS Basics](./assets/crsp_in_wrds_thumbnail.png)](https://wrds-www.wharton.upenn.edu/pages/grid-items/crsp-basics/) + + +## How do these compare with Bloomberg or Datastream? + +Choosing between financial databases like CRSP, Bloomberg, or Datastream depends on the specific requirements of the research or analysis being conducted. Each of these platforms has unique strengths and features that make them suitable for different purposes. Here are some reasons why someone might opt for CRSP over Bloomberg or Datastream: + +- **Historical Depth:** CRSP is renowned for its extensive historical data, particularly for U.S. securities. It offers data going back as far as 1925, which is invaluable for long-term historical research and analysis. This level of historical depth might not be matched by Bloomberg or Datastream. +- **Survivorship Bias-Free Data:** CRSP's data includes companies that have ceased to exist, which is crucial for accurate historical analyses. This feature helps in avoiding survivorship bias, making it a robust choice for academic studies that require comprehensive historical perspectives. +- **Data Consistency and Quality:** CRSP is known for its high standards in data accuracy, consistency, and cleanliness, which are critical for reliable academic research. + +On the other hand, there are some drawbacks of CRSP relative to Bloomberg or Datastream. + +- **Limited Scope**: CRSP has a limited scope relative to Bloomberg or Datastream. It primarily focuses on US markets and lacks the global coverage found in Bloomberg. +- **Real-Time Data**: Does not offer real-time data, which is essential for current market analysis. +- **Less Comprehensive**: Fewer types of financial data compared to Bloomberg (e.g., lacks extensive international data, commodities, real-time news). + +Broadly speaking, CRSP is more suited for academic research focused on historical analysis of the U.S. stock market, offering in-depth and high-quality data with a bias-free historical perspective. Bloomberg, on the other hand, excels in providing a wide range of real-time global financial data and tools, catering more to finance professionals and analysts who require real-time data and sophisticated analysis tools. The choice between them largely depends on the specific needs, goals, and resources of the user. + +## WRDS Web Queries + +To familiarize yourselves to using WRDS, please [watch the following video](https://vimeo.com/436447434) about WRDS Web Queries. While we will be automating the query process using the WRDS Python package [`wrds`](https://pypi.org/project/wrds/), using the web query system is a good way for initial exploration of the data. + +[![WRDS Web Queries](./assets/wrds_web_queries.png)](https://wrds-www.wharton.upenn.edu/pages/grid-items/introduction-web-queries-wrds/) + + diff --git a/docs_src/assets/Compustat_Logo.png b/docs_src/assets/Compustat_Logo.png new file mode 100644 index 0000000000000000000000000000000000000000..c0122ef2f364588f70c2a49c53555ea1fa687a9c GIT binary patch literal 25290 zcmdSBbxd4s6eo)B ztc$9Y7)<2^=|1!T!BSK~6b7ay9{te-5qga3D6QiH1M}AP^#!|TLE#AlV`?PxSybKA z@F*KO3m~yHn7f!h-d4SBAo}gSF#<=J+L?UX)hSk8c#b8nP)sSIvI@>@dSCe6C#8Wb z6?v2&C=n<>s3YapNG(gcyrZn`-P6-C$C&F*AMfx4vVWERDl5zJf2jBxZx--I423e} z%`%@%e%HTuVz%?rgy_&yVkjmFtoZ-V^}aJqS8FB~Z*Z&pf%G z@RyloWMr;&<^J7vLCYCoP5RW=@55$v%*=z2cNZ!K24qX`8zsJd|2|!z$Fse?-MxDe zI*0bU4W|&3B%x9!H)g87GwS)pg}7dSqqLzR`Elz3GpW~h;;DB>e#5`VQc&teH|td0 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\n", + " \n", + " \"QuantEcon\"\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "84b7ac3c", + "metadata": {}, + "source": [ + "# 1.3 An Introductory Example\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "adf85dfd", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "- [An Introductory Example](#An-Introductory-Example) \n", + " - [Overview](#Overview) \n", + " - [The Task: Plotting a White Noise Process](#The-Task:-Plotting-a-White-Noise-Process) \n", + " - [Version 1](#Version-1) \n", + " - [Alternative Implementations](#Alternative-Implementations) \n", + " - [Another Application](#Another-Application) \n", + " - [Exercises](#Exercises) " + ] + }, + { + "cell_type": "markdown", + "id": "123a514b", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "We’re now ready to start learning the Python language itself.\n", + "\n", + "In this lecture, we will write and then pick apart small Python programs.\n", + "\n", + "The objective is to introduce you to basic Python syntax and data structures.\n", + "\n", + "Deeper concepts will be covered in later lectures.\n", + "\n", + "You should have read the [lecture](https://python-programming.quantecon.org/getting_started.html) on getting started with Python before beginning this one." + ] + }, + { + "cell_type": "markdown", + "id": "dd04aadf", + "metadata": {}, + "source": [ + "## The Task: Plotting a White Noise Process\n", + "\n", + "Suppose we want to simulate and plot the white noise\n", + "process $ \\epsilon_0, \\epsilon_1, \\ldots, \\epsilon_T $, where each draw $ \\epsilon_t $ is independent standard normal.\n", + "\n", + "In other words, we want to generate figures that look something like this:\n", + "\n", + "![https://python-programming.quantecon.org/_static/lecture_specific/python_by_example/test_program_1_updated.png](https://python-programming.quantecon.org/_static/lecture_specific/python_by_example/test_program_1_updated.png)\n", + "\n", + " \n", + "(Here $ t $ is on the horizontal axis and $ \\epsilon_t $ is on the\n", + "vertical axis.)\n", + "\n", + "We’ll do this in several different ways, each time learning something more\n", + "about Python.\n", + "\n", + "We run the following command first, which helps ensure that plots appear in the\n", + "notebook if you run it on your own machine." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cc7e562", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "1671a1d3", + "metadata": {}, + "source": [ + "## Version 1\n", + "\n", + "\n", + "\n", + "Here are a few lines of code that perform the task we set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9ef3835", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = (5,3)\n", + "\n", + "ϵ_values = np.random.randn(100)\n", + "plt.plot(ϵ_values)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "948de567", + "metadata": {}, + "source": [ + "Let’s break this program down and see how it works.\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "f801fb81", + "metadata": {}, + "source": [ + "### Imports\n", + "\n", + "The first two lines of the program import functionality from external code\n", + "libraries.\n", + "\n", + "The first line imports [NumPy](https://python-programming.quantecon.org/numpy.html), a favorite Python package for tasks like\n", + "\n", + "- working with arrays (vectors and matrices) \n", + "- common mathematical functions like `cos` and `sqrt` \n", + "- generating random numbers \n", + "- linear algebra, etc. \n", + "\n", + "\n", + "After `import numpy as np` we have access to these attributes via the syntax `np.attribute`.\n", + "\n", + "Here’s two more examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a783ea17", + "metadata": {}, + "outputs": [], + "source": [ + "np.sqrt(4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6adab364", + "metadata": {}, + "outputs": [], + "source": [ + "np.log(4)" + ] + }, + { + "cell_type": "markdown", + "id": "ed236510", + "metadata": {}, + "source": [ + "We could also use the following syntax:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80d2daad", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "\n", + "numpy.sqrt(4)" + ] + }, + { + "cell_type": "markdown", + "id": "71c05b11", + "metadata": {}, + "source": [ + "But the former method (using the short name `np`) is convenient and more standard." + ] + }, + { + "cell_type": "markdown", + "id": "b581441f", + "metadata": {}, + "source": [ + "#### Why So Many Imports?\n", + "\n", + "Python programs typically require several import statements.\n", + "\n", + "The reason is that the core language is deliberately kept small, so that it’s easy to learn and maintain.\n", + "\n", + "When you want to do something interesting with Python, you almost always need\n", + "to import additional functionality." + ] + }, + { + "cell_type": "markdown", + "id": "a4540370", + "metadata": {}, + "source": [ + "#### Packages\n", + "\n", + "\n", + "\n", + "As stated above, NumPy is a Python *package*.\n", + "\n", + "Packages are used by developers to organize code they wish to share.\n", + "\n", + "In fact, a package is just a directory containing\n", + "\n", + "1. files with Python code — called **modules** in Python speak \n", + "1. possibly some compiled code that can be accessed by Python (e.g., functions compiled from C or FORTRAN code) \n", + "1. a file called `__init__.py` that specifies what will be executed when we type `import package_name` \n", + "\n", + "\n", + "You can check the location of your `__init__.py` for NumPy in python by running the code:" + ] + }, + { + "cell_type": "markdown", + "id": "7c7e5c25", + "metadata": { + "hide-output": false + }, + "source": [ + "```ipython\n", + "import numpy as np\n", + "\n", + "print(np.__file__)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "368a8338", + "metadata": {}, + "source": [ + "#### Subpackages\n", + "\n", + "\n", + "\n", + "Consider the line `ϵ_values = np.random.randn(100)`.\n", + "\n", + "Here `np` refers to the package NumPy, while `random` is a **subpackage** of NumPy.\n", + "\n", + "Subpackages are just packages that are subdirectories of another package.\n", + "\n", + "For instance, you can find folder `random` under the directory of NumPy." + ] + }, + { + "cell_type": "markdown", + "id": "48753714", + "metadata": {}, + "source": [ + "### Importing Names Directly\n", + "\n", + "Recall this code that we saw above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d501633b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "np.sqrt(4)" + ] + }, + { + "cell_type": "markdown", + "id": "a6648e84", + "metadata": {}, + "source": [ + "Here’s another way to access NumPy’s square root function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fafee420", + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import sqrt\n", + "\n", + "sqrt(4)" + ] + }, + { + "cell_type": "markdown", + "id": "63fb5694", + "metadata": {}, + "source": [ + "This is also fine.\n", + "\n", + "The advantage is less typing if we use `sqrt` often in our code.\n", + "\n", + "The disadvantage is that, in a long program, these two lines might be\n", + "separated by many other lines.\n", + "\n", + "Then it’s harder for readers to know where `sqrt` came from, should they wish to." + ] + }, + { + "cell_type": "markdown", + "id": "a668dae2", + "metadata": {}, + "source": [ + "### Random Draws\n", + "\n", + "Returning to our program that plots white noise, the remaining three lines\n", + "after the import statements are" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcc4d204", + "metadata": {}, + "outputs": [], + "source": [ + "ϵ_values = np.random.randn(100)\n", + "plt.plot(ϵ_values)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c2a56d62", + "metadata": {}, + "source": [ + "The first line generates 100 (quasi) independent standard normals and stores\n", + "them in `ϵ_values`.\n", + "\n", + "The next two lines genererate the plot.\n", + "\n", + "We can and will look at various ways to configure and improve this plot below." + ] + }, + { + "cell_type": "markdown", + "id": "c9889d3d", + "metadata": {}, + "source": [ + "### Note: What is a Random Seeds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11e485ed", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d8223a1", + "metadata": {}, + "outputs": [], + "source": [ + "ϵ_values = np.random.randn(100)\n", + "plt.plot(ϵ_values)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4010760d", + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f12df22", + "metadata": {}, + "outputs": [], + "source": [ + "ϵ_values = np.random.randn(100)\n", + "plt.plot(ϵ_values)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ed227fb", + "metadata": {}, + "source": [ + "## Alternative Implementations\n", + "\n", + "Let’s try writing some alternative versions of [our first program](#ourfirstprog), which plotted IID draws from the standard normal distribution.\n", + "\n", + "The programs below are less efficient than the original one, and hence\n", + "somewhat artificial.\n", + "\n", + "But they do help us illustrate some important Python syntax and semantics in a familiar setting." + ] + }, + { + "cell_type": "markdown", + "id": "a864e098", + "metadata": {}, + "source": [ + "### A Version with a For Loop\n", + "\n", + "Here’s a version that illustrates `for` loops and Python lists.\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1676245d", + "metadata": {}, + "outputs": [], + "source": [ + "ts_length = 100\n", + "ϵ_values = [] # empty list\n", + "\n", + "for i in range(ts_length):\n", + " e = np.random.randn()\n", + " ϵ_values.append(e)\n", + "\n", + "plt.plot(ϵ_values)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "093877de", + "metadata": {}, + "source": [ + "In brief,\n", + "\n", + "- The first line sets the desired length of the time series. \n", + "- The next line creates an empty *list* called `ϵ_values` that will store the $ \\epsilon_t $ values as we generate them. \n", + "- The statement `# empty list` is a *comment*, and is ignored by Python’s interpreter. \n", + "- The next three lines are the `for` loop, which repeatedly draws a new random number $ \\epsilon_t $ and appends it to the end of the list `ϵ_values`. \n", + "- The last two lines generate the plot and display it to the user. \n", + "\n", + "\n", + "Let’s study some parts of this program in more detail.\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "85eba02a", + "metadata": {}, + "source": [ + "### Lists\n", + "\n", + "\n", + "\n", + "Consider the statement `ϵ_values = []`, which creates an empty list.\n", + "\n", + "Lists are a *native Python data structure* used to group a collection of objects.\n", + "\n", + "Items in lists are ordered, and duplicates are allowed in lists.\n", + "\n", + "For example, try" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee1f60d5", + "metadata": {}, + "outputs": [], + "source": [ + "x = [10, 'foo', False]\n", + "type(x)" + ] + }, + { + "cell_type": "markdown", + "id": "abb82775", + "metadata": {}, + "source": [ + "The first element of `x` is an [integer](https://en.wikipedia.org/wiki/Integer_%28computer_science%29), the next is a [string](https://en.wikipedia.org/wiki/String_%28computer_science%29), and the third is a [Boolean value](https://en.wikipedia.org/wiki/Boolean_data_type).\n", + "\n", + "When adding a value to a list, we can use the syntax `list_name.append(some_value)`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc1b46c9", + "metadata": {}, + "outputs": [], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ce1d842", + "metadata": {}, + "outputs": [], + "source": [ + "x.append(2.5)\n", + "x" + ] + }, + { + "cell_type": "markdown", + "id": "2621e155", + "metadata": {}, + "source": [ + "Here `append()` is what’s called a *method*, which is a function “attached to” an object—in this case, the list `x`.\n", + "\n", + "We’ll learn all about methods [later on](https://python-programming.quantecon.org/oop_intro.html), but just to give you some idea,\n", + "\n", + "- Python objects such as lists, strings, etc. all have methods that are used to manipulate the data contained in the object. \n", + "- String objects have [string methods](https://docs.python.org/3/library/stdtypes.html#string-methods), list objects have [list methods](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists), etc. \n", + "\n", + "\n", + "Another useful list method is `pop()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20b510ae", + "metadata": {}, + "outputs": [], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0f4985c", + "metadata": {}, + "outputs": [], + "source": [ + "x.pop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7840fbbc", + "metadata": {}, + "outputs": [], + "source": [ + "x" + ] + }, + { + "cell_type": "markdown", + "id": "5bc40e53", + "metadata": {}, + "source": [ + "Lists in Python are zero-based (as in C, Java or Go), so the first element is referenced by `x[0]`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "537085ff", + "metadata": {}, + "outputs": [], + "source": [ + "x[0] # first element of x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7137175e", + "metadata": {}, + "outputs": [], + "source": [ + "x[1] # second element of x" + ] + }, + { + "cell_type": "markdown", + "id": "8e4c3bb9", + "metadata": {}, + "source": [ + "### The For Loop\n", + "\n", + "\n", + "\n", + "Now let’s consider the `for` loop from [the program above](#firstloopprog), which was" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c9df0ed", + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(ts_length):\n", + " e = np.random.randn()\n", + " ϵ_values.append(e)" + ] + }, + { + "cell_type": "markdown", + "id": "e9a328dd", + "metadata": {}, + "source": [ + "Python executes the two indented lines `ts_length` times before moving on.\n", + "\n", + "These two lines are called a `code block`, since they comprise the “block” of code that we are looping over.\n", + "\n", + "Unlike most other languages, Python knows the extent of the code block *only from indentation*.\n", + "\n", + "In our program, indentation decreases after line `ϵ_values.append(e)`, telling Python that this line marks the lower limit of the code block.\n", + "\n", + "More on indentation below—for now, let’s look at another example of a `for` loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3989d19d", + "metadata": {}, + "outputs": [], + "source": [ + "animals = ['dog', 'cat', 'bird']\n", + "for animal in animals:\n", + " print(\"The plural of \" + animal + \" is \" + animal + \"s\")" + ] + }, + { + "cell_type": "markdown", + "id": "6f6c25df", + "metadata": {}, + "source": [ + "This example helps to clarify how the `for` loop works: When we execute a\n", + "loop of the form" + ] + }, + { + "cell_type": "markdown", + "id": "dbbce3d9", + "metadata": { + "hide-output": false + }, + "source": [ + "```python3\n", + "for variable_name in sequence:\n", + " \n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "24e8c514", + "metadata": {}, + "source": [ + "The Python interpreter performs the following:\n", + "\n", + "- For each element of the `sequence`, it “binds” the name `variable_name` to that element and then executes the code block. \n", + "\n", + "\n", + "The `sequence` object can in fact be a very general object, as we’ll see\n", + "soon enough." + ] + }, + { + "cell_type": "markdown", + "id": "cd045b26", + "metadata": {}, + "source": [ + "### A Comment on Indentation\n", + "\n", + "\n", + "\n", + "In discussing the `for` loop, we explained that the code blocks being looped over are delimited by indentation.\n", + "\n", + "In fact, in Python, **all** code blocks (i.e., those occurring inside loops, if clauses, function definitions, etc.) are delimited by indentation.\n", + "\n", + "Thus, unlike most other languages, whitespace in Python code affects the output of the program.\n", + "\n", + "Once you get used to it, this is a good thing: It\n", + "\n", + "- forces clean, consistent indentation, improving readability \n", + "- removes clutter, such as the brackets or end statements used in other languages \n", + "\n", + "\n", + "On the other hand, it takes a bit of care to get right, so please remember:\n", + "\n", + "- The line before the start of a code block always ends in a colon \n", + " - `for i in range(10):` \n", + " - `if x > y:` \n", + " - `while x < 100:` \n", + " - etc., etc. \n", + "- All lines in a code block **must have the same amount of indentation**. \n", + "- The Python standard is 4 spaces, and that’s what you should use. " + ] + }, + { + "cell_type": "markdown", + "id": "e210f170", + "metadata": {}, + "source": [ + "### While Loops\n", + "\n", + "\n", + "\n", + "The `for` loop is the most common technique for iteration in Python.\n", + "\n", + "But, for the purpose of illustration, let’s modify [the program above](#firstloopprog) to use a `while` loop instead.\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c9effd1", + "metadata": {}, + "outputs": [], + "source": [ + "ts_length = 100\n", + "ϵ_values = []\n", + "i = 0\n", + "while i < ts_length:\n", + " e = np.random.randn()\n", + " ϵ_values.append(e)\n", + " i = i + 1\n", + "plt.plot(ϵ_values)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bae1c9a2", + "metadata": {}, + "source": [ + "A while loop will keep executing the code block delimited by indentation until the condition (`i < ts_length`) is satisfied.\n", + "\n", + "In this case, the program will keep adding values to the list `ϵ_values` until `i` equals `ts_length`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f9acae6", + "metadata": {}, + "outputs": [], + "source": [ + "i == ts_length #the ending condition for the while loop" + ] + }, + { + "cell_type": "markdown", + "id": "f6a4977e", + "metadata": {}, + "source": [ + "Note that\n", + "\n", + "- the code block for the `while` loop is again delimited only by indentation. \n", + "- the statement `i = i + 1` can be replaced by `i += 1`. " + ] + }, + { + "cell_type": "markdown", + "id": "9fe1230f", + "metadata": {}, + "source": [ + "## Another Application\n", + "\n", + "Let’s do one more application before we turn to exercises.\n", + "\n", + "In this application, we plot the balance of a bank account over time.\n", + "\n", + "There are no withdraws over the time period, the last date of which is denoted\n", + "by $ T $.\n", + "\n", + "The initial balance is $ b_0 $ and the interest rate is $ r $.\n", + "\n", + "The balance updates from period $ t $ to $ t+1 $ according to $ b_{t+1} = (1 + r) b_t $.\n", + "\n", + "In the code below, we generate and plot the sequence $ b_0, b_1, \\ldots, b_T $.\n", + "\n", + "Instead of using a Python list to store this sequence, we will use a NumPy\n", + "array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3463484", + "metadata": {}, + "outputs": [], + "source": [ + "r = 0.025 # interest rate\n", + "T = 50 # end date\n", + "b = np.empty(T+1) # an empty NumPy array, to store all b_t\n", + "b[0] = 10 # initial balance\n", + "\n", + "for t in range(T):\n", + " b[t+1] = (1 + r) * b[t]\n", + "\n", + "plt.plot(b, label='bank balance')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9278f1b9", + "metadata": {}, + "source": [ + "The statement `b = np.empty(T+1)` allocates storage in memory for `T+1`\n", + "(floating point) numbers.\n", + "\n", + "These numbers are filled in by the `for` loop.\n", + "\n", + "Allocating memory at the start is more efficient than using a Python list and\n", + "`append`, since the latter must repeatedly ask for storage space from the\n", + "operating system.\n", + "\n", + "Notice that we added a legend to the plot — a feature you will be asked to\n", + "use in the exercises." + ] + }, + { + "cell_type": "markdown", + "id": "0e323dec", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "Now we turn to exercises. It is important that you complete them before\n", + "continuing, since they present new concepts we will need." + ] + }, + { + "cell_type": "markdown", + "id": "bf406a06", + "metadata": {}, + "source": [ + "## Exercise 3.1\n", + "\n", + "Your first task is to simulate and plot the correlated time series\n", + "\n", + "$$\n", + "x_{t+1} = \\alpha \\, x_t + \\epsilon_{t+1}\n", + "\\quad \\text{where} \\quad\n", + "x_0 = 0\n", + "\\quad \\text{and} \\quad t = 0,\\ldots,T\n", + "$$\n", + "\n", + "The sequence of shocks $ \\{\\epsilon_t\\} $ is assumed to be IID and standard normal.\n", + "\n", + "In your solution, restrict your import statements to" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4da50a38", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "53e448a3", + "metadata": {}, + "source": [ + "Set $ T=200 $ and $ \\alpha = 0.9 $." + ] + }, + { + "cell_type": "markdown", + "id": "8eeda2bc", + "metadata": {}, + "source": [ + "## Solution to[ Exercise 3.1](https://python-programming.quantecon.org/#pbe_ex1)\n", + "\n", + "Here’s one solution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4578e185", + "metadata": {}, + "outputs": [], + "source": [ + "α = 0.9\n", + "T = 200\n", + "x = np.empty(T+1)\n", + "x[0] = 0\n", + "\n", + "for t in range(T):\n", + " x[t+1] = α * x[t] + np.random.randn()\n", + "\n", + "plt.plot(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b1ee91d3", + "metadata": {}, + "source": [ + "## Exercise 3.2\n", + "\n", + "Starting with your solution to exercise 1, plot three simulated time series,\n", + "one for each of the cases $ \\alpha=0 $, $ \\alpha=0.8 $ and $ \\alpha=0.98 $.\n", + "\n", + "Use a `for` loop to step through the $ \\alpha $ values.\n", + "\n", + "If you can, add a legend, to help distinguish between the three time series.\n", + "\n", + "Hints:\n", + "\n", + "- If you call the `plot()` function multiple times before calling `show()`, all of the lines you produce will end up on the same figure. \n", + "- For the legend, noted that the expression `'foo' + str(42)` evaluates to `'foo42'`. " + ] + }, + { + "cell_type": "markdown", + "id": "682f5011", + "metadata": {}, + "source": [ + "## Solution to[ Exercise 3.2](https://python-programming.quantecon.org/#pbe_ex2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c74097b2", + "metadata": {}, + "outputs": [], + "source": [ + "α_values = [0.0, 0.8, 0.98]\n", + "T = 200\n", + "x = np.empty(T+1)\n", + "\n", + "for α in α_values:\n", + " x[0] = 0\n", + " for t in range(T):\n", + " x[t+1] = α * x[t] + np.random.randn()\n", + " plt.plot(x, label=f'$\\\\alpha = {α}$')\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6cd8434a", + "metadata": {}, + "source": [ + "Note: `f'\\$\\\\alpha = {α}\\$'` in the solution is an application of [f-String](https://docs.python.org/3/tutorial/inputoutput.html#tut-f-strings), which allows you to use `{}` to contain an expression.\n", + "\n", + "The contained expression will be evaluated, and the result will be placed into the string." + ] + }, + { + "cell_type": "markdown", + "id": "710b5d7c", + "metadata": {}, + "source": [ + "## Exercise 3.3\n", + "\n", + "Similar to the previous exercises, plot the time series\n", + "\n", + "$$\n", + "x_{t+1} = \\alpha \\, |x_t| + \\epsilon_{t+1}\n", + "\\quad \\text{where} \\quad\n", + "x_0 = 0\n", + "\\quad \\text{and} \\quad t = 0,\\ldots,T\n", + "$$\n", + "\n", + "Use $ T=200 $, $ \\alpha = 0.9 $ and $ \\{\\epsilon_t\\} $ as before.\n", + "\n", + "Search online for a function that can be used to compute the absolute value $ |x_t| $." + ] + }, + { + "cell_type": "markdown", + "id": "1cd2da19", + "metadata": {}, + "source": [ + "## Solution to[ Exercise 3.3](https://python-programming.quantecon.org/#pbe_ex3)\n", + "\n", + "Here’s one solution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8996551f", + "metadata": {}, + "outputs": [], + "source": [ + "α = 0.9\n", + "T = 200\n", + "x = np.empty(T+1)\n", + "x[0] = 0\n", + "\n", + "for t in range(T):\n", + " x[t+1] = α * np.abs(x[t]) + np.random.randn()\n", + "\n", + "plt.plot(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "97ff965f", + "metadata": {}, + "source": [ + "## Exercise 3.4\n", + "\n", + "One important aspect of essentially all programming languages is branching and\n", + "conditions.\n", + "\n", + "In Python, conditions are usually implemented with if–else syntax.\n", + "\n", + "Here’s an example, that prints -1 for each negative number in an array and 1\n", + "for each nonnegative number" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6080e122", + "metadata": {}, + "outputs": [], + "source": [ + "numbers = [-9, 2.3, -11, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "165fb094", + "metadata": {}, + "outputs": [], + "source": [ + "for x in numbers:\n", + " if x < 0:\n", + " print(-1)\n", + " else:\n", + " print(1)" + ] + }, + { + "cell_type": "markdown", + "id": "e5a8f5fd", + "metadata": {}, + "source": [ + "Now, write a new solution to Exercise 3 that does not use an existing function\n", + "to compute the absolute value.\n", + "\n", + "Replace this existing function with an if–else condition." + ] + }, + { + "cell_type": "markdown", + "id": "4cadf82a", + "metadata": {}, + "source": [ + "## Solution to[ Exercise 3.4](https://python-programming.quantecon.org/#pbe_ex4)\n", + "\n", + "Here’s one way:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da4679e0", + "metadata": {}, + "outputs": [], + "source": [ + "α = 0.9\n", + "T = 200\n", + "x = np.empty(T+1)\n", + "x[0] = 0\n", + "\n", + "for t in range(T):\n", + " if x[t] < 0:\n", + " abs_x = - x[t]\n", + " else:\n", + " abs_x = x[t]\n", + " x[t+1] = α * abs_x + np.random.randn()\n", + "\n", + "plt.plot(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cd087f1b", + "metadata": {}, + "source": [ + "Here’s a shorter way to write the same thing:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84d67d27", + "metadata": {}, + "outputs": [], + "source": [ + "α = 0.9\n", + "T = 200\n", + "x = np.empty(T+1)\n", + "x[0] = 0\n", + "\n", + "for t in range(T):\n", + " abs_x = - x[t] if x[t] < 0 else x[t]\n", + " x[t+1] = α * abs_x + np.random.randn()\n", + "\n", + "plt.plot(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1c7fc965", + "metadata": {}, + "source": [ + "## Exercise 3.5\n", + "\n", + "Here’s a harder exercise, that takes some thought and planning.\n", + "\n", + "The task is to compute an approximation to $ \\pi $ using [Monte Carlo](https://en.wikipedia.org/wiki/Monte_Carlo_method).\n", + "\n", + "Use no imports besides" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c65f875", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "133925e8", + "metadata": {}, + "source": [ + "Your hints are as follows:\n", + "\n", + "- If $ U $ is a bivariate uniform random variable on the unit square $ (0, 1)^2 $, then the probability that $ U $ lies in a subset $ B $ of $ (0,1)^2 $ is equal to the area of $ B $. \n", + "- If $ U_1,\\ldots,U_n $ are IID copies of $ U $, then, as $ n $ gets large, the fraction that falls in $ B $, converges to the probability of landing in $ B $. \n", + "- For a circle, $ area = \\pi * radius^2 $. " + ] + }, + { + "cell_type": "markdown", + "id": "e25a7b53", + "metadata": {}, + "source": [ + "## Solution to[ Exercise 3.5](https://python-programming.quantecon.org/#pbe_ex5)\n", + "\n", + "Consider the circle of diameter 1 embedded in the unit square.\n", + "\n", + "Let $ A $ be its area and let $ r=1/2 $ be its radius.\n", + "\n", + "If we know $ \\pi $ then we can compute $ A $ via\n", + "$ A = \\pi r^2 $.\n", + "\n", + "But here the point is to compute $ \\pi $, which we can do by\n", + "$ \\pi = A / r^2 $.\n", + "\n", + "Summary: If we can estimate the area of a circle with diameter 1, then dividing\n", + "by $ r^2 = (1/2)^2 = 1/4 $ gives an estimate of $ \\pi $.\n", + "\n", + "We estimate the area by sampling bivariate uniforms and looking at the\n", + "fraction that falls into the circle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e59ff3dc", + "metadata": {}, + "outputs": [], + "source": [ + "n = 1000000 # sample size for Monte Carlo simulation\n", + "\n", + "count = 0\n", + "for i in range(n):\n", + "\n", + " # drawing random positions on the square\n", + " u, v = np.random.uniform(), np.random.uniform()\n", + "\n", + " # check whether the point falls within the boundary\n", + " # of the unit circle centred at (0.5,0.5)\n", + " d = np.sqrt((u - 0.5)**2 + (v - 0.5)**2)\n", + "\n", + " # if it falls within the inscribed circle, \n", + " # add it to the count\n", + " if d < 0.5:\n", + " count += 1\n", + "\n", + "area_estimate = count / n\n", + "\n", + "print(area_estimate * 4) # dividing by radius**2" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/01_python_jupyter_demo.ipynb b/src/01_python_jupyter_demo.ipynb index 20b184d..dd2dc6d 100644 --- a/src/01_python_jupyter_demo.ipynb +++ b/src/01_python_jupyter_demo.ipynb @@ -6,8 +6,7 @@ "id": "J09tr3ds09ky" }, "source": [ - "Python for Data Science Demo\n", - "============================\n", + "# 1.2 Python for Data Science Demo\n", "\n", "This notebook is designed to show off some of the features of using Python for data science that you'll encounter throughout the year. Although Jupyter Notebooks are not always the right medium for your code, it will also demonstrate some of the features of Jupyter and Jupyter Notebooks that make them useful for data exploration and visualization.\n", "\n", @@ -29,7 +28,7 @@ }, "source": [ "\n", - "# 1. Set up Environment\n", + "## 0. Set up Environment\n", "\n", "We'll first start by discussing the Python interpreter, Anaconda vs Conda, Jupyter, and Google Colaboratory. We'll defer an in-depth discussion until next week, but we'll mention the basics today. Today, we'll run everything in Google Colaboratory. Next week we'll run our code locally in Jupyter. The following week, we'll discuss text editors. In particular, we'll write code in [Visual Studio Code](https://code.visualstudio.com/).\n", "\n", @@ -52,7 +51,7 @@ "if IN_COLAB:\n", " !pip install plotly==5.9.0\n", "else:\n", - " print(\"Be sure to install the required packages manually if not in Colab\")\n" + " print(\"Be sure to install the required packages manually if not in Colab\")" ] }, { @@ -78,7 +77,7 @@ "id": "qOKX9_RL09k8" }, "source": [ - "# 2. NumPy\n", + "## 1. NumPy\n", "\n", "NumPy a library designed to add support \"for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.\"\n", "\n", @@ -86,7 +85,7 @@ "\n", "The basic functionality of NumPy is the efficient management of arrays, with syntax as follows:\n", "
\n", - "\n", + "\n", "
" ] }, @@ -196,7 +195,7 @@ "id": "oy8f0YOD09lC" }, "source": [ - "# 2. SciPy\n", + "## 2. SciPy\n", "\n", "SciPy is a library \"used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.\"\n", "\n", @@ -231,7 +230,7 @@ "id": "fXUSbyiz09lD" }, "source": [ - "# 3. Matplotlib\n", + "## 3. Matplotlib\n", "\n", "Matplotlib is the most plotting library for Python. Even other plotting libraries build off of Matplotlib as a foundation. Even if you use other plotting libraries, it is important to understand the basics of Matplotlib.\n", "\n", @@ -363,7 +362,7 @@ "id": "j63HvaTz09lG" }, "source": [ - "# 4. Pandas\n", + "## 4. Pandas\n", "\n", "From Wikipedia, \"`pandas` is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term \"panel data\", an econometrics term for data sets that include observations over multiple time periods for the same individuals. Its name is a play on the phrase \"Python data analysis\" itself. Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010.\"" ] @@ -427,7 +426,7 @@ "id": "Y9Ku8QcY1gMM" }, "source": [ - "# 5. StatsModels\n", + "## 5. StatsModels\n", "\n", "\"`statsmodels` is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration.\"" ] @@ -488,9 +487,9 @@ "id": "usHlQ_zd09lH" }, "source": [ - "# 6. IPyWidgets\n", + "## 6. IPyWidgets\n", "\n", - "#### 6.1 Lorenz Attractor: Lorenz System of Differential Equations\n" + "### 6.1 Lorenz Attractor: Lorenz System of Differential Equations\n" ] }, { diff --git a/day_02/03.00-Introduction-to-Pandas.ipynb b/src/02_00-Introduction-to-Pandas.ipynb similarity index 100% rename from day_02/03.00-Introduction-to-Pandas.ipynb rename to src/02_00-Introduction-to-Pandas.ipynb diff --git a/day_02/03.01-Introducing-Pandas-Objects.ipynb b/src/02_01-Introducing-Pandas-Objects.ipynb similarity index 100% rename from day_02/03.01-Introducing-Pandas-Objects.ipynb rename to src/02_01-Introducing-Pandas-Objects.ipynb diff --git a/day_02/03.02-Data-Indexing-and-Selection.ipynb b/src/02_02-Data-Indexing-and-Selection.ipynb similarity index 100% rename from day_02/03.02-Data-Indexing-and-Selection.ipynb rename to src/02_02-Data-Indexing-and-Selection.ipynb diff --git a/day_02/03.03-Operations-in-Pandas.ipynb b/src/02_03-Operations-in-Pandas.ipynb similarity index 100% rename from day_02/03.03-Operations-in-Pandas.ipynb rename to src/02_03-Operations-in-Pandas.ipynb diff --git a/day_02/03.04-Missing-Values.ipynb b/src/02_04-Missing-Values.ipynb similarity index 100% rename from day_02/03.04-Missing-Values.ipynb rename to src/02_04-Missing-Values.ipynb diff --git a/day_02/Using_Interact.ipynb b/src/02_Using_Interact.ipynb similarity index 57% rename from day_02/Using_Interact.ipynb rename to src/02_Using_Interact.ipynb index 4e4bf5b..190d036 100644 --- a/day_02/Using_Interact.ipynb +++ b/src/02_Using_Interact.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Using Interact" + "# 2.4 Using Interact" ] }, { @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -66,24 +66,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "327689dacfef4c9f8e28da3894ec26a6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), Output()), _dom_classes=('widget-…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=10);" ] @@ -99,24 +84,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "51ca0221fd294cfd85304d3905190d28", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Checkbox(value=True, description='x'), Output()), _dom_classes=('widget-interact',))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=True);" ] @@ -130,24 +100,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "265a588b228f461ea9775a1dda63350e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Text(value='Hi there!', description='x'), Output()), _dom_classes=('widget-interact',))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x='Hi there!');" ] @@ -161,24 +116,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5729df6bf28f42399ab9b5df85f0d062", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Checkbox(value=True, description='x'), FloatSlider(value=1.0, description='y', max=3.0, …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "@interact(x=True, y=1.0)\n", "def g(x, y):\n", @@ -201,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -218,24 +158,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5785850fb1ae42b5aaadc74fbadf93c1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=5, description='p', max=15, min=-5), Output()), _dom_classes=('widget-in…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(h, p=5, q=fixed(20));" ] @@ -269,24 +194,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "db485082dedf413887345fd62a816cc0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), Output()), _dom_classes=('widget-…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=widgets.IntSlider(min=-10,max=30,step=1,value=10));" ] @@ -323,24 +233,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "82ee153b9ad147c6bcac12485b49db34", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=2, description='x', max=4), Output()), _dom_classes=('widget-interact',)…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=(0,4));" ] @@ -354,24 +249,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3d1de1678eaa4424adbb04ec6680a6a3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=4, description='x', max=8, step=2), Output()), _dom_classes=('widget-int…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=(0,8,2));" ] @@ -385,24 +265,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1215bfe82858483ea2d3cd60d6432718", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=5.0, description='x', max=10.0), Output()), _dom_classes=('widget-inte…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=(0.0,10.0));" ] @@ -416,24 +281,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "968d609db24c4384ad7338578146ea13", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=5.0, description='x', max=10.0, step=0.01), Output()), _dom_classes=('…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=(0.0,10.0,0.01));" ] @@ -447,24 +297,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f68e0e63301947eeb378d8a4285f45a5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=5.5, description='x', max=20.0, step=0.5), Output()), _dom_classes=('w…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "@interact(x=(0.0,20.0,0.5))\n", "def h(x=5.5):\n", @@ -480,24 +315,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "36fc1fa21d3f40658bfe957c99da9d26", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Dropdown(description='x', options=('apples', 'oranges'), value='apples'), Output()), _do…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=widgets.Dropdown(options=['apples','oranges']));" ] @@ -511,24 +331,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e2d136a6c1504f1fa3b9ebaae11f7514", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(Dropdown(description='x', options=(('one', 10), ('two', 20)), value=10), Output()), _dom…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "interact(f, x=widgets.Dropdown(options=[('one', 10), ('two', 20)]));" ] @@ -556,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -573,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -589,20 +394,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ipywidgets.widgets.interaction.interactive" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "type(w)" ] @@ -616,22 +410,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(IntSlider(value=10, description='a', max=30, min=-10),\n", - " IntSlider(value=20, description='b', max=60, min=-20),\n", - " Output())" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "w.children" ] @@ -645,24 +426,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b9cc27aca8db41a994c8ab2239ca4d2a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=10, description='a', max=30, min=-10), IntSlider(value=20, description='…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from IPython.display import display\n", "display(w)" @@ -679,20 +445,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'a': 10, 'b': 20}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "w.kwargs" ] @@ -706,71 +461,17 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "30" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "w.result" ] } ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false + "name": "python" } }, "nbformat": 4, diff --git a/src/02_functions.ipynb b/src/02_functions.ipynb new file mode 100644 index 0000000..358a4e6 --- /dev/null +++ b/src/02_functions.ipynb @@ -0,0 +1,1447 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d01b84d5", + "metadata": { + "id": "d01b84d5" + }, + "source": [ + "# Functions\n", + "\n", + "**Prerequisites**\n", + "\n", + "- [Getting Started](https://datascience.quantecon.org/../introduction/getting_started.html) \n", + "- [Basics](https://datascience.quantecon.org/basics.html) \n", + "- [Collections](https://datascience.quantecon.org/collections.html) \n", + "- [Control Flow](https://datascience.quantecon.org/control_flow.html) \n", + "\n", + "\n", + "**Outcomes**\n", + "\n", + "- Economic Production Functions \n", + " - Understand the basics of production functions in economics \n", + "- Functions \n", + " - Know how to define your own function \n", + " - Know how to find and write your own function documentation \n", + " - Know why we use functions \n", + " - Understand scoping rules and blocks \n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "ddecc742", + "metadata": { + "id": "ddecc742" + }, + "source": [ + "## Application: Production Functions\n", + "\n", + "Production functions are useful when modeling the economics of firms producing\n", + "goods or the aggregate output in an economy.\n", + "\n", + "Though the term “function” is used in a mathematical sense here, we will be making\n", + "tight connections between the programming of mathematical functions and Python\n", + "functions." + ] + }, + { + "cell_type": "markdown", + "id": "33438a42", + "metadata": { + "id": "33438a42" + }, + "source": [ + "### Factors of Production\n", + "\n", + "The [factors of production](https://en.wikipedia.org/wiki/Factors_of_production)\n", + "are the inputs used in the production of some sort of output.\n", + "\n", + "Some example factors of production include\n", + "\n", + "- [Physical capital](https://en.wikipedia.org/wiki/Physical_capital), e.g.\n", + " machines, buildings, computers, and power stations. \n", + "- Labor, e.g. all of the hours of work from different types of employees of a\n", + " firm. \n", + "- [Human Capital](https://en.wikipedia.org/wiki/Human_capital), e.g. the\n", + " knowledge of employees within a firm. \n", + "\n", + "\n", + "A [production function](https://en.wikipedia.org/wiki/Production_function)\n", + "maps a set of inputs to the output, e.g. the amount of wheat produced by a\n", + "farm, or widgets produced in a factory.\n", + "\n", + "As an example of the notation, we denote the total units of labor and\n", + "physical capital used in a factory as $ L $ and $ K $ respectively.\n", + "\n", + "If we denote the physical output of the factory as $ Y $, then a production\n", + "function $ F $ that transforms labor and capital into output might have the\n", + "form:\n", + "\n", + "$$\n", + "Y = F(K, L)\n", + "$$\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "4774ddc2", + "metadata": { + "id": "4774ddc2" + }, + "source": [ + "### An Example Production Function\n", + "\n", + "Throughout this lecture, we will use the\n", + "[Cobb-Douglas](https://en.wikipedia.org/wiki/Cobb%E2%80%93Douglas_production_function)\n", + "production function to help us understand how to create Python\n", + "functions and why they are useful.\n", + "\n", + "The Cobb-Douglas production function has appealing statistical properties when brought to data.\n", + "\n", + "This function is displayed below.\n", + "\n", + "$$\n", + "Y = z K^{\\alpha} L^{1-\\alpha}\n", + "$$\n", + "\n", + "The function is parameterized by:\n", + "\n", + "- A parameter $ \\alpha \\in [0,1] $, called the “output elasticity of\n", + " capital”. \n", + "- A value $ z $ called the [Total Factor Productivity](https://en.wikipedia.org/wiki/Total_factor_productivity) (TFP). " + ] + }, + { + "cell_type": "markdown", + "id": "bcd32baf", + "metadata": { + "id": "bcd32baf" + }, + "source": [ + "## What are (Python) Functions?\n", + "\n", + "In this class, we will often talk about `function`s.\n", + "\n", + "So what is a function?\n", + "\n", + "We like to think of a function as a production line in a\n", + "manufacturing plant: we pass zero or more things to it, operations take place in a\n", + "set linear sequence, and zero or more things come out.\n", + "\n", + "We use functions for the following purposes:\n", + "\n", + "- **Re-usability**: Writing code to do a specific task just once, and\n", + " reuse the code by calling the function. \n", + "- **Organization**: Keep the code for distinct operations separated and\n", + " organized. \n", + "- **Sharing/collaboration**: Sharing code across multiple projects or\n", + " sharing pieces of code with collaborators. " + ] + }, + { + "cell_type": "markdown", + "id": "fed78915", + "metadata": { + "id": "fed78915" + }, + "source": [ + "## How to Define (Python) Functions?\n", + "\n", + "The basic syntax to create our own function is as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e1bdbe8", + "metadata": {}, + "outputs": [], + "source": [ + "def function_name(inputs):\n", + " # step 1\n", + " # step 2\n", + " # ...\n", + " return outputs" + ] + }, + { + "cell_type": "markdown", + "id": "57878b06", + "metadata": { + "id": "57878b06" + }, + "source": [ + "Here we see two new *keywords*: `def` and `return`.\n", + "\n", + "- `def` is used to tell Python we would like to define a new function. \n", + "- `return` is used to tell Python what we would like to **return** from a\n", + " function. \n", + "\n", + "\n", + "Let’s look at an example and then discuss each part:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a3fb19f", + "metadata": {}, + "outputs": [], + "source": [ + "def mean(numbers):\n", + " total = sum(numbers)\n", + " N = len(numbers)\n", + " answer = total / N\n", + "\n", + " return answer" + ] + }, + { + "cell_type": "markdown", + "id": "e87d1e82", + "metadata": { + "id": "e87d1e82" + }, + "source": [ + "Here we defined a function `mean` that has one input (`numbers`),\n", + "does three steps, and has one output (`answer`).\n", + "\n", + "Let’s see what happens when we call this function on the list of numbers\n", + "`[1, 2, 3, 4]`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8115113e", + "metadata": {}, + "outputs": [], + "source": [ + "x = [1, 2, 3, 4]\n", + "the_mean = mean(x)\n", + "the_mean" + ] + }, + { + "cell_type": "markdown", + "id": "556b1f1a", + "metadata": { + "id": "556b1f1a" + }, + "source": [ + "Additionally, as we saw in the [control flow](https://datascience.quantecon.org/control_flow.html) lecture, indentation\n", + "controls blocks of code (along with the [scope](#scope) rules).\n", + "\n", + "To see this, compare a function with no inputs or return values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "304fc73d", + "metadata": {}, + "outputs": [], + "source": [ + "def f():\n", + " print(\"1\")\n", + " print(\"2\")\n", + "f()" + ] + }, + { + "cell_type": "markdown", + "id": "c7c7283d", + "metadata": { + "id": "c7c7283d" + }, + "source": [ + "With the following change of indentation…" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63b91a05", + "metadata": {}, + "outputs": [], + "source": [ + "def f():\n", + " print(\"1\")\n", + "print(\"2\")\n", + "f()" + ] + }, + { + "cell_type": "markdown", + "id": "370040f1", + "metadata": { + "id": "370040f1" + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "71eb966e", + "metadata": { + "id": "71eb966e" + }, + "source": [ + "### Scope\n", + "\n", + "Notice that we named the input to the function `x` and we called the output\n", + "`the_mean`.\n", + "\n", + "When we defined the function, the input was called `numbers` and the output\n", + "`answer`… what gives?\n", + "\n", + "This is an example of a programming concept called\n", + "[variable scope](http://python-textbok.readthedocs.io/en/1.0/Variables_and_Scope.html).\n", + "\n", + "In Python, functions define their own scope for variables.\n", + "\n", + "In English, this means that regardless of what name we give an input variable (`x` in this example),\n", + "the input will always be referred to as `numbers` *inside* the body of the `mean`\n", + "function.\n", + "\n", + "It also means that although we called the output `answer` inside of the\n", + "function `mean`, that this variable name was only valid inside of our\n", + "function.\n", + "\n", + "To use the output of the function, we had to give it our own name (`the_mean`\n", + "in this example).\n", + "\n", + "Another point to make here is that the intermediate variables we defined inside\n", + "`mean` (`total` and `N`) are only defined inside of the `mean` function\n", + "– we can’t access them from outside. We can verify this by trying to see what\n", + "the value of `total` is:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11844389", + "metadata": {}, + "outputs": [], + "source": [ + "def mean(numbers):\n", + " total = sum(numbers)\n", + " N = len(numbers)\n", + " answer = total / N\n", + " return answer # or directly return total / N\n", + "\n", + "# uncomment the line below and execute to see the error\n", + "# total" + ] + }, + { + "cell_type": "markdown", + "id": "5bd0615d", + "metadata": { + "id": "5bd0615d" + }, + "source": [ + "This point can be taken even further: the same name can be bound\n", + "to variables inside of blocks of code and in the outer “scope”." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "388a4029", + "metadata": {}, + "outputs": [], + "source": [ + "x = 4\n", + "print(f\"x = {x}\")\n", + "def f():\n", + " x = 5 # a different \"x\"\n", + " print(f\"x = {x}\")\n", + "f() # calls function\n", + "print(f\"x = {x}\")" + ] + }, + { + "cell_type": "markdown", + "id": "de1ca784", + "metadata": { + "id": "de1ca784" + }, + "source": [ + "The final point we want to make about scope is that function inputs and output\n", + "don’t have to be given a name outside the function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5dae5c00", + "metadata": {}, + "outputs": [], + "source": [ + "mean([10, 20, 30])" + ] + }, + { + "cell_type": "markdown", + "id": "a52ba1d5", + "metadata": { + "id": "a52ba1d5" + }, + "source": [ + "Notice that we didn’t name the input or the output, but the function was\n", + "called successfully.\n", + "\n", + "Now, we’ll use our new knowledge to define a function which computes the output\n", + "from a Cobb-Douglas production function with parameters $ z = 1 $ and\n", + "$ \\alpha = 0.33 $ and takes inputs $ K $ and $ L $." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f01d0518", + "metadata": {}, + "outputs": [], + "source": [ + "def cobb_douglas(K, L):\n", + "\n", + " # Create alpha and z\n", + " z = 1\n", + " alpha = 0.33\n", + "\n", + " return z * K**alpha * L**(1 - alpha)" + ] + }, + { + "cell_type": "markdown", + "id": "ac6912bc", + "metadata": { + "id": "ac6912bc" + }, + "source": [ + "We can use this function as we did the mean function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a533ffc0", + "metadata": {}, + "outputs": [], + "source": [ + "cobb_douglas(1.0, 0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "46005624", + "metadata": { + "id": "46005624" + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "56e2a4de", + "metadata": { + "id": "56e2a4de" + }, + "source": [ + "### Re-using Functions\n", + "\n", + "Economists are often interested in this question: how much does output\n", + "change if we modify our inputs?\n", + "\n", + "For example, take a production function $ Y_1 = F(K_1,L_1) $ which produces\n", + "$ Y_1 $ units of the goods.\n", + "\n", + "If we then multiply the inputs each by $ \\gamma $, so that\n", + "$ K_2 = \\gamma K_1 $ and $ L_2 = \\gamma L_1 $, then the output is\n", + "\n", + "$$\n", + "Y_2 = F(K_2, L_2) = F(\\gamma K_1, \\gamma L_1)\n", + "$$\n", + "\n", + "How does $ Y_1 $ compare to $ Y_2 $?\n", + "\n", + "Answering this question involves something called *returns to scale*.\n", + "\n", + "Returns to scale tells us whether our inputs are more or less productive as we\n", + "have more of them.\n", + "\n", + "For example, imagine that you run a restaurant. How would you expect the amount\n", + "of food you could produce would change if you could build an exact replica of\n", + "your restaurant and kitchen and hire the same number of cooks and waiters? You\n", + "would probably expect it to double.\n", + "\n", + "If, for any $ K, L $, we multiply $ K, L $ by a value $ \\gamma $\n", + "then\n", + "\n", + "- If $ \\frac{Y_2}{Y_1} < \\gamma $ then we say the production function has\n", + " decreasing returns to scale. \n", + "- If $ \\frac{Y_2}{Y_1} = \\gamma $ then we say the production function has\n", + " constant returns to scale. \n", + "- If $ \\frac{Y_2}{Y_1} > \\gamma $ then we say the production function has\n", + " increasing returns to scale. \n", + "\n", + "\n", + "Let’s try it and see what our function is!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba4280b8", + "metadata": {}, + "outputs": [], + "source": [ + "y1 = cobb_douglas(1.0, 0.5)\n", + "print(y1)\n", + "y2 = cobb_douglas(2*1.0, 2*0.5)\n", + "print(y2)" + ] + }, + { + "cell_type": "markdown", + "id": "dce2a0e7", + "metadata": { + "id": "dce2a0e7" + }, + "source": [ + "How did $ Y_1 $ and $ Y_2 $ relate?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92f17f1b", + "metadata": {}, + "outputs": [], + "source": [ + "y2 / y1" + ] + }, + { + "cell_type": "markdown", + "id": "0bc96ca2", + "metadata": { + "id": "0bc96ca2" + }, + "source": [ + "$ Y_2 $ was exactly double $ Y_1 $!\n", + "\n", + "Let’s write a function that will compute the returns to scale for different\n", + "values of $ K $ and $ L $.\n", + "\n", + "This is an example of how writing functions can allow us to re-use code\n", + "in ways we might not originally anticipate. (You didn’t know we’d be\n", + "writing a `returns_to_scale` function when we wrote `cobb_douglas`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b1315cd", + "metadata": {}, + "outputs": [], + "source": [ + "def returns_to_scale(K, L, gamma):\n", + " y1 = cobb_douglas(K, L)\n", + " y2 = cobb_douglas(gamma*K, gamma*L)\n", + " y_ratio = y2 / y1\n", + " return y_ratio / gamma" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13d23ac8", + "metadata": {}, + "outputs": [], + "source": [ + "returns_to_scale(1.0, 0.5, 2.0)" + ] + }, + { + "cell_type": "markdown", + "id": "63637c4c", + "metadata": { + "id": "63637c4c" + }, + "source": [ + "### Exercise\n", + "\n", + "See exercise 1 in the [exercise list](#ex2-4).\n", + "\n", + "It turns out that with a little bit of algebra, we can check that this will\n", + "always hold for our [Cobb-Douglas example](#cobb-douglas-example) above.\n", + "\n", + "To show this, take an arbitrary $ K, L $ and multiply the inputs by an\n", + "arbitrary $ \\gamma $.\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + " F(\\gamma K, \\gamma L) &= z (\\gamma K)^{\\alpha} (\\gamma L)^{1-\\alpha}\\\\\n", + " &= z \\gamma^{\\alpha}\\gamma^{1-\\alpha} K^{\\alpha} L^{1-\\alpha}\\\\\n", + " &= \\gamma z K^{\\alpha} L^{1-\\alpha} = \\gamma F(K, L)\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "For an example of a production function that is not CRS, look at a\n", + "generalization of the Cobb-Douglas production function that has different\n", + "“output elasticities” for the 2 inputs.\n", + "\n", + "$$\n", + "Y = z K^{\\alpha_1} L^{\\alpha_2}\n", + "$$\n", + "\n", + "Note that if $ \\alpha_2 = 1 - \\alpha_1 $, this is our Cobb-Douglas\n", + "production function." + ] + }, + { + "cell_type": "markdown", + "id": "be199e69", + "metadata": { + "id": "be199e69" + }, + "source": [ + "### Exercise\n", + "\n", + "See exercise 2 in the [exercise list](#ex2-4).\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "6abfcb1f", + "metadata": { + "id": "6abfcb1f" + }, + "source": [ + "### Multiple Returns\n", + "\n", + "Another valuable element to analyze on production functions is how\n", + "output changes as we change only one of the inputs. We will call this the\n", + "marginal product.\n", + "\n", + "For example, compare the output using $ K, L $ units of inputs to that with\n", + "an $ \\epsilon $ units of labor.\n", + "\n", + "Then the marginal product of labor (MPL) is defined as\n", + "\n", + "$$\n", + "\\frac{F(K, L + \\varepsilon) - F(K, L)}{\\varepsilon}\n", + "$$\n", + "\n", + "This tells us how much additional output is created relative to the additional\n", + "input. (Spoiler alert: This should look like the definition for a partial\n", + "derivative!)\n", + "\n", + "If the input can be divided into small units, then we can use calculus to take\n", + "this limit, using the partial derivative of the production function relative to\n", + "that input.\n", + "\n", + "In this case, we define the marginal product of labor (MPL) and marginal product\n", + "of capital (MPK) as\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "MPL(K, L) &= \\frac{\\partial F(K, L)}{\\partial L}\\\\\n", + "MPK(K, L) &= \\frac{\\partial F(K, L)}{\\partial K}\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "In the [Cobb-Douglas](#cobb-douglas-example) example above, this becomes\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "MPK(K, L) &= z \\alpha \\left(\\frac{K}{L} \\right)^{\\alpha - 1}\\\\\n", + "MPL(K, L) &= (1-\\alpha) z \\left(\\frac{K}{L} \\right)^{\\alpha}\\\\\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "Let’s test it out with Python! We’ll also see that we can actually return\n", + "multiple things in a Python function.\n", + "\n", + "The syntax for a return statement with multiple items is return item1, item2, ….\n", + "\n", + "In this case, we’ll compute both the MPL and the MPK and then return both." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46654832", + "metadata": {}, + "outputs": [], + "source": [ + "def marginal_products(K, L, epsilon):\n", + "\n", + " mpl = (cobb_douglas(K, L + epsilon) - cobb_douglas(K, L)) / epsilon\n", + " mpk = (cobb_douglas(K + epsilon, L) - cobb_douglas(K, L)) / epsilon\n", + "\n", + " return mpl, mpk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44284da4", + "metadata": {}, + "outputs": [], + "source": [ + "tup = marginal_products(1.0, 0.5, 1e-4)\n", + "print(tup)" + ] + }, + { + "cell_type": "markdown", + "id": "eca892b8", + "metadata": { + "id": "eca892b8" + }, + "source": [ + "Instead of using the tuple, these can be directly unpacked to variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b2e4f7e", + "metadata": {}, + "outputs": [], + "source": [ + "mpl, mpk = marginal_products(1.0, 0.5, 1e-4)\n", + "print(f\"mpl = {mpl}, mpk = {mpk}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0c65f1f6", + "metadata": { + "id": "0c65f1f6" + }, + "source": [ + "We can use this to calculate the marginal products for different `K`, fixing `L`\n", + "using a comprehension." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91124022", + "metadata": {}, + "outputs": [], + "source": [ + "Ks = [1.0, 2.0, 3.0]\n", + "[marginal_products(K, 0.5, 1e-4) for K in Ks] # create a tuple for each K" + ] + }, + { + "cell_type": "markdown", + "id": "490312cd", + "metadata": { + "id": "490312cd" + }, + "source": [ + "### Documentation\n", + "\n", + "In a previous exercise, we asked you to find help for the `cobb_douglas` and\n", + "`returns_to_scale` functions using `?`.\n", + "\n", + "It didn’t provide any useful information.\n", + "\n", + "To provide this type of help information, we need to\n", + "add what Python programmers call a “docstring” to our functions.\n", + "\n", + "This is done by putting a string (not assigned to any variable name) as\n", + "the first line of the *body* of the function (after the line with\n", + "`def`).\n", + "\n", + "Below is a new version of the template we used to define functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcfe31d5", + "metadata": {}, + "outputs": [], + "source": [ + "def function_name(inputs):\n", + " \"\"\"\n", + " Docstring\n", + " \"\"\"\n", + " # step 1\n", + " # step 2\n", + " # ...\n", + " return outputs" + ] + }, + { + "cell_type": "markdown", + "id": "f210bf45", + "metadata": { + "id": "f210bf45" + }, + "source": [ + "Let’s re-define our `cobb_douglas` function to include a docstring." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5078fd27", + "metadata": {}, + "outputs": [], + "source": [ + "def cobb_douglas(K, L):\n", + " \"\"\"\n", + " Computes the production F(K, L) for a Cobb-Douglas production function\n", + "\n", + " Takes the form F(K, L) = z K^{\\alpha} L^{1 - \\alpha}\n", + "\n", + " We restrict z = 1 and alpha = 0.33\n", + " \"\"\"\n", + " return 1.0 * K**(0.33) * L**(1.0 - 0.33)" + ] + }, + { + "cell_type": "markdown", + "id": "aa4f9b57", + "metadata": { + "id": "aa4f9b57" + }, + "source": [ + "Now when we have Jupyter evaluate `cobb_douglas?`, our message is\n", + "displayed (or use the Contextual Help window with Jupyterlab and `Ctrl-I` or `Cmd-I`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c30fe52", + "metadata": {}, + "outputs": [], + "source": [ + "cobb_douglas?" + ] + }, + { + "cell_type": "markdown", + "id": "c0326dc6", + "metadata": { + "id": "c0326dc6" + }, + "source": [ + "We recommend that you always include at least a very simple docstring for\n", + "nontrivial functions.\n", + "\n", + "This is in the same spirit as adding comments to your code — it makes it easier\n", + "for future readers/users (including yourself) to understand what the code does." + ] + }, + { + "cell_type": "markdown", + "id": "cfc8949e", + "metadata": { + "id": "cfc8949e" + }, + "source": [ + "### Exercise\n", + "\n", + "See exercise 3 in the [exercise list](#ex2-4)." + ] + }, + { + "cell_type": "markdown", + "id": "05110f75", + "metadata": { + "id": "05110f75" + }, + "source": [ + "### Default and Keyword Arguments\n", + "\n", + "Functions can have optional arguments.\n", + "\n", + "To accomplish this, we must these arguments a *default value* by saying\n", + "`name=default_value` instead of just `name` as we list the arguments.\n", + "\n", + "To demonstrate this functionality, let’s now make $ z $ and $ \\alpha $\n", + "arguments to our cobb_douglas function!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ec51f0d", + "metadata": {}, + "outputs": [], + "source": [ + "def cobb_douglas(K, L, alpha=0.33, z=1):\n", + " \"\"\"\n", + " Computes the production F(K, L) for a Cobb-Douglas production function\n", + "\n", + " Takes the form F(K, L) = z K^{\\alpha} L^{1 - \\alpha}\n", + " \"\"\"\n", + " return z * K**(alpha) * L**(1.0 - alpha)" + ] + }, + { + "cell_type": "markdown", + "id": "be2590fd", + "metadata": { + "id": "be2590fd" + }, + "source": [ + "We can now call this function by passing in just K and L. Notice that it will\n", + "produce same result as earlier because `alpha` and `z` are the same as earlier." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9546cb37", + "metadata": {}, + "outputs": [], + "source": [ + "cobb_douglas(1.0, 0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "e4dfe474", + "metadata": { + "id": "e4dfe474" + }, + "source": [ + "However, we can also set the other arguments of the function by passing\n", + "more than just K/L." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "780070a8", + "metadata": {}, + "outputs": [], + "source": [ + "cobb_douglas(1.0, 0.5, 0.35, 1.6)" + ] + }, + { + "cell_type": "markdown", + "id": "d421b4f4", + "metadata": { + "id": "d421b4f4" + }, + "source": [ + "In the example above, we used `alpha = 0.35`, `z = 1.6`.\n", + "\n", + "We can also refer to function arguments by their name, instead of only their\n", + "position (order).\n", + "\n", + "To do this, we would write `func_name(arg=value)` for as many of the arguments\n", + "as we want.\n", + "\n", + "Here’s how to do that with our `cobb_douglas` example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05eb1bbc", + "metadata": {}, + "outputs": [], + "source": [ + "cobb_douglas(1.0, 0.5, z = 1.5)" + ] + }, + { + "cell_type": "markdown", + "id": "6a4f28fe", + "metadata": { + "id": "6a4f28fe" + }, + "source": [ + "### Exercise\n", + "\n", + "See exercise 4 in the [exercise list](#ex2-4).\n", + "\n", + "In terms of variable scope, the `z` name within the function is\n", + "different from any other `z` in the outer scope.\n", + "\n", + "To be clear," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "557cf5df", + "metadata": {}, + "outputs": [], + "source": [ + "x = 5\n", + "def f(x):\n", + " return x\n", + "f(x) # \"coincidence\" that it has the same name" + ] + }, + { + "cell_type": "markdown", + "id": "f1b2022f", + "metadata": { + "id": "f1b2022f" + }, + "source": [ + "This is also true with named function arguments, above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5d13655", + "metadata": {}, + "outputs": [], + "source": [ + "z = 1.5\n", + "cobb_douglas(1.0, 0.5, z = z) # no problem!" + ] + }, + { + "cell_type": "markdown", + "id": "d868edfb", + "metadata": { + "id": "d868edfb" + }, + "source": [ + "In that example, the `z` on the left hand side of `z = z` refers\n", + "to the local variable name in the function whereas the `z` on the\n", + "right hand side refers to the `z` in the outer scope." + ] + }, + { + "cell_type": "markdown", + "id": "94841288", + "metadata": { + "id": "94841288" + }, + "source": [ + "### Aside: Methods\n", + "\n", + "As we learned earlier, all variables in Python have a type associated\n", + "with them.\n", + "\n", + "Different types of variables have different functions or operations\n", + "defined for them.\n", + "\n", + "For example, I can divide one number by another or make a string uppercase.\n", + "\n", + "It wouldn’t make sense to divide one string by another or make a number\n", + "uppercase.\n", + "\n", + "When certain functionality is closely tied to the type of an object, it\n", + "is often implemented as a special kind of function known as a **method**.\n", + "\n", + "For now, you only need to know two things about methods:\n", + "\n", + "1. We call them by doing `variable.method_name(other_arguments)`\n", + " instead of `function_name(variable, other_arguments)`. \n", + "1. A method is a function, even though we call it using a different\n", + " notation. \n", + "\n", + "\n", + "When we introduced the core data types, we saw many methods defined on\n", + "these types.\n", + "\n", + "Let’s revisit them for the `str`, or string type.\n", + "\n", + "Notice that we call each of these functions using the `dot` syntax\n", + "described above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f794c03", + "metadata": {}, + "outputs": [], + "source": [ + "s = \"This is my handy string!\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2fefb42", + "metadata": {}, + "outputs": [], + "source": [ + "s.upper()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fa57a97", + "metadata": {}, + "outputs": [], + "source": [ + "s.title()" + ] + }, + { + "cell_type": "markdown", + "id": "6f69a383", + "metadata": { + "id": "6f69a383" + }, + "source": [ + "## More on Scope (Optional)\n", + "\n", + "Keep in mind that with mathematical functions, the arguments are just dummy names\n", + "that can be interchanged.\n", + "\n", + "That is, the following are identical.\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + " f(K, L) &= z\\, K^{\\alpha} L^{1-\\alpha}\\\\\n", + " f(K_2, L_2) &= z\\, K_2^{\\alpha} L_2^{1-\\alpha}\n", + "\\end{eqnarray}\n", + "$$\n", + "\n", + "The same concept applies to Python functions, where the arguments are just\n", + "placeholder names, and our `cobb_douglas` function is identical to" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2688518b", + "metadata": {}, + "outputs": [], + "source": [ + "def cobb_douglas2(K2, L2): # changed dummy variable names\n", + "\n", + " # Create alpha and z\n", + " z = 1\n", + " alpha = 0.33\n", + "\n", + " return z * K2**alpha * L2**(1 - alpha)\n", + "\n", + "cobb_douglas2(1.0, 0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "ac370bdb", + "metadata": { + "id": "ac370bdb" + }, + "source": [ + "This is an appealing feature of functions for avoiding coding errors: names of variables\n", + "within the function are localized and won’t clash with those on the outside (with\n", + "more examples in [scope](#scope)).\n", + "\n", + "Importantly, when Python looks for variables\n", + "matching a particular name, it begins in the most local scope.\n", + "\n", + "That is, note that having an `alpha` in the outer scope does not impact the local one." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0a3f795", + "metadata": {}, + "outputs": [], + "source": [ + "def cobb_douglas3(K, L, alpha): # added new argument\n", + "\n", + " # Create alpha and z\n", + " z = 1\n", + "\n", + " return z * K**alpha * L**(1 - alpha) # sees local argument alpha\n", + "\n", + "print(cobb_douglas3(1.0, 0.5, 0.2))\n", + "print(\"Setting alpha, does the result change?\")\n", + "alpha = 0.5 # in the outer scope\n", + "print(cobb_douglas3(1.0, 0.5, 0.2))" + ] + }, + { + "cell_type": "markdown", + "id": "b670be91", + "metadata": { + "id": "b670be91" + }, + "source": [ + "A crucial element of the above function is that the `alpha` variable\n", + "was available in the local scope of the function.\n", + "\n", + "Consider the alternative where it is not. We have removed the `alpha`\n", + "function parameter as well as the local definition of `alpha`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f6e8ae7", + "metadata": {}, + "outputs": [], + "source": [ + "def cobb_douglas4(K, L): # added new argument\n", + "\n", + " # Create alpha and z\n", + " z = 1\n", + "\n", + " # there are no local alpha in scope!\n", + " return z * K**alpha * L**(1 - alpha)\n", + "\n", + "alpha = 0.2 # in the outer scope\n", + "print(f\"alpha = {alpha} gives {cobb_douglas4(1.0, 0.5)}\")\n", + "alpha = 0.3\n", + "print(f\"alpha = {alpha} gives {cobb_douglas4(1.0, 0.5)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3ca2c57c", + "metadata": { + "id": "3ca2c57c" + }, + "source": [ + "The intuition of scoping does not apply only for the “global” vs. “function”\n", + "naming of variables, but also for nesting.\n", + "\n", + "For example, we can define a version of `cobb_douglas` which\n", + "is also missing a `z` in its inner-most scope, then put the function\n", + "inside of another function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46ae03fa", + "metadata": {}, + "outputs": [], + "source": [ + "z = 1\n", + "def output_given_alpha(alpha):\n", + " # Scoping logic:\n", + " # 1. local function name doesn't clash with global one\n", + " # 2. alpha comes from the function parameter\n", + " # 3. z comes from the outer global scope\n", + " def cobb_douglas(K, L):\n", + " return z * K**alpha * L**(1 - alpha)\n", + "\n", + " # using this function\n", + " return cobb_douglas(1.0, 0.5)\n", + "\n", + "alpha = 100 # ignored\n", + "alphas = [0.2, 0.3, 0.5]\n", + "# comprehension variables also have local scope\n", + "# and don't clash with the alpha = 100\n", + "[output_given_alpha(alpha) for alpha in alphas]" + ] + }, + { + "cell_type": "markdown", + "id": "31407dd3", + "metadata": { + "id": "31407dd3" + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "a2fb7cae", + "metadata": { + "id": "a2fb7cae" + }, + "source": [ + "## Exercises" + ] + }, + { + "cell_type": "markdown", + "id": "e26e52d6", + "metadata": { + "id": "e26e52d6" + }, + "source": [ + "### Exercise 1\n", + "\n", + "What happens if we try different inputs in our Cobb-Douglas production\n", + "function?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a30f1f7c", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute returns to scale with different values of `K` and `L` and `gamma`" + ] + }, + { + "cell_type": "markdown", + "id": "d9e2b3ce", + "metadata": { + "id": "d9e2b3ce" + }, + "source": [ + "([back to text](#dir2-4-1))" + ] + }, + { + "cell_type": "markdown", + "id": "d5a4a39b", + "metadata": { + "id": "d5a4a39b" + }, + "source": [ + "### Exercise 2\n", + "\n", + "Define a function named `var` that takes a list (call it `x`) and\n", + "computes the variance. This function should use the mean function that we\n", + "defined earlier.\n", + "\n", + "$ \\text{variance} = \\frac{1}{N-1} \\sum_i (x_i - \\text{mean}(x))^2 $" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d25d314a", + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here." + ] + }, + { + "cell_type": "markdown", + "id": "42b55c73", + "metadata": { + "id": "42b55c73" + }, + "source": [ + "([back to text](#dir2-4-2))" + ] + }, + { + "cell_type": "markdown", + "id": "746199cb", + "metadata": { + "id": "746199cb" + }, + "source": [ + "### Exercise 3\n", + "\n", + "Redefine the `returns_to_scale` function and add a docstring.\n", + "\n", + "Confirm that it works by running the cell containing `returns_to_scale?` below.\n", + "\n", + "*Note*: You do not need to change the actual code in the function — just\n", + "copy/paste and add a docstring in the correct line." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d575966", + "metadata": {}, + "outputs": [], + "source": [ + "# re-define the `returns_to_scale` function here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63efd956", + "metadata": {}, + "outputs": [], + "source": [ + "# test it here\n", + "\n", + "returns_to_scale?" + ] + }, + { + "cell_type": "markdown", + "id": "d070816f", + "metadata": { + "id": "d070816f" + }, + "source": [ + "([back to text](#dir2-4-3))" + ] + }, + { + "cell_type": "markdown", + "id": "fe3b042a", + "metadata": { + "id": "fe3b042a" + }, + "source": [ + "### Exercise 4\n", + "\n", + "Experiment with the `sep` and `end` arguments to the `print` function.\n", + "\n", + "These can *only* be set by name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94454380", + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here." + ] + }, + { + "cell_type": "markdown", + "id": "b8d7ac0e", + "metadata": { + "id": "b8d7ac0e" + }, + "source": [ + "([back to text](#dir2-4-4))" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/02_interactive_plot_example.ipynb b/src/02_interactive_plot_example.ipynb deleted file mode 100644 index 2fb78e1..0000000 --- a/src/02_interactive_plot_example.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Interactive Visualization Example\n", - "\n", - "This example comes from here: https://jupyterbook.org/en/stable/interactive/interactive.html" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.io as pio\n", - "import plotly.express as px\n", - "import plotly.offline as py\n", - "\n", - "df = px.data.iris()\n", - "fig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\", size=\"sepal_length\")\n", - "fig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/day_02/occupations--with-solutions.ipynb b/src/02_occupations--with-solutions.ipynb similarity index 100% rename from day_02/occupations--with-solutions.ipynb rename to src/02_occupations--with-solutions.ipynb diff --git a/day_04/occupations.ipynb b/src/02_occupations.ipynb similarity index 83% rename from day_04/occupations.ipynb rename to src/02_occupations.ipynb index a0c4cef..38b2369 100644 --- a/day_04/occupations.ipynb +++ b/src/02_occupations.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -22,40 +22,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%%HTML\n", "

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zAtVD7(u5E~2qA=IfI^xOLI@#*kPJ{r6G8|fgb6j6j P00000NkvXXu0mjfbfAG_ literal 0 HcmV?d00001 diff --git a/docs_src/conf.py b/docs_src/conf.py index 760745a..80c9371 100644 --- a/docs_src/conf.py +++ b/docs_src/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- -project = "Blank Project" +project = "FINM August Review: Python" copyright = "2024, Jeremiah Bejarano" author = "Jeremiah Bejarano" @@ -107,7 +107,7 @@ "colab_url": "", }, "path_to_docs": "docs_src", - "repository_url": "https://github.com/jmbejara/blank_project", + "repository_url": "https://github.com/jmbejara/finm-python-crash-course", "repository_branch": "master", "extra_footer": "", "home_page_in_toc": True, @@ -118,7 +118,7 @@ "use_issues_button": True, } html_logo = "../assets/logo.png" -html_title = "Blank Project Template" +html_title = "FINM August Review: Python" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". diff --git a/docs_src/discussion_01.md b/docs_src/discussion_01.md new file mode 100644 index 0000000..16e1a69 --- /dev/null +++ b/docs_src/discussion_01.md @@ -0,0 +1,33 @@ +# 1. Agenda + +- Introduction: Who am I? What's the goal of this review? +- **Course Page on GitHub** + - Review course page on GitHub: https://github.com/jmbejara/finm-python-crash-course + - Course textbook: https://jeremybejarano.com/finm-python-crash-course/ +- [**Set up Environment**](./01_setting_up_environment.md): Today we will make sure that everyone has their computational environment set +up correctly. This includes Python (via the Anaconda distribution), Visual +Studio Code, Git and GitHub, and a WRDS class count for this course. +- **Various Method of Interacting with Python**: Throughout the course, we'll +discuss the various ways of interacting with Python: Google Collab, Jupyter +Notebooks through the standard Jupyter server, Jupyter Notebooks in VS Code, +using IPython in the command line, and running Python scripts directly from the +command line (`.py` files). +- Discuss assignment for next week (installing software). (Assignments are not + graded. This is an optional review.) + - List of software to install is on the main README: + https://github.com/jmbejara/finm-python-crash-course/blob/main/README.md + - Helpful text to understand the process of setting up your environment: + https://datascience.quantecon.org/introduction/local_install.html +- [Run Python Demo Notebook in Google + Colab](https://colab.research.google.com/github/jmbejara/finm-python-crash-course/blob/main/week_1/Part_1_Python_Jupyter_demo.ipynb) +- Start HW1 as a group. Discuss how the Jupyter notebook can be used for HW. + Formatting is important! [Work through problems together + here.](https://colab.research.google.com/github/jmbejara/finm-python-crash-course/blob/main/HW/HW-1-numpy-scipy/HW1.ipynb) +- With the remaining time, we'll take a step back and go over some of the more + basic aspects of Python. We'll go through some simpler examples in the + following notebooks (which can be accessed in Google Colab). +- [Python + Fundamentals](https://datascience.quantecon.org/python_fundamentals/index.html) + - [Basics](https://datascience.quantecon.org/python_fundamentals/basics.html) + - [Collections](https://datascience.quantecon.org/python_fundamentals/collections.html) + diff --git a/docs_src/discussion_02.md b/docs_src/discussion_02.md new file mode 100644 index 0000000..4ac5415 --- /dev/null +++ b/docs_src/discussion_02.md @@ -0,0 +1,51 @@ +# 2. Agenda + +- Questions about HW1? Did anyone attempt? +- Follow-up on previous assignment, HW 0: Installation of software on the main + [README](https://github.com/jmbejara/finm-python-crash-course/blob/main/README.md) + - Today we will use Jupyter locally to do all of our coding. We will use + Jupyter notebooks. Next week we will use notebooks within VS Code. The + week after than we will move away from notebooks and write `.py` files + directly. + - Did anyone have any trouble installing Anaconda and VS Code? Share screen + if there are issues. + - Review HW 2 from last time. + - Who tried the HW? Any questions? + - Show location of solutions notebook. +- What are some gotcha's when using Jupyter notebooks? + - What is the terminal/command prompt? What is bash? + - Spin up Jupyter notebook. Show how it can't go above the root folder. + - Discuss the importance of maintaining a reasonable folder structure. + Folder for all course work, separate folder for each course, for each + project, etc. + - Google Colab vs locally-running Jupyter server, Jupyter Notebooks vs VS + Code + - Difference between Python and Anaconda? + - Difference between Anaconda and Conda. + - Demo the use of conda for installing packages and using conda + environments. + - What is the purpose of a conda environment? +- Skim over the [./src/02_Using_Interact.ipynb](./_notebook_build/_02_Using_Interact.ipynb) + - We're not going to cover it, but those that are interested can learn more + about how to use it here. +- Continue with introductory Python topics: + - To learn about "Control Flow" in the context of generating pseudo-random + time series, let's use the ["Introductory Example" or "Python by + Example"](https://python-programming.quantecon.org/python_by_example.html) + notebook found here: + [./src/01_python_by_example.ipynb](./_notebook_build/_01_python_by_example.ipynb) +- Start with discussion of Pandas. Start going over the Pandas chapter from + ["Python Data Science + Handbook"](https://jakevdp.github.io/PythonDataScienceHandbook) + - `02_00-Introduction-to-Pandas.ipynb` + - `02_01-Introducing-Pandas-Objects.ipynb` + - `02_02-Data-Indexing-and-Selection.ipynb` + - Break for an set of in-class exercises: + [./src/02_occupations.ipynb](./_notebook_build/_02_occupations.ipynb) + - `02_03-Operations-in-Pandas.ipynb` + - `02_04-Missing-Values.ipynb` +- Homework for next time: See HW 2 folder. These are a series of short exercises + to practice using Pandas. + + + diff --git a/docs_src/discussion_03.md b/docs_src/discussion_03.md new file mode 100644 index 0000000..906fa7e --- /dev/null +++ b/docs_src/discussion_03.md @@ -0,0 +1,20 @@ +# 3. Agenda + +- Today we will use notebooks within VS Code. We'll also begin the discussion of writing `.py` files directly. The week after that we will move away from notebooks entirely. +- Discuss the features of using the Python and Jupyter extensions within VS Code. + - Overview: https://code.visualstudio.com/docs/datascience/overview + - Variable explorer and data viewer: https://code.visualstudio.com/docs/datascience/jupyter-notebooks#_variable-explorer-and-data-viewer + - Custom notebook diffing: https://code.visualstudio.com/docs/datascience/jupyter-notebooks#_custom-notebook-diffing +- Demonstration of Git and GitHub + - VS Code especially makes Git diffs of Jupyter notebooks easy. Demonstrate why they are otherwise difficult. +- Finish discussion of Pandas from previous lecture: + - Set of in-class exercises: [./src/occupations.ipynb](./_02_occupations.ipynb) + - `03.03-Operations-in-Pandas.ipynb` + - `03.04-Missing-Values.ipynb` +- Demonstrate Pandas in the context of factor analysis/principal components analysis of a panel (Note from 2023. Ran out of time at the beginning of discussing this notebook.) +of economic and financial time series. [./src/factor_analysis_demo.ipynb](./_notebook_build/_03_factor_analysis_demo.ipynb) +- Very quick review of Numpy, Matplotlib, and Scipy, with emphasis on plotting + - Introduction to [NumPy](https://python-programming.quantecon.org/numpy.html) + - Introduction to [Matplotlib](https://python-programming.quantecon.org/matplotlib.html) + - Compare Matplotlib to other plotting libraries: [./src/comparing_plotting_libraries.ipynb](./_notebook_build/_03_comparing_plotting_libraries.ipynb) + - Introduction to [SciPy](https://python-programming.quantecon.org/scipy.html) diff --git a/docs_src/discussion_04.md b/docs_src/discussion_04.md new file mode 100644 index 0000000..e69f9ad --- /dev/null +++ b/docs_src/discussion_04.md @@ -0,0 +1,22 @@ +# 4. Agenda + +- Today we move away from Jupyter notebooks entirely and focus on writing `.py` files directly. We'll focus on writing our own modules, discuss automating tasks by using the command line, we'll discuss task management software (Python's `doit` package and Makefiles), and discuss the importance of conda environments (and hint at Docker containers). +- Give an overview of GitKraken and GitHub. + - Create a new repository on GitHub and clone it in GitKraken. + - Create a commit and push to GitHub + - Make edits to code and view the diffs. + - Discuss pull requests and the open source model (delegating oversight) +- Now, let's briefly move away from notebooks and write `.py` files directly. We'll discuss the pros and cons of working with Notebooks vs `.py` files. + - To do this, complete again the `Occupations` exercises the following in-class Pandas exercises within a `.py` file. Complete using the %% cells. + - Once the assignment is complete, remove the %% cells for comparison. + - Show how to use the debugger. + - Show how to run the script from the command line. + - Use the script to print to the command line. + - Use the script to save a figure. + - Write a shell script to run several Python scripts. +- Discussion of writing our own modules + - Start with a review of functions in Python: review the ["Functions"](https://datascience.quantecon.org/python_fundamentals/functions.html) chapter found here: [./src/02_functions.ipynb](./_notebook_build/_02_functions.ipynb.ipynb) + - Demonstrate my own, very simple module that I use, called `config` +- Write an end-to-end automatically-run program using a conda environment, the command line, and Python's `doit`. This should download data on it's own, store it somewhere as a cached data set, run the analysis, generate the charts, and insert the charts into a PDF document (do this using a Jupyter notebook). + - Do this by looking at the structure of my `blank-project` repository. + \ No newline at end of file diff --git a/docs_src/index.md b/docs_src/index.md index ce04abc..8c74c1e 100644 --- a/docs_src/index.md +++ b/docs_src/index.md @@ -1,39 +1,100 @@ -# Welcome to My Blank Project's documentation! +# FINM August Review: Python -The purpose of the project is to serve as a template for creating a new project. -The idea is that you can substitute your own code and documentation into -the placeholders here. + +## Summary + +The FINM August Review is a series of lectures designed for incoming students to prepare for starting with the Financial Mathematics program. The Python Introduction and Review portion is designed to be a refresher or short introduction to the Python programming language. No prior experience is necessary. Even though some incoming students may have extensive prior experience with Python, this review is designed for those with little experience. The aim is to introduce you to what you need to know for the upcoming FINM program. The academic lectures of September Launch and autumn quarter will assume students have mastered the concepts covered throughout August Review, and so it’s critical that all students enter the year with a solid grasp of this material. + +```{attention} Pardon my dust! These notes will change frequently as I update it with new content before the course begins. +``` + + +## Course Info + +* **Class:** + - Discussion 1: Tuesday, July 30: 6-9pm CT on Zoom + - Discussion 2: Friday, August 2: 6-9pm CT on Zoom + - Discussion 3: Tuesday, August 6: 6-9pm CT on Zoom + - Discussion 4: Friday, August 9: 6-9pm CT on Zoom + +* **Lecturer:** Jeremy Bejarano, jeremiah.bejarano@gmail.com +* **Website:** + - Canvas: https://canvas.uchicago.edu/courses/57668 will be used for grades. + - Lecture notes will be hosted here: https://jeremybejarano.com/finm-python-crash-course/ + - Code for the course will be hosted on GitHub: https://github.com/jmbejara/finm-python-crash-course + +**Required Software** +Each lecture after this will use the following software. Please make sure to install these before then. If you need help installing this software, please ask for help in the discussion section on Canvas. + + - Python 3.11 or greater, Anaconda Distribution + - For this class, please download the [Anaconda distribution of Python](https://www.anaconda.com/products/distribution). Be sure to download current version, with Python version 3.9. or greater. When you install Anaconda, be sure to install the full Anaconda distribution. + The MiniConda version is nice, but I only recommend it for advanced users. Nice instructions for installing and using Anaconda can be found (here.)[https://datascience.quantecon.org/introduction/local_install.html] + - The Visual Studio Code (VS Code) text editor + - A good text editor is important for software development. Some of your classes will use a fully-fledged Integrated Development Environment (IDE) like PyCharm. For this review, I suggest Visual Studio Code. You can download it here: https://code.visualstudio.com/ + - There are several VS Code extensions that I recommend installing. To learn about extensions, see [here.](https://code.visualstudio.com/docs/editor/extension-marketplace) I recommend installing at least these two extensions: the [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) and [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) VS Code extensions. + - Git + - Although there are many different Git clients and Git GUI's that you could use, + I prefer that you install GitHub Desktop. You will need to install both + Git (link here: https://git-scm.com/downloads) + and GitHub Desktop (link here: https://github.com/apps/desktop). + - Some classes will use GitHub. GitHub is a website that allows you to store, interact with, and share your Git repositories online. [Please register an account with GitHub](https://github.com/) if you don't already have one. + +*NOTE:* It's also important that you have a quality laptop. I recommend a laptop with at least 16GB of RAM and at least 500 GB of storage (at a minimum). +So much of your schooling and of your job will revolve around your laptop. +It's important to invest in a good one. If you have any questions about your laptop, please ask in the discussion section on Canvas. + +**WRDS Account** + +This course requires that you create a WRDS account. WRDS is a comprehensive data research platform that provides access to a wide range of financial, economic, and marketing data. +Follow the instructions [here](./01_setting_up_environment.md#wrds-how-do-i-sign-up) to sign up. + + + +## Helpful References + +A lot of my lecture material will use content from the following helpful books: + +* [Introduction to Economic Modeling and Data Science](https://datascience.quantecon.org/), by Thomas J. Sargent and John Stachurski (QuantEcon) +* Note, the whole lectures series on QuantEcon's website is very good: [Quantitative Economics](https://lectures.quantecon.org/), by Thomas J. Sargent and John Stachurski (QuantEcon) +* [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/), by Jake VanderPlas (PDSH) +* [Python for Data Analysis, 2nd Edition](https://github.com/wesm/pydata-book), by Wes McKinney (PDA) + +## Table of Contents / Schedule + ```{toctree} -:maxdepth: 2 -:caption: Contents -_notebook_build/_01_example_notebook.ipynb +:maxdepth: 1 +:caption: Discussion 1 +discussion_01.md +01_setting_up_environment.md _notebook_build/_01_python_jupyter_demo.ipynb -_notebook_build/_02_interactive_plot_example.ipynb +_notebook_build/_01_python_by_example.ipynb +``` + +```{toctree} +:maxdepth: 1 +:caption: Discussion 2 +discussion_02.md +WRDS_intro_and_web_queries.md +_notebook_build/_02_Using_Interact.ipynb +_notebook_build/_02_occupations.ipynb +``` + +```{toctree} +:maxdepth: 1 +:caption: Discussion 3 +discussion_03.md +_notebook_build/_03_comparing_plotting_libraries.ipynb +``` + +```{toctree} +:maxdepth: 1 +:caption: Discussion 4️ +discussion_04.md myst_markdown_demos.md -notebooks.md apidocs/index ``` -## Notes - -- Note that I have included the notebooks here twice. This is just to - demonstrate how you can create subsections with child pages in the table of - contents. You can read more about this - [here.](https://myst-parser.readthedocs.io/en/latest/syntax/organising_content.html#using-toctree-to-include-other-documents-as-children) -- Note that you can segment your TOC in a fun way with emojis as done here: - [MyST-Parser documentation](https://myst-parser.readthedocs.io/en/latest/index.html). See the `.md` - source [here](https://github.com/executablebooks/MyST-Parser/blob/d448abf395c29bb649f81fba5c1a2bc49e195cc0/docs/index.md?plain=1) - to see how to do this. -- Because we're using Sphinx with the MySt extention, we can use Markdown almost - everywhere. However, we still need to use it at least on the `index.rst` file. - Here is a link to a [RestructuredText - Cheatsheet](https://github.com/ralsina/rst-cheatsheet/blob/master/rst-cheatsheet.rst). -- I'm using `autodoc2` to create the API documentation. This is a fork of the - original `autodoc` extension that allows you to use Markdown in your docstrings. - You can read more about it [here](https://sphinx-autodoc2.readthedocs.io/en/latest/). - The differences between this and the original `autodoc` are documented [here](https://sphinx-autodoc2.readthedocs.io/en/latest/autodoc_diff.html). - ## Indices and tables diff --git a/docs_src/notebooks.md b/docs_src/notebooks.md deleted file mode 100644 index b13bdc0..0000000 --- a/docs_src/notebooks.md +++ /dev/null @@ -1,34 +0,0 @@ -# Notebooks 📖 - -Here is a demo of two things: - - - Demonstrating the inclusion of notebooks into the documentation. - - Demonstrating the creation of a page with subsections, where the subsections appear - as children in the main table of contents on the main page. - -```{toctree} -_notebook_build/_01_example_notebook.ipynb -_notebook_build/_02_interactive_plot_example.ipynb -``` - -Vestibulum interdum orci ac viverra porta. Maecenas ut nunc id metus placerat -condimentum vel at ante. Ut cursus consectetur malesuada. Quisque nunc dolor, -varius in commodo ut, ultrices maximus nisi. Fusce et magna orci. Curabitur -mauris lorem, dapibus id viverra sed, blandit in diam. Curabitur id molestie -elit. Quisque feugiat mollis sem ut tempus. Proin iaculis aliquet luctus. -Pellentesque et finibus mi. Donec sit amet turpis ut nisi cursus porttitor vel -convallis ex. Sed feugiat massa nec blandit mollis. Fusce vitae pretium mi, sed -congue lectus. Quisque feugiat enim id dui vehicula mattis. Donec id elit non -lectus tincidunt luctus a ac nibh. - -Phasellus cursus at lacus at pulvinar. In posuere malesuada accumsan. Curabitur -elementum, metus vel imperdiet sodales, leo lorem accumsan dui, quis blandit -lacus mauris eu lectus. Etiam non ipsum sem. Ut aliquam elit sit amet est -malesuada ornare. Fusce aliquam erat a sagittis tristique. Aliquam ac felis -tellus. Vestibulum vestibulum ut felis ac condimentum. - -Curabitur orci tellus, iaculis eu libero vel, sagittis viverra nunc. Aliquam sed -neque vulputate, hendrerit justo ac, semper purus. Praesent risus massa, dapibus -nec diam in, consequat mollis ante. Duis at est euismod nisi aliquet egestas. -Aliquam eleifend interdum nisi hendrerit congue. Vestibulum consectetur commodo -libero vel malesuada. Integer non urna elit. diff --git a/dodo.py b/dodo.py index ace7823..40b0640 100644 --- a/dodo.py +++ b/dodo.py @@ -184,11 +184,27 @@ def task_pull_fred(): "file_dep": [], "targets": [], }, - "01_example_notebook.ipynb": { - "file_dep": ["./src/load_fred.py"], - "targets": [Path(OUTPUT_DIR) / "GDP_graph.png"], + "01_python_by_example.ipynb": { + "file_dep": [], + "targets": [], + }, + "02_Using_Interact.ipynb": { + "file_dep": [], + "targets": [], }, - "02_interactive_plot_example.ipynb": { + "02_occupations.ipynb": { + "file_dep": [], + "targets": [], + }, + "02_functions.ipynb": { + "file_dep": [], + "targets": [], + }, + "03_factor_analysis_demo.ipynb": { + "file_dep": [], + "targets": [], + }, + "03_comparing_plotting_libraries.ipynb": { "file_dep": [], "targets": [], }, @@ -257,22 +273,30 @@ def task_run_notebooks(): "./docs/html/index.html", "./docs/html/myst_markdown_demos.html", "./docs/html/apidocs/index.html", + "./docs/html/WRDS_intro_and_web_queries.html" +] + +sphinx_file_dep = [ + "./docs_src/conf.py", + "./docs_src/index.md", + "./docs_src/myst_markdown_demos.md", + "./docs_src/WRDS_intro_and_web_queries.md", + "./docs_src/01_setting_up_environment.md", + "./docs_src/discussion_01.md", + "./docs_src/discussion_02.md", + "./docs_src/discussion_03.md", + "./docs_src/discussion_04.md", ] def task_compile_sphinx_docs(): """Compile Sphinx Docs""" - file_dep = [ - "./docs_src/conf.py", - "./docs_src/index.md", - "./docs_src/myst_markdown_demos.md", - "./docs_src/notebooks.md", - ] + return { "actions": ["sphinx-build -M html ./docs_src/ ./docs"], # Use docs as build destination # "actions": ["sphinx-build -M html ./docs/ ./docs/_build"], # Previous standard organization "targets": sphinx_targets, - "file_dep": file_dep, + "file_dep": sphinx_file_dep, "task_dep": ["run_notebooks"], "clean": True, } @@ -337,6 +361,6 @@ def task_copy_built_docs_to_publishing_dir(): copy_build_files_to_docs_publishing_dir, ], "targets": targets, - "file_dep": file_dep, + "file_dep": sphinx_file_dep, "clean": True, } diff --git a/requirements.txt b/requirements.txt index 954b204..d96045f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -50,4 +50,5 @@ vega_datasets>=0.9.0 wrds>=3.2.0 xbbg>=0.7.7 xlrd>=2.0.1 +yfinance zstandard>=0.22.0 \ No newline at end of file diff --git a/src/01_example_notebook.ipynb b/src/01_example_notebook.ipynb deleted file mode 100644 index 64f4293..0000000 --- a/src/01_example_notebook.ipynb +++ /dev/null @@ -1,63 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example Notebook\n", - "\n", - "This notebook is designed demonstrate a number of goals:\n", - "\n", - " - The notebook is part of the automated analytical pipeline, as it is run programmatically by the build system, as in the dodo.py file.\n", - " - It is tracked by version control via Git. To avoid large files and the problems associated with non-text files, the notebook is stripped of its output. \n", - " - In order to avoid re-running the notebook every time it changes (it changes often, even by the act of opening it) and to only rerun it if meaningful changes have been made, the build system only looks for changes in the plaintext version of the notebook. That is, the notebook is converted to a Python script via [nbconvert](https://nbconvert.readthedocs.io/en/latest/), which is often packaged with Jupyter.\n", - " Then, DoIt looks for changes to the Python version. If it detects a difference, then the notebook is re-run. (Note, that you could also convert to a Markdown file with \n", - " [JupyText](https://github.com/mwouts/jupytext). However, this package is often not packaged with Jupyter.)\n", - " - Since we want to use Jupyter Notebooks for exploratory reports, we want to keep fully-computed versions of the notebook (with the output intact). However, earlier I said that I strip the notebook of its output before committing to version control. Well, to keep the output, every time PyDoit runs the notebook, it outputs an HTML version of the freshly run notebook and saves that HTML report in the `output` directory. That way, you will be able to view the finished report at any time without having to open Jupyter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "import config\n", - "from pathlib import Path\n", - "OUTPUT_DIR = Path(config.OUTPUT_DIR)\n", - "\n", - "import load_fred" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = load_fred.load_fred()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df[[\"GDP\"]].dropna().plot()\n", - "filepath = Path(OUTPUT_DIR) / 'GDP_graph.png'\n", - "plt.savefig(filepath)" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/01_python_by_example.ipynb b/src/01_python_by_example.ipynb new file mode 100644 index 0000000..a0fedda --- /dev/null +++ b/src/01_python_by_example.ipynb @@ -0,0 +1,1268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "56ff7eda", + "metadata": {}, + "source": [ + "\n", + "\n", + "