From a81c8cbd18f453c7469d5da0dc84ba062173af44 Mon Sep 17 00:00:00 2001 From: Annie Tor Date: Thu, 13 Sep 2018 15:55:15 -0400 Subject: [PATCH 01/18] Adding chap 4 notebook --- code/chap04mine.ipynb | 1138 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1138 insertions(+) create mode 100644 code/chap04mine.ipynb diff --git a/code/chap04mine.ipynb b/code/chap04mine.ipynb new file mode 100644 index 00000000..7e535191 --- /dev/null +++ b/code/chap04mine.ipynb @@ -0,0 +1,1138 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 4\n", + "\n", + "Copyright 2017 Allen Downey\n", + "\n", + "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure Jupyter so figures appear in the notebook\n", + "%matplotlib inline\n", + "\n", + "# Configure Jupyter to display the assigned value after an assignment\n", + "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", + "\n", + "# import functions from the modsim library\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Returning values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a simple function that returns a value:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def add_five(x):\n", + " return x + 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's how we call it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = add_five(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you run a function on the last line of a cell, Jupyter displays the result:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add_five(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But that can be a bad habit, because usually if you call a function and don't assign the result in a variable, the result gets discarded.\n", + "\n", + "In the following example, Jupyter shows the second result, but the first result just disappears." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add_five(3)\n", + "add_five(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you call a function that returns a variable, it is generally a good idea to assign the result to a variable." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8 10\n" + ] + } + ], + "source": [ + "y1 = add_five(3)\n", + "y2 = add_five(5)\n", + "\n", + "print(y1, y2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write a function called `make_state` that creates a `State` object with the state variables `olin=10` and `wellesley=2`, and then returns the new `State` object.\n", + "\n", + "Write a line of code that calls `make_state` and assigns the result to a variable named `init`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def make_state():\n", + " state = State(olin = 10, wellesley = 2)\n", + " return state" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "olin 10\n", + "wellesley 2\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "init = make_state()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the code from the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def step(state, p1, p2):\n", + " \"\"\"Simulate one minute of time.\n", + " \n", + " state: bikeshare State object\n", + " p1: probability of an Olin->Wellesley customer arrival\n", + " p2: probability of a Wellesley->Olin customer arrival\n", + " \"\"\"\n", + " if flip(p1):\n", + " bike_to_wellesley(state)\n", + " \n", + " if flip(p2):\n", + " bike_to_olin(state)\n", + " \n", + "def bike_to_wellesley(state):\n", + " \"\"\"Move one bike from Olin to Wellesley.\n", + " \n", + " state: bikeshare State object\n", + " \"\"\"\n", + " if state.olin == 0:\n", + " state.olin_empty += 1\n", + " return\n", + " state.olin -= 1\n", + " state.wellesley += 1\n", + " \n", + "def bike_to_olin(state):\n", + " \"\"\"Move one bike from Wellesley to Olin.\n", + " \n", + " state: bikeshare State object\n", + " \"\"\"\n", + " if state.wellesley == 0:\n", + " state.wellesley_empty += 1\n", + " return\n", + " state.wellesley -= 1\n", + " state.olin += 1\n", + " \n", + "def decorate_bikeshare():\n", + " \"\"\"Add a title and label the axes.\"\"\"\n", + " decorate(title='Olin-Wellesley Bikeshare',\n", + " xlabel='Time step (min)', \n", + " ylabel='Number of bikes')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a modified version of `run_simulation` that creates a `State` object, runs the simulation, and returns the `State` object." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def run_simulation(p1, p2, num_steps):\n", + " \"\"\"Simulate the given number of time steps.\n", + " \n", + " p1: probability of an Olin->Wellesley customer arrival\n", + " p2: probability of a Wellesley->Olin customer arrival\n", + " num_steps: number of time steps\n", + " \"\"\"\n", + " state = State(olin=10, wellesley=2, \n", + " olin_empty=0, wellesley_empty=0)\n", + " \n", + " for i in range(num_steps):\n", + " step(state, p1, p2)\n", + " \n", + " return state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now `run_simulation` doesn't plot anything:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "olin 0\n", + "wellesley 12\n", + "olin_empty 1\n", + "wellesley_empty 0\n", + "dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state = run_simulation(0.4, 0.2, 60)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But after the simulation, we can read the metrics from the `State` object." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state.olin_empty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run simulations with different values for the parameters. When `p1` is small, we probably don't run out of bikes at Olin." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state = run_simulation(0.2, 0.2, 60)\n", + "state.olin_empty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When `p1` is large, we probably do." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state = run_simulation(0.6, 0.2, 60)\n", + "state.olin_empty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More for loops" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`linspace` creates a NumPy array of equally spaced numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.25, 0.5 , 0.75, 1. ])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p1_array = linspace(0, 1, 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use an array in a `for` loop, like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.25\n", + "0.5\n", + "0.75\n", + "1.0\n" + ] + } + ], + "source": [ + "for p1 in p1_array:\n", + " print(p1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will come in handy in the next section.\n", + "\n", + "`linspace` is defined in `modsim.py`. You can get the documentation using `help`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function linspace in module modsim:\n", + "\n", + "linspace(start, stop, num=50, **options)\n", + " Returns an array of evenly-spaced values in the interval [start, stop].\n", + " \n", + " start: first value\n", + " stop: last value\n", + " num: number of values\n", + " \n", + " Also accepts the same keyword arguments as np.linspace. See\n", + " https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html\n", + " \n", + " returns: array or Quantity\n", + "\n" + ] + } + ], + "source": [ + "help(linspace)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`linspace` is based on a NumPy function with the same name. [Click here](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html) to read more about how to use it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** \n", + "Use `linspace` to make an array of 10 equally spaced numbers from 1 to 10 (including both)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linspace(1, 10, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** The `modsim` library provides a related function called `linrange`. You can view the documentation by running the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function linrange in module modsim:\n", + "\n", + "linrange(start=0, stop=None, step=1, **options)\n", + " Returns an array of evenly-spaced values in the interval [start, stop].\n", + " \n", + " This function works best if the space between start and stop\n", + " is divisible by step; otherwise the results might be surprising.\n", + " \n", + " By default, the last value in the array is `stop-step`\n", + " (at least approximately).\n", + " If you provide the keyword argument `endpoint=True`,\n", + " the last value in the array is `stop`.\n", + " \n", + " start: first value\n", + " stop: last value\n", + " step: space between values\n", + " \n", + " Also accepts the same keyword arguments as np.linspace. See\n", + " https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html\n", + " \n", + " returns: array or Quantity\n", + "\n" + ] + } + ], + "source": [ + "help(linrange)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `linrange` to make an array of numbers from 1 to 11 with a step size of 2." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sweeping parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`p1_array` contains a range of values for `p1`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p2 = 0.2\n", + "num_steps = 60\n", + "p1_array = linspace(0, 1, 11)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following loop runs a simulation for each value of `p1` in `p1_array`; after each simulation, it prints the number of unhappy customers at the Olin station:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 0\n", + "0.1 0\n", + "0.2 0\n", + "0.30000000000000004 0\n", + "0.4 0\n", + "0.5 8\n", + "0.6000000000000001 5\n", + "0.7000000000000001 18\n", + "0.8 31\n", + "0.9 35\n", + "1.0 38\n" + ] + } + ], + "source": [ + "for p1 in p1_array:\n", + " state = run_simulation(p1, p2, num_steps)\n", + " print(p1, state.olin_empty)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can do the same thing, but storing the results in a `SweepSeries` instead of printing them.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "0.0 0\n", + "0.1 0\n", + "0.2 0\n", + "0.3 1\n", + "0.4 6\n", + "0.5 7\n", + "0.6 16\n", + "0.7 17\n", + "0.8 37\n", + "0.9 29\n", + "1.0 38\n", + "dtype: int64" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sweep = SweepSeries()\n", + "\n", + "for p1 in p1_array:\n", + " state = run_simulation(p1, p2, num_steps)\n", + " sweep[p1] = state.olin_empty\n", + " \n", + "sweep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And then we can plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file figs/chap02-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(sweep, label='Olin')\n", + "\n", + "decorate(title='Olin-Wellesley Bikeshare',\n", + " xlabel='Arrival rate at Olin (p1 in customers/min)', \n", + " ylabel='Number of unhappy customers')\n", + "\n", + "savefig('figs/chap02-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "**Exercise:** Wrap this code in a function named `sweep_p1` that takes an array called `p1_array` as a parameter. It should create a new `SweepSeries`, run a simulation for each value of `p1` in `p1_array`, store the results in the `SweepSeries`, and return the `SweepSeries`.\n", + "\n", + "Use your function to plot the number of unhappy customers at Olin as a function of `p1`. Label the axes." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sweep_p1(p1_array):\n", + " sweep = SweepSeries() #initialize new SweepSeries\n", + " for p1 in p1_array: #for each value in p1_array, run simulation\n", + " state = run_simulation(p1, p2, num_steps)\n", + " sweep[p1] = state.olin_empty\n", + " return sweep\n", + " \n", + "run_simulation()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(sweep_p1(p1_array), label='Olin')\n", + "\n", + "decorate(title='Olin-Wellesley Bikeshare',\n", + " xlabel='People going from Olin to Wellesley (p1 in customers/min)', \n", + " ylabel='Number of unhappy customers')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write a function called `sweep_p2` that runs simulations with `p1=0.5` and a range of values for `p2`. It should store the results in a `SweepSeries` and return the `SweepSeries`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "def sweep_p2(p1_array):\n", + " sweep = SweepSeries()\n", + " for p2 in p1_array:\n", + " state = run_simulation(p1, p2, num_steps)\n", + " sweep[p2] = state.olin_empty\n", + " return sweep" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(sweep_p2(p1_array), label='Wellesley')\n", + "\n", + "decorate(title='Olin-Wellesley Bikeshare',\n", + " xlabel='People going from Wellesley to Olin (p2 in customers/min)', \n", + " ylabel='Number of unhappy customers')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional exercises\n", + "\n", + "The following two exercises are a little more challenging. If you are comfortable with what you have learned so far, you should give them a try. If you feel like you have your hands full, you might want to skip them for now.\n", + "\n", + "**Exercise:** Because our simulations are random, the results vary from one run to another, and the results of a parameter sweep tend to be noisy. We can get a clearer picture of the relationship between a parameter and a metric by running multiple simulations with the same parameter and taking the average of the results.\n", + "\n", + "Write a function called `run_multiple_simulations` that takes as parameters `p1`, `p2`, `num_steps`, and `num_runs`.\n", + "\n", + "`num_runs` specifies how many times it should call `run_simulation`.\n", + "\n", + "After each run, it should store the total number of unhappy customers (at Olin or Wellesley) in a `TimeSeries`. At the end, it should return the `TimeSeries`.\n", + "\n", + "Test your function with parameters\n", + "\n", + "```\n", + "p1 = 0.3\n", + "p2 = 0.3\n", + "num_steps = 60\n", + "num_runs = 10\n", + "```\n", + "\n", + "Display the resulting `TimeSeries` and use the `mean` function provided by the `TimeSeries` object to compute the average number of unhappy customers." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Continuting the previous exercise, use `run_multiple_simulations` to run simulations with a range of values for `p1` and\n", + "\n", + "```\n", + "p2 = 0.3\n", + "num_steps = 60\n", + "num_runs = 20\n", + "```\n", + "\n", + "Store the results in a `SweepSeries`, then plot the average number of unhappy customers as a function of `p1`. Label the axes.\n", + "\n", + "What value of `p1` minimizes the average number of unhappy customers?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + } + ], + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2c63d131285b6f07277b3398c1d021790c2e4a48 Mon Sep 17 00:00:00 2001 From: Annie Tor Date: Fri, 14 Sep 2018 16:49:34 -0400 Subject: [PATCH 02/18] Adding chapter 6 --- code/chap06.ipynb | 533 +++++++++++++++++++++--- code/chap06mine.ipynb | 949 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 1422 insertions(+), 60 deletions(-) create mode 100644 code/chap06mine.ipynb diff --git a/code/chap06.ipynb b/code/chap06.ipynb index 20ac99f5..5d9f68ef 100644 --- a/code/chap06.ipynb +++ b/code/chap06.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -55,9 +55,26 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 2.525149\n", + "1951 2.572851\n", + "1952 2.619292\n", + "1953 2.665865\n", + "1954 2.713172\n", + "Name: un, dtype: float64" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "un = table2.un / 1e9\n", "un.head()" @@ -65,9 +82,26 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 2.557629\n", + "1951 2.594940\n", + "1952 2.636772\n", + "1953 2.682053\n", + "1954 2.730228\n", + "Name: census, dtype: float64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "census = table2.census / 1e9\n", "census.head()" @@ -75,9 +109,20 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07224800083333333" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t_0 = get_first_label(census)\n", "t_end = get_last_label(census)\n", @@ -106,9 +151,67 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "results = run_simulation1(system)\n", "plot_results(census, un, results, 'Constant growth model')" @@ -208,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -256,9 +372,29 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file figs/chap03-fig03.pdf\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "results = run_simulation2(system)\n", "plot_results(census, un, results, 'Proportional model')\n", @@ -288,10 +424,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 60, + "metadata": {}, "outputs": [], "source": [ "def update_func1(pop, t, system):\n", @@ -303,6 +437,9 @@ " \n", " returns: population next year\n", " \"\"\"\n", + " print(t)\n", + " print(pop)\n", + " \n", " births = system.birth_rate * pop\n", " deaths = system.death_rate * pop\n", " return pop + births - deaths" @@ -317,9 +454,20 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "update_func1" ] @@ -333,9 +481,20 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "function" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(update_func1)" ] @@ -349,10 +508,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 63, + "metadata": {}, "outputs": [], "source": [ "def run_simulation(system, update_func):\n", @@ -381,9 +538,72 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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values
t_01950.000000
t_end2016.000000
p_02.557629
birth_rate0.027000
death_rate0.010000
\n", + "
" + ], + "text/plain": [ + "t_0 1950.000000\n", + "t_end 2016.000000\n", + "p_0 2.557629\n", + "birth_rate 0.027000\n", + "death_rate 0.010000\n", + "dtype: float64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t_0 = get_first_label(census)\n", "t_end = get_last_label(census)\n", @@ -398,11 +618,160 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1950.0\n", + "2.557628654\n", + "1951.0\n", + "2.601108341118\n", + "1952.0\n", + "2.645327182917006\n", + "1953.0\n", + "2.6902977450265952\n", + "1954.0\n", + "2.7360328066920476\n", + "1955.0\n", + "2.7825453644058125\n", + "1956.0\n", + "2.829848635600711\n", + "1957.0\n", + "2.8779560624059233\n", + "1958.0\n", + "2.926881315466824\n", + "1959.0\n", + "2.9766382978297603\n", + "1960.0\n", + "3.0272411488928666\n", + "1961.0\n", + "3.078704248424045\n", + "1962.0\n", + "3.131042220647254\n", + "1963.0\n", + "3.184269938398258\n", + "1964.0\n", + "3.2384025273510284\n", + "1965.0\n", + "3.293455370315996\n", + "1966.0\n", + "3.349444111611368\n", + "1967.0\n", + "3.406384661508761\n", + "1968.0\n", + "3.46429320075441\n", + "1969.0\n", + "3.523186185167235\n", + "1970.0\n", + "3.583080350315078\n", + "1971.0\n", + "3.6439927162704344\n", + "1972.0\n", + "3.7059405924470314\n", + "1973.0\n", + "3.768941582518631\n", + "1974.0\n", + "3.833013589421448\n", + "1975.0\n", + "3.8981748204416125\n", + "1976.0\n", + "3.9644437923891203\n", + "1977.0\n", + "4.031839336859735\n", + "1978.0\n", + "4.100380605586351\n", + "1979.0\n", + "4.170087075881319\n", + "1980.0\n", + "4.240978556171301\n", + "1981.0\n", + "4.313075191626214\n", + "1982.0\n", + "4.3863974698838595\n", + "1983.0\n", + "4.460966226871885\n", + "1984.0\n", + "4.536802652728707\n", + "1985.0\n", + "4.613928297825096\n", + "1986.0\n", + "4.692365078888122\n", + "1987.0\n", + "4.77213528522922\n", + "1988.0\n", + "4.8532615850781164\n", + "1989.0\n", + "4.935767032024445\n", + "1990.0\n", + "5.0196750715688605\n", + "1991.0\n", + "5.1050095477855315\n", + "1992.0\n", + "5.191794710097886\n", + "1993.0\n", + "5.28005522016955\n", + "1994.0\n", + "5.369816158912433\n", + "1995.0\n", + "5.461103033613944\n", + "1996.0\n", + "5.553941785185382\n", + "1997.0\n", + "5.648358795533533\n", + "1998.0\n", + "5.744380895057604\n", + "1999.0\n", + "5.8420353702735826\n", + "2000.0\n", + "5.941349971568234\n", + "2001.0\n", + "6.042352921084894\n", + "2002.0\n", + "6.145072920743337\n", + "2003.0\n", + "6.249539160395973\n", + "2004.0\n", + "6.355781326122704\n", + "2005.0\n", + "6.46382960866679\n", + "2006.0\n", + "6.573714712014126\n", + "2007.0\n", + "6.685467862118366\n", + "2008.0\n", + "6.799120815774378\n", + "2009.0\n", + "6.9147058696425425\n", + "2010.0\n", + "7.032255869426465\n", + "2011.0\n", + "7.151804219206714\n", + "2012.0\n", + "7.273384890933229\n", + "2013.0\n", + "7.397032434079093\n", + "2014.0\n", + "7.522781985458438\n", + "2015.0\n", + "7.650669279211232\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "results = run_simulation(system, update_func1)\n", "plot_results(census, un, results, 'Proportional model, factored')" @@ -438,10 +807,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, + "execution_count": 66, + "metadata": {}, "outputs": [], "source": [ "def update_func2(pop, t, system):\n", @@ -466,9 +833,22 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Xpy75/IQYPZqamtiwYQMOhzlknsViYe7cuYSFhQ1rOsZMgBJCCPH51dfXU1BQ4CvRCAoKIj8/n8TExGFPi1TaDDOlFCUlPYcfvOSSS3j55Zd9719//XUuueQS8vLyWLBgAddeey07d+7s99jr16/nqquuYtGiRSxcuJBvfetbFBQUDPo1CCHGptraWjZs2OALTiEhISxevHhEghNIgBqVCgoKuP/++7nnnnvYvHkz69evZ968eXz729+mvr6+131eeukl7rjjDq688krWr1/Pxx9/zKpVq7juuuvYtGnTMF+BEOJUU1VVRUFBAW63OVReaGgoS5YsYfz48SOWJglQo9C2bdtIS0tj/vz5WK1WwsLCuOGGG7j44ot7DVB2u51f/epX/PznP2fFihXYbDZsNhtf+9rXuP7669m7dy9gNiZ56qmnOPfcc1m0aBG33nqr73gbN27k4osv5sEHH2TRokWceeaZPPbYY75z/P3vf2fFihUsWLCAr3zlK3z00Ue+/fLz8/3Sc8455/Duu+/2u58QYvSoqKigqKiIzvkBIyIiWLp0KbGxsSOarjFbB1Wws4aCXTUALJyZzMIc/2k6Pt52kK1lRwBYOieFPOWfhX1/8wF2VtQBcPb8KeSkx/utf3tDJbsPNPR67M9r+fLlPPnkk6xevZoVK1Ywf/58srKyuO+++3rdvqioCKfTyVlnndVj3Q033OB7/ec//5nXXnuNZ599lsTERB544AF+8IMf8PzzzwNQXl7OihUr+Pjjj9m4cSPXXHMNF110EXFxcdx99928+uqrZGRk8L//+7/cf//9vP322/1eR319fZ/7SaMIIUaeYRiUlpZSXl7uWxYdHc3ixYuHvUFEbyQHNQplZGTwj3/8g6ysLNauXctll13G0qVL+f3vf09vMyDX19cTGxs7YFP7devWcfPNNzN16lTCwsK4/fbbKSwsZN++fYDZUufGG28kJCSEZcuWkZCQQGVlJTabjeDgYF566SWKi4u57LLLAgoyJ7ufEGLoGYbB9u3b/YLT+PHjWbp06agITiABatjZbDY6Ojp6LHe73YSGHh/sPTU1lZ/85Cf885//5LPPPuP73/8+zzzzDGvXru2xb0JCAo2Njb32I2pubsbpNCctrq6u5q677iI/P5/8/HzOPPNMgoODfaM4REVF+aUhJCQEj8dDVFQUzz//PJWVlaxevZply5bx9NNP9xosuzrZ/YQQQ8vtdrNp0yb279/vW5aUlMTixYtHVZ/SMVvEtzCn/6K3ZbmTWJY7qc/1Z8+fwtnzp/S5/vzFqZy/OPWE05WcnEx1dTWzZ8/2Levo6KC6uto3W/A3v/lNzjvvPK666irAfKq5/PLLKS4uRmvd45h5eXmEhYXx4YcfsmLFCr91Dz30EJWVlTz//PMkJiZyzz338IUvfMG3vqysjLS0NIqKivpM87Fjx3z1Vy6Xi08++YRbbrmF/Px8goKC/AKjYRgcO3ZswP2611sJIYaH0+mkoKCAhoYG37IpU6aQm5s76ko3JAc1zC6++GIef/xx9uzZA5jDND344IMkJCQwZ84cAC688EKeeuop3n33XZxOJ06nk40bN/Lhhx9y7rnn9jimzWbjtttu45577uG9996jo6ODtrY2nn32Wf7xj39w0003AXDppZfyxBNPcOjQIdxuN8888wxXXHEF7e3t/aa5vr6eq6++moKCAkJCQkhKSsJisRAbG8vUqVNxOp28+eabuN1unn/+ed+gvf3tJ4QYfm1tbXzyySd+wWnGjBmjMjjBGM5BjVY33XQTQUFBXH/99dTV1REaGsrixYt57rnnfFnrb33rW4SFhfHkk09yxx13YBgG06dP55577mHZsmW9Hvcb3/gG0dHRPP3009x5550YhsHMmTNZs2YNCxYsAOC6666jo6ODK664gsbGRjIzM1mzZs2AA7ROmzaN++67j7vvvpva2lri4uL46U9/SkZGBgA//elPeeihh/jZz37GJZdcwrx58wLaTwgxfJqamti4caPvgdRisZCTk8O0adNGOGV9s5wK9QFKqTRg73vvvcfkyZN7rC8pKSE7O3vY0yUGl3yOQgyNuro6CgoKfPXfVquVvLw8UlJShi0NVVVVnSVA07TW+wLZR3JQQggxhh08eJCtW7fi8XgACA4OZsGCBcM+dcbJkAAlhBBjkGEYVFRUsGvXLt+ysLAwFi1a1GexvuNQBbaEKViCR0dLPglQQggxxhiGwc6dO32jyIDZAXfhwoVERET03N7tomXnJ7RXlRKakkF07jmjotGEBCghhBhD3G43W7ZsoaamxrcsPj6eBQsW9NrHqaOlgeYt/6ajxRz2zFG9m+D4yexuH09cdChTk0dulmsJUEIIMUY4HA4KCwv9mpGnpKQwd+5cgoKCemzffrCMlh3rMdzH+zI6x6XyznYnDW0HGR8TxuWJ0VitI5ObCihAKaWCgPlAPpAIuIEaoFBrvXXokieEECIQLS0tbNy40W9a9unTp5Odnd2juM5tb6G1dAOOQ8eHObJYg4jKWYZ7wnRa3zYHBKhvakdXNpA9bWRGNO83QCml4oDvATcA8UAFUAcEAROAVKXUIeAp4AmtdePQJlcIIUR3dXV1FBYW+kZ16a+PU9ueItrKN2O4jw+5FhQZS0zeeQTHmINiz1OJFOla8rOTyJw6bnguohd9Biil1Grgv4D3gKuBd7XWjm7bxABfAK4Adiil7tJaPz+E6RVCCNFF92bkQUFBzJs3zzd0Wncepx3D3YHbY3C0wU5w8nQyl16INdjm22ZuZgKz0uMJCx3ZWqD+zr4AWKC1PtLXBlrrJuCfwD+VUhOBuwAJUEIIMcQMw6C8vJzS0lLfstDQUBYuXMi4cX3neiJmzKdhTwn6kJ0DEbm425KYZgQR2mWb4CArwUEjPxJenynQWt/SX3DqZftDWuubBydZY1cgU74/9thjzJkzx28YfOh9csBOL7/8MtnZ2eTl5ZGXl0dubi4XXngha9as8Rs9/JxzzmHOnDm+7Tr/zj//fN82lZWV3HTTTSxYsIC8vDxWrlzJCy+80Ot5L7roIs4888xeR1IXQgwNj8fDtm3b/IJTdHQ0y5Yt8wUnj8tBS8mnuO0tfvtaQ0JJ/MKlVKcsp9UWT7uzg9K9vc/UPdICzr8ppRKBOUAI4FfjprV+c5DTddpzOBzcdtttrFu3DpvNNvAOQGZmJq+++ipgPl0VFRVx22230dTUxA9+8APfdr/+9a97jHreyePxcO2117Jy5UoefvhhwsLC2Lp1KzfddBM2m42vf/3rvm07B4FNSEjgnXfe4eKLL/4cVyyECITT6WTTpk3U1dX5lnVtRm4YHtoPlNJWVoDH2Y7H0UZ07rl+DSVCY+NZPNvKhh2HWDxrIhlTRq6eqT8B5eGUUlcDB4B3MIv03ujy9/qQpe40tmTJEtra2nj00UdPan+LxcK8efO4//77WbNmDU1NTQHt19DQQGVlJStXriQ8PByLxUJeXh533HFHj23Xrl3LF7/4Rb761a/yl7/85aTSKYQIXGtrK5988olfcJoyZYpvHidXQw2Nn7xMy46P8Djbcbk87NlaxIaCXT2OlTFlHN88P4vMqXGjolNubwLNQd0O/AG4U2vdfDInUkpdATzdbXE48J7W+ryTOWZ/WssKaSvfHNC2YVOyiZ7tP116c/GHtB/oWRTXXcSM+URmLjipNPZ73IgIHnzwQa688krOOussFi1adFLHWbJkCVarlaKiol6nhO8uPj6ehQsX8p3vfIdVq1b5ivkuvfRSv+3q6+t57733+Ne//kV0dDQPPvggxcXFfvNcCSEGT319PYWFhb4JSAGysrKYMWMGHkcbTaUbcFTv9q1zODsoPeSkOmoWrQdczMxxEBt1vKbJYrEQHDQ6A1OnQAPUFOC3JxucALTWfwX+2vleKZWHmSO7/WSPOdbNnTuX66+/nh//+Me+orsT1Tn/UkvL8XLo2267jeBg/4/+qquu4uabzSrENWvWsG7dOv7973/z5z//mY6ODs466yzuueceX8ugV155hSVLljBx4kQAVq1axQsvvMADDzxwUukUQvStqqqKbdu2+bXUmzt3LhOTk7BXbKWtfItfZ1uLNYi4mfNpioqiqcEBBuytPsbczMSRuoSTEmiAegc4F9g90IaBUEqFYAare7XW2wbjmKeKQKd873TjjTfy8ccfc9999/nV/wTK7XbT1NTkCyQADz/8cJ91UJ1pvPLKK7nyyitxOp1s3ryZRx99lFtvvZW1a9diGAbr1q2jtraWpUuXAmadmcPh4I477iA+Pv6E0ymE6MkwDLTW7N59/Kc3NDSUBQsWEOlupeGjdbjbjoEBHsPAarUQmpxOZNYSgiKiOSO+lfVbD7J0TgopCVEjeCUnJ9AAtQ34tVJqFVAGOLuu1Fr3rKDo302AHXjyBPcLWGTmgs9V9BY9+6wexX6DIZAp37sKCgrioYce4pJLLul1kMeBFBQUYBgGmZmZAW2/du1aXnzxRV555RXADFZLliwhJCSEa665BoANGzbQ2NjIW2+9hdV6vBrz+uuv58UXX/TN4CuEOHkdHR0UFRX5janXdcBXZ20T7rZj2B0dHK5vxQgbx9zzL8Y24ficecnxkXz1nIxRW8c0kEAbup8FbMSsM8rF20fK+9d7u+c+KKVsmMV692qtR/9siYMskCnfu5s6dSo/+clPWLduXcDn8Xg8FBQUcPfdd3P99dcTFRXY09NZZ53F/v37eeCBB6irq8MwDA4cOMCzzz7rm25+7dq1XHDBBSQlJZGQkOD7+/KXv8yLL74oTc6F+JzsdjuffvqpX3BKSkpi2bJlvgdVW2IqntgUdh9qozw4k4KQhRylZ2u8UzU4QYA5KK312YN4zgsAD2ZrwNNOIFO+9+ZrX/saH374IRs2bOhzm7KyMvLy8gBzUrJJkyZxzTXXcPnll/tt98Mf/rDXgSPfeustkpOT+dvf/sZjjz3GypUrsdvtxMXFccEFF/C9732Puro63n33XZ577rke+69cuZIHHniAt99+m5UrVwZ4R4QQXTU0NFBYWIjDcXzgnimxYWSnJvaoO54w7xw8KOqq7VgtFo7U20mZcOoV5fUl4CnflVJJwM1ADmbOqwT4g9a64kROqJRaAzRqrX90AvukIVO+j3nyOYrTXffGEIarnemhdhItbVjCorAtuIyYaP+i/qZWJ59sr2bxrGTiosNGItkBOZkp3wPtB7UQs+7py8BR4AiwEtiulDqhIj5gMfDJCe4jhBBjlmEYlJSUUFRUhMfjMQdyPVZDlnGYBNpobHZQtvsAn7z9f3TPVMRE2rhwSdqoDk4nK9BGEo8A/wPc0LXeSCn1OPAQcCJFgGlA9QlsL4QQY1ZHRwdbtmzh8OHDYBh0NNcRam9gVkIY4SHBuN0equvaOByezlF3MmX7G1CpIzP9xXALNEDlA9f00qjhMSCw3rBeWuvIE9leCCHGqtbWVgoLC2lubsbT1oyr8TDjbQbZyeEEeycJjJg0g9CUadRWtBJqC8LtOX3algUaoA5h5nx0t+XpwEl33hVCiNPVkSNH2Lx5M057G676ajyONpLCgpgUFUyw1UJw9HgiZy7FFj+JuY4OgiPqmDNjAraQng2cxqpAA9RfgGeUUt8HOpuRLQF+410nhBAiAIZhUFFRQUlJCYZhYLFa8DjtjLd68LS5OOgKJTH/HKLScrBYzGYCYaHB5GcnjXDKh1+gAeoXQAqwDrNhhQVwYRbx3TU0SRNCiLHF7Xazfft2qqqqfMvCIqLIW7yEfRs+piYijdrITMLaElhoGfn5mEZaoP2gnMC1SqnbAIU5CkS51to+lIkTQoixoq2tjYIP36Oh/ijBMRMAGD9+PPn5+dhCgmkOTWHrzibU1Diyp50ejSAG0t+U7xcB/9Zau7yvu5uilAJkPighhOjP4f17KPjg37Q3N+Fye+josJE7dxazZ8/2DRemsqYxYWI7CXHhI5za0aO/HNQbQDJQ633dFwM4fWrthBAiQG5HG/rTf7OrVOPxGLTaXbg6PEQHNZI+I8tvLEur1SLBqZs+A5TW2trbazE6PfbYY5SUlPDkk/2Pv9vZm7uwsJCYmJhhSp0Qpxecyvp9AAAgAElEQVTD46Zl73a2fraemqZ2ACxYsALR4ybTGJdDYUkt5+RPGdmEjnIBT/kuhBCif4Zh4Dy8l6PFn7K9soYWp8e3btz4OObOXc47O9vImTaehTk9Zy8Q/vqrgzqCWXw3IK31qTUL1giqqqri0ksv5dZbb+Wpp57C5XJxyy23YLVaeeqpp3A6ndxwww1cddVVfPrppzzyyCPs3buXSZMmcdNNN3HBBRf4jnPXXXexfft20tPTSU9P9zvPunXrWLNmDfX19cyZM4d7772XKVPkaU2IoeJxOWja/DaHq/ZTVN2Cs8NDeFgwlmAbaVmzmHvGcoKDg0ma6iQm0jbSyT0l9JeDup0AA9Ros2fPHsrKynqdGHCwBQcHk5mZyfTp0wPep7m5mR07dvD+++/z7rvvctttt3HJJZfw3nvvsX79em655RZmz57N9ddfzwMPPMB5551HYWEhN954IwkJCcyfP59bb72VmTNn8oc//AGtNVdffTX5+eawiO+88w6/+93vePrpp8nIyODZZ5/l2muv5Y03+qtKFEJ8LkEhVBxuYOveRjrcHgyLlbAJE8j/wnLS0qb5pr2Q4BS4/uqgnhvGdAyqioqKYQlOYI6jVVFRcUIBCsyZcjsnA3S73axevRqbzcbZZ5+N2+3mT3/6E4sWLeKii8wGlEuWLOFLX/oSr7zyComJiezYsYM//vGP2Gw2Zs+ezSWXXMLBgwcBM/e0evVqcnJyALjuuuv4y1/+wsaNG0lNTR3cGyCEwOVysXXrVqo7IrBYLbRbonGExpOYMpdp09IHPoDoVX9FfAHPjqe1PvG5yIdQenr6sOaguhevBWLcOHNisc55maKjowF8rXra29uZNGmS3z6TJ09m48aNHDlyhNDQUOLi4vzWdQao6upqnnzySZ555hnfepfLRXV1tQQoIT4nw/DgOFhG+8HdxORfSGubncLCQlpbW7HawoieqmhosLA4P59Fs3tODyQC118RX+uwpWKQTZ8+/YRzNMNtoFku8/Pz2bRpk9+yAwcOMGHCBJKSknA4HNTV1REfHw9gjoTslZiYyOrVq/0mKtyzZw8pKSnU1dUN4lUIcXpxHq2iteQzXE1HOdrYzgd7X8MWE4bb7fZtkz0zm5UzMgkL7XsCUhGY/or4vjOcCRH+li1bxlNPPcWbb77J+eefT0FBAa+//jqPP/44kyZNYuHChTzwwAPcd999VFZW8vLLLzNv3jwALr30Un7/+9+zYMEC0tPTeeONN7jzzjt54403eszIKYQYWEdzPa2lG3Ae2Y9hGJQdaOBgq5t6tyZmaiYxkTaCg4PJzc0lJSVlpJM7ZvRXxPcgcJ/WutX7ui+G1vo/Bz9pp7fg4GCefPJJHnnkEe666y6SkpK47777WLZsGQC/+c1vuOuuuzjjjDNISUlhxYoV1NfXA2aAampq4sYbb6S2tpapU6fyxBNPkJaW5jcGmBCifx5HG61lhbQfKKWzzZjDbXDIFUQNkbSHxuBpdpCSZA5Z1FlULwZHf4/TC4CQLq/7ckq29BspkydPRuvjs5bExMT4vQf83i9durTX40yYMIGnn366z/OsXr2a1atXD3h+IURPRoeLtr3bsFdsw+1yYvXOzVTX1sHu9jCCU2bgqW0jOSaMmSqdvLm5UjoxBPor4ju7t9dCCDGWGYaHho9for2pgSMNbbS2u5g+KZYqI4aD1jAssaEEAxlTQsnJySEtLW3AOmVxcgIO+UqpSOByIAdwAjuBtd6RzoUQYkywWKzYJs6gpPRdXB0eWqwRVLckEhoZisVbphQWFsb8+fMZP15GHR9KAY2xp5SaDewH7scMUPOBhwGtlMoauuQJIcTQcrf3bLAcMT2X8Skp7A3LZKc7gSOtLt+6hIQEzjzzTAlOwyDQHNQfgH8A/09r7QJQSoUDfwSeBs4amuQJIcTQcLc10aoLcNRUYJ13CfFJx2estQSFQMYi7LVFpCZFEBkegsViITMzk4yMDCnSGyaBBqhcYHVncALQWtuVUj8HioYkZSfIMAz50pzCPB7PwBsJMQg8znba9myhfd8OWtsc1NS3cfTQm5z9zSuJCAvB4XCwZcsWjh49ypSkKABCQ0OZN28eEyZMGOHUn14CDVBbgC8AZd2W5wO7BjVFJyEsLMzXaVWC1KnFMAxcLheHDx8mMjJypJMjxjDD3YF9XzFte4owOpxgQPXRVhwuN+5QD5t2VpM9NYKioiIcDodvv/j4eObNm0dYWNgIpv701F8/qBu7vC0AHldK5QMbADcwB7gBeGBIUxiAyZMnU1VVxZEjR0Y6KeIkBAcHExsbK0+nYkh0Dk3UWlaIp2t9kwWSUlP5oC6J9tDxGEer2XioDsMwe85YLBYyMjLIzMyUB98RMtBo5l3VABd4/zodBb6L2XhixISEhDBt2rSRTIIQYpQxDAPX0QPmCBDH6mi1u4j2jiQeFBFLpFpEfFIa2cVV2BsqaW1u9HXqlCK90aG/flDyiy+EOKW1lhVSe7CaIw12OtweZkxLJn7WYsKmZGGxBlFTU0NTTQlO5/HeMhMmTGDevHmEhoaOYMoF9NPMXCn17RM5kFLKopT67udPkhBCfH4Wi4XIrCU0tzpxeizURmawPXoZ4ak5eAzYsWMHhYWFvuBksVhQSrF48WIJTqNEf0V8eUqpHwFPAa9orQ/1tpFSKhH4JnAd8G5/J1NKTQR+D5wNtAPPaK3vPpmECyFEJ4/TTvv+EsKnz8ViOf7cbYtPYdKic9m8s4PQyChmT0mgubmZLVu20NTU5NsuPDycefPmSd+mUaa/Ir7vextF3AM8qpTahTl6xFHAAiRgNj/PBP4FXKW1LhjgfK8Cm4EkYCLwoVKqRGv9t899JUKI047hdmHfW0xbRRHt9nZqD7WTt2yJX6OGibPmc974Y0xOjOJQ9UHWr9/hNz1GcnIyubm52Gwy0+1o028zc631JmCVUmoacCFms/IZgAez0cTvgDe11vsHOpFSahGQDiz19qfaq5RaDtg/1xUIIU47huHBUaVp3b0Jj72VQ3WtNDS14zz4GZXTs0hL8c8JTU6IYPu2rVRXV/uWWa1WcnJySE1NlVZ6o1RA/aC01nuBJz/nueYDxcC9SqmrMIv4ntRaP/I5jyuEOE0YhoGztpJWvRF3S4O50GIubw+KpCY6i7odtaROjPMFnbq6OoqKirDbjz8LR0dHM2/ePGJiYkbiMkSAhnN8+PGYnX0/xMxJZQFvKaUOSRGfEGIgrsZaWks/w1XvXx1uDY0gdekZbNhukDg+kqVzUrBYLHg8Hnbv3s3u3bt9fZsAUlNTycnJISgoaLgvQZyg4QxQDqBJa32v9/02pdQfgcsACVBCiF4ZhkHL9vdpryqjxe6kodnB5MRorCEhRKTPJTxtDpbgEC5PdhATacNisdDa2kpRURENDQ2+49hsNubMmcPEiRNH8GrEiRjOAFUKRCilbF2m6JAZvoQQ/bJYLFhCwzlQ20xTqxMDC+Hhqaily7GGhvu2i40KxTAMDhw4wI4dO+jo6PCti4+PJy8vj/Dw8N5OIUap4QwQ/waOAI94m68r4GrM4ZKEEALofeDniPQ8wrZt4UBHPIejFFWO8WTZ/MfGczqdbN++nUOHjhcBWiwWsrKymD59ujSEOAWdyISFeZgNHUIwm5n7aK0HbEChtW5XSp0FPAYcwmwk8aDW+u8nlGIhxJhkGAaOQ3toK99MTP5FBEdE+9ZZbWHM+NJVbPqgksykaBbmJPsFnCNHjrB161ba29t9y6KiosjLy2PcuHHDeh1i8AQUoJRSdwE/B+qB5m6rDQJs4ae1rgAuPpEECiHGPldDDa0ln+FsOExjczubX3mNM79+OaEhxxsy2CIi+eb5WQQHHe+I63a7KSkpYe/evX7HS01NZebMmQQHSy3CqSzQT+8a4G6t9S+GMjFCiNOLu62ZVr0Rx6FyAPbXNNFid2GxuthcfIAz5qX5bd81ODU2NlJUVERLS4tvWWhoKHPmzCE5OXlY0i+GVqABKh5YN5QJEUKcPjwdTux7irDv3Y7hOT6qw7iYCPZZ4qmNnEH0YTuL3B6CgvyHDDUMg/LycrTWfs3Hk5KSyM3NlXH0xpBAA9RLwBXAvUOXFCHEWGcYBo6D2uxo226na7uF0OR00tQidm4+ysKEKHIzEnoEp5aWFrZu3erXfDw4OJicnBymTJkiDSHGmEADlB24Uyn1NWA34Oy6Umv99cFOmBBi7Gne+h7t1eU0NLdztMFOWkoMEROSico+g5DxZv+kVV+I6RFoDMNg3759lJSU+I2jFxcXR15enszGPEYFGqAikM60QojPKTRlBhXbttLY4sBlDaUsdBbLzzjTLyB1D052u52tW7dy9OhR3zKr1UpmZiYzZsyQXNMYFuhYfN8Z6oQIIcYWw+MGi9UvgNgSU4mflkFZRTtHI6YT5YnA4XITZuv5U2QYBlVVVezcuROXy+VbHhMTQ15enoyjdxo4kX5Qs4E7gBzMiQ5Lgd9prT8dorQJIU5BvgFdd31CmFpCREq6b53FYmHymatIiKoiPcLG3MwEv5Z5ndrb29m+fTuHDx/223f69OkopbBa+5xrVYwhgfaDuhB4DXM0iJcwO+qegTmf08Va63eGLolCiFNFR0sjrSWf4Kg9QGNLOwfLX0etuoqUpFjfNhaLhbPnT+l1f8MwOHjwIDt27PDLNUVGRjJ37lyZUPA0E2gO6hfA/Vrr+7ouVEr9FLMDrwQoIU5jRoeLtvIt2PduwzA81Na3cfSYHY8lhIJNpVxy0cIB64ocDgfFxcV+QxUBTJs2jaysLOl0exoK9BPPBnprqfci8JPBS44Q4lRiGAbOmgpaSj7F097qWz4+NoxyVyKHIjMJNyJpanUSG9V7/6TOXNPOnTtxOo83EI6IiGDu3LnEx8cP+XWI0SnQALUfyAPKuy2fD9QOaoqEEKeEjpZGWnd9jOPIASxYfCN0hsQlE5ezjMkH3aR4DPJUAiHBvc+91N7eTnFxMTU1NX7LZagiAYEHqCeAp5RSk4EN3mVLgLuAB4ciYUKI0ctxaA9NW9+jqaWdmrpWksZHEDd+HJFZiwidpLBYLOT308iur7qm8PBwcnNzSUhIGIarEKNdoM3Mf6eUigbuBCZ4F1cDP9NaPz5UiRNCjE7BccnUNzs5VGuOHa0dSaxYuorQ8IgB97Xb7Wzfvp3aWv/Cl7S0NLKzsyXXJHwC/iZ4B4r9hVIqEbBrrbuPai6EOE0EhUWSPG8Z+z/cQGV4NkTG09BmkNzPfICGYVBZWUlJSYnfZIIRERHk5uYyYcKEvncWp6U+A5RS6kbgT955nG7sZb3vdSDzQQkhTj2G4aG9cicel4OIGfP9WuLFTJ9DashUbHVtLMpJJiy07+fd1tZWtm3bRl1dnW+ZxWIhLS1NWuiJPvX3rbgdWIs5seDt/WwX8HxQQohTR0fTUZqLP6L16CFq6toIao4mb97xB1OLxUpm6ngyU/vum2QYBnv27KGsrMxvDL2oqChyc3OlX5PoV58BSms9rbfX3SmlZCAsIcYQo8NFa/km7Hu3Y7e72HvoGIYBzVs3kJE1jagIW0DHOXbsGNu2bePYsWO+ZRaLhRkzZpCRkUFQUO8t+4ToFOhIEhVAvta6vtvyFGArkDgEaRNCDDPn0Spaij/EbTermMNCg7HZQjgQnMbRqAwqa5rJSe+/X5Lb7UZrTUVFhd98TbGxseTm5hIbG9vP3kIc118d1CpgmfdtGvBfSqm2bpvNGKJ0CSGGkcfloLXkU9qrtN9yW3wKU2Z9ieo9bXw1bxKJcf230jty5AjFxcW0th7vtBsUFERmZibp6ekyhp44If3loLYB38fX/Y48/OeBMoAW4NtDkzQhxHBw1FTQVPwRtYfr8RgGyfGRWEJCicpeQugkxTiLhUlTjX6HKnI4HOzatYuqqiq/5RMmTGDOnDkyX5M4Kf3VQVUC5wAopZ4FbtVaNw1XwoQQQ88wPBwrLaS84hBOlweAuDRFysJzsIYezy31FZw6p8TYtWuX3zBFISEhzJw5U2a5FZ9LwPNBKaWClVKTgM6aTQsQCszXWv/PUCVQCDF0LBYrcXnnEFKxnza3heroHDzhs5gcOnCH25aWFoqLi/0mEgSYNGkSOTk5hIb2PvaeEIEKtJHESuBZoLc2oQ2ABCghTgEeRxsWW7hfriYkNoH05at4o7iN+bOmBNQIory8nPLycjwej295REQEs2fPJjFR2kyJwRFo77hfYk6p8WvgPWAVkAz8BvjR0CRNCDFYDMOg/UAJddvWUxM9k/lnLfMLUvHpiitTPQT1MnlgV701grBYLKSnp5OZmSkdbsWgCvTblAF8VWutlVJbgEit9TqllAv4Kea0G0KIUcjd1kxz8QdU7d5NXaOdDsun7JmSzozpk/y26y849dUIYty4ccyZM0eajoshEWiAsgOdefkyIBf4F7AZyByCdAkhPifDMGjfv4vW0g0YbhdutwcD6LCGsql4P+nTUrBa+2/A0Dl+Xmlpqd+o4yEhIWRlZZGamiqNIMSQCTRAfQTcrZT6HrAJuFYp9QiwHAi4ZZ9S6rvA04Cjy+KbtNbPB3oMIcTA3G1NNBd/gKuu2rcsKT6SyqCpdEzM5bwFqQMGp8bGRoqLi2lsbPRbLo0gxHAJNED9CHgd+C7wFGb/qCbAhjknVKDmAY9orX98IokUQgTGMAza9u2gevOHxIYH05m5CYocx7g5yzkrJI6YSFu/uR6Xy0VJSQn79+/3GwkiMjKS2bNny1xNYtgE2sy8HMhWSoVrre1KqYXA2cBRrfXGEzjffOC3J5FOIcQA3PYWDn32Jgf37MXu6KBjfAQTxkUQkZ5LREY+lqBg+qspMgyDAwcOUFJS4tenKSgoiIyMDKZPny4jQYhh1d9QR712hOiy/P3O91rr7kMg9bZfEDAH+JZS6tdAG/BH4AGttdHvzkKIAVlCbDTVNWB3mHMtVTUHkbx8JZEpkwbY0yzO27FjBw0NDX7LExMTmT17NhERA/eLEmKw9ZeDasEczigQgQxLnIBZf/U8cBmQDbyKWVQo03UI8TlZg21MO/MCjr36Igdt05iav5SYpIn97uN0OiktLe1RnBceHk5OTg7JycnSCEKMmP4C1NmDeSKtdQ1wVpdFW5VSjwFfQQKUECfEcHdwdK8meorymygwPCmV6V/6LtlRMYyL7rsRg2EY7N+/n9LSUr/iPKvVyvTp02U6DDEq9DcW34eDeSKlVA7wda31z7ostmFOiCiECFBb7QHKP3yLhtpagnNWsHT5Ir/1iRP7b8RQX1/Pjh07/OZpArM4b9asWTKwqxg1Ah3qqJB+ivu01gsDOEwj8COlVBWwBnN09O8BNweSBiFOdx6Xg9bSDRwp3Ub9YXO+praSjzk8U5GUOG7A/dvb2ykpKenR2TYiIoKcnBySkpKkOE+MKoE2M3+jl/3SgYuBewM5gNb6oHeOqQcxh0g6Cvxca/1SgGkQ4rRkGAaO6nJaSz7F47QTHWEjKjyEY+0G7omzCLL1P8Ot2+2moqKC8vJyOjo6fMuDgoKYMWMG06dPl+I8MSoF2sz8vt6WezvergIeDfA4/wfkB5w6IU5zrQ11tO5cj6Wp5vhCC0zKyiZyQh5ZmZP6nQqjpqaGXbt20dbm39B24sSJzJw5U1rniVHt847s+H/AY4ORECHEce6ODvRn6zm6YyMRNiupyTFgAWtYJFEzl2JLmsbEforjmpub2bFjR4+pMKKjo5k1axYTJkwY6ksQ4nMLtA6qt8esWMyBYg8NaoqEEBzd9gm12z4FoMXuptnuInHmPCIyF2AN7rtIz+FwUFZWRmVlpV+zcRk7T5yKAs1B9dUnqh34zuAlRwgBMCEnn7iSrTQ0tuCJGI8193yiMtL73N7j8bB37152797tN6irxWIhLS2NzMxMbAPUVQkx2gQaoLr3iTIAJ7BTa908uEkS4vTSZnfS2uYgIT7atywoLJLUxWdjHGwkZ+kZhPQxz5JhGBw+fJhdu3b5zdEEkJCQQE5ODtHR0b3uK8RoF2gjiQ8BlFJRgALc5mJtH8K0CTGmuT0Gu7YUc3jLB3RETeSLl3+VoC4jjI+bMYe5M/rev7GxkV27dlFXV+e3PCoqipkzZ5KYmCjFeeKUFmgdVCjmaA9XYHauBbArpf4A/Ehr7R6i9AkxJrnbmmkoXk/D5q0EewyC6lso2VHOrDkZA+5rt9spLS3t0Z8pJCSEzMxM0tLSZFBXMSYEWsT3O8y5n74BFABWYBHwMObcTv85FIkTYqwx3B207SnCXrEVw+MmYVw4h+vbsNlCiLL0P+ZyR0cH5eXlVFRU4HYffyaUeiYxVgUaoL4OfElr/XGXZS8rpeqBdUiAEqJfdoeL+opSwmu24ba3+JbHx4bBhHRmLD2HkMje64o8Hg/79++nrKwMh8Phty45OZns7GyioqKGNP1CjIQTmfLd1cvyY70sE0J4eTwGJTvKOFj4PuGOejKmjCMoyCx+C46ZQFTOMhLjknvdt7MBRElJCS0tLX7rYmNjycnJIT4+fsivQYiREmiAuhP4o1LqemCD1tqjlJqFWS/1i679pAKZG0qI04WzoYbGT1/B1uHGDRxptJMyMZ6IzAWETcnGYum9rqihoYGSkpIeDSDCw8PJyspi0qS+R5AQYqwINEA9CkQB6wG3UsoDhAAWYCHw6y7byqBeQniFjk8mKXUqVXv2EhIcRPi0OcQtOhNrSO9TYbS0tFBaWsqhQ/7934ODg8nIyGDatGkybp44bQQaoC4d0lQIMQZ0dLipOVzP5EnHp7uwWCxMXXwubuvHpC05m9CY8b3u297eTllZWY+JA6UBhDidnWg/qHAgA7MV3x7ppCuEWVdUUVrO/g3/h8vpIPI/riUuJty33haXhDr/K73u63K52LNnT4+WeQApKSlkZWXJ/EzitBVoP6gg4L+BWzletOdUSj0H3Ky17uhndyHGLHfrMVr1RuqLtmKxu7ABRR99wjkrV/S/n9vNvn37KC8v95vRFmDChAlkZ2czbtzAczwJMZYFWsT3C+BKYDXwMWaAWorZD+oe758Qpw2Ps5228s20V+7EMDwkxUVQYT+GNSiIxNhgDMPotRGDYRgcOHAArTXt7f6TScfExJCdnU1CQoI0gBCCwAPUauAarfWbXZatU0o1A88gAUqcJg4faeJg8SamduzD6Die8wkPC2aiymFy/plExvWsZzIMg0OHDqG17tFkPCIigqysLFJSUiQwCdFFoAEqCijvZXkFIBPLiDHP43Gz/p31OCs2E+Jpp2ViDJHhIQCExCUTmb2EhHFJPfYzDIPa2lpKS0tpamryWxcaGkpmZiZTp06VoYmE6EWgAaoQuAmzDqqrm4HNg5oiIUYhw9lObG0RjR6zWK62vo0ZGVOJzFqELWlarzmfuro6SktLqa+v91seEhLC9OnTmTZtGsF9jFIuhAg8QP0n8IFSajmwwbtsMZAGXDD4yRJiZHWvQwoKi2Ty3IUcW/8BkdHRTM1fRlxWLhZrzz5JDQ0NlJaW9pjNNigoiPT0dNLT06XJuBABCLSZ+Sal1DzgOmAm5tBHrwNPaK1lRl0xZrQ7Oti2vZy6qv1ccMkXsXaZ/mJ8dj6zgkKIU3N7ndW2sbERrTW1tbV+y61WK6mpqWRkZBAa2nsHXSFETwGXL2ity4DblFLxgFtr3Th0yRJi+Llam3n/5dcIP7aPcCyUlSmyslJ9660hocTnLOyxX1NTE1prampq/JZbLBamTJlCRkYGERERPfYTQvQv4ACllLobuAFI8r4/ADyqtX50iNImxLDwOO207SmivXInqdZmagELBkd3bIAuAaq7pqYmysrKegxLZLFYmDRpEpmZmdLJVojPIdCOur/ALN67H//5oH6qlArRWj80dEkUYvB1uD3U1TcT3VhO296tGB3mYP3xseG0tLkYn5pO6qLlve7b3NxMWVkZ1dXVPdZNnDgRpZRMsy7EIAg0B3UN8O1u/aA+UUqVA08AEqDEKcHt9rBzTy17NhcSd6yM7MkRfvVMoeOTmLtkFbb4ST327QxMhw4d8hsvD8zAlJmZSUxMzJBfgxCni0ADVDBwoJfl5YA8KopThru9hYaP1hLXbnaWrTtmISEugqCoOCIzF/TaZLyvojwwJwzMzMwkNjZ2WNIvxOkk0AD1EPCkUuqbWusDAEqp8cCvMIc7EuKUEBIRzcSkOKorWwgOshIcEU30nC8QOimzx9xMx44dY/fu3b0GpqSkJDIzM2W8PCGGUKAB6htANlDhbRzRAUwFbMBipdT3OjfUWicOeiqFOEEej0HZgQZaWuzk5xwvrrNYLExduBwc/2Ti3MVETZuNJcj/36CxsZGysjIOHz7c47jJyclkZGRIYBJiGJzIhIWDQik1DtgO3KO1fm6wjitEp7Z2F2+8u43QQ9sJ9bRxLPVaYqOO9z8KTZzCjEuv7tGXqb6+nt27d/foxwRSlCfESAi0o+7zg3jOp4CeNdBCDAKPow13+WZSaz7D7jBb5u0q2MSSc5b6trFYLFi8wckwDI4ePcru3bt7TK9usVh8gUkaPwgx/IZ1IDCl1LeBGKB4OM8rxja324PF6MBesQ373u0YbhdJceHsP9xBfGwYKQmeHvsYhsHhw4fZvXs3jY3+fc4tFgspKSlkZGRIc3EhRtCwBSil1DTgZ8AZwFvDdV4xdtU3tbOh+CARjRXMDq3G47T71kWGhzAzdyaxOUsI6TLKuGEYHDx4kD179vQYXdxisTB58mRmzJhBVFTUsF2HEKJ3wxKgvDPyvgDcprWuUUoNx2nFGNbU6uDN1z4gobkE3G20psQSHmZ+nYOjxhOZtZiQhCm+JuNut5sDBw6wZ88e2tra/I5ltVqZOnUq06dPlyGJhBhFhisHdTegtdYvD9P5xBgXaXUys2MnTW5z+ovWdgfum/8AABjOSURBVBeR42KJzFxI6KQMX5Nxl8tFZWUlFRUVOBwOv2MEBweTmppKeno6YWFhw34NQoj+9RmglFJ/CvQgWuvvDrDJ5UCKUuoy7/tozH5VC7XWNwZ6HnF66nB7aLW7/FriBYVHM2l2Psa2jSQmxBGfs4Dw1Fm+JuPt7e3s3buXffv20dHR4Xc8m83GtGnTSEtLk2kvhBjF+stBda0dtgFfAkoxJy90AvOAXOBvA51Ea53V9b1SaivmQLPPnWB6xWnE4zEoraxn8/a9jKOFlavO8hvlIWH2QqIiQwlPn4vVZuaAWlpa2LNnD1VVVXg8/o0jwsLC/n97dx4d11UnePxbqiqVSpu1S6WlJFm2f04cnI2EBLrZ0plmCQTSTZgG0k2AsDTNTIcJzJCwdgcSQjeHNDM0SwcGOkPn0KSTGZJAw0kIWXHbcezYsfOLV22WZMmyrV2lWuaP+xSXZVuWZUtVJf0+5+hEfve9V/eXUr1f3fvuu5e2tjai0agtFGhMDjjlp1RV3zP9u4h8H5dQPp2+j4jcDqydeawx58LoyBg7n3iUhuE9AOzeu5rVbceeUMjLD1O09grAPcO0Z88e+vr6Tpgnr7i4mLa2NhobG21pdWNyyFy/Rv4ZrsU004+BLWf6oqp60ZkeY5aPVDLBRPuLxHY/h+QdZoAkAX8evs4tkJagpoeK79mz54Rl1QHKy8tZtWoVtbW1J12S3RiT3eaaoHqBNwO7Zmy/Bmg/pzUyy9LQaIzh0Ukq432M6QYS48MAVJUVkJfno6apgdLzLwTciLyuri727t3LyMjICeeqqalh1apVVFRUWGIyJofNNUH9DXCPiLwZ2Az4cOtBvQ14z2wHGjOb2FSCTTv72L19B43jyrravOOWv8gvXsHKSy4jVL+aWCyGqrJ//35isdhx58nLy6OhoYG2tjZ7uNaYJWKuUx39s4h0AR8DbvA2vwC8XlU3LFTlzNKXnBxneNPDRMfc/HeHjhZSXR4mLxgivOoSws0XMDI6hr7wwkkHPgQCAVpaWmhtbbWh4sYsMXMeyqSqvwV+u4B1MctQqKiQlgo/PWMQDgUoKiqgsO1iClov5NCRIbZt3HTSyVsLCwtpbW21EXnGLGGzPQd111xPoqqfPTfVMUtVKpWio2+YkdEY69qqXtnu8+URfc0bCcYfplrWE1p5MT0Dh9n3zO8ZHh4+4TxlZWW0tbURiUTs/pIxS9xsXz0vm+M5UqffxSxn45Nxfv30LibbX6AkcZTmyAcpLjz2gGxBXSs1b7qerv7DtD/17An3l6ZnFW9tbbWBD8YsI7M9B/Wm6d9F5Abgl6o6sCi1MktGKhEn1b2d6r2PEZtwk7m+8B+bee0b3fNLhw8fZt++fRw4cOCE55cCgQDRaJTW1labI8+YZWiunff/AFwBWIIyp+USTYrJrpcZ3bWR5MQokbIAHb1QVhKiLjRIV1cX+/btO2GpC3D3l1paWohGowSDwcUPwBiTFeaaoDYA7wbuXMC6mBw3NjHFczv6SA22c0Gwg8To0VfKisP5NK9sYLgsypahKWLPP3/C8ZWVlbS2tlJXV2fdeMaYOSeoJPA1Efk8sA8YTy9U1cvPdcVMbhkZi/Hgg09QObSTcHyI0YYVFIQCpFIphlNB+kO1DExC6tDoccdNP7/U2tpqy6kbY45zJi0oe97JnFLYN8l5k1sYjrslLQaHJ/El8jjoL2fcXwgTx+9fUFDwSjdeKBQ6yRmNMcvdXB/U/cr07yJSCuSp6ok3D8yyMD4ZZ3wyTkXpsQdj/eESIudfyMjzm0iGQnQEyiFYDnn+446trKykpaWFuro6m7jVGDOrOT/hKCKfAG4F6r1/HwTuVlW7L7VMTMTiPK8HeXnHbqpDMd567VX4fD5SqRS9vb3sj+VzqLCKYGkV+I/9afn9fhobG2lpaaG0tDSDERhjcsmcEpSI3IJbFferwFO4ufheB/wPERlX1bsXroomW0we7ufQhkdonugj6fOza/caSE3Q3t7OxITrwwuW172yf1FRES0tLTQ1NdloPGPMGZtrC+qTwMdV9V/Stj0tIu3A7YAlqCVs6nAvY7s3E+vvoCk4QtdIitFUgoNP/oqi6shx+/p8Pmpra2lubqa6utpG4xlj5m2uCaoat5LuTM8BjeeuOiYbjE/G2fLyQSo4St34HmIDXcQSSXpHpugaTzHi81OwooyCFZWvHBMKhYhGo0SjUXuo1hhzTsw1QW3HLatxx4zt78UtA2+WiK6Dwzz+6AbKh3aRSB6G6hJ6RqYYGIuTwg2GKKmpxhd0I+8qKytpbm4mEonYoAdjzDk11wT1ReBhEbkSeNbbdiXwFuC6haiYyYzyeD8Ngxs4PJlkVzxFe2yE/KAff+EKAqVV+IL5BINBmpqaaG5upri4ONNVNsYsUXMdZv5rEbkK+BRuPahxYCdwmapuXcD6mQU0NjFFwJ9HftBPKpXi4MGDdHQP0JXIJ5aYoCCUT7is3CWmQD4VFRVEo1Hq6+vx+/2nfwFjjDkLsy238RbgSVUdBVDVJ4AnFqtiZuFMTMZ5fvt+erY9R6Q1SkVdBZ2dnYyPuwlCiqvrSE1NEiitIL+gkMbGRpqbm22lWmPMopqtBfUwEBeRTcBj3s8zqjq5KDUzCyJ+tJ/9//EM/S9uY2wqxZb+diJr1uJPW2Y9UFxGZWUl0WiUSCRirSVjTEbMlqCqcM86XQn8AXAzkCciv+dYwtqgqokFr6U5K6lkgsnefRx8aTNd3d30jcQZjKVIJFPk+yeZGh/DX1REKBSisbGRaDRq95aMMRk323pQh4GHvB9ExA9cgktYVwIfAqpE5ElVffsi1NWcobHhEbY9/RR5R/cxMDzKSCz5Slm4IEAqGKa4sobaaAvNzc3U1tbaSDxjTNaY81RHqpoQkS6gBzgIdAA1wMoFqpuZp2QyycZHHmCP7mR0Kkkw6KcofGwmB39hKWXV9TSvXktTUxPhcDiDtTXGmJObNUGJSDHwRuCPgKuBtUAn8FvgHuB9qnpggeto5mhoaIjOzk66u7s53NfPyJRrMcWmEoTDIQrKqmhctZbmlauorKy0WR6MMVlttlF8TwKXA4eBx3HTGT2mqrsXp2rmdIbHYuzWPezTl6ioq2B4eOiVsnB5FSOD/STyQtQ2NXPhZZfR0Ghz4hljcsdsLajXAV3AD3EDIp5V1amzeTERuQb4GtCK6ya8S1W/dzbnXI7iUzE6dzzP0088y9joECl8TKbWUlx8bPmLcMkKLnnD1bStPd9mEDfG5KTZEtRKXNfeVcAngCIReQp4FJewNqtqaq4vJCIR4OfAu1X1lyJyCW7C2Y2qunneESxxsakEHX3DrCjKJzncx/4XN9PT2c5UPE5iyn1f8JFi7MghVqyIUldXR1NTE1VVVdaFZ4zJabON4tsP/JP3g4hcCLwZl7C+AEyJyOPAo6r6ndO9kKr2iEi1qg6LSB5QCcSB4bMNYqnauqufpzbtwn9kLyXJAfJ9sePK8wN+Ekkor6hi9YUXI+svti48Y8yScSaj+LYCW0XkbuBS4EbctEfvAk6boLxzDItIIXDUe+2vq+quM671Ejc+Ps6BAwdo37yRQMdufKkk4z4f+cUhtxIXUFgQIrpyNS3rL6OkojqzFTbGmAVw2gQlIo3Aa4ArvP9egmv5PINbC+rxM3zNCaAIWA88IiK7VPWeMzzHkjEVT7K78wjd/UdZU+enq6uLwcFBUqkUSZL4SeHz+wgG/AT8PuojEVrWXUT1yvPIy7MZHowxS9dso/juxyWkCK4b7mngF8AtwHPznUFCVZNADNgkIt8HrsUNWV92YrEpfvhvvyc22I5vrJ/BlmbCofTnlYopW1FETWkB0dXn0bDu1QQLbT48Y8zyMFsLKgR8C9dC2uwllnkTkTcA31TVS2e8xpGzOW+uSSaT9Pf307V/L917XybU20vepJvecOjQIOH6Wnw+H1VVVTQ0NFDz+teRX1phAx6MMcvObIMkrjnHr7UFaBCRT+OeqXoN8GHg3ef4dbLOoaPjbNq2h8TYIKmhbiaOHiI5OQZAQSBFYspHKOinujjFmnXrqK+vp6Cg4DRnNcaYpW3OgyTOlqoeFZG3Af8AfAk3I8VHVPV3i1WHxZRKpRgcHGTLi7t4YfM2AlNDhBlnRWH+KwMdAEpDAVbXlBBdtZbyVa8iuKImc5U2xpgssmgJCsB73ukPFvM1F1MymeTIkSMcOHCAnp4eJiYmiI8coXiyh1QKkkA8maQo309NUT6N0ShVbRcQqmvF51/Ut8IYY7KeXRXPUiqVoqOrjy0v7qZj/z4aaksJBo7NCB4oLCU/GMBPioYVIVoaa6lZdQEFDWvwFxRlsObGGJPdLEHNw3T3XU9PDwc6O9i7p53k2FFCyRhDoRYqq8oBCIVCRCIRLqzOp7yslHCj4C+xSVqNMWYuLEHNUSqVorfvIB2d3Rwd6GXsyACJsSGSk2OUJBKMJt20Q1PDR2m+9CLq6+vTZgx/VWYrb4wxOcgS1CySySQDAwPs3d/JC9t3MX70EIVMUBY6/hGwwnw/JX5oLAvTuu58ytevz1CNjTFm6bAENUM8Hqe/v5+enh76+vqIx+PERo7Awf2ESZECksEQoUAe1UUBqgvzqW5oItywhvy6VvKCoUyHYIwxS4IlKCAWi7Gvoxvd1U53VzeRmhL8ecfuEwULigj4fZBMURbys7qygMbmKAX1qwhF2sgLFWaw9sYYszQt2wQ1NjZGX18fPT09DA7009nRQ3J8iEBikuGCVZSVFQNQWFhIJBKhLTDCisIQFa1rCdWvshF4xhizwJZNgkomk3T3HmLnrv1MjBzBFx8lMTZEYmyY5OQoxVNxxhNxABJjI6y5/BIikQglJSX4fD5SssaeVTLGmEW0LK64o6OjPPKbJ9jb3kN+YpwS/yQrgvHj9qkoCuLL91FfGiIiLdSKHFduyckYYxbXsrjqdnZ2wnAfZePd+EiR8vnwBUOUhwNUFQaoDAcorq4nVLeS/LqV+MPFma6yMcYse8siQdXV1bG3tJSRXh9lIT+1JSFaqsIUVtUTirS5pGT3lIwxJqssiwRVVlbGW9/5bg79dhR/qIhQpI1QpM1aSsYYk8WWRYIC8Pl8VLz+veQF8jNdFWOMMXOQd/pdlg5LTsYYkzuWVYIyxhiTOyxBGWOMyUqWoIwxxmQlS1DGGGOykiUoY4wxWSlXhpn7AXp7ezNdD2OMMfOQdv32z/WYXElQEYD3v//9ma6HMcaYsxMB9sxlx1xJUBuBPwR6gMRp9jXGGJN9/LjktHGuB/hSqdTCVccYY4yZJxskYYwxJitZgjLGGJOVLEEZY4zJSpagjDHGZCVLUMYYY7KSJShjjDFZyRKUMcaYrGQJyhhjTFbKlZkkTklELgceUtUa79/VwN3AHwOTwA+BL6lqwiv/CXA9EE87zXpV3SsiUeAe4ArgIPApVX0ki2N5rVd+HnAAuFVVf+6VZSyWM4lDRL4LfGDGKYqA21T1azn4ntwIfB6oAl4C/puqPuWV5VosnwJuBiqBZ4BPqureTMYiIlcDdwKrvdf9hqp+T0TKgH8CrgZGgM+r6o+8Y3zA3wIfBfKBHwGfUdW4V3498DXcLAe/Az6oqgezMZa0Y/OA+4Hfqeq30ra/CfeetgFbgRtUdU7TCmWjnG1BiYhPRD4C/Br3Rzftx0AN7qJ9AXA58Ddp5ZcA71LV4rSfvV7ZfcALuA/kTcB9IrJygUOZVywiEgEeBv4nUAJ8ErjXu3BkJJb5xKGqH09/L4DPADu8uDISx3xjEZH1wDeBa4Ey4F7gQe9ikmuxXA98FbjRq+//A34jIgWZikVEmnAX5dtx/3//DLhDRP4Y+EfcNGgR4O3AnSLyBu/QjwLX4T77q4HLgFu9c56PS7Qf9GLZ5cW2oM4iFkSkBfgF8K4Z56wCHgS+4p3zAeDf0/7+ck7OVhz3JnwC9wYDICKFwFuAm1X1oKoOAl8AbvI+pGFgLbBl5slEZA3wauCLqhpT1cdwH8oPL3woZx4L8OfAE6r6Y1VNqepvcBeYwxmMZT5xkLZvG3AX8D5VHcrB92Q1xz5TPtxFZtw7Ntdi+RPgB6r6O1WNq+o/AjHgqgzG0gL8VFUfUNWkqm4EHgeuAv4U+IKqjqnqFuAHuMQE8BfAt1S1S1X7gS8DH/PKPgD8QlWfUtUJ4HPA60RkdTbGIiLFwHO41tEzM855HfCiqt6vqlOq+g0g5J0zJ+Vygvquql4KbErbNh3PaNq2BFCN+0ZxEa5r7wci0i8im0XkGm+/84EOVU0/9iXgVQtS++PNJ5ZLgf0icp+IDIjI80Cdqg6TuVjmE0e6v8ddFLd6/8619+Tfge3ANtzF/OvAe1U1Se7FkjejbLp8DRmKRVWfVNWPT/9bRCo4Nol0Ctf6OVl9zse1ytPL6r3jjytT1TGgk+yNZQI4T1VvBaZmnHZmnADK4vyNLYicTVCqeuAk20Zw3Rh3iUiFiFQCX/SKw7iusCdx3yjrcV0YPxORC4FiYGzGKceAwoWJ4Lh6zyeWClzXyr24roA7gAe8VkhGYplnHACIyAW4Pvevpx2ea+9JAe6CcAXuPtp/x3Xx1ZF7sdwPfFREXi0iQRG5Cdf7ECaDsUwTkRW4VtsGXItiQlXTZ75Or8/M+k7/XniSspnHLrgzicVrzZ7q/ljGYznXcjZBzeIG3LfXnbgbng9624+o6q9V9WpV3eQ1ge8HHgPeifu2GJ5xrkLcTcpMOWUsuJvav1LVh7xYfgZsBt5K9sUyWxzTPgQ8qKp9aduyLQ6YPZYvA72qukFVJ1X1O8B+4D3kWCyqeh/uS899uBbFBcBvgMNkOBavi/H3QB+uO2wYKJjRZZxen5n1nb5gj5ykbOaxC2oescwmG//GzspSTFAR4GOqWquqFwDdwE5VHRORd4jIX8zYPx/XbN4BRL37VNPWcmKTeTGdMhZcs798xv7TozKzLZbZ4ph2LSfenM62OGD2WJpwff7p4riumJyKxRuE8wtVXaWqdcCncUnqOTIYi4i8HtfSeBD4U+++0S7cPb/WU9RnByAzynpU9cjMMu/eXJTsjWU2M+M8k2OzUs4PMz+JbwLbReQW3B/a1zk2IswP3C0iO3EftPcCrwU+oqodIrIV+KqIfM7bfi1w5WIHkGa2WH4C/JWIfAD4Ke5b+nrgelXtzLJYZotjerjzSuDp9INUVbMsDpg9lodwXWY/wy3K9n5gHfBwFr4nMHssVwFfEpE/BIZw3X8HgI2qmspELF739UO4RxC+Pb1dVUdE5AHcKLgP44ZY34QbmQfwz8AtIvIorpXxZW8buM/OUyLyRuBZXKvxeVV9OUtjmc0DuL+/673f/yuQxA2+yElLMUHdhBv1Mojrdvm219WCqj4oIrcB/wLU4Voh16hqh3fsnwDfxz2TMAB8WFW3L3L9080Wy1YReRvuovIdoAO4TlU7vWOzKZZTxuFpASa9kWQzZVMcMPt78gPvfsJPcc9B7QDelqXvCcz+vvwf3M31rbhehkeBd6bdG8lELJ/E3Ue+Q0TuSNv+v3Cj8r4DtON6RL6qqr/0yr8L1OJGvRUC/4p3v01Vt4nIh7x9GnAtmvcscBww/1hOSVUPisg7cM9B3QO8CLxDVWPnuvKLxVbUNcYYk5WW4j0oY4wxS4AlKGOMMVnJEpQxxpisZAnKGGNMVrIEZYwxJitZgjLGGJOVLEEZcw55k/f2e5N/ziy7TURGvOUSjDGnYQnKmHPrr3EPwKc/fImItAK34WYO2J+BehmTcyxBGXMOqWovbk2hj4jIa9KKvo1bh+zbJz3QGHMCm0nCmHPMm4n6adwUQZfhZsv/GXCxqu7w9rkRt6prA2428dtU9VdeWQC3RPn7cMvCHMJNz3WLqiZE5F7cHGvn4eZqu05VH1+0AI1ZJNaCMuYc8+ar+yhu8t4bgb8Dbk9LTm/HTdR6m7fPPbi1vC73TvE53BLgN+BW6f0c8F+Aa9Je5gO4Odeuwi3XYMySsxQnizUm41R1u4h8E/geroV0Z1rxrcCd3hpeALtF5NW4JS3+M25F3g+q6hNe+Y9E5LO4FVP/r7dth6reu9BxGJNJlqCMWThfwa2q+7eqmr489/nApSLyhbRtQbx1e7xZ968SkW/gllhfj5vx3Z+2/56FrLgx2cC6+IxZIKo67v06PqMoAHwGuCjtZx1uTSVE5HbckhB53n//EzBzKYuZ5zRmybEWlDGL7yWgWVV3T2/wWlMTwDeAvwT+WlV/4pWFcQsK+k5yLmOWLEtQxiy+u4CfiIgCjwFXA1/C3X8Ct4DgNSLyDLAC11VYyonLyRuzpFkXnzGLTFX/FbgZ+CzuvtPNwMdU9efeLn+OG723Dfg3YDfwv4FLF72yxmSQPQdljDEmK1kLyhhjTFayBGWMMSYrWYIyxhiTlSxBGWOMyUqWoIwxxmQlS1DGGGOykiUoY4wxWckSlDHGmKz0/wE2ti8NqF9QlAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "system.alpha = system.birth_rate - system.death_rate\n", "\n", @@ -492,24 +872,57 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 92, "metadata": { - "collapsed": true, "scrolled": false }, "outputs": [], "source": [ - "# Solution goes here" + "def updatefunc3(pop, t, system):\n", + " \n", + " system.alpha1 = 0.018\n", + " system.alpha2 = 0.015\n", + " \n", + " if t < 1980:\n", + " growth = system.alpha1 * results[t]\n", + " return pop + growth\n", + " \n", + " if t >= 1980:\n", + " growth = system.alpha2 * results[t]\n", + " return pop + growth\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UqFRQUMBDDz3E/fffT2FhIZ988glz587l61//OnV1dX3mee2117j77ru55ppr+OSTT1i7di2XXXYZN910Exs3bhzmVyCEGEsMw2Dnzp1+F8GRkZEsXbp0RAcuSYAahbZu3crkyZOZN28eVquVkJAQbrnlFi699NI+A1R7ezs//elP+fGPf8zy5ctxOBw4HA6uuuoqbr75Zvbu3QuYg0meffZZzj//fBYuXMidd97pO9769eu59NJLeeSRR1i4cCFnn302Tz75pO8cf/7zn1m+fDnz58/ni1/8Ih9//LEvX35+vl95zjvvPD744IMB8wkhRge3201hYaHvdwIgNjaWM888k9DQ0BEs2TjugyrYeZCCXQcBWJCTzIJc/2U61m6tZEvxYQCWzE4hTyX67f+o8AA799QCsGxeOrlT4/z2/31dOSUH6vs89qk699xzeeaZZ1i5ciXLly9n3rx5ZGdn8+CDD/aZfvPmzTidTs4555xj9t1yyy2+x7/73e946623ePHFF0lMTOThhx/mW9/6Fi+//DIApaWlLF++nLVr17J+/XpuuOEGLrnkEmJiYrjvvvv4y1/+QmZmJn/605946KGH+Pvf/z7g66irq+s3nwyKEGLkOZ1OCgoKqK+v921LSUnhjDPOwGazjWDJTFKDGoUyMzN58803yc7OZtWqVVx55ZUsWbKEX/3qVxiGcUz6uro6oqOjjzvUfvXq1dx+++1kZGQQEhLCd7/7XTZs2MC+ffsAcyTdrbfeSlBQEEuXLiUhIYHy8nIcDgd2u53XXnuN7du3c+WVVwYUZE42nxBi6LW2trJ27Vq/4DR16lTmzp07KoITSIAadg6Hwzd0sye3201wcLDv+aRJk/j+97/PX//6V/7973/zzW9+k+eff55Vq1YdkzchIYGGhoY+7yNqbm7G6XQCUFVVxb333kt+fj75+fmcffbZ2O123ywOERERfmUICgrC4/EQERHByy+/THl5OStXrmTp0qU899xzfQbLnk42nxBiaNXV1bF27Vrf9HAWi4WZM2eSm5s7qi4gx20T34LcgZvels5JZemc1H73L5uXzrJ56f3uv3DRJC5cNOmEy5WcnExVVRWzZs3ybevq6qKqqsq3WvBXv/pVLrjgAq699lrAbA/+yle+wvbt29FaH3PM7vsT1qxZw/Lly/32Pfroo5SXl/Pyyy+TmJjI/fffz1lnneXbX1xczOTJk9m8eXO/ZW5sbPT1X7lcLj799FPuuOMO8vPzsdlsfoHRMAwaGxuPm693v5UQYnhUVVWxefNm30g9m81GXl4eyUmJGIYxqgKU1KCG2aWXXspTTz1FWVkZYE7T9Mgjj5CQkMDs2bMBuPjii3n22Wf54IMPcDqdOJ1O1q9fz5o1azj//POPOabD4eCuu+7i/vvv58MPP6Srq4u2tjZefPFF3nzzTW677TYArrjiCp5++mmqq6txu908//zzXH311b47xftTV1fH9ddfT0FBAUFBQSQlJWGxWIiOjiYjIwOn08m7776L2+3m5Zdf9l2VDZRPCDG8DMOgpKSEwsJCX3AKDg5m8eLFJEZH0PDvN2nfs2WES+lv3NagRqvbbrsNm83GzTffTG1tLcHBwSxatIiXXnrJ14f0ta99jZCQEJ555hnuvvtuDMNg2rRp3H///SxdurTP4375y18mMjKS5557jnvuuQfDMMjJyeGFF15g/vz5ANx00010dXVx9dVX09DQQFZWFi+88MJxh5FOmTKFBx98kPvuu4+amhpiYmL4wQ9+QGZmJgA/+MEPePTRR/nhD3/I5Zdfzty5cwPKJ4QYHh6Ph+3bt7N//37ftoiICBYuXIi95Qj1Gz/EcHXS1XgEe3QCjvi0ESztUZax0B+glJoM7P3www9JSzv2jSsqKmLGjBnDXi4xuOT/KMTgc7lcFBYWcvjwYd+2uLg48vPzce3bSlvpJsCMAwYWqqNnkzbzDOKiB3eIeUVFRXcL0BSt9b5A8kgNSgghxqnW1lYKCgp8s5EDpKWlkTsljbbCd3HVH/Rtb/c4WN+lOFQVSrq7isvOmjri/VHSByWEEONQ90i9nsEpc9pUpge10PjZ637BKSgulbCFV1DTFQHAgUPN7D/UPOxl7k1qUEIIMc5UVFSwdetW32AIq9VKTmosUVUb6XC2H01osRA2bS5hmfOwWKzMnNpG8YF6FsxIJi0xcoRKf5QEKCGEGCcMw0BrTUlJiW9bcHAw+fn5WIvX4nK2YxgGdU0dhCSkk7bofOwRMb60C2eat+eEBo+O0CBNfEIIMQ50dXVRWFjoF5y6J3yNjY0lIncpHU4Pu6s7KejK5lNXLpawCX7HCHHYR01wggBrUEopGzAPyAcSATdwENigtR5dA+eFEOI0097ezoYNG3w3yXvamkmaNIX8/PnY7ebPvD0ylqj8i9AFjXS6LdDSye59dcfMMzqaDBiglFIxwDeAW4A4YA9QC9iAeGCSUqoaeBZ4WmvdMLTFFUII0VN9fT0bNmygs7MTw9WJq/4gKcFdzEqY4QtO3aLTpjKvtYbCokPMz0kie1JMP0cdHfoNUEqplcCPgA+B64EPtNadvdJEAWcBVwM7lFL3aq1f7ud4VwPP9docCnyotb7g5F+CEEKcnnyDIdxddDUewd1SS1ZsCMkRIRzY+ClGVzwzlP+UbXOmx5M9KYawkIEnlx4NBqpBzQfma60P95dAa90E/BX4q1JqInAv0GeA0lr/AfhD93OlVB7wPvDdkyi3EEKctgzDoKioiLKyMtxtTXQ1HMJuuJmTFEaYzUJpRSNVQek07TrClMkTCenRr2SzWQmzjY3hB/2WUmt9x0DBqY/01Vrr2wNJq5QKwgxWD2ittwZ6jvEgkCXfn3zySWbPnk1paalfmr4WB+z2+uuvM2PGDPLy8sjLy2POnDlcfPHFvPDCC36zh5933nnMnj3bl67778ILL/SlKS8v57bbbmP+/Pnk5eWxYsUKfv/73/d53ksuuYSzzz67z5nUhRCDz+VyUVBQQOnuIpw1+3HVVhJq9TB3YjgTQuyEJqSxP/kcDkbm0NZlYVvpkZEu8kkLeLiGUioRmA0EAX63F2ut3z3B894GtAPPnGC+00ZnZyd33XUXq1evxuFwBJQnKyuLv/zlL4B5hbV582buuusumpqa+Na3vuVL97Of/eyYWc+7eTwebrzxRlasWMFjjz1GSEgIW7Zs4bbbbsPhcPClL33Jl7Z7EtiEhATef/99Lr300lN4xUKI42lpaaFg/Toaq8rpajYXVI0LtTMjIRRHaBjh2WcSnDKd/MpG/rnxAPkzkpgzPX6ES33yAh3Fdz1mMOmr0dLAHDQREKWUA7NZ77+01qN/IsARsnjxYqqqqnjiiSe4++67Tzi/xWJh7ty5PPTQQ9x0001cf/31x50UFswO1/LyclasWOFb7jkvL4+7777bt65Ut1WrVvG5z32OuLg4XnnlFQlQQgyhmpoaNm3aRHvdQTM4GRDrsJDkgMipswnLzMcaZK7nNi01mrSECL+mvbEo0NJ/F/g1cI/W+lTnv7gI8GD2XQ2Z1uINtJUWBpQ2JH0GkbP8l0tv3r6GjgPHNsX1FjZ9HuFZ80+qjAMeNyyMRx55hGuuuYZzzjmHhQsXntRxFi9ejNVqZfPmzX0uCd9bXFwcCxYs4LrrruOyyy7zNfNdccUVfunq6ur48MMP+dvf/kZkZCSPPPII27dv91vnSghx6gzDoKysjN27d2MYBvaIWNzN9US42unwTGCdbRap6UeDE5gXqGM9OEHgN+qmA78YhOAEcDmwWmvtGYRjjWtnnHEGN998M9/73vdoamo6qWN0r7/Ucz6uu+66y7doYPffU0895dv/wgsvcOONN7Jt2zbuuOMOFi1axK233srBg0fn7nrjjTdYvHgxEydOJCIigssuu6zffiohxMlxdbRTuH4dRUVFvr7k0LAwzv7cRRjpC9kbs5h2WySbi2tGuKRDI9AQ+z5wPlByvIQBWATcNwjHGZMCXfK926233sratWt58MEH/fp/AuV2u2lqamLixIm+bY899li/fVDdZbzmmmu45pprcDqdFBYW8sQTT3DnnXeyatUqDMNg9erV1NTUsGTJEsDsM+vs7OTuu+8mLm703vgnxFhgGAYNe3eybs0/aTNsOBIyALOFY968eQQHBzN3wiQqPi5jXnYSczLHbj/TQAINUFuBnymlLgOKAb/OCK31iXSSTAaqTiD9SQnPmn9KTW+Rs845ptlvMASy5HtPNpuNRx99lMsvv5ywsLATPl9BQQGGYZCVlRVQ+lWrVvHqq6/yxhtvAGawWrx4MUFBQdxwww0ArFu3joaGBt577z2s1qOV8JtvvplXX33Vt4KvEOLEdTXXs7/gH2wtKcfp9tDR2UV7p4MlSxeRm5vr+84lxYZx7YocguwBDwEYcwJt4jsHWI95Y+0cvPdIef/6HvfcD611uNZ63YnkGU8CWfK9t4yMDL7//e+zevXqgM/j8XgoKCjgvvvu4+abbyYiIiKgfOeccw779+/n4Ycfpra2FsMwOHDgAC+++KJvuflVq1Zx0UUXkZSUREJCgu/vC1/4Aq+++qoMORfiJHi6nDTv+pSt77xCod6Hy+2hqcVJhwus9mjikqf4XRAC4zo4QYA1KK31sqEuyOkikCXf+3LVVVexZs0a1q3rP7YXFxeTl5cHgN1uJzU1lRtuuIGvfOUrfum+/e1vY7Md+8F+7733SE5O5o9//CNPPvkkK1asoL29nZiYGC666CK+8Y1vUFtbywcffMBLL710TP4VK1bw8MMP8/e//50VK1YE+I4IcXozDIPOqhIad33Krsp6jrSZXQAWiwVHVDxNETlYQqIp3l9PSkJgF5rjRcBLviulkoDbgVzMmlcR8Gut9Z6hK57v3JORJd/HPfk/itNNV1MtLbvW0niogu2H2mjvMn+PrcFhJE5RqDkL+WhzNfNnJDMlJWrEV7g9FUO25LtSagHwD+AA8BnmjborgDuUUudqrTeeVImFEOI05W5rpv7T16hq6GBrVQsuj0F0VBj2CUlMz51DTk4OVquVL50fOaYD06kIdJDE48D/Abf0vLlWKfUU8CggTYBCCHECrKER7HNHs2V/DR4DOu2RBEelce6SRX4tRadrcILAA1Q+cEMfMz88CQR2N6wQQpzGPK5O3820nZ2dFBYWUusOxhERRY0rDCMonKTJs/vsxjhdBRqgqjGHh+te26cCg3HzrhBCjEuejlZa9Xqchw8QseQqWts7KCwspKOjA6x2JqRPxd1q43PLFpOePLrXZxpugQaoV4DnlVLfBLqHkS0Gfu7dJ4QQogfD46Z933baSgtxdXZyqLaVot/9H+Epib7Zti0WCznZiunTp5/WTXn9CTRA/QRIAVZjjuCzAC7MJr57h6ZoQggxNjlr9tNS9Cnu1kYwoKyykf0tXdQZ9bSFRJAYG4bD4WDu3LkkJCSMdHFHrUDvg3ICNyql7gIU5lIZpVrr9qEsnBBCjCVdLQ20Fn2G8/B+37Zmp5uDhFJtC8FlDcHa5WHChAnk5+f7VgwQfRtoyfdLgH9orV3ex72lK6WAk1oPSgghxg1Pl5O20kI69m6nq6sLm82KYRgcbDfY1xVJcMoEwg+3Eh0RzKzcLN8QcjGwgWpQ7wDJQI33cX9OaD0oIYQYTwzDoHHdW3TW11BT305DSydTUqKpsMVxJCgES7D58zgpZQJz5swhJSVlhEs8dvQboLTW1r4ei9HpySefpKioiGeeGXiR4u67uTds2BDQAoZCiIFZLBZCp8ymVL9Ja7uLBmsUZQ2xxMeF+2Y7jYqKYt68eQHPiSlMY39FKyGEGEYeZztWh3/fUXBKJonTFO8Xt1Pb0Um400OcARaLOdnzzJkz+5z/UgxsoD6ow5jNd8eltU4ctBKNcxUVFVxxxRXceeedPPvss7hcLu644w6sVivPPvssTqeTW265hWuvvZbPPvuMxx9/nL1795Kamsptt93GRRdd5DvOvffey7Zt25g6dSpTp071O8/q1at54YUXqKurY/bs2TzwwAOkp6ePxEsWYlwwuly07dlC+54tBM2+gOiUSb59breb2qh0CC4hPSqCqHAHNpuNWbNmyffz7SYWAAAgAElEQVTuFAxUg/ouAQao0aasrIzi4uI+FwYcbHa7naysLKZNmxZwnubmZnbs2MFHH33EBx98wF133cXll1/Ohx9+yCeffMIdd9zBrFmzuPnmm3n44Ye54IIL2LBhA7feeisJCQnMmzePO++8k5ycHH7961+jteb6668nP99c+eT999/nl7/8Jc899xyZmZm8+OKL3HjjjbzzzkBdiUKIvnTPNt6q1+Nqbaamvp3qt95izlXXkRwXQVNTE4WFhbS0tJAca67ZFhkZybx584iMjBzh0o9tA/VBvTSM5RhUe/bsGZbgBOZig3v27DmhAAXmSrndiwG63W5WrlyJw+Fg2bJluN1ufvvb37Jw4UIuucQcQLl48WI+//nP88Ybb5CYmMiOHTv4zW9+g8PhYNasWVx++eVUVlYCZu1p5cqV5ObmAnDTTTfxyiuvsH79eiZNmtRvmYQQ/lz1B2nZ9RldjeaS6tW1rTS2OPHYHfx7YxlzMiMpKirC4/H48kiT3uAZqIkv4NXxtNYnvhb5EJo6deqw1qB6N68FYsKECQC+D3H3lVb30NOOjg5SU1P98qSlpbF+/XoOHz5McHAwMTExfvu6A1RVVRXPPPMMzz//vG+/y+WiqqpKApQQAXC3NdOq19NZXeq3PSkpjiJbIrVBSYTWVrKtowub1ZwBwm63M2vWLJlLbxAN1MTXOmylGGTTpk074RrNcDvetCb5+fls3Oi/ismBAweIj48nKSmJzs5OamtriYuLA+DQoUO+dImJiaxcudJvocKysjJSUlKora0dxFchxPji6XLSXraZ9r3b6OhwEhxkAwtYrDZCp8wmdFoe6Vv24agoIdjmAe+kRdHR0cydO1dG6Q2ygZr4rhvOggh/S5cu5dlnn+Xdd9/lwgsvpKCggLfffpunnnqK1NRUFixYwMMPP8yDDz5IeXk5r7/+OnPnzgXgiiuu4Fe/+hXz589n6tSpvPPOO9xzzz2888472O0ycFOI/rTu+ozmfbuoqW+jobmT9MRIEqZnE569CGtoJCUlJTQeLCbYdrR7furUqcyYMUNuvB0CAzXxPQI8qLVu9T7uj6G1/u/BL9rpzW6388wzz/D4449z7733kpSUxIMPPsjSpUsB+PnPf869997LmWeeSUpKCsuXL6eurg4wA1RTUxO33norNTU1ZGRk8PTTTzN58mQqKipG8mUJMaqFTctj3/YtNDR30m6PptA+hyvnLKGzs4PNn33m+44BOBwO5syZQ3Jy8giWeHwb6HJ6PhDU43F/xuRIv5GSlpaG1kdXLYmKivJ7Dvg9X7JkSZ/HiY+P57nnnuv3PCtXrmTlypXHPb8Qp6uulgZsIeFY7EG+bbbwaNLzl1K06TA1tmQmJ0azf38FxXoXLpfLly4uLo68vDyZS2+IDdTEt6yvx0IIMZZ5nB20lRbSXr6DzoQckvOWYLcdbZ6boPLJC6snyAYNNeXs3HHAt89isaCULI8xXALukFBKhQNfAXIBJ7ATWOWd6VwIIUY1w+Omo3wnraWFdLa1caiujYaytRx2pJE3e4pf2oRIC5s2baK19ehYsbCwMObOnes3elYMrYAClFJqFvAvzMC0DXOGqeuAHymlLtZa7x6yEgohxCkwDAPnoX207l6Hu60RgJY2F02tTtqDYtlffBCVlUZYSBCGYVBSUkJxcTGGcbT3IjU1lVmzZhEUFNTfacQQCLQG9WvgTeC/tNYuAKVUKPAb4DngnKEpnhBCnDxXYw2tRetw1VX5bY9LTmRXcDKVXTFkpccC0NbWxubNm/0GQnTf25SamipNeiMg0AA1B1jZHZwAtNbtSqkfA5uHpGQnyDAM+QCNYT3vxBfiVLnbW2grLqCjopjmdiehwXbsNiuWoGDCp88jZFIuC+vasVosJMWGUVlZyfbt2/1u7o+NjSUvL4+wsLARfCWnt0AD1CbgLKC41/Z8YNeglugkhISE+G5alSA1thiGgcvl4tChQ4SHh490ccQ44azZR+PeXVTXttHa7iImKoRp+YsImz4PqyMEgJT4CJxOJ4WFhVRXV/vyWiwWsrKyyMzMlN+TETbQfVC39nhaADyllMoH1gFuYDZwC/BwoCdTSk0EfgUsAzqA57XW951Euf2kpaVRUVHB4cOHT/VQYgTY7Xaio6OJj48f6aKIcSIkIwfXjk20tjfSFJxEiWMGaalzifAGJ4Camhq2bt1KR0eHb1t4eDh5eXkyEGKUON5s5j0dBC7y/nU7Avwn8FCA5/sLUAgkAROBNUqpIq31HwPM36egoCCmTJly/IRCiHHHefgA1uBQ7FFHL3AsFiupi5az27afAy2h5E6JJTTY/Lnr6uqiqKiIffv2+R1n0qRJ5OTkyGwro8hA90EN6i++UmohMBVY4u3L2quUOhdoH8zzCCFOD13NdbTu/jfOmgM0WKKIXfIF4qKP3jjriEth0ZJYFngM3/b6+no2b97sN3w8ODiYOXPmkJSUNOyvQQxsoCa+r2utXw70QEopC3Cd1vq3/SSZB2wHHlBKXYvZxPeM1vrxEyivEOI05+lso7VkIx37i+jodFF1pJX2zlr2UsAFl/gPKI6JNJv0PB4PxcXFlJaW+g0fT05OZvbs2QQHBw/raxCBGagum6eU+g7wLPCG1rq6r0RKqUTgq8BNwAcDHC8Wc6DFGsyaVDbwnlKq+lSb+IQQ45/h7qJ933bayjZhdJkDii0WaO90UxeaTk1TMOUHm5iUHOWXr6mpic2bN9PU1OTbZrfbmTlzJmlpaTIQYhQbqInvm95BEfcDTyildmHOHnEEc475BMzh51nA34BrtdYFA5yrE2jSWj/gfb5VKfUb4EpAApQQok+GYdBZXUabXo+7vdlvX2TKZKwJkzh00MMZWQlMjAv3y1daWkpxcbHfbQzx8fHMmTNHho+PAQP2BmqtNwKXKaWmABdjDiufDngwB038EnhXa70/gHPtBsKUUo4e0yNJb6QQol9dTbW07PgYZ/0hGpo7cATZCA8NwhYRQ0T2YoIS0snv7OIMt0FUuMOXr7m5mS1bttDQ0ODbZrPZmDFjBpMnT5Za0xgRUIDQWu8FnjnFc/0DOAw87m06VMD1mEPVhRCiTy01VVTUNNPhdGMPCWXuvGWETsrBYjEneA0LOTr9kGEYlJWVobX2qzVNmDCBvLw8WVBwjBm2GozWukMpdQ7wJFCNOUjiEa31n4erDEKIscUeFUfopBw6q9ZxJGwqNeHTifFMJNdy7OKALS0tbNmyhfr6et82q9WKUopp06ZJrWkMGtYmNq31HuDS4TynEGL06+5nwt1FSHq2376Y3EVEWjPYXdbK/OxEsjJijsnbX63pjDPOIDIyclhegxh80gckhBhRXY2Hadn1Ka66gzS0ezCc0WRNm+jbb3WEMnvmFLKy3ESE+s8m3ldfk9VqJSsrS9ZsGgckQAkhRoSns53W4vV0HNC4XG4O1DTT3tlF02cfk5H+RUIcR3+e7DYrEaFHm/U8Hg+lpaWUlJQcU2uaM2cOUVH+Q83F2HQiCxbmYd5sG4Q5zNxHa32qAyiEEKcJw/DQsW8HrSUbMbrMAb02mwWXG7OfyTGNLcWHWTRzYp/5Gxoa2Lp1q999TdLXND4FumDhvcCPgTqguddug1Mf4SeEOA04a6to3bmWrpY6v+0hSZNIVrls39ZAXlYi87ITj8nrdrspLi6mrKzMbzaImJgY5syZI31N41CgNagbgPu01j8ZysIIIcYnj7ODlp1r6awupbXdRafLTWxUCLawaCJyluBIzCDKMEhOnUhEmOOY/LW1tWzdutVvDj2bzUZ2djZTpkyRWtM4FWiAigNWD2VBhBDjl8Vmo7O2mopDzTS2OjGsdmJmnklMzjwsVpuZxmI5Jji5XC6KioooLy/32x4XF8ecOXNkDbFxLtAA9RpwNfDA0BVFCDFeWWxBROaeiXPvazSEpHAwYgadzYlc5A1OfamurmbHjh1+6zXZ7XZycnLIyMiQWtNpINAA1Q7co5S6CigBnD13aq2/NNgFE0KMTe6OVpzVZYROme23PTh5KhkXfpWNBXVkpk/gzNkpfebv6Ohg+/btHDx40G97cnIys2bNIiQkpM98YvwJNECFIRO6CiEGcHR03gY62zuoPeJm9vw8336LxcLEjHSujklkQuSxy1sYhkF5eTlFRUV0dXX5tgcHBzNz5kwmTpwotabTTKBz8V031AURQoxdroYaWnZ8TFfjEQ7Vt1Hb2E5H1b+ImzSN1ET/e5L6Ck5NTU1s27bNb5oiMFe5nTFjBkFBQcfkEePfidwHNQu4G8gFrJizk/9Sa/3ZEJVNCDHKeVydtOkC2vfvAgywgNPlpsMaTlXUTBq2VvOl5ZH91ny6urooKSk5Zuh4REQEs2fPJi4ubpheiRiNAr0P6mLgLcwZyV/DvFH3TGCNUupSrfX7Q1dEIcRo0z13XmvRp3g6233bLVYbkxacw0btICY6jHPy+l8QsKamhu3bt9PW1ubbZrVamT59OtOnT8dm638AhTg9BFqD+gnwkNb6wZ4blVI/wLyBVwKUEKcJd1szLTs/prNmPw3Nneay6hZwJGQQkbsUW1gUX0xrJzYqpM/g1NHRwY4dO6iu9l+kOy4ujlmzZskNt8In0AA1A+hrpN6rwPcHrzhCiNGuVa+jrryMqiOtuLo8WILDyFi0HEfy0Rtm46JDj8lnGAZ79+5Fa+03CMLhcJCTkyPLr4tjBBqg9gN5QGmv7fOAmkEtkRBiVAvPXsy+XbtwdXmoDZ3EXksuX42fNGBwqa+vZ9u2bX7z5wGkpaWRk5NDcPCxAyeECDRAPQ08q5RKA9Z5ty0G7gUeGYqCCSFGnuE2azoW29GfCltoBNPOvphdBTU026M5c1YKDvuxCwgCOJ1Odu/ezf79+/0GQURGRjJr1iwZBCEGFOgw818qpSKBe4B47+Yq4Ida66eGqnBCiJHjrK2iZfsaOiJTSTxjKXbb0SAUnp7FsvA0IsOC/JZc72YYBvv372f37t04nUfv67fZbGRlZTF16lSs1r6DmhDdAh5m7p0o9idKqUSgXWvde1ZzIcQ44Oly0rp7Ha17d3Covp36pr1UGwnMy5/hly4pNqzP/A0NDWzfvt1vEUGApKQkZs6cSVhY3/mE6K3fAKWUuhX4rda6w/u4937fY1kPSojxwXl4P83b1+DpaKWp1Ul9Uwdui50ivZ/p2VOJjui/r6i/5rywsDBmzpxJUlLScLwEMY4MVIP6LrAK6PA+7o+sByXEGOdxddJa9G86Knb7tk2IDKHSHUOJTZGSmtDvIIj+mvPkniZxqvoNUFrrKX097k0pJeNChRjDnIf307RtDe72FqxW8+tsdYQQkbOUnOCJZLS7mJoa3WeAqqurY8eOHTQ2NvptT0xMZObMmbIchjglgc4ksQfI11rX9dqeAmwBjl3+UggxqhldLlqKPqW+dAdVh1sIDbaTkhBBcPJUInLPwhocShLQV8NcR0cHRUVFVFRU+G0PCwsjNzeXpKQkuadJnLKB+qAuA5Z6n04GfqSUauuVbPoQlUsIMdSsVpoOVbGn0qz9tHTZiJ2/hIScWf1mcbvd7Nmzh9LSUr+bbW02G9OnT2fatGnSnCcGzUA1qK3ANzHn3QPzRt2e60AZQAvw9aEpmhBiKFmsNpIWXMChAy9xoCuGmgmzSQ1N7TOtYRgcOnSInTt3+s2dBzBx4kRycnJkdJ4YdAP1QZUD5wEopV4E7tRaN/WXXggxunU1HcEWGefX9GaPimfaipUcLGnlS7NT+hyl19zczM6dOzl8+LDf9sjISGbOnEl8fPwxeYQYDAGvB6WUsiulUoHu+rsFCAbmaa3/b6gKKIQ4NYa7i1a9noM7CqmOmsXSC8/1C1LRCYlcknBsPqfTidaa8vJyv2HjQUFBKKWYPHmy9DOJIRXoIIkVwItAbB+76wEJUEKMQq6GGpq2/JN9e/bT3ObCc3g9umQa2VkZ/ebxeDzs27eP4uJiXC6Xb7vFYiEjI4Ps7GwcDsdwFF+c5gKdSeJ/MZfU+BnwIXAZkAz8HPjO0BRNCHGyDI+bttJNtJVtAsPAEWQDXLQGxbKjrA6VmX5M7ae7n6moqIiWlha/ffHx8eTm5hIV5b86rhBDKdAAlQn8h9ZaK6U2AeFa69VKKRfwA8xlN45LKfWfwHNAZ4/Nt2mtXz6RQgsh+tfVVEvzto/oajri25aUEEVZ8Axip89i0ayUY4JTY2Mju3bt4siRI37bw8PDycnJkWHjYkQEGqDaAY/3cTEwB/gbUAhkncD55gKPa62/dwJ5hBABMAwPrSWbqdi0lthIhy+gBMVOJHL2Mi51hBNk9x8C3tHRwe7du6moqPDrZ7Lb7WRlZTFlyhSZ1FWMmEAD1MfAfUqpbwAbgRuVUo8D5wInMrJvHvCLEyqhEOK43G3NVH76DpV7yul0ufG4Q0mMiyRMLSB08mwsFgs9Q1NXVxdlZWWUlZXhdrt92y0WC5MmTSIrK0vWaBIjLtAA9R3gbeA/gWcx749qAhyYa0Idl1LKBswGvqaU+hnQBvwGeFhrbQyYWQgxIEuQg9bGJjpdZrDZ3xrMxOWfJywp2S9d97x5Wms6Ozv99iUlJTFjxgxZcl2MGoEOMy8FZiilQrXW7UqpBcAy4IjWen2A50rArH29DFyJuYz8XzADnUw2K8QpsAYFM+Wsi2l8exWVwdPIWrSUCYlHx44bhkFNTQ1FRUU0N/uvlBMVFUVOTg4JCX2MNRdiBA001VGft4X32P5R93Otde8pkI6htT4InNNj0xal1JPAF5EAJUTADMND3b4ywlOmEBJ89CsckpRB5hX/ycywSCLCjg4Db2hoYNeuXdTW1vodJyQkhOzsbNLS0mQAhBiVBqpBtWBOZxSI406+pZTKBb6ktf5hj80OzOU8hBAB6Gw4Qsm//kptZQXWnPM5a9kiv/1x8UeXUG9tbWX37t1UVVX5pbHb7UyfPp2pU6fKvHliVBsoQC0b5HM1AN9RSlUAL2DO7fcN4PZBPo8Q447hcdNWtpmabes4Um1O7tpRtJbK7ExSJ8b5pe3s7KS4uPiYGSBkAIQYawaai2/NYJ5Ia13pnSH9EcwbfI8AP9ZavzaY5xFivHHVH6R5+xrcLfVEhNqJCnfQ2NqFOzGLoOCQo+lcLsrKyti7d6/fTOMAKSkpZGdny/pMYkwJdKqjDQzQ3Ke1XhDIcbTW/wTyAyuaEKe3LmcnjbvW4anaTc+vX+qUyQTF5ZGbOw2r1YLb7aa8vJySkhK/FW0B4uLiyMnJYcKECcNbeCEGQaDDzN/pI99U4FLggcEskBAC9u/aSfm/PyDE6GDyxGiwgMUWRLhaQMikXJIsVt+Q8eLiYtrb2/3yR0VFkZ2dTWJiogyAEGNWoMPMH+xru3fqosuAJwazUEKczup2b2LvP/+GAbQCja2dJEzJJCL3LGxhkRiGQVVVFVrrY+bMCwsLQylFamqqBCYx5gVag+rPP4EnB6MgQgjThMmKmJi11NU3Y9gcOKcsJSp/LgA1NTXs3r2bxsZGvzzBwcFkZmYyadIkmZpIjBuB9kH1dU9UNOZEsdWDWiIhTiOGYdDS7iKyx31L1pBwJi9aRueuErLP+RyRUZHU1taye/du6urq/PJ3DxmfMmUKdvupXm8KMboE+onu756oDuC6wSuOEKePw4fr2fHRP+joggu+/B/YbEdrPlHTZjF/2izq6+tZt27dMavZ2mw2pkyZwrRp02RtJjFuBRqget8TZQBOYKfWurmP9EKIfhiGQdv+3ex8769YXJ2EAlu3aObOm+FL09jYiNaaQ4cO+eW1Wq1kZGSQmZlJSEgIQoxngQ6SWAOglIoAFOA2N+v2ATMKIfx0NdXSsvMTXPUHmRhlp7q2EwtgrSsHZtDY2EhxcTEHDx70y2exWEhPTyczM5OwsD5nIRNi3Am0DyoYc768qzGnJwJoV0r9GviO1trdb2YhBG0trXjKN9Fevovu1vKYqBDacZC64Dwc8RPZuHEj1dX+XboWi4WUlBSysrKIiIgYgZILMXICbeL7JebaT18GCgArsBB4DHN13P8eisIJMda1tjvZ/MlnuPZsJDs1DLu3n8lisRI2dQ5TkzIp2bOXql0fH5O3OzDJ8hfidBVogPoS8Hmt9doe215XStUBq5EAJcQx3J1tFK7+PZ7mw1iAmjpISYjAEZ+GO20ORVWHqC759Jh8EydOJCsri6ioqOEvtBCjyIks+e7qY3tjH9uEEIDVEUp6chTlzeYIPKc1FGP6mZQ0Oaku3HJM+qSkJJRSREdHD3dRhRiVAg1Q9wC/UUrdDKzTWnuUUjMx+6V+0vM+qUDWhhJiPHK7PX5DxS0WC+mLzqf58EGs6Tm0hUSxXpcfky85OZmsrCwJTEL0EmiAegKIAD4B3EopDxAEWIAFwM96pJUFZsRppcvtQW/eSsWOLcz/wpeJjT46yq7ZY6dt6nwO19ZBk/+9TBKYhBhYoAHqiiEthRBjVFdTLVv/8S7N1fsJBgr/9Qnnf/5z1NfXU1JScswNtmD2MWVmZkpgEuI4TvQ+qFAgE3MUX5ncpCtOV57OdlpLNtCxv4gkm4tmzBtwnVU7+deaEFpbmvzSWywWX2CSwQ9CBCbQ+6BswP8Ad3K0ac+plHoJuF1r3TVAdiHGjdr6FkLqSmgv24TRZY4bcgTZsIc6qLZOwDEh3i84WSwWUlNTmT59ugwXF+IEBdrE9xPgGmAlsBYzQC3BvA/qfu+fEONWY0snWz8roKNsA9Pj7USGm/ertzrd7HOG0BQzhZCgo8uoW61W0tPTmTZtmqxiK8RJCjRArQRu0Fq/22PbaqVUM/A8EqDEOGYYBmXvr8ZTtR8HcKjOhiPYzoEOG4ctcVhCw+leeclms5GRkcG0adMIDQ0dyWILMeYFGqAigNI+tu8B4gevOEKMPhaLhckqkx0HD+B2e6g37NS0RWINj8biDU0Wi4WMjAyUUgQHBx/niEKIQAS6stkG4LY+tt8OFA5ecYQYWW6PQVFZDW63x2/7hKy5OGJiaIhJxT0xE2v4BPAGp/j4eM4++2xmz54twUmIQRRoDeq/gX8ppc4F1nm3LQImAxcNfrGEGH57DtSy89O1hNVqXGd/kdmzpgFQX1/Pzp07qY9Ixc7RZdQjIiLIyckhMTFRllcXYggEOsx8o1JqLnATkIM59dHbwNNaa1lRV4xphuGhs7KYzvVriDxyBIDKjR+Tnp7AnrISqqqqvCnNIORwOMjKypLl1YUYYgGvEa21LgbuUkrFAW6tdcPQFUuIoWcYBq4jB2jdvY6u5jriQg3q7Facbg9Wo5W1H/8LetSMrFYrU6ZMITMzk6CgoJEruBCniYADlFLqPuAWIMn7/ADwhNb6iSEqmxBD4nB9O1u27OYMRzk0Hl0Y0GOAJTKSJtsErOH+szykpKQwY8YMWSxQiGEU6I26P8Fs3nsI//WgfqCUCtJaPzp0RRRi8KwrLOPg5k+Y0FHJwegQkuPCMQyDQ+0GBzyRuCOjsFqONtvFxsaSk5NDTEzMCJZaiNNToDWoG4Cv97oP6lOlVCnwNCABSowJiUc20tFRCUBtYzvWYAcVROEMjgLr0a9DREQEM2bMICkpSQZACDFCAg1QduBAH9tLAZm/RYxKri4PQXb/QQxp+WdxeP8+nAa0hMVSZo/FEuTw7Q8ODkYpRUZGhgQmIUZYoEOQHgWeUUqld29QSsUCP8Wc7ihgSqkJSqn9SqlrTySfEIFyutwU7KjmT699RF2j//JkHUERdCRNpi52Ml3Ryb7gZLfbyc7O5rzzzmPSpEkSnIQYBQKtQX0ZmAHs8Q6O6AIyAAewSCn1je6EWuvE4xzrWSD1JMoqREA+WbuVjuL1pLjq2PqZlWUXL6O1tRWtNZWVlWAJw+q9n9ZqtTJ58mSmT58uN9kKMcqcyIKFp0wp9XUgCtg+GMcToid3WzOtxevJbNjNHlejufHAFjYVRlFVXY1hGL603bOMK6VkZJ4Qo1SgN+q+fKonUkpNAX4InAm8d6rHEwLMIeNxkTbaSzfTvm8bhsdNaLCdqMhg6l3QHBxJS0UF9LihNjk5mezsbFn+QohRLuD7oE6Fdz2p3wN3aa0PKqWG47RiHKtr6uDfWyto2LOTJTE1hNvdALjcBhVNnVQTiSUuHpv96ACIuLg4ZsyYIUPGhRgjhiVAAfcBWmv9+jCdT4xzOwq3Yt+9jlR3Cwc7bUyaGE1ls5PKDhtETcQadbTZbsKECWRnZxMfHy+DH4QYQ4YrQH0FSFFKXel9Hok5KnCB1vrWYSqDGEdyYp3s9LTgNgya3LD+YAdEJWBLODoDRGRkJNnZ2XIvkxBj1LAEKK11ds/nSqktmNMkvTQc5xdjl2EY7KlsZEpKNFbr0SATreZh3bKJI21OrFHx2CPjfPPmhYeHo5QiJSVFApMQY1i/AUop9dtAD6K1/s/BKY4QR1UdbmHt5nKMyl10Ll5ITnYGbreb8vJySktLaQ9PxjHB4ZsBIiwsjKysLNLS0iQwCTEODFSD6jnEyQF8HtiNuXihE5gLzAH+eKIn1VqfcaJ5xOnFMAwO6u3ElH5GkKeDvevasdvOZN/ePXR2dgJgDTb7mUJDQ8nMzCQ9PV2WvxBiHOk3QGmtr+p+rJR6HrNJ7ts90yilHgKye+cV4lQ4a6toLfqM1IbDtNLJEZeHjoYKdm3dhC041JcuJCSEzMxMMjIyJDAJMQ4F2gf1/zBrTL29DGwZvOKI05HbY1C0t5bp8RY6SzbgrNmH22NQ2ezkkMeGJySY0AmJvuDUHZjS09Ox2WwjXHohxFAJNEAdBM4DSnptXwGUD2qJxGnlwKFmPi3cQ1D1dgg5TFx0CJVNTiqanLgMsE9IIDgqDixWCUxCnGYCDVA/Al5QSp0HbMJc+3ohcAlw1UAZhRhIa3kRCXs/Ao8L3WhAo5lxbHwAABYvSURBVAsPYAuPJjg6EYvNTmhoKNOnT5fAJMRpJtCpjl75/+3de3xcZZnA8d9M5pZ7k7RJc2lom6QP16LlrlxkWbwgiqLiroCK3HRZd8VFdwURXbmJu35EdvGC6MqyygdlYRfwuiD3i1xKAUsfWtpcJknTpk3STpNJJjOzf7wn7RDakIYkM5M8388nnybnzDnzPj2T8+S9nPcVkShwMXCut/lF4ERVfXqmCmfmvrraCjQ5ytZ4mnCwgMJQEeGKGnyhCEVFRbsTk/UxGTP/TPo5KFX9I/DHGSyLmcMSo0lWv7oVaaygvCTM4OAgGzdupL29nXhhGWXhUcIVi/EXllBcXExzczMNDQ2WmIyZxyZ6DuqGyZ5EVb88PcUxc9GmrgEef2ot5b0vMVDfxKL6KqLR6O7ZxYuql4C/gNLSUlpaWuwBW2MMMHEN6qhJniP95i8x81UyvovApidZ2PEs2xNpOvt66R86iEh4z0evvKKSlpYWFi9ebInJGLPbRM9BnTz2vYicC/xGVXtnpVQm76USwwy+tpquV56nvW+I7oSPZDJNacRHMBUHSqiqqqKlpcUmcTXG7NVk+6C+BxwLWIIy+9S3I85TL3VySPE2YptW07ZtJ7GRFABFhQEKIiWEKmpY3NBIc3MzlZWVWS6xMSaXTTZBPQ18GLh+Bsti8tjajb0899hTRPrW8sjoCOFIyD2MAPiDEcIVNTQsb6G5uZmysrLsFtYYkxcmm6BSwLUi8lVgEzCUuVNVj57ugpn8MTw8zK7XnmSkezVDXo9kQShFMBQmXFnD0gMPo6mpyZZWN8bsl/2pQdnzTgaAoeFRwsEChobcUPGOjg6SiSSBUBB/MklhYZiSRbU0H7aKZcubCIfD2S6yMSYPTfZB3W+MfS8iZYBfVftnrFQmJ8VHRlmtW3h5zcvUVvogNbx7qDj+AsqqaykMFrDi7cfQuGw5gcBsrYdpjJmLJn0HEZHPAZcDdd7PW4AbVdX6peaBdDrN448/y2urn4KRHbRvLaWhuXmsm4ny8nKaVq2yZ5iMMdNmUglKRC4DrgSuAR7DdX+/E/gnERlS1RtnrogmmxKJBJt0LRtefJZYXy++xAhpIDy6i0Q8TkNjI01NTVRVVVliMsZMq8nWoC4BPquqv8jY9riItAFXA5ag5pDRZIoX1nXij3XS8epLjOzsA8Dn81EUCeDzwbIlDRz0jneyoKYuy6U1xsxVk01Qi3Ar6Y73HNAwfcUx2ZROp3nu5Y08/dRzpPs7KA+MEAnvmT086PfR1NTIiqNOoqTGLrsxZmZNNkG9jFtW47px2z+OWwbe5LFkMklnZycbN24kul4JbusBIJ7yEQ75KQkVsHRJA8uPPIHIwvosl9YYM19MNkF9DbhfRI4DnvS2HQe8FzhzJgpmZt7Q0BCtra20t7czMjICQEl5KfG+LaTTUFMa5OAVy2hcdQKhytosl9YYM99Mdpj570XkFODzuPWghoBXgKNUdc0Mls9Ms3Q6zbZt23j2hbW8pkpt3SKCgT3NeOHSClY0VrOsrprFhx1HsGJxFktrjJnPJlpu473Ao6q6C0BVHwEema2Cmek1OjpKZ2cnmzZtZMO69SQGthFMJ9jWA4vrF1NUVMTSpUtpbGwk4ANfIJjtIhtj5rmJalD3A6Mi8izwoPf1hKoOz0rJzLSIxWK0trbS0dZKvH8rydh2ikcS7EwnAAgN9XP4205jSYM9v2SMyS0TJaiFuGedjgOOBy4F/CLyFHsS1tOqmpzxUpr9kk6n2bx5M21tbUTb2/APDZAcHABv1odw0E9RcYDasiKaVx1FRV2NJSdjTM6ZaD2oPuA+7wsRKQBW4RLWccBngIUi8qiqvn8WymreRDwep729nba2Nrb1bCbWu5VUPEZpcYhAgZ+ioJ+60hB1VeWULF9JYePB+IM2T54xJjdNeqojVU2KSBToBrYA7UA1sHyGymYmIZ1O09vbS1tbG5s3b3Zz46VTDG+Nkhp2zXjBVIrD6kpYVLOYomUrCdc24fMXvMmZjTEmuyZMUCJSArwL+EvgVOBAoAP4I3Ar8AlV7ZrhMpq9GB4eJhqNsmlTK9v7dlAYybiUPj9lVdUke7soD/gorVvGASeeSHih9TMZY/LHRKP4HgWOBvqAh3DTGT2oqhum+mYicjpwLbAMVwu7QVV/ONXzzTfpdJrt27fT1tZGZ2cnnR2bSe3qI+0vYEnLCnw+qKqqYunSpSwqLeTVPz1NzaFHsLBmUbaLbowx+22iGtQ7gSjwE9yAiCdVNTHVNxKRWuBXwIdV9Tcisgo3n98zqvr8VM87H8TjcaLRKB0dHcR29DMa6ycZ6yMQ20Uqlcaf8lFWXskRq1ZSWlq6+7hDTnlvFkttjDFvzUQJajmuae8U4HNAsYg8BjyAS1jPq2p6sm+kqt0iskhVd4qIH6gCRoGdUy79HJZOp9nQ2snqF1+ls7OLxWUQTuwkObTnv2tBJEAwmWRhUQG1FcWvS07GGJPvJhrF1wr82PtCRA4H/gKXsK4EEiLyEPCAqt48mTfzklMRMOC997dUdf1bCWCuGRoaIhqN0t7ezvpN3cQH+oiMxhiJpwhEghT4oKYkSG1JiMJIIQW1QpUcTkFhSbaLbowx02p/RvGtAdaIyI3AEcB5uGmPPgRMKkF54kAxsBL4tYisV9Vb9+P4OSeZTNIR7WZLTxdbtmzZvUpteaoff8ItdRFM+5GqCNXFQSJVtUQaDyFcu9xG4xlj5qw3TVAi0gAcAxzr/bsK1zT3BG4tqIf25w1VNQWMAM+KyI+AM3AjAuedvv4B/rR6HbphE6nRBE0NC163v2xhNeWjMRoXRKiuLKFoiRBpPIRAaWWWSmyMMbNnolF8d+ESUi2un+hx4F7gMuC5/Z1BQkROAr6jqkdkbA4D/ftb6HyWSCTo6uqivb2dbdu3s3HTZkKJnYRSwwzGiymKBFm4cCGNjY3U1FSz64X/I1R9AJG6Fpsfzxgzr0xUgwoD38XVkJ73aj5vxQtAvYh8ETdk/RjgfODDb/G8OS+dTtPTs4WOaJQtPZtJJkZI7uonGeunKjnIcDJJ0A9VRRGOe9dJFBcX7z62/Mj3ZbHkxhiTPRMNkjh9Ot9IVQdE5DTge8BVuAd+L1DVh6fzfXJJLBZj/WutvLR2A1u3DbCwMEmJb4hUfBcAPqB+QZjSAh+NlRFKl5W/LjkZY8x8NulBEtPBe97p+Nl8z9k2MjJCV1cXHR0d9Pf309+/g4GeHkqSgyQG06SKw5SE/dSWhKguDhCOFBKuX0Gk4UACZVXZLr4xxuSMWU1Qc1UqlaKnp4eNm9rp7+slldrTGlpSkCSe3IWfNAuCBRxWHaGiOExoYT3hhgMJ1yzFV2CXwRhjxrM74xSl02n6+vqIRqP8ed0mercPMDq4kwOWNRAM+AHw+/00NK2gNtFLVaSAyupFFC4RwvUrKCi0h2qNMWYilqD2UywWo7Ozk2g0yuCuXaTiMWLdPYSGYoRJM9BXQnPLcpYsWUJdXR2hUIih2lICJRUEKmptslZjjJkkS1CTMDZz+PrX2hgY6CfCCMnBHSQHd0A6RRFJhv1pygM+ahYUcfwJJ7zu+MLGQ7JUcmOMyV+WoPYhkUjQ3d3taktdPXR390J8J8W+OP5CN3tDwO9jUXGQ6uJCksOjVNXXU7rMkpExxkwHS1AZkskkPT09dHV10dPTs3uwgz/eTyTmlr1KARULQtSVhagsDBAqLiNc10K4voVASUUWS2+MMXPLvE9QqVSKrVu3sm59KxtbO9i5YyeN9VUU+Pf0FYWKyyiPFFBImtrSEPV1FZQ1riBc10xgQY31KxljzAyYlwlqbJn0rq4uuru7GRnaRVd0M+mhHURSCXaUhqlYUEp5eTn19fXU1dUx8FwBkZJSihpWEKyqw+fzZzsMY4yZ0+ZNgkqlUmxo7WLdq60MD/YR9o+SHBwgNbiD1Eic4uQoQ6lRgn4oSqc5+eSTKSnZs4RF4fFnZLH0xhgz/8yLBBWLxbj/dw/RGt1CaHSQsoJhykJ75rqNBHzUFkcoSCRYVF7MwoOWvi45GWOMmX3zIkFFo1H8g70sGOrER5rUKIQKC6kuDrKoKEB5JEio+gDCtU2Eag7AHwhlu8jGGDPvzYsEVVtbS9uCCoa2RKkMB6gtD9NQUUikegnhumZCNcvwB8PZLqYxxpgM8yJBlZeX8+7TP0jfQzH84SLCdc2Ea5vwh4uyXTRjjDH7MC8SFIDP52PBCR+z5jtjjMkT82qstCUnY4zJH/MqQRljjMkflqCMMcbkJEtQxhhjcpIlKGOMMTnJEpQxxpiclC/DzAsANm/enO1yGGOMmYKM+3fBZI/JlwRVC3D22WdnuxzGGGPemlrgtcm8MF8S1DPACUA3kHyT1xpjjMk9Bbjk9MxkD/Cl0+mZK44xxhgzRTZIwhhjTE6yBGWMMSYnWYIyxhiTkyxBGWOMyUmWoIwxxuQkS1DGGGNykiUoY4wxOckSlDHGmJyULzNJ7JOIHA3cp6rV3s+LgBuB9wDDwE+Aq1Q16e2/DTgLGM04zUpV3SgijcCtwLHAFuDzqvrrHI7lHd7+g4Au4HJV/ZW3L2ux7E8cIvID4JxxpygGrlDVa/PwmpwHfBVYCKwD/kFVH/P25VssnwcuBaqAJ4BLVHVjNmMRkVOB64EW732/rao/FJEFwI+BU4EY8FVV/al3jA/4JnAREAJ+CnxJVUe9/WcB1+JmOXgY+LSqbsnFWDKO9QN3AQ+r6ncztp+Mu6ZNwBrgXFWd1LRCuShva1Ai4hORC4Df4z50Y34GVONu2ocCRwP/nLF/FfAhVS3J+Nro7bsDeBH3C3khcIeILJ/hUKYUi4jUAvcD/waUApcAt3s3jqzEMpU4VPWzmdcC+BKw1osrK3FMNRYRWQl8BzgDWADcDtzj3UzyLZazgGuA87zy/i/wBxGJZCsWEVmCuylfjfv//WvgOhF5D/B93DRotcD7getF5CTv0IuAM3G/+y3AUcDl3jkPxiXaT3uxrPdim1FvIRZEZClwL/ChcedcCNwDfMM7593A7zI+f3knbwuOuwifw11gAESkCHgvcKmqblHV7cCVwIXeL2khcCDwwviTicgK4Ejga6o6oqoP4n4pz5/5UPY/FuCTwCOq+jNVTavqH3A3mL4sxjKVOMh4bRNwA/AJVd2Rh9ekhT2/Uz7cTWbIOzbfYvkIcIuqPqyqo6r6fWAEOCWLsSwFfq6qd6tqSlWfAR4CTgE+ClypqoOq+gJwCy4xAXwK+K6qRlV1K/B14GJv3znAvar6mKrGga8A7xSRllyMRURKgOdwtaMnxp3zTODPqnqXqiZU9dtA2DtnXsrnBPUDVT0CeDZj21g8uzK2JYFFuL8o3oZr2rtFRLaKyPMicrr3uoOBdlXNPHYdcNiMlP71phLLEUCriNwhIr0ishpYrKo7yV4sU4kj07/iboprvJ/z7Zr8DngZeAl3M/8W8HFVTZF/sfjH7Rvbv4IsxaKqj6rqZ8d+FpFK9kwincbVfvZWnoNxtfLMfXXe8a/bp6qDQAe5G0scOEhVLwcS4047Pk4AZXY+YzMibxOUqnbtZVsM14xxg4hUikgV8DVvdyGuKexR3F+UdbgmjDtF5HCgBBgcd8pBoGhmInhduacSSyWuaeV2XFPAdcDdXi0kK7FMMQ4ARORQXJv7tzIOz7drEsHdEI7F9aP9I66JbzH5F8tdwEUicqSIBEXkQlzrQyFZjGWMiJTjam1P42oUcVXNnPk6szzjyzv2fdFe9o0/dsbtTyxebXZf/WNZj2W65W2CmsC5uL9eX8F1eN7jbe9X1d+r6qmq+qxXBb4LeBD4IO6vxcJx5yrCdVJmyz5jwXVq/1ZV7/NiuRN4HngfuRfLRHGM+Qxwj6r2ZGzLtThg4li+DmxW1adVdVhVbwZagY+RZ7Go6h24P3ruwNUoDgX+APSR5Vi8JsangB5cc9hOIDKuyTizPOPLO3bDju1l3/hjZ9QUYplILn7G3pK5mKBqgYtVtUZVDwU6gVdUdVBEPiAinxr3+hCu2rwWaPT6qcYcyBurzLNpn7Hgqv0V414/Nioz12KZKI4xZ/DGzulciwMmjmUJrs0/0yiuKSavYvEG4dyrqs2quhj4Ii5JPUcWYxGRE3E1jXuAj3r9RutxfX7L9lGetYCM29etqv3j93l9c43kbiwTGR/n/hybk/J+mPlefAd4WUQuw33QvsWeEWEFwI0i8gruF+3jwDuAC1S1XUTWANeIyFe87WcAx812ABkmiuU24G9F5Bzg57i/0lcCZ6lqR47FMlEcY8OdlwOPZx6kqppjccDEsdyHazK7E7co29nAIcD9OXhNYOJYTgGuEpETgB245r8u4BlVTWcjFq/5+j7cIwg3jW1X1ZiI3I0bBXc+boj1hbiReQD/CVwmIg/gahlf97aB+915TETeBTyJqzWuVtVXczSWidyN+/yd5X3/90AKN/giL83FBHUhbtTLdlyzy01eUwuqeo+IXAH8AliMq4Wcrqrt3rEfAX6EeyahFzhfVV+e5fJnmiiWNSJyGu6mcjPQDpypqh3esbkUyz7j8CwFhr2RZOPlUhww8TW5xetP+DnuOai1wGk5ek1g4uvyX7jO9TW4VoYHgA9m9I1kI5ZLcP3I14nIdRnb/x03Ku9moA3XInKNqv7G2/8DoAY36q0I+CVef5uqviQin/FeU4+r0XxshuOAqceyT6q6RUQ+gHsO6lbgz8AHVHVkugs/W2xFXWOMMTlpLvZBGWOMmQMsQRljjMlJlqCMMcbkJEtQxhhjcpIlKGOMMTnJEpQxxpicZAnKmGnkTd671Zv8c/y+K0Qk5i2XYIx5E5agjJleX8A9AJ/58CUisgy4AjdzQGsWymVM3rEEZcw0UtXNuDWFLhCRYzJ23YRbh+ymvR5ojHkDm0nCmGnmzUT9OG6KoKNws+XfCbxdVdd6rzkPt6prPW428StU9bfevgBuifJP4JaF2YabnusyVU2KyO24OdYOws3VdqaqPjRrARozS6wGZcw08+aruwg3ee95wL8AV2ckp/fjJmq9wnvNrbi1vI72TvEV3BLg5+JW6f0K8HfA6Rlvcw5uzrVTcMs1GDPnzMXJYo3JOlV9WUS+A/wQV0O6PmP35cD13hpeABtE5EjckhZ/hVuR99Oq+oi3/6ci8mXciqn/421bq6q3z3QcxmSTJShjZs43cKvqflNVM5fnPhg4QkSuzNgWxFu3x5t1/xQR+TZuifWVuBnfCzJe/9pMFtyYXGBNfMbMEFUd8r4dGrcrAHwJeFvG1yG4NZUQkatxS0L4vX/fDYxfymL8OY2Zc6wGZczsWwccoKobxjZ4tak48G3gb4AvqOpt3r5C3IKCvr2cy5g5yxKUMbPvBuA2EVHgQeBU4Cpc/xO4BQRPF5EngHJcU2EZb1xO3pg5zZr4jJllqvpL4FLgy7h+p0uBi1X1V95LPokbvfcS8N/ABuA/gCNmvbDGZJE9B2WMMSYnWQ3KGGNMTrIEZYwxJidZgjLGGJOTLEEZY4zJSZagjDHG5CRLUMYYY3KSJShjjDE5yRKUMcaYnPT/Klv2Uzv1mVQAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation(system, updatefunc3)\n", + "plot_results(census, un, results, 'Proportional model, combined birth and death')" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# Solution goes here" - ] + "source": [] } ], "metadata": { diff --git a/code/chap06mine.ipynb b/code/chap06mine.ipynb new file mode 100644 index 00000000..5d9f68ef --- /dev/null +++ b/code/chap06mine.ipynb @@ -0,0 +1,949 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 6\n", + "\n", + "Copyright 2017 Allen Downey\n", + "\n", + "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure Jupyter so figures appear in the notebook\n", + "%matplotlib inline\n", + "\n", + "# Configure Jupyter to display the assigned value after an assignment\n", + "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", + "\n", + "# import functions from the modsim.py module\n", + "from modsim import *\n", + "\n", + "from pandas import read_html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Code from the previous chapter\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'data/World_population_estimates.html'\n", + "tables = read_html(filename, header=0, index_col=0, decimal='M')\n", + "table2 = tables[2]\n", + "table2.columns = ['census', 'prb', 'un', 'maddison', \n", + " 'hyde', 'tanton', 'biraben', 'mj', \n", + " 'thomlinson', 'durand', 'clark']" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 2.525149\n", + "1951 2.572851\n", + "1952 2.619292\n", + "1953 2.665865\n", + "1954 2.713172\n", + "Name: un, dtype: float64" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "un = table2.un / 1e9\n", + "un.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 2.557629\n", + "1951 2.594940\n", + "1952 2.636772\n", + "1953 2.682053\n", + "1954 2.730228\n", + "Name: census, dtype: float64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census = table2.census / 1e9\n", + "census.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07224800083333333" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_0 = get_first_label(census)\n", + "t_end = get_last_label(census)\n", + "elapsed_time = t_end - t_0\n", + "\n", + "p_0 = get_first_value(census)\n", + "p_end = get_last_value(census)\n", + "total_growth = p_end - p_0\n", + "\n", + "annual_growth = total_growth / elapsed_time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### System objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can rewrite the code from the previous chapter using system objects." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
values
t_01950.000000
t_end2016.000000
p_02.557629
annual_growth0.072248
\n", + "
" + ], + "text/plain": [ + "t_0 1950.000000\n", + "t_end 2016.000000\n", + "p_0 2.557629\n", + "annual_growth 0.072248\n", + "dtype: float64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = System(t_0=t_0, \n", + " t_end=t_end,\n", + " p_0=p_0,\n", + " annual_growth=annual_growth)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can encapsulate the code that runs the model in a function." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "def run_simulation1(system):\n", + " \"\"\"Runs the constant growth model.\n", + " \n", + " system: System object\n", + " \n", + " returns: TimeSeries\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " \n", + " for t in linrange(system.t_0, system.t_end):\n", + " results[t+1] = results[t] + system.annual_growth\n", + " \n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also encapsulate the code that plots the results." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_results(census, un, timeseries, title):\n", + " \"\"\"Plot the estimates and the model.\n", + " \n", + " census: TimeSeries of population estimates\n", + " un: TimeSeries of population estimates\n", + " timeseries: TimeSeries of simulation results\n", + " title: string\n", + " \"\"\"\n", + " plot(census, ':', label='US Census')\n", + " plot(un, '--', label='UN DESA')\n", + " plot(timeseries, color='gray', label='model')\n", + " \n", + " decorate(xlabel='Year', \n", + " ylabel='World population (billion)',\n", + " title=title)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we run it." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation1(system)\n", + "plot_results(census, un, results, 'Constant growth model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Proportional growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a more realistic model where the number of births and deaths is proportional to the current population." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "def run_simulation2(system):\n", + " \"\"\"Run a model with proportional birth and death.\n", + " \n", + " system: System object\n", + " \n", + " returns: TimeSeries\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " \n", + " for t in linrange(system.t_0, system.t_end):\n", + " births = system.birth_rate * results[t]\n", + " deaths = system.death_rate * results[t]\n", + " results[t+1] = results[t] + births - deaths\n", + " \n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I picked a death rate that seemed reasonable and then adjusted the birth rate to fit the data." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "system.death_rate = 0.01\n", + "system.birth_rate = 0.027" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what it looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file figs/chap03-fig03.pdf\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation2(system)\n", + "plot_results(census, un, results, 'Proportional model')\n", + "savefig('figs/chap03-fig03.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model fits the data pretty well for the first 20 years, but not so well after that." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Factoring out the update function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`run_simulation1` and `run_simulation2` are nearly identical except the body of the loop. So we can factor that part out into a function." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "def update_func1(pop, t, system):\n", + " \"\"\"Compute the population next year.\n", + " \n", + " pop: current population\n", + " t: current year\n", + " system: system object containing parameters of the model\n", + " \n", + " returns: population next year\n", + " \"\"\"\n", + " print(t)\n", + " print(pop)\n", + " \n", + " births = system.birth_rate * pop\n", + " deaths = system.death_rate * pop\n", + " return pop + births - deaths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The name `update_func` refers to a function object." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "update_func1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which we can confirm by checking its type." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "function" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(update_func1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`run_simulation` takes the update function as a parameter and calls it just like any other function." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Simulate the system using any update function.\n", + " \n", + " system: System object\n", + " update_func: function that computes the population next year\n", + " \n", + " returns: TimeSeries\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " \n", + " for t in linrange(system.t_0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " \n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we use it." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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t_01950.000000
t_end2016.000000
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\n", + "
" + ], + "text/plain": [ + "t_0 1950.000000\n", + "t_end 2016.000000\n", + "p_0 2.557629\n", + "birth_rate 0.027000\n", + "death_rate 0.010000\n", + "dtype: float64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_0 = get_first_label(census)\n", + "t_end = get_last_label(census)\n", + "p_0 = census[t_0]\n", + "\n", + "system = System(t_0=t_0, \n", + " t_end=t_end,\n", + " p_0=p_0,\n", + " birth_rate=0.027,\n", + " death_rate=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1950.0\n", + "2.557628654\n", + "1951.0\n", + "2.601108341118\n", + "1952.0\n", + "2.645327182917006\n", + "1953.0\n", + "2.6902977450265952\n", + "1954.0\n", + "2.7360328066920476\n", + "1955.0\n", + "2.7825453644058125\n", + "1956.0\n", + "2.829848635600711\n", + "1957.0\n", + "2.8779560624059233\n", + "1958.0\n", + "2.926881315466824\n", + "1959.0\n", + "2.9766382978297603\n", + "1960.0\n", + "3.0272411488928666\n", + "1961.0\n", + "3.078704248424045\n", + "1962.0\n", + "3.131042220647254\n", + "1963.0\n", + "3.184269938398258\n", + "1964.0\n", + "3.2384025273510284\n", + "1965.0\n", + "3.293455370315996\n", + "1966.0\n", + "3.349444111611368\n", + "1967.0\n", + "3.406384661508761\n", + "1968.0\n", + "3.46429320075441\n", + "1969.0\n", + "3.523186185167235\n", + "1970.0\n", + "3.583080350315078\n", + "1971.0\n", + "3.6439927162704344\n", + "1972.0\n", + "3.7059405924470314\n", + "1973.0\n", + "3.768941582518631\n", + "1974.0\n", + "3.833013589421448\n", + "1975.0\n", + "3.8981748204416125\n", + "1976.0\n", + "3.9644437923891203\n", + "1977.0\n", + "4.031839336859735\n", + "1978.0\n", + "4.100380605586351\n", + "1979.0\n", + "4.170087075881319\n", + "1980.0\n", + "4.240978556171301\n", + "1981.0\n", + "4.313075191626214\n", + "1982.0\n", + "4.3863974698838595\n", + "1983.0\n", + "4.460966226871885\n", + "1984.0\n", + "4.536802652728707\n", + "1985.0\n", + "4.613928297825096\n", + "1986.0\n", + "4.692365078888122\n", + "1987.0\n", + "4.77213528522922\n", + "1988.0\n", + "4.8532615850781164\n", + "1989.0\n", + "4.935767032024445\n", + "1990.0\n", + "5.0196750715688605\n", + "1991.0\n", + "5.1050095477855315\n", + "1992.0\n", + "5.191794710097886\n", + "1993.0\n", + "5.28005522016955\n", + "1994.0\n", + "5.369816158912433\n", + "1995.0\n", + "5.461103033613944\n", + "1996.0\n", + "5.553941785185382\n", + "1997.0\n", + "5.648358795533533\n", + "1998.0\n", + "5.744380895057604\n", + "1999.0\n", + "5.8420353702735826\n", + "2000.0\n", + "5.941349971568234\n", + "2001.0\n", + "6.042352921084894\n", + "2002.0\n", + "6.145072920743337\n", + "2003.0\n", + "6.249539160395973\n", + "2004.0\n", + "6.355781326122704\n", + "2005.0\n", + "6.46382960866679\n", + "2006.0\n", + "6.573714712014126\n", + "2007.0\n", + "6.685467862118366\n", + "2008.0\n", + "6.799120815774378\n", + "2009.0\n", + "6.9147058696425425\n", + "2010.0\n", + "7.032255869426465\n", + "2011.0\n", + "7.151804219206714\n", + "2012.0\n", + "7.273384890933229\n", + "2013.0\n", + "7.397032434079093\n", + "2014.0\n", + "7.522781985458438\n", + "2015.0\n", + "7.650669279211232\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation(system, update_func1)\n", + "plot_results(census, un, results, 'Proportional model, factored')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember not to put parentheses after `update_func1`. What happens if you try?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** When you run `run_simulation`, it runs `update_func1` once for each year between `t_0` and `t_end`. To see that for yourself, add a print statement at the beginning of `update_func1` that prints the values of `t` and `pop`, then run `run_simulation` again." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combining birth and death" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since births and deaths get added up, we don't have to compute them separately. We can combine the birth and death rates into a single net growth rate." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "def update_func2(pop, t, system):\n", + " \"\"\"Compute the population next year.\n", + " \n", + " pop: current population\n", + " t: current year\n", + " system: system object containing parameters of the model\n", + " \n", + " returns: population next year\n", + " \"\"\"\n", + " net_growth = system.alpha * pop\n", + " return pop + net_growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how it works:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Xpy75/IQYPZqamtiwYQMOhzlknsViYe7cuYSFhQ1rOsZMgBJCCPH51dfXU1BQ4CvRCAoKIj8/n8TExGFPi1TaDDOlFCUlPYcfvOSSS3j55Zd9719//XUuueQS8vLyWLBgAddeey07d+7s99jr16/nqquuYtGiRSxcuJBvfetbFBQUDPo1CCHGptraWjZs2OALTiEhISxevHhEghNIgBqVCgoKuP/++7nnnnvYvHkz69evZ968eXz729+mvr6+131eeukl7rjjDq688krWr1/Pxx9/zKpVq7juuuvYtGnTMF+BEOJUU1VVRUFBAW63OVReaGgoS5YsYfz48SOWJglQo9C2bdtIS0tj/vz5WK1WwsLCuOGGG7j44ot7DVB2u51f/epX/PznP2fFihXYbDZsNhtf+9rXuP7669m7dy9gNiZ56qmnOPfcc1m0aBG33nqr73gbN27k4osv5sEHH2TRokWceeaZPPbYY75z/P3vf2fFihUsWLCAr3zlK3z00Ue+/fLz8/3Sc8455/Duu+/2u58QYvSoqKigqKiIzvkBIyIiWLp0KbGxsSOarjFbB1Wws4aCXTUALJyZzMIc/2k6Pt52kK1lRwBYOieFPOWfhX1/8wF2VtQBcPb8KeSkx/utf3tDJbsPNPR67M9r+fLlPPnkk6xevZoVK1Ywf/58srKyuO+++3rdvqioCKfTyVlnndVj3Q033OB7/ec//5nXXnuNZ599lsTERB544AF+8IMf8PzzzwNQXl7OihUr+Pjjj9m4cSPXXHMNF110EXFxcdx99928+uqrZGRk8L//+7/cf//9vP322/1eR319fZ/7SaMIIUaeYRiUlpZSXl7uWxYdHc3ixYuHvUFEbyQHNQplZGTwj3/8g6ysLNauXctll13G0qVL+f3vf09vMyDX19cTGxs7YFP7devWcfPNNzN16lTCwsK4/fbbKSwsZN++fYDZUufGG28kJCSEZcuWkZCQQGVlJTabjeDgYF566SWKi4u57LLLAgoyJ7ufEGLoGYbB9u3b/YLT+PHjWbp06agITiABatjZbDY6Ojp6LHe73YSGHh/sPTU1lZ/85Cf885//5LPPPuP73/8+zzzzDGvXru2xb0JCAo2Njb32I2pubsbpNCctrq6u5q677iI/P5/8/HzOPPNMgoODfaM4REVF+aUhJCQEj8dDVFQUzz//PJWVlaxevZply5bx9NNP9xosuzrZ/YQQQ8vtdrNp0yb279/vW5aUlMTixYtHVZ/SMVvEtzCn/6K3ZbmTWJY7qc/1Z8+fwtnzp/S5/vzFqZy/OPWE05WcnEx1dTWzZ8/2Levo6KC6uto3W/A3v/lNzjvvPK666irAfKq5/PLLKS4uRmvd45h5eXmEhYXx4YcfsmLFCr91Dz30EJWVlTz//PMkJiZyzz338IUvfMG3vqysjLS0NIqKivpM87Fjx3z1Vy6Xi08++YRbbrmF/Px8goKC/AKjYRgcO3ZswP2611sJIYaH0+mkoKCAhoYG37IpU6aQm5s76ko3JAc1zC6++GIef/xx9uzZA5jDND344IMkJCQwZ84cAC688EKeeuop3n33XZxOJ06nk40bN/Lhhx9y7rnn9jimzWbjtttu45577uG9996jo6ODtrY2nn32Wf7xj39w0003AXDppZfyxBNPcOjQIdxuN8888wxXXHEF7e3t/aa5vr6eq6++moKCAkJCQkhKSsJisRAbG8vUqVNxOp28+eabuN1unn/+ed+gvf3tJ4QYfm1tbXzyySd+wWnGjBmjMjjBGM5BjVY33XQTQUFBXH/99dTV1REaGsrixYt57rnnfFnrb33rW4SFhfHkk09yxx13YBgG06dP55577mHZsmW9Hvcb3/gG0dHRPP3009x5550YhsHMmTNZs2YNCxYsAOC6666jo6ODK664gsbGRjIzM1mzZs2AA7ROmzaN++67j7vvvpva2lri4uL46U9/SkZGBgA//elPeeihh/jZz37GJZdcwrx58wLaTwgxfJqamti4caPvgdRisZCTk8O0adNGOGV9s5wK9QFKqTRg73vvvcfkyZN7rC8pKSE7O3vY0yUGl3yOQgyNuro6CgoKfPXfVquVvLw8UlJShi0NVVVVnSVA07TW+wLZR3JQQggxhh08eJCtW7fi8XgACA4OZsGCBcM+dcbJkAAlhBBjkGEYVFRUsGvXLt+ysLAwFi1a1GexvuNQBbaEKViCR0dLPglQQggxxhiGwc6dO32jyIDZAXfhwoVERET03N7tomXnJ7RXlRKakkF07jmjotGEBCghhBhD3G43W7ZsoaamxrcsPj6eBQsW9NrHqaOlgeYt/6ajxRz2zFG9m+D4yexuH09cdChTk0dulmsJUEIIMUY4HA4KCwv9mpGnpKQwd+5cgoKCemzffrCMlh3rMdzH+zI6x6XyznYnDW0HGR8TxuWJ0VitI5ObCihAKaWCgPlAPpAIuIEaoFBrvXXokieEECIQLS0tbNy40W9a9unTp5Odnd2juM5tb6G1dAOOQ8eHObJYg4jKWYZ7wnRa3zYHBKhvakdXNpA9bWRGNO83QCml4oDvATcA8UAFUAcEAROAVKXUIeAp4AmtdePQJlcIIUR3dXV1FBYW+kZ16a+PU9ueItrKN2O4jw+5FhQZS0zeeQTHmINiz1OJFOla8rOTyJw6bnguohd9Biil1Grgv4D3gKuBd7XWjm7bxABfAK4Adiil7tJaPz+E6RVCCNFF92bkQUFBzJs3zzd0Wncepx3D3YHbY3C0wU5w8nQyl16INdjm22ZuZgKz0uMJCx3ZWqD+zr4AWKC1PtLXBlrrJuCfwD+VUhOBuwAJUEIIMcQMw6C8vJzS0lLfstDQUBYuXMi4cX3neiJmzKdhTwn6kJ0DEbm425KYZgQR2mWb4CArwUEjPxJenynQWt/SX3DqZftDWuubBydZY1cgU74/9thjzJkzx28YfOh9csBOL7/8MtnZ2eTl5ZGXl0dubi4XXngha9as8Rs9/JxzzmHOnDm+7Tr/zj//fN82lZWV3HTTTSxYsIC8vDxWrlzJCy+80Ot5L7roIs4888xeR1IXQgwNj8fDtm3b/IJTdHQ0y5Yt8wUnj8tBS8mnuO0tfvtaQ0JJ/MKlVKcsp9UWT7uzg9K9vc/UPdICzr8ppRKBOUAI4FfjprV+c5DTddpzOBzcdtttrFu3DpvNNvAOQGZmJq+++ipgPl0VFRVx22230dTUxA9+8APfdr/+9a97jHreyePxcO2117Jy5UoefvhhwsLC2Lp1KzfddBM2m42vf/3rvm07B4FNSEjgnXfe4eKLL/4cVyyECITT6WTTpk3U1dX5lnVtRm4YHtoPlNJWVoDH2Y7H0UZ07rl+DSVCY+NZPNvKhh2HWDxrIhlTRq6eqT8B5eGUUlcDB4B3MIv03ujy9/qQpe40tmTJEtra2nj00UdPan+LxcK8efO4//77WbNmDU1NTQHt19DQQGVlJStXriQ8PByLxUJeXh533HFHj23Xrl3LF7/4Rb761a/yl7/85aTSKYQIXGtrK5988olfcJoyZYpvHidXQw2Nn7xMy46P8Djbcbk87NlaxIaCXT2OlTFlHN88P4vMqXGjolNubwLNQd0O/AG4U2vdfDInUkpdATzdbXE48J7W+ryTOWZ/WssKaSvfHNC2YVOyiZ7tP116c/GHtB/oWRTXXcSM+URmLjipNPZ73IgIHnzwQa688krOOussFi1adFLHWbJkCVarlaKiol6nhO8uPj6ehQsX8p3vfIdVq1b5ivkuvfRSv+3q6+t57733+Ne//kV0dDQPPvggxcXFfvNcCSEGT319PYWFhb4JSAGysrKYMWMGHkcbTaUbcFTv9q1zODsoPeSkOmoWrQdczMxxEBt1vKbJYrEQHDQ6A1OnQAPUFOC3JxucALTWfwX+2vleKZWHmSO7/WSPOdbNnTuX66+/nh//+Me+orsT1Tn/UkvL8XLo2267jeBg/4/+qquu4uabzSrENWvWsG7dOv7973/z5z//mY6ODs466yzuueceX8ugV155hSVLljBx4kQAVq1axQsvvMADDzxwUukUQvStqqqKbdu2+bXUmzt3LhOTk7BXbKWtfItfZ1uLNYi4mfNpioqiqcEBBuytPsbczMSRuoSTEmiAegc4F9g90IaBUEqFYAare7XW2wbjmKeKQKd873TjjTfy8ccfc9999/nV/wTK7XbT1NTkCyQADz/8cJ91UJ1pvPLKK7nyyitxOp1s3ryZRx99lFtvvZW1a9diGAbr1q2jtraWpUuXAmadmcPh4I477iA+Pv6E0ymE6MkwDLTW7N59/Kc3NDSUBQsWEOlupeGjdbjbjoEBHsPAarUQmpxOZNYSgiKiOSO+lfVbD7J0TgopCVEjeCUnJ9AAtQ34tVJqFVAGOLuu1Fr3rKDo302AHXjyBPcLWGTmgs9V9BY9+6wexX6DIZAp37sKCgrioYce4pJLLul1kMeBFBQUYBgGmZmZAW2/du1aXnzxRV555RXADFZLliwhJCSEa665BoANGzbQ2NjIW2+9hdV6vBrz+uuv58UXX/TN4CuEOHkdHR0UFRX5janXdcBXZ20T7rZj2B0dHK5vxQgbx9zzL8Y24ficecnxkXz1nIxRW8c0kEAbup8FbMSsM8rF20fK+9d7u+c+KKVsmMV692qtR/9siYMskCnfu5s6dSo/+clPWLduXcDn8Xg8FBQUcPfdd3P99dcTFRXY09NZZ53F/v37eeCBB6irq8MwDA4cOMCzzz7rm25+7dq1XHDBBSQlJZGQkOD7+/KXv8yLL74oTc6F+JzsdjuffvqpX3BKSkpi2bJlvgdVW2IqntgUdh9qozw4k4KQhRylZ2u8UzU4QYA5KK312YN4zgsAD2ZrwNNOIFO+9+ZrX/saH374IRs2bOhzm7KyMvLy8gBzUrJJkyZxzTXXcPnll/tt98Mf/rDXgSPfeustkpOT+dvf/sZjjz3GypUrsdvtxMXFccEFF/C9732Puro63n33XZ577rke+69cuZIHHniAt99+m5UrVwZ4R4QQXTU0NFBYWIjDcXzgnimxYWSnJvaoO54w7xw8KOqq7VgtFo7U20mZcOoV5fUl4CnflVJJwM1ADmbOqwT4g9a64kROqJRaAzRqrX90AvukIVO+j3nyOYrTXffGEIarnemhdhItbVjCorAtuIyYaP+i/qZWJ59sr2bxrGTiosNGItkBOZkp3wPtB7UQs+7py8BR4AiwEtiulDqhIj5gMfDJCe4jhBBjlmEYlJSUUFRUhMfjMQdyPVZDlnGYBNpobHZQtvsAn7z9f3TPVMRE2rhwSdqoDk4nK9BGEo8A/wPc0LXeSCn1OPAQcCJFgGlA9QlsL4QQY1ZHRwdbtmzh8OHDYBh0NNcRam9gVkIY4SHBuN0equvaOByezlF3MmX7G1CpIzP9xXALNEDlA9f00qjhMSCw3rBeWuvIE9leCCHGqtbWVgoLC2lubsbT1oyr8TDjbQbZyeEEeycJjJg0g9CUadRWtBJqC8LtOX3algUaoA5h5nx0t+XpwEl33hVCiNPVkSNH2Lx5M057G676ajyONpLCgpgUFUyw1UJw9HgiZy7FFj+JuY4OgiPqmDNjAraQng2cxqpAA9RfgGeUUt8HOpuRLQF+410nhBAiAIZhUFFRQUlJCYZhYLFa8DjtjLd68LS5OOgKJTH/HKLScrBYzGYCYaHB5GcnjXDKh1+gAeoXQAqwDrNhhQVwYRbx3TU0SRNCiLHF7Xazfft2qqqqfMvCIqLIW7yEfRs+piYijdrITMLaElhoGfn5mEZaoP2gnMC1SqnbAIU5CkS51to+lIkTQoixoq2tjYIP36Oh/ijBMRMAGD9+PPn5+dhCgmkOTWHrzibU1Diyp50ejSAG0t+U7xcB/9Zau7yvu5uilAJkPighhOjP4f17KPjg37Q3N+Fye+josJE7dxazZ8/2DRemsqYxYWI7CXHhI5za0aO/HNQbQDJQ633dFwM4fWrthBAiQG5HG/rTf7OrVOPxGLTaXbg6PEQHNZI+I8tvLEur1SLBqZs+A5TW2trbazE6PfbYY5SUlPDkk/2Pv9vZm7uwsJCYmJhhSp0Qpxecyvp9AAAgAElEQVTD46Zl73a2fraemqZ2ACxYsALR4ybTGJdDYUkt5+RPGdmEjnIBT/kuhBCif4Zh4Dy8l6PFn7K9soYWp8e3btz4OObOXc47O9vImTaehTk9Zy8Q/vqrgzqCWXw3IK31qTUL1giqqqri0ksv5dZbb+Wpp57C5XJxyy23YLVaeeqpp3A6ndxwww1cddVVfPrppzzyyCPs3buXSZMmcdNNN3HBBRf4jnPXXXexfft20tPTSU9P9zvPunXrWLNmDfX19cyZM4d7772XKVPkaU2IoeJxOWja/DaHq/ZTVN2Cs8NDeFgwlmAbaVmzmHvGcoKDg0ma6iQm0jbSyT0l9JeDup0AA9Ros2fPHsrKynqdGHCwBQcHk5mZyfTp0wPep7m5mR07dvD+++/z7rvvctttt3HJJZfw3nvvsX79em655RZmz57N9ddfzwMPPMB5551HYWEhN954IwkJCcyfP59bb72VmTNn8oc//AGtNVdffTX5+eawiO+88w6/+93vePrpp8nIyODZZ5/l2muv5Y03+qtKFEJ8LkEhVBxuYOveRjrcHgyLlbAJE8j/wnLS0qb5pr2Q4BS4/uqgnhvGdAyqioqKYQlOYI6jVVFRcUIBCsyZcjsnA3S73axevRqbzcbZZ5+N2+3mT3/6E4sWLeKii8wGlEuWLOFLX/oSr7zyComJiezYsYM//vGP2Gw2Zs+ezSWXXMLBgwcBM/e0evVqcnJyALjuuuv4y1/+wsaNG0lNTR3cGyCEwOVysXXrVqo7IrBYLbRbonGExpOYMpdp09IHPoDoVX9FfAHPjqe1PvG5yIdQenr6sOaguhevBWLcOHNisc55maKjowF8rXra29uZNGmS3z6TJ09m48aNHDlyhNDQUOLi4vzWdQao6upqnnzySZ555hnfepfLRXV1tQQoIT4nw/DgOFhG+8HdxORfSGubncLCQlpbW7HawoieqmhosLA4P59Fs3tODyQC118RX+uwpWKQTZ8+/YRzNMNtoFku8/Pz2bRpk9+yAwcOMGHCBJKSknA4HNTV1REfHw9gjoTslZiYyOrVq/0mKtyzZw8pKSnU1dUN4lUIcXpxHq2iteQzXE1HOdrYzgd7X8MWE4bb7fZtkz0zm5UzMgkL7XsCUhGY/or4vjOcCRH+li1bxlNPPcWbb77J+eefT0FBAa+//jqPP/44kyZNYuHChTzwwAPcd999VFZW8vLLLzNv3jwALr30Un7/+9+zYMEC0tPTeeONN7jzzjt54403eszIKYQYWEdzPa2lG3Ae2Y9hGJQdaOBgq5t6tyZmaiYxkTaCg4PJzc0lJSVlpJM7ZvRXxPcgcJ/WutX7ui+G1vo/Bz9pp7fg4GCefPJJHnnkEe666y6SkpK47777WLZsGQC/+c1vuOuuuzjjjDNISUlhxYoV1NfXA2aAampq4sYbb6S2tpapU6fyxBNPkJaW5jcGmBCifx5HG61lhbQfKKWzzZjDbXDIFUQNkbSHxuBpdpCSZA5Z1FlULwZHf4/TC4CQLq/7ckq29BspkydPRuvjs5bExMT4vQf83i9durTX40yYMIGnn366z/OsXr2a1atXD3h+IURPRoeLtr3bsFdsw+1yYvXOzVTX1sHu9jCCU2bgqW0jOSaMmSqdvLm5UjoxBPor4ju7t9dCCDGWGYaHho9for2pgSMNbbS2u5g+KZYqI4aD1jAssaEEAxlTQsnJySEtLW3AOmVxcgIO+UqpSOByIAdwAjuBtd6RzoUQYkywWKzYJs6gpPRdXB0eWqwRVLckEhoZisVbphQWFsb8+fMZP15GHR9KAY2xp5SaDewH7scMUPOBhwGtlMoauuQJIcTQcrf3bLAcMT2X8Skp7A3LZKc7gSOtLt+6hIQEzjzzTAlOwyDQHNQfgH8A/09r7QJQSoUDfwSeBs4amuQJIcTQcLc10aoLcNRUYJ13CfFJx2estQSFQMYi7LVFpCZFEBkegsViITMzk4yMDCnSGyaBBqhcYHVncALQWtuVUj8HioYkZSfIMAz50pzCPB7PwBsJMQg8znba9myhfd8OWtsc1NS3cfTQm5z9zSuJCAvB4XCwZcsWjh49ypSkKABCQ0OZN28eEyZMGOHUn14CDVBbgC8AZd2W5wO7BjVFJyEsLMzXaVWC1KnFMAxcLheHDx8mMjJypJMjxjDD3YF9XzFte4owOpxgQPXRVhwuN+5QD5t2VpM9NYKioiIcDodvv/j4eObNm0dYWNgIpv701F8/qBu7vC0AHldK5QMbADcwB7gBeGBIUxiAyZMnU1VVxZEjR0Y6KeIkBAcHExsbK0+nYkh0Dk3UWlaIp2t9kwWSUlP5oC6J9tDxGEer2XioDsMwe85YLBYyMjLIzMyUB98RMtBo5l3VABd4/zodBb6L2XhixISEhDBt2rSRTIIQYpQxDAPX0QPmCBDH6mi1u4j2jiQeFBFLpFpEfFIa2cVV2BsqaW1u9HXqlCK90aG/flDyiy+EOKW1lhVSe7CaIw12OtweZkxLJn7WYsKmZGGxBlFTU0NTTQlO5/HeMhMmTGDevHmEhoaOYMoF9NPMXCn17RM5kFLKopT67udPkhBCfH4Wi4XIrCU0tzpxeizURmawPXoZ4ak5eAzYsWMHhYWFvuBksVhQSrF48WIJTqNEf0V8eUqpHwFPAa9orQ/1tpFSKhH4JnAd8G5/J1NKTQR+D5wNtAPPaK3vPpmECyFEJ4/TTvv+EsKnz8ViOf7cbYtPYdKic9m8s4PQyChmT0mgubmZLVu20NTU5NsuPDycefPmSd+mUaa/Ir7vextF3AM8qpTahTl6xFHAAiRgNj/PBP4FXKW1LhjgfK8Cm4EkYCLwoVKqRGv9t899JUKI047hdmHfW0xbRRHt9nZqD7WTt2yJX6OGibPmc974Y0xOjOJQ9UHWr9/hNz1GcnIyubm52Gwy0+1o028zc631JmCVUmoacCFms/IZgAez0cTvgDe11vsHOpFSahGQDiz19qfaq5RaDtg/1xUIIU47huHBUaVp3b0Jj72VQ3WtNDS14zz4GZXTs0hL8c8JTU6IYPu2rVRXV/uWWa1WcnJySE1NlVZ6o1RA/aC01nuBJz/nueYDxcC9SqmrMIv4ntRaP/I5jyuEOE0YhoGztpJWvRF3S4O50GIubw+KpCY6i7odtaROjPMFnbq6OoqKirDbjz8LR0dHM2/ePGJiYkbiMkSAhnN8+PGYnX0/xMxJZQFvKaUOSRGfEGIgrsZaWks/w1XvXx1uDY0gdekZbNhukDg+kqVzUrBYLHg8Hnbv3s3u3bt9fZsAUlNTycnJISgoaLgvQZyg4QxQDqBJa32v9/02pdQfgcsACVBCiF4ZhkHL9vdpryqjxe6kodnB5MRorCEhRKTPJTxtDpbgEC5PdhATacNisdDa2kpRURENDQ2+49hsNubMmcPEiRNH8GrEiRjOAFUKRCilbF2m6JAZvoQQ/bJYLFhCwzlQ20xTqxMDC+Hhqaily7GGhvu2i40KxTAMDhw4wI4dO+jo6PCti4+PJy8vj/Dw8N5OIUap4QwQ/waOAI94m68r4GrM4ZKEEALofeDniPQ8wrZt4UBHPIejFFWO8WTZ/MfGczqdbN++nUOHjhcBWiwWsrKymD59ujSEOAWdyISFeZgNHUIwm5n7aK0HbEChtW5XSp0FPAYcwmwk8aDW+u8nlGIhxJhkGAaOQ3toK99MTP5FBEdE+9ZZbWHM+NJVbPqgksykaBbmJPsFnCNHjrB161ba29t9y6KiosjLy2PcuHHDeh1i8AQUoJRSdwE/B+qB5m6rDQJs4ae1rgAuPpEECiHGPldDDa0ln+FsOExjczubX3mNM79+OaEhxxsy2CIi+eb5WQQHHe+I63a7KSkpYe/evX7HS01NZebMmQQHSy3CqSzQT+8a4G6t9S+GMjFCiNOLu62ZVr0Rx6FyAPbXNNFid2GxuthcfIAz5qX5bd81ODU2NlJUVERLS4tvWWhoKHPmzCE5OXlY0i+GVqABKh5YN5QJEUKcPjwdTux7irDv3Y7hOT6qw7iYCPZZ4qmNnEH0YTuL3B6CgvyHDDUMg/LycrTWfs3Hk5KSyM3NlXH0xpBAA9RLwBXAvUOXFCHEWGcYBo6D2uxo226na7uF0OR00tQidm4+ysKEKHIzEnoEp5aWFrZu3erXfDw4OJicnBymTJkiDSHGmEADlB24Uyn1NWA34Oy6Umv99cFOmBBi7Gne+h7t1eU0NLdztMFOWkoMEROSico+g5DxZv+kVV+I6RFoDMNg3759lJSU+I2jFxcXR15enszGPEYFGqAikM60QojPKTRlBhXbttLY4sBlDaUsdBbLzzjTLyB1D052u52tW7dy9OhR3zKr1UpmZiYzZsyQXNMYFuhYfN8Z6oQIIcYWw+MGi9UvgNgSU4mflkFZRTtHI6YT5YnA4XITZuv5U2QYBlVVVezcuROXy+VbHhMTQ15enoyjdxo4kX5Qs4E7gBzMiQ5Lgd9prT8dorQJIU5BvgFdd31CmFpCREq6b53FYmHymatIiKoiPcLG3MwEv5Z5ndrb29m+fTuHDx/223f69OkopbBa+5xrVYwhgfaDuhB4DXM0iJcwO+qegTmf08Va63eGLolCiFNFR0sjrSWf4Kg9QGNLOwfLX0etuoqUpFjfNhaLhbPnT+l1f8MwOHjwIDt27PDLNUVGRjJ37lyZUPA0E2gO6hfA/Vrr+7ouVEr9FLMDrwQoIU5jRoeLtvIt2PduwzA81Na3cfSYHY8lhIJNpVxy0cIB64ocDgfFxcV+QxUBTJs2jaysLOl0exoK9BPPBnprqfci8JPBS44Q4lRiGAbOmgpaSj7F097qWz4+NoxyVyKHIjMJNyJpanUSG9V7/6TOXNPOnTtxOo83EI6IiGDu3LnEx8cP+XWI0SnQALUfyAPKuy2fD9QOaoqEEKeEjpZGWnd9jOPIASxYfCN0hsQlE5ezjMkH3aR4DPJUAiHBvc+91N7eTnFxMTU1NX7LZagiAYEHqCeAp5RSk4EN3mVLgLuAB4ciYUKI0ctxaA9NW9+jqaWdmrpWksZHEDd+HJFZiwidpLBYLOT308iur7qm8PBwcnNzSUhIGIarEKNdoM3Mf6eUigbuBCZ4F1cDP9NaPz5UiRNCjE7BccnUNzs5VGuOHa0dSaxYuorQ8IgB97Xb7Wzfvp3aWv/Cl7S0NLKzsyXXJHwC/iZ4B4r9hVIqEbBrrbuPai6EOE0EhUWSPG8Z+z/cQGV4NkTG09BmkNzPfICGYVBZWUlJSYnfZIIRERHk5uYyYcKEvncWp6U+A5RS6kbgT955nG7sZb3vdSDzQQkhTj2G4aG9cicel4OIGfP9WuLFTJ9DashUbHVtLMpJJiy07+fd1tZWtm3bRl1dnW+ZxWIhLS1NWuiJPvX3rbgdWIs5seDt/WwX8HxQQohTR0fTUZqLP6L16CFq6toIao4mb97xB1OLxUpm6ngyU/vum2QYBnv27KGsrMxvDL2oqChyc3OlX5PoV58BSms9rbfX3SmlZCAsIcYQo8NFa/km7Hu3Y7e72HvoGIYBzVs3kJE1jagIW0DHOXbsGNu2bePYsWO+ZRaLhRkzZpCRkUFQUO8t+4ToFOhIEhVAvta6vtvyFGArkDgEaRNCDDPn0Spaij/EbTermMNCg7HZQjgQnMbRqAwqa5rJSe+/X5Lb7UZrTUVFhd98TbGxseTm5hIbG9vP3kIc118d1CpgmfdtGvBfSqm2bpvNGKJ0CSGGkcfloLXkU9qrtN9yW3wKU2Z9ieo9bXw1bxKJcf230jty5AjFxcW0th7vtBsUFERmZibp6ekyhp44If3loLYB38fX/Y48/OeBMoAW4NtDkzQhxHBw1FTQVPwRtYfr8RgGyfGRWEJCicpeQugkxTiLhUlTjX6HKnI4HOzatYuqqiq/5RMmTGDOnDkyX5M4Kf3VQVUC5wAopZ4FbtVaNw1XwoQQQ88wPBwrLaS84hBOlweAuDRFysJzsIYezy31FZw6p8TYtWuX3zBFISEhzJw5U2a5FZ9LwPNBKaWClVKTgM6aTQsQCszXWv/PUCVQCDF0LBYrcXnnEFKxnza3heroHDzhs5gcOnCH25aWFoqLi/0mEgSYNGkSOTk5hIb2PvaeEIEKtJHESuBZoLc2oQ2ABCghTgEeRxsWW7hfriYkNoH05at4o7iN+bOmBNQIory8nPLycjwej295REQEs2fPJjFR2kyJwRFo77hfYk6p8WvgPWAVkAz8BvjR0CRNCDFYDMOg/UAJddvWUxM9k/lnLfMLUvHpiitTPQT1MnlgV701grBYLKSnp5OZmSkdbsWgCvTblAF8VWutlVJbgEit9TqllAv4Kea0G0KIUcjd1kxz8QdU7d5NXaOdDsun7JmSzozpk/y26y849dUIYty4ccyZM0eajoshEWiAsgOdefkyIBf4F7AZyByCdAkhPifDMGjfv4vW0g0YbhdutwcD6LCGsql4P+nTUrBa+2/A0Dl+Xmlpqd+o4yEhIWRlZZGamiqNIMSQCTRAfQTcrZT6HrAJuFYp9QiwHAi4ZZ9S6rvA04Cjy+KbtNbPB3oMIcTA3G1NNBd/gKuu2rcsKT6SyqCpdEzM5bwFqQMGp8bGRoqLi2lsbPRbLo0gxHAJNED9CHgd+C7wFGb/qCbAhjknVKDmAY9orX98IokUQgTGMAza9u2gevOHxIYH05m5CYocx7g5yzkrJI6YSFu/uR6Xy0VJSQn79+/3GwkiMjKS2bNny1xNYtgE2sy8HMhWSoVrre1KqYXA2cBRrfXGEzjffOC3J5FOIcQA3PYWDn32Jgf37MXu6KBjfAQTxkUQkZ5LREY+lqBg+qspMgyDAwcOUFJS4tenKSgoiIyMDKZPny4jQYhh1d9QR712hOiy/P3O91rr7kMg9bZfEDAH+JZS6tdAG/BH4AGttdHvzkKIAVlCbDTVNWB3mHMtVTUHkbx8JZEpkwbY0yzO27FjBw0NDX7LExMTmT17NhERA/eLEmKw9ZeDasEczigQgQxLnIBZf/U8cBmQDbyKWVQo03UI8TlZg21MO/MCjr36Igdt05iav5SYpIn97uN0OiktLe1RnBceHk5OTg7JycnSCEKMmP4C1NmDeSKtdQ1wVpdFW5VSjwFfQQKUECfEcHdwdK8meorymygwPCmV6V/6LtlRMYyL7rsRg2EY7N+/n9LSUr/iPKvVyvTp02U6DDEq9DcW34eDeSKlVA7wda31z7ostmFOiCiECFBb7QHKP3yLhtpagnNWsHT5Ir/1iRP7b8RQX1/Pjh07/OZpArM4b9asWTKwqxg1Ah3qqJB+ivu01gsDOEwj8COlVBWwBnN09O8BNweSBiFOdx6Xg9bSDRwp3Ub9YXO+praSjzk8U5GUOG7A/dvb2ykpKenR2TYiIoKcnBySkpKkOE+MKoE2M3+jl/3SgYuBewM5gNb6oHeOqQcxh0g6Cvxca/1SgGkQ4rRkGAaO6nJaSz7F47QTHWEjKjyEY+0G7omzCLL1P8Ot2+2moqKC8vJyOjo6fMuDgoKYMWMG06dPl+I8MSoF2sz8vt6WezvergIeDfA4/wfkB5w6IU5zrQ11tO5cj6Wp5vhCC0zKyiZyQh5ZmZP6nQqjpqaGXbt20dbm39B24sSJzJw5U1rniVHt847s+H/AY4ORECHEce6ODvRn6zm6YyMRNiupyTFgAWtYJFEzl2JLmsbEforjmpub2bFjR4+pMKKjo5k1axYTJkwY6ksQ4nMLtA6qt8esWMyBYg8NaoqEEBzd9gm12z4FoMXuptnuInHmPCIyF2AN7rtIz+FwUFZWRmVlpV+zcRk7T5yKAs1B9dUnqh34zuAlRwgBMCEnn7iSrTQ0tuCJGI8193yiMtL73N7j8bB37152797tN6irxWIhLS2NzMxMbAPUVQkx2gQaoLr3iTIAJ7BTa908uEkS4vTSZnfS2uYgIT7atywoLJLUxWdjHGwkZ+kZhPQxz5JhGBw+fJhdu3b5zdEEkJCQQE5ODtHR0b3uK8RoF2gjiQ8BlFJRgALc5mJtH8K0CTGmuT0Gu7YUc3jLB3RETeSLl3+VoC4jjI+bMYe5M/rev7GxkV27dlFXV+e3PCoqipkzZ5KYmCjFeeKUFmgdVCjmaA9XYHauBbArpf4A/Ehr7R6i9AkxJrnbmmkoXk/D5q0EewyC6lso2VHOrDkZA+5rt9spLS3t0Z8pJCSEzMxM0tLSZFBXMSYEWsT3O8y5n74BFABWYBHwMObcTv85FIkTYqwx3B207SnCXrEVw+MmYVw4h+vbsNlCiLL0P+ZyR0cH5eXlVFRU4HYffyaUeiYxVgUaoL4OfElr/XGXZS8rpeqBdUiAEqJfdoeL+opSwmu24ba3+JbHx4bBhHRmLD2HkMje64o8Hg/79++nrKwMh8Phty45OZns7GyioqKGNP1CjIQTmfLd1cvyY70sE0J4eTwGJTvKOFj4PuGOejKmjCMoyCx+C46ZQFTOMhLjknvdt7MBRElJCS0tLX7rYmNjycnJIT4+fsivQYiREmiAuhP4o1LqemCD1tqjlJqFWS/1i679pAKZG0qI04WzoYbGT1/B1uHGDRxptJMyMZ6IzAWETcnGYum9rqihoYGSkpIeDSDCw8PJyspi0qS+R5AQYqwINEA9CkQB6wG3UsoDhAAWYCHw6y7byqBeQniFjk8mKXUqVXv2EhIcRPi0OcQtOhNrSO9TYbS0tFBaWsqhQ/7934ODg8nIyGDatGkybp44bQQaoC4d0lQIMQZ0dLipOVzP5EnHp7uwWCxMXXwubuvHpC05m9CY8b3u297eTllZWY+JA6UBhDidnWg/qHAgA7MV3x7ppCuEWVdUUVrO/g3/h8vpIPI/riUuJty33haXhDr/K73u63K52LNnT4+WeQApKSlkZWXJ/EzitBVoP6gg4L+BWzletOdUSj0H3Ky17uhndyHGLHfrMVr1RuqLtmKxu7ABRR99wjkrV/S/n9vNvn37KC8v95vRFmDChAlkZ2czbtzAczwJMZYFWsT3C+BKYDXwMWaAWorZD+oe758Qpw2Ps5228s20V+7EMDwkxUVQYT+GNSiIxNhgDMPotRGDYRgcOHAArTXt7f6TScfExJCdnU1CQoI0gBCCwAPUauAarfWbXZatU0o1A88gAUqcJg4faeJg8SamduzD6Die8wkPC2aiymFy/plExvWsZzIMg0OHDqG17tFkPCIigqysLFJSUiQwCdFFoAEqCijvZXkFIBPLiDHP43Gz/p31OCs2E+Jpp2ViDJHhIQCExCUTmb2EhHFJPfYzDIPa2lpKS0tpamryWxcaGkpmZiZTp06VoYmE6EWgAaoQuAmzDqqrm4HNg5oiIUYhw9lObG0RjR6zWK62vo0ZGVOJzFqELWlarzmfuro6SktLqa+v91seEhLC9OnTmTZtGsF9jFIuhAg8QP0n8IFSajmwwbtsMZAGXDD4yRJiZHWvQwoKi2Ty3IUcW/8BkdHRTM1fRlxWLhZrzz5JDQ0NlJaW9pjNNigoiPT0dNLT06XJuBABCLSZ+Sal1DzgOmAm5tBHrwNPaK1lRl0xZrQ7Oti2vZy6qv1ccMkXsXaZ/mJ8dj6zgkKIU3N7ndW2sbERrTW1tbV+y61WK6mpqWRkZBAa2nsHXSFETwGXL2ity4DblFLxgFtr3Th0yRJi+Llam3n/5dcIP7aPcCyUlSmyslJ9660hocTnLOyxX1NTE1prampq/JZbLBamTJlCRkYGERERPfYTQvQv4ACllLobuAFI8r4/ADyqtX50iNImxLDwOO207SmivXInqdZmagELBkd3bIAuAaq7pqYmysrKegxLZLFYmDRpEpmZmdLJVojPIdCOur/ALN67H//5oH6qlArRWj80dEkUYvB1uD3U1TcT3VhO296tGB3mYP3xseG0tLkYn5pO6qLlve7b3NxMWVkZ1dXVPdZNnDgRpZRMsy7EIAg0B3UN8O1u/aA+UUqVA08AEqDEKcHt9rBzTy17NhcSd6yM7MkRfvVMoeOTmLtkFbb4ST327QxMhw4d8hsvD8zAlJmZSUxMzJBfgxCni0ADVDBwoJfl5YA8KopThru9hYaP1hLXbnaWrTtmISEugqCoOCIzF/TaZLyvojwwJwzMzMwkNjZ2WNIvxOkk0AD1EPCkUuqbWusDAEqp8cCvMIc7EuKUEBIRzcSkOKorWwgOshIcEU30nC8QOimzx9xMx44dY/fu3b0GpqSkJDIzM2W8PCGGUKAB6htANlDhbRzRAUwFbMBipdT3OjfUWicOeiqFOEEej0HZgQZaWuzk5xwvrrNYLExduBwc/2Ti3MVETZuNJcj/36CxsZGysjIOHz7c47jJyclkZGRIYBJiGJzIhIWDQik1DtgO3KO1fm6wjitEp7Z2F2+8u43QQ9sJ9bRxLPVaYqOO9z8KTZzCjEuv7tGXqb6+nt27d/foxwRSlCfESAi0o+7zg3jOp4CeNdBCDAKPow13+WZSaz7D7jBb5u0q2MSSc5b6trFYLFi8wckwDI4ePcru3bt7TK9usVh8gUkaPwgx/IZ1IDCl1LeBGKB4OM8rxja324PF6MBesQ373u0YbhdJceHsP9xBfGwYKQmeHvsYhsHhw4fZvXs3jY3+fc4tFgspKSlkZGRIc3EhRtCwBSil1DTgZ8AZwFvDdV4xdtU3tbOh+CARjRXMDq3G47T71kWGhzAzdyaxOUsI6TLKuGEYHDx4kD179vQYXdxisTB58mRmzJhBVFTUsF2HEKJ3wxKgvDPyvgDcprWuUUoNx2nFGNbU6uDN1z4gobkE3G20psQSHmZ+nYOjxhOZtZiQhCm+JuNut5sDBw6wZ88e2tra/I5ltVqZOnUq06dPlyGJhBhFhisHdTegtdYvD9P5xBgXaXUys2MnTW5z+ovWdgfum/8AABjOSURBVBeR42KJzFxI6KQMX5Nxl8tFZWUlFRUVOBwOv2MEBweTmppKeno6YWFhw34NQoj+9RmglFJ/CvQgWuvvDrDJ5UCKUuoy7/tozH5VC7XWNwZ6HnF66nB7aLW7/FriBYVHM2l2Psa2jSQmxBGfs4Dw1Fm+JuPt7e3s3buXffv20dHR4Xc8m83GtGnTSEtLk2kvhBjF+stBda0dtgFfAkoxJy90AvOAXOBvA51Ea53V9b1SaivmQLPPnWB6xWnE4zEoraxn8/a9jKOFlavO8hvlIWH2QqIiQwlPn4vVZuaAWlpa2LNnD1VVVXg8/o0jwsLC/n97dx4d11UnePxbqiqVSpu1S6WlJFm2f04cnI2EBLrZ0plmCQTSTZgG0k2AsDTNTIcJzJCwdgcSQjeHNDM0SwcGOkPn0KSTGZJAw0kIWXHbcezYsfOLV22WZMmyrV2lWuaP+xSXZVuWZUtVJf0+5+hEfve9V/eXUr1f3fvuu5e2tjai0agtFGhMDjjlp1RV3zP9u4h8H5dQPp2+j4jcDqydeawx58LoyBg7n3iUhuE9AOzeu5rVbceeUMjLD1O09grAPcO0Z88e+vr6Tpgnr7i4mLa2NhobG21pdWNyyFy/Rv4ZrsU004+BLWf6oqp60ZkeY5aPVDLBRPuLxHY/h+QdZoAkAX8evs4tkJagpoeK79mz54Rl1QHKy8tZtWoVtbW1J12S3RiT3eaaoHqBNwO7Zmy/Bmg/pzUyy9LQaIzh0Ukq432M6QYS48MAVJUVkJfno6apgdLzLwTciLyuri727t3LyMjICeeqqalh1apVVFRUWGIyJofNNUH9DXCPiLwZ2Az4cOtBvQ14z2wHGjOb2FSCTTv72L19B43jyrravOOWv8gvXsHKSy4jVL+aWCyGqrJ//35isdhx58nLy6OhoYG2tjZ7uNaYJWKuUx39s4h0AR8DbvA2vwC8XlU3LFTlzNKXnBxneNPDRMfc/HeHjhZSXR4mLxgivOoSws0XMDI6hr7wwkkHPgQCAVpaWmhtbbWh4sYsMXMeyqSqvwV+u4B1MctQqKiQlgo/PWMQDgUoKiqgsO1iClov5NCRIbZt3HTSyVsLCwtpbW21EXnGLGGzPQd111xPoqqfPTfVMUtVKpWio2+YkdEY69qqXtnu8+URfc0bCcYfplrWE1p5MT0Dh9n3zO8ZHh4+4TxlZWW0tbURiUTs/pIxS9xsXz0vm+M5UqffxSxn45Nxfv30LibbX6AkcZTmyAcpLjz2gGxBXSs1b7qerv7DtD/17An3l6ZnFW9tbbWBD8YsI7M9B/Wm6d9F5Abgl6o6sCi1MktGKhEn1b2d6r2PEZtwk7m+8B+bee0b3fNLhw8fZt++fRw4cOCE55cCgQDRaJTW1labI8+YZWiunff/AFwBWIIyp+USTYrJrpcZ3bWR5MQokbIAHb1QVhKiLjRIV1cX+/btO2GpC3D3l1paWohGowSDwcUPwBiTFeaaoDYA7wbuXMC6mBw3NjHFczv6SA22c0Gwg8To0VfKisP5NK9sYLgsypahKWLPP3/C8ZWVlbS2tlJXV2fdeMaYOSeoJPA1Efk8sA8YTy9U1cvPdcVMbhkZi/Hgg09QObSTcHyI0YYVFIQCpFIphlNB+kO1DExC6tDoccdNP7/U2tpqy6kbY45zJi0oe97JnFLYN8l5k1sYjrslLQaHJ/El8jjoL2fcXwgTx+9fUFDwSjdeKBQ6yRmNMcvdXB/U/cr07yJSCuSp6ok3D8yyMD4ZZ3wyTkXpsQdj/eESIudfyMjzm0iGQnQEyiFYDnn+446trKykpaWFuro6m7jVGDOrOT/hKCKfAG4F6r1/HwTuVlW7L7VMTMTiPK8HeXnHbqpDMd567VX4fD5SqRS9vb3sj+VzqLCKYGkV+I/9afn9fhobG2lpaaG0tDSDERhjcsmcEpSI3IJbFferwFO4ufheB/wPERlX1bsXroomW0we7ufQhkdonugj6fOza/caSE3Q3t7OxITrwwuW172yf1FRES0tLTQ1NdloPGPMGZtrC+qTwMdV9V/Stj0tIu3A7YAlqCVs6nAvY7s3E+vvoCk4QtdIitFUgoNP/oqi6shx+/p8Pmpra2lubqa6utpG4xlj5m2uCaoat5LuTM8BjeeuOiYbjE/G2fLyQSo4St34HmIDXcQSSXpHpugaTzHi81OwooyCFZWvHBMKhYhGo0SjUXuo1hhzTsw1QW3HLatxx4zt78UtA2+WiK6Dwzz+6AbKh3aRSB6G6hJ6RqYYGIuTwg2GKKmpxhd0I+8qKytpbm4mEonYoAdjzDk11wT1ReBhEbkSeNbbdiXwFuC6haiYyYzyeD8Ngxs4PJlkVzxFe2yE/KAff+EKAqVV+IL5BINBmpqaaG5upri4ONNVNsYsUXMdZv5rEbkK+BRuPahxYCdwmapuXcD6mQU0NjFFwJ9HftBPKpXi4MGDdHQP0JXIJ5aYoCCUT7is3CWmQD4VFRVEo1Hq6+vx+/2nfwFjjDkLsy238RbgSVUdBVDVJ4AnFqtiZuFMTMZ5fvt+erY9R6Q1SkVdBZ2dnYyPuwlCiqvrSE1NEiitIL+gkMbGRpqbm22lWmPMopqtBfUwEBeRTcBj3s8zqjq5KDUzCyJ+tJ/9//EM/S9uY2wqxZb+diJr1uJPW2Y9UFxGZWUl0WiUSCRirSVjTEbMlqCqcM86XQn8AXAzkCciv+dYwtqgqokFr6U5K6lkgsnefRx8aTNd3d30jcQZjKVIJFPk+yeZGh/DX1REKBSisbGRaDRq95aMMRk323pQh4GHvB9ExA9cgktYVwIfAqpE5ElVffsi1NWcobHhEbY9/RR5R/cxMDzKSCz5Slm4IEAqGKa4sobaaAvNzc3U1tbaSDxjTNaY81RHqpoQkS6gBzgIdAA1wMoFqpuZp2QyycZHHmCP7mR0Kkkw6KcofGwmB39hKWXV9TSvXktTUxPhcDiDtTXGmJObNUGJSDHwRuCPgKuBtUAn8FvgHuB9qnpggeto5mhoaIjOzk66u7s53NfPyJRrMcWmEoTDIQrKqmhctZbmlauorKy0WR6MMVlttlF8TwKXA4eBx3HTGT2mqrsXp2rmdIbHYuzWPezTl6ioq2B4eOiVsnB5FSOD/STyQtQ2NXPhZZfR0Ghz4hljcsdsLajXAV3AD3EDIp5V1amzeTERuQb4GtCK6ya8S1W/dzbnXI7iUzE6dzzP0088y9joECl8TKbWUlx8bPmLcMkKLnnD1bStPd9mEDfG5KTZEtRKXNfeVcAngCIReQp4FJewNqtqaq4vJCIR4OfAu1X1lyJyCW7C2Y2qunneESxxsakEHX3DrCjKJzncx/4XN9PT2c5UPE5iyn1f8JFi7MghVqyIUldXR1NTE1VVVdaFZ4zJabON4tsP/JP3g4hcCLwZl7C+AEyJyOPAo6r6ndO9kKr2iEi1qg6LSB5QCcSB4bMNYqnauqufpzbtwn9kLyXJAfJ9sePK8wN+Ekkor6hi9YUXI+svti48Y8yScSaj+LYCW0XkbuBS4EbctEfvAk6boLxzDItIIXDUe+2vq+quM671Ejc+Ps6BAwdo37yRQMdufKkk4z4f+cUhtxIXUFgQIrpyNS3rL6OkojqzFTbGmAVw2gQlIo3Aa4ArvP9egmv5PINbC+rxM3zNCaAIWA88IiK7VPWeMzzHkjEVT7K78wjd/UdZU+enq6uLwcFBUqkUSZL4SeHz+wgG/AT8PuojEVrWXUT1yvPIy7MZHowxS9dso/juxyWkCK4b7mngF8AtwHPznUFCVZNADNgkIt8HrsUNWV92YrEpfvhvvyc22I5vrJ/BlmbCofTnlYopW1FETWkB0dXn0bDu1QQLbT48Y8zyMFsLKgR8C9dC2uwllnkTkTcA31TVS2e8xpGzOW+uSSaT9Pf307V/L917XybU20vepJvecOjQIOH6Wnw+H1VVVTQ0NFDz+teRX1phAx6MMcvObIMkrjnHr7UFaBCRT+OeqXoN8GHg3ef4dbLOoaPjbNq2h8TYIKmhbiaOHiI5OQZAQSBFYspHKOinujjFmnXrqK+vp6Cg4DRnNcaYpW3OgyTOlqoeFZG3Af8AfAk3I8VHVPV3i1WHxZRKpRgcHGTLi7t4YfM2AlNDhBlnRWH+KwMdAEpDAVbXlBBdtZbyVa8iuKImc5U2xpgssmgJCsB73ukPFvM1F1MymeTIkSMcOHCAnp4eJiYmiI8coXiyh1QKkkA8maQo309NUT6N0ShVbRcQqmvF51/Ut8IYY7KeXRXPUiqVoqOrjy0v7qZj/z4aaksJBo7NCB4oLCU/GMBPioYVIVoaa6lZdQEFDWvwFxRlsObGGJPdLEHNw3T3XU9PDwc6O9i7p53k2FFCyRhDoRYqq8oBCIVCRCIRLqzOp7yslHCj4C+xSVqNMWYuLEHNUSqVorfvIB2d3Rwd6GXsyACJsSGSk2OUJBKMJt20Q1PDR2m+9CLq6+vTZgx/VWYrb4wxOcgS1CySySQDAwPs3d/JC9t3MX70EIVMUBY6/hGwwnw/JX5oLAvTuu58ytevz1CNjTFm6bAENUM8Hqe/v5+enh76+vqIx+PERo7Awf2ESZECksEQoUAe1UUBqgvzqW5oItywhvy6VvKCoUyHYIwxS4IlKCAWi7Gvoxvd1U53VzeRmhL8ecfuEwULigj4fZBMURbys7qygMbmKAX1qwhF2sgLFWaw9sYYszQt2wQ1NjZGX18fPT09DA7009nRQ3J8iEBikuGCVZSVFQNQWFhIJBKhLTDCisIQFa1rCdWvshF4xhizwJZNgkomk3T3HmLnrv1MjBzBFx8lMTZEYmyY5OQoxVNxxhNxABJjI6y5/BIikQglJSX4fD5SssaeVTLGmEW0LK64o6OjPPKbJ9jb3kN+YpwS/yQrgvHj9qkoCuLL91FfGiIiLdSKHFduyckYYxbXsrjqdnZ2wnAfZePd+EiR8vnwBUOUhwNUFQaoDAcorq4nVLeS/LqV+MPFma6yMcYse8siQdXV1bG3tJSRXh9lIT+1JSFaqsIUVtUTirS5pGT3lIwxJqssiwRVVlbGW9/5bg79dhR/qIhQpI1QpM1aSsYYk8WWRYIC8Pl8VLz+veQF8jNdFWOMMXOQd/pdlg5LTsYYkzuWVYIyxhiTOyxBGWOMyUqWoIwxxmQlS1DGGGOykiUoY4wxWSlXhpn7AXp7ezNdD2OMMfOQdv32z/WYXElQEYD3v//9ma6HMcaYsxMB9sxlx1xJUBuBPwR6gMRp9jXGGJN9/LjktHGuB/hSqdTCVccYY4yZJxskYYwxJitZgjLGGJOVLEEZY4zJSpagjDHGZCVLUMYYY7KSJShjjDFZyRKUMcaYrGQJyhhjTFbKlZkkTklELgceUtUa79/VwN3AHwOTwA+BL6lqwiv/CXA9EE87zXpV3SsiUeAe4ArgIPApVX0ki2N5rVd+HnAAuFVVf+6VZSyWM4lDRL4LfGDGKYqA21T1azn4ntwIfB6oAl4C/puqPuWV5VosnwJuBiqBZ4BPqureTMYiIlcDdwKrvdf9hqp+T0TKgH8CrgZGgM+r6o+8Y3zA3wIfBfKBHwGfUdW4V3498DXcLAe/Az6oqgezMZa0Y/OA+4Hfqeq30ra/CfeetgFbgRtUdU7TCmWjnG1BiYhPRD4C/Br3Rzftx0AN7qJ9AXA58Ddp5ZcA71LV4rSfvV7ZfcALuA/kTcB9IrJygUOZVywiEgEeBv4nUAJ8ErjXu3BkJJb5xKGqH09/L4DPADu8uDISx3xjEZH1wDeBa4Ey4F7gQe9ikmuxXA98FbjRq+//A34jIgWZikVEmnAX5dtx/3//DLhDRP4Y+EfcNGgR4O3AnSLyBu/QjwLX4T77q4HLgFu9c56PS7Qf9GLZ5cW2oM4iFkSkBfgF8K4Z56wCHgS+4p3zAeDf0/7+ck7OVhz3JnwC9wYDICKFwFuAm1X1oKoOAl8AbvI+pGFgLbBl5slEZA3wauCLqhpT1cdwH8oPL3woZx4L8OfAE6r6Y1VNqepvcBeYwxmMZT5xkLZvG3AX8D5VHcrB92Q1xz5TPtxFZtw7Ntdi+RPgB6r6O1WNq+o/AjHgqgzG0gL8VFUfUNWkqm4EHgeuAv4U+IKqjqnqFuAHuMQE8BfAt1S1S1X7gS8DH/PKPgD8QlWfUtUJ4HPA60RkdTbGIiLFwHO41tEzM855HfCiqt6vqlOq+g0g5J0zJ+Vygvquql4KbErbNh3PaNq2BFCN+0ZxEa5r7wci0i8im0XkGm+/84EOVU0/9iXgVQtS++PNJ5ZLgf0icp+IDIjI80Cdqg6TuVjmE0e6v8ddFLd6/8619+Tfge3ANtzF/OvAe1U1Se7FkjejbLp8DRmKRVWfVNWPT/9bRCo4Nol0Ctf6OVl9zse1ytPL6r3jjytT1TGgk+yNZQI4T1VvBaZmnHZmnADK4vyNLYicTVCqeuAk20Zw3Rh3iUiFiFQCX/SKw7iusCdx3yjrcV0YPxORC4FiYGzGKceAwoWJ4Lh6zyeWClzXyr24roA7gAe8VkhGYplnHACIyAW4Pvevpx2ea+9JAe6CcAXuPtp/x3Xx1ZF7sdwPfFREXi0iQRG5Cdf7ECaDsUwTkRW4VtsGXItiQlXTZ75Or8/M+k7/XniSspnHLrgzicVrzZ7q/ljGYznXcjZBzeIG3LfXnbgbng9624+o6q9V9WpV3eQ1ge8HHgPeifu2GJ5xrkLcTcpMOWUsuJvav1LVh7xYfgZsBt5K9sUyWxzTPgQ8qKp9aduyLQ6YPZYvA72qukFVJ1X1O8B+4D3kWCyqeh/uS899uBbFBcBvgMNkOBavi/H3QB+uO2wYKJjRZZxen5n1nb5gj5ykbOaxC2oescwmG//GzspSTFAR4GOqWquqFwDdwE5VHRORd4jIX8zYPx/XbN4BRL37VNPWcmKTeTGdMhZcs798xv7TozKzLZbZ4ph2LSfenM62OGD2WJpwff7p4riumJyKxRuE8wtVXaWqdcCncUnqOTIYi4i8HtfSeBD4U+++0S7cPb/WU9RnByAzynpU9cjMMu/eXJTsjWU2M+M8k2OzUs4PMz+JbwLbReQW3B/a1zk2IswP3C0iO3EftPcCrwU+oqodIrIV+KqIfM7bfi1w5WIHkGa2WH4C/JWIfAD4Ke5b+nrgelXtzLJYZotjerjzSuDp9INUVbMsDpg9lodwXWY/wy3K9n5gHfBwFr4nMHssVwFfEpE/BIZw3X8HgI2qmspELF739UO4RxC+Pb1dVUdE5AHcKLgP44ZY34QbmQfwz8AtIvIorpXxZW8buM/OUyLyRuBZXKvxeVV9OUtjmc0DuL+/673f/yuQxA2+yElLMUHdhBv1Mojrdvm219WCqj4oIrcB/wLU4Voh16hqh3fsnwDfxz2TMAB8WFW3L3L9080Wy1YReRvuovIdoAO4TlU7vWOzKZZTxuFpASa9kWQzZVMcMPt78gPvfsJPcc9B7QDelqXvCcz+vvwf3M31rbhehkeBd6bdG8lELJ/E3Ue+Q0TuSNv+v3Cj8r4DtON6RL6qqr/0yr8L1OJGvRUC/4p3v01Vt4nIh7x9GnAtmvcscBww/1hOSVUPisg7cM9B3QO8CLxDVWPnuvKLxVbUNcYYk5WW4j0oY4wxS4AlKGOMMVnJEpQxxpisZAnKGGNMVrIEZYwxJitZgjLGGJOVLEEZcw55k/f2e5N/ziy7TURGvOUSjDGnYQnKmHPrr3EPwKc/fImItAK34WYO2J+BehmTcyxBGXMOqWovbk2hj4jIa9KKvo1bh+zbJz3QGHMCm0nCmHPMm4n6adwUQZfhZsv/GXCxqu7w9rkRt6prA2428dtU9VdeWQC3RPn7cMvCHMJNz3WLqiZE5F7cHGvn4eZqu05VH1+0AI1ZJNaCMuYc8+ar+yhu8t4bgb8Dbk9LTm/HTdR6m7fPPbi1vC73TvE53BLgN+BW6f0c8F+Aa9Je5gO4Odeuwi3XYMySsxQnizUm41R1u4h8E/geroV0Z1rxrcCd3hpeALtF5NW4JS3+M25F3g+q6hNe+Y9E5LO4FVP/r7dth6reu9BxGJNJlqCMWThfwa2q+7eqmr489/nApSLyhbRtQbx1e7xZ968SkW/gllhfj5vx3Z+2/56FrLgx2cC6+IxZIKo67v06PqMoAHwGuCjtZx1uTSVE5HbckhB53n//EzBzKYuZ5zRmybEWlDGL7yWgWVV3T2/wWlMTwDeAvwT+WlV/4pWFcQsK+k5yLmOWLEtQxiy+u4CfiIgCjwFXA1/C3X8Ct4DgNSLyDLAC11VYyonLyRuzpFkXnzGLTFX/FbgZ+CzuvtPNwMdU9efeLn+OG723Dfg3YDfwv4FLF72yxmSQPQdljDEmK1kLyhhjTFayBGWMMSYrWYIyxhiTlSxBGWOMyUqWoIwxxmQlS1DGGGOykiUoY4wxWckSlDHGmKz0/wE2ti8NqF9QlAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "system.alpha = system.birth_rate - system.death_rate\n", + "\n", + "results = run_simulation(system, update_func2)\n", + "plot_results(census, un, results, 'Proportional model, combined birth and death')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercises\n", + "\n", + "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, is changing over time. So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n", + "\n", + "Write an update function that takes `pop`, `t`, and `system` as parameters. The system object, `system`, should contain two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`. It should use `t` to determine which growth rate to use. Note: Don't forget the `return` statement.\n", + "\n", + "Test your function by calling it directly, then pass it to `run_simulation`. Plot the results. Adjust the parameters `alpha1` and `alpha2` to fit the data as well as you can.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "def updatefunc3(pop, t, system):\n", + " \n", + " system.alpha1 = 0.018\n", + " system.alpha2 = 0.015\n", + " \n", + " if t < 1980:\n", + " growth = system.alpha1 * results[t]\n", + " return pop + growth\n", + " \n", + " if t >= 1980:\n", + " growth = system.alpha2 * results[t]\n", + " return pop + growth\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UqFRQUMBDDz3E/fffT2FhIZ988glz587l61//OnV1dX3mee2117j77ru55ppr+OSTT1i7di2XXXYZN910Exs3bhzmVyCEGEsMw2Dnzp1+F8GRkZEsXbp0RAcuSYAahbZu3crkyZOZN28eVquVkJAQbrnlFi699NI+A1R7ezs//elP+fGPf8zy5ctxOBw4HA6uuuoqbr75Zvbu3QuYg0meffZZzj//fBYuXMidd97pO9769eu59NJLeeSRR1i4cCFnn302Tz75pO8cf/7zn1m+fDnz58/ni1/8Ih9//LEvX35+vl95zjvvPD744IMB8wkhRge3201hYaHvdwIgNjaWM888k9DQ0BEs2TjugyrYeZCCXQcBWJCTzIJc/2U61m6tZEvxYQCWzE4hTyX67f+o8AA799QCsGxeOrlT4/z2/31dOSUH6vs89qk699xzeeaZZ1i5ciXLly9n3rx5ZGdn8+CDD/aZfvPmzTidTs4555xj9t1yyy2+x7/73e946623ePHFF0lMTOThhx/mW9/6Fi+//DIApaWlLF++nLVr17J+/XpuuOEGLrnkEmJiYrjvvvv4y1/+QmZmJn/605946KGH+Pvf/z7g66irq+s3nwyKEGLkOZ1OCgoKqK+v921LSUnhjDPOwGazjWDJTFKDGoUyMzN58803yc7OZtWqVVx55ZUsWbKEX/3qVxiGcUz6uro6oqOjjzvUfvXq1dx+++1kZGQQEhLCd7/7XTZs2MC+ffsAcyTdrbfeSlBQEEuXLiUhIYHy8nIcDgd2u53XXnuN7du3c+WVVwYUZE42nxBi6LW2trJ27Vq/4DR16lTmzp07KoITSIAadg6Hwzd0sye3201wcLDv+aRJk/j+97/PX//6V/7973/zzW9+k+eff55Vq1YdkzchIYGGhoY+7yNqbm7G6XQCUFVVxb333kt+fj75+fmcffbZ2O123ywOERERfmUICgrC4/EQERHByy+/THl5OStXrmTp0qU899xzfQbLnk42nxBiaNXV1bF27Vrf9HAWi4WZM2eSm5s7qi4gx20T34LcgZvels5JZemc1H73L5uXzrJ56f3uv3DRJC5cNOmEy5WcnExVVRWzZs3ybevq6qKqqsq3WvBXv/pVLrjgAq699lrAbA/+yle+wvbt29FaH3PM7vsT1qxZw/Lly/32Pfroo5SXl/Pyyy+TmJjI/fffz1lnneXbX1xczOTJk9m8eXO/ZW5sbPT1X7lcLj799FPuuOMO8vPzsdlsfoHRMAwaGxuPm693v5UQYnhUVVWxefNm30g9m81GXl4eyUmJGIYxqgKU1KCG2aWXXspTTz1FWVkZYE7T9Mgjj5CQkMDs2bMBuPjii3n22Wf54IMPcDqdOJ1O1q9fz5o1azj//POPOabD4eCuu+7i/vvv58MPP6Srq4u2tjZefPFF3nzzTW677TYArrjiCp5++mmqq6txu908//zzXH311b47xftTV1fH9ddfT0FBAUFBQSQlJWGxWIiOjiYjIwOn08m7776L2+3m5Zdf9l2VDZRPCDG8DMOgpKSEwsJCX3AKDg5m8eLFJEZH0PDvN2nfs2WES+lv3NagRqvbbrsNm83GzTffTG1tLcHBwSxatIiXXnrJ14f0ta99jZCQEJ555hnuvvtuDMNg2rRp3H///SxdurTP4375y18mMjKS5557jnvuuQfDMMjJyeGFF15g/vz5ANx00010dXVx9dVX09DQQFZWFi+88MJxh5FOmTKFBx98kPvuu4+amhpiYmL4wQ9+QGZmJgA/+MEPePTRR/nhD3/I5Zdfzty5cwPKJ4QYHh6Ph+3bt7N//37ftoiICBYuXIi95Qj1Gz/EcHXS1XgEe3QCjvi0ESztUZax0B+glJoM7P3www9JSzv2jSsqKmLGjBnDXi4xuOT/KMTgc7lcFBYWcvjwYd+2uLg48vPzce3bSlvpJsCMAwYWqqNnkzbzDOKiB3eIeUVFRXcL0BSt9b5A8kgNSgghxqnW1lYKCgp8s5EDpKWlkTsljbbCd3HVH/Rtb/c4WN+lOFQVSrq7isvOmjri/VHSByWEEONQ90i9nsEpc9pUpge10PjZ637BKSgulbCFV1DTFQHAgUPN7D/UPOxl7k1qUEIIMc5UVFSwdetW32AIq9VKTmosUVUb6XC2H01osRA2bS5hmfOwWKzMnNpG8YF6FsxIJi0xcoRKf5QEKCGEGCcMw0BrTUlJiW9bcHAw+fn5WIvX4nK2YxgGdU0dhCSkk7bofOwRMb60C2eat+eEBo+O0CBNfEIIMQ50dXVRWFjoF5y6J3yNjY0lIncpHU4Pu6s7KejK5lNXLpawCX7HCHHYR01wggBrUEopGzAPyAcSATdwENigtR5dA+eFEOI0097ezoYNG3w3yXvamkmaNIX8/PnY7ebPvD0ylqj8i9AFjXS6LdDSye59dcfMMzqaDBiglFIxwDeAW4A4YA9QC9iAeGCSUqoaeBZ4WmvdMLTFFUII0VN9fT0bNmygs7MTw9WJq/4gKcFdzEqY4QtO3aLTpjKvtYbCokPMz0kie1JMP0cdHfoNUEqplcCPgA+B64EPtNadvdJEAWcBVwM7lFL3aq1f7ud4VwPP9docCnyotb7g5F+CEEKcnnyDIdxddDUewd1SS1ZsCMkRIRzY+ClGVzwzlP+UbXOmx5M9KYawkIEnlx4NBqpBzQfma60P95dAa90E/BX4q1JqInAv0GeA0lr/AfhD93OlVB7wPvDdkyi3EEKctgzDoKioiLKyMtxtTXQ1HMJuuJmTFEaYzUJpRSNVQek07TrClMkTCenRr2SzWQmzjY3hB/2WUmt9x0DBqY/01Vrr2wNJq5QKwgxWD2ittwZ6jvEgkCXfn3zySWbPnk1paalfmr4WB+z2+uuvM2PGDPLy8sjLy2POnDlcfPHFvPDCC36zh5933nnMnj3bl67778ILL/SlKS8v57bbbmP+/Pnk5eWxYsUKfv/73/d53ksuuYSzzz67z5nUhRCDz+VyUVBQQOnuIpw1+3HVVhJq9TB3YjgTQuyEJqSxP/kcDkbm0NZlYVvpkZEu8kkLeLiGUioRmA0EAX63F2ut3z3B894GtAPPnGC+00ZnZyd33XUXq1evxuFwBJQnKyuLv/zlL4B5hbV582buuusumpqa+Na3vuVL97Of/eyYWc+7eTwebrzxRlasWMFjjz1GSEgIW7Zs4bbbbsPhcPClL33Jl7Z7EtiEhATef/99Lr300lN4xUKI42lpaaFg/Toaq8rpajYXVI0LtTMjIRRHaBjh2WcSnDKd/MpG/rnxAPkzkpgzPX6ES33yAh3Fdz1mMOmr0dLAHDQREKWUA7NZ77+01qN/IsARsnjxYqqqqnjiiSe4++67Tzi/xWJh7ty5PPTQQ9x0001cf/31x50UFswO1/LyclasWOFb7jkvL4+7777bt65Ut1WrVvG5z32OuLg4XnnlFQlQQgyhmpoaNm3aRHvdQTM4GRDrsJDkgMipswnLzMcaZK7nNi01mrSECL+mvbEo0NJ/F/g1cI/W+lTnv7gI8GD2XQ2Z1uINtJUWBpQ2JH0GkbP8l0tv3r6GjgPHNsX1FjZ9HuFZ80+qjAMeNyyMRx55hGuuuYZzzjmHhQsXntRxFi9ejNVqZfPmzX0uCd9bXFwcCxYs4LrrruOyyy7zNfNdccUVfunq6ur48MMP+dvf/kZkZCSPPPII27dv91vnSghx6gzDoKysjN27d2MYBvaIWNzN9US42unwTGCdbRap6UeDE5gXqGM9OEHgN+qmA78YhOAEcDmwWmvtGYRjjWtnnHEGN998M9/73vdoamo6qWN0r7/Ucz6uu+66y7doYPffU0895dv/wgsvcOONN7Jt2zbuuOMOFi1axK233srBg0fn7nrjjTdYvHgxEydOJCIigssuu6zffiohxMlxdbRTuH4dRUVFvr7k0LAwzv7cRRjpC9kbs5h2WySbi2tGuKRDI9AQ+z5wPlByvIQBWATcNwjHGZMCXfK926233sratWt58MEH/fp/AuV2u2lqamLixIm+bY899li/fVDdZbzmmmu45pprcDqdFBYW8sQTT3DnnXeyatUqDMNg9erV1NTUsGTJEsDsM+vs7OTuu+8mLm703vgnxFhgGAYNe3eybs0/aTNsOBIyALOFY968eQQHBzN3wiQqPi5jXnYSczLHbj/TQAINUFuBnymlLgOKAb/OCK31iXSSTAaqTiD9SQnPmn9KTW+Rs845ptlvMASy5HtPNpuNRx99lMsvv5ywsLATPl9BQQGGYZCVlRVQ+lWrVvHqq6/yxhtvAGawWrx4MUFBQdxwww0ArFu3joaGBt577z2s1qOV8JtvvplXX33Vt4KvEOLEdTXXs7/gH2wtKcfp9tDR2UV7p4MlSxeRm5vr+84lxYZx7YocguwBDwEYcwJt4jsHWI95Y+0cvPdIef/6HvfcD611uNZ63YnkGU8CWfK9t4yMDL7//e+zevXqgM/j8XgoKCjgvvvu4+abbyYiIiKgfOeccw779+/n4Ycfpra2FsMwOHDgAC+++KJvuflVq1Zx0UUXkZSUREJCgu/vC1/4Aq+++qoMORfiJHi6nDTv+pSt77xCod6Hy+2hqcVJhwus9mjikqf4XRAC4zo4QYA1KK31sqEuyOkikCXf+3LVVVexZs0a1q3rP7YXFxeTl5cHgN1uJzU1lRtuuIGvfOUrfum+/e1vY7Md+8F+7733SE5O5o9//CNPPvkkK1asoL29nZiYGC666CK+8Y1vUFtbywcffMBLL710TP4VK1bw8MMP8/e//50VK1YE+I4IcXozDIPOqhIad33Krsp6jrSZXQAWiwVHVDxNETlYQqIp3l9PSkJgF5rjRcBLviulkoDbgVzMmlcR8Gut9Z6hK57v3JORJd/HPfk/itNNV1MtLbvW0niogu2H2mjvMn+PrcFhJE5RqDkL+WhzNfNnJDMlJWrEV7g9FUO25LtSagHwD+AA8BnmjborgDuUUudqrTeeVImFEOI05W5rpv7T16hq6GBrVQsuj0F0VBj2CUlMz51DTk4OVquVL50fOaYD06kIdJDE48D/Abf0vLlWKfUU8CggTYBCCHECrKER7HNHs2V/DR4DOu2RBEelce6SRX4tRadrcILAA1Q+cEMfMz88CQR2N6wQQpzGPK5O3820nZ2dFBYWUusOxhERRY0rDCMonKTJs/vsxjhdBRqgqjGHh+te26cCg3HzrhBCjEuejlZa9Xqchw8QseQqWts7KCwspKOjA6x2JqRPxd1q43PLFpOePLrXZxpugQaoV4DnlVLfBLqHkS0Gfu7dJ4QQogfD46Z933baSgtxdXZyqLaVot/9H+Epib7Zti0WCznZiunTp5/WTXn9CTRA/QRIAVZjjuCzAC7MJr57h6ZoQggxNjlr9tNS9Cnu1kYwoKyykf0tXdQZ9bSFRJAYG4bD4WDu3LkkJCSMdHFHrUDvg3ICNyql7gIU5lIZpVrr9qEsnBBCjCVdLQ20Fn2G8/B+37Zmp5uDhFJtC8FlDcHa5WHChAnk5+f7VgwQfRtoyfdLgH9orV3ex72lK6WAk1oPSgghxg1Pl5O20kI69m6nq6sLm82KYRgcbDfY1xVJcMoEwg+3Eh0RzKzcLN8QcjGwgWpQ7wDJQI33cX9OaD0oIYQYTwzDoHHdW3TW11BT305DSydTUqKpsMVxJCgES7D58zgpZQJz5swhJSVlhEs8dvQboLTW1r4ei9HpySefpKioiGeeGXiR4u67uTds2BDQAoZCiIFZLBZCp8ymVL9Ja7uLBmsUZQ2xxMeF+2Y7jYqKYt68eQHPiSlMY39FKyGEGEYeZztWh3/fUXBKJonTFO8Xt1Pb0Um400OcARaLOdnzzJkz+5z/UgxsoD6ow5jNd8eltU4ctBKNcxUVFVxxxRXceeedPPvss7hcLu644w6sVivPPvssTqeTW265hWuvvZbPPvuMxx9/nL1795Kamsptt93GRRdd5DvOvffey7Zt25g6dSpTp071O8/q1at54YUXqKurY/bs2TzwwAOkp6ePxEsWYlwwuly07dlC+54tBM2+gOiUSb59breb2qh0CC4hPSqCqHAHNpuNWbNmyffz7SYWAAAgAElEQVTuFAxUg/ouAQao0aasrIzi4uI+FwYcbHa7naysLKZNmxZwnubmZnbs2MFHH33EBx98wF133cXll1/Ohx9+yCeffMIdd9zBrFmzuPnmm3n44Ye54IIL2LBhA7feeisJCQnMmzePO++8k5ycHH7961+jteb6668nP99c+eT999/nl7/8Jc899xyZmZm8+OKL3HjjjbzzzkBdiUKIvnTPNt6q1+Nqbaamvp3qt95izlXXkRwXQVNTE4WFhbS0tJAca67ZFhkZybx584iMjBzh0o9tA/VBvTSM5RhUe/bsGZbgBOZig3v27DmhAAXmSrndiwG63W5WrlyJw+Fg2bJluN1ufvvb37Jw4UIuucQcQLl48WI+//nP88Ybb5CYmMiOHTv4zW9+g8PhYNasWVx++eVUVlYCZu1p5cqV5ObmAnDTTTfxyiuvsH79eiZNmtRvmYQQ/lz1B2nZ9RldjeaS6tW1rTS2OPHYHfx7YxlzMiMpKirC4/H48kiT3uAZqIkv4NXxtNYnvhb5EJo6deqw1qB6N68FYsKECQC+D3H3lVb30NOOjg5SU1P98qSlpbF+/XoOHz5McHAwMTExfvu6A1RVVRXPPPMMzz//vG+/y+WiqqpKApQQAXC3NdOq19NZXeq3PSkpjiJbIrVBSYTWVrKtowub1ZwBwm63M2vWLJlLbxAN1MTXOmylGGTTpk074RrNcDvetCb5+fls3Oi/ismBAweIj48nKSmJzs5OamtriYuLA+DQoUO+dImJiaxcudJvocKysjJSUlKora0dxFchxPji6XLSXraZ9r3b6OhwEhxkAwtYrDZCp8wmdFoe6Vv24agoIdjmAe+kRdHR0cydO1dG6Q2ygZr4rhvOggh/S5cu5dlnn+Xdd9/lwgsvpKCggLfffpunnnqK1NRUFixYwMMPP8yDDz5IeXk5r7/+OnPnzgXgiiuu4Fe/+hXz589n6tSpvPPOO9xzzz2888472O0ycFOI/rTu+ozmfbuoqW+jobmT9MRIEqZnE569CGtoJCUlJTQeLCbYdrR7furUqcyYMUNuvB0CAzXxPQI8qLVu9T7uj6G1/u/BL9rpzW6388wzz/D4449z7733kpSUxIMPPsjSpUsB+PnPf869997LmWeeSUpKCsuXL6eurg4wA1RTUxO33norNTU1ZGRk8PTTTzN58mQqKipG8mUJMaqFTctj3/YtNDR30m6PptA+hyvnLKGzs4PNn33m+44BOBwO5syZQ3Jy8giWeHwb6HJ6PhDU43F/xuRIv5GSlpaG1kdXLYmKivJ7Dvg9X7JkSZ/HiY+P57nnnuv3PCtXrmTlypXHPb8Qp6uulgZsIeFY7EG+bbbwaNLzl1K06TA1tmQmJ0azf38FxXoXLpfLly4uLo68vDyZS2+IDdTEt6yvx0IIMZZ5nB20lRbSXr6DzoQckvOWYLcdbZ6boPLJC6snyAYNNeXs3HHAt89isaCULI8xXALukFBKhQNfAXIBJ7ATWOWd6VwIIUY1w+Omo3wnraWFdLa1caiujYaytRx2pJE3e4pf2oRIC5s2baK19ehYsbCwMObOnes3elYMrYAClFJqFvAvzMC0DXOGqeuAHymlLtZa7x6yEgohxCkwDAPnoX207l6Hu60RgJY2F02tTtqDYtlffBCVlUZYSBCGYVBSUkJxcTGGcbT3IjU1lVmzZhEUFNTfacQQCLQG9WvgTeC/tNYuAKVUKPAb4DngnKEpnhBCnDxXYw2tRetw1VX5bY9LTmRXcDKVXTFkpccC0NbWxubNm/0GQnTf25SamipNeiMg0AA1B1jZHZwAtNbtSqkfA5uHpGQnyDAM+QCNYT3vxBfiVLnbW2grLqCjopjmdiehwXbsNiuWoGDCp88jZFIuC+vasVosJMWGUVlZyfbt2/1u7o+NjSUvL4+wsLARfCWnt0AD1CbgLKC41/Z8YNeglugkhISE+G5alSA1thiGgcvl4tChQ4SHh490ccQ44azZR+PeXVTXttHa7iImKoRp+YsImz4PqyMEgJT4CJxOJ4WFhVRXV/vyWiwWsrKyyMzMlN+TETbQfVC39nhaADyllMoH1gFuYDZwC/BwoCdTSk0EfgUsAzqA57XW951Euf2kpaVRUVHB4cOHT/VQYgTY7Xaio6OJj48f6aKIcSIkIwfXjk20tjfSFJxEiWMGaalzifAGJ4Camhq2bt1KR0eHb1t4eDh5eXkyEGKUON5s5j0dBC7y/nU7Avwn8FCA5/sLUAgkAROBNUqpIq31HwPM36egoCCmTJly/IRCiHHHefgA1uBQ7FFHL3AsFiupi5az27afAy2h5E6JJTTY/Lnr6uqiqKiIffv2+R1n0qRJ5OTkyGwro8hA90EN6i++UmohMBVY4u3L2quUOhdoH8zzCCFOD13NdbTu/jfOmgM0WKKIXfIF4qKP3jjriEth0ZJYFngM3/b6+no2b97sN3w8ODiYOXPmkJSUNOyvQQxsoCa+r2utXw70QEopC3Cd1vq3/SSZB2wHHlBKXYvZxPeM1vrxEyivEOI05+lso7VkIx37i+jodFF1pJX2zlr2UsAFl/gPKI6JNJv0PB4PxcXFlJaW+g0fT05OZvbs2QQHBw/raxCBGagum6eU+g7wLPCG1rq6r0RKqUTgq8BNwAcDHC8Wc6DFGsyaVDbwnlKq+lSb+IQQ45/h7qJ933bayjZhdJkDii0WaO90UxeaTk1TMOUHm5iUHOWXr6mpic2bN9PU1OTbZrfbmTlzJmlpaTIQYhQbqInvm95BEfcDTyildmHOHnEEc475BMzh51nA34BrtdYFA5yrE2jSWj/gfb5VKfUb4EpAApQQok+GYdBZXUabXo+7vdlvX2TKZKwJkzh00MMZWQlMjAv3y1daWkpxcbHfbQzx8fHMmTNHho+PAQP2BmqtNwKXKaWmABdjDiufDngwB038EnhXa70/gHPtBsKUUo4e0yNJb6QQol9dTbW07PgYZ/0hGpo7cATZCA8NwhYRQ0T2YoIS0snv7OIMt0FUuMOXr7m5mS1bttDQ0ODbZrPZmDFjBpMnT5Za0xgRUIDQWu8FnjnFc/0DOAw87m06VMD1mEPVhRCiTy01VVTUNNPhdGMPCWXuvGWETsrBYjEneA0LOTr9kGEYlJWVobX2qzVNmDCBvLw8WVBwjBm2GozWukMpdQ7wJFCNOUjiEa31n4erDEKIscUeFUfopBw6q9ZxJGwqNeHTifFMJNdy7OKALS0tbNmyhfr6et82q9WKUopp06ZJrWkMGtYmNq31HuDS4TynEGL06+5nwt1FSHq2376Y3EVEWjPYXdbK/OxEsjJijsnbX63pjDPOIDIyclhegxh80gckhBhRXY2Hadn1Ka66gzS0ezCc0WRNm+jbb3WEMnvmFLKy3ESE+s8m3ldfk9VqJSsrS9ZsGgckQAkhRoSns53W4vV0HNC4XG4O1DTT3tlF02cfk5H+RUIcR3+e7DYrEaFHm/U8Hg+lpaWUlJQcU2uaM2cOUVH+Q83F2HQiCxbmYd5sG4Q5zNxHa32qAyiEEKcJw/DQsW8HrSUbMbrMAb02mwWXG7OfyTGNLcWHWTRzYp/5Gxoa2Lp1q999TdLXND4FumDhvcCPgTqguddug1Mf4SeEOA04a6to3bmWrpY6v+0hSZNIVrls39ZAXlYi87ITj8nrdrspLi6mrKzMbzaImJgY5syZI31N41CgNagbgPu01j8ZysIIIcYnj7ODlp1r6awupbXdRafLTWxUCLawaCJyluBIzCDKMEhOnUhEmOOY/LW1tWzdutVvDj2bzUZ2djZTpkyRWtM4FWiAigNWD2VBhBDjl8Vmo7O2mopDzTS2OjGsdmJmnklMzjwsVpuZxmI5Jji5XC6KioooLy/32x4XF8ecOXNkDbFxLtAA9RpwNfDA0BVFCDFeWWxBROaeiXPvazSEpHAwYgadzYlc5A1OfamurmbHjh1+6zXZ7XZycnLIyMiQWtNpINAA1Q7co5S6CigBnD13aq2/NNgFE0KMTe6OVpzVZYROme23PTh5KhkXfpWNBXVkpk/gzNkpfebv6Ohg+/btHDx40G97cnIys2bNIiQkpM98YvwJNECFIRO6CiEGcHR03gY62zuoPeJm9vw8336LxcLEjHSujklkQuSxy1sYhkF5eTlFRUV0dXX5tgcHBzNz5kwmTpwotabTTKBz8V031AURQoxdroYaWnZ8TFfjEQ7Vt1Hb2E5H1b+ImzSN1ET/e5L6Ck5NTU1s27bNb5oiMFe5nTFjBkFBQcfkEePfidwHNQu4G8gFrJizk/9Sa/3ZEJVNCDHKeVydtOkC2vfvAgywgNPlpsMaTlXUTBq2VvOl5ZH91ny6urooKSk5Zuh4REQEs2fPJi4ubpheiRiNAr0P6mLgLcwZyV/DvFH3TGCNUupSrfX7Q1dEIcRo0z13XmvRp3g6233bLVYbkxacw0btICY6jHPy+l8QsKamhu3bt9PW1ubbZrVamT59OtOnT8dm638AhTg9BFqD+gnwkNb6wZ4blVI/wLyBVwKUEKcJd1szLTs/prNmPw3Nneay6hZwJGQQkbsUW1gUX0xrJzYqpM/g1NHRwY4dO6iu9l+kOy4ujlmzZskNt8In0AA1A+hrpN6rwPcHrzhCiNGuVa+jrryMqiOtuLo8WILDyFi0HEfy0Rtm46JDj8lnGAZ79+5Fa+03CMLhcJCTkyPLr4tjBBqg9gN5QGmv7fOAmkEtkRBiVAvPXsy+XbtwdXmoDZ3EXksuX42fNGBwqa+vZ9u2bX7z5wGkpaWRk5NDcPCxAyeECDRAPQ08q5RKA9Z5ty0G7gUeGYqCCSFGnuE2azoW29GfCltoBNPOvphdBTU026M5c1YKDvuxCwgCOJ1Odu/ezf79+/0GQURGRjJr1iwZBCEGFOgw818qpSKBe4B47+Yq4Ida66eGqnBCiJHjrK2iZfsaOiJTSTxjKXbb0SAUnp7FsvA0IsOC/JZc72YYBvv372f37t04nUfv67fZbGRlZTF16lSs1r6DmhDdAh5m7p0o9idKqUSgXWvde1ZzIcQ44Oly0rp7Ha17d3Covp36pr1UGwnMy5/hly4pNqzP/A0NDWzfvt1vEUGApKQkZs6cSVhY3/mE6K3fAKWUuhX4rda6w/u4937fY1kPSojxwXl4P83b1+DpaKWp1Ul9Uwdui50ivZ/p2VOJjui/r6i/5rywsDBmzpxJUlLScLwEMY4MVIP6LrAK6PA+7o+sByXEGOdxddJa9G86Knb7tk2IDKHSHUOJTZGSmtDvIIj+mvPkniZxqvoNUFrrKX097k0pJeNChRjDnIf307RtDe72FqxW8+tsdYQQkbOUnOCJZLS7mJoa3WeAqqurY8eOHTQ2NvptT0xMZObMmbIchjglgc4ksQfI11rX9dqeAmwBjl3+UggxqhldLlqKPqW+dAdVh1sIDbaTkhBBcPJUInLPwhocShLQV8NcR0cHRUVFVFRU+G0PCwsjNzeXpKQkuadJnLKB+qAuA5Z6n04GfqSUauuVbPoQlUsIMdSsVpoOVbGn0qz9tHTZiJ2/hIScWf1mcbvd7Nmzh9LSUr+bbW02G9OnT2fatGnSnCcGzUA1qK3ANzHn3QPzRt2e60AZQAvw9aEpmhBiKFmsNpIWXMChAy9xoCuGmgmzSQ1N7TOtYRgcOnSInTt3+s2dBzBx4kRycnJkdJ4YdAP1QZUD5wEopV4E7tRaN/WXXggxunU1HcEWGefX9GaPimfaipUcLGnlS7NT+hyl19zczM6dOzl8+LDf9sjISGbOnEl8fPwxeYQYDAGvB6WUsiulUoHu+rsFCAbmaa3/b6gKKIQ4NYa7i1a9noM7CqmOmsXSC8/1C1LRCYlcknBsPqfTidaa8vJyv2HjQUFBKKWYPHmy9DOJIRXoIIkVwItAbB+76wEJUEKMQq6GGpq2/JN9e/bT3ObCc3g9umQa2VkZ/ebxeDzs27eP4uJiXC6Xb7vFYiEjI4Ps7GwcDsdwFF+c5gKdSeJ/MZfU+BnwIXAZkAz8HPjO0BRNCHGyDI+bttJNtJVtAsPAEWQDXLQGxbKjrA6VmX5M7ae7n6moqIiWlha/ffHx8eTm5hIV5b86rhBDKdAAlQn8h9ZaK6U2AeFa69VKKRfwA8xlN45LKfWfwHNAZ4/Nt2mtXz6RQgsh+tfVVEvzto/oajri25aUEEVZ8Axip89i0ayUY4JTY2Mju3bt4siRI37bw8PDycnJkWHjYkQEGqDaAY/3cTEwB/gbUAhkncD55gKPa62/dwJ5hBABMAwPrSWbqdi0lthIhy+gBMVOJHL2Mi51hBNk9x8C3tHRwe7du6moqPDrZ7Lb7WRlZTFlyhSZ1FWMmEAD1MfAfUqpbwAbgRuVUo8D5wInMrJvHvCLEyqhEOK43G3NVH76DpV7yul0ufG4Q0mMiyRMLSB08mwsFgs9Q1NXVxdlZWWUlZXhdrt92y0WC5MmTSIrK0vWaBIjLtAA9R3gbeA/gWcx749qAhyYa0Idl1LKBswGvqaU+hnQBvwGeFhrbQyYWQgxIEuQg9bGJjpdZrDZ3xrMxOWfJywp2S9d97x5Wms6Ozv99iUlJTFjxgxZcl2MGoEOMy8FZiilQrXW7UqpBcAy4IjWen2A50rArH29DFyJuYz8XzADnUw2K8QpsAYFM+Wsi2l8exWVwdPIWrSUCYlHx44bhkFNTQ1FRUU0N/uvlBMVFUVOTg4JCX2MNRdiBA001VGft4X32P5R93Otde8pkI6htT4InNNj0xal1JPAF5EAJUTADMND3b4ywlOmEBJ89CsckpRB5hX/ycywSCLCjg4Db2hoYNeuXdTW1vodJyQkhOzsbNLS0mQAhBiVBqpBtWBOZxSI406+pZTKBb6ktf5hj80OzOU8hBAB6Gw4Qsm//kptZQXWnPM5a9kiv/1x8UeXUG9tbWX37t1UVVX5pbHb7UyfPp2pU6fKvHliVBsoQC0b5HM1AN9RSlUAL2DO7fcN4PZBPo8Q447hcdNWtpmabes4Um1O7tpRtJbK7ExSJ8b5pe3s7KS4uPiYGSBkAIQYawaai2/NYJ5Ia13pnSH9EcwbfI8AP9ZavzaY5xFivHHVH6R5+xrcLfVEhNqJCnfQ2NqFOzGLoOCQo+lcLsrKyti7d6/fTOMAKSkpZGdny/pMYkwJdKqjDQzQ3Ke1XhDIcbTW/wTyAyuaEKe3LmcnjbvW4anaTc+vX+qUyQTF5ZGbOw2r1YLb7aa8vJySkhK/FW0B4uLiyMnJYcKECcNbeCEGQaDDzN/pI99U4FLggcEskBAC9u/aSfm/PyDE6GDyxGiwgMUWRLhaQMikXJIsVt+Q8eLiYtrb2/3yR0VFkZ2dTWJiogyAEGNWoMPMH+xru3fqosuAJwazUEKczup2b2LvP/+GAbQCja2dJEzJJCL3LGxhkRiGQVVVFVrrY+bMCwsLQylFamqqBCYx5gVag+rPP4EnB6MgQgjThMmKmJi11NU3Y9gcOKcsJSp/LgA1NTXs3r2bxsZGvzzBwcFkZmYyadIkmZpIjBuB9kH1dU9UNOZEsdWDWiIhTiOGYdDS7iKyx31L1pBwJi9aRueuErLP+RyRUZHU1taye/du6urq/PJ3DxmfMmUKdvupXm8KMboE+onu756oDuC6wSuOEKePw4fr2fHRP+joggu+/B/YbEdrPlHTZjF/2izq6+tZt27dMavZ2mw2pkyZwrRp02RtJjFuBRqget8TZQBOYKfWurmP9EKIfhiGQdv+3ex8769YXJ2EAlu3aObOm+FL09jYiNaaQ4cO+eW1Wq1kZGSQmZlJSEgIQoxngQ6SWAOglIoAFOA2N+v2ATMKIfx0NdXSsvMTXPUHmRhlp7q2EwtgrSsHZtDY2EhxcTEHDx70y2exWEhPTyczM5OwsD5nIRNi3Am0DyoYc768qzGnJwJoV0r9GviO1trdb2YhBG0trXjKN9Fevovu1vKYqBDacZC64Dwc8RPZuHEj1dX+XboWi4WUlBSysrKIiIgYgZILMXICbeL7JebaT18GCgArsBB4DHN13P8eisIJMda1tjvZ/MlnuPZsJDs1DLu3n8lisRI2dQ5TkzIp2bOXql0fH5O3OzDJ8hfidBVogPoS8Hmt9doe215XStUBq5EAJcQx3J1tFK7+PZ7mw1iAmjpISYjAEZ+GO20ORVWHqC759Jh8EydOJCsri6ioqOEvtBCjyIks+e7qY3tjH9uEEIDVEUp6chTlzeYIPKc1FGP6mZQ0Oaku3HJM+qSkJJRSREdHD3dRhRiVAg1Q9wC/UUrdDKzTWnuUUjMx+6V+0vM+qUDWhhJiPHK7PX5DxS0WC+mLzqf58EGs6Tm0hUSxXpcfky85OZmsrCwJTEL0EmiAegKIAD4B3EopDxAEWIAFwM96pJUFZsRppcvtQW/eSsWOLcz/wpeJjT46yq7ZY6dt6nwO19ZBk/+9TBKYhBhYoAHqiiEthRBjVFdTLVv/8S7N1fsJBgr/9Qnnf/5z1NfXU1JScswNtmD2MWVmZkpgEuI4TvQ+qFAgE3MUX5ncpCtOV57OdlpLNtCxv4gkm4tmzBtwnVU7+deaEFpbmvzSWywWX2CSwQ9CBCbQ+6BswP8Ad3K0ac+plHoJuF1r3TVAdiHGjdr6FkLqSmgv24TRZY4bcgTZsIc6qLZOwDEh3i84WSwWUlNTmT59ugwXF+IEBdrE9xPgGmAlsBYzQC3BvA/qfu+fEONWY0snWz8roKNsA9Pj7USGm/ertzrd7HOG0BQzhZCgo8uoW61W0tPTmTZtmqxiK8RJCjRArQRu0Fq/22PbaqVUM/A8EqDEOGYYBmXvr8ZTtR8HcKjOhiPYzoEOG4ctcVhCw+leeclms5GRkcG0adMIDQ0dyWILMeYFGqAigNI+tu8B4gevOEKMPhaLhckqkx0HD+B2e6g37NS0RWINj8biDU0Wi4WMjAyUUgQHBx/niEKIQAS6stkG4LY+tt8OFA5ecYQYWW6PQVFZDW63x2/7hKy5OGJiaIhJxT0xE2v4BPAGp/j4eM4++2xmz54twUmIQRRoDeq/gX8ppc4F1nm3LQImAxcNfrGEGH57DtSy89O1hNVqXGd/kdmzpgFQX1/Pzp07qY9Ixc7RZdQjIiLIyckhMTFRllcXYggEOsx8o1JqLnATkIM59dHbwNNaa1lRV4xphuGhs7KYzvVriDxyBIDKjR+Tnp7AnrISqqqqvCnNIORwOMjKypLl1YUYYgGvEa21LgbuUkrFAW6tdcPQFUuIoWcYBq4jB2jdvY6u5jriQg3q7Facbg9Wo5W1H/8LetSMrFYrU6ZMITMzk6CgoJEruBCniYADlFLqPuAWIMn7/ADwhNb6iSEqmxBD4nB9O1u27OYMRzk0Hl0Y0GOAJTKSJtsErOH+szykpKQwY8YMWSxQiGEU6I26P8Fs3nsI//WgfqCUCtJaPzp0RRRi8KwrLOPg5k+Y0FHJwegQkuPCMQyDQ+0GBzyRuCOjsFqONtvFxsaSk5NDTEzMCJZaiNNToDWoG4Cv97oP6lOlVCnwNCABSowJiUc20tFRCUBtYzvWYAcVROEMjgLr0a9DREQEM2bMICkpSQZACDFCAg1QduBAH9tLAZm/RYxKri4PQXb/QQxp+WdxeP8+nAa0hMVSZo/FEuTw7Q8ODkYpRUZGhgQmIUZYoEOQHgWeUUqld29QSsUCP8Wc7ihgSqkJSqn9SqlrTySfEIFyutwU7KjmT699RF2j//JkHUERdCRNpi52Ml3Ryb7gZLfbyc7O5rzzzmPSpEkSnIQYBQKtQX0ZmAHs8Q6O6AIyAAewSCn1je6EWuvE4xzrWSD1JMoqREA+WbuVjuL1pLjq2PqZlWUXL6O1tRWtNZWVlWAJw+q9n9ZqtTJ58mSmT58uN9kKMcqcyIKFp0wp9XUgCtg+GMcToid3WzOtxevJbNjNHlejufHAFjYVRlFVXY1hGL603bOMK6VkZJ4Qo1SgN+q+fKonUkpNAX4InAm8d6rHEwLMIeNxkTbaSzfTvm8bhsdNaLCdqMhg6l3QHBxJS0UF9LihNjk5mezsbFn+QohRLuD7oE6Fdz2p3wN3aa0PKqWG47RiHKtr6uDfWyto2LOTJTE1hNvdALjcBhVNnVQTiSUuHpv96ACIuLg4ZsyYIUPGhRgjhiVAAfcBWmv9+jCdT4xzOwq3Yt+9jlR3Cwc7bUyaGE1ls5PKDhtETcQadbTZbsKECWRnZxMfHy+DH4QYQ4YrQH0FSFFKXel9Hok5KnCB1vrWYSqDGEdyYp3s9LTgNgya3LD+YAdEJWBLODoDRGRkJNnZ2XIvkxBj1LAEKK11ds/nSqktmNMkvTQc5xdjl2EY7KlsZEpKNFbr0SATreZh3bKJI21OrFHx2CPjfPPmhYeHo5QiJSVFApMQY1i/AUop9dtAD6K1/s/BKY4QR1UdbmHt5nKMyl10Ll5ITnYGbreb8vJySktLaQ9PxjHB4ZsBIiwsjKysLNLS0iQwCTEODFSD6jnEyQF8HtiNuXihE5gLzAH+eKIn1VqfcaJ5xOnFMAwO6u3ElH5GkKeDvevasdvOZN/ePXR2dgJgDTb7mUJDQ8nMzCQ9PV2WvxBiHOk3QGmtr+p+rJR6HrNJ7ts90yilHgKye+cV4lQ4a6toLfqM1IbDtNLJEZeHjoYKdm3dhC041JcuJCSEzMxMMjIyJDAJMQ4F2gf1/zBrTL29DGwZvOKI05HbY1C0t5bp8RY6SzbgrNmH22NQ2ezkkMeGJySY0AmJvuDUHZjS09Ox2WwjXHohxFAJNEAdBM4DSnptXwGUD2qJxGnlwKFmPi3cQ1D1dgg5TFx0CJVNTiqanLgMsE9IIDgqDixWCUxCnGYCDVA/Al5QSp0HbMJc+3ohcAlw1UAZhRhIa3kRCXs/Ao8L3WhAo5lxbHwAABYvSURBVAsPYAuPJjg6EYvNTmhoKNOnT5fAJMRpJtCpjl75/+3de3xcZZnA8d9M5pZ7k7RJc2lom6QP16LlrlxkWbwgiqLiroCK3HRZd8VFdwURXbmJu35EdvGC6MqyygdlYRfwuiD3i1xKAUsfWtpcJknTpk3STpNJJjOzf7wn7RDakIYkM5M8388nnybnzDnzPj2T8+S9nPcVkShwMXCut/lF4ERVfXqmCmfmvrraCjQ5ytZ4mnCwgMJQEeGKGnyhCEVFRbsTk/UxGTP/TPo5KFX9I/DHGSyLmcMSo0lWv7oVaaygvCTM4OAgGzdupL29nXhhGWXhUcIVi/EXllBcXExzczMNDQ2WmIyZxyZ6DuqGyZ5EVb88PcUxc9GmrgEef2ot5b0vMVDfxKL6KqLR6O7ZxYuql4C/gNLSUlpaWuwBW2MMMHEN6qhJniP95i8x81UyvovApidZ2PEs2xNpOvt66R86iEh4z0evvKKSlpYWFi9ebInJGLPbRM9BnTz2vYicC/xGVXtnpVQm76USwwy+tpquV56nvW+I7oSPZDJNacRHMBUHSqiqqqKlpcUmcTXG7NVk+6C+BxwLWIIy+9S3I85TL3VySPE2YptW07ZtJ7GRFABFhQEKIiWEKmpY3NBIc3MzlZWVWS6xMSaXTTZBPQ18GLh+Bsti8tjajb0899hTRPrW8sjoCOFIyD2MAPiDEcIVNTQsb6G5uZmysrLsFtYYkxcmm6BSwLUi8lVgEzCUuVNVj57ugpn8MTw8zK7XnmSkezVDXo9kQShFMBQmXFnD0gMPo6mpyZZWN8bsl/2pQdnzTgaAoeFRwsEChobcUPGOjg6SiSSBUBB/MklhYZiSRbU0H7aKZcubCIfD2S6yMSYPTfZB3W+MfS8iZYBfVftnrFQmJ8VHRlmtW3h5zcvUVvogNbx7qDj+AsqqaykMFrDi7cfQuGw5gcBsrYdpjJmLJn0HEZHPAZcDdd7PW4AbVdX6peaBdDrN448/y2urn4KRHbRvLaWhuXmsm4ny8nKaVq2yZ5iMMdNmUglKRC4DrgSuAR7DdX+/E/gnERlS1RtnrogmmxKJBJt0LRtefJZYXy++xAhpIDy6i0Q8TkNjI01NTVRVVVliMsZMq8nWoC4BPquqv8jY9riItAFXA5ag5pDRZIoX1nXij3XS8epLjOzsA8Dn81EUCeDzwbIlDRz0jneyoKYuy6U1xsxVk01Qi3Ar6Y73HNAwfcUx2ZROp3nu5Y08/dRzpPs7KA+MEAnvmT086PfR1NTIiqNOoqTGLrsxZmZNNkG9jFtW47px2z+OWwbe5LFkMklnZycbN24kul4JbusBIJ7yEQ75KQkVsHRJA8uPPIHIwvosl9YYM19MNkF9DbhfRI4DnvS2HQe8FzhzJgpmZt7Q0BCtra20t7czMjICQEl5KfG+LaTTUFMa5OAVy2hcdQKhytosl9YYM99Mdpj570XkFODzuPWghoBXgKNUdc0Mls9Ms3Q6zbZt23j2hbW8pkpt3SKCgT3NeOHSClY0VrOsrprFhx1HsGJxFktrjJnPJlpu473Ao6q6C0BVHwEema2Cmek1OjpKZ2cnmzZtZMO69SQGthFMJ9jWA4vrF1NUVMTSpUtpbGwk4ANfIJjtIhtj5rmJalD3A6Mi8izwoPf1hKoOz0rJzLSIxWK0trbS0dZKvH8rydh2ikcS7EwnAAgN9XP4205jSYM9v2SMyS0TJaiFuGedjgOOBy4F/CLyFHsS1tOqmpzxUpr9kk6n2bx5M21tbUTb2/APDZAcHABv1odw0E9RcYDasiKaVx1FRV2NJSdjTM6ZaD2oPuA+7wsRKQBW4RLWccBngIUi8qiqvn8WymreRDwep729nba2Nrb1bCbWu5VUPEZpcYhAgZ+ioJ+60hB1VeWULF9JYePB+IM2T54xJjdNeqojVU2KSBToBrYA7UA1sHyGymYmIZ1O09vbS1tbG5s3b3Zz46VTDG+Nkhp2zXjBVIrD6kpYVLOYomUrCdc24fMXvMmZjTEmuyZMUCJSArwL+EvgVOBAoAP4I3Ar8AlV7ZrhMpq9GB4eJhqNsmlTK9v7dlAYybiUPj9lVdUke7soD/gorVvGASeeSHih9TMZY/LHRKP4HgWOBvqAh3DTGT2oqhum+mYicjpwLbAMVwu7QVV/ONXzzTfpdJrt27fT1tZGZ2cnnR2bSe3qI+0vYEnLCnw+qKqqYunSpSwqLeTVPz1NzaFHsLBmUbaLbowx+22iGtQ7gSjwE9yAiCdVNTHVNxKRWuBXwIdV9Tcisgo3n98zqvr8VM87H8TjcaLRKB0dHcR29DMa6ycZ6yMQ20Uqlcaf8lFWXskRq1ZSWlq6+7hDTnlvFkttjDFvzUQJajmuae8U4HNAsYg8BjyAS1jPq2p6sm+kqt0iskhVd4qIH6gCRoGdUy79HJZOp9nQ2snqF1+ls7OLxWUQTuwkObTnv2tBJEAwmWRhUQG1FcWvS07GGJPvJhrF1wr82PtCRA4H/gKXsK4EEiLyEPCAqt48mTfzklMRMOC997dUdf1bCWCuGRoaIhqN0t7ezvpN3cQH+oiMxhiJpwhEghT4oKYkSG1JiMJIIQW1QpUcTkFhSbaLbowx02p/RvGtAdaIyI3AEcB5uGmPPgRMKkF54kAxsBL4tYisV9Vb9+P4OSeZTNIR7WZLTxdbtmzZvUpteaoff8ItdRFM+5GqCNXFQSJVtUQaDyFcu9xG4xlj5qw3TVAi0gAcAxzr/bsK1zT3BG4tqIf25w1VNQWMAM+KyI+AM3AjAuedvv4B/rR6HbphE6nRBE0NC163v2xhNeWjMRoXRKiuLKFoiRBpPIRAaWWWSmyMMbNnolF8d+ESUi2un+hx4F7gMuC5/Z1BQkROAr6jqkdkbA4D/ftb6HyWSCTo6uqivb2dbdu3s3HTZkKJnYRSwwzGiymKBFm4cCGNjY3U1FSz64X/I1R9AJG6Fpsfzxgzr0xUgwoD38XVkJ73aj5vxQtAvYh8ETdk/RjgfODDb/G8OS+dTtPTs4WOaJQtPZtJJkZI7uonGeunKjnIcDJJ0A9VRRGOe9dJFBcX7z62/Mj3ZbHkxhiTPRMNkjh9Ot9IVQdE5DTge8BVuAd+L1DVh6fzfXJJLBZj/WutvLR2A1u3DbCwMEmJb4hUfBcAPqB+QZjSAh+NlRFKl5W/LjkZY8x8NulBEtPBe97p+Nl8z9k2MjJCV1cXHR0d9Pf309+/g4GeHkqSgyQG06SKw5SE/dSWhKguDhCOFBKuX0Gk4UACZVXZLr4xxuSMWU1Qc1UqlaKnp4eNm9rp7+slldrTGlpSkCSe3IWfNAuCBRxWHaGiOExoYT3hhgMJ1yzFV2CXwRhjxrM74xSl02n6+vqIRqP8ed0mercPMDq4kwOWNRAM+AHw+/00NK2gNtFLVaSAyupFFC4RwvUrKCi0h2qNMWYilqD2UywWo7Ozk2g0yuCuXaTiMWLdPYSGYoRJM9BXQnPLcpYsWUJdXR2hUIih2lICJRUEKmptslZjjJkkS1CTMDZz+PrX2hgY6CfCCMnBHSQHd0A6RRFJhv1pygM+ahYUcfwJJ7zu+MLGQ7JUcmOMyV+WoPYhkUjQ3d3taktdPXR390J8J8W+OP5CN3tDwO9jUXGQ6uJCksOjVNXXU7rMkpExxkwHS1AZkskkPT09dHV10dPTs3uwgz/eTyTmlr1KARULQtSVhagsDBAqLiNc10K4voVASUUWS2+MMXPLvE9QqVSKrVu3sm59KxtbO9i5YyeN9VUU+Pf0FYWKyyiPFFBImtrSEPV1FZQ1riBc10xgQY31KxljzAyYlwlqbJn0rq4uuru7GRnaRVd0M+mhHURSCXaUhqlYUEp5eTn19fXU1dUx8FwBkZJSihpWEKyqw+fzZzsMY4yZ0+ZNgkqlUmxo7WLdq60MD/YR9o+SHBwgNbiD1Eic4uQoQ6lRgn4oSqc5+eSTKSnZs4RF4fFnZLH0xhgz/8yLBBWLxbj/dw/RGt1CaHSQsoJhykJ75rqNBHzUFkcoSCRYVF7MwoOWvi45GWOMmX3zIkFFo1H8g70sGOrER5rUKIQKC6kuDrKoKEB5JEio+gDCtU2Eag7AHwhlu8jGGDPvzYsEVVtbS9uCCoa2RKkMB6gtD9NQUUikegnhumZCNcvwB8PZLqYxxpgM8yJBlZeX8+7TP0jfQzH84SLCdc2Ea5vwh4uyXTRjjDH7MC8SFIDP52PBCR+z5jtjjMkT82qstCUnY4zJH/MqQRljjMkflqCMMcbkJEtQxhhjcpIlKGOMMTnJEpQxxpiclC/DzAsANm/enO1yGGOMmYKM+3fBZI/JlwRVC3D22WdnuxzGGGPemlrgtcm8MF8S1DPACUA3kHyT1xpjjMk9Bbjk9MxkD/Cl0+mZK44xxhgzRTZIwhhjTE6yBGWMMSYnWYIyxhiTkyxBGWOMyUmWoIwxxuQkS1DGGGNykiUoY4wxOckSlDHGmJyULzNJ7JOIHA3cp6rV3s+LgBuB9wDDwE+Aq1Q16e2/DTgLGM04zUpV3SgijcCtwLHAFuDzqvrrHI7lHd7+g4Au4HJV/ZW3L2ux7E8cIvID4JxxpygGrlDVa/PwmpwHfBVYCKwD/kFVH/P25VssnwcuBaqAJ4BLVHVjNmMRkVOB64EW732/rao/FJEFwI+BU4EY8FVV/al3jA/4JnAREAJ+CnxJVUe9/WcB1+JmOXgY+LSqbsnFWDKO9QN3AQ+r6ncztp+Mu6ZNwBrgXFWd1LRCuShva1Ai4hORC4Df4z50Y34GVONu2ocCRwP/nLF/FfAhVS3J+Nro7bsDeBH3C3khcIeILJ/hUKYUi4jUAvcD/waUApcAt3s3jqzEMpU4VPWzmdcC+BKw1osrK3FMNRYRWQl8BzgDWADcDtzj3UzyLZazgGuA87zy/i/wBxGJZCsWEVmCuylfjfv//WvgOhF5D/B93DRotcD7getF5CTv0IuAM3G/+y3AUcDl3jkPxiXaT3uxrPdim1FvIRZEZClwL/ChcedcCNwDfMM7593A7zI+f3knbwuOuwifw11gAESkCHgvcKmqblHV7cCVwIXeL2khcCDwwviTicgK4Ejga6o6oqoP4n4pz5/5UPY/FuCTwCOq+jNVTavqH3A3mL4sxjKVOMh4bRNwA/AJVd2Rh9ekhT2/Uz7cTWbIOzbfYvkIcIuqPqyqo6r6fWAEOCWLsSwFfq6qd6tqSlWfAR4CTgE+ClypqoOq+gJwCy4xAXwK+K6qRlV1K/B14GJv3znAvar6mKrGga8A7xSRllyMRURKgOdwtaMnxp3zTODPqnqXqiZU9dtA2DtnXsrnBPUDVT0CeDZj21g8uzK2JYFFuL8o3oZr2rtFRLaKyPMicrr3uoOBdlXNPHYdcNiMlP71phLLEUCriNwhIr0ishpYrKo7yV4sU4kj07/iboprvJ/z7Zr8DngZeAl3M/8W8HFVTZF/sfjH7Rvbv4IsxaKqj6rqZ8d+FpFK9kwincbVfvZWnoNxtfLMfXXe8a/bp6qDQAe5G0scOEhVLwcS4047Pk4AZXY+YzMibxOUqnbtZVsM14xxg4hUikgV8DVvdyGuKexR3F+UdbgmjDtF5HCgBBgcd8pBoGhmInhduacSSyWuaeV2XFPAdcDdXi0kK7FMMQ4ARORQXJv7tzIOz7drEsHdEI7F9aP9I66JbzH5F8tdwEUicqSIBEXkQlzrQyFZjGWMiJTjam1P42oUcVXNnPk6szzjyzv2fdFe9o0/dsbtTyxebXZf/WNZj2W65W2CmsC5uL9eX8F1eN7jbe9X1d+r6qmq+qxXBb4LeBD4IO6vxcJx5yrCdVJmyz5jwXVq/1ZV7/NiuRN4HngfuRfLRHGM+Qxwj6r2ZGzLtThg4li+DmxW1adVdVhVbwZagY+RZ7Go6h24P3ruwNUoDgX+APSR5Vi8JsangB5cc9hOIDKuyTizPOPLO3bDju1l3/hjZ9QUYplILn7G3pK5mKBqgYtVtUZVDwU6gVdUdVBEPiAinxr3+hCu2rwWaPT6qcYcyBurzLNpn7Hgqv0V414/Nioz12KZKI4xZ/DGzulciwMmjmUJrs0/0yiuKSavYvEG4dyrqs2quhj4Ii5JPUcWYxGRE3E1jXuAj3r9RutxfX7L9lGetYCM29etqv3j93l9c43kbiwTGR/n/hybk/J+mPlefAd4WUQuw33QvsWeEWEFwI0i8gruF+3jwDuAC1S1XUTWANeIyFe87WcAx812ABkmiuU24G9F5Bzg57i/0lcCZ6lqR47FMlEcY8OdlwOPZx6kqppjccDEsdyHazK7E7co29nAIcD9OXhNYOJYTgGuEpETgB245r8u4BlVTWcjFq/5+j7cIwg3jW1X1ZiI3I0bBXc+boj1hbiReQD/CVwmIg/gahlf97aB+915TETeBTyJqzWuVtVXczSWidyN+/yd5X3/90AKN/giL83FBHUhbtTLdlyzy01eUwuqeo+IXAH8AliMq4Wcrqrt3rEfAX6EeyahFzhfVV+e5fJnmiiWNSJyGu6mcjPQDpypqh3esbkUyz7j8CwFhr2RZOPlUhww8TW5xetP+DnuOai1wGk5ek1g4uvyX7jO9TW4VoYHgA9m9I1kI5ZLcP3I14nIdRnb/x03Ku9moA3XInKNqv7G2/8DoAY36q0I+CVef5uqviQin/FeU4+r0XxshuOAqceyT6q6RUQ+gHsO6lbgz8AHVHVkugs/W2xFXWOMMTlpLvZBGWOMmQMsQRljjMlJlqCMMcbkJEtQxhhjcpIlKGOMMTnJEpQxxpicZAnKmGnkTd671Zv8c/y+K0Qk5i2XYIx5E5agjJleX8A9AJ/58CUisgy4AjdzQGsWymVM3rEEZcw0UtXNuDWFLhCRYzJ23YRbh+ymvR5ojHkDm0nCmGnmzUT9OG6KoKNws+XfCbxdVdd6rzkPt6prPW428StU9bfevgBuifJP4JaF2YabnusyVU2KyO24OdYOws3VdqaqPjRrARozS6wGZcw08+aruwg3ee95wL8AV2ckp/fjJmq9wnvNrbi1vI72TvEV3BLg5+JW6f0K8HfA6Rlvcw5uzrVTcMs1GDPnzMXJYo3JOlV9WUS+A/wQV0O6PmP35cD13hpeABtE5EjckhZ/hVuR99Oq+oi3/6ci8mXciqn/421bq6q3z3QcxmSTJShjZs43cKvqflNVM5fnPhg4QkSuzNgWxFu3x5t1/xQR+TZuifWVuBnfCzJe/9pMFtyYXGBNfMbMEFUd8r4dGrcrAHwJeFvG1yG4NZUQkatxS0L4vX/fDYxfymL8OY2Zc6wGZczsWwccoKobxjZ4tak48G3gb4AvqOpt3r5C3IKCvr2cy5g5yxKUMbPvBuA2EVHgQeBU4Cpc/xO4BQRPF5EngHJcU2EZb1xO3pg5zZr4jJllqvpL4FLgy7h+p0uBi1X1V95LPokbvfcS8N/ABuA/gCNmvbDGZJE9B2WMMSYnWQ3KGGNMTrIEZYwxJidZgjLGGJOTLEEZY4zJSZagjDHG5CRLUMYYY3KSJShjjDE5yRKUMcaYnPT/Klv2Uzv1mVQAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation(system, updatefunc3)\n", + "plot_results(census, un, results, 'Proportional model, combined birth and death')" + ] + }, + { + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e98d392e802e13e5bdeaf069ba6bb8c8534c25f8 Mon Sep 17 00:00:00 2001 From: Annie Tor Date: Fri, 14 Sep 2018 16:50:43 -0400 Subject: [PATCH 03/18] Adding chapter 5 --- code/chap05mine.ipynb | 1603 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1603 insertions(+) create mode 100644 code/chap05mine.ipynb diff --git a/code/chap05mine.ipynb b/code/chap05mine.ipynb new file mode 100644 index 00000000..92482c80 --- /dev/null +++ b/code/chap05mine.ipynb @@ -0,0 +1,1603 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 5\n", + "\n", + "Copyright 2017 Allen Downey\n", + "\n", + "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure Jupyter so figures appear in the notebook\n", + "%matplotlib inline\n", + "\n", + "# Configure Jupyter to display the assigned value after an assignment\n", + "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", + "\n", + "# import functions from the modsim.py module\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading data\n", + "\n", + "Pandas is a library that provides tools for reading and processing data. `read_html` reads a web page from a file or the Internet and creates one `DataFrame` for each table on the page." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pandas import read_html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data directory contains a downloaded copy of https://en.wikipedia.org/wiki/World_population_estimates\n", + "\n", + "The arguments of `read_html` specify the file to read and how to interpret the tables in the file. The result, `tables`, is a sequence of `DataFrame` objects; `len(tables)` reports the length of the sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filename = 'data/World_population_estimates.html'\n", + "tables = read_html(filename, header=0, index_col=0, decimal='M')\n", + "len(tables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can select the `DataFrame` we want using the bracket operator. The tables are numbered from 0, so `tables[2]` is actually the third table on the page.\n", + "\n", + "`head` selects the header and the first five rows." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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United States Census Bureau (2017)[28]Population Reference Bureau (1973–2016)[15]United Nations Department of Economic and Social Affairs (2015)[16]Maddison (2008)[17]HYDE (2007)[24]Tanton (1994)[18]Biraben (1980)[19]McEvedy & Jones (1978)[20]Thomlinson (1975)[21]Durand (1974)[22]Clark (1967)[23]
Year
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\n", + "
" + ], + "text/plain": [ + " United States Census Bureau (2017)[28] \\\n", + "Year \n", + "1950 2557628654 \n", + "1951 2594939877 \n", + "1952 2636772306 \n", + "1953 2682053389 \n", + "1954 2730228104 \n", + "\n", + " Population Reference Bureau (1973–2016)[15] \\\n", + "Year \n", + "1950 2.516000e+09 \n", + "1951 NaN \n", + "1952 NaN \n", + "1953 NaN \n", + "1954 NaN \n", + "\n", + " United Nations Department of Economic and Social Affairs (2015)[16] \\\n", + "Year \n", + "1950 2.525149e+09 \n", + "1951 2.572851e+09 \n", + "1952 2.619292e+09 \n", + "1953 2.665865e+09 \n", + "1954 2.713172e+09 \n", + "\n", + " Maddison (2008)[17] HYDE (2007)[24] Tanton (1994)[18] \\\n", + "Year \n", + "1950 2.544000e+09 2.527960e+09 2.400000e+09 \n", + "1951 2.571663e+09 NaN NaN \n", + "1952 2.617949e+09 NaN NaN \n", + "1953 2.665959e+09 NaN NaN \n", + "1954 2.716927e+09 NaN NaN \n", + "\n", + " Biraben (1980)[19] McEvedy & Jones (1978)[20] Thomlinson (1975)[21] \\\n", + "Year \n", + "1950 2.527000e+09 2.500000e+09 2.400000e+09 \n", + "1951 NaN NaN NaN \n", + "1952 NaN NaN NaN \n", + "1953 NaN NaN NaN \n", + "1954 NaN NaN NaN \n", + "\n", + " Durand (1974)[22] Clark (1967)[23] \n", + "Year \n", + "1950 NaN 2.486000e+09 \n", + "1951 NaN NaN \n", + "1952 NaN NaN \n", + "1953 NaN NaN \n", + "1954 NaN NaN " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table2 = tables[2]\n", + "table2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`tail` selects the last five rows." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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United States Census Bureau (2017)[28]Population Reference Bureau (1973–2016)[15]United Nations Department of Economic and Social Affairs (2015)[16]Maddison (2008)[17]HYDE (2007)[24]Tanton (1994)[18]Biraben (1980)[19]McEvedy & Jones (1978)[20]Thomlinson (1975)[21]Durand (1974)[22]Clark (1967)[23]
Year
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201471699681857.238184e+097.243784e+09NaNNaNNaNNaNNaNNaNNaNNaN
201572478927887.336435e+097.349472e+09NaNNaNNaNNaNNaNNaNNaNNaN
201673259967097.418152e+09NaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " United States Census Bureau (2017)[28] \\\n", + "Year \n", + "2012 7013871313 \n", + "2013 7092128094 \n", + "2014 7169968185 \n", + "2015 7247892788 \n", + "2016 7325996709 \n", + "\n", + " Population Reference Bureau (1973–2016)[15] \\\n", + "Year \n", + "2012 7.057075e+09 \n", + "2013 7.136796e+09 \n", + "2014 7.238184e+09 \n", + "2015 7.336435e+09 \n", + "2016 7.418152e+09 \n", + "\n", + " United Nations Department of Economic and Social Affairs (2015)[16] \\\n", + "Year \n", + "2012 7.080072e+09 \n", + "2013 7.162119e+09 \n", + "2014 7.243784e+09 \n", + "2015 7.349472e+09 \n", + "2016 NaN \n", + "\n", + " Maddison (2008)[17] HYDE (2007)[24] Tanton (1994)[18] \\\n", + "Year \n", + "2012 NaN NaN NaN \n", + "2013 NaN NaN NaN \n", + "2014 NaN NaN NaN \n", + "2015 NaN NaN NaN \n", + "2016 NaN NaN NaN \n", + "\n", + " Biraben (1980)[19] McEvedy & Jones (1978)[20] Thomlinson (1975)[21] \\\n", + "Year \n", + "2012 NaN NaN NaN \n", + "2013 NaN NaN NaN \n", + "2014 NaN NaN NaN \n", + "2015 NaN NaN NaN \n", + "2016 NaN NaN NaN \n", + "\n", + " Durand (1974)[22] Clark (1967)[23] \n", + "Year \n", + "2012 NaN NaN \n", + "2013 NaN NaN \n", + "2014 NaN NaN \n", + "2015 NaN NaN \n", + "2016 NaN NaN " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table2.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Long column names are awkard to work with, but we can replace them with abbreviated names." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "table2.columns = ['census', 'prb', 'un', 'maddison', \n", + " 'hyde', 'tanton', 'biraben', 'mj', \n", + " 'thomlinson', 'durand', 'clark']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the DataFrame looks like now. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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censusprbunmaddisonhydetantonbirabenmjthomlinsondurandclark
Year
195025576286542.516000e+092.525149e+092.544000e+092.527960e+092.400000e+092.527000e+092.500000e+092.400000e+09NaN2.486000e+09
19512594939877NaN2.572851e+092.571663e+09NaNNaNNaNNaNNaNNaNNaN
19522636772306NaN2.619292e+092.617949e+09NaNNaNNaNNaNNaNNaNNaN
19532682053389NaN2.665865e+092.665959e+09NaNNaNNaNNaNNaNNaNNaN
19542730228104NaN2.713172e+092.716927e+09NaNNaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " census prb un maddison hyde \\\n", + "Year \n", + "1950 2557628654 2.516000e+09 2.525149e+09 2.544000e+09 2.527960e+09 \n", + "1951 2594939877 NaN 2.572851e+09 2.571663e+09 NaN \n", + "1952 2636772306 NaN 2.619292e+09 2.617949e+09 NaN \n", + "1953 2682053389 NaN 2.665865e+09 2.665959e+09 NaN \n", + "1954 2730228104 NaN 2.713172e+09 2.716927e+09 NaN \n", + "\n", + " tanton biraben mj thomlinson durand \\\n", + "Year \n", + "1950 2.400000e+09 2.527000e+09 2.500000e+09 2.400000e+09 NaN \n", + "1951 NaN NaN NaN NaN NaN \n", + "1952 NaN NaN NaN NaN NaN \n", + "1953 NaN NaN NaN NaN NaN \n", + "1954 NaN NaN NaN NaN NaN \n", + "\n", + " clark \n", + "Year \n", + "1950 2.486000e+09 \n", + "1951 NaN \n", + "1952 NaN \n", + "1953 NaN \n", + "1954 NaN " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first column, which is labeled `Year`, is special. It is the **index** for this `DataFrame`, which means it contains the labels for the rows.\n", + "\n", + "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n", + "\n", + "`NaN` is a special value that indicates missing data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Series\n", + "\n", + "We can use dot notation to select a column from a `DataFrame`. The result is a `Series`, which is like a `DataFrame` with a single column." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 2557628654\n", + "1951 2594939877\n", + "1952 2636772306\n", + "1953 2682053389\n", + "1954 2730228104\n", + "Name: census, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census = table2.census\n", + "census.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "2012 7013871313\n", + "2013 7092128094\n", + "2014 7169968185\n", + "2015 7247892788\n", + "2016 7325996709\n", + "Name: census, dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like a `DataFrame`, a `Series` contains an index, which labels the rows.\n", + "\n", + "`1e9` is scientific notation for $1 \\cdot 10^9$ or 1 billion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From here on, we will work in units of billions." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 2.525149\n", + "1951 2.572851\n", + "1952 2.619292\n", + "1953 2.665865\n", + "1954 2.713172\n", + "Name: un, dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "un = table2.un / 1e9\n", + "un.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 2.557629\n", + "1951 2.594940\n", + "1952 2.636772\n", + "1953 2.682053\n", + "1954 2.730228\n", + "Name: census, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census = table2.census / 1e9\n", + "census.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what these estimates look like." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file figs/chap03-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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paSlz5swBzGdmzc3N3H333SQnJ3c7TiEGOk9FMbXbP20pvw4QGxPFdv9IKlzDiCpvpL7R0+4uarALepi5UioNmAg4AEvbfVrrf/RwXGctesz0s+p6i50w/6Ruv54QTMn3tmw2G4899hhLly4lKiqq2++3bt06DMNgzJgxQR3/6quv8sorr/Dmm28CZrKaPXs2DoeDm2++GYA1a9ZQVVXFu+++i9Xa+hjztttu45VXXmmp4CuEAMProV6vpbFwB9A6otaRnEX2hPlk7Kwm3TCYMylzQM9pOhNBjVtUSt0EHMYsMPgO8Pc2X3/rtegGoGBKvneUk5PDPffcw7Jly4J+H7/fz7p167j//vu57bbbiIkJblXj+fPnc+jQIR555BHKy8sxDIPDhw/z/PPPt5Sbf/XVV7n44otJT08nNTW15eurX/0qr7zyigw5FyLAXXaEypXLaDj4BeXVjTS7vVjsDmLy5xE/Ywm2qDgWTBnKwuk5kpw6Eez/kZ8AfwJ+prWu7cV4BrxgSr535qqrrmL58uWsWbPmlMfs2bOHgoICAOx2O1lZWdx888184xvfaHfcj370I2y2kwuXvfvuu2RkZPDyyy/z5JNPsmTJEhobG0lMTOTiiy/m+9//PuXl5Xz44Ye88MILJ52/ZMkSHnnkEd577z2WLFkS5P8RIQYub/VxGqqrOHq8jsZmLyRmMeeCK3FEx7Yc03ahV9FeUCXflVL1wGSt9d7eD6nT989FSr4PePI5ioHGMPwUf/xXdutCimLGURWZxfnTcxg3fPA9pz2Tku/B3kG9DywEQpKghBAi3PmbGzB83nZl1i0WK+mzLqYopYLd+2qYMTYdlZMYwij7l2AT1FbgcaXUZcAewN12p9b65FmcQggxCBiGQfPRPdTtWo3FlYCRfxEpCa0DmmzR8RTkxzJyuJvEuMgQRtr/BJug5gNrARcwqcM+KdsuhBiUfA011H2xAnfZEWrr3RTvK6HsqJNLr7wYh731Oa/NZpXkdAaCSlBa6/N6OxAhhOgvDMNP48HtNOxZj+Hz4vcb5kAISyQV3gjWfFHCuZOzQh1mv9edeVDpmBNxx2MOT98F/Elrvb+XYusWwzCwWGQ0TH8VzGAdIcKBt6ac2u2f4q1uXWTHarWSnj+VD4uTiXBFkp7U/TmL4mTBrmY+A/gAcy7UasyJukuA7ymlFmitN/ReiKdns9nweDxBr/otwk9jY2OXw+yFCDXD56Vh30Ya9m/B7/O3DA+3xyYRM2EBKfGp1O05zrjcJCIjZE5TTwj2/+J/Av8L3K61bvlTVyn1e+AxIKRdgAkJCRw7doysrKx2KxuI8GcYBo2NjRw9epT09PRQhyNEpwyfl8pVf8VbV8XxqkYqapoYlZ1MfN50XCMmtZTLmKLSQhzpwBJsgpoG3Nw2OQU8CQS3ZHgvSklJ4ciRI2itQx2KOAMOh4P09HTi4uJOf7AQIWCx2XGmZHPgyyPUNripdySxPfYcLhwlq/f3pmATVDGQC3TMACOAkK8sYbVaycnJCXUYQogBLErNIOXwQXYfi6PSlU26NwK3x4fTcfKqLKJnBJug/gI8q5T6IXBirZ3ZwP8L7BNCiAHB11BD/e41xIyfizWidbCD1e4k56JryNlSRH6MkwkjU2SZol4WbIJ6GMgElmGO4LMAHswuvnt7JzQhhOg7ht/XMnS8udnN9i/LGbPoclITXS3HWCwWzi2Q4eN9Jdh5UG7gFqXUXYACGoF9WuvG3gxOCCH6gqeqlLrty/HWllNT5+bI8VoMo4pVa3aw9KKpcqcUIl2VfL8U+EBr7Qm87ihbKQWEZz0oIYQ4Hb/XTYNe165WU0SEjSZ7HEdi82mqd1BcXk9WanDlakTP6uoO6u9ABlAaeH0qBiBPCYUQ/UpzyQHqdq7C31Tfss1itZGUP5PEUelUFtWyeMpQMpKjQxjl4NZVyXdrZ6+FEKI/8zXWUbdjFe5jB6lpcGOzWoh2OXCmDCUmfx62qDim+vxMHTsEm3TthZRMdxZCDCre6uM0FO2nqKyO2gYPVqeLKZdcSPTQMS3Lpdls8jd5OOjqGdRxglypXGst06eFEP1CRMZwHGnDaDiylQpXNiUxY3FWxzErW+6Wwk1Xd1A/QUppCCH6McPrwddYiz02qd32xEnzSY0ewdbdzeSPSGbymNQQRSi60tUzqBf6MA4hhOhRzccOUrdjJX4suMctISsjoWWfzRWDys8jJbuJlARXF1cRodRVF9+yYC+itb66Z8IRQoiz0zIIovSgWUSwrJ7SI+9zwVVfJcbVumK+xWKR5BTmuuriq+9inxBChJXWIoIbMHweDANKKuppNOzUEc3KLUe5ZHZuqMMU3dBVF9+NPf1mSqkhwB8xy3M0Ac9qre/v6fcRQgwunqpS6r5YgbemrGWbxQJDJ0zhncOJOF1RjMiMk8Km/UxXXXyPAg9qresDr0/F0Fr/W5Dv9zZmeY50YAiwXCm1S2v9ctARCyFEQNuVILw+H/bA8HBbTCKx+fNITRrCggMVDM+MkyKC/VBXn9h0wNHm9akENdJPKTUTszzHHK21BziglFqAua6fEEJ0i2H4qfrsdTy1VZRWNlBR08SIockkj5+Oa8TkliKCY4cnneZKIlx11cV3Xmevz8JUYDvwC6XUDZhdfE9prf+zB64thBhkLBYrrpzxFC5/j+o6N3XOFDZETGfpiAIssgLEgBD0Pa9SKhr4BjAecAM7gFcDK50HIwk4F1iOeSeVB7yrlCqWLj4hxOl09vwoMjefjKMH2HnQQaVzCENdsbg9PunOGyCC+hSVUhOATzET0zbMmlA3Av+ulLpEa707iMs0AzVa618Evt+qlPov4ApAEpQQ4pQ8VaXU7VhFzIR5OOJSWrZbLFaGzF1KftpxIpw21LBEGQQxgAS74NSfgLeAHK31RVrrCzBLwK8GngnyGruBKKWUs802+TNHCHFKfq+bup2fUbX6TeqPF7P27Tc5UFR90nGTxqSSl5skyWmACTZBTQIeCQxuACBQrPCXwIwgr/EBcBz4T6WUM3BXdhPwv92IVwgxSDQfO0jlildpPLid2vpmvjxahbu6jDVrd+Lx+kIdnugDwSaoTZjPjzqaBuwM5gJa6yZgPubzp2LgXeBRrfXrQcYghBgE/E311Gx6n5qN77bUaoqKtNMYmcbelPlU+qIoOi7rCAwGXc2D+k6bb9cBv1dKTQPWAD5gInA78Eiwb6a13g8sPrNQhRADmWEYNB3eRf3uNRje1rFXVqeL2LHnkOtJom5/BedNHUpyvCxRNBicbjXztkqAiwNfJ5QB3wYe6uG4hBCDiK++mtrty/FUFFFb78bnN0iIjSByqCI6bzZWZyTKMFDD5DnTYNLVPKjhfRmIEGLw8nuaaCo7StHxOmrq3fgc0aTOvZjYrNyWYyQxDT6nfAallPpWdy6klLIopb599iEJIQYbR0I6rtwJNLn9HI8awe7Ec9lYJFVtB7uuuvgKlFI/Bp4G3tRaF3d2kFIqDfgmcCvwYc+HKIQYSAy/D19dFfa45Hbb4/JmMiw6m80bq8gblsiciZkhilCEi666+H4YGBTxAPCEUmon5uoRZYAFSMUcfj4G+Cdwg9Z6Xe+HLITorzzVpdRt+xRfUz3+iZeRlt6apCx2B1nDh/HNlHQSYyNDGKUIF11OlNVabwAuU0oNBy7BHFY+CvBjDpr4HfAPrfWh3g5UCNF/GT4vDfs20rB/Cx63j6NldZQUvsWsK/+F1MSodsdKchInBLWSg9b6APBUL8cihBiAPFWl1G77BF9dJQBFZXXUNvmpi0ni4/WH+NoihU0WdxWdkKWGhBC9wvD7aNizgYb9W2hblSdr1Ei2HsukyRrFpPRYDMPAfGogRHuSoIQQPc5TXUrd1k/x1la05B6LzUF03iwic8Yxp7CShNgIMpKjQxuoCGuSoIQQPaph/xbq9Vqam70UHa8jNdFFYnYusRPOwxYVC0BerhQRFKcnEw2EED3K5oqlts5c3LXObbDFOwJXwaUtyUmIYHWnYGEBZlVcBx06jLXWMoBCCAFAxJCRxA0bQ3NNIQej8vE6YigubyB3SFyoQxP9TLAFC+/FLK1RAdR22G0gI/yEGJR8DTUYnmbs8anttidPWcjwjFqq9pSxcHq2LO4qzkiwd1A3A/drrR/uzWCEEP1Dy8rjuz6nGQfN4y5lRHabSrd2B6OyExk5NBGrDCEXZyjYBJUMLOvNQIQQ/YO/uZHa7ctpPnaQ8upGSisbqCp+j7SrryImqrVgtsViQdZ3FWcj2EESrwHX9GYgQojw5y49ROWqZbhLD2IYBhU1TTRZoylzZrF805FQhycGmGDvoBqBnymlrgL2Au62O7XWV/d0YEKI8GH4PNTvXkNj4Y6WbVarhZzJ0/nb4SRSEmOYNWFICCMUA1GwCSoKeLk3AxFChCdvTTk1Wz7EW1vRUpPJGuEidsJ5pKbl8JXhtWSmxshyRaLHBbsW3429HYgQIvw0Fn5B/a7Pqa5tpKSsnpyMWOJzRhGTPx9rhDkyLztd5jeJ3tGdeVATgLuB8ZjPrnYDv9Nar+6l2IQQIWZ4PZSW11Fa2YDfYmOrMZqLJy3CareFOjQxCAQ1SEIpdQmwCXM032vAX4FYYLlS6sLeC08IEUquEZNJzM6l2RnPvqS5lDmHUtvoCXVYYpAI9g7qYeAhrfWDbTcqpe7DnMD7fk8HJoToW4bhx/C4sTpb6zFZLBbSZ11CRloV9lo3cyZlEumUJTxF3wj2J20s0NlIvVeAe3ouHCFEKPga66jd8iE19W7sky4hM7X1uZLVGclEld4yQEKIvhJsgjoEFAD7OmyfCpT2aERCiD7VfOwgNVs/obS0kuNVjdSW2kj62tJ2d0qSnEQoBJug/gA8rZQaCqwJbJsN3As82huBCSF6l+H3Ua/X0nhgGx6vn4qaJgws1Lth9bZizp+WHeoQxSAX7DDz3ymlYoGfAScW3CoCfq61/n1vBSeE6B2+hlpqt3yAp8rsAHHYrWRmpbG8bgRJQ4cxY3xGiCMUohvDzAMLxT6slEoDGrXWHVc1F0L0A83HDlK79WMMb+uCMM60YQyfeB72Cjc56bHSpSfCwikTlFLqO8B/a62bAq877m95LfWghAh/J7r0ynZupKS8npyMOBwOG9F5s3DlTsRisTAsI/L0FxKij3R1B/UT4FWgKfD6VKQelBD9QOP+rRzZspaS8gYADlX6mfKVK3AmSXeeCE+nTFBa6+Gdve5IKSV9AUL0A67hE4jatxPKC6mNSGN/3FSUIwHn6U8VIiSCrai7H5imta7osD0T2AKk9UJsQogeZLE5yJy7hOPOzVRZs7l65jBiXI5QhyXEKXX1DOoyYG7g21zg35VSDR0OG9VLcQkhzoK/qZ6ag7sgK5+E2NbnSvaYBAoWLGCKReY2ifDX1R3UVuCHwImf4gLa14EygDrgW8G+mVLq28AzQHObzXdorV8M9hpCiK65y45Q/Pk/OXKkjKr0Mi5eeiE2W+uym1KCXfQXXT2DKgTOB1BKPQ/8QGtdc5bvNwX4T631T8/yOkKIDgzDT8PejdTu2UDhoQr8BriKt7B26xjOmTIi1OEJ0W1B14NSStmVUlnAiXX2LUAEMFVr/b9Bvt9U4LfdD1MI0RV/cwM1Wz7CU34Um9VCWlI0Ryo9lKZMY3ZGcqjDE+KMBDtIYgnwPJDUye5K4LQJSillAyYC1ymlHgcagP8CHtFaG0FHLIRox112hNqtH+FvbmzZljF8BDV5+czKy5aBEKLfCnYliV9hltR4HPgIuAzIAP4f8OMgr5EKbABeBK7AXCH9baAGmUclRLcZhp/Knes4vPEzMpKiAs+WLESNmkLU6KnMsARV7k2IsBVsghoNfE1rrZVSm4BorfUypZQHuA+z7EaXtNYlwPw2m7YopZ4ErkQSlBDd4muqp3D53zm6/0t8PgPD7yd7aBqxkxfiTBka6vCE6BHB/onVCPgDr/cAkwKvNwJjgrmAUmq8UurBDpudmCtVCCG6wWK1YjTV4POZveNHm2Pw5S+W5CQGlGAT1ArgfqVUAmY33VeVUg5gAWYXXTCqgB8rpW5RSlmVUlOB72M+2xJCdIPV6SJ77mLiYyOHsvgTAAAenklEQVSpThrHhKXfJC0j5fQnCtGPBJugfgxMB74NvAxEYyam54Eng7mA1voo5rOr2wLnvg78Umv9WjdjFmLQaa6vpaHJ026bMzkT9dWbuOhrXyEnIz5EkQnRe4IdZr4PGKuUcmmtG5VSM4DzgDKt9dpg30xr/TEw7cxCFWLwMQyDI9s2ULjmE+qyz+GiS+a0WwEiKi4hhNEJ0bu6Wuoo6jTbPznxvda64xJIQoiz5G9upGzDB+zfvB0DsB/8nB16GPl5UulWDA5d3UHVYS5nFAzb6Q8RQgTLXVpI7bZPsbgbSY53UVbdiOFw4bT4T3+yEANEVwnqvD6LQggBgOH1ULdrNU2Hd7VsS0ty0ZA0mrELLiA2WgoKisGjq7X4lvdlIEIMdvv1Xo6tfZ+RydaW50zWCBexE89nRqp064nBJ9iljtbTRXef1npGj0UkxCBj+H2sefddmvdvAeC41UVaUhQRGSOIyZ+H1Sl3TWJwCnYlib93ct4IYDHwi54MSIjBxu9uIqm+kOLA9xX1PnLnn0fU0DFSs0kMasEOM++4AgTQUt/pMuCJngxKiMHEFhlNzuyFVL/7Fpb4IYy/4DKiEmT4uBDB3kGdyscEOVFXCGHOa9q7/xg5Q1OIjGj95xeZNYb8S6/CNWS43DUJERDsM6jO5kTFYy4UW9zJPiFEB7UNzWz88GP8h7dSlH8hC+ZPadlnsViIypSigkK0Fewd1KnmRDUBN/ZcOEIMTN7aCo5/9i4cOoAVaN65goOjc8nN7KzEmhACgk9QHedEGYAb2KG1ru3ZkIQYOAy/j8b9W2jYu5FYw098tJPqejepybFkxMj8diG6EuwgieUASqkYQAE+c7Nu7PJEIQYpwzBorDxO884VeGvKWrYPSYsjOSOf7IJZWKySoIToSrDPoCIwiwpeg1nDCaBRKfUn4Mdaa18vxSdEv1Nb28D6Dz8ktkKTmxELgTEP9vg0EicuwB4r3XpCBCPYLr7fYdZ++jqwDrNMx0zgN0Az8G+9EZwQ/U1dWQlr31iG3V1LPVBR00RyYjRRY6bjGj4Ri5RhFyJowSaoq4GvaK1Xtdn2hlKqAliGJCghAIiMjCA92k+52/ze7UomYe5XsMfIvCYhuivYBNUIeDrZXt2DsQjR79ljEsmdfi7Nq1eQMWUeWROmyLwmIc5QsAnqZ8B/KaVuA9Zorf1KqXzM51IPt50nJbWhxGBx6HApu7drzrvwHBz21gEPMaMmU5CtsLliQhidEP1fsAnqCSAGWAn4lFJ+wIH5+HcG8HibY2VokhjQDMNgzYfLadizDrvhY9PWdGZOHdOy32K1SXISogcEm6Au79UohOgnvLUV1O1YSXLFAZoNs9e7auty3BNH4HSc7cphQoi2ujsPygWMxhzF96VM0hWDheHz0LBvEw37t4BhkBgTSWVNM/boOArmXyDJSYheEOw8KBvwH8APaO3acyulXgC+q7X29lqEQoRQQ5OHzWs2M8qjsXrqW3dYLYydM49YNR2LTZKTEL0h2H9ZDwPXAtcDqzAT1BzMeVAPBL6EGFD2fnmUPSs+ILr+KEUxToamxQLgSMwgJn+eTLgVopcFm6CuB27WWv+jzbZlSqla4FkkQYkByLl/FdH1RwGornOTmmIjZdJcIobmydBxIfpAsAkqBtjXyfb9QErPhSNE+Micei4VRw7R6PYyJG8imbPOxxrhCnVYQgwawSao9cAdmM+g2vousLFHIxKij3l9frbsPEp2VhLpSdEt2x1JmQyfOY/I1CwiU4eGMEIhBqdgE9S/AZ8qpRYAawLbZgG5wMU9H5YQfaPoeB1rP1lFbNl2jmdN4eLLzm/XfZcwbmYIoxNicAtq5Uqt9QZgCvABkI3Zrfc3IE9r/XnvhSdE7/HWlGPZ8S5Jxzdi97txHN3E/sLSUIclhAgIenys1noPcJdSKhnwaa2rei8sIXqP4fVQv28DjQe2YTcMkuNcVNY2kZGRQHaCrDYuRLgIOkEppe4HbgfSA98fBp7QWj/RS7EJ0WMMw2DXgXJcDcXElW7B39Q6pyk1KYqsglkk5E3HYneEMEohRFvBTtR9GLgVeIj29aDuU0o5tNaP9V6IQpydqtpmPl61E8fhDST5y4jOTmh5zuRIyiRm/LnYYxNDHKUQoqNg76BuBr7VYR7UZ0qpfcAfAElQImzZaotIOfg+fp8XD1Be3URaWhLRY2cTkTla5jQJEaaCTVB24HAn2/cBsd15Q6VUArANeEBr/UJ3zhXiTESnDCE9NZ6SY+WkJLgYMr6AuHGzsToiQh2aEKILwT4Rfgx4SimVfWKDUioJ+DXmckfd8TSQ1c1zhAjK4WO16MKKdtuszkiyZyxAjRvJmMXXkDBpgSQnIfqBYO+gvg6MBfYHBkd4gRzACcxSSn3/xIFa67RTXUQp9S0gDth+xhEL0Ykmt5flG49wfO92Ymgg++oriIpsHfDgyla4ssdgscgoPSH6i+4ULDwrSqnhwM+Bc4B3z/Z6QrRlaazGoT9gaJ05j2njuu2cO29K636LBXONYyFEfxFsPagXz+ZNAuU6XgLu0lqXKKXO5nJCtDB8Xhr2baJx/xZGxDZzsA7iY5wMtR/FnFsuhOiv+qqQzf2A1lq/0UfvJwawpmYvR47XkRNZR90XK/A11AAQ7XIwKjuRRDWFqFFTQxylEOJs9VWC+gaQqZS6IvB9LOagixla6+/0UQyinzMMA32okjWb9pNY/gWRKXVEOFt/hB0J6STmz8MelxzCKIUQPaVPEpTWOq/t90qpLZirULzQF+8vBgbDMDi0ZSPZRZuxGR6KjtsZnhmPxRlBtJpBZPY4mdMkxAAitapFv2GxWMhPqGPvEQ92m5WUBBcRmaOIGXcO1oioUIcnhOhhp0xQSqn/DvYiWutvd+dNtdaTu3O8GJzKqhpJSWgtEGixWEibeh7N5UXEJMQTP2E+ztTsLq4ghOjPurqDartChBP4CrAbs3ihG3OI1CTg5V6LTgxKDU0ePttaxJF9eznvvKnkZrauk2ePSSBr/uU4EtKx2KQDQIiB7JT/wrXWV514rZR6FvOZ0Y/aHqOUegjI63iuEGdj/ZYDNGxfxfDmYrYvryPrqstw2Fsn2DqTZSESIQaDYP8E/Rc6n1TyIrCl58IRg5lh+Gk6tAtVsZq9nuP4gKzm/bhrKnEkycg8IQabYBNUCXA+sLfD9iVAYY9GJAYVn8+P1WrBV1tO7fYVeKtLsQGZqTFYLZAyOh9XtAyAEGIwCjZB/TvwnFLqfGAT5poxM4FLgau6OlGIUykuq2f5+v1MijxKSmMhYLTsS0xNJSb/XJwpQ0MXoBAipIJd6ugvSqkjwG3AdYHN24B5Wuu1vRWcGLj2H61i1cefM6R2B8dxEz80AYfdisVqwzViMlEjC2QQhBCDXNC/AbTWnwCf9GIsYhBJN0oZ1bAFt98PFnM18qj04WZ125iEUIcnhAgDXc2DejTYi2it7+6ZcMRgEZU5kszsLMpLjpE5JJnEiXOluq0Qop2u7qCmB3kN4/SHiMHK4/WzYdcx8PuYPan1eZLFaiNz5iJSir8kSs2QAoJCiJN0NQ/qvBOvlVLXAf/UWpf1SVRiQKhr9PD2h9uJPrYVh99N2bDrSEloHZHnTM7EmZwZwgiFEOEs2PKivwNkIooImmH4sZbsYtTxT0loKiLaXcbezZtDHZYQoh8JNkGtBb7am4GIgcNTWULVZ29Qv2s1WUkR2G1WMlOjyU+T3mAhRPCCHcXnB/5DKXUfcABobLtTaz2jpwMT/YdhGBwoquHI0eMURB6m6Yhu2RfhtDE2bxixE+bJnCYhRLcEm6DWBr6EaMfnN3hn1T7qDuwgvW4PZemRxEQ5AXMgRNSoqbhGTMJitYU4UiFEfxPsRN0HT7xWSsUBVq11Va9FJfoNK34yi1dQV1sCQGmlQUyUE2daLjHjzsEWFRfiCIUQ/VXQE3WVUrcD9wCZge9Lgd9qrX/dS7GJfsBitTF89HB2lh8jLiaCIVkZxE84F2daTqhDE0L0c0ElKKXUXcD9wMPAKsy1+OYAP1VKNWqtf9t7IYpwUVhcw879ZVw0ezhWa+uE2sT82ajyI8QMH49r+CRZokgI0SOC/U1yB/CvWuv/bbPtM6VUIfAQIAlqADMMg/c+P0jpl7tJrd/HjuSvMiGvdcCD1ekideE18pxJCNGjgh1mnopZSbejjYAMzRrgvNXHGXr8M3KqN+Hy1nBkwwr8/vZDxiU5CSF6WrB3UF9gltX4VYftX8csAy8GIF9DLfV71tJctI80u0G13UqMy0FeShP43GCV5YmEEL0n2AT1APCOUmo28Hlg22zgYuCK3ghMhEZhSQ2bdxxhQUY13sNfYPh9AFgsFkblJBGdm0/UqKmydp4QotcFO8z8faXUQuB7mPWgGoFdwHSt9dZejE/0oY/XHaR050ZS67/kcJGdjOTWdfMi0ocTnTcLW3R8CCMUQgwmXZXbuBhYqbWuB9BarwBW9FVgou9lV6zFWrcPgKo6L2mJLpyJ6cSMnYUjSRZ1FUL0ra7uoN4BvEqpDcDHga/VWuvmPolM9LnsSdOoPHyQCKeNjKwM4sfNImLISKnRJIQIia4SVArmXKfZwFzgTsCqlFpDa8Jaq7X29XqUokcdKa1l89otzDtvBvExrc+SIjJGMGbyRCLSsonMHisj84QQIdVVPahK4O+BL5RSNmAKZsKaDXwbSFFKrdRaL+6DWMVZMgyDTZ9vonTbamK9NWxcbeX8C+e07LdYLCRMvySEEQohRKtg50ERuFM6AhQDpcChwPkjeic00VMMw6D52EGqVr9BasnnuLw1APgObqK2XnpshRDhqctRfEqpGGABsAi4AMgDDgOfAM8B39RaF/VyjOIM+f1+PMcO0rBvI97acgBckXbiY5xgsZE9uYCYSOnGE0KEp65G8a0EZgCVwKeYyxl9rLXe1zehiTPl8/k4sG0rxVvXkBPnJyqy9WO2WG2Mmjab6FEFWCOjQxilEEJ0ras7qDmYXXr/jTkg4nOttadPohJnzDD87Hr7RcqKigEo8dgZkRmPxW4nMmc8USMmYY2IOs1VhBAi9LpKUCMwu/YWArcD0UqpVcBHmAlrk9ZaaniHGYvFSkbucMqLizEMaPCAd8g40sdPxxrhCnV4QggRtK5G8R0E/ivwhVJqEnA+ZsK6H/AopT4FPtJaP9XrkYp2quua+fLAMYoOHOD8C88hKtLRsi8xbyqJeife1NGMmTmbmNjYEEYqhBBnJujCPYEljbYqpX4LTAVuxFz26HIgqASllFoC/AcwHHMk4KNa62e6G/Rg56uvZuM/3ofSfUQDXx4cwYS87Jb9tqg4Jnz9X2UekxCiXzttglJKDQVmArMC/50CeIHVmLWgPg3mjZRSQ4DXgK9qrf+plJqCWVNqvdZ605mFP/A1NHlwe/wkxEbgqSqlcf8WmksOkOVrpBg/AMd3boI2CQqk/IUQov/rahTf65gJaQhQC3wG/A24C9jY3RUktNbFSqlUrXWtUsoKJGMmutozDX4gq6hpYt2OEvYfqWJMXD3T4svwVBS37I+LcVLX6CY2fQhDJuWHMFIhhOgdXd1BRQBPYN4hbdJa+8/2zQLJKQqoDrz3I1rrvWd73YHI43ZTvmcbIxv24yitpyknEZutdV51VHoOE2ZPxpGcJWvlCSEGpK4GSSzppfdsAqKBicA/lFJ7tdbP9dJ79QtNzV4inLZ2iSbBV8Fozy4afF5cEXa8PgOb3UJk5mhcwydhj0sOYcRCCNH7gh4k0VMCd2JuYINS6llgKeaqFINOTb2bLXtK2XWggkvOySUnI65lnzMthyFDh2BpqiEyykVkzjhcwyZgc8WEMGIhhOg7fZaglFLzgce11lPbbI4AqvoqhnCzeXcJB3fuZGjDAXZvrCBn8YKWfRaLldQJs/G7G4gcmicVbIUQg05f3kFtAbKUUj/CXDZpJnAT8NU+jCEs+D3NNB3ZzZjyrRjVhwGwlO/F4z0Xh7119F1k1uhQhSiEECHXZwlKa12tlLoU+B3wc8xFZ2/WWi/vqxhCwe832Hekir2HKlmUH4f78A6ai/Zi+LzYgbTEKFwRNmKi/Fib68AuJdWFEAL6+BlUYL7T3L58z1B745M9NBTtJ7nhIIeOukmIbd9Vl56WQGT2WCKH5cvzJSGEaKPPB0kMJoZhMLZqJcerjwFQUWNvSVD22CQih+UTmTUai83R1WWEEGJQkgTVA/x+g0PHaqlv9DB+ROvwb4vFQsaIkVSVHichLoKkeBcRQ0biGjYee+IQmb8khBBdkAR1luoa3Lz94Xbs5fuJsPkZnf01nI7WgQ7xIyeSV36IyJxxROaMwyY1mIQQIiiSoM6QYfjxHD+M79BOhhdvw+3xYmBFf1nChLysluPscckkLbwOi8XaxdWEEEJ0JAkqSE1uL3sOVZLu8hNTV0jTUY2/qR6A5LgISit9JMRGMMQ4BmS1O1eSkxBCdJ8kqCDoA6VsXL2RuPpC/M5astLa11dKjIsgZdhwooeNIyJjeIiiFEKIgUUSVBBiijaQWbkZgGoPZPj82GxWrE4XEUPHEDl0LPaYhBBHKYQQA4skqIC6Bjf7jlRxuKSaxXNHYbW2jrBLHjmWQzu2YQES41w403KIHjYOZ9owqbskhBC9RBIU4Pf7+Oc7K4moOojLU8nhUTcxLDOxZX9EWg6j8kYTnZlLRJaSCbVCCNEHBlWCMgyDytpmbFYL8TEReGsraTq6m+aje8hrKqO8uQmAgzt2MixzTst5FquN1HlXhipsIYQYlAZNgjpQVM1nW4uoqamnIKWRsVHleCpLWvbHx0TQ5PYRH+MkNSOEgQohhAAGUYKyN1URVbSeIU3F+Cv8eHISoc1CDtFxsYwbO42IoQp7TOKpLySEEKJPDJoElWSpIbX5CAYGrkgnPsPAbrXhTM8lcqjCkZot85WEECKMDJoE5coaTW5WIhF2sMckmiuIZ43BGuEKdWhCCCE6MWgSlNUZSUrBPOyxydgT0mWhViGECHODJkEBuHLGhzoEIYQQQZKHLkIIIcKSJCghhBBhSRKUEEKIsCQJSgghRFiSBCWEECIsSYISQggRlvrLMHMbQElJyemOE0IIEYba/P4OukZRf0lQQwCuueaaUMchhBDi7AwBvgzmwP6SoNYD5wLFgC/EsQghhOg+G2ZyWh/sCRbDMHovHCGEEOIMySAJIYQQYUkSlBBCiLAkCUoIIURYkgQlhBAiLEmCEkIIEZYkQQkhhAhLkqCEEEKEJUlQQgghwlJ/WUnilJRSM4C/a63TAt+nAr8FLgKagf8Gfq619gX2/xm4GvC2ucxErfV+pVQO8BwwCygFvqe1/kcYt+WcwP6xQBFwj9b6tcC+kLWlO+1QSj0NXNvhEtHAvVrr/+iHn8mNwH1ACrAb+LHWelVgX39ry/eAO4FkYDVwh9Z6fyjbopS6APg1MDrwvo9prZ9RSiUA/wVcANQB92mtnw+cYwF+CdwKOIHngZ9orb2B/VcD/4G5ysFy4AatdWk4tqXNuVbgdWC51vqJNtvPw/xMRwJbgeu01kEtKxSO+u0dlFLKopS6GXgf84fuhBeBNMxf2vnADODf2+yfAlyutY5p87U/sO8VYBvmP8hbgFeUUiN6uSln1Bal1BDgHeD3QCxwB/BS4BdHSNpyJu3QWv9r288C+AmwM9CukLTjTNuilJoIPA4sBRKAl4C3Ar9M+ltbrgYeBm4MxPt/wAdKqchQtUUplY35S/khzP+//wL8Sil1EfBHzGXQhgCLgV8rpeYHTr0VuALz3/5oYDpwT+Ca4zAT7Q2BtuwNtK1XnUVbUErlAn8DLu9wzRTgLeDBwDXfBN5r8/PX7/TbwDE/hNsxP2AAlFJRwMXAnVrrUq11BXA/cEvgH6kLyAO2dLyYUmoMMA14QGvt1lp/jPmP8qbeb0r32wJcD6zQWr+otTa01h9g/oKpDGFbzqQdtDl2JPAo8E2tdU0//ExG0/pvyoL5S6YxcG5/a8uVwJ+01su11l6t9R8BN7AwhG3JBV7WWr+ptfZrrdcDnwILga8B92utG7TWW4A/YSYmgG8BT2itj2itjwO/AG4L7LsW+JvWepXWugn4GTBHKTU6HNuilIoBNmLeHa3ucM0rgB1a69e11h6t9WNAROCa/VJ/TlBPa62nAhvabDvRnvo223xAKuZfFJMxu/b+pJQ6rpTapJRaEjhuHHBIa9323N3AhF6Jvr0zactU4KBS6hWlVJlSajOQobWuJXRtOZN2tPWfmL8Utwa+72+fyXvAF8B2zF/mjwBf11r76X9tsXbYd2L/GELUFq31Sq31v574XimVROsi0gbm3U9n8YzDvCtvuy8zcH67fVrrBuAw4duWJmCs1voewNPhsh3bCaDpm5+xXtFvE5TWuqiTbXWY3RiPKqWSlFLJwAOB3S7MrrCVmH9RZmJ2YSxTSk0CYoCGDpdsAKJ6pwXt4j6TtiRhdq28hNkV8CvgzcBdSEjacobtAEAplY/Z5/5Im9P722cSifkLYRbmc7R/w+ziy6D/teV14Fal1DSllEMpdQtm74OLELblBKVUPOZd21rMO4omrXXbla/bxtMx3hOvozrZ1/HcXtedtgTuZk/1fCzkbelp/TZBdeE6zL9ed2E+8HwrsL1Ka/2+1voCrfWGwC3w68DHwGWYfy26OlwrCvMhZaicsi2YD7Xf1Vr/PdCWZcAm4BLCry1dteOEbwNvaa2PtdkWbu2ArtvyC6BEa71Wa92stX4KOAhcRT9ri9b6Fcw/el7BvKPIBz4AKglxWwJdjGuAY5jdYbVAZIcu47bxdIz3xC/suk72dTy3V51BW7oSjj9jZ2UgJqghwG1a63StdT5wFNiltW5QSn1FKfWtDsc7MW+bdwI5gedUJ+Rx8i1zXzplWzBv+xM7HH9iVGa4taWrdpywlJMfTodbO6DrtmRj9vm35cXsiulXbQkMwvmb1nqU1joD+BFmktpICNuilJqHeafxFvC1wHOjvZjP/IafIp6dgOqwr1hrXdVxX+DZXA7h25audGxnd84NS/1+mHknHge+UErdhfmD9gitI8JswG+VUrsw/6F9HTgHuFlrfUgptRV4WCn1s8D2pcDsvm5AG1215c/Ad5VS1wIvY/6VPhG4Wmt9OMza0lU7Tgx3HgF81vYkrbUOs3ZA1235O2aX2TLMomzXAOOBd8LwM4Gu27IQ+LlS6lygBrP7rwhYr7U2QtGWQPf13zGnIDx5YrvWuk4p9SbmKLibMIdY34I5Mg/gL8BdSqmPMO8yfhHYBua/nVVKqQXA55h3jZu11nvCtC1deRPz5+/qwOsfAH7MwRf90kBMULdgjnqpwOx2eTLQ1YLW+i2l1L3A/wIZmHchS7TWhwLnXgk8izknoQy4SWv9RR/H31ZXbdmqlLoU85fKU8Ah4Aqt9eHAueHUllO2IyAXaA6MJOsonNoBXX8mfwo8T3gZcx7UTuDSMP1MoOvP5X8wH65vxexl+Ai4rM2zkVC05Q7M58i/Ukr9qs32P2COynsKKMTsEXlYa/3PwP6ngXTMUW9RwF8JPG/TWm9XSn07cEwW5h3NVb3cDjjztpyS1rpUKfUVzHlQzwE7gK9ord09HXxfkYq6QgghwtJAfAYlhBBiAJAEJYQQIixJghJCCBGWJEEJIYQIS5KghBBChCVJUEIIIcKSJCghelBg8d7jgcU/O+67VylVFyiXIIQ4DUlQQvSsH2JOgG87+RKl1HDgXsyVAw6GIC4h+h1JUEL0IK11CWZNoZuVUjPb7HoSsw7Zk52eKIQ4iawkIUQPC6xE/RnmEkHTMVfLXwYUaK13Bo65EbOqaxbmauL3aq3fDeyzY5Yo/yZmWZhyzOW57tJa+5RSL2GusTYWc622K7TWn/ZZA4XoI3IHJUQPC6xXdyvm4r03Ar8BHmqTnBZjLtR6b+CY5zBrec0IXOJnmCXAr8Os0vsz4PvAkjZvcy3mmmsLMcs1CDHgDMTFYoUIOa31F0qpx4FnMO+Qft1m9z3ArwM1vAD2KaWmYZa0+AZmRd4btNYrAvufV0rdjVkx9e3Atp1a65d6ux1ChJIkKCF6z4OYVXV/qbVuW557HDBVKXV/m20OAnV7AqvuL1RKPYZZYn0i5orvtjbHf9mbgQsRDqSLT4heorVuDLxs7LDLDvwEmNzmazxmTSWUUg9hloSwBv57IdCxlEXHawox4MgdlBB9bzcwTGu978SGwN1UE/AY8B3gh1rrPwf2uTALClo6uZYQA5YkKCH63qPAn5VSGvgYuAD4OebzJzALCC5RSq0G4jG7CuM4uZy8EAOadPEJ0ce01n8F7gTuxnzudCdwm9b6tcAh12OO3tsOvAHsA14ApvZ5sEKEkMyDEkIIEZbkDkoI8f/bq2MBAAAAgEH+1tPYURLBkqAAWBIUAEuCAmBJUAAsCQqAJUEBsCQoAJYCqM7gT7XJ7uAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(census, ':', label='US Census')\n", + "plot(un, '--', label='UN DESA')\n", + " \n", + "decorate(xlabel='Year',\n", + " ylabel='World population (billion)')\n", + "savefig('figs/chap03-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following expression computes the elementwise differences between the two series, then divides through by the UN value to produce [relative errors](https://en.wikipedia.org/wiki/Approximation_error), then finds the largest element.\n", + "\n", + "So the largest relative error between the estimates is about 1.3%." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.3821293828998855" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(abs(census - un) / un) * 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Break down that expression into smaller steps and display the intermediate results, to make sure you understand how it works.\n", + "\n", + "1. Compute the elementwise differences, `census - un`\n", + "2. Compute the absolute differences, `abs(census - un)`\n", + "3. Compute the relative differences, `abs(census - un) / un`\n", + "4. Compute the percent differences, `abs(census - un) / un * 100`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 0.032480\n", + "1951 0.022089\n", + "1952 0.017480\n", + "1953 0.016188\n", + "1954 0.017056\n", + "1955 0.020448\n", + "1956 0.023728\n", + "1957 0.028307\n", + "1958 0.032107\n", + "1959 0.030321\n", + "1960 0.016999\n", + "1961 0.001137\n", + "1962 -0.000978\n", + "1963 0.008650\n", + "1964 0.017462\n", + "1965 0.021303\n", + "1966 0.023203\n", + "1967 0.021812\n", + "1968 0.020639\n", + "1969 0.021050\n", + "1970 0.021525\n", + "1971 0.023573\n", + "1972 0.023695\n", + "1973 0.022914\n", + "1974 0.021304\n", + "1975 0.018063\n", + "1976 0.014049\n", + "1977 0.011268\n", + "1978 0.008441\n", + "1979 0.007486\n", + " ... \n", + "1987 -0.018115\n", + "1988 -0.023658\n", + "1989 -0.028560\n", + "1990 -0.031861\n", + "1991 -0.037323\n", + "1992 -0.038763\n", + "1993 -0.040597\n", + "1994 -0.042404\n", + "1995 -0.042619\n", + "1996 -0.041576\n", + "1997 -0.040716\n", + "1998 -0.040090\n", + "1999 -0.039403\n", + "2000 -0.039129\n", + "2001 -0.038928\n", + "2002 -0.038837\n", + "2003 -0.039401\n", + "2004 -0.040006\n", + "2005 -0.041050\n", + "2006 -0.041964\n", + "2007 -0.043192\n", + "2008 -0.044599\n", + "2009 -0.046508\n", + "2010 -0.057599\n", + "2011 -0.061999\n", + "2012 -0.066201\n", + "2013 -0.069991\n", + "2014 -0.073816\n", + "2015 -0.101579\n", + "2016 NaN\n", + "Length: 67, dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = census - un" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 0.032480\n", + "1951 0.022089\n", + "1952 0.017480\n", + "1953 0.016188\n", + "1954 0.017056\n", + "1955 0.020448\n", + "1956 0.023728\n", + "1957 0.028307\n", + "1958 0.032107\n", + "1959 0.030321\n", + "1960 0.016999\n", + "1961 0.001137\n", + "1962 0.000978\n", + "1963 0.008650\n", + "1964 0.017462\n", + "1965 0.021303\n", + "1966 0.023203\n", + "1967 0.021812\n", + "1968 0.020639\n", + "1969 0.021050\n", + "1970 0.021525\n", + "1971 0.023573\n", + "1972 0.023695\n", + "1973 0.022914\n", + "1974 0.021304\n", + "1975 0.018063\n", + "1976 0.014049\n", + "1977 0.011268\n", + "1978 0.008441\n", + "1979 0.007486\n", + " ... \n", + "1987 0.018115\n", + "1988 0.023658\n", + "1989 0.028560\n", + "1990 0.031861\n", + "1991 0.037323\n", + "1992 0.038763\n", + "1993 0.040597\n", + "1994 0.042404\n", + "1995 0.042619\n", + "1996 0.041576\n", + "1997 0.040716\n", + "1998 0.040090\n", + "1999 0.039403\n", + "2000 0.039129\n", + "2001 0.038928\n", + "2002 0.038837\n", + "2003 0.039401\n", + "2004 0.040006\n", + "2005 0.041050\n", + "2006 0.041964\n", + "2007 0.043192\n", + "2008 0.044599\n", + "2009 0.046508\n", + "2010 0.057599\n", + "2011 0.061999\n", + "2012 0.066201\n", + "2013 0.069991\n", + "2014 0.073816\n", + "2015 0.101579\n", + "2016 NaN\n", + "Length: 67, dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = abs(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.013821293828998854" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z = max (y / un)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.3821293828998855" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z * 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`max` and `abs` are built-in functions provided by Python, but NumPy also provides version that are a little more general. When you import `modsim`, you get the NumPy versions of these functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Constant growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can select a value from a `Series` using bracket notation. Here's the first element:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.557628654" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census[1950]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the last value." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.325996709" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census[2016]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But rather than \"hard code\" those dates, we can get the first and last labels from the `Series`:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1950" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_0 = get_first_label(census)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2016" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_end = get_last_label(census)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "66" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elapsed_time = t_end - t_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can get the first and last values:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.557628654" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_0 = get_first_value(census)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.325996709" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_end = get_last_value(census)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can compute the average annual growth in billions of people per year." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.768368055" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_growth = p_end - p_0" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07224800083333333" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annual_growth = total_growth / elapsed_time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TimeSeries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's create a `TimeSeries` to contain values generated by a linear growth model." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Series([], dtype: float64)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = TimeSeries()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initially the `TimeSeries` is empty, but we can initialize it so the starting value, in 1950, is the 1950 population estimated by the US Census." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "1950 2.557629\n", + "dtype: float64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[t_0] = census[t_0]\n", + "results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After that, the population in the model grows by a constant amount each year." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "for t in linrange(t_0, t_end):\n", + " results[t+1] = results[t] + annual_growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the results looks like, compared to the actual data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file figs/chap03-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(census, ':', label='US Census')\n", + "plot(un, '--', label='UN DESA')\n", + "plot(results, color='gray', label='model')\n", + "\n", + "decorate(xlabel='Year', \n", + " ylabel='World population (billion)',\n", + " title='Constant growth')\n", + "savefig('figs/chap03-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model fits the data pretty well after 1990, but not so well before." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercises\n", + "\n", + "**Optional Exercise:** Try fitting the model using data from 1970 to the present, and see if that does a better job.\n", + "\n", + "Hint: \n", + "\n", + "1. Copy the code from above and make a few changes. Test your code after each small change.\n", + "\n", + "2. Make sure your `TimeSeries` starts in 1950, even though the estimated annual growth is based on later data.\n", + "\n", + "3. You might want to add a constant to the starting value to match the data better." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file figs/chap03-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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ffTQ1NZGQkMB3v/tdCgoKAPjud7/Lo48+yve+9z2uuuoq5s2bF9J1QoiTF+jrwVW9lSi1eKDrDsAWGUP+FTew6Z3d0OUm3Gmj2+U9rpl5LpeL8vJy9u/fT0xMDEuXLj2ly8pZxiNDWCnlALYBv9Ba/yKE83OBmrfffpvMzMwhx8vLyykuLh71eopTS/4ehQidYRj07S2np2I9hs9DRP48ImYuwHbETLy9B7poONjDvKLUkGfpeb1eqqqqqK6uJhA4PHnivPPOO+EekPr6+kM9QDO01ntCuWa8xqBuB3qBJ8fp+UIIMWn5utvo3vE+3tYGANweH9Xr1uDrSmDpguAeiqy0mJAnQRiGQW1tLVprPB5P0LGcnJxTvh3OKQ9QSiknZrfef2qtx2eBJyGEmISMgJ/e3Vtx7f4YI2COCfW5fVQ0eqmPnUvvHhdFBb0kxUUc330Ng+bmZsrKyujq6go6Fh8fT2lpKYmJiaP2OUI1Hi2oS4AA8D/j8GwhhJiUvG2NdG9/H1/3oNVkLBYSihfQE59Az8FerEBji+u4AlRnZydlZWU0NzcHlUdERFBcXMz06dPHLS1kPALUVcBqrfXEywoTQogJJuB146rcSG9tGWCAAVjAHpdKzKxl2GOTOG96H2u37uPcMzNIjA0P6b5utxutNXV1dUELANjtdmbOnEleXh42m21sPlSIxiNAnQXcNw7PFUKIScddr+mt3UkgYNDU1osnYKFk2XLCc0qxWMxJD4mx4Vx1Xn5I9/P7/VRXV1NVVRW0aIDFYiE7OxulVFDC/ngKKUAppWzAfGABkAr4gUZgk9Z663E+MxfYf5zXCCHEaSk8p5SeugqqynbTaktmf+wZJIflkGM5vjRWwzDYt28fFRUV9PYGb0KYmppKSUkJMTETa0WJEQOUUioB+CpwK5AEVAMtgA1IBnKUUg3AU5hTxtuP9UCtddTJVloIIaYiwwhgeD1YnYe76SxWG3Fzzgd/BrUHI8FiYdfeNnKmhT6jrqWlhbKyMtrbg7+iY2JiKCkpITU19ShXjq+jBiil1Erg+8DbwI3AW1pr9xHnxALnAtcCO5RS92qtnx/D+gohxJTk62yhe8casNqJnH85Tsfh8R9HfCpnLkmg5r0qFpSkhbyJYE9PD+Xl5TQ0NASVh4WFoZQiOzt7Qq+LOVILaiGwUGvdfLQTtNadmLPx/kcpNQ24F5AAJYQQITL8Xly7NuOq2Ybf5+dAq4va+jBWfPZirNbDwSMqwsG1lxSFFFC8Xi+VlZXs2bMnKNHWarWSn5/PzJkzsdsn/lKsR+3E1FrfOVJwGub8Bq31HaNTrakrlC3fH3/8cWbPnk1VVVXQOcNtDnjIyy+/THFxMXPnzmXu3LnMmTOHSy+9lGeeeSZohs6FF17I7NmzB8479OvTn/70wDm1tbXcfvvtLFy4kLlz57JixQp+//vfD/vcyy67jPPOOy9owVghRGg8zXW0vb8aV/VWjECA3fs6aO3y0tPdQ/meoZuTHis4BQIBampqeOedd4asApGRkcEFF1xAUVHRpAhOcByz+JRSqcBswAEE/SlprV8b5Xqd9txuN3fffTerV6/G6XSGdE1hYSF/+9vfAHNAdMuWLdx99910dnbyta99beC8n/70p0NWPT8kEAhw8803s2LFCn784x8THh7O1q1buf3223E6nXzhC18YOPfQIrApKSm8+eabXH755SfxiYU4fQTcLrrLPsDdcPiHUIvFQty0LD5w5eKxR3OgtWfIRqlHYxgGBw4coLy8nO7u7qBjiYmJlJaWEh8/sVcuH06os/huxFyWaLhVAg3MSRNiFC1ZsoT9+/fz2GOPcc899xz39RaLhXnz5vHQQw9xyy23cOONN4a0TElbWxu1tbWsWLGCiAgz2W/u3Lncc889Q5Y+eemll/jUpz5FUlISL7zwggQoIY5hYP08vQHDe3hI3+III7poCQnTCtj9wR5m5SczY3pokyA6OjrYuXMnLS0tQeWRkZGUlJSQnp4+oceZRhJqC+qbwK+Bb2utu4518kTQU7kJV9XmkM4NzyomZlbwduld29fQt3doV9yRImfOJ6pw4QnVccT7RkbyyCOPcN1117Fs2TIWL158QvdZsmQJVquVLVu2DLsl/JGSkpJYtGgRN9xwA1deeeVAN9/VV18ddF5raytvv/02r7/+OjExMTzyyCNs3749aJ8rIcRhRsBPx8ZX8bY20O3y0NzeS056LBGZhUQXn401zPyBMNR8pr6+PioqKqivrw/qxnc4HBQUFDBjxgys1sm9aXqoASoL+D+TJThNFWeeeSarVq3iW9/61kDX3fE6tP/S4Gb/3XffPaQP+vrrr+eOO8whxGeeeYbVq1fzr3/9i9/97nf4fD6WLVvG/fffP7Cp4iuvvMKSJUuYNm0aAFdeeSW///3vefjhh0+onkJMdRarDXtMIrV6F21dbjy2SALxizjrzPnHdR+fz8fu3bvZvXt30B5NFouF3NxcCgsLQx4WmOhCDVBvAhcBu8awLqeFULd8P+S2225j3bp1PPjgg0HjP6Hy+/10dnYOBBKAH//4x0cdgzpUx+uuu47rrrsOj8fD5s2beeyxx7jrrrt46aWXMAyD1atX09TUxNKlSwFzzMztdnPPPfeQlBRav7kQp5vIwkVE7qqgMhBDU1QB4a1hzPP6g6aUH41hGNTX11NRUTFkk9G0tDRKSkqIjo4eq6qPi1AD1Dbgp0qpK4FKIGgwQmt9/IMkYyyqcOFJdb3FzFo2pNtvNISy5ftgNpuNRx99lKuuuorIyMjjft7GjRsxDIPCwsJjn4w5rvTiiy/yyiuvAGawWrJkCQ6Hg5tuugmA9evX097ezj//+c+gLoRVq1bx4osvDuzgK8Tpyt/Xg6tyI1HqrIGuOwCrI4z8Fdez8991FEU6WDJrWkjB6eDBg5SVldHR0RFUHhsbS2lpKcnJyaP+GSaCUDsolwEbgAhgDv05Uv2/hp/3LIYVypbvR8rOzuY73/kOq1evDvk5gUCAjRs3ct9997Fq1aqQf7JatmwZdXV1PPzww7S0tGAYBnv37uXZZ58d2G7+pZde4pJLLiEtLY2UlJSBX5/5zGd48cUXZcq5OG0ZRoDemk9oe/9FemrL2fzP/6HbFTy5yGp3cMU5M7hoYfYxd7ft7u5m06ZNfPjhh0HBKSwsjDlz5nDeeedN2eAEIbagtNYXjHVFThehbPk+nM9//vOsWbOG9evXH/WcyspK5s6dC5grEmdkZHDTTTdxzTXXBJ339a9/fdhViv/5z3+Snp7OH//4Rx5//HFWrFhBb28vCQkJXHLJJXz1q1+lpaWFt956i+eee27I9StWrODhhx/mjTfeYMWKFSH+iQgxNXjbD9C9Yy2+zoN09XjY19yNP9DGBxvKufiCOUHnHrnr7ZE8Hs9Aou3gCRA2m438/Hzy8/MnTS7TyQh5y3elVBpwB1CK2fIqB36tta4eu+oNPDsX2fJ9ypO/RzEZBbxuevQG+urKMbNuwNXno+KAj/2xs+hxJvH5iwpJSzx2F30gEGDPnj1UVlYO6YnIzMykqKhoIP1jshmzLd+VUouAfwF7gQ8wE3VXAHcqpc7XWn90QjUWQohJyjAM3Pt30VP+IQHP4dXBLVYbKbMXUdWSgK2ll8vPzDhmcDIMg8bGRsrLy+np6Qk6lpSURElJyaRMtD1ZobYRfwL8X+DWwdu0K6WeAB4FpAtQCHHa8HW10r1zLd6WBtq73YQ5bUSE2XGmZBNdeg62yFiWZvuwWa047CN357W3t1NWVjYk0TYqKoqSkhLS0tImbaLtyQo1QC0AbhocnPo9DoSWDSuEEFOEv7udngP17GvuxtXnwxEZw7xLPkX4tLyBYBLuHPnrtbe3dyDRdjCHw0FhYSG5ubmTPtH2ZIUaoBowNxrUR5TnAZK8K4Q4rTjTZ2BLyqK3fgfNkXk0RxUQ3xdPaQgtHZ/PR1VVFdXV1UMSbWfMmEFBQcGUSbQ9WaEGqBeAXyml/gs4NI1sCfCz/mNCCDEl+brbMbx9OBIO5ylaLBZS5p1PfUQhzTVu5hSmUJA18hjRoZSNiooK3O6grfVIT0+npKSEqCjZz3WwUAPUD4HpwGrMGXwWwIvZxXfv2FRNCCHGj+H34qraQm/NNtyWMPyzryB72uGNAm2RsZx5ZjR5BR4SY8NHuBM0NzdTVlZGZ2dnUHlcXBylpaWy+spRhJoH5QFuVkrdDSigF6jSWveOfKUQQkwuhmHgOVBDT/kHeHu6aGpz0drRR0fLu0z7wlU47IdzCO0264jBqauri7KyMpqamoLKw8PDKS4uJiMj47SdABGKkbZ8vwz4l9ba2//6SFlKKUD2gxJCTA3+ng66y9bhad4LmMGqo9tDjyOeJpLYVHaAs2dPP+Z93G43lZWV1NbWBiXa2u32gUTb4ZLlRbCRWlCvAulAU//ro5H9oIQQk9rg7jwjcHjigiMikpQFC9ha4yAjNQaVkzDCXczFmWtqati1a1fQotAWi4WsrCyUUoSHj9wdKA47aoDSWluHey0mpscff5zy8nKefPLJEc87lM29adOmkDYwFGIqG9yd5+nupM/jJyrC3DQ8IruEyMKFJDrCCM/sIjs95qjdcYZh0NDQQHl5OS6XK+hYcnIypaWl8v/tBEz9xZyEEOIoDE8fXdveobW9mwOtLoyAgSotJHHuMhxxqQPn5Uw7enBpa2tj586dtLW1BZVHR0dTUlJCamqqjDOdoJHGoJo5tLDUMWitU499lgCzBXP11Vdz11138dRTT+H1ernzzjuxWq089dRTeDwebr31Vq6//no++OADfvKTn1BTU0NGRga33347l1xyycB97r33Xj755BPy8vLIy8sLes7q1at55plnaG1tZfbs2TzwwANkZWWNx0cWYsKyhkUQkT+f1nfewG04OBBThMt5BpfEHfsrzeVyUVFRwb59+4LKnU4nSimys7NP+0TbkzVSC+qbhBigJprdu3dTWVk57MaAo81ut1NYWEh+fmjbNIM5s2fHjh28++67vPXWW9x9991cddVVvP3226xdu5Y777yTWbNmsWrVKh5++GEuvvhiNm3axG233UZKSgrz58/nrrvuoqSkhF//+tdorbnxxhtZsMDc+eTNN9/k5z//OU8//TQFBQU8++yz3Hzzzbz66khDiUJMbYZh4O9qwR4bvD1FZN5sMtp7+MfuMCKjIpmZPfI4k9frHUi0DQQCA+VWq3Ug0XaknQlE6EYag3ruFNZjVFVXV5+S4ARmVnh1dfVxBSgwd8o9tBmg3+9n5cqVOJ1OLrjgAvx+P7/97W9ZvHgxl11mTqBcsmQJV1xxBa+88gqpqans2LGD3/zmNzidTmbNmsVVV1018JPc6tWrWblyJaWlpQDccsstvPDCC2zYsIGcnJzR/QMQYhIw185bh7e1AU/JZWTkHu5NsFhtZC04h0+ltZE7LTZoGvlghmFQV1eH1npIou20adMoLi6WRNtRNlIXX8i742mtj38v8jGUl5d3SltQR3avheLQysSHpprGxMQADHQJ9PX1kZGREXRNZmYmGzZsoLm5mbCwMBISEoKOHQpQ+/fv58knn+RXv/rVwHGv18v+/fslQInTSsDrxrXrI3prd9Db56XhYA8H61/D/rnrSEsKDiYFWcO3nAzDGEi07eoKXtktPj6e0tJSEhMTx+wznM5G6uLrGeHYhHYoz2AiO9ag6YIFC/joo+BdTPbu3UtycjJpaWm43W5aWloGMtAPHDgwcF5qaiorV64M2qhw9+7dTJ8+fciKyUJMRYZh4N6n6anYMLAVRnObC5fbT29kDO9vqeN/XVR0zP+HnZ2dlJWV0dzcHFQeERFBcXEx06dPlwkQY2ikLr4bTmVFRLBzzjmHp556itdee41Pf/rTbNy4kX/84x888cQTZGRksGjRIh5++GEefPBBamtrefnll5k3bx4AV199Nb/85S9ZuHAheXl5vPrqq3z729/m1VdfPS124RSnN29HEz07/423/UBQecbMArbtT8HtiGH+tAQCAQObbfjg4na70VpTV1c3JNG2oKCAGTNmSKLtKTBSF98jwINa657+10djaK3/e/Srdnqz2+08+eST/OTeG8N2AAAgAElEQVQnP+Hee+8lLS2NBx98kHPOOQeAn/3sZ9x7772cffbZTJ8+neXLl9Pa2gqYAaqzs5PbbruNpqYmsrOz+cUvfkFubu6Qpf2FmCoCnl569Eb69lbQ5/aa211YwBoeRXTREpzT8jlnbztpiZHERYcNew+/3091dTVVVVVDEm2zs7NRShEWNvy1YvSN9OP0QsAx6PXRhDzTTyk1Dfgl5gaHfcCvtNb3hXr9VJCZmYnWh3ctiY2NDXoPBL1funTpsPdJTk7m6aefPupzVq5cycqVK4/5fCGmiq5t7+BqqKWxtYeObg9Z6XGkz1pEZP5cLHbzq6zwKDP0DMNg3759VFRU0NsbvMRoSkoKpaWlA+PE4tQZqYvvguFen6S/YW5wmAZMA9Yopcq11n8cpfsLIU5TkQULqavQdHR76HKmst42my/kL8ByjB1tW1paKCsro729Pag8JiZmINFWjI+QBySUUlHANUAp4AF2Ai/1r3QeyvWLMTc4XKq19gI1SqnzMVdGF0KIkAXcLizOcCyWw8HHEZ9K1vyl7NzexUFrMvnp8Xh9/qNuud7T00N5eTkNDQ1B5WFhYQOJtjIBYnyFFKCUUrOA9zAD0yeYe0LdAHxfKXWp1roihNvMB7YDDyilrsfs4ntSa/2TE6i3EOI0ZAT89O7ZjmvXZqx5C4nLOwOb7XAAii9ZzMLYdpwOG1lpw3fJeb1edu3aRU1NzZBE27y8PAoKCmQy0QQR6t/Cr4G/Av/Z3/pBKRUB/AZ4GlgWwj0SgXOBNZgtqSLgn0qpBuniE0Ici+dgPd1l/8bX1UpLRx+N1W8Qd14C80qDl/DKzxx+Z9tAIEBtbS2VlZV4PMEdPxkZGRQVFREZGTlm9RfHL9QANQdYeSg4AWite5VSPwC2hHgPN9CptX6g//02pdRvgM8CJx2gDMOQ5vgkNvgnWSEG8/d201P+Ae7GagDau9wcaHXhsUXzyc46CvPSiY44+tJChmFw4MABysvL6e7uDjqWmJhISUlJUNK7mDhCDVAfY7Z+Ko8oXwCUhXiPCiBSKeUcNG41Ku3o8PDwgaRVCVKTi2EYeL1eDhw4IMvEiCBGwE9vzSe4qjZj+A9P+U5IiKHSNpM9xnSS4iLpc/uOGqA6OjooKyvj4MGDQeWRkZEUFxczbdo0+c6YwEbKg7pt0NuNwBNKqQXAesAPzAZuBR4O8Vn/ApqBnyilvoG5dfyN/fc4KZmZmdTX1w/J9haTg91uJy4ujuTk5GOfLE4LnoP1dO9ci6+7A8MwsFrNIBI2vYDoorOY12WQ2eZi1swUbNahAaavr4+Kigrq6+uHJNoWFhaSm5sribaTwLFWMx+sEbik/9chB4H/AB461oO01n1KqWXA40AD5iSJR7TWfzmuGg/D4XAwY8aMk72NEGIC6NtXaeY09XrZf7CHqAgHWTOyiS5diiPR3G59ejhMT4kecu2hxZurqqrw+w/vjGuxWMjJyaGwsFASbSeRkfKgRv0bX2tdDVw+2vcVQkwdzrRceg0nNQ0t+C12ao080ko+hSNxaEA6xDAM6uvrqaiooK+vL+hYWloaxcXFkmg7CY3UxfcVrfXzod5IKWUBbtBa/3ZUaiaEOC0cOcHJaneSOvdcans+oiIwA0tYJJ0uL2lJw1/f0tLCzp076ejoCCqPjY2lpKSElJSUsay+GEMjdfHN7R8regp4RWvdMNxJSqlU4EvALcBbo19FIcRUFPD00aM3YPi9xMy5KChIhU8vYNal2fSWNXL27OnDToLo7u6mvLycxsbGoPKwsDCKiorIysqSCRCT3EhdfP/VPynifuAxpVQZ5uoRBwELkII5/bwQeB24Xmu9ceyrLISYzAzDwL1/Fz3lH+Lrc9HU1ktTczQXLl8cFFDiY8K4ePHQ/cs8Hs9Aou3gCRA2m21gqx1JtJ0aRvxb1Fp/BFyplJoBXIo5rXwmEMCcNPFz4DWtdd1YV1QIMfn5ezro3rkWz8F6AgGDqvp2vL4A7Z4qqvcVHTXJFsxcuT179lBZWYnX6w06lpmZSVFREREREWP9EcQpFNKPGVrrGuDJMa6LEGKKMgJ+equ3mTlNAXN2ndVqITI2jk/8eXSFpRG7r2PYAGUYBo2NjZSXl9PTE7yPalJSEiUlJQM7VIupRdrBQogx5W1rpHv7+/i6WweVWojIPYOi3LlUrK1jeVEqapitMNrb2ykrKxuyE3RUVBTFxcWkp6fLONMUJgFKCDEmjICfnvIP6a3dSZ/bS2tnH9OSo3HEJRM96zwcceY2FtdcrIYEmd7e3oFE28EcDsdAoq3VOvI2GmLykwAlhBgbFit+VydNrT0cbO/Fb7FhzSvmjKXnBm2TMTg4+Xw+qqqqqK6uHpJoO2PGDAoKCnA6naf0Y4jxIwFKCDEmLBYL0Weci2X3bjqd0eyPPYPq1liK/OA44pvHMAz27t1LRUUFbrc76Fh6ejrFxcVERx89UVdMTcezYeFczD2dHJjTzAdorWUChRCnMcMw8DTW4EzNxmI7/LVii4hh5mXXse2DRlLDHZw/P3PIBoLNzc2UlZXR2dkZVB4XF0dpaSlJSUfJ0BVTXqgbFt4L/ABoBbqOOGwgM/yEOG35e7vp3vE+nqY6WmILyF9yPg774YVYnbEJXHV+NBFh9qDuvK6uLsrLyzlw4EDQ/cLDwykuLiYjI0MmQJzmQm1B3QTcp7X+4VhWRggxeRiGQd/eMnoq1tPn6mN/czc9NRtosaexdMkZQedGhh9eCcLtdlNZWUltbe2QRNuZM2eSn58vK40LIPQAlQSsHsuKCCEmD7+rk67t7+Ft2Q9Ar9uHy+2jNSKHpr1uCpSL1MTg3Wn9fv9Aoq3Pd3h/J4vFQlZWFkopwsPDT+nnEBNbqAHqz8C1wANjVxUhxERnGAZ9dWaryfAfXs0hKS2V3QlLONAVxnyVSmJceNA1DQ0NlJeX43K5gu6XnJxMaWkpsbGxp+wziMkj1ADVC3xbKfV5YBfgGXxQa/2F0a6YEGJi8bu66Nr+Lu7mfRiGgc1mBSxE5s0hsmABZ/cFWOjzkxR3eLmhtrY2du7cSVtbW9C9oqOjKSkpITU1VcaZxFGFGqAigT+OZUWEEBOXt72Jjg3/oLvbxb7mbiLC7OTmZREz50Ic8WbCbWzU4fNdLhcVFRXs27cv6D5OpxOlFNnZ2ZJoK44p1LX4bhjrigghJi57bBIeeyR7GsytLfY7M4nJvYjE+OC9lnw+H7t27aK6uppAIDBQbrVaBxJtHY6hW2cIMZzjyYOaBdwDlAJWoAL4udb6gzGqmxBigrBYbaQuvJjGg6+wxZ9PICoZrIe/PgzDoK6uDq31kETb6dOnU1xcTGRk5JG3FWJEoeZBXQr8HfgX5oQJC3A2sEYpdbnW+s2xq6IQ4lQKeHrp21tBRN6ZQeND9tgkSj5zPe2fNLCoNJ3oCAeGYQwk2nZ1BadIxsfHU1paSmJi4qn+CGKKCLUF9UPgIa31g4MLlVLfxUzglQAlxBTgaa6ja9u7tLa0U1t2kAsvX47VOminW6edCxdkAdDZ2UlZWRnNzc1B94iIiKCoqEgSbcVJCzVAFQPDzdR7EfjO6FVHCDEeDL+PHr2B3prt1B3oosvlwWrZzLZyxdzS7KBz3W43Wmvq6uqCEm3tdjszZ84kLy9PEm3FqAg1QNUBc4GqI8rnA02jWiMhxCnl62yha+vb5n5NFggPs9HW52Rf7Gwa613MKTawWi34/X6qq6upqqoakmibnZ2NUoqwsLBx/CRiqgk1QP0CeEoplQms7y9bAtwLPDIWFRNCjC3DMOjd8wkuvXFgl1uAjMIiKtqyyEtN5Kwz0rFYYN++fZSXl9Pb2xt0j9TUVEpKSoiJiTnV1RengVCnmf9cKRUDfBtI7i/eD3xPa/3EWFVOCDE2/H09dH/yLi111USFO7BaLVisNqJKziY8q4SrA2YibmtrKzt37qS9vT3o+piYmIFEWyHGSsjTzPsXiv2hUioV6NVaH7mquRBiEvC2NdK64TX2N7bQ0e0hKS6czLxcYuZchD06HgC3u5eysjIaGhqCrg0LCxtItJUJEGKsHTVAKaVuA36rte7rf33k8YHXsh+UEJOHLTqBzj4/Hd3mimUV3gwSZi4nIToOr9fLrl27qKmpGZJom5eXR0FBAXa77HMqTo2R/qV9E3gJ6Ot/fTSyH5QQk4jVEUbOOZfR+torlNmKySqYSWJcBDU1NVRWVuLxBC21SUZGBkVFRZJoK065owYorfWM4V4fSSkl7XwhJijDMPC2NmBPmBaUz+RMzqDkczeR0t5HpK2PDR/+m+7u7qBrExMTKSkpISEh4VRXWwgg9JUkqoEFWuvWI8qnA1sBGSkVYoIJuF20b3mbvZWVHJx2NhcuPyto3Cjgc3OgrpyDBw8GXRcZGUlxcTHTpk2TcSYxrkYag7oSOKf/bS7wfaWU64jTZh7Pw5RS/wE8DQxerOt2rfXzx3MfIcTIPM11tG95m6qaRjzeAEb3OnbNyKFw5nT6+vqoqKigvr5+SKJtYWEhM2bMkJXGxYQwUgtqG/BfmOvugZmoO7hz2gC6ga8cx/PmAT/RWn/reCophAjNwIoQe7ZjAaLCHXi8btrDMzGaXRCopKqqCr//cN6TxWIhJyeHwsJCSbQVE8pIY1C1wIUASqlngbu01p0n+bz5wP85yXsIIYbh62qja+tb+LpaBsqmTUumJj6P9OlJ9LbXoA/2BV2TlpZGcXGxJNqKCSnk/aCUUnalVAZwaJEtCxAGzNda/99j3UMpZQNmA19WSv0UcAG/AR7WWhsjXiyEOCrDMHDV7KB+03skRDs4NGzkTMnGkzmHxF27aWusDromNjaWkpISUlJShrmjEBNDqJMkVgDPAsOtm98GHDNAASnAR8DzwGcxF6D9G9CJTFMX4oQE3L3s3/Ame8vLcXv9BPyRpCRGQ+5cKrsMGj/6OOj8sLAwioqKyMrKkgkQYsILNePu/8fcUuOnwNvAlUA68DPgG6HcQGvdCCwbVLRVKfU48DkkQAlxQoyAj879e3F7zTGlPR1WOnNKaK5qDJoAYbPZyM/PJz8/XxJtxaQR6lSdAuD7WuvNwMdAlNZ6NXAHIyfxDlBKlSqlHjyi2ImZCCyEOAG2iBhyl34Kp8PKXlsarQlZNLW2BgWnzMxMLrjgApRSEpzEpBLqv9Ze4NC6J5XAHOB1YDNQGOI92oFvKKXqgWcwZwV+FTPICSFC4O7pwhoWicNuDgUbhkG7LYa29BIi/eB0HP6ZMykpiZKSEuLj48erukKclFBbUO8D9yml4jHHkT6jlHIA52OOIR2T1nofZtfgqv5r/gL8QGv95+OttBCnGyPgp3bDe2z6/ZNsWL8DgPb2dj788EM2b95MwGodCE5RUVEsWLCAJUuWSHASk1qoLahvAP8A/gN4CjM/qhOzi+7eUB+mtX4HWHCcdRTitObrPEjDh29Qs2sPAD3b3uEdXxc9XW1B5zkcDgoLC8nNzZVEWzElhDrNvAooVkpFaK17lVKLgAuAg1rrDWNaQyFOU0bAT+/urfRUfUR4wCAywk5dh4eDAR+x+/cTExMBmIm2ubm5FBYW4nQ6x7nWQoyekZY6Gnbp4kHl7x56r7U+cgkkIcRJ8Ha20L39PXwdzRiGQWO3l31eK+0RqaRNS8duM1tI6enpFBcXEx0dPc41FmL0jdSC6sZczigUtmOfIoQ4Fp/Xy461a/DXbiU7NZrWPh/VbX24CMORlkea3WwhxcXFUVpaSlJS0jjXWIixM1KAuuCU1UIIQV/7QTa88ieMnlbcAYNGdwCXYcEel4IzxgxE4eHhFBUVkZmZKYm2YsobaS2+NaeyIkKc7uw2K9HWXna7A7R7DWx+Cyk5eVgcTmw2GzNnziQvL09ymcRpI9SljjYxQnef1nrRqNVIiNOQ3++ntqmNekcSnQEX4UnpxKelYbVaycrKQilFeHj4eFdTiFMq1B/FXh3mujzgcuCB0ayQEKeDltZOyj7RLDl3Pk0HGikvL8flcmGNSSBlZjRWu5Pk5GRKSkqIi4sb7+oKMS5CnWZ+5BJFwMAGhFcCj41mpYSYyrZs2ELLx+/g8bh5uXk/EeGDc5YsxMabW62npqbKOJM4rZ1sZ/Y7wOOjUREhprqA20V32QdYKrfR3OOiy2fg31PJtEKFzWrB6XRSWFhITk6OJNoKQehjUMPlRMUB3wUaRrVGQkwxhmHQt7ecjvL11B7sZG+XH5cBNrud+PQ0HHYbM2bMoKCgAIfDMd7VFWLCCLUFdbScqD7ghtGrjhBTh2EY6PIaEg9+TEPDfmra3HgD5n+j2KRkwpLSmZ6RRXFxMVFRUeNcWyEmnlAD1JE5UQbgAXZqrbtGt0pCTH5t7T18/PZb9O7dQTcWbE7zv5rF7sCRMI3E9AxKS0tJTBxuD1AhBIQ+SWINgFIqGlCA3yzWvWNYNyEmrb3//gcN1btw+c0WU4zNRnhCCtFpmZSUlDJ9+nSZACHEMYQ6BhWGuevttZgrmAP0KqV+DXxDa+0fo/oJMam43W601uzpteC1WACDsKhoYrJyUaWzyMvLw2aTlcGECEWoXXw/x9z76YvARsx9pBYDPwbcwH+PReWEmAx6+zz0un20NO2jqqoKn8+HJSyS6JQ0DKsTNftMioqKCAsLG++qCjGphBqgvgBcobVeN6jsZaVUK7AaCVDiNOQPGOzYsoOaD9+i2R5LekZq0PEsNYvS0lJiYmLGqYZCTG7Hs+W7d5jyjlGsixCTRqCvh6p//4ttH2+nz28QsLjoioslJjqcmJiYgURbIcSJCzVAfRv4jVJqFbBeax1QSp2BOS71w8F5UrI3lJjKjICfVv0x2z/6gOYuN4bNCn4/Nis4LH5mzZpFTk6OTIAQYhSEGqAeA6KBtYBfKRUAHIAFWAT8dNC5MgIsppw+t4+WvbvZt/V99ja30Z/ORESYHVtELHPOOpuiM2ZLoq0QoyjUAHX1mNZCiAkqEDDYUV5D2ftv0Nt9kOhIJ/S3jix2JzlFszhjwRIiI4fdgFoIcRKONw8qAijAnMW3W5J0xVRmGAY1O7bw8Ruv4fEHAOjz+gkPc5KckcOcpReQmJQ8zrUUYuoKNQ/KBvxv4C4Od+15lFLPAXdorX1jVkMhxkFHRwdlZWUcbGrC5nRArxur1UJkXBKLz19ORm6ejDMJMcZC7eL7IXAdsBJYhxmglmLmQd3f/0uISc3V52XfgTZc7Q3s3bsXwzDAaiU6dRq21oOcueQc1Jz5kmgrxCkSaoBaCdyktX5tUNlqpVQX8CskQIlJzO8P8NH2WvQH/6K7r4fp+fnYrP3jTBYLM8+YS0HBTMLDI8a5pkKcXkINUNFA1TDl1YB0wotJKxDwU/3R+5Sv+xCP14cdONjcSlpaEmlpaRQXF0uirRDjJNQAtQm4HXMMarA7gM2jWiMhTpGGqp1s/2ANHd3dOOwWPF6wWS0khQU466yzSElJGe8qCnFaCzVA/TfwnlLqfGB9f9lZQC5wyehXS4ix0dbVR01VDT3VG2k8cGCg3Gm34YgLY95ZZ5M3Z5HsaCvEBBDqNPOPlFLzgFuAEsylj/4B/EJrLTvqignP7w+wZuNuajavwd/TQGyUc2CcyWa1kl9UQsnZF+FwyoKuQkwUobag0FpXAncrpZIAv9a6feyqJcToCQQCVO+qoO7Dv+P3eADodfuIjnCQmZnJrPM+TXR80jjXUghxpJADlFLqPuBWIK3//V7gMa31Y2NUNyFOimEYNDY2Ul5eTk9PD+HRUXhaPdjtVtKTE1h4/qdIzikY72oKIY4i1ETdH2J27z1E8H5Q31VKObTWj4b6QKVUPPAJcL/W+rnjrrEQx9BwsIfyqnrCAy20tLQMlEemTCccH2cuWkz27LNknEmICS7UFtRNwFeOyIP6t1KqCvgFEHKAAp4CMo7jfCFCEggYvL5OU/fJBxiuJhJyCsy18wCHw0FhaSk52Vdis8uCrkJMBqEGKDuwd5jyKiDkJBGl1FeAWGB7qNcIEQqfz4feuonmLeuw9fYC0H6ggZi8XGbMmEFBQQFOp3OcaymEOB6hBqhHgSeVUl/SWu8FUEolAj/CXO7omJRSM4DvAWcD/zyBugoxhGEY1O7exY4Na+jtaCPCZuC1gMNuIzPByuJl5xETEzve1RRCnIBQA9QXgWKgun9yhA/IBpzAWUqprx46UWs9ZBvR/sVmfw/crbVuVEqddMXF5OL3++nq6iI6Ohq7PeS5OUfV1OZi09ZdhLWX09ZYD4a52rjVaiEjMYrZ8xeQMWcpFtvJP0sIMT6OZ8PCk3EfoLXWL5/kfcQkYxgG9fX1VFRU0NfXR2xsLEuXLj2pIPXW+l2Ub16PvbuemHAIc5iLt4bZLBTm5zHz7E9hj5RWkxCTXaiJus+f5HOuAaYrpT7b/z4Gs8twkdb6tpO8t5igDh48SFlZGR0dHQNlnZ2dVFVVUVRUdNz3c7vdVFZWsu/j9wjragOgr89ChNPGjLQkipYuJzItZ9TqL4QYX6ek/0NrHfRtpJTaiplD9dypeL44tbq7uykvL6exsXHY47t37yYrK4uoqKiQ7hcIBKipqWHXrl14vV5i4mLwdrVjs1nITYnmzLPOIb5gLharbIMhxFQiHfRi1Hg8HiorK9mzZ4+5l1I/m81Gfn4+TU1NtLe3EwgE2LlzJ4sWLRrxfl09btZsKCfMf5C+vt6BckdsItnTejijMI/0ucuwhsl260JMReMSoLTWZ47Hc8XYCAQC7Nmzh8rKSrxe70C5xWIhMzOToqIiwsPDSUtLY+3atQAcOHCApqYmUlOHzKkBYP22ajZ8sB579z4iktJJSYkHIDo6mpKSElISL8HqkGnjQkxl0oISJ+zIpYQGS0pKorS0lLi4uIGy+Ph4srOzqaurA2DHjh2cf/75QSs6uFwuyst2UrVlM+EdzVgw6GtpwJKWRGlJMTk5ObIChBCniaMGKKXUb0O9idb6P0anOmKyaG9vZ+fOnbS2tgaVR0VFUVJSQlpaGhaLZch1RUVFNDQ04PV66enpobq6mpkzZ+Lz+di1axe7tm/B09ZImM+H22ZeX5hsY9HiOUQmywIkQpxORmpBDV4hwglcAVRgbl7oAeYBc4A/jlntxITT29tLRUUF9fX1QeUOh4PCwkJyc3NHbOGEhYWhlGLHjh0AVFZWUlXfgbe9Dk9LPQF3/1iTBXKTIijKyyZt3gXYY2XjZiFON0cNUFrrzx96rZT6Feasu68PPkcp9RBw/POFxaTj8/moqqqiuroav98/UG61WsnNzT2upYRyc3Opra3lQHMb+5tasPdWEGPpJSrCXCMvxmmjID2OjLnnETa9YNiWmBBi6gt1DOr/w2wxHel5YOvoVUdMNIZhUFdXh9Yat9sddGzatGkUFxeHPF38EIvFwqxZs2h87X+I7K7HYgTwADERdlRKFDlnzCOqYAFWu0yCEOJ0FmqAagQuBHYdUb4CqB3VGokJo7m5mbKyMjo7O4PK4+PjKSkpISnpxDf5S0pKoqgwn61Ne/H5LeQnhjO7tIi4Wedij44/2aoLIaaAUAPU94FnlFIXAh8DFsz9oC4DPj/ShWLy6erqoqysjKampqDy8PBwiouLycjIOK5uN8MwqKpvJz46nJSEiIHyOYvOJsHXgb2rmeS5y3Cm5kh3nhBiQKhLHb2glKoHVgFf7i/+BDhPa71hrConTi23243Wmrq6uqBEW7vdzsyZM8nLy8NmO77VGtq6+lizaQ+e2k+ISYjnkqs+NRCELBYLWYsuwmK1yaKuQoghQv5W0Fq/C7w7hnUR48Tv9w8sJeTz+QbKLRYLWVlZKKUIDw8/7vsahoH/QDXR+nXs/j78LgeV1bNQ+dMGzrE6wkblMwghpp6R8qAeCfUmWut7Rqc64lQyDIOGhgbKy8txuVxBx5KTkyktLSU29sRWBfd2NNFT9gFGWyPp0RZaOiA11kaatx6YdszrhRBipBbUwhDvYRz7FDHRtLW1sXPnTtra2oLKDy0llJqaetzjTNX7OrD4eknprKCvvpJD/zRSEiJISIojadZSwjILR/NjCCGmsJHyoC449Fop9WXgda31wVNSKzFmXC4X5eXl7N+/P6jc6XSilCI7O/u4lxLq7PHw1voavHu3M91dQ3Rm9KBxJitRM2cROXO+TBsXQhyXUMegfg6cBUiAmqS8Xu9Aom0gEBgot1qt5OXlMXPmTBwOxwnd2+7uIGH3a1g8LvxAS0cfyfEROFNziC4+G1tU3DHvIYQQRwo1QG0APgP8aAzrIsaAYRjU1taitcbj8QQdmz59OsXFxURGntx2FRFx8UxLiuJAg4vEuHCSp6UTN+tcnMmZJ3VfIcTpLdQAFQD+t1Lqu0AN0Dv4oNZ65I19xClnGMZAom1XV1fQsfj4eEpLS0lMTDyue3p9AbbtasZmgblFaQPlFpuDnEXLiN2+joQzziI8qxiLRVYcF0KcnONpQUm+0yTR2dlJWVkZzc3NQeUREREUFxczffr0406Ibe9y8/d3dhLVUk644UblfpnI8MNdguGZhaSn58q0cSHEqAk1UffBQ6+VUrGAVWvdPma1EifE7XZTUVHx/9q78/i4quuA47/RNhotliVZkmXJQrYsHXlklmB2kkDjkKQsISGEpAGSEJYkpWlDStICIZAGwtbyCaUlCyFpKE34kFCcAlkLYV/KEoxtSUdesGXLli1kydq3mekf98mekWwhCUszY53v56OP7XfnvbnHI72je99d2LZt27iJttXV1SxZsmTKE20BIiPDpO1cx7Ldf2LIW4+v4Y16Vp509L7X+Hw+fJacjDGH0KQn6orIl4FrgUXev3cDd6mqPZeKs1AoxObNm9m4ceO4ibYVFRWICH7/1JJHOBzBR5iB5nr6Nr5OeKifhfkZ7Ggbpjg/wMLsvYc6DGOMiTGpBCUiV+2tOjUAABR4SURBVAPXAzcDz+HW4jsV+EcR6VfVu2auiuZgIpEILS0tNDY20t8f81iQ4uJigsEgubm5Bzn7wDq7B3lpbQs5vdsJpm0j1L//+VVuIIPa5UXk1p5ERknloQjBGGMOarItqCuBL6nqL6KOPS8iW4GbAEtQs6y9vZ36+no6O2N7WnNzc/dNtJ2qzu5+Hlv9J4p6mhgJ9dJXnoc/w32LpGRmk11zPP6yGhsAYYyZFZNNUEW4nXTHeg2wscSzqLe3l4aGBnbu3BlzfHSn2oqKimmvCJ5DP7VD6+gJDQPQ3TdMIDubwLJjCVTU2YKuxphZNdk7zjrcthq3jDn+Kdw28GaGDQ8P09TUxJYtW8ZNtK2qqmLZsmWkpU0+gYTDEQaGRmJG4qXlFrBIgrRoPSVFeRTWHktg6dG2AoQxJi4me0f7FvC4iJwMvOgdOxn4CHDeTFTMOOFwmK1bt9LU1DRuom1ZWRm1tbVTmmgbiUR4a0cna198hUCGjzPO+WBMi6v46FPIKyp2iclG5Rlj4miyw8z/ICKrgK/g9oPqBxqA41V1zQzWb86KRCLs2rWLhoYGenp6YsoKCgoIBoPk5+dP7ZrhEHs2rmfTE0+QF+oj5EtnS8uxLCnfvzNuWm4BaWLzro0x8TfRdhsfAZ5V1V4AVX0GeGa2KjaX7d27l/Xr19Pe3h5zPCsri2AwyMKFC6f0nCk8MsRAcz39W9YSHuilODtMRxek+0YYbG6A8vce6hCMMeZdm6gF9TgwIiKvAk96Xy+o6uCs1GwOGhgYoLGxke3bt8dMtE1PT9830XYqK433dHXRteENMvZsIjK8/2Mrzg/gS8ug/JgTmF99zCGNwRhjDpWJEtQC3Fynk4H3AlcBKSLyEvsT1suqGprxWh7mRkZG2LRpE5s2bSIU2v/f6fP5qKyspKamhoyMyQ9U6O1op/75Z+ltbiQjNcKy8nxGG1wp/gB5lUdRckSdDX4wxiS0ifaD6gAe874QkVTgWFzCOhn4ArBARJ5V1bNmoa6HnUgkwvbt22lsbGRgYCCmrKSkhGAwSE5OztQvvLeVwW0NEA4zFIaOrgGKSksILD2azLIaGy5ujEkKk75TqWpIRLYDO4HdQDNQDCydobod1t5++23q6+vZuzd2yaB58+ZRV1fHggUL3vEakUiE9r19pKSkUDAvsO941mIhvyCPtrYOfDmFZB55Cvl1K2yCrTEmqUyYoEQkBzgd+CBwBlALbAP+BNwHfEZVdxz0AuOvdzbwXWAJLsndrqo/nFbNk1RPTw8NDQ20trbGHPf7/dTW1rJ48eJJDYDYsq2NdS+8iL99A/6603j/acftK/OlplF+4gfIHUqlfFnVlHfINcaYRDDRKL5ngROADuAp3HJGT6rqxum8kYiUAr8CPq6qvxWRY3HLJb2iqq9P55rJZGhoaN9E2+gBEKmpqVRVVVFVVTWpibYjPZ0MbF1Lysb15LS5UX69m98k/L6VpKTsT2x5lbXYPrbGmGQ20R3xVGA78BPcgIgXVXV4um+kqjtFpEhVu0UkBSgERoDudzg1qYXDYbZs2UJTUxPDw7H/feXl5dTW1hIIBA547u6OPhq37GFXey8fPSrAwNZ1DLU1AxBIg/TUFELhMMVpvQz095OV/e52xjXGmEQyUYJaiuvaWwV8GcgWkeeAJ3AJ63VVjUxw/jhecsoC9nrvfZuqbphWzRNcJBKhtbWVhoYGent7Y8oKCwsJBoPMnz//oOcPj4RY/WQjOT3bKOx7i11daQQyoz4uH1Quq2B+zXvIKreBD8aYw89Eo/i2AD/2vhCRo4EP4BLW9cCwiDwFPKGq90zhPQeAbOAo4DciskFV75tW7RNUZ2cn69evZ8+ePTHHs7OzCQaDlJSUjHvOFIlEYo6lEeaE/ufo9LZr7+oLeAnKR0ZxBYHKI0kvLJv2wrDGGJPopjKKbw2wRkTuAlYCl+CWPfoYMOkEpaphYAh4VUR+BJyLG3CR9Pr7+/dNtI2Wnp5OTU0NlZWVMQMWIpEILW09rGlqo7wkl6Ori/aV+dLSKTpiKZGRBvJzM8nOySKwuJbAEStIzbanS8aYw987JigRKQdOBE7y/jwW9+zoBdxeUE9N5o1E5DTgTlVdGXXYDyT91vEjIyNs3LiRzZs3j5tou2TJEqqrqw840XbDtk6eeLGJ/P7tbN2ezpFV58QMdCgKvodAqJtA5Qr85WITa40xc8pEo/gexiWkUtxAhueBR4GrgdemsYLEG0CZiHwNNyLwROBS4OPTqHdCiEQiNDc3o6oMDsauAFVaWsry5cvJzs4+4HkjnbsoaltL3Z7XCIdChHrT2dn2fspK9j+XSi9YRP5pn7ZuPGPMnDRRC8oPfA/XQnrd65qbNlXdKyJnAv8K3ICbT3WZqj79bq4bL21tbdTX19PV1RVzPC8vj7q6OgoL968Q3tbRz5sb2zhBCkjb8xYDzQ2M9LjnUwvm+RkeCVGYF6BwZDewP0FZYjLGzGUTDZI4+1C/mTffKamXzu7u7qa+vp7du3fHHM/MzGT58uWUlcUOXHjuje3o+g0U9DezdUMnJfNj91gqyg+QPr+YzCNW4F9oi3IYY8woG5s8SYODgzQ1NbF169aYibZpaWn7JtqmpqaOO6+048+MdLwJQMewj+K8DHw+H77UNPyLlhGoqCMtr2jcecYYM9dZgnoHoVCIt956iw0bNjAyMrLvuM/nY/HixYgIGRl+mnd109rey0krSmPOL126lFZdR5Y/jcK8AOnzFpBZEcRfVm2DHowxZgKWoA4iEomwc+dOGhoa6OvriylbsGABdXV1zJs3j1AozEOPvUpK+2ayhjvYs/hzFOTtXxkis6waqSrFX1JJZkWQtLwie7ZkjDGTYAnqADo6Oli/fj0dHR0xx3NycggGgxQXFxMJDTOwrZGB7Y3UtG+gq3cIAF2vnHzK/k0AU9IyKFx1Mb6U8d1/xhhjDs4SVJS+vj4aGxtpaWmJOZ6RkcHCskr2DmXS0dpGYOc6hlo3Ewm7kfb5uX56B4aZn5NJcUbHuOtacjLGmKmzBAUMDw/vm2gbDu8fTZ+SkuK2WcfPupf+j7yBFvamhygsi13JISfLz5HHVRNYXEtGccVsV98YYw5LczpBHWiibTgcISXFx6JFi1i+fDlZWVl0Nr7Crv7NRCLQPwiDQyP4M9JIyy3AXy5kLqomxW8riRtjzKE0JxNUJBLZN9G221uMtaN7kK6eAcKDw3zi/LMoKy3e9/rcCmFe1lPgg/yC+eRVBQksriVtXuFB3sEYY8y7NecSVFdXF/X19bS1te07Fh7oIdTexuJQD/NSw+xp745JUKlZ86g59XTS55eQXlRuW6cbY8wsmDMJqq+vn5dfe5ONm7cQyEhjnj9MqHcv9HdRkZtKTWGYXXsigI/ebRtgRVXM+dk1x8en4sYYM0fNiQTV3d3Nrx//X3a07iEj1EdKSj+DfliUm05laSYZqSmMhMKk+HzMLy4mr6Yy3lU2xpg5b04kqJaWFvzDe8kb2IGPCJlpPlYuzCfX26E2xR8gd1E1RWU1pOYW2kRaY4xJAHMiQZWVlbFzaxGR9hbKctMpmx8gO+DHX7qUzLIa0heU2XMlY4xJMHMiQeXm5nL6h86kI7OLFH8WmWVCRulSWwvPGGMS2JxIUOAWd80/9Xx8aenxrooxxphJmFP9WpacjDEmecypBGWMMSZ5WIIyxhiTkCxBGWOMSUiWoIwxxiQkS1DGGGMSUrIMM08FaG1tjXc9jDHGTEPU/XvSO7gmS4IqBbjwwgvjXQ9jjDHvTimwaTIvTJYE9QrwPmAnEIpzXYwxxkxdKi45vTLZE3yRSGTmqmOMMcZMkw2SMMYYk5AsQRljjElIlqCMMcYkJEtQxhhjEpIlKGOMMQnJEpQxxpiEZAnKGGNMQrIEZYwxJiEly0oSByUiJwCPqWqx9+8i4C7gw8Ag8BPgBlUNeeX3AxcAI1GXOUpVN4tIBXAfcBKwG/iKqv4mgWM5xStfDuwArlXVX3llcYtlKnGIyA+Ai8ZcIhu4TlW/m4SfySXAN4EFQCPw96r6nFeWbLF8BbgKKAReAK5U1c3xjEVEzgBuBaq9971DVX8oIvOBHwNnAD3AN1X1p945PuA7wBVABvBT4OuqOuKVXwB8F7fKwdPA51V1dyLGEnVuCvAw8LSqfi/q+F/gPtMqYA1wsapOalmhRJS0LSgR8YnIZcAfcN90o34GFONu2iuAE4B/iio/FviYquZEfW32yh4E3sT9QF4OPCgiS2c4lGnFIiKlwOPAvwG5wJXAA96NIy6xTCcOVf1S9GcBfB2o9+KKSxzTjUVEjgLuBM4F5gMPAKu9m0myxXIBcDNwiVff/wH+KCKZ8YpFRBbjbso34f5//wq4RUQ+DHwftwxaKXAWcKuInOadegVwHu5nvxo4HrjWu2YQl2g/78WywYttRr2LWBCRSuBR4GNjrrkAWA1827vmI8Dvo77/kk7SVhz3IXwZ9wEDICJZwEeAq1R1t6ruAa4HLvd+SANALfDG2IuJSA1wHPAtVR1S1SdxP5SXznwoU48F+CzwjKr+TFUjqvpH3A2mI46xTCcOol5bBdwOfEZVu5LwM6lm/8+UD3eT6ffOTbZYPgHcq6pPq+qIqn4fGAJWxTGWSuDnqvqIqoZV9RXgKWAVcD5wvar2qeobwL24xATwOeB7qrpdVduAG4EvemUXAY+q6nOqOgBcA5wqItWJGIuI5ACv4VpHL4y55nnAelV9WFWHVfUOwO9dMyklc4L6gaquBF6NOjYaT2/UsRBQhPuN4hhc1969ItImIq+LyNne64JAs6pGn9sIHDkjtY81nVhWAltE5EEReVtE/gwsVNVu4hfLdOKI9i+4m+Ia79/J9pn8HlgHrMXdzG8DPqWqYZIvlpQxZaPlNcQpFlV9VlW/NPpvESlg/yLSEVzr50D1CeJa5dFli7zzY8pUtQ/YRuLGMgAsV9VrgeExlx0bJ4AyO99jMyJpE5Sq7jjAsR5cN8btIlIgIoXAt7ziAK4r7Fncb5SLcF0YD4nI0UAO0Dfmkn1A1sxEEFPv6cRSgOtaeQDXFXAL8IjXColLLNOMAwARWYHrc78t6vRk+0wycTeEk3DP0f4B18W3kOSL5WHgChE5TkTSReRyXO9DgDjGMkpE8nCttpdxLYoBVY1e+Tq6PmPrO/r3rAOUjT13xk0lFq81e7DnY3GP5VBL2gQ1gYtxv7024B54rvaOd6rqH1T1DFV91WsCPww8CXwU99tiYMy1snAPKePloLHgHmr/TlUf82J5CHgd+EsSL5aJ4hj1BWC1qu6KOpZoccDEsdwItKrqy6o6qKr3AFuAT5Jksajqg7hfeh7EtShWAH8EOohzLF4X40vALlx3WDeQOabLOLo+Y+s7esPuOUDZ2HNn1DRimUgifo+9K4djgioFvqiqJaq6AmgBGlS1T0TOEZHPjXl9Bq7ZXA9UeM+pRtUyvsk8mw4aC67Znz/m9aOjMhMtloniGHUu4x9OJ1ocMHEsi3F9/tFGcF0xSRWLNwjnUVVdpqoLga/hktRrxDEWEXk/rqWxGjjfe260AffMb8lB6lMPyJiynaraObbMezZXQeLGMpGxcU7l3ISU9MPMD+BOYJ2IXI37RruN/SPCUoG7RKQB94P2KeAU4DJVbRaRNcDNInKNd/xc4OTZDiDKRLHcD/yNiFwE/Bz3W/pRwAWqui3BYpkojtHhzkuB56NPUlVNsDhg4lgew3WZPYTblO1CoA54PAE/E5g4llXADSLyPqAL1/23A3hFVSPxiMXrvn4MNwXh7tHjqtojIo/gRsFdihtifTluZB7AfwJXi8gTuFbGjd4xcD87z4nI6cCLuFbjn1W1KUFjmcgjuO+/C7y//x0Qxg2+SEqHY4K6HDfqZQ+u2+Vur6sFVV0tItcBvwAW4lohZ6tqs3fuJ4Af4eYkvA1cqqrrZrn+0SaKZY2InIm7qdwDNAPnqeo279xEiuWgcXgqgUFvJNlYiRQHTPyZ3Os9T/g5bh5UPXBmgn4mMPHn8l+4h+trcL0MTwAfjXo2Eo9YrsQ9R75FRG6JOv7vuFF59wBbcT0iN6vqb73yHwAluFFvWcAv8Z63qepaEfmC95oyXIvmkzMcB0w/loNS1d0icg5uHtR9wHrgHFUdOtSVny22o64xxpiEdDg+gzLGGHMYsARljDEmIVmCMsYYk5AsQRljjElIlqCMMcYkJEtQxhhjEpIlKGMOIW/x3jZv8c+xZdeJSI+3XYIx5h1YgjLm0PoqbgJ89ORLRGQJcB1u5YAtcaiXMUnHEpQxh5CqtuL2FLpMRE6MKrobtw/Z3Qc80Rgzjq0kYcwh5q1E/TxuiaDjcavlPwS8R1XrvddcgtvVtQy3mvh1qvo7rywNt0X5Z3DbwrTjlue6WlVDIvIAbo215bi12s5T1admLUBjZom1oIw5xLz16q7ALd57CfDPwE1Ryeks3EKt13mvuQ+3l9cJ3iWuwW0BfjFul95rgL8Fzo56m4twa66twm3XYMxh53BcLNaYuFPVdSJyJ/BDXAvp1qjia4FbvT28ADaKyHG4LS0+jduR9/Oq+oxX/lMR+QZux9Rfe8fqVfWBmY7DmHiyBGXMzPk2blfd76hq9PbcQWCliFwfdSwdb98eb9X9VSJyB26L9aNwK76nRr1+00xW3JhEYF18xswQVe33/to/pigN+DpwTNRXHW5PJUTkJtyWECnenx8Cxm5lMfaaxhx2rAVlzOxrBI5Q1Y2jB7zW1ABwB/DXwFdV9X6vLIDbUNB3gGsZc9iyBGXM7LsduF9EFHgSOAO4Aff8CdwGgmeLyAtAHq6rcB7jt5M35rBmXXzGzDJV/SVwFfAN3HOnq4AvquqvvJd8Fjd6by3w38BG4D+AlbNeWWPiyOZBGWOMSUjWgjLGGJOQLEEZY4xJSJagjDHGJCRLUMYYYxKSJShjjDEJyRKUMcaYhGQJyhhjTEKyBGWMMSYh/T/s82YyMAIDqQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results[t_0 + 20] = census[t_0 + 20]\n", + "\n", + "for t in linrange(t_0 + 20, t_end):\n", + " results[t+1] = results[t] + annual_growth\n", + "\n", + "plot(census, ':', label='US Census')\n", + "plot(un, '--', label='UN DESA')\n", + "plot(results, color='gray', label='model')\n", + "\n", + "decorate(xlabel='Year', \n", + " ylabel='World population (billion)',\n", + " title='Constant growth')\n", + "savefig('figs/chap03-fig02.pdf')" + ] + }, + { + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5ba646a4d10e476ab1eaf4b8a19a9559c7a4ccbb Mon Sep 17 00:00:00 2001 From: Annie Tor Date: Sun, 23 Sep 2018 12:51:48 -0400 Subject: [PATCH 04/18] chap08mine.ipynb --- code/chap08.ipynb | 313 ++++++++++++++-- code/chap08mine.ipynb | 812 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 1089 insertions(+), 36 deletions(-) create mode 100644 code/chap08mine.ipynb diff --git a/code/chap08.ipynb b/code/chap08.ipynb index 0f16233f..f89540d6 100644 --- a/code/chap08.ipynb +++ b/code/chap08.ipynb @@ -16,9 +16,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Configure Jupyter so figures appear in the notebook\n", @@ -43,9 +41,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_results(census, un, timeseries, title):\n", @@ -68,9 +64,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def run_simulation(system, update_func):\n", @@ -100,9 +94,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "filename = 'data/World_population_estimates.html'\n", @@ -117,7 +109,20 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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XLAaXy0UoFCIuLo7nn3+eZ555hhtuuAGPx8ONN97IkiVL2r3W9vaTJCVE9wn5vFSsfoVgTTkAwWAIh8NBzLCJxAyfhM1h3vrPn5xDlMtBbmZ8JMNtU59NUNPGtF/1NntCNrMnZLe5/oLJuVwwObfN9Z+ZMYjPzBh02nENGDCAo0ePMm7cuMZlgUCAo0ePNs4W/KUvfYmLL76Ym266CYCUlBSuvfZatmzZgtb6lGNOnDgRj8fDihUrmD9/frN1jzzyCAUFBTz//PNkZGRw3333cd555zWu37VrF4MHD2bDhg1txlxZWdn4/Mrv9/PRRx/x9a9/nSlTpuBwOJp1sDUMg8rKyg73a/ncSgjRdWwuD47oOALV5ZRX11NQ4yZr2gWMU/nNthuekxShCK05a1V8SqnFSqmaFq+gUurtsxVDT7BgwQKefPJJ9u7dC5jDND388MOkp6czfvx4AC699FKefvpp3nnnHXw+Hz6fjzVr1rBixQouvPDCU47pdru58847ue+++3j33XcJBALU1dXx7LPP8vLLL3PHHXcAcOWVV/Kb3/yGwsJCgsEgv/vd71i8eDH19fXtxlxWVsZXvvIV1q5di8vlIjMzE5vNRmJiInl5efh8Pt544w2CwSDPP/9846C97e0nhOg+NpuNuDFzKK+38WnDIHYlzOS/+xqoq+9do7WctRKU1vovwF9OfFZKTQTeBr53tmLoCe644w4cDgdLliyhtLSUqKgoZsyYwXPPPddYPXf99dfj8Xh46qmnuOuuuzAMg2HDhnHfffcxe/bsVo/7hS98gfj4eJ555hl+8IMfYBgGo0ePZunSpUydOhWAr371qwQCARYvXkxFRQX5+fksXbq0wwFahwwZwv3338+9997L8ePHSU5O5p577mHEiBEA3HPPPTzyyCP86Ec/YtGiRUyaNMnSfkKIMxeqr6Vu30Zi1fTGqjsAR0w8wy6/mXXv7YXqBjxuBzV1/oi3zDsdtkj0EFZKuYBNwG+01r+xsP1gYP+7775LTk7OKet37NjBqFGjujxOcXbJ71EI6wzDoP7QDmp3rsYI+IgeNono4VNwtGiJd+hYNYUltUwamRHRVnqHDx8+UQM0RGt9wMo+kXoGdQfgBZ6K0PmFEKLXCtSUU7P1Q/xlhQA0+ALsW7WCQHUys6Y0r6HIzYzvsY0gOnLWE5RSyo1Zrfc/WuvIDPAkhBC9kBEK4t27kbq9n2KEggDUNwTYWeTncMJEvAfqGDnCS2pidIQj7RqRKEFdAoSA1yNwbiGE6JX85UXUbPmQQE2T0WRsNpJHTaE2KZnaEi92oKi0ThLUGVgELNda97xeYUII0cOE/A3U7VqLt2A7YIAB2MCZmEH8uLk4E1KZM7CelRuPcN452aQkeCIdcpeJRIKaAdwbgfMKIUSv03BY4y3YRihkcLzciy9kY/Tc+XgGjcFmMxs9pCR4WDRnWIQj7XqWEpRSygFMBqYAGUAQKALWaa03nuY5BwNHT3MfIYTolzyDxlB7cCd7tu+lzJHG0YSxpEUNYpCt749U126CUkolA98AbgNSgX1AKeAA0oBBSqlC4GnMJuMVHZ1Qax17pkELIURfZBghDL8Pu/tkNZ3N7iBxwvkQzKagJAZsNnYfKmdQVvv9F/uCNhOUUuoG4CfAu8BXgHe01g0ttkkAzgMWA1uVUndrrZ/vxniFEKJPClSVUrN1BdidxExegNvlaFznSsrgnJnJ7P9gD1NGZ0Z0EsGzqb0S1FRgqta6uK0NtNZVmK3xXldKZQF3A5KghBDCIiPop273eur2byIYCHKsrI6Cw1EsvPpi7PaTgyrHRrtYfMnIfjXQcpuVmFrrr7eXnFrZvlBr/bWuCavvsjLl+xNPPMH48ePZs2dPs21amxzwhJdeeolRo0YxceJEJk6cyIQJE7j00ktZunRpsyk65s2bx/jx4xu3O/H6zGc+07hNQUEBd9xxB1OnTmXixIksXLiQP//5z62e97LLLmPOnDnNBowVQljjKz5I+YfLqdu3ESMUYu+RSsqq/dTW1LLjwKmTk/an5ASn0YpPKZUBjAdcQLOfktb6jS6Oq99raGjgzjvvZPny5bjdbkv75Ofn869//Qswh0HZsGEDd955J1VVVXz7299u3O6xxx47ZdTzE0KhELfeeisLFy7k0UcfxePxsHHjRu644w7cbjef//znG7c9MQhseno6b7/9NgsWLDiDKxai/wg11FGz/WMaCk9+CbXZbCRm5fJx3WB8zjiOldWeMlFqf2OpGYhS6ivAIczBXV8HXmvyerXbouvHZs6cSV1dHY8//nin9rfZbEyaNIkHHniApUuXUlVVZWm/8vJyCgoKWLhwIdHR0dhsNiZOnMhdd911yrYvvPACF110EZ/73Of405/+1Kk4hehPzKnXt1P24QvNk5Mrivhx5zNywRfJzM7isnOHtDvdT39htQT1PeD3wA+01tXdGE+Xqd21jro96y1t68kdRfy45tOlV29ZQf2hU6viWooZPpnY/KmdirHd48bE8PDDD3Pdddcxd+5cpk+f3qnjzJw5E7vdzoYNG1qdEr6l1NRUpk2bxs0338wVV1zRWM135ZVXNtuurKyMd999lzfffJP4+HgefvhhtmzZ0myeKyHESUYoSOXa1/CXFVJT56O4wsugAQlE5+QTN+pc7FHm6A99sT9TZ1ltSJ8L/Kq3JKe+4pxzzmHJkiV8//vft1wCaunE/Es1NTWNy+68887GSQNPvJ588snG9UuXLuXWW29l8+bNfP3rX2fGjBncfvvtFBWdnDb6n//8JzNnziQrK4u4uDiuuOKKNp9TCSHM5uLO+BSOFtdQUFRNhd/N/qRpJJxzYWNyEs1ZLUG9DVwI7O7GWPoFq1O+n3D77bezatUq7r///mbPf6wKBoNUVVWRlZXVuOzRRx9t8xnUiRivu+46rrvuOnw+H+vXr+fxxx/nm9/8Ji+88AKGYbB8+XKOHz/OrFmzAPOZWUNDA3fddRepqf273lyItsTkTyNm9052heI5HjsCT1kUk/zBZk3KxUlWE9Qm4DGl1BXALsDXdKXW+tQHFBEWmz/1jKre4sfNPaXarytYmfK9KYfDwSOPPMKiRYuIiYk57fOtXbsWwzDIz8/veGPM50rLli3jn//8J2Amq5kzZ+JyubjlllsAWL16NRUVFfz73//Gbj9ZCF+yZAnLli1rnMFXiP4qWF9L3a61xKoZzUpHdlcUwxbexLaPDjIyxsXMcVmSnNphtYpvLrAGiAYmEO4jFX613u5ZtMrKlO8t5eXl8cMf/pDly5dbPk8oFGLt2rXce++9LFmyhLi4OEv7zZ07l4MHD/LQQw9RWlqKYRgcOnSIZ599tnG6+RdeeIFLLrmEzMxM0tPTG19XXXUVy5Ytkybnot8yjBDe/Zsp/3AZtQU7WP/v16mpa/Z9HrvTxeWzh3Dh1LxeNbttJFgqQWmtL+juQPoLK1O+t+aaa65hxYoVrF69us1tdu3axcSJEwFwOp1kZ2dzyy23cO211zbb7jvf+Q4Ox6nf2v79738zYMAA/vrXv/LEE0+wcOFCvF4vycnJXHLJJXzjG9+gtLSUd955h+eee+6U/RcuXMhDDz3EW2+9xcKFCy3+RIToG/wVx6jZupJAVQnVtT6OFNcQDJXz8ZodXHzBhGbbtpz1VrTO8pTvSqlM4GvAGMyS1w7g91rrfd0XXuO5ByNTvvd58nsUvVHI30CtXkP9wR2Yc2FAXX2AnccCHE0YR607lWsuzCcz5fSr6PuSzkz5brUf1DTMZ09XASVAMbAQ2KyUkio+IUS/YxgG9Ud2Ub5iGfUHw3M1YbbWSx9/Ls5Ji3AkZ7Fg1pB+n5w6y2ojiV8CfwNuazpNu1LqSeARQKoAhRD9RqC6jJptK/GXFlJR00CU20F0lBN3eh5xY2bjiElgVl4Ah92OyynVeZ1lNUFNAW5pmpzCngCs9YYVQog+IlhTQe2xwxwprqGuPoArJp5Jl1yEJ2to43h5Hnck5oPtW6ym9kLMiQZbGgpI510hRL/iHjAER2ou3oYgxTFD2RR7Lvvqk/rdYK7dzWqK/xPwO6XUt4ATzchmAv8bXhdxhmHIH0cvZrWxjhBnW6CmAsNfjyv5ZD9Fm81G+qTzORydT/H+BibkpzMiNymCUfZNVhPUg8BAYDlmqcsG+DGr+O7untCsczgc+P1+y6N+i57H6/W228xeiLPNCPqp27MB7/5NNNiiCI6/nLyskxMFOmISOOecOIaO8JGS4GnnSKKzrPaD8gG3KqXuBBTgBfZorb3dGZxVSUlJHDt2jOzs7GYjG4iezzAMvF4vR44cITMzM9LhCIFhGPiO7ad2x8f4a6s5Xl5HWWU9laXvk/X5RbicJ/sQOh12SU7dqL0p3y8D/qO19offt5SrlAIiPx9UWloahw8fRmsdyTBEJ7lcLjIzM0lISIh0KKKfC9ZWUrN9Fb7iQ4CZrCprfNS6kjhOKuu2H+Pc8QMjHGX/0V4J6jVgAHA8/L4tBhDRwaTsdjt5eXmRDEEI0Ys1rc4zQsHG5a7oGNKnTGHjfhfZGfGoQcntHEV0tTYTlNba3tp7IYToK5pW5/lqqqj3BYmNNicNj84bTUz+VFJcUXhyqskbEC8Nsc4yaagvhOi3DF891Zveo6yihmNldRghAzUmn5SJc3ElZjRuNyhLqp8job1nUMWcGLujA1rrjI63EkKInsUeFU30sMmUvfcWDYaLY/EjqXOP5ZJEuaX1BO2VoL6HxQQlhBA9nWEYBKtLcSakNVseM3Q82RW1vLo3ipjYGIbnyXOmnqK9Z1DPncU4hBCi25hj563CX1aIb/RlZA/ObVxnszvInTKbizLLGZyV0KwZuYis9qr4LM+Op7U+/bnIhRCim4X8DdTt/gRvwVa89X4KS2opOfwGzs9eR2ZqbLNtR+RKyamnaa+Kr/asRSGEEF3IMAwajmhqd64h5DPHEygur6OuIYg3Jp4PNxzkcxeOlFZ5PVx7VXw3n81AhBCiK/grj1O77SP8FceaLc8ePoJNR9NpcMUzOSuZUMjA4ZAE1ZO1V8X3MHC/1ro2/L4thtb6/3V9aEIIYV3I56VWr6X+0E7qG/zmdBc2sHtiiRs5E3fWMGYfqiAzJYbEuKhIhyssaK+KbyrgavK+LZZb+imlsoDfYk5wWA/8Tmt9r9X9hRCiLdWb3qOusICisloqa3zkDkhkwLhpxAybiM1p3srypYVer9JeFd8Frb0/Q//CnOAwE8gCViildmit/9pFxxdC9FMxI6ZycKemssZHtTuD1Y7xfH7YFGwyo22vZXkkCaVULHAtMAbwAduAF8IjnVvZfzrmBIeztNZ+YL9S6nzMkdGFEMKyUEMdNrcHm+1k8nElZZA7eRbbtlRTYk9j2IAk/IGgTLnei1lKUEqpccAHmIlpM+acUDcDP1FKXaq13mnhMJOBLcCPlVI3YVbxPaW1/mUn4hZC9ENGKIj3wBbqdq/HPnQqiUPH4nCcTEBJo6czNaECt8tBbmZ8BCMVXcFqCer3wMvA/4RLPyilooE/AM8Acy0cIwU4D1iBWZIaCfxbKVUoVXxCiI74Sg5Ts/0jAtVllFbWU7TvLRLnJDNpTG6z7YblyMy2fYXVsu8E4KETyQkgPFnhT4FpFo/RAFRprX+stW7QWm/CTHBXn07AQoj+JeitoerTt6lc+xrBmnIqqhs4VlaHDzebtx2kxuvv+CCiV7JagvoUs/Szq8XyKcB2i8fYCcQopdxNnlvJaOpCiFYZoSDe/Zup27MeIxhoXJ6cHM8ux3AOGANJTYyhviFAXLSrnSOJ3qq9flC3N/m4FnhSKTUFWA0EgfHAbcBDFs/1H6AY+KVS6ruYU8d/JXwMIYRo5Cs5TM22lQRqKjEMA7vd7FAbNXAEcSNnMKnaIKe8jnHD03HYpbNtX9XRaOZNFQGXhF8nlABfBh7o6ERa63ql1FzgCaAQs5HEw1rrf5xWxEKIPq3+yC6zT5PXz9GSWmKjXeQOySNuzCxcKeZ06wM9MDA9LsKRiu7WXj+oIV19Mq31PmBBVx9XCNF3uDMH4zXc7C8sJWhzUmAMJXP0RbhSJCH1N202klBK3Xg6B1JK2ZRSXz7zkIQilizEAAAgAElEQVQQ/YlhNB+Mxu50kzHxPBwZQ9mdOpfKhGFU1UlDiP6ovSq+ieFnRU8D/9RaF7a2kVIqA/gS8FXgna4PUQjRF4V89dTqNRhBP/ETLmw2srhn4AjGXZqHd3sR544fKI0g+qn2qvi+FW4UcR/wuFJqO+boESWADUjHbH6eD7wJ3KS1Xtv9IQshejPDMGg4upvaHf8lUF/H8XIvx4vjmDd/erMklRQfxcXTB0UwUhFp7Tbz1lp/AlyhlBoCXIrZrHw4EMJsNPFr4A2t9cHuDlQI0fsFayup2bYSX8lhQiGDPYcr8AdCVPj2sO/ISOlkK5qx1A9Ja70feKqbYxFC9FFGKIh33yazT1MoCIDdbiMmIZHNwaFUR2WScKRSEpRoRjrKCiG6lb+8iJotHxKoKWuy1Eb04LGMHDyRnSsPMn9kBkqmwhAtSIISQnQLIxSkdsd/8RZso77BT1lVPVlpcbgS04gbNwdXYgYA116sZOp10SpJUEKI7mGzE6yr4nhZLSUVXoI2B/ahoxg767xm02RIchJtkYlShBDdwmazETf2PGxOF1XudHanzmFNWSqBYKQjE73F6UxYOBFzTicXZjPzRlpraUAhRD9mGAa+ov24M/KwOU7eVhzR8Qy/7Do2fVxEhsfF+ZNzZAJBYZnVCQvvxpxaowyobrHaQFr4CdFvBb011Gz9EN/xg5QmjGDYzPNxOR2N690JySw6P47oKKdU54nTYrUEdQtwr9b6we4MRgjRexiGQf2h7dTuXE19XT1Hi2uo3b+GUmcms2aObbZtjEdGghCnz2qCSgWWd2cgQojeI1hXRfWWD/CXHgXA2xCgriFAWfQgjh9qYISqIyMlJsJRit7OaoJ6EVgM/Lj7QhFC9HSGYVB/0Cw1GcGTA7imZmawN3kmx6qjmKwySEn0RDBK0VdYTVBe4AdKqWuA3YCv6Uqt9ee7OjAhRM8SrKumesv7NBQfwTAMHA47YCNm6ARiRkzh3PoQUwNBUhOjIx2q6COsJqgY4K/dGYgQoufyVxyncs2r1NTUcaS4hugoJ4OH5hI/YR6uJLPDbUJshIMUfY7Vsfhu7u5AhBA9lzMhFZ8zhgOFRQAcdecQP/hCUpLSIxyZ6MtOpx/UOOAuYAxmB9+dwK+11h93U2xCiB7CZneQMfViikr+yYbgMEKxaWCXgWhE97LUY04pdSnwKWZrvheBvwPxwAql1MXdF54Q4mwL+bzU7d1wyky3zoRURl91E0Pyh/PFi0cyIlcGdxXdy+pXoAeBB7TW9zddqJS6B7MD79tdHZgQ4uzzFR+ketP7lJVWULC9hHkL5mO3N5np1u1k3pTcCEYo+hOrY46MAv7SyvJlwLiuC0cIEQlGMEDN9o+oXPsGBw4e50hxDfaD69m041CkQxP9mNUEdRCY2MryycDxrgtHCHG2BapKqfjoJbwHtoANPFEOAnY3hxInsuNwHaGQ0fFBhOgGVqv4fgM8rZTKAVaHl80E7gYe7o7AhBDdyzAMvAc2U6fXNs5yC5CdP5Kd5bkMzUhhxtgBzar4hDibrDYz/7VSKh74AZAWXnwU+JHW+snuCk4I0T2C9bXUbH6f0oP7iPW4sNtt2OwOYkefiyd3NFeGTnTEFSJyLLcTDQ8U+6BSKgPwaq1bjmouhOgF/OVFlK15g6NFpVTW+EhN9JAzdDDxEy7EGZcEgMMhpSYReW0mKKXU7cD/aa3rw+9brm98L/NBCdF7OOKSqaoPUlljjli2059N8vD5JMclRjgyIZprrwT1PeAFoD78vi0yH5QQvYjdFcWg2ZdR9sY/2e4YRe6I4aQlyzhFoudpM0FprYe09r4lpZTUBQjRQxmGgb+sEGdyVrPGDu60bEZ/9hYyKhsYMlBKTqJnsjqSxD6lVEorywcCx7o8KiHEGQs11FG+5jV2vP433n939SkjQ8THeiQ5iR6tvWdQVwCzwx8HAz9RStW12Gz46ZxMKfVl4BmgocniO7TWz5/OcYQQ7fMVH6Riw7vs2V+Ezx/CqFnF7iGDyB8+MNKhCWFZe8+gNgHfAk7UC0yk+TxQBlAD3Hga55sE/FJr/f3TCVIIYY0RDFCr1+A9sAUbEOtx4fM3UOHJwVbaQP5pfaUUIrLaewZVAMwDUEo9C3xTa111huebDPzqDI8hhGhFoLqc6o3vEKgubVyWlZXGgeRhjJ0wBpUng7uK3sXyfFBKKadSKhtwhBfbgChgstb6bx0dQynlAMYD1yulHgPqgD8AD2mtZSwVITrJMAzq9m/l8LoPSI5zYQvXebjT84gffwFXuD3YbNKWSfQ+lhKUUmoh8CxwSkMJoBzoMEEB6cAnwPPA1ZgD0P4LqEKaqQvRKaEGL0fXvM2hHTto8AcJBWNIT4kjdtRMPHljJDGJXs3qSBI/x5xS4zHgXeAKYADwv8B3rRxAa10EzG2yaKNS6gngs0iCEqJTjFCAqqOHaPCbY+kdqnYwYN7lRA8YEOHIhDhzVgfbGgH8RGu9HnPiwlit9XLga7TfibeRUmqMUur+FovdmB2BhRCd4IiOZ/Csi/C4HVTEDSXrgi+QlJkZ6bCE6BJWS1BeIBR+vwuYALwJrAfyLR6jAviuUuowsBSzVeA3MJOcEMKChtpq7FExuJyOxmXROfkMX3A9IxPSSIqPimB0QnQtqyWoD4F7lVJJmM+RrlJKuYDzMZ8hdUhrfQSzanBJeJ9/AD/VWr94ukEL0d8YoSAFaz5g3Z+fYs3qrc3W2Ww20rKzJTmJPsdqCeq7wKvAl4GnMftHVWFW0d1t9WRa6/eAKacZoxD9WqCqhML/vsX+3QcAaNjyPkeG55E9QJqNi77NajPzPcAopVS01tqrlJoGXACUaK3XdGuEQvRTRiiId+9Gavd8gidkEB/jorrOD+5oGrwNHR9AiF6uvaGOYjpY/v6Jz1rrlkMgCSHOgL+qlJotHxCoLDYX2GBgRiL7XMOZOuc8YqPdkQ1QiLOgvRJUDeZwRlY4Ot5ECNGRgN/P1pUrCBZsJC8jrnGgMVdSJikT5pEVK4O7iv6jvQR1wVmLQghBfUUJa/75d4zaMgAqa10kJcQQkz+V6CHjsdlkCnbRv7Q3Ft+KsxmIEP2d02En2VlPWfhzSSCWwbM+izNeGkOI/snqUEfraKe6T2s9rcsiEqKfcsanMGjqbGo+fJ/E0TMYOXMWDofUnov+y2oz89da2W8osAD4cVcGJER/UFpWxfbNmnPnTMHRZKbbhPxJTM0ZgSsuKYLRCdEzWG1m3nKIIqBxAsIrgMe7Migh+rINazZQ+ul72IM+NiclM/Gck5M02ewOSU5ChJ3pU9f3gIu6IhAh+rpQQx1VG97Bs/cDnMF67IQ49sl71PsCkQ5NiB7J6jOo1vpEJQL3AIVdGpEQfYxhGNQf2kGtXoPhbyA1MZrKmgYMZxSjZp9LlEueMwnRGqvPoNrqE1UP3Nx14QjRdxiGgd6xn9TSDVBd3LjcZoPhEyeRPH42jqhW+8MLIbCeoFr2iTIAH7BNa13dtSEJ0fuVV9Ty6bvvYC/cRkO8m4HpcQA4YhKIGzsHd1pOhCMUouez2khiBYBSKg5QQNBcrL3dGJsQvVbpun/jKNwJQHl1A8kJ0aSOnkrM8EnYHFa/FwrRv1l9BhWFOevtYswRzAG8SqnfA9/VWge7KT4heqW8STOpPLSXmjo/SQNzyTj/MjzJaZEOS4hexepXuV9jzv30BWAtZuu/6cCjQAPw/7ojOCF6A2+9D1/AIDHu5HxM7tSBDJo8E78rgQGjxmOz2do5ghCiNVYT1OeBy7XWq5ose0kpVQYsRxKU6IeCIYNtG7dR/Mn7BLNGc9HCec0SUdqE8yIYnRC9n9V+UF7A38ryyi6MRYheI1RfS/Hatyj/+F84fFXYD33K/kPFHe8ohLDMagnqB8AflFJLgNVa65BSaizmc6kHm/aTkrmhRF9mhILUF2yjdvc6HAE/yQkeyqrq8TjBXlcOZEQ6RCH6DKsJ6nEgDlgJBJVSIcCFOVvNNOCxJttKr0PR59Q3BKgqLMB58BOCNeWNyzOSo3FkDGHEefNxxyZEMEIh+h6rCerKbo1CiB4qFDLYtrOAQ2veJ8lXxPCcpMbnTI7YJBLHzGaA9GkSolucbj+oaGAE5rOrvdJJV/R1NYUHKFvxAjHBID6grKqetJQEYkZMJnrwOGx2qTAQortY7QflAH4GfJOTVXs+pdRzwNe01jLapeiT4jIGkpGWyLFjZbicdtwDR5Ay/QLsnthIhyZEn2e1iu9B4DrgBmAVZoKahdkP6r7wS4hera7eT0mFl7wBJ58l2V1R5E2dA5s+YfCsi4hOGxjBCIXoX6wmqBuAW7TWbzRZtlwpVQ38DklQohcLBkNs2H6YI+tX4iBIxhe/hCfq5H+NmEGjGTloFDbbmc5OI4Q4HVb/x8UBe1pZvg+Q8VtEr2UYIeoLtlK/+u8k1hwgruYQn36ytdk2NptNkpMQEWD1f9064I5Wln8NWN914Qhx9vhKDlOx8kXqdn5MVpILgCiXgyxKIhyZEAKsV/H9P+ADpdT5wOrwshnAYOCSrg9LiO5RXl1P4eEicup20nDsQOPyhBg3uXlZZE+9gKgBgyMWnxDiJKvNzD9RSk0CvgqMxhz66FXgN1prmVFX9HjBYIhVnx6keOtaUuv2kpCT0DiTrc3hImb4JNKGjJdm40L0IJYnptFa7wLuVEqlAkGtdUX3hSVEF/N7idr2Kmm1VQAcK6sjLzMeT3Y+MWo6Dmk2LkSPY/nJr1LqXqXUUeA4UKqUOqCU+lb3hSZE17FHxZAzJBeA2Ggnmbm5JM28kvgJ8yQ5CdFDWe2o+yBm9d4DNJ8P6h6llEtr/YjVEyqlkoDNwH1a6+dOO2IhOlBYUsuBo5XMHH+yz5LNZmPA5POhtpzUcTPw5IyUOZqE6OGsVvHdAtzYoh/UR0qpPcBvAMsJCngayD6N7YWwJBQy+M+aA5Tu2kSy9zAH0xeTl5XcuN4Zl0T2JTfIcyYhegmrVXxO4FAry/cA8VZPppS6EUgAtljdRwirghWFpBa8w8DqbUQHKtnx0cpTtpHkJETvYTVBPQI8pZTKPbFAKZUC/AJzuKMOKaWGAD8Cvny6QQrRnmB9LVUb36Fi9SsMjPFjt0FirJvxSTUYRijS4QkhOslqFd8XgFHAPqXUISAA5AFuYIZS6hsnNtRanzJjW3iw2T8Dd2qti5RSZxy46N+Ol9exbc9xpiWX4937KUbQnPDZ5bSTPziNBDWF6CETZAQIIXqx05mw8EzcC2it9UtneBwhWLnxCPu27WBA1VaOpEByvKdxXdSAocSOmokj2nLNsxCih7LaUff5MzzPtcBApdTV4c/xmFWG07TWt5/hsUU/k3B0LYPKtwNQXGYnKc6DKz6F2NHn4pbJA4XoMyx31D0TWuuRTT8rpTYCj0szc9EZQ0cMZnPBTqLcDrIyk4gfPQPPoDHSAEKIPuasJCghOqOmzsf6ncc5d3wWLufJ5BM3bDzDD+4gJj2LWDUde1RMBKMUQnSXiCQorfU5kTiv6D227C3hk093kVa+jQ3B2UybOqpxnc3uIGPuNdicrghGKITobtLESfQ4oYAP95GNDD72AfG+45RsWEGt19dsG0lOQvR9bZaglFL/Z/UgWmvp2yTOmGEYNBzdTe3O1aTU11ER5SBkGAxOC+D2VUD0KT0YhBB9WHtVfE3b6bqBy4GdmJMX+oBJwATgr90WnejzgsEQm3aXMCwliLF3Df7yInOFDXO08bQs4seehzNBJm4Wor9pM0Fpra858V4p9TvMVnffabqNUuoBYGTLfYWworCklvfX7MZTtAWb4xg5GXGN6+xR0SRPmEnUwBEyqKsQ/ZTVRhJfxCwxtfQ8sLHrwhH9SaD4AJkH3sZh+KkEUhI8xEa78QwZR8zwydid7kiHKISIIKsJqgiYB+xusXwhUNClEYl+Y8DAdEpi7VTX2chIjiExezBxY2bjjEuKdGhCiB7AaoL6CbBUKTUP+BSwYc4HdRlwTXs7CmEYBnsOV5AU5yE9ObpxuSsxg9xxEwmWHSVp/Hm4MwZJdZ4QopHVoY7+pJQ6DCwBrg8v3gzM0Vqv6a7gRO9XXl3PinUH8BVsJj45iUsWXdQsCaWMn43N7sDmkD7jQojmLN8VtNbvA+93YyyijzEMg+CxfcTpN3EG6wnWudi1bxxqWFbjNnZXVAQjFEL0ZO31g3rY6kG01nd1TTiir/BXHqd2+8cY5UUMiLNRWgkZCQ4y/YeBrA73F0KI9kpQUy0ew+iKQETvZhgG+45UYgt4Sa/aSf3hXZz400hPjiY5NZHUcbOIysmPbKBCiF6jvX5QF5x4r5S6HnhTa11yVqISvUpVrY93Vu/Hf2gLAxv2E5cT1/icyWazEztcmo0LIU6f1WdQvwZmAJKgxCmcDZUk730Dm6+OIFBaWU9aUjTujEHEjToXR2xipEMUQvRCVgeLXQNc1Z2BiN4rOjGJrNRYbEBqooe0rAEkTltI4pRLJTkJITrNagkqBPxMKXUPsB/wNl2ptZ7W1YGJnscfCLFpdzEOG0wcmdm43OZwMWjaXBK2rCJ57Aw8uaOw2WSgfCHEmbGaoNaEX6Kfqqhu4JX3thFbugOP0YAafD0xnpNTXnhy8hkwYLA0GxdCdBmrHXXvP/FeKZUA2LXWFd0WlehRjIAfZ+FWhh9/H19DAwA7Nm5n8owJjdvYbDZskpyEEF3IckddpdRtwA+BgeHPx4Ffaa1/0U2xiQgKhQxshKg/uJ26PZ8S8nkZkOzmaLGfjORoBsRWRjpEIUQfZylBKaXuBO4FHgRWYY7FNwv4vlLKq7X+VfeFKM6miuoGVm85QlztYUY7DxH0Vjeui492M3JUOvEjZ+DOHBy5IIUQ/YLVEtQdwP9orf/WZNlHSqkC4AFAElQfUFHt5bWX3ye9ZheBYC11OYlEuc0/Ebsnltj8qURl50sDCCHEWWE1QaVjzqTb0nogp+vCEZEUh5eRvq3UBP0AVNf5iY6NJXr4JKLzxsiArkKIs8rqHWcr5rQaP2+x/AuY08CLXiYUMqj3BZq1xHPGpzBQjeaI3k5meiKpIycRPXSCjAAhhIgIqwnqPuB1pdRM4L/hZTOBS4CruyMw0T0Mw2D/0Qq2/Hcd0W4bF10+v9n0FxkTziUxPcNMTNIqTwgRQZYeJmit3wYuBBow54P6HFAFTNVav9Z94YmuZISClO3ewt5XniOxaB2OQxs4cKSs2TbO+BRi1TRJTkKIiGtvuo1LgJVa61oArfWHwIdnKzDRdUIBH/UHt+M9sIVQfS0ZsSHKq8BlC9BwcAfkzI50iEIIcYr2qvheBwJKqU+A98Kvj7XWDWclMnHGaqqqqNq9EXfZXgz/yV9bRnI0NqebnHOmkTTinAhGKIQQbWsvQaVh9nWaCcwGvg3YlVKrOZmw1mitg90epTgtteWlbP9oJbUHd+J2GAzPSebEYyZ7VDSJg8eTOWiMNH4QQvRo7c0HVQ68Fn6hlHIAkzAT1kzgy0CaUmql1nrBWYhVWFVZRMOhHRAK4QtBeVU96VmZRA+dgCc7X5qLCyF6Bcs9LsMlpcNAIXAcOBjef2j3hCY6YhgGJRW1lFU1G1yemFxFcoo5zYUtLhXPuHkkz/0C0XmjJTkJIXqNdu9WSqk44HxgPnARMBI4BLwPLAW+pLU+avVkSqmFwM+AIZhJ7mGt9TOdiryfO3ComK0f/5eo0t1EjZnLnLlTGtfZHE5yps8j3ucgZ/gw7HYZ+UEI0fu014pvJTANKAc+wBzO6D2t9Z7OnEgplQW8CFyltX5TKTUJc7ikdVrrTztzzP4oUFNBfcEW7Hu2EVdcCkDtvs2EzpuM3X6yP1Pi4JHIVIFCiN6svRLULMwqvf/DbBDxX621v7Mn0loXKqXStdbVSik7kAoEgOoOdu23jpfXsfNAGcdKa7lifDT1BVvxFR8EINoJLoedYChEhrOWeq+XmNiYCEcshBBdp70ENRSzau9C4DYgVim1CngXM2F9qrU2Tudk4eQUA1SGz/2Q1np3pyLv4/yBIC+/t5O4mkOk1u3nWJWTaE+TX5cNBg/PIyl/IjE50vBBCNH3tNeK7wDwh/ALpdQEYB5mwroX8CulPgDe1Vo/dRrnrAdigfHAG0qp3VrrpZ2Kvg8xDKPZkENOQkzzrqKi2ixgVtVFhxOUDXdGHtGDx+FKzW62jxBC9CWWv3ZrrTcBm5RSvwImAzdjDnt0JWA5QWmtQ4AP+EQp9TtgEWaDi37HMAyOFNewaVcxOZnxTBiR3rjO5nSRPmgoRmAHyfEeYuNiiM4dSfSgsThi5emSEKLv6zBBKaVygOnAjPC/kzCfHX2MORfUB1ZOpJSaCzymtZ7cZHEU0G+njt99qIJ3/7uLZO9hCg67GDfs8mYNHdJHTyQ6WE304LFE5SjpWCuE6Ffaa8X3D8yElIXZkOEj4FXgTmB9J0aQ2AhkK6W+g9kicDrwFeCqTsTdqxmGQaDiGOnFWxhTtp5QMEiw1kVh8RyyM5Mat3OlDCR57rVSjSeE6JfaK0FFAY9jlpA+DVfNdZrWulIpdRnwa+BHmP2pbtFarziT4/YGxeVeNu8pZppKwVm2n/qDOwjUmKOIpyVE4Q8ESU2MJjVwHDiZoCQxCSH6s/YaSSzs6pOF+zv1q6GzV208jN62mxTvQQp2V5CZ1Hwai/TkaFxJGXgGjSVqgAzKIYQQJ0jb5G6WVb6BQPlmAMr9NjIS3dhsNmwOJ1EDhxOdNwZnYnoHRxFCiP5HElQXCIUMDh6rpqi0lhljs5qtyxo6lCK9lZgoJ6mJ0bgS0vDkjSYqe4Q0ehBCiHZIgjpDwWCIv7++HnvpPmL85ZTl3khKYnTjek/2CNSwLKIyB+PJG40zMV2eLQkhhAWSoDopFPDhK9xH/eGd5JfupqrWB4Deppl57slJAO1ON6kXXo/N7ohUqEII0StJgrKopMLLjv0lpNsrGWgcw1e0DyNktrRPjo+itt5PUpyHDHf5KftKchJCiNMnCcqCPbsK2LjqYxLrj1DpCpKa3Xwkh7iYKMZNGUF07kjcGXkRilIIIfoWSVAt+PxB3K7mJZ600HHSvfswDPA2QIMvQJTbiTM+hagchWfgCOxRMpK4EEJ0JUlQYdv2lbL3YAmVRwr47GcvIMbjalwXn6dIiPkAbJCckkTisNFE547EmZAauYCFEKKP6/cJyjBC+EsOU7LuI+JKCkgwguzdM4xxY4c1buOISSB/1vm4kjJxpedgs8kMtUII0d36TYIKBkMcLanlQGEVmSkxDE0KUn9Y01C4h1CDlwGhegoNs9FD7aHd0CRBAcTmT41E2EII0W/1mwS1fX8ZH32yl6T6I7icxSSnNJ9rMSHO7DSblJFBYv7gCEQohBCiqX6ToLICh1Al72LDoMEGoaSUxqkt7FHRxA8cQXp2Po74VOlIK4QQPUC/SVCJmVkkxDhxuxzEx7ixO5xEZQ3Fk52PKy1bnisJIUQP028SlDMpkyHDcrFHxeDJVrizhspYeEII0YP1mwRls9lInvU5bE5XxxsLIYSIuH5VryXJSQgheo9+laCEEEL0HpKghBBC9EiSoIQQQvRIkqCEEEL0SJKghBBC9Ei9pZm5A6CoqCjScQghhOiEJvdvyzO49pYElQWwePHiSMchhBDizGQBe61s2FsS1DrgPKAQCEY4FiGEEKfPgZmc1lndwWYYRsdbCSGEEGeZNJIQQgjRI0mCEkII0SNJghJCCNEjSYISQgjRI0mCEkII0SNJghJCCNEjSYISQgjRI0mCEkII0SP1lpEk2qSUmga8prXOCH9OB34FfAZoAP4P+JHWOhhe/0fg80CgyWHGa633KaXygKXADOA48HWt9Rs9+FrODa8fBRwFfqi1fjG8LmLXcjrXoZR6GriuxSFigbu11j/rhb+Tm4F7gDRgJ/BdrfWq8Lredi1fB74NpAIfA3dorfdF8lqUUhcBvwBGhM/7iNb6GaVUEvAH4CKgBrhHa/1seB8b8FPgq4AbeBb4ntY6EF7/eeBnmKMcrABu0lof74nX0mRfO/APYIXW+vEmyy/A/J0OAzYB12utLQ0r1BP12hKUUsqmlLoFeBvzj+6E54EMzJv2WGAa8JMm6ycBV2qt45q89oXXLQM2Y/6HvBVYppQa2s2X0qlrUUplAa8DTwLxwB3An8M3johcS2euQ2v9P01/F8D3gO3h64rIdXT2WpRS44HHgEVAEvBn4OXwzaS3XcvngQeBm8PxvgL8RynlidS1KKVyMW/KD2D+fL8I/Fwp9Rngt5jDoGXx/9s79xirqisOf4q2zsRoSm2kmiq2xaVIfQSsVmM1mWisgjb4aig+efVhVBq0gQmKqYaXmhoi2hLTllJKsHTGQFOtkVhfrfGBBBz8VZpQaH3hK0oHRAX/WPuGw2HmGsa5955zs75kMnf2PnvP/uXcc9ZZa++zF5wPzDKzM1PTicBo/NofApwMTEt9DsUN7VVJyytJW035HFows8HAcuD7uT4PATqBW1OfHcDDme9f6SjtwPGT8GP8BANgZq3AucBkSW9KegeYDkxIF2kLcAzwYr4zMzsaGAHcLGm7pJX4RTmu9lL2XgtwBfC4pN9J2inpEfwG824DtfRFB5ljvwHMAcZIer+E52QIu66pffCbzNbUtmxaLgIWSPq7pI8l3QtsB9oaqGUwsFhSh6Qdkp4FHgPagIuB6ZK6Jb0ILMANE8CVwC8l/VfSZmAGMCnVjQWWS3pS0jZgKnC6mQ0pohYzOxB4HveOns71ORp4SdIySR9Jmgt8MfVZSspsoO6TNBx4LlNW0fP/TNknwFfwJ4oT8dDeAjPbbGYvmNnIdNxQYKOkbNuXgW/VZPS70xctw4ENZrbEzN4ys1XAIEkf0DgtfdGR5U78prg6/V22c/IwsBZYg9/MZwOXSdpB+fHKlOQAAAXoSURBVLTsm6ur1B9Ng7RIekLSjyp/m9lAdm0ivRP3fnoaz1DcK8/WHZba71YnqRvYRHG1bAOOlTQN+CjXbV4ngKjPd6wmlNZASXq1h7IteBhjjpkNNLMvAzen6hY8FPYE/kR5GB7CWGpmJwAHAt25LruB1too2G3cfdEyEA+tLMJDATOBjuSFNERLH3UAYGbD8Jj77Ezzsp2TA/Abwqn4PNrP8RDfIMqnZRkw0cxGmNn+ZjYBjz600EAtFczsYNxrewb3KLZJyu58nR1PfryVz6091OXb1py90ZK82d7mxxqupb8prYGqwuX40+s6fMKzM5W/J+lvks6W9FxygZcBK4EL8KfFllxfrfgkZaPoVQs+qf2QpBVJy1LgBeB7FE9LNR0VrgE6Jb2RKSuaDqiuZQbwuqRnJH0oaT6wAbiEkmmRtAR/6FmCexTDgEeAd2mwlhRi/CfwBh4O+wA4IBcyzo4nP97KDXtLD3X5tjWlD1qqUcTv2OeiGQ3UV4FJkg6VNAz4H7BOUreZjTKzK3PHfwF3m7uAI9I8VYVj2NNlrie9asHd/i/ljq+syiyalmo6KlzInpPTRdMB1bV8DY/5Z/kYD8WUSktahLNc0jclDQJ+hhup52mgFjP7Lu5pdAIXp3mjV/A5v6N6GU8XYLm61yS9l69Lc3NHUFwt1cjr3Ju2haT0y8x74C5grZlNwb9os9m1ImwAcLeZrcMvtMuA04Dxkjaa2WrgdjObmsovBL5TbwEZqmlZCFxrZmOBxfhT+vHApZI2FUxLNR2V5c5fB57KNpKkgumA6lpW4CGzpXhSth8CxwF/KeA5gepa2oBbzOwM4H08/Pcq8KyknY3QksLXK/BXEOZVyiVtMbMOfBXcOHyJ9QR8ZR7A74EpZvYo7mXMSGXg186TZnYW8A/ca1wl6V8F1VKNDvz7d2n6fD2wA198UUqa0UBNwFe9vIOHXealUAuSOs2sHfgjMAj3QkZK2pjaXgT8Gn8n4S1gnKS1dR5/lmpaVpvZefhNZT6wERgtaVNqWyQtvepIDAY+TCvJ8hRJB1Q/JwvSfMJi/D2oLuC8gp4TqH5e/oBPrq/GowyPAhdk5kYaoeWn+DzyTDObmSm/B1+VNx/4Dx4RuV3SX1P9fcCh+Kq3VuAB0nybpDVmdk065nDco7mkxjqg71p6RdKbZjYKfw/qfuAlYJSk7f09+HoRGXWDIAiCQtKMc1BBEARBExAGKgiCICgkYaCCIAiCQhIGKgiCICgkYaCCIAiCQhIGKgiCICgkYaCCoB9Jm/duTpt/5uvazWxLSpcQBMFnEAYqCPqXG/AX4LMvX2JmRwHt+M4BGxowriAoHWGggqAfkfQ6nlNovJmdkqmah+chm9djwyAI9iB2kgiCfibtRP0UvkXQyfhu+UuBkyR1pWOuxrO6Ho7vJt4u6aFUtx+eonwMnhbmbXx7rimSPjGzRfgea8fie7WNlvRY3QQGQZ0IDyoI+pm0X91EfPPeq4E7gNsyxul8fKPW9nTM/Xgur2+nLqbiKcAvx7P0TgWuA0Zm/s1YfM+1NjxdQxA0Hc24WWwQNBxJa83sLuBXuIc0K1M9DZiVcngBrDezEXhKix/gGXmvkvR4qv+Nmd2EZ0x9MJV1SVpUax1B0EjCQAVB7bgVz6r7C0nZ9NxDgeFmNj1Ttj8pb0/adb/NzObiKdaPx3d8H5A5/t+1HHgQFIEI8QVBjZC0NX3cmqvaD7gRODHzcxyeUwkzuw1PCbFv+n0OkE9lke8zCJqO8KCCoP68DBwpaX2lIHlT24C5wE+AGyQtTHUteELBfXroKwialjBQQVB/5gALzUzASuBs4BZ8/gk8geBIM3saOBgPFR7Enunkg6CpiRBfENQZSQ8Ak4Gb8HmnycAkSX9Kh1yBr95bA/wZWA/8Fhhe98EGQQOJ96CCIAiCQhIeVBAEQVBIwkAFQRAEhSQMVBAEQVBIwkAFQRAEhSQMVBAEQVBIwkAFQRAEhSQMVBAEQVBIwkAFQRAEheRTfmloFmOy4cYAAAAASUVORK5CYII=\n", + "text/plain": [ + "
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\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
values
t_01950.000000
t_end2016.000000
p_02.557629
alpha0.025000
beta-0.001800
\n", + "
" + ], + "text/plain": [ + "t_0 1950.000000\n", + "t_end 2016.000000\n", + "p_0 2.557629\n", + "alpha 0.025000\n", + "beta -0.001800\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "system = System(t_0=t_0, \n", " t_end=t_end,\n", @@ -213,7 +290,20 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "results = run_simulation(system, update_func_quad)\n", "plot_results(census, un, results, 'Quadratic model')" @@ -239,7 +329,27 @@ "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file figs/chap04-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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OOINPPvmEgoICrrvuOs4991xSU1O58847eeONN8jJyeGf//wn9957L++++67P4ygpKWlxPT3JdAy1tbUcOnSowePIkSOtTkQOh4OEhAQSExNJTEwkISGhQUKKjY3V/4kuqksmqI4sJyeH119/nb/97W8sXryY++67j9TUVK666ip+8pOfNPkil5SU0L17d6Kjo31ud8mSJcyaNYt+/foB8Ktf/YoJEyawdetWwDqJ3HTTTURHR3PiiSfSs2dPioqKyMzMJCoqipdffplp06Zx8cUXc8kllxz1hBITE3NM66nQcDqdlJaWUlJS4klGFRUVfq/vcDhITEwkKSmpQSJKTEzUG+hVizRBhZGYmJhm7/dxOp3ExsZ6Xvfv35/bb78dsBLQe++9x/z580lNTeWyyy5rsG7Pnj0pLS2ltra2SZI6fPiw5xr9rl27mDNnDnPnzvXMj4qKYufOnURFRZGUlNQghujoaM/d5c8//zxPPfUUM2bMIC4ujquuuoqZM2f6PFZf6+nJKvQqKys5ePAgJSUlHDx4kEOHDvlVMnInouTkZJKSkkhOTiY5OZnExMR27YFAdQ5dMkHljWx6Wc/biWOyOHFMVovzTx3fl1PH921x/lmT+3PW5P6tjqtXr17s2rWLUaO+64qwrq6OXbt2eUYL/tGPfsSZZ57J1VdfDUBaWhqXXXYZ69atwxjTZJu5ubnExcWxbNmyBnVQAA8++CBFRUU8//zzZGRkMHfuXE466STP/I0bNzJgwADWrFnTYsyHDh3C6XTy5JNPUltby6effsqsWbOYMGECkZGRDe41cblcHDp06KjraaON9lddXc2BAwc8D39KRxEREXTr1o3u3bt7HsnJyZqIVMB0yQQVrs477zwee+wxhgwZwuDBgyktLWXBggX07NmT0aNHA3DOOefw+OOPk52d7WlQsGbNGpYtW8b999/fZJsxMTHMnj2buXPn4nA4mDp1KjU1NSxevJjXX3+dRYsWATB9+nQef/xxhgwZQkZGBk8//TQLFy5k6dKlPmMuKSnh2muvZeHCheTl5ZGZmYnD4fCcsGpqanj77bc566yzePHFFzly5MhR11PBV1dX1yAhHT58+KjrJCcnk5aWRkpKiicZaQe+Kpg0QYWRm2++mcjISGbOnElxcTGxsbFMnjyZ5557znN57sorryQuLo4FCxZw66234nK5GDx4MHPnzuXEE09sdrs//OEPSU5O5qmnnuK2227D5XIxYsQInn76aSZOnAjADTfcQF1dHZdffjmlpaUMHTqUp59+mm7duvmMeeDAgcybN48777yTffv2kZqayh133EFOTg4Ad9xxBw8++CB33XUXF154IePGjfNrPRV4FRUV7N27l3379nHgwAHq6+tbXDYqKoqUlBTS0tJITU0lNTX1qPWYSgWaI5QdDwaCiAwAvl26dCnZ2dlN5m/YsKHJja+q49HPsfVcLhelpaXs2bOHvXv3+iwlRUREkJqaSnp6Ounp6aSkpGjpSAXUjh07OP300wEGGmO2+rOOlqCU6kTcSWnXrl3s3r2bysrKFpft1q0bGRkZpKenk5aWpnVHKuxoglKqEygrK2PHjh3s3r27xQYOERERpKenk5mZSWZmpvbyr8KeJiilOqiamhp27tzJ9u3bPa0jG4uJiaFXr15kZmaSnp7e7sMlKNUW+t+qVAficrnYt28f27dvZ+/evc02dIiOjqZ379707t2b9PR0rUtSHZYmKKU6gOrqarZt20ZRUVGz9UqRkZH06tWL7OxsTUqq0/ArQYlIJDAemIA1+q0T2AOsMMasDV54SnVd7gYPW7duZdeuXc2WltLS0sjOzqZPnz7aDFx1Oj4TlIikAj8DbgR6AFuAYiASSAf6i8hu4EngcWNMaXDDVarzc7lc7Nmzh82bN3Pw4MEm82NiYujXrx99+/YlKSkpBBEq1T5aTFAiMgP4LbAUuBb4rzGmutEy3YCTgMuB9SIyxxjzfBDjVarTqq+vZ8eOHWzevJny8vIm81NTUxkwYAC9e/fWJuGqS/BVgpoITDTG7G9pAWNMGfBv4N8i0huYA2iCUqoV6urq2Lp1K1u2bKG6usFvQCIiIsjKymLAgAGkpDQ7CLVSnVaLCcoYM6s1GzLG7AZ+2uaIujB/hnx/9NFHWbhwIa+++ipDhgzxLNPc4IBur776KnPmzCEuLg6wfqn36dOHSy65hGuuucbTe/hpp53GgQMHmvw6z8jI8IzvVFRUxAMPPEBhYSF1dXVkZWVx2WWXccUVVzTZ77nnnkt5eTlLly7V+pFmOJ1OioqK2LRpU5PEFB0dTf/+/Rk4cKDnc1Oqq/G7FZ+IZACjgWigwXgIxpi3AxyX8qG6uprZs2ezZMkSYmJi/Fpn6NChvPHGG4BVx7FmzRpmz55NWVkZv/jFLzzL/eEPf2jS67lbfX09119/PdOmTeOhhx4iLi6OtWvXcvPNNxMTE8MPfvADz7KFhYVER0fTs2dP3nvvPc4777w2HHHnUl9f70lMjYc7j4uLY9CgQfTv31/vWVJdnr+t+K4FFmAlp8ZcWI0mVDuZMmUKu3bt4pFHHuHWW29t9foOh4Nx48Zx7733csMNN3DttdcetVNYgIMHD1JUVMS0adM8vRDk5uZy6623UlNT02DZxYsX873vfY8ePXrw4osvaoLC+mGwY8cOjDFNmorHx8eTk5ND3759tYm4UjZ/f6L9ClgI3GaMOXq//GHuyMYVVGxa5deycX2Hkzyq4XDph9cto2r7Br/WTxgynsShE1sdo89tJiTwwAMPcMUVVzB16lQmTZp0TNuZMmUKERERrFmzptkh4Rvr0aMHeXl5/PjHP+aCCy5g4sSJ5ObmMn369AbLlZSUsHTpUt555x2Sk5N54IEHWLduXYNxrrqa4uJivvzyyyY9PsTFxZGTk0O/fv00MSnViL8Jqi/wp86QnDqLsWPHMnPmTH7zm994Lt21lnv8Je8WY7Nnz25yaenqq6/mpz+1qheffvpplixZwvvvv88LL7xAXV0dU6dOZe7cuZ5BFV977TWmTJlC7969Abjgggv461//yvz5848pzo7syJEjfPXVV+zZs6fB9NjYWIYMGUL//v21RZ5SLfA3Qb0HnA58E8RYujx/h3x3u+mmm/jkk0+YN29eg/offzmdTsrKyjyJBOChhx5qsQ7KHeMVV1zBFVdcQU1NDatWreKRRx7hlltuYfHixbhcLpYsWcK+ffs44YQTAKvOrLq6mltvvZUePXq0Os6OqK6uDmMM3377bYOh0iMjIxk8eDCDBw/WOialjsLfb8jnwB9E5AJgI9CgwsEY06qKEBHJA94yxmTYrzOAP2ElQQfwDnCLMabpXYoBkDh0YpsuuyWPmtrksl8g+DPku7fIyEgefPBBLrzwQhISElq9v8LCQlwuF0OHDvVr+cWLF/PSSy/x2muvAVaymjJlCtHR0Vx33XUA5OfnU1payn/+858Gl6xmzpzJSy+9xM0339zqODsS902269evb9IAIjs7m2HDhmkv4kr5yd+L3lOBAiAeGIN9j5T9mODvzkTEISLXYZXIvJufLQLqgIFADpAKPO7vdjsL95DvmzdvBqC0tJQHHnigwZDvjfXr14/bb7+dJUuW+L2f+vp6CgsLufPOO5k5c6bfvRFMnTqVbdu2MX/+fIqLi3G5XGzfvp1nn33WPRAZixcv5uyzzyYzM5OePXt6HhdddBEvvfQStbW1fsfZ0VRUVFBYWMjKlSsbJKcePXpw0kknkZubq8lJqVbwqwRljDk1QPubB5wH3AvcASAiEUA9MM8Yc8SethB4LED77DD8GfK9OZdeeinLli0jPz+/xWU2btxIbm4uYA3nnZWVxXXXXcdll13WYLlf/vKXzdaJ/Oc//6FXr178/e9/59FHH2XatGlUVlaSmprK2Wefzc9+9jOKi4v573//y3PPPddk/WnTpjF//nzeffddpk2b5uc70jHU19ezZcsWNm7ciNPp9EyPjY1l5MiR9OnTx3OvmVLKf34P+S4imVg34o7EKnltABYaY7b4uzMR6WOM2SUipwCvG2OavTVeRJ4BehhjLvRjmwPQId87vXD9HMvKyli7dm2D1nkOh4P+/fszbNgwvUFZKVvQhny364zeB7YDn2HVE00DZonIKcaYpt0XNMMYs8uPfc0GpgPH+7NNpULB5XKxadMmNm7c2KCX8e7duzNq1ChSU1NDGJ1SnYO/jSQeBv4B3GiM8RS5ROQx4EGgzZcARSQaeBQ4HzjNGPN1W7epVDCUl5ezZs0aSku/67w/MjISEWHQoEF6OU+pAPE3QU0ArvNOTrZHAf/uePVBRJKBN4FkIM8Ys7Ot21Qq0FwuF99++y1ff/11g7qmlJQUcnNzdegLpQLM3wS1GxgAmEbTBwGBuHn3Jax6rZOMMRUB2J5SAVVdXc3atWvZt2+fZ1pERARDhw5lyJAhWmpSKgj8TVAvAn8RkZ8D7qZiU4A/2vOOmYiMBs4FqoF9IuKeVWqMadrqQal2VlxczOrVqxs0He/evTtjx471qw9DpdSx8TdB3Qf0AZZglXQcQC3WJb45rd2pMeYjIMV+/gWNekdXKhy4XC42btzIN99806A3iMGDBzNs2DDtO0+pIPP3Pqga4Hq7hZ0AlcAmY0yl7zWV6piqqqpYvXo1xcXFnmkxMTHk5uaSkZERwsiU6jp8Dfl+LvC+MabWft5YX/flOB0PSnUmJSUlrFy5ssEggj169GDcuHE6eKBS7chXCeotoBewz37eEh0PSnUaRUVFrF+/3nNvk8PhYOjQoeTk5GhDCKXama8h3yOae646nkcffZQNGzawYMECn8u57/ResWJFl6v8r6+vZ/369RQVFXmmxcTEMH78eNLT00MYmVJdl/b3r7q8qqoqVq5cycGD33We3717dyZMmHBMvcQrpQLDVx3UfqzLd0flHjZDtc2OHTuYPn06t9xyC08++SS1tbXMmjWLiIgInnzySWpqarjxxhu5+uqr+eyzz3j44Yf59ttvycrK4uabb+bss8/2bGfOnDl88cUXDBo0iEGDBjXYz5IlS3j66acpKSlh9OjR3H333fTt2zcUhxxyhw4dorCwsEET8qysLMaMGaMDCSoVYr5KUL/CzwTVkWzevJmNGzc2OzBgMERFRTF06FAGDx7s1/KHDx9m/fr1fPjhh/z3v/9l9uzZXHjhhSxdupSPP/6YWbNmMWrUKGbOnMn8+fM588wzWbFiBTfddBM9e/Zk/Pjx3HLLLYwYMYKFCxdijOHaa69lwgRrVJT33nuPP//5zzz11FPk5OTw7LPPcv311/PWW76qGTunvXv3snr1as//gsPhYMSIEQwcOFDrm5QKA77qoJ5rxzjazZYtW9otOYE14OCWLVv8TlBgjZTrHgzQ6XQyY8YMYmJiOPXUU3E6nTzzzDNMmjSJc8+1GldOmTKF888/n9dee42MjAzWr1/PokWLiImJYdSoUVx44YXs3Gn1HrVkyRJmzJjByJEjAbjhhht48cUXKSgooH///oF/A8LU1q1bWb9+vef+pujoaMaPH0/Pnj1DHJlSys3XJT6/R8AzxrR+vPEQGTRoULuXoBpfYjualBRrFBL3Jabk5GQAz42hVVVVZGVlNVgnOzubgoIC9u/fT2xsbIPetLOzsz0JateuXSxYsIC//OUvnvm1tbXs2rWrSyQol8vFV199xZYt340Sk5CQQF5enud9VkqFB1+X+I60WxTtaPDgwa0qzYTC0S4vTZgwgZUrG45wsn37dtLT08nMzKS6upri4mJ69OgBWJey3DIyMpgxY0aDgQo3b95Mnz59GtyU2hk5nU7WrFnD7t27PdNSUlLIy8sjNjY2hJEppZrj6xLfj9szEOW/E088kSeffJK3336bs846i8LCQt58800ee+wxsrKyyMvLY/78+cybN4+ioiJeffVVxo0bB8D06dN54oknmDhxIoMGDeKtt97itttu46233iIqqvM26qytraWwsJCSkhLPtN69e5Obm6uNIZQKU74u8T2APQy7/bwlLmPMrwMfmmpJVFQUCxYs4OGHH2bOnDlkZmYyb948TjzxRAD++Mc/MmfOHI4//nj69OnDGWec4TnRXgbiAAAgAElEQVQxT58+nbKyMm666Sb27dtHv379ePzxxxkwYAA7duwI5WEFTXV1Nfn5+ZSVlXmmDRo0iBEjRmhjCKXCmK+fzBOBaK/nLel0Lf1CJTs7G2O+G9GkW7duDV4DDV6fcMIJzW4nPT2dp556qsX9zJgxgxkzZhx1/51BRUUF+fn5HDny3RXrkSNHtrpeUCnV/nxd4ju1uedKdRSHDx8mPz/fc4+Tw+FgzJgxXfaeL6U6Gr8rHUQkEbgMGAnUAF8Ci+2ezpUKK6WlpRQUFFBTY/17RkREMH78eHr16hXiyJRS/vKrjz0RGQVsA+7FSlDjgYcAIyLDgheeUq1XUlLC8uXLPckpKiqKSZMmaXJSqoPxtxPYhcDrQD9jzFnGmO9hDQH/GdByZYdS7aykpISCggLPfW7uG561w1elOh5/E9QYYL4xptY9wR6s8B4gLxiBBZJ76ATVMXmPZutLcXFxg+QUGxvL8ccf77nxWSnVsfiboFYDJzUzfQLwVeDCCbzExER27txJTU2N3yc6FT5cLhfFxcVHHSiwuLiYwsLCBslpypQp2juEUh2Yr/ugbvJ6WQg8JiITgHzACYwGbgTmBzXCNsrOzubAgQMUFRW1ax98KnDi4uLIzs5ucb675OR0OoHvSk5JSUntFaJSKgiO1pu5tz3A2fbD7QBwDVbjibAUERFBRkYGGRk6Ikhn1Dg5xcXFMWXKFE1OSnUCvu6DGtiegSjVWgcPHqSwsFCTk1KdVIt1UCJyVWs2JCIOEbmm7SEpdXRlZWUNGkTExcXpZT2lOhlfl/hyReT/AU8Crxljdje3kIhkAD8CbgD+689ORSQPeMs9Eq+IxACPAZdg1W/9wRjze7+PQnUp5eXl5OfnU1trNSqNiYlh8uTJJCYmhjgypVQg+brE93O7UcRc4BER+Qqr94gDgAPoidX8fCjwDnC1MabQ185ExAFci3WTr7d5gACDge7Af0RkpzHmhWM6KtVpufvWq66uBqyBBidPnqyt9ZTqhHx2dWSMWQlcICIDgXOwmpUPAeqxGk38GXjbGLPNz/3NA87DalRxh9f0q7AS3EHgoIg8BMwENEEpj6qqKvLz86msrASsAR3z8vLo3r17iCNTSgWDX33xGWO+BRYEYH9PGmPmisgp7gkikgL0puH9VF8DowKwP9VJ1NbWNuiVPCIigokTJ5KWlhbiyJRSweLvjboBYYzZ1cxkd612hde0CiAh+BGpjsDpdFJYWMjhw4cBq1fy8ePH07NnzxBHppQKpnZNUC1wD9QT7zUtASgPQSwqzLhcLlavXt1gJNyxY8dqx69KdQEhT1B2vdMerEYSbsMI8y6UVPC5XC7WrVvHnj17PNNGjhzps1cJpVTn4fd4UEH2InCXiHyBdclvNvCn0IakQu2bb76hqKjI83rw4ME6Eq5SXUhrBizMxRoHKhqrmbmHMaatDSjmAg9jNWOPAP6Cdf+V6qK2bdvWYPj5rKwshg8fHsKIlFLtza8EJSJzsIbWKAEON5rtopUt/IwxHwEpXq+rgJvth+ri9u7dyxdffOF53bNnT8aOHYvD4fCxllKqs/G3BHUdcKcx5r5gBqPUoUOHWL16tWdolO7duzNhwgQiIkJeXaqUamf+fut7AEuCGYhSVVVVDcZ0SkhIYNKkSURFhUtVqVKqPfmboF4GLg9mIKprq6uro7CwkKqqKsDqwigvL4/Y2NgQR6aUChV/f5pWAreJyKXAN0CN90xjzA8CHZjqOlwuF2vWrOHQoUPAdzfiav96SnVt/iaoBODvwQxEdV0bNmxocK/TqFGjtJcIpZTfffH9ONiBqK6pqKiIzZs3e14PHjyY/v37hzAipVS4aM19UKOAW4GRWHVXXwN/NsZ8FqTYVCe3f/9+1q1b53ndq1cvvddJKeXhVyMJETkHWI3Vmu9l4J9AMrBMRM4MXniqszp8+DArV670NCdPSUkhNzdX73VSSnn4W4K6D7jXGDPPe6KI3IF1A+97gQ5MdV61tbWsWLHC05w8Pj6eiRMnanNypVQD/jYzHw78rZnpL6HjNqlWcPdO7h7XKTIykokTJxIXFxfiyJRS4cbfBLUNyG1m+nhgX+DCUZ3dhg0b2Lfvu3+ZsWPH6oi4Sqlm+XtN5XHgSRHJBvLtaVOAOcADwQhMdT47duxo0GIvJyeHPn36hDAipVQ487eZ+Z9FJBm4DUi3J+8C7jLGPBas4FTnUVpa2qAD2MzMTETExxpKqa7O71ppu6PY+0QkA6g0xjTu1VypZlVXV7NixQqcTicAycnJjBs3TlvsKaV8ajFBichNwDPGmCr7eeP5nucBGA9KdVL19fWsXLmyQR972mJPKeUPX2eJXwGLgSr7eUtaPR6U6jrWr19PSUkJ8F0fe4mJiSGOSinVEbSYoIwxA5t73piI6HUa1axt27Y1GLJ9xIgR2seeUspv/vYksUVE0pqZ3gfYG/CoVIdXWlraoBuj7OxsBg5s8XeOUko14asO6gLgRPvlAOC3IlLRaLEhQYpLdWA1NTWsXLmS+vp6ALp168bo0aO1UYRSqlV81UF9DvwccJ9Vcmk4DpQLKAeuCk5oqiNy9xRRWVkJWI0iJkyYQGRkZIgjU0p1NL7qoIqA0wBE5FngFmNMWXsFpjomYwz79+/3vM7NzdVGEUqpY+L3eFAiEiUiWYD7p7ADiAXGG2P+EawAVcexZ88evvnmG8/roUOHkpmZGcKIlFIdmV8JSkSmAc8CTRpKAAcBTVBdXHl5OWvWrPG8zsjIYOjQoSGMSCnV0fnbWezvsYbUyAMOA6cC/wfsAX4anNBUR1FXV8fKlSs9w2ckJCTo2E5KqTbzN0HlAL81xqzCGrgw0RizBCs5+bqJ128iMllECkXkkIhsEpHrArFdFVwul4vPP/+cw4etnq8iIyOZMGECMTExIY5MKdXR+ZugKoF6+/lGYIz9fBXQ5us4IhIBvIE1hHx3rNLZYyIyxveaKtS2bt3Krl27PK9HjRqlw2copQLC3wT1P+BOEUkBVgIXiUg0cAoQiJZ9qUAG4LB7pnABdTRs1q7CTGlpKV999ZXndf/+/enbt28II1JKdSb+Jqj/B0wErgH+DiRiJaZngUfbGoQxphh4DHgeqAVWALcbYza0ddsqOGpqali1apXnZtyUlBSOO+64EEellOpM/EpQxphNxpjhwBPGmAqsxhKXAMcbY+5vaxD2Jb4q4EdAPFbJ7C4RObOt21aB53K5WLt2LRUVVsci0dHRjB8/nogIf3/vKKXU0fnq6ijhKNM/dL+2k1ZbXAycYIxxN7hYJiJPAzOxWg+qMLJlyxb27v2uC8axY8eSkNDsv4tSSh0zX/dBlWPVBfmjrf3Y9MW66ddbHdblPhVGSkpK2LDhuyuvgwYNolevXiGMSCnVWflKUKe2WxRWKen3InIDsBAYB1wPaFPzMFJdXc2qVatwuazfLampqQwfPjzEUSmlOitfffEta68gjDFfisjFwD3Ag1g3AP/GGPNGe8WgfHO5XKxZs8YzMm5MTIzWOymlgsrfro5W4ONynzEmr62BGGPeBt5u63ZUcGzatKlJJ7Dx8fEhjEgp1dn5laCAt5pZbxBwHnB3IANS4efAgQMYYzyvc3JyyMjICGFESqmuwN/ezOc1N11ErgEuAB4JZFAqfFRVVbF69WpPvVOPHj0QkRBHpZTqCtpagfAB8L1ABKLCj3vwwerqagBiY2MZN26cdgKrlGoX/tZBNXeTS3fgDmB3QCNSYcMYQ3FxMQAOh4Nx48YRFxcX4qiUUl2Fv3VQLd0TVQX8OHDhqHCxb9++JoMPpqenhzAipVRX42+CanxPlAurI9cvjTGHAxuSCrXKykpW5X9KbU090bEx9OzZk5ycnFCHpZTqYvxtJLEMQESSAAGc1mRTGcTYVAjU19ezsmA5ZTs2U1ZeRUxKJqedeqrWOyml2p1fjSREJNbuG+8AVk/jq4EDIvKIiLS1myMVRr7+6iv2fLOO8vJKXPX1pFUd5JM120MdllKqC/K3Fd+fsXoY/yGQhdV33pVYTcx/F5TIVLvbvXs3X6/6FGqqiIyMoEdMBCU9JzBquI7xpJRqf/7WQf0AON8Y84nXtFdFpARYAvw64JGpdlVRUcGqTz7AeeQQOBz0S4unx6AJxPY/jj7pSaEOTynVBfmboCppvmfxQwGMRYVIfX09Kz75iMr9OwGIjXQwdvQoUsedpHVPSqmQ8TdB3QYsEpGZQL4xpl5EjgMWAPd53ycVgLGhVDtbv3Y1+zZbQ2g4gNGDskgdqw0jlFKh5W+CegRIAj4GnCJSD0Rjnc/ygD94LauNJjqQXTu281XhpxwuqyAuNorjslLoe+L5OCL9/ddQSqng8PcsND2oUaiQKC8vJ3/pOxw+VIbL5SKiro7a7MlExieHOjSllGr1fVDxQA5W67/NepNux+V0Olm1ahX1kdFERDiIcLmIzjiOQcOHhTo0pZQC/O+LLxKrOfktfHdpr0ZEngN+aoypC1qEKii+/PJLysrKiO2eTs+4BJIdEQw+4XTSU3SMJ6VUePD3Et99wBXADOATrAR1AvAQMNd+qA5i586dFBUVeV6PnjCJAQMGhC4gpZRqhr8JagZwnT3qrdsSETkM/AVNUB1GWWkJa9es8bzOysqif//+IYxIKaWa529PEknApmambwG0i+sOoq6ulmVv/JMdX39JeXklSUlJjB49WpuTK6XCkr8JagVwczPTfwqsClw4Kpjy33mNvXv24airpmz7Jrr3yCYqSpuTK6XCk79np18DH4nIKUC+PW0yMAA4O/BhqUDbtPITdmzd4nmdmNKbAQO0jz2lVPjyqwRljFkJjAPex+ooNh14ExhmjFkevPBUIBQXbeTzFcuJjIwgOTGGbt3TOGHaRaR109FxlVLhy+/rO8aYjcBsEekBOI0xpcELSwVKVekB8v/7b+pd1oDI3bolc/qlVxMTn3CUNZVSKrT8rYNCRO4UkV3APqBYRLaKyM+DF5pqK2d1BSve/icV1VY/v5FR0Uw552JNTkqpDsHfG3XvA24A7gUKsRLbJOAOEYk2xjzY1kBEpDfwBNbw8lXAX4wxd7Z1u12Vq97JqreWsHHHAZISYoiIjGDcyWeQ0rNXqENTSim/+HuJ7zrgqkb3QX0qIpuAx4E2JyjgDawWgZlAb2CZiGwwxvw9ANvuUlwuF5uXvcUXm7ZT73JRdqSGoWMmMGDYqFCHppRSfvM3QUUBzY37vQloc8+iIjIJGAScYIypBb61WwxWtnXbXVFtVSWfb95OPVa9kzMhgzEnnBLaoJRSqpX8rYN6EFggIp52ySKSBtyP1d1RW40H1gF3i8hOEdkMXGSM2R2AbXcpLpeLtevWE5PZj2490qmP68ZZ0y8iJVn72FNKdSz+lqB+CAwHtojIdqAO6AfEAJNF5GfuBY0xGccQRxpwErAMqyQ1DPiPiOzWS3yts2nTJvbu3Qs4SMzsywln59I3+1g+EqWUCq3WDFgYTNVAmTHmbvv15yKyCLgY0ATlB1e9k/0HijHGeKYNHjyYvtnZIYxKKaWOnb/jQT0f5Di+BhJEJMYYU2NP0z54/OSqq8W88w8+3V5JelZvIiIc9OjRg+HDh4c6NKWUOmZ+3wcVZO8D+4GHRSRGREYB1wL/CG1Y4c/lcrG74D1WmCKcZXvZtWULjshoxo8fr53AKqU6tLBIUMaYKmAqVv3TbuA/wAPGmFdCGlgHULllLevWf0llndVir84RQ25uLrGxsSGOTCml2iZsLqMZY7YA54U6jo6k5sAONq/6hDInJMZHU1oXzwmnnkpW78xQh6aUUm0WNglKtY6zspzd+e+wsdi6VSwuKYkJx00id/SwEEemlFKB0WKCEpFn/N2IMeaawISj/OGqd1Ky8l3W7TpEvQsckVGk9R/G+PHjtN5JKdVp+KqDSvZ69ACuxhoDKhJwAmOwhoLXUlg727f2Yz77YhOVtfUAxGf0Y+LkKTr4oFKqU2nxjGaMudT9XET+AjxijPml9zIici/WTbWqnRzZtYWCTz5lb4WT6KgIuvXKInfS8SQlJYU6NKWUCih/f3L/H9aAhY09D6wNXDjKF5ezjjXvv8WeCicAFa5Yhg0dS58+fUIcmVJKBZ6/zcz3AKc1M30aUBS4cJQvh49UUNoti9j4OOodkWQNG83kiWNDHZZSSgWFvyWo3wJPi8hpwGrAgTUe1LnApb5WVIFRU1PDihUrcEXFkDZQoN7F2Wefpo0ilFKdll8lKGPMi8BZgAu4ErgCayiMk40x/wpeeAqs3iJWr15NRUUFAFHRMZxy+ulER0eHODKllAoev5t9GWM+BD4MYiyqGS5nHQWFq9h/YL9n2tixY0lObvMwXEopFdZ83Qf1gL8bMcbcGphwVGPL//0G6zZsICqtNz17ZSBDh9K7d+9Qh6WUUkHnqwQ10c9tuAIRiGpq0xdr+HLDlzhc4CzeSW33VIYOHRrqsJRSql34ug/qVPdzEbkSeMcYc6BdolIcOVTC+vwPiYqMoLaunqj4ZM45/1xtFKGU6jL8bWb+Z6zeJFQ7qK2t4dN/v0xtXR1J8TEkJsbxvYt+QEJ8XKhDU0qpduNvgioALgpmIMricrkoePcNDpWWAuBwwGlnn0vfflkhjkwppdqXv6346oHficgdwLdYTcw9jDF5gQ6sq1r72TJ2b/vW83rUmDH0zjkuhBEppVRo+JugCuyHCqLP13zOik8/JTE2gpjoSAZm9Wbo8d8LdVhKKRUSfiUoY8w893MR6QZEGGNKgxZVF7R923YKlr4D9U6OVDpJjosl96zv43CExaDHSinV7vw++4nIjSKyHTgIFIvIbhH5TfBC6zqOHDnCquWfEllfC0BsZAQTz7qIyLiEEEemlFKh41eCEpHZwP3Ao8BJwMnAH4FbReSW4IXX+dXU1FBQUEBkQiJp/QcRHxtN3smn03vAwFCHppRSIeVvHdTNwE+MMf/wmvapiBQB9wJ/CnhkXYDT6aSwsJAjR44AEJOUwrk/OpUemTp8hlJK+ZugegIrmpm+CsgOXDhdh8vlYsXKVRw8eBAAh8PBuHHj6JGp3RgppRT4Xwe1nuaH1fgh8HXgwukaXC4Xyz78H2s++R8VFVUAjBw5UvvYU0opL/6WoOYC/xaRKcBye9oU4Gzg4mAE1pmtWrUWs/IzIutrOVj0Db3zTmLgQK1zUkopb/6OB/UecDpQjTUe1CVAGTDRGPNWIAMSkRQR2SYiVwdyu+Fix7YiNq38HxGuOgBSouC4HE1OSinVmK/hNs4GPjbGHAEwxvwP+F87xPQk0Cn79dm7dw8rPniHyPpquiXG4Kqp45Tzp5OWpdV4SinVmK9LfP8G6kRkJfCB/fjMGFMdrGBE5CqgG7AuWPsIlZKSEvL/+zZ1FWUAJMdGcsLZZ9J9yIgQR6aUUuHJV4JKB07Aqms6EfgFECEi+XyXsAqMMc5ABCIiA4G7gOOB/wRim+HiYOkhPn3/HWoOWaOVxEY6yMubSPchuSGOTCmlwpev8aAOAm/ZD0QkEhiHlbCmANcA6SLysTHmvLYEYW/7r8BsY8weEWnL5sJKeXk5r7/yBvUl20iKjyY2KoKJo4bRY/RJoQ5NKaXCmt9dHdklpR3AbmAfsM1ef1AA4rjT2oV5NQDbChtVVVX861/vUHNgG05nPUcqahg1MItek87WPvaUUuoofDYzF5Ek4BTgDOB7wDBgO/Ah8DTwI2PMrgDEcRnQR0TcTdaTgQUikmeMuSkA2293NTU15OfnU1+2B4erHgcwuGd3Bky9EEdkdKjDU0qpsOerFd/HQB5W57AfYXVn9IExZlOggzDGDGu077XAI8aY5wK9r/ZQW1tLfn4+hw8fpnuf/kRFRpDpqmTyRf9HZFxiqMNTSqkOwVcJ6gSsS3rPYDWIWG6MqW2XqDowd3I6dOgQAI6ICE4850J6pSQSlZQa4uiUUqrj8JWgBmFd2jsduBFIFJFPgKVYCWu1McYVjKCMMWODsd1gq6ur47PlyymzkxPA6NGjyc7W+5yUUqq1fLXi2wossh+IyBjgNKyEdSdQKyIfAUuNMQuCHmmYq6urY/nyfDZ88SURDsjsm8XYMWPo169fqENTSqkOqTWt+D43xvwRuAArSS0GzsQaI6pLq6uro6CggA3rNxBRvh8O76eu+CBZWZ2yQwyllGoXR+0sVkSygUnAZPvvOKAO+AxrLKiPghhf2Kurq6OwsJADu7YTV3WAKqBnrIOctEgiHaGOTimlOi5frfhewUpIvYHDwKfAm8BsYFWgepDoyGpraykoKKB47y5qDuwgPjaSfslRpCSnMPzs7+OI9LezeKWUUo35OoPGAo9glZBWG2Pq2yWiDqK6upqCggIO7t9Lzb5t4KpnUGosAzLT6D55OpGx8aEOUSmlOjRfjSSmtWcgHUlVVRXLly/nwN49cHAnuOrJSYujb3p3uudNIyohOdQhKqVUh6fXoFqpoqKC5cuXs2fnLsp3F5EQE8GYPklkpSVbyUnvdVJKqYDQBNUK5eXl5OfnU7J3N+W7t+JwuUhxQJQjmu6TLiCqW49Qh6iUUp2GJig/lZSUsGLFCmpqaoiNiyM2KpL0yHq6JyXR97Tva3JSSqkA0y61/bBnzx7y8/OpqakBICYhidOnnU9mZiajL7qS5PSMEEeolFKdj5agjmLr1q0UrlxDfKz1VsXGxpKXl0dKSgrZQ0fqsBlKKRUkmqBa4HK52LDhaz7LX0nNgZ0kZ2aT1bcPkyZNIjHR6pFck5NSSgWPnmGbUV9fz9q1a1mRX4BzfxFR9TU4929jwIAcT3JSSikVXFqCaqS6upoVK1awf6shvvIAdREu4nCQ0yOGzPi6UIenlFJdhiYoL4cOHaJw+aeU7dxCfXUFDoeDIT0TSYuPR878PjGp2hhCKaXaiyYoW9H2HXz24QdEVeyHequbwcGpsQwaNIhuY88gQrsuUkqpdtXlE5TL5eKjj1ewYeWnRNUcJjkhhtjoCEZmJNB3zBTiB4/D4dBuyZVSqr11+QRVVLSVrWuWEVVTCUBtdS3H52TTZ/I5RKf2CnF0SinVdXX5Vnzl5Ufolp6BwwFJ0RHkjR1F3zN+pMlJKaVCrMuXoIYMGYLL5eJIchwjRo0iecBwvaSnlFJhoMsnqLi4OEaNGgWjRoU6FKWUUl66/CU+pZRS4UkTlFJKqbCkCUoppVRYCps6KBH5HnA/kAPsAx40xjwV2qiUUkqFSliUoESkL/AKcC+QAvwf8HsROSukgSmllAqZcClBDQD+box5zX69QkQ+Ak4A3g1VUEoppUInLBKUMeZj4GP3axFJA04CXvRj9UiwRr1VSikVnrzO0ZH+rhMWCcqbiHQH/gUUAG/4sUpvgMsvvzyYYSmllAqM3sBmfxYMqwQlIkOxktJXwOXGmHo/VluBVdraDTiDGJ5SSqljF4mVnFb4u4LD5XIFL5xWEJGTsZLTk8DtxpjwCEwppVRIhEWCEpHBwBpgjjHm0VDHo5RSKvTCJUH9AfgFcKTRrMeNMb8OQUhKKaVCLCwSlFJKKdVYWNyoq5RSSjWmCUoppVRY0gSllFIqLGmCUkopFZY0QSmllApLYdWTRKCJSB7wljEmw37dE/gTcBZQDTwD3GWMcdrzXwB+ANR5bWa0MWaLiPQDngYmYw0HMssY83a7HYytpWFJRCQFWAR8DygH7jDGPGuv4wDuAW4AYoBngV8ZY+rs+T8Afod1l/cy4GpjzL5wPy57vW1AD8DdHHWnMUbseadifd6Dgc+BK40xfnWxEkhHG0pGRHpg3V1/sTFmrdf0WcCvge5YN7HPNMYcsed19GP7HzCRhr2/dDfGOEVkDNYN+6OBLcA1xhi/ex8IFB//k4OBR7HOBdXAEuBWY0x1R/iu2XG0+tjs9dr1+9YpS1Ai4hCR64D3sP5J3J4HMoDhwHFAHvBbr/njgOnGmCSvxxZ73kvAF1gfzvXASyIyKMiH0sBRhiV5AuvL3hs4D7hfRKbaq94AXIx1fDlYJ4bb7W2OwEq8V2Md2zdYx9pujvW4RCQdyAIyvD4v8Zr3OjDP3uZrwLsi0q7/80cbSsbuQeVTYGCj9c4C5mD9mOoNxGGdODrDsTmAsUBuo++aU0RisJLxYnub9wHviUi39jouO0Zfx/YasA7ohfWdmgzcZa8a1t81O45jOrZQfN86ZYLCepNuxPoAABCRBOBs4BfGmH3GmBLgTuB6O6HFA8OAtY03ZvcROAGYa4ypMcZ8gNWh7bXBP5QGBmAPS2KMqbd/VX4EnA5cAtxpjKmwf6kuxPqyAFwFPGKM2WGM2Q/cDcy0510BvGmM+cQYUwXcBpwgIjntdVAc+3GNB75xlyoauRj40hjzijGm1hjzIBBrb7M9DaD5YztBRE7HOkHd08x6VwHPGGO+NMaUA78BfiQiSXT8YxuK1S/bxmbmnQJEG2MesY/tJeBL4IdBiN+XATR/bNOw+v28xz4X7Ab+Bpxorxfu3zU49mNr9+9bZ73E96QxZq6InOI1zZ2Mvd9cJ9ATK+MPw7q0t1BEJgPbsRLSW8AIYFujD+ZrrBJYu/ExLMnHWEXubxrFd4H9fARWB7ze8/rY648AVnrto0JEtgOjGm0vaNpwXOOACBEpxPqVvhr4uTFmA02PGcBgHdf7QTiMZh1lKJm1wED70tBfG606AvC+hLwZ6394KB3/2MYBh4GlInIcVuy/MsYsxzq2DY2W/xrr2NqNj2O7wRgzy2u6A7gQq6s2CPPvmr3fYz22dv++dcoSlDFmVzPTyrEu+T0gImn2tfG59ux4IBnrQ5sH9MG6tLDEvh6eBFQ02mQFkBCcIzi6RsOSrAKqGnWw6x1f4/jdzxOamdd43XbVyuNyAoVYv976Y32R3rFLy2F1XNB0KBljTLH72n4zGsRvvwdVhOFnBq0+tmh7uZ8A2VglrXdEpBU4aTsAAAWiSURBVA8d4Ni8pjv4rs7lPntyh/muQauPrd2/b521BNWSK7He9A3AfuAPwPlAqTHmPawE5vaKiPwY69f6eqwk5i0Bq9K+3TUelgSrTi1ORBxeJ3Pv+I7QMH73P015M/Mar9tuWntcxpgHGq1/G3AT1qWIIzT9coTNZ2aOPpRMg8/FPmHE8d1n1mGPzRjzAvCC16THROQnWJeDwub/EVo+NhFJxiopDgWmejV06BDfNWj9sYXi+9YpS1A+9MZqCZVpjDkO2AlssIva54vIVY2Wj8H61foV0M+up3IbRtMi7f9v7/5CrKqiOI5/bZSgqMiXCMEmevhRQQUWofQmFZFGZP+QlEyth6DGmCwboj+jaFlCWFKBFBUVKtpDpA/9I8oKgqgRczETqC9F/zAylILoYe2J48XJ1O655zq/D8iMd9+re925e9Y5++yzV9uVC8+fkxckbyxz2cPABA69GF3t305ALW3fRcS+1rZyNDSVmmM7lrgk9Um6otLWQx50jf7MqjEf8to6jRHbkbT2/zzyvRg+TBt0UWyS5ku6ruXh6lhrdGySzga2k8lmekTsqbys8WOt/N9HHVsnxtt4O4NaA+yQ1E9+MJ4Ani1tPcAzkr4hp5ZuAWYAiyJir6SvgBXlqGEGOTc7vc7OlyWgb9NSliQi9kvaQq7EWUj+MltMrhaCPBrql/QeeaTzaHkM4HXg43K97lNgJfBlRBzuAnZbHEdcvcA8SbOAfeTPc5icG99DTufeTK4ouhf4i7wYXJuxYvsPXgXWS9pELrVeBWyOiN/Le9LNsU0GBstY201WMjgN2EYubZ4gaQk5NueQy823/I9dP6KxYpM0CdhK/uKdG+UWlYpGjzU4rth6qXm8jbcEtZhcBfYL+QavjYh1ABHxlqQB4A1yieUuYFZE7C2vnQO8SN4z8BOwMCJ21Nz/u8mBvFLSysrjz5ErhdaRH5SDwIqI2FranwfOIo+MTgE2Uq6/RcSQpDvKc6aQR1U3tT+UQxxrXA8CT5Nz4aeS95XMLgPrB0mzySnd9eRKsNkR8UcN8VSNGVv8SymZiHhH0iA5BTMZeJeyGiwiujo2st9nAh+Ur18AV0fEbwCSriE/j4+TCez6siKuTmPFNgxcTE5//Sr9c9LwdUTMoPljDY49ttrHm8ttmJlZI423a1BmZtYlnKDMzKyRnKDMzKyRnKDMzKyRnKDMzKyRnKDMzKyRnKDM2kTSm5J+LJtxtrYNSNovqbcDXTPrCk5QZu3TR94MX70ZEknnkrWeBiJidwf6ZdYVnKDM2iQividr/iySdHmlaS1ZjuJotgcyG3e8k4RZG5VdyD8hN0O9jNwdfwNZTXZ009sFZNXVKeRO+wMRsa20TSSL/s0ly8D8TG7H1R9ZgfY1cs+z88m9Cm+IiA9rC9CsjXwGZdZGpUzIneSGpwuAp4DlleR0LbmJ8UB5znpgi6TRYpjLyJLc88gS4suAe8jqp6NuI/dAmwl81uaQzGoz3jaLNatdROyQtAZ4gTxDWlVpfghYFREbyt9HJF0K3AfcCgwBt0fER6X9JUlLyQqmowXmdkZEa9Vas67nBGVWj8eAB4DBiPiz8vgFwDRJD1cem0Spo1N22Z8paTW5y/RFZNmDnsrzv21nx806xVN8ZjWIiAPl2wMtTROB+4FLKn8uJOuNIWk5WbLhpPL1KrLCc1Xrv2l2QvAZlFln7QLOiYiR0QfK2dRBYDVZUruvlEmnVHWeSlbXNTuhOUGZddaTwCuSAngfuBJ4hLz+BFlcc5ak7cAZ5FTh6cDJHeirWa08xWfWQRGxkSx5vpS87rQEuCsiNpWnzCdX7w0Bm4ER4GVgWu2dNauZ74MyM7NG8hmUmZk1khOUmZk1khOUmZk1khOUmZk1khOUmZk1khOUmZk1khOUmZk1khOUmZk10t8QnHlvpzUN3wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "system.t_end = 2250\n", "results = run_simulation(system, update_func_quad)\n", @@ -258,7 +368,18 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "13.856665141368708" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results[system.t_end]" ] @@ -267,7 +388,18 @@ "cell_type": "code", "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "13.88888888888889" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "-system.alpha / system.beta" ] @@ -281,11 +413,26 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [ - "# Solution goes here" + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "system.p_0 = 20\n", + "results = run_simulation(system, update_func_quad)\n", + "plot_results(census, un, results, 'World population projection')" ] }, { @@ -304,9 +451,107 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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United States Census Bureau (2015)[28]Population Reference Bureau (1973-2015)[15]United Nations Department of Economic and Social Affairs (2015)[16]
Year
20167.334772e+09NaN7.432663e+09
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\n", + "
" + ], + "text/plain": [ + " United States Census Bureau (2015)[28] \\\n", + "Year \n", + "2016 7.334772e+09 \n", + "2017 7.412779e+09 \n", + "2018 7.490428e+09 \n", + "2019 7.567403e+09 \n", + "2020 7.643402e+09 \n", + "\n", + " Population Reference Bureau (1973-2015)[15] \\\n", + "Year \n", + "2016 NaN \n", + "2017 NaN \n", + "2018 NaN \n", + "2019 NaN \n", + "2020 NaN \n", + "\n", + " United Nations Department of Economic and Social Affairs (2015)[16] \n", + "Year \n", + "2016 7.432663e+09 \n", + "2017 NaN \n", + "2018 NaN \n", + "2019 NaN \n", + "2020 7.758157e+09 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "table3 = tables[3]\n", "table3.head()" @@ -321,10 +566,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ "table3.columns = ['census', 'prb', 'un']" @@ -339,10 +582,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ "def plot_projections(table):\n", diff --git a/code/chap08mine.ipynb b/code/chap08mine.ipynb new file mode 100644 index 00000000..f89540d6 --- /dev/null +++ b/code/chap08mine.ipynb @@ -0,0 +1,812 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 8\n", + "\n", + "Copyright 2017 Allen Downey\n", + "\n", + "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure Jupyter so figures appear in the notebook\n", + "%matplotlib inline\n", + "\n", + "# Configure Jupyter to display the assigned value after an assignment\n", + "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", + "\n", + "# import functions from the modsim.py module\n", + "from modsim import *\n", + "\n", + "from pandas import read_html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Functions from the previous chapter" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_results(census, un, timeseries, title):\n", + " \"\"\"Plot the estimates and the model.\n", + " \n", + " census: TimeSeries of population estimates\n", + " un: TimeSeries of population estimates\n", + " timeseries: TimeSeries of simulation results\n", + " title: string\n", + " \"\"\"\n", + " plot(census, ':', label='US Census')\n", + " plot(un, '--', label='UN DESA')\n", + " plot(timeseries, color='gray', label='model')\n", + " \n", + " decorate(xlabel='Year', \n", + " ylabel='World population (billion)',\n", + " title=title)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Simulate the system using any update function.\n", + " \n", + " system: System object\n", + " update_func: function that computes the population next year\n", + " \n", + " returns: TimeSeries\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " \n", + " for t in linrange(system.t_0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " \n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading the data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'data/World_population_estimates.html'\n", + "tables = read_html(filename, header=0, index_col=0, decimal='M')\n", + "table2 = tables[2]\n", + "table2.columns = ['census', 'prb', 'un', 'maddison', \n", + " 'hyde', 'tanton', 'biraben', 'mj', \n", + " 'thomlinson', 'durand', 'clark']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XLAaXy0UoFCIuLo7nn3+eZ555hhtuuAGPx8ONN97IkiVL2r3W9vaTJCVE9wn5vFSsfoVgTTkAwWAIh8NBzLCJxAyfhM1h3vrPn5xDlMtBbmZ8JMNtU59NUNPGtF/1NntCNrMnZLe5/oLJuVwwObfN9Z+ZMYjPzBh02nENGDCAo0ePMm7cuMZlgUCAo0ePNs4W/KUvfYmLL76Ym266CYCUlBSuvfZatmzZgtb6lGNOnDgRj8fDihUrmD9/frN1jzzyCAUFBTz//PNkZGRw3333cd555zWu37VrF4MHD2bDhg1txlxZWdn4/Mrv9/PRRx/x9a9/nSlTpuBwOJp1sDUMg8rKyg73a/ncSgjRdWwuD47oOALV5ZRX11NQ4yZr2gWMU/nNthuekxShCK05a1V8SqnFSqmaFq+gUurtsxVDT7BgwQKefPJJ9u7dC5jDND388MOkp6czfvx4AC699FKefvpp3nnnHXw+Hz6fjzVr1rBixQouvPDCU47pdru58847ue+++3j33XcJBALU1dXx7LPP8vLLL3PHHXcAcOWVV/Kb3/yGwsJCgsEgv/vd71i8eDH19fXtxlxWVsZXvvIV1q5di8vlIjMzE5vNRmJiInl5efh8Pt544w2CwSDPP/9846C97e0nhOg+NpuNuDFzKK+38WnDIHYlzOS/+xqoq+9do7WctRKU1vovwF9OfFZKTQTeBr53tmLoCe644w4cDgdLliyhtLSUqKgoZsyYwXPPPddYPXf99dfj8Xh46qmnuOuuuzAMg2HDhnHfffcxe/bsVo/7hS98gfj4eJ555hl+8IMfYBgGo0ePZunSpUydOhWAr371qwQCARYvXkxFRQX5+fksXbq0wwFahwwZwv3338+9997L8ePHSU5O5p577mHEiBEA3HPPPTzyyCP86Ec/YtGiRUyaNMnSfkKIMxeqr6Vu30Zi1fTGqjsAR0w8wy6/mXXv7YXqBjxuBzV1/oi3zDsdtkj0EFZKuYBNwG+01r+xsP1gYP+7775LTk7OKet37NjBqFGjujxOcXbJ71EI6wzDoP7QDmp3rsYI+IgeNono4VNwtGiJd+hYNYUltUwamRHRVnqHDx8+UQM0RGt9wMo+kXoGdQfgBZ6K0PmFEKLXCtSUU7P1Q/xlhQA0+ALsW7WCQHUys6Y0r6HIzYzvsY0gOnLWE5RSyo1Zrfc/WuvIDPAkhBC9kBEK4t27kbq9n2KEggDUNwTYWeTncMJEvAfqGDnCS2pidIQj7RqRKEFdAoSA1yNwbiGE6JX85UXUbPmQQE2T0WRsNpJHTaE2KZnaEi92oKi0ThLUGVgELNda97xeYUII0cOE/A3U7VqLt2A7YIAB2MCZmEH8uLk4E1KZM7CelRuPcN452aQkeCIdcpeJRIKaAdwbgfMKIUSv03BY4y3YRihkcLzciy9kY/Tc+XgGjcFmMxs9pCR4WDRnWIQj7XqWEpRSygFMBqYAGUAQKALWaa03nuY5BwNHT3MfIYTolzyDxlB7cCd7tu+lzJHG0YSxpEUNYpCt749U126CUkolA98AbgNSgX1AKeAA0oBBSqlC4GnMJuMVHZ1Qax17pkELIURfZBghDL8Pu/tkNZ3N7iBxwvkQzKagJAZsNnYfKmdQVvv9F/uCNhOUUuoG4CfAu8BXgHe01g0ttkkAzgMWA1uVUndrrZ/vxniFEKJPClSVUrN1BdidxExegNvlaFznSsrgnJnJ7P9gD1NGZ0Z0EsGzqb0S1FRgqta6uK0NtNZVmK3xXldKZQF3A5KghBDCIiPop273eur2byIYCHKsrI6Cw1EsvPpi7PaTgyrHRrtYfMnIfjXQcpuVmFrrr7eXnFrZvlBr/bWuCavvsjLl+xNPPMH48ePZs2dPs21amxzwhJdeeolRo0YxceJEJk6cyIQJE7j00ktZunRpsyk65s2bx/jx4xu3O/H6zGc+07hNQUEBd9xxB1OnTmXixIksXLiQP//5z62e97LLLmPOnDnNBowVQljjKz5I+YfLqdu3ESMUYu+RSsqq/dTW1LLjwKmTk/an5ASn0YpPKZUBjAdcQLOfktb6jS6Oq99raGjgzjvvZPny5bjdbkv75Ofn869//Qswh0HZsGEDd955J1VVVXz7299u3O6xxx47ZdTzE0KhELfeeisLFy7k0UcfxePxsHHjRu644w7cbjef//znG7c9MQhseno6b7/9NgsWLDiDKxai/wg11FGz/WMaCk9+CbXZbCRm5fJx3WB8zjiOldWeMlFqf2OpGYhS6ivAIczBXV8HXmvyerXbouvHZs6cSV1dHY8//nin9rfZbEyaNIkHHniApUuXUlVVZWm/8vJyCgoKWLhwIdHR0dhsNiZOnMhdd911yrYvvPACF110EZ/73Of405/+1Kk4hehPzKnXt1P24QvNk5Mrivhx5zNywRfJzM7isnOHtDvdT39htQT1PeD3wA+01tXdGE+Xqd21jro96y1t68kdRfy45tOlV29ZQf2hU6viWooZPpnY/KmdirHd48bE8PDDD3Pdddcxd+5cpk+f3qnjzJw5E7vdzoYNG1qdEr6l1NRUpk2bxs0338wVV1zRWM135ZVXNtuurKyMd999lzfffJP4+HgefvhhtmzZ0myeKyHESUYoSOXa1/CXFVJT56O4wsugAQlE5+QTN+pc7FHm6A99sT9TZ1ltSJ8L/Kq3JKe+4pxzzmHJkiV8//vft1wCaunE/Es1NTWNy+68887GSQNPvJ588snG9UuXLuXWW29l8+bNfP3rX2fGjBncfvvtFBWdnDb6n//8JzNnziQrK4u4uDiuuOKKNp9TCSHM5uLO+BSOFtdQUFRNhd/N/qRpJJxzYWNyEs1ZLUG9DVwI7O7GWPoFq1O+n3D77bezatUq7r///mbPf6wKBoNUVVWRlZXVuOzRRx9t8xnUiRivu+46rrvuOnw+H+vXr+fxxx/nm9/8Ji+88AKGYbB8+XKOHz/OrFmzAPOZWUNDA3fddRepqf273lyItsTkTyNm9052heI5HjsCT1kUk/zBZk3KxUlWE9Qm4DGl1BXALsDXdKXW+tQHFBEWmz/1jKre4sfNPaXarytYmfK9KYfDwSOPPMKiRYuIiYk57fOtXbsWwzDIz8/veGPM50rLli3jn//8J2Amq5kzZ+JyubjlllsAWL16NRUVFfz73//Gbj9ZCF+yZAnLli1rnMFXiP4qWF9L3a61xKoZzUpHdlcUwxbexLaPDjIyxsXMcVmSnNphtYpvLrAGiAYmEO4jFX613u5ZtMrKlO8t5eXl8cMf/pDly5dbPk8oFGLt2rXce++9LFmyhLi4OEv7zZ07l4MHD/LQQw9RWlqKYRgcOnSIZ599tnG6+RdeeIFLLrmEzMxM0tPTG19XXXUVy5Ytkybnot8yjBDe/Zsp/3AZtQU7WP/v16mpa/Z9HrvTxeWzh3Dh1LxeNbttJFgqQWmtL+juQPoLK1O+t+aaa65hxYoVrF69us1tdu3axcSJEwFwOp1kZ2dzyy23cO211zbb7jvf+Q4Ox6nf2v79738zYMAA/vrXv/LEE0+wcOFCvF4vycnJXHLJJXzjG9+gtLSUd955h+eee+6U/RcuXMhDDz3EW2+9xcKFCy3+RIToG/wVx6jZupJAVQnVtT6OFNcQDJXz8ZodXHzBhGbbtpz1VrTO8pTvSqlM4GvAGMyS1w7g91rrfd0XXuO5ByNTvvd58nsUvVHI30CtXkP9wR2Yc2FAXX2AnccCHE0YR607lWsuzCcz5fSr6PuSzkz5brUf1DTMZ09XASVAMbAQ2KyUkio+IUS/YxgG9Ud2Ub5iGfUHw3M1YbbWSx9/Ls5Ji3AkZ7Fg1pB+n5w6y2ojiV8CfwNuazpNu1LqSeARQKoAhRD9RqC6jJptK/GXFlJR00CU20F0lBN3eh5xY2bjiElgVl4Ah92OyynVeZ1lNUFNAW5pmpzCngCs9YYVQog+IlhTQe2xwxwprqGuPoArJp5Jl1yEJ2to43h5Hnck5oPtW6ym9kLMiQZbGgpI510hRL/iHjAER2ou3oYgxTFD2RR7Lvvqk/rdYK7dzWqK/xPwO6XUt4ATzchmAv8bXhdxhmHIH0cvZrWxjhBnW6CmAsNfjyv5ZD9Fm81G+qTzORydT/H+BibkpzMiNymCUfZNVhPUg8BAYDlmqcsG+DGr+O7untCsczgc+P1+y6N+i57H6/W228xeiLPNCPqp27MB7/5NNNiiCI6/nLyskxMFOmISOOecOIaO8JGS4GnnSKKzrPaD8gG3KqXuBBTgBfZorb3dGZxVSUlJHDt2jOzs7GYjG4iezzAMvF4vR44cITMzM9LhCIFhGPiO7ad2x8f4a6s5Xl5HWWU9laXvk/X5RbicJ/sQOh12SU7dqL0p3y8D/qO19offt5SrlAIiPx9UWloahw8fRmsdyTBEJ7lcLjIzM0lISIh0KKKfC9ZWUrN9Fb7iQ4CZrCprfNS6kjhOKuu2H+Pc8QMjHGX/0V4J6jVgAHA8/L4tBhDRwaTsdjt5eXmRDEEI0Ys1rc4zQsHG5a7oGNKnTGHjfhfZGfGoQcntHEV0tTYTlNba3tp7IYToK5pW5/lqqqj3BYmNNicNj84bTUz+VFJcUXhyqskbEC8Nsc4yaagvhOi3DF891Zveo6yihmNldRghAzUmn5SJc3ElZjRuNyhLqp8job1nUMWcGLujA1rrjI63EkKInsUeFU30sMmUvfcWDYaLY/EjqXOP5ZJEuaX1BO2VoL6HxQQlhBA9nWEYBKtLcSakNVseM3Q82RW1vLo3ipjYGIbnyXOmnqK9Z1DPncU4hBCi25hj563CX1aIb/RlZA/ObVxnszvInTKbizLLGZyV0KwZuYis9qr4LM+Op7U+/bnIhRCim4X8DdTt/gRvwVa89X4KS2opOfwGzs9eR2ZqbLNtR+RKyamnaa+Kr/asRSGEEF3IMAwajmhqd64h5DPHEygur6OuIYg3Jp4PNxzkcxeOlFZ5PVx7VXw3n81AhBCiK/grj1O77SP8FceaLc8ePoJNR9NpcMUzOSuZUMjA4ZAE1ZO1V8X3MHC/1ro2/L4thtb6/3V9aEIIYV3I56VWr6X+0E7qG/zmdBc2sHtiiRs5E3fWMGYfqiAzJYbEuKhIhyssaK+KbyrgavK+LZZb+imlsoDfYk5wWA/8Tmt9r9X9hRCiLdWb3qOusICisloqa3zkDkhkwLhpxAybiM1p3srypYVer9JeFd8Frb0/Q//CnOAwE8gCViildmit/9pFxxdC9FMxI6ZycKemssZHtTuD1Y7xfH7YFGwyo22vZXkkCaVULHAtMAbwAduAF8IjnVvZfzrmBIeztNZ+YL9S6nzMkdGFEMKyUEMdNrcHm+1k8nElZZA7eRbbtlRTYk9j2IAk/IGgTLnei1lKUEqpccAHmIlpM+acUDcDP1FKXaq13mnhMJOBLcCPlVI3YVbxPaW1/mUn4hZC9ENGKIj3wBbqdq/HPnQqiUPH4nCcTEBJo6czNaECt8tBbmZ8BCMVXcFqCer3wMvA/4RLPyilooE/AM8Acy0cIwU4D1iBWZIaCfxbKVUoVXxCiI74Sg5Ts/0jAtVllFbWU7TvLRLnJDNpTG6z7YblyMy2fYXVsu8E4KETyQkgPFnhT4FpFo/RAFRprX+stW7QWm/CTHBXn07AQoj+JeitoerTt6lc+xrBmnIqqhs4VlaHDzebtx2kxuvv+CCiV7JagvoUs/Szq8XyKcB2i8fYCcQopdxNnlvJaOpCiFYZoSDe/Zup27MeIxhoXJ6cHM8ux3AOGANJTYyhviFAXLSrnSOJ3qq9flC3N/m4FnhSKTUFWA0EgfHAbcBDFs/1H6AY+KVS6ruYU8d/JXwMIYRo5Cs5TM22lQRqKjEMA7vd7FAbNXAEcSNnMKnaIKe8jnHD03HYpbNtX9XRaOZNFQGXhF8nlABfBh7o6ERa63ql1FzgCaAQs5HEw1rrf5xWxEKIPq3+yC6zT5PXz9GSWmKjXeQOySNuzCxcKeZ06wM9MDA9LsKRiu7WXj+oIV19Mq31PmBBVx9XCNF3uDMH4zXc7C8sJWhzUmAMJXP0RbhSJCH1N202klBK3Xg6B1JK2ZRSXz7zkIQilizEAAAgAElEQVQQ/YlhNB+Mxu50kzHxPBwZQ9mdOpfKhGFU1UlDiP6ovSq+ieFnRU8D/9RaF7a2kVIqA/gS8FXgna4PUQjRF4V89dTqNRhBP/ETLmw2srhn4AjGXZqHd3sR544fKI0g+qn2qvi+FW4UcR/wuFJqO+boESWADUjHbH6eD7wJ3KS1Xtv9IQshejPDMGg4upvaHf8lUF/H8XIvx4vjmDd/erMklRQfxcXTB0UwUhFp7Tbz1lp/AlyhlBoCXIrZrHw4EMJsNPFr4A2t9cHuDlQI0fsFayup2bYSX8lhQiGDPYcr8AdCVPj2sO/ISOlkK5qx1A9Ja70feKqbYxFC9FFGKIh33yazT1MoCIDdbiMmIZHNwaFUR2WScKRSEpRoRjrKCiG6lb+8iJotHxKoKWuy1Eb04LGMHDyRnSsPMn9kBkqmwhAtSIISQnQLIxSkdsd/8RZso77BT1lVPVlpcbgS04gbNwdXYgYA116sZOp10SpJUEKI7mGzE6yr4nhZLSUVXoI2B/ahoxg767xm02RIchJtkYlShBDdwmazETf2PGxOF1XudHanzmFNWSqBYKQjE73F6UxYOBFzTicXZjPzRlpraUAhRD9mGAa+ov24M/KwOU7eVhzR8Qy/7Do2fVxEhsfF+ZNzZAJBYZnVCQvvxpxaowyobrHaQFr4CdFvBb011Gz9EN/xg5QmjGDYzPNxOR2N690JySw6P47oKKdU54nTYrUEdQtwr9b6we4MRgjRexiGQf2h7dTuXE19XT1Hi2uo3b+GUmcms2aObbZtjEdGghCnz2qCSgWWd2cgQojeI1hXRfWWD/CXHgXA2xCgriFAWfQgjh9qYISqIyMlJsJRit7OaoJ6EVgM/Lj7QhFC9HSGYVB/0Cw1GcGTA7imZmawN3kmx6qjmKwySEn0RDBK0VdYTVBe4AdKqWuA3YCv6Uqt9ee7OjAhRM8SrKumesv7NBQfwTAMHA47YCNm6ARiRkzh3PoQUwNBUhOjIx2q6COsJqgY4K/dGYgQoufyVxyncs2r1NTUcaS4hugoJ4OH5hI/YR6uJLPDbUJshIMUfY7Vsfhu7u5AhBA9lzMhFZ8zhgOFRQAcdecQP/hCUpLSIxyZ6MtOpx/UOOAuYAxmB9+dwK+11h93U2xCiB7CZneQMfViikr+yYbgMEKxaWCXgWhE97LUY04pdSnwKWZrvheBvwPxwAql1MXdF54Q4mwL+bzU7d1wyky3zoRURl91E0Pyh/PFi0cyIlcGdxXdy+pXoAeBB7TW9zddqJS6B7MD79tdHZgQ4uzzFR+ketP7lJVWULC9hHkL5mO3N5np1u1k3pTcCEYo+hOrY46MAv7SyvJlwLiuC0cIEQlGMEDN9o+oXPsGBw4e50hxDfaD69m041CkQxP9mNUEdRCY2MryycDxrgtHCHG2BapKqfjoJbwHtoANPFEOAnY3hxInsuNwHaGQ0fFBhOgGVqv4fgM8rZTKAVaHl80E7gYe7o7AhBDdyzAMvAc2U6fXNs5yC5CdP5Kd5bkMzUhhxtgBzar4hDibrDYz/7VSKh74AZAWXnwU+JHW+snuCk4I0T2C9bXUbH6f0oP7iPW4sNtt2OwOYkefiyd3NFeGTnTEFSJyLLcTDQ8U+6BSKgPwaq1bjmouhOgF/OVFlK15g6NFpVTW+EhN9JAzdDDxEy7EGZcEgMMhpSYReW0mKKXU7cD/aa3rw+9brm98L/NBCdF7OOKSqaoPUlljjli2059N8vD5JMclRjgyIZprrwT1PeAFoD78vi0yH5QQvYjdFcWg2ZdR9sY/2e4YRe6I4aQlyzhFoudpM0FprYe09r4lpZTUBQjRQxmGgb+sEGdyVrPGDu60bEZ/9hYyKhsYMlBKTqJnsjqSxD6lVEorywcCx7o8KiHEGQs11FG+5jV2vP433n939SkjQ8THeiQ5iR6tvWdQVwCzwx8HAz9RStW12Gz46ZxMKfVl4BmgocniO7TWz5/OcYQQ7fMVH6Riw7vs2V+Ezx/CqFnF7iGDyB8+MNKhCWFZe8+gNgHfAk7UC0yk+TxQBlAD3Hga55sE/FJr/f3TCVIIYY0RDFCr1+A9sAUbEOtx4fM3UOHJwVbaQP5pfaUUIrLaewZVAMwDUEo9C3xTa111huebDPzqDI8hhGhFoLqc6o3vEKgubVyWlZXGgeRhjJ0wBpUng7uK3sXyfFBKKadSKhtwhBfbgChgstb6bx0dQynlAMYD1yulHgPqgD8AD2mtZSwVITrJMAzq9m/l8LoPSI5zYQvXebjT84gffwFXuD3YbNKWSfQ+lhKUUmoh8CxwSkMJoBzoMEEB6cAnwPPA1ZgD0P4LqEKaqQvRKaEGL0fXvM2hHTto8AcJBWNIT4kjdtRMPHljJDGJXs3qSBI/x5xS4zHgXeAKYADwv8B3rRxAa10EzG2yaKNS6gngs0iCEqJTjFCAqqOHaPCbY+kdqnYwYN7lRA8YEOHIhDhzVgfbGgH8RGu9HnPiwlit9XLga7TfibeRUmqMUur+FovdmB2BhRCd4IiOZ/Csi/C4HVTEDSXrgi+QlJkZ6bCE6BJWS1BeIBR+vwuYALwJrAfyLR6jAviuUuowsBSzVeA3MJOcEMKChtpq7FExuJyOxmXROfkMX3A9IxPSSIqPimB0QnQtqyWoD4F7lVJJmM+RrlJKuYDzMZ8hdUhrfQSzanBJeJ9/AD/VWr94ukEL0d8YoSAFaz5g3Z+fYs3qrc3W2Ww20rKzJTmJPsdqCeq7wKvAl4GnMftHVWFW0d1t9WRa6/eAKacZoxD9WqCqhML/vsX+3QcAaNjyPkeG55E9QJqNi77NajPzPcAopVS01tqrlJoGXACUaK3XdGuEQvRTRiiId+9Gavd8gidkEB/jorrOD+5oGrwNHR9AiF6uvaGOYjpY/v6Jz1rrlkMgCSHOgL+qlJotHxCoLDYX2GBgRiL7XMOZOuc8YqPdkQ1QiLOgvRJUDeZwRlY4Ot5ECNGRgN/P1pUrCBZsJC8jrnGgMVdSJikT5pEVK4O7iv6jvQR1wVmLQghBfUUJa/75d4zaMgAqa10kJcQQkz+V6CHjsdlkCnbRv7Q3Ft+KsxmIEP2d02En2VlPWfhzSSCWwbM+izNeGkOI/snqUEfraKe6T2s9rcsiEqKfcsanMGjqbGo+fJ/E0TMYOXMWDofUnov+y2oz89da2W8osAD4cVcGJER/UFpWxfbNmnPnTMHRZKbbhPxJTM0ZgSsuKYLRCdEzWG1m3nKIIqBxAsIrgMe7Migh+rINazZQ+ul72IM+NiclM/Gck5M02ewOSU5ChJ3pU9f3gIu6IhAh+rpQQx1VG97Bs/cDnMF67IQ49sl71PsCkQ5NiB7J6jOo1vpEJQL3AIVdGpEQfYxhGNQf2kGtXoPhbyA1MZrKmgYMZxSjZp9LlEueMwnRGqvPoNrqE1UP3Nx14QjRdxiGgd6xn9TSDVBd3LjcZoPhEyeRPH42jqhW+8MLIbCeoFr2iTIAH7BNa13dtSEJ0fuVV9Ty6bvvYC/cRkO8m4HpcQA4YhKIGzsHd1pOhCMUouez2khiBYBSKg5QQNBcrL3dGJsQvVbpun/jKNwJQHl1A8kJ0aSOnkrM8EnYHFa/FwrRv1l9BhWFOevtYswRzAG8SqnfA9/VWge7KT4heqW8STOpPLSXmjo/SQNzyTj/MjzJaZEOS4hexepXuV9jzv30BWAtZuu/6cCjQAPw/7ojOCF6A2+9D1/AIDHu5HxM7tSBDJo8E78rgQGjxmOz2do5ghCiNVYT1OeBy7XWq5ose0kpVQYsRxKU6IeCIYNtG7dR/Mn7BLNGc9HCec0SUdqE8yIYnRC9n9V+UF7A38ryyi6MRYheI1RfS/Hatyj/+F84fFXYD33K/kPFHe8ohLDMagnqB8AflFJLgNVa65BSaizmc6kHm/aTkrmhRF9mhILUF2yjdvc6HAE/yQkeyqrq8TjBXlcOZEQ6RCH6DKsJ6nEgDlgJBJVSIcCFOVvNNOCxJttKr0PR59Q3BKgqLMB58BOCNeWNyzOSo3FkDGHEefNxxyZEMEIh+h6rCerKbo1CiB4qFDLYtrOAQ2veJ8lXxPCcpMbnTI7YJBLHzGaA9GkSolucbj+oaGAE5rOrvdJJV/R1NYUHKFvxAjHBID6grKqetJQEYkZMJnrwOGx2qTAQortY7QflAH4GfJOTVXs+pdRzwNe01jLapeiT4jIGkpGWyLFjZbicdtwDR5Ay/QLsnthIhyZEn2e1iu9B4DrgBmAVZoKahdkP6r7wS4hera7eT0mFl7wBJ58l2V1R5E2dA5s+YfCsi4hOGxjBCIXoX6wmqBuAW7TWbzRZtlwpVQ38DklQohcLBkNs2H6YI+tX4iBIxhe/hCfq5H+NmEGjGTloFDbbmc5OI4Q4HVb/x8UBe1pZvg+Q8VtEr2UYIeoLtlK/+u8k1hwgruYQn36ytdk2NptNkpMQEWD1f9064I5Wln8NWN914Qhx9vhKDlOx8kXqdn5MVpILgCiXgyxKIhyZEAKsV/H9P+ADpdT5wOrwshnAYOCSrg9LiO5RXl1P4eEicup20nDsQOPyhBg3uXlZZE+9gKgBgyMWnxDiJKvNzD9RSk0CvgqMxhz66FXgN1prmVFX9HjBYIhVnx6keOtaUuv2kpCT0DiTrc3hImb4JNKGjJdm40L0IJYnptFa7wLuVEqlAkGtdUX3hSVEF/N7idr2Kmm1VQAcK6sjLzMeT3Y+MWo6Dmk2LkSPY/nJr1LqXqXUUeA4UKqUOqCU+lb3hSZE17FHxZAzJBeA2Ggnmbm5JM28kvgJ8yQ5CdFDWe2o+yBm9d4DNJ8P6h6llEtr/YjVEyqlkoDNwH1a6+dOO2IhOlBYUsuBo5XMHH+yz5LNZmPA5POhtpzUcTPw5IyUOZqE6OGsVvHdAtzYoh/UR0qpPcBvAMsJCngayD6N7YWwJBQy+M+aA5Tu2kSy9zAH0xeTl5XcuN4Zl0T2JTfIcyYhegmrVXxO4FAry/cA8VZPppS6EUgAtljdRwirghWFpBa8w8DqbUQHKtnx0cpTtpHkJETvYTVBPQI8pZTKPbFAKZUC/AJzuKMOKaWGAD8Cvny6QQrRnmB9LVUb36Fi9SsMjPFjt0FirJvxSTUYRijS4QkhOslqFd8XgFHAPqXUISAA5AFuYIZS6hsnNtRanzJjW3iw2T8Dd2qti5RSZxy46N+Ol9exbc9xpiWX4937KUbQnPDZ5bSTPziNBDWF6CETZAQIIXqx05mw8EzcC2it9UtneBwhWLnxCPu27WBA1VaOpEByvKdxXdSAocSOmokj2nLNsxCih7LaUff5MzzPtcBApdTV4c/xmFWG07TWt5/hsUU/k3B0LYPKtwNQXGYnKc6DKz6F2NHn4pbJA4XoMyx31D0TWuuRTT8rpTYCj0szc9EZQ0cMZnPBTqLcDrIyk4gfPQPPoDHSAEKIPuasJCghOqOmzsf6ncc5d3wWLufJ5BM3bDzDD+4gJj2LWDUde1RMBKMUQnSXiCQorfU5kTiv6D227C3hk093kVa+jQ3B2UybOqpxnc3uIGPuNdicrghGKITobtLESfQ4oYAP95GNDD72AfG+45RsWEGt19dsG0lOQvR9bZaglFL/Z/UgWmvp2yTOmGEYNBzdTe3O1aTU11ER5SBkGAxOC+D2VUD0KT0YhBB9WHtVfE3b6bqBy4GdmJMX+oBJwATgr90WnejzgsEQm3aXMCwliLF3Df7yInOFDXO08bQs4seehzNBJm4Wor9pM0Fpra858V4p9TvMVnffabqNUuoBYGTLfYWworCklvfX7MZTtAWb4xg5GXGN6+xR0SRPmEnUwBEyqKsQ/ZTVRhJfxCwxtfQ8sLHrwhH9SaD4AJkH3sZh+KkEUhI8xEa78QwZR8zwydid7kiHKISIIKsJqgiYB+xusXwhUNClEYl+Y8DAdEpi7VTX2chIjiExezBxY2bjjEuKdGhCiB7AaoL6CbBUKTUP+BSwYc4HdRlwTXs7CmEYBnsOV5AU5yE9ObpxuSsxg9xxEwmWHSVp/Hm4MwZJdZ4QopHVoY7+pJQ6DCwBrg8v3gzM0Vqv6a7gRO9XXl3PinUH8BVsJj45iUsWXdQsCaWMn43N7sDmkD7jQojmLN8VtNbvA+93YyyijzEMg+CxfcTpN3EG6wnWudi1bxxqWFbjNnZXVAQjFEL0ZO31g3rY6kG01nd1TTiir/BXHqd2+8cY5UUMiLNRWgkZCQ4y/YeBrA73F0KI9kpQUy0ew+iKQETvZhgG+45UYgt4Sa/aSf3hXZz400hPjiY5NZHUcbOIysmPbKBCiF6jvX5QF5x4r5S6HnhTa11yVqISvUpVrY93Vu/Hf2gLAxv2E5cT1/icyWazEztcmo0LIU6f1WdQvwZmAJKgxCmcDZUk730Dm6+OIFBaWU9aUjTujEHEjToXR2xipEMUQvRCVgeLXQNc1Z2BiN4rOjGJrNRYbEBqooe0rAEkTltI4pRLJTkJITrNagkqBPxMKXUPsB/wNl2ptZ7W1YGJnscfCLFpdzEOG0wcmdm43OZwMWjaXBK2rCJ57Aw8uaOw2WSgfCHEmbGaoNaEX6Kfqqhu4JX3thFbugOP0YAafD0xnpNTXnhy8hkwYLA0GxdCdBmrHXXvP/FeKZUA2LXWFd0WlehRjIAfZ+FWhh9/H19DAwA7Nm5n8owJjdvYbDZskpyEEF3IckddpdRtwA+BgeHPx4Ffaa1/0U2xiQgKhQxshKg/uJ26PZ8S8nkZkOzmaLGfjORoBsRWRjpEIUQfZylBKaXuBO4FHgRWYY7FNwv4vlLKq7X+VfeFKM6miuoGVm85QlztYUY7DxH0Vjeui492M3JUOvEjZ+DOHBy5IIUQ/YLVEtQdwP9orf/WZNlHSqkC4AFAElQfUFHt5bWX3ye9ZheBYC11OYlEuc0/Ebsnltj8qURl50sDCCHEWWE1QaVjzqTb0nogp+vCEZEUh5eRvq3UBP0AVNf5iY6NJXr4JKLzxsiArkKIs8rqHWcr5rQaP2+x/AuY08CLXiYUMqj3BZq1xHPGpzBQjeaI3k5meiKpIycRPXSCjAAhhIgIqwnqPuB1pdRM4L/hZTOBS4CruyMw0T0Mw2D/0Qq2/Hcd0W4bF10+v9n0FxkTziUxPcNMTNIqTwgRQZYeJmit3wYuBBow54P6HFAFTNVav9Z94YmuZISClO3ewt5XniOxaB2OQxs4cKSs2TbO+BRi1TRJTkKIiGtvuo1LgJVa61oArfWHwIdnKzDRdUIBH/UHt+M9sIVQfS0ZsSHKq8BlC9BwcAfkzI50iEIIcYr2qvheBwJKqU+A98Kvj7XWDWclMnHGaqqqqNq9EXfZXgz/yV9bRnI0NqebnHOmkTTinAhGKIQQbWsvQaVh9nWaCcwGvg3YlVKrOZmw1mitg90epTgtteWlbP9oJbUHd+J2GAzPSebEYyZ7VDSJg8eTOWiMNH4QQvRo7c0HVQ68Fn6hlHIAkzAT1kzgy0CaUmql1nrBWYhVWFVZRMOhHRAK4QtBeVU96VmZRA+dgCc7X5qLCyF6Bcs9LsMlpcNAIXAcOBjef2j3hCY6YhgGJRW1lFU1G1yemFxFcoo5zYUtLhXPuHkkz/0C0XmjJTkJIXqNdu9WSqk44HxgPnARMBI4BLwPLAW+pLU+avVkSqmFwM+AIZhJ7mGt9TOdiryfO3ComK0f/5eo0t1EjZnLnLlTGtfZHE5yps8j3ucgZ/gw7HYZ+UEI0fu014pvJTANKAc+wBzO6D2t9Z7OnEgplQW8CFyltX5TKTUJc7ikdVrrTztzzP4oUFNBfcEW7Hu2EVdcCkDtvs2EzpuM3X6yP1Pi4JHIVIFCiN6svRLULMwqvf/DbBDxX621v7Mn0loXKqXStdbVSik7kAoEgOoOdu23jpfXsfNAGcdKa7lifDT1BVvxFR8EINoJLoedYChEhrOWeq+XmNiYCEcshBBdp70ENRSzau9C4DYgVim1CngXM2F9qrU2Tudk4eQUA1SGz/2Q1np3pyLv4/yBIC+/t5O4mkOk1u3nWJWTaE+TX5cNBg/PIyl/IjE50vBBCNH3tNeK7wDwh/ALpdQEYB5mwroX8CulPgDe1Vo/dRrnrAdigfHAG0qp3VrrpZ2Kvg8xDKPZkENOQkzzrqKi2ixgVtVFhxOUDXdGHtGDx+FKzW62jxBC9CWWv3ZrrTcBm5RSvwImAzdjDnt0JWA5QWmtQ4AP+EQp9TtgEWaDi37HMAyOFNewaVcxOZnxTBiR3rjO5nSRPmgoRmAHyfEeYuNiiM4dSfSgsThi5emSEKLv6zBBKaVygOnAjPC/kzCfHX2MORfUB1ZOpJSaCzymtZ7cZHEU0G+njt99qIJ3/7uLZO9hCg67GDfs8mYNHdJHTyQ6WE304LFE5SjpWCuE6Ffaa8X3D8yElIXZkOEj4FXgTmB9J0aQ2AhkK6W+g9kicDrwFeCqTsTdqxmGQaDiGOnFWxhTtp5QMEiw1kVh8RyyM5Mat3OlDCR57rVSjSeE6JfaK0FFAY9jlpA+DVfNdZrWulIpdRnwa+BHmP2pbtFarziT4/YGxeVeNu8pZppKwVm2n/qDOwjUmKOIpyVE4Q8ESU2MJjVwHDiZoCQxCSH6s/YaSSzs6pOF+zv1q6GzV208jN62mxTvQQp2V5CZ1Hwai/TkaFxJGXgGjSVqgAzKIYQQJ0jb5G6WVb6BQPlmAMr9NjIS3dhsNmwOJ1EDhxOdNwZnYnoHRxFCiP5HElQXCIUMDh6rpqi0lhljs5qtyxo6lCK9lZgoJ6mJ0bgS0vDkjSYqe4Q0ehBCiHZIgjpDwWCIv7++HnvpPmL85ZTl3khKYnTjek/2CNSwLKIyB+PJG40zMV2eLQkhhAWSoDopFPDhK9xH/eGd5JfupqrWB4Deppl57slJAO1ON6kXXo/N7ohUqEII0StJgrKopMLLjv0lpNsrGWgcw1e0DyNktrRPjo+itt5PUpyHDHf5KftKchJCiNMnCcqCPbsK2LjqYxLrj1DpCpKa3Xwkh7iYKMZNGUF07kjcGXkRilIIIfoWSVAt+PxB3K7mJZ600HHSvfswDPA2QIMvQJTbiTM+hagchWfgCOxRMpK4EEJ0JUlQYdv2lbL3YAmVRwr47GcvIMbjalwXn6dIiPkAbJCckkTisNFE547EmZAauYCFEKKP6/cJyjBC+EsOU7LuI+JKCkgwguzdM4xxY4c1buOISSB/1vm4kjJxpedgs8kMtUII0d36TYIKBkMcLanlQGEVmSkxDE0KUn9Y01C4h1CDlwGhegoNs9FD7aHd0CRBAcTmT41E2EII0W/1mwS1fX8ZH32yl6T6I7icxSSnNJ9rMSHO7DSblJFBYv7gCEQohBCiqX6ToLICh1Al72LDoMEGoaSUxqkt7FHRxA8cQXp2Po74VOlIK4QQPUC/SVCJmVkkxDhxuxzEx7ixO5xEZQ3Fk52PKy1bnisJIUQP028SlDMpkyHDcrFHxeDJVrizhspYeEII0YP1mwRls9lInvU5bE5XxxsLIYSIuH5VryXJSQgheo9+laCEEEL0HpKghBBC9EiSoIQQQvRIkqCEEEL0SJKghBBC9Ei9pZm5A6CoqCjScQghhOiEJvdvyzO49pYElQWwePHiSMchhBDizGQBe61s2FsS1DrgPKAQCEY4FiGEEKfPgZmc1lndwWYYRsdbCSGEEGeZNJIQQgjRI0mCEkII0SNJghJCCNEjSYISQgjRI0mCEkII0SNJghJCCNEjSYISQgjRI0mCEkII0SP1lpEk2qSUmga8prXOCH9OB34FfAZoAP4P+JHWOhhe/0fg80CgyWHGa633KaXygKXADOA48HWt9Rs9+FrODa8fBRwFfqi1fjG8LmLXcjrXoZR6GriuxSFigbu11j/rhb+Tm4F7gDRgJ/BdrfWq8Lredi1fB74NpAIfA3dorfdF8lqUUhcBvwBGhM/7iNb6GaVUEvAH4CKgBrhHa/1seB8b8FPgq4AbeBb4ntY6EF7/eeBnmKMcrABu0lof74nX0mRfO/APYIXW+vEmyy/A/J0OAzYB12utLQ0r1BP12hKUUsqmlLoFeBvzj+6E54EMzJv2WGAa8JMm6ycBV2qt45q89oXXLQM2Y/6HvBVYppQa2s2X0qlrUUplAa8DTwLxwB3An8M3johcS2euQ2v9P01/F8D3gO3h64rIdXT2WpRS44HHgEVAEvBn4OXwzaS3XcvngQeBm8PxvgL8RynlidS1KKVyMW/KD2D+fL8I/Fwp9Rngt5jDoGXx/9s79xirqisOf4q2zsRoSm2kmiq2xaVIfQSsVmM1mWisgjb4aig+efVhVBq0gQmKqYaXmhoi2hLTllJKsHTGQFOtkVhfrfGBBBz8VZpQaH3hK0oHRAX/WPuGw2HmGsa5955zs75kMnf2PnvP/uXcc9ZZa++zF5wPzDKzM1PTicBo/NofApwMTEt9DsUN7VVJyytJW035HFows8HAcuD7uT4PATqBW1OfHcDDme9f6SjtwPGT8GP8BANgZq3AucBkSW9KegeYDkxIF2kLcAzwYr4zMzsaGAHcLGm7pJX4RTmu9lL2XgtwBfC4pN9J2inpEfwG824DtfRFB5ljvwHMAcZIer+E52QIu66pffCbzNbUtmxaLgIWSPq7pI8l3QtsB9oaqGUwsFhSh6Qdkp4FHgPagIuB6ZK6Jb0ILMANE8CVwC8l/VfSZmAGMCnVjQWWS3pS0jZgKnC6mQ0pohYzOxB4HveOns71ORp4SdIySR9Jmgt8MfVZSspsoO6TNBx4LlNW0fP/TNknwFfwJ4oT8dDeAjPbbGYvmNnIdNxQYKOkbNuXgW/VZPS70xctw4ENZrbEzN4ys1XAIEkf0DgtfdGR5U78prg6/V22c/IwsBZYg9/MZwOXSdpB+fHKlOQAAAXoSURBVLTsm6ur1B9Ng7RIekLSjyp/m9lAdm0ivRP3fnoaz1DcK8/WHZba71YnqRvYRHG1bAOOlTQN+CjXbV4ngKjPd6wmlNZASXq1h7IteBhjjpkNNLMvAzen6hY8FPYE/kR5GB7CWGpmJwAHAt25LruB1too2G3cfdEyEA+tLMJDATOBjuSFNERLH3UAYGbD8Jj77Ezzsp2TA/Abwqn4PNrP8RDfIMqnZRkw0cxGmNn+ZjYBjz600EAtFczsYNxrewb3KLZJyu58nR1PfryVz6091OXb1py90ZK82d7mxxqupb8prYGqwuX40+s6fMKzM5W/J+lvks6W9FxygZcBK4EL8KfFllxfrfgkZaPoVQs+qf2QpBVJy1LgBeB7FE9LNR0VrgE6Jb2RKSuaDqiuZQbwuqRnJH0oaT6wAbiEkmmRtAR/6FmCexTDgEeAd2mwlhRi/CfwBh4O+wA4IBcyzo4nP97KDXtLD3X5tjWlD1qqUcTv2OeiGQ3UV4FJkg6VNAz4H7BOUreZjTKzK3PHfwF3m7uAI9I8VYVj2NNlrie9asHd/i/ljq+syiyalmo6KlzInpPTRdMB1bV8DY/5Z/kYD8WUSktahLNc0jclDQJ+hhup52mgFjP7Lu5pdAIXp3mjV/A5v6N6GU8XYLm61yS9l69Lc3NHUFwt1cjr3Ju2haT0y8x74C5grZlNwb9os9m1ImwAcLeZrcMvtMuA04Dxkjaa2WrgdjObmsovBL5TbwEZqmlZCFxrZmOBxfhT+vHApZI2FUxLNR2V5c5fB57KNpKkgumA6lpW4CGzpXhSth8CxwF/KeA5gepa2oBbzOwM4H08/Pcq8KyknY3QksLXK/BXEOZVyiVtMbMOfBXcOHyJ9QR8ZR7A74EpZvYo7mXMSGXg186TZnYW8A/ca1wl6V8F1VKNDvz7d2n6fD2wA198UUqa0UBNwFe9vIOHXealUAuSOs2sHfgjMAj3QkZK2pjaXgT8Gn8n4S1gnKS1dR5/lmpaVpvZefhNZT6wERgtaVNqWyQtvepIDAY+TCvJ8hRJB1Q/JwvSfMJi/D2oLuC8gp4TqH5e/oBPrq/GowyPAhdk5kYaoeWn+DzyTDObmSm/B1+VNx/4Dx4RuV3SX1P9fcCh+Kq3VuAB0nybpDVmdk065nDco7mkxjqg71p6RdKbZjYKfw/qfuAlYJSk7f09+HoRGXWDIAiCQtKMc1BBEARBExAGKgiCICgkYaCCIAiCQhIGKgiCICgkYaCCIAiCQhIGKgiCICgkYaCCoB9Jm/duTpt/5uvazWxLSpcQBMFnEAYqCPqXG/AX4LMvX2JmRwHt+M4BGxowriAoHWGggqAfkfQ6nlNovJmdkqmah+chm9djwyAI9iB2kgiCfibtRP0UvkXQyfhu+UuBkyR1pWOuxrO6Ho7vJt4u6aFUtx+eonwMnhbmbXx7rimSPjGzRfgea8fie7WNlvRY3QQGQZ0IDyoI+pm0X91EfPPeq4E7gNsyxul8fKPW9nTM/Xgur2+nLqbiKcAvx7P0TgWuA0Zm/s1YfM+1NjxdQxA0Hc24WWwQNBxJa83sLuBXuIc0K1M9DZiVcngBrDezEXhKix/gGXmvkvR4qv+Nmd2EZ0x9MJV1SVpUax1B0EjCQAVB7bgVz6r7C0nZ9NxDgeFmNj1Ttj8pb0/adb/NzObiKdaPx3d8H5A5/t+1HHgQFIEI8QVBjZC0NX3cmqvaD7gRODHzcxyeUwkzuw1PCbFv+n0OkE9lke8zCJqO8KCCoP68DBwpaX2lIHlT24C5wE+AGyQtTHUteELBfXroKwialjBQQVB/5gALzUzASuBs4BZ8/gk8geBIM3saOBgPFR7Enunkg6CpiRBfENQZSQ8Ak4Gb8HmnycAkSX9Kh1yBr95bA/wZWA/8Fhhe98EGQQOJ96CCIAiCQhIeVBAEQVBIwkAFQRAEhSQMVBAEQVBIwkAFQRAEhSQMVBAEQVBIwkAFQRAEhSQMVBAEQVBIwkAFQRAEheRTfmloFmOy4cYAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "un = table2.un / 1e9\n", + "census = table2.census / 1e9\n", + "plot(census, ':', label='US Census')\n", + "plot(un, '--', label='UN DESA')\n", + " \n", + "decorate(xlabel='Year', \n", + " ylabel='World population (billion)',\n", + " title='Estimated world population')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running the quadratic model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the update function for the quadratic growth model with parameters `alpha` and `beta`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def update_func_quad(pop, t, system):\n", + " \"\"\"Update population based on a quadratic model.\n", + " \n", + " pop: current population in billions\n", + " t: what year it is\n", + " system: system object with model parameters\n", + " \"\"\"\n", + " net_growth = system.alpha * pop + system.beta * pop**2\n", + " return pop + net_growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extract the starting time and population." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.557628654" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_0 = get_first_label(census)\n", + "t_end = get_last_label(census)\n", + "p_0 = get_first_value(census)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize the system object." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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values
t_01950.000000
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\n", + "
" + ], + "text/plain": [ + "t_0 1950.000000\n", + "t_end 2016.000000\n", + "p_0 2.557629\n", + "alpha 0.025000\n", + "beta -0.001800\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = System(t_0=t_0, \n", + " t_end=t_end,\n", + " p_0=p_0,\n", + " alpha=0.025,\n", + " beta=-0.0018)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the model and plot results." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation(system, update_func_quad)\n", + "plot_results(census, un, results, 'Quadratic model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating projections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To generate projections, all we have to do is change `t_end`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file figs/chap04-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "system.t_end = 2250\n", + "results = run_simulation(system, update_func_quad)\n", + "plot_results(census, un, results, 'World population projection')\n", + "savefig('figs/chap04-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The population in the model converges on the equilibrium population, `-alpha/beta`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13.856665141368708" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[system.t_end]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13.88888888888889" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "-system.alpha / system.beta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** What happens if we start with an initial population above the carrying capacity, like 20 billion? Run the model with initial populations between 1 and 20 billion, and plot the results on the same axes." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "system.p_0 = 20\n", + "results = run_simulation(system, update_func_quad)\n", + "plot_results(census, un, results, 'World population projection')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparing projections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compare the projection from our model with projections produced by people who know what they are doing." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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United States Census Bureau (2015)[28]Population Reference Bureau (1973-2015)[15]United Nations Department of Economic and Social Affairs (2015)[16]
Year
20167.334772e+09NaN7.432663e+09
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" + ], + "text/plain": [ + " United States Census Bureau (2015)[28] \\\n", + "Year \n", + "2016 7.334772e+09 \n", + "2017 7.412779e+09 \n", + "2018 7.490428e+09 \n", + "2019 7.567403e+09 \n", + "2020 7.643402e+09 \n", + "\n", + " Population Reference Bureau (1973-2015)[15] \\\n", + "Year \n", + "2016 NaN \n", + "2017 NaN \n", + "2018 NaN \n", + "2019 NaN \n", + "2020 NaN \n", + "\n", + " United Nations Department of Economic and Social Affairs (2015)[16] \n", + "Year \n", + "2016 7.432663e+09 \n", + "2017 NaN \n", + "2018 NaN \n", + "2019 NaN \n", + "2020 7.758157e+09 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table3 = tables[3]\n", + "table3.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`NaN` is a special value that represents missing data, in this case because some agencies did not publish projections for some years." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "table3.columns = ['census', 'prb', 'un']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function plots projections from the UN DESA and U.S. Census. It uses `dropna` to remove the `NaN` values from each series before plotting it." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_projections(table):\n", + " \"\"\"Plot world population projections.\n", + " \n", + " table: DataFrame with columns 'un' and 'census'\n", + " \"\"\"\n", + " census_proj = table.census / 1e9\n", + " un_proj = table.un / 1e9\n", + " \n", + " plot(census_proj.dropna(), 'b:', label='US Census')\n", + " plot(un_proj.dropna(), 'g--', label='UN DESA')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the model until 2100, which is as far as the other projections go." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "system = System(t_0=t_0, \n", + " t_end=2100,\n", + " p_0=p_0,\n", + " alpha=0.025,\n", + " beta=-0.0018)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "results = run_simulation(system, update_func_quad)\n", + "\n", + "plot_results(census, un, results, 'World population projections')\n", + "plot_projections(table3)\n", + "savefig('figs/chap04-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "People who know what they are doing expect the growth rate to decline more sharply than our model projects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "**Optional exercise:** The net growth rate of world population has been declining for several decades. That observation suggests one more way to generate projections, by extrapolating observed changes in growth rate.\n", + "\n", + "The `modsim` library provides a function, `compute_rel_diff`, that computes relative differences of the elements in a sequence. It is a wrapper for the NumPy function `ediff1d`:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "%psource compute_rel_diff" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we can use it to compute the relative differences in the `census` and `un` estimates:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "alpha_census = compute_rel_diff(census)\n", + "plot(alpha_census)\n", + "\n", + "alpha_un = compute_rel_diff(un)\n", + "plot(alpha_un)\n", + "\n", + "decorate(xlabel='Year', label='Net growth rate')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other than a bump around 1990, net growth rate has been declining roughly linearly since 1965. As an exercise, you can use this data to make a projection of world population until 2100.\n", + "\n", + "1. Define a function, `alpha_func`, that takes `t` as a parameter and returns an estimate of the net growth rate at time `t`, based on a linear function `alpha = intercept + slope * t`. Choose values of `slope` and `intercept` to fit the observed net growth rates since 1965.\n", + "\n", + "2. Call your function with a range of `ts` from 1960 to 2020 and plot the results.\n", + "\n", + "3. Create a `System` object that includes `alpha_func` as a system variable.\n", + "\n", + "4. Define an update function that uses `alpha_func` to compute the net growth rate at the given time `t`.\n", + "\n", + "5. Test your update function with `t_0 = 1960` and `p_0 = census[t_0]`.\n", + "\n", + "6. Run a simulation from 1960 to 2100 with your update function, and plot the results.\n", + "\n", + "7. Compare your projections with those from the US Census and UN." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Related viewing:** You might be interested in this [video by Hans Rosling about the demographic changes we expect in this century](https://www.youtube.com/watch?v=ezVk1ahRF78)." + ] + }, + { + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 02dc266eee49edab1c266b099287e6e4ba20e86f Mon Sep 17 00:00:00 2001 From: Annie Tor Date: Wed, 26 Sep 2018 22:29:51 -0400 Subject: [PATCH 05/18] Turning in ModSim Project 1 Snapshot --- code/ModSimProject1.ipynb | 1412 +++++++++++++++++++++++++++++++++++++ 1 file changed, 1412 insertions(+) create mode 100644 code/ModSimProject1.ipynb diff --git a/code/ModSimProject1.ipynb b/code/ModSimProject1.ipynb new file mode 100644 index 00000000..2d935108 --- /dev/null +++ b/code/ModSimProject1.ipynb @@ -0,0 +1,1412 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "#Project 1: What would have happened to China's population if the one-child policy was never introduced?" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "#Usable Data: https://docs.google.com/document/d/1gnod7zwaCUK6Ah32k0DPGqsEXfnV07-yvn9CYvjle1M/edit?usp=sharing" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [], + "source": [ + "#To-Do\n", + " #Figure out how to import data into Jupyter\n", + " #Design a preliminary model\n", + " #Focus on sex imbalance\n", + " #Analyze data" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [], + "source": [ + "from pandas import read_html\n", + "from pandas import read_csv\n", + "\n", + "# Configure Jupyter so figures appear in the notebook\n", + "%matplotlib inline\n", + "\n", + "# Configure Jupyter to display the assigned value after an assignment\n", + "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "filename = r'C:\\Users\\ator\\Desktop\\ModSim\\ModSimProject1Data.csv'\n", + "tables = read_csv(filename, header=0, index_col=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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200975.03251.46054568507365148.53945564618634913312600007.0812.13102.01374.9651.166
201075.23651.47119868853278548.52880264917221513377050007.1111.90100.46873.6271.164
201175.42551.48228469198881848.51771665214118213441300007.1411.9398.92472.2901.162
201275.60251.49342969551916948.50657165517583113506950007.1512.1097.37970.9531.160
201375.76951.50404169910554848.49595965827445213573800007.1612.0896.06369.9861.158
201475.93251.51334070278104548.48666066148895513642700007.1612.3794.74669.0191.156
201576.09251.52077770646319348.47922366475680713712200007.1112.0793.42968.0521.154
201676.25251.52618171037342248.47381966829157813786650007.3012.0092.11267.0851.152
2017NaN51.52970971440530348.4702916719896971386395000NaNNaNNaNNaN1.150
\n", + "
" + ], + "text/plain": [ + " lebt popmp popm popfp popf popt death \\\n", + "Year \n", + "1960 43.725 51.544741 343839501 48.455259 323230499 667070000 25.43 \n", + "1961 44.051 51.516616 340179667 48.483384 320150333 660330000 14.24 \n", + "1962 44.783 51.492137 342819197 48.507863 322950803 665770000 10.02 \n", + "1963 45.972 51.469899 351197134 48.530101 331137866 682335000 10.04 \n", + "1964 47.592 51.448006 359289719 48.551994 339065281 698355000 11.50 \n", + "1965 49.549 51.425395 367786712 48.574605 347398288 715185000 9.50 \n", + "1966 51.696 51.401772 378008633 48.598228 357391367 735400000 8.83 \n", + "1967 53.847 51.378155 387673867 48.621845 366876133 754550000 8.43 \n", + "1968 55.843 51.356285 397759563 48.643715 376750437 774510000 8.21 \n", + "1969 57.603 51.338248 408665290 48.661752 387359710 796025000 8.03 \n", + "1970 59.085 51.325203 420001834 48.674797 398313166 818315000 7.60 \n", + "1971 60.303 51.317166 431631246 48.682834 409473754 841105000 7.32 \n", + "1972 61.344 51.313197 442335153 48.686803 419694847 862030000 7.61 \n", + "1973 62.281 51.312222 452543013 48.687778 429396987 881940000 7.04 \n", + "1974 63.134 51.312862 461995352 48.687138 438354648 900350000 7.34 \n", + "1975 63.915 51.314088 470239739 48.685912 446155261 916395000 7.32 \n", + "1976 64.631 51.315789 477588355 48.684211 453096645 930685000 7.25 \n", + "1977 65.278 51.317977 484162022 48.682023 459292978 943455000 6.87 \n", + "1978 65.857 51.319985 490703734 48.680015 465461266 956165000 6.25 \n", + "1979 66.377 51.321069 497303725 48.678931 471701275 969005000 6.21 \n", + "1980 66.843 51.320778 503577431 48.679222 477657569 981235000 6.34 \n", + "1981 67.256 51.318869 510050546 48.681131 483834454 993885000 6.36 \n", + "1982 67.618 51.315619 517584727 48.684381 491045273 1008630000 6.60 \n", + "1983 67.933 51.311744 525078211 48.688256 498231789 1023310000 6.90 \n", + "1984 68.210 51.308234 531976602 48.691766 504848398 1036825000 6.82 \n", + "1985 68.451 51.305803 539244515 48.694197 511795485 1051040000 6.78 \n", + "1986 68.661 51.304493 547311204 48.695507 519478796 1066790000 6.86 \n", + "1987 68.842 51.304140 556154832 48.695860 527880168 1084035000 6.72 \n", + "1988 69.002 51.304938 565190587 48.695062 536439413 1101630000 6.64 \n", + "1989 69.150 51.307071 573946550 48.692929 544703450 1118650000 6.54 \n", + "1990 69.293 51.310590 582470121 48.689410 552714879 1135185000 6.67 \n", + "1991 69.437 51.315781 590531742 48.684219 560248258 1150780000 6.70 \n", + "1992 69.587 51.322457 597891229 48.677543 567078771 1164970000 6.64 \n", + "1993 69.750 51.329666 604889315 48.670334 573550685 1178440000 6.64 \n", + "1994 69.935 51.336150 611842204 48.663850 579992796 1191835000 6.49 \n", + "1995 70.156 51.341118 618586032 48.658882 586268968 1204855000 6.57 \n", + "1996 70.427 51.344128 625140432 48.655872 592409568 1217550000 6.56 \n", + "1997 70.747 51.345689 631590489 48.654311 598484511 1230075000 6.51 \n", + "1998 71.115 51.347107 637697691 48.652893 604237309 1241935000 6.50 \n", + "1999 71.521 51.350177 643281642 48.649823 609453358 1252735000 6.46 \n", + "2000 71.955 51.356104 648445282 48.643896 614199718 1262645000 6.45 \n", + "2001 72.401 51.365289 653289427 48.634711 618560573 1271850000 6.43 \n", + "2002 72.840 51.377145 657832960 48.622855 622567040 1280400000 6.41 \n", + "2003 73.257 51.390671 662117402 48.609329 626282598 1288400000 6.40 \n", + "2004 73.642 51.404430 666239967 48.595570 629835033 1296075000 6.42 \n", + "2005 73.987 51.417387 670338757 48.582613 633381243 1303720000 6.51 \n", + "2006 74.291 51.429159 674246565 48.570841 636773435 1311020000 6.81 \n", + "2007 74.562 51.440000 677920041 48.560000 639964959 1317885000 6.93 \n", + "2008 74.808 51.450264 681538500 48.549736 643116500 1324655000 7.06 \n", + "2009 75.032 51.460545 685073651 48.539455 646186349 1331260000 7.08 \n", + "2010 75.236 51.471198 688532785 48.528802 649172215 1337705000 7.11 \n", + "2011 75.425 51.482284 691988818 48.517716 652141182 1344130000 7.14 \n", + "2012 75.602 51.493429 695519169 48.506571 655175831 1350695000 7.15 \n", + "2013 75.769 51.504041 699105548 48.495959 658274452 1357380000 7.16 \n", + "2014 75.932 51.513340 702781045 48.486660 661488955 1364270000 7.16 \n", + "2015 76.092 51.520777 706463193 48.479223 664756807 1371220000 7.11 \n", + "2016 76.252 51.526181 710373422 48.473819 668291578 1378665000 7.30 \n", + "2017 NaN 51.529709 714405303 48.470291 671989697 1386395000 NaN \n", + "\n", + " birth mortm mortf sex \n", + "Year \n", + "1960 20.86 499.659 397.316 NaN \n", + "1961 18.02 497.798 396.271 NaN \n", + "1962 37.01 495.937 395.226 1.070 \n", + "1963 43.37 459.408 371.219 NaN \n", + "1964 39.14 422.879 347.213 NaN \n", + "1965 37.88 386.349 323.206 NaN \n", + "1966 35.05 349.820 299.200 NaN \n", + "1967 33.96 313.291 275.193 1.070 \n", + "1968 35.59 297.064 257.750 NaN \n", + "1969 34.11 280.838 240.308 NaN \n", + "1970 33.43 264.612 222.865 NaN \n", + "1971 30.65 248.386 205.422 NaN \n", + "1972 29.77 232.160 187.979 1.070 \n", + "1973 27.93 221.847 179.878 NaN \n", + "1974 24.82 211.534 171.778 NaN \n", + "1975 23.01 201.221 163.677 NaN \n", + "1976 19.91 190.908 155.577 NaN \n", + "1977 18.93 180.596 147.476 1.070 \n", + "1978 18.25 177.449 144.034 NaN \n", + "1979 17.82 174.302 140.592 NaN \n", + "1980 18.21 171.155 137.150 NaN \n", + "1981 20.91 168.008 133.709 NaN \n", + "1982 22.28 164.861 130.267 1.070 \n", + "1983 20.19 161.681 127.030 NaN \n", + "1984 19.90 158.501 123.793 NaN \n", + "1985 21.04 155.321 120.556 NaN \n", + "1986 22.43 152.141 117.319 NaN \n", + "1987 23.33 148.961 114.083 1.080 \n", + "1988 22.37 148.383 112.916 NaN \n", + "1989 21.58 147.805 111.749 NaN \n", + "1990 21.06 147.228 110.583 1.104 \n", + "1991 19.68 146.650 109.416 NaN \n", + "1992 18.27 146.073 108.249 1.120 \n", + "1993 18.09 144.644 106.486 NaN \n", + "1994 17.70 143.216 104.722 NaN \n", + "1995 17.12 141.788 102.958 NaN \n", + "1996 16.98 140.360 101.194 NaN \n", + "1997 16.57 138.932 99.431 1.140 \n", + "1998 15.64 133.831 96.886 NaN \n", + "1999 14.64 128.730 94.342 NaN \n", + "2000 14.03 123.629 91.798 NaN \n", + "2001 13.38 118.528 89.254 NaN \n", + "2002 12.86 113.427 86.710 1.160 \n", + "2003 12.41 111.762 84.895 NaN \n", + "2004 12.29 110.097 83.081 NaN \n", + "2005 12.40 108.432 81.267 NaN \n", + "2006 12.09 106.767 79.453 NaN \n", + "2007 12.10 105.102 77.639 1.170 \n", + "2008 12.14 103.557 76.302 1.168 \n", + "2009 12.13 102.013 74.965 1.166 \n", + "2010 11.90 100.468 73.627 1.164 \n", + "2011 11.93 98.924 72.290 1.162 \n", + "2012 12.10 97.379 70.953 1.160 \n", + "2013 12.08 96.063 69.986 1.158 \n", + "2014 12.37 94.746 69.019 1.156 \n", + "2015 12.07 93.429 68.052 1.154 \n", + "2016 12.00 92.112 67.085 1.152 \n", + "2017 NaN NaN NaN 1.150 " + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tables.columns = ['lebt', 'popmp', 'popm', 'popfp', 'popf', 'popt', 'death', 'birth', 'mortm', 'mortf', 'sex']\n", + "tables" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [], + "source": [ + "#Before anything, we need to establish some common variables that we'll use throughout the notebook.\n", + "popt = tables.popt;\n", + "t_0 = get_first_label(popt);\n", + "t_ocp = 1979; #when the one-child policy was enacted\n", + "t_end = get_last_label(popt);\n", + "p_0 = popt[t_0];\n", + "p_ocp = popt[t_ocp];\n", + "p_end = popt[t_end];\n", + "death_rate = tables.death / 1000;\n", + "birth_rate = tables.birth / 1000;\n", + "growth_rate = birth_rate - death_rate;" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(popt, ':', label='China Population')\n", + " \n", + "decorate(xlabel='Year',\n", + " ylabel='Total population (billion)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Linear Growth Model\n", + "# \n", + "# For this model, we'll use the average growth per year from 1960-1979 to simulate the growth of the population if it hadn't \n", + "# been affected by the one-child policy.\n", + "\n", + "# First, we find the average growth per year from 1960-1979, before the one-child policy was enacted.\n", + "growth = (popt[t_ocp] - popt[t_0]) / (t_ocp - t_0)\n", + "\n", + "#Establishing the system and its parameters (see previous cells for more information)\n", + "system = System(t_0 = t_0,\n", + " t_ocp = t_ocp,\n", + " t_end = t_end,\n", + " p_0 = p_0,\n", + " p_end = p_end,\n", + " growth = growth)\n", + "\n", + "# Now for the update function. We're just going to add the avg growth per year to the year previous:\n", + "def update_func_linear(pop, t, system):\n", + " return pop + system.growth;\n", + "\n", + "# Our simulation function runs through each year and then puts our model outputs into a TimeSeries so we can plot it.\n", + "def run_simulation(system, update_func):\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " \n", + " for t in linrange(system.t_0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " \n", + " return results\n", + "\n", + "# Lastly, we run and then plot the model.\n", + "results = run_simulation(system, update_func_linear)\n", + "\n", + "plot(popt, ':', label='China pop')\n", + "plot(results, '--', label='Linear model of pop')" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# TO-DO 9/26/18\n", + "# talk to ninja person\n", + " # find out how to take the average growth rate of just 1960-1979\n", + " # confirm proportional growth model still works\n", + " # quadratic model" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
values
t_01960
t_end2017
t_ocp1979
p_0667070000
p_end1386395000
growth_rateYear\n", + "1960 -0.00457\n", + "1961 0.00378\n", + "1962 0...
avg_gr0.0130493
\n", + "
" + ], + "text/plain": [ + "t_0 1960\n", + "t_end 2017\n", + "t_ocp 1979\n", + "p_0 667070000\n", + "p_end 1386395000\n", + "growth_rate Year\n", + "1960 -0.00457\n", + "1961 0.00378\n", + "1962 0...\n", + "avg_gr 0.0130493\n", + "dtype: object" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def find_avg(system):\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " growth_total = 0\n", + "\n", + " for t in linrange(system.t_0, system.t_end):\n", + " growth_total = system.growth_rate[t] + growth_total\n", + " \n", + " return growth_total/(system.t_end - system.t_0)\n", + " \n", + " #return results\n", + "\n", + "avg_gr = find_avg(system2)\n", + "\n", + "system2 = System(t_0=t_0,\n", + " t_end=t_end,\n", + " t_ocp=t_ocp,\n", + " p_0=p_0,\n", + " p_end=p_end,\n", + " growth_rate=growth_rate,\n", + " avg_gr = avg_gr) " + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [], + "source": [ + "def prop(pop, t, system):\n", + " if (t <= 1979): \n", + " \n", + " growth = growth_rate[t] * pop\n", + " return pop + growth\n", + " else: \n", + " growth = avg_gr * pop\n", + " return pop + growth" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def run_simulation(system, prop):\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " \n", + " for t in linrange(system.t_0, system.t_end):\n", + " results[t+1] = prop(results[t], t, system)\n", + " \n", + " return results\n", + "\n", + "# Lastly, we run and then plot the model.\n", + "results = run_simulation(system, prop)\n", + "\n", + "plot(popt, ':', label='China pop')\n", + "plot(results, '--', label='Linear model of pop')" + ] + }, + { + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 33981db18a2d9c1004b7464e52014e662583a309 Mon Sep 17 00:00:00 2001 From: Annie Tor Date: Fri, 28 Sep 2018 21:00:53 -0400 Subject: [PATCH 06/18] Adding ModSim Project 1 Final file --- code/ModSim Project 1.ipynb | 1774 +++++++++++++++++++++++++++++++++++ 1 file changed, 1774 insertions(+) create mode 100644 code/ModSim Project 1.ipynb diff --git a/code/ModSim Project 1.ipynb b/code/ModSim Project 1.ipynb new file mode 100644 index 00000000..b8f14d51 --- /dev/null +++ b/code/ModSim Project 1.ipynb @@ -0,0 +1,1774 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ModSim Project 1\n", + "\n", + "Maya Al-Ahmad and Annie Tor" + ] + }, + { + "cell_type": "code", + "execution_count": 362, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure Jupyter so figures appear in the notebook\n", + "%matplotlib inline\n", + "\n", + "# Configure Jupyter to display the assigned value after an assignment\n", + "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", + "\n", + "# import functions from the modsim.py module\n", + "from modsim import *\n", + "\n", + "# import function that will let us read in our data\n", + "from pandas import read_csv\n", + "\n", + "# reading in the data, putting it as table\n", + "filename = r'C:\\Users\\ator\\Desktop\\ModSim\\ModSimProject1Data.csv'\n", + "table = read_csv(filename, header=0, index_col=0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What would've happened to China's population if the one-child policy was never instated?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In 1979, the Chinese government introduced a radical law that limited families to having only one child. This rule, known as the one-child policy, was an extreme attempt at radically cutting down China's fast-growing population. The one-child policy existed in many forms and variations until 2016.\n", + "\n", + "The goal of this project is to predict what might have happened to China's population if it had never established the one-child policy. To do this, we created models based off population data from 1960 to 2017 from the World Bank. In our models, we attempted to fit population growth from 1960 through 1979 (when the policy was introduced) and then continued the growth we observed from then for 1980 through 2016." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": true + }, + "source": [ + "Here's the data as a table. We'll only be using the population total, the birth rates, and the death rates for this project." + ] + }, + { + "cell_type": "code", + "execution_count": 364, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Life expectancy at birth, total (years) Population, male (% of total) \\\n", + "Year \n", + "1960 43.725 51.544741 \n", + "1961 44.051 51.516616 \n", + "1962 44.783 51.492137 \n", + "1963 45.972 51.469899 \n", + "1964 47.592 51.448006 \n", + "1965 49.549 51.425395 \n", + "1966 51.696 51.401772 \n", + "1967 53.847 51.378155 \n", + "1968 55.843 51.356285 \n", + "1969 57.603 51.338248 \n", + "1970 59.085 51.325203 \n", + "1971 60.303 51.317166 \n", + "1972 61.344 51.313197 \n", + "1973 62.281 51.312222 \n", + "1974 63.134 51.312862 \n", + "1975 63.915 51.314088 \n", + "1976 64.631 51.315789 \n", + "1977 65.278 51.317977 \n", + "1978 65.857 51.319985 \n", + "1979 66.377 51.321069 \n", + "1980 66.843 51.320778 \n", + "1981 67.256 51.318869 \n", + "1982 67.618 51.315619 \n", + "1983 67.933 51.311744 \n", + "1984 68.210 51.308234 \n", + "1985 68.451 51.305803 \n", + "1986 68.661 51.304493 \n", + "1987 68.842 51.304140 \n", + "1988 69.002 51.304938 \n", + "1989 69.150 51.307071 \n", + "1990 69.293 51.310590 \n", + "1991 69.437 51.315781 \n", + "1992 69.587 51.322457 \n", + "1993 69.750 51.329666 \n", + "1994 69.935 51.336150 \n", + "1995 70.156 51.341118 \n", + "1996 70.427 51.344128 \n", + "1997 70.747 51.345689 \n", + "1998 71.115 51.347107 \n", + "1999 71.521 51.350177 \n", + "2000 71.955 51.356104 \n", + "2001 72.401 51.365289 \n", + "2002 72.840 51.377145 \n", + "2003 73.257 51.390671 \n", + "2004 73.642 51.404430 \n", + "2005 73.987 51.417387 \n", + "2006 74.291 51.429159 \n", + "2007 74.562 51.440000 \n", + "2008 74.808 51.450264 \n", + "2009 75.032 51.460545 \n", + "2010 75.236 51.471198 \n", + "2011 75.425 51.482284 \n", + "2012 75.602 51.493429 \n", + "2013 75.769 51.504041 \n", + "2014 75.932 51.513340 \n", + "2015 76.092 51.520777 \n", + "2016 76.252 51.526181 \n", + "2017 NaN 51.529709 \n", + "\n", + " Population, male Population, female (% of total) Population, female \\\n", + "Year \n", + "1960 343839501 48.455259 323230499 \n", + "1961 340179667 48.483384 320150333 \n", + "1962 342819197 48.507863 322950803 \n", + "1963 351197134 48.530101 331137866 \n", + "1964 359289719 48.551994 339065281 \n", + "1965 367786712 48.574605 347398288 \n", + "1966 378008633 48.598228 357391367 \n", + "1967 387673867 48.621845 366876133 \n", + "1968 397759563 48.643715 376750437 \n", + "1969 408665290 48.661752 387359710 \n", + "1970 420001834 48.674797 398313166 \n", + "1971 431631246 48.682834 409473754 \n", + "1972 442335153 48.686803 419694847 \n", + "1973 452543013 48.687778 429396987 \n", + "1974 461995352 48.687138 438354648 \n", + "1975 470239739 48.685912 446155261 \n", + "1976 477588355 48.684211 453096645 \n", + "1977 484162022 48.682023 459292978 \n", + "1978 490703734 48.680015 465461266 \n", + "1979 497303725 48.678931 471701275 \n", + "1980 503577431 48.679222 477657569 \n", + "1981 510050546 48.681131 483834454 \n", + "1982 517584727 48.684381 491045273 \n", + "1983 525078211 48.688256 498231789 \n", + "1984 531976602 48.691766 504848398 \n", + "1985 539244515 48.694197 511795485 \n", + "1986 547311204 48.695507 519478796 \n", + "1987 556154832 48.695860 527880168 \n", + "1988 565190587 48.695062 536439413 \n", + "1989 573946550 48.692929 544703450 \n", + "1990 582470121 48.689410 552714879 \n", + "1991 590531742 48.684219 560248258 \n", + "1992 597891229 48.677543 567078771 \n", + "1993 604889315 48.670334 573550685 \n", + "1994 611842204 48.663850 579992796 \n", + "1995 618586032 48.658882 586268968 \n", + "1996 625140432 48.655872 592409568 \n", + "1997 631590489 48.654311 598484511 \n", + "1998 637697691 48.652893 604237309 \n", + "1999 643281642 48.649823 609453358 \n", + "2000 648445282 48.643896 614199718 \n", + "2001 653289427 48.634711 618560573 \n", + "2002 657832960 48.622855 622567040 \n", + "2003 662117402 48.609329 626282598 \n", + "2004 666239967 48.595570 629835033 \n", + "2005 670338757 48.582613 633381243 \n", + "2006 674246565 48.570841 636773435 \n", + "2007 677920041 48.560000 639964959 \n", + "2008 681538500 48.549736 643116500 \n", + "2009 685073651 48.539455 646186349 \n", + "2010 688532785 48.528802 649172215 \n", + "2011 691988818 48.517716 652141182 \n", + "2012 695519169 48.506571 655175831 \n", + "2013 699105548 48.495959 658274452 \n", + "2014 702781045 48.486660 661488955 \n", + "2015 706463193 48.479223 664756807 \n", + "2016 710373422 48.473819 668291578 \n", + "2017 714405303 48.470291 671989697 \n", + "\n", + " Population, total Death rate, crude (per 1,000 people) \\\n", + "Year \n", + "1960 667070000 25.43 \n", + "1961 660330000 14.24 \n", + "1962 665770000 10.02 \n", + "1963 682335000 10.04 \n", + "1964 698355000 11.50 \n", + "1965 715185000 9.50 \n", + "1966 735400000 8.83 \n", + "1967 754550000 8.43 \n", + "1968 774510000 8.21 \n", + "1969 796025000 8.03 \n", + "1970 818315000 7.60 \n", + "1971 841105000 7.32 \n", + "1972 862030000 7.61 \n", + "1973 881940000 7.04 \n", + "1974 900350000 7.34 \n", + "1975 916395000 7.32 \n", + "1976 930685000 7.25 \n", + "1977 943455000 6.87 \n", + "1978 956165000 6.25 \n", + "1979 969005000 6.21 \n", + "1980 981235000 6.34 \n", + "1981 993885000 6.36 \n", + "1982 1008630000 6.60 \n", + "1983 1023310000 6.90 \n", + "1984 1036825000 6.82 \n", + "1985 1051040000 6.78 \n", + "1986 1066790000 6.86 \n", + "1987 1084035000 6.72 \n", + "1988 1101630000 6.64 \n", + "1989 1118650000 6.54 \n", + "1990 1135185000 6.67 \n", + "1991 1150780000 6.70 \n", + "1992 1164970000 6.64 \n", + "1993 1178440000 6.64 \n", + "1994 1191835000 6.49 \n", + "1995 1204855000 6.57 \n", + "1996 1217550000 6.56 \n", + "1997 1230075000 6.51 \n", + "1998 1241935000 6.50 \n", + "1999 1252735000 6.46 \n", + "2000 1262645000 6.45 \n", + "2001 1271850000 6.43 \n", + "2002 1280400000 6.41 \n", + "2003 1288400000 6.40 \n", + "2004 1296075000 6.42 \n", + "2005 1303720000 6.51 \n", + "2006 1311020000 6.81 \n", + "2007 1317885000 6.93 \n", + "2008 1324655000 7.06 \n", + "2009 1331260000 7.08 \n", + "2010 1337705000 7.11 \n", + "2011 1344130000 7.14 \n", + "2012 1350695000 7.15 \n", + "2013 1357380000 7.16 \n", + "2014 1364270000 7.16 \n", + "2015 1371220000 7.11 \n", + "2016 1378665000 7.30 \n", + "2017 1386395000 NaN \n", + "\n", + " Birth rate, crude (per 1,000 people) \\\n", + "Year \n", + "1960 20.86 \n", + "1961 18.02 \n", + "1962 37.01 \n", + "1963 43.37 \n", + "1964 39.14 \n", + "1965 37.88 \n", + "1966 35.05 \n", + "1967 33.96 \n", + "1968 35.59 \n", + "1969 34.11 \n", + "1970 33.43 \n", + "1971 30.65 \n", + "1972 29.77 \n", + "1973 27.93 \n", + "1974 24.82 \n", + "1975 23.01 \n", + "1976 19.91 \n", + "1977 18.93 \n", + "1978 18.25 \n", + "1979 17.82 \n", + "1980 18.21 \n", + "1981 20.91 \n", + "1982 22.28 \n", + "1983 20.19 \n", + "1984 19.90 \n", + "1985 21.04 \n", + "1986 22.43 \n", + "1987 23.33 \n", + "1988 22.37 \n", + "1989 21.58 \n", + "1990 21.06 \n", + "1991 19.68 \n", + "1992 18.27 \n", + "1993 18.09 \n", + "1994 17.70 \n", + "1995 17.12 \n", + "1996 16.98 \n", + "1997 16.57 \n", + "1998 15.64 \n", + "1999 14.64 \n", + "2000 14.03 \n", + "2001 13.38 \n", + "2002 12.86 \n", + "2003 12.41 \n", + "2004 12.29 \n", + "2005 12.40 \n", + "2006 12.09 \n", + "2007 12.10 \n", + "2008 12.14 \n", + "2009 12.13 \n", + "2010 11.90 \n", + "2011 11.93 \n", + "2012 12.10 \n", + "2013 12.08 \n", + "2014 12.37 \n", + "2015 12.07 \n", + "2016 12.00 \n", + "2017 NaN \n", + "\n", + " Mortality rate, adult, male (per 1,000 male adults) \\\n", + "Year \n", + "1960 499.659 \n", + "1961 497.798 \n", + "1962 495.937 \n", + "1963 459.408 \n", + "1964 422.879 \n", + "1965 386.349 \n", + "1966 349.820 \n", + "1967 313.291 \n", + "1968 297.064 \n", + "1969 280.838 \n", + "1970 264.612 \n", + "1971 248.386 \n", + "1972 232.160 \n", + "1973 221.847 \n", + "1974 211.534 \n", + "1975 201.221 \n", + "1976 190.908 \n", + "1977 180.596 \n", + "1978 177.449 \n", + "1979 174.302 \n", + "1980 171.155 \n", + "1981 168.008 \n", + "1982 164.861 \n", + "1983 161.681 \n", + "1984 158.501 \n", + "1985 155.321 \n", + "1986 152.141 \n", + "1987 148.961 \n", + "1988 148.383 \n", + "1989 147.805 \n", + "1990 147.228 \n", + "1991 146.650 \n", + "1992 146.073 \n", + "1993 144.644 \n", + "1994 143.216 \n", + "1995 141.788 \n", + "1996 140.360 \n", + "1997 138.932 \n", + "1998 133.831 \n", + "1999 128.730 \n", + "2000 123.629 \n", + "2001 118.528 \n", + "2002 113.427 \n", + "2003 111.762 \n", + "2004 110.097 \n", + "2005 108.432 \n", + "2006 106.767 \n", + "2007 105.102 \n", + "2008 103.557 \n", + "2009 102.013 \n", + "2010 100.468 \n", + "2011 98.924 \n", + "2012 97.379 \n", + "2013 96.063 \n", + "2014 94.746 \n", + "2015 93.429 \n", + "2016 92.112 \n", + "2017 NaN \n", + "\n", + " Mortality rate, adult, female (per 1,000 female adults) \\\n", + "Year \n", + "1960 397.316 \n", + "1961 396.271 \n", + "1962 395.226 \n", + "1963 371.219 \n", + "1964 347.213 \n", + "1965 323.206 \n", + "1966 299.200 \n", + "1967 275.193 \n", + "1968 257.750 \n", + "1969 240.308 \n", + "1970 222.865 \n", + "1971 205.422 \n", + "1972 187.979 \n", + "1973 179.878 \n", + "1974 171.778 \n", + "1975 163.677 \n", + "1976 155.577 \n", + "1977 147.476 \n", + "1978 144.034 \n", + "1979 140.592 \n", + "1980 137.150 \n", + "1981 133.709 \n", + "1982 130.267 \n", + "1983 127.030 \n", + "1984 123.793 \n", + "1985 120.556 \n", + "1986 117.319 \n", + "1987 114.083 \n", + "1988 112.916 \n", + "1989 111.749 \n", + "1990 110.583 \n", + "1991 109.416 \n", + "1992 108.249 \n", + "1993 106.486 \n", + "1994 104.722 \n", + "1995 102.958 \n", + "1996 101.194 \n", + "1997 99.431 \n", + "1998 96.886 \n", + "1999 94.342 \n", + "2000 91.798 \n", + "2001 89.254 \n", + "2002 86.710 \n", + "2003 84.895 \n", + "2004 83.081 \n", + "2005 81.267 \n", + "2006 79.453 \n", + "2007 77.639 \n", + "2008 76.302 \n", + "2009 74.965 \n", + "2010 73.627 \n", + "2011 72.290 \n", + "2012 70.953 \n", + "2013 69.986 \n", + "2014 69.019 \n", + "2015 68.052 \n", + "2016 67.085 \n", + "2017 NaN \n", + "\n", + " Sex ratio at birth (male births per female births) \n", + "Year \n", + "1960 NaN \n", + "1961 NaN \n", + "1962 1.070 \n", + "1963 NaN \n", + "1964 NaN \n", + "1965 NaN \n", + "1966 NaN \n", + "1967 1.070 \n", + "1968 NaN \n", + "1969 NaN \n", + "1970 NaN \n", + "1971 NaN \n", + "1972 1.070 \n", + "1973 NaN \n", + "1974 NaN \n", + "1975 NaN \n", + "1976 NaN \n", + "1977 1.070 \n", + "1978 NaN \n", + "1979 NaN \n", + "1980 NaN \n", + "1981 NaN \n", + "1982 1.070 \n", + "1983 NaN \n", + "1984 NaN \n", + "1985 NaN \n", + "1986 NaN \n", + "1987 1.080 \n", + "1988 NaN \n", + "1989 NaN \n", + "1990 1.104 \n", + "1991 NaN \n", + "1992 1.120 \n", + "1993 NaN \n", + "1994 NaN \n", + "1995 NaN \n", + "1996 NaN \n", + "1997 1.140 \n", + "1998 NaN \n", + "1999 NaN \n", + "2000 NaN \n", + "2001 NaN \n", + "2002 1.160 \n", + "2003 NaN \n", + "2004 NaN \n", + "2005 NaN \n", + "2006 NaN \n", + "2007 1.170 \n", + "2008 1.168 \n", + "2009 1.166 \n", + "2010 1.164 \n", + "2011 1.162 \n", + "2012 1.160 \n", + "2013 1.158 \n", + "2014 1.156 \n", + "2015 1.154 \n", + "2016 1.152 \n", + "2017 1.150 " + ] + }, + "execution_count": 364, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll rename the column names for the sake of brevity. We'll also be converting the population totals into billions and the birth and death rates into percentages for the same reason." + ] + }, + { + "cell_type": "code", + "execution_count": 367, + "metadata": {}, + "outputs": [], + "source": [ + "table.columns = ['lebt', 'popmp', 'popm', 'popfp', 'popf', 'popt', 'death', 'birth', 'mortm', 'mortf', 'sex']\n", + "popt = table.popt / 1e9;\n", + "death_rate = table.death / 1000;\n", + "birth_rate = table.birth / 1000;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll establish some common variables that we'll use throughout the notebook. These variables include the year our data starts, the year the one-child policy was introduced, the year the one-child policy was ended, the populations at all these times, and the growth rates." + ] + }, + { + "cell_type": "code", + "execution_count": 369, + "metadata": {}, + "outputs": [], + "source": [ + "t_0 = get_first_label(popt);\n", + "t_ocp = 1979; \n", + "t_end = 2016;\n", + "p_0 = popt[t_0];\n", + "p_ocp = popt[t_ocp];\n", + "p_end = popt[t_end];\n", + "growth_rate = birth_rate - death_rate;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Growth Model\n", + "\n", + "For the linear growth model, we'll use the average growth per year from 1960 to 1979 to simulate the growth of the population if it hadn't been affected by the one-child policy.\n", + "First, we find the average growth (in number of births - deaths) per year from 1960-1979, before the one-child policy was enacted." + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.01589131578947368" + ] + }, + "execution_count": 370, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "growth = (popt[t_ocp] - popt[t_0]) / (t_ocp - t_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we establish the system and its parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 388, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "system = System(t_0 = t_0,\n", + " t_ocp = t_ocp,\n", + " t_end = t_end,\n", + " p_0 = p_0,\n", + " p_end = p_end,\n", + " growth = growth);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the update function, we're just going to add the average growth per year to the previous year." + ] + }, + { + "cell_type": "code", + "execution_count": 389, + "metadata": {}, + "outputs": [], + "source": [ + "def linear(pop, t, system):\n", + " return pop + system.growth;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation function runs the update function through each year and then puts our model outputs into a TimeSeries so we can plot it. We'll be using this same function for all the other types of models we test." + ] + }, + { + "cell_type": "code", + "execution_count": 391, + "metadata": {}, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " \n", + " for t in linrange(system.t_0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " \n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we run and then plot the model." + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation(system, linear)\n", + "\n", + "plot(popt, ':', label='China pop')\n", + "plot(results, '--', label='Linear model of pop')\n", + "\n", + "decorate(title= 'Linear Model',\n", + " xlabel='Year',\n", + " ylabel='Total population (billions)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Proportional Growth Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the proportional growth model, we'll use the average growth rate per year from 1960 to 1979 to simulate the growth of the population if it hadn't been affected by the one-child policy. Unlike the last model, the proportional growth model accounts for the fact that larger populations will grow more, instead of at a constant rate. First, we find the average growth rate per year from 1960-1979, before the one-child policy was enacted. It's important to note that the last model used average growth, and this model uses average growth *rate*." + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "metadata": {}, + "outputs": [], + "source": [ + "def find_avg(system):\n", + " results = TimeSeries()\n", + " results[system.t_0] = system.p_0\n", + " growth_total = 0\n", + "\n", + " for t in linrange(system.t_0, system.t_ocp):\n", + " growth_total = system.growth_rate[t] + growth_total\n", + " \n", + " return growth_total/(system.t_ocp - system.t_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we establish the system and its parameter, like we did in the last model." + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "metadata": {}, + "outputs": [], + "source": [ + "avg_gr = find_avg(system2)\n", + "\n", + "system2 = System(t_0=t_0,\n", + " t_end=t_end,\n", + " t_ocp=t_ocp,\n", + " p_0=p_0,\n", + " p_end=p_end,\n", + " growth_rate=growth_rate,\n", + " avg_gr = avg_gr); " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the update function, we're just going to multiply the average growth rate per year by the population that year, then add it back to the population. Before 1979, we'll use the actual growth rates per year, and after 1979, we'll use the average growth rate we found earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 399, + "metadata": {}, + "outputs": [], + "source": [ + "def prop(pop, t, system):\n", + " if (t <= 1979): \n", + " growth = growth_rate[t] * pop\n", + " return pop + growth\n", + " else: \n", + " growth = avg_gr * pop\n", + " return pop + growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we run and then plot the model. We're using the same simulation function as with the linear model." + ] + }, + { + "cell_type": "code", + "execution_count": 402, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation(system, prop)\n", + "\n", + "plot(popt, ':', label='China pop')\n", + "plot(results, '--', label='Proportional model of pop')\n", + "decorate(title= 'Proportional Model',\n", + " xlabel='Year',\n", + " ylabel='Total population (billions)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quadratic Growth Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the quadratic growth model, we'll use the two parameters, alpha and beta, to simulate the growth of the population if it hadn't been affected by the one-child policy. Unlike the previous models, the quadratic growth model accounts for the fact that one day, population growth will be forced to slow because of a lack of resources or space. First, we establish the system and the numbers we've chosen for alpha and beta. The number we've chosen for alpha is 0.022 and beta is -0.0015, which means that our predicted carrying capacity for this model is 14.67 billion people. " + ] + }, + { + "cell_type": "code", + "execution_count": 406, + "metadata": {}, + "outputs": [], + "source": [ + "system3 = System(t_0=t_0,\n", + " t_end=t_end,\n", + " t_ocp=t_ocp,\n", + " p_0=p_0,\n", + " p_end=p_end,\n", + " alpha = 0.022,\n", + " beta = -0.0015);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now for the update function. We're just going to multiply alpha by the population, and add that to beta multiplied by the population squared." + ] + }, + { + "cell_type": "code", + "execution_count": 407, + "metadata": {}, + "outputs": [], + "source": [ + "def quad(pop, t, system):\n", + " net_growth = system.alpha * pop + system.beta * pop**2\n", + " return pop +net_growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we run and then plot the model." + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results = run_simulation(system3, quad)\n", + "\n", + "plot(popt, ':', label='China pop')\n", + "plot(results, '--', label='Quadratic model of pop')\n", + "decorate(title= 'Quadratic Model',\n", + " xlabel='Year',\n", + " ylabel='Total population (billions)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interpretation\n", + "\n", + "\n", + "China’s one child policy was never truly a one child policy. The actual number of children allowed fluctuated between 1979 and 2016 (when the policy was introduced and ended respectively). Certain people could also have more children – ethnic minorities were allowed two, and farmers could have a second child, provided that first child was a girl. The policy was also not equally enforced, and those in cities often felt its effects most strongly. All that being said, the Chinese government estimates that about 400 million births were prevented thanks to the policy, which was an attempt to curb overpopulation. Did it work?\n", + "\n", + "For the purposes of this project, we decided to assume that the death rate would be proportional to the birth rate. In reality, this may not have been true at all. We also ignored the existence of the two child policy, (from 1970 to 1979, and re-implemented in 2016), as it wasn’t enforced. Additionally, we treated the one-child policy as if it had truly been a purely one-child policy, ignoring its many variations.\n", + "\n", + "In this project, we tested three different types of models: linear, proportional, and quadratic. This was so we could find the model type that best fit the data from 1960 to 1979, thus giving us the most accurate prediction for the years following.\n", + "Interestingly, the linear model seemed to fit the data incredibly well from the 1980s to the 1990s. Perhaps this was the time when the one child policy was best enforced, making the growth most linear. Maybe it’s just a coincidence. Regardless, populations don’t grow linearly, which is why this model was ruled out immediately.\n", + "\n", + "The proportional model fits the data well, and seems to follow realistic growth after the real plot and the model diverge. However, it assumes that the population will continue to grow proportionally forever, ignoring the fact that space and resources are finite.\n", + "\n", + "In the end, we decided the most accurate model was the Quadratic model. It fit the data well, and a quadratic model projects an eventual plateau in the growth rate, which logically makes sense (at some point, China will run out of resources and ways to create new ones, meaning that population growth will be forced to slow). This model, like the Proportional model, predicts a dramatically larger population than what China currently has. According to our model, had the one child policy never been implemented, China’s population would be about 2 billion in 2016. The actual population of China in 2016 was 1.4 billion.\n", + "As a result of the one-child policy, China was able to curb their population growth significantly." + ] + } + ], + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c13e3edb58e59c49f54ade1851e7da038dca1228 Mon Sep 17 00:00:00 2001 From: Annie Tor Date: Mon, 15 Oct 2018 16:21:12 -0400 Subject: [PATCH 07/18] Adding all notebooks --- code/China _ Data.html | 70 + code/China _ Data_files/1458892451075570 | 23 + .../WebStatsUtil.js.download | 57 + .../XzQsWhTqmnE8dYDOJ0HjJQ.js.download | 3 + code/China _ Data_files/adsct | 1 + code/China _ Data_files/adsct(1) | 1 + 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China 

196019802000Trillion 012China
+
China (1992)
+
426.916 Billion
+
196019802000Billion 0.71.4China
+
China (1972)
+
862,030,000
+
196019802000 100130China
+
China (1976)
+
121.58
+
196019802000 08China
+
China (2014)
+
7.544
+
196019802000 218China
196019802000 4080China
196019802000Thousand 09China
196019802000 5880China

Global Economic Prospects - Forecasts

  • Annual GDP Growth (%)
    20152016201720182019202066.57China
    China

Projects & Operations

Finances

Surveys

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',this.widgets=[],this.options=t,this.processOptions()}return t.prototype.push=function(t){this.widget=t,this.widgets.push(t)},t.prototype.pop=function(){1!==this.widgets.length&&(this.widgets.pop(),this.widget=this.widgets[this.widgets.length-1])},t.prototype.render=function(){this.el&&this.el.parentNode&&this.el.parentNode.removeChild(this.el);var t=new Image,e=this;L.on(t,"load",function(){e.createElement(),e.show(),G.pendingAutoprompt&&(G.pendingAutoprompt=!1,G.autoprompt())}),t.src=this.options.imgSrc,b()},t.prototype.createElement=function(){var t=this.el=h(d(this.template,this.options)),e=this.btnEl=t.getElementsByTagName("a")[0],o=this;L.addClass(t,"uv-tab uv-slide-"+this.edge),t.setAttribute("aria-label","Open Feedback Widget"),this.dimensions=L.dimensions(t),this.hide(!1),this.rotation&&(t.style.marginTop=["-",Math.round(this.dimensions.height/2),"px"].join("")),L.on(e,"click",function(t){return t.preventDefault&&t.preventDefault(),o.visibleWidget?o.visibleWidget.hide():o.widget.show(),!1}),this.onCreateElement&&this.onCreateElement(this)},t.prototype.remove=function(){this.el&&(this.widget.hide(),this.hide())},t.prototype.hide=function(t){t=t===o?!0:t,t&&r(this.el),L.addClass(this.el,"uv-is-hidden"),this.el.offsetWidth},t.prototype.show=function(){r(this.el),L.removeClass(this.el,"uv-is-hidden"),this.el.offsetWidth},t.prototype.processOptions=function(){var t={trigger_position:"right",trigger_background_color:"CC6D00",tab_label:"feedback",tab_inverted:!1},e=T({},t,this.options);e.trigger_position.match(/^((top|bottom|middle)-(left|right)|left|right)$/)||(e.trigger_position=t.trigger_position),e.trigger_position.match(/^(right|left)$/)&&(e.trigger_position="middle-"+e.trigger_position),"string"==typeof e.trigger_background_color&&e.trigger_background_color.match(/^#/)&&(e.trigger_background_color=e.trigger_background_color.substring(1));var o=/([^\-]+)-([^\-]+)/.exec(e.trigger_position),i=o[1],s=o[2],n="middle"===o[1]?90:0,r=["tab-",e.inverted?"light-":"dark-",e.trigger_position].join(""),a=[n?"link-vertical":"link-horizontal","-no-bullhorn"].join(""),l=[W,"/dcache","/widget/feedback-tab.png?t=",encodeURIComponent(e.tab_label),"&c=",e.tab_inverted?encodeURIComponent(e.trigger_background_color):"ffffff","&r=",encodeURIComponent(n),e.tab_inverted?"&i=yes":""].join(""),p=e.trigger_position.replace(/middle-/,"").replace(/(bottom|top)-(right|left)/,"horizontal");p=["tab-",p,e.tab_inverted?"-light":"-dark","-no-bullhorn"].join(""),p=[W,UserVoice.manifest[p]].join(""),e.bgImage=p,e.imgSrc=l,e.tab_label=u(e.tab_label),r=D[r],a=D[a],(I.ie6||I.ieQuirks)&&(r+="position:absolute !important;","top"===i?r+="top: expression(((document.documentElement.scrollTop || document.body.scrollTop) + (!this.offsetHeight && 0)) + 'px');":"middle"===i?r+="top: expression(((document.documentElement.scrollTop || document.body.scrollTop) + ((((document.documentElement.clientHeight || document.body.clientHeight) + (!this.offsetHeight && 0)) / 2) >> 0)) + 'px');":"bottom"===i&&(r+="top: expression(((document.documentElement.scrollTop || document.body.scrollTop) + (document.documentElement.clientHeight || document.body.clientHeight) - this.offsetHeight) + 'px');")),e.tabStyle=d(r,{color:e.trigger_background_color,bgImage:e.bgImage}),e.linkStyle=a,"top"===i?this.edge="top":"bottom"===i?this.edge="bottom":"left"===s?this.edge="left":"right"===s&&(this.edge="right"),this.rotation=n,this.options=e},t}(),B=function(){function i(){}return i.template='
',i.prototype.show=function(t){t=t===o?!0:t,G.hideActive(),G.active=this,this.el||this.render(),this.trigger&&(this.trigger.visibleWidget=this),this.trigger&&this.trigger.popoverWillShow&&this.trigger.popoverWillShow(),t?this.animateIn():L.removeClass(this.el,"uv-is-hidden"),this.iframe.poke({show:!0}),this.iframe.poke({focus:null})},i.prototype.hide=function(t){if(t=t===o?!0:t,G.active=null,this.el||this.render(),this.trigger&&(this.trigger.visibleWidget=null),this.trigger&&this.trigger.popoverWillHide&&this.trigger.popoverWillHide(),t?this.animateOut():L.addClass(this.el,"uv-is-hidden"),this.temp){var e=this.el;setTimeout(function(){e.parentNode.removeChild(e)},500)}},i.prototype.toggle=function(t){this.el||this.render(),this.visible()?this.hide(t):this.show(t)},i.prototype.animateIn=function(){this.position(),r(this.el),L.removeClass(this.el,"uv-is-hidden"),this.el.offsetWidth,this.createIframe()},i.prototype.animateOut=function(){r(this.el),L.addClass(this.el,"uv-is-hidden")},i.prototype.visible=function(){return!L.hasClass(this.el,"uv-is-hidden")},i.prototype.paneOpened=function(t,e){e.needsReload&&(this.needsReload=!0)},i.prototype.position=function(){var t=this.visible();L.addClass(this.el,"uv-no-transition"),t||(L.addClass(this.el,"uv-is-invisible"),this.el.style.left="-1000px"),L.removeClass(this.el,"uv-scale-\\S+"),this.el.offsetWidth,this.calculatePosition(),this.el.offsetWidth,t||L.removeClass(this.el,"uv-is-invisible"),L.removeClass(this.el,"uv-no-transition"),this.el.offsetWidth},i.prototype.createIframe=function(){var e=L(this.el,"uv-popover-iframe-container"),o=L(this.el,"uv-popover-loading"),i=this;(!this.iframe||this.needsReload)&&(this.iframe&&(e.removeChild(this.iframe.el),this.needsReload=!1),this.iframe=new q("popover",this.options.widgetType,T({height:"100%"},UserVoice.globalOptions,this.options)),this.iframe.render(),L.addClass(this.iframe.el,"uv-popover-iframe"),e.appendChild(this.iframe.el),I.ie8&&L.addClass(this.el,"uv-ie8"),L.on(t,"resize",function(){i.visible()&&i.calculatePosition()}),o&&o.parentNode&&(this.iframe.loaded||I.ie&&I.version<10?o.parentNode.removeChild(o):L.one(this.iframe.el,"load",function(t){o.parentNode.removeChild(o)})))},i.prototype.createElement=function(t){var o=this.el=e.createElement("div");o.innerHTML=this.template,o.setAttribute("data-html2canvas-ignore","true"),o.setAttribute("aria-label","Feedback Widget"),this.options.width&&!I.mobile&&(L(o,"uv-popover-content").style.width=this.options.width),this.options.height&&!I.mobile&&(L(o,"uv-popover-content").style.height=this.options.height),L.addClass(this.el,"uv-popover uv-is-hidden"),t.appendChild(o)},i}(),J=function(){function o(t){this.template=B.template,this.options=t,b()}return o.prototype=T({},B.prototype),o.prototype.render=function(){this.createElement(e.body)},o.prototype.show=function(){var e,o,i;I.mobile&&p(),B.prototype.show.apply(this,arguments),I.match(/iPhone OS 7/)&&(e=this,(i=function(){e.visible()&&(o!==y().height?(t.scrollBy(0),o=y().height,setTimeout(i,80)):setTimeout(i,800))})())},o.prototype.hide=function(){B.prototype.hide.apply(this,arguments),I.mobile&&(c(),this.needsReload=!0)},o.prototype.calculatePosition=function(){var e=this.options.position.match(/^(top|bottom)-(left|right)$/)?this.options.position:"bottom-right",o=/(\w+)-(\w+)/.exec(e);if(this.el.style.zIndex=100003,I.mobile){var i=L(this.el,"uv-popover-content"),s=t.innerWidth,n=325,r=10,a=s/(n+2*r),l=Math.round(a*r);i.style.width="100%",i.style.height="100%",this.el.style.position="fixed",this.el.style.top=l+"px",this.el.style.left=l+"px",this.el.style.bottom=l+"px",this.el.style.right=l+"px"}else{var r=20;this.el.style.left="",this.el.style[o[1]]=r+"px",this.el.style[o[2]]=r+"px"}L.addClass(this.el,"uv-"+("bottom"===o[1]?"top":"bottom")),L.addClass(this.el,"uv-scale-"+e)},o}(),H=function(){function t(t,e){this.template=B.template+'
',this.options=t,this.trigger=e,b()}return t.prototype=T({},B.prototype),t.prototype.render=function(){this.target="self"===this.options.target?this.trigger.el:L.element(this.options.target),G.systemTrigger&&this.target===G.systemTrigger.el?(this.container=this.target,this.isContained=!0):this.container=e.body,this.createElement(this.container)},t.prototype.paneOpened=function(t,e){B.prototype.paneOpened.apply(this,arguments),this.setTailColor(e.backgroundColor)},t.prototype.defaultPosition=function(){var t=this.target,o=L.offset(t),i=L.dimensions(t),s=t.currentStyle?t.currentStyle.position:L.getComputedStyle(t,null).position,n=100;return"fixed"!==s&&(o.top-=e.body.scrollTop,o.left-=e.body.scrollLeft),o.topy().height?"top":o.lefty().width?"left":o.top>o.top+i.height-y().height?"bottom":"top"},t.prototype.calculatePosition=function(){var t=L(this.el,"uv-popover-tail"),e=this.target,o="automatic"===this.options.position?this.defaultPosition():this.options.position,i="top"===o?"bottom":"right"===o?"left":"left"===o?"right":"top",s="top"===o||"bottom"===o?"left":"top",r="left"===s?"width":"height",a="left"===s?"top":"left",l="width"===r?"height":"width",p=L.dimensions(this.el),c=L.dimensions(e),d=L.offset(e),h=n(d[s]+c[r]/2,p[r],0,y()[r],10),u=n(d[s]-h+c[r]/2,L.dimensions(t)[r],0,p[r],15),g=1*(u-L.dimensions(t)[r]/2)/p[r],m=1/3>=g?"left"===s?"-left":"-top":2/3>=g?"":"left"===s?"-right":"-bottom",f=e.currentStyle?e.currentStyle.position:L.getComputedStyle(e,null).position;this.currentPosition=o,this.el.style.position="fixed"!==f||this.isContained?"absolute":"fixed",this.el.style[s]=h-(this.isContained?d[s]:0)+"px",t.style[s]=u+"px",L.removeClass(this.el,"uv-(bottom|top|left|right)"),L.addClass(this.el,"uv-"+o),L.addClass(this.el,"uv-scale-"+i+m),"top"===o||"left"===o?this.el.style[a]=(this.isContained?0:d[a])-p[l]-14+"px":("bottom"===o||"right"===o)&&(this.el.style[a]=(this.isContained?0:d[a])+c[l]+14+"px")},t.prototype.setTailColor=function(t){"transparent"!==t&&("#ffffff"===t||"rgb(255, 255, 255)"===t||"white"===t||""===t?L.removeClass(this.el,"uv-reversed"):L.addClass(this.el,"uv-reversed"),L(this.el,"uv-popover-content").style.backgroundColor=t,L(this.el,"uv-popover-tail").style["border"+this.currentPosition[0].toUpperCase()+this.currentPosition.slice(1)+"Color"]=t)},t}(),K=function(){function t(t){var e={trigger_background_color:"rgba(46, 49, 51, 0.6)",trigger_position:"bottom-right"};this.options=T({},e,t),this.options.trigger_position.match(/^(bottom|top)-(left|right)$/)||(this.options.trigger_position=e.trigger_position),this.widgets=[],I.touch||v(),b()}return t.icon_images={contact:'\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n\r\n',feedback:'\n\n \n\n\n \n \n \n\n\n',simple_feedback:'\n\n \n\n\n \n \n \n\n\n',post_suggestion:'\n\n \n\n\n \n \n \n\n\n',smartvote:'\n\n \n\n\n \n \n \n\n\n',satisfaction:'\r\n\r\n \r\n\r\n\r\n \r\n \r\n \r\n\r\n\r\n'},t.prototype.remove=function(){this.widget.hide();try{e.body.removeChild(this.el)}catch(t){}},t.prototype.push=function(t){this.widget=t,this.widgets.push(t)},t.prototype.pop=function(){1!==this.widgets.length&&(this.widgets.pop(),this.widget=this.widgets[this.widgets.length-1])},t.prototype.render=function(){var e=this,o="classic_widget"===this.widget.options.mode?"contact":this.widget.options.mode;this.el||this.createElement(),this.btnEl.innerHTML=t.icon_images[o],I.ie8||(this.options.trigger_color&&(L(this.el,"path","uv-bubble-foreground").style.fill=this.options.trigger_color),this.options.trigger_background_color&&(L(this.el,"path","uv-bubble-background").style.fill=this.options.trigger_background_color)),L.on(this.el,"click",function(){e.visibleWidget?e.visibleWidget.hide():e.widget.show()})},t.prototype.createElement=function(){var t=this,o=this.el=e.createElement("div"),i=this.btnEl=e.createElement("a");i.className="uv-icon-trigger",i.href="javascript:void(0);",i.setAttribute("aria-label","Open Feedback Widget"),i.addEventListener("keydown",function(e){13===e.keyCode&&(e.preventDefault(),t.visibleWidget?t.visibleWidget.hide():t.widget.show())}),o.appendChild(i),o.setAttribute("data-html2canvas-ignore","true"),L.addClass(this.el,"uv-icon uv-"+this.options.trigger_position),I.ie8&&L.addClass(this.el,"uv-ie8"),e.body.appendChild(this.el),this.onCreateElement&&this.onCreateElement(this)},t.prototype.popoverWillShow=function(){L.addClass(this.el,"uv-is-selected")},t.prototype.popoverWillHide=function(){L.removeClass(this.el,"uv-is-selected")},t}(),$=function(){function t(t,e){this.el=t,this.options=e,L.data(this.el,"trigger",this)}return t.prototype.render=function(){var t=this;this.clickHandler=function(e){return t.options.trigger_prevent_default_enabled&&e.preventDefault&&e.preventDefault(),t.widget.toggle(),t.options.trigger_prevent_default_enabled?!1:void 0},L.on(this.el,"click",this.clickHandler)},t.prototype.remove=function(){this.removed=!0,L.off(this.el,"click",this.clickHandler)},t}(),q=function(){function i(t,e,o){this.options=T({css:"display: block; background: transparent; padding: none; margin: none; border: none; width: #{width}; height: #{height}",cdn:"omnibox"===e,embed_type:t,type:e,width:"100%",height:"100%"},o),this.options.mode.match(/contact|instant/)?this.options.mode="contact":this.options.mode.match(/post_idea|post_suggestion/)?this.options.mode="feedback":"satisfaction"===this.options.mode&&(this.options.survey_lockout=!1),this.pokes=[],this.loaded=!1,this.setLocation(),this.src=[this.location.baseUrl,this.queryString()].join("?"),"object"==typeof o.config&&this.poke({event:"config",data:o.config}),this.sendUserData(),i.all.push(this)}return i.all=[],i.prototype.setLocation=function(){var t="https";UserVoice.account.deactivated?this.location={host:UserVoice.account.widget2_domain,protocol:t,baseUrl:t+"://"+UserVoice.account.widget2_domain+"/widget_environment/deactivated"}:this.options.src?this.location={host:UserVoice.account.subdomain_ssl_host,protocol:t,baseUrl:t+"://"+UserVoice.account.subdomain_ssl_host+this.options.src}:this.options.cdn?this.location={host:UserVoice.account.widget2_domain,protocol:t,baseUrl:[t,"://",UserVoice.account.widget2_domain,"/",this.options.type,"/",encodeURIComponent(this.options.locale||"0"),"/",encodeURIComponent(this.options.accent_color||"0"),"/",UserVoice.account.client_key].join("")}:this.location={host:UserVoice.account.subdomain_ssl_host,protocol:t,baseUrl:t+"://"+UserVoice.account.subdomain_ssl_host+"/clients/widgets/"+this.options.type},this.location.origin=[this.location.protocol,"://",this.location.host].join("")},i.prototype.doLoad=function(){this.loaded=!0;var t=this.el.contentWindow;setTimeout(function(){t.focus()},0),this.poke()},i.prototype.poke=function(e){if(e&&("object"==typeof e&&(e=JSON.stringify(e)),this.pokes.push(e)),this.loaded&&this.el&&this.el.contentWindow){for(var o=this.el.contentWindow,i=0,s=this.pokes.length;s>i;i++)if("postMessage"in t)o.postMessage(this.pokes[i],this.location.origin);else try{o.location.href=[this.src,(+new Date).toString()+"&"+this.pokes[i]].join("#")}catch(n){}this.pokes=[]}},i.prototype.render=function(){this.loaded=!1,this.options.startIframeLoad=+new Date;var t=this.el=e.createElement("iframe"),o=this,i={height:this.options.height,width:this.options.width},s=d(this.options.css,i);if(t.attachEvent?t.attachEvent("onload",function(){o.doLoad()}):t.onload=function(){o.doLoad()},t.name="uvw-iframe-"+this.options.id,L.addClass(t,"uvw-dialog-iframe"),I.ie?t.style.setAttribute("cssText",s,0):t.setAttribute("style",s),t.setAttribute("allowtransparency","true"),t.setAttribute("frameBorder","0"),t.frameBorder=0,!(I.ie&&I.version<9)){t.style.visibility="hidden";var n=t.onload;t.onload=function(){"function"==typeof n&&n(),t.style.visibility="visible"}}return t.src=this.src,t},i.prototype.queryString=function(){var e={},i=["sess","mode","locale","link_color","topic_id","forum_id","article_id","suggestion_id","feedback_tab_name","support_tab_name","contact_us","email","name","status_ids","smartvote_status_ids","states","category_ids","smartvote_category_ids","survey_lockout","primary_color","accent_color","trigger_color","trigger_background_color","header","border","custom_template_id","design_settings_id","allow_tests","demo","startIframeLoad","org_name","post_idea_title","contact_title","smartvote_title","context","embed_type","trigger_method","menu","screenshot_enabled","mobile","twitter_demo","twitter_demo_username","twitter_demo_message","twitter_demo_satisfaction_thanks_mode","twitter_demo_satisfaction_thanks_message","permalinks_enabled","strings","instant_answers","smartvote","satisfaction","contact_enabled","feedback_enabled"];"classic_widget"===this.options.type&&i.push("sso");for(var s=0;so?"325px":"100%",contact_enabled:!0,post_suggestion_enabled:!0,smartvote_enabled:!0,feedback_enabled:!0},s=G.processModeOptions(T(i,UserVoice.globalOptions,e)),n=new q("inline",s.widgetType,s),r=t.currentStyle?t.currentStyle.position:L.getComputedStyle(t,null).position;for("static"===r&&(t.style.position="relative");t.firstChild;)t.removeChild(t.firstChild);t.appendChild(n.render()),n.poke({show:!0})}return t}(),Z=function(){function o(t,e){this.iframe=new q("lightbox",t.widgetType,T({css:"display: block; border: none; -moz-border-radius: 3px; -webkit-border-radius: 3px; height: 100%; padding: none; position: absolute; top: 0; right: 0; bottom: 0; left: 0; width: 100%;-webkit-transform: translate3d(0,0,0);"},t)),this.template='",this.id=L.uniqId(),this.dialog_id="uvw-dialog-"+this.id,this.dialog_close_id="uvw-dialog-close-"+this.id,this.dialog_content_id="uvw-dialog-content-"+this.id,this.options=t}return o.prototype.toggle=function(){this.show()},o.prototype.show=function(){I.ie6||I.touch||I.ieMobile||I.iOS||I.ieQuirks?t.open(this.iframe.src,"uservoice_widget","height=500,width=888,resizable=yes,scrollbars=1"):(this.initPopup(),this.overlay.style.visibility="visible",this.overlay.style.display="block",this.dialog.focus(),L.addClass(x(),"uvw-dialog-open"))},o.prototype.hide=function(){this.iframe.poke("reset"),this.overlay&&(this.overlay.style.display="none"),L.removeClass(x(),"uvw-dialog-open")},o.prototype.initPopup=function(){if(this.overlay||(f(P),this.overlay=h(d(this.template,this))),this.iframe.poke("opened"),!this.dialog||this.dialog.getAttribute("data-widget-key")!==this.id){for(this.iframe.render(),this.dialogContent=e.getElementById(this.dialog_content_id);this.dialogContent.firstChild;)this.dialogContent.removeChild(this.dialogContent.firstChild);this.dialogContent.appendChild(this.iframe.el),this.dialog=e.getElementById(this.dialog_id), +this.dialog.setAttribute("data-widget-key",this.id);var t=this;L.on(e.getElementById(this.dialog_close_id),"click",function(e){return t.hide()})}},o}(),G=function(){var i={session:!1,syncedSession:!1};return i.setUvts=function(t){i.uvts=t,q.sendUserData()},i.setEmail=function(t){i.email=t,q.sendUserData()},i.setName=function(t){i.name=t,q.sendUserData()},i.setSSO=function(t){i.sso=t,q.sendUserData()},i.addExternalUserId=function(t){i.external_user_ids||(i.external_user_ids=[]),i.external_user_ids.push(t),q.sendUserData()},i.setSession=function(){i.session=!0,i.pendingAutoprompt&&(i.pendingAutoprompt=!1,i.autoprompt())},i.syncSession=function(){i.syncedSession=!0,i.pendingAutoprompt&&(i.pendingAutoprompt=!1,i.autoprompt())},i.tracker=new O(UserVoice.account.subdomain_id,{channel:e.location.host===UserVoice.account.subdomain_site_host?"site2":"external",onUvts:i.setUvts,onSession:i.setSession,onSync:i.syncSession,v2:!0}),i.jsonp=function(){var o={},i=0;return 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fbq.loadPlugin("opttracking"); +fbq.set("experiments", {"0":{"name":"beacon","range":[0,0],"code":"b","passRate":0.5},"1":{"name":"logDataLayer","range":[0,0],"code":"d","passRate":0},"2":{"name":"button_click_send_beacon","range":[0,0.1],"code":"e","passRate":0.5},"3":{"name":"button_click_send_beacon_all_browser","range":[0.11,0.12],"code":"f","passRate":0.5},"4":{"name":"all_event_send_beacon","range":[0.12,0.13],"code":"g","passRate":0.5},"5":{"name":"send_coalescence_telemetry","range":[0,0],"code":"h","passRate":0.5}});instance.configLoaded("global_config"); }}); \ No newline at end of file diff --git a/code/China _ Data_files/fs.frame.html b/code/China _ Data_files/fs.frame.html new file mode 100644 index 00000000..d7cfe909 --- /dev/null +++ b/code/China _ Data_files/fs.frame.html @@ -0,0 +1,12 @@ + + + + + + ForeSee Cloud Services Storage Utility Frame + + + +

This frame is used to deliver ForeSee functionality to your website.

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Thursday, May 18th, 2017, 9:26:12 AM +* (c) Copyright 2016, ForeSee. http://www.foresee.com +* Patents pending. +**/ +!function(){function trimDots(ary){var i,part;for(i=0;i0&&(ary.splice(i-1,2),i-=2)}}function __eB(element,type,handler){element.addEventListener?element.addEventListener(type,handler,!1):element.attachEvent("on"+type,handler)}function getAllScripts(){return _D.getElementsByTagName("script")}function mixin(target,source,force,deepStringMixin){return source&&eachProp(source,function(value,prop){!force&&hasProp(target,prop)||(!deepStringMixin||"object"!=typeof value||!value||isArray(value)||isFunction(value)||value instanceof RegExp?target[prop]=value:(target[prop]||(target[prop]={}),mixin(target[prop],value,force,deepStringMixin)))}),target}function each(ary,func){if(ary){var i;for(i=0;i-1&&(!ary[i]||!func(ary[i],i,ary));i-=1);}}function defaultOnError(err){}function getGlobal(value){if(!value)return value;var g=global;return 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outerLoop}!foundStarMap&&starMap&&getOwn(starMap,nameSegment)&&(foundStarMap=getOwn(starMap,nameSegment),starI=i)}!foundMap&&foundStarMap&&(foundMap=foundStarMap,foundI=starI),foundMap&&(nameParts.splice(0,foundI,foundMap),name=nameParts.join("/"))}return pkgMain=getOwn(config.pkgs,name),pkgMain?pkgMain:name}function removeScript(name){each(getAllScripts(),function(scriptNode){if(attr(scriptNode,"data-requiremodule")===name&&attr(scriptNode,"data-requirecontext")===context.contextName)return scriptNode.parentNode.removeChild(scriptNode),!0})}function hasPathFallback(id){var pathConfig=getOwn(config.paths,id);if(pathConfig&&isArray(pathConfig)&&pathConfig.length>1)return pathConfig.shift(),context.require.undef(id),context.makeRequire(null,{skipMap:!0})([id]),!0}function splitPrefix(name){var prefix,index=name?name.indexOf("!"):-1;return index>-1&&(prefix=name.substring(0,index),name=name.substring(index+1,name.length)),[prefix,name]}function makeModuleMap(name,parentModuleMap,isNormalized,applyMap){var url,pluginModule,suffix,nameParts,prefix=null,parentName=parentModuleMap?parentModuleMap.name:null,originalName=name,isDefine=!0,normalizedName="";return name||(isDefine=!1,name="_@r"+(requireCounter+=1)),nameParts=splitPrefix(name),prefix=nameParts[0],name=nameParts[1],prefix&&(prefix=normalize(prefix,parentName,applyMap),pluginModule=getOwn(defined,prefix)),name&&(prefix?normalizedName=pluginModule&&pluginModule.normalize?pluginModule.normalize(name,function(name){return 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onError(err,errback){err.requireModules;errback&&errback(err)}function takeGlobalQueue(){globalDefQueue.length&&(apsp.apply(defQueue,[defQueue.length,0].concat(globalDefQueue)),globalDefQueue=[])}function cleanRegistry(id){delete registry[id],delete enabledRegistry[id]}function breakCycle(mod,traced,processed){var id=mod.map.id;mod.error?mod.emit("error",mod.error):(traced[id]=!0,each(mod.depMaps,function(depMap,i){var depId=depMap.id,dep=getOwn(registry,depId);!dep||mod.depMatched[i]||processed[depId]||(getOwn(traced,depId)?(mod.defineDep(i,defined[depId]),mod.check()):breakCycle(dep,traced,processed))}),processed[id]=!0)}function checkLoaded(){var usingPathFallback,waitInterval=1e3*config.waitSeconds,expired=waitInterval&&context.startTime+waitInterval<(new Date).getTime(),noLoads=[],reqCalls=[],stillLoading=!1,needCycleCheck=!0;inCheckLoaded||(inCheckLoaded=!0,eachProp(enabledRegistry,function(mod){var map=mod.map,modId=map.id;if(mod.enabled&&(map.isDefine||reqCalls.push(mod),!mod.error))if(!mod.inited&&expired)hasPathFallback(modId)?(usingPathFallback=!0,stillLoading=!0):(noLoads.push(modId),removeScript(modId));else if(!mod.inited&&mod.fetched&&map.isDefine&&(stillLoading=!0,!map.prefix))return needCycleCheck=!1}),expired&&noLoads.length||(needCycleCheck&&each(reqCalls,function(mod){breakCycle(mod,{},{})}),expired&&!usingPathFallback||!stillLoading||checkLoadedTimeoutId||(checkLoadedTimeoutId=setTimeout(function(){checkLoadedTimeoutId=0,checkLoaded()},50)),inCheckLoaded=!1))}function callGetModule(args){hasProp(defined,args[0])||getModule(makeModuleMap(args[0],null,!0)).init(args[1],args[2])}function removeListener(node,func,name,ieName){node.detachEvent&&!isOpera?ieName&&node.detachEvent(ieName,func):node.removeEventListener(name,func,!1)}function getScriptData(evt){var node=evt.currentTarget||evt.srcElement;return removeListener(node,context.onScriptLoad,"load","onreadystatechange"),removeListener(node,context.onScriptError,"error"),{node:node,id:node&&attr(node,"data-requiremodule")}}function intakeDefines(){var args;for(takeGlobalQueue();defQueue.length;){if(args=defQueue.shift(),null===args[0])return;callGetModule(args)}}var inCheckLoaded,Module,context,handlers,checkLoadedTimeoutId,config={waitSeconds:7,baseUrl:"./",paths:{},bundles:{},pkgs:{},shim:{},config:{}},registry={},enabledRegistry={},undefEvents={},defQueue=[],defined={},urlFetched={},bundlesMap={},requireCounter=1,unnormalizedCounter=1;return handlers={require:function(mod){return mod.require?mod.require:mod.require=context.makeRequire(mod.map)},exports:function(mod){if(mod.usingExports=!0,mod.map.isDefine)return mod.exports?defined[mod.map.id]=mod.exports:mod.exports=defined[mod.map.id]={}},module:function(mod){return mod.module?mod.module:mod.module={id:mod.map.id,uri:mod.map.url,config:function(){return getOwn(config.config,mod.map.id)||{}},exports:mod.exports||(mod.exports={})}}},Module=function(map){this.events=getOwn(undefEvents,map.id)||{},this.map=map,this.shim=getOwn(config.shim,map.id),this.depExports=[],this.depMaps=[],this.depMatched=[],this.pluginMaps={},this.depCount=0},Module.prototype={init:function(depMaps,factory,errback,options){options=options||{},this.inited||(this.factory=factory,errback?this.on("error",errback):this.events.error&&(errback=proxy(function(err){this.emit("error",err)},this)),this.depMaps=depMaps&&depMaps.slice(0),this.errback=errback,this.inited=!0,this.ignore=options.ignore,options.enabled||this.enabled?this.enable():this.check())},defineDep:function(i,depExports){this.depMatched[i]||(this.depMatched[i]=!0,this.depCount-=1,this.depExports[i]=depExports)},fetch:function(){if(!this.fetched){this.fetched=!0,context.startTime=(new Date).getTime();var map=this.map;return this.shim?void 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err.requireMap=this.map,err.requireModules=this.map.isDefine?[this.map.id]:null,err.requireType=this.map.isDefine?"define":"require",onError(this.error=err)}else exports=factory;this.exports=exports,this.map.isDefine&&!this.ignore&&(defined[id]=exports,req.onResourceLoad&&req.onResourceLoad(context,this.map,this.depMaps)),cleanRegistry(id),this.defined=!0}this.defining=!1,this.defined&&!this.defineEmitted&&(this.defineEmitted=!0,this.emit("defined",this.exports),this.defineEmitComplete=!0)}}else this.fetch()}},callPlugin:function(){var map=this.map,id=map.id,pluginMap=makeModuleMap(map.prefix);this.depMaps.push(pluginMap),on(pluginMap,"defined",proxy(function(plugin){var load,normalizedMap,normalizedMod,bundleId=getOwn(bundlesMap,this.map.id),name=this.map.name,parentName=this.map.parentMap?this.map.parentMap.name:null,localRequire=context.makeRequire(map.parentMap,{enableBuildCallback:!0});return this.map.unnormalized?(plugin.normalize&&(name=plugin.normalize(name,function(name){return normalize(name,parentName,!0)})||""),normalizedMap=makeModuleMap(map.prefix+"!"+name,this.map.parentMap),on(normalizedMap,"defined",proxy(function(value){this.init([],function(){return value},null,{enabled:!0,ignore:!0})},this)),normalizedMod=getOwn(registry,normalizedMap.id),void(normalizedMod&&(this.depMaps.push(normalizedMap),this.events.error&&normalizedMod.on("error",proxy(function(err){this.emit("error",err)},this)),normalizedMod.enable()))):bundleId?(this.map.url=context.nameToUrl(bundleId),void this.load()):(load=proxy(function(value){this.init([],function(){return value},null,{enabled:!0})},this),load.error=proxy(function(err){this.inited=!0,this.error=err,err.requireModules=[id],eachProp(registry,function(mod){0===mod.map.id.indexOf(id+"_unnormalized")&&cleanRegistry(mod.map.id)}),onError(err)},this),load.fromText=proxy(function(text,textAlt){var moduleName=map.name,moduleMap=makeModuleMap(moduleName),hasInteractive=useInteractive;textAlt&&(text=textAlt),hasInteractive&&(useInteractive=!1),getModule(moduleMap),hasProp(config.config,id)&&(config.config[moduleName]=config.config[id]);try{req.exec(text)}catch(e){return}hasInteractive&&(useInteractive=!0),this.depMaps.push(moduleMap),context.completeLoad(moduleName),localRequire([moduleName],load)},this),void plugin.load(map.name,localRequire,load,config))},this)),context.enable(pluginMap,this),this.pluginMaps[pluginMap.id]=pluginMap},enable:function(){enabledRegistry[this.map.id]=this,this.enabled=!0,this.enabling=!0,each(this.depMaps,proxy(function(depMap,i){var id,mod,handler;if("string"==typeof depMap){if(depMap=makeModuleMap(depMap,this.map.isDefine?this.map:this.map.parentMap,!1,!this.skipMap),this.depMaps[i]=depMap,handler=getOwn(handlers,depMap.id))return void(this.depExports[i]=handler(this));this.depCount+=1,on(depMap,"defined",proxy(function(depExports){this.defineDep(i,depExports),this.check()},this)),this.errback&&on(depMap,"error",proxy(this.errback,this))}id=depMap.id,mod=registry[id],hasProp(handlers,id)||!mod||mod.enabled||context.enable(depMap,this)},this)),eachProp(this.pluginMaps,proxy(this,function(pluginMap){var mod=getOwn(registry,pluginMap.id);mod&&!mod.enabled&&context.enable(pluginMap,this)},this)),this.enabling=!1,this.check()},on:function(name,cb){var cbs=this.events[name];cbs||(cbs=this.events[name]=[]),cbs.push(cb)},emit:function(name,evt){each(this.events[name],function(cb){cb(evt)}),"error"===name&&delete this.events[name]}},context={config:config,contextName:contextName,registry:registry,defined:defined,urlFetched:urlFetched,defQueue:defQueue,Module:Module,makeModuleMap:makeModuleMap,nextTick:req.nextTick,onError:onError,configure:function(cfg){cfg.baseUrl&&"/"!==cfg.baseUrl.charAt(cfg.baseUrl.length-1)&&(cfg.baseUrl+="/");var shim=config.shim,objs={paths:!0,bundles:!0,config:!0,map:!0};eachProp(cfg,function(value,prop){objs[prop]?(config[prop]||(config[prop]={}),mixin(config[prop],value,!0,!0)):config[prop]=value}),cfg.bundles&&eachProp(cfg.bundles,function(value,prop){each(value,function(v){v!==prop&&(bundlesMap[v]=prop)})}),cfg.shim&&(eachProp(cfg.shim,function(value,id){isArray(value)&&(value={deps:value}),!value.exports&&!value.init||value.exportsFn||(value.exportsFn=context.makeShimExports(value)),shim[id]=value}),config.shim=shim),cfg.packages&&each(cfg.packages,function(pkgObj){var location,name;pkgObj="string"==typeof pkgObj?{name:pkgObj}:pkgObj,name=pkgObj.name,location=pkgObj.location,location&&(config.paths[name]=pkgObj.location),config.pkgs[name]=pkgObj.name+"/"+(pkgObj.main||"main").replace(currDirRegExp,"").replace(jsSuffixRegExp,"")}),eachProp(registry,function(mod,id){mod.inited||mod.map.unnormalized||(mod.map=makeModuleMap(id))}),(cfg.deps||cfg.callback)&&context.require(cfg.deps||[],cfg.callback)},makeShimExports:function(value){function fn(){var ret;return value.init&&(ret=value.init.apply(global,arguments)),ret||value.exports&&getGlobal(value.exports)}return fn},makeRequire:function(relMap,options){function localRequire(deps,callback,errback){var id,map,requireMod;if(options.enableBuildCallback&&callback&&isFunction(callback)&&(callback.__requireJsBuild=!0),"string"==typeof deps){if(isFunction(callback))return;if(relMap&&hasProp(handlers,deps))return handlers[deps](registry[relMap.id]);if(req.get)return req.get(context,deps,relMap,localRequire);if(map=makeModuleMap(deps,relMap,!1,!0),id=map.id,!hasProp(defined,id))return;return defined[id]}return intakeDefines(),context.nextTick(function(){intakeDefines(),requireMod=getModule(makeModuleMap(null,relMap)),requireMod.skipMap=options.skipMap,requireMod.init(deps,callback,errback,{enabled:!0}),checkLoaded()}),localRequire}return options=options||{},mixin(localRequire,{toUrl:function(moduleNamePlusExt){var ext,index=moduleNamePlusExt.lastIndexOf("."),segment=moduleNamePlusExt.split("/")[0],isRelative="."===segment||".."===segment;return index!==-1&&(!isRelative||index>1)&&(ext=moduleNamePlusExt.substring(index,moduleNamePlusExt.length),moduleNamePlusExt=moduleNamePlusExt.substring(0,index)),context.nameToUrl(normalize(moduleNamePlusExt,relMap&&relMap.id,!0),ext,!0)},defined:function(id){return hasProp(defined,makeModuleMap(id,relMap,!1,!0).id)},specified:function(id){return id=makeModuleMap(id,relMap,!1,!0).id,hasProp(defined,id)||hasProp(registry,id)}}),relMap||(localRequire.undef=function(id){takeGlobalQueue();var map=makeModuleMap(id,relMap,!0),mod=getOwn(registry,id);removeScript(id),delete defined[id],delete urlFetched[map.url],delete undefEvents[id],eachReverse(defQueue,function(args,i){args[0]===id&&defQueue.splice(i,1)}),mod&&(mod.events.defined&&(undefEvents[id]=mod.events),cleanRegistry(id))}),localRequire},enable:function(depMap){var mod=getOwn(registry,depMap.id);mod&&getModule(depMap).enable()},completeLoad:function(moduleName){var found,args,mod,shim=getOwn(config.shim,moduleName)||{},shExports=shim.exports;for(takeGlobalQueue();defQueue.length;){if(args=defQueue.shift(),null===args[0]){if(args[0]=moduleName,found)break;found=!0}else args[0]===moduleName&&(found=!0);callGetModule(args)}if(mod=getOwn(registry,moduleName),!found&&!hasProp(defined,moduleName)&&mod&&!mod.inited){if(!(!config.enforceDefine||shExports&&getGlobal(shExports)))return hasPathFallback(moduleName),void 0;callGetModule([moduleName,shim.deps||[],shim.exportsFn])}checkLoaded()},nameToUrl:function(moduleName,ext,skipExt){var paths,syms,i,parentModule,url,parentPath,bundleId,pkgMain=getOwn(config.pkgs,moduleName);if(pkgMain&&(moduleName=pkgMain),bundleId=getOwn(bundlesMap,moduleName))return context.nameToUrl(bundleId,ext,skipExt);if(req.jsExtRegExp.test(moduleName))url=moduleName+(ext||"");else{for(paths=config.paths,syms=moduleName.split("/"),i=syms.length;i>0;i-=1)if(parentModule=syms.slice(0,i).join("/"),parentPath=getOwn(paths,parentModule)){isArray(parentPath)&&(parentPath=parentPath[0]),syms.splice(0,i,parentPath);break}url=syms.join("/"),url+=ext||(/^data\:|\?/.test(url)||skipExt?"":".js"),url=("/"===url.charAt(0)||url.match(/^[\w\+\.\-]+:/)?"":config.baseUrl)+url}return config.urlArgs?url+((url.indexOf("?")===-1?"?":"&")+config.urlArgs):url},load:function(id,url){req.load(context,id,url)},execCb:function(name,callback,args,exports){return callback.apply(exports,args)},onScriptLoad:function(evt){if("load"===evt.type||readyRegExp.test((evt.currentTarget||evt.srcElement).readyState)){interactiveScript=null;var data=getScriptData(evt);context.completeLoad(data.id)}},onScriptError:function(evt){var data=getScriptData(evt);!hasPathFallback(data.id)}},context.require=context.makeRequire(),context}function getInteractiveScript(){return interactiveScript&&"interactive"===interactiveScript.readyState?interactiveScript:(eachReverse(getAllScripts(),function(script){if("interactive"===script.readyState)return interactiveScript=script}),interactiveScript)}function subtractFromURL(base,notches){var pref=base.substr(0,base.indexOf("//"))+"//",suff=base.substr(pref.length),dom=suff.substr(suff.indexOf("/")+1),tail=dom.substr(dom.lastIndexOf("/")+1);dom=dom.substr(0,dom.length-tail.length-1),suff=suff.substr(0,suff.indexOf("/"));var bits=dom.split("/");return bits.length-=Math.min(bits.length,notches),(pref+suff+"/"+bits.join("/")+tail).replace(/\/\/\//g,"//")}var _moduleLocationOverride,_W=window,_D=_W.document,supportsDomStorage=!!_W.sessionStorage,skipInit=!1,_HD=_D.getElementsByTagName("head"),isOpera="undefined"!=typeof opera&&"[object Opera]"===opera.toString(),gatewayVersion=2.03;if(!isOpera){_HD=_HD&&_HD.length>0?_HD[0]:_D.body;try{supportsDomStorage&&sessionStorage.setItem("_","")}catch(e){supportsDomStorage=!1}var globalConfig={},productConfig={};/** + * @preserve + * [GENERAL_CONFIG] + */ +if("undefined"==typeof _W._fsDefine&&JSON&&!(document.documentMode<10)){var stg=function(){};stg.prototype.has=function(itm){return null!==this.get(itm)},stg.prototype.get=function(itm){var res=null;return supportsDomStorage&&(res=localStorage.getItem(itm),res||(res=sessionStorage.getItem(itm))),res},stg.prototype.set=function(itm,val){if(supportsDomStorage)try{localStorage.setItem(itm,val.toString())}catch(e){try{sessionStorage.setItem(itm,val.toString())}catch(e){}}};var require,define,req,s,head,baseElement,interactiveScript,currentlyAddingScript,op=Object.prototype,hasOwn=(op.toString,op.hasOwnProperty),ap=Array.prototype,hasProp=function(obj,prop){return hasOwn.call(obj,prop)},getOwn=function(obj,prop){return hasProp(obj,prop)&&obj[prop]},eachProp=function(obj,func){var prop;for(prop in obj)if(hasProp(obj,prop)&&func(obj[prop],prop))break},isDefined=function(obj){return null!==obj&&"undefined"!=typeof obj},isFunction=function(obj){return"function"==typeof obj},isObject=function(obj){return"object"==typeof obj},isArray=function(obj){return"[object Array]"==Object.prototype.toString.call(obj)},isDate=function(obj){return obj instanceof 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Object(result)===result?result:self})},dispose=function(obj){if(obj){if(obj.length)for(var i=obj.length-1;i>=0;i--)obj[i]=null;for(var prop in obj){var tob=typeof obj[prop];"function"!=tob&&"object"!=tob||(obj[prop]=null)}}obj=null},getParam=function(parm){var vars={},vrl=(_W.location.href.replace(/[?&]+([^=&]+)=([^&]*)/gi,function(m,key,value){vars[key]=value}),vars[parm]);return vrl?decodeURIComponent(vrl):vrl},nextTick=function(cb){setTimeout(cb||function(){},20)},ext=function(){var options,name,copy,a=arguments,target=a[0]||{},i=1,lnt=a.length,surface=arguments[arguments.length-1]===!1;for("object"==typeof target||isFunction(target)||(target={}),lnt===i&&(target=this,--i);i-1?"&":"?"):"";if(params)for(var nm in params)pm=params[nm],isString(pm)||(pm=JSON.stringify(pm)),pList+=encodeURIComponent(nm)+"="+encodeURIComponent(pm)+"&";return pList},compute=function(vstr){var f=new[].constructor.constructor(vstr);return f.call(_W)},toLowerCase=function(str){return 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this.bitindex>0&&(e[r]<<=8-this.bitindex,e[r]=P.BitStream.ReverseTable[e[r]],r++),_?t=e.subarray(0,r):(e.length=r,t=e),t},P.BitStream.ReverseTable=function(t){return t}(function(){var t,e=new(_?Uint8Array:Array)(256);for(t=0;t<256;++t)e[t]=function(t){var e=t,r=7;for(t>>>=1;t;t>>>=1)e<<=1,e|=1&t,--r;return(e<>>0}(t);return e}()),P.Huffman={},P.Huffman.buildHuffmanTable=function(t){var e,r,i,n,s,a,o,h,f,u,l,c=t.length,p=0,d=Number.POSITIVE_INFINITY;for(h=0,f=c;hp&&(p=t[h]),t[h]>=1;for(l=i<<16|h,u=a;u0&&(i=this.getParent(r),s[r]>s[i]);)n=s[r],s[r]=s[i],s[i]=n,n=s[r+1],s[r+1]=s[i+1],s[i+1]=n,r=i;return this.length},P.Heap.prototype.pop=function(){var t,e,r,i,n,s=this.buffer;for(e=s[0],t=s[1],this.length-=2,s[0]=s[this.length],s[1]=s[this.length+1],n=0;;){if((i=this.getChild(n))>=this.length)break;if(i+2s[i]&&(i+=2),!(s[i]>s[n]))break;r=s[n],s[n]=s[i],s[i]=r,r=s[n+1],s[n+1]=s[i+1],s[i+1]=r,n=i}return{index:t,value:e,length:this.length}},P.RawDeflate=function(t,e){this.compressionType=P.RawDeflate.CompressionType.DYNAMIC,this.lazy=0,this.freqsLitLen,this.freqsDist,this.input=_&&t instanceof Array?new Uint8Array(t):t,this.output,this.op=0,e&&(e.lazy&&(this.lazy=e.lazy),"number"==typeof e.compressionType&&(this.compressionType=e.compressionType), +e.outputBuffer&&(this.output=_&&e.outputBuffer instanceof Array?new Uint8Array(e.outputBuffer):e.outputBuffer),"number"==typeof e.outputIndex&&(this.op=e.outputIndex)),this.output||(this.output=new(_?Uint8Array:Array)(32768))},P.RawDeflate.CompressionType={NONE:0,FIXED:1,DYNAMIC:2,RESERVED:3},P.RawDeflate.Lz77MinLength=3,P.RawDeflate.Lz77MaxLength=258,P.RawDeflate.WindowSize=32768,P.RawDeflate.MaxCodeLength=16,P.RawDeflate.HUFMAX=286,P.RawDeflate.FixedHuffmanTable=function(){var t,e=[];for(t=0;t<288;t++)switch(!0){case t<=143:e.push([t+48,8]);break;case t<=255:e.push([t-144+400,9]);break;case t<=279:e.push([t-256+0,7]);break;case t<=287:e.push([t-280+192,8]);break;default:throw"invalid literal: "+t}return e}(),P.RawDeflate.prototype.compress=function(){var t,e,r,i=this.input;switch(this.compressionType){case P.RawDeflate.CompressionType.NONE:for(e=0,r=i.length;e>>8&255,h[f++]=255&s,h[f++]=s>>>8&255,_)h.set(t,f),f+=t.length,h=h.subarray(0,f);else{for(a=0,o=t.length;a257&&0===h[s-1];s--);for(a=30;a>1&&0===u[a-1];a--);for(c=this.getTreeSymbols_(s,h,a,u),p=this.getLengths_(c.freqs,7),w=0;w<19;w++)x[w]=p[v[w]];for(o=19;o>4&&0===x[o-1];o--);for(d=this.getCodesFromLengths_(p),m.writeBits(s-257,5,!0),m.writeBits(a-1,5,!0),m.writeBits(o-4,4,!0),w=0;w=16){switch(w++,g){case 16:y=2;break;case 17:y=3;break;case 18:y=7;break;default:throw"invalid code: "+g}m.writeBits(c.codes[w],y,!0)}return this.dynamicHuffman(n,[f,h],[l,u],m),m.finish()},P.RawDeflate.prototype.dynamicHuffman=function(t,e,r,i){var n,s,a,o,h,f,u,l;for(h=e[0],f=e[1],u=r[0],l=r[1],n=0,s=t.length;n256)i.writeBits(t[++n],t[++n],!0),o=t[++n],i.writeBits(u[o],l[o],!0),i.writeBits(t[++n],t[++n],!0);else if(256===a)break;return i},P.RawDeflate.prototype.fixedHuffman=function(t,e){var r,i,n;for(r=0,i=t.length;r256)e.writeBits(t[++r],t[++r],!0),e.writeBits(t[++r],5),e.writeBits(t[++r],t[++r],!0);else if(256===n)break;return e},P.RawDeflate.Lz77Match=function(t,e){this.length=t,this.backwardDistance=e},P.RawDeflate.Lz77Match.LengthCodeTable=function(t){return _?new Uint32Array(t):t}(function(){var t,e,r=[];for(t=3;t<=258;t++)e=function(t){switch(!0){case 3===t:return[257,t-3,0];case 4===t:return[258,t-4,0];case 5===t:return[259,t-5,0];case 6===t:return[260,t-6,0];case 7===t:return[261,t-7,0];case 8===t:return[262,t-8,0];case 9===t:return[263,t-9,0];case 10===t:return[264,t-10,0];case t<=12:return[265,t-11,1];case t<=14:return[266,t-13,1];case t<=16:return[267,t-15,1];case t<=18:return[268,t-17,1];case t<=22:return[269,t-19,2];case t<=26:return[270,t-23,2];case t<=30:return[271,t-27,2];case t<=34:return[272,t-31,2];case t<=42:return[273,t-35,3];case t<=50:return[274,t-43,3];case t<=58:return[275,t-51,3];case t<=66:return[276,t-59,3];case t<=82:return[277,t-67,4];case t<=98:return[278,t-83,4];case t<=114:return[279,t-99,4];case t<=130:return[280,t-115,4];case t<=162:return[281,t-131,5];case t<=194:return[282,t-163,5];case t<=226:return[283,t-195,5];case t<=257:return[284,t-227,5];case 258===t:return[285,t-258,0];default:throw"invalid length: "+t}}(t),r[t]=e[2]<<24|e[1]<<16|e[0];return r}()),P.RawDeflate.Lz77Match.prototype.getDistanceCode_=function(t){var e;switch(!0){case 1===t:e=[0,t-1,0];break;case 2===t:e=[1,t-2,0];break;case 3===t:e=[2,t-3,0];break;case 4===t:e=[3,t-4,0];break;case t<=6:e=[4,t-5,1];break;case t<=8:e=[5,t-7,1];break;case t<=12:e=[6,t-9,2];break;case t<=16:e=[7,t-13,2];break;case t<=24:e=[8,t-17,3];break;case t<=32:e=[9,t-25,3];break;case t<=48:e=[10,t-33,4];break;case t<=64:e=[11,t-49,4];break;case t<=96:e=[12,t-65,5];break;case t<=128:e=[13,t-97,5];break;case t<=192:e=[14,t-129,6];break;case t<=256:e=[15,t-193,6];break;case t<=384:e=[16,t-257,7];break;case t<=512:e=[17,t-385,7];break;case t<=768:e=[18,t-513,8];break;case t<=1024:e=[19,t-769,8];break;case t<=1536:e=[20,t-1025,9];break;case t<=2048:e=[21,t-1537,9];break;case t<=3072:e=[22,t-2049,10];break;case t<=4096:e=[23,t-3073,10];break;case t<=6144:e=[24,t-4097,11];break;case t<=8192:e=[25,t-6145,11];break;case t<=12288:e=[26,t-8193,12];break;case t<=16384:e=[27,t-12289,12];break;case t<=24576:e=[28,t-16385,13];break;case t<=32768:e=[29,t-24577,13];break;default:throw"invalid distance"}return e},P.RawDeflate.Lz77Match.prototype.toLz77Array=function(){var t,e=this.length,r=this.backwardDistance,i=[],n=0;return t=P.RawDeflate.Lz77Match.LengthCodeTable[e],i[n++]=65535&t,i[n++]=t>>16&255,i[n++]=t>>24,t=this.getDistanceCode_(r),i[n++]=t[0],i[n++]=t[1],i[n++]=t[2],i},P.RawDeflate.prototype.lz77=function(t){function e(t,e){var r,i,n=t.toLz77Array();for(r=0,i=n.length;r0)o.push(r);else{for(;o.length>0&&r-o[0]>c;)o.shift();if(r+P.RawDeflate.Lz77MinLength>=i){for(f&&e(f,-1),n=0,s=i-r;n0?(h=this.searchLongestMatch_(t,r,o),f?f.lengthP.RawDeflate.Lz77MinLength){for(o=f;o>P.RawDeflate.Lz77MinLength;o--)if(t[i+o-1]!==t[e+o-1])continue t;s=f}for(;sf&&(n=i,f=s),s===P.RawDeflate.Lz77MaxLength)break}return new P.RawDeflate.Lz77Match(f,e-n)},P.RawDeflate.prototype.getTreeSymbols_=function(t,e,r,i){var n,s,a,o,h,f,u=new(_?Uint32Array:Array)(t+r),l=new(_?Uint32Array:Array)(316),c=new(_?Uint8Array:Array)(19);for(s=0,n=0;n0;)l[h++]=0,c[0]++;else for(;a>0;)f=a<138?a:138,f>a-3&&f0;)l[h++]=u[n],c[u[n]]++;else for(;a>0;)f=a<6?a:6,f>a-3&&f0&&h.push(s,t[s]);if(r=new Array(h.length/2),i=new(_?Uint32Array:Array)(h.length/2),1===r.length)return f[h.pop().index]=1,f;for(s=0,a=h.length/2;s2*f[s-1]+u[s]&&(f[s]=2*f[s-1]+u[s]),c[s]=new Array(f[s]),p[s]=new Array(f[s]);for(n=0;n=0;--s){for(n=0,o=0,h=d[s+1],a=0;at[n]?(c[s][a]=o,p[s][a]=e,h+=2):(c[s][a]=t[n],p[s][a]=n,++n);d[s]=0,1===u[s]&&i(s)}return l},P.RawDeflate.prototype.getCodesFromLengths_=function(t){var e,r,i,n,s=new(_?Uint16Array:Array)(t.length),a=[],o=[],h=0;for(e=0,r=t.length;e>>=1;return s};var H=P.Huffman.buildHuffmanTable;P.RawInflate=function(t,e){switch(this.buffer,this.blocks=[],this.bufferSize=ZLIB_RAW_INFLATE_BUFFER_SIZE,this.totalpos=0,this.ip=0,this.bitsbuf=0,this.bitsbuflen=0,this.input=_?new Uint8Array(t):t,this.output,this.op,this.bfinal=!1,this.bufferType=P.RawInflate.BufferType.ADAPTIVE,this.resize=!1,this.prev,!e&&(e={})||(e.index&&(this.ip=e.index),e.bufferSize&&(this.bufferSize=e.bufferSize),e.bufferType&&(this.bufferType=e.bufferType),e.resize&&(this.resize=e.resize)),this.bufferType){case P.RawInflate.BufferType.BLOCK:this.op=P.RawInflate.MaxBackwardLength,this.output=new(_?Uint8Array:Array)(P.RawInflate.MaxBackwardLength+this.bufferSize+P.RawInflate.MaxCopyLength);break;case P.RawInflate.BufferType.ADAPTIVE:this.op=0,this.output=new(_?Uint8Array:Array)(this.bufferSize),this.expandBuffer=this.expandBufferAdaptive,this.concatBuffer=this.concatBufferDynamic,this.decodeHuffman=this.decodeHuffmanAdaptive;break;default:throw new Error("invalid inflate mode")}},P.RawInflate.BufferType={BLOCK:0,ADAPTIVE:1},P.RawInflate.prototype.decompress=function(){for(;!this.bfinal;)this.parseBlock();return this.concatBuffer()},P.RawInflate.MaxBackwardLength=32768,P.RawInflate.MaxCopyLength=258,P.RawInflate.Order=function(t){return _?new Uint16Array(t):t}([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]),P.RawInflate.LengthCodeTable=function(t){return _?new Uint16Array(t):t}([3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,258,258]),P.RawInflate.LengthExtraTable=function(t){return _?new Uint8Array(t):t}([0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0]),P.RawInflate.DistCodeTable=function(t){return _?new Uint16Array(t):t}([1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577]),P.RawInflate.DistExtraTable=function(t){return _?new Uint8Array(t):t}([0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13]),P.RawInflate.FixedLiteralLengthTable=function(t){return t}(function(){var t,e,r=new(_?Uint8Array:Array)(288);for(t=0,e=r.length;t>>=1){case 0:this.parseUncompressedBlock();break;case 1:this.parseFixedHuffmanBlock();break;case 2:this.parseDynamicHuffmanBlock();break;default:throw new Error("unknown BTYPE: "+t)}},P.RawInflate.prototype.readBits=function(t){for(var e,r=this.bitsbuf,i=this.bitsbuflen,n=this.input,s=this.ip,a=n.length;i=a)throw new Error("input buffer is broken");r|=n[s++]<>>=t,i-=t,this.bitsbuf=r,this.bitsbuflen=i,this.ip=s,e},P.RawInflate.prototype.readCodeByTable=function(t){for(var e,r,i=this.bitsbuf,n=this.bitsbuflen,s=this.input,a=this.ip,o=s.length,h=t[0],f=t[1];n=o);)i|=s[a++]<>>16,this.bitsbuf=i>>r,this.bitsbuflen=n-r,this.ip=a,65535&e},P.RawInflate.prototype.parseUncompressedBlock=function(){var t,e,r,i=this.input,n=this.ip,s=this.output,a=this.op,o=i.length,h=s.length;if(this.bitsbuf=0,this.bitsbuflen=0,n+1>=o)throw new Error("invalid uncompressed block header: LEN");if(t=i[n++]|i[n++]<<8,n+1>=o)throw new Error("invalid uncompressed block header: NLEN");if(e=i[n++]|i[n++]<<8,t===~e)throw new Error("invalid uncompressed block header: length verify");if(n+t>i.length)throw new Error("input buffer is broken");switch(this.bufferType){case P.RawInflate.BufferType.BLOCK:for(;a+t>s.length;){if(r=h-a,t-=r,_)s.set(i.subarray(n,n+r),a),a+=r,n+=r;else for(;r--;)s[a++]=i[n++];this.op=a,s=this.expandBuffer(),a=this.op}break;case P.RawInflate.BufferType.ADAPTIVE:for(;a+t>s.length;)s=this.expandBuffer({fixRatio:2});break;default:throw new Error("invalid inflate mode")}if(_)s.set(i.subarray(n,n+t),a),a+=t,n+=t;else for(;t--;)s[a++]=i[n++];this.ip=n,this.op=a,this.output=s},P.RawInflate.prototype.parseFixedHuffmanBlock=function(){this.decodeHuffman(P.RawInflate.FixedLiteralLengthTable,P.RawInflate.FixedDistanceTable)},P.RawInflate.prototype.parseDynamicHuffmanBlock=function(){function t(t,e,r){var i,n,s,a=this.prev;for(s=0;s=h&&(this.op=i,r=this.expandBuffer(),i=this.op),r[i++]=n;else for(s=n-257,o=P.RawInflate.LengthCodeTable[s],P.RawInflate.LengthExtraTable[s]>0&&(o+=this.readBits(P.RawInflate.LengthExtraTable[s])),n=this.readCodeByTable(e),a=P.RawInflate.DistCodeTable[n],P.RawInflate.DistExtraTable[n]>0&&(a+=this.readBits(P.RawInflate.DistExtraTable[n])),i>=h&&(this.op=i,r=this.expandBuffer(),i=this.op);o--;)r[i]=r[i++-a];for(;this.bitsbuflen>=8;)this.bitsbuflen-=8,this.ip--;this.op=i},P.RawInflate.prototype.decodeHuffmanAdaptive=function(t,e){var r=this.output,i=this.op;this.currentLitlenTable=t;for(var n,s,a,o,h=r.length;256!==(n=this.readCodeByTable(t));)if(n<256)i>=h&&(r=this.expandBuffer(),h=r.length),r[i++]=n;else for(s=n-257,o=P.RawInflate.LengthCodeTable[s],P.RawInflate.LengthExtraTable[s]>0&&(o+=this.readBits(P.RawInflate.LengthExtraTable[s])),n=this.readCodeByTable(e),a=P.RawInflate.DistCodeTable[n],P.RawInflate.DistExtraTable[n]>0&&(a+=this.readBits(P.RawInflate.DistExtraTable[n])),i+o>h&&(r=this.expandBuffer(),h=r.length);o--;)r[i]=r[i++-a];for(;this.bitsbuflen>=8;)this.bitsbuflen-=8,this.ip--;this.op=i},P.RawInflate.prototype.expandBuffer=function(t){var e,r,i=new(_?Uint8Array:Array)(this.op-P.RawInflate.MaxBackwardLength),n=this.op-P.RawInflate.MaxBackwardLength,s=this.output;if(_)i.set(s.subarray(P.RawInflate.MaxBackwardLength,i.length));else for(e=0,r=i.length;ee&&(this.output.length=e),t=this.output),this.buffer=t,this.buffer},P.RawInflate=function(t,e){switch(this.buffer,this.blocks=[],this.bufferSize=32768,this.totalpos=0,this.ip=0,this.bitsbuf=0,this.bitsbuflen=0,this.input=_?new Uint8Array(t):t,this.output,this.op,this.bfinal=!1,this.bufferType=P.RawInflate.BufferType.ADAPTIVE,this.resize=!1,this.prev,!e&&(e={})||(e.index&&(this.ip=e.index),e.bufferSize&&(this.bufferSize=e.bufferSize),e.bufferType&&(this.bufferType=e.bufferType),e.resize&&(this.resize=e.resize)),this.bufferType){case P.RawInflate.BufferType.BLOCK:this.op=P.RawInflate.MaxBackwardLength,this.output=new(_?Uint8Array:Array)(P.RawInflate.MaxBackwardLength+this.bufferSize+P.RawInflate.MaxCopyLength);break;case P.RawInflate.BufferType.ADAPTIVE:this.op=0,this.output=new(_?Uint8Array:Array)(this.bufferSize),this.expandBuffer=this.expandBufferAdaptive,this.concatBuffer=this.concatBufferDynamic,this.decodeHuffman=this.decodeHuffmanAdaptive}},P.RawInflate.BufferType={BLOCK:0,ADAPTIVE:1},P.RawInflate.prototype.decompress=function(){for(;!this.bfinal;)this.parseBlock();return this.concatBuffer()},P.RawInflate.MaxBackwardLength=32768,P.RawInflate.MaxCopyLength=258,P.RawInflate.Order=function(t){return _?new Uint16Array(t):t}([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]),P.RawInflate.LengthCodeTable=function(t){return _?new Uint16Array(t):t}([3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,258,258]),P.RawInflate.LengthExtraTable=function(t){return _?new Uint8Array(t):t}([0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0]),P.RawInflate.DistCodeTable=function(t){return _?new Uint16Array(t):t}([1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577]),P.RawInflate.DistExtraTable=function(t){return _?new Uint8Array(t):t}([0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13]),P.RawInflate.FixedLiteralLengthTable=function(t){return t}(function(){var t,e,r=new(_?Uint8Array:Array)(288);for(t=0,e=r.length;t>>=1){case 0:this.parseUncompressedBlock();break;case 1:this.parseFixedHuffmanBlock();break;case 2:this.parseDynamicHuffmanBlock();break;default:throw new Error("unknown BTYPE: "+t)}},P.RawInflate.prototype.readBits=function(t){for(var e,r=this.bitsbuf,i=this.bitsbuflen,n=this.input,s=this.ip,a=n.length;i=a)throw new Error("input buffer is broken");r|=n[s++]<>>=t,i-=t,this.bitsbuf=r,this.bitsbuflen=i,this.ip=s,e},P.RawInflate.prototype.readCodeByTable=function(t){for(var e,r,i=this.bitsbuf,n=this.bitsbuflen,s=this.input,a=this.ip,o=s.length,h=t[0],f=t[1];n=o);)i|=s[a++]<>>16,this.bitsbuf=i>>r,this.bitsbuflen=n-r,this.ip=a,65535&e},P.RawInflate.prototype.parseUncompressedBlock=function(){var t,e,r,i=this.input,n=this.ip,s=this.output,a=this.op,o=i.length,h=s.length;if(this.bitsbuf=0,this.bitsbuflen=0,n+1>=o)throw new Error("invalid uncompressed block header: LEN");if(t=i[n++]|i[n++]<<8,n+1>=o)throw new Error("invalid uncompressed block header: NLEN");if(e=i[n++]|i[n++]<<8,t===~e)throw new Error("invalid uncompressed block header: length verify");if(n+t>i.length)throw new Error("input buffer is broken");switch(this.bufferType){case P.RawInflate.BufferType.BLOCK:for(;a+t>s.length;){if(r=h-a,t-=r,_)s.set(i.subarray(n,n+r),a),a+=r,n+=r;else for(;r--;)s[a++]=i[n++];this.op=a,s=this.expandBuffer(),a=this.op}break;case P.RawInflate.BufferType.ADAPTIVE:for(;a+t>s.length;)s=this.expandBuffer({fixRatio:2});break;default:throw new Error("invalid inflate mode")}if(_)s.set(i.subarray(n,n+t),a),a+=t,n+=t;else for(;t--;)s[a++]=i[n++];this.ip=n,this.op=a,this.output=s},P.RawInflate.prototype.parseFixedHuffmanBlock=function(){this.decodeHuffman(P.RawInflate.FixedLiteralLengthTable,P.RawInflate.FixedDistanceTable)},P.RawInflate.prototype.parseDynamicHuffmanBlock=function(){function t(t,e,r){var i,n,s,a=this.prev;for(s=0;s=h&&(this.op=i,r=this.expandBuffer(),i=this.op),r[i++]=n;else 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B.FSEvent,this.st=document.createElement("script"),this.st.type="text/javascript",this.st.src=t,e&&(this.st.id=e),this.br=new B.Browser,void 0!==this.st.addEventListener?this._loadOnOthers():void 0!==this.st.attachEvent&&this._loadOnIE()};if(U.prototype._loadOnIE=function(){var t=this,e=this.st;e.onreadystatechange=function(){3==e.readyState&&(e.onreadystatechange=function(){t.loadSuccess.fire(e.src),t.loadFailure=null},t.loadFailure&&t.loadFailure.fire(e.src))},document.body.appendChild(e)},U.prototype._loadOnOthers=function(){this.st.addEventListener("load",e.proxy(function(){this.loadSuccess.fire(this.st.src)},this),!1),this.st.addEventListener("error",e.proxy(function(){this.loadFailure.fire(this.st.src)},this),!1),document.body.appendChild(this.st)},!window.btoa){var M="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/",j=new Array(-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,62,-1,-1,-1,63,52,53,54,55,56,57,58,59,60,61,-1,-1,-1,-1,-1,-1,-1,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,-1,-1,-1,-1,-1,-1,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,-1,-1,-1,-1,-1);window.btoa=function(t){var e,r,i,n,s,a;for(i=t.length,r=0,e="";i>r;){if(n=255&t.charCodeAt(r++),r==i){e+=M.charAt(n>>2),e+=M.charAt((3&n)<<4),e+="==";break}if(s=t.charCodeAt(r++),r==i){e+=M.charAt(n>>2),e+=M.charAt((3&n)<<4|(240&s)>>4),e+=M.charAt((15&s)<<2),e+="=";break}a=t.charCodeAt(r++),e+=M.charAt(n>>2),e+=M.charAt((3&n)<<4|(240&s)>>4),e+=M.charAt((15&s)<<2|(192&a)>>6),e+=M.charAt(63&a)}return e},window.atob=function(t){var e,r,i,n,s,a,o;for(a=t.length,s=0,o="";a>s;){do{e=j[255&t.charCodeAt(s++)]}while(a>s&&-1==e);if(-1==e)break;do{r=j[255&t.charCodeAt(s++)]}while(a>s&&-1==r);if(-1==r)break;o+=String.fromCharCode(e<<2|(48&r)>>4);do{if(61==(i=255&t.charCodeAt(s++)))return o;i=j[i]}while(a>s&&-1==i);if(-1==i)break;o+=String.fromCharCode((15&r)<<4|(60&i)>>2);do{if(61==(n=255&t.charCodeAt(s++)))return o;n=j[n]}while(a>s&&-1==n);if(-1==n)break;o+=String.fromCharCode((3&i)<<6|n)}return o}}B.b64EncodeUnicode=function(t){return btoa(e.enc(t).replace(/%([0-9A-F]{2})/g,function(t,e){return String.fromCharCode("0x"+e)}))},B.b64DecodeUnicode=function(t){return decodeURIComponent(Array.prototype.map.call(atob(t).split(""),function(t){return"%"+("00"+t.charCodeAt(0).toString(16)).slice(-2)}).join(""))};var P={Util:{stringToByteArray:function(t){var e,r,i=t.split("");for(e=0,r=i.length;e>>0;return i}}};P.CompressionMethod={DEFLATE:8,RESERVED:15},P.BitStream=function(t,e){if(this.index="number"==typeof e?e:0,this.bitindex=0,this.buffer=t instanceof(_?Uint8Array:Array)?t:new(_?Uint8Array:Array)(P.BitStream.DefaultBlockSize),2*this.buffer.length<=this.index)throw new Error("invalid index");this.buffer.length<=this.index&&this.expandBuffer()},P.BitStream.DefaultBlockSize=32768,P.BitStream.prototype.expandBuffer=function(){var t,e=this.buffer,r=e.length,i=new(_?Uint8Array:Array)(r<<1);if(_)i.set(e);else for(t=0;t1&&(t=e>8?function(t){return P.BitStream.ReverseTable[255&t]<<24|P.BitStream.ReverseTable[t>>>8&255]<<16|P.BitStream.ReverseTable[t>>>16&255]<<8|P.BitStream.ReverseTable[t>>>24&255]}(t)>>32-e:P.BitStream.ReverseTable[t]>>8-e),e+a<8)o=o<>e-i-1&1,8==++a&&(a=0,n[s++]=P.BitStream.ReverseTable[o],o=0,s===n.length&&(n=this.expandBuffer()));n[s]=o,this.buffer=n,this.bitindex=a,this.index=s},P.BitStream.prototype.finish=function(){var t,e=this.buffer,r=this.index;return this.bitindex>0&&(e[r]<<=8-this.bitindex,e[r]=P.BitStream.ReverseTable[e[r]],r++),_?t=e.subarray(0,r):(e.length=r,t=e),t},P.BitStream.ReverseTable=function(t){return t}(function(){var t,e=new(_?Uint8Array:Array)(256);for(t=0;t<256;++t)e[t]=function(t){var e=t,r=7;for(t>>>=1;t;t>>>=1)e<<=1,e|=1&t,--r;return(e<>>0}(t);return e}()),P.Huffman={},P.Huffman.buildHuffmanTable=function(t){var e,r,i,n,s,a,o,h,f,u,l,c=t.length,p=0,d=Number.POSITIVE_INFINITY;for(h=0,f=c;hp&&(p=t[h]),t[h]>=1;for(l=i<<16|h,u=a;u0&&(i=this.getParent(r),s[r]>s[i]);)n=s[r],s[r]=s[i],s[i]=n,n=s[r+1],s[r+1]=s[i+1],s[i+1]=n,r=i;return this.length},P.Heap.prototype.pop=function(){var t,e,r,i,n,s=this.buffer;for(e=s[0],t=s[1],this.length-=2,s[0]=s[this.length],s[1]=s[this.length+1],n=0;;){if((i=this.getChild(n))>=this.length)break;if(i+2s[i]&&(i+=2),!(s[i]>s[n]))break;r=s[n],s[n]=s[i],s[i]=r,r=s[n+1],s[n+1]=s[i+1],s[i+1]=r,n=i}return{index:t,value:e,length:this.length}},P.RawDeflate=function(t,e){this.compressionType=P.RawDeflate.CompressionType.DYNAMIC,this.lazy=0,this.freqsLitLen,this.freqsDist,this.input=_&&t instanceof Array?new Uint8Array(t):t,this.output,this.op=0,e&&(e.lazy&&(this.lazy=e.lazy),"number"==typeof e.compressionType&&(this.compressionType=e.compressionType), +e.outputBuffer&&(this.output=_&&e.outputBuffer instanceof Array?new Uint8Array(e.outputBuffer):e.outputBuffer),"number"==typeof e.outputIndex&&(this.op=e.outputIndex)),this.output||(this.output=new(_?Uint8Array:Array)(32768))},P.RawDeflate.CompressionType={NONE:0,FIXED:1,DYNAMIC:2,RESERVED:3},P.RawDeflate.Lz77MinLength=3,P.RawDeflate.Lz77MaxLength=258,P.RawDeflate.WindowSize=32768,P.RawDeflate.MaxCodeLength=16,P.RawDeflate.HUFMAX=286,P.RawDeflate.FixedHuffmanTable=function(){var t,e=[];for(t=0;t<288;t++)switch(!0){case t<=143:e.push([t+48,8]);break;case t<=255:e.push([t-144+400,9]);break;case t<=279:e.push([t-256+0,7]);break;case t<=287:e.push([t-280+192,8]);break;default:throw"invalid literal: "+t}return e}(),P.RawDeflate.prototype.compress=function(){var t,e,r,i=this.input;switch(this.compressionType){case P.RawDeflate.CompressionType.NONE:for(e=0,r=i.length;e>>8&255,h[f++]=255&s,h[f++]=s>>>8&255,_)h.set(t,f),f+=t.length,h=h.subarray(0,f);else{for(a=0,o=t.length;a257&&0===h[s-1];s--);for(a=30;a>1&&0===u[a-1];a--);for(c=this.getTreeSymbols_(s,h,a,u),p=this.getLengths_(c.freqs,7),w=0;w<19;w++)x[w]=p[v[w]];for(o=19;o>4&&0===x[o-1];o--);for(d=this.getCodesFromLengths_(p),m.writeBits(s-257,5,!0),m.writeBits(a-1,5,!0),m.writeBits(o-4,4,!0),w=0;w=16){switch(w++,g){case 16:y=2;break;case 17:y=3;break;case 18:y=7;break;default:throw"invalid code: "+g}m.writeBits(c.codes[w],y,!0)}return this.dynamicHuffman(n,[f,h],[l,u],m),m.finish()},P.RawDeflate.prototype.dynamicHuffman=function(t,e,r,i){var n,s,a,o,h,f,u,l;for(h=e[0],f=e[1],u=r[0],l=r[1],n=0,s=t.length;n256)i.writeBits(t[++n],t[++n],!0),o=t[++n],i.writeBits(u[o],l[o],!0),i.writeBits(t[++n],t[++n],!0);else if(256===a)break;return i},P.RawDeflate.prototype.fixedHuffman=function(t,e){var r,i,n;for(r=0,i=t.length;r256)e.writeBits(t[++r],t[++r],!0),e.writeBits(t[++r],5),e.writeBits(t[++r],t[++r],!0);else if(256===n)break;return e},P.RawDeflate.Lz77Match=function(t,e){this.length=t,this.backwardDistance=e},P.RawDeflate.Lz77Match.LengthCodeTable=function(t){return _?new Uint32Array(t):t}(function(){var t,e,r=[];for(t=3;t<=258;t++)e=function(t){switch(!0){case 3===t:return[257,t-3,0];case 4===t:return[258,t-4,0];case 5===t:return[259,t-5,0];case 6===t:return[260,t-6,0];case 7===t:return[261,t-7,0];case 8===t:return[262,t-8,0];case 9===t:return[263,t-9,0];case 10===t:return[264,t-10,0];case t<=12:return[265,t-11,1];case t<=14:return[266,t-13,1];case t<=16:return[267,t-15,1];case t<=18:return[268,t-17,1];case t<=22:return[269,t-19,2];case t<=26:return[270,t-23,2];case t<=30:return[271,t-27,2];case t<=34:return[272,t-31,2];case t<=42:return[273,t-35,3];case t<=50:return[274,t-43,3];case t<=58:return[275,t-51,3];case t<=66:return[276,t-59,3];case t<=82:return[277,t-67,4];case t<=98:return[278,t-83,4];case t<=114:return[279,t-99,4];case t<=130:return[280,t-115,4];case t<=162:return[281,t-131,5];case t<=194:return[282,t-163,5];case t<=226:return[283,t-195,5];case t<=257:return[284,t-227,5];case 258===t:return[285,t-258,0];default:throw"invalid length: "+t}}(t),r[t]=e[2]<<24|e[1]<<16|e[0];return r}()),P.RawDeflate.Lz77Match.prototype.getDistanceCode_=function(t){var e;switch(!0){case 1===t:e=[0,t-1,0];break;case 2===t:e=[1,t-2,0];break;case 3===t:e=[2,t-3,0];break;case 4===t:e=[3,t-4,0];break;case t<=6:e=[4,t-5,1];break;case t<=8:e=[5,t-7,1];break;case t<=12:e=[6,t-9,2];break;case t<=16:e=[7,t-13,2];break;case t<=24:e=[8,t-17,3];break;case t<=32:e=[9,t-25,3];break;case t<=48:e=[10,t-33,4];break;case t<=64:e=[11,t-49,4];break;case t<=96:e=[12,t-65,5];break;case t<=128:e=[13,t-97,5];break;case t<=192:e=[14,t-129,6];break;case t<=256:e=[15,t-193,6];break;case t<=384:e=[16,t-257,7];break;case t<=512:e=[17,t-385,7];break;case t<=768:e=[18,t-513,8];break;case t<=1024:e=[19,t-769,8];break;case t<=1536:e=[20,t-1025,9];break;case t<=2048:e=[21,t-1537,9];break;case t<=3072:e=[22,t-2049,10];break;case t<=4096:e=[23,t-3073,10];break;case t<=6144:e=[24,t-4097,11];break;case t<=8192:e=[25,t-6145,11];break;case t<=12288:e=[26,t-8193,12];break;case t<=16384:e=[27,t-12289,12];break;case t<=24576:e=[28,t-16385,13];break;case t<=32768:e=[29,t-24577,13];break;default:throw"invalid distance"}return e},P.RawDeflate.Lz77Match.prototype.toLz77Array=function(){var t,e=this.length,r=this.backwardDistance,i=[],n=0;return t=P.RawDeflate.Lz77Match.LengthCodeTable[e],i[n++]=65535&t,i[n++]=t>>16&255,i[n++]=t>>24,t=this.getDistanceCode_(r),i[n++]=t[0],i[n++]=t[1],i[n++]=t[2],i},P.RawDeflate.prototype.lz77=function(t){function e(t,e){var r,i,n=t.toLz77Array();for(r=0,i=n.length;r0)o.push(r);else{for(;o.length>0&&r-o[0]>c;)o.shift();if(r+P.RawDeflate.Lz77MinLength>=i){for(f&&e(f,-1),n=0,s=i-r;n0?(h=this.searchLongestMatch_(t,r,o),f?f.lengthP.RawDeflate.Lz77MinLength){for(o=f;o>P.RawDeflate.Lz77MinLength;o--)if(t[i+o-1]!==t[e+o-1])continue t;s=f}for(;sf&&(n=i,f=s),s===P.RawDeflate.Lz77MaxLength)break}return new P.RawDeflate.Lz77Match(f,e-n)},P.RawDeflate.prototype.getTreeSymbols_=function(t,e,r,i){var n,s,a,o,h,f,u=new(_?Uint32Array:Array)(t+r),l=new(_?Uint32Array:Array)(316),c=new(_?Uint8Array:Array)(19);for(s=0,n=0;n0;)l[h++]=0,c[0]++;else for(;a>0;)f=a<138?a:138,f>a-3&&f0;)l[h++]=u[n],c[u[n]]++;else for(;a>0;)f=a<6?a:6,f>a-3&&f0&&h.push(s,t[s]);if(r=new Array(h.length/2),i=new(_?Uint32Array:Array)(h.length/2),1===r.length)return f[h.pop().index]=1,f;for(s=0,a=h.length/2;s2*f[s-1]+u[s]&&(f[s]=2*f[s-1]+u[s]),c[s]=new Array(f[s]),p[s]=new Array(f[s]);for(n=0;n=0;--s){for(n=0,o=0,h=d[s+1],a=0;at[n]?(c[s][a]=o,p[s][a]=e,h+=2):(c[s][a]=t[n],p[s][a]=n,++n);d[s]=0,1===u[s]&&i(s)}return l},P.RawDeflate.prototype.getCodesFromLengths_=function(t){var e,r,i,n,s=new(_?Uint16Array:Array)(t.length),a=[],o=[],h=0;for(e=0,r=t.length;e>>=1;return s};var H=P.Huffman.buildHuffmanTable;P.RawInflate=function(t,e){switch(this.buffer,this.blocks=[],this.bufferSize=ZLIB_RAW_INFLATE_BUFFER_SIZE,this.totalpos=0,this.ip=0,this.bitsbuf=0,this.bitsbuflen=0,this.input=_?new Uint8Array(t):t,this.output,this.op,this.bfinal=!1,this.bufferType=P.RawInflate.BufferType.ADAPTIVE,this.resize=!1,this.prev,!e&&(e={})||(e.index&&(this.ip=e.index),e.bufferSize&&(this.bufferSize=e.bufferSize),e.bufferType&&(this.bufferType=e.bufferType),e.resize&&(this.resize=e.resize)),this.bufferType){case P.RawInflate.BufferType.BLOCK:this.op=P.RawInflate.MaxBackwardLength,this.output=new(_?Uint8Array:Array)(P.RawInflate.MaxBackwardLength+this.bufferSize+P.RawInflate.MaxCopyLength);break;case P.RawInflate.BufferType.ADAPTIVE:this.op=0,this.output=new(_?Uint8Array:Array)(this.bufferSize),this.expandBuffer=this.expandBufferAdaptive,this.concatBuffer=this.concatBufferDynamic,this.decodeHuffman=this.decodeHuffmanAdaptive;break;default:throw new Error("invalid inflate mode")}},P.RawInflate.BufferType={BLOCK:0,ADAPTIVE:1},P.RawInflate.prototype.decompress=function(){for(;!this.bfinal;)this.parseBlock();return this.concatBuffer()},P.RawInflate.MaxBackwardLength=32768,P.RawInflate.MaxCopyLength=258,P.RawInflate.Order=function(t){return _?new Uint16Array(t):t}([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]),P.RawInflate.LengthCodeTable=function(t){return _?new Uint16Array(t):t}([3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,258,258]),P.RawInflate.LengthExtraTable=function(t){return _?new Uint8Array(t):t}([0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0]),P.RawInflate.DistCodeTable=function(t){return _?new Uint16Array(t):t}([1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577]),P.RawInflate.DistExtraTable=function(t){return _?new Uint8Array(t):t}([0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13]),P.RawInflate.FixedLiteralLengthTable=function(t){return t}(function(){var t,e,r=new(_?Uint8Array:Array)(288);for(t=0,e=r.length;t>>=1){case 0:this.parseUncompressedBlock();break;case 1:this.parseFixedHuffmanBlock();break;case 2:this.parseDynamicHuffmanBlock();break;default:throw new Error("unknown BTYPE: "+t)}},P.RawInflate.prototype.readBits=function(t){for(var e,r=this.bitsbuf,i=this.bitsbuflen,n=this.input,s=this.ip,a=n.length;i=a)throw new Error("input buffer is broken");r|=n[s++]<>>=t,i-=t,this.bitsbuf=r,this.bitsbuflen=i,this.ip=s,e},P.RawInflate.prototype.readCodeByTable=function(t){for(var e,r,i=this.bitsbuf,n=this.bitsbuflen,s=this.input,a=this.ip,o=s.length,h=t[0],f=t[1];n=o);)i|=s[a++]<>>16,this.bitsbuf=i>>r,this.bitsbuflen=n-r,this.ip=a,65535&e},P.RawInflate.prototype.parseUncompressedBlock=function(){var t,e,r,i=this.input,n=this.ip,s=this.output,a=this.op,o=i.length,h=s.length;if(this.bitsbuf=0,this.bitsbuflen=0,n+1>=o)throw new Error("invalid uncompressed block header: LEN");if(t=i[n++]|i[n++]<<8,n+1>=o)throw new Error("invalid uncompressed block header: NLEN");if(e=i[n++]|i[n++]<<8,t===~e)throw new Error("invalid uncompressed block header: length verify");if(n+t>i.length)throw new Error("input buffer is broken");switch(this.bufferType){case P.RawInflate.BufferType.BLOCK:for(;a+t>s.length;){if(r=h-a,t-=r,_)s.set(i.subarray(n,n+r),a),a+=r,n+=r;else for(;r--;)s[a++]=i[n++];this.op=a,s=this.expandBuffer(),a=this.op}break;case P.RawInflate.BufferType.ADAPTIVE:for(;a+t>s.length;)s=this.expandBuffer({fixRatio:2});break;default:throw new Error("invalid inflate mode")}if(_)s.set(i.subarray(n,n+t),a),a+=t,n+=t;else for(;t--;)s[a++]=i[n++];this.ip=n,this.op=a,this.output=s},P.RawInflate.prototype.parseFixedHuffmanBlock=function(){this.decodeHuffman(P.RawInflate.FixedLiteralLengthTable,P.RawInflate.FixedDistanceTable)},P.RawInflate.prototype.parseDynamicHuffmanBlock=function(){function t(t,e,r){var i,n,s,a=this.prev;for(s=0;s=h&&(this.op=i,r=this.expandBuffer(),i=this.op),r[i++]=n;else 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moduleName=map.name,moduleMap=makeModuleMap(moduleName),hasInteractive=useInteractive;textAlt&&(text=textAlt),hasInteractive&&(useInteractive=!1),getModule(moduleMap),hasProp(config.config,id)&&(config.config[moduleName]=config.config[id]);try{req.exec(text)}catch(e){return}hasInteractive&&(useInteractive=!0),this.depMaps.push(moduleMap),context.completeLoad(moduleName),localRequire([moduleName],load)},this),void plugin.load(map.name,localRequire,load,config))},this)),context.enable(pluginMap,this),this.pluginMaps[pluginMap.id]=pluginMap},enable:function(){enabledRegistry[this.map.id]=this,this.enabled=!0,this.enabling=!0,each(this.depMaps,proxy(function(depMap,i){var id,mod,handler;if("string"==typeof depMap){if(depMap=makeModuleMap(depMap,this.map.isDefine?this.map:this.map.parentMap,!1,!this.skipMap),this.depMaps[i]=depMap,handler=getOwn(handlers,depMap.id))return void(this.depExports[i]=handler(this));this.depCount+=1,on(depMap,"defined",proxy(function(depExports){this.defineDep(i,depExports),this.check()},this)),this.errback&&on(depMap,"error",proxy(this.errback,this))}id=depMap.id,mod=registry[id],hasProp(handlers,id)||!mod||mod.enabled||context.enable(depMap,this)},this)),eachProp(this.pluginMaps,proxy(this,function(pluginMap){var mod=getOwn(registry,pluginMap.id);mod&&!mod.enabled&&context.enable(pluginMap,this)},this)),this.enabling=!1,this.check()},on:function(name,cb){var cbs=this.events[name];cbs||(cbs=this.events[name]=[]),cbs.push(cb)},emit:function(name,evt){each(this.events[name],function(cb){cb(evt)}),"error"===name&&delete this.events[name]}},context={config:config,contextName:contextName,registry:registry,defined:defined,urlFetched:urlFetched,defQueue:defQueue,Module:Module,makeModuleMap:makeModuleMap,nextTick:req.nextTick,onError:onError,configure:function(cfg){cfg.baseUrl&&"/"!==cfg.baseUrl.charAt(cfg.baseUrl.length-1)&&(cfg.baseUrl+="/");var shim=config.shim,objs={paths:!0,bundles:!0,config:!0,map:!0};eachProp(cfg,function(value,prop){objs[prop]?(config[prop]||(config[prop]={}),mixin(config[prop],value,!0,!0)):config[prop]=value}),cfg.bundles&&eachProp(cfg.bundles,function(value,prop){each(value,function(v){v!==prop&&(bundlesMap[v]=prop)})}),cfg.shim&&(eachProp(cfg.shim,function(value,id){isArray(value)&&(value={deps:value}),!value.exports&&!value.init||value.exportsFn||(value.exportsFn=context.makeShimExports(value)),shim[id]=value}),config.shim=shim),cfg.packages&&each(cfg.packages,function(pkgObj){var location,name;pkgObj="string"==typeof pkgObj?{name:pkgObj}:pkgObj,name=pkgObj.name,location=pkgObj.location,location&&(config.paths[name]=pkgObj.location),config.pkgs[name]=pkgObj.name+"/"+(pkgObj.main||"main").replace(currDirRegExp,"").replace(jsSuffixRegExp,"")}),eachProp(registry,function(mod,id){mod.inited||mod.map.unnormalized||(mod.map=makeModuleMap(id))}),(cfg.deps||cfg.callback)&&context.require(cfg.deps||[],cfg.callback)},makeShimExports:function(value){function fn(){var ret;return value.init&&(ret=value.init.apply(global,arguments)),ret||value.exports&&getGlobal(value.exports)}return fn},makeRequire:function(relMap,options){function localRequire(deps,callback,errback){var id,map,requireMod;if(options.enableBuildCallback&&callback&&isFunction(callback)&&(callback.__requireJsBuild=!0),"string"==typeof deps){if(isFunction(callback))return;if(relMap&&hasProp(handlers,deps))return handlers[deps](registry[relMap.id]);if(req.get)return req.get(context,deps,relMap,localRequire);if(map=makeModuleMap(deps,relMap,!1,!0),id=map.id,!hasProp(defined,id))return;return defined[id]}return intakeDefines(),context.nextTick(function(){intakeDefines(),requireMod=getModule(makeModuleMap(null,relMap)),requireMod.skipMap=options.skipMap,requireMod.init(deps,callback,errback,{enabled:!0}),checkLoaded()}),localRequire}return options=options||{},mixin(localRequire,{toUrl:function(moduleNamePlusExt){var ext,index=moduleNamePlusExt.lastIndexOf("."),segment=moduleNamePlusExt.split("/")[0],isRelative="."===segment||".."===segment;return-1!==index&&(!isRelative||index>1)&&(ext=moduleNamePlusExt.substring(index,moduleNamePlusExt.length),moduleNamePlusExt=moduleNamePlusExt.substring(0,index)),context.nameToUrl(normalize(moduleNamePlusExt,relMap&&relMap.id,!0),ext,!0)},defined:function(id){return hasProp(defined,makeModuleMap(id,relMap,!1,!0).id)},specified:function(id){return id=makeModuleMap(id,relMap,!1,!0).id,hasProp(defined,id)||hasProp(registry,id)}}),relMap||(localRequire.undef=function(id){takeGlobalQueue();var map=makeModuleMap(id,relMap,!0),mod=getOwn(registry,id);removeScript(id),delete defined[id],delete urlFetched[map.url],delete undefEvents[id],eachReverse(defQueue,function(args,i){args[0]===id&&defQueue.splice(i,1)}),mod&&(mod.events.defined&&(undefEvents[id]=mod.events),cleanRegistry(id))}),localRequire},enable:function(depMap){getOwn(registry,depMap.id)&&getModule(depMap).enable()},completeLoad:function(moduleName){var found,args,mod,shim=getOwn(config.shim,moduleName)||{},shExports=shim.exports;for(takeGlobalQueue();defQueue.length;){if(args=defQueue.shift(),null===args[0]){if(args[0]=moduleName,found)break;found=!0}else args[0]===moduleName&&(found=!0);callGetModule(args)}if(mod=getOwn(registry,moduleName),!found&&!hasProp(defined,moduleName)&&mod&&!mod.inited){if(!(!config.enforceDefine||shExports&&getGlobal(shExports)))return void hasPathFallback(moduleName);callGetModule([moduleName,shim.deps||[],shim.exportsFn])}checkLoaded()},nameToUrl:function(moduleName,ext,skipExt){var paths,syms,i,parentModule,url,parentPath,bundleId,pkgMain=getOwn(config.pkgs,moduleName);if(pkgMain&&(moduleName=pkgMain),bundleId=getOwn(bundlesMap,moduleName))return context.nameToUrl(bundleId,ext,skipExt);if(req.jsExtRegExp.test(moduleName))url=moduleName+(ext||"");else{for(paths=config.paths,syms=moduleName.split("/"),i=syms.length;i>0;i-=1)if(parentModule=syms.slice(0,i).join("/"),parentPath=getOwn(paths,parentModule)){isArray(parentPath)&&(parentPath=parentPath[0]),syms.splice(0,i,parentPath);break}url=syms.join("/"),url+=ext||(/^data\:|\?/.test(url)||skipExt?"":".js"),url=("/"===url.charAt(0)||url.match(/^[\w\+\.\-]+:/)?"":config.baseUrl)+url}return config.urlArgs?url+(-1===url.indexOf("?")?"?":"&")+config.urlArgs:url},load:function(id,url){req.load(context,id,url)},execCb:function(name,callback,args,exports){return callback.apply(exports,args)},onScriptLoad:function(evt){if("load"===evt.type||readyRegExp.test((evt.currentTarget||evt.srcElement).readyState)){interactiveScript=null;var data=getScriptData(evt);context.completeLoad(data.id)}},onScriptError:function(evt){hasPathFallback(getScriptData(evt).id)}},context.require=context.makeRequire(),context}function getInteractiveScript(){return interactiveScript&&"interactive"===interactiveScript.readyState?interactiveScript:(eachReverse(getAllScripts(),function(script){if("interactive"===script.readyState)return interactiveScript=script}),interactiveScript)}function subtractFromURL(base,notches){var pref=base.substr(0,base.indexOf("//"))+"//",suff=base.substr(pref.length),dom=suff.substr(suff.indexOf("/")+1),tail=dom.substr(dom.lastIndexOf("/")+1);dom=dom.substr(0,dom.length-tail.length-1),suff=suff.substr(0,suff.indexOf("/"));var bits=dom.split("/");return bits.length-=Math.min(bits.length,notches),(pref+suff+"/"+bits.join("/")+tail).replace(/\/\/\//g,"//")}var _W=window,_D=_W.document,supportsDomStorage=!!_W.sessionStorage,skipInit=!1,_HD=_D.getElementsByTagName("head"),isOpera="undefined"!=typeof opera&&"[object Opera]"===opera.toString();if(!isOpera){_HD=_HD&&_HD.length>0?_HD[0]:_D.body;try{supportsDomStorage&&sessionStorage.setItem("_","")}catch(e){supportsDomStorage=!1}var globalConfig={},productConfig={}; +globalConfig = {"codeVer":"19.3.4","storage":"COOKIE","alwaysOnLatest":0,"deferredLoading":0,"brainUrl":"https://brain.foresee.com","recUrl":"https://record.foresee.com/rec/","surveyUrl":"https://survey.foreseeresults.com/survey/display","analyticsUrl":"https://analytics.foresee.com/ingest/events","staticUrl":"https://static.foresee.com","products":{"trigger":true},"modernSurveyUrl":"https://cxsurvey.foresee.com/sv","customerId":"xYpMVkA5xg9Bx5llpBsANQ==","surveyAsyncCurl":"s.foresee.com","siteKey":"worldbank-org","environment":"production"}; + +productConfig = {}; +productConfig.trigger = ({ "check": function () { var triggerconfig = { id: 'xYpMVkA5xg9Bx5llpBsANQ==', site_id: 'worldbank.org', surveyAsyncCurl: 'i.4see.mobi', hasReplay: 'false', triggerDelay: 0, inviteDelay: 0, repeatDays: { decline: 90, accept: 90 }, trackerConvertsAfter: 1000 * 10, trackerHeartbeatTimeout: 1000 * 10, trackerHeartbeatLongTimeout: 1000 * 12, onExitMobileHeartbeatInterval: 1000 * 60, reinviteDelayAfterInviteAbandon: 1000 * 60 * 60 * 24 * 90, workInIframes: 'dontRunOtherIframes', ignoreNavigationEvents: false, publicApiName: "FSR", globalExclude: { urls: [], referrers: [], userAgents: [], browsers: [], cookies: [], variables: [] }, inviteExclude: { urls: [], referrers: [], userAgents: [], browsers: [], cookies: [], variables: [] }, browser_cutoff: { IE: 10, Safari: 5.2, Firefox: 25, Chrome: 30, Opera: 1000 }, platform_cutoff: { Android: 4.4, Winphone: 9, iPod: 9, iPhone: 9, iPad: 9 }, device_blacklist: ['HTC_Rezound', 'blackberry'], replay_pools: [{ path: '.', sp: 100 }], replay_repools: [], cpps: { BetaSite: { source : 'url', patterns : [ { "regex" : 'beta', "value" : 'Y' } ], init: 'N' } } }; var surveydefs = 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campaign repeated"); + wb.campaign = cmpnId.substr(0,unescape(_satellite.readCookie('new_cidcookie')).length); + } + wb.campaign = wb.getValOnce(wb.campaign,'new_cidcookie'); +//} + +// Percent page View +wb.eVar9 = wb.prop7 = wb.getPreviousValue(wb.pageName,'s_ppn'); +if (wb.pageName) { + var ppv = wb.getPercentPageViewed(wb.pageName); +} +if (ppv && typeof ppv == 'object' && typeof ppv.length == 'number' && ppv.length > 1 && ppv[0] == wb.prop7) { + var initalPercentViewed = ppv[1]; + var finalPercentViewed = ppv[2]; + wb.prop16 = initalPercentViewed + '|' + finalPercentViewed; +} + +//Performance Timing +wb.performanceTiming(); + +//PageLoad time +s_getLoadTime(); + +//Get page load time +var pageLoadTime = s_getLoadTime()/10; +if(pageLoadTime >0 && pageLoadTime <= 90){ + wb.prop25=pageLoadTime; +}else if(pageLoadTime > 90){ + wb.prop25 = 90; +}else{ + wb.prop25 = '0'; +} + +//set device type +if (navigator.userAgent.match(/mobile/i) && navigator.userAgent.indexOf('iPad') == -1) { + wb.eVar30 = 'mobile'; +} else if (navigator.userAgent.match(/iPad|Android|Touch/i)) { + wb.eVar30 = 'tablet'; +} else { + wb.eVar30 = 'desktop'; +} + +//Screen Orientation +if(wb.eVar30 != "desktop"){ + if (screen.height < screen.width) { + wb.eVar27 = "landscape" + } else { + wb.eVar27 = "portrait" + } +} + +// Customized Link Tracking + +customLinkObject = wb.linkObject; +linkType = wb.linkType; +if(customLinkObject !== undefined && wb.linkType == "e"){ + //linkType = wb.linkType; + _satellite.getVar('CustomLinkClick'); +}else if(wb.linkType == "d"){ + //linkType = wb.linkType; + _satellite.getVar('DownloadClick'); + _satellite.getVar('FBPixelDownload'); + _satellite.getVar('TwitterPixelDownload'); + _satellite.getVar('GAdwordsTagDownload'); +} + +/* convert all the values to lowercase for consistent reporting purpose*/ +var hasNumber = /\d/; +for (var a = 1; a <= 100; a++) { + if (hasNumber.test(wb["prop" + a]) == false) { + wb["prop" + a] && (wb["prop" + a] = wb["prop" + a].toLowerCase().replace(/^d=/, 'D=')); + } + if (hasNumber.test(wb["eVar" + a]) == false) { + wb["eVar" + a] && (wb["eVar" + a] = wb["eVar" + a].toLowerCase().replace(/^d=/, 'D=')); + } + if (hasNumber.test(wb["hier" + a]) == false) { + wb["hier" + a] && (wb["hier" + a] = wb["hier" + a].toLowerCase().replace(/^d=/, 'D=')); + } +}; +for (var b = ["products", "pageName", "channel", "campaign"], a = 0; a < b.length; a++) { + if (hasNumber.test(wb[b[a]]) == false) { + wb[b[a]] && (wb[b[a]] = wb[b[a]].toLowerCase().replace(/^d=/, "D=")); + } +}; + +} +wb.doPlugins = s_doPlugins +wb.trackingServer="worldbankgroup.sc.omtrdc.net"; +//wb.trackingServerSecure="wbgroup.omtrdc.net"; + + +/* +===============Plugin section Start============ +*/ + +/* plugin: //Page load time definition */ + +function s_getLoadTime() +{ +if(!window.s_loadT) +{ +var b=new Date().getTime(), +o=window.performance?performance.timing:0,a=o?o.requestStart:window.inHeadTS||0; +s_loadT=a?Math.round((b-a)/100):'' +} +return s_loadT +} + +/* + * Plugin: downloadLinkHandler 0.8 - identify and report download links + */ +wb.downloadLinkHandler=new Function("p","e","" ++"var s=this,o=s.p_gh(),h=o.href,n='linkDownloadFileTypes',i,t;if(!h|" ++"|(s.linkType&&(h||s.linkName)))return'';i=h.indexOf('?');t=s[n];s[n" ++"]=p?p:t;if(s.lt(h)=='d')s.linkType='d';else h='';s[n]=t;return e?o:" ++"h;"); +wb.p_gh=new Function("","" ++"var s=this;if(!s.eo&&!s.lnk)return'';var o=s.eo?s.eo:s.lnk,y=s.ot(o" ++"),n=s.oid(o),x=o.s_oidt;if(s.eo&&o==s.eo){while(o&&!n&&y!='BODY'){o" ++"=o.parentElement?o.parentElement:o.parentNode;if(!o)return'';y=s.ot" ++"(o);n=s.oid(o);x=o.s_oidt;}}return o?o:'';"); + +/* + * Plugin: getTimeParting 3.4 + */ +wb.getTimePartingNew=new Function("h","z","" ++"var s=this,od;od=new Date('1/1/2000');if(od.getDay()!=6||od.getMont" ++"h()!=0){return'Data Not Available';}else{var H,M,D,U,ds,de,tm,da=['" ++"Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturda" ++"y'],d=new Date();z=z?z:0;z=parseFloat(z);if(s._tpDST){var dso=s._tp" ++"DST[d.getFullYear()].split(/,/);ds=new Date(dso[0]+'/'+d.getFullYea" ++"r());de=new Date(dso[1]+'/'+d.getFullYear());if(h=='n'&&d>ds&&dde||d=12){U=' P" ++"M';H=H-12;}if(H==0){H=12;}D=da[D];tm=H+':'+M+U;return(tm+'|'+D);}"); + + +/* + * Plugin: getNewRepeat 1.2 - Returns whether user is new or repeat + */ +wb.getNewRepeat=new Function("d","cn","" ++"var s=this,e=new Date(),cval,sval,ct=e.getTime();d=d?d:30;cn=cn?cn:" ++"'s_nr';e.setTime(ct+d*24*60*60*1000);cval=s.c_r(cn);if(cval.length=" ++"=0){s.c_w(cn,ct+'-New',e);return'New';}sval=s.split(cval,'-');if(ct" ++"-sval[0]<30*60*1000&&sval[1]=='New'){s.c_w(cn,ct+'-New',e);return'N" ++"ew';}else{s.c_w(cn,ct+'-Repeat',e);return'Repeat';}"); + + +/* + * Plugin: getAndPersistValue 0.3 - get a value on every page + */ +wb.getAndPersistValue=new Function("v","c","e","" ++"var s=this,a=new Date;e=e?e:0;a.setTime(a.getTime()+e*86400000);if(" ++"v)s.c_w(c,v,e?a:0);return s.c_r(c);"); + + +/* Utility Function: split v1.5 - split a string (JS 1.0 compatible) */ +wb.split = new Function("l", "d", "" ++ "var i,x=0,a=new Array;while(l){i=l.indexOf(d);i=i>-1?i:l.length;a[x" ++ "++]=l.substring(0,i);l=l.substring(i+d.length);}return a"); + +/* + * Plugin Utility: Append to List v1.2 + */ +wb.apl=new Function("l","v","d","u","" ++"var s=this,m=0;if(!l)l='';if(u){var i,n,a=l.split(d),al=a.length;fo" ++"r(i=0;i=0){x=x.substring(0,i)+n+x." ++ "substring(i+o.length);i=x.indexOf(o,i+l)}return x"); + +/* + * Plugin Utility: Join v1.0 + */ +wb.join=new Function("v","p","" ++"var s=this;var f,b,d,w;if(p){f=p.front?p.front:'';b=p.back?p.back" ++":'';d=p.delim?p.delim:'';w=p.wrap?p.wrap:'';}var str='';for(var x=0" ++";x-1?c.split(',',10):[''],i;a" ++"[0]=o?unescape(a[0]||''):I;for(i=1;i<9&&(i9||!o)a[9]=a[9]&&a[9]!='L'&&a[9" ++"]!='LP'&&a[9]!='PL'?'P':a[9];return a};s.c_w(P,s.c_r(K)||'');s.c_w(" ++"K,escape(I)+',0,0,0,0,0,0,0,0');W.s_PPVevent=function(e){var W=wind" ++"ow,D=document||{},B=D.body,E=D.documentElement||{},S=window.screen|" ++"|{},Ho='offsetHeight',Hs='scrollHeight',Ts='scrollTop',Wc='clientWi" ++"dth',Hc='clientHeight',M=Math,C=100,J='object',N='number',Z=',',s=W" ++".s_Obj||W.s||0;e=e&&typeof e==J?e.type||'':'';if(!e.indexOf('on'))e" ++"=e.substring(2);if(W.s_PPVt&&!e){clearTimeout(s_PPVt);s_PPVt=0}if(s" ++"&&typeof s==J&&B&&typeof B==J){var h=M.max(B[Hs]||E[Hs],B[Ho]||E[Ho" ++"],B[Hc]||E[Hc]||1),X=W.innerWidth||E[Wc]||B[Wc]||1,Y=W.innerHeight|" ++"|E[Hc]||B[Hc]||1,x=S.width||1,y=S.height||1,r=M.round(C*(W.devicePi" ++"xelRatio||1))/C,b=(D.pageYOffset||E[Ts]||B[Ts]||0)+Y,p=h>0&&b>0?M.r" ++"ound(C*b/h):1,O=W.orientation,o=!isNaN(O)?M.abs(o)%180:Y>X?0:90,a=s" ++".s_PPVg(n),L=(e=='load')||(a[1]<1),t,V=function(u,v,f,n){v=typeof v" ++"!=N?u:v;v=f||(u>v)?u:v;return n?v:v>C?C:v<0?0:v};if(new RegExp('(iP" ++"od|iPad|iPhone)').exec((window.navigator&&navigator.userAgent)||'')" ++"&&o){t=x;x=y;y=t}o=o?'L':'P';a[9]=L||!a[9]?o:a[9].substring(0,1);if" ++"(a[9]!='L'&&a[9]!='P')a[9]=o;s.c_w(K,escape(a[0])+Z+V(a[1],p,!L)+Z+" ++"V(a[2],p,L)+Z+V(a[3],b,L,1)+Z+X+Z+Y+Z+x+Z+y+Z+r+Z+a[9]+(a[9]==o?'':" ++"o))}if(!W.s_PPVt&&e!='unload')W.s_PPVt=setTimeout(W.s_PPVevent,333)" ++"};for(var f=W.s_PPVevent,i=0;i-1)return n?n:'[[';}" ++ "return 0;"); + +/* + * Plugin Utility: pt - runs function in f argument against list of + * variables declared in x (delimited by d), with a as an optional + * argument to be included in f function call + */ +wb.pt=new Function("x","d","f","a","" ++"var s=this,t=x,z=0,y,r;while(t){y=t.indexOf(d);y=y<0?t.length:y;t=t" ++".substring(0,y);r=s[f](t,a);if(r)return r;z+=y+d.length;t=x.substri" ++"ng(z,x.length);t=z30*60*1000){if(d>30*da" ++"y){s.c_w(c,ct,e);s.c_w(c+'_s',f2,es);}else if(d<30*day+1 && d>7*day" ++"){s.c_w(c,ct,e);s.c_w(c+'_s',f3,es);}else if(d<7*day+1 && d>day){s." ++"c_w(c,ct,e);s.c_w(c+'_s',f4,es);}else if(d-1)return n?n:'[[';}" ++ "return 0;"); + + + /* + * Plugin: getVisitNum - version 3.0 + */ + wb.getVisitNum=new Function("tp","c","c2","" + +"var s=this,e=new Date,cval,cvisit,ct=e.getTime(),d;if(!tp){tp='m';}" + +"if(tp=='m'||tp=='w'||tp=='d'){eo=s.endof(tp),y=eo.getTime();e.setTi" + +"me(y);}else {d=tp*86400000;e.setTime(ct+d);}if(!c){c='s_vnum';}if(!" + +"c2){c2='s_invisit';}cval=s.c_r(c);if(cval){var i=cval.indexOf('&vn=" + +"'),str=cval.substring(i+4,cval.length),k;}cvisit=s.c_r(c2);if(cvisi" + +"t){if(str){e.setTime(ct+1800000);s.c_w(c2,'true',e);return str;}els" + +"e {return 'unknown visit number';}}else {if(str){str++;k=cval.substri" + +"ng(0,i);e.setTime(k);s.c_w(c,k+'&vn='+str,e);e.setTime(ct+1800000);" + +"s.c_w(c2,'true',e);return str;}else {s.c_w(c,e.getTime()+'&vn=1',e)" + +";e.setTime(ct+1800000);s.c_w(c2,'true',e);return 1;}}"); + wb.dimo=new Function("m","y","" + +"var d=new Date(y,m+1,0);return d.getDate();"); + wb.endof=new Function("x","" + +"var t=new Date;t.setHours(0);t.setMinutes(0);t.setSeconds(0);if(x==" + +"'m'){d=s.dimo(t.getMonth(),t.getFullYear())-t.getDate()+1;}else if(" + +"x=='w'){d=7-t.getDay();}else {d=1;}t.setDate(t.getDate()+d);return " + +"t;"); + + +/* Plugin: Performance Timing Tracking - 0.11 BETA */ +wb.performanceTiming=new Function("v","" ++"var s=this;if(v)s.ptv=v;if(typeof performance!='undefined'){if(perf" ++"ormance.timing.loadEventEnd==0){s.pi=setInterval(function(){s.perfo" ++"rmanceWrite()},250);}if(!s.ptc||s.linkType=='e'){s.performanceRead(" ++");}else{s.rfe();s[s.ptv]='';}}"); +wb.performanceWrite=new Function("","" ++"var s=this;if(performance.timing.loadEventEnd>0)clearInterval(s.pi)" ++";try{if(s.c_r('s_ptc')==''&&performance.timing.loadEventEnd>0){try{" ++"var pt=performance.timing;var pta='';pta=s.performanceCheck(pt.fetc" ++"hStart,pt.navigationStart);pta+='^^'+s.performanceCheck(pt.domainLo" ++"okupStart,pt.fetchStart);pta+='^^'+s.performanceCheck(pt.domainLook" ++"upEnd,pt.domainLookupStart);pta+='^^'+s.performanceCheck(pt.connect" ++"End,pt.connectStart);pta+='^^'+s.performanceCheck(pt.responseStart," ++"pt.connectEnd);pta+='^^'+s.performanceCheck(pt.responseEnd,pt.respo" ++"nseStart);pta+='^^'+s.performanceCheck(pt.loadEventStart,pt.domLoad" ++"ing);pta+='^^'+s.performanceCheck(pt.loadEventEnd,pt.loadEventStart" ++");pta+='^^'+s.performanceCheck(pt.loadEventEnd,pt.navigationStart);" ++"s.c_w('s_ptc',pta);if(sessionStorage&&navigator.cookieEnabled&&s.pt" ++"v!='undefined'){var pe=performance.getEntries();var tempPe='';for(v" ++"ar i=0;i-1?pe[i].name.split('?')[0]:pe[i].name;tempPe+='|'+(Math.round(pe[" ++"i].startTime)/1000).toFixed(1)+'|'+(Math.round(pe[i].duration)/1000" ++").toFixed(1)+'|'+pe[i].initiatorType;}sessionStorage.setItem('s_pec" ++"',tempPe);}}catch(err){return;}}}catch(err){return;}"); +wb.performanceCheck=new Function("a","b","" ++"if(a>=0&&b>=0){if((a-b)<60000&&((a-b)>=0)){return((a-b)/1000).toFix" ++"ed(2);}else{return 600;}}"); +wb.performanceRead=new Function("","" ++"var s=this;if(performance.timing.loadEventEnd>0)clearInterval(s.pi)" ++";var cv=s.c_r('s_ptc');if(s.pte){var ela=s.pte.split(',');}if(cv!='" ++"'){var cva=s.split(cv,'^^');if(cva[1]!=''){for(var x=0;x<(ela.lengt" ++"h-1);x++){s.events=s.apl(s.events,ela[x]+'='+cva[x],',',2);}}s.even" ++"ts=s.apl(s.events,ela[ela.length-1],',',2);}s.linkTrackEvents=s.apl" ++"(s.linkTrackEvents,s.pte,',',2);s.c_w('s_ptc','',0);if(sessionStora" ++"ge&&navigator.cookieEnabled&&s.ptv!='undefined'){s[s.ptv]=sessionSt" ++"orage.getItem('s_pec');sessionStorage.setItem('s_pec','',0);}else{s" ++"[s.ptv]='sessionStorage Unavailable';}s.ptc=true;"); +/* Remove from Events 0.1 - Performance Specific, +removes all performance events from s.events once being tracked. */ +wb.rfe=new Function("","" ++"var s=this;var ea=s.split(s.events,',');var pta=s.split(s.pte,',');" ++"try{for(x in pta){s.events=s.rfl(s.events,pta[x]);s.contextData['ev" ++"ents']=s.events;}}catch(e){return;}"); +/* Plugin Utility - RFL (remove from list) 1.0*/ +wb.rfl=new Function("l","v","d1","d2","ku","" ++"var s=this,R=new Array(),C='',d1=!d1?',':d1,d2=!d2?',':d2,ku=!ku?0:" ++"1;if(!l)return'';L=l.split(d1);for(i=0;i-1){C=L[i].split(':');C[1]=C[0]+':'+C[1];L[i]=C[0];}if(L[i" ++"].indexOf('=')>-1){C=L[i].split('=');C[1]=C[0]+'='+C[1];L[i]=C[0];}" ++"if(L[i]!=v&&C)R.push(C[1]);else if(L[i]!=v)R.push(L[i]);else if(L[i" ++"]==v&&ku){ku=0;if(C)R.push(C[1]);else R.push(L[i]);}C='';}return s." ++"join(R,{delim:d2})"); + +/*utility function to map language code with language*/ +function langMapping(langCode) { + var mappedLang; + var paramMap = { + 'ab': 'abkhazian', + 'aa': 'afar', + 'af': 'afrikaans', + 'ak': 'akan', + 'sq': 'albanian', + 'am': 'amharic', + 'ar': 'arabic', + 'an': 'aragonese', + 'hy': 'armenian', + 'as': 'assamese', + 'av': 'avaric', + 'ae': 'avestan', + 'ay': 'aymara', + 'az': 'azerbaijani', + 'bm': 'bambara', + 'ba': 'bashkir', + 'eu': 'basque', + 'be': 'belarusian', + 'bn': 'bengali', + 'bh': 'bihari languages', + 'bi': 'bislama', + 'bs': 'bosnian', + 'br': 'breton', + 'bg': 'bulgarian', + 'my': 'burmese', + 'ca': 'catalan', + 'ch': 'chamorro', + 'ce': 'chechen', + 'ny': 'chichewa', + 'zh': 'chinese', + 'cv': 'chuvash', + 'kw': 'cornish', + 'co': 'corsican', + 'cr': 'cree', + 'hr': 'croatian', + 'cs': 'czech', + 'da': 'danish', + 'dv': 'divehi', + 'nl': 'dutch', + 'dz': 'dzongkha', + 'en': 'english', + 'eo': 'esperanto', + 'et': 'estonian', + 'ee': 'ewe', + 'fo': 'faroese', + 'fj': 'fijian', + 'fi': 'finnish', + 'fr': 'french', + 'ff': 'fulah', + 'gl': 'galician', + 'ka': 'georgian', + 'de': 'german', + 'el': 'greek (modern)', + 'gn': 'guaraní', + 'gu': 'gujarati', + 'ht': 'haitian', + 'ha': 'hausa', + 'he': 'hebrew (modern)', + 'hz': 'herero', + 'hi': 'hindi', + 'ho': 'hiri motu', + 'hu': 'hungarian', + 'ia': 'interlingua', + 'id': 'indonesian', + 'ie': 'interlingue', + 'ga': 'irish', + 'ig': 'igbo', + 'ik': 'inupiaq', + 'io': 'ido', + 'is': 'icelandic', + 'it': 'italian', + 'iu': 'inuktitut', + 'ja': 'japanese', + 'jv': 'javanese', + 'kl': 'kalaallisut', + 'kn': 'kannada', + 'kr': 'kanuri', + 'ks': 'kashmiri', + 'kk': 'kazakh', + 'km': 'central khmer', + 'ki': 'kikuyu', + 'rw': 'kinyarwanda', + 'ky': 'kirghiz', + 'kv': 'komi', + 'kg': 'kongo', + 'ko': 'korean', + 'ku': 'kurdish', + 'kj': 'kuanyama', + 'la': 'latin', + 'lb': 'luxembourgish', + 'lg': 'ganda', + 'li': 'limburgan', + 'ln': 'lingala', + 'lo': 'lao', + 'lt': 'lithuanian', + 'lu': 'luba-katanga', + 'lv': 'latvian', + 'gv': 'manx', + 'mk': 'macedonian', + 'mg': 'malagasy', + 'ms': 'malay', + 'ml': 'malayalam', + 'mt': 'maltese', + 'mi': 'maori', + 'mr': 'marathi', + 'mh': 'marshallese', + 'mn': 'mongolian', + 'na': 'nauru', + 'nv': 'navajo', + 'nd': 'north ndebele', + 'ne': 'nepali', + 'ng': 'ndonga', + 'nb': 'norwegian bokmål', + 'nn': 'norwegian nynorsk', + 'no': 'norwegian', + 'ii': 'sichuan yi', + 'nr': 'south ndebele', + 'oc': 'occitan', + 'oj': 'ojibwa', + 'cu': 'church slavic', + 'om': 'oromo', + 'or': 'oriya', + 'os': 'ossetian', + 'pa': 'panjabi', + 'pi': 'pali', + 'fa': 'persian', + 'pl': 'polish', + 'ps': 'pashto', + 'pt': 'portuguese', + 'qu': 'quechua', + 'rm': 'romansh', + 'rn': 'rundi', + 'ro': 'romanian', + 'ru': 'russian', + 'sa': 'sanskrit', + 'sc': 'sardinian', + 'sd': 'sindhi', + 'se': 'northern sami', + 'sm': 'samoan', + 'sg': 'sango', + 'sr': 'serbian', + 'gd': 'gaelic', + 'sn': 'shona', + 'si': 'sinhala', + 'sk': 'slovak', + 'sl': 'slovenian', + 'so': 'somali', + 'st': 'southern sotho', + 'es': 'spanish', + 'su': 'sundanese', + 'sw': 'swahili', + 'ss': 'swati', + 'sv': 'swedish', + 'ta': 'tamil', + 'te': 'telugu', + 'tg': 'tajik', + 'th': 'thai', + 'ti': 'tigrinya', + 'bo': 'tibetan', + 'tk': 'turkmen', + 'tl': 'tagalog', + 'tn': 'tswana', + 'to': 'tonga (tonga islands)', + 'tr': 'turkish', + 'ts': 'tsonga', + 'tt': 'tatar', + 'tw': 'twi', + 'ty': 'tahitian', + 'ug': 'uighur', + 'uk': 'ukrainian', + 'ur': 'urdu', + 'uz': 'uzbek', + 've': 'venda', + 'vi': 'vietnamese', + 'vo': 'volapük', + 'wa': 'walloon', + 'cy': 'welsh', + 'wo': 'wolof', + 'fy': 'western frisian', + 'xh': 'xhosa', + 'yi': 'yiddish', + 'yo': 'yoruba', + 'za': 'zhuang', + 'zu': 'zulu' + + }; + + if(typeof paramMap[langCode] !== "undefined"){ + mappedLang = paramMap[langCode]; + }else{ + mappedLang = langCode; + } + return mappedLang; +} +/* Utility function to map the language name to its language code */ +function langMappingToCodes(language) { + var mappedLang; + var paramMap = { + 'abkhazian': 'ab', + 'afar': 'aa', + 'afrikaans': 'af', + 'akan': 'ak', + 'albanian': 'sq', + 'amharic': 'am', + 'arabic': 'ar', + 'aragonese': 'an', + 'armenian': 'hy', + 'assamese': 'as', + 'avaric': 'av', + 'avestan': 'ae', + 'aymara': 'ay', + 'azerbaijani': 'az', + 'bambara': 'bm', + 'bashkir': 'ba', + 'basque': 'eu', + 'belarusian': 'be', + 'bengali': 'bn', + 'biharilanguages': 'bh', + 'bislama': 'bi', + 'bosnian': 'bs', + 'breton': 'br', + 'bulgarian': 'bg', + 'burmese': 'my', + 'catalan': 'ca', + 'chamorro': 'ch', + 'chechen': 'ce', + 'chichewa': 'ny', + 'chinese': 'zh', + 'chuvash': 'cv', + 'cornish': 'kw', + 'corsican': 'co', + 'cree': 'cr', + 'croatian': 'hr', + 'czech': 'cs', + 'danish': 'da', + 'divehi': 'dv', + 'dutch': 'nl', + 'dzongkha': 'dz', + 'english': 'en', + 'esperanto': 'eo', + 'estonian': 'et', + 'ewe': 'ee', + 'faroese': 'fo', + 'fijian': 'fj', + 'finnish': 'fi', + 'french': 'fr', + 'fulah': 'ff', + 'galician': 'gl', + 'georgian': 'ka', + 'german': 'de', + 'greek(modern)': 'el', + 'guaraní': 'gn', + 'gujarati': 'gu', + 'haitian': 'ht', + 'hausa': 'ha', + 'hebrew(modern)': 'he', + 'herero': 'hz', + 'hindi': 'hi', + 'hirimotu': 'ho', + 'hungarian': 'hu', + 'interlingua': 'ia', + 'indonesian': 'id', + 'interlingue': 'ie', + 'irish': 'ga', + 'igbo': 'ig', + 'inupiaq': 'ik', + 'ido': 'io', + 'icelandic': 'is', + 'italian': 'it', + 'inuktitut': 'iu', + 'japanese': 'ja', + 'javanese': 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6"))a.Oa=!0}}catch(x){}a.Z=function(){a.aa&&(k.clearTimeout(a.aa),a.aa=p);a.k&&a.K&&a.k.dispatchEvent(a.K);a.v&&("function"==typeof a.v?a.v(): +a.k&&a.k.href&&(a.d.location=a.k.href));a.k=a.K=a.v=0};a.Ma=function(){a.b=a.d.body;a.b?(a.u=function(c){var b,d,f,e,g;if(!(a.d&&a.d.getElementById("cppXYctnr")||c&&c["s_fe_"+a._in])){if(a.wa)if(a.useForcedLinkTracking)a.b.removeEventListener("click",a.u,!1);else{a.b.removeEventListener("click",a.u,!0);a.wa=a.useForcedLinkTracking=0;return}else a.useForcedLinkTracking=0;a.clickObject=c.srcElement?c.srcElement:c.target;try{if(!a.clickObject||a.N&&a.N==a.clickObject||!(a.clickObject.tagName||a.clickObject.parentElement|| +a.clickObject.parentNode))a.clickObject=0;else{var h=a.N=a.clickObject;a.da&&(clearTimeout(a.da),a.da=0);a.da=setTimeout(function(){a.N==h&&(a.N=0)},1E4);f=a.Da();a.track();if(f -1) s.events=s.apl(s.events,"event8",",",2); + + /* Download Tracking */ + var url=s.downloadLinkHandler(); if(url){ + s.prop21 = url.substring(url.lastIndexOf("/")+1,url.length); + s.eVar21 = url; + s.events=s.apl(s.events,"event12",",",2); + s.prop22 = "D=pageName"; + s.prop23 = s.prevPageName; + s.linkTrackVars="prop1,prop4,prop6,prop7,prop10,prop14,prop17,prop21,prop22,prop23,prop24,prop25,prop26,prop27,prop53,prop54,prop57,prop60,prop69,prop70,prop75,eVar4,eVar6,eVar7,eVar12,eVar17,eVar21,eVar23,eVar24,eVar25,eVar26,eVar47,eVar58,products"; + s.linkTrackEvents="event12,event19"; + } + else { + if(s.events.match(/,*?event12\b/)==null) s.prevPageName = s.getPreviousValue(s.pageName,'gpv_p5',''); + } + + s.prop27 = s.getNewRepeat(); + //Determine bounce rate for all visits + s.visitstart=s.getVisitStart('s_vs'); + if(s.visitstart&&s.visitstart==1){ + s.firstPage='firstpage'; + } + s.clickPast(s.firstPage,'event23','event24'); + + /* S_Code version */ + s.prop55 = SCODEVERSION; + + /* Campaign Pathing */ + if(s.campaign) + s.prop61 = 'D=v0+"|"+pageName'; + else + s.prop61 = "D=pageName"; + + /* URL */ + s.eVar75 = "D=g"; + + var tpA = s.getTimeParting('n','-5'); + s.prop59 = tpA[2]; + + s.prop72 = 'D=c53+":"+pageName'; + s.prop73 = 'D=c53+":"+c4+":"+pageName' + s.prop74 = 'D=c53+":"+c4'; + s.prop75 = 'D=g'; + + /*prop to eVar copies */ + s.eVar1="D=c1"; + s.eVar2="D=c15"; + s.eVar4="D=c4"; + s.eVar5="D=c5"; + s.eVar6="D=c6"; + s.eVar7="D=c7"; + s.eVar8="D=c8"; + s.eVar9="D=c9"; + s.eVar12="D=c60"; + s.eVar13="D=c13"; + s.eVar15="D=c2"; + s.eVar16="D=c16"; + s.eVar17="D=c17"; + s.eVar18="D=c18"; + s.eVar19="D=c19"; + s.eVar20="D=c20"; + + s.eVar23="D=c14"; // Section + s.eVar24="D=c24"; + s.eVar25="D=c25"; + s.eVar26="D=c26"; + s.eVar27="D=c71"; // Multimedia type + //s.eVar29=""; // Hold + + s.eVar30="D=pageName"; + + + s.eVar36="D=c27"; // New vs repeat + + s.eVar43 = "D=c59"; // Day of week + + s.eVar50="D=c50"; + s.eVar51="D=c51"; + s.eVar52="D=c52"; + + //s.eVar54 = "D=c54"; + + if(s.prop53) s.eVar53 = "D=c53"; + + s.eVar72 = 'D=c53+":"+pageName'; + s.eVar73 = 'D=c53+":"+c4+":"+pageName' + s.eVar74 = 'D=c53+":"+c4'; + s.eVar75 = 'D=g'; + + /* copy eVars to props */ + s.prop62 = "D=v39"; + if(s.eVar35) { + s.prop63 = "D=v34"; + s.linkTrackVars=s.apl(s.linkTrackVars,"prop63",",",2); + } + + /* Page view count */ + s.events=s.apl(s.events,"event11",",",2); + + /* Engagement Score */ + s.engagementScore = 0; + if(s.events.match(/,*?event13\b/)!=null) s.engagementScore += s.engagementScoreMap['event13']; + if(s.events.match(/,*?event32\b/)!=null) s.engagementScore += s.engagementScoreMap['event32']; + if(s.events.match(/,*?event14\b/)!=null) s.engagementScore += s.engagementScoreMap['event14']; + if(s.events.match(/,*?event33\b/)!=null) s.engagementScore += s.engagementScoreMap['event33']; + if(s.events.match(/,*?event15\b/)!=null) s.engagementScore += s.engagementScoreMap['event15']; + if(s.events.match(/,*?event1\b/)!=null) s.engagementScore += s.engagementScoreMap['event1']; + if(s.events.match(/,*?event2\b/)!=null) s.engagementScore += s.engagementScoreMap['event2']; + if(s.events.match(/,*?event4\b/)!=null) s.engagementScore += s.engagementScoreMap['event4']; + if(s.events.match(/,*?event5\b/)!=null) s.engagementScore += s.engagementScoreMap['event5']; + if(s.events.match(/,*?event6\b/)!=null) s.engagementScore += s.engagementScoreMap['event6']; + if(s.events.match(/,*?event7\b/)!=null) s.engagementScore += s.engagementScoreMap['event7']; + if(s.events.match(/,*?event10\b/)!=null) s.engagementScore += s.engagementScoreMap['event10']; + if(s.events.match(/,*?event11\b/)!=null) s.engagementScore += s.engagementScoreMap['event11']; + if(s.events.match(/,*?event12\b/)!=null) s.engagementScore += s.engagementScoreMap['event12']; + if(s.events.match(/,*?event18\b/)!=null) s.engagementScore += s.engagementScoreMap['event18']; + //if(s.events.match(/,*?event20\b/)!=null) s.engagementScore += s.engagementScoreMap['event20']; + //if(s.events.match(/,*?event22\b/)!=null) s.engagementScore += s.engagementScoreMap['event22']; + //if(s.events.match(/,*?event23\b/)!=null) s.engagementScore += s.engagementScoreMap['event23']; + //if(s.events.match(/,*?event24\b/)!=null) s.engagementScore += s.engagementScoreMap['event24']; + + + if(s.events.match(/,*?event11\b/)!=null){ + if(s.linkTrackEvents.match(/,*?event11\b/) == null) + if(s.linkTrackEvents != "event19") s.engagementScore--; + } + s.eVar47 = "+"+s.engagementScore; + s.products = ";;;;event19="+s.engagementScore; + s.events=s.apl(s.events,"event19",",",2); + s.linkTrackVars=s.apl(s.linkTrackVars,"events",",",2); + s.linkTrackVars=s.apl(s.linkTrackVars,"products",",",2); + s.linkTrackVars=s.apl(s.linkTrackVars,"evar47",",",2); + s.linkTrackEvents=s.apl(s.linkTrackEvents,"event19",",",2); + + /* event List ContextData */ + s.contextData['eventList'] = s.events; + s.linkTrackVars=s.apl(s.linkTrackVars,"contextData.eventList",",",2); + +} +s.doPlugins=s_doPlugins; + + + +/* PLUGINS */ +/* + * Plugin: getNewRepeat 1.2 - Returns whether user is new or repeat + */ +s.getNewRepeat=new Function("d","cn","" ++"var s=this,e=new Date(),cval,sval,ct=e.getTime();d=d?d:30;cn=cn?cn:" ++"'s_nr';e.setTime(ct+d*24*60*60*1000);cval=s.c_r(cn);if(cval.length=" ++"=0){s.c_w(cn,ct+'-New',e);return'New';}sval=s.split(cval,'-');if(ct" ++"-sval[0]<30*60*1000&&sval[1]=='New'){s.c_w(cn,ct+'-New',e);return'N" ++"ew';}else{s.c_w(cn,ct+'-Repeat',e);return'Repeat';}"); + +/* + * Utility Function: split v1.5 (JS 1.0 compatible) + */ +s.split=new Function("l","d","" ++"var i,x=0,a=new Array;while(l){i=l.indexOf(d);i=i>-1?i:l.length;a[x" ++"++]=l.substring(0,i);l=l.substring(i+d.length);}return a"); + +/* + * Plugin: getVisitStart v2.0 - returns 1 on first page of visit + * otherwise 0 + */ +s.getVisitStart=new Function("c","" ++"var s=this,v=1,t=new Date;t.setTime(t.getTime()+1800000);if(s.c_r(c" ++")){v=0}if(!s.c_w(c,1,t)){s.c_w(c,1,0)}if(!s.c_r(c)){v=0}return v;"); + +/* +* Plugin: clickPast - version 1.0 +*/ +s.clickPast=new Function("scp","ct_ev","cp_ev","cpc","" ++"var s=this,scp,ct_ev,cp_ev,cpc,ev,tct;if(s.p_fo(ct_ev)==1){if(!cpc)" ++"{cpc='s_cpc';}ev=s.events?s.events+',':'';if(scp){s.events=ev+ct_ev" ++";s.c_w(cpc,1,0);}else{if(s.c_r(cpc)>=1){s.events=ev+cp_ev;s.c_w(cpc" ++",0,0);}}}"); + +s.p_fo=new Function("n","" ++"var s=this;if(!s.__fo){s.__fo=new Object;}if(!s.__fo[n]){s.__fo[n]=" ++"new Object;return 1;}else {return 0;}"); + +/* + * Plugin Utility: apl v1.1 + */ +s.apl=new Function("l","v","d","u","" ++"var s=this,m=0;if(!l)l='';if(u){var i,n,a=s.split(l,d);for(i=0;i-1?t.substring(0,t.indexOf('#')):t;}if(t)v+=v?d+t:t;p=p.substrin" ++"g(i==p.length?i:i+1)}return v"); +s.p_gpv=new Function("k","u","h","" ++"var s=this,v='',q;j=h==1?'#':'?';i=u.indexOf(j);if(k&&i>-1){q=u.sub" ++"string(i+1);v=s.pt(q,'&','p_gvf',k)}return v"); +s.p_gvf=new Function("t","k","" ++"if(t){var s=this,i=t.indexOf('='),p=i<0?t:t.substring(0,i),v=i<0?'T" ++"rue':t.substring(i+1);if(p.toLowerCase()==k.toLowerCase())return s." ++"epa(v)}return''"); + +/* + * Plugin: getTimeParting 3.3 + */ +s.getTimeParting=new Function("h","z","" ++"var s=this,od;od=new Date('1/1/2000');if(od.getDay()!=6||od.getMont" ++"h()!=0){return'Data Not Available';}else{var H,M,D,W,U,ds,de,tm,tt," ++"da=['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Sa" ++"turday'],d=new Date(),a=[];z=z?z:0;z=parseFloat(z);if(s._tpDST){var" ++" dso=s._tpDST[d.getFullYear()].split(/,/);ds=new Date(dso[0]+'/'+d." ++"getFullYear());de=new Date(dso[1]+'/'+d.getFullYear());if(h=='n'&&d" ++">ds&&dde||d=12){U='PM';H=H-12;}if(H==0){H=12;}if(D==6||D==0){W='Wee" ++"kend';}D=da[D];tm=H+':'+M+U;tt=H+':'+((M>30)?'30':'00')+U;a=[tm,tt," ++"D,W];return a;}"); + +/* + * Plugin: linkHandler 0.8 - identify and report custom links + */ +s.linkHandler=new Function("p","t","e","" ++"var s=this,o=s.p_gh(),h=o.href,i,l;t=t?t:'o';if(!h||(s.linkType&&(h" ++"||s.linkName)))return'';i=h.indexOf('?');h=s.linkLeaveQueryString||" ++"i<0?h:h.substring(0,i);l=s.pt(p,'|','p_gn',h.toLowerCase());if(l){s" ++".linkName=l=='[['?'':l;s.linkType=t;return e?o:h;}return'';"); +s.p_gh=new Function("","" ++"var s=this;if(!s.eo&&!s.lnk)return'';var o=s.eo?s.eo:s.lnk,y=s.ot(o" ++"),n=s.oid(o),x=o.s_oidt;if(s.eo&&o==s.eo){while(o&&!n&&y!='BODY'){o" ++"=o.parentElement?o.parentElement:o.parentNode;if(!o)return'';y=s.ot" ++"(o);n=s.oid(o);x=o.s_oidt;}}return o?o:'';"); +s.p_gn=new Function("t","h","" ++"var i=t?t.indexOf('~'):-1,n,x;if(t&&h){n=i<0?'':t.substring(0,i);x=" ++"t.substring(i+1);if(h.indexOf(x.toLowerCase())>-1)return n?n:'[[';}" ++"return 0;"); + +/* + * Utility manageVars v1.4 - clear variable values (requires split 1.5) + */ +s.manageVars=new Function("c","l","f","" ++"var s=this,vl,la,vla;l=l?l:'';f=f?f:1 ;if(!s[c])return false;vl='pa" ++"geName,purchaseID,channel,server,pageType,campaign,state,zip,events" ++",products,transactionID';for(var n=1;n<76;n++){vl+=',prop'+n+',eVar" ++"'+n+',hier'+n;}if(l&&(f==1||f==2)){if(f==1){vl=l;}if(f==2){la=s.spl" ++"it(l,',');vla=s.split(vl,',');vl='';for(x in la){for(y in vla){if(l" ++"a[x]==vla[y]){vla[y]='';}}}for(y in vla){vl+=vla[y]?','+vla[y]:'';}" ++"}s.pt(vl,',',c,0);return true;}else if(l==''&&f==1){s.pt(vl,',',c,0" ++");return true;}else{return false;}"); +s.clearVars=new Function("t","var s=this;s[t]='';"); +s.lowercaseVars=new Function("t","" ++"var s=this;if(s[t]&&t!='events'){s[t]=s[t].toString();if(s[t].index" ++"Of('D=')!=0){s[t]=s[t].toLowerCase();}}"); + +/* + * channelManager v2.85 - Tracking External Traffic + */ +s.channelManager=new Function("a","b","c","d","e","f","g","" ++"var s=this,h=new Date,i=0,j,k,l,m,n,o,p,q,r,t,u,v,w,x,y,z,A,B,C,D,E" ++",F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T;h.setTime(h.getTime()+1800000);if(e)" ++"{i=1;if(s.c_r(e))i=0;if(!s.c_w(e,1,h))s.c_w(e,1,0);if(!s.c_r(e))i=0" ++";if(f&&s.c_r('s_tbm'+f))i=0;}j=s.referrer?s.referrer:document.refer" ++"rer;j=unescape(j.toLowerCase());if(!j)k=1;else {l=j.indexOf('?')>-1" ++"?j.indexOf('?'):j.length;m=j.substring(0,l);n=s.split(j,'/');n=s.sp" ++"lit(n[2],'?');o=n[0].toLowerCase();p=s.linkInternalFilters.toLowerC" ++"ase();p=s.split(p,',');for(q=0;q'+s._extraSearchEngines;if(d==1){m=s.repl(m,'oogle'," ++"'%');m=s.repl(m,'ahoo','^');j=s.repl(j,'as_q','*');}y=s.split(x,'>'" ++");for(z=0;z-1){if(A[2])E=v=A" ++"[2];else E=o;if(d==1){E=s.repl(E,'#',' - ');j=s.repl(j,'*','as_q');" ++"E=s.repl(E,'^','ahoo');E=s.repl(E,'%','oogle');}F=s.split(A[1],',')" ++";for(G=0;G-1||j.indexOf('http" ++"s://www.google.')==0||j.indexOf('http://r.search.yahoo.com')==0)H=1" ++";I=s.getQueryParam(F[G],'',j).toLowerCase();if(H||I)break;}}if(H||I" ++")break;}if(H||I)break;}}if(!r||g!='1'){J=s.split(a,',');K=0;while(!" ++"T&&K');for(L=0;L-1){w=M[0];break;}}if(R>-1)break;}}" ++"J=s._channelParameter;if(J){K=s.split(J,'>');for(L=0;L');for(L=0;Lyahoo.com,yahoo.co.jp|p,va|Yahoo!>bing.com|q|Bing>altavista." 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src + '&enablejsapi=1'; + } + }else{ + src = src + '?enablejsapi=1'; + } + + iFrames[i].setAttribute('src', src); + iFrames[i].setAttribute('id', 'ytplayer' + n); + n++ + } + } + } +} + +if (n > 0) { + /* Loads IFrame Player API Code asynchronously */ + var tag = document.createElement('script'); + tag.src = "https://www.youtube.com/iframe_api"; + var firstScriptTag = document.getElementsByTagName('script')[0]; + firstScriptTag.parentNode.insertBefore(tag, firstScriptTag); + /* End YoutTube Player Mapping */ + + /* YouTube Player Mapping (https://developers.google.com/youtube/iframe_api_reference) */ + var playerInfoList = new Array(); + + if (document.getElementsByTagName('iframe')) { + for (i = 0; i < document.getElementsByTagName('iframe').length; i++) { + if (document.getElementsByTagName('iframe')[i].id.indexOf('ytplayer') > -1) { + playerInfoList.push(document.getElementsByTagName('iframe')[i]); + } + } + } + + var players = new Array(); + window.playersaem = players; + window.onYouTubeIframeAPIReady = function () { + for (x = 0; x < playerInfoList.length; x++) { + players[x] = new YT.Player(playerInfoList[x], { + events: { + onStateChange: onPlayerStateChange + } + }); + } + } + + //Player state change handler and video analytics tracker + function onPlayerStateChange(event) { + + var videoPlayer = "YouTube"; + var mediaOffset = Math.floor(event.target.getCurrentTime()); + var mediaLength = Math.floor(event.target.getDuration()); + console.log("event: "+event.data); + console.log("YT.PlayerState.PLAYING: "+YT.PlayerState.PLAYING); + console.log("YT.PlayerState.PAUSED: "+YT.PlayerState.PAUSED); + console.log("mediaOffset: "+mediaOffset); + console.log("media length: "+mediaLength) + var videoTitle = event.target.getVideoData().title; + + //console.log("media length: "+mediaLength); + //console.log("mediaOffset: "+mediaOffset); + //console.log("event.data: "+event.data); + //console.log("YT.PlayerState.PLAYING: "+YT.PlayerState.PLAYING); + //console.log("YT.PlayerState.PAUSED: "+YT.PlayerState.PAUSED); + //console.log("YT.PlayerState.BUFFERING: "+YT.PlayerState.BUFFERING); + //console.log("YT.PlayerState.ENDED: "+YT.PlayerState.ENDED); + + //wb.Media.playerName = videoPlayer; + /* + if (event.data == YT.PlayerState.PLAYING && mediaLength == 0) { + videoContentType = "live:youtube video"; + wb.media.trackSeconds = 60; + wb.Media.open(videoTitle, mediaLength, videoPlayer); + console.log("60 seconds tracking set"); + }*/ + if (event.data == 3 && mediaOffset == 0 && mediaLength == 0) { + videoContentType = "live:youtube video"; + wb.Media.trackSeconds = 60; + wb.Media.open(videoTitle, mediaLength, videoPlayer); + wb.Media.play(videoTitle, mediaOffset); + //console.log("60 seconds tracking set"); + } + + if (event.data == YT.PlayerState.PLAYING) { + if (mediaOffset == 0 && mediaLength > 0) { + videoContentType = "replay:youtube video"; + wb.Media.segmentByMilestones = true; + wb.Media.trackMilestones = '25,50,75'; + //console.log("milestone tracking set"); + wb.Media.open(videoTitle, mediaLength, videoPlayer); + wb.Media.play(videoTitle, mediaOffset); + _satellite.getVar('FBPixelVideoReplay'); + _satellite.getVar('TwitterPixelVideoReplay'); + _satellite.getVar('GAdwordsTagVideoReplay'); + } else { + wb.Media.play(videoTitle, mediaOffset); + }; + }else if (event.data == YT.PlayerState.PAUSED) { + wb.Media.stop(videoTitle, mediaOffset); + }else if (event.data == YT.PlayerState.BUFFERING) { + wb.Media.stop(videoTitle, mediaOffset); + }else if (event.data == YT.PlayerState.ENDED) { + wb.Media.stop(videoTitle, mediaOffset); + wb.Media.close(videoTitle); + mediaOffset = 0; + } + } +} +}); diff --git a/code/China _ Data_files/satellite-5a0b584c64746d703f006e37.js.download b/code/China _ Data_files/satellite-5a0b584c64746d703f006e37.js.download new file mode 100644 index 00000000..9d4ecb4d --- /dev/null +++ b/code/China _ Data_files/satellite-5a0b584c64746d703f006e37.js.download @@ -0,0 +1,77 @@ +_satellite.pushAsyncScript(function(event, target, $variables){ + if (typeof kWidget !== "undefined") { + kWidget.addReadyCallback(function (playerId) { + var kdp = document.getElementById(playerId); + + //console.log("player "+kdp); + //console.log("Name "+kdp.evaluate('{mediaProxy.entry.name}')); + //console.log("Duration "+kdp.evaluate('{duration}')); + //console.log("Current Time "+kdp.evaluate('{video.player.currentTime}')); + /* + kdp.kBind('playerStateChange', function (event) { + console.log("event "+event); + kulturaPlayerHandler("playing", kdp.evaluate('{mediaProxy.entry.name}'), kdp.evaluate('{duration}'), kdp.evaluate('{video.player.currentTime}')); + });*/ + kdp.kBind("playerPlayed", function (MediaPlayerState) { + kulturaPlayerHandler("playing", kdp.evaluate('{mediaProxy.entry.name}'), kdp.evaluate('{duration}'), kdp.evaluate('{video.player.currentTime}')); + }); + kdp.kBind("seek", function (MediaPlayerState) { + kulturaPlayerHandler("seeking", kdp.evaluate('{mediaProxy.entry.name}'), kdp.evaluate('{duration}'), kdp.evaluate('{video.player.currentTime}')); + }); + kdp.kBind("seeked", function (MediaPlayerState) { + kulturaPlayerHandler("seeked", kdp.evaluate('{mediaProxy.entry.name}'), kdp.evaluate('{duration}'), kdp.evaluate('{video.player.currentTime}')); + }); + kdp.kBind("playerPaused", function (MediaPlayerState) { + kulturaPlayerHandler("paused", kdp.evaluate('{mediaProxy.entry.name}'), kdp.evaluate('{duration}'), kdp.evaluate('{video.player.currentTime}')); + }); + kdp.kBind("playerPlayEnd", function (MediaPlayerState) { + kulturaPlayerHandler("ended", kdp.evaluate('{mediaProxy.entry.name}'), kdp.evaluate('{duration}'), kdp.evaluate('{video.player.currentTime}')); + }); + }); +} + +function kulturaPlayerHandler(playerState,mediaName,videoDuration,videoCurrentTime){ + var videoPlayer = "Kaltura"; + var mediaOffset = videoCurrentTime>0?Math.floor(videoCurrentTime):0; + var mediaLength = Math.floor(videoDuration); + + wb.Media.playerName = videoPlayer; + + //console.log("mediaOffset - "+mediaOffset); + //console.log("mediaLength - "+mediaLength); + //console.log("playerState - "+playerState); + + if(playerState == "playing" && mediaLength == 0){ + videoContentType = "live:kaltura video"; + wb.Media.trackSeconds = 60; + wb.Media.open(mediaName, mediaLength, videoPlayer); + //wb.Media.play(mediaName, mediaOffset);*/ + } + + if(playerState == "playing"){ + if (mediaOffset == 0 && mediaLength > 0){ + videoContentType = "replay:kaltura video"; + wb.Media.segmentByMilestones = true; + wb.Media.trackMilestones = '25,50,75'; + //console.log("milestone tracking set"); + wb.Media.open(mediaName, mediaLength, videoPlayer); + wb.Media.play(mediaName, mediaOffset); + _satellite.getVar('FBPixelVideoReplay'); + _satellite.getVar('TwitterPixelVideoReplay'); + _satellite.getVar('GAdwordsTagVideoReplay'); + }else{ + wb.Media.play(mediaName, mediaOffset); + } + }else if(playerState == "seeking"){ + wb.Media.stop(mediaName, mediaOffset); + }else if(playerState == "seeked"){ + wb.Media.play(mediaName, mediaOffset); + }else if(playerState == "paused"){ + wb.Media.stop(mediaName, mediaOffset); + }else if(playerState == "ended"){ + wb.Media.stop(mediaName, mediaOffset); + wb.Media.close(mediaName); + mediaOffset = 0; + } +} +}); diff --git a/code/China _ Data_files/satellite-5abd109d64746d01bc00b52f.js.download b/code/China _ Data_files/satellite-5abd109d64746d01bc00b52f.js.download new file mode 100644 index 00000000..1ddc23da --- /dev/null +++ b/code/China _ Data_files/satellite-5abd109d64746d01bc00b52f.js.download @@ -0,0 +1,23 @@ +_satellite.pushAsyncScript(function(event, target, $variables){ + // +!function(f,b,e,v,n,t,s) +{if(f.fbq)return;n=f.fbq=function(){n.callMethod? +n.callMethod.apply(n,arguments):n.queue.push(arguments)}; +if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0'; +n.queue=[];t=b.createElement(e);t.async=!0; +t.src=v;s=b.getElementsByTagName(e)[0]; +s.parentNode.insertBefore(t,s)}(window,document,'script', +'https://connect.facebook.net/en_US/fbevents.js'); +fbq('init', '1458892451075570'); +fbq('track', 'PageView'); +fbq('track', 'ViewContent'); +// + +}); diff --git a/code/China _ Data_files/satellite-5abd109d64746d01bc00b530.html b/code/China _ Data_files/satellite-5abd109d64746d01bc00b530.html new file mode 100644 index 00000000..7f63d9ad --- /dev/null +++ b/code/China _ Data_files/satellite-5abd109d64746d01bc00b530.html @@ -0,0 +1,48 @@ + + + + + + + \ No newline at end of file diff --git a/code/China _ Data_files/satellite-5abd109d64746d01bc00b54e.js.download b/code/China _ Data_files/satellite-5abd109d64746d01bc00b54e.js.download new file mode 100644 index 00000000..140a2f31 --- /dev/null +++ b/code/China _ Data_files/satellite-5abd109d64746d01bc00b54e.js.download @@ -0,0 +1,11 @@ +_satellite.pushAsyncScript(function(event, target, $variables){ + +!function(e,t,n,s,u,a){e.twq||(s=e.twq=function(){s.exe?s.exe.apply(s,arguments):s.queue.push(arguments); +},s.version='1.1',s.queue=[],u=t.createElement(n),u.async=!0,u.src='//static.ads-twitter.com/uwt.js', +a=t.getElementsByTagName(n)[0],a.parentNode.insertBefore(u,a))}(window,document,'script'); +// Insert Twitter Pixel ID and Standard Event data below +twq('init','nwaxc'); +twq('track','PageView'); + + +}); diff --git a/code/China _ Data_files/satellite-5abd109d64746d01bc00b54f.js.download b/code/China _ Data_files/satellite-5abd109d64746d01bc00b54f.js.download new file mode 100644 index 00000000..531d7228 --- /dev/null +++ b/code/China _ Data_files/satellite-5abd109d64746d01bc00b54f.js.download @@ -0,0 +1,8 @@ +_satellite.pushAsyncScript(function(event, target, $variables){ + !function(e,t,n,s,u,a){e.twq||(s=e.twq=function(){s.exe?s.exe.apply(s,arguments):s.queue.push(arguments); +},s.version='1.1',s.queue=[],u=t.createElement(n),u.async=!0,u.src='//static.ads-twitter.com/uwt.js', +a=t.getElementsByTagName(n)[0],a.parentNode.insertBefore(u,a))}(window,document,'script'); +// Insert Twitter Pixel ID and Standard Event data below +twq('init','ny9zz'); +twq('track','PageView'); +}); diff --git a/code/China _ Data_files/satellite-5abd109d64746d01bc00b550.js.download b/code/China _ Data_files/satellite-5abd109d64746d01bc00b550.js.download new file mode 100644 index 00000000..0b36912d --- /dev/null +++ b/code/China _ Data_files/satellite-5abd109d64746d01bc00b550.js.download @@ -0,0 +1,9 @@ +_satellite.pushAsyncScript(function(event, target, $variables){ + !function(e,t,n,s,u,a){e.twq||(s=e.twq=function(){s.exe?s.exe.apply(s,arguments):s.queue.push(arguments); +},s.version='1.1',s.queue=[],u=t.createElement(n),u.async=!0,u.src='//static.ads-twitter.com/uwt.js', +a=t.getElementsByTagName(n)[0],a.parentNode.insertBefore(u,a))}(window,document,'script'); +// Insert Twitter Pixel ID and Standard Event data below + +twq('init','nya03'); +twq('track','PageView'); +}); diff --git a/code/China _ Data_files/satellite-5abd109d64746d01bc00b551.js.download b/code/China _ Data_files/satellite-5abd109d64746d01bc00b551.js.download new file mode 100644 index 00000000..2f095a06 --- /dev/null +++ b/code/China _ Data_files/satellite-5abd109d64746d01bc00b551.js.download @@ -0,0 +1,8 @@ +_satellite.pushAsyncScript(function(event, target, $variables){ + !function(e,t,n,s,u,a){e.twq||(s=e.twq=function(){s.exe?s.exe.apply(s,arguments):s.queue.push(arguments); +},s.version='1.1',s.queue=[],u=t.createElement(n),u.async=!0,u.src='//static.ads-twitter.com/uwt.js', +a=t.getElementsByTagName(n)[0],a.parentNode.insertBefore(u,a))}(window,document,'script'); +// Insert Twitter Pixel ID and Standard Event data below +twq('init','nyz9o'); +twq('track','PageView'); +}); diff --git a/code/China _ Data_files/satellite-5ac5fe4564746d2458008f32.js.download b/code/China _ Data_files/satellite-5ac5fe4564746d2458008f32.js.download new file mode 100644 index 00000000..2dd01b52 --- /dev/null +++ b/code/China _ Data_files/satellite-5ac5fe4564746d2458008f32.js.download @@ -0,0 +1,65 @@ +_satellite.pushAsyncScript(function(event, target, $variables){ + if(_satellite.settings.isStaging){ + if (typeof FSR!="object"){ + // Instructions: please embed this snippet directly into every page of your website template. + // For optimal performance, this must be embedded directly into the template, not referenced + // as an external file. + + // ForeSee Embed Script v2.00 + // DO NOT MODIFY BELOW THIS LINE ***************************************** + ;(function (g) { + var d = document, am = d.createElement('script'), h = d.head || d.getElementsByTagName("head")[0], fsr = 'fsReady', + aex = { + "src": "//gateway.answerscloud.com/worldbank-org/staging/gateway.min.js", + "type": "text/javascript", + "async": "true", + "data-vendor": "fs", + "data-role": "gateway" + }; + for (var attr in aex){am.setAttribute(attr, aex[attr]);}h.appendChild(am);g[fsr] = function () {var aT = '__' + fsr+ '_stk__';g[aT] = g[aT] || [];g[aT].push(arguments);}; + })(window); + // DO NOT MODIFY ABOVE THIS LINE ***************************************** + } + //ForeSee Page Level Code + var WorldBank = {lang: _satellite.getVar('SiteLanguage')}; + + fsReady(function() { + //FSR.CPPS.set('S_channel variable:',wb.channel); + FSR.CPPS.set('S_Channel Variable',_satellite.getVar('ChannelInfo').toLowerCase()); + //if (_satellite.getVar('o:subSection3')!=undefined){ + // FSR.CPPS.set('s.prop26 variable:',_satellite.getVar('o:subSection3').toLowerCase());} + }); + +} + +else { + if (typeof FSR!="object"){ + // Instructions: please embed this snippet directly into every page of your website template. + // For optimal performance, this must be embedded directly into the template, not referenced + // as an external file. + + // ForeSee Embed Script v2.00 + // DO NOT MODIFY BELOW THIS LINE ***************************************** + ;(function (g) { + var d = document, am = d.createElement('script'), h = d.head || d.getElementsByTagName("head")[0], fsr = 'fsReady', + aex = { + "src": "//gateway.answerscloud.com/worldbank-org/production/gateway.min.js", + "type": "text/javascript", + "async": "true", + "data-vendor": "fs", + "data-role": "gateway" + }; + for (var attr in aex){am.setAttribute(attr, aex[attr]);}h.appendChild(am);g[fsr] = function () {var aT = '__' + fsr + '_stk__';g[aT] = g[aT] || [];g[aT].push(arguments);}; + })(window); + // DO NOT MODIFY ABOVE THIS LINE ***************************************** +} + //ForeSee Page Level Code + var WorldBank = {lang: _satellite.getVar('SiteLanguage')}; + fsReady(function() { + //FSR.CPPS.set('S_channel variable:',wb.channel); + FSR.CPPS.set('S_Channel Variable',_satellite.getVar('ChannelInfo').toLowerCase()); + //if (_satellite.getVar('o:subSection3')!=undefined){ + // FSR.CPPS.set('s.prop26 variable',_satellite.getVar('o:subSection3').toLowerCase());} + }); +} +}); diff --git a/code/China _ Data_files/satellite-5b32978d64746d60500041fa.js.download b/code/China _ Data_files/satellite-5b32978d64746d60500041fa.js.download new file mode 100644 index 00000000..0e6c5a2a --- /dev/null +++ b/code/China _ Data_files/satellite-5b32978d64746d60500041fa.js.download @@ -0,0 +1,128 @@ +_satellite.pushAsyncScript(function(event, target, $variables){ + var n = 0; + +if(typeof JQuery != 'undefined'){ +jQuery('iframe').each(function () { + + var src = jQuery(this).attr('src'); + if (src) { + if (src.indexOf('ustream.tv') > -1) { + jQuery(this).attr('src', src); + jQuery(this).attr('id', 'UstreamIframe' + n); + n++ + } + } + +}); +}else{ + var iFrames = document.getElementsByTagName('iframe'); + for(i=0;i -1){ + iFrames[i].setAttribute('src', src); + iFrames[i].setAttribute('id', 'UstreamIframe' + n); + n++ + } + } + } +} + +if(n>0){ + /** include the ustream player API **/ + + var 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wb.Media.play(videoTitle, mediaOffset); + startTime = new Date(); + } + prevEvent = event; + }else if(!data && event === 'playing' && prevEvent != "paused"){ + mediaOffset += Math.round((new Date() - startTime)/1000); + wb.Media.stop(videoTitle, mediaOffset); + prevEvent = "paused"; + }else if(data && (event === 'finished' || event === 'offline')){ + mediaOffset += Math.round((new Date() - startTime)/1000); + wb.Media.stop(videoTitle, mediaOffset); + wb.Media.close(videoTitle); + mediaOffset = 0; + prevEvent = event; + } + } + /** Get the video title by calling ustream RESTful service **/ + function getVideoTitle(videoId,videoType){ + var videoTitle = videoId; + var videoLength = 0; + var xhr = new XMLHttpRequest(); + + xhr.onreadystatechange = function(){ + if(this.readyState == 4 && this.status == 200){ + var response = JSON.parse(xhr.responseText); + if(videoType === 'videos'){ + videoTitle = response.video.title; + videoLength = Math.round(response.video.length); + } + else 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All rights reserved. + * + * You are hereby granted a non-exclusive, worldwide, royalty-free license to use, + * copy, modify, and distribute this software in source code or binary form for use + * in connection with the web services and APIs provided by Facebook. + * + * As with any software that integrates with the Facebook platform, your use of + * this software is subject to the Facebook Platform Policy + * [http://developers.facebook.com/policy/]. This copyright notice shall be + * included in all copies or substantial portions of the software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS + * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR + * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER + * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + */ +try {window.FB|| (function(window, fb_fif_window) { var apply = Function.prototype.apply; function bindContext(fn, thisArg) { return function _sdkBound() { return apply.call(fn, thisArg, arguments); }; } var global = { __type: 'JS_SDK_SANDBOX', window: window, document: window.document }; var sandboxWhitelist = [ 'setTimeout', 'setInterval', 'clearTimeout', 'clearInterval' ]; for (var i = 0; i < sandboxWhitelist.length; i++) { global[sandboxWhitelist[i]] = bindContext( window[sandboxWhitelist[i]], window ); } (function() { var self = window; var __DEV__ = 0; function emptyFunction() {}; var __transform_includes = {}; var __annotator, __bodyWrapper; var __w, __t; var undefined; var __p; with (this) { 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