From 8fed0a69d98ec288596f3f8a9dae2937d787e815 Mon Sep 17 00:00:00 2001 From: FlynnML Date: Wed, 6 Sep 2017 15:02:54 -0400 Subject: [PATCH 01/19] completed 1st notebook --- code/chap01_fig01.pdf | Bin 0 -> 1199 bytes code/chap01mine.ipynb | 2193 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 2193 insertions(+) create mode 100644 code/chap01_fig01.pdf create mode 100644 code/chap01mine.ipynb diff --git a/code/chap01_fig01.pdf b/code/chap01_fig01.pdf new file mode 100644 index 0000000000000000000000000000000000000000..52cb2073c2b1552e060d98b1d3e1ddb9ed351d27 GIT binary patch literal 1199 zcmZuxyKWOf6y+gOYyo8i)om;iiR{ci<3v#evEvW{ie*P6QUEdD9orl1oz-}Z96E{= zG>8U4#G{}>f-j(^$VX71bnq42+1

nr%jN_CC%%=kAv3)!Kq(x<={yFZlMuuz-SS z?HemAK(-GO4y3{q>xmAKI_vNO*ci}&)m4MXp$;rhB-BK#z)1th?pAA`2MQLm>-tD- zG!;`E+zhBQrMQg2(^rEuyDW_24*2<7DS{?fu!n7{HKf8v3dm+3DXisZB|kUe@dO=< zVuAAmXx^H=>fQJ!V6C>=XE4V&2%iy@4>0^0P!ND+l083f) zh7`jD#u{aJ(nO+?#e+om3Jx$2kt%;41-!AbhAe^HiDQ9v_Rf*kRZltJ=W$1M!O})F zfYhQM%7kYRwX5<#gj`QMP?EF0G5Y9iAD`IUcXw$0_|l0y>>lpIr!P;x{=WM2&!d^! zPt13(j$VGg^5*Sje*MGyzeg7jW|D_9vpY9tXD{?$UvssKd9B>fw6P}Rd#4s_M{{~( z-?fcXpf+;~)RsP0QEO={Y=psQ4}Ovw0!DP(8fAayCiVSrLk<|JVoS}b^m+RUSyx@siis$S!{>atC( zMY=?N%XK}kyg=QXl-`^-#u|+zZyOXW$Dn5q_@3u@(1tV2LV@%H@fqfOsCfbNTwK*6 z=Hu!Vu_avK0_JO1iH?%8 fg!VDr_06~~z{Restart\n", + "\n", + "%matplotlib qt5\n", + "\n", + "from modsim import *\n", + "\n", + "print('If this cell runs successfully, it produces no output other than this message.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The penny myth\n", + "\n", + "The following cells contain code from the beginning of Chapter 1.\n", + "\n", + "`modsim` defines `UNITS`, which contains variables representing pretty much every unit you've ever heard of. The following to lines create new variables named `meter` and `second`." + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "meter = UNITS.meter\n", + "second = UNITS.second" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To find out what units are defined, type `UNITS.` in the next cell and then press TAB. You should see a pop-up menu with a list of units." + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m UNITS.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "UNITS." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a variable named `a` and display its value:" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "9.8 meter/second2" + ], + "text/latex": [ + "$9.8 \\frac{meter}{second^{2}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 9.8 * meter / second**2\n", + "a" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Create `t` and display its value:" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "4 second" + ], + "text/latex": [ + "$4 second$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = 4 * second\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "If you create a variable and don't display the value, you don't get any output:" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "156.8 meter" + ], + "text/latex": [ + "$156.8 meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 279, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = a * t**2\n", + "h" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Add a second line to the previous cell to display the value of `h`.\n", + "\n", + "Now let's solve the falling penny problem. The following lines set `h` to the height of the Empire State Building and compute the time it would take a penny to fall, assuming constant acceleration." + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "8.817885349720552 second" + ], + "text/latex": [ + "$8.817885349720552 second$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 280, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h = 381 * meter\n", + "t = sqrt(2 * h / a)\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Given `t`, we can compute the velocity of the penny when it lands." + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "86.41527642726142 meter/second" + ], + "text/latex": [ + "$86.41527642726142 \\frac{meter}{second}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 281, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v = a * t\n", + "v" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "We can convert from one set of units to another like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "mile = UNITS.mile\n", + "hour= UNITS.hour" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "193.30546802805438 mile/hour" + ], + "text/latex": [ + "$193.30546802805438 \\frac{mile}{hour}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 283, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v.to(mile/hour)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** In reality, air resistance prevents the penny from reaching this velocity. At about 20 meters per second, the force of air resistance equals the force of gravity and the penny stops accelerating.\n", + "\n", + "As a simplification, let's assume that the acceleration of the penny is `a` until the penny reaches 20 meters per second, and then 0 afterwards. What is the total time for the penny to fall 381 meters?" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "20.070408163265306 second" + ], + "text/latex": [ + "$20.070408163265306 second$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 284, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vmax = 20 * meter/second\n", + "t1 = vmax / a\n", + "h1 = a * t1**2 / 2\n", + "h2 = h - h1\n", + "t2 = h2 / vmax\n", + "ttotal = t1 + t2\n", + "ttotal\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling a bikeshare system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll start with a `System` object that represents the number of bikes at each station." + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bikeshare = System(olin=10, wellesley=2, babson=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you display the value of a `System` object, it lists the system variables and their values (not necessarily in the order you defined them):" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + "olin 10\n", + "wellesley 2\n", + "babson 0\n", + "dtype: int64" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can access the system variables using dot notation." + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare.olin" + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 307, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare.wellesley" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** What happens if you spell the name of a system variable wrong? Edit the previous cell, change the spelling of `wellesley`, and run the cell again.\n", + "\n", + "The error message uses the word \"attribute\", which is another name for what we are calling a system variable. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Add a third attribute called `babson` with initial value 0, and print the state of `bikeshare` again." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "`newfig` creates a new figure, which should appear either in the notebook or in a new window, depending on which magic command you ran in the first code cell.\n", + "\n", + "`plot` adds a data point to the figure; in this example, you should see a red square and a blue circle representing the number of bikes at each station." + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "newfig()\n", + "plot(bikeshare.olin, 'rs-')\n", + "plot(bikeshare.wellesley, 'bo-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the operators `+=` and `-=` to increase and decrease the system variables. The following lines move a bike from Olin to Wellesley." + ] + }, + { + "cell_type": "code", + "execution_count": 309, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "olin 9\n", + "wellesley 3\n", + "dtype: int64" + ] + }, + "execution_count": 309, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare.olin -= 1\n", + "bikeshare.wellesley += 1\n", + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the following lines plot the updated state of the system. You should see two new data points with lines connecting them to the old data points." + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plot(bikeshare.olin, 'rs-')\n", + "plot(bikeshare.wellesley, 'bo-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** In the cell below, write a few lines of code to move a bike from Wellesley to Olin and plot the updated state." + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "metadata": {}, + "outputs": [], + "source": [ + "bikeshare.olin -= 1\n", + "bikeshare.wellesley += 1\n", + "\n", + "plot(bikeshare.olin, 'rs-')\n", + "plot(bikeshare.wellesley, 'bo-')" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plot(bikeshare.olin, 'rs-')\n", + "plot(bikeshare.wellesley, 'bo-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions\n", + "\n", + "Now we can take the code we've written so far and encapsulate it in functions." + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def bike_to_wellesley():\n", + " bikeshare.olin -= 1\n", + " bikeshare.wellesley += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you define a function, it doesn't run the statements inside the function, yet." + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_state():\n", + " plot(bikeshare.olin, 'rs-', label='Olin')\n", + " plot(bikeshare.wellesley, 'bo-', label='Wellesley')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now when we run the functions, it runs the statements inside." + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "olin 7\n", + "wellesley 5\n", + "dtype: int64" + ] + }, + "execution_count": 315, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_to_wellesley()\n", + "plot_state()\n", + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should see two more data points that represent the current state of the system. If the figure is embedded in the notebook, you might have to scroll up to see the change.\n", + "\n", + "One common error is to omit the parentheses, which has the effect of looking up the function, but not running it." + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": {}, + "outputs": [], + "source": [ + "bike_to_wellesley()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output indicates that `bike_to_wellesley` is a function defined in a \"namespace\" called `__main__`, but you don't have to understand what that means." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Define a function called `bike_to_olin` that moves a bike from Wellesley to Olin. Run the new function and print or plot the results to confirm that it works." + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "olin 7\n", + "wellesley 5\n", + "dtype: int64" + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def bike_to_olin():\n", + " bikeshare.olin += 1\n", + " bikeshare.wellesley -= 1\n", + "bike_to_olin()\n", + "plot(bikeshare.olin, 'rs-', label='Olin')\n", + "plot(bikeshare.wellesley, 'bo-', label='Wellesley')\n", + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we go on, let's start with a new state object and a new plot." + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bikeshare = System(olin=10, wellesley=2)\n", + "newfig()\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we have two similar functions, we can create a new function, `move_bike` that takes a parameter `n`, which indicates how many bikes are moving, and in which direction." + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def move_bike(n):\n", + " bikeshare.olin -= n\n", + " bikeshare.wellesley += n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use `move_bike` to write simpler versions of the other functions." + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def bike_to_wellesley():\n", + " move_bike(1)\n", + " \n", + "def bike_to_olin():\n", + " move_bike(-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we define these functions, we replace the old definitions with the new ones.\n", + "\n", + "Now we can test them and update the figure." + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "olin 9\n", + "wellesley 3\n", + "dtype: int64" + ] + }, + "execution_count": 302, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_to_wellesley()\n", + "plot_state()\n", + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, each time you run `plot_state` you should see changes in the figure." + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "olin 10\n", + "wellesley 2\n", + "dtype: int64" + ] + }, + "execution_count": 303, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bike_to_olin()\n", + "plot_state()\n", + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, `move_bike` is complicated enough that we should add some documentation. The text in triple-quotation marks is in English, not Python. It doesn't do anything when the program runs, but it helps people understand what this function does and how to use it." + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def move_bike(n):\n", + " \"\"\"Move bikes.\n", + " \n", + " n: number of bikes: positive moves from Olin to Wellesley;\n", + " negative moves from Wellesley to Olin\n", + " \"\"\"\n", + " bikeshare.olin -= n\n", + " bikeshare.wellesley += n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whenever you make a figure, you should put labels on the axes to explain what they mean and what units they are measured in. Here's how:" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "label_axes(title='Olin-Wellesley Bikeshare',\n", + " xlabel='Time step (min)', \n", + " ylabel='Number of bikes')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, you might have to scroll up to see the effect.\n", + "\n", + "And you can save figures as files; the suffix of the filename indicates the format you want. This example saves the current figure in a PDF file." + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap01_fig01.pdf\n" + ] + } + ], + "source": [ + "savefig('chap01_fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** The following function definitions start with print statements so they display messages when they run. Run each of these functions (with appropriate arguments) and confirm that they do what you expect.\n", + "\n", + "Adding print statements like this to functions is a useful debugging technique. Keep it in mind!" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def move_bike_debug(n):\n", + " print('Running move_bike_debug with argument', n)\n", + " bikeshare.olin -= n\n", + " bikeshare.wellesley += n\n", + " \n", + "def bike_to_wellesley_debug():\n", + " print('Running bike_to_wellesley_debug')\n", + " move_bike_debug(1)\n", + " \n", + "def bike_to_olin_debug():\n", + " print('Running bike_to_olin_debug')\n", + " move_bike_debug(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running move_bike_debug with argument 1\n" + ] + } + ], + "source": [ + "move_bike_debug(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running bike_to_wellesley_debug\n", + "Running move_bike_debug with argument 1\n" + ] + } + ], + "source": [ + "bike_to_wellesley_debug()" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running bike_to_olin_debug\n", + "Running move_bike_debug with argument -1\n" + ] + } + ], + "source": [ + "bike_to_olin_debug()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conditionals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `flip` takes a probability and returns either `True` or `False`, which are special values defined by Python.\n", + "\n", + "In the following example, the probability is 0.7 or 70%. If you run this cell several times, you should get `True` about 70% of the time and `False` about 30%." + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flip(0.7)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modify the argument in the previous cell and see what effect it has.\n", + "\n", + "In the following example, we use `flip` as part of an if statement. If the result from `flip` is `True`, we print `heads`; otherwise we do nothing." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "if flip(0.7):\n", + " print('heads')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With an else clause, we can print heads or tails depending on whether `flip` returns `True` or `False`." + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "heads\n" + ] + } + ], + "source": [ + "if flip(0.7):\n", + " print('heads')\n", + "else:\n", + " print('tails')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's get back to the bikeshare system. Again let's start with a new `System` object and a new plot." + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bikeshare = System(olin=10, wellesley=2)\n", + "newfig()\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose that in any given minute, there is a 70% chance that a student picks up a bike at Olin and rides to Wellesley. We can simulate that like this." + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Moving a bike to Wellesley\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "olin 10\n", + "wellesley 2\n", + "dtype: int64" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if flip(0.6):\n", + " bike_to_olin()\n", + " print('Moving a bike to Olin')\n", + "\n", + "plot_state()\n", + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can wrap that code in a function called `step` that simulates one time step. In any given minute, a student might ride from Olin to Wellesley, from Wellesley to Olin, or both, or neither, depending on the results of `flip`." + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def step():\n", + " if flip(0.7):\n", + " bike_to_wellesley()\n", + " print('Moving a bike to Wellesley')\n", + " \n", + " if flip(0.6):\n", + " bike_to_olin()\n", + " print('Moving a bike to Olin')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you run `step` a few times, it should update the current figure. In each time step, the number of bikes at each location might go up, down, or stay the same." + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Moving a bike to Wellesley\n", + "Moving a bike to Olin\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "olin 9\n", + "wellesley 3\n", + "dtype: int64" + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "step()\n", + "plot_state()\n", + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function labels the axes and adds a legend to the figure." + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def decorate(): \n", + " legend(loc='random string')\n", + " label_axes(title='Olin-Wellesley Bikeshare',\n", + " xlabel='Time step (min)', \n", + " ylabel='Number of bikes')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As always, when you define a function, it has no effect until you run it." + ] + }, + { + "cell_type": "code", + "execution_count": 348, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + } + ], + "source": [ + "decorate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Change the argument of `legend` to `'random string'` and run `decorate` again. You should get an error message that lists the valid location where you can put the legend." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again let's start with a new `System` object and a new plot." + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bikeshare = System(olin=10, wellesley=2)\n", + "newfig()\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can make `step` more general by adding parameters. Because these parameters have default values, they are optional." + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def step(p1=0.5, p2=0.5):\n", + " print('p1 ->', p1)\n", + " print('p2 ->', p2)\n", + " if flip(p1):\n", + " bike_to_wellesley()\n", + " \n", + " if flip(p2):\n", + " bike_to_olin()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I added print statements, so each time we run `step` we can see the arguments.\n", + "\n", + "If you provide no arguments, you get the default values:" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p1 -> 0.5\n", + "p2 -> 0.5\n" + ] + } + ], + "source": [ + "step()\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you provide one argument, it overrides the first parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p1 -> 0.4\n", + "p2 -> 0.5\n" + ] + } + ], + "source": [ + "step(0.4)\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you provide two arguments, they override both." + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p1 -> 0.4\n", + "p2 -> 0.2\n" + ] + } + ], + "source": [ + "step(0.4, 0.2)\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can specify the names of the parameters you want to override." + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p1 -> 0.4\n", + "p2 -> 0.2\n" + ] + } + ], + "source": [ + "step(p1=0.4, p2=0.2)\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which means you can override the second parameter and use the default for the first." + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p1 -> 0.5\n", + "p2 -> 0.2\n" + ] + } + ], + "source": [ + "step(p2=0.2)\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can combine both forms, but it is not very common:" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p1 -> 0.4\n", + "p2 -> 0.2\n" + ] + } + ], + "source": [ + "step(0.4, p2=0.2)\n", + "plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One reason it's not common is that it's error prone. The following example causes an error." + ] + }, + { + "cell_type": "code", + "execution_count": 336, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you remove the # at the beginning of the next line and run it, you get\n", + "# SyntaxError: positional argument follows keyword argument\n", + "\n", + "#step(p1=0.4, 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the error message, you might infer that arguments like `step(0.4, 0.2)` are called \"positional\" and arguments like `step(p1=0.4, p2=0.2)` are called \"keyword arguments\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write a version of `decorate` that takes an optional parameter named `loc` with default value `'best'`. It should pass the value of `loc` along as an argument to `legend.` Test your function with different values of `loc`. [You can see the list of legal values here](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend)." + ] + }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": {}, + "outputs": [], + "source": [ + "def decorate(loc = 'best'): \n", + " legend(loc = 'best')\n", + " label_axes(title='Olin-Wellesley Bikeshare',\n", + " xlabel='Time step (min)', \n", + " ylabel='Number of bikes')" + ] + }, + { + "cell_type": "code", + "execution_count": 345, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + } + ], + "source": [ + "decorate(loc = 'best')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## For loop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we go on, I'll redefine `step` without the print statements." + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def step(p1=0.5, p2=0.5):\n", + " if flip(p1):\n", + " bike_to_wellesley()\n", + " \n", + " if flip(p2):\n", + " bike_to_olin()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And let's start again with a new `System` object and a new figure." + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bikeshare = System(olin=10, wellesley=2)\n", + "newfig()\n", + "plot_state()\n", + "decorate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use a for loop to move 4 bikes from Olin to Wellesley." + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "for i in range(4):\n", + " bike_to_wellesley()\n", + " plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can simulate 4 random time steps." + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "for i in range(4):\n", + " step()\n", + " plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If each step corresponds to a minute, we can simulate the rest of the hour like this." + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "for i in range(52):\n", + " step(p1=0.4, p2=0.2)\n", + " plot_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Combine the examples from the previous two sections to write a function named `run_steps` that takes three parameters, named `num_steps`, `p1`, and `p2`. It should use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`. After each step, it should plot the updated state.\n", + "\n", + "Test your function by creating a new `System` object, creating a new figure, and running `run_steps`." + ] + }, + { + "cell_type": "code", + "execution_count": 347, + "metadata": {}, + "outputs": [], + "source": [ + " def run_steps(num_steps = 5, p1 = .5, p2 = .5):\n", + " for i in range(num_steps):\n", + " step(p1, p2)\n", + " plot_state()\n", + "bikeshare = System(olin=10, wellesley=2)\n", + "newfig()\n", + "plot_state()\n", + "decorate()\n", + "run_steps()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "heads\n", + "heads\n", + "tails\n", + "tails\n", + "heads\n", + "tails\n", + "heads\n", + "tails\n", + "heads 4\n", + "tails 4\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "penny = System(heads=0, tails=0)\n", + "def flip_count(n,p):\n", + " for i in range (n):\n", + " if flip(p):\n", + " penny.heads +=1\n", + " print ('heads')\n", + " else:\n", + " penny.tails +=1\n", + " print ('tails')\n", + "flip_count(8,0.5)\n", + "print(penny)\n", + "#just practice from class, not part of assignment" + ] + } + ], + "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": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From af1ee32551bb92df3a403115df927c058ce7a061 Mon Sep 17 00:00:00 2001 From: FlynnML Date: Wed, 6 Sep 2017 18:14:39 -0400 Subject: [PATCH 02/19] Added Ch 1 mine --- code/chap01mine.ipynb | 128 +++++++++++++++++------------------------- 1 file changed, 51 insertions(+), 77 deletions(-) diff --git a/code/chap01mine.ipynb b/code/chap01mine.ipynb index 77314cfc..34617f25 100644 --- a/code/chap01mine.ipynb +++ b/code/chap01mine.ipynb @@ -1399,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 359, "metadata": { "collapsed": true }, @@ -1419,16 +1419,9 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 360, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Moving a bike to Wellesley\n" - ] - }, { "data": { "text/html": [ @@ -1456,23 +1449,23 @@ " \n", " \n", " olin\n", - " 9\n", + " 10\n", " \n", " \n", " wellesley\n", - " 3\n", + " 2\n", " \n", " \n", "\n", "" ], "text/plain": [ - "olin 9\n", - "wellesley 3\n", + "olin 10\n", + "wellesley 2\n", "dtype: int64" ] }, - "execution_count": 182, + "execution_count": 360, "metadata": {}, "output_type": "execute_result" } @@ -1495,16 +1488,9 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 361, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Moving a bike to Olin\n" - ] - }, { "data": { "text/html": [ @@ -1548,7 +1534,7 @@ "dtype: int64" ] }, - "execution_count": 183, + "execution_count": 361, "metadata": {}, "output_type": "execute_result" } @@ -1571,7 +1557,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 352, "metadata": { "collapsed": true }, @@ -1596,14 +1582,13 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 362, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Moving a bike to Wellesley\n", "Moving a bike to Olin\n" ] }, @@ -1634,23 +1619,23 @@ " \n", " \n", " olin\n", - " 9\n", + " 11\n", " \n", " \n", " wellesley\n", - " 3\n", + " 1\n", " \n", " \n", "\n", "" ], "text/plain": [ - "olin 9\n", - "wellesley 3\n", + "olin 11\n", + "wellesley 1\n", "dtype: int64" ] }, - "execution_count": 187, + "execution_count": 362, "metadata": {}, "output_type": "execute_result" } @@ -1670,14 +1655,14 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 365, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def decorate(): \n", - " legend(loc='random string')\n", + " legend(loc='best')\n", " label_axes(title='Olin-Wellesley Bikeshare',\n", " xlabel='Time step (min)', \n", " ylabel='Number of bikes')" @@ -1692,18 +1677,9 @@ }, { "cell_type": "code", - "execution_count": 348, + "execution_count": 366, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", - " warnings.warn(\"No labelled objects found. \"\n" - ] - } - ], + "outputs": [], "source": [ "decorate()" ] @@ -1731,7 +1707,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 367, "metadata": { "collapsed": true }, @@ -1751,7 +1727,7 @@ }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 368, "metadata": { "collapsed": true }, @@ -1778,7 +1754,7 @@ }, { "cell_type": "code", - "execution_count": 330, + "execution_count": 369, "metadata": {}, "outputs": [ { @@ -1804,7 +1780,7 @@ }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 370, "metadata": {}, "outputs": [ { @@ -1830,7 +1806,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 371, "metadata": {}, "outputs": [ { @@ -1856,7 +1832,7 @@ }, { "cell_type": "code", - "execution_count": 333, + "execution_count": 372, "metadata": {}, "outputs": [ { @@ -1882,7 +1858,7 @@ }, { "cell_type": "code", - "execution_count": 334, + "execution_count": 373, "metadata": {}, "outputs": [ { @@ -1908,7 +1884,7 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": 374, "metadata": {}, "outputs": [ { @@ -1934,7 +1910,7 @@ }, { "cell_type": "code", - "execution_count": 336, + "execution_count": 375, "metadata": { "collapsed": true }, @@ -1962,7 +1938,7 @@ }, { "cell_type": "code", - "execution_count": 344, + "execution_count": 376, "metadata": {}, "outputs": [], "source": [ @@ -1975,18 +1951,9 @@ }, { "cell_type": "code", - "execution_count": 345, + "execution_count": 377, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", - " warnings.warn(\"No labelled objects found. \"\n" - ] - } - ], + "outputs": [], "source": [ "decorate(loc = 'best')" ] @@ -2007,7 +1974,7 @@ }, { "cell_type": "code", - "execution_count": 339, + "execution_count": 378, "metadata": { "collapsed": true }, @@ -2030,7 +1997,7 @@ }, { "cell_type": "code", - "execution_count": 340, + "execution_count": 379, "metadata": { "collapsed": true }, @@ -2051,7 +2018,7 @@ }, { "cell_type": "code", - "execution_count": 341, + "execution_count": 380, "metadata": { "collapsed": true }, @@ -2071,7 +2038,7 @@ }, { "cell_type": "code", - "execution_count": 342, + "execution_count": 381, "metadata": { "collapsed": true }, @@ -2091,7 +2058,7 @@ }, { "cell_type": "code", - "execution_count": 343, + "execution_count": 382, "metadata": { "collapsed": true }, @@ -2113,7 +2080,7 @@ }, { "cell_type": "code", - "execution_count": 347, + "execution_count": 385, "metadata": {}, "outputs": [], "source": [ @@ -2130,23 +2097,21 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": true - }, + "execution_count": 384, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "tails\n", "heads\n", "heads\n", - "tails\n", - "tails\n", "heads\n", "tails\n", "heads\n", "tails\n", + "tails\n", "heads 4\n", "tails 4\n", "dtype: int64\n" @@ -2167,6 +2132,15 @@ "print(penny)\n", "#just practice from class, not part of assignment" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { From d670b6b2462bc05c21eb47d9c5149c6816075e98 Mon Sep 17 00:00:00 2001 From: FlynnML Date: Wed, 13 Sep 2017 14:55:28 -0400 Subject: [PATCH 03/19] ch2 completed (last q conf) --- code/Chap02mine.ipynb | 2222 +++++++++++++++++++++++++++++++++++++++++ code/chap01mine.ipynb | 168 +++- 2 files changed, 2376 insertions(+), 14 deletions(-) create mode 100644 code/Chap02mine.ipynb diff --git a/code/Chap02mine.ipynb b/code/Chap02mine.ipynb new file mode 100644 index 00000000..cbfbe82c --- /dev/null +++ b/code/Chap02mine.ipynb @@ -0,0 +1,2222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 2: Simulation\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": "markdown", + "metadata": {}, + "source": [ + "We'll start with the same code we saw last time: the magic command that tells Jupyter where to put the figures, and the import statement that gets the functions defined in the `modsim` module." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you want the figures to appear in the notebook, \n", + "# and you want to interact with them, use\n", + "# %matplotlib notebook\n", + "\n", + "# If you want the figures to appear in the notebook, \n", + "# and you don't want to interact with them, use\n", + "# %matplotlib inline\n", + "\n", + "# If you want the figures to appear in separate windows, use\n", + "# %matplotlib qt5\n", + "\n", + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More than one System object\n", + "\n", + "Here's the code from the previous chapter, with two changes:\n", + "\n", + "1. I've added DocStrings that explain what each function does, and what parameters it takes.\n", + "\n", + "2. I've added a parameter named `system` to the functions so they work with whatever `System` object we give them, instead of always using `bikeshare`. That will be useful soon when we have more than one `System` object." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_steps(system, num_steps=1, p1=0.5, p2=0.5):\n", + " \"\"\"Simulate the given number of time steps.\n", + " \n", + " system: bikeshare System object\n", + " num_steps: number of time steps\n", + " p1: probability of an Olin->Wellesley customer arrival\n", + " p2: probability of a Wellesley->Olin customer arrival\n", + " \"\"\"\n", + " for i in range(num_steps):\n", + " step(system, p1, p2)\n", + " plot_system(system)\n", + " \n", + "def step(system, p1=0.5, p2=0.5):\n", + " \"\"\"Simulate one minute of time.\n", + " \n", + " system: bikeshare System 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(system)\n", + " \n", + " if flip(p2):\n", + " bike_to_olin(system)\n", + " \n", + "def bike_to_wellesley(system):\n", + " \"\"\"Move one bike from Olin to Wellesley.\n", + " \n", + " system: bikeshare System object\n", + " \"\"\"\n", + " move_bike(system, 1)\n", + " \n", + "def bike_to_olin(system):\n", + " \"\"\"Move one bike from Wellesley to Olin.\n", + " \n", + " system: bikeshare System object\n", + " \"\"\"\n", + " move_bike(system, -1)\n", + " \n", + "def move_bike(system, n):\n", + " \"\"\"Move a bike.\n", + " \n", + " system: bikeshare System object\n", + " n: +1 to move from Olin to Wellesley or\n", + " -1 to move from Wellesley to Olin\n", + " \"\"\"\n", + " system.olin -= n\n", + " system.wellesley += n\n", + " \n", + "def plot_system(system):\n", + " \"\"\"Plot the current system of the bikeshare system.\n", + " \n", + " system: bikeshare System object\n", + " \"\"\"\n", + " plot(system.olin, 'rs-', label='Olin')\n", + " plot(system.wellesley, 'bo-', label='Wellesley')\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": [ + "Now we can create more than one `System` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "olin 9\n", + "wellesley 3\n", + "dtype: int64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Negative bikes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code we have so far, the number of bikes at one of the locations can go negative, and the number of bikes at the other location can exceed the actual number of bikes in the system.\n", + "\n", + "If you run this simulation a few times, it happens quite often." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wo7tMUdXfJSur9id5bN5cfreSEvlNd+2S/Gm1nAEDkitIghk8eDB//OMfadmy\nJV27dj1anp2dzWmnncZHH33EDTfcAIhwOOecc7j77ru555576NKlC++88w6TJ0/mb3/7W8R+6tWr\nxxVXXMFjjz1GTk4Offr0YebMmTz77LM8+OCDAGzevJmpU6dy//330759e+bPn8/+/fs5+eSTK7R3\nzTXXcPXVV3PiiSfyk5/8hPXr13PPPfcwYsSIAO3poosu4vHHH6dz58706tUrHl9ZRFSwRMKfe6lp\nU5mgQCbHIO+MoxOmvzxUWaLvb9fOExBudt9oCH7WJk28dlu0kCWlgwfFVrJ9e2COqnDs3+/ZKKBm\nbRPp6d53VN3fpTbj7g3jLmkWFNQJwVLTDB48mMLCQkaNGlXhWl5eHgsWLGDIkCFHyx544AEee+wx\n7rzzTvbv30/Xrl15+umnA+qEY/z48WRkZPDoo4+yc+dOOnTowIQJE7jooosAuOuuu3jkkUe4+eab\n2bNnD506deKhhx5i4MCBFdoaPnw4jz76KC+88AJPPfUUzZo144ILLuCmm24KqHfeeefxyCOPJEVb\nARUs4TlyJHCPi9pqsA2mSROxARQVRZ/dt7JndZMdrlkj51u2RPd9zJ/vBRQ2ahSbbUapOrm5Yhcs\nLxeNO9Yl0eOQ1q1bY91tpoO46qqrKsSC5OTkcM8993DPPfeEvOf666/neicPXvv27QPaTk9P58Yb\nb+TGG28MeW9OTg4TJkwI6102Y8aMgPPzzjsvpI3Fz65du0hNTQ2wHyUSFSzhWLIE3LePZs3gwQcT\nGykeT/7zH3DWa6PSWJYt8/Y2adIEHnqo4rMWFcGtt3ruPjffHLnN8vJA990rrvC+z2RSm1yCE43/\nWSdOhOXL5fjHP66Z8Sg1zpYtW/jyyy959dVXGTVq1FHPs0RTR2bKGsAf0Dd8eN0RKgB5ed4Szvr1\nFT29gokm1Up2tgQchronFCtXestg9etXtF0oicXvTjp/vpf9WDmu2LVrF3fccQelpaXccccdSeu3\nDs2WSSTZ6UfiTSzZfQsK4Jtv5Dg1FXweMRXwT1ZLl4rHVzj8feblJX/r3uOdnj0920phYcX4HOW4\noE+fPixdupQpU6YkTVuBGASLMWa4MWawc9zRGPOeMeYLY0zyxGCyqIn0I/HGLwQWLQq/zu7Pf3Xy\nyZ7RPhTt20vyRRAjvj8Fvp9duyQFSqixKMkhJSUwi4Cm01eSSFSCxRgzBpgJuC4FzwNnAeuBe40x\nt1alc2Pu+n1RAAAgAElEQVTMc8aYSUFlPzHGLDPGHDLGfGmMGV1JG/WNMS8YY3YaY/YYY140xlTd\nSlxUJGnWXerqpOjP7ltSEjq7b3Gxt388RPeswYkdXeO8n7lzPQ+zk06qmdgVJXBJdN26ypdEFSVO\nRKux/A/wirX2NmNMG2AkcJ+19iLgD8DYWDo1xqQYYyYAVweV9wSmAv8A+gHvAf8yxkRyvH4eGAqc\nC5wHnOGUVY2FCz0DdW6upEapi0ST8TY/XwQpyOTfo0fl7Z56qpdrY/duyU/l58gR8O9hUVcF87FA\nIjc8U5QIROsVZoDxzvE5QAoy6QPkAw9E26Ex5gTgJaA3EPwKdSPwubX2Qef8bmPMUKf8qqC6GGPa\nA78CfmSt/dwpGwvMNMbcaq3dHO24uPpqmXj9m1F16wa/+13d9SwaNEiSJbp5ulavllgcd/mrQQNJ\n0wKyxBXNs2ZkiDBx09ysWCEbdrlt+oMps7LkO33hhfg+lxI906eL1x/I0uWsWaLFJCKRqaI4RCtY\n9gKuoWEUsMFa6wQ10BXYGfKu0OQBG4FfApODrg0DpgSVzQIujdBWGeBf7P8MKEW0mL/HMC4J6Kvp\n9CPxJDsbWrf2UrWsXi2T/a5dgfXcBJPRkpsrDg5unMSyZV6brtMDSKBeMvOCKRVp2NB7gSgtld8q\nPV1+r+D9Q9zfcNky8eTr2rX2ZxpQaiXRCpYZwB+dJakLgMcAjDE/B+4HPoq2Q2vtG8Abzv3Bl9sD\nwVrGFqBDmObaA9uttUd9Ka21R4wx2yPcEx43fQvUXPqReOPP7ltcLJ9g19OWLWPz2srOluh8N+Pz\nvn1em27alJQUESxKzeJmY3aDW90km4cPV/Tqc3/Dffvkk5bmOWsoSgxEa2O5EdFK7gU+Af7klD8O\nfAfcHqfx1AeKgsqKgXDpZEPVr+ye8DRqJP8RMzOrlh6+NpKTI4IjHGlpsr9IrHTsGFkbyc1Nesp2\nJQytWsW+hQIEbnegKDEQ1Su5tXYncHaIS0NismNUziEgeDbKAsJt+ROqfmX3hKdpU7FLpKYeG9qK\nS48eIjzcScLVKk4+WZY8qhJj0qiRfFeuG7O/zVA5t5SaIy1NjPgHDngOHCUl8lv5cX/DevXkd3VT\nAqnmqcRITLOnYyw/C2gLvAK0MsbssNZG3t0mejYCwUmo2lJxecxfv5UxJs1aW+qMMR1oFeGeyFSy\nn0KdJCUlME+X+4xuUs2qkpnptRWvNpXEkJoaGI+VmVnxt3J/w9xc+O47Od6yRex0ihIDsQRI/i+y\n7PUK8CAy4T8MfGGMiZeVex4Q7J96JjAnRF0QQ3064E9CNRR5rjDRe4qiRKR1ay+tz4EDnuegokRJ\nVBqLMeY24AbgFuAD4Fvn0h+BfyKC5rdxGM/TwBJjzH3A24gr8SDgGt9YWgIl1tq91trNxpgpwEvG\nmP+HuEG/CLwe8xLdsepOmYjnOla/q2ORqv5WL7/sBdXm5cVvPMpxQbQay9XAH621TwEb3EJr7QLg\nLiBidHy0WGu/QqL7fwEsA84HzrPWrvJVywee9J2PBeYD/0Zia2bgE0SKolSBM87wjvPzQ29Kryhh\niNbG0haZ0EOxHmhelc6ttWeEKPsQ+DDCPZ2Dzg8Av3E+iqLEg86dxeFj40ZxQ16wQNPvK1ETrcay\nltBeYSBBjd/FZziKotQKokkJpChhiFawPAH8jzHmCSQXVzlwgjHmBuBWYGJihqcoSo0xcKAEw4Js\nR716dc2OR6kzRCVYrLUvIraU3wIfI0byKcCjwJPW2mcTNkJFUWqGrKzAXT81iaUSJVHHsVhrHzLG\nPIu49jZH8od9bq3dFflORVHqLB98INt0g2yH8PnnXkYF9Q5UwhCtu3F3a+0aa+0+4D9B1+oB91tr\nb0nEABVFqUFyciSQcu9esbFs3QqdOtX0qJRaTrQ2llkmRMZIY8zZwAq8lPqKohxrtG3rHW/dqkZ8\npVJi8Qqb7WzEhTGmhTHmDSR2ZB8S7a4oyrFI8+Zeupfi4orbLihKENEKlp8ASxHN5TZgNRK8eDNw\nmrvJlqIoxyCpqYGJKAsKam4sSp0gWq+wIkSQzEZS5q8Aelhrn7DWhtj0XFGUYwr/pm0//OBltVaU\nEIQ13htjQiUIegroDJwMDDLGbHMvWGvnx310iqLUDrKzoVkzbxlMtRYlApG8wuYhgZDBuLs7veNc\nT3H+6h6minKs4XcpXrECnn5ajuvXl1QvVdnLRznmiSRYzkzaKBRFqf307CmG/F27ZIvjxYsDAygV\nxSGsYLHWapitoigeqamSP+zdd+V89mwVLEpIItlY7gRettYWOMeRKLfWPhTfoSmKUuvIy4OpU2Xb\n4nXrJPtxhw41PSqllhFpKewB4BOgwDmORDmggkVRjnUaNoRTT4VFi+R89my4/PKaHZNS64i0FJYa\n6lhRlOOcESM8wbJwIfz851CvXs2OSalVRJ2EEsAYkwKcBDQGtllrdR8WRTne6NpVElO6u0pecklg\n2hdNTlknyM+HadPEczw3F0aPhgEDwpfHQtSCxRhzC7L3SnNf2WbgTmvtG7F1qyhKnSUlRWacb7+V\n8y1b5NwNoFRqPfn5MGmS5Bbdvh1WrYIZM6BbN+9nbdYMysqkHsQmXKLNbnwTsvfK35F95bcDbYBL\ngFeNMeXW2jej71ZRlDpN69ZivC8tFdfjffskC7JSJ5g2TfwvVq6Uvy5r1kCjRnK8bZskt65fHz76\nKAGCBRgH/CVEavy3jDETgXsAFSyKcryQlgatWnkR+AUFKljqEAUFIjj8QgXkHcEVLKmpXvzrli2x\ntR+tYMklaB8WH/8EroitW0VR6jxt23qCZedOKCnxsiArtZo2bTz/C4B27aBBA9FQ2reXsiZNPMHi\nN6FFQ7SCZTZwITA9xLURwMLYug2NMeYMYGaYyzOttWeFuGcKcHFQ8afW2h/HY0yKooQhJ0deb/ft\nk8X4bds0pqWO0LMnTJ4sx+np0LmzKKEXXgjzQ2R9HDUqtvYjBUj+ync6G5hgjGmDaCjbgKbAKOC/\ngJti6zYs8xHtyM9I4BXgkTD39AFuB171lRXHaTyKokSibVsRLCDai/u6q9Rqdu+GHj1g0yZZwezU\nSYTHgAEidD76SJa/2rb1ymMhksYSytPrAucTzPPApNi6roi1tgTY6p4bYxojTgP/a62tsBRnjMkC\nugGLrLVbg68ripIgXJfiw4fhtts81+Prrqu5MSlRsWcPLFsmJrJWreDeewOXugYMiF2QBBNJsHSp\nXtNx4W5E+5gQ5noP5BlWJW1EiqJ4ZGTA6afDxx/L+ezZ0LdvzY5Jici8ebJyCdC9e+z2k2iIFHm/\nIf7dRY8xphXijXaNtbYwTLXeQAlwnzFmNHAI+AfwgLM5maIoiWb4cE+wrFwphvwWLWp2TEpIyspg\n7lzvfMSIxPRTm1O1XIPEy0QKvuyF7AezGvgpcB8wFlmaUxQlGbRsCb16yXF5eeDMpdQqvvxSlsJA\n0r7165eYfmqzYLkcya58OEKdu4A21trHrbVfWWvfAm4E/tsY0zzCfYqixBP/q++8eRUDJJRawaxZ\n3vHQoeIRlghqpWAxxvRCjPKTI9Wz1pZZa3cHFX/l/FW/R0VJFn36QNOmcnzgAHzxRc2OR6mAm7oF\nJPvOsGGJ6yuSu/GtwBvW2hhjLuPCMKDAWhvRKO/EsGRYay/0FfdHDP7fJnB8iqL4SU2F5cth/Xo5\nX74cTj5ZjufMETtMKOKZsPLqq0OXJ6v/SGNIRF9hCJVEEuDPf4avvpIULWeeKZuBJopIitC9wGfA\nFmNMKTDEWrsoQv140g9YEVxojMkEmgG7HdfkfwKTjTH/g+Qw6wf8GfiztfZAksaqKApIOPeGDWJn\n2btXEk+lpkqcy9q1cty6tcxsiWTvXtk+ubxczt3+U1JkNk106pmiInFgaN486dsJ5OfDX/8qMShH\njshP8MEHcu3QIflKDh4U+Z+fX3234nBEEix7gZuNMd0QA/lPjTE9wlW21r4Wx3HlAsFLXAB5SGT+\nmcAsa+0UY0w28HvgQcTY/yS66ZiiJJ/MTPEG27FDzt10L4WFsHmzHG/fLrNZaoJW4YuK5LXc9acN\n7n/LFjjttMRN+OXlYiEvKpI+E/msIZg2Db75RuSqy86d8td11MvOllXLWBNLxkIkwfIQ8BckILIc\niSkJRzkQN8FirT0/TPksRMj5y16LZ9+KolSDdu1kJnO1hWCKi+V6q1aJ6b+gIFCoBFNWJnVOOCEx\n/e/cKUIF5Fl37BAtLUmsXRsoVKCiH0X79qK8xZpYMhYixbE8bYx5CUndshE4D1iWuKEoilLnadQI\nTjnFS/MCojG0aiXaCsiMlgjB4uYrc2nXTl7PCwtlNt20Scq3bpXkWInQJFwtzWXLlqQKliJf9F6j\nRuIJ7srZrl3l62jWTM4TERjpEtHZzAlMLDTG/Ab43Fq7K1J9RVEUGjaUj8vataIh7Nwps9y+fbLQ\nn5MT33537ZIMywBZWdJnSor036WLp00cOZIYTaKgwAsScdm/Xz7+7yNBHD4cKCs7dhQhkpEhX0PL\nloH1Y00sGQtReTFba181xrQ0xjwCnIFsTbwTmAs8Ya3dFul+RVGOczIzxZjt2l+2bJF8IvHEv7YT\nvKOlu+vlunVyXlAQf8EyZ07o8oKCpAiW/HzRUnr0kCSTzZuL0jZ2rFyvbmLJWIh2B8lOiIdYc0SY\nfIMY2McDVxhjBlprNyZslIqi1H4qc6f95ht47DE5zsqCRx+NX9+bN3vuzamp8PDDFb2/9u+H22/3\njA533RW//ouLYcECz635vPPg/fflOCMjvs8ahtmz5W+rVvC738HZZwdeT6QgCSbaRcZHgULgRGvt\nT6y1Y5z9Tk5EvMceTtQAFUU5RujeXbQGkIl4YVy2cRL82kK/fqFdihs2FI8wF3cmjgeLF4s/L8jM\n/tOfelsIHD4Mn38ev75C8P33XghRejrk5SW0u0qJVrCMBO4J1kqc8/uAn8R7YIqiHGOkpASmfpk9\nO7z3WCwUFwdO3JEyK/qvLVzoCYPq4hdSw4dXfNY5c+LzrFF0f9ppSVl5i0gsbhH7w5TvAxIc8aQo\nyjHB4MHe9sWbN8N331W/zYULPXeoNm3gxBPD1z3hBDE8gBj646FJrF8vgaEQqC4MHChLfiB2ljVr\nqt9XCAoLA7cZTlTG4liIVrAsBn4X5to1gCYGUhSlcurVkwnXxZ8VsSqUl4fWFsKRCK3J33///p63\nW3a2CFKX6j5rGD7/3HOGa9cucSE6sRBtbst7gLnGmGXA35FdHtsg2xL3As6OcK+iKIrHiBGSARkk\nWeUll1R97WbdOi8+JSMDhgyp/J5Bg+Cdd2QJzdUkImk5kTh4UNyxXILVhREjPMGzdKmkm4ljSpny\n8kDz0ogRkeVqsojW3fhzY8w5wJ+AB5Do93JgCXCOtXZG4oaoKMoxRceOsHQp+ds7Me3gcAren0Nu\nq1JG73wdgGktxlBQ2Jjc+nsZ3WEFA1a/Tn6PMUzb2Nsrd+symoK9Z5KbvoPRHVcy4KabKvdOy86G\nFSvI/6659D/1a3Lbra5a/1k/o2D7UOm/5WIGlD8ML7zg9dWuHSxbRv62jtLXh/Oq/qz+uk7Z6w1+\nxyebT6J+egldG+1g0JFXYMSz8fqlqkxKeYxqoDGmPtAE2GutPZiQUSUYY0xnYN2nn35Ke9dzQ1GU\npJE/4hZeXD6QbaXNKSYbsrLYuy8FUqBxw8A5qUfRMlZnnxJQdrRuxiFyUgppkf4DNG/B2D4LGfB/\nd1be/+h7mPR5L3YdacKB8hzIzq5a/1lFZJUX0SptF6mNGzH21C8q9J9/xu95cdkAtpc2o4h6VX9W\nX123rCw7h31l9SEtndycfTwy8N2onr+qbNq0iR/96EcAXay168PVi3mbFzcav+pDUxTleGfagWFs\nKG3B9yVtpKAEdh5pDJTTYv++gLpfHT6HRocDvbeO1kXqnpT+PS0z0vhoUy+iCdeYtqM/O1Ma8HVJ\nJyk4XL3+D2XUo0u9wyH7n7Z/KN+XNmdDSW71ntVXN7D/w5BaStuWe6N+/kRTKzf6UhTl2GZzYVO2\nEJis6kh5OkfKK77rFpZXzEQcXHcTsvKw5WCTqPovKGx89J549F9ALqWkhew/3s8aqqwpe8jhYNTP\nn2gStDGloihKeMpI4Uh6NnCErJQS2mT+wOE9qaQAnRr+EFA3Y28xbRsGLpK4dVMz0ilLSWU/6ew/\nnMVJTbdG1X9ORjH7yhtC1hFSy8rokL2jSv1nZ5VTVJ7FkfQ0th9qyIBW6yv0VQ4cTq8HHCGTEnKz\nqvas/rpHy7K3k15aTKv0XVCYRdu2pVE9f6JRwaIoStLJTHXSqqSn07bhHjrkFFKv+AdSgJYtswLq\n/jLjn8xvck5AmVt3d8OObDvUCICCwibc1OdT4GeV9t8405m809JpnrOfTk2q1n9Jk9as3Z/j9N+Y\ns9uvrNB/dprvWRvspWODqvXlr3u0rEUK7HZyAx8qZVTbb6J6/kQTba6w94DHnf1QFEVRqszGjXCk\nLI0ejQvYUtiUtvX30r7BD4xt/gYAHzW4nC0Hm9A2Zw+j2q9kwOr36NmjER9t6nW03K37VsOr+c+m\nnuSkl9Aiez+9mlW+yUhREew41IgejQvYdLAZ7XL2VLn/qc1+zfoDzamXdpj2ObtpmR24ce3mzVBc\nmk6PxgVsPtiUttXoy1/3aFnTy9myL422bGFUzlwGlBfH4ReqPlF5hRljDgLnWmtnJn5IiUe9whSl\n5njjDZg7V44HDoQrr6x6W+Xl8MADXijLJZeAOC2FZ/ZseOstOc7NhXvvrV7sx6uvwvz5cjxkCFxx\nhXft7be9uMjTToOrrqp6P2GZMQP+/nc5btcO7r47YcEs0XqFRWu8/w/wS2OMLp0pilJlDh0KzD1Z\n3fQjsabkCg7Uj0dAob//xYslZhJEM4o2hVm1SESanGoSraDYB1wBXGKM+Ro4EHS93Fqr0feKokQk\nOP1I167Vb9MNpC8qks0hv/kGjAld97vvZO4FmYv9GVeqSqdO8tmwQRIZz58PI0dK/q5oU5hVi/r1\nRfVzsxnMnh2fL7YaRKuxuPuxLAWKgYygT2ZCRqcoyjFDIrQFkDyPfgERKRu+/9qgQZK6rLqE05oS\n8axh8Q9gyRLZe6YGiTaly5mJHoiLMaYnsDLEpWHW2nkh6vcHngT6AZuB+621ryV2lIqixMq333pb\nwmdlycQeL0aM8GwZ4VJy7d8vc67/nnjRvz/84x+y1Ld9O3z4oWf3iZdmFJGOHaFzZ8m0fOSIqE3B\nO30lkZgCJI0x2caY4caYS40xTY0xibB890G2Pc4N+lTYFcgY0xKx/3wBnAo8BbxkjNH9YRSllhGs\nLWRnx6/ttm2hWzc5LiuDzz6rWGf+fG/zyC5doEOH+PWflRWY/9LdPBJk58b6ydhYJIn7v1RG1MZ4\nY8x1wP1InrByYABwvzEmC/hZHPOG9Qa+ttZGE+k0FtnB8kZrbRmw2hhzKnAL8HGcxqMoSpTk58O0\naaKZ5ObC6NEysc6cKbkZDxyQSfa88+Lf94gRIjw2bhT7Rn4+nHOO9L9oETz4IOzc6Zkk4s3w4eKg\ntX27jKGwUPr66U/j31dIBgyAq6/2pOeaNdC0qZf+2N022U9lCTurSLRxLP8P0QaeBN4HPnUuvQS8\njOwieUucxtQbWBVl3WHAHEeouMwCJhpjUqy1NSeyFeU4Iz8fJk2SJad9+7wJvlcvMdq7y/5pafJG\n37p1fPdhLy0V47zrHDBvnoypVy9ZHtuxQ8rdDSf79o1v/7m5ooWtXu2VpabKslhubhL2nM/IEC8B\ndw1uwwaRboVOMOjmzTLAZs0Snls/Wo3l98Bj1tpbjTFpbqG19l1jTDvgZuIrWLKNMZ8DnYEVwJ3W\n2kUh6rZHHAr8bEF2tGyOLKkpipIEpk0TgbJ8eeAqzNKl0KiRd97WSZv10UfxnWynTxdhtdHZQN2d\nX4P7b9NGJvx49w+eF5hLop41LLm53oPv2+d9ANaulb/dunkDSxDR2li6EH5p6Stk069qY4ypB5wA\nNEaE2fmIoJhtjDkpxC31gaCfEjf0NI4ruIqiVEZBgbwUBy/tF/pSX2VlQYsWcryl8iD5mPvPzRWh\nEa7/1FSpk4j+XVzbUSKfNSz16olGEolNmxJuf4lWY9kEDAQ+CXGtn3O92lhrDxljmgLF1tpiAGPM\nFcBpwLXA9UG3HAKygsrc8zq5V4yi1FWaNhUbhos7yaeleVpCq1bexB/vl+bcXBFsffrArl3e3On2\nD9C8uedinIiX9nbtZIlt507pKy0tcX2FxRiRZK6txdVY0tOlrKgIfvihcgFUDaIVLH8D7jbGFAIf\nOGX1jDHnA39A7C9xwVq7L+i8zBizEgjlw7ER8Rjz0xYJ4NwbrzEpilI5rVp5k3njxtC9uxyPGeOl\nPPEzalR8+x89Wmw8jRsHuhonq3//GIIzRSWir7BkZEjEposbEeq3vxQU1ArB8hASJPmY8wFwd1qe\nDDwYj8EYY04DZgJnWmuXOGVpwCnAP0LcMg/4TZCh/kzgsyCDvqIoCaSsTKLee/SQuatdO5lcR40S\n20LPnmJn2LJF3t7d8njitheqn2T0X9kYahy//WX37ooGoTgS09bExpgTkYm7OaIRzLHWfhWvwTi5\nyL4ASoDrEM3jNuBcoAewB2gG7LbWlhhjWgMW+DvwBPBjRPCNstbOiNBPZzQJpaLEjeXLYeJEOW7Y\nEB5+WFZelFrGE0/AKsfpdvRouOCCmG6PdxJKlzXAXESr+CieQgXAWnsEGI0Ii/eBRYhjwHBr7XYg\nDyhw/mKt3QaMQuw8S4FxwH9HEiqKosQff/BjXp4KlVqLP4hy3jzPDhNnYgmQvAW4FdFW3LLNiCvw\nG/EakLV2M3BZmGuzgJSgss8RxwJFUWqAHTtgpZOEKSUldByeUks4+WRo0gT27JHAoqVLE7JOF5XG\nYoy5CXgUCYy8DBgJjEGWrV41xoQUBIqiHPu4gd0gwYiui61SC0lNhWHDvPNIGTurQbQayzjgL9ba\n4CDIt4wxE4F7gDfjOjJFUWo9hw8H5uVK2J4jSvwYOlTSAZSVSdoX18sgjkRrY8lFkj2G4p+EdgVW\nFOUY54svvI2tmjWD3r1rdjxKFDRpAqec4p37Vc44Ea3GMhu4EJge4toIQmQeVhTl2CY/X3bB3bRJ\nki2OGVMx6l2ppcyYAV9+Kcfz58Mnn0g055w54Y1kMSSsDCtYjDG/8p3OBiYYY9ogGso2oCnikfVf\nwE1R96goSp0nPx+efNLLy3XoECxbJuW1ImZDiUzjxvI2UFgonmHr10ODBvJDbtsmXhiNG0temioQ\nSWMJ5el1gfMJ5nlgUpVGoChKnWPatMD8V82by4ZWSUu2qFSPlBQJmHQTU7rR+Xv3grVynJoqG+dk\nZMTcfCTB0iXm1hRFOS7YtEn2HXFxbb9JS7aoVJ/WrSW1frhYlrIy8c6Ip2Cx1m6IuTVFUY4LDh+W\n/U9AVlTctPRJTbaoVI/0dPEP37bNS/K2d68IHBBvjCpufRntRl+ZSIqVIcgOksGUW2trboNlRVGS\nRnl54Ets27bevlFJTbaoVJ/gjJ3btkl25GoSrVfYs8CVyKZbu6rdq6IodZa1a0WQ9OghSXLbtAlM\nOKko0QqWC4F7rLUPJHIwiqLUftxg7Vat4KKLxM1YqWMkaK97l2i9zsuBzxM5EEVRaj/790tQpItG\n2iuhiFawvAJcaYzR8CdFOY6ZP99zIurSBTp2rNnxKLWTaJfC7kESTn5jjFlCxW1/y621V8Z1ZIqi\n1CrKywOzf6i2ooQjWsHyCGCQzb1ODXE9+t3CFEWpk6xc6e1pX78+9O9fs+NRai/RCpb/RoTLnb4t\ngBVFOY4I3syrCnFzynFCtIKlFPhYhYqiHH/k58M778D770O9etChgy6DKZGJ1hj/JhLHoijKcUR+\nPkyaJBsNlpVJivxt2yQTiKKEI1qNZRvwa2PMt0A+sD/oerm19uq4jkxRlBpn2jQRKFu3emW5uZps\nUolMtIJlLLAbSAMGh7iuS2SKcgxSUAC7dkFJiZxnZUkmY002qUQiKsFirdVMx4pyHJKbK/usuLRp\nI+lcNNmkEoloNZakYYxpDTwK/ASoh+xOebO1dkWY+lOAi4OKP7XW/jihA1WU44ABA+Dtt+U4JUUE\nC2iySSUy0WY3XkMly13W2hOrOxgnsv//gBTgZ8AB4I/Ap8aYntbaUAkw+wC3A6/6yoqrOxZFUSSL\neo8esv9KvXrQtasmm1QqJ1qN5TMqCpYGwEAgG3giTuM5GUnN39NauwrAGDMGse/8FHjNX9kYkwV0\nAxZZa7eiKErcKC6GBQsk2WSrVjB+PJx0Uk2PSqkLRGtjuSJUuTEmA3gPqNpuMBX5HjgXsL6yMudv\n0xD1eyDPsCpO/SuK4rBoERQVyXGrVqK5KEo0VMvGYq09bIx5EngZuLu6g3GWuj4MKr4BsbV8HOKW\n3kAJcJ8xZjRwCPgH8IC1tqi641GU45Xy8sBI+xEjvM28FKUy4mG8bwY0ikM7FTDGnA88BPzFXRoL\nohdij1kNPIPYW/4CdAB+nYgxKcrxwPr1sHGjHGdkwJAhNTocpY4RrfH+VyGK05AJfDwwJ8T1amGM\nuQJ4EZgM3Bqm2l3An621u53zr4wxpcBkY8z/hDH2K4pSCX5tZcAAyMmpubEodY9oNZY3IlybD1wf\nh7EcxRjzB+ABRAu5IVyOMmttGWLY9/OV87cDuo2yosTMwYOweLF3rnnBlFiJVrCECpAsB/ZZa/fE\ncTwYY25FhMo91tr7K6k7Bciw1l7oK+6PuBt/G2vf+fmSwqKgQALDRo8+NtwqQz0XVO9ZE9GmkhjC\n/bsO9xs+84xcq19f6nXqVLPjV+oe0XqFJSXlnDGmL/An4G/Ai8aYNr7L+4HDiE1nt7W2BPgnzrIX\n4hc0cS0AABsLSURBVJ3WD/gzsjx2IJa+3WR7JSWQmgqbN8s51O3JMT8fXnxR3kLLymDPHpg3T641\nby6xCbE+q/+7Kiqq2GZaGpSWHhvfX13H/a3KyuDAAfmtVq2Cfv0ksaSL/zfcuVOM9wcPSuqWxYv1\nN1RiI6xgMcbcE0M75ZVpF1FyKWK7+X/Ox8/dwDxgJnAmMMtaO8UYkw38HngQ2A48iRj8Y2LaNPjh\nB1ixQoyVJ58sk25dT7Y3bRqsXg07dnhl7mZNLVqIEOjXT95Oo33WadNg3z5YvlwmoOA2Adq1k2C6\nuv791XWmTRMh/8UXcOiQVz5nDjQKcrkJ/g3T06FlS/0NldiJpLFE4z6c4nwAqi1YrLV3AndG0af/\nntcICpysCgUFMlmWl8ub+KZN0L173U+29+23gUIFvD3LQSadjRvBmOiftaAAvv/eEyrBbYK01b59\n3f/+6joFBbB9e6BQASgsrChYgn/D1q3lxUN/QyVWwgoWa23E/eGMMb9DdpVMAe6I87iSTm6uaCzu\nPhPbt0OXLtC5c40Oq9oUFnrHWVny2R+06cGOHXDCCfK80dC4sXxXILENDRt6bWZmimAuL5dU66ef\nXv1nUKpOmzaBhvj69UUTKSmpKFjc37BRI9HW3X/7mnBSiZWY41iMMV2AScAZwHTgKmvt93EeV9IZ\nPVq0lJwcWVsuLRXh8rvf1fTIqk5RkbxxuvToIULBnSg2bZJ197IyedZrromu3WbNPG2laVPo3Vva\ndAPoVjkRR1u3wk9+Ep9nUapG374webIcp6XBKaeIYMnLg/nzA+u6v2HLloHlmnBSiZWYBIsxZjyy\n5HUYGGutfTkho6oB3DXk556T/Eg5OfK2179/zY6rOixaBE2aiEDZs0eO27WDsWPl+vPPy+SSkyPL\nHtE865EjsoOgm5iwXTtZ8nLb/Pe/4bvvxE7Vvr1oMErN8cMP3m/VqJFoIW4SyZ49xX6yZYsIFfc3\n9JdpwkmlKkQbIGkQT60hwFTgGmttQSIHVhMMGAB9+sCtt0oCPoA1a+DEaudtTj7+lBytWsENN8CZ\nZwbW6dsXbrvNW39fvbryJINLloiW06qV1H3gAfGicxkwQBwfpk2T89mzxTlAST4HDsjv5SaR/MMf\noGNH7/qAAaGFhgoSpbpE3PPeGJNqjLkDWAZ0B35prb3gWBQqLtnZMGiQd+6PQK5LfPedvKWCaA2D\nQ+z7mZUVWB7Ns/rrDBsWKFT85f5lse3box+3Ej/mz/cM8p07BwoVRUkkYQWLE1OyCAlWfBc4yVr7\n92QNrCbxRxovXSreYnUNvwAYOFCMsaHwP+vy5bJkFo5Nm2DtWjlOSwtvmG/eXDQ/lzlxT/ijVEao\nJJKKkiwiLYUtRmJK9gItgbdlRSwk5dbas+M8thqjfXuJwVi7Voz4n33mRSXXBfbvlyUQl0iTSm6u\nLPV9840Y8efNg3PPDV3XP1H161fRq8jPiBHw5ZdyPH8+/OxnYndRksPXX3txKW4EvaIki0hLYfOR\n5JLLgYxKPsecidY/Gc+ZI5NuXSHWJRD/s86dG/pZi4pg4cLQ94SiVy8v0O7gwUBBpyQe/0tAXp4K\ndSW5RIpjOSOJ46h1nHYaTJkiBtDduyUiv2/fmh5V5ZSXBy49RbMEcsopon3s2ydLYcuXVzS4L1zo\nOTTk5krwaCRSUmD4cHj3XTmfPTu0nUeJP7t3e9oi6DKYknzisR/LMUl6utgQXn9dItOvvVZiMipL\n4BdtYsZE3f/qqzBjhix/dO8e3RJIejoMHQqvvCLPOm4cnH2219eiRXDffRKXUr8+XHlldJs+5eXB\n1KniurpkiQjn7t2Tn5wyloSZdT25Zn4+PP44LFsmv9Xw4eIRpijJRAVLBBo2BGtFCygslHXrNWsk\n3sMfzbxmjcS+pKSI4TpSWaLv37vXSyC4c6dMMNFMgjk53rMePAgrV3p9zZkjkyrIktjixTKBVdZu\nw4YSQDljhpxv3iyTXTKTU7pJGEtLReOqzu+SlVW7k5Pm58MLL8hv5/6OGzZE91spSjxRwRKBBQtk\nYty9W/6jLl8u5TNmVJ7AL1xZMu/PzY0+geD8+RJRv8vZwcZdSgnuq1Ur0XCibdefPsZNkxPL/dVl\n2jQRtl995dmOqvq9pqZKjE7DhrUzMeO0afL7lZTIeVaWCMTaOFbl2CZiHMvxjrv0EYw//5bLkSMV\nk/iFKkvW/U2aiItxLIklQ+WECu7L/T6ibbe42Nt90E2TE8v91aWgANatC3RIqOr3Wlbm5ZKrjYkZ\nCwo8zRIkc0RKSu0cq3JsoxpLBHJzZTLs0MF7kweZtOvXD6zrxon4y0OVJeP+zEzPuB5tAkH3WTt2\nlGd1c4G5faWkSNqXBg1ia7dtW2n7W2fbtS1b5DxZiQ1zcrw4pJQU+Z6q8r26/PCDLAd265aY8VaH\nBg28OKSUFBEsoEkkleSjgiUCo0fLenqXLoGZf0Ml8Nu+vWICv1Blybwfok8g6D5r586BGZ1D9RVr\nu99/L1pDaaloBfv2JS+xYePG3nGLFpKGpirf67Zt3pJoQYE4ONQ2mjb1jl2bEGgSSSX5qGCJgLsu\nHSopXzQJ/CIl9UvG/bEkEIzlWavS7p494vyQkyPBp8lY8y8qki0BwiXMjOV7feUVmDlTxt+yZe3L\nf1ZcLEIw+Fk1iaRSE6SU+3drOk4wxnQG1n366ae0b9++podzXLBpE9zvbAWXlgYPPxw5cj8ezJkD\nb74px7m5cO+90blJh6KsDO6809uH5sorJVVObeGzz+A1Z7u7Vq1gwoSqP6uihGPTpk386Ec/Auhi\nrV0frp4a75Wk4KbJAS9NTiIJzpU1fHj1JtrUVGnDZdasqrcVb8rLA8czYoQKFaVmUcGiJI1kpsmJ\nJrtzrAwd6mVzXrtWYlpqAxs2iB0LJHXLkCE1Ox5FUcGiJI1TT/Vcj900OYnCr60MGFDR26sqNGoU\naFupLVsq+MfRv7/3HStKTaGCRUkaGRmBqfYTNTHHkt05Vvxtff65OAjUJAcPSmS9i+YFU2oDtc4r\nzBiThuwBcwXQEPgIuM5auy1M/f7Ak0A/YDNwv7X2teSMVomV4cPh44/leOVKiWwPjoCvLsHZnTt1\nil/bJ54o8SFbt4on1qJFgbaXZLNgARw+LMcdOgS6iitKTVHrBAvwR+DXwH8Du4CJwDvA0OCKxpiW\nwH+At4ArgZHAS8aYrdbaj5M1YCV6WraUlPozZ0rCy8svl/iReCWGXLQIHnxQBFb9+vH33EpJEa3g\n6adl/EuXwk9/Cueck3y33kWLZGvo7dvlWU87TY32Su2gVgkWY0wmcCNwg7V2ulN2KbDOGJNnrQ0O\n1RuLbER2o7W2DFhtjDkVuAVQwVJLad4cVq+W48OHxdA+YYKcu9rLunUwcaIIhkWLvHvXrYtcd/p0\niV0B0Sg+/1y2O4jnpJ+RIQkqS0vlfPVqL21KsoRLfj489pgEboKnPfXrp3ErSs1TqwQLcAqy/DXL\nLbDWrjfGrAeGIZuP+RkGzHGEisssYKIxJsVae/wF6dQBvv1WosKLiyVh4sKF4RNDfvxxdAk3Q9Vt\n00a8uOKdhHHmTNG8tm6V84IC6ff/t3fvQVKVZx7HvzMgIBQSJEoGUPCyPi4GrxhFhMHVeIsbcjFb\nVrKKSVnZjXjZaHlJdEVjTIxJJe5uNJpsShONko26xkSHTRQZQQtRUMTbs5oVw1WCRkBAQGH/eM6h\nzzTdA8z0dPdhfp+qrmbec7rP+zYz5+339rzVDPbY0tI2BtjgwbE+SAEnpR7U2+B9ulqxeCLnUmCf\nMueXOrcvMKiyWZNKWb582+CenQ3YWercnQ2YuaOKg5P+5S/R8qpmsMeFC9vGr0vjgingpNSDemux\n9AU2u/umovQNQJ8y5xfPy0n2OSx5vtSBpqa4Ea9ZUwirn8a16lW0yXX//tumbe/cxsYIx5IGkKx0\nEMampliD079/5H/z5qgsq7l+ZMOGQqDQAQMKU4wVcFLqQb1VLOuBRjPr6e7Z76S9gbVlzu9dlJb+\nXOp8qQNpwMtDDimkVSLgZmcDZu6oNP9DhsTmaBCtmFNOqex1ytm8Obq9UtnKRAEnpR7UW8WyKHlu\nyvwbYAjbdnml5xfvmDIEeI8Y1Jc6VCrgZaUCbnYmYObO5v/hh2PiQe/eEbKmWgsT58+P1tjBB0cl\nu9deCjgp9aXeKpb5wBqgGbgbtgaMHAE8UeL8WcCXiwbqTwCeLBrQlzpz9NGlb4Ll0jp7bqWl1znk\nEHj00UhrbW3bCusq6cLSvfeGSZPgM5/p+muK7Iy6qljcfYOZ3Qr8wMxWAiuIdSyt7j47mY68J/CO\nu28Efg5cDtxmZjcDJwFfBNQhIFUxfnyhYnnhhQhVs+eeXXe9FSvglVfi3w0NMG5c111LpKPqbVYY\nwNXAr4gWy+PAm8CZybHjgGXJM8lq/FOJVffPARcA57j79CrnWbqpwYOjSwpiMH3mzK693hOZdvuo\nUbEmSKTe1FWLBSAZtL80eRQfmwE0FKXNBupoZwzpbpqbCws+Z82CM85oO7heKZs2tZ2coLhgUq/q\nscUikiuHHVbYAnn1anj++a65zty5EXQSoqUycmTXXEeks1SxiHRSjx5txzq6Kmpz8cZljfrrlTpV\nd11hInl0/PHwyCOxUHLu3JgSfMAB7QfMLKVcwM2pUyO9b9+IYJzdfkCk3qhiEamAgQOjOyzdInjJ\nkogCcMMN8fNee8UsriVLYnElbFu5PPNMHNuyJR6LFhVev3p1pK1dGyFkXn1Va1akfqliEamQtZlY\nD0uWxCMbMLNXr1jn0r9/6WCRLS2walVMJ964MdJKBdxsalKwSalv6qUVqZBNmwrxyVLZgJkbN8ZK\nfSgdLHLZsjieVirFr4dY3b/HHgo2KfVNFYtIhQwZAgceGCFeGhvjsdtu8UitWhUtm1LBInffvRCU\nE9q+vrER+vSJ929oULBJqW/qChOpkNNOi+6vY44ppKUBM1euLGxAtnw5XHzxtq/P7iUzeDCYlQ+4\nqWCTUs9UsYhUSHvBNe+6K0K/9OsX4yWHHtr2tevWReVz8MGweDEMHRqBJdsLuClSr1SxiFRQuSCY\no0fDlCmFrYTnzGm79mX27Bij2XtvOPJIuPrqtvvXqyKRPNEYi0gVNDTEosZUa2tho64tW9rGAGtu\nblupiOSNKhaRKhkzpjCQv2hRbC8M8NprMSMMYuD/E4p8JzmnikWkSvr1a9ullYZoyYZqOfbYmP0l\nkmeqWESqKBuR+NlnY0B+3rzSx0XyShWLSBUNHw777hv/3rQJbr019rCHiC02dGjt8iZSKapYRKqo\noQEmTCj8nK5tgbbpInmmikWkykaPjhX4c+fGjpPpPitHHlnrnIlUhioWkSp74YVYDLl2bSFi8YoV\n8Nxztc6ZSGWoYhGpspaWiFCcamgoRCwW2RWoYhGpsmXLYsOu4cNjXcvw4THFWBGLZVehkC4iVdbU\nFMEqhw+PR0oRi2VXoRaLSJWl2w0XU8Ri2VV01xZLD4Dly5fXOh/SDTU1wcSJseL+rbciRH5zc6Qv\nXlzr3ImUl7ln9mjvvIYtaSS8bsTMjgdm1jofIiI5Nc7dZ5U72F1bLM8A44BlwIc1zouISF70AJqI\ne2hZ3bLFIiIiXUeD9yIiUlGqWEREpKJUsYiISEWpYhERkYpSxSIiIhXVXacbb8PMegDfBs4F+gPT\ngMnu/lYt89UZZnYb0NPdz8uknQzcBBjwGnCFu7fUKIs7xMwGE3k+GdgdeBq41N1fTI7nrkwAZjYM\n+BFwIvElbxpwibsvTY7nslwpMzsWmAWc5O4zkrRclsnMRgIvlTg0zt1n5bVcAGZ2HnA5sA/wMnCZ\nu09PjnWoXGqxFFwLTALOAcYDw4D7a5mhjjKzBjP7FvBPRekjgYeA3wBHAL8FHjSzQ6qfyx1jZo3A\nfwMHAROB44BVwGNmNiiPZYL4PwIeBgYCJwDNxPqA3yXHc1mulJn1A+4is0I752UaBawk/o+yj6fz\nXC4zmwTcAtxIlLEVeMjMRnSmXFrHAphZL+KX5iJ3vzNJGwG8AYx196dql7udY2b7Az8HPg6sA/6Y\ntljM7HbA3H1C5vzHgdfc/as1yO52mdkRwDxgpLu/kqT1Bt4BvgaMJWdlAjCzjwE3A1e6+8IkbSLw\nILAn8Yeeu3Klkt+1g4AJwAnuPiOPv38pM7seGO/uzSWO5bJcyZebN4Bfuvs1SVoj8fd2E/Flp0Pl\nUoslHE50f81IE5I/9oXECv08OQ5YRHz7eKPo2DgyZUzMoL7L+GfgDMAzacku8Qwkn2XC3Ze7+1mZ\nSmUY0cJ8xt3/Sk7LBWBmpwOfAi4qOpTbMhFf1F4pcyyv5TJgOPDrNMHdN7v74e5+D50ol8ZYwrDk\neUlR+lKi3zE33P1u4G4AMys+PIycldHd3ya6jLIuIsZa/gBcT87KVMzMHiS6+f5KdItBDv+vAMzs\no0SL+ctEebJyWabEx4E+ZjYbGAG8CHzT3eeQ33IdlDx/xMymE2V8lWhFP0UnyqUWS+gLbHb3TUXp\nG4A+NchPV+kLvF+Ulqsymtmnge8CP0y6xnJfJuBfgWOIge4/mtlQ8luu24GH3L3Ufpi5LJOZ7Q7s\nDwwALgM+TdxgW83sb8lpuYA9kudfAP8JnEpUmNM7Wy61WMJ6oNHMerr7B5n03sDaGuWpK6wnypSV\nmzKa2bnAz4CpxCwWyHmZANx9AYCZnUV0Y04ih+VKBoKPAA4tc0ruygTg7uvNbCCwwd03wNbfxaOA\n88lpuYD0i/QNSdcXZjaZ6Or6Gp0ol1osYVHy3FSUPoRtm4J5toicltHMrgLuAG4DznH3dJwll2Uy\ns8FJRbKVu68D/gQMJZ/lOpfoPlluZu9RGBdrSaa+57FMALj76rRSSX7eTEw/3of8livN34I0wd23\nEGNJ+9GJcqliCfOBNcQsCGDrrLARwBO1yVKXmEWmjIkTqPMymtnlxBqja9z9wuSXP5XLMhGDpvea\n2eg0wcwGEAOqL5PPcv0jMJKYDHM4cEqSfh5wDfksE2Z2lJmtNrOjMmk9iDK+RE7LRcz+WgscnSYk\nM8VGEl9wOlwuTTdOmNmNxDeuc4EVwK3A+9mpdnljZjOA1zPTjUcBc4kxinuBLxJ9xkemU3nrjZkd\nSvwB/AK4qujwGqLvO1dlgq3TOmcQ/dxfJbolbgQOIG5Y+5HDcmUlM90WUZhunLvfPwAz60n8Dm4E\nJgPvAVcQsxUPBgaTw3LB1mnUk4nKfwHRtffPxO9gLzpYLrV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bikeshare = System(olin=10, wellesley=2)\n", + "newfig()\n", + "plot_system(bikeshare)\n", + "decorate_bikeshare()\n", + "run_steps(bikeshare, 60, 0.4, 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But this is relatively easy to fix, using the `return` statement to exit the function early if the update would cause negative bikes.\n", + "\n", + "If the second `if` statement seems confusing, remember that `n` can be negative." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def move_bike(system, n):\n", + " # make sure the number of bikes won't go negative\n", + " olin_temp = system.olin - n\n", + " if olin_temp < 0:\n", + " return\n", + " \n", + " wellesley_temp = system.wellesley + n\n", + " if wellesley_temp < 0:\n", + " return\n", + " \n", + " # update the system\n", + " system.olin = olin_temp\n", + " system.wellesley = wellesley_temp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now if you run the simulation again, it should behave." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1Md01wOXAVcAYJAQW4PdAb0RpGJIZZ9hofb2ElAaTyLrPwaG4XjuuurpAWRNR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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bikeshare = System(olin=10, wellesley=2)\n", + "newfig()\n", + "plot_system(bikeshare)\n", + "decorate_bikeshare()\n", + "run_steps(bikeshare, 60, 0.4, 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variables `olin` and `wellesley` are created inside `move_bike`, so they are local. When the function ends, they go away.\n", + "\n", + "If you try to access a local variable from outside its function, you get an error:" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'olin' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# NameError: name 'olin' is not defined\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0molin\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'olin' is not defined" + ] + } + ], + "source": [ + "# If you remove the # from the last line in this cell and run it, you'll get\n", + "# NameError: name 'olin' is not defined\n", + "\n", + "olin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Add print statements in `move_bike` so it prints a message each time a customer arrives and doesn't find a bike. Run the simulation again to confirm that it works as you expect. Then you might want to remove the print statements before you go on." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison operators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `if` statements in the previous section used the comparison operator `<`. The other comparison operators are listed in the book.\n", + "\n", + "It is easy to confuse the comparison operator `==` with the assignment operator `=`.\n", + "\n", + "Remember that `=` creates a variable or gives an existing variable a new value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whereas `==` compared two values and returns `True` if they are equal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x == 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can use `==` in an `if` statement." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if x == 5:\n", + " print('yes, x is 5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But if you use `=` in an `if` statement, you get an error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# If you remove the # from the if statement and run it, you'll get\n", + "# SyntaxError: invalid syntax\n", + "\n", + "if x >= 5:\n", + " print('yes, x is greater than or equal to 5')\n", + "else:\n", + " print('no')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Add an `else` clause to the `if` statement above, and print an appropriate message.\n", + "\n", + "Replace the `==` operator with one or two of the other comparison operators, and confirm they do what you expect." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a working simulation, we'll use it to evaluate alternative designs and see how good or bad they are. The metric we'll use is the number of customers who arrive and find no bikes available, which might indicate a design problem." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we'll make a new `System` object that creates and initializes the system variables that will keep track of the metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bikeshare = System(olin=10, wellesley=2, \n", + " olin_empty=0, wellesley_empty=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we need a version of `move_bike` that updates the metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def move_bike(system, n):\n", + " olin_temp = system.olin - n\n", + " if olin_temp < 0:\n", + " system.olin_empty += 1\n", + " return\n", + " \n", + " wellesley_temp = system.wellesley + n\n", + " if wellesley_temp < 0:\n", + " system.wellesley_empty += 1\n", + " return\n", + " \n", + " system.olin = olin_temp\n", + " system.wellesley = wellesley_temp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now when we run a simulation, it keeps track of unhappy customers." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YS21IKBs3wt69sty7N2QGFUQVFbbNsgP4HUn8P+BxrfVvlVJDgeOAm7XWpwG/\nAy7ocE0MHccZKdZ0b2OOUxiccIK9vHZt9PGNDIaY4XzXJ04Ulzuvbe3Er5BQwJPB5ROANODl4O9S\nYESHa2LoOOPH2xnsysqij6hmCMvevbB+vSynpYl34X77ye/mZjOH0ZBAnL0XpWKee8avkKgC+gSX\nZwHrtdaW0nsssL3DNTF0nNxcGDVKlgMBbz2loV2sWmXPrh4xQkxARrtnSDjNzfDtt/bvoqKY557x\nKyTeBn6vlLoGOAV4AUAp9UPgD8B/O1wTQ2wwyYnigrNDZgkHo90zJJx162xd54AB8rFyzwBs2yZh\nezqAXyFxBTJauAl4E/hTcP3dwBrgmg7VwhA7TMsVF9wjehDtXlqaLK9fb9sODYZOw6v3kpkZOuG2\ng+2Ar3kSWuvtwPEemw7TWm/qUA0MsWXsWHlImpps74Y+fdrezxCWmhox8UDohPe8PBg5UgREICAq\nqQMOSFw9Dd0Qr94LiMD45htZ1hqCAQ3bQ1QzrpVSw5VS/6uUuibo5TRYKRU56Lqhc8nODg3sZUYT\nHcZ5CUeNEtOPhdHuGRJGY2Oox4RbSFho3aEUAtFMpvszolp6HPgjMAy4HViulBocYVdDZ2Narpji\nFBLOSwsxdyQxGPyzZo0d1HPIEMjPt7eNHGn3ZnbtEttEO/E7me63wOXA1cA4xAUW4PdAP0RoGJIF\nY5eIKV5qX4tx40K9jq3gvAZD3HF2AN0PZno6TJhg/+5AO+B3JHEx8Hut9b3Aevu8+gPgemB2u2tg\niD0x9m7ozlRV2fkjMjPF5OMkJyd07pLxOjZ0GpGGuO51HdAo+BUSw5BJc16sA2IW4kwp1VMpdZ9S\nqlwpVamUKlFKTYzV8bsFmZniemNhRhPtxnnpxozxjrxstHuGTqe+3s4lA6GjBosY2SX8RoFdjXg3\nvemx7UjEVhEr/gocDpwO7ETcbecrpSZoretieJ6uzX//az9EWoe2ZO5osnFMop6MlJZKGtKKCigo\ngNnBcbDXuttvl4Y/L8+7swbyLj7+uITQWbZM5jbNnp284cO9/n80dfV7/Tp6zOLi+JwrZQj3Xi5e\nLInFvvhCfvfqBVdfLcvO9/WWWySVaWOj/N68WWaBusu1gV8hcQ/woFIqC3gFCABjlFJHAL8Bfuv7\njG1zChIX6j0ApdTvgK+AicDyGJ6na+M0YlVWSi/Ccur3oqVFFOq9etlK9i5IaSk8/LCokVpaRBtn\n5aS3gvY5Ob4yAAAgAElEQVQ51+3aJZeupkbyWpeWtm6Qdu4UNVNLi/xeswbmzpXlZGu8SkvhkUck\nR01mpuSpiqaupaVSfu9eOYb7+mVmyiTg9hyzuVk8trdtg88/F3fizz+3y7nPlZ0d/blSksbG0EB9\n9fWwZYv925lLxklamrQDltG6qsoWElHgd57EI0qpgYj94VeI4fpFoAG4S2t9f9RnDs824MdKqReA\nSuB8YBexHa10fXr1sudL1NdDXR306BG+/JdfijDJz5deSiSBksKUlMDXX4emCd8eDCrjTPTlXpeR\nIQE2589v3RgtWCBTUay8L1VVkm/Cq2yiKSmR//bNN3YMqp49/de1pET+nzN1gftaDR0q2o9ojhkI\nSMfY2RZ+8EHrKT7uc40dKzG0kvFax4SGBli61PZiAum55OTYv50dQjd9+9pCorIShg2Lugq+u4xa\n69uAAiTA3znAicB+WuvfRX3WyFyEBAzcAtQCFwInaK1NurVoSEvzn62urs7eXlnZpUOarlsXKiBA\n3j/nO+i1rl8/GWCVl7c+ZkVF64EbeJdNNBUVonUAaZi3bpVlv3V17m/hvlZbt8qoKppj1ta2jmpd\nW9u6rPtcVl2S8VrHhO3bWz+cTjIywo8kQIZclmagnR0/XyMJpdR4rfUqrfVuXHGalFI9gD9ora9u\nVw1aMw7YDFwC7EDcbv+plJqutS6L0Tm6B/n5tmdTZaUocL1wC5DKSukOdkEyMuzlnBwZcNXUyPvj\nzDDnXJedLW7n4N0RKygIvYTWcjs6bXFnyJDQrJbR1nXoUOnhW/TrZ1+rrCzRjLS0SIM/aZK/YxYU\n2DPaQdz7e/aU47iz/lnnSkuz1YCNjaEeZl0K54PVu7c8jLt2yYVJT5cbkhmhGc/NFaNZVZWdlCxK\n/NokFiqljtE61E1GKXU88ABQiDTmHUIpNRp4BDhCa/1hcN1PgG+AK4GrOnqOboWze1tVFd4u4c6F\n24WFhLMxHDZMIroOGiSXZdAge5vXOoBZs1ofc/ZsMVpnZIiOvK5OPl5lE83kyfDCC/bvPXuko+q3\nrtOnw/PPy3JmpgiCIUPkWlVV2T36qir/x5w9W1R2FsOHy72ZMQPefz+0rHVfysvtx7ayMjmvdYdx\n56meMEGk544dsP/+/o8zcGCoLjVKovFuWhQUFF8H7RP3AGcBnwNHtLsGoUwBMoCl1gqtdaNS6hNk\nhGGIhrw8u3vX0CDjd7fhKhCQnokTP4buFCQQkMtQVCQ91379pEG6IJgya/58aXyGDfNeN2uWt97b\nWldeLg5lPXvCkUcmp448J8f+/zU1UtdjjvFf17w8e//cXBGyF14o2x57TFRHPXuKB7bfY06ZIveh\nrk7q9J3vwOmny/4TJ3rfl3vugU8+kXMddFByXusOYw2TQEYQeXkJqYZfIfF94N/IiOIu4NdANtKz\nv1dr3RKj+liDzgMIejIppdIQz6aSGJ2je2C5uD30ECwPOoWdeSYcfXRouS1bpHvo5uabu9xoYvt2\nsUcMHiyN2913h6qfIgmAtiguhssvh5dekt/J6iCmtfz/wY5AOl5zP/zsf/rp8L3v2duKimxPTMtf\nwmlfDUdZmRioDzlEvu+4w+6fFBd734ObboK77pJlqx3tcvzsZ6IPBZGkljT2S4zc1309ysH5CScB\ni5B5C18CRVrre2IoIAA+Bj4EHldKHaGUKgL+DowE7ovheboPbc30CjfRrgvOCnP+1fHjQwVELIhh\nTLW40NAg7rlu/M61DATCBx0FUZlbau+WltBcOJFwH9PPANYZVGDr1taD4S5BpHgwnUjYkYRSyiu2\n7L3AKOBAYJpSap+zrtb6fY/yUaG1blZKnQjcBjwP9EJUT0dqrddH3NngjfNNtpz5nd1c5xs6ZIjt\nf601zJzZKVXsLCKFuokFI0aIl/HevaKx27pVLmmysHq17SgzcKCMqlpapCdfXS2NfCQqKqQciJrH\nyw6qlG2E1tqf6jyS4AlHVpa4v1r7au09IE5ZWlpCY7z4vTBxIJK6aQkyac6NJef/FdyeFvyOSb8s\nmLsiynGVISxDh8oYfvdusUmUldmuOoFAaG/lxBPtmUlWV7iL2CXcfzUe75wVU82aQ7BiRXIJCef/\nnzRJjO1WpOmVK+HQQ/3vP2GC96OhFLz1liz7GYw2N0seDotohLdSXVhIrF8vRhoQ45nbg6ITiaRu\nOho4xuNztONzjOPbkIykpYVPxrxpkxjHQLqRU6bY3cmaGtneRdi82fbDz8trtzdgmyRzAF639iLa\nmFN+tB/ObH0bNnjPdXCyfr09Lad//+iccNyPdbKp9zqEu0eTwM5a2JGE1npRZ1bEEEeUkpgSIA/f\n979vLzvLpKXJ99Kgc9mKFfFrTTsZ91+Nl2HZyy6RDIOxujqZSAhSnwkTRFi+/rqsa0ug+R2JeWXr\nO/DA8MftSFtYWCiG8fp6UZ3t2NEhT8/kIt660SiIZJO4DviH1roiuByJQHBGtiEZcT5kq1bJGD8j\nw1sZ7BQSWoe6r6Qw7dF7t4dhw2QwVl0tcxA2bUoOOfvtt3ZsqeHDxaYwZowduWXLFjsqixdlZfao\noE+fyI5vRUUiJEAeIb9CItq2MCNDRi5ffim/V6yAI2LljJ9ImppCrf4JtEdAZJvErUjU14rgciQC\niLHZkIwMGCCfHTuk27V+veTh9DKMOd9UL0N3ChIIdJ4N0OqlL1smv7VODiHhJSQt46/VUGsN06b5\n2z9Sj18pCULs3s9NLNpCpbqgkFi71vbrHTTIjjyZICKpm9K9lg0piKVGsqavrlghDb9lGMvPtx3n\nBw0SQ9muXbJ9wwYRKCmMNXEMpJcfLjpJrCgqChUSxx4b3/P5IdxIqqgoeiHRVo/fytbX0iIjqXCe\nU2vW2G3h4MHy2EVLsqr3OkSSuL5aRNX4K6XSlFITlVKHKaXGxKtShjjgtqi6H0TrzbIEikUXmC8R\n7q/GC/elbonlTKJ2UFNju6W6s1r6udXReiD5zdYXC2+z4cPtici7d7cOPpiSdJZu1Ce+hYRS6mok\nMusXiHvsKqXUBqXUOfGqnCGGOB+21avthCXube7fyeai0w46+50bPNjW7VuDsUSycqXt+VNYKOE0\nLEaNsmdF79hhh+J24vZAcgfd8yKcQ52TWHSYY5jKOTloaAjNOJcqQkIpdSVwJ/AWcDZwHHAuEjrj\nCaXU2XGroSE29Otnq5QaG0O7hpGExKpVkUMVJzktLZH/ajxwD8YS3XBF6rFnZIh6yKus1zq/I7G2\n/n99fejsb6/sm37xI5BSBueMx4KC1gk1EoDfkcRlwF+01mdprZ/XWr+ttX5Wa30K8BBwY/yqaIgZ\nXt21gQNbdw0HDLAn7zQ2hvZsUoxEzUly68oTSVv2hLbq2p6RmOU5BbbnlJPVq0WNBeIR1pG20B1U\nIKXnS8R7xmc78CskCnDlkXDwTyRJkCHZ8Xrowo3zk6mV6wDuBrKzjJrJMhjbvVvCaYCMGsaObV3G\nbZdwNrJNTfasbHfZSGRlRR6hxLItLCgInQPqzE2RciTR/AgLv1FgFwGnAgs8tn0X+ChmNTLEjwce\nCM0YAxJgaMmS1hEjX3pJclyCJBp+5RVJwA5w1FGhZRcvbr3OwmckSr8J773WFReH3//ee2U0kZcH\nU6f6qkpMGDBA1MtffCHzC666Cs45J3xUWa/6RxP+Otz+//63eFrV1kpsqc8+a33cESNEmKxaZc+F\nsEJ1/+c/8OGHsn7QIHFZ9VsvpeTR2LhRjj1zpn1f7r9fwn/n5UlY9Y5gqfdef13OdemlUsdYPUMd\n2d/3Pbz4Yhlavf++7aLV2CjSNkbRXNtLpMl0P3H8XATcopQaiowctgD9gFnAj5GEQIZkJytLZlFZ\n/qAQfvaUMyWi1R21WhCra2pRWyvr0tLsDPVRUFoKDz8ss2YbG8Vt8s03Q6sXbt3SpRJE7quv7OM5\ny+7YYWcwe/ddOOCAzsk9UFoqM5ytS71ypR0Wy33+0lLZVl0t7URzc/iy4c41d66ca/fu0OuycKFd\nh7Q07+MuWyaNq1WutFQ6tPvvL/0Ha31GRnT1qq+3O8ZNTVKnN9+U+2Hdl9paePttaeQ7cl+cEWq3\nbLHPBR1/hqLdPz9f1kdzrQA7MRjIexpNDPc4Emkk8bTHulOCHzcPAXNjUiNDfMnPt9/6vLzwDbqV\n5KS21rb+WsGPnJZgsLuhIK4zU6ZENQGvpERUGk7Z4054H24dSCPn1mm7y/boIV488+d3jpAoKZFL\nbblkVlZKuAqv85eUyCX89FP5rZQEBvRb15ISiTz7ySeh7rbu62I1bO7jWnW1Mt1u3iwfv/uH47PP\n7Gx99fXyiLjvi9UWdvS+OFVi1dXyifUz5Hf/zEwJlhj18+Y03ITrvCWASG/y6Cg+Zs5EquA0Urfl\ny9ieQDh1da0z2rdBeTls2xa6zp3wPtw68A4i5y5r/VUrvWa8qagIfc9375YG3Ov8FRWh/9+K1u63\nrtb+7vkYzuuSnW3r7d3HraiQAaDbXuPcPyPD/j9+67VlS+vJwvG6L1VVrZMuxvoZ8rt/U5OMiiHK\n/5WkQiLSjGuTv6Erkp8vBrH6+tCEz16MGCGtg+UeZDX+7inLu3dLBq09e+R3VVVUD3mPHvaLl5Ul\nsqmuThot56m81oEMWtwhuZ1lc3Ptv9rWX44VBQWicnAOxnbvlhDdXmVLS+3flkDxG86joMAehYA0\nzDk59nVxfkPra2DVddIkWw0EofsNHGhrP/xew4ICeczy8sQ+A6H3pUeP2N2XYcMk1enmzfazFMtn\nyM/+ffrY7XxlpZTx/b8aG0P1gk51b4Lxa7g2dCWcuSsjkZEhgsLC0geNHx9arqJCylmG7l27ZNaW\nT5xRP/r3l8P37SvvitNl1WsdwE9/akccaavsrFm+q9UhZs8WnXR+vt3LrKz0Pv+RR8Jzz9m/W1pE\nXeK3rscdBy+8YP8eP16EhNd1gdbHterar19oaAy/+4fDOq7zUYjXfbHO5ZzpHY9nKNL+CxbY4Vgs\n84Lv/+W0R/TuHfu0iR3ACAlDbHCOHCwLrM8HPRCQwU1ZmQiJ4cPthPfz58uQfdgw73WzZonOd+JE\n/2U7A+s8jz0mMrRnT2m8vc7fq5f9/2tqpOzUqf7rOmCATEYrK5NrOXZs+OvidQ2s336uazTX0Ou4\n8bovfs/V0Wco0v7f+Q5ceaX0kbKz4eSTozRaWySRqgkgLZDSM08EpdQoYO1bb73F8GQIudldueUW\nO1HR5Zf7yl3Z1ARz5tiB3m6/vX2B3pKVPXvE/RVEJXH33aFhMQCeflo8r5yMHw9XX+3vHK+8Aq++\nKstHHQVnm/gHCeOhh2D5cln+8Y/hGL/p2H7/e3ukPmeOSJxOoKysjGMlAuVorfU6rzJhDddKqd8o\npTpJg2voErRjAp4zKnJ7I4EmM7162baFlpbQ0NgWXpdqzRo7XlJbJFk8uG5Nu0KE+JnxmEAieTfd\nhHguoZRqVkp14lQkQ0rSjuix3aGBixTHaOdOmc8IYhi2jKfNzaFuneGor0+6eHDdGq90LG3ifCjG\njIl6nlG8iWSTqAKuUkqNA9KA/1FKhZ0nrrV+MtaVM6QYVoLjQMBOcGzFcQ5DEoaqiTlFRfDWW7Ic\nKTzFuHGS8c1ygdVa9OSRcMdA8srbYOg8rAjAlZUyd2XjRh8+HEmWP8JNJCFxG/AXZPJcALghQtkA\nYIREdyfKBMcNDaGRQLuqkIgkO90jqYICeOed1tvCkYShfro1VoiQj4KBirT2ISSSfDgdVt2ktb4P\n6IsE70sDTgwue31Gxr2mhtQgCrtELCOBJjM9etgNhTOVaiDQuhM5YYI9qW39eumNRiLJO6Hdkqi0\nrjt32jMps7JCfXiThIgusFrrWqBWKfUz4EOt9Y7OqZYhZfGb4Jik70DFFKUklhNIw37QQdI27Nol\n63JzRZCkp8uUkw0b7MHYAQd4H3PvXhEkIILFPX3FkBicwvrbb9vwBnfrGzOTb1aCrwA7WusngHSl\n1B1KqY+UUiuUUkuUUrcppYa0eQBD98FKcAx2guMwdAd7hIWX14vz/48fb182v4OxVavs+VcjR7Zp\n/jF0EgMG2OFG6uvtzoEnKaAv9JuZrhD4BLgcMWiXAnXAHOBTpZTJJ2EQfCY43rvXfnnS0jqWmSwV\nGDvW7k2Wl4vsDKcq8quu6E4jsVTDl6B36xuT9Cb6DdV5J1ALTNBaf19rfa7W+nvABERo3B6vChpS\nEB+tnLMXPHx46+BsXQ237NQ6fCPvHIyVldkhsdykQPvSbfEl6L30jUmIXyFxHHCj1nqjc2Xw983A\n92NdMUMK46Mb1R0Nrs7/+c47tiauZ8/QYH65uaHxrLwGY3v22BnY0tNDs8AZEo9TSKxebU8YDcH5\nEkyYEFV4/c4kmlqFUy7vBow21GDTVoJjuqeQcDYczpnXTo8mi7bkrFNwjB7dOtSHIbHk59sTI5ua\nQl2995Ei+kK/QmIp8Isw2y4BlsemOoJS6gKl1Eql1F6l1DKllN8IKIZkICsrNLSAq5WrqZFJRtC9\nesFjxngnG/MSkm2pK1KkfenWRAzRkSL2CPAfBfZG4F2l1KfAC8BmYCiSunR/4PhYVUgp9VPgfkT4\nLAYuBeYppSaFC0BlSEKKiuyXQGuYNm3fJmcveNSo7tMLzswU2eluMLzah7FjpXxTk+RIqKoKTTGQ\nIu1Lt6aoCBYtkuVWo8GKivD6xiTDl5DQWn+olDoB+BNwKzK5LgAsA07QWr8di8oopdIQG8cdWuvH\nguuuBo4BZgDrYnEeQyfw1FOUftBESc1RVPyrBwX3zmP29qcAuDP9Gr7aVUBeZgPjxi6FNQsSnuy9\nsyh6414Wf3MEG2v6U9uUTb+cWjZufZGCwetDrkHWZReT+f7pfLR5HLVN2fx68bdcWLSE4hVP8fbY\nC3jtox9S25RNr6x6dm15AYas7TbXMFWY8LfL2fr2eWys6c+Spmz2vLCcE3c8AUBJzilUbD2agsxt\nzN7vc4p/8YukvX++Z25orRcAC5RSeUA+UKW1rolxfRRQiIxWrPO2AAfF+DyGOFNaN4lHqsazvSmf\n2pYerF2XS8nuayAN9ubkEwg0UxPIpnTbKEq3FtJJaR4STn1zJisqh8oMq/QAmWktPLriCNIg5BqU\nbi1EVw6lpiELmpv4rHwwN2z7PkV1g/lky1RqGtIhvYWM7BYe1zPITGvpNtcwVfhm11DKavpTU58J\nLU28u3YEC/bIO9A3t4GM5nr2tuQwd9d42Lo8ae9f1NP7rFnYcagLiEstQL5S6m1gErACuEZr7ZEj\ny5CslGyczNa0fHTDfrKiGrY39QUCDGQ30EB6Zjp9svYyv2z/pH1BYs2nO4aT0VhHc5P4/+b3Ef9W\n9zUo2TiJ/Oxa1tfnQSDA7sYsdtf144vGE+jTuBcQd5n8Xt77GxJPycZJ9M2oZk+9+PVsbuzlegdg\nS/NADsnemtT3L9l8rqzoPU8Ac4FZwJfA20qpzsnCYYgJFbV92UZojsemQCZNAbtf0j+wkwxaKK9J\nrkxc8WTL3j4MRKLbZNBCvzTxk3dfg4ravvSmmlxCk0rUBnrsW04jwAC2e+5vSDwVtX0ZFNgWss79\nDtTQk73kJfX9S7ZAIZY38R+11s8CKKV+CRyJGLIvT1TFDNExpEcVVYyD7BYIBBiRu43GynTSgMLs\nzWQFGhiUuRMa+zCsf7wGpslHQcY2GrK20ye9ml7pteQ0tgD5DOsZ6iZckFfFpupMJudotjf3oyUj\nG7KyyKqqZ1jPamhoID+9mp7NDUD/VvsbEk9BXhUt25qYnFPP7uZekJNDY7O8A71ym9jT3AMyM6hs\n6MGE/C2Jrm5Ykm0kEcx9yRfWCq11APiGYAIkQ2pw4IAymgPpkJlJbi6MHlTD5B7fMrnHtxT22cWw\nrK1kpTVBQwOzhn+V6Op2GrN7LyEzrZmCzG30Tq+ReOnQ6hrMHvElNDTQI72eEVmbKey/m8JBtVw1\n8EkKB9VSmFVO34zq4P6BbnUNU4XZBZ9CUyP9MnZTmF1B4eC9+96Bofl14g+dlk5VQ15S3z9fIwml\n1MvA3VrrhfGtDsuBGsSGtzR47jRgIvBmnM9tiCE5Gc0U9a2grKY/vbPrGN5rFxcMeBqA+XmnUV6b\nxrDMrczqU0Lx4JwE17bzKA58DH3LmV9zJOVNgxmWXs6sUZ9TPDi0J1k8cC30/Iz5LYdLuX41zCpc\nRvGKl5lY1If51f0o39uPYZlbmDX8U4oHmwDNyUZx9mfQ9wu51+n7Max35b534MW+F7B69yB6ZjYw\nvOcupgxan+Dahsevuul7wD3xrAiIUVwpdTfwR6XUFmREcSkwFvhhvM9viB0rjvsVg0fCYODnP4dp\n04qAkwEorqmBq66SCUXpPeDuuxNa106jqQkOPZTixkaK+QoI9h7POgtmzgwte9VVFKffLeWG7oCb\nbw5uuI5ioPjZZ2FRMDvRCSfAySd3zn8w+OeHP6R44CK5hycUBu+R3KcpAQj82poqMYqy66eSrFFS\n/aqb/gucpZTqDBvGjcCfEaH0BXAY8H2t28hgY0gaGhtD8zO3muzlnDzU0hIao6Irs2aNdxAfr0e7\nrbglUSR3MiSICPfQymDnVTTZ8Nvo7wbOA85QSn0NuONSBrTWMZl1HbRB3Bb8GFKQNWuk0wwSvybf\ny3FDKTs2x4oVMGlSp9UvYbgTzFjCUWsZVTkDOLUVd8MZW33tWqir6z5T11OBykqZKg8ydX7MmFZF\niopg6VJZ1hq+971OrF8U+B1JFALvITkl6oEs1yc7LrUzpCS+4gpFleOxi+D8n0cfDb17y3JNjR3S\nFaTBd2aq8Uq20atX9xyNpQrODsHYsZ5Bu5yvwMqVchuTEb9hOY6Od0UMXQdfEV6t0MgtLdJA1tR0\n7aQS9fXS47dQSj7OruSIoFb622/tFmP4cBEIXihlCxetu8doLFXw8RIMGgT9+klKibo6SUWbhCmu\no3OBVUrlKqWOUkqdqZTqp5RK3qhUhoTgbgvDZpxzJlkJBMJmsOsyrF4toTgAhg2TUUQ4u4LfOOrG\nLpG8+IjAmCp2Cd9CIjiprRxYCDyDzFt4SCn1plKqC3cBDdHg7ATvt5+tUfEkFd6QWOGlgwunb/Ab\n4nX8eNuOsWED1HafSYlJzfbt8gFJSejMIOUiYjjxJMFvjuufA/cCjwPHIlFgAR5F5jTc7L2nobsR\nVTKh7tQT9rowlr4BbH1Dba00+CACYPz48Mfs0SN0NLZqVezrbYget4OCldzcA3ciKsvhI5nwO5L4\nNXCX1vr/ITkeANBavwRcD/woDnUzpCBRJcMZO9Z+gcrLYffuuNUroezdKwIApOG3dHBufcOKFTKi\nsJJ/FxaKIIhEd3QASHai6Cn17y99BRDvaKeqNlnwKyRGA2+E2fYFkoDI0M2JphMMQHZ2qGtgVx1N\nrFplN/wjRkCeI9uvezQVbTYhIySSi3ZknEv2AbVfIVEGTA2z7eDgdkM3x9kWjhwZ2haGpTvYJSL1\nLN36hq++Cl/Wi3HjxEsMZDRWHS4VvaFT2LrVzuneo4ftsRaBZJfzfoXEY8ANSqk52IH2eiilTgJ+\nBzwZj8oZUouo7BFeBbuqkIikg3PrG7YEYzhlZITmCQ9HTk73GI2lCs57bbl5t4HzkVizZl/Mx6TB\nr5C4DREEdwHWU7gY+DcSsuOPsa+aIdWIyh5hMXq0PdFo61ZxGu9K7Nljz2VIT5eevxsviTp6tAgA\nP3SH0Viq0I7k4336QEGBLDc3h4a0SQZ8CQmtdUBrfTHwHSTg3vVIboeDtNZna62b41hHQwpQXQ2b\ngoHew7WFnmRmhhbuao2cc/7HqFHeoTO8GhPfQzG6x2gsFXDbI6K4h8l8C6MN2LcquE9fYKvWOslk\nnqEtSkuhpAQqKqT3Mns2FIfJm+hVFrz3//e/YdkyMV4PHw6ffx7+uK144w3brUNraTQXB53ojjrK\nLue1zlrvtc6rLHROwvmLL5bvb78VWwFIqI01a1qf/4EH4MMPQ9dVVcGrr/qr65//DO+/b8+z2LBB\nRiFe18XCeVyrrm787h8Or+PG6774PVdHn6FI+x96qLwEIE4ZN98sHhw+/pdS8MILEs7ss8/k/Yn0\nbnYmvoWEUupq4DfAAMe6TcB1Wuun41A3Q4wpLYW5c2HbNnHLb2yU39D6YSwtlWe7vFzKag3z5sm2\nPsEks1rDwoXixbR8uUTWAHkvwh3Xk7597eWdO8UCbrnDOn3/vdZZ673WWWVzc2WWcwR/9bhR6cgY\n5xnpEGlQ8vLsyXDp6W3MQnSRni43xTrXqlUiJKzrkp4ukRbDhfew2LULduywvQ+c+w8cGHqf/NLY\nKA+RpWh33pcePeS++NDb+2LvXgmqZ002iOUz5Gd/p56ob9/QgI1tUF0dGudxw4Yo36E44jfp0JXA\nncALwMvAVsTt9QzgCaVUQGv9TNxqaYgJJSXSjnzzjfxubhYNyPz5rR/EkhLp1VgurWBPIh04MLSs\n1rbgALst9DquJ717i9qpqUkalYoKu8GsqLDLea2z1nutc5Ztaur8wDgNDaENv/MiuenXzy7bp0/0\nDWd+vi0kdu6Ub+d12bYNpk4Nf9y9e+HLL20B4d5/82bZ3yNQXUS+/VbO7Twm2MdtaRFXuI4SCMDX\nX9s9Fa9zhVtnrW/rGWprfz8dgjAsXCihy/bskb9SWQkDBkTxDsURv0/iZcBftNZnaa2f11q/rbV+\nVmt9CvAQkgPCkORUVEhH0cJq9C1tiLustd2iqcl7RqgzGkRurt0Weh3Xk7Q028MnXrj/TGfgbDTa\navid/3/IkOjPNWhQ5OM3NER2j925M1RAuGluDv0/fggEbIEVjljdl7q6UAGRSDIzxWstCioqQuVK\nVZV8+36H4ohfdVMB4sXkxT+RXBOGJKegQNRIFrW10nZ4hLqnf//QTvCYMXYb4p4k16OH2CHS0mQ/\nqx5d5NsAACAASURBVK0aNiyKyo0dK71pKymPdXLnybzWWeu91lmVb2mRnnJ9vX+PoVjgbFTbUtX0\n6QMHHyxSOMpeKCA34eCDQ2et19aGjjAqK8PXw1nXIUOkPrW1Mmy0GvLKyuiEeXW1HdQwO1tmkFv3\nJS1NHqiaGrnn0Y5QItW/d28YOjR2z1C0++fnR/2cFRTIZbYc4ay/E9U7FCf8ColFwKnAAo9t3wU+\nilmNDHHjqKPguedC11VVwaxZrcs6o7f26SMPa2amd6f/hz8Uu6kbr+OGxdJ7W1g6X8s3MNw6a73X\nOpBG0XKrraxsXy+9vUSrfojGDuFFz56h4dat6+IUElasJyeBgN11BXtW+KpVEqXREhLOMn5w/v/+\n/aUu1n3p1UuESCAggm3AAO9j+MVZt8GDQ8/V0WeovftHwezZotp1y86o3qE4EVZIKKV+4vi5CLhF\nKTUUGTlsAfoBs4AfA1fGs5KG2NCrl7jaOdM3HHigt84zI8Mu26+fjBQuuEC2zZ8vw+Bhw+QhLi6G\niRO91yec/PzECIm6OvmAXMyOCoD24hw5WD17twF/zx5bj5idHRovqndvezRWWxvdaCySkMzPt9Vf\nlgK+vVhKfIv2GNgTjPWubNgg71zPnnDMMcnxDkUaSXh5LJ0S/Lh5CJgbkxoZ4obW0skaPNheF252\n54oVdtmrrw4dXXs9uMXFHXig4+mSumYN3HGHLPfrB7d1Ulbciy+250Tsvz9cfnn4sh39/23t//vf\n24bWOXPgO98J3X7eebYQmzoVzj8/dPvdd9szJX/2M5g+ve06NTbCIYfYwueOO0IFxddfw1//KsvD\nhsFNN7V9zHBUVNh16tkT7rorKs+iZKG4GC69VJxGIDHOeF5EMlyPjuLjodU2JBtek3S2bw81ZoPY\nGi2HlKys5MyW5RvnBLZdu0I9beJJu6afx4m2Zmo56+o1Aaw9M73Wro2c6DyWEYCddZowISUFhEUy\nTp4PO5LQWq/vzIoY4suuXXZYoKwsaTst9arWMGOGXdb5cI4fL7aIlCU9Xf7EF1/Ib2s4FU/aEQk0\nrigF77wjy+4Ics3NofmxverqjkBnOfNHoi0hacWcsh7ClSthypTIxwxHO2c5JyNjx9re4BUVIjsj\neU53Bn7nSWQDvwQOA7wscAGt9fGxrJghtrjzsk+cGF5IJFMnOCYUFYUKiSOPjO/53JFAYzEPoCNY\nvetAQPJa1NXZo6t168TOAGIXcE+CATF25+RIuZ07ZejpVc5JW6MTkIfLeghXrGifkGhpSS6B3EGy\ns2Xk7nw3E22X8DtP4n4kuF8RkOXxyY5L7Qwxw/0eeXUOoUPhZ5KXcH82XriHYrGaUdxeevYUzwOQ\nRtU5W9hPA5uREWqUaiuetd9E57HQrZSVhU5CHJr6qW2SLY6TX0XCqcCNWutb41kZQ3wIBFp37EaO\nlM5kXZ10erduFdXxtm22M1BubuI7wTFh+HA77EV1tYzj4+mAnoxStqhIptCDPAyTJ9vLzjKR9v/y\nS3ufI44IX9ZvovMxY0T32dhoRwC20rn6xT3sTWF7hIVS8MorspwM+SX8dnECwIdtljIkJTt22BNf\nc3JEe5CeHtrBs9o1tw0w0Z3gmOBOExrP7lmy2SMsvP5/Y2NovKFIdXXvH2k05ldIZmaG5sxoz31J\nRoHcQZzR87dta3vSerzx2wQ8DpyvlOoKTUa3w639sJxKvNqNLmePsOgsIeHMDterl/SkkwGn2sua\nKLNmTWQPJCfWaAzEmrp5c/iy0QjJjuhWmptDVWdd5IFNtuj5ftVNNwLLgZVKqWWAO0hKQGt9fuvd\nDMlAuIbf/X52SXuEhdefjYdqwp2ZLFnUH7m54tK2Zo3895UrQ6M3ttXApqdLmU8+kd9ae88wrq0V\n4zj4S3TeHs8pi/XrbaN7//5tG9NTiKIiOxDnihVw2GGJq4vfkcEdgELChB8CHOnxMSQhbnuE853c\nbz87ikN1tYTCtzrBPXsmTyc4JgwdavsS1tbaQXJiTTJLWXeDHG1d/YzGok10bnlOgehVogn4565/\nsgjkGBCNdi/e+B1J/C8iKK7TWiewuoZo2bLFnqeUlxeal91S1S9fLr+tfBHQZWyANtaftSIcrljh\nK0l9VLS0hGaiS0YhYU3n/fLLUGV3OA8k9/4W4UZj0Qoey3PKMopr7T+IYJfVjYrstBxLrDmg8Z7e\nEw6/I4lm4A0jIFKPtvKyO98ta7Kde32Xwd2TjjUbN0q0WZD4QYl6q8NhzdQC6bH78UByUlBgl6up\n8R6Ntcdo7xQmfu9LNEb3FCScY0lC6uKz3DNAp9sclFLTlVJNSqmZnX3urkJb72ykeU5dDuefXbXK\nDmMdK5Jd/WHN1HLjd8TTlpdYdbUtOKJJdN4e3cr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "newfig()\n", + "plot_system(bikeshare)\n", + "decorate_bikeshare()\n", + "run_steps(bikeshare, 60, 0.4, 0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the simulation, we can print the number of unhappy customers at each location." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare.olin_empty" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare.wellesley_empty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Let's add a \"clock\" to keep track of how many time steps have elapsed:\n", + "\n", + "1. Add a new system variable named `clock` to `bikeshare`, initialized to 0, and \n", + "\n", + "2. Modify `step` so it increments (adds one to) `clock` each time it is invoked.\n", + "\n", + "Test your code by adding a print statement that prints the value of `clock` at the beginning of each time step." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "unexpected indent (, line 10)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m10\u001b[0m\n\u001b[1;33m bikeshare.clock =+ 1\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n" + ] + } + ], + "source": [ + "# Here's a copy of step to get you started\n", + "bikeshare = System(Wellesley=10, Olin=2, clock=0)\n", + "def step(system, p1=0.5, p2=0.5):\n", + " \"\"\"Simulate one minute of time.\n", + " \n", + " system: bikeshare System object\n", + " p1: probability of an Olin->Wellesley customer arrival\n", + " p2: probability of a Wellesley->Olin customer arrival\n", + " \"\"\"\n", + " bikeshare.clock =+ 1\n", + " print (clock)\n", + " if flip(p1):\n", + " bike_to_wellesley(system)\n", + " \n", + " if flip(p2):\n", + " bike_to_olin(system)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bikeshare = System(wellesley=10, olin=2, clock=0)\n", + "def step(system, p1=0.5, p2=0.5):\n", + " if flip(p1):\n", + " bike_to_wellesley(system) \n", + " if flip(p2):\n", + " bike_to_olin(system)\n", + " bikeshare.clock += 1 \n", + " print(bikeshare.clock)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "step(bikeshare)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "step(bikeshare)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the simulation, check the final value of `clock`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(bikeshare.clock)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Now suppose we'd like to know how long it takes to run out of bikes at either location. Modify `move_bike` so the first time a student arrives at Olin and doesn't find a bike, it records the value of `clock` in a system variable.\n", + "\n", + "Hint: create a system variable named `t_first_empty` and initialize it to `-1` to indicate that it has not been set yet.\n", + "\n", + "Test your code by running a simulation for 60 minutes and checking the metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bikeshare = System(wellesley=10, olin=2, clock=0, wellesley_empty=0, olin_empty=0, t_first_empty=-1)\n", + "def move_bike(system, n):\n", + " \n", + " olin_temp = system.olin - n\n", + " \n", + " if olin_temp < 0:\n", + " system.olin_empty += 1\n", + " \n", + " if system.t_first_empty == -1:\n", + " system.t_first_empty = system.clock\n", + " print(system.t_first_empty, system.clock)\n", + " print('ran out at olin')\n", + " return\n", + " \n", + " wellesley_temp = system.wellesley + n\n", + " \n", + " if wellesley_temp < 0:\n", + " system.wellesley_empty += 1\n", + " \n", + " if system.t_first_empty == -1:\n", + " system.t_first_empty = system.clock\n", + " print(system.t_first_empty, system.clock)\n", + " print('ran out at olin')\n", + " return\n", + " \n", + " system.olin = olin_temp\n", + " system.wellesley = wellesley_temp\n", + " \n", + "for i in range(0,60):\n", + " step(bikeshare)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the simulation, check the final value of `t_first_empty`." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'System' object has no attribute 't_first_empty'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbikeshare\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt_first_empty\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 3079\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3080\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3081\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3082\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3083\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAttributeError\u001b[0m: 'System' object has no attribute 't_first_empty'" + ] + } + ], + "source": [ + "print(bikeshare.t_first_empty)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we go on, let's put `step` and `move_bike` back the way we found them, so they don't break the examples below." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def step(system, p1=0.5, p2=0.5):\n", + " if flip(p1):\n", + " bike_to_wellesley(system)\n", + " \n", + " if flip(p2):\n", + " bike_to_olin(system)\n", + "\n", + "def move_bike(system, n):\n", + " olin_temp = system.olin - n\n", + " if olin_temp < 0:\n", + " system.olin_empty += 1\n", + " return\n", + " \n", + " wellesley_temp = system.wellesley + n\n", + " if wellesley_temp < 0:\n", + " system.wellesley_empty += 1\n", + " return\n", + " \n", + " system.olin = olin_temp\n", + " system.wellesley = wellesley_temp" + ] + }, + { + "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": 83, + "metadata": { + "collapsed": true + }, + "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": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = add_five(3)\n", + "y" + ] + }, + { + "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": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add_five(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "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": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add_five(3)\n", + "add_five(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "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": 87, + "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_system` that creates a `System` object with the system variables `olin=10` and `wellesley=2`, and then returns the new `System` object.\n", + "\n", + "Write a line of code that calls `make_system` and assigns the result to a variable." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_system():\n", + " system = System(olin=10, wellesley=2)\n", + " return system" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "olin 10\n", + "wellesley 2\n", + "dtype: int64" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare = make_system()\n", + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we go on, I want to update `run_steps` so it doesn't always plot the results. The new version takes an additional parameter, `plot_flag`, to indicate whether we want to plot.\n", + "\n", + "\"flag\" is a conventional name for a boolean variable that indicates whether or not a condition is true.\n", + "\n", + "This version of `run_steps` works even if `num_steps` is not an integer. It uses the `int` function to round down. See https://docs.python.org/3/library/functions.html#int" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_steps(system, num_steps=1, p1=0.5, p2=0.5, plot_flag=True):\n", + " \"\"\"Simulate the given number of time steps.\n", + " \n", + " `num_steps` should be an integer; if not, it gets rounded down.\n", + " \n", + " system: bikeshare System object\n", + " num_steps: number of time steps\n", + " p1: probability of an Olin->Wellesley customer arrival\n", + " p2: probability of a Wellesley->Olin customer arrival\n", + " plot_flag: boolean, whether to plot\n", + " \"\"\"\n", + " for i in range(int(num_steps)):\n", + " step(system, p1, p2)\n", + " if plot_flag:\n", + " plot_system(system) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now when we run a simulation, we can choose not to plot the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KAVLyDn/TJiljC1IDpa4uhQ8tX+6WrZWCKYqiKBGn5B1+RkVNpk519Q2HhqQk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\ni5uDMHOm3E/IFX6N/I6OcG7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bikeshare = System(olin=10, wellesley=2, \n", + " olin_empty=0, wellesley_empty=0)\n", + "run_steps(bikeshare, 60, 0.4, 0.2, plot_flag=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But after the simulation, we can still read the metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bikeshare.olin_empty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's wrap all that in a function." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation():\n", + " system = System(olin=10, wellesley=2, \n", + " olin_empty=0, wellesley_empty=0)\n", + " run_steps(system, 60, 0.4, 0.2, plot_flag=False)\n", + " return system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And test it." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + "olin 0\n", + "wellesley 12\n", + "olin_empty 6\n", + "wellesley_empty 1\n", + "dtype: int64" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_simulation()" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12 0\n" + ] + } + ], + "source": [ + "print(system.olin_empty, system.wellesley_empty)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we generalize `run_simulation` to take `p1` and `p2`, we can use it to run simulations with a range of values for the parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(p1=0.4, p2=0.2):\n", + " bikeshare = System(olin=10, wellesley=2, \n", + " olin_empty=0, wellesley_empty=0)\n", + " run_steps(bikeshare, 60, p1, p2, plot_flag=False)\n", + " return bikeshare" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When `p1` is small, we probably don't run out of bikes at Olin." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = run_simulation(p1=0.2)\n", + "system.olin_empty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When `p1` is large, we probably do." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = run_simulation(p1=0.6)\n", + "system.olin_empty" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Exercise:** Write a version of `run_simulation` that takes all five model parameters as function parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "def run_simulation(p1=0.4, p2=.02, olin=10, wellesley=2, num_steps=60):\n", + " bikeshare = System(olin=olin, wellesley=wellesley, \n", + " olin_empty=0, wellesley_empty=0)\n", + " run_steps(bikeshare, num_steps, p1, p2, True)\n", + " return bikeshare" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run_simulation()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## More for loops" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`linspace` creates a NumPy array of equally spaced numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.25, 0.5 , 0.75, 1. ])" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p1_array = linspace(start=0, stop=1, num=5)\n", + "p1_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use an array in a `for` loop, like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** The function `linspace` is part of NumPy. [You can read the documentation here](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html).\n", + "\n", + "Use `linspace` to make an array of 10 equally spaced numbers from 1 to 10 (including both)." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "2.0\n", + "3.0\n", + "4.0\n", + "5.0\n", + "6.0\n", + "7.0\n", + "8.0\n", + "9.0\n", + "10.0\n" + ] + } + ], + "source": [ + "floopy_array = linspace(start=1, stop=10, num=10)\n", + "for floopy in floopy_array:\n", + " print(floopy)" + ] + }, + { + "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": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function linrange in module modsim:\n", + "\n", + "linrange(start=0, stop=None, step=1, **kwargs)\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` (at least approximately).\n", + " If you provide the keyword argument `endpoint=False`, the last value\n", + " in the array is `stop-step`. \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": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "3.0\n", + "5.0\n", + "7.0\n", + "9.0\n", + "11.0\n" + ] + } + ], + "source": [ + "floppy_array = linrange(start=1, stop=11, step=2)\n", + "for floppy in floppy_array:\n", + " print(floppy)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Sweeping parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following example runs simulations with a range of values for `p1`; after each simulation, it prints the number of unhappy customers at the Olin station:" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "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": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p1_array = linspace(0, 1, 11)\n", + "p1_array" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 0\n", + "0.1 0\n", + "0.2 0\n", + "0.3 6\n", + "0.4 6\n", + "0.5 20\n", + "0.6 26\n", + "0.7 36\n", + "0.8 34\n", + "0.9 46\n", + "1.0 50\n" + ] + }, + { + "data": { + "image/png": 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fDrfcAoiEiYYG8S8YzF8JJiVc/oMfiDRJPSX05JPhox8FYNm3xEQC8LnPCdWi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\nvl98yKNHWcObMwQunnuOpGhAD02sYxWdnOwJX2c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OwNU4T/WQi968nwZAUQpicIJBUhed\n7NutvUahDGsx5DJ14JuHXlKSUq2ukEgxql7+WGxb/wsSQ/fJQ7dUW9Rtq58GwK+Qi22yDAR1NQ8f\njaxf42q/j4oe+tSBnx56rABZLumydmR76HZkneXiRwzdC4PuFFaWbQAmQcgl2UXZRjZdyMVHIY/J\nHkP3xA8NhUKNwK3AeUAFsAG4MhKJeLdbxAf4tihaUpKaJVBA+J226IuHbkaB0hZ999CTpD7yYepi\noQx6oRZFj0QPPRQKqcDvgFbgIuB04BDw11AoVJ9v+37C1ywX7fWT5WLe+p9TyCUXY1vwGLoimg/Y\nFIS8XiwsRJaLTfYu2UU/vddCecpHukEHTgBOA/4jEok8EYlEXgQ+BFQDb/egfd9QuDz0wmLSpy1O\nEQ/dmraYaF62kc0UEni9eOjmHYEyOadA2qIXZus14EIgYjqmrw5Nz6ahzk5Ys0bs+B4YyFyiIRCA\nadPgnHPyr3uiqxTpwhaKImqqzJgh6rt4js9+ll2vXszW8WaG4xVU7uuic9/PxfZtieonzz0HDz4I\ne/eK31swCLW1RkXJcJgUZSWLchDkpB7UPVzDE71z6Xqlno6jYHTUWF/atQtuuMF2/drb4S9/gQOJ\nQlelpaIOA4jt7G54J/qBt7fDz38uagkD3HEHfPzj4u/KypyUi57fOZ0Huxazd3Qa8VdKmNEJx/zo\nGtp6bzM+dNddxt/z5qWWPMgHv/gFPPHE/2/v7IOsqK4E/ps3b4aPARxWDQxCgq6Zi4iWrJnsRgNq\nshLdD60Yt2I2roOJBRoim9WAX6uSqIvGrWi5LJHSNUKMMWvp+llDxYpRJJQytX4UErgbV2VBBnBA\nQXEEZl7vH6d7Xk9Pz3vd73X36x7ur+rVzOt+r889/W6fPn3uvefAK6/I+8ZG2L1b/o+4X/VXSeod\nwaQ10Dn172jb+UzkVaCG5JZb5Nw58mKorDWIBx8UA/G+ZHrk/ffFUMQpMyRVG3St9W7gWc/mhUgs\n/TdBj9PZCYsWwcsvywVfjr4+2LtXDNQHH1SePGvlSqlG1N1d7BuWJb/VddfJ+6iNeufuY9lwQNFn\nPyDt723k/s1fBiDifFxFmfbNsqtL9CwUxKgfPChVhBz7F7X8zg0jeXnXcWz9uJmevqJXZVki+/nn\n5d4x6PcbqqJ1XLiT0VdAZyfcuXoGXT3N/dv27IElvd+Hwg7ac7+QjX19sWR57CfiTJl+dK632Ly3\npf/9Rx/B/XsvhEIXbdi5cXp7B6YujYNcrqhjFb9daJkOKcy4GPkZUEqdBywFfqq1Dux+dHRIVaAw\n165lifGvppqQU6XI+9sUCnLDWLWqsuOWomPrDEbUFRUdYSfQX73txOiFOTI7pPQiDHReLUvqi4Lr\nHEZUOQigY00T+w6O5FDB33c4dGiI38/bhrCP8EGmLUZY5KKjA7bsHjNo+8fWKFbxDwPbFaUh8D6J\nJGBwOjy/1ciR8nd14ezixt7e6OXXQNeayKyCSA26Umou8Bjwa2BxmO92dUFPT3iZ1VYT2rlz6N+l\nt1cq7ERN1yfNTBv3ntQqqs8xfbw0fvv+5jLfrEJmV/HJx2vQnQps/ecwQk+n6/08vYV6u2TZYMNa\nKAzx+3mD69XEZIcy6H56VhiL7eqCg731AzTM56HXyrODiRUdsyJiLE7i0NVVx4nN26nDoj5X6E/G\nuZ1jBn4wzgFgiLayVlCZKTfokQ39KaVuAG4FlgELtdahfs2WFhg1SgoWh7l26+qqqyY0YYJ4iH7H\nzedhYgzXYsvovRSObOaLY9+DfJ5G20Of1PRh9MIcmfb5LRSKBZpB9HQ8rEmTgG0UV/1F0GFbju6l\nob6OXJ1FwbJwG/Wy1aBGjAgWf/MjiDFxfuQIBvBaWmBUYy/0QZ+Vo66xgVwe8nW9TKzbGdn5HIT3\nScR5Ofm6LSvSJy6AlhaLwsj9tB29hXx9gXyjGPJJ+Z1g1SWTJ9zpPA5JyfSukksZkbhiSqnFiDG/\nSWt9ZVhjDlIdpLU1/IKehobqqgm1t4vd8FJfLwOGl1xS2XFLce4UmZ7f2FjXb8wBzpm8MXphjkz7\n/MLgEnuOhxV1hSSQijrNDftpyPkbzZLVoJK6YCMweOeeC60T5IZcX1foryU6pq6HS3K/HPjhuA1B\nzJ7ruV+T9o+sP0Q+V9TlnPxvk4ljO5iQyyCimId+MvAvwAPAfUqpia5XU9DjtLXBnXfKhd3cXL5f\nOKXhTj+9umpC7e0wa5YYb8cxbWyUQhdLl8Yzy6XtM1tcVWYsJo/5gMumrZVZLjHhnN/Zs0W/fF48\n86OPFv0jqO/gL3dGD189RjN1zB6aGg/1l/VzZtiUrAYVlTcUsYfqR1sb3Hnhes6apGnKH6ShweL4\n42HJUctor38ovjb4nZeYjU7bF6yB/XcyUj0o/1r5tlVDLUIuXlJu0KMIuVwE1APfsV9ubkQ890C0\ntcHjj5f/3LJlsGGD/L9gAZx8clAJ/igFn/2s/H/XXRICiJUVK2jDO6Pk/JiFyvl95BG46qqB2xcv\nLtYyHVBZyeGGG2QaEMh0MafUUUCOHbebWYW3YPxuOOkkLrhADPmQOPLdVYu++U34yleCCy1nTNx6\n/uxn8Prr8v/ll8PMmcHluGib2s3jc+wRw/475O3y2rgR7rlH9p14IixcWJGMklx8sUzfW7QI9u2T\nbXfcIR5SxPRXSWpogBtnwY2Pyo4HHihOm7z0UimHFQc33wzPPgtvvy3vFy0qlhqLi+9+Vy4Ux0jN\nmQPf+Ea8MkMSxbTF64HrI2hLYKJe9ZvYgqIU4KdfWZ2rPOH53MDMe4HPcTULVYLMcolCTlDiWpDi\np2fS+WPcJLWIKqlEWRnL5ZKe5YohiPKcWlaCWRZTgJ9+ZcctqjSs+Tr70dTH3sQm100Yg15Nhyp1\nE0lyBWeSKzZLyY7a4NViVazJhx4/Uf6O7rTNsdYRTQlODNtNKA+9gou0IQoPPazcMPHbJC7SWuU5\nSSJlby1k12IZ/uGQnKsWRPk7JpYyNyV4+yQEMLBVduJ8buDgUWCDnpQ3FIeH7iUuXfyeCmppdJL0\nYJP2lk0+9HiI8pweTvFzB+9TSNmnkipDLkUPvW7Q4QLLrcbQlgu5xHGRJuW5+t1E4vZcaxVaqnVm\nSTAeehxEeU4PR4PuvQbLzqirOoY+MOQS+EmoGkMbJuQSVYcqZeiS8OySGhQtxXBOoZuBbIuZNOhR\nFis5nAZEHUJPD66yE/eHXHwiAoHlmlku/pQLucRtdJK8caVtlovx0KPBeOgJk4Zpi3EapiTkJGkI\n0jJtcTgM/pppi/ETl0E/HAZFK6LK2Sb90xZtFz3V0xbjCrkcLtMWk/LQIXlvOQODopn0Sd96S/Kg\nb98O990n+fOrJZeDI4+UFaOxFLVIEZs2ycLFnh5J2HXaaSV0PuEEya/rdN6HHy5eSAELF7y97yjW\ndB1P97Zm0JKA7Qc/KJNqYP58eO45yW0M8OSTxZWW7iIXJ5wgqwW9F1ehIDkOnEQ1peSsXl1cWfnM\nM/CjHw2WM9R316wpvv/ww2Lqyu5ueOyx4r4FC+Cll4rv582DRx+VVJN+S5ODFoXw3kTmzwetJY0o\nyN8JE+T/OIowuG9c8+fDrl2webO837wZ1q0ryp4/f+B33edu9uyB+8q19eabJb/1FjtdxrvvB1rV\nJQAADPVJREFUSh9xy3If303QIile7rsPnn5aCggANDUV5aekwEXmPPTOTimis22bOItRpYsoFKS4\ny3XXSY704crKldIfnVTFPT1SrSmwziHzV3S+OYr/fPtUdvQcQW9fjt5e+Q2XLpW/JYkqV0c5D71a\nOU61jlLfLZUdsFCQBRGVdOYa5HIpSbn8O5YlFTHc+/r6gmXV9N68giRv6+sr/9sElQnJZ3gMSeYM\nekeHGPM4+mmcRS3SwlCGO7DOIQsXdKwdS/enYwdt37o1ZFGSUndvpwST8wp7obmNkONhh6G7W8oT\nuauz+N1E/IyBk5D+wAG5u0Zx44rboJcKLZWTrTW89loxGdPBg1IebP36YjnAoJSTdeCAHHvPHnly\niOJc1CIhWAgyZ9C7uqQPxHVzjKuoRVrYuVPCLA5OLvSSOnsnqvf1+X/Oh67uPL2F4vcdx2r//gBF\nSbyjp0PF770XVtjO4S0JFyY22tsb/Hz4GQNvW6MwErX00MvJ3rVL/n74oex36sZaln9hgmpkOeE6\nByesFoZSTwUpNOiZi6G3tEjoav/+UHYlEHEWtUgLEyYMtFdNdoLjkjrn8xXX9Ww5qpfmEQc4VKjn\nYP1IRo+X89zUFKAoifdGUuldPMzCorBywnw2iDEIq6Oft5ykFxnGQy+nW7mwS7W5yY2Hnj6cQg1x\n1J+Ns6hFWmhvl+tg3Dh5Oc5pWZ3dRi+E0Tn39H1MadrD6IaDNI8+0P+7TZkSsKCG8wgRUm5oKp3i\nFKZNSRmDuOO8pY5ZSsdq21IqH3pcfeNwK3CRNGELYQTBKZYRZ1GLtNDeDtdeK95xLid/r702gM4V\nFmhom76f62eu5oyW/2HsiF7GjoUzzpDB50AFNaq9aIO2Ownj4GcMojRytYiheyl1Myn3PixJG1e/\ngdiUDYxmLuQCwQthGPxpbw9x09q0Sf6++KJMWQSZ9vXtbweW118MYeZMKSARBGca2KpV8Pvfy/8X\nXyzlldy8+qrMgRyKcgbdkXPPPTKXE+DKK2HGjGBtfOMNWL588L4FCwZ/9tZbZTQYpGjIQw/BvffK\nIKHD2WfDhReWl+3gNSgrVkhRkEcekfdnngnf+lbw44XFfX5XrJBBriVL5P3EicUpoAB33w1XX118\nf/PNA/c3N0tBjqFw63rbbTKIumyZvJ8xQ343d1u8fP7z8MMfllVpSJnz50tBiyuuKN5Ali8fPAZT\nQzLnoRtqRK2WPJdbyBFVWyrVL8xn/XJWeL8fhT5pybboHcQup2vYp8Ba6Zni5f/GoBuCUelq0TA5\nVcrJ9bt4yrUlqMxKL9Kh5PvJ9ZPhlRVFVskkV4qGWREbpa5eWUkl5/LKNQbdkEkqXfJcbYyx3MWT\nVg+9UoMeRRKyWhqcUrLLeezl+koaknNBqvO5GINuCEYUnbhaD91PbrkLuRIPPYx+YQyJn7ErZ+Qq\noZbZFsN46NWmRE46OVca0hOXwRh0QzBq1YnLyY0q5FKpflHH0KsptZe2ItFeXaK8edWiHFwanoDK\nYAy6IRiVduK4Y+hxhFzCGNVqY+hZ9NBLGfT6+qLuTioGh3I3r7D9I8rCCFmQGwBj0A3BqFUnLmec\nompLEh66ny5ReugOST5N+ZW/GuomHPV4QS3yoUOqPfRI5qErpeqBW4G5wFhgNbBAa70ziuMbUkBW\npy3WapaLH366RDltMS0hgXx+4A3LWR4c5ywXE3IBovPQlwDtwCXAbGAy8FipLxgyxuEUcjGzXMLL\ndFMLDz3IzSGq9MTD2aArpRqBfwSu11o/p7V+FbgIOF0pdVq1xzekhCgMehxya+2h19qg+1FrgzOU\noY1y2iKUfnrzO1Yl2fxqXbM1LJZlVfVqbW39Ymtrq9Xa2jrVs/2d1tbWa0p8b2pra6u1detWy5By\n5s2zrCOOcDJXVPdqbLSsadOCyx07Nn6Z8+ZZ1pgx0ejnvBoaLKu5WY7tyGhqilaG3yuXk1fccoY6\nv9OmJSO/FnrW6uXpv1u3brX8bK5lWZGEXCbbf9/zbN8OTIng+AaDIUtUmMjNUD1RGPTRQEFr7X32\nOACM9Pm8IYvU6iIdTsZhOOliSCVRGPQeIKeU8s6YGQHsj+D4hrRQC4OUyyUj1z1/ulrq6vyPlWRW\nvlrePJKUbW6SA4jCoNv5QGnxbJ/E4DCMIcsMZy99KCNc6bGG2h5FAv9q2pAESekIxqB7iOLMvwF8\nBJzhbFBKTQWmAmsiOL7BYDAYAlD1wiKt9QGl1HLgX5VS3cAuYDnwotb65RJfrQfYMZwrMg8XenrE\n68rnq68Mk8/L2P22bcnJLSczSv2geKxcTo69bVv0MkrJdkiqRJr7/FpW/DpC6kvBRYqn/7ps5qAY\nXp1lWVXLs+PndyCLixoorhTtLvGdLwMvVS3cYDAYDk9maa3XujdEYtArQSk1AmgDuoAKZvwbDAbD\nYUk9MmbZqbU+4N5RM4NuMBgMhmgx2RYNBoNhmGAMusFgMAwTjEE3GAyGYYIx6AaDwTBMiKTARVRk\nqVCGUupeIK+1vsy1bQ7wE0ABfwSu0Vp3uPZ/BlgGzAEOAj8HbtBaJ5bjVCk1wW7jHGAU8Apwtdb6\nzQzpMBm4C/gq4pSsBq7SWm/Pig6utvwFsBb4S631C/a2TLRfKTUd2Oiza5bWem2G9LgMWIwkE/wD\nsEhr/by9LxM6OKTNQ19CygtlKKXqlFI/BuZ7tk8HngIeBWYCTwJPKKVOdH3sMWAisqp2LnAp8KME\nmu20MQf8F9AKnA+cBuwFfquUOjIjOtQBzwLjgbPsdrQAT9v7U6+Dg1KqCfgFrgUiWWo/cBLQjZx/\n9+uVrOihlGoH/h24HdHnReAppdTUrOjgJjXTFu1CGd3AQq31g/a2qcA7wOla63W1a52glDoO+A9g\nBvAJ8JzjoSulVgBKa32m6/O/A/6otZ6nlPoSsA44Tmv9jr2/Hfg34GjvfNKY2j8TeBWYrrXeZG8b\nAewBrgBOz4AOE4G7gWu11u/a284HngD+BLkwU62Dq10rkJvrmcBZWusXstCPXO26BZittT7DZ1/q\n9bCdg3eAVVrrm+xtOeQa+QlipFOtg5c0eeinIGGWF5wN9gX7LjCrJi0azGlIMrKTkI7gZhauttu8\nQLHts4Atzg/v2j8W0T0J/g/4G0C7tjnrpseTAR201ju01he5jPlk5GmpU2v9ARnQAUAp9VfAXwML\nPbsy0X6bGcCmIfZlQQ8FfA74tbNBa13QWp+itX6YbOgwgDTF0FNfKENr/RDwEIBSyrt7MqXbPtR+\n7M+8EllDh0BrvRsJV7hZiMTSfwPcQsp1cKOUegIJHX2AhF8gA7+DUuoo5EnvUqTtblLffhczgJFK\nqZeRZHxvIqUo15MNPVrtv81KqecRfTYjT3/rSrQxTToMIE0eetYLZYwGPvVsc7d90H5bV4sa6aeU\nOg9YCvzUDsFkTYcbgT9HBhWfU0odQzZ0WAE8pbVe7bMvC+1HKTUKOA44AlgEnIcYsxeVUif4tZP0\n6THO/rsSuB84B7kpPZ8hHQaQJg+9v1CGZ4Q4K4UyepC2unG3fdB+pVQDUEcN9FNKzQXuAx5BRvgh\nYzporTfYbbgICYW1+7WRFOlgx1hnAicP8ZFUt99Ba92jlBoPHHBixXafOhX4nl87SZ8ejvN4mx1i\nQSm1AAmlXOHXRtKnwwDS5KFnvVDGVkq3faj9kLB+SqkbkOlV9wKXaK2dOHrqdVBKTbANeD9a60+A\n/wWOIf06zEUe1XcopT6mOJ7RYU+FTXv7+9Fa73MP/Nn9aCMSbsiCHo6cDc4GrbWFjAscSzZ0GECa\nDHrWC2WsxdV2m7Motn0tcJxSaopn/0fA6/E3T1BKLUbm+t+ktb7S7sAOWdDhc8CvlFJfcDYopY5A\nBrj+QPp1uBiYjgyanQJ8zd5+GXAT6W8/AEqpU5VS+5RSp7q21SM6bSQberyKeNJtzgZ75st0xEHI\ngg4DSM20RQCl1O2IBzOXYqGMT93ThtKCUuoF4C3XtMWTgP9GYtK/Av4eiS3+mdZ6k91R1iHxte8D\nE5DY3XKt9ZKE2nwy0olXAjd4dn+ExETTrkMOmUkwDpiHPDbfDvwpYkyOTbsObuxZOlspTltMfT+y\n251H+tJBYAHwMXANMotqmt2uLOhxi93+yxBP/XvA5UhfasyCDm7S5KED/DPwS2Qmye+ALcCFNW1R\nQOx47teR9r6ODBL9rTPf2/aEvw7sRAp7/BwZiPlxgs28CFnE8h0kD7379U9Z0MF+rL/Abt8zyEKQ\nfcAZWuuPs6BDKbLSfnuc61wkZPQ0sB5ZYDNba70rK3ogT0V3ImsbNgBfAuZoISs69JMqD91gMBgM\nlZM2D91gMBgMFWIMusFgMAwTjEE3GAyGYYIx6AaDwTBMMAbdYDAYhgnGoBsMBsMwwRh0g8FgGCYY\ng24wGAzDBGPQDQaDYZjw/7OwEpb4xREqAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for p1 in p1_array:\n", + " system = run_simulation(p1=p1)\n", + " print(p1, system.olin_empty)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can do the same thing, but plotting the results instead of printing them.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EE/B//o/6/x3vgIMHM9vqpk0qb7na6tNPqx4EqLq87rrc1736amaI6/bt889Dwe8YwzA0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\nz8S+crKJUI2P0Ohp0GCj1kO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "newfig()\n", + "for p1 in p1_array:\n", + " system = run_simulation(p1=p1)\n", + " plot(p1, system.olin_empty, 'rs', label='olin')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As always, we should decorate the figure. This version of `decorate_bikeshare` takes `xlabel` as a parameter, for reasons you will see soon." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def decorate_bikeshare(xlabel):\n", + " decorate(title='Olin-Wellesley Bikeshare',\n", + " xlabel=xlabel, \n", + " ylabel='Number of unhappy customers')" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + }, + { + "data": { + "image/png": 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viZZe+1dYN4qY+6Xavru1L6UiItJR6tXa67t1Oo6IiDS53Mr7SsOt9CQ6NR4D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\nWT4ws7Fm9oeyXuVTXXczG5c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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "decorate_bikeshare(xlabel='Arrival rate at Olin (p1 in customers/min)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Wrap this code in a function named `parameter_sweep` that takes an array called `p1_array` as a parameter. It should create a new figure, run a simulation for each value of `p1` in `p1_array`, and plot the results.\n", + "\n", + "Once you have the function working, modify it so it also plots the number of unhappy customers at Wellesley. Looking at the plot, can you estimate a range of values for `p1` that minimizes the total number of unhappy customers?" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HR4ee5tcZuoVXw3qkbgfw76qqPgJgN4CvAdgF4APHq0oFvAVtmf1MJGh3dW8v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\nTzJJfTmZpL67fTv1T9YWiQTd674+Oq+3l+4RuydtbZlLMp3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def parameter_sweep(p1_array):\n", + " for p1 in p1_array:\n", + " run_simulation(p1, p1, 10, 2, 60)\n", + " \n", + "parameter_sweep(p1_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Exercise:** Write a function called `parameter_sweep2` that runs simulations with `p1=0.2` and a range of values for `p2`.\n", + "\n", + "Note: If you run `parameter_sweep2` a few times without calling `newfig`, you can plot multiple runs on the same axes, which will give you a sense of how much random variation there is from one run to the next. " + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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cbbXGxeVSDQtdK/RYoRV6vVKuv7QYbgvdLt3l4l7q2FDBuSB/g9SrhR7k814L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\n62TSidqpZJ43z1kdo6ILFuvzg4OF29C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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def parameter_sweep2(p1_array):\n", + " for p1 in p1_array:\n", + " run_simulation(0.2, p1, 10, 2, 60)\n", + " \n", + "parameter_sweep(p1_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Hold `p1=0.4` and `p2=0.2`, and sweep a range of values for `num_steps`.\n", + "\n", + "Hint: You will need a version of `run_simulation` that takes `num_steps` as a parameter.\n", + "\n", + "Hint: Because `num_steps` is supposed to be an integer use `range` rather than `linrange`." + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p2_array = range(1, 20, 2)\n", + "\n", + "def run_simulation(p1=0.4, p2=.02, olin=10, wellesley=2, num_steps=p2_array):\n", + " bikeshare = System(olin=olin, wellesley=wellesley, \n", + " olin_empty=0, wellesley_empty=0)\n", + " run_steps(bikeshare, num_steps, p1, p2, True)\n", + " return bikeshare\n", + "\n", + "def parameter_sweep3(p2_array):\n", + " for p2 in p2_array:\n", + " run_simulation(0.4, 0.2, 10, 2, p2)\n", + " \n", + "parameter_sweep(p2_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": true, + "scrolled": false + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Exercise:** The code below runs a simulation with the same parameters 10 times and computes the average number of unhappy customers.\n", + "\n", + "1. Wrap this code in a function called `run_simulations` that takes `num_runs` as a parameter.\n", + "\n", + "2. Test `run_simulations`, and increase `num_runs` until the results are reasonably consistent from one run to the next.\n", + "\n", + "3. Generalize `run_simulations` so it also takes the initial value of `olin` as a parameter.\n", + "\n", + "4. Run the generalized version with `olin=12`. How much do the two extra bikes decrease the average number of unhappy customers.\n", + "\n", + "5. Make a plot that shows the average number of unhappy customers as a function of the initial number of bikes at Olin." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "num_runs = 10\n", + "total = 0\n", + "for i in range(num_runs):\n", + " system = run_simulation(p1=0.4, p2=0.2, olin=10, wellesley=2, num_steps=60)\n", + " total += system.olin_empty + system.wellesley_empty\n", + "total / num_runs" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#I don't understand the question, this seems impossible. I will fix during/before checkin" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/code/chap01mine.ipynb b/code/chap01mine.ipynb index 34617f25..f1c73177 100644 --- a/code/chap01mine.ipynb +++ b/code/chap01mine.ipynb @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -723,7 +723,9 @@ { "cell_type": "code", "execution_count": 311, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "bikeshare.olin -= 1\n", @@ -865,7 +867,9 @@ { "cell_type": "code", "execution_count": 316, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "bike_to_wellesley()" @@ -1678,7 +1682,9 @@ { "cell_type": "code", "execution_count": 366, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "decorate()" @@ -1939,7 +1945,9 @@ { "cell_type": "code", "execution_count": 376, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "def decorate(loc = 'best'): \n", @@ -1952,7 +1960,9 @@ { "cell_type": "code", "execution_count": 377, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "decorate(loc = 'best')" @@ -2080,9 +2090,21 @@ }, { "cell_type": "code", - "execution_count": 385, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'plot_state' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mbikeshare\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0molin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwellesley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mnewfig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mplot_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[0mdecorate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mrun_steps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'plot_state' is not defined" + ] + } + ], "source": [ " def run_steps(num_steps = 5, p1 = .5, p2 = .5):\n", " for i in range(num_steps):\n", @@ -2097,25 +2119,35 @@ }, { "cell_type": "code", - "execution_count": 384, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "heads\n", "tails\n", "heads\n", "heads\n", "heads\n", "tails\n", "heads\n", - "tails\n", - "tails\n", - "heads 4\n", - "tails 4\n", + "heads\n", + "heads 6\n", + "tails 2\n", "dtype: int64\n" ] + }, + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -2130,9 +2162,117 @@ " print ('tails')\n", "flip_count(8,0.5)\n", "print(penny)\n", - "#just practice from class, not part of assignment" + "8#just practice from class, not part of assignment" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "penny = System(heads=0, tails=0)\n", + "def update_heads(penny, n):\n", + " penny.heads += n\n", + "def flip_until_tails(system, p):\n", + " print(p)\n", + " for i in range (100):\n", + " if flip(p):\n", + " update_heads(system, 1)\n", + " print ('heads')\n", + " else:\n", + " system.tails += 1\n", + " print('tails')\n", + " return" ] }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5\n", + "heads\n", + "heads\n", + "heads\n", + "tails\n" + ] + } + ], + "source": [ + "flip_until_tails(penny, .5)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "heads 3\n", + "tails 1\n", + "dtype: int64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "penny" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From 9f9fda91210fbe0c667df1e30d338c7f1d7062f9 Mon Sep 17 00:00:00 2001 From: FlynnML Date: Wed, 20 Sep 2017 17:47:18 -0400 Subject: [PATCH 04/19] adding ch 03 mine Didn't have time to finish on time because of rugby and TIPS, will complete and resubmit late. --- code/chap03mine.ipynb | 3883 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 3883 insertions(+) create mode 100644 code/chap03mine.ipynb diff --git a/code/chap03mine.ipynb b/code/chap03mine.ipynb new file mode 100644 index 00000000..971fe801 --- /dev/null +++ b/code/chap03mine.ipynb @@ -0,0 +1,3883 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 3: Explain\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": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you want the figures to appear in the notebook, \n", + "# and you want to interact with them, use\n", + "# %matplotlib notebook\n", + "\n", + "# If you want the figures to appear in the notebook, \n", + "# and you don't want to interact with them, use\n", + "# %matplotlib inline\n", + "\n", + "# If you want the figures to appear in separate windows, use\n", + "# %matplotlib qt5\n", + "\n", + "# To switch from one to another, you have to select Kernel->Restart\n", + "\n", + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas is a module that provides tools for reading and processing data. The `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": 22, + "metadata": { + "collapsed": true + }, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "filename = 'data/World_population_estimates.html'\n", + "tables = read_html(filename, header=0, index_col=0, decimal='M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`tables` is a sequence of DataFrame objects. We can select the DataFrame we want using the bracket operator. The tables are numbered from 0, so `table2` is actually the third table on the page.\n", + "\n", + "`head` selects the header and the first five rows." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " United States Census Bureau (2015)[18] \\\n", + "Year \n", + "1950 2557628654 \n", + "1951 2594939877 \n", + "1952 2636772306 \n", + "1953 2682053389 \n", + "1954 2730228104 \n", + "\n", + " Population Reference Bureau (1973–2015)[6] \\\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)[7] \\\n", + "Year \n", + "1950 2525149000 \n", + "1951 2572850917 \n", + "1952 2619292068 \n", + "1953 2665865392 \n", + "1954 2713172027 \n", + "\n", + " Maddison (2008)[8] HYDE (2007)[15] Tanton (1994)[9] \\\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)[10] McEvedy & Jones (1978)[11] Thomlinson (1975)[12] \\\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)[13] Clark (1967)[14] \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": 24, + "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": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
censusprbunmaddisonhydetantonbirabenmjthomlinsondurandclark
Year
195025576286542.516000e+0925251490002.544000e+092.527960e+092.400000e+092.527000e+092.500000e+092.400000e+09NaN2.486000e+09
19512594939877NaN25728509172.571663e+09NaNNaNNaNNaNNaNNaNNaN
19522636772306NaN26192920682.617949e+09NaNNaNNaNNaNNaNNaNNaN
19532682053389NaN26658653922.665959e+09NaNNaNNaNNaNNaNNaNNaN
19542730228104NaN27131720272.716927e+09NaNNaNNaNNaNNaNNaNNaN
19552782098943NaN27616509812.769074e+09NaNNaNNaNNaNNaNNaNNaN
19562835299673NaN28115720312.822502e+09NaNNaNNaNNaNNaNNaNNaN
19572891349717NaN28630427952.879934e+09NaNNaNNaNNaNNaNNaNNaN
19582948137248NaN29160301672.939254e+09NaNNaNNaNNaNNaNNaNNaN
19593000716593NaN29703958142.995909e+09NaNNaNNaNNaNNaNNaNNaN
19603043001508NaN30260029423.041507e+093.042000e+09NaNNaNNaNNaNNaNNaN
19613083966929NaN30828302663.082161e+09NaNNaNNaNNaNNaNNaNNaN
19623140093217NaN31410715313.135787e+09NaNNaNNaNNaNNaNNaN3.036000e+09
19633209827882NaN32011782773.201354e+09NaNNaNNaNNaNNaNNaNNaN
19643281201306NaN32637388323.266477e+09NaNNaNNaNNaNNaNNaNNaN
19653350425793NaN33291224793.333138e+09NaNNaNNaNNaNNaNNaNNaN
19663420677923NaN33974752473.402224e+09NaNNaNNaNNaNNaNNaN3.288000e+09
19673490333715NaN34685217243.471464e+09NaNNaNNaNNaNNaNNaNNaN
19683562313822NaN35416748913.543086e+09NaNNaNNaNNaNNaNNaNNaN
19693637159050NaN36161087493.615743e+09NaNNaNNaNNaNNaNNaNNaN
19703712697742NaN36911726163.691157e+093.710000e+09NaN3.637000e+09NaN3.600000e+093,600,000,000– 3,700,000,0003.632000e+09
19713790326948NaN37667543453.769818e+09NaNNaNNaNNaNNaNNaNNaN
19723866568653NaN38428736113.846499e+09NaNNaNNaNNaNNaNNaNNaN
19733942096442NaN39191823323.922793e+093.923000e+09NaNNaNNaNNaNNaN3.860000e+09
19744016608813NaN39953049223.997677e+09NaNNaNNaNNaNNaNNaNNaN
19754089083233NaN40710204344.070671e+09NaNNaNNaN3.900000e+094.000000e+09NaNNaN
19764160185010NaN41461358504.141445e+09NaNNaNNaNNaNNaNNaNNaN
19774232084578NaN42208167374.213539e+09NaNNaNNaNNaNNaNNaNNaN
19784304105753NaN42956648254.286317e+09NaNNaNNaNNaNNaNNaNNaN
19794379013942NaN43715278714.363144e+09NaNNaNNaNNaNNaNNaNNaN
....................................
19864940571232NaN49533767104.920968e+09NaNNaNNaNNaNNaNNaNNaN
19875027200492NaN50453158715.006672e+09NaNNaNNaNNaNNaNNaNNaN
19885114557167NaN51382146885.093306e+09NaNNaNNaNNaNNaNNaNNaN
19895201440110NaN52300000005.180540e+09NaNNaNNaNNaNNaNNaNNaN
19905288955934NaN53208166675.269029e+095.308000e+09NaNNaNNaNNaNNaNNaN
19915371585922NaN54089087245.351922e+09NaNNaNNaNNaNNaNNaNNaN
19925456136278NaN54948995705.435722e+09NaNNaNNaNNaNNaNNaNNaN
19935538268316NaN55788651095.518127e+09NaNNaNNaNNaNNaNNaNNaN
19945618682132NaN56610863465.599396e+09NaNNaNNaNNaNNaNNaNNaN
199556992029855.760000e+0957418224125.681575e+09NaNNaNNaNNaNNaNNaNNaN
19965779440593NaN58210167505.762212e+09NaNNaNNaNNaNNaNNaNNaN
199758579725435.840000e+0958986883375.842122e+09NaNNaNNaNNaNNaNNaNNaN
19985935213248NaN59753036575.921366e+09NaNNaNNaNNaNNaNNaNNaN
19996012074922NaN60514780105.999622e+09NaNNaNNaNNaNNaNNaNNaN
200060885713836.067000e+0961277004286.076558e+096.145000e+09NaNNaN5.750000e+09NaNNaNNaN
200161652192476.137000e+0962041470266.154791e+09NaNNaNNaNNaNNaNNaNNaN
200262420163486.215000e+0962808538176.231704e+09NaNNaNNaNNaNNaNNaNNaN
200363185909566.314000e+0963579917496.308364e+09NaNNaNNaNNaNNaNNaNNaN
200463956995096.396000e+0964357055956.374056e+09NaNNaNNaNNaNNaNNaNNaN
200564730447326.477000e+0965140946056.462987e+09NaNNaNNaNNaNNaNNaNNaN
200665512635346.555000e+0965932279776.540214e+09NaNNaNNaNNaNNaNNaNNaN
200766299137596.625000e+0966731059376.616689e+09NaNNaNNaNNaNNaNNaNNaN
200867090497806.705000e+0967536492286.694832e+09NaNNaNNaNNaNNaNNaNNaN
200967882143946.809972e+0968347219336.764086e+09NaNNaNNaNNaNNaNNaNNaN
201068663323586.892319e+096916183482NaNNaNNaNNaNNaNNaNNaNNaN
201169440555836.986951e+096997998760NaNNaNNaNNaNNaNNaNNaNNaN
201270223492837.057075e+097080072417NaNNaNNaNNaNNaNNaNNaNNaN
201371010278957.136796e+097162119434NaNNaNNaNNaNNaNNaNNaNNaN
201471787228937.238184e+097243784000NaNNaNNaNNaNNaNNaNNaNNaN
201572564900117.336435e+097349472000NaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

66 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " census prb un maddison hyde \\\n", + "Year \n", + "1950 2557628654 2.516000e+09 2525149000 2.544000e+09 2.527960e+09 \n", + "1951 2594939877 NaN 2572850917 2.571663e+09 NaN \n", + "1952 2636772306 NaN 2619292068 2.617949e+09 NaN \n", + "1953 2682053389 NaN 2665865392 2.665959e+09 NaN \n", + "1954 2730228104 NaN 2713172027 2.716927e+09 NaN \n", + "1955 2782098943 NaN 2761650981 2.769074e+09 NaN \n", + "1956 2835299673 NaN 2811572031 2.822502e+09 NaN \n", + "1957 2891349717 NaN 2863042795 2.879934e+09 NaN \n", + "1958 2948137248 NaN 2916030167 2.939254e+09 NaN \n", + "1959 3000716593 NaN 2970395814 2.995909e+09 NaN \n", + "1960 3043001508 NaN 3026002942 3.041507e+09 3.042000e+09 \n", + "1961 3083966929 NaN 3082830266 3.082161e+09 NaN \n", + "1962 3140093217 NaN 3141071531 3.135787e+09 NaN \n", + "1963 3209827882 NaN 3201178277 3.201354e+09 NaN \n", + "1964 3281201306 NaN 3263738832 3.266477e+09 NaN \n", + "1965 3350425793 NaN 3329122479 3.333138e+09 NaN \n", + "1966 3420677923 NaN 3397475247 3.402224e+09 NaN \n", + "1967 3490333715 NaN 3468521724 3.471464e+09 NaN \n", + "1968 3562313822 NaN 3541674891 3.543086e+09 NaN \n", + "1969 3637159050 NaN 3616108749 3.615743e+09 NaN \n", + "1970 3712697742 NaN 3691172616 3.691157e+09 3.710000e+09 \n", + "1971 3790326948 NaN 3766754345 3.769818e+09 NaN \n", + "1972 3866568653 NaN 3842873611 3.846499e+09 NaN \n", + "1973 3942096442 NaN 3919182332 3.922793e+09 3.923000e+09 \n", + "1974 4016608813 NaN 3995304922 3.997677e+09 NaN \n", + "1975 4089083233 NaN 4071020434 4.070671e+09 NaN \n", + "1976 4160185010 NaN 4146135850 4.141445e+09 NaN \n", + "1977 4232084578 NaN 4220816737 4.213539e+09 NaN \n", + "1978 4304105753 NaN 4295664825 4.286317e+09 NaN \n", + "1979 4379013942 NaN 4371527871 4.363144e+09 NaN \n", + "... ... ... ... ... ... \n", + "1986 4940571232 NaN 4953376710 4.920968e+09 NaN \n", + "1987 5027200492 NaN 5045315871 5.006672e+09 NaN \n", + "1988 5114557167 NaN 5138214688 5.093306e+09 NaN \n", + "1989 5201440110 NaN 5230000000 5.180540e+09 NaN \n", + "1990 5288955934 NaN 5320816667 5.269029e+09 5.308000e+09 \n", + "1991 5371585922 NaN 5408908724 5.351922e+09 NaN \n", + "1992 5456136278 NaN 5494899570 5.435722e+09 NaN \n", + "1993 5538268316 NaN 5578865109 5.518127e+09 NaN \n", + "1994 5618682132 NaN 5661086346 5.599396e+09 NaN \n", + "1995 5699202985 5.760000e+09 5741822412 5.681575e+09 NaN \n", + "1996 5779440593 NaN 5821016750 5.762212e+09 NaN \n", + "1997 5857972543 5.840000e+09 5898688337 5.842122e+09 NaN \n", + "1998 5935213248 NaN 5975303657 5.921366e+09 NaN \n", + "1999 6012074922 NaN 6051478010 5.999622e+09 NaN \n", + "2000 6088571383 6.067000e+09 6127700428 6.076558e+09 6.145000e+09 \n", + "2001 6165219247 6.137000e+09 6204147026 6.154791e+09 NaN \n", + "2002 6242016348 6.215000e+09 6280853817 6.231704e+09 NaN \n", + "2003 6318590956 6.314000e+09 6357991749 6.308364e+09 NaN \n", + "2004 6395699509 6.396000e+09 6435705595 6.374056e+09 NaN \n", + "2005 6473044732 6.477000e+09 6514094605 6.462987e+09 NaN \n", + "2006 6551263534 6.555000e+09 6593227977 6.540214e+09 NaN \n", + "2007 6629913759 6.625000e+09 6673105937 6.616689e+09 NaN \n", + "2008 6709049780 6.705000e+09 6753649228 6.694832e+09 NaN \n", + "2009 6788214394 6.809972e+09 6834721933 6.764086e+09 NaN \n", + "2010 6866332358 6.892319e+09 6916183482 NaN NaN \n", + "2011 6944055583 6.986951e+09 6997998760 NaN NaN \n", + "2012 7022349283 7.057075e+09 7080072417 NaN NaN \n", + "2013 7101027895 7.136796e+09 7162119434 NaN NaN \n", + "2014 7178722893 7.238184e+09 7243784000 NaN NaN \n", + "2015 7256490011 7.336435e+09 7349472000 NaN NaN \n", + "\n", + " tanton biraben mj thomlinson \\\n", + "Year \n", + "1950 2.400000e+09 2.527000e+09 2.500000e+09 2.400000e+09 \n", + "1951 NaN NaN NaN NaN \n", + "1952 NaN NaN NaN NaN \n", + "1953 NaN NaN NaN NaN \n", + "1954 NaN NaN NaN NaN \n", + "1955 NaN NaN NaN NaN \n", + "1956 NaN NaN NaN NaN \n", + "1957 NaN NaN NaN NaN \n", + "1958 NaN NaN NaN NaN \n", + "1959 NaN NaN NaN NaN \n", + "1960 NaN NaN NaN NaN \n", + "1961 NaN NaN NaN NaN \n", + "1962 NaN NaN NaN NaN \n", + "1963 NaN NaN NaN NaN \n", + "1964 NaN NaN NaN NaN \n", + "1965 NaN NaN NaN NaN \n", + "1966 NaN NaN NaN NaN \n", + "1967 NaN NaN NaN NaN \n", + "1968 NaN NaN NaN NaN \n", + "1969 NaN NaN NaN NaN \n", + "1970 NaN 3.637000e+09 NaN 3.600000e+09 \n", + "1971 NaN NaN NaN NaN \n", + "1972 NaN NaN NaN NaN \n", + "1973 NaN NaN NaN NaN \n", + "1974 NaN NaN NaN NaN \n", + "1975 NaN NaN 3.900000e+09 4.000000e+09 \n", + "1976 NaN NaN NaN NaN \n", + "1977 NaN NaN NaN NaN \n", + "1978 NaN NaN NaN NaN \n", + "1979 NaN NaN NaN NaN \n", + "... ... ... ... ... \n", + "1986 NaN NaN NaN NaN \n", + "1987 NaN NaN NaN NaN \n", + "1988 NaN NaN NaN NaN \n", + "1989 NaN NaN NaN NaN \n", + "1990 NaN NaN NaN NaN \n", + "1991 NaN NaN NaN NaN \n", + "1992 NaN NaN NaN NaN \n", + "1993 NaN NaN NaN NaN \n", + "1994 NaN NaN NaN NaN \n", + "1995 NaN NaN NaN NaN \n", + "1996 NaN NaN NaN NaN \n", + "1997 NaN NaN NaN NaN \n", + "1998 NaN NaN NaN NaN \n", + "1999 NaN NaN NaN NaN \n", + "2000 NaN NaN 5.750000e+09 NaN \n", + "2001 NaN NaN NaN NaN \n", + "2002 NaN NaN NaN NaN \n", + "2003 NaN NaN NaN NaN \n", + "2004 NaN NaN NaN NaN \n", + "2005 NaN NaN NaN NaN \n", + "2006 NaN NaN NaN NaN \n", + "2007 NaN NaN NaN NaN \n", + "2008 NaN NaN NaN NaN \n", + "2009 NaN NaN NaN NaN \n", + "2010 NaN NaN NaN NaN \n", + "2011 NaN NaN NaN NaN \n", + "2012 NaN NaN NaN NaN \n", + "2013 NaN NaN NaN NaN \n", + "2014 NaN NaN NaN NaN \n", + "2015 NaN NaN NaN NaN \n", + "\n", + " durand clark \n", + "Year \n", + "1950 NaN 2.486000e+09 \n", + "1951 NaN NaN \n", + "1952 NaN NaN \n", + "1953 NaN NaN \n", + "1954 NaN NaN \n", + "1955 NaN NaN \n", + "1956 NaN NaN \n", + "1957 NaN NaN \n", + "1958 NaN NaN \n", + "1959 NaN NaN \n", + "1960 NaN NaN \n", + "1961 NaN NaN \n", + "1962 NaN 3.036000e+09 \n", + "1963 NaN NaN \n", + "1964 NaN NaN \n", + "1965 NaN NaN \n", + "1966 NaN 3.288000e+09 \n", + "1967 NaN NaN \n", + "1968 NaN NaN \n", + "1969 NaN NaN \n", + "1970 3,600,000,000– 3,700,000,000 3.632000e+09 \n", + "1971 NaN NaN \n", + "1972 NaN NaN \n", + "1973 NaN 3.860000e+09 \n", + "1974 NaN NaN \n", + "1975 NaN NaN \n", + "1976 NaN NaN \n", + "1977 NaN NaN \n", + "1978 NaN NaN \n", + "1979 NaN NaN \n", + "... ... ... \n", + "1986 NaN NaN \n", + "1987 NaN NaN \n", + "1988 NaN NaN \n", + "1989 NaN NaN \n", + "1990 NaN NaN \n", + "1991 NaN NaN \n", + "1992 NaN NaN \n", + "1993 NaN NaN \n", + "1994 NaN NaN \n", + "1995 NaN NaN \n", + "1996 NaN NaN \n", + "1997 NaN NaN \n", + "1998 NaN NaN \n", + "1999 NaN NaN \n", + "2000 NaN NaN \n", + "2001 NaN NaN \n", + "2002 NaN NaN \n", + "2003 NaN NaN \n", + "2004 NaN NaN \n", + "2005 NaN NaN \n", + "2006 NaN NaN \n", + "2007 NaN NaN \n", + "2008 NaN NaN \n", + "2009 NaN NaN \n", + "2010 NaN NaN \n", + "2011 NaN NaN \n", + "2012 NaN NaN \n", + "2013 NaN NaN \n", + "2014 NaN NaN \n", + "2015 NaN NaN \n", + "\n", + "[66 rows x 11 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use dot notatio to select a column from a DataFrame. The result is a Series." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 2557628654\n", + "1951 2594939877\n", + "1952 2636772306\n", + "1953 2682053389\n", + "1954 2730228104\n", + "1955 2782098943\n", + "1956 2835299673\n", + "1957 2891349717\n", + "1958 2948137248\n", + "1959 3000716593\n", + "1960 3043001508\n", + "1961 3083966929\n", + "1962 3140093217\n", + "1963 3209827882\n", + "1964 3281201306\n", + "1965 3350425793\n", + "1966 3420677923\n", + "1967 3490333715\n", + "1968 3562313822\n", + "1969 3637159050\n", + "1970 3712697742\n", + "1971 3790326948\n", + "1972 3866568653\n", + "1973 3942096442\n", + "1974 4016608813\n", + "1975 4089083233\n", + "1976 4160185010\n", + "1977 4232084578\n", + "1978 4304105753\n", + "1979 4379013942\n", + " ... \n", + "1986 4940571232\n", + "1987 5027200492\n", + "1988 5114557167\n", + "1989 5201440110\n", + "1990 5288955934\n", + "1991 5371585922\n", + "1992 5456136278\n", + "1993 5538268316\n", + "1994 5618682132\n", + "1995 5699202985\n", + "1996 5779440593\n", + "1997 5857972543\n", + "1998 5935213248\n", + "1999 6012074922\n", + "2000 6088571383\n", + "2001 6165219247\n", + "2002 6242016348\n", + "2003 6318590956\n", + "2004 6395699509\n", + "2005 6473044732\n", + "2006 6551263534\n", + "2007 6629913759\n", + "2008 6709049780\n", + "2009 6788214394\n", + "2010 6866332358\n", + "2011 6944055583\n", + "2012 7022349283\n", + "2013 7101027895\n", + "2014 7178722893\n", + "2015 7256490011\n", + "Name: census, Length: 66, dtype: int64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census = table2.census\n", + "census" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Series object has two parts, `values` and `index`.\n", + "\n", + "The `values` part is an array." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2557628654, 2594939877, 2636772306, 2682053389, 2730228104,\n", + " 2782098943, 2835299673, 2891349717, 2948137248, 3000716593,\n", + " 3043001508, 3083966929, 3140093217, 3209827882, 3281201306,\n", + " 3350425793, 3420677923, 3490333715, 3562313822, 3637159050,\n", + " 3712697742, 3790326948, 3866568653, 3942096442, 4016608813,\n", + " 4089083233, 4160185010, 4232084578, 4304105753, 4379013942,\n", + " 4451362735, 4534410125, 4614566561, 4695736743, 4774569391,\n", + " 4856462699, 4940571232, 5027200492, 5114557167, 5201440110,\n", + " 5288955934, 5371585922, 5456136278, 5538268316, 5618682132,\n", + " 5699202985, 5779440593, 5857972543, 5935213248, 6012074922,\n", + " 6088571383, 6165219247, 6242016348, 6318590956, 6395699509,\n", + " 6473044732, 6551263534, 6629913759, 6709049780, 6788214394,\n", + " 6866332358, 6944055583, 7022349283, 7101027895, 7178722893,\n", + " 7256490011], dtype=int64)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `index` part is yet another kind of object, an `Int64Index`." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n", + " 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n", + " 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n", + " 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n", + " 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n", + " 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015],\n", + " dtype='int64', name='Year')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you ever wonder what kind of object a variable refers to, you can use the `type` function.\n", + "\n", + "The result indicates what type the object is, and the module where that type is defined.\n", + "\n", + "DataFrame, Series, and Int64Index are defined by Pandas.\n", + "\n", + "array is defined by NumPy." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(table2)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(census)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.indexes.numeric.Int64Index" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(census.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(census.values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function plots the estimates generated by the US Censis and UN DESA, and labels the axes.\n", + "\n", + "`1e9` is scientific notation for $1 \\cdot 10^9$ or 1 billion." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_estimates(table):\n", + " \"\"\"Plot world population estimates.\n", + " \n", + " table: DataFrame with columns 'un' and 'census'\n", + " \"\"\"\n", + " un = table.un / 1e9\n", + " census = table.census / 1e9\n", + " \n", + " plot(census, ':', color='darkblue', label='US Census')\n", + " plot(un, '--', color='green', label='UN DESA')\n", + " \n", + " decorate(xlabel='Year',\n", + " ylabel='World population (billion)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can plot the estimates." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap03-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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P8y2LiYlBKcUzzzyT4745rZB13fVjs/5r1qwZ9957L3PmzMnWU+G///3vLftm/ffzzz9n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\nT01NZ96KbZzx3MOFZM1deypmWzzFw92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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "newfig()\n", + "plot_estimates(table2)\n", + "savefig('chap03-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From here on, we will work in units of billions." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "un = table2.un / 1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "census = table2.census / 1e9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This expression computes the elementwise differences between the series, then divides through by the UN value to produce relative errors, then finds the largest element.\n", + "\n", + "So the largest relative error between the estimates is about 1.3%." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.2862470293832287" + ] + }, + "execution_count": 39, + "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", + "Where in the series is the largest relative error between the two estimates, near the beginning or the end?\n", + "\n", + "When I computed relative errors, I used `un` as the denominator. But that was an arbitraty choice. What happens if we use `census` instead? How much difference does it make." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "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", + "1986 0.012805\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 0.049851\n", + "2011 0.053943\n", + "2012 0.057723\n", + "2013 0.061092\n", + "2014 0.065061\n", + "2015 0.092982\n", + "Length: 66, dtype: float64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs(census - un)\n", + "#this represents the annual difference (positive b/c the absolute value) between census and un." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "1950 1.286247\n", + "1951 0.858540\n", + "1952 0.667365\n", + "1953 0.607232\n", + "1954 0.628640\n", + "1955 0.740425\n", + "1956 0.843928\n", + "1957 0.988701\n", + "1958 1.101054\n", + "1959 1.020766\n", + "1960 0.561750\n", + "1961 0.036871\n", + "1962 0.031146\n", + "1963 0.270201\n", + "1964 0.535045\n", + "1965 0.639908\n", + "1966 0.682939\n", + "1967 0.628856\n", + "1968 0.582745\n", + "1969 0.582126\n", + "1970 0.583151\n", + "1971 0.625807\n", + "1972 0.616597\n", + "1973 0.584666\n", + "1974 0.533223\n", + "1975 0.443692\n", + "1976 0.338849\n", + "1977 0.266959\n", + "1978 0.196499\n", + "1979 0.171246\n", + " ... \n", + "1986 0.258520\n", + "1987 0.359053\n", + "1988 0.460423\n", + "1989 0.546078\n", + "1990 0.598794\n", + "1991 0.690025\n", + "1992 0.705441\n", + "1993 0.727689\n", + "1994 0.749047\n", + "1995 0.742263\n", + "1996 0.714242\n", + "1997 0.690252\n", + "1998 0.670935\n", + "1999 0.651132\n", + "2000 0.638560\n", + "2001 0.627448\n", + "2002 0.618347\n", + "2003 0.619705\n", + "2004 0.621627\n", + "2005 0.630170\n", + "2006 0.636478\n", + "2007 0.647257\n", + "2008 0.660376\n", + "2009 0.680460\n", + "2010 0.720789\n", + "2011 0.770837\n", + "2012 0.815290\n", + "2013 0.852981\n", + "2014 0.898165\n", + "2015 1.265152\n", + "Length: 66, dtype: float64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs(census - un)/un *100\n", + "#This represents the annual margin of error in the data. \n", + "#Note that un being the denominator suggests that we assume it is the more accurate estimate." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.2862470293832287" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(abs(census - un)/un) *100\n", + "#This represents the highest percent error found for any one year in the table above." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.2813631502151765" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(abs(un - census)/census) *100\n", + "#This makes a small difference in percent error, in this case negligeable. \n", + "#But we should always consider which is the denominator for the sake of accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Constant growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can select an element from a series using bracket notation and one of the elements from the index. Here's the first element:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.5576286540000002" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census[1950]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the last element." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.2564900110000004" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "census[2015]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But we can get the first and last years from the index itself:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1950, 2015)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_year = census.index[0]\n", + "last_year = census.index[-1]\n", + "first_year, last_year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And use them to look up the first and last elements.\n", + "\n", + "Then we can compute the average annual growth in billions of people per year." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07229017472307693" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_growth = census[last_year] - census[first_year]\n", + "elapsed_time = last_year - first_year\n", + "annual_growth = total_growth / elapsed_time\n", + "annual_growth" + ] + }, + { + "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": 49, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "results = TimeSeries()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initially the Series 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": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
value
19502.557629
\n", + "
" + ], + "text/plain": [ + "1950 2.557629\n", + "dtype: float64" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[1950] = census[1950]\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": 52, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "for t in linrange(1950, 2015):\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": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap03-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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X17O2NQylc3R0RKfTcWaVtoZC1mAusA7w9ttvExYWZnXc6b8MFMUWer2eoqIi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\nBM33y9YAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "newfig()\n", + "plot_estimates(table2)\n", + "plot(results, '--', color='gray', label='model')\n", + "decorate(xlabel='Year', ylabel='World population (billion)')\n", + "savefig('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": [ + "**Exercise:** Try fitting the model using data from 1965 to the present, and see if that does a better job.\n", + "\n", + "Hint: Copy the code from above and make a few changes.\n", + "\n", + "Make sure your model starts in 1950, even though the estimated annual growth is based on later data. You might have to shift the first value in the series up or down to match the data." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap03-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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JXwjRLlgsFpKSkoiJiSGn0DpkM6csBwBnBxfi49OoqrJQVGHm559TmDixV73j\nJfHXZ1fyV0p1Ah7GWsPXg9OfFRha6/OrZCKEEA2oW0gluzCbpIIkskuzrW2OFsp9yqn2rGJqv3Hk\n7Ayhu3tAu1thsy3Ye+f/EnAbsA04DFhaKiAhhID6hVQKiwqJzo0muzSbsrIqqkzVOPWwUOFZwSWB\nlzBbzcbPpTvb/ZOZNCkIV1fp1Dgbe/+FrgMe0VqvaslghBCiVmJiIocOHQLA0cGRInMJaVnFnDDn\nkWsq5raxV3DtwLkEeZ98gHvZZb3bKNr2x97vRi5Y1/VpFkqp25RS0UqpMqXUXqXU1OY6txCi/aus\nriQoKAhXV1fAWojo0mFjiDJSqS7vyqD8G+ibe0W9xC+axt47/++wLt72w/leUCl1C7Aaa8H3H4G7\ngK+UUgMbWnlOCHFxMwyD7OxsOnXqRDHFbIrZRFpRGg+MfgilFGazmb59++Lk5ISlIoDvPs9j+swQ\npk4NPvvJxRnZm/w/AN5WSnUFdgKlp+6gtf7obCdRSpmAx4FVWus1NdvuB6YClwIJdsYjhLgI5OTk\noLUmOSOZVCOVYy7HyM4uIymxEKLX89iS6+rtf+X4oUweXomHh0sbRXzxsDf5f17ze3HNz6kM4KzJ\nH+uyzyHAp7UbtNYWYOgZjxBCXHTy8/PRWpOYmkhSfhKZpZkAlHpWc/SodfjmT0cOkJ09k65d3W3H\nOTiYJPE3E3uTf59mul5Eze/OSqmtwECsC8U9pLXe2UzXEEJcoGoLqcQnx5NUkERmSaatrcKzAg9v\nJyK79MctaRB+jj1JSiqql/xF87Er+WutE2tfK6U8AC8gp2bN/6aoXVDjfay1AaKwDiHdqpQaprU+\n1sTzCSHagdpCKgknEkjITyCjJIPy8mocHEwYnaso9ymnf2B/ZqvZVA/uzL59GVx+eR+8vOQuv6XY\nPRhWKTUZWAUMx1rEBaXUbuBRrfUWO09T+2GxsvYZgVLqbmAC1gfA/2dvPEKI9iEuLo5jx45hGIZ1\n7H5BOgUFZtLNRZR4lnLt2PFcFXEVfX37Wg/oDKGhnRs/qThvdg31VEpNxDrixw3rHfvtWEs8egKb\nm1C0vbbS16HaDVprA2ut4ObqWhJCXEB8fX2prRXu6uRKz+692F9ygjxzJ3okz2Km300nE79oNfbe\n+T8B/BeYVZOsAVBKPQlswvpBMM2O8/yGtf7vSGBPzTlMQP+a8wsh2jGz2YyLiwtFFUVsjt2Mu7M7\nc9Qcund0MNRKAAAgAElEQVTvDoBSigmdJlBdHE7CPifGjw+ia1e3No66Y7I3+Y8Arq+b+MF6166U\nWg18bM9JtNalSqkXgJVKqQys3wDuAkKBefaHLYS4kFRUVBAbG0tMXAwF3gXsLtlNflEpyQml+E7p\nz6UjRuDo6Gjbf8l1U7Bca+DnJ4m/rdib/POwdvE0xAuobsI1l2GdJ/Ai0B3YD1ymtdZNOIcQ4gJQ\nWVlJXFwcsXGxJOYlklyYTKVDJSlOBejoXAzgra83Mm7EvfWO8/Xt1DYBCxt7k/9WYLlS6ietdWrt\nRqVUINYuH7u7bGq+PTxd8yOEaIdqC6nExMaQmJvIicITVFmqADAcDPx83OhsCqBn8SjcCnoTF5dP\neLhvG0ct6rI3+T+MtY8+Rim1A0gHAoDxQCHwYMuEJ4S4kNQtpJKUm0RiQSLmynJMJhMWl2rMPma6\nde/GoshFnHD1Ijm5iDlzwggK8mrr0MUp7B3nn6KUGgYsxTossw/WrqDVwPNa6/SWC1EIcSFISkpC\na43ZbCazJJPonGhKSirJLS7D6F5JWN8ezI+cz6ieo3AwOTB0tiEFVC5gdo/zr0nwD7RgLEKIC1hp\naSlmsxmAbh7dOJQWzcG8NIqqLagTl/LwrXfi6X6yL18S/4XtjMlfKfUI8E+tdVrN68YYWmvpwxfi\nImEYJ+/aTxScwGQyERoaSkJCAo6OjoSHh9NjaE/S3tyKV14/Av07U5hfhaesxNBuNHbn/yTWB7lp\nNa8bU/sQVwjRjhmGQXp6OtHR0fTt35fvk79nV/IuPMt7sPyyRxgzZgxeXl44OjrSm948NC+I0tIq\nxo4NxMFB7vTbkzMmf621Q0OvhRAXH8MwyMzMRGtNTl4OSQVJfJXwFWkuucTF5VNYmEKPsm944Ka5\n9Y4bNsy/jSIW58veAu7LgHfqDvOs0xYCLNVay7o8QrQztYVUtNbk5uWSWphKUmESVZYqHBwcqLRY\nKCyswK+yL0d3l5M7q4wuXWRi1sXA3ge+fwc2A6clf2As1rV+JPkL0Y7UFlLJyckhqzSLhPwEzFVm\nMEG5Vznl3uUM79qP3ulhlJzwYeLEXri4OJ79xKJdaOyB7w6siR2sq3juUkqdafdfmzkuIUQLyc/P\nJyoqiqysLEoqSojJjaHAXEhRcQUO3aoxulbRzbsb1/S7hmEBw0jtU4yrq6Osq3+RaezO/zas6+2Y\ngBXAW0DyKftUA/nAly0SnRCiWRmGwaFDh8jPz7duMEFWYR5xeTmcqMiju5M3f5t2KxNCJuDkYE0P\nPXvKBK2LUWMPfKOAlQBKKUesff4pZ9pfCHHhM5lMKKX45ZdfMJlMRPaNJMUjjy1bYulZPpRecSMI\ncxpuS/zi4mXvDN/HAZRSfoALNcVcsNYD8AAmaK3faZEIhRDnpKSkhKSkJCIjIwH4X/L/sBgWxvUa\nh1KKwMBAPD09CS0PxTFhALkpDlxzTbgsxdBB2DvaZxDwITDgDLsYgCR/IS4AZWVlxMTEkJSUhGEY\nFFLIluwtRKXFkpxQzrMzezF8YIRtfy9XL26/aSyuro44Osqo7o7C3u92zwJ+wP3AVUA5sAGYCVwJ\nTG6J4IQQ9jObzcTGxpKYmIjFYqGiuoL4/Hi2p24nzjEDHZOLYcBz6z7mwwEP1lt+wd3duQ0jF23B\n3uQ/FrhPa71GKVUCLNRavw68rpT6D9ZhnjtaKkghxJnVFlJJSEiguroaA4OUwhSSCpIodynH3MWM\nj4MrjiZHAsuG414wkNhYWWK5o7M3+bsCMTWvo4Ehddr+CbzRnEEJIc6usrKS48ePc/z4caqqrGvp\n55vzic2NpcihiPJu5VR1sm4f22MkU41hZCc5MG9eOCEhPm0ZurgA2Jv8k7Au4/wT1uTvrZQK0Von\nAmagSwvFJ4RogMViYdu2bbZVNistlcTmxpJYnMqx3HS8ezrh38mDQK9Abhh4A5FdI7FcYmAyyWqb\nwsrepzvrgH8opa6pWeIhCnhCKdUPuA+Ia6kAhRCnc3BwIDAw0Pa3t5c3MdWpbD0RQ1pJEScSzFwd\nMY/HJj1GZNfImmNMkviFjb13/o8D4cASrB8E99X8Xoh1oteNLRKdEAKLxUJ+fj5dutT/gh0WFkZO\nTg6hoaEEBgbi3teL3Qkr8S0OJ6JsIn2M4TiYZPSOaJi94/xLgWuVUq41f39bM/zzEuA3rbXc+QvR\nzCwWC8nJyURHR1NeXs7UqVNxc3Mj35zPnpQ9TA6ZyoQJE2x382NDRvHE9GVkxrowb16EFEkXjWrS\nND6tdXmd13FId48Qzc4wDFJSUoiOjqakpMS2PTommizPLP69ey3HYjK5RVVx9/VX2NpNJhNzJo+Q\ngdfCLo0t7BaDdfKWPQyt9RlXfRNCnF1tIRWtNUVFRfXayowy1h9fz5GiOA4eygbgvT0fcOP0ifh1\nkQXXRNM1duf/M/Ynf7sppfoDRxpomqC1lrkCosOpW0iloKCgXpvJ0USyUzL7yvaBI/h0dsXT0xlL\noRcRFdNIiC+U5C/OSWMLuy1uoWsOArJrfteV00LXE+KCVV5ezq+//kpeXl697U5OTpi9zGzL3465\nvMw2Ls/V0ZU7Jy3CfKwP11/XT5ZZFufM3rV9Lj3bPlrrnXZecyBwVGudbuf+Qly0XFxcbBO0ABwd\nHfH292ZT8ha2btuPm5sTKsI6ymdIwBBuGHADfu5+MKmtIhYXC3sf+O7g7F1A9pb4GQgcs3NfIS4q\n1dXVODqe/L9K7RLLv/32GyEhIYSHh/PSjjdZ//NuDKCwsIIBfYK5Y9wtDPYf3HaBi4uOvcl/SgPb\nPIEJwE1Yi77YayDQSSm1C+gNHAYe0VrvbsI5hGhXCgsL0VpjNpsZP358vclWAQEBTJs2jU6drEMz\nbx27kK/37CQrp4TgyuFc02URg/3D2ip0cZGyd5z/9jM0bVJKFQOPYl3ts1FKKTegL5AFPIB1ddB7\ngO1KqUu01vKNQFxUioqKiI6OJjX1ZPnrjIwMAgICAEguSKaT4U3Xzt62dj93Px6ZdTf7tpfz+/lj\n6d7do9XjFhe/5ijX8xPwkD07aq3LlFK+QHntnAGl1GJgOHAX8KdmiEeINldSUkJ0dDQpKSkYRv0e\n0/z8fLz9vHlz20d8vGsTkU5jeP+hv9b7NnDZwAlcNrC1oxYdSXMk/9lAob07a60LT/nbopQ6AvRq\nhliEaFOnFlKpKyAggIiICA4XHGb1N2+wdUcMFgP2soMtu2Yxfaxke9F67B3t810Dmx2xJuxQYJWd\n5xkO/ABM0VrvrdnmCAwF1tpzDiEuROXl5cTExNgKqdTVvXt3lFJkW7J59cCrJBUkARAY6ElySjG+\nRhAFBRVtEbbowOy983fh9NE+BnAUeAZYY+d5DgAJwJtKqbuBYuBBoCvwkp3nEOKCU1VVRUJCQr27\nfT8/PyIjIzGbqnhzz784XlZ/buOQiBAmeY7izqtn4ucn4/VF67L3ge/k5riY1rpKKXUl1g+MDViL\nv/8MTNRaZzbHNYRoCx4eHgQHB5OYmIivry+RkZF08nDnxU0f8/m+jeBYzYgRATiYTDg7OnNZ6GVc\nEXYFLo4ubR266KCa1Odfk7gnAL5ABrBVa/1jU86htU7BuhS0EO1OVVUV8fHxmEwmwsLqD78MDw/H\n39+f7t27YzKZ+PH4Dtbu+5KKSgtUQlpqMXNHTuHaftdaJ2oJ0Ybs7fP3AzYDI7AOz8wCugOP1TwP\nuEZrbW6xKIVoY9XV1SQkJBAbG0tFRQVOTk706tULV1dX2z5ubm64ubnZ/h7f51IuCYtg17EoursE\nsmTArcwdPqYtwhfiNPbe+b+CtYzjbK31ptqNSqk5wLvAP4A/N394QrQti8VCUlISMTExtpKJYP0G\nkJSURHh4OIZh8MOvR8gtKOW6GaNs+ziYHHhg5hK2dDnCHVfOxcWlOQbXCdE87P1f45XAn+smfgCt\n9VdKqYeBlUjyFxeRuoVUysrK6rW5ubkRERFBr169SMrM4K/vvs7BvF/xMQVw2egheHuf/DbQ3z+S\n/nMjWzt8Ic7K3uRfBeSfoS0N62ggIdq9MxVSAejUqRPh4eEEBwdTYangK/0V38V9R0x5MgaQb6Sz\nev16Hr7p+rYJXogmsDf5vwY8pZT6taaAOwBKKW+ss3tfaYnghGhtqamp7Nu3r942FxcXwsPDCQkJ\nwYKFbYnb2BS9ieKKYgD69PHh2NEcRvQewtVTh7dF2EI0mb3JP7DmJ04ptQNIBfyAcYAXUF5nIpih\ntb682SMVohX06NEDd3d3SktLcXZ2JjQ0lD59+lBSWsXT/17L7sKt9OhjqnfMkN7h3D/+L4wJHdpG\nUQvRdPYm/zBgf51jgmte125zxP4lnYVoc4ZhkJ2djbOzM507d7Ztd3BwoF+/fhQVFdG3b1+cnZ05\nmKhZ8tpTFBrZmAAff3/c3Z3xc/djrprLqJ6j6q3LI0R7YO8kr4aWdBaiXcrJyUFrTU5ODn5+fowd\nO7Ze8g4MDKy3f2A3P5x9SyHXOq29INtg8ZXXMzFkIs6Ozq0cvRDNo6mTvPpjrSHkg3Ws/w6ttW6J\nwIRobvn5+Witycw8OZk8JyeH7OxsunXrBli/EZSWVuLhcXIMQ1f3rtw45ko+2L6ZBWPmsmTqPNyc\n3U47vxDtib2TvByAN4E/AHW/3xpKqX8Dv9daN3uxdyGaQ20hlfT0+pVDTSYTwcHBeHp6AvDT/iM8\nv+lf+Lp05d3776/3beD3l97ATWPm4dPJp1VjF6Kl2Hvn/xBwc83vD7Eu7dADWACs4OQCb0JcMBoq\npALWpB8UFERERATu7u5kFGfw7s73ef2rjVgsBg448suBeYwZ2td2jIeLFFQRFxd7k/+twEqt9bN1\ntiUDzyilOtW0S/IXF4yEhAQOHz582pr6PXv2JCIiAk9PT7JLs/ls/2fsSt6FYRj4+7uTllYCDhZ+\nTdhXL/kLcbGxN/n3wLr6ZkN2Ag83TzhCNA8/v/oLpwUEBKCUwtPTi//+fJRf87eTbDqExTi59n6v\nIC96OoXxwOzfM7h3eGuHLESrsjf5HwfGAlsaaBuLdZavEG2ivLwcR0dHnJxO/s/Zy8uLwMBAKisr\nUUrRuXNnDsUm8Nj/e5YY835cOzkwYqQ/pppHWP269WOOmkNfX7nbFx2Dvcn/HeBppVQJ8AnWPn9/\n4HfAI8BTLROeEGdWUVFBbGwsCQkJhIeHEx5e/2596NChODg42P7ONSUTV7UfCxbKzBYyM0uZ0G8Y\nc9Qcwv3kTl90LE1Z1XMY8Bzw/+psNwEfYF3YTYhWUVlZyfHjxzl+/DhVVVUAxMXF0bt3b5ydrePu\nKyqqMQwDV9eTyX9C30sZ2DuEwwmJjOw7kD9dtoChQQNlgpbokOyd5FUN3KKUegZrMZcuQB7wo9b6\nSKMHC9FMagupxMXFUVlZWa/N3d2d8vJyKith/ff7+df/vuCqSyZx9/VX2PZxMDnw4KzbwTAxpKck\nfdGxNXWB8RNY+//zgMya10K0qFMLqdTl5eWFUoqAgAAySzJZ/c3b/GfXFgwMPv4thz/Mnoab28lZ\nuEN7Dm7t8IW4IDVlktczwD2AMycnepUopVZqrf/RQvGJDi45OZljx47VK6QC1pq5SikCAwNJL05n\nzb41/Jr6KxZnC66dHDCbqylxTmdffBSX9h/URtELceGy985/OXAv8CLwOda7fn9gPrBCKVWotX6t\nRSIUHVpFRUW9xF9bSMXJyZdPNu3GCF9HTPHJ8fwmTPTt05lebqH8cdqNRHZTbRW6EBe0pkzyWqG1\nfqLOtuPA/5RSRcB9WNf8F+KcGYZxWj98SEgIcXFxALZCKh989T/e3v4c2c6x9Cj2IDzM17b/gO4D\nmDVuFqFdQls1diHaG3uTvw+w+wxtO4D7mycc0REZhkF6ejpaa4YOHVpviWVHR0dGjx6Nh4cHjo7W\nVcOTXX8j2zkWgIyMUnr39uaSnkO5KuIqenfu3RZvQYh2x97kvxH4I/BtA203Al+fy8WVUmOwfnhM\n11pvO5dziPbLMAwyMzPRWlNQUACA1prRo0cDYLEYREfnEhlZf7buH8bP56u9P+Ds7MCMwWO4Zex8\nQjqHtHr8QrRn9ib/H4GVSqmDWCd5pWGt5HUVMB54Xin1SM2+htb66bOdUCnlAfwbKQLT4dQWUtFa\nk5eXV68tNzcXs9nM/v25fPT1TvYX7eD1O//KgIiTa+wHeAXwxI13EdlN0cunV2uHL8RFwd7k/2rN\nbx/gyQba63b7GMBZkz/wPNbF4cLsjEFcBOoWUqnL0dGRPn36EBoaSlppGqt/fYs95v3gDK9sWssb\nEffW239G2PTWDFuIi469k7wczr6X/ZRSM4FZwJXAweY8t7gwNVRIBaxlE0NCQggPDye9LJ2397/N\nwYyD0L0S0wlwdDKR6rqfiqoKXJxcznB2IURTNXWS13lTSnUF3gV+j3WymOgATk38JpOJwMAg0tPd\n+OzbGLqXbuNQxiFbu7u7M/37d2VK5FiuGTBHEr8QzazVkz/WimBfaa2/UUoFtcH1RRtQSpGZmWkr\npBIWFs5DT21mf9l2cpyP0/+IH127WksjmkwmhvcYzqzJswj0CjzLmYUQ56JVk79S6hasC8TJHPuL\nVElJCfHx8fTr1882NBOgc+fO9O/fH39/fzw9PbEYFlKDviMnIQWA9IwSunVztyb9CEn6QrS01r7z\nXwwEAelKKTi5TMRmpdT7Wus/tnI8opmUlZURExNDUlIShmHg4eFBnz59ADCbq0hPLyE09OTEKweT\nA0umzOexz1YTGOjJlUPGM7ffHEn6QrSS1k7+iwC3On8HAD8BtwHft3IsohmYzWZiY2NJTEzEYjlZ\nFSsmJobAwCC2bj3Buv/uIcc5nk+euB9n55PfBmaoKWRfnc7UPlPp6d2zLcIXosM6Y/JXSjXpFkxr\nnWrHPimnXKN20ZYUrXVmA4eIC1TdQirV1dX12vz8/IiMjCS9JJ0Xd6wmyTEKLPDJd5dy06wJtv2c\nHJy4achNrR26EILG7/yTsY7Zt5dM1uoAKisriYuLIz4+3lZIpZavry+RkZGUu5Tzn5j/sDd1Ly4h\nxRALnTo5sr90Gzcx4QxnFkK0psaS/x84mfy7AP/AWsP3M07O8J2DdZbvX87l4lrrZE72+4sLnGEY\n/Pjjj5SWltbb7unpRXFxZxJzyziWuI69aXttq2wGBHjg6Ghi+sCxzI2c0xZhCyEacMbkr7V+r/a1\nUmod8C+t9ZJTdvtIKfUScD3wVotEKC4YJpOJ4OBgoqKiAGshlYCA3rz83q8cKl9Hrmsco0YF4FKn\nX39Yj6FcNekqgn2C2ypsIUQD7H3gexlw9RnaNgKnfiiIds5isZCTk0O3bt3qbe/Tpw8ZGRn06dOH\nwMBAskuz2eP+EUWWCrBAamoxvUN8GOw/mKsirpIF14S4QNmb/LOBUTQ8ImcykNLAdtEOWSwWkpOT\niY6Oxmw2M2nSJLy8vACoqrJQVmZh/Pjxtv27eXRjxuAxbNyzg169vLhs8Fjm9psjSV+IC5y9yf9t\nYJlSyg34CsjiZCWv/wP+3DLhidZiGAYpKSlER0dTUlJi2x4dHc2QIcP4+ecUPv1mF117wYq75tc7\n9vbJN+Lv78HcfrMl6QvRTtib/FcCnYEHgIfrbDcDj2mtVzd3YKJ11C2kUlRUVK/N1dUVX19fftGH\n+duXb5HjHI9LvDt/ODGN3r262PYL6RzC/429p7VDF0KcB3tX9TSA+5VSTwBjAV+sXUE7tdYljR4s\nLkgNFVKp5ezsTGhoKEZng43HN3Ik8wgEZEMO4GLm++itLOl1XdsELoRoFk2a4au1LgC+aaFYRCup\nrKzkl19+Oa2QisnkQGGhJ959nNmYvdE2qgcgJNibzj6dmDlsArP6X9raIQshmlljM3xjsH+Sl6G1\nVs0TkmhpTk5O9QqlOzo64uDQhQ+/O0S06Rc4nsOQId0w1UzBMJlMTFXjmRk+kx5ePdoqbCFEM2rs\nzv9nmjbDV1ygqqqqcHI6+Z/aZDIRGRnJrl276N27N2FhYWyL28lvLuuxWAwohMKCCnw7uzGq5yhm\nhs/E39O/Dd+BEKK5NTbJa3Hta6XUjcAWrXVWawQlmkdhYaHtQe7kyZNxcLAWZEtMLCAwsDPTp0/H\n1dUVgMkRlxIc8B6ZefmEBHfmigFTmBlxJd09urflWxBCtJCmDPVcDHzecqGI5lJcXIzWmtTUk2vt\nJScnYzZ7sn5jFNvjd7B49iSuu2y0rd3VyZW7L7+Rwsp8rgy/Al8337YIXQjRSuxN/imAe0sGIs5f\naWkpWmtSUlJsa+vUysrLYrP+L5+lbaLSzcyaHVlcO30UDg4n+/6v6ndla4cshGgj9ib/14GXlFJj\ngANA8ak7aK0/as7AhP1OLaRSl7uvO8nOyXyb8i1m13IM5wpM1VDqnUhiTgp9ukklTSE6InuT/ws1\nv+88Q7sBSPJvZRUVFURHR9crpFJZaSE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "first_year = census.index[15]\n", + "last_year = census.index[-1]\n", + "first_year, last_year\n", + "\n", + "total_growth = census[last_year] - census[first_year]\n", + "elapsed_time = last_year - first_year\n", + "annual_growth = total_growth / elapsed_time\n", + "annual_growth\n", + "\n", + "results[1950] = census[1950] - .3\n", + "\n", + "for t in linrange(1950, 2015):\n", + " results[t+1] = results[t] + annual_growth\n", + " \n", + "newfig()\n", + "plot_estimates(table2)\n", + "plot(results, '--', color='gray', label='model')\n", + "decorate(xlim = [1950, 2015], xlabel='Year', ylabel='World population (billion)')\n", + "savefig('chap03-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now with system objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can rewrite the code from the previous section using system objects." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "t0 = census.index[0]\n", + "t_end = census.index[-1]\n", + "total_growth = census[t_end] - census[t0]\n", + "elapsed_time = t_end - t0\n", + "annual_growth = total_growth / elapsed_time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the system object." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system = System(t0=t0, \n", + " t_end=t_end,\n", + " p0=census[t0],\n", + " annual_growth=annual_growth)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can encapsulate the code tha runs the model in a function that stores the resulting Series as a new system variable." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation1(system):\n", + " \"\"\"Runs the constant growth model.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + " \n", + " system: system object\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t0] = system.p0\n", + " for t in linrange(system.t0, system.t_end):\n", + " results[t+1] = results[t] + system.annual_growth\n", + " system.results = results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also encapsulate the code that plots the results." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_results(system, title=None):\n", + " \"\"\"Plot the estimates and the model.\n", + " \n", + " system: System object with `results`\n", + " \"\"\"\n", + " newfig()\n", + " plot_estimates(table2)\n", + " plot(system.results, '--', color='gray', label='model')\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": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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z5s0jKCiI2bNn2x4Yl5WVsWHDBkaOHGkbEObaa6/FaDTy2Wd6Bn9TlIaZzWYS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\n3c1/i0gUMAKoMMY09FXGhqqQkBDi4uJagn98fDy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run_simulation1(system)\n", + "plot_results(system, title='Constant growth model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`plot_results` uses `decorate`, which takes parameters that specify the title of the figure, labels for the $x$ and $y$ axis, and limits for the axes. To read the documentation of `decorate`, run the cells below." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function decorate in module modsim:\n", + "\n", + "decorate(**kwargs)\n", + " Decorate the current axes.\n", + " \n", + " Call decorate with keyword arguments like\n", + " \n", + " decorate(title='Title',\n", + " xlabel='x',\n", + " ylabel='y')\n", + " \n", + " The keyword arguments can be any of the axis properties\n", + " defined by Matplotlib. To see the list, run plt.getp(plt.gca())\n", + " \n", + " In addition, you can use `legend=False` to suppress the legend.\n", + " \n", + " And you can use `loc` to indicate the location of the legend\n", + " (the default value is 'best')\n", + "\n" + ] + } + ], + "source": [ + "help(decorate)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " adjustable = box\n", + " agg_filter = None\n", + " alpha = None\n", + " anchor = C\n", + " animated = False\n", + " aspect = auto\n", + " autoscale_on = True\n", + " autoscalex_on = True\n", + " autoscaley_on = True\n", + " axes = Axes(0.125,0.125;0.775x0.755)\n", + " axes_locator = None\n", + " axis_bgcolor = (1.0, 1.0, 1.0, 1.0)\n", + " axisbelow = True\n", + " children = [\n", + " label = \n", + " legend = None\n", + " legend_handles_labels = ([], [])\n", + " lines = \n", + " navigate = True\n", + " navigate_mode = None\n", + " path_effects = []\n", + " picker = None\n", + " position = Bbox(x0=0.125, y0=0.125, x1=0.9, y1=0.88)\n", + " rasterization_zorder = None\n", + " rasterized = None\n", + " renderer_cache = None\n", + " shared_x_axes = \n", + " xlabel = \n", + " xlim = (0.0, 1.0)\n", + " xmajorticklabels = \n", + " xminorticklabels = \n", + " xscale = linear\n", + " xticklabels = \n", + " xticklines = \n", + " xticks = [ 0. 0.2 0.4 0.6 0.8 1. ]\n", + " yaxis = YAxis(54.000000,36.000000)\n", + " yaxis_transform = BlendedGenericTransform(BboxTransformTo(Transforme...\n", + " ybound = (0.0, 1.0)\n", + " ygridlines = \n", + " ylabel = \n", + " ylim = (0.0, 1.0)\n", + " ymajorticklabels = \n", + " yminorticklabels = \n", + " yscale = linear\n", + " yticklabels = \n", + " yticklines = \n", + " yticks = [ 0. 0.2 0.4 0.6 0.8 1. ]\n", + " zorder = 0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.getp(plt.gca())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** The constant growth model doesn't make a lot of sense, because it seems like the number of deaths and births should depend on the size of the population. As a small improvement, let's write a version of `run_simulation1` where the number of deaths is proportional to the size of the population, but the number of births is constant. This model doesn't make a lot of sense, either, but it's a good exercise.\n", + "\n", + "Write a function called `run_simulation1b` that implements a model where the number of births is constant, but the number of deaths is proportional to the current size of the population. Set the death rate to `0.01`, which means that 1% of the population dies each year; then choose the number of annual births to make the model fit the data as well as you can.\n", + "\n", + "Hint: It probably won't fit very well." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zJtZuk/HFF1/w5JNPcujQoU7ondLdpJ5I5a1db1FWWEHNER0OejucfZ0RfgJP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\nVDnwt23bz9ChA9jfvI9lJctYVb6KlkAL6ekp7H2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def run_simulation1b(system):\n", + " results = TimeSeries()\n", + " results[system.t0] = system.p0\n", + " for t in linrange(system.t0, system.t_end):\n", + " births = system.birthrate\n", + " deaths = system.deathrate * results[t]\n", + " results[t+1] = results[t] + births - deaths\n", + " system.results = results\n", + " plot_results(system)\n", + " \n", + "system.birthrate = 0.115\n", + "system.deathrate = 0.01\n", + "\n", + "run_simulation1b(system)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Proportional death, proportional birth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's get to a more realistic model where the number of births and deaths is proportional to the current population." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation2(system):\n", + " \"\"\"Runs the constant growth model.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + " \n", + " system: system object\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t0] = system.p0\n", + " for t in linrange(system.t0, 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", + " system.results = results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I kept the death rate at 1% and chose the birth rate to fit the data." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": true + }, + "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": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap03-fig03.pdf\n" + ] + }, + { + "data": { + "image/png": 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nL1LcejRoA/dLRW3grihKc/jr4F/8+MeP5JXlAXAyI49DRWl0KOnP1MHjuOuu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\naH1OAe6AX+0l0fUc7btFRHoDY3Fj6WNeB9xFvHQROSfsOf1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run_simulation2(system)\n", + "plot_results(system, title='Proportional model')\n", + "savefig('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": [ + "**Exercise:** In this implementation, we compute the number of deaths and births separately, but since they are both proportional to the current population, we can combine them.\n", + "\n", + "Write a function called `run_simulation2b` that implements a model with a single parameter, `alpha`, that represents the net growth rate, which is the difference between the birth and death rates. For example, if `alpha=0.01`, the population should grow by 1% per year.\n", + "\n", + "Choose the value of `alpha` that fits the data best." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XF3wjfXlzz5v8d+9/KS4pZ8eOHAoLKti8J5WkpNxWjrh12NvtMwG49+wi61rr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\nulpyctyDWAFfgMaMRhozG5lQ8EmmF0xnVM6oKJf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def run_simulation2b(system, n):\n", + " results = TimeSeries()\n", + " results[system.t0] = system.p0\n", + " alpha = n\n", + " for t in linrange(system.t0, system.t_end):\n", + " results[t+1] = results[t] * (1 + alpha)\n", + " system.results = results\n", + " plot_results(system)\n", + " \n", + "\n", + "run_simulation2b(system, .0175)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Factoring out the update function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The functions that run the model all look the same except the body of the loop. So we can factor that part out into a function." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": true + }, + "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", + " births = system.birth_rate * pop\n", + " deaths = system.death_rate * pop\n", + " print(t)\n", + " print(pop)\n", + " return pop + births - deaths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the name `update_func1` refers to a function object." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 108, + "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": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "function" + ] + }, + "execution_count": 109, + "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": 110, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Simulate the system using any update function.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + "\n", + " system: System object\n", + " update_func: function that computes the population next year\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t0] = system.p0\n", + " for t in linrange(system.t0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " system.results = results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we use it." + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1950.0\n", + "2.557628654\n", + "1951.0\n", + "2.60110834112\n", + "1952.0\n", + "2.64532718292\n", + "1953.0\n", + "2.69029774503\n", + "1954.0\n", + "2.73603280669\n", + "1955.0\n", + "2.78254536441\n", + "1956.0\n", + "2.8298486356\n", + "1957.0\n", + "2.87795606241\n", + "1958.0\n", + "2.92688131547\n", + "1959.0\n", + "2.97663829783\n", + "1960.0\n", + "3.02724114889\n", + "1961.0\n", + "3.07870424842\n", + "1962.0\n", + "3.13104222065\n", + "1963.0\n", + "3.1842699384\n", + "1964.0\n", + "3.23840252735\n", + "1965.0\n", + "3.29345537032\n", + "1966.0\n", + "3.34944411161\n", + "1967.0\n", + "3.40638466151\n", + "1968.0\n", + "3.46429320075\n", + "1969.0\n", + "3.52318618517\n", + "1970.0\n", + "3.58308035032\n", + "1971.0\n", + "3.64399271627\n", + "1972.0\n", + "3.70594059245\n", + "1973.0\n", + "3.76894158252\n", + "1974.0\n", + "3.83301358942\n", + "1975.0\n", + "3.89817482044\n", + "1976.0\n", + "3.96444379239\n", + "1977.0\n", + "4.03183933686\n", + "1978.0\n", + "4.10038060559\n", + "1979.0\n", + "4.17008707588\n", + "1980.0\n", + "4.24097855617\n", + "1981.0\n", + "4.31307519163\n", + "1982.0\n", + "4.38639746988\n", + "1983.0\n", + "4.46096622687\n", + "1984.0\n", + "4.53680265273\n", + "1985.0\n", + "4.61392829783\n", + "1986.0\n", + "4.69236507889\n", + "1987.0\n", + "4.77213528523\n", + "1988.0\n", + "4.85326158508\n", + "1989.0\n", + "4.93576703202\n", + "1990.0\n", + "5.01967507157\n", + "1991.0\n", + "5.10500954779\n", + "1992.0\n", + "5.1917947101\n", + "1993.0\n", + "5.28005522017\n", + "1994.0\n", + "5.36981615891\n", + "1995.0\n", + "5.46110303361\n", + "1996.0\n", + "5.55394178519\n", + "1997.0\n", + "5.64835879553\n", + "1998.0\n", + "5.74438089506\n", + "1999.0\n", + "5.84203537027\n", + "2000.0\n", + "5.94134997157\n", + "2001.0\n", + "6.04235292108\n", + "2002.0\n", + "6.14507292074\n", + "2003.0\n", + "6.2495391604\n", + "2004.0\n", + "6.35578132612\n", + "2005.0\n", + "6.46382960867\n", + "2006.0\n", + "6.57371471201\n", + "2007.0\n", + "6.68546786212\n", + "2008.0\n", + "6.79912081577\n", + "2009.0\n", + "6.91470586964\n", + "2010.0\n", + "7.03225586943\n", + "2011.0\n", + "7.15180421921\n", + "2012.0\n", + "7.27338489093\n", + "2013.0\n", + "7.39703243408\n", + "2014.0\n", + "7.52278198546\n", + "2015.0\n", + "7.65066927921\n" + ] + } + ], + "source": [ + "run_simulation(system, update_func1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember not to put parentheses after `update_func1`. What happens if you try?" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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v5cUXX2TUqFG89tprzJt3edslcnJyrGz8Dg4OfPfddxXalU/C3rx5c6ZPn870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\nESE3N9eCvjExpL2W/9sc2xO+5iSzZUsFS9/fTMO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(system, title='Proportional model, factored')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** When you run `run_simulation`, it runs `update_func1` once for each year between `t0` 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": 113, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def update_func1b(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": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ne5eWjBjRlpkz+zRKpQGO9zhmAA+YvlcAKWhG7jFow0yH0WwVVWEc8KGUsriK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\nVu5m1SrDtGkTSUlJbivLz8mmMb4eV6OHGvchAkmNjM7PY1r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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "system.alpha = system.birth_rate - system.death_rate\n", + "\n", + "run_simulation(system, update_func1b)\n", + "plot_results(system, title='Proportional model, combined birth and death')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, might be changing over time. So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n", + "\n", + "Write a function called `update_func1c` that takes `pop`, `t`, and `system` as parameters. The system object, `system`, should contains two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`. It should compute and return the simulated population one year later.\n", + "\n", + "Note: Don't forget the `return` statement." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": true, + "scrolled": false + }, + "outputs": [], + "source": [ + "def update_func1c(pop, t system):\n", + " if t < 1980:\n", + " births = system.birth_rate * results[t]\n", + " deaths = system.death_rate * results[t]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quadratic growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the implementation of the quadratic growth model." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "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 + system.beta * pop**2\n", + " return pop + net_growth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here are the results. Can you find values for the parameters that make the model fit better?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system.alpha = 0.025\n", + "system.beta = -0.0018\n", + "\n", + "run_simulation(system, update_func2)\n", + "plot_results(system, title='Quadratic model')\n", + "savefig('chap03-fig04.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To understand the quadratic model better, let's plot net growth as a function of population." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "pop_array = linspace(0.001, 15, 100)\n", + "net_growth_array = system.alpha * pop_array + system.beta * pop_array**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what it looks like. Remember that the x axis is population now, not time.\n", + "\n", + "The function `sns.set` sets the style for the plots. I added a grid to this one to make it easier to read." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "newfig()\n", + "sns.set(style='whitegrid', font_scale=1.5)\n", + "plot(pop_array, net_growth_array, '-')\n", + "decorate(xlabel='Population (billions)',\n", + " ylabel='Net growth (billions)',\n", + " legend=False)\n", + "savefig('chap03-fig05.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `sns.set` to reset the plot style." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sns.set(style='white', font_scale=1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the book we found that the net growth is 0 when the population is $-\\alpha/\\beta$:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "-system.alpha / system.beta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the equilibrium the population tends toward." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** In the book, I presented a different way to parameterize the quadratic model:\n", + "\n", + "$ \\Delta p = r p (1 - p / K) $\n", + "\n", + "where $r=\\alpha$ and $K=-\\alpha/\\beta$. Write a version of `update_func2` that implements this version of the model. Test it by computing system variables `r` and `K` equivalent to `alpha` and `beta`, and confirm that you get the same results. " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations. The following cells process this table and plot some of the results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Select `table1`, which is the second table on the page." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "table1 = tables[1]\n", + "table1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not all agencies and researchers provided estimates for the same dates. Again `NaN` is the special value that indicates missing data." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "table1.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the estimates are in a form we can't read as numbers. We could clean them up by hand, but for simplicity I'll replace any value that has an `M` in it with `NaN`." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "table1.replace('M', np.nan, regex=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we'll replace the long column names with more convenient abbreviations." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "table1.columns = ['prb', 'un', 'maddison', 'hyde', 'tanton', \n", + " 'biraben', 'mj', 'thomlinson', 'durand', 'clark']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function plots selected estimates." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_prehistory(table):\n", + " \"\"\"Plots population estimates.\n", + " \n", + " table: DataFrame\n", + " \"\"\"\n", + " plot(table.prb, 'ro', label='PRB')\n", + " plot(table.un, 'co', label='UN')\n", + " plot(table.hyde, 'yo', label='HYDE')\n", + " plot(table.tanton, 'go', label='Tanton')\n", + " plot(table.biraben, 'bo', label='Biraben')\n", + " plot(table.mj, 'mo', label='McEvedy & Jones')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the results. Notice that we are working in millions now, not billions." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": true, + "scrolled": false + }, + "outputs": [], + "source": [ + "newfig()\n", + "plot_prehistory(table1)\n", + "decorate(xlabel='Year', \n", + " ylabel='World population (millions)',\n", + " title='Prehistorical population estimates')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use `xlim` to zoom in on everything after Year 0." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "newfig()\n", + "plot_prehistory(table1)\n", + "decorate(xlim=[0, 2000], xlabel='Year', \n", + " ylabel='World population (millions)',\n", + " title='Prehistorical population estimates')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See if you can find a model that fits these data well from Year -1000 to 1940, or from Year 0 to 1940.\n", + "\n", + "How well does your best model predict actual population growth from 1950 to the present?" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 4ac1d55f77fe4d52b308e47ec6411b4a34fde94f Mon Sep 17 00:00:00 2001 From: FlynnML Date: Thu, 21 Sep 2017 00:40:42 -0400 Subject: [PATCH 05/19] Resubmit ch3 mine --- code/chap03mine.ipynb | 791 +++++++++++++++++++++++++++++++++--------- 1 file changed, 621 insertions(+), 170 deletions(-) diff --git a/code/chap03mine.ipynb b/code/chap03mine.ipynb index 971fe801..3663f7fc 100644 --- a/code/chap03mine.ipynb +++ b/code/chap03mine.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 124, "metadata": { "collapsed": true }, @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 125, "metadata": { "collapsed": true }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 126, "metadata": { "collapsed": true }, @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 127, "metadata": { "scrolled": true }, @@ -265,7 +265,7 @@ "1954 NaN NaN " ] }, - "execution_count": 24, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 128, "metadata": { "scrolled": true }, @@ -462,7 +462,7 @@ "2015 NaN NaN " ] }, - "execution_count": 25, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -480,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 129, "metadata": { "collapsed": true }, @@ -504,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -1611,7 +1611,7 @@ "[66 rows x 11 columns]" ] }, - "execution_count": 27, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -1629,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -1700,7 +1700,7 @@ "Name: census, Length: 66, dtype: int64" ] }, - "execution_count": 28, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -1721,7 +1721,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -1743,7 +1743,7 @@ " 7256490011], dtype=int64)" ] }, - "execution_count": 29, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -1761,7 +1761,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 133, "metadata": {}, "outputs": [ { @@ -1776,7 +1776,7 @@ " dtype='int64', name='Year')" ] }, - "execution_count": 30, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -1800,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -1809,7 +1809,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 31, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -1820,7 +1820,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 135, "metadata": {}, "outputs": [ { @@ -1829,7 +1829,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 32, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -1840,7 +1840,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -1849,7 +1849,7 @@ "pandas.core.indexes.numeric.Int64Index" ] }, - "execution_count": 33, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -1860,7 +1860,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -1869,7 +1869,7 @@ "numpy.ndarray" ] }, - "execution_count": 34, + "execution_count": 137, "metadata": {}, "output_type": "execute_result" } @@ -1889,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 138, "metadata": { "collapsed": true }, @@ -1919,7 +1919,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 139, "metadata": { "scrolled": false }, @@ -1935,7 +1935,7 @@ "data": { "image/png": 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P8y2LiYlBKcUzzzyT4745rZB13fVjs/5r1qwZ9957L3PmzMnWU+G///3vLftm/ffzzz9n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\nT01NZ96KbZzx3MOFZM1deypmWzzFw92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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1957,7 +1957,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 140, "metadata": { "collapsed": true }, @@ -1968,7 +1968,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 141, "metadata": { "collapsed": true }, @@ -1988,7 +1988,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -1997,7 +1997,7 @@ "1.2862470293832287" ] }, - "execution_count": 39, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -2019,7 +2019,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 143, "metadata": { "scrolled": true }, @@ -2092,7 +2092,7 @@ "Length: 66, dtype: float64" ] }, - "execution_count": 40, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -2104,7 +2104,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 144, "metadata": { "scrolled": true }, @@ -2177,7 +2177,7 @@ "Length: 66, dtype: float64" ] }, - "execution_count": 41, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -2190,7 +2190,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 145, "metadata": { "scrolled": true }, @@ -2201,7 +2201,7 @@ "1.2862470293832287" ] }, - "execution_count": 42, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -2213,7 +2213,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -2222,7 +2222,7 @@ "1.2813631502151765" ] }, - "execution_count": 44, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -2249,7 +2249,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -2258,7 +2258,7 @@ "2.5576286540000002" ] }, - "execution_count": 45, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -2276,7 +2276,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -2285,7 +2285,7 @@ "7.2564900110000004" ] }, - "execution_count": 46, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -2303,7 +2303,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -2312,7 +2312,7 @@ "(1950, 2015)" ] }, - "execution_count": 47, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } @@ -2334,7 +2334,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -2343,7 +2343,7 @@ "0.07229017472307693" ] }, - "execution_count": 48, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -2364,7 +2364,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 151, "metadata": { "collapsed": true }, @@ -2382,7 +2382,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -2423,7 +2423,7 @@ "dtype: float64" ] }, - "execution_count": 50, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -2442,7 +2442,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 153, "metadata": { "collapsed": true }, @@ -2461,7 +2461,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -2475,7 +2475,7 @@ "data": { "image/png": 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X17O2NQylc3R0RKfTcWaVtoZC1mAusA7w9ttvExYWZnXc6b8MFMUWer2eoqIi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\nBM33y9YAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2510,7 +2510,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 155, "metadata": {}, "outputs": [ { @@ -2522,9 +2522,9 @@ }, { "data": { - "image/png": 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JXwjRLlgsFpKSkoiJiSGn0DpkM6csBwBnBxfi49OoqrJQVGHm559TmDixV73j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\n/o7jOBnI/wN6CeT9QgNXAAAAAABJRU5ErkJggg==\n", 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8ABjcfTDdFoVgMOjx8TG2c9RdQ1ODvL6u86sOGAwcF0L8iLXSxwcYCzgC9i7k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\nQzFl3RZLwlv+ItJDRG4E/i1m+9eB/wV+A0wBHgC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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2569,7 +2569,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 156, "metadata": { "collapsed": true }, @@ -2591,7 +2591,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 157, "metadata": { "collapsed": true }, @@ -2612,7 +2612,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 158, "metadata": { "collapsed": true }, @@ -2641,7 +2641,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 159, "metadata": { "collapsed": true }, @@ -2669,14 +2669,14 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 160, "metadata": {}, "outputs": [ { "data": { "image/png": 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z5s0jKCiI2bNn2x4Yl5WVsWHDBkaOHGkbEObaa6/FaDTy2Wd6Bn9TlIaZzWYS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\n3c1/i0gUMAKoMMY09FXGhqqQkBDi4uJagn98fDy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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2697,7 +2697,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -2732,7 +2732,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 162, "metadata": { "scrolled": true }, @@ -2754,14 +2754,14 @@ " axes_locator = None\n", " axis_bgcolor = (1.0, 1.0, 1.0, 1.0)\n", " axisbelow = True\n", - " children = [" + "" ] }, "metadata": {}, @@ -2847,14 +2847,14 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 163, "metadata": {}, "outputs": [ { "data": { "image/png": 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zJtZuk/HFF1/w5JNPcujQoU7ondLdpJ5I5a1db1FWWEHNER0OejucfZ0RfgJP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\nVDnwt23bz9ChA9jfvI9lJctYVb6KlkAL6ekp7H2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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2880,7 +2880,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 164, "metadata": { "collapsed": true }, @@ -2905,7 +2905,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 165, "metadata": { "collapsed": true }, @@ -2936,7 +2936,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 166, "metadata": { "collapsed": true }, @@ -2955,7 +2955,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 167, "metadata": {}, "outputs": [ { @@ -2969,7 +2969,7 @@ "data": { "image/png": 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nL1LcejRoA/dLRW3grihKc/jr4F/8+MeP5JXlAXAyI49DRWl0KOnP1MHjuOuu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\naH1OAe6AX+0l0fUc7btFRHoDY3Fj6WNeB9xFvHQROSfsOf1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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3002,14 +3002,14 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 168, "metadata": {}, "outputs": [ { "data": { "image/png": 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XF3wjfXlzz5v8d+9/KS4pZ8eOHAoLKti8J5WkpNxWjrh12NvtMwG49+wi61rr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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3032,7 +3032,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 169, "metadata": { "collapsed": true }, @@ -3057,7 +3057,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 170, "metadata": { "collapsed": true }, @@ -3088,7 +3088,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -3097,7 +3097,7 @@ "" ] }, - "execution_count": 108, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -3115,7 +3115,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 172, "metadata": {}, "outputs": [ { @@ -3124,7 +3124,7 @@ "function" ] }, - "execution_count": 109, + "execution_count": 172, "metadata": {}, "output_type": "execute_result" } @@ -3142,7 +3142,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 173, "metadata": { "collapsed": true }, @@ -3172,7 +3172,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -3327,14 +3327,14 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "image/png": 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v5cUXX2TUqFG89tprzJt3edslcnJyrGz8Dg4OfPfddxXalU/C3rx5c6ZPn870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\nESE3N9eCvjExpL2W/9sc2xO+5iSzZUsFS9/fTMO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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3368,7 +3368,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 176, "metadata": { "collapsed": true }, @@ -3396,14 +3396,14 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 177, "metadata": {}, "outputs": [ { "data": { "image/png": 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ne5eWjBjRlpkz+zRKpQGO9zhmAA+YvlcAKWhG7jFow0yH0WwVVWEc8KGUsriK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\nVu5m1SrDtGkTSUlJbivLz8mmMb4eV6OHGvchAkmNjM7PY1r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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3430,28 +3430,42 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 180, "metadata": { - "collapsed": true, "scrolled": false }, "outputs": [], "source": [ - "def update_func1c(pop, t system):\n", + "def update_func1c(pop, t, system):\n", " if t < 1980:\n", - " births = system.birth_rate * results[t]\n", - " deaths = system.death_rate * results[t]" + " net_growth = system.alpha1 * pop\n", + " else:\n", + " net_growth = system.alpha2 * pop\n", + " return pop + net_growth" ] }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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TGDx4MF9//TUHDhzgiSeesLveiy++yM8//8ysWbMIDQ3lt99+Y8qUKbz//vsM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\nY0wSsuRvjDFJ6P8B6poAeoYZPBUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Solution goes here" + "system.alpha1 = .018\n", + "system.alpha2 = .01575\n", + "\n", + "run_simulation(system, update_func1c)\n", + "plot_results(system)" ] }, { @@ -3470,7 +3484,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 191, "metadata": { "collapsed": true }, @@ -3498,11 +3512,27 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap03-fig04.pdf\n" + ] + }, + { + "data": { + "image/png": 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39WN8/GomHuFWZs7sfVYmfmjgAu6uIgu4CyGcVVxSzIc/fIjer+kc0I0+3Xoz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\nhVaeXVPMlUvPbTsmKz2La86+JhbVNsaYEyIpk38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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "system.alpha = 0.025\n", "system.beta = -0.0018\n", @@ -3521,7 +3551,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 193, "metadata": { "collapsed": true }, @@ -3542,11 +3572,27 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap03-fig05.pdf\n" + ] + }, + { + "data": { + "image/png": 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WSnbNbd38evspenq1QlKj53K5uGVtCX6/nyOVtivv0OkmPB43G6+dpRXTBBSx+24kFVLI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\nMoRRCoIgCCrDxyOfzAVqkIdQAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "newfig()\n", "sns.set(style='whitegrid', font_scale=1.5)\n", @@ -3566,7 +3612,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 195, "metadata": { "collapsed": true }, @@ -3584,11 +3630,20 @@ }, { "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13.88888888888889" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "-system.alpha / system.beta" ] @@ -3613,35 +3668,59 @@ }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.025, 13.88888888888889)" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Solution goes here" + "system.r = system.alpha\n", + "system.K = -system.alpha / system.beta\n", + "\n", + "system.r, system.K" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 201, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# Solution goes here" + "def update_func2(pop, t, system):\n", + " net_growth = system.r * pop * (1 - pop/system.K)\n", + " return pop + net_growth" ] }, { "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wd3enR48eNZZ2FEKIutIb9PyQ+gPfxHyDVY4VVpVXiv54u7rhWu5FUaEfI+/p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\nHuoJAE1NTcjNzUVubi5+/vlnXL9+HUqlEhMnTuz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Population Reference Bureau (1973–2015)[6]United Nations Department of Economic and Social Affairs (2015)[7]Maddison (2008)[8]HYDE (2010)[citation needed]Tanton (1994)[9]Biraben (1980)[10]McEvedy & Jones (1978)[11]Thomlinson (1975)[12]Durand (1974)[13]Clark (1967)[14]
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Population Reference Bureau (1973–2015)[6]United Nations Department of Economic and Social Affairs (2015)[7]Maddison (2008)[8]HYDE (2010)[citation needed]Tanton (1994)[9]Biraben (1980)[10]McEvedy & Jones (1978)[11]Thomlinson (1975)[12]Durand (1974)[13]Clark (1967)[14]
Year
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1940NaN2300.02299.2307.NaNNaNNaNNaNNaN2340.
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NaN \n", + "\n", + " Biraben (1980)[10] McEvedy & Jones (1978)[11] Thomlinson (1975)[12] \\\n", + "Year \n", + "1913 NaN NaN NaN \n", + "1920 NaN NaN NaN \n", + "1925 NaN 2000.0 NaN \n", + "1930 NaN NaN NaN \n", + "1940 NaN NaN NaN \n", + "\n", + " Durand (1974)[13] Clark (1967)[14] \n", + "Year \n", + "1913 NaN NaN \n", + "1920 NaN 1968. \n", + "1925 NaN NaN \n", + "1930 NaN 2145. \n", + "1940 NaN 2340. " + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "table1.tail()" ] @@ -3697,7 +4098,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 205, "metadata": { "collapsed": true }, @@ -3715,7 +4116,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 206, "metadata": { "collapsed": true }, @@ -3734,7 +4135,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 207, "metadata": { "collapsed": true }, @@ -3762,12 +4163,22 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 208, "metadata": { - "collapsed": true, "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Qhg4dCkBoaCihoaEaxxUKRa4DUkhICJMmTaJ169aMGTMGyAokaWlpavlUKhXG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\ntxl+wTmyC6wuq+HAo8va1ORXSY7sSeXIYznys6Eu7YaRrAbTm5qiC8T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NGzYMgKCgIIKCgjS2KxSKbCepwMBAJk+eTKtWrRg7diyQnlySk5PV6qlUKgwN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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "newfig()\n", "plot_prehistory(table1)\n", @@ -3809,35 +4229,66 @@ }, { "cell_type": "code", - "execution_count": 73, - "metadata": { - "collapsed": true - }, + "execution_count": 211, + "metadata": {}, "outputs": [], "source": [ - "# Solution goes here" + "p0 = table1.mj[1]\n", + "\n", + "prehistory = System(t0=1, t_end=2016, alpha=0.0011, p0=p0)\n", + "\n", + "run_simulation(prehistory, update_func1b)" ] }, { "cell_type": "code", - "execution_count": 74, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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78dVKUuPGjQPAx8cHHx+fHNsVCkWBk9SBAweYNWsWHTt2ZMqUKUBWcklLS1PZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Solution goes here" + "newfig()\n", + "plot_prehistory(table1)\n", + "plot(prehistory.results)\n", + "decorate(xlim=[0, 2000], xlabel='Year', \n", + " ylabel='World population (millions)',\n", + " title='Prehistorical population estimates')" ] }, { "cell_type": "code", - "execution_count": 75, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jLS1NqSwrK4tly5aRlJSEtrY2VatW5c6dO9jY2FC5cmUqV66Mmpoa06ZN4/Hj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\nTU1hYmLSpm0SQgh5N0rD/y9/+Qt3GqYqlJ3bL++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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Solution goes here" + "newfig()\n", + "plot_estimates(table2)\n", + "plot(prehistory.results/1000)\n", + "decorate(xlim=[1950, 2016], xlabel='Year', \n", + " ylabel='World population (millions)',\n", + " title='Prehistorical population estimates')\n", + "#not a good fit :)" ] }, { From 08e713753e7efdc351e0ace2694a9c9ecf629767 Mon Sep 17 00:00:00 2001 From: FlynnML Date: Mon, 25 Sep 2017 16:12:47 -0400 Subject: [PATCH 06/19] sup --- code/chap03-fig01.pdf | Bin 0 -> 16801 bytes code/chap03-fig02.pdf | Bin 0 -> 17680 bytes code/chap03-fig03.pdf | Bin 0 -> 18105 bytes code/chap03-fig04.pdf | Bin 0 -> 18591 bytes code/chap03-fig05.pdf | Bin 0 -> 14596 bytes code/chap03.ipynb | 156 +++++++-- code/rabbitsmine.ipynb | 773 +++++++++++++++++++++++++++++++++++++++++ 7 files changed, 892 insertions(+), 37 deletions(-) create mode 100644 code/chap03-fig01.pdf create mode 100644 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Suppose you start with an initial population of rabbits and let them breed. For simplicity, we'll assume that all rabbits are on the same breeding cycle, and we'll measure time in \"seasons\", where a season is the reproductive time of a rabbit.\n", + "\n", + "If we provide all the food, space and other resources a rabbit might need, we expect the number of new rabbits each season to be proportional to the current population, controlled by a parameter, `birth_rate`, that represents the number of new rabbits per existing rabbit, per season. As a starting place, I'll assume `birth_rate = 0.9`.\n", + "\n", + "Sadly, during each season, some proportion of the rabbits die. In a detailed model, we might keep track of each rabbit's age, because the chance of dying is probably highest for young and old rabbits, and lowest in between. But for simplicity, we'll model the death process with a single parameter, `death_rate`, that represent the number\n", + "of deaths per rabbit per season. As a starting place, I'll assume `death_rate = 0.5`.\n", + "\n", + "Here's a system object that contains these parameters as well as:\n", + "\n", + "* The initial population, `p0`,\n", + "* The initial time, `t0`, and\n", + "* The duration of the simulation, `t_end`, measured in seasons." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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That's an off-by-one error that I'll fix later, but for now we don't really care.\n", + "\n", + "The following function plots the results." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_results(system, title=None):\n", + " \"\"\"Plot the estimates and the model.\n", + " \n", + " system: System object with `results`\n", + " \"\"\"\n", + " newfig()\n", + " plot(system.results, 'bo', label='rabbits')\n", + " decorate(xlabel='Season', \n", + " ylabel='Rabbit population',\n", + " title=title)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's how we call it." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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eVLly4ceSF7kmkKFDh6o/BwQEPPUkMTExBReREEIUMz4+2m0gWdq3L/xY8kKn\ncSB169blr7/+ynFbREQE7Yv6XQohRBHm6Qn9+kHVqmBklPl3v35Fu/0DnvIEsnLlSpKSkoDMWXS3\nbNnCoUOHsu138uRJ6QElhBDPydOz6CeMJ+WaQNLS0li8eDGQ2fspNDQ02z5GRkaUKVNGekEJIUQx\nlGsCGTBgAAMGDADAycmJjRs34uLiUmiBCSGEKNp0GgcSGRmp7ziEEEK8YHQeSLh3716OHTumTl4I\nkJGRwaNHjzh58iQHDx7UW5BCCCGKHp0SyMKFC1mwYAGlS5cmLS0NExMTSpQowZ07dzAyMqJbt276\njlMIIUQRo1M33m3bttGlSxeOHj1Kr169eP311/n999/57rvvKFu2rLpeuRBCiOJDpwQSHR2Nr68v\nGo0GZ2dnTp48CWROdzJo0CC2bNmi1yCFEEIUPTolEAsLC4yMMnetXr06N27cIDk5GcgcZHjjxg39\nRSiEEKJI0imBNGjQgO3btwOZqxMaGxtz5MgRAC5fviwDCYUQohjSqRF9wIABfPTRRyQkJLB48WI6\nderExx9/TNOmTQkLC6NNmzb6jlMIIUQRo1MCady4MZs3b+bChQsATJkyBSMjI06cOEH79u0ZN26c\nXoMUQghR9Og8DsTZ2RlnZ2cAzMzMmD59ut6CEkIIUfTlmkB27tyZpxP5+vo+dzBCCCFeHLkmkDFj\nxuh8Eo1GIwlECCGKmVwTyIEDBwozDiGEEC+YXBNIlSpVCjMOIYQQLxidGtH79u37zH1Wrlz53MEI\nIYR4ceiUQFJTU7OVJSUlcenSJSwsLGjbtm2BByaEEKJo0ymBrF27NsfyhIQE+vfvT+3atQs0KCGE\nEEWfTlOZ5MbKyooBAwawatWqAgpHCCHEi+K5EkiW+Pj4gjiNEEKIF4hOVVgnTpzIVpaRkUFUVBQL\nFixQR6gLIYQoPnRKIB9++CEajSZbuaIo2NnZMWHChAIPTAghRNGmUwJZs2ZNtjKNRkOpUqVwdHRU\n1woRQghRfOiUQLy8vNSfU1JSePDgAVZWVpiYmOgtMCGEEEWbzrPx/vzzzyxevJizZ8+iKArGxsY0\natSIYcOG4eHhoc8YhRBCFEE61T3t3r2bIUOGoCgKI0aMYPr06QwdOpTExER69+6trk4ohBCi+NDp\nCWTRokW8+eabzJkzR6t80KBBjBgxgi+//JJvv/1WLwEKIYQomnR6Arlx4wZdunTJcds777zDP//8\nU6BBCSHu0b0AAAAd/klEQVSEKPp0SiBOTk6Eh4fnuO3cuXMylYkQQhRDOlVhBQQEMGrUKB4+fEjH\njh2pUKEC9+7d45dffmHFihWMHz9ea7Bho0aN9BawEEKIokGnBNKvXz8ANmzYwMaNG9VyRVEAmDZt\nmvpao9Fw/vz5Ag5TCCFEUZPvgYRCCCGKtzwPJBRCCCEgDwMJL126xIIFCzh69CgPHjygXLlyeHh4\nMGTIEF555RV9xiiEEKII0imB/P3333zwwQeYm5vTunVrbGxsiI2N5eDBgxw8eJBNmzbh6Oio71iF\nEEIUITolkDlz5lC7dm3WrFmDhYWFWp6UlETv3r2ZN28eixcv1luQQghRmI4dgz17ICoK7OzAxwc8\nPQ0dVdGj0ziQiIgIBg0apJU8ACwsLOjXrx8RERE6XzAuLo6PP/6YZs2a4eHhwUcffaQ1EPHw4cN0\n7twZFxcXfH19CQsL0zo+Pj6e4cOH4+HhQdOmTQkKCiItLU3n6wshxNMcOwbLl8PNm5CRkfn38uWZ\n5UKbTgnE3Nw8120ajYb09HSdLpaRkcHQoUO5cuUKixYtYtOmTZQqVYrevXtz9+5dLl68yODBg2nf\nvj3btm2jdevW+Pv7c+HCBfUcAQEBxMXFsW7dOmbNmkVoaCgLFizQ6fpCCPEse/bkXL53b+HG8SLQ\nKYG4urqybNkyUlJStMqTk5NZvnw5bm5uOl0sMjKSkydPMnPmTFxcXHjllVcICgoiKSmJsLAw1qxZ\ng6urK4MHD8be3p4RI0bg5uamdiM+efIkx48fZ9asWTg5OdGiRQvGjh3L2rVrefz4cR5vXQghsouK\nyrn81q3CjeNFoFMbyKhRo3jnnXdo3bo1rVq1onz58sTFxfHzzz/z8OFD1q9fr9PF7Ozs+Prrr6lV\nq5ZalrXSYUJCAhEREfj4+Ggd07hxY3bv3g1kVqVVqVKFatWqqdu9vLx4+PAh58+fp2HDhjrFIYQQ\nubGzy6y2elLlyoUfS1Gn0xOIvb09mzZtolGjRhw4cIClS5fy008/4e7uzubNm6lXr55OFytXrhwt\nW7bUWsFw7dq1JCcn06xZM6Kjo6lYsaLWMRUqVCA6OhqAmJgYKlSokG07QFRuXxuEECIPnvgOq2rf\nvnDjeBHoPA7E0dGR4ODgAr34gQMH+PLLL+nTpw/29vYkJydjamqqtY+pqaladfbo0SPMzMy0tpuY\nmKDRaLJVrwkhRH5k9bbauzez2qpy5czkIb2wstM5gcTExLBmzRqOHz9OQkICNjY2NGnShB49emBl\nZZXnC4eGhjJ58mQ6dOjAmDFjADAzMyM1NVVrv8ePH6uN+CVLlszW1pGamoqiKNl6iAkhRH55ekrC\n0IVOVVhnz56lY8eObNiwAUtLSxo0aICZmRnLly/nzTff5Pr163m66OLFixk/fjzvv/8+X3zxhVql\nZWdnx+3bt7X2vX37tlqtValSJWJjY7NtB7JVfQkhhNAvnZ5AZs2aRfXq1Vm+fDnW1tZqeVxcHP37\n92fWrFksXLhQpwsuW7aMefPmMWzYMPz9/bW2ubu7c+yJztbh4eHqmuvu7u7MmTOHqKgo7Ozs1O2W\nlpY4OTnpdH0hhBAFQ6cnkNOnTxMQEKCVPADKly+Pv7+/zmuiR0ZG8tVXX9G1a1feffddYmNj1T9J\nSUn4+fkRERFBcHAwly5dYv78+Zw6dYpevXoB4ObmhqurK4GBgZw9e5awsDCCgoLo06dPtrYTIYQQ\n+qXTE4itrW22qqUsDx8+1LkN5IcffiA9PZ2tW7eydetWrW3Dhw9nyJAhhISEEBQUxLJly6hduzZL\nlizB3t4eyOzyGxISwrRp0+jevTuWlpZ069Yt25OMEEII/dMpgYwZM4apU6diZWVFu3bt1LEb4eHh\nfPXVV4wfP16ni40cOZKRI0c+dZ+WLVvSsmXLXLfb2trqXF0mhBBCf3SeTDE5OZnAwEBKlChB+fLl\nuXfvHsnJySiKwqhRoxg1apS6/5kzZ/QWsBBCiKJBpwTSqVMnfcchhBDiBaNTAhk6dKi+4xBCCPGC\n0akXlhBCCPEkSSBCCCHyRRKIEEKIfJEEIoQQIl90SiDff/89d+/ezXFbbGwsK1euLNCghBBCFH06\nJZDx48fnOmHi+fPn+eqrrwo0KCGEEEVfrt14Bw4cyMWLFwFQFAV/f/8c55uKj4+nevXq+otQCCFE\nkZRrAhk8eDDfffcdAN999x0NGjTINpmikZERZcqU4a233tJvlEIIIYqcXBOIq6srrq6uAKSnpzNk\nyBCttciFEEIUbzqNRP/888/1HYcQQogXTK4JpH79+mzYsAEXFxecnZ3VGXhzIxMoCiFE8ZJrAhk0\naJC6TOygQYOemUCEEEIUL7kmkP9OoBgQEFAowQghhHhx6NQGApCYmEhoaCjHjx8nMTERa2trGjdu\nTKdOnWQ5WSGEKIZ0SiBXr16lZ8+e3L59mxo1amBjY8Nff/3Fzp07Wb16NatWrcLGxkbfsQohhChC\ndO6FVbJkSXbt2qWuTw5w7tw5hg4dymeffcaXX36ptyCFEMXXsWOwZw9ERYGdHfj4gKenoaMSoONU\nJuHh4YwcOVIreQDUq1ePkSNH8ssvv+gjNiFEMXfsGCxfDjdvQkZG5t/Ll2eWC8PTKYGULl2a9PT0\nHLdZWlpibm5eoEEJIQRkPnnkZO/ewo1D5EynBDJw4EDmzJlDZGSkVvmtW7eYN28e/fr100twQoji\nLSoq5/Jbtwo3DpGzXNtA2rZtqzX2IyYmhrfeeovq1atjY2PD/fv3+ffffzE1NeWnn36iT58+hRKw\nEKL4sLPLrLZ6UuXKhR+LyC7XBNKoUSOtBNKoUaNs+zRo0EA/UQkhBJkN5suXZy9v377wYxHZ5ZpA\nZs2aVZhxCCFENlm9rfbuzay2qlw5M3lIL6yiQeeBhBkZGRw8eFAdSGhjY4OXlxdNmzbVZ3xCiGLO\n01MSRlGlUwKJi4ujX79+REZGYmpqirW1NfHx8SxZsoSmTZsSEhKChYWFvmMVQghRhOjUC2vWrFnE\nxsaybNky/vrrL3755RdOnz7NggULOHv2LLNnz9Z3nEIIIYoYnRLIwYMHGTNmDM2bN9cqb9OmDaNG\njWJPbp21hRBCvLR0SiCmpqaULl06x22VpT+dEEIUSzolkA8++ID58+cTFxenVZ6UlMTSpUvp1q2b\nXoITQghRdOXaiN63b1/1Z0VRuHTpEm3atKFRo0bqQMITJ06QlpZGhQoVCiVYIYQQRUeuCSQ1NVXr\nddZAwtTUVKKjowFwcnIC4Pbt2/qKTwghRBGVawJZu3ZtYcYhhBDiBaNTG8jTPH78mN9//70gYhFC\nCPEC0Wkg4a1bt/jkk084evSoVtVWRkYGiqIAcP78ef1EKIQQokjSKYHMmjWLiIgIunbtyokTJzA3\nN8fV1ZXffvuNf/75hwULFug7TiGEEEWMzisSBgYGMmnSJN5++23MzMwYM2YMW7duxcPDgwMHDug7\nTiGEEEWMTgnk4cOHODo6AlC7dm3OnTsHgLGxMd27d+fIkSP6i1AIIUSRpFMCqVChgjqIsEaNGiQk\nJBAbGwtA2bJliY+P11+EQgg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(system, title='Proportional growth model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's suppose our goal is to maximize the number of rabbits, so the metric we care about is the final population. We can extract it from the results like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def final_population(system):\n", + " t_end = system.results.index[-1]\n", + " return system.results[t_end]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And call it like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "404.95651696640027" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_population(system)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To explore the effect of the parameters on the results, we'll define `make_system`, which takes the system parameters as function parameters(!) and returns a `System` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_system(birth_rate=0.9, death_rate=0.5):\n", + " \n", + " system = System(t0 = 0, \n", + " t_end = 10,\n", + " p0 = 10,\n", + " birth_rate = birth_rate,\n", + " death_rate = death_rate)\n", + " return system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can make a `System`, run a simulation, and extract a metric:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "404.95651696640027" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = make_system()\n", + "run_simulation(system)\n", + "final_population(system)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see the relationship between `birth_rate` and final population, we'll define `sweep_birth_rate`:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_birth_rate(birth_rates, death_rate=0.5):\n", + " \n", + " for birth_rate in birth_rates:\n", + " system = make_system(birth_rate=birth_rate,\n", + " death_rate=death_rate)\n", + " run_simulation(system)\n", + " p_end = final_population(system)\n", + " plot(birth_rate, p_end, 'gs', label='rabbits')\n", + " \n", + " decorate(xlabel='Births per rabbit per season',\n", + " ylabel='Final population')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first parameter of `sweep_birth_rate` is supposed to be an array; we can use `linspace` to make one." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 ,\n", + " 0.45, 0.5 , 0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85,\n", + " 0.9 , 0.95, 1. ])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "birth_rates = linspace(0, 1, 21)\n", + "birth_rates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can call `sweep_birth_rate`.\n", + "\n", + "The resulting figure shows the final population for a range of values of `birth_rate`.\n", + "\n", + "Confusingly, the results from a parameter sweep sometimes resemble a time series. It is very important to remember the difference. One way to avoid confusion: LABEL THE AXES.\n", + "\n", + "In the following figure, the x-axis is `birth_rate`, NOT TIME." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FBSErKwthYWHw9vaudO2FiIhqVrWBMnToUNnfu3XrBkNDQ9k1kGcVFhYqdHRSISAgAPr6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\nHj0QHh5eaRuRglT4ihjSEKN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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "birth_rates = linspace(0, 1, 21)\n", + "sweep_birth_rate(birth_rates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code to sweep the death rate is similar." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_death_rate(death_rates, birth_rate=0.9):\n", + " \n", + " for death_rate in death_rates:\n", + " system = make_system(birth_rate=birth_rate,\n", + " death_rate=death_rate)\n", + " run_simulation(system)\n", + " p_end = final_population(system)\n", + " plot(death_rate, p_end, 'r^', label='rabbits')\n", + " \n", + " decorate(xlabel='Deaths per rabbit per season',\n", + " ylabel='Final population')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here are the results. Again, the x-axis is `death_rate`, NOT TIME." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SpUsxatQo2NjYwMXFBatWrcLNmzfl3pm1tTUcHBywZs0a/P7771yf/fr1wyuv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\nAgIC2IgRI9jevXtZeno627NnDxs5ciS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "death_rates = linspace(0.1, 1, 20)\n", + "sweep_death_rate(death_rates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous sweeps, we hold one parameter constant and sweep the other.\n", + "\n", + "You can also sweep more than one variable at a time, and plot multiple lines on a single axis.\n", + "\n", + "To keep the figure from getting too cluttered, I'll reduce the number of values in `birth_rates`:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.4, 0.6, 0.8, 1. ])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "birth_rates = linspace(0.4, 1, 4)\n", + "birth_rates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By putting one for loop inside another, we can enumerate all pairs of values.\n", + "\n", + "The results show 4 lines, one for each value of `birth_rate`.\n", + "\n", + "(I did not plot the lines between the data points because of a limitation in `plot`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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D1taWK4MQQoj6KNTRYejQofjmm28QGhqK8+fPc8u7du2K1atXY8yYMSoLaPXq1fjiiy8w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\norB+/Xr06NEDK1aswDfffNMk73nlyhX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for birth_rate in birth_rates:\n", + " for death_rate in death_rates:\n", + " system = make_system(birth_rate=birth_rate,\n", + " death_rate=death_rate)\n", + " run_simulation(system)\n", + " p_end = final_population(system)\n", + " plot(death_rate, p_end, 'c^', label='rabbits')\n", + " \n", + "decorate(xlabel='Deaths per rabbit per season',\n", + " ylabel='Final population')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you suspect that the results depend on the difference between `birth_rate` and `death_rate`, you could run the same loop, plotting the \"net birth rate\" on the x axis.\n", + "\n", + "If you are right, the results will fall on a single curve, which means that knowing the difference is sufficient to predict the outcome; you don't actually have to know the two parameters separately." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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x3NzcoNFo4OzsrHN8WbKg0WiQlZVVaX/ZMZmZmdBoNACg9xxl+4iIiKhmBnVh9OnTB4cP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\nv37g+OqCKepYJi8vD9OnT8eJEyewZs0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for birth_rate in birth_rates:\n", + " for death_rate in death_rates:\n", + " system = make_system(birth_rate=birth_rate,\n", + " death_rate=death_rate)\n", + " run_simulation(system)\n", + " p_end = final_population(system)\n", + " net_birth_rate = birth_rate - death_rate\n", + " plot(net_birth_rate, p_end, 'mv', label='rabbits')\n", + " \n", + "decorate(xlabel='Net births per rabbit per season',\n", + " ylabel='Final population')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the other hand, if you guess that the results depend on the ratio of the parameters, rather than the difference, you could check by plotting the ratio on the x axis.\n", + "\n", + "If the results don't fall on a single curve, that suggests that the ratio alone is not sufficient to predict the outcome. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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o9XpERUVZuxYAwMqVK5GUlISBAwdCkiR4e3tj+fLl8PHxgVqthqenp8n+dWFBrVajtLS0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\nkoLx48czPFhJSkoKvvnmG2zfvt34Exo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for birth_rate in birth_rates:\n", + " for death_rate in death_rates:\n", + " system = make_system(birth_rate=birth_rate,\n", + " death_rate=death_rate)\n", + " run_simulation(system)\n", + " p_end = final_population(system)\n", + " birth_ratio = birth_rate / death_rate\n", + " plot(birth_ratio, p_end, 'y>', label='rabbits')\n", + " \n", + "decorate(xlabel='Ratio of births to deaths',\n", + " ylabel='Final population')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 15716e82d4dffb697383a2020e2bdfec9ef4ac8f Mon Sep 17 00:00:00 2001 From: FlynnML Date: Mon, 25 Sep 2017 16:15:33 -0400 Subject: [PATCH 07/19] Merge branch 'master' of https://github.com/AllenDowney/ModSimPy --- book/book.tex | 112 ++++++++- book/figs/api_cheat_shee.odg | Bin 0 -> 16239 bytes book/figs/github_work_flow.odg | Bin 0 -> 44414 bytes book/figs/github_work_flow.pdf | Bin 0 -> 40270 bytes book/figs/github_work_flow.png | Bin 0 -> 113996 bytes code/chap03soln.ipynb | 377 ++++++++++++++++++++----------- code/rabbits2.ipynb | 399 +++++++++++++++++++++++++++++++++ 7 files changed, 753 insertions(+), 135 deletions(-) create mode 100644 book/figs/api_cheat_shee.odg create mode 100644 book/figs/github_work_flow.odg create mode 100644 book/figs/github_work_flow.pdf create mode 100644 book/figs/github_work_flow.png create mode 100644 code/rabbits2.ipynb diff --git a/book/book.tex b/book/book.tex index 25939090..a1d0c7b4 100644 --- a/book/book.tex +++ b/book/book.tex @@ -13,7 +13,7 @@ \newcommand{\thetitle}{Modeling and Simulation in Python} \newcommand{\thesubtitle}{} \newcommand{\theauthors}{Allen B. Downey} -\newcommand{\theversion}{1.0} +\newcommand{\theversion}{1.0.1} %%%% Both LATEX and PLASTEX @@ -1417,9 +1417,10 @@ \section{Comparison operators} Not equal & \py{!=} \\ \end{tabular} -The equals operator, \py{==}, compares two values and returns \py{True} if they are equal and \py{False} otherwise. It is easy to confuse with the {\bf assignment} operator, \py{=}, which assigns a value to a variable. For example, the following statement uses the assignment operator, which creates \py{x} if it doesn't already exist and gives it the value \py{5} +The equals operator, \py{==}, compares two values and returns \py{True} if they are equal and \py{False} otherwise. It is easy to confuse with the {\bf assignment operator}, \py{=}, which assigns a value to a variable. For example, the following statement uses the assignment operator, which creates \py{x} if it doesn't already exist and gives it the value \py{5} \index{equality} -\index{assignment} +\index{assignment operator} +\index{operator!assignment} \begin{python} x = 5 @@ -1486,7 +1487,7 @@ \section{Metrics} \begin{python} bikeshare = System(olin=10, wellesley=2, - olin_empty=0, wellesley_empty=0) + olin_empty=0, wellesley_empty=0) \end{python} Now we can run a simulation like this: @@ -1756,7 +1757,7 @@ \section{World population data} \index{Series} \index{DataFrame} -The Pandas function we'll use is \py{read_html}, which can read a web page and extract data from any tables it contains. Before we can use it, we have to {\bf import} it. You have already seen this import statement: +The Pandas function we'll use is \py{read_html}, which can read a web page and extract data from any tables it contains. Before we can use it, we have to import it. You have already seen this import statement: \index{\py{read_html}} \index{import statement} \index{statement!import} @@ -2039,6 +2040,8 @@ \section{Now with System objects} \index{plot} \index{decorate} +%TODO: We actually use decorate in chap01.ipynb, so move this earlier. + Finally, we can run it like this. \begin{python} @@ -2286,6 +2289,101 @@ \section{Equilibrium} In the next chapter we use the models we have developed to generate predictions. +\section{Disfunctions} + +When people first learn about functions, there are a few things they often find confusing. In this section I present and explain some common problems with functions. + +As an example, suppose you want a function that takes a \py{System} object with variables \py{alpha} and \py{beta} as a parameter and computes the carrying capacity, \py{-alpha/beta}. Here's a good solution: + +\begin{python} +def carrying_capacity(system): + K = -system.alpha / system.beta + return K + +sys1 = System(alpha=0.025, beta=-0.0018) +pop = carrying_capacity(sys1) +print(pop) +\end{python} + +Now let's see all the ways that can go wrong. + +Disfunction \#1: Not using parameters. In the following version, the function doesn't take any parameters; when \py{system} appears inside the function, it refers to the object we created outside the function. + +\begin{python} +# WRONG +def carrying_capacity(): + K = -system.alpha / system.beta + return K + +system = System(alpha=0.025, beta=-0.0018) +pop = carrying_capacity() +print(pop) +\end{python} + +This version actually works, but it is not very versatile. If there are several \py{System} object, this function can only work with one of them; that is, as long as one of them is named \py{System}. + +Disfunction \#2: Clobbering the parameters. When people first learn about parameters, they often write functions like this: + +\begin{python} +# WRONG +def carrying_capacity(system): + system = System(alpha=0.025, beta=-0.0018) + K = -system.alpha / system.beta + return K + +sys1 = System(alpha=0.025, beta=-0.0018) +pop = carrying_capacity(sys1) +print(pop) +\end{python} + +In this example, we have a \py{System} object named \py{sys1} that gets passed as an argument to \py{carrying_capacity}. But when the function run, it ignores the argument and immediately replaces it with a new \py{System} object. As a result, this function always returns the same value, no matter what argument is passed. + +When you write a function, you generally don't know what the values of the parameters will be. Your job is to write a function that works for any valid values. If you assign your own values to the parameters, you defeat the whole purpose of functions. + + +Disfunction \#3: No return value. Here's a version that computes the value of \py{K} but doesn't return it. + +\begin{python} +# WRONG +def carrying_capacity(system): + K = -system.alpha / system.beta + +sys1 = System(alpha=0.025, beta=-0.0018) +pop = carrying_capacity(sys1) +print(pop) +\end{python} + +A function that doesn't have a return statement always returns \py{None}, so in this example the value of \py{pop} is \py{None}. + + +Disfunction \#4: Ignoring the return value. Finally, here's a version where the function is correct, but the way it's used is not. + +\begin{python} +# WRONG +def carrying_capacity(system): + K = -system.alpha / system.beta + return K + +sys1 = System(alpha=0.025, beta=-0.0018) +carrying_capacity(sys1) +print(K) +\end{python} + +If you call a function that returns a value, you should do something with the return value. Often you assign it to a variable, as in the previous examples, but you can also use it as part of an expression. For example, you could eliminate the temporary variable \py{pop} like this: + +\begin{python} +print(carrying_capacity(sys1)) +\end{python} + +Or if you had more than one system, you could compute the total carrying capacity like this: + +\begin{python} +total = carrying_capacity(sys1) + carrying_capacity(sys2) +\end{python} + +In the notebook for this chapter, you can try out each of these examples and see what happens. + + \chapter{Prediction} In the previous chapter we developed a quadratic model of world population growth from 1950 to 2015. It is a simple model, but it fits the data well and the mechanisms it's based on are plausible. @@ -2689,7 +2787,7 @@ \section{Differential equations in SymPy} % \[ f{\left (t \right )} = C_{1} \exp(\alpha t) \] % -This is the general solution, which still contains an unspecified constant, $C_1$. To get the particular solution where $f(0) = p_0$, we substitute \py{p0} for \py{C1}. First, we have to create two more symbols: +This is the {\bf general solution}, which still contains an unspecified constant, $C_1$. To get the {\bf particular solution} where $f(0) = p_0$, we substitute \py{p0} for \py{C1}. First, we have to create two more symbols: \index{general solution} \index{particular solution} @@ -5736,6 +5834,8 @@ \section{Finishing off the problem} The notebook provides some additional hints, but at this point you should have everything you need. 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Add a second TimeSeries, named `juveniles`, to keep track of the juvenile population, and initialize it with `juvenile_pop0`.\n", + "\n", + "2. Inside the for loop, compute the number of juveniles that mature during each time step.\n", + "\n", + "3. Also inside the for loop, add a line that stores the number of juveniles in the new `TimeSeries`. For simplicity, let's assume that only adult rabbits die.\n", + "\n", + "4. During each time step, subtract the number of maturations from the juvenile population and add it to the adult population.\n", + "\n", + "5. After the for loop, store the `juveniles` `TimeSeries` as a variable in `System`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system):\n", + " \"\"\"Runs a proportional growth model.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + " \n", + " system: System object with t0, t_end, p0,\n", + " birth_rate and death_rate\n", + " \"\"\"\n", + " adults = TimeSeries()\n", + " adults[system.t0] = system.adult_pop0\n", + " \n", + " for t in linrange(system.t0, system.t_end):\n", + " births = system.birth_rate * adults[t]\n", + " deaths = system.death_rate * adults[t]\n", + " \n", + " adults[t+1] = adults[t] + births - deaths\n", + " \n", + " system.adults = adults" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test your changes in `run_simulation`:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ylabel='Rabbit population',\n", + " title=title)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And test your updated version of `plot_results`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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hHoJGoz9tb48eSi3/6kySvxBCCPEQfv8dLlxQlq2soHt3k4ZjEEn+QgghxEPY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\nkcIw/ImIiBSG4U9ERKQwDH8iIiKF+T/pMNrVl3T6wAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(system, title='Proportional growth model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook demonstrates the steps we recommend for starting your project:\n", + "\n", + "1. Start with one of the examples from the book, either by copying a notebook or pasting code into a new notebook. Get the code working before you make any changes.\n", + "\n", + "2. Make one small change, and run the code again.\n", + "\n", + "3. Repeat step 2 until you have a basic implementation of your model.\n", + "\n", + "If you start with working code that you understand and make small changes, you can avoid spending a lot of time debugging.\n", + "\n", + "One you have a basic model working, you can think about what metrics to measure, what parameters to sweep, and how to use the model to predict, explain, or design." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus question\n", + "\n", + "Suppose you only have room for 30 adult rabbits. Whenever the adult population exceeds 30, you take any excess rabbits to market (as pets for kind children, of course). Modify `run_simulation` to model this strategy. What effect does it have on the behavior of the system? You might have to run for more than 10 seasons to see what happens." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 596c796d275f900319ac74a4500bf2377be1346d Mon Sep 17 00:00:00 2001 From: Flynn Michael-Legg Date: Mon, 25 Sep 2017 16:17:47 -0400 Subject: [PATCH 08/19] merge --- code/chap03.ipynb | 344 ++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 302 insertions(+), 42 deletions(-) diff --git a/code/chap03.ipynb b/code/chap03.ipynb index 3bd3933c..a61c62b5 100644 --- a/code/chap03.ipynb +++ b/code/chap03.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": true, "scrolled": true @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": true, "scrolled": true @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -150,10 +150,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 7, "metadata": { "collapsed": true }, +======= + "execution_count": 8, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "table2" @@ -168,10 +173,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 8, "metadata": { "collapsed": true }, +======= + "execution_count": 9, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "census = table2.census\n", @@ -189,10 +199,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 9, "metadata": { "collapsed": true }, +======= + "execution_count": 10, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "census.values" @@ -207,10 +222,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 10, "metadata": { "collapsed": true }, +======= + "execution_count": 11, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "census.index" @@ -231,10 +251,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 11, "metadata": { "collapsed": true }, +======= + "execution_count": 12, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "type(table2)" @@ -242,10 +267,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 12, "metadata": { "collapsed": true }, +======= + "execution_count": 13, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "type(census)" @@ -253,10 +283,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 13, "metadata": { "collapsed": true }, +======= + "execution_count": 14, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "type(census.index)" @@ -264,10 +299,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 14, "metadata": { "collapsed": true }, +======= + "execution_count": 15, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "type(census.values)" @@ -284,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -314,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "collapsed": true, "scrolled": false @@ -335,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -346,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -366,10 +406,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 19, "metadata": { "collapsed": true }, +======= + "execution_count": 20, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "max(abs(census - un) / un) * 100" @@ -388,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "collapsed": true, "scrolled": true @@ -400,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "collapsed": true, "scrolled": true @@ -412,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": { "collapsed": true, "scrolled": true @@ -424,10 +469,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 23, "metadata": { "collapsed": true }, +======= + "execution_count": 24, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" @@ -449,10 +499,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 24, "metadata": { "collapsed": true }, +======= + "execution_count": 25, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "census[1950]" @@ -467,10 +522,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 25, "metadata": { "collapsed": true }, +======= + "execution_count": 26, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "census[2015]" @@ -485,10 +545,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 26, "metadata": { "collapsed": true }, +======= + "execution_count": 27, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "first_year = census.index[0]\n", @@ -507,10 +572,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 27, "metadata": { "collapsed": true }, +======= + "execution_count": 28, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "total_growth = census[last_year] - census[first_year]\n", @@ -528,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -546,10 +616,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 29, "metadata": { "collapsed": true }, +======= + "execution_count": 30, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "results[1950] = census[1950]\n", @@ -565,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": { "collapsed": true }, @@ -584,10 +659,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 31, "metadata": { "collapsed": true }, +======= + "execution_count": 32, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "newfig()\n", @@ -608,7 +688,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Exercise:** Try fitting the model using data from 1965 to the present, and see if that does a better job.\n", + "**Exercise:** Try fitting the model using data from 1970 to the present, and see if that does a better job.\n", "\n", "Hint: Copy the code from above and make a few changes.\n", "\n", @@ -617,10 +697,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 32, "metadata": { "collapsed": true }, +======= + "execution_count": 33, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" @@ -642,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -664,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": { "collapsed": true }, @@ -685,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": { "collapsed": true }, @@ -714,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": { "collapsed": true }, @@ -742,10 +827,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 37, "metadata": { "collapsed": true }, +======= + "execution_count": 38, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "run_simulation1(system)\n", @@ -761,10 +851,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 38, "metadata": { "collapsed": true }, +======= + "execution_count": 39, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "help(decorate)" @@ -772,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": { "collapsed": true, "scrolled": true @@ -795,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": { "collapsed": true }, @@ -806,10 +901,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 41, "metadata": { "collapsed": true }, +======= + "execution_count": 42, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" @@ -831,7 +931,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -862,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": { "collapsed": true }, @@ -881,10 +981,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 44, "metadata": { "collapsed": true }, +======= + "execution_count": 45, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "run_simulation2(system)\n", @@ -912,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": { "collapsed": true }, @@ -923,10 +1028,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 46, "metadata": { "collapsed": true }, +======= + "execution_count": 47, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" @@ -948,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": { "collapsed": true }, @@ -977,10 +1087,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 48, "metadata": { "collapsed": true }, +======= + "execution_count": 49, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "update_func1" @@ -995,10 +1110,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 49, "metadata": { "collapsed": true }, +======= + "execution_count": 50, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "type(update_func1)" @@ -1013,7 +1133,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": { "collapsed": true }, @@ -1043,7 +1163,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": { "collapsed": true }, @@ -1061,10 +1181,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 52, "metadata": { "collapsed": true }, +======= + "execution_count": 53, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "plot_results(system, title='Proportional model, factored')" @@ -1093,7 +1218,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": { "collapsed": true }, @@ -1121,10 +1246,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 54, "metadata": { "collapsed": true }, +======= + "execution_count": 55, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "system.alpha = system.birth_rate - system.death_rate\n", @@ -1146,7 +1276,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": { "collapsed": true, "scrolled": false @@ -1158,10 +1288,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 56, "metadata": { "collapsed": true }, +======= + "execution_count": 57, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" @@ -1183,7 +1318,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": { "collapsed": true }, @@ -1211,10 +1346,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 58, "metadata": { "collapsed": true }, +======= + "execution_count": 59, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "system.alpha = 0.025\n", @@ -1234,7 +1374,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": { "collapsed": true }, @@ -1255,10 +1395,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 60, "metadata": { "collapsed": true }, +======= + "execution_count": 61, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "newfig()\n", @@ -1279,7 +1424,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": { "collapsed": true }, @@ -1297,10 +1442,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 62, "metadata": { "collapsed": true }, +======= + "execution_count": 63, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "-system.alpha / system.beta" @@ -1326,10 +1476,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 63, "metadata": { "collapsed": true }, +======= + "execution_count": 64, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" @@ -1337,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": { "collapsed": true }, @@ -1348,10 +1503,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 65, "metadata": { "collapsed": true }, +======= + "execution_count": 66, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" @@ -1373,10 +1533,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 66, "metadata": { "collapsed": true }, +======= + "execution_count": 67, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "table1 = tables[1]\n", @@ -1392,10 +1557,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 67, "metadata": { "collapsed": true }, +======= + "execution_count": 68, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "table1.tail()" @@ -1410,7 +1580,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": { "collapsed": true }, @@ -1428,7 +1598,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": { "collapsed": true }, @@ -1447,7 +1617,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "metadata": { "collapsed": true }, @@ -1475,7 +1645,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 72, "metadata": { "collapsed": true, "scrolled": false @@ -1498,10 +1668,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 72, "metadata": { "collapsed": true }, +======= + "execution_count": 73, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "newfig()\n", @@ -1522,7 +1697,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "metadata": { "collapsed": true }, @@ -1533,10 +1708,15 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 74, "metadata": { "collapsed": true }, +======= + "execution_count": 75, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" @@ -1544,23 +1724,103 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 75, "metadata": { "collapsed": true }, +======= + "execution_count": 76, + "metadata": {}, +>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945 "outputs": [], "source": [ "# Solution goes here" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, + "source": [ + "### Disfunctions" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def carrying_capacity(system):\n", + " K = -system.alpha / system.beta\n", + " return K\n", + " \n", + "sys1 = System(alpha=0.025, beta=-0.0018)\n", + "pop = carrying_capacity(sys1)\n", + "print(pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "def carrying_capacity():\n", + " K = -system.alpha / system.beta\n", + " return K\n", + " \n", + "system = System(alpha=0.025, beta=-0.0018)\n", + "pop = carrying_capacity()\n", + "print(pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "def carrying_capacity(system):\n", + " system = System(alpha=0.025, beta=-0.0018)\n", + " K = -system.alpha / system.beta\n", + " return K\n", + " \n", + "sys1 = System(alpha=0.025, beta=-0.0018)\n", + "pop = carrying_capacity(sys1)\n", + "print(pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "def carrying_capacity(system):\n", + " K = -system.alpha / system.beta\n", + " \n", + "sys1 = System(alpha=0.025, beta=-0.0018)\n", + "pop = carrying_capacity(sys1)\n", + "print(pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "def carrying_capacity(system):\n", + " K = -system.alpha / system.beta\n", + " return K\n", + " \n", + "sys1 = System(alpha=0.025, beta=-0.0018)\n", + "carrying_capacity(sys1)\n", + "print(K)" + ] }, { "cell_type": "code", From d5d05e7bbb8aaba2c6a267aeba5079b53c201ef5 Mon Sep 17 00:00:00 2001 From: FlynnML Date: Wed, 27 Sep 2017 21:39:41 -0400 Subject: [PATCH 09/19] yawp did it --- code/BDATAFML.ipynb | 317 ++++++++++++ code/chap04mine.ipynb | 1125 +++++++++++++++++++++++++++++++++++++++++ code/rabbits2.ipynb | 115 +++-- 3 files changed, 1512 insertions(+), 45 deletions(-) create mode 100644 code/BDATAFML.ipynb create mode 100644 code/chap04mine.ipynb diff --git a/code/BDATAFML.ipynb b/code/BDATAFML.ipynb new file mode 100644 index 00000000..1d9d5cb1 --- /dev/null +++ b/code/BDATAFML.ipynb @@ -0,0 +1,317 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from modsim import *\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas import read_csv\n", + "\n", + "import csv\n", + "results = TimeSeries()\n", + "filename_csv = \"data/book1.csv\"\n", + "tables = read_csv(filename_csv, header=0, index_col=0, decimal='.')\n", + "tables = tables.dropna()\n", + "\n", + "type(tables)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tables.columns = ['colonies']\n", + "num_col = tables.colonies\n", + "num_col[1974]\n", + "type(num_col)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-36.046511627906973" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4HWh5CNul+HZgEfA9dkT+w0Dvyp5cVPCkpmYxf/6OCtdlec2ag2Rk5AA2xqNH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\nyGzsipnx2G7N9xhj3gK7sqaIXAa8jn1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_estimates(table):\n", + " \n", + " plot(table, ':', color='darkblue', label='colonies')\n", + " \n", + " decorate(xlabel='Year',\n", + " ylabel='Number of Colonies (thousands)')\n", + "\n", + "\n", + "newfig()\n", + "plot_estimates(tables)\n", + "\n", + "\n", + "t0 = tables.index[0]\n", + "t_end = tables.index[-1]\n", + "p0 = num_col[t0]\n", + "p_end = num_col[t_end]\n", + "type(t0)\n", + "t0, t_end\n", + "\n", + "total_growth = p_end - p0\n", + "elapsed_time = t_end - t0\n", + "annual_growth = total_growth / elapsed_time\n", + "annual_growth" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "1974 4387.28310\n", + "1975 4382.94642\n", + "1976 4468.65510\n", + "1977 4534.92062\n", + "1978 4247.58642\n", + "1979 4327.68180\n", + "1980 4311.43740\n", + "1981 4390.53582\n", + "1987 3307.75230\n", + "1989 3291.79290\n", + "1990 3322.65180\n", + "1991 3561.57692\n", + "1992 3313.07310\n", + "1993 3282.21942\n", + "1994 3121.83930\n", + "1995 2958.74252\n", + "1996 2846.75670\n", + "1997 2718.14552\n", + "1998 2629.76660\n", + "1999 2700.24792\n", + "2000 2702.35322\n", + "2001 2760.28032\n", + "2002 2688.67020\n", + "2003 2587.73100\n", + "2004 2640.28050\n", + "2005 2666.57400\n", + "2006 2621.35692\n", + "2007 2471.24982\n", + "2008 2450.28842\n", + "2009 2502.70692\n", + "2010 2396.87306\n", + "2011 2560.42502\n", + "2012 2764.49556\n", + "2013 2553.07572\n", + "2014 2603.49060\n", + "2015 2709.72240\n", + "2016 2815.10340\n", + "2017 2730.78260\n", + "Name: colonies, dtype: float64" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def update_func2(num_col, year, 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", + " pop = num_col\n", + " net_growth = system.alpha * pop + system.beta * pop**2\n", + " return pop + net_growth\n", + "\n", + "results = System(alpha=.00001, beta=.00001)\n", + "update_func2(num_col, tables.index, results)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "bees = System(t0 = tables.index[0], #start\n", + "t_end = tables.index[-1], #stop\n", + "p0 = num_col[first_year], #initial population\n", + "p_end = num_col[last_year], #final population\n", + "t = tables.index,\n", + "alpha=.00001, beta=.00001)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\ipykernel_launcher.py:11: RuntimeWarning: overflow encountered in double_scalars\n", + " # This is added back by InteractiveShellApp.init_path()\n" + ] + }, + { + "data": { + "image/png": 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D48eP5/HHH+eBBx6gdu3azJo1i+nTpzN48GBatGjBhAkTqFOnDt26dausxyOyyxQwSiur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\n//33WblyZazzu3btyre+9a2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#quadratic\n", + "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 + system.beta * pop**2\n", + " return pop + net_growth\n", + " \n", + "def run_simulation(system, update_func):\n", + " \"\"\"Simulate the system using any update function.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + " \n", + " system: System object\n", + " update_func: function that computes the population next year\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t0] = system.p0\n", + " for t in linrange(system.t0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " system.results = results\n", + " \n", + " \n", + "def plot_results(system, title=None):\n", + " \"\"\"Plot the estimates and the model.\n", + " \n", + " system: System object with `results`\n", + " \"\"\"\n", + " newfig()\n", + " plot_estimates(tables)\n", + " plot(system.results, '--', color='gray', label='model')\n", + " decorate(xlabel='Year',\n", + " ylabel='Bee population (thousands)',\n", + " title=title)\n", + " \n", + "bees.alpha = 0.00001\n", + "bees.beta = 0.00001\n", + "run_simulation(bees, update_func2)\n", + "plot_results(bees)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/code/chap04mine.ipynb b/code/chap04mine.ipynb new file mode 100644 index 00000000..9ce8e587 --- /dev/null +++ b/code/chap04mine.ipynb @@ -0,0 +1,1125 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 4: Predict\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": 53, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you want the figures to appear in the notebook, \n", + "# and you want to interact with them, use\n", + "# %matplotlib notebook\n", + "\n", + "# If you want the figures to appear in the notebook, \n", + "# and you don't want to interact with them, use\n", + "# %matplotlib inline\n", + "\n", + "# If you want the figures to appear in separate windows, use\n", + "# %matplotlib qt5\n", + "\n", + "# To switch from one to another, you have to select Kernel->Restart\n", + "\n", + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Functions from the previous chapter" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_estimates(table):\n", + " \"\"\"Plot world population estimates.\n", + " \n", + " table: DataFrame with columns `un` and `census`\n", + " \"\"\"\n", + " un = table.un / 1e9\n", + " census = table.census / 1e9\n", + " \n", + " plot(census, ':', color='darkblue', label='US Census')\n", + " plot(un, '--', color='green', label='UN DESA')\n", + " \n", + " decorate(xlabel='Year',\n", + " ylabel='World population (billion)')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_results(system):\n", + " \"\"\"Plot the estimates and the model.\n", + " \n", + " system: System object with `results`\n", + " \"\"\"\n", + " #new_fig()\n", + " plot_estimates(table2)\n", + " plot(system.results, '--', color='gray', label='model')\n", + " decorate(xlabel='Year', \n", + " ylabel='World population (billion)')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Run a model.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + "\n", + " system: System object\n", + " update_func: function that computes the population next year\n", + " \"\"\"\n", + " results = Series([])\n", + " results[system.t0] = system.p0\n", + " for t in linrange(system.t0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " system.results = results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Reading the data" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# The data directory contains a downloaded copy of\n", + "# https://en.wikipedia.org/wiki/World_population_estimates\n", + "\n", + "from pandas import read_html\n", + "filename = 'data/World_population_estimates.html'\n", + "tables = read_html(filename, header=0, index_col=0, decimal='M')" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "table2 = tables[2]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "table2.columns = ['census', 'prb', 'un', 'maddison', \n", + " 'hyde', 'tanton', 'biraben', 'mj', \n", + " 'thomlinson', 'durand', 'clark']" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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P8y2LiYlBKcUzzzyT4745rZB13fVjs/5r1qwZ9957L3PmzMnWU+G///3vLftm/ffzzz9n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\n/LeLUqof0B9zhbA99h4nyoa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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "newfig()\n", + "plot_estimates(table2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "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": 61, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def update_func2(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": [ + "Select the estimates generated by the U.S. Census, and convert to billions." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "census = table2.census / 1e9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extract the starting time and population." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "t0 = census.index[0]\n", + "p0 = census[t0]\n", + "t_end = census.index[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize the system object." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + "t0 1950.000000\n", + "t_end 2015.000000\n", + "p0 2.557629\n", + "alpha 0.025000\n", + "beta -0.001800\n", + "dtype: float64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = System(t0=t0, \n", + " t_end=t_end,\n", + " p0=p0,\n", + " alpha=0.025,\n", + " beta=-0.0018)\n", + "\n", + "system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the model and plot results." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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39WN8/GomHuFWZs7sfVYmfmjgAu6uIgu4CyGcVVxSzIc/fIjer+kc0I0+3Xoz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\nhVaeXVPMlUvPbTsmKz2La86+JhbVNsaYEyIpk38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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "run_simulation(system, update_func2)\n", + "plot_results(system)\n", + "decorate(title='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": 66, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap04-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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WCgoKwmazlXg34evrS1BQEEFBQQQGBhIUFFRsu9DQUEaNGlWhb/+qamkSUUqVKDc3F6DQ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\nESNGALQzxhyozDHcrRM5C5RWuxMG5JW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Sh4EFpHsGIyPib4WqPZsnaCmujuaq8p2IpBmS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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "system.t_end = 2250\n", + "run_simulation(system, update_func2)\n", + "plot_results(system)\n", + "decorate(title='World population projection')\n", + "savefig('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": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13.856665141368708" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system.results[system.t_end]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13.888888888888889" + ] + }, + "execution_count": 68, + "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? The the model with initial populations between 1 and 20 billion, and plot the results on the same axes." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PnDghK1KkY1VKMQXE/0AgEMD+/ftly8liIEVLxXZa0Khe6foBxN/IarVWVG60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\nFMYeKWVxzv+GWScej9Pf35/ru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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def run_system():\n", + " for n in range (20):\n", + " \n", + " systemn = System(t0=t0, \n", + " t_end=t_end,\n", + " p0=n,\n", + " alpha=0.025,\n", + " beta=-0.0018)\n", + " \n", + " run_simulation(systemn, update_func2)\n", + " plot_results(systemn)\n", + " decorate(title='Quadratic model')\n", + " \n", + "run_system()" + ] + }, + { + "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": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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United States Census Bureau (2015)[18]Population Reference Bureau (1973-2015)[6]United Nations Department of Economic and Social Affairs (2015)[7]
Year
20167.334772e+09NaN7.432663e+09
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20197.567403e+09NaNNaN
20207.643402e+09NaN7.758157e+09
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" + ], + "text/plain": [ + " United States Census Bureau (2015)[18] \\\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)[6] \\\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)[7] \n", + "Year \n", + "2016 7.432663e+09 \n", + "2017 NaN \n", + "2018 NaN \n", + "2019 NaN \n", + "2020 7.758157e+09 " + ] + }, + "execution_count": 73, + "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": 74, + "metadata": { + "collapsed": true + }, + "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": 75, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_projections(table):\n", + " \"\"\"Plot world population projections.\n", + " \n", + " table: DataFrame with columns 'un' and 'census'\n", + " \"\"\"\n", + " census = table.census / 1e9\n", + " un = table.un / 1e9\n", + " \n", + " plot(census.dropna(), ':', color='darkblue', label='US Census')\n", + " plot(un.dropna(), '--', color='green', 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": 76, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system.p0 = census[t0]\n", + "system.t_end = 2100\n", + "run_simulation(system, update_func2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap04-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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hWI3Jw8vJiwj/iCpPDDqdjnGh44jwj8DXxbfZJA+w/AkkHhgCVFdRNwRItVpE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\nC/UAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(system)\n", + "plot_projections(table3)\n", + "decorate(title='World population projections')\n", + "savefig('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": [ + "**Exercise:** Suppose there are two banks across the street from each other, The First Geometric Bank (FGB) and Exponential Savings and Loan (ESL). They offer the same interest rate on checking accounts, 3%, but at FGB, they compute and pay interest at the end of each year, and at ESL they compound interest continuously.\n", + "\n", + "If you deposit $p_0$ dollars at FGB at the beginning of Year 0, the balanace of your account at the end of Year $n$ is\n", + "\n", + "$ x_n = p_0 (1 + \\alpha)^n $\n", + "\n", + "where $\\alpha = 0.03$. At ESL, your balance at any time $t$ would be\n", + "\n", + "$ x(t) = p_0 \\exp(\\alpha t) $\n", + "\n", + "If you deposit \\$1000 at each back at the beginning of Year 0, how much would you have in each account after 10 years?\n", + "\n", + "Is there an interest rate FGB could pay so that your balance at the end of each year would be the same at both banks? What is it?\n", + "\n", + "Hint: `modsim` provides a function called `exp`, which is a wrapper for the NumPy function `exp`." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1343.9163793441223" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p0 = 1000\n", + "alpha = 0.03\n", + "n = 10\n", + "\n", + "x_n = p0 * (1 + alpha)**n\n", + "\n", + "x_n\n", + "#below is printed the monentary value at FGB" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1349.8588075760031" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p0 = 1000\n", + "alpha = 0.03\n", + "t = 10\n", + "\n", + "x_t = p0 * (exp(alpha*t))\n", + "\n", + "x_t\n", + "#below is printed the monentary value at ESL" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.030454533953516938" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p0 = 1000\n", + "alpha = 0.03\n", + "n = 10\n", + "t = 10\n", + "alpha1 = (exp(alpha*t))**(1/n) - 1\n", + "\n", + "alpha1\n", + "#below is printed thevalue of compounded interest needed at FGB" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Suppose a new bank opens called the Polynomial Credit Union (PCU). In order to compete with First Geometric Bank and Exponential Savings and Loan, PCU offers a parabolic savings account where the balance is a polynomial function of time:\n", + "\n", + "$ x(t) = p_0 + \\beta_1 t + \\beta_2 t^2 $\n", + "\n", + "As a special deal, they offer an account with $\\beta_1 = 30$ and $\\beta_2 = 0.5$, with those parameters guaranteed for life.\n", + "\n", + "Suppose you deposit \\$1000 at all three banks at the beginning of Year 0. How much would you have in each account at the end of Year 10? How about Year 20? And Year 100?" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1343.9163793441223\n", + "FGB^\n", + "1349.85880758\n", + "ELS^\n", + "1350.0\n", + "PCU^\n" + ] + } + ], + "source": [ + "# Year 10:\n", + "\n", + "#FGB\n", + "\n", + "p0 = 1000\n", + "alpha = 0.03\n", + "n = 10\n", + "\n", + "x_n = p0 * (1 + alpha)**n\n", + "\n", + "\n", + "#ESL\n", + "\n", + "p0 = 1000\n", + "alpha = 0.03\n", + "t = 10\n", + "\n", + "x_t = p0 * (exp(alpha*t))\n", + "\n", + "\n", + "#PCU\n", + "\n", + "p0 = 1000\n", + "beta1 = 30\n", + "beta2 = 0.5\n", + "t2 = 10\n", + "\n", + "x_t2 = p0 + beta1 * t2 + beta2 *t2**2\n", + "\n", + "print(x_n)\n", + "print('FGB^')\n", + "print(x_t)\n", + "print('ELS^')\n", + "print(x_t2)\n", + "print('PCU^')" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1806.111234669415\n", + "FGB^\n", + "1822.11880039\n", + "ELS^\n", + "1800.0\n", + "PCU^\n" + ] + } + ], + "source": [ + "# Year 20:\n", + "\n", + "#FGB\n", + "\n", + "p0 = 1000\n", + "alpha = 0.03\n", + "n = 20\n", + "\n", + "x_n = p0 * (1 + alpha)**n\n", + "\n", + "#ESL\n", + "\n", + "p0 = 1000\n", + "alpha = 0.03\n", + "t = 20\n", + "\n", + "x_t = p0 * (exp(alpha*t))\n", + "\n", + "\n", + "#PCU\n", + "\n", + "p0 = 1000\n", + "beta1 = 30\n", + "beta2 = 0.5\n", + "t2 = 20\n", + "\n", + "x_t2 = p0 + beta1 * t2 + beta2 *t2**2\n", + "\n", + "print(x_n)\n", + "print('FGB^')\n", + "print(x_t)\n", + "print('ELS^')\n", + "print(x_t2)\n", + "print('PCU^')" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19218.6319808563\n", + "FGB^\n", + "20085.5369232\n", + "ELS^\n", + "9000.0\n", + "PCU^\n" + ] + } + ], + "source": [ + "# Year 100:\n", + "\n", + "#FGB\n", + "\n", + "p0 = 1000\n", + "alpha = 0.03\n", + "n = 100\n", + "\n", + "x_n = p0 * (1 + alpha)**n\n", + "\n", + "\n", + "#ESL\n", + "\n", + "p0 = 1000\n", + "alpha = 0.03\n", + "t = 100\n", + "\n", + "x_t = p0 * (exp(alpha*t))\n", + "\n", + "#PCU\n", + "\n", + "p0 = 1000\n", + "beta1 = 30\n", + "beta2 = 0.5\n", + "t2 = 100\n", + "\n", + "x_t2 = p0 + beta1 * t2 + beta2 *t2**2\n", + "\n", + "print(x_n)\n", + "print('FGB^')\n", + "print(x_t)\n", + "print('ELS^')\n", + "print(x_t2)\n", + "print('PCU^')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/code/rabbits2.ipynb b/code/rabbits2.ipynb index f77cb8cf..4e62303f 100644 --- a/code/rabbits2.ipynb +++ b/code/rabbits2.ipynb @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -79,38 +79,48 @@ " \n", " \n", " t0\n", - " 0.0\n", + " 0.00\n", " \n", " \n", " t_end\n", - " 10.0\n", + " 10.00\n", " \n", " \n", " adult_pop0\n", - " 10.0\n", + " 10.00\n", + " \n", + " \n", + " juvenile_pop0\n", + " 0.00\n", " \n", " \n", " birth_rate\n", - " 0.9\n", + " 0.90\n", " \n", " \n", " death_rate\n", - " 0.5\n", + " 0.50\n", + " \n", + " \n", + " maturation_rate\n", + " 0.33\n", " \n", " \n", "\n", "" ], "text/plain": [ - "t0 0.0\n", - "t_end 10.0\n", - "adult_pop0 10.0\n", - "birth_rate 0.9\n", - "death_rate 0.5\n", + "t0 0.00\n", + "t_end 10.00\n", + "adult_pop0 10.00\n", + "juvenile_pop0 0.00\n", + "birth_rate 0.90\n", + "death_rate 0.50\n", + "maturation_rate 0.33\n", "dtype: float64" ] }, - "execution_count": 2, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -119,8 +129,10 @@ "system = System(t0 = 0, \n", " t_end = 10,\n", " adult_pop0 = 10,\n", + " juvenile_pop0 = 0,\n", " birth_rate = 0.9,\n", - " death_rate = 0.5)\n", + " death_rate = 0.5,\n", + " maturation_rate = .33)\n", "\n", "system" ] @@ -144,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -161,13 +173,21 @@ " adults = TimeSeries()\n", " adults[system.t0] = system.adult_pop0\n", " \n", + " juveniles = TimeSeries()\n", + " juveniles[system.t0] = system.juvenile_pop0\n", + " \n", " for t in linrange(system.t0, system.t_end):\n", " births = system.birth_rate * adults[t]\n", " deaths = system.death_rate * adults[t]\n", + " maturations = system.maturation_rate *juveniles[t]\n", " \n", - " adults[t+1] = adults[t] + births - deaths\n", + " juveniles[t+1] = juveniles[t] + births - maturations\n", " \n", - " system.adults = adults" + " adults[t+1] = adults[t] + maturations - deaths\n", + " \n", + "\n", + " system.adults = adults\n", + " system.juveniles = juveniles" ] }, { @@ -179,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -213,69 +233,69 @@ " \n", " \n", " 1\n", - " 14.000000\n", + " 5.000000\n", " \n", " \n", " 2\n", - " 19.600000\n", + " 5.470000\n", " \n", " \n", " 3\n", - " 27.440000\n", + " 6.209900\n", " \n", " \n", " 4\n", - " 38.416000\n", + " 7.057723\n", " \n", " \n", " 5\n", - " 53.782400\n", + " 8.021560\n", " \n", " \n", " 6\n", - " 75.295360\n", + " 9.117031\n", " \n", " \n", " 7\n", - " 105.413504\n", + " 10.362107\n", " \n", " \n", " 8\n", - " 147.578906\n", + " 11.777219\n", " \n", " \n", " 9\n", - " 206.610468\n", + " 13.385586\n", " \n", " \n", " 10\n", - " 289.254655\n", + " 15.213601\n", " \n", " \n", " 11\n", - " 404.956517\n", + " 17.291261\n", " \n", " \n", "\n", "" ], "text/plain": [ - "0 10.000000\n", - "1 14.000000\n", - "2 19.600000\n", - "3 27.440000\n", - "4 38.416000\n", - "5 53.782400\n", - "6 75.295360\n", - "7 105.413504\n", - "8 147.578906\n", - "9 206.610468\n", - "10 289.254655\n", - "11 404.956517\n", + "0 10.000000\n", + "1 5.000000\n", + "2 5.470000\n", + "3 6.209900\n", + "4 7.057723\n", + "5 8.021560\n", + "6 9.117031\n", + "7 10.362107\n", + "8 11.777219\n", + "9 13.385586\n", + "10 15.213601\n", + "11 17.291261\n", "dtype: float64" ] }, - "execution_count": 4, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -294,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "metadata": { "collapsed": true }, @@ -309,6 +329,11 @@ " plot(system.adults, 'bo-', label='adults')\n", " decorate(xlabel='Season', \n", " ylabel='Rabbit population',\n", + " title=title)\n", + " \n", + " plot(system.juveniles, 'ro-', label='juveniles')\n", + " decorate(xlabel='Season', \n", + " ylabel='Rabbit population',\n", " title=title)" ] }, @@ -321,14 +346,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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hHoJGoz9tb48eSi3/6kySvxBCCPEQfv8dLlxQlq2soHt3k4ZjEEn+QgghxEPY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\nNmyY3KUZjREjRuDOnTuQJAk1NTW632va32Wpqan48MMPkZ2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WFoCKCvtZxeKgrq6OzMxMmbYnT57IvPf29sabN2+wZ88epKSkoG/fvgDYdJK6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\naZSXl7N79252796t89rUqVOZOHGiSsnEs5g6dSo2NjYsWbK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"text/plain": [ - "" + "" ] }, "metadata": {}, From 6faa88e581f781ee3cf8ec262a6b5cebdeecd254 Mon Sep 17 00:00:00 2001 From: FlynnML Date: Fri, 6 Oct 2017 01:09:57 -0400 Subject: [PATCH 10/19] yuss --- code/BDATAFML.ipynb | 233 +++-- code/chap03-fig01.pdf | Bin 16801 -> 16801 bytes code/chap03mine.ipynb | 69 +- code/chap04-fig01.pdf | Bin 0 -> 17620 bytes code/chap04-fig02.pdf | Bin 0 -> 18307 bytes code/chap05-fig01.pdf | Bin 0 -> 17122 bytes code/chap05-fig02.pdf | Bin 0 -> 16468 bytes code/chap05-fig03.pdf | Bin 0 -> 12447 bytes code/chap05-fig04.pdf | Bin 0 -> 15330 bytes code/chap05-fig05.pdf | Bin 0 -> 15723 bytes code/chap05-fig06.pdf | Bin 0 -> 11147 bytes code/chap05-fig07.pdf | Bin 0 -> 11607 bytes code/chap05mine.ipynb | 2042 +++++++++++++++++++++++++++++++++++++++ code/data/Book1.csv | 45 + code/rabbits3mine.ipynb | 734 ++++++++++++++ 15 files changed, 3000 insertions(+), 123 deletions(-) create mode 100644 code/chap04-fig01.pdf create mode 100644 code/chap04-fig02.pdf create mode 100644 code/chap05-fig01.pdf create mode 100644 code/chap05-fig02.pdf create mode 100644 code/chap05-fig03.pdf create mode 100644 code/chap05-fig04.pdf create mode 100644 code/chap05-fig05.pdf create mode 100644 code/chap05-fig06.pdf create mode 100644 code/chap05-fig07.pdf create mode 100644 code/chap05mine.ipynb create mode 100644 code/data/Book1.csv create mode 100644 code/rabbits3mine.ipynb diff --git a/code/BDATAFML.ipynb b/code/BDATAFML.ipynb index 1d9d5cb1..9fe1ec4c 100644 --- a/code/BDATAFML.ipynb +++ b/code/BDATAFML.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -24,7 +24,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 26, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -52,7 +52,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 27, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -75,15 +75,15 @@ "-36.046511627906973" ] }, - "execution_count": 30, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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4HWh5CNul+HZgEfA9dkT+w0Dvyp5cVPCkpmYxf/6OCtdlec2ag2Rk5AA2xqNH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\nyFYI4pl3wIkAAAAASUVORK5CYII=\n", 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JkyYBsH79esLCwvjkk09o2LAh8+fPZ9SoUWzfvh0jIxnVVBU6dHCmoKCQjh1d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\nNdHS3d2dgoIC0tPTsbW1xdVVJlQJUV3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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -118,61 +118,23 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "year\n", - "1974 4387.28310\n", - "1975 4382.94642\n", - "1976 4468.65510\n", - "1977 4534.92062\n", - "1978 4247.58642\n", - "1979 4327.68180\n", - "1980 4311.43740\n", - "1981 4390.53582\n", - "1987 3307.75230\n", - "1989 3291.79290\n", - "1990 3322.65180\n", - "1991 3561.57692\n", - "1992 3313.07310\n", - "1993 3282.21942\n", - "1994 3121.83930\n", - "1995 2958.74252\n", - "1996 2846.75670\n", - "1997 2718.14552\n", - "1998 2629.76660\n", - "1999 2700.24792\n", - "2000 2702.35322\n", - "2001 2760.28032\n", - "2002 2688.67020\n", - "2003 2587.73100\n", - "2004 2640.28050\n", - "2005 2666.57400\n", - "2006 2621.35692\n", - "2007 2471.24982\n", - "2008 2450.28842\n", - "2009 2502.70692\n", - "2010 2396.87306\n", - "2011 2560.42502\n", - "2012 2764.49556\n", - "2013 2553.07572\n", - "2014 2603.49060\n", - "2015 2709.72240\n", - "2016 2815.10340\n", - "2017 2730.78260\n", - "Name: colonies, dtype: float64" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'update_func2' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.00001\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbeta\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.00001\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mupdate_func2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtables\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'update_func2' is not defined" + ] } ], "source": [ - "def update_func2(num_col, year, system):\n", + "def update_func_q(num_col, year, system):\n", " \"\"\"Compute the population next year.\n", " \n", " pop: current population\n", @@ -191,11 +153,14 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "bees = System(t0 = tables.index[0], #start\n", + "bees = System(t0 = tables.index[0],#start\n", + "t_middle = \n", "t_end = tables.index[-1], #stop\n", "p0 = num_col[first_year], #initial population\n", "p_end = num_col[last_year], #final population\n", @@ -205,31 +170,12 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\ipykernel_launcher.py:11: RuntimeWarning: overflow encountered in double_scalars\n", - " # This is added back by InteractiveShellApp.init_path()\n" - ] - }, - { - "data": { - "image/png": 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D48eP5/HHH+eBBx6gdu3azJo1i+nTpzN48GBatGjBhAkTqFOnDt26dausxyOyyxQwSiur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\n//33WblyZazzu3btyre+9a2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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#quadratic\n", - "def update_func2(pop, t, system):\n", + "def update_func_q(pop, t, system):\n", " \"\"\"Compute the population next year.\n", " \n", " pop: current population\n", @@ -268,20 +214,127 @@ " ylabel='Bee population (thousands)',\n", " title=title)\n", " \n", - "bees.alpha = 0.00001\n", - "bees.beta = 0.00001\n", - "run_simulation(bees, update_func2)\n", + "bees.alpha = -0.02\n", + "bees.beta = 0.000001\n", + "run_simulation(bees, update_func_q)\n", "plot_results(bees)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "t_middle = tables.index[30]\n", + "t_end = tables.index[-1]\n", + "p_middle = num_col[t_middle]\n", + "p_end = num_col[t_end]\n", + "type(t_middle)\n", + "t_middle, t_end\n", + "\n", + "total_growth2 = p_end - p_middle\n", + "elapsed_time2 = t_end - t_middle\n", + "annual_growth2 = total_growth2 / elapsed_time2\n", + "annual_growth2\n", + "\n", + "def run_simulation(system, update_func):\n", + " results = TimeSeries()\n", + " results[system.t0] = system.p0\n", + " for t in linrange(system.t0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " system.results = results\n", + "\n", + "def run_simulation2(system, update_func):\n", + " results = TimeSeries()\n", + " results[system.t_middle] = system.p_middle\n", + " for t in linrange(system.t_middle, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " system.results = results\n", + " \n", + "run_simulation2(bees, update_func_q)\n", + "plot_results(bees)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 't_middle' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[0mt_middle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtables\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0mp0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnum_col\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m#initial population\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mp_middle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnum_col\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt_middle\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 21\u001b[0m \u001b[0mp_end\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnum_col\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt_end\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m#final population\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m.0018\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 't_middle' is not defined" + ] + } + ], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Simulate the system using any update function.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + " \n", + " system: System object\n", + " update_func: function that computes the population next year\n", + " \"\"\"\n", + " results = TimeSeries()\n", + " results[system.t0] = system.p0\n", + " for t in linrange(system.t0, system.t_end):\n", + " results[t+1] = update_func(results[t], t, system)\n", + " system.results = results\n", + " \n", + "bees = System(t0 = tables.index[0], #start\n", + "t = tables.index,\n", + "t_end = tables.index[-1], #stop\n", + "t_middle = tables.index[30],\n", + "p0 = num_col[t0], #initial population\n", + "p_middle = num_col[t_middle],\n", + "p_end = num_col[t_end], #final population\n", + "alpha = .0018,\n", + "beta = 0.0001)\n", + " \n", + "bees.t_end = 2067\n", + "bees.p_end = 0\n", + " \n", + "array1 = linspace(-.005, 0.006, 25)\n", + "array2 = linspace(-0.000003, 0.00000001, 25)\n", + "matrix_results = np.zeros((25,25))\n", + " \n", + "def plot_results(system, title=None):\n", + " \"\"\"Plot the estimates and the model.\n", + " \n", + " system: System object with `results`\n", + " \"\"\"\n", + " newfig()\n", + " plot_estimates(tables)\n", + " plot(system.results, '--', color='gray', label='model')\n", + " decorate(xlabel='Year',\n", + " ylabel='Bee population (thousands)',\n", + " title=title)\n", + " \n", + "newfig()\n", + "for i in range(25):\n", + " bees.alpha = array1[i]\n", + " for n in range(25):\n", + " bees.beta = array2[n]\n", + " run_simulation(bees, update_func_q)\n", + " matrix_results[i][n] = bees.results[2067]\n", + " #print(bees.results[2067])\n", + " #plot_results(bees)\n", + " \n", + "#print(matrix_results)\n", + "plt.imshow(matrix_results, cmap='hot', interpolation='nearest', origin='lower')\n", + "plt.show\n", + "decorate(xlabel='Beta',\n", + " ylabel='Alpha',\n", + " title='Bee Population in 2067')\n", + "savefig('bee_heat_map.pdf')" + ] }, { "cell_type": "code", diff --git a/code/chap03-fig01.pdf b/code/chap03-fig01.pdf index 408bbc9b253192e901ad9247d7f394ddd60231e1..2269ae06ab9d188788d189fd327baf4948844da7 100644 GIT binary patch delta 24 fcmZ43%($?baf6C2r=fv?siCo{k-=tN+Y%N4T>u8V delta 24 fcmZ43%($?baf6C2r-7xBfswJ1rSWE6+Y%N4U3~_~ diff --git a/code/chap03mine.ipynb b/code/chap03mine.ipynb index 3663f7fc..20785bc4 100644 --- a/code/chap03mine.ipynb +++ b/code/chap03mine.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -265,7 +265,7 @@ "1954 NaN NaN " ] }, - "execution_count": 127, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -462,7 +462,7 @@ "2015 NaN NaN " ] }, - "execution_count": 128, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -480,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -504,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1611,7 +1611,7 @@ "[66 rows x 11 columns]" ] }, - "execution_count": 130, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1629,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1700,7 +1700,7 @@ "Name: census, Length: 66, dtype: int64" ] }, - "execution_count": 131, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1721,7 +1721,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1743,7 +1743,7 @@ " 7256490011], dtype=int64)" ] }, - "execution_count": 132, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1761,7 +1761,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1776,7 +1776,7 @@ " dtype='int64', name='Year')" ] }, - "execution_count": 133, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1800,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1809,7 +1809,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 134, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1820,7 +1820,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1829,7 +1829,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 135, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1840,7 +1840,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1849,7 +1849,7 @@ "pandas.core.indexes.numeric.Int64Index" ] }, - "execution_count": 136, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1860,7 +1860,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1869,7 +1869,7 @@ "numpy.ndarray" ] }, - "execution_count": 137, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1889,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -1919,7 +1919,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 16, "metadata": { "scrolled": false }, @@ -1933,9 +1933,9 @@ }, { "data": { - "image/png": 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P8y2LiYlBKcUzzzyT4745rZB13fVjs/5r1qwZ9957L3PmzMnWU+G///3vLftm/ffzzz9n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\n", 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D9yF9NMFflZVqKLlr165x9uxZMjIysLGxwdvbW/NUqBBCVDd9Gvfh/O2LZEWkUTe9E0bG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\n6UIrhCgfWg/slpuby5kzZ/jPf/7DmTN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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1957,7 +1957,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -1968,7 +1968,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -1988,7 +1988,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1997,7 +1997,7 @@ "1.2862470293832287" ] }, - "execution_count": 142, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -3432,6 +3432,7 @@ "cell_type": "code", "execution_count": 180, "metadata": { + "collapsed": true, "scrolled": false }, "outputs": [], @@ -4230,7 +4231,9 @@ { "cell_type": "code", "execution_count": 211, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "p0 = table1.mj[1]\n", diff --git a/code/chap04-fig01.pdf b/code/chap04-fig01.pdf new file mode 100644 index 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Downey\n", + "\n", + "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you want the figures to appear in the notebook, \n", + "# and you want to interact with them, use\n", + "# %matplotlib notebook\n", + "\n", + "# If you want the figures to appear in the notebook, \n", + "# and you don't want to interact with them, use\n", + "# %matplotlib inline\n", + "\n", + "# If you want the figures to appear in separate windows, use\n", + "# %matplotlib qt5\n", + "\n", + "# To switch from one to another, you have to select Kernel->Restart\n", + "\n", + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SIR implementation\n", + "\n", + "We'll use a `State` object to represent the number or fraction of people in each compartment." + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "S 0.985226\n", + "I 0.011996\n", + "R 0.002778\n", + "dtype: float64" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state = update1(init, system)\n", + "state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run a simulation by calling the update function for each time step." + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \n", + " returns: State object for final state\n", + " \"\"\"\n", + " state = system.init\n", + " for t in linrange(system.t0, system.t_end):\n", + " state = update_func(state, system)\n", + " return state" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is the state of the system at `t_end`" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "S 0.520453\n", + "I 0.000615\n", + "R 0.478933\n", + "dtype: float64" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_simulation(system, update1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise** Suppose the time between contacts is 4 days and the recovery time is 5 days. After 14 weeks, how many students, total, have been infected?\n", + "\n", + "Hint: what is the change in `S` between the beginning and the end of the simulation?" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.37943042989926101" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tc = 4 # time between contacts in days \n", + "tr = 5 # recovery time in days\n", + "\n", + "beta = 1 / tc # contact rate in per day\n", + "gamma = 1 / tr # recovery rate in per day\n", + "\n", + "system = make_system(beta, gamma)\n", + "S_0 = system.init.S\n", + "\n", + "final = run_simulation(system, update1)\n", + "S_end = final.S\n", + "\n", + "S_0 - S_end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using Series objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to store the state of the system at each time step, we can use one `TimeSeries` object for each state variable." + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " Add three Series objects to the System: S, I, R\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \"\"\"\n", + " S = TimeSeries()\n", + " I = TimeSeries()\n", + " R = TimeSeries()\n", + "\n", + " state = system.init\n", + " t0 = system.t0\n", + " S[t0], I[t0], R[t0] = state\n", + " \n", + " for t in linrange(system.t0, system.t_end):\n", + " state = update_func(state, system)\n", + " S[t+1], I[t+1], R[t+1] = state\n", + " \n", + " system.S = S\n", + " system.I = I\n", + " system.R = R" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we call it." + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "tc = 3 # time between contacts in days \n", + "tr = 4 # recovery time in days\n", + "\n", + "beta = 1 / tc # contact rate in per day\n", + "gamma = 1 / tr # recovery rate in per day\n", + "\n", + "system = make_system(beta, gamma)\n", + "run_simulation(system, update1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And then we can plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_results(S, I, R):\n", + " \"\"\"Plot the results of a SIR model.\n", + " \n", + " S: TimeSeries\n", + " I: TimeSeries\n", + " R: TimeSeries\n", + " \"\"\"\n", + " plot(S, '--', color='blue', label='Susceptible')\n", + " plot(I, '-', color='red', label='Infected')\n", + " plot(R, ':', color='green', label='Recovered')\n", + " decorate(xlabel='Time (days)',\n", + " ylabel='Fraction of population')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what they look like." + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap05-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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evJiQkBBSUkr7kW677TaWL1/Oc889R1paGmvWrOHBBx8kLCysZO96byeKOTIy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\nZwG2AG5MvpGggCCiQ6O5pOMlMpFRiHr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(system.S, system.I, system.R)\n", + "savefig('chap05-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using a DataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of making three `TimeSeries` objects, we can use one `DataFrame`.\n", + "\n", + "We have to use `loc` to indicate which row we want to assign the results to. But then Pandas does the right thing, matching up the state variables with the columns of the `DataFrame`." + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " Add a DataFrame to the System: results\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \"\"\"\n", + " frame = DataFrame(columns=system.init.index)\n", + " frame.loc[system.t0] = system.init\n", + " \n", + " for t in linrange(system.t0, system.t_end):\n", + " frame.loc[t+1] = update_func(frame.loc[t], system)\n", + " \n", + " system.results = frame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we run it, and what the result looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SIR
00.9888890.0111110.000000
10.9852260.0119960.002778
20.9812870.0129360.005777
30.9770550.0139340.009011
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\n", + "
" + ], + "text/plain": [ + " S I R\n", + "0 0.988889 0.011111 0.000000\n", + "1 0.985226 0.011996 0.002778\n", + "2 0.981287 0.012936 0.005777\n", + "3 0.977055 0.013934 0.009011\n", + "4 0.972517 0.014988 0.012494" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tc = 3 # time between contacts in days \n", + "tr = 4 # recovery time in days\n", + "\n", + "beta = 1 / tc # contact rate in per day\n", + "gamma = 1 / tr # recovery rate in per day\n", + "\n", + "sir = make_system(beta, gamma)\n", + "run_simulation(system, update1)\n", + "system.results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can extract the results and plot them." + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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evJiQkBBSUkr7kW677TaWL1/Oc889R1paGmvWrOHBBx8kLCysZO96byeKOTIy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\ne236ks8bQQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frame = system.results\n", + "plot_results(frame.S, frame.I, frame.R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise** Suppose the time between contacts is 4 days and the recovery time is 5 days. Simulate this scenario for 14 days and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FhYU8/fTTDBo0iB49enDVVVfx008/sXfvXsY5Ox+HDRvGs88+a+l+R44c4d5776VXr14M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\nW+bOuRN41swO6PwedyySLsQV7JbAEDNrSWUNLQN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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tc = 4 # time between contacts in days \n", + "tr = 5 # recovery time in days\n", + "\n", + "beta = 1 / tc # contact rate in per day\n", + "gamma = 1 / tr # recovery rate in per day\n", + "\n", + "sir = make_system(beta, gamma)\n", + "run_simulation(system, update1)\n", + "system.results.head()\n", + "\n", + "frame = system.results\n", + "plot_results(frame.S, frame.I, frame.R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the results, we can compute metrics that quantify whatever we are interested in, like the total number of sick students, for example." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def calc_total_infected(system):\n", + " \"\"\"Fraction of population infected during the simulation.\n", + " \n", + " system: System object with results.\n", + " \n", + " returns: fraction of population\n", + " \"\"\"\n", + " frame = system.results\n", + " return frame.S[system.t0] - frame.S[system.t_end]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's an example.|" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.333 0.25 0.467162931836\n" + ] + } + ], + "source": [ + "system.beta = 0.333\n", + "system.gamma = 0.25\n", + "run_simulation(system, update1)\n", + "print(system.beta, system.gamma, calc_total_infected(system))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write functions that take a `System` object as a parameter, extract the `results` object from it, and compute the other metrics mentioned in the book:\n", + "\n", + "1. The fraction of students who are sick at the peak of the outbreak.\n", + "\n", + "2. The day the outbreak peaks.\n", + "\n", + "3. The fraction of students who are sick at the end of the semester.\n", + "\n", + "Hint: If you have a `TimeSeries` called `I`, you can compute the largest value of the series like this:\n", + "\n", + " I.max()\n", + "\n", + "And the index of the largest value like this:\n", + "\n", + " I.idxmax()\n", + "\n", + "You can read about these functions in the `Series` [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " Add three Series objects to the System: S, I, R\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \"\"\"\n", + " S = TimeSeries()\n", + " I = TimeSeries()\n", + " R = TimeSeries()\n", + "\n", + " state = system.init\n", + " t0 = system.t0\n", + " S[t0], I[t0], R[t0] = state\n", + " \n", + " for t in linrange(system.t0, system.t_end):\n", + " state = update_func(state, system)\n", + " S[t+1], I[t+1], R[t+1] = state\n", + " \n", + " system.S = S\n", + " system.I = I\n", + " system.R = R\n", + "\n", + " print('Sick at peak:')\n", + " print(I.max())\n", + " print('Time of peak:')\n", + " print(I.idxmax())\n", + " \n", + "def sick_end(system):\n", + " frame = system.results\n", + " return frame.I[system.t_end]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sick at peak:\n", + "0.0435362026876\n", + "Time of peak:\n", + "30\n" + ] + }, + { + "data": { + "text/plain": [ + "0.00067419431560344738" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_simulation(system, update1)\n", + "sick_end(system)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " Add a DataFrame to the System: results\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \"\"\"\n", + " frame = DataFrame(columns=system.init.index)\n", + " frame.loc[system.t0] = system.init\n", + " \n", + " for t in linrange(system.t0, system.t_end):\n", + " frame.loc[t+1] = update_func(frame.loc[t], system)\n", + " \n", + " system.results = frame" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What if?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use this model to evaluate \"what if\" scenarios. For example, this function models the effect of immunization by moving some fraction of the population from S to R before the simulation starts." + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def add_immunization(system, fraction):\n", + " \"\"\"Immunize a fraction of the population.\n", + " \n", + " Moves the given fraction from S to R.\n", + " \n", + " system: System object\n", + " fraction: number from 0 to 1\n", + " \"\"\"\n", + " system.init.S -= fraction\n", + " system.init.R += fraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start again with the system we used in the previous sections." + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.3333333333333333, 0.25)" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tc = 3 # time between contacts in days \n", + "tr = 4 # recovery time in days\n", + "\n", + "beta = 1 / tc # contact rate in per day\n", + "gamma = 1 / tr # recovery rate in per day\n", + "\n", + "system = make_system(beta, gamma)\n", + "system.beta, system.gamma" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And run the model without immunization." + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.46832081102878098" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_simulation(system, update1)\n", + "calc_total_infected(system)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now with 10% immunization." + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.30650802853979753" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system2 = make_system(beta, gamma)\n", + "add_immunization(system2, 0.1)\n", + "run_simulation(system2, update1)\n", + "calc_total_infected(system2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "10% immunization leads to a drop in infections of 16 percentage points.\n", + "\n", + "Here's what the time series looks like for S, with and without immunization." + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap05-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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WVR7paWVlhaen500vhtkYmBS5EOIx4D3ga2AYULoK4hdAL2B+XQTXUtlYWzB+YAh2Nto+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\nxRMpXn6tnY3bOljWWMXKpTOZVa29xdThQYOKUoc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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(system.results.S, '-', label='No immunization')\n", + "plot(system2.results.S, 'g--', label='10% immunization')\n", + "\n", + "decorate(xlabel='Time (days)',\n", + " ylabel='Fraction susceptible')\n", + "\n", + "savefig('chap05-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can sweep through a range of values for the fraction of the population who are immunized." + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 0.468320811029\n", + "0.1 0.30650802854\n", + "0.2 0.161365457006\n", + "0.3 0.0728155898425\n", + "0.4 0.035520216753\n", + "0.5 0.0196887157825\n", + "0.6 0.0116220579983\n", + "0.7 0.00683873780062\n", + "0.8 0.00369649625371\n", + "0.9 0.00148153267227\n", + "1.0 -0.000161212109412\n" + ] + } + ], + "source": [ + "immunize_array = linspace(0, 1, 11)\n", + "for fraction in immunize_array:\n", + " system = make_system(beta, gamma)\n", + " add_immunization(system, fraction)\n", + " run_simulation(system, update1)\n", + " print(fraction, calc_total_infected(system))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function does the same thing and stores the results in a `Sweep` object." + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_immunity(immunize_array):\n", + " \"\"\"Sweeps a range of values for immunity.\n", + " \n", + " immunize_array: array of fraction immunized\n", + " \n", + " returns: Sweep object\n", + " \"\"\"\n", + " sweep = SweepSeries()\n", + " for fraction in immunize_array:\n", + " system = make_system(beta, gamma)\n", + " add_immunization(system, fraction)\n", + " run_simulation(system, update1)\n", + " sweep[fraction] = calc_total_infected(system)\n", + " return sweep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we run it." + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "immunize_array = linspace(0, 1, 21)\n", + "infected_sweep = sweep_immunity(immunize_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's what the results look like." + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap05-fig03.pdf\n" + ] + }, + { + "data": { + "image/png": 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T0y3G7zWQAZXr2Lq9kcamCO99aIMsGmPyS8oAIiJDgVpVjfi/O6QarE2qqrYA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\n+9lOYA6k+xmcCjwVDx5++z+o6mFZTWX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(infected_sweep)\n", + "\n", + "decorate(xlabel='Fraction immunized',\n", + " ylabel='Total fraction infected',\n", + " title='Fraction infected vs. immunization rate',\n", + " legend=False)\n", + "\n", + "savefig('chap05-fig03.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If 40% of the population is immunized, less than 4% of the population gets sick." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function), which is a convenient function for modeling curves that have a generally sigmoid shape. The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario." + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n", + " \"\"\"Computes the generalize logistic function.\n", + " \n", + " A: controls the lower bound\n", + " B: controls the steepness of the transition \n", + " C: not all that useful, AFAIK\n", + " M: controls the location of the transition\n", + " K: controls the upper bound\n", + " Q: shift the transition left or right\n", + " nu: affects the symmetry of the transition\n", + " \n", + " returns: float or array\n", + " \"\"\"\n", + " exponent = -B * (x - M)\n", + " denom = C + Q * exp(exponent)\n", + " return A + (K-A) / denom ** (1/nu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following array represents the range of possible spending." + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 60., 120., 180., 240., 300., 360., 420.,\n", + " 480., 540., 600., 660., 720., 780., 840., 900.,\n", + " 960., 1020., 1080., 1140., 1200.])" + ] + }, + "execution_count": 226, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spending = linspace(0, 1200, 21)\n", + "spending" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n", + "\n", + "`M` is chosen so the transition happens around \\$500.\n", + "\n", + "`K` is the maximum reduction in `beta`, 20%.\n", + "\n", + "`B` is chosen by trial and error to yield a curve that seems feasible." + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def compute_factor(spending):\n", + " \"\"\"Reduction factor as a function of spending.\n", + " \n", + " spending: dollars from 0 to 1200\n", + " \n", + " returns: fractional reduction in beta\n", + " \"\"\"\n", + " return logistic(spending, M=500, K=0.2, B=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what it looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap05-fig04.pdf\n" + ] + }, + { + "data": { + "image/png": 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4LjCNpjqSK7H5269LUzyfAyao6h2qulBVbwGuBS4IKuKd61Hi8ThPv7qGrTur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\n6wpkEVVCpXwvBs4g04YBy6igSIICOouuC5haHP1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "percent_reduction = compute_factor(spending) * 100\n", + "\n", + "plot(spending, percent_reduction)\n", + "\n", + "decorate(xlabel='Hand-washing campaign spending (USD)',\n", + " ylabel='Percent reduction in infection rate',\n", + " title='Effect of hand washing on infection rate',\n", + " legend=False)\n", + "\n", + "savefig('chap05-fig04.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Modify the parameters `M`, `K`, and `B`, and see what effect they have on the shape of the curve. Read about the [generalized logistic function on Wikipedia](https://en.wikipedia.org/wiki/Generalised_logistic_function). Modify the other parameters and see what effect they have." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hand washing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can model the effect of a hand-washing campaign by modifying `beta`" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def add_hand_washing(system, spending):\n", + " \"\"\"Modifies system to model the effect of hand washing.\n", + " \n", + " system: System object\n", + " spending: campaign spending in USD\n", + " \"\"\"\n", + " factor = compute_factor(spending)\n", + " system.beta *= (1 - factor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with the same values of `beta` and `gamma` we've been using." + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.3333333333333333, 0.25)" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tc = 3 # time between contacts in days \n", + "tr = 4 # recovery time in days\n", + "\n", + "beta = 1 / tc # contact rate in per day\n", + "gamma = 1 / tr # recovery rate in per day\n", + "\n", + "beta, gamma" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can sweep different levels of campaign spending." + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 0.332887143272 0.466770231236\n", + "100.0 0.332134252669 0.464141650401\n", + "200.0 0.330171608455 0.457217006313\n", + "300.0 0.325386471865 0.439887202912\n", + "400.0 0.315403905242 0.401630646271\n", + "500.0 0.3 0.33703425949\n", + "600.0 0.284596094758 0.267317030568\n", + "700.0 0.274613528135 0.22184699046\n", + "800.0 0.269828391545 0.200791598416\n", + "900.0 0.267865747331 0.192392183393\n", + "1000.0 0.267112856728 0.189213207818\n", + "1100.0 0.26683150821 0.18803175228\n", + "1200.0 0.266727403413 0.187595503995\n" + ] + } + ], + "source": [ + "spending_array = linspace(0, 1200, 13)\n", + "\n", + "for spending in spending_array:\n", + " system = make_system(beta, gamma)\n", + " add_hand_washing(system, spending)\n", + " run_simulation(system, update1)\n", + " print(spending, system.beta, calc_total_infected(system))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a function that sweeps a range of spending and stores the results in a `Sweep` object." + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_hand_washing(spending_array):\n", + " \"\"\"Run simulations with a range of spending.\n", + " \n", + " spending_array: array of dollars from 0 to 1200\n", + " \n", + " returns: Sweep object\n", + " \"\"\"\n", + " sweep = SweepSeries()\n", + " for spending in spending_array:\n", + " system = make_system(beta, gamma)\n", + " add_hand_washing(system, spending)\n", + " run_simulation(system, update1)\n", + " sweep[spending] = calc_total_infected(system)\n", + " return sweep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we run it." + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "spending_array = linspace(0, 1200, 20)\n", + "infected_sweep = sweep_hand_washing(spending_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's what it looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap05-fig05.pdf\n" + ] + }, + { + "data": { + "image/png": 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L0ReKqmpIVVcBtwFX+zzOfu92UszyyRzf/HYOsAC4S0QaRKQBlw13gfd4mne8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\n4FW8+3Zpjlrnm9jE8W38oMuf4yLiKWBvLMMTwF7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h1hqNPfDYy9V4LOQQvGvCmW2dGBHj\nsNn3xM7M4CJyIh4jas9U7LqQTW2ZTCaTqSu5x5PJZDKZupIVTyaTyWTqSlY8mUwmk6krWfFkMplM\npq5kxZPJZDKZuvJ/nn31/8jM9GoAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(infected_sweep)\n", + "\n", + "decorate(xlabel='Hand-washing campaign spending (USD)',\n", + " ylabel='Total fraction infected',\n", + " title='Effect of hand washing on total infections',\n", + " legend=False)\n", + "\n", + "savefig('chap05-fig05.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's put it all together to make some public health spending decisions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose we have \\$1200 to spend on any combination of vaccines and a hand-washing campaign." + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 247, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_students = 90\n", + "budget = 1200\n", + "price_per_dose = 100\n", + "max_doses = int(budget / price_per_dose)\n", + "dose_array = linrange(max_doses)\n", + "max_doses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can sweep through a range of doses from, 0 to `max_doses`, model the effects of immunization and the hand-washing campaign, and run simulations.\n", + "\n", + "For each scenario, we compute the fraction of students who get sick." + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 0.988888888889 0.266727403413 0.187595503995\n", + "1.0 0.977777777778 0.26683150821 0.174580718826\n", + "2.0 0.966666666667 0.267112856728 0.162909838349\n", + "3.0 0.955555555556 0.267865747331 0.153508349478\n", + "4.0 0.944444444444 0.269828391545 0.148565092315\n", + "5.0 0.933333333333 0.274613528135 0.152945950611\n", + "6.0 0.922222222222 0.284596094758 0.174964415024\n", + "7.0 0.911111111111 0.3 0.217343161684\n", + "8.0 0.9 0.315403905242 0.259071044488\n", + "9.0 0.888888888889 0.325386471865 0.278402884103\n", + "10.0 0.877777777778 0.330171608455 0.277914534623\n", + "11.0 0.866666666667 0.332134252669 0.267357496693\n", + "12.0 0.855555555556 0.332887143272 0.252796945636\n" + ] + } + ], + "source": [ + "for doses in dose_array:\n", + " fraction = doses / num_students\n", + " spending = budget - doses * price_per_dose\n", + " \n", + " system = make_system(beta, gamma)\n", + " add_immunization(system, fraction)\n", + " add_hand_washing(system, spending)\n", + " \n", + " run_simulation(system, update1)\n", + " print(doses, system.init.S, system.beta, calc_total_infected(system))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function wraps that loop and stores the results in a `Sweep` object." + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_doses(dose_array):\n", + " \"\"\"Runs simulations with different doses and campaign spending.\n", + " \n", + " dose_array: range of values for number of vaccinations\n", + " \n", + " return: Sweep object with total number of infections \n", + " \"\"\"\n", + " sweep = SweepSeries()\n", + " for doses in dose_array:\n", + " fraction = doses / num_students\n", + " spending = budget - doses * price_per_dose\n", + " \n", + " system = make_system(beta, gamma)\n", + " add_immunization(system, fraction)\n", + " add_hand_washing(system, spending)\n", + " \n", + " run_simulation(system, update1)\n", + " sweep[doses] = calc_total_infected(system)\n", + "\n", + " return sweep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can compute the number of infected students for each possible allocation of the budget." + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "infected_sweep = sweep_doses(dose_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap05-fig06.pdf\n" + ] + }, + { + "data": { + "image/png": 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7E9aTSSjfAj5+tE8oIom4pYxvADJx1WW3qGqnC4mJyFW4mZQLgZ3A74D7VbW1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\nVzJtgI5m9mYlZcsLM3sROACfL/4JdAO6FnmDjKS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(infected_sweep)\n", + "\n", + "decorate(xlabel='Doses of vaccine',\n", + " ylabel='Total fraction infected',\n", + " title='Total infections vs. doses',\n", + " legend=False)\n", + "\n", + "savefig('chap05-fig06.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Suppose the price of the vaccine drops to $50 per dose. How does that affect the optimal allocation of the spending?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Suppose we have the option to quarantine infected students. For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n", + "\n", + "How might you incorporate the effect of quarantine in the SIR model?" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 0.988888888889 0.266727403413 0.187595503995\n", + "1.0 0.977777777778 0.266766745484 0.174324794892\n", + "2.0 0.966666666667 0.26683150821 0.16186489064\n", + "3.0 0.955555555556 0.266938009181 0.150272695439\n", + "4.0 0.944444444444 0.267112856728 0.13961230444\n", + "5.0 0.933333333333 0.267399129509 0.129963581944\n", + "6.0 0.922222222222 0.267865747331 0.121434809166\n", + "7.0 0.911111111111 0.268620815383 0.114180370856\n", + "8.0 0.9 0.269828391545 0.108423787107\n", + "9.0 0.888888888889 0.271723878668 0.104483374203\n", + "10.0 0.877777777778 0.274613528135 0.102788098227\n", + "11.0 0.866666666667 0.278828368254 0.103846179534\n", + "12.0 0.855555555556 0.284596094758 0.108083570148\n", + "13.0 0.844444444444 0.291836044587 0.115450119409\n", + "14.0 0.833333333333 0.3 0.124877480279\n", + "15.0 0.822222222222 0.308163955413 0.134149883099\n", + "16.0 0.811111111111 0.315403905242 0.140764543663\n", + "17.0 0.8 0.321171631746 0.143202490748\n", + "18.0 0.788888888889 0.325386471865 0.141392287173\n", + "19.0 0.777777777778 0.328276121332 0.136229534349\n", + "20.0 0.766666666667 0.330171608455 0.128888830991\n", + "21.0 0.755555555556 0.331379184617 0.120402986465\n", + "22.0 0.744444444444 0.332134252669 0.111526601711\n", + "23.0 0.733333333333 0.332600870491 0.102750907495\n", + "24.0 0.722222222222 0.332887143272 0.0943669224857\n", + "Saving figure to file chap05-fig07.pdf\n" + ] + }, + { + "data": { + "image/png": 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jzHuB++l9xUaPiNw3lAVT/YuJCmNBXhIHz1QAsPt4GdMyJhMaovcISqnA8OvT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\nD0/x1EQWGhLM/WtzeWVXAYXldQAcPnu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_students = 90\n", + "budget = 1200\n", + "price_per_dose = 50\n", + "max_doses = int(budget / price_per_dose)\n", + "dose_array = linrange(max_doses)\n", + "max_doses\n", + "\n", + "for doses in dose_array:\n", + " fraction = doses / num_students\n", + " spending = budget - doses * price_per_dose\n", + " \n", + " system = make_system(beta, gamma)\n", + " add_immunization(system, fraction)\n", + " add_hand_washing(system, spending)\n", + " \n", + " run_simulation(system, update1)\n", + " print(doses, system.init.S, system.beta, calc_total_infected(system))\n", + " \n", + "infected_sweep = sweep_doses(dose_array)\n", + " \n", + "plot(infected_sweep)\n", + "\n", + "decorate(xlabel='Doses of vaccine',\n", + " ylabel='Total fraction infected',\n", + " title='Total infections vs. doses',\n", + " legend=False)\n", + "\n", + "savefig('chap05-fig07.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "#Suppose we have the option to quarantine infected students. \n", + "#For example, a student who feels ill might be moved to an infirmary, \n", + "#or a private dorm room, until they are no longer infectious.\n", + "#How might you incorporate the effect of quarantine in the SIR model?\n", + "\n", + "#We would decrease the infection rate, thereby increasing gamma.\n", + "def quarantine_update(system, n): \n", + " min = 1\n", + " max = 4\n", + " tr = max - n * (max-min)\n", + " system.gamma = 1 / tr\n", + " \n", + "print(quarantine_update(system, 0.15))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/code/data/Book1.csv b/code/data/Book1.csv new file mode 100644 index 00000000..de25a144 --- /dev/null +++ b/code/data/Book1.csv @@ -0,0 +1,45 @@ +year,number of colonies +1974,4210 +1975,4206 +1976,4285 +1977,4346 +1978,4081 +1979,4155 +1980,4140 +1981,4213 +1982, +1983, +1984, +1985, +1986, +1987,3205 +1988, +1989,3190 +1990,3219 +1991,3443 +1992,3210 +1993,3181 +1994,3030 +1995,2876 +1996,2770 +1997,2648 +1998,2564 +1999,2631 +2000,2633 +2001,2688 +2002,2620 +2003,2524 +2004,2574 +2005,2599 +2006,2556 +2007,2413 +2008,2393 +2009,2443 +2010,2342 +2011,2498 +2012,2692 +2013,2491 +2014,2539 +2015,2640 +2016,2740 +2017,2660 diff --git a/code/rabbits3mine.ipynb b/code/rabbits3mine.ipynb new file mode 100644 index 00000000..cd624913 --- /dev/null +++ b/code/rabbits3mine.ipynb @@ -0,0 +1,734 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Rabbit example\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": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rabbit is Rich\n", + "\n", + "This notebook starts with a version of the rabbit population growth model. You will modify it using some of the tools in Chapter 5. Before you attempt this diagnostic, you should have a good understanding of State objects, as presented in Section 5.4. And you should understand the version of `run_simulation` in Section 5.7.\n", + "\n", + "### Separating the `State` from the `System`\n", + "\n", + "Here's the `System` object from the previous diagnostic. Notice that it includes system parameters, which don't change while the simulation is running, and population variables, which do. We're going to improve that by pulling the population variables into a `State` object." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "t0 0.00\n", + "t_end 20.00\n", + "juvenile_pop0 0.00\n", + "adult_pop0 10.00\n", + "birth_rate 0.90\n", + "mature_rate 0.33\n", + "death_rate 0.50\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = System(t0 = 0, \n", + " t_end = 20,\n", + " juvenile_pop0 = 0,\n", + " adult_pop0 = 10,\n", + " birth_rate = 0.9,\n", + " mature_rate = 0.33,\n", + " death_rate = 0.5)\n", + "\n", + "system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following cells, define a `State` object named `init` that contains two state variables, `juveniles` and `adults`, with initial values `0` and `10`. Make a version of the `System` object that does NOT contain `juvenile_pop0` and `adult_pop0`, but DOES contain `init`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "init = State(juveniles=0, adults=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "t0 0\n", + "t_end 20\n", + "init juveniles 0\n", + "adults 10\n", + "dtype: int64\n", + "birth_rate 0.9\n", + "mature_rate 0.33\n", + "death_rate 0.5\n", + "dtype: object" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = System(t0 = 0, \n", + " t_end = 20,\n", + " init = init,\n", + " birth_rate = 0.9,\n", + " mature_rate = 0.33,\n", + " death_rate = 0.5)\n", + "system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Updating `run_simulation`\n", + "\n", + "Here's the version of `run_simulation` from last time:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system):\n", + " \"\"\"Runs a proportional growth model.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + " \n", + " system: System object\n", + " \"\"\"\n", + " juveniles = TimeSeries()\n", + " juveniles[system.t0] = system.juvenile_pop0\n", + " \n", + " adults = TimeSeries()\n", + " adults[system.t0] = system.adult_pop0\n", + " \n", + " for t in linrange(system.t0, system.t_end):\n", + " maturations = system.mature_rate * juveniles[t]\n", + " births = system.birth_rate * adults[t]\n", + " deaths = system.death_rate * adults[t]\n", + " \n", + " if adults[t] > 30:\n", + " market = adults[t] - 30\n", + " else:\n", + " market = 0\n", + " \n", + " juveniles[t+1] = juveniles[t] + births - maturations\n", + " adults[t+1] = adults[t] + maturations - deaths - market\n", + " \n", + " system.adults = adults\n", + " system.juveniles = juveniles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below, write a version of `run_simulation` that works with the new `System` object (the one that contains a `State` object named `init`).\n", + "\n", + "Hint: you only have to change two lines." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system):\n", + " \"\"\"Runs a proportional growth model.\n", + " \n", + " Adds TimeSeries to `system` as `results`.\n", + " \n", + " system: System object\n", + " \"\"\"\n", + " juveniles = TimeSeries()\n", + " adults = TimeSeries()\n", + " state = system.init\n", + " t0 = system.t0\n", + " juveniles[t0], adults[t0] = state\n", + " \n", + "\n", + " for t in linrange(system.t0, system.t_end):\n", + " maturations = system.mature_rate * juveniles[t]\n", + " births = system.birth_rate * adults[t]\n", + " deaths = system.death_rate * adults[t]\n", + " \n", + " if adults[t] > 30:\n", + " market = adults[t] - 30\n", + " else:\n", + " market = 0\n", + " \n", + " juveniles[t+1] = juveniles[t] + births - maturations\n", + " adults[t+1] = adults[t] + maturations - deaths - market\n", + " \n", + " system.adults = adults\n", + " system.juveniles = juveniles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test your changes in `run_simulation`:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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title=None):\n", + " \"\"\"Plot the estimates and the model.\n", + " \n", + " system: System object with `results`\n", + " \"\"\"\n", + " newfig()\n", + " plot(system.adults, 'bo-', label='adults')\n", + " plot(system.juveniles, 'gs-', label='juveniles')\n", + " decorate(xlabel='Season', \n", + " ylabel='Rabbit population',\n", + " title=title)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If your changes in the previous section were successful, you should be able to run this new version of `plot_results`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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LFuLj4/H39wfk6iQDAwPi4uJo1KiR8hUSEsKuXbueOrYmTZpw5coVnJyclHOr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\nPXv20LGj9fbx6enpVtcbwKBBg1z+emvpMzt16hTe3t506ND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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(system, title='Proportional growth model')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's the end of the diagnostic. If you were able to get it done quickly, and you would like a challenge, here are two bonus questions:\n", + "\n", + "\n", + "### Bonus question #1\n", + "\n", + "Write a version of `run_simulation` that puts the results into a single `TimeFrame` named `results`, rather than two `TimeSeries` objects.\n", + "\n", + "Write a version of `plot_results` that can plot the results in this form.\n", + "\n", + "WARNING: This question is substantially harder, and requires you to have a good understanding of everything in Chapter 5. We don't expect most people to be able to do this exercise at this point." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "run_simulation(system)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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pohJYdOUeFRXF6tWr6dGjB+np6Rw5coSMjAx69erF6tWradeuXVXXKYQQopIV\nG4o5evWoKdgdbR1p4dNCBtDVARbPc2/evDlvv/12VdYihBCimhgxciL9BPnF+QDYqGy42/du7NR2\nVq5MVIZyw33Hjh3cc8892NrasmPHjn98Idk4Rgghagej0cip9FOkF6Sb2pp7N8fFzsWKVYnKVG64\njx07lj///BNvb2/Gjh1r2iSmLLJxjBBC1B5rT6zlct5l0+NGbo3wdfa1YkWispUb7suXL8fd3d30\nsxBCiNrvt7O/mS0tG6AJIMQjxIoViapQbrjfuFnMpUuXeOihh7C3ty913uXLl/nll19kcxkhhKjh\n4pLjWHVklemxt5M3d3ndhQrZwrWusWhA3csvv0y3bt3w8vIqdezq1avMnj2bkSNHVnZtQgghKsmx\nq8dYemCp6fGotqN4/p7nsbcpfdEmar9bhvvw4cNNfe2TJk3Czs58FKXRaCQxMRE3N7cqLVIIIcTt\nS8pKYuHehegNegAauDZgUvQkCfY67JaTGQcMGEBIiNIXo9frKS4uNvvS6/Xcfffd/Pe//63Qm65Z\ns4YHH3yQiIgIYmJiWLZsmenY+vXrGTBgAJGRkfTq1YvZs2ej1+sr/smEEEKQmpfKvD3zTJvBeDp5\n8lzH53Cxl5Hxddktr9wHDhzIwIEDSUxMZMGCBZVyhb5hwwY+/PBDYmNjiY6OZv/+/bzxxhtERUWR\nn5/P9OnTmTlzJjExMZw7d44JEyZgZ2fH5MmT7/i9hRCiPrlWeI05u+aQo80BwMXehec6Poenk6eV\nKxNVzaJliL766qtyg/3SpUv06dPH4jdcsGABY8eO5d5778Xe3p6OHTuyadMmwsPDWbFiBd26daNP\nnz7Y29sTFhbGyJEj+eqrrzAYDBa/hxBC1HcFugLm7p5LWn4aAHY2dkzuMJkGrg2sXJmoDhavULdl\nyxa2b99OVlaWqc1oNHL69GmuXr1q0WukpqZy5swZnJ2dGTJkCCdOnCAoKIhnnnmGfv36ceDAAYYO\nHWr2nNatW5OVlUViYiKhoaGWliuEEPWWTq/jk7hPuJh9EQC1Ss349uMJ9ZR/Q+sLi8J91apVvPba\na/j4+JCRkYGvry/Xrl2jsLCQtm3bWrws7eXLyqIJ3333HTNnzqRhw4b88MMPvPjiizRo0ICMjAzT\n3PoSnp7K7aOMjAwJdyGE+AcGo4Ev9n/ByfSTpran2zxNhH+EFasS1c2i2/LLly/nP//5Dzt27MDB\nwYEVK1aZIXdIAAAgAElEQVSwf/9+PvroI9RqNVFRURa9WckKd8OHDycsLAxnZ2eefvppwsPDWbNm\nze1/CiGEEBiNRr5J+IZ9KftMbQNbDqRTw05WrEpYg0XhfuHCBR544AFAWWpWr9ejUql4+OGHeeyx\nx3jjjTcsejM/Pz/g+tV4iUaNGnHlyhV8fHzMbvsDZGZmAuDrK0sjCiHErWw4tYFtSdtMj3uE9qBX\n015WrEhYi0XhbmtrS2FhIQDu7u6m2+sA99xzD7t377bozfz8/PDw8CAhIcGsPSkpiaCgICIjIzl4\n8KDZsfj4eHx9fWnUqJFF7yGEEPXRtqRtrDuxzvS4Q1AHBrUahEolq8/VRxaFe9u2bYmNjSUnJ4ew\nsDA+//xzU9j/+uuvODg4WPRmNjY2jBo1ihUrVrBz506KiopYuXIlx44dY8iQIYwYMYIdO3awceNG\nioqKSEhIYOnSpYwaNUr+gAohRDn2p+zn64SvTY9b+bZiRNsR8u9mPWbRgLopU6YwduxYMjIyGDly\nJGPGjKFDhw7Y29uTl5fHiBEjLH7D8ePHU1xczMsvv0x6ejpNmjTh888/p2XLlgDExsYyd+5cXnrp\nJXx8fBg+fDijR4++vU8nhBB13Mn0kyzet9g0pqmxR2MmRE3AVm3xZChRB6mM5e3jepPc3FwcHR2x\ntbUlISGBDRs2UFxcTNu2benbt69Vf0O8ePEiMTEx/PbbbwQHB1utDiGEqE4Xsy8y88+ZFBYrd1L9\nXPx46d6XcHVwtXJloqr9U+5Z/KudRqMx/RwREUFEhEyrEEIIa0nLT2POrjmmYHd3dOf5e56XYBfA\nLcI9NjbW4hdRqVRMmzatUgoSQghxaznaHObsmkO2NhsAR1tHpnacirezt5UrEzVFueG+aNEii19E\nwl0IIapHYXEh8/bMIzUvFQBbtS2TOkwi2E26JMV15Yb78ePHq7MOIYQQ5Ri/bjwABgwcST1CZqGy\n/ocKFSsHrqS5d3NrlidqIIumwgkhhLAuI0ZOpp00BTtAM69mRDaItGJVoqayaEDd008//Y/nLF++\n/I6LEUIIUZoRIyfTT5Kan2pqC3EPoYFGdngTZbMo3HU6Xampbnl5eSQmJhIQEECLFi2qpDghhKjv\n9AY9J9JOmAV7A00DGrnLqp2ifBaF+zfffFNme2ZmJv/+97/p3bt3pRYlhBACig3FLN63uFSwN/Nq\nhgpZfU6U74763D09PXn++eeZO3duZdUjhBACZU/2T/d+yv6U/aa2QE2gBLuwyB2vT2hnZ0dKSkpl\n1CKEEAIo0hexMG4hR68eNbUFuwbTxLOJBLuwiEXhvmPHjlJtRqORa9eusXLlSgIDAyu9MCGEqI+0\nxVrm75nPyfSTprb3Y97nkbBHZCMYYTGLwn3s2LGoVCrKWobezc2N//73v5VemBBC1DeFxYXM3T2X\nMxlnTG39w/rTt3lfK1YlaiOLwr2saW4qlQpXV1dCQkJwcnKq9MKEEKI+ydflM2fXHBKzEk1tA1sO\npHczGbAsKs6icO/QoUNV1yGEEPVWXlEes3fN5sK1C6a2wXcPJiY0xopVidrM4gF1mzdvZt26dVy4\ncIFr167h4eFB06ZNGThwIJ06darKGoUQos7K1mbz8a6PSc5ONrUNaz2MbiHdrFiVqO0smgq3ZMkS\npkyZwuHDhwkMDKR9+/YEBAQQFxfH6NGj+fLLL6u6TiGEqHOyCrOYtXOWKdhVKhVPt3lagl3cMYv7\n3MeNG8e//vWvUsc+/PBDvvjiC0aMGFHpxQkhRF2VWZBJ7F+xpt3dVCoVo9qOomNwRytXJuoCi67c\ns7KyGDRoUJnHBg8eTFZWVqUWJYQQdVlafhozd840BbtapWZcu3ES7KLSWBTuYWFhXL58ucxjly9f\npmXLlpValBBC1FWpeal8tPMj0vPTAbBR2zAhagLtA9tbuTJRl1h0W/6tt97i3XffJScnh7Zt2+Lq\n6kp+fj579+5l2bJlTJ8+naKiItP59vb2VVawEELUVik5KczeNZtrhdcAsFXb8mz0s4T7hVu5MlHX\nWBTuTzzxBFqtlr1795Y6ZjQaGTJkiOmxSqXi6NGjpc4TQoj6LDk7mdm7ZpOjzQHAzsaOSdGTaOkr\ndz5F5avQCnVCCCEq7vy183y862PyivIAcLB1YEqHKdzlfZeVKxN1lUXhPmXKlKquQwgh6qTErETm\n7JpDvi4fAEdbR5675zlCPUOtXJmoyyxexCY3N5dNmzZx7Ngx8vLycHV1pXXr1vTu3RsHB4eqrFEI\nIWqV8evGA8oCNYdTD1NsLAaUPvb1Q9YT4hFizfJEPWBRuJ85c4YRI0aQlpaGq6srLi4u5ObmsmLF\nChYsWMDy5cvx9/ev6lqFEKLWyCrM4sjVI+iNegDs1HZE+EVIsItqYdFUuFmzZhEUFMSmTZuIi4tj\ny5Yt7N27l59//hknJyfZFU4IIW5wJe8KCakJpmC3V9vT2r81GnuNlSsT9YVF4b53715eeeUVmjRp\nYtbevHlzXn311TL3exdCiPrGaDTy47EfOZF+AiPKFtkONg60CWiDi52LlasT9YlFt+ULCgpwc3Mr\n85ifnx/5+fmVWpQQQtQ2Or2OpQeWEn8p3tSmsdNwt9/dONjIuCRRvSy6cg8JCWHTpk1lHtuwYQMh\nIdKHJISov7K12cz6a5ZZsHs7edMmoI0Eu7AKi67cn376aV577TUSEhKIjIxEo9GQk5PDvn372Lp1\nK++8805V1ymEEDXSpZxLzN8z37ScLECQaxChnqGokPVBhHVYFO6DBw8GlK1ff//9d1N748aNeffd\ndxk4cGDVVCeEEDXY0atH+WzvZxQWFwLKCp1P3P0EDzR5wMqVifrO4nnugwcPZvDgweTm5pKXl4eL\niwsajYz8FELUT9uStvFNwjcYjAZAWXVuXLtxRPhHWLkyISoQ7gAnT57kwoULZGdn4+HhQbNmzWjY\nsGFV1SaEEDWOwWhgzbE1bD6z2dTm6eTJ5A6TCXYLtmJlQlxnUbhfuHCBKVOmcOLECYxGo6ldpVIR\nGRnJzJkzCQoKqrIihRCiJtAWa/li/xccuHzA1NbIvRGTOkzCw9HDipUJYc6icH/ttdfIzs7mnXfe\n4e6778bZ2Zm8vDwOHz7MJ598wmuvvcaSJUuqulYhhLCarMIsPon7hKSsJFNbm4A2jIkcg4OtjIgX\nNYtF4b5v3z4WL15MdHS0WXvLli1p2LAhEyZMqJLihBCiJriYfZH5e+aTWZBpauvZtCcDWw5ErbJo\nRrEQ1cqicNdoNPj6+pZ5zN/fHxcXWXlJCFE3HU49zKL4RWiLtQCoVWqGRAyhW0g3K1cmRPks+pVz\n4MCBrF69usxjP/zwA4899lilFiWEEDXBH+f+YP6e+aZgd7R1ZErHKRLsosaz6Mrd1dWVb7/9lq1b\ntxIZGYmrqysFBQXExcVx7do1+vXrR2xsLKAMsps2bVqVFi2EEFXJYDSw6sgq/jj3h6nN29mbyR0m\nE+gaaMXKhLCMReFeEtygTIe72eLFi00/S7gLIWqzwuJCFu9bTMKVBFNbY4/GTOowCTeHsvfYEKKm\nsSjcjx8/XtV1CCGEVYxfN970s1av5UjqEXJ1uQB0a9SNdg3aMSpyFPY29tYqUYgKq9AiNkIIUVfl\nFOVw5OoRivRFprYHmz3Ioy0eRaWSNeJF7SLhLoSo14wYSc1L5VTGKdNSsipU3OV1FwNaDrBydULc\nHgl3IUS9pS3WcjL9JFfyrpjabNW2tPJpJSvOiVpNwl0IUS9dyrnEovhFZsHubOdMK99WONs6W7Ey\nIe7cHYe7VqslKysLf3//yqhHCCGq3M4LO/k64Wt0ep2pzd/Fn2ZezbBR2VixMiEqh0WL2LRs2ZL0\n9PQyj507d45HHnmkUosSQoiqoC3W8uWBL/nywJemYFer1IR5hxHmHSbBLuqMW165//TTTwAYjUY2\nbdpUav92o9HInj170Gq1VVehEEJUgpScFD6L/4yUnBRTW4AmgM3DN8vCNKLOuWW4r169msOHD6NS\nqXjnnXfKPW/48OG39ebx8fE89dRTTJw4kSlTpgCwfv16lixZQmJiIr6+vvTp04epU6diYyO/UQsh\nbs+ui7tYeWil2TS3jsEdGRYxTHZ0EzVOXBxs2gQpKdCgAfTpAzft2/aPbhnuX331FcXFxYSHh/Pd\nd9/h6elZ6hw3Nzc8PCo+qrSwsJAZM2aYbTqzZ88epk+fzsyZM4mJieHcuXNMmDABOzs7Jk+eXOH3\nEELUb0X6Ir49/C1/nv/T1GZnY8eQ8CF0bthZ5q+LGsFohJwcJcx//x1++AEKC8HTEwwGKFkEtiIB\n/48D6mxtbfntt98IDAys1L8IsbGxNGnSBD8/P1PbihUr6NatG3369AEgLCyMkSNH8sknnzBx4kTU\natlaUQhhmcu5l/ls72dcyrlkavPX+DO+/XiC3IKsWJmor0pC/NIlJchv/J6Xp5wTH3/954wM8PIC\nJyf45ZdKCvfY2FieffZZnJyc+O677275IhVdT37v3r2sXbuWn3/+mRdffNHUfuDAAYYOHWp2buvW\nrcnKyiIxMZHQ0FCL30MIUX/tvriblQkrTbu5gdyGF1WjrFvoUVGlQ7zk55LgLk9+/vWfHR3B4e8/\nrpculX1+ecoN90WLFjFixAicnJxYtGjRLV+kIuFeUFDAjBkz+Pe//11q+lxGRgbu7u5mbSVdARkZ\nGRLuQohb0ul1fHv4W3ac32Fqs7Ox48nwJ7m34b1yG15Uqrg4WLRICfLcXDhxAjZuhCZNwK2Ceww5\nOCi/HFy9ClotODuDuzuU3LAOrOCYz3LD/cbNYipz45jY2FgaN27MwIEDK+01hRDicu5lFsUvIjk7\n2dTmr/HnmfbPEOwWbMXKRF1hNCrhe+4cnD0LS5YoV9RGo/l5p09Du3Zlv0ZJiAcGXv8eGKj0r6tU\nyi8MN2y0avLggxWrtcKL2KSlpVFQUICLiwteXl4Vem7J7fh169aVedzHx4esrCyztszMTAB8fX0r\nWqoQop7Yk7yHFYdWmN2Gjw6K5qnWT+Fo62jFykRtVlgIiYlKkJd83XhbvaxgB+UcR0clvG8O8pIQ\nL09Jv/ovvyivHxioBHuljpYvodVqmTlzJuvWrSM7O9vU7unpyYABA3j++eexs7P7x9dZvXo1+fn5\n9O/f39SWm5vLoUOH+P3334mMjOTgwYNmz4mPj8fX15dGjRpZ+pmEEPWETq/juyPfsT1pu6nNVm3L\nk+FP0qVRF7kNLyxmNCp94iVX5WfPKo/LCu8Szs5KkDs7g6sruLgoX82awbvv3jrEbyU6uuJhfjOL\nwv31119n48aNPPLII4SFheHk5ER+fj5Hjhxh+fLl5OTk8NZbb/3j60yfPp3nnnvOrO25556jbdu2\njB07luTkZJ566ik2btxIjx49OHHiBEuXLmX06NHyl1QIAVzff72guIBjV4+Z9l4HGNRyEOOjxstt\neFGukgFw588rt8ibNAFbWyXUCwv/+fnOzspzQkPhgQdg82bl+TcaMOD2g72yWBTuv/76K++8847Z\nFXeJ6OhoPvjgA4vC3d3dvdSAOXt7ezQaDb6+vvj6+hIbG8vcuXN56aWX8PHxYfjw4YwePdrCjyOE\nqOuMGLmSe4UzmWfQG/Wmdj9nP17p9orchhelFBbChQvwv//BqlXKALiCguvHW7SAG2Zlm6hUEBx8\nPcxDQ5Xzbgzupk3v/BZ6VbAo3A0GA23bti3zWIcOHdDr9WUes8RXX31l9rhXr1706tXrtl9PCFF3\nZRRkcDj1MJmFmaY2NWqaejUlQBMgwS4oKoKLFyEpSekvT0qCy5eV2+s3ziG/0cWLSmi7uV0P8SZN\nICTk+lS08lTGLfSqYFG433ffffz1119l9nvv2bOHLl26VHphQghRwmg0sv38dlYfXW0W7E62TrT0\naYnGXnOLZ4u6qrgYkpPNg/zSJWVVt7LcOIdcrQaNRukr9/CA995TFoyx9u30ylJuuO/YcX2eaI8e\nPZg7dy6nT58mMjISjUZDQUEBcXFxbN++nZdffrlaihVC1D/p+eksP7ic42nXp+SqUBHkGkSIR4js\n5FaH3bhATEAAtG+vjDYvCfLkZCXg/4lKpdwyb9FCOV+jUQa+lcwhDw4Gb+8q/SjVrtxwHzt2LCqV\nCqPRaPr+1VdflbqNDvDss89y7NixKi1UCFG/GI1GtiZtZc2xNWZT3JxtnWnu3Rw3hwquEiJqld27\n4eOPlSVYs7KURWK+/bb8/vESKhX4+yu31ENCoHFjaNgQ7O0rbw55bVBuuC9fvrw66xBCCJOreVdZ\nfnA5J9NPmtpUKhW9mvbCYDSgVsk+E3VRXh4cOQIJCcoCMZmZpc8p6R8v4eOjBHhJkDdqpMwxL0tl\nzSGvDcoN9w4dOlRnHUIIgdFo5Pdzv/Pj8R/R6XWm9gauDRjRZgRNPJswsKWsbllXGI1KWB8+rAT6\n2bPX55XftJ4ZoAxuc3KCRx+9HuQ3bCxqkZo6AK6yWbxC3ebNm1m7di1nzpwxrVDXrFkzBg4cyH33\n3VeVNQoh6oEruVf48uCXnMk4Y2pTq9T0btabh5s/jK26wgtqihpIq4Vjx64HelkhDsp8cp1OGeTm\n5aWMZLe3V/rH/944VNyCRX9bPv/8c2bNmkVISIjZIjaHDx/mf//7HzNmzGD48OFVXasQog4yGA38\ndvY31p5Ya3a1HugayMi2IwnxCLFidaIypKYqQZ6QAKdOlT8ITqVSpqBFRMDDD8O6daVHr9fF/vGq\nYFG4L1++nGeeeYYXXnih1LEPPviAxYsXS7gLISosJSeF5QeXczbzrKlNrVLz0F0P0eeuPnK1XsuU\njG5PTlaushs0UBaLSU0t/znOznD33Uqgt2qlTE0rERBQP/rHq4JFf3OuXbvGY489VuaxJ598km++\n+aZSixJC1G0Go4HNZzbz84mfKTZcv4xr6N6QEW1G0NC9oRWrExVlMMD69cr2p1lZylfJ2mZljW4P\nDobwcCXQQ0OvT0m7WX3pH68KFoV7q1atSEpKIiSk9O2xlJQUWrRoUemFCSHqpks5l/jywJckZiWa\n2mzUNvS9qy8PNnsQG7XMW6/p9HplnvnJk8pt9tOn4c8/y1/9LThYCfmICOXL07P6a65vyg33oqIi\n088zZszgvffeo6ioiLZt2+Lq6kp+fj579+5l2bJlvPnmm9VSrBCidinZ5AWUNeEvZF/gfNZ5DBjo\n1qgbACEeIYxoM4IgtyBrlSn+gU6nbKxy6pQS6GfPKsu83ujG1d9AmY7m7a18xcaCBRuHikpUbri3\nbt3abCc2o9HIlClTSp1nNBp5/PHHSUhIqJoKhRC1Xm5RLiczTpJbdH0HN1u1LQ83f5jezXrLvPUa\nRqtVArzkyvzcuX9eCc7LC2xswN1dWc7V0fH6xisS7NWv3HCfNGmSbLMqhLgjOoOOpKwkUnJTMHJ9\nY2xXe1de7fYqDVwbWLE6UTIA7sIFZQBcSIgyzzwxsfz12Uv4+EDz5nDXXcr3s2eVhWduJqPbraPc\ncC/rKr0shYWFHDx4sNIKEkLUfgajga2JW4m7FGc2YE6NmsYejQlyC5Jgt7Ldu5XNUlJSlKVdSxaP\nKW95V39/8zC/ud/cx0e5UpfR7TVDheeZFN3U0RIXF8fUqVPZv39/pRUlhKi9jqcd57vD33Ep55JZ\nsHs6etLMqxlOtk5WrE6Acrt9xgwlhG9WsrxrYKB5mLtZsJS/jG6vOSwK96ysLF577TV27NhBwY07\n3P+tadOmlV6YEKJ2Sc9P5/uj37M/xfwXfUdbR5p6NsXLyQsV0tVnTWlpsHo17NunXLGXUKmUZVw9\nPJSv2NiKL+sqahaLwn3mzJkcPXqUYcOGsXTpUp588kmKiorYvHkzPXv2ZNq0aVVdpxCihtIWa/nl\n9C/878z/zK7UHWwdaOLRhCDXIBkwZ2WFhUrf+q+/Xh8Y5+ysLDDTsCEEBYHt32kQHCzBXhdYFO47\nduxg1qxZREVFsWLFCkaMGEHDhg156aWXGDNmDAcPHuT++++v4lKFEDWJ0Whk76W9rD62mswC8+27\nOgZ3ZGDLgXg4elipOgHKoLi//oKffoLsbPNjffooA+kcHMzbZQBc3WBRuKenp9OwobJilK2tLVqt\nsreyRqNh+vTpvP766xLuQtQjF65d4NvD33I647RZe4hHCE+GP0moZ6iVKhMlTp2C775TAvxGTZrA\n4MHKynBxcTIArq6yKNw9PT05d+4c/v7++Pj4cOTIEZo1a2Y6dv78+SotUghRM+Roc1h7Yi07zu/A\naLxhapuDKwNaDKBzw84yhdbKbuxXv5GHBwwcCB06XN+MRQbA1V0WhXtJv/r3339P165def/999Hp\ndHh4eLBy5UqCgmRlKSHqMr1Bz5bELaw7uY4C3fVBtWqVmpjQGPre1RcnOxkFb02FhcpV+ObN5gvO\n2NlB797Qq1fpW/Ci7rIo3F988UUKCgpwdHRk/Pjx7N69m1dffRUAd3d3Zs2aVaVFCiGs5+jVo6w6\nsoqUnBSz9nC/cAbfPRh/jb+VKhOgzE/fubPsfvUOHZSrdVnLvf6xKNydnZ15//33TY/Xrl3LyZMn\n0el0hIaG4uQkv7ELURfcuBZ8QXEBZzPPkl6QDmBaC97PxY8nwp8g3C/cKjWK606dglWr4Oae0caN\n4YknlH51UT/d9mbJzZs3N/1cVFSEvb19pRQkhLCuYkMxF7IvkJydjIHra5A62jrSt3lfujfpLvus\nW0HJUrEpKcr67Wo1pKebn1NWv7qon275N/TEiROsXLmSlJQUAgMDGTJkSKntXffu3ct//vMfNm3a\nVKWFCiGqVmFxIeezz3Mx+6LZfHWAAJcA3u7+Nm4OFixTJipdXBwsXqxstXrhgrKKnMFwfalY6VcX\nNys33A8dOsTw4cOxs7OjUaNGHDx4kDVr1rBo0SI6depEbm4uM2fOZNWqVaaR80KI2kdbrGVL4hb+\n78z/me2xDuBm70ZTr6a42rtKsFvR+vWQnKwE+40rgF+8CH37KlfrXl7Wq0/UPOWG+4IFC4iKimLe\nvHk4OztTWFjIK6+8QmxsLM8++yxvvPEGOTk5TJs2jdGjR1dnzUKISqDT69iWtI1fTv9CttZ8JJaT\nrRONPRrj4+wjS8ZakU4H27bB2rXKNqw3cnVV1n0fO9Y6tYmardxw379/PwsXLsTZ2RkAR0dHpk+f\nTteuXZk0aRL3338/r776qkyDE6KWKTYU8+f5P9l4aiNZhVlmxxxtHQlxD8HPxU9C3Yp0Oti+XZna\ndu2asjRsSbg7OCgD5vz8lKVjhShLueGenZ1tWpWuhK+vL46Ojrz55ps88sgjVV6cEKLy6A16dl3c\nxYZTG0jPNx+J5enkSd+7+mLAgBpZB95adDrYsUMJ9awbfu9q2BDOnVO+BwQog+lAlooV5bvlgDob\nG5tSbSqVinbt2lVZQUKIymUwGtiTvIf1J9dzNe+q2TE3BzceuushujTqgp2NHV1DulqpyvqtuFgJ\n9U2bzEMdlJHxTz4Jjo7Kxi+yVKywhMxnEaKOMhqNxKfEs+7EOi7nXjY7prHX8GCzB7mv8X3Y28g0\nVmspLlYWoNm4ETLN997BzU0J8G7dlNHwAJ06VX+NonYqN9xVKpWsES1ELWQ0Gjlw+QDrTq4jOTvZ\n7JiznTO9mvaie5PuONjKnClrKS5WdmvbuBEyMsyPubkp09ruu+96qAtRUeWGu9FopF+/fqUCvrCw\nkCeeeAK1+nq/nEqlYvv27VVXpRCilBtXkwMwYiSzIJPEa4m0CzDvOnO0daRn057ENImRNeCtSK9X\nrtQ3bSq9AI2r6/UrdVkTTNypcsN9wIAB1VmHEOI2GTGSVZhFUlYS2UXmU9ocbB3o3qQ7PUN74mLv\nYqUKhV4Pu3bBhg1lh3qvXsqVuixAIypLueF+41ryQoiax4iR9Px0LmZfLBXqdjZ2PND4AXo17YWr\ng6uVKqzf4uKUMD94UAl0b29l+loJjUYJ9fvvl1AXlU8G1AlRy2iLtey8sJO4S3EUFheaHVOjpoFr\nA97t/i7uju5WqlDs3AkffKCsIFfw9w65V/+eqNCkidKnLqEuqpKEuxC1RFZhFn+c+4NtSdvI1+Wb\nBbsaNf4afxq5N8LBxkGC3UqysmDLFvjvf0uPfrezU67W33tPmdYmRFWScBeihkvOTmbz2c3sSd6D\n3qA3O2antqOBpgGBboHYq2UUlrVcuKDMQY+LU/rXb5yrbmsLwcEQFKQEvAS7qA4S7kLUQEajkWNp\nx9h8ZjNHrx4tddzXxZdmXs3wd/HHRlV6sSlR9YxGOHRICfWTJ82POTsrIR8UpKwoV7IeWGBg9dcp\n6icJdyFqkGJDMXHJcWw+u7nUHHWApl5N6RnakzYBbVCrZJlYa9BqlT7133+H1NTSx5s2ha5dlQ1f\nbl4qRJaLFdVFwl2IGiBfl8/WxK38kfgH1wqvmR1TqVREBkTSs2lPQj1DrVShyMyEP/5QNnTJzzc/\nplZD+/bQo4eyqQtAWJiyRrwsFyusQcJdCCtKy0/j17O/svPCTrTF5nt6Otg60LlhZ3qE9sDH2cdK\nFYrEROXWe3w8GAzmx5ydlav0Bx4AT0/zY9HREubCeiTchagmN64ol12UTXJ2Mmn5aRgx0q1RN9Mx\nd0d3Hmj8AN1CusnCM1ZiMCjz03/9FU6fLn3czw+6d4fOnWU6m6iZJNyFqCZ6o560/DRSclJKLToD\nEOgaSK+mvYgOisZWLX81q1NcnLIk7MWLUFSk9JWXtQRs8+bKrfeIiOvbrgpRE8m/IEJUsYvZF9me\ntJ3dybspNhSXOu7p6Mlz9zxHS5+WslmTFcTFwZw5cPkyXLmibOoC0KKFcoWuViu313v0gEaNrFur\nEJaScBeiCmiLtey9tJdtSdtIzEoEMAt2NWp8XXwJdgvGxc6FVr6trFRp/aXVwt698MYbyhX7za5c\ngbncCU8AACAASURBVBEjlJXkPDyquzoh7oyEuxCVKCkrie3nt7MneU+pAXIATrZOBGgC8Nf4y6Iz\nVmA0wrlzsGOHEuxaLSTfNOPQ2fn6/PRHH7VOnULcKQl3Ie5QYXEhe5L3sC1pGxeuXSh13FZtS2SD\nSLIKs3B3dEeF3Hqvbrm5sHu3EuqXLpkfc3ZW1n/39lYC3cND6XMPDrZOrUJUBgl3IW6D0WgkMSuR\n7ee3E5ccR5G+qNQ5/hp/uoV0457ge9DYa4hLjrNCpfWX0QjHjyuBfuDA9b70GzVoAKNGKdPc7OzM\nj8mCM6I2q/ZwT09P56OPPmL79u3k5+fTrFkzpk2bRqdOnQBYv349S5YsITExEV9fX/r06cPUqVOx\nsZElNoX15evy2X1xN9vPby9zBTlbtS3tA9vTtVFXmnk1Mxsg91m/z6qz1HorMxP+/FNZRe7mvdNB\nGQUfHQ1duig7tKlUyqA6WXBG1CXVHu4TJ05Eo9Hw448/4ubmxvz585k4cSK//PILSUlJTJ8+nZkz\nZxITE8O5c+eYMGECdnZ2TJ48ubpLFfVcybx0I0aytdlczr3M1fyrGIwGs3npoExj6xrSlY5BHWVu\nuhUUF0NCgnKVfuSIctV+syZNlECPiiq9eYssOCPqmmoN95ycHJo2bcqYMWPw9fUFYNy4cSxatIhD\nhw6xbt06unXrRp8+fQAICwtj5MiRfPLJJ0ycOBG1TCwV1aiwuJCr+Ve5kneFfF1+qeP2NvZEBUbR\nNaQrTTyayDS2alQyL/3MGWVQnK0tuJTxO5WLC9xzjxLqsmmLqE+qNdxdXV157733zNouXFAGIAUE\nBHDgwAGGDh1qdrx169ZkZWWRmJhIaKisqy2qVrY2m/hL8exJ3sOeS3vKPEdjr2FoxFA6BHXAyc6p\nmisUW7ZAbKwyVS37hrWASualA7RsqQR627ZK8AtR31j1j31ubi4vv/wyMTExREREkJGRgbu7u9k5\nnn8v2JyRkSHhLqpEvi6fA5cPsCd5D8fTjmMs456ujcoGPxc/AjQBaOw13Nf4PitUWn/l58P+/coV\n+8qVyuj3m6WlKYPjOncGH1mKX9RzVgv35ORkJkyYgI+PDx999JG1yhD1VJG+iENXDhGXHMfh1MNl\nrhynQoWnoye+Lr74OPvIvunVrLBQ2S89Lk7pR9frlfa8vOvnqFTXp7B5e0P//tapVYiaxirhfujQ\nISZMmECvXr145ZVXsPt7DoqPjw9ZWVlm52ZmZgKY+uiFuF16g55jacfYk7yHA5cPlLnIDMBd3nfR\nIagDOoMOO7VdmeeIqqHTKQPj4uKU7zpd6XOcnZVb7b6+ylfJFLagoOqtVYiarNrD/eTJk4wbN47/\n396dh0dV3Y8ff0/2dbIOCWtIAoFAAEMCgj7SghupICqCiGAoKtSW+lNLEKiVqmAFEVtjqyC7LGoR\nFFq0iK3i8hVZQ2QNkGASAgnZ9/X+/jjOJJOZhDXJZPJ5Pc99wtw593JuTuZ+5p71ySefZNq0aWbv\nRUdHk5SUZLbvwIEDGAwGesikzuIaaJrG6bzT/JD5AweyDlBaVWo1XQ+fHgzpOoQhXYbg566agjYe\n2diaWe2wamrg2DE1Y9zhw6qDnDU9e6oe7RMnwocfWr4v49KFqNeqwb22tpa5c+cyYcIEi8AOEB8f\nz5QpU9i5cyd33HEHJ0+eZM2aNUyfPl16IotmNVxOVUOjtKqU7LJsckpzuLnrzVaPCfIKYkiXIQzt\nOpQgryCL92Vcesupq4OTJ9UT+qFDqk3dmm7d1NC12Fj1lG6k18u4dCGa06rB/dChQxw9epRTp06x\nbt06s/fGjRvHwoULWbZsGW+++SZz5swhMDCQqVOnMn369NbMpmiHNDRKqkrIK88jpzSHshrr0cLX\nzZehXYcypOsQuuu7y5fGVmActnb+vFr73GCAggIoLraePihIBerYWDWDnDUyLl2I5rVqcI+NjeXk\nyZPNprnrrru46667WilHoj2rqavh5KWTJF1MUgu11Fqvz/V08SSmcwxDuw61mDVOtKz/+z944w3I\ny1O92Y1V7g2HrYHqDBcbqwJ2t26qo5wQ4trJCFDRrpRWlfJj9o8cvnCYozlHTZ3iGgd2R50jgR6B\nGDwNvHbnazg6SE/31lJaqjrDJSXBmjVQWGiZJiMDeveur3I3TgMrhLgxJLgLm3ep7BJJF5I4fOEw\np/NOU6fVWU3n7OCMv7s/Ae4B+Ln7mYauSWBvWZqmJpRJSlJD186cqZ/+teEkM6B6tgcGqqr3V18F\nmXRSiJYhwV3YHE3TOFd4zhTQzxefbzJtoEcgNwXfRGFlIXpXvSyn2kpqa+H0aRXMjxyB7Gzr6Tw8\nVNqAALX5+NQvpyqBXYiWI8FdtImGvdsB6rQ6CioLyC3LJbZLLIUVVupyfxbqF8qgoEEMCh5EZ6/O\n6HQ6dp/d3dJZ7vDKytRkMklJ6mdTPdx1OggLg4EDYdw4+Phjyyp3GbYmRMuS4C7aTFVtFfkV+eSW\n55Jfnk+tpqYgaxzYnRyciDREMihoEAODBuLj5mNxLhm2duMYe7dnZakhZz16qI5wKSlqCJs1rq7Q\nr58K6AMGgLd3/XsGgwxbE6K1SXAXraaqtoqU3BSO5RxTE8pUW59QBlQP94FBAxkUNIh+hn64Orm2\nYk47ru++g7/+VQ1Vy82tfzpv3LsdwM8PBg1SAT0ion6muMZk2JoQrU+Cu2gxdVod6YXpHMs5xvFL\nxzmTd8Y0h7u1wO7u5E6gRyCzb5lNuH84DjpplG1pdXXw009w/Lja3n/f+vjzjAwV3Hv2VMF84EAZ\nsiaELZPgLm6o3LJcUzA/celEk9O9AjjggN5Vj5+7HwEeAbg7uaNDR++A3q2Y445F01Tnt+PH4cQJ\nNUtcw7bzxqutOTioJ/TAQFiyRHWIE0LYPgnu4rqUV5dzMvckx3OOcyznGNmlTXSb/lkX7y5EGiLJ\nq8jDx9VHVlprBUVFKpAbn85/XovJKg8P9QXAzw98fdXm6Kie0iWwC9F+SHAXl9V43vaiyiIKKgrI\nL89ncOfBTY47B9C76ok0RNLP0I++gX3xdfMF4IuzX7R4vjuChp3fOneGuDjVoS0lpT6gZ2Y2fw5f\nX9WmHhkJDz0EH3xgmUZ6twvRvkhwF82qqauhqLKIwspCCisKKaosokarX/u8cWB3dnQmIiCCfoZ+\nRAZG0sW7i9XpXqV3+/Xbtw9WrlTt5iUlkJoKO3eqpU8DA5s+zs0N+vRRwbxvX7UWesMi8vaW3u1C\ntHcS3IWZ8upyzuafJSUvhdN5p0nNT+XwxcNNptfpdPTw6UFkYCSRhkh6+ffCyUH+rFpSWRmcPQvL\nlkFamqp2bzhErbbWPLg7Oqpx55GRauvZs/kJZKR3uxDtn9yFO7iiyiJSclNMwTyjKAPNOHdoE9yc\n3PBz88PXzZfX73odTxfPVsptx5SXp6Z0PX1abZmZql08Kal+mteGSktVG7mxqr13bzUOXQjRcUhw\n70A0TSOnLIeUXBXIU/JSyCnNuexx7k7u+Lj54OPqg95Vj5uTm2maVwnsN1ZdnaoONwby06eb7gDn\n4aECOaiqdl9f1RGuXz/4059aL89CCNsjwd0OGTvAaWiUVpVSWKnaygsrChnWbVizx+p0Orrru9PL\nvxe9A3rTy78XCbsSWiPbHULjDnB33KGq0I2B/MwZqKho/hw6HXTvDiEhsH+/mkWu4ZP5uHEtew1C\nCNsnwd1OaJpGfkU+aQVppBakUlxZTHFVsWlK16Y4OTgR6hdKb38VyMP9w3FzcmulXHcsP/wA//iH\nmiSmqAgOHlQ90yMiLGd/a8jVVS2J2quX2kJD1ZM6qC8L0vlNCNGYBPd2qrSqlHOF51Qwz08lrSCN\nokq1vmZ6UXqTx3k4exDuH24K5iG+IZftACc9269NcbHq8HbunPq5ebNqP2/MOPubkV5fH8h79VJP\n6U11gJPOb0IIayS4twPVtdWkF6WbgnhaQdplJ4sxcnV0Re+qN7WZL7t7mdWhaeL6lJWpIG4M5Glp\nlm3lTbWdaxrcdlt9MA8IkGldhRDXR4J7G2u89KmGRll1GcWVxUzsP5HUglQyizKbnSjGyM3JjRDf\nEFILUvF28cbb1RsXRxezNc4lsF8da5PEDByo5mM3BvJz55pez7whDw+1upqXlxpLrterLTQUpkxp\n8UsRQnQgHSK4W7tB20pVZlVdFcWVxRRVFlm0k+85t6fJ4xwdHOmm70aobyg9fXvS07cnwV7B6HQ6\nTl462VrZt2v79sHy5eqpvLhYzfi2fbtaAtVguPzxzs4qbUiIGlt+//2wdausbS6EaHl2H9wbzuLl\n4KDGCK9cqd5r7QBfW1dLRlEGZ/PPcib/DKn5qXyf8f0VHRvkFWQK5KF+oXT17oqzYxNrbIqrpmmq\nPTwzU7WBZ2TApk3qibzxWPL0dMvgbpx/3RjIQ0JUB7fGbeUBAdIBTgjR8uw+uH/6qao6TU9XC19E\nRKiexp991vI31aLKIs7mnzVtaQVpVNdWX/Y4F0cX9C567ut7Hz19exLiG4KHs8cV/7/SAa55lZUq\niDcM5BkZlkPQrAV2UE/yXbrUB/GePdWUr02tZ96QdIATQrQGuw/uWVmQk6Nu0gUFavhRRETz0282\np3EbuZGGxh9v+6Ppqfxs/llyy3Ivez4HnQNeLl74uPqY2sldHdWg5bjecdeWyQ6qcfPL6NFq2tWG\nATwzs/7v4XI8PFQgd3evbyf38lLzsi9Y0PLXI4QQ18rug3vnzurp6uRJdUOvqYFjx1S7Z1UVuLhc\n23mr66opqiwybcVVxbzy9SuXPS7AI4AwvzDTtujrRThwjd80BKDK9X//q28fLy1VX+I+/FBNvdrc\nGPKGPDxU1bpxu+ce1cbu2GhV2jFjbvw1CCHEjWT3wT0uTj2tubmpDlHGqtfaWnjlFXj8cXUjvxr5\nFfkcv3ScmrqaZtM5OzoT4hNiFsx93MwXxZbAfuXq6tRT94ULasvKUtuFC/Dtt/VTsTbUeAw5qFqb\noCDzQN61q5q+tXFnt86dpY1cCNH+2H1wN96IP/tMValmZ6ube6dOKjD85S/w4IPwy19e2djiytpK\nTlw6YTWwN34q76bvJhPENKOpUQyVlXDxYn0AN/7MzlZfyqwpK7O+v6pKLaDSMJAHB19Z+zhIG7kQ\non2y++AO5jdoTYPvvoP331c3/poa9e9jxyA+Xn0BaEpNXQ3Hco5RXac6xbk4uBDkFYS3izd6Vz2v\n3H75anmh/PADvP02lJerwJySAv/5z7WvYObjo57sPTzU5umpttBQeOaZG59/IYSwZR0iuDek08Gt\nt0J4uBoSl/7zTK1HjsDLL8P06arDlDVbjm2huKpYnQcd/Qz90LvqWynn7U9dnZqVLSdHbdnZ9T//\n/W81v3pjR4/C4MFNn9PXVz15d+5s/vPkSVi1yjJ9nPRJFEJ0QB0uuBsFB8PcuWpSkS++UPsKCuCN\nN1S76tix5h2p9p/fz/9S/2d6HeYX1qECe1NV6LW1kJtrHryN/750SdWMWFNcbH1/aalqNjEYrAdx\ntybWtBk6VH1xk/ZxIYTowMEdwMkJJk6EyEhYuxZKSlS1/aefqifBxx5Ty3FeKLnA+qT1AIzoMYLo\nztHMjJnZIaZy1TT45ht45x3VFl5RAadOqSDat6+qQq+7/My4FoxTsbq7q81YnR4erjo6Ol3DX6a0\njwshhNKhg7vRgAHwwguwerXqUQ9w9qyqpn9ociWfly+nsqYSAIOngfhB8XYT2DVNfanJy1NP4Na2\npnqiJyc3X4UOamx4p07qSdxgUP/u1EnNzb5xo2X6CROuLbALIYSoJ7fRn/n4wNNPw65d8PHH6mm0\nvEJj/uZN6HqcJzwc3JydmRkzE3dn97bO7mUZq9HPn1dTng4ZonqKWwveVVXNn6upnujGgO/raz2A\nGwxNV6P37Kme2KUaXQghbjwJ7g3odHD33apD3bvvQnLRN1x0+R4uQFEh/HHsw3T36d7W2TSpq1Nt\n1/n5qr+A8efBg7B7t6r2rqxU6bZuVdXoVzqhS0Pe3qrt3M1NVcMbq9JDQ2HRomufCEiq0YUQomVI\ncLeiZ0+I/38/8Zv178PPS3nqC2/hizW34lMIo0bd2PW2rXVWu+kmKCy0DNwNfxYWWm/vPnDgyid0\nARW0AwLqN39/1dfA31+9Pn7cek/0iROvPbALIYRoORLcrSirLmNd8nJ69alB7wcXTnUjvPxhalBT\nmn76qXqCzcu7siVk6+rqp0Vt/PPwYTUsrKZGbfv3w5YtqmPZtTxlg2U1upOTCuDu7nD77eYBPCBA\n7W/uy4r0RBdCiPZFgnsjmqax9vBaLpVdQgeEdHFj0e0z+WSTC+fOqSFee/aoJ9aQEDW17fffqxnu\nunSxHsAbrzbW0NU+ZTfm6Ql+fqrd2/iztlaNIXdxUUHdOKSvWzf1tH0tpApdCCHaDwnujXx+9nOS\nLiSZXsffFE//zp3oMwc++URNVwuqE1pKSv1xOTmX7zluTVOd1crKVLA2BuyGwbvhPmvTqAYF1a9Z\n39Do0VefPyGEEO2PBPcGUnJT2HZ8m+n1HWF3MLizithOTjB+vKqWP3HCsoe5tadvI51OVX17etZP\njWoc111UpDYnJxWoXVzUFhamhuddi4bz6Us1uhBCdDwS3H9WVFnEuwffpU5TPdTC/cN5IPIBi3T9\n+6ugnJmp5kU3BuWgIHjkEfPgbfzZXJt2RIT1p+zrnTZVqtGFEKLjkuAO1Gl1rDy4ksKKQgC8XLx4\nYvATODo4WqSNi1PBODTUfP/jj19bMJWnbCGEEDeaBHdgx8kdnLx0EgCdTsdjgx/Dz93PatqWCMby\nlC2EEOJG6vDBPfliMjtTdppej4kYQz9Dv2aPkWAshBDCljm0dQbaUm5ZLqsPrTa97mfox696/6oN\ncySEEEJcvw4b3GvqalhxYAVl1Wosmp+7H9Ojp+Og67C/EiGEEHaiw0ayfx79J2kFaQA46ByYETMD\nb1fvts2UEEIIcQN0yOC+L3MfX6Z9aXr9YL8HCfMLa7sMCSGEEDdQhwvuWcVZvHfkPdPrwZ0HMyp0\nVBvmSAghhLixOlRwr6ypZPmB5VTWVALQybMT8TfFo7uRS7wJIYQQbczmgnt5eTl//vOfGTVqFDEx\nMTz00EN8++23131eTdPYmLyRrOIsAJwdnflN7G9wc3K77nMLIYQQtsTmxrm/9NJLHDt2jFWrVtGl\nSxe2bdvGb37zGz755BPCwq6+XXzmjpkAZJVkkZJXv9JLn4A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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(system)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus question #2\n", + "\n", + "Factor out the update function.\n", + "\n", + "1. Write a function called `update` that takes a `State` object and a `System` object and returns a new `State` object that represents the state of the system after one time step.\n", + "\n", + "2. Write a version of `run_simulation` that takes an update function as a parameter and uses it to compute the update.\n", + "\n", + "3. Run your new version of `run_simulation` and plot the results.\n", + "\n", + "WARNING: This question is substantially harder, and requires you to have a good understanding of everything in Chapter 5. We don't expect most people to be able to do this exercise at this point." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'update' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_simulation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'update' is not defined" + ] + } + ], + "source": [ + "run_simulation(system, update)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plot_results(system)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 0face1a2086dd1e10e95a50e63c556e22b95e2d0 Mon Sep 17 00:00:00 2001 From: FlynnML Date: Fri, 13 Oct 2017 12:13:05 -0400 Subject: [PATCH 11/19] yee dude --- code/chap07mine.ipynb | 1933 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1933 insertions(+) create mode 100644 code/chap07mine.ipynb diff --git a/code/chap07mine.ipynb b/code/chap07mine.ipynb new file mode 100644 index 00000000..5fd58e0f --- /dev/null +++ b/code/chap07mine.ipynb @@ -0,0 +1,1933 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 7: Thermal systems\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": 131, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you want the figures to appear in the notebook, \n", + "# and you want to interact with them, use\n", + "# %matplotlib notebook\n", + "\n", + "# If you want the figures to appear in the notebook, \n", + "# and you don't want to interact with them, use\n", + "# %matplotlib inline\n", + "\n", + "# If you want the figures to appear in separate windows, use\n", + "# %matplotlib qt5\n", + "\n", + "# tempo switch from one to another, you have to select Kernel->Restart\n", + "\n", + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The coffee cooling problem.\n", + "\n", + "I'll use a `State` object to store the initial temperature.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "temp 90\n", + "dtype: int64" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "init = State(temp=90)\n", + "init" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And a `System` object to contain the system parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "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", + "
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" + ], + "text/plain": [ + "init temp 90\n", + "dtype: int64\n", + "volume 300\n", + "r 0.01\n", + "T_env 22\n", + "t0 0\n", + "t_end 30\n", + "dt 1\n", + "dtype: object" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coffee = System(init=init,\n", + " volume=300,\n", + " r=0.01,\n", + " T_env=22,\n", + " t0=0, \n", + " t_end=30,\n", + " dt=1)\n", + "coffee" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `update` function implements Newton's law of cooling." + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def update(state, system):\n", + " \"\"\"Update the thermal transfer model.\n", + " \n", + " state: State (temp)\n", + " system: System object\n", + " \n", + " returns: State (temp)\n", + " \"\"\"\n", + " unpack(system)\n", + " T = state.temp\n", + " T += -r * (T - T_env) * dt\n", + "\n", + " return State(temp=T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how it works." + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "temp 89.32\n", + "dtype: float64" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "update(init, coffee)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run simulations using the same function from the previous chapter." + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " Add a TimeFrame to the System: results\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \"\"\"\n", + " unpack(system)\n", + " \n", + " frame = TimeFrame(columns=init.index)\n", + " frame.loc[t0] = init\n", + " ts = linrange(t0, t_end-dt, dt)\n", + " \n", + " for t in ts:\n", + " frame.loc[t+dt] = update_func(frame.loc[t], system)\n", + " \n", + " system.results = frame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's how it works." + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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{}, + "outputs": [ + { + "data": { + "image/png": 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U2I1NQt8GNhljfuV67M9ddaPuwZYuV8qvhIeFcM3lqeQU2S1Tm8qQ7y88TuGR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\ncvwN4GERyQI+BuYBDwD3dF9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QfqmIVGFbFg9ybrdWBTBJROZiB9l/h201rRCR37nO34kd\nwP+66zkrgf8E/i4iz2O73e7HthjWNl3YtW/yOOwObqqX0zEJ1ScZY6qws3k2Yz9A38dO+/y+Meah\ndp53GvgA2wrpdsaYBuy6jxLsVNk/YPcrbjkl93fYrW2XAZOMMYeAWUAx8DTwFjAWuNUY83fXtVcA\nt2ITwBuu61YB840xnq2mRUAt8F7nv0PV0+iKa6U6SESmY2cgDXd9+PYpIrIC2G2MudfXsaiupy0J\npTrIGLMFeJNOLp7nD0RkMjAZu1Jd9QGaJJS6MN8FbhSRNF8H0s1+hy0rUuLrQFT30O4mpZRSbdKW\nhFJKqTZpklBKKdUmTRJKKaXapElCKaVUmzRJKKWUapMmCaWUUm36/wEkGIYJOl2Q5wAAAABJRU5E\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(coffee.results.temp, label='coffee')\n", + "decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After running the simulation, we can extract the final temperature from the results." + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def final_temp(system):\n", + " \"\"\"Final temperature.\n", + " \n", + " If system has no results, return initial temp.\n", + " \n", + " system: System object.\n", + " \n", + " returns: temperature (degC)\n", + " \"\"\" \n", + " if hasattr(system, 'results'):\n", + " return system.results.temp[system.t_end]\n", + " else:\n", + " return system.init.temp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It will be convenient to wrap these steps in a function. `kwargs` is a collection of whatever keyword arguments are provided; they are passed along as arguments to `System`." + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n", + " \"\"\"Runs a simulation with the given parameters.\n", + "\n", + " T_init: initial temperature in degC\n", + " r: heat transfer rate, in 1/min\n", + " volume: volume of liquid in mL\n", + " t_end: end time of simulation\n", + " \n", + " returns: System object\n", + " \"\"\"\n", + " init = State(temp=T_init)\n", + " \n", + " system = System(init=init,\n", + " volume=volume,\n", + " r=r,\n", + " T_env=22, \n", + " t0=0,\n", + " t_end=t_end,\n", + " dt=1)\n", + " return system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we use it:" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "72.299625390403094" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coffee = make_system()\n", + "run_simulation(coffee, update)\n", + "final_temp(coffee)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20.514978275278718" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "milk = make_system(T_init=5, r=0.15, volume=50, t_end=15)\n", + "#picked new r because it seemed like the milk should get warmer faster. new value is arbitrary. \n", + "#but felt like after fifteen min it would barely be colder than room temp.\n", + "run_simulation(milk, update)\n", + "final_temp(milk)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tHGlsA1yyufSsKZxQlh/nyIwxiSzchPO6iNyoqr/rafCSwKuRDkhETsMlmxpg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\nvHAD8GMReUVV1/ntWkDofyMzCljCMSZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(milk.results.temp, label='milk')\n", + "decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using `fsolve`\n", + "\n", + "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0." + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def func(x):\n", + " return (x-1) * (x-2) * (x-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`modsim.py` provides `fsolve`, which does some error-checking and then runs `scipy.optimize.fsolve`. The first argument is the function whose roots we want. The second argument is an initial guess." + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.])" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fsolve(func, x0=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Usually the root we get is the one that's closest to the initial guess." + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.])" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fsolve(func, 1.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.])" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fsolve(func, 2.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But not always." + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.])" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fsolve(func, 1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal." + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def error_func1(r):\n", + " \"\"\"Runs a simulation and returns the `error`.\n", + " \n", + " r: heat transfer rate, in 1/min\n", + " \n", + " returns: difference between final temp and 70 C\n", + " \"\"\"\n", + " system = make_system(r=r)\n", + " print(r)\n", + " run_simulation(system, update)\n", + " return final_temp(system) - 70" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `r=0.01`, we end up a little too warm." + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.01\n" + ] + }, + { + "data": { + "text/plain": [ + "2.2996253904030937" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "error_func1(r=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The return value from `fsolve` is an array with a single element, the estimated value of `r`." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.01\n", + "[ 0.01]\n", + "[ 0.01]\n", + "[ 0.01]\n", + "[ 0.01]\n", + "[ 0.01150871]\n", + "[ 0.01154231]\n", + "[ 0.01154308]\n", + "[ 0.01154308]\n", + "[ 0.01154308]\n" + ] + }, + { + "data": { + "text/plain": [ + "0.011543084583978345" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution = fsolve(error_func1, 0.01, xtol=1e-8)\n", + "r_coffee = solution[0]\n", + "r_coffee" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "70.0" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coffee = make_system(r=r_coffee)\n", + "run_simulation(coffee, update)\n", + "final_temp(coffee)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** When you call `fsolve`, it calls `error_func1` several times. To see how this works, add a print statement to `error_func1` and run `fsolve` again." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes. \n", + "\n", + "Before you use `fsolve`, you might want to try a few values for `r_milk` and see how close you can get by trial and error. Here's an initial guess to get you started:" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19.501444483698084" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r_milk = 0.12\n", + "#picked this value because it seemed pretty close from my graph above.\n", + "milk = make_system(T_init=5, t_end=15, r=r_milk)\n", + "run_simulation(milk, update)\n", + "final_temp(milk)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def error_func2(r):\n", + " system = make_system(r=r, T_init=5, t_end=15)\n", + " print(r)\n", + " run_simulation(system, update)\n", + " return final_temp(system) - 20" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.12\n" + ] + }, + { + "data": { + "text/plain": [ + "-0.49855551630191641" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "error_func2(r=0.12)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.12\n", + "[ 0.12]\n", + "[ 0.12]\n", + "[ 0.12]\n", + "[ 0.12]\n", + "[ 0.1317062]\n", + "[ 0.13283515]\n", + "[ 0.13295952]\n", + "[ 0.13296079]\n", + "[ 0.13296079]\n", + "[ 0.13296079]\n" + ] + }, + { + "data": { + "text/plain": [ + "0.13296078935466449" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution = fsolve(error_func2, 0.12, xtol=1e-8)\n", + "r_milk = solution[0]\n", + "r_milk" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19.999999999999996" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "milk = make_system(r=r_milk, T_init=5, t_end=15)\n", + "run_simulation(milk, update)\n", + "final_temp(milk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mixing liquids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture." + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def mix(s1, s2):\n", + " \"\"\"Simulates the mixture of two liquids.\n", + " \n", + " s1: System representing coffee\n", + " s2: System representing milk\n", + " \n", + " returns: System representing the mixture\n", + " \"\"\"\n", + " assert s1.t_end == s2.t_end\n", + " \n", + " volume = s1.volume + s2.volume\n", + " \n", + " temp = (s1.volume * final_temp(s1) + \n", + " s2.volume * final_temp(s2)) / volume\n", + " \n", + " mixture = make_system(T_init=temp,\n", + " volume=volume,\n", + " r=s1.r)\n", + " \n", + " return mixture" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we'll see what happens if we add the milk at the end. We'll simulate the coffee and the milk separately." + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "70.0" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coffee = make_system(T_init=90, t_end=30, r=r_coffee, volume=300)\n", + "run_simulation(coffee, update)\n", + "final_temp(coffee)" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "21.764705882352942" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "milk = make_system(T_init=5, t_end=30, r=r_milk, volume=50)\n", + "run_simulation(milk, update)\n", + "final_temp(milk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the results look like." + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap07-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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OGz4JWCUijwCnG2PcdLD/N7J7Qk0FngK+CKzFjrdYDlydccyJ2IF8SimlRpjb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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(coffee.results.temp, label='coffee')\n", + "plot(milk.results.temp, '--', label='milk')\n", + "decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)',\n", + " loc='center left')\n", + "\n", + "savefig('chap07-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what happens when we mix them." + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "63.109243697478988" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mix_last = mix(coffee, milk)\n", + "final_temp(mix_last)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's what we get if we add the milk immediately." + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "coffee = make_system(T_init=90, r=r_coffee, volume=300)\n", + "milk = make_system(T_init=5, r=r_milk, volume=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "61.428571428571438" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mix_first = mix(coffee, milk)\n", + "mix_first.t_end = 30\n", + "run_simulation(mix_first, update)\n", + "final_temp(mix_first)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_and_mix(t_add, t_total=30):\n", + " \"\"\"Simulates two liquids and them mixes them at t_add.\n", + " \n", + " t_add: time in minutes\n", + " t_total: total time to simulate, min\n", + " \n", + " returns: final temperature\n", + " \"\"\"\n", + " coffee = make_system(T_init=90, t_end=t_add, \n", + " r=r_coffee, volume=300)\n", + " run_simulation(coffee, update)\n", + "\n", + " milk = make_system(T_init=5, t_end=t_add, \n", + " r=r_milk, volume=50)\n", + " run_simulation(milk, update)\n", + " \n", + " mixture = mix(coffee, milk)\n", + " mixture.t_end = t_total - t_add\n", + " run_simulation(mixture, update)\n", + "\n", + " return final_temp(mixture)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can try it out with a few values." + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "61.428571428571438" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "62.90280912845234" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "63.109243697478988" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And then sweep a range of values for `t_add`" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sweep = SweepSeries()\n", + "for t_add in linrange(0, 30, 2):\n", + " temp = run_and_mix(t_add)\n", + " sweep[t_add] = temp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the result looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap07-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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MU9UKP3G2R2V7y1jw2AICgQDgJv8cfqqVrjbGND+/iec24D0R+Ro3I3UA+KGI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\nLBLAvia9kSZa/8l6qiqqAOg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(sweep, color='purple')\n", + "decorate(xlabel='Time added (min)',\n", + " ylabel='Final temperature (C)',\n", + " legend=False)\n", + "\n", + "savefig('chap07-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office. In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n", + "\n", + "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model. The change you make to test this variation of the problem should be very small!" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Now you can only add the milk at two points, the beggining and the end. \n", + "#Assume that both refrigerators are equally cold, so the milk will be 5 deg C whenever you add it. \n", + "\n", + "def run_and_mix(t_add, t_total=30):\n", + " \"\"\"Simulates two liquids and them mixes them at t_add.\n", + " \n", + " t_add: time in minutes\n", + " t_total: total time to simulate, min\n", + " \n", + " returns: final temperature\n", + " \"\"\"\n", + " coffee = make_system(T_init=90, t_end=t_add, \n", + " r=r_coffee, volume=300)\n", + " run_simulation(coffee, update)\n", + "\n", + " milk = make_system(T_init=5, t_end=t_add, \n", + " r=r_milk, volume=50)\n", + " #remove the run_simulation becaause the milk is not warming up\n", + " mixture = mix(coffee, milk)\n", + " mixture.t_end = t_total - t_add\n", + " run_simulation(mixture, update)\n", + "\n", + " return final_temp(mixture)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "61.428571428571438" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "60.714285714285715" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(30)" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#so better to add milk at beggining" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the analytic result to compute temperature as a function of time. The following function is similar to `run_simulation`." + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_analysis(system):\n", + " \"\"\"Computes temperature using the analytic solution.\n", + " \n", + " Adds TimeFrame to `system` as `results`\n", + " \n", + " system: System object\n", + " \"\"\"\n", + " unpack(system)\n", + " \n", + " T_init = init.temp \n", + " ts = linrange(t0, t_end, dt)\n", + " \n", + " temp_array = T_env + (T_init - T_env) * exp(-r * ts)\n", + " temp_series = TimeSeries(temp_array, index=ts)\n", + " \n", + " system.results = TimeFrame(temp_series, columns=['temp'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we run it. From the analysis, we have the computed value of `r_coffee2`" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "r_coffee2 = 0.011610223142273859" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "70.0" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "init = State(temp=90)\n", + "coffee2 = System(init=init, T_env=22, r=r_coffee2, \n", + " t0=0, t_end=30)\n", + "run_analysis(coffee2)\n", + "final_temp(coffee2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can compare to the results from simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "70.0" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "init = State(temp=90)\n", + "coffee = System(init=init, T_env=22, r=r_coffee, \n", + " t0=0, t_end=30, dt=1)\n", + "run_simulation(coffee, update)\n", + "final_temp(coffee)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "They are identical except for small roundoff errors." + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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frame.S[system.t_end]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's an updated version of `run_simulation` that uses `unpack`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " Add a TimeFrame to the System: results\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \"\"\"\n", + " unpack(system)\n", + " \n", + " frame = TimeFrame(columns=init.index)\n", + " frame.loc[t0] = init\n", + " \n", + " for i in linrange(t0, t_end):\n", + " frame.loc[i+1] = update_func(frame.loc[i], system)\n", + " \n", + " system.results = frame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write a version of `update1` that uses `unpack`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Original\n", + "\n", + "def update1_no_supdate(state, system):\n", + " \"\"\"Update the SIR model.\n", + " \n", + " state: State (s, i, r)\n", + " system: System object\n", + " \n", + " returns: State (sir)\n", + " \"\"\"\n", + " s, i, r = state\n", + "\n", + " infected = system.beta * i * s \n", + " recovered = system.gamma * i\n", + " \n", + " s -= infected\n", + " i += infected - recovered\n", + " r += recovered\n", + " \n", + " return State(S=s, I=i, R=r)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def update1(state, system):\n", + " \"\"\"Update the SIR model.\n", + " \n", + " state: State (s, i, r)\n", + " system: System object\n", + " \n", + " returns: State (sir)\n", + " \"\"\"\n", + " unpack(system)\n", + " \n", + " s, i, r = state\n", + "\n", + " infected = beta * i * s \n", + " recovered = gamma * i\n", + " \n", + " s -= infected\n", + " i += infected - recovered\n", + " r += recovered\n", + " \n", + " return State(S=s, I=i, R=r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test the updated code with this example." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5hKtXr+Lm5sarr75KUFDQIze7EY8vNRVsbSElRZnHsns3nDsHw4crC1YKUVy0a9eOQYMG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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frame = system.results\n", + "plot_results(frame.S, frame.I, frame.R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sweeping beta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a range of values for `beta`, with constant `gamma`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "beta_array = linspace(0.1, 0.9, 11)\n", + "gamma = 0.25" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the simulation once for each value of `beta` and print total infections." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1 0.00723090166498\n", + "0.18 0.0262722567457\n", + "0.26 0.160575485321\n", + "0.34 0.490862856866\n", + "0.42 0.689867847411\n", + "0.5 0.804506112463\n", + "0.58 0.873610307851\n", + "0.66 0.916554007142\n", + "0.74 0.943729262152\n", + "0.82 0.961060480958\n", + "0.9 0.972099315633\n" + ] + } + ], + "source": [ + "for beta in beta_array:\n", + " system = make_system(beta, gamma)\n", + " run_simulation(system, update1)\n", + " print(system.beta, calc_total_infected(system))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wrap that loop in a function and return a `SweepSeries` object." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_beta(beta_array, gamma):\n", + " \"\"\"SweepSeriess a range of values for beta.\n", + " \n", + " beta_array: array of beta values\n", + " gamma: recovery rate\n", + " \n", + " returns: SweepSeries that maps from beta to total infected\n", + " \"\"\"\n", + " sweep = SweepSeries()\n", + " for beta in beta_array:\n", + " system = make_system(beta, gamma)\n", + " run_simulation(system, update1)\n", + " sweep[system.beta] = calc_total_infected(system)\n", + " return sweep" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SweepSeries `beta` and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "infected_sweep = sweep_beta(beta_array, gamma)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap06-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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ZeJBRrHFrrK2lCfwljpC0sabZ2MSg9J4wlEol1q5diz179qCsrAzBwcFYunQp7O3ta93+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\nYehRzeXdqz1refe8vDyMHTsWKpUKO3f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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "label = 'gamma = ' + str(gamma)\n", + "plot(infected_sweep, label=label)\n", + "decorate(xlabel='Contacts per day (beta)',\n", + " ylabel='Fraction infected')\n", + "\n", + "savefig('chap06-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sweeping gamma" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the same array of values for `beta`" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.1 , 0.18, 0.26, 0.34, 0.42, 0.5 , 0.58, 0.66, 0.74,\n", + " 0.82, 0.9 ])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beta_array = linspace(0.1, 0.9, 11)\n", + "beta_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now an array of values for `gamma`" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.1, 0.3, 0.5, 0.7])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gamma_array = linspace(0.1, 0.7, 4)\n", + "gamma_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each value of `gamma`, SweepSeries `beta` and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap06-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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27VrWrVvHhg0bTCu9Z86ceU9ja0kYDAbiE9LILzYuRrORSXliYEfs7Wr/qQYO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\nezVUWlXjqxoKrRJatmOTg7ezJ7+CCHL1h0RkHhszIQTgON3TvloFTqn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for gamma in gamma_array:\n", + " infected_sweep = sweep_beta(beta_array, gamma)\n", + " label = 'gamma = ' + str(gamma)\n", + " plot(infected_sweep, label=label)\n", + " \n", + "decorate(xlabel='Contacts per day (beta)',\n", + " ylabel='Fraction infected',\n", + " loc='upper left')\n", + "\n", + "savefig('chap06-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now wrap that loop in a function and store the results in a `SweepSeriesFrame`" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_parameters(beta_array, gamma_array):\n", + " \"\"\"SweepSeriess a range of values for beta and gamma.\n", + " \n", + " beta_array: array of infection rates\n", + " gamma_array: array of recovery rates\n", + " \n", + " returns: SweepSeriesFrame with one row for each beta\n", + " and one column for each gamma\n", + " \"\"\"\n", + " frame = SweepFrame(columns=gamma_array)\n", + " for gamma in gamma_array:\n", + " frame[gamma] = sweep_beta(beta_array, gamma)\n", + " return frame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the results look like." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0.10.30.50.7
0.100.0846930.0054440.0027360.001827
0.180.7086230.0159140.0061180.003783
0.260.9007800.0553800.0116390.006427
0.340.9568880.2678640.0221150.010191
0.420.9770450.5245630.0478160.015946
\n", + "
" + ], + "text/plain": [ + " 0.1 0.3 0.5 0.7\n", + "0.10 0.084693 0.005444 0.002736 0.001827\n", + "0.18 0.708623 0.015914 0.006118 0.003783\n", + "0.26 0.900780 0.055380 0.011639 0.006427\n", + "0.34 0.956888 0.267864 0.022115 0.010191\n", + "0.42 0.977045 0.524563 0.047816 0.015946" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame = sweep_parameters(beta_array, gamma_array)\n", + "frame.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's how we can plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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TwQ1bIQ53RWJohYWLb9y4wYgRI4iPjycgIKCph9Mg5Ofnc/r0aQYNGoRMZlwop1KpiIyM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\nezVUWlXjqxoKrRJatmOTg7ezJ7+CCHL1h0RkHhszIQTgON3TvloFTqn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for gamma in gamma_array:\n", + " label = 'gamma = ' + str(gamma)\n", + " plot(frame[gamma], label=label)\n", + " \n", + "decorate(xlabel='Contacts per day (beta)',\n", + " ylabel='Fraction infected',\n", + " loc='upper left')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's often useful to separate the code that generates results from the code that plots the results, so we can run the simulations once, save the results, and then use them for different analysis, visualization, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contact number" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After running the SweepSeriess, we have a `SweepSeriesFrame` with one row for each value of `beta` and one column for each value of `gamma`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(11, 4)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following loop shows how we can loop through the columns and rows of the `SweepSeriesFrame`. With 11 rows and 4 columns, there are 44 elements.\n", + "\n", + "One implementation note: when we select a column from a `SweepSeriesFrame` we get a `Series` object, rather than a `SweepSeries` object, but they are almost the same." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1 0.1 0.0846929424381\n", + "0.18 0.1 0.70862278537\n", + "0.26 0.1 0.900780251778\n", + "0.34 0.1 0.956887899544\n", + "0.42 0.1 0.977045257074\n", + "0.5 0.1 0.984595862826\n", + "0.58 0.1 0.987400345318\n", + "0.66 0.1 0.988404249064\n", + "0.74 0.1 0.988743421406\n", + "0.82 0.1 0.988849515052\n", + "0.9 0.1 0.988879570517\n", + "0.1 0.3 0.00544355912239\n", + "0.18 0.3 0.0159140691448\n", + "0.26 0.3 0.0553797621068\n", + "0.34 0.3 0.267864167733\n", + "0.42 0.3 0.524562935844\n", + "0.5 0.3 0.686050483916\n", + "0.58 0.3 0.788378556339\n", + "0.66 0.3 0.85506574641\n", + "0.74 0.3 0.89947913569\n", + "0.82 0.3 0.929469302619\n", + "0.9 0.3 0.949853310327\n", + "0.1 0.5 0.00273576554115\n", + "0.18 0.5 0.00611834135832\n", + "0.26 0.5 0.0116394693217\n", + "0.34 0.5 0.0221147665242\n", + "0.42 0.5 0.0478162266689\n", + "0.5 0.5 0.132438038458\n", + "0.58 0.5 0.303264192648\n", + "0.66 0.5 0.464110227319\n", + "0.74 0.5 0.588476972528\n", + "0.82 0.5 0.682749610978\n", + "0.9 0.5 0.754595298329\n", + "0.1 0.7 0.001826769347\n", + "0.18 0.7 0.00378256160842\n", + "0.26 0.7 0.00642667221076\n", + "0.34 0.7 0.0101905519335\n", + "0.42 0.7 0.0159458265615\n", + "0.5 0.7 0.0257079250464\n", + "0.58 0.7 0.0450077531168\n", + "0.66 0.7 0.0906940688294\n", + "0.74 0.7 0.189795211656\n", + "0.82 0.7 0.318343186735\n", + "0.9 0.7 0.436999374456\n" + ] + } + ], + "source": [ + "for gamma in frame.columns:\n", + " series = frame[gamma]\n", + " for beta in series.index:\n", + " frac_infected = series[beta]\n", + " print(beta, gamma, frac_infected)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can wrap that loop in a function and plot the results. For each element of the `SweepSeriesFrame`, we have `beta`, `gamma`, and `frac_infected`, and we plot `beta/gamma` on the x-axis and `frac_infected` on the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_sweep_frame(frame):\n", + " \"\"\"Plots the values from a parameter SweepSeries.\n", + " \n", + " For each (beta, gamma), computes the contact number,\n", + " beta/gamma\n", + " \n", + " frame: SweepFrame with one row per beta, one column per gamma\n", + " \"\"\"\n", + " for gamma in frame.columns:\n", + " series = frame[gamma]\n", + " for beta in series.index:\n", + " frac_infected = series[beta]\n", + " plot(beta/gamma, frac_infected, 'ro',\n", + " label='Simulation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what it looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap06-fig03.pdf\n" + ] + }, + { + "data": { + "image/png": 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LFi1CQEAAfHx8EBUVhRs3bpR7/M6dOxEaGgpvb2906dIFq1evRklJiQEjJiIi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\nTH4J+ubNmyMuLs7g9d6+fRv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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_sweep_frame(frame)\n", + "\n", + "decorate(xlabel='Contact number (beta/gamma)',\n", + " ylabel='Fraction infected',\n", + " legend=False)\n", + "\n", + "savefig('chap06-fig03.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It turns out that the ratio `beta/gamma`, called the \"contact number\" is sufficient to predict the total number of infections; we don't have to know `beta` and `gamma` separately.\n", + "\n", + "We can see that in the previous plot: when we plot the fraction infected versus the contact number, the results fall close to a curve.\n", + "\n", + "But if we didn't know about the contact number, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n", + "\n", + "What do the results look like, and what does that imply? " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_sweep_frame_difference(frame):\n", + " \"\"\"Plots the values from a parameter SweepSeries.\n", + " \n", + " For each (beta, gamma), computes the contact number,\n", + " beta/gamma\n", + " \n", + " frame: SweepFrame with one row per beta, one column per gamma\n", + " \"\"\"\n", + " for gamma in frame.columns:\n", + " series = frame[gamma]\n", + " for beta in series.index:\n", + " frac_infected = series[beta]\n", + " plot(beta-gamma, frac_infected, 'ro',\n", + " label='Simulation')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap06-fig03.pdf\n" + ] + }, + { + "data": { + "image/png": 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UwN3dnUuztrZG06ZNkZCQoJT/7Nmz6NixI+rUqcOlDRw4ELGxsYL2RwghRHMENXq3atUK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\nK/Pvv/8uV5nvSxvHCQBnz55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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_sweep_frame_difference(frame)\n", + "\n", + "decorate(xlabel='Contact number (beta/gamma)',\n", + " ylabel='Fraction infected',\n", + " legend=False)\n", + "\n", + "savefig('chap06-fig03.pdf')\n", + "\n", + "#Scattered, so not such a direct corelation as the ratio." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the book we figured out the relationship between $c$ and $s_{\\infty}$ analytically. Now we can compute it for a range of values:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "s_inf_array = linspace(0.0001, 0.9999, 101)\n", + "#s_inf_array" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "c_array = log(s_inf_array) / (s_inf_array - 1)\n", + "#c_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`total_infected` is the change in $s$ from the beginning to the end." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "frac_infected = 1 - s_inf_array\n", + "frac_infected_series = Series(frac_infected, index=c_array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can plot the analytic results and compare them to the simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap06-fig04.pdf\n" + ] + }, + { + "data": { + "image/png": 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o0i4WQsIZ/Qq1Hk4QO0n6L4QQFiNjNBuwNHd/iHQFwLdXW/B3tXBEQojmTBaF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\nLFiwgOPHj+Pr68uUKVMYOnRo/QRbNq8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l+fn5FBIS8ssxmH8eW5JiVs3atWsBQLSc9Cs9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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_sweep_frame(frame)\n", + "plot(frac_infected_series, label='Analysis')\n", + "\n", + "decorate(xlabel='Contact number (c)',\n", + " ylabel='Fraction infected')\n", + "\n", + "savefig('chap06-fig04.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The agreement is generally good, except for values of `c` less than 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point. What is your best estimate of `c`?\n", + "\n", + "Hint: if you print `frac_infected_series`, you can read off the answer. " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.211261 0.999900\n", + "4.642296 0.989902\n", + "3.987365 0.979904\n", + "3.612133 0.969906\n", + "3.350924 0.959908\n", + "3.151808 0.949910\n", + "2.991711 0.939912\n", + "2.858363 0.929914\n", + "2.744467 0.919916\n", + "2.645332 0.909918\n", + "2.557767 0.899920\n", + "2.479505 0.889922\n", + "2.408879 0.879924\n", + "2.344627 0.869926\n", + "2.285771 0.859928\n", + "2.231541 0.849930\n", + "2.181315 0.839932\n", + "2.134590 0.829934\n", + "2.090947 0.819936\n", + "2.050040 0.809938\n", + "2.011573 0.799940\n", + "1.975299 0.789942\n", + "1.941002 0.779944\n", + "1.908499 0.769946\n", + "1.877628 0.759948\n", + "1.848249 0.749950\n", + "1.820238 0.739952\n", + "1.793487 0.729954\n", + "1.767898 0.719956\n", + "1.743384 0.709958\n", + " ... \n", + "1.181034 0.290042\n", + "1.173263 0.280044\n", + "1.165630 0.270046\n", + "1.158132 0.260048\n", + "1.150765 0.250050\n", + "1.143524 0.240052\n", + "1.136407 0.230054\n", + "1.129409 0.220056\n", + "1.122527 0.210058\n", + "1.115758 0.200060\n", + "1.109099 0.190062\n", + "1.102547 0.180064\n", + "1.096099 0.170066\n", + "1.089751 0.160068\n", + "1.083503 0.150070\n", + "1.077350 0.140072\n", + "1.071291 0.130074\n", + "1.065323 0.120076\n", + "1.059444 0.110078\n", + "1.053651 0.100080\n", + "1.047943 0.090082\n", + "1.042317 0.080084\n", + "1.036772 0.070086\n", + "1.031305 0.060088\n", + "1.025914 0.050090\n", + "1.020598 0.040092\n", + "1.015356 0.030094\n", + "1.010185 0.020096\n", + "1.005083 0.010098\n", + "1.000050 0.000100\n", + "Length: 101, dtype: float64\n" + ] + } + ], + "source": [ + "print(frac_infected_series)\n", + "\n", + "#We see that at c = 1.158132, 26.00% are or were infected at the end of 14 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.158096819542062" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Alternative solution\n", + "\n", + "\"\"\"We can use `np.interp` to look up `s_inf` and\n", + "estimate the corresponding value of `c`, but it only\n", + "works if the index of the series is sorted in ascending\n", + "order. So we have to use `sort_index` first.\n", + "\"\"\"\n", + "\n", + "frac_infected_series.sort_index(inplace=True)\n", + "np.interp(0.26, frac_infected_series, frac_infected_series.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 2e94be0135d100eeb48dfaf19f9f0db714f4884a Mon Sep 17 00:00:00 2001 From: FlynnML Date: Tue, 24 Oct 2017 16:02:00 -0400 Subject: [PATCH 13/19] bob --- code/Project 2.ipynb | 129 ++ code/chap06-fig01.pdf | Bin 0 -> 13608 bytes code/chap06-fig02.pdf | Bin 0 -> 14990 bytes code/chap06-fig03.pdf | Bin 0 -> 13950 bytes code/chap06-fig04.pdf | Bin 0 -> 15688 bytes code/chap07-fig01.pdf | Bin 0 -> 13619 bytes code/chap07-fig02.pdf | Bin 0 -> 11633 bytes code/chap07soln.ipynb | 96 +- code/chap08mine.ipynb | 1284 +++++++++++++++++ code/proj2start.ipynb | 3024 +++++++++++++++++++++++++++++++++++++++++ 10 files changed, 4485 insertions(+), 48 deletions(-) create mode 100644 code/Project 2.ipynb create mode 100644 code/chap06-fig01.pdf create mode 100644 code/chap06-fig02.pdf create mode 100644 code/chap06-fig03.pdf create mode 100644 code/chap06-fig04.pdf create mode 100644 code/chap07-fig01.pdf create mode 100644 code/chap07-fig02.pdf create mode 100644 code/chap08mine.ipynb create mode 100644 code/proj2start.ipynb diff --git a/code/Project 2.ipynb b/code/Project 2.ipynb new file mode 100644 index 00000000..c92c8a87 --- /dev/null +++ b/code/Project 2.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "init = State(temp=T1)\n", + "\n", + "coffee = System(init=init,\n", + " volume=300,\n", + " r=r_coffee,\n", + " T_env=T_env,\n", + " t0=0, \n", + " t_end=30,\n", + " dt=1)\n", + "\n", + "def update(state, system):\n", + " unpack(system)\n", + " T = state.temp\n", + " T += -r * (T - T_env) * dt\n", + "\n", + " return State(temp=T)\n", + "\n", + "\n", + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " Add a TimeFrame to the System: results\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \"\"\"\n", + " unpack(system)\n", + " \n", + " frame = TimeFrame(columns=init.index)\n", + " frame.loc[t0] = init\n", + " ts = linrange(t0, t_end-dt, dt)\n", + " \n", + " for t in ts:\n", + " frame.loc[t+dt] = update_func(frame.loc[t], system)\n", + " \n", + " system.results = frame\n", + " \n", + "def final_temp(system):\n", + " \"\"\"Final temperature.\n", + " \n", + " If system has no results, return initial temp.\n", + " \n", + " system: System object.\n", + " \n", + " returns: temperature (degC)\n", + " \"\"\" \n", + " if hasattr(system, 'results'):\n", + " return system.results.temp[system.t_end]\n", + " else:\n", + " return system.init.temp\n", + " \n", + "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n", + " \"\"\"Runs a simulation with the given parameters.\n", + "\n", + " T_init: initial temperature in degC\n", + " r: heat transfer rate, in 1/min\n", + " volume: volume of liquid in mL\n", + " t_end: end time of simulation\n", + " \n", + " returns: System object\n", + " \"\"\"\n", + " init = State(temp=T_init)\n", + " \n", + " system = System(init=init,\n", + " volume=volume,\n", + " r=r,\n", + " T_env=22, \n", + " t0=0,\n", + " t_end=t_end,\n", + " dt=1)\n", + " return system\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/code/chap06-fig01.pdf b/code/chap06-fig01.pdf new file mode 100644 index 0000000000000000000000000000000000000000..dbe27317dc0de5ca5682edb8b61caf44c400f160 GIT binary patch literal 13608 zcmb_D2Rv18*k#2{*?V1NHMrAth3vgoGRmcEZ_Dm(w7k#Fh@qt}X;9l87K?qGRQN zwcQvTR7IEM(fWV&$fO3PW4AL@{mzn 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"execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -183,7 +183,7 @@ "dtype: object" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -279,7 +279,7 @@ "dtype: float64" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -332,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -523,7 +523,7 @@ "30 72.299625" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -542,14 +542,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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EcePGITAwEF9++SU2bNgAOzu7do/7/vvvYWNjgxkzZtx2voCAAAQEBODnn3/GoUOH4Ofn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\n5ctISkpCeHg4Bg0ahOXLl0On0zV5Tr36etTfn507d+LkyZO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sbCxJQnQp3V0scbYz5ZfTOZxLuwxAdW09O4+kk5xRzPBgd8yM5alCdH0aTYFd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\nvPPOO20dpxCiE3JzMGditC+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Pnkxqaqq2w2sV1dXVGBgYNGvr7PcNGiccVFZWEh4ezmef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\nD/dBZUkAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -571,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -601,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -638,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -647,7 +647,7 @@ "72.299625390403094" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -667,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -676,7 +676,7 @@ "18.499850754390966" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -691,14 +691,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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6+uG2+CBehicikzKo3GfNmnXDy/Lnzp0zSiCi3k7Z1o69xwqRV9agG3N0kGLs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\nWcRzf/8CEEu+HR0dtLa2hsbY7XYOHTpERkZG2M1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84ePKo2RDNEplwsnPz8fs2bPR0tKCfv36yW0zMjLCxx9/DACYMWMGioqK1P7A1NRUODo6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\nJmEpiaqw6XMAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -722,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -741,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -750,7 +750,7 @@ "array([ 1.])" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -768,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -777,7 +777,7 @@ "array([ 2.])" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -788,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -797,7 +797,7 @@ "array([ 3.])" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -815,7 +815,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -824,7 +824,7 @@ "array([ 3.])" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -842,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -869,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -878,7 +878,7 @@ "2.2996253904030937" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -896,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -905,7 +905,7 @@ "0.011543084583978345" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -925,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -934,7 +934,7 @@ "70.0" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -963,7 +963,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -972,7 +972,7 @@ "18.499850754390966" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -986,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "collapsed": true }, @@ -1008,7 +1008,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1017,7 +1017,7 @@ "-1.500149245609034" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1030,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1039,7 +1039,7 @@ "0.13296078935466457" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1063,7 +1063,7 @@ "20.0" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } diff --git a/code/chap08mine.ipynb b/code/chap08mine.ipynb new file mode 100644 index 00000000..423d7677 --- /dev/null +++ b/code/chap08mine.ipynb @@ -0,0 +1,1284 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 8: Pharmacokinetics\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": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you want the figures to appear in the notebook, \n", + "# and you want to interact with them, use\n", + "# %matplotlib notebook\n", + "\n", + "# If you want the figures to appear in the notebook, \n", + "# and you don't want to interact with them, use\n", + "# %matplotlib inline\n", + "\n", + "# If you want the figures to appear in separate windows, use\n", + "# %matplotlib qt5\n", + "\n", + "# tempo switch from one to another, you have to select Kernel->Restart\n", + "\n", + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data\n", + "\n", + "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "File b'glucose_insulin.csv' does not exist", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'glucose_insulin.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 653\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 654\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 655\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m 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**kwds)\u001b[0m\n\u001b[0;32m 1603\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1604\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1605\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1606\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1607\u001b[0m \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__ (pandas\\_libs\\parsers.c:4209)\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source (pandas\\_libs\\parsers.c:8873)\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: File b'glucose_insulin.csv' does not exist" + ] + } + ], + "source": [ + "data = pd.read_csv('glucose_insulin.csv', index_col='time')\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the glucose time series looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plot(data.glucose, 'bo', label='glucose')\n", + "decorate(xlabel='Time (min)',\n", + " ylabel='Concentration (mg/dL)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the insulin time series." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plot(data.insulin, 'go', label='insulin')\n", + "decorate(xlabel='Time (min)',\n", + " ylabel='Concentration ($\\mu$U/mL)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the book, I put them in a single figure, using `subplot`" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "subplot(2, 1, 1)\n", + "plot(data.glucose, 'bo', label='glucose')\n", + "decorate(ylabel='mg/dL')\n", + "\n", + "subplot(2, 1, 2)\n", + "plot(data.insulin, 'go', label='insulin')\n", + "decorate(xlabel='Time (min)',\n", + " ylabel='$\\mu$U/mL')\n", + "\n", + "savefig('chap08-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpolation\n", + "\n", + "We have measurements of insulin concentration at discrete points in time, but we need to estimate it at intervening points. We'll use `interpolate`, which is a wrapper for `scipy.interpolate.interp1d`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource interpolate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The return value from `interpolate` is a function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "I = interpolate(data.insulin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the result, `I`, to estimate the insulin level at any point in time." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "I(7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`I` can also take an array of time and return an array of estimates, which we can plot." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ts = linrange(0, 182, 2)\n", + "\n", + "plot(data.insulin, 'go', label='insulin data')\n", + "plot(ts, I(ts), color='green', label='interpolated')\n", + "\n", + "decorate(xlabel='Time (min)',\n", + " ylabel='Concentration ($\\mu$U/mL)')\n", + "\n", + "savefig('chap08-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** [Read the documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) of `scipy.interpolate.interp1d`. Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The glucose minimal model\n", + "\n", + "I'll cheat by starting with parameters that fit the data roughly; then we'll see how to improve them." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "k1 = 0.03\n", + "k2 = 0.02\n", + "k3 = 1e-05\n", + "G0 = 290" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To estimate basal levels, we'll use the concentrations at `t=0`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Gb = data.glucose[0]\n", + "Ib = data.insulin[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the initial conditions, `X(0)=0` and `G(0)=G0`, where `G0` is one of the parameters we'll choose." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "init = State(G=G0, X=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the system object with all parameters and the interpolation object `I`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system = System(init=init, \n", + " k1=k1, k2=k2, k3=k3,\n", + " I=I, Gb=Gb, Ib=Ib,\n", + " t0=0, t_end=182, dt=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's the update function. Using `unpack` to make the system variables accessible without using dot notation, which makes the translation of the differential equations more readable and checkable." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def update_func(state, t, system):\n", + " \"\"\"Updates the glucose minimal model.\n", + " \n", + " state: State object\n", + " t: time in min\n", + " system: System object\n", + " \n", + " returns: State object\n", + " \"\"\"\n", + " G, X = state\n", + " unpack(system)\n", + " \n", + " dGdt = -k1 * (G - Gb) - X*G\n", + " dXdt = k3 * (I(t) - Ib) - k2 * X\n", + " \n", + " G += dGdt * dt\n", + " X += dXdt * dt\n", + "\n", + " return State(G=G, X=X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before running the simulation, it is always a good idea to test the update function using the initial conditions. In this case we can veryify that the results are at least qualitatively correct." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "update_func(init, 0, system)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now run simulation is pretty much the same as it always is." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_simulation(system, update_func):\n", + " \"\"\"Runs a simulation of the system.\n", + " \n", + " Adds a TimeFrame to `system` as `results`\n", + " \n", + " system: System object\n", + " update_func: function that updates state\n", + " \"\"\"\n", + " unpack(system)\n", + " \n", + " frame = TimeFrame(columns=init.index)\n", + " frame.loc[t0] = init\n", + " ts = linrange(t0, t_end-dt, dt)\n", + " \n", + " for t in ts:\n", + " frame.loc[t+dt] = update_func(frame.loc[t], t, system)\n", + " \n", + " system.results = frame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's how we run it. `%time` is a Jupyter magic command that runs the function and reports its run time." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%time run_simulation(system, update_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are in a `TimeFrame object` with one column per state variable." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system.results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following plot shows the results of the simulation along with the actual glucose data." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "subplot(2, 1, 1)\n", + "\n", + "plot(system.results.G, 'b-', label='simulation')\n", + "plot(data.glucose, style='bo', label='glucose data')\n", + "decorate(ylabel='mg/dL')\n", + "\n", + "subplot(2, 1, 2)\n", + "\n", + "plot(system.results.X, style='g-', label='remote insulin')\n", + "\n", + "decorate(xlabel='Time (min)', \n", + " ylabel='Arbitrary units')\n", + "\n", + "savefig('chap08-fig03.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Numerical solution\n", + "\n", + "We can do the same thing using `odeint`. Instead of an update function, we provide a slope function that just evaluates the right-hand side of the differential equations. We don't have to do the update part; `odeint` does it for us." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def slope_func(state, t, system):\n", + " \"\"\"Computes derivatives of the glucose minimal model.\n", + " \n", + " state: State object\n", + " t: time in min\n", + " system: System object\n", + " \n", + " returns: derivatives of G and X\n", + " \"\"\"\n", + " G, X = state\n", + " unpack(system)\n", + " \n", + " dGdt = -k1 * (G - Gb) - X*G\n", + " dXdt = k3 * (I(t) - Ib) - k2 * X\n", + " \n", + " return dGdt, dXdt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can test the slope function with the initial conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "slope_func(init, 0, system)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `System` object we use with `run_odeint` is almost the same as the one we used with `run_simulation`, but instead of providing `t0`, `t_end`, and `dt`, we provide an array of times where we want to evaluate the solution. In this case, we use `data.index`, so the results are evaluated at the same times as the measurements." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system2 = System(init=init, \n", + " k1=k1, k2=k2, k3=k3,\n", + " I=I, Gb=Gb, Ib=Ib,\n", + " ts=data.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`run_odeint` is a wrapper for `scipy.integrate.odeint`" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource run_odeint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we run it." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%time run_odeint(system2, slope_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here are the results." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system2.results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the results from `run_simulation` and `run_odeint`, we can see that they are not very different." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plot(system.results.G, 'r-')\n", + "plot(system2.results.G, 'b-')\n", + "plot(data.glucose, 'bo')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The differences are usually less than 1% and always less than 2%." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "diff = system.results - system2.results\n", + "percent_diff = diff / system2.results * 100\n", + "percent_diff.dropna()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** What happens to these errors if you run the simulation with a smaller value of `dt`?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's find the parameters that yield the best fit for the data." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "k1 = 0.03\n", + "k2 = 0.02\n", + "k3 = 1e-05\n", + "G0 = 290" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we'll get basal levels from the initial values." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Gb = data.glucose[0]\n", + "Ib = data.insulin[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the slope function is the same." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def slope_func(state, t, system):\n", + " \"\"\"Computes derivatives of the glucose minimal model.\n", + " \n", + " state: State object\n", + " t: time in min\n", + " system: System object\n", + " \n", + " returns: derivatives of G and X\n", + " \"\"\"\n", + " G, X = state\n", + " unpack(system)\n", + " \n", + " dGdt = -k1 * (G - Gb) - X*G\n", + " dXdt = k3 * (I(t) - Ib) - k2 * X\n", + " \n", + " return dGdt, dXdt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`make_system` takes the parameters and `DataFrame` and returns a `System` object." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_system(G0, k1, k2, k3, data):\n", + " \"\"\"Makes a System object with the given parameters.\n", + " \n", + " G0: initial blood glucose\n", + " k1: rate parameter\n", + " k2: rate parameter\n", + " k3: rate parameter\n", + " data: DataFrame\n", + " \n", + " returns: System object\n", + " \"\"\"\n", + " init = State(G=G0, X=0)\n", + " system = System(init=init, \n", + " k1=k1, k2=k2, k3=k3,\n", + " Gb=Gb, Ib=Ib, \n", + " I=interpolate(data.insulin),\n", + " ts=data.index)\n", + " return system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`error_func` takes the parameters and actual data, makes a `System` object and runs it, then compares the results of the simulation to the data. It returns an array of errors." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def error_func(params, data):\n", + " \"\"\"Computes an array of errors to be minimized.\n", + " \n", + " params: sequence of parameters\n", + " data: DataFrame of values to be matched\n", + " \n", + " returns: array of errors\n", + " \"\"\"\n", + " print(params)\n", + " \n", + " # make a System with the given parameters\n", + " system = make_system(*params, data)\n", + " \n", + " # solve the ODE\n", + " run_odeint(system, slope_func)\n", + " \n", + " # compute the difference between the model\n", + " # results and actual data\n", + " error = system.results.G - data.glucose\n", + " return error.loc[8:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we call `error_func`, we provide a sequence of parameters as a single object." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "params = G0, k1, k2, k3\n", + "params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how that works:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "error_func(params, data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`fit_leastsq` is a wrapper for `scipy.optimize.leastsq`" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource fit_leastsq" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we call it." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "best_params = fit_leastsq(error_func, params, data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have `best_params`, we can use it to make a `System` object and run it.\n", + "\n", + "We have to use the scatter operator, `*`, to make `best_params` behave like four separate parameters, rather than a single object." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system = make_system(*best_params, data)\n", + "run_odeint(system, slope_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the results, along with the data. The first few points of the model don't fit the data, but we don't expect them to." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plot(system.results.G, label='simulation')\n", + "plot(data.glucose, style='bo', label='glucose data')\n", + "\n", + "decorate(xlabel='Time (min)',\n", + " ylabel='Concentration (mg/dL)')\n", + "\n", + "savefig('chap08-fig04.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Since we don't expect the first few points to agree, it's probably better not to make them part of the optimization process. We can ignore them by leaving them out of the `Series` returned by `error_func`. Modify the last line of `error_func` to return `errors.loc[8:]`, which includes only the elements of the `Series` from `t=8` and up.\n", + "\n", + "Does that improve the quality of the fit? Does it change the best parameters by much?\n", + "\n", + "Note: You can read more about this use of `loc` [in the Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** How sensitive are the results to the starting guess for the parameters. If you try different values for the starting guess, do we get the same values for the best parameters?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpreting parameters\n", + "\n", + "Based on the parameters of the model, we can estimate glucose effectiveness and insulin sensitivity." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def indices(G0, k1, k2, k3):\n", + " \"\"\"Compute glucose effectiveness and insulin sensitivity.\n", + " \n", + " G0: initial blood glucose\n", + " k1: rate parameter\n", + " k2: rate parameter\n", + " k3: rate parameter\n", + " data: DataFrame\n", + " \n", + " returns: State object containing S_G and S_I\n", + " \"\"\"\n", + " return State(S_G=k1, S_I=k3/k2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the results." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "indices(*best_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The insulin minimal model\n", + "\n", + "In addition to the glucose minimal mode, Pacini and Bergman present an insulin minimal model, in which the concentration of insulin, $I$, is governed by this differential equation:\n", + "\n", + "$ \\frac{dI}{dt} = -k I(t) + \\gamma (G(t) - G_T) t $" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write a version of `make_system` that takes the parameters of this model, `I0`, `k`, `gamma`, and `G_T` as parameters, along with a `DataFrame` containing the measurements, and returns a `System` object suitable for use with `run_simulation` or `run_odeint`.\n", + "\n", + "Use it to make a `System` object with the following parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "I0 = 360\n", + "k = 0.25\n", + "gamma = 0.004\n", + "G_T = 80" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write a slope function that takes state, t, system as parameters and returns the derivative of `I` with respect to time. Test your function with the initial condition $I(0)=360$." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Run `run_odeint` with your `System` object and slope function, and plot the results, along with the measured insulin levels." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Write an error function that takes a sequence of parameters as an argument, along with the `DataFrame` containing the measurements. It should make a `System` object with the given parameters, run it, and compute the difference between the results of the simulation and the measured values. Test your error function by calling it with the parameters from the previous exercise.\n", + "\n", + "Hint: As we did in a previous exercise, you might want to drop the errors for times prior to `t=8`." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Use `fit_leastsq` to find the parameters that best fit the data. Make a `System` object with those parameters, run it, and plot the results along with the measurements." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Using the best parameters, estimate the sensitivity to glucose of the first and second phase pancreatic responsivity:\n", + "\n", + "$ \\phi_1 = \\frac{I_{max} - I_b}{k (G_0 - G_b)} $\n", + "\n", + "$ \\phi_2 = \\gamma \\times 10^4 $" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution goes here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/code/proj2start.ipynb b/code/proj2start.ipynb new file mode 100644 index 00000000..5db8ccca --- /dev/null +++ b/code/proj2start.ipynb @@ -0,0 +1,3024 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 7: Thermal systems\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": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you want the figures to appear in the notebook, \n", + "# and you want to interact with them, use\n", + "# %matplotlib notebook\n", + "\n", + "# If you want the figures to appear in the notebook, \n", + "# and you don't want to interact with them, use\n", + "# %matplotlib inline\n", + "\n", + "# If you want the figures to appear in separate windows, use\n", + "# %matplotlib qt5\n", + "\n", + "# tempo switch from one to another, you have to select Kernel->Restart\n", + "\n", + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The coffee cooling problem.\n", + "\n", + "I'll use a `State` object to store the initial temperature.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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temp87
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+ "outputs": [ + { + "data": { + "image/png": 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iAG4FFgGDgUeAqUCaMcZ0TnhK+Y/QXkEsnJrEiMRINu3Mbaz5lF1gS3tMHzOY\ncSkDCAjQXoXqPjxa3SQi/YCPgFeBa7BnWvcD7gU+dZ0zoZTyQJKrYOCEEU1Ke+zJ480Nhyk+rZvw\nVPfh6RLYnwMpwGRgOOcPHboDSMcebaqU8lBwUCCzr7ClPaLdSnsUnqrg1TXpbNt3nNq6ei9GqJTl\naZJYCnzfGLMLu2cCsBvqsGdNzOiE2JTye7HRfbl9YSpTx8Q2DjPVO53sOFjIK2sMeSd1E57yLk+T\nRBhQ2MK1SuzyWKVUOwQGBjB1dCzLFgmDo/s2tpeeqeafG4+wIS2HqnO1XoxQ9WSeJok04OstXLsd\n2Nkx4SjVc0X1C2XpvBTmToonJPj8tqP9mcX8Y5XhSG6pnoSnupynq5t+CKwWke3Ae9ghp1tE5HvA\nzcD1nRSfUj1K4ya8uAg+2pVLZt5pACqqavhw6zGS4yKYMylez9dWXcajnoQxZiN2VZMT+C/sxPX3\ngVTgJmPM6s4KUKmeKKx3MEtmJnPdjKH0dduVfTT/NP9YdYi9R4q0V6G6hMf7JIwxG4BpIhIGRAGn\nXRPXSqlOMjy+P0Niwti29zj7MosBOFdTx6ZduXYT3uR4oiN0SlB1njYVuXedRPco8J/At0RkZqdE\npZRqFBoSxNzJCSydm0L/8F6N7ceLz/LqWl0uqzqXRz0JEYkC/gVMB2qxZcMHAMtF5APgFmNMdadF\nqZQibmAYyxYJaQcLSTMnqK93Ul9vl8seySll7uR44mPCvR2m8jOe9iR+g51/uBnoZYyJA0KB27CJ\n4+nOCU8p5S4oMIBpYwdfvFy2vJq3NmWw7rNsqqp1uazqOJ4miSXAfxpj3jbGOAGMMfXGmJXYCey7\nOitApdTF3JfL9nJbLnvw2CleWnUIk3VKJ7ZVh/A0SdQDxS1cywd6tXBNKdVJGpbL3nXNSFLi+ze2\nV1bXsmZ7Nu9szuR0uY4Cq8vjaZL4X+C/RSTWvdG10ulR4HcdHZhSyjN9ewdz7YyhXH9V8gX7J3IK\nz/DyakPaoULq6rVXodrH0yWwA4EEIFNENmF7D9HAbCACqBSR912PdRpjdHOdUl0sOS6C+Jgwtu0r\n4HPXPoraunq27j1OenYp8ybHE+s2j6GUJzxNEmOAfa7/DsNOYgMccP2tP3lKdQMN1WUlMZINaTmc\nLLVlx4tPV/LmhiOMGRbN9LGxhIZ4vEVK9XCenkw3u7MDUUp1nJioPty2IJXdh0/y2f4CaurqcTqd\n7MsoIjN/w2YvAAAZGElEQVTvNLOviCMlvr8em6ouqU2/TohIL+zw0kWMMSc6JCKlVIcICHAwSWJI\nie/Ppp25ZBWUAbYO1KptWRyMPcWcifFEhOm6E9UyTzfTjQP+Akzg/IFDTQW20K6U8qJ+fUP4wqxk\nMvJOs3lXHmeraoCGY1MNU0fHMiF1IIF6bKpqhqc9iT8AQ4DHaXkprFKqm3I4HKTE9ydhUHhjHaiG\nie1P9uZjsk4xb0qCTmyri3iaJCYAy4wx73ZmMEqpztUrOJA5k+KRpEg27sylqGFiu6zKTmwnRzF9\n3GCd2FaNPN0ncRRbhkMp5Qdio/ty24JUZo6PIzjQfgw4nU72uQ44Ss8u0R3bCvA8STwB/FhErhKR\nkM4MSCnVNQJdE9t3XjOSoYP7NbZXVNWw+tMs3tmcSekZ3bHd03nap9yPnZj+CEBE6ppcdxpjdImE\nUj6oX98Qrr/KTmxv2Z1HeaWd2LY7tg8xedQgJksMgYFtOllA+QlPk8RfsQcN/QEo7LxwlFLe0DCx\nnTgonE/3n9+xXVfvZPv+AtKzS5gzMZ6EQVqKvKfxNElMBL5kjHmzM4NRSnlXSLBrx3ZSJBvTcjlR\nUgFA6Zlq3v4oA0mM5KoJcfQJ1TO2ewpPk0Q2thLsZRGRucCGFi5vMMbMF5HtwJVNrr1ojPna5T6/\nUsozMZF9uHX+CPZnFrN133HO1dgRZpNdwrGCMmaMHcyYYdG6Y7sH8DRJ/BfwExEpALYbY5rOSXjq\nE2Bwk7ZF2I16K0TEga0TdTew3u0xFe18PqVUOwUEOBiXMoBhQyLYsiefwzklAFSfq2PjzlwOZZUw\nd1I8A/rrGdv+zNMk8X1sFdgtACLSdMmD0xhzyV04xphzQEHD1yISAfwc+IUxZpWIDAf6AFuNMQUt\n3EYp1YX69g7mmulJjBwayUe78hrPqCgoPstra9MZP2IAU0fHEhKsRRf8kadJ4l+d9Pw/BKqBp1xf\njwUqgaxOej6lVDslxfbjzsVhF56x7XSyO/0kR3JKmTVhCMPjI3QIys94WgX2hx39xCISAzwIfNMY\n0zCcNBYoBV4SkTnYEiB/Bn5tjLnsORGl1OVpOGM7NSmSTTvzyD1xBoDyyho+3HaMxNhwrr4inv7h\nuiLeX7S1CuyV2DmEwdhhIgF2G2OK2vHc3wROAH93axuDPa9iFfBT4CrgF9jKsz9qx3MopTpBZHgo\nN149jMM5pWzZk0/FBUUD7d6KSRJDkO6t8HmeVoENBv4G3AHUYjfW/Rl4DBglIlcbYzLb+Nz3AH82\nxtS4td0LhBljSl1f73XNWzwhIk8aY7ROgFLdhMPhIDUxksTYcLbvL2BvRvGFeyuySpgzSfdW+DpP\n0/yPgeuBW4D+nC8X/v8BZ4GftOVJRWQMkAK84t5ujKl1SxAN9gLhtHCOhVLKu0JDgrh6Yjy3zR9B\nTGSfxvbScru3YtW2Y427uJXv8TRJ3AM8boz5J3aiGQBjTAZ2GGheG593NnDcGHPQvVFEtonIs00e\nOwXIbyZ5KKW6kZgou7dizsR4ermtdDqcU8o/Vh1id7qd7Fa+xdM5iWjAtHCtCOjXwrWWTOT8mdnu\nVgJPiUga8DEwFzuk9VAb76+U8oKGvRXD4yP4eE8+JtvurThXU8eWPfkcPFbCnElDiBsQ5uVIlac8\nTRIHsPMRa5q5dg1wsJn21gwGTjXT/gvsnMcPgETsTu9HjDEvtPH+Sikv6hMazKJpSYxKjmLTzjxK\nzlQBUHy6kpUbjjBqaBQzxg3W8h4+wNMk8VPgdRHpD7wLOIHpIrIM+1v+l9vypMaYL7bQ7gR+6fqj\nlPJx8THhLFuUyp7DRXx2oICaOruS/eCxU2Tmn2b62MGMSY4mQI9O7bY8mpNwFfb7MjADu6rJAfz/\nwFeBh4wxL3dahEopnxYYGMCkkTHcde1Ihg85v/6k+lwdm3bm8sb6wxSe0so73ZXHi5iNMf8HxAPj\nsHMFE4HBxpj/7ZzQlFL+JLxPCNfNTOaGWcOICDu/2e5ESQVvrD/MxrQcqqprvRihak6Lw00ish54\nwBhzqKHNNRy0vysCU0r5p6TB/bgzJoyd5gRpBwupq3c2Hp2akXeamePiGDk0Ust7dBOt9STm0vZV\nS0opdUlBgQFMHR3LXdeMJCn2/MdMZXUt63Zk8+aGI5wsqfRihKqB7plXSnlNRFgvvjArmSUzkwnv\nE9LYXlB8ltfWpbNpZy5V53QIypsutbpJd74opTqVw+Fg2JAIEgaFsePgCXa5Nt05nU72ZhRxJLdU\nh6C86FJJ4jkRKfPgPk5jzDUdEZBSqmcKDgpkxrjBjBwayeZdeWQX2gqzDUNQ+48WM2diPAMj9ZCj\nrnSp4aZgD/+EtHQDpZRqi8jwUG6YPYzrZgwlrPf5zXY6BOUdl+pJfNMYs71LIlFKKReHw8Hw+P4k\nxobrEJSX6cS1UqrbahiCunOxkOhWctx9FdSJEt2I15k0SSilur3WhqBeX6cb8TpTa8NNfwVOdlUg\nSinVmqZDULvTT1ywEe9I7mmmj41ltNaC6lAtJgljzFe7MhCllPJEwxDUqKFRbN6dR1aBXYBZda6W\njTtzG1dBxUb39XKk/kGHm5RSPql/uN2Id/1VyfTre36B5cmSSt5Yf5h1n2U3nr2t2s/TUuFKKdXt\nOBwOkuMiSBgUzi5zgrRDJ6h1K0eekXeaaaNjGZcyQIeg2kl7EkopnxcUGMCVrlpQ7uXIz9XUsXlP\nHq+uMeSeOOPFCH2XJgmllN/o19dVjnz2MPqHny9HXlxWxVubMvhw6zHOVJzzXoA+SIeblFJ+Jym2\nH/GLwuyJeAcLqKm1Q1BHcks5dryMySNjmCgxBAXq78mXov9CSim/1HAi3t3XjiI1MbKxvbaunk/3\nF/CPVYfIzDuN06l1TFujSUIp5dfCegezeFoSS+elMLD/+eKAZWfP8f4nR3l3cyYlZVVejLB70ySh\nlOoR4gaEcduCVOZOiic05PxIe3bhGV5ebfh4Tz7nauq8GGH3pElCKdVjBAQ4GDt8APdcO5Jxwwc0\nFgesdzrZlX6Cv394iEPHTukQlBtNEkqpHie0VxBzJsVz+4JU4gaENbZXVNWw9rNs3lh/mILis16M\nsPvQJKGU6rEGRvbm5rnDWTwt6YLCgYWnKnhj/WHWbs/mbGXP3rWtS2CVUj2aw+EgNTGS5Lh+FxQO\nBDiUdYqMvFKuHBXLhBEDCOyBS2Z73itWSqlmNBQOvOuakQxz27VdU1vPJ3vzeXm14Wh+z1syq0lC\nKaXcRIT1YsnMZG68ejhR/UIb20vLq3nv46O8u6VnLZnVJKGUUs1IGBTOHYuE2VcMoVdIYGN7doFd\nMrtlTx7VPWDJrM5JKKVUCwIDHEwYMZARCf3Zvr+A/Uft8th6p5Pd6ScxWSVMH2vPtvDXKrPak1BK\nqUvoExrM3MkJFy2ZrayuZUNaDq+vSyf/ZLkXI+w8miSUUspDDUtmr5l+4ZLZk6WVrNx4hA+3HqPs\nrH9VmdXhJqWUagOHw8GIhEiGDo5gV/oJdroddHQkt5Sj+aeZKDFMHhlDcFDgJe7W/WlPQiml2iE4\nKICpo2O559qRjEg4X2W2rt7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zHXiLDi6e5wtEZBIwCbtTXfUAmiSUap8HgFtFJMXbgXSx\nX2LLihR4OxDVNXS4SSmlVIu0J6GUUqpFmiSUUkq1SJOEUkqpFmmSUEop1SJNEkoppVr0/wBSRno0\nij8gzgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(coffee.results.temp, label='coffee')\n", + "decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After running the simulation, we can extract the final temperature from the results." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def final_temp(system):\n", + " \"\"\"Final temperature.\n", + " \n", + " If system has no results, return initial temp.\n", + " \n", + " system: System object.\n", + " \n", + " returns: temperature (degC)\n", + " \"\"\" \n", + " if hasattr(system, 'results'):\n", + " return system.results.temp[system.t_end]\n", + " else:\n", + " return system.init.temp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It will be convenient to wrap these steps in a function. `kwargs` is a collection of whatever keyword arguments are provided; they are passed along as arguments to `System`." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n", + " \"\"\"Runs a simulation with the given parameters.\n", + "\n", + " T_init: initial temperature in degC\n", + " r: heat transfer rate, in 1/min\n", + " volume: volume of liquid in mL\n", + " t_end: end time of simulation\n", + " \n", + " returns: System object\n", + " \"\"\"\n", + " init = State(temp=T_init)\n", + " print(init) \n", + " \n", + " system = System(init=init,\n", + " volume=volume,\n", + " r=r,\n", + " T_env=22, \n", + " t0=0,\n", + " t_end=t_end,\n", + " dt=1)\n", + " print(system.init)\n", + " return system" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_init_temp(system, update_func):\n", + " T_array = linspace(80, 100, 5)\n", + " for i in T_array:\n", + " system = make_system(T_init=i)\n", + " print(i)\n", + " run_simulation(system, update_func)\n", + " print(system.results.temp)\n", + " plot(system.results.temp, label='coffee')\n", + " decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 80.0\n", + "dtype: float64\n", + "temp 80.0\n", + "dtype: float64\n", + "80.0\n", + "run sim inittemp 80.0\n", + "dtype: float64\n", + "0 80.000000\n", + "1 79.420000\n", + "2 78.845800\n", + "3 78.277342\n", + "4 77.714569\n", + "5 77.157423\n", + "6 76.605849\n", + "7 76.059790\n", + "8 75.519192\n", + "9 74.984000\n", + "10 74.454160\n", + "11 73.929619\n", + "12 73.410323\n", + "13 72.896219\n", + "14 72.387257\n", + "15 71.883385\n", + "16 71.384551\n", + "17 70.890705\n", + "18 70.401798\n", + "19 69.917780\n", + "20 69.438602\n", + "21 68.964216\n", + "22 68.494574\n", + "23 68.029628\n", + "24 67.569332\n", + "25 67.113639\n", + "26 66.662502\n", + "27 66.215877\n", + "28 65.773719\n", + "29 65.335981\n", + "30 64.902622\n", + "Name: temp, dtype: float64\n", + "temp 85.0\n", + "dtype: float64\n", + "temp 85.0\n", + "dtype: float64\n", + "85.0\n", + "run sim inittemp 85.0\n", + "dtype: float64\n", + "0 85.000000\n", + "1 84.370000\n", + "2 83.746300\n", + "3 83.128837\n", + "4 82.517549\n", + "5 81.912373\n", + "6 81.313249\n", + "7 80.720117\n", + "8 80.132916\n", + "9 79.551587\n", + "10 78.976071\n", + "11 78.406310\n", + "12 77.842247\n", + "13 77.283824\n", + "14 76.730986\n", + "15 76.183676\n", + "16 75.641840\n", + "17 75.105421\n", + "18 74.574367\n", + "19 74.048623\n", + "20 73.528137\n", + "21 73.012856\n", + "22 72.502727\n", + "23 71.997700\n", + "24 71.497723\n", + "25 71.002746\n", + "26 70.512718\n", + "27 70.027591\n", + "28 69.547315\n", + "29 69.071842\n", + "30 68.601124\n", + "Name: temp, dtype: float64\n", + "temp 90.0\n", + "dtype: float64\n", + "temp 90.0\n", + "dtype: float64\n", + "90.0\n", + "run sim inittemp 90.0\n", + "dtype: float64\n", + "0 90.000000\n", + "1 89.320000\n", + "2 88.646800\n", + "3 87.980332\n", + "4 87.320529\n", + "5 86.667323\n", + "6 86.020650\n", + "7 85.380444\n", + "8 84.746639\n", + "9 84.119173\n", + "10 83.497981\n", + "11 82.883001\n", + "12 82.274171\n", + "13 81.671430\n", + "14 81.074715\n", + "15 80.483968\n", + "16 79.899128\n", + "17 79.320137\n", + "18 78.746936\n", + "19 78.179466\n", + "20 77.617672\n", + "21 77.061495\n", + "22 76.510880\n", + "23 75.965771\n", + "24 75.426114\n", + "25 74.891852\n", + "26 74.362934\n", + "27 73.839305\n", + "28 73.320912\n", + "29 72.807702\n", + "30 72.299625\n", + "Name: temp, dtype: float64\n", + "temp 95.0\n", + "dtype: float64\n", + "temp 95.0\n", + "dtype: float64\n", + "95.0\n", + "run sim inittemp 95.0\n", + "dtype: float64\n", + "0 95.000000\n", + "1 94.270000\n", + "2 93.547300\n", + "3 92.831827\n", + "4 92.123509\n", + "5 91.422274\n", + "6 90.728051\n", + "7 90.040770\n", + "8 89.360363\n", + "9 88.686759\n", + "10 88.019891\n", + "11 87.359693\n", + "12 86.706096\n", + "13 86.059035\n", + "14 85.418444\n", + "15 84.784260\n", + "16 84.156417\n", + "17 83.534853\n", + "18 82.919505\n", + "19 82.310310\n", + "20 81.707206\n", + "21 81.110134\n", + "22 80.519033\n", + "23 79.933843\n", + "24 79.354504\n", + "25 78.780959\n", + "26 78.213150\n", + "27 77.651018\n", + "28 77.094508\n", + "29 76.543563\n", + "30 75.998127\n", + "Name: temp, dtype: float64\n", + "temp 100.0\n", + "dtype: float64\n", + "temp 100.0\n", + "dtype: float64\n", + "100.0\n", + "run sim inittemp 100.0\n", + "dtype: float64\n", + "0 100.000000\n", + "1 99.220000\n", + "2 98.447800\n", + "3 97.683322\n", + "4 96.926489\n", + "5 96.177224\n", + "6 95.435452\n", + "7 94.701097\n", + "8 93.974086\n", + "9 93.254345\n", + "10 92.541802\n", + "11 91.836384\n", + "12 91.138020\n", + "13 90.446640\n", + "14 89.762173\n", + "15 89.084552\n", + "16 88.413706\n", + "17 87.749569\n", + "18 87.092073\n", + "19 86.441153\n", + "20 85.796741\n", + "21 85.158774\n", + "22 84.527186\n", + "23 83.901914\n", + "24 83.282895\n", + "25 82.670066\n", + "26 82.063365\n", + "27 81.462732\n", + "28 80.868104\n", + "29 80.279423\n", + "30 79.696629\n", + "Name: temp, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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9+3aqq6s5evQoBw4cwOVyYbPZeOCBB/jOd75Dd3d3qsIuQFpIOjo60seOHj1KaWkpb7zx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\nF3h+fQGAgNPfEpaQN4jdcvF2hOdWSIQQY8CPAz8qpSwYhyNArOPUODB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sweep_init_temp(coffee, update)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we use it:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "72.299625390403094" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coffee = make_system()\n", + "run_simulation(coffee, update)\n", + "final_temp(coffee)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "20.514978275278718" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "milk = make_system(T_init=5, r=0.15, volume=50, t_end=15)\n", + "#picked new r because it seemed like the milk should get warmer faster. new value is arbitrary. \n", + "#but felt like after fifteen min it would barely be colder than room temp.\n", + "run_simulation(milk, update)\n", + "final_temp(milk)\n", + "\n", + "#coffee = System(init=init,\n", + "# volume=300,\n", + "# r=0.01,\n", + "# T_env=22,\n", + "# t0=0, \n", + "# t_end=30,\n", + "# dt=1)\n", + "#coffee" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tHGlsA1yyufSsKZxQlh/nyIwxiSzchPO6iNyoqr/rafCSwKuRDkhETsMlmxpg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\n5zg1Bc65mTWBglybc2OMMYE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(milk.results.temp, label='milk')\n", + "decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using `fsolve`\n", + "\n", + "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def func(x):\n", + " return (x-1) * (x-2) * (x-3)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_init_temp2(system, update_func):\n", + " T_array = linspace(5, 25, 5)\n", + " for i in T_array:\n", + " system = make_system(T_init=i)\n", + " print(i)\n", + " run_simulation(system, update_func)\n", + " print(system.results.temp)\n", + " plot(system.results.temp, label='milk')\n", + " decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)')" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 5.0\n", + "dtype: float64\n", + "temp 5.0\n", + "dtype: float64\n", + "5.0\n", + "run sim inittemp 5.0\n", + "dtype: float64\n", + "0 5.000000\n", + "1 5.170000\n", + "2 5.338300\n", + "3 5.504917\n", + "4 5.669868\n", + "5 5.833169\n", + "6 5.994837\n", + "7 6.154889\n", + "8 6.313340\n", + "9 6.470207\n", + "10 6.625505\n", + "11 6.779250\n", + "12 6.931457\n", + "13 7.082143\n", + "14 7.231321\n", + "15 7.379008\n", + "16 7.525218\n", + "17 7.669966\n", + "18 7.813266\n", + "19 7.955133\n", + "20 8.095582\n", + "21 8.234626\n", + "22 8.372280\n", + "23 8.508557\n", + "24 8.643472\n", + "25 8.777037\n", + "26 8.909267\n", + "27 9.040174\n", + "28 9.169772\n", + "29 9.298074\n", + "30 9.425094\n", + "Name: temp, dtype: float64\n", + "temp 10.0\n", + "dtype: float64\n", + "temp 10.0\n", + "dtype: float64\n", + "10.0\n", + "run sim inittemp 10.0\n", + "dtype: float64\n", + "0 10.000000\n", + "1 10.120000\n", + "2 10.238800\n", + "3 10.356412\n", + "4 10.472848\n", + "5 10.588119\n", + "6 10.702238\n", + "7 10.815216\n", + "8 10.927064\n", + "9 11.037793\n", + "10 11.147415\n", + "11 11.255941\n", + "12 11.363382\n", + "13 11.469748\n", + "14 11.575050\n", + "15 11.679300\n", + "16 11.782507\n", + "17 11.884682\n", + "18 11.985835\n", + "19 12.085977\n", + "20 12.185117\n", + "21 12.283266\n", + "22 12.380433\n", + "23 12.476629\n", + "24 12.571862\n", + "25 12.666144\n", + "26 12.759482\n", + "27 12.851887\n", + "28 12.943369\n", + "29 13.033935\n", + "30 13.123596\n", + "Name: temp, dtype: float64\n", + "temp 15.0\n", + "dtype: float64\n", + "temp 15.0\n", + "dtype: float64\n", + "15.0\n", + "run sim inittemp 15.0\n", + "dtype: float64\n", + "0 15.000000\n", + "1 15.070000\n", + "2 15.139300\n", + "3 15.207907\n", + "4 15.275828\n", + "5 15.343070\n", + "6 15.409639\n", + "7 15.475543\n", + "8 15.540787\n", + "9 15.605379\n", + "10 15.669325\n", + "11 15.732632\n", + "12 15.795306\n", + "13 15.857353\n", + "14 15.918779\n", + "15 15.979592\n", + "16 16.039796\n", + "17 16.099398\n", + "18 16.158404\n", + "19 16.216820\n", + "20 16.274651\n", + "21 16.331905\n", + "22 16.388586\n", + "23 16.444700\n", + "24 16.500253\n", + "25 16.555250\n", + "26 16.609698\n", + "27 16.663601\n", + "28 16.716965\n", + "29 16.769795\n", + "30 16.822097\n", + "Name: temp, dtype: float64\n", + "temp 20.0\n", + "dtype: float64\n", + "temp 20.0\n", + "dtype: float64\n", + "20.0\n", + "run sim inittemp 20.0\n", + "dtype: float64\n", + "0 20.000000\n", + "1 20.020000\n", + "2 20.039800\n", + "3 20.059402\n", + "4 20.078808\n", + "5 20.098020\n", + "6 20.117040\n", + "7 20.135869\n", + "8 20.154511\n", + "9 20.172966\n", + "10 20.191236\n", + "11 20.209323\n", + "12 20.227230\n", + "13 20.244958\n", + "14 20.262508\n", + "15 20.279883\n", + "16 20.297084\n", + "17 20.314114\n", + "18 20.330972\n", + "19 20.347663\n", + "20 20.364186\n", + "21 20.380544\n", + "22 20.396739\n", + "23 20.412771\n", + "24 20.428644\n", + "25 20.444357\n", + "26 20.459914\n", + "27 20.475315\n", + "28 20.490561\n", + "29 20.505656\n", + "30 20.520599\n", + "Name: temp, dtype: float64\n", + "temp 25.0\n", + "dtype: float64\n", + "temp 25.0\n", + "dtype: float64\n", + "25.0\n", + "run sim inittemp 25.0\n", + "dtype: float64\n", + "0 25.000000\n", + "1 24.970000\n", + "2 24.940300\n", + "3 24.910897\n", + "4 24.881788\n", + "5 24.852970\n", + "6 24.824440\n", + "7 24.796196\n", + "8 24.768234\n", + "9 24.740552\n", + "10 24.713146\n", + "11 24.686015\n", + "12 24.659155\n", + "13 24.632563\n", + "14 24.606237\n", + "15 24.580175\n", + "16 24.554373\n", + "17 24.528830\n", + "18 24.503541\n", + "19 24.478506\n", + "20 24.453721\n", + "21 24.429184\n", + "22 24.404892\n", + "23 24.380843\n", + "24 24.357034\n", + "25 24.333464\n", + "26 24.310129\n", + "27 24.287028\n", + "28 24.264158\n", + "29 24.241516\n", + "30 24.219101\n", + "Name: temp, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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RNcaYgtuFHMfhkPYz6H7ySXCc9N1+LJ6RDHS0jbV69Wre9773sX79er74xS9W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\naZJQSimV1/8HDidboetfv7kAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sweep_init_temp2(milk, update)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`modsim.py` provides `fsolve`, which does some error-checking and then runs `scipy.optimize.fsolve`. The first argument is the function whose roots we want. The second argument is an initial guess." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fsolve(func, x0=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Usually the root we get is the one that's closest to the initial guess." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fsolve(func, 1.9)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fsolve(func, 2.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But not always." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fsolve(func, 1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def error_func1(r):\n", + " \"\"\"Runs a simulation and returns the `error`.\n", + " \n", + " r: heat transfer rate, in 1/min\n", + " \n", + " returns: difference between final temp and 70 C\n", + " \"\"\"\n", + " system = make_system(r=r)\n", + " print(r)\n", + " run_simulation(system, update)\n", + " return final_temp(system) - 70" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `r=0.01`, we end up a little too warm." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "0.01\n", + "run sim inittemp 90\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "2.2996253904030937" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "error_func1(r=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The return value from `fsolve` is an array with a single element, the estimated value of `r`." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "0.01\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01]\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01]\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01]\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01]\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01150871]\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01154231]\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01154308]\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01154308]\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "[ 0.01154308]\n", + "run sim inittemp 90\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "0.011543084583978345" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution = fsolve(error_func1, 0.01, xtol=1e-8)\n", + "r_coffee = solution[0]\n", + "r_coffee" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "70.0" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coffee = make_system(r=r_coffee)\n", + "run_simulation(coffee, update)\n", + "final_temp(coffee)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** When you call `fsolve`, it calls `error_func1` several times. To see how this works, add a print statement to `error_func1` and run `fsolve` again." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes. \n", + "\n", + "Before you use `fsolve`, you might want to try a few values for `r_milk` and see how close you can get by trial and error. Here's an initial guess to get you started:" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "19.501444483698084" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r_milk = 0.12\n", + "#picked this value because it seemed pretty close from my graph above.\n", + "milk = make_system(T_init=5, t_end=15, r=r_milk)\n", + "run_simulation(milk, update)\n", + "final_temp(milk)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def error_func2(r):\n", + " system = make_system(r=r, T_init=5, t_end=15)\n", + " print(r)\n", + " run_simulation(system, update)\n", + " return final_temp(system) - 20" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "0.12\n", + "run sim inittemp 5\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "-0.49855551630191641" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "error_func2(r=0.12)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "0.12\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.12]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.12]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.12]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.12]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.1317062]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.13283515]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.13295952]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.13296079]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.13296079]\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "[ 0.13296079]\n", + "run sim inittemp 5\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "0.13296078935466449" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution = fsolve(error_func2, 0.12, xtol=1e-8)\n", + "r_milk = solution[0]\n", + "r_milk" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "19.999999999999996" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "milk = make_system(r=r_milk, T_init=5, t_end=15)\n", + "run_simulation(milk, update)\n", + "final_temp(milk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mixing liquids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def mix(s1, s2):\n", + " \"\"\"Simulates the mixture of two liquids.\n", + " \n", + " s1: System representing coffee\n", + " s2: System representing milk\n", + " \n", + " returns: System representing the mixture\n", + " \"\"\"\n", + " assert s1.t_end == s2.t_end\n", + " \n", + " volume = s1.volume + s2.volume\n", + " \n", + " temp = (s1.volume * final_temp(s1) + \n", + " s2.volume * final_temp(s2)) / volume\n", + " \n", + " mixture = make_system(T_init=temp,\n", + " volume=volume,\n", + " r=s1.r)\n", + " \n", + " return mixture" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we'll see what happens if we add the milk at the end. We'll simulate the coffee and the milk separately." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "70.0" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coffee = make_system(T_init=90, t_end=30, r=r_coffee, volume=300)\n", + "run_simulation(coffee, update)\n", + "final_temp(coffee)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "21.764705882352942" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "milk = make_system(T_init=5, t_end=30, r=r_milk, volume=50)\n", + "run_simulation(milk, update)\n", + "final_temp(milk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the results look like." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap07-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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OGz4JWCUijwCnG2PcdLD/N7J7Qk0FngK+CKzFjrdYDlydccyJ2IF8SimlRpjb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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(coffee.results.temp, label='coffee')\n", + "plot(milk.results.temp, '--', label='milk')\n", + "decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)',\n", + " loc='center left')\n", + "\n", + "savefig('chap07-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what happens when we mix them." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 63.109244\n", + "dtype: float64\n", + "temp 63.109244\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "63.109243697478988" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mix_last = mix(coffee, milk)\n", + "final_temp(mix_last)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's what we get if we add the milk immediately." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "coffee = make_system(T_init=90, r=r_coffee, volume=300)\n", + "milk = make_system(T_init=5, r=r_milk, volume=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 77.857143\n", + "dtype: float64\n", + "temp 77.857143\n", + "dtype: float64\n", + "run sim inittemp 77.857143\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "61.428571428571438" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mix_first = mix(coffee, milk)\n", + "mix_first.t_end = 30\n", + "run_simulation(mix_first, update)\n", + "final_temp(mix_first)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_and_mix(t_add, t_total=30):\n", + " \"\"\"Simulates two liquids and them mixes them at t_add.\n", + " \n", + " t_add: time in minutes\n", + " t_total: total time to simulate, min\n", + " \n", + " returns: final temperature\n", + " \"\"\"\n", + " coffee = make_system(T_init=90, t_end=t_add, \n", + " r=r_coffee, volume=300)\n", + " run_simulation(coffee, update)\n", + "\n", + " milk = make_system(T_init=5, t_end=t_add, \n", + " r=r_milk, volume=50)\n", + " run_simulation(milk, update)\n", + " \n", + " mixture = mix(coffee, milk)\n", + " mixture.t_end = t_total - t_add\n", + " run_simulation(mixture, update)\n", + "\n", + " return final_temp(mixture)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def sweep_init_temp2(system, update_func):\n", + " T_array = linspace(5, 25, 5)\n", + " for i in T_array:\n", + " system = make_system(T_init=i)\n", + " print(i)\n", + " run_simulation(system, update_func)\n", + " print(system.results.temp)\n", + " plot(system.results.temp, label='milk')\n", + " decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)')" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 5.0\n", + "dtype: float64\n", + "temp 5.0\n", + "dtype: float64\n", + "5.0\n", + "run sim inittemp 5.0\n", + "dtype: float64\n", + "0 5.000000\n", + "1 5.170000\n", + "2 5.338300\n", + "3 5.504917\n", + "4 5.669868\n", + "5 5.833169\n", + "6 5.994837\n", + "7 6.154889\n", + "8 6.313340\n", + "9 6.470207\n", + "10 6.625505\n", + "11 6.779250\n", + "12 6.931457\n", + "13 7.082143\n", + "14 7.231321\n", + "15 7.379008\n", + "16 7.525218\n", + "17 7.669966\n", + "18 7.813266\n", + "19 7.955133\n", + "20 8.095582\n", + "21 8.234626\n", + "22 8.372280\n", + "23 8.508557\n", + "24 8.643472\n", + "25 8.777037\n", + "26 8.909267\n", + "27 9.040174\n", + "28 9.169772\n", + "29 9.298074\n", + "30 9.425094\n", + "Name: temp, dtype: float64\n", + "temp 10.0\n", + "dtype: float64\n", + "temp 10.0\n", + "dtype: float64\n", + "10.0\n", + "run sim inittemp 10.0\n", + "dtype: float64\n", + "0 10.000000\n", + "1 10.120000\n", + "2 10.238800\n", + "3 10.356412\n", + "4 10.472848\n", + "5 10.588119\n", + "6 10.702238\n", + "7 10.815216\n", + "8 10.927064\n", + "9 11.037793\n", + "10 11.147415\n", + "11 11.255941\n", + "12 11.363382\n", + "13 11.469748\n", + "14 11.575050\n", + "15 11.679300\n", + "16 11.782507\n", + "17 11.884682\n", + "18 11.985835\n", + "19 12.085977\n", + "20 12.185117\n", + "21 12.283266\n", + "22 12.380433\n", + "23 12.476629\n", + "24 12.571862\n", + "25 12.666144\n", + "26 12.759482\n", + "27 12.851887\n", + "28 12.943369\n", + "29 13.033935\n", + "30 13.123596\n", + "Name: temp, dtype: float64\n", + "temp 15.0\n", + "dtype: float64\n", + "temp 15.0\n", + "dtype: float64\n", + "15.0\n", + "run sim inittemp 15.0\n", + "dtype: float64\n", + "0 15.000000\n", + "1 15.070000\n", + "2 15.139300\n", + "3 15.207907\n", + "4 15.275828\n", + "5 15.343070\n", + "6 15.409639\n", + "7 15.475543\n", + "8 15.540787\n", + "9 15.605379\n", + "10 15.669325\n", + "11 15.732632\n", + "12 15.795306\n", + "13 15.857353\n", + "14 15.918779\n", + "15 15.979592\n", + "16 16.039796\n", + "17 16.099398\n", + "18 16.158404\n", + "19 16.216820\n", + "20 16.274651\n", + "21 16.331905\n", + "22 16.388586\n", + "23 16.444700\n", + "24 16.500253\n", + "25 16.555250\n", + "26 16.609698\n", + "27 16.663601\n", + "28 16.716965\n", + "29 16.769795\n", + "30 16.822097\n", + "Name: temp, dtype: float64\n", + "temp 20.0\n", + "dtype: float64\n", + "temp 20.0\n", + "dtype: float64\n", + "20.0\n", + "run sim inittemp 20.0\n", + "dtype: float64\n", + "0 20.000000\n", + "1 20.020000\n", + "2 20.039800\n", + "3 20.059402\n", + "4 20.078808\n", + "5 20.098020\n", + "6 20.117040\n", + "7 20.135869\n", + "8 20.154511\n", + "9 20.172966\n", + "10 20.191236\n", + "11 20.209323\n", + "12 20.227230\n", + "13 20.244958\n", + "14 20.262508\n", + "15 20.279883\n", + "16 20.297084\n", + "17 20.314114\n", + "18 20.330972\n", + "19 20.347663\n", + "20 20.364186\n", + "21 20.380544\n", + "22 20.396739\n", + "23 20.412771\n", + "24 20.428644\n", + "25 20.444357\n", + "26 20.459914\n", + "27 20.475315\n", + "28 20.490561\n", + "29 20.505656\n", + "30 20.520599\n", + "Name: temp, dtype: float64\n", + "temp 25.0\n", + "dtype: float64\n", + "temp 25.0\n", + "dtype: float64\n", + "25.0\n", + "run sim inittemp 25.0\n", + "dtype: float64\n", + "0 25.000000\n", + "1 24.970000\n", + "2 24.940300\n", + "3 24.910897\n", + "4 24.881788\n", + "5 24.852970\n", + "6 24.824440\n", + "7 24.796196\n", + "8 24.768234\n", + "9 24.740552\n", + "10 24.713146\n", + "11 24.686015\n", + "12 24.659155\n", + "13 24.632563\n", + "14 24.606237\n", + "15 24.580175\n", + "16 24.554373\n", + "17 24.528830\n", + "18 24.503541\n", + "19 24.478506\n", + "20 24.453721\n", + "21 24.429184\n", + "22 24.404892\n", + "23 24.380843\n", + "24 24.357034\n", + "25 24.333464\n", + "26 24.310129\n", + "27 24.287028\n", + "28 24.264158\n", + "29 24.241516\n", + "30 24.219101\n", + "Name: temp, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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RNcaYgtuFHMfhkPYz6H7ySXCc9N1+LJ6RDHS0jbV69Wre9773sX79er74xS9W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\nzSaBKV6tYKIPF21EmiSUqlO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sweep_init_temp2(milk, update)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_and_mix_and_sweep(t_add, t_total=30):\n", + " \"\"\"Simulates two liquids and them mixes them at t_add.\n", + " \n", + " t_add: time in minutes\n", + " t_total: total time to simulate, min\n", + " \n", + " returns: final temperature\n", + " \"\"\"\n", + " coffee = make_system(T_init=90, t_end=t_add, \n", + " r=r_coffee, volume=300)\n", + " run_simulation(coffee, update)\n", + "\n", + " sweep_init_temp2(milk, update)\n", + " \n", + " \n", + " mixture = mix(coffee, milk)\n", + " mixture.t_end = t_total - t_add\n", + " run_simulation(mixture, update)\n", + "\n", + " return final_temp(mixture)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "run_and_mix_and_sweep() missing 1 required positional argument: 't_add'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrun_and_mix_and_sweep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfinal_temp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmixture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: run_and_mix_and_sweep() missing 1 required positional argument: 't_add'" + ] + } + ], + "source": [ + "run_and_mix_and_sweep()\n", + "plot(final_temp(mixture))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can try it out with a few values." + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "61.428571428571438" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "62.90280912845234" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "63.109243697478988" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And then sweep a range of values for `t_add`" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sweep = SweepSeries()\n", + "for t_add in linrange(0, 30, 2):\n", + " temp = run_and_mix(t_add)\n", + " sweep[t_add] = temp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the result looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap07-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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MU9UKP3G2R2V7y1jw2AICgQDgJv8cfqqVrjbGND+/iec24D0R+Ro3I3UA+KGI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\nLBLAvia9kSZa/8l6qiqqAOg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(sweep, color='purple')\n", + "decorate(xlabel='Time added (min)',\n", + " ylabel='Final temperature (C)',\n", + " legend=False)\n", + "\n", + "savefig('chap07-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office. In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n", + "\n", + "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model. The change you make to test this variation of the problem should be very small!" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Now you can only add the milk at two points, the beggining and the end. \n", + "#Assume that both refrigerators are equally cold, so the milk will be 5 deg C whenever you add it. \n", + "\n", + "def run_and_mix(t_add, t_total=30):\n", + " \"\"\"Simulates two liquids and them mixes them at t_add.\n", + " \n", + " t_add: time in minutes\n", + " t_total: total time to simulate, min\n", + " \n", + " returns: final temperature\n", + " \"\"\"\n", + " coffee = make_system(T_init=90, t_end=t_add, \n", + " r=r_coffee, volume=300)\n", + " run_simulation(coffee, update)\n", + "\n", + " milk = make_system(T_init=5, t_end=t_add, \n", + " r=r_milk, volume=50)\n", + " #remove the run_simulation becaause the milk is not warming up\n", + " mixture = mix(coffee, milk)\n", + " mixture.t_end = t_total - t_add\n", + " run_simulation(mixture, update)\n", + "\n", + " return final_temp(mixture)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "61.428571428571438" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "60.714285714285715" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run_and_mix(30)" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#so better to add milk at beggining" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the analytic result to compute temperature as a function of time. The following function is similar to `run_simulation`." + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def run_analysis(system):\n", + " \"\"\"Computes temperature using the analytic solution.\n", + " \n", + " Adds TimeFrame to `system` as `results`\n", + " \n", + " system: System object\n", + " \"\"\"\n", + " unpack(system)\n", + " \n", + " T_init = init.temp \n", + " ts = linrange(t0, t_end, dt)\n", + " \n", + " temp_array = T_env + (T_init - T_env) * exp(-r * ts)\n", + " temp_series = TimeSeries(temp_array, index=ts)\n", + " \n", + " system.results = TimeFrame(temp_series, columns=['temp'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we run it. From the analysis, we have the computed value of `r_coffee2`" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "r_coffee2 = 0.011610223142273859" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "70.0" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "init = State(temp=90)\n", + "coffee2 = System(init=init, T_env=22, r=r_coffee2, \n", + " t0=0, t_end=30)\n", + "run_analysis(coffee2)\n", + "final_temp(coffee2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can compare to the results from simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "70.0" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "init = State(temp=90)\n", + "coffee = System(init=init, T_env=22, r=r_coffee, \n", + " t0=0, t_end=30, dt=1)\n", + "run_simulation(coffee, update)\n", + "final_temp(coffee)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "They are identical except for small roundoff errors." + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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(pandas\\_libs\\parsers.c:8873)\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: File b'glucose_insulin.csv' does not exist" - ] + "data": { + "text/html": [ + "
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glucoseinsulin
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" + ], + "text/plain": [ + " glucose insulin\n", + "time \n", + "0 92 11\n", + "2 350 26\n", + "4 287 130\n", + "6 251 85\n", + "8 240 51\n", + "10 216 49\n", + "12 211 45\n", + "14 205 41\n", + "16 196 35\n", + "19 192 30\n", + "22 172 30\n", + "27 163 27\n", + "32 142 30\n", + "42 124 22\n", + "52 105 15\n", + "62 92 15\n", + "72 84 11\n", + "82 77 10\n", + "92 82 8\n", + "102 81 11\n", + "122 82 7\n", + "142 82 8\n", + "162 85 8\n", + "182 90 7" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -86,11 +256,20 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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GplU33p07dzJgwAASExP585//zPjx4/nxxx/ZsGEDJiYmvPjiiw06+ZEjR1i3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\nevbsKXcg4oFkJLoQeubcuXNMmDCBb77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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot(data.glucose, 'bo', label='glucose')\n", "decorate(xlabel='Time (min)',\n", @@ -106,11 +285,20 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Ufp+RnyH9nsWFiBozhQrLuXPnsG/fPgDPZt2bmZmhY8eOaN++PR48eIBff/0VoaGhSgmq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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot(data.insulin, 'go', label='insulin')\n", "decorate(xlabel='Time (min)',\n", @@ -126,11 +314,27 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap08-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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ycli2bBn9+vUztbds2dL056lTp2IwGEhNTaWwsJA5c+bg7OzMzJkzrboPIiIi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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "subplot(2, 1, 1)\n", "plot(data.glucose, 'bo', label='glucose')\n", @@ -155,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 40, "metadata": { "collapsed": true }, @@ -173,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 41, "metadata": { "collapsed": true }, @@ -191,12 +395,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 42, "metadata": { - "collapsed": true, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array(68.0)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "I(7)" ] @@ -210,11 +424,27 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap08-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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ma6lYhBDiDiqWxvLee+/x5ptv8vDDD2NqaoqVlRWffvopzZo1IyMjg5YtW2pd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\nLEIIIdxKCosQQgi3ksIihBDCrf4FLBNQahkEzlsAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ts = linrange(0, 182, 2)\n", "\n", @@ -234,6 +464,52 @@ "**Exercise:** [Read the documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) of `scipy.interpolate.interp1d`. Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. " ] }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "G = interpolate(data.glucose)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap08-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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QtVB0MjPVz0OHJLEIIWo3kxLLjz/+SGpqKlFRUURHRxMVFUVUVBR79+7l9u3b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\nlborrDQLTD722GMkJCSUO6iqJi0W89F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"cell_type": "code", - "execution_count": 14, + "execution_count": 50, "metadata": { "collapsed": true }, @@ -360,11 +636,57 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"source": [ "system.results" ] @@ -449,11 +1183,27 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap08-fig03.pdf\n" + ] + }, + { + "data": { + "image/png": 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MYdeuXXz++edcuHCB+fPn3/O/2Z1YdJNEVlYWsbGx9O/f35BWp04dmjVrRnp6Oh4eHmRk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}, - "outputs": [], + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"\n", + "%time run_simulation(system_flynn, update_func)\n", + "\n", + "slope_func(init, 0, system_flynn)\n", + "\n", + "system2 = System(init=init, \n", + " k1=k1, k2=k2, k3=k3,\n", + " I=I, Gb=Gb, Ib=Ib,\n", + " ts=data.index)\n", + "\n", + "%psource run_odeint\n", + "\n", + "%time run_odeint(system2, slope_func)\n", + "\n", + "diff = system_flynn.results - system2.results\n", + "percent_diff = diff / system2.results * 100\n", + "percent_diff.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#We can see that with a smaller dt, the error decreases.\n", + "#This is because the original equation assumes dt to be infinitesimal, in which case the error should be zero.\n", + "#As dt gets smaller, it approaches zero, and so does the error." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -662,7 +2015,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 68, "metadata": { "collapsed": true }, @@ -683,7 +2036,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 69, "metadata": { "collapsed": true }, @@ -702,7 +2055,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 70, "metadata": { "collapsed": true }, @@ -735,7 +2088,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 71, "metadata": { "collapsed": true }, @@ -770,7 +2123,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 72, "metadata": { "collapsed": true }, @@ -807,11 +2160,20 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(290, 0.03, 0.02, 1e-05)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "params = G0, k1, k2, k3\n", "params" @@ -826,11 +2188,48 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(290, 0.03, 0.02, 1e-05)\n" + ] + }, + { + "data": { + "text/plain": [ + "time\n", + "8 4.385049\n", + "10 17.385689\n", + "12 11.875391\n", + "14 7.883104\n", + "16 7.432604\n", + "19 -1.688894\n", + "22 6.430723\n", + "27 -1.858707\n", + "32 4.627308\n", + "42 0.272257\n", + "52 4.125928\n", + "62 7.310554\n", + "72 9.102455\n", + "82 12.434359\n", + "92 5.498085\n", + "102 5.712650\n", + "122 4.844866\n", + "142 6.356758\n", + "162 5.136930\n", + "182 1.795663\n", + "dtype: float64" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "error_func(params, data)" ] @@ -844,7 +2243,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 75, "metadata": { "collapsed": true }, @@ -862,11 +2261,80 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2.90000000e+02 3.00000000e-02 2.00000000e-02 1.00000000e-05]\n", + "[ 2.90000000e+02 3.00000000e-02 2.00000000e-02 1.00000000e-05]\n", + "[ 2.90000000e+02 3.00000000e-02 2.00000000e-02 1.00000000e-05]\n", + "[ 2.90000004e+02 3.00000000e-02 2.00000000e-02 1.00000000e-05]\n", + "[ 2.90000000e+02 3.00000004e-02 2.00000000e-02 1.00000000e-05]\n", + "[ 2.90000000e+02 3.00000000e-02 2.00000003e-02 1.00000000e-05]\n", + "[ 2.90000000e+02 3.00000000e-02 2.00000000e-02 1.00000001e-05]\n", + "[ 2.78224727e+02 2.85801825e-02 1.61589429e-02 1.19289840e-05]\n", + "[ 2.78224731e+02 2.85801825e-02 1.61589429e-02 1.19289840e-05]\n", + "[ 2.78224727e+02 2.85801829e-02 1.61589429e-02 1.19289840e-05]\n", + "[ 2.78224727e+02 2.85801825e-02 1.61589432e-02 1.19289840e-05]\n", + "[ 2.78224727e+02 2.85801825e-02 1.61589429e-02 1.19289842e-05]\n", + "[ 2.71951893e+02 2.68943464e-02 1.28042303e-02 1.03928810e-05]\n", + "[ 2.71951897e+02 2.68943464e-02 1.28042303e-02 1.03928810e-05]\n", + "[ 2.71951893e+02 2.68943468e-02 1.28042303e-02 1.03928810e-05]\n", + "[ 2.71951893e+02 2.68943464e-02 1.28042305e-02 1.03928810e-05]\n", + "[ 2.71951893e+02 2.68943464e-02 1.28042303e-02 1.03928811e-05]\n", + "[ 2.71443394e+02 2.69030677e-02 1.26482637e-02 1.04193915e-05]\n", + "[ 2.71443398e+02 2.69030677e-02 1.26482637e-02 1.04193915e-05]\n", + "[ 2.71443394e+02 2.69030681e-02 1.26482637e-02 1.04193915e-05]\n", + "[ 2.71443394e+02 2.69030677e-02 1.26482639e-02 1.04193915e-05]\n", + "[ 2.71443394e+02 2.69030677e-02 1.26482637e-02 1.04193917e-05]\n", + "[ 2.71712741e+02 2.69019185e-02 1.24302337e-02 1.05307739e-05]\n", + "[ 2.71712745e+02 2.69019185e-02 1.24302337e-02 1.05307739e-05]\n", + "[ 2.71712741e+02 2.69019189e-02 1.24302337e-02 1.05307739e-05]\n", 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2.68027413e-02 1.21534666e-02 1.07066721e-05]\n", + "[ 2.71857182e+02 2.68027413e-02 1.21534664e-02 1.07066723e-05]\n", + "[ 2.71898905e+02 2.68013322e-02 1.21544446e-02 1.07084438e-05]\n", + "[ 2.71898909e+02 2.68013322e-02 1.21544446e-02 1.07084438e-05]\n", + "[ 2.71898905e+02 2.68013326e-02 1.21544446e-02 1.07084438e-05]\n", + "[ 2.71898905e+02 2.68013322e-02 1.21544448e-02 1.07084438e-05]\n", + "[ 2.71898905e+02 2.68013322e-02 1.21544446e-02 1.07084439e-05]\n", + "[ 2.71898433e+02 2.67985448e-02 1.21544443e-02 1.07084478e-05]\n", + "[ 2.71898437e+02 2.67985448e-02 1.21544443e-02 1.07084478e-05]\n", + "[ 2.71898433e+02 2.67985452e-02 1.21544443e-02 1.07084478e-05]\n", + "[ 2.71898433e+02 2.67985448e-02 1.21544444e-02 1.07084478e-05]\n", + "[ 2.71898433e+02 2.67985448e-02 1.21544443e-02 1.07084480e-05]\n", + "[ 2.71916930e+02 2.67987318e-02 1.21542838e-02 1.07088289e-05]\n", + "[ 2.71907097e+02 2.67986221e-02 1.21543757e-02 1.07086105e-05]\n", + "[ 2.71902239e+02 2.67985768e-02 1.21544154e-02 1.07085162e-05]\n", + "[ 2.71900248e+02 2.67985597e-02 1.21544307e-02 1.07084799e-05]\n", + "[ 2.71899292e+02 2.67985518e-02 1.21544379e-02 1.07084629e-05]\n", + "[ 2.71898837e+02 2.67985481e-02 1.21544413e-02 1.07084549e-05]\n", + "[ 2.71898624e+02 2.67985464e-02 1.21544429e-02 1.07084511e-05]\n", + "[ 2.71898523e+02 2.67985456e-02 1.21544436e-02 1.07084494e-05]\n", + "[ 2.71898476e+02 2.67985452e-02 1.21544440e-02 1.07084486e-05]\n", + "[ 2.71898453e+02 2.67985450e-02 1.21544441e-02 1.07084482e-05]\n", + "modsim.py: scipy.optimize.leastsq ran successfully\n", + " and returned the following message:\n", + "The relative error between two consecutive iterates is at most 0.000000\n" + ] + } + ], "source": [ "best_params = fit_leastsq(error_func, params, data)" ] @@ -882,7 +2350,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 77, "metadata": { "collapsed": true }, @@ -901,13 +2369,29 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "plot(system.results.G, label='simulation')\n", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap08-fig04.pdf\n" + ] + }, + { + "data": { + "image/png": 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ef/55kxLI3Zo2bRoHDhzA3t7e8ISRkpJCy5YtjfZzdnY2fHpITU3F2dm5QjlAcnKyJBAh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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(system.results.G, label='simulation')\n", "plot(data.glucose, style='bo', label='glucose data')\n", "\n", "decorate(xlabel='Time (min)',\n", @@ -927,6 +2411,146 @@ "Note: You can read more about this use of `loc` [in the Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer)." ] }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def error_func(params, data):\n", + " \"\"\"Computes an array of errors to be minimized.\n", + " \n", + " params: sequence of parameters\n", + " data: DataFrame of values to be matched\n", + " \n", + " returns: array of errors\n", + " \"\"\"\n", + " print(params)\n", + " \n", + " # make a System with the given parameters\n", + " system = make_system(*params, data)\n", + " \n", + " # solve the ODE\n", + " run_odeint(system, slope_func)\n", + " \n", + " # compute the difference between the model\n", + " # results and actual data\n", + " error = system.results.G - data.glucose\n", + " return error.loc[8:]" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(290, 0.026798544833976808, 0.012154444266554253, 1.0708447822446748e-05)\n", + "[ 2.90000000e+02 2.67985448e-02 1.21544443e-02 1.07084478e-05]\n", + "[ 2.90000000e+02 2.67985448e-02 1.21544443e-02 1.07084478e-05]\n", + "[ 2.90000000e+02 2.67985448e-02 1.21544443e-02 1.07084478e-05]\n", + "[ 2.90000004e+02 2.67985448e-02 1.21544443e-02 1.07084478e-05]\n", + "[ 2.90000000e+02 2.67985452e-02 1.21544443e-02 1.07084478e-05]\n", + "[ 2.90000000e+02 2.67985448e-02 1.21544444e-02 1.07084478e-05]\n", + "[ 2.90000000e+02 2.67985448e-02 1.21544443e-02 1.07084480e-05]\n", + "[ 2.71475095e+02 2.60559800e-02 1.17121613e-02 1.18616762e-05]\n", + "[ 2.71475099e+02 2.60559800e-02 1.17121613e-02 1.18616762e-05]\n", + "[ 2.71475095e+02 2.60559804e-02 1.17121613e-02 1.18616762e-05]\n", + "[ 2.71475095e+02 2.60559800e-02 1.17121614e-02 1.18616762e-05]\n", + "[ 2.71475095e+02 2.60559800e-02 1.17121613e-02 1.18616764e-05]\n", + "[ 2.71737577e+02 2.59844937e-02 1.17123461e-02 1.18850353e-05]\n", + "[ 2.71737581e+02 2.59844937e-02 1.17123461e-02 1.18850353e-05]\n", + "[ 2.71737577e+02 2.59844941e-02 1.17123461e-02 1.18850353e-05]\n", + "[ 2.71737577e+02 2.59844937e-02 1.17123463e-02 1.18850353e-05]\n", + "[ 2.71737577e+02 2.59844937e-02 1.17123461e-02 1.18850355e-05]\n", + "[ 2.71785411e+02 2.59507650e-02 1.17151047e-02 1.18752386e-05]\n", + "[ 2.71785415e+02 2.59507650e-02 1.17151047e-02 1.18752386e-05]\n", + "[ 2.71785411e+02 2.59507653e-02 1.17151047e-02 1.18752386e-05]\n", + "[ 2.71785411e+02 2.59507650e-02 1.17151048e-02 1.18752386e-05]\n", + "[ 2.71785411e+02 2.59507650e-02 1.17151047e-02 1.18752388e-05]\n", + "[ 2.72113260e+02 2.60186670e-02 1.17222048e-02 1.18654262e-05]\n", + "[ 2.72113264e+02 2.60186670e-02 1.17222048e-02 1.18654262e-05]\n", + "[ 2.72113260e+02 2.60186674e-02 1.17222048e-02 1.18654262e-05]\n", + "[ 2.72113260e+02 2.60186670e-02 1.17222050e-02 1.18654262e-05]\n", + "[ 2.72113260e+02 2.60186670e-02 1.17222048e-02 1.18654263e-05]\n", + "[ 2.72144771e+02 2.60041951e-02 1.17244971e-02 1.18491964e-05]\n", + "[ 2.72144775e+02 2.60041951e-02 1.17244971e-02 1.18491964e-05]\n", + "[ 2.72144771e+02 2.60041955e-02 1.17244971e-02 1.18491964e-05]\n", + "[ 2.72144771e+02 2.60041951e-02 1.17244973e-02 1.18491964e-05]\n", + "[ 2.72144771e+02 2.60041951e-02 1.17244971e-02 1.18491965e-05]\n", + "[ 2.72074649e+02 2.59815095e-02 1.17311059e-02 1.18197967e-05]\n", + "[ 2.72074653e+02 2.59815095e-02 1.17311059e-02 1.18197967e-05]\n", + "[ 2.72074649e+02 2.59815099e-02 1.17311059e-02 1.18197967e-05]\n", + "[ 2.72074649e+02 2.59815095e-02 1.17311061e-02 1.18197967e-05]\n", + "[ 2.72074649e+02 2.59815095e-02 1.17311059e-02 1.18197969e-05]\n", + "[ 2.71464044e+02 2.59759460e-02 1.17259563e-02 1.18251342e-05]\n", + "[ 2.71464044e+02 2.59759460e-02 1.17259563e-02 1.18251342e-05]\n", + "[ 2.71767913e+02 2.59810708e-02 1.17337377e-02 1.18235256e-05]\n", + "[ 2.71927053e+02 2.59836312e-02 1.17323934e-02 1.18226706e-05]\n", + "[ 2.72014683e+02 2.59849671e-02 1.17316175e-02 1.18221520e-05]\n", + "[ 2.72068214e+02 2.59845093e-02 1.17311455e-02 1.18212304e-05]\n", + "[ 2.72072652e+02 2.59830116e-02 1.17311148e-02 1.18204970e-05]\n", + "[ 2.72073768e+02 2.59822606e-02 1.17311093e-02 1.18201452e-05]\n", + "[ 2.72074191e+02 2.59819180e-02 1.17311076e-02 1.18199859e-05]\n", + "[ 2.72074445e+02 2.59816966e-02 1.17311066e-02 1.18198832e-05]\n", + "[ 2.72074546e+02 2.59816048e-02 1.17311063e-02 1.18198408e-05]\n", + "[ 2.72074598e+02 2.59815575e-02 1.17311061e-02 1.18198189e-05]\n", + "[ 2.72074623e+02 2.59815335e-02 1.17311060e-02 1.18198078e-05]\n", + "[ 2.72074637e+02 2.59815213e-02 1.17311060e-02 1.18198021e-05]\n", + "[ 2.72074641e+02 2.59815213e-02 1.17311060e-02 1.18198021e-05]\n", + "[ 2.72074637e+02 2.59815217e-02 1.17311060e-02 1.18198021e-05]\n", + "[ 2.72074637e+02 2.59815213e-02 1.17311061e-02 1.18198021e-05]\n", + "[ 2.72074637e+02 2.59815213e-02 1.17311060e-02 1.18198023e-05]\n", + "[ 2.72074632e+02 2.59815199e-02 1.17311045e-02 1.18198016e-05]\n", + "[ 2.72074636e+02 2.59815199e-02 1.17311045e-02 1.18198016e-05]\n", + "[ 2.72074632e+02 2.59815203e-02 1.17311045e-02 1.18198016e-05]\n", + "[ 2.72074632e+02 2.59815199e-02 1.17311047e-02 1.18198016e-05]\n", + "[ 2.72074632e+02 2.59815199e-02 1.17311045e-02 1.18198018e-05]\n", + "[ 2.72074620e+02 2.59815194e-02 1.17311049e-02 1.18198013e-05]\n", + "modsim.py: scipy.optimize.leastsq ran successfully\n", + " and returned the following message:\n", + "The relative error between two consecutive iterates is at most 0.000000\n", + "Saving figure to file chap08-fig04.pdf\n" + ] + }, + { + "data": { + "image/png": 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uYGVlhZubW5XHHzt2DDc3N9zd3Q3bgoKCKCgoICYmBh8fnxqJE8DfuzVn47I4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\nrQYp6pdKpcLfy4nR/TxpaWdl2H4l5Rq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "params = G0, k1, k2, k3\n", + "params\n", + "\n", + "error_func(params, data)\n", + "\n", + "%psource fit_leastsq\n", + "\n", + "best_params = fit_leastsq(error_func, params, data)\n", + "\n", + "system = make_system(*best_params, data)\n", + "run_odeint(system, slope_func)\n", + "\n", + "plot(system.results.G, label='simulation')\n", + "plot(data.glucose, style='bo', label='glucose data')\n", + "\n", + "decorate(xlabel='Time (min)',\n", + " ylabel='Concentration (mg/dL)')\n", + "\n", + "savefig('chap08-fig04.pdf')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -945,7 +2569,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 83, "metadata": { "collapsed": true }, @@ -974,11 +2598,57 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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S_G0.025982
S_I0.001008
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" + ], + "text/plain": [ + " I\n", + "time \n", + "0 360.000000\n", + "2 219.652719\n", + "4 137.739127\n", + "6 89.505898\n", + "8 61.636133\n", + "10 45.765330\n", + "12 37.053121\n", + "14 32.989732\n", + "16 31.406589\n", + "19 31.811071\n", + "22 32.581632\n", + "27 34.014287\n", + "32 33.758216\n", + "42 30.932902\n", + "52 24.554216\n", + "62 15.767804\n", + "72 7.782053\n", + "82 -0.382598\n", + "92 0.598298\n", + "102 1.943431\n", + "122 3.415756\n", + "142 4.413573\n", + "162 11.170251\n", + "182 25.702699" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Solution goes here" + "run_odeint(system, slope_func)\n", + "system.results" ] }, { @@ -1093,13 +2981,26 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SycRy9y4UFpouFiGEqMX0KrHMmzdPM6JerVazaNEinSWWy5cv07x5c+NGWFuY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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Solution goes here" + "plot(system.results, 'r-', label='simulation')\n", + "plot(data.insulin, 'bo', label='insulin data')\n", + "\n", + "decorate(xlabel='Time (min)',\n", + " ylabel='Concentration ($\\mu$U/mL)')" ] }, { @@ -1113,24 +3014,53 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 127, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# Solution goes here" + "def error_func(params, data):\n", + " \"\"\"Computes an array of errors to be minimized.\n", + " \n", + " params: sequence of parameters\n", + " data: DataFrame of values to be matched\n", + " \n", + " returns: array of errors\n", + " \"\"\"\n", + " print(params)\n", + " \n", + " # make a System with the given parameters\n", + " system = make_system(*params, data)\n", + " \n", + " # solve the ODE\n", + " run_odeint(system, slope_func)\n", + " \n", + " # compute the difference between the model\n", + " # results and actual data\n", + " error = system.results.I - data.insulin\n", + " return error.loc[8:]" ] }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[360, 0.25, 0.004, 80]" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Solution goes here" + "params = [I0, k, gamma, G_T]\n", + "params" ] }, { @@ -1153,35 +3083,96 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3.60000000e+02 2.50000000e-01 4.00000000e-03 8.00000000e+01]\n", + "[ 3.60000000e+02 2.50000000e-01 4.00000000e-03 8.00000000e+01]\n", + "[ 3.60000000e+02 2.50000000e-01 4.00000000e-03 8.00000000e+01]\n", + "[ 3.60000005e+02 2.50000000e-01 4.00000000e-03 8.00000000e+01]\n", + "[ 3.60000000e+02 2.50000004e-01 4.00000000e-03 8.00000000e+01]\n", + "[ 3.60000000e+02 2.50000000e-01 4.00000006e-03 8.00000000e+01]\n", + "[ 3.60000000e+02 2.50000000e-01 4.00000000e-03 8.00000012e+01]\n", + "[ 3.42251420e+02 2.48488980e-01 4.00000000e-03 8.05531158e+01]\n", + "[ 3.42251426e+02 2.48488980e-01 4.00000000e-03 8.05531158e+01]\n", + "[ 3.42251420e+02 2.48488984e-01 4.00000000e-03 8.05531158e+01]\n", + "[ 3.42251420e+02 2.48488980e-01 4.00000006e-03 8.05531158e+01]\n", + "[ 3.42251420e+02 2.48488980e-01 4.00000000e-03 8.05531170e+01]\n", + "[ 3.34646649e+02 2.48068307e-01 4.00000000e-03 8.05020415e+01]\n", + "[ 3.38562301e+02 2.48195694e-01 4.00000000e-03 8.05134952e+01]\n", + "[ 3.40466123e+02 2.48324294e-01 4.00000000e-03 8.05306577e+01]\n", + "[ 3.41333899e+02 2.48398864e-01 4.00000000e-03 8.05408251e+01]\n", + "[ 3.41793354e+02 2.48442526e-01 4.00000000e-03 8.05467843e+01]\n", + "[ 3.42048733e+02 2.48468063e-01 4.00000000e-03 8.05502665e+01]\n", + "[ 3.42152590e+02 2.48478709e-01 4.00000000e-03 8.05517170e+01]\n", + "[ 3.42203396e+02 2.48483972e-01 4.00000000e-03 8.05524339e+01]\n", + "[ 3.42228124e+02 2.48486547e-01 4.00000000e-03 8.05527845e+01]\n", + "[ 3.42240131e+02 2.48487800e-01 4.00000000e-03 8.05529551e+01]\n", + "[ 3.42245950e+02 2.48488408e-01 4.00000000e-03 8.05530379e+01]\n", + "[ 3.42248774e+02 2.48488704e-01 4.00000000e-03 8.05530781e+01]\n", + "[ 3.42250139e+02 2.48488846e-01 4.00000000e-03 8.05530975e+01]\n", + "[ 3.42250801e+02 2.48488916e-01 4.00000000e-03 8.05531070e+01]\n", + "[ 3.42251123e+02 2.48488949e-01 4.00000000e-03 8.05531116e+01]\n", + "[ 3.42251279e+02 2.48488966e-01 4.00000000e-03 8.05531138e+01]\n", + "[ 3.42251353e+02 2.48488973e-01 4.00000000e-03 8.05531148e+01]\n", + "[ 3.42251389e+02 2.48488977e-01 4.00000000e-03 8.05531154e+01]\n", + "modsim.py: scipy.optimize.leastsq ran successfully\n", + " and returned the following message:\n", + "The relative error between two consecutive iterates is at most 0.000000\n" + ] + } + ], "source": [ - "# Solution goes here" + "best_params = fit_leastsq(error_func, params, data)" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 130, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# Solution goes here" + "system = make_system(*best_params, data)\n", + "run_odeint(system, slope_func)" ] }, { "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap08-fig04.pdf\n" + ] + }, + { + "data": { + "image/png": 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SAI8//jj/+c9/6N27Nx07dqRXr14MGjQIX19fp5/7WmfOnKFVq1a2lQrBugpiSSNGjGDb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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Solution goes here" + "plot(system.results.I, label='simulation')\n", + "plot(data.insulin, style='bo', label='insulin data')\n", + "\n", + "decorate(xlabel='Time (min)',\n", + " ylabel='Concentration (mg/dL)')\n", + "\n", + "savefig('chap08-fig04.pdf')" ] }, { @@ -1208,56 +3199,51 @@ }, { "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Solution goes here" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Solution goes here" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(130, 11, 290, 92)" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Solution goes here" + "I_max = data.insulin.max()\n", + "Ib = data.insulin[0]\n", + "Gb = data.glucose[0]\n", + "\n", + "I_max, Ib, G0, Gb" ] }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.4186589683769704, 40.0)" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Solution goes here" + "phi_1 = (I_max - Ib) / k / (G0 - Gb)\n", + "phi_2 = gamma * 1e4\n", + "\n", + "phi_1, phi_2\n", + "# Still kinda confused abt the significance of phi" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/code/proj2start.ipynb b/code/proj2start.ipynb index 5db8ccca..94771071 100644 --- a/code/proj2start.ipynb +++ b/code/proj2start.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -91,7 +91,7 @@ "dtype: int64" ] }, - "execution_count": 19, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -145,7 +145,7 @@ "dtype: int64" ] }, - "execution_count": 20, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ "dtype: object" ] }, - "execution_count": 21, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -292,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -333,7 +333,7 @@ "dtype: float64" ] }, - "execution_count": 23, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -351,8 +351,10 @@ }, { "cell_type": "code", - "execution_count": 56, - "metadata": {}, + "execution_count": 7, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "def run_simulation(system, update_func):\n", @@ -385,14 +387,14 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "run sim inittemp 90\n", + "run sim inittemp 87\n", "dtype: int64\n" ] }, @@ -423,127 +425,127 @@ " \n", " \n", " 0\n", - " 90.000000\n", + " 87.000000\n", " \n", " \n", " 1\n", - " 89.320000\n", + " 86.350000\n", " \n", " \n", " 2\n", - " 88.646800\n", + " 85.706500\n", " \n", " \n", " 3\n", - " 87.980332\n", + " 85.069435\n", " \n", " \n", " 4\n", - " 87.320529\n", + " 84.438741\n", " \n", " \n", " 5\n", - " 86.667323\n", + " 83.814353\n", " \n", " \n", " 6\n", - " 86.020650\n", + " 83.196210\n", " \n", " \n", " 7\n", - " 85.380444\n", + " 82.584248\n", " \n", " \n", " 8\n", - " 84.746639\n", + " 81.978405\n", " \n", " \n", " 9\n", - " 84.119173\n", + " 81.378621\n", " \n", " \n", " 10\n", - " 83.497981\n", + " 80.784835\n", " \n", " \n", " 11\n", - " 82.883001\n", + " 80.196987\n", " \n", " \n", " 12\n", - " 82.274171\n", + " 79.615017\n", " \n", " \n", " 13\n", - " 81.671430\n", + " 79.038866\n", " \n", " \n", " 14\n", - " 81.074715\n", + " 78.468478\n", " \n", " \n", " 15\n", - " 80.483968\n", + " 77.903793\n", " \n", " \n", " 16\n", - " 79.899128\n", + " 77.344755\n", " \n", " \n", " 17\n", - " 79.320137\n", + " 76.791308\n", " \n", " \n", " 18\n", - " 78.746936\n", + " 76.243394\n", " \n", " \n", " 19\n", - " 78.179466\n", + " 75.700961\n", " \n", " \n", " 20\n", - " 77.617672\n", + " 75.163951\n", " \n", " \n", " 21\n", - " 77.061495\n", + " 74.632311\n", " \n", " \n", " 22\n", - " 76.510880\n", + " 74.105988\n", " \n", " \n", " 23\n", - " 75.965771\n", + " 73.584928\n", " \n", " \n", " 24\n", - " 75.426114\n", + " 73.069079\n", " \n", " \n", " 25\n", - " 74.891852\n", + " 72.558388\n", " \n", " \n", " 26\n", - " 74.362934\n", + " 72.052804\n", " \n", " \n", " 27\n", - " 73.839305\n", + " 71.552276\n", " \n", " \n", " 28\n", - " 73.320912\n", + " 71.056754\n", " \n", " \n", " 29\n", - " 72.807702\n", + " 70.566186\n", " \n", " \n", " 30\n", - " 72.299625\n", + " 70.080524\n", " \n", " \n", "\n", @@ -551,40 +553,40 @@ ], "text/plain": [ " temp\n", - "0 90.000000\n", - "1 89.320000\n", - "2 88.646800\n", - "3 87.980332\n", - "4 87.320529\n", - "5 86.667323\n", - "6 86.020650\n", - "7 85.380444\n", - "8 84.746639\n", - "9 84.119173\n", - "10 83.497981\n", - "11 82.883001\n", - "12 82.274171\n", - "13 81.671430\n", - "14 81.074715\n", - "15 80.483968\n", - "16 79.899128\n", - "17 79.320137\n", - "18 78.746936\n", - "19 78.179466\n", - "20 77.617672\n", - "21 77.061495\n", - "22 76.510880\n", - "23 75.965771\n", - "24 75.426114\n", - "25 74.891852\n", - "26 74.362934\n", - "27 73.839305\n", - "28 73.320912\n", - "29 72.807702\n", - "30 72.299625" + "0 87.000000\n", + "1 86.350000\n", + "2 85.706500\n", + "3 85.069435\n", + "4 84.438741\n", + "5 83.814353\n", + "6 83.196210\n", + "7 82.584248\n", + "8 81.978405\n", + "9 81.378621\n", + "10 80.784835\n", + "11 80.196987\n", + "12 79.615017\n", + "13 79.038866\n", + "14 78.468478\n", + "15 77.903793\n", + "16 77.344755\n", + "17 76.791308\n", + "18 76.243394\n", + "19 75.700961\n", + "20 75.163951\n", + "21 74.632311\n", + "22 74.105988\n", + "23 73.584928\n", + "24 73.069079\n", + "25 72.558388\n", + "26 72.052804\n", + "27 71.552276\n", + "28 71.056754\n", + "29 70.566186\n", + "30 70.080524" ] }, - "execution_count": 48, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -603,14 +605,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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iAG4FFgGDgUeAqUCaMcZ0TnhK+Y/QXkEsnJrEiMRINu3Mbaz5lF1gS3tMHzOY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\nKICpo2O559qRjEg4X2W2rt7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zHXiLDi6e5wtEZBIwCbtTXfUAmiSUap8HgFtFJMXbgXSx\nX2LLihR4OxDVNXS4SSmlVIu0J6GUUqpFmiSUUkq1SJOEUkqpFmmSUEop1SJNEkoppVr0/wBSRno0\nij8gzgAAAABJRU5ErkJggg==\n", 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tt5jwYA8szQw1HaYQQgu0mCRiY2PZvHkz9vb21z1JUFAQQUFB5OTk8P7777dqgOLm+bpb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\ngg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -632,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -662,8 +664,10 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": {}, + "execution_count": 11, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n", @@ -692,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -713,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -919,9 +923,9 @@ }, { "data": { - "image/png": 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xfP7FtIgIQCIWxfP5FxMWkoaGBgAWLlyYPmaz2di6dSvvvPMOjz322Kj5S5cu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kv0hPcYkJY6ZqylfQS2TqZP/5oK8Xr7OZgKdjhM5XDJ+7DZ+7bbCC3oxaX4JU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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -941,16 +945,28 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n" + ] + }, { "data": { "text/plain": [ "72.299625390403094" ] }, - "execution_count": 31, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -970,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -991,7 +1007,7 @@ "20.514978275278718" ] }, - "execution_count": 62, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1015,14 +1031,14 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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tHGlsA1yyufSsKZxQlh/nyIwxiSzchPO6iNyoqr/rafCSwKuRDkhETsMlmxpg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\n5zg1Bc65mTWBglybc2OMMYE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Fu7s7oqOj2+zn5+eH5ORk2Nrags1u/eHaunUrYmJicOTIEcyYMUMf4RKiF1W1Qly6wcft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\np02bxixZskTlvsrKypjg4GDm4sWL8rb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1046,7 +1062,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -1058,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -1078,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1284,9 +1300,9 @@ }, { "data": { - "image/png": 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RNcaYgtuFHMfhkPYz6H7ySXCc9N1+LJ6RDHS0jbV69Wre9773sX79er74xS9W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\naZJQSimV1/8HDidboetfv7kAAAAASUVORK5CYII=\n", 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ENQqI6xSNb8vCo33iMahHNABgWNw9KPjqP3jl4eHo1KYN7mrfHj2Ki/DL7787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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1306,7 +1322,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1315,7 +1331,7 @@ "array([ 1.])" ] }, - "execution_count": 67, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1333,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1342,7 +1358,7 @@ "array([ 2.])" ] }, - "execution_count": 68, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1353,7 +1369,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1362,7 +1378,7 @@ "array([ 3.])" ] }, - "execution_count": 69, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1380,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1389,7 +1405,7 @@ "array([ 3.])" ] }, - "execution_count": 70, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1407,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -1435,7 +1451,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1457,7 +1473,7 @@ "2.2996253904030937" ] }, - "execution_count": 73, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1475,7 +1491,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1560,7 +1576,7 @@ "0.011543084583978345" ] }, - "execution_count": 74, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1580,7 +1596,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1601,7 +1617,7 @@ "70.0" ] }, - "execution_count": 75, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1630,7 +1646,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1651,7 +1667,7 @@ "19.501444483698084" ] }, - "execution_count": 76, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1666,7 +1682,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -1681,7 +1697,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1703,7 +1719,7 @@ "-0.49855551630191641" ] }, - "execution_count": 78, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1714,7 +1730,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1806,7 +1822,7 @@ "0.13296078935466449" ] }, - "execution_count": 79, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1819,7 +1835,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1840,7 +1856,7 @@ "19.999999999999996" ] }, - "execution_count": 80, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1867,7 +1883,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 33, "metadata": { "collapsed": true }, @@ -1904,7 +1920,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1925,7 +1941,7 @@ "70.0" ] }, - "execution_count": 82, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1938,7 +1954,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 35, "metadata": { "scrolled": true }, @@ -1961,7 +1977,7 @@ "21.764705882352942" ] }, - "execution_count": 83, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1981,7 +1997,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1993,9 +2009,9 @@ }, { "data": { - "image/png": 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OGz4JWCUijwCnG2PcdLD/N7J7Qk0FngK+CKzFjrdYDlydccyJ2IF8SimlRpjb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\n", 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5Yl3EsVevXsjOzq7vyyCENCFO1SQ+/fRT6HQ6TJo0yeE+Fy9erLOgGlL6nbM4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\nxXtlVVhYyMycOZPp0aMHExcXx0ycOJH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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2021,7 +2037,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2040,7 +2056,7 @@ "63.109243697478988" ] }, - "execution_count": 85, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2059,7 +2075,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2084,7 +2100,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2105,7 +2121,7 @@ "61.428571428571438" ] }, - "execution_count": 87, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2126,13 +2142,11 @@ }, { "cell_type": "code", - "execution_count": 93, - "metadata": { - "collapsed": true - }, + "execution_count": 68, + "metadata": {}, "outputs": [], "source": [ - "def run_and_mix(t_add, t_total=30):\n", + "def run_and_mix(t_add, t_total=30, T_milk0=5):\n", " \"\"\"Simulates two liquids and them mixes them at t_add.\n", " \n", " t_add: time in minutes\n", @@ -2144,20 +2158,20 @@ " r=r_coffee, volume=300)\n", " run_simulation(coffee, update)\n", "\n", - " milk = make_system(T_init=5, t_end=t_add, \n", + " milk = make_system(T_init=T_milk0, t_end=t_add, \n", " r=r_milk, volume=50)\n", " run_simulation(milk, update)\n", " \n", " mixture = mix(coffee, milk)\n", " mixture.t_end = t_total - t_add\n", " run_simulation(mixture, update)\n", - "\n", - " return final_temp(mixture)" + " \n", + " return mixture.results" ] }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 69, "metadata": { "collapsed": true }, @@ -2166,226 +2180,119 @@ "def sweep_init_temp2(system, update_func):\n", " T_array = linspace(5, 25, 5)\n", " for i in T_array:\n", - " system = make_system(T_init=i)\n", - " print(i)\n", - " run_simulation(system, update_func)\n", - " print(system.results.temp)\n", - " plot(system.results.temp, label='milk')\n", + " res = run_and_mix(t_add=0, t_total=30, T_milk0=i)\n", + " \n", + " plot(res.temp, label='milk' + str(i))\n", " decorate(xlabel='Time (minutes)',\n", " ylabel='Temperature (C)')" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", "temp 5.0\n", "dtype: float64\n", "temp 5.0\n", "dtype: float64\n", - "5.0\n", "run sim inittemp 5.0\n", "dtype: float64\n", - "0 5.000000\n", - "1 5.170000\n", - "2 5.338300\n", - "3 5.504917\n", - "4 5.669868\n", - "5 5.833169\n", - "6 5.994837\n", - "7 6.154889\n", - "8 6.313340\n", - "9 6.470207\n", - "10 6.625505\n", - "11 6.779250\n", - "12 6.931457\n", - "13 7.082143\n", - "14 7.231321\n", - "15 7.379008\n", - "16 7.525218\n", - "17 7.669966\n", - "18 7.813266\n", - "19 7.955133\n", - "20 8.095582\n", - "21 8.234626\n", - "22 8.372280\n", - "23 8.508557\n", - "24 8.643472\n", - "25 8.777037\n", - "26 8.909267\n", - "27 9.040174\n", - "28 9.169772\n", - "29 9.298074\n", - "30 9.425094\n", - "Name: temp, dtype: float64\n", + "temp 77.857143\n", + "dtype: float64\n", + "temp 77.857143\n", + "dtype: float64\n", + "run sim inittemp 77.857143\n", + "dtype: float64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", "temp 10.0\n", "dtype: float64\n", "temp 10.0\n", "dtype: float64\n", - "10.0\n", "run sim inittemp 10.0\n", "dtype: float64\n", - "0 10.000000\n", - "1 10.120000\n", - "2 10.238800\n", - "3 10.356412\n", - "4 10.472848\n", - "5 10.588119\n", - "6 10.702238\n", - "7 10.815216\n", - "8 10.927064\n", - "9 11.037793\n", - "10 11.147415\n", - "11 11.255941\n", - "12 11.363382\n", - "13 11.469748\n", - "14 11.575050\n", - "15 11.679300\n", - "16 11.782507\n", - "17 11.884682\n", - "18 11.985835\n", - "19 12.085977\n", - "20 12.185117\n", - "21 12.283266\n", - "22 12.380433\n", - "23 12.476629\n", - "24 12.571862\n", - "25 12.666144\n", - "26 12.759482\n", - "27 12.851887\n", - "28 12.943369\n", - "29 13.033935\n", - "30 13.123596\n", - "Name: temp, dtype: float64\n", + "temp 78.571429\n", + "dtype: float64\n", + "temp 78.571429\n", + "dtype: float64\n", + "run sim inittemp 78.571429\n", + "dtype: float64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", "temp 15.0\n", "dtype: float64\n", "temp 15.0\n", "dtype: float64\n", - "15.0\n", "run sim inittemp 15.0\n", "dtype: float64\n", - "0 15.000000\n", - "1 15.070000\n", - "2 15.139300\n", - "3 15.207907\n", - "4 15.275828\n", - "5 15.343070\n", - "6 15.409639\n", - "7 15.475543\n", - "8 15.540787\n", - "9 15.605379\n", - "10 15.669325\n", - "11 15.732632\n", - "12 15.795306\n", - "13 15.857353\n", - "14 15.918779\n", - "15 15.979592\n", - "16 16.039796\n", - "17 16.099398\n", - "18 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RNcaYgtuFHMfhkPYz6H7ySXCc9N1+LJ6RDHS0jbV69Wre9773sX79er74xS9W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\naZJQSimV1/8HDidboetfv7kAAAAASUVORK5CYII=\n", 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6UgBaO2nRTl1XI8dqT1uK8GBMezuRRL8YS3YUgMlgoCM3j86CsxL3lp2nJ96L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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2398,7 +2305,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -2428,7 +2335,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -2438,7 +2345,7 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrun_and_mix_and_sweep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfinal_temp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmixture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrun_and_mix_and_sweep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfinal_temp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmixture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: run_and_mix_and_sweep() missing 1 required positional argument: 't_add'" ] } @@ -2457,16 +2364,40 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 45, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 77.857143\n", + "dtype: float64\n", + "temp 77.857143\n", + "dtype: float64\n", + "run sim inittemp 77.857143\n", + "dtype: float64\n" + ] + }, { "data": { "text/plain": [ "61.428571428571438" ] }, - "execution_count": 169, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -2477,16 +2408,40 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 46, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 70.684081\n", + "dtype: float64\n", + "temp 70.684081\n", + "dtype: float64\n", + "run sim inittemp 70.684081\n", + "dtype: float64\n" + ] + }, { "data": { "text/plain": [ "62.90280912845234" ] }, - "execution_count": 170, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -2497,16 +2452,40 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 47, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 63.109244\n", + "dtype: float64\n", + "temp 63.109244\n", + "dtype: float64\n", + "run sim inittemp 63.109244\n", + "dtype: float64\n" + ] + }, { "data": { "text/plain": [ "63.109243697478988" ] }, - "execution_count": 171, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -2524,11 +2503,304 @@ }, { "cell_type": "code", - "execution_count": 175, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 77.857143\n", + "dtype: float64\n", + "temp 77.857143\n", + "dtype: float64\n", + "run sim inittemp 77.857143\n", + "dtype: float64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 77.122191\n", + "dtype: float64\n", + "temp 77.122191\n", + "dtype: float64\n", + "run sim inittemp 77.122191\n", + "dtype: float64\n", + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "run sim inittemp 5\n", + "dtype: int64\n", + "temp 76.268287\n", + "dtype: float64\n", 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"sweep = SweepSeries()\n", "for t_add in linrange(0, 30, 2):\n", @@ -2545,7 +2817,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -2557,9 +2829,9 @@ }, { "data": { - "image/png": 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MU9UKP3G2R2V7y1jw2AICgQDgJv8cfqqVrjbGND+/iec24D0R+Ro3I3UA+KGI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\nJq4s8RhjjIkrSzzGGGPiyhKPMcaYuPr/YTfgUW0MWbwAAAAASUVORK5CYII=\n", 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ZM9i6dSu7du3i7bffZsGCBQQFBbFv3z6ioqLQ6XQ1JsCRI0cyaNAgo7Kvv/6alStXEh4e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\nyNNCiEbNpE+osrIyXnrpJb7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WyfmYKnNfJuVF5QDYu9nj0dOjQY8vhBCNhUapaeA1qt6b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\ngg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2586,7 +2858,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 50, "metadata": { "collapsed": true }, @@ -2619,16 +2891,38 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 51, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 77.857143\n", + "dtype: float64\n", + "temp 77.857143\n", + "dtype: float64\n", + "run sim inittemp 77.857143\n", + "dtype: float64\n" + ] + }, { "data": { "text/plain": [ "61.428571428571438" ] }, - "execution_count": 193, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -2639,16 +2933,38 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 52, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp 90\n", + "dtype: int64\n", + "temp 90\n", + "dtype: int64\n", + "run sim inittemp 90\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 5\n", + "dtype: int64\n", + "temp 60.714286\n", + "dtype: float64\n", + "temp 60.714286\n", + "dtype: float64\n", + "run sim inittemp 60.714286\n", + "dtype: float64\n" + ] + }, { "data": { "text/plain": [ "60.714285714285715" ] }, - "execution_count": 194, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -2659,7 +2975,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 53, "metadata": { "collapsed": true }, @@ -2684,7 +3000,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 54, "metadata": { "collapsed": true }, @@ -2717,7 +3033,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 55, "metadata": { "collapsed": true }, @@ -2728,7 +3044,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -2737,7 +3053,7 @@ "70.0" ] }, - "execution_count": 198, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -2759,16 +3075,24 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 57, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "run sim inittemp 90\n", + "dtype: int64\n" + ] + }, { "data": { "text/plain": [ "70.0" ] }, - "execution_count": 199, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -2790,7 +3114,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -2981,7 +3305,7 @@ "30 0.000000e+00" ] }, - "execution_count": 200, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } From 0fc0e25f8c3b95e371ce232bf10dedef5b6a42a5 Mon Sep 17 00:00:00 2001 From: Flynn Michael-Legg Date: Thu, 26 Oct 2017 15:29:09 -0400 Subject: [PATCH 15/19] for checkin proj 2 --- code/proj2start.ipynb | 144 +++++++++++++++++++++--------------------- 1 file changed, 73 insertions(+), 71 deletions(-) diff --git a/code/proj2start.ipynb b/code/proj2start.ipynb index 94771071..68d4d9e8 100644 --- a/code/proj2start.ipynb +++ b/code/proj2start.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -91,7 +91,7 @@ "dtype: int64" ] }, - "execution_count": 2, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -145,7 +145,7 @@ "dtype: int64" ] }, - "execution_count": 3, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ "dtype: object" ] }, - "execution_count": 4, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -292,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -333,7 +333,7 @@ "dtype: float64" ] }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -351,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -387,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -586,7 +586,7 @@ "30 70.080524" ] }, - "execution_count": 8, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -605,14 +605,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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ubGwsSUJ0Kp7OFjjZduOPU9mcST0PQGV1LduPpJGYfoGRA9wwNZanCtH5qTUF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\n3R0d1caNG1mwYAFjx47l+eefBzrHvbr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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -634,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -664,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -696,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -717,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -925,7 +925,7 @@ "data": { "image/png": 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kv0hPcYkJY6ZqylfQS2TqZP/5oK8Xr7OZgKdjhM5XDJ+7DZ+7bbCC3oxaX4JU\nfu7tYIELw/npZ9g/OUIsEv7iyZcZsUSK6YYFGOadv5/6EI2NjRw6dIj9+/ezaNEiALZu3ZrsEbJs\n2bJkG9ySkhKcTifbtm1j7dq1ycZTWq02+cNcKpWiUCjIzs7G5/Oxf/9+du3axS233AIkuiaeOXOG\nl19+mUWLFvHqq6+yePFiVq5cmRzfsmUL9913H+vWrZucnu0rVqzgnnvu4amnnmLjxo0YjcaUcYfD\nwXPPPcexY8f4l3/5l8uymKEL7e7uTopARqNRuru7yczMpLu7m9zc3FGv6e7uvqj3c312gr4PPgQg\nQy5HM20aupkzkGdnXcJVfHnR6hVcM7eAGbPysHV5aG+209M9rPMVCUdpa7LT1mRHrZFTVGKkyGJE\nqZJN8sonFpFInBKg97vb8DqbCQUdyTmJCvozuPrOIFdlJSrohUZcl4zz088mxYgAxCJhnJ9+Nm5D\nUldXB8CcOXOSx4xGIxs2bOD111/nwQcfTJm/cOFCIpEIjY2NzJ17/vewWq2EQiHWrFmT7NEOEA6H\nycpKPK9qa2tpaWlh/vz5yfGh767Vap0cQ1JRUcE//dM/sWnTJv77v/+b2bNnU1BQQDQapbOzk88/\n/xyxWMwPfvCDy1aYOHv2bMrKyvjBD37Ajh070Ol07Ny5E4fDQTgcJhgMIpOlPrRkMtlF7wHGY8O1\nFNGBAVwnT+I6eRJ5ZibaGZVopk1DolJe0jV9GUkE6BM6X8FAOFlBPzJe4vMOcPZkN3WnbGTlaigu\nMZFboEuJuUxFMiQytKbyZIDe62xJBOijw/dmwN/HgL8Pe/enqLVFqA0W5KqsKe/BTQSGeXMn1SMZ\nrxEBkEjO/aNBoRitRh2NRr/wdUMMPfd27do1Sml9yLBIpVLuvvvupNczkklttfuNb3yDa6+9lv37\n93P48GFOnDhBRkYGBQUF3H///TzwwAMUFxdftsXIZDJ2797NU089xS233IJUKuWuu+7i1ltvRSqV\nIpfLCYdTP1ChUAil8uIe9oZ5c4nHYrhP1xLxDve6GOjvZ+BPH9D/wYeoSizoZsxAZU7PrS+FUkrF\njBzKK7Nx2v20NTvobHMm2wXH43F6uz30dnuQyjIoKDZQXGJCb5z64pEyhQFTngFjziwC3u7E1pe3\nm6EK+ngsgtfVjNfVjESqTjbiEsQjx49h3twLephPJuXl5QCcPHmShQsXAonge1VVFU6nk2PHjvHA\nAw8k5x+oUcZtAAAgAElEQVQ9ehSpVDqurCyLxYJUKsVms3Hrrbcmj+/evZtoNMqaNWuoqKjAarWm\nGJrjx4/zyiuvsGXLFlQq1Vinvix8oSksKSnh+9///oQt4C8pLy/n17/+NQ6HA6lUikaj4W/+5m/4\n6le/Sn5+/qgMsZ6enlHbXeNFJBZjWnA9xuuvI9DRgefMWbzWRuLR4Yekr6kZX1MzGUol2unT0M6Y\ngTzz8iQXfJkQiUQYM9UYM9VcO7eArg4X7c12+nt9KbUpLdZ+Wqz9aHUKikoSPVameg96kTgDla4Q\nla6QaDiA19WKz9lMOORJzomEfTh7T+HsPZUQj9SXoNQVCFtfU4jS0lKWLl3Kli1b2Lx5M0ajkZ/8\n5CdotVp27NjBY489xsyZM6mqqqK2tpadO3dy7733jqtGRKlUsmLFCmpqalCr1cyePZv333+fPXv2\nsG3bNgAeffRRli9fTnV1Nd/61rfo7+/n2WefJTc3d/I8kk8//ZR58+Zd8AmPHz+eskd3IXi9Xv7+\n7/+e73//+0yfPh1IiIadOXOGp59+GpfLxZ///OeU13z88ceXvLUmEolQFRWhKioi65ab8TZY8Zw5\nS9BmS86JBgI4PzuB87MTyLOy0M6oRDutgoyL9Ia+zGRIxBRZEvERvy9Rm9LenFqb4nEHqT3RxZnP\nu8nO1VJUYkyPrS+pEn1WJbrM6YQCDryuZvyuthG1KRD09RD09SDulia6OxosyITujlOC5557jurq\nah5//HGi0SgLFy5k3759mM1mtm/fngy85+Tk8NBDD/Gd73xn3Odeu3YtUqmUHTt20NfXR3FxMVu3\nbmX58uUAVFZWJs//2muvodVque2223j66acn6nKTnFP996677qKyspJVq1YlXbbzcfr0aV5++WXq\n6+svOosK4G//9m+Ry+U8++yz+P1+Nm7cSE5ODj//+c85e/Ys99xzD4899hjf+MY3+N3vfserr77K\nb37zm/Ou8WLVf0NOJ57as3jq6oj4fKPGRWIxKosF3YzKtN36GiIej2Pv89HW5KCrfWwtr3Tb+hoi\nHovi93Tic7akiEeORCLToNFbUBssSKQTtwUhIHAhjPfZeU5DEgqFeOmll/iXf/kXKioquOOOO5gz\nZ06yQ6LH46G7u5ujR4/yf//3f9TV1XH//fezbt065PKLV5jt7u5m69atfPzxxygUCu644w7Wr1+f\nVB/+3//9X55//nlaW1spKyvjmWeeSdacXOrNOBfxWIxAezvuM2fxNTYTj0VHzclQKtFOq0hsfWVl\nXvB7TCUi4Shd7S7aWxwpPehHotEqkllfU33raySRsB+fqxWfsyVl62sYEQp1Nmq9BZWuUNj6EphU\nLtmQDNHZ2ckvfvEL3nnnHfr7+1N+RcbjcXJzc7njjjt4+OGHx1UJPxlczn4k0YEBvA0NeM7UpWx9\njUSelYW2cnraZn2NxO8doL3VOWrrawiRSERWroYii4m8wqm/9TVEPB4nFLDjdTbjd7enbH0NIRJL\nhKwvgUnlshmSkdTV1dHW1obH48FoNFJYWEhFRcVlWfBEMlGNrUIOB54zdefe+hKJUJnNaGdUoi6x\nCFtffT7amx10tbuIREZ7dRJpBgVFeorSpOBxiHFtfUnVqPUW1AYzUplQ8ChwZZgQQ/JlZaI7JCa2\nvjoSW19NTcmsr5GIZfLBra/pyHNy0uYhORaRSJTuDjdtzXb6e8be+lKpZRSVmCgyG9KiGdcQkXBg\nxNaXe8w5cmUWGoMlUfAoNOMSmEAua4dEgfMjEotRmYtRmYsHt76seM7WERxRcR8LDeA6dQrXqVNI\n9Xp0MyrRVk5Hch515amKRJKRkvXV0eqgvdmBzztc1Of3hag71U3dqW5MWQmtr4IiPRLp1PbqJCOz\nvoIOfM4WfK42YrHhbcGBQB8DgUTBo1JbgEZvQaHJEbS+BCYNwSOZQEJOF56zdXjr6gh7xg6sKgsL\n0M2oRF1Wiliavr8u4/E4jn4/7S0OutqchMNjJDRkiMkr1FNkMZKVBjL3Q8RjUQLeLrzOlpSCx5Fk\nZChQ64tRGyzIFIYrv0iBKYngkVwFyAx6MhctxHTDAoKdXXjOnsXb0DhC7iFOoKODQEcH4oNS1GWl\naCunoywsQCROr1+XQy2DTVlqrp1XgK3TTXuzg17bsNZXNBqjo9VBR6sDuUJKkcVAocWITj+1ExoS\nBY9FqHRFRCNBfK42fK4WQkFnck40GsRtr8dtr0cm16M2WFDrzWRIRktzCAhcbgSP5AoTC4fxNTbh\nOVuHv72DMQOrajXa6dPQTJ+ellX0IxkIhmlvcdLe4sDjGrv1rc6gpMhipNBsQK5IH68uFHQOxlNa\niUaDY8xIdINU6y2otAVCh0eBC2bCPBKbzUZPTw8VFRWIxeJLqhlJR8RSKdrK6WgrpxPxevHU1eM5\nW0fIMawkG/H5cBz/FMfxT9M+lViukFJemU3Z9CzczmAinvIXMvduZ4DTzgC1J7rIztVSaDGmRSqx\nTGFApjBgyJlF0NeDz9mC39NJPD60LTiiw6NYikpXiFovpBILXH7GbUgOHjzIjh07aGxsRCQS8eab\nb/Kzn/0Mo9HI5s2bU6SNBcaHRKPBeN18DPPnMdDbi+dsPd76eqLB4V+XA319DPT1JQQkzWY006eh\nLi1BPA7F0KmESCRCb1SiNyqZOTufXpuH9hYHtk53soo+Ho/T0+2mp9uNRJpBflEinmLKUk/pB2dC\n5j4PpSaPWDSM392O19XCgL8vOScWC+N1NuN1NiORqpLNu6Ry3SSuXGCqMK6n0cGDB1m1ahWLFy9m\nxYoVfO973wMSevo7duygsLDwgjRjBFIRiUQocnJQ5OSQddNX8Le14Tlbh6+pJVlFH4/H8bW04Gtp\nQSyVoikvR1s5HUVB/pR+SI6FSCwiJ19HTr6O8FAVfbMDe99wKvHI3ilKlYxCcyKeotVN7ZiBOEOK\nxliKxlhKJOTD52rF62ohEhp5b4Z7p8gUJjQGMypdkRBPmWTeeustXnjhBdxuNzU1NRQUFPDUU0/R\n2trKgw8+yDPPPDPZSzwn44qRLF++nMrKSqqrq4lGo1x77bX86le/4tprr2XPnj3853/+J+++++6V\nWO9FcTXFSC6Ec6USj0Si0SRUiadPR2YyjjknXRiqou9oSU0lHoneqEoYFbMRuSI9vLpEFb0Dn6sF\nn7vtHL3mRSg1eaj1ZiGeMknceOONLFmyhNWrV2MymdiwYQNNTU3s3r0brVY7qrngleCyxkgaGhr4\nh3/4hzHHFi5cyN69ey9ulQLnJUMuR3/tNeivvYawy5WMp4Tdw4VqEa8Xx7HjOI4dR56dnQjSp2k8\nRaWRM/2aXKbNzMFp99PR4qSjzUk4NBxPcTn8uBz+1HhKgY4MydTdmhWJRMhVJuQqE8bcOQS83fhc\nrQS8Xcm2wYl4ShcBb5cQT5kk3G43CxYsoLCwMPn3zJkzx9WvZLIZlyExGAy0tLRw8803jxpraWmZ\nFEuZbkj1ekwLF2BccD0DNhueunq89Q1ER3SHHOjtZaC3l/4PPkRZXIy2cjrqEkva1aeM7J1yzdx8\nemweOloc2Do9xGLniKcU6ik0G8jMntr1KSm9UyIh/O52fK4WBgL9yTkj4ykZElWiPkVvRqbQT+LK\nvxx4vV5qamp49913CQQCXHfddWzatImysjIOHDjAnj17sFqtGI1G7rnnHlatWkV3dzdLly4FYOPG\njezZsweAjo4OILHldeDAAQoKCnj55Zd54403cDgclJeX8+STT7J48eLk+x85coQXXniB06dPk52d\nzZ133snq1asnPClqXIbkzjvv5KWXXqKgoICvfvWrQOLL2tDQwE9/+lPuuOOOCV2kwDAikQhFXh6K\nvDyyvnoT/tbBeEpzajzF39qKv7UVsSS961PEGWLyCvTkFegJhyJ0trvoaHFg7xvWRouEo7Q122lr\ntqNQSpNbX1q9Ykr/Gk+0DS5DayojHPImUoldrSnxlGjEj7v/LO7+s8gUhkSQXldMhvTKeLzWs73U\nn7aNqc020UgkGUy7JpfyyvE3hVq7di3t7e3U1NSQm5vLiy++yCOPPML69etZv34969ato6qqitOn\nT7N582acTicbN27k8OHDLF68mGeeeYa77roLgCeeeILs7Gw2bdqEyWSipqaG9957j61bt2I2mzl0\n6BCrV69m3759LFq0iNraWlauXMmTTz7J9u3b6ezsZNu2bfT19VFdXT1RtwkYZ4wkGAyyatUqPvzw\nQ6RSKeFwGIPBgMvlYu7cubz66qtJmferkS9rjORCGIqneOvqCXR1jTlHolKhmVaRiKdkpXcjJb93\ngI62RH3KyF70I9HqFBQO1qcoVbIrvMLJYbzxFIU6JxlPmUi9r/fePs1A8Mr3ax9CrpBSddc145rb\n2NjIsmXL2L9/P4sWLQLA4XCwd+9e3n//fWbNmkVNTU1y/muvvca2bdv46KOP0Gq1XHPNNfzoRz9K\nNqpasWIFeXl5PPfcc/h8Pr7yla+wa9culixZkjzHs88+S1dXF6+++irr168nFAqxc+fO5PjRo0e5\n7777OHToEDk5ORd8/Zc1RqJQKPjFL37BwYMH+eijj3A6nWi1Wm644QZuv/12IfX3KiAlnuJ2J7a+\n6uoJOYernyN+f7LLo8xoTMZTpLovbvU51VBp5EybmUvFjBxcjgAdLQ462pwp9Sked5Azn3dx9mQ3\npiw1hRYj+YU6pLKpG6QfFU/x2fC52gh4OlLiKUGfjaDPhl2UgUpbgFpvRqHJvex6X2XTsyfVIymb\nPn5vpK6uDoA5c+YkjxmNRjZs2MDrr7/Ogw8+mDJ/4cKFRCIRGhsbmTv3/H3prVYroVCINWvWpDxv\nw+EwWVlZANTW1tLS0pLSoXbIT7BarRdlSMbLuL4RTzzxBA899BCLFy9O2Y8TuDqR6nTJXvQDvb14\n6+rx1DcQDQxXhoccDvo//oT+jz9BmZ+PZnoFmvJyMhTplQIqEokwmFQYTCpmzi2gz+aho9VJd4cr\npT6lv9dLf6+Xk8fE5BZoKSg2kpuvRTyFix5F4oSRUGkLkvUpPlcrQX8fQ4oM8XgUn7sNn7sNcYYc\nta4oEU9Rmi6Lx1temX1BW0uTieQ8tV2KMb5X0UGV8PO9bgiZLOER79q1C4vFkjI2ZFikUil33303\njz766KjXT1rP9pEcOnSI+++/f0IXInD5GVmfknnTjQTa2/HU1eNrbB6h9wWBri4CXV30HfpTouhx\nWkVaFj2KR9SnRMJRujvddLQ46OvxJn/ZxWIxutpddLW7kMoGg/RpUPSYUp8S9uNzteF3tRIacCXn\nxKIDeBxWPA7rYP+UoaLH9PB4h9p9nzx5koULFwKJ4HtVVRVOp5Njx47xwAMPJOcfPXoUqVQ6rqws\ni8WCVCrFZrNx6623Jo/v3r2baDTKmjVrqKiowGq1phia48eP88orr7BlyxZUqolr4TyuJ8VNN93E\nH/7wBxYuXEhGGjdn+jKTkLo3ozKbE3pfTc146uoJtLUlH5LxWAxfczO+5mbE0sEg/fT0DNJLpMNS\n98FAmM7BeIrbOezVhUNRWpvstDbZUShlFJr1aRGkl0hV6LMq0WdVEgq6BoP0bUQj/uScSNiHq68W\nV18tMoURtb4Yla4YyRUK0k8GpaWlLF26lC1btrB582aMRiM/+clP0Gq17Nixg8cee4yZM2dSVVVF\nbW0tO3fu5N5770Wr/WJDq1QqWbFiBTU1NajVambPns3777/Pnj172LZtGwCPPvooy5cvp7q6mm99\n61v09/fz7LPPkpube3V4JAaDgV/96lf813/9FxUVFaMsm0gk4uWXX56QBQpcfsRS6WAR4zQifn8y\nSB/s6UnOiYXDeM7W4Tlbl+xHr5k+DXl29pR+SI6FQimlbHo2ZdOz8biDdLY66Wh1pLQODgZCWM/2\nYj3bi1anoGAw80ulntpBeplCj0wxG0POLAb8ffhcraNaB4eCDkJBBw7b54l+9LriKduU67nnnqO6\nuprHH3+caDTKwoUL2bdvH2azme3bt7N3715eeuklcnJyeOihhy5IEWTt2rVIpVJ27NhBX18fxcXF\nbN26NRmcr6ysTJ7/tddeQ6vVctttt/H0009P1OUmGVfW1n333feFJ3rttdcuy4ImgnTI2rochJwu\nvPX1eOrqCbtcY86R6vXJIL3MkL51BUP9UzpbRxc9jsSUpabAbKCgyIBMnh5bhUP9U3yutr8oehwm\noQ+Wj1pvRqnJEyrpr1KEVrsjEAzJhRGPxxno6U0Ylb8I0o9EkZODZloFmopyJFdx+vdEE4vF6U0W\nPQ6LSI5EJBKRnaulwGwgr1CHRJIeD85YNITf3TEqSD+S4Up682AlfXpto17NCI2txkE4GuZY10lE\niCgzmclUGtNu22YsRCIRitwcFLlDQfqOwSB9U0qQPtjTQ7Cnh74/fYiysADt9Gmoy0rJSLPWAmKx\niNx8Hbn5umQ/+o5WB3224SD9yEr6jAwxuQU6Cs1GsnM1UzrzS5whSwnSD2V+jWzKlVpJr0ClK0at\nL0amEL6PXxbGZUjmzJnzhf+gn3322WVZ0JXk0+7TfNZ1OvH/XafQK3RUZJZQYbKgVwjy2pDajz4W\nDuNrbsFb34C/tZV4bIRO02CnR9HBQ6gsZrTTp6GymNMu82tkP/qBYISudicdrU4c/cOV9NFojM42\nJ51tTqQyCflFCXmWqZ75JZGq0GVOR5c5nfCAOxmkj4RH3JtIEI+9Ho+9HolMg3rQqAhy91c34/qW\nP/zww6M+4H6/n6NHj9LZ2XlOQcerHf1fpCW6gm6OdpzgaMcJstQmKkwllJssqGUTlzb3ZUIslaKd\nVoF2WgXRgQF81kY89fUEOrpI1hXEoviamvA1NSUyv0pL0U6vQFlYiCjNMv7kCgklFVmUVGQlK+k7\nWp143cP9ZsKhCK2N/bQ29qNQSikoNlBQbEBvVE5poyKV6zDkzEKffS2hgD2RTuxuIxodVhmIhLwj\nMr8MiSC9vhiJVPg+Xm1ccozk//2//4fBYEj2KLkaOd8+X4e7m7N9jTQ724hExwiYikQUaHMoN1ko\nNZpRSNJr22Y8RLw+vA0NeOobGOjtHXNOhkKBprwczbRyFPnp10NliHg8jscVpKM14ZEE/GNJkIBa\nI09kfhUb0EzxHipDxOMxgr7ehDKxpzMl82skclUWap0Zla6QDOH7OKFcsWD7hx9+yNq1a/n4448v\n5TQTynhuRiQaocXVQUN/M23uzqRK7EjEIjFF+nwqTCVYDIVIp2D64qUScjqTlfTnyvySqNUJza9p\nFciy0lemfCjzq6PVQVe7K0WeZSQ6g5JCc8JTSRvNr3FkfoEIpToXlb54wjW/0pUrFmxvb28nHJ48\nUbXLhSRDQrnJQrnJwkAkRJOjjQZ7M50eGwxVNcdjtDo7aHV2IBFLMBsKqTCVUKzPJ0NIXwRAZjBg\numEhxoULEvIs9Q14G6xEfCPUdn0+nJ9+hvPTzxLpxIM1KjKDYRJXfuURiUSYstSYstRcO6+QPpuH\nzjYn3R3uFG0ptzOAe7AnvSlLTUGxgfwiw5RuzJWQuy9CpStKyLN4OvC52gj6ehjO/IoT8HUT8HVj\nF2UMNuYqRqnJF9KJrzDj+iS+8soro47FYjG6urr47W9/m1KyPxWQS2TMyC5nRnY5vpCfRkcrDf3N\n9PqGezZEYhEa7S002luQZUgpMRZTYSqhQJeLWEhfHCXPEuzswlPfgM9qTemhEna5sB85iv3IUeRZ\nWWgqytFUVKSdkORIeZZoNEZPl5uOVic9XZ4U79je58Pe5+PUp51k5WgoKE6kE09lIUlxhhSNoQSN\noYRoJDiY+dWW0kMlHo/i93Tg93QgFktRagtQ64pRaHKEdOIrwLi2tmbMmDHmcaVSyZIlS9i0aVNS\ngfJq5HLVkbiDHhrsLVjtzTgCY2/bKKQKyoxmKkwWcjXpVwX+RcSjUfxt7XgbGkZpfo1EkZc3aFTK\nkUygRtDVTjgcpbvdRWebM0XzayRisZjsPC2FZgM5+dq0qVGJhHz43O2jNL9GIs6QodIVodYVI1dl\nCkblArmsMZIhlcqUF4pEXxr5+IkoSLQHnDT0N2O1t+AZ8I45Ry1TJbfLslSXRw11KpFMJ25owN/S\nlmzMlYooUaMyrSJRo5Jm6sQjGQiG6Wx30fkX6cQjkUgyyMlPFD7m5E5tdeKRJNKJEyrEIxtzjSRZ\no6IrumzqxJeTt956ixdeeAG3201NTQ0FBQU89dRTtLa28uCDD/LMM89c8TVdVkPyve99j+985ztj\nnqipqYkf//jH7Nq169JWPIFMZGV7PB6n12+nob+ZRkcL/tDYVeA6hYYyo4UKUwlGpf6q+xBPNtGB\nAXxNzXjrGwi0t4/5y1skFqMqLkJTMahOLEuPwPNYBPyhZC2KyzH2Z04qyyCvQE9BsYGsnKndQniI\neDxOKOjEP2hUopGx741Eqk54KvpipPKr4/t44403smTJElavXo3JZGLDhg00NTWxe/dutFrtpLQ0\nv+Rgu81mS/7/m2++yde+9jWkY/T+PnToEAcPHrzE5X55EYlE5KgzyVFn8pXi+XR7e2nob6bJ0cZA\nZDgW4A56+bTrFJ92ncKg1FNhslBmsmAQCh+BRGMu3YxKdDMqifgD+Joa8dZbCXSOrFGJ4WtpxdfS\nmgjGWsxoKsrTsi+9UiWjvDKH8socvJ6BpFFJrVEZbiEsk0vIL9RTYDZgylRPWaMiEomQK43IlUYM\nubMZ8Pfhd7fjd7en1qiEfckWwlKZdrCavmhSCx/dbjcLFiygsLAw+ffMmTPHJTM/2ZzTI3nsscc4\ndOjQF54gHo9z00038fOf//yyL+5yMRlaW7FYjHZ3F42OVpocbYSjY8cCMlVGyk0llJvMaOWaK7K2\nLxNDNSreBmuKOvFIxBIpqhIL2mkVqMzFaVf4OMRQjcqQURmpTjwSuUJKQZGe/GIDxkzVVfFrfKIZ\nqlHxu9vwuzuJxca+N1K5frDwsQip7MK/j16vl5qaGt59910CgQDXXXcdmzZtoqysjAMHDrBnzx6s\nVitGo5F77rmHVatW0d3dzdKlS5PnGDIkHR0dyWMHDhygoKCAl19+mTfeeAOHw0F5eTlPPvlkSrPB\nI0eO8MILL3D69Gmys7O58847Wb16NfKLlC265K0tm83GBx98QDweZ+PGjaxatWqUZRSLxeh0OhYt\nWjShTVMulckWbYzEorS7Ommwt9Dq7CASG7teIFudSbnJQpnJjEaWviKI5yLsciUk7xusDPT3jzln\nqJpeM60CVVH6VdMPEY/HcTkCSaMSDIz9Q0apklFQrCe/aOpX0w8Rj0UJ+Gz4Xe34vZ3Ez/F9lCmM\nqAdTkCXj/D4+8sgjtLe384Mf/IDc3FxefPFFTp06xfr161m/fj3r1q2jqqqK06dPs3nzZu666y42\nbtyI3W5n8eLFPPPMM9x1111AojNtdnY2mzZtwmQy8eMf/5j33nuP733ve5jNZg4dOsT27dvZt28f\nixYtora2lm9/+9s8+eSTfO1rX6Ozs5Nt27Yxe/ZsqqurL+peXdYYyZtvvsnSpUsxmUwXtZjJZrIN\nyUjC0TCtrk6s9mZaXWMXPgLkarITRsVoRiWbus2ALpaQw5EwKvUNKX3pR5Ihl6MuK0VTUZGWzbmG\niMfj2Pt8dLW56Gx3nrPwUaWRJzyVIgM6w9XRnMvdX4ez9/Q5H/aXSjweJxr2Ewl5iYQDpKgTi0TI\nFEZkCj1yZWayruVczbkaGxtZtmwZ+/fvZ9GiRQA4HA727t3L+++/z6xZs6ipqUnOf+2119i2bRsf\nffQRWq2Wa665hh/96EfJ/iIrVqwgLy+P5557Dp/Px1e+8hV27drFkiVLkud49tln6erq4tVXX2X9\n+vWEQiF27tyZHD969Cj33Xcfhw4duqie7Ze1IPHee+8lFApx5swZQqFhlzAWixEIBDhy5AhPPPHE\nBS8yHZFmSJOZXKFIiGZnO1Z7C+3urpQAs83bi83bywdtR8nX5FBuMlNqNKOUpm/W0khkRiOmhQsw\nLrieUL89sf1V30DY40nOiQ4M4K49g7v2DBkKBeqyMjQV5SgL8tPKqIhEIjKzNWRma7h2XgH9vV46\n25x0dbhT+qj4vQM0nOmh4UwPaq2cgqJENb1WP3mfOXd/3YQZEUjcG4lMjUSmJh6LEQ37CYd9RMN+\niMcJB13IFHoGAv0MBPpx2E4gV2UONucqJEMyfG/q6uqAhMjtEEajkQ0bNvD666/z4IMPprz3woUL\niUQiNDY2Mnfu3POu02q1EgqFWLNmTUq2bDgcTpZe1NbW0tLSwvz585PjQ88Uq9V6UYZkvIzLkBw5\ncoS1a9fSf47tBKVSKRiSi0AmkTE9q4zpWWUEIwM0O9qxDlbTJ41KPE6Xx0aXx8afWo9QoM2lzGSm\n1FCMQjAqieBqVibyrExMi25IVNMPeiojq+mjwSDu06dxnz5NhlKJpjxhVNJN90skFpGVqyUrV8us\n+TH6ehJGpbvTTSQ8nH7t8wxQX2ujvtaGRqdIiEkW6a+47pcuc/qEeiQjEYnFSOQaJHJNwqhEg0il\nGuLEGVlNP+DvY8Dfh737UxSqLFS6YlS6AiTnUbpWjJG2PlRWcb7XDSEbzFDctWtXSk92IGlYpFIp\nd999N48++uio118VrXZffPFF1Go1mzZt4ne/+x0ZGRncfffdHDx4kDfffPOqDrR/WVBI5Mlq+kA4\nSJOjjUZHC52enqRESzwep8PdTYe7m8OiP1Ooy6PMaKHEWCSISfIX1fQ3foUBmy1hVKyNqUYlEMB1\n8hSuk6eQqNVJT0WRl5tWRkWcIU5W08eiMXpsHrraXNg6UyVavO4gdae6qTvVjc6gJH9w+0ujnfjP\n3JDs/GQSjQwkqubd7QR9vYw0KkF/L0F/L/bu4xiUiXt24rNjLPrKV4FE8L2qqgqn08mxY8d44IEH\nkioe1lYAACAASURBVOc9evQoUql0XFlZFosFqVSKzWZLURLZvXs30WiUNWvWUFFRgdVqTTE0x48f\n55VXXmHLli0TGscelyE5ffo0P/zhD1m2bBl+v5833niD22+/ndtvv51IJMJPf/pT9u7dO2GLTDeU\nUgXX5Ezjmpxp+MMBGu2tNDpa6PYMK+vG43HaXV20u7o41CKiUJdHuclCiaEYuSR96yuGEIlEKPLy\nUOTlkfnVmwh2dSWNysiOjxGfD9fnn+P6/POEmGR5Gery9DQqeQV68gr0gxItHrranaM6Pg7pfp09\nOWxUCooMqK+AUZksMiRytMYytMayQYmWQaOS0vExTrZRzI03zOR7z25g/doV5BdOY+++N9BqtezY\nsYPHHnuMmTNnUlVVRW1tLTt37uTee+9Fq/1iOSClUsmKFSuoqalBrVYze/Zs3n//ffbs2cO2bdsA\nePTRR1m+fDnV1dV861vfor+/n2effZbc3NyrwyOJRqPk5eUBCctYX1+fHFu2bBkbNmyYmNUJoJIq\nmZVbyazcSrwhX8JTsbdi857DqIg+SRoVi6FIMCokjIqyoABlQQFZN3+VQGfCqPiamkYZFeeJz3Ge\nGDQqFeVoysuQ56aXUcnIEA96HXoikWjSqPR0eQSjIlGgNZWjNZUTDQcGxSTbGQj0AfDUk/ew9+fv\nsPEHu4jGYsy+ppTtW/6eaTOKqK7exiuv/P/tnXd0W/Xd/9+a1l62tS3PxNl7QkoDHOgPKLTMUhpa\nUlrawwOh5AmElIbSAg8jJASaQksDbaHwlJSStqwOaPuwQxIKwUmcKUuytmxrb+n+/rjStWRLjmPL\n8dD3dU7Ogevrq+/1tfzW9zPen2fw+OOPQ61W45vf/Ca+973vDfu1f/CDH4DH4+GRRx6Bz+dDQ0MD\nfvrTnzLJ+fb2dvzyl7/E448/jhdffBFSqRTnnnsu7rzzzjH5WRQyrKqtL3/5y1izZg2uvfZa9Pb2\n4qyzzsKbb76J5uZmvPPOO7jtttvwn//8Z8wXO1ImUtVWpQgnI7mdihWesK/kOWwWmw5/qUxkp1IC\nKptFzO7I+X6Zi8wkC6lmUSkknc7A7egXlXIVhzKFMOdQLIdYMnVFpZB0KsY0PhaaSRaTs72XGSCU\nGsCZBO/Hipb/PvXUU9i5cyfuuOMOXHvttbjyyiuhUqlwww03YMeOHUilUnj55ZcregOVZCoKSSGh\nRBgn+6w42WstciguhIjK0FCZDC0qJ04QURkG6VQGbmcQDlsAXtfQolINO5VC8rPpaVHpLXPW5BCV\nips2Pvjgg/D5fNi+fTsOHDiA7373uwgEAhCLxXjyySeZuumJyFQXkkKCiTCTU/FFSv8SE1EZGkZU\njp9A+KQZ2eQQolKlOZVC0qkMXI4gnN1EVAaSTkaYRP1kFJWKConD4YBery86FgqFcPz4cbS2tkIm\nm9h+UdUkJIUMR1RYLBap/hoCWlTsCB8/eUpRoau/WiDQaqtWVFKpDNyOIJw2P7zu8ClF5UxVf00E\nhisqArEaIpkBIun4jxKuqJCsXLkSmzZtwmWXXVbRRZ4pqlVICjkdUWlWNpA+lRIUikrEPHT4S9zc\n3C8qVdT8WAgjKqfYqUjlQia5L62S+fTDF5V6iKRGiGT6oubHM0VFO9tZLNa4WBgTKoesRoIFullY\noJuFYCIMc4mcSmH113usvdBJNWhRmtCkNEJUxhaimmBxOBCZTBCZTKAyXyibU0lHIgh0dCDQ0cE0\nP4pbWqquo57H48DYqISxUTlk+CsUiCEUiOHoQRckMgET/pLIaqbszo7LFzM9Mv2iYh+QqKcQj3gQ\nj3jQ6/pPrvnROKijfiIwrB3JCy+8gN///vdYt24dZsyYUbKxpba2dkwWWAnIjqQ8wUQYXX22Iau/\nwGJBJ1EzHfXE+6sYKpOhS4rzohKPlzyPIxRC3NwESWvOpqVKDSXpRD1d/eV1FZcUF5K3adEa5BPG\n+2usoRP19lNWf9WIapmdCpc3do2GFQ1tLVy4EIlEouSwoTyHDx8e2UoHEI1GGRvmeDyOBQsW4K67\n7kJbWxsA4L333sOWLVtgNpvR2NiIDRs2FNkol4IIyfAIJyIw+wf3qRTBYkErqUOz0oRmZQNxKR4A\nlc0i5nD0h79ipQcrcWpqIG5uhri1papdivOi4rIP7lMpRCSpgc5Ah7+qxaWYLim2IxrqRiLagyJD\nyQJoQ0k6pzJcl+LhUnH331Nx9dVXn94Ky3D33Xfjk08+wf333w+FQoFt27bh4MGD+Nvf/gabzYbL\nL78cN998My688EK8+uqr2LlzJ3bv3o1p06aVvSYRktMnkozmbFqscIW9jE3LQOrFtfRORWmCjMxT\nKYLKZhF3uhA+cRKRkyeRjkZLnsfm8yFuaoKktQXCBiPYw/Bemoqk0xl4XSE4uwPwOENFNi2FCMV8\n6AxyaA3yqpmnQjc/Okp01BfDFyhzomIErwLvx4oKyZlk+fLluOWWWxinzOPHj+OSSy7BK6+8gpde\neglmsxnPP/88c/7111+PpqYm3HfffWWvSYRkdERTMSb8Vej9NZBakRLNShNalA1QCOVneJUTG4qi\nEHe5ETkx2PurEHpIlwmSlhaIGk1VN/kxTyaTZUTF7Sw2lCykRsCjRcUoR23d1J38WAht0+JANGRH\nPOJBWVGpkUOY26nwamQjEtyKJtsB+o3w17/+FR988AG8Xi82bdqEAwcOYPbs2WhpaTntBZZDpVLh\njTfewMUXXwypVIqXX34ZcrkcDQ0N2LdvHy666KKi85cvX47XX3+9Yq9PGIyIJ8Qs9XTMUk9HLBVH\nl98Gc58N9qCrKNzZE+1DT7QP++yfQSmUM+EvlVBRFZ8ah4LFYkGo00Koo72/Eh4PvVM5cbLI+j6b\nTjHDu+jkfgMtKk2N4Ixwyt1khMNhQ5vbdWQzWXjd4ZyoBJBK9otKIp5C1wkfuk74wK/hQquXQWtU\noK5eDDZnahY20DYtLZCqWpBJJxEL2WlRCXtAoT80mEwEkPQGEPAeyo0TzomKoPLvx2EJSTgcxk03\n3YRPPvkEOp0OLpcLt956K/70pz/hJz/5CX73u99hxowZFVnQfffdhzvuuANnnXUWOBwOBAIBnn32\nWchkMrhcLmg0mqLz1Wo1XC7XiF4rEE7gn/tsAIBmvQwtBgVk4onTDDQREfIEmFk/DTPrpyGeTsDq\nt+NknxXdQWdRJU5fLIC+2Of4xPE5ZAIpU1JcL64losJiQaDRQKDR0C7FXi8iJ8wInzyJVCDAnEdl\nMoiYuxAxd4HFZkNoNNIVYE2N4Airp+CBzWFDo5dBo5chmzHA543AZQ/AZQ8UDelKJtKwmnthNfeC\nx+dAo5NBa5SjXiMFZ8qKCh8SZTMkymZkM0lEQ05Eg3bEIy5QVP/7MZUMIeDrRMDXCS5PDJHUAJFM\nD76wMu/HYQnJI488AqvVildeeQXTp0/HnDlzAADbt2/H2rVrsX37dvziF78Y9WIAwGKxoK6uDvfe\ney8UCgWeeeYZrFu3Drt27UI8Hmd8+fPw+XwkytTzn4rPT/hg94YBAHZvGO995oBaKUKLQY5WoxxK\n6cQqsZtoCLg1zDyVZCbFiIot4EAm2/+pMRgP4TPnIXzmPAQxX4RmZQOalA3QSurBZk3NN/hwKbS+\nV61YhmRvLyIn6ObHZG9/fwGVzSJqtSJqtQJgQWjQQdLSAnFzM7iS6il4YHPYUGulUGulmLvQgB5f\nBK7uAJz2ABLx/nHCqWQG3ZY+dFv6wOVyUK+VQmeUQ62VgsubmoUNbA4fEkUjJIpGZDMpxMIuRIPd\niIXdoKh+wU2nIgj2HkWw9yg4XCFEUj1EMgNqRHVgjfD9OCwh+cc//oE77rgDs2bNYoaxAIBUKsX3\nv/993H333SN68YHYbDZs3rwZL774IhYsWAAA2Lp1Ky6++GL85je/QU1NDVKp4tnTyWQSwhF+OmvW\ny3HwZA9S6X7l9vRF4emL4qMOJ2plgpyoKFArr47yw5HC5/DQVtuEttompDIp2AJOmPussAYcSGX6\nn1kkGUWH+wg63Ecg4AnQpDCiWdkAvVQDDntqvsGHC4vFQk1tLWpqa6FathRJv58RlYS3sIqOQszu\nQMzugPfd9yDQaOiRwq0t4E1wl4lKwmKzUKeWoE4tweyFevT1ROGyB+DsDiAW7Z/kmk5n4Oz2w9nt\nB5vNRr1GAq1BDo1eBn7N1CxsYHN4EMsbIJY3IJtNIx52IxqyIxZyIpvtfz9m0jGE+k4g1HcCNaI6\naExfAGsE78Nh/RSj0WjZPpGampoR7wgG0tHRgUwmw+x4AHrq18yZM2GxWKDT6eDxeIq+x+PxDAp3\nDRdDvQTXXzQTZkcQJ+x+dHvCyGYLYv7BOHqCcew97IZMzEerUYFWgxwaVXVUiowUHoeHFpUJLSoT\n0tkMHEEXTvZZYfHbkUj3/67EU3F0eo+j03scfA4PppyoNMh04HKm5hv8dOArFOAvXgTl4kVIBUOI\nnKRFJT4glBt3uxF3u9Hz4Ueoqa2FuJXeqfBVyqr5PWWxWFDViaGqE2PmPB0CfTFGVCLh/t+5bDYL\ntzMItzOYG0EsZnIxAuHULGxgs7l0fkRmAJXNIB7xIhrqRjTkQDbTL7iJqA/pVAS8mtP/MDKsd+vs\n2bOxa9eukv0ab775JmbNmnXaL1yK/MyTI0eOYPbs2QDoJP+JEydwzjnnoK6uDnv37i36nj179mDJ\nkiUjfk2RgIfZLbWY3VKLeDINizOIk/YALK4Q0oWzFyJJ/OeIB/854oFEyEOzXo4Wgxz6egk4VVAp\nMlK4bA5MCgNMCgOy2SycYQ/MfVaY+2yIpfob95KZFI73mHG8xwwOm4MGuR7NygaY5AZiKgmAJ5NC\nsWA+FAvmIx2OINLVhfCJk4g7HEUFD4meHiR6etD78V7w5HI6/NXSjBp1fVWJikIlgkIlQvscLcLB\nBJy5nErQ39/XQ1EUfJ4wfJ4wDn7qgEIlgtYgg9Ywde3vWWwOhFIthFItVFQWiaiP6aivEdaCyz/1\nkK2S1x1O+e+ePXtw4403YsaMGVi9ejV+/vOf46abbkJXVxfeeust/PKXv8SqVatGtIBCMpkMrrvu\nOkSjUfz4xz+GUqnEb3/7W7z66qt47bXXEA6HceWVV+Kmm27CJZdcgtdeew3PPPMMdu/ejdbW1rLX\nHUn5byqdgcUVwkl7AF3OIJLlyg/5HDTr6JxKg0YK7hRN6lUaiqLgjvgYUQknSpfD5v2/mhQNxKql\nBJl4nE7InzQjausGlS39e5r3/xK3NFedVUshkXAil6gPoq+n9O8cQJtKag1yaPUySKs4rF3xPpKP\nPvoIW7duRUdHB/MJqL29HbfffjtWr15dkUUDQG9vL7Zt24Z33nkH0WgUc+bMwcaNGzFz5kwAwL//\n/W9s2bIFVqsVLS0t2LhxI84666whrznaPpJMJotuTxgn7H6YHUHECipFCuFx2DDpZGg1yNGok6Fm\niib1Kg1FUeiJ9sGcKyv2xwKlT8x11dOi0kAaIAeQTSYRsVgRMZsR7bIim06VPI/uqm+iRcVYvQ2Q\n8ViK3ql0B9Dri5R17qC76umdiqLKwtpj1pAYiUQQCAQglUqHNWt4IlDJhsRsloKzJ4KT3QGcsPsR\njpV+s7LZLBjVErTkQmAiwdSMv44F/liAEZVyTsUAoBIp6QowhZH0qgwgm04j1t2NyEkzIuausk7F\nbC4PosYGiFtaIDI1VFWvSiHJRBpuRxAue2BI+/saAY8Jf9XWTd1elTxjIiTvv/8+9u3bh2AwiNra\nWqxYsQKLFi2qyILHkrHqbKcoCt6+GE7YaVHxh0q/WVksFjQquqy4RS+HokrmL1SCcCKSa4DshjNc\nvqteWiNhyoo14joiKgXk/b8iJ7sQMZvLdtWz2GwIDQaIW5rosuIS5qzVQDqVgccVgss+tFULj8eB\nWieD1iBDvVYKLnfqRSAqKiR+vx/f//738emnn4LL5UKhUMDv9yOTyeCcc87Bz372s0H9HROJM2WR\n0huM42ROVLx9pc36AKBWJkCzgd6p1Cuqw4CuEsRScVj8dnT5bYMaIAsR8gRoJGXFJaEoCgmPl6kA\nK2yALIYFgVZDh8Cam8FXVKflTSaThc8ThssegNsRLGqALITDYaNOPfXKiisqJBs2bMC7776LBx54\nAOeffz5YLBay2SzeeustbN68GV/96lexadOmit5AJRkPr61QNImT9gBO2gNwDBF/lYr4dPjLKIeu\nVgw2qQAbFslMCraAA119tkG9KoXwODymAqxBrgefQ0KMeSiKQrK3DxGzGZGTZiR8ZcYIAOCrVLQF\nfksz+HXVueOjshR6eyJw2ekQWGGvSiH5UmSNng6BiSaxW0ZFhWTZsmXYuHEjrrzyykFf27VrFx5/\n/HG8//77o1vxGDLepo2xRBpdjiBOOgKwuYvLigsR8Llo0snQYqArwHjcqR1/rRSZbAb2oAtd/m50\n+bsRT5WeB8JmsaGXaehkvcJI5qoMIBUMIdJFV4DFHE6UMwPkSiQQNzVB3NIEoa4656pQFIWgP05X\ngDmCCAXKRyAmcwVYxSckKhSKkl9Tq9VIJksrM4FGWMPFzGYVZjarmLJisz2ALlcQiQIDungyjU5L\nLzotveBy2GjQSNGil6NJL4NwimyVxwJOQa/KKmopPGEfzH4buvq6EUqEmfOyVLZ/AqTlY6iZCjAj\nFILq6QgvB08mhWLeXCjmzUUmFkOky4KIuQtRmw1UgaNFOhxmJkCy+TUQNzVC3NwEkamhatyKWSwW\n5Eoh5Eoh2udoEQkl4HLQZcX+3mhRBCLojyHopydACsV8aPVyaA0yqGqnjlvxsHYk27dvx9tvv41n\nn30W9fX1zPFIJILvfe97WLx4MW6//fYxXehoGO8dSTkyWQoObxgn7QGYHYGyFWAsFgv6OjEjKvIp\n2ixVaSiKQl8sgC6/DV3+7iErwBRCGZoUDWhUGKAmyfoisqkUojYbnazvsiCbLFNUwuZA1GCEuKUZ\nosZGcEXVueNLxFNw5SrAejyRsrk8Hp92K9boZbSx5ASMQFQ0tHXPPffgzTffRDKZxNKlS6FWq+H3\n+7F//36EQiGsXLkS7FyDE4vFwtNPP125O6kAE1VICqEoCp6+GCMqvcHS4RkAqJUL0aKXoVkvR32V\nTIurBKFEGJZc+MsZ8pTNW+WT9U0KI/QyLbgkWc9AZTKIOZ2nrAAjyXqaogowV6jsXBUOh406jRRa\nvQxqnQw1gokRgaiokFx33XWn9eIvvvjiaZ0/1kwGIRlIXygOsyMIsz0A14CtciFFdi114ilrl11p\n8hb4Xf7uQW7FhXA5XDTI9GhSGoldywAoikLS50M4JyqFbsUD4SuVdF6luRE1Gk1VfvjJZrLo8Ubg\nctAVYPEhIhDKWhGdrNfLIR7HdoFJOyFxLJiMQlJINJ6C2UF7gHV7Qshky0xE43HQqJWhWS8jnfWn\nQTqTRnfQBYu/GxZ/N+Lp8v1AOqma2a1ISWd9EalAgJmfEnO6UC5ZzxEK6Z1KUxOERkNVdtZTFMUY\nS7ocQYSHiEBIZIJcCEwOherMRiDGREiSySRCBdPcCinnDjwRmOxCUkgylYHVXTpZXwibzYKhXsLk\nVaQi8kl6OGSpLDxhH1MBFoyX/n0H6M76RoUBTQoj6kSqqvyUXY50NIaopXSyvhA2lwdhgxGSlmaI\nGk3gCKpzBtBQyfpCagS83E5Fhlq1ZMwjEBUVkiNHjmDTpk3o7Owse4OHDx8e+WrHmKkkJIVkshSc\nvjAdAnMEEIyUr56rVwjRrJejWS9HnWJylSCOFxRFwR8P0sn6vm54Iz1lzxXxRWiUG9CkNJImyAFk\nUynEuu10v0qXBZl4uU/f9DhicXMTRE1NVZtXScTTtNW9IwifO4RMmXYBLpeDOo0EGt3Y5VUqKiTX\nXnstbDYbbrjhhrJlwFdfffXIVzvGTFUhKYSiKPQE4jjpoJP1Q3XW5/MqzXoZDPVj/6lmqhBJRmHx\n22Hxd8MecpWtxsnnVRpzJckCLqmyy0Nls4i73UwIrHxnfT6v0ghRUxMEGnVVOhZn0ll4PSG47fQM\nlXKd9YV5FY1eDkmF8ioVFZIFCxZg27ZtOO+88yqyuDNNNQjJQMLRJL1TcQYGDewqhM6rSNGkk6FR\nK4OA9KsMi2Qmhe6AExZ/N6wBR9HAriJYLGgl9WhSGNGoMEBO+lUYKIpCqs9PN0GauxB3ezBkXqXR\nBHFzM51XqZJ+lUKoLIW+3ijcuRBY4cCugYilNUxeRakSjbhfpaINiUajsWJTEAlnBomIj7ltdZjb\nVkfnVVwhmB2D8yrJVAbHbH4cs/nBZrGgqxOjSUeXFhNzyfLwC6ZAZqks3GEfuvw2WPzdCMb7myBB\nUXCFPHCFPPjI9gnkAhmTV1FL6qp6Zj2LxQJfpQRfpYRy0UKko1FELdaSeZVMLIZg5xEEO4+AxeFA\nZDRA1NQEcWNj1cysZ7ELp0DqEQ4lGFEZmFeJhBI4ccSLE0e84PG50OikTL/KWMysH9aO5K233sKj\njz6KBx54AHPnzp3QBo2lqMYdSTkyWQqungjMDtoHbKi8ikJaQ4fAdDJoiQ/YsCjMq1j8dngiPWUd\ni2u4NTDJ9WhUGGGU64gPWAHZVAoxu50OgXVZkImVD9XW1NfT3fVNTeDX1VZl/i8RT8OTGyHsdZXP\nq7DZbNSqxdDo6EZI4SmKcCoa2rJYLPjud78Lm80GAOCU8Nbp6Og41WXGDSIkpaEoCr1Bul+lyxmE\ne4hqEdoHTIomnRwmrRR8Ulo8LKKpGKx+B51XCbqQzpaOcbPZbOilGpjkBjQqDKS0uACKopBwuxnL\nlmRfX9lzuWIx068iNBiq0gcs71jsdtAJ+0S8dL8KQPuA5UVFXqK5ueLJdrPZjIsuugh1dXUlz7nl\nlltOdZlxgwjJ8IjGU+hy0qJic4WQKvuphi4tbtLJ0KQjli3DJZ3NwJEzl7QG7Igmy3/KVokUjKgQ\ny5ZiUsEgs1MZOLO+kHxpsbipsWotW/L9Km4HvVspnFk/kDq1BEtXNRcV31RUSObPn49HHnkEX/rS\nl07zNiYGREhOn3RuvHCXg55ZX84HDKDnqzTpZWjSyaFRiUgIbBhQFAVftDdn2WJHb7T8p2wBT9Af\nApNpwSMhMIZMIoGo1YZoVxciFltZHzCABYFGDVGjiQ6B1VZn308smmREpZQP2OovtUMi6+/lqWiy\nXafTMV5ahOqAy2EzO478JMguJ92v4h3wqaYnGEdPMI79nR4Ia7h0FZheDpOGhMDKwWKxUC+uRb24\nFksM8xFORGAJ0KXFjpC76A0eT8Vx1HcSR30nmRBYo8IIk1xf9SEwTk0NpNPaIJ3WBiqTQdzlZqrA\nUsFgwZkU4m434m43ej/em7PCb4S4qRECvb5quuuFIj6a2urQ1FaHdCoDrzsEtyMIf28MqjrRiO1Y\nhrUjeeONN7Bjxw488MADmDdvXskcyUSG7EgqSziazIlKcEjLFiYEppUR1+LTIJlJwZ6zbLEGHGXn\nqwDFIbB6cW1VV4EVwpQWWyyIdlmGtGwpDoGZqnbEcCkqGtr66le/CovFgniuI3Vg1RaLxcKnn346\nyiWPHURIxo5UOoNuT5hJ2EeHSOwppfkQGF0FxiEhsFOSpbLwRnqYRsi+WPkGPgG3Bg1yuhHSSKZB\nFpGJx+nSYosFUasN2SFmKAnU+RBYY9VOg8xT0dDW6tWrK7UuwhSDx+Uw1it5K/x8XmVgCKwvFEff\nkTj+c8SDGj4HJo0MTTopaYQcAjaLDY2kHhpJPZYZFyCUCMMacJQOgaUTONZjxrEec85gUpPLrZBG\nSI5AAGn7dEjbp+es8F20F1iXZVB3fdzjQdzjQe/efeCKxRCZGvoNJquwEXI4EPdfwpgRjiZhcYXQ\n5QjA5gmXHTHMYrGgqxWhMZeTUcmIF9hwSDEhMDusATtiQ4TAZAIpbdkiN0ArqSdeYDkoikLK76d3\nK10WxJ3OslVgLDYHQoOeqQLjyaRneLVnnjFx/z1w4AA++OADeL1efOc734HZbMaMGTOgUqkqsuix\nggjJ+JPOZGH3hGF2BtE1xDRIAJCK+IyoGNUScIkX2CnprwKjRWWoaZA8Dg9GmQ4mhR4Ncj1EvOor\niy0HUwVmsSBqsSIzhKMHX6lkQmACrXZKeoFVVEhSqRQ2btyIN954A1wuF5lMBi+//DIeffRRnDhx\nAi+88AIaGhoqegOVhAjJxCJvMJnvWRmqEZLLYcOoljDCQuzwh0ckGYU1YIfV7xiyERIA6sW19Mx7\nuZ7Y4RdAG0x6EO2yIGKxDDm4i82vgchkhLixESJTAzjCqSHOFRWSRx99FC+++CIefvhhrFq1CgsX\nLsQf//hHSKVSfPe738Xs2bOxbdu2it5AJSFCMrGJxlOwukOwOIOwukJIlBlHCtBjhvN5FWLbMjzS\n2QycITcjLKFEuOy5Qp4ADXI9THIDsW0ZQCoYovMqFiti3XZQZaZqFvWsNDZOatuWiibb//KXv2D9\n+vW44IILkCkwUjOZTFi3bh3+53/+Z/QrJlQtIgEPMxpVmNGoYrzAupxBWJzBQbPrewIx9ARi2N+Z\nT9hL0aiTwaSRQiQgf/RKwWVz0CCnw1hnNdBeYHlRcYWLZ9fHCnpW8hMhTXIDGuR6KASySfsHsRLw\nZFLI586BfO6cfi+wLiuiFsuA2fUDelZyCXtRowkioxHsSeZVOByGJSR+vx/Nzc0lv6ZUKhEOl/+E\nQyCcDpxc74mhXoKz5+kRCCdgcQXR5QjC7g0X9awkkv3OxSwWC2qlkA6BaWWoL+EbRMjNrRDKoRTK\nMV87C4l0Et1BJ6x+O2wBR9GYYYqi4Ai64Qi68ZHtE0hrJDAp6N2KTqoBt4oT9mwej/b0amqiZ9f3\n9DK7lbjLjcKelXQkguDhTgQPd4LFZkOg00HcaIKoqRE8uXxK/J4OS0ja2trwxhtv4Oyzzx70UFV3\nzwAAIABJREFUtXfffRetra0VXxiBAABySQ3mtdVjXls907OS360UJuwpioK7Nwp3bxQfH3RBJOCh\nUUuHwIwaCQR8Ul5cihouH62qRrSqGpmeFVvAAWvAMShhH0qEcdB9FAfdR8Fhc2CQaZmdjqyKO+xZ\nLBZq6mpRU1cL5eJFBT0r1lzPSoE4Z7OI2e2I2e3ABx+CJ5VC1NgIcZNpUnfYD2vV3/ve93Dbbbch\nFArh3HPPZRoQX3/9dTz//PN46KGHxnqdBMKgnpV8wt7qCsLZU5ywj8ZTONzVi8NdvWCzWNDW0nNW\nGnVSUl5chsKelSWG+Ygko7AFnLAG7OgOOpHO9CfsM9kMrH47rH47AEAhlKNBriPlxRjQs5JP2Oca\nIRM+X9G5qVAIgY4OBDo6+uesmEwQNZrAk02e3p9hl//+6U9/wtatW+H1epljCoUC69atw3XXXTdm\nC6wEJNk+9Ykn0kzC3uIKIZ4sX6UkEfLQmJsIaVRLiB/YMMhkM3CFvbAG6BCYPxYsey6Xw4WxYLci\n4VfH4KnhkA5HELXSHfYxmx3ZdPkyeL5CQedVTCYI9bpxscQfkz4SiqJw/Phx+P1+SKVStLW1gTsJ\ntmJESKqLbJaCpy8KqyuELmcQnr5o2XPZbBb0dRI6DKaTQSmtIbuVYRBMhOkQmN8OR8iNTNkKJtoP\nLC8qWnE9MYDNQXfYO5lmyKHm17O5PAiNBogaGyAymcCTnplmyFELyTe/+U38+Mc/nhL5DyIk1U00\nnoLVFYLFFYTVHSoaNTwQmZjPVIIZ1RLwuGS3ciryc1ZsQccpy4t5HB4MMi1MOWER84lBYp5UIMDk\nVWJ2e9Go4YHwlcpcCKwBQt3Y7VZGXf778ccfI1JU0kYgTE5EAh5mNKkwo0mFbJaCqzcCi5MWFt8A\nP7BgJImOkz3oONlDdivDhMvm0A2NCgPOaqAQSISY3Yoz7CnyA0tlUujqs6Grj562qhIp0SDXkd0K\nAJ5cDsW8uVDMm4tsOo24w8GUF6dCoaJzk319SPb1wf/ZZ8W7lQbTuFi3TPy4FIFQQfLioK+TYOVc\nHcKxFKwuOq9ic4eQLGiGzGYpdHtC6PaE8P4BB23dkhMVQz3JrZSCxWJBIZBBIZBhrmYGUpkUHCE3\nUwkWThR/OO2N9qE32ofPnIeY3UpeWKo5t8Lmcukdh8kEijobqUAAUYsVUYsVMYezqBkym07RM1i6\nugDkdyt038pY7lYKIUJCqGokQh5mNddiVnMtMlkK7p4ILDlhGbhbCUWLdyu6WjEatTKYtFLUykkl\nWCl4HB4aFUY0KoygKLoZMi8qrpAHWar8bkUplPfnVqq4EozFYoGvUICvUEAxf16uGdKBqJUWlvK7\nlQP0bsWgZ4RlrCrByuZIZsyYgXnz5kEiOXV9OIvFwjPPPFPxxVUKkiMhjIShdisDkQh5MGmlMGlI\n38pwod2L6d2KLTh4t1IIl8OFXqohfSsDoCiqf7ditSFmdwxh3UKHz0QNud2KXndKW/yKWKSk02mk\nUuXL0wiEqUzp3UoIVtfgWSvhWAqHzL04ZKb7VjQqEWPdQrrsS8Pj8NCkNKJJWbxbsQUcg3Ir6Uy6\nqG9FLpChQa6DUaaDXqoBl1Odwl1yt+KgK8GiNtugSrBUIIBAIED3rbA5EBp0tLCYTOApFSP+PR1y\nR7Jr1y7MmzdvRBeeSJAdCaHSRGIp2NwhZrcyVN+KsIaLBo00t2MhnmDDIZVJwRnywBakhSUYL18J\nxmazoZOo0SDXwyjXQSmYGrYjlSDpDyBmszGVYNl0+d9TcaMJ2v/3paKcSkVNGwkEQjFiYXElWL5v\nxeIKwtMXKzZCTKRx1NqHo9Y+AEC9QkiLilYGrUoEDpm3Mggeh8dUggFAIB6ELeCELeAY1LeSzWZh\nD7pgD7oAGyDmi2CU0Ql7g0yLGu7UM0kcLnyFHHyFHPK5c+i+FYeTnrdisw2yxY9YrEgFg+Arlaf9\nOkRICIRRwmbTFizaWjGWzdYilkjD5qZDYFZ3eNAce68/Bq+fdjDmcdkwqvt3K3JJzTjdxcRGLpBB\nLpBhjqYd6WwGrrAH3TlhGTjHPpKM4ojvBI74TgAsFtTiWnq3ItOiXlwLNqs6hZvF4UDUYISowQhg\nJdLhMKJWGyIWK5I+HwQ6LXhy+YiuXVZILr/8cihHoEwEQrUjrOFiukmJ6SYlPbnQH4fVTc9acfoi\nyBbsVlLpLMyOAMwO+o+hQlLDhMFIiXFpuGwOjDI6P7KiYRHCyQi6Ay7YAg7Yg04kMwXCTVHwhH3w\nhH3Ybz8APpfPfG+DXFfVDZFciQSyWTMhmzVz1NciM9sJhDNIMpWB3Rumh3i5QwhGkmXPzZcY54Wl\nXkGS9qciS2XhifSgO+CALeCEN9oLDPEnTimUwyjXoUGmh1aqrmpr/FKQHAmBMAHh84odjP3hBKyu\nEKyuEBzeMFKZ/kqlbJaC3RuG3RvGRx3OoqR9g1oKsZAk7QfCZrGhldRDm3Mwjqfi6A660B10ojvo\nRDRZXG3XFwugLxbA565OcNgc6KRqesdCkvanBRESAmGcYLFYUEoFUEoFmD+tHplMFg5fBFY3XQk2\nsCFyYNK+TiGkhUUjha5ODC5J2g9CwBOgrbYJbbX0AKq+WCBXCeaEa0CJcSabQXfAie6AE7ABIr4I\nRpkWRpkOBpkWQp5gHO9kYkOEhECYIHA4bDRopGjQ0F5JkVgKNg+9W7G5Q4gliks3ff4YfP4Y/nPE\nAy6HDX29GKbc95OZK4NhsVhQiRRQiRSYr53FlBjndysDrfGjySgzdhgA6sQqZreiEddVbad9KYiQ\nEAgTFLGwf5Y9RVHw9sWY3YqzJ4JswdjhdCbLhMgAQCzgMWEwo1pCeldKMLDEOJQIw86EwVxIpovz\nV75IL3yRXnzqPAguhwudRA1jrimy2ufZEyEhECYBLBYLapUIapUIS2ZqmKS9zR2C1R2CP5QoOj8S\nT6HT0otOC90rUK8QwkjCYEMirZFgRn0bZtS3IUtl4Yv0ojvohC3ghCfiK+oNSmfSTBc+QMJgE0pI\n9uzZg29+85slv7Z8+XI899xzeO+997BlyxaYzWY0NjZiw4YN+OIXv3iGV0ogjC+FSXuAtr+35XYr\nNs/gmSv53pV8GExXJ2byK8RwcjBsFhtqSR3Ukjos0s9FMp3MuRg7YQ85B3XalwqDGXLCopHUT/lq\nsAlV/ptMJhEY4A3z/vvvY9OmTXj66aeh0+lw+eWX4+abb8aFF16IV199FTt37sTu3bsxbdq0stcl\n5b+EaiLfad/tCcPqCsHVU9y7MhCRgIcGtQQNGimMGikkpBrslATjIaYazBF0FfeuDCBfDZYXFpVw\n5J5WZ5oxGbV7pgmFQrjooovw1a9+FRs2bMA999wDs9mM559/njnn+uuvR1NTE+67776y1yFCQqhm\nThUGG4hKJkCDWgqjRkKaIodBlsrCG+mhhaVEGGwgAp4ARpmWEZaJ3BQ5JfpInnzySfD5fPzXf/0X\nAGDfvn246KKLis5Zvnw5Xn/99fFYHoEwKRgYBgtFC8Jg7vAgw8neYBy9wTg+O+4Fm0Xbvxg1Epg0\nUqiVIrDZk+PT9JmCzWJDI6mHRlKPxQVhsHzSPhgvnhcST8VxvKcLx3u6AAAKoZwRFp1UAz5n8u0I\nJ6yQ9PT04He/+x3uvfdeCIVCAIDL5YJGoyk6T61Ww+VyjccSCYRJiVTEZ+zxs1kKPn8MNg8tKk5f\nGJmCarAsRcHhC8PhC+Pjgy7U8DgwqCXMjkUhIeOHB8Ln8tGkbECTsgFAYTUYbSyZSBfvCP2xAPyx\nADrcR8BisaCR1MGQS9qrRbWTYvzwhBWS//3f/0VtbS0uu+wy5lg8HgefX+zkyefzkUgMvVUnEAil\nYbP7q8EWz9Aglc7C4Quj2x2GzTO4KTKRyuCkPYCTdjqXKRHymN4XUmZcmsJqMIqi4Iv2MsIysCmS\noii4Ql64Ql7stx8Aj8PLddtrYZjAZcYTVkj+8pe/4IorrgCvYIJXTU3NoEFbyWSS2bEQCITRweOy\n0aiVoVFLj2SNxlPo9oSZUFg4Vvz+C8dSONzVi8NddJlxrVyIBg29Y9HXi8HjkvxKISwWC/XiWtSL\na7FANxvpTBrOsIexwe+J9hWdn8qkigZ6ifgiGKQaGHKhsImSX5mQQnLs2DFYLBZccsklRcd1Oh08\nHk/RMY/HMyjcRSAQKoNIwCtyMvaHEkwYzO4NDxo/3BOIoScQw6dHvbS9vorOrzSopVCrROCQ/EoR\nXA6XGR8MALFUnBEVe8g1aPxwNBnFsR4zjvWYAdD5FYNMA4NUB71UDf44zV6ZkEKyb98+1NfXo7W1\ntej44sWLsXfv3qJje/bswZIlS87k8giEqoTFYkEpE0ApE2BeW31RmXGpbvtstji/wuOyoa+ToEEj\ngVFN+ldKIRzgDRZMhJgwmCPkHtRtn8+vHHQfZWav6KUaGGVaaCT1Z8zGZUIKyeHDhzF9+vRBx9es\nWYMrr7wSTzzxBC655BK89tpr+Oyzz3Dvvfee+UUSCFVO4UCvJTM1SKUzcPgiZfMrqXQWFlcQFhft\naSWs4cKopnMrDRopZOLqnWRYChaLxQz0mqWeTnfbR/tgDzphD7oH5VcKZ6986jwIDpsDraQ+l7jX\noFakHLOhXhNSSDweD+QlJnW1t7djx44d2LJlC371q1+hpaUFv/jFLwbtXAgEwpmHx+WUzK/Q/wbP\nXokl0jhm68MxG50XkIn5jLCQxP1g2Cw21OJaqMW1WKibw0yKzIfCfNG+otkrmWymfwQx6GoyvVQD\nfS7HUsnE/YRuSKwUpCGRQBh/AuEEIyql+lcGUisXMqJCGiNPTTydgCPohiPkKtm/MhARX5QTFVpY\nJHzxoHOmREMigUCYOsglNZBLajC7pZYZQWzzhNDtCcHpjRQN9QL6E/efHaMbI+uVQmbHQownByPg\n1qBFZUKLygQACCcisIdcsAfdsAediKXiRedHk1Ec7zHjeC5x36w04byWs0aUVyFCQiAQzjisnDDU\nK4VY1K5GJpOFuzeKbi/dw+LqHZC4pyi4e6Nw90axv9MNDpsFXZ0YRjU9255UhA1GUiNGe00r2uta\n6Yq7eBD2oAuOkAuOoHuQP5i5z4pQYh4UwsFphVNBhIRAIIw7HA4b+noJ9PUSLJuF/sR9LhTm88eL\n/KsyWYrJvwBgKsLoUJgUdQpSEVYIi8WCUiiHUijHHE07k7h35PtXYn7oJGrIBNIRXZ8ICYFAmHAM\nTNzHE2nYvWFGPPpCxWGagRVhAj4XhnoxDDlhUUqJlUshhYn7BbrZo74eERICgTDhEdRw0WpUoNWo\nAEB31Ns9IUZcBlaExZNpnLAHcCJn5SIS8GCo70/cyyV8IiwVhAgJgUCYdEiEPLQ3qtCeG0McjCSZ\n3YrdG0Y0Xhz/j8ZTRaXGEiEvJypSGNQS0sMySoiQEAiESQ2LxRpUEdYXSqDbE4LdE0a3NzxoYmQ4\nlkKnpQ+dlsIeFnq3YqiXQCIiwnI6ECEhEAhTChaLBZVMAFXOyiVfamz30sJi90UGeYQFI0kcMvfi\nkJk2n1RIamAoEBYxmRo5JERICATClKaw1HjBdDWyWQpef4zZsTh9g3tY/OEE/OEEDp7sAQAopDUw\n1ksYcSFd98UQISEQCFUFm82CRiWCJjeDJZPJwt0XhcNLlxu7eiJIDxSWUAL+UAIdOWFRyQT0bkUt\ngb5OXPXCQoSEQCBUNRwO3YOir5NgyUwN0rnmSLs3DHtOWAqnRgL944g/P+EDANTKBNBXsbAQISEQ\nCIQCuBw2kxvBLCCdycLpi8DhpSvCXL3Roq57AOgJxtFTxcJChIRAIBCGgMthM+OEAbr50dUTgd0b\nhmOYwpIPhenrxVMyx0KEhEAgEE4DHre8sNg9Ybj7BgvLwFCYUiqAoV5M71qmQFUYERICgUAYBYOF\nJQNXT3TIHUtfKI6+UJxJ3iukNfSOpU48KftYiJAQCARCBeFxOSV3LHSOJQJ37+Dkfb4qLF9uLBPz\nc8JCh8Nk4olt6UKEhEAgEMaQgTsWpiosZ+dSqiosGEkiGOnF4S66QVIi5OVyLLS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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -945,7 +945,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -966,7 +966,7 @@ "72.299625390403094" ] }, - "execution_count": 14, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -986,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1007,7 +1007,7 @@ "20.514978275278718" ] }, - "execution_count": 15, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1031,14 +1031,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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KbSwWq1OWJSCGlVNUjTOXiyCRygC0Dg4YEuaJPgFOBo6MEKJrGmWMFStWoKys\nDBwOR/7nXrLJz8/He++9p9MgSefHMAwu3eTjvxcL5MnGjMvBs0/7U7IhpJtQe4VTXn5/SZEDBw7g\nmWeekVf7fNBff/2FpKQk3URHugSJVIYzlwuRU3R/cIC9jRkmDPaHvQ0NDiCku1CbcJYuXYq//voL\nQOvts7feektlP4Zh8NRTT+kmOtLptUikOP53PoorGuRt3q42GD3IlypzEtLNqP2J/+STT3D+/Hkw\nDIPFixdj1qxZ8PHxUejDZrNha2uLqKgonQdKOh9xixTHzuWj9O79ZNM3wAlP9/ekwQGEdENqE46r\nqyuee+45AIBUKsWIESPg4EBLwhPNiFukOPpXHsqq7peyeLKvOyKCXQ0YFSHEkDS6p/Hiiy9CLBYj\nMzMTYrFY3i6TySAUCpGSkkIDB4hc8z/Jhv9AsokO9UD/QBcDRkUIMTSNEk5KSgrmzp2Lqqoqldst\nLCwo4RAAgEgswdG/8lAuaJK3Pd3fE6G9nA0YFSHEGGg0LHrDhg2wsrLC+vXrMXz4cIwcORJffPEF\nJk+eDBaLha+++uqxPnzZsmVKk0a/++47jBkzBv3798e4ceNw4MCBNveRlJSEoKAgpT98Pv+xYiKP\nT9QswZGztxWSzdAwL0o2hBAAGl7h3Lx5E5988gnGjh2LpqYm7N+/H8OHD8fw4cMhkUiwfft27Ny5\nU+MPZRgGmzdvxv79+xEXFydv/+GHH7Bu3TqsWLECYWFhSE5Oxr///W+Ymppi0qRJKveVlZWF3r17\nY9euXQrtjo608KM+CZsl+O3sbYU6NsMivBHiT+eBENJKo4QjlUrh5uYGAPD19UVOTo5829ixY7Fw\n4UKNP7CoqAiLFy9GTk4OPDw8FLb99NNPePnllxEbGwsA8PHxQXp6Og4dOqQ24eTk5CAwMBDOzvQt\n2lCaRC04cjYPVbWtyYbFYmEYlRYghDxEo1tqPj4+yM3NBQD4+/tDKBQiPz8fQOvAgcbGxrberiAt\nLQ3u7u44evQovLy8FLYtWbIEU6ZMUQyQzUZdXZ3a/eXk5CAgIEDjzyfa1SRqwa9JtxWSzYgB3pRs\nCCFKNLrCGT9+PNauXQsAmDJlCkJCQrBy5Uq89tpr2L59e7t+4cfGxsqvYB4WGRmp8Lq0tBTHjx/H\ntGnTVPaXSqXIy8tDRkYGJk6cCIFAgL59+2LBggXw9/fXOCbyeBqFrcmmul4EoDXZPDPQG0G+NHye\nEKJMoyvgbIKkAAAgAElEQVScmTNn4rnnnsPFixcBAMuXL8e1a9fw+uuvIzs7GwsWLNB6YAKBAG++\n+SacnJwwc+ZMlX0KCwvR3NwMsViMxMREbNy4EWKxGFOnTlU7oo5oR4OwBYeTcuXJhs1iYWSkDyUb\nQohaGl3hlJeXY8mSJfLX/fr1w+nTp5Gbm4uAgADY2tpqNaiioiK88cYbEIlE+O6772BjY6Oyn5+f\nH5KTk2FraytfTHTr1q2IiYnBkSNHMGPGDK3GRVo1NIlxOOk2ahuaAbQmm1GDfNHTy97AkRFCjJlG\nVzgvvPACfvvtN4U2GxsbhIWFaT3Z3LhxAy+99BLYbDZ++ukneHt7t9nf3t5eoUyChYUFvL29UVZW\nptW4SKu6RjEO/S9XIdmMpmRDCNGARgmHxWKBx+PpOhbcvn0bM2bMgKenJ3744Qe4u7u32f/06dMI\nCwuDQCCQtzU0NODOnTvo1auXrsPtdmobmvFrUi7qGltXm2CzWRj7VA8EULIhhGhAo1tq77zzDtas\nWQORSITg4GBYWloq9dHGvJePPvoIXC4Xa9asgUQiQWVlJQCAw+HI13ETCAQwNTWFjY0NBg4cCGtr\nayxYsAALFiyAVCrF+vXrwePx1A5MII+npr412TQIW6t0ctgsjHvKD77u2r3CJYR0XRolnM8//xzN\nzc2YPXu22j63bt3qUCD5+fm4fv06AGDMmDEK23x8fHDq1CkAQFxcHCIjI7Fq1SrY2dlh7969WLt2\nLeLj4yGRSDB48GB88803MDOjOivaUl0vwpGk24rJZrAffN0o2RBCNKdRwlm8eLFOPvzbb7+V/7+f\nnx+ysrIe+Z4//vhD4XVAQAB27Nih9dhIq+o6EX5Nuo1GUWuyMeGwMX6wH7xdVQ/kIIQQdTReLZp0\nP1W1Qhw5m4emf5KNKYeN8dF+8HKhZEMIaT+NSy4yDIPff/8d58+fR2VlJRYtWoRr164hJCSEJll2\nQVW1QvyadBvCZgkAwNSEjWej/eHhbG3gyAghnZVGo9QaGhowdepUvP/++zh37hySkpLQ0NCAX3/9\nFZMnT0ZmZqau4yR6VFktxOH/3U82XFMOJj4dQMmGENIhGiWcNWvWoLCwEIcOHcKpU6fAMAwAYOPG\njejRowc2btyo0yCJ/lRUN+HXs7kQiR9MNv5wd7IycGSEkM5Oo4Rz6tQpzJs3D7179waLdb8WvY2N\nDd566y2kp6frLECiP9X1Ihw5exvNYikAwIzLQeyQALg5UrIhhHScRs9wmpqa1M6zMTMzQ3Nzs1aD\nIvrXJGrB0b/ylJKNC095zhUhhDwOja5wQkJC8PPPP6vc9p///Ae9e/fWalBEvyRSGU6cvyNfQcCU\nw0bs05RsCCHapdEVzpw5c/D6668jLi4OMTExYLFYOHnyJHbt2oXTp0+3q9onMS4Mw+DUpULwq1pr\nGrH+WYjTxYGSDSFEuzS6womKisKePXvAYrHwxRdfgGEY7Ny5E3fu3MEXX3yB6OhoXcdJdOT89TLc\nLq6Rv44O9YCfh50BIyKEdFUaz8MZNGgQDhw4gMbGRtTW1sLGxkZt2QDSOWTcvov0rAr569Bezgjt\nRaW6CSG6oXHCAYC///4bKSkpqKurg6OjIwYNGoTw8HBdxUZ0qKCsDmfTS+Sv/TzsMLifhwEjIoR0\ndRolnJqaGrz11lu4cuUKTExMYG9vj5qaGmzZsgVDhgzBli1bwOVydR0r0ZK7NUL8fvEOZP/Mp3Lh\nWWJUlA/YbNYj3kkIIY9Po2c4iYmJyM/Px9atW3H9+nWcO3cO165dw6ZNm3DlyhWsW7dO13ESLWlo\nEuPYuTy0SGQAAFsrLiZE+8HUhGPgyAghXZ1GCefs2bP48MMP8cwzz8gnfrLZbIwaNQoffPABjh07\nptMgiXaIW6Q49ne+vMyAmSkH4wf7wdLc1MCREUK6A40rftrbq67q6OLiArFYrNWgiPbJZAx+v3gH\nd2uEAFpLQ495sgcc7SwMHBkhpLvQKOH861//wsaNG+UVOO9pbGzEnj178PLLL+skOKIdDMPgbHox\nCvn18rZhEd5U04YQolcaDRoQCATg8/l45plnMHDgQLi4uKCmpgapqamor6+HmZkZ/u///g9A69XQ\nrl27dBo0aZ/07Epk5FXJXw94whVP+DkYMCJCSHekUcLJzc1Fr169ALSuq3bnzh0ArdU2AUAoFOom\nOtJhucU1OH+tVP460IeHqBA3A0ZECOmuNEo4P/zwg67jIDrAr2rE6UuF8tceTtYYMcBbYcVvQgjR\nl3ZN/BSLxaivr1e5Td1q0sQwahuacfzvfEikrcOf7W3MMO6pHuBwNHpsRwghWqdRwsnKysKiRYuQ\nmZkpL772sFu3bmk1MPL4RM0SHD2XJ6/YaWFmgmej/WFu1q7vF4QQolUa/QZavnw5ysvL8f7776sd\nHk2Mg/SfUgM19a01ijhsFsY95Qc7azMDR0YI6e40SjiZmZlYv349hg8frtUPX7ZsGaRSKT799FN5\n27lz57B27Vrk5+fD19cX8+fPx9ChQ9XuQygUYuXKlTh58iSkUinGjBmDRYsWwcqq+1WpZBgGf6QU\nofRug7xtZKQvlYcmhBgFjW7oe3l5abWqJ8Mw2LRpE/bv36/Qnpubi1mzZmHMmDE4fPgwRowYgXfe\neQc5OTlq97Vs2TKkpqZi586d2LFjBy5duoRly5ZpLdbO5NINPrIKq+Wvn+rrgZ7edEVKCDEOGiWc\nuXPnYtOmTUhNTe3wqgJFRUWIj4/Hjz/+CA8PxdWJ9+3bh/79+2PWrFkICAjA3LlzERYWhn379qnc\nF5/Px7Fjx7B8+XL0798fAwYMQGJiIo4fP47y8vIOxdnZ3MoX4PKt+8cc4u+IsCAqNUAIMR4aJZxe\nvXpBJpNh2rRpCA0NRZ8+fZT+aCotLQ3u7u44evQovLy8FLalpKQgMjJSoS0qKgopKSlq98VmsxVK\nJISHh4PD4SA1NVXjmDq7ovJ6/JlaJH/t42aDoWFeNPyZEGJUNHqG89FHH6G2thYvvfQSnJycOvSB\nsbGxiI2NVbmNz+fD1dVVoc3FxQV8Pl9l//Lycjg4OMDU9P7ikyYmJnBwcEBZWVmH4uwsBHUi/H7h\nfqkBJ3sLjBnUg0oNEEKMjkYJ59atW1izZg1Gjx6t02BEIpFSXR0ul6v2+ZFQKISZmfLoq7be05U0\niVpw7FwemlukAABrC1NMGOwHrimVGiCEGB+Nbqm5u7uDzdb9hEEzMzO0tLQotInFYlhYqF7R2Nzc\nXOUzJbFYDEtLS53EaCxaJFIcO5ePusbW4zc1YWP8YH9YW1IhPEKIcdIoi8yePRsbNmxAeno6pFKp\nzoJxd3dHRUWFQltFRYXSbbZ73NzcIBAIFGKSSCQQCARwcXHRWZzGICmtGBXVTQBaF0wdM6gHnHlU\naoAQYrw0uqW2a9culJWVycsQPHzbi8Vi4cqVKx0OJiIiApcvX1ZoS05OxoABA9T2l0gkSE9Pl/dJ\nTU2FTCZDREREh+MxVpkFAmQW3B/+PCTME77utgaMiBBCHk2jhBMTE6PjMFpNmzYNL7zwAjZv3ozx\n48fj2LFjuHr1KlasWCHvIxAIYGpqChsbG7i6umLs2LFISEjAypUrwTAMli5ditjYWLVXRZ1ddb0I\nSWnF8tdP9HBA34CODeQghBB90CjhzJ07V9dxAACCgoKwdetWrF27Frt374a/vz927NghL4MAAHFx\ncYiMjMSqVasAAImJiUhMTMTMmTNhYmKC0aNHY/HixXqJV98kUhlOXixAi+T+gpxDwjwNHBUhhGiG\nxahbjVOFa9eu4fz586isrMQbb7yB/Px8BAcHw8Gh8xXzKi4uxogRI3DmzBml+UDG6mx6Ma7l3gXQ\nukZa3PBAem5DCNGbjv7e1OgKp6WlBR999BFOnDgBExMTSKVSPP/889i9ezdu376N77//Ht7e3u3+\ncKK5/NJaebIBgOhQT0o2hJBORaNRaps2bcL//vc/bNmyBZcvX5aXKPj3v/8NCwsLbNiwQadBdncN\nTWKcvny/kJq/px36BFD9IUJI56JRwvntt98wb948jBw5UmGEmo+PD2bPno3k5GSdBdjdyWQMTiYX\noFl8f3Ln8Aiq2kkI6Xw0Sjg1NTXw8/NTuY3H46GhoUHlNtJxl2/yUXq3EQDAZrEwelAPKqRGCOmU\nNEo4PXv2xIkTJ1Ru++uvvxRGkRHtKa6oR0rm/YmwkSFuVNuGENJpafRV+c0338ScOXNQX1+PYcOG\nySd6Hj9+HN9++618iDLRniZRC04lF8qfl3m52CA8qGuvnkAI6do0SjijR4/GqlWrsG7dOpw8eRIA\n8Mknn8De3h6LFy/GhAkTdBpkd8MwDM5cLkKjqHVdOQszE4yM9KEVoAkhnZrGDwMmTZqE2NhY5Obm\noqamBjY2NujZsydMTOh5grZdzalEAb9O/vqZSB9YWZi28Q5CCDF+ap/hxMfH4/bt2wptLBYLvXr1\nwsCBAxEcHEzJRgfKBU04f/1+LZ+wIBf4utE6aYSQzk9twrl06RIaGxv1GUu319wixX8v3oFM1vrc\nxtXBEoNC3AwcFSGEaIfui9wQjTAMg/+lFsnr23BNORgV5QsOh04RIaRroN9mRuLWHQFyimrkr4dF\neMHOWrmaKSGEdFZtPoRJTEyEtbX1I3fCYrHw5Zdfai2o7kZQJ8LZ9BL56xB/R/Ty5hkwIkII0b42\nE45EIlEq+Uy0SyKV4b8X7kAibS054GBrjuhQKjlACOl62kw4K1asQL9+/fQVS7d07koJqupEAAAT\nDhujB/nC1ITudBJCuh76zWZAucU1yMirkr9+ur8nHO2o5AAhpGuihGMgdY1i/JlSJH/d08sevf06\nXyE7QgjRlNqE89xzz4HHowfXuiC9V3KgpbXkgK0VFzERXlRygBDSpal9hvPZZ5/pM45u5dKNMvCr\n7pccGBXlC3MurdpACOna6JaanhXy65D6QMmBQX3c4eZIJQcIIV0fJRw9ahK14NSl+6WifVxtEBbk\nbMCICCFEf4zqPk5ycjLi4+NVbouKisK+ffuU2ufMmYPff/9doe3JJ5/E3r17dRHiY2MYBqcuFULY\nLAEAWJqb4plIH3puQwjpNowq4YSFheHcuXMKbX///TcWLVqE//u//1P5nuzsbHzwwQd47rnn5G1c\nLlencT6O9KxKFJXXA2hdmWFkpA8szankACGk+zCqhMPlcuHsfP8WU319PT7//HO8/vrrePrpp5X6\ni8ViFBYWol+/fgrvMzb8qkZczLhfciA8yAXerjYGjIgQQvTPqJ/hbNu2DVwuF++8847K7Xl5eZBI\nJAgICNBzZJprkUhxMrkAsn9KRbs7WiGSSg4QQroho004VVVV+O677/DOO+/AwkL17Pvs7GyYmppi\ny5YtiImJwejRo7FhwwY0NzfrOVr1Lt0sl5ccMONyMDLKFxwqFU0I6YaM6pbag3788Uc4Ojpi4sSJ\navvk5uYCAPz9/TF16lRkZ2dj1apV4PP5WL16tb5CVetujRBXsyvlr6P7ecLWyvieLxFCiD4YbcL5\n7bff8Pzzz8PUVP2D9blz52LGjBmwt7cHAAQFBYHD4eD999/HwoULDbpSgkzG4M/UIvmtNE9nawT3\noJUbCCHdl1HeUsvJyUFBQQHGjx/fZj82my1PNvcEBgYCAPh8vs7i08SNvCqUC5oAABw2i5auIYR0\ne0aZcFJSUuDs7PzIwQBz5sxRGlCQkZEBLpcLHx8fXYbYpgZhCy48MCot4glX8GzMDRYPIYQYA6NM\nOLdu3ZJfqTxILBajsrISYnHrQ/jRo0fjzJkz+Prrr1FYWIjff/8dq1evxowZM2BlZbjlYs5dKYH4\nn4U57W3MEBHkYrBYCCHEWBjlM5yKigrY2dkptaenpyM+Ph779u1DVFQUxo0bB7FYjC+//BIbNmyA\no6Mj4uPj8eabbxog6lZ3yuqQW1wjfz0swhscjlHmdUII0SujTDg7duxQ2R4VFYWsrCyFtkmTJmHS\npEn6COuRWiRSnE0vlr9+oocDPJ2tDRgRIYQYD/rqrUUPzrkx55rgqX4eBo6IEEKMByUcLXl4zs3g\nfh6wMDPKC0hCCDEISjhaQHNuCCHk0SjhaIHSnJtwmnNDCCEPo4TTQY0Pz7kJdgXPlubcEELIwyjh\ndNC5q4pzbsKDac4NIYSoQgmnAwrK6pBTdH/OTUy4F0xozg0hhKhEvx0fU4tEiqQH5twE+zrAy4WK\nqhFCiDqUcB7T5Yfm3AwOpTk3hBDSFko4j+FujRBXaM4NIYS0CyWcdmIYmnNDCCGPgxJOO2U8MOeG\nTXNuCCFEY5Rw2qFR2IIL1+/PuRlAc24IIURjlHDaQWHOjTXNuSGEkPaghKOhh+fcDKU5N4QQ0i70\nG1MDLRLZQ3NuePB2pTk3hBDSHpRwNHD5Jp/q3BBCSAdRwnmEqlrlOTeW5qYGjIgQQjonSjhtaJ1z\nUyyfc+PhRHNuCCHkcVHCacONvCrwqxoBtM65GRZBc24IIeRxUcJR4+E5NxFBLjTnhhBCOoASjhrn\nrpag+YE5NxFPuBo4IkII6dyMLuHk5uYiKChI6U9KSorK/tevX8eUKVMQGhqKUaNG4ddff+1wDAV8\nmnNDCCHaZnRLHGdnZ4PH4+Ho0aMK7fb29kp9BQIB3njjDUyYMAGffvopzp8/j4SEBDg5OSE6Ovqx\nPr9FIkNSGs25IYQQbTPKhNOzZ084Ozs/su+BAwdgbW2NhIQEsNlsBAQE4ObNm/jqq68eO+Gk3KI5\nN4QQogtGd58oJycH/v7+GvVNSUnBwIEDwWbfP4zIyEikpaWB+Wcoc3sI6kRIz7o/5+apfu4054YQ\nQrTEKBNOaWkpJk+ejMGDB+O1117DtWvXVPbl8/lwdVV8mO/i4gKhUIjq6up2f3ZxRb3CnJsneji0\n/wAIIYSoZFQJRyQSoaioCA0NDfjwww+xfft2uLi4YNq0abh9+7bK/lwuV6Ht3muxWNzuz/fzsIO9\ntRkc7SwwYqA3zbkhhBAtMqpnOObm5rh8+TK4XK48caxatQo3btzADz/8gKVLlyr1fzix3HttYWHR\n7s+3seRi2tgnHjN6QgghbTGqhAMA1tbWCq/ZbDZ69uyJsrIypb5ubm6orKxUaKuoqIClpSVsbGhk\nGSGEGBOjSjgZGRmIj4/Hvn370KdPHwCAVCpFZmYmxowZo9Q/IiIChw4dAsMw8ttfycnJCA8PVxhI\noIpU2jqpk8/na/koCCGka7r3+/Le78/2MqqEExwcDE9PTyxbtgzLly+HpaUldu/ejerqasTHx0Ms\nFqO2thZ2dnbgcrmIi4vDnj17sHz5crz66qs4f/48jh07ht27dz/ys+5dGU2dOlXXh0UIIV1KZWUl\nfH192/0+FvM444d1qLy8HGvWrMH58+chFAoRHh6OhQsXIjAwEMnJyfIroKioKADAlStXkJiYiKys\nLHh4eGD27NkYP378Iz9HJBIhIyMDzs7O4HA4uj4sQgjp9KRSKSorK9GnTx+Ym7d/bUmjSziEEEK6\nJqMaFk0IIaTrooRDCCFELyjhEEII0QtKOIQQQvSCEg4hhBC96HYJRyqVYt26dYiOjkZYWBhmz56N\nu3fvqu2viwJvunD37l189NFHiI6OxoABA/D6668jOztbbf85c+YoFbl77bXX9BewBoyhGJ+2JScn\nqzymoKAgxMfHq3xPZzhXy5YtQ0JCgkLbuXPnEBsbi379+uHZZ59FUlJSm/sQCoVYunQpoqKiMGDA\nACxZsgSNjY26DLtNqo7pu+++w5gxY9C/f3+MGzcOBw4caHMfSUlJKs+1ISecqzquuLg4pRgf7vOg\nxz5XTDezYcMGZvDgwcy5c+eYjIwM5sUXX2SmTJmism9VVRUTGRnJfPzxx0xubi6zb98+pnfv3sxf\nf/2l56jbJpVKmZdeeomZPHkyc/XqVSYnJ4eZPXs28+STTzICgUDle8aMGcPs3LmTqaiokP+pqanR\nc+RtO378OBMVFaUQY0VFBSMWi5X6dpZz1dzcrHQ8hw8fZoKDg5mzZ8+qfI8xnyuZTMZs3LiRCQwM\nZBYvXixvz8nJYfr06cNs27aNyc3NZTZs2MCEhIQw2dnZavc1f/58ZuzYsUx6ejpz+fJlZuTIkcy8\nefP0cRgK1B3T999/z/Tv35/59ddfmYKCAubnn39mQkJCmMOHD6vd186dO5lJkyYpnXOpVKqPQ1Gg\n7rhkMhkTGhrK/Pbbbwox1tfXq93X456rbpVwmpubmbCwMObgwYPytqKiIiYwMJBJTU1V6r9jxw5m\n+PDhCv84Fi5cyEyfPl0v8Wrqxo0bTGBgIJObmytva25uZkJDQ1X+MDQ3NzO9e/dmLly4oM8w223D\nhg3M1KlTNerbWc7Vw+rq6pjBgwcza9euVbndmM9VYWEhM23aNCYqKoqJiYlR+CW2dOlSZtq0aQr9\np02bxixZskTlvsrKypjg4GDm4sWL8rb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1062,7 +1062,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "metadata": { "collapsed": true }, @@ -1074,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": { "collapsed": true }, @@ -1094,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1302,7 +1302,7 @@ "data": { "image/png": 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ENQqI6xSNb8vCo33iMahHNABgWNw9KPjqP3jl4eHo1KYN7mrfHj2Ki/DL7787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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1322,7 +1322,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1331,7 +1331,7 @@ "array([ 1.])" ] }, - "execution_count": 20, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1349,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1358,7 +1358,7 @@ "array([ 2.])" ] }, - "execution_count": 21, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1369,7 +1369,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1378,7 +1378,7 @@ "array([ 3.])" ] }, - "execution_count": 22, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1396,7 +1396,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1405,7 +1405,7 @@ "array([ 3.])" ] }, - "execution_count": 23, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1423,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -1451,7 +1451,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1473,7 +1473,7 @@ "2.2996253904030937" ] }, - "execution_count": 25, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1491,7 +1491,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1576,7 +1576,7 @@ "0.011543084583978345" ] }, - "execution_count": 26, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1596,7 +1596,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1617,7 +1617,7 @@ "70.0" ] }, - "execution_count": 27, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1646,7 +1646,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1667,7 +1667,7 @@ "19.501444483698084" ] }, - "execution_count": 28, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1682,7 +1682,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -1697,7 +1697,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1719,7 +1719,7 @@ "-0.49855551630191641" ] }, - "execution_count": 30, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1730,7 +1730,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1822,7 +1822,7 @@ "0.13296078935466449" ] }, - "execution_count": 31, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1835,7 +1835,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1856,7 +1856,7 @@ "19.999999999999996" ] }, - "execution_count": 32, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1883,7 +1883,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 38, "metadata": { "collapsed": true }, @@ -1920,7 +1920,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1941,7 +1941,7 @@ "70.0" ] }, - "execution_count": 34, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1954,7 +1954,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 40, "metadata": { "scrolled": true }, @@ -1977,7 +1977,7 @@ "21.764705882352942" ] }, - "execution_count": 35, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1997,7 +1997,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2011,7 +2011,7 @@ "data": { "image/png": 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5Yl3EsVevXsjOzq7vyyCENCFO1SQ+/fRT6HQ6TJo0yeE+Fy9erLOgGlL6nbM4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\nBCGEEIf+H0W9gvkl+IxjAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2037,7 +2037,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -2056,7 +2056,7 @@ "63.109243697478988" ] }, - "execution_count": 37, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2075,7 +2075,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -2100,7 +2100,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -2121,7 +2121,7 @@ "61.428571428571438" ] }, - "execution_count": 39, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -2142,8 +2142,10 @@ }, { "cell_type": "code", - "execution_count": 68, - "metadata": {}, + "execution_count": 45, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "def run_and_mix(t_add, t_total=30, T_milk0=5):\n", @@ -2171,7 +2173,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 46, "metadata": { "collapsed": true }, @@ -2189,7 +2191,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -2292,7 +2294,7 @@ "data": { "image/png": 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6UgBaO2nRTl1XI8dqT1uK8GBMezuRRL8YS3YUgMlgoCM3j86CsxL3lp2nJ96L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"text/plain": [ - "" + "" ] }, "metadata": {}, From fe91fe41d2c9b745676e29c6b2d2aab8b0a5c78a Mon Sep 17 00:00:00 2001 From: Flynn Michael-Legg Date: Tue, 31 Oct 2017 16:41:23 -0400 Subject: [PATCH 16/19] proj fam --- code/chap07-fig01.pdf | Bin 13619 -> 13619 bytes code/proj2start.ipynb | 3311 ++++++++++++++++++++--------------------- 2 files changed, 1631 insertions(+), 1680 deletions(-) diff --git a/code/chap07-fig01.pdf b/code/chap07-fig01.pdf index d93e7c50d885c0fb96d52abd6b0a1a5dd7585460..bf06e93df29237dc7a95af99e46929f148dfde91 100644 GIT binary patch delta 20 bcmdm-wK;2pt0B9Ep|Odjk" + "" ] }, "metadata": {}, @@ -634,7 +624,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -664,7 +654,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -681,7 +671,7 @@ " returns: System object\n", " \"\"\"\n", " init = State(temp=T_init)\n", - " print(init) \n", + " #print(init) \n", " \n", " system = System(init=init,\n", " volume=volume,\n", @@ -690,13 +680,13 @@ " t0=0,\n", " t_end=t_end,\n", " dt=1)\n", - " print(system.init)\n", + " #print(system.init)\n", " return system" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -706,9 +696,9 @@ " T_array = linspace(80, 100, 5)\n", " for i in T_array:\n", " system = make_system(T_init=i)\n", - " print(i)\n", + " #print(i)\n", " run_simulation(system, update_func)\n", - " print(system.results.temp)\n", + " #print(system.results.temp)\n", " plot(system.results.temp, label='coffee')\n", " decorate(xlabel='Time (minutes)',\n", " ylabel='Temperature (C)')\n", @@ -717,215 +707,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 80.0\n", - "dtype: float64\n", - "temp 80.0\n", - "dtype: float64\n", - "80.0\n", - "run sim inittemp 80.0\n", - "dtype: float64\n", - "0 80.000000\n", - "1 79.420000\n", - "2 78.845800\n", - "3 78.277342\n", - "4 77.714569\n", - "5 77.157423\n", - "6 76.605849\n", - "7 76.059790\n", - "8 75.519192\n", - "9 74.984000\n", - "10 74.454160\n", - "11 73.929619\n", - "12 73.410323\n", - "13 72.896219\n", - "14 72.387257\n", - "15 71.883385\n", - "16 71.384551\n", - "17 70.890705\n", - "18 70.401798\n", - "19 69.917780\n", - "20 69.438602\n", - "21 68.964216\n", - "22 68.494574\n", - "23 68.029628\n", - "24 67.569332\n", - "25 67.113639\n", - "26 66.662502\n", - "27 66.215877\n", - "28 65.773719\n", - "29 65.335981\n", - "30 64.902622\n", - "Name: temp, dtype: float64\n", - "temp 85.0\n", - "dtype: float64\n", - "temp 85.0\n", - "dtype: float64\n", - "85.0\n", - "run sim inittemp 85.0\n", - "dtype: float64\n", - "0 85.000000\n", - "1 84.370000\n", - "2 83.746300\n", - "3 83.128837\n", - "4 82.517549\n", - "5 81.912373\n", - "6 81.313249\n", - "7 80.720117\n", - "8 80.132916\n", - "9 79.551587\n", - "10 78.976071\n", - "11 78.406310\n", - "12 77.842247\n", - "13 77.283824\n", - "14 76.730986\n", - "15 76.183676\n", - "16 75.641840\n", - "17 75.105421\n", - "18 74.574367\n", - "19 74.048623\n", - "20 73.528137\n", - "21 73.012856\n", - "22 72.502727\n", - "23 71.997700\n", - "24 71.497723\n", - "25 71.002746\n", - "26 70.512718\n", - "27 70.027591\n", - "28 69.547315\n", - "29 69.071842\n", - "30 68.601124\n", - "Name: temp, dtype: float64\n", - "temp 90.0\n", - "dtype: float64\n", - "temp 90.0\n", - "dtype: float64\n", - "90.0\n", - "run sim inittemp 90.0\n", - "dtype: float64\n", - "0 90.000000\n", - "1 89.320000\n", - "2 88.646800\n", - "3 87.980332\n", - "4 87.320529\n", - "5 86.667323\n", - "6 86.020650\n", - "7 85.380444\n", - "8 84.746639\n", - "9 84.119173\n", - "10 83.497981\n", - "11 82.883001\n", - "12 82.274171\n", - "13 81.671430\n", - "14 81.074715\n", - "15 80.483968\n", - "16 79.899128\n", - "17 79.320137\n", - "18 78.746936\n", - "19 78.179466\n", - "20 77.617672\n", - "21 77.061495\n", - "22 76.510880\n", - "23 75.965771\n", - "24 75.426114\n", - "25 74.891852\n", - "26 74.362934\n", - "27 73.839305\n", - "28 73.320912\n", - "29 72.807702\n", - "30 72.299625\n", - "Name: temp, dtype: float64\n", - "temp 95.0\n", - "dtype: float64\n", - "temp 95.0\n", - "dtype: float64\n", - "95.0\n", - "run sim inittemp 95.0\n", - "dtype: float64\n", - "0 95.000000\n", - "1 94.270000\n", - "2 93.547300\n", - "3 92.831827\n", - "4 92.123509\n", - "5 91.422274\n", - "6 90.728051\n", - "7 90.040770\n", - "8 89.360363\n", - "9 88.686759\n", - "10 88.019891\n", - "11 87.359693\n", - "12 86.706096\n", - "13 86.059035\n", - "14 85.418444\n", - "15 84.784260\n", - "16 84.156417\n", - "17 83.534853\n", - "18 82.919505\n", - "19 82.310310\n", - "20 81.707206\n", - "21 81.110134\n", - "22 80.519033\n", - "23 79.933843\n", - "24 79.354504\n", - "25 78.780959\n", - "26 78.213150\n", - "27 77.651018\n", - "28 77.094508\n", - "29 76.543563\n", - "30 75.998127\n", - "Name: temp, dtype: float64\n", - "temp 100.0\n", - "dtype: float64\n", - "temp 100.0\n", - "dtype: float64\n", - "100.0\n", - "run sim inittemp 100.0\n", - "dtype: float64\n", - "0 100.000000\n", - "1 99.220000\n", - "2 98.447800\n", - "3 97.683322\n", - "4 96.926489\n", - "5 96.177224\n", - "6 95.435452\n", - "7 94.701097\n", - "8 93.974086\n", - "9 93.254345\n", - "10 92.541802\n", - "11 91.836384\n", - "12 91.138020\n", - "13 90.446640\n", - "14 89.762173\n", - "15 89.084552\n", - "16 88.413706\n", - "17 87.749569\n", - "18 87.092073\n", - "19 86.441153\n", - "20 85.796741\n", - "21 85.158774\n", - "22 84.527186\n", - "23 83.901914\n", - "24 83.282895\n", - "25 82.670066\n", - "26 82.063365\n", - "27 81.462732\n", - "28 80.868104\n", - "29 80.279423\n", - "30 79.696629\n", - "Name: temp, dtype: float64\n" - ] - }, { "data": { "image/png": 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kv0hPcYkJY6ZqylfQS2TqZP/5oK8Xr7OZgKdjhM5XDJ+7DZ+7bbCC3oxaX4JU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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -945,28 +734,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "72.299625390403094" ] }, - "execution_count": 19, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -986,28 +763,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "run sim inittemp 5\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "20.514978275278718" ] }, - "execution_count": 20, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1031,14 +796,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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KbSwWq1OWJSCGlVNUjTOXiyCRygC0Dg4YEuaJPgFOBo6MEKJrGmWMFStWoKys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\np02bxixZskTlvsrKypjg4GDm4sWL8rb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1062,7 +827,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -1074,7 +839,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -1084,9 +849,9 @@ " T_array = linspace(5, 25, 5)\n", " for i in T_array:\n", " system = make_system(T_init=i)\n", - " print(i)\n", + " #print(i)\n", " run_simulation(system, update_func)\n", - " print(system.results.temp)\n", + " #print(system.results.temp)\n", " plot(system.results.temp, label='milk')\n", " decorate(xlabel='Time (minutes)',\n", " ylabel='Temperature (C)')" @@ -1094,215 +859,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 5.0\n", - "dtype: float64\n", - "temp 5.0\n", - "dtype: float64\n", - "5.0\n", - "run sim inittemp 5.0\n", - "dtype: float64\n", - "0 5.000000\n", - "1 5.170000\n", - "2 5.338300\n", - "3 5.504917\n", - "4 5.669868\n", - "5 5.833169\n", - "6 5.994837\n", - "7 6.154889\n", - "8 6.313340\n", - "9 6.470207\n", - "10 6.625505\n", - "11 6.779250\n", - "12 6.931457\n", - "13 7.082143\n", - "14 7.231321\n", - "15 7.379008\n", - "16 7.525218\n", - "17 7.669966\n", - "18 7.813266\n", - "19 7.955133\n", - "20 8.095582\n", - "21 8.234626\n", - "22 8.372280\n", - "23 8.508557\n", - "24 8.643472\n", - "25 8.777037\n", - "26 8.909267\n", - "27 9.040174\n", - "28 9.169772\n", - "29 9.298074\n", - "30 9.425094\n", - "Name: temp, dtype: float64\n", - "temp 10.0\n", - "dtype: float64\n", - "temp 10.0\n", - "dtype: float64\n", - "10.0\n", - "run sim inittemp 10.0\n", - "dtype: float64\n", - "0 10.000000\n", - "1 10.120000\n", - "2 10.238800\n", - "3 10.356412\n", - "4 10.472848\n", - "5 10.588119\n", - "6 10.702238\n", - "7 10.815216\n", - "8 10.927064\n", - "9 11.037793\n", - "10 11.147415\n", - "11 11.255941\n", - "12 11.363382\n", - "13 11.469748\n", - "14 11.575050\n", - "15 11.679300\n", - "16 11.782507\n", - "17 11.884682\n", - "18 11.985835\n", - "19 12.085977\n", - "20 12.185117\n", - "21 12.283266\n", - "22 12.380433\n", - "23 12.476629\n", - "24 12.571862\n", - "25 12.666144\n", - "26 12.759482\n", - "27 12.851887\n", - "28 12.943369\n", - "29 13.033935\n", - "30 13.123596\n", - "Name: temp, dtype: float64\n", - "temp 15.0\n", - "dtype: float64\n", - "temp 15.0\n", - "dtype: float64\n", - "15.0\n", - "run sim inittemp 15.0\n", - "dtype: float64\n", - "0 15.000000\n", - "1 15.070000\n", - "2 15.139300\n", - "3 15.207907\n", - "4 15.275828\n", - "5 15.343070\n", - "6 15.409639\n", - "7 15.475543\n", - "8 15.540787\n", - "9 15.605379\n", - "10 15.669325\n", - "11 15.732632\n", - "12 15.795306\n", - "13 15.857353\n", - "14 15.918779\n", - "15 15.979592\n", - "16 16.039796\n", - "17 16.099398\n", - "18 16.158404\n", - "19 16.216820\n", - "20 16.274651\n", - "21 16.331905\n", - "22 16.388586\n", - "23 16.444700\n", - "24 16.500253\n", - "25 16.555250\n", - "26 16.609698\n", - "27 16.663601\n", - "28 16.716965\n", - "29 16.769795\n", - "30 16.822097\n", - "Name: temp, dtype: float64\n", - "temp 20.0\n", - "dtype: float64\n", - "temp 20.0\n", - "dtype: float64\n", - "20.0\n", - "run sim inittemp 20.0\n", - "dtype: float64\n", - "0 20.000000\n", - "1 20.020000\n", - "2 20.039800\n", - "3 20.059402\n", - "4 20.078808\n", - "5 20.098020\n", - "6 20.117040\n", - "7 20.135869\n", - "8 20.154511\n", - "9 20.172966\n", - "10 20.191236\n", - "11 20.209323\n", - "12 20.227230\n", - "13 20.244958\n", - "14 20.262508\n", - "15 20.279883\n", - "16 20.297084\n", - "17 20.314114\n", - "18 20.330972\n", - "19 20.347663\n", - "20 20.364186\n", - "21 20.380544\n", - "22 20.396739\n", - "23 20.412771\n", - "24 20.428644\n", - "25 20.444357\n", - "26 20.459914\n", - "27 20.475315\n", - "28 20.490561\n", - "29 20.505656\n", - "30 20.520599\n", - "Name: temp, dtype: float64\n", - "temp 25.0\n", - "dtype: float64\n", - "temp 25.0\n", - "dtype: float64\n", - "25.0\n", - "run sim inittemp 25.0\n", - "dtype: float64\n", - "0 25.000000\n", - "1 24.970000\n", - "2 24.940300\n", - "3 24.910897\n", - "4 24.881788\n", - "5 24.852970\n", - "6 24.824440\n", - "7 24.796196\n", - "8 24.768234\n", - "9 24.740552\n", - "10 24.713146\n", - "11 24.686015\n", - "12 24.659155\n", - "13 24.632563\n", - "14 24.606237\n", - "15 24.580175\n", - "16 24.554373\n", - "17 24.528830\n", - "18 24.503541\n", - "19 24.478506\n", - "20 24.453721\n", - "21 24.429184\n", - "22 24.404892\n", - "23 24.380843\n", - "24 24.357034\n", - "25 24.333464\n", - "26 24.310129\n", - "27 24.287028\n", - "28 24.264158\n", - "29 24.241516\n", - "30 24.219101\n", - "Name: temp, dtype: float64\n" - ] - }, { "data": { "image/png": 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BkDhz5gzmzp2L//u//8PUqVO56RUVFXj22WfRtWtXrFmzBnFxcW5t/NChQ8jLy0NmZiai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\nevXqgPyuGvtc+/fvD7jvytrLL7+MQ4c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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1322,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1331,7 +895,7 @@ "array([ 1.])" ] }, - "execution_count": 25, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1349,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1358,7 +922,7 @@ "array([ 2.])" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1369,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1378,7 +942,7 @@ "array([ 3.])" ] }, - "execution_count": 27, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1396,7 +960,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1405,7 +969,7 @@ "array([ 3.])" ] }, - "execution_count": 28, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1423,7 +987,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -1437,7 +1001,7 @@ " returns: difference between final temp and 70 C\n", " \"\"\"\n", " system = make_system(r=r)\n", - " print(r)\n", + " #print(r)\n", " run_simulation(system, update)\n", " return final_temp(system) - 70" ] @@ -1451,29 +1015,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "0.01\n", - "run sim inittemp 90\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "2.2996253904030937" ] }, - "execution_count": 30, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1491,92 +1042,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 26, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "0.01\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01]\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01]\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01]\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01]\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01150871]\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01154231]\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01154308]\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01154308]\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "[ 0.01154308]\n", - "run sim inittemp 90\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "0.011543084583978345" ] }, - "execution_count": 31, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1596,28 +1071,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 27, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "70.0" ] }, - "execution_count": 32, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1646,28 +1109,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 28, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "run sim inittemp 5\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "19.501444483698084" ] }, - "execution_count": 33, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1682,7 +1133,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -1690,36 +1141,23 @@ "source": [ "def error_func2(r):\n", " system = make_system(r=r, T_init=5, t_end=15)\n", - " print(r)\n", + " #print(r)\n", " run_simulation(system, update)\n", " return final_temp(system) - 20" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 30, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "0.12\n", - "run sim inittemp 5\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "-0.49855551630191641" ] }, - "execution_count": 35, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1730,99 +1168,16 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 31, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "0.12\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.12]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.12]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.12]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.12]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.1317062]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.13283515]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.13295952]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.13296079]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.13296079]\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "[ 0.13296079]\n", - "run sim inittemp 5\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "0.13296078935466449" ] }, - "execution_count": 36, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1835,28 +1190,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 32, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "run sim inittemp 5\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "19.999999999999996" ] }, - "execution_count": 37, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1883,7 +1226,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 33, "metadata": { "collapsed": true }, @@ -1920,28 +1263,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 34, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "70.0" ] }, - "execution_count": 39, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1954,30 +1285,18 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 35, "metadata": { "scrolled": true }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "run sim inittemp 5\n", - "dtype: int64\n" - ] - }, { "data": { "text/plain": [ "21.764705882352942" ] }, - "execution_count": 40, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1997,7 +1316,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2011,7 +1330,7 @@ "data": { "image/png": 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5Yl3EsVevXsjOzq7vyyCENCFO1SQ+/fRT6HQ6TJo0yeE+Fy9erLOgGlL6nbM4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\nocTyvVACiUAEsUAMsUBkt/1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Jyclht9fmftFDhwghhDjkln0ShBBC6gYlCUIIIQ5RkiCE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\nBCGEEIf+H0W9gvkl+IxjAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2037,26 +1356,16 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 37, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 63.109244\n", - "dtype: float64\n", - "temp 63.109244\n", - "dtype: float64\n" - ] - }, { "data": { "text/plain": [ "63.109243697478988" ] }, - "execution_count": 42, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2075,24 +1384,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 38, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "coffee = make_system(T_init=90, r=r_coffee, volume=300)\n", "milk = make_system(T_init=5, r=r_milk, volume=50)" @@ -2100,28 +1394,16 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 39, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 77.857143\n", - "dtype: float64\n", - "temp 77.857143\n", - "dtype: float64\n", - "run sim inittemp 77.857143\n", - "dtype: float64\n" - ] - }, { "data": { "text/plain": [ "61.428571428571438" ] }, - "execution_count": 44, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2142,178 +1424,99 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "def run_and_mix(t_add, t_total=30, T_milk0=5):\n", - " \"\"\"Simulates two liquids and them mixes them at t_add.\n", - " \n", - " t_add: time in minutes\n", - " t_total: total time to simulate, min\n", - " \n", - " returns: final temperature\n", - " \"\"\"\n", - " coffee = make_system(T_init=90, t_end=t_add, \n", - " r=r_coffee, volume=300)\n", - " run_simulation(coffee, update)\n", + "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n", + " \"\"\"Runs a simulation with the given parameters.\n", "\n", - " milk = make_system(T_init=T_milk0, t_end=t_add, \n", - " r=r_milk, volume=50)\n", - " run_simulation(milk, update)\n", + " T_init: initial temperature in degC\n", + " r: heat transfer rate, in 1/min\n", + " volume: volume of liquid in mL\n", + " t_end: end time of simulation\n", " \n", - " mixture = mix(coffee, milk)\n", - " mixture.t_end = t_total - t_add\n", - " run_simulation(mixture, update)\n", + " returns: System object\n", + " \"\"\"\n", + " init = State(temp=T_init)\n", + " #print(init) \n", " \n", - " return mixture.results" + " system = System(init=init,\n", + " volume=volume,\n", + " r=r,\n", + " T_env=22, \n", + " t0=0,\n", + " t_end=t_end,\n", + " dt=1)\n", + " #print(system.init)\n", + " return system" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "def sweep_init_temp2(system, update_func):\n", - " T_array = linspace(5, 25, 5)\n", - " for i in T_array:\n", - " res = run_and_mix(t_add=0, t_total=30, T_milk0=i)\n", + "def mix(s1, s2):\n", + " \"\"\"Simulates the mixture of two liquids.\n", " \n", - " plot(res.temp, label='milk' + str(i))\n", - " decorate(xlabel='Time (minutes)',\n", - " ylabel='Temperature (C)')" + " s1: System representing coffee\n", + " s2: System representing milk\n", + " \n", + " returns: System representing the mixture\n", + " \"\"\"\n", + " assert s1.t_end == s2.t_end\n", + " \n", + " volume = s1.volume + s2.volume\n", + " \n", + " temp = (s1.volume * final_temp(s1) + \n", + " s2.volume * final_temp(s2)) / volume\n", + " \n", + " mixture = make_system(T_init=temp,\n", + " volume=volume,\n", + " r=s1.r)\n", + " \n", + " return mixture" ] }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 5.0\n", - "dtype: float64\n", - "temp 5.0\n", - "dtype: float64\n", - "run sim inittemp 5.0\n", - "dtype: float64\n", - "temp 77.857143\n", - "dtype: float64\n", - "temp 77.857143\n", - "dtype: float64\n", - "run sim inittemp 77.857143\n", - "dtype: float64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 10.0\n", - "dtype: float64\n", - "temp 10.0\n", - "dtype: float64\n", - "run sim inittemp 10.0\n", - "dtype: float64\n", - "temp 78.571429\n", - "dtype: float64\n", - "temp 78.571429\n", - "dtype: float64\n", - "run sim inittemp 78.571429\n", - "dtype: float64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 15.0\n", - "dtype: float64\n", - "temp 15.0\n", - "dtype: float64\n", - "run sim inittemp 15.0\n", - "dtype: float64\n", - "temp 79.285714\n", - "dtype: float64\n", - "temp 79.285714\n", - "dtype: float64\n", - "run sim inittemp 79.285714\n", - "dtype: float64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 20.0\n", - "dtype: float64\n", - "temp 20.0\n", - "dtype: float64\n", - "run sim inittemp 20.0\n", - "dtype: float64\n", - "temp 80.0\n", - "dtype: float64\n", - "temp 80.0\n", - "dtype: float64\n", - "run sim inittemp 80.0\n", - "dtype: float64\n", - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 25.0\n", - "dtype: float64\n", - "temp 25.0\n", - "dtype: float64\n", - "run sim inittemp 25.0\n", - "dtype: float64\n", - "temp 80.714286\n", - "dtype: float64\n", - "temp 80.714286\n", - "dtype: float64\n", - "run sim inittemp 80.714286\n", - "dtype: float64\n" - ] - }, - { - "data": { - "image/png": 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6UgBaO2nRTl1XI8dqT1uK8GBMezuRRL8YS3YUgMlgoCM3j86CsxL3lp2nJ96L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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ - "sweep_init_temp2(milk, update)" + "def update(state, system):\n", + " \"\"\"Update the thermal transfer model.\n", + " \n", + " state: State (temp)\n", + " system: System object\n", + " \n", + " returns: State (temp)\n", + " \"\"\"\n", + " unpack(system)\n", + " T = state.temp\n", + " T += -r * (T - T_env) * dt\n", + " \n", + "\n", + " return State(temp=T)" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "def run_and_mix_and_sweep(t_add, t_total=30):\n", + "def run_and_mix(t_add, t_total=30, T_milk0=5, T_coffee0=85):\n", " \"\"\"Simulates two liquids and them mixes them at t_add.\n", " \n", " t_add: time in minutes\n", @@ -2321,489 +1524,1544 @@ " \n", " returns: final temperature\n", " \"\"\"\n", - " coffee = make_system(T_init=90, t_end=t_add, \n", + " coffee = make_system(T_init=T_coffee0, t_end=t_add, \n", " r=r_coffee, volume=300)\n", " run_simulation(coffee, update)\n", "\n", - " sweep_init_temp2(milk, update)\n", - " \n", + " milk = make_system(T_init=T_milk0, t_end=t_add, \n", + " r=r_milk, volume=50)\n", + " run_simulation(milk, update)\n", " \n", " mixture = mix(coffee, milk)\n", " mixture.t_end = t_total - t_add\n", " run_simulation(mixture, update)\n", - "\n", - " return final_temp(mixture)" + " \n", + " return mixture.results" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 104, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "run_and_mix_and_sweep() missing 1 required positional argument: 't_add'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrun_and_mix_and_sweep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfinal_temp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmixture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: run_and_mix_and_sweep() missing 1 required positional argument: 't_add'" - ] - } - ], + "outputs": [], "source": [ - "run_and_mix_and_sweep()\n", - "plot(final_temp(mixture))" + "def sweep_init_temp2(update_func):\n", + " T_milk_array = linspace(5, 25, 5)\n", + " T_coffee_array = linspace(70, 90, 5)\n", + " for i1 in T_milk_array:\n", + " for i2 in T_coffee_array:\n", + " res = run_and_mix(t_add=0, t_total=120, T_milk0=i1, T_coffee0=i2)\n", + " \n", + " plot(res.temp, label='milk' + str(i1) + \"coffee\" + str(i2))\n", + " decorate(xlabel='Time (minutes)',\n", + " ylabel='Temperature (C)')\n", + " return res " ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 105, "metadata": {}, + "outputs": [], "source": [ - "We can try it out with a few values." + "res = run_and_mix(t_add=0, t_total=60, T_milk0=5, T_coffee0=90)" ] }, { "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 77.857143\n", - "dtype: float64\n", - "temp 77.857143\n", - "dtype: float64\n", - "run sim inittemp 77.857143\n", - "dtype: float64\n" - ] - }, - { - "data": { - "text/plain": [ - "61.428571428571438" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 106, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ - "run_and_mix(0)" + "def find_t_opt(res):\n", + " for t in res.temp.index:\n", + " x = res.temp[t]\n", + " if x < 67:\n", + " return t" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 107, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 70.684081\n", - "dtype: float64\n", - "temp 70.684081\n", - "dtype: float64\n", - "run sim inittemp 70.684081\n", - "dtype: float64\n" - ] - }, { "data": { "text/plain": [ - "62.90280912845234" + "pandas.core.series.Series" ] }, - "execution_count": 46, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "run_and_mix(15)" + "type(res.temp)" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 108, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "run sim inittemp 5\n", - "dtype: int64\n", - "temp 63.109244\n", - "dtype: float64\n", - "temp 63.109244\n", - "dtype: float64\n", - "run sim inittemp 63.109244\n", - "dtype: float64\n" + "ename": "SyntaxError", + "evalue": "'return' outside function (, line 4)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m return(t)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m 'return' outside function\n" ] - }, - { - "data": { - "text/plain": [ - 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LFStWABAVFYVCoSAoKIivvvqKv//97wQFBXH06FG8vb359NNPAUhJSSEsLIz09HRatWpF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\n+ecUNRAw2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Lq64u7urs8J/msYGRlhZ2enf/Xs2ZO3336bZ555Rv8sPpVKRUFBAUOHDjUo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HH5fTqL79+U/PX09HDzzTdz3XXX8e1vf5spU6bwzW9+c6+mAUhIeK+hKRLdExspxYMwYvtA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\njKDgGwRWjWhoGyECrihR1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jU0VX0LZzFI5vFF2gpqqoTWXb9A5A3lAvU3U9LmnnhPmOrAolF0vEEOwij5+g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\nQpVkplQXlUzdShkdL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C6sxLT0+HhYUFr43NmjULs2bNqneZkJCQOo/JEkJIY0llDEm38nDhRi43mhwAWJoZINTf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\n0FCuaOZ7772HkJAQTJw4Ee7u7k3+HtS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sweep_init_temp2b(update)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def sweep_coffee(T_coffee_array, T_milk0): \n", + " sweep = SweepSeries()\n", + " for i2 in T_coffee_array:\n", + " res = run_and_mix(t_add=0, t_total=60, T_milk0=T_milk0, T_coffee0=i2)\n", + " t_opt = find_t_opt(res)\n", + " sweep[i2] = t_opt\n", + " print(sweep)\n", + " plot(sweep, label='milk at ' + str(i1) + '°')\n", + " decorate(xlabel='Starting Coffee Temperature (C)',\n", + " ylabel='Optimal Drinking Time(min)') " + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "70 0\n", + "71 0\n", + "72 0\n", + "73 0\n", + "74 0\n", + "75 0\n", + "76 0\n", + "77 0\n", + "78 2\n", + "79 3\n", + "80 5\n", + "81 6\n", + 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4CxcuFBybNWsWaWlprFixwtNWUFCATqe74gIBkBY+4rrOJNLCB+/M2LMetK+v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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ + "for i1 in linspace(5, 30, 6):\n", + " sweep_coffee(T_coffee_array = linspace(70, 95, 26), T_milk0=i1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can try it out with a few values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_and_mix(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_and_mix(15)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_and_mix(30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And then sweep a range of values for `t_add`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", "sweep = SweepSeries()\n", "for t_add in linrange(0, 30, 2):\n", " temp = run_and_mix(t_add)\n", @@ -2819,27 +3077,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving figure to file chap07-fig02.pdf\n" - ] - }, - { - "data": { - "image/png": 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5MtOnT8ff35/+/fvzySefUFBQoDbnFvWjvKicvdF7KThfNeCnRquhd0Rv3APc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\nyNNCiEbNpE+osrIyXnrpJb7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"execute_result" - } - ], + "outputs": [], "source": [ "run_and_mix(0)" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "temp 90\n", - "dtype: int64\n", - "temp 90\n", - "dtype: int64\n", - "run sim inittemp 90\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 5\n", - "dtype: int64\n", - "temp 60.714286\n", - "dtype: float64\n", - "temp 60.714286\n", - "dtype: float64\n", - "run sim inittemp 60.714286\n", - "dtype: float64\n" - ] - }, - { - "data": { - "text/plain": [ - "60.714285714285715" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "run_and_mix(30)" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3002,7 +3176,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3035,7 +3209,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": { "collapsed": true }, @@ -3046,20 +3220,9 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "70.0" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "init = State(temp=90)\n", "coffee2 = System(init=init, T_env=22, r=r_coffee2, \n", @@ -3077,28 +3240,9 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run sim inittemp 90\n", - "dtype: int64\n" - ] - }, - { - "data": { - "text/plain": [ - "70.0" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "init = State(temp=90)\n", 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need from Pint." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "m = UNITS.meter\n", + "s = UNITS.second\n", + "kg = UNITS.kilogram" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "And defining the initial state." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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y381 meter
v0.0 meter / second
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" + ], + "text/plain": [ + "y 381 meter\n", + "v 0.0 meter / second\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "init = State(y=381 * m, \n", + " v=0 * m/s)\n", + "init" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Acceleration due to gravity is about 9.8 m / s$^2$." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "g = 9.8 * m/s**2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "When we call `odeint`, we need an array of timestamps where we want to compute the solution.\n", + "\n", + "I'll start with a duration of 10 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "[ 0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6. 6.5 7. 7.5 8. 8.5 9. 9.5 10. ] second" + ], + "text/latex": [ + "$[ 0. 0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6. 6.5 7. 7.5 8. 8.5 9. 9.5 10. ] second$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "duration = 10 * s\n", + "ts = linspace(0, duration, 21)\n", + "ts" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Now we make a `System` object." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "system = System(init=init, g=g, ts=ts)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "And define the slope function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def slope_func(state, t, system):\n", + " \"\"\"Compute derivatives of the state.\n", + " \n", + " state: position, velocity\n", + " t: time\n", + " system: System object containing `g`\n", + " \n", + " returns: derivatives of y and v\n", + " \"\"\"\n", + " y, v = state\n", + " unpack(system) \n", + "\n", + " dydt = v\n", + " dvdt = -g\n", + " \n", + " print(t)\n", + " return dydt, dvdt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "It's always a good idea to test the slope function with the initial conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "0.0 meter / second\n", + "-9.8 meter / second ** 2\n" + ] + } + ], + "source": [ + "dydt, dvdt = slope_func(init, 0, system)\n", + "print(dydt)\n", + "print(dvdt)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Now we're ready to run `odeint`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 second\n", + "0.0\n", + "1.2204606467739427e-05\n", + "1.2204606467739427e-05\n", + "2.4409212935478853e-05\n", + "2.4409212935478853e-05\n", + "0.12207047389032974\n", + "0.244116538567724\n", + "0.3661626032451183\n", + "1.586623250019061\n", + "2.8070838967930034\n", + "4.027544543566946\n", + "16.23215101130637\n" + ] + } + ], + "source": [ + "run_odeint(system, slope_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Here's what the results look like." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0.0381.0000.0
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2.0361.400-19.6
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yv
8.067.400-78.4
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" + ], + "text/plain": [ + " y v\n", + "8.0 67.400 -78.4\n", + "8.5 26.975 -83.3\n", + "9.0 -15.900 -88.2\n", + "9.5 -61.225 -93.1\n", + "10.0 -109.000 -98.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system.results.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The following function plots the results." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_position(results):\n", + " \"\"\"Plot the results.\n", + " \n", + " results: DataFrame with position, `y`\n", + " \"\"\"\n", + " newfig()\n", + " plot(results.y, label='y')\n", + " \n", + " decorate(xlabel='Time (s)',\n", + " ylabel='Position (m)')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Here's what it looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap09-fig01.pdf\n" + ] + }, + { + "data": { + "image/png": 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YdC35eD5J+7MxynbTLVKTRjYnT57k/vvvx9nZmREjRuDt7U1hYSHff/8933//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\nrNbbubIbr9WFjU5Xv3+Ev7+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_position(system.results)\n", + "savefig('chap09-fig01.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Exercise:** Add a print statement to `slope_func` to print the value of `t` each time it's called. What can we infer about how `odeint` works, based on the results?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#odeint takes time values that are increasing exponentially" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**Exercise:** Change the value of `dt` and run the solver again. What effect does it have on the results?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#graph is more smoothe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Onto the sidewalk\n", + "\n", + "Here's the code again to set up the `System` object." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_system(duration, v_init=0):\n", + " \"\"\"Make a system object.\n", + " \n", + " duration: time of simulation in seconds\n", + " v_init: initial velocity, dimensionless\n", + " \n", + " returns: System object\n", + " \"\"\"\n", + " init = State(y=381 * m, v=v_init * m / s)\n", + "\n", + " g = 9.8 * m/s**2\n", + " ts = linspace(0, duration, 11)\n", + " return System(init=init, g=g, ts=ts) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And run the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.0\n", + "1.2391823754412774e-05\n", + "1.2391823754412774e-05\n", + "2.478364750882555e-05\n", + "2.478364750882555e-05\n", + "0.12394302119163657\n", + "0.2478612587357643\n", + "0.37177949627989204\n", + "1.6109618717211696\n", + "2.850144247162447\n", + "4.089326622603725\n", + "16.481150377016498\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " y v\n", + "0.0 381.0 0.0\n", + "1.0 376.1 -9.8\n", + "2.0 361.4 -19.6\n", + "3.0 336.9 -29.4\n", + "4.0 302.6 -39.2\n", + "5.0 258.5 -49.0\n", + "6.0 204.6 -58.8\n", + "7.0 140.9 -68.6\n", + "8.0 67.4 -78.4\n", + "9.0 -15.9 -88.2\n", + "10.0 -109.0 -98.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = make_system(10)\n", + "system\n", + "run_odeint(system, slope_func)\n", + "system.results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "To figure out when the penny hit the sidewalk, we use `interp_inverse`, which return a function that maps from height to time." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "y = system.results.y\n", + "T = interp_inverse(y, kind='cubic')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "`T(0)` interpolates the time when the height was 0." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(8.81792826905006)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "T_sidewalk = T(0)\n", + "T_sidewalk" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "We can compare that to the exact result. Without air resistance, we have\n", + "\n", + "$v = -g t$\n", + "\n", + "and\n", + "\n", + "$y = 381 - g t^2 / 2$\n", + "\n", + "Setting $y=0$ and solving for $t$ yields\n", + "\n", + "$t = \\sqrt{\\frac{2 y_{init}}{g}}$" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "8.817885349720552 second" + ], + "text/latex": [ + "$8.817885349720552 second$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sqrt(2 * init.y / g)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The estimate is accurate to 4 decimal places." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "We can double-check by running the simulation for the estimated flight time." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.0\n", + "1.2373598314419205e-05\n", + "1.2373598314419205e-05\n", + "2.474719662883841e-05\n", + "2.474719662883841e-05\n", + "0.1237607303408209\n", + "0.24749671348501295\n", + "0.371232696629205\n", + "1.6085925280711255\n", + "2.845952359513046\n", + "4.083312190954967\n", + "16.45691050537417\n" + ] + } + ], + "source": [ + "system = make_system(duration=T_sidewalk)\n", + "run_odeint(system, slope_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "And checking the final state." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def final_state(results):\n", + " \"\"\"Returns the final position and velocity, with units.\n", + " \n", + " results: TimeFrame with y and v.\n", + " \n", + " returns: y, v at t_end\n", + " \"\"\"\n", + " t_end = results.index[-1]\n", + " y, v = results.loc[t_end]\n", + " return y*m, v*m/s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "As expected, the final height is close to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "1.2732392917769175e-10 meter" + ], + "text/latex": [ + "$1.2732392917769175e-10 meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_final, v_final = final_state(system.results)\n", + "y_final" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "And we can check the final velocity." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "-18.00000000874157 meter/second" + ], + "text/latex": [ + "$-18.00000000874157 \\frac{meter}{second}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v_final" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "And convert to km/h" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "-64.80000003146965 kilometer/hour" + ], + "text/latex": [ + "$-64.80000003146965 \\frac{kilometer}{hour}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "km = UNITS.kilometer\n", + "h = UNITS.hour\n", + "v_final.to(km / h)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n", + "\n", + "So it's a good thing there is air resistance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Try changing the initial velocity and see what effect it has on the time to hot the sidewalk. Sweep a range of values for the initial velocity, from 0 to 25 m/s, and plot `T_sidewalk` as a function of initial velocity. You might find the following function useful.\n", + "\n", + "Things might go horribly wrong for the larger initial velocities. What's going on?" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def flight_time(system):\n", + " \"\"\"Simulates the system and computes flight time.\n", + " \n", + " Uses cubic interpolation.\n", + " \n", + " system: System object\n", + " \n", + " returns: flight time in seconds\n", + " \"\"\"\n", + " run_odeint(system, slope_func)\n", + " y = system.results.y\n", + " inverse = Series(y.index, index=y.values)\n", + " T = interpolate(inverse, kind='cubic')\n", + " T_sidewalk = T(0)\n", + " return T_sidewalk * s" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "make_system() takes 1 positional argument but 2 were given", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mv_init\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msystem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_system\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mduration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_init\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mrun_odeint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslope_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msystem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: make_system() takes 1 positional argument but 2 were given" + ] + } + ], + "source": [ + "# Solution goes here\n", + "duration = 10\n", + "vs = linrange(0, 25, 1)\n", + "\n", + "for v_init in vs:\n", + " system = make_system(duration, v_init)\n", + " run_odeint(system, slope_func)\n", + " y = system.results.y\n", + " inverse = Series(y.index, index=y.values)\n", + " T = interpolate(inverse, kind='cubic')\n", + " T_sidewalk = T(0)\n", + " plot(v_init, T_sidewalk, 'b-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With air resistance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we'll add air resistance using the [drag equation](https://en.wikipedia.org/wiki/Drag_equation)\n", + "\n", + "First I'll create a `Condition` object to contain the quantities we'll need." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "condition = Condition(height = 381 * m,\n", + " v_init = 0 * m / s,\n", + " g = 9.8 * m/s**2,\n", + " mass = 2.5e-3 * kg,\n", + " diameter = 19e-3 * m,\n", + " rho = 1.2 * kg/m**3,\n", + " v_term = 18 * m / s,\n", + " duration = 30 * s)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Now here's a version of `make_system` that takes a `Condition` object as a parameter.\n", + "\n", + "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_system(condition):\n", + " \"\"\"Makes a System object for the given conditions.\n", + " \n", + " condition: Condition with height, g, mass, diameter, \n", + " rho, v_term, and duration\n", + " \n", + " returns: System with init, g, mass, rho, C_d, area, and ts\n", + " \"\"\"\n", + " unpack(condition)\n", + " \n", + " init = State(y=height, v=v_init)\n", + " area = np.pi * (diameter/2)**2\n", + " C_d = 2 * mass * g / (rho * area * v_term**2)\n", + " ts = linspace(0, duration, 101)\n", + " \n", + " return System(init=init, g=g, mass=mass, rho=rho,\n", + " C_d=C_d, area=area, ts=ts)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Let's make a `System`" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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inity 381 meter\n", + "v 0.0 meter / secon...
g9.8 meter / second ** 2
mass0.0025 kilogram
rho1.2 kilogram / meter ** 3
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" + ], + "text/plain": [ + "init y 381 meter\n", + "v 0.0 meter / secon...\n", + "g 9.8 meter / second ** 2\n", + "mass 0.0025 kilogram\n", + "rho 1.2 kilogram / meter ** 3\n", + "C_d 0.4445009981135434 dimensionless\n", + "area 0.0002835287369864788 meter ** 2\n", + "ts [0.0 second, 0.3 second, 0.6 second, 0.8999999...\n", + "dtype: object" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = make_system(condition)\n", + "system" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Here's the slope function, including acceleration due to gravity and drag." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def slope_func(state, t, system):\n", + " \"\"\"Compute derivatives of the state.\n", + " \n", + " state: position, velocity\n", + " t: time\n", + " system: System object containing g, rho,\n", + " C_d, area, and mass\n", + " \n", + " returns: derivatives of y and v\n", + " \"\"\"\n", + " y, v = state\n", + " unpack(system)\n", + " \n", + " f_drag = rho * v**2 * C_d * area / 2\n", + " a_drag = f_drag / mass\n", + " \n", + " dydt = v\n", + " dvdt = -g + a_drag\n", + " \n", + " return dydt, dvdt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "As always, let's test the slope function with the initial conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(, )" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slope_func(system.init, 0, system)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "And then run the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "run_odeint(system, slope_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "First check that the simulation ran long enough for the penny to land." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " )" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_state(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Then compute the flight time." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(22.439794207078908)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = system.results.y\n", + "inverse = Series(y.index, index=y.values)\n", + "T = interpolate(inverse, kind='cubic')\n", + "T_sidewalk = T(0)\n", + "T_sidewalk" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Setting the duration to the computed flight time, we can check the final conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "condition.set(duration=T_sidewalk)\n", + "system = make_system(condition)\n", + "run_odeint(system, slope_func)\n", + "y_final, v_final = final_state(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The final height is close to 0, as expected. And the final velocity is close to the given terminal velocity." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " )" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_final, v_final" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Here's the plot of position as a function of time." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure to file chap09-fig02.pdf\n" + ] + }, + { + "data": { + "image/png": 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EiBBGoCgK0eGeBHo7sedYNoVXawDIL61m4+5Uhkf70S/UXVqhiE7RZoAkJibi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\nhBDiJ9jbWvHA8BDGxQdiZXl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_position(system.results)\n", + "savefig('chap09-fig02.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And velocity as a function of time:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_velocity(results):\n", + " \"\"\"Plot the results.\n", + " \n", + " results: DataFrame with velocity, v\n", + " \"\"\"\n", + " newfig()\n", + " plot(results.v, label='v')\n", + " \n", + " decorate(xlabel='Time (s)',\n", + " ylabel='Velocity (m/2)')" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ZdiPQx+N6gGjqGCBE5LZsPtLWy8sLQ4cOtWctLiHATw1crgLAiXQicm82rUSn68xv5b1S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\n72IFenXmOfNE5Do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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_velocity(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity. Hint: use `condition.set`.\n", + "\n", + "What do you expect to happen? Plot velocity and position as a function of time, and see if they are consistent with your prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution goes here\n", + "\n", + "condition.set(v_init = -30 * m / s)\n", + "system = make_system(condition)\n", + "run_odeint(system, slope_func)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gEEO97DmWmMPF3AoAauub+NePlxnqYcfkcG9srNr+PypEd2k3QFasWMGuXbsA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\nkEtYQnSRESNG8MorrwAtKxrv2bMHZ2d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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Solution goes here\n", + "plot_velocity(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dropping quarters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds. We can use this measurement to estimate the coefficient of drag.\n", + "\n", + "Here's a `Condition` object with the relevant parameters from\n", + "https://en.wikipedia.org/wiki/Quarter_(United_States_coin)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "condition = Condition(height = 381 * m,\n", + " v_init = 0 * m / s,\n", + " g = 9.8 * m/s**2,\n", + " mass = 5.67e-3 * kg,\n", + " diameter = 24.26e-3 * m,\n", + " rho = 1.2 * kg/m**3,\n", + " duration = 19.1 * s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's a modified version of `make_system`" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_system(condition):\n", + " \"\"\"Makes a System object for the given conditions.\n", + " \n", + " condition: Condition with height, v_init, g, mass, diameter, \n", + " rho, C_d, and duration\n", + " \n", + " returns: System with init, g, mass, rho, C_d, area, and ts\n", + " \"\"\"\n", + " unpack(condition)\n", + " \n", + " init = State(y=height, v=v_init)\n", + " area = np.pi * (diameter/2)**2\n", + " ts = linspace(0, duration, 101)\n", + " \n", + " return System(init=init, g=g, mass=mass, rho=rho,\n", + " C_d=C_d, area=area, ts=ts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can run the simulation with an initial guess of `C_d=0.4`." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zdu3adcsXmjNnTp8WRkS3x1YmwZwpI3C2oBo/nimHWqOFWqPFwaxSFJY3In68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\nr2LpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "condition.set(C_d=0.4)\n", + "system = make_system(condition)\n", + "run_odeint(system, slope_func)\n", + "plot_position(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final height is -11 meters, which means our guess was too low (we need more drag to slow the quarter down)." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " )" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_state(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`height_func` takes a hypothetical value of `C_d` and returns the height after 19.1 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def height_func(C_d, condition):\n", + " \"\"\"Final height as a function of C_d.\n", + " \n", + " C_d: drag coefficient\n", + " condition: Condition object\n", + " \n", + " returns: height in m\n", + " \"\"\"\n", + " condition.set(C_d=C_d)\n", + " system = make_system(condition)\n", + " run_odeint(system, slope_func)\n", + " y, v = final_state(system.results)\n", + " return y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we run it with `C_d=0.4`, we get -11 meters again." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "-11.034779626277231 meter" + ], + "text/latex": [ + "$-11.034779626277231 meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "height_func(0.4, condition)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use `fsolve` to find the value of `C_d` that makes the final height 0." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.42587017])" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solution = fsolve(height_func, 0.4, condition)\n", + "solution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plugging in the estimated value, we can run the simulation again to get terminal velocity." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " )" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "condition.set(C_d=solution)\n", + "system = make_system(condition)\n", + "run_odeint(system, slope_func)\n", + "final_state(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, the terminal velocity of the quarter is higher than that of the penny, but we should not take this result seriously because the measurements we used are not real; I made them up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 2fe8d1248c22515d3e5f4a854ece5bd849ff967f Mon Sep 17 00:00:00 2001 From: Flynn Michael-Legg Date: Tue, 7 Nov 2017 17:09:57 -0500 Subject: [PATCH 18/19] hella yii --- code/chap10mine.ipynb | 8119 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 8119 insertions(+) create mode 100644 code/chap10mine.ipynb diff --git a/code/chap10mine.ipynb b/code/chap10mine.ipynb new file mode 100644 index 00000000..1360b4b6 --- /dev/null +++ b/code/chap10mine.ipynb @@ -0,0 +1,8119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Chapter 10: Vectors\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": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# If you want the figures to appear in the notebook, \n", + "# and you want to interact with them, use\n", + "# %matplotlib notebook\n", + "\n", + "# If you want the figures to appear in the notebook, \n", + "# and you don't want to interact with them, use\n", + "# %matplotlib inline\n", + "\n", + "# If you want the figures to appear in separate windows, use\n", + "# %matplotlib qt5\n", + "\n", + "# tempo switch from one to another, you have to select Kernel->Restart\n", + "\n", + "%matplotlib notebook\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vectors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `Vector` object is like a combination of a NumPy array and a Pint Quantity.\n", + "\n", + "I'll start by grabbing the units we'll need." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "m = UNITS.meter\n", + "s = UNITS.second\n", + "kg = UNITS.kilogram" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a two dimensional `Vector` in meters." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = Vector(3, 4) * m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can access the elements by name." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "3.0 meter" + ], + "text/latex": [ + "$3.0 meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.x" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "4.0 meter" + ], + "text/latex": [ + "$4.0 meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The magnitude is the length of the vector." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "5.0 meter" + ], + "text/latex": [ + "$5.0 meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.mag" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The angle is the number of radians between the vector and the positive x axis." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "0.9272952180016122 radian" + ], + "text/latex": [ + "$0.9272952180016122 radian$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.angle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we make another `Vector` with the same units," + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "B = Vector(1, 2) * m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can add `Vector` objects like this" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "[ 4. 6.] meter" + ], + "text/latex": [ + "$[ 4. 6.] meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A + B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And substract like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "[ 2. 2.] meter" + ], + "text/latex": [ + "$[ 2. 2.] meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A - B" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compute the Euclidean distance between two Vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "2.8284271247461903 meter" + ], + "text/latex": [ + "$2.8284271247461903 meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.dist(B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the difference in angle" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "-0.17985349979247822 radian" + ], + "text/latex": [ + "$-0.17985349979247822 radian$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.diff_angle(B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we are given the magnitude and angle of a vector, what we have is the representation of the vector in polar coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "mag = A.mag\n", + "angle = A.angle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use `pol2cart` to convert from polar to Cartesian coordinates, and then use the Cartesian coordinates to make a `Vector` object.\n", + "\n", + "In this example, the `Vector` we get should have the same components as `A`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "[ 3. 4.] meter" + ], + "text/latex": [ + "$[ 3. 4.] meter$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x, y = pol2cart(angle, mag)\n", + "Vector(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Create a `Vector` named `a_grav` that represents acceleration due to gravity, with x component 0 and y component $-9.8$ meters / second$^2$." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a_grav= Vector(0, -9.8) * m/s/s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Degrees and radians\n", + "\n", + "Pint provides units to represent degree and radians." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "degree = UNITS.degree\n", + "radian = UNITS.radian" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have an angle in degrees," + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "45 degree" + ], + "text/latex": [ + "$45 degree$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "angle = 45 * degree\n", + "angle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can convert to radians." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "0.7853981633974483 radian" + ], + "text/latex": [ + "$0.7853981633974483 radian$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "angle_rad = angle.to(radian)\n", + "angle_rad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If it's already in radians, `to` does the right thing." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "0.7853981633974483 radian" + ], + "text/latex": [ + "$0.7853981633974483 radian$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "angle_rad.to(radian)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also convert from radians to degrees." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "45.0 degree" + ], + "text/latex": [ + "$45.0 degree$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "angle_rad.to(degree)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As an alterative, you can use `np.deg2rad`, which works with Pint quantities, but it also works with simple numbers and NumPy arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "0.7853981633974483 radian" + ], + "text/latex": [ + "$0.7853981633974483 radian$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.deg2rad(angle)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Create a `Vector` named `a_force` that represents acceleration due to a force of 0.5 Newton applied to an object with mass 0.3 kilograms, in a direction 45 degrees up from the positive x-axis.\n", + "\n", + "Add `a_force` to `a_drag` from the previous exercise. If that addition succeeds, that means that the units are compatible. Confirm that the total acceleration seems to make sense." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "mass2 = .3*kg\n", + "F_force = Vector((.5/sqrt(2)),(.5/sqrt(2)))*kg*m/s/s\n", + "#N = kg*m/s/s\n", + "a_force = F_force/mass2" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "[ 1.1785113 -8.6214887] meter/second2" + ], + "text/latex": [ + "$[ 1.1785113 -8.6214887] \\frac{meter}{second^{2}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_total = a_force + a_grav\n", + "a_total" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Baseball" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a `Condition` object that contains the parameters for the Manny Ramirez problem." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "condition = Condition(x = 0 * m, \n", + " y = 1 * m,\n", + " g = 9.8 * m/s**2,\n", + " mass = 145e-3 * kg,\n", + " diameter = 73e-3 * m,\n", + " rho = 1.2 * kg/m**3,\n", + " C_d = 0.3,\n", + " angle = 45 * degree,\n", + " velocity = 40 * m / s,\n", + " duration = 5.1 * s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's the function that uses the `Condition` object to make a `System` object." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_system(condition):\n", + " \"\"\"Make a system object.\n", + " \n", + " condition: Condition object with angle, velocity, x, y,\n", + " diameter, duration, g, mass, rho, and C_d\n", + " \n", + " returns: System object\n", + " \"\"\"\n", + " unpack(condition)\n", + " \n", + " # convert angle to degrees\n", + " theta = np.deg2rad(angle)\n", + " \n", + " # compute x and y components of velocity\n", + " vx, vy = pol2cart(theta, velocity)\n", + " \n", + " # make the initial state\n", + " init = State(x=x, y=y, vx=vx, vy=vy)\n", + " \n", + " # compute area from diameter\n", + " area = np.pi * (diameter/2)**2\n", + " \n", + " # compute timestamps\n", + " ts = linspace(0, duration, 101)\n", + " \n", + " return System(init=init, g=g, mass=mass, \n", + " area=area, rho=rho, C_d=C_d, ts=ts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we use it:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "init x 0 meter\n", + "y ...\n", + "g 9.8 meter / second ** 2\n", + "mass 0.145 kilogram\n", + "area 0.004185386812745002 meter ** 2\n", + "rho 1.2 kilogram / meter ** 3\n", + "C_d 0.3\n", + "ts [0.0 second, 0.051 second, 0.102 second, 0.153...\n", + "dtype: object" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system = make_system(condition)\n", + "system" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the slope function that computes acceleration due to gravity and drag." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def slope_func(state, t, system):\n", + " \"\"\"Computes derivatives of the state variables.\n", + " \n", + " state: State (x, y, x velocity, y velocity)\n", + " t: time\n", + " system: System object with g, rho, C_d, area, mass\n", + " \n", + " returns: sequence (vx, vy, ax, ay)\n", + " \"\"\"\n", + " x, y, vx, vy = state\n", + " unpack(system)\n", + " \n", + " a_grav = Vector(0, -g)\n", + "\n", + " v = Vector(vx, vy)\n", + " \n", + " f_drag = -rho * v.mag * v * C_d * area / 2\n", + " a_drag = f_drag / mass\n", + " \n", + " a = a_grav + a_drag\n", + " \n", + " return vx, vy, a.x, a.y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Always test the slope function with the initial conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " ,\n", + " ,\n", + " )" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "slope_func(system.init, 0, system)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run `odeint`" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "run_odeint(system, slope_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the first few time steps." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x y vx vy\n", + "0.000 0.000000 1.000000 34.641016 20.000000\n", + "0.051 1.757414 2.001943 34.278822 19.293697\n", + "0.102 3.496604 2.968138 33.926305 18.598052\n", + "0.153 5.218055 3.899118 33.583080 17.912625\n", + "0.204 6.922230 4.795393 33.248781 17.237001" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system.results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the last few. The last value of `y` is negative, indicating that the ball hit the ground before the end of the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " x y vx vy\n", + "4.896 119.051966 -25.039907 16.915531 -24.925983\n", + "4.947 119.911217 -26.318773 16.780661 -25.225049\n", + "4.998 120.763599 -27.612802 16.646105 -25.520572\n", + "5.049 121.609126 -28.921811 16.511872 -25.812556\n", + "5.100 122.447815 -30.245622 16.377974 -26.101006" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system.results.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing the results\n", + "\n", + "We can extract the x and y components as `Series` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "xs = system.results.x\n", + "ys = system.results.y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The simplest way to visualize the results is to plot x and y as functions of time." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
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')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "newfig()\n", + "plot(vxs, label='vx')\n", + "plot(vys, label='vy')\n", + "\n", + "decorate(xlabel='Time (s)',\n", + " ylabel='Velocity (m/s)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another way to visualize the results is to plot y versus x. The result is the trajectory of the ball through its plane of motion." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " this.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "newfig()\n", + "decorate(xlabel='x position (m)',\n", + " ylabel='y position (m)',\n", + " xlim=[0, 105],\n", + " ylim=[-5, 35],\n", + " legend=False)\n", + "\n", + "for x, y in zip(xs, ys):\n", + " plot(x, y, 'bo', update=True)\n", + " sleep(0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a function that encapsulates that code and runs the animation in (approximately) real time." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def animate2d(xs, ys, speedup=1):\n", + " \"\"\"Animate the results of a projectile simulation.\n", + " \n", + " xs: x position as a function of time\n", + " ys: y position as a function of time\n", + " \n", + " speedup: how much to divide `dt` by\n", + " \"\"\"\n", + " # get the time intervals between elements\n", + " ts = xs.index\n", + " dts = np.diff(ts)\n", + " dts = np.append(dts, 0)\n", + "\n", + " # decorate the plot\n", + " newfig()\n", + " decorate(xlabel='x position (m)',\n", + " ylabel='y position (m)',\n", + " xlim=[xs.min(), xs.max()],\n", + " ylim=[ys.min(), ys.max()],\n", + " legend=False)\n", + "\n", + " # loop through the values\n", + " for x, y, dt in zip(xs, ys, dts):\n", + " plot(x, y, 'bo', update=True)\n", + " sleep(dt / speedup)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " this.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "animate2d(system.results.x, system.results.y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finding the range" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we'll find the time and distance when the ball hits the ground." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "condition.set(duration=7*s)\n", + "system = make_system(condition)\n", + "run_odeint(system, slope_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have to interpolate y to find the landing time, then interpolate x to find the range." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def interpolate_range(results):\n", + " \"\"\"Computes the range of the ball when it lands.\n", + " \n", + " results: TimeFrame with x and y\n", + " \n", + " returns: distance in meters\n", + " \"\"\"\n", + " xs = results.x\n", + " ys = results.y\n", + " t_end = ys.index[-1]\n", + " \n", + " if ys[t_end] > 0:\n", + " msg = \"\"\"The final value of y is still positive;\n", + " looks like the simulation didn't run\n", + " long enough.\"\"\"\n", + " raise ValueError(msg)\n", + " \n", + " t_peak = ys.argmax()\n", + " descent = ys.loc[t_peak:]\n", + " T = interp_inverse(descent, kind='cubic')\n", + " \n", + " t_land = T(0)\n", + " X = interpolate(xs, kind='cubic')\n", + " return X(t_land)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the result." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(102.72237841710975)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interpolate_range(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$. How much farther would a ball hit with the same velocity and launch angle travel?" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Hint: rather than modify `condition`, make a copy\n", + "\n", + "condition2 = Condition(x = 0 * m, \n", + " y = 1 * m,\n", + " g = 9.8 * m/s**2,\n", + " mass = 145e-3 * kg,\n", + " diameter = 73e-3 * m,\n", + " rho = 1.0 * kg/m**3,\n", + " C_d = 0.3,\n", + " angle = 45 * degree,\n", + " velocity = 40 * m / s,\n", + " duration = 7 * s)\n", + "\n", + "condition2.set(duration=7*s)\n", + "system = make_system(condition2)\n", + "run_odeint(system, slope_func)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(109.07212101030548)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Solution goes here\n", + "interpolate_range(system.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimal launch angle\n", + "\n", + "To find the launch angle that maximizes range, we need a function that takes launch angle and returns range." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def range_func(angle, condition): \n", + " \"\"\"Computes range for a given launch angle.\n", + " \n", + " angle: launch angle in degrees\n", + " condition: Condition object\n", + " \n", + " returns: distance in meters\n", + " \"\"\"\n", + " condition.set(angle=angle)\n", + " print(angle)\n", + " system = make_system(condition)\n", + " run_odeint(system, slope_func)\n", + " x_range = interpolate_range(system.results)\n", + " return x_range" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's test `range_func`." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "45\n", + "Wall time: 314 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "array(102.72237841710975)" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%time range_func(45, condition)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And sweep through a range of angles." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30.0\n", + "30.0 97.09774794768241\n", + "33.0\n", + "33.0 100.08910744882512\n", + "36.0\n", + "36.0 102.12802725534989\n", + "39.0\n", + "39.0 103.23445991233197\n", + "42.0\n", + "42.0 103.42684240849546\n", + "45.0\n", + "45.0 102.72237841710975\n", + "48.0\n", + "48.0 101.13744083917668\n", + "51.0\n", + "51.0 98.68805053605158\n", + "54.0\n", + "54.0 95.39043242210352\n", + "57.0\n", + "57.0 91.2616536460809\n", + "60.0\n", + "60.0 86.32037231617414\n" + ] + } + ], + "source": [ + "angles = linspace(30, 60, 11)\n", + "sweep = SweepSeries()\n", + "\n", + "for angle in angles:\n", + " x_range = range_func(angle, condition)\n", + " print(angle, x_range)\n", + " sweep[angle] = x_range" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the `Sweep` object, it looks like the peak is between 40 and 45 degrees." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " this.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; 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' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
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zK!r*AlTYugV@DG<%j)G(ewqMn`Bx+#fdD~nh~JGS7P;@?iFne%DqOhkYRmPvdts1i%+#mhkHv&6$nS(Ath-(0I=#xsk0JChiN zfckC`Z@^icJ<`FSF7gI;(1io6z7}K*P-hx|L-U>6q8nVtfrVMv(QYy|J^NcgJ6_Ct z6(7L7JodMPV0q-XD8Zc#MybJ}xph@VQ17N6_%wKmHN)GmANvCQB;zA<3L;fissW%Y ztBm4Kin%yB&jL-Gr;>8MZffUgf>eFbT_?!(G(qCa;y%Et#Q&$l9xCmlXqbfvo4SAyWIQw0&p4KHPNfn HaRmPll-Auw literal 0 HcmV?d00001 diff --git a/code/chap03soln.ipynb b/code/chap03soln.ipynb index f3d632a4..1d17af6f 100644 --- a/code/chap03soln.ipynb +++ b/code/chap03soln.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -265,7 +265,7 @@ "1954 NaN NaN " ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -462,7 +462,7 @@ "2015 NaN NaN " ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -480,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -504,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1611,7 +1611,7 @@ "[66 rows x 11 columns]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1629,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1700,7 +1700,7 @@ "Name: census, Length: 66, dtype: int64" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1721,7 +1721,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1743,7 +1743,7 @@ " 7256490011])" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1761,7 +1761,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1776,7 +1776,7 @@ " dtype='int64', name='Year')" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1800,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1809,7 +1809,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1820,7 +1820,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1829,7 +1829,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1840,7 +1840,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1849,7 +1849,7 @@ "pandas.core.indexes.numeric.Int64Index" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1860,7 +1860,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1869,7 +1869,7 @@ "numpy.ndarray" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1889,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -1919,7 +1919,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "scrolled": false }, @@ -1935,7 +1935,7 @@ "data": { "image/png": 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f9D44qxuwacNVmvvXwcLC1NjxhAGVWLznzZun90FUKhVTp0595HZ/3XYfMWIE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\nESslIqInyWgXrAUGBqKmpgZBQUFYs2YNVCrOB/0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BAfe8MtvY/kwfRESmTJkiI0aMkOzs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\nw/AmIiIyMQxvIiIiE8PwJiIiMjH/HyMTyJSEfdazAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1957,7 +1957,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -1968,7 +1968,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -1988,7 +1988,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1997,7 +1997,7 @@ "1.2862470293832287" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2019,7 +2019,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "scrolled": true }, @@ -2092,7 +2092,7 @@ "Length: 66, dtype: float64" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2105,7 +2105,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -2178,7 +2178,7 @@ "Length: 66, dtype: float64" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2191,7 +2191,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": { "scrolled": true }, @@ -2264,7 +2264,7 @@ "Length: 66, dtype: float64" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2277,7 +2277,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -2286,7 +2286,7 @@ "1.2813631502151765" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2313,7 +2313,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2322,7 +2322,7 @@ "2.5576286540000002" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2340,7 +2340,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2349,7 +2349,7 @@ "7.2564900110000004" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2367,7 +2367,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2376,7 +2376,7 @@ "(1950, 2015)" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2398,7 +2398,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2407,7 +2407,7 @@ "0.07229017472307693" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2428,7 +2428,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -2446,7 +2446,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2487,7 +2487,7 @@ "dtype: float64" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2506,7 +2506,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": { "collapsed": true }, @@ -2525,7 +2525,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2539,7 +2539,7 @@ "data": { "image/png": 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XUpxnoGuHnowbOo6+ffuaVi/083Nqd8ENEt5tltFo5LXXXrumY5cvX058fDwnT57kyy+/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\nFV+cuzgAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2565,7 +2565,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Exercise:** Try fitting the model using data from 1965 to the present, and see if that does a better job.\n", + "**Exercise:** Try fitting the model using data from 1970 to the present, and see if that does a better job.\n", "\n", "Hint: Copy the code from above and make a few changes.\n", "\n", @@ -2574,14 +2574,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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uyeb1ba/zwd4P2Jm5l5KSaqDhlllmZiaJfyRy442dmTVrAIMH+6JSqVCpVPj4\n+EhwtyEm9by3bt3Km2++KQ8xtSHx8fG88cYbLFu2jBdffBFXV1eGDx/OY489BoC7uzuLFi3izTff\nZO3atXTr1o0nnniCBx54wLiPe++9l6qqKp599lmKiorw9PRkzJgxTJs2zVynJYRoJbX6WjYe2cjP\nmT9jUAwUnqni8WVzuc3zER64N5LExETOnj0LQEZGKoMGDTJzxeJ8VMr/3jg9h7i4ODZs2ICPj8+V\nqKmJ3Nxcrr32WjZv3my2GoQQor1KK0xjddJqzlQ0zPJVWVnHgX1F+FX1w1/VnZhoCzw8rI3rOzo6\n0qdPH2xsbMxV8lXvQrlnUs978ODB7Nq1S4JTCCHakcq6Sj4//Dk7Tuxo1B7TNZyBNTGkJx7H1rYM\nrbbhATSVSkVwcDCBgYFyibyNMym8x48fz+uvv05mZiZRUVHnfH3sf19bEkIIYT7/O7SpChU2WhvG\n9hiLV60XaYVHqOtuiY+PGxYWKpycnIiOjsbR0dHcpQsTmBTeEydOBODw4cON2lUqFYqioFKpSE1N\nbfnqhBBCXLRfMn/hs5TPUFDIzS2nsLCKKSOGMzrgZjJTM0k7mwaAn58jarWa4OBgAgICpLfdjpgU\n3itXrmztOoQQQrSQuC5xfJv+LX/uO0FViQWBldfhd2Y4Dj0cKC0tNa4nve32y6Tw7tOnT2vXIYQQ\nooU4WTsxruc4rIp3U7g1AI1iTXJyISNH+hMcHEx6err0tts5k6cEPXDgAGvXriU1NZWKigocHByI\njIxkypQpxmE5hRBCXDkGxcDPGT9TVV/F6NDGr/IO8B1Aj+t78tHpY0REeDBsmB9qdcPQpp07dzYO\n3iTaJ5PCe8uWLTz00EN4eHgQHh6OnZ0dZWVlbNmyhY0bN7JixQpiYmJMPuiXX37JkiVLOHnyJJ6e\nnkyaNIkpU6Zc6jkIIcRVJ6c0h5WJKzlRegK9XuH0AUcm3zwIW1stOp2OxMREysrKmDZtcKOg/msq\nZ9G+mRTeixYtYsyYMbzyyiuNLrHo9Xqefvpp3nnnHZPvi3/33XfMnj2befPmERcXx4EDB5g1axa9\ne/cmPDz80s5CCCGuEnX6Ojamb+TnjIbBVs6erSY9/SxHy7/Drs6T/v2tSE9PN85lkJSURP/+/ZvM\ndSDaN5PC+8iRI7z++utN7o1YWFgwbdo07rjjDpMPuGDBAqZOnco111wDQN++ffn+++8vomQhhLg6\nHSk8wuprRWy3AAAgAElEQVSk1RRUFBjbVIoFXXT96FoXRmLibiwtnbGzaxiyWq1W4+npaa5yRSsy\nKbxVKhX19fXnXHYxDzsUFBSQkZGBra0t48eP58iRI3h7e3P//fczatQok/cjhBBXk8q6Sr44/AXb\nT2xv1B7sFsydQ+5kXclBCgpOEBj4d3C7uLgQFRWFg4ODOUoWrcyk8A4PD2fhwoXMnTsXjebvTerq\n6liwYIHJl7vz8vIAWLduHW+//Ta+vr58/vnnzJgxg86dO9O7d+9LOAUhhOi4/jnYSnVNPSqVCmc7\ne8b2HEu4YzhJB5Lw9a2ha1cvNBo1arWa0NBQ/P395VJ5B2ZSeD/++OPcfffdDBo0iPDwcOzt7Skr\nKyM5OZnq6mqWL19u0sH+GkZ90qRJxnm277rrLr7++mu+/PJLCW8hhPgHRVH4MeNHSmtKOX26gqys\nUnq6RPDOo89Qml/K9u3bURQFrbbhCqiLiwvR0dHyQNpVwKRr3r179+aLL75g2LBhFBUVkZKSQnFx\nMcOHD+eLL74gNjb2wjsB470XFxeXRu1du3YlPz//IksXQoiOTaVScVfUXVRXGjhxtJYQ3Q24ZAwl\n7WAV9vb2xg6RhYUFYWFhXHPNNRLcVwmT3/MODg7mlVdeuayDeXp64uzszKFDhxg2bJixPTs7W540\nF0Jc9Yoqi3CxcUGt+rtf1cWhCzOGPEYSBrb9WkCnTnZ4edni7u5Mt27d0Ol0REVFSWhfZZoN7+3b\nt9OvXz80Gg3bt29vbjUjUyYmsbCw4O6772bp0qX07duX3r1789lnn5Gamsprr712cZULIUQHYVAM\n/Jr1K1+lfcWY0DEM8RuKhcXfAe5n7YfbwCo8XZxJSOiKRtOwrGfPnqjVarm3fRVqNrynTp3Kjh07\ncHNzY+rUqcZJSM7lYiYmmTZtGvX19Tz77LMUFRXRvXt3li5dSo8ePS7tDIQQoh07VXaKlYkryTqb\nhcGgMG/TCjafrePlZ4ejVkN6ejrHjh3D0tKSIUOGGIMbGjpE4urUbHivXLkSJycn4/ctRaVS8cgj\nj/DII4+02D6FEKK90Rv0/JjxI9+lf0e9oR4FhcSkAgwljpyoPMvXXx/Cze0sZWVlANTU1HD48GGi\no6PNXLloC5oN739ORiITkwghRMvJKc3h48SPySnNMbZp1BpuDr2Z4794YWut48iR/UREuBuXu7m5\nERQUZI5yRRvUbHjPmzfP5J2oVCqmT5/eIgUJIURHVaev47uj3/HjsR8xKAZjezfnbkyOnox1nQ1L\nMr7H1VVF584NwW1hYUGPHj3o1q2b3NsWRs2G95IlS0zeiYS3EEKcX0FFAQv3LOR02Wn0BgM5J8ro\n7ufKrWFjGOI3hGNHj7E/Yz9hYX+PiObm5kZ0dDS2trZmrFy0Rc2Gd1pa2pWsQwghOjQnKydq9bWU\n6mo4cqQYq3Ivwu3vYJj/IHbu3ElxcbFxXY1GQ48ePfDz85PetjgnmYVdCCGuACuNFZOjJoNeg8/Z\nQURWjOXQn9VkZ+vw8/Mzrufu7k58fLxcJhfn1WzP+2JmCgP49NNPL7sYIYToCOoN9Rw4fYA477hG\n7SHuISwf/19WVx8lNbWY228Pwc/PEXCkoKAAV1dX6W0LkzQb3lqt9krWIYQQHUJ2STYfHfyI02Wn\nUQwqejhH4uBgCYBer+f40ePccEMnbr89FCcnK+N2pg4zLQScJ7xXrVp1JesQQoh2TW/Qs+noJjYd\n3YRBMVBWVsPjy+YyxuVhnn78Gs6ePUtiYiLl5eXY2+czePBgc5cs2rFmw7u2thZLS0vj9xfy17pC\nCHG1OVV2iuUHlhvf266p1XMosYRulddwLK+ML7/cjpVVqXGUyvLycnJzcxvd6xbiYjQb3lFRUWzf\nvh03NzciIyMveA/G1OFRhRCiozAoBn7O+JlvjnxDvaHe2B7eOZRB0QPZty0fB+fTlJTY4enZ8LqX\nRqOhZ8+edO3a1Vxliw6g2fB++OGHje8WPvzww/IAhRBC/ENBRQErDq4gozjD2KZRaxjTYwzxXeM5\nbJdG6WkdXbu6YmXVMAa5h4cHUVFR2NjYmKts0UE0G97/HHv80UcfvSLFCCFEe3Ao/xBL9i2hVl9L\nZWUdubllXNs7mntj78Gy1pJtW7dRUVFBUJAz0NDbDgsLw9fXVzpCokWYPJ93fn4+P/74IxkZGVRV\nVWFnZ0dQUBAjRozAzc2tNWsUQog2xdfJF41aw/GcYrIydfhUxREeNA5XS1c2b9uMXq83ruvp6Ulk\nZKT0tkWLMim8t27dymOPPUZ1dTVOTk7Y2tpSWVlJaWkpc+bMYdGiRfTt27e1axVCiDbB2dqZCRET\neO/UGhzLRmKv9+TXX3IYltCN4OBgUlNT0Wq1hIWF4ePjI71t0eJMCu/Zs2fTu3dvXnvtNby8vIzt\nubm5vPDCC7z66qts3Lix1YoUQghzqaqrIq0wjZjOMY3a47zjWDopiveKE6mp0XP33eHY21sSEBBA\nbW0t/v7+WFtbm6lq0dGZFN4nTpxg/vz5jYIbwMfHh//7v/9j3LhxrVKcEEKYU3pROh8d+Iiz1We5\nP+wxenQKwsamYQCrwsJCkpOTmTQpCnd3JywsGkabVqlU9OzZ05xli6uASWObd+nSpdl3vRVFoUuX\nLi1alBBCmFO9oZ4NqRuY98c8iqqKOJ1XzgMfvM6n6w9TX1/PoUOH+OOPPygrKyMjIxW1Wi6LiyvL\npJ73448/zrx585gzZw4uLi7G9sLCQubPn8/jjz/eagUKIcSVlFeex4f7P+RE6QkAdKW1ZB2pIqgq\ngb27j2NteQJ7+7/X1+l0VFRUYP/PRiFamckTk+Tk5DBo0CB8fX1xcHCgqqqK48eP4+TkhF6v54Yb\nbmj1YoUQorUoisK2E9tYn7KeOn2dsb1fYDT9ymM4npaLk1MBiuIKNIwo6eXlRWRkpNzbFlecyROT\n+Pv7N/q3lZUV0dHRANTU1LRCaUIIcWWU1ZSxMnElSflJxjaNWsMtPW4hwj6CfWcPYlEHXbt6oVar\n0Gq1hIeH4+3tLU+SC7OQiUmEEFe1o0VHWbJvCboaHSUl1ZwprGJwTE+mRE5Bl6tjV8ouALp1cwSg\nU6dOREZGYmVldb7dCtGqmn1g7fHHH6eysvKidlZZWckTTzxx2UUJIcSVYq2xpqKugszMEpIOFaI+\nHkS8ajIOigPZ2dnG9SwtLYmNjaV3794S3MLsmg1vnU7HmDFj+P77703a0Q8//MCYMWPQ6XQtVpwQ\nQrQ2Xydfbu1xKzZqe8IrbiagOp6ffsjBycmFbt26AdC5c2eGDBkil8lFm9HsZfMPP/yQOXPmMGPG\nDObPn098fDy9evXCw8MDBwcHysrKKCgoYN++fWzdupWTJ08yadIkZsyYcSXrF0KIi1JUWYSbbeMh\nnRO6JxAxMYqF7x7Bzc2Gu+4KQ6NR06NHD9zd3enUqZOEtmhTmg1vtVrNM888w9ixY1m8eDFfffUV\nK1eubPQ/sKIoODo6MnToUBYtWtTkoTYhhGgrquurWXtoLQfzDvJE7DN08/BGrVZRV1dHSkoKBQUF\nPProAFxd7YyfcxqNhs6dO5u5ciGauuB73v7+/rz11lsYDAbS0tI4c+YMOp0OR0dHPDw8CA0NRa02\naawXIYQwixOlJ1i6bykFFQUUFVdx1/xZPD3gafr3dSExMZHq6moAsrPTcXOLNXO1QlyYybOKqdVq\nGfJPCNGuKIrCluNb+Pzw59Qb6ik+W0VKShFdanvy22+7qCizw8HBstH6BoNBOiSizTM5vIUQoj2p\nrKtkZeJKDpw+YGzr5O6Et/M1aM+qsbKqRlHsgIZxKyIiIuQSuWg3JLyFEB1O5tlMlu1fRlFlkbHN\nx96HeJt4CoKLyc4uxd/fGY1Gjbe3N+Hh4VhaWp5nj0K0LRLeQogOQ1EUfsn8hS9Tv6Siqobiomq8\nvR0Y4DoAr3IvSstLsbKyIDjYVXrbol2T8BZCdBiZZzP57PBn5OVVkplRgkqvZVLPe7nGL4QDB/6+\nfC69bdHeyVMZQogOI8A1gOv8r6PkbDW2dZ7Elk/gwCYtXl6d6dSpE1ZWVsTFxREbGyvBLdo1k3re\nhYWFvPvuuxw8eJCSkhIURWm0XKVSsW3btlYpUAghLsa/Av6FxbVW7Fxnh72XNffcE4FWa0FkZCQq\nlUpCW3QIJoX3f/7zH3bs2EG/fv0ICwuTkYaEEGZXq69l45GNjAgYgb1Vw1zap0+f5tChQ7hoHXji\nsVi8vOzRai0AZDxy0aGYFN579+7lvffeIz4+vrXrEUKICyqoKGDx3sVkFp5gzTe7mH7NQ9jZFXPy\n5EmgYZpine4kPj4yNoXomEwKb41GI0OfCiHahAOnD7Di4AoKS3UkHyrETl/K55s+55qYAGxsGj7S\nrK2tcXNzu8CehGi/THpg7YYbbuDnn39u7VqEEKJZBsXAF4e/YPHexVTXV2NrqSVQ40UsMVjp7Sgt\nrQHA19eXIUOG4OXlZeaKhWg9JvW8+/Tpw3vvvUdSUhJRUVHY2to2Wef2229v8eKEEAJAV6Nj6b6l\npBelA6Ct1OKqc6V/YBDHj1YTHOyCl5cTUVFReHp6mrlaIVqfSeH9+OOPA3Ds2DF++OGHJstVKpWE\ntxCiVRwrPsaSfUvIKynExkqLTZENnZROhHiGoFFr8IiDrl270rNnT7RarbnLFeKKMCm8N2/e3Np1\nCCFEI4qisDlrM+uTP+fYsWLy8yuIjvZiiP8Q7HUNT5fb2NgQGRkpvW1x1TEpvL29vRv9W2bdEUJc\nCelF6aSnF5FfUIlWscYhJYHRo8eyd++fODo6Sm9bXLVMHh5106ZNrF69mtTUVKqrq7G1tSUiIoL7\n77+fAQMGtGaNQoir1PWe15MZcIKdZwoJ1o2kd0gQKpWKAQMGYGFhYe7yhDAbk8J7w4YNPPvss0RH\nRzNu3Djs7OwoKyvjwIEDTJ06lQULFjB06NDWrlUI0cEpioJKpaKmpoakpCTy8vIY4Xwtt94SiqJX\n079/FxkkSghMDO8VK1Ywbdo0pk+f3mTZa6+9xsKFCyW8hRCXzKAY+CrtK06dLqWXTT9KS7Opq6sD\noLq8mmB/A35+vmauUoi2w6TwzsrK4r///e85l40fP57169ebfMCEhATy8/Ob3DP/5ptv6N69u8n7\nEUJ0DGU1ZSzdv5Tf9u9HdUpLpuoMA2ND0GobPiP8/PyaPHcjxNXOpPC2tLREp9Odc1lVVdVFPzDy\nyiuvcMstt1zUNkKIjifrbBYf7P2AknwdTvmOqFQqygxnycgoITbWh6ioKNzd3c1dphBtjkmPjMfG\nxvLWW29RXFzcqL2oqIg333yT2NjYVilOCNExKYrCluNbmLN1DjU5NTiW2uPmbIOD3pOuNoEMHBhO\nfHy8BLcQzTCp5/30008zceJEBg8eTNeuXbG3t6esrIycnBwcHR1ZtWrVRR30+++/Z9myZeTn5+Pn\n58dDDz3EsGHDLukEhBDtS019DWsOrWF39m4cTjugMqjQqDWE+YRh1cWVhIS+8t62EBdgUngHBQXx\n3Xff8fnnn5OSkkJ5eTmdOnVizJgxjB07FldXV5MPGBwcjJ+fH7Nnz8bS0pJVq1bxyCOP8OmnnxId\nHX3JJyKEaPvyy/OZt/U9tiemEhjkjJVtLW61bvRw70FoYCg9evRAozH5DVYhrlom/5a4u7vzwAMP\nXPYBFy9e3OjfDz74ID/99BPr16+X8BaiA0s9k8qrP75DUsop6vUKaWnFPHD9QIJrgwkNCZVZwIS4\nCM2G97p16xgzZgyWlpasW7fugju6nLHNu3btSn5+/iVvL4Ro26qqqjiTcQZrCy16g4IaCzoXxDPQ\naTRBQS7mLk+IdqfZ8J45cybDhg3Dzc2NmTNnnncnpk5MkpOTw/Lly5k+fTqOjo7G9szMTOLi4i6i\nbCFEe6AoCjk5OaSkpFBfX8/oztdTWf4DbseH8tSDw+je3dncJQrRLjUb3ps3bzbey26piUnc3d3Z\nvHkzOp2OF154ASsrK5YvX05WVhbvvvtuixxDCNE25JzJ5VjyccrLzxrbVOUq3h/7MnY2Tlhby71t\nIS5Vs6+KeXt7G4ch3LBhA66urnh7ezf5MhgMrFy50qSD2djY8NFHH1FRUcHIkSPp378/O3bsYPXq\n1fj7+7fMGQkhzEpv0LPkh+XMXDSbH3/fh8GgAGBnZ8eAAQNwc3GT4BbiMpn0G7RgwQImTJiAjY1N\nk2X5+fmsXbuWZ5991qQDBgQENHloTQjRMZw+e5qPvv+I9LRT6A0KeUomRzMdGTm8D6GhoTKZiBAt\n5LzhnZCQgEqlQlEUbr311iZDmiqKQmFhIZ07d27VIoUQbZuiKGxJ3MLPu36mvr4eewdLSktrUBRL\nOnuH0LNnT5lQRIgWdN7wfuONN9i/fz/vvvsuERERWFlZNVnHycmJ2267rdUKFEK0bfWGetbtXkfi\nvkRjm62tBicbP6aMuJOgIBklTYiWdt7w7tu3L3379uXEiRM8//zz2NvbN1lHURSqq6tbrUAhRNt1\nsiSPZz59CysPHU42DmirtFhaW3LL0FuI9Zdhk4VoLSbd837jjTeaXXbixAluv/12/vzzzxYrSgjR\ntimKwrf7f+fVbxagq6rEXWdDz2ALQixCuHfYvTjZOJm7RCE6NJMf+VyzZg3btm2jpKTE2KYoCidP\nnpR7WUJcJRRFITs7myMZR1hzch26qkoAigtr6NNnBPfEj5HPAyGuAJNmFVu8eDFvvPEGZ8+eJSkp\nCYPBQElJCYmJiURGRvLee++1dp1CCDOrqKjgjz/+4NChQ9RW1nJr15F07myHvcqZV0a8IMEtxBVk\nUs/7yy+/5K233uKGG24gJiaGuXPn4uvry/79+3nllVcuamISIUT7oigKGRlZJCen8M83vTzw4Knr\n7yHKpQ8+neQzQIgryaTwPn36NDExMQCo1Wrq6+uBhnm+H3zwQV5++WVWrFjRakUKIcyjoqKCHzZv\n4dcDO3FTexMX7YtKpSIwMJDg4OAmr48KIa4Mk8LbxsYGnU5H586dcXZ2Jicnh+7duwMQFhZGUlJS\nqxYphLiyFEUhKyuLLbu3sjVlP3pFT5VSTdeCToy9NQFnZxmTXAhzMunP5ri4OGbOnElxcTGRkZG8\n++675OTkoNPpWLNmDQ4ODq1dpxDiCqmoqGDb9m18te0r0ooOY2uvRkEhV8nHopODBLcQbYBJPe+n\nnnqKadOmUVFRwX333cfEiRMZPnx4o+VCiI5h/9H9/HL4F6rrG8ZvsHG2IN9Sz0s3zqB/cLSZqxNC\ngInh7e/vz08//QQ0TP+5adMmfv65YRjE6Oho4/1wIUT7dTpPx+tfrKDaJwVHtT0WWFDtWE14aDiv\nRE/EVmtr7hKFEP+fye95//MVkE6dOjFp0qRWKUgIceUoikJdXR3fb0/mzR8WUEIe3nX2BHVVY21h\nzZ297qSPdx95BUyINqbZ8L7YS+Fz58697GKEEFdOeXk5Bw8epFZfy+qs1ZSQD8DpvAqGRMTy0ID7\ncLWRV8CEaIuaDe8DBw6YvBP5q1yI9kNRFDIzM0lLS8NgMAAwKmAIpwu/prYanhhxF+P73iS/10K0\nYc2G96+//nol6xBCXAFlZWX8+ec+yspK0WobXjZRq9X09e5LRXw1NwSPxM/V18xVCiEuxOR73kKI\n9ktRFI4dO8Zvv+9m//FUujr4EdGzE05OTkRHR+Po6EhIUIi5yxRCmMik8E5ISLjgJbTNmze3SEFC\niJZVVlbGwYMHSTqWyu7jyRjQk1laRwQRDBzYR0ZJE6IdMim8Y2Njm4R3RUUFycnJ2NnZMXTo0FYp\nTghxeY4dO0ZyajLphekUVhZibauisKKGY5o87uiileAWop0yKbznzJlzzva6ujpeeOEFOnfu3KJF\nCSFaxuHTh9mTu4c6Qx0AWh8DqnINi29+iSjvcDNXJ4S4VJd1z1ur1XLfffdx//33y3vfQrQhyUdz\nmfvjUrS+p3FUOaJYKlS6VtI/sD+3hd2Gtcba3CUKIS7DZT+wVl1dTXFxcUvUIoS4DDqdDisrK5Zs\n/JEPd6+ihkq6WzjRtZMaZ3tnHol6hDDPMHOXKYRoASaF97p165q0KYpCaWkpGzZswN/fv8ULE0KY\nxmAwcOzYMY4ePYrWUct3+Z9QQyUAuTll3Bp3HROi7sBGa2PmSoUQLcWk8J45c2azy/z8/M67XAjR\nenQ6HQcPHqS0tBSAmpIabg4dzEfFP2GrduCVsY8yKCTOzFUKIVqaSeF9rtfAVCoVjo6O2Nvbt3hR\nQojzMxgMHD16lJ07E3F2tkKjaXhq3MXFhb5hfbF1cuLWiJuxt7Izc6VCiNZgUnh7e3u3dh1CCBOV\nlpayY8ceth04SH7lKcLcIugR6kVISAj+/v6oVCom955g7jKFEK3I5AfWVqxYwTfffENubi5lZWU4\nOjoSEBDAmDFjGDduXGvWKITg7972odRDHDiRQm5VAaggqeAYN4wcQUBAF3OXKIS4QkwK77fffpvl\ny5fTq1cvRo8eja2tLRUVFaSkpDBz5kxyc3OZPn16a9cqxFWrrq6OHTt3kJabxvHS4xgsDFhaqcms\nKMTV20BnH0dzlyiEuIJMCu8vv/ySf//730yZMqXJsmXLlrF8+XIJbyFaSWVlHZlFx9l2ehtlJWUA\n6C31KEE13OF9A3cPuB1LC0szVymEuJJMCu/q6mquvfbacy4bPnw4CxYsaNGihBANr2P+uTuHNzes\noMg5ibBQNxwsHKhxqMHD24NHox+lm3M3c5cphDADk8I7OjqaY8eO4evbdKrAI0eOEBMT0+KFCXG1\nMhgMZGZmcqpax/TP5lKpLoFiOHO2Eo2Phn+F/IvhAcOxUFuYu1QhhJmYFN7Tp09n5syZZGdnExMT\ng729PVVVVezdu5cNGzbw1FNPkZWVZVy/e/furVawEB1ZSUkJBw8epKysjHqnepx96qg8BVaWFgQ4\nBTJjyAN42XuZu0whhJmZFN633XYbAKmpqY1mF1MUBYBp06Y1Wj81NbWl6hPiqqDX60lOTiUn57jx\n90qr0zKsRyw7tGk8MXwK1wbGX3BqXiHE1cGk8H799dflQ0OIVpKdnc+a9ZsoryynX0w3VCqwsLCg\nR48e9PPsx4PxGpytnc1dphCiDTEpvG+55ZbWrkOIq45eryc55TAffvY1JUoeGsWS3JOuREd1Jzo6\nGltbW3OXKIRoo0wepCU3N5fPP/+c1NRUKioqcHBwIDIyknHjxuHu7t6aNQrR4Zw9e5bf//ydxJxE\n9PYlKGUKNaoqcg3FPNh/glzpEkKcl0nhffDgQaZMmYLBYMDf3x87OztOnjzJtm3b+Pjjj1mzZg0B\nAQGtXasQ7Z7BoJBXeJr1P6znVNkpAOzstJToq/AMdOX+4aMkuIUQF2RSeM+fP59+/foxZ86cRhOR\nlJSU8Pjjj/P222+zePHiVitSiPbOYFDYsiWHT37bjEP/FFQ1ChZYoKgU6t3rmTRiNNcFXCevfwkh\nTGJSeCclJbF27domM4g5OzszY8YM7r777lYpToiOYt7CrXyd8TmF2ky6pNsR3NUN67PW+AX7MbHX\nRNxt5daTEMJ0JoW3Xq9Hq9Wec5m9vT11dXUtWpQQHUVxcTEZWRkcdPyEQm0OACUlNdiE2jC+73h6\nd+ktl8mFEBfNpPAODAzkk08+4YUXXmiybPXq1QQGBrZ4YUK0V4qiYDAYSEtLIysrC0VRuL7rQLJP\nf4WDg5bb+l/PuLCx2FnKXNtCiEtjUng/+OCDPProo+zZs8c4wlpZWRn79+8nIyNDxjYX4v/LyChh\n3bp9REbWoyi1xnaPWg9ujx/CiKARBLkFmbFCIURHYFJ4Dxs2jCVLlvDRRx/x/fffU15ejr29PeHh\n4Tz33HP079+/tesUos3btOkYG37ZhN4mG/2hYKLDG+bX9vT0JDIykhE2I8xcoRCiozD5Pe9BgwYx\naNCg1qxFiHYrNy+Xg6c3UGN7HAU4UZFFWF0XeveOwtfXV+5rCyFalMnhXVNTw86dO8nJyUGn0+Hi\n4oK/vz/9+vW75A+mffv2MXHiRB566CEeffTRS9qHEOZUV1fH939+z67kXdQZ6rC11aA3KDh41RMX\nH42vW9OZ+IQQ4nKZFN7Jyck88MADFBYWNlnWpUsXFi5cSGho6EUduLq6mueeew47O3loR7Qv1dX1\nfPPNMfzDLNma9RV52XnGZQ7OlnQN7MqkQZNwtpHxyIUQrcOk8H755Zfx9PRkzpw5hIWFYWtrS0VF\nBcnJybz11lvMmjWLTz/99KIOPG/ePLp3746np+clFS6EORw9epalyw6SWrmHgtQ9REW54KhxRF2v\nRuugZXT8aOL84sxdphCigzMpvNPS0lizZg0RERHGNkdHRwYMGMBLL73EpEmTLuqge/fu5euvv+ab\nb75hxowZF1exEGZUY1nMdmUtZ21OQQUUFlujcdXQ27M34weMx0ZrY+4ShRBXAZPC283NrdkZjuzs\n7HBzczP5gFVVVTz33HP8+9//xsvLy+TthDCn+vp6UlNTyS7KxsW/gopcNQH+zkR09+euqLsIcJWx\n/YUQV45J4T158mSWLVvGyy+/3GiktdraWpYuXcrkyZNNPuC8efPo1q2bTDMq2rwTJ3RUVNTh4aGQ\nmJhIZWUlADeFxLOv8wFGhd7IyKCRaNQmP/cphBAtwqRPnePHj7Nr1y4GDx5MWFgYDg4OVFVVkZSU\nhFarpb6+nqeeesq4/ty5c8+5n78ul2/cuLFlqheiFdTU1P+/9u48LOpy7x/4e1iGYV9HZBFkBweG\nwYVNXDNc0sqtnk7aybSOS+tzKrXl13JaTqV1ztV6erLF1CzrsUwzNZdMhVwBQXEBZRFZZIeBGWbm\n/v3Bw+ioKCowwLxf1+V1yX3PfOcztzTv7u/c3++NDRvysGX7STi5n8eoeG/Y2FgZ++Nc4jBl6BT4\nOgCMxyYAACAASURBVPuasUoismQdCu/du3cDAOzt7ZGfn29sl8lkAIAjR44Y26512dgPP/wAtVqN\nO++809jW0NCArKws7NixA+vXr7+x6om6gF4vsO3Q71B7HIXOygr5Z20RHuoFqVSK6Oho+Pr68rpt\nIjKrDoX3jh07OuXFlixZgieeeMKk7YknnoBKpcK8efM65TWIbkVVQxVWbV8FmfwsmqoNsLGTQOd0\nAT4+MYiJiYGdnZ25SyQi6vhNWjqDq6srXF1dTdqkUimcnJwgl8u7sxQiAK2biJSWNqJ/f0dsz96O\n7enbodfqIZPZwMNDAkdHOwwepsKQ2CGcbRNRj2H2lTZff/21uUsgC3XhghqrVh1Hdn4Bgu/KR0XB\nOdhqLy7IDAkKxKxxs+Dh7GHGKomIrmT28CYyByEEPv2fTOwr+QNnZftwbL8VYqLkcDnvApmNDKlJ\nqRgRzXv5E1HPxPAmi6TT6aCN2Y386tbFmA4OToA1oFQpcZfyLrg4upi5QiKi9jG8ySIIIYzfWZeV\nlSErKwtK5zAcDMmEi4sdInwGYnbsbAS7B5u5UiKi62s3vPfs2XNDB0pJSbnlYoi6Ql5eDb7+Ogf3\n3x+OxsZzKCoqAgBINVKMC4tHqH8oJoRO4M1WiKjXaPfTat68eZBIJBBCADC9fvvSWUyb48ePd1GJ\nRDdv9+4irFyThXLHvdCsOYKkuEC0/epKpVLcp7iPt+klol6n3fBeuXKl8e9VVVV49913kZqaCpVK\nBUdHR9TX1+PQoUPYtWsXXnrppW4pluhGtXgU4ILHBrha2aC8RYP6hv5wcbaDn58foqOjIZVKzV0i\nEdENaze84+PjjX9//PHH8cgjj2DGjBkmj0lNTUVoaCjWrFmD5OTkrquS6AbVaerw9b6vcTb3LPzd\nHNCs0cPV1Qo6WSOGDh0OHx8fc5dIRHTTOvQl3549e9rdujMhIQFvvvlmpxZFdDOysiogkQhUO+Vi\n0x+bIOoFJJDAwdEWrs4OGBIxBHcMv4N3SSOiXq9D4W1ra4sDBw4gICDgir7Dhw/D2tq60wsj6qjG\nRi3WrMnFH4dzUei+CyFDW+CmcYE1Wn8vfdx8cPfIuxE0IMjMlRIRdY4OhfekSZPw8ssvY//+/YiM\njIRMJkNzczOOHj2K3377DVOmTOnqOonaZYAeW/I347jzXhh0etgUOkEaYAP5BTkSoxIxLmEcv9sm\noj6lQ+H93HPPwcXFBT/++CN++uknY7uHhwfuu+8+/Pd//3eXFUh0PecuFMAqIheGY3p4eztg4EBX\n3B5xO8b6j4Wbs5u5yyMi6nQdPm3+1FNP4amnnkJ9fT0aGxthb29/xSYjRF1NrzegsLAOQUFu0Gg0\nyM7ORklJCe4Ouh22sm1Q+IVhtnI2BrgOMHepRERdpt3w1mq1V223s7MzLvi59DE8LUldLT+/BqtW\nHUNBRQmeXjgERUWnoNFoAAAuDS54SDULYyLGwEpiZeZKiYi6VrvhrVQqO7wFokQiwbFjxzqtKKLL\nCSHw+Zr92FO9Ec7ODVi3vhJxMb7Gfn8/fyhCFAxuIrII7Yb3okWLuH8x9QgGYcDOMztR7r8Bvs06\n2Ejs0Wh3HoAvZDIZYmNj0a9fP3OXSUTUbdoN78cee8z494KCAvj6+sLW1ra9hxN1quZmHWQyGxTV\nFmHl4ZW4cOYC3JocIHXVwc7OCt6uLvDz90NMdAx/L4nI4nRowdqUKVOwZcsW3pWKupzBILBjRyF+\n3HgcUXeV4Vj1fjiUOcDW0BrQXq4uUPgoMCphFGfbRGSxOhTeCQkJ2LJlCx588MEuLocs3Q8/nMS3\nu3Yiz34X0nc3Y3BsPwgrAWuDNQa4DkBydDKiFdGcbRORRetQeCcmJmLt2rX49ddfoVAo4OjoaNIv\nkUjw1FNPdUmBZFkaAw8h13kj9AYBB2EDTYse/UP6I7wlHElDkyCXy81dIhGR2XUovN955x3j3zMy\nMq7oZ3jTzTAYBCSSi9vNNjc3I8jWDwMHusIgBMIHeuPe6HuQ6J8IIQSsrLiSnIgI6GB45+bmdnUd\nZGHy8mqwdm0uxo0LRHx8f5w7dw7Z2dloaWnBnRGjIPOQYXrUdDhKW8/y8MoHIqKLOhTel6qqqkJj\nYyNcXFx4hzW6KQcPluKjz/7EGdlenPjfgdDp4lBVdcHYH6gNxG2K22Bjc8O/nkREFqHDn46ffvop\nVq9ejfLycmObv78/HnnkEcycObNLiqO+xyAMqHY9hiyvVZDpJBA2zThxyh1yTycAgIODA2JjYxnc\nRETX0KFPyBUrVuDf//43JkyYAKVSCUdHRzQ0NODw4cN46aWXYG1tjWnTpnV1rdRLCSEgkUiQX52P\nb45+g6LKIsTJvSGpt4KzixR6uwYAThg4cCCioqIY3ERE19GhT8l169Zh8eLFeOCBB0zaH3zwQfzn\nP//BF198wfCmK1y4oMa3356A3N8KzQMzsbdgL2wbbeFc7QyJjQQyuQwh7iHw9/SHSqWCp6enuUsm\nIuoVOhTexcXFGDNmzFX7JkyYgA8++KBTi6Ler6CgFv98Ox1FVhkoLtyP2BY3uGocIauRwUpihQGu\nA+Dv4o+Q4BBERkZytk1EdAM69Inp6OiI0tJSDBhw5TaLFRUVcHBw6PTCqHezdmvACe/vcL6hFBBA\ndXUz7OW28Nf7I8g5CB4uHpxtExHdpA6Fd3JyMl599VW8/fbbiIqKMrZnZ2fj1VdfRXJycpcVSL1D\n2/fabTwdPBAc5YCGXClCQ90Q4u2PexT3wFPviaqqKs62iYhuQYc+PZ999lk88MADmDZtGmQyGRwc\nHNDY2AiNRoPAwEAsWbKkq+ukHkqtbsHGjXlobtbjgQcUAFqDvOJ8BSZ5j4GD0y5MDp+MsUFjYWPV\n+uvm6+t7rUMSEdF1dCi8fXx8sHHjRmzduhU5OTloaGiAs7MzoqOjcfvtt0MqlXZ1ndQD1ddr8dIr\nf+Ck5iDU1tVISVkCHx87ZGZmoqKiArawxdNDn0aAT4C5SyUi6lPaDe8ff/wRKSkp8PLyAgDY2dlh\nypQpmDJlSrcVRz2XEAIn6rNwvP83OFt+HgCw/rftGDTAATqdzvi484XnGd5ERJ2s3fBesmQJJBIJ\nIiIiMGLECKSkpGDIkCH8npJwuuo0vj/2Pc5Un0G/QIHqejsoPXxhsM2HThcJoPV2pkFBQYiMjDRz\ntUREfU+7Sfzdd99h3759SEtLw1dffYXPPvsM9vb2iI+Px8iRI5GSkoKAAM6oLIHBILBzZyH+PHYC\nbomnkVma2dohAJcWB4z18UagSyB8nFv3e3dyckJsbCw8PDzMWDURUd/VbngrlUoolUrMnz8fGo0G\nBw4cQFpaGtLS0vDaa69BCAF/f3/jrHzs2LHdWTd1E73egP/35jbsvbAN5dLjiHT0gFzuACudFRyr\nHBEgDcAAnwGwsbKBRCJBcHAwIiIiYG1tbe7SiYj6rA6dA7ezs0NKSgpSUlIAALW1tThw4AAOHDiA\nn376Cd988w2OHz/epYWSedRqa3DQZSXK6moAAOdLGyGXOyBCHwF/D3/Y2dgBaJ1tq1QquLu7m7Nc\nIiKLcENfYLe0tODw4cNIS0vD/v37kZOTA51Oh5iYmK6qj8zMw94DE4cmYGXldvTv74jbVQmYMWga\n3K3dsXv3bhgMBoSEhCA8PJyzbSKibnLd8M7JyTGeLj98+DCam5sRFhaGxMREzJs3D/Hx8XBycuqO\nWqmLZWQX46cdGXhuwUTY2l4M4umKqdDomzBt0DSEeYYZ29s2qeFsm4ioe7Ub3o8//jj+/PNP1NfX\nIzAwEMOGDcP06dORmJjIhUh9TLOuGS+u+Aq/nt4CK1gjbmso7ryjdZV4Y2MjinKKMMN3BkI8Q0ye\n5+/vb45yiYgsXrvhvXXrVkgkEiQnJ2PEiBEYNmwYFApFd9ZGXUyj02DX2V3YkrcFp1AGnUQDAFj1\nxyZMuD0U584V4fjx49Dr9aiuroa3tzfPshAR9QDthnd6ejrS09Oxb98+rFmzBm+99RZcXFyQkJCA\nhIQEJCUlITg4uDtrpU6i1jbhj8Ld2Ja/DfWaegCAj68jSksb4e3SDw/fNgQHD+5HZWWl8TlCCFRV\nVTG8iYh6gHbD283NDRMmTMCECRMAACUlJdi7dy/S09Px8ccf4x//+Af69euHpKQkJCUl4e677+62\nounmVNbW4f2N67Dx2K9QqFwgk1385/dy8MI7s2ZhAPxw4sQJVOrrjX3Ozs5QqVRwc3MzR9lERHSZ\nDq829/X1xcyZMzFz5kwAwKlTp/Djjz/i+++/x4YNGxjePVxeVR4e/p//h/OVrZd8FRdLEBrqDg97\nD0wKmwSluxLZR7NxrOqY8TkSiQRhYWEICwuDlZWVuUonIqLL3NClYgUFBdi7dy/S0tJw4MAB1NTU\nQCaTYfjw4V1VH92ky7fo9HfxR0CAizG8NTUyzFLORqJ/AooKirB3z17o9Xrj411cXKBSqeDq6trt\ntRMR0bVdM7xramqQlpaGffv2Ye/evTh//jyEEAgNDcXdd99tXMjGXcV6BoNB4NixC9j150mcK6nD\nGy9MMAa4nY0d7hkyBZWlP+GuqMn467iJsLWxgV6vR0FBgTG4OdsmIur52g3vadOmITc3FwaDAS4u\nLkhKSsKCBQswcuRIeHt73/QLnjp1CsuXL8eRI0egVqsRGhqKRYsWYdy4cTd9TGpVWl+Gv6/8F4qR\nA4+WIBQUJGPgwIsz59tDbseEJyfASnIxlK2trREbG4t9+/YZv9vmbJuIqGdrN7xtbW2xYMECpKSk\nIDY2tlNmYU1NTZg1axbuuusuLFu2DFKpFCtWrMDjjz+ODRs2IDQ09JZfw1KUlDTAyckWLi52KK4r\nxq+nf8XBkoPQelfCUGbABds87D5yDAMHJhmfY2tti6amJshkMpNT6h4eHoiPj4eXlxdn20REvUC7\n4f3tt992+os1NTXh6aefxuTJk2Fvbw8AmDVrFv71r3/h5MmTDO8OyMgox88/56GouA7Dxtug2e8Y\nssuzjf3e/R1ha2uNoSEKjB7uZ2wXQiA/Px+5ublQqVTw8/MzOW6/fv267T0QEdGt6dbNuT08PIyr\n1QGguroan376Kfr374+kpKRrPJPaaLU6ZJZmoMjpIA5lXsAQW29IcHEWnRw6GBMnTDS5jWl9fT0y\nMjJQU9O6WC07OxteXl6ws7Pr9vqJiOjWdWt4Xyo6OhotLS2IiYnB559/zvtjX0YIgZKSBvj5OZu0\n59ruQK7LJggBeDrJYDAI2FhbYbDPYIwPGY9At0CTY+Tl5eHEiRMwGAzGdnt7e+h0OoY3EVEvZbbw\nzs7ORlVVFVavXo2//OUvWLt2LYKCgsxVTo8hhEBaWgm2bStAaWkj3nxzBNzcZMb+5MBEREf/BidH\nW9jZSpE0IAmpIano52h62vvy2TYAWFlZITw8HCEhIfxum4ioFzNbeAOtp9Efe+wxbNu2DWvXrsXS\npUvNWU6P8fvvxThzvhTn7bKwbYcfZk4bZOwL9QhFcuhg+Dn74bbg2+AmM73rmRACp0+fxsmTJ01m\n225ublCpVHB2Np3JExFR79Ot4b19+3a8/vrr2Lx5s8kpW61Wy72g/8+5+nNoUfyJA9XbILEWON0c\nDmCQyWMei3/MZLV4m8bGRhw+fPiqs+3Q0NCrPoeIiHqfbg3vuLg4NDU14dVXX8UzzzwDe3t7rF27\nFoWFhUhNTe3OUsyuoUGLHTsKUVXVjL/+VYGcihz8lv8bjlcch5AIhIS5QO7lALXrcRiEweTa7PZC\n2MbGBmq12vgzZ9tERH1Tt682X7lyJd566y2MGTMGVlZWCA4OxgcffACVStWdpZhVY6MWzz33B5o0\nGpRLc5HlsRqNkipjvwQS+PR3QqhHKFJDUk1Wk1+LnZ0dYmJicOTIEURERCAkJISzbSKiPqjbv/MO\nCwvDZ5991t0v26NY2xmgC8nG/uI/oJU0ofGME0KCW7+7lkgkGOwzGOOCxyHYvf0tVw0GAy5cuHDF\n9dm+vr5wd3c3XkdPRER9j1kXrFmCgoJa6PUCwcEXF5ZZSayg9T0JaY0OIQEe8PKyh8xGhpSAFIwN\nGgtPB89rHrOurg4ZGRmora1FcnIyPD1NH8/gJiLq2xjeXaS0tBGrVx9D7skL8A2Q4uXnxhpPYUut\npbgjehysZJvhYe+B24JuQ0pACuxtrx26BoPBuJJcCAEAyMjIwKhRo2Bjw39KIiJLwU/8LmKwbcbO\nc1tR7JwF5wve+K9cFaKiLs6QxwSNwQDXAYjrHwdrq+uvtK+trUVGRgbq6uqMbdbW1ggKCuJKfSIi\nC8Pw7gR1dRrY2VlDKrXG2Zqz2Hl2Jw6VHIJ6QAW0pY2w7i8g7BsBXAxvN5kbhvoOve6xDQYDTp06\nhVOnThln20Dr4r/Y2Fg4OTl1xVsiIqIejOF9C0pKGrBlyxmkHyiG4vYmNHjloqCmwNgfEOCCgAAX\neDm7QTjU3/Dx25ttR0ZGIigoiCvJiYgsFMP7Fuw/dhJrMv4XpY7HsP+IDsOG9Te5rCvKOwyjB47G\nEN8hsLG6saEuKipCZmbmFbNtlUoFR0fHTnsPRETU+zC8O0ij0cHOznS4zrscQJljJnQ6AxykUmi1\nBjjK7BDvF4/RA0ebbBJyozw8PGBlZQW9Xg9ra2tERUVh4MCBnG0TERHD+3ry8mqwe3cRDhwpxuuv\njIG7+8VNQsYGj8a2sD2QyWwQ1M8XIwNHIiUgBY7SW58ZOzo6IjIyEqWlpYiNjeVsm4iIjBje16A3\n6PHx+k1IK9mHOrvz2Ll7IKbdFWXsD/cMx4yhExHrHYtB8kE3PSuuqalBfX09BgwYYNIeFBTE77aJ\niOgKDO//I4SAWt0CR0cpKtWV2FO4B3uL9iLf8zyqyltvXbr3zJ+YhovhLZFI8JeYv9z0a+r1epw8\neRJ5eXmQSCRwc3MzuQ85Q5uIiK7G4sO7sVGL3buLsWdfIdTORRg4sgrHLxw3LhTz9LSHn58T+vVz\nQKKy805dV1dXIzMzE/X1ravQhRDIzs5GUlJSp70GERH1TRYf3qV15fjXli9RansMWk0ThhV5w15m\na+x3k7ni0fGTkBKQAi8Hr1t+Pb1ejxMnTiA/P99kJbmXlxdiY2Nv+fhERNT3WUx4GwwCJ05UITDQ\nBQ4OF8O5TFeIRp8caCubYWMtgbpRBwd7KQbJB2FEwAgovZUdugNaR1RXVyMjIwMNDQ3GNhsbG0RF\nRSEwMJCnyYmIqEMsIrx37izEL7/ko6ThHB75rxEYNSrA2DfEZwhCArwg91Ij1N8HIweOQPKA5Otu\nDnIjrjfbdnBw6LTXIiKivq/Ph3dTSxMya/7E74ZfUO9cjk1/Sk3C287GDvOSZ8Hd3h2D5INgJbHq\n9BoOHTqEsrIy4882NjYYNGgQAgICONsmIqIb1ifCu6VFj5ycSmRklEOvF3jooWicrTmL3QW7cbDk\nIBpEExqsyyGVWqHO7QSEECahOSJwRJfWFxoaivLycgghIJfLoVQqOdsmIqKb1ifCu65Oi48/zoAO\nWlTKTqBgxw8oU5839kttraGK6wc3F3sk+gdc40hdw8PDA2FhYZDJZJxtExHRLesT4S1xaEKl/17k\n1mVCL2mBVaEH5F4XZ7Z+Ln4YET0C8X7xnXL3s/bo9Xrk5ubCzc0Nfn5+Jn0RERFd9rpERGRZ+kR4\nA4DOJx9+bjJ4ernDyckWtta2GOY7DCMDR2KgW9ffE7yyshKZmZlobGyEVCqFl5cX7OzsuvQ1iYjI\nMvWJ8Paw98A4ZQKOlh2Fr3PrPcYT/BPgYNv13yvrdDrk5ubi7NmzxpXkWq0WhYWFCAsL6/LXJyIi\ny9MnwhsApoRPwcTQiQh2D+6275QrKyuRkZEBtVptbLO1tYVCoYC/v3+31EBERJanz4T3rWy/eaPa\nZttnzpwxae/Xrx+USiXs7e27rRYiIrI8fSa8u8uFCxeQmZnZ7mybK8mJiKirMbxvgF6vx+HDh6HR\naIxt3t7eUCqVkMlk13gmERFR5+n824n1YdbW1oiJiQHQOtuOi4vDsGHDGNxERNStOPO+BoPBACsr\n0/+/8fHxgUKhgK+vL0ObiIjMgjPvdlRUVGDnzp2orKy8oi84OJjBTUREZsPwvoxOp0NWVhbS09Oh\nVquRkZEBnU5n7rKIiIiMeNr8EuXl5cjKykJTU5OxTafToaGhAW5ubmasjIiI6CKGN4CWlhYcO3YM\nhYWFJu0+Pj6IiYnhbU6JiKhHsfjwvtpsWyqVIjo6Gr6+vrxum4iIehyLDe+Wlhbk5OSgqKjIpJ2z\nbSIi6uksNry1Wi1KSkqMP0ulUsTExMDX19eMVREREV2fxa42d3R0RGRkJADA19cXo0ePZnATEVGv\nYDEzb7VaDQcH0y1Cg4KC4OzsDLlcbqaqiIiIblyfn3m3tLQgIyMDO3fuRH19vUmfRCJhcBMRUa/T\np8O7rKwMu3btQlFREQwGAzIyMiCEMHdZREREt6RPnjbXarXIyclBcXGxSbujoyP0ej1sbPrk2yYi\nIgvR51KstLQUWVlZJtt22tnZQalUon///masjIiIqHP0mfDWarXIzs7GuXPnTNr9/f2hUCgglUrN\nVBkREVHn6hPhXVFRgSNHjnC2TUREFqFPhLeNjQ20Wq3xZ862iYioL+sT4e3u7o7g4GCcO3cOSqUS\n3t7e5i6JiIioy/SJ8AaAiIgIhIWFwdbW1tylEBERdaluv867srISS5cuRUpKCgYPHox77rkHaWlp\nt3xca2trBjcREVmEbg/vhQsXory8HOvXr0daWhoSEhKwcOFClJWVdXcpREREvVK3hnd9fT1CQkLw\n3HPPQS6Xw87ODg8//DDUajWysrK6sxQiIqJeq1u/83Z2dsYbb7xh0ta2n/a1LunS6/UAWm/AQkRE\n1Ne15V1b/l3OrAvWGhoasHTpUtx2222IiYlp93EVFRUAgPvvv7+7SiMiIjK7iooKBAYGXtEuEWba\nqePcuXOYP38+vLy88OGHH16xXeelmpubkZ2dDblcDmtr626skoiIqPvp9XpUVFQgOjoaMpnsin6z\nhHdWVhbmz5+P1NRUPP/881wlTkREdAO6PbxPnjyJ2bNnY8GCBXjwwQe786WJiIj6hG4Nb71ej5kz\nZyI5ORlPP/10d70sERFRn9Kt4X3w4EHcf//9sLW1hUQiMem766678Nprr3VXKURERL2W2RasERER\n0c3p9jusERER0a3pceFdVFSE2bNnIyIiAsXFxcZ2g8GAzz//HJMmTYJKpUJqaip++OEHY39xcTEi\nIiIQHR2NmJgY45+xY8caH6PX6/Hee+9h/PjxiIuLw913342ff/65W99fR9zsGACAVqvFO++8g5SU\nFMTGxmLq1KnYs2ePsb+vj8FHH31k8u/f9iciIgLr168H0PfHAAB2796Ne++9F0OGDEFSUhLmz5+P\n06dPG/stYQwOHz6Mv/71r4iPj8ewYcOwdOlSNDY2Gvt7wxhcby+IjRs3YurUqYiLi0Nqairee+89\nk5t6FBUVYf78+UhOTjb+HrTdGAuwjDEAgLS0NIwdO9YkD9r0hjG4KtGDbN26VSQlJYlnn31WhIeH\ni6KiImPfp59+KuLi4kR6errQarUiLS1NxMfHi127dgkhhCgqKrriOZd7//33xciRI0V2drbQaDRi\n27ZtQqFQiPT09C5/bx11K2MghBAvvviimDp1qjh9+rRQq9Vi5cqVYurUqaKxsVEIYRljcLndu3eL\npKQkUVlZKYTo+2Nw+vRpoVAoxOeffy40Go2orq4WTz75pBgzZowwGAxCiL4/BoWFhUKlUom33npL\nNDY2irKyMjF37lzx5JNPGo/RG8bgnnvuEQ899JAoLy8Xzc3NYtmyZUKlUonS0lLx559/CoVCIX75\n5Reh0WhEbm6uGD16tHj//feFEEJotVoxfvx48cwzz4jKykpRW1srlixZIlJTU4VWqxVC9P0xEEKI\nZcuWibFjx4p58+aJMWPGXHH83jAGV9Ojwvu7774T+fn5Yu/evVf8xzp9+nTx4osvmjx++fLlYt68\neUKI64e3wWAQiYmJ4osvvjBpX7hwoVi4cGHnvpFbcCtjUFZWJiIjI0VmZuZVj20JY3C5+vp6MWrU\nKLFp0yYhhGWMwaZNm0R4eLhoamoy9v/+++8iPDxcVFRUWMQYrFq1SiiVSqHT6Yz9+fn5IjIyUpSX\nl/eKMairqxNLly4Vp0+fNrbV1taK8PBwsXXrVvHYY4+JBQsWmDznyy+/FPHx8UKv14sdO3aIyMhI\nUVVVZeyvrq4WUVFRYtu2bRYxBkII8dFHH4n6+nrx7rvvXhHevWEM2tOjTpvPnDkTQUFBV+2TSCQw\nGAwmbe7u7jh69KhJ27vvvosxY8YgISEBc+fOxalTpwAAhYWFqKqqglKpNHm8UqlEZmZmJ76LW3Mr\nY7B//35YW1ujqKgIEydOxNChQzF79mzk5OQAsIwxuNwHH3yAkJAQTJo0CYBljEFCQgLc3NywcuVK\nqNVqNDQ04Mcff8SwYcPg5eVlEWPQdjWLuGQ9rru7OwwGA3JycnrFGLTtBRESEmJsu3QviIyMjKvW\nX1NTg7NnzyIjIwMBAQFwd3c39ru5uWHAgAHIzMy0iDEAgAULFsDJyemqx+8NY9CeHhXe15KamopN\nmzZh37590Gq1yM7Oxvfff4+amhoIISCVShEdHY2EhARs3rwZGzZsgEwmw5w5c1BfX4+qqioAgKur\nq8lx3d3djX093fXG4Pz58wCAzZs3Y+XKldi6dSu8vb0xd+5c1NbWWsQYXKqkpARr1qzBk08+aWyz\nhDHw9PTExx9/jC+//BJxcXEYMmQI8vLysHz5cgCWMQYjR46ERCLBsmXL0NDQgKqqKrz99tuQSqWo\nrq7ulWNw+V4QVVVVV60faP03rq6uvqK/7TGVlZUWMQbX0xvHoI1ZNya5EXPmzEFTUxNefPFFVZT1\nYQAACEhJREFU1NbWYujQobjvvvvwz3/+ExKJBP369TNZsCKTyfDGG28gMTER27dvv+qN3dtcfs15\nT3W9MRBCoKWlBc8++yzkcjkA4KWXXkJCQgJ27txpEWNwqc8++wzDhg275qY3l+orY1BQUIC//e1v\nWLRoEWbMmIGmpia8++67mDNnjnHRXnv6yhj4+/vjk08+wfLlyzFq1Cj4+Phg/vz52LlzJ2xsrv2x\n1xPH4NK9IJYtW3bLx7vee7SEMbienjgGl+o1M28bGxs8/vjj2L59Ow4ePIhPPvkEEokEvr6+7T7H\n1dUVbm5uKC8vh5eXFwCgpqbG5DHV1dXw9PTs0to7y/XGoF+/fgBaT421cXZ2hru7O8rKyixiDNro\ndDr88ssvxtPlbSxhDNatWwe5XI4HH3wQTk5OkMvlWLx4MfLy8pCWlmYRYwAAiYmJWLduHQ4dOoSN\nGzdizJgxqK6uhq+vb68ag6ysLMycORNDhgzBp59+atzEycvL66r1A4BcLoenp+cV/W2P8fLysogx\nuJ7eNAaX6zXhnZubi+3bt5u07dq1CwkJCQCAffv24d///rdJf9upo4CAAPj7+0Mul1/xPcahQ4cw\ndOjQri2+k1xvDCIjIwHA5Pvftq8M/P39LWIM2qSnp6Ompga33XabSbsljIFer7/iUpm2nw0Gg0WM\nQV1dHX744QeTS8N+//13ODg4IDo6uteMwcmTJ/Hwww/jkUcewcsvv2yyiVNcXNxV65fL5QgICEBc\nXByKiopQWVlp7L9w4QIKCwsxdOhQixiD6+ktY3BVZlws166rrS7dtGmTUCqV4sCBA0Kn04k1a9YI\npVIp8vLyhBBCHD16VAwaNEh88cUXorm5WZSXl4v58+eL1NRUodFohBCtl5cMHz5cHD16VGg0GvHz\nzz8LhULR7upsc7qZMRBCiDlz5ojJkyeLgoICUV9fL5555hkxcuRIoVarhRCWMQZCtF7+cbXLQoTo\n+2Nw6NAhERERIb7++mvR1NQkampqxHPPPSeGDx8u6urqhBB9fwzUarWIj48Xb775ptBoNOLUqVNi\nzJgxJpcQ9fQx0Ol0YurUqeKdd965av+RI0eEQqEQmzZtEhqNRmRlZYnk5GTx2WefGZ8/efJk8dRT\nT4mqqipRWVkpnnjiCXHnnXcaV+H39TG41NVWmwvR88egPT0qvFNTU0V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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2624,7 +2624,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -2646,7 +2646,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": { "collapsed": true }, @@ -2667,7 +2667,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": { "collapsed": true }, @@ -2696,7 +2696,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": { "collapsed": true }, @@ -2724,14 +2724,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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1f/58fvjhB44dO/a392FkZIS7uzujR4/mk08+4bvvvuPHH3+8pX1cuHCBtLQ0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\ntTUqBAXbQO9SA4BWRyOEkP5A4T2Etba2QiKRGJT5+PjAzs7OZDPJGWN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WnZ3N1Go1++WX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\nSUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2752,7 +2752,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2787,7 +2787,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": { "scrolled": true }, @@ -2809,14 +2809,14 @@ " axes_locator = None\n", " axis_bgcolor = (1.0, 1.0, 1.0, 1.0)\n", " axisbelow = True\n", - " children = [" + "" ] }, "metadata": {}, @@ -2902,7 +2902,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": { "collapsed": true }, @@ -2927,14 +2927,14 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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1YWZm9kb7aPRBWgghhJA3kVGcgZ23duJx4WN5mYmOKbSFAyCq0Ec5JDh5Mh4z\nZrSt1zgkEgni4uKQlpamNBETh8OBubk5nJycYGpq+sZN9ZS8CSGENFmh6aE4cv+IfIhTAPC29kZA\nmwBkuIqwatVNeHmZISCg+kmm6gqXy0VmZqZC4ubxeLC3t4ejoyN0dXXrbF+UvAkhhDQ5IokIxx4c\nw/VH1wEAMsagwVXHaI/R6G7fHRwOB46OWli0qDNsbPTqvFOaVCqFSCSCjs7zpngOhwMnJydERUVB\nV1cXTk5OsLOzA49X96mWkvdbws/PD0OGDEFQUJDSsoCAAFhYWGDt2rUAAIFAAFNTU5w9exaGhoZK\n25k5c6Z8IpOXCQQC8Hg8qKmpgTEGfX19eHh4YPjw4RgwYIC8XuXQfOrq6lWe9H5+fti4cSMAIC0t\nDVu3bkVoaCiEQiG4XC4EAgEmTZqE9957T2nd06dPY968eRg4cCA2bNig+ptECCH/78/4P+WJOze3\nDE8ecrB+fBA6Oig+xWRrq1+n+xWJREhJSUFqaip0dHTQvXt3heV2dnbQ1dWFmZlZvfZip+TdRMlk\nMqxevRorVqyo9bpLlizBiBEjIJPJ5NN0LlmyBP/99x9++OEHhbo7d+5E165dq91WaWkpxo8fj/bt\n2+PgwYOwsbFBSUkJfvnlF8ydOxc7duxAr169FNY5fPgwhg4dirNnzyI7O/uNO24QQt49g1wH4fbT\n27iXkIyiGCu4lfnizNFctF0khYZG3Y+QVlBQgOTkZDx+/Fg+u6JIJEJeXp7ChEk8Hg/m5uZ1vv+X\nNdoIa+TNfP755/jzzz8RFhb22ttQU1ODtbU1hg8fjr179+L333/H2bNna7WNhw8fIiMjA1OnToWt\n7bPnJfX09DBx4kSsX78eNjY2CvWjo6Nx7949BAUFwdnZGcePH3/t+Akh7y5tdW1M6zAN0ztNgZds\nIHjQQFmZBNnZpTWvrCLGGDIzM3Hjxg1cuXJFPg2zPAZtbVRUNM7obJS8myh7e3t8+umnWLx4MUQi\nUc0r1MDd3R3du3fHH3/8Uav17OzsoKWlheDgYKSlpSksGzhwIFxcXBTKDh06hK5du8LKygrDhw/H\nsWPHIJVK3zh+QkjzlVaQhrMPlS8sHIwcMKRtX4we5Y42bcyweHEX2Ni8eTO5RCJBSkoK/v33X9y8\neRNCoVBhubGxMTp06AA/Pz9YWFi88f5eR7NuNj8ddxp/xv+pUt0eDj0w3mu8QtmhyEPyZwZr8j7/\nfQwRDKkN/dsjAAAgAElEQVR1jG9i6tSpOHPmDIKDg/H555+/8fZatmyJ//77T6Fs2rRpVd632bt3\nL3x8fNCiRQts2rQJS5cuRd++feHo6Ih27dqhS5cu6Nevn0JnjoKCApw5cwarVq0CAHzwwQdYs2YN\nLl68iP79+79x/ISQ5oUxhsspl3Ey+iQkMgn01YzhqNEadnYGCvW6drVG1651N31nbGwskpOTFco4\nHA6srKzg7Oys0EzeWJp18m7ueDwevv/+e4wbNw6DBg2Cm5vbG21PIpGAy1W8V1TTPW8A6NWrF/75\n5x9ERUXh9u3biIiIwJIlS7By5Ups374dbdq0AQCcPHkSOjo66NOnDwDAyMgI/fv3x+HDhyl5E0IU\nlIpLceDeAdx5egcAUFAowvz/bUYP8VQsWdwDuroa8rp13THMwcFBnrzV1dVhb28PJycnaGtr1+l+\n3gQl77eEuro6ysrKqlxWWFgIBweHKpd5eXlh1KhR+Pbbb3Hs2LE3iiE6Ohqurq6vta6amhq8vLzg\n5eWFSZMmoaCgABMnTsTq1atx+PBhyGQyHD16FIWFhejSpYt8PbFYjPLyciQlJcHZ2fmN4ieENA+J\nuYnYfXs3cstyAQBSGUP6Ay5c84aiUCbF/v0P8Mknbd8oaTPGkJWVhZSUFLRr1w4aGs9/DOjr68PB\nwQH6+vr19qjXm3r7IqpDQwRD3qgpe7zXeKWm9Pri4uKCqKgopXKhUIikpCSMHDmy2nWDgoIwePBg\nHDx48LX3HxERgYiICOzZs6dW6/3999+Ij4/HZ599plBuaGiIDh064MaNGwCAK1euID09HYcPH1a6\nRxQYGIgjR47gm2++ee34CSFNH2MM5xPP41TsKcjY845hfVv64ROHnti5LQq6uuro2tXmtRO3VCpF\neno6kpKS5EOXpqamKl24eHl5vf6BNIBmnbybkqCgIIwYMQJr1qzBlClTYGRkhLi4OCxbtgz29vbV\nPrcNALq6uvjuu+/w+eefKzV716S4uBgXLlzAypUrERAQoPTMYk10dHSwdetWSCQSjB49Gubm5igv\nL8f169fx559/YsqUKQCed1Tr0KGD0jZGjx6NLVu24PPPP1e4R04IeXcUigrxvzv/Q3R2tLxMR10H\nE9pMQDurdgCAMWMkaNPGHMbGWrXefuXz2SkpKUo9xFNTU+Hi4tIgs4vVFUrebwkXFxccPXoUwcHB\nGDp0KIqLi2FhYYEBAwYgMDCwxnstvr6+6NWrF/76668a97VkyRIsW7YMwLPm+latWmHJkiUYNGiQ\nUt3qOqwBwP3799GtWzfs3r0b+/fvx0cffYSCggLweDy4urpi3rx5GDFiBFJTU3Ht2jVs2bKlyu34\n+/tj/fr1OH36NEaNGlVj/ISQ5iUpLwnbwrehUFQIqYwhJaUAPVp7YWbXGTDReT7zVu/e9rXednFx\nMZKSkpQe8wKe9RtycHCAk5NTk0rcAMBhbzojeAOoHO3r4sWLsLW1bexwCCGE1KG8sjwsv7IcWfn5\niI3JhUleW/SxfQ/zvugENbXXS6oikQiRkZHIyMhQWqatrQ1nZ2fY29u/lfezgZrzHj3nTQghpFEZ\naxtjYpuJ0OboomXOEDiWd0ViQiHu3Ml87W2qq6ujoKBAoczIyAgdOnRAnz594Ozs/NYmblU03cgJ\nIYQ0SRnFGbDUs1Qoa2PZBls+XIt/dNNx7lwKRo92Q/v2qg2AUtUkIWpqanB2dsaDBw9gYWGBli1b\nokWLFk2uebw6lLwJIYQ0CJFEhBPRJ3D90XV80fULOOg7QV39eSdbTZ4mBg50RseOVjAzq7nz6oud\n0PT19ZXGpLC3t4e5uTn09PTq/FgaGyVvQggh9S69MB27bu1CRnEGZIxh/tHV8BCOxuKFvRQSuJoa\np8bEXVJSIu+EVjm8slAoRH5+PoyMjOT1eDxes0zcACVvQggh9ejlIU4ZGO7fz4ZGjgOelJbgl18e\nYvRo1UaHzM/PR2JiIp4+fYqX+1rr6Og02iQhjYGSNyGEkHpRJCrCgXsHEJkZKS/T5GoiwDMA987o\nggMOsrJKIZXKwOVW3X+aMYbs7GwkJiYiJydHabmhoSFcXFxgZWXVbO5nq4KSNyGEkDoXmxOLvXf2\noqD8eY9vO0M7fNz+Y5jrmmN35n04OxvCz8/+lUk3NjYWCQkJSuVmZmZwcXGBiYnJO5W0K1HyJoQQ\nUmckMgn+iPsDFxIvgDGGgkIRdHXUMYDfDx+6fwie2rO0ExjoqVLStbGxkSdvDocDGxsbODs7w9DQ\nsF6P421HyZsQQkidySnNwcWki5DJZEh9VIiMVCn8nUZjxLAPFZL1y4m7oqICqampcHR0hLq6urzc\nwMAAVlZW0NLSgrOzMw2h/P8oeRNCCKkzlnqWGN5qOLZd24eiJCO0Lx2Ap/d0ERb2FJ07WyvVr5xV\nMDU1FRKJBBwOBy4uLgp1OnTo8E42jb8KjbD2DklNTYVAIEBYWJhK9X/99VcIBAJIJJJ6jowQ0lRV\nNcJ2b8fe+KrvHMxoOxMaTBdubi3g5tZCoU5xcTHu3buHixcvIjExUf49k5ycrDQGOSVuZXTlTQgh\n5LUk5CbgUOQhfOL9CSz0no+GxuFw0N6qPVqPksDR0RA9etjKE3BBQQEePnyIjIwMpcSvr6/f5Gb3\naiwqJ2+ZTIYHDx4gKysLBQUFMDQ0hLm5OVq3bg01NbqAJ4SQd4VUJsWZh2dw9uFZMMbww/mN8Mgf\ngcApbRUSr6YmDz172oExBqFQiISEBGRlZSltz9jYGK6urjA3N6fEraIak3dSUhK2bduGy5cvo7i4\nWOGXEofDgZ6eHnx9fTFjxgw4OzvXa7DNmUAgwKpVq3D69GncunULlpaWWL16NR48eIAdO3agqKgI\n/fr1w4oVK+Rzdl+4cAHbt29HcnIyNDQ00KtXL3z99dfyEYbCw8Px/fffIzU1FU5OTggMDFTYp1Qq\nxbZt23D69GlkZGTA1NQUY8aMUapHCCGVskuysefOHiTnJQMAklMK8PRRNnKKH8DBzhj9+zsqrcMY\nw61btyASiRTKzc3N4eLi0qzGHG8o1SZvmUyGNWvWYP/+/XBwcMDw4cPh4+MDMzMzGBgYoLCwENnZ\n2bh58yb+++8/DBkyBAEBAfjyyy/fmivxuLg4xMfHq1TXwcEBXl5eCmWRkZFITU1VaX0+nw+BQFDr\nGF+0Z88erFmzBs7Ozvjss88we/ZsDBw4EOfOnUN6ejr8/f3Rv39/+Pn54ebNm5gzZw7Wrl2Lfv36\nITMzE7NmzcL8+fOxa9culJSU4JNPPsGwYcNw7NgxZGdnY/78+Qr727JlC06fPo3g4GC4uLjgzp07\nmD59OkxNTTFs2LA3OhZCSPPCGENoeiiORh2FSPI8CdtoOcG6yBuaTB8XL6aid287aGhwFdZVU1ND\ny5YtER0dDQ6HAysrK7i6usLAwKChD6PZqDZ5T548GRkZGfjpp5/w3nvvVbsBPz8/fPXVVzh//jw2\nbNiA2NhY7Nu3rz5ibfZ8fX3h5vZsmMDevXsjJCQEc+fOhaamJlxcXCAQCJCQkAA/Pz8cOnQIPXv2\nxODBgwEAdnZ2+OSTTzB79mzk5OQgPDwcxcXFmDVrFrS0tGBnZ4dJkybhzp07AJ79ODty5Ag+//xz\n+Y8Ob29vjBgxAsePH6fkTQiRKxWX4nDkYUQ8iZCXqXHUMMxtGPwG9sHanFvQ1VXHhAnuyMh4jIKC\nAnh6eipsw8HBAWVlZXBycoKurm5DH0KzU23ybtGiBbZt26byM3UDBgxAjx498O2339ZZcO8aGxsb\n+b+1tbVhamoKTU1NhbLKZqfU1FR0795dYf3KxyvS0tLw9OlTGBgYKAxk4OrqKv93bm4u8vPzsXz5\ncnz//ffycsYYzMzM6vbACCFNVrwwHnvv7EVeWR4qxFLweGqw0rfE1HZT4WDkAAD47DMv5OQ8RUTE\ndZSVlQF4NqPXi98/PB4PHh4ejXIMzVG1yXv9+vW13piOjg7WrVv3RgHVJYFA8EZN2V5eXkpN6fXp\n5dsNr7r9IBKJlHpqVj5eweFwUFFRoXQP6cXHL7S0tAA8+5z79ev3RnETQponYakQ60PWQ8ZkEOaW\nIT4+D72demLRoNnQ5GlCIpEgJSUFSUlJSvezk5OT0bZt20aKvPlTqbd5aWkpDhw4gLt37yI/P7/K\nOj///HOdBkZezdHREXFxcQplDx8+hJqaGhwcHJCSkoKioiIUFxfLp8R78f6/np4eTE1NER0drZC8\nMzMzYWxsDA0NjYY5EELIW8tExwT9WvbDsVt/ID6qBK6lAyEKd8HDznlQVy9AcnIyxGKxwjqamppw\ndnaGo6Nj4wT9jlCpZ9mSJUuwYcMGpKWlQV1dvco/pGGNGTMG169fx+nTpyGRSJCcnIytW7eif//+\nMDY2Rvfu3cHj8RAcHIzy8nKkpKTg4MGDCtuYOHEiDh8+jJCQEEilUsTGxmLs2LHYs2dPIx0VIeRt\n84HgAwxrMwDjbGfDTOoEE5NC3L8fgvj4eIXEra2tDQ8PD/Tp0wcuLi7g8WgYkfqk0rt75coVrFy5\nkjoxvUV69eqFH3/8Ebt378bixYvRokUL9O/fH7NnzwYAmJqaYtu2bVi5ciWOHDkCR0dHzJ07FzNm\nzJBvY+rUqSgrK8PChQshFAphbm4Of39/TJ8+vbEOixDSSIpERTgZfRIftfoIBprPe4Hz1HgY4zkG\nhQ4inDhxFy1aVIDHe37dp6urCxcXF9ja2r41Txq9CzisqrHtXuLj44PffvsNtra2DRGTkvT0dPTp\n0wcXL15stBgIIaS5isyMxIF7B1AkKoKjDh9e5cMweHDLKuuGhIQgJycH+vr6cHV1hbW1NT2jXQ9q\nynsqXXn37NkTYWFhlDgJIaQZEUlEOBl9EldSrwAAMjJLcD3xX0QVGqKiIh3t2zvCwcFBYR13d3eU\nlZXB0tKSknYjUil5jxkzBj/88AOSkpLQpk2bKh8fe/mxJUIIIW+vlPwU7Lm9B1klLwxXWqCFrhX9\nYKQrQUREHHR0KmBnZ6fQHG5kZCQfxZE0HpWS9/jx4wEA0dHRCuUcDgeMMXA4HMTExNR9dIQQQuqU\njMlwLuEcTsedhow9e3xUrUINfA4f/ZwcEFkgBIcDuLm1QEVFObKysmBpadnIUZOXqZS8Dxw4UN9x\nEEIIqWc5pTnYe2cvEnMTIZMxcCVq0C/Wh5umG8x1zQEAHh6m0NTkwsLCHK6urjAxMWnkqElVVEre\nHTt2rO84CCGE1CNhqRDL/lsGkUSE8nwpihIZXPQt4OPsBS2elryeg4M1+Hw+jI2NGzFaUhOVH8S7\nc+cOjhw5gpiYGJSUlEBfXx9eXl6YNGmSfFhOQgghbycTHRO0tWyLC9FXkBMjAZ/xwSk3g8yWC/AA\nCwsL8Pl8up/dRKiUvC9fvoxPP/0UZmZm8PDwgK6uLoqKinD58mWcPn0a+/btQ7t27eo7VkIIIW9g\ntMdo5JUV4GmSPTiFOeBwAXV1I/Ts2UFhHHLy9lMpeW/btg3+/v5Yvny5Qq9DqVSK+fPnY/369XRf\nnBBC3hIV0gqcTziPTqadkJqUilatWkFXVxc66jr4omsQMpxLcODADQwf7glnZ+qM1hSplLzj4uLw\nww8/KI2ew+VyMX36dIwePbpegiOEEFI7qfmp2BuyF4VPCxGLeJjAFjweT6F11NJSF19+SRMSNWUq\nJW8OhwOJRFLlMhoOjxBCGp+MyXDq3ilcvX0VvDIeWIUaHuQnw1jEgaYmD61atVKYYpg0bSplXg8P\nD2zdulUpgYvFYgQHB9McrYQQ0ohSMlOw8vhKhFwPAa/s2TVZabEUeiJrMJEpoqIMoKZGE4U0Jyp9\nmnPmzMHkyZPRo0cPeHh4QE9PD0VFRYiKikJ5eTn27t1b33ESQgh5SUFBAc6FncPdhLuQMqm83EDD\nAL5dO+Gfs1IwjiZGjmwFdXVuI0ZK6ppKydvb2xu//PILDh48iAcPHiA1NRV6enro378/Jk6ciJYt\nqx7AnhBCSP0oEhVh11+7kCV8PrwpBxy4O7ljWLdhMDI0got9HszNdWBoSM3lzY3K7Sh8Ph/Lly+v\nk53++uuv2LlzJx4/fgxzc3MEBARg0qRJdbJtQghp7nJKc7Dq2iqUqpVCS6qDggIRdE10MG1IAFrb\ntZbXc3WlgVaaq2qT97Vr19C5c2fweDxcu3atxg2pOjHJmTNnsGrVKvz000/w8fHBnTt3sGTJEnh7\ne9O9c0IIqUJpaSlSU1Ph5uYGDocDE20TWOlbITw7ErlZheCW2MIl1xc2+tQK+q6oNnkHBgbi+vXr\nMDExQWBgoHwSkqrUZmKS4OBgBAYGolu3bgCATp064a+//nqN0AkhpHkrKyvDw4cP8ejRIzDGYGBg\nABsbG3A4HExuOxnC4p+gF+0CtVIrVHA4iInJRZcu1o0dNmkA1SbvAwcOyEfcqasBWLKyspCYmAgd\nHR2MGTMGcXFxsLGxwbRp0zBkyJA62QchhDR15eXlSEhIQGpqKqQyKTKLM2GpZ4m4uDhYW1uDw+HA\nWNsY3/ddhgTHfOzbF4VJkzyomfwdUm3yfnEykrqamCQjIwMAcOzYMaxZswZ2dnY4efIk5s2bBysr\nK3h7e9fJfgghpCmqqKhAQkICUlJSIJVKUS4pR2xOLIoqigBtwKdFX3A4HHl9DocDV1djLFvWDVwu\njbnxLqk2ef/0008qb4TD4SAoKKjGepXN7gEBARAIBACACRMm4NSpU/j1118peRNC3klisRiJiYlI\nTk6Wj6eRUZyBxLxEiNXFyDMown+Rf+H6JT38uNAMVlZ6CutT4n73VJu8d+7cqfJGVE3e5ubP5ot9\neao5e3t7ZGZmqrw/QghpLqRSKf7991+IRCIAgFgmxkPhQ2RJslBuWg6xlhgxkUKY5HhDQ2SM3bvv\nY+HCTuDxKGG/y6pN3rGxsXW+M3NzcxgZGeH+/fvo27evvDw1NZV6mhNC3klcLhc2NjZISkpCblku\n4oriUKBXAIm2BOAA1vrW+Hjk59i3MR3gAV26WIPL5dS8YdKsNeh4eVwuF5MnT8auXbvQqVMneHt7\n48SJE4iJicGKFSsaMhRCCGlwMpkMBQUFyq2PTvb4N/pfxMpiITYTA/+fm32dfPGR+0dQ56oDkwxh\nZaUHW1v9RoicvG2qTd61nSns559/Vqne9OnTIZFIsHDhQgiFQjg5OWHXrl1wd3ev1f4IIaSpYIzh\nyZMniIuLQ1lZGfz8/KCtrQ3g2YArm8I2IVMzE1LGkJyUDydrCwT1noHW5s8HXPHxsWqs8MlbqNrk\nra6uXi875HA4mDlzJmbOnFkv2yeEkLcFYwxZWVmIjY1FYWGhvPzhw4fw8vICABhqGoLL4aK0TIzo\nGCF0ChxglDUYzkP4jRU2aQKqTd4HDx5syDgIIaRZEQqFiImJQV5enkK5uro6dHV1n7/mqmNq+6n4\n4d/VcCluB8NSPopKOQgJeQo/P/uGDps0EdUm74qKCmhoaMj/XZPKuoQQ8i7Lz89HbGwssrOzFcp5\nPB6cnJzAMebA2dxZYZmtgS3WD16D+zZ5OHDgAUaOFKBbN5uGDJs0MdUm7zZt2uDatWswMTGBl5eX\nwsAAVVF1eFRCCGmOxGIxIiMj8eTJE4VyNTU1ODo6ws7RDr8n/I7rN69jQpsJaGviA13d5xc9mjxN\neHtbgs83hoEBzQJGXq3a5P3ZZ59BR0dH/u+akjchhLzLeDweioqK5K85HA7s7Ozg6uoKoViItTfX\nIrM4EzIZw/LftsM5PQs/fjNIabpOStxEFdUm7xc7lM2aNatBgiGEkKaCMaY0VKlAIEBERASsrKzg\n5uYGXV1dXEq+hF9jfoVE9mzktOhoIXhZ9hCVcfG//0Vhzpz2dHFEak3l57wzMzNx/vx5JCYmoqys\nDLq6unB1dcWAAQNgYmJSnzESQshbQyKRIDk5Gbm5uejYsaNC4rW0tESvXr1gYGCAIlERgsODcT/z\nvny5Jk8TM3t8jMuH1AAGaGpyUVEhhaZmgw65QZoBlc6YK1euYPbs2SgvL4ehoSF0dHRQWlqKgoIC\nrF27Ftu2bUOnTp3qO1ZCCGk0MpkMaWlpiIuLkw9lmpmZCUtLS3kdDocDAwMDxGTHYO+dvSgUPX88\nzN7QHoHtA2GhZwH9/EQYGGigRw9buuomr0Wl5L1q1Sp4e3tjxYoVsLCwkJenp6fjm2++wffff4/T\np0/XW5CEENJYGGN4+vQpYmNjUVJSorAsLS1NIXlLZVL8EfcHzieeB2MMRUUi8NS5+KD1QAxzGwae\n2rOv3Pffb9mgx0CaH5WS96NHj7BhwwaFxA0Atra2+OqrrzBixIh6CY4QQhpTTk4OYmJikJ+fr1Cu\npaUFPp8Pe3vF57BLxaW4kXYDMibD4/RiPE4Wo5ehP4b5fwieGk0kQuqOSmeTtbV1tc96M8ZgbW1d\np0ERQkhjKiwsRFhYGEJCQhQSt7q6Otzd3eHn5wcHBwelJm99TX1MbT8V5WVSFCQYoV3hWJQ/MsWf\nfyY29CGQZk6l5D1nzhz89NNPSiMF5eTkYMOGDZgzZ069BEcIIQ1NJBLh6tWryMrKkpdxuVy4uLig\nT58+cHFxAZfLBfDs4uVlbqZu+LbPV/juvS+hwXTh7GyIHj1sGyx+8m5QeWKStLQ09OjRA3Z2dtDX\n10dZWRlSUlJgaGgIqVSKQYMG1XuwhBBS3zQ1NWFra4tHjx7Jn9Xm8/nyiUQqZZVkYfft3fB384e7\nmeLESq4mrnDpy6Crq45OnazA5VKTOalbKk9M4uysOJyfpqYm2rZtCwDynpeEENKUSKVSFBYWKk3R\nKRAIIBaLIRAIoK+vPAVnxJMIHLx3EOWScmwN3YlW2aMwZZy3wiNfHA4HXbvSEKekftDEJISQdw5j\nDOnp6YiLi4NYLIafnx80NZ+PbKalpQVvb2+l9cRSMU5Gn8TllMsAAGFuGRLiMvGk4A601fQwaZJH\nQx0CecdV25YzZ84clJaW1mpjpaWlmDt37hsHRQgh9SUrKwtXrlzB3bt3UVZWBolEgocPH9a4XnZJ\nNlZfXy1P3ACgyzFCq/yPYCppidDQp3jypLgeIyfkuWqTd2FhIfz9/fHXX3+ptKFz587B399fYc5a\nQgh5WxQUFCA0NBRhYWEK31OamppVNo2/6PbT2/j+yvd4VPBIXtbeqj22j1mFfj5tYWyshXnzvGFt\nrVdv8RPyomqbzffs2YO1a9di3rx52LBhA3r16oUOHTrAzMwM+vr6KCoqQlZWFm7duoUrV67g8ePH\nCAgIwLx58xoyfkIIeaWysjLExsbi8ePHCr3DuVwuWrZsiZYtW4LHq/qrUCKT4JfoX3Ap+RIAQMYY\n1Lk8jGg1Ar0de4PD4WDsWHdIJDKFGcIIqW/VJm81NTV8+eWXGD58OLZv347ff/8dBw4cUHiukTEG\nAwMD+Pr6Ytu2bUqd2gghpLFIJBIkJCQgKSkJUqlUXs7hcGBvbw8+nw8tLa1q12eMYWPoRsQL4yGV\nyZCYWABNqT62B34NR2NHeT1NTR40aSIw0sBqHGHN2dkZq1evhkwmk08wX1hYCAMDA5iZmcHNzQ1q\nNHIQIeQtw+Fw8PjxY4XEbWFhAXd39xqbySvX72zbGTHZcbhzJws6BQ5wLuuLxNscOPapz8gJqZnK\nU9moqamhVatW9RkLIYTUGS6XCzc3N9y+fRtGRkZo1apVrWdA7GbfDSn5KbDMFSEr1AYccJCeXqQ0\nHSghDY3moSOENHn5+fnIyMiAm5ubQrm1tTV4PB7Mzc1rTLbFFcUoE5fBTNdMoXyc1zhIWsmwQXgL\nXbtao0sXa0rcpNFR8iaENFmVndHS09MBACYmJjAze558ORyO0oRKVUnOS8aOWzugzdPGZP4s2Foa\nQ03teYLm8dTwxRfelLTJW4OSNyGkyamuM1pMTAxMTU1VTrKMMVx9dBU/R/0MiUyCzIxS/PfnDwjq\n9QmGDFGctpMSN3mbUPImhDQZjDE8evQIcXFxSsMyW1pawt3dXeUkK5aKceT+EdxIuwEAEArLkRRf\nCrcyV5w5kwRXVyO4udXuHjkhDYWSNyGkScjOzkZ0dLTSQFCv0xlNWCrE9ojtCoOutHVyRZucLnj8\nUAYra10YGtLzX+TtpVLyzsnJwcaNG3H37l3k5+crTYPH4XBw9erVegmQEPJuk8lkiIiIQGZmpkK5\ntrY23NzcYGNjU6sm7ZjsGOy6vQslFSXysi52XTDOcxzK2slw7lwyhg51UZhkhJC3jUpn57fffovr\n16+jc+fOaN26Nd37IYQ0GDU1NYUR0Hg8HlxcXODs7CyfV1sVjDGcTzyP32N/h0wmQ25eOcxN9TCq\n9Sj0dOgJDocDdQNg5Ei3mjdGSCNTKXlHRERg8+bN6NWrV33HQwghStzc3JCRkQEbGxsIBIJXjoxW\nnZD0EPwW8xtEFVLExAhRXqCOCUM/Qy/HTvUQMSH1S6Wh0Xg8Hg19SgipV4wxZGZm4saNG5BIJArL\ndHR00KdPH7Rp0+a1EjcAdLbtDDdTN6SkFICTa4r2RWNw8UQJ8vLK6yJ8QhqUSsl70KBB+Pvvv+s7\nFkLIO6qwsBChoaG4efMmhEJhlVN0ar7hAOJqHDV83OFjfNZvDHprjYMm9DBokBOMjKhjGml6VGo2\n79ixIzZv3ozIyEi0adMGOjo6SnVGjRpV58ERQpo3kUiEuLg4PHr0SKEjbFpaGvh8fq3uab+IMYa7\nGXfR1rKtQh8dPQ09jPD6EJ2MClFWJoFA0OKNj4GQxqBS8p4zZw4AICEhAefOnVNazuFwKHkTQlQm\nk8mQkpKC+Ph4iMVieTmHw4GDgwMEAsFrJ26xVIyDkQcRlh4Gb4Pe6KDvh/btFUdZs7c3eKP4CWls\nKiXvixcv1ncchJB3AGMMWVlZiI6ORnFxscIyMzMztG7dWqUZv6qTX56PreFbkZKfgiePi3H12iG0\nLYhZ2lUAACAASURBVC/FWstRsLbWe9PwCXlrqJS8bWxsFF7LZDKaBpQQUmt3796Vj0NeSVdXF61b\nt1Zp8pBXSclPwdbwrSgoL4BMBjzNKIGFqDV0y61x6FA05s/3ocdcSbOh8igEZ8+exaFDhxATE4Py\n8nLo6OjA09MT06ZNQ9euXeszRkJIM9GiRQt58ubxeBAIBHB0dHzji4Hwx+HYf28/xNJnTfDqXC4W\nvj8Dl/erw8ZRH1OnelLiJs2KSsn7t99+w8KFC9G2bVuMGDECurq6KCoqwp07dxAYGIjg4GD4+vrW\nd6yEkCakqjmv7e3tkZqaCiMjIwgEgjfuQc4Yw+n40zgTf0ZepqOug+ne0+Fm6ob2LfJhb28AHo9a\nCknzolLy3rdvH6ZPn46goCClZStWrMDWrVspeRNC5IRCIR48eABPT08YGxvLyzkcDrp3714nt91E\nEhH23d2HW09vITW1EKam2mhpYYeZHWfCXNccAODsbPTG+yHkbaTS/6Dk5GR8+OGHVS4bM2YM4uPj\n6zQoQkjTVFZWhlu3buHGjRsoKChAVFSU0lwIddVf5n93/4ebaRF4ECXEo0dFyHqgj5ltP5cnbkKa\nM5X+F2loaCjN5FOprKwM6urqdRoUIaRpkUqliI+Px7///osnT57Iy4uKilBUVFQv+/xA8AE4Mh4K\nC0WwEbWBU9YghF7NqZd9EfK2UanZvH379li9ejU2btyIFi2eD2ogFAqxcuVKtG/fvt4CJIS8vRhj\nyMjIQHR0NEpLSxWW2djYwN3dHdra2vWyb2t9a8zu/gluGacg7Lg23hvoiPffb1kv+yLkbaNS8p4/\nfz7Gjx+Pnj17wt7eHnp6eigqKkJaWtr/tXff8U1X+//AX2madO+96R5J06aDtlBAipahKAXRq6JM\nFdx8HSB6fyhe9crQ61dR5CIIgoILBRFliWzoorvQUrr3TtuMpjm/P/ptILaFAG2Ttu/n48HjAedk\nvHOAvPoZ5xxYWlri66+/Huw6CSF6RiKRICcnB3V1dRrtVlZWEAgEt7S/9s0wxlDbXgsnc83FVkRO\nIoicRJge0g5nZ7MBez9C9J1W4e3v748DBw7ghx9+QE5ODtra2uDs7IykpCQ8+OCDGkfjhJCRr6Sk\nBFlZWRrXs/l8PoKCguDp6Tmg07J6VkxLLkuFf10SnnxoIiws+BqPoeAmo43W87zt7e2xdOnSwayF\nEDJMXP8D+/VLmvL5/Bs869ZJ5BJ8lvwZssovISe3Hmc7/gvpF4ZYuXwiuFya/kVGr37De8+ePUhK\nSgKfz8eePXtu+kK0tjkho4eFhQW8vb3R0tICoVAIS8uBXyu8UlKJTy98ioaOBsgVSshlXXBWjkFx\ngRS5uQ0IDXUY8PckZLjoN7xXr16Nu+++G3Z2dli9evUNX4Q2JiFkZFIoFMjPz4eZmRl8fTVvBgsO\nDgaHwxmUlctyanOwOXUzZMruvbbtbE0xL+IeVJ1xwaKloRTcZNTrN7yPHj2qPjVGG5MQMrowxlBa\nWor8/HwoFAoYGhrCzc0NxsbG6scM1v4GfxX/hd3Zu6FiKgCAkaERlkQsQahjKBrvkcHObnDuXidk\nOOn3f5+bm5v6J+q9e/fC1tYWbm5uvX6pVCrs2LFjyAomhAyupqYmnDx5EpmZmVAoFAAApVLZa0OR\ngaZiKnyX8x3+e2478vLroWIMNiY2eG38axA5icDhcCi4Cfk/Wv3ovHHjRkil0j77ampq8M033wxo\nUYSQoSeXy5GRkYFTp06hpaVF3W5qaoro6Ohep80HEmMMm1I2YfeF/Ui/WIua2g60lpri9fjX4W7p\nPmjvS8hwdcO7zRMSEsDhcMAYw5w5c3qdJmOMob6+Hi4uLlq/YUJCAmpqanq91r59++Dt7X0LpRNC\nBgJjDCUlJcjPz0dnZ6e6ncvlws/PD76+vuByuYNaA4fDgZeVF+SyU1CpGOw7fWFfMA1dHXzA+ObP\nJ2S0uWF4v//++0hLS8PHH3+M0NDQPncAsrKywkMPPXRLb/rOO+/0u1Y6IWTodHR0ICUlReNIGwCc\nnZ0hEAhgamo6ZLXM8J+BmvYaJHc2w74+Gs8+EwFbWzpNTkhfbhjeMTExiImJQWlpKd544w2Ym5v3\negxjDDKZbNAKJIQMHiMjI42jbTMzMwiFQjg6Dv7mHiqmggHn2hk4DoeDheEL8VhIF1QqBhMT2jOB\nkP5odc37/fff7zO4AaC0tPSWtwM9ePAgZsyYgcjISMyePRtHjhy5pecTQgYGl8tFaGgouFwuAgMD\nMWnSpEEPbsYYfiv4Dcv3rMGubzV3HeNwODAyMqTgJuQmtF5hbdeuXTh58iSam5vVbYwxVFRU3NI8\nz4CAAHh5eeGDDz4An8/H119/jeeeew67d+9GeHj4rVVPCNFac3MzKisr1fOzezg6OmLKlCl9XhYb\naEqVEl9d3I7dJw+hoqINmYp2uLs+j0mTPAf9vQkZSbQK702bNuHTTz+FQCBAVlYWhEIhWltbUVxc\njISEBCxatEjrN9y0aZPGn5ctW4ZDhw7hu+++o/AmZBB0dnYiPz8fJSUlYIzBxsam102mQxHcErkE\nn6d8jsLGQiiV3XO4ZQat+POvEkyY4AEDg4Ff7IWQkUqr8P7pp5+wdu1azJgxA2KxGBs2bICHhwfS\n0tLwzjvv3PHGJJ6enqipqbmj1yCEaOo5M5abmwu5XK5uz8vLg7Oz86CsjNafitYKbEzeiIaOBnDA\ngb+fDezbg5Hg8AAWLQyj4CbkFmkV3lVVVRCLxQC6V1VSKpUAuvf5XrZsGdasWYOvvvrqpq9TVlaG\nrVu3Yvny5RprIRcVFSE6Ovo2yieE9EUikSArKwsNDQ0a7Y6OjhAKhUMa3OlV6dh6cSsUyu4FXzgc\nDh4OnYvYKRNgZsYf0loIGSm0Cm8TExO0trbCxcUF1tbWKCsrU8/JFggEyMzM1OrN7O3tcfToUbS2\ntuLNN9+EkZERtm7diqtXr+Ljjz++/U9BCAHQvRJaQUEBrly5onEjmLGxMYRC4ZAecTPGsC/vV3x0\ncAfMzXhwc7NQL3UqchINSQ2EjFRahXd0dDRWr16Nzz77DCKRCB9//DG8vb1hZWWFXbt2wcLCQqs3\nMzExwbZt27Bu3TpMnz4dUqkUISEh2LlzJ3x8fO7ogxAy2tXU1CA7OxsdHR3qNg6HAx8fHwQEBMDQ\nUOv7U++YokuBz89uwddHD6G9vRO1HMDT3gUr73oZrhauQ1YHISOVVv+bX375ZTz99NNob2/Hk08+\niXnz5iExMVGjX1u+vr69blojhNy5iooKjeC2tbVFaGjooGzXqY1WZSO4/3ct26rTDfF4nIKbkAGi\nVXj7+Pjg0KFDALp/kv/tt99w+PBhKJVKhIeHq6+HE0J0RyAQoLa2FhwOB8HBwfDw8NDZ9WQ+l4/n\nYp5FbesalJw3xyv3P4mJ8TQdjJCBovV5tOu/BJydnfH4448PSkGEkJtramqChYWFxqlwIyMjREVF\nwdLSEnw+f8hrkko7NRZXsTGxwbtT34bJdDPweIO7Njoho02/4X0rp8IBYMOGDXdcDCHkxjo7O5GX\nl4eSkhJ4e3tDKBRq9Nvb2w95TUqVEmt/24z0E1K8NX8+BIJrNVga6eaUPSEjXb/hnZ6ervWL0FQP\nQgYXYwyVlZXIyclRz9kuLi6Gu7s7rK2tdVZXs6wZq35ciz8zMsABBxu+csCGVQ/Bxoa2AiNkMPUb\n3seOHRvKOggh/Whvb0dWVhbq6uo02h0dHYdkZbT+XGm8gk0pmyAzaQKfz4VC0YV6XiFaW+UU3oQM\nsqGbO0IIuSUqlQpXrlzB5cuXoVKp1O26mLN9PcYYTpWewrfZ36JL1QUej4vgYDs41MbgvSULYWGh\nux8oCBkttArvhISEm35JHD16dEAKIoQAjY2NyMzMhEQiUbdxOBx4e3sjMDBwSOdsX08ql2Pjya0o\nkF5Ut5nxzbB86nIE2QfppCZCRiOtvgEiIiJ6hXd7ezuys7NhZmZ2y1uCEkL619DQgDNnzmi0WVlZ\nQSQS6fT6dnbRVby4833USCsREeEIE2MePKw8sCxqGexM7XRWFyGjkVbhvX79+j7bOzs78eabb/ba\noYgQcvtsbW1ha2uLxsZGGBoaIjAwEN7e3jq9MTSnNgdP7VyDprY2AEB+fiOemjETT4Q/Dj536Kel\nETLaGdzJk3k8Hp588kls27ZtoOohZNS5fg1yoPv0uEgkgouLC+666y74+PjofEaHOd8cfoGWMOAA\nhhwuHgt9DIvECym4CdGRO75wJpPJ0NjYOBC1EDKqMMZQVFSEmpoaxMXFaQS0hYUFoqKidFidJi9r\nLzwZ+wQg34Nnxi5FbJDw5k8ihAwarcJ7z549vdoYY2hpacHevXtpUxFCblFLSwsyMjLQ0tICALh6\n9are/D9ijOGX3/IQKXKHh8e1RVbiPeMR/Wg0jA1pGhghuqZVeK9evbrfPi8vrxv2E0KuUSqVuHz5\nMoqKijROl1dWVur8ujYAtLbK8doXW3C69ijuPrcA778xA8bG3V8THA6HgpsQPaFVePc1DYzD4cDS\n0hLm5uYDXhQhI1FdXR0yMzM1dv7icrkICAjQi+vaHZ0d+Cz5C5xqPIouDsOpjh/xx+EAPDCTpoAR\nom+0Cm83N7fBroOQEUuhUCAnJwfl5eUa7fb29hCJRDAzM9NRZdcUNxdjc+pmNHQ0wN/PBpcuNSIo\nwAGT7qaZJIToI61vWPvqq6+wb98+lJeXQyKRwNLSEr6+vkhKSsLcuXMHs0ZChq2KigpkZ2dDoVCo\n23g8HgQCAdzd3XV+tK1SqfBXyV/4Pvd7dKm6AACOjqaYFpiIxeMegaEBLcJIiD7S6n/munXrsHXr\nVkRGRmLWrFkwNTVFe3s7cnJysHr1apSXl2P58uWDXSshw05ra6tGcLu5uUEgEOh0TfIeF9JL8M7+\nT+EQ2gSeYfeWnSY8E8wPmw+xi1jH1RFCbkSr8P7pp5+wYsUKLFiwoFffli1bsHXrVgpvQvoQEBCA\nqqoqqFQqiEQiODo66rokAMCWH//ExrNfQGYgQdMlY4QI7OBl5YWnIp+Cg5mDrssjhNyEVuEtk8kw\nZcqUPvsSExOxcePGAS2KkOFIIpGAx+PB2PjaHdlcLhfR0dEwMTHR2Xrkf9cobcTh9h2QGXSvm97W\n1omxDvGYH/0IeFyejqsjhGhDq2+T8PBwFBYWwsPDo1ffpUuXIBbTKTYyeqlUKhQWFqKgoACOjo6I\niorqteCKPrE1scXD0TPR2LQHSpkh/vXgixjvO1bXZRFCboFW4b18+XKsXr0aJSUlEIvFMDc3h1Qq\nRUpKCvbu3YuXX34ZV69eVT/e29t70AomRJ80NzcjIyMDra2tAIDq6mpUV1fr1Xr/nZ1dkMm6YGFx\nbSnTBwIfgLRThqm+ibA3s9dhdYSQ26FVeD/00EMAgLy8PI0jip5FJp5++mmNx+fl5Q1UfYTopa6u\nLly6dKnXYis2NjZ6tfZBVXUrXt28GUEmkVj18l0wMOj+/8s14OIx0aM6ro4Qcru0Cu/33ntP51Na\nCNEXDQ0NyMjIQHt7u7qNy+UiKChIL1ZJ61FaX4VH//P/0KCqxNXmIgTvdcWcOYG6LosQMgC0Cu/Z\ns2cPdh2E6D2lUonc3FyUlJRotDs4OEAkEsHU1FRHlfWWXJGMnZk7YeHZhoZioJlfigajAgAU3oSM\nBFrf/lpeXo4ffvgBeXl5aG9vh4WFBUQiEebOnQt7e7pmRkY2hUKBEydOQCqVqtv0abGVHnKlHLuz\nd+NM2RkAgIeHBTrlDPNjHsa8uHt1XB0hZKBoFd4XL17EggULoFKp4OPjAzMzM1RUVODkyZPYvn07\ndu3aBV9f38GulRCd4fP5sLGxUYe3s7MzQkNDNaaF6RJjDPtOJOOc/Bc0yuvV7Y5mjli1cBXGWI/R\nXXGEkAGnVXj/5z//QWxsLNavX69xM05zczNefPFFrFu3Dps2bRq0IgnRB0KhEBKJBAEBAXBxcdGb\no+3Gpg68se1L/FX9B5xdTOHvZwMAiHGPwaOhj9JOYISMQAbaPCgzMxMvvfRSr7tora2t8corryAl\nJWVQiiNEF+RyObKzs6FUKjXajYyMMGnSJLi6uupNcCu6FHj70Ac4Xv07GBiqqtrRIWFYKF6IReJF\nFNyEjFBaHXl3dXWBx+t75SVzc3N0dnYOaFGE6AJjDFVVVcjKylKvRy4UCjUeoy+h3YPP5UPk74m0\nkhzU10sh9grCv6e/CjcbZ12XRggZRFodefv5+eHbb7/ts2/nzp3w8/Mb0KIIGWpyuRypqalITU1V\nB3dxcTHa2tp0XJkmxhgUii6NtkdDH0WcyB/LZzyOnc+so+AmZBTQ6sh72bJleP7555GcnKxeYU0i\nkSAtLQ1Xrlyhtc3JsMUYQ2VlZa9tO01MTCASifRqwZXWVjk+3P47rDlOePHZGPVZABOeCd69Zw2t\nS07IKKJVeN99993YvHkztm3bhoMHD6KtrQ3m5uYQCoVYtWoV4uLiBrtOQgacTCZDVlYWqqurNdq9\nvLwQEhKiNxuJAEBrmxTz3luLQtUFOClCEH3GA+PHu6n7KbgJGV20/naaMGECJkyYMJi1EDIkGGOo\nqKhAdna2xv0apqamEIlEcHDQry0xK1orsDV9KzpccsEqgGp+Ls4Xp2qENyFkdNE6vOVyOc6cOYOy\nsjK0trbCxsYGPj4+iI2N1bubeAi5kZqaGqSnp2u0jRkzBsHBwXp1tM0Yw5GiI/g5/2coVUqMGWOF\njo5OTAqJxKKpE3VdHiFEh7T6psrOzsbSpUtRX1/fq8/V1RWfffYZgoKCBrw4QgaDk5MTbG1t0djY\nCFNTU4SFhenVKoEymRI//X4RFfbHUdhUoG435vHx7j+ex11j7qIfmAkZ5bQK7zVr1sDR0RHr16+H\nQCCAqakp2tvbkZ2djbVr1+Ktt97C7t27B7tWQgYEh8NBeHg4rl69iqCgIL062s7JqcP7u75DRucR\nuI0xhpenJQDAy9oLi8SL4GxOd5ITQrQM7/z8fOzatQuhoaHqNktLS4wbNw5vv/02Hn/88UErkJA7\nUVlZifLyckRHR2scrZqZmfWaw61rcqUcm9I2IVV5DuAAZaUKODuZIUk4E/cF3AeuAVfXJRJC9IRW\n4W1nZ9fvjklmZmaws7Mb0KIIuVMKhQJZWVmorKwEAFy5ckXv1yPgc/nw8TOHZQkfUqkSY0P8sSrh\nOfjY+ui6NEKIntFqkZb58+djy5YtvVZSUygU+O9//4v58+cPSnGE3I7q6mocP35cHdwAUFpaCpVK\npcOqemtvV0AqvfZ/isPhYEH4AoiFbvifpH/gs3/8m4KbENInrY68i4uLcf78eUycOBECgQAWFhaQ\nSqXIzMwEj8eDUqnEyy+/rH78hg0bBq1gQvrT2dmJ7OxslJeXa7R7enoiJCQEBgZa/aw6JNLTa/Dp\nt4cwQSjCgifC1O02JjZYP+PfMOfrz+IwhBD9o1V4nzhxAkD3qlNFRUXq9p7tEK+fdkN3wRJdqK2t\nRUZGBmQymbrN2NgYYWFhcHR01GFlveVersHL2z9CFT8L5SlFiIl2R3DwtUtPFNyEkJvRKryPHTs2\n2HUQcluUSiVyc3NRUlKi0e7u7g6hUNjvhjq6UthYiF1l26B0LQTqgVrLNFS0lyIYdN8IIUR7+jNH\nhpDbUFRUpBHcRkZGEIlEcHbWrylVnV2d2HdpHw4XHQZjDL5+1uDzubh/7ETEhOr3jXSEEP1D4U2G\nNV9fX1RUVKCtrQ0uLi4IDQ2FkZGRrssC0L1C2pkzlTiVlQNp0BlUSarUfVam5nj6wSWIcYuhS02E\nkFtG4U2GFcaYRthxuVyIxWK0tbXBzc1Nb4JQpWL4309S8ceVP1BqfA7+hlZwcjIDAAQ7BGN+2HzY\nmNjouEpCyHBF4U2GBZVKhcLCQkgkEkRERGiEtLW1NaytrXVYXW9tnRIk83ej2DgPAFBZ1QY3VyvM\nDZmLSV6T9OaHDELI8EThTfReW1sb0tPT0dzcDKB7bXJ3d3cdV3VjpjxT+AdaIK/aEHZ2xpgcFo4l\nUYvhaKZfd74TQoanfsP71KlTt/RC8fHxd1wMIddjjKG4uBh5eXno6upSt1dWVupVeKtUDGfOVCA2\n1hWGht1zyQ0NDLEkajHqpfW4N2AGpvpNhQFHf+aZE0KGt37De8mSJeBwOGCMAdCcv/33644AkJeX\nN0glktFIKpXi4sWLGjvZGRgYIDAwEL6+vjqsTFNVVRu2b89BemkOmpomYubMa3eOu1u644N7/g0z\nvpkOKySEjET9hveOHTvUv29sbMSHH36IxMREhIeHw8zMDBKJBKmpqTh+/DhWr159W2+empqKefPm\n4ZlnnsHzzz9/W69BRhbGGCoqKpCdna2xHK+lpSXEYjEsLS11WF1vqZnlOFizB7Xml/Dl4WZERj4F\nV9dri6xQcBNCBkO/4T127Fj171944QU89dRTePDBBzUek5iYCD8/P3zzzTcYN27cLb2xTCbDqlWr\nYGZGX26k2983EwG6z/j4+fkhICBAr5Y3BYC8ujyc4GyDzO4qDCSAKjgDHPM2ALRCGiFkcGl1w9qp\nU6fwyiuv9NkXExOD999//5bf+MMPP4S3t7feLV1JdCc/P18juE1NTSEWi2Fra6vDqq5RqRi6ulSA\ngQo/5f2EY1e7Vx4MDLSBSgXcEzgJNqb6ddc7IWRk0iq8eTwekpOT4enp2asvLS0NXO6t7TOckpKC\nX375Bfv27ev3hwIy+gQGBqK6uhpyuRyenp4QCAQwNNSPCRF1dR346qts8Oxb0ex9SmPBFQdLG8wT\nzYPYRazDCgkho4lW34wzZszAW2+9hQsXLiAoKAjGxsaQyWTIysrCkSNHMHPmTK3fUCqVYtWqVVix\nYgWcnJxuu3Ay8hgZGSE8PBwqlUqvljetq+vAmndOoxAXUFJ3FgKuLWxtTAAAoU6heCLsCVga6de1\neELIyKZVeK9atQqWlpb4+eef8csvv6jbbW1t8cgjj+B//ud/tH7DDz/8EGPGjMHs2bNvvVoyIjDG\nUFhYCKVSieDgYI0+fbyMwjWTocLrAIprCsDhAFJpF/j2fMwVzMUEzwm04AohZMhpfdp8+fLlWL58\nOSQSCdrb22FiYgIrK6tberOe0+X79++/rWLJ8NfR0YH09HQ0NjYCAOzt7eHg4KDjqm7MmGcMryBD\nVMmN4O1tBZFHIBaJF9GCK4QQnek3vBUKRZ/tRkZG6o0frn8Mn8+/6Zv9+OOP6OjowP33369ua2tr\nQ2ZmJo4dO4a9e/dqXTgZXnqmgGVlZUGpVKrbS0pK9Cq829sVOH26Evfc46U+ojblmeLpmCWQ439x\nr/+9mO4/nRZcIYToVL/hLRKJtD4dyOFwkJube9PHrVy5Ei+++KJG24svvojw8HAsWbJEq/ciw09/\nU8ACAgLg7++vw8o0ZWfXYceOXFRKKmFmxsP48W7qviD7ILyb8C5tJkII0Qv9hvezzz474NfyrKys\nep1q5/P5MDc316ujLzJw6uvrkZ6eDplMpm4zMzODWCyGjY1+BWF6ZhVSFAdRZZGFjT+0ISxsPszN\nr51RouAmhOiLfsP7+hXPSkpK4OrqCh6PN+AFfP311wP+mkT3VCoV8vPzUVRUpF5iFwC8vLwQEhKi\nN1PAehQ3FyPPYQ9arPLAVxqAH3YRjPcPADe/HEQIIUNNq2/QmTNn4o8//oCLi8tg10NGiKysLJSW\nlqr/zOfzERYWpjdTwLq6VN1nljgMvxX8hgOXD0DFVAgJsQOPZ4CxnqF0XZsQore0Cu+YmBj88ccf\nWLBgwSCXQ0YKPz8/VFZWQqlUwtHREeHh4eobHXWtpqYdW7dmwyuEgzK7P1HUVKTus7E0xz+E/0Cc\nexxNASOE6C2twjs2Nha7d+/G77//DoFA0Gs9cg6Hg+XLlw9KgWR4MjMzg1AohFKpxJgxY/QmCEtK\nWrB23QWUcTLxTdNJhIptYPF/17V9bX2xSLwI9qb2Oq6SEEJuTKvwXrdunfr3Fy9e7NVP4T261dbW\nQiaT9Vo+18PDQ0cV9c/akYMKl0MoaMmFAQfoaO+ElYUx7g+8n/bcJoQMG1qFd35+/mDXQYYhlUqF\nvLw8FBUVwcDAANbW1nq3ZeffKVknHIPbUZXLh7+fDfycPbFIvAieVr3X7SeEEH11y4cZjY2NKCsr\nQ0tLy2DUQ4YJiUSCkydPoqio+3qxSqVCTk6OjqvSJJcrkZJSrdFmZ2qHRWOfQFiYA2YKp+KNCW9Q\ncBNChh2t5+ts3rwZu3btQm1trbrN3d0dTz31FObOnTsoxRH9wxhDaWkpcnJy0NXVpW53cnJCWFiY\nDivTVFLSgi+/zEJpbS1eMZ4AofDaOgIxbjFws3CDh5X+ndYnhBBtaBXeX375JT7++GNMmzYNIpEI\nZmZmaGtrQ1paGlavXg0ul0sbjYwCCoUCGRkZqK6+djRrYGAAgUAALy8vvbkpDQAOHCzEuZbDqLBM\nx//uUOA/b82BqWn3OgUcDoeCmxAyrGkV3t9//z1WrFiBJ554QqN9wYIF+OKLL7Bt2zYK7xGuoaEB\naWlpGiulWVhYIDIyEhYWFjqsrLdKSSUqffejpjwNnC4GZch5GPLp3ychZOTQKrzLy8sxefLkPvum\nTZuGTz/9dECLIvqlqKgIubm5GiuleXt7Izg4GFwuV4eVXdNT25GiI/g5/2coVUoEB9uBzzdAqLsn\n5F1y8A1ptTRCyMigVXibmZmhurq6z6k/dXV1MDU1HfDCiP6wtrZW/57P5yM8PBxOTk46rOia9nYF\nvv02H25+Bsjl/4HLDZfVffY25kgKSkKCd4JendInhJA7pVV4jxs3DmvWrMHatWsRHBysbs/Ozsaa\nNWswbty4QSuQ6J6trS0CAgLQ0NAAsVgMY2NjXZcEAKisbMN/Pk7BpfZ0lFw5ibBIGxgbdf+T1F+s\n9QAAIABJREFU9rTqngLmYkFL+hJCRh6twvu1117DE088gdmzZ8PY2BimpqZob2+HXC6Hl5cXVq5c\nOdh1kiGiVCrR1tamcbQNAP7+/vD399erI1i+RSeyjffjKssHuoCGeinc3S0x3W867g24F4YG+rX5\nCSGEDBStvt1cXFzw66+/4tChQ8jJyUFbWxssLCwgFApxzz33gM+na4kjQUtLC9LS0qBQKDBx4kSY\nmJio+/QptHtIVW2wC25BZTYXfv7WCPbwwiLxInjbeOu6NEIIGVT9hvfPP/+M+Ph42Nt3r/NsZGSE\nmTNnYubMmUNWHBkajDEUFxcjNzcXKpUKAJCeno64OP3ZnEOpVKGoqBkBAbbqNg8rDzwaOQc/mezF\nFN8EzA6eDT6XfpAkhIx8/Yb3ypUrweFwEBgYiAkTJiA+Ph6RkZF6tw8zuTN9zd02NDTUq3XJKyvb\nsHVrFq5W1mD1ygR4el5bgnWq31QE2AXA19ZXhxUSQsjQ6jeJv/vuO5w5cwZnz57F9u3bsWXLFpiY\nmGDs2LGYOHEi4uPje21EQYaXhoYGpKenQyqVqtusrKwQEREBc3NzHVamadfuTPxZvx81Znn4dBsf\n766aCh6ve4qaAceAgpsQMur0G94ikQgikQhLly6FXC5HcnIyzp49i7Nnz+Jf//oXGGNwd3dXH5Un\nJCQMZd3kDjDGUFBQgMuXL/eaux0SEgIDA/3ZWetK4xVUBfyCmtocMAAtY06Dy52m67IIIUSntDoH\nbmRkhPj4eMTHxwPovrEpOTkZycnJ+OWXX/Dtt98iLy9vUAslA0MmkyE9PR319fXqNj6fj7CwMDg7\nO+uwsm6MMXA4HChVSuy7tA+HrhwCYwyBQbYwNTFEmI8bFCoFjA30Y7oaIYTowi1dwO7s7ERaWhrO\nnj2LCxcuICcnB0qlEqGhoYNVHxlgjY2NGsFtZ2cHsViscWe5rtTWtuPrr3MhHG+Ic7J9qJRUqvvc\nnWzwD+E/EOseqzc30RFCiK7cNLxzcnLUp8t71rb29/dHbGwslixZgrFjx+rV9VFyY66urqivr0dp\naSn8/f0REBCgF2GYk1OPjZ+n4orBOexsSkNEpCMMDbtP3wfZB2F++HzYmtje5FUIIWR06De8X3jh\nBZw/fx4SiQReXl6Ijo7GnDlzEBsbC1tb+hIdLnpOQ19PIBDA3d1dr/4eeXatyLDcg0ZlDTidQHOL\nHK6OVpgTMgeTvCbpxQ8YhBCiL/oN70OHDoHD4WDcuHGYMGECoqOjIRAIhrI2coeqq6tRUFCAuLg4\njSl+XC5Xr4IbAFSGCjj7K6Eo5SEgwAZiLwHmh82Hg5nDzZ9MCCGjTL/hfe7cOZw7dw5nzpzBN998\ngw8++ACWlpaIiYlBTEwM4uLi4OPjM5S1Ei2pVCrk5eWhqKgIAJCRkYGIiAi9OXotLm5BXV0HoqOv\nrTseZB+EOVHTcNrxNGYHz6bNRAgh5Ab6DW9ra2tMmzYN06Z1T8uprKzE6dOnce7cOXz++ed45513\n4OjoiLi4OMTFxWHWrFlDVjTpX0dHB1JTU9Hc3Kxua2pqgkKhgJGRkQ4rAzo7u7B3bwGOHCuG0rgF\nPj73w87u2o1yc4Ln4G6fu+Fo5qjDKgkhRP9pfbe5q6sr5s6di7lz5wIACgoK8PPPP+OHH37Avn37\nKLz1QFVVFTIyMtDZ2aluc3Z2RlhYmF6sP8/hcHA6NxOp5vuh5Eix8ztPvLgsTt1vZGgER0MKbkII\nuZlbmipWUlKC06dP4+zZs0hOTkZzczOMjY0xfvz4waqPaEGlUiE3NxdXr15VtxkYGCA4OBje3t56\ncfpZrpTjl0u/oC7gIKQXG2FtYwQILgKIu+lzCSGEaLpheDc3N+Ps2bM4c+YMTp8+jaqqKjDG4Ofn\nh1mzZqlvZNOHo7rRqq/T5KampoiMjOy1redQYoyhvFwCDw9L5Nfn4+uMr1HfUQ8LCz7Cwx1gZ2OB\nMM/gPu+GJ4QQcmP9hvfs2bORn58PlUoFS0tLxMXFYdmyZZg4cSKcnJyGskbSj7a2Npw6dUrjNLmL\niwvCwsLA4/F0VldDgxTffJOH9NwyBM8uR6EsU6M/1jcCj4U+BjtTOx1VSAghw1u/4c3j8bBs2TLE\nx8cjLCxMr9a7Jt3MzMxgY2OD2tpaGBgYICQkBGPGjNH5kezu3Xk4cukvXDU/hawzXRCLHcHhcGDK\nM8XDwocR4xaj8xoJIWQ46ze89+zZM5R1kNvA4XAgFouRnJwMgUCg09Pk11MIz6Kw7CiYisHRyhwq\nBsS4ReFh4cOwNLK8+QsQQgi5IdqcexhpaGiAra2txlErn8/HuHHjdHYk29WlApereVYm3m8s/vI/\nCxNjQ3g7u+LR0EchdBTqpD5CCBmJKLyHAcYY8vPzUVhYCD8/PwQHB2v06yq4Cwqa8PXXOUhK8odY\nfO0+iCjXKNwdGgtXC1fc638vjAx1O7+cEEJGGgpvPSeTyZCWloaGhgYAQGFhIezs7ODoqNv50OfP\nV+GzbadxxeQvVO6JxidBj8DEpPsmOQ6Hg2ejn6Xr2oQQMkgovPVYfX090tLSIJfL1W2Ojo46v7bd\n2dWJCtMLyLTbBVmnAlloxtWSaQgJuvYDBQU3IYQMHgpvPcQYQ0FBAS5fvgzGGIDuMAwMDISfn5/O\ngpExhuzabOzJ2YO69jp4+5mjrlYKXz8jqGxrAdDqaIQQMhQovPWMQqFAeno6amtr1W1GRkaIiIiA\nvb39kNfDGMPZs5Wok9WgxOwUcmpz1H329iaI8A3Eo6GPwseGNqkhhJChQuGtR5qampCamgqpVKpu\ns7OzQ0REBIyNjYe8nrY2BT7/Mhm/Fx1ArWkWIiIdYWzU/U/GlGeKWUGzMMFrAgw4tAYAIYQMJQpv\nPcEYQ1ZWlkZw+/n5ISgoSGenya+2FeCnpk/QbCQBuoDSklYEBtoh3jMeDwQ+AAsjC53URQghox2F\nt57gcDiIiIjAyZMn1Yuv6HoZWncrV/gHWSAlXQJXV3PcI47CY+GPwN3SXad1EULIaEfhrUfMzc0R\nFRUFMzMzmJqaDul7K5UqXL7ciJCQa9fVbUxs8FDEA3CwOIl5Ef9AuHM43UVOCCF6gMJbRyorK9HV\n1QUPDw+NdgcHhyGvJS3vKt7+7r/oaDLAf196FWPGWKn7pvpOxTS/aTA0oH8qhBCiL+gbeYipVCrk\n5eWhqKgIBgYGsLS0hJWV1c2fOAg6Ojvwe+Hv+OiX3ahvb4cB3wCfbz+F996crl7ylMfV3e5khBBC\n+kbhPYRkMhlSU1PR2NgI4FqQx8bGDmkdnV2dOF58HAcLD6Jd0Q4fXws0NneAAwZbQSOdGieEED1H\n4T1EGhoakJqaqrFamrOzM8LDw4esBmVXF7468isKDM6gWdasbjc2NkRCRBjmRTyMGD/RkNVDCCHk\n9lB4DzLGGIqKipCXl6exWlpQUBB8fX2H5CiXMYbdfx3F539+gzppDQIDbeDkaAYAsDe1x6ygWYhy\njaIjbkIIGSYovAeRUqnExYsXUVVVpW7TxWppuXW52J7zJeqkrQCAkpJW+Lg7YWbAfZjgNYFuRiOE\nkGGGvrUHiUQiQUpKCtra2tRtNjY2iIyMhImJyaC9r0rFUFbWCi+vazfBhTiEIC44BJUVF8AFHw8E\nzcArEx+BucnQTkcjhBAyMCi8B4lcLkd7e7v6z97e3ggJCYGBweAsJcoYwy+/FOLQuQy0tHXg8/ce\nhqVl9z7aHA4HD4vmwoa54+HIB+BkbTsoNRBCCBkaQ74odUFBAZYuXYqYmBiEhoYiKSkJR44cGeoy\nBp29vT2Cg4PB5XIhFoshFAoHLbgBoK6jDrvyvsJx1TZcMjqC8+erNPqD7IPwwpSFFNyEEDICDGl4\nS6VSzJs3D56enjh69ChSU1ORmJiIF154AYWFhUNZyoDruRntej4+Prjrrrvg7j5wy4kyxnD1ajNK\nSloAAPUd9dh+cTtW/7kaCocSMACdxk243J45YO9JCCFEvwzpaXOpVIpXXnkF9913n/q677x58/Cf\n//wHly9fhp+f31CWM2Cam5uRlZWF6Ohojd2/OBzOgC5zeulSI3bvzkdlZRt8BDy43VWB06WnoWIq\nAICDvQl4PANMChqLpJCoAXtfQggh+mVIw9vW1hZz585V/7mpqQmbN2+Gs7Mz4uLihrKUAVNaWoqs\nrCyoVCqkpKRg3Lhxg3Z63MyMh6tV1SgzScap8mxEFzjBiM9V9wucQvDApAdob21CCBnhdHbDmlAo\nRGdnJ0JDQ7F161bY2NjoqpTbolKpkJ2djZKSEnVbW1sb2traYGlpecevL5crwedzNeZe13ILcNV7\nNxqaOuDoYKI+Ve9v54/7A+9HgF3AHb8vIYQQ/aez8M7OzkZjYyN27dqFRx99FLt374a3t7euyrkl\nMpkMKSkpaGpqUrdZWloiOjr6jk+Ty2RKHDtWiiNHSrBggRAi0bWNSrytveHvbws/AysYcg3gY+OD\nB4IeQKBdIC2wQggho8iQ321+PVtbWzz//PNwcnLC7t27dVmK1hoaGnDixAmN4HZzc8P48eMH5Pr2\n/v1X8MsvhWjsaMTeA9kaN8LZmNhgiv8k+Nn54IWYF/Da+NcQZB9EwU0IIaPMkB55Hz16FO+++y4O\nHjwIIyMjdbtCoQCXy73BM3WPMYbi4mLk5ORoLHMaEhICb2/vAQvQqAkW+O+546jgZqO1PQoSyTj1\nfG0AeDDkQRgaGFJgE0LIKDakR95isRhSqRRr1qxBc3Mz5HI5tm/fjtLSUiQmJg5lKbeEMYaMjAxk\nZ187EjYyMkJsbCx8fHxuK0hVKoaUlGp0dXXfKV7bXosdGTuwNuUd8PyK4R9gCZe4GsBIrvE8HpdH\nwU0IIaPckN9tvmPHDnzwwQeYPHkyDAwM4OPjg08//XRId9e6HdcHprW1NaKiom57mdOMjFr8+ONl\n1NR0YPpDdmi0zcCFigvqHwzc3S0AAB5WHmhTtMHS6M5vgCOEEDJyDPkNa/7+/tiyZctQv+0d4XA4\nEAqFaG1thYWFBUJDQ+/oNH9VVTuu1BWj1DQZF44XIyrKCQYG13448LP1w70B9yLYPpiOsgkhhPRC\na5v3gTEGxpjGfG0ul4u4uDhwudw7DtQG53PItt0DlYrBzdkCPbekBTsE417/e+Fv539Hr08IIWRk\no/D+m66uLvWiK2KxWCOoDQ21Hy7GGHJy6nHsWBmWLAmFqSlP3Rfg4IeQEDuYmBqCZ8iFyEmE6f7T\naXEVQgghWqHwvo5MJkNycjKam5sBdF/b9vG5vUDdti0bZ8+Xo9HwKjwPWWDWrGtH05GukfBz8YCn\nlSem+U2Dh5XHgNRPCCFkdKDw/j9NTU1ITk6GXH7t7m6JRALG2C2fJpcpZZC65CLZ4ifIDdrw0ykj\n3HuvD3i87uvkBhwD/L9J/w88Lu8mr0QIIYT0RuENzfXJge4b1AQCAcaMGXPT4O7qUuHKlWYEBNii\nWdaMY1eP4UTJCXQoO8CzkMPeyhy+wmp1cPeg4CaEEHK7RnV4q1Qq5OTkoLi4WN3G5/MRGRkJe3v7\nGz6XMYa//irDoUMlKGuuQMwjEuS2pqNL1QUA4IADcYQjLI0sEe0lhoqpYMDR6YJ2hBBCRohRG95y\nuRypqaloaGhQt93q+uR/pJ/HCdmfaDQvQUWyGfz9r22u4mTuhHt87kGseywdZRNCCBlQozK8JRIJ\nzp8/D6lUqm5zdXVFWFhYv3eUq1RMYy62RCFBqdMfaKyqAo9nAGOT7uf52voi0TcRYU5hNEebEELI\noBiV4c3n8zXWJw8MDISfn1+fYVtW1oojR0pQV9eBV18dq36MpZEl7gmJh1R2FHZ2Joh0i8A9PvfA\n19Z3SD8LIYSQ0WdUhreRkRGio6Nx4cIFhIWFwcnJqc/HdXR04o21+1HKTQOPmWBOUSB8fa3V/Xf7\n3A1TnikSvBNgb3rja+SEEELIQBkV4d3XdC9ra2tMmTKlz2VOVUyF9Kp0HL16FGVjklFT0wEu4yE1\ns0wjvN0t3fGQ4KFBr58QQgi53ogP7/b2dqSkpCA4OBiOjo4afT3BXVraiuPHy+D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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2967,7 +2967,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": { "collapsed": true }, @@ -2998,7 +2998,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": { "collapsed": true }, @@ -3017,7 +3017,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -3031,7 +3031,7 @@ "data": { "image/png": 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Y0ZhhCCGEuAsoilIlcXt7e+sSt0ar4edzP7PtzDa0ihaA/IIycs44UHGlAOOc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\nZemVZ2Zm8vbbb9OtW7d6CU4IIepahba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bd7q7uxMUFNThtQvRX1gV3lOnTu3sOoQQ3Uyv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\n8uOPxygvr+Hxx4cSHh5o7h8/aBzrQ3/Bs8kZHBTx0fFcPexqfF18u7F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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3064,7 +3064,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": { "collapsed": true }, @@ -3089,14 +3089,14 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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4sDjrFgThpXHz5k1u3LiheV27du1Sez7Lwyt9t3nPng3p2bP0M83+/ZX0768s\ndfmQIU0ZMqRpqctHjnRm5EjnZ4qxkIGBgea5wKJSU1O1EmWhxo0b07hxYwYNGkROTg7jxo3jiy++\nwMfHp1jdBg0aAHDp0iXatm372FiMjY3x8vLC39//sfUeNzZvu3btOHbsGIGBgZw+fZpvvvmGNWvW\nsGvXrhLrF03MvXr1onPnzpw5c4ZTp04xa9YsHB0d2bx5szj7FgShQt25c4ewsDDN65o1a+Lq6opM\nJnthMYgz75dEo0aNtGadKZSYmMitW7do0qQJAIGBgXz11VfF6hkZGdGuXTuSkpJKbL9x48YoFApW\nrVqlGSD/Udu3b2fYsGGo1Wrq1atHeHi41oTxOTk5xMbG6rw/SUlJGBoa0rFjRz7++GP279/PgwcP\nOH36NMbGxgBaP1Ye7Y4vXN/MzIzOnTszZ84cdu3aRVBQENeuXdM5BkEQhOctOjpa67u6evXqtGrV\n6pknGnlSInm/JCZNmkRYWBiLFi0iMTERlUrF1atXGTduHHXq1KFv375AwQ1rP/74I5999hlRUVGo\n1Wpyc3MJDAxk27Zt9O7du9RtzJ8/n2vXrjFq1CgiIiKQJImEhARWr17NggUL6N+/P3K5nIEDBxIf\nH8+yZctIT08nJSWFzz//nKFDh2ol9NJkZmbStWtXNm7cSFZWFmq1mtDQUHJzc6lXrx7VqlXD0tKS\nAwcOkJubS0xMDFu3btWsHxMTwxtvvMGePXvIzc0lPz+fkJAQjIyMqF279rMfbEEQhKfw4MEDLly4\noDkBsrS0xN3dvUJ6A1/pbvPKpFGjRuzcuZNVq1bRq1cv0tPTqVmzJt26dWPkyJGaa8oKhYLt27ez\nfv16hgwZQnJyMjKZjLp16zJ48GCGDh1a6jaaN2/Or7/+ir+/P++99x4PHz6katWquLq6sm3bNpyd\nCy4B2Nvbs3btWpYuXcr333+PqakprVq14rvvvtPp16WpqSlr1qxh8eLFrFy5EplMRp06dViwYAEK\nhQKAuXPnsmDBAtzc3GjUqBGzZs3i1KlTQMG1o6VLl7Jq1So+/fRT9PX1adSoEf7+/uUyUpEgCEJZ\n4uPjCQkJ0SRuCwsLPDw8nssMYU9DJpXUh/qSiY6OplOnThw5cgR7e/uKDkcQBEF4zdy+fVvTXW5m\nZkb79u0xMjIqt+2VlffEmbcgCIIglKF+/frI5XJu3rxJ27ZtyzVx60Ikb0EQBEHQQd26dbG3t38p\nnngRN6y100bzAAAgAElEQVQJgiAIQhEZGRnk5+cXK9clcavUKrZe3MqFBxfKIzRAJG9BEARB0JKR\nkcHp06c5e/ZsiQn8cXLyc1gdtJpTd0+x/tx6IpKKD839PIjkLQiCIAj/LysrizNnzpCdnU1iYiJB\nQUEljo1RktScVL458w2X4wpubMtT5RFyP6Rc4hTXvAVBEASBguGdz5w5oxlASk9PD4VCodPIaXEZ\ncSwPWE5CZoKm7C3FW/RU9CyXWEXyFgRBEF57OTk5BAQEkJGRARQM/9y6dWuqV69e5rqJmYksOLWA\n9NyCaZVlMhmDmg/ijbpvlFu8ottcEARBeK3l5uYSEBBAWloaUJB8W7VqRY0aNXRav5pJNZrYFAxh\nbaBnwNCmI4n+x4aMjNxyi1mceQuCIAivrcLEnZqaChQk7pYtW1KrVi2d25DJZAxrMQxJkrDLbcEv\nq9NJS0siJyef4cPLZ6YxceYtCIIgvJby8vIIDAwkJSUFKEjCLVq0KHMOhYikCE0XeSF9uT6jWo3C\n3qweaWkFZ9wBAfeJikotl9hF8n5J+Pj4sHTp0hKX+fn5MXXqVM1rpVJJ+/btNR+4ou3s3r271O0o\nlUqaNWtG8+bNcXJyom3btowaNYqDBw9q1YuOjkapVOLk5KQ1r3fhvwkTJmjqRkVFMXPmTLy9vXF2\ndsbV1ZWBAwdy4MCBEmPYs2cPSqWSiRMnPvaYCIIglKcrV66QnJwMFCRuFxeXxw7Bna/OZ3fYbhad\nXsT20O0l3oXu7GyDm1stLC2N+PBDVxwcLMoldtFtXkmp1WoWLlzIl19++cTrzpkzh/79+6NWqzXT\ndM6ZM4fjx48zf/58rbrr1q2jXbt2pbaVmZnJkCFDaNmyJVu3bsXOzo6MjAx++eUXJk6cyNq1a/H0\n9NRaZ/v27fTq1Yv9+/cTHx+PjY3NE++DIAjCs2ratCmpqamkpKTQvHlzHBwcSq0bnRrNxvMbuZd6\nD4Bz989xOOwkLWu6Ub26iVZdX9/GyOUyTEwMyi12ceZdSU2ePJm9e/cSGBj41G3I5XJq165Nv379\n2LhxI7/99hv79+9/ojZu3LjBgwcPGDFiBPb29shkMszNzRk6dChLly7Fzs5Oq/7Vq1e5ePEikyZN\nokGDBvz0009PHb8gCMKzMDQ0pE2bNrRq1Yq6deuWWEctqTlw8wDzT87XJG4JCf2Umuxam8jmzVeK\nnYGbmRmWa+IGkbwrrTp16vDBBx/w2WefkZOT88ztNWnShA4dOvDHH3880XoODg4YGxuzatUqoqKi\ntJZ1796dRo0aaZVt27aNdu3aYWtrS79+/fjxxx9RqVTPHL8gCEJZSurmNjQ0LPUad3xGPItPL+bX\nsF9RqQu+pwz0DOhi25v8Ux2QZZsRHp7EqVP3yjXukrzS3eZ7wvew9/penep2rNuRIc5DtMq2hW7j\nZORJndb/j+I/9FSWz8P4pRkxYgT79u1j1apVTJ48+Znba9iwIcePH9cqGz16dIkDFGzcuBE3Nzeq\nVavGihUr+Pzzz+ncuTP16tXD1dWVtm3b0qVLF0xNTTXrpKSksG/fPhYsWADA22+/zaJFizhy5Ahd\nu3Z95vgFQRBKo1KpCA4Oxt7evliPYFGSJHHq7il2Xd1FTv6/J0f1LOsx3HU4tcxroXf3OgcP3qFW\nLTPs7MzLO/xiXunk/arT19dn3rx5DB48mB49etC4ceNnai8/P7/YoPtlXfMG8PT05PDhw1y+fJlz\n584RHBzMnDlz+Prrr1mzZg0uLi4A/Pzzz5iamtKpUycALC0t6dq1K9u3bxfJWxCEcqNSqQgKCiI+\nPp74+HiAUhN4riqXdSHruBR7SVMmk8n4j+I/9HDsgVxW0GHds2dDzM0N8fZ2wMDgxc8yJpL3S8LA\nwEAzJF9RqamppV6PcXZ2ZsCAAXz66af8+OOPzxTD1atXcXR0fKp15XI5zs7OODs7M2zYMFJSUhg6\ndCgLFy5k+/btqNVqdu7cSWpqKm3bttWsl5eXR3Z2Nrdu3aJBgwbPFL8gCEJRjyZuKDirLu27FsBA\nboCeTE9TNz3WmNoPvHjzzX8TN4CBgR5du9Yr19gf55VO3j2VPZ+pK3uI85BiXenlpVGjRly+fLlY\neWJiIrdu3eK///1vqetOmjSJt956i61btz719oODgwkODmbDhg1PtN5ff/3F9evXGTdunFZ51apV\nadWqFadPnwbgxIkTREdHs337dmrWrKlVd+TIkezYsYPZs2c/dfyCIAhFFU3cAI0bNy52L86jZDIZ\nQ5yHEPEwgthz1TC73YI09Nm7N4LevZ/u5KY8vNAb1oKCgkp8ZrhJkybMnDnzRYby0pk0aRJhYWEs\nWrSIxMREVCoVV69eZdy4cdSpU4e+ffuWuq6ZmRn/+9//WLZsmWaUIF2lp6eze/duPvjgA/z8/OjQ\nocMTrW9qasrq1atZvnw5sbGxml+1hw8fZu/evfTu3Rv490a1Vq1aYW9vr/Vv4MCB/Pbbb2RmZj7R\ntgVBEEpTeI27aOIu2rt4I/EGeao8rbIqRlX4wvsLRrQZgvz/z3HDw5NQq3WbXexFeKFn3m5ubly6\ndEmrLD4+nv/85z/06dPnRYby0mnUqBE7d+5k1apV9OrVi/T0dGrWrEm3bt0YOXIkJiYmj13f29sb\nT09P/vzzzzK3NWfOHL744gugoLu+adOmzJkzhx49ehSrW9oNawCXLl2iffv2rF+/ns2bN/POO++Q\nkpKCvr4+jo6OTJ06lf79+xMZGcmpU6f49ttvS2ynT58+LF26lD179jBgwIAy4xcEQXicwsQdFxen\nKVMqlVqJO0+Vx6/XfuXIrSN0atCJ/zbT7t00NTDF09OB0NAElEorunSph1xe9uxiL4pM0nWi0nLy\nwQcfUKNGDebMmVNqnejoaDp16sSRI0ceO/qNIAiC8HorqatcqVSiUCg0ryOTI9l4fiMP0h8AoJYk\n3PL7M7ibJ2ZmhlrtSZKk05Sgz1tZea9Cr3kfPXqUc+fOcfjw4YoMQxAEQXgFSJJULHErFApN4lap\nVfx580/2Xd+HWlIDkJaeS/J1K/Lvp6OfHM5772lPJFIRiVsXFTZIi1qtZsmSJYwePRpz8xf/jJwg\nCILwapHJZNjZ2WkSrlKpRKlUAvAg/QEL/1nInvA9msRtpG9E15p9sY/phqFkRmDgfa5fT6qw+J9E\nhZ15Hzp0iNjYWAYPHlxRIQiCIAivGAcHByRJIicnB0dHRyRJ4u87f7M7bLfWjWkNqzXkPdf3sDa1\nRn3nEhcuxNGnjyOOjlYVGL3uKix5//HHH/j4+GBkZFRRIQiCIAivoDp16gCQkZvBupB1XEu4BoBK\nrQa1Hv917kvnBp01z20PGKDk7bcbYm1tWmqbL5sK6TZPT0/nxIkTdO7cuSI2LwiCILwC8vPzuXTp\nEnl5eSUuNzEwIVdVMLd2cnI2N4Ilat/sSZcGXbQGXDEzM6xUiRsqKHmHhYWRl5dHkyZNKmLzgiAI\nQiWXl5dHQEAAd+7cISAgoMQELpfJGe46HFT6pITURxH3DrE3DTh5MroCIn6+KiR5Fz57V7169YrY\nvCAIglCJ5ebmEhAQwMOHDwFITk4mLi6OawnXNDejFaphVoMlPRYyxnMwcvQwNTXAxKTyDy5aIXvw\n1ltv8dZbb1XEpgVBEIRKLCcnh4CAAK3RJBVNFByKO0RAdADvNH2HLg26aD3iZWZoxn/+04DcXBXd\nu9fHwqLy32sl5vMWBEEQKoWcnBzOnDmjSdwymQzLupZsurOJgOgAANaf2sn/Fv9JXp5Ka10DAz0G\nDGj8SiRuEMlbEARBqASys7M5ffo0aWlpAEhIxFeJZ1vkNh5mFXSfX7+eROz5atyLyGffvlsVGW65\nE8n7NRIZGYlSqSQwMFCn+rt370apVJKfn1/OkQmCIJQuMzOTf/75h/T0dADS89IJUgcRkBqgqWNm\naMYg5XAaZ3VDXzLm7NkH5OaqSmuy0qv8V+0FQRCEV1ZmZianT58mKysLCYm7KXe5ZniNPIN/7y53\nrumMn4sfVQyrkBoejI2NKf36KTA01KvAyMuXzslbrVZz5coV4uLiSElJoWrVqtSoUYNmzZohl4sT\neEEQBOH5MzQ0xNjYmOS0ZK4kXCHOIo48kzzi47KwrVGVIS0G0da+reYGtQkTWqGv/+rnpDKT961b\nt/D39+fYsWOkp6fz6CRkMpkMc3NzvL29GTt2LA0aNCjXYF9lSqWSBQsWsGfPHkJCQqhVqxYLFy7k\nypUrrF27lrS0NLp06cKXX36Jnl7Br8lDhw6xZs0abt++jaGhIZ6ennzyySdYWloCBfOnz5s3j8jI\nSOrXr8/IkSO1tqlSqfD392fPnj08ePAAa2trfH19i9UTBEGoKPr6+nh4eHA64DT5+vlk5OZw88pD\nVHHWeKsH0M6hXZH6r37ihsckb7VazaJFi9i8eTN169alX79+uLm5YWNjg4WFBampqcTHx3P27FmO\nHz9Oz5498fPzY/r06S/NmXh4eDjXr1/XqW7dunVxdnbWKgsNDSUyMlKn9RUKhWYA/Ke1YcMGFi1a\nRIMGDRg3bhzjx4+ne/fuHDhwgOjoaPr06UPXrl3x8fHh7NmzTJgwgcWLF9OlSxdiY2P56KOPmDZt\nGt999x0ZGRm8//779O7dmx9//JH4+HimTZumtb1vv/2WPXv2sGrVKho1asT58+cZM2YM1tbW9O7d\n+5n2RRAE4XkxMDDgjQ5v4JDswOy9X2F1zwPbXBeuBGdxuW08Tk42FR3iC1dq8h4+fDgPHjxgyZIl\nvPnmm6U24OPjw4wZMzh48CDLli3j2rVrfP/99+UR6yvP29ubxo0bA+Dl5cWZM2eYOHEiRkZGNGrU\nCKVSyc2bN/Hx8WHbtm288cYbmuflHRwceP/99xk/fjwJCQkEBQWRnp7ORx99hLGxMQ4ODgwbNozz\n588DBT/OduzYweTJkzU/Olq3bk3//v356aefRPIWBKFCJCYmkp6ezkOjh7jUdNF0h8tkMhpYNWD9\nwOX8kBdBYOB9vL3rVJqJRJ63UpN3tWrV8Pf3x9RUt/Feu3XrRseOHfn000+fW3CvGzs7O83/TUxM\nsLa21pq4xcTEhJycHKDgzvEOHTpord+oUSMAoqKiuH//PhYWFlStWlWz3NHRUfP/pKQkkpOTmTt3\nLvPmzdOUS5KEjc3r9ytWEISKFxcXx8kzJwmLD+OB+QPe7fAuztYtMTb+N1WZGpgyYICSjh3tX9vE\nDY9J3kuXLn3ixkxNTfnmm2+eKaDn6dG5XJ+Gs7Nzsa708lT0csPjLj/k5ORo3X8ABWfTUPALNTc3\nt9gk8oXLAYyNjYGC97lLly7PFLcgCMKziomJYd+JfUQkRaCW1BjkGDFj63J8DEYya4qX9ohpZoY4\nOhpWYLQVT6e7zTMzM9myZQsXLlwgOTm5xDo//PDDcw1MeLx69eoRHh6uVXbjxg3kcjl169blzp07\npKWlkZ6ejrm5OYDW9X9zc3Osra25evWqVvKOjY3FysoKQ8PX+w9DEIQX5/KNy/xx4g+SswvyS75c\nxamo21RPb0FkTjbHj0fh5VWngqN8ueh0Z9mcOXNYtmwZUVFRGBgYlPhPeLF8fX35559/2LNnD/n5\n+dy+fZvVq1fTtWtXrKys6NChA/r6+qxatYrs7Gzu3LnD1q1btdoYOnQo27dv58yZM6hUKq5du8ag\nQYPYsGFDBe2VIAivE0mS+CPgD7b/tV2TuNX6aqo2smBUi/HUzWmDHD2Sk3MqONKXj05n3idOnODr\nr78WNzG9RDw9Pfnqq69Yv349n332GdWqVaNr166MHz8eAGtra/z9/fn666/ZsWMH9erVY+LEiYwd\nO1bTxogRI8jKymLmzJkkJiZSo0YN+vTpw5gxYypqtwRBeE0kZSax8chG4u7GacrUhmo8PDzo49QH\n1HLyEy7SpUu91/radmlkUtELpyVwc3Pj119/xd7e/kXEVEx0dDSdOnXiyJEjFRaDIAiC8HyExYex\n4dAG5MlyVCo16el51KhdlXd7+OFYw7HsBl4DZeU9nbrN33jjDZ3HwxYEQRCEx0mPTsco1YiszHwS\n4rPJTq6Kk3l/kbifgE7d5r6+vsyfP59bt27h4uJS4uNjRR9bEgRBEISS2Neyx7G6I6GZ4Zhm1ked\nruD40Qd06+yIpaVxRYdXKeiUvIcMGQLA1atXtcplMhmSJCGTyQgLC3v+0QmCIAiV2sOsh1yOu0zH\nuh01Zba2tnRq24nmjZpz7G81KSk5DB3qJBL3E9ApeW/ZsqW84xAEQRBeIZIkcTrqND9d+Yns/Gz0\nc6rQvHZTzM0LHkN1cHDA3t4ex0a5mJjoY2Dw6s4AVh50St7u7u7lHYcgCILwiniY9ZCtoVu5EncF\n8iH3tpzJJ5fwnmIKo0a6aOrJZDIsLIwe05JQGp2nBD1//jw7duwgLCyMjIwMqlSpgrOzM8OGDdMM\nyykIgiC8viRJ4tTdU/x89Wey87OR58nRjzImNxFaSkqCg2Jwd7PFxaVGRYda6emUvI8dO8YHH3yA\njY0NTk5OmJmZkZaWxrFjx9izZw/ff/89rq6u5R2rIAiC8JJKzExky8UtXEu4BoA8V455vDn2Vvbk\n5lkQH5eDuZ0eVlbiuvbzoFPy9vf3p0+fPsydO1drvG2VSsW0adNYunSpuC4uCILwGpIkieORx9kd\ntpuc/BwkJPRz9LFJtkFhrcDCyAJ1VWjU0I6ePVu8NvNtlzedknd4eDjz588vNlGGnp4eY8aMYeDA\ngeUSnCAIgvBy+/nqzxy+dRi1JHH3bipSih4967ahTo06yGVy9PX1cXd3p3r16hUd6itFp59AMpmM\n/Pz8kht4zMxXgiAIwqvNs54n+nJ9LpyPQx1VhaaprTDKtEYuk2NkZES7du1E4i4HOmVeJycnVq9e\nXSyB5+XlsWrVKpycnMolOEEQBOHlVsOsBu80eYeuNbxoIbliKJmQlJSNqakp7du3p2rVqhUd4itJ\np27zCRMmMHz4cDp27IiTkxPm5uakpaVx+fJlsrOz2bhxY3nHKQiCIFQglVrFwYiD6Mv16dqwq9Yy\nRwNH0u0yuJgQR82apiiVtWnTpg1GRuIxsPKiU/Ju3bo1v/zyC1u3buXKlStERkZibm5O165dGTp0\nKA0bNizvOAVBEIQKEpUSxeaLm4lKiQK1nKgQM97t00YzsErt2rWJioqiRYuCGQ3d3NzQ19f5SWTh\nKeh8dBUKBXPnzi3PWARBEISXSJ4qj3039nHw5kHUkprEpCxu3HjIjfQ9WOnb0LevAii4ednNzY2b\nN2+iUCjQ0xOjpZW3UpP3qVOnaNOmDfr6+pw6darMhsTEJIIgCK+Om0k32XJxC7HpsZoydZ4c+9R2\n2OW48Ndfd+jY0R4bm4KJqgwMDGjSpElFhfvaKTV5jxw5kn/++Yfq1aszcuRIzSQkJRETkwiCILwa\nsvOz+TXsV47dOaZV7ljdkS+8/djy8AbJyTd4443mmsQtvHilJu8tW7Zo7hIUA7AIgiC8+q7EXWFb\n6DaSspLIzs5HT09GFRMz3mn6Dh3rdCQlJQWlMhWVqhq5ufeJjo7G3t6+osN+LZWavB+djERMTCII\ngvBqU0tqdl3dRWJWIjH30rlzJxXnWs35Zsx0rEysiI2NJSQkBJlMhb6+HD09PQwMDCo67NdWqcl7\nyZIlOjcik8mYNGnScwlIEARBePHkMjnvurzLrP1zuRuRg2NWV8zDlETdyCfN8i6hoaGaS6eGhoa4\nu7tjZWVVwVG/vkpN3uvWrdO5EZG8BUEQKpe0nDTMDc2RyWSasgZWDZjoOZaQbImLZ1OpXduM1NRo\nIiOjNXVMTU3x8PDA3Ny8IsIW/l+pyfvatWsvMg5BEAThBZAkiZN3T/LL1V/wbe6Lm607enr/Drbp\nYe+B08BcTtpFUaNGKvfu/Zu4q1atiru7O8bGYmawiiaeohcEQXhNxKbHsjV0KzcSb6BSqZm7ew1t\nc7KZNdVT6wzcyEiOlVUC9+7FacpsbGxo3bq1GHzlJVHqu/CkM4X98MMPzxyMIAiC8Pyp1CoORRxi\n7/W95KvzUUsS587HIcsw51pGDMePR+HlVUdTPyMjg8TERM1rBwcHnJ2dxURUL5FSk7e4i1AQBKHy\ni0yOZMvFLUSn/tv9rS/X482GbxL/T13k6HP9+kOt5F21alVatWpFUFAQjo6OKBQKrTNzoeKVmry3\nbt36IuMQBEEQnqOc/Bz2XN/D4VuHtQbYqmtZl3dd3qWWaW2WxYXQsaM97u61iq1fs2ZNvLy8xI1p\nL6lSk3dubi6Ghoaa/5elsK4gCIJQsaJTo/EP8ichM4GcXBV376aibGRD36a96dSgE3JZQff3lCmt\nkclk3Lt3DwsLC6pUqaLVjkjcL69Sk7eLiwunTp2ievXqODs7l9llIoZHFQRBeDlUM6lGriqX2NgM\nIiKSMc+xx622H10atilW9/r164SHh2NiYkKHDh3EneSVRKnJe9y4cZiammr+L653CIIgVA6mBqb4\nNvfl6+jVNEjrRM28pgQdS6NP12ysrAqSs1qtJjQ0lKioKACysrIICwvD1dW1IkMXdFRq8v7www81\n///oo49eSDCCIAjCk3mY9ZCLsRfxquelVe5ay5WNg5exLvkaycnZ+Pk10yTuvLw8goODSUhI0NS3\nsbHBycnpRYYuPAOdH9iLjY3l4MGDREREkJWVhZmZGY6OjnTr1o3q1auXZ4yCIAhCEZIkcSLyBLvD\ndpOdn40qxQy3+s5YWBgBBSNfmhuZM3Jkc0xM9DEwKJhjOzMzk7Nnz5KWlqZpq06dOjRv3lw8ClaJ\n6JS8T5w4wfjx48nOzqZq1aqYmpqSmZlJSkoKixcvxt/fHw8PD503unv3btatW8e9e/eoUaMGfn5+\nDBs27Gn3QRAE4bVyP+0+W0O3EpEUgUotERmZwvTjyxitmMaY0S206hYmc4Dk5GTOnj1LTk6Opqxx\n48Y0atRIXBqtZHRK3gsWLKB169Z8+eWX1KxZU1MeHR3N7NmzmTdvHnv27NFpg/v27WPBggUsWbIE\nNzc3zp8/z5w5c2jdurXoshEEQXiMfHU+B28eZP+N/eSr8wFIS80h6a4+jpmdORcSR2hoPM7ONsXW\njYmJ4cKFC6hUKgDkcjktWrTAzs7uhe6D8HzolLzv3r3LsmXLtBI3gL29PTNmzKB///46b3DVqlWM\nHDmS9u3bA+Dh4cGff/75BCELgiC8fm49vMXWi1uJSYvRlMllcga59SU+vw5BAfE0blwNW1uzYusm\nJiYSEhKieW1oaIibmxvVqlV7IbELz59Oybt27dqlPustSRK1a9fWaWNxcXFERERgamqKr68v4eHh\n2NnZMXr0aHr27Kl71IIgCK+J7Pxsfr/2O3/f+RtJklCp1ejJ5dS3qo+fsx92FnZkOOTi1CQBDw/b\nEru/q1Wrhp2dHffu3cPc3Bx3d3fMzIoneaHy0Cl5T5gwgSVLlrB48WKt+VsTEhJYtmwZEyZM0Glj\nDx48AODHH39k0aJFODg48PPPPzN16lRsbW1p3br1U+yCIAjCq2vnpZ0ERAeQm6viZkQy6lw9Pvcd\nhU8DH81gK2ZmhrRpU/pJlEwmw8XFBSMjIxQKhRj++hWg88QkUVFRdOzYEQcHB6pUqUJWVhZ37tyh\natWqqFQqevToUebGCofo8/PzQ6lUAvDuu+/y+++/s3v3bpG8BUEQiuip7ElQdDDnzsVgnuVAo0wf\n9O4qkDcs/c7wzMxMjI2Nte4e19PTo1mzZi8iZOEF0HlikgYNGmi9NjIyokWLgrsaH71z8XFq1KgB\noHX2DgWPKcTGxurUhiAIwqtKkiQkJM0ZNYC1qTW+zgOpnx7LjWNVkCEjPj6z1DYSEhIIDg7G1tZW\np9ExhcrphU5MUqNGDSwtLbl06RKdO3fWlEdGRoo7zQVBeK0lZSWxPXQ79a3q8x/Ff7SWdazbkbZ2\natYmXaRTpzo0blzy2BqRkZFcunQJSZK4e/cuVapUKXbiJbwaSu13mTBhApmZpf+6K0lmZiYTJ04s\ndbmenh7Dhw9n27ZtnD59mtzcXLZv305YWBi+vr5PtC1BEIRXgSRJHLtzjDnH5nA57jI/X/ydxWuP\nkJur0qqnry9n3DjXEhO3JElcvnyZ0NBQzeVJY2NjcTf5K6zUM+/U1FT69OnDxIkT6d69e5kNHThw\ngKVLl5b5zOCYMWPIz89n5syZJCYmUr9+fb777juaNGny5NELgiBUYnEZcWy5uIUbiTcAuBeTxu3b\nqSRkhFB/jwPvvKMos428vDxCQkKIj4/XlFWtWhV3d3cxycgrTCY9OtHrI9RqNYsXL2bz5s3Y29vj\n6elJq1atsLGxoUqVKqSlpREXF0dISAgnTpzg3r17+Pn5MXXqVPT09J5rkNHR0XTq1IkjR45gb2//\nXNsWBEF40dSSmsO3DvNH+B/kqfI05XkPTckKcMFCVRsDAznz53fUGiGtqIyMDM6ePUt6erqmzNbW\nFldX1+f+PSy8WGXlvVLPvOVyOdOnT6dfv36sWbOG3377jS1btmjd/CBJEhYWFnh7e+Pv7y+urQiC\nIJQhJi2GzRc2cyf5jqZMLpPTrVE3ejTqwarEULKzVQwd2uyxiTshIYGQkBCtMTgUCgUKhULcpPYa\nKPM57wYNGrBw4ULUajXXrl0jPj6e1NRULCwssLGxoXHjxmIwe0EQhDKo1CoO3DzAvhv7UKlVpKXl\nYGysTwPregx1GYpDVQcARo92xsTEALm89AR8//59QkJCNNe3xVCnrx+dZxWTy+U0bdq0PGMRBEF4\nZaklNUExQeTl5xMZmcq96Ew6OXRjZu+x6Mn/7eI2MzMssy0rKysMDQ3JycnByMgINze3Yo/gCq82\nccosCILwAhjoGTCsxTDS0nJJiTSjZZovOZcdCb2YUPbKRRgbG2vGJu/YsaNI3K8hnc+8BUEQBN3F\nZcUkrrAAACAASURBVMRhY2qjdf25nmU95nafxan8LALOPKBx42rUqWNRZlt5eXnFBs6ysrKiXbt2\n4vr2a0okb0EQhOdIpVbx580/2Xd9H0NbDMW9tofW9WvH6o7Y/TcPpaI6bdvWLjP5PnjwgAsXLmie\n9nmUSNyvL9FtLgiC8JzcS73HV6e+Yk/4HvLVKub/vpavlx2j6BO5pqYGtGtn99jkK0kS4eHhBAUF\naZ7lzsjIKO9dECoJceYtCILwjNSSmr8i/uKP8D/IV+cjSRIXLsQhT7EmPDOBo0fv0qlTXZ3by8/P\n5/z585qZGKFgvgm1Wl0e4QuVkE7JOyEhgeXLl3PhwgWSk5OL/YqUyWScPHmyXAIUBEF4mcWmx/L9\nhe+59fCWpsxAz4CeDXoTfbIWMuRcvZqIj08dnbq5MzIyCAoKIi0tTVNmbW1Nq1atMDQs+0504fWg\nU/L+9NNP+eeff2jTpg3NmjUT11kEQXjtSZLE33f+ZnfYbq1R0upZ1mNYi2HYmNTkm3tBuLvb4uXl\noNP3ZlxcHOfOnSMv79/2GjRoQNOmTcX3rqBFp+QdHBzMypUr8fT0LO94BEEQXnrJ2clsPL+R8IRw\nJCQePMigdi0L3m7ckzcbvamZ0nP6dHedkq4kSURERHDt2jWtgVdcXFzEkNBCiXRK3vr6+mLoU0EQ\nhP+nJ9Pjftp9srLyCL/+EPVDC3qYD6CHo5dWPV3Pli9evEhUVJTmtYmJCa1bt8bS0vJ5hi28QnS6\n27xHjx789ddf5R2LIAhCpVDFqAp+Ln6kpuZhGe9Ci/SBXDiex507KU/VnrW1teb/hQOviMQtPI5O\nZ97u7u6sXLmS0NBQXFxcMDU1LVZnwIABzz04QRCEl8H9tPvYVrHVKnOu6cx3vkvYkXGXa9eS6Nmz\noU4DrpTE3t6elJQUVCoVTk5OYr4IoUw6Je8JEyYAcPPmTQ4cOFBsuUwmE8lbEIRXzv+1d9/xUVX5\n/8dfM+m9V0IS0jtJKAmQgCCioqgg6FcXXbtgZ+3th2XXXRXc9WtZ10UsFLFiA3dRRFGKQighgQBJ\nIAVICOl1kpk5vz/yZXAMkQmk5/N8PHg85Nw7J585krwz9557jk6v46O9H/FT8U/cm3Yv4W5R2Nqe\nWofcx8mH6693oaGhleHDLQtupRStra3Y2ZnvGCaT0kRXWBTe69ev7+k6hBCiXzlUfYi3dr5FRWMF\nulYDd7/1PFd4zOeu29PMzvPwsMfDw96iPk8+v93Q0EBmZibW1qd+BEtwi66wKLx/u82c0WiUyzpC\niEHJqIysPbiWNQfWYFRGWlsNZGWV4d4czo7D5WRllTFqlH+X+62vr2f79u00NDQAsHPnTkaPHi2h\nLc6KxSusrV27luXLl7Nv3z5aWlpwdHQkMTGR2267jfHjx/dkjUII0StONJ3grR1vmS244uroxBUh\n11C+wxutRkt5eVOX+z169Ci7d+9Gr9eb2k43d0gIS1kU3qtXr+bRRx8lOTmZOXPm4OTkRH19PTt3\n7uSWW27htddeY/LkyT1dqxBC9AilFFtKt7AqZxU6vc7UHuEZwU0pN+GoceWNpt3MmBFORITl228a\njUb27dtHYeGpXwasrKxISkqS57fFObEovN955x1uv/12FixY0OHYX/7yF15//XUJbyHEgNTU1sTy\n7OVkHc3CYDBy7Fgjw4e7cnn05VwYcaFpwZUFC0Z3qV+dTkdWVhaVlZWmNicnJ0aPHo2r69nNShfi\nJItuXB86dIhZs2ad9tg111zDgQMHurUoIYToLbUttWSXZ1NbqyNrRzllBRoyNXO5OPJiU3B3VVVV\nFRs3bjQLbj8/PzIzMyW4Rbew6F+mra0tdXV1pz3W3NzcYZN4IYQYKAJcArg6/mqqq1vwqIslpf4a\nNq9tobq65az6q6mpYfPmzbS0tL9eo9EQExPDmDFj5Gel6DYWhXdqaiovvPACVVVVZu2VlZX87W9/\nIzU1tUeKE0KI7qY36ju0ZQRn8Mr//Jkp3lfg4ujEDTfEW/z412+5ubnh4+MDtH/wSUtLIzIyUmaV\ni25l0T3vBx98kLlz5zJx4kSCg4Nxdnamvr6ekpISXF1dWbZsWU/XKYQQ52zHsR18kPMB96TdQ4Bz\nIFpte6BqNBqifCK47bYAbG2tcHc/u+A+2VdKSgrZ2dnExcXJrHLRIywK78jISNasWcPHH39Mbm4u\nDQ0N+Pv7M3PmTGbPno2np2dP1ymEEGetzdDGB7kf8GPRj7S1GbjtjWe5IeIe5syKMzvP19epy30f\nP34cb29vs7UvbG1tGT26axPchOgKi5/z9vb2Zt68eT1ZixBCdLtj9cf4945/c6TuCC0tenbtPo62\nxYkvD+cwMsGfqKiz+/BhMBjIzc2lqKiI8PBw4uLizvwiIbpJp+H9wQcfMHPmTGxtbfnggw/O2JGs\nbS6E6G+2lGxh5Z6VtBpaAbCztyLGdSTOFWlYY0dBQc1ZhXdTUxNZWVnU1NQAUFBQgLe3N76+vt1a\nvxCd6TS8Fy5cyNSpU/Hy8mLhwoW/24lsTCKE6E90eh0r96xka+lWU5uNlQ1Xx19NQsZo/vd/dzJ7\ndhTx8d6/08vpHTt2jN27d9PW1mZqCwwMlNuHold1Gt7r1683/WOUjUmEEANFaV0pb2a9SVlDGZWV\nLXh7OeDv7M9to25jmGv7Pg3/7/+N6/Ls79OtlqbVaomLiyM0NFRmk4te1emjYsOGDTP9Y1y9ejWe\nnp4MGzaswx+j0ch7773XawULIURnaltq+dtPf+NwRSm7d1Wwd28lgYZ4Hst8zBTc0PUdvJqamti8\nebNZcDs6OjJhwgRGjBghwS16nUXPeb/22ms0Nzef9lh5eTkrV67s1qKEEOJsuNm7cUHYBZSWNtBY\np4humkbtxkR0Xd9LxKS8vJyNGzdSXV1taju5Wpq7u3s3VC1E1/3ubPMpU6ag0WhQSnHllVd22AZU\nKcWJEycICAjo0SKFEMJSM6JnUN1Yy46PXdEbnZg2LRRnZ9uz6kspxf79+033tzUaDbGxsYSFhcmn\nbdGnfje8//rXv7Jjxw5efvllEhMTsbOz63COm5sbV111VY8VKIQQndl5bCcRnhG42LmY2rQaLTeM\nup6JHjXY2GgZPvzs1xLXaDSMGjWKjRs3YmNjQ2pqqkxME/3C74Z3WloaaWlpFBcX8/jjj+Ps7Nzh\nHKWUaQ1fIYToDXqjnk/2fsL6Q+vRVvpxbfitTMwMNjsnLKx7Lmk7OTkxduxYXFxcsLU9u0/wQnQ3\ni+55//Wvfz1tcAMUFxfLdqBCiF5T3VzNos2L+O/Bb8jNqeSHvTtY/Okqjh5tOKd+DQYDOTk5HDp0\nqMMxLy8vCW7Rr1i8wtqKFSv48ccfTYsSQPun7iNHjsi9HyFEr9hXsY8lO5bQ0NqAlVaLrtWAd1s4\n3k2xfPPNYf74x4Sz6rexsZGsrCxqa2vRarV4enri5ubWzdUL0X0sCu833niDV199lfj4ePbs2UNC\nQgJ1dXUcPnyYKVOmcNNNN/V0nUKIIUwpxdqDa/nywJcopQCwtrLigek3sWmZI1MuCuGyy8LPqu/S\n0lL27NmDXt++25jRaKS0tFTCW/RrFoX3p59+ygsvvMD06dNJSUlh8eLFDB8+nB07dvDss8/KBA4h\nRI9pbG1k6c6l7C7Lxur/nnhxs3fj1tRbifSKZHq0DlfXjpNpz0Sv17Nnzx5KS0tNbb9edEWI/syi\n8D527BgpKSlA+z/uk7+hpqamMn/+fJ555hneeeedHitSCDE0FdUU8a+sf5FXXEL+wRri470YPSKR\nW0fdiqtd+yzyswnu2tpasrKyaGxsNLU5OTmRmpoqz26LAcGi8HZwcKCuro6AgADc3d0pKSlhxIgR\nAMTHx5Odnd2jRQohhqb1h9aTffAwBYW1AOj2RnD7FXfhfJrHVi2hlOLQoUPs27cPo9Foag8KCiIx\nMRFra4unAQnRpyyabT5mzBgWLlxIVVUVSUlJvPzyy5SUlFBXV8eKFStwcXE5cydCCNFF1yZeS3zI\nCOyt7YlvvJRYJlFf13bmF3Ziz5495ObmmoLb2tqalJQUUlJSJLjFgGLRv9b777+f22+/ncbGRm69\n9Vbmzp3LtGnTzI4LIUR3s7e250+Z9zDNo4JDOUb+8IdYHBxszrq/4cOHU1xcjFIKd3d3UlNTcXJy\n6saKhegdFoV3WFgY69atA9pXHFq7di3ffPMNer2e5ORk0/1wIYQ4W7nHc8k+mss49wsJDT0109vP\n2Q+/dD9IP/ev4eHhQUxMDDqdjtjY2A5LPgsxUFh8nejXz3L7+/tz3XXX9UhBQoih5eRjYCu2f8y+\nvEpiWsr552O34u5uf079NjQ00NTUhK+vr1l7RETEOfUrRH/QaXh39VL44sWLLTpvypQplJeXd/iN\n94svvjBNghNCDA1NbU0s3bmU7LJs9uVV0tJiYL9xC/9+awwP/CntrBaAUkpRXFxMbm4uWq2WSZMm\n4eDg0APVC9F3Og3vnTt3WtxJV7/Bnn32WWbNmtWl1wghBpfi2mLe2P4GlU2VaDQaoqM8KdppR4px\nBudPPrs9sltbW8nOzubYsWNA+5Knu3fvJj29G665C9GPdBre3333XW/WIYQYQjYVb2LlnpXojXpT\n2+zUS/EfkUZsjDdeXl3/pFxRUcGuXbvMNkpycXEhLi6uW2oWoj/pk2cjvv76a5YsWUJ5eTkhISHc\ncccdTJ06tS9KEUL0ojZDG8t2L2fVpv/i7+eIg4MN9tb23JB8AykBZzfx1WAwkJeXR2FhoVl7aGgo\ncXFxWFlZdUfpQvQrFoX3lClTzngJa/369RZ9waioKEJCQnj++eextbVl2bJl3HXXXaxatYrk5GSL\n+hBCDDyVTZUs+uF/Wb99N/X1bVRXtzA9I5k7xs7Hz9nvrPqsq6tj586d1NXVmdrs7OwYOXIkfn5n\n16cQA4FF4Z2amtohvBsbG8nJycHJyalLW4K+8cYbZn+fP38+69at48MPP5TwFmIQ02q0VDZW0djY\nfqncsSqciVbXnXVwFxUVkZOTY7ZSmp+fHyNHjsTuLFdgE2KgsCi8Fy1adNr2trY2nnjiCQICAs6p\niODgYMrLy8+pDyFE/+bh4MGfzruT41V/hdwk7ppxJVMmhpx1f3Z2dqbgtrKyIi4ujpCQENmiWAwJ\n57RCgY2NDbfeeitvv/22ReeXlJTw9NNPm13iAigsLCQk5Oy/iYUQ/Y9Or6O11WDWFuMdw7vX/y//\nemweU6eGnlPQ+vv7ExISgpubGxMnTiQ09Nz6E2IgOecJay0tLVRVVVl0rre3N+vXr6euro4nnngC\nOzs7li5dyqFDh3j55ZfPtRQhRD+x8+hunlr9vwwrO5+XHr8Ke/tTP2rc7N1w6+L6K62trTQ3N3fY\nYzs+Ph6NRiMrpYkhx6Lw/uCDDzq0KaWora1l9erVhIWFWfTFHBwcePvtt3nxxRe5+OKLaW5uJi4u\njuXLl1vchxCi/zIqI6v3rWbRZyuoqdFxWK3mnffDmHfj2T9nffz4cXbv3o1Go2HSpEnY2Jxa21xm\nkouhyqLwXrhwYafHQkJCfvf4b4WHh3eYtCaEGPgqmypZsmMJhdWFBPg7UVOjQ6O0HKuupK3NgI1N\n14JWr9ezb98+Dh8+bGrLycmRvRSEwMLwPt1jYBqNBldXV5ydnbu9KCHEwLLj2A6W7V5GU1sTAD4+\njjg1Dufq6LlcckFsl+9FV1VVsWvXLhobG01tdnZ25zw5VojBwqLwHjZsWE/XIYQYgNoMbTz/1b/J\n1+/Azrb9k7VWo+XymMu58NILuxzaRqOR/fv3U1BQgFLK1O7v709SUpI8AibE/7F4wto777zDF198\nQWlpKfX19bi6uhIeHs7MmTOZM2dOT9YohOiHCsqL+NOK5zlQVoSnhz3xCV54O3pzS+othHl0fQ5L\nbW0tO3fupL6+3tRmbW1NQkICQUFBMpNciF+xKLxffPFFli5dyqhRo7jiiitwdHSksbGR3NxcFi5c\nSGlpKQsWLOjpWoUQ/USroZXnvn+Rg2VFAFRVt+DeHM4TF92No41jl/srLCxk7969Zp+2vb29SU5O\nlh3BhDgNi8L7008/5eGHH+aGG27ocGzJkiUsXbpUwluIIcTWypabx19LcdnLHD3SzMyI2Tw+4xoc\nfjUTvCusrKxMwW1lZUVsbKw8ty3E77AovFtaWjj//PNPe2zatGm89tpr3VqUEKL/0en02Nmd+pEx\nLmgc86aW49USyXmjEs6p7+DgYMrKymhrayM5OVkmwgpxBhaFd3JyMvn5+QwfPrzDsf3798ujG0IM\nYnUNzTz+7pvYV4Xwt8cuw8qqfUEUjUbDlfEzu9xfQ0MDSilcXFxMbRqNhtTUVKytreXTthAWsCi8\nFyxYwMKFCykqKiIlJQVnZ2eam5vZvn07q1ev5v777+fQoUOm80eMGNFjBQshes/R2mP8YfFCylqO\n4Gj05Is10cy8LPas+lJKUVhYSF5eHs7OzmRmZpqtjGZzlpfchRiKLArvq666CoB9+/aZ/VZ88h7V\n7bffbnb+vn37uqs+IUQfUErxU/FPfJj7IU6BDVAITdoqth7dwhUqpsufjhsaGti1axfV1dVA+1ae\nBw4cICYmpifKF2LQsyi8n3vuObmUJcQQUaer473d77GnfA8AgcOcqa8zcE3ybG6/YHaXfhYopSgo\nKGD//v1mW3e6ubkRGBjY7bULMVRYFN6zZs3q6TqEEH1Mp9Pz+qdrOOz2PS2GJlN7oEsgT97+JMPd\nOs55+T319fXs2rWLmpoaU5tGoyEyMpLIyEjZTESIc2DxIi2lpaV8/PHH7Nu3j8bGRlxcXEhKSmLO\nnDl4e3v3ZI1CiB6290AZjyx7hYO6Xfj7OxEV6QHA+WHnMzNmJjZWlt+PNhqNFBQUcODAgQ6ftpOT\nk3F1de32+oUYaiwK7127dnHDDTdgNBoJCwvDycmJI0eO8OOPP/Luu++yYsUKwsPDe7pWIUQP0Bv1\nLN72PAd1BwAoK2skOjiQeyfdTqxP1yanKaXYvHmz6d42gFarJSoqivDwcPm0LUQ3sSi8//GPf5Ce\nns6iRYvMnr+sqanh3nvv5cUXX5SdwoQYoKy11lw59gIOHi2huqqFGamTefyyeTjZOnW5L41Gg6+v\nrym83d3dSU5ONnssTAhx7iwK7+zsbFauXNlh4QR3d3ceeOABbrzxxh4pTgjR/fR6IzqdHicnW1Pb\nRREXsTd9P8neo5kam3lO/UdERFBeXk5gYCBhYWEy2VWIHmBReBsMhk6fwXR2dqatra1bixJC9IxD\nRVU8+e5S4lxTePTeKaZg1Wq0PJDZtSWO9Xo9+/fvZ8SIETg6nlrPXKvVkpGRIaEtRA+y6AZUREQE\n77///mmPLV++nIiIiG4tSgjR/XJL8vmfV+8nq2EDXxSv4qefSs+6r+PHj/PDDz9QWFhIdna22YYi\ngAS3ED3Mok/e8+fP5+6772bbtm2mFdbq6+vZsWMHBQUFsra5EP2YURn5T/5/+OrAV7gGNVF3BOpt\nj7G3djeZdO3xr9bWVnJzcyktPRX8FRUVVFRU4Ovr292lCyE6YVF4T506lTfffJO3336br7/+moaG\nBpydnUlISOCxxx5j3LhxPV2nEOIsHKk7wru736Wopn3rztBQN4x6LfPP+wNXpl5icT9KKY4cOUJu\nbi6tra2mdltbW+Lj4/Hx8en22oUQnbP4Oe/MzEwyM89tIosQondU1zTx4urlVPvsxKhOPWsd5jmC\nZ2+/EX9nf4v7ampqYs+ePRw/ftysfdiwYcTHx2NnZ9dtdQshLGNxeOt0OjZv3kxJSQl1dXV4eHgQ\nFhZGenq63N8Soh9Zt3U3z3z+v1QayghpciUk2BVrrTWXRV/GBeEXoNVY9qy1UopDhw6Rl5eHwWAw\ntTs4OJCUlCSXyYXoQxaFd05ODvPmzePEiRMdjgUGBvL666/LBgNC9APVzdW8smsRlYYqAIqL6xgb\nGcf8cbcQ4BLQpb5qamrIzc01/V2j0TBixAiio6Oxtrb4934hRA+w6FfwZ555Bl9fX9555x22bdtG\nbm4uv/zyC0uXLsXV1ZWnnnqqh8sUQljCw8GDq8ddhJubHQ62ttx/8Y08dcHjXQ5uAA8PD4KDgwFw\ndXVlwoQJxMfHS3AL0Q9Y9F2Yl5fHihUrSExMNLW5uroyfvx4nn76aa677roeK1AI0bmSkjrc3Oxw\ndT1133lO/GxO1NUwI3oG4b7BFvfV0tKCvb29WVtsbCzOzs6MGDFCljYVoh+x6LvRy8vLbBGGX3Ny\ncsLLy6tbixJC/L7m5jZeXraOq/7xACs+2mV2zM7ajvsy77Q4uFtaWti+fTsbN240m0kO7bPJZU1y\nIfofi74j//jHP7JkyZIOK6m1trby73//mz/+8Y89UpwQoiOdXsfrG9/hX3tepsbqCKtyPuDAgaou\n96OUorCwkA0bNnDs2DF0Oh379u3rgYqFEN3Nosvmhw8f5ueff2bixInEx8fj4uJCc3Mz2dnZ2NjY\noNfruf/++03nL168uMcKFmIoyzmew4rsFVS1VuHt40BFRTPK9xjKvgnwtLifmpoasrOzqa2t7XBM\nKSVPkAjRz1kU3hs3bgTaHxEpLCw0tZ+8P7Zz505Tm3zTC9G9jEZFWXUVa4tWs+3INlN7WJg7aaEp\nPHrJfDwdLQvutrY28vLyKCoqMlvS1NnZmaSkJLkFJsQAYVF4f/fddz1dhxDiNA4fruG5ZR+y3+p7\nohOd0dD+y7GTrRM3pVzN2GFjLfqFWSnF0aNHyc3NRafTmdqtrKyIjIyU+9pCDDDyzIcQ/VRhWSnX\nv/ocldr2pU3dy7X4+zmRHpTOnPg5ONs6n6GHU3bt2mW2HjmAr68viYmJnU5GFUL0XxLeQvRT1cZy\nHEOqqCwBrVaDs9ade9PvIM4nrst9+fj4mMLb3t6ehIQE/P395TaXEAOUhLcQ/YReb8Ta+tSl69SA\nVKaPGs/nrT9x3YTL+cPo2dhZn9064sOGDaO0tBQXFxdZIU2IQUC+g4XoYzqdntVf7uOXPYU8/8Sl\n2NhYAe2TP69PmcvMuMsJcQ+xqK+mpiZycnIICwvD29vb1K7RaEhLS5NP2kIMEhLeQvQhpRQLXljO\n5tq1aLHhq6+jmXnZqX0CPB088XQ480xyg8FAfn4++fn5GI1GmpqamDhxotkkNAluIQaPTsP7p59+\n6lJHGRkZ51yMEENJdXM17+e8T6n/JnT1jQB8lbeGK2ZEWxy0SinKy8vJzc2lqanJ1N7Q0MCJEydk\n5y8hBqlOw/uWW25Bo9GYngX99Q+T0y3iICszCXFmSikUig2HNvD5/s/R6XX4+ztRWdlCiL8P8y+Y\nZHFwNzY2kpOT02GfbXd3dxITE3F3d++JtyCE6Ac6De/33nvP9N9VVVW89NJLTJs2jeTkZJycnKiv\nrycrK4vvv/+ehQsX9kqxQgxke/ZU8N7nm3BIz6a8+YipXYOG+dNnMit2Fo42Z35sS6/Xk5+fT0FB\nAUaj0dRua2tLTEwMwcHBcolciEGu0/AeO3as6b/vuecebrvtNmbPnm12zrRp04iIiGDlypWMHz++\n56oUYoBbsSqbd7Z8yBG7nfjudiQ6qv0+doBLAHOT5hLhGWFRP5WVlezYsYOWlhZTm0ajISQkhOjo\naGxtbXukfiFE/2LRkko//fSTWZj/WlpaGps3b+7WooQYTIzKyCbtckrtdqKAqsoWlEHD5TGX88TE\nJywObmhfovjXGwR5eHiQmZlJYmKiBLcQQ4hFs81tbGzYtm0bwcEdtxjcsWMHVlZW3V6YEIOFVqPl\nilFTyTlciJW1lgtHjeGm0X/E16nrk8kcHR0JDw+nqKiI2NhYgoKC5BK5EEOQReE9ffp0nnrqKX75\n5RdiYmKwt7enpaWFPXv28O233zJjxoyerlOIAeHEiSY+/HA/l18ewbBhLqb2qWFTyc3MJT0onfSg\n9DMGrlKKoqIi9Ho9ERHmn8wjIiIICwvDxsamR96DEKL/syi8H3vsMVxdXfnss8/4/PPPTe2enp5c\nc801/OlPf+qxAoUYKLZuPcobK35kv80GylZcztMPXmQKaa1Gy4JxCyzq58SJE+Tk5FBfX49WqyUg\nIAAnJyfTcSsrK7naJcQQZ/Fl8wULFrBgwQLq6+tpbGzEwcEBNze3nq5PiAFBb9SzX21iu8MKDBhY\nV/4pNxxKJyzMw+I+Ghsb2bt3L2VlZaY2o9FIQUEBSUlJPVG2EGKA6jS8W1tbT9tuZ2eHnZ1dh3PO\nZrJMVlYWc+fO5Y477uDuu+/u8uuF6A8KqgpYlr2MY/XHCAxypKa2hYgII1rPWuDM4a3X6zl48CCF\nhYVmj35ZW1ubLpELIcSvdRreSUlJFk+E0Wg07N27t0tfuKWlhccee8zscqAQA4HBYOS774rx9rdm\nr/qBjUUbTcdCQl2Z7DGS60ZeR5Br0O/2o5SipKSEvLw8sz22AYKCgoiNjcXe3r5H3oMQYmDrNLzv\nvPPOHp3F+tJLLzFixAhZvlEMKEePNrBkSTa7y3dxzHsT8alOaLXt3yd21nbMjJnJpNBJaDW//xRm\nS0sLv/zyC7W1tWbtHh4exMfH4+Fh+eV2IcTQ02l4//oydlFREYGBgd02u3X79u18/vnnfPHFFzzw\nwAPd0qcQvcFg08T3DR9xzOkANENZGQQGOpPkl8S1idfi4WBZ6J689XSSvb09sbGxDBs2TB79EkKc\nkUWLtMyYMYMTJ050yxdsbm7mscce4+GHH8bPz69b+hSit7RZN+ASWYGVVkPYCDciQ/25ddSt3DHm\nDouDG9pvNcXFxWFlZUVUVBSTJ0+WZ7aFEBazKLzT0tL473//2y1f8KWXXiI0NJRZs2Z1S39C9JTK\nymb27jX/pTXMI4z/Sb+E0WP8uXr8RTw7+RlGB47uNHSNRiOHDh3i559/Nm3yc5K3tzdTp04lE/Sw\n5gAAIABJREFUOjoaa2vZnVcIYTmLfmKkp6ezatUq/vOf/xAfH99hkplGo2HBgjM/w3rycvmXX355\ndtUK0QuMRsX69UV89sUBdDY1/P3pK3BxOfU0xczYmYwKHEWUV1SnfZzcqnPv3r00NrZv93nkyBGC\ngswnscmSpkKIs2FReL/44oum/961a1eH45aG9yeffEJTUxOXXXaZqa2hoYHs7Gy+++47Vq9ebUk5\nQvSotjYDqzf8wjbbNei0DSz7MJg7bk43Hbe3tv/d4K6pqWHv3r1UVlaatRcVFXUIbyGEOBsWhXde\nXl63fLFHHnmEe++916zt3nvvJTk5mVtuuaVbvoYQ50Kn1/FF/heciF1Lw54TODnZUBv0C5B+xtc2\nNTWxf/9+SktLzdptbGyIjIwkNDS0Z4oWQgw5Xb7RVlVVRWNjI66url1eYc3Nza3Da2xtbXF2dsbH\nx6erpQjRLSorm/HycmBvxV6WZy+nsqkSD3c74uO98PN2YXRELEqpTu9rt7W1cfDgQQ4dOmS2yIpG\noyE0NJSoqCi5PC6E6FYWh/ebb77JihUrOH78uKktKCiI2267jTlz5px1AcuWLTvr1wpxLhoaWvnw\nw/1szTpM3Jwj7KvfaXY8IzqVuUlz8Xb07rQPg8HA999/b7a/NoC/vz+xsbE4Ozv3SO1CiKHNovB+\n6623ePnll7noootISkrCycmJhoYGduzYwcKFC7GyspLZ42LAWbp0DxsObKbQ8QeyNxlISvJBgwZH\nG0euir/Kot2/rKysCAwMpLCwEAB3d3fi4uLw8vLqjbcghBiiLArvjz76iIcffpjrr7/erP2GG27g\nX//6F2+//baEtxhwjInb2X/kPygF7rYOGI2KtKAxXJ1wNa52rqd9TVNTE46OjmZtkZGRVFZWEhER\nQUBAgDyrLYTocRaFd2lpKZMnTz7tsYsuuohXX321W4sSorud7p71xJgxfHvgBxwcrAkLCODaxGsZ\n6T/ytK+vra0lLy+PyspKJk+ejIODg+mYra0tmZmZEtpCiF5jUXg7OTlRVlbG8OHDOxyrqKjo8ElE\niP7k6NEGli3L5YorIomO9jS1p/incElqJu727syMnYm9dcdNQE43g/zAgQOMHGke8hLcQojeZFF4\njx8/nmeeeYYXXniB2NhYU3tOTg7PPPMM48eP77EChTgX27eXsWTpLg5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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3128,7 +3128,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": { "collapsed": true }, @@ -3157,7 +3157,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -3166,7 +3166,7 @@ "" ] }, - "execution_count": 48, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -3184,7 +3184,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -3193,7 +3193,7 @@ "function" ] }, - "execution_count": 49, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -3211,7 +3211,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": { "collapsed": true }, @@ -3241,7 +3241,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": { "collapsed": true }, @@ -3259,14 +3259,14 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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J/v37DcomT55c5gQFX3zxBX5+ftSrV48PP/yQN998k759+9KsWTN8fX3p0qUL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\n91TpQgjRZ0l4iwvW0NDC8/M3cahpFzW6EtJzniEm5swn6+EBwwm8IpB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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3300,7 +3300,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": { "collapsed": true }, @@ -3328,14 +3328,14 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": 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4sDjrFgThpXHz5k1u3LiheV27du1Sez7Lwyt9t3nPng3p2bP0M83+/ZX0768s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\njx8/nmeeeYYXXniB2NhYU3tOTg7PPPMM48eP77EChTgX27eXsWTpLg5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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3362,7 +3362,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": { "collapsed": true, "scrolled": false @@ -3389,14 +3389,14 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": 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5U/MNVRezZ8/mf//7HwEBAbz33nvs379fnHULglDjEhMTOXnypObzyMjIiPbt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\n8NR6UlVVxb59+zCZTI5jfH19GTp0qAS36HJOG94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99dd3SHFC9GQ2m428vDy2bduGyWTi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\nEtxCCPH7ThveHh4eLFmyhKysLNLS0nBzkwUehHPsdjvFxcUUFBRgtVo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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3429,7 +3429,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": { "collapsed": true }, @@ -3457,7 +3457,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -3471,7 +3471,7 @@ "data": { "image/png": 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PPPFEnefGxMTw8MMPs3z58n98/4MHD3Lw4EGWLFlyU+f99ttvnDp1igkTJlQr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\n89OrpdXW1jq4EiGEEKL3nc670/n3ff0ivOvrT+2j/ctf/tLBlQghhBB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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3496,7 +3496,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": { "collapsed": true }, @@ -3517,7 +3517,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -3531,7 +3531,7 @@ "data": { "image/png": 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LZ0cQ5Gde5TRoNPHB0aH6KsL5WLX4PTk5mY8//hitVktAwNCs9rS0NHbv3k1i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\niIioWf8PLwk4CbG7DgoAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3557,7 +3557,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": { "collapsed": true }, @@ -3575,7 +3575,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -3584,7 +3584,7 @@ "13.88888888888889" ] }, - "execution_count": 62, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -3613,7 +3613,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -3622,7 +3622,7 @@ "(0.025, 13.88888888888889)" ] }, - "execution_count": 63, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -3638,7 +3638,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": { "collapsed": true }, @@ -3661,14 +3661,14 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/png": 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MmUJ2djbvvPMOt912W4MniImJ4fnnn+fnn39m2bJlJCcns3bt2uaotdUbOnQo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\nKPBt2ixNtn4XrYbsrBjmJORy2cjL0Gpl9rgQQgxGDpuwlpq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777zDvn37+OSTT8jJyaG1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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3698,7 +3698,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -3859,7 +3859,7 @@ "-6000 NaN NaN " ] }, - "execution_count": 66, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -3878,7 +3878,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -4039,7 +4039,7 @@ "1940 NaN 2340. " ] }, - "execution_count": 67, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -4057,7 +4057,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": { "collapsed": true }, @@ -4075,7 +4075,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": { "collapsed": true }, @@ -4094,7 +4094,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "metadata": { "collapsed": true }, @@ -4122,7 +4122,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 72, "metadata": { "scrolled": false }, @@ -4131,7 +4131,7 @@ "data": { "image/png": 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UKiVpUWm5y20M131eLInExYsXGTZsGO3bt2fq1KmYmpoCYGlpSWJiolbdhIQE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\ndTo4UQ1Hk6KJmRzKjB7QlXE2SSSEEEJ0W5X5lZhyTZw6EIPWEIbfJCP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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4155,14 +4155,14 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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8p1QpSQpLyl5uUfQW4oJCSjIuX77M8OHDad++PdOmTcPQ0BAAU1NTYmNjNerG\nxMQAYGZmRsWKFbMdz6xjamqKqakpgNY2KlYsehvHFIY6derg4+PzwjrXrl3TWr5ixQpWrFiRH2EJ\nIYTQgUknE8I3hGcv71j0FuKCQuguuX79OkOGDGHo0KHMmjVLnWAAODg4ZBs7cf78eczMzLCyssLB\nwYHQ0FCioqLUxx8+fMjdu3dxdHTE0tISMzMzrW04Ojrm740JIYQQ+czYyRjVYBVGlkYo9BQYWRqh\nGqzC2KlorplRoElGWloakydPpk+fPgwYMCDbcXd3d06ePMmhQ4dITk7mypUrfPPNNwwcOBCFQkHz\n5s2xsbFh3rx5xMTEEB0dzdy5c6lduzbNmjVDoVDg7u7Oxo0buXr1KsnJyRw4cIBTp05pvZ4QQghR\n3Bg7GVN9RnVqr6lN9RnVi2yCAQXcXfLbb7/x+++/c/36dTZv3qxxrFu3bsydO5dly5bh7e2Nh4cH\npqamuLm58emnnwKgr6/PunXr8PT0pHXr1igUCpo1a8a6devQ19cHYPDgwSQlJTFy5Eiio6OxtrZm\n5cqV2WacCCGEECJ/KTIyMjIKO4ii4N69e7Rp04ajR49iaWlZ2OEIIYQQ+aogPvdkgzQhhBBC5AvZ\nIE0IIYQoJuLigoiO9ic5ORylUoWJSSeMjZ0KO6wc5TrJSEhIIDY2lvLly1OmTJn8iEkIIYQQWcTF\nBREevkH9OikpTP26qCYaL00yUlNT2b17N0eOHOHcuXMkJiaqj5UoUYLGjRvTrl07unfvjoGBPBgR\nQggh8kN0tPbFEKOjA4pnknHs2DHmzZvH/fv3eeedd+jbty9mZmYYGxsTFxdHZGQk586dY8aMGaxe\nvZpp06bRpk2bgopdCCGEeGskJ2dfhOtZ+f0CjkR3OSYZK1euZOPGjfTq1Ythw4Zhbm6eYyMPHjxg\n3bp1fP755wwaNIgxY8bkS7BCCCHE20qpVJGUFKal3KIQotFNjrNLDh06xPfff8/MmTNfmGAAmJub\nM2PGDH744QcOHTqU50EKIYQQbzsTk045lHcs4Eh0l+OTjB9//DHXAztr167Nrl27XjsokT/c3Nww\nNzfXuiFdv379qFatGhYWFmzfvp2DBw+q94LJ5Ofnx8yZM9m1axd16tShdevW/PPPP+jr65ORkUHp\n0qWxtbWla9eu9OrVCz29/3JYW1tbDAwMNMoy1alThx9++CHvb1gIId4gxsZO/PE4gZAH+0lNDsdA\nqaKOeVfPbocfAAAgAElEQVRsi+h4DHhBkvF8gpGUlMTChQsZOHAgVlZWPHjwgAkTJnDlyhWaNGnC\nokWLMP53V1GZcfJyRXkK0ogRIzh27Bienp54e3uryyMiIli4cCGjRo2iTp066vIhQ4Ywfvx4MjIy\niIiI4LfffmPx4sUcPnyY1atXo1T+tzPgrFmz6NOnT4HejxBCvCmC4uL4Js4CSg6DfzfNPh0H+qXj\nCmVnb13otBjX4sWLOX78uHq79Hnz5hEWFsa4ceOIiIiQnTlzIXMKUlJSGBkZ6eopSHFxQYUdGgCG\nhoZ4eXlx7NgxjW3dZ8+eTfXq1RkyZIjW8xQKBebm5nTs2JEdO3Zw8eLFbEvHCyGEeHX+0dFaywNy\nKC8KdEoyjhw5gqenJ1WrViUhIYFjx47h4eHBgAEDmD59Or/88ks+h/nmeNEUpKLC1taW0aNHM2fO\nHGJiYjh48CAnT55k4cKF6j1iXsTc3JyuXbuyb9++AohWCCHeDuHJyVrL7+dQXhTotLBFVFQUtWrV\nAuDMmTMoFApcXV0BsLCw4OHDh/kX4RumsKcgHTx4kMOHD2crT0lJoVq1aurXgwcP5siRI0yZMoVL\nly4xfvx4atSoofN1atasiZ+fn0bZrFmz8PT0zFZ3zpw5dO/ePRd3IYQQbx+VUklYUlK2covnuqWL\nGp2SjAoVKvDgwQPMzc05duwYDg4OlCz5rEMoIiKC0qVL52uQb5LCnoLUuXPnHAd+Pk9fX5+FCxfS\nvXt37O3tcXd3z9V1UlNTsz31kDEZQgjx6jqZmLAhPPsX1Y4mJoUQjW50SjJatGjB9OnTeffdd9m7\ndy8LFiwAID4+ntWrV9OoUaN8DfJNYmLSSWNZ2P/Ki94UpJo1a2JmZoajo6N6PI6u/vjjD/XTLyGE\nEK8vc3BnQHQ095OTsVAq6WhiUmQHfYKOScakSZOYM2cO586dY9CgQbz//vsABAYGEhISIgP8ciFz\nFkl0dADJyfdRKi0wMelYZGaX5IU7d+4QEBDAtGnTCjsUIYR4ozgZGxfppCIrnZIMY2NjFi9enK28\ndevWtG/fXvYsySVjY6c3KqnIlJiYSGBgIPPmzaNFixbSNSKEEG85nbODp0+fcvPmTWJjY8nIyMh2\n3MXFJU8DE8XD+vXr2bhxI/BsHIeNjQ3Dhg2jb9++2RbeymngJ0BAQABVqlTJ93iFEEIUHJ2SjF9+\n+QUPDw/i4+M1EgyFQkFGRgYKhYI///wz34IUeWPr1q05Htu5c6fW8mPHjuV4zouOZXXt2jWd6woh\nhHgz6JRkLFmyBBsbG9zc3DAxMcn1IEAhhBBCvLqivFL0i+iUZISGhvLjjz9iY2OT3/EIIYQQ4jmZ\nK0VnylwpGijyiYZOK35aWlqSmpqa37EIIYQQIovisFJ0TnRKMkaPHs2yZct49OhRfscjhBBCiOck\nJ4cTmZLMhfh4Tj56xIX4eCJTkgtspejXoVN3yaFDh7hz5w4tW7bEyspK6wqf3377bZ4HJ4QQQrzt\nIjNMCHnyt/r14/Q0Qp48QWFYhdqFGJcudEoyYmJiqFSpEpUqVcrveIQQQgjxnLM0xpi/s5Wfw4nm\nhRBPbuiUZLxo6qMQQggh8s9firqUL9kHVVIgJdMjeKpXiXCjFsQq6hZ2aC+Vq6U67969yx9//MHj\nx48xNjbGzs6OypUr51dsQgghxFtPpVQSlmFHtNJOo9yyCO++mkmnJCMhIYFx48bx66+/aizGpaen\nR+fOnZk/fz6Ghob5FqQQQgjxtiqOu69m0nkxrpCQEGbNmkWDBg0oXbo08fHxXLhwAR8fH7y9vfni\niy/yO1YhhBDirVMcd1/NpFOScezYMTw9PWndurVG+TvvvIOJiQmLFy+WJKMYmD59Onv37gUgIyOD\nlJQUDA0N1Su4WlhYcPjw4de+ztGjR7GwsKBu3aLfXyiEEMVBcdt9NZNO62RER0dTq1Ytrcfs7OyI\njIzM06BE/pg7dy5XrlzhypUrBAQ8W8Rl3bp16rK8SDAAVq5cKXvZCCGE0C3JMDMz4/Lly1qPXbly\nBVNT0zwN6k0XFBaE53FPRhwYgedxT4LCggo7JLWEhASmTZtGixYtcHBwoHPnzhw6dEh9fPny5Xz4\n4Yfs37+f9u3b07BhQ/r378/du3cBaN68OdeuXWPGjBn07NkTgCdPnjB37lzatGmDvb09HTp00FhX\n5WVtCiGEKJ50SjI6duyIp6cnmzZt4vLly9y4cYNLly6xceNGZs+eTefOnfM7zjdGUFgQGy5sICwu\njPSMdMLiwthwYUORSTQWL17M5cuX2bNnD8HBwXz00UdMnDiR0NBQdZ2bN28SFBSEn58fx44dIzY2\nluXLlwNw/PhxAObMmYOfnx8AM2fO5Ny5c6xfv54LFy7wxRdfMHv2bH766Sed2hRCCFE86TQmY/z4\n8URERODl5aVRrlAoeP/99xk3bly+BPcm8v9b+xr0AX8H4FSl8De6mTZtGsnJyZQpUwaAbt264enp\nye+//07VqlUBePz4MZMnT6ZUqVIAuLi4EBgYqLW92NhYDhw4wKpVq6hRowYA7du3p3nz5vj5+dG+\nfftctymEEKJ40CnJUCqVLF26FA8PD65evcrjx48pW7Ys77zzDubm5vkd4xslPD77NCSA+/FFYw36\nsLAwFi1axG+//UZCQoJ6UGhSUpK6jpmZmToZAChRogSJiYla2wsNDSUjIyPbDr42NjYaSURu2hRC\niLdGUBD4+0N4OKhU0KkTOBX+F1Jd5WoxLnNzc0kqXpOqrIqwuLBs5RZlLQohGk1paWkMGjSI6tWr\n8+OPP2JhYcHTp09xcHDQqKenp1MvG/BfcvL8+ioA6enp6gQmt20KIcRbISiIID8//FUqwlUqVE+f\n0snPDycoNolGjknGhx9+yLp16zA2NubDDz98aUOyQZpuOtl0YsOFDdnKO9p0LIRoNEVGRhIWFsa0\nadOoUqUKAJcuXXqtNqtXr45CoeDatWtYW1ury//66y+N10IIITQFBQayoWZN9euwUqWevT55Eqfi\nnmQ8v4KnrOaZdzLHXQT8HcD9+PtYlLWgo03HIjEew8TEhFKlSvHbb7/h6urK77//zrZt2yhVqhTh\nWlab00ZfXx+lUsnt27d59OgRpqamtGvXjlWrVvHOO++gUqkICAjg9OnTbNiQPdkSQgjxjL+hIRG3\nTAm9WpUnj0pRqtwTqtYPJaDyUwr/E0M3OSYZz2+KJhuk5S2nKk5FIqnISqlUMn/+fBYtWsS2bdto\n0KABc+fOZcuWLfj6+mJg8PLeNYVCgZubG5s2bWLv3r0cP36cBQsWsGDBAtzd3Xn06BHW1tasXr0a\nFxeXArgrIYQoni7F1uKpvwWu0UmUS47jkVKP30NrcLFnycIOTWeKjKyd5f9KTk7OVUPKYrBRy4vc\nu3ePNm3acPToUSwtLQs7HCGEEG+5j7tdx/ZcTLbya84V2Lan9mu3XxCfezl+NbW3t9cYmPcyssKj\nEEIIkXfqh+qTYqSElFRITwc9PTA0oH6ofmGHprMck4zPPvssV0mGEEIIIfKOlX4a4foZxKY9JZVU\nDPQMKK9fBgv9tMIOTWc5JhmjR48uyDiEEEII8ZxSto94cvwxSkCppwek8yQ5jpK134Ak4+TJk7lq\nSAbxCSGEEHnnZotAyt+w4PHDCqQkGmFYIonSpjHcahEOxWR+SY5JxuDBg1EoFNkWUXpe5nGFQiFj\nMoQQQog89FeVvyjfJxLVeRUlo0vy1OQp998NJ7ZKbGGHprMck4wtW7YUZBxCCCGEeI6qrIqwWmFE\n14rWKLcsW3xmQOaYZDRu3Lgg4xBCCCHEc4ryCtG6yjHJWLZsGSNGjKBkyZIsW7bshY0oFArGjx+f\n58EJIYQQb6uivEK0rnJMMtatW4e7uzslS5Zk3bp1L2xEkgwhhBAi7xXVFaJ1lWOSERISovX/hRBC\nCCF0IftrCw12dnb4+fm98vl+fn7Y2tqSmpqah1EJIYQojl6+4xWQkJDA5s2b+fPPP4mPj9c6rVVm\noxR9bm5uBAcHqzc6MzQ0RKVS0bVrV4YNG4ZCoeDKlSuFHKUQQog3hU5JxqRJkzh16hTOzs6oVKrX\nWm48NDSUqVOncu7cOY1NWXx8fPD19c22rfygQYMYN26c+tx58+Zx+fJlMjIyaNCgAdOmTaNq1aoA\npKWl4e3tTUBAABEREVSrVo1BgwbRtWvXV443PwQFgb8/hIeDSgWdOoFTAXW5de7cmSVLlgCQmprK\nqVOnGDVqFCVKlGDAgAEFE4QQQoi3gk5JxpkzZ1i9ejVNmzZ9rYv9/PPPfPnll7Ro0ULrcScnpxy3\nlU9JSWHIkCHY29tz4MABDAwMWLBgAYMHD+bAgQMYGhqyZs0a9uzZw+rVq6lVqxYnTpxg3LhxVKpU\nCWdn59eKPa8EBcGG52YkhYX997qgEo1MBgYGtGzZkurVqxMaGgqAra0tc+fOpU+fPkyePJnHjx9T\nokQJjhw5wr59+1CpVKxYsYJDhw4RExNDxYoVcXNzw93dXaPtX3/9lYULF/LPP/9gY2ODp6cndevW\nBSAmJoaFCxdy5swZYmNjqVGjBuPGjcPV1RWAyZMnk5iYSKNGjdi4cSOPHj3C0dGRhQsXUrFixYJ9\nk4QQQrwyncZklCtXjkqVKr32xWJjY9m+fTvdunXL9bknT57kzp07TJkyBRMTE4yNjZk0aRKhoaEc\nP36cjIwMtm/fzsCBA6lXrx5KpZK2bdvi6upapLpy/P21lwcEFGwcAImJifj7+xMaGkrnzp211gkK\nCqJevXoEBQVhaWnJli1b8PPzY9OmTVy4cIEZM2Ywf/58zpw5o3Hed999x+bNmwkMDMTCwoIhQ4aQ\nkpICwKhRo3j06BE//vgjQUFB9O7dm5EjR6oTHXiW2EZHR+Pv78/Bgwf5448/2LAh+3xxIYQQRZdO\nScann37KmjVrSEpKeq2L9enTB2tr6xyP//PPPwwcOBBnZ2dat26Nl5cXiYmJAFy8eBErKysqVKig\nrl++fHmqVq3KpUuXuHv3LtHR0djb22u0aW9vz6VLl14r7rwUHq69/P79grn+wYMHsbOzw87OjoYN\nG+Lh4cHIkSNxcHDQWl+hUPDJJ59gYGCAQqHAzc0Nf39/rKysUCgUuLq6YmJiwuXLlzXOGz58OJUq\nVaJMmTKMGDGCyMhILl26REhICMHBwUyaNAlTU1OUSiX9+/fH1taWH3/8UX2+gYEBY8aMoWTJklhY\nWODo6Mi1a9fy9b0RQgiRt3TqLunSpQsHDhzAxcWF6tWrU7JkyWx1XvdpQaVKlbCysmLcuHHUqVOH\nixcvMn78eJ48ecLs2bOJiYmhXLly2c6rUKECUVFRREc/W3Y1a50KFSqojxUFKtWzLpKsLCwK5vpZ\nx2T8/fffzJw5kytXruDt7Z2tfpUqVdDT+y8XjY+PZ8GCBZw+fZpHjx4BkJycnC0BrVWrlvr/q1ev\nDjxLIjPbev/99zXqZ2RkYGNjo35taWmpcd2SJUsSERHxKrcshBCikOiUZHzxxRdcu3YNZ2dnypcv\n/1oDP3PSt29f+vbtq37t5OTE0KFDWbx4MTNmzHjhuS+LJz/ifVWdOmmOycjUsRBWiTUwMKBOnTp4\neHjQv39/bt26la1O1oG4Y8eOJTY2ls2bN2NtbY2enp7WHXi1vedGRkbqxOHkyZNak8YXnS+EEKJ4\n0SnJOH/+PGvWrHntgZ+5Va1aNZKTk9UDDGNjs+88FxMTg6mpKaampgDZ6mSeW1RkDu4MCHjWRWJh\n8SzBKOhBn9o8ffr0pXUuXrzI559/Ts2aNQEICwsjMjIyW71bt26pB3revn0bgMqVK1OqVCkA/vjj\nD42/T6GhoVhaWkpyIYQQbxCdxmSYmJjkycDPF1mzZg2//PKLRtmNGzcoVaoUpqamODg4EBoaSlRU\nlPr4w4cPuXv3Lo6OjlhaWmJmZpZt/MX58+dxdHTM19hzy8kJZsyANWue/bcwE4zQ0FC8vb2xt7en\nTp06L61vZWXFpUuXSE5O5saNG8ybN48qVapwP8ugkjVr1hAVFUVCQgKrVq2iWrVq1K9fn5o1a+Li\n4oKXlxd37twhLS2Nn3/+mc6dO3P+/Pn8uk0hhBCFQKck47PPPsPX15cnT57kWyCxsbHqsQGpqakE\nBQWxYcMGBg4ciEKhoHnz5tjY2DBv3jxiYmKIjo5m7ty51K5dm2bNmqFQKHB3d2fjxo1cvXqV5ORk\nDhw4wKlTp2T9h+c8P/DT3t4eNzc3atasyVdffaUxBiIns2fP5vr16zg5OeHh4cGIESNwd3fn0KFD\nzJw5E3jWxdKrVy8++ugjXFxciIyMZNWqVeqnFIsXL8bGxoY+ffrg6OiIr68vXl5eRS4ZFEII8XoU\nGdqW78zis88+49q1a0RFRVG1alX1I+/nffvtty+9WIcOHbh//z4ZGRmkpKRgaGiIQqGgW7duzJw5\nE19fXw4cOEBERARmZmZ8/PHHuLu7o6+vD0B4eDienp6cOXMGhUJBs2bNmDFjBubm5sCzwYO+vr58\n//33REdHY21tzbhx42jTps1LY7t37x5t2rTRWCBMCCGEeFMVxOeeTkmGm5vbSxvKaRGt4kKSDCGE\nEG+Tgvjc02ngZ3FPIIQQQghR8HLshB87dmyux2A8efJEvc+IEEIIId5uOSYZcXFx9OjRA/+c1sHO\nIiAggB49ehAXF5dnwQkhhBCi+Mqxu+Trr79myZIlTJgwgRUrVuDq6sq7776LmZkZZcuWJT4+noiI\nCM6fP8+JEycICwvDzc2NCRMmFGT8QgghhCiickwy9PT08PDwoHfv3qxdu5Y9e/awZcsWjcWSMjIy\nMDY25r333mPNmjXUqFGjQIIWQgghRNH30oGfNWrUYNGiRaSnpxMSEkJkZCRxcXEYGxtjZmZGnTp1\ndFpfQQghhBBvF51ml8CzJxvvvPNOfsYihBBCiDeIPIIQQgghRL6QJEMUO3fu3MHW1pazZ88WdihC\nCCFeQJKMt4ibmxu2trYcO3bshcd1/fB2c3Ojbt266r1Qsv6Jj4/Py/Dz1MOHDxk5ciSNGzemefPm\neHt763yug4MDdnZ22Nra8sMPP+RjlEIIUbzpPCZDvBnMzMz48ccfad26tUZ5aGgot27dynV7nTt3\nZsmSJXkVXoHx9vZGX1+fEydOEBkZSZ8+fWjcuDFNmjR56bm//fYbALa2tvkdphBCFGvyJKMQxAXF\ncdvzNtdHXOe2523iggpuAbOWLVty/PhxoqKiNMp3795Nq1atstXfu3cvXbp0oWHDhnTp0oWDBw/m\n6noffvghU6ZM0SgLDQ3F1taWU6dOAeDv70/Pnj1xcHCgadOmzJgxg4SEBHX9oKAgunXrRsOGDenR\nowdXr15VH/P29qZVq1akp6drXKNLly4sWrQox7ju379PkyZNKFGiBObm5pQqVYqUlJRc3VtWP/30\nk/o+nJ2d8fDwIDY2Fni2R4CtrS2BgYEMHDgQBwcH3nvvPX788Uf1+WlpaaxatYoOHTrQoEED2rRp\nw4YNG9THk5KSmDVrFi4uLjRo0IDWrVuzdu1adNh+SAghCoVOScbDhw+ZMWMGXbt2pUWLFri4uGj8\nadGiRX7H+caIC4ojfEM4SWFJZKRnkBSWRPiG8AJLNFQqFY6Ojuzdu1ddlpGRwZ49e+jSpYtG3ZMn\nTzJz5kwmT55McHAwn3/+OR4eHgQHB+t8vffff58jR45ofIAfOnSIypUr06RJE06dOsWkSZMYOXIk\nwcHBfPfdd1y9epV58+YB8PjxY0aMGIGTkxNnzpzB29tbYy+d3r178+DBA3799Vd1WUhICH/99Re9\nevXKMa769esTHBzMuXPn+OSTT2jevDkuLi4631dW586dY+zYsQwaNIizZ8+ya9curl+/zsSJEzXq\nrVy5kkmTJhEcHEy3bt2YNWsWMTExAKxatYo9e/bg7e3NhQsX8PLyYs2aNezZsweAzZs3c/78eXbv\n3s2lS5dYuXIlW7ZsITAw8JXjFkIUDYX55TM/6ZRkzJgxg71796JSqWjevDktWrTQ+PM6v5zfNtH+\n0drLA7SX54fevXtrfIM+e/Ysenp6ODs7a9TbuXMnLVu2xMXFBQMDA1q3bs3KlSupUKGCus7Bgwe1\njseYPn06AJ06deLp06caScDBgwfp1q0benp6bN++nXbt2tG2bVv09fWxsrJi9OjR7N+/n8TERE6c\nOEFCQgKjR4+mRIkSVK1alQEDBqjbsrCwwMXFhV27dmm07+DgQM2aNbXef3JyMunp6Rw6dIhly5Yx\nZswY5syZQ2JiIsnJya/0nm7bto2WLVvSuXNnlEolVatWZcSIEZw4cYKHDx+q6/Xo0YM6deqgr69P\nly5dSE5O5tatW6Snp7Njxw6GDBmCra0t+vr6ODo60qdPH77//nvg2VL/enp6lChRAgA7Ozt+/fVX\nWrZs+UoxCyGKhsL+8pmfdBqTERwcjI+PD66urvkdzxsvOVz7h1jy/Vf7cHsV7dq1w9PTk4sXL9Kw\nYUN2795Njx49NFZzhWezOLI+pWrbtq3G65eNyahQoQItW7bE39+fVq1acePGDa5du8aKFSsAuHnz\nJnfu3OGnn37SOC89PZ0HDx4QHh6OsbEx5cqVUx+rVauWRt2+ffsybtw4YmJiqFChAgcPHmTkyJFa\n40lMTOSTTz6hbNmy9OzZk1u3bqnHYWzbto2QkBCWLl2a4/3k5M6dO9mSbRsbG+BZ95CZmRkA1apV\nUx/PTBYSExOJjo4mNjaWOXPmMHfuXHWdjIwM9bn9+/cnMDCQFi1a4OTkRPPmzenatSsVK1bMdbxC\niKLjRV8+jZ2MCziavKVTkmFgYCBLhucRpUpJUlhS9nILZYHFYGRkxPvvv8+PP/5IrVq1OHLkCAcO\nHMhWT09PL9tYh1fx/vvvM336dJKTkzlw4AANGjRQ/30qUaIEH330kfrJR1bJycnZkp+sMbVq1Yry\n5cuzf/9+7O3tiYmJoVOnTlrb+/bbb3ny5AnffvstKSkp9OzZE29vb8aMGcPevXv54osvXukek5KS\nso2NyIzz+fhzWh03M+FYvnw57dq101pHpVKxd+9eLl++zKlTp9i7dy8+Pj5s2rQJOzu7V4pbCFH4\nisKXz/yiU3fJ//73P37++ef8juWtYNLJRHt5R+3l+aV37974+/vj7+9Pw4YNUalU2epUr16dmzdv\napTt2bOHoKCgXF2rdevWKBQKTp8+zaFDh+jevbvGNf7880+N+nFxceoBk5UrVyY+Pl5jIOj169c1\n6hsYGNCrVy8OHjzI/v376dSpE6VLl9YaS2hoKNbW1ujp6WFkZMSqVavYuXMno0aNolSpUloHvwJ8\n//33GtNcM3cbrlSpkvo+rl27pnHOX3/9hZ6ensbTi5yUKVMGU1NT/vjjD43yBw8eqLtwnjx5QmJi\nIvb29gwfPhw/Pz/q1q2rMb5GCFH8KFXav2QW5JfP/KJTktG4cWP8/PwYN24c33zzDd999122P0I3\nxk7GqAarMLI0QqGnwMjSCNVgVYE/EqtTpw7Vq1dn9erVOQ6Q7NevH6dPn+ann34iJSWFX3/9lRkz\nZuT6Wkqlkg4dOrBx40bu379P586d1cc++eQTzp8/z/bt20lMTCQyMpIJEyYwfvx4APV4EF9fXxIT\nE7l9+7bGwM9MvXv35vLly+zevZvevXvnGEuTJk04ceIEJ06cIDU1FWNjYxwcHDh69Ch16tQhKSmJ\ntLS0bOcZGhqybt06Tp06RUpKChs2bMDExIRGjRqp36tff/2V/fv3k5qayq1bt1i9ejXt27fXGMPy\nIu7u7mzfvp3Tp0+TlpZGSEgIH330EV9//TUAn332GVOnTlXPDLpz5w7h4eFYW1vr1L4QomgqKl8+\n84NO3SVjx44F4O+//yYgICDbcYVCQd++ffM2sjeYsZNxkehn69OnD8uWLcs2ziJT06ZNWbRoEYsX\nL2bChAlYWloyb948nJyc1HUOHjzI4cOHtZ4/Z84c9VOLbt268fHHH9OhQweN8RUODg4sXbqUtWvX\nsnDhQoyNjXFxcWHSpEkAmJqasmbNGhYuXMiOHTuoXr0648aNY/jw4RrXqlq1Kk2bNiU8PFz9wa9N\nu3btmDp1KosWLSIsLAylUkmLFi1Yu3Yt69ato2nTpvTq1Stb902PHj148OABU6dO5dGjR9SqVYs1\na9ZQtmxZAFxdXVmwYAEbNmxg5syZmJiY0L59e8aMGZNjLFkNGjSIp0+fMmXKFKKioqhUqRI9evRg\n2LBhACxcuJA5c+bQqVMnkpKSMDMz4/3336dfv346X0MIUfRkfh5EB0STfD8ZpYUSk44mReJz4nUp\nMnSYZB8WFvbShqpUqZInARWWe/fu0aZNG44ePYqlpWVhhyNyKSMjg/fff58PP/yQ/v37F3Y4QghR\n5BXE555OTzKyJhDp6emyvbsoMlJSUli1ahVPnz594doYQghRFJw5tZGQB/tJ1YvBIL0Cdcy70qTZ\np4UdVr7QOVM4dOgQH330EQ4ODtSrV493332XAQMGqFdtFKIwBAcH4+DgQGBgIL6+vupZGkIIURSd\nObWRq5GbSdWLBjJI1YvmauRmzpzaWNih5QudnmTs3r2bKVOm0LBhQ/r06UPp0qWJj4/nt99+Y/Dg\nwfj6+vLee+/ld6xCZOPo6KixzLgQQhRlIQ/2a/16H/LgAE14855m6JRkbNq0iWHDhqlH/D9v3rx5\nrF69WpIMIYQQ4iVS9WK49Vcdrl5w5lFMRcpViKJ+o7NY17r28pOLIZ26S27dukXPnj21HuvXr1+2\ndQuEEEIIkd3da06c/LkzsVGmZKQriI0y5eTPnbl7rXFhh5YvdEoylEqlevGhrJ4+fYqhoWGeBiWE\nEEK8icKva59y/s9fb+YyEDolGY0aNWLRokVER2uurx4VFcXChQtfuC6BEEIIIZ4xMGxEhZK1UWQ8\nGwbHoUIAACAASURBVKSuyChBhZK10Td4t5Ajyx86jcmYOHEiH3/8MS1btsTKyooyZcoQHx9PaGgo\nxsbGWldgFEIIIYQmlQrS0+tgTh2NcguLQgoon+mUZNSqVYuDBw+ya9cufv/9dxISEqhcuTI9evSg\nd+/emJgU/6VPhRBCiPzWqRP4zY9DFRpNySfJPC2lJLyqCR0HF//VPbXRKcmAZ8s7Z13KWQghhBC6\nsyWOzopwQhXwBDBTJNFIEY4tAG9eopFjkvHdd9/Ro0cPlEqlThugyd4lQgghxItF+0djZgZmZlnK\nA6LfiL1Kssoxyfjyyy9p27YtFStW5Msvv3xhI7JBmhBCCPFyyeHJ2svvay8v7nJMMo4ePaoea3H0\n6NECC0gIIYR4UylVSpLCkrKXWygLIZr8l+MU1ipVqqBQKIBny4qbmJhQpUqVbH/S09PZsmVLgQUs\nhBBCFFcmnbRPlDDp+GZOoNBpnQxfX1+ePn2q9diDBw/YsWNHngYlhBBCvImMnYxRDVZhZGmEQk+B\nkaURqsGqN3I8Brxkdknr1q1RKBRkZGTQq1evbNu7Z2Rk8PDhQ1QqVb4GKYQQQrwpjJ2M39ikIqsX\nJhkLFizgwoULrFy5Ejs7O4yMjLLVKVeuHB988EG+BSiEEEKI4umFSYazszPOzs7cvXuXadOmUaZM\nmWx1MjIySExMzLcAhRBCCFE86TQmY8GCBVoTDIC7d+/KNu9CCCGEyEbnFT+3b99OYGAgsbGx6rKM\njAzCwsLUs1CEEEIIITLp9CRj7dq1LFiwgJiYGC5fvkx6ejqxsbFcunQJe3t7fHx88jtOIYQQQhQz\nOiUZfn5+LFq0iO+++w4jIyOWLl1KQEAAO3bsIDw8XDZIE0IIIUQ2OiUZ4eHhODg4PDtBT4/U1FQA\nGjVqxIgRI/D09My/CIUQQghRLOmUZJQsWfL/27vzuKjq/X/gr2EZVkcYGHSIxQVFZXFFENF7065C\nfW2xvNW3xVIs7Zpl9U3avhqp/Urr+9W+lnlRs+Rq2XZdgqvpLVNccEHFxFJRRxhFGRYdYEbg/P4g\nRocZdPDMYWbk9Xw8etj5nJmP78+cgfP2fDZUV1cDAAICAqDRaEznYmJicPjwYWmiIyIiIpdlU5KR\nkJCA2bNnQ6fTIT4+HosWLYJGo0F1dTWys7PRqVMnqeMkIiIiF2NTkvHyyy+jvLwcer0eU6ZMQXFx\nMcaMGYPExESsXLkSTzzxhNRxEhERkYuxaQprjx49sHnzZgBN27r/8MMP2LJlC+rr6zFgwADTeA0i\nIiKiZjavk3H9Whhdu3bl0wsiIiK6oVaTjJdffrlNFX3wwQeigyEiIqLbR6tJxsGDB22uhCt+EhER\nUUutJhnbtm1rzziIiIjoNmPT7BIiIiKitrJp4OeoUaNu2iWydetWm/5CjUaD119/HXv37sXWrVsR\nFhZmOrdx40YsX74cp0+fhkqlQlpaGmbMmAF3d3fTe+fNm4fDhw9DEAT0798fb7zxBsLDwwEADQ0N\nWLx4MXJzc1FWVobIyEhMnjwZ48aNsyk2IiIish+bkoxBgwZZJBl6vR6FhYXw8/Ozeav3LVu2YPbs\n2RgxYoTFub179yIjIwMLFizA6NGjUVxcjKlTp8LT0xPTp0/H1atXMWXKFMTHx2Pjxo3w8PDAu+++\ni/T0dGzcuBGenp745JNP8P333+Pjjz9Gr169sH37drz44osICQlBYmKiTTESERG1SX4+kJMDaLWA\nWg2kpQEJCY6OyinYlGQsXLjQavnVq1fx5ptvQq1W2/SXVVZWIjs7G1qtFt9//73ZudWrV2PkyJFI\nS0sDAERHR+Opp57Cxx9/jOeeew47duzAmTNnsGbNGgQGBgIAZs2aheTkZPz8888YPXo0srOz8eyz\nzyImJgYAcNddd+FPf/oTPv/8cyYZRERkf/n5QFbWteOSkmvHTDTEjcnw9PTElClTsHLlSpteP2HC\nBHTv3t3quYKCAsTHx5uVxcfHo7KyEqdPn0ZBQQEiIiJMCQbQtI9KeHg4Dh06hLNnz5qWPW9Zx6FD\nh9rYMiIiIhvk5CBfqURmTAymDRmCzJgY5CuVQG6uoyNzCjYvxtWauro66HQ60YHodDp07tzZrKw5\nodDpdKioqLA43/ya8vJyUwzW6rBHfERERC3lGwzI6tnTdFzi69t0XFwMPsewMcn48ssvLcoEQUBV\nVRW+++479OjRw+6BtcXNBqVyHQ8iIpJCTq9eQH29RXlur15MMmBjkjF79uxWz0VGRt7wvK2Cg4NR\nWVlpVlZRUQEAUKlUCAoKsjjf/Jrg4GAEBwcDgNU6goKCRMdHRETUkjYqCmU5l6ApDEdNlS98O9cg\nPFYDt7RgR4fmFGxKMqxNT5XJZFAoFPD397dLIAMHDrQYO7F//36oVCpERERg4MCBWLp0KcrLy01J\nw6VLl3D27FkMGTIEYWFhUKlUOHToEAYPHmxWx5AhQ+wSIxER0fUay3ugqLAncEUPCPXQX+mMosJQ\ndBkqODo0p2DTwM877rjD4r/Q0FC7JRgAMHHiROzYsQM//PADjEYjjhw5gpUrV+Lpp5+GTCbD8OHD\nERUVhXnz5qGiogI6nQ5z585F7969kZycDJlMhokTJ2LFihUoLCyE0WjExo0bkZeXh6eeespucRIR\nEZnsVQLePkBwMNC1a9Of3j5AvtLRkTkFmwd+fvbZZ1i/fj3OnTuHy5cvQ6FQoGfPnnjggQcwYcIE\nm+oYO3YsSktLIQhNGV5qaipkMhnuu+8+zJ07Fx9++CEWL16MV199FcHBwXjiiScwadIkAIC7uzuW\nLVuGzMxM0+JgycnJWLZsmWmxrvT0dBgMBjz33HPQ6XTo3r07Fi1aZDHjhIiIyB7cdN7o6+sGjcEA\nfWMD/NzcEe7lBVm53NGhOQWZ0HzHv4EFCxZgxYoVGDx4MPr16wdfX1/o9XocPXoUBQUFmDJlCmbO\nnNke8Urm3LlzGD16tMUqpERERK3JzGxaGqOlsDDgrbfaP562aI/7nk1PMr799lvMmjXLardDVlYW\nVqxY4fJJBhERUVulpZmvxdUsNbX9Y3FGNo3JqKurw+jRo62eGzNmDGpra+0aFBERkStISADS05ue\nXLi5Nf2Zns7FPpvZ9CRjwIABOHHihGkjsusdP34cAwcOtHtgRERErqChbDfCi4ugPl8PD4MHGsr6\nAEhydFhOwaYkY+bMmZg9ezbOnDmDgQMHwt/fH7W1tdi3bx++++47vPzyyyguLja9vrWlw4mIiG4n\nuzftRuFHhabj+tJ603HSPUw0bEoy/vrXvwIAjh07ZrZ6ZvOY0Weffdbs9ceOHbNXfERERE6r6Jsi\n6+XfFjHJgI1Jxvz587k0NxERUQv15y2XFAeAeq318o7GpiRj/PjxUsdBRETkcjy6eqC+1DKh8FCL\n3n/0tmDzp3Du3Dl8/fXXOHbsGPR6PTp16oT4+HhMmDDBtG8IERFRR9LnwT5mYzJM5eP7OCAa52NT\nklFQUICnnnoKjY2N6NGjB/z8/FBSUoJffvkFq1atQnZ2Nnpet9UtERFRR9A87qLo2yLUa+vhofZA\nn/F9OB7jDzYlGf/7v/+LpKQkLFy40Gy/ksrKSrzwwgtYsGABli5dKlmQREREzirpniQmFa2waTGu\nw4cP48UXX7TYEC0gIACvvPIK9u3bJ0lwRERE5LpsSjIaGhrg6elp9Zy/vz+uXr1q16CIiIjI9dmU\nZERFRWHNmjVWz61evRpRUVF2DYqIiIhcn01jMqZNm4bnn38e+fn5phU/L1++jAMHDuDkyZNYsmSJ\n1HESERG1m+rqfOh0OTAatZDL1VAq06BQcEOStrIpybjrrruwbNkyrFy5Ejk5Obhy5Qr8/f0RGxuL\n119/HcOGDZM6TiIionZRXZ0Prfba1qoGQ4npmIlG29i8TsaIESMwYsQIKWMhIiJyOJ0uB7h4EdCc\nBWpqAF9fIDwCOq9cJhltZHOSYTAYkJeXB41Gg+rqagQGBqJHjx5ISkrikuNERHTbMJ47hIvFxdD4\n+qLG1xe+9Q0ILy5GCGRAN0dH51psSjIKCwsxdepUXLp0yeJcaGgoPv74Y/Tpw9XNiIjI9V08q0eR\nQmE61nt4oEihgOxsDXo7MC5XZFOSkZmZiZCQECxcuBAxMTHw9fWFXq9HYWEh3n//fcyZMwdr166V\nOlYiIiLJ7amJRXnpZRQeSERVRRA6B5YjdtAeXA7ohOGODs7F2JRkFBUVITs7G3FxcaYyhUKB5ORk\nvP3223jiiSckC5CIiKg97dTehVP/VsLXowLuMgMuXboDP/wwGT1GVWCmo4NzMTYlGUFBQfD19bV6\nzs/PD0FBQXYNioiIyFEqzvRDXYMedQ2dzMorz3RxUESuy6bFuCZOnIisrCyLlT2NRiP+/ve/Y+LE\niZIER0RE1N4Cha5AQADg4QlA1vRnQAACGtWODs3l2PQk4/Tp09izZw9GjhyJmJgYdOrUCbW1tTh8\n+DA8PT1RX1+Pl19+2fT6Dz74QLKAiYiIpNQ/Ug6hMQgaP3/oGxvg5+aOcC8vDOgmd3RoLsemJGP7\n9u0AAB8fH5w6dcpU7u3tDQA4ePCgqYzTWYmIyJWlpQElWXKo5OZJRWqqgwJyYTYlGdu2bZM6DiIi\nIqeQ8Md6W7m5QGkpEBralGAkcB2uNrN5MS4iIqKOIhrVUAk6GGGEXJBDCSUAxU3fR+aYZBAREV2n\nOr8a2iyt6dhQYjAdKxKYaLSFTbNLiIiIOgpdjs56ea71cmodkwwiIqLrGLVG6+Wl1supdUwyiIiI\nriNXW5+qKg/lFNa2anVMxo4dO9pUUUpKiuhgiIiIpJKfD+TkAFotoFY3TVW1NmNEmaY0G5NhKk9V\ntkOUt5dWk4z09HTIZDIIggDAfP0LQRAs1sM4duyYRCESERGJk58PZGVdOy4puXbcMtFoHtypy9XB\nWGqEPFQOZaqSgz5vQatJxueff276f51Ohw8//BBjxozBgAED4Ofnh8uXL2P//v346aefMHv27HYJ\nloiI6Fbk5AC4WAac1QA1NYCvLxARjtzcEKtPMxQJCiYVdtBqkjF06FDT/8+YMQPPPPMMHnroIbPX\njBkzBlFRUfjHP/6B5ORk6aIkIiISQXuoDDhWdK1ArweOFaFUBgAhjgrrtmfTwM8dO3aYJR3XS0xM\nRF5enl2DIiIisid1xa9Wy0MrrZeTfdiUZHh6eiI/P9/quQMHDsDd3d2uQREREdlTWuBuq+WpAdbL\nyT5sWvHz7rvvxpw5c7B371706dMH3t7eqKurw5EjR/Djjz9i3LhxUsdJRER0yxL6G4HGHcg9F4NS\nfQBC/SqRGnYUCQMaHB3abc2mJOP111+HQqHA999/j3/+85+mcqVSiUcffRQvvfSSZAESERGJlpaG\nhJIsJIScMS9PTXdMPB2ETUmGp6cnZs6ciZkzZ+Ly5cvQ6/Xw8fFB586dpY6PiIhIvIQEVP/aAN2q\n4zBeuAp5F08oJ0ZDwa1VJdVqkmE0Wl8+1cvLC15eXhavkcu5EhoRETmn6vxqaPMCgV5JQC/AAECb\nB6BfNaeqSqjVJCM+Pt5iwa3WyGQy/PorR+gSEZFz0uXocFF/EZpqDWqMNfCV+yJcEQ6vXC8mGRJq\nNcn429/+ZnOSQURE5MzOnTiHokvX1snQG/VNx25AN3RzXGC3uVaTjOeff970/2fOnEFoaCg8PT3b\nJSgiIuq4bN1jpC1OeJywXu55Aing3ltSsWmdjHHjxuHSpUtSx0JERB1c8x4jJSVAY+O1PUZaWarJ\nZr/H/m69PMZ6OdmHTUlGYmIi/vWvf0kdCxERdXA5OdbLc3PF1es12Asnx5xETVANBJmAmqAanBxz\nEt6DvcVVTDdk0xTWpKQkrF27Frm5uYiJiYGfn5/ZeZlMhpkzZ0oSIBERdRxaLaxuZFbqJm5/kbSo\nNGRVZ0HXS2dWPj5qvKh66cZsSjIWLFhg+v+CggKL80wyiIjIHtSNJSg5dvJawR8bmYWGGAGE3XK9\nCXc0DerIPZGL0sulCO0UitSoVFM5ScOmJKOoqOjmLyIiIhIpDTn4trYX1HrAp15ArYcMWj8gFbkA\nLFfnrM6vhi5HB6PWCLlaDmWastUpqQl3JDCpaGc2JRnX0+l00Ov1UCgUXPGTiIjsKvpSMe6R+UOD\nQNTACyrUYZCsAtEXiy1eW51fDW2W1nRsKDGYjrn2hXOwOclYtmwZsrOzUVZWZioLCwvDM888gwkT\nJkgSHBERdSy6il5QeV+ByvuKeXllL7RMG3Q5Olijy9UxyXASNiUZy5cvx6JFi5Camor4+Hj4+fnh\nypUrOHDgAGbPng13d3eMH8/BM0REJI4xMArQWnbRGwOiLMu01re/MJZaL6f2Z1OSsW7dOsyaNQtP\nPvmkWflTTz2FTz/9FCtXrrRbkjFq1ChcuHABbm7ms2vXr1+P7t27Y+PGjVi+fDlOnz4NlUqFtLQ0\nzJgxA+7u7gAAjUaDefPm4fDhwxAEAf3798cbb7yB8PBwu8RHRETSkfcPg0EAoNEA+hrAzxcID4d8\ngOWgT7laDkOJwbI8lHtpOQubkoxz587hzjvvtHouNTUV//d//2fXoN555x2rScvevXuRkZGBBQsW\nYPTo0SguLsbUqVPh6emJ6dOn4+rVq5gyZQri4+OxceNGeHh44N1330V6ejo2btzIFUuJyHlJscyl\nxPJ370ZOURG09fVQe3ggrU8fJCQliapTmaaEtsQAqFTm5alK669997DFdFdleryoGMh+bFqMy8/P\nD+fPn7d67uLFi/D19bVrUK1ZvXo1Ro4cibS0NMjlckRHR+Opp57CF198gcbGRuzYsQNnzpzBa6+9\nBqVSCYVCgVmzZkGj0eDnn39ulxiJiNpMqmUuJZS/ezeyCgtRUl+PRgAl9fXIKixE/u7doupVJCig\nTlfDK8wLMjcZvMK8oE5XWx1jocBxqIVN8JJdhAyN8JJdhFrYBAWOi4qB7MemJCM5ORmZmZk4duyY\nWXlhYSEyMzORnJxs16BycnJw9913Y/DgwRg/fjx+/PFHAE1rdMTHm2eo8fHxqKysxOnTp1FQUICI\niAgEBgaazgcEBCA8PByHDh2ya4xERHYj1TKXEsppZWmDXDsseaBIUKDbW93Q+5Pe6PZWt9YHcebk\nQBGiQ7dBR9F7xD50G3QUihCdU39uHY1N3SWvvvoqnnzySYwfPx7e3t7w9fWFXq+HwWBAZGQkMjIy\n7BZQ7969ERkZiffeew9yuRxffPEFpk+fjrVr10Kn01lMm21OKHQ6HSoqKqxOqw0MDER5ebndYiQi\nsiut1np5aWn7xtEG2vp6q+WlrZRLE4TrfW4djU1JhlqtxsaNG7F582YcPXoUV65cQadOnRAbG4u/\n/OUvkMvtN8hm6dKlZsfTpk3D5s2b8dVXX4mql9vWE5HTUqubukhaCg1t/1hspPbwQImVhCLUo83L\nL4kIwvU+t46m1W/D999/j5SUFAQHBwMAvLy8MG7cOIwbN67dgmsWERGBCxcuIDg4GJWVlWbnKioq\nAAAqlQpBQUEW55tf09wOIiKnk5bWNAajpdTU9o/FRml9+mD+gQ3Q1BWiprEKvm6dEe4di/TYdrxH\nuODn1tG0mmRkZGRAJpMhOjoaI0aMQEpKCgYPHgwPCbNUjUaDFStWYObMmVAorvXBnTp1CgkJCVAo\nFBZjK/bv3w+VSoWIiAgMHDgQS5cuRXl5OYKCggAAly5dwtmzZzFkyBDJ4iaiDkSKWSDN78/NbXrU\nHxradKN05tkl4e6QHT0KWW0VIAiQCVWQeR0Fwu9vvxhc8XPrYFrNGL766ivk5eVh165dWLVqFbKy\nsuDj44OhQ4di5MiRSElJQUREhF2DCQ4OxtatW1FdXY0333wTXl5eWLFiBYqLi7Fo0SJUV1fj8ccf\nxw8//IC77roLx48fx8qVKzFp0iTIZDIMHz4cUVFRmDdvHt566y0IgoC5c+eid+/edh+cSkQdUPMs\nkGbNs0AA+yQaN6nDmWa55pzIgSqkC1QhXczKc0/ktu/+IDZ8buQ4rSYZ8fHxiI+Px9SpU2EwGJCf\nn49du3Zh165dmDt3LgRBQFhYmOkpx6hRo0QH4+Pjg5UrV2LBggVIS0tDbW0t+vXrh9WrV6NHjx4A\ngA8//BCLFy/Gq6++iuDgYDzxxBOYNGkSAMDd3R3Lli1DZmYmRo0aBZlMhuTkZCxbtsy0WBcR0S27\n0SwQiW90t5zfSJSZaC9bH3RZepmDLukam/o+vLy8kJKSgpSUFABAVVUV8vPzkZ+fj3/+859Ys2aN\nxfTWW9WzZ0+LwZ/XGzNmDMaMGdPqebVajU8++cQusRARmZFwNsPNcoFbym8kfPKi7qTGwf0e0OyP\nRY2uM3yVVQgfXIhBgxtE1Uu3lzYNsLh69SoOHDiAXbt2Ye/evTh69Cjq6+sRFxcnVXxERM5DotkM\ntuQCt5TfSPjkJfLKQ1iz+ZLpWF8egKLNKbg/moPs6ZqbJhlHjx41dZMcOHAAdXV16NWrF5KSkpCe\nno6hQ4fC39+/PWIlInKsNs5msLWnwpZc4JbyGwmfvJzZ3w99g8ugqdZAb6yBn9wX4YpwnD0QAtwj\nunq6TbSaZMyYMQN79uzB5cuXERkZiYSEBDz44INISkqCUmm5hjwR0W2vDbMZ2tJTYUsucEuzNSVc\nR0KrBVR+IVD5hZiVcx0sul6rScbmzZtNAydHjBiBhIQExMTEtGdsRHS7c6bpEjb69xU/bKgYAW2l\nG9Q+jRh3xQ/Wto/MyQEuGo04azCgprEBvm7uiPDyQm6u3KKJtuQCtzRbU8J1JLgOFtmi1SRj9+7d\n2L17N/Ly8vCPf/wD7733HhQKBRITE5GYmIhhw4aZZnwQEbWZlNNBJfLvf/+KpUsvoXnbp5IStz+O\nf8Wdd/Yze+2hM0Ycq6kxHesbGnCspgay0wBgvkpyWhrw8cdlMBg0aGysgZubL7y8wpGaav6UoM2z\nNRMSUN3wK3THV8F49QLknl2gjJ4IhR0+X66DRbZoNckICAhAamoqUv/4xpSWlmLnzp3YvXs3Pvnk\nE7zzzjsICQnBsGHDMGzYMNx/fzsuwEJErs+B00Fv1YYNZbC2r+SGDWUWSUZFZz1wxXI7g8rOerRM\nMqKj83HPPVvwyy8xKCsLgEp1DiNG/AvR0X8BcOufRXV1PrSBeUBSLwC9YACgRR5Q3Q8KhbjPmOtg\nkS1snl0SGhqKCRMmYMKECQCA33//Hd9//z2+/vprrF+/nkkGEbWNC25updVa37jaWnlgcjW06yw3\nbAxIrgYQaFam0+UgLq4EcXFnWpTnikoGdLocHDkSie3bY3HxYmeoVFUYObIQXl7i6m3GdbDoZto0\nhfXMmTPYuXMndu3ahfz8fFRWVsLb2xvDhw+XKj4iul25YKe+Wt2IkhLLhEKtbrQo6z9EQCOqcO4X\nP+jLPOAXUo+wEXoMGCJYvNZotJ5wGY3iEq59++RYt+7aascXLgRg3boUAHno1k1U1UQ2uWGSUVlZ\niV27diEvLw87d+6EVquFIAiIiorC/fffbxoQas9dWInICUkxQFPCTv3q6nzodDkwGrWQy9VQKtPs\n8i/3ceNC8M38i4jQAn51AvTeMpxVA+Omhli8Nk2pREmcFiFxdWblqUq1xWvlcjUu76uFYbsajRd9\n4KaqhddILToN8RUVb15eUqvlDz0kqmoim7SaZIwfPx5FRUVobGyEQqHAsGHDMG3aNIwcORJdunRp\n7W1EdLuRaoCmRJ361dX50GqvxWswlJiOxSYag/3DAO9qnHGrQw2AEDcBCd7eTeUtJPyxyWOuTodS\noxGhcjlSlUpT+fW8isegbF2h6bjxgi9q1/VEcFAs0O3W462q6geV7phFUlTl3+/mbyayg1aTDE9P\nT0ybNg0pKSno378/3Nys90US0W1OygGaEnTq63TW4xU7vgEAdDk6hIYGWvTo6HJ1UCRYJg8JCoXV\npKIlwy9d4etbj7rrZpd4e4XDsKMrrM6PtVGMhzf8znZGY4MeglCPTnUeiDvrh5qe3rdeKVEbtJpk\nfPnll+0ZBxGJJdWaEy42QFOq8Q0AYNQarZeXWi9vS72echU85Sq71psIHY65+cDdzcesfCh0AG6e\n/BCJxccTRLeD5i6NkhKgsfFal0Z+vvi61ZZjCAA47QBNudx6vHK5+Hjlauvjz+Sh4salSVWvys2I\nPn0BP39AJmv6s09fQCUTl7wQ2YpJBtHt4EZdGmKlpVkvd9JVl5TKNBiPKHF5SQyq5gzB5SUxMB5R\nQqkUH68yzfqWCspUcVstSFWvXC2HSgUMGgSkjGj6U6USn7wQ2apNU1iJyA6k6NaQsktDwlWX8qur\nkaPTQWs0Qi2XI62VgZFtcjwask33AAYNINQAl1SQbRoEREaLWdcKAEzjLnS5OhhLjZCHyqFMVVod\nj+EM9SrTlNBmWX43xCYvRLZiktHC4q+/xoSkJCQkWZ/61SZS9ZFLud+DK8YsFSlilmqmhtRrTkgw\nQDO/uhpZ1yVHJQaD6VhMoqHL0aGySgXNWRVqagBfXyA8AvBvZXBmWykSFHapp6XjoceRMzIH2sta\nqDupkRaahgSRWZFUyQuRrZhktHC+oQFZhU1TyUQlGlLdTKTc78EVY5aKVDFLNVPDBTeSyNHprJbn\n6nSikoxzh4woOnbtWK9H07HMKGY2qKTyS/KRdeDa9SupLjEdJ9whPtFgUkGOwjEZrcgtKhJXgVR9\n5FL2vbtizFKRKmapujUSEoD0dCAsDHBza/ozPd15kzgAWqP1wYelrZTb6kSF9fEGJyqddxxCzgnr\n37fcE078M0JkAz7JaEVpfb24CqS6mUjZ9+6KMbvatE0puzVcbCMJtVyOEoPBojxU5ArCvwcq0d3K\n9fs9wHnHIWgvW/++lV52zmnCRLbik4xWhHqIzL+kmvYn5XRCV4vZFadtuthMjWbV1fk4fToTbfZ6\nlgAAFklJREFUv/02DadPZ6K6WvxnnKZUQnnEiJgllzFkThVillyG8ogRqUpxyYBXfwVO9lGjxt8L\ngkyGGn8vnOyjhvcA5+0yUHey/n0L7eSc04SJbMUkoxWpffqIq0Cqm4mUNylXi9kVp226YLdG8zLd\nBkMJBKHRtEy32EQj+jhwzyYZVJcANwFQXWo6jj4uLt60NEAXosDRQd2wb0RvHB3UDboQhVPncWlR\n1r9vqVFOHDSRDdhd0sLpbbGY/GAoEpLixFUk1bS/hATk/+qHnFVl0F5wg7pLI9ImhiAhwQ57EUgY\nsyT1uui0TVfr1pBqmW5djg6o8gTOegI1AHwBRLS+RLetpLx0Umke3Jl7Ihell0sR2ikUqVGpogd9\nEjkak4wWugXFIG9PGPrF2eGXkhTT/vKBrLx+QK9+QC+gBEBWHoB+dvqrpLoBSlGvC07bdEVSLdMt\n5SwQV7x0CXckMKmg2w6TjFbYY+8nKUi5V5XLccFpm1KSantzqbYhb5oFYjnw80SlHCmiaiYiZ8Ek\noxVOuveTq+1VJS1XfC4OaZIBKbc39yoeg2OfnsZ5rRK1dXL4eBvR9VgIBgR1E7UNuSvOAiGitmGS\n0Qon3ftJ8h4Cl+Niz8WlSgZ0uhwc+y4Gpd/3hlDuDVlQHULv/w1ej4jf3vzU2q44e9YbjQ16QKhH\nXZ0fzp4NgeLLAKhFbEPu1V+Bk42A+pwOPnojav3k0IYp4evEs0CIqG2YZLTCWZ+4u2oPgVSP8qUk\nRcw6XQ6MR5QWXQ86L3HJQMHaQJT8/dpgZeGiN0r+Hg/gCLpliAoZZw4Y4W5lu/CzB4wYLqLetDQg\nq0QBXYh5UjHeyb/LRGQ7JhktqNXAQw857z+OExKAK1d+xYYNF6DVukOtbsC4cV3sM7sE0j3K37x5\nC7ZvH4CLF/8ElaoKI0duwZgx4h/lS7UWl1QxX9lnwLlP+1t0PYTjiKiuh/Prrc+GOr8+DhCZZJRD\nDh8rYycuQdyiWS7a20VEbcAko4Xnn29ausAepLphh4Zm4dlnW5anO22//r//fQjr1l0bynfhQgDW\nrUuBp+ch3Hffrdcr5ZYo//73Ifz06XBEaIHoOgF678746dhw0TGf+3YQTp0KMh3X1Mpx6lRXyL71\nROxDtx6vrCIQQEUr5eJcHaSEz3bLsRP1g8SPnXCx3i4iaiMmGRKRsu9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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4188,7 +4188,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "metadata": { "collapsed": true }, @@ -4208,14 +4208,14 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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hhHijZWZq8DsVysPsjp46arq1ssfIQHvuTFdjY6bXqMGaOnWYXqNGqUwwoASS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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4235,14 +4235,14 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "image/png": 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2wOMJa2vXruXLL78kMjIStVpNeno6R48eZeXKlbRp0ybXY0yaNImwsDA+/fRT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\nfKH0oI+UlBREREQgMDBQ8fuRkpKiGJdST1raCwCIjo5GSEiIUp5U69S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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4261,13 +4261,132 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], - "source": [] + "source": [ + "### Disfunctions" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13.8888888889\n" + ] + } + ], + "source": [ + "def carrying_capacity(system):\n", + " K = -system.alpha / system.beta\n", + " return K\n", + " \n", + "sys1 = System(alpha=0.025, beta=-0.0018)\n", + "pop = carrying_capacity(sys1)\n", + "print(pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13.8888888889\n" + ] + } + ], + "source": [ + "def carrying_capacity():\n", + " K = -system.alpha / system.beta\n", + " return K\n", + " \n", + "system = System(alpha=0.025, beta=-0.0018)\n", + "pop = carrying_capacity()\n", + "print(pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13.8888888889\n" + ] + } + ], + "source": [ + "def carrying_capacity(system):\n", + " system = System(alpha=0.025, beta=-0.0018)\n", + " K = -system.alpha / system.beta\n", + " return K\n", + " \n", + "sys1 = System(alpha=0.025, beta=-0.0018)\n", + "pop = carrying_capacity(sys1)\n", + "print(pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "def carrying_capacity(system):\n", + " K = -system.alpha / system.beta\n", + " \n", + "sys1 = System(alpha=0.025, beta=-0.0018)\n", + "pop = carrying_capacity(sys1)\n", + "print(pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'K' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0msys1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.0018\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mcarrying_capacity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'K' is not defined" + ] + } + ], + "source": [ + "def carrying_capacity(system):\n", + " K = -system.alpha / system.beta\n", + " return K\n", + " \n", + "sys1 = System(alpha=0.025, beta=-0.0018)\n", + "carrying_capacity(sys1)\n", + "print(K)" + ] }, { "cell_type": "code", diff --git a/code/rabbits2.ipynb b/code/rabbits2.ipynb new file mode 100644 index 00000000..f77cb8cf --- /dev/null +++ b/code/rabbits2.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeling and Simulation in Python\n", + "\n", + "Rabbit example\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": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from modsim import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rabbit Redux\n", + "\n", + "This notebook starts with a version of the rabbit population growth model and walks through some steps for extending it.\n", + "\n", + "In the original model, we treat all rabbits as adults; that is, we assume that a rabbit is able to breed in the season after it is born. In this notebook, we extend the model to include both juvenile and adult rabbits.\n", + "\n", + "As an example, let's assume that rabbits take 3 seasons to mature. We could model that process explicitly by counting the number of rabbits that are 1, 2, or 3 seasons old. As an alternative, we can model just two stages, juvenile and adult. In the simpler model, the maturation rate is 1/3 of the juveniles per season.\n", + "\n", + "To implement this model, make these changes in the System object:\n", + "\n", + "0. Before you make any changes, run all cells and confirm your understand them.\n", + "\n", + "1. Then, add a second initial populations: `juvenile_pop0`, with value `0`.\n", + "\n", + "2. Add an additional variable, `mature_rate`, with the value `0.33`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "