{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to programming for Geoscientists through Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 5 solutions\n", "## [Gerard J. Gorman (g.gorman@imperial.ac.uk)](http://www.imperial.ac.uk/people/g.gorman), [Nicolas Barral (n.barral@imperial.ac.uk)](http://www.imperial.ac.uk/people/n.barral)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Fill lists with function values**
\n", "A function with many applications in science is defined as:

\n", "$h(x) = \\frac{1}{\\sqrt{2\\pi}}\\exp(-0.5x^2)$

\n", "Fill lists *xlist* and *hlist* with *x* and *h(x)* values for uniformly spaced *x* coordinates in [−4, 4]. You may adapt the first example in the lecture 4 notes." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "xlist = [-4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0]\n", "ylist = [0.00013383022576488537, 0.0044318484119380075, 0.05399096651318806, 0.24197072451914337, 0.3989422804014327, 0.24197072451914337, 0.05399096651318806, 0.0044318484119380075, 0.00013383022576488537]\n" ] } ], "source": [ "from math import *\n", "\n", "# Function h(x)\n", "def h(x):\n", " return (1.0/sqrt(2*pi)) * exp(-0.5 * x**2)\n", "\n", "# Generate n points in [-4,4]\n", "n = 9 # number of uniformly distributed points in xlist\n", "dx = 8.0/(n-1) # x spacing\n", "xlist = [-4.0+i*dx for i in range(n)] # Python lists\n", "\n", "ylist = [h(x) for x in xlist]\n", "\n", "print('xlist =', xlist)\n", "print('ylist =', ylist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Fill arrays; loop version**
\n", "The aim is to fill two arrays *x* and *y* with *x* and *h(x)* values, respectively, where *h(x)* is defined above. Let the *x* values be uniformly spaced in [−4, 4]. Use list comprehensions to create the *x* and *y* arrays." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x = [-4. -3. -2. -1. 0. 1. 2. 3. 4.]\n", "y = [ 1.33830226e-04 4.43184841e-03 5.39909665e-02 2.41970725e-01\n", " 3.98942280e-01 2.41970725e-01 5.39909665e-02 4.43184841e-03\n", " 1.33830226e-04]\n" ] } ], "source": [ "from math import *\n", "from numpy import *\n", "\n", "# Function h(x)\n", "def h(x):\n", " return (1.0/sqrt(2*pi)) * exp(-0.5 * x**2)\n", "\n", "# Generate n points in [-4,4]\n", "n = 9 # number of uniformly distributed points in x\n", "dx = 8.0/(n-1) # x spacing\n", "x = array([-4.0+i*dx for i in range(n)]) # list created using list comprehension and then converted to an array using the array function\n", "\n", "y = array([h(xi) for xi in x]) # \n", "\n", "print('x =', x) # x is an array\n", "print('y =', y) # y is an array = h(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Fill arrays; vectorized version**
\n", "Vectorize the code in the previous exercise by creating the *x* values using the *linspace* function and by evaluating *h(x)* for an array argument." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x = [-4. -3. -2. -1. 0. 1. 2. 3. 4.]\n", "y = [ 1.33830226e-04 4.43184841e-03 5.39909665e-02 2.41970725e-01\n", " 3.98942280e-01 2.41970725e-01 5.39909665e-02 4.43184841e-03\n", " 1.33830226e-04]\n" ] } ], "source": [ "from math import *\n", "from numpy import *\n", "\n", "# Function h(x)\n", "def h(x):\n", " return (1.0/sqrt(2*pi)) * exp(-0.5 * x**2)\n", "\n", "# Generate n points in [-4,4]\n", "n = 9\n", "x = linspace(-4.0, 4.0, n)\n", "\n", "y = h(x)\n", "\n", "print('x =', x) # x is an array\n", "print('y =', y) # y is an array = h(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Apply a function to a vector**
\n", "Given a vector $v = (2, 3, −1)$ and a function $f(x) = x^3 + xe^x + 1$, apply $f$ to each element in $v$. Then calculate $f(v)$ as $v^3 + ve^v + 1$ using vector computing rules. Show that the two results are equal." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v = [ 2. 3. -1.]\n", "f(v) element-wise = 23.7781121979 88.2566107696 -0.367879441171\n", "f(v) = [ 23.7781122 88.25661077 -0.36787944]\n" ] } ], "source": [ "from math import *\n", "from numpy import *\n", "\n", "# Function f(x)\n", "def f(x):\n", " return x**3 + x*exp(x) + 1.0\n", "\n", "v = array([2.,3.,-1.]) # vector v\n", "\n", "# applying function f(x) on each element of v\n", "y1 = f(v[0]) \n", "y2 = f(v[1])\n", "y3 = f(v[2])\n", "\n", "# applying f(x) on v\n", "y = f(v)\n", "\n", "print('v =', v)\n", "print('f(v) element-wise = ', y1, y2, y3)\n", "print('f(v) = ', y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Simulate by hand a vectorized expression**
\n", "Suppose *x* and *t* are two arrays of the same length, entering a vectorized expression:\n", "\n", "```python\n", "y = cos(sin(x)) + exp(1/t)\n", "```\n", "\n", "If *x* holds two elements, 0 and 2, and *t* holds the elements 1 and 1.5, calculate by hand (using a calculator) the *y* array. Thereafter, write a program that mimics the series of computations you did by hand (use explicit loops, but at the end you can use NumPy functionality to check the results)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y1 = [ 3.71828183 2.56203432]\n", "y2 = [ 3.71828183 2.56203432]\n" ] } ], "source": [ "from math import *\n", "from numpy import *\n", "\n", "# Function f(x,t)\n", "def f(x,t):\n", " return cos(sin(x)) + exp(1.0/t)\n", "\n", "# Defining x and t arrays\n", "x=array([0.,2.])\n", "t=array([1.,1.5])\n", "\n", "# calculating y1 explicitly\n", "y1=zeros(len(x))\n", "for i in range(len(x)):\n", " y1[i] = f(x[i],t[i])\n", "\n", "# calculating y directly using vectorization feature of numpy\n", "y2=f(x,t) # y2 is an array\n", "print('y1 = ', y1)\n", "print('y2 = ', y2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Demonstrate array slicing**
\n", "Create an array *w* with values 0, 0.1, 0.2, ..., 3. Write out *w[:]*, *w[:-2]*, *w[::5]*, *w[2:-2:6]*. Convince yourself in each case that you understand which elements of the array are printed." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "w[:] = [ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 1.1 1.2 1.3 1.4\n", " 1.5 1.6 1.7 1.8 1.9 2. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9\n", " 3. ]\n", "w[:-2] = [ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 1.1 1.2 1.3 1.4\n", " 1.5 1.6 1.7 1.8 1.9 2. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8]\n", "w[::5] = [ 0. 0.5 1. 1.5 2. 2.5 3. ]\n", "w[2:-2:6] = [ 0.2 0.8 1.4 2. 2.6]\n" ] } ], "source": [ "from numpy import *\n", "\n", "w=arange(0,3.1,0.1) # creates the array starting at 0, ending at (but not containing) 3.1, with a step size of 0.1\n", "\n", "print('w[:] =', w[:])\n", "print('w[:-2] =', w[:-2])\n", "print('w[::5] =', w[::5])\n", "print('w[2:-2:6] =', w[2:-2:6])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Plot a formula**
\n", "Make a plot of the function $y(t) = v_0t − 0.5gt^2$ for $v_0 = 10$, $g = 9.81$, and $t \\in [0, 2v_0/g]$. The label on the *x* axis should be 'time (s)' and the label on the *y* axis should be 'height (m)'." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/dist-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['gamma', 'f']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "\n", "v0=10.\n", "g=9.81\n", "\n", "n=50 # number of points to be plotted on the graph\n", "t=linspace(0,2*v0/g,n) # generate n points between 0 and 2*v0/g\n", "y=v0*t - 0.5*g*t**2\n", "\n", "plot(t,y)\n", "xlabel('time (s)')\n", "ylabel('height (m)')\n", "show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Specify the x and y axes**
\n", "Extend the program from the previous exercise such that the minimum and maximum *x* and *y* values are computed, and use the extreme values to specify the extent of the *x* and *y* axes. Add some space above the heighest curve." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "image/png": 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Oq+OcEpcXfRFpDXwK/MEY81/nPRljXjfGZBhjMuLi4lwdxyHFFXXM/D6Ps3u3\nZ3CXGKvjKKU8wOnd4xiVFsvL3+VQVtNgdZyT5tKiLyLB2Ar+v4wxn7lyXc700uIcahubuXei9spX\nSv2f+yelc6C6gX98773tGVxW9MW2T+RNYJM3TaKeX1zJB6t2cMXQRLrGtbY6jlLKg/Tp1IYLB3bi\nrR+3eW17BleO9EcCVwPjRGSt/XaOC9fnFM/O30JIUAB3jdd2C0qp/3bPWd0xBp5fsNXqKCfFlWfv\n/GiMEWNMP2PMAPvtG1etzxnW7TzIvCxbu4W4yFCr4yilPFDn6HCuHWFrz5Czr8LqOCdMTz5v4bkF\nW4gOD+bG0SlWR1FKebBbx6QRERLEC4u8b7SvRd/u5/wSluXs59YxXYkMC7Y6jlLKg8VEhHD9qBS+\n2bCXrF3eNZ+uFn1sTdWeX7CV+MhQrh6WbHUcpZQXuHF0Cm1aBfP8gi1WRzkhWvSBH3L2s6qglNvH\npdEqROe9VUodX1RYMDefkcqSLcWs3l5qdRyH+X3Rt43yt9CpbSsuH5JkdRyllBe5bkQysa1DeW6+\n9+zb9/uiv2DjPtYXlnHXhG6EBPn95lBKnYDwkCBuG9uVn/JLWJ673+o4DvHrKtfUbPjbgq2kxkVw\n0UCv6QWnlPIgVwxNIqFNGM/O34Ixnt962a+L/lfrd7NlXwV/nNCdIG2drJQ6CWHBgdw5vhtrdx7k\nu81FVsc5Lr+tdI1NzbywcCvpHSI5t2+C1XGUUl5s6uDOdGkXznMLtnr8RCt+W/Q//bWQgpJq7jmr\nBwEB2jpZKXXyggMD+MOEbmzaU843WZ490YpfFv26xiZeWpxL/8S2TOgZb3UcpZQPOL9/J7rFt+Zv\nC7fS2NRsdZyj8sui/+Gqnew6WMOfzuqhE6QopZwiMEC456zu5BdX8e+1u62Oc1R+V/RrG5p4ZUku\np6XEMDKtndVxlFI+5OzeHejbqQ0vLt5Kg4eO9v2u6H+4agfFFXX88czuOspXSjmViHDX+G7sLK3h\n32t2WR3niPyq6Nc2NDHj+zxOS4lhWKqO8pVSzje+Zzy9O0bx6pJcj9y371dF/5PMnewrr+Ou8d2s\njqKU8lEiwp3ju1FQUs2X6zxv377fFP26xiZeW5pHRpdohnfVUb5SynXO6tWenglRvPJdLk0edt6+\n3xT9OasL2VNWy10Tuum+fKWUS4kId45LI39/FV+t96zRvl8U/frGZl5bksfApLaMSou1Oo5Syg+c\n3bsDPdrYeLncAAAPTElEQVRH8rKHjfb9ouh/9mshuw7WcOd4HeUrpdwjIEC4Y3wauUWVzPOgq3R9\nvug3NDXz6tJc+nduw5jucVbHUUr5kUl9EkiLb81Li3M8piePzxf9z9fsYmepjvKVUu4XGCDcMS6N\nrfsqmZ+91+o4gI8X/camZl5dkkufTlGMS9ceO0op9zuvX0dS4yJ40UNG+z5d9L9Yu5vtJdXcOU5H\n+Uopaxwa7W/eW8HCTfusjuO7Rb+p2fDKklx6JkRxZq/2VsdRSvmxyf06ktwunJcW51g+u5bPFv25\n63azbX8Vd41P01G+UspSQYEB3D6uG9m7y1m8ydrZtXyy6Dc3G15dkkuP9pGc1auD1XGUUooLBnQk\nKSacl5fkWjra98miv2jTPnKKKvn92K46K5ZSyiMEBQYw/fRU1u08yE/5JZbl8Lmib4zhtaV5JMa0\n0rlvlVIeZergzsRFhjJjaZ5lGVxW9EXkLREpEpEsV63jSFbml7J250Gmn96VoECf+z9NKeXFwoID\nuWFUCsty9rOhsMySDK6sirOBiS78/CN6bWkusa1DuWRwZ3evWimljuvK05KIDAtixve5lqzfZUXf\nGPMDUOqqzz+SDYVlLMvZz/WjkgkLDnTnqpVSyiGRYcFcM7wL87L2kldc6fb1+9T+j398n0dkaBBX\nDetidRSllDqqaSNTCAkM4PXv892+bsuLvohMF5FMEcksLi4+6c/JL67km6w9XD28C1FhwU5MqJRS\nzhXbOpTLhiTy2ZpC9pTVuHXdlhd9Y8zrxpgMY0xGXNzJd8Gc+X0+IYEBTBuZ4sR0SinlGjeNTqXZ\nwKxl29y6XsuLvjPsLavlszWFXJqRSFxkqNVxlFLquBJjwjm/f0c+WLWDA1X1bluvK0/Z/AD4Cegh\nIoUicoOr1jVrWT7NBqafnuqqVSillNPdOqYr1fVNvPNTgdvW6cqzd64wxiQYY4KNMZ2NMW+6Yj0H\nqup5f9UOJvdLIDEm3BWrUEopl+jePpIJPdsze0UBVXWNblmn1+/eeeenAqrrm7h1TJrVUZRS6oTd\nOqYrB6sb+GDVDresz6uLfnV9I7NXFDChZzw9OkRaHUcppU7Y4C7RnJYSw6xl26hvbHb5+ry66H+4\naicHqxu4dUxXq6MopdRJu3VMV/aW1/LvNbtcvi6vLfqNTc28+eM2hiRHM7hLjNVxlFLqpJ3RPY6e\nCVG8sSzf5W2Xvbboz8vay66DNdw0Ws/YUUp5NxHhptEp5BRVsnTryV+k6givLPrGGGYtyyclNoIJ\nPXUqRKWU9zuvX0faR4Uya5lrWzN4ZdFfta2UdYVl3DAqRSdJUUr5hJAgW0eB5bklZO92Xdtlryz6\nbyzbRnR4MBcP0vbJSinfccXQJCJCAl3amsHrin5ecSWLN+/j6mFdaBWi7ZOVUr6jTatgLh2SyNx1\nu13WiM3riv6bP24jODCAq4cnWx1FKaWc7vqRKTQbw+zlBS75fK8q+iWVdXy6upCLBnbSxmpKKZ+U\nGBPOpL4JvL9qB5UuaM3gVUX/vZU7qGts5sbR2j5ZKeW7po9OpaK2kY9+2en0z/aaol/b0MQ/fypg\nXHo8afHackEp5bv6J7ZlaHIMb/24jcYm57Zm8Jqi//maXZRU1esoXynlF24cncKugzXMy9rr1M/1\niqLf3Gx4Y1k+fTpFMTy1ndVxlFLK5Sb0bE9KbITTWzN4RdFfsqWI/OIqbhqdiohejKWU8n0BAcIN\no1JYX1jGqm2lzvtcp32SC72xLJ+ObcI4p2+C1VGUUsptLh7UmZiIEN5w4sVaHl/0NxSWsTK/lGkj\nUwgO9Pi4SinlNK1CArlqWBcWbdpHXnGlUz7T46vo28u3ERESyGVDE62OopRSbnfN8C6EBAbwzooC\np3yeRxf9oopa5q7fzSUZiUSFBVsdRyml3C62dSiT+3dkzupCymoaTvnzPLrov//zDhqaDNeOSLY6\nilJKWWbayGSq65v4JPPUL9by2KJf19jEeyt3MLZHHCmxEVbHUUopy/Tp1IYhydG881MBTc2ndvqm\nxxb9r9fvYX9lHdNG6sVYSik1bWQKO0trWLxp3yl9jkcWfWMMby8vIC2+NaO7xVodRymlLHdWr/Z0\nbBPG7FM8oOuRRf/XHQfYsKuMa0ck68VYSikFBNlbyq/IK2Hz3vKT/hyPLPpvLS8gKiyIiwd1sjqK\nUkp5jCuGJhIWHHBKvfY9rujvKavh26y9XD40ifCQIKvjKKWUx2gbHsKFAzvz+ZpdHKiqP6nP8Lii\n/+5P2zHGcPWwLlZHUUopj3PdiGTqGpv54JcdJ/V+jyr6xsAHq3ZwZq/2JMaEWx1HKaU8To8OkYxM\na8e7P20/qV77HlX0D1bXc6C6getG6GmaSil1NNeNSGFPWS3zs0/89E2XFn0RmSgiW0QkV0TuP97y\n+yvrSe8QybDUGFfGUkoprzYuPZ6kmHDeXn7i3TddVvRFJBB4FZgE9AKuEJFex3pPbWMT149M0dM0\nlVLqGAIDhGtHJJO5/cAJv9eVI/2hQK4xJt8YUw98CEw51hsCA4TzB3R0YSSllPINl2R0JiIk8ITf\n58qi3wlo2R2o0P7cUcVEhBAWfOJ/CaWU8jdRYcFMHdz5hN9n+YnwIjIdmG5/WCciWVbmcUAssN/q\nEMehGZ1DMzqHZnSOo2U8ofPbXVn0dwEtZz7pbH/uPxhjXgdeBxCRTGNMhgsznTLN6Bya0Tk0o3P4\nU0ZX7t75BegmIikiEgJcDnzpwvUppZQ6DpeN9I0xjSJyOzAfCATeMsZku2p9Simljs+l+/SNMd8A\n35zAW153VRYn0ozOoRmdQzM6h99kFGNObRYWpZRS3sOj2jAopZRyLbcU/eO1YxCRUBH5yP76zyKS\n3OK1B+zPbxGRsy3MeLeIbBSR9SKyWES6tHitSUTW2m8uO1jtQMbrRKS4RZYbW7x2rYjk2G/XWpjx\nhRb5torIwRavuWs7viUiRUc7PVhsXrL/HdaLyKAWr7lrOx4v45X2bBtEZIWI9G/xWoH9+bUikmlh\nxjEiUtbiZ/pQi9dOqEWLCzP+qUW+LPt3MMb+mru2Y6KILLHXl2wRuesIyzjvO2mMcekN20HcPCAV\nCAHWAb0OW+b3wD/s9y8HPrLf72VfPhRIsX9OoEUZxwLh9vu3Hspof1zpIdvxOuCVI7w3Bsi3/xlt\nvx9tRcbDlr8D2wF+t21H+3pOBwYBWUd5/RxgHiDAMOBnd25HBzOOOLRubK1Ofm7xWgEQ6wHbcQzw\n1al+T1yZ8bBlJwPfWbAdE4BB9vuRwNYj/Nt22nfSHSN9R9oxTAHesd+fA4wXEbE//6Exps4Ysw3I\ntX+e2zMaY5YYY6rtD1diu+7AnU64rUULZwMLjTGlxpgDwEJgogdkvAL4wAU5jskY8wNQeoxFpgD/\nNDYrgbYikoD7tuNxMxpjVtgzgDXfR0e249Gcynf5hJxgRqu+j3uMMb/a71cAm/jv7gVO+066o+g7\n0o7ht2WMMY1AGdDOwfe6K2NLN2D7X/eQMBHJFJGVInKBC/KB4xkvtv/6N0dEDl0c53Hb0b57LAX4\nrsXT7tiOjjja38Nd2/FEHf5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "\n", "v0=10.\n", "g=9.81\n", "\n", "n=50 # number of points to be plotted on the graph\n", "t=linspace(0,2*v0/g,n) # generate n points between 0 and 2*v0/g\n", "y=v0*t - 0.5*g*t**2\n", "\n", "#find minimum and maximum t and y values\n", "tmin = min(t)\n", "tmax = max(t)\n", "ymin = min(y)\n", "ymax = max(y)\n", "\n", "# plot graph\n", "plot(t,y)\n", "xlabel('time (s)')\n", "ylabel('height (m)')\n", "axis([tmin, tmax, ymin, ymax+0.1*(ymax-ymin)]) # specify the extent of the axes [tmin, tmax, ymin, ymax]\n", "show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Plot a formula for several parameters**
\n", "Make a program that reads a set of $v_0$ values using iPython widgets and plots the corresponding curves $y(t) = v_0t − 0.5gt^2$ in the same figure (set $g = 9.81$). Let $t \\in [0, 2v_0/g$] for each curve, which implies that you need a different vector of $t$ coordinates for each curve." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0779ddb6a3f04681b6e6f3e9c87ccd9a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "A Jupyter Widget" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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smPsd+dlZ9J30CiY6nSYxFOQXcnZvDOFbrpGdno+7vz2dRgTi28JFsy6niqAz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\nWNM09l8whRCC7iPCKMor5eg2y1h4UQhBx6jhZCUnkXb8mNL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KOVEIcQcwWkp5uxCiDfAd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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%pylab inline\n", "from ipywidgets import widgets\n", "from IPython.display import display, clear_output\n", "\n", "g=9.81\n", "\n", "n=50 # number of points to be plotted for each curve\n", "\n", "# Let the user enter v0 values\n", "def plot_functions(sender):\n", "\n", " try:\n", " v0_strings = sender.value.split(\",\")\n", " v0_list = [float(v0_string) for v0_string in v0_strings]\n", " except:\n", " print(\"ERROR: invalid input, expects a series of numbers separated by a ','\")\n", " return 1\n", " \n", " n_curves = len(v0_list)\n", " v0 = array(v0_list)\n", "\n", " t=zeros((n_curves,n))\n", " y=zeros((n_curves,n))\n", " for xx in range(n_curves):\n", " t[xx]=linspace(0,2*v0[xx]/g,n) # generate n points between 0 and 2*v0/g\n", " y[xx]=v0[xx]*t[xx] - 0.5*g*t[xx]**2\n", " \n", " # plot graph\n", " for i in range(n_curves):\n", " ll='v0='+str(v0[i]) # text for curve label in legend\n", " plt=plot(t[i],y[i],label=ll)\n", "\n", " legend()\n", " xlabel('time (s)')\n", " ylabel('height (m)')\n", " show()\n", "\n", "\n", "widget_plot = widgets.Text()\n", "widget_plot.on_submit(plot_functions)\n", "display(widget_plot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Plot another formula**
\n", "The function

\n", "$f(x, t) = \\exp(-(x - 3t)^2)\\sin(3\\pi(x - t))$\n", "

\n", "describes, for a fixed value of *t*, a wave localized in space. Make a program that visualizes this function as a function of *x* on the interval [−4, 4] when *t* = 0." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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0gPdddV7F1/zi0BjrO2NsX5fw/T4buuJk8gXGZjNlM6Zi3rhDkN/qE1UMyqcy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\nTF1YQDHGGFMX/z/FYGOdZ/rKwQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pylab import *\n", "\n", "def f(x,t):\n", " return exp(-(x-3*t)**2)*sin(3*pi*(x-t))\n", "\n", "n=500 # number of points to be plotted on the graph\n", "x=linspace(-4.,4.,n) \n", "y=f(x,0.0)\n", "\n", "# plot graph\n", "plot(x,y)\n", "xlabel('x')\n", "ylabel('f')\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Implement matrix-vector multiplication**
\n", "A matrix $\\mathbf{A}$ and a vector $\\mathbf{b}$, represented in Python as a 2D array and a 1D array respectively, are given by:\n", "\n", "$$\n", "\\mathbf{A} = \\left\\lbrack\\begin{array}{ccc}\n", "0 & 12 & -1\\cr\n", "-1 & -1 & -1\\cr\n", "11 & 5 & 5\n", "\\end{array}\\right\\rbrack\n", "$$\n", "\n", "$$\n", "\\mathbf{b} = \\left\\lbrack\\begin{array}{c}\n", "-2\\cr\n", "1\\cr\n", "7\n", "\\end{array}\\right\\rbrack\n", "$$\n", "\n", "Multiplying a matrix by a vector results in another vector $\\mathbf{c}$, whose components are defined by the general rule\n", "\n", "$$\\mathbf{c}_i = \\sum_j\\mathbf{A}_{i,j}\\mathbf{b}_j$$\n", "\n", "Define $\\mathbf{A}$ and $\\mathbf{b}$ as NumPy arrays, and multiply them together using the above rule." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The vector c = [ 5. -6. 18.]\n" ] } ], "source": [ "from numpy import *\n", "A = array([[0, 12, -1], [-1, -1, -1], [11, 5, 5]])\n", "b = array([-2, 1, 7])\n", "\n", "if A.shape[0] != A.shape[1] or A.shape[0] != b.shape[0]:\n", " raise RunTimeError(\"A should be square and b a vector of the same size as A\")\n", "\n", "c = zeros(b.shape[0])\n", "for i in range(0, b.shape[0]):\n", " s = 0 # s holds the sum of A[i][j]*b[j] over the index j\n", " for j in range(0, b.shape[0]):\n", " s += A[i][j]*b[j]\n", " c[i] = s\n", " \n", "print(\"The vector c = \", c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* **Plot meteorological data**
\n", "The file data/precipitation.csv contains monthly precipitation data for South-East England between 1916 and 2016 provided by the [Met Office](https://www.metoffice.gov.uk/hadobs/hadukp/data/download.html). Each row represents a year, and each column a month.\n", "To open the data file, use the command below:\n", "\n", "```python\n", "import numpy as np\n", "data = np.loadtxt(fname='data/precipitation.csv', delimiter=',')\n", "```\n", "\n", " - Open the data file, and print its content. Observe that it is a multi-dimensional Numpy array. Print its shape. How many years are represented ?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "[[ 37.5 106.6 110.7 ..., 107.6 106.7 87.9]\n", " [ 37.3 27.6 50.2 ..., 107.4 31.1 32.9]\n", " [ 77.5 30.3 24.2 ..., 40.3 57.8 65.3]\n", " ..., \n", " [ 181.9 130.3 38.7 ..., 108.5 135.3 55.8]\n", " [ 93.1 56. 26.8 ..., 54. 75.2 70.2]\n", " [ 114.1 49.1 79.7 ..., 28.7 94.9 17.4]]\n", "shape: (101, 12)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/dist-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['f']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "%pylab inline\n", "\n", "data = loadtxt(fname='data/precipitation.csv', delimiter=',')\n", "\n", "print(data)\n", "print(\"shape: \", data.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " - Write a function that converts a length in mm into a length in inches. Use this function to convert the precipitation data into inches in a vectorized way." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mm_to_inches(x):\n", " return x/25.4\n", "\n", "data = mm_to_inches(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \n", " - With a `for` loop, print (in a formatted way) for each month, the year where the monthly precipitation was the highest. A line like \n", "`The maximal precipitation for January is 181.9 mm/month and was in 2014.` \n", "is expected.\n", "Hint: you should use the [max]() and [argmax]() functions provided by Numpy." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The maximal precipitation for January is 7.2 inches/month and was in 2014\n", "The maximal precipitation for February is 5.4 inches/month and was in 1951\n", "The maximal precipitation for March is 5.9 inches/month and was in 1947\n", "The maximal precipitation for April is 5.7 inches/month and was in 2000\n", "The maximal precipitation for May is 4.7 inches/month and was in 1932\n", "The maximal precipitation for June is 5.3 inches/month and was in 1971\n", "The maximal precipitation for July is 4.6 inches/month and was in 1920\n", "The maximal precipitation for August is 5.3 inches/month and was in 1917\n", "The maximal precipitation for September is 6.5 inches/month and was in 1974\n", "The maximal precipitation for October is 7.8 inches/month and was in 1987\n", "The maximal precipitation for November is 8.0 inches/month and was in 1940\n" ] } ], "source": [ "months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n", "for month in range(data.shape[1]-1):\n", " data_month = data[:,month]\n", " max_precip = amax(data_month)\n", " year = argmax(data_month)\n", " print(\"The maximal precipitation for %s is %.1f inches/month and was in %d\" % (months[month], max_precip, year+1916))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " - Plot the [average](), [maximal]() and [minimal]() monthly precipitation with respect to the year. Don't forget to decorate the plot properly (add a plot title, legends and axis labels).\n", " " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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6pCzJOi2DfaKNmbo9YZOj3IwgYhrKkI/ATPBePoLu5mjURaa1dGMWmrp09JzF\n4DjLx1HUUK75CGxncQwjaJExF5eLIOhtTU+OxkBPtPf3QEjTUEmVsPniirwgmHDoPiAaRLJ1WgZ7\n5eYpr5MbKLQgaM2uachMusZHYE8cRkuHLDCCrfI+ZYkcK5v+pYEuS4OuGWdRQ5PknhxMoVFSOmGP\nz/YRdB8UsxBEE/vSkUsw0CPPoioI7yMw48lR01CmO5TVKqXuVEqtU0q9ppQ6IZPHyyn0O71NKyYn\nn1w12Cehp0oJKwjbds+PEWRaEJRapqHBXjmPg1ui62WaEbRug+oZ8t/rocxnU9uIYQQB4+7HCiPI\ntZ4EA13yfBQURpPdhofFR1CRAUHQ3yX+iOLKcKah7uboeEoqc1IQxPURKKXKgLcCJwMzgB5gLfAX\nrfUrAfb/A+BerfU7lFIlQIgqTWMcxkFU2ZD8A2QYAYggaAsrCNqjQgiiE1Smoob63KYhK3Km1WYE\nGZ5IDm6FurnRcfRbWnqmYQuC0knQtjPxNmOGEVjMtnLy6IzJhl34rWwSkUZIPS2iuYM4iyE9fQmM\n/6ekMpxpqKclKgiKy7NvrgwAX0aglPoK8DhwAvA0cANwBzAIfFMpdZ9S6qg429cApwA3AWit+7XW\nrX7rjzt0H5D3lExD/VFtbNLMcKahwX5x3MYIAsMIMu0jsMJHQZjJwW1iX1WFmX8QDm6VUg2GmWQz\nhHSgOzpxltUE61s8lvIIIHf8BCZCCyzW3e6YYowgMIwgDSGkRsiXhMwO7m6ReQBy1jQUjxE8o7X+\nss+y7yqlpgBz4mw/H2gCfqmUWgasAT6ptY6ZBZRSlwOXA8yZE293SeD5m6F+AcwdBYtUJGSswXqA\nUmEEM6UWzfCQUOFEMJOfmQwh2skpXYygr1Mm9eqpsccsscJHQSbD1m2ipQ8NZNY0NNgnArNuXlQg\nZVMDM1ojBDcNmQm3v8MR/iEbEGUKvowgR0xD/d2xZjgQIWVr4BFGkA4fQZccL2wuQIyPoEKev+Fh\nKMgdF63vSLTWf3H/ppQqUEpNcpbv11o/F2ffRcAxwE+11kcDXcDVHse5UWu9Qmu9orGxMfQJ+GJ4\nCP56FTz14/TtMwy6rNjhZBt/D/bFmob0UPAb2jysJZYgKE6zj+CfX4Nfvin6PeIsdpuGHEZQO1cE\nUyY19NYdgI4VBNnKJdB6pJba157YWT3QE80v6c0h0jzYC1gJjbnWk8AWujYj6LGcxYXF0ZDPdB2v\npCK4cjHBVud3AAAgAElEQVQ0IIqQzQggu36rAEgokpRStyilJimlKhH/wKtKqasC7HsnsFNr/bTz\n/U5EMGQHzZtEgqerFnlYGNNQ5WTR4EuqUmcEENxh7MUIzASVLtPQvlckdtvsz5xfqRU+CvJgtu1w\nJuck/ocwMBFDtbaPIEuCwAjYEit8VA8nPr7dZSuXHMZ2TgQkz2wzBaOhQ7SwYvsuSfiyG9FUTZVu\ndanAFvJhQkBNclvER+AoJzlmHgrCTQ7XWrcDFwB/Q0w+70u0kdZ6L7BDKeUUBedM4NVkBxoae16U\n92xVu3Sju1ns4eYGLa0OZi820Fps/MY+a5paBHE+guW4rY7+VlgsY0qXacg4gI1wcoePGkawf508\nnHVZYAQmOmk0TEN27RsIrkEP9ERzRXLJYTzYG73/IAd9BG5nMdHrb3wEkJ56Q7aQLwkRNWQnt4EV\nwp1bDuMggqBYKVWMCIJ7tNYDQNDA7P8H3KyUeglYDnwjuWEmgb2OIOjcm76epWHQ5eQQ2NpUGEpt\nyk4bRmA0xrCmodLq2N/T5awaGogKJXeBt2IrjwBg78vybrT0TJpqWrfJ5FU1NTZqKBuwa99AsInT\nNH8xgiCnGIGrzWau9S12R2hBlBGW24xgWuo+AlvIF1cEn8iNr7DcLQjGHiO4AdgKVAKPKKXmAoHu\nBK31C479/yit9QVa6+yFRRhGMDwodXqyje7maE16cBJefCh18yb49sLoTQwjIzYq6sWOHPRc+l1m\nGoN0CYK2ndEOaq07nGN2ihAwTjBTb2fvS/LdhHRmcmI+uFXKDxcUjJ5paAQjiMMEzTbG9JdTjKAn\nlhEUl4m/IFd8BP1dI/9rk7gYYxpyGEEqiYVm4o9EDQW8hyPlJYyPwJhncyu7OKEg0Fr/UGs9U2v9\nZi3YBpyehbElD61FEJjQsY5RMA91N0cvPsSv5b7/VfEpNG+M/mZS4o2jrqBQ9tcVlBG4IngMisrT\n46iy8wLaLEFgzDEgk3FptZiOVIE07y6tymwegQkdBcs0lC1B4GIExiwYb+I01yIMIxjogcd/mPnW\nlm5GALlVb8h2FheVScl1IwjKXT6Cob7UHPF2BFWYhLJI9KBJKDOCYIwxAqXUVKXUTUqpvznfDwcu\nzfjIUkHrdtHCDj1LvmdKELTthNf+7L3MmIYM4rX5Mw4l++ZyMwKQAnZdB4KNLeIsdpuG0tSu0jxw\nhSXRwnJ25VGDUsdPMGmm+CgyaRoyhe2MICguD18OIBXYRdAgmGnIXIuKesmSDcIIXr0H7vsibPpn\n8mMNAjcjADmnHc/Aw9+W1841mR1DPNgRWkoJK2h3zJUxPoI0ZBfb17bECR8NwjDsEtRme3t/OYIg\npqHVwN+RzGKA9cB/ZGpAaYExCy1yQhvD1ugBmZR7EmgQz/wcbn8vbHti5DK3aSheJUojCOybw9Rz\ncQuCwD4CP0aQJkHQuk0czzOOtkxDXbGMAKIO49q58p5JZ3Fvq0y6tU4+ilKZN0XZcAuCIKYhW+BX\n1AdjBFsekfeWLfHXMxgegr9/AQ5sTLyuDS9GMP0o2PcyPPh1ef39c+H26YUtj8KfPhnedDPQHdWw\nQYSUMVe6ncWQmsPYKGkljmkIHew56m4WFm7Gaf7PHGtXGUQQNGit7wCGAbTWg0AS7bayiD0vyiS1\n8HTRCJNhBP/4b/jt2+KvY2jfXz8b24FseMipd2IxgrKaOIzAETj2hBWZIKz+A5WN4XwEhSUjk5NM\nQkuqOLgNamZB3XyXacjFQMxkWOcIgpJqcYRnonCZEZLVVqnvkspR8BFYmcUQzEdQXC5aY4/lRrNL\nd9vYagRBwN4Au9bAk/8Lr49IDYoPL0bwzl/Bl1rkdfT7Ys2ZyeLVu2HN6ujzFARDA+L/swWVuddK\na6QroIG5H5JRCA1inMUmBDTAZO6eB4rHqGkI6FJKTcaJFFJKHQ+ErJ6WZex9CRoPk0mgampygqB1\nW+KGFj0HpVHFvpflRrZ/R0dL1oJoKwNd3o3jPRmBY/+1BUHVlOCCoK9zpFkIHNNQmnwEdXOhdrY8\nYEODI30E4M0IIDOTs/lvbCZWUhnfFPXo9ekzsbidxcZuHc80FI8RvP5X+MGyKMMF8YEYU1xQQbD5\nIXlPxHDdGOiNJiEaKCX+qoJCmHyITN5h9+uGCT8O00DGHaEGUVNcRV3surVzABWcQXnB9v8Y7T4R\n0+xtk7nIHk8kamjsMYJPA/cAC5VSjwO/RsJCcxd7XoTpy+Rz9bTkcgl62+UBjkfhettg1kqYd7Jk\n2ZqH2Njx7ciFeKF3cX0ENiNokAk0CK3s6xhpFgKhqenyEdTOFQewHpKHub/Lw0dgGME8ec+kAzci\nCKwM9ZLK+A/sY9+HF29Pz/HdzmJjt47nLI5hBHWxPoJd/wI0vHhb9DdjFppyeGxF13jY/LC894QM\n2hvsiZYl8cLkhfLesincft0wYchh2IWbfUFU6Sh3CYLiMrlPUxmnbfYLYuff9gT89CTY9yqs/LA1\nljHKCLTW/wJOBU4EPgIs1Vq/lOmBJY2OvWILjAiCGckxAkPn41Ut7GmVm+6cb8n6D3xV6Hwkq9gV\nPgre5iETzWDHJg8ZRmD7CBxbZxBW0B+HEaRqGurvkuglwwhAzEN9cRhBxDTkCIpMOIyNADb/E4gp\nyk8QDA85tWnSFNVs25ENSicl7yNoWifva38fZZJbHpHzW/QmYQZx+ljLmLpgh5PcH/Y8vRiBjXpH\nEDSn2L7SMILmEIzA7Y+BqNJhRwwZTF4gYdrJwu0sBn+F7Nn/g9VvEdb0gb/DG6zYmjHsLAY4FliG\nlIh4t1LqkswNKUXscWTUdKcw6qTpydkGI4IgjnO256CECE5dCsdeDmt+CT8/A175oyyvcDmLIQQj\n8DANGU03SOSQHyMorkjdNGRME7Xzoiaf1h2Os9h1TCMAI6Yh53/IGCNQsUwsno/AXON0CQKvyaks\nQXOaET6ClqjTtGmdbN+5D7Y8LL9veQTmnyLa+PBg1D/jh+1PwvCAmDDTzQjqnRayKWnaPVHfQBhn\n9oCH0DX3WoWHIKhfIONMNpcgJnzUmIZ87qs1q2HaUfDRR2H2ythlJrt/rAkCpdRvgO8AJwErndeK\nDI8reRh76rQj5b16umjcYamYmSTi1SrqbY3S0LOuld7C3c3w7M/lN3f4KHgzAmNjHfAyDVkaWZUR\nBAEih/o9QjnN/lJlBMaBWTcvmgjVul0c1G5BcOjZcMyl0RC+CCPIQCx6V5P853Z11niCwEyM6Uri\nMsXjCi0HfSLTkJsRDA/KfzPYJz6AN1wmzs+Xfyc29M59MP9kcdJDYrv35odkPHNOSEIQ9MVnBMXl\nMGlWapq2UdJUQUhG4GEaiscI6hfKM51s5naE7VVauQA+k3nXAZl/vBi5Uo4yllumoSDN61cg9Yay\n2O8vBex5QZxY5iKYRJ2OPaIVBMFgf3Sy9GMEQwMywZjm8IXFsOL9Ekmx9k55OKqnRde3qyO64Rk1\n5EooA4sRBDAN9XVGtXAb6cgsNslkdXNloqiaCgfWS+ie2zQ0e2WsVpRpZ7HtH4D4PgJjkkuWEbTt\nkonc2MpNyQNTVgTkunfFMZ24GQGIYOrrlP9z+jI4/Dx45S7xC4AwAqOpt2xGynj5YPPDMOtYifDy\nikDyg9Yjaw15YfKC1BiB8Q/MOEaUuKHB2IgfP3g5iw0jcPsIwPJnbE6uqc5At7CqwuLoMb3uK629\n70MbJbnXtziIaWgtMC3hWrmCvS8LLTMwk3EYh7FN5f18BGbyNtmjBoVFsOxiOOMLsROCn49geCha\njC4RIzA3V5BcAl8fgSMIUpHrB7fJhGfGUzsnas/2OqaNTNb/6ToQ65cx4/E7Vo8lCJKpR3XnB+B3\nlv3XbkpjUFYbzkcAorWa/7PxMDjqIrmej14vTs+6+XJfF5XHZwRdzfI8LDjVcUSHEHhe958X6hem\nyAgc/8CC08SE1RpQWMVjBJ6moRQd2wM9UQFQEid8tLdVWJ37PrSRg81p4nUo+5NS6h6gASk9/Xel\n1D3mlb0hhsBgv5goGhZFf6u2GEFQ2A+uX/lao016aR9esJu0+B0rxkfgkVBWXC7Oz6A+Aq9JuagM\n0NGidsmgdZtM/kbQ1cwWRgAjGYEbGXUW+zECnwb25hrq4fCF1A5ugx1PQcvW6G92pqtBWU24PAIQ\nRtC0TmzJkw+BuavEBNfbKmxAKXnVz48fObT1EUDLJFteJ6a7RM5lr3HFw+SFMq54JpfhYUkau+dK\nuO9LsctM+9X5p8h70MghT39MHNNQ3TzH/JSsILDancZzFkcCFuIwguIQtYqyhHgc7DtZG0W60OY0\nJTGZpSDOYgjnMLZrkvhlIxrtqrzWe7kbfs1pbC3NjhryCh8F0TQS+QiGnRr4ns5iq/qhe99BYUJH\nDWpnRwWL1zFtRExDGfARdPoIAj0cW7LYwI5/7zkY/FqCRPKAnEdvm0z4XoKgok6uhd1kyIbd/CXC\nCA7C/tfElGm2OfId8PgPohMmyPJ4E+fmh0VxmHEM7H5Bfutti6+txoyLYIwAxOTipYm/eg/ce3VU\n8y8ogtO/ED2v9p0SVGF8egc2wKKzE4/P01nsRKi58whAEitTCSG16xpFIn88JvMuj4hBN8YSI9Ba\nP6y1fhjYDjxtfX8GCGFszCJMNEudNUmVThJKF6ZBjdHgymoSm4aCMoLicnkI3KYhs5+i8sRRQxAs\nqczcoH7OYkj+RtQ6mkxmYAveRIygqEw03XQzgsE+YVsjBEEcU1SvSxCEwdrfy3lAVKu1JwuDcsvc\n4wW7+UsMI3gdGhdH11v5ITjsrdH6WSCMoGWLv1lr80Mwb5WYK819GvQ8wzAC8Ne0X7xVWMjbb4Jz\nfyhmE1t4te2SXhsV9XL+QR3G/R6MYO4qOOOLktfjhfoUQkj7u6P3dlGpU8PKixF45LK4kYPO4iA+\ngt/hlJdwMOT8lnsw9kV7YlJKWEFHGEbgCIKGRf6mIfNAuX0EflDKuwKp2c+kGSN9BKpAhIeNykbR\nfOPBr84QRB8cr8ihgd7EoaU9B516PpYgqLH+70Q+AqXi1xs6sBF+ckL4jlJ+mli8BLYYRhAimmT/\na7BvLSy9UL4bh6ddH9/ARI757d92yJbXAkrMmC2bYcqS6Hq1c+Dim2O17rr5km/iZfZs3y1mI8Mg\nwgqCoIzAmFz8NO2OPRLKfeQ7YOYb5Lf9r1nj3BWNPJt8SPAQUi/TUFEpnPKf/kx38kIRnMn4x2z/\nj1L+fYuNIKiIxwjGprO4SGsdMSg7n3Oku7YLrdtl4jQ3lkH19HDO4oggWCzJYXYdocg6hhGEMCd4\nVSA1D2bNzFit1XQnsx3OEKzekF/lUbD6FntM+HdcAn/48MjfbZieCXUu05BBIkYA8ZO8dj8vZbl3\nPpt4Pzb8NLHAjCBOmYThodh74OU7ZfI78Qr5bipeejmLjSDwq6NjF3YrKBQWuuNZydZuPMx/TBCN\ngvMqNWF8NlOPkPfQjMC5PxIxgqJSiUjy07Q79kYDNhoOFRZlC4I2SxA0HBqcEQz0ACqcebN+obDG\nMDWN7OPZ/4VfT4JIVYE4kUljyTRkoUkpdZ75opQ6HwhUC1kptVUp9bJS6gWlVLxG9+mBKYRmx5GD\nCIJQjMDR2hsXiX3Za+L1ixqKB68uZWYymjRrZK0hr5u8slFuZK+aRQZejesNiiwfgRsHXpe6O/H2\nHWFdNiMIKwgq4xTgczTnMDHl4O+ki9eusrfNMsf4TJDbn4YfHQM/O0kmO60lPHj+qRKdpgrjm4Yi\ndn+fycdd2K28DnY5j0oqgsCYXyYf4uzXuU8DMwLn/kjECEAmWC9GMDwkPjZT9K2oVLRyExHV1yET\nc43FCDr3+edd2Pel8ce4FaV4SGTGioeBrthQVT+Hb1eTzAnuYo82xigj+CjweaXUdqXUDuC/kFIT\nQXG61nq51jrzSWgmmsWNSdNFMwlKCXvb5AE3D5qXw7jnoGi2QWKeDby6lEVMQ9OFjhvN0y+Gu2oK\noOObMvzaVEKUEXiZhroOCJvY/4r/viPJZLYfpio6obqrj3ohnmnI/B9hSyZ7FZwDixH4JPKZ7Fi3\nDX9oEB78BvzyTWKD79gDPz8dHvuusKIj3yEKR/X0qGnItiMbRBiBn4/AVeq5oj5qFjSTuB9qZjnN\nWDwih5o3yYRjJuFMMQKQCbZ588jnq6tJFCmTTAgi3AwjMAEck2bJe8Ohztg9rv2G++Fbc6MC312C\nOghSCSEdwQh8TEPdB+L7B8BhBGNMEGitN2mtjwcOB5ZorU/UWqeh9mwG0LrdO4mqeoZEtQTNKuxt\nk0nb9An2slfbWcVBUTppZPioESgm4sHcIIN9sclkBmaii2ceipiG4vgI3Kah/u7odtuf8t936zY5\nbzNeA2MeCsQI4jSnMRNVaEbgZxqKxwhaxZZbOmnkBPnHj8HD35IY/o89Dh9+UCasB74qDWSWnCvr\n1cyKRsR4mYYSOYtHMAJn/foF8bN6QQRR3VwfRrBJ9mHahpbVACp4pdCwjMDL5GICNOyy4FMOl/EO\n9EQFaIQRxBEEu5+X+9NUDuj3+K8ToW6uKHhJMQKX/8eXEXjksrgxFk1DSqlSpdR7gCuBTyulvqSU\n+lKi7Rxo4B9KqTVKqct99n+5Uuo5pdRzTU0p9BYe6BHN3UsQmBDSoOYhEw4YaWjhETnU0wrlNSN/\njwev5jQ9rULbI/VLjCDwYQSmoFq8pLKIs9gvj4CRjKDbsvZtf9J/3we3ev/HNbPFPxPEZltanZgR\nhK1z39UkE7SbBZXG8RGY/768dqQg2PooHPF2uPBnohTUz4cP3QfHXAKrrowKwpqZ8Z3FRSVyHYL4\nCCBqSkpkFjKoX+BvGjKmEHD8Dx4Czw9hGQGMnGA9BcFhgBYfhhGgxkdQP1+YkFc5auOHMWalge5Y\nU00QFBaLxSBo+W4b/V2xDMSPEXQ1JRYEYTqcZQlBTEN3A+cDg0CX9QqCk7TWxwDnAJ9QSp3iXkFr\nfaPT4H5FY2MCShUPpktWnRcjMLkEAR3GEUFgWtz5mIbC+AfA3zRUXmtlKzp/7WC/v48ALIrcCzec\nCq/fG13Hr3E9xOYR2DCRSGW1wgj8btKWLVFzio0ZR0sUSxCbbTxGYDTn7uZwdWG6HEruPn4iZ3FZ\n7cjyz0ODcs3rF8auX1IJ5/0Izvjv6G+TZoqJY3jYmxGATO6+UUM+jCCoIKibL0lt9vUaGhCB7TYt\nhckuDssIYKTJxUQz2aVWGp1IqP2vOb4VFS0DU1QqE7UXGzR+mIgg8PDHBIEpPhcWXqYhv/DRIKYh\nSE/v8DQhiCCYpbW+SGv9ba319eYVZOda613O+37gLqSKaWbg5cQ0MIIgaHZxX7sIguIymSjSZhpy\nwkfth9ZMRkEZgbvw3M5npb7SdqtdZrzwUb88AmNaWfxm+Z9MdJCNIafaZZ2HIDjpU/DRx0b+7oVE\nPgITMhuGFfhpYkbAjijtMSzsrLx2ZGewzn1i2zYTVDzUzJYIr/adgPYRBJMTMALrOifDCPo7YrPN\nW7dL1JFbkIURBGEYgZ/JpWMvoKLMGoQ9FBSLIGjfKcpWYbG1/FDv627Yw36bEYT0EZjje/kz4mF4\nSK6x21nsTigbHhLlJaEgcD3rOYAgguAJpdSRYXeslKpUSlWbz8BZSN2izMBMXF7O4uppROKzg8Aw\nApAb1Y8RhAkdBbFFDw/GagI9B+UBddcv8YsaKquVidJM3KZRiS2s+jsB5W2vj+QRuLQRs7/DnQAx\nLz9B+04ZvxcjKChMbNM2SFQR1IQ8hulY1dUUO+EYFJV5N7DvawO0XGf3BGnuk0CCYGbsWL3MFRX1\nCaKGvExDi73Xd8OrFLQ7YsggU4wgYnLxYASVDbETfWGxOIWb1kWTyWw0HCoCxZ0kZzMCrZNzFoMI\nx/6O4J3+YGTDIXDCR10TeXcLIzoTeiEHu5QFEQQnAWuUUq8rpV5ywkGDNKaZCjymlHoRyUb+i9b6\n3gTbJI/W7WIjtiMUDAqLRUoHLTPR2yalfwGq/QRBkowAYv0ERhBEtARjGur1FgRKxSaVbX1U3m0h\nZ9pUeplpInkErpvQPBjzTpbJ0ctPYAqcmW5jySJe3+KeFpixXIRdGIdxl0+0hlLeeQt2+K+7IYzR\nPm3bth8muQWBHyOIFzVkTbaHXwBnfT0qDBPBVCQ1JSQgPYIgDCMwx3Jr8p37Ys1CBlOWSK6InUwW\n2c9CuTdtv1wkzHS2MOr23ck5i83+IZzD2CvL2iuhzC9yzY0c7FIWRBCcAxyKaPTnAm913uNCa71Z\na73MeS3VWn89taEmQOs2iVwp8Dml6UdJk+wgwsDNCNzlKQZ6hCqG9hG4Yrm1jjos3TXOh/r9tTGT\nVNbfFU28soWVX1MasPII3IzggNzcpVUw+3hvRmDCFL1MQ2HgV4p6eFj+j8opcoygjCBS+tfnAfRi\nIHZCYHmdUzXS0ULbwzACJ1rKJHB5mSviCQI3I6hskEQ1v/vYjdrZoo0bhQBkkiurGVn7JywjKCge\nmZPjBy9NvmOPtzBtXCKKm8n7seGVG2Ge2UOccttNr8VWAw2DeLkXfjBKhM2wTUKZbWIKUl4CcrJL\nWbzqo04pPzp8XrkFdyE0N875tkyud18R3z5omrDHmIb2x24TtuCcQYTGOzehESjldVaNc9tH4BOB\nU9koPoLtTzqmmoWxwqq/w9tRDDLBFJaOjBqyJ9I5x0tyWZfLnHFwq4S0Bpkg48GvOY0x15TXRSeW\nIOjrkP/R7wH0EgQ2Iyivi61A2rHbKQIXoG59Rb0I7OY4jKC8Xq6JFwNK1A4yCOadLD1yzSTcvFE0\ndDcjdAu8eHBHMyXC5ENkYrMj8+ysYhumdMZQ38h7yWuiNlFZC40geN3fMZ8IhoGESTD1ZAQVgI7V\n6gMLgjhJnaOEeGrHLc77GuA5532N9T230LrdO2LIYPJCOOtrsOkB6SnqBzMZGEFQPU0mTbtGUNiC\nc5ExOFTdaI92KesIIzCmoT5/RlA1RTT4LY+K1rb0gtgubH0+lUcNiss8GIEV7TDnBHk3vW4NWraI\nsA2qJfrBjxFEBGyd/Fctm73Le7iR6AH0ak4TwwisYm8gjKB6erAIKKVkcjEJcH5RQ/b+DYaHnVIi\nSUxoNuaucgrVOYlazZu8k9HKakXgBan86o5mSgSTDGZY3NCgKFBejMCuoeQ2DdXMlnvaVgKMqW76\nUWJ/3/9a8oKguEyud5iSMxFB4AofhVit3viBApuGcqcUdbzqo2913udrrRc47+YVsNVXltDXIQ+C\nl6PYxooPilbxjy/6a5t25VGI+hxsZ2zYgnMG5bXRbl7u/XhFDXkllIHcaJ37pY/trJUjM6D92lQa\nFHlkNto29hlHy7HtSCQQ01Cq/gGI5je4J+duSxA0HCqTpKkoGw+JbLNezWncjACi16N9dzjWUzMr\nqmF6Oej96g0ZVpYyIzhJ3rc+JpNW+05vQRAmuzgsU3Eng3XtB7Q3I6ibFxUybtNQQaEsj2EETphp\n9QzHv+AIgiDJi16onh6uP4mZsN0JZRB7X3U1SWBCIgVxLDECpdS8eBsqwax462QNkWbqcRgBiPZ2\n/o8lyeefX/NeJyIIHMuYVy5B2KY0NhoWjRQEMVFDARhB5RSZJHc/L9UlIxnQjnmorzN+qYeympGT\nQdf+aGhqcZnUsN9mOYy1lnh1r4ihsPAL6TRjqqiPn2XqRiBGkMBHYB+/Y3cwR7GBPZnFYwRuQWBY\nWaqMoG6uVIDd+lh0AvVqyxpGECRqXO9G9TRhoebeNvdilYcgKCiMNo9yMwJwYv2tshntO4UFF5VI\nWO0+J/gwGUYAkmAapj+JJyPwsPN79cz2QuRZHwOCAPgfpdTvlVKXKKWWKqWmKKXmKKXOUEp9DXgc\nWBJn++zhYJwcAjcmTZdJztgd3fBjBLYg6LEmkbAwgsA4is1+CkskFrs/QfgoxE5480+Oal3m4ev3\n6U5mUDcvtn/t8PDIqJs5x0s6vxlPd4vsN1VHMQQ3DUEwh3EQQeBOYOtpFRNEcUVsQxitxWwQhhHY\nk5mfsxhGOozTxQhA+g5se9w/YggyywiUcspIO9crklXs0+V2yhK5372Wm2xp45ezK5Q2Lo6GPifj\nLIYkGIFHE5xIoqItCALUGYKxFT6qtX4n8EVgMfBj4FEky/hDwOvAGVrr+7IxyISwm6kHQXmtf80V\ntyCo9hIESZqGQARBb5uYduyJT6nY1POheD4C52YrKhPTkHmYzBj74jiLwREEW6MPWm+rJCDFCIIT\npIfs7n/JdxMxlBZG4NOu0tjQy+vEzFNWEyyENFL6N17UkIePoLzWaQhjTZC9rTJBhzIN2YLAJ3wU\n/BlBMolRbsw7Sfa/7i/yffLCketkkhGA4+B3BFEkq9iHWR3/MTjnW97ac/0CYcamjEq7lW9g+xeS\nZgQzZN9B23b2e+QReNn5g5SXsPdj9rvmV3DP/4tf9TfDiBujprV+VWv9Ba31aVrrxVrro7XW79Fa\n/1ZrnTv50a3bRTsIEuUBTkPxgIKgrFaibOyonN5WsQWaPsRh0OhQ4gPrYwUBRAtZRbqT+fkIjFP3\neGEN5fUSd28evkTO4vr5ot2biclLo55znLwb81C6cgggylb8GEGZM0HbWaYb7oPrD4O9HjmJXU1y\nvfz+r5Iqbx+BEeR2WK8xGSRtGvKY1P0Kz4VJ2koE4yd45Y/CYr0YYSYZAYiS07ZDJriOvfKM+GnI\nM46GY316X9iRQ1o7jMD5jxvTIAiqpwPavw2t1tJfefND8t2rCU6Jy6cHopAkSiaz92P2++JtsOv5\ncJWM04yAwco5joOuZuqJYOKpvcJI3VFDSo3MLu45KMuDxnrbaLAEQW+r0GMzaZc4dcoTdYeqngEo\nqYkPMo6qaeLQHuwTTT4RI4Do5O7lbC2vk2Qlk1h2MI2CoCSOaai0JvpANBwq0TibH4Lb3iuCzjyc\nNoyuFFEAACAASURBVBLVdympGtnA3jACkOOV1ggjCZNDYDApgSAwhefcUUNhk7bioXaujGOoz798\ndaQngaMEaQ33fRl2PDNy3WQYgTluyyYnq3hKcpObHWbd2yZat2EElZOj1zpZZ7G5tn6RQwe3wr9+\nBc/9Ur57+QiKXT49CG4aMmbggR5RDnY8BYvPCXUK6cb4EASJQkfdKK+V+Hu/ZiWoWGerO7s4maxi\ng0kz5SYyjMCYhUB+7++ONoL38xFUNcKlfxJ6HTPGvfErjxoYO78py+FnY59zvEwSw0OybvX09Exa\nRaXefYu7W2L9LpMPEcftre+Wz5VTYK9HUnuiB7CkEon5trS33rZY056pQNqRDCMwpqE4HbO8ykyk\nkxEoFWUFXmYhkLEVV0QZQdtOePz78NB1I9dNihFYIaSd+6Jm1bConSsMt2XTyAqlEK3DlBIjwD+X\nwCRTbnsiWs7CfbwSV9SQX89sLygV7Vu84T4J6V38pvDnkUaMfUFgmqknCh21YSYAL/NQb5uYfGxt\nv2pqbPhob2ty/gGQm6DhUEcQuARKiVPIKki/2Pknx96Y1U7znXiVRw2M0DRavl93rzknyP72vSLs\nIR2OYvDvW2wEo4HRMGtmwSV3w8xjovXobSSyzXr1JOhpjRU6FfXJm4ZKnX4S8TpmeQmCdDICiAoC\nd7E5G+V1UUaw02ECmx8aWVhxsDc8IzDHbd7on1UcBIVF0XLRpsaQbX6LCIIMMQLDgrv2O70TusU8\nbPsz3Amgfj2z/WCa07z+V5lfph8d7hzSjECCQCk1Uyl1olLqFPPK9MACQw/DmV+Cw88Pvo2bItuw\ny0sYmLhm8+AmU3DORsMi0Zrc+yl2ClkZH0FhiH6sphSG0bLjRQ0Vl8tDGsMI1Egfi0ks2/5k+nII\nDEqqPZzFB2PLIiw8HU64Ai65R1jQ9GUiQN3FvoKYhiBW8LiFeXmdMJL23bKveK0GvTBpVvwJ3avM\nRDoZAUgJhooGmHui/zp2mYkdz4rmrYdh7e+j67TtdCZyn4gfP5RUSELYgfX+WcVBYSKHTB8CmxFM\nSZERVEwW80w8RmCE2rbHRXN3F7hzJ4B2JyEIettg4wOw6OzkzMxpRJDGNN9CQkX/G7jKef1nhscV\nHAWF4nQy2lAQuGv+2PASBPNOEturqeuTimkIok619l0uRlDp8hGEEATV08QGbbTOeM5icISbwwg6\n93vHP9fOlglu04MyMaQjYsigtGpkhqubEZTVwNlfjzYWmr5MJq19VivNocHEpX/dzWmGh51+xS5B\n0HMweU22Zmb86B+vUtTpZgSTZsBnN8HsONXebUGw8xmYfZz8ry/fEV3n8R/I+4oPhB/D5EMk4aur\nKXlGANFcgrZd4nS2i0kufRucenVsBFEYKCXPixcj6G6R8ipHv1eu2bYnnAJ37oZDZYCyGEHA8hIG\nxRWw+UF5BhaNrn8AgjGCC4DFWus3a63PdV7nJdwql2EmG0/TUPtIQTD3RLkZTcnnZJrS2LAjh8rc\njMCOGgqhKRrty5QCjscIQMw8NiPwu4HnHA8b74tuky54NafpaYkvYKcvk/c9VqXN7gOADuAjICoI\n+jtEoNjX2fQkCJtDYHDsR+Ck//BfXl6feUYQBMYXMtAjZrZZK+HId0ly4oENYiJa8ytY9u5o+9Ew\naDg0KqhTZQR97TLG6umxTueKejj9c6mVOqme4Z1LYMqqzDlBnvttj3uXs7DDvcHfvOqHEsdXU1QG\nC05L5gzSiiCCYDNQnHCtsYSwpqGyGgl32/KI+CR621JnBJGxuH0EPZYgCGMach46U/MmESOony/U\neKAnfp/VuSeIY91sky6UukI6TeXReP/rpJkyodp+gq1OM5xpcVpmuPMW7PISBqYgW/vO5ATBoW+E\nlR/0X14xeWThuXQzgiAwjGD3C3JdZx8rLTlR8NId8MQPJers5E8nt/+GRUiHWryzioPChJBuf9I7\n+zhV+GUXb39SzEYzjoE5J0ogSvMGb7Zn56cELUFtYPY3/9Tk+iqkGUFiu7qBF5RSDwB95ket9ZUZ\nG1WmkchZXOaRHzD/FHjiR073qqHUfAT1CyRqRg/FTnzFbtNQEozAxN3HcxZD1N5/cJvcxEbbdsP4\nCext0oGSqti+y5HKo/W+m6CUjNMWBK/9ScwGs+KYQyKMwBEEdnkJA1OBtOegE56bZtiF58y1GhVG\n4Ag84yiedaz4XxacCi/cIuM78p3eJSqCwA5dTZURgFwzd/OadKB6Bqz/uyh2toN/+1Oi9BWXRX0t\ne1+W0uxuVDRI+e/uFnmGCoqD5xYZ4T/KYaMGQRjBPcDXgCeIrUAaCEqpQqXU80qpPyc3xAygtFom\n4qA+ApBSv8OD4uWH1ExDRaXRSdWejEyN84ggCOGwjAgCJxM3oY/ACiGNF37ZuERi7EuqgyfsBYHb\nNNRtZRXHw/RlYoMe7I+G3x32lvjONnffYi9GYDupJ6Vg2/aDV72hgSQEfqooq5X7a8sjcg+YLPUj\n3yVsaKAHTkqSDUA0hBRS8xHUzhVzLGSGEVRPE6XLJJCCnPuuf4k5FIRlmjBsL639zd8WH8bN7xTm\n4NUz2w9GECwa3bBRg4SCQGv9K+BWogLgFue3oPgk8Fpyw8sQlPIuMzE8HO1X7Mac40Xiv3qPfE/F\nNARR81AMI3BqnJsOZmEmiIoGEW6mhlBCH8E8eW9aJ9p4lY8gKCiAhafJQxH0Jg8Ct7M4aGnv6cvE\ndNH0mjixB7pgSYI+SW5B4McIDFKZwPzgVW9osMcJS8xixIg5zy2PxjqVl5wrjPTw86NROcmgeoYT\nRlsY3EzihaKSaNMfd4XSdMCY/2w/we7n5d4yLLigMJph72UamncSvOMXUobllbvCne+8k8UPkwml\nIwkEiRo6DdiA1Bv6CbA+aPioU530LUCcBgCjBK8yE/0dRPrYulFSKY410wkqFdMQRB3GtlZqTBgm\nAzWMj6DAiazQQyJAChO4dSobZILc5bSWiOfkOv8n8O5bg48lCNyMwF1uww8Rh/GLYhYqq5GHKu6x\njGnIETyePgKbEWTCNORRbygdTWnCwvy/Q31yPxuUTYLLH4TzfpTa/gsKJKGtamrqfSuMeSgjjMAk\nlVmCwCSSzT4u+psxD/n5cZa8Fc51oqyCOopBIh0v/Fnw9TOMID6C64GztNavAyilFiEM4Q0Btv0+\n8FkggXo6CvBiBO46Q27MPyVaoz9tjMAVNQRRrTGsyaB6qjiAE5mFQLT7unkSSw7Bwi/TidIq0b4G\n+0X7s0tQx0PdfKHru9aImW7xmxMLvaJSmYiNbyERI8iEICj3MA0lU8Yh5XFY5+kOM21cnJ5jLH5z\nNCM4FdQvkBDLTPgIjCbe7hIEDYtj78G5q+Q9XmjwMZfI8lR8IqOMIJy02AgBAK31egJEESml3grs\n11rH9ScopS5XSj2nlHquqakpwHDShLLakT4CIwj8HD7zLc0zFR8ByMNy4pXimDIwdkjDCMIklEFU\nywk6cdfNizYJD6PNpAOR5jQmkiegj6CgQDpVvXSHTOiJzEIgQu+oi2HdX8Uf0uOq8WQft7gyuWKC\nieDVpWw0GUFxJUxZmpljnP556fuRKhoPEz9BkPLyYeEuMzE8JDV/5ricwjOOlvskkYJy5DvC5TLl\nGIIIgueUUv+nlDrNef2cYK0qVwHnKaW2ArcBZyilfuteSWt9o9Z6hdZ6RWNjFicjEz1hIxEjmLUy\nqqWnahqqqJfWmbb5x6StdydhGoJo0k28OkM27CigVOy5ycAczzQVClPae/oyJ7a7AhaeEex4x7xP\nGMhLt8eWoDYw13NSwBaVYVFUKtfF7SMYLUYw85hRrXYZCMdcAh96IDP3ZnG5/BeGEWx9VJ7/hafH\nrldUCh+6H1Z9Mv1jyCEEEQQfA14FrnRerzq/xYXW+nNa61la63nAxcA/tdb/nsJY04tkTENFpaIx\nFJakp4a8GxFG4EyKoU1DDjUNygjsvIBsMwLjkNvmmNrclUfjYdpR8n7ovwWPwZ+yBGaugH/9OrYE\ntUFBoVz3TDiKDSrqRt9HUFEvbMit+eYiistEYGUKdlLZS78TQe0VxTNlSeqm4BxHwqdOa90HfNd5\njR8YZ/HwcDRqI5EgADjuY0KpM6E1FlvOYlUYXmMzgiCIjwCiIaRFZcG3SRdqZooNeOtjcMLHR1Ye\njYfZx4rJ4Mh3hjvmMZfAn66UydirSGH9Aim9nSm46w0lU9gtVZRUwqX3RIXpRIZJKhvogVfvhsPP\ny25yXw7Bd6ZRSt2htX6XUuplIqmCUWitA99JWuuHgIeSGWDGUF4rCUT9HdGJ34RtxhMEi9+UuZKx\nhhF0H0wuttxkciYKHTUwpqHKKZkRbIkw7yR5AIeHRtYZiofJC+Ezr0sf2zA44m1w7+ck+cdrIrz0\nTxIinCm46w0N9HgnL2YaY9iWnVZUT5dksfX3yjxw1LtGe0SjhngqpzGKvTUbA8k6IoXnWi1BkMBZ\nnGkUW87isP4BCG8aqp3jdJHKsn/AYN7JYqrZtzacIIDwQgBEQC69EF74rTf7CCpAk0V5fWwP5sFe\nKEqyZn8eqcO0rHzhFlGiEoUhj2PE61ls4qo+rrXeZr+Aj2dneBmEV+G53jYxkYyWE83Euw90J8cI\nIqahgBNaYbEk7SQzqaYDJjRv62MjS1BnCsdcIu+pRn0lA7dpaKAn+z6CPKIwLSs3/EOiflLNexjD\nCOIs/jeP33KjQEYqiBSes0JI/cpLZAu2AzoZRlDZKAIuTNXI838Mp38h/LHSAdtPkKjyaLow+1g4\n+n2jk9pfM1NMEF2OeWg0fAR5RGHni0xgsxDE9xF8DNH8Fyil7P6A1Uh/grEN2zRk0Ns6emYhcBxV\nCtDJMYKCQrhiTTi78/xRpsPGT9Dbnh1BoBSc/7+ZP44Xpjpx+/tfkeTEPCMYXRgG3bB4wjvP4zGC\nW4BzkaJz51qvN+RUGGiyMIzANg21bs9MFmNQmF6mICGqyaBycuJM21zCvJMd34we9yF6kQSufa/K\n+2BvdgvO5RGL2jkSHLD8PaMTLJFD8GUEWus2oA14N4BSagpQBlQppaq01tuzM8QMwUw6hhFoLa3x\n7LLLowHTt3iiTBDGTwDxS1CPB1RNkeKA+9Y6TdF7Jmy4Yk6gvA4+8XR6y6uPUQQpOneuUmoDsAV4\nGNgK/C3D48o8iitEGzA+gs79Uu5gcpzG39kaFyTnIxiLMH4CGP+MQCkxD+17BYb6SdoEmEf6MHnh\nhHYSGwRxFl8LHA+s11rPB84EnsroqLIBU4ramIZMi8f6URYEJnJoIk0QJmxvvAsCgKlHSD8FUxI7\nzwjyyAEEEQQDWutmoEApVaC1fhBYkeFxZQdlVpmJZkcQ5BlB9rHobMmkTqZH7ljD1MOlxtB+p0XH\nRBL4eeQsggiCVqVUFfAIcLNS6gdAV4JtxgbswnMtm8RUVDPKk1HJBBQEh70F/nN9Wss/DwwNc+b1\nD3HvWo8G5aMJEzlk+kDkGUEeOYAgguB8pG/xp4B7gU2Ml2zjcqsUdfNGcRqNdkXG4gloGoK0Zzfv\n7+hjU1MXz271aEc6mjCllXc6gmCiXec0Y8uBLs790WPsb+8d7aEkRHf/IO29A6M9DE8EEQRf0loP\na60Htda/0lr/EPivTA8sK4gxDW0efbMQjGtGMDysaevOzoNwoKMPgD1tPVk5XmAUl4sfatea6Pc8\nksZjGw/w8q42Ht1wYLSHkhBfuGstH/jls6M9DE9M3MxiiDqLh4cldHS0HcVg+QjGn6b4h+d3ceI3\nH8iKVtTkCIJdrTmoKU5dGu3gNQ6vczaxcZ+0H31+R44xPw+8uKOV1/a0o/WIGp6jDl9BoJT6mFN5\ndLFS6iXrtQV4yW+7MYWyWslobd8lDrzJC0Z7RNGooWQTynIYa7YdpKt/iE37OxOvnCKaOh1G0Jpj\njAAkcsggzwhSwsYmuZde2NGaYM3RRf/gMNtauunqH+JgllhxGMQziN+C5AtcB1xt/d6htW7x3mSM\nobwO0LD7efmeZwQZxXpHe9tyoIuj52Q2VNQwgqbOPvoHhykpCkJ+s4SpVs+DcXids4mNjlKxbk8H\nPf1DlJfkZk7AtuYuhoaFCexo6aa+MrcUvXhPh9ZabwU+AXRYL5RS4yMF1JSZMPbayYeM3lgMxqmP\nQGsdEQSbmzIfdHbAYQRaw75ccyROtXoF5xlB0mjvHWBfex8r5tYxOKxZu7tttIcEQEfvAJ19gzG/\nbWqKsuAdB7uzPaSESFRrCGAN0qN4jfVK2LNYKVWmlHpGKfWiUuoVpdRXUh5tulFmCYKiMpiUfJ2h\n3oGhmIudNMZp1NC+9j46euXh2HIg84LAMAKA3blmHqqZEy0VPs6uczZh2MA73jALgBe254Z56P/d\n+jz/cdvzMb9ttMyhO1py7H4kfj+Ctzrv87XWC5x38wpiTO8DztBaLwOWA29SSuVWo1TDCHY/L20b\nC5I3H/zi8S2c8/1Hae3uT21M45QRvO6wgUllRWzOkiCYOkn+w925FjlUUCB9cCHPCFKAmVyPXzCZ\nWXXlOeMn2LCvk6c2t0RMQSBjnVFTRl1F8ZhjBBEopd6mlPquUup6pdQFQbbRAiMGi51XbrnLTUmD\nNNQYWrurjf6hYZ7ZkqL7JMIIxpcg2OAIgjOXTGXrgS6GhzN7KzR19nHkTBH0u3M1cgigqIzBoeHc\nYy1jABv3d1JSVMDs+gqOnlPH89ujkUPr93Vw3DfuZ93e9qyOaWhYs6+9l86+wRgLwaamLhZOqWJ2\nfQU7WsagIFBK/QT4KPAysBb4qFLqx0F2rpQqVEq9AOwH7tNaP53KYNMOu0tVfWoRQ+v3yUV/cnNz\ngjUToGR8OovX7+ugoaqEN8yto2dgiL0Zttsf6OhjTn0FdRXFuTnJHvE2OOytUFLFXc/v4vTvPJQ6\nm5xg2Li/kwUNlRQWKJbPrmV3W2/EH/SD+zewr72PF7PMEpo7+xh0lBxjqhoe1mxq6mRhYxWz68ao\nIADOAM7WWv9Sa/1L4M3ObwmhtR7SWi8HZgHHKqWOcK+jlLpcKfWcUuq5pqamMGNPHXbf2jiM4NZn\ntvPJ2573jf/tGxyK2L2f3JSiIBintYZe39fJoqnVLGgQxpNJP0FX3yBd/UM0VpcyvaacPW05yAjm\nnwIX3wwFBWw50EXf4HB6fEwTCBv2d3DIFOnPvXy2PMvPb29l/b4O/uqUFsl2Hol9rz3vCKG97b10\n9w9xyJQqZtWXs6u1J8ZslAsIIgg2AnOs77Od3wJDa90KPAiM6A+otb5Ra71Ca72isbExzG5TR3E5\nFDoTbpzQ0V89sZW7X9jNc9u8k1a2HJDQsMVTq1m3t4ODXSlodqZ/sEdt/r7BIc9N/rX94IgohVyC\n1pqN+zpYNLWa+Y0iCDLpJzARQ43VpcyoLctNRmDBOLazEU01XtA7MMTOgz0RQbB0xiSKCxUv7Gjl\nR//cSEVx4aiwQSMIGqpKIz4L48s4ZIowgoEhnXORbEEEQTXwmlLqIaXUg8CrwCSl1D1KqXv8NlJK\nNSqlap3P5UiG8rp0DDqtMH4Cn9DRfe29rNsr9u3Vj2/1XOd1Z/klJ84F4OktKbCCqUvh8odh7okx\nPzd19LH8K/eNKKK2o6Wbt//0CX79pPfYcgG7Wnvo6h/i0KlVTK0uo7y4kC1pnPR2tHTH+BzMxNpQ\nVcKM2vKUJ4MfPrCBmx7bktI+4sEkv2Ujmmq8YFNTJ1oTEQRlxYUcPn0Sf1u7hz+/tJtLTpzHgsaq\nURAEcryzl07l9b3tdPcPRgTBwsYq5tQL488181CgWkNISYkvA9cgpqEvAdc7Lz9MBx50+h0/i/gI\n/pzSaH2gtaZ3wFtbTojyWnHQmv6lLjyyXsxVpyxq5N5X9nreWBv2dVJYoLhg+UzKiwtTNw/NWD6i\ndd5LO1vpGRjid8/tjPn976/sRWvYuC93zQomf2Dx1GoKChTzGyrZfCA9493e3M1p33mIP78cFZA2\nI5heU0577yBdSTKmJzYe4Lv3ree6v77GtubMTNRGcG3N0P5zBVprDnT2sXF/J2u2taQk+MzkeuiU\n6shvy2fXsq25m7KiQj500vy0KAFhsbetl5LCAs44bArDGl7e2campk5qyotpqCphthEEB3OLpSYU\nBFrrh+O94mz3ktb6aK31UVrrI7TWX03v0AW9A0Mcf90D3PDw5uR2UF4vjmKfnqUPr29iSnUpX7/g\nCLTW/OapbSPWeX1fB/MbKqksLWLFvLrUHcYeMKzk0Q0HYmr13Lt2LwBbPCaR9t6BtNsif3D/Bj76\nmzWhtjGO9EOnykM7v7EybdrvU1uaGRrWvLwz6hQ0E6sxDUFyxef6B4f577vXMquunOLCAr573/q0\njNmNVExDPf1DXPe311IzR2YBWms+dfsLrLj2ft743Yd5+0+f5LwfPUb/4HBS+9u4v5MCBfMaKiK/\nmWz1S06Yy+QqYxbszXiEmo09bb1MqymL+Cxe3NnKxv2d/5+9845vq7z+//tqeMiWZXmveM/sOM7e\nIYEAARJCCz9WoVBWKB2U0tJvgUKhjBYKLZRd2kJLKEmZSYCEDMh29vTee0jyHpLu7w/5XkuWbEuO\nnTjg9+vFi1jj3kfSvc95zjmfcx6SQv0QBIGoQB8E4QLyCARB+Kbn/82CIDTZ/dcsCMK51WQNgI9a\nSXiADzvzhphovuhhuPQpl09ZrCJf59WzMDWUcUEaLh4fwX/2l9Le5eh95NU0kxpuc1HnJAWTW9Mi\nr0qHi5zqZlQKgS6LlS2nagCobergYKkBtVKguM/EKooiy5/bwWOfnBzWcXx+spqvcmo9MjC5Nc2E\nB3ij81UDkBjiR1lj25AnAXsO9rSZti/YqWvuRCFAsJ83UYE2nf5Qkoavf11IYV0rj6+ayK3z4vn4\naCWnq4b30rdYRRp6JvHiBs9ltVvP1PDqjkL+vX90byH+4tZ8PjxSyQ/mxPHCdVO5f3kqzZ1mB8mn\nJ+TVtBAf7Ie3qrelxEUZYdw2P4G7F9vyfdGBvnRZrNS3Du+9OBDVpg4idT4E+3szLshW21BQ1yqH\nsLxVSiICfEZdLcFABWXze/6vFUUxwO4/rSiKAeduiIOzMCWUI2VGTO1DaOYUNwfi57t86mi57ZiL\nUm1J7FvmxWNs6+ajIxXya9q7LJQ0tsku6uzEYAD2FQ5vO6ac6mbmp4QQqfNh43GbF/D5qRpEEa6a\nGo2hrdtBflhhbKemqZN395UO2+qj02whr7aZLrPVo2Pm9iSKJRJC/LCKUDoM48ousX3P+XaKm7qW\nToL8vFAqBCJ1PR6BhyGCssY2Xtyax6UTI1iSFsadC5PQeqv44+c58muqTO1n3Qff0NYlCw06uq0e\ny2p394QhPzxcMSq7WgJsOl7F81tyuTozmkevnMBVU6O5eW48CgF2DTGMml/XQlLP5Cqh9VHz25Xj\nCdTY+vhE9ywChlpHYmzr4kBxI+9nl7kdFqxqapevuanj9OwuaKC+pVM2BADj9BrKR1l1sTt1BLMF\nQdDa/a0VBGHWyA7LMxamhmKxiuzOH96e5Dty6lAIMD/ZtmnKrIQgMiID+PuuYvmmk5JWaRG2r2hS\ntA4/LyV7CodvLF090sL0iAAunRjJzrw6mju6+fxENYkhflw8PhxwTDZKcXmzVeSFrXnDMo68mha6\nLbbPne9mB1GrVSS/tsXBECSG2m6KQhdySVEU5eT7YDS2dlFQ10qAj4pyQ7ucJ6pr7iLE36YGCw/w\nQSF43mbiD5tOo1IIPHyFrUGcTqPmzkVJbD1TyxtfF/LDtw8w96mvuOnN/R4dty9SWGhmgk0l1tez\nG4y9BQ14KRXk1bbI4UNXDDmHdpacrDTx8/ePMi02kCdXT0LoCcHqfNVMitZ5dM9K91y3xUpxfavD\n5OqKKNkQePbbd5mtrPnbbqY+9iXfe2UPv/zgGJe98DWfHRt4tzurVaTG1EmEznbeqeMCMfZ0Gk0K\n7R1rTJDvheMR2PE3wP6Obe15bNQwLTYQf2/V0MND/bAjt44p4wLR93QKFASBW+bGkVPTLEtJpUlL\nCg2plQpmJASxdxg9gsL6FsxWkYxILZdPjqDLbGX9wXL2FDawYmIEiT2STPtkY051bx+WDYfKXU66\nnnLSrqlXvpvHKzO00dFtlb8fgITg/msJtufUccmfd7pVCHSw5zdYPS0aUext7FXX0kmo1mYI1EoF\nYVofKj2oJbBaRXbm1rM6M5pIXW8LiFvnxRPi783vPzvN8QoTM+ODyKlppvos6hQkQzCjxxD0J6u1\nWkX2FzU6rPqrTO0U1rdy+4IEVAqBj45Uunzv+9llTHr0c/53uNzl865Yf7CcbWdq3X59f7ywJQ+N\nl5JXb5qOj9qxM+jc5BCOlBndkj7vyK0j/bebuenNffzlq3zMVpGUETIEG49XcbDEwJ0LE/n7LTP4\n9MfzSYvQsvbfh/jdJyf7DWk2tnXRZbHaeQS9dUp9PYLqpo5+5eDnA3cMgSDaXX2iKFoZuH31OUet\nVDAvOZidufXD5h4bWrs4Wm6Uw0ISV0yJQuut4t2epHFubTNeSgVxPZMbwJzEYPJrWygfJqsvGZu0\nCC3TxumJCPDh2c9zsFhFVkyMYFyQBoUARfW958utaSZS58ODK9LxVimHxSs4WdmEv7eKEH9vtz2C\nXkPZ6xHoNGqC/bxcGoJj5TZjs9eNhHt2SSNqpcDVmbamY9KY6pt7DQFAZKCPR8ni4oZWWjrNTI4J\ndHhc46XirVuyePWm6ez+1VJ+u9LmLewuGLr3JxmCiVEBNlltP4bg85PVfP/VPWw93Ts5S+q0yydH\nsiAlhE+OVjrkGKxWkWc2n+GXHxyj2yLKwoLBaOro5pfrj3Hr2wd49OOTQ56wRFHkUKntHgrTOlfK\nz08OwWwV2e+G3FrKi5Ub2nmx51q2v6ZcEeCjwt9bRYUHhkAURd7aVURSqB8PrkhnSXoYE6N17e/D\njQAAIABJREFUvHfHHG6dF8/fdxXzzGbXKviqnhBURI8hkGobvFQKYvS9Se1xQRpEcXS1PnHHEBQK\ngnCfIAjqnv9+AgxRojNyLEwNpcLYPmzVmV/n1yOKOBkCjZeKqzOj2Xi8msbWLvJqWkgM9UOt7P0q\nL5sUCdDvCs1TzvQkihND/FEoBFZMjKC1y0J0oC+TonV4q5REBfo6hBVyqm1x+VCtNzfPjePjo5Vy\nuGionKgwMT4qgJQwf48NQUqfm9YmIXWe9HJrba/vr3jPnoPFBiZG60iP1KIQoKC2BVEUqWvuJNS/\n1xDYZITu33QnKm0J4YlROqfnJscEcsmECNRKBeMjA9Br1OzKH7pKTKohCA/wIT6kfzXV/mKbh/nu\nvl7V2u6CBgI1ajIiAlg1LZoKY7v8vbV3Wfjxe4d5eXsB/2/mOK7OjHZqhNYfu/LqsVhFLkoP4+3d\nxaz52+4h5ZkqjO3Ut3QyNTbQ5fPT4/R4qRRufX8HSwzMiA/iq/sX8cXPFvLKjdOZEDVwqlJS6Xji\nEWSXGDhWbuLWeQkoFL1KQi+VgkeumMDy8eFsPF7lcsEpLTYkj0CqbZDaYEiM09s8leHIkQ0X7hiC\nu4C5QAVQDswC7hjJQQ2FhSm2CXtH7tnH5kVRZP3BcgI1aqdVIcANs+Poslj54GCZPOHaMy5Iw8z4\nIDYcKh8WDyWnupmkUH95c5XLJ9sMzYqJEXLMNcFuEjFbrOTXtch5izsXJuHnpWLNy7t5cuPpIUkp\nLVaR01XNTIgKIDnMX550B+NouZHEUD/8vR2dyMRQP5dyydwew3GoxDDg8Tu6LRwrN5EVp8dbpSQ2\nSEN+XQtNHWa6LFYHjyBKZ5sM3P0tTlaY8FIqSAkfOPSgUAjMSQpmd4GjJ2q1ul/XUtfcicZLiZ+3\nioQQTb85gkM9fWu259ZRbmhDFEX2FDQwOyEYhUJgWUY4vmolHx2poNrUwbWv7WHj8Sp+fWk6T66e\nxKLUUEzt3ZyqHFz1tD2nDq2Pildvms5rN02npL6N33x4wq3PY8/hnjFPG+d6EyIftZKsOD27BskT\ntHSaOVPdRGacHkEQSA3XOlz7A+HpIuCtb4rQ+aq5OtN1S/rlGeFUmjo4XeW8qJIS/fbhxCevnsTT\nayY7vC42ePQVlblTR1AriuJ1oiiGiaIYLori9aIonn3wcJgZF6QhMcRPLgADqG3u4L/ZZfz8/SMs\neOYrHv/0lFvHent3MTty67h3SbKDJZdIDdcyMz6If+wuocLY7hD/llidGU1BXSvHK85+s4yc6mZ5\nUgeYHqvn4ZXjuWNhb6O8hBA/iutbEUWRkh5ppmSggvy8+ODuOSxKC+WNrwtZ8PQ23vjaM6euqL6F\n9m4LE6N0JIf509xpprZ5YFmeKIocKTM6xEp7x+tPfUunQ02E1LMpVOtNQ2sXxQ393yhSt9eseFts\nPbnHS7GvIZCI1PnSabbS6KbW/niFifRIrYOX1x9zkkKoMnU4jPW3H51gqZtN5OrswlgJIX6UNrbR\nbXGMQXd0WzhVaeLKKVEArDtQRlljOxXGduYm21Rqft4qlo8P55OjlVz512/Ir23htZuyuHNREoJg\nM1gAu+zCWBaryO3/yHYIGYmiyI7cOhakhKBSKrh4QgQ3zYljV369x5LoI2VGvFUK0iP7D+HMSw7h\nTHXzgMc+UmrEKkJWnOe72nlSVFbW2MbnJ6u5flYsGi/X0e8l6bYWMFtP1zg9V2XqQK0UCLbbfWxC\nlI4pfa7/cK0PXkrFqEoYu6MaShUEYasgCCd6/p4sCML/jfzQPGdhaij7ihro6LbwfnYZi5/dzgMf\nHGN7Th0atYq/7yoaVJFyrNzIkxtPc1G6TZPcHzfMjpVjj65ilZdNisRLpWDDoV6pabmhjT9+nuOR\nzLWpo5sKY7uDIVAoBH44P4HwgN64a3ywH82dZhpau+RVdZrduNIjAvjr9ZnseGAJi1JD+cOmMx51\nZjzZs5KcEB0gJ74cN9toc0oulhvaqW/pYpoLQ5DRMzmcKO81lEX1rZitItdmjQMgu7j/hLsUApne\nMzkkhflTVN8qJ277hoYAt5rPiaLIiQoTE1yEhVwxT5pge1a1FcZ21h0oo9LUwWNuLDzsw1gJIf6Y\nrSLlfapOT1SY6LaIrJwcyeLUUNYdKGNHjzBiTo9cGeCqqVE0dZhRKxWsv3suy3vUZABhWh9Swvxl\nuSnAV2dq2XK6hmc+PyPnFs5UN1Pd1MHi1DD5dVdOjcJiFdl4fGDVTF+OlBmZGK0b0KDO61Hk7R5A\nRnqwxIAg0G+IaSCiA31paO1yqv1xxT/3FCMIAjfPiev3NaFab6aMC2SLi0R6tanDplJzsXi0R6EQ\niNb7jioJqTuhodeBXwPdYKsYBq4byUENlYWpIXR0W7n2tb388oNjTI7R8dl988n+zTLeu2M2fl4q\nnv28/3ZHTR3d3Pvvw4T6e/PH700Z0PVcMTFC3nfUlSHQ+apZlhHGJ0cr6bZYae00c9vb2fx1Wz43\nvrHPYbUoiiK1za4nKWlST48YODGW0NPVs7i+lZyaZgQBl/K6cUEanrt2KmFab37+/hG3QxgnKkx4\nqRQkhfq7NARPfHaaH/0z28HISU23proIDUyL1SMIjrkAyUhfOimCAB8VhwYoNsouNpAQ4ifLRJND\n/em2iPJ7HEJDPdXF7qwMyw3tNHWYmRTtniFICPEjUucjJ4wlT+v7WTFsOFTBtpyBnWd7hVOC3JnV\nMf8ifabMOD3Xz4qjtrmTv2zNI8Tf2+E3XpIWxgvXTeXje+eREekcP5+XHMKBokZZ9fLO3hKUCoHC\nulbZsGzPsf1/UVpvbiw9IoDUcH8+9iDn1WW2crzC5HIRYM+kaB1aH9WAMtKDpQbSwrUE+KjdPr+E\n/NsPEg7tNFt470AZl02KdAjtuGJZehhHy4wOu+CBLUcg5QcGQ9pIZ9PxKhqGufh0KLhjCDSiKPYV\nS4/KVpezE4PxUio4WWHilyvSePf22UyI0qFQCOj9vLhrcRJbTtdyoJ+V5h8/z6HC2M5frp8mS0b7\nw1ul5MbZcYRqveX+IX1ZNTWahtYudubW8Yv/HiWvtpn7liaTU9PMda/tpb6lk205tax6eTczn9jq\ncoV+xk4xNBDxdu2dc2uaiQvS9LuRt85XzbPXTKGgrpVnNue4fE1fTlY2kRGh7ZFjeqP1VsmGoL3L\nwvbcWsxW0SHeO1BoQOerJjVMK0tAobdnU3KYP5lxerKLXRsCURQ5WNIoewPQa/QkJU2Iv2NoCFwb\ngje/KWKfnULpRE8ob2K0ezWTUthlT0EDja1dvLe/jCunRPH4qomkhPnzmw3Hae7o3wPsGxoCR/UX\nwKESI7FBGkL8vVmSFkpEgA+1zZ3MTQp2WKwoFAJXTY0m2N91C/O5ScG0d1s4XGqgpKGVHbl13LUo\nkfAAb97qaaq3PaeWjMgAB28T4MopUWSXGNxW4JypbqLLbB10Fa9UCMxODOabfNeKP4tV5HCJweG3\n9oSoPr+9KIp8dqzKSbJ6tMxEc4eZK3rybwNxUYbN0+rrAVebOuQagsG4ZEIEja1d3P3uIab/fguX\nvfD1eW1N7Y4hqBcEIYme3cUEQbgG8MxHPEdovFS88YMsPlw7j3sWO8f3fzgvgTCtN09tOuPyojta\nbmJOYjDT45xbQLvipxelsOOBxS7zCACL08LQa9T88oNjbDpRza8vzeDnF6fx5g+yKG5oZcHT27j1\n7weo71lZuHKPc6qb0Xqr5CrJ/ojR+6JSCBTVt7pMYPdlfkoIN8+J461dRYM2yZPCJeN7wiWCIJBk\npxzakVtHR7cVQbCFGyQGCw1kxuk5VGqQwxJSzyZvlS2JmFfbgqnNcRKta+7k1xuOY2jrZmZ87+8k\nVZlKLTekdhZg60Ia4u/Nh0cc5ZVf59Xx+KeneOTjk/L1cLzChEohDPr92TMvKQRDWzcPbThOe7eF\nOxcl4a1S8vQ1k6lq6uDpfuSGnWYLpvZuOTSk16jR+aodPAJRFDlYaiCzZ0JVKRVcO8MWOpPi/u4y\nKzEYhWC7zv69rxSlQuCm2fHcPCeer/PqOVhi4GCJgcVpoU7vvaInP/HJUfe8AskblPr/DMSClBDK\nDe0uFVN5tc00d5qHbAii9Y6GYH9RI2v/fcipW+++wgYEobewbyAyIrVE6XzYeqY3TyCKIlU97SXc\n4cbZcRx79GLW3z2XX65IY05ScL/zyLnAHUOwFngVSBcEoQL4KTYl0ahkYWooE/tx6329lPx0WSoH\nSwxsOe3sslca2wedcO1RKIR+k0pgk5ytnBxFQ2sXq6dFc/sCW85hQUoob986k8kxOp66ehLbfrGY\nxBA/l6GQnOpmUiO0gyok1Erbln25Nc0UN7QN6kEA/OrSdOKCNfzfh8cxW/rv+yOFS+xXyclh/nJR\n2RcnqwnUqFkxIYLtOXVYrSLdFisnKkwuE8USWXF6mjvMsmQ0t6ZZzmtIxlj6TswWKy9ty2fxs9v4\n4GA5t8yNZ9W0XmVHgI+a8ABvusxWQvy9HeK0giDw60vTOVJm5L0DZYAtdPHIxydRKwXOVPcWCJ6o\nbCIlXOtU/DQQUsJ288lqlqaHyd99ZqyeW+bG8+6+UnmrTnvqW2zhQckjEATBQf0Ftu++rrmTTLuJ\n8OY5cVybNY5LJ7rumNsfUjXv9pxa3s8uY3lGOBE6H66fGYuPWsFP1x3GbBVZnOpsCOKC/Zg6LtDt\n8NDhUqOt6Z8bE6OUj9iW41wQerBPLshTpMpyqdeUlLPb0edc+4oaSQvXyu0pBkIQBJZmhPF1Xr0c\nWjW0ddNptrptCMB2z06P03PP4mS5JuV8MaAhEARBAWSJorgMCAXSRVGcL4qicwvOC4TvZ8UQG6Rx\n6iLaabZQ19wpJxaHi7VLkvnpshT+cPUkh8l8dmIw6+6cw3UzY/FSKeQ9V+09FVEUOVPd5NakDhAf\nrOGbfJsG3J0VrcZLxUOXZVBQ18q67LJ+Xycniu0SqMlh/tQ1d9LQ0smW0zUsywhn+fhw6ls6OVFp\nIqe6mU6zdWBDEG+7uQ+WGGjrMlPa2CaPe8o4HUqFwMEeGelvPzrJs5/nMD8lhC9/vohHr5wgy2nt\nxwSO+QGJqzOjmZUQxFObTlPf0smb3xRRWNfKC9dNI8BHxT/3lCCKIicrTExyMywkEanzlXdeu2uR\n4wZHP16agkat5M9bnAv6XCmcEkP8HPZqkPMDdivrYH9vnr5msluTVl/mJodwtNyEoa2bG2fbkqJ6\nPy+uzoyhrLEdrbfKwejYc+WUKE5VNblVQyKpxdyReMYGa0gK9WO7i3zKwWIDIf7ech9/T1ErFYQH\n2OTDHd0WNh6vQq20XVdSyK7LbCW7pFHuE+YOF6WH09ZlkQsf+9YQXGgMaAh6qoh/2fPvVlEUz64i\naRSgUiqYHKNzaiIlqU2k5NJwEaHz4afLUgddYU6LDaS+pYsyOyVBpamDpg4zGe4aghA/OrptK3t3\njcfF48OZEa/n+S/z+u3Zf7LShFIhOCSsk3t6p/xrbwlNHWZWTIhgUWooggDbztTJ2/QNZAhscW8v\nDhYbyK+19WySpLgaLxUTogLILmnkxa35/Gd/KfcsTuLVm7LkWHpfpDGFuIiRC4LAE6sn0t5t4Zcf\nHOMvX+WxLCOcyyZF8r2scWw+UcXxChMNrV39epQDcf2sWK6aGsWMeMdJNMjPix/OT+Cz41VOGn5X\nhiA+xI9KU4ecdD9casRXrRxULOAuc3vCSQkhfvK/AW6dGw/YQob9hfJWTo5EIcCGQwO3qjC0dlFU\n38o0D1Q+S9LC2FfY6HQNHiw1MD3OPYPSH5KE9MtTNTR3mrl7cXJPPss2iR+vMNLRbWWWG2EhiTlJ\nwfiqlXzcEyqT5g93cwSjDXdCQ1sEQfiFIAjjBEEIkv4b8ZGNILFBGioMjvuGSkkwT0JDw4m04jtc\n1hsektxXd3MW0qpUrRSID3Y9WfZFEAR+fVkG9S2dvLbTubbgaE84JT3CMVwirb7/vqsYjZeS+Skh\nBPt7MyUmkG05tRwpNRLi70WMvv/vUxAEpsfpOVhq6G1FYV8vEafnQLGB57fksiYzhgcuSRvws8ge\nQT/J0uQwLXcsTOSrM7Y22o/0NJS7cXYc3Rab1wG4LR215/YFibxw3TSXE9bt8xPR+qh4fovjfgau\nDMG85BCUCoFb/74fU3s3h0oNTI7RoXKjpsEdZsQHERHgw50LEx3CZynhWv74vSn8bHlqv+8NC/Bh\nxcQIXt1ZyBcn+29XcaR88EVAX5akh9FlsTrkyeqaOylpaCPLzeu/PyRDsOFQOVE6H9YuScLfW8WO\nnpojqS+YO/kBCR+1khtnx7LhUAU7cutkafK30iPo4VpseYKdwMGe/7IHe1OP4dgmCMIpQRBO9rSm\nGBWMC9JgtooOFbZS9eFwh4bcJS1Ci8ZLySE7Fc2mE1XEBWtkzf1gSMqhxBB/p7DJQGTG6rl8UiSv\nf13o0Fb546OVfP/VPXirFDz3/akO7xkXpMFLpcDU3s2S9DDZSCxJC+NouZFv8uvcCg1Mj9NT0tDG\nnoIGvFQK4uxCANPj9FisIotSQ3lqzaRBj5U0QGhI4t4lKcxKCOKhyzJktVdCiB8LUkI4WmZEIeD2\n9+0uOo2aHy1I5MtTNRxzsYFOsF/veKfH6Xnp+mkcrzBx/et7OVXZ1G+oZij4qJXsfegirpsZ6/Tc\nNdNjBg0pPnvNFCZF67j3P4f77bF0pNT2Pbqqyu+PrHg9fl5KB7mtpO47288fFehDuaGdnXn1XDUt\nGm+VkrlJwezMrUMURfYVNZIa7t+v2qo/7r84jZQwfx7471HOVDehVAguvdELAXcqixNc/Jc42Puw\nSUzvF0VxPDAbWCsIwvnNiPTQu2+ovSGw/TviPFl0pUJgSkyg3ErA2NbFnoIGt0vpAdkLSB1CGOGB\nS9LotlhZ/fJurn55F99/ZQ/3/ecwU2IC+WjtPKdQk1IhyB7Iigm9Scsl6aGIItQ0dbq1IpS8nU+P\nV5Ec6u+w8l0+PpzHr5rAyzdkulXlmxau7WkA2H882ddLybo75/CDnlCIxM1zbH8nhfoPKAAYKrfO\niydQo3bY5ayupQO9Ru1ktFdMjOS1m7LIr7V1nc10Q3lzrvDzVvH3W2YQH6zhR//Idir6255Ty993\nFTEpWufUVmQgvFVK5iWHsP1Mrbz17J++yCFK5+N2TUd/RAf6YraKWKwiV/cIDBal2XqT5dQ0c7C4\nkVkJnimwwGZUn792Ko2tXby7r5Rwrfd5Vf6cDe5UFvsIgvBzQRA2CIKwXhCEnwqCMOhsKYpilSiK\nh3r+3QycBlw38DjHjNM79/qoNLYT4u/tkVpkuMmMC+R0VRPtXRa+PFWD2Spy6cTBdc0SUYG+xAZp\n5GpXT4gP8eOZayYzPioAP28VFlHktvkJvHP7rH5XSslh/ngpFXLZPdgatUmrIleFZH2ZGB2Al0pB\nl9nqZGy8VUpumhOPn5sTSrC/N1/9YhGrp3l+mS1NDyMxxI9ZiSMT9dT6qLltXgLbc+oo7WlHUden\nS6o9S9LDePvWmVw1Ncohlj8a0Pt58a/bZhHk78U1r+zhR//M5nCpgZe25XPr2weI1mv46/WZHh93\nSXoYlaYO8mpbeO7LXFudyzVTPPJuXSHVEkyO0cnND6Vmki9tK6C1yzLk331itI6fXJSCKJ6/ReRw\n4M4d9k+gGfhLz9/XA/8CvufuSQRBiAemAftcPHcHPU3sYmOd3dWRIDLQJimz7/VRaeoY9kSxp2TG\n6jFbRY5XmNh8opoonQ9TYtxfDSkVAjt/uWTI5189LYbV02Lcfv19F6Vw1dRoh5WfQiGwOC2U9YfK\nmTxu8LF7q5RMjtaRXWLwSLvfH/btfj1BqRD49L75qBTDE4t3xerMaP70ZS6fHKtk7ZLkAQ0B2BKS\nntYKnCvCA3z45N75/H1XMW/vLubLnjbRV0yJ4pk1k/stZhwIqX7h+S9z2XyymhtmxTI/JeSsxyrt\na3y13QIhRq8hOcxfrovwJD/Ql7sXJ3GgxOCx2mw04Y4hmNgT3pHYJgiCe93bAEEQ/IH1wE9FUXRq\nfSiK4mvAawBZWVnnpLROrVQQFejr5BEkhw7cbXKkkYpvvs6r4+u8em6cHXdWaomRJjVc63Lyvv/i\nVC6ZEOF2S4Dp8foeQ3B+v/+RCAnZE6PXMD1OzydHewxBSyfTR1HYx1MCNV78bHkqP1qYyHv7S/FW\nK7lxVuyQr9lInS/pEVo2nagmRu/Lry/LGJZxJodp+c+PZjspuhalhpJfa2sj72q/BHdRKRX884cz\nz3aY5xV3lj+HBEGYLf3Rs03loMninteqsRmBd0VR3DC0IY4M4/QauR+4KIpUGtvPW6JYIsjPi/hg\nDX/fVUyXxcqlkzwrGBotROp8HRqeDcYlEyKI0ft6pDK5ULlyShRnqpvJrWke1CO4UPD3VnH7gkRu\nGoaFy0UZtjDjM9dM9ijHMBhzkoKdlFdSeGgo+YFvG+4YgunAbkEQigVBKAb2ADMEQTguCMKx/t4k\n2K6IN4HToig+NyyjHUbGBflS1tPl0dTeTVuX5byHhsAWHmrpNBOq9b6gV4uekBmr55sHl3qs2rgQ\nuWySTYv/732ldHRbvxWGYDi5Z3EyH62dx9yksw8JDcbMhCCWZYRxzXT3w6HfVtwxuSuGeOx5wE3A\ncUEQjvQ89pAoihuHeLxhJTZIQ11zJ+1dlvNeQ2DPtDg9Gw5XsGJCxKDtbMe48AjVejMvOYT3eyq5\nxwyBI37eKqf+/SOFj1rJGz+YcU7ONdoZ1BAMtZ2EKIrfAKN2JpM05OWGtvNeQ2DPguQQtD4q1oyt\nUkYlX5V+Rbe1m0viLxnyMa6YEsXXeTYNfqj/+fdCxxhj5CQSoxxJXVJmaJNrCEaDIYgP8eP4o5d8\nJ+LlFyJvHn+Tvx7+61kd45IJEXj1xKvHPIIxRgPfWUMgFZWVNtgMgZdK4bDF3BhjuKKmrYay5jK6\nLe7vMtcXna9a3vhlzBCMMRoYWb3cKCbE3wtftZIyQzs1TR1E6QbfYm6M7zZW0Up9ez0W0UJpcylJ\ngUmDv6kffrw0mRi9L3qN57tujTHGcPOdNQSCIBCjt9US1LcMf/vpMb59NHY0YhFt/ecLTYVnZQgm\nxwR61ItnjDFGku9saAhs4aHSRluyeMwQjDEYNW29O1IVGAvO40jGGGN4+U4bgnE9hqC2ecwQjDE4\nta29nTELTc4tu8cY40LlO28I2rosWEWIHgXFZEPBbDVz55d3srdq7/keyree2jabIUjTp1FkKjrP\noxljME41nHK5N/kYzny3DYHdpikXqkdQ1VLF7srd7Kty6uc3xjBT216LUlAyI2IGRaYiLFbL+R7S\nGP1wpPYI1356LXsq95zvoVwQfLcNgd0mKBeqIShrtlWoNrQ3DPLKMc6W2rZagn2DSQ5MptPSSWWr\nexu5j3HukQxAcVPx+R3IBcKYIegh6gLda7S8xbZ/bH27692ixhg+attqCfMNIzHQti/TWHho9LK/\nej8A1a39b6k5Ri/faUPg760iyM8LvUY9pP7po4HyZpshaOgY8whGmtq2WsI0YSTqbIag0DiWMB6N\ndJg7OFp3FICq1qrzPJoLg++0IQCbV3ChhoWg1yM4V6GhV4++ys+3//ycnGu0IRkCnbeOIJ+gMeXQ\nKOVY3TG6rd14KbzGDIGbfGcLyiR+fWk61gtYWWDvEYiiOOIb2eyr3sexumNYRSsK4buzjugwd9DU\n1US4n22fhaTApDFDMErZX70fpaBkYcxCjtX32yl/DDu+O3dyP8xODB6W3udVLVX8L+9/wzAi9xFF\nkbLmMlSCCrPVTFOX0wZww05lSyWdls4hrbSO1h3lvq/uO6s+PecLSToa6mvrEZSoS6TQWDgmTxyF\nHKg+wPjg8STrk6lrq6PbeuFdb+ea77whGC7+k/MfHt798DldJZo6TbR0t5ARbNvSb6QTxharhZpW\nW3Vticnz7uRfFH/BtrJt5Bpzh3toI45UVRymse2glaBLoLm7eSxJP8poN7dzrP4YWRFZRPpFIiLK\nRnyM/vlWGILtZdspNJ3f1ZmUOPyq9Ktzdk4pPzAldAow8nmC+vZ6zKIZgKImzxUzeYY8AHIac4Z1\nXOeCurY6AMI1ttCQnDAeCw+NKo7UHsFsNTMzYiYRfratXqtahi9PYOww0mnpHLbjjRZGzBAIgvCW\nIAi1giCcGKlzAHRburl/+/1c9eFVLFi3gLVb13Kg+sBIntIlkpRwa8nWc3ZOKT8wNWwqMPIegX04\nqKTJc48gz2gzBKcbTg/bmM4V0qpS8gjGDMH5o627jQ9yP3BZ0Heg+gBKQcm0sGlE+kUCw6ccMnWa\nuOqjq/j93t8Py/FGEyPpEbzN0Le5dBuVQsV/r/wvj819jItiLyKnMYd7ttzDkdojg795mOi0dFLe\nUk6AVwAnGk6cM+2yVEwmewQjLCGtbLEVUPmr/Sk2FXv03saORtlQnWk8M6zjOll/ErPVPKzH7EtN\nWw2+Kl/81H6AzSD4qf3Gms+dBzYVbeJ3e37Hl6VfOj13oPoAE0Im4Kf2kz0C+/ux29LNo7sfHdLv\n9tKRl2jsaGRT0aZzko87l4yYIRBFcSfQOFLHlxAEgURdIqtTVvO7ub/jvZXvEaYJY+3WteQb8kf6\n9IBtdWwVrdyQcQNw7sJD5S3lBPsEE64JR6VQjbhHIFXSzoiY4bFHIIWFEnWJ5Bhyhq09Q3lzOdd9\ndh0bi0Z2K+zatlrCNeGyKksQBJJ0SWNFZeeBfKPtvn7vzHsOj7d1t3Gi/gQzwm37EPuqfNF76x08\ngpMNJ1mft57f7/29R6HkXEMu63LWMTNiJp2WTjYVbhqGTzJ6OO85AkEQ7hAEIVsQhOy6urqzPl6I\nbwivLn8VL6UXd26506P44OHaw+ws3+nxOaXwwNLYpSToEs6dIWguJ0YbgyAIBPsEj3iahpp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BQqNhZtJNA7UI7R3jH5DpQKJc8dfI7q1moKjAWIiE6yvozgDCpaKs4qmW2/WovRxjgki3Mac9Co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\n2cu8vvx1p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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_data = mean(data, axis=1)\n", "max_data = amax(data, axis=1)\n", "min_data = amin(data, axis=1)\n", "\n", "number_years = data.shape[0]\n", "years = linspace(1916, 2016, number_years)\n", "\n", "plot(years, avg_data, years, max_data, years, min_data)\n", "legend((\"average\", \"max\", \"min\"), loc='best')\n", "title(\"Monthly precipitation between 1916 and 2016\")\n", "xlabel(\"year\")\n", "ylabel(\"precipitation (inches)\")\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " - Plot the same quantities, but only for the last twenty years.\n", " " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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qeejuFBdD3GLVFjSgsf3GvZhxu8stejsbCl9vkmNVwlTDTvYbs91g+PtNZacv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\nkMj+kdf1oOoqCw8tZOmxpSRlJV2bV6ViFZrXbE5zv+bXEnx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(years[-20:], avg_data[-20:], years[-20:], max_data[-20:], years[-20:], min_data[-20:])\n", "legend((\"average\", \"max\", \"min\"), loc='best')\n", "title(\"Monthly precipitation for the last 20 years\")\n", "xlabel(\"year\")\n", "ylabel(\"precipitation (inches)\")\n", "show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ " - Plot the same quantities, but only for years ending in 6." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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0tDQnCmYwGAx5xygDOxgyZAht27alWbNmTJ8+nc+nTWXixGegeBCUrcfs+QsZ//B9AMyd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\nTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(years[::10], avg_data[::10], years[::10], max_data[::10], years[::10], min_data[::10])\n", "legend((\"average\", \"max\", \"min\"), loc='best')\n", "title(\"Monthly precipitation every 10 years for the last 100 years\")\n", "xlabel(\"year\")\n", "ylabel(\"precipitation (inches)\")\n", "show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }