From 2501fbe4893efe1786c53116fd253bbd94331498 Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 15:44:46 +0100 Subject: [PATCH 01/10] delete ipynb_checkpoints --- .../py_exp_comp_s4_sol-checkpoint.ipynb | 364 ----- .../py_exp_comp_wm1_sol-checkpoint.ipynb | 1377 ----------------- .../py_exp_comp_wm2_sol-checkpoint.ipynb | 762 --------- 3 files changed, 2503 deletions(-) delete mode 100644 notebook_s4/.ipynb_checkpoints/py_exp_comp_s4_sol-checkpoint.ipynb delete mode 100644 notebook_wm1/.ipynb_checkpoints/py_exp_comp_wm1_sol-checkpoint.ipynb delete mode 100644 notebook_wm2/.ipynb_checkpoints/py_exp_comp_wm2_sol-checkpoint.ipynb diff --git a/notebook_s4/.ipynb_checkpoints/py_exp_comp_s4_sol-checkpoint.ipynb b/notebook_s4/.ipynb_checkpoints/py_exp_comp_s4_sol-checkpoint.ipynb deleted file mode 100644 index 0c26002..0000000 --- a/notebook_s4/.ipynb_checkpoints/py_exp_comp_s4_sol-checkpoint.ipynb +++ /dev/null @@ -1,364 +0,0 @@ -{ - "metadata": { - "name": "" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - "\n", - "# Exploratory Computing with Python\n", - "*Developed by Mark Bakker*\n", - "## Statistics Notebook 4: Linear regression and curve fitting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook we will perform linear regression on some of the data in the data set on wooden beams, and we perform do curve fitting on a data set of groundwater head observations" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%pylab inline" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Linear regression\n", - "We apply linear regression to fit a straight line through a set of data. The function `polyfit` fits a polynomial of arbitrary degree through a set of data (`polyfit` is part of the `numpy` package). The input arguments are `x,y,degree`. When the degree of the polynomial is 1, it fits a straight line of the form $y=p[0]*x+p[1]$ and it returns the array of parameters `p`. The parameters are obtained by `polyfit` by minimizing the sum of the squares of the errors between the data (the $y$-values) and the fitted polynomial. For example, consider the `xdata` and `ydata` below. The slope and $y$-intercept of the best-fit line are computed and both the data and best-fit line are drawn." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "xdata = array([0.0,1.0,2.0,3.0,4.0,5.0]) # Observed value of x\n", - "ydata = array([1.0,3.0,2.0,5.0,5.0,6.0]) # Observed value of y\n", - "a,b = polyfit(xdata,ydata,1)\n", - "print 'fitted slope: ',a\n", - "print 'fitted y-intercept: ',b\n", - "plot(xdata, ydata, 'bo', label='observed')\n", - "yfit = a*xdata + b\n", - "error = ydata - yfit # Error\n", - "plot(xdata, yfit, 'r', label='fit')\n", - "xlabel('xdata')\n", - "ylabel('ydata')\n", - "legend(loc='best')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "fitted slope: 0.971428571429\n", - "fitted y-intercept: 1.2380952381\n" - ] - }, - { - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 1. Straight line fit between `Edyn` and `Estat`\n", - "The data set of experiments on wooden beams contains two measurements of the elasticity modulus. The column labeled `Estat` contains measurements of the elasticity modulus using a standard static bending experiment. The column labeled `Edyn` contains measurements of the elasticity modulus using a dynamic mechanical analysis where an oscillatory force is applied. The two experiments don't give exactly the same value. You are asked to determine the linear relationship between the two measurements. Let's first assume that the measurement of `Estat` is much more accurate than the measurement of `Edyn` (we will consider the reverse some other time).\n", - "\n", - "Plot the `Edyn` data on the $y$-axis vs. the `Estat` data on the $x$-axis using blue markers. Use `polyfit` to determine the parameters of the best-fit straight line. Add the best-fit straight line as a red line to the graph. Label the axes and add a legend." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from pandas import read_csv\n", - "w = read_csv('douglas_data.csv',skiprows=[1],skipinitialspace=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a,b = polyfit(w.Estat,w.Edyn,1)\n", - "xfit = linspace(w.Estat.min(),w.Estat.max(),100)\n", - "yfit = a * xfit + b\n", - "plot(w.Estat,w.Edyn,'bo',label='observed')\n", - "plot(xfit,yfit,'r',label='best-fit line')\n", - "xlabel('Estat')\n", - "ylabel('Edyn')\n", - "legend(loc='best')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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Qrx+8/jps2wb//S/4+NhG0DpCKZ16SqdOnfjqq6/q5NobNmygW7du3H777fz222+4u7tX\n6Bmoz5kzZ7C3t6ekpASA+++/n/fee8+W4ioaINYIrAvQvn17M+WeVZbJWowePZS1a0MICYli2LBo\nQkKiWLt2ZMXea6mpMGYMPPEELFwI334LgwbVmsy1iYoyXU+xRVrq6Oho0tLSKlQCBQUFLF68mOPH\nj9O5c2dA4/auJSgoiAkTJjB16lSLrvn555/XTGjFLcncucGkpS0x2LyomQ2MrFI7t9/uRlKScfkd\nd7jXVMQaMXr0UMtcpK9cgeeeg+3b4ZlnNIE5XVxsL2AdopSOwoCjR4/i7u6uUzjlUdGiFdZAOyCv\nXx9FXp4DLi7FzJlTyWzABNZSXrVOXh6sWwerVsFjj8GJE3DbbXUtVe1gE0tRPcNcN+tz9zt16iTr\n16+Xfv36SZcuXeSNN96QgoIC3fHjx4/LjBkzpEOHDrJgwQI5e/as7tjbb78tAwcOlObNm4uvr698\n9dVX8sUXX4izs7M4OTmJm5ubBAQEGF1z79694urqKvb29uLm5iaTJ0+W06dPi52dnRQVFUlkZKQ4\nODiIi4uLuLm5yZw5c4za0NbXBhodNmyYLr7e5s2bZdCgQfLcc8/J7bffLiEhIfK///1Pd+7Nmzfl\n7bffln79+smgQYPkww8/lBIzexHq82enqF2sERW+1igpEdm2TaRTJ5G//EXk5Mm6lsgstvqNNYpf\nbkNUOh07dhRfX185ePCg/PDDDxIYGCgbN24UEZErV66Ih4eHfPLJJ/LHH3/Iiy++KPfcc4+IaIKr\nenl5yc8//ywiImfPntVl8YyOjq402nNcXJxBmoPySiQoKEjeeecds+dXVH/z5s3i7Owszz33nFy9\nelWWLVsmgwcP1p07f/58CQsLk9OnT8sPP/wgPXr0kNjYWJPXqc+fnUJhkq+/FunfX6R3b5EDB+pa\nmkqx1W9MORJUhJ2ddf6qdWk7Hn30UQYPHkyvXr2YOXMmu3fvBuDjjz9m/PjxjBkzhubNm/PMM89w\n6tQpLl26hJ2dHbm5ufz8888UFhbi7e2tS74m5cIGmaKy45bWMUezZs1YunQpHh4ezJgxg8TERHJy\nchARdu7cyapVq+jUqRO9evVi6tSpfPLJJ9W+lqJhEROTQEjIUoKCogkJWUpMTEJdi2Qd0tJg/HjN\nMtqcOXDkCDTiMFzKplMRNRhcrYE2SjdAYGAgkZGRgCZd9O7du/nvf/+rO15YWEhCQgLjx4/nvffe\n45VXXmHSpEmEh4ezZMkS2rRpY9T+wYMHuf/++wGNt9xPP/1kkVw1set0794de3vNs0779u0pKirS\nBWo9d+4cPXv21NUtKSkxa1tS3FrExCQwb94eA9tMWprG7dhqMctqm6tXYflyeO89WLAA3n0Xmjat\na6nqHDXTqcccO3ZM9/ro0aPcc889gCZd9MSJEw3SRWdnZzN+/HgARo0axb59+0hJSeH06dOsWrUK\n0KSk1p+lDBkyhBs3bnDjxg2LFY6Dg4POHdqa+Pr64uXlRUpKiq5Pf/zxh0FqbsWtS30Nw18tCgrg\n1VehWzfIzYXkZIiMVAqnFKV06ikiwkcffcQ333zDjz/+yJtvvskDDzwAwKOPPsrHH3/MJ598Qk5O\nDjk5OcTExJCdnc3PP//M/v37yc/Px9nZmSZNmuDurnEf7dOnDykpKTVK/9CnTx+OHTtWoyU2U9jb\n2xMaGsqzzz7LiRMnKCkpIS0tjYSEW2SJRVEh9TUMf5UQgY8+An9/2LdPs9Fz40Zo166uJatXKKVT\nT7GzsyMiIoIFCxbw0EMPMXXqVJ544gkAPDw82LNnDwcOHOCuu+7izjvv5N133wUgPz+fxYsX06ZN\nG/r27UvLli2ZP38+AMOGDeOuu+6ic+fO9O3bt8Jrm3sfHh7OqVOnaNOmDX/7298sOl+/vKK2o6Oj\nuffee5k1axatWrXikUceIT093aycCttTW3YWa20WrTMSE2HIEHj+eY2iiYnRKB+FEXZi7UfWeoid\nnZ3ZfD2NoPu3JOqzsz2m7Cw+PktYuzakynaWyvLLmL5WZOU7+a1MlfPgnDkDixdDQgK88AJMnAgO\nDWh2VgG2+o0pRwKFQmES83aWqCopAkucBKy1WbQmVMmZ4fp1ePFFeOcdjUfa22+DXpJKRQXYxBG7\nnmGum42k+7ck6rOzPdYK0W/1/DI2wiI5CwpE1q8XadtWZMoUkQsX6k5gG2Or35ia6SgUCpNYy87S\nUJwEKpRTBD77TBMfrUMHiI2FXr1qWcJbA6V0FAqFScrimoUAsYAjrq4nGDhwWJXaaShOAubk7JF/\nHoYPh0uXNK7QI0dWe9O3wobea7/99hv33nsv3bt3JygoiA8++ADQRCweM2YM3t7ePPTQQ2RnZ+vO\nWbduHXfeeSf+/v58/fXXuvITJ07Qu3dvunTpwpIlZXkqCgsLmTp1Kh07diQoKEh5OikUVmT06KGE\nh9+Bq+sHwAtANLm5O9i69UKVvNjM5ZcZOLC9zjOud+9p9O49u0pecqY862ribVdeTi9+42O3u3k5\ndSeEhcHx4zBqlFI4NcUmi3YicvHiRTl27JiIaOKBde7cWbKysuSf//ynPPXUU5KXlycRERGyevVq\nERHJyMgQX19fOXv2rMTFxUlgYKCurVGjRsn27dvlypUrMmjQIDly5IiIiOzYsUPGjRsnOTk5snLl\nSomIiDApi7lu2rD7ChujPrvawVr2mPJBOZct26CXrjpeYL5B+56e82XZsg0SHLxEhg1bJsHBSwwC\neZpKd+3pOV88PafUKAX27t3x8tCfF8q73kPkDydX+Tl0gkhWVpX6eqtgq99Yrf1yH3jgAfnqq69k\n3LhxOmX0/fffy/jx40VEZNeuXTJv3jxd/YCAALlx44aIiHTp0kVX/sorr8hrr70mIiILFiyQnTt3\niohIZmam9O3b1+S1zd08Dw8PAdRfA/zz8PCo4TdSYQnWciYoj6Eym2XiGvHi7PykWQViThnCUqMy\nixVkYaHIv/8t0r69yIQJIufO1aiPDR1bKZ1a2Rx66tQpkpOT6d+/P0eOHKFbt24AdOvWjcOHDwOQ\nmJiIn5+f7hxfX18SExM5deoUbdu21ZX7+/tz6NAhAA4fPox/6QasVq1a6WJ4WcrVq1d1QTDVX8P6\nu3r1ao2/l4rKsZU9xtBon2OiRiwFBW8alOiHxfn992wT5wDcMCqp1GFBBL74AgIC4IMPNA4D776r\ncRhQWB2bOxLcuHGD0NBQXn31Vdzc3BARi881tbNdRHTl2gFI/5g5oqOjda+DgoIIasRRXhUKS7FV\nkjRDZWbqQdH00HThwg1CQpZy8mSamZaN7boVKsgff4S//x3OnoXVq+HBBxutzSYuLo64uDibX8em\nSqewsJBx48YxYcIExowZA0C/fv04ceIEgYGBnDhxgn79+gEwYMAA9u3bpzv35MmT9OvXD3d3dzIy\nMnTlKSkpDBgwQHdOSkoKvr6+XL16lXbt2tGkSROTsugrHYVCYRm22rRpqMzcgCWA/kbUEybPS0u7\nSFLSDmCGiXMicXK6SWFhWYlZBXnxIkRFaWY1UVEwYwY4OdWoTw2d8g/jzz33nE2uYzOlIyJMnTqV\nHj16GMToGjBgAJs2bWLVqlVs2rSJgQMHAtC/f38WLlzIuXPn+PXXX7G3t9cFquzWrRvbt2/nvvvu\nY+fOnaxZs0bX1tatWwkODubNN9/UtaVQKKzH6NFDrR4ZQNveP/4RwU8/XaKwMAuIAhyAYpo2vUnz\n5gtIT/+X7hxX1xnk5kaUvmsDBBucAyO5++4/aNOmAgWZkwMvv6xJFT1tGqSmQsuWVu2bohLERhw8\neFDs7OykV69eEhAQIAEBAfLFF19IVlaW/OUvf5EOHTrImDFjdM4CIiJr1qwRHx8f8fPzk4SEBF15\ncnKyBAYGSqdOnWTRokW68oKCApk8ebJ06NBBhg0bJhcvXjQpiw27qVAoasju3fESGDhVPDzCxMNj\novTuPVt274438njr3n26gaMBRJZzNFhs3lOtqEhk0yaRO+4QCQsTOX26VvvYELHVuNmoA34qFLcC\nVQ5SaaVrADa5rrn+hIQsJTb2Bb2aCcBePDzO0b+/N3PmjDB9/X37NHYbNzd45RUoXZ5XVIzNxk2b\nqLJ6RiPppqIRYrxfJV5cXR+VHj3mGe1tsd41RDw9p4in5/xyM42q7Ymp/FrxAkvExWWCBAbOKre3\nx4LZTXKyyP33i/j4iPzf/4mUlNRItsaGrcZNNdNRKBowhk//CcAe9I3rlqQiqGymZHyNWOA3oAMa\nu4p+3Si+/HK5Ffpjui/h4Xdw6NBFPZuNidnNpUuwbJkmodrixRARAc7O1ZapsaJSGygUCiMM97vE\nYujNVXkqAkvC+Zddw1gRaDzIQKt4ahrEs+xapvty6FAFSi03F9as0SyhTZwIJ09Cq1Y1kkdhfZTS\nUSgaAOZmI4b7XaoezVmTMycEWFp6fhFpaSFERb2ru15S0gnKZjgryrWwAo0HmUbp1HTTaFl/qtCX\nkhLNps7ISOjfHw4dgq5daySHwnYopaNQ1HMqmo0Y7nepevSACxcuYzx7WUBS0g0KC9/WlTg6zqSo\nyMVMKxpFYI1No2X9Mb1B06gv8fHw9NOabJ0ffACDB9fo+opawCaWonpGI+mm4halsqCbWtfi7t2n\ni6vrDMsN7SLSuvWjFscwc3R8wGRdD48wCQlZahWnBW1/AgOniovLTPN9SU0VGTNGpGNHkW3blJOA\nDbDVuKlmOgpFPaeyJGj6mzdjYhKqFD2gffv2ZGaaOmK8jOXrezt5ecYhcdaunWU1F+2YmASiorZz\n+nQhDg4XcXd/iI4dO3HHHe6avgzwh7lzNbOahQth+3ZwMTcDU9RHlNJRKOo5VQm6WdXoAbff7kZS\nkqkjxm17ebVlzpwRVgmJY27fz7Rpn5Ce/rpezZmkpv5KMwdPuny0DZ74PwgNhRMnoE2bKl9XUfco\nl2mFop4TE5PAtGlbSE9vj9bY7+n5O2+//USNB/ysrPNcvNjcINyMp+d8IIv09Hd0ZZoZTc1jrmmv\nX95G5eOzhObNr3Hs2OvlaguPMo6XOMavTZ1weCWKoJkTaiyDonKUy7RC0cCwbqSAFmiyd2pZUG2Z\nygZ8jUeak9Mp3N3H4u3dpnQ28zBQFuQzK+s84Mzq1ftZty62Wv3QvxdJSSfIzIygzCPOkbQ0O5o1\nu2Bwzp/4H6/wNM6cZQrvE3fzXkI+iSJoZrW6rqgv2MRSVM9oJN1U1CNM7eKv7o59a2XvNGzLVOwy\nY/ms0Q9TbcAUKZ8t1M5uikC8dOGUfMh4OYeXTGCL2FHW/5omj1NYjq3GzVpJ4qZQNDY0+19MbdTc\nW+W2KnMkqF5b5jaSGspnjX4YtpGAZk9QDvAvg3ot5GVetZ/GYfpznF74ksp7nEQI1tW5ceOyxddV\n1E+U0lEobIA1FYU1s3dWdfOlNfphHNHgBaCb7rgTBcxlLan40rapA4/d/RdW2h8ll3BgJGVhdiIR\nsTwzsKJ+opSOQmEDrKko5s4NxsdniUGZZiPmiBq0dcnk8e+/TyIkZCkxMQmAdfpR1ob+7KoIEB7m\nY5Lpzki+ZDj7mdukF7E/bmbIkJ7A34C9QDSaqAcjad7cy+Lr1oSYmARCQpYSFBRtcD8UNUc5EigU\nNsCaaZ6tmb1Te05Y2EtkZxtn3szOnkts7FAzEQ+q14+yNrSZORPox0+8wu20oA2zeZ19jAAi6eim\nmUFpFNVQ9IOJAri4VH15sqpYEo9OUX2Uy7RCYSM0GzX3VhwRuZrt1tQrLigomvj44WhmEucAb2AE\npiJGW6MfMTEJTJq0gWaZ41jJcwRxjSgm8B8cKeFnNPuC2uDo+DuffLIQwIRbtfXctivCOG+Ptrxm\nEbQbGg0yn87kyZOlbdu20qNHD11ZcnKyjB49Wnr16iUPPPCApKSk6I6tXbtWunbtKn5+fnLw4EFd\neUpKigQGBkrnzp0lMjJSV15QUCBTpkwRb29vlTlU0SiwllecoUfcMpPeceU9xXbvjpfg4CUybNgy\ng1w95soNuH5dfhn/uFzBWaL5hzTjRqVedOUzh1orzE5lDBtm2f241bHVuGnT0TghIUGOHj1qoHRC\nQ0Nlx44nOlnqAAAgAElEQVQdIiLywQcfSFhYmIiIZGRkiK+vr5w9e1bi4uIkMDBQd86oUaNk+/bt\ncuXKFRk0aJAcOXJERER27Ngh48aNk5ycHFm5cqVERESYlEMpHcWtgrXcpw2VV+VtmlN2phOr6SnB\nggKR114TadtWZPJkGe47tdx1rOcObi2s6aLekLHVuGlTm86QIUM4c+aMQVmLFi3IzMykpKSEzMxM\nPDw8AEhMTGTkyJF4e3vj7e2NiJCdnY2bmxupqamEhoYCMHbsWBITE+nbty+JiYmEh4fTtGlTpk+f\nTkhIiC27o1DUOdbyitO3E50/f5lff51Jbu5G3XEfn0gGDvQiJGRpuQ2dZaSlreC110LJzNxhVL5+\n3VJGl/yhiY/m5QV79kBAAI4hSyFVv7b1vPyshTXtcQpjat2RYPXq1fTv359FixbRvn17vvvuOwAO\nHz6Mn5+frp6vry+JiYl07NiRtm3b6sr9/f15//33iYiI4PDhw8yYMQOAVq1akZGRQX5+Pk2aNKnd\nTikUVqQim401veIqChQ6cKAXW7deKLdHZwmQBPyONhxPbq6xA2wAx3jx8Pvw7MeahGr33w92mlQF\nxgO69fpjLazpuKEwptaVzpQpU5gzZw4zZsxgw4YNTJkyhQ8//NCkwcrOzjinhojoykWzPGhwzBzR\n0dG610FBQQQFBVW/EwqFjajMc8pWT+HlA4WGhCw12hSq8XQLBcpmNnl5D+te38F5VrCEEPbw4e29\n6X18FzgaDjHlB/SsrHQuXlxgEPutPswqqho49VYgLi6OuLg421/IJot2epw+fdrAptOuXTu5efOm\niIjcuHFD2rVrJyIiu3btkrlz5+rq9erVS7KyskREpHPnzrryl19+WV577TUREVmwYIF8/PHHIiKS\nmZkpffr0MSlDLXRTobAKltgTasPAbs6Ybux0EC9tXCbL8yyVK7SSF4iUXp0XVDlMTl04DCgqxlbj\nZq3PdO6991527dpFaGgon376KSNGaDa49e/fn4ULF3Lu3Dl+/fVX7O3tcXd3B6Bbt25s376d++67\nj507d7JmzRoABgwYwNatWwkODubNN99k4MCBtd0dhcKqWGKzqY2ncHPLePopD+wpZgqprCjawU/t\nuzDLeyJZLe1ZMWdMleRrjLOKRo1NVFkpYWFh0r59e3FychIvLy/ZtGmTJCUlSVhYmPTs2VMef/xx\nOXHihK7+mjVrxMfHR/z8/CQhIUFXnpycLIGBgdKpUydZtGiRrrygoEAmT54sHTp0UC7TiluCwMCp\npR5dy0r/x1vkOWWR23IVWLZsgzg6zig3q5mskyeYL+VHekgcQ+WpgVNsKouibrDVuKk2hyoU9QRN\n3pxPDOwbsARPz4sV5s4xl58mPPwOvv32d5MOCZVtMNVskAxGs3nUAc0Mpz19mxxgef4NuvArz7CK\npC6JrF03yqBdU7KsXRuiZjMNDFuNm0rpKG5prJvTxraY2wnfu3cE33+/oQrnJQDbsbO7jkgXIBgY\niqvrTLp0EZydi40St5VXDJqIBdG64+1IZzlRjHV4nx13DuWjNv1waopRdAK1m//WQSVxUyiqiKmn\n7oSEWfj5vcvy5RPrnfIxZ89xd684LbPhedpIzq9TNl5ovN9yczeSnBwFCIYJ4TR7ayZNCqVHj/00\naVJEVlY6AK7c5GleYR5r2cxkpg+L4KOvVjO7in2oy303ivqFUjqKWxZTuWDy8t7g2LEo5s3bA9Sf\nAI4xMQkkJZ0weayyPSuGRn/jPDma91Fo4qpdAvJMtpOZ6aeb3bRvN5+5zYeyMOtXvmEQ/TiCg89b\nrF1g6MpcfiapVVZV7YM1aEiz2saMUjqKWxZzT93gQFractavj7JoULL1YKadkWl2/BtGfrZkz4rh\n3h3zfdbMguyADmbqaBTDveznlYw47Jql8/KAkfzg0hFfl01GGyRNzSQ9PRfg6TmV9PR3qtSHmqIi\nQzcclNJpxNzqT4aVuf1asuRTG4OZ8YwsCnCgdeuTrF07u9Lr6G+4PHz4F65dM1WrGHgJ6A1cBmYC\nG/WOR9KNbqziQfxJYREvcblPEnHxz1VBbkhP/xe9e0fQq5flu/mt8T00n+HUsgcLRS1iE5+4ekYj\n6WaVsFa04vqMqT7CYovdkEVqJ/ijNaMam+qzk9M0cXAIEnjSYFMnPCowT9o7hMgGxsglbpP5vCLO\n5FnUR2vIba3voYoMbX1sNW6qmU4jpTE8GWr78Y9/RJCScoO8vI5o0x9buuRTG4Zxa8dTg/JxwyYQ\nFbWdY8de16s5FBf68TdG8yzfsrPF3XT74yRXaQ1YtiRmDbmt9T205j1U2BaldBopjcXLSLvbvSwR\n2X5cXPZaHMCxNgazqsZTq2w5ytQO/9Wr9+te21HCY2zjRSI5Zg/H3niHtrd70W/9mioFuLRGHDhr\nfQ9VZOiGg1I6jZTG9mRY3VArtTGYVSWqcXVtTJrPO4HBvM2/+ApBCGcxNwNO8v2Tj1d6fk3lrlgu\nY6r6PVSRoRsOanNoI8X0zvHaSQfc0DCVrhmoEyeM6m6+XD9nGV4b3iVQhEheZDthODjOZsmSnkRH\nm9t1Y3ts/T281Z1lbInaHKqwKurJ0HLKz5Lq0j23ouUo7QB74cJl0tOv0759e3xvs2e1+zkmfvk5\nL8oyHmMe+bgAUFS0kUOHomwqb2XY8nuo3KjrKTZxT6hnNJJu3tLUpyCSgYGz6iydsTlvut69Z5d6\ngcULRIozefI0q+USt8l7zXvLkG5PNjrvLpV2umbYatxUMx1Fvac+PbHGxCRw4kS2yWOJiecICoq2\n2QZS7SzG1dU4tbRIfun9WcIj9OIl/EimO0NJ4GSWH62dQk22e6va8KDxOMs0NJTSUdR76pN797p1\nseTleZs8dv26ty6MjDWVorHSTcDVNRQfn/bccYc7c+aMZPXq/QzkW17hXVy5jSd5i/38WdeGp2dL\nWrZsXN5djc1ZpqGglI6i3mOrJ9aqGpljYhL45ptjQC7wEBAAFKGJ4vwB8Liubk2Vor5sSUknSkPk\naBlKbu5Q7rij1Hng9Gmcjr+LH/9iKT15j2EITgbteXm1Zc6cEY3KhqfcqOspNlm0K2Xy5MnStm1b\ng3TVIiKbNm2Sbt26ib+/vzzzzDO68rVr10rXrl3Fz89PDh48qCtPSUmRwMBA6dy5s0RGRurKCwoK\nZMqUKeLt7a2SuN3C2GJtvqo74XfvjhdPzykCUwTKRzl4UmCs1ewlpiMpaO01ZWWj73lG5OmnJd+9\nufzT7U/iSo7J+j4+i2+pSBNVQaXCrj62GjdtOhonJCTI0aNHDZTOTz/9JAMHDpSff/5ZREQuXbok\nIiIZGRni6+srZ8+elbi4OAkMDNSdM2rUKNm+fbtcuXJFBg0aJEeOHBERkR07dsi4ceMkJydHVq5c\nKRERESblUEqn4WDKYcC0gqjZQFpVRaapr/0zPg8eEv1MnzVRiuZkg6WasDbkyxzWylXnZiJPPilh\nw+aarO/mNq5BDLT1yUlEUYatxk2bLq8NGTKEM2fOGJR98cUXTJ06lTvvvBOANm00uUISExMZOXIk\n3t7eeHt7IyJkZ2fj5uZGamoqoaEaQ+jYsWNJTEykb9++JCYmEh4eTtOmTZk+fTohISG27I7Cxphz\nGFi7NoS1a0MqXRqqynJZVZfszEes1tILiEabu8bH50uLlnFMyWz+WvY8xE7+ybOkuxbx0+r1DI2Y\nzMWgaJO1+/TpwZdfmj5WHblssRRXn5xEFLWETVSZHqdPnzaY6dx3330yb9486dOnj0ydOlWSk5NF\nRGTp0qWyceNGXb3Q0FDZt2+f/PLLLzJw4EBd+RdffCHh4eEiIjJ48GBJTU3VHevQoYPk5eUZyVAL\n3VRYgZoso1V1uczwWvGls5Rl0rr1oybPqXymM1vXhqPjA7Js2YZqy2zKJbsvh+V/jm0krVlbiezz\nmIGM5u6bh0dYtWYOtRkMVrk1119sNW7a17aSy8vL4+rVqxw8eJAxY8bw1FNPaZWfUV07OzujMhHR\nlYtmedDgmKLhUhOHAfMebntN1p87NxgfnyWUZdp8AYgmM3MH8+btISYmAdA8iYeELOX337Nxdk4C\nUtDOZsqIQJMYTdNGUdFnbN16QddGxTKHAEvRzJKWlr4vKJUNOnCO9whnt8Nwms2cRJc/fmfFdx8Y\nzALK+qJPJNeuzSI29gWD/lhCVe9lTVBuzY2PWvdeGzhwIEFBQbi6uvLggw8yY8YM8vLyGDBgAPv2\n7dPVO3nyJP369cPd3Z2MjAxdeUpKCgMGDABgwIABpKSk4Ovry9WrV2nXrh1NmjQxed3o6Gjd66Cg\nIIKCgmzSv8aItZZiauLiWtXBSyvfpEkbyMzcYXBM63kGGC39ODs/iZ3dtxQXj6GkxImSknZAPvCO\nyTYqug8nT6YCVzDMa7OEggIHNqwczPVnBzHyt2Ps9u7DsX/+l5HjRxrd6z/96Xa+/fZ3XFyu0Lp1\nKHl59uTkdEUbTbt8fyz5nKqjCKr7HVBuzfWHuLg44uLibH8hm8yf9Ci/vPbRRx9JRESElJSUyKFD\nh2Tw4MEiIpKenq5zJDhw4ICRI8G2bdvk8uXLRo4EY8eOlezsbOVIUEdYcymmJg4D1Vmm2b07Xlq2\nnGjyPK1Ru6I2y+Qtn8tFs1zXosUks8tbu3fHi739Q0ZtO1Aof2/WR6RdO5FJk0R++62C+xMvjo4z\nDM53cZkp5b3cQKRHj3lG99bVdYbJZcCq3suafAds4SSisA62GjcrbXXv3r1y7733SosWLcTNzU3c\n3NzE3d3dosbDwsKkffv24uzsLF5eXrJp0yYpKiqSGTNmSLdu3eShhx6Sw4cP6+qvWbNGfHx8xM/P\nTxISEnTlycnJEhgYKJ06dZJFixbpygsKCmTy5MnSoUMH5TJdR1h7Tb66Lq5VHbzK6puX35LEYLt3\nx0vr1o+WUziVD8Ca+6bffomM5jNJoZt828xL5OhRI5mN73XFXm76f4Yy6iseYxuWqXvp6jpdunef\nblKJWvIdqMhDTbk110/qTOn06dNHvv76aykuLraJALWBUjq2w5YZG6vqSluVwatsoDSlJBbrrm2J\nQjUcpC07R3PfNHV7cUz2MVxS6Caj+UxCgpeYlLlHj3nl2jV9711cJhj1p3v36WYU1DKTDwjae9mj\nxzxxdX3UYPZUXolW9h1oDFlqb0VsNW5WatNxdnamT58+2NvXus+BogFgqzX56rjSViVnTpndQls/\nCnDAwyOVtWtn6dqxZEe7fqTkQ4fO88cfxtcrbw9p0qSI2wnkBXoxigyeYxlvMw0n16f479y/mpT5\n4sWL5UpM33t/f3fatDF0L1+3LpbkZFO1i03aarT3MiRkKUlJpm1e2n5X9h2oT2GMFHVPpUpnyJAh\nPPTQQzzyyCO0bNkS0HiVjR071ubCKeo/tgo1YuuBynCgHIpW+fTvX9Z+VcLu6w/SsbHG1zNQwtnZ\nvNbqN26z/xcbS8biSzBZZODqGs4zzwwz2z9Pz5ZkZi4BtPclGJiJviOCj08kzz8farKNgwcNA4VC\nJDASFxfzXmmWOBVU9h1QHmoKfSpVOhkZGXh6evL1118blCulowDb5UOx9UBlqbKsasbRuXOD+fHH\nBaSn/0tX5uk5nzlzHobiYti8Gf7xD+689172v/0f4nf8RKDuvkVUeK077mhDcnIw2lkZFAM9ad06\njB49ulWqFJ95JolVq0LJzfUrPXdkpZtYLZnJVvYdUB5qCn0qVTovv/wyt912W23IomigVDcVdEVY\nY6CqyI23usrSMtfgPzBUDFncdvQILIoADw/49FPo14/hwPDJj1rcH42iNJVlc7ZF9z86ejb9+vXQ\ny4K6t9I+W0M5q8CbCgMqM/p07dpVxo8fLzExMVJSUmITw5KtsaCbinpGTV1pbWG81gT9nG/Qpqfn\n/AqjA3TnJ/mCEEmzd5Ml3R+V4BE1l6G2Pb2scU3lodbwsNW4aVfauFlKSkrYt28fmzZt4siRIzz6\n6KNMnjyZu+66q3a0ohWwVa5vhW2JiUnQeyovZs6cERbHW9PYVl4wajMkpDQdgIlrVTaD6d17NseO\nvW50bu/eEXz//QYAgoKiiY+Pph3pPM8/eIhPeIGlbOQyhYwAYnFxOYefnxvLl4cpQ7qi3mKrcbPS\n5TV7e3uCg4MJDg5m//79hIeH8/rrr9O/f39Wr15NQECA1YVSKLRov/SmvvwVebgZ24QSgFgOHTpP\nSMhSA6Viqafc6dM5JmU8fbosk2hzx1yW8AJ/Yw3/4Ql8SeU6PwEvoYk+8AZ5eXDsGMybpwJbKhoh\nlU2FLl++LGvWrJHevXvLqFGj5KOPPpKCggL55ptvpHv37jaZflkbC7qpqGdYsjxmbh9N796zSzdD\navfCbKhww6al+3E8PEJN1nN0/Ivs3nVAZMsWuXlbG9ndzE86k6a3D2iGxft3FIr6gq3GzUpnOvfc\ncw/h4eF8+umneHl5GZUrFLbAEpdpcx5uKSk3yMvT31sSCpjfa2Kpp1ynTm5cu6bvsgwQyeCiQjqN\nD+Na59Z47PoUrhZy1/rNeOc58M0331NU9BmagJ7mr1FbqQQUirqmUqWTmppqMtozwKJFi6wukEIB\n8Pvv2SbL9RWBOQ+3vDx3YDaQgyYYp+mNzdq2KvOU0yqEggIHnJ1/oaAgCriBL6ms4hg9uMmiggWc\ncv2NNtExpYpDCApqy3ffuVNUBOY2crq4FNcop4xSVoqGhlml8+CDD+pelzco2dnZsWvXLttKpmi0\nxMQkkJZWfve9Bn2XaVOuuDAZzQD/nl7Z/WjSBziWHgsGhuraqsil11ghJHAb/yCayzzKJV5iEY/w\nFAXMxinZnsIfypwX9u+fSVFRy9J3wWhSIhhfo7obYVUCNEVDxKzSefrppwHYs2cPP/zwgy5z54cf\nfkivXr1qRzpFo2TdulhycyMoP0i7us5gzpyyEDHl99okJZ0gM/MG8LleawlANzS5brQswdPzP8yZ\n84TJdvT37ISELNUN6k3IYx7fspBDvE9PujGZq1wDEoH2FBYaessVFW0EppXrRxQuLmfx93fXRQ5Y\nvXq/yftQ2UbYhhxeRs3QGi9mlY4238z8+fP5+uuvadasGQCPPPIIgwcPZuXKlbUioKJuqMtBQWNj\nMYyJBsV06WL6CV47Cy8psQPuKHc0FvhXubIV3H674e5/c5sb8/MdsaOEMLbzIpEcowP38DC/sE2v\n1hI0nmmm8AKGUxbb7RTvvTfL4FrV3QjbUMPLqBla46ZSm06rVq1ITk6mf//+gCaJWuvWrW0umKLu\nqOtBoWwQLouJBuDlFWVQz5ScGqcBfUx/xd3d25i9vr7CdT78FYfYgB3OTOTPHMQNKL9XZ4WJ62op\nxlxsNy2mlvdcXWdw/jxG7t36NNTwMg15hqawApW5tx0+fFh69uwpd999t9x9990SEBBgkAOnIWBB\nNxV61HXeekujEZiW8xmBJ/TeVy8hmQ+/yP8xVs7gLY8xRuw4UHruBJPtOTmFGkUrcHScXi4lQMX5\nfSxNJVDV+1TfsGU6DIX1sNW4WelMp1+/fhw/fpzz588DGLhNK25N6nrZxpSNZeBAL9ati2X16v26\n5T5jOV9HE/dsMmXLconY20+mpGSzrlZFcb82v/IpT6UVE85AXubvhLOVPFxL2wsCvE2ed/fdrXn+\n+YfKydyLQ4f2kpe3n6ys84Azq1fvZ926WKPZS1VSCVR0n6wRbNXWNNQZmsJKmNNG//znP3WvP/zw\nQ4NjixcvtkijTZ48Wdq2bWuQrlrLyy+/LHZ2dpKZmakrW7t2rXTt2lX8/Pzk4MGDuvKUlBQJDAyU\nzp07S2RkpK68oKBApkyZIt7e3lbNHFrV5GG3GnU90ymPuY2igYFTy8loOjumm9v9Fcb92r07Xkbf\n96ys8hwiGTjLBsZIGzLKtaN9Oo8XMLyuo+N0k2mfK5Pf1PeqMcwCGuoMrbFR1XHT4nbNHQgICDD5\n2tR7cyQkJMjRo0eNlM65c+ckJCREOnXqpFM6GRkZ4uvrK2fPnpW4uDgJDAzU1R81apRs375drly5\nIoMGDZIjR46IiMiOHTtk3LhxkpOTIytXrpSIiAjTnazCzatvWQ7rQgFaY1CwptwVRR4wlHOSyXot\nWkwyL+dncfJUu4flFF1kFw9IN1JEE70gvlw7+imgp5b+hQpMFJglgYFTqyy/Vonr3ytNJIXy19b0\n9VZCBQCt/zRIpSMicvr0aSOlM378eDl+/LiB0tm1a5fMmzfP4Bo3btwQEZEuXbroyl955RV57bXX\nRERkwYIFsnPnThERyczMlL59+5qUoSo3rz495delAjQ3KFiiTCyVW7+twMCpEhg4y6jd3bvjpWXL\niWaf/vXldHR8wGS91q1DTXfy228lqaWXHCVAhrOvAiWzWKcIXFxmiKmwOi4uM81+LhXNXkzdKwcH\nQ1sQLBZPzylqYFbUKrZSOpXadKyNNpxOz549DcoPHz6Mn5+f7r2vry+JiYl07NiRtm3b6sr9/f15\n//33iYiI4PDhw8yYMQPQeNllZGSQn59PkyZNqi1fXdsz9KlLLx9TLsSWerVZIrdhWwnAHvT35KSl\nLeHIkSS2br3A9esdTMro4lJsIGd09OusWDGzdH+MBkfHGTz1VLl7dfo0LFoE33zDZ+36suT6R5RQ\n/vM9B0zExeU6fn5tad58Py4ue7l0qYhjx37HMBQO5OW9YfZzqciGYepeFRf/GxiDxkblBkwkPX2o\n8u5S3BKYVTo//vgj7u7uAOTm5upea99Xh5s3b/Liiy+yd29ZelyNQi37r4+p8DsioisXzUzNqK2a\nUJ+MnNVVgJbusanqXhxLlaAlchu2FUv5QTwtbQWvvRZKZuYONErJ9G5+faKjZwOv89prYRQVueDo\nmMdTTw0tLQeuX4cVK2DTJvjb32DTJg6MXUlJqqn76Q0sp1mzMI4efVtXGhOTwPjxb5OXV3H/9Kko\n4oG5jaEQiCZe2xKj9qv6uamNmIr6hFmlU1xs/UE2LS2NM2fO6CIanD9/nj59+pCYmMiAAQPYt2+f\nru7Jkyfp168f7u7uZGRk6MpTUlIYMGAAAAMGDCAlJQVfX1+uXr1Ku3btzM5yoqOjda+DgoJ0m1/L\nU5+yHFZHAVo6G6nOXhxLlaBGbk0qAf3QM/pyG7al/7rsvOvX80rfa+WJALJxcLhO8+am99lER88u\nUzKl/bx/xGJCzvzAxHMHuREUhHdSErRvT0xMApcvp+Pk9CSFhW/ptRIJaD7v9u09DdofPXoofn7b\nOXbM+No//phqcl9NRV5m69bFmuyHZn8PaBRtFNqwPVX93Op6z5Wi4RAXF0dcXJztL2STRTs9TNl0\ntOjbdNLT03WOBAcOHDByJNi2bZtcvnzZyJFg7Nixkp2dbTVHApH6Y+SsjkHfUptUdWxXlp6zbNkG\ncXScYVDH0XGGgYeXYVva1/FGtpIyo77xscrsW7s/i5MZnuMklTvlS4Llbo7rzim7t/ECU0Tj+bas\n1JYTX+H9MPW56Nt9fHwiZdmyDRY5UlTWluZvme5zr+rnVp9slIqGha3Ug02VTlhYmLRv316cnZ3F\ny8tLNm3aZHC8c+fOBi7Ta9asER8fH/Hz85OEhARdeXJysgQGBkqnTp1k0aJFuvKCggKZPHmydOjQ\nwaou0/WJqipAS11uq+OaW7NNm4YDnWFbG0oHfdP5ajSKwHIPsODgJXLw1X/LDx7e8hPdJYQvjM4p\nk9G8wrNkM6fGycFQUUG8uLrOKNeWeQVZcVsaRwjtuVX93BqDC7bCNjRIpVNfaMhKp6rYcqYjYpkS\n7NFjnsEArBnYl4mb2zgDr7TAwFnSrFmY2Nk9XFrP9ADp4TFRWrSYZHbw1FdgXpyTdwmXdAc3WXb7\nn8WBQpPnlA3Gy8rJulRgmXh4hFk0wzU9qFf/3lam1NVMR1Fb2GrcrHXvNYVtsdQmZUk9cwboymwB\nFy9q0xIYeqVlZ2tSNGu90tLS9GOYLQGumWyvf39vRIRYE+YPrQfYpbRnWUEkM/g3rzObrsW/0yR/\nGsU4Ut6+lJWVXnr2UuC30v+adAcVxUgzhWm7W/UcQCyJMFBVm2N9slEqFGBBwE9Fw8LS0CiV1auJ\nAdrTsyWZmUsAO0x5pa1aFUxubn803lna/DYrME4DYDhAmho8586+j5N//xf/wZdYgunFcS6gCdXU\n0bMlTk5TSU/3NGjzzJmnEPmD8ukONAyt0qBsOljnCUw5eFbmAFKm4IWFC4ebvM9VDX3TUEPlKG5d\n7EqnUbc05ZPQKSomJiaBSZM2lLorGxISEsWXXy6v8PyQkKXExgYD7wBbyh1NKC17R69sCRAC7EeT\nBmAvHh7n6N/fmzlzRhgowvXr92oGzyZFLP+TO/0+3MLx9JtMvvYxx+htJOulS5kcO1Y+KjRoPMIM\n++HmNp5Bg/yYM2cEgMVuxgZyuRQzcGD70pmcoYJcu9b0YG9Kwfv4LGHt2hClHBR1hs3GTZss2tUz\nGkk3rUKZXaH6BuiyNkzZE0zbGDS2lKVmDfj6jgIz/zRNLgX0EfH1Fdm1S3Z/FmfWFmLOkG6qf9q+\nGdtW4sXe/iFp2jRMWrd+tMI4a/ryWuoAouwuivqIrcZNtbx2i1LdDYFlmzaXmjxuySZZ7XWiot7l\nxIlZ5OW9oXf0lJmzfqF79xZ4eUXplrZCQpaSn+9IVtZ5Ll5sjn360ywnivv5nA239aL/26u4/8Hh\njAaws2P9+ijOn79Eevp1XF3bs25dLFlZV81cz7gf8fHf4+T0IE5OeeTmajcwa+xSJSU7uXkTbt6E\nFStmAq8b7AcydQ8snaXUpygYCoWtUUrnFqS69piYmASOHDmFxtZyFZiK/jJYVWwd2kFXs/Skn046\n32R9d/c8kpK2m5S/Gc+wEFeeoidv8SS+pJJ1pQUhb0Rx/5jhOgV74cJlfv3VjtzcHWRmQlISeHpO\nxW8NfkwAACAASURBVNNzAenpZdlDPT3nA1mkp+tLEAkspKhoKEVF0yjblGocLaGoaCMrVjxIXNwl\nixV6RQ8B9SkKhkJha5TSuQWpTsw27UB/7dp2vdIFaIz7XrRufZK1a2dXeL52UNXmjmnevK3BABsU\nFE18fFtgJrBR7+xpLFgQbCS/PcU8wX94no3E8wB9+J6zdNLVy8tzKKeglmLoHADp6e8QGDiNXr30\nDekPAxrj+ldfnaSoqBuaCATavr2NNgqAuZ9IUVEf4uOjS+9txQq9soeAP/3pdg4eDCU31w+tY4WP\nz5fKw0xxS6KUzi1IdZZrTCkq+BcQhY9PQaUKp2xQfR04AZQNoGlpewDtE/3s0jphgAuQR5Mml/j2\nW09iYhIAOHz4N+5jLy/zd7JozsOM4wibja5rHDDTdL+bN/fiyy+jjcpHjx5Ky5ZP8Mcfphwj0kr/\nm56F6C/PVabQK3oIANi69QK5uWVOG/b2UykqytOFyFHOBIpbCaV0bExdBFusznKNsaLS7G1xcPiF\n5s1bV3i9skE1AfgR0Pd6W0JaWgjr1+8t90TfFY2r9Jfk588mNnYoP/64AN/CM3xw/Thd+R/P8k92\n8jBwkPKu1M7OTzJnzoRyATPN99vc5+DoaC547U1atw7Dzc2Bs2cNlxn1Y7NpqUihV/QQYEohlZS8\nw9mzUZw9u1zFSVPcciilY0PqKtiipRsC9QfipKQTekfKNnUWF8OxY5pNnebkLhtUYzFcNgNtwMoL\nF26wdWuJ3hN9AvAqcBsQSzsyeS49m4f4nBd5gDF0pZCxpXWHAv9BE/CzDVCMk9MlEwEzgymvnDw9\n5/PLL78zfvxZ8vK80bhkD9V9DsHBndi27UlAP+DnNJo2vcmWLQsYPXoo0dFlkatzcjIpKlpI2VKc\nhooUekUPAXl55n6CGiVWW6ksFIraQikdG1JX+XAs2RBorBATcHTU5qIxnWqg8nwx5gfQM2d+Jju7\nNxonhUtAPrATV24yn1eZz1/ZwoP4Mo/rOAJ3oLGrpAK+wBPoD/R5eWPo3Xsaf/xRgIvLxFKFEgyE\nYG//MC4uLjg45JOXB+npH+vJssSgP5cu3QAmlF7LAc2yWW9ELrN69X7WrYtl7txgrlyZXe6+lcmi\nVejmZlMVPQRUHmVaebEpbi2U0rEhdekKW5nLrrFCHEpREbi7P8TNm00wldmi8nwxxvmPAJydfyA/\n/w70jfx2RBJOJCt4j0MMZAA/8Sv/KT26nLLNm9OAEjQKRxvO5hzFxTc5dqw5GruTBien6djZnaeg\nYBE3b2r7vgTD9AhlqQLy8hw4fToH/fA32llebu6nxMdrSvRnp+YUOlDprNbcQ0B5hVR++U55sSlu\nJZTSsSH12RXWnEIsLGxPcbFpG05Fcjdvfo1mzS5w8+ZURMrsH87Oj+DgUExu7h1oY5wNo4RX2EMh\nGYTyId9yT2ntn4HWaAZ+jYLz9CwhL+8c169PA5wA7Z4fY0+1wsI30SipWDTRDYrQRDrYi+FymIOu\nP4WFV0rb0ub9SUfjvVZG+VmeKYUeErK0wlmtuYcAfYV04cIN0tIukpsboZNXxUlT3GoopWND6nOw\nRdMKMbZ0I6dlmTpjYhKIitrOiRPZpctbYYBG0Tg5OQJZFBbeTm6uxl5yF6ms4kF6coNFrOFDUkCn\ncLSEAXto1uwHBg+OYs6cJwBMhOUx9dVNQKOY9JXRTCCzXL1ifHwiGTjQiwMHbi9XfxaGMyMNlc1O\nazKr1VdIZSF19qs4aYpbEqV0bEh9DrZoSiG6uJwrTcOslU9j5/DwSGXt2llmbELlI0WHUFDwXwoK\notAM9q9zG5dZxnOEsoNVPEMoV8kntLR9LVOAPDTLZcH4+l43iPHWo8d+3XKXBtNKs2wmpGUjMBat\nInFxmYmfXxHLl09k3bpYCgreKlf/Dcr26JRR2ezUWrPaqkQyUCgaIkrp2Jj6OoiYUoiXLrnppWE2\nHeZfM7t5l59+yqCo6LNyrZbZS+ASTchmLqtYyGq28Rh+nCCT24BonJ2nUVDwKzAejQJZQNlA/yT5\n+fYGLRsP6saeanDGTG974uHxBv3772XOnMd1fTF0ty7DweFXA5uWp+d83YZSc9TnWa1CUZ+wqdKZ\nMmUKMTExtG3blp9++gmAhQsXsnv3blxdXRk6dCgrV67E1dUVgHXr1rF+/XqcnJx48803GTx4MAAn\nTpzgr3/9K9evX+exxx5jxQrND7uwsJCZM2eyb98+OnfuzPbt2/H09DQtjMKI8goxOvp1kpPHU1DQ\nA+3GTk/PnboBNyYmgWnTtpSmCvAy06oDIITyIys5wXFyGMzX/IyvXp0jFBS0B/qhURQOwBogCc3m\n0bdITw8zaNV4UDd2o4bmZmQqpmdPX6MNouZmJyUlWRh6s2WZabeM+jyrVSjqFTYJI1pKQkKCHD16\nVHr06KEri42NleLiYikuLpZp06bJ22+/LSIiGRkZ4uvrK2fPnpW4uDgJDAzUnTNq1CjZvn27XLly\nRQYNGiRHjhwREZEdO3bIuHHjJCcnR1auXCkREREm5bBxN28Jdu+OF0/P+eUiHc+Qli0f1kVI1kRD\nLp/m2fDvHibJt9wuR2grQ1kr5VNAw4zSDJ0bBKaVOzZNYLzAEunY8XGTMmojN/fuPVs8PaeUO3+y\nwJPlyhYLxJuM2GwqU6emjXhREZ8VjR1bjZs2nekMGTKEM2fOGJSNGDFC9zokJIRdu3YxdepUEhMT\nGTlyJN7e3nh7azJFZmdn4+bmRmpqKqGhoQCMHTuWxMRE+vbtS2JiIuHh4TRt2pTp06cTEhJiy+7c\n0qxbF2sQFFPDRq5fj2L9+r2MHj20nLE8GP0Yal1I45+MoD8ZLHPowZbi+xDmorGl6M8aitDMUjZg\nGLkANBs0w4AXuHRpJjExCQYzhfIzM20w0cTEc1y/7o1mL08SEIomDE8xMNIgjln5vTTh4Xdw6JB+\nQNKWlLfngNoro1BYizq16bz11ltMmzYNgMOHD+Pn56c75uvrS2JiIh07dqRt27a6cn9/f95//30i\nIiI4fPgwM2bMAKBVq1ZkZGSQn59PkyZNarcjdYi1wuyY874Ch1LngvLLUUOBJDx4iKVkMJHj/Iu/\nsqxzSx6Z2JmAXUf10hpo5YkEJpa+djVzPRcAcnM3mtyMaqq/eXn7dcE3NdfqAeylRYvf8PHZgUg+\nq1fvJypqOxcv5pOeXubSnZAwCz+/QpYvn8jq1RAfX3/d3BWKW4H/b+/cw6qs0ob/wwAhxUOUoIPI\nQeNQppIcqlGwDPRVojx84GT1Kn5TjIqT5lQqiTKOo+bkKa006mucsqaJiUsTLecD9HoTUPnGUVDz\nlOMkB8EDZwXX98cDm73Ze8tB2Bvs/l3XvqK117Oe+3lcrJu17pPVlM7y5ctxcnJi6tSpACYr1NnY\nGAcbKqV07Uopg+tMjdFAYmKi7uewsDDCwsLaKHnnoT3T7Jizb0AdDg7avYqLS7GzK+XmzWjs+DWz\nqeFNMvh7t75MefB5HAa5srq+0mdiomYj+tOfJlFebodSlYB++hhzOc9KaPA0a7q7MPe8vXoV0hg4\n2hBvE46X1ydcu9anSfyMYbBodfUWcnMTmDdvT/04L9LUQcHR8WXmzn2+mTcoCF2b9PR00tPTO/5G\nHXJop8e5c+cMbDpKKfXRRx+pxx9/XFVVVenaUlNTVXx8vO7/hw0bpq5fv66UUsrT01PX/vbbb6tN\nmzYppZSaP3+++uqrr5RSSpWUlKhHH33UpAwWeEyr0J4VJ03bdN5Urq4z1NKl7+rZPm6pSXypfsBJ\n7b6nn5rsF2OyKqap6puGNpx3FcQ2ud+v69sXmbTDmHteL69oZWv7skGbre3Lysvrf92mSqlxFdGA\ngN/Uy5xR32epcnRsWaVQQbjb6Kh10+I7nbS0NNasWUNmZiYODg669qCgIBYuXMiFCxc4e/Ys3bp1\nw8nJCQBfX1927NjB2LFjSUlJYd26dQAEBwezfft2wsPD+eCDDwgJCbH041iV9kyzM2HCaLZtg7fe\nms25c+XADTw8epCU9N+6lDlBZLGWBfSknFf4CtuxGQaxNEB9cswMrl6l3u14M5pHmrazcHaOoX9/\nV06dOsWNG7ZAJFoWgur6PlqOM0fHaObOnd2i571yhfqccY3U1r7HlSvTzDxt0/ejHZ05OT3A8uVP\n1gdnNnigzRYPNEFoRzpU6UybNo2MjAwuX77MwIEDWbZsGStXruTGjRuMHTsWgMcee4zNmzfj4uJC\nXFwcTz75JPb29rz//vu6cd5++22mT5/Om2++SUxMDCNHjgTgueeeIy0tDT8/P7y8vNixY4dJOe5W\nDI/EGo+Xjh3L1xnhb2fzMfXd4cPvGt3nz0lf8SnTGMV+EkjiE17kFvcQWn3AoF9i4mZWrDhKba2+\ng8AUIB3wB2pxde3NgAE9OXYsAC0TQGL9xxBv7/5Gi725I8CyMtP2FqXsTbYblqpuzHPm4FDXaeOq\nBOFuwaZ+G3VXY2Njc1t7T1el0cYRQUMpggbl4+BwgQEDoLLSzsBw7u29mPXrNS+/pvaRhu8alNWH\na1OZcvp7Ii4eYr1azFoWUEkPXX9n52gefthPp7CMU9U0lkhowNHxFby8FMeP90dTNsY51AAiIhJI\nS0syqkh66VKvJl52i4CraDsqQwICZhvZdFxdX+Xeey/x00/2VFcPAp4GRuPtvYjp0934/vufLFr7\nSBA6Kx21borS6eJou4vd9dkBjBf5htQ0+m7AEREJKKXYu9f0Yh8fN4acWat45fL/YycTSWACxbZ7\nDY6wbG1fprb2ed24Dg5x1NT8iFLf6I1mWqE4O0dTUjKk/jtjmV1dX6V//zJu3uxplADT1TWWiooS\nysqGo+1YGlzwDcfw9l7E+vXaDsbwuOxpnVLVbw8J6c/27f8xq4TvFqxRVFDomnTUuilpcCxIe//C\n79qVyfbt/6G29tH6FuM6OIapaTTM23wUTv93L1573sGOxwhnL0cZpn1Vez89e07Azs6JsrI6amsf\nMLhSc42e0GQ809Orf//+2NldoqBA30ssBijHxqY7hYU1FBT8Tk/mxfX/HU1BwYf07TsNU0dyfftO\n45FHfIyyAZjL7qzf3lyW6LsBaxUVFAR9ROlYiI74hW+sibOkvuX2VSgbcHCoM/oLZgRHWEss/W78\nyKtEkcZ2wNBlvbbWnfJy/YSar9CYugZgCFoOtYbjL9M2mF/8wok//nESCQmfcP78NCori6mp8QR2\n0ChWo6JpqjhN22pGExT0rVGqm5ZizdpHlsJaRQUFQZ9uzXcR2gPzv/DftnnMxoWyIfml+VibBrQk\nlE8THx+Oq2ssbsTzfxjGLkazA8UwikjDk6YKp7HsgT7vARloR2QAN4FraAoiEa02zf82uEJLnqkd\niT3wgCuPPOJDXZ0jhuWiQVM0W9AUamN9HQAvr554ey826N3wXG2lM9c+ai9+DopV6PzITsdCdMQv\nfONC2fBX6ido9WAalUODfaRXr0TKyop10fm1V86y4GoeMzjHFuLw4XeUEYc2JYwzODeWPWiKH1qR\ntDS0EtQfNvk+E+3ozBeoo6zsFAkJnzRxCEg084Q+9d8tBgoBTbksX66lRLqT5JpNjzofe2zAXZ8l\n+uegWIXOjygdM7S3/aUjfuENMy9rpQhcXWcyYMBsnJweqF+Mn9MZzufN28P5M8uI5Q0S+Svf8iDD\neZ6LhKNlaPasH9mwno6t7WH8/AbqlT3Qpw64AMSiVetsyuj69kQAKioSyc2txdDBoLkd2gp69Hiu\nvqjb7W01LcHcUad+Hra7MUu0lF8QOgOidEzQEfaXjviFN51O/79Nyrhh/R6GnPklf+dBLlPHRA5w\nhAYHhIajqnAad0oN9XQW4eMzgKSkGKZOfYWqKv0gzIYYl3X1ffeakbRY7+c6tGmnn7amFE1p6e+S\nGuNnAEaOHNZme01TzB11HjyYYBTsejch5ReEzoC4TJsgImKJSXfiESNm8cADrm3e/TR1021w321v\nmu7SFk/04t63VuB01Zbf8QipfIGxzSYBSAJmAf1pzAr9NBER35KWlkRi4maWL/8OpR6h0V05DXCj\nb9/9DBjQix9+uNWkGucitKOxl9CO/wAuAcMx9LSbDxwBBgHuNMTPNNAQt9MehIUl6iUIbSQ0NJH0\ndON2Qfg5Ii7TFsS0/SWT/Hw7cnMblVFrdz/NRbu3x5Ge/i6tPz+RRAL++97hw/7DWHI1g1pWYKxw\nAO7B1fVVQFFQ0Li46+/GEhN/Q2rqEXJz69CU0rdou5HRBAVdIi0tiYCAWeTm6pcy0L7X3KnvA/6M\n5u3W1LX7Tzg4PEd1dSxazE3jczs4vMLcub9q1Xu4HWLbEATrIUrHBKYXJWPvrda6mzaXkqY9jvQ2\nbNjLpTOLeItlxLOBbczCu+4nBvdbxKDuiZw5Y0rhgLPzCbZt01yfb3f8kpT0Yr2cphVTr15umHYM\ncERTOAD9THwPDg69qK42tCdBHX5+te26IxTbhiBYD1E6JjC1KJnz3mqp91lzSqVdYijq6njy/L9I\n5kEyGc2jHOZHPAC4ccOO9esjSEj4RK/OjYYWvf+bZg30DUrT0bECZ+doXF374ObWz0AxmdtF9OwJ\n5eUN/2e6j5dXT65d03eM0GRLSnrRZP+2IrYNQbAeonRMYGpRKirqadJ7q6VHMs0plTt1qT644k/0\n++MqQqtuMYmdZBPc5F6XADhyZJuu4mZLF9xduzJJSNhBfn451dXuwCRgNHZ2sdjbl7BmzT/YsGEv\n8fHhZncRvXr10Xt/xi7Z7eUK3VIksacgWAdROmYwVRp53ry2H8k0p1TabGc4fpyil2Lp/89TzK/d\nylfcD6SCgdJZRFXVbF3Z6dYsuI07NP2EmouBYxQUuFJQYLhzW78+gvXrI4wUB6D3/rR7OzpG4+3d\nn1/8wqlZV2jJGSYIdweidFrInR7JNKdUWm1nKCyEpUvhb3/jK+fhxNcWcJOG9DA2aDaRfLQsAbVA\nERcvtj4Q1dQOTduhRAOfG7Q27NzS0pLMvhfD99eyWjWSM0wQ7iI6pDRcJ6MzPKZxJU2lvL3fNKi6\nuXOnVi0zNHSpiohYYrIip6qsVGrFCqWcnZV69VWlSkpUaOhSMxUy9dsXKXv7qabHvA3mx37JZHto\n6NI7fFPGtGeFVEEQWkZHrZuy07EQLdkp3fbY69Yt+MtfYPFiCAqCgwdh8GDA/C7KsFjZCm7cSNAd\nsbUU82NXmWztCLdjyRkmCHcPHZrwc+bMmbi4uDB06FBdW1lZGVFRUbi7u/Pss89S3ujSxIYNGxgy\nZAj+/v4cONBYlTI/P5+AgAC8vLxYvLgx0ePNmzeJjY1l0KBBhIWFUVBQ0JGPc8dMmDCatLQk0tMT\nb3sEZURGhqZo3n0XPvsMvvxSp3B27cqkuLgUB4cXaUyOCVpQZtMEmPe0eqGOjw83Sq7p4PAKXl61\nuLrON2i/06Sb5pC4GkG4i+iQ/VM9mZmZ6siRI+rhhx/Wta1atUrNmTNHVVdXq9mzZ6s1a9YopZQq\nLCxUPj4+6scff1Tp6elqxIgRumvGjx+vduzYoS5fvqyeeOIJlZOTo5RS6vPPP1eTJ09WFRUVauXK\nlWr27Nkm5Wivx9y5M0OFhy9WoaFLVXj44lYdVbXp2pMnlYqKUsrDQ6lPP1Xq1i2jMZse2Tk4vKJ6\n9hyvIMPEkdSSNh1JmTv2a9FxYDvQkqNJQRDal45SDx1u7Dh37pyB0pk8ebLKzc1VSil1+PBhNWXK\nFKWUUqmpqWrevHm6fsOHD1dlZWVKKaW8vLx07WvXrlWbNm1SSik1f/58lZKSopRSqqSkRI0cOdKk\nDO3x8kwvfItatPC1+triYqXmzlXq/vuVWrVKqaoqk93M2ToCAn6jXF1fbdL+pnJ1ndFlF2pLKThB\nEDQ6SulY3KaTk5ODr68vAL6+vmRnZwOQlZWFn5+frp+Pjw9ZWVkMGjSIfv0aI9j9/f35y1/+wuzZ\ns8nOzubll18G4L777qOwsJCamhq6d+/e7nLfSfBmi6+troaNG2H1aoiOhrw8eOABzGHO1uHk9ADb\ntj3JW2/N5ty5cuAGHh49SEpqTAaq74J8/fpFwJ5evfrp0vynph7h/PlylOqOp2cPkpJirOopJnE1\ngnB3YHGlo1qRQM7Gxjhli1JK1660nVqLxk5MTNT9HBYWRlhYWIvlgJYZs03FkgBkZ//79tcqBV98\nAW++CUOHwoED4OPTrEy3s3XcbpE2dEHORMt11qAUM/nuu03cujUE2AZAbi7MmjWfbdu6jouyxPUI\nQutIT08nPT29w+9jcaUTGBhIfn4+I0aMID8/n8DAQACCg4P57rvvdP1OnDhBYGAgTk5OFBYW6trz\n8vIIDg7WXZOXl4ePjw+lpaW4uLiY3eXoK5220Jwx21QsydGj84FrXL060Py1//M/sGAB14pLWeH8\nONnXBtM9/s8tWiTbmkPMcOe1F8Pkm3u5detBDOvdQEHBn7pMWWOJ6xGE1tP0j/Fly5Z1yH0srnSC\ng4NJTk5m9erVJCcnExISAkBQUBALFy7kwoULnD17lm7duuHk5ARox3A7duxg7NixpKSksG7dOt1Y\n27dvJzw8nA8++EA3VkfQ3AJv6ghNq4yZgOZFZpj2Jfj+GczPyqDwyQ1sch3Jh9V+XDqTrPu+JYtk\nWwJWd+3KJCfnNFpSzloMa93A7aZEZ3dRbtjd5OSc5sqVHQbftTqPnSAIHUOHWIrqiYmJUf3791f2\n9vbKzc1NJScnq+vXr6tnnnlGDRw4UEVFRemcBZRSat26dcrb21v5+fmpzMxMXfvx48fViBEjlIeH\nh3rjjTd07Tdu3FAzZsxQAwcOVKGhoerSpUsm5Wivx7ydMbv5AM0MBUtUH36n1tt6qxKb7moRv1eO\nVOiCN5t6nLV38KMphwaYqiCuXs7FCmLr/9u1gjENn830v0VHBK4Kwt1KR6mHDt3pfPbZZybbv/76\na5Pt8+bNY968eUbt/v7+HDlyxKjdzs6O5ORko/aO4nZ2kuYCNO0IIY5cFrOC3fc44Vf7I0W46PVb\ngbYrahz/TncWTe0axcUFnDmzTa9HJjAE42Jqh2ncmWkVPu3szlNU1ItduzI75W7BcKcpcT2C0FmR\njAQtoCVGaVPHb66ur4K6xmOFX7GK1/mBIbw0MIqLvXtQdMyl6W3Q6sc0Ym6RbIk8puwaDg5xNAaP\n7gX+DQysb2u4/k94e79Er16FnDo1gcrK/ii1jZs3NYeCefM6p23E0NHDdBZrqZcjCNZHlE4ztNQo\nbcq+siTcl4Hr11B2z1cs7B7EAUcn5swcwa3vf+LYMVN3K0bLKmCLo2M+ISGhbZbHlI1Jq6EzC3DB\ncHfTkHFAu97NzZP09ESTZbs7q23EcKfZWAiub98LBAW5S70cQegsdMihXSfjTh6zTckmz59Xato0\nVXXf/er1ByaobtQaBIUuXfqukW2lb9/fKHv7qc0GkLZUHnM2JhubZ8zYnpYYjWVujM5oG5GsBYLQ\nvnSUepCdTjO0KtnktWuwciVs3Qpz5xJd6EbqP1YbdDlzZgUHDyYY1ZwpKqohN/cLo75vvaWl/284\nUsvKuoi2GwrndvYfczame+/tQUWFqW+06/WPobpSzjOpBioIXQNROvWYs5O0aOG9eVNTNMuXw4QJ\n8K9/wYABXAtLNHltdfU9Rk4JYWb65uWVkZi4me3b/9PkuMzwSKypImhZBc9G+vY9SVBQgsFC3dY4\nIGshWQsEofMjSofb20luu/AqBTt3wsKFMHAg7NkDw4bp+rVmp2Cub3X1IDZtyqCk5PMm3zR6u5lS\nBOb+8gdMVkBdvz7OaMGW3YMgCO2NTf3Z3V2NjY3NbVPkmDKYa+1aFcxduzLZuPFbvYX3aSYMcIIF\nC6CgANasgf/6L2iStseUMtMWeOOFe9euTKZM+aze2N/AImAcvXsnc+3ax0by9e7933h52aKfN60l\nmQxMPo8oEkEQ9Ghu3WwrstOhebuNwbHNxYuwZIm2q1m6FGbNAlvT17dmpzBhwmj8/D4hNzcBzb5S\nB4wDRmNr+67J8b29e3DtWp9Wp3uRYyhBEKxFhxZx6yq06BisrAwSErTjswED4ORJeOUVswoHGu1E\n1dX30L17bbM7iqSkF/H2voWWoiaJhqOzOXNCjQqpeXsvQqkaM9mrv23miQVBEKyD7HRoxm5TWwvJ\nyZCYCE89pUVIurs3O+auXZnMmvX3+vxrGkeP3j5T8+12RoGBmUbta9b8w+Q4pjzrJOuyIAidAbHp\n1GPSznFPJbz2Gjg7w9q1MHJki+8ZEPAbcnM3m2ifzeHDpo/LWktztqgGTNuWFrN+fYQoHkEQTNJR\nNh0JDjXF0aNKhYcrNWSIUikpRmWiW0KfPi+aDKzs2/fFVo9ljpYGRLYpwFUQhJ81HaUe5HhNn6Ii\nWLwYUlM1Z4FXXgE7uzYNZWNTY+abG22XrwktdVRoVYCrIAhCByJKR5+KCujbV3MS6NPnjoby8OjJ\nlSuGSSdhER4ePe5o3Ka0xBOtK2UWEATh7ka81/Tx9ITVq+9Y4YDmiebqWoAWwJkIJODqWkBS0ot3\nPHZriY8PN+n9Nnfu0xaXRRCEnzfiSNCBdKYgzM4kiyAInZ+OWjetonS2bt3KRx99RE1NDaNGjWLd\nunWUlZUxffp0cnNzCQgIYPv27fTs2ROADRs2sHHjRuzs7Pjggw/45S9/CUB+fj7PP/88V69eZdq0\naaxYscLk/ayldARBELoqHbVuWvx4rbS0lD/84Q98++235OTkcOrUKfbs2cOWLVtwd3fnhx9+wM3N\njffeew+AoqIiNm/ezL59+9iyZQvx8fG6sRYsWMDrr79OTk4OGRkZHDp0yNKPIwiCILQCiysdR0dH\nlFJcu3aNqqoqKisr6dOnD9nZ2cTGxtK9e3dmzpxJVlYWAFlZWYwbNw53d3dCQ0NRSlFeXg7AkRfu\newAADfRJREFUyZMniY6OxtnZmUmTJumuEQRBEDonVlE6W7ZswcPDA1dXV5544gmCg4PJycnB19cX\nAF9fX7KzswFN6fj5+emu9/HxISsri9OnT9OvXz9du7+/PwcPHrTsw7SCXbsyiYhYQliYVpFz167M\n5i8SBEG4y7C4y3RxcTFxcXHk5eXRt29fpk6dys6dO1t1dmjTJJsz0Oz1iYmJup/DwsIICwtr8f3u\nlJaWmBYEQbAW6enppKend/h9LK50srOzCQkJYfDgwQBMnTqV/fv3ExgYSH5+PiNGjCA/P5/AwEAA\ngoOD+e6773TXnzhxgsDAQJycnCgsLNS15+XlERISYva++krH0mzYsNdMYs4EUTqCIHQKmv4xvmzZ\nsg65j8WP10aNGsWhQ4coLS2lpqaG3bt3Ex4eTnBwMMnJyVRVVZGcnKxTIEFBQezZs4cLFy6Qnp5O\nt27dcHJyArRjuB07dnD58mVSUlIIDg629OO0CMkIIAiCoGHxnU6vXr1YsmQJzz33HJWVlYwbN44x\nY8YQFBTE9OnT8fHxISAggFWrVgHg4uJCXFwcTz75JPb29rz//vu6sd5++22mT5/Om2++SUxMDCNb\nkZDTkkhGAEEQBA0JDrUArakgKgiC0Bm4q4JDLY21lQ5IRgBBELoWonTugM6gdARBELoSHbVuSpbp\nNiKVOAVBEFqPKJ02IHE3giAIbUNKG7QB83E331pJIkEQhK6BKJ02IHE3giAIbUOUThuQuBtBEIS2\nIUqnDUglTkEQhLYhLtNtROJuBEG4m5E4nTtA4nQEQRBax11TOVQQBEH4+SJKRxAEQbAYonQEQRAE\niyFKRxAEQbAYonQEQRAEi2E1pVNRUcFLL73Egw8+iL+/P1lZWZSVlREVFYW7uzvPPvss5eXluv4b\nNmxgyJAh+Pv7c+DAAV17fn4+AQEBeHl5sXjxYlO3EgRBEDoJVlM6S5cuxd3dnaNHj3L06FF8fX3Z\nsmUL7u7u/PDDD7i5ufHee+8BUFRUxObNm9m3bx9btmwhPj5eN86CBQt4/fXXycnJISMjg0OHDlnr\nkVpFenq6tUUwQmRqGZ1RJuiccolMLaMzytRRWE3pfPfddyxatAgHBwdsbW3p3bs32dnZxMbG0r17\nd2bOnElWVhYAWVlZjBs3Dnd3d0JDQ1FK6XZBJ0+eJDo6GmdnZyZNmqS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- } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 2. Error analysis of linear regression of `Edyn` and `Estat`\n", - "Compute the difference between the fitted and observed values of `Edyn`; these are called the errors. Compute the mean and standard deviation of the error (if you have done the fit correctly, the mean should be very close to zero). Create a histogram of the errors. Add to the same graph the Normal distribution using the sample mean and sample standard deviation you just computed. On the same graph, add vertical lines for the 2.5 and 97.5 percentiles according to the Normal distribution." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.stats import norm\n", - "error = w.Edyn - (a*w.Estat + b)\n", - "mu = mean(error)\n", - "sig = std(error)\n", - "hist(error,normed=True)\n", - "x = linspace(-6000,6000,100)\n", - "y = norm.pdf(x,loc=mu,scale=sig)\n", - "plot(x,y,'r')\n", - "x025 = norm.ppf(0.025,loc=mu,scale=sig)\n", - "x975 = norm.ppf(0.975,loc=mu,scale=sig)\n", - "axvline(x025,color='k')\n", - "axvline(x975,color='k')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "" - ] - }, - { - "output_type": "display_data", - "png": 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zM0VE5NChQ7Jp0ybJysqShQsXNqjnkksukTVr1khZWZmkpqZKQUFBo1hb0BzV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\n" - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Count how many data points fall outside the 95% interval according to the corresponding Normal distribution. The data points outside the 95% interval are potential outliers. Recreate the plot you made in Exercise 3, but now plot the data points inside the 95% interval with black circles and the data points outside the 95% interval with red circles (refer to Notebook 4 of quarter 1 if you forgot how to do that)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'number of points outside 95 percentile: ',sum(abs(error)>x975)\n", - "outside = abs(error)>x975\n", - "plot(w.Estat[~outside],w.Edyn[~outside],'ko')\n", - "plot(w.Estat[outside],w.Edyn[outside],'ro')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "number of points outside 95 percentile: 19\n" - ] - }, - { - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "[]" - ] - }, - { - "output_type": "display_data", - "png": 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7hfOOHz+OzMxMREdHo6OjQxhvbW2FyWQCAJhMJrS2tiIpKQmXLl3CrFmzJEPw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\nAKdOnRL9rL+/HwcOHAAgHfr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- } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 3. Fit `Estat` vs `Edyn` or the other way around?\n", - "In the previous two exercises, we assumed that `Estat` is more accurate than `Edyn` so we fit: `Edyn = a1 * Estat + b1`; let's call this line 1. Next, we assume `Edyn` is more accurate than `Estat` so we fit `Estat = a2 * Edyn + b2`; let's call this line 2. Plot the `Edyn` data on the $y$-axis vs. the `Estat` data on the $x$-axis using blue markers. Plot the two best-fit lines you computed using red (line 1) and green (line 2), label the axes and add a legend. Report the slope and intercept of the best fit lines as they are shown on the graph (Note: that requires a bit of algebra for line 2 as it needs to be reworked in the form `Edyn = slope * Estat + intercept`)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a1,b1 = polyfit(w.Estat,w.Edyn,1)\n", - "a2,b2 = polyfit(w.Edyn,w.Estat,1)\n", - "print 'a1,b1: ',a1,b1\n", - "print 'a2,b2: ',1.0/a2,-b2/a2\n", - "plot(w.Estat,w.Edyn,'bo',label='observed')\n", - "x1 = array([w.Estat.min(),w.Estat.max()])\n", - "y1 = a1*x1 + b1\n", - "plot(x1,y1,'r',label='line 1')\n", - "y2 = 1.0/a2 * x1 - b2/a2\n", - "plot(x1,y2,'g',label='line 2')\n", - "xlabel('Estat')\n", - "ylabel('Edyn')\n", - "legend(loc='best')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "a1,b1: 0.836674348143 3010.55026869\n", - "a2,b2: 1.11078518985 -625.844196038\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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/+OADMXPmTPHgwQMxY8YM8dFHHwkhhIiPjxf29vbi6tWr4tChQ8LZ2Vl1zeDB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\n1yHTxo72gIA50rLlOOnR41GLIb/Vz2f5s9zCPPkzd8kBPOU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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 4. Error analysis of `Estat` vs `Edyn` and the other way around.\n", - "In the previous exercise, two straight lines were fit of the form `Edyn = slope * Estat + intercept`. Compute and report the mean error and the square root of the mean squared error of both straight lines, where the error is defined as the measured `Edyn` minus the fitted `Edyn`. Plot the errors vs. `Estat` for the two fitted lines with red and green dots, respectively. Does either of the errors show a trend?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "error1 = w.Edyn - a1 * w.Estat - b1\n", - "error2 = w.Edyn - 1/a2 * w.Estat + b2/a2\n", - "print 'mean error line 1: ',mean(error1)\n", - "print 'mean error line 2: ',mean(error2)\n", - "print 'mean squared error line 1: ',sqrt(mean(error1**2))\n", - "print 'mean squared error line 2: ',sqrt(mean(error2**2))\n", - "#\n", - "plot(w.Estat,error1,'ro')\n", - "plot(w.Estat,error2,'go')\n", - "title('Line 2 (green dots) shows a trend')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "mean error line 1: 8.57377589649e-12\n", - "mean error line 2: 7.05113869914e-12\n", - "mean squared error line 1: 1536.36115\n", - "mean squared error line 2: 1770.23176695\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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bj74exMx8qU35bezR0hWR69PWLUtNJAHbgTcALl6EHTuaZc4aGxVFemoqM1at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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Curve fitting\n", - "To be added" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebook_wm1/.ipynb_checkpoints/py_exp_comp_wm1_sol-checkpoint.ipynb b/notebook_wm1/.ipynb_checkpoints/py_exp_comp_wm1_sol-checkpoint.ipynb deleted file mode 100644 index 3fd53ec..0000000 --- a/notebook_wm1/.ipynb_checkpoints/py_exp_comp_wm1_sol-checkpoint.ipynb +++ /dev/null @@ -1,1377 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:7dc77826410c48786733116f1a5fbf5bcd911b8a87e49aa2255935a95da41dc6" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - "\n", - "# Exploratory Computing with Python\n", - "*Developed by Mark Bakker*\n", - "## Water Management Notebook 1: Dealing with time series of data" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# We will probably need these\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Loading real data with Pandas\n", - "Real data is often stored in CSV files (Comma Separated Values).\n", - "So far, we have loaded csv files with the `loadtxt` command. \n", - "The `loadtxt` function has some basic functionality and works just fine, but when we have more elaborate data sets we want more sophisticated functionality. \n", - "The most powerful and advanced package for data handling and analysis is called `pandas`. We will use only a few functions of the `pandas` package here. Full information on `pandas` can be found on the pandas website.\n", - "\n", - "Consider the following dataset, which is stored in the file `transport.csv`. It shows the percentage of transportation kilometers by car, bus or rail for four countries. The dataset has four columns. \n", - "\n", - "`country, car, bus, rail` \n", - "`some more explanations, yada yada yada` \n", - "`France, 86.1, 5.3, 8.6` \n", - "`Germany, 85.2, 7.1, 7.7` \n", - "`Netherlands, 86.4, 4.6, 9` \n", - "`United Kingdom, 88.2, 6.5, 5.3` \n", - "\n", - "This data file is loaded with the `read_csv` function of the `pandas` package. The `read_csv` function has many options. We will only use three here. The rows that need to be skipped are defined with the `skiprows` keyword (in this case row 1 with the `yada yada` text). The `skipinitialspace` keyword is set to `True` so that the column name ' car' is loaded without the initial space that is in the data file. And the `index_col` keyword is set to indicate that the names in column 0 can be used as an index to select a row." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from pandas import read_csv\n", - "tran = read_csv('transport.csv', skiprows=[1], skipinitialspace=True, index_col=0)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you have loaded the data, you can print it to the screen" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print tran" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " car bus rail\n", - "country \n", - "France 86.1 5.3 8.6\n", - "Germany 85.2 7.1 7.7\n", - "Netherlands 86.4 4.6 9.0\n", - "United Kingdom 88.2 6.5 5.3\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When the DataFrame is large, you can still print it to the screen (`pandas` is smart enough not to show the entire DataFrame when it is very large), or you can simply print the first 5 lines of the DataFrame with the `.head()` function. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Basic DataFrame manipulation\n", - "The `read_csv` function returns what is called a DataFrame. The variable `tran` in the code above is now a DataFrame. The rows and columns of a DataFrame may have names, as you can see from printing the DataFrame to the screen. To find out which names are used for the columns, use the `keys` function, which is accessible with the dot syntax. You can loop through the names of the columns if you wish." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Names of columns:'\n", - "print tran.keys()\n", - "for key in tran.keys():\n", - " print key" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Names of columns:\n", - "Index([u'car', u'bus', u'rail'], dtype='object')\n", - "car\n", - "bus\n", - "rail\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each DataFrame may be indexed just like an array, by specifying the row and column number using the `.iloc` syntax (which stands for *index location*), where column 0 is the column labeled `car` (since the column labeled as `country` was stored as an index when reading the csv file; more on that later)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print tran.iloc[0,1] # Gives the bus data for France\n", - "print tran.iloc[1,0] # Gives the car data for Germany\n", - "print tran.iloc[2,2] # Gives the rail data for Netherlands\n", - "print tran.iloc[3] # All data for United Kindom\n", - "print tran.iloc[:,1] # All data for bus" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "5.3\n", - "85.2\n", - "9.0\n", - "car 88.2\n", - "bus 6.5\n", - "rail 5.3\n", - "Name: United Kingdom, dtype: float64\n", - "country\n", - "France 5.3\n", - "Germany 7.1\n", - "Netherlands 4.6\n", - "United Kingdom 6.5\n", - "Name: bus, dtype: float64\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, values in a DataFrame may be selected by specifying the indices by name, using the `.loc` syntax. This is a bit more typing but *much* more explicit. The equivalent of the code cell above, but using indices by name is" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print tran.loc['France','bus']\n", - "print tran.loc['Germany','car']\n", - "print tran.loc['Netherlands','rail']\n", - "print tran.loc['United Kingdom']\n", - "print tran.loc[:,'bus']" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "5.3\n", - "85.2\n", - "9.0\n", - "car 88.2\n", - "bus 6.5\n", - "rail 5.3\n", - "Name: United Kingdom, dtype: float64\n", - "country\n", - "France 5.3\n", - "Germany 7.1\n", - "Netherlands 4.6\n", - "United Kingdom 6.5\n", - "Name: bus, dtype: float64\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are two alternative ways to access all the data in a column. First, you can simply specify the column name as an index, without having to use the `.loc` syntax. Second, the dot syntax may be used, like syntax `.column_name`, where `column_name` is the name of the column. Hence, the following three are equivalent" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print tran.loc[:,'car'] # All rows of 'car' column\n", - "print tran['car'] # 'car' column \n", - "print tran.car" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "country\n", - "France 86.1\n", - "Germany 85.2\n", - "Netherlands 86.4\n", - "United Kingdom 88.2\n", - "Name: car, dtype: float64\n", - "country\n", - "France 86.1\n", - "Germany 85.2\n", - "Netherlands 86.4\n", - "United Kingdom 88.2\n", - "Name: car, dtype: float64\n", - "country\n", - "France 86.1\n", - "Germany 85.2\n", - "Netherlands 86.4\n", - "United Kingdom 88.2\n", - "Name: car, dtype: float64\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###`numpy` functions for DataFrames\n", - "Series can be pretty much treated as arrays, especially when they contain data. Many `numpy` functions also work on DataFrames, but they can also be accessed with the *dot* syntax, like `dataframe_name.function()`. Simply type `tran.` in a code cell and then hit the [tab] key to see all the functions that are available. In the code cell below, we compute the maximum value of transportation by car, the country corresponding to the maximum value of transportation by car, and the mean value of all transportation by car. You can also find all values larger than a specified value, just like for arrays:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'maximum car travel percentage:', tran.car.max()\n", - "print 'country with maxum car travel percentage:', tran.car.argmax()\n", - "print 'mean car travel percentage:', tran.car.mean()\n", - "print 'all rail travel above 8 percent:'\n", - "print tran.rail[ tran.rail > 8 ]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "maximum car travel percentage: 88.2\n", - "country with maxum car travel percentage: United Kingdom\n", - "mean car travel percentage: 86.475\n", - "all rail travel above 8 percent:\n", - "country\n", - "France 8.6\n", - "Netherlands 9.0\n", - "Name: rail, dtype: float64\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 1. Average annual rainfall by country\n", - "The file `annual_precip.csv` contains the average yearly rainfall and total land area for all the countries in the world (well, there are some missing values); the data is available on the website of the world bank. Open the data file to see what it looks like. Load the data with the `read_csv` function of `pandas`, making sure that the names of the countries can be used to select a row, and perform the following tasks:\n", - "\n", - "* Print the first 5 lines of the DataFrame to the screen with the `.head()` function.\n", - "* Print the average annual rainfall for Panama and make sure to include the units.\n", - "* Report the total land area of the Netherlands and make sure to include the units.\n", - "* Report all countries with an average annual rainfall less than 200 mm/year\n", - "* Report all countries with an average annual rainfall more than 2500 mm/year\n", - "* Report all countries with an average annual rainfall that is within 50 mm/year of the average annual rainfall in the Netherlands" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Adding a column to a DataFrame\n", - "A column may be added to a DataFrame by simply specifying the name and values of the new column using the syntax `DataFrame['newcolumn']=something`. For example, let's add a column named `public_transport`, which is the sum of the `bus` and `rail` columns, and then find the country with the largest percentage of public transport" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tran['public_transport'] = tran.bus + tran.rail\n", - "print 'Country with largest percentage public transport:',tran.public_transport.argmax()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Country with largest percentage public transport: Germany\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Plotting DataFrames\n", - "You can plot the column or row of a DataFrame with the `matplotlib` functions, as we have done in previous Notebooks, but `pandas` has also implemented its own, much more convenient, plotting functions (still based on `matplotlib` in the background, of course). First, we have to tell the IPython Notebook to put the figures inline" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plotting capabilities of `pandas` also use the *dot* syntax, like `dataframe.plot()`. All columns can be plotted simultaneously (note that the names appear on the axes and the legend is added automatically!)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tran.plot() # plot all columns" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also plot one column at a time. The style of the plot may be specified with the `kind` keyword (the default is `'line'`). Check out `tran.plot?` for more options. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tran['bus'].plot(kind='bar')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Sorting DataFrames\n", - "DataFrames may be sorted with the `.sort` function. The keyword `inplace=True` replaces the values in the DataFrame with the new sorted values (when `inplace=False` a new DataFrame is returned, which you can store in a separate variable so that you have two datasets, one sorted and one unsorted). The `sort` funcion has several keyword arguments, including `columns` which is either the name of one column to sort by or a list of columns so that data is sorted by the first specified column and when values are equal they are sorted by the next column in the list. Another keyword is `ascending`, which you can use to specify whether to sort in ascending order (`ascending=True`), or descending order (`ascending=False`)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Data sorted by car use:'\n", - "print tran.sort(columns='car')\n", - "print 'Data sorted by bus use:'\n", - "print tran.sort(columns='bus')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Data sorted by car use:\n", - " car bus rail public_transport\n", - "country \n", - "Germany 85.2 7.1 7.7 14.8\n", - "France 86.1 5.3 8.6 13.9\n", - "Netherlands 86.4 4.6 9.0 13.6\n", - "United Kingdom 88.2 6.5 5.3 11.8\n", - "Data sorted by bus use:\n", - " car bus rail public_transport\n", - "country \n", - "Netherlands 86.4 4.6 9.0 13.6\n", - "France 86.1 5.3 8.6 13.9\n", - "United Kingdom 88.2 6.5 5.3 11.8\n", - "Germany 85.2 7.1 7.7 14.8\n" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 2. Average annual rainfall by country continued\n", - "Continue with the average yearly rainfall and total land area for all the countries in the world and perform the following tasks:\n", - "\n", - "* Add a new column that stores the total average annual freshwater influx in km$^3$/year for each country. Make sure you don't convert your units correctly. \n", - "* Sort the data on the total average annual freshwater influx in ascending order using the `.sort` syntax and report the 5 countries with the largest annual freshwater influx using the `iloc` syntax. (In older versions of IPython, the missing values may be sorted to the top of the DataFrame. To remove the missing values type: `dataframe_name.dropna()`)\n", - "* Make a bar graph of the 10 countries with the largest annual freshwater influx." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Time series data\n", - "In time series data, one of the columns represents dates, sometimes including times, together referred to as datetimes. `pandas` can be used to read csv files where one of the columns includes datetime data. You need to tell `pandas` which column contains datetime values and `pandas` will try to convert that column to datetime objects. Datetime objects are very convenient as specifics of the datetime object may be assessed with the dot syntax: `.year` returns the year, `.month` returns the month, etc.\n", - "\n", - "For example, consider the following data stored in the file `timeseries1.dat`\n", - "\n", - "`date, conc` \n", - "`2014-04-01, 0.19` \n", - "`2014-04-02, 0.23` \n", - "`2014-04-03, 0.32` \n", - "`2014-04-04, 0.29` \n", - "\n", - "The file may be read with `read_csv` using the keyword `parse_dates=[0]` so that column number 0 is converted to datetimes" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data = read_csv('timeseries1.dat', parse_dates=[0])\n", - "print data" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " date conc\n", - "0 2014-04-01 0.19\n", - "1 2014-04-02 0.23\n", - "2 2014-04-03 0.32\n", - "3 2014-04-04 0.29\n", - "4 2014-04-05 0.32\n" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The rows of the DataFrame `data` are numbered, as we have not told `pandas` what column to use as the index of the rows. The first column of the DataFrame `data` has datetime values. We can access, for example, the year, month, or day with the dot syntax" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'datetime of row 0:', data.iloc[0,0]\n", - "print 'year of row 0:', data.iloc[0,0].year\n", - "print 'month of row 0:', data.iloc[0,0].month\n", - "print 'day of row 0:', data.iloc[0,0].day" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "datetime of row 0: 2014-04-01 00:00:00\n", - "year of row 0: 2014\n", - "month of row 0: 4\n", - "day of row 0: 1\n" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Time series data may also contain the time in addition to the date. For example, the data of the file `timeseries2.dat`, shown below, contains the day and time. You can access the `hour` or `minutes`, but also the time of a row of the DataFrame with the `.time()` function.\n", - "\n", - "`date, conc` \n", - "`2014-04-01 12:00:00, 0.19` \n", - "`2014-04-01 13:00:00, 0.20` \n", - "`2014-04-01 14:00:00, 0.23` \n", - "`2014-04-01 15:00:00, 0.21` " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data = read_csv('timeseries2.dat', parse_dates=[0])\n", - "print data\n", - "print 'hour of row 0:', data.iloc[0,0].hour\n", - "print 'minute of row 0:', data.iloc[0,0].minute\n", - "print 'time of row 0:', data.iloc[0,0].time()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " date conc\n", - "0 2014-04-01 12:00:00 0.19\n", - "1 2014-04-01 13:00:00 0.20\n", - "2 2014-04-01 14:00:00 0.23\n", - "3 2014-04-01 15:00:00 0.21\n", - "hour of row 0: 12\n", - "minute of row 0: 0\n", - "time of row 0: 12:00:00\n" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 3, Load and plot daily rainfall\n", - "Rainfall data for the Netherlands may be obtained from the website of the Royal Dutch Meteorological Society KNMI . Daily rainfall for the weather station Rotterdam is stored in the file `rotterdam_rainfall_2012.txt`. First open the file in a text editor to see what the file looks like (Notepad, Textedit, even Word if you have to). At the top of the file, an explanation is given of the data in the file. Read this. Load the data file with the `read_csv` function of `pandas`. Use the keyword `skiprows` to skip all rows except for the row with the names of the columns. Use the keyword `parse_dates` to give either the name or number of the column that should be converted to a datetime. Don't forget the `skipinitialspace` keyword, else the names of the columns may start with a bunch of spaces. Perform the following tasks:\n", - "* Some rainfall values in the dataset may be -1 (read the header of the file to learn why); set all rainfall values that are less than zero to zero. \n", - "* Use the `plot` function of `pandas` to create a line plot of the daily rainfall with the number of the day (so not the date) along the vertical axis. \n", - "* Use `matplotlib` functions to add labels to the axes and set the limits along the horizontal axis from 0 to 365. \n", - "* Determine the maximum daily rainfall and the date of the maximum daily rainfall and print them to the screen." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rain = read_csv('rotterdam_rainfall_2012.txt',skiprows=9,\n", - " parse_dates=['YYYYMMDD'],skipinitialspace=True)\n", - "rain.iloc[:,2] = rain.iloc[:,2] * 0.1\n", - "rain.RH[rain.RH<0] = 0\n", - "rain.RH.plot()\n", - "plt.xlabel('day')\n", - "plt.ylabel('daily rainfall (mm/day)')\n", - "plt.xlim(0,365)\n", - "print 'Maximum daily rainfall',rain.RH.max()\n", - "print 'Date of maximum daily rainfall',rain.YYYYMMDD[rain.RH.argmax()]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Maximum daily rainfall 22.4\n", - "Date of maximum daily rainfall 2012-12-22 00:00:00\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Ot77lOOefT5/985+Os88+/vu/+urFzv77O05nZ+3nn/qU45x7ruMcfrjjXH+9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\nOaS0Z7CJ3wGTRYK2s7O+9BIAXvWqkpLsdcpetnH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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 4, Compute monthly rainfall from daily rainfall\n", - "In this exercise we are going to compute the total monthly rainfall for 2012 in the City of Rotterdam using the daily rainfall measurements we loaded in the previous Exercise; later on in this Notebook we learn convenient functions from `pandas` to do this, but here we are going to do this with a loop. Create an array of 12 zeros to store the monthly totals and loop through all the days in 2012 to compute the total rainfall for each month. The month associated with each row of the DataFrame may be obtained with the `.month` syntax, as shown above. Print the monthly totals (in mm/month) to the screen and create a bar graph of the total monthly rainfall (in mm/month) vs. the month using the `plt.bar` function (type `help(plt.bar)`). " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "monthlyrain = np.zeros(12)\n", - "for i in range(len(rain)):\n", - " month = rain.iloc[i,1].month\n", - " monthlyrain[month-1] += rain.iloc[i,2]\n", - "print monthlyrain\n", - "#\n", - "plt.bar(np.arange(12),monthlyrain,width=1)\n", - "plt.xlabel('month')\n", - "plt.ylabel('monthly rainfall (mm/month)')\n", - "plt.xticks(np.arange(12)+0.5,['J','F','M','A','M','J','J','A','S','O','N','D'])\n", - "plt.xlim(0,12)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 83. 24.3 21.9 57.6 76.5 119. 121.6 93.4 52. 132.6\n", - " 63.3 149.5]\n", - "total rainfall in 2012: 994.7 mm\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "(0, 12)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Date times as index\n", - "The datetime of a dataset may also be used as the index of the DataFrame by specifying the column with the dates as the column the `index_col`. Note that datetimes are given as YearMonthDay, so `2012-04-01` means April 1, 2012." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data = read_csv('timeseries1.dat', parse_dates=[0], index_col=0)\n", - "print data\n", - "print 'data on April 1:',data.loc['2014-04-01']\n", - "print 'data on April 2:',data.loc['2014-04-02']" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " conc\n", - "date \n", - "2014-04-01 0.19\n", - "2014-04-02 0.23\n", - "2014-04-03 0.32\n", - "2014-04-04 0.29\n", - "2014-04-05 0.32\n", - "data on April 1: conc 0.19\n", - "Name: 2014-04-01 00:00:00, dtype: float64\n", - "data on April 2: conc 0.23\n", - "Name: 2014-04-02 00:00:00, dtype: float64\n" - ] - } - ], - "prompt_number": 29 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Resampling\n", - "DataFrames have a very powerful feature called resampling. Downsampling refers to going from high frequency to low frequency. For example, going from daily data to monthly data. Upsampling refers to going from low frequency to high frequency. For example going from monthly data to daily data. For both upsampling and downsampling, you need to tell `pandas` how to perform the resampling. Here we discuss downsampling, as we did in Exercise 4, where we computed monthly totals from daily values. First we reload the daily rainfall in Rotterdam in 2012 and specify the dates as the index. We resample the rain to monthly totals using the `resample` function. You have to tell `resample` to resample to what frequency. Common ones are `'A'` for yearly, `'M'` for monthly, `'W'` for weekly, `'D'` for daily, and `'H'` for hourly, but there are many other ones (see here). The keyword argument `how` is used to tell `pandas` how to compute the resampled data. This can be many things, like `'mean'` for the mean (that is the default), `'sum'` for the total, `'min'` and `'max'`, etc. The keyword argument `kind` is used to tell `pandas` where to assign the computed value to. You can assign the computed value to the last day of the period, or first day, or to the entire period (in this case the entire month). The latter is done by specifying `kind='period'`, which is what we will do here. Calculating the montly totals and making a bar graph can now be done with `pandas` as follows. (Compare these answers to your answer for Exercise 4 and make sure you got the right answer!)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rain = read_csv('rotterdam_rainfall_2012.txt',skiprows=9,\n", - " parse_dates=['YYYYMMDD'],index_col='YYYYMMDD',skipinitialspace=True)\n", - "rain.RH[rain.RH<0] = 0\n", - "rain.RH = rain.RH * 0.1 # Convert to mm/day\n", - "monthlyrain = rain.RH.resample('M', how='sum', kind='period')\n", - "print monthlyrain\n", - "monthlyrain.plot(kind='bar')\n", - "plt.ylabel('mm/month')\n", - "plt.xlabel('month')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "YYYYMMDD\n", - "2012-01 83.0\n", - "2012-02 24.3\n", - "2012-03 21.9\n", - "2012-04 57.6\n", - "2012-05 76.5\n", - "2012-06 119.0\n", - "2012-07 121.6\n", - "2012-08 93.4\n", - "2012-09 52.0\n", - "2012-10 132.6\n", - "2012-11 63.3\n", - "2012-12 149.5\n", - "Freq: M, Name: RH, dtype: float64\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 32, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 32 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 5, Resample weather data\n", - "The file `rotterdam_weather_2000_2010.txt` contains daily weather data at the weather station Rotterdam for the period 2000-2010 (again from the KNMI). Open the data file in an editor to see what is in it. Perform the following tasks:\n", - "* Load the data making sure the dates are used as index. \n", - "* Convert the rain and evaporation to mm/day, and the temperature to degrees Celcius. \n", - "* Set any negative rainfall (explained in the file) to zero. \n", - "* Compute total yearly rainfall, total yearly evaporation, and mean yearly temperature. \n", - "* Make a line plot of the yearly rainfall, yearly evaporation, and mean temperature using the `plot` function of `pandas`. Make sure to plot the mean temperature on the secondary $y$-axis (use the help function to find out how). " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "weather = read_csv('rotterdam_weather_2000_2010.txt',skiprows=11,\n", - " parse_dates=['YYYYMMDD'],index_col='YYYYMMDD',skipinitialspace=True)\n", - "weather.TG = 0.1 * weather.TG\n", - "weather.RH = 0.1 * weather.RH\n", - "weather.EV24 = 0.1 * weather.EV24\n", - "weather.RH[weather.RH<0] = 0\n", - "yearly_rain = weather.RH.resample('A', how='sum', kind='period')\n", - "yearly_evap = weather.EV24.resample('A', how='sum', kind='period')\n", - "yearly_temp = weather.TG.resample('A', how='mean', kind='period')\n", - "yearly_rain.plot()\n", - "yearly_evap.plot()\n", - "yearly_temp.plot(secondary_y=True)\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Rain/evap (mm/year)')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 33, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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E2+BwKDVvnlItW0pO9fDhMr4oAypWlJnMk0gz7nH5siikZk21d/5OVaaMUj/+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\nGk0mY+ZM+OILqZDtho4BaSyPUoovV37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- "text": [ - "" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 4" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "weather = read_csv('rotterdam_weather_2000_2010.txt',skiprows=11,\n", - " parse_dates=['YYYYMMDD'],index_col='YYYYMMDD',skipinitialspace=True)\n", - "weather.TG = 0.1 * weather.TG\n", - "weather.RH = 0.1 * weather.RH\n", - "weather.EV24 = 0.1 * weather.EV24\n", - "weather.RH[weather.RH<0] = 0\n", - "weather['rain_min_evap'] = weather.RH - weather.EV24\n", - "yearly_rain_min_evap = weather.RH.resample('M', how='sum', kind='period')\n", - "yearly_rain_min_evap.plot()\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Rain min evap (mm/year)')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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5xF8cNtUEJk3KrumrNIEoi8p4jROA4v9ZT4/YVMfg7iJ6993w4Q8Hsx0nu3bJ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\no87TcXJnxdU1gb4++VzvdjhkiGwPqwlA9HSQ+r1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- "text": [ - "" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Answers to the exercises" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from pandas import read_csv\n", - "rain = read_csv('annual_precip.csv',skiprows=2,index_col=0,)\n", - "#\n", - "print 'First five lines of rain dataset:'\n", - "print rain.head()\n", - "#\n", - "print\n", - "print 'Average annual rainfall in Panama is',rain.loc['Panama','precip'],'mm/year'\n", - "#\n", - "print\n", - "print 'Land area of the Netherlands is', rain.loc['Netherlands','area'], 'thousand km^2/year'\n", - "#\n", - "print\n", - "print 'Countries where average rainfall is below 200 mm/year'\n", - "print rain[ rain.precip < 200 ]\n", - "#\n", - "print\n", - "print 'Countries where average rainfall is above 2500 mm/year'\n", - "print rain[ rain.precip > 2500 ]\n", - "#\n", - "print\n", - "print 'Countries with almost the same rainfall as the Netherlands'\n", - "print rain[ abs(rain.loc['Netherlands','precip'] - rain.precip) < 50 ]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "First five lines of rain dataset:\n", - " precip area\n", - "country \n", - "Afghanistan 327 652.2\n", - "Albania 1485 27.4\n", - "Algeria 89 2381.7\n", - "American Samoa NaN 0.2\n", - "Andorra NaN 0.5\n", - "\n", - "Average annual rainfall in Panama is 2692.0 mm/year\n", - "\n", - "Land area of the Netherlands is 33.7 thousand km^2/year\n", - "\n", - "Countries where average rainfall is below 200 mm/year\n", - " precip area\n", - "country \n", - "Algeria 89 2381.7\n", - "Bahrain 83 0.8\n", - "Egypt, Arab Rep. 51 995.5\n", - "Jordan 111 88.8\n", - "Kuwait 121 17.8\n", - "Libya 56 1759.5\n", - "Mauritania 92 1030.7\n", - "Niger 151 1266.7\n", - "Oman 125 309.5\n", - "Qatar 74 11.6\n", - "Saudi Arabia 59 2149.7\n", - "Turkmenistan 161 469.9\n", - "United Arab Emirates 78 83.6\n", - "Yemen, Rep. 167 528.0\n", - "\n", - "Countries where average rainfall is above 2500 mm/year\n", - " precip area\n", - "country \n", - "Bangladesh 2666 130.2\n", - "Brunei Darussalam 2722 5.3\n", - "Colombia 2612 1109.5\n", - "Costa Rica 2926 51.1\n", - "Fiji 2592 18.3\n", - "Indonesia 2702 1811.6\n", - "Malaysia 2875 328.6\n", - "Panama 2692 74.3\n", - "Papua New Guinea 3142 452.9\n", - "Sao Tome and Principe 3200 1.0\n", - "Sierra Leone 2526 71.6\n", - "Solomon Islands 3028 28.0\n", - "\n", - "Countries with almost the same rainfall as the Netherlands\n", - " precip area\n", - "country \n", - "Burkina Faso 748 273.6\n", - "Lesotho 788 30.4\n", - "Mexico 752 1944.0\n", - "Netherlands 778 33.7\n", - "Slovak Republic 824 48.1\n", - "Swaziland 788 17.2\n" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Back to Exercise 1\n", - "\n", - "Answers to Exercise 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Countries with almost the same rainfall as Netherlands'\n", - "print rain[ abs(rain.loc['Netherlands','precip'] - rain.precip) < 50 ]\n", - "#\n", - "print\n", - "rain['totalq'] = rain.precip * rain.area * 1e-3\n", - "print 'Country with largest influx of rain'\n", - "print rain.totalq.argmax(), rain.totalq.max(), 'km^3 per year'\n", - "#\n", - "print\n", - "#rain = rain.dropna() # Drop the NaN values\n", - "rain.sort(columns='totalq',ascending=False,inplace=True)\n", - "print rain[:5]\n", - "#\n", - "rain.totalq[:10].plot(kind='bar')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Countries with almost the same rainfall as Netherlands\n", - " precip area\n", - "country \n", - "Burkina Faso 748 273.6\n", - "Lesotho 788 30.4\n", - "Mexico 752 1944.0\n", - "Netherlands 778 33.7\n", - "Slovak Republic 824 48.1\n", - "Swaziland 788 17.2\n", - "\n", - "Country with largest influx of rain\n", - "Brazil 15074.6508 km^3 per year\n", - "\n", - " precip area totalq\n", - "country \n", - "Brazil 1782 8459.4 15074.6508\n", - "Russian Federation 460 16376.9 7533.3740\n", - "United States 715 9147.4 6540.3910\n", - "China 645 9327.5 6016.2375\n", - "Indonesia 2702 1811.6 4894.9432\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Back to Exercise 2\n", - "\n", - "Answers to Exercise 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "from pandas import read_csv\n", - "rain = read_csv('rainfall_delft_2012.dat',skiprows=23,parse_dates=['YYYYMMDD'])\n", - "#\n", - "monthlyrain = np.zeros(12)\n", - "for i in range(len(rain)):\n", - " month = rain.iloc[i,1].month\n", - " monthlyrain[month-1] += rain.iloc[i,2]\n", - "print monthlyrain\n", - "#\n", - "plt.bar(np.arange(12),monthlyrain/10,width=1)\n", - "plt.xlim(0.5,12.5)\n", - "plt.xlabel('month')\n", - "plt.ylabel('monthly rainfall (mm/month)')\n", - "#a=xticks(arange(12)+0.5,['J','F','M','A','M','J','J','A','S','O','N','D'])\n", - "plt.xlim(0,12)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 949. 275. 288. 588. 590. 1132. 1389. 1026. 549. 1879.\n", - " 673. 1546.]\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "(0, 12)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from pandas import read_csv\n", - "rain = read_csv('rainfall_delft_2012.dat',skiprows=23,skipinitialspace=True,parse_dates=['YYYYMMDD'], index_col=['YYYYMMDD'])\n", - "s = rain.resample('M',how='mean',kind='period')\n", - "print s.head()\n", - "s.RD.plot()\n", - "print s['2012-01']\n", - "s.index\n", - "#\n", - "#d = rain.resample('M',how='sum')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " STN RD SX Unnamed: 4\n", - "YYYYMMDD \n", - "2012-01 449 30.612903 0.000000 NaN\n", - "2012-02 449 9.482759 0.793103 NaN\n", - "2012-03 449 9.290323 0.000000 NaN\n", - "2012-04 449 19.600000 0.000000 NaN\n", - "2012-05 449 19.032258 0.000000 NaN\n", - " STN RD SX Unnamed: 4\n", - "YYYYMMDD \n", - "2012-01 449 30.612903 0 NaN\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 26, - "text": [ - "\n", - "freq: M\n", - "[2012-01, ..., 2012-12]\n", - "length: 12" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Back to Exercise 2\n", - "\n", - "Answers to Exercise 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from pandas import read_csv\n", - "##rain = read_csv('rainfall_delft_all.dat',skiprows=23,skipinitialspace=True,parse_dates=['YYYYMMDD'],index_col='YYYYMMDD')\n", - "rain = read_csv('rainfall_delft_all.dat',skiprows=23,skipinitialspace=True,parse_dates=['YYYYMMDD'])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rain['month'] = [d.month for d in rain['YYYYMMDD']]\n", - "marchrain = rain.RD[rain.month==3]\n", - "marchrain = reshape(marchrain,(62,31))\n", - "marchrain = sum(marchrain,1)\n", - "marchrain = marchrain / 10.0 # To convert units to mm\n", - "years = arange(1952,1952+len(marchrain))\n", - "imax = argmax(marchrain)\n", - "print 'Wetted March in dataset: ',years[imax],'with total rainfall: ',marchrain[imax],'mm'\n", - "bar( years[ marchrain>100 ], marchrain[ marchrain>100 ], color='b')\n", - "bar( years[ (marchrain<100) & (marchrain>50) ], marchrain[ (marchrain<100) & (marchrain>50) ], color='g')\n", - "bar( years[ marchrain<50 ], marchrain[ marchrain<50 ], color='k')\n", - "xlabel('year')\n", - "ylabel('Total rainfall in March in mm')\n", - "xt = xticks(rotation=40)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'reshape' is not defined", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\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 1\u001b[0m \u001b[0mrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'month'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'YYYYMMDD'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mmarchrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmarchrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarchrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m62\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mmarchrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarchrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmarchrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmarchrain\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m10.0\u001b[0m \u001b[0;31m# To convert units to mm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'reshape' is not defined" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#s = rain.groupby([rain.index.year,rain.index.month]).sum()" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rain.loc['Russian Federation']" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebook_wm2/.ipynb_checkpoints/py_exp_comp_wm2_sol-checkpoint.ipynb b/notebook_wm2/.ipynb_checkpoints/py_exp_comp_wm2_sol-checkpoint.ipynb deleted file mode 100644 index 5382418..0000000 --- a/notebook_wm2/.ipynb_checkpoints/py_exp_comp_wm2_sol-checkpoint.ipynb +++ /dev/null @@ -1,762 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:48111e112fd64084e75ff1b319dc96b3d4269ed40b43b7a2ec8b550fc1c50ab6" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - "\n", - "# Exploratory Computing with Python\n", - "*Developed by Mark Bakker*\n", - "## Water Management Notebook 2: Manning's equation and emptying reservoir" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Exercise 1: Manning's equation\n", - "The average velocity of water flowing in a canal may be estimated with Manning's equation. Manning's equation for the average velocity $v$ is\n", - "\n", - "$v=\\frac{1}{n}R_h^{2/3}S^{1/2}$\n", - "\n", - "where $n$ is Manning's coefficient, $R_h$ is the hydraulic radius, and $S$ is the slope of the bottom of the canal. \n", - "A detailed description of Manning's equation can be found here.\n", - "\n", - "\n", - "We will consider canals with a trapezoidal cross-section as shown below. The slope of the banks is 1:1.\n", - "\n", - "\n", - "\n", - "Our goal in this exercise is to compute the water height $h$ for a given design discharge $Q_d$ given the width $w$, the roughness coefficient $n$, and the bottom slope $S$. This exercise consists of 6 steps." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 1*. Write a function that computes the hydraulic radius. The function should take two arguments: the height $h$ of the water above the bottom of the canal, and the width $w$ of the canal at the bottom (see Figure). The function should return the hydraulic radius. To test your function, the hydraulic radius for $h=2$ m, and $w=4$ m, is $R_h=1.2426$ m. (Note: in engineering practice it is, of course, ridiculous to give an answer with so many digits. But for testing code it is useful.)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 2*. Write a function that computes the average velocity in the canal. The function should take four arguments: the height $h$, the width $w$, the roughness coefficient $n$, and the bottom slope $S$. Inside the function, call the function for the hydraulic radius that you wrote for step 1. To test your function, the average velocity for $h=2$ m, $w=4$ m, $n=0.025$, $S=0.005$ is $v=3.2692$ m/s. Remember that `2/3=0`, while `2.0/3=0.666...`. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 3*. Write a function that computes the total discharge in the canal. The function should take four arguments: the height $h$, the width $w$, the roughness coefficient $n$, and the bottom slope $S$. Inside the function, call the function for the average velocity. To test your function, the total discharge for $h=2$ m, $w=4$ m, $n=0.025$, $S=0.005$ is $Q=39.23$ m$^3$/s." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 4*. Make a graph of total discharge $Q$ vs. the height of the water $h$, where $h$ varies from 1 to 4 m. Draw three lines for three different width $w=4$ m, $w=6$ m, $w=8$ m. Otherwise, use $n=0.025$, and $S=0.005$. Add a legend, labels on the axes, etc. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 5*. When you know the design discharge $Q_d$, you can find the corresponding water height $h$ from the graph you just produced. This is not very convenient though. Especially since the graph is for specific values of the roughness coefficient $n$ and the slope $S$. We want to find the water height that corresponds to a given $Q$. We need one additional function, let's call it `Qdiff` that computes the difference between the discharge computed with the discharge function you wrote in Step 3 and the design discharge you want. Inside the function, call the function for the total discharge that you wrote in Step 3. The input arguments of `Qdiff` are the height $h$, the width $w$, the roughness coefficient $n$, the bottom slope $S$, and the design discharge $Q_d$. `Qdiff` should return the difference between the design discharge and the discharge you compute with the specified values of $h$, $w$, $n$, and $S$. Test your `Qdiff` function. Use `fsolve` (see previous Notebook) to find the water height $h$ corresponding to the design discharge and the parameters of the canal $w$, $n$, $S$. Show that your code produces the correct result. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 6*. Compute the water height for $Q_d$ = 20, 40, 60, 80, 100 m$^3$/s, for $w=6$ m, $n=0.02$, $S=0.004$ and print them to the screen. \n", - "Make a graph for $h$ vs $Q_d$." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Polynomial fit\n", - "The function `polyfit` may be used to fit a polynomial through a set of data. Given a set of $x$ and $y$ values, `polyfit` computes the parameters $p$ of a polynomial of degree $N$ that goes through the data\n", - "\n", - "$f(x) = p_0 x^N + p_1 x^{N-1} + ... + p_{N-1}x + p_N$ \n", - "\n", - "Once you have computed the parameters $p_n$, you can evaluate the polynomial using the `polyval` function:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = np.array([1,3,4,6,8,9])\n", - "y = np.array([1,4,2,6,5,7])\n", - "p = np.polyfit(x,y,5)\n", - "print p\n", - "x2 = np.linspace(0,10,100)\n", - "y2 = np.polyval(p,x2)\n", - "plt.plot(x,y,'bo')\n", - "plt.plot(x2,y2)\n", - "plt.ylim(-1,8)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 3.01587302e-02 -7.90873016e-01 7.68174603e+00 -3.36662698e+01\n", - " 6.42880952e+01 -3.65428571e+01]\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "(-1, 8)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When the degree of the polynomial is equal to the number of data points minus one, the polynomial goes exactly through the data points as shown above where there are 6 data points and a polynomial of degree 5 (hence, 6 $p$ values) is fitted through the data. Higher order polynomials often wiggle through the data and may give unreasonable values outside the range of the data. A smoother polynomial is obtained when the degree of the polynomial is smaller than the number of data points minus 1. The best fit is obtained by computing the parameters such that the sum of squared differences between the fitted line and the data is minimized. For example, let's fit a polynomial of order 3 through the data of the previous example" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = np.array([1,3,4,6,8,9])\n", - "y = np.array([1,4,2,6,5,7])\n", - "p = np.polyfit(x,y,3)\n", - "print 'the values of p are:',p\n", - "x2 = np.linspace(0,10,100)\n", - "y2 = np.polyval(p,x2)\n", - "plt.plot(x,y,'bo')\n", - "plt.plot(x2,y2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "the values of p are: [ 0.00943775 -0.16406679 1.47407525 -0.22559713]\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 19, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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ErkJggg==\n", - "text": [ - "" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***\n", - "###Exercise 2. Emptying a reservoir \n", - "Consider a reservoir filled with water that is emptied by opening a gate at the bottom of the reservoir. \n", - "The water level in the reservoir is measured with a pressure transducer on the bottom of the reservoir while it is being emptied. \n", - "The rate of change of the water level $h$ in the reservoir is theoretically related to the water level in the reservoir as\n", - "\n", - "$\\frac{\\text{d}h}{\\text{d}t} = \\alpha \\sqrt{h-h_0}$\n", - "\n", - "where $\\alpha$ and $h_0$ are constants. Rearrangement of the differential equation gives\n", - "\n", - "$\\frac{1}{\\sqrt{h-h_0}}\\text{d}h = \\alpha \\text{d}t$\n", - "\n", - "and integration gives\n", - "\n", - "$\\sqrt{h-h_0} = 2\\alpha t + \\beta$\n", - "\n", - "where $\\beta$ is a constant of integration. Rearrangment gives\n", - "\n", - "$ h = 4\\alpha^2 t^2 + 4\\beta\\alpha t + \\beta^2 + h_0$\n", - "\n", - "Hence, $h$ vs. $t$ is a parabola.\n", - "\n", - "*Step 1*. The raw data of the experiment is provided in the file `emptying_reservoir.csv`. A `csv` file is an ascii file that you can open in a standard editor (csv stands for comma separated values). Load the column labeled as 'Pressure' from the data file. Note the units of pressure. Pressure measurements include both air pressure and the pressure caused by the water column. Note: if you want to specify one colomn to read with the `usecols` keyword, put square brackets around the column number to make it a list. This may be a bit confusing, but `usecols` expects a sequence. `(3)` is not a sequence, it is just the number 3, while `[3]` is a sequence as it is the data type list. Alternatively, the somewhat funny syntax `(3,)` is a tuple and would work. If you want to read more than one column, for example columns 2 and 3, you can type either `(2,3)` (which is a tuple of two values) or `[2,3]` (which is a list of two values)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 2* The pressure transducer starts recording pressures before the start of the experiment (when the reservoir is openened and starts to empty) and is removed after a bit more than an hour. The pressure is recorded for some time after the pressure transducer is removed from the reservoir. Plot the pressure and determine at what index the experiment starts and at what index the experiment ends. Zooming is, unfortunately, not yet supported for inline graphs in IPython notebooks (v.1.0). Use the `xlim` and `ylim` functions to zoom in on the beginning and end of the experiment to determine the index of the beginning and end of the experiment. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 3*. Create a new array that only contains the pressure data during the experiment. Make a graph for pressure against time. Set time equal to zero at the beginning of the experiment and determine the time between measurements from the data file." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 4*. Fit a parabola through the data using polyfit. Plot in one graph the data of the experiment and the fitted polynomial. Do you get a nice fit?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Step 5*. Write a Python function for the change of water level with time $\\text{d}h/\\text{d}t$ based on the parabola of Step 4. Plot $\\text{d}h/\\text{d}t$ vs. $t$ based on the parabola. Next, compute the derivative numerically using the measured water levels during the experiment. Plot the numerical derivative based on the data on the same graph. Do you still get a good match between the model and the data?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Answers to the exercises" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Answers to Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 1\n", - "def Rh(h,w):\n", - " A = (w * h) + (h * h) # m^2, is the cross sectional area of the flow\n", - " P = w + 2 * h * np.sqrt(2.) # m, is the wetted perimeter of the channel\n", - " rv = A / P\n", - " return rv\n", - "Rh(2,4)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 20, - "text": [ - "1.2426406871192852" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 2\n", - "def Vavg(h,w,n,S):\n", - " rv = Rh(h,w)**(2.0/3) * np.sqrt(S) / n\n", - " return rv\n", - "Vavg(2,4,0.025,0.005)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "3.2692041364531312" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 3\n", - "def Qtot(h,w,n,S):\n", - " A = (w+h) * h\n", - " return A * Vavg(h,w,n,S)\n", - "Qtot(2,4,0.025,0.005)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "39.230449637437573" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 4\n", - "h = np.linspace(1,4,100)\n", - "Q = Qtot(h,4,0.025,0.005)\n", - "plt.plot(h,Q,label='w=4')\n", - "Q = Qtot(h,6,0.025,0.005)\n", - "plt.plot(h,Q,label='w=6')\n", - "Q = Qtot(h,8,0.025,0.005)\n", - "plt.plot(h,Q,label='w=8')\n", - "plt.legend(loc='best')\n", - "plt.xlabel('h (m)')\n", - "plt.ylabel('Q (m$^3$/s)')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 23, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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S1nb4sLQyatSQabhZs5odUbJduH2BN5a+wY27N9jx5g5K5S5ldkiP5PZjHh4eHqRJk0aP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\nYkiuq1dh4UJJGEFBUlX+t9+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- "text": [ - "" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 5\n", - "def Qdiff(h,w,n,S,Qd):\n", - " Qt = Qtot(h,w,n,S)\n", - " return Qd - Qt\n", - "from scipy.optimize import fsolve\n", - "h50 = fsolve(Qdiff,3,args=(4,0.025,0.005,50))\n", - "print 'design discharge is 50'\n", - "print 'computed water height is ',h50\n", - "print 'discharge computed with Qtot is ',Qtot(h50,4,0.025,0.005)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "design discharge is 50\n", - "computed water height is [ 2.28088116]\n", - "discharge computed with Qtot is [ 50.]\n" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 6\n", - "w = 6.0\n", - "n = 0.02\n", - "S = 0.004\n", - "h = np.zeros(5)\n", - "Qd = np.arange(20,101,20)\n", - "for i in range(5):\n", - " h[i] = fsolve(Qdiff,1,args=(w,n,S,Qd[i]))\n", - "print 'Design discharge is ',Qd\n", - "print 'Corresponding water height is ',h\n", - "plt.plot(Qd,h,'b-o')\n", - "plt.xlabel('Design discharge (m$^3$/d)')\n", - "plt.ylabel('Water height (m)')\n", - "plt.title('w=6 m, n=0.02, S=0.004')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Design discharge is [ 20 40 60 80 100]\n", - "Corresponding water height is [ 1.03196415 1.54829374 1.95427027 2.29943954 2.60427215]\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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M8/n5/ejZ8wyPohMRP0vkCmnJQitXWg2k/HyYMwcaNuxIXh4MG3YXxcU1qFt3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\niIiIiIiIiIiIiIiIJOD/AUK5iUf/BI6oAAAAAElFTkSuQmCC\n", - "text": [ - "" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Back to Exercise 1\n", - "\n", - "Answers to Exercise 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 1\n", - "head = np.loadtxt('emptying_reservoir.csv',skiprows=54,usecols=[1],delimiter=\";\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 26 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 2\n", - "plt.subplot(211)\n", - "plt.plot(head)\n", - "plt.xlim(170,190)\n", - "plt.ylim(1115,1120)\n", - "plt.subplot(212)\n", - "plt.plot(head)\n", - "plt.xlim(325,335)\n", - "plt.ylim(1040,1080)\n", - "print 'Data from index 184 to 332'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Data from index 184 to 332\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 27 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 3\n", - "headnew = head[184:332]\n", - "print len(headnew)\n", - "time = np.arange(0,len(headnew)*30,30)\n", - "plt.plot(time,headnew)\n", - "plt.xlabel('time (seconds')\n", - "plt.ylabel('head (cm)')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "148\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 28, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 4\n", - "a = np.polyfit(time,headnew,2)\n", - "hfit = np.polyval(a,time)\n", - "plt.plot(time,headnew,'b',label='data')\n", - "plt.plot(time,hfit,'r',label='parabola')\n", - "plt.xlabel('time (seconds')\n", - "plt.ylabel('head (cm)')\n", - "plt.legend(loc='best')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 29, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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WmBiO9X+HnFPeZ2mhRznbbzAPPFaQokXtDkyyEk8mk5xAe8wU9P7WPk5gRHpP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\nO0lMMpDYNXgfMN7tUYuIiIi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- "text": [ - "" - ] - } - ], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Step 5\n", - "def dhdt(t):\n", - " return 2*a[0]*t + a[1]\n", - "dhdtnum = (headnew[1:] - headnew[:-1]) / 30.0\n", - "plt.plot(0.5*(time[1:]+time[:-1]), dhdtnum,label='numerical 1') \n", - "dhdtnum2 = (headnew[2:] - headnew[:-2]) / 60.0\n", - "plt.plot(time[1:-1],dhdtnum2,'r',label='numerical 2')\n", - "dhdtpoly = dhdt(time)\n", - "plt.plot(time,dhdtpoly,'k',label='parabola')\n", - "plt.legend(loc='best')\n", - "plt.xlabel('time (seconds')\n", - "plt.ylabel('head (cm)')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 30, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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4/2kbG4WROdref02NWBWu0fjP7dwJ7gLxkDNm+CdQNTb6lVMgDoe4X9Nso64s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\n7XXpVs5ke0Um8TwrhR2H4ORJxjceQDe5f0+mpQXa2739ScxmOOyy4tb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- "text": [ - "" - ] - } - ], - "prompt_number": 30 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Back to Exercise 2" - ] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file From 552044c785acc2538f7d3361e85b11b628f58227 Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 17:10:14 +0100 Subject: [PATCH 02/10] add pyqt --- pyproject.toml | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..f20ed85 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,29 @@ +[build-system] +requires = ["setuptools>=64.0.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "exploratory-computing-with-python" +version = "0.1.0" +description = "Python for Exploratory Computing Notebooks" +readme = "README.md" +requires-python = ">=3.11" +dependencies = ["ipykernel", "matplotlib", "numpy", "pandas", "pyqt6"] + +maintainers = [{ name = "M. Bakker", email = "m.bakker@tudelft.nl" }] +classifiers = [ + 'Intended Audience :: Science/Research', + 'Programming Language :: Python :: 3 :: Only', +] + +[project.optional-dependencies] +ci = ["ruff", "pytest", "papermill"] + +[tool.ruff] +extend-include = ["*.ipynb"] +lint.extend-select = ["I"] +show-fixes = true +fix = true + +[tool.setuptools] +packages = [] From 4edd09a46e67d242e56f92ff31f37c8a8a33c9e9 Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 17:27:36 +0100 Subject: [PATCH 03/10] update dependencies --- pyproject.toml | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f20ed85..b92840c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,15 @@ version = "0.1.0" description = "Python for Exploratory Computing Notebooks" readme = "README.md" requires-python = ">=3.11" -dependencies = ["ipykernel", "matplotlib", "numpy", "pandas", "pyqt6"] +dependencies = [ + "ipykernel", + "ipywidgets", + "matplotlib", + "numpy", + "pandas", + "pyqt6", + "scipy", +] maintainers = [{ name = "M. Bakker", email = "m.bakker@tudelft.nl" }] classifiers = [ @@ -17,13 +25,8 @@ classifiers = [ ] [project.optional-dependencies] -ci = ["ruff", "pytest", "papermill"] +ci = ["pytest", "papermill"] -[tool.ruff] -extend-include = ["*.ipynb"] -lint.extend-select = ["I"] -show-fixes = true -fix = true [tool.setuptools] packages = [] From f75c4afa027450dc2d62190a73be9b891ff703d6 Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 17:27:44 +0100 Subject: [PATCH 04/10] update gitignore --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 86bc903..edbe86e 100644 --- a/.gitignore +++ b/.gitignore @@ -90,3 +90,7 @@ ENV/ .ropeproject .DS_Store + +*.egg-info/ +*.ipynb_checkpoints +*.lock \ No newline at end of file From 0c3b307534a3ceff9edb195ab4f2d4339f4f162b Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 17:57:52 +0100 Subject: [PATCH 05/10] update notebooks with bugs --- .../py_exploratory_comp_3_sol.ipynb | 559 +++--------------- .../py_exploratory_comp_8_sol.ipynb | 222 +++---- .../py_exploratory_comp_9_sol.ipynb | 58 +- notebook_adv5/py_exp_comp_adv5_sol.ipynb | 363 ++++++++---- .../py_exploratory_comp_5_sol.ipynb | 542 ++++++++++------- 5 files changed, 784 insertions(+), 960 deletions(-) diff --git a/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb b/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb index 186cd18..0172ea4 100644 --- a/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb +++ b/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -41,21 +41,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello world, the value of i is 0\n", - "Hello world, the value of i is 1\n", - "Hello world, the value of i is 2\n", - "Hello world, the value of i is 3\n", - "Hello world, the value of i is 4\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "for i in [0, 1, 2, 3, 4]:\n", " print('Hello world, the value of i is', i)" @@ -70,23 +58,11 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x, xsquare 0 0\n", - "x, xsquare 1 1\n", - "x, xsquare 2 4\n", - "x, xsquare 3 9\n", - "We are done with the loop\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "for x in [0, 1, 2, 3]: \n", + "for x in [0, 1, 2, 3]:\n", " xsquared = x ** 2\n", " print('x, xsquare', x, xsquared)\n", "print('We are done with the loop')" @@ -101,19 +77,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the value of data is: 20\n", - "the value of data is: mark\n", - "the value of data is: 3.1622776601683795\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "for data in [20, 'mark', np.sqrt(10)]:\n", " print('the value of data is:', data)" @@ -128,26 +94,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the value of i is: 0\n", - "the value of i is: 1\n", - "the value of i is: 2\n", - "the value of i is: 3\n", - "the value of i is: 4\n", - "the value of i is: 5\n", - "the value of i is: 6\n", - "the value of i is: 7\n", - "the value of i is: 8\n", - "the value of i is: 9\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "for i in range(10):\n", " print('the value of i is:', i)" @@ -162,19 +111,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a range with 10 values: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", - "a range from 10 till 20 [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n", - "a range from 10 till 20 with steps of 2: [10, 12, 14, 16, 18]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('a range with 10 values:', list(range(10)))\n", "print('a range from 10 till 20', list(range(10, 20)))\n", @@ -190,22 +129,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "x = np.linspace(0, 2 * np.pi, 100)\n", "y = np.zeros_like(x) # similar to zeros(shape(x))\n", @@ -257,20 +183,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting value: 4\n", - "changing data in the first if-statement\n", - "value after the first if-statement: 6\n", - "value after the second if-statement: 6\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "data = 4\n", "print('starting value:', data)\n", @@ -301,17 +216,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a is not smaller than 3\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "a = 4\n", "if a < 3:\n", @@ -329,17 +236,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a is equal to 4\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "a = 4\n", "if a < 4:\n", @@ -359,55 +258,13 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Enter a value: 3\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the entered value is smaller than 4\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Enter a value: 7\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the entered value is larger than 4\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Enter a value: 4\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the entered value is equal to 4\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "for i in range(3): # do this 3 times\n", - " a = float(input('Enter a value: '))\n", + " # a = float(input('Enter a value: '))\n", + " a = np.random.rand() * 10 # override `input` for testing purposes\n", " if a < 4:\n", " print('the entered value is smaller than 4')\n", " elif a > 4:\n", @@ -477,25 +334,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "i, datasum: 0 1\n", - "i, datasum: 1 4\n", - "i, datasum: 2 6\n", - "i, datasum: 3 11\n", - "i, datasum: 4 18\n", - "i, datasum: 5 21\n", - "i, datasum: 6 25\n", - "i, datasum: 7 27\n", - "total sum of data: 27\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "data = np.array([1, 3, 2, 5, 7, 3, 4, 2])\n", "datasum = 0\n", @@ -552,18 +393,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the maximum value is 7\n", - "the index of the maximum value is 4\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "maxvalue = -1e8\n", "maxindex = None\n", @@ -584,18 +416,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "the maximum value is 7\n", - "the index of the maximum value is 4\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('the maximum value is ', np.max(data))\n", "print('the index of the maximum value is ', np.argmax(data))" @@ -633,17 +456,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[11. 22. 18.]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "data = np.array([[1, 2, 3, 5],\n", " [4, 8, 6, 4],\n", @@ -668,19 +483,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sum of entire array: 51\n", - "sum rows (axis=0): [ 8 15 13 15]\n", - "sum columns (axis=1): [11 22 18]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('sum of entire array:', np.sum(data))\n", "print('sum rows (axis=0):', np.sum(data, axis=0))\n", @@ -700,17 +505,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a is between 5 and 8\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "x = [1, 4, 5, 8, 9]\n", "a = 6\n", @@ -729,17 +526,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a is between 5 and 8\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "x = [1, 4, 5, 8, 9]\n", "a = 6\n", @@ -789,19 +578,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "21\n", - "G\n", - "Goodmorning \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "text1 = 'Goodmorning everybody'\n", "print(len(text1))\n", @@ -818,18 +597,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Goodmorning everybody in the class\n", - "The magic number is 7\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "text1 = 'Goodmorning everybody'\n", "newtext = text1 + ' in the class' # Adding two srings\n", @@ -848,21 +618,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "True\n", - "False\n", - "True\n", - "True\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "print('delft' < 'eindhoven') # True as 'd' is smaller than 'e'\n", "print('dalft' < 'delft') # True as 'a' is smaller than 'e'\n", @@ -880,19 +638,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TU Delft\n", - "tu delft\n", - "TU DELFT\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "TU = 'TU Delft'\n", "print(TU)\n", @@ -909,28 +657,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is the sentence: This is a sentence containing a number of words\n", - "This is the split sentence: ['This', 'is', 'a', 'sentence', 'containing', 'a', 'number', 'of', 'words']\n", - "All words may be printed seperately:\n", - "This\n", - "is\n", - "a\n", - "sentence\n", - "containing\n", - "a\n", - "number\n", - "of\n", - "words\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sentence = 'This is a sentence containing a number of words'\n", "print('This is the sentence:', sentence)\n", @@ -980,28 +709,9 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The number of days in January is 31\n", - "The number of days in February is 28\n", - "The number of days in March is 31\n", - "The number of days in April is 30\n", - "The number of days in May is 31\n", - "The number of days in June is 30\n", - "The number of days in July is 31\n", - "The number of days in August is 31\n", - "The number of days in September is 30\n", - "The number of days in October is 31\n", - "The number of days in November is 30\n", - "The number of days in December is 31\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "months = ['January', 'February', 'March', 'April',\\\n", " 'May','June', 'July', 'August', 'September',\\\n", @@ -1022,22 +732,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "x = np.linspace(-2 * np.pi, 2 * np.pi, 100)\n", "y = np.zeros_like(x)\n", @@ -1061,28 +758,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "average monthly temperature in month 1 is less than 10 degrees\n", - "average monthly temperature in month 2 is less than 10 degrees\n", - "average monthly temperature in month 3 is less than 10 degrees\n", - "average monthly temperature in month 4 is less than 10 degrees\n", - "average monthly temperature in month 5 is more than 10 degrees\n", - "average monthly temperature in month 6 is more than 10 degrees\n", - "average monthly temperature in month 7 is more than 10 degrees\n", - "average monthly temperature in month 8 is more than 10 degrees\n", - "average monthly temperature in month 9 is more than 10 degrees\n", - "average monthly temperature in month 10 is more than 10 degrees\n", - "average monthly temperature in month 11 is less than 10 degrees\n", - "average monthly temperature in month 12 is less than 10 degrees\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "temperature = np.loadtxt('holland_temperature.dat')\n", "for i in range(len(temperature)):\n", @@ -1103,19 +781,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data values: [1 3 2 5 7 3 4 2]\n", - "running total: [ 1 4 6 11 18 21 25 27]\n", - "running total with numpy: [ 1 4 6 11 18 21 25 27]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "data = np.array([1, 3, 2, 5, 7, 3, 4, 2])\n", "runningtotal = np.zeros_like(data)\n", @@ -1138,20 +806,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 3.1 3.3 6.2 9.2 13.1 15.6 17.9 17.5 14.5 10.7 6.7 3.7]\n", - "Number of month closest to 15 degrees, temp: 9 14.5\n", - "Alternative method:\n", - "Number of month closest to 15 degrees, temp: 9 14.5\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "temperature = np.loadtxt('holland_temperature.dat')\n", "print(temperature)\n", @@ -1178,31 +835,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The oil price exceeds 40 euros for the first time in April of 2005\n", - "The oil price exceeds 60 euros for the first time in December of 2007\n", - "The oil price exceeds 80 euros for the first time in June of 2008\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "oilprice = np.loadtxt('oil_price_monthly.dat', delimiter=',')\n", "plt.plot(oilprice[:,2], 'b-')\n", @@ -1229,17 +864,9 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Guido is between Franka and Joe\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "x = ['Aaldrich', 'Babette', 'Chris', 'Franka', 'Joe', 'Louisa', 'Pierre', 'Simone', 'Tarek', 'Yvonne', 'Zu']\n", "myname = 'Guido'\n", @@ -1259,7 +886,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "exploratory-computing-with-python (3.13.5)", "language": "python", "name": "python3" }, @@ -1273,7 +900,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.13.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/notebook8_pandas/py_exploratory_comp_8_sol.ipynb b/notebook8_pandas/py_exploratory_comp_8_sol.ipynb index 0b88037..670970c 100644 --- a/notebook8_pandas/py_exploratory_comp_8_sol.ipynb +++ b/notebook8_pandas/py_exploratory_comp_8_sol.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -216,7 +216,7 @@ "output_type": "stream", "text": [ "Names of columns:\n", - "Index(['car', 'bus', 'rail'], dtype='object')\n", + "Index(['car', 'bus', 'rail'], dtype='str')\n", "car\n", "bus\n", "rail\n" @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -319,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -349,7 +349,7 @@ ], "source": [ "print(tran.loc[:, 'car']) # all rows of 'car' column\n", - "print(tran['car']) # 'car' column \n", + "print(tran['car']) # 'car' column\n", "print(tran.car)" ] }, @@ -362,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -374,7 +374,7 @@ "Name: France, dtype: float64" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -425,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -454,14 +454,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['France' 'Netherlands']\n" + "\n", + "['France', 'Netherlands']\n", + "Length: 2, dtype: str\n" ] } ], @@ -508,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -534,19 +536,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { 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\n", 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", 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\n", 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -593,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -786,7 +784,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -813,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -897,7 +895,7 @@ } ], "source": [ - "tran.rename(columns={'bus': 'BUS', \n", + "tran.rename(columns={'bus': 'BUS',\n", " 'rail': 'train'}, inplace=True)\n", "display(tran)" ] @@ -911,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1045,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1130,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1160,7 +1158,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1171,10 +1169,10 @@ "2 3\n", "3 4\n", "4 5\n", - "Name: date, dtype: int64" + "Name: date, dtype: int32" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1198,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1290,7 +1288,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1419,7 +1417,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1476,13 +1474,13 @@ "" ], "text/plain": [ - " conc\n", - "date \n", - "2014-04-01 0.19\n", - "2014-04-02 0.23\n", - "2014-04-03 0.32\n", - "2014-04-04 0.29\n", - "2014-04-05 0.32" + " conc\n", + "date \n", + "2014-04-01 0.19\n", + "2014-04-02 0.23\n", + "2014-04-03 0.32\n", + "2014-04-04 0.29\n", + "2014-04-05 0.32" ] }, "metadata": {}, @@ -1492,9 +1490,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "data on April 1: conc 0.19\n", + "data on April 1: conc 0.19\n", "Name: 2014-04-01 00:00:00, dtype: float64\n", - "data on April 2: conc 0.23\n", + "data on April 2: conc 0.23\n", "Name: 2014-04-02 00:00:00, dtype: float64\n" ] } @@ -1520,26 +1518,39 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_54525/618857844.py:4: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.\n", + "Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).\n", + "\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.\n", + "\n", + "See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment\n", + " rain.RH[rain.RH<0] = 0 # remove negative values\n" + ] + }, { "data": { "text/plain": [ "YYYYMMDD\n", - "2012-01 83.0\n", - "2012-02 24.3\n", - "2012-03 21.9\n", - "2012-04 57.6\n", - "2012-05 76.5\n", - "2012-06 119.0\n", - "2012-07 121.6\n", - "2012-08 93.4\n", - "2012-09 52.0\n", - "2012-10 132.6\n", - "2012-11 63.3\n", - "2012-12 149.5\n", - "Freq: M, Name: RH, dtype: float64" + "2012-01-31 82.3\n", + "2012-02-29 23.9\n", + "2012-03-31 21.5\n", + "2012-04-30 57.1\n", + "2012-05-31 76.1\n", + "2012-06-30 118.5\n", + "2012-07-31 121.4\n", + "2012-08-31 93.2\n", + "2012-09-30 51.7\n", + "2012-10-31 132.2\n", + "2012-11-30 63.3\n", + "2012-12-31 149.3\n", + "Freq: ME, Name: RH, dtype: float64" ] }, "metadata": {}, @@ -1547,14 +1558,12 @@ }, { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1564,7 +1573,7 @@ " skipinitialspace=True)\n", "rain.RH[rain.RH<0] = 0 # remove negative values\n", "rain.RH = rain.RH * 0.1 # convert to mm/day\n", - "monthlyrain = rain.RH.resample('M', kind='period').sum()\n", + "monthlyrain = rain.RH.resample('ME').sum()\n", "display(monthlyrain)\n", "monthlyrain.plot(kind='bar')\n", "plt.ylabel('mm/month')\n", @@ -1608,19 +1617,17 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1652,7 +1659,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2121,7 +2128,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2213,14 +2220,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2245,7 +2250,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -2258,14 +2263,12 @@ }, { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2273,7 +2276,7 @@ "rain = pd.read_csv('rotterdam_rainfall_2012.txt', skiprows=9,\n", " parse_dates=['YYYYMMDD'], skipinitialspace=True)\n", "# convert to mm/d\n", - "rain.iloc[:,2] = rain.iloc[:,2] * 0.1\n", + "rain[\"RH\"] = rain[\"RH\"].multiply(0.1)\n", "# set negative values to zero\n", "rain.loc[rain.RH < 0, 'RH'] = 0\n", "rain.RH.plot()\n", @@ -2295,7 +2298,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2307,14 +2310,12 @@ }, { "data": { - "image/png": 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1nYwkabzaDF67Gnha14lIksZvpltSv0jvNNHmwLVJLgHun9xeVQd1n54kaZRmOn30gZFlIUmaF2a6JfXbAEneX1VHD25L8n7g2x3nJkkasTbXFF4yRdtLh52IJGn8pi0KSf46yVXALkmuHHjcBFy1sYGTbJLkB0m+1LzfMsnXk/y4eX7KxsaQJG2YmXoKnwFeAZzfPE8+/qSqXj+E2H8LXDfwfgVwYVXtTG9G1hVDiCFJ2gDTFoWq+lVV3VxVhwFrgN/TuxtpsyQ7bEzQJNsBBwKnDDQfDJzWvD4NeOXGxJAkbbg2I5rfDhwH3AE81DQXsMdGxP0g8B56t7tO2qaqbgeoqtuTbD1NPsuB5QA77LBRtUmStJ42I5r/Dtilqu4aRsAkLwfWVtWlSfbZ0M9X1SpgFcCyZcscZS1JQ9SmKNwKDHPdhBcAByV5GfA4YIsk/wzckWRp00tYCqwdYkxJUgttisKNwEVJLuDhI5pPnkvAqjoGOAag6Sm8u6rekORE4HBgZfN83lyOL0mauzZF4Zbm8Zjm0ZWVwNlJjmziHdJhLEnSFGYtClV1PECSzXtv69fDCl5VFwEXNa/vAvYf1rElSRtu1hHNSZ6V5Af0Zku9JsmlSXbvPjVJ0qi1meZiFXBUVe1YVTsC7wI+3m1akqRxaFMUnlhV35p805zyeWJnGUmSxqbV3UdJ/idwevP+DcBN3aUkSRqXNj2FNwNLgHOBf2leH9FlUpKk8Whz99EvgHeMIBdJ0pi1mftoGfBeYGJw/6ramLmPJEnzUJtrCmcAf09vDYWHZtlXkvQI1qYorKuq8zvPRJI0dm2KwrFJTqG38M3g3EfndpaVJGks2hSFI4BdgUfz8PUULAqStMC0KQp7VtV/7TwTSdLYtRmncHGS3TrPRJI0dm16Ci8EDk9yE71rCqE3W6q3pErSAtOmKBzQeRaSpHmhzYjmn44iEUnS+LXpKWgjTKy4YOjHvHnlgUM/piRBuwvNkqRFwqIgSeob+emjJNsDnwaeRm8w3Kqq+lCSLYHP0pt472bgdc0MrWrB01SShmEcPYUHgHdV1X8B/hR4WzMOYgVwYVXtTG9KjRVjyE2SFrWRF4Wqur2qLmte3wNcB2wLHAyc1ux2GvDKUecmSYvdWK8pJJkAngN8H9imqm6HXuEAtp7mM8uTrE6yet26daNKVZIWhbEVhSSbAecAf1dVd7f9XFWtqqplVbVsyZIl3SUoSYvQWIpCkkfTKwhnDEzBfUeSpc32pcDaceQmSYvZyItCkgCfAK6rqpMHNp0PHN68Phw4b9S5SdJiN44RzS8A/hK4KsnlTdt7gZXA2UmOBG4BDhlDbpK0qI28KFTVd+nNtDqV/UeZiyTp4RzRLEnqsyhIkvosCpKkPouCJKnPoiBJ6rMoSJL6LAqSpD6LgiSpz6IgSeqzKEiS+iwKkqQ+i4Ikqc+iIEnqsyhIkvosCpKkPouCJKnPoiBJ6rMoSJL6LAqSpL55VxSSHJDk+iQ3JFkx7nwkaTGZV0UhySbAh4GXArsBhyXZbbxZSdLiMa+KArA3cENV3VhVvwPOAg4ec06StGikqsadQ1+S1wIHVNVfNe//EnhuVb19YJ/lwPLm7S7A9SNIbSvgzgUSZyF9F+PM3xjGmd9xdqyqJVNt2LTjwBsqU7Q9rGpV1Spg1WjS6UmyuqqWLYQ4C+m7GGf+xjDO/I8znfl2+mgNsP3A++2A28aUiyQtOvOtKPw7sHOSnZI8BjgUOH/MOUnSojGvTh9V1QNJ3g78K7AJcGpVXTPmtGB0p6tGEWchfRfjzN8Yxpn/caY0ry40S5LGa76dPpIkjZFFQZLUZ1FoIcmvOz7+g0kuH3hMDPn4leT0gfebJlmX5EvDjDNw/Fc1MXft6Pgj/T5NjE5/B0YVp+ufzUCc9yW5JsmVze/0czuIsV2S85L8OMlPknyouUFlmDEqyUkD79+d5LhhxmiOO/k34JokVyQ5KslY/j5bFOaH+6rq2QOPm4d8/N8Az0ry+Ob9S4CfDTnGoMOA79K7e6wLo/4+C0nXPxuSPA94ObBXVe0BvBi4dcgxApwLfKGqdgb+GNgMOGGYcYD7gVcn2WrIx13f5N+A3en9Pr8MOLbjmFOyKCweXwEObF4fBpzZRZAkmwEvAI6kwz88jOj7LCQj/NksBe6sqvsBqurOqhr2eKP9gN9W1SebGA8C7wTenOQJQ4zzAL27gd45xGPOqKrW0pu14e1N8Rspi8L88PiBU0f/0lGMs4BDkzwO2AP4fkdxXgl8tap+BPw8yV4dxRnV91lIRvWz+RqwfZIfJflIkhd1EGN34NLBhqq6G7gFeOaQY30YeH2SJw35uNOqqhvp/X3eelQxJ1kU5ofB00ev6iJAVV0JTND7r/rLXcRoHEbvDzbN82FdBBnh91lIRvWz+TXwJ/T+210HfDbJm4YcJqw3Bc4s7XPWFJtPA+8Y5nFbGHkvAebZ4DV17nzgA8A+wFOHffAkT6XXrX9WkqI3ALGSvKe6GRDT6fdZSEb9s2lO51wEXJTkKuBw4FNDDHEN8JrBhiRb0Jsm5ydDjDPpg8BlwCc7OPYfSPJ04EFg7SjiDbKnsLicCvxjVV3V0fFfC3y6qnasqomq2h64CXhhR/G6/j4Lych+Nkl2SbLzQNOzgZ8OOcyFwBOSvLGJuQlwEvCpqrp3yLGoqp8DZ9O7HtOpJEuAjwH/1NE/UzOyKMwiyab07kB4xKuqNVX1oQ5DHAasf03kHOAvugg2gu8DjO53oOM4o/zZbAacluTaJFfSWzDruGEGaP5Yvgo4JMmPgR8BvwXeO8w46zmJ3rTWXZi8rngN8A1612WO7yjWjJzmYhZJ9gQ+XlV7jzsXjceofgf8XdN8YE9hBkneQu9Wx38Ydy4aj1H9Dvi7pvnCnoIkqc+egiSpz6IgSeqzKEiS+iwK0ggleXKStw6836fL2V2lDWVRkEbrycBbZ91LGhOLgjSNJBNJfpjklCRXJzkjyYuT/Fszh//eSbZM8oVm3YCLk+zRfPa4JKcmuSjJjUkm581ZCTyjGah0YtO2WZLPN7HOGMfMmNIk5z6SZvZM4BB6k7v9O70RwC8EDqI3evZW4AdV9cok+9GbOO3ZzWd3BfYFNgeuT/JRYAXwrKp6NvROHwHPoTfr523Av9Gb3vq7o/hy0vrsKUgzu6mqrqqqh+hNwnZhM8XCVfRmaX0hcDpAVX0TeOrAFMsXVNX9VXUnvYnNtpkmxiXNlB0PAZc3x5XGwqIgzWxwLqKHBt4/RK+nPdWpnskRoYOffZDpe+Zt95M6Z1GQNs53gNdD/1TQnc38+9O5h97pJGle8j8SaeMcB3yymQ30XnrrBkyrqu5qLlRfTW9J0Qu6T1Fqz7mPJEl9nj6SJPVZFCRJfRYFSVKfRUGS1GdRkCT1WRQkSX0WBUlS3/8HSfpBEaC/p2IAAAAASUVORK5CYII=\n", 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", 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+ "image/png": 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yE7YFs+39lmhJJZoOSxCqpQuxF/j7/t9IJVK+bP4lEon4f61QLeNatHNox/Ho42y/tZ3JzSdrOqRq7+7du2RlZdGlS5ci9+fl5eHr64uPjw8NGjRgy5YtfPnll5w4cYKEhATeffdd1bFHjhxhzpw53Lx5k7S0NAoKCsjJySErKwtDQ+VFB21tbZo1a6Z6Tv369TE3N+fGjRtlThLT09NJS0tT/aynp/fcBMzf3x9/f//njjV48GAAIiIiin3+zZs3F/l51apV/P777xw9epQhQ4YUicvW1rbY45Y3kSRq2B/BMUzcHoJMrqCHpy2L+vmiq135VgFoSSUs7OuD/8+nVN1YPurgrumwBKHaKZAXqPoz963bl3qW9TQcUeXTr34/jkcf54+7fzDOdxyGOlV4ZkPHUHlVTxPnLabCtYN//vlnkate8O+VrkGDBqmSxC1bttC9e3dq1FA2fYiIiOCNN95g7NixzJ49G0tLS06fPs3IkSPJy8tTJYnlqWHDhkV+/uabb/j222/L/bzPk5WVRX5+PpaWlkXuP378ONbW1lhYWNCxY0dmzZql+j1qgkgSNei3C5F8tTsUhQLebuzAvLc90daqfAliIUdLQ77p2ZAvdl7hp8O3aVfXCo9aZpoOSxCqle23tnM35S5memZ87PuxpsOplFrZt8LJxInI9Ej+DP+Td+u++/InVVYSSbGnfTWlYcOG6OnpERkZSbt27Z55zMCBA5k6dSpBQUHs3LmT5cuXqx4LCgpCLpezYMECpFLlZ9z27dufGqOgoIDAwEDVVcNbt26RkpJCgwYNyvwarl+/XiTB1fQ07uTJk7G3t6dz586q+7p3706fPn1wdXXl3r17fPXVV/j7+3Pu3Dm0tDQzuyiSRA1ZfTqcmfuvAzC4hTPfvdkIaRWYvn2niQP/3EzgwNU4xm8LZv+41pVualwQqqrknGR+Cf4FgE98P8FMT3wJexapRErfen35MfBHtt7cyjt13hFT8uXIxMSEzz//nAkTJiCXy2ndujWpqamcOXMGU1NThg4diouLC61atWLkyJHIZDLefPNN1fPd3d3Jz89nyZIl9OzZkzNnzhRJIgvp6Ogwbtw4Fi9ejLa2Nh9//DEtWrRQy3pEExMTTE3LvxlFccydO5etW7dy/Phx9PX/rV7y5CYdT09PvLy8qF27NsePH6dTp06aCFXsbq5oCoWCJUfvqBLE99u5MaNX1UgQoWg3lrsJGcw9cFPTIQlCtbHk8hLS89KpZ1GPt+u8relwKrXe7r3R19LndvJtghODNR1OtTdz5kymTZvGnDlzaNCgAd27d+fPP//E1fXfjUODBg0iJCSEt956CwODf3fje3t7s3DhQubNm4eHhwebN29mzpw5T53D0NCQyZMnM3DgQF577TWMjY3Ztm1bhby+ivLjjz8yd+5c/v77b7y8vF54rJubGzVr1uTu3bsVFN3TRJJYgRQKBXMP3mTB4dsAfNalLl92r1/lvgE/2Y1l3dkI0Y1FENTgxqMb7Ly9E4ApflPQkoor9C8i+jlXLIlEwqeffsrNmzfJy8sjISGBgwcP0rZtW9UxY8eORaFQsH79+qeeP2HCBB48eEBWVhYHDx5k8ODBKBQKzM3NixzXp08f7t27R05ODocPH8bJyam8X1qFmT9/PjNnzuTgwYM0bdr0pcdHR0fz6NEj7OzsKiC6ZxNJYgWRyxVM/+Mav54IA2Dq6w0Y16lOlUsQC7Wra8XQlsr6V6IbiyCUjUKhYO6FuShQ4O/qTxObJpoOqUoo7Od8+P5h0c9ZUJuMjAyCg4NV9Q/Dw8MJDg4mMjISgKSkJIKDg7l+XTkjeOvWLYKDg4mLi1ONMWTIEKZMmaL6ed68eUybNo01a9bg4uJCXFwccXFxqk1BGRkZfPHFF5w/f56IiAiOHj1Kr169cHd3p1s3zZXAEkliBSiQyfli5xU2nr+PRALfv+XJqDZumg6rzL70b0BtKyMS0nMfb8AR3VgEoTQOhB/gUsIlDLQNmNhkoqbDqTIa1miIl5Wyn/OuO7s0HY5QTQQGBuLr64uvry8AEydOxNfXl+nTpwOwd+9efH19ef311wHlWkJfX98i6ywjIyOJjY1V/bxs2TLy8vJ45513sLOzU91+/PFHALS0tLhy5QpvvvkmdevWZeTIkTRp0oRTp05pdJONRCE+2V8qOjoaR0dHoqKicHAoWe/UvAI547dd5q/QOLSkEha8601v31ovf2IVcTUmld5Lz1AgV/Dju9680+TV7i0rCCWVlZ9Fzz09SchK4BPfTxjtVbzivILSvnv7+Or0V9gY2nDw7YNoSyvvfsycnBzCw8NxdXUtsmFBKJsX/V7L8vktiCuJ5SonX8b7GwP5KzQOXS0pSwc2rlYJIvzbjQXg273XiErK0nBEglC1rApdRUJWAg7GDgxpNOTlTxCK6OrSFQs9C+Kz4jkRJfo5C4I6iSSxnGTkFjB87UWO3UpEX0fKyqFN6e5Reaqoq9MH7WrT1NmCjNwCJm4PRiYXF6cFoTii0qJYd20dAF80+wI9Lc3WbquK9LT06FNH2Trtt1tiA4sgqJNIEstBalY+g1cHcC7sEcZ62qwf3px2da00HVa50ZJK+KmfD8Z62lyMSObXk/c0HZIgVAnzA+eTL8+nlX0rOjh20HQ4VVbfen2RSqQExAYQlhqm6XAEodoQSaKaPcrIZcDK81yOTMHMQIfNo/zwc9NcS52KUtiNBeCnw7e5GpOq4YgEoXI7HXOa41HH0ZZoM7nZ5Cpb6aAysDe2p62DshTL9ltPd/IQBKF0RJKoRnGpOfT99RzXY9OoaazL1jEt8HY013RYFeadJg50b2RLvkzB+G3B5OTLNB2SIFRK+bJ85l2YB8DABgNxM6/61Q40rX89ZTmcP+7+QVa+WBstCOogkkQ1iUrK4t1fz3IvMRM7M322v9+SBnaVowVQRZFIJHzfxxMr0Y1FEF5oy80tRKRFYKlvyQfeH2g6nGqhpX1LnEycyMjPYH/Yfk2HIwjVgkgS1eBuQgbvLj9HVFI2zjUM2f5+S9ysjDUdlkZYGunywzvKVkPrzkZwUnRjEYQiHmY/ZFnIMgDGNx6Pia6JhiOqHqQSKf3q9QNg662tom6rIKiBSBLL6PqDNPr9eo64tBzqWBuz/f2WOFoaajosjWpfz5ohj7uxfC66sQhCEYuCFpGZn4lHDQ96uffSdDjVSi/3Xuhr6XMn+Q6XEy5rOpxqpX379owfP17TYQgVTCSJZXA5Mpn+K87xKDOPRvambHu/JTamokAqwJQnurF8vUd0YxEEgCuJV/jj3h+Asj+zVCLegtXJTM+M192UXTC23tyq4WgEoeoT71CldO7eI95bFUBaTgFNnC3YMroFlka6mg6r0jDQ1eLn/r5oSyX8FRrHrksxmg5JEDRKrpAzJ2AOAL1q98LLykvDEVVPhVPOhyNFP2d1GTZsGCdOnODnn39GIpEgkUiIiIjg6tWr+Pv7Y2xsjI2NDYMHD+bhw39/5+3bt2fcuHGMHz8eCwsLbGxsWLlyJZmZmQwfPhwTExPc3d05cOCA6jnHjx9HIpHw559/4uXlhb6+Pi1atODq1auaeOmvPJEklsKxWwkMW3uBzDwZrWrXYMOI5pgZ6Gg6rErnyW4s34huLMIr7o+7f3D10VWMdIwY32S8psOpthrUaIC3lTcF8gJ23t6p6XBeSqFQkJWfVeG3kszu/Pzzz7Rs2ZLRo0cTGxtLbGwsJiYmdOzYEV9fXwIDAzl48CDx8fH07du3yHPXr19PzZo1uXDhAuPGjWPs2LG8++67tGrVikuXLtG1a1cGDx5MVlbRz4cvvviCBQsWcPHiRaysrOjZsyf5+flq+Z0LxVd5m1xWUgdCY/lk62XyZQo61bdm6aDG6OtoaTqsSuuDdrU5djOBwPvJTNwezNYxLdGSinpwwqslPS+dRZcWATDWeyw1DWpqNqBqrn/9/oQkhrDj9g5GeY6q1P2cswuy8dviV+HnDRgYgKFO8dbPm5mZoauri6GhIba2ys5hs2bNwtfXl++//1513Jo1a3B0dOT27dvUrau8QODt7c3UqVMBmDJlCnPnzqVmzZqMHq3sUT59+nSWLVvGlStXaNGihWqsb775hi5dugDKRNPBwYHdu3c/lYQK5UujVxJPnjxJz549sbe3RyKRsGfPniKPKxQKpk+fjp2dHQYGBnTu3Jk7d+4UOSYpKYlBgwZhamqKubk5I0eOJCMjo8gxV65coU2bNujr6+Po6Mj8+fNLFe+B0Fg+2nKJfJmC173sWD64iUgQX6KwG4uRrpboxiK8spaHLCcpJwkXUxcG1h+o6XCqva7OXbHUtyQhK4HjUcc1HU61FBISwrFjxzA2Nlbd6tevD8C9e/++z3t5/busQktLixo1auDp6am6z8bGBoCEhIQi47ds2VL1Z0tLS+rVq8eNGzfK5bUIz6fRr1eZmZl4e3szYsQI+vTp89Tj8+fPZ/Hixaxfvx5XV1emTZtGt27duH79Ovr6yg0igwYNIjY2lsOHD5Ofn8/w4cMZM2YMW7ZsASAtLY2uXbvSuXNnli9fTmhoKCNGjMDc3JwxY8aUKN7v9l1H27Qm7zZxYO7bXuKKWDE5WhryzZuNmLTzCj8dvk3bOlZ41DLTdFiCUCHCUsPYckP5fjS5+WR0tMTSlPKmq6VLnzp9WBW6iq03t9LZubOmQ3ouA20DAgYGaOS8ZZGRkUHPnj2ZN2/eU4/Z2dmp/qyjU/Tfu0QiKXJfYachuVxepniE8qHRJNHf3x9/f/9nPqZQKFi0aBFTp06lVy9lmYgNGzZgY2PDnj176N+/Pzdu3ODgwYNcvHiRpk2bArBkyRJ69OjBjz/+iL29PZs3byYvL481a9agq6tLo0aNCA4OZuHChc9NEnNzc8nNzVX9nJ6ervrzsFYuTH+jIVKRIJbIu00cOHojnkPX4hm/LZj941qLq7BCtadQKJh3YR4FigLaO7anda3Wmg7pldG3bl/WXF1DQFwAYSlhlbarjUQiKfa0rybp6uoik/3bRatx48b8/vvvuLi4oK2t/lTi/PnzODk5AZCcnMzt27dp0KCB2s8jvFil3bgSHh5OXFwcnTv/+w3QzMwMPz8/zp07B8C5c+cwNzdXJYgAnTt3RiqVEhAQoDqmbdu26Or+u/O4W7du3Lp1i+Tk5Geee86cOZiZmaluDRsqexIPaenMNz1FglgaEomEOX28RDcW4ZVyPOo4Zx+cRUeqw6SmkzQdzivFztiOdg7tANh2a5uGo6n6XFxcCAgIICIigocPH/LRRx+RlJTEgAEDuHjxIvfu3ePQoUMMHz68SDJZWjNmzODo0aNcvXqVYcOGUbNmTXr37l32FyKUSKVNEuPi4oB/1ysUsrGxUT0WFxeHtbV1kce1tbWxtLQscsyzxnjyHP81ZcoUUlNTVbfr168D8GEHd9WlcaHk/tuN5dQd0Y1FqL5yZbnMv6hc/zys0TAcTR01HNGrp399ZT/nvff2in7OZfT555+jpaVFw4YNsbKyIi8vjzNnziCTyejatSuenp6MHz8ec3NzpNKypxZz587l008/pUmTJsTFxbFv374iF3uEilF5t3xpkJ6eHnp6eqqf09LSNBhN9VLYjWXDuft8viOEg5+2xULUlxSqoQ3XNhCdEY21oTWjPEdpOpxXUgu7FjibOnM/7T77w/bTt57YGVtadevWVc3iPWnXrl3Pfc7x48efui8iIuKp+55Vjqd169aiNmIlUGmvJBZus4+Pjy9yf3x8vOoxW1vbp3ZEFRQUkJSUVOSYZ43x5DmEilXYjSU+TXRjEaqnuMw4VoauBGBik4lVYs1ZZRCflsNXu0PZHHCfjNyCMo/3ZD/n327+Jt5rBKGEKm2S6Orqiq2tLUePHlXdl5aWRkBAgGprfMuWLUlJSSEoKEh1zD///INcLsfPz091zMmTJ4sU4Tx8+DD16tXDwsKiRDGl5KWU4RUJhQx0tVjUT3RjEaqvhUELyS7Ixtfalx6uPTQdTpWgUCj4fEcIWwIi+Xr3VfxmH+Gr3aFcjUkt07i93HthoG3A3ZS7BMUHvfwJgiCoaDRJzMjIIDg4mODgYEC5WSU4OJjIyEgkEgnjx49n1qxZ7N27l9DQUIYMGYK9vb1q8WqDBg3o3r07o0eP5sKFC5w5c4aPP/6Y/v37Y29vD8DAgQPR1dVl5MiRXLt2jW3btvHzzz8zceLEEse74toKdb30V56ngxnjO9cBRDcWoXoJig/iQPgBJEiY0nyKWMdcTHuCYzh15yG62lLcrIzIzJOxJSCSN5acptfSM2y/GEVWXsmvLprqmqoSdbGBpfJr3749CoUCc3NzTYcioOEkMTAwEF9fX3x9fQGYOHEivr6+TJ8+HYBJkyYxbtw4xowZQ7NmzcjIyODgwYOqGokAmzdvpn79+nTq1IkePXrQunVrVqz4N5kzMzPj77//Jjw8nCZNmvDZZ58xffr0EtdIBDgSc4SA2IqvZ1VdfdCuNk2cLcjILeCz7SHI5GIqSKjaZHKZqj/zO3XfoUENUbKjOJIy85i5X1ko+dNOdTg6sR2/jW5BT297dLQkhESlMOn3K/jNPso3f1zlVlz6S0YsqnADy5H7R0jMEhvmBKG4JAqxSOOloqOjcXR0pMGyBtS2qc3vb/6OrpbYbKEOkY+y8P/5JJl5MiZ3r8/Y9rU1HZIglNr2W9uZeX4mJrom/PnWn1jol2xJy6tq4rZgdl2Oob6tCfvGtUZH69/rFw8zctkZFM1vFyK5/+jfGYcmzhYMbO7E6152xaq5OvivwQQnBvOhz4eM9R5bLq/jZXJycggPD8fFxQUDg7IVsxb+lZ2dTUREBK6urkUuIsG/n99RUVE4ODhoKMLylZKSwu7duzl16hT3798nKysLKysrfH196datG61atSr12JV2TWJlZKlrSURaBKtDV2s6lGrDqYayGwvAwsO3yrz+SBA0JTU3lcWXFwPwsc/HIkEsplN3Etl1OQaJBOb08SySIALUNNZT9oD/rD2bRvrh72GLtlRC0P1kPtsRgt/3R5mx7zp3EzKecwalwquJO2/tJF+e/8Jjy0thp5GsLLG8Rp3y8vIAZdu/V8mDBw8YNWoUdnZ2zJo1i+zsbHx8fOjUqRMODg4cO3aMLl260LBhQ7ZtK91SC1ECpwQ+8PiAeTfmsTJ0JT3ceuBs6qzpkKqFJ7uxTNgWzD7RjUWogn65/Aupuam4m7uLUivFlJ0n4+vdyjInQ1u64Ov0/MRaKpXQuk5NWtepSUJaDtsDo/jtQhQxKdmsORPOmjPh+LlaMtDPie4etuhpF30P6eLchfkX55OQrezn3MW5S3m+tGfS0tLC3NxcVZXD0NBQrFktI7lcTmJiIoaGhuXS+aUy8/X1ZejQoQQFBamafvxXdnY2e/bsYdGiRURFRfH555+X6BxiurkYCi9XR0ZGMvvmbM48OIOfnR8ru6wU/4OrSVJmHt0WnSQxPZdhrVz49vHVRUGoCm4l3aLv/r7IFXJWd11Nc7vmmg6pSpjz1w1+PRmGvZk+f09sh7FeyT7kZXIFJ+8ksvl8JP/cjKdwWbOlkS7vNnFgQHMnXGoaqY5ffGkxK0NX0ty2Oau7aWZGSKFQEBcXR0pKikbOXx1JpVJcXV2fWWy7Ok83P3r0iBo1apTb8SCSxGJ58h+ZwlTBW3vfIleWy5w2c3jD7Q1Nh1dtHLuVwPC1FwHYOLI5bepYaTgiQXg5hULBiEMjCIwPpKtzVxa0X6DpkKqEqzGp9Fp6BplcweqhTenUwOblT3qB2NRstl2MYuuFKOLSclT3t3avyUA/J7o0tOFhdjzdd3VHrpCzp9ceaptrbg20TCYrUppNKD1dXd3ndnmpzkliRRBJYjH89x/ZiisrWHJ5CZb6luztvRczPTNNh1htTNtzlY3n72Njqseh8W0xNxQbhITK7WDEQb448QX6Wvr80fsP7I3tNR1SpVcgk/PW/84SGpPK6152LB3YWK1jH7uVyOaA+5y4nUjhJ1xNYz36NXPgDr9wLu4E/ev15+sWX6vtvELlVN2TxKCgID7//HP++OMPTE1NizyWmppK7969WbRoEd7e3qUaX2xcKYXhjYbjZuZGUk4SP1/6WdPhVCtf9WiAW2E3lt1XRYcEoVLLys9iQaDyyuEIzxEiQSymdWcjCI1JxVRfm296PnstVWlpa0np0tCGdcObc/KLDnzcwR0rEz0eZuSy9Ng9/rngDsDuO3tJzSlZKR1BqGwWLFhAx44dn0oQQVkCsEuXLvzwww+lHl8kiaWgo6XDtBbTANhxewfBCcGaDagaUXZj8UFbKuHP0Fh2XxbdWITKa83VNcRlxmFvZM/wRsM1HU6VEJWUxYK/bwPKL4XWJvoveUbpOVoa8nm3epz9siPLBjWmTZ2aFGS6I8u1IleeRcdfF/LT4dvEpmaXWwyCUJ4CAgLo1avXcx/v2bMnZ8+eLfX4Ikkspaa2Tent3huAGednaKykQnXk5WD+bzeWP64RnVy0XIS4uihUBtHp0ay9uhaAL5p9gb52+SU71YVCoWDqnqtk58vwc7WkXzPHCjmvjpYUf087No704/jnHWhW43UAsvVP8fPR27w29x9Grb/IsZsJoqi/wMmTJ+nZsyf29vZIJBL27NlT5PFdu3bRtWtXatSogUQiUXWNe5kdO3ZQv3599PX18fT05K+//iryuEKhYPr06djZ2WFgYEDnzp25c+fOC8eMiYnBxMTkuY8bGxsTGxtbrPieRSSJZTCxyUTM9cy5k3yHTdc3aTqcaqWwG0t6bgETn+jGcjzqOO22tWPuhbnI5DLNBim80hYELiBPnoefnR+dnDppOpwqYW/IA07cTkRXW8qcPp4aqQ7hUtOIJT1Ho6+lj5Z+PJ61HyJXwJEbCQxfd5G284+x5OgdEp7Y/CK8WjIzM/H29mbp0qXPfbx169bMmzev2GOePXuWAQMGMHLkSC5fvkzv3r3p3bs3V69eVR0zf/58Fi9ezPLlywkICMDIyIhu3bqRk/P8f4tWVlbcunXruY/fvHmTmjVrFjvO/xIbV4rhRQtfd9/ZzfSz0zHQNmBPrz1iTZIa/bcbS++mhry9723S85TriDo6dmRe23niCo5Q4c49OMeYw2PQkmixs+dO3C3cNR1SpZecmUfnhSd4lJnHZ13qMq5THY3G892579h5eyfdXLrxQYNv+e1CJDuDoknNVs4KaUsldG5gw6AWTrxWuyZSqSh3VhWVdeOKRCJh9+7d9O7d+6nHCru8XL58GR8fnxeO069fPzIzM9m/f7/qvhYtWuDj48Py5ctRKBTY29vz2WefqWoZpqamYmNjw7p16+jfv/8zxx0+fDh3797l1KlTTz2mUCho06YNderUYe3atcV/0U8QVxLLqLd7b5rYNCG7IJs5AXPEVKgaOdUw5Juehd1YrvPJ0S9Iz0vH2dQZXaku/0T9w5jDY0jNFV1ahIqTL89n3gXlFYQB9QeIBLGYZv15g0eZedSzMeH9dppvv9m/nvJD9+j9o5gZZzPtjYYEfNWJhX29aepsQYFcwcFrcQxefYH2Px5n2fF7PMzI1XDUQmmlp6eTlpamuuXmVuzf5blz5+jcuXOR+7p168a5c+cACA8PJy4ursgxZmZm+Pn5qY55lqlTpxIaGoqfnx/bt28nJCSEkJAQtm3bhp+fH1evXuXrr0u/i18kiWUkkUiY3mI62lJtjkcf55/IfzQdUrXyblMHuja0QWLxDzdTQjDSNmJZp2X82uVXTHRMuJxwmSEHhhCbUfo1F4JQEttubuNe6j0s9CwY66OZHsBVzZm7D/n9UrSy9d7bnuhqa/6jp55lPXytfSlQFLDz9k4A9HW06NPYgZ1jW3FofFuGtnTGRF+byKQs5h28Scs5R/l4yyXO3nsoLghUMQ0bNsTMzEx1mzNnToWePy4uDhuborVAbWxsiIuLUz1eeN/zjnmW2rVrc+TIETIzM+nfvz+NGzemcePGDBgwgKysLA4fPoy7e+m/yGr+/9RqwM3cTbWz8fsL35OZn6nhiKoPiUTCgLYy9GoeBaCh3jAcTR1patuU9f7rsTG0ISw1jPf+eo9bSc9flyEI6vAo+xH/C/4fAJ80/gRT3afLTghF5eTL+Gp3KABDWjjT+AWt9ypa4dXEHbd3PLX5sJ6tCd/18iDgq07Mf9sLb0dz8mUK9l+JZeDKADotPMGqU2EkZ+ZpInShhK5fv05qaqrqNmXKFE2HpDZNmzbl6tWrXLp0ia1bt/Lbb79x6dIlrl69SrNmzco0tkgS1WSM1xgcjB1IyEpgafCzF7sKJZeam8r3gVNBoiA/1Zd/Ah05dScRgDoWddjUYxPu5u4kZCcw7OAwLsZd1HDEQnW25PIS0vPTaWDZgLfc39J0OFXCoiN3uP8oC1tTfT7vVk/T4RTRxbkLNfRrkJid+NxZIENdbfo2c+SPj15j/7jWDPRzwkhXi7DETGb9eQO/OUeZsC2YixFJ4upiJWZiYoKpqanqpqenV6Hnt7W1JT4+vsh98fHx2Nraqh4vvO95x7yMj48P7777Ln379n3pGsniEkmimuhr66uq92++sZkbj25oOKKqT6FQ8N2574jLjMPRxJHejh8D8PmOEFKylN/ebY1sWdd9HY2tG5ORn8H7h9/nYPhBTYYtVFPXHl5j151dAEzxm4KWVEvDEVV+1x+ksfJUGAAze3tgoq+j4YiK0tHS4e26bwOw7da2lx7vUcuM79/yJODrznz/lieN7E3JK5Cz+3IM7y4/R7dFJ1l3Jly1+UUQCrVs2ZKjR48Wue/w4cO0bNkSAFdXV2xtbYsck5aWRkBAgOoYTRBJohq1rtWabi7dkCvkzDg3Q5RoKaNdd3Zx+P5htCXazG87n29eb6LqxrL46F3VcWZ6ZqzouoIuzl3Il+fzxckv2Hh9owYjF6obuULOnAtzUKDgDbc38LX21XRIlZ5MrmDKrivI5Ap6eNrSpWHZejOXl3frvotUIuVi3EXuJt99+RMAYz1tBvo5sX9ca/746DX6NXXEQEeL2/EZfLvvOn7fH+HbvdfIl8nLOXqhPGRkZBAcHKyqfxgeHk5wcDCRkZEAJCUlERwczPXr1wG4desWwcHBRdYODhkypMiU9qeffsrBgwdZsGABN2/e5NtvvyUwMJCPP1Ze/JBIJIwfP55Zs2axd+9eQkNDGTJkCPb29s/cWV1RRJKoZpObTcZYx5irj66y4/YOTYdTZYWlhDH3wlwAxjUeh0dNDwx0tfi8q3K66lzYoyLH62np8UPbH1RrjOZfnM+CwAXIFeJNWii7P8P+JCQxBANtAyY0maDpcKqEdWcjCIlOxURfm28fVymojGyNbOng2AGArbe2lui5EokEb0dz5r3jRcDXnZjRqxH1bEzIyZez7mwEvwdFl0fIQjkLDAzE19cXX1/ll8GJEyfi6+vL9OnTAdi7dy++vr68/rqyKHv//v3x9fVl+fLlqjEiIyOLFLFu1aoVW7ZsYcWKFXh7e7Nz50727NmDh4eH6phJkyYxbtw4xowZQ7NmzcjIyODgwYPo62uuzJuok1gMJa2z9NvN3/g+4HuMdYzZ23svVoZWFRBl9ZEry2XQn4O4lXyLFnYt+LXLr0glyu8z8Wk5+H1/FKkErn7XDUNd7SLPVSgUrL66WtVT+3W315nZaiY6WpVrmkuoOjLzM3lj9xs8zH7I+MbjGek5UtMhVXrRyVl0/ekkWXkyvn/Lk4F+TpoO6YXOx55n9N+jMdQ25Oi7RzHWNS71WAqFgqXH7vLj37dxtzbm8IS2GikaLiiVtU7iq05cSSwHfev2xaOGBxn5Gcy/OF/T4VQ5PwX9xK3kW1joWfB96+9VCSKAjak+tqb6yBVwNSbtqedKJBJGeY5iduvZaEu0+TPsTz48+iEZeRkV+RKEauTXK7/yMPshTiZODG44WNPhVHqFrfey8mQ0d7GkfwW13isLP1s/XM1cySrIYn/Y/pc/4QUkEglDW7lgrKfN3YQMjt9OVFOUgvB8V65ceeYtNDSUO3fulLoupEgSy4GWVItpLachlUg5GHGQMzFnNB1SlXEi6gSbb2wGYFbrWc+8CuvtaAZASFTKc8d5s/abLOm0BANtA87Hnmf4oeE8zH5YLjEL1VdEaoRqfevk5pPR1dLVcESV374rsRy/lYiulpTv+3hWiU4lEomEfvX6AbD15tYy71I20ddRJcerT4WXOT5BeBkfHx/VFPmTNx8fH+rXr4+ZmRlDhw59YYu/ZxFJYjlpWKMhA+sPBGDW+VnkFIg+oC+TmJXItDPTAHivwXu0dWj7zON8HJV11oJfkCSCciPR2m5rsdS35GbSTd776z0iUiPUGbJQzc2/OJ8CeQFtarV57r9H4V8pWXnM2HcNgI86uONuXfpp24r2Zu03MdA24F7qPQLjA8s83rDXXNCSSjh99yHXHzw96yEI6rR7927q1KnDihUrVJtuVqxYQb169diyZQurV6/mn3/+YerUqSUaVySJ5ehj34+xMbQhOiOaFVdWaDqcSk2ukPPV6a9Izk2mnkW9F24OKLyS+LIkEaBRzUZs8t+Eo4kjMRkxDD4wmCuJV9QVtlCNnYw+yamYU2hLtZnUbJKmw6kSvv/rBg8z8qhjbczY9ppvvVcSJromvOH2BqBcV15WDhaG+Hso69utPi2uJgrla/bs2fz888+MHDkST09PPD09GTlyJD/99BMLFixg0KBBLFmyhN27d5doXJEkliMjHSOmNFdugV97bS1hKWEajqjyWndtHedjz6Ovpc/8tvNfOK3nWcsMiQRiUrJJTH/5OgtHU0c2+m+kUY1GpOSmMPLQSE5EnVBn+EI1kyfLU/VnHtxwMC5mLpoNqAo4e+8h2wOVu3nnVpLWeyXVv76yOsI/kf+QkJVQ5vFGtXEDYG9IDAlpYjZJKD+hoaE4Ozs/db+zszOhocqORz4+PkV2XBdH1fu/uIrp6NSRdg7tKJAXMOP8DFGR/xlCE0NZcmkJAF82/xI3c7cXHm+ir0Odx9NYL1qX+KQaBjVY020NrWu1JkeWwyfHPuH327+XKW6h+tp4fSOR6ZHUNKjJ+17vazqcSi8nX8ZXu5QfRO+1cKKJs6WGIyqduhZ1aWzdGJlCpurnXBY+juY0c7EgX6Zg/bmIsgcoCM9Rv3595s6dS17ev20i8/PzmTt3LvXr1wcgJibmqd7QLyOSxHImkUj4yu8rDLQNCIoP4o97f2g6pEolIy+DyacmU6AooItzF/rU6VOs53k7mAMQEp1S7HMZ6hiyuONietXuhVwh59tz37IsZJlI3IUiErISVMtDJjaZiJGOkYYjqvwWH71DxKMsbEz1mNS9vqbDKZPCq4k7b+98qp9zaYxsrfzSuzkgkqy8gjKPJwjPsnTpUvbv34+DgwOdO3emc+fOODg4sH//fpYtWwZAWFgYH374YYnGFUliBbA3tmes91gAFgQuIDknWcMRVR6zA2YTlR6FnZEd37T8ptj1xLwdzYHirUt8ko5Uh5mvzWS052gA/hf8P2acn0GBXLx5C0qLghaRVZCFt5U3r7u9rulwKr0bsWmsOKlcSjOjlwemlaz1Xkl1duqs6ud8NPLoy5/wEl0a2uBcw5CUrHx+vxSjhggF4WmtWrUiPDycGTNm4OXlhZeXFzNmzCA8PJwWLVoAMHjwYL744osSjSuSxAryXsP3qGNRh5TcFBYGLdR0OJXCvnv72B+2H6lEyry28zDTMyv2c30eJ4khUSnI5SW7EiiRSPik8Sd87fc1EiTsvL2TCccnkF2QXaJxhOonOCGYfWH7kCBhSvMpRWp0Ck+TyRV8uSuUArmCbo1s6NbIVtMhlZmOlg7v1H0HgG03X97P+WW0pBJGvOYKwJrT4SV+vxKE4jIxMeGDDz5g4cKFLFy4kPfffx8TE5MyjSneASuIjlSH6S2ULX323N1DYFzZSyxUZZFpkcw6PwuAD7w/KHEv3Hq2JuhpS0nLKSDiUWapYuhfvz8/tf8JXakux6OOM/rv0aTkpJRqLKHqk8llzLkwB4A+dfrQqGblbSVXWWw4F0FIVAometrM6OXx8idUEe/UfQctiRaB8YHcSb5T9vGaOGCqr034w0yO3iz7hhhBeJaNGzfSunVr7O3tuX//PgA//fQTf/xR+mVuIkmsQD7WPqpvqDPPzyRfVvb1LlVRviyfyScnk1WQRWPrxozxHFPiMXS0pHjUKn4pnOfp5NyJlV1XYqJrQkhiCIMPDCYmQ0wJvYr23N3D9UfXMdExYZzvOE2HU+nFpGTzw6FbAEz2r4+Nqeb6y6rbk/2ct90q+9VEIz1tBvopd56uPCWqXAjqt2zZMiZOnIi/vz/JycnIZDIALCwsWLRoUanHFUliBRvfeDyW+paEpYax7to6TYejEb8E/8LVR1cx1TVlbpu5aEm1SjXOk1POZdHYpjEb/Tdia2RLRFoEg/8azM2km2UaU6ha0vLSVP2+x/qMpYZBDQ1HVLkpFAqmP26919TZgoHNK3dv5tIo3MCy794+tbT1HNbKBW2phAvhSVwpwYY7QSiOJUuWsHLlSr7++mu0tbVV9zdt2lRVAqc0RJJYwcz0zPiimXLh6K9XfiUqLUrDEVWscw/OsebqGgC+a/UddsZ2pR5LtXklOrXMcdU2r81G/424m7uTmJ3IsIPDCIgNKPO4QtWwLHgZybnJuJm5qZID4fn+DI3l6M0EdLQkzKkirfdKqrltc9zM3MgqyGLvvb1lHs/WTJ+e3vaAKK4tqF94eDi+vk8v29LT0yMzs3RLskAkiRrxuuvr+Nn5kSvLZXbA7FemBEtSThJfnf4KUK756ezcuUzj+T5OEm88SCO3QFbW8LA1smW9/3qa2jQlMz+TD458wIHwA2UeV6jc7ibfVXXYmNx8MjrSqr07t7ylZuXz7d7rAHzY3p06NmVbGF9ZPdnPedutbWp5nx7ZWrmBZf+VWB6kiI1ygvq4uroSHBz81P0HDx6kQYMGpR5XJIkaIJFImOo3FR2pDmcenOHQ/UOaDqncKRQKpp2ZxsPsh7iZuamlzZmDhQGWRrrkyeTciE1XQ5RgqmvK8i7L6erclQJ5AZNOTmL9tfVqGVuofOIz4xl/fDwyhYxOTp1oZd9K0yFVenMO3OBhRi61rYz4sEPVar1XUj1r98RA24Cw1DAuxl0s83getcxo6VYDmVzB+rMRZQ9QEB6bOHEiH330Edu2Kb/QXLhwgdmzZzNlyhQmTSr9561IEjXExcxFVatv3oV5pOepJ8mprLbc3MLJ6JPoSnWZ33Y+BtoGZR5TIpHg7fB480qk+mpP6mnp8UO7HxjUYBAAPwb+yA8Xf0CukKvtHILmxWXGMeLQCO6n3cfeyJ4vm3+p6ZAqvfNhj9h6UblEZu7bXuhpl249cVVhomtCT7eeAGy9tVUtY45qo7yauOVCJBm5oj6roB6jRo1i3rx5TJ06laysLAYOHMiyZcv4+eef6d+/9EtoRJKoQSM9R+Ji6sLD7IcsvrRY0+GUm5tJN1kQuACAz5p+Rj3Lemob28fRAoAQNaxLfJJUImVys8lMbDIRgA3XN/DlqS/Jk+W95JlCVVCYIEamR1LLuBZru6/F1qjq1/grT0+23hvo50Qzl6rZeq+knuznHJcZV+bxOtSzxs3KiPScArZffLXWpAvla9CgQdy5c4eMjAzi4uKIjo5m5MiRZRpTJIkapKuly9QWUwHlmpfQxNLvQKqssvKzmHRyEvnyfNo7tGdA/QFqHd/bUXklsaw7nJ9FIpEw3GM437f+Hm2JNgfCD/DhkQ/VstNR0JzYjFiGHxxOVHqUMkHsthZ7Y3tNh1XpLT12l7CHmVib6PGlf9VuvVcSdSzq0MSmCTKFjN/vlL3fu1QqUa1NXHMmHJkori2omaGhIdbW1moZSySJGuZn58cbbm+gQMHM8zOrXXu4+RfnE54ajpWBFTNem1HstnvFVdjDOexhJqlZ5VN3smftnizttBRDbUMC4gIYdnAYCVmiIG5V9CDjAcMPDSc6IxoHYwfWdV9Xph32r4pbceksO34PgBm9GlX51nslVaSfsxrq277d2AELQx2ik7M5dK3sVyeFV5Ovry+NGzcu1q20RJJYCXze9HNMdU25kXRDtcuyOvg74m9+v/M7EiTMaTMHC30LtZ/DwkgXlxqGAISUY+2xVrVasbb7Wmro1+BW8i0G/zWYsFRRFLcqicmIYcShEcRkxOBo4iimmItJ2XrvCgVyBV0aVo/WeyXVybETNQ1q8jD7oVr6OevraDG4hbK49ipRXFsopd69e9OrVy969epFt27duHfvHnp6erRv35727dujr6/PvXv36NatW6nPIZLESqCGQQ0mNJkAwC+Xf1HLuhdNe5DxgG/PfQso11762fmV27m81VRU+2Ua1mjIxh4bcTZ15kHmA4YcGEJwQnC5nlNQj+j0aEYcVCaITiZOrO0mEsTi2nT+PpcjUzDW02ZmLw+1zwZUBU/2c1bXF/n3WjqjqyXlUmQKQffVt/FOeHV88803qltiYiKffPIJ586dU/VuPnv2LOPHjyc+Pr7U5xBJYiXRp04ffKx8yCrIYu6FuZoOp0wK5AV8eepL0vPS8arpxYc+H5br+QqnnMvSnq+4HE0c2eC/Ac+anqTmpjLq71EcizxW7uetrEKjU0nKrNybeaLSoxhxaAQPMh/gbOrMmm5rsDGy0XRYVcKDlGzmH1R2H5rcvR62ZtWn9V5JvVNH2c/5UsIlbiffLvN41ib69PYtLK4triYKZbNjxw6GDBny1P3vvfcev/9e+rW0IkmsJKQSKdNaTkNbos3RyKMcjzqu6ZBKbcWVFVxOuIyRjhFz284t9+LEPk7mgHK6uSIKk1vqW7Kq6yraOrQlV5bL+OPj2XF7R7mft7K5EZvGm0tP0+PnU8Sn5Wg6nGeKSlMmiLGZsbiYuogEsQQUCgXT/7hGZp6Mxk7mDHrce/hVZWNkQ0enjgBsu1n2fs4AI1u7AXDwahxRSVlqGVN4NRkYGHDmzJmn7j9z5gz6+qX/cieSxEqkrkVdBjcaDMD3Ad+TlV/13jSC4oP49cqvAExrMQ1HE8dyP2dDO1N0tCQ8zMgjpoK6GBjqGPJzh595y/0t5Ao5M87NYGnw0lemew7AiduJKBQQl5bD6A2BZOeVveuNOkWmRTL80HDiMuNUCaK1oXp2/L0KDlyN48iNeHS0JMx926tatt4rqf71HvdzDtunltq29WxNaFvXCrlCudNZEEpr/PjxjB07lk8++YRNmzaxadMmxo0bx0cffcSECRNKPW6pksTIyEhOnTrFoUOHuHTpErm5uaUOQCjqA68PsDeyJzYzluUhyzUdTomk5qby5akvkSvkvFn7TV53e71Czquvo0UDO1OgYqacC2lLtfmu1Xe87/U+AMtDlvPdue+q3Q7157kYnqT685XoVD7bEYy8kpTzuJ92n+GHhhOfFY+rmStru6/FytBK02FVGanZ+Xyz9xoAY9vVpm41bb1XUs1sm1HbrDbZBdlq6ecMMOpxOZztF6NIzS6fCg1C9ffll1+yfv16goKC+OSTT/jkk0+4dOkSa9eu5csvS98ooNhJYkREBJMnT8bZ2RlXV1fatWuHv78/TZs2xczMjC5durBjxw7kctGVoiwMdQz5yk/Z33jD9Q3cSrql4YiKR6FQ8O3Zb4nLjMPJxEn1GipK4brE8t688l8SiYSPfT9mWotpSCVSfr/zO+OPja+SV4FLQi5XEPh4sf3U1xugqyXlr9A4fjpS9rVaZRWRGsGIgyNIyEqgtllt1nRbQ02DmpoOq0qZe+Amiem5uFkZ8WEHd02HU2lIJBL61VdvP+c2dWpSz8aEzDwZWy9Elnk84dXVt29fzpw5Q1JSEklJSZw5c4a+ffuWacxiJYmffPIJ3t7ehIeHM2vWLK5fv05qaip5eXnExcXx119/0bp1a6ZPn46XlxcXL5a9x+WrrJ1jOzo7dUamkDHz/Mwq0Q5u552dHIk8grZUm/lt52OkY1Sh5y/c4VyRVxKf1LdeX35q/xN6WnqciD7B6L9Hk5xTfXcs3knIIDU7HwMdLYa2cmFOH08Alvxzl92XozUWV3hqOCMOjSAhOwF3c3dWdVslEsQSCgh7xG+Pk5U5b3mir1O9W++VVE+3nhhqGxKeGs6FuAtlHk8ikTDycau+dWcjyJdV/vd7oXKoiOVNxUoSjYyMCAsLY/v27QwePJh69ephYmKCtrY21tbWdOzYkW+++YYbN27w448/EhUlWg2V1eTmkzHUNiQkMUQtVf7L072Ue8y/MB+AT30/pVHNRhUeg8/jJDE0JpUCDb3JdnTqyKquqzDTM+PKwysMOTCE6HTNJUzl6UKEcqq5sbM5OlpS3m7iwIftawMweWcogRFJL3p6uQhLDWPEoREkZicqE8SuIkEsqZx8GVN2Kzs/DWjuiJ9bDQ1HVPkY6xrTs/bjfs431dPPuZePPTWN9YhNzeGv0Fi1jClUf40aNWLr1q3k5b24wsSdO3cYO3Ysc+eWvHJKsZLEOXPmUKOG8s0iMjKSnJzn72Ts3r07ffr0KXEgQlG2RrZ87PsxAD8F/cTD7IcajujZcmW5fHHyC3JkObSyb8WQRk9vwa8IbjWNMNHTJidfzu14zbXN87H2YUP3DdgZ2RGRFsHgA4O58eiGxuIpL4XrEZs6/9u/9/Ou9ejeyJY8mZwxG4MqdLdmWEoYIw6O4GH2Q+pY1GF1t9XUMBAJTkn97/g9whIzsTLR40v/BpoOp9Iq3MByLOqYWura6mlrMaRlYXHt8FdqA5xQekuWLOHHH3/E1taWfv368cMPP7B582Z+//13Vq1axcSJE2nevDk+Pj6YmpoyduzYEp+jRBtX5HI57u7u4kphBRlQfwANLBuQnpfOj4E/ajqcZ1oYuJA7yXew1LdkduvZSCWa2TAvlUrwetzHWVNTzoXczN3Y1GMTdS3q8jD7IcMODuPsg7MajUmdFAoFFx9fKWzu+m+SKJVKWNjPG89aZiRl5jFi3UXScsp/If69lHsMPzScRzmPqGtRl9VdV2Opb/nyJwpF3I5PZ9nxuwB827MRZgavVuu9knC3cKepTVNkChk7b+9Uy5jvtXBGT1tKaEwqF8Ir/kq8UPV06tSJwMBA9u7di7W1NZs3b+bjjz9m0KBBfPvtt9y5c4chQ4YQHR3NvHnzMDMzK/E5SvSJLpVKqVOnDo8ePSrxiYSS05ZqM73ldCRI+DPsT87Hntd0SEUcjzrOlptbAJj12iyNT+35VFDnleKwNrRmXfd1NLdtTlZBFh8d+Yj9Yfs1HZZaRCdnE5uag7ZUgu/jGpWFDHW1WTmkKTametxJyGDclsvlOv1/N/kuIw6NICknifqW9VnddXW5tH+s7uRyBVN2hZIvU9C5gTU9PEU3mpdRdz9nSyNd3m7iAMDKU6IcjlB8rVu3ZsmSJQQHB5OcnExOTg7R0dHs27ePjz/+GAuL0r8nlviyz9y5c/niiy+4evVqqU8qFJ9HTQ/Vm9Gs87PIlVWOckMJWQlMOzMNgPcavEcbhzYajuiJHc7l2MO5JEx0TVjWeRndXbpToChgyqkprLu6rspPJQXeV17laFTLDENd7acetzXTZ/XQZhjoaHHidiKz/iyf6fY7yXcY+fdIknKSaGDZgJVdVmKub14u56ruNl+IJOh+Mka6Wsx4RVvvlVRHp45YGVjxKOcRRyKPqGXMkY/L4Ry9GU9YouaWzYByxiAuM448WeXuqFQeTp48Sc+ePbG3t0cikbBnz54ijysUCqZPn46dnR0GBgZ07tyZO3fuvHBMFxcXJBLJU7ePPvpIdUz79u2fevyDDz4oj5dYbCVOEocMGcKFCxfw9vbGwMAAS0vLIjdB/cb5jsPKwIr7afdZHbpa0+Egk8v46tRXpOSmUN+yvqrvtKYVXkm8HZ9OZm7lqFWoq6XLvLbzGNxQWSR9QdAC5l+cXyV2rD/PhXDlru3mLs//dupRy4yf+vkAyh2bG85FqDWG28m3GXnoiQSxq0gQSysuNYd5B5St977oVg97cwMNR1Q16Ej/7eesrg0sta2M6VTfGoUC1p6JUMuYJRGfGc/ee3v5+vTXdN7ZmS47u/DZ8c8qPA5Ny8zMxNvbm6VLlz7z8fnz57N48WKWL19OQEAARkZGdOvW7YX7NS5evEhsbKzqdvjwYQDefffdIseNHj26yHHz589X3wsrhacvA7zEokWLyiEM4UVMdE2Y1HwSX5z4glWhq/B39cfVzFVj8ay9tpaAuAAMtA2Y33Y+ulq6GovlSdam+tib6fMgNYfQmFRaVJKdmVKJlEnNJmFjaMOPgT+y6cYmErMT+b7195Xmd1cShesRm7m8+Ethdw9bJnevz7yDN/lu33VcahjRtm7ZC1rfSrrFqL9HkZKbQsMaDVnRZQVmeiVfayMoTf/jKhm5Bfg4mjO4pYumw6lS3qn7DiuvrORSwiVuJd2inmW9Mo85so0rR28msCMoiold6mJhVH7vEam5qQTGBXIu9hwBsQFEpEU8dczpB6fJleWip6VXbnGUp7yCkneC8vf3x9/f/5mPKRQKFi1axNSpU+nVqxcAGzZswMbGhj179tC/f/9nPs/Kquh739y5c6lduzbt2rUrcr+hoSG2tpVnuUeJk8ShQ4eWRxzCS3Rz7saeWns4E3OG2edns7LrSo1MCV1JvMLSy8pvV1OaT9Fosvos3o7mPEiNIyQqpdIkiYWGNhpKTYOaTD0zlUMRh3iU/YiZr83EwcRB06EVW1JmHncTlNNgTV+SJAJ80M6NuwkZ/H4pmo82X2LXh62oU4buHTeTbjL679Gk5KbgUcODX7v+iqmuaanHe9UdvBrL39fj0ZZKmPu2J1qi9V6JWBta09GpI3/f/5ttt7YxveX0Mo/Z0q0GjexNufYgjS0XIvlIjcXMcwpyuJRwiYDYAAJiA7iRdKPIrIYECQ1rNMTPzg8/Oz+mnJpCUk4SNx7dwMfaR21xVKTVp5XrO9PT00lLS1Pdr6enh55eyRPf8PBw4uLi6Ny5s+o+MzMz/Pz8OHfu3HOTxCfl5eWxadMmJk6c+NTn+ObNm9m0aRO2trb07NmTadOmYWhoWOI41aVMW1FzcnJIS0srchPKh0Qi4Wu/r9HT0iMgLkAjmyAy8jKYdHISBYoCurl0o7d77wqP4WU0XVT7ZV53e51lnZdhpGNEYHwgvfb04udLP5OZn6np0Iql8Cqiu7UxlsW4wiGRSPi+jwfNXSxJzy1gxPqLPMoo3braG49uqK4getb0FAliGaXl5DP9D2XrvQ/a1aa+rfhdlkbhmvH9YfvV0s9ZIpEw6oni2rmluBJWqEBeQEhiCCuurGDkoZG89ttrvH/4fdZcXcO1R9eQK+S4mrnSv15/FrVfxKn+p9j6xlYmNJlAK/tWeFl5ARD6MLTMr0sTrkSnsOm8sjB8w4YNMTMzU93mzJlTqjHj4pQlj2xsbIrcb2Njo3rsZfbs2UNKSgrDhg0rcv/AgQPZtGkTx44dY8qUKWzcuJH33nuvVHGqS4mTxMzMTD7++GOsra0xMjLCwsKiyE3d0tPTGT9+PM7OzhgYGNCqVasiHV2Ks4A0KSmJQYMGYWpqirm5OSNHjiQjQ7OLgkvD0cRR1Sf4x8AfSc1NrbBzKxQKZp6fSUxGDPZG9spd15VwcXtl2uH8PC3sWrClxxZa2LUgT57HqtBVvLH7Dfbc3VPp1yoW1kd82VTzk/S0tVg+uAlOloZEJWXzwaagEn/wXX90nVF/jyI1NxWvml782kUkiGU178BNEtJzca1pxMcdReu90mpq0xR3c3e19nN+3dMeW1N9EtNz2RdS/OLaCoWCu8l32XR9E+OOjqPN1ja899d7LLm8hAtxF8iT52FtaM2btd9kduvZHHnnCHt77+XrFl/TybnTU8s2vGoqk8QriVfU8roqUm6BjM93hCB73E++sFNc4W3KlCkai2316tX4+/tjb29f5P4xY8bQrVs3PD09GTRoEBs2bGD37t3cu3evWOPeu3ePqVOnMmDAABISEgA4cOAA165dK3WsJU4SJ02axD///MOyZcvQ09Nj1apVfPfdd9jb27Nhw4ZSB/I8o0aN4vDhw2zcuJHQ0FC6du1K586diYmJAYq3gHTQoEFcu3aNw4cPs3//fk6ePMmYMWPUHmtFGNZoGLXNapOUk8RPQT9V2Hn3he3jr/C/0JJoMa/tvEr7Ae1ZywypBB6k5pCQ9vxFxJrmZu7Gii4rWNxhMU4mTjzMfsi0M9MY+OdALidc1nR4z/VvfcSSfSG0NNJlzbCmmOhrczEimSm7Qou9y/vao2uM+nsUaXlpeFt582uXXzHRLf2UtaD8e9wcoLzC8r1ovVcmEomEfvWU/Zy33tyqluoFutpShrZyAWDVqbAXjvkg4wG77+xm8snJdNzRkbf2vsW8i/M4Hn2cjPwMTHVN6ezUma/9vmZv770ceecIs1vP5s3ab2JjZPPccQE8rZTtNqvilcTFR+9wOz4DSyNlvU8TExNMTU1Vt9JMNQOq9YLx8fFF7o+Pjy/WWsL79+9z5MgRRo0a9dJj/fz8ALh79+5Ljz1x4gSenp4EBASwa9cu1YWwkJAQvvnmm5c+/7kUJeTo6Kg4duyYQqFQKExMTBR37txRKBQKxYYNGxT+/v4lHe6FsrKyFFpaWor9+/cXub9x48aKr7/+WiGXyxW2traKH374QfVYSkqKQk9PT/Hbb78pFAqF4vr16wpAcfHiRdUxBw4cUEgkEkVMTMwzz5uTk6NITU1V3QrHiIqKUuvrK63AuECFxzoPhcc6D8Xl+Mvlfr6I1AhF803NFR7rPBTLg5eX+/nKqttPJxTOk/crDl2N1XQoxZJbkKtYG7pW0WJzC9Xf6xfHv1A8SH+g6dCKyMzNV7hN+VPhPHm/Iiops1RjnLydoBpj6bE7Lz0+NDFU0XJLS4XHOg/Fe3++p0jPTS/VeYV/5eQXKDotOK5wnrxfMWlHiKbDqRYy8jIUfpv9FB7rPBTnHpxTy5gpmXmKBtMOKJwn71ecup2ouj8pO0lxMPyg4tuz3yr8f/dXvWcU3ppubKoYfWi0YnXoasXVh1cVBbKCUseQnpuu8FznqfBY56F4mPVQHS+rQoREJaveZzYcCSrT5zeg2L17t+rnwrzjxx9/VN2XmppaJO94kW+++UZha2uryM/Pf+mxp0+fVgCKkJCX/3/aokULxYIFCxQKhUJhbGysuHfvnkKhUCgCAgIUtWrVeunzn6fEVxKTkpJwc3MDwNTUlKQk5ZWF1q1bc/LkydJnq89QUFCATCZDX1+/yP0GBgacPn36pQtIAc6dO4e5uTlNmzZVHdO5c2ekUikBAQHPPO+cOXOKrF1o2LChWl9XWTWxacJb7m8B8N2578iXl19Xi3xZPpNOTiKrIIumNk0Z5fnybz+aVtnqJb6MrpYuwzyGse+tfbxd520kSDgQcYA397zJ0uClZOVXXHu7F7kcmYJMrsDeTB8Hi9ItpG5Tx4pv31T29p5/8BYHrz5/Ki00MZQxf48hPS8dX2tflndZjrGucanOK/xr2fF73E3IoKaxLlN61Nd0ONWCkY4RPd3U28/ZzFCHvk0dQZLLgtN/8OPFH3l337u03daWz098zs7bO4lKj0JLooW3lTdjvMawptsazgw4w4quKxjhMYJGNRqhJS39VWJjXWPczJSf91XlauKT08w9ve3pUM+6xGNkZGQQHBxMcHAwoNysEhwcTGRkJBKJhPHjxzNr1iz27t1LaGgoQ4YMwd7ent69e6vG6NSpE7/88kuRceVyOWvXrmXo0KFoaxfdN3zv3j1mzpxJUFAQERER7N27lyFDhtC2bVu8vLxeGnNoaChvvfXWU/dbW1vz8GHp2/qWOEl0c3MjPFy5W6h+/fps374dgH379mFubl7qQJ7FxMSEli1bMnPmTB48eIBMJmPTpk2cO3eO2NjYYi0gjYuLw9q66D8SbW1tLC0tn7vIdMqUKUXWLly/fl2tr0sdJjSZgLmeOXdT7rLx+sZyO8+Sy0u4/ug6prqmzGkzp0xvOBWlsm9eeZ6aBjX5ttW3bHtjG01smpAjy2F5yHJ67unJ/rD9Gi/CXdgqrJlr2eqhDm7hzLDHU2njtwUTGv302toriVcYc3gM6fnpNLZurNrsI5TN3YR0/ndMub7pm56NMDeseiWYKqvCKeey9nPOl+dzKf4Sy4KXcVMyF+N6M7grXcT66+u5maSsZ+lu7s57Dd7jl46/cLr/aTb12MQ433E0s22m9rJahVPOVWVdYuE0c01jXb57/IW0pAIDA/H19cXX1xeAiRMn4uvry/Tpyt3rkyZNYty4cYwZM4ZmzZqRkZHBwYMHi1zQunfv3lPJ2ZEjR4iMjGTEiBFPnVNXV5cjR47QtWtX6tevz2effcbbb7/Nvn37ihWzubk5sbFPf+m+fPkytWrVKvZr/68Sl8AZPnw4ISEhtGvXji+//JKePXvyyy+/kJ+fz8KFC0sdyPNs3LiRESNGUKtWLbS0tGjcuDEDBgwgKChI7ecq9N+t8ZVx17aFvgWfNf2MaWemsSx4Gd1culHLuPT/EJ7lbMxZ1l5bC8CMVjOwNao8tZtepHDzypWoVORyBdIqVtajQY0GrO22lsP3D7MwaCExGTFMOTWF327+xpfNvlS9aVe04tZHLI6przcg4lEmx28lMmrDRf74qDW2Zso32OCEYD448gGZ+ZmqBNFQR3MlIKoLuVzBl7+HkieT07G+NW942Wk6pGrF3cKdZrbNuBh3ke23tvNJ40+K9Ty5Qs7t5NsExAZwPvY8QfFBZBdkqx6XSECeZ4GzoTcft/SnuV3zCm2B6mXlxZ67e7jysPIniVeiU1h+IgyAWb09sDTSJSu55OO0b9/+hV/KJRIJM2bMYMaMGc89JiIi4qn7unbt+txxHR0dOXHiRIljLdS/f38mT57Mjh07kEgkyOVyzpw5w+eff86QIUNKPW6Jk8QJE/7trtG5c2du3rxJUFAQ7u7uxbokWlK1a9fmxIkTZGZmkpaWhp2dHf369cPNza3IAlI7u3/f8OLj4/Hx8QGUi0wLd/kUKigoICkpqVIVrCyNXrV78cfdPwiMD+T7gO/5peMvattx/Cj7EV+d/gqAvnX70sm5k1rGrQh1bYzR15GSnltA2MNM3K2r3hSlRCKhq0tX2jm2Y8O1DawMXcmVxCsM/GsgPd168mnjT1+66Fyd8mVyLkemAOpJErW1pCwZ4Mvby85yOz6DkesvsuODltxOuapKEJvaNGVpp6UiQVST3y5GEng/GUNdLWb2Fq33ykP/ev25GHeR3+/8zljvseho6Tx1jEKhIDo9mvNx5wmIDeBC7AWSc4tmMhZ6FjS3a04LuxYYyurz4fr73NWW0rxXR2oaVGxR68IdztceKkvmSCVlqpxXbv47zdzd49X6EvT999/z0Ucf4ejoiEwmo2HDhshkMgYOHMjUqVNLPW6Jk8Qn5eTk4OzsjLOzc1mGKRYjIyOMjIxITk7m0KFDzJ8/H1dXV2xtbTl69KgqKUxLSyMgIICxY8cC0LJlS1JSUggKCqJJkyYA/PPPP8jlctXOoapKIpEwrcU03t73NiejT3I08iidnTu//IkvIVfImXpmKo9yHuFu7s4Xzb5QQ7QVR1tLimctMy5GJBMclVIlk8RCelp6jPYaTS93ZT3Fvff2si9sH0cijzDKcxRDGg5BX1v/5QOV0dWYVLLzZZgZ6FBHTb9PE30dVg9tRu+lZ7j2II2RW3cQprWIrIIsmtk245eOv4gEUU3i03KY+5dyqvLzrvWoJVrvlYsOTh2wNrAmITuBw/cP08OtBwAPsx9yIfYC52OVieGDzAdFnmegbUBTm6b42fnRwq4FdSzqqJIxhUKBt2MqIVEpbDx3nwld6lboa6ptXhsDbQMy8jMITw2ntnntCj1/caljmrmqUigUxMXFsXjxYqZPn05oaCgZGRn4+vpSp06dMo1d4q8EMpmMmTNnUqtWLYyNjQkLU17anTZtGqtXq7+v8KFDhzh48CDh4eEcPnyYDh06UL9+fYYPH16sBaQNGjSge/fujB49mgsXLnDmzBk+/vhj+vfv/1SNoqrIzdyN4Y2GAzDnwhy1FGXefGMzp2NOoytV9h2uiCRE3apCvcSSsDa0Znbr2fz2+m/4WPmQXZDNkstL6LWnF4ciDpX7esXACOWVjmYuFmqdvne0NGTFkCboGd8nVPYjWQVZ+Nn6iSuIavbNH9dIzy3A29FcVVpFUL8n+zmvubqGeRfm8dYfb9Fhewcmn5rM7ru7eZD5AG2pNo2tG/Oh94ds8N/AmQFn+F/n/zG00VDqWdYrcrVOIpEw+nFx7U3n75OTX/ri2qWhLdWmYQ3l5s3Kui7xWdPMrxKFQoG7uzvR0dE4OjrSo0cP+vbtW+YEEUqRJM6ePZt169Yxf/58dHX//Yvw8PBg1apVZQ7ov1JTU/noo4+oX78+Q4YMoXXr1hw6dAgdHeVl/OIsIN28eTP169enU6dO9OjRg9atW7NixQq1x6opY7zG4GjiSEJWAr9c/uXlT3iBG49uqOovftHsC+paVOy3VnUp3LxSVXY4F5dHTQ82+G9gXpt52Bja8CDzAZ+f+JxhB4dx49GNcjvvBTWuR/wvhV4Yxs5rkUjzKMh0p6PFZAy0xZUudTl0LY6D1+KUrff6iNZ75e2duu+gLdHmVvItNt3YxN2Uu0iQ0MCyAcMaDWNZ52Wc6X+G9f7rGeszFl9rX3SkT09LP6l7I1tqmRvwKDOP3ZdjKuiV/Ktwyrky7nB+1aeZAaRSKXXq1OHRo0dqH1uiKOElCHd3d3799Vc6deqEiYkJISEhuLm5cfPmTVq2bElycilWiVZyhdl5VFQUDg6Vs8/u2ZizvH/kfaQSKb+9/pvqm19JZOVn0W9/PyLSIujg2IGfO/xcZdctRSVl0Wb+MXS0JIR+261aFgvOLshm3dV1rLm6hhxZDhIk9HbvzSeNP1Hrwna5XEGTWYdJzspn14etaOykvs5KF+Mu8tHRj8guyMZe14tbV95FR6rLppF++FWy3ttVUXpOPl0WniQuLYex7WszubsoeVMRfg35lb/v/423lTct7FrQ3LY55vrmZRpz1akwZv15A3drY/4e37ZCN+QduX+ECccnUN+yPjt67qiw8xbHD4dusvTYPWoa6/L3hHZPXUWsCp/f6rBv3z7mz5/PsmXL8PDwUNu4Jb6SGBMTg7v70y2c5HI5+fnlV69PeLFWtVrh7+KPXCFnxrkZyOQln5KYd3EeEWkRWBtaM6PVjCqbIAI4WBhQ01iXfJmC67GVb3e6OhhoGzDWZyz73tpHD9ceKFCw++5u3tj9BmuuriFPlqeW89xLzCA5Kx99HSke9mYvf0IxPZkgtrJvxe63V/G6hxP5MgUfbAri/qOq0c+6Mpt/8BZxaTm41DDk005ln3oSiud97/f5/c3fmd5yOl1dupY5QQTo18wRYz1t7iZkcOJOYtmDLAHPmsqKCreTb1eauq0gppmfNGTIEC5cuIC3tzcGBgZYWloWuZVWiTeuNGzYkFOnTj21WWXnzp2qmkKCZnzR7AtOxZzi2qNrbLu1jYENBhb7uQcjDrLrzi4kSJjTeo5a3tQ0SSKR4O1gztGbCQRHpqj16ldlY2tky7y28xhQfwDzLszj6qOr/BT0Eztv7+Szpp/R0bFjmRL+wqlmH0dzdLXVs7MxIDaAj49+TI4sh9dqvcbPHX5GT0uPH9/1Jjo5i5DoVEasu8iuD1/DzODFU3HCswXdT2JTwH1AtN6rDkz0dejfzJFVp8NZdSqsVEWiS8vGyAZrQ2sSshK4/ug6TW2bvvxJ5UxMMxe1aNGichm3xEni9OnTGTp0KDExMcjlcnbt2sWtW7fYsGED+/fvL48YhWKyMrTi08afMjtgNosvL6azc2esDV/+RhKTEcOMs8p6T6M8R9Hcrnl5h1ohvB2VSWJ1W5f4PD7WPmx+fTP7w/azKGgRUelRjD82Hj9bPyY1n1Tq9aUXHxfRbq6m9YjnY88z7ug4cmQ5tK7VmkUdFqGnpSzrYaCrxcohTem19Az3EjP5eMsl1gxrho5W5Sy7UVnlFcj58vdQFAp4p4kDrdwrrq6eUH6GvebC2rMRnLn7iOsP0mhob1ph5/aq6cWRyCOEPgytFEniq7yb+VmGDh1aLuOW+J23V69e7Nu3jyNHjmBkZMT06dO5ceMG+/bto0uXLuURo1AC79Z9F8+anmTmZzL/4vyXHl8gL+DLk1+Snp+Ol5UXY33GVkCUFaO67XAuDqlEypu132T/W/sZ7TkaXakuAXEBvLvvXWaem0lSTlKJx7xYuLO5jJ1WAM49OKe6gtjWoa3qCuKTrE31WTW0KYa6Wpy685Dv9l3TeLeZqmb5iXvcScighpEuX/dooOlwBDVxsDDE30NZ33fV6bAKPXdhEf/KsHlFTDM/LTIy8oW30irV1/M2bdpw+PBhEhISyMrK4vTp03Tt2rXUQQjqoyXVYlqLaUglUg5FHOJ0zOkXHr88ZDnBicEY6xgzr828l+6yq0oKezhHPMoiOVM96/OqCkMdQz5p/Al/9P6Drs5dkSvkbL+9nTd2vcGGaxvIlxVv/XBMSjYxKdloSSVlnrI/G3OWcf+MI1eWSzuHdvzU/qfnthBrZG/Gon4+SCSw6Xwk689GlOncr5K7CRn88s9dAKb3bIiF+ACtVka1UfZS3hfygPi0nAo7b+EOZ02XwRHTzM/m4uKCq6vrc2+lVeIkcejQoZw8ebLUJxTKX4MaDRjUYBAAs87PKtLi6UkX4y6yMnQlANNaTMPBpHrt/DIz1MGtprLf76sy5fxfDiYOLGi/gLXd1lLfsj7p+en8EPgDffb24WT0yZdeoQt8vB6xkb0pRnqlr71/JuaMKkFs79iehe0XvrTHbNdGtkzxV+7GnbH/OsduJbzweEG5E/2rXcrWe+3rWfGmd9WvBSsU5eNoTjMXC/JlCjaci6iw8zas0RAtiRbxWfHEZ8ZX2Hn/S0wzP9vly5e5dOmS6hYQEMDy5cupW7cuO3aUfkd6iZPE1NRUOnfuTJ06dfj++++Jian4mk3Cy33s8zE2hjbEZMSw4srTNSFTc1OZcmoKcoWcXrV7qToDVDeqeolRqZoNRMOa2jZl6+tb+bblt1jqWxKRFsFHRz9i7JGxhKU8f9rqQnjZ6yOeij7FJ/98Qp48jw6OHVjY7uUJYqHRbdzo19QRuQLGbbnMrbj0UsfxKtgWGMWFiCQMdLSY2Uu03quuRrZWXk3cdD6SrLyCCjmnoY4h7ubKyiaamnIuOs3sKaaZn+Dt7V3k1rRpU0aPHs2PP/7I4sWLSz1uiZPEPXv2EBMTw9ixY9m2bRsuLi74+/uzc+dOUQKnEjHUMWSK3xQA1l1dx93ku6rHFAoF35z9hviseJxNnfnK7ytNhVnuvB2UJVuCo6pf/c6S0pJq8Xbdt/nzrT8Z3mg42lJtzjw4Q5+9fZgTMIfU3KcT6YtlLKJ9Mvoknx77lDx5Hp2cOrGg3YJn9rN9HolEwszeHvi5WpKRW8CIdRd5mJFbqliqu4S0HL7/S1lQ/bOudXG0FB1rqqsuDW1wrmFIanY+vwdFV9h5C9clXnlY8VPOT08z21Z4DFVRvXr1uHjxYqmfX6o1iVZWVkycOJGQkBACAgJwd3dn8ODB2NvbM2HCBO7cuVPqgAT16ejYkfYO7SlQFDDz/EzkCjkAO27v4GjkUbSl2sxvO79atz/zebyOLiQ6VWx+eMxY15iJTSfyR68/6ODYAZlCxpabW3h99+tsubGFArnyykRyZh634zMAaOpS8vWIJ6JOMP7YePLl+XRx7sIP7X4oUYJYSFdbyvL3muBSw5CYlGzGbAis8NZklV1KVh6Tf79Cek4BXg5mDH+t9GuQhMpPSyphxOO/49Wnw5HJK+a9TdV5JbHirySKaeYXS0tLK3JLTU3l5s2bTJ06tUzt+cpUVyI2NpbDhw9z+PBhtLS06NGjB6GhoTRs2JCffvqpLEMLaiCRSJjiNwUDbQMuJVzij7t/cDf5rmrX8/jG40vVmaUqaWBngo6WhKTMPKKTn70281XlZOrE4o6LWdl1Je7m7qTmpjLnwhze2fsOZ2POEnhfefXVzcqImsZ6LxmtqONRxxl//N8EcV7bsm2KsjDSZfWwZpjqa3MpMoUvf78ikn4gNSufhX/fovW8Yxy7lYiWVMIc0XrvlfBOEwdM9bWJeJTF0RsVs0bQy0qZJF57dE31ZbIiiGnmlzM3N8fCwkJ1s7S0pGHDhpw7d45ly5aVetwSr0TPz89n7969rF27lr///hsvLy/Gjx/PwIEDMTVV1mzavXs3I0aMYMKECaUOTFAPe2N7PvT+kAVBC1gQtAArAytyZbm8Zv8agxsO1nR45U5PW4uGdqaERKdyOSpFTME9Qwu7FuzouYPfb//OL8G/cC/1Hu8feR973cZIdDvQ3MWxROP9E/kPn534jAJ5Ad1cujGnzRy17JqvbWXMsveaMHTNBfYEP6C2lTHjXtEuImk5+aw9HcGq02Gk5yg/rBvYmfJ1jwY0UmNXHKHyMtLTZlALZ5Ydv8eq0+F0bVT+06+uZq4Y6xiTkZ/BvZR71LOsV+7nFNPMxXPs2LEiP0ulUqysrHB3d0dbu/SbDkv8TDs7O+RyOQMGDODChQv4+Pg8dUyHDh0wNzcvdVCCeg1qOIh9Yfu4nXyb1NxULPUtmdV6FlLJq1Gg2MfRnJDoVEKiUsRuz+fQlmrTr34/urt2Z3nIcrbe3MqDvEsYuYWQpNeLtDwXTHVfXrj36P2jfH7icwoUBfi7+PN9m+/Rlpb+Deq/XnOvyYxeHny1O5QFh2/jamXEG16vzt9pRm4B689GsOJkGKnZyjXg9WxMmNClDl0b2lZoP19B84a2dGHlyTAuhCdxJToFr8dlv8qLVCKlUc1GBMQGcOXhlQpJEsU0c/FIJBJatWr1VEJYUFDAyZMnadu2banGLXGW8NNPP/HgwQOWLl36zAQRlJc9w8PDSxWQoH46Uh2mtZiGBOUHyOzWs6lp8Op0YCjc4Rz8ChXVLi0zPTMmN5/Mlh47kGXURyKRce7RLnru7sn2W9tf2BP8yP0j/yaIrupPEAsN9HNiZGvleqzPtoe8En+vmbkFLDt+jzbz/uGHQ7dIzc7H3dqYpQMbc+DTNnT3sBMJ4ivI1kxf9cV31amK+cytyHWJYpq5+Dp06EBS0tPNElJTU+nQoUOpxy12kujk5MTHH3+MjY1NmS5dCprhY+3DL51+YUnHJbSu1VrT4VSowiTxakwq+TK5ZoOpIpJTzcmKGobew/dxNXMlKSeJmedn0m9/Py7GPb1T7vD9w6oEsYdrD75vXT4JYqGvejSgY31rcgvkjN4QyIOU6rneNDtPxsqTYbSdf4x5B2+SnJWPW00jfu7vw6HxbXndSySHr7oRj78w/RkaS0wF/H/gWbNiOq+IaeaSUSgUzyx59ejRI4yMjEo9brHfxTdu3MjevXv56KOPSExMpFu3brz55pu8/vrrYmq5imjrULrLzVWdaw0jTPW1Scsp4FZcOh61xJqtl7kYrty00tL+NRa++T7bb21nafBSbiXfYsShEXR26szEphNxNHHkUMQhJp+cjEwh4w23N5j12iy0pFrlGp+WVMLiAb68s+wsN+PSGbk+kJ0ftCxTwe/KJCdfxpaASP53/J6q5I9zDUM+7VSHN73t0Ra9rIXHPGqZ0dKtBufCHrH+bARflXMbxsIyOPdS7pGRl4GxrnG5nEdMMxdPnz59AOV087Bhw9DT+3eToUwm48qVK7Rq1arU4xf7naZdu3YsWLCAO3fucObMGXx8fFiyZAm2trZ07NiRRYsWERZWsb0kBaE4pFKJmHIuoX/rI1qgI9VhUINB/PnWn/Sr1w+pRMqRyCP02tOLr059pUoQe7r1rJAEsZCxnjarhjalprEuN2LT+HRrcIWVAikvuQUyNpyLoN0Px5ix/zoPM3JxsDBg/jteHJ3Yjj6NHUSCKDxldFvl1cTfAiLJyC3fXcc1DWpSy7gWChRcfXS1XM4hppmLz8zMDDMzMxQKBSYmJqqfzczMsLW1ZcyYMWzatKnU45fqa3ejRo1o1KgRU6ZMITY2lv3797N3716++uor3NzcmDdvHq+//nqpgxIEdfNxNOfUnYeERKXwXgtnTYdTqRXI5FyKVF5JfLKItoW+BVNbTKVfvX7Mvzif87Hn2Re2D4A3a7/JjFYzKixBLORgYciKIU3pv+I8R27EM+/gzXK/klIe8grkbA+MYumxu8SmKvvx1jI34OOO7rzTxAEdkRgKL9C+rjVuVkaEJWay7WKUas1uefGs6UlMRgyhiaG0sGuh1rHFNHPJrF27FlD2bv7888/LNLX8LGV+57Gzs2P06NHs27ePhw8fMnPmzCKXOwWhMvB+vOvvVe3hXBLXY9PIypNhqq9NPRuTpx6vY1GHFV1WsLjDYrxqejG44WCNJIiFGjtZ8OO73gCsOBnGtouRGomjNPJlcrZeiKTDj8eZuucqsak52JrqM7O3B/983o4BzZ1Egii8lFQqYdTjVn1rz4RTUM5rrwvXJZZH5xUxzVw633zzjdoTRCjllUSAhIQEEhISkMuL/mN86623yhyUIKhb4XTznYQM0nPyMdEve92+6qqwX3NTF8vnboqQSCR0cOpAB6fS75pTpze97QlLzGDRkTt8vfsqTpZGtKxdQ9NhPVeBTM7uyzEs+ecukUlZAFib6PFRB3f6NXNEX0czCbdQdfVpXIsfDt0kOjmbQ9fied3LrtzOVVhUOzQx9LkbJkpDTDOXzc6dO9m+fTuRkZHk5eUVeezSpUulGrPEX1GDgoLw8PDAzs4OLy8vfHx8VDdfX99SBSEI5c3KRI9a5gYoFBAa83SPYuFfZe3XrCmFmzoK5Ao+2BRE+MNMTYf0FJlcwe7L0XReeIIvdl4hMimLmsa6THujIScndWBoKxeRIAqloq+jxeDHS2lWnS7f/QH1LeujLdHmUc4jYjNj1TKmmGYum8WLFzN8+HBsbGy4fPkyzZs3p0aNGoSFheHv71/qcUucJI4YMYK6dety9uxZwsLCCA8PV93ExhWhMvN5fDUxJEokic+jUCgIjFCuR2zuWvJ+zZokkUiY/44XPo7mpGbnM3LdRVKy8l7+xAogkyvYG/KArj+dYMK2ECIeZWFppMtXPepzclIHRrZ2FcmhUGaDW7qgqyXlcmQKQY/bapYHfW19VSFtdU05i2nmsvnf//7HihUrWLJkCbq6ukyaNInDhw/zySefkJpa+s+8EieJYWFhzJ8/Hz8/P1xcXHB2di5yE4TKyttRWfomOKr83jyrunuJmTzKzENPW1olSwXp62ixYkgTapkbEPYwkw83X9JobUy5XMFfobH4/3yST367zL3ETMwNdZjUvR6nJnVgTNvaGOpWj7I9guZZmejR27ewuHb5XrRRrUtMLHuSKKaZyy4yMlJV6sbAwID09HQABg8ezG+//VbqcUucJHbq1ImQkJBSn1AQNMXHUXllTFxJfL7CqWZvR3P0tKvmlS1rE31WDW2Kka4WZ+89Yvof11AoKrY0jkKh4ODVOHosPsWHmy9xOz4DU31tPu9al1OTOvBhe/dqU9NRqFxGPt7AcuhaHJGPssrtPE+uSyyLJ6eZ3xTTzKVma2ur6rji5OTE+fPnAQgPDy/T+1+J36VWrVrF0KFDuXr1Kh4eHujoFN0A8Oabb5Y6GEEoTx61TJFKIC4th7jUHGzN9DUdUqVTmCQ2r2LrEf+rgZ0pSwb6Mmp9IL9diKS2lRGj2riV+3kVCgVHbyTw05HbXHuQBoCJnjYj27gy/DVXzAzEhimhfNWzNaFtXStO3k5kzZlwvi2nqdvCK4k3km6QL89HR1q6f9s/H/l3mrm8Yn0VdOzYkb179+Lr68vw4cOZMGECO3fuJDAwUFVwuzRKnCSeO3eOM2fOcODAgacek0gkyGTP7+0qCJpkqKtNXRsTbsalExyVQncz8Y31v1SbVlyrdpII0LG+DV/1aMCsP28w+68buNQwonNDm3I5l0Kh4PjtRH46fJsr0cor1Ua6Woxo7cqo1m6YGYrkUKg4o1q7cvJ2ItsDo5jQpW65fDlxNnXGVNeUtLw0biffplGNkid4IVEpLD9xDxDTzGW1YsUKVbWZjz76iBo1anD27FnefPNN3n///VKPW+Lp5nHjxvHee+8RGxuLXC4vchMJolDZ+TqZA6Je4rPEpeYQlZSNVAKNH/+eqrqRrV0Z0NwJhQI+3XqZG7Fpah1foVBw8nYifZadZfjai1yJTsVQV4ux7WtzanJHPutaTySIQoVrU6cm9WxMyMqTsfVC+dQNlUgkqhZ9pZlyzi2Q8cXOEOQKxDRzGRUUFDBr1izi4uJU9/Xv35/Fixczbtw4dHVLn3yXOEl89OgREyZMwMamfL6RC0J5KiyqHRyZotE4KqMLj68iNrQ3rTZ1JCUSCTN6NaJV7Rpk5skYtT6QhPQctYx99u5D3l1+jiFrLnA5MgV9HSlj2rpxclIHJnevL66KCBojkUgY2UbZdWXd2Yhy27zlVVO5LrE0m1cq8zTzyZMn6dmzJ/b29kgkEvbs2VPkcYVCwfTp07Gzs8PAwIDOnTtz586dF4757bffIpFIitzq169f5JicnBzVVUBjY2Pefvtt4uPjXxqvtrY28+fPp6BA/S0ZS5wk9unTh2PHjqk9EEGoCIVFtUNjUqt8n191uxheNesjvoyOlpRlg5rgVtOImJRsxmwIIie/9LMeAWGP6PfrOQauCiDwfjJ62lJGvObKyUkd+KpHA2oai45Tgub18rGnprEesak5/BWqnlqG/1W4LjH0YcmuJFb2aebMzEy8vb1ZunTpMx+fP38+ixcvZvny5QQEBGBkZES3bt3IyXnxF9BGjRoRGxurup0+fbrI4xMmTGDfvn3s2LGDEydO8ODBg2KvJ+zUqRMnTpwo3gssgRKvSaxbty5Tpkzh9OnTeHp6PrVx5ZNPPlFbcIKgbnVtTDDU1SIjt4CwxAzqPKPt3KuqqhbRLg4zQx1WD2tG76VnCI5K4fMdISwZ4FuiThFB95NYePg2Z+4+AkBXS8pAPyfGtq+NjanYBCVULnraWgxt6cyCw7dZeSqMN73t1dYZpVBhkhiRFkFqbipmei8vm6Wpaeb09HTS0v5dbqKnp/fcFsL+/v7PLUCtUChYtGgRU6dOpVevXgBs2LABGxsb9uzZQ//+/Z8bg7a2Nra2z369qamprF69mi1bttCxY0dA2Ze5QYMGnD9/nhYtXtwj29/fny+//JLQ0FCaNGnyVIu+0m4qLtXuZmNjY06cOPFU1iqRSESSKFRqWlIJHrXMuBCexOWoFJEkPpaalc+teGVdreqYJAK41jRi+XtNGLw6gP1XYqltZcyELnVf+rzLkcn8dOQOJ28nAqCjJaFfM0c+bO+OvblBeYctCKU2qIUzS4/f5WpMGgHhSbRwU2+rSnN9c5xMnIhMj+Tqw6u8Vuu1lz5HU9PMDRs2LPLzN998w7ffflviccLDw4mLi6Nz586q+8zMzPDz8+PcuXMvTBLv3LmDvb09+vr6tGzZkjlz5uDk5AQou9nl5+cXGbd+/fo4OTlx7ty5lyaJH374IQALFy586rGybCoucZIYHh5eqhMJQmXh62jOhfAkQqJS6NvUUdPhVApBkUkoFMpEysqk+k6Xtqxdg9lveTD591B+PnoHNysjevnUeuaxodGp/HTkNv/cTABAWyrh3aYOfNTBHQcLw4oMWxBKxdJIl7cbO7A5IJJVp8LVniQCeFp5EpkeyZWHV16aJGpymvn69evUqvXv/+vPu4r4MoWbQ/67L8PGxqbIxpH/8vPzY926ddSrV4/Y2Fi+++472rRpw9WrVzExMSEuLg5dXV3Mzc1LNG6hwp3N6iaquQqvnMJ1iWKH878uhCu70DRzqVqt+EqjXzMnwhIz+fVkGF/svIKjpSGNnf593dcepPLT4TscuaFcMK4lldDHtxbjOtbBqYZIDoWqZURrVzYHRHL0ZjxhiRm4WRmrdXyvml78GfbnSzevaHo3s4mJCaamphV6zic9OX3t5eWFn58fzs7ObN++nZEjR6r1XDk5Oejrq2cJTImTRIVCwc6dOzl27BgJCQlPZa+7du1SS2CCUF4Kk8Sbsenk5MtEz1yq93rEZ5nUvT73EjM5ciOeMRsC2fPRa2TkFrDo8B0OXlN+a5dKoLdPLcZ1qoNrTaOXjCgIlVNtK2M61bfm6M0E1pwJZ1ZvT7WOX9h55erDqygUiueue6zMu5lLonBNYXx8PHZ2dqr74+Pj8fHxKfY45ubm1K1bl7t376rGzcvLIyUlpcjVxPj4+OeuY3ySTCbj+++/Z/ny5cTHx3P79m3c3NyYNm0aLi4upU5ES7y7efz48QwePJjw8HCMjY0xMzMrchOEys7eTB8rEz0K5AquPRAt+nLyZVx5fFW1eTUool0cWlIJP/f3oYGdKQ8z8njzlzN0X3SKg9fikEiUO0MPT2zHwn4+IkEUqrzCbkM7g6JJzsxT69j1LOqhK9UlJTeFqPSoZx5T2Xczl4Srqyu2trYcPXpUdV9aWhoBAQG0bNmy2ONkZGRw7949VaLZpEkTdHR0iox769YtIiMjizXu7NmzWbduHfPnzy9SF9HDw4NVq1YVO67/KvGVxI0bN7Jr1y569OhR6pMKgiZJJBK8Hcw5ciOey5EpNHF+NRKj5wmOSiFfpsDaRA8ny1dnOtVIT5vVQ5vSa+kZEtNzAXjdy47xneqIDU1CtdLCzZJG9qZce5DG5oD7fNyxjtrG1tHSoX6N+lxJvMKVh1dwMnUq8rimp5lLIyMjQ3WFD5R7MYKDg7G0tMTJyYnx48cza9Ys6tSpg6urK9OmTcPe3p7evXurntOpUyfeeustPv74YwA+//xzevbsibOzMw8ePOCbb75BS0uLAQMGAMrNLyNHjmTixIlYWlpiamrKuHHjaNmy5Us3rYByh/WKFSvo1KkTH3zwgep+b29vbt68WerfRYmTRDMzM9zcyr8HqiCUJx9HM47ciCckWlxJfLI+orpLZFR29uYG/Dbaj12XYujpbU8DO82tWRKE8iKRSBjdxo3x24JZf+4+o9u6oaetvmU2XjW9uJJ4hdDEUN5we6PIY09OM39XRaaZAwMD6dChg+rniRMnAjB06FDWrVvHpEmTyMzMZMyYMaSkpNC6dWsOHjxYZB3gvXv3ePjwoern6OhoBgwYwKNHj7CysqJ169acP38eKysr1TE//fQTUqmUt99+m9zcXLp168b//ve/YsUcExODu7v7U/fL5XLy8/NL/DsoVOIk8dtvv+W7775jzZo1GBiI8g9C1eTjqNyoEBKVotlAKoGL91+dTSvP4m5twqTu9V9+oCBUYT087Zh74CZxaTnsDX7Au2qs7OBl5QU3nu688t9pZosqMs3cvn17FIrnN1uQSCTMmDGDGTNmPPeYiIiIIj9v3br1pefV19dn6dKlzy3i/SINGzbk1KlTODs7F7l/586d+Pr6lni8QiVOEvv27ctvv/2GtbU1Li4uTxXTvnTpUqmDEYSK4umgXD8bmZTFo4xcaryiXTJkcgWXCpPEV2Q9oiC8inS1pQxt5cK8gzdZfTqcd5o4qG3moLCo9s3km+TKctHT0quS08xV2fTp0xk6dCgxMTHI5XJ27drFrVu32LBhA/v37y/1uCVOEocOHUpQUBDvvfceNjY2r9z0lFA9mBno4GZlRFhiJleiU+lQ31rTIWnEjdg0MnILMNHTpr6tmGoVhOpsYHMnlvxzh5tx6Zy5+4jWdWqqZdxaxrWw1LckKSeJm0k38bbyrpLTzFVZr1692LdvHzNmzMDIyIjp06fTuHFj9u3bR5cuXUo9bomTxD///JNDhw7RunXrUp9UECoDH0dzwhIzCY5KeWWTxAuP1yM2cbFASyq+8AlCdWZmqEPfpo6sOxvBylNhaksSJRIJnjU9ORF9gtDEUMhxrpLTzFVdmzZtOHz4sFrHLHEJHEdHR40WpBQEdfF5XC8x+BVel/iq1UcUhFfdiNdckUjgxO1Ebj9uxakOhVPOwQkhYppZgwIDA9m4cSMbN24kKCiozOOVOElcsGABkyZNempRpiBUNd4O5oCy88qLFilXVwqFQpUkvir1EQXhVedUw5BuDZWJ2+pT6muzW1hU+0z0ZTHNrAHR0dG0adOG5s2b8+mnn/Lpp5/SrFkzWrduTXR0dKnHLXGS+N5773Hs2DFq166NiYkJlpaWRW6CUFU0sDNFV0tKSlY+kUlZmg6nwoU/zORhRh66WlI8a4lC+ILwqhjVxhWA3cExqhqhZeVR0wMJEjJk8Ui0MsQ0cwUbNWoU+fn53Lhxg6SkJJKSkrhx4wZyuZxRo0aVetwSr0lctGhRqU8mCJWJrraUhvamBEelEByVgnONV6uzRmCEclezt6OZaE0oCK+QJs4W+DiaExyVwqbz95nQpW6Zx9SVGiItsEGmHUfLhplimrmCnThxgrNnz1KvXj3VffXq1WPJkiW0adOm1OOWanezIFQXhW+UwVEp9PKppelwKtQFsR5REF5JEomEUW1c+XjLZTaev8/Y9rXL/EXx5yN3yMmohY55HL7uaWqKVCguR0fHZxbNlslk2Nvbl3rcYk03Z2ZmlmjQkh4vCJryKm9eUW1aEesRBeGV072RLbXMDUjKzGP35ZgyjVVYNFuWrSzQfSf1ujpCFErghx9+YNy4cQQGBqruCwwM5NNPP+XHH38s9bjFShLd3d2ZO3cusbGxzz1GoVBw+PBh/P39Wbx4cakDEoSK5P04Sbz2II28Arlmg6lACWk53H+UhUSinHoSBOHVoq0lZfhrLgCsOhWGXF66zXu5BTI+36HczdzasQkAVx9eRa54dd5PK4Nhw4YRHByMn58fenp66Onp4efnx6VLlxgxYkSp944Ua7r5+PHjfPXVV3z77bd4e3vTtGlT7O3t0dfXJzk5mevXr3Pu3Dm0tbWZMmUK77//fqlepCBUNJcahpgZ6JCanc+tuHRVJ5bqrnCquYGtKab6Oi85WhCE6qhfM0d+PnKHe4mZnLidWKp6sT8fucOdBOVu5vlvduD1vd+Tnp9ORGoEbuZu5RC18CzltV+kWElivXr1+P3334mMjGTHjh2cOnWKs2fPkp2dTc2aNfH19WXlypX4+/ujpSUWwAtVh0QiwdvRnJO3EwmOSn5lksSL4aL0jSC86kz0dejf3JGVp8JZdTqsxEnif3szW5kY0sCyAZcSLnHl4RWRJFag8tovUqKNK05OTnz22Wd89tln5RKMIGiCj4PZ4yQxlcEtNR1Nxbj4eGdzUxcx1SwIr7KhrVxYcyaCM3cfce1BKo3si/dF+clp5ieLZntZeXEp4RKhiaH0du9djpELz5KQkEBCQgJyedHpfi8vr1KNV+LdzYJQ3fg4mQPKotqvgrScfG7EKXcfNhc7mwXhleZgYYi/hy37r8Sy+nQ4C/v6FOt5/04z6xUpml3YeSX0YWh5hCs8R1BQEEOHDuXGjRtPNYeQSCTIZLJSjVviYtqCUN14Pe68ci8xg7Scp0sIVDdB95NRKMC5hiHWpvqaDkcQBA0b1UY5Lbwv5AHxaTkvPf7JaebZb3kUKZpd2HnldvJtsguyyyFa4VlGjBhB3bp1OXv2LGFhYYSHh6tuYWFhpR5XJInCK6+msR4OFgYoFBAanarpcMpd4XpEUR9REARQlgJr5mJBvkzB+rMRLzz2v9PM3RoVLZptY2iDtYE1MoWM649EKZyKEhYWxvz58/Hz88PFxQVnZ+cit9ISSaIg8GrVS1T1axZJoiAIjxVeTdwcEElWXsFzj3veNHMhiUSCp9XjKedEMeVcUTp16kRISIjax63USaJMJmPatGm4urpiYGBA7dq1mTlzZpH5doVCwfTp07Gzs8PAwIDOnTtz586dIuMkJSUxaNAgTE1NMTc3Z+TIkWRkZFT0yxEqsVclSczJlxESpbxaKopoC4JQqHMDG5xrGJKanc/OoOhnHvOiaeYnFa5LvPLwSvkEKzxl1apVrFmzhu+++47ff/+dvXv3FrmVVqk2riQnJ7N69Wpu3LgBQIMGDVTFGtVp3rx5LFu2jPXr19OoUSMCAwMZPnw4ZmZmfPLJJwDMnz+fxYsXs379elxdXZk2bRrdunXj+vXr6Osr11sNGjSI2NhYDh8+TH5+PsOHD2fMmDFs2bJFrfEKVdeTSaJCoUAikWg2oHJyJTqVPJmcmsZ6uNQw1HQ4giBUElpSCSNec+WbvddYczqcQX7OaEn/fR982TTzkwrXJYrNKxXn3LlznDlzhgMHDjz1WIVuXDl58iSurq4sXryY5ORkkpOTWbJkCa6urpw8ebJUQTzP2bNn6dWrF6+//jouLi688847dO3alQsXLgDKq4iLFi1i6tSp9OrVCy8vLzZs2MCDBw/Ys2cPADdu3ODgwYOsWrUKPz8/WrduzZIlS9i6dSsPHjx45nlzc3NJS0tT3dLT09X6uoTKp5G9GVpSCYnpucSmvnzhdlWlasXnYlFtE2FBEErn3aYOmBnoEPEoiyM34os89rJp5ic1qtEIqURKXGYcCVkJ5Rmy8Ni4ceN47733iI2NRS6XF7mVNkGEUiSJH330EX379iU8PJxdu3axa9cuwsLC6N+/Px999FGpA3mWVq1acfToUW7fvg1ASEgIp0+fxt/fH4Dw8HDi4uLo3Lmz6jlmZmb4+flx7tw5QJldm5ub07RpU9UxnTt3RiqVEhAQ8MzzzpkzBzMzM9WtYcOGan1dQuVjoKtFPRsTQDmlUl39mySKqWZBEIoy1NVmoJ8TAKtPhavuL+40s2ocHUPczd0BsS6xojx69IgJEyZgY2Oj1nFLnCTevXuXzz77rEhnFS0tLSZOnMjdu3fVGtyXX35J//79qV+/Pjo6Ovj6+jJ+/HgGDRoEQFxcHMBTvxQbGxvVY3FxcVhbF60ir62tjaWlpeqY/5oyZQqpqamq2/XrYofWq6CwXmJwNa2XKJMrCHpcRFt0WhEE4VmGtXJBR0vChYgkQqJSSjTN/CSxLrFi9enTh2PHjql93BKvSWzcuDE3btygXr16Re6/ceMG3t7eagsMYPv27WzevJktW7bQqFEjgoODGT9+PPb29uXWggZQNcculJaWVm7nEioPHwdztgREEhyZoulQysXNuDTScwsw1tOmgZ2ppsMRBKESsjHVp6eXPbsux7DqdDiOFgbFnmZ+kpeVF7/f+V2sS6wgdevWZcqUKZw+fRpPT090dHSKPF64j6OkSpwkfvLJJ3z66afcvXuXFi1aAHD+/HmWLl3K3LlzuXLl328NpW0DU+iLL75QXU0E8PT05P79+8yZM4ehQ4dia6v8RhMfH4+dnZ3qefHx8fj4+ABga2tLQkLRNREFBQUkJSWpni8IAN6PN6+ExqQikyuKLNquDgrrIzZ2tqh2r00QBPUZ2caVXZdj+Cs0VlVNpDjTzE8qvJJ47eE1ZHIZWlKtlzxDKItVq1ZhbGzMiRMnOHHiRJHHJBJJxSWJAwYMAGDSpEnPfEwikah2h5ZlsSRAVlYWUmnRGXEtLS1VT0JXV1dsbW05evSoKilMS0sjICCAsWPHAtCyZUtSUlIICgqiSZMmAPzzzz/I5XL8/PzKFJ9QvbhbG2Okq0Vmnoy7CRnUszXRdEhqVdivubno1ywIwgv8v707j4uq3B84/plhB9mUHRVxX0BwTytvpknapplbmqZlN1PTtM3KJfNq1/qVerPMUsxSS8u61S1NLS3T3FFwXzAEWURk35nz++M4gwgqywwzA9/36zUvnXOeeeY5HJ358izfp0OAO71aNGL3uStA1YaZ9Zq7N8fZ1pnc4lzOZZyjtWdrUzRVXBMbG3v7QtVQ5SDRVA2pyEMPPcS//vUvmjZtSocOHTh8+DDvvfce48ePB9ToeNq0acyfP59WrVoZUuAEBAQwaNAgQE3Pc//99zNhwgSWL19OUVERkydPZsSIEQQEBNTatQjLZ6PVENrYnb/OpxF18WqdChIVRWHftUUrXWXRihDiNib0bs7uc1eqPMysZ6O1IdQrlL1Je4m+HC1BYi0pLCwkNjaWFi1aYGtbrSyHZVS5hpps71JV//nPf5g1axbPPfccKSkpBAQE8M9//pPZs2cbyrz88svk5OTwzDPPkJ6ezl133cXmzZsNORIB1q5dy+TJk+nbty9arZYhQ4awdOnSWrsOYT3CmnhcCxIzGN7N3K0xnri0XC5nFWBnozHkhBRCiJvp08aHyHHdCG7kUqVh5uuFeqtB4tHUowxpPcTILRTXy83NZcqUKXz22WcAnD59mubNmzNlyhQCAwN59dVXq1VvtcPM48ePExcXR2FhYZnjDz/8cHWrLMfV1ZXFixezePHim5bRaDTMmzePefPm3bRMw4YNJXG2qJRO1wKoupYGZ9+1+YgdG3vgaCdzg4QQt9enjc/tC92CYYXzZVnhbGozZ87kyJEj7Nixg/vvv99wvF+/fsydO7f2gsTz588zePBgoqOjDfMPAUNi3prOQxTCnPSLV04lZ5FXWIKTfd0IqCQ/ohCitul3XjmXfo6cohxc7FzM3KK667vvvuOrr77ijjvuKLNRQocOHTh37ly1661ynsSpU6cSHBxMSkoKzs7OHDt2jN9//52uXbuyY8eOajdECEvg5+aIj6sDJTqFmEsZ5m6O0RgWrQTLohUhRO3wcvLC38UfBYVjqcfM3Zw67fLly+VyQgPk5OTUaHetKgeJe/bsYd68eXh5eaHVatFqtdx1110sXLiw2kushbAUGk3pnL26ki8xJSuf2NQcNBroEiQ9iUKI2iNJtWtH165d+d///md4rg8MP/30U3r27FnteqscJJaUlODqqq769PLyMux/HBQUxKlTp6rdECEshX7Iua7svHLgWi9iG19X3J3sblNaCCGMRz/kbE3zEn///XceeughAgIC0Gg0fPfdd2XOK4rC7Nmz8ff3x8nJiX79+nHmzJlb1rlw4UK6deuGq6srPj4+DBo0qFzMdM8996DRaMo8nn322Uq1ecGCBbz22mtMnDiR4uJilixZQv/+/YmMjORf//pXla7/elUOEkNCQjhy5AgAPXr0YNGiRfz555/MmzeP5s2bV7shQliKurZ4ReYjCiHMRR8kRqdGG9YwWLqcnBzCwsJYtmxZhecXLVrE0qVLWb58OXv37sXFxYWIiAjy8/NvWufOnTuZNGkSf/31F1u3bqWoqIj+/fuTk5NTptyECRNITEw0PBYtWlSpNt91111ERUVRXFxMaGgov/zyCz4+PuzZs8eQI7o6qrxw5Y033jBc1Lx583jwwQe5++67adSoEV999VW1GyKEpQhp7I5GA/FX80jNLsCrgcPtX2TBDEGi7NcshKhl7Rq2w1ZjS2peKkk5Sfg38L/9i0wgKyurzBa7N26/e70BAwYwYMCACs8pisLixYt54403eOSRRwBYs2YNvr6+fPfdd4Yd4m60efPmMs9Xr16Nj48PBw8epHfv3objzs7O1d4NrkWLFnzyySfVeu3NVLknMSIigkcffRSAli1bcvLkSVJTU0lJSeHee+81auOEMAc3RztaeDcArL83MSu/iOOX1A/G7tKTKISoZY62jrTybAWYd15i+/btcXd3NzwWLlxYrXpiY2NJSkqiX79+hmPu7u706NGDPXv2VLqejAx1YWTDhmU/l9euXYuXlxchISHMnDmT3NzcStVnY2NTbgtigCtXrmBjU/0sHVXuSfziiy8YPHgwLi6lS9lvvEghrF14Ew/OpmRz5GI6fdv5mrs51XYoLh2dAk0aOuHn7nj7FwghhJF19O7IibQTRF+OJqJZhFnacPz4cQIDAw3Pb9aLeDtJSUkA+PqW/V7w9fU1nLsdnU7HtGnTuPPOOwkJCTEcf/zxxwkKCiIgIICjR4/yyiuvcOrUKTZt2nTbOm82lF9QUIC9ffWSoUM1gsQXXniBZ599locffpjRo0cTERFRoyhVCEsU1sSDrw/Gc9jKexL3x8p8RCGEeXX07shXp74ya0+iq6srbm5uZnv/602aNImYmBh27dpV5vgzzzxj+HtoaCj+/v707duXc+fO0aJFiwrr0u8ep9Fo+PTTT2nQoIHhXElJCb///jtt27atdlurHCQmJiayefNm1q9fz7Bhw3B2dmbo0KGMGjWKXr16VbshQliS8MYegDrcrChKjfJMmZN+v2YZahZCmIs+Dc7xK8cp0hVhp7XeLAv6+YLJycn4+5fOr0xOTiY8PPy2r588eTI//vgjv//+O40bN75l2R49egBw9uzZmwaJ77//PqD2JC5fvrxMp529vT3NmjVj+fLlt23XzVQ5SLS1teXBBx/kwQcfJDc3l2+//ZZ169bRp08fGjduXKPM3kJYirb+rtjbasnML+bClVyCvaxvp4CC4hKirvWEyqIVIYS5BLkF4WrvSlZhFmeunqF9o/bmblK1BQcH4+fnx/bt2w1BYWZmJnv37mXixIk3fZ2iKEyZMoVvv/2WHTt2EBwcfNv3ioqKAigTjN4oNjYWgD59+rBp0yY8PY27YUKVF65cz9nZmYiICAYMGECrVq24cOGCkZolhHnZ2WgJCVCHJqIuXjVza6onJiGDwmIdjVzsaW6FQa4Qom7QarSG3sToy9Fmbs3tZWdnExUVZQjSYmNjiYqKIi4uDo1Gw7Rp05g/fz7ff/890dHRjBkzhoCAAAYNGmSoo2/fvnzwwQeG55MmTeKLL75g3bp1uLq6kpSURFJSEnl5eQCcO3eOt956i4MHD3LhwgW+//57xowZQ+/evenYseNt2/zbb78ZPUCEagaJubm5rF27loEDBxIYGMjixYsZPHgwx47Jtjui7ggz5Eu0zu359sWqwW3XZp5WO1wuhKgbrGnnlQMHDtCpUyc6deoEwPTp0+nUqROzZ88G4OWXX2bKlCk888wzdOvWjezsbDZv3oyjY+niwHPnzpGammp4/tFHH5GRkcE999yDv7+/4aFPHWhvb8+2bdvo378/bdu2ZcaMGQwZMoQffvihFq+8vCoPN48YMYIff/wRZ2dnhg0bxqxZs2q05YsQlkq/PZ+1Ll6RJNpCCEthTTuv3HPPPbdM/K3RaJg3bx7z5s27aZkbR1Zvl0i8SZMm7Ny5s0rtrA1VDhJtbGzYsGGDrGoWdZ4+SDxxKZOC4hIcbK3n37tOp3BAv2hF5iMKIcxM35N4IfMCGQUZuDu4m7lFojKqPNysH2a2sbG55RY0Qli7pg2d8XS2o7BEx8nELHM3p0pOJWeRmV+Mi70N7f0tI+2DEKL+8nT0pIlrEwCOpcrUNGtR5SBRp9Px1ltvERgYSIMGDTh//jwAs2bNYuXKlUZvoBDmotFoDPMSo6xsyFk/1Nw5yBNbmxqtTxNCCKOwpnmJ1qZZs2bMmzePuLg4o9Zb5W+P+fPns3r1ahYtWlQmi3dISAiffvqpURsnhLmFXZcv0Zrsv6AuWpH5iEIIS6GflxidavkrnK3NtGnT2LRpE82bN+e+++7jyy+/pKCgoMb1VjlIXLNmDStWrGDUqFFl5iSGhYVx8uTJGjdICEsS3tQDgKj4dLO2oyoURTHstNK1mfFTIgghRHV09LoWJF6Ovu1CDlE106ZNIyoqin379tGuXTumTJmCv78/kydP5tChQ9Wut8pBYkJCAi1btix3XKfTUVRUVO2GCGGJ9D2J5y/nkJFrHf++46/mkZSZj52Nhk5NJEgUQliGNg3bYKe142rBVeKz4s3dnDqpc+fOLF26lEuXLjFnzhw+/fRTunXrRnh4OKtWrapycF7lILF9+/b88ccf5Y5//fXXhpxCQtQVDV3sadrQGYCjCenmbUwl7bvWixgS6I6TvfWsyBZC1G32Nva0a9gOkHmJplJUVMSGDRt4+OGHmTFjBl27duXTTz9lyJAhvPbaa4waNapK9VU5Bc7s2bMZO3YsCQkJ6HQ6Nm3axKlTp1izZg0//vhjVasTwuKFN/EgLi2XIxfTubuVt7mbc1v7Zb9mIYSFCvUO5WjqUaJTo3mg+QPmbk6dcejQISIjI1m/fj1arZYxY8bw/vvv07ZtW0OZwYMH061btyrVW+WexEceeYQffviBbdu24eLiwuzZszlx4gQ//PAD9913X1WrE8LiWdsK532SRFsIYaGsaXs+a9KtWzfOnDnDRx99REJCAu+++26ZABHUfadHjBhRpXor3ZN4/vx5mjdvDsDdd9/N1q1bq/RGQlir8CZq0teoixkoimLRW9ylZhdw/nIOIItWhBCWR7945UTaCQpLCrG3sb/NK0RlnD9/nqCgoFuWcXFxITIyskr1VronsWPHjoSEhPDaa6+xd+/eKr2JENasQ4A7tloNqdkFJKTnmbs5t3TgWuqb1r4N8HCWD18hhGVp7NoYTwdPinRFnEyTjCjGcrsAsboqHSSmpqaycOFCUlJSeOSRR/D392fChAn88MMPsvOKqNMc7Wxo6+8KwJGLGWZuza3Jfs1CCEum0WgI9b425Cz5EmvE09OThg0bVupRXZUebnZ0dOShhx7ioYceQlEU9uzZw/fff88rr7zCyJEj6devHw8//DAPPfQQ3t6WP7lfiKoIa+xBTEImR+LTeaCjv7mbc1P7Zb9mIYSFC/UK5ff43zl6+Sij2lVtta0otXjxYpO/R5VXN4P6m0CvXr3o1asXb7/9NmfOnOH7779n9erVTJw4kffee49JkyYZu61CmE14Ew/W7o0jKi7d3E25qZyCYo5dygSkJ1EIYbkMSbWlJ7FGxo4dC0BxcTHr1q0jIiICX19fo76HUTZ1bdWqFTNmzOD333/n0qVL9O/f3xjVCmExwq+tcI5OyKC4RGfextzEobirlOgUAj2cCPBwMndzhBCiQiHeIQBczLrI1fyrZm6N9bO1teXZZ581ydS/avUknjlzht9++42UlBR0utIvTI1Gw6xZs2jUqJHRGiiEJWjh3YAGDrZkFxRzJiWbdv5u5m5SOfqt+GSoWQhhydzs3Qh2DyY2I5bo1Gh6N+5t7iZZve7du3P48GGjL2CpcpD4ySefMHHiRLy8vPDz8yuTDkQfJApR12i1Gjo2dmf3uStEXUy3zCDx2spmGWoWQli6UK9QYjNiOXr5qASJRvDcc88xY8YM4uPj6dKlCy4uLmXOd+zYsVr1VjlInD9/Pv/617945ZVXqvWGQlirsCYe7D53hSMX0xnZvam5m1NGYbGOwxf1QaLkRxRCWLaOXh35/tz3Mi/RSPRJsp9//nnDMY1GY8jtW1JSUq16qxwkXr16laFDh1brzYSwZuEWvPNKzKUM8ot0eDrb0dKngbmbI4QQt3R9GhydokOrMcoSiXorNjbWJPVWOUgcOnQov/zyC88++6wp2iOExdIHiaeTs8gpKMbFoVpTek1CPx+xa7OGFr0jjBBCALTybIWDjQNZhVn8nfk3we7B5m6SVTNVMu0qf8u1bNmSWbNm8ddffxEaGoqdnV2Z89d3dQpRl/i6OeLn5khSZj4xCRn0aG45C7QM+RFlPqIQwgrYae3o0KgDh1IOcfTyUQkSjeT48ePExcVRWFhY5vjDDz9crfqqHCSuWLGCBg0asHPnTnbu3FnmnEajkSBR1GnhTTzYfCyJqIvpFhMk6nRK6aIVWdkshLASoV6hHEo5RHRqNI+0fMTczbFq58+fZ/DgwURHRxvmIgKGkaVam5NoqnFvIaxB2LUg8Uh8urmbYnAmJZuMvCKc7GzoEGB5q66FEKIi+nmJRy8fNXNLrN/UqVMJDg5m+/btBAcHs2/fPq5cucKMGTN49913q12v5UyqEsIKhDVxByxrD2f9UHPnIA/sbGTytxDCOuh3Xjlz9Qz5xfk42jqauUXWa8+ePfz66694eXmh1WrRarXcddddLFy4kOeff57Dhw9Xq95KBYnTp0/nrbfewsXFhenTp9+y7HvvvVethghhDTo29kCjgYT0PFKy8vFxNf+Hmj5I7BokQ81CCOvh5+KHl5MXqXmpnEg7QSefTuZuktUqKSnB1dUVAC8vLy5dukSbNm0ICgri1KlT1a63UkHi4cOHKSoqMvz9ZmRVpajrGjjY0sqnAaeTszlyMYP72ltAkCg7rQghrJBGoyHUK5TfLv7G0ctHJUisgZCQEI4cOUJwcDA9evRg0aJF2Nvbs2LFCpo3b17teisVJP72228V/l2I+iissce1IDGd+9obdzP1qoq/msuljHxstRo6NfUwa1uEEKKqOnp3NASJovreeOMNcnJyAJg3bx4PPvggd999N40aNeKrr76qdr0yJ1GIKgpv6sHGg/EWkVRbP9TcIdAdZ3v57yyEsC76eYmy80rNREREGP7esmVLTp48SVpaGp6enjUa5a3Wt8qBAwfYsGFDhbl4Nm3aVO3GCGENwhp7AHAkPh2dTkGrNd80i32xauqb7rIVnxDCCnXw6oAGDYk5iaTmpeLl5GXuJlm1s2fPcu7cOXr37k3Dhg0NqXCqq8pLIb/88kt69erFiRMn+PbbbykqKuLYsWP8+uuvuLu716gxQliDNn6uONppycovJvZKjlnbcuBaT2I3SaIthLBCLnYutPBoAUgqnJq4cuUKffv2pXXr1gwcOJDExEQAnnrqKWbMmFHteqscJC5YsID333+fH374AXt7e5YsWcLJkycZNmwYTZs2rXZDhLAWdjZaQgLUX4ii4tLN1o6rOYWcSckG1O34hBDCGnX0tqwh599//52HHnqIgIAANBoN3333XZnziqIwe/Zs/P39cXJyol+/fpw5c+a29S5btoxmzZrh6OhIjx492LdvX5nz+fn5TJo0iUaNGtGgQQOGDBlCcnJypdr8wgsvYGdnR1xcHM7Ozobjw4cPZ/PmzZWqoyJVDhLPnTvHAw88AIC9vT05OTloNBpeeOEFVqxYUe2GCGFNwq7t42zOpNr6+YgtfRrQ0MXebO0QQoia0M9LtJSexJycHMLCwli2bFmF5xctWsTSpUtZvnw5e/fuxcXFhYiICPLz829a51dffcX06dOZM2cOhw4dIiwsjIiICFJSUgxlXnjhBX744Qc2btzIzp07uXTpEo8++mil2vzLL7/w73//m8aNG5c53qpVK/7+++9K1VGRKgeJnp6eZGVlARAYGEhMTAwA6enp5ObmVrshQliT8GtBojkXr+yXoWYhRB2g33klJjWGEl31to8zpgEDBjB//nwGDx5c7pyiKCxevJg33niDRx55hI4dO7JmzRouXbpUrsfxeu+99x4TJkxg3LhxtG/fnuXLl+Ps7MyqVasAyMjIYOXKlbz33nvce++9dOnShcjISHbv3s1ff/112zbn5OSU6UHUS0tLw8HBofIXf4MqB4m9e/dm69atAAwdOpSpU6cyYcIERo4cSd++favdECGsiT5IPJGYSX6ReT7U9l3br7l7sCxaEUJYrxbuLXC2dSa3OJfzGedN8h5ZWVlkZmYaHgUFBdWqJzY2lqSkJPr162c45u7uTo8ePdizZ0+FryksLOTgwYNlXqPVaunXr5/hNQcPHqSoqKhMmbZt29K0adOb1nu9u+++mzVr1hieazQadDodixYtok+fPlW+Tr0qr27+4IMPDF2qr7/+OnZ2duzevZshQ4bwxhtvVLshQliTxp5ONHSxJy2nkBOJmXRqWruBWm5hMccS1K0BpSdRCGHNbLQ2dPDqwP6k/USnRtPKs5XR36N9+/Zlns+ZM4e5c+dWuZ6kpCQAfH3L5sj19fU1nLtRamoqJSUlFb7m5MmThnrt7e3x8PCodL3XW7RoEX379uXAgQMUFhby8ssvc+zYMdLS0vjzzz8re3nlVDlIbNiw9AtJq9Xy6quvGp7n5eVVuyFCWBONRkN4Ew9+PZlC1MX0Wg8SD8elU6xTCHB3pLFn+SEGIYSwJqFeoexP2s/Ry0d5tFXl5uFVxfHjxwkMDDQ8r8kQrCUKCQnh9OnTfPDBB7i6upKdnc2jjz7KpEmT8Pf3r3a9Rsm+W1BQwLJly1i0aFGlIl4h6oKwxmqQeMQM8xIN8xFlKz4hRB2gX+F8NNU0i1dcXV1xc3OrcT1+fn4AJCcnlwm+kpOTCQ8Pr/A1Xl5e2NjYlFupnJycbKjPz8+PwsJC0tPTy/QmXl/mdtzd3Xn99dercDW3V+k5iQUFBcycOZOuXbvSq1cvwwTNyMhIgoODef/993nhhReM2jghLFlYEzUNzpH4jFp/b1m0IoSoS/QrnM+lnyO3yHIXwQYHB+Pn58f27dsNxzIzM9m7dy89e/as8DX29vZ06dKlzGt0Oh3bt283vKZLly7Y2dmVKXPq1Cni4uJuWu/1mjVrxrx587h48WJ1L61ClQ4SZ8+ezUcffUSzZs24cOECQ4cO5ZlnnuH999/nvffe48KFC7zyyitGbZwQlky/eCU2NYf03MJbFzaiohIdh/5OByRIFELUDd7O3vi5+KFTdBy7csysbcnOziYqKoqoqChAXawSFRVFXFwcGo2GadOmMX/+fL7//nuio6MZM2YMAQEBDBo0yFBH3759+eCDDwzPp0+fzieffMJnn33GiRMnmDhxIjk5OYwbNw5QewGfeuoppk+fzm+//cbBgwcZN24cPXv25I477rhtm6dNm8amTZsIDg7mvvvu48svv6z24pzrVTpI3LhxI2vWrOHrr7/ml19+oaSkhOLiYo4cOcKIESOwsbGpcWOEsCYezvY0a6TOB6zN3sRjlzLJKyrB3cmOVj4Nau19hRDClEK91FQ45s6XeODAATp16kSnTp0ANcDr1KkTs2fPBuDll19mypQpPPPMM3Tr1o3s7Gw2b96Mo6OjoY5z586RmppqeD58+HDeffddZs+eTXh4OFFRUWzevLnMYpb333+fBx98kCFDhtC7d2/8/PwqvdXxtGnTiIqKYt++fbRr144pU6bg7+/P5MmTOXToUPV/GEol2dnZKfHx8Ybnjo6OytGjRyv78moLCgpSgHKP5557TlEURcnLy1Oee+45pWHDhoqLi4vy6KOPKklJSWXq+Pvvv5WBAwcqTk5Oire3t/Liiy8qRUVFlW7DxYsXFUC5ePGiUa9NWL/n1x9Sgl75UVmy7XStveeKneeUoFd+VJ5ava/W3lMIIUwtMjpSCVkdokz9darR6qyv39+FhYXK4sWLFQcHB0Wr1SphYWHKypUrFZ1OV6V6Kt2TWFJSgr196a4Otra2NGhg+l6M/fv3k5iYaHhcn6MRbp+hvKSkhAceeIDCwkJ2797NZ599xurVqw2/EQhRE+ZIqr1P5iMKIeogw+KVy0dRFMXMrbFORUVFbNiwgYcffpgZM2bQtWtXPv30U4YMGcJrr73GqFGjqlRfpVc3K4rCk08+aVg2np+fz7PPPouLi0uZcpXtGq0sb2/vMs/ffvttWrRowT/+8Q9DhvJ169Zx7733AupCmnbt2vHXX39xxx138Msvv3D8+HG2bduGr68v4eHhvPXWW7zyyivMnTu3TOCrV1BQUGYsX7/DjBA3MmzPdzEdRVHQaDQmfT9FUTggK5uFEHVQu0btsNHYcDnvMsm5yfi5VG5Vr4BDhw4RGRnJ+vXr0Wq1jBkzhvfff5+2bdsaygwePJhu3bpVqd5K9ySOHTsWHx8f3N3dcXd3Z/To0QQEBBie6x+mVFhYyBdffMH48ePRaDSVylC+Z88eQkNDy4z7R0REkJmZybFjFU+OXbhwYZlrujEJpxB67f3dsLPRcCWnkPirps8Teu5yNldzi3C00xISYNr/b0IIUZucbJ1o7dkaMP+8RGvTrVs3zpw5w0cffURCQgLvvvtumQAR1JXZI0aMqFK9le5JjIyMrFLFpvDdd9+Rnp7Ok08+CVQuQ3lSUlKFWc715yoyc+ZMpk+fbniekJAggaKokKOdDe383Tgan0HUxXSaNDRtYut9sepWfOFNPLC3rfKumkIIYdFCvUI5kXaC6NRo+jfrb+7mWI3z588TFBR0yzIuLi5VjuWs6ltm5cqVDBgwgICAAJO+j4ODA25uboaHq6urSd9PWLewxh4AtZJUW58fsbvMRxRC1EGh3paxwtna3C5ArC6rCRL//vtvtm3bxtNPP204dn2G8uvdmMW8oizn+nNC1FRtLl7ZFyvzEYUQdZc+qfbxK8cp1hWbuTXCaoLEyMhIfHx8eOCBBwzHKpOhvGfPnkRHR5OSkmIos3XrVtzc3GQIWRiFfvFKzKUMikp0JnufS+l5JKTnYaPV0LmW94oWQoja0My9Ga52ruSX5HPm6hlzN6fes4ogUafTERkZydixY7G1LZ1GWZkM5f3796d9+/Y88cQTHDlyhC1btvDGG28wadKkOrfBtzCP5l4uuDrakl+k43Sy6VbC64eaOwS44eJglG3XhRDComg1WkK8QgCITo02c2uEVQSJ27ZtIy4ujvHjx5c7d7sM5TY2Nvz444/Y2NjQs2dPRo8ezZgxY5g3b15tXoKow7RajWFeoimHnGW/ZiFEfSDzEi2HVXRH9O/f/6aJNR0dHVm2bBnLli276euDgoL46aefTNU8IQhr4s6us6kcuZjOqB6mmUC8/9rKZgkShRB1mX5eovQkVl5JSQmrV69m+/btpKSkoNOVnfr066+/VqteqwgShbB0pSucTbOHc3puIaeuDWV3bSbzEYUQdZe+J/F8xnkyCzNxs3czc4ss39SpU1m9ejUPPPAAISEhRtvYQYJEIYxAv8L5dEoW2QXFNDDynMEDF9RexObeLng1kLm0Qoi6q6FjQxo3aEx8djwxqTH0Cuhl7iZZvC+//JINGzYwcOBAo9ZrFXMShbB0Pm6OBLg7oigQHW/83kTJjyiEqE/0vYnRl2XIuTLs7e1p2bKl0euVIFEIIwkzYb7EfbJoRQhRj8i8xKqZMWMGS5Ysuen6jeqS4WYhjCS8iQc/xyQZfeeVvMISYhLU3snukkRbCFEPGHoSU6NRFMVoc+zqql27dvHbb7/x888/06FDB+zs7Mqcvz7rS1VIkCiEkeh7Eo/Epxu13qiL6RSVKPi5OdLY08modQshhCVq27Attlpb0vLTSMhOoLFrY3M3yaJ5eHgwePBgo9crQaIQRhIa6I5WA4kZ+SRn5uPr5miUeg35EYMbym/TQoh6wcHGgXYN2xGdGs3Ry0clSLyNyMhIk9QrcxKFMBIXB1ta+7oCxp2XWJpEW1LfCCHqj1Cv0iFnYR7SkyiEEYU19uBkUhZHLqYT0cGvxvUVl+g49Lck0RZC1D+h3qFwEo6mys4rlfH111+zYcMG4uLiKCwsLHPu0KFD1apTehKFMKLwph6A8XoSjydmklNYgpujLW2u9VIKIUR9oF/hfPLKSYpKiszcGsu2dOlSxo0bh6+vL4cPH6Z79+40atSI8+fPM2DAgGrXK0GiEEak33nlaHwGOl3NUxHsi1WHmrs2a4hWK/MRhRD1RxPXJng4eFCoK+TU1VPmbo5F+/DDD1mxYgX/+c9/sLe35+WXX2br1q08//zzZGRUP3evBIlCGFFr3wY42dmQXVDM+dTsGten32lFhpqFEPWNRqMhxCsEgCOXj5i5NZYtLi6OXr3UnWmcnJzIylK3cX3iiSdYv359teuVIFEII7K10RIa6A7A4bj0GtWlKErpTivBsmhFCFH/dPSWpNqV4efnR1qa+n3RtGlT/vrrLwBiY2NrlGBbgkQhjCysiRok1jRf4vnUHK7kFOJgqyXkWuAphBD1iWHnFdme75buvfdevv/+ewDGjRvHCy+8wH333cfw4cNrlD9RVjcLYWTG2p5v/7X5iGFNPHCwtalhq4QQwvroh5vjsuJIz0/Hw9HDvA2yUCtWrECn0wEwadIkGjVqxO7du3n44Yf55z//We16JUgUwsjCrwWJJxOzyC8qwdGuegGefr/m7jIfUQhRT7k7uNPMrRkXMi8QnRrN3Y3vNneTLJJWq0WrLR0cHjFiBCNGjKh5vTWuQQhRRqCHE14N7CnWKRy7lFnteq7faUUIIeorSapdOX/88QejR4+mZ8+eJCQkAPD555+za9euatcpQaIQRqbRaAy9idUdck7KyOdiWh5aDXS+lntRCCHqI/3ilaOXJan2zXzzzTdERETg5OTE4cOHKSgoACAjI4MFCxZUu14JEoUwAX2+xCPVDBL1vYjtA9xwdbQzUquEEML6hHqX9iTWZKVuXTZ//nyWL1/OJ598gp1d6XfGnXfeWe3dVkCCRCFMQr94pbornEv3a5ahZiFE/dbaszUONg5kFmbyd+bf5m6ORTp16hS9e/cud9zd3Z309PRq1ytBohAmoO9J/PtKLmk5hbcuXAH9TisSJAoh6js7rR3tGrYDZF7izfj5+XH27Nlyx3ft2kXz5s2rXa8EiUKYgLuzHc29XICb9CamX4TzO6CCoZOMvCJOJavZ8iVIFEKI0iHn2piXmJWVxbRp0wgKCsLJyYlevXqxf//+m5Z/8skn0Wg05R4dOnQwlJk7d265823btjVamydMmMDUqVPZu3cvGo2GS5cusXbtWl588UUmTpxY7XolBY4QJhLWxIPzqTkcuZhOnzY+pSf+3gPrhkFBJnR9Cga+A9rSNDkH/05DUSDYywVvVwcztFwIISyLYfFKqumDxKeffpqYmBg+//xzAgIC+OKLL+jXrx/Hjx8nMDCwXPklS5bw9ttvG54XFxcTFhbG0KFDy5Tr0KED27ZtMzy3tTVeCPbqq6+i0+no27cvubm59O7dGwcHB1588UWmTJlS7XqlJ1EIE6lwhfPpX+DzwWqACHBgJWwcC0X5hiL7YvX7NctWfEIIAaU7r5xOO01+cf5tSldfXl4e33zzDYsWLaJ37960bNmSuXPn0rJlSz766KMKX+Pu7o6fn5/hceDAAa5evcq4cePKlLO1tS1TzsvLy2jt1mg0vP7666SlpRETE8Nff/3F5cuXeeutt2pUrwSJQpiIYfHKxXR1RV701/DlSCjOg1b9YfDHYGMPJ35QA8c8NTg8IItWhBCiDH8Xfxo5NqJYKeZk2skqvz4rK4vMzEzDQ58i5kbFxcWUlJTg6OhY5riTk1Ol8w2uXLmSfv36ERQUVOb4mTNnCAgIoHnz5owaNYq4uLgqX8ft2Nvb0759e7p3706DBg1qXJ8MNwthIu38XbG30XI1t4i0nR/RaMdrgAIhj8Hg5WBjB26B8OXjELcbVg0gf8QGjsZnANBdkmgLIQSg9pSFeoey4+IOjl4+SrhPeJVe3759+zLP58yZw9y5c8uVc3V1pWfPnrz11lu0a9cOX19f1q9fz549e2jZsuVt3+fSpUv8/PPPrFu3rszxHj16sHr1atq0aUNiYiJvvvkmd999NzExMbi6ulbpWq43fvz4SpVbtWpVteqXIFEIE3GwtaGdvyt3Jn5Gox0b1INdn4KB74J++6Tgu2Hcz7D2Mbh8As3K/gTpXiDDtQVNGzqbr/FCCGFhOnp1ZMfFHdVa4XzjfEIHh5vP9/78888ZP348gYGB2NjY0LlzZ0aOHMnBgwdv+z6fffYZHh4eDBo0qMzxAQMGlF5Hx4706NGDoKAgNmzYwFNPPVXl69FbvXo1QUFBdOrUySQ5JCVIFMJUFIWXtV9wp921ALH3S9DnddBoypbzC4GnfoEvhuCQepqv7eey0vdtNDeWE0KIeqwmO6+4urri5uZWqbItWrRg586d5OTkkJmZib+/P8OHD79tKhlFUVi1ahVPPPEE9vb2tyzr4eFB69atK0xbUxUTJ05k/fr1xMbGMm7cOEaPHk3DhsYbhZI5iUKYQkkxfD+ZO1PWAxDZ4Gm4943yAaKeR1MYv4Uz9u1x1+Ty/KWX4MSPtdhgIYSwbB0adUCDhks5l0jNSzX5+7m4uODv78/Vq1fZsmULjzzyyC3L79y5k7Nnz1aqZzA7O5tz587h7+9fozYuW7aMxMREXn75ZX744QeaNGnCsGHD2LJli1F6FiVIFMLYigvg6yfh8BcoGi0vFT3DwvR+FJXobvmyEkdPRhbMZGtJZ2x1BbDhCdi/snbaLIQQFq6BfQNaeLQAIPqy6ZJqb9myhc2bNxMbG8vWrVvp06cPbdu2NaxWnjlzJmPGjCn3upUrV9KjRw9CQkLKnXvxxRfZuXMnFy5cYPfu3QwePBgbGxtGjhxZ4/Y6ODgwcuRItm7dyvHjx+nQoQPPPfcczZo1Izs7u0Z1S5AohDEVZMPaoeqKZRt7lKGfscWuH4XFOk4lZd3ypScSM0ktsOEl7UvoOo0BRQf/mw6/Lagw6bYQQtQ3oV6l+zibSkZGBpMmTaJt27aMGTOGu+66iy1bthj2RE5MTCy3MjkjI4Nvvvnmpr2I8fHxjBw5kjZt2jBs2DAaNWrEX3/9hbe3t1HbrtVq0Wg0KIpCSUlJjeuTOYlCGEtumhogJhwAOxcYuQ5t83sIa7KXP86kcvhiOiGB7jd9uX6/5vBmXmgfXgpu/rDz3+ojKxEeeB9s5L+sEKL+CvUO5duz35o0qfawYcMYNmzYTc+vXr263DF3d3dyc3Nv+povv/zSGE2rUEFBAZs2bWLVqlXs2rWLBx98kA8++ID7778frbZmfYHSkyiEMWQmQuRANUB08oSxP0Dze4DSpNpHrk+qXYH91+dH1Gigz2vwwHug0cKhNfDVaCi8+YeQEELUdfqk2jGpMZToat5TZu2ee+45/P39efvtt3nwwQe5ePEiGzduZODAgTUOEEF6EoWoubTzsOYRSI8DV3944lvwaWc4HdbYA7h1kKgoimGnlTL5Ebs9BQ184Oun4PTP6vs8/hU4Sw5FIUT908KjBU62TuQU5RCbEUtLz9vnLqzLli9fTtOmTWnevDk7d+5k586dFZbbtGlTteqXIFGImkiKgS8ehexk8AyGMd+BZ7MyRfQ7r5y9nE1WfhGujnblqrlwJZfU7ALsbbSE3jgk3e4hGPNfWD8c4vfBqggY/Y26IloIIeoRW60tHRp14EDyAaJTo+t9kDhmzBiTpkuTIFGI6orbC+uGQn4G+IbA6E3g6luumLerA4EeTiSk5xEdn0GvluX369wfqw41hzVxx9HOpvx7BfWE8VvgiyGQehpW9lcDRd8ORr8sIYSwZKHeoRxIPsDR1KMMbjXY3M0xq4rmRxqTzEkUojrOboPPB6kBYpMe8OSPFQaIevp5iYdvMuS8rzL7Nfu0U5Nue7dVF7KsGgAXKreXqBBC1BX6eYmmTIMjVBIkClFVMZtg3QgoyoWW/dQ5iE6et3zJ7RavHNAHibfbr9m9MYzfDE17QUEGfD4Yjn1XxQsQQgjrpU+Dcyb9DLlFspjPlCRIFKIqDq6Gr8eDrgg6DIYR68He5bYv089LPBKfXu5cSlY+F67kotFAl6BbB5uAGpA+sQnaPgglhbDxSdi7okqXIYQQ1srXxRdfZ190io5jV46Zuzl1mgSJQlTWrvfhh6mAAl2ehCErwfbW+3PqhQS6YaPVkJxZQGJGXplz+6+tam7n54ZbBYtaKmTnBMPWQNen1Pb8/BJsnydJt4UQ9YJ+H2dTJtUWEiQKcXuKAltnw7a56vO7XoAHF4O2ggUmN+Fsb0trX1eg/JCzPj9i99sNNd9IawMP/J+6JzTAH/8H/50EJUVVq0cIIayMYecVmZdoUhIkCnEruhK19/DPJerz++ZBv7lqsusqCm+ipraJuphR5vi+ayubuzarxFDzjTQa6P0SPPwf0NhA1FpYPxIKc6pelxBCWAl9kGjKnVeEBIlC3FxxoTr/8NBn6q4nDy2FO6dWuzp9Uu2oi1cNxzLziziRlAlA91utbL6dzmNgxDqwdYKzW2H1g5CTWv36hPXISoZTm2HH27Bnmdx3UXO6Ejj2rbow7uun4Mw29ZgFad+oPTYaG1JyU0jKSTJ3c+osyZMoREUKc9Rt8M79Clo7GPIpdBhUoyrDm3oAEB2fQYlOwUar4eDfV1EUCGrkjI+bY83a3OZ+dTvAdUPh0qHSXIoNg2tWr7AceemQGAUJhyDhIFw6DJkJZctsmwvtB6m79TTpUa1eb1FPlRRB9Eb44z24cqb0eMzX6m5SHYdB2OPg09Z8bbzG2c6ZVp6tOJl2kujUaPxc/MzdpDpJgkQhbpR3FdYOU3c3sXOG4V9Ay741rraVjyvO9jbkFJZw7nI2rX1dS1Pf1KQX8XpNusH4X9Sk22nnrgWKX4N/mHHqF7WnKA8Sj6oBf8Ih9c8rZysoqAHvNhDQCS6fUstFb1AfviHQdbz65e7gWuuXIKxEUT5EfaFOq0mPU485ekD3Z6AgE45uUHOz/rlEfQR2gbCREDLErFuEhnqFqkHi5WjuC7rPbO2oyyRIFOJ6WUnw+aOQcgwc3WHU19Cku1GqttFqCA10Z29sGlFx6bT2dTWsbK7RUPONvFurSbfXPgbJMRD5AAz/HFr0Md57COMqKYKU46XBYMJh9blSwRCfRxAEdoaAzuqf/mFlA8CEQ3BgJUR/o97//02HrXMgbLi6Gt63fe1dl7BshTlwIBJ2/weyrw3ZunhDz8lqT7T+39V9b8GZLRC1Xv0z4aD62PIatBkI4Y9Di75gU7shRahXKBtPb5R5iSYkQaIQelcvwJpBcDUWGviqSbKNvO1deBMPNUiMT+eRTgFEXcubeNsk2lXl5g/jfoIvR8GFP2DtUBi8HEIfM+77iKrT6dQewet7CJOioTi/fFkXn+sCwi5qb6FLo1vXH3gteOw/H458Cfs/Vd9v/6fqo2lP6Pa0uie4rYNprlFYtrx02PcJ/PUh5KmjGbg1Vudcd35CTbF1PVt79d9Lu4cg+7I6JH1knfrv9vh36qOBL4QOhfBRtfaLiD4NzvErxynWFWOrlZDG2OQnKgRA8nF1knZ2ktpTM+a/JpnLp0+qHRWXztH4DAqLdXg1cKBZI2ejvxeO7uqcxE3PqB/i3zwF2cnQc5Lx30tUTFEg4+J1PYSHIPGIOoR3Iwd3CAgv20voFlj9OYVOnnDHROjxLMT+rgaIJ/8HcXvUh7OXuuCpy5PgGVSTqxTWIidVXdy0/9PSf4MNm6tpvTqOqFze1wbe0PM59ZF4FI6sV4ejs5Nhzwfqwz9c7V0Meez2v9TUQLB7MA3sGpBdlM259HO0adjGZO9VX0mQKET8AXUOX346+LSH0ZvUnjgT0G/Pdyo5iz9OXwagWzNPNKZaXGDrAI9FwmZf2PexOjyUlQj95oFWkhsYXfblsj2ECYcgt4LVxrZO4N+xNBgM6Kx+WZvinmg00Pwf6iPzEhxao+4clJUIu95Tk8S3jlCHolv2rVL+T2ElMi+pQ8oHIqH4WjJ/n/Zw9wx1kVN1h4n9O6qP++bBma1qCq7TW9TFVYlRsOV1dUFd2OPQ6j6wqeRmAZWk1Wjp4NWBvYl7OXL5iASJJiBBoqjfzv2mDskW5UDjbvD4BpNOxPZ3d8Tb1YHLWQWs26dOEDfaopWb0WphwL/VwHfbXPXLIisZHllW6R1jRAXyM+BS1HVB4WG11/BGWlv1C/n6HkLvdrU+fwsAtwC451W4+0U4/bPao3R+B5zerD48mqoLXTo9AS5etd8+YVxpsfDnYohap27hCeq/wd4vQusBxvulxMYO2g5UHzlX1NXQUWvVXvMTP6gPZy/oOBzCR4JfqHHeF+jo1ZG9iXuJTo1mWJthRqtXqCRIFPXX8e/VIdiSQmjeR13F7NDApG+p0WgIa+zBthPJpGarH9pV3mmlem+sDik18IX/TlZXvuZcVhe0yKrX2yvKV+df6QPChINlU4QYaMCrVdkeQr+Q8nO8zM3GtnSOWepZOBgJh79QV7Zumwu/LYD2j6hzFyWNjvVJOan2Ekd/Xbr4KehOteewxb2mvZ8ujaDHP9VH8jE1QD26AXJS4K9l6sMvVJ27GDq0xr+MGLbnk51XTEKjKLLZ6+3Ex8fTpEkTLl68SOPGjc3dHGEMhz6HH54HRQftHlbzINbSJP5lv53lnS2nAGjgYMuROf2x0dbil/CZrbBhDBTlqitjR30NDXxq7/0tXUkxXD5Rdsg45TjoisuXdW8KgZ2uW2kcDo5utd5koyjKg5hN6srohIOlx306QLfxai+Q/EJh2RKPwO/vqj13XPtqb9lP7TkO6mm+dpUUw7ntau/iqZ9LezW1ttAqQp2/2Kp/tUY2ruRd4Z4N96BBw+6Ru2lgX/YXffn+rhkJEitB/pHVMbv/A79c2++40xPw0JJanYe160wqo1fuBaB3a2/WjDdOip0qiT+oJt3OvQKezdR5mI1a1H47LEHmJbiwqzQoTDxaOm/rei7eZXsIAzqpk/jrokuHYf9KtSdK/7Owb6AGit2eMvqqf1FDcX+pweHZraXH2j6oDisHdDJfuyqSmwYx36g9jJcOlR53bnRtdfTj4NexSr2d939zPwnZCXzS/xPu8L+jzDn5/q4ZGW4W9YeiwK9vwR//pz7vNUXN/1XLQ2mhjd0Nf+9enf2ajaFxF3hqq7qi++oFNen2qI1qAFTXFWTD33+q81HP/Qqpp8qXcXBTVxpfHxS6N64/w64BneCRD6D/W9fS6KxUh9cPrFQfTXuqC13aPyxpdMxFUdT5pH/8n5rmCtTtQ0Meg7ung087szbvppwbQvcJ6iPlpJpK58hXamaJvcvVh08HNVjsOKxSoxyhXqEkZCdw9PLRckGiqBmLX96YkJDA6NGjadSoEU5OToSGhnLgwAHDeUVRmD17Nv7+/jg5OdGvXz/OnCk7VygtLY1Ro0bh5uaGh4cHTz31FNnZ2bV9KcKcdCVqUmF9gNh3jlkCRAB3JztCA9VA8a5WZuyJatRCDRT9OqorcFc/CGe3ma89pqIrUYdPf39XTSz+72awbhjs/UgNEDVaNQjs/k8Y/DFM2g+v/K1ucXjfm+rcPI8m9SdAvJ4+jc7k/TDme/VnobFRU+hsehrea6/OYbz6t7lbWn8oCpz8CT7tC58PUgNErR10HgtTDsKQTyw3QLyRT1t1ZfQLx9RpLx0eBRsHdTODX16H/2sL64bD8f9CccFNq5F5iaZj0cPNV69epVOnTvTp04eJEyfi7e3NmTNnaNGiBS1aqENj//73v1m4cCGfffYZwcHBzJo1i+joaI4fP46jo7oX7oABA0hMTOTjjz+mqKiIcePG0a1bN9atW1epdkh3tZUrLoTvnlWHONDAg++pKzjN6GJaLhev5tKrhQWsIC3IUvepPr9DnSP0yDIIG2HuVtVMelxpT2HsTnWrxet5NFUn8DfvA8G9zbq1mNXJTLwujc6lawc16pyybk+pc+AkjY7x6Urg2Lfqvsopx9Rjtk7QZaw6KuJeR76b8q6qc2OPrIf4/aXHnTzVXtLwx9We7ut+aYtKieKJn5+goWNDdgzbUSalmHx/14xFB4mvvvoqf/75J3/88UeF5xVFISAggBkzZvDiiy8CkJGRga+vL6tXr2bEiBGcOHGC9u3bs3//frp27QrA5s2bGThwIPHx8QQEBJSrt6CggIKC0t9aEhISaN++vfwjs0aFueoijbNb1QDo0RXqfqOirOJC+O9z6k4KAP3eVHdfsJbes/xMdV7huV/h/G/l9zh2cFODweb3qMFhw+bWc22WqqT4WhqdlerPXM+jKXQZp873ratzNmtTcSEc/UrNZ5l2Tj1m7wrdn4Y7JtXtn/Hl06XD0YZfSADvtteGo4eDqx8FJQXcse4OinXFbB6ymcAGgYaiEiTWjEUHie3btyciIoL4+Hh27txJYGAgzz33HBMmTADg/PnztGjRgsOHDxMeHm543T/+8Q/Cw8NZsmQJq1atYsaMGVy9WtqTUFxcjKOjIxs3bmTw4MHl3nfu3Lm8+eab5Y7LPzIrk5cO60eoQ2O2TmqKm1b9zN0qy6XTwdZZ6o4JAD0mQsQCy0y6XVKsLq7QB4UX95Xd51hjA427lvYWBnYxT17C+uLKOTiwSk2jk5+uHtPaQYdB6tzFpndIUF5VRXnqz/PPJaX5N5081f+XPZ5R/15f6ErUkY6odXDyx9ItLDVadc/o8McZEfslx9JO8E7vd7g/+H7DSyVIrBmL/tQ8f/48H330EdOnT+e1115j//79PP/889jb2zN27FiSktQNyX19fcu8ztfX13AuKSkJH5+yE19tbW1p2LChocyNZs6cyfTp0w3P9T2Jwopkp8Dnj0JytLrd2agN6heVuDmtFiL+Ba5+6urvvR+pk8kHf2wZixPSzl83hPwHFGSUPd+w+XVDyHer2xKK2tGohfpv5943yqbRid6oPnzaq0PRkkbn9gqy1IB79wdqbkFQ85v2nKxOkzFxLleLpLVRdwNq2VdNYn/sWzVgvLhXHSU6u5VQH1+OuThwNHYr9zeLkF9KjMSig0SdTkfXrl1ZsGABAJ06dSImJobly5czduxYk72vg4MDDg6lX4qZmRXssyos19W/1QndaefBxQee2GTUDP91Xq8p0MAPvpuofhjnpMKItbUfdOWlq3sO63sLr14oe97RXR0+bt4HWvRRU/kI87Jzgk6j1Mf1aXRSjsP/ZsDWOZJG52byrsLeFfDXh6W9se5N1GkfnZ4AO0ezNs9iOLqr+413eVJNBH9kPRz5ko45aXzp4sDRMz/A8d8hbKT1z622ABYdJPr7+5frwWvXrh3ffPMNAH5+fgAkJyfj71+6125ycrJh+NnPz4+UlJQydRQXF5OWlmZ4vahDUk6qaV2yLqmJjsd8V3/z/9VEx2s7IXw1Wl09GfkAjP5a7WU0lZIidR9tfVCYcFBNdq6ntVV3/2jeR+0xDAiXBRKWzJBGZ776RS5pdCqWnQJ7lqk/n8Is9VjDFmoam47Djb7fcZ3i1RL6zoI+r9PxxNdw4C1OONhTlHgau+1vqinPPHqYu5VWzaKDxDvvvJNTp8rmMDt9+jRBQUEABAcH4+fnx/bt2w1BYWZmJnv37mXixIkA9OzZk/T0dA4ePEiXLl0A+PXXX9HpdPToIf946pSEg/DFY5CXpk5sfuJbda9aUT0t+sCT/4O1Q9Vh+0/vU3tlvVoZp35FUeey6YPC2D9KvyT1vFqXBoXN7pShSmvk5KGm0enxrPoLx/5P4eT/1LnCcXtgsxd0fkJd7OIZZO7W1p6MePhzKRz6rHSOnW+IGhy2HyS/AFWFVkvT9kNxj15KRkEGp/u9TofTv0HcbjUnqqg2iw4SX3jhBXr16sWCBQsYNmwY+/btY8WKFaxYsQJQ98GdNm0a8+fPp1WrVoYUOAEBAQwaNAhQex7vv/9+JkyYwPLlyykqKmLy5MmMGDGiwpXNwkrF/g7rR0JhtprzbvQ3ktbEGALC4alf4ItH1eF7fdLtxl2rV19umjoB/fxv6vxC/YR8PaeGpSuQW/SpO2k9hDpHLLi3+rgxjc6u92HXYmh1nzrvzi8UHD3A3qXuzS27cg7+XAxR60FXpB4L7AK9X4LW99e9660lGo2GEK8Q/kz4k6NeTehw18/qZ9a2j4FF5m6e1bLo1c0AP/74IzNnzuTMmTMEBwczffp0w+pmUNPgzJkzhxUrVpCens5dd93Fhx9+SOvWrQ1l0tLSmDx5Mj/88ANarZYhQ4awdOlSGjSo3ARgWR1l4U7+DzaOg5IC9QtoxDrpcTK27MtqAupLh9SV4sM+g9YRt39dcaE6uVzfW3gpCsOesgA29uoQsj4o9AuzzNXUwjRKiuH0ZrV38fo0OnpaW3UOmuHhof7p5HGT555ln1vSUG3KCTWZf8w3pdMomt0Nd89QfzGS4LDGPoz6kI+OfMRDzR9iwd3qWobqfH9nZWUxa9Ysvv32W1JSUujUqRNLliyhW7duFZbfsWMHffr0KXc8MTGxzLS2ZcuW8c4775CUlERYWBj/+c9/6N7dDNuyVoHFB4mWwOqDxMJcdYXhwdXqPrXODdUeGyeP0r8bjnnecMzTsj5obxS1Hv47SU1/0vZBGLJSJnibSkE2bByr7sqisVH3vO78RNkyigKXT5UGhRd2QVFu2TI+7UuHkIN6qr1FQujT6Bz7FrKTQVdc8zrtnCsRWHqUBqHXH3NwNU7glnBIDQ5P/lh6rOV96r7KknHBqHYl7GLitokEuQXx42D1512d7+/hw4cTExPDRx99REBAAF988QXvv/8+x48fJzAwsFx5fZB46tQp3NzcDMd9fHzQXvul96uvvmLMmDEsX76cHj16sHjxYjZu3MipU6fKZWCxJBIkVoLVBomG3GWfq2kDqsveFZw9bxNM6o9dK+fobvrfjP/6CDa/qv49fBQ8tFRy4ZlaSRF8P0VdiABqypPOT5YdQr4+6S2Ai3dpUNj8HnDzR4hbUhT1l4u8dPWzKz9DXfGbn3HdsRufX3eswAgZKTTaW/RgXn/Ms+Iy+q0gz23XVwjtHlJ7DgPCa94+UU5GQQZ3fXkXALtG7MLdwd3w/X1jgHdjFhO9vLw8XF1d+e9//8sDDzxgON6lSxcGDBjA/Pnzy71GHyRevXoVDw+PCtvWo0cPunXrxgcfqHlodTodTZo0YcqUKbz66qs1uWyTkm/UukanU/NG7ftE/VPPIwi6PQ3N7lI/SHPT1JQLeVev/T2t7N9z064Floq6mKAwS93qrLI0NuqHZ0U9kzcNMBuqKTRuR1Fgx0LY+W/1+R2T1BWUMkxpejZ2MOgjdZXzrvfh1/nq43q2jurKVf0Qsk8HuTeiajQatYfZ3gXcy/fc3FZJsRoo3iywvGWwmQ4lheqQsP4zskbXYgOhj8Fd09W9ioXJuDu4E+QWxN+ZfxOdGs1dgXcZzt2YKWXOnDnMnTu3XB3FxcWUlJQYtvXVc3JyYteuXbd8//DwcAoKCggJCWHu3LnceeedABQWFnLw4EFmzpxpKKvVaunXrx979uyp6mXWKgkS64rcNDU7/4GVZfPJtbwPuk+o3n6quhL1Q/P6wLGiYDLv6rW/X/uzKFcd/s1NVR9XqvCetk63Hw6P36cOnYPak3X3izKfpzZpNNBvLrj6w8+vAAr4hqoBYYs+aoBYmWBfCFOxsVU/M6qzeE1R1NXGt+3FTK+4TH4moKjzbcMfhzunQcNgo12auLVQr1A1SLxcNkisqCexIq6urvTs2ZO33nqLdu3a4evry/r169mzZw8tW7as8DX+/v4sX76crl27UlBQwKeffso999zD3r176dy5M6mpqZSUlFS48cfJkyeNcNWmI0GitbsUBfs/URPW6tMoOLqryVe7jq9ZjkCtTfU+aIvyrwscbxFM3tiLqSuG4jzITFAftzPwXTUAFubR45/Qqr/a29PAcufUCFElGo36S46dU/WmRuhK1F1TbOxkvq0ZhHqF8uP5HzmaerTMcVdX1zLzBW/l888/Z/z48QQGBmJjY0Pnzp0ZOXIkBw8erLB8mzZtaNOmjeF5r169OHfuHO+//z6ff/559S/GAkiQaI2KC+D4f9Uh5fh9pcd9Q9WgKXQo2Dubr312jmDnX7UPWEVRh4fK9FJerTjYLClUr7P9I6a7BlE50kMiRFlaG3UURJhFR++OAESnRlPdJRctWrRg586d5OTkkJmZib+/P8OHD6d58+aVrqN79+6G4WkvLy9sbGxITk4uUyY5OdniN/WQINGaZMTDgUg1+WrOZfWY1k4NlrpPUFOJWOuwq0ZTOiFctlcTQghRDW0822CvtSejIIO4rDhsqH5SchcXF1xcXLh69Spbtmxh0aLK51uMiooy7ARnb29Ply5d2L59uyGHs06nY/v27UyePLna7asNEiRaOkVRE0Xv/wRO/qTO9QNwDYCu46DzWHD1vXUdQgghRD1gZ2NHu0btOHL5CEcvH6WTQ6cq17FlyxYURaFNmzacPXuWl156ibZt2zJu3DgAZs6cSUJCAmvWrAFg8eLFBAcH06FDB/Lz8/n000/59ddf+eWXXwx1Tp8+nbFjx9K1a1e6d+/O4sWLycnJMdRpqSRItFT5mXD0K3VIOfW6rQmb3a2uUm77gGXnLxRCCCHMINQrlCOXjxCdGk2nwKoHiRkZGcycOZP4+HgaNmzIkCFD+Ne//oWdnfqdm5iYSFxcabaPwsJCZsyYQUJCAs7OznTs2JFt27aVSbA9fPhwLl++zOzZs0lKSiI8PJzNmzeXW8xiaSRPYiXUap7ElJNqr+GRL9Ut5gDsXCBshBoc+ra/9euFEEKIeuzn2J95+feXCWkUwjvh71hnnmMLIT2JlqCkGE79BPtWwIU/So83aqXONQwboc7VE0IIIcQthXqFAnDy6kkKSwrN3BrrJkGiOWWnqItQDkSWpnzRaKHNQLXXUPbzFEIIIaoksEEgDR0bkpafxrmMc+ZujlWTILG2KQrE71d7DY99B7oi9bhzI3URStfx4NHErE0UQgghrJVGo6GjV0d2xO/gRPoJczfHqkmQWFsKcyHma3UhStJ1ST4Du17L+TdIzS8ohBBCiBoJ9Q5lR/wOTqZb9o4mlk6CRFNLOw/7V6pb5uWnq8dsHNS9PLs9DYGdzdo8IYQQoq7Rz0s8dfXUbUqKW5Eg0RR0Oji3XR1SPrMVuLaA3KMpdH1K3TLPpZFZmyiEEELUVSFeIWjQkJiXaO6mWDUJEo0pNw2i1qo9h1djS4+36KsOKbfqr27ZJIQQQgiTcbV3pbl7c07nnTZ3U6yaBInGkHhEnWsY/TUU56nHHNyh0yi159CrpXnbJ4QQQtQzod6hnE6SILEmJEisruJCOP5fNfH1xb2lx31D1F7D0KFg72K+9gkhhBD1WKhXKN/wjbmbYdUkSKyqjAQ4GAkHP4OcFPWY1hbaPQzdn4Gmd0huQyGEEMLMOnp3NHcTrJ4EiVXx/fNweQcoJerzBn7QdRx0eRJc/czZMiGEEEJcp6VHSxxtJLVcTUiQWBVnt4KbFoLuVIeU2z4INnbmbpUQQgghbmCrtaWVeysOc9jcTbFaEiRWRegwGDgdfDuYuyVCCCGEuI22nm3N3QSrpjV3A6zKfW9KgCiEEEJYiVGtRpm7CVZNgkQhhBBC1EnOts7mboJVkyBRCCGEEEKUI0GiEEIIIYQoR4JEIYQQQghRjgSJQgghhBCiHAkShRBCCCFEORIkCiGEEEKIciRIFEIIIYQQ5UiQKIQQQgghypEgUQghhBBClCNBohBCCCGEKEeCRCGEEEIIUY4EiUIIIYQQohwJEoUQQgghRDm25m6ANdDpdAAkJiaauSVCCCGEqCz997b+e1xUjQSJlXDx4kUAunfvbuaWCCGEEKKqLl68SNOmTc3dDKujURRFMXcjLF1aWhqNGjUiJiYGd3d3czenRu655x527Nhh7mbUSFZWFu3bt+f48eO4urqauznVJvfCcsi9sCzWfj/kXliOjIwMQkJCuHLlCg0bNjR3c6yO9CRWgq2t+mNq0qQJbm5uZm5Nzdjb29O4cWNzN6NGMjMzAQgMDLTq+yH3wnLIvbAs1n4/5F5YDv3PX/89LqpGFq7UM5MmTTJ3E8Q1ci8sh9wLyyL3w3LIvajfZLi5EjIzM3F3dycjI8PqfyusC+R+WA65F5ZD7oXlkHthOeRe1Iz0JFaCg4MDc+bMwcHBwdxNEcj9sCRyLyyH3AvLIffCcsi9qBnpSRRCCCGEEOVIT6IQQgghhChHgkQhhBBCCFGOBIlCCCGEEKIcCRKFEEIIIUQ59SZIXLhwId26dcPV1RUfHx8GDRrEqVOnypTJz89n0qRJNGrUiAYNGjBkyBCSk5PLlImLi+OBBx7A2dkZHx8fXnrpJYqLi8uU2bFjB507d8bBwYGWLVuyevVqU1+eVamte7Fp0ybuu+8+vL29cXNzo2fPnmzZsqVWrtFa1Ob/C70///wTW1tbwsPDTXVZVqk270VBQQGvv/46QUFBODg40KxZM1atWmXya7QmtXk/1q5dS1hYGM7Ozvj7+zN+/HiuXLli8mu0Fsa6F88//zxdunTBwcHhpp8/R48e5e6778bR0ZEmTZqwaNEiU12WdVDqiYiICCUyMlKJiYlRoqKilIEDBypNmzZVsrOzDWWeffZZpUmTJsr27duVAwcOKHfccYfSq1cvw/ni4mIlJCRE6devn3L48GHlp59+Ury8vJSZM2caypw/f15xdnZWpk+frhw/flz5z3/+o9jY2CibN2+u1eu1ZLV1L6ZOnar8+9//Vvbt26ecPn1amTlzpmJnZ6ccOnSoVq/XktXWvdC7evWq0rx5c6V///5KWFhYbVyi1ajNe/Hwww8rPXr0ULZu3arExsYqu3fvVnbt2lVr12oNaut+7Nq1S9FqtcqSJUuU8+fPK3/88YfSoUMHZfDgwbV6vZbMGPdCURRlypQpygcffKA88cQTFX7+ZGRkKL6+vsqoUaOUmJgYZf369YqTk5Py8ccfm/oSLVa9CRJvlJKSogDKzp07FUVRlPT0dMXOzk7ZuHGjocyJEycUQNmzZ4+iKIry008/KVqtVklKSjKU+eijjxQ3NzeloKBAURRFefnll5UOHTqUea/hw4crERERpr4kq2Wqe1GR9u3bK2+++aaJrsT6mfpeDB8+XHnjjTeUOXPmSJB4G6a6Fz///LPi7u6uXLlypRavxvqZ6n688847SvPmzcu819KlS5XAwEBTX5LVqs69uN7NPn8+/PBDxdPTs8zn1iuvvKK0adPG+BdhJerNcPONMjIyAAwbfh88eJCioiL69etnKNO2bVuaNm3Knj17ANizZw+hoaH4+voaykRERJCZmcmxY8cMZa6vQ19GX4coz1T34kY6nY6srCzZ5P0WTHkvIiMjOX/+PHPmzKmNS7F6proX33//PV27dmXRokUEBgbSunVrXnzxRfLy8mrr0qySqe5Hz549uXjxIj/99BOKopCcnMzXX3/NwIEDa+vSrE517kVl7Nmzh969e2Nvb284FhERwalTp7h69aqRWm9d6uWO1zqdjmnTpnHnnXcSEhICQFJSEvb29nh4eJQp6+vrS1JSkqHM9f/Z9ef1525VJjMzk7y8PJycnExxSVbLlPfiRu+++y7Z2dkMGzbMyFdRN5jyXpw5c4ZXX32VP/74A1vbevmxUyWmvBfnz59n165dODo68u2335Kamspzzz3HlStXiIyMNPGVWSdT3o8777yTtWvXMnz4cPLz8ykuLuahhx5i2bJlJr4q61Tde1EZSUlJBAcHl6tDf87T07NmjbdC9fLTetKkScTExLBr1y5zN6Xeq617sW7dOt58803++9//4uPjY9L3slamuhclJSU8/vjjvPnmm7Ru3dqodddVpvx/odPp0Gg0rF27Fnd3dwDee+89HnvsMT788EP5RbYCprwfx48fZ+rUqcyePZuIiAgSExN56aWXePbZZ1m5cqXR38/ayfd37ap3w82TJ0/mxx9/5LfffqNx48aG435+fhQWFpKenl6mfHJyMn5+foYyN66W0j+/XRk3Nzf58L2Bqe+F3pdffsnTTz/Nhg0byk0FECpT3ousrCwOHDjA5MmTsbW1xdbWlnnz5nHkyBFsbW359ddfTXtxVsbU/y/8/f0JDAw0BIgA7dq1Q1EU4uPjTXFJVs3U92PhwoXceeedvPTSS3Ts2JGIiAg+/PBDVq1aRWJiogmvzPrU5F5URlW+V+oNc0+KrC06nU6ZNGmSEhAQoJw+fbrcef3E16+//tpw7OTJkxVOQk5OTjaU+fjjjxU3NzclPz9fURR14UpISEiZukeOHCkLV65TW/dCURRl3bp1iqOjo/Ldd9+Z8IqsV23ci5KSEiU6OrrMY+LEiUqbNm2U6OjoMisU67Pa+n/x8ccfK05OTkpWVpahzHfffadotVolNzfXVJdndWrrfjz66KPKsGHDytS9e/duBVASEhJMcWlWxxj34nq3W7hSWFhoODZz5sx6vXCl3gSJEydOVNzd3ZUdO3YoiYmJhsf1H4rPPvus0rRpU+XXX39VDhw4oPTs2VPp2bOn4bw+nUH//v2VqKgoZfPmzYq3t3eFKXBeeukl5cSJE8qyZcskBc4NauterF27VrG1tVWWLVtW5n3S09Nr9XotWW3dixvJ6ubyauteZGVlKY0bN1Yee+wx5dixY8rOnTuVVq1aKU8//XStXq+lq637ERkZqdja2ioffvihcu7cOWXXrl1K165dle7du9fq9VoyY9wLRVGUM2fOKIcPH1b++c9/Kq1bt1YOHz6sHD582LCaOT09XfH19VWeeOIJJSYmRvnyyy8VZ2dnSYFTHwAVPiIjIw1l8vLylOeee07x9PRUnJ2dlcGDByuJiYll6rlw4YIyYMAAxcnJSfHy8lJmzJihFBUVlSnz22+/KeHh4Yq9vb3SvHnzMu8hau9e/OMf/6jwfcaOHVtLV2r5avP/xfUkSCyvNu/FiRMnlH79+ilOTk5K48aNlenTp0sv4g1q834sXbpUad++veLk5KT4+/sro0aNUuLj42vjMq2Cse7Fzb4TYmNjDWWOHDmi3HXXXYqDg4MSGBiovP3227V0lZZJoyiKYtTxayGEEEIIYfXq3cIVIYQQQghxexIkCiGEEEKIciRIFEIIIYQQ5UiQKIQQQgghypEgUQghhBBClCNBohBCCCGEKEeCRCGEEEIIUY4EiUIIIYQQohwJEoUQQgghRDkSJAohaoWiKPTr14+IiIhy5z788ENsbGywsbHh7NmzZc5dunQJT09P5s+fj5+fHwsWLCj3+mHDhnHHHXcwbtw4QkNDKSwsLHP+p59+wt7enkOHDnHhwgU0Gg02NjYkJCSUKZeYmIitrS0ajYYLFy4AVLu8/uHq6kqHDh2YNGkSZ86cKfP61atXG8rZ2Njg6elJjx49mDdvHhkZGZX6uQohhKlIkCiEqBUajYbIyEj27t3Lxx9/bDgeGxvLyy+/zOrVq3nkkUd48skn0el0hvMTJkygS5cuvP7666xYsYI333yT6Ohow/mNGzfy448/8tlnn7FkyRKysrKYM2eO4Xx6ejoTJkxg1qxZdO7c2XA8MDCQNWvWlGnjZ599RmBgYIXtr2r5bdu2kZiYyJEjR1iwYAEnTpwgLCyM7du3lynn5uZGYmIi8fHx7N69m2eeeYY1a9YQHh7OpUuXbvbjFEII0zPz3tFCiHpm9erVSoMGDZTz588rOp1O6dOnjzJ48GBFURQlJSVF8fb2Vt555x1FURQlMjJScXd3V+Li4gyvf/LJJ5VOnTophYWFhvJLliwxnP/1118VOzs75a+//lIURVHGjh2rdOvWTSkqKlIURVFiY2MVQHnjjTeUVq1alWlb69atlVmzZimAEhsbW6Pyhw8fLlO2pKREueeee5SgoCCluLi4zPXdKDk5WfHy8lJGjRpVhZ+sEEIYlwSJQoha98gjjyj33HOPsnTpUsXb21tJSUkxnPv2228VR0dH5ZdfflE8PDyU1atXl3ltRkaG0rRpU2XWrFnKY489pvTp00fR6XRlykydOlVp06aNsmHDBsXJyUk5ceKE4Zw+iNu3b5/i5eWl/PHHH4qiKMoff/yheHt7K/v27asw6Ktq+RuDRP21AcrevXsVRbl5kKi/BldXV0NAKYQQtU2Gm4UQtW7FihXExMQwbdo0VqxYgbe3t+HcoEGDGDZsGPfffz//+Mc/GDt2bJnXurm5ERkZyYIFC/jll1+IjIxEo9GUKbNw4UIARowYwYIFC2jbtm25NtjZ2TF69GhWrVoFwKpVqxg9ejR2dnYVtrmq5Suib4d+/uLtymZlZXHlypVK1y+EEMYkQaIQotb5+Pjwz3/+k3bt2jFo0KBy52fNmoVOp+ONN96o8PX33nsvd9xxB0888QRBQUHlzjs5OfHiiy/i7OzM1KlTb9qO8ePHs3HjRpKSkti4cSPjx4+/ZburWv5GiqIAlAtqa1pWCCFMQYJEIYRZ2NraYmtre9Nz1/9Z1dfrz9vY2NwyyAoNDaVt27aMHDmSdu3aERIScss2V7X8jU6cOAFAcHBwpcq6ubnRqFGjKr2HEEIYiwSJQoh6bfz48ezYsaPSvYJVLa+n0+lYunQpwcHBdOrU6ZZlU1JSWLduHYMGDUKrlY9pIYR53PzXcCGEqAcmTJjA0KFD8fDwMGr5K1eukJSURG5uLjExMSxevJh9+/bxv//9DxsbG0M5RVFISkpCURTS09PZs2cPCxYswN3dnbfffrsGVyaEEDUjQaIQol6ztbXFy8vL6OX79esHgLOzM0FBQfTp04cVK1bQsmXLMuUyMzPx9/dHo9Hg5uZGmzZtGDt2LFOnTsXNza1qFyOEEEakUfSzo4UQQgghhLhGJrsIIYQQQohyJEgUQgghhBDlSJAohBBCCCHKkSBRCCGEEEKUI0GiEEIIIYQoR4JEIYQQQghRjgSJQgghhBCiHAkShRBCCCFEORIkCiGEEEKIciRIFEIIIYQQ5UiQKIQQQgghyvl/Rx0UHVr1K/wAAAAASUVORK5CYII=", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2365,9 +2364,9 @@ "weather.RH = 0.1 * weather.RH\n", "weather.EV24 = 0.1 * weather.EV24\n", "weather.loc[weather.RH < 0, 'RH'] = 0\n", - "yearly_rain = weather.RH.resample('A', kind='period').sum()\n", - "yearly_evap = weather.EV24.resample('A', kind='period').sum()\n", - "yearly_temp = weather.TG.resample('A', kind='period').mean()\n", + "yearly_rain = weather.RH.resample('YE').sum()\n", + "yearly_evap = weather.EV24.resample('YE').sum()\n", + "yearly_temp = weather.TG.resample('YE').mean()\n", "ax1 = yearly_rain.plot()\n", "ax1 = yearly_evap.plot()\n", "plt.ylabel('Rain/evap (mm/year)')\n", @@ -2384,11 +2383,16 @@ "source": [ "Back to Exercise 5" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "exploratory-computing-with-python (3.13.5)", "language": "python", "name": "python3" }, @@ -2402,7 +2406,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.13.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/notebook9_discrete_random_variables/py_exploratory_comp_9_sol.ipynb b/notebook9_discrete_random_variables/py_exploratory_comp_9_sol.ipynb index a8fea41..84ddece 100644 --- a/notebook9_discrete_random_variables/py_exploratory_comp_9_sol.ipynb +++ b/notebook9_discrete_random_variables/py_exploratory_comp_9_sol.ipynb @@ -48,7 +48,7 @@ { "data": { "text/plain": [ - "array([0, 1, 1, 0, 1, 1, 1, 0, 1, 0])" + "array([0, 1, 0, 1, 1, 1, 1, 0, 1, 1])" ] }, "execution_count": 2, @@ -75,7 +75,7 @@ { "data": { "text/plain": [ - "array([0, 0, 1, 0, 1, 0, 1, 1, 0, 1])" + "array([0, 1, 0, 1, 0, 0, 1, 0, 0, 0])" ] }, "execution_count": 3, @@ -311,7 +311,7 @@ { "data": { "text/plain": [ - "True" + "np.True_" ] }, "execution_count": 10, @@ -324,7 +324,7 @@ "flips1 = rnd.randint(low=0, high=1 + 1, size=5)\n", "rnd.seed(55)\n", "flips2 = rnd.choice(range(2), size=5, replace=True)\n", - "np.alltrue(flips1 == flips2) # Check whether all values in the two arrays are equal" + "np.all(flips1 == flips2) # Check whether all values in the two arrays are equal" ] }, { @@ -342,14 +342,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", 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", 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\n", 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", 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fdUqSttVbQCRZBFwAvAZYBpyWZNlQszcB91XVc4EPAO9ut10GnAq8EDgB+Jt2f5KkXaTPM4gjgA1VtbGqHgMuBU4aanMScEk7fRlwbJK0yy+tqker6ofAhnZ/kqRdZPce930AcPfA/CbgyO21qaqtSe4HntUuv2Fo2wOGPyDJmcCZ7exDSW7fOaX3Zn/gJ+MuYidZKMeyUI4DPJa5qvdjybt3aPPnbG9FnwGRjmU1YptRtqWqLgIumnlp45FkbVVNjLuOnWGhHMtCOQ7wWOaq+XwsfXYxbQIOGpg/ENi8vTZJdgf2Be4dcVtJUo/6DIg1wNIkhyTZg+ai8+qhNquB09vpk4Grq6ra5ae2dzkdAiwFvt1jrZKkIb11MbXXFM4CrgQWAauqan2SlcDaqloNfBz4VJINNGcOp7bbrk/yOeA2YCvw1qp6vK9ad6F50x02goVyLAvlOMBjmavm7bGk+YNdkqSf5ZPUkqROBoQkqZMBsQskWZXkniTfHXctOyLJQUmuSfK9JOuT/Mm4a5qtJHsl+XaS77TH8q5x17SjkixKclOS/zHuWnZEkjuT3Jrk5iRrx13PbCV5RpLLkny//X/m5eOuaaa8BrELJDkGeAj4ZFX96rjrma0kzwaeXVU3Jnk6sA743aq6bcylzVj7xP7TquqhJE8Brgf+pKpumGbTOSvJOcAEsE9VvXbc9cxWkjuBiaqa1w/KJbkEuK6qPtbeyfnUqvqXcdc1E55B7AJV9TWau7Tmtar6cVXd2E4/CHyPjifc54NqPNTOPqX9mrd/LSU5EPgd4GPjrkWQZB/gGJo7Namqx+ZbOIABoVlqR949HPjWeCuZvbZL5mbgHuCqqpq3xwJ8EPhT4IlxF7ITFPDlJOva4XTmo0OBLcAn2m6/jyV52riLmikDQjOWZG/gC8DbquqBcdczW1X1eFUtp3lS/4gk87L7L8lrgXuqat24a9lJjqqql9CMBP3Wtot2vtkdeAnwkao6HPi/wDavPJjrDAjNSNtf/wXg76vqH8Zdz87QnvpfSzO0/Hx0FHBi23d/KfBbSf5uvCXNXlVtbr/fA3yR+TmS8yZg08BZ6WU0gTGvGBAaWXth9+PA96rq/eOuZ0ckWZzkGe30LwDHAd8fb1WzU1XvrKoDq2oJzWgEV1fVG8Zc1qwkeVp7AwRtl8zxwLy7+6+q/jdwd5LntYuOpRkZYl7pczRXtZJ8BvhNYP8km4Dzqurj461qVo4C/hC4te27B/iPVXXFGGuarWcDl7QvotoN+FxVzevbQxeIXwK+2Pwtwu7Ap6vqS+Mtadb+A/D37R1MG4E/GnM9M+ZtrpKkTnYxSZI6GRCSpE4GhCSpkwEhSepkQEiSOhkQ6lWSJdsbxTbJyiTHTbP9XyR5x06o44wkvzxCu3+tN8lEkg/thM+e9jiH2i9P8ts7+rlD+1yS5A8G5s9I8tc78zO08PgchMamqs7dhR93Bs0DV5tH3aCq1gI7PNz0LI5zOc2orDvz+ZIlwB8An57JRkkWLZDX/WoWPIPQrrAoyd+27134cvvkMkkuTnJyO/3b7bj51yf50NA7DZYluTbJxiRnTy5M8ob2nQ43J7mwHXxvUbvf77bvFHh7+xkTNA8t3Tz5+QP7eWn7XohvAm8dWP6bk3Uk2TvJJ9p93pLkde3y45N8M8mNST7fjlPF0P4Hj/POJO9q29+a5PlDbfcAVgKntLWe0rZ7Rhr/nOSNbdtPJTkuzbstJmu7KckrO/4Nzgde0e7z7e2yX07ypSR3JHnPQA0PtWc93wJenuTYdr+3pnm3yZ5JjkjyD237k5I8nGSPtpaN7fKzk9zW/rwu3d5/HJq7DAjtCkuBC6rqhcC/AK8bXJlkL+BC4DVVdTSweGj75wOvphmT57wkT0nyAuAUmoHdlgOPA6+n+ev7gKr61ao6DPhEVV1Gcybw+qpaXlUPD+3/E8DZVTXVC13+E3B/VR1WVS8Crk6yP/DnwHHt4HJrgXNG+Hn8pG3/EeBnus+q6jHgXOCzba2fBb5O8xT7C2meyH1F2/zXgBtoQ6093tNonhDfa+gzV9C8m2B5VX2gXbac5md4GE0gHdQufxrw3ao6sj2mi4FT2v3vDvwxcCPNaL609XwXeBlwJE+O8LsCOLz9eb15hJ+L5hgDQrvCD6tqcmiOdTTdHYOeD2ysqh+2858ZWn95VT3avkDmHprhGI4FXgqsaYf9OJZmiOWNwKFJPpzkBGDK0WaT7As8o6q+2i761HaaHgdcMDlTVffR/IJeBny9reF04DlTfV5rcpDDrp9Fl+to3i1wDE2oHJbkAODe9p0WR0/WXVXfB34E/MoI+/1KVd1fVY/QjBM0WfvjNAMyAjyP5t/vn9r5S4BjqmorsKEN6iOA97f1vaKtF+AWmrO2NwBbR6hHc4wBoV3h0YHpx9n22ldmsX2AS9q/iJdX1fOq6i/aX9wvphmd9a1M/wKdMNqLgrraheY9EpM1LKuqN42wr8nj6fpZdPkazS/eV9Ac1xbgZJ78RTzdz2+6OoZreWTgusNU+76OZkju/wf8T5qgOrqtF5oXGF1AE+TrknjNc54xIDQXfJ/mr/4l7fwpI2zzFeDkJL8IkOSZSZ7TdvvsVlVfoOkWmhxi+UHg6cM7aYf6vj/J0e2i12/n874MnDU5k2Q/mu6do5I8t1321CSj/OU+nZ+ptaruBvYHllbVRprXo76DJwPia5N1t59/MHD7VPucge8DSyaPkWawxsmzra8BbwO+WVVbgGfRnA2uT7IbcFBVXUPzIqNnANtcn9HcZkBo7NprAm8BvpTkeuD/APdPs81tNP3/X05yC3AVzQitBwDXtl0+FwPvbDe5GPho10VqmlE2L2gvUg9fn5j0V8B+7cXv7wCvbH8pngF8pq3hBppfkDvqGpoL8zcnmQzLbwGT3TzX0Rzn9e3839DcCHAr8FngjKoaPDuAprtna3sx/u2MqO1++iPg8+3+nwA+OlDTL/HkGcMtwC3VjAC6CPi7dpubgA/Mx1du/rxzNFfNCUn2rqqHkoSmW+KOgYupksbAMwjNFf++/at/PbAvzV1NksbIMwhJUifPICRJnQwISVInA0KS1MmAkCR1MiAkSZ3+P1RI1U3GbT8rAAAAAElFTkSuQmCC\n", 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QUEAIAgDgGlbmANS7d295eXlJkvr06VNV9VRY9+7d7X9HRkYqJiZG9evX1/vvv69BgwaVuo+Xl5d9bgAA4NpX5gCUkpJS6t/lFRQUJHd3d+Xl5Tm05+XlXdH6nT9Tu3ZtNW3aVLt37660MQEAQPVWoTVAGzZs0Lx58zRv3jxlZWVd0b6enp5q37690tPT7W0lJSVKT09XbGxsRcpycPr0ae3Zs0ehoaGVNiYAAKjeyvX78EOHDql///5as2aNateuLUk6efKkOnXqpAULFuiGG24o0zhJSUkaOHCgoqOj1bFjR02ZMkWFhYX2X4UNGDBA9erVU2pqqqRfF05///339r8PHz6s7Oxs1apVS40bN5YkPfXUU+rVq5fq16+vI0eOKCUlRe7u7urfv395pgoAAK5B5ToDNHjwYJ07d07btm3TiRMndOLECW3btk0lJSUaPHhwmcfp27evJk6cqOTkZEVFRSk7O1vLli2zL4w+cOCAcnJy7P2PHDmitm3bqm3btsrJydHEiRPVtm1bh8+8EM6aNWume+65R3Xq1NHatWt1/fXXl2eqAADgGmQzxpgr3cnHx0dff/212rZt69CelZWlLl266MyZM5VWoDMUFBTI399f+fn58vPzc3Y5QJnwLDAA1cX+CT2qZNwr+f4u1xmg8PBw+5Phf6+4uFhhYWHlGRIAAOCqKVcAevXVVzVs2DBt2LDB3rZhwwYNHz5cEydOrLTiAAAAqkKZF0EHBAQ43PywsLBQMTEx9udsnT9/Xh4eHnrwwQdd+j5BAAAAZQ5AU6ZMqcIyAAAArp4yB6CBAwdWZR0AAABXTbnuA/R7v/zyi86ePevQxi+nAACAKyvXIujCwkINHTpUdevWVc2aNRUQEOCwAQAAuLJyBaBnnnlG//nPfzR9+nR5eXlp5syZGjt2rMLCwjR37tzKrhEAAKBSlesS2Mcff6y5c+eqa9euSkxMVJcuXdS4cWPVr19f7733nu69997KrhMAAKDSlOsM0IkTJ9SwYUNJv673OXHihCTplltu0erVqyuvOgAAgCpQrgDUsGFD7du3T5LUvHlzvf/++5J+PTN04eGoAAAArqpcASgxMVHffvutJGnkyJGaNm2avL299cQTT+jpp5+u1AIBAAAqW7nWAD3xxBP2v+Pj47Vt2zZt3LhRjRs3VmRkZKUVBwAAUBUqfB8gSYqIiFBERERlDAUAAFDlynUJTJLS09PVs2dPNWrUSI0aNVLPnj31xRdfVGZtAAAAVaJcAejNN99Ut27d5Ovrq+HDh2v48OHy8/PT//zP/2jatGmVXSMAAEClKtclsPHjx2vy5MkaOnSove3xxx9X586dNX78eD322GOVViAAAEBlK9cZoJMnT6pbt24Xtd9+++3Kz8+vcFEAAABVqVwB6I477tDixYsvav/oo4/Us2fPChcFAABQlcp8CWzq1Kn2v1u2bKlx48YpIyNDsbGxkqS1a9dqzZo1evLJJyu/SgAAgEpkM8aYsnRs0KBB2Qa02bR3794KFeVsBQUF8vf3V35+vvz8/JxdDlAmESM/dXYJAFAm+yf0qJJxr+T7u8xngC48+gIAAKC6K/d9gC4wxqiMJ5EAAABcQrkD0Ny5c9W6dWv5+PjIx8dHkZGRmjdvXmXWBgAAUCXKdR+gSZMm6fnnn9fQoUPVuXNnSdJXX32lhx9+WMeOHXN4VhgAAICrKVcAev311zV9+nQNGDDA3nbHHXfopptu0pgxYwhAAADApZXrElhOTo46dep0UXunTp2Uk5NT4aIAAACqUrkCUOPGjfX+++9f1L5w4UI1adKkwkUBAABUpXJdAhs7dqz69u2r1atX29cArVmzRunp6aUGIwAAAFdSrjNAf/vb37R+/XoFBQVpyZIlWrJkiYKCgrR+/Xr99a9/rewaAQAAKtUVnwE6d+6c/t//+396/vnn9e6771ZFTQAAAFXqis8AXXfddfrwww+rohYAAICrolyXwPr06aMlS5ZUcikAAABXR7kWQTdp0kQvvPCC1qxZo/bt26tmzZoO7z/++OOVUhwAAEBVKFcAevvtt1W7dm1lZWUpKyvL4T2bzUYAAgAALq1cAej3T4a/8CBUm81WORUBAABUsXI/DPXtt99Wq1at5O3tLW9vb7Vq1UozZ86szNoAAACqRLnOACUnJ2vSpEkaNmyYYmNjJUmZmZl64okndODAAb3wwguVWiQAAEBlKlcAmj59utLS0tS/f3972x133KHIyEgNGzaMAAQAAFxauS6BnTt3TtHR0Re1t2/fXufPn69wUQAAAFWpXAHo/vvv1/Tp0y9qf+utt3TvvfdWuCgAAICqVK5LYNKvi6BXrFihm2++WZK0bt06HThwQAMGDFBSUpK936RJkypeJQAAQCUqVwDasmWL2rVrJ0nas2ePJCkoKEhBQUHasmWLvR8/jQcAAK6oXAFo5cqVlV0HAADAVVPu+wABAABUVwQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOU4PQNOmTVNERIS8vb0VExOj9evXX7Lv1q1b9be//U0RERGy2WyaMmVKhccEAADW49QAtHDhQiUlJSklJUUbN25UmzZtlJCQoKNHj5ba/8yZM2rYsKEmTJigkJCQShkTAABYj1MD0KRJkzRkyBAlJiaqZcuWmjFjhmrUqKF33nmn1P4dOnTQq6++qn79+snLy6tSxgQAANbjtAB09uxZZWVlKT4+/rdi3NwUHx+vzMzMqzpmUVGRCgoKHDYAAHDtcloAOnbsmIqLixUcHOzQHhwcrNzc3Ks6Zmpqqvz9/e1beHh4uT4fAABUD05fBO0KRo0apfz8fPt28OBBZ5cEAACqkIezPjgoKEju7u7Ky8tzaM/Ly7vkAueqGtPLy+uSa4oAAMC1x2lngDw9PdW+fXulp6fb20pKSpSenq7Y2FiXGRMAAFx7nHYGSJKSkpI0cOBARUdHq2PHjpoyZYoKCwuVmJgoSRowYIDq1aun1NRUSb8ucv7+++/tfx8+fFjZ2dmqVauWGjduXKYxAQAAnBqA+vbtqx9//FHJycnKzc1VVFSUli1bZl/EfODAAbm5/XaS6siRI2rbtq399cSJEzVx4kTFxcUpIyOjTGMCAADYjDHG2UW4moKCAvn7+ys/P19+fn7OLgcok4iRnzq7BAAok/0TelTJuFfy/c2vwAAAgOUQgAAAgOU4dQ0Q8Ge4rAMAqAqcAQIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJbjEgFo2rRpioiIkLe3t2JiYrR+/frL9v/ggw/UvHlzeXt7q3Xr1lq6dKnD+w888IBsNpvD1q1bt6qcAgAAqEacHoAWLlyopKQkpaSkaOPGjWrTpo0SEhJ09OjRUvt//fXX6t+/vwYNGqRNmzapT58+6tOnj7Zs2eLQr1u3bsrJybFv//rXv67GdAAAQDVgM8YYZxYQExOjDh066I033pAklZSUKDw8XMOGDdPIkSMv6t+3b18VFhbqk08+sbfdfPPNioqK0owZMyT9egbo5MmTWrJkSZlqKCoqUlFRkf11QUGBwsPDlZ+fLz8/vwrMDhUVMfJTZ5cAAKhk+yf0qJJxCwoK5O/vX6bvb6eeATp79qyysrIUHx9vb3Nzc1N8fLwyMzNL3SczM9OhvyQlJCRc1D8jI0N169ZVs2bN9Mgjj+j48eOXrCM1NVX+/v72LTw8vAKzAgAArs6pAejYsWMqLi5WcHCwQ3twcLByc3NL3Sc3N/dP+3fr1k1z585Venq6Xn75Za1atUrdu3dXcXFxqWOOGjVK+fn59u3gwYMVnBkAAHBlHs4uoCr069fP/nfr1q0VGRmpRo0aKSMjQ7fddttF/b28vOTl5XU1SwQAAE7k1DNAQUFBcnd3V15enkN7Xl6eQkJCSt0nJCTkivpLUsOGDRUUFKTdu3dXvGgAAFDtOTUAeXp6qn379kpPT7e3lZSUKD09XbGxsaXuExsb69Bfkj7//PNL9pekQ4cO6fjx4woNDa2cwgEAQLXm9J/BJyUlKS0tTXPmzNG2bdv0yCOPqLCwUImJiZKkAQMGaNSoUfb+w4cP17Jly/Taa69p+/btGjNmjDZs2KChQ4dKkk6fPq2nn35aa9eu1f79+5Wenq7evXurcePGSkhIcMocAQCAa3H6GqC+ffvqxx9/VHJysnJzcxUVFaVly5bZFzofOHBAbm6/5bROnTpp/vz5+sc//qHRo0erSZMmWrJkiVq1aiVJcnd313fffac5c+bo5MmTCgsL0+23364XX3yRdT4AAECSC9wHyBVdyX0EULW4DxAAXHssfx8gAAAAZyAAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAy3H6w1CtiOdbAQDgXJwBAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAlkMAAgAAluMSAWjatGmKiIiQt7e3YmJitH79+sv2/+CDD9S8eXN5e3urdevWWrp0qcP7xhglJycrNDRUPj4+io+P165du6pyCgAAoBpxegBauHChkpKSlJKSoo0bN6pNmzZKSEjQ0aNHS+3/9ddfq3///ho0aJA2bdqkPn36qE+fPtqyZYu9zyuvvKKpU6dqxowZWrdunWrWrKmEhAT98ssvV2taAADAhdmMMcaZBcTExKhDhw564403JEklJSUKDw/XsGHDNHLkyIv69+3bV4WFhfrkk0/sbTfffLOioqI0Y8YMGWMUFhamJ598Uk899ZQkKT8/X8HBwZo9e7b69ev3pzUVFBTI399f+fn58vPzq6SZ/iZi5KeVPiYAANXF/gk9qmTcK/n+9qiSCsro7NmzysrK0qhRo+xtbm5uio+PV2ZmZqn7ZGZmKikpyaEtISFBS5YskSTt27dPubm5io+Pt7/v7++vmJgYZWZmlhqAioqKVFRUZH+dn58v6dcDWRVKis5UybgAAFQHVfX9emHcspzbcWoAOnbsmIqLixUcHOzQHhwcrO3bt5e6T25ubqn9c3Nz7e9faLtUnz9KTU3V2LFjL2oPDw8v20QAAECZ+U+p2vFPnTolf3//y/ZxagByFaNGjXI4q1RSUqITJ06oTp06stlsTqzs6igoKFB4eLgOHjxYJZf8riUcq7LjWF0ZjlfZcazKzmrHyhijU6dOKSws7E/7OjUABQUFyd3dXXl5eQ7teXl5CgkJKXWfkJCQy/a/8J95eXkKDQ116BMVFVXqmF5eXvLy8nJoq1279pVM5Zrg5+dnif+BVAaOVdlxrK4Mx6vsOFZlZ6Vj9Wdnfi5w6q/APD091b59e6Wnp9vbSkpKlJ6ertjY2FL3iY2NdegvSZ9//rm9f4MGDRQSEuLQp6CgQOvWrbvkmAAAwFqcfgksKSlJAwcOVHR0tDp27KgpU6aosLBQiYmJkqQBAwaoXr16Sk1NlSQNHz5ccXFxeu2119SjRw8tWLBAGzZs0FtvvSVJstlsGjFihF566SU1adJEDRo00PPPP6+wsDD16dPHWdMEAAAuxOkBqG/fvvrxxx+VnJys3NxcRUVFadmyZfZFzAcOHJCb228nqjp16qT58+frH//4h0aPHq0mTZpoyZIlatWqlb3PM888o8LCQj300EM6efKkbrnlFi1btkze3t5XfX7VgZeXl1JSUi66DIiLcazKjmN1ZTheZcexKjuO1aU5/T5AAAAAV5vT7wQNAABwtRGAAACA5RCAAACA5RCAAACA5RCALGz16tXq1auXwsLCZLPZ7M9Tw8VSU1PVoUMH+fr6qm7duurTp4927Njh7LJc0vTp0xUZGWm/8VpsbKw+++wzZ5dVLUyYMMF+Kw84GjNmjGw2m8PWvHlzZ5flsg4fPqz77rtPderUkY+Pj1q3bq0NGzY4uyyXQgCysMLCQrVp00bTpk1zdikub9WqVXrssce0du1aff755zp37pxuv/12FRYWOrs0l3PDDTdowoQJysrK0oYNG/SXv/xFvXv31tatW51dmkv75ptv9L//+7+KjIx0diku66abblJOTo59++qrr5xdkkv66aef1LlzZ1133XX67LPP9P333+u1115TQECAs0tzKU6/DxCcp3v37urevbuzy6gWli1b5vB69uzZqlu3rrKysnTrrbc6qSrX1KtXL4fX48aN0/Tp07V27VrddNNNTqrKtZ0+fVr33nuv0tLS9NJLLzm7HJfl4eFxycck4Tcvv/yywsPDNWvWLHtbgwYNnFiRa+IMEFAO+fn5kqTAwEAnV+LaiouLtWDBAhUWFvIomst47LHH1KNHD8XHxzu7FJe2a9cuhYWFqWHDhrr33nt14MABZ5fkkv79738rOjpad999t+rWrau2bdsqLS3N2WW5HM4AAVeopKREI0aMUOfOnR3uQI7fbN68WbGxsfrll19Uq1YtLV68WC1btnR2WS5pwYIF2rhxo7755htnl+LSYmJiNHv2bDVr1kw5OTkaO3asunTpoi1btsjX19fZ5bmUvXv3avr06UpKStLo0aP1zTff6PHHH5enp6cGDhzo7PJcBgEIuEKPPfaYtmzZwvqDy2jWrJmys7OVn5+v//u//9PAgQO1atUqQtAfHDx4UMOHD9fnn3/Oo3r+xO8v10dGRiomJkb169fX+++/r0GDBjmxMtdTUlKi6OhojR8/XpLUtm1bbdmyRTNmzCAA/Q6XwIArMHToUH3yySdauXKlbrjhBmeX47I8PT3VuHFjtW/fXqmpqWrTpo3++c9/Orssl5OVlaWjR4+qXbt28vDwkIeHh1atWqWpU6fKw8NDxcXFzi7RZdWuXVtNmzbV7t27nV2KywkNDb3oXzZatGjBJcM/4AwQUAbGGA0bNkyLFy9WRkYGCwqvUElJiYqKipxdhsu57bbbtHnzZoe2xMRENW/eXM8++6zc3d2dVJnrO336tPbs2aP777/f2aW4nM6dO190m46dO3eqfv36TqrINRGALOz06dMO//a0b98+ZWdnKzAwUDfeeKMTK3M9jz32mObPn6+PPvpIvr6+ys3NlST5+/vLx8fHydW5llGjRql79+668cYbderUKc2fP18ZGRlavny5s0tzOb6+vhetI6tZs6bq1KnD+rI/eOqpp9SrVy/Vr19fR44cUUpKitzd3dW/f39nl+ZynnjiCXXq1Enjx4/XPffco/Xr1+utt97SW2+95ezSXIuBZa1cudJIumgbOHCgs0tzOaUdJ0lm1qxZzi7N5Tz44IOmfv36xtPT01x//fXmtttuMytWrHB2WdVGXFycGT58uLPLcDl9+/Y1oaGhxtPT09SrV8/07dvX7N6929lluayPP/7YtGrVynh5eZnmzZubt956y9kluRybMcY4KXsBAAA4BYugAQCA5RCAAACA5RCAAACA5RCAAACA5RCAAACA5RCAAACA5RCAAACA5RCAAACA5RCAABfStWtXjRgx4rJ9bDablixZUuYxMzIyZLPZdPLkyQrVVlV+P5/9+/fLZrMpOzu7yj6vLMe4OhkzZoyioqKcXQZQ7RCAgGomJydH3bt3d3YZDiIiIjRlypQKjxMeHq6cnJwqfQ7WokWL9OKLL1ZoDGeFqCsNvwAujYehAtVMSEiIs0uoMu7u7lU+v8DAwCodvzo4e/asPD09nV0G4FScAQJcTElJiZ555hkFBgYqJCREY8aMcXj/j2cBvv76a0VFRcnb21vR0dFasmRJqZeRsrKyFB0drRo1aqhTp07asWOHw/sfffSR2rVrJ29vbzVs2FBjx47V+fPnJUnGGI0ZM0Y33nijvLy8FBYWpscff1zSr2dDfvjhBz3xxBOy2Wyy2WyXnNuuXbt06623ytvbWy1bttTnn3/u8H5pl8C2bt2qnj17ys/PT76+vurSpYv27Nljf3/mzJlq0aKFvL291bx5c7355puXPb5/PHsTERGh8ePH68EHH5Svr69uvPHGyz41+4EHHtCqVav0z3/+0z7f/fv3Kzo6WhMnTrT369Onj6677jqdPn1aknTo0CHZbDbt3r1bkvTTTz9pwIABCggIUI0aNdS9e3ft2rXrkp8bEREhSfrrX/8qm81mf33BvHnzFBERIX9/f/Xr10+nTp1ymPPQoUM1YsQIBQUFKSEhQZK0atUqdezYUV5eXgoNDdXIkSPt/8w/+eQT1a5dW8XFxZKk7Oxs2Ww2jRw50j7u4MGDdd9990mSfvjhB/Xq1UsBAQGqWbOmbrrpJi1duvSS8wGczskPYwXwO3FxccbPz8+MGTPG7Ny508yZM8fYbDaHp6lLMosXLzbGGJOfn28CAwPNfffdZ7Zu3WqWLl1qmjZtaiSZTZs2GWOMWblypZFkYmJiTEZGhtm6davp0qWL6dSpk33M1atXGz8/PzN79myzZ88es2LFChMREWHGjBljjDHmgw8+MH5+fmbp0qXmhx9+MOvWrbM/Xfr48ePmhhtuMC+88ILJyckxOTk5pc6tuLjYtGrVytx2220mOzvbrFq1yrRt29ZhPvv27XOo/dChQyYwMNDceeed5ptvvjE7duww77zzjtm+fbsxxph3333XhIaGmg8//NDs3bvXfPjhhyYwMNDMnj37ssf4909br1+/vgkMDDTTpk0zu3btMqmpqcbNzc3+GX908uRJExsba4YMGWKf7/nz501SUpLp0aOHMcaYkpISExgYaIKCgsxnn31mr7VevXr2ce644w7TokULs3r1apOdnW0SEhJM48aNzdmzZ0v93KNHjxpJZtasWSYnJ8ccPXrUGGNMSkqKqVWrlrnzzjvN5s2bzerVq01ISIgZPXq0w5xr1aplnn76abN9+3azfft2c+jQIVOjRg3z6KOPmm3btpnFixeboKAgk5KSYp+nm5ub+eabb4wxxkyZMsUEBQWZmJgY+7iNGzc2aWlpxhhjevToYf77v//bfPfdd2bPnj3m448/NqtWrbrkPwfA2QhAgAuJi4szt9xyi0Nbhw4dzLPPPmt//fvAMH36dFOnTh3z888/299PS0srNQB98cUX9j6ffvqpkWTf77bbbjPjx493+Nx58+aZ0NBQY4wxr732mmnatOklv5zr169vJk+efNm5LV++3Hh4eJjDhw/b2z777LPLBqBRo0aZBg0aXPJzGzVqZObPn+/Q9uKLL5rY2NhL1lFaALrvvvvsr0tKSkzdunXN9OnTyzyGMcb8+9//Nv7+/ub8+fMmOzvbhISEmOHDh9v/2Q0ePNj8/e9/N8YYs3PnTiPJrFmzxr7/sWPHjI+Pj3n//fcv+bm/P1YXpKSkmBo1apiCggJ729NPP+0QVOLi4kzbtm0d9hs9erRp1qyZKSkpsbdNmzbN1KpVyxQXFxtjjGnXrp159dVXjTHG9OnTx4wbN854enqaU6dOmUOHDhlJZufOncYYY1q3bm0PzEB1wCUwwMVERkY6vA4NDdXRo0dL7btjxw5FRkbK29vb3taxY8c/HTc0NFSS7ON+++23euGFF1SrVi37NmTIEOXk5OjMmTO6++679fPPP6thw4YaMmSIFi9ebL9UUlbbtm1TeHi4wsLC7G2xsbGX3Sc7O1tdunTRddddd9F7hYWF2rNnjwYNGuRQ90svveRwiawsfn9sbDabQkJCLnnML6VLly46deqUNm3apFWrVikuLk5du3ZVRkaGpF8vN3Xt2lXSr8fCw8NDMTEx9v3r1KmjZs2aadu2bVf0udKvl8d8fX3tr0v770z79u0dXm/btk2xsbEOlyw7d+6s06dP69ChQ5KkuLg4ZWRkyBijL7/8UnfeeadatGihr776SqtWrVJYWJiaNGkiSXr88cf10ksvqXPnzkpJSdF33313xfMAriYCEOBi/vhlb7PZVFJSUqnjXvjSuzDu6dOnNXbsWGVnZ9u3zZs3a9euXfL29lZ4eLh27NihN998Uz4+Pnr00Ud166236ty5cxWu63J8fHwu+d6FtTVpaWkOdW/ZskVr1669os+pjGNeu3ZttWnTRhkZGfawc+utt2rTpk3auXOndu3apbi4uCsas6zKUn/NmjWveNyuXbvqq6++0rfffqvrrrtOzZs3t4e6CyHvgsGDB2vv3r26//77tXnzZkVHR+v1118v34SAq4AABFRjzZo10+bNm1VUVGRv++abb654nHbt2mnHjh1q3LjxRZub26//N+Hj46NevXpp6tSpysjIUGZmpjZv3ixJ8vT0tC+WvZQWLVro4MGDysnJsbf9WVCJjIzUl19+WWrQCg4OVlhYmPbu3XtRzQ0aNLjSQ3BFLjXfuLg4rVy5UqtXr1bXrl0VGBioFi1aaNy4cQoNDVXTpk0l/Xoszp8/r3Xr1tn3PX78uHbs2KGWLVte8nOvu+66Pz3OZdWiRQtlZmbKGGNvW7NmjXx9fXXDDTdI+u2s1uTJk+1h50IAysjIsJ/RuiA8PFwPP/ywFi1apCeffFJpaWmVUitQFQhAQDX297//XSUlJXrooYe0bds2LV++3P5LpMv9GuuPkpOTNXfuXI0dO1Zbt27Vtm3btGDBAv3jH/+QJM2ePVtvv/22tmzZor179+rdd9+Vj4+P6tevL+nXSzCrV6/W4cOHdezYsVI/Iz4+Xk2bNtXAgQP17bff6ssvv9Rzzz132bqGDh2qgoIC9evXTxs2bNCuXbs0b948+y/Yxo4dq9TUVE2dOlU7d+7U5s2bNWvWLE2aNKnMcy+PiIgIrVu3Tvv379exY8fsZ1u6du2q5cuXy8PDQ82bN7e3vffeew5nS5o0aaLevXtryJAh9jMs9913n+rVq6fevXtf9nPT09OVm5urn376qUJzePTRR3Xw4EENGzZM27dv10cffaSUlBQlJSXZQ29AQIAiIyP13nvv2cPOrbfeqo0bN2rnzp0OcxoxYoSWL1+uffv2aePGjVq5cqVatGhRoRqBqkQAAqoxPz8/ffzxx8rOzlZUVJSee+45JScnS5LDuqA/k5CQoE8++UQrVqxQhw4ddPPNN2vy5Mn2gFO7dm2lpaWpc+fOioyM1BdffKGPP/5YderUkSS98MIL2r9/vxo1aqTrr7++1M9wc3PT4sWL9fPPP6tjx44aPHiwxo0bd9m66tSpo//85z86ffq04uLi1L59e6Wlpdkv+QwePFgzZ87UrFmz1Lp1a8XFxWn27NlVfgboqaeekru7u1q2bKnrr79eBw4ckPTrGZOSkhKHYNC1a1cVFxdfdLZk1qxZat++vXr27KnY2FgZY7R06dJS1ztd8Nprr+nzzz9XeHi42rZtW6E51KtXT0uXLtX69evVpk0bPfzwwxo0aJA99F4QFxfnUH9gYKBatmypkJAQNWvWzN6vuLhYjz32mFq0aKFu3bqpadOmf3pLAsCZbOb35z8BVHvvvfeeEhMTlZ+ff9k1NABgZdwJGqjm5s6dq4YNG6pevXr69ttv9eyzz+qee+4h/ADAZRCAgGouNzdXycnJys3NVWhoqO6+++4/vbQEAFbHJTAAAGA5LIIGAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACW8/8Bf8OFJ0pKy/sAAAAASUVORK5CYII=", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -965,7 +955,7 @@ "poll = rnd.choice([0, 1], 1000, p=[0.49, 0.51]) # A=0, B=1\n", "polled_for_A = np.count_nonzero(poll == 0)\n", "print('polled for A:', polled_for_A)\n", - "if polled_for_A > 500: \n", + "if polled_for_A > 500:\n", " print('The Dog will predict the wrong winner')\n", "else:\n", " print('The Dog will predict the correct winner')" @@ -991,7 +981,7 @@ "for i in range(1000):\n", " poll = rnd.choice([0, 1], 1000, p=[0.49, 0.51])\n", " polled_for_A = np.count_nonzero(poll == 0)\n", - " if polled_for_A > 500: \n", + " if polled_for_A > 500:\n", " Awins += 1\n", " else:\n", " Bwins += 1\n", @@ -1019,7 +1009,7 @@ "for i in range(1000):\n", " poll = rnd.choice([0, 1], 5000, p=[0.49, 0.51])\n", " polled_for_A = np.count_nonzero(poll == 0)\n", - " if polled_for_A > 2500: \n", + " if polled_for_A > 2500:\n", " Awins += 1\n", " else:\n", " Bwins += 1\n", @@ -1037,7 +1027,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "exploratory-computing-with-python (3.13.5)", "language": "python", "name": "python3" }, @@ -1051,7 +1041,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.13.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/notebook_adv5/py_exp_comp_adv5_sol.ipynb b/notebook_adv5/py_exp_comp_adv5_sol.ipynb index 763b0f7..d30eae7 100644 --- a/notebook_adv5/py_exp_comp_adv5_sol.ipynb +++ b/notebook_adv5/py_exp_comp_adv5_sol.ipynb @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -63,16 +63,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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IpFTwIiKRUsGLiERKBS8iEikVvIhIpFTwIiKRUsGLiERKBS8iEqmKC97MPm1m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\n4BBxAAAAAElFTkSuQmCC\n", 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", 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" ] }, "metadata": {}, @@ -100,16 +100,30 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -117,8 +131,8 @@ } ], "source": [ - "from IPython.html.widgets import interact\n", - "interact(plot_circle,r=(1,5)); " + "from ipywidgets import interact\n", + "interact(plot_circle,r=(1,5));" ] }, { @@ -137,16 +151,30 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -161,7 +189,7 @@ " ax1.set_ylim(-5,5)\n", " c = Circle((x0,0),radius=r)\n", " ax1.add_patch(c)\n", - " \n", + "\n", "interact(plot_circle,r=(1.0,5,0.1),x0=(-5.0,5.0,0.1));" ] }, @@ -175,16 +203,30 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -237,7 +293,7 @@ " ax1.set_ylim(-5,5)\n", " c = Circle((x0,0),radius=r,color=color)\n", " ax1.add_patch(c)\n", - " \n", + "\n", "interact(plot_circle,\n", " r = (1.0, 5, 0.1),\n", " x0 = (-5.0, 5.0, 0.1),\n", @@ -254,16 +310,30 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -280,8 +350,8 @@ " e = Ellipse(xy=(0,0),width=a,height=b,angle=angle,color=color)\n", " ax1.add_patch(e)\n", "interact(plot_ellipse,\n", - " a=(1, 5, 0.1), \n", - " b=(1, 5, 0.1), \n", + " a=(1, 5, 0.1),\n", + " b=(1, 5, 0.1),\n", " angle=(-180, 180),\n", " color=['Aqua', 'Chocolate', 'Fuchsia']);" ] @@ -293,48 +363,45 @@ "###Different types of widgets\n", "There are many different types of widgets. The slider and dropdown widget are the easiest to use with `interact`, as you simply specify a range of values (for a slider) or a list of words (for a dropdown widget). All other widgets must be imported from the `IPython.html.widgets` package and be called directly, but still within the `interact` command. A current list of IPython widgets may be found [here](http://nbviewer.ipython.org/github/datadave/ipython-in-depth/blob/master/notebooks/Interactive%20Widgets/Widget%20List.ipynb)\n", "\n", - "The slider and dropwdown widget can be used in the same fashion as the other widgets. For example, the `FloatSlider` widget and `Dropdown` widget can be called explicitly to create the same interactive graph with a circle, as shown above Exercise 2. Note that there is slightly different syntax for IPython2 and IPython3." + "The slider and dropwdown widget can be used in the same fashion as the other widgets. For example, the `FloatSlider` widget and `Dropdown` widget can be called explicitly to create the same interactive graph with a circle, as shown above Exercise 2." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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+ "application/vnd.jupyter.widget-view+json": { + "model_id": "cbee1fa43eb345a69b9cdf6cadc00fd7", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "interactive(children=(FloatSlider(value=2.0, description='r', max=5.0, min=1.0), FloatSlider(value=0.0, descri…" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# IPython2 code\n", - "import IPython.html.widgets as widgets\n", - "interact(plot_circle,\n", - " r = widgets.FloatSliderWidget(min = 1, max = 5, step = 0.1, value = 2),\n", - " x0 = widgets.FloatSliderWidget(min = -5, max = 5, step = 0.1, value = 0),\n", - " color = widgets.DropdownWidget(values = ['red','blue','green']) );" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, { "data": { - "image/png": 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+ "image/png": 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -342,12 +409,13 @@ } ], "source": [ - "# IPython3 code\n", - "import IPython.html.widgets as widgets\n", + "import ipywidgets as widgets\n", + "\n", "interact(plot_circle,\n", - " r = widgets.FloatSlider(min = 1, max = 5, step = 0.1, value = 2),\n", - " x0 = widgets.FloatSlider(min = -5, max = 5, step = 0.1, value = 0),\n", - " color = widgets.Dropdown(options = ['red','blue','green'], description = 'Color:') );" + " r = widgets.FloatSlider(min=1, max=5, step=0.1, value=2),\n", + " x0 = widgets.FloatSlider(min=-5, max=5, step=0.1, value=0),\n", + " color = widgets.Dropdown(options=['red', 'blue', 'green'])\n", + " )" ] }, { @@ -360,42 +428,40 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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+ "application/vnd.jupyter.widget-view+json": { + "model_id": "3c8eed093444448295b54e7d1bd0bceb", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "interactive(children=(FloatText(value=2.0, description='r'), BoundedFloatText(value=0.0, description='x0', max…" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# Ipython2 code\n", - "interact(plot_circle,\n", - " r = widgets.FloatTextWidget(value = 2, min = 1, max = 5),\n", - " x0 = widgets.BoundedFloatTextWidget(value = 0, min = -5, max = 5),\n", - " color = widgets.DropdownWidget(values = ['red','blue','green']) );" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "data": { - "image/png": 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"text/plain": [ - "" + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -403,11 +469,12 @@ } ], "source": [ - "# Ipython3 code\n", + "# Modernized widget names and parameters\n", "interact(plot_circle,\n", - " r = widgets.FloatText(value = 2, min = 1, max = 5),\n", - " x0 = widgets.BoundedFloatText(value = 0, min = -5, max = 5),\n", - " color = widgets.Dropdown(options = ['red','blue','green'], description = 'Color:') );" + " r = widgets.FloatText(value=2, min=1, max=5),\n", + " x0 = widgets.BoundedFloatText(value=0, min=-5, max=5),\n", + " color = widgets.Dropdown(options=['red', 'blue', 'green'])\n", + " )" ] }, { @@ -420,16 +487,30 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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", 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" ] }, "metadata": {}, @@ -440,10 +521,8 @@ "def plot_line(showlegend=True):\n", " plt.plot([0,1,2],[0,1,0],'b',label='Data')\n", " if showlegend: plt.legend(loc='best')\n", - "#IPython2 code\n", - "interact(plot_line, showlegend=widgets.CheckboxWidget(value=True));\n", - "#IPython3 code\n", - "#interact(plot_line, showlegend=widgets.Checkbox(value=True, description='Show Legend:'));" + "\n", + "interact(plot_line, showlegend=widgets.Checkbox(value=True, description='Show Legend:'));" ] }, { @@ -456,16 +535,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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+ "application/vnd.jupyter.widget-view+json": { + "model_id": "2e02c909bc574e719174cce7f14ba6f7", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "interactive(children=(BoundedFloatText(value=2.0, description='Water Table Depth:', max=10.0), BoundedFloatTex…" ] }, "metadata": {}, @@ -477,27 +560,37 @@ " gammadry = 16.0\n", " gammawet = 20.0\n", " gammawater = 10.0\n", - " z = np.array([0,d,10])\n", - " sig1 = sig0 + d*gammadry\n", - " sig2 = sig1 + (10-d) * gammawet\n", + " z = np.array([0, d, 10])\n", + "\n", + " # Stress calculations\n", + " sig1 = sig0 + d * gammadry\n", + " sig2 = sig1 + (10 - d) * gammawet\n", " sig = np.array([sig0, sig1, sig2])\n", - " p = np.array([0, 0, (10-d) * gammawater])\n", + "\n", + " p = np.array([0, 0, (10 - d) * gammawater])\n", " sigp = sig - p\n", - " if plotsig: plt.plot(sig, z, 'k', label=r\"$\\sigma$\")\n", - " if plotsigp: plt.plot(sigp, z, 'r', label=r\"$\\sigma'$\")\n", - " if plotp: plt.plot(p, z, 'b', label = 'p' )\n", - " plt.ylim(10,0)\n", - " plt.xlim(0,300)\n", + "\n", + " # Plotting\n", + " # plt.figure(figsize=(6, 8)) # Added for better visibility\n", + " if plotsig: plt.plot(sig, z, 'k', label=r\"$\\sigma$ (Total)\")\n", + " if plotsigp: plt.plot(sigp, z, 'r', label=r\"$\\sigma'$ (Effective)\")\n", + " if plotp: plt.plot(p, z, 'b', label = 'p (Pore Water)' )\n", + "\n", + " plt.ylim(10, 0) # Depth starts at 0 at the top\n", + " plt.xlim(0, 300)\n", " plt.xlabel('stress (kN/m$^2$)')\n", - " plt.ylabel('z ( m)')\n", - " plt.legend(loc='best')\n", + " plt.ylabel('z (m)')\n", + " # plt.grid(True, linestyle='--', alpha=0.6) # Helps visualize the values\n", + " plt.legend(loc='upper right')\n", + " # plt.show()\n", "\n", "interact(soilstress,\n", - " d = widgets.BoundedFloatTextWidget(value = 2, min = 0, max = 10),\n", - " sig0 = widgets.BoundedFloatTextWidget(value = 50, min = 0, max = 100),\n", - " plotsig = widgets.CheckboxWidget(value=True),\n", - " plotsigp = widgets.CheckboxWidget(value=True),\n", - " plotp = widgets.CheckboxWidget(value=True));" + " d = widgets.BoundedFloatText(value=2, min=0, max=10, description='Water Table Depth:'),\n", + " sig0 = widgets.BoundedFloatText(value=50, min=0, max=100, description='Surcharge:'),\n", + " plotsig = widgets.Checkbox(value=True, description='Total Stress'),\n", + " plotsigp = widgets.Checkbox(value=True, description='Effective Stress'),\n", + " plotp = widgets.Checkbox(value=True, description='Pore Pressure')\n", + " );" ] }, { @@ -526,16 +619,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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21sKN5jU6ORKNvjP+uusewE35cJld/E0yWQIwaUGVO4FHeei9Q1A5I5giOnhP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\nX9Om1FcCAEBEbsCNipwFxKu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+ "application/vnd.jupyter.widget-view+json": { + "model_id": "e700b67557b94a5192571e7aaab20dcf", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "interactive(children=(FloatSlider(value=0.04, description='Diffusivity:', max=0.06, min=0.02, step=0.001), Bou…" ] }, "metadata": {}, @@ -543,42 +640,54 @@ } ], "source": [ - "def plot_T(alpha=0.04,delT=10.0):\n", + "def plot_T(alpha=0.04, delT=10.0):\n", " Tm = 10\n", " t0 = 365.0\n", - " z = np.linspace(0,20,100)\n", - " lab = 2 * np.sqrt(t0*np.pi*alpha)\n", - " plt.figure()\n", - " for t in np.linspace(0,t0,11):\n", - " T = Tm + delT * np.exp(-2*np.pi*z/lab) * np.cos(2*np.pi*(t/t0 - z/lab))\n", - " plt.plot(T,z)\n", - " plt.xlim(-5,25)\n", - " plt.ylim(15,0)\n", - " plt.xlabel('Temperature (Celcius)')\n", + " z = np.linspace(0, 20, 100)\n", + "\n", + " # Thermal wavelength/damping depth calculation\n", + " lab = 2 * np.sqrt(t0 * np.pi * alpha)\n", + "\n", + " plt.figure(figsize=(7, 9))\n", + "\n", + " # Plotting temperature profiles for 11 different times of the year\n", + " for t in np.linspace(0, t0, 11):\n", + " T = Tm + delT * np.exp(-2*np.pi * z / lab) * np.cos(2*np.pi * (t/t0 - z/lab))\n", + " plt.plot(T, z)\n", + "\n", + " plt.xlim(-5, 25)\n", + " plt.ylim(15, 0)\n", + " plt.axvline(Tm, color='k', linestyle='--', alpha=0.5, label='Mean Temp')\n", + " plt.xlabel('Temperature (°C)')\n", " plt.ylabel('Depth (m)')\n", - "interact(plot_T, \n", - " alpha=(0.02,0.06,0.001), \n", - " delT = widgets.BoundedFloatTextWidget(value = 10, min = 5, max = 15) );" + " # plt.title(r'Soil Temperature Profiles (Thermal Diffusivity $\\alpha$ = ' + f'{alpha})')\n", + " # plt.grid(True, alpha=0.3)\n", + " # plt.show()\n", + "\n", + "interact(plot_T,\n", + " alpha=widgets.FloatSlider(value=0.04, min=0.02, max=0.06, step=0.001, description='Diffusivity:'),\n", + " delT=widgets.BoundedFloatText(value=10, min=5, max=15, description='Temp Amp:')\n", + " );" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "exploratory-computing-with-python (3.13.5)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, diff --git a/notebook_pandas/py_exploratory_comp_5_sol.ipynb b/notebook_pandas/py_exploratory_comp_5_sol.ipynb index 44c09b9..84ce542 100644 --- a/notebook_pandas/py_exploratory_comp_5_sol.ipynb +++ b/notebook_pandas/py_exploratory_comp_5_sol.ipynb @@ -82,20 +82,82 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " car bus rail\n", - "country \n", - "France 86.1 5.3 8.6\n", - "Germany 85.2 7.1 7.7\n", - "Netherlands 86.4 4.6 9.0\n", - "United Kingdom 88.2 6.5 5.3\n" - ] + "data": { + "text/html": [ 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carbusrail
country
France86.15.38.6
Germany85.27.17.7
Netherlands86.44.69.0
United Kingdom88.26.55.3
\n", + "
" + ], + "text/plain": [ + " car bus rail\n", + "country \n", + "France 86.1 5.3 8.6\n", + "Germany 85.2 7.1 7.7\n", + "Netherlands 86.4 4.6 9.0\n", + "United Kingdom 88.2 6.5 5.3" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print tran" + "tran" ] }, { @@ -125,7 +187,7 @@ "output_type": "stream", "text": [ "Names of columns:\n", - "Index([u'car', u'bus', u'rail'], dtype='object')\n", + "Index(['car', 'bus', 'rail'], dtype='str')\n", "car\n", "bus\n", "rail\n" @@ -133,10 +195,10 @@ } ], "source": [ - "print 'Names of columns:'\n", - "print tran.keys()\n", + "print ('Names of columns:')\n", + "print (tran.keys())\n", "for key in tran.keys():\n", - " print key" + " print (key)" ] }, { @@ -174,11 +236,11 @@ } ], "source": [ - "print tran.iloc[0,1] # Gives the bus data for France\n", - "print tran.iloc[1,0] # Gives the car data for Germany\n", - "print tran.iloc[2,2] # Gives the rail data for Netherlands\n", - "print tran.iloc[3] # All data for United Kindom\n", - "print tran.iloc[:,1] # All data for bus" + "print(tran.iloc[0,1]) # Gives the bus data for France\n", + "print(tran.iloc[1,0]) # Gives the car data for Germany\n", + "print(tran.iloc[2,2]) # Gives the rail data for Netherlands\n", + "print(tran.iloc[3]) # All data for United Kindom\n", + "print(tran.iloc[:,1]) # All data for bus" ] }, { @@ -216,11 +278,11 @@ } ], "source": [ - "print tran.loc['France','bus']\n", - "print tran.loc['Germany','car']\n", - "print tran.loc['Netherlands','rail']\n", - "print tran.loc['United Kingdom']\n", - "print tran.loc[:,'bus']" + "print(tran.loc['France','bus'])\n", + "print(tran.loc['Germany','car'])\n", + "print(tran.loc['Netherlands','rail'])\n", + "print(tran.loc['United Kingdom'])\n", + "print(tran.loc[:,'bus'])" ] }, { @@ -263,9 +325,9 @@ } ], "source": [ - "print tran.loc[:,'car'] # All rows of 'car' column\n", - "print tran['car'] # 'car' column \n", - "print tran.car" + "print(tran.loc[:,'car']) # All rows of 'car' column\n", + "print(tran['car']) # 'car' column\n", + "print(tran.car)" ] }, { @@ -292,8 +354,8 @@ "output_type": "stream", "text": [ "maximum car travel percentage: 88.2\n", - "country with maximum car travel percentage: United Kingdom\n", - "mean car travel percentage: 86.475\n", + "country with maximum car travel percentage: 3\n", + "mean car travel percentage: 86.47500000000001\n", "all rail travel above 8 percent:\n", "country\n", "France 8.6\n", @@ -303,11 +365,11 @@ } ], "source": [ - "print 'maximum car travel percentage:', tran.car.max()\n", - "print 'country with maximum car travel percentage:', tran.car.argmax()\n", - "print 'mean car travel percentage:', tran.car.mean()\n", - "print 'all rail travel above 8 percent:'\n", - "print tran.rail[tran.rail > 8]" + "print('maximum car travel percentage:', tran.car.max())\n", + "print('country with maximum car travel percentage:', tran.car.argmax())\n", + "print('mean car travel percentage:', tran.car.mean())\n", + "print('all rail travel above 8 percent:')\n", + "print(tran.rail[tran.rail > 8])" ] }, { @@ -339,59 +401,58 @@ "First five lines of rain dataset:\n", " precip area\n", "country \n", - "Afghanistan 327 652.2\n", - "Albania 1485 27.4\n", - "Algeria 89 2381.7\n", + "Afghanistan 327.0 652.2\n", + "Albania 1485.0 27.4\n", + "Algeria 89.0 2381.7\n", "American Samoa NaN 0.2\n", "Andorra NaN 0.5\n", "\n", "Average annual rainfall in Panama is 2692.0 mm/year\n", - "\n", "Land area of the Netherlands is 33.7 thousand km^2/year\n", "\n", "Countries where average rainfall is below 200 mm/year\n", " precip area\n", "country \n", - "Algeria 89 2381.7\n", - "Bahrain 83 0.8\n", - "Egypt, Arab Rep. 51 995.5\n", - "Jordan 111 88.8\n", - "Kuwait 121 17.8\n", - "Libya 56 1759.5\n", - "Mauritania 92 1030.7\n", - "Niger 151 1266.7\n", - "Oman 125 309.5\n", - "Qatar 74 11.6\n", - "Saudi Arabia 59 2149.7\n", - "Turkmenistan 161 469.9\n", - "United Arab Emirates 78 83.6\n", - "Yemen, Rep. 167 528.0\n", + "Algeria 89.0 2381.7\n", + "Bahrain 83.0 0.8\n", + "Egypt, Arab Rep. 51.0 995.5\n", + "Jordan 111.0 88.8\n", + "Kuwait 121.0 17.8\n", + "Libya 56.0 1759.5\n", + "Mauritania 92.0 1030.7\n", + "Niger 151.0 1266.7\n", + "Oman 125.0 309.5\n", + "Qatar 74.0 11.6\n", + "Saudi Arabia 59.0 2149.7\n", + "Turkmenistan 161.0 469.9\n", + "United Arab Emirates 78.0 83.6\n", + "Yemen, Rep. 167.0 528.0\n", "\n", "Countries where average rainfall is above 2500 mm/year\n", " precip area\n", "country \n", - "Bangladesh 2666 130.2\n", - "Brunei Darussalam 2722 5.3\n", - "Colombia 2612 1109.5\n", - "Costa Rica 2926 51.1\n", - "Fiji 2592 18.3\n", - "Indonesia 2702 1811.6\n", - "Malaysia 2875 328.6\n", - "Panama 2692 74.3\n", - "Papua New Guinea 3142 452.9\n", - "Sao Tome and Principe 3200 1.0\n", - "Sierra Leone 2526 71.6\n", - "Solomon Islands 3028 28.0\n", + "Bangladesh 2666.0 130.2\n", + "Brunei Darussalam 2722.0 5.3\n", + "Colombia 2612.0 1109.5\n", + "Costa Rica 2926.0 51.1\n", + "Fiji 2592.0 18.3\n", + "Indonesia 2702.0 1811.6\n", + "Malaysia 2875.0 328.6\n", + "Panama 2692.0 74.3\n", + "Papua New Guinea 3142.0 452.9\n", + "Sao Tome and Principe 3200.0 1.0\n", + "Sierra Leone 2526.0 71.6\n", + "Solomon Islands 3028.0 28.0\n", "\n", "Countries with almost the same rainfall as Netherlands\n", " precip area\n", "country \n", - "Burkina Faso 748 273.6\n", - "Lesotho 788 30.4\n", - "Mexico 752 1944.0\n", - "Netherlands 778 33.7\n", - "Slovak Republic 824 48.1\n", - "Swaziland 788 17.2\n" + "Burkina Faso 748.0 273.6\n", + "Lesotho 788.0 30.4\n", + "Mexico 752.0 1944.0\n", + "Netherlands 778.0 33.7\n", + "Slovak Republic 824.0 48.1\n", + "Swaziland 788.0 17.2\n" ] } ], @@ -399,26 +460,25 @@ "from pandas import read_csv\n", "rain = read_csv('annual_precip.csv', skiprows=2, index_col=0)\n", "#\n", - "print 'First five lines of rain dataset:'\n", - "print rain.head()\n", + "print('First five lines of rain dataset:')\n", + "print(rain.head())\n", "#\n", - "print\n", - "print 'Average annual rainfall in Panama is',rain.loc['Panama','precip'],'mm/year'\n", + "print()\n", + "print('Average annual rainfall in Panama is',rain.loc['Panama','precip'],'mm/year')\n", "#\n", - "print\n", - "print 'Land area of the Netherlands is', rain.loc['Netherlands','area'], 'thousand km^2/year'\n", + "print('Land area of the Netherlands is', rain.loc['Netherlands','area'], 'thousand km^2/year')\n", "#\n", - "print\n", - "print 'Countries where average rainfall is below 200 mm/year'\n", - "print rain[ rain.precip < 200 ]\n", + "print()\n", + "print('Countries where average rainfall is below 200 mm/year')\n", + "print(rain[ rain.precip < 200 ])\n", "#\n", - "print\n", - "print 'Countries where average rainfall is above 2500 mm/year'\n", - "print rain[ rain.precip > 2500 ]\n", + "print()\n", + "print('Countries where average rainfall is above 2500 mm/year')\n", + "print(rain[ rain.precip > 2500 ])\n", "#\n", - "print\n", - "print 'Countries with almost the same rainfall as Netherlands'\n", - "print rain[abs(rain.loc['Netherlands','precip'] - rain.precip) < 50]" + "print()\n", + "print('Countries with almost the same rainfall as Netherlands')\n", + "print(rain[abs(rain.loc['Netherlands','precip'] - rain.precip) < 50])" ] }, { @@ -440,13 +500,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Country with largest percentage public transport: Germany\n" + "Country with largest percentage public transport: 1\n" ] } ], "source": [ "tran['public_transport'] = tran.bus + tran.rail\n", - "print 'Country with largest percentage public transport:',tran.public_transport.argmax()" + "print('Country with largest percentage public transport:',tran.public_transport.argmax())" ] }, { @@ -466,19 +526,9 @@ "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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mMyuapG8BL6NZA/DWhccj4sfZQnXkPWYzK9217XJS1fAes5kVTdJf0JzLfApw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\nAElFTkSuQmCC\n", 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" ] }, "metadata": {}, @@ -555,10 +605,10 @@ } ], "source": [ - "print 'Data sorted by car use:'\n", - "print tran.sort(columns='car')\n", - "print 'Data sorted by bus use:'\n", - "print tran.sort(columns='bus')" + "print('Data sorted by car use:')\n", + "print(tran.sort_values(by='car'))\n", + "print('Data sorted by bus use:')\n", + "print(tran.sort_values(by='bus'))" ] }, { @@ -587,8 +637,8 @@ ], "source": [ "firstdictionary = {'goals': 20, 'city': 'Delft'}\n", - "print firstdictionary['goals']\n", - "print firstdictionary['city']" + "print(firstdictionary['goals'])\n", + "print(firstdictionary['city'])" ] }, { @@ -608,7 +658,20 @@ { "data": { "text/html": [ - "
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Germany 85.2 7.1 7.7 14.885.27.17.714.8
Netherlands 86.4 4.6 9.0 13.686.44.69.013.6
United Kingdom 88.2 6.5 5.3 11.888.26.55.311.8
\n", @@ -674,7 +737,7 @@ } ], "source": [ - "tran.rename(columns={'bus': 'BUS', \n", + "tran.rename(columns={'bus': 'BUS',\n", " 'rail': 'train'}, inplace=True)\n", "tran" ] @@ -696,7 +759,20 @@ { "data": { "text/html": [ - "
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France 86.1 5.3 8.6 13.986.15.38.613.9
Germany 85.2 7.1 7.7 14.885.27.17.714.8
Netherlands 86.4 4.6 9.0 13.686.44.69.013.6
United Kingdom 88.2 6.5 5.3 11.888.26.55.311.8
\n", @@ -790,21 +866,21 @@ "output_type": "stream", "text": [ "Country with largest influx of rain\n", - "Brazil 15074.6508 km^3 per year\n", + "26 15074.6508 km^3 per year\n", "\n", " precip area totalq\n", "country \n", - "Brazil 1782 8459.4 15074.6508\n", - "Russian Federation 460 16376.9 7533.3740\n", - "United States 715 9147.4 6540.3910\n", - "China 645 9327.5 6016.2375\n", - "Indonesia 2702 1811.6 4894.9432\n" + "Brazil 1782.0 8459.4 15074.6508\n", + "Russian Federation 460.0 16376.9 7533.3740\n", + "United States 715.0 9147.4 6540.3910\n", + "China 645.0 9327.5 6016.2375\n", + "Indonesia 2702.0 1811.6 4894.9432\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -813,9 +889,9 @@ }, { "data": { - "image/png": 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QTCsi5h8EQRAMo486/6JpgSziiJCHRw4OkIdHDg6Qh0cODpCHR7Yxf0nHSrpR0g2SzpK0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\n6fWXgzqxAAAAAElFTkSuQmCC\n", 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -824,12 +900,12 @@ ], "source": [ "rain['totalq'] = rain.precip * rain.area * 1e-3\n", - "print 'Country with largest influx of rain'\n", - "print rain.totalq.argmax(), rain.totalq.max(), 'km^3 per year'\n", + "print('Country with largest influx of rain')\n", + "print(rain.totalq.argmax(), rain.totalq.max(), 'km^3 per year')\n", "#\n", - "print\n", - "rain.sort(columns='totalq', ascending=False, inplace=True)\n", - "print rain[:5]\n", + "print()\n", + "rain.sort_values(by='totalq', ascending=False, inplace=True)\n", + "print(rain[:5])\n", "#\n", "rain.totalq[:10].plot(kind='bar')" ] @@ -874,7 +950,7 @@ ], "source": [ "data = read_csv('timeseries1.dat', parse_dates=[0])\n", - "print data" + "print(data)" ] }, { @@ -903,10 +979,10 @@ } ], "source": [ - "print 'datetime of row 0:', data.iloc[0,0]\n", - "print 'year of row 0:', data.iloc[0,0].year\n", - "print 'month of row 0:', data.iloc[0,0].month\n", - "print 'day of row 0:', data.iloc[0,0].day" + "print('datetime of row 0:', data.iloc[0,0])\n", + "print('year of row 0:', data.iloc[0,0].year)\n", + "print('month of row 0:', data.iloc[0,0].month)\n", + "print('day of row 0:', data.iloc[0,0].day)" ] }, { @@ -946,10 +1022,10 @@ ], "source": [ "data = read_csv('timeseries2.dat', parse_dates=[0])\n", - "print data\n", - "print 'hour of row 0:', data.iloc[0,0].hour\n", - "print 'minute of row 0:', data.iloc[0,0].minute\n", - "print 'time of row 0:', data.iloc[0,0].time()" + "print(data)\n", + "print('hour of row 0:', data.iloc[0,0].hour)\n", + "print('minute of row 0:', data.iloc[0,0].minute)\n", + "print('time of row 0:', data.iloc[0,0].time())" ] }, { @@ -975,7 +1051,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Maximum daily rainfall 22.4\n", + "Maximum daily rainfall 22.400000000000002\n", "Date of maximum daily rainfall 2012-12-22 00:00:00\n" ] }, @@ -983,17 +1059,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", + "/tmp/ipykernel_62939/2789095368.py:4: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.\n", + "Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).\n", + "\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" + "See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment\n", + " rain.RH[rain.RH<0] = 0\n" ] }, { "data": { - "image/png": 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79eoydu1yjjd1/kpFHo/Ll6/E2WefvXd9g4OAEPL/P/CAnH9sTO6LK6+8EgCw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\np9ooXjYOINvrF7+QB7iq7P/7v4Ef/MC79FJV9mNju/CSlzj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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1003,14 +1082,14 @@ "source": [ "rain = read_csv('rotterdam_rainfall_2012.txt',skiprows=9,\n", " parse_dates=['YYYYMMDD'],skipinitialspace=True)\n", - "rain.iloc[:,2] = rain.iloc[:,2] * 0.1\n", + "rain.RH = rain.RH.multiply(0.1)\n", "rain.RH[rain.RH<0] = 0\n", "rain.RH.plot()\n", "plt.xlabel('day')\n", "plt.ylabel('daily rainfall (mm/day)')\n", "plt.xlim(0,365)\n", - "print 'Maximum daily rainfall',rain.RH.max()\n", - "print 'Date of maximum daily rainfall',rain.YYYYMMDD[rain.RH.argmax()]" + "print('Maximum daily rainfall',rain.RH.max())\n", + "print('Date of maximum daily rainfall',rain.YYYYMMDD[rain.RH.argmax()])" ] }, { @@ -1032,14 +1111,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 83. 24.3 21.9 57.6 76.5 119. 121.6 93.4 52. 132.6\n", - " 63.3 149.5]\n" + "[ 82.3 23.9 21.5 57.1 76.1 118.5 121.4 93.2 51.7 132.2 63.3 149.3]\n" ] }, { "data": { "text/plain": [ - "(0, 12)" + "(0.0, 12.0)" ] }, "execution_count": 22, @@ -1048,9 +1126,9 @@ }, { "data": { - "image/png": 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+ "image/png": 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zJSkpKZo8ebIpOQEAgHczXHZefvlln79qcnJyspKSkpzPCwoKFBUV5cFEAADAXcpcdjZt2qR77rlH/fv3d2Oc/xceHi5Jys7OVkREhHN5dna2mjVr5twmJyfH5XWXLl1Sbm6u8/VXEhgYqMDAQPNDAwAAr1PmOTtPPPGEatSooccee0xvv/222+e5xMTEKDw8XBs3bnQuKygo0Pbt2xUXFydJiouLU15ennbv3u3cZtOmTSopKVHLli3dmg8AAPiGMpedw4cP6+OPP1bDhg01a9Ys2e12dejQQXPnztXx48ev6ZsXFhZq79692rt3r6RfJiXv3btXx48fl81m06hRozR16lStWrVK+/btU9++fRUZGalu3bpJkho0aKCOHTtq8ODB2rFjhz777DMNHz5cvXv35kwsAAAgyeDZWE2aNNG4ceO0Y8cOHTp0SD169NDatWtVr149NWvWTBMmTNCuXbvKPN6uXbsUGxur2NhYSVJSUpJiY2M1YcIESdKYMWM0YsQIDRkyRHfeeacKCwu1bt065zV2JOnNN99U/fr11b59e3Xu3Fl33XWXFi5caOTHAgAAFmZzOByO8g5y9uxZrV27VqtWrdJHH32kpKQk/fnPfzYj33VRUFCgkJAQ5efnm3YNIcAbRY9d4+kIlnB0ehdPR7AMX/hM+sp/7z/ie1nWv9+Gz8a6ksqVK+vhhx/Www8/rOLiYtNOPwcAACivayo7O3fu1ObNm5WTk6OSkhLncpvNplmzZqlGjRqmBQQAACgPw2XnxRdf1Lhx41SvXj3Z7XaXa+74+vV3AACA9RguO6+88ooWL1583a63AwAAUB6G743l5+enNm3auCMLAACA6QyXndGjR+vVV191RxYAAADTGT6M9eyzz6pLly6qU6eOGjZsWOpGoCtWrDAtHAAAQHkZLjsjR47U5s2b1a5dO1WrVo1JyQAAwKsZLjtpaWl677331KWLb1xkCQAA/LEZLjthYWGqU6eOO7IAgE/whSvVSr5z5V/A3QxPUJ40aZImTpyoc+fOuSMPAACAqQzv2UlNTdWhQ4dkt9sVHR1daoLynj17TAsHAABQXobLTrdu3dwQAwAAwD0Ml52JEye6IwcAAIBblOuu54WFhS43ApX0u7dYBwAAuN4MT1A+cuSIunTposqVKyskJERVq1ZV1apVFRoaqqpVq7ojIwAAwDUzvGfn8ccfl8Ph0OLFi0vd9RwAAMDbGC47X375pXbv3q169eq5Iw8AAICpDB/GuvPOO5WZmemOLAAAAKYzvGfn73//u4YOHaqTJ0+qUaNGpa6z06RJE9PCAQAAlJfhsvP999/r0KFDGjBggHOZzWaTw+GQzWZTcXGxqQEBAADKw3DZGThwoGJjY7V8+XImKAMAAK9nuOwcO3ZMq1atUt26dd2RBwAAwFSGJyjfe++9+vLLL92RBQAAwHSG9+w88MADGj16tPbt26fGjRuXmqDctWtX08IBAACUl+GyM3ToUEnSlClTSq1jgjIAAPA2hsvOb++FBQAA4M0Mz9kBAADwJWUqO2+99VaZB8zMzNRnn312zYEAAADMVKayM3/+fDVo0EAzZ85URkZGqfX5+fn66KOP9Nhjj+n222/Xjz/+aHpQAACAa1GmOTtbtmzRqlWrNHfuXCUnJ6ty5cqy2+0KCgrSmTNnlJWVperVq6t///76+uuvZbfb3Z0bAACgTMo8Qblr167q2rWrfvjhB3366ac6duyYfv75Z1WvXl2xsbGKjY2Vnx9TgAAAgHcxfDZW9erV1a1bNzdEAQAAMB+7YgAAgKVRdgAAgKUZPoxlZY0mrpdf4A2mjHV0ehdTxgEAAOXj1Xt2iouLNX78eMXExKhSpUqqU6eOXnjhBTkcDuc2DodDEyZMUEREhCpVqqT4+HgdOHDAg6kBAIA38eqyM2PGDM2fP1/z5s1TRkaGZsyYoZkzZ2ru3LnObWbOnKnU1FQtWLBA27dvV+XKlZWQkKDz5897MDkAAPAWZTqMlZSUVOYBZ8+efc1hfuvzzz/Xgw8+qC5dfjkkFB0dreXLl2vHjh2SftmrM2fOHI0bN04PPvigJOmNN96Q3W7XypUr1bt3b9OyAAAA31SmsvPFF1+UaTCbzVauML/VunVrLVy4UN99953+67/+S19++aU+/fRTZ6E6cuSIsrKyFB8f73xNSEiIWrZsqfT09KuWnaKiIhUVFTmfFxQUmJobAAB4jzKVnc2bN7s7xxWNHTtWBQUFql+/vipUqKDi4mJNmzZNiYmJkqSsrCxJKnXFZrvd7lx3JSkpKZo8ebL7ggMAAK/h1XN23nnnHb355ptatmyZ9uzZo7S0NL300ktKS0sr17jJycnKz893PjIzM01KDAAAvE2Z9ux07969zAOuWLHimsP81nPPPaexY8c6D0c1btxYx44dU0pKivr166fw8HBJUnZ2tiIiIpyvy87OVrNmza46bmBgoAIDA03LCQAAvFeZyk5ISIi7c1zRuXPnSt1vq0KFCiopKZEkxcTEKDw8XBs3bnSWm4KCAm3fvl1PPvnk9Y4LAAC8UJnKzpIlS9yd44oeeOABTZs2TbVq1dJtt92mL774QrNnz9bAgQMl/TIhetSoUZo6dapuvfVWxcTEaPz48YqMjOT+XQAAQJKXX0F57ty5Gj9+vJ566inl5OQoMjJSf/rTnzRhwgTnNmPGjNHZs2c1ZMgQ5eXl6a677tK6desUFBTkweQAAMBbXFPZeffdd/XOO+/o+PHjunDhgsu6PXv2mBJMkqpUqaI5c+Zozpw5V93GZrNpypQpmjJlimnfFwAAWIfhs7FSU1M1YMAA2e12ffHFF2rRooWqVaumw4cPq1OnTu7ICAAAcM0Ml53XXntNCxcu1Ny5c1WxYkWNGTNGGzZs0MiRI5Wfn++OjAAAANfM8GGs48ePq3Xr1pKkSpUq6aeffpIk9enTR61atdK8efPMTQj4gOixazwdAQBwFYb37ISHhys3N1eSVKtWLW3btk3SL7du+PXdyAEAALyB4bJz7733atWqVZKkAQMGaPTo0erQoYN69eqlhx56yPSAAAAA5WH4MNbChQudF/UbNmyYqlWrps8//1xdu3bVn/70J9MDAgAAlEeZ9ux0797deWfw//mf/1FxcbFzXe/evZWamqoRI0aoYsWK7kkJAABwjcpUdlavXq2zZ89K+uXQFWddAQAAX1Gmw1j169dXcnKy2rVrJ4fDoXfeeUfBwcFX3LZv376mBgQAACiPMpWdBQsWKCkpSWvWrJHNZtO4ceNks9lKbWez2Sg7AADAq5Sp7LRu3dp5irmfn5++++471axZ063BAAAAzGD41PMjR46oRo0a7sgCAABgOsOnnteuXVt5eXnasWOHcnJynKehX8ZhLAAA4E0Ml50PP/xQiYmJKiwsVHBwsMvcHebsAAAAb2P4MNYzzzyjgQMHqrCwUHl5eTpz5ozzcfk2EgAAAN7CcNk5efKkRo4cqRtuuMEdeQAAAExluOwkJCRo165d7sgCAABgOsNzdrp06aLnnntO33zzjRo3bqyAgACX9V27djUtHAAAQHkZLjuDBw+WJE2ZMqXUOpvN5nLfLAAAAE8zXHZ+e6o5AACANzM8ZwcAAMCXlGnPTmpqqoYMGaKgoCClpqb+7rYjR440JRgAAIAZylR2Xn75ZSUmJiooKEgvv/zyVbez2WyUHQAA4FXKVHaOHDlyxa8BAAC8HXN2AACApRk+G0uSTpw4oVWrVun48eO6cOGCy7rZs2ebEgwAAMAMhsvOxo0b1bVrV91yyy3av3+/GjVqpKNHj8rhcOj22293R0YAAIBrZvgwVnJysp599lnt27dPQUFBeu+995SZmal77rlHPXv2dEdGAACAa2a47GRkZKhv376SJH9/f/3888+68cYbNWXKFM2YMcP0gAAAAOVhuOxUrlzZOU8nIiJChw4dcq774YcfzEsGAABgAsNzdlq1aqVPP/1UDRo0UOfOnfXMM89o3759WrFihVq1auWOjAAAANfMcNmZPXu2CgsLJUmTJ09WYWGh3n77bd16662ciQUAALyOobJTXFysEydOqEmTJpJ+OaS1YMECtwQDAMAbRI9d4+kIKCdDc3YqVKig++67T2fOnHFXHgAAAFMZnqDcqFEjHT582B1ZAAAATGe47EydOlXPPvusVq9erdOnT6ugoMDlAQAA4E0MT1Du3LmzJKlr166y2WzO5Q6HQzabTcXFxealAwAAKCfDZWfz5s3uyHFVJ0+e1PPPP6+1a9fq3Llzqlu3rpYsWaLmzZtL+qVkTZw4Ua+//rry8vLUpk0bzZ8/X7feeut1zQkAALyT4bJzzz33uCPHFZ05c0Zt2rRRu3bttHbtWtWoUUMHDhxQ1apVndvMnDlTqampSktLU0xMjMaPH6+EhAR98803CgoKum5ZAQCAd7qmu55fLzNmzFBUVJSWLFniXBYTE+P82uFwaM6cORo3bpwefPBBSdIbb7whu92ulStXqnfv3lcct6ioSEVFRc7nzDUCAMC6DE9Qvp5WrVql5s2bq2fPnqpZs6ZiY2P1+uuvO9cfOXJEWVlZio+Pdy4LCQlRy5YtlZ6eftVxU1JSFBIS4nxERUW59ecAAACe49Vl5/Dhw875N+vXr9eTTz6pkSNHKi0tTZKUlZUlSbLb7S6vs9vtznVXkpycrPz8fOcjMzPTfT8EAADwKK8+jFVSUqLmzZvrxRdflCTFxsbq66+/1oIFC9SvX79rHjcwMFCBgYFmxQQAAF7M8J6diRMn6tixY+7IUkpERIQaNmzosqxBgwY6fvy4JCk8PFySlJ2d7bJNdna2cx0AAPhjM1x2PvjgA9WpU0ft27fXsmXLXCb6mq1Nmzb69ttvXZZ99913ql27tqRfJiuHh4dr48aNzvUFBQXavn274uLi3JYLAAD4DsNlZ+/evdq5c6duu+02Pf300woPD9eTTz6pnTt3mh5u9OjR2rZtm1588UUdPHhQy5Yt08KFCzVs2DBJks1m06hRozR16lStWrVK+/btU9++fRUZGalu3bqZngcAAPiea5qgHBsbq9TUVJ06dUqLFi3SiRMn1KZNGzVp0kSvvPKK8vPzTQl355136v3339fy5cvVqFEjvfDCC5ozZ44SExOd24wZM0YjRozQkCFDdOedd6qwsFDr1q3jGjsAAEBSOScoOxwOXbx4URcuXJDD4VDVqlU1b948jR8/Xq+//rp69epV7oD333+/7r///quut9lsmjJliqZMmVLu7wXvFD12jacjAAB82DXt2dm9e7eGDx+uiIgIjR49WrGxscrIyNCWLVt04MABTZs2TSNHjjQ7KwAAgGGGy07jxo3VqlUrHTlyRIsWLVJmZqamT5+uunXrOrd59NFH9f3335saFAAA4FoYPoz1yCOPaODAgbrpppuuuk316tVVUlJSrmAAAABmMFx2xo8f744cAAAAblGmspOUlFTmAWfPnn3NYQAAAMxWprLzxRdflGkwm81WrjAAAABmK1PZ2bx5s7tzAAAAuIVX3/UcAACgvAxPUD579qymT5+ujRs3Kicnp9RZV4cPHzYtHAAAQHkZLjtPPPGEtmzZoj59+igiIoJ5OgAAwKsZLjtr167VmjVr1KZNG3fkAQAAMJXhOTtVq1ZVWFiYO7IAAACYzvCenRdeeEETJkxQWlqabrjhBndkAgCYgJvoAr8oU9mJjY11mZtz8OBB2e12RUdHKyAgwGXbPXv2mJsQAACgHMpUdrp16+bmGAAAAO5RprIzceJEd+cAAABwC8MTlG+55Rb9+OOPpZbn5eXplltuMSUUAACAWQyXnaNHj6q4uLjU8qKiIp04ccKUUAAAAGYp89lYq1atcn69fv16hYSEOJ8XFxdr48aNiomJMTcdAABAOZW57FyepGyz2dSvXz+XdQEBAYqOjtasWbNMDQcAAFBeZS47l++BFRMTo507d6p69epuCwUAAGAWwxcVPHLkiDtyAAAAuIXhsiNJGzduvOpdzxcvXmxKMAAAADMYLjuTJ0/WlClT1Lx5c+56DgAAvJ7hsrNgwQItXbpUffr0cUceAAAAUxm+zs6FCxfUunVrd2QBAAAwneGy88QTT2jZsmXuyAIAAGA6w4exzp8/r4ULF+p///d/1aRJk1J3PZ89e7Zp4QAAAMrLcNn56quv1KxZM0nS119/7bKOycoAAMDbGC47mzdvdkcOAAAAtzA8Z+fXTpw4wc0/AQCAVzNcdkpKSjRlyhSFhISodu3aql27tkJDQ/XCCy+UusAgAACApxk+jPWXv/xFixYt0vTp09WmTRtJ0qeffqpJkybp/PnzmjZtmukhAQAArpXhspOWlqa///3v6tq1q3NZkyZNdNNNN+mpp56i7AAAAK9i+DBWbm6u6tevX2p5/fr1lZuba0ooAAAAsxguO02bNtW8efNKLZ83b56aNm1qSigAAACzGC47M2fO1OLFi9WwYUMNGjRIgwYNUsOGDbV06VL99a9/dUdGp+nTp8tms2nUqFHOZefPn9ewYcNUrVo13XjjjerRo4eys7PdmgMAAPgOw2Xnnnvu0XfffaeHHnpIeXl5ysvLU/fu3fXtt9/qv//7v92RUZK0c+dO/e1vf1OTJk1clo8ePVoffvih/vnPf2rLli06deqUunfv7rYcAADAtxieoCxJkZGR13UicmFhoRITE/X6669r6tSpzuX5+flatGiRli1bpnvvvVeStGTJEjVo0EDbtm1Tq1atrjheUVGRioqKnM8LCgrc+wMAAACPuaayc/78eX311VfKyckpdW2dX5+lZZZhw4apS5cuio+Pdyk7u3fv1sWLFxUfH+9cVr9+fdWqVUvp6elXLTspKSmaPHmy6TkBAID3MVx21q1bp759++qHH34otc5ms6m4uNiUYJe99dZb2rNnj3bu3FlqXVZWlipWrKjQ0FCX5Xa7XVlZWVcdMzk5WUlJSc7nBQUFioqKMi0zAADwHobn7IwYMUI9e/bU6dOnVVJS4vIwu+hkZmbq6aef1ptvvqmgoCDTxg0MDFRwcLDLAwAAWJPhspOdna2kpCTZ7XZ35HGxe/du5eTk6Pbbb5e/v7/8/f21ZcsWpaamyt/fX3a7XRcuXFBeXl6pjOHh4W7PBwAAvJ/hsvPwww/r448/dkOU0tq3b699+/Zp7969zkfz5s2VmJjo/DogIEAbN250vubbb7/V8ePHFRcXd10yAgAA72Z4zs68efPUs2dPffLJJ2rcuLECAgJc1o8cOdK0cFWqVFGjRo1cllWuXFnVqlVzLh80aJCSkpIUFham4OBgjRgxQnFxcVednAwAAP5YDJed5cuX61//+peCgoL08ccfy2azOdfZbDZTy05ZvPzyy/Lz81OPHj1UVFSkhIQEvfbaa9c1AwAA8F42h8PhMPKC8PBwjRw5UmPHjpWfn+GjYF6poKBAISEhihr1jvwCbzBlzKPTu5gyDqTosWs8HQEAYAKz/zZe/vudn5//uycbGW4rFy5cUK9evSxTdAAAgLUZbiz9+vXT22+/7Y4sAAAApjM8Z6e4uFgzZ87U+vXr1aRJk1ITlGfPnm1aOAAAgPIyXHb27dun2NhYSdLXX3/tsu7Xk5UBAAC8geGys3nzZnfkAAAAcAtmGQMAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEuj7AAAAEvz93QAq4oeu8bTEcrk6PQuno4AAIBbsWcHAABYGmUHAABYGmUHAABYGmUHAABYGmUHAABYmlefjZWSkqIVK1Zo//79qlSpklq3bq0ZM2aoXr16zm3Onz+vZ555Rm+99ZaKioqUkJCg1157TXa73YPJfYevnDUGAMC18uo9O1u2bNGwYcO0bds2bdiwQRcvXtR9992ns2fPOrcZPXq0PvzwQ/3zn//Uli1bdOrUKXXv3t2DqQEAgDexORwOh6dDlNX333+vmjVrasuWLbr77ruVn5+vGjVqaNmyZXr44YclSfv371eDBg2Unp6uVq1alWncgoIChYSEKGrUO/ILvMGdPwIAAH9YZl/b7fLf7/z8fAUHB191O6/es/Nb+fn5kqSwsDBJ0u7du3Xx4kXFx8c7t6lfv75q1aql9PT0q45TVFSkgoIClwcAALAmnyk7JSUlGjVqlNq0aaNGjRpJkrKyslSxYkWFhoa6bGu325WVlXXVsVJSUhQSEuJ8REVFuTM6AADwIJ8pO8OGDdPXX3+tt956q9xjJScnKz8/3/nIzMw0ISEAAPBGXn021mXDhw/X6tWrtXXrVt18883O5eHh4bpw4YLy8vJc9u5kZ2crPDz8quMFBgYqMDDQnZEBAICX8Oo9Ow6HQ8OHD9f777+vTZs2KSYmxmX9HXfcoYCAAG3cuNG57Ntvv9Xx48cVFxd3veMCAAAv5NV7doYNG6Zly5bpgw8+UJUqVZzzcEJCQlSpUiWFhIRo0KBBSkpKUlhYmIKDgzVixAjFxcWV+UwsAABgbV5ddubPny9Jatu2rcvyJUuWqH///pKkl19+WX5+furRo4fLRQUBAAAkLy87ZbkEUFBQkF599VW9+uqr1yERAADwNV49ZwcAAKC8KDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSKDsAAMDSLFN2Xn31VUVHRysoKEgtW7bUjh07PB0JAAB4AUuUnbfffltJSUmaOHGi9uzZo6ZNmyohIUE5OTmejgYAADzMEmVn9uzZGjx4sAYMGKCGDRtqwYIFuuGGG7R48WJPRwMAAB7m7+kA5XXhwgXt3r1bycnJzmV+fn6Kj49Xenr6FV9TVFSkoqIi5/P8/HxJUknROfeGBQDgD6ygoMAt4zkcjt/dzufLzg8//KDi4mLZ7XaX5Xa7Xfv377/ia1JSUjR58uRSy0/O7++OiAAAQFLIHPeM+9NPPykkJOSq632+7FyL5ORkJSUlOZ+XlJQoNzdX1apVk81mK/f4BQUFioqKUmZmpoKDg8s9nrv4Qk4ymscXcpLRPL6Qk4zm8YWc7sjocDj0008/KTIy8ne38/myU716dVWoUEHZ2dkuy7OzsxUeHn7F1wQGBiowMNBlWWhoqOnZgoODvfZD92u+kJOM5vGFnGQ0jy/kJKN5fCGn2Rl/b4/OZT4/QblixYq64447tHHjRueykpISbdy4UXFxcR5MBgAAvIHP79mRpKSkJPXr10/NmzdXixYtNGfOHJ09e1YDBgzwdDQAAOBhlig7vXr10vfff68JEyYoKytLzZo107p160pNWr5eAgMDNXHixFKHyryNL+Qko3l8IScZzeMLOcloHl/I6cmMNsd/Ol8LAADAh/n8nB0AAIDfQ9kBAACWRtkBAACWRtkBAACWRtlxk/79+6tbt26ejnFF/fv3l81mK/U4ePCgp6M5sw0dOrTUumHDhslms6l///7XP9jvSE9PV4UKFdSlSxdPR3Hhi++lN//e/Jq35/TWz+Rl33//vZ588knVqlVLgYGBCg8PV0JCgj777DNPR3ORmZmpgQMHKjIyUhUrVlTt2rX19NNP68cff/R0NEn//zs+ffp0l+UrV6405W4AZvj135uAgADZ7XZ16NBBixcvVklJyXXLQdn5g+rYsaNOnz7t8oiJifF0LElSVFSU3nrrLf3888/OZefPn9eyZctUq1YtDya7skWLFmnEiBHaunWrTp065ek4LnztvYQ5vPkzKUk9evTQF198obS0NH333XdatWqV2rZt6zUlQpIOHz6s5s2b68CBA1q+fLkOHjyoBQsWOC9Ym5ub6+mIkqSgoCDNmDFDZ86c8XSUq7r89+bo0aNau3at2rVrp6efflr333+/Ll26dF0yWOI6OzDu8v9NeaPbb79dhw4d0ooVK5SYmChJWrFihWrVquU1heyywsJCvf3229q1a5eysrK0dOlS/fnPf/Z0LCdfei9hDm//TObl5emTTz7Rxx9/rHvuuUeSVLt2bbVo0cLDyVwNGzZMFStW1L/+9S9VqlRJklSrVi3FxsaqTp06+stf/qL58+d7OKUUHx+vgwcPKiUlRTNnzvR0nCv69d+bm266SbfffrtatWql9u3ba+nSpXriiSfcnoE9O/BKAwcO1JIlS5zPFy9e7JVXxH7nnXdUv3591atXT48//rgWL14sb7t0la+8lzCHt38mb7zxRt14441auXKlioqKPB3ninJzc7V+/Xo99dRTzqJzWXh4uBITE/X22297xftaoUIFvfjii5o7d65OnDjh6Thldu+996pp06ZasWLFdfl+lJ0/qNWrVzv/0bnxxhvVs2dPT0dy8fjjj+vTTz/VsWPHdOzYMX322Wd6/PHHPR2rlEWLFjlzdezYUfn5+dqyZYuHU7nylfcS5vD2z6S/v7+WLl2qtLQ0hYaGqk2bNvrzn/+sr776ytPRnA4cOCCHw6EGDRpccX2DBg105swZff/999c52ZU99NBDatasmSZOnOjpKIbUr19fR48evS7fi7LzB9WuXTvt3bvX+UhNTfV0JBc1atRQly5dtHTpUi1ZskRdunRR9erVPR3LxbfffqsdO3bo0UcflfTLP+K9evXSokWLPJzMlS+8lzCHr3wme/TooVOnTmnVqlXq2LGjPv74Y91+++1aunSpp6O58IY9N2U1Y8YMpaWlKSMjw9NRyszhcFy3idTM2fmDqly5surWrevpGL9r4MCBGj58uCTp1Vdf9XCa0hYtWqRLly4pMjLSuczhcCgwMFDz5s1TSEiIB9O58vb3Eubwpc9kUFCQOnTooA4dOmj8+PF64oknNHHiRK84Q7Bu3bqy2WzKyMjQQw89VGp9RkaGqlatqho1angg3ZXdfffdSkhIUHJysle8h2WRkZFx3eYOsmcHXqtjx466cOGCLl68qISEBE/HcXHp0iW98cYbmjVrlssesi+//FKRkZFavny5pyO68Ob3Eubwtc/kbzVs2FBnz571dAxJUrVq1dShQwe99tprLmcySlJWVpbefPNN9erVy2tO775s+vTp+vDDD5Wenu7pKP/Rpk2btG/fPvXo0eO6fD/27MBrVahQwblLtkKFCh5O42r16tU6c+aMBg0aVOr/lnv06KFFixZd8fo2nuLN7yXM4SufyR9//FE9e/bUwIED1aRJE1WpUkW7du3SzJkz9eCDD3o6ntO8efPUunVrJSQkaOrUqYqJidG///1vPffcc7rppps0bdo0T0cspXHjxkpMTPS6aQlFRUXKyspScXGxsrOztW7dOqWkpOj+++9X3759r0sG9uy4SUlJifz96ZLlFRwcrODgYE/HKGXRokWKj4+/4mGBHj16aNeuXV414VLy3vfy13zl98Ybc/rKZ/LGG29Uy5Yt9fLLL+vuu+9Wo0aNNH78eA0ePFjz5s3zdDynW2+9Vbt27dItt9yiRx55RHXq1NGQIUPUrl07paenKywszNMRr2jKlCnX9WJ9ZbFu3TpFREQoOjpaHTt21ObNm5WamqoPPvjguv3Pl83hSzOwfEjHjh1Vt25dr/rlBbydr/ze+EpOAL9gz47Jzpw5o9WrV+vjjz9WfHy8p+MAPsFXfm98JScAV961H9YCBg4cqJ07d+qZZ57xquPPgDfzld8bX8kJwBWHsQAAgKVxGAsAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcArmDSpElq1qyZp2MAMAFlB8Afns1m08qVKz0dA4CbUHYAAIClUXYAeI22bdtqxIgRGjVqlKpWrSq73a7XX39dZ8+e1YABA1SlShXVrVtXa9eudb5my5YtatGihQIDAxUREaGxY8fq0qVLLmOOHDlSY8aMUVhYmMLDwzVp0iTn+ujoaEnSQw89JJvN5nx+2T/+8Q9FR0crJCREvXv31k8//eTOtwCAG1B2AHiVtLQ0Va9eXTt27NCIESP05JNPqmfPnmrdurX27Nmj++67T3369NG5c+d08uRJde7cWXfeeae+/PJLzZ8/X4sWLdLUqVNLjVm5cmVt375dM2fO1JQpU7RhwwZJ0s6dOyVJS5Ys0enTp53PJenQoUNauXKlVq9erdWrV2vLli2aPn369XszAJiC20UA8Bpt27ZVcXGxPvnkE0lScXGxQkJC1L17d73xxhuSpKysLEVERCg9PV0ffvih3nvvPWVkZMhms0mSXnvtNT3//PPKz8+Xn59fqTElqUWLFrr33nudxcVms+n9999Xt27dnNtMmjRJf/3rX5WVlaUqVapIksaMGaOtW7dq27Zt1+PtAGAS9uwA8CpNmjRxfl2hQgVVq1ZNjRs3di6z2+2SpJycHGVkZCguLs5ZdCSpTZs2Kiws1IkTJ644piRFREQoJyfnP2aJjo52Fh0jrwPgXSg7ALxKQECAy3Obzeay7HKxKSkpKdeYZXn9tb4OgHeh7ADwWQ0aNFB6erp+fTT+s88+U5UqVXTzzTeXeZyAgAAVFxe7IyIAL0DZAeCznnrqKWVmZmrEiBHav3+/PvjgA02cOFFJSUny8yv7P2/R0dHauHGjsrKydObMGTcmBuAJlB0APuumm27SRx99pB07dqhp06YaOnSoBg0apHHjxhkaZ9asWdqwYYOioqIUGxvrprQAPIWzsQAAgKWxZwcAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcAAFgaZQcAAFja/wHmi7Aw5DjufgAAAABJRU5ErkJggg==", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1062,7 +1140,7 @@ "for i in range(len(rain)):\n", " month = rain.iloc[i,1].month\n", " monthlyrain[month-1] += rain.iloc[i,2]\n", - "print monthlyrain\n", + "print(monthlyrain)\n", "#\n", "plt.bar(np.arange(12),monthlyrain,width=1)\n", "plt.xlabel('month')\n", @@ -1090,25 +1168,25 @@ "name": "stdout", "output_type": "stream", "text": [ - " conc\n", - "date \n", - "2014-04-01 0.19\n", - "2014-04-02 0.23\n", - "2014-04-03 0.32\n", - "2014-04-04 0.29\n", - "2014-04-05 0.32\n", - "data on April 1: conc 0.19\n", + " conc\n", + "date \n", + "2014-04-01 0.19\n", + "2014-04-02 0.23\n", + "2014-04-03 0.32\n", + "2014-04-04 0.29\n", + "2014-04-05 0.32\n", + "data on April 1: conc 0.19\n", "Name: 2014-04-01 00:00:00, dtype: float64\n", - "data on April 2: conc 0.23\n", + "data on April 2: conc 0.23\n", "Name: 2014-04-02 00:00:00, dtype: float64\n" ] } ], "source": [ "data = read_csv('timeseries1.dat', parse_dates=[0], index_col=0)\n", - "print data\n", - "print 'data on April 1:',data.loc['2014-04-01']\n", - "print 'data on April 2:',data.loc['2014-04-02']" + "print(data)\n", + "print('data on April 1:',data.loc['2014-04-01'])\n", + "print('data on April 2:',data.loc['2014-04-02'])" ] }, { @@ -1131,25 +1209,38 @@ "output_type": "stream", "text": [ "YYYYMMDD\n", - "2012-01 83.0\n", - "2012-02 24.3\n", - "2012-03 21.9\n", - "2012-04 57.6\n", - "2012-05 76.5\n", - "2012-06 119.0\n", - "2012-07 121.6\n", - "2012-08 93.4\n", - "2012-09 52.0\n", - "2012-10 132.6\n", - "2012-11 63.3\n", - "2012-12 149.5\n", - "Freq: M, Name: RH, dtype: float64\n" + "2012-01-31 82.3\n", + "2012-02-29 23.9\n", + "2012-03-31 21.5\n", + "2012-04-30 57.1\n", + "2012-05-31 76.1\n", + "2012-06-30 118.5\n", + "2012-07-31 121.4\n", + "2012-08-31 93.2\n", + "2012-09-30 51.7\n", + "2012-10-31 132.2\n", + "2012-11-30 63.3\n", + "2012-12-31 149.3\n", + "Freq: ME, Name: RH, dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_62939/3507311271.py:4: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.\n", + "Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).\n", + "\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.\n", + "\n", + "See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment\n", + " rain.RH[rain.RH<0] = 0\n" ] }, { "data": { "text/plain": [ - "" + "Text(0.5, 0, 'month')" ] }, "execution_count": 24, @@ -1158,9 +1249,9 @@ }, { "data": { - "image/png": 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X+aBg9tlnn33j6KrSl9yZgKSjJP2+8vHEY35UyJMXW88+++yzrymuWn1R8sBH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\nxfx/1dQRXuSV/YUAAAAASUVORK5CYII=\n", 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", 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" ] }, "metadata": {}, @@ -1173,8 +1264,8 @@ " skipinitialspace=True)\n", "rain.RH[rain.RH<0] = 0\n", "rain.RH = rain.RH * 0.1 # Convert to mm/day\n", - "monthlyrain = rain.RH.resample('M', how='sum', kind='period')\n", - "print monthlyrain\n", + "monthlyrain = rain.RH.resample('ME').sum()\n", + "print(monthlyrain)\n", "monthlyrain.plot(kind='bar')\n", "plt.ylabel('mm/month')\n", "plt.xlabel('month')" @@ -1204,17 +1295,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:6: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", + "/tmp/ipykernel_62939/1593482493.py:6: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.\n", + "Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).\n", + "\n", + "Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.\n", "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" + "See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment\n", + " weather.RH[weather.RH<0] = 0\n" ] }, { "data": { - "image/png": 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0bVv67RITRR56SKR5c0e/2Ul+oM2bHRMhY44eFfnwQ7UBNWqkYc8+fOsnTdLu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\nTkSuQmCC\n", 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1228,9 +1322,9 @@ "weather.RH = 0.1 * weather.RH\n", "weather.EV24 = 0.1 * weather.EV24\n", "weather.RH[weather.RH<0] = 0\n", - "yearly_rain = weather.RH.resample('A', how='sum', kind='period')\n", - "yearly_evap = weather.EV24.resample('A', how='sum', kind='period')\n", - "yearly_temp = weather.TG.resample('A', how='mean', kind='period')\n", + "yearly_rain = weather.RH.resample('YE').sum()\n", + "yearly_evap = weather.EV24.resample('YE').sum()\n", + "yearly_temp = weather.TG.resample('YE').mean()\n", "yearly_rain.plot()\n", "yearly_evap.plot()\n", "yearly_temp.plot(secondary_y=True)\n", @@ -1241,21 +1335,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "exploratory-computing-with-python (3.13.5)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.13.5" } }, "nbformat": 4, From 15b0526799da2783776f7bc31c8cd88fe55bd392 Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 17:58:03 +0100 Subject: [PATCH 06/10] run function for notebooks --- .github/workflows/test_notebooks.py | 49 +++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 .github/workflows/test_notebooks.py diff --git a/.github/workflows/test_notebooks.py b/.github/workflows/test_notebooks.py new file mode 100644 index 0000000..0092bf5 --- /dev/null +++ b/.github/workflows/test_notebooks.py @@ -0,0 +1,49 @@ +#%% +# depenencies +from pathlib import Path +import papermill as pm +import pytest +from shutil import rmtree + +#%% +# paths +BASE_DIR = Path(__file__).parent.parent.parent +OUTPUT_DIR = Path(__file__).parent / ".nb_test_outputs" + +if OUTPUT_DIR.exists(): + rmtree(OUTPUT_DIR) + +OUTPUT_DIR.mkdir(exist_ok=False) + +#%% +# Collect all notebooks you want to test +notebooks = [] +for path in BASE_DIR.iterdir(): + if path.is_dir(): + if path.name.endswith("_bugs"): + continue + for nb in path.rglob("*_sol.ipynb"): + notebooks.append(nb.resolve()) +#%% +# test function +def test_notebook_runs(nb_path: Path) -> None: + out_path = OUTPUT_DIR / nb_path.name + print(f"Testing notebook: {nb_path.name}") + pm.execute_notebook( + input_path=str(nb_path), + output_path=str(out_path), + kernel_name="python3", + log_output=True, + cwd=str(nb_path.parent), + ) + +#%% +@pytest.mark.parametrize("nb_path", notebooks, ids=lambda p: str(p)) +def test_notebook_runs_pytst(nb_path: Path) -> None: + test_notebook_runs(nb_path) + +# local test +# for nb_path in sorted(notebooks): + # test_notebook_runs(nb_path) +# %% +rmtree(OUTPUT_DIR) \ No newline at end of file From 3f199ee50295efd656a2618fbd3e29dcc3b9bbad Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 18:07:33 +0100 Subject: [PATCH 07/10] create ci --- .github/workflows/test_notebooks.py | 19 ++++++++++-------- .github/workflows/test_notebooks.yml | 30 ++++++++++++++++++++++++++++ 2 files changed, 41 insertions(+), 8 deletions(-) create mode 100644 .github/workflows/test_notebooks.yml diff --git a/.github/workflows/test_notebooks.py b/.github/workflows/test_notebooks.py index 0092bf5..b16809d 100644 --- a/.github/workflows/test_notebooks.py +++ b/.github/workflows/test_notebooks.py @@ -7,20 +7,23 @@ #%% # paths -BASE_DIR = Path(__file__).parent.parent.parent -OUTPUT_DIR = Path(__file__).parent / ".nb_test_outputs" +CWD = Path(__file__).parent.resolve() +BASE_DIR = CWD.parent.parent +OUTPUT_DIR = CWD / ".nb_test_outputs" +print(f"Base dir: {OUTPUT_DIR}") -if OUTPUT_DIR.exists(): - rmtree(OUTPUT_DIR) +# if OUTPUT_DIR.exists(): + # rmtree(OUTPUT_DIR) -OUTPUT_DIR.mkdir(exist_ok=False) +OUTPUT_DIR.mkdir(exist_ok=True) #%% # Collect all notebooks you want to test +notebooks_to_ignore = ["bugs", "wm", "s3", "s4"] notebooks = [] for path in BASE_DIR.iterdir(): if path.is_dir(): - if path.name.endswith("_bugs"): + if any(ignored in path.name for ignored in notebooks_to_ignore): continue for nb in path.rglob("*_sol.ipynb"): notebooks.append(nb.resolve()) @@ -43,7 +46,7 @@ def test_notebook_runs_pytst(nb_path: Path) -> None: test_notebook_runs(nb_path) # local test -# for nb_path in sorted(notebooks): - # test_notebook_runs(nb_path) +for nb_path in sorted(notebooks): + test_notebook_runs(nb_path) # %% rmtree(OUTPUT_DIR) \ No newline at end of file diff --git a/.github/workflows/test_notebooks.yml b/.github/workflows/test_notebooks.yml new file mode 100644 index 0000000..1024721 --- /dev/null +++ b/.github/workflows/test_notebooks.yml @@ -0,0 +1,30 @@ +name: Test Notebooks + +on: + push: + branches: + - main + schedule: + # Run on the first day of every month at 00:00 UTC + - cron: '0 0 1 * *' + +jobs: + test-notebooks: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.x' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -e .[ci] + + - name: Run notebook tests + run: python .github/workflows/test_notebooks.py From 729b8b70b2e0d0ed611cfd1f5ae2fa8cbd4dd595 Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 18:08:21 +0100 Subject: [PATCH 08/10] rerun notebook --- .../py_exploratory_comp_3_sol.ipynb | 511 +++++++++++++++--- 1 file changed, 423 insertions(+), 88 deletions(-) diff --git a/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb b/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb index 0172ea4..2e46113 100644 --- a/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb +++ b/notebook3_for_and_if/py_exploratory_comp_3_sol.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -41,9 +41,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world, the value of i is 0\n", + "Hello world, the value of i is 1\n", + "Hello world, the value of i is 2\n", + "Hello world, the value of i is 3\n", + "Hello world, the value of i is 4\n" + ] + } + ], "source": [ "for i in [0, 1, 2, 3, 4]:\n", " print('Hello world, the value of i is', i)" @@ -58,9 +70,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x, xsquare 0 0\n", + "x, xsquare 1 1\n", + "x, xsquare 2 4\n", + "x, xsquare 3 9\n", + "We are done with the loop\n" + ] + } + ], "source": [ "for x in [0, 1, 2, 3]:\n", " xsquared = x ** 2\n", @@ -77,9 +101,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the value of data is: 20\n", + "the value of data is: mark\n", + "the value of data is: 3.1622776601683795\n" + ] + } + ], "source": [ "for data in [20, 'mark', np.sqrt(10)]:\n", " print('the value of data is:', data)" @@ -94,9 +128,26 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the value of i is: 0\n", + "the value of i is: 1\n", + "the value of i is: 2\n", + "the value of i is: 3\n", + "the value of i is: 4\n", + "the value of i is: 5\n", + "the value of i is: 6\n", + "the value of i is: 7\n", + "the value of i is: 8\n", + "the value of i is: 9\n" + ] + } + ], "source": [ "for i in range(10):\n", " print('the value of i is:', i)" @@ -111,9 +162,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a range with 10 values: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "a range from 10 till 20 [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n", + "a range from 10 till 20 with steps of 2: [10, 12, 14, 16, 18]\n" + ] + } + ], "source": [ "print('a range with 10 values:', list(range(10)))\n", "print('a range from 10 till 20', list(range(10, 20)))\n", @@ -129,9 +190,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "x = np.linspace(0, 2 * np.pi, 100)\n", "y = np.zeros_like(x) # similar to zeros(shape(x))\n", @@ -183,9 +255,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting value: 4\n", + "changing data in the first if-statement\n", + "value after the first if-statement: 6\n", + "value after the second if-statement: 6\n" + ] + } + ], "source": [ "data = 4\n", "print('starting value:', data)\n", @@ -216,9 +299,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a is not smaller than 3\n" + ] + } + ], "source": [ "a = 4\n", "if a < 3:\n", @@ -236,9 +327,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a is equal to 4\n" + ] + } + ], "source": [ "a = 4\n", "if a < 4:\n", @@ -258,9 +357,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the entered value is smaller than 4\n", + "the entered value is larger than 4\n", + "the entered value is larger than 4\n" + ] + } + ], "source": [ "for i in range(3): # do this 3 times\n", " # a = float(input('Enter a value: '))\n", @@ -334,9 +443,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i, datasum: 0 1\n", + "i, datasum: 1 4\n", + "i, datasum: 2 6\n", + "i, datasum: 3 11\n", + "i, datasum: 4 18\n", + "i, datasum: 5 21\n", + "i, datasum: 6 25\n", + "i, datasum: 7 27\n", + "total sum of data: 27\n" + ] + } + ], "source": [ "data = np.array([1, 3, 2, 5, 7, 3, 4, 2])\n", "datasum = 0\n", @@ -393,9 +518,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the maximum value is 7\n", + "the index of the maximum value is 4\n" + ] + } + ], "source": [ "maxvalue = -1e8\n", "maxindex = None\n", @@ -416,9 +550,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the maximum value is 7\n", + "the index of the maximum value is 4\n" + ] + } + ], "source": [ "print('the maximum value is ', np.max(data))\n", "print('the index of the maximum value is ', np.argmax(data))" @@ -456,9 +599,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[11. 22. 18.]\n" + ] + } + ], "source": [ "data = np.array([[1, 2, 3, 5],\n", " [4, 8, 6, 4],\n", @@ -483,9 +634,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sum of entire array: 51\n", + "sum rows (axis=0): [ 8 15 13 15]\n", + "sum columns (axis=1): [11 22 18]\n" + ] + } + ], "source": [ "print('sum of entire array:', np.sum(data))\n", "print('sum rows (axis=0):', np.sum(data, axis=0))\n", @@ -505,9 +666,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a is between 5 and 8\n" + ] + } + ], "source": [ "x = [1, 4, 5, 8, 9]\n", "a = 6\n", @@ -526,9 +695,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a is between 5 and 8\n" + ] + } + ], "source": [ "x = [1, 4, 5, 8, 9]\n", "a = 6\n", @@ -578,9 +755,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n", + "G\n", + "Goodmorning \n" + ] + } + ], "source": [ "text1 = 'Goodmorning everybody'\n", "print(len(text1))\n", @@ -597,9 +784,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Goodmorning everybody in the class\n", + "The magic number is 7\n" + ] + } + ], "source": [ "text1 = 'Goodmorning everybody'\n", "newtext = text1 + ' in the class' # Adding two srings\n", @@ -618,9 +814,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n", + "False\n", + "True\n", + "True\n" + ] + } + ], "source": [ "print('delft' < 'eindhoven') # True as 'd' is smaller than 'e'\n", "print('dalft' < 'delft') # True as 'a' is smaller than 'e'\n", @@ -638,9 +846,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TU Delft\n", + "tu delft\n", + "TU DELFT\n" + ] + } + ], "source": [ "TU = 'TU Delft'\n", "print(TU)\n", @@ -657,9 +875,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is the sentence: This is a sentence containing a number of words\n", + "This is the split sentence: ['This', 'is', 'a', 'sentence', 'containing', 'a', 'number', 'of', 'words']\n", + "All words may be printed seperately:\n", + "This\n", + "is\n", + "a\n", + "sentence\n", + "containing\n", + "a\n", + "number\n", + "of\n", + "words\n" + ] + } + ], "source": [ "sentence = 'This is a sentence containing a number of words'\n", "print('This is the sentence:', sentence)\n", @@ -709,9 +946,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of days in January is 31\n", + "The number of days in February is 28\n", + "The number of days in March is 31\n", + "The number of days in April is 30\n", + "The number of days in May is 31\n", + "The number of days in June is 30\n", + "The number of days in July is 31\n", + "The number of days in August is 31\n", + "The number of days in September is 30\n", + "The number of days in October is 31\n", + "The number of days in November is 30\n", + "The number of days in December is 31\n" + ] + } + ], "source": [ "months = ['January', 'February', 'March', 'April',\\\n", " 'May','June', 'July', 'August', 'September',\\\n", @@ -732,9 +988,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "x = np.linspace(-2 * np.pi, 2 * np.pi, 100)\n", "y = np.zeros_like(x)\n", @@ -758,9 +1025,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "average monthly temperature in month 1 is less than 10 degrees\n", + "average monthly temperature in month 2 is less than 10 degrees\n", + "average monthly temperature in month 3 is less than 10 degrees\n", + "average monthly temperature in month 4 is less than 10 degrees\n", + "average monthly temperature in month 5 is more than 10 degrees\n", + "average monthly temperature in month 6 is more than 10 degrees\n", + "average monthly temperature in month 7 is more than 10 degrees\n", + "average monthly temperature in month 8 is more than 10 degrees\n", + "average monthly temperature in month 9 is more than 10 degrees\n", + "average monthly temperature in month 10 is more than 10 degrees\n", + "average monthly temperature in month 11 is less than 10 degrees\n", + "average monthly temperature in month 12 is less than 10 degrees\n" + ] + } + ], "source": [ "temperature = np.loadtxt('holland_temperature.dat')\n", "for i in range(len(temperature)):\n", @@ -781,9 +1067,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data values: [1 3 2 5 7 3 4 2]\n", + "running total: [ 1 4 6 11 18 21 25 27]\n", + "running total with numpy: [ 1 4 6 11 18 21 25 27]\n" + ] + } + ], "source": [ "data = np.array([1, 3, 2, 5, 7, 3, 4, 2])\n", "runningtotal = np.zeros_like(data)\n", @@ -806,9 +1102,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3.1 3.3 6.2 9.2 13.1 15.6 17.9 17.5 14.5 10.7 6.7 3.7]\n", + "Number of month closest to 15 degrees, temp: 9 14.5\n", + "Alternative method:\n", + "Number of month closest to 15 degrees, temp: 9 14.5\n" + ] + } + ], "source": [ "temperature = np.loadtxt('holland_temperature.dat')\n", "print(temperature)\n", @@ -835,9 +1142,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The oil price exceeds 40 euros for the first time in April of 2005\n", + "The oil price exceeds 60 euros for the first time in December of 2007\n", + "The oil price exceeds 80 euros for the first time in June of 2008\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "oilprice = np.loadtxt('oil_price_monthly.dat', delimiter=',')\n", "plt.plot(oilprice[:,2], 'b-')\n", @@ -864,9 +1191,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Guido is between Franka and Joe\n" + ] + } + ], "source": [ "x = ['Aaldrich', 'Babette', 'Chris', 'Franka', 'Joe', 'Louisa', 'Pierre', 'Simone', 'Tarek', 'Yvonne', 'Zu']\n", "myname = 'Guido'\n", From 9c8e2b59080285f891fba3df185d695336449def Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 18:23:50 +0100 Subject: [PATCH 09/10] update tests --- .github/workflows/test_notebooks.py | 20 +++++++++----------- .github/workflows/test_notebooks.yml | 6 +++--- 2 files changed, 12 insertions(+), 14 deletions(-) diff --git a/.github/workflows/test_notebooks.py b/.github/workflows/test_notebooks.py index b16809d..930f416 100644 --- a/.github/workflows/test_notebooks.py +++ b/.github/workflows/test_notebooks.py @@ -9,12 +9,7 @@ # paths CWD = Path(__file__).parent.resolve() BASE_DIR = CWD.parent.parent -OUTPUT_DIR = CWD / ".nb_test_outputs" -print(f"Base dir: {OUTPUT_DIR}") - -# if OUTPUT_DIR.exists(): - # rmtree(OUTPUT_DIR) - +OUTPUT_DIR = CWD / ".ipynb_checkpoints" OUTPUT_DIR.mkdir(exist_ok=True) #%% @@ -25,6 +20,8 @@ if path.is_dir(): if any(ignored in path.name for ignored in notebooks_to_ignore): continue + if ".github" in str(path): + continue for nb in path.rglob("*_sol.ipynb"): notebooks.append(nb.resolve()) #%% @@ -42,11 +39,12 @@ def test_notebook_runs(nb_path: Path) -> None: #%% @pytest.mark.parametrize("nb_path", notebooks, ids=lambda p: str(p)) -def test_notebook_runs_pytst(nb_path: Path) -> None: - test_notebook_runs(nb_path) +def test_notebook_runs_pytest(nb_path: Path) -> None: + test_notebook_runs(nb_path=nb_path) # local test -for nb_path in sorted(notebooks): - test_notebook_runs(nb_path) +# for nb_path in sorted(notebooks): +# test_notebook_runs(nb_path) +# break # %% -rmtree(OUTPUT_DIR) \ No newline at end of file +rmtree(OUTPUT_DIR) diff --git a/.github/workflows/test_notebooks.yml b/.github/workflows/test_notebooks.yml index 1024721..9d3c3f6 100644 --- a/.github/workflows/test_notebooks.yml +++ b/.github/workflows/test_notebooks.yml @@ -14,10 +14,10 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@v6 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v6 with: python-version: '3.x' @@ -27,4 +27,4 @@ jobs: pip install -e .[ci] - name: Run notebook tests - run: python .github/workflows/test_notebooks.py + run: pytest .github/workflows/test_notebooks.py From 4558837863b017db81228347d2990e244cfd32bf Mon Sep 17 00:00:00 2001 From: martinvonk Date: Thu, 29 Jan 2026 18:32:23 +0100 Subject: [PATCH 10/10] last update to the tests --- .github/workflows/test_notebooks.py | 28 +++++++++------------------- .github/workflows/test_notebooks.yml | 2 +- 2 files changed, 10 insertions(+), 20 deletions(-) diff --git a/.github/workflows/test_notebooks.py b/.github/workflows/test_notebooks.py index 930f416..8396057 100644 --- a/.github/workflows/test_notebooks.py +++ b/.github/workflows/test_notebooks.py @@ -3,14 +3,11 @@ from pathlib import Path import papermill as pm import pytest -from shutil import rmtree #%% # paths CWD = Path(__file__).parent.resolve() BASE_DIR = CWD.parent.parent -OUTPUT_DIR = CWD / ".ipynb_checkpoints" -OUTPUT_DIR.mkdir(exist_ok=True) #%% # Collect all notebooks you want to test @@ -26,25 +23,18 @@ notebooks.append(nb.resolve()) #%% # test function -def test_notebook_runs(nb_path: Path) -> None: - out_path = OUTPUT_DIR / nb_path.name - print(f"Testing notebook: {nb_path.name}") +@pytest.mark.parametrize("path", notebooks, ids=lambda p: str(p)) +def test_notebook_runs(path: Path) -> None: + print(f"Testing notebook: {path.name}") pm.execute_notebook( - input_path=str(nb_path), - output_path=str(out_path), - kernel_name="python3", + input_path=str(path), + output_path=None, log_output=True, - cwd=str(nb_path.parent), + kernel_name="python3", + cwd=str(path.parent), ) #%% -@pytest.mark.parametrize("nb_path", notebooks, ids=lambda p: str(p)) -def test_notebook_runs_pytest(nb_path: Path) -> None: - test_notebook_runs(nb_path=nb_path) - # local test -# for nb_path in sorted(notebooks): -# test_notebook_runs(nb_path) -# break -# %% -rmtree(OUTPUT_DIR) +# for path in sorted(notebooks): +# test_notebook_runs(path=path) diff --git a/.github/workflows/test_notebooks.yml b/.github/workflows/test_notebooks.yml index 9d3c3f6..1b53e55 100644 --- a/.github/workflows/test_notebooks.yml +++ b/.github/workflows/test_notebooks.yml @@ -3,7 +3,7 @@ name: Test Notebooks on: push: branches: - - main + - master schedule: # Run on the first day of every month at 00:00 UTC - cron: '0 0 1 * *'