lecture04-python-scientific-ecosystem/lecture04.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Use this for larger plots:\n",
    "#matplotlib.rc('figure', figsize=(15, 10))\n",
    "matplotlib.rc('figure', figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div style=\"text-align:center\">\n",
    "<h1>Numpy and the Python Scientific Ecosystem</h1>\n",
    "\n",
    "<br><br>\n",
    "<img src=\"numpy-logo.png\" style=\"width: 50%; margin: auto;\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Example from last lecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 1.0, 2.0]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1, 10, 100]\n",
    "\n",
    "# Take log10 of all list elements:\n",
    "[math.log(i, 10) for i in l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1, 10, 100]\n",
    "\n",
    "# take log10 of all list elements:\n",
    "np.log10(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Another example from last lecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "from pathlib import Path\n",
    "\n",
    "def read_infected_cases():\n",
    "    \"\"\"Read number of infected cases from CSV file and return as list of int.\"\"\"\n",
    "    \n",
    "    fname = Path('..') / 'lecture03-python-introduction' / 'austria_covid_19_data.csv'\n",
    "    with open(fname, 'r') as read_obj:\n",
    "        # pass the file object to reader() to get the reader object\n",
    "        csv_reader = csv.reader(read_obj)\n",
    "        # Pass reader object to list() to get a list of lists\n",
    "        list_of_rows = list(csv_reader)\n",
    "\n",
    "    # get second column\n",
    "    infected_cases = [i[1] for i in list_of_rows]\n",
    "\n",
    "    # remove first line, as it contains header\n",
    "    infected_cases = infected_cases[1:]\n",
    "\n",
    "    # convert to integer, as it is string\n",
    "    infected_cases = [int(i) for i in infected_cases]\n",
    "\n",
    "    return infected_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "infected_cases = read_infected_cases()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "plt.plot([math.log(i, 10) for i in infected_cases], label = 'log(infections)')\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('log(infections)')\n",
    "\n",
    "plt.title('Log(infections)')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The same thing with numpy a bit shorter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAHwCAYAAADuJ7gwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd3xUVeL+8c9JBxISeg+h9x6a+kUR1wIqiliQagHR3dVd6+7q2lZXV13FtrIUFaQIggXrShEpKhB6CSR0QksgpAAJKXN+f2TgFzHAAJnczMzzfr3yMjP3zp0nQJLHc88911hrEREREZGyFeR0ABEREZFApBImIiIi4gCVMBEREREHqISJiIiIOEAlTERERMQBKmEiIiIiDlAJExGfYoypYYzZYoyJcD/+1hgz3MPX1jLGLDLGZBtj/l3KuTYaY64ozWOedvz2xpifvHV8ESl7IU4HEBH/ZYzZCdxrrZ1Xiof9C/CBtTYXwFp73Xm8dhRwCKhsL2KRRGPMh0CKtfapk89Za9tc6PE8Ya1dZ4zJMMbcYK390pvvJSJlQyNhIuIzjDHhwHBgygUeoiGw6WIKmMOmAvc5HUJESodKmIiUOWPMSGPMVmNMujFmjjGmbrFtV7tPN2YaY/5jjPnRGHOve3N3IMNam1Js/4UntxtjRhhjlhhjXjPGHDHG7DDGXOfe9iFFBe5xY8xRY8xVxpggY8xfjDHbjDGHjTEzjTFVix37MmPMT+4RqD3u448CBhc7zpfufXcaY65yfx5ujBljjNnn/hjjLpAYY64wxqQYYx4xxqQaY/YbY+4q9p59jTGb3KdM9xpjHi32R7cQ6HPyWCLi21TCRKRMGWOuBF4CbgPqALuAj93bqgOzgL8C1YAtwCXFXt7O/dzZdHfvUx14BZhojDHW2hEUjSS9Yq2NdJ8ifRC4CbgcqAscAd51Z4kFvgXeBmoAHYE11tpxpx3nhhIyPAn0cL+mA9ANeKrY9tpANFAPuAd41xhTxb1tInCftTYKaAssOPkia+1eIB9ocY4/AxHxASphIlLWBgPvW2tXWWtPUFS4ehpj4oC+wEZr7afW2gLgLeBAsdfGANnnOP4ua+14a20hMImiolfrDPveBzxprU1xZ3kWGGiMCXHnnGetnW6tzbfWHrbWrjmPr/F5a22qtTYNeA4YWmx7vnt7vrX2G+Ao/79Y5QOtjTGVrbVHrLWrTjt2NkV/DiLi41TCRKSs1aVo9AsAa+1R4DBFo0J1gT3FtlkgpdhrjwBR5zj+qdJmrT3u/jTyDPs2BD5zn27MABKBQopKWwNgmwdfT0l+9TW6P69b7PFhd8k86XixjLdQVEZ3uU/F9jzt2FFAxgXmEpFyRCVMRMraPorKDwDGmEoUnXrcC+wH6hfbZoo/BtYBzUsxyx7gOmttTLGPCPdpvz1AkzO87lwT+3/1NQKx7ufOyVq7wlrbH6gJfA7MPLnNPXcujHOfkhURH6ASJiLeFmqMiTj5QVGpuMsY09E9wfyfwDJr7U7ga6CdMeYm9ynB31M0f+qk5UCMMaZeKWUbC7xojGkIp9Yg6+/eNhW4yhhzmzEmxBhTzRjT0b3tIND4LMedDjzlPl514Gk8uKLTGBNmjBlsjIm21uYDWRSNzJ10BbDAfepURHycSpiIeNs3QE6xj/8D/g7MpmjkqwlwB4C19hBwK0UT6g8DrYEE4IR7ex7wITCklLK9CcwBvjfGZAO/UDSxH2vtbopOCz4CpANrKJpkD0WT51u7T2N+XsJxX3DnXgesB1a5n/PEUGCnMSYLGM2vv9bBFBVHEfEDxneXyxERf2eMCaJoTthga+0P7udqAIuBTtbaHCfzlSVjTDtgnLX29DliIuKjVMJEpFwxxlwDLKNo1Owxik5JNg6kwiUigUGnI0WkvOlJ0VWJh4AbgJtUwETEH2kkTERERMQBGgkTERERcYBKmIiIiIgDQpwOcL6qV69u4+LinI4hIiIick4rV648ZK2tUdI2nythcXFxJCQkOB1DRERE5JyMMbvOtE2nI0VEREQcoBImIiIi4gCVMBEREREH+NycsJLk5+eTkpJCbm6u01HkHCIiIqhfvz6hoaFORxEREXGUX5SwlJQUoqKiiIuLwxjjdBw5A2sthw8fJiUlhUaNGjkdR0RExFF+cToyNzeXatWqqYCVc8YYqlWrphFLERER/KSEASpgPkJ/TyIiIkX8poQ5LTIy8qJeP3DgQLZv3w5A3759ycjIOOv+mzdvpmPHjnTq1Ilt27ad13stXLiQn3766dTjsWPHMnny5PMPfQZ5eXn06tWLgoKCUjumiIiIv1EJKwc2btxIYWEhjRs3BuCbb74hJibmrK/5/PPP6d+/P6tXr6ZJkybn9X6nl7DRo0czbNiw8w9+BmFhYfTp04cZM2aU2jFFRET8jUpYKbPW8thjj9G2bVvatWt3qoi4XC4eeOAB2rRpw/XXX0/fvn2ZNWsWAFOnTqV///6njhEXF8ehQ4fYuXMnrVq1YuTIkbRp04arr76anJwcvvnmG8aMGcOECRPo3bs3AFOmTKFbt2507NiR++67j8LCQgC+++47OnfuTIcOHejTpw87d+5k7NixvPHGG3Ts2JHFixfz7LPP8tprrwGwZs0aevToQfv27bn55ps5cuQIAFdccQVPPPEE3bp1o3nz5ixevBgoKpAn37d9+/YkJycDcNNNNzF16tQy+BMXERHxTX5xdWRxz325kU37skr1mK3rVuaZG9p4tO+nn37KmjVrWLt2LYcOHaJr16706tWLpUuXsnPnTtavX09qaiqtWrXi7rvvBmDp0qUMGjSoxOMlJyczffp0xo8fz2233cbs2bMZMmQIo0ePJjIykkcffZTExERmzJjB0qVLCQ0N5YEHHmDq1Klcd911jBw5kkWLFtGoUSPS09OpWrXqr14LMH/+/FPvN2zYMN5++20uv/xynn76aZ577jnGjBkDQEFBAcuXL+ebb77hueeeY968eYwdO5aHHnqIwYMHk5eXd6r8tW3blhUrVlzwn7mIiIi/87sS5rQlS5YwaNAggoODqVWrFpdffjkrVqxgyZIl3HrrrQQFBVG7du1TI1gA+/fvp0aNEu/tSaNGjejYsSMAXbp0YefOnb/ZZ/78+axcuZKuXbsCkJOTQ82aNfnll1/o1avXqeUgqlatetbsmZmZZGRkcPnllwMwfPhwbr311lPbBwwY8JscPXv25MUXXyQlJYUBAwbQrFkzAIKDgwkLCyM7O5uoqKhz/bGJiIgEHL8rYZ6OWHmLtfa8ngeoUKHCGZdtCA8PP/V5cHAwOTk5JR57+PDhvPTSS796fs6cOaV6NeLJLMHBwacm3d955510796dr7/+mmuuuYYJEyZw5ZVXAnDixAkiIiJK7f1FRET8ieaElbJevXoxY8YMCgsLSUtLY9GiRXTr1o3LLruM2bNn43K5OHjwIAsXLjz1mlatWrF169YLfs8+ffowa9YsUlNTAUhPT2fXrl307NmTH3/8kR07dpx6HiAqKors7OzfHCc6OpoqVaqcmu/10UcfnRoVO5Pt27fTuHFjHnzwQW688UbWrVsHwOHDh6lRo4ZWxhcRETkDvxsJc9rNN9/Mzz//TIcOHTDG8Morr1C7dm1uueUW5s+fT9u2bWnevDndu3cnOjoagH79+rFw4UKuuuqqC3rP1q1b88ILL3D11VfjcrkIDQ3l3XffpUePHowbN44BAwbgcrmoWbMmc+fO5YYbbmDgwIF88cUXvP3227861qRJkxg9ejTHjx+ncePGfPDBB2d97xkzZjBlyhRCQ0OpXbs2Tz/9NAA//PADffv2vaCvR0REJBCYs50mK4/i4+NtQkLCr55LTEykVatWDiXy3NGjR4mMjOTw4cN069aNpUuXUrt2bXJycujduzdLly4lODjY6ZilYsCAAbz00ku0aNHiN9t85e9LRETkYhljVlpr40vappGwMnT99deTkZFBXl4ef//736lduzZQNCfsueeeY+/evcTGxjqc8uLl5eVx0003lVjAREREyoPc/EJy8wuJqRjmWAavlzBjTDCQAOy11l5/2rZwYDLQBTgM3G6t3entTE4pPg/sdNdcc03ZBfGysLCwUl38VURE5EK5XJaUIzlsPpDF5gPZbDmQzeYDWew4dIxhPeN49kbnLugri5Gwh4BEoHIJ2+4Bjlhrmxpj7gD+BdxeBplERETEz2QczytWtIrKVtKBbI7lFZ7ap2G1irSoFUW/dnW4pGl1B9N6uYQZY+oD/YAXgYdL2KU/8Kz781nAO8YYYy9gopq1VjeH9gG+NgdRRETKn7wCF9vSjrLlQDaJB7KKStf+bA5k/f/lnmIqhtKiVhS3xjegRe0oWtaOonmtKCqFl5+ZWN5OMgZ4HDjTap31gD0A1toCY0wmUA04dD5vEhERweHDh6lWrZqKWDlmreXw4cNaO0xERDxWUOhiy8FsVu3OYPXuI2zcm8W2tKMUuIr+pz402NCkRiQ9m1Q7VbZa1q5Mrcrh5b4TeK2EGWOuB1KttSuNMVecabcSnvvNUIkxZhQwCihx4nr9+vVJSUkhLS3twgNLmYiIiKB+/fpOxxARkXLq0NETrN6dwardR1i9+wjrUjI57j6dWD0yjHb1ounTqiYtakfRqk5lGlWvRGiwby576s2RsEuBG40xfYEIoLIxZoq1dkixfVKABkCKMSYEiAbSTz+QtXYcMA6Klqg4fXtoaOipW/OIiIiIb8gvdJG4P6tY6cpgd/pxAEKCDK3rVua2+AZ0io2hc2wV6lepUO5Ht86H10qYtfavwF8B3CNhj55WwADmAMOBn4GBwIILmQ8mIiIi5V9qVu6psrXKPcp1osAFQK3K4XSOrcKQHrF0jq1C23rRRIT6x9qZZ1Lms9OMMc8DCdbaOcBE4CNjzFaKRsDuKOs8IiIi4h2Zx/P5ftMBfkxKY/XuDPZmFN3/OCw4iLb1KjOkR0M6x1ahU2wMdaIj/GqUyxN+sWK+iIiIlA9ZufnM3XiQr9fvZ3FyGvmFltqVI4iPq0Kn2Cp0jo2hdd3KhIf49yjXSVoxX0RERLwmOzefeYkH+XrdfhYlHSKv0EW9mArcdWkj+rWrQ/v60QE3yuUJlTARERE5b0dPFDA/8SBfrdvPj0lp5BW4qBsdwbCeDenXvg4dG8SoeJ2DSpiIiIh45NiJAuZvTuXrdfv4YUtR8apdOYIh3YuKV6cGMQQFqXh5SiVMREREzuh4XgELNqfy9br9LNicyokCF7UqhzO4eyz92tWhc2wVFa8LpBImIiIiv5Jf6GLupqI5XvM3HyQ330WNqHAGdYulX/s6dFHxKhUqYSIiIgJAocvy+eq9vLUgmV2Hj1M9Mpzb4hvQr10d4uOqEqziVapUwkRERAKcy2X5av1+xsxLYnvaMdrUrcz4YfFc2bKmipcXqYSJiIgEKJfL8r+NB3hjXhJJB4/SolYUY4d05po2tXVlYxlQCRMREQkw1lrmJabyxtwkNu3PokmNSrw9qBP92tXRXK8ypBImIiISIKy1/JiUxhtzk1ibkknDahV5/bYO9O9YT6cdHaASJiIi4uestfy07TCvz01i5a4j1IupwL9uaceAzvUJDQ5yOl7AUgkTERHxY8t3pPPv77ewbEc6tStH8MJNbbktvgFhISpfTlMJExER8UOrdh/h9e+TWLL1EDWiwnn2htbc0S2WiNDAuHG2L1AJExER8SPrUjJ4Y24SP2xJo2qlMJ7s24ohPRpSIUzlq7xRCRMREfEDmw9k8e/vk5i76SDRFUJ5/NoWDO8ZR6Vw/aovr/Q3IyIi4sO2px3ljXnJfLVuH5FhIfz5qubcfVkcURGhTkeTc1AJExER8UEpR47z1vxkZq/aS1hwEKMvb8J9vRoTUzHM6WjiIZUwERERH5Kancu7C7YyffkeAIb2aMgDvZtQMyrC4WRyvlTCREREfMCRY3n8d9F2PvxpB/mFllu71OePfZpRL6aC09HkAqmEiYiIlGPZuflMXLKDiYt3cDSvgBs71OVPVzWnUfVKTkeTi6QSJiIiUg7l5BUy+eedjP1xG0eO53NNm1o8/LsWtKgd5XQ0KSUqYSIiIuVIXoGLj1fs5p0FW0nNPkGv5jV49OrmtK8f43Q0KWUqYSIiIuVAQaGLT1fv5c15yezNyKFbXFXeHtSJ7o2rOR1NvEQlTERExEEul+Xr9ft5Y14S29OO0a5eNP8c0I5ezapjjHE6nniRSpiIiIgDXC7L95sOMmZeEpsPZNO8ViRjh3Thmja1VL4ChEqYiIhIGTpZvt6cn0zi/iwaVa/EmNs7ckOHugQHqXwFEpUwERGRMmCtu3zNS2aTu3y9flsHbuxQl5DgIKfjiQNUwkRERLzIWsvcTQcZ4y5fcdUqqnwJoBImIiLiFSfL15vzk9m4r6h8/fvWDvTvqPIlRVTCRERESpG1lnmJqYyZl8TGfVk0rFaR127twE0qX3IalTAREZFSYK1lfmIqY+YnsWFvFrFVK/LqwPbc3KmeypeUSCVMRETkIlhrWbA5lTHzklm/N5PYqhV5xV2+QlW+5CxUwkRERC7A6eWrQdUKKl9yXlTCREREzoPLVVS+3lqQzLoUd/m6pT03d1b5kvOjEiYiIuKB/EIXX67dx9gft5F08Cj1q1TgX7e0Y0Dn+ipfckFUwkRERM4iJ6+QGSt2M37xDvZm5NC8ViRv3N6B69vXVfmSi6ISJiIiUoLM4/lM/nknH/y0k/RjeXRpWIXn+7ehd4uaBOn2QlIKVMJERESKOZCZy8Ql25m2bDfH8gq5smVN7r+iCV3jqjodTfyMSpiIiAiwLe0o437czqerU3BZuKF9He67vAmt6lR2Opr4KZUwEREJaOtSMnhv4Ta+23iAsOAgBnWLZeT/NaZB1YpORxM/pxImIiIBx1rL0q2Hee/HrSzdepjKESH8/oqmjLg0juqR4U7HkwChEiYiIgGj0GX538YDvLdwG+v3ZlIzKpy/9W3JoG6xREWEOh1PAoxKmIiIBIQlyYd4+osNbD90jEbVK/HygHbc3Lke4SHBTkeTAKUSJiIifs3lsvxn4Vb+PTeJxtUr8Z/BnbmmTW2CtcyEOEwlTERE/Fbm8XwenrmG+ZtTualjXf45oB0Vw/SrT8oH/UsUERG/tGFvJvdPXcmBzFz+0b8NQ3o0xBiNfkn5oRImIiJ+Z2bCHv7++QaqVgpjxn096RxbxelIIr+hEiYiIn4jN7+Q577cyPTle7i0aTXeuqMT1bTkhJRTKmEiIuIX9qQf54Gpq1i/N5Pf927Cw79rocn3Uq6phImIiM/7YUsqf/p4DS5rGT8snt+1ruV0JJFzUgkTERGf5XJZ3pyfzFsLkmlZuzJjh3SmYbVKTscS8YhKmIiI+KQjx/L404w1/JiUxi2d6/PCTW2pEKaFV8V3qISJiIjPWZeSwf1TVpGWfYJ/3tyOQd0aaPkJ8TkqYSIi4jOstUxfvodn52ykRlQ4n4zuSYcGMU7HErkgKmEiIuITcvMLeerzDcxamUKv5jV48/aOVKkU5nQskQumEiYiIuXersPHGD1lFYn7s3iwTzMe6tNMy0+Iz1MJExGRcm1+4kH+NGMNQcbwwYiu9G5Z0+lIIqVCJUxERMql43kF/OvbzUz6eRdt61XmvcFdaFC1otOxREqNSpiIiJQ7K3el88jMtew8fJy7Lo3jiWtbEhGq5SfEv6iEiYhIuXGioJA35iYzbtE26kRXYPrIHvRsUs3pWCJeoRImIiLlwoa9mTw8cw1JB48yqFsDnuzXmshw/ZoS/6V/3SIi4qj8Qhf/+WEbby9IpmqlMD64qyu9W2jyvfg/lTAREXFM8sFsHp65lvV7M7mpY12evbENMRW19pcEBpUwEREpc4Uuy8Ql23nt+yQiw0N4b3BnrmtXx+lYImVKJUxERMrUzkPHePSTtSTsOsLVrWvxzwHtqB4Z7nQskTKnEiYiImXC5bJMXbaLf36zmZBgw+u3deDmTvV0420JWCphIiLidXszcnhi1jqWbD3E/zWrzisD21MnuoLTsUQcpRImIiJeY61l1soUnv9yE4XW8uLNbbmzW6xGv0TwYgkzxkQAi4Bw9/vMstY+c9o+I4BXgb3up96x1k7wViYRESk7qdm5/O3T9cxLTKVbXFVeu7UDsdV02yGRk7w5EnYCuNJae9QYEwosMcZ8a6395bT9Zlhr/+DFHCIiUoastcxZu49n5mzkeF4hT/Vrxd2XNiIoSKNfIsV5rYRZay1w1P0w1P1hvfV+IiLiLGstP2xJ5fW5SWzYm0WH+tH8+7YONK0Z5XQ0kXLJq3PCjDHBwEqgKfCutXZZCbvdYozpBSQBf7bW7vFmJhERKV3WWhYlH+L1uUms3ZNBg6oVeHVge27uVI+Q4CCn44mUW14tYdbaQqCjMSYG+MwY09Zau6HYLl8C0621J4wxo4FJwJWnH8cYMwoYBRAbG+vNyCIi4iFrLT9tO8zrc5NYuesI9WIq8PKAdtzSpT6hKl8i52SKzhqWwRsZ8wxwzFr72hm2BwPp1trosx0nPj7eJiQkeCOiiIh46JftReVr+Y506kRH8PveTbktvgFhISpfIsUZY1Zaa+NL2ubNqyNrAPnW2gxjTAXgKuBfp+1Tx1q73/3wRiDRW3lEROTiJexM5/W5Sfy07TA1o8J57sY23N61ARGhwU5HE/E53jwdWQeY5B7hCgJmWmu/MsY8DyRYa+cADxpjbgQKgHRghBfziIjIBVq9+wivz01icfIhqkeG8ffrWzO4e6zKl8hFKLPTkaVFpyNFRMrO+pRM3piXxILNqVStFMboyxszpEdDKoZprW8RTzhyOlJERHzXxn2ZjJmXzNxNB4mpGMrj17ZgeM84KoXr14ZIadF3k4iInLLlQDZj5iXx7YYDVI4I4ZHfNWfEpXFERYQ6HU3E76iEiYgI+zNzePnbzcxZu4/IsBAe6tOMuy9rRHQFlS8Rb1EJExEJYLn5hUxcsoN3Fmyl0FpGX96E+3o1JqZimNPRRPyeSpiISACy1jIvMZV/fLWJ3enHubZNbZ7s14oGVXWDbZGyohImIhJgtqYe5fmvNrEoKY1mNSOZck93LmtW3elYIgFHJUxEJEBk5+bz9oKtvL9kBxXCgnn6+tYM7dlQtxgScYhKmIiIn3O5LJ+u3svL327m8LET3B7fgEevaUH1yHCno4kENJUwERE/tnZPBs/M2ciaPRl0io3h/RHxtK8f43QsEUElTETEL6Vln+DV/21mZkIKNaLC+fetHbi5Uz2CgozT0UTETSVMRMSP5Be6mPTTTt6cl0xuQSH39WrMH65sqsVWRcohlTARET+xODmN577cxNbUo1zevAZP39CaJjUinY4lImegEiYi4uP2pB/nha838b+NB2lYrSITh8dzZcuaGKNTjyLlmUqYiIiPys0v5L2F23jvx22EBBkev7YF91zWiPCQYKejiYgHVMJERHzQwi2pPDNnI7sOH+fGDnX5W99W1I6OcDqWiJwHlTARER+yPzOH57/cxLcbDtC4RiWm3dudS5pqtXsRX6QSJiLiA/ILXXy4dCdvzEvCZS2PXdOCe/9Ppx5FfJlKmIhIObdiZzpPfbaBLQez6dOyJs/e2EY32hbxAyphIiLl1OGjJ3j52818sjKFejEVGDe0C1e3qe10LBEpJSphIiLljMtl+XjFHv713WaOnSjg/iua8Mcrm1IxTD+yRfyJvqNFRMqRDXszeerzDazZk0GPxlX5R/+2NKsV5XQsEfEClTARkXIgKzef179PYvLPO6laKYw3bu/ATR3racFVET+mEiYi4iBrLXPW7uOFrxM5dPQEQ3s05JGrWxBdQfd6FPF3KmEiIg7ZlnaUp7/YwNKth2lfP5qJw+NpXz/G6VgiUkZUwkREylhufiHvLNjKfxdtIyI0mH/c1JY7u8USHKRTjyKBRCVMRKQMbTmQzYPTV7PlYDYDOtXjr31bUSMq3OlYIuIAlTARkTJgrWXKL7t44etEoiJC+PCurlzRoqbTsUTEQSphIiJeln4sj8dnrWVeYipXtKjBqwM7aPRLRFTCRES8aUnyIR6euYaM4/k8c0NrRlwSp2UnRARQCRMR8Yq8Ahevfb+FcYu207RmJB/e1Y3WdSs7HUtEyhGVMBGRUrY97SgPfryaDXuzGNw9lqf6taZCWLDTsUSknFEJExEpJdZaPklI4Zk5GwkPDeK/Q7twjW64LSJnoBImIlIKMo/n87fP1vP1+v30bFyNN27vSO3oCKdjiUg5phImInKRlu9I508fryY1+wRPXNuSUb0aa+FVETknlTARkQtUUOjirfnJvPPDVmKrVmT2/ZfQoYFuOyQinlEJExG5AHvSj/PQx6tZtTuDgV3q8+yNbYgM149UEfGcfmKIiJynL9bs5anPNgDw1qBO3NihrsOJRMQXqYSJiHgoOzefZ77YyKer99KlYRXG3N6RBlUrOh1LRHyUSpiIiAfWp2Tyh+mrik5D9mnGH69sSkhwkNOxRMSHqYSJiJzFyRtv/+OrRKpHhjHjvp50javqdCwR8QMqYSIiZ5Cdm89fP13PV+v207tFDV6/rSNVKoU5HUtE/IRKmIhICTbty+L301axO/04j1/bgtG9mhCktb9EpBSphImIFGOtZcaKPTwzZyPRFUKZdm93ujeu5nQsEfFDKmEiIm7H8wp46rMNfLp6L5c1rc6YOzpSPTLc6Vgi4qdUwkREgOSD2TwwdRVb047y56ua84crm+rWQyLiVSphIhLwPl2VwpOfbaBSeDBT7unOpU2rOx1JRAKASpiIBKzc/EKe+WIjMxL20L1RVd4e1ImalSOcjiUiAUIlTEQC0va0ozwwdRWbD2Tz+95N+PNVzbX4qoiUKZUwEQk4X67dx19mryMsJIgP7upK7xY1nY4kIgFIJUxEAkZufiEvfL2JKb/spkvDKrw9qBN1Yyo4HUtEApRKmIgEhN2Hj/PAtJVs2JvFqF6NeeyaFoTq9KOIOEglTET83ncbDvDYrLUYYPyweH7XupbTkUREVMJExH+5XJaXvk1k/OIddKgfzTt3dqZB1YpOxxIRAVTCRMRPFbosT8xex93vV94AACAASURBVKyVKQzr2ZCn+rUmLESnH0Wk/FAJExG/k1/o4pGZa5mzdh9/vqo5D/ZpijFa/V5EyheVMBHxK3kFLv44fRX/23iQv1zXktGXN3E6kohIiVTCRMRv5OYX8sDUVSzYnMozN7TmrksbOR1JROSMVMJExC/k5BUy6qMElmw9xD9vbsed3WOdjiQiclYqYSLi846eKODuD1eQsDOdVwd2YGCX+k5HEhE5J5UwEfFpmTn5jPhgOetSMhlzRydu7FDX6UgiIh5RCRMRn3XkWB7D3l/O5gNZvHtnZ65tW9vpSCIiHlMJExGfdOjoCYZMWMb2Q8cYNzSe3i11E24R8S0qYSLicw5m5XLn+F/Ym5HD+8O7clmz6k5HEhE5byphIuJT9mbkMHj8L6Rln2DSXd3o3ria05FERC6ISpiI+Izdh48zaPwvZOXm89G93ekcW8XpSCIiF0wlTER8wva0o9w5fhm5BYVMu7cH7epHOx1JROSiqISJSLmXdDCbO8cvw1rL9JE9aFWnstORREQumkqYiJRrG/dlMnTickKCDNNG9aBpzSinI4mIlIogpwOIiJzJmj0ZDBr3CxEhQcy8r6cKmIj4Fa+VMGNMhDFmuTFmrTFmozHmuRL2CTfGzDDGbDXGLDPGxHkrj4j4loSd6QyZsIzoiqHMuK8ncdUrOR1JRKRUeXMk7ARwpbW2A9ARuNYY0+O0fe4BjlhrmwJvAP/yYh4R8RE/JqUx7P3l1IwKZ+Z9PWlQtaLTkURESp3X5oRZay1w1P0w1P1hT9utP/Cs+/NZwDvGGON+rYgEmINZubz0TSKfr9lH81qRTLm3OzWjIpyOJSLiFV6dmG+MCQZWAk2Bd621y07bpR6wB8BaW2CMyQSqAYdOO84oYBRAbGysNyOLiAPyC118sHQHb85LJr/Q8scrm/LAFU2pEBbsdDQREa/xagmz1hYCHY0xMcBnxpi21toNxXYxJb2shOOMA8YBxMfHa5RMxI8s3XqIZ+ZsZGvqUa5sWZOnr2+t+V8iEhDKZIkKa22GMWYhcC1QvISlAA2AFGNMCBANpJdFJhFx1r6MHF78OpGv1++nQdUKTBgWz1WtazkdS0SkzHithBljagD57gJWAbiK3068nwMMB34GBgILNB9MxL+dKChkwuIdvLNgKy5refh3zRnVqzERoTr1KCKBxZsjYXWASe55YUHATGvtV8aY54EEa+0cYCLwkTFmK0UjYHd4MY+IOGzhllSe+3ITOw4d45o2tXiqX2td+SgiAcubV0euAzqV8PzTxT7PBW71VgYRKR/2pB/n+a82MXfTQRpVr8Sku7txefMaTscSEXGUblskIl6Tm1/I2B+38d7CbQQHGZ64tiV3XxZHeIhOPYqInFcJM8ZUAnLdVz2KiJTIWsu8xFSe/2oje9JzuL59HZ7s14o60RWcjiYiUm6ctYQZY4Iomqc1GOhK0Sr44caYNOAbYJy1NtnrKUXEZ+w8dIxnv9zIwi1pNKsZybR7u3NJ0+pOxxIRKXfONRL2AzAP+CuwwVrrAjDGVAV6Ay8bYz6z1k7xbkwRKe9y8gp594etjFu0nbCQIJ7q14rhl8QRGuzNu6OJiPiuc5Wwq6y1+ac/aa1NB2YDs40xoV5JJiI+Iys3nxHvL2fV7gwGdKrHX65rSc3Kut2QiMjZnLWEnSxgxpgmQIq19oQx5gqgPTDZWptRUkkTkcCRcTyPYe8vJ3F/Fu8N7sx17eo4HUlExCd4ep5gNlBojGlK0dpejYBpXkslIj4h/Vged45fxub92Ywd0kUFTETkPHhawlzW2gLgZmCMtfbPFC3GKiIBKi37BHeM+5ltaUcZPzyePq10yyERkfPh6RIV+caYQRTdYugG93OaCyYSoA5m5XLn+F/Yl5HLByO66upHEZEL4OlI2F1AT+BFa+0OY0wjQFdEigSgfRk53P7fnzmQmcuku7upgImIXCCPRsKstZuAB4s93gG87K1QIlI+7Uk/zqDxv5B5PJ/J93SnS8MqTkcSEfFZHpUwY8ylwLNAQ/drDGCttY29F01EypMdh44xePwvHMsrZOrI7rSvH+N0JBERn+bpnLCJwJ+BlYBuWSQSYLamZnPn+GUUuCzTR/agdd3KTkcSEfF5npawTGvtt15NIiLl0pYD2Qye8Atg+HhUD5rXinI6koiIX/C0hP1gjHkV+JSi+0cCYK1d5ZVUIlIubNibydCJywgLCWLayB40qRHpdCQREb/haQnr7v5vfLHnLHBl6cYRkfJi7Z4Mhk5cRmR4CNNG9iCueiWnI4mI+BVPr47s7e0gIlJ+rNyVzoj3VxBTKZRp9/agQdWKTkcSEfE7Hq0TZoyJNsa8boxJcH/82xgT7e1wIlL2lm0/zNCJy6keFc6MUT1VwEREvMTTxVrfB7KB29wfWcAH3golIs5YuvUQwz9YTt2YCswY1YO6MRWcjiQi4rc8nRPWxFp7S7HHzxlj1ngjkIg4Y+GWVO77aCWNqldiyr3dqR4Z7nQkERG/5ulIWI4x5rKTD9yLt+Z4J5KIlLW5mw4yavJKmtaMZPrIHipgIiJlwNORsPuBSe55YAZIB0Z4K5SIlJ1v1u/nwemraVMvmsl3dSO6YqjTkUREAoKnV0euAToYYyq7H2d5NZWIlInPVqfw6Cfr6Ngghg/v6kpUhAqYiEhZOWsJM8YMsdZOMcY8fNrzAFhrX/diNhHxosk/7+TpLzbSs3E1JgyPp1K4pwPjIiJSGs71U/fk6owl3afElnIWESkD1lr+s3Abr/5vC79rXYu3B3UiIjTY6VgiIgHnrCXMWvtf96fzrLVLi29zT84XER9ireWlbzczbtF2bu5Uj1cGtic02NPrc0REpDR5+tP3bQ+fE5FyqtBl+eun6xm3aDvDejbk37d2UAETEXHQueaE9QQuAWqcNi+sMqDzFyI+Iq/AxZ9nruHrdfv5Q++mPHJ181NzO0VExBnnmhMWBkS69ys+LywLGOitUCJSenLyChk9ZSU/JqXxt74tGdWridORRESEc88J+xH40RjzobV2VxllEpFSkpWbzz0friBh1xFeHtCOO7rFOh1JRETcPJ0QMsEYE3PygTGmijHmf17KJCKl4NDREwwa9wtr9mTw9qBOKmAiIuWMpwsDVbfWZpx8YK09Yoyp6aVMInKR9mXkMGTiMvZl5DB+WDxXtNC3q4hIeeNpCXMZY2KttbsBjDEN0TphIuXS9rSjDJ24nKycfD66pztd46o6HUlERErgaQl7ElhijPnR/bgXMMo7kUTkQm3al8Ww95dhLUwf1YO29aKdjiQiImfg6b0jvzPGdAZ6UHQD7z9baw95NZmInJeVu9IZ8cEKIsNDmHJvd5rUiHQ6koiInIVHE/NN0YJC1wKdrbVfAhWNMd28mkxEPLY4OY0hE5ZTPTKcT0b3VAETEfEBnl4d+R+gJzDI/TgbeNcriUTkvHy7fj93f7iCuOqVmHlfT+pXqeh0JBER8YCnc8K6W2s7G2NWw6mrI8O8mEtEPDAzYQ9/mb2OTrFVeH9EV6IrhDodSUREPORpCcs3xgTjviLSGFMDcHktlYic08QlO/jHV5v4v2bV+e/QLlQM8/TbWUREygNPf2q/BXwG1DTGvEjRLYue8loqETkjay1j5iXz5vxkrmtbmzF3dCQ8RLdyFRHxNee6gXcja+0Oa+1UY8xKoA9FV0feZK1NLJOEIvIrk37ayZvzk7m1S31eGtCOkGBPp3aKiEh5cq6RsFlAF2PMfGttH2BzGWQSkTNYuCWV57/axFWtavHyLe0JDjJORxIRkQt0rhIWZIx5BmhujHn49I3W2te9E0tETpd8MJs/TltNi9qVefOOjipgIiI+7lznMe4Acikqa1ElfIhIGUg/lsfdk1YQHhrMhOHxVArXJHwREV931p/k1totwL+MMeustd+WUSYRKeZEQSGjP1rJwawTzBjVg3oxFZyOJCIipcDT/51eYIy5E4gr/hpr7fPeCCUiRay1PPnZBpbvTOfNOzrSKbaK05FERKSUeFrCvgAygZXACe/FEZHixi3azqyVKTzYpxn9O9ZzOo6IiJQiT0tYfWvttV5NIiK/MnfTQV7+bjP92tfhT32aOR1HRERKmacLDP1kjGnn1SQicsrGfZk89PFq2teL5t+3diBIV0KKiPgdT0fCLgNGGGN2UHQ60gDWWtvea8lEAlRqdi4jJyVQOSKU8cPiiQjVavgiIv7I0xJ2nVdTiAgAufmFjJq8kiPH8/lkdE9qVo5wOpKIiHjJuW5bVNlamwVkl1EekYBlreXxWetYsyeDsUO60LZetNORRETEi841EjYNuJ6iqyItRachT7JAYy/lEgk4by/Yypy1+3j82hZc27a203FERMTLzrVY6/Xu/zYqmzgigemrdft4fW4SAzrX4/7LmzgdR0REysBZr440xsSdY7sxxtQvzUAigWbtngwembmW+IZVeGlAO4zRlZAiIoHgXKcjXzXGBFG0WOtKIA2IAJoCvYE+wDNAijdDivir/Zk5jJycQI2ocP47tAvhIboSUkQkUJzrdOStxpjWwGDgbqAOkAMkAl8DL1prc72eUsQPHc8r4N5JCRzPK+Sje7pTLTLc6UgiIlKGzrlEhbV2E/BkGWQRCRgul+XhGWtJ3J/FxOFdaVE7yulIIiJSxjxaJ8wYM6CEpzOB9dba1NKNJOL/Xvt+C99tPMDfr29N75Y1nY4jIiIO8HSx1nuAnsAP7sdXAL8AzY0xz1trP/JCNhG/9OmqFP6zcBuDusVy96VxTscRERGHeFrCXEAra+1BAGNMLeA9oDuwCFAJE/FAws50/jJ7PT0bV+P5/m10JaSISADz9AbecScLmFsq0Nxamw7kl34sEf+zJ/049320knpVKvDekM6EBnv67SciIv7I05GwxcaYr4BP3I8HAouMMZWADK8kE/EjufmFjJycQH6hi4nD44mpGOZ0JBERcZinJez3wADgMopuXTQJmG2ttRStFyYiZ/HC15vYfCCbD+/qSuMakU7HERGRcsCjEmattcaYJUAeRfeMXO4uYCJyDt9t2M+UX3YzqldjrmihKyFFRKSIR5NSjDG3AcspOg15G7DMGDPQm8FE/MHejBwen7WODvWjefTqFk7HERGRcsTT05FPAl1PrglmjKkBzANmeSuYiK8rKHTx0PTVuCy8NagTYSGaiC8iIv+fpyUs6LRFWQ/j+ZWVIgHprfnJJOw6wpt3dKRhtUpOxxERkXLG0yL1nTHmf8aYEcaYERTdN/Kbs73AGNPAGPODMSbRGLPRGPNQCftcYYzJNMascX88ff5fgkj58/O2w7z9w1YGdqlP/471nI4jIiLlkKcT8x8zxtwCXErR1ZHjrLWfneNlBcAj1tpVxpgoYKUxZq77XpTFLbbWXn/eyUXKqSPH8vjzjDU0qlaJ525s43QcEREppzw9HYm1djYw+zz23w/sd3+ebYxJBOoBp5cwEb9hreWxWWtJP5bHhOGXUCnc428xEREJMGf9DWGMyaZoSYrfbKJo5YrKnryJMSYO6AQsK2FzT2PMWmAf8Ki1dqMnxxQpjyb9tJN5iak8fX1r2taLdjqOiIiUY2ctYdbaqIt9A2NMJEUjaH+y1madtnkV0NBae9QY0xf4HGhWwjFGAaMAYmNjLzaSiFds3JfJP7/ZTJ+WNblLN+YWEZFz8OoVjsaYUIoK2FRr7aenb7fWZllrj7o//wYINcZUL2G/cdbaeGttfI0aNbwZWeSCHM8r4I/TV1OlUiiv3tpBN+YWEZFz8loJM0W/hSYCidba18+wT233fhhjurnzHPZWJhFveXbORnYcOsYbt3ekaiXdF1JERM7Nm7OGLwWGAuuNMWvcz/0NiAWw1o6laAX++40xBUAOcIduhyS+Zs7afcxMSOGPVzblkia/GcgVEREpkddKmLV2CUUT+M+2zzvAO97KIOJtuw8f52+frqdLwyo81Oc30xlFRETOSKvei1yg/EIXf/x4NUEG3ryjIyHB+nYSERHPaREjkQv02vdbWLsng/cGd6Z+lYpOxxERER+j/3UXuQCLktL474/bubN7LNe1q+N0HBER8UEqYSLnKS37BA/PXEvzWpE8fX1rp+OIiIiP0ulIkfPgclke+WQt2bn5TL23OxGhwU5HEhERH6WRMJHzMGHJdhYlpfH361vTovZF31BCREQCmEqYiIfW7sngle+2cF3b2gzurttniYjIxVEJE/FAdm4+f5y+mlqVI3h5QHvdlkhERC6a5oSJnIO1lqc+38DejBxmjOpBdMVQpyOJiIgf0EiYyDnMXrWXL9bs4099mhEfV9XpOCIi4idUwkTOYnvaUZ7+YgM9Glflgd5NnY4jIiJ+RCVM5AwOHz3BvZMTCA8JYsztnQgO0jwwEREpPZoTJlKC7Nx8Rnywgr1Hcvjonu7Ujo5wOpKIiPgZjYSJnCY3v5B7JyWQuD+L94Z0plsjzQMTEZHSp5EwkWLyC138Ydoqlu9MZ8ztHbmyZS2nI4mIiJ/SSJiIm8tleWLWOuYlpvJ8/7b071jP6UgiIuLHVMJEKFoL7PmvNvHp6r088rvmDO3R0OlIIiLi51TCRIC35m/lw592cs9ljfjDlVqKQkREvE8lTALeh0t38Ma8JAZ2qc+TfVvplkQiIlImVMIkoH22OoVnv9zE1a1r8fKAdgRpLTARESkjKmESsOZtOsijn6zjkibVeGtQJ0KC9e0gIiJlR791JCD9vO0wD0xbRdu6lRk3LJ6I0GCnI4mISIBRCZOAsz4lk5GTE4itWpEP7+pGZLiWyxMRkbKnEiYBZWvqUYZ/sJzoCqF8dE83qlQKczqSiIgEKJUwCRh7M3IYNnEZQQam3NudOtEVnI4kIiIBTOdhJCAcOnqCoROWkX2igI9H9aBR9UpORxIRkQCnkTDxe1m5+Qx/fzn7MnN4f0RX2tSNdjqSiIiISpj4t9z8Qu6dlMCWA9m8N6QLXeOqOh1JREQE0OlI8WP5hS5+P3UVK3am8+YdnejdoqbTkURERE7RSJj4JZfL8visdczfnMo/+rflxg51nY4kIiLyKyph4nestTz/1SY+W72Xx65pwZAeDZ2OJCIi8hsqYeJ33v1hKx/+tJOR/9eIB65o4nQcERGREqmEiV/5cu0+Xvs+iZs71eNvfVthjG7ILSIi5ZNKmPiNlbuO8Mgna+kaV4WXb2mnAiYiIuWaSpj4hT3pxxk1OYE60RH8d2g84SG6IbeIiJRvKmHi87Jy87n7wxXkF7p4f0RXqup+kCIi4gO0Tpj4tJNrge04dIzJ93SjSY1IpyOJiIh4RCVMfJa1lmfmbGRx8iFeGdieS5pUdzqSiIiIx3Q6UnzWxCU7mLZsN6Mvb8Jt8Q2cjiMiInJeVMLEJ83ddJAXv0nk2ja1efyaFk7HEREROW8qYeJzNuzN5KGPV9OuXjRv3N6RoCAtRSEiIr5HJUx8yoHMXO6dlEBMhVAmDIunQpiWohAREd+kifniM46dKOCeSSvIzs1n1v2XULNyhNORRERELphKmPiEQpfloY/XkLg/i4nDu9KqTmWnI4mIiFwUnY4Un/Dyt4nMSzzIMze0oXfLmk7HERERuWgqYVLuTV22i/GLdzDikjiGXxLndBwREZFSoRIm5dri5DSe/mIjvVvU4Kl+rZyOIyIiUmpUwqTcSj6YzQNTVtGsZiRv39mZkGD9cxUREf+h32pSLh06eoK7PlxBRFgwE0d0JTJc15CIiIh/UQmTcic3v5CRkxM4dPQEE4bFUy+mgtORRERESp2GF6Rccbksj36yltW7M3hvcGc6NIhxOpKIiIhXaCRMypUx85L4at1+nri2Jde1q+N0HBEREa9RCZNy47PVKby1YCu3xddn9OWNnY4jIiLiVSphUi4sSkrjiVnr6dm4Gi/c1A5jdFNuERHxbyph4rhFSWmMnJxAk5qRjB3ShbAQ/bMUERH/p9924qiTBaxxjUim3tud6IqhTkcSEREpEyph4pjTC1jVSmFORxIRESkzKmHiCBUwEREJdCphUuZUwERERFTCpIwtTlYBExERAZUwKUOLk9O4d1ICjapXUgETEZGApxImZaJ4AZs2socKmIiIBDyVMPE6FTAREZHfUgkTr1IBExERKZlKmHiNCpiIiMiZqYSJVyxJPqQCJiIichYqYVLqliQf4p5JK1TAREREzkIlTEqVCpiIiIhnvFbCjDENjDE/GGMSjTEbjTEPlbCPMca8ZYzZaoxZZ4zp7K084n0qYCIiIp4L8eKxC4BHrLWrjDFRwEpjzFxr7aZi+1wHNHN/dAfec/9XfIwKmIiIyPnx2kiYtXa/tXaV+/NsIBGod9pu/YHJtsgvQIwxpo63Mol3qICJiIicvzKZE2aMiQM6ActO21QP2FPscQq/LWpSjqmAiYiIXBivlzBjTCQwG/iTtTbr9M0lvMSWcIxRxpgEY0xCWlqaN2LKBZi9MoW7VcBEREQuiFdLmDEmlKICNtVa+2kJu6QADYo9rg/sO30na+04a228tTa+Ro0a3gkrHisodPH8l5t45JO1dImtwnQVMBERkfPmtYn5xhgDTAQSrbWvn2G3OcAfjDEfUzQhP9Nau99bmeTipR/L4w/TVvHTtsPcdWkcT/ZtRUiwVjoRERE5X968OvJSYCiw3hizxv3c34BYAGvtWOAboC+wFTgO3OXFPHKRNu3LYtRHCaRmn+C1WzswsEt9pyOJiIj4LK+VMGvtEkqe81V8Hwv83lsZpPR8uXYfj81aS0yFMD65rycdGsQ4HUlERMSneXMkTPxAocvy2vdbeG/hNuIbVuE/QzpTMyrC6VgiIiI+TyVMzijzeD4PfryaH5PSuLN7LM/e0IawEM3/EhERKQ0qYVKi5IPZjJycwN6MHF68uS2Duzd0OpKIiIhfUQmT3/jfxgM8PGMNFcJCmD6yB/FxVZ2OJCIi4ndUwuQUl8vy5vxk3pyfTIf60Ywd2oU60RWcjiUiIuKXVMIEgOzcfB6euZa5mw5yS+f6vHhzWyJCg52OJSIi4rdUwoQdh44xcnICOw4d45kbWjPikjiK1toVERERb1EJC3A/bEnlwemrCQkyfHRPNy5pUt3pSCIiIgFBJSxAWWt578dtvPq/LbSqXZn/Du1Cg6oVnY4lIiISMFTCAtDxvAIem7WOr9ft54YOdXnllvZUCNP8LxERkbKkEhZgThQUMnjCMtbsyeAv17Xkvl6NNf9LRETEASphAeb5LzexencG797ZmX7t6zgdR0REJGDpHjQBZPbKFKYu2819lzdWARMREXGYSliASNyfxZOfr6d7o6o8dnULp+OIiIgEPJWwAJCVm8/9U1ZSOSKUt+/sREiw/tpFREScpjlhfs5ay6Mz17LnSA4fj+pBzagIpyOJiIgIGgnze+MXb+f7TQf563Ut6aobcYuIiJQbKmF+7Jfth/nXd1vo264291zWyOk4IiIiUoxKmJ9KzcrlD9NW07BaRf51S3utBSYiIlLOaE6YH8ovdPH7aas4dqKAaSO7ExUR6nQkEREROY1KmB965bvNrNh5hDG3d6R5rSin44iIiEgJdDrSz3yzfj/jF+9gWM+G3NSpntNxRERE5AxUwvzI9rSjPD5rHR0bxPBkv1ZOxxEREZGzUAnzE8fzCrh/yipCgw3/GdyZ8JBgpyOJiIjIWWhOmB+w1vLkZxtISs1m8t3dqBtTwelIIiIicg4aCfMDU5bt5rPVe3n4/7V358FR1nccxz9fAgS5BEwkKiByKoIgSaCgVugg2lbrDSqnyiCKona0dVqvsVqtY0WtHRERFRCUKh619ahHUWcUQwhCBDlEhACScBOuQPLtH1mmJCYgkt1fdvf9msns7rML++HHb5YPz/Pb5xnQSWd1TA8dBwAA/AiUsDg3f/UW3ffPr9S/c7rG9u8QOg4AAPiRKGFxbNOOEt0wLVctmzbQ+ME9VKcOJ2QFACBesCYsTpWWuW5+KU8bikv06vV91axh/dCRAADAYaCExaknPlimT5Zt0IOXdFO3VkeHjgMAAA4ThyPj0EdLCvXEh8t0WWYrXZHdOnQcAADwE1DC4szqTTt168vzdXJGU/3pwq5cmBsAgDhFCYsju/eW6oYX56m0zPXUkJ46qj4nZAUAIF6xJiyO3PfWIi1cs1UTh2WqbVqj0HEAAMARYE9YnJiZs1rT56zSmLPba+CpGaHjAACAI0QJiwPv5K/THbMW6KyOabptYKfQcQAAQA2ghNVy/11SqJtm5On0Ns01YWim6qbwVwYAQCLgX/Ra7PMVG3Xd1Fx1atlEk0dmq1EqS/gAAEgUlLBaKm/VZl37fI7atGioqdf21tFH1QsdCQAA1CBKWC20aO02jZj8hdKapGraqN5q0YhLEgEAkGgoYbXM8sJiDXt2jhqn1tWLo3qrZdMGoSMBAIAooITVIqs27tSQSZ/LzDRtVG+1at4wdCQAABAllLBaYt3WXRry7Ofas69M00b1Urv0xqEjAQCAKKKE1QIbivdoyKQ52rxjr6Zc00snZzQNHQkAAEQZJSywLTtLNHTSHK3dskvPXZ2t01o1Cx0JAADEACUsoOI9+zTiuRytKNqhZ4ZnKbtti9CRAABAjHD2z0B2lZTqmudzlL9mqyYMzdRZHdNDRwIAADHEnrAA9uwr1ZhpucpZuUnjB/fQOV1aho4EAABijBIWY/tKyzRuRp5mLy3SXy45Tb/pfnzoSAAAIABKWAyVlbluf2WB3v1qve65oIsGZbcOHQkAAARCCYsRd9edb+Trtbw1uv3czrr6jJNCRwIAAAFRwmLA3fXAvxZr+pxVGtu/vcb27xA6EgAACIwSFgOPvb9Mkz79ViP7ttVtAzuHjgMAAGoBSliUPT37Gz3+wTINzmqtu8/vIjMLHQkAANQClLAo+sfc1Xrw7a91Qffj9edLuqlOHQoYAAAo3YmZ6AAACBJJREFURwmLknmrNuuPr+XrzA5penRQd6VQwAAAwAEoYVGwfttujZmaq4yjG+jJq05XvRSGGQAAVEQ7qGH7z4ZfvGefnhmepWYN64eOBAAAaiGuHVmD3F13vZ6vvFVbNGFoT3XOaBI6EgAAqKXYE1aDpnz2nWbOLdC4X3TQeV2PCx0HAADUYpSwGvLZNxt131uLNOCUlrplQKfQcQAAQC1HCasBBZt3auz0eToprZHGD+7OqSgAAMAhUcKO0K6SUo2ekqu9pWWaOCxTTRrUCx0JAADEARbmHwF31+2vfKnF32/TcyOz1S69cehIAAAgTrAn7AhMmL1Cby1Yp9+de7L6dT42dBwAABBHKGE/0UdLCvXwu+WXJBpzdrvQcQAAQJyhhP0EK4qKNW5Gnk7JaKqHLz2Ni3IDAIDDRgk7TNt379Xoqbmql1JHE4dn6qj6KaEjAQCAOMTC/MNQVua69eX5+nbDDk27trdaNW8YOhIAAIhTUdsTZmaTzazQzPKreb6fmW01s/mRn7ujlaWmPPb+Ur2/uFB3/foU9Wl/TOg4AAAgjkVzT9jzkp6UNOUgr/nE3c+PYoYa807+Oj3x4XJdntlKI/q2DR0HAADEuajtCXP3jyVtitbvH0tff79Nv535pXq0bqb7L+7KQnwAAHDEQi/M72NmX5rZ22Z2anUvMrPRZjbXzOYWFRXFMp+27CzR6Cm5apxaV08Py1RqXRbiAwCAIxeyhM2TdKK7d5f0N0mvV/dCd5/o7lnunpWenh6zgPtKy3Tj9Dx9v3W3JgzLVMumDWL23gAAILEFK2Huvs3diyP3/y2pnpmlhcpTlYfe/lqfLt+g+y/qqp5tmoeOAwAAEkiwEmZmGRZZXGVmvSJZNobKU9mseQWa9Om3GtHnRA3Kbh06DgAASDBR+3akmc2Q1E9SmpkVSLpHUj1JcvcJki6TdL2Z7ZO0S9IV7u7RynM4FhRs0R2zFupn7VrozvO7hI4DAAASUNRKmLtfeYjnn1T5KSxqlaLte3Td1FylN07V36/qqXopob+7AAAAEhENo5Ktu0rUOLWuJg7P1DGNU0PHAQAACYrLFlXS4dgmeveWn6tOHc4FBgAAooc9YVWggAEAgGijhAEAAARACQMAAAiAEgYAABAAJQwAACAAShgAAEAAlDAAAIAAKGEAAAABUMIAAAACoIQBAAAEQAkDAAAIgBIGAAAQACUMAAAgAEoYAABAAJQwAACAAChhAAAAAVDCAAAAAqCEAQAABGDuHjrDYTGzIknfxeCt0iRtiMH7oCLGPfYY8zAY9zAY9zCSedxPdPf0qp6IuxIWK2Y2192zQudINox77DHmYTDuYTDuYTDuVeNwJAAAQACUMAAAgAAoYdWbGDpAkmLcY48xD4NxD4NxD4NxrwJrwgAAAAJgTxgAAEAAlLBKzOw8M1tiZsvN7I7QeZKFma00s4VmNt/M5obOk6jMbLKZFZpZ/gHbWpjZf8xsWeS2eciMiaiacb/XzNZE5vx8M/tVyIyJyMxam9lHZrbYzL4ys5sj25nzUXSQcWfOV8LhyAOYWYqkpZLOkVQgKUfSle6+KGiwJGBmKyVluXuynkcmJszs55KKJU1x966RbQ9L2uTuD0X+49Hc3X8fMmeiqWbc75VU7O6PhMyWyMzsOEnHufs8M2siKVfSRZJGijkfNQcZ90FizlfAnrCKekla7u4r3L1E0kuSLgycCagx7v6xpE2VNl8o6YXI/RdU/mGJGlTNuCPK3H2du8+L3N8uabGkE8Scj6qDjDsqoYRVdIKk1Qc8LhATJ1Zc0ntmlmtmo0OHSTIt3X2dVP7hKenYwHmSyY1mtiByuJJDYlFkZm0lnS5pjpjzMVNp3CXmfAWUsIqsim0cr42NM9y9p6RfShobOXwDJLKnJLWX1EPSOkl/DRsncZlZY0mvSrrF3beFzpMsqhh35nwllLCKCiS1PuBxK0lrA2VJKu6+NnJbKOk1lR8aRmysj6zh2L+WozBwnqTg7uvdvdTdyyQ9I+Z8VJhZPZUXgRfdfVZkM3M+yqoad+b8D1HCKsqR1NHMTjKz+pKukPRm4EwJz8waRRZvyswaSRooKf/gvwo16E1JIyL3R0h6I2CWpLG/BERcLOZ8jTMzk/SspMXu/ugBTzHno6i6cWfO/xDfjqwk8pXZxySlSJrs7g8EjpTwzKydyvd+SVJdSdMZ9+gwsxmS+klKk7Re0j2SXpc0U1IbSaskXe7uLCKvQdWMez+VH5ZxSSslXbd/nRJqhpmdKekTSQsllUU2/0Hl65OY81FykHG/Usz5CihhAAAAAXA4EgAAIABKGAAAQACUMAAAgAAoYQAAAAFQwgAAAAKoGzoAAESDmR0j6YPIwwxJpZKKIo93unvfIMEAIIJTVABIeGZ2r6Rid38kdBYA2I/DkQCSjpkVR277mdlsM5tpZkvN7CEzG2JmX5jZQjNrH3ldupm9amY5kZ8zwv4JACQCShiAZNdd0s2SukkaJqmTu/eSNEnSTZHXPC5pvLtnS7o08hwAHBHWhAFIdjn7L51iZt9Iei+yfaGk/pH7AyR1Kb8kniSpqZk1cfftMU0KIKFQwgAkuz0H3C874HGZ/v8ZWUdSH3ffFctgABIbhyMB4NDek3Tj/gdm1iNgFgAJghIGAIc2TlKWmS0ws0WSxoQOBCD+cYoKAACAANgTBgAAEAAlDAAAIABKGAAAQACUMAAAgAAoYQAAAAFQwgAAAAKghAEAAARACQMAAAjgfyd8ZNoWtTMMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "plt.plot(np.log10(infected_cases), label = 'log(infections)')\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('log(infections)')\n",
    "\n",
    "plt.title('Log(infections)')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The Python Scientific Ecosystem\n",
    "\n",
    "<br>\n",
    "<div style=\"text-align:center\">\n",
    "<img alt=\"Python scientific ecosystem\" src=\"python-scientific-ecosystem.png\" style=\"height:650px;\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<small>Source: https://speakerdeck.com/jakevdp/the-state-of-the-stack-scipy-2015-keynote?slide=8</small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Numpy: the ndarray data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 5.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1., 2., 5.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5944470690>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arbitrary_values = np.array([1., 2., 5.])\n",
    "\n",
    "plt.plot(arbitrary_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Accessing elements in Numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 5.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrary_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrary_values[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrary_values[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrary_values[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 5.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrary_values[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arbitrary_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** do not use `np.ndarray()` to create a new `ndarray` object, use `np.array()`!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Dimensions and shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "unity_matrix = np.array([[1., 0., 0.],\n",
    "                         [0., 1., 0.],\n",
    "                         [0., 0., 1.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unity_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unity_matrix[2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 1.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unity_matrix[2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unity_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arbitrary_values.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Elementwise operations and broadcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, with Python list you cannot combine numbers and lists arithmetically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "#[1, 2, 3] + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 12, 13])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3]) + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3]) * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(np.arange(5) * 2 * np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Let's do exercise 1!\n",
    "\n",
    "Solve exercise 1 in `lecture04-python-scientific-ecosystem/exercises04.ipynb`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Let's do something with real data\n",
    "\n",
    "Public toilets in Vienna:\n",
    "https://www.data.gv.at/katalog/dataset/d9f5e582-3773-4f0b-8403-5d34718f6cf7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Note: we use the order (longitude, latitude) here. This is the order used in shape files and necessary for plotting too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "import os.path\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "\n",
    "def read_public_toilets():\n",
    "    \"\"\"Download CSV with geocordinates of public toilets in Vienna, parse it and return a numpy\n",
    "    array of shape (N,2), where each point is (longitude_x, latitude_y).\"\"\"\n",
    "    fname = Path('..') / 'data' / 'public-toilets.csv'\n",
    "    if not os.path.exists(fname):\n",
    "        URI = ('https://data.wien.gv.at/daten/geo?'\n",
    "               'service=WFS&request=GetFeature&version=1.1.0&'\n",
    "               'typeName=ogdwien:WCANLAGEOGD&srsName=EPSG:4326&outputFormat=csv')\n",
    "        urllib.request.urlretrieve(URI, filename=fname)\n",
    "        \n",
    "    d = pd.read_csv(fname)\n",
    "    return d.SHAPE.str.extract(r'POINT \\((\\d+\\.\\d+) (\\d+\\.\\d+)\\)').astype(float).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "public_toilets = read_public_toilets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 2)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.29172236, 48.23330501],\n",
       "       [16.42051555, 48.29787686],\n",
       "       [16.38666464, 48.29154054],\n",
       "       [16.36199004, 48.19799353],\n",
       "       [16.3595375 , 48.1972726 ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "stephansplatz = np.array([16.372223, 48.208432])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# Length of one degree longitude/latitude on stephansplatz:\n",
    "LON_TO_KM = 74.1\n",
    "LAT_TO_KM = 111.19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Doing calculations with longitude/latitude is difficult. Let's project the points to cartesian coordinates in kilometers with origin at the Stephansplatz. Of course this it's not very accurate to project longitude/latitude that way, but for a small area like Vienna it should be good enough:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_km(locations):\n",
    "    return (locations - stephansplatz) * [LON_TO_KM, LAT_TO_KM]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "public_toilets_km = to_km(public_toilets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(public_toilets_km.T[0], public_toilets_km.T[1], 'ko', markersize=3, label='Public toilet');\n",
    "plt.plot([0], [0], 'P', label='Stephansplatz', color='chocolate')\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.legend();\n",
    "plt.title('Map of public toilets in Vienna')\n",
    "plt.xlabel('Longitude [km]')\n",
    "plt.ylabel('Latitude[km]')\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Let's add a simple (inaccurate) map of Vienna (adapted version from [this one](https://commons.wikimedia.org/wiki/Category:Maps_of_Vienna#/media/File:Gemeindebezirke_Wiens.svg), license: public domain). See also [interactive map of public toilets](https://m.wien.gv.at/stadtplan/#base=karte&zoom=12&lat=48.2023&lon=16.4142&layer=wc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "vienna = plt.imread(str(Path('..') / 'data' / 'vienna-map.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(695, 925, 4)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vienna.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# distance from Stephansplatz to borders of the PNG file in km\n",
    "left = -13.682179147809752\n",
    "right = 16.639373238835283\n",
    "bottom = -9.81358722568525\n",
    "top = 12.989326274442831\n",
    "extent = left, right, bottom, top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5944496b10>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(public_toilets_km.T[0], public_toilets_km.T[1], 'ko', markersize=3, label='Public toilet');\n",
    "plt.plot([0], [0], 'P', label='Stephansplatz', color='chocolate')\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.legend();\n",
    "plt.title('Map of public toilets in Vienna')\n",
    "plt.xlabel('Longitude [km]')\n",
    "plt.ylabel('Latitude[km]')\n",
    "plt.grid();\n",
    "\n",
    "plt.imshow(vienna, extent=extent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Hint: there are [better ways to plot geographic maps](https://matplotlib.org/basemap/users/examples.html). This is just a simple example to play with Numpy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Distances between locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(point1, point2):\n",
    "    \"\"\"Calculate eukledian distance between two points. Points are passed\n",
    "    as lists or arrays of length 2. Numpy arrays of many dimensions are \n",
    "    supported: axis=0 must be the dimension for x/y coordinates.\"\"\"\n",
    "    return ((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "a = [1, 1]\n",
    "b = [0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4142135623730951"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "you_are_here =  np.array([16.357709, 48.232303])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Unfortunately the array has the wrong shape, the first _axis_ must be the one for x/y coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 2)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets_km.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Transposing the array solves this problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 180)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets_km.T.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Why is this necessary? We can look up how `distance()` can be used by uncommenting the following lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# distance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# distance??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.96509716,  2.76563039],\n",
       "       [ 3.57847811,  9.94537442],\n",
       "       [ 1.07012568,  9.2408389 ],\n",
       "       [-0.75826205, -1.16065303]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets_km[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.96509716,  3.57847811,  1.07012568, -0.75826205],\n",
       "       [ 2.76563039,  9.94537442,  9.2408389 , -1.16065303]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets_km[:4].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.96509716,  2.76563039])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets_km[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -5.96509716,   3.57847811,   1.07012568,  -0.75826205,\n",
       "        -0.93999559,   1.16174873,   2.11561918,  -0.30798692,\n",
       "         6.15564258,  -3.29553743,  -1.56848278,  -2.56921897,\n",
       "         0.5443434 ,  -3.61216863,  -2.03132104,  -3.41949438,\n",
       "         0.38501993,   0.78140082,   2.27726148,  -2.90579073,\n",
       "        -7.37747855,  -4.64686001,  -3.82225644,   9.50149747,\n",
       "         5.58736846,  -4.82282265,  -2.20424985,  -1.60433438,\n",
       "         1.53370862,   3.31194067,   6.92794872,  -2.04913763,\n",
       "        -3.77166136,  -3.12152892,  -7.49755917,  -2.51779185,\n",
       "         3.33151814,   0.92843446,   9.59892594,   2.2461946 ,\n",
       "        -1.61366426,  10.1038895 ,   8.82024742,  -0.48237077,\n",
       "         9.92407519,   2.1390669 ,   3.39814573,   4.15271023,\n",
       "         3.81112657,   0.24650761,   6.8120831 ,   4.52144465,\n",
       "         2.27878046,   1.4159545 ,   1.87267573,   3.20642789,\n",
       "        -1.66707792,  -1.01642321,  -2.50464918,   1.06701018,\n",
       "       -10.65564377,  -5.74019077,  -4.25885322,  -2.88784404,\n",
       "        -3.07669093,  -7.02342449,  -3.51358884,  -4.38683327,\n",
       "        -2.2959695 ,  -0.77058771,  -1.74063666,   0.59413017,\n",
       "         1.71397008,  -1.80944152,   1.96210444,  -0.801495  ,\n",
       "        -6.66182765,   1.88368913,  -0.87589786,  -0.7062898 ,\n",
       "         5.63655676,   2.19686039,  -1.64945123,  -0.33980241,\n",
       "        -0.6197607 ,  -0.22570439,   2.17513722,   1.45941209,\n",
       "         0.88543256,   2.60229865,   7.93676815,  -0.01595658,\n",
       "        -7.49768851,   0.34497558,   1.93658402,   3.00760527,\n",
       "        -0.75554086,  -2.08742803,   0.66391652,   2.14629215,\n",
       "         1.03188924,  -0.18968975,   3.80921595,  -2.55175596,\n",
       "         0.60913728,  -0.96326765,  -1.01920382,  -8.54543866,\n",
       "         3.61377814,   1.65817507,  -7.22114996,  -2.65025616,\n",
       "         2.18094684,   1.3693413 ,   4.68800562,   2.29973445,\n",
       "         1.09922982,   3.40616455,   2.95059814,  -8.07951406,\n",
       "        -1.79933229,   2.16683349,  -2.85582236,   3.66828804,\n",
       "         3.47186202,   2.44001905,  -1.23766558,   1.50916181,\n",
       "        -0.04025198,   0.83194054,  -4.59222558,   2.42281306,\n",
       "        -7.1780674 ,  -7.74243214,  -0.66347781,  -7.62594364,\n",
       "         2.84792337,   2.88968266,  -2.86211059,  -4.68981116,\n",
       "        -2.69891769,  -2.78569068,  -2.62920526,  -3.78057809,\n",
       "        -1.23125343,  -2.79649715,  -2.30739719,  -1.47044462,\n",
       "        -1.2584903 ,  -8.61955535,  -1.1639967 ,   7.95814429,\n",
       "        -2.42728471,  -0.3504987 ,  -0.58380843,  -5.16327878,\n",
       "        -8.27647527,   0.44258462,  -5.91518635,  -6.6998293 ,\n",
       "         1.04777519,   1.8091567 ,   0.90201342,   2.40698792,\n",
       "         2.08098926,  -8.93115738,  -6.86616349,  -8.40788027,\n",
       "        -9.29394012,  -7.00598388,   3.372613  ,  -0.2665495 ,\n",
       "         2.68074674,  -0.05053288,  -4.54919468,  -4.55463502,\n",
       "        -3.89786561,  -2.43772988,  -0.36017306,  -0.35179768])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets_km.T[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "distances_to_me = distance(public_toilets_km.T, to_km(you_are_here))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.89087892, 8.64987739, 6.92728303, 3.82803623, 3.89738561])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances_to_me[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "There is a toilet very close, less than 500m!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4462247392318615"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances_to_me.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Often there is another way to call Numpy functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4462247392318615"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(distances_to_me)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "But where is the toilet?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closest_idx = distances_to_me.argmin()\n",
    "closest_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.36326918, 48.23076184])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_toilets[closest_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def google_maps_link(location):\n",
    "    \"\"\"Return link to turbine in Google maps.\n",
    "    \n",
    "    See documentation:\n",
    "    https://developers.google.com/maps/documentation/urls/guide\n",
    "    https://stackoverflow.com/questions/47038116/google-maps-url-with-pushpin-and-satellite-basemap\n",
    "\n",
    "    \"\"\"\n",
    "    xlong, ylat = location\n",
    "    \n",
    "    # alternative API which does not allow marker\n",
    "    # f\"https://www.google.com/maps/@?api=1&map_action=map&center={ylat},{xlong}&basemap=satellite\"\n",
    "    \n",
    "    # alternative API which does not allow sattelite\n",
    "    # f\"https://www.google.com/maps/search/?api=1&query={ylat},{xlong}\"\n",
    "    \n",
    "    # zoom level z=xxx seems to be broken somehow (?)\n",
    "    return f\"http://maps.google.com/maps?q=loc:{ylat}+{xlong}&z=13\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://maps.google.com/maps?q=loc:48.2307618445136+16.36326918334209&z=13\n"
     ]
    }
   ],
   "source": [
    "print(google_maps_link(public_toilets[closest_idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://maps.google.com/maps?q=loc:48.232303+16.357709&z=13\n"
     ]
    }
   ],
   "source": [
    "print(google_maps_link(you_are_here))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def osm_location_link(location, zoom_level):\n",
    "    \"\"\"Return link to location in openstreetmaps.\"\"\"\n",
    "    xlong, ylat = location\n",
    "    return f\"https://www.openstreetmap.org/?mlat={ylat}&mlon={xlong}#map={zoom_level}/{ylat}/{xlong}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.openstreetmap.org/?mlat=48.2307618445136&mlon=16.36326918334209#map=15/48.2307618445136/16.36326918334209\n"
     ]
    }
   ],
   "source": [
    "print(osm_location_link(public_toilets[closest_idx], 15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(public_toilets_km.T[0], public_toilets_km.T[1], 'ko', markersize=3, label='Public toilet');\n",
    "plt.plot([0], [0], 'P', label='Stephansplatz', color='chocolate')\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.title('Map of public toilets in Vienna')\n",
    "plt.xlabel('Longitude [km]')\n",
    "plt.ylabel('Latitude[km]')\n",
    "plt.grid();\n",
    "\n",
    "plt.imshow(vienna, extent=extent)\n",
    "\n",
    "plt.plot(public_toilets_km.T[0][closest_idx], public_toilets_km.T[1][closest_idx],\n",
    "         'mv', label='Closest toilet');\n",
    "plt.plot(*to_km(you_are_here), '*g', label='You are here');\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Let's do exercise 2!\n",
    "\n",
    "Solve exercise 2 in `lecture04-python-scientific-ecosystem/exercises04.ipynb`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How many close toilets are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False,  True,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False,  True, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "        True, False, False, False, False, False, False, False, False])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances_to_me < 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(distances_to_me < 1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Let's do exercise 3!\n",
    "\n",
    "Solve exercise 3 in `lecture04-python-scientific-ecosystem/exercises04.ipynb`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Why numpy? Performance!\n",
    "\n",
    "One reason for using Numpy instead of pure Python is performance. \n",
    "\n",
    "<img src=\"benchmarks.svg\" style=\"width:750px\">\n",
    "\n",
    "<small>Note that this comparison is not very fair. The Python tests are mostly done with pure Python. One can easily speed-up tests by using numpy or other libraries. Adding ``@numba.jit`` to the Fibonacci test causes a speed up of factor 50 (tested with $n=30$).</small>\n",
    "\n",
    "<small>Source: https://julialang.org/benchmarks/</small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Also: vector and matrix operations are way simpler to write, many numerical algorithms implemented"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How fast is Numpy in comparison to pure Python?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "numbers = list(range(10**7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.9 ms ± 7.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "numbers_np = np.array(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.16 ms ± 536 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit numbers_np.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "A factor 10 speedup comes usually for free with good simple code, but sometimes a speedup factor of 500 is also not uncommon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Numpy is not good at: Handling non-gridded data\n",
    "\n",
    "Imagine somebody wants to collect the age of each person infected with Covid-19 for each day. One could use a list of lists, i.e. a Python list where each element is a list of integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "infected_age_per_day = [\n",
    "    [16, 41],\n",
    "    [73],\n",
    "    [40, 24, 83, 29],\n",
    "    [],\n",
    "    [64, 13],    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[40, 24, 83, 29]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected_age_per_day[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The number of infected person varies, so this is not a regular array with rectangular shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "One can convert the this lists of lists to a `numpy` array, but it is of dtype `object` and not `int`. This should be avoided!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([list([16, 41]), list([73]), list([40, 24, 83, 29]), list([]),\n",
       "       list([64, 13])], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(infected_age_per_day)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "rise": {
   "center": false,
   "controls": false,
   "scroll": true,
   "slideNumber": "c/t",
   "theme": "serif",
   "transition": "none"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}