DataVis_Day3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 1610,
     "status": "ok",
     "timestamp": 1665494588979,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "Xm4ybHu3eYRR"
   },
   "outputs": [],
   "source": [
    "# importing packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1574,
     "status": "ok",
     "timestamp": 1665494592953,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "g2cYFs7ZfMp6",
    "outputId": "f71ea224-4c8d-4517-d857-00a4b3f336b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(344, 7)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"penguins\")\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rjcVHjyufOvZ"
   },
   "source": [
    "# Heatmaps \n",
    "\n",
    "In many areas, we usually end up having lots of variables and interdependencies that we might be interested in. On `Day 1` we used `pandas` and its `.corr()` functionality to derive a `correlation matrix` from our 🐧 datatable. \n",
    "\n",
    "\n",
    "If we want to visualize those correlations, we can for example use `heatmaps`.\n",
    "\n",
    "[Heatmaps in Seaborn](https://seaborn.pydata.org/generated/seaborn.heatmap.html?highlight=heatmap#seaborn.heatmap) take rectangular data and return a color coded matrix, ie. we can directly use the output of `df.corr()` as input in `sns.heatmap()`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 325
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1664974605596,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "qxIdDpJ88RUt",
    "outputId": "7acae199-01e2-4606-b4c4-c8c17adb0217"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c17d6b290>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.heatmap(df.corr(), xticklabels=False, cmap='crest') # no xtickslabel, since the correlation matrix is symmetric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nFqJvBX08bXc"
   },
   "source": [
    "This looks nice, however the colormap `crest` might not be an ideal choice, as our data contains `positive` and `negative` values, we better use a `diverging` color palette."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 325
    },
    "executionInfo": {
     "elapsed": 598,
     "status": "ok",
     "timestamp": 1664974606181,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "0nmUGv3l-aml",
    "outputId": "a982fbdd-be2c-4e6a-8a0e-609abf5dd12e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c059e3c90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(df.corr(), xticklabels=False, cmap='vlag', center=0, square=True) # try to find a diverging colorpalette for seaborn and set center=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lW4tjyA5-czF"
   },
   "source": [
    "This is already very informative, but sometimes it is even better to `annotate` heatmaps for example with the `correlation values`.\n",
    "\n",
    "Try to update our `heatmap` from above and annotate the correlation values rounded to 3 decimals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 325
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1664974606182,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "DL2aEqqDELeB",
    "outputId": "51c7e1de-1b85-4976-eba7-05bd17c831f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c02cf7a10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcUAAAEiCAYAAACIgMm+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd5xV1bXA8d+a3oEpMMOA1AFhBEG6FLGQIGJNYonGSrBGozFGYyxPfZonib74YgNEiRqNNRJFLFiQPiAgzNCLAtML0/us98c9DHeGoZ5pXNf387kf7tlnn332vsPcddc++9wRVcUYY4wx4NfWHTDGGGPaCwuKxhhjjMOCojHGGOOwoGiMMcY4LCgaY4wxDguKxhhjjMOCojHGmHZHROaISLaIbDjEfhGRZ0Rkm4h8JyKnNcd5LSgaY4xpj14BJh9m/7lAkvOYDjzfHCe1oGiMMabdUdVFQP5hqlwI/EM9lgMdRSTB7XktKBpjjDkRJQK7vbb3OGWuBLhtwLSut0aPs+/lc/Sf83pbd6FdKX+tWWaPfEZgRHhbd6FdGfbHB6Sl2j6e96XLViy5Ec+0534zVXVm8/Xq+FhQNMYY0+qcAOgmCO4Funttd3PKXLHpU2OMMSeiecDVzirU0UChqma4bdQyRWOMMe74Nf/MrIi8AUwEYkVkD/AQEAigqi8A84EpwDagDLiuOc5rQdEYY4wrIs0/6aiqVxxhvwK3Nvd5LSgaY4xxRVogU2wrdk3RGGOMcVimaIwxxhVfyhQtKBpjjHGnBa4pthULisYYY1yxTNEYY4xxiPhOUPSdnNcYY4xxyTJFY4wxroif7+RXFhSNMca4Y9cUjTHGGA9fuqZoQdEYY4wrvjR96jsjMcYYY1yyTNEYY4wrvjR9apmiMcYY47BM0RhjjDu2+tQYY4zxaIm/p9hWLCgaY4xxxZe++9R3wrsxxhjjkmWKxhhjXPGl+xQtKBpjjHHHh27JsKBojDHGFV+6pmhB0RhjjCu+tPrUd0ZijDHGuGSZojHGGHds+tT4ihH330fC2NOpLCjgkyuvbrLO0LvuIH7MGGorK1j56OPs27wFgB5TJjPwumsASHt5Lt/PXwBAp/79GfHAH/EPDiZz2TLWPPW31hlMM1FVXn7pOdasTiE4OJhbfnM3vfskNahTWVnBUzMeIyszHT8/f4YNH82VV98AwKcLPuSTj+fh5+dHSEgoN97yW7p170F2diZ3/mYaXbt2AyCp3wCm33xHq4/vWHVI6kfPqecjfkJ2Sgrpi74+qE70oEF0O/scUCjLzGDbv94EYNRjj1OWmQlAZeE+trz6j/pjuk/6CdGDBkGdkrViOZnLlrbOgFyK6t2H7pN+CiLkrltDVqN+xwwaTOLZ51BdXAxA9qoU8tatBSDxzLPp0LcvABmLv6FgYxoAPaZeQORJJ1FbWQnArv/Mozw7q7WG5JovffdpqwRFEekJfKiqpzQqnw08pappIrILGK6quSJSoqoRx9JWM/RxIlClqkud7Vec87zTnOdpb3Z+NJ+t77zLqAf/1OT++DGjiejenY9/cTnRyckMu+duFt4wnaCoSJJvuJ7Pr7sBVZj0ykukf7OE6uJiTrvnd6x64knyU1MZ//RfiB8zmsxly1t5ZMdvzbcpZKbv5ZnnXmbrlk3MfvEZHn/y/w6qd/6FP+eUQUOoqa7mkYf+wJrVKxk6bCTjJpzJTyZPBWDVymXMfflF7n/wcQDiuyQw4+kXWnU8rojQ64IL2TjnJaqKCjnlltso2LSR8uzs+iohMTEknnEmqS+8QG1FOQHh4fX76qqrWf/3Zw5qNu60YQR16Mi6p58C1QbHtGsinPTTyWx543Wqi4o4+bppFG7dQkVuboNqBWlp7P50QYOyqD59CYuPJ232TPwCAuh35dUUbt9GXVUVAHu+WMi+TRtbbSjNyZduyWjTkajqNFVNa8s+eJkInN7WnWhtuWvXUVVUdMj9iRPGs8vJAPNTUwmMiCAkJoYuo0aRtTKFqqJiqouLyVqZQvzoUYTExBAYHk5+aioAu+YvIHHC+FYZS3NZtXIpE86chIjQr/8ASktLKcjPa1AnODiEUwYNASAgMJBevfuSl+d5YwwLO/AGX1FZwYn8GTqiW3cq8vKoLMhHa2vJ+24dnQYMbFCn84iRZC5fRm1FOQA1paVHbLfLqNHs+WIhqB71Me1BeNeuVBQUULVvH1pXR0FaKh2T+h/VsaGxcRT/8AOoUlddTXl2Fh369G3hHrcSkWN/HFWzMllENovINhG5t4n9J4nIlyKyRkS+E5EpbofSmtOnASLyOnAakApcDcwH7lbVVcfToIj4A3/GE9CCgWdV9UUn63sYyAVOAVYDV6mqOi/aU0ApsAToDdwG3ATUishVwG+cU0wQkbuAeOCeQ2WNzvn+C9gHDALeAtYDdwChwEWqut3JPsuBoUBn4HrndRgDrFDVa4/ndWhJoXGxDbKC8uxsQuNiCYuLo6xReVhcnKd+Ts5B9U8k+Xl5xMbE1W/HxMSSn59Hp+iYJuuXlpawetVypky9uL5swfx5fDTvXWpqqnnwkRn15dnZmdxz182EhoZx+ZXXMmDgoJYbSDMI6hBFVWFh/XZVYSER3bs3qBMS6/n5Jt94E4gfexZ+TuFWzxS7X0AAp9xyG1pXR/rXX9VPFwbHRBMzeDDRA5OpKS1l14fzqMhr+MGjPQqMjKLa60NkVXER4V0TD6rX6eSTiTjpJCrz89n92adUFxdRlp1F13ETyFqxDL/AQCJ79KTcK8NMPONMEsaNp3jXTvZ++QVaW9sqY2qvnPf3Z4FJwB4gRUTmNUqk/gS8parPi8hAPDGlp5vztmZQ7A/coKpLRGQOcEsztHkDUKiqI0QkGFgiIp86+4YCyUA6nuA3VkRWAS8CE1R1p4i8AaCqu0TkBaBEVf8CICI3AAnAOOBkYB5wuKnUU4EBQD6wA5itqiNF5A48Qfa3Tr1OeILgBU6bY4FpeH7gQ1R1retXxbSa2tpa/vbXxzn3vIvoEp9QXz55ygVMnnIBixd9wbtvv85td9xDp07RPDfzdSKjotixfQsznniYvz4zq0FmeSISPz9CYmJJmzWToA4dGPjrG/numf+ltqKCb2f8D9VFRQR3imbgtF9TlpVJZX4+fv4BaE0NG577O52Sk+n9s5+TNvPFth5Ks9i3bSv5aalobS2xQ0+j5/kXsPWfr1G8cweFCV05+ZrrqCkro2TvHtA6APZ++QU1pSWIvz89zj2P+DGnk7H4mzYeydFroenTkcA2Vd0BICJvAhcC3kFRgSjneQc87/eutOb06W5VXeI8fw1PsHHrJ8DVIrIWWAHEAPtXRKxU1T2qWgesxfPp4WRgh6rudOq8cYT2/62qdc4nky5HqJuiqhmqWglsB/YH5/U0/OTyH1VVpzxLVdc7fUzlEJ9wRGS6iKwSkVWfZ2ceoRvNqzwnl9DOneu3Qzt3pjwnl7KcHMIalZfl5Hjqx8UdVL+9WzB/Hr+/8yZ+f+dNdOwUTW7egWw3Ly+X6ENkiS8+97/Ed03kvPMvaXL/6eMmkrLSsxAjMDCIyCjP72/vPv3oEt+VjPS9zTyS5lVVWERQhw7120EdOhw03V5VWEjBxjS0ro7KggIq8nIJifFkj/uzqsqCfIp27CC8a1fPMUWF5KduAKAgNZUwrw8U7Vl1cRGBUVH120GRUfULavarLS+vz/Jy164h3GtsmUsXs/GlWWx943VEhIr8fABqSksA0Npacr9bR1hC15YeSrMSkeN51L+vOY/pjZpNBHZ7be9xyrw9DFwlInvwZIm/waXWDIp6hO3jIcBvVHWI8+ilqvuDUaVXvVqOLyv2buNIk+Dedeu8tusanbuyiTpN1aunqjNVdbiqDj+nc/wRO92c0r9ZTM8pkwGITk6muqSEirw8slasoMuoEQRGRhIYGUmXUSPIWrGCirw8qktLiU5OBqDnlMnsXdT+P/FOnnIBM55+gRlPv8DIUaez6MvPUFW2bN5IWFh4k1Onb77+MmVlpVx7/c0Nyr0D3berV5CQ4Pk9LircR53zZpmVmUFGxl66dGndn+exKtm7h5DYGII7dUL8/YkZfGr9FOh++WlpRPXuDUBAWBghMbFU5ufjHxKK+PvXl0f06FE/Fe85pg8AUb16U5Gbw4mgND2dkE7RBHXoiPj50WlgMvucqeL9AsIPrBHsmNSPcudaMyL4h4YCEBrXmdDOnSnasf3gY/r1pyLnxHg93PB+X3MeM4+jmSuAV1S1GzAFeFVcfpNAa06fniQiY1R1GfBLYDFwvss2PwFuFpEvVLVaRPoBh/vovRnoLSI9VXUXcJnXvmIOpOE/GqMfeZi404YQ3LEjU+e9R+qsl/AL8Py32P7+B2QsXUbC6WOY8s6/qKmoIOUxzyrKqqJiNs6ZyzlzZgGQ9tIrVBV5PjF/O+OvjHzgfvyDg8lYtvyEWnkKMHTYSL5dvZLbb76WIOeWjP1+f+dNzHj6BfJyc3jvnTdITOzOH37nuRIwecqFnD3pXBbM/4D1363B39+fiIhIbr399wCkpa3nrTf+gb+/P35+fvz6ptuJiGzn/+Xq6tg1bx4nX3c9In5kr15FeXY23c6ZROmePRRs2kjh1i10TEpi8G/vhDrlhwXzqSkvI+Kkk+h90SWoKiJC+tdf1QfF9K+/ou+ll5Mwdhy1VZXseO+9Nh7oUVLlh08XkHT5LxE/IXfdOipyc0iYcAZlGRkUbt1C5xEj6ZjUD62ro7ainF0fzgM8U4z9f+W5ham2spKdH/y7fqFRrwsvIjAsHATKsrL44eOP2myIx6OFvuZtL+B9AbsbB7+/3wBMBlDVZSISAsQC2RwnUW2OhO0IJ/HcRrEAWAUMwzMn/Cu8Ftoczy0ZzieCx/AEVwFygIvwXE+8W1WnOsf8HVilqq+IyPnADDwLbVKASFW90gmo7+DJ2H6D58WuvyXjCH2a2Oh8X3mNq36f920ejW8tOdpbQN4aPa7lf2AniP5zXm/rLrQr5a8939ZdaFcCI07sa7XNbdgfH2ixhdALr7/umN+Xzp7z8mH7IyIBwBbgbDzBMAX4paqmetX5GPiX894+AFgIJKqLwNYqQbE9EZEIVS0Rz92mzwJbVfXptu7X0bKgeIAFxYYsKDZkQbGhEy0oAjh3C/wv4A/MUdX/FpFH8CQ585wVp7OACDyX5O7xuoR2XH6M32jzaxG5BggC1uBZjWqMMeY4tdRfyVDV+XhmFL3LHvR6noZnBX+zabdBUUQGAa82Kq5U1VFu2nWywuPKDFuqT8YYcyLzpb+S0W6DoqquB4a0dT+8tcc+GWNMm7MvBDfGGGM8fOkLwX0n5zXGGGNcskzRGGOMK770VzIsKBpjjHHHh6ZPLSgaY4xxxZcyRd8ZiTHGGOOSZYrGGGPcselTY4wxxsOXpk8tKBpjjHHFl+5TtKBojDHGHR/6RhvfyXmNMcYYlywoGmOMMQ6bPjXGGOOK/ZUMY4wxxtFSf0+xLVhQNMYY445lisYYY4yHL2WKvhPejTHGGJcsUzTGGOOKLbQxxhhj9vOh6VMLisYYY1yxTNEYY4zZz4cyRd8J78YYY4xLlikaY4xxxZemT31nJMYYY9qE+MkxP46qXZHJIrJZRLaJyL2HqHOpiKSJSKqI/NPtWCxTNMYY404L/D1FEfEHngUmAXuAFBGZp6ppXnWSgPuAsapaICKd3Z7XguIJpv+c19u6C+3G5uuvbOsutCvf3/pkW3ehXencIbitu9CuDGvBtsWvRSYdRwLbVHUHgIi8CVwIpHnV+TXwrKoWAKhqttuT2vSpMcaY9igR2O21vccp89YP6CciS0RkuYhMdntSyxSNMca0OhGZDkz3KpqpqjOPsZkAIAmYCHQDFonIIFXdd7z9sqBojDHGFTmOa4pOADxcENwLdPfa7uaUedsDrFDVamCniGzBEyRTjrlDDps+NcYY446f37E/jiwFSBKRXiISBFwOzGtU5994skREJBbPdOoON0OxTNEYY4wrx5MpHomq1ojIbcAngD8wR1VTReQRYJWqznP2/URE0oBa4PeqmufmvBYUjTHGuNNCN++r6nxgfqOyB72eK3CX82gWNn1qjDHGOCxTNMYY48rRfkPNicCCojHGGFd86btPLSgaY4xxxzJFY4wxxsOXMkXfGYkxxhjjkmWKxhhjXLGFNsYYY8x+PjR9akHRGGOMK5YpGmOMMfv5UKboOyMxxhhjXLJM0RhjjCs2fWqMMcY47D5FY4wxxgdZpmiMMcYdH5o+tUzRGGOMcVimaIwxxhXx8538yoKiMcYYd8R3pk8tKBpjjHHFMkVjjDHGIT6UKfpOeDfGGGNcskzRGGOMOzZ9aowxxnj40vSpBUWDqvLyS8+xZnUKwcHB3PKbu+ndJ6lBncrKCp6a8RhZmen4+fkzbPhorrz6BgA+XfAhn3w8Dz8/P0JCQrnxlt/SrXsPsrMzufM30+jatRsASf0GMP3mO1p9fMdixP33kTD2dCoLCvjkyqubrDP0rjuIHzOG2soKVj76OPs2bwGgx5TJDLzuGgDSXp7L9/MXANCpf39GPPBH/IODyVy2jDVP/a11BtOMzhrRi95dO1JTW8f8pdvIzi89qM5lk5KJCA2iprYOgLcXplFWUU1kWBBTxiYRHOSPnwhff/s9O9P31R8XGRbE9RcMZel3u0lJS2+1MR0vVeWzD/7B9k1rCQgM4vzLbiK+W69D1n/75b9QkJfN9LufBOD9154hLzsDgMqKUoJDwpl21xPs3LKeL+e/QW1tLf7+/pw19Up69k1ulTG5ZQttXBCRnsCHqnpKo/LZwFOqmiYiu4DhqporIiWqGnGUbb/itP3OcfRrIlClqkvdtnWiWfNtCpnpe3nmuZfZumUTs198hsef/L+D6p1/4c85ZdAQaqqreeShP7Bm9UqGDhvJuAln8pPJUwFYtXIZc19+kfsffByA+C4JzHj6hVYdjxs7P5rP1nfeZdSDf2pyf/yY0UR0787Hv7ic6ORkht1zNwtvmE5QVCTJN1zP59fdgCpMeuUl0r9ZQnVxMafd8ztWPfEk+ampjH/6L8SPGU3msuWtPLLj16trRzpFhjD7gzUkxEYwaVRvXv94fZN1P1y8haxGAXPM4G5s/j6XtVuyiOkQys/OGsDM97+t33/m8F7sTC9o0TE0p+2b1pKfm8lNf3iK9B+2seC9OVx7+6NN1t20fiWBQSENyi6+6vb655//5zWCQ8IACA2P5BfX/Z7IDp3IztzNm7P+zO0PPNtyA2lO9t2nzU9Vp6lqWht2YSJwehuev82sWrmUCWdOQkTo138ApaWlFOTnNagTHBzCKYOGABAQGEiv3n3Jy8sFICwsvL5eRWUFJ/JESu7adVQVFR1yf+KE8exyMsD81FQCIyIIiYmhy6hRZK1MoaqomOriYrJWphA/ehQhMTEEhoeTn5oKwK75C0icML5VxtJckrpHk7ojB4CM3BJCAgMIDw08+gYUggI9n7+DA/0pKauq39W3ezSFJRXk7itv1j63pC2pqxk0bDwiQmKPJCoqyigpOjioV1VWsHLRfMaec1GT7agqG9ctJ3nIGADiE3sS2aETAHFdulFTXUVNTXXLDeQEICKTRWSziGwTkXsPU+9nIqIiMtztOdtq+jRARF4HTgNSgauB+cDdqrrqaBsRz0T2/wGTgN1Alde+YcBTQASQC1yrqhki8hWwDjgDz/ivB7KBm4BaEbkK+I3TzAQRuQuIB+45VNboZJn/BewDBgFvAeuBO4BQ4CJV3e5kn+XAUKCzc+6rgTHAClW99mjH3pzy8/KIjYmr346JiSU/P49O0TFN1i8tLWH1quVMmXpxfdmC+fP4aN671NRU8+AjM+rLs7MzueeumwkNDePyK69lwMBBLTeQVhAaF0t5dnb9dnl2NqFxsYTFxVHWqDwsLs5TPyfnoPonkoiwIIpLK+u3i8sqiQgNorT84Dfsc0/viyps+SGPZev3ALDku9384uyBnNY/nsAAf9763PMBITDAj1HJibz1eSojBia2zmCaQUlRAVEdo+u3IztEU1xYQERUpwb1vv7kbUZNOI/AwOAm29m9cxPhkR2Ijks4aN+m9SuJT+xJQMAxfPhoQy3xp6NExB94Fs/7+x4gRUTmNU6eRCQSz3vtiuY4b1tliv2B51R1AFAE3HKc7VzstDUQT3A5HUBEAvEEy5+r6jBgDvDfXseFqeoQ57xzVHUX8ALwtKoOUdVvnHoJwDhgKvDnI/TlVDyBdQDwK6Cfqo4EZnMgyAJ0whME7wTmAU8DycAgERlyrC9Aa6utreVvf32cc8+7iC7xB36ZJ0+5gP97YS5XXj2Nd99+HYBOnaJ5bubrPPnU81xz/Y0889QTlJUdfC3K+IaPFm/llQ/X8c9P1tOtcxTJvT0ftAb0jGXD9hxeeG81736RxpSxnuvVYwd3Z9XGdKpr6tqy2y0ia+8u9uVl0X/QiEPWSV2zlOQhB09O5WTu4cuP3uDcn01ryS42L/E79seRjQS2qeoOVa0C3gQubKLeo8D/ABXNMZS2yhR3q+oS5/lrwO2Hq3wYE4A3VLUWSBeRL5zy/sApwGfOqih/IMPruDcAVHWRiESJSMdDtP9vVa0D0kSkyxH6kqKqGQAish341ClfD5zpVe8/qqoish7IUtX1zjGpQE9gbeOGRWQ6MB3gTw89zs8v/eURunJkC+bPY+Fn8wHo07c/uXkHspm8vFyiD5Elvvjc/xLfNZHzzr+kyf2nj5vIrBefASAwMIjAwCAAevfpR5f4rmSk76VP336u+99WynNyCe3cuX47tHNnynNyKcvJofNpQxuUZ3+7xlM/Lu6g+u3d0H7xDE7y/JfPyCshMjwYcooBiAwLpqS86qBj9pdV19SRtjOH+JgIUnfkMKhvF95Z6Plwn55bQoC/H2EhgSTERtKvRwxnnNaD4KAAVJWa2jrWbM5spVEevVVLPmXtii8B6Nq9N0X78uv3FRfm10977rfn+61k7NnBs4/fTl1dHaUlhbz2/KNcdfMDANTV1rJ5QwrX3/HfDY4r2pfHu3Of4vzLb6ZT7JHectqPFvojw4l4ZgD32wOManBekdOA7qr6kYj8vjlO2lZBUY+w7ZYAqao6xuX5K72eH+mn7l23zmu7joavc2UTdZqqd6BzqjOBmQDr0r5vltdq8pQLmDzlAgC+XbWCBfM/YOy4iWzdsomwsPAmp07ffP1lyspKuenWOxuUZ6TvJaGrZ/rr29UrSEjwPC8q3EdERCR+/v5kZWaQkbGXLl3im6P7bSb9m8X0/cXP2P3Z50QnJ1NdUkJFXh5ZK1Yw6ObpBEZGAtBl1AjWP/+C5xpjaSnRycnkp6bSc8pktr7V/tdurdmSyZotnuDUO7ETQ/vHs2lXLgmxEVRW1xw0dSoCIUEBlFfW4CdCn27RfJ/hWWFaVFrJSfEdSN2RQ3RUKAH+fpRVVPPGpxvqjz99cHeqa2rbZUAEGD72Jwwf+xMAtm1cw6olnzJwyBjSf9hGcEjoQVOnw06fxLDTJwGwLz+Ht+bMqA+IADu3biCmc1eiOh74PasoL+WtOTOYOOVyuvfq3wqjaj7H80eGvT/sO2Y673VHe7wfnktk1x7zyQ+jrYLiSSIyRlWXAb8EFgPnH0c7i4AbRWQunmt0ZwL/BDYDcfvP4Uyn9lPVVOe4y4AvRWQcUKiqhSJSDES5HNcJaeiwkXy7eiW333wtQc4tGfv9/s6bmPH0C+Tl5vDeO2+QmNidP/zOM9s9ecqFnD3pXBbM/4D1363B39+fiIhIbr3d84EtLW09b73xD/z9/fHz8+PXN91ORGT7folHP/IwcacNIbhjR6bOe4/UWS/hF+D5Ndn+/gdkLF1GwuljmPLOv6ipqCDlMc8q26qiYjbOmcs5c2YBkPbSK1QVeTKrb2f8lZEP3I9/cDAZy5afUCtPAXbsLaB3Ykd+fdFpVNfU8vHSbfX7rjnvVOZ+tI4Afz9+fvZA/P0EEeH7jH18ty0LgK9W7+Kno/swfEBXAD5eurVNxtFc+pw8hG0b1/L8n+8kMCiYqZfeWL9v9lP3Me2uJ47YRtraZQdNna5a8ikFuVks/ux9Fn/2PgBXTL+X8IgOzTuAdsL7w/4h7AW6e213c8r2i8QzI/iVMyMYD8wTkQuOZW1KY6La3EnaEU7ouSVjAbAKGAak4bkGV7/Q5mhvyWi00OYHoBrPNcJ3nOtzzwAd8AT//1XVWc5Cm7V4FtoEAter6koR6Qe8gydj+w1wA163ZByhHxOdvk91tr/yGkv9Pu/bPBrfmnK0t4A0V6boCzZff2Vbd6Fd+f7WJ9u6C+1K5w5NL3D5sbrmgmEttjB8+7tvH/P7Up+f/eKw/RGRAGALcDaeYJgC/NIruWlc/yuOcbFmU1o9U3QWtZzcxK6JXnV6ej0/5D2K6onotx1i31o81xyb8pqq/rZR/S3AYK+ibxrtP1w/vgK+8tqe2NQ+79Wlzutwitd2/T5jjDmhtMA1RVWtEZHbgE/wrAuZo6qpIvIIsEpV5zX7SbFvtDHGGOPS8VxTPBqqOh/PLKJ32YOHqDuxOc55QgRFERkEvNqouFJVRzVV/3DcvHDN2Q9jjPEVLbT6tE2cEEHRuW2hze/hay/9MMaYdsW+5s0YY4zxPSdEpmiMMab9sr+SYYwxxuxnf0/RGGOM8bBM0RhjjHH40upT3wnvxhhjjEuWKRpjjHHHh27JsKBojDHGFV+aPrWgaIwxxpWW+pq3tmBB0RhjjDs+tPrUd0ZijDHGuGRB0RhjjHHY9KkxxhhXxL7RxhhjjPGwb7Qxxhhj9rNM0RhjjPHwpUzRd0ZijDHGuGSZojHGGFd8KVO0oGiMMcYdu6ZojDHGePhSpug7IzHGGGNcskzRGGOMK3bzvjHGGOOw6VNjjDFmPz+/Y38cBRGZLCKbRWSbiNzbxP67RCRNRL4TkYUi0sP1UNw2YIwx5sdNRI75cRRt+gPPAucCA4ErRGRgo2prgOGqOhh4B3jS7Vhs+vQEU/7a823dhXbj+1td///3KT2evaetu9CuhMfHtnUX2pcL/t3WPThWI4FtqroDQETeBC4E0vOuEkwAAB6zSURBVPZXUNUvveovB65ye1ILisYYY1xpoWuKicBur+09wKjD1L8B+NjtSS0oGmOMcUeOPSiKyHRgulfRTFWdeVynF7kKGA6ccTzHe7OgaIwxptU5AfBwQXAv0N1ru5tT1oCInAPcD5yhqpVu+2VB0RhjjCvi1yL3KaYASSLSC08wvBz4ZYPzigwFXgQmq2p2c5zUgqIxxhhXWuKaoqrWiMhtwCeAPzBHVVNF5BFglarOA2YAEcDbzorWH1T1AjfntaBojDGmXVLV+cD8RmUPej0/p7nPaUHRGGOMK/Y1b8YYY8x+PvQ1bxYUjTHGuCLHcUtGe2VB0RhjjCsttPq0TfhOeDfGGGNcskzRGGOMK770p6MsKBpjjHHHrikaY4wxHr50TdGCojHGGFd8afWp74zEGGOMcckyRWOMMa7YQhtjjDFmP7umaIwxxnjYNUVjjDHGB1mmaIwxxhVfuiXDMkVjjDHGYZmiMcYYd3zomqIFRWOMMa740vSpBUVjjDGu+NLqUwuKxhhj3BHfyRR9J7wbY4wxLlmmaIwxxhXxoUzRgqIxxhh37LtPjS/pkNSPnlPPR/yE7JQU0hd9fVCd6EGD6Hb2OaBQlpnBtn+9CcCoxx6nLDMTgMrCfWx59R/1x3Sf9BOiBw2COiVrxXIyly1tnQE1g7NG9KJ3147U1NYxf+k2svNLD6pz2aRkIkKDqKmtA+DthWmUVVQTGRbElLFJBAf54yfC199+z870ffXHRYYFcf0FQ1n63W5S0tJbbUzHY8T995Ew9nQqCwr45Mqrm6wz9K47iB8zhtrKClY++jj7Nm8BoMeUyQy87hoA0l6ey/fzFwDQqX9/RjzwR/yDg8lctow1T/2tdQbTTOKGDmXgtGmInx+7P/uM7e+912B/SGwsQ+64g4DwcMTPj02vvkrO6tV0nTCB3hdfXF8vqkcPFv/udxTt3En/K68k8cwzCQwP55MrrmjtIbn3Y8oUReR24GYgCnhfVW8TkZuAMlX9x+GPbn4isgsYrqq5zdhmT+B0Vf2ns32tc47bmusc7ZYIvS64kI1zXqKqqJBTbrmNgk0bKc/Orq8SEhND4hlnkvrCC9RWlBMQHl6/r666mvV/f+agZuNOG0ZQh46se/opUG1wTHvXq2tHOkWGMPuDNSTERjBpVG9e/3h9k3U/XLyFrEYBc8zgbmz+Ppe1W7KI6RDKz84awMz3v63ff+bwXuxML2jRMTSXnR/NZ+s77zLqwT81uT9+zGgiunfn419cTnRyMsPuuZuFN0wnKCqS5Buu5/PrbkAVJr3yEunfLKG6uJjT7vkdq554kvzUVMY//Rfix4wmc9nyVh7ZcfLzI/nGG1nx0ENU5OUxbsYMslaupGTPnvoqSZdeSvqSJfywYAER3box4sEH+XL6dNIXLSJ90SIAInv0YNh991G0cycAWSkp7Jo/n4nPPdcmw3LPd4Li0eS8twCTgPv3F6jqCy0ZEMWjNfPxnsAvW/F87UZEt+5U5OVRWZCP1taS9906Og0Y2KBO5xEjyVy+jNqKcgBqSg/OmhrrMmo0e75YCKpHfUx7kdQ9mtQdOQBk5JYQEhhAeGjg0TegEBTo+bwZHOhPSVlV/a6+3aMpLKkgd195s/a5peSuXUdVUdEh9ydOGM8uJwPMT00lMCKCkJgYuowaRdbKFKqKiqkuLiZrZQrxo0cREhNDYHg4+ampAOyav4DECeNbZSzNoWNSEmUZGZRnZaE1NaQvXkyXUaMa1FFVAkJDAQgID6cyP/+gdrqOH0/GN9/Ub+/bsoXKghPjg5KvO2zgEZEXgN7Ax0Anr/KHReRu5/lXIvI3EVkrIhtEZKRXnVdFZJmIbBWRX3sd/3sRSRGR70Tkv5yyniKyWUT+AWwAuh+p8yJylYisdM79ooj4O+UlIvLfIrJORJaLSBenvI+zvV5EHhOREqepPwPjnXbudMq6isgCp+9PHqEfJSIyQ0RSReRzERnpvC47ROQCp861IvJvEflMRHaJyG0icpeIrHH6FH2k8baEoA5RVBUW1m9XFRYSFBXVoE5IbCyhsbEk33gTyTfdQoekfvX7/AICOOWW20i+6ZYGwTQ4JpqYwYM55ZbbOPma6wiJiWn5wTSTiLAgiksr67eLyyqJCA1qsu65p/flmvNOZcygbvVlS77bzcBesdx0yTB+dtZAFqZ4soHAAD9GJSey9LvdLTuAVhQaF9tgVqE8O5vQuFjC4uIoa1QeFhfnqZ+Tc1D9E0VIdDTluQcmqSry8giJbviru/XNN0mcOJGzZs9m5AMPsGHWrIPaSRg3jr1eQfGEJ8fxOJpmRSY7cWGbiNzbxP5gEfmXs3+FM+vnymGDoqreBKQDZwKH+xgTpqpD8GSVc7zKBwNnAWOAB0Wkq4j8BEgCRgJDgGEiMsGpnwQ8p6rJqvr94fomIgOAy4CxzrlrgSud3eHAclU9FVgE7A/IfwP+pqqDgD1ezd0LfKOqQ1T1aadsiNP+IOAyETlckA4HvlDVZKAYeAxPdn0x8IhXvVOAS4ARwH/jmYIeCiwDmr5g0w6Inx8hMbGkzZrJtn+9Qe+LL8E/JASAb2f8Dxue+zvb/vUmPaeeT7DzBuHnH4DW1LDhub+TtWolvX/287YcQov4aPFWXvlwHf/8ZD3dOkeR3DsOgAE9Y9mwPYcX3lvNu1+kMWVsEgBjB3dn1cZ0qmvq2rLbpoV1HT+ePV98wRfTprHy0UcZ8tvfNrjm1jEpidrKSkp++KENe9nMRI79ccQmxR94FjgXGAhcISIDG1W7AShQ1b7A08D/uB1Kc01RvgGgqouAKBHp6JR/oKrlzvW/L/EEwp84jzXAt8DJeIIhwPeqerQXF84GhgEpIrLW2e7t7KsCPnSer8YzPQqe4Py28/yfR2h/oaoWqmoFkAb0OEzdKmCB83w98LWqVjvPe3rV+1JVi1U1BygE/uN1jHe9BkRkuoisEpFV/16z9gjdPjZVhUUEdehQvx3UocNB02VVhYUUbExD6+qoLCigIi+XkBjPp/tqp25lQT5FO3YQ3rWr55iiQvJTNwBQkJpKWHxCs/a7uQ3tF881553KNeedSkl5NZHhwfX7IsOCKSmvOuiY/WXVNXWk7cwhPiYCgEF9u7D5e082kZ5bQoC/H2EhgSTERnLGaT2YfvFpDBuQwKhTEhnaP74VRtdyynNyCe3cuX47tHNnynNyKcvJIaxReVlOjqd+XNxB9U8UFfn5hMYeyGxDYmKoaDQ92v2cc8hYsgSAfZs34x8Y2GD2JWH8eNJ9KUtsOSOBbaq6Q1WrgDeBCxvVuRCY6zx/BzhbXN4f0lxBUQ+x3VS5AE84WdkQVe2rqi85+4/lwpMAc73a6a+qDzv7qlV1/7lrOb5VtpVez4/Uhvf56vYfq6p1jY7zbrPOa7txvQZUdaaqDlfV4RcNHXKU3T86JXv3EBIbQ3CnToi/PzGDT6VgY1qDOvlpaUT19nzeCAgLIyQmlsr8fPxDQhF///ryiB496qfSPMf0ASCqV28qcnNoz9ZsyWTuR+uY+9E6tu3Or8/6EmIjqKyuobS8ukF9EQgN9vzI/ETo0y2a3H1lABSVVnJSvOeDRnRUKAH+fpRVVPPGpxuY+f63zHz/W1ZvzGDFhr2s2ZzZiqNsfunfLKbnlMkARCcnU11SQkVeHlkrVtBl1AgCIyMJjIyky6gRZK1YQUVeHtWlpUQnJwPQc8pk9i46cQJE4dathCckENq5MxIQQNdx48haubJBnfKcHGIHDwYgols3/IKCDlyiEKHr2LE+GBRbZP40EfC+1rDHKWuyjqrW4Ek2XF2raa5bMi4DvhSRcUChqhY6wfpCEXkCz/TiRDzTlOXAoyLyuqqWiEgiUH2Idg9nIfCBiDytqtnONbnII0y7Lgd+BvwLuNyrvBiIPI4+nPjq6tg1bx4nX3c9In5kr15FeXY23c6ZROmePRRs2kjh1i10TEpi8G/vhDrlhwXzqSkvI+Kkk+h90SWoKiJC+tdf1QfF9K+/ou+ll5Mwdhy1VZXsaLRsvT3bsbeA3okd+fVFp1FdU8vHS7fV77vmvFOZ+9E6Avz9+PnZA/H3E0SE7zP28d22LAC+Wr2Ln47uw/ABnqz546Vb22QczWH0Iw8Td9oQgjt2ZOq890id9RJ+AZ63je3vf0DG0mUknD6GKe/8i5qKClIeexyAqqJiNs6ZyzlzPNfT0l56haqiYgC+nfFXRj5wP/7BwWQsW37irDwFtK6ODbNmMfKhhxB/f/Z8/jklu3fT74or2LdtG9kpKWx8+WUG3Xorvc4/HwXWPXNgdXZ0cjLlubmUZ2U1aPfka66h6/jx+AcHc9bs2ez+/HO2vvlmK4+udYnIdGC6V9FMVZ3ZVv3ZTw4kOIeo4NwCAUzFuU1BRB4GSlT1LyLyFbAWOAMIBK5X1ZVOnd54pkZjgSdVdZbT5h3ANOcUJcBVeLKxD1X1lKPpj6rmishlwH14Mt5q4FZVXS4iJaoa4dT/OTBVVa8VkSTgNSAUz3TnlaqaKCKBwCd4PmG8guf6af0tGSLyIfAXVf3qEH3yPl/9a+O9r/FtHo3G0WDf4Sz/472H/4H9iHwz4IK27kK70uPZe9q6C+1KePyJs4CnNZz373+32H0T1WVlx/y+FBgWdtj+iMgY4GFV/amzfR+Aqj7hVecTp84yEQkAMoE4PVJgO9x5XRy7v1NfAXer6qpG5Q/jFRzaAxEJA8pVVUXkcuAKVW08R92uWVA8wIJiQxYUG7Kg2NAJGBQDgC141ovsBVKAX6pqqledW4FBqnqT855+iapeeqx98fZj+0abYcDfnQux+4Dr27g/xhhzwmuJT+qqWiMit+GZxfMH5qhqqog8AqxS1XnAS8CrIrINyKfhZbHj4jooqurEQ5Q/7KZdEVkBBDcq/pWqNv3VIkdBVb8BTm1PfTLGGNM0VZ0PzG9U9qDX8wrgF815znabKarqqCPXal3tsU/GGGOaT7sNisYYY04MLpemtCsWFI0xxriiLXJVsW1YUDTGGOOKZYrGGGOMw5eCou/8uWRjjDHGJcsUjTHGuFLnQ6miBUVjjDGuuP1mtPbEgqIxxhhXfCgmWlA0xhjjji9Nn9pCG2OMMcZhmaIxxhhX7JqiMcYY46ir852gaNOnxhhjjMMyRWOMMa7Y9KkxxhjjsNWnxhhjjA+yTNEYY4wrvrTQxoKiMcYYV3xo9tSCojHGGHd86ZqiBUVjjDGu+NL0qS20McYYYxyWKRpjjHHF7lM0xhhjHHZN0RhjjHH4UlC0a4rGGGNc0To95ocbIhItIp+JyFbn305N1BkiIstEJFVEvhORy46mbcsUTzCBEeFt3YV2o3OH4LbuQrsSHh/b1l1oV0ozc9u6C6bl3AssVNU/i8i9zvYfGtUpA65W1a0i0hVYLSKfqOq+wzVsmaIxxhhX6lSP+eHShcBc5/lc4KLGFVR1i6pudZ6nA9lA3JEatkzRGGOMK21wSbGLqmY4zzOBLoerLCIjgSBg+5EatqBojDHGleO5eV9EpgPTvYpmqupMr/2fA/FNHHq/94aqqogcsgMikgC8ClyjqnVH6pcFRWOMMa3OCYAzD7P/nEPtE5EsEUlQ1Qwn6GUfol4U8BFwv6ouP5p+2TVFY4wxrrTBNcV5wDXO82uADxpXEJEg4H3gH6r6ztE2bEHRGGPMiebPwCQR2Qqc42wjIsNFZLZT51JgAnCtiKx1HkOO1LBNnxpjjHGltb/mTVXzgLObKF8FTHOevwa8dqxtW1A0xhjjii99o40FRWOMMa740p+OsqBojDHGFR9KFG2hjTHGGLOfZYrGGGNcsWuKxhhjjMOuKRpjjDGO1r4loyXZNUVjjDHGYZmiMcYYV3xo9tSCojHGGHd8afrUgqIxxhhXbPWpMcYY4/Cl1ae20MYYY4xxWFA0xhhjHDZ9aowxxhUfuqRoQdEYY4w7ttDGGGOMcdgtGcYYY4zDVp8aY4wxPsgyRWOMMa74UKJoQdEYY4w7dk3RGGOMcdjq0xYgIj2BD1X1lOM4diJwt6pObeZu/ShE9e5D90k/BRFy160ha9nSBvtjBg0m8exzqC4uBiB7VQp569YCkHjm2XTo2xeAjMXfULAxDYAeUy8g8qSTqK2sBGDXf+ZRnp3VWkNyRVX57IN/sH3TWgICgzj/spuI79brkPXffvkvFORlM/3uJwF4/7VnyMvOAKCyopTgkHCm3fUEO7es58v5b1BbW4u/vz9nTb2Snn2TW2VMbsQNHcrAadMQPz92f/YZ2997r8H+kNhYhtxxBwHh4YifH5tefZWc1avpOmECvS++uL5eVI8eLP7d7yjauZP+V15J4plnEhgezidXXNHaQzpuI+6/j4Sxp1NZUMAnV17dZJ2hd91B/Jgx1FZWsPLRx9m3eQsAPaZMZuB11wCQ9vJcvp+/AIBO/fsz4oE/4h8cTOayZax56m+tM5hm5EMxsf0ERdNGRDjpp5PZ8sbrVBcVcfJ10yjcuoWK3NwG1QrS0tj96YIGZVF9+hIWH0/a7Jn4BQTQ78qrKdy+jbqqKgD2fLGQfZs2ttpQmsv2TWvJz83kpj88RfoP21jw3hyuvf3RJutuWr+SwKCQBmUXX3V7/fPP//MawSFhAISGR/KL635PZIdOZGfu5s1Zf+b2B55tuYE0Bz8/km+8kRUPPURFXh7jZswga+VKSvbsqa+SdOmlpC9Zwg8LFhDRrRsjHnyQL6dPJ33RItIXLQIgskcPht13H0U7dwKQlZLCrvnzmfjcc20yrOO186P5bH3nXUY9+Kcm98ePGU1E9+58/IvLiU5OZtg9d7PwhukERUWSfMP1fH7dDajCpFdeIv2bJVQXF3PaPb9j1RNPkp+ayvin/0L8mNFkLlveyiMz+7W31acBIvK6iGwUkXdEJExEzhaRNSKyXkTmiEgwgIhMFpFNIvItcIlT5iciW0Ukzmt72/7txkTkFRF5XkSWi8gOEZnonGOjiLziVe95EVklIqki8l9e5X8WkTQR+U5E/uKU/UJENojIOhFZdKiBOmN7yzn+fRFZISLDm+NFPBbhXbtSUVBA1b59aF0dBWmpdEzqf1THhsbGUfzDD6BKXXU15dlZdOjTt4V73PK2pK5m0LDxiAiJPZKoqCijpKjgoHpVlRWsXDSfsedc1GQ7qsrGdctJHjIGgPjEnkR26ARAXJdu1FRXUVNT3XIDaQYdk5Ioy8igPCsLrakhffFiuowa1aCOqhIQGgpAQHg4lfn5B7XTdfx4Mr75pn5735YtVBYc/Jq2d7lr11FVVHTI/YkTxrPLyQDzU1MJjIggJCaGLqNGkbUyhaqiYqqLi8lamUL86FGExMQQGB5OfmoqALvmLyBxwvhWGUtzqqvTY360V+0tKPYHnlPVAUARcBfwCnCZqg7Ck9neLCIhwCzgfGAYEA+gqnXAa8CVTnvnAOtUNecw5+wEjAHuBOYBTwPJwCARGeLUuV9VhwODgTNEZLCIxAAXA8mqOhh4zKn7IPBTVT0VuOAw570FKFDVgcADzjhaXWBkFNVev+RVxUUERkYeVK/TySczYNp0el/ycwIjowAoy86iQ+8+SEAA/qGhRPboWb8PIPGMMxkwbTrdzpmE+Pu3/GCaSUlRAVEdo+u3IztEU1x48Bv415+8zagJ5xEYGNxkO7t3biI8sgPRcQkH7du0fiXxiT0JCAhsvo63gJDoaMq9Zg0q8vIIiY5uUGfrm2+SOHEiZ82ezcgHHmDDrFkHtZMwbhx7vYKirwqNi6U8O7t+uzw7m9C4WMLi4ihrVB4WF+epn5NzUP0TTZ3qMT/cEJFoEfnMSYI+E5FOh6kbJSJ7ROTvR9N2ewuKu1V1ifP8NeBsYKeqbnHK5gITgJOd8q3qWfb0mlcbc4D9k/3XAy8f4Zz/cdpYD2Sp6nonuKYCPZ06lzoZ6Ro8AXMgUAhUAC+JyCVAmVN3CfCKiPwaOFwkGAe8CaCqG4DvjtDPNrNv21bWP/t/bJw9k6KdO+h5vifWF+/cQeH2bZx8zXX0vugSSvbuAa0DYO+XX5D64nNsevklAkJCiR9zelsOodll7d3Fvrws+g8accg6qWuWkjzk4HHnZO7hy4/e4NyfTWvJLraaruPHs+eLL/hi2jRWPvooQ377WxCp398xKYnaykpKfvihDXtpWpLqsT9cuhdYqKpJwEJn+1AeBQ45a9dYewuKjV+qfcfcgOpuIEtEzgJGAh8f4ZBK5986r+f7twNEpBdwN3C2kxF+BISoao3T/jvAVGCBc/6bgD8B3YHVTkbpiohMd6ZvV723cpXb5hqoLi4iMOpAdhcUGVW/oGa/2vJytLYWgNy1awiPP5D5ZC5dzMaXZrH1jdcRESqcqbOa0hIAtLaW3O/WEZbQtVn73dxWLfmU2U/dx+yn7iMisiNF+w5MARYX5tdPe+635/utZOzZwbOP386rz/0X+bkZvPb8geuOdbW1bN6QwoBTRzc4rmhfHu/OfYrzL7+ZTrFdWnZQzaAiP5/Q2AOZS0hMTP3PeL/u55xDxhLPZ9l9mzfjHxhIkNf/qYTx40n/EWSJAOU5uYR27ly/Hdq5M+U5uZTl5BDWqLwsJ8dTPy7uoPonmtbOFIEL8SRJOP82eQ1DRIYBXYBPj7bh9hYUTxKRMc7zXwKrgJ4isv9C1a+Ar4FNTnkfp7zx8rXZeLLHt1W11mWfooBSoFBEugDnAohIBNBBVefjmXo91Snvo6orVPVBIAdPcGzKEuBS55iBwKBDdUBVZ6rqcFUdfsnI5r3sWJqeTkinaII6dET8/Og0MJl9W7c0qBMQHlH/vGNSP8rznF9aEfyda0mhcZ0J7dyZoh3bDz6mX38qcg43g932ho/9CdPueoJpdz1Bv1OGs371N6gqe7/fSnBIKBFRDYPisNMncfsDz3HrH5/hV7c8RHRsAlfd/ED9/p1bNxDTuStRHQ98JqooL+WtOTOYOOVyuvc6uuu2ba1w61bCExII7dwZCQig67hxZK1c2aBOeU4OsYMHAxDRrRt+QUFUFRZ6dorQdezYH01QTP9mMT2nTAYgOjmZ6pISKvLyyFqxgi6jRhAYGUlgZCRdRo0ga8UKKvLyqC4tJTrZswq555TJ7F3043itXOqiqhnO80w8ga8BEfED/oonqTlq7W316WbgVhGZA6QBtwPLgbdFJABIAV5Q1UoRmQ58JCJlwDeA94WweXimTY80dXpEqrpORNbgCcS78QQznPN94FzfFDzXPwFmiEiSU7YQWHeIpp8D5opImtN2Kp4p2dalyg+fLiDp8l8ifkLuunVU5OaQMOEMyjIyKNy6hc4jRtIxqR9aV0dtRTm7PpwHgPj50f9XniXmtZWV7Pzg3/XzIr0uvIjAsHAQKMvK4oePP2r1oR2vPicPYdvGtTz/5zsJDApm6qU31u+b/dR9TLvriSO2kbZ22UFTp6uWfEpBbhaLP3ufxZ+9D8AV0+8lPKJD8w6gGWldHRtmzWLkQw8h/v7s+fxzSnbvpt8VV7Bv2zayU1LY+PLLDLr1Vnqdfz4KrHvmmfrjo5OTKc/NpTyr4e04J19zDV3Hj8c/OJizZs9m9+efs/XNN1t5dMdu9CMPE3faEII7dmTqvPdInfUSfgGet9Ht739AxtJlJJw+hinv/IuaigpSHnscgKqiYjbOmcs5czzXW9NeeoWqIs+MzLcz/srIB+7HPziYjGXLT8iVp8dz877zHj7dq2imqs702v85znqRRu5vdG4VkaY6cAswX1X3iNd0/hH75UvfRLCfs4rzaVVtt8u4RMQfCFTVCifj/Rzor6pVhztu9eOP+t4P7DhtOGVKW3ehXYmd0/RtIz9WpZkn3jRkS7p0+eKjjwzH6N8Lvzvm96WLzh583P0Rkc3ARFXNEJEE4CtV7d+ozuvAeDyXwiKAIDwLOQ93/bHdZYquici9wM0cWIHaXoUBX4pIIJ6s8pYjBURjjGmP/r+9O7ZxIgrCAPy/nq4HYgJikEjogRjJdEEMEWTXAz0gCqAFhsCjQ/KBTvCwfRq+L7G0WmltJ79mdmb3ChsWn5I8T3Loz4+nJ1TVXQastV4kuXkoEJOBoVhVhxz/qDtrrddJnp2c+qGq3pz7+6y1niR5e3L4S1U9TXLxvUSAf+0Ke4eHJO/XWi+TfM3P+YybJK+q6q9Hu8eF4q90+J09AH9z7dskt9e4NsAlXPouXFV9y3Fl7/T45yT3ArGq3uW48/6gxzZ9CgBX819UigCcj7dkAECbtMUgFAHY8oif7/3HhCIAWya1Tw3aAEBTKQKwpV+OM4JQBGDLpPapUARgy6BMFIoA7JlUKRq0AYCmUgRgiz1FAGieaAMAzT1FABhIKAJA0z4FYMug7qlQBGDP90Hjp0IRgC2DMlEoArBnUvvUoA0ANJUiAFsm7SkKRQC2DMpEoQjAHpUiALRJ06cGbQCgqRQB2DLpLRlr0o8BgB3apwDQhCIANKEIAE0oAkATigDQhCIAtB+P/j8zvnkKAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(df.corr(), xticklabels=False, cmap='vlag', center=0, square=True, annot=True,\n",
    "            fmt=\".3f\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dxmgqKEmFSI_"
   },
   "source": [
    "If you want to change the `fontcolor`, `fontsize`, `orientation` etc of your annotations, check the \n",
    "`annot_kws` keyword arguments for [matplotlib.axes.Axes.text()](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text), or [here](https://stackoverflow.com/questions/72168450/changing-annotation-text-color-in-seaborn-heat-map). \n",
    "\n",
    "\n",
    "-----\n",
    "\n",
    "------\n",
    "\n",
    "For the 🐧 dataframe our `y-ticks-labels` are perfectly aligned, but sometimes, especially if you have a lot of variables, your labels might get messed up. For this it is often useful to `rotate` and `align` your tick-labels manually. For that we can for example directly use the `pyplot` interface, again proving that we can have full control about pretty much all objects in our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 325
    },
    "executionInfo": {
     "elapsed": 528,
     "status": "ok",
     "timestamp": 1664974606694,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "vqQQRg3OIyMH",
    "outputId": "95a23a6b-61d1-42e4-b6ec-1986dc97e228"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5, 2.5, 3.5]), <a list of 4 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(df.corr(), xticklabels=False, cmap='vlag', center=0, square=True, annot=True,\n",
    "            fmt=\".3f\")\n",
    "\n",
    "plt.yticks(rotation=45, ha='right', va=\"center\") # ha= horizontal alignment, va= vertical alignment "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QQkEXeyuM4lo"
   },
   "source": [
    "A nice extensions to `heatmaps` are `clustermaps`, which add a `hierarchical clustering` to your heatmap and thus rearranges your cells to clusters. Compare the `clustermap` result to the heatmap one above! For more info on the clustermap, see e.g., [here](https://pythontic.com/visualization/seaborn/clustermap)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 767
    },
    "executionInfo": {
     "elapsed": 869,
     "status": "ok",
     "timestamp": 1664974607546,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "3apAaVmgJg0c",
    "outputId": "9acf1a81-2aac-4efc-f7c7-7b18cec882b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f3c02ac5590>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,4))\n",
    "sns.clustermap(df.corr(), yticklabels=False, cmap='vlag', center=0,\n",
    "               row_cluster=True)\n",
    "\n",
    "## try to plot a clustermap with no yticklabels, your color palette centered at 0 and showing clusters both on the columns and rows \n",
    "## combine to your heatmap "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mnly6osELHdx"
   },
   "source": [
    "# Saving figures \n",
    "\n",
    "Usually we are creating figures for talks or papers - for the latter, journals often ask us to provide high resolution figures (e.g., `scalable vector graphics, SVG`) with a specific number of `dots per inch, DPI`.\n",
    "\n",
    "Saving a file can be done as easy as `plt.savefig(\"filename\")`. This functionality also allows us to specify the file format and the DPI, allowing to save high-resolution figures. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1664974607547,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "wcaEmyV-Srnz",
    "outputId": "423ca915-596e-4b59-edec-6e61d4ae7632"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x=\"flipper_length_mm\", y=\"body_mass_g\")\n",
    "\n",
    "## 1. try to save a \n",
    "\n",
    "plt.savefig(\"scatter.svg\", dpi=300, format=\"svg\") #save plot to working directory\n",
    "\n",
    "\n",
    "# Note: an object-oriented approach would also allow to do:\n",
    "# my_plot = plt.figure()\n",
    "# my_plot.savefig(\"filename.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QpHvHo0tWuMm"
   },
   "source": [
    "# Downloading files from google Colab\n",
    "\n",
    "Since our current working directory is in google colab, we need to download the files to our local machine. \n",
    "\n",
    "- First let´s use the `os` module to check which files are currently written to our working dir\n",
    "- Afterwards, we can download the file in a very similar way as we did for the upload in session 1. \n",
    "\n",
    "\n",
    "Note: If you want to download multiple files at the same time, one needs to do a little [workaround](https://predictivehacks.com/?all-tips=how-to-download-files-and-folders-from-colab) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1664974607548,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "9pcDzufKXNri",
    "outputId": "3c5090c2-2a5f-4177-ff9b-40f93f561cd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.config', 'scatter.svg', 'sample_data']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.listdir() #shows files in current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1664974607550,
     "user": {
      "displayName": "Dominik Kraft",
      "userId": "18275469635399983658"
     },
     "user_tz": -120
    },
    "id": "dqLAyouSUzfl",
    "outputId": "34083a9d-f9fc-4601-aa32-edde0dec72fc"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "    async function download(id, filename, size) {\n",
       "      if (!google.colab.kernel.accessAllowed) {\n",
       "        return;\n",
       "      }\n",
       "      const div = document.createElement('div');\n",
       "      const label = document.createElement('label');\n",
       "      label.textContent = `Downloading \"${filename}\": `;\n",
       "      div.appendChild(label);\n",
       "      const progress = document.createElement('progress');\n",
       "      progress.max = size;\n",
       "      div.appendChild(progress);\n",
       "      document.body.appendChild(div);\n",
       "\n",
       "      const buffers = [];\n",
       "      let downloaded = 0;\n",
       "\n",
       "      const channel = await google.colab.kernel.comms.open(id);\n",
       "      // Send a message to notify the kernel that we're ready.\n",
       "      channel.send({})\n",
       "\n",
       "      for await (const message of channel.messages) {\n",
       "        // Send a message to notify the kernel that we're ready.\n",
       "        channel.send({})\n",
       "        if (message.buffers) {\n",
       "          for (const buffer of message.buffers) {\n",
       "            buffers.push(buffer);\n",
       "            downloaded += buffer.byteLength;\n",
       "            progress.value = downloaded;\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
       "      const a = document.createElement('a');\n",
       "      a.href = window.URL.createObjectURL(blob);\n",
       "      a.download = filename;\n",
       "      div.appendChild(a);\n",
       "      a.click();\n",
       "      div.remove();\n",
       "    }\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "download(\"download_118824be-9041-41f5-a738-945a2073a3a7\", \"scatter.svg\", 71995)"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from google.colab import files\n",
    "\n",
    "files.download(\"scatter.svg\") # we need to specify the filename\n",
    "# this file should now be in your Downloads folder on your local machine\n",
    "# SVG can be opened with Google Chrome for example "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q9ddW6oaU8pT"
   },
   "source": [
    "Feel free to compare a `figure.png` with a `figure.svg`, especially if you zoom into your plot! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2WsyZfZzYuEL"
   },
   "source": [
    "# The End 🎉\n",
    "\n",
    "\n",
    "![](https://i.imgflip.com/6vp5c6.jpg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SNfgKR4pZIPy"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNWscn4Ika40VEMT0VI9hM1",
   "collapsed_sections": [],
   "provenance": []
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}