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@mlaves
Created January 26, 2023 10:29
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rug_plot_matplotlib.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyPbj8gEcvw6rYSBNAeKnVJY",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/mlaves/0753ad223abcb3e7273db470c57da029/rug_plot_matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Rug plot\n",
"\n",
"A rug plot is used to visualize the distribution of a single random variable using tick\n",
"marks along the x-axis. It is often used to show the location of individual observations\n",
"in kernel density estimation."
],
"metadata": {
"id": "1BABlzEongsg"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Dc-8rmmtnbDp"
},
"outputs": [],
"source": [
"from scipy import stats\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"source": [
"rvs = stats.norm.rvs(loc=-2, size=(100,)).tolist() + stats.norm.rvs(loc=2, size=(100,)).tolist()\n",
"\n",
"gauss_kde = stats.gaussian_kde(rvs)\n",
"mean, std = stats.norm.fit(rvs)"
],
"metadata": {
"id": "n-BiO6Dwnpqn"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"minimax = 8\n",
"plot_min, plot_max = -minimax, minimax\n",
"x = np.linspace(plot_min, plot_max, 1000)\n",
"\n",
"ax.plot(x, gauss_kde(x))\n",
"\n",
"ax.set_title(\"rug plot for mixture of Gaussians\")\n",
"ax.set_ylabel(\"density\")\n",
"\n",
"# rugs\n",
"y = list(filter(lambda x: plot_min < x < plot_max, rvs))\n",
"ax.plot(y, np.full_like(y, -0.005), '|k', markeredgewidth=1)\n",
"\n",
"fig.patch.set_alpha(0.0)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281
},
"id": "5JjPy5Jqnql-",
"outputId": "a30ef7b8-f521-4f26-984a-fd067f8b60b0"
},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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axHef2JgQjeZvltUB718lhIOIML9oGKWVNoI80VjiMHHlidJqSqua+OGF08kd0u3t6AEYkZXBrRfN4N2qJh5cUzmAEXrjrV31jB0+KOxVSvOLhlFR30LdkbawlmuimyUOEzfaO3385uWdzC7M5rI+7qENcMmcAk6fkMudL+2kubWjz+1jVZdPWV1ezxkTwldN5Te/aBiAXXUkGEscJm48WVpNdeMxvnnulJAagEWEWy44hYaj7dz1WvkAROiNLfsOc7i1kzMmha+aym/mmGzSkpNYV2WJI5FY4jBxQVW5543dzBqTzdIpoU+nMbswhwtmjeK+tyri9qrjrV1u+8YJzobbm4zUZGaOybIrjgQT0cQhIitEZLuIlInIzd2sP1tESkWkU0QuD1rXJSLr3cczAcvHi8gat8xHRSS0GyabuPbWrnrKao5w3RnF/e5u+qUlE2lu7eThd6oiFJ231lY0Mj5vMCOyMiJS/vyiYWyoPkRbZ1dEyjfRJ2KJQ0SSgd8B5wPTgatEZHrQZlXAdcBD3RRxTFXnuI+LA5b/ErhDVScBjcD1YQ/exJw/v1XB8MFpXHgCNyeaXZjDGRNzueeN3XH35aeqlFY1Hm+LiIT5RcNo7/Sxed/hiB3DRJdIXnEsBMpUtVxV24FHgEsCN1DVClXdCIR0KzFxTiWXAU+4i/4MXBq+kE0sOnColZe2HuTTp439wJiN/vjikokcPNzGP947EObovFVed5SGo+0siGDimDfOGsgTTSQTxxhgT8DrandZqDJEpEREVouIPznkAk2q2tlXmSJyg7t/SW1tbX9jNzHkmQ178SlcsWDsCZdx1qQ8xg3P5KE18VVdtc69S9+C4sgljhFZGYzOzmDT3kMRO4aJLtHcOF6kqguAzwB3isjE/uysqnep6gJVXZCff3L3HjDR7el39zFnbA7j8wafcBlJScKVC8eyZncDu2qPhDE6b5VUNpCTmcqEvPDMT9WTWWOy2VhtiSNRRDJx7AUCTwEL3WUhUdW97s9yYBUwF6gHckTEP8dWv8o08WfbgcNs3X+YT8ztz8Vs9z41fywpScLDcXTVUVLZyPxxw0g6wdvEhurUsTnsrjvKoWPx2TPNfFAkE8daYLLbCyoNuBJ4po99ABCRYSKS7j7PA84EtqiqAq8A/h5Y1wJ/DXvkJmb8df0+kpOEj59Ao3iw/KHpnDdjJE+UVsdFI3nD0XbKa48yP4LVVH6zC51p6t+z6qqEELHE4bZD3Ai8AGwFHlPVzSJyu4hcDCAip4lINfAp4A8istndfRpQIiIbcBLFL1R1i7vu+8C3RKQMp83jnki9BxP9XnjvAGdMzO11epH++NT8sTS1dPDq9thvF1vnNlYvKBoe8WPNcu9vsqG6KeLHMt6L6LTqqvoc8FzQsh8HPF+LU90UvN9bwKweyizH6bFlElxZzRHK647y+TOLw1bmRybnMXxwGn/dsI/zZsT2lOsllQ2kJScdvxqIpJzMNIpyM9lk7RwJIZobx43p1YtbnK6z50wfGbYyU5OTuHDWaF7acpAjbZ197xDF1lU0MnNM1gl3Ue4vayBPHJY4TMx6cfNBZhdmMzp7UFjLvXRuAW2dPl6I4TEdrR1dbKw+xILiyFdT+Z1amMPepmM2U24CsMRhYtLBw62s39PEeWG82vCbN24YhcMG8dcN+8Je9kB5b+8h2rt8ER0xHsxfJWbVVfHPEoeJSS9vrQHg3Onhb4cQES6ZU8AbO2tj9uy5xG0YH8jEMWNMNiLWQJ4ILHGYmPTqjhrG5AxiysjIDGz7+OwCfAr/3HIwIuVHWok7sWFemHqbhWJIegqT8odYO0cCsMRhYk5nl4+3yuo5a3Jev2fCDdUpo4ZSlJsZk3NXDcTEhj2ZVZhtU48kAEscJuZsqG6iua2Ts/tx343+EhFWzBjFW7vqYm409EBMbNiTGQXZ1Da3UdPcOuDHNgPHEoeJOa/uqCNJ4MyJ4b8VaqCPzRxFR5fyyraaiB4n3AZiYsOeTB+dBTh3HTTxyxKHiTmv76zl1LE5ZGemRvQ4cwpzGJmVHnPVVQM1sWF3phc4icPuzRHfLHGYmHKopYMNe5o4a3LkZzxOShI+NmMUq3bUcKw9duauGqiJDbuTPSiVscMH2RVHnLPEYWLKm7vq8CmcPTmy1VR+K2aOorXDx6s7YmPuqoGc2LAnM0Zns3mfNZDHM0scJqa8UVbH0PQU5ozNGZDjLSwezrDMVF7cHBvVVf6JDU8bwBHjwWYUZFFR30Jza2x1KjChs8RhYsrq8npOGz+clOSB+dNNSU7io1NH8Mr2Grp8OiDHPBn+iQ39s9V6YcYYp51j24Fmz2IwkWWJw8SMmuZWymuPsmj8wJ5NL5s2gsaWDt6tiv57ag/0xIbdmT7aSVqbbTxH3LLEYWLGO7sbAFg8IXdAj3v2lHxSkoSXtkZ3t1wvJjbszsisdHIHp1nPqjhmicPEjDXlDQxJT2GG2+VzoGRlpLJw/HBWbovu6Ue8mNiwOyLC9IIsSxxxzBKHiRmry+uZXzRswNo3Ai2fNpIdB4+wp6FlwI8dKi8mNuzJjIJsdtY0097p8zoUEwER/R8oIitEZLuIlInIzd2sP1tESkWkU0QuD1g+R0TeFpHNIrJRRD4dsO4+EdktIuvdx5xIvgcTHeqPtLGz5giLJnhTDXPOtBEAvLQ1eq86vJjYsCczCrLo6FJ21lgDeTyKWOIQkWTgd8D5wHTgKhGZHrRZFXAd8FDQ8hbgc6o6A1gB3Ckigf0vv6uqc9zH+oi8ARNVvGrf8CvKHczE/MGsjNLpR1SVdZUNnsxP1Z0ZNoI8rkXyimMhUKaq5araDjwCXBK4gapWqOpGwBe0fIeq7nSf7wNqgMgPFTZRa83uBjLTkj3tZnrOtJGsLq+PyvEJu2qP0tjS4cn8VN0pzh1MZlqyjSCPU5FMHGOAPQGvq91l/SIiC4E0YFfA4p+7VVh3iEi31+UicoOIlIhISW1tbIz6NT3zt2+ketC+4bfslBF0dClv7KzzLIaerKt0rsi87lHll5QkTBudZSPI41RUN46LyGjgfuDzquq/KrkFOAU4DRgOfL+7fVX1LlVdoKoL8vPtYiWWNR5tZ9uBZs+qqfzmFw0je1BqVHbLXVvRyPDBaUzIG+x1KMfNKMhiy77D+GJg4KTpn0gmjr3A2IDXhe6ykIhIFvB34N9UdbV/uaruV0cbcC9OlZiJY2srnLPphQM88C9YSnISS6fmR+Uo8pKKBuYXDYvYja1OxIyCLI62d1EVxT3RzImJZOJYC0wWkfEikgZcCTwTyo7u9k8Df1HVJ4LWjXZ/CnAp8F5YozZRZ11Vo+fTaPgtnzaShqPtrN8TPffVrm1uo6K+hdOipH3Db0aBO4Lc2jniTsQSh6p2AjcCLwBbgcdUdbOI3C4iFwOIyGkiUg18CviDiGx2d78COBu4rptutw+KyCZgE5AH/CxS78FEh3crm5jh8TQafksm55OcJLwcRd1y/e0b84uio33Db/LIIaQkibVzxKGUSBauqs8BzwUt+3HA87U4VVjB+z0APNBDmcvCHKaJYu2dPjZUN3HN4iKvQwEgOzOV04qH8fLWGr634hSvwwGc8RvpKUnMHDOwI+r7kp6SzKQRQ+yKIw5FdeO4MVv3H6at0/tpNAKdM20k2w82R80o8rWVjZxamEN6ivdXZMFmFGRb4ohDljhMVPPfX2LeuOhJHMunjQSiYxT5sfYuNu89FDXjN4LNKMii7kgbNYdbvQ7FhJElDhPVSqsaGZMziFHZGV6Hctz4PGcU+ctR0C13/Z4mOn0a1YkDYPN+u+qIJ5Y4TFQrrWxk7riBudtff5wzbSRrdns/ivx4w/i46GoY95vmJg4bQR5fLHGYqLX/0DH2HWqNqmoqv+XTRtLRpby2w9tR5Gt2NzB15FCyM1M9jaMnWRmpjBueaT2r4owlDhO1SiudsRLR1DDuN29cDjmZqZ52y23v9FFS0cjpE70dUd+XGXZvjrhjicNErdIqp5vptNHR1c0UnFHky6aOYOX2Gjq7vLnnxMbqJo51dLHYo6nmQzWjIIvK+hYOR+HkkObEWOIwUau0yulmmpYSnX+my6eNpKmlg9Iqb0aRry6vB2DR+Gi/4nBGkG+1q464EZ3/I03Ca+3o4r29h5hbFH0N435nT8kjNdm7UeRvl9dzyqihDBuc5snxQ+XvWbXFelbFDUscJipt3neIji5lfhQ2jPsNzUhl0fhcT8ZztHV2sa4y+ts3APKHppM3JM3aOeKIJQ4TlY4P/IvChvFA50wbwa7ao+yuOzqgx92w5xCtHT7Pp5oPhYgw3UaQxxVLHCYqlVY2MW54ZlTcP7s3/lHkA11d9fauekRgcZS3b/jNKMhi58Fm2jq7vA7FhIElDhN1VJV1VY1R2Q032NjhmUwdOXTAq6tWl9czfXRW1I7fCDajIItOn7Lz4BGvQzFhYInDRJ3qxmPUNrcxLwpHjHdn+bQRrK1o5FDLwHQ3be3oorSqMSaqqfz8PatsBHl8sMRhok5pVWy0b/gtnzaSLp+yasfAzF21tqKBtk4fH5mUNyDHC4ei4ZkMTku2EeRxwhKHiTqllY1kpiUzdeRQr0MJydyxOYwYms7zmw4MyPFe3V5LWkoSi6J84F+gpCRh2mgbQR4vLHGYqLOuqpE5Y3NISY6NP8+kJOH8maN4ZXsNR9s6I36813bWsrB4OJlpEb0PW9jNKMhi6/7D+KLsfu2m/2Ljf6ZJGC3tnWzd3xyVExv25oJZo2nr9LFyW2Srq/Y1HWPHwSMsmZIf0eNEwoyCbI62d1FRP7Bdl034RTRxiMgKEdkuImUicnM3688WkVIR6RSRy4PWXSsiO93HtQHL54vIJrfM34iIRPI9mIG1sfoQXT5lXhSPGO/OguLh5A1J5/n39kf0OK/tqAVgydTYSxzT/ffmsOqqmBdS4hCRi0SkX0lGRJKB3wHnA9OBq0RketBmVcB1wENB+w4HbgUWAQuBW0XEfwr6e+ALwGT3saI/cZno5h/4N3dsbF1xJLvVVSu31dDSHrnqqtd21jIqK4PJI4ZE7BiRMnnkEFKSxKYeiQOhJoNPAztF5D9F5JQQ91kIlKlquaq2A48AlwRuoKoVqroRCJ5e9GPAP1W1QVUbgX8CK0RkNJClqqtVVYG/AJeGGI+JAe9WNTIhf3DUz7/UnQtmjaa1w8cr22ojUn57p4/Xd9SxZEo+sXihnZ6SzOSRQ+2KIw6ElDhU9RpgLrALuE9E3haRG0Skt24vY4A9Aa+r3WWh6GnfMe7zPst04ysRkZLa2sj8RzbhpaqUVjVF9fxUvVk43qmuem5TZKqrVpfX09zWyXkzRkak/IEwoyCLLfsO4Zz3mVgVcvWTqh4GnsC5chgNfAIoFZGbIhTbSVHVu1R1gaouyM+PvfrgRFRR30LD0faYGb8RzF9d9fK2gxG5pewLmw+QmZbMmTE0fiPYjIIs6o60U9Pc5nUo5iSE2sZxiYg8DawCUoGFqno+cCrw7R522wuMDXhd6C4LRU/77nWfn0iZJsr52zdiYaqRnlw2bwytHb6wX3X4fMo/txxk6dR8MlKTw1r2QPKPILeBgLEt1CuOy4A7VHWWqv6XqtYAqGoLcH0P+6wFJovIeBFJA64EngnxeC8A54nIMLdR/DzgBVXdDxwWkcVub6rPAX8NsUwT5UqrGhmakcKk/Nhr+PWbMzaHCfmDeXJdeM9n1lc3UdPcxsdmjApruQNt2mindtumHoltoSaOA6r6WuACEfklgKq+3N0OqtoJ3IiTBLYCj6nqZhG5XUQudss4TUSqgU8BfxCRze6+DcBPcZLPWuB2dxnAV4C7gTKcNpfnQ32zJrqVVjoD/5KSYq/h109E+OS8Qt6paKCqviVs5b6w+QApScLSqSPCVqYXhmakUpSbaQ3kMS7UxHFuN8vO72snVX1OVaeo6kRV/bm77Meq+oz7fK2qFqrqYFXNVdUZAfv+SVUnuY97A5aXqOpMt8wb1VrZ4kJzawfbDzbHdDWV32XzxiACT5ZW971xCHw+5e8b93PmpDyyB8XGbLi9mVFgU4/Eul4Th4h8WUQ2AaeIyMaAx25g48CEaBLB+j1NqJMJQ+QAAB+aSURBVBJzI8a7Mzp7EGdOzOPJ0uqwTK9RUtlIdeMxPjE31E6J0W1GQTZVDS0DNpuwCb++rjgeAi7CaUe4KOAx3+2ia0xYlFY2IQJzYmQq9b5ccdpYqhuP8eqOk+8K/lRpNZlpyTHdDTfQqYXO73jj3iaPIzEnqq/EoapaAXwVaA54+Ed3GxMW66oamTJiKFkZsV8VA3D+zFGMzErn3rcqTqqc1o4u/r5pPytmjoq5SQ17MmuM07NqY7X1rIpVoVxxAKwDStyf6wJeG3PSfD7l3arGmB2/0Z3U5CSuWVTEaztqKas58bve/eO9AzS3dnLZ3MK+N44R2ZmpFOdmsmGPXXHEql4Th6p+3P05XlUnuD/9jwkDE6KJd2W1R2hu7YyLhvFAVy0aR1pyEn8+iauOe9+qYEL+YM6YGDt3+wvF7MIcNu21K45YFeoAwDNFZLD7/BoR+ZWIjItsaCZRxMPAv+7kDUnnkjkFPL5uDzXNrf3e/92qRjbsaeK6M4pjuotyd2YXZrP/UOsJfS7Ge6F2x/090CIi/pHiu4D7IxaVSSjrKhsZPjiN4txMr0MJu69+dBIdXcr/rirv9773vlnB0PQULpsXP9VUfqeOdRvI99hVRywKNXF0uuMlLgF+q6q/A2Ljvp4m6pVWNjJv3LCYnPG1L8V5g7ls7hgeWFPJgUOhn13vPNjMsxv38ZlF4xiSHh+N4oFmFGSRJLCx2to5YlGoiaNZRG4BrgH+7t6bIz66vxhPNRxtp7zuaNxVUwX62vLJoPDLf2wLeZ87XtpBZmoyX1wyMYKReSczLYUpI4eywXpWxaT+3I+jDbheVQ/gTC74XxGLyiSM0jht3wg0dngmN5w9gaff3cvbu+r73P6tsjqe23SA68+awPAYvC9JqGYXZrOxusmmWI9Bod6P44Cq/kpVX3dfV6nqXyIbmkkEpVWNpCQJswuzvQ4lor760UmMG57Jdx7fQFNLe4/bHWvv4panN1Gcm8lXlsbn1Ybf7MIcGls6qG485nUopp9C7VV1mXvv70MiclhEmkXEJpsxJ21dZSMzCrJieqrwUAxKS+Z/rppLTXMrX39kPW2dXR/aRlX5/pMbqWpo4d8vmxX3n4l/BPkGa+eIOaFWVf0ncLGqZqtqlqoOVdWsSAZm4l9Hl48N1U1xNfCvN6eOzeGnl8zk1R21fPmBUg4de3+uptaOLm5+chPPbNjHd86byhkTY/dmTaGaOmooaclJbLJ2jpgTaneNg6q6NaKRmISzdf9hWjt8cd2+EezKhePo9Cm3PrOZ5f/9KhefWkB6ahJ/37ifqoYWblo2Ke6rqPzSUpKYVpDFuzaCPOaEmjhKRORR4P9wGskBUNWnIhKVSQjxOvCvL9csLmLWmGzueGkHD66ppNOnzB2bw08vncmSKYl1m+N543J4+J0qOrp8pCaHfCdr47FQE0cW0IJzJz4/BSxxmBO2rrKRguwMRmcP8jqUAXfq2Bzu+/xCutxp15PjbGR4qOYXDePeNyvYuv8wswvjY2bkRBBS4lDVz0c6EJN4Sivja2LDE5GoCcPPf7VZWtloiSOGhNqraoqIvCwi77mvZ4vIDyMbmoln+w8dY9+h1oSrpjIfNDp7EAXZGayrsnaOWBJqpeIfgVuADgBV3Qhc2ddOIrJCRLaLSJmI3NzN+nQRedRdv0ZEit3lV4vI+oCHT0TmuOtWuWX618X2TZgTVGml80URD3f8MydnbtGw4wNBTWwINXFkquo7Qcs6e9tBRJKB3+Hcm3w6cJWITA/a7HqgUVUnAXcAvwRQ1QdVdY6qzgE+C+xW1fUB+13tX6+qNSG+BxNFSiobyEhNYnqB9epOdPPHDWNv07F+zeVlvBVq4qgTkYk4DeKIyOXA/j72WQiUqWq5qrYDj+BMkhjoEuDP7vMngOXy4ZnurnL3NXFkbUUDc8cOs5405v12jiq76ogVof6v/SrwB+AUEdkLfAP4Uh/7jAH2BLyudpd1u42qdgKHgOA71nwaeDho2b1uNdWPukk0AIjIDSJSIiIltbUnf99nEz7NrR1s2XeY08bb3YcNTBudRXpK0vHu2Sb69dqrSkS+FfDyOeAVnGRzFPgk8KvIhQYisghoUdX3AhZfrap7RWQo8CROVdaH5s1S1buAuwAWLFhgs6hFkXWVjfgUFlniMDgDAU8tzLErjhjS1xXHUPexAPgyMAzIwbnamNfHvnuBsQGvC91l3W4jIilANhA4feiVBF1tqOpe92czzj3RF/YRh4ky7+xuICVJmDvOul8ax7yiYby39xCtHR+ew8tEn77uOf4TVf0Jzpf+PFX9jqp+G5gP9HXr2LXAZBEZLyJpOEngmaBtngGudZ9fDqx0bxiFe8+PKwho3xCRFBHJc5+nAh8H3sPElLUVDcwck01mWvzdoMicmPlFw+joUjbY9CMxIdQ2jpFA4FzQ7e6yHrltFjcCLwBbgcdUdbOI3C4iF7ub3QPkikgZ8C0gsMvu2cAeVQ2852Y68IKIbATW41yx/DHE92CiQGtHFxv2HGKhVVOZAAuLhyMCa3Y3eB2KCUGop3x/Ad4Rkafd15cC9/W1k6o+h9M2ErjsxwHPW4FP9bDvKmBx0LKjOFc7JkZt2NNEe5ePhcWWOMz7sjNTmT46i9Xl9c4dE01UC/VGTj8HPg80uo/Pq+p/RDIwE5/ecc8oFxTbwD/zQYvG57KusrHbe5WY6BJyJ3pVLVXVX7uPdyMZlIlf71Q0cMqooeRkxu8tUc2JWTxhOG2dPjba/Tmino2+MgOms8tHaWUjp1k1lenGwvFOO8fqEO7LbrxlicMMmC37D3O0vcsaxk23cjLTmDYqi9W7LXFEO0scZsD42zcscZieLJownHWVjbR3+rwOxfTCEocZMKvLGyjKzWRkVobXoZgotXhCLq0dPjZW23iOaGaJwwyIzi4fa8rrOWNi8FRkxrxvkdvO8ba1c0Q1SxxmQGzed5jmtk5On5jndSgmiuVkpjF9dBZvlNV5HYrphSUOMyDe3OV8EZw+wa44TO/OmpzPuspGjrT1essf4yFLHGZAvL2rnqkjh5I/NN3rUEyUO3tKHp0+teqqKGaJw0RcW2cXaysaON3aN0wIFhQNJzMtmdd22H10opUlDhNx71Y10drhs4ZxE5K0lCROn5DLazstcUQrSxwm4t7aVU+SwCJr3zAhOmtyHpX1LVTWH/U6FNMNSxwm4t7eVcesMdlkD0r1OhQTI86ekg9g1VVRyhKHiaiW9k7erWqybrimX8bnDaZw2CBetcQRlSxxmIh6Z3cDnT619g3TLyLCslNG8EZZHcfabZr1aGOJw0TU6zvrSEtJshlxTb+dO30krR0+XrdG8qhjicNE1Ks7alk0fjiD0pK9DsXEmEXjcxmansJLWw96HYoJEtHEISIrRGS7iJSJyM3drE8XkUfd9WtEpNhdXiwix0Rkvfv434B95ovIJnef34iIRPI9mBNX3dhCWc0RlrgNncb0R1pKEktPGcHLW2vo8qnX4ZgAEUscIpIM/A44H5gOXCUi04M2ux5oVNVJwB3ALwPW7VLVOe7jSwHLfw98AZjsPlZE6j2Yk+Nv2Fw61RKHOTHnTh9J/dF23q1q9DoUEyCSVxwLgTJVLVfVduAR4JKgbS4B/uw+fwJY3tsVhIiMBrJUdbWqKvAX4NLwh27C4dXttYzJGcTE/CFeh2Ji1NKp+aQmC//cYtVV0SSSiWMMsCfgdbW7rNttVLUTOAT4u9+MF5F3ReRVETkrYPvqPsoEQERuEJESESmprbXGtYHW3unjrV31LJmaj9UmmhOVlZHK4gm5vLD5AM65ookG0do4vh8Yp6pzgW8BD4lIVn8KUNW7VHWBqi7Iz7eqkoFWWuXMbmrtG+ZkXThrNBX1LWzed9jrUIwrkoljLzA24HWhu6zbbUQkBcgG6lW1TVXrAVR1HbALmOJuX9hHmSYKvLqjlpQksfEb5qStmDmKlCTh2Q37vA7FuCKZONYCk0VkvIikAVcCzwRt8wxwrfv8cmClqqqI5LuN64jIBJxG8HJV3Q8cFpHFblvI54C/RvA9mBP0yrYa5hcNY2iGTTNiTk5OZhpnT8nn2Q378FnvqqgQscThtlncCLwAbAUeU9XNInK7iFzsbnYPkCsiZThVUv4uu2cDG0VkPU6j+ZdUtcFd9xXgbqAM50rk+Ui9B3Ni9jS0sO1AM+dMG+l1KCZOXHTqaPYdaqXUeldFhZRIFq6qzwHPBS37ccDzVuBT3ez3JPBkD2WWADPDG6kJJ38PmHOnW+Iw4XHu9FGkp2zi2Q37WGCzEHguWhvHTQx7ccsBpowcQnHeYK9DMXFiSHoKy6eN4O+b9tPR5fM6nIRnicOEVVNLO2srGjlv+iivQzFx5tI5Y6g70s6q7da93muWOExYrdzmTA9h1VQm3D56ygjyh6bz6No9fW9sIsoShwmrFzcfZGRWOrPGZHsdiokzqclJfHJeIa9sr6HmcKvX4SQ0SxwmbFo7unhtZy3nTh9JUpKNFjfhd8WCQrp8ypOlNnzLS5Y4TNi8vrOOlvYuzrX2DRMhE/KHsLB4OI+X7LEpSDxkicOEzbMb9jEsM9VGi5uI+vRpYymvO8pbu+q9DiVhWeIwYdHS3sk/txzk/FmjSU22PysTORfOHk3u4DTufXO316EkLPsfbsJi5bYajnV0cdHsAq9DMXEuIzWZqxeN4+VtNVTWH/U6nIRkicOExbMb9jFiaDoLx9uoXhN51ywuIiVJuO+tCq9DSUiWOMxJO9zawSvba7lw9miSrTeVGQAjsjK4cNZoHi+pprm1w+twEo4lDnPSXnjvAO2dPj5u1VRmAF3/kQkcaevkwTVVXoeScCxxmJP2+LpqxucNZt64HK9DMQlkVmE2Z0/J5+7XyznW3uV1OAnFEoc5KbvrjvLO7gYun19ot4g1A+6mZZOoO9LOQ+/YVcdAssRhTsoT6/aQJPDJeYV9b2xMmJ1WPJzFE4Zz12u7aO2wq46BYonDnLAun/Lkur0smZLPqOwMr8MxCepryyZz8HAbj9hVx4CxxGFO2Os7azlwuJUrFozte2NjIuT0ibmcPiGX36wssx5WA8QShzlhD6yuJHdwGsvtFrHGQyLCLRecQsPRdu56rdzrcBJCRBOHiKwQke0iUiYiN3ezPl1EHnXXrxGRYnf5uSKyTkQ2uT+XBeyzyi1zvfsYEcn3YLpXWX+Ul7fV8JlF40hLsfMP463ZhTlcdGoBf3y9nIM25XrERex/vIgkA78DzgemA1eJyPSgza4HGlV1EnAH8Et3eR1wkarOAq4F7g/a72pVneM+aiL1HkzP/vxWJckiXLO4yOtQjAHgu+dNpcun/PIf27wOJe5F8lRxIVCmquWq2g48AlwStM0lwJ/d508Ay0VEVPVdVd3nLt8MDBKR9AjGavrhSFsnj5fs4YJZoxmZZY3iJjqMy83kC2dN4KnSvawpt5lzIymSiWMMEHiPx2p3WbfbqGoncAgInpP7k0CpqrYFLLvXrab6kfQweEBEbhCREhEpqa21exSH05Prqmlu6+TzZxZ7HYoxH3DTssmMyRnED//vPTq6fF6HE7eiunJaRGbgVF99MWDx1W4V1lnu47Pd7auqd6nqAlVdkJ+fH/lgE0RHl48/vl7O3HE5zB03zOtwjPmAQWnJ/OTiGeysOcI9b9i065ESycSxFwjsp1noLut2GxFJAbKBevd1IfA08DlV3eXfQVX3uj+bgYdwqsTMAHn63b1UNx7jpmWTvA7FmG6dM30k504fyZ0v7WB3nU27HgmRTBxrgckiMl5E0oArgWeCtnkGp/Eb4HJgpaqqiOQAfwduVtU3/RuLSIqI5LnPU4GPA+9F8D2YAJ1dPv7fK2XMKMjio1OtM5uJXj+7dCbpKcl889H1dFqVVdhFLHG4bRY3Ai8AW4HHVHWziNwuIhe7m90D5IpIGfAtwN9l90ZgEvDjoG636cALIrIRWI9zxfLHSL0H80HPbtxHRX0LNy2bZPNSmag2MiuDn106k/V7mvj9ql1972D6RRLhhu8LFizQkpISr8OIaa0dXSz/71fJHpTK3276CEl23w0TA256+F2e37Sfp75yBrMLbfbm/hKRdaq6IHh5VDeOm+jxl7cr2Nt0jB9cMM2ShokZP71kBiOGpvOVB0tpamn3Opy4YYnD9KnxaDu/XVnG0qn5fGRyntfhGBOynMw0/t8186k53MY3Hl2Pzxf/NSwDwRKH6dMv/7GNo+1d3HL+NK9DMabf5ozN4ccXTWfV9lp+s3Kn1+HEBUscplfv7G7gkbV7+NePjGfqqKFeh2PMCbl60Tg+Oa+QO1/ayV/XB48KMP2V4nUAJnq1dXbxg6c3MSZnEF8/Z7LX4RhzwkSEf79sJnubWvju4xsZmZXB4gnBk1SYUNkVh+nRL57fRlnNEX7+iZlkptk5holt6SnJ/OGaBYzLzeSGv5Swdf9hr0OKWZY4TLdWbjvIvW9WcN0ZxSy1wX4mTmRnpnLf509jcHoKV9+9hu0Hmr0OKSZZ4jAfsqehhe88vpFTRg3l5vNP8TocY8KqcFgmD31hManJwmf+uJqdBy159JclDvMBza0dXP/ntc70IlfPIyM12euQjAm78XmDeegLi0lKEq74w9uUVjV6HVJMscRhjmvr7OIrD5ayq/Yov79mPhPyh3gdkjERMzF/CI9/8XSyBqXymT+u5qUtB70OKWZY4jAAtHf6+PIDpby+s47/uGwWZ06ygX4m/hXnDebJL5/BlJFDueH+Ev731V0kwjRMJ8sSh+FIWydf+EsJK7fV8LNLZ3LFgrF972RMnMgbks7DX1jM+bNG84vnt/GlB9ZxuLXD67CimiWOBLe36Rif+t+3eaOsjl9cNsvuIW4S0uD0FH571Vx+eOE0Xtpaw/l3vs5bZXVehxW1LHEksBc2H+CCX7/OnoYW7rl2AVcuHOd1SMZ4RkT417Mm8NgXTyc9JYnP3L2GH/7fJpscsRs2rXoCOnColZ/9fQt/27if2YXZ/ObKuRTnDfY6LGOiRmtHF//1wnbufXM3WYNS+eY5U/jMonGkJifWuXZP06pb4kggDUfbuffN3fzpjd10+pQvL53IV5ZOIi0lsf4zGBOqbQcO89O/beHNsnrG5AzihrMn8OnTxiZMN3VLHAmaOFSVjdWHeHzdHp4q3UtLexcXzBrFzSumMS430+vwjIl6qsqqHbX8bmUZJZWN5GSmcumcMVyxYCzTC7K8Di+iPEkcIrIC+DWQDNytqr8IWp8O/AWYD9QDn1bVCnfdLcD1QBfwNVV9IZQyu5NoiaO908e6ykZWba/h5W01lNUcIT0liQtnjeZLSycyZaTNcmvMiVhTXs/9qyt5cfNB2rt8TBoxhOXTRrD8lJHMGZsTd1fvA544RCQZ2AGcC1QDa4GrVHVLwDZfAWar6pdE5ErgE6r6aRGZDjwMLAQKgJeAKe5uvZbZnXhNHD6fsv9wK5V1R6lsaGHr/sNsqD7E1n2Hae/ykZosnFY8nAtmjeaiUwvIHpTqdcjGxIWmlnb+un4fL245wJryBjp9SlpKEjMLspg7bhhTRw1lYv5gJuQNYdjgNK/DPWE9JY5ITnm6EChT1XI3gEeAS4DAL/lLgNvc508AvxURcZc/oqptwG4RKXPLI4Qyw6ai7iiHWztQBcW5ZHV+AmjA8g+uU5wVPvd58P4ELg9a197po7Wji9ZOH63tXe7zLlrau2g82k790XYaW9ppONJO3ZF22rt8x+MdnJbMzDHZXHdmMfOLhnHmpDyGpNustsaEW05mGteeUcy1ZxRzuLWDt8rqWFfZyLtVTdy/upL2zvf/Xw5JT2HE0HTyhqaTPzSdYZmpDE5LYXB6CplpyQxJTyEjNZmUZCElSUhOSiIlSUhJFpKThJSkJJIERACc2zaLe/dm/02cxV3w/mv/emHyyCFhb5OJ5LfKGGBPwOtqYFFP26hqp4gcAnLd5auD9h3jPu+rzLC57dnNrNpeG6niQ5acJAxKTWbY4FSGZ6aRPySdqSOzyBuaRtHwwRTlZlKUm8no7EEk2/3AjRlQWRmprJg5mhUzRwPQ2eWjuvEY5XVHKK89yt6mY9Q0t1Hb3MaWfYc5dKyDo22dtAUkl0h66VtLmDQivNMHxe3pqIjcANwAMG7ciY1P+PryyVyzqAhxs70guP8QEfens1zc5QS8TpL3l/vPFqSX/QHSU5LISE0mPdX5OSg1OeG6ABoTy1KSkyjOG0xx3mCW9TK5dEeXj5b2Lo62dXKsowufT+n0KZ1dSqfPR5f7ust9+BsV/M0LxxsZ1P/DXe5/7f4clZ0R1vcHkU0ce4HAuSsK3WXdbVMtIilANk4jeW/79lUmAKp6F3AXOG0cJ/IG5o4bdiK7GWNMn1KTk8gelBSTbY+RPJVdC0wWkfEikgZcCTwTtM0zwLXu88uBleqk02eAK0UkXUTGA5OBd0Is00TAbbfdFpYy/I+lS5eydOnS4+UuXbr0Q9sE8m/r386/rLi4mOLi4uPrb7vtNoqLi0lJcc6JcnJyWLp0KSkpKRQXFwN8aPvAMv3L/Pv5t/fvGxizf7n/deDP4PfR3XG6+3x6+8y6KzuwTP/7Cvws/T/96wLjDvxM/Z+l/3VxcTEZGRksXbqUjIyM4/vk5OQcLy/wEfh5+j+n4PfkL7u7zyv4M+juM+ztc+pufX/3M6GLdHfcC4A7cbrO/klVfy4itwMlqvqMiGQA9wNzgQbgyoCG738D/gXoBL6hqs/3VGZfccRrr6qBJCInPWuovwEvmKoeLz9wm8Djdbe8p/KCy+1rWW9lBm7f0/Pe3kN3+/mPE/x59rTsRGLv7TPt63M7WcHHDiW+4M+lu8/Qr6+/xe7KDWU/82E2ANASx0mxxGGJI1SWOOJHT4nDWl2NMcb0iyUOY4wx/WKJwxhjTL/E7TgOE1633nprWMtYtWoV8H7vmyVLlvR6nCVLlrB06dLj+/mXVVRUAO/3KAK47777qK6uBiA7O5s5c+bwxhtvUFhYCEBRUdEHtg8s03/8O++8kzlz5hzfPjiOwOXXXXfdB/bt7j3ceuut3R4neJu+lgW/DizT/76Ct7311lu57777jq/zx+3/DPxlVFRUcN1117Fq1SoqKio4cOAAixcvZvXq1YwaNQqApqYmvvGNb3Dfffd9II7AzxO6/336PwP/dt19Xr19hj19Bj2t7+uzMyfOGseNMcZ0yxrHjTHGhIUlDmOMMf1iicMYY0y/WOIwxhjTL5Y4jDHG9EtC9KoSkVqg8gR3zwPqwhhOuFhc/WNx9Y/F1T/xGleRquYHL0yIxHEyRKSku+5oXrO4+sfi6h+Lq38SLS6rqjLGGNMvljiMMcb0iyWOvt3ldQA9sLj6x+LqH4urfxIqLmvjMMYY0y92xWGMMaZfLHEYY4zpF0scIRCROSKyWkTWi0iJiCz0OiY/EblJRLaJyGYR+U+v4wkkIt8WERWRPK9jARCR/3I/q40i8rSI5HgczwoR2S4iZSJys5ex+InIWBF5RUS2uH9TX/c6pkAikiwi74rI37yOxU9EckTkCfdva6uInO51TAAi8k33d/ieiDwsIhnhKtsSR2j+E/iJqs4Bfuy+9pyIfBS4BDhVVWcA/5/HIR0nImOB84Aqr2MJ8E9gpqrOBnYAt3gViIgkA78DzgemA1eJyHSv4gnQCXxbVacDi4GvRklcfl8HtnodRJBfA/9Q1VOAU4mC+ERkDPA1YIGqzgSSgSvDVb4ljtAokOU+zwb2eRhLoC8Dv1DVNgBVrfE4nkB3AN/D+eyigqq+qKqd7svVQKGH4SwEylS1XFXbgUdwTgI8par7VbXUfd6M8yU4xtuoHCJSCFwI3O11LH4ikg2cDdwDoKrtqtrkbVTHpQCDRCQFyCSM31uWOELzDeC/RGQPzlm9Z2eqQaYAZ4nIGhF5VURO8zogABG5BNirqhu8jqUX/wI87+HxxwB7Al5XEyVf0H4iUgzMBdZ4G8lxd+KcjPi8DiTAeKAWuNetQrtbRAZ7HZSq7sX5rqoC9gOHVPXFcJVvt451ichLwKhuVv0bsBz4pqo+KSJX4JxdnBMFcaUAw3GqFE4DHhORCToAfaz7iOsHONVUA663uFT1r+42/4ZTJfPgQMYWS0RkCPAk8A1VPRwF8XwcqFHVdSKy1Ot4AqQA84CbVHWNiPwauBn4kZdBicgwnCvY8UAT8LiIXKOqD4SjfEscLlXtMRGIyF9w6lYBHmcAL5X7iOvLwFNuonhHRHw4k5rVehWXiMzC+WPdICLgVAeVishCVT3gVVwB8V0HfBxYPhAJthd7gbEBrwvdZZ4TkVScpPGgqj7ldTyuM4GLReQCIAPIEpEHVPUaj+OqBqpV1X9V9gRO4vDaOcBuVa0FEJGngDOAsCQOq6oKzT5gift8GbDTw1gC/R/wUQARmQKk4fEMnaq6SVVHqGqxqhbj/MeaNxBJoy8isgKnquNiVW3xOJy1wGQRGS8iaTgNl894HBPiZPt7gK2q+iuv4/FT1VtUtdD9m7oSWBkFSQP373qPiEx1Fy0HtngYkl8VsFhEMt3f6XLC2GhvVxyh+QLwa7eRqRW4weN4/P4E/ElE3gPagWs9PouOdr8F0oF/uldDq1X1S14EoqqdInIj8AJOj5c/qepmL2IJcibwWWCTiKx3l/1AVZ/zMKZodxPwoHsCUA583uN4cKvNngBKcapl3yWM04/YlCPGGGP6xaqqjDHG9IslDmOMMf1iicMYY0y/WOIwxhjTL5Y4jDHG9IslDmOMMf1iicMYY0y//P/OkM3VAoxrQQAAAABJRU5ErkJggg==\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "jjvAyS4Anrp0"
},
"execution_count": null,
"outputs": []
}
]
}
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