wldmr commited on
Commit
3d5129b
·
1 Parent(s): 846f4d1
app.py CHANGED
@@ -1,7 +1,7 @@
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  import subprocess
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- notebook='basics.ipynb'
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- #notebook='seriesheat_voila.ipynb'
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  subprocess.run("jupyter trust "+notebook+"; voila --VoilaConfiguration.file_whitelist favicon.ico --show_tracebacks=True --port=7860 --no-browser --Voila.ip=0.0.0.0 "+notebook, shell=True)
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  import subprocess
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+ #notebook='basics.ipynb'
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+ notebook='seriesheat_voila.ipynb'
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  subprocess.run("jupyter trust "+notebook+"; voila --VoilaConfiguration.file_whitelist favicon.ico --show_tracebacks=True --port=7860 --no-browser --Voila.ip=0.0.0.0 "+notebook, shell=True)
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datasets/df_episode.tsv.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:7bc6569e5d95e1e14d70bd3772dbbd874ce38a205124fb2499c9c2e5cf0e0437
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+ size 5969125
datasets/df_series.tsv.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:2a9cde4eb49415b1a466311e6302836cdd98f9bbc7d667ac703e81b29a7d1852
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+ size 505013
seriesheat_voila.ipynb CHANGED
@@ -7,12 +7,12 @@
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  "source": [
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  "# Episodifier\n",
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  "Re-implementation of [\"Series Heat\"](https://vallandingham.me/seriesheat/) by Jim Vallandingham \n",
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- "Data source is [IMDB Interfaces](https://www.imdb.com/interfaces/)"
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  ]
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 1,
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  "id": "f5ea98b4-320c-4173-a5bc-b2bbdadbaf3e",
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  "metadata": {},
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  "outputs": [],
@@ -35,20 +35,20 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 12,
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  "id": "d9915649-069d-4b6a-b619-53f3fbed5c01",
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  "metadata": {},
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  "outputs": [],
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  "source": [
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- "path_episode= '/Users/hujo/Downloads/SeriesHeat/df_episode.tsv'\n",
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  "df_episode = pd.read_csv(path_episode, sep='\\t')\n",
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- "path_series = '/Users/hujo/Downloads/SeriesHeat/df_series.tsv'\n",
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  "df_series = pd.read_csv(path_series, sep='\\t')"
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  ]
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 16,
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  "id": "47ea178f-34c9-4c72-bb7f-2032758e7cc5",
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  "metadata": {},
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  "outputs": [
@@ -56,7 +56,7 @@
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  "name": "stdout",
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  "output_type": "stream",
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  "text": [
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- "Last data load on: 2022-12-25\n"
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  ]
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  }
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  ],
@@ -69,15 +69,15 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 4,
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  "id": "61e6604d-dd72-4ac0-9f41-395c981dbefa",
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  "metadata": {},
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  "outputs": [
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  {
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  "data": {
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- "image/png": 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4cJ7zFQoFVapUifz8/IR9Q/QuSKtYsSI1b95cmKa8uOnatatKPrkDgZs3b5K2tjZNnz5dlO6vv/4iHR0dYfrFixcJAG3atKnAbcnN19eXAFBsbKzKvNevX6tMGzBgABkYGNCbN2+Eaa1btyZ7e3uVtHkFAsoA7H01atQgLy8v4feWLVtUgtmcnBxq0qSJKM+nT58SAIqIiNB0kwXu7u4UFBQk/K5Zs6YQ7CQmJhIR0datWwmAcAFOJA4EiN5dXAKgw4cPq6zD3t6eANCxY8eEaQ8ePCCpVEqjR4/Ot3wPHz5UuZBV0nQ/Hj9+nADQ2rVrRen27Nmjdnpe6xk6dKgwTaFQUOvWrUlPT48ePnxIRETbt28nADRt2jTR8p06dSKJRELXr18noqIFAr179xala9++PZmbmwu/le3noEGDROm6deumkqe6On3q1CkCQKtXrxamKduHr776irKzs0XpcwcCL1++JFNTU+rXr58oXVpaGpmYmAjTP6Suenp6UtmyZenx48fCtMuXL5OWlhb16tVLmHb48GGN24Jbt25RixYtKCYmhnbu3EmRkZFUoUIF0tLSUrnIVEcul6v8bYiIdu3aRQBoz549Gm7dv6pXr04GBgZkYGBAQ4cOpS1bttDQoUMJAHXp0iXfZZ88eUIAyNzcnAwNDSkiIoI2bNhALVu2zLONK4oFCxYQAPrf//6nUfrvvvuOxo0bRxs2bKBffvlFOKZ8fHwoKyurwOVz19nMzEyqWrUqNWnSRDQdAOnp6QnHGtG7OgJAFAgFBASQTCYT3QRMSEggbW1tjQKB9PR0CgoKIolEQgAIABkYGAg3kArSu3dvCgsLoy1bttDq1aupbdu2BEDUFrOCcSDwmbtz5w4BoB49emiUfsaMGQSAEhISRNPv3btHAEQXDLkDgRYtWpC1tbXKxfovv/xCAOjXX38lon9PwKGhoRqVKSIiggDQxYsX80yzbt06tQ3i27dvydjYmDp27EhE7+5UqWuIMzMzycTERLQ9bdu2JXd3d3r48KHo37Vr19ReaLxPuZ7cd0SJiFxcXEQXSIUJBOzt7alWrVp09+5dtfMvXLhAAGjVqlUq5e7bty9JpVLh76O8uFF3BzB3IDBnzhySSCSUnJyskq+rqys1a9aMiIhSU1OFQPL9u5qa8PX1JalUKgrQ1Hnx4gU9fPiQ1qxZQwDo0qVLwryiBAK5g9Fhw4aRmZmZ8Ltfv36kq6ursj3KAEGZ55s3b0hPT49at25NT5480XCr3xk4cCDZ2NgI26etrU379+8nCwsLWrx4MRERjRw5kkxNTUXHV2EDATc3N5Xp1atXp/bt2+dbPk0CgYL247Bhw8jExIQePHigUocMDQ1Vbj7ktZ6kpCTR9N27dxMA+uWXX4iIqH///qStrS3ceFBSXmQrL0SKEgicOXNGlG7OnDkEgJ4/f05E/7afV69eFaU7c+ZMnvuP6F378+jRI3r48CGZmpqKnlwo24dVq1apLJc7EFAGi4cOHVLZxy1atCBnZ2ciKnpd/eeff9Q+BSEi8vPzIwsLC+F3YQIBdR4/fkxWVlZUpUqVAtNqaWnRN998ozL94MGDBEB4MlgYjo6OBIAGDhwomj5gwAACQNeuXctz2du3bwsXpuvXrxem5+TkkJubG5UvX77Q5VHH29ubLC0tNbqIz8v06dNFx4+mnjx5Qg8fPqRvvvmGTE1NRfMA0Ndff62yjLGxMY0cOZKIiLKzs0lfX19tUPX1119rFAhkZWXRpEmTKDAwkH755Rdas2YNNWzYkAwNDenUqVOF2h6lfv36EYAiL18acR+Bz5yxsTGAd++Wa+LWrVvQ0tKCs7OzaLq1tTVMTU1x69atfJetVKmSSqcqV1dXYf77KlasqFGZUlJSoKWlBTc3t3zXDQBVqlQRTdfT04Ojo6MwX/nf3KNI6OrqwtHRUTQtOTkZ8fHxsLS0FP2rXLkygH87wxWmPADg4uKS737Mz08//YQrV67Azs4OderUQVhYGFJTU0VlBoDg4GCVci9duhRv375VeVdfk79DcnIyiAiVKlVSyTcxMVHYFxUrVsSoUaOwdOlSWFhYwM/PDwsXLiywf4BSuXLl1HYYj4+PR/v27WFiYgJjY2NYWloKHbw0zVsdmUwmvP+uZGZmJnpn/datW7CxsVHptJz7GJFKpZg1axZ2794NKysrNGzYED/99BPS0tIKLEeDBg1w7949XL9+HSdPnoREIoG3tzcaNGgg9F85fvw4fHx8Ct1p8X0VKlRQmZZ7e4tCk/2YnJyM58+fo2zZsip1KD09Pd/jSUlLS0vlOFUej8p322/dugVbW1sYGRmJ0uXVDhVG7v1nZmYGAMJ2KttPJycnUTp17UBGRgZ++OEHoR+DhYUFLC0t8ezZM7V1WtPjFACaNGmiso/37dsn7OOi1tX82jVXV1c8evQIr169KrCcmihTpgxCQ0ORlJRU4Cg9+vr6avsBvHnzRphfWMplunbtKprerVs3AMCpU6cKXFZXV1fovAy8q7+dO3fG33//jdu3bxe6TO9LTU3FqVOn0Llz5w8a2GHkyJHQ0tLCgQMHCkz722+/oV69epDJZChTpgwsLS0RExOjtr4W1NY8fPgQGRkZakd0Ule/1BkyZAh+/fVXrF+/Hl26dEH37t1x4MAB2NjYiPo9Fsbo0aMBQKP9wd4p/mFFWL6MjY1ha2uLK1euFGq5/+JjHkVpnP9LCoUC1apVw5w5c9TOt7Oz+49L9G4kjAYNGmDbtm3Yt28fIiIiMGvWLGzduhWtWrUSOrNFRETA09NTbR65P8qkyd9BoVBAIpFg9+7dakcpeT/P2bNnIyQkBDt27MC+ffswbNgwzJw5E3/88YfQyTEv6sry7Nkz+Pr6wtjYGFOmTIGTkxNkMhkuXLiAcePGFaoDX24fe8SVESNGwN/fH9u3b8fevXvx/fffY+bMmTh06BBq1KiR53LKTtrHjh1DamoqatasCblcjgYNGmD+/PlIT0/HxYsXMX369A8qX17bS7k6236sfN+nUChQtmxZrF27Vu383IHEp5ZXG5e7g/P7Pub+Gzp0KFasWIERI0bA29sbJiYmkEgk6NKli9o6relxCgBxcXGwtrZWmf/+BWNR6+p/SdnGPnnyJN+2w8bGBvfu3VOZrpxWlOEgbW1tER8fDysrK9H0smXLAkC+wXOZMmUgk8lgamqqUmfeX17dxbKmlCMhaTpaUF6UHdWfPHmSb7rjx4+jbdu2aNiwIaKjo2FjYwNdXV2sWLFC7ahMn6qtUcrMzMSyZcswduxY0c0RXV1dtGrVClFRUcjMzCz0SITv1zmmGQ4ESoA2bdpg8eLFOHXqVIFjMtvb20OhUCA5OVm4gwYA9+/fx7Nnz/L8IIxy2T///BMKhUJ0YF69elWYXxROTk5QKBRISEjI8+JWmXdSUpLojmFmZiZu3LghjM+vTJecnIwmTZoI6bKysnDjxg14eHiI1nv58mU0bdq00IHR++V5fz3KaUXdF8C7k96gQYMwaNAgPHjwADVr1sT06dPRqlUr4U6ksbHxR/0mgZOTE4gIFStWFO7A5qdatWqoVq0aJk2ahJMnT8LHxwexsbGYNm1aodd95MgRPH78GFu3bkXDhg2F6Tdu3FBJ+ykCWHt7exw+fFhlKNPr16+rTe/k5ITRo0dj9OjRSE5OhqenJ2bPni0M86dOhQoVUKFCBRw/fhypqalo0KABAKBhw4YYNWoUNm3ahJycHNH2q/OpAviPka+TkxMOHDgAHx+fIt8EUCgUSE1NFdXBa9euAYAwQpS9vT0OHDiAly9fip4K5G6HlHfzc48q9iFPDJTtZ0pKiuiuZlJSkkrazZs3Izg4GLNnzxamvXnz5oO+Kq48/suWLavR8V/Yuvp+u5bb1atXYWFh8VE/hqh82llQkOjp6Ynjx4+rnHtOnz4NAwMDjdqs3Ly8vLB//37cvXtX9Lf8559/CiyTlpYWPD09cfbsWZWLUU2W18S6devg5OSEevXqfVA+L1++xKNHjwosz5YtWyCTybB3717RKD0rVqwo0notLS2hr68vPMV6n7r6ldvjx4+RnZ2tNnDPysqCQqHIN6jPi6Z1jv2LXw0qAcaOHQu5XI6+ffvi/v37KvNTUlIwb948ABA+5pL766zKu+KtW7fOcz1ff/010tLSsGHDBmFadnY2FixYAENDQ2E4ucIKCAiAlpYWpkyZonKnTHl3oVmzZtDT08P8+fNFdxyWLVuG58+fC+WuVasWLC0tERsbKxqibuXKlSon4KCgINy9exdLlixRKVNGRka+j8Br1aqFsmXLIjY2VvTIevfu3UhMTMx3P+YlJydH5RFs2bJlYWtrK6zDy8sLTk5O+Pnnn9UOh/bw4cNCrxcAOnToAG1tbYSHh6vc0SEiPH78GMC74dyys7NF86tVqwYtLa0iDeEH/Htn6f31ZmZmIjo6WiWtXC7/oFeF1PHz80NWVpaoHigUCixcuFCU7vXr18KrCEpOTk4wMjLSaNsbNGiAQ4cO4cyZM0Ig4OnpCSMjI/z444/CcH35UV6EfcjFpDrKAOhD8g0KCkJOTg6mTp2qMi87O1vjvKOiooT/JyJERUVBV1cXTZs2BfCuHcrJyRGlA4C5c+dCIpGgVatWAN4FyxYWFjh27Jgonbp6pSll3rm/PKvua9fa2toqx9KCBQuKdPGi5OfnB2NjY8yYMQNZWVkq85XHf1Hrqo2NDTw9PbFq1SrR3+vKlSvYt2+fysfANKWuXbp79y6WL1+O6tWri4a9vnfvHq5evSravk6dOuH+/fvYunWrMO3Ro0fYtGkT/P39i/S9jaCgIADvziHvW7p0KXR0dETDod6+fVsINJU6d+6MnJwc0fDZb968wdq1a+Hm5vZBH626ePEiEhMThdeU1ElJSUFKSopo3epeEZ46dSqIqMCv6Wpra0MikYjq582bN7F9+/bCb8D/5+fn54ft27eLXpNKTEzE3r17C1y+bNmyMDU1xbZt20Tn8vT0dPz6669wcXER3XC4evWqaD0vXrxQqetEJNys8vPzK9J2lUb8RKAEcHJywrp169C5c2e4urqKviys/AiLcixyDw8PBAcHY/HixcIrGWfOnMGqVasQEBCAxo0b57me/v37Y9GiRQgJCcH58+fh4OCAzZs348SJE4iMjFR5Z1dTzs7OmDhxIqZOnYoGDRqgQ4cOkEqlOHv2LGxtbTFz5kxYWlpi/PjxCA8PR8uWLdG2bVskJSUhOjoatWvXFt4n19XVxbRp0zBgwAA0adIEnTt3xo0bN7BixQqVd4979uyJjRs3YuDAgTh8+DB8fHyQk5ODq1evYuPGjdi7dy9q1aqltsy6urqYNWsWQkND4evri65du+L+/fuYN28eHBwcivSBmZcvX6J8+fLo1KkTPDw8YGhoiAMHDuDs2bPCXUUtLS0sXboUrVq1gru7O0JDQ1GuXDncvXsXhw8fhrGxMX799ddCr9vJyQnTpk3D+PHjcfPmTQQEBMDIyAg3btzAtm3b0L9/f4wZMwaHDh3CkCFDEBgYiMqVKyM7OxtxcXHQ1tZGx44dC71eAKhfvz7MzMwQHByMYcOGQSKRIC4uTu0jZi8vL2zYsAGjRo1C7dq1YWhoCH9//yKtVykgIAB16tTB6NGjcf36dbi4uGDnzp3Co2Pl3fJr166hadOmCAoKgpubG3R0dLBt2zbcv38fXbp0KXA9DRo0wNq1ayGRSIRXhbS1tVG/fn3s3bsXjRo1KvAxt6enJ7S1tTFr1iw8f/4cUqkUTZo0EV5HKCp9fX24ublhw4YNqFy5MsqUKYOqVauiatWqGufh6+uLAQMGYObMmbh06RJatGgBXV1dJCcnY9OmTZg3b57ofWp1ZDIZ9uzZg+DgYNStWxe7d+/Grl27MGHCBOEOnr+/Pxo3boyJEyfi5s2b8PDwwL59+7Bjxw6MGDFC9P5+37598eOPP6Jv376oVasWjh07JjxhKApPT0907doV0dHReP78OerXr4+DBw+qfXrUpk0bxMXFwcTEBG5ubjh16hQOHDgAc3PzIq/f2NgYMTEx6NmzJ2rWrIkuXbrA0tISt2/fxq5du+Dj44OoqKgPqqsRERFo1aoVvL290adPH2RkZGDBggUwMTFR+baEpsaOHYuUlBQ0bdoUtra2uHnzJhYtWoRXr14JN6mUxo8fj1WrVuHGjRvCU6BOnTqhXr16CA0NRUJCgvBl4ZycHOGL2EohISEqy6tTo0YN9O7dG8uXL0d2djZ8fX1x5MgRbNq0CePHjxddyPfq1QtHjx4VtUkDBgzA0qVLMXjwYFy7dg0VKlRAXFwcbt26pdIGN2rUSGX5/Chfr8vvtSBlYKzsO5OWloYaNWqga9eucHFxAQDs3bsX//vf/9CyZcsCv0PQunVrzJkzBy1btkS3bt3w4MEDLFy4EM7OzqJvoBRGeHg49uzZgwYNGmDQoEHCjUN3d/cC89TW1saYMWMwadIk1KtXD7169UJOTg6WLVuGv//+W+Wplqurq/A3BIALFy6ga9eu6Nq1K5ydnZGRkYFt27bhxIkT6N+/P2rWrFmkbSqV/vPuyazIrl27Rv369SMHBwfS09MjIyMj8vHxoQULFoiGYMzKyqLw8HCqWLEi6erqkp2dHY0fP16Uhkh11CAiovv371NoaChZWFiQnp4eVatWTWVUDuVoHYUdum758uVUo0YNkkqlZGZmRr6+vrR//35RmqioKHJxcSFdXV2ysrKib775Rhjj/n3R0dFUsWJFkkqlVKtWLTp27Jja7cnMzKRZs2aRu7u7sF4vLy8KDw8XRgrJz4YNG4QylylThrp3705///23KI2mowa9ffuWvv32W/Lw8CAjIyOSy+Xk4eGhdmSiixcvUocOHcjc3JykUinZ29tTUFAQHTx4UEijHAlFOezi+9R9R4Do3Ug5X331FcnlcpLL5eTi4kKDBw8WRnJJTU2l3r17k5OTE8lkMipTpgw1btyYDhw4UOC+8vX1JXd3d7XzTpw4QfXq1SN9fX2ytbWlsWPH0t69e1VGyElPT6du3bqRqakpARBGEMpr1CC5XK7Rtj98+JC6detGRkZGZGJiQiEhIXTixAnRqCCPHj2iwYMHk4uLC8nlcjIxMaG6devSxo0bC9x2IqL4+HgCQK6urqLp06ZNI0A8Vr5S7lGDiIiWLFlCjo6OwhB8yv1jb29PrVu3VslDXb1X5+TJk+Tl5SWMua0cAacw+5Ho3VC9Xl5epK+vT0ZGRlStWjUaO3Ys/fPPP/muX7melJQU4fseVlZWNHnyZJWRyl6+fEkjR44kW1tb0tXVpUqVKlFERIRoSF2id8Mh9unTh0xMTMjIyIiCgoKE7zWoGzUo97Gibhz/jIwMGjZsGJmbm5NcLid/f39h9Lb383z69KnQVhoaGpKfnx9dvXpV5W+aX/ugbv1E70bs8fPzIxMTE5LJZOTk5EQhISF07tw5IvrwunrgwAHy8fEhfX19MjY2Jn9/f5WR5gozatC6deuoYcOGZGlpSTo6OmRhYUHt27en8+fPq6RVjh6Ve5ufPHlCffr0IXNzczIwMCBfX1+1+6xjx46kr6+v9ryQW2ZmJoWFhZG9vT3p6uqSs7MzzZ07VyWdcujj3O7fv0/BwcFUpkwZkkqlVLduXbVDmXp5eZG1tXWB5SF6N/JQuXLlqGbNmvmms7e3F42g9vTpU+rRowc5OzuTgYEBSaVScnd3pxkzZmg8bv6yZcuoUqVKJJVKycXFhVasWKH2OAegdphrde3V0aNHhXbF0dGRYmNj82w71Fm7di3VqVOHTE1NSV9fn+rWrUubN29WSYdcw4OnpqZSYGAgOTg4kEwmIwMDA/Ly8qLY2FiVdoLlT0L0kXp+MMZYCbJ9+3a0b98ev//+O3x8fIq7OIwxDVhZWaFXr16IiIgo7qIAePekt0yZMoiMjMTgwYOLuziMFRr3EWCMffEyMjJEv3NycrBgwQIYGxvzI2TGSoj4+HhkZGRg3LhxxV0UwbFjx1CuXDn069evuIvCWJHwEwHG2Bevb9++yMjIgLe3N96+fYutW7fi5MmTmDFjBsaPH1/cxWOMMcaKBQcCjLEv3rp16zB79mxcv34db968gbOzM7755hsMGTKkuIvGGGOMFRsOBBhjjDHGGCuFuI8AY4wxxhhjpRAHAowxxhhjjJVCn/0Hxd68eSP66hxjjDHGGGMsb3p6epDJZAWm+6wDgTdv3og+Mc0YY4wxxhjLn7W1NW7cuFFgMPBZBwL8JIB9qOfFXQBWsi0r7gKwEq1VcReAlWT1Thd3CVhJpXgNJHVPQ2ZmZskOBBj7UMbFXQBWshkUdwFYicYNEPsA2vLiLgErDbizMGOMMcYYY6UQBwKMMcYYY4yVQhwIMMYYY4wxVgpxIMAYY4wxxlgpxIEAY4wxxhhjpRAHAowxxhhjjJVCHAgwxhhjjDFWCnEgwBhjjDHGWCnEgQBjjDHGGGOlEAcCjDHGGGOMlUIcCDDGGGOMMVYKcSDAGGOMMcZYKcSBAGOMMcYYY6UQBwKMMcYYY4yVQhwIMMYYY4wxVgpxIMAYY4wxxlgpxIEAY4wxxhhjpRAHAowxxhhjjJVCHAgwxhhjjDFWCnEgwBhjjDHGWCnEgQBjjDHGGGOlEAcCjDHGGGOMlUIcCDDGGGOMMVYKcSDAGGOMMcZYKcSBAGOMMcYYY6UQBwKMMcYYY4yVQjrFXQDGPqUXxV0AVrK9Lu4CsBKNGyD2AXJeFXcJWEmlKMS567MOBIgIhoaGSE9PL+6isBLKpLgLwEq2PsVdAMYYY6zwDA0NQUQFpvusAwGJRIL09HTcuXMHxsbGxV0cVsK8ePECdnZ2XH9YkXD9YR+C6w/7EFx/2IdQ1h+JRFJg2s86EFAyNjbmA4EVGdcf9iG4/rAPwfWHfQiuP+xT487CjDHGGGOMlUIcCDDGGGOMMVYKfdaBgFQqxeTJkyGVSou7KKwE4vrDPgTXH/YhuP6wD8H1h32IwtQfCWnSpZgxxhhjjDH2RfmsnwgwxhhjjDHGPg0OBBhjjDHGGCuFOBBgjDHGGGOsFPrsAoGQkBAEBAQUdzFYKRMWFgZPT8/iLgYrgbjNYp+CRCLB9u3bi7sYjLEvXJECgbS0NAwfPhzOzs6QyWSwsrKCj48PYmJi8Pr1649dRsYEISEhkEgkwj9zc3O0bNkSf/75Z3EXjf3H3q8Lurq6qFixIsaOHYs3b94Ud9HYF4LPdayoirPurFy5Eqampp90HezLUegvC6empsLHxwempqaYMWMGqlWrBqlUir/++guLFy9GuXLl0LZt20IXJCcnR6NPITPWsmVLrFixAsC7xnbSpElo06YNbt++XcwlY/81ZV3IysrC+fPnERwcDIlEglmzZhV30QolKysLurq6xV0M9p5Pda5jX76i1h1uB1hxKPQTgUGDBkFHRwfnzp1DUFAQXF1d4ejoiHbt2mHXrl3w9/cHAMyZMwfVqlWDXC6HnZ0dBg0ahPT0dCEfZcS6c+dOuLm5QSqVii7kwsPDYWlpCWNjYwwcOBCZmZnCvD179uCrr76CqakpzM3N0aZNG6SkpIjKefLkSXh6ekImk6FWrVrYvn07JBIJLl26JKS5cuUKWrVqBUNDQ1hZWaFnz5549OhRYXcJ+49JpVJYW1vD2toanp6e+O6773Dnzh08fPgQADBu3DhUrlwZBgYGcHR0xPfff4+srCxRHj/++COsrKxgZGSEPn368F3kEkpZF+zs7BAQEIBmzZph//79AACFQoGZM2eiYsWK0NfXh4eHBzZv3ixafufOnahUqRJkMhkaN26MVatWQSKR4NmzZwDUvzIWGRkJBweHPMtUUPt08+ZNSCQSbNiwAb6+vpDJZFi7du1H2R/s49H0XHf79m20a9cOhoaGMDY2RlBQEO7fvy/KKyYmBk5OTtDT00OVKlUQFxcnmp+cnIyGDRtCJpPBzc1NqMOsZNK07kgkEsTExKBt27aQy+WYPn06AGDHjh2oWbMmZDIZHB0dER4ejuzsbCH//K6vjhw5gtDQUDx//lx4YhoWFvaf7wNWchQqEHj8+DH27duHwYMHQy6Xq02jvKuvpaWF+fPnIz4+HqtWrcKhQ4cwduxYUdrXr19j1qxZWLp0KeLj41G2bFkAwMGDB5GYmIgjR47gl19+wdatWxEeHi4s9+rVK4waNQrnzp3DwYMHoaWlhfbt20OhUAAAXrx4AX9/f1SrVg0XLlzA1KlTMW7cONG6nz17hiZNmqBGjRo4d+4c9uzZg/v37yMoKKgwu4QVs/T0dKxZswbOzs4wNzcHABgZGWHlypVISEjAvHnzsGTJEsydO1dYZuPGjQgLC8OMGTNw7tw52NjYIDo6urg2gX0kV65cwcmTJ6GnpwcAmDlzJlavXo3Y2FjEx8dj5MiR6NGjB44ePQoAuHHjBjp16oSAgABcvnwZAwYMwMSJEz+4HAW1T0rfffcdhg8fjsTERPj5+X3wetnHo+m5TqFQoF27dnjy5AmOHj2K/fv3IzU1FZ07dxbSbdu2DcOHD8fo0aNx5coVDBgwAKGhoTh8+DCAdwFrhw4doKenh9OnTyM2NlblfMVKjsJcJwHvbja0b98ef/31F3r37o3jx4+jV69eGD58OBISErBo0SKsXLlSCBKA/K+v6tevj8jISBgbG+PevXu4d+8exowZ82k3mpVsVAh//PEHAaCtW7eKppubm5NcLie5XE5jx45Vu+ymTZvI3Nxc+L1ixQoCQJcuXRKlCw4OpjJlytCrV6+EaTExMWRoaEg5OTlq83748CEBoL/++ktIb25uThkZGUKaJUuWEAC6ePEiERFNnTqVWrRoIcrnzp07BICSkpIK2BOsuAQHB5O2trZQ3wCQjY0NnT9/Ps9lIiIiyMvLS/jt7e1NgwYNEqWpW7cueXh4fKpis0/g/boglUoJAGlpadHmzZvpzZs3ZGBgQCdPnhQt06dPH+ratSsREY0bN46qVq0qmj9x4kQCQE+fPiUiosmTJ6vUi7lz55K9vb2oHO3atcuznLnbpxs3bhAAioyMLNqGs09O03Pdvn37SFtbm27fvi2kiY+PJwB05swZIiKqX78+9evXT5RPYGAgff3110REtHfvXtLR0aG7d+8K83fv3k0AaNu2bZ9oC9mnUpjrJAA0YsQIUbqmTZvSjBkzRNPi4uLIxsYmz3Wqu74yMTH5wC1hpcVHGTXozJkzuHTpEtzd3fH27VsAwIEDB9C0aVOUK1cORkZG6NmzJx4/fizqJKOnp4fq1aur5Ofh4QEDAwPht7e3N9LT03Hnzh0A7x6jdu3aFY6OjjA2NhYe0ytfLUpKSkL16tUhk8mEPOrUqSNax+XLl3H48GEYGhoK/1xcXABA5TUj9nlp3LgxLl26hEuXLuHMmTPw8/NDq1atcOvWLQDAhg0b4OPjA2traxgaGmLSpEmi184SExNRt25dUZ7e3t7/6Tawj0NZF06fPo3g4GCEhoaiY8eOuH79Ol6/fo3mzZuLjvHVq1cLx3dSUhJq164tyi93O1EUBbVPSrVq1frgdbH/Vu5zXWJiIuzs7GBnZyekcXNzg6mpKRITEwG8a298fHxE+fj4+Ijm29nZwdbWVpjP7dGXR911EqDaDly+fBlTpkwRtVv9+vXDvXv3hOsnTa6vGNNUoToLOzs7QyKRICkpSTTd0dERAKCvrw/g3Tuwbdq0wTfffIPp06ejTJky+P3339GnTx9kZmYKF/n6+vpF6iDs7+8Pe3t7LFmyBLa2tlAoFKhataqoH0FB0tPT4e/vr7ZToY2NTaHLxP47crkczs7Owu+lS5fCxMQES5YsQevWrdG9e3eEh4fDz88PJiYmWL9+PWbPnl2MJWafyvt1Yfny5fDw8MCyZctQtWpVAMCuXbtQrlw50TJSqVTj/LW0tEBEomm5+5vkpmn7lNdrA6z4aXquYyy3wtad3O1Aeno6wsPD0aFDB5W8ZTKZxtdXjGmqUE8EzM3N0bx5c0RFReHVq1d5pjt//jwUCgVmz56NevXqoXLlyvjnn380Xs/ly5eRkZEh/P7jjz9gaGgIOzs7PH78GElJSZg0aRKaNm0KV1dXPH36VLR8lSpV8Ndff4mi7rNnz4rS1KxZE/Hx8XBwcICzs7PoH5+gSxaJRAItLS1kZGTg5MmTsLe3x8SJE1GrVi1UqlRJeFKg5OrqitOnT4um/fHHH/9lkdknoKWlhQkTJmDSpEmiAQhyH9/Ku7dVqlTBuXPnRHnkbicsLS2RlpYmCgbeH3AgN03aJ/b50/Rc5+rqijt37ghPqwEgISEBz549g5ubm5DmxIkTouVOnDghmn/nzh3cu3dPmM/tUcmlad3JS82aNZGUlKTSbjk7O0NLS0uj6ys9PT3k5OR8rE1iX7hCvxoUHR2N7Oxs1KpVCxs2bEBiYiKSkpKwZs0aXL16Fdra2nB2dkZWVhYWLFiA1NRUxMXFITY2VuN1ZGZmok+fPkhISMD//vc/TJ48GUOGDIGWlhbMzMxgbm6OxYsX4/r16zh06BBGjRolWr5bt25QKBTo378/EhMTsXfvXvz8888A/u2kM3jwYDx58gRdu3bF2bNnkZKSgr179yI0NJQPoM/c27dvkZaWhrS0NCQmJmLo0KHCE55KlSrh9u3bWL9+PVJSUjB//nxs27ZNtPzw4cOxfPlyrFixAteuXcPkyZMRHx9fTFvDPqbAwEBoa2tj0aJFGDNmDEaOHIlVq1YhJSUFFy5cwIIFC7Bq1SoAwIABA3D16lWMGzcO165dw8aNG7Fy5UoA/7YTjRo1wsOHD/HTTz8hJSUFCxcuxO7du/NcvybtEysZNDnXNWvWDNWqVUP37t1x4cIFnDlzBr169YKvr6/wyse3336LlStXIiYmBsnJyZgzZw62bt0qdOBs1qwZKleujODgYFy+fBnHjx//KJ3WWfHRpO7k5YcffsDq1asRHh6O+Ph4JCYmYv369Zg0aRIAaHR95eDggPT0dBw8eBCPHj3iV4ZY/orSseCff/6hIUOGUMWKFUlXV5cMDQ2pTp06FBERIXTynTNnDtnY2JC+vj75+fnR6tWrRZ3w8urMoux498MPP5C5uTkZGhpSv3796M2bN0Ka/fv3k6urK0mlUqpevTodOXJEpWPViRMnqHr16qSnp0deXl60bt06AkBXr14V0ly7do3at29PpqampK+vTy4uLjRixAhSKBRF2S3sPxAcHEwAhH9GRkZUu3Zt2rx5s5Dm22+/FepO586dae7cuSp1bfr06WRhYUGGhoYUHBxMY8eO5c7CJUxenXRnzpxJlpaWlJ6eTpGRkVSlShXS1dUlS0tL8vPzo6NHjwppd+zYQc7OziSVSqlRo0YUExNDAEQDDcTExJCdnR3J5XLq1asXTZ8+Pd/OwgW1T8rOwsqBC9jnS5Nz3a1bt6ht27Ykl8vJyMiIAgMDKS0tTZRPdHQ0OTo6kq6uLlWuXJlWr14tmp+UlERfffUV6enpUeXKlWnPnj3cWbiE06Tu5PU33rNnD9WvX5/09fXJ2NiY6tSpQ4sXLxbmF3R9RUQ0cOBAMjc3JwA0efLkT7y1rCSTEOV6AfYLtXbtWmFsXX6/kzGmzvTp0xEbGyt61YMxxhj7UhX6y8IlxerVq+Ho6Ihy5crh8uXLGDduHIKCgjgIYIwJoqOjUbt2bZibm+PEiROIiIjAkCFDirtYjDHG2H/iiw0E0tLS8MMPPyAtLQ02NjYIDAwUfZCDMcaSk5Mxbdo0PHnyBBUqVMDo0aMxfvz44i4WY4wx9p8oNa8GMcYYY4wxxv71UT4oxhhjjDHGGCtZOBBgjDHGGGOsFOJAgDHGGGOMsVKIAwHGGGOMMcZKIQ4EGGOMMcYYK4U4EGCMMcYYY6wU4kCAMcYYY4yxUogDAcYYY4wxxkohDgQYY4wxxhgrhf4Pprd72grUxLgAAAAASUVORK5CYII=\n",
79
  "text/plain": [
80
- "<Figure size 800x300 with 1 Axes>"
81
  ]
82
  },
83
  "metadata": {},
@@ -86,7 +86,7 @@
86
  ],
87
  "source": [
88
  "# Make a figure and axes with dimensions as desired.\n",
89
- "fig = plt.figure(figsize=(8, 3))\n",
90
  "ax2 = fig.add_axes([0.05, 0.15, 0.9, 0.1])\n",
91
  "\n",
92
  "# Set the colormap and norm to correspond to the data for which\n",
@@ -124,14 +124,14 @@
124
  },
125
  {
126
  "cell_type": "code",
127
- "execution_count": 5,
128
  "id": "14453a04-aace-4aac-afc2-fb3d82df0400",
129
  "metadata": {},
130
  "outputs": [
131
  {
132
  "data": {
133
  "application/vnd.jupyter.widget-view+json": {
134
- "model_id": "46cf173095224722ad101268b0f68640",
135
  "version_major": 2,
136
  "version_minor": 0
137
  },
 
7
  "source": [
8
  "# Episodifier\n",
9
  "Re-implementation of [\"Series Heat\"](https://vallandingham.me/seriesheat/) by Jim Vallandingham \n",
10
+ "Data source is [IMDb Interfaces](https://www.imdb.com/interfaces/)"
11
  ]
12
  },
13
  {
14
  "cell_type": "code",
15
+ "execution_count": 18,
16
  "id": "f5ea98b4-320c-4173-a5bc-b2bbdadbaf3e",
17
  "metadata": {},
18
  "outputs": [],
 
35
  },
36
  {
37
  "cell_type": "code",
38
+ "execution_count": 27,
39
  "id": "d9915649-069d-4b6a-b619-53f3fbed5c01",
40
  "metadata": {},
41
  "outputs": [],
42
  "source": [
43
+ "path_episode= 'datasets/df_episode.tsv.zip'\n",
44
  "df_episode = pd.read_csv(path_episode, sep='\\t')\n",
45
+ "path_series = 'datasets/df_series.tsv.zip'\n",
46
  "df_series = pd.read_csv(path_series, sep='\\t')"
47
  ]
48
  },
49
  {
50
  "cell_type": "code",
51
+ "execution_count": 28,
52
  "id": "47ea178f-34c9-4c72-bb7f-2032758e7cc5",
53
  "metadata": {},
54
  "outputs": [
 
56
  "name": "stdout",
57
  "output_type": "stream",
58
  "text": [
59
+ "Last data load on: 2022-12-28\n"
60
  ]
61
  }
62
  ],
 
69
  },
70
  {
71
  "cell_type": "code",
72
+ "execution_count": 29,
73
  "id": "61e6604d-dd72-4ac0-9f41-395c981dbefa",
74
  "metadata": {},
75
  "outputs": [
76
  {
77
  "data": {
78
+ "image/png": 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\n",
79
  "text/plain": [
80
+ "<Figure size 600x300 with 1 Axes>"
81
  ]
82
  },
83
  "metadata": {},
 
86
  ],
87
  "source": [
88
  "# Make a figure and axes with dimensions as desired.\n",
89
+ "fig = plt.figure(figsize=(6, 3))\n",
90
  "ax2 = fig.add_axes([0.05, 0.15, 0.9, 0.1])\n",
91
  "\n",
92
  "# Set the colormap and norm to correspond to the data for which\n",
 
124
  },
125
  {
126
  "cell_type": "code",
127
+ "execution_count": 30,
128
  "id": "14453a04-aace-4aac-afc2-fb3d82df0400",
129
  "metadata": {},
130
  "outputs": [
131
  {
132
  "data": {
133
  "application/vnd.jupyter.widget-view+json": {
134
+ "model_id": "0f7db2b3b739455b8c12ad7df9162ab7",
135
  "version_major": 2,
136
  "version_minor": 0
137
  },