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update
Browse files- favicon.ico +0 -0
- seriesheat_voila.ipynb +31 -23
favicon.ico
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seriesheat_voila.ipynb
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]
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},
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{
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"cell_type": "
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"
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"id": "47ea178f-34c9-4c72-bb7f-2032758e7cc5",
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"metadata": {},
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"outputs": [
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{
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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: 2022-12-28\n"
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]
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}
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],
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"source": [
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"
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"dt = datetime.fromtimestamp(timestamp)\n",
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"date_string = dt.strftime(\"%Y-%m-%d\")\n",
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"print('Last data load on: ',date_string)"
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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":
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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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\n",
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"text/plain": [
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-
"<Figure size
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]
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},
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"metadata": {},
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],
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"source": [
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"# Make a figure and axes with dimensions as desired.\n",
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-
"fig = plt.figure(figsize=(
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"ax2 = fig.add_axes([0.05, 0.15, 0.9, 0.1])\n",
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"\n",
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"# Set the colormap and norm to correspond to the data for which\n",
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" #ticks=bounds, # optional\n",
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" #spacing='proportional',\n",
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" orientation='horizontal')\n",
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-
"cb2.ax.set_title('The color coding of the series ratings has the limits of 5.0, 6.6, 7.5 and 8.5')\n",
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"# Change the tick labels from numbers to letters\n",
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-
"cb2.set_ticklabels(['Garbage', 'Bad', 'Regular', 'Good', 'Great', ''])\n",
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"
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"\n",
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"plt.show()"
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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": 30,
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]
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},
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{
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+
"cell_type": "markdown",
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+
"id": "27c06436-8d65-43d6-a717-758333f2f0c9",
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"metadata": {},
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"source": [
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+
"The color coding of the series ratings has the limits of 5.0, 6.6, 7.5 and 8.5"
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]
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},
|
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{
|
| 58 |
"cell_type": "code",
|
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+
"execution_count": 41,
|
| 60 |
"id": "61e6604d-dd72-4ac0-9f41-395c981dbefa",
|
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"metadata": {},
|
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"outputs": [
|
| 63 |
{
|
| 64 |
"data": {
|
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+
"image/png": "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\n",
|
| 66 |
"text/plain": [
|
| 67 |
+
"<Figure size 400x200 with 1 Axes>"
|
| 68 |
]
|
| 69 |
},
|
| 70 |
"metadata": {},
|
|
|
|
| 73 |
],
|
| 74 |
"source": [
|
| 75 |
"# Make a figure and axes with dimensions as desired.\n",
|
| 76 |
+
"fig = plt.figure(figsize=(4, 2))\n",
|
| 77 |
"ax2 = fig.add_axes([0.05, 0.15, 0.9, 0.1])\n",
|
| 78 |
"\n",
|
| 79 |
"# Set the colormap and norm to correspond to the data for which\n",
|
|
|
|
| 101 |
" #ticks=bounds, # optional\n",
|
| 102 |
" #spacing='proportional',\n",
|
| 103 |
" orientation='horizontal')\n",
|
| 104 |
+
"#cb2.ax.set_title('The color coding of the series ratings has the limits of 5.0, 6.6, 7.5 and 8.5', fontsize=10)\n",
|
| 105 |
"# Change the tick labels from numbers to letters\n",
|
| 106 |
+
"cb2.set_ticklabels(['Garbage', 'Bad', 'Regular', 'Good', 'Great', ''], fontsize=10)\n",
|
| 107 |
+
"plt.tick_params(bottom=False)\n",
|
| 108 |
"\n",
|
| 109 |
"plt.show()"
|
| 110 |
]
|
| 111 |
},
|
| 112 |
+
{
|
| 113 |
+
"cell_type": "code",
|
| 114 |
+
"execution_count": 28,
|
| 115 |
+
"id": "47ea178f-34c9-4c72-bb7f-2032758e7cc5",
|
| 116 |
+
"metadata": {},
|
| 117 |
+
"outputs": [
|
| 118 |
+
{
|
| 119 |
+
"name": "stdout",
|
| 120 |
+
"output_type": "stream",
|
| 121 |
+
"text": [
|
| 122 |
+
"Last data load on: 2022-12-28\n"
|
| 123 |
+
]
|
| 124 |
+
}
|
| 125 |
+
],
|
| 126 |
+
"source": [
|
| 127 |
+
"timestamp = os.path.getmtime(path_episode) \n",
|
| 128 |
+
"dt = datetime.fromtimestamp(timestamp)\n",
|
| 129 |
+
"date_string = dt.strftime(\"%Y-%m-%d\")\n",
|
| 130 |
+
"print('Last data load on: ',date_string)"
|
| 131 |
+
]
|
| 132 |
+
},
|
| 133 |
{
|
| 134 |
"cell_type": "code",
|
| 135 |
"execution_count": 30,
|