Commit d7493678 authored by Hubert Degaudenzi's avatar Hubert Degaudenzi
Browse files

April update

parent 4e0e2eaf
This diff is collapsed.
This diff is collapsed.
......@@ -662,7 +662,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
"version": "3.8.7"
},
"varInspector": {
"cols": {
......
......@@ -104,7 +104,7 @@
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"<thead><tr><th>x</th><th>y</th><th>FWHM</th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
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......@@ -177,7 +177,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 6,
"metadata": {},
"outputs": [
{
......@@ -234,7 +234,7 @@
"EXTNAME = 'Grid image' / The grid PSF image "
]
},
"execution_count": 9,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
......@@ -247,7 +247,7 @@
},
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"cell_type": "code",
"execution_count": 8,
"execution_count": 7,
"metadata": {},
"outputs": [
{
......@@ -257,7 +257,7 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-647a1169e9a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-7-647a1169e9a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m 2728\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2729\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2730\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 2731\u001b[0m \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2732\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1445\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1446\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1449\u001b[0m \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m 5521\u001b[0m resample=resample, **kwargs)\n\u001b[1;32m 5522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5523\u001b[0;31m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5524\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5525\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
......@@ -309,7 +309,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.8.6"
}
},
"nbformat": 4,
......
......@@ -13,7 +13,7 @@
},
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -23,7 +23,7 @@
},
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"execution_count": null,
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"outputs": [],
"source": [
......@@ -38,61 +38,18 @@
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"execution_count": null,
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"outputs": [
{
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"outputs": [],
"source": [
"y_np"
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{
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{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
......@@ -102,19 +59,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /home/isdc/degauden/.local/tmp/conda/envs/TensorFlow/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"non-resource variables are not supported in the long term\n"
]
}
],
"outputs": [],
"source": [
"# Generate tensorflow graph\n",
"import tensorflow.compat.v1 as tf\n",
......
This diff is collapsed.
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -25,7 +25,17 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"logs_base_dir = \"/tmp/degauden/tensorboard/example/logs\"\n",
"os.makedirs(logs_base_dir, exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -36,26 +46,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"1875/1875 [==============================] - 18s 10ms/step - loss: 0.5017 - accuracy: 0.8216 - val_loss: 0.4047 - val_accuracy: 0.8563\n",
"Epoch 2/5\n",
"1875/1875 [==============================] - 18s 10ms/step - loss: 0.3839 - accuracy: 0.8595 - val_loss: 0.3871 - val_accuracy: 0.8647\n",
"Epoch 3/5\n",
"1875/1875 [==============================] - 18s 10ms/step - loss: 0.3495 - accuracy: 0.8721 - val_loss: 0.3710 - val_accuracy: 0.8673\n",
"Epoch 4/5\n",
"1875/1875 [==============================] - 17s 9ms/step - loss: 0.3284 - accuracy: 0.8784 - val_loss: 0.3756 - val_accuracy: 0.8628\n",
"Epoch 5/5\n",
"1875/1875 [==============================] - 17s 9ms/step - loss: 0.3123 - accuracy: 0.8838 - val_loss: 0.3789 - val_accuracy: 0.8706\n"
]
}
],
"outputs": [],
"source": [
"def create_model():\n",
" return tf.keras.models.Sequential([\n",
......@@ -86,38 +79,12 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe id=\"tensorboard-frame-4fbf182c38483361\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
" </iframe>\n",
" <script>\n",
" (function() {\n",
" const frame = document.getElementById(\"tensorboard-frame-4fbf182c38483361\");\n",
" const url = new URL(\"/\", window.location);\n",
" url.port = 6006;\n",
" frame.src = url;\n",
" })();\n",
" </script>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"logs_base_dir = \"./logs\"\n",
"os.makedirs(logs_base_dir, exist_ok=True)\n",
"%tensorboard --logdir {logs_base_dir}"
]
},
......
This diff is collapsed.
......@@ -4,8 +4,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compartmental models in epidemiology\n",
"Some more informations can be found [here](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology \"Compartmental models in epidemiology\")"
"# Compartmental Models in Epidemiology\n",
"Some more informations can be found [here](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology \"Compartmental models in epidemiology\") and [here](https://en.wikipedia.org/wiki/Mathematical_modelling_of_infectious_disease \"Mathematical modelling of infectious disease\") "
]
},
{
......@@ -66,7 +66,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "77a37c411ebb4b4abfaaca94f9ddccd0",
"model_id": "1834001ebe034cfd9aa2f2043eda7a28",
"version_major": 2,
"version_minor": 0
},
......@@ -138,7 +138,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d438c404a92b4a8cb684aa43f9074b2e",
"model_id": "3973cd82d52f4ad09aff7f9534a9ce99",
"version_major": 2,
"version_minor": 0
},
......@@ -226,7 +226,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"version": "3.8.6"
}
},
"nbformat": 4,
......
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