fix pics
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@ -16,7 +16,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 6,
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"id": "e2abc871-bf82-4cf6-993e-cd904c7e7468",
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"metadata": {
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"editable": true,
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@ -43,7 +43,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 7,
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"id": "529de80c-dd7f-45eb-b3c1-753b33d2bdb7",
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"metadata": {
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"editable": true,
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@ -52,7 +52,23 @@
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},
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"tags": []
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},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1b0e443e40374de8adfd2915d57fca0b",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(VBox(children=(Dropdown(description='Région', options=(('Ville de Genève', 'geneva'), ('Région …"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"places = {\n",
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" \"geneva\": {\"shorthand\": \"geneva\", \"friendly\": \"Ville de Genève\", \"label\": \"Geneva, Switzerland\", \"cars\": 44_000, \"node\": 2809410},\n",
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@ -165,7 +181,7 @@
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" \n",
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" counter = 0\n",
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" nodes = [place[\"node\"]];\n",
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" shoteach = vehicles / 10;\n",
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" shoteach = vehicles / 5;\n",
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" \n",
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" edges = edges_reset.copy()\n",
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" \n",
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@ -225,7 +241,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 8,
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"id": "f4440649-731e-421e-b43d-17576bb8ad3f",
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"metadata": {
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"editable": true,
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@ -234,11 +250,27 @@
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},
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"tags": []
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},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "fd849bc25b9b422398025911f0ae564a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(VBox(children=(HBox(children=(IntSlider(value=1, description='8800 véhic.', max=10, min=1, read…"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"w_slider = widgets.IntSlider(\n",
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" min=1,\n",
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" max=10,\n",
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" max=5,\n",
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" step=1,\n",
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" readout=False,\n",
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" description='0 véhic.',\n",
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@ -248,7 +280,7 @@
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"\n",
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"def compute(*ignore):\n",
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" \n",
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" shoteach = np.ceil(places[w_place.value][\"cars\"] * mots[\"car\"][\"ppv\"] / mots[w_mot.value][\"ppv\"]) / 10;\n",
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" shoteach = np.ceil(places[w_place.value][\"cars\"] * mots[\"car\"][\"ppv\"] / mots[w_mot.value][\"ppv\"]) / 5;\n",
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" w_slider.description = f\"{w_slider.value * shoteach:.0f} véhic.\"\n",
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" file = open(f\"./outputs/{places[w_place.value][\"shorthand\"]}/plot_{w_mot.value}_{w_slider.value}.svg\", \"rb\")\n",
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" read = file.read()\n",
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@ -265,10 +297,32 @@
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},
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{
|
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"cell_type": "code",
|
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"execution_count": null,
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"execution_count": 9,
|
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"id": "adf24613-a4ab-433f-8a1d-6e1d168163aa",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/html": [
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"\n",
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"<style>\n",
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"h1, p {\n",
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" display: block !important;\n",
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" max-width: 700px !important;\n",
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" margin: 0 auto !important;\n",
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" overflow: hidden !important; /*chrome bug patch*/\n",
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"}\n",
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"</style>\n"
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],
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"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
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]
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},
|
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"execution_count": 9,
|
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"metadata": {},
|
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"output_type": "execute_result"
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}
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],
|
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"source": [
|
||||
"from IPython.core.display import HTML\n",
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"HTML(\"\"\"\n",
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