notebook + environment + stack
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3 changed files with 354 additions and 0 deletions
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Dockerfile
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Dockerfile
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FROM continuumio/miniconda3 AS base-deps
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WORKDIR /root/home/
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ADD . /root/home/remplis-ta-ville
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RUN apt update
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RUN cd remplis-ta-ville && conda env create && conda init
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FROM base-deps
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EXPOSE 8866
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ENTRYPOINT cd remplis-ta-ville && conda run -n gis voila remplis-ta-ville.ipynb --no-browser --Voila.ip=0.0.0.0
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14
environment.yml
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environment.yml
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name: gis
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channels:
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- conda-forge
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- defaults
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dependencies:
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- osmnx
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- geopandas
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- networkx
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- pandas
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- numpy
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- jupyter
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- ipython
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- ipywidgets
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- voila
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330
remplis-ta-ville.ipynb
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remplis-ta-ville.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4af40662-b782-4904-ba32-224960d16d38",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"# Remplis ta ville"
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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": "e2abc871-bf82-4cf6-993e-cd904c7e7468",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import ipywidgets as widgets\n",
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"from IPython import display\n",
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"import os"
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]
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},
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{
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"cell_type": "markdown",
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"id": "caf2f2f9-05de-4ea7-8efb-55e5eeaffc59",
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"metadata": {},
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"source": [
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"Trouves-tu que ta région n'est pas assez encombrée par le trafic de transit, qu'elle n'est pas assez bruyante, que l'air et les eaux n'y sont pas assez pollués ? Eh bien, tu te trouves sur la bonne page ! Choisis ta région parmi la liste et laisse-moi te montrer ce que représente la quantité de trafic automobile supplémentaire estimée par l'Office Fédéral des Routes (OFROU) engendrée par l'extension de la largeur des autoroutes."
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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": 2,
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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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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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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": "a65109866b884b0c8bde3165b2fb39fb",
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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": 2,
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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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" \"coppet\": {\"shorthand\": \"coppet\", \"friendly\": \"Région de Coppet\", \"label\": [\"Commugny, Switzerland\",\"Coppet, Switzerland\",\"Founex, Switzerland\"], \"cars\": 8_800, \"node\": 6514705484},\n",
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" \"nyon\": {\"shorthand\": \"nyon\", \"friendly\": \"Ville de Nyon\", \"label\": \"Nyon, Switzerland\", \"cars\": 7_000, \"node\": 311240719},\n",
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"}\n",
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"\n",
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"mots = {\n",
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" \"car\": {\"shorthand\": \"car\", \"friendly\": \"Voiture\", \"ppv\": 1.5, \"length\": 7},\n",
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" \"bus\": {\"shorthand\": \"bus\", \"friendly\": \"Bus\", \"ppv\": 132, \"length\": 21},\n",
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" \"bike\": {\"shorthand\": \"bike\", \"friendly\": \"Vélo\",\"ppv\": 1, \"length\": 0.5},\n",
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"}\n",
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"\n",
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"w_place = widgets.Dropdown(\n",
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" options=[(places[place][\"friendly\"],places[place][\"shorthand\"]) for place in places.keys()],\n",
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" description='Région',\n",
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")\n",
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"\n",
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"w_mot = widgets.Dropdown(\n",
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" options=[(mots[mot][\"friendly\"],mots[mot][\"shorthand\"]) for mot in mots.keys()],\n",
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" description='Véhicule',\n",
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")\n",
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"\n",
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"w_submit = widgets.Button(\n",
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" description='Remplir !',\n",
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" disabled=False\n",
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")\n",
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"\n",
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"w_progress = widgets.IntProgress(\n",
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" min=0,\n",
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" description=' ',\n",
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" orientation='horizontal'\n",
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")\n",
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"\n",
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"def simulate(*ignore):\n",
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" \n",
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" import geopandas as gpd\n",
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" import pandas as pd\n",
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" import networkx as nx\n",
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" import osmnx as ox\n",
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" import numpy as np\n",
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" \n",
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" place = places[w_place.value]\n",
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" mot = mots[w_mot.value]\n",
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" \n",
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" people = place[\"cars\"] * mots[\"car\"][\"ppv\"]\n",
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" \n",
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" vehicles = np.ceil(people / mot[\"ppv\"])\n",
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"\n",
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" # Download regional roads graph and convert it to GeoDataFrame\n",
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" \n",
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" utw = [\"name\",\"highway\",\"access\",\"length\",\"lanes\",\"lanes:forward\",\"lanes:backward\",\"lanes:psv\",\"lanes:psv:forward\",\"lanes:psv:backward\",\"lanes:bus\",\"lanes:bus:forward\",\"lanes:bus:backward\",\"motor_vehicle\"]\n",
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" ox.settings.useful_tags_way=utw\n",
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"\n",
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" w_progress.description = \"Chargement du graphe\"\n",
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" w_progress.value = 0\n",
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" G = ox.graph_from_place(place[\"label\"], network_type=\"drive\")\n",
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" \n",
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" # Estimate and clean:\n",
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" # \n",
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" # * Lanes dedicated to motorized individual transport\n",
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" # * Lanes length\n",
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" # * Lanes cars capacity\n",
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"\n",
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" w_progress.description = \"Annotation du graphe\"\n",
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" edges = ox.graph_to_gdfs(G, nodes=False, edges=True)\n",
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"\n",
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" w_progress.description = \"Estimation des capacités\"\n",
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" edges[\"lanes:forward\"] = edges[\"lanes:forward\"].fillna(0) if \"lanes:forward\" in edges.columns else 0\n",
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" edges[\"lanes:forward\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes:forward\"]).astype(int)), axis=1)\n",
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" edges[\"lanes:backward\"] = edges[\"lanes:backward\"].fillna(0) if \"lanes:backward\" in edges.columns else 0\n",
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" edges[\"lanes:backward\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes:backward\"]).astype(int)), axis=1)\n",
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" edges[\"lanes\"] = edges[\"lanes\"].fillna(0) if \"lanes\" in edges.columns else 0\n",
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" edges[\"lanes\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes\"]).astype(int)), axis=1)\n",
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"\n",
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" edges[\"lanes:psv\"] = edges[\"lanes:psv\"].fillna(0) if \"lanes:psv\" in edges.columns else 0\n",
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" edges[\"lanes:psv\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes:psv\"]).astype(int)), axis=1)\n",
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" edges[\"lanes:psv:forward\"] = edges[\"lanes:psv:forward\"].fillna(0) if \"lanes:psv:forward\" in edges.columns else 0\n",
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" edges[\"lanes:psv:forward\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes:psv:forward\"]).astype(int)), axis=1)\n",
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" edges[\"lanes:psv:backward\"] = edges[\"lanes:psv:backward\"].fillna(0) if \"lanes:psv:backward\" in edges.columns else 0\n",
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" edges[\"lanes:psv:backward\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes:psv:backward\"]).astype(int)), axis=1)\n",
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"\n",
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" edges[\"lanes:bus\"] = edges[\"lanes:bus\"].fillna(0) if \"lanes:bus\" in edges.columns else 0\n",
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" edges[\"lanes:bus\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes:bus\"]).astype(int)), axis=1)\n",
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" edges[\"lanes:bus:forward\"] = edges[\"lanes:bus:forward\"].fillna(0) if \"lanes:bus:forward\" in edges.columns else 0\n",
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" edges[\"lanes:bus:forward\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes:bus:forward\"]).astype(int)), axis=1)\n",
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" edges[\"lanes:bus:backward\"] = edges[\"lanes:bus:backward\"].fillna(0) if \"lanes:bus:backward\" in edges.columns else 0\n",
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" edges[\"lanes:bus:backward\"] = edges.apply(lambda row: np.min(np.array(row[\"lanes:bus:backward\"]).astype(int)), axis=1)\n",
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"\n",
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" edges[\"lanes:mit\"] = edges.apply(lambda row: np.max([row[\"lanes\"],row[\"lanes:forward\"]+row[\"lanes:backward\"],2]) - np.max([row[\"lanes:psv\"],row[\"lanes:psv:forward\"]+row[\"lanes:psv:backward\"],row[\"lanes:bus:backward\"]]), axis=1)\n",
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"\n",
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" edges[\"highway\"] = edges[\"highway\"] if \"highway\" in edges.columns else \"\"\n",
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" edges[\"motor_vehicle\"] = edges[\"motor_vehicle\"] if \"motor_vehicle\" in edges.columns else \"\"\n",
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" edges[\"access\"] = edges[\"access\"] if \"access\" in edges.columns else \"\"\n",
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" \n",
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" edges[\"lanes:mit\"] = edges.apply(lambda row: (0 if np.isin(row[\"highway\"],[\"residential\",\"living_street\"]).any() else row[\"lanes:mit\"]), axis=1)\n",
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" edges[\"lanes:mit\"] = edges.apply(lambda row: (0 if np.isin(row[\"motor_vehicle\"],[\"no\",\"private\",\"destination\",\"agricultural\",\"forestry\",\"delivery\"]).any() else row[\"lanes:mit\"]), axis=1)\n",
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" edges[\"lanes:mit\"] = edges.apply(lambda row: (0 if np.isin(row[\"access\"], [\"no\",\"destination\"]).any() else row[\"lanes:mit\"]), axis=1)\n",
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"\n",
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" edges[\"length\"] = edges[\"length\"].fillna(0)\n",
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" \n",
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" edges[\"capacity\"] = edges.apply(lambda row: np.floor(row[\"length\"] * row[\"lanes:mit\"] / mot[\"length\"]), axis=1)\n",
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" \n",
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" edges_reset = edges.copy()\n",
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" \n",
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" # print(f\"Occupancy : {vehicles / sum(edges[\"capacity\"])}\")\n",
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"\n",
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" # Colorize the plotted roads graph:\n",
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"\n",
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" w_progress.description = \"Remplissage des rues\"\n",
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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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" \n",
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" edges = edges_reset.copy()\n",
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" \n",
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" edges_copy = pd.DataFrame(index=edges.index)\n",
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" edges_copy[\"occupancy\"] = 0\n",
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" edges_copy[\"capacity\"] = edges[\"capacity\"]\n",
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" lastshot = 0\n",
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"\n",
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" w_progress.max = vehicles\n",
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" \n",
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" while counter < vehicles and nodes:\n",
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" \n",
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" idx = nodes.pop(0)\n",
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" if idx in edges.index.get_level_values(0).values:\n",
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" nodedges = edges.loc[idx]\n",
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" nodedges_copy = edges_copy.loc[idx]\n",
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" for i,r in nodedges.iterrows():\n",
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" #print(nodes,counter,r[\"capacity\"],nodedges_copy.loc[i,\"occupancy\"],edges.shape[0])\n",
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" if counter >= vehicles:\n",
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" break\n",
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" \n",
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" if not nodedges_copy.loc[i,\"occupancy\"]:\n",
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" nodedges_copy.loc[i,\"occupancy\"] = r[\"capacity\"]\n",
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" counter += r[\"capacity\"]\n",
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" w_progress.value = counter\n",
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" nodes.append(i[0])\n",
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" edges.drop(idx, inplace=True)\n",
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" if counter // shoteach > lastshot:\n",
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" lastshot += 1\n",
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" \n",
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" edges_copy[\"color\"] = edges_copy.apply(lambda row: 'darkred' if row[\"occupancy\"] else 'darkseagreen', axis=1)\n",
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" edges_copy[\"color\"] = edges_copy.apply(lambda row: 'darkgray' if not row[\"capacity\"] else row[\"color\"], axis=1)\n",
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" \n",
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" colors = edges_copy.loc[:,\"color\"].to_numpy()\n",
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" \n",
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" fig, ax = ox.plot_graph(G, edge_color=colors, node_size=15, edge_linewidth=5, filepath=f\"./outputs/{place[\"shorthand\"]}/plot_{mot[\"shorthand\"]}_{lastshot}.svg\", save=True, show=False, close=True)\n",
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" \n",
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" w_progress.description = \"Terminé\"\n",
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"\n",
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"w_submit.on_click(simulate)\n",
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"\n",
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"if os.environ.get('SERVER_SOFTWARE','jupyter').startswith('voila'):\n",
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" toolbar = widgets.HBox([widgets.VBox([w_place, w_mot])],layout=widgets.Layout(justify_content=\"center\"))\n",
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"else:\n",
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" toolbar = widgets.HBox([widgets.VBox([w_place, w_mot, w_submit, w_progress])],layout=widgets.Layout(justify_content=\"center\"))\n",
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"\n",
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"toolbar"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c16c39c7-c442-4460-97c6-318b01c357db",
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"metadata": {},
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"source": [
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"Pour te donner une idée concrète de la quantité que cela représente, je vais les placer dans ta région. Imagine-toi ta région vide de voitures. Maintenant, remplis les routes de ta région une par une jusqu'à atteindre l'augmentation de la quantité de voitures prédite par l'OFROU en cas d'extension des autoroutes. Les routes complètement remplies sont représentées en rouge, celles encore vides en vert. Les routes représentées en gris sont des routes essentiellement résidentielles, elles ne sont donc pas prises en compte dans le calcul."
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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": 3,
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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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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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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": "d7f46e8330ab475481525e195b871224",
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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, max=10, min=1),), layout=Layout(justify_conten…"
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]
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},
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"execution_count": 3,
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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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"slider = widgets.IntSlider(min=1, max=10, step=1)\n",
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"\n",
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"image = widgets.Image(format='svg+xml')\n",
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"\n",
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"def compute(*ignore):\n",
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" file = open(f\"./outputs/{places[w_place.value][\"shorthand\"]}/plot_{w_mot.value}_{slider.value}.svg\", \"rb\")\n",
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" read = file.read()\n",
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" image.value = read\n",
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"\n",
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"slider.observe(compute, 'value')\n",
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"\n",
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"widgets.HBox([widgets.VBox([widgets.HBox([slider], layout=widgets.Layout(justify_content=\"center\")), image], layout=widgets.Layout(max_width=\"700px\"))], layout=widgets.Layout(justify_content=\"center\"))"
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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": null,
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"id": "adf24613-a4ab-433f-8a1d-6e1d168163aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.core.display import HTML\n",
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"HTML(\"\"\"\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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"}\n",
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"</style>\n",
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"\"\"\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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},
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"voila": {
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"template": "lab",
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"theme": "dark"
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}
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},
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"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in a new issue