mirror of
https://github.com/gsi-upm/soil
synced 2024-10-31 17:11:42 +00:00
356 lines
10 KiB
Plaintext
356 lines
10 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "7641396c-a602-477e-bf03-09e1191ff549",
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload"
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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": "4f12285c-78db-4ee8-b9c6-7799d34f10f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"%autoreload 1"
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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": "7710bb03-0cb9-413a-a407-fe48855ff917",
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"metadata": {},
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"outputs": [],
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"source": [
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"%aimport markov_sim"
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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": 10,
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"id": "2dffca0f-da9e-4f69-ac43-7afe52ad2d32",
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"metadata": {},
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"outputs": [],
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"source": [
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"%aimport soil\n",
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"%aimport soil.visualization"
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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": 11,
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"id": "12871006-70ca-4c6f-8a3e-0aae1d0bce31",
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"metadata": {},
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"outputs": [],
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"source": [
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"G = markov_sim.load_city_graph(\"Chamberi, Madrid\", network_type=\"drive\")"
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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": 12,
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"id": "31e96cc5-b703-4d2a-a006-7b9a2cedc365",
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"metadata": {},
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"outputs": [],
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"source": [
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"# env = markov_sim.CityEnv(G=G, n_assets=20, side=10, max_weight=1, seed=10)"
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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": 13,
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"id": "5e070b36-0ba6-4780-8fd4-3c72fa3bb240",
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"metadata": {},
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"outputs": [],
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"source": [
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"# for i in range(2):\n",
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"# env.step()"
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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": 35,
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"id": "56f8b997-65b0-431d-9517-b93edb1cfcd8",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/j/.cache/pypoetry/virtualenvs/soil-cCX5yKRx-py3.10/lib/python3.10/site-packages/osmnx/plot.py:955: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
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" plt.show()\n",
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"/home/j/.cache/pypoetry/virtualenvs/soil-cCX5yKRx-py3.10/lib/python3.10/site-packages/osmnx/plot.py:955: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
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" plt.show()\n"
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]
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},
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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": "86e45bd44e434674b11805fd94e98414",
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"version_major": 2,
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"version_minor": 0
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},
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"text/html": [
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"Cannot show widget. You probably want to rerun the code cell above (<i>Click in the code cell, and press Shift+Enter <kbd>⇧</kbd>+<kbd>↩</kbd></i>)."
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],
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"text/plain": [
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"Cannot show ipywidgets in text"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from soil.visualization import JupyterViz, GeoNetworkDrawer, Controller\n",
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"from soil import visualization\n",
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"from matplotlib import colors\n",
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"from matplotlib import colormaps\n",
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"plasma = colormaps.get_cmap('plasma')\n",
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"model_params = {\n",
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" \"n_assets\": {\n",
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" \"type\": \"SliderInt\",\n",
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" \"value\": 100,\n",
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" \"label\": \"Number of assets:\",\n",
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" \"min\": 1,\n",
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" \"max\": 1000,\n",
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" \"step\": 1,\n",
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" },\n",
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" \"max_weight\": {\n",
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" \"type\": \"SliderInt\",\n",
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" \"value\": 3,\n",
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" \"label\": \"Maximum edge weight:\",\n",
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" \"min\": 1,\n",
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" \"max\": 20,\n",
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" \"step\": 1,\n",
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" },\n",
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" \"ratio_lazy\": {\n",
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" \"type\": \"SliderFloat\",\n",
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" \"value\": 0,\n",
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" \"label\": \"Ratio of lazy agents (they prefer shorter streets):\",\n",
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" \"min\": 0,\n",
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" \"max\": 1,\n",
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" \"step\": 0.05,\n",
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" },\n",
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" \"side\": {\n",
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" \"type\": \"SliderInt\",\n",
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" \"value\": 10,\n",
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" \"label\": \"Size of the side:\",\n",
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" \"min\": 2,\n",
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" \"max\": 20,\n",
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" \"step\": 1,\n",
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" },\n",
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" \"gradual_move\": {\n",
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" \"type\": \"Checkbox\",\n",
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" \"value\": True,\n",
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" \"label\": \"Use gradual movement\",\n",
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" }, \n",
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" \"lockstep\": {\n",
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" \"type\": \"Checkbox\",\n",
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" \"value\": True,\n",
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" \"label\": \"Run in locksteps\",\n",
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" },\n",
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" \"G\": G,\n",
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" # \"width\": 10,\n",
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"}\n",
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"\n",
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"def colorize(d):\n",
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" # print(d)\n",
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" if any(a.waiting for a in d):\n",
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" return 'red'\n",
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" else:\n",
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" return 'blue'\n",
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"\n",
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"def network_portrayal(graph, spring=True):\n",
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" global pos, l\n",
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" node_size = [10*(len(node[1][\"agent\"])) for node in graph.nodes(data=True)]\n",
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" node_color = [colorize(d[\"agent\"]) for (k, d) in graph.nodes(data=True)]\n",
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" # pos = {node: (d[\"x\"], d[\"y\"]) for node, d in graph.nodes(data=True)}\n",
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" edge_width = [graph.edges[k]['travel_time']/100 for k in graph.edges]\n",
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" # print(edge_width)\n",
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" weights = [graph.edges[k]['occupation'] for k in graph.edges]\n",
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" norm = colors.Normalize(vmin=0, vmax=max(weights))\n",
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" color = plasma(norm(weights))\n",
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" # print(color)\n",
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" return dict(node_size=node_size, node_color=node_color, edge_linewidth=edge_width, edge_color=color)\n",
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"\n",
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"page = visualization.JupyterViz(\n",
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" markov_sim.CityEnv,\n",
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" model_params,\n",
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" measures=[\"NodeGini\", \"EdgeGini\", \"EdgeOccupation\"],\n",
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" name=\"City Environment\",\n",
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" space_drawer=GeoNetworkDrawer,\n",
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" agent_portrayal=network_portrayal,\n",
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" columns=3,\n",
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")\n",
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"# This is required to render the visualization in the Jupyter notebook\n",
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"page"
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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": 39,
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"id": "70da18d7-66bd-4710-89a6-aca14707c56e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>NodeGini</th>\n",
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" <th>EdgeGini</th>\n",
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" <th>EdgeOccupation</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.866567</td>\n",
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" <td>0.927276</td>\n",
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" <td>0.087624</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0.866567</td>\n",
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" <td>0.933494</td>\n",
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" <td>0.081301</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0.863867</td>\n",
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" <td>0.933163</td>\n",
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" <td>0.078591</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>0.866567</td>\n",
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" <td>0.929943</td>\n",
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" <td>0.084914</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0.869433</td>\n",
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" <td>0.934949</td>\n",
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" <td>0.076784</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>127</th>\n",
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" <td>0.880367</td>\n",
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" <td>0.934185</td>\n",
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" <td>0.075881</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>128</th>\n",
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" <td>0.881400</td>\n",
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" <td>0.933038</td>\n",
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" <td>0.078591</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>129</th>\n",
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" <td>0.881400</td>\n",
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" <td>0.936299</td>\n",
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" <td>0.078591</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>130</th>\n",
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" <td>0.881400</td>\n",
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" <td>0.929784</td>\n",
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" <td>0.086721</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>131</th>\n",
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" <td>0.876733</td>\n",
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" <td>0.932746</td>\n",
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" <td>0.082204</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>132 rows × 3 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" NodeGini EdgeGini EdgeOccupation\n",
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"0 0.866567 0.927276 0.087624\n",
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"1 0.866567 0.933494 0.081301\n",
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"2 0.863867 0.933163 0.078591\n",
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"3 0.866567 0.929943 0.084914\n",
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"4 0.869433 0.934949 0.076784\n",
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".. ... ... ...\n",
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"127 0.880367 0.934185 0.075881\n",
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"128 0.881400 0.933038 0.078591\n",
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"129 0.881400 0.936299 0.078591\n",
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"130 0.881400 0.929784 0.086721\n",
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"131 0.876733 0.932746 0.082204\n",
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"\n",
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"[132 rows x 3 columns]"
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]
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},
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"execution_count": 39,
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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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"page.controller.model.datacollector.get_model_vars_dataframe()"
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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": "d9a7d3c8-2f87-47d5-8d27-a7387ea3457d",
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|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3 (ipykernel)",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.10.12"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|