{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Geneva Heat Demand\nIn this tutorial, we will learn how to display Geneva heat demand data in 3D.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first import geneva's 3D buildings datasets\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ipybuilding.datasets import geneva\n\nbase_path, facade_path, roof_path = geneva.load_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then load them with geopandas\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import geopandas\n\nbases = geopandas.read_file(base_path)\nfacade = geopandas.read_file(facade_path)\ntoits = geopandas.read_file(roof_path)\n\nbases.columns = [s.lower() for s in bases.columns]\nfacade.columns = [s.lower() for s in facade.columns]\ntoits.columns = [s.lower() for s in toits.columns]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next step is to load Geneva's heat demand data\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n\nDATA_URL = \"https://raw.githubusercontent.com/vferat/ipybuilding/main/tutorials/Geneva/SCANE_INDICE_DERNIER.csv\"\nheat_demand = pd.read_csv(DATA_URL, sep=';')\nheat_demand.columns = [s.lower() for s in heat_demand.columns]\nheat_demand.egid = heat_demand.egid.astype(int)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then merge both datasets based on bulding's EGID\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bases = bases.merge(heat_demand, on='egid')\nfacade = facade.merge(heat_demand, on='egid')\ntoits = toits.merge(heat_demand, on='egid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create a new column to store buidling colors based on heat demand indice:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib as mpl\n\nnorm = mpl.colors.Normalize(vmin=1, vmax=toits.indice.quantile(0.95))\ncmap = mpl.cm.get_cmap('RdYlGn_r')\n\ncmap_val = cmap(norm(toits.indice)) \nbases['color'] = (cmap_val* 255).astype(int).tolist()\n\ncmap_val = cmap(norm(toits.indice)) \ntoits['color'] = (cmap_val* 255).astype(int).tolist()\n\ncmap_val = cmap(norm(facade.indice)) \nfacade['color'] = (cmap_val* 255).astype(int).tolist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Only display a subset of building based on initial view\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def filtered_buildings_geom(bbox):\n return (bases.cx[bbox[0]: bbox[2], bbox[1]:bbox[3]],\n facade.cx[bbox[0]: bbox[2], bbox[1]:bbox[3]],\n toits.cx[bbox[0]: bbox[2], bbox[1]:bbox[3]])\n\nstart_point = (6.1402,46.208742)\ndelta = 0.001\nbbox = (start_point[0] - delta, start_point[1]-delta, start_point[0] + delta, start_point[1]+delta) \n\nf_base, f_face, f_toit = filtered_buildings_geom(bbox)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now plot our data thanks to pydeck\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\nimport pydeck\n\nMAPBOX_API_KEY = os.environ[\"MAPBOX_API_KEY\"]\n\ncoords = f_base.centroid.map(lambda p: [p.x, p.y])\nINITIAL_VIEW_STATE = pydeck.data_utils.compute_view(coords.values)\nINITIAL_VIEW_STATE.zoom = 16\nINITIAL_VIEW_STATE.max_zoom = 16\n\n# AWS Open Data Terrain Tiles\nTERRAIN_IMAGE = \"https://s3.amazonaws.com/elevation-tiles-prod/terrarium/{z}/{x}/{y}.png\"\n\n# Define how to parse elevation tiles\nELEVATION_DECODER = {\"rScaler\": 256, \"gScaler\": 1, \"bScaler\": 1 / 256, \"offset\": -32768}\nSURFACE_IMAGE = f\"https://api.mapbox.com/v4/mapbox.satellite/{{z}}/{{x}}/{{y}}@2x.png?access_token={MAPBOX_API_KEY}\"\n\n\nterrain_layer = pydeck.Layer(\n \"TerrainLayer\", elevation_decoder=ELEVATION_DECODER, texture=SURFACE_IMAGE, elevation_data=TERRAIN_IMAGE,\n # light_settings=lights\n\n)\n\n\nbases_layer = pydeck.Layer('GeoJsonLayer', \n data=f_base, get_fill_color=[255,100,255], pickable=True\n )\nfacade_layer = pydeck.Layer('GeoJsonLayer', \n data=f_face, \n get_fill_color='color', \n line_width_scale=0.1,\n pickable=True,\n filled=True,\n wireframe=True,\n )\ntoits_layer = pydeck.Layer('GeoJsonLayer', \n data=f_toit, \n get_fill_color='color', \n pickable=True,\n stroked=False,\n filled=True,\n wireframe=True,\n )\n\nr = pydeck.Deck(\n layers=[\n terrain_layer,\n bases_layer, \n facade_layer, \n toits_layer\n ],\n initial_view_state=INITIAL_VIEW_STATE,\n map_style='satellite',\n tooltip={\n 'text': 'IDC: {indice} MJ/m2\u00b7a'\n }\n)\nr.to_html()\n\n# .. image:: download.png" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 0 }