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mangecoeur/tessa_data_vizualisation.ipynb

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tessa_data_vizualisation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "knIH9zvkfLai",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# Filter view\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"executionInfo": {
"elapsed": 2175,
"status": "ok",
"timestamp": 1663413091697,
"user": {
"displayName": "Jon Chambers",
"userId": "14157858456610911265"
},
"user_tz": -120
},
"id": "7Zeofpmpj2rz",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# Libraries\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import ipywidgets as widgets\n",
"from math import pi\n",
"import numpy as np\n",
"import random\n",
"import requests\n",
"# import plotly.express as px\n",
"import pandas as pd\n",
"# import plotly.graph_objects as go\n",
"\n",
"# !pip install ipyleaflet\n",
"\n",
"# make plot with selection\n",
"import matplotlib\n",
"import folium\n",
"import ipyleaflet\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from requests.auth import HTTPBasicAuth\n",
"from matplotlib.patches import Circle, RegularPolygon\n",
"from matplotlib.path import Path\n",
"from matplotlib.projections.polar import PolarAxes\n",
"from matplotlib.projections import register_projection\n",
"from matplotlib.spines import Spine\n",
"from matplotlib.transforms import Affine2D\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"executionInfo": {
"elapsed": 13,
"status": "ok",
"timestamp": 1663413093464,
"user": {
"displayName": "Jon Chambers",
"userId": "14157858456610911265"
},
"user_tz": -120
},
"id": "Fg5n9J1xykfY",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"def radar_factory(num_vars, frame='circle'):\n",
" \"\"\"\n",
" Create a radar chart with `num_vars` axes.\n",
"\n",
" This function creates a RadarAxes projection and registers it.\n",
"\n",
" Parameters\n",
" ----------\n",
" num_vars : int\n",
" Number of variables for radar chart.\n",
" frame : {'circle', 'polygon'}\n",
" Shape of frame surrounding axes.\n",
"\n",
" \"\"\"\n",
" # calculate evenly-spaced axis angles\n",
" theta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)\n",
"\n",
" class RadarTransform(PolarAxes.PolarTransform):\n",
"\n",
" def transform_path_non_affine(self, path):\n",
" # Paths with non-unit interpolation steps correspond to gridlines,\n",
" # in which case we force interpolation (to defeat PolarTransform's\n",
" # autoconversion to circular arcs).\n",
" if path._interpolation_steps > 1:\n",
" path = path.interpolated(num_vars)\n",
" return Path(self.transform(path.vertices), path.codes)\n",
"\n",
" class RadarAxes(PolarAxes):\n",
"\n",
" name = 'radar'\n",
" # use 1 line segment to connect specified points\n",
" RESOLUTION = 1\n",
" PolarTransform = RadarTransform\n",
"\n",
" def __init__(self, *args, **kwargs):\n",
" super().__init__(*args, **kwargs)\n",
" # rotate plot such that the first axis is at the top\n",
" self.set_theta_zero_location('N')\n",
"\n",
" def fill(self, *args, closed=True, **kwargs):\n",
" \"\"\"Override fill so that line is closed by default\"\"\"\n",
" return super().fill(closed=closed, *args, **kwargs)\n",
"\n",
" def plot(self, *args, **kwargs):\n",
" \"\"\"Override plot so that line is closed by default\"\"\"\n",
" lines = super().plot(*args, **kwargs)\n",
" for line in lines:\n",
" self._close_line(line)\n",
"\n",
" def _close_line(self, line):\n",
" x, y = line.get_data()\n",
" # FIXME: markers at x[0], y[0] get doubled-up\n",
" if x[0] != x[-1]:\n",
" x = np.append(x, x[0])\n",
" y = np.append(y, y[0])\n",
" line.set_data(x, y)\n",
"\n",
" def set_varlabels(self, labels):\n",
" self.set_thetagrids(np.degrees(theta), labels)\n",
"\n",
" def _gen_axes_patch(self):\n",
" # The Axes patch must be centered at (0.5, 0.5) and of radius 0.5\n",
" # in axes coordinates.\n",
" if frame == 'circle':\n",
" return Circle((0.5, 0.5), 0.5)\n",
" elif frame == 'polygon':\n",
" return RegularPolygon((0.5, 0.5), num_vars,\n",
" radius=.5, edgecolor=\"k\")\n",
" else:\n",
" raise ValueError(\"Unknown value for 'frame': %s\" % frame)\n",
"\n",
" def _gen_axes_spines(self):\n",
" if frame == 'circle':\n",
" return super()._gen_axes_spines()\n",
" elif frame == 'polygon':\n",
" # spine_type must be 'left'/'right'/'top'/'bottom'/'circle'.\n",
" spine = Spine(axes=self,\n",
" spine_type='circle',\n",
" path=Path.unit_regular_polygon(num_vars))\n",
" # unit_regular_polygon gives a polygon of radius 1 centered at\n",
" # (0, 0) but we want a polygon of radius 0.5 centered at (0.5,\n",
" # 0.5) in axes coordinates.\n",
" spine.set_transform(Affine2D().scale(.5).translate(.5, .5)\n",
" + self.transAxes)\n",
" return {'polar': spine}\n",
" else:\n",
" raise ValueError(\"Unknown value for 'frame': %s\" % frame)\n",
"\n",
" register_projection(RadarAxes)\n",
" return theta"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"executionInfo": {
"elapsed": 12,
"status": "ok",
"timestamp": 1663413093465,
"user": {
"displayName": "Jon Chambers",
"userId": "14157858456610911265"
},
"user_tz": -120
},
"id": "LHYaAi73v8E8",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"def extract_statistics(df:pd.DataFrame):\n",
" stat_dict = {}\n",
" stat_dict[\"mean_q_h_kwh_m2\"] = df[\"q_h_kwh_m2\"].mean()\n",
" stat_dict[\"mean_ww_h_kwh_m2\"] = df[\"q_ww_kwh_m2\"].mean()\n",
" stat_dict[\"sum_q_h_kwh\"] = df[\"q_h_kwh\"].sum()\n",
" stat_dict[\"sum_q_ww_kwh\"] = df[\"q_ww_kwh\"].sum()\n",
" stat_dict[\"sum_co2eq_hww_kg\"] = df[\"co2eq_hww_kg\"].sum()\n",
" stat_dict[\"co2eq_kg_per_kwh\"] = df[\"q_h_kwh\"].sum()/df[\"co2eq_hww_kg\"].sum()\n",
" stat_dict[\"nb_buildings\"] = len(df)\n",
"\n",
" return stat_dict\n",
"\n",
"\n",
"def generate_filtered(df):\n",
" filters = {\n",
" \"1970 and newer\": ((df[\"is_heated\"]==True) & (~df[\"const_period\"].str.contains(\"1961-1970\"))),\n",
" \"1980 and newer\": ((df[\"is_heated\"]==True) & (~df[\"const_period\"].str.contains(\"1961-1970\")) & (~df[\"const_period\"].str.contains(\"1971-1980\"))),\n",
" \"1990 and newer\": ((df[\"is_heated\"]==True) & (~df[\"const_period\"].str.contains(\"1961-1970\")) & (~df[\"const_period\"].str.contains(\"1971-1980\")) & \n",
" (~df[\"const_period\"].str.contains(\"1981-1990\"))),\n",
" \"q_hww > 2000\": ((df[\"is_heated\"]==True) & (df[\"q_hww_kwh\"] > 2000)),\n",
" \"co2 > 2000\": ((df[\"is_heated\"]==True) & (df[\"co2eq_hww_kg\"] > 2000))\n",
" }\n",
"\n",
" frames = {key: df[filter] for key, filter in filters.items()}\n",
"\n",
" stats = {key:extract_statistics(frame) for key, frame in frames.items()}\n",
" stats_df = pd.DataFrame(stats).T\n",
" norm_df = stats_df / stats_df.iloc[0]\n",
"\n",
" return stats_df, norm_df, frames\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"executionInfo": {
"elapsed": 270,
"status": "ok",
"timestamp": 1663413711168,
"user": {
"displayName": "Jon Chambers",
"userId": "14157858456610911265"
},
"user_tz": -120
},
"id": "Erx48fHO_vjy",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"def draw_one(df_norm):\n",
" global out\n",
" N = len(df_norm.columns)\n",
"\n",
" theta = radar_factory(N, frame='polygon')\n",
"\n",
" spoke_labels = df_norm.columns\n",
" with out:\n",
" out.clear_output()\n",
" fig, axs = plt.subplots(figsize=(9, 9), nrows=1, ncols=1,\n",
" subplot_kw=dict(projection='radar'))\n",
" fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)\n",
"\n",
" colors = ['b', 'r', 'g', 'm', 'y']\n",
" # Plot the four cases from the example data on separate axes\n",
" ax = axs\n",
" ax.set_rgrids([0.2, 0.4, 0.6, 0.8])\n",
" # ax.set_title('TITLE', weight='bold', size='medium', position=(0.5, 1.1),\n",
" # horizontalalignment='center', verticalalignment='center')\n",
"\n",
" n_to_plot = min(5, len(df_norm))\n",
" df_to_plot = df_norm.iloc[:n_to_plot].copy()\n",
" alphas = np.linspace(0.1, 0.25, n_to_plot)\n",
" for i, ((_, series), color, alpha) in enumerate(zip(df_to_plot.iterrows(), colors, alphas)):\n",
" ax.plot(theta, series.values, color=color, alpha=alpha)\n",
" \n",
" ax.fill(theta, series.values, facecolor=color, alpha=alpha, label=df_norm.index[i])\n",
" ax.set_varlabels(spoke_labels)\n",
" if len(df_norm) > 0:\n",
" ax.legend()\n",
"\n",
" plt.show()\n",
"\n",
"def load_next():\n",
" \n",
" global stats_df\n",
" global idx\n",
" if idx < len(stats_df):\n",
" row = stats_df.iloc[idx].values\n",
" idx = idx + 1\n",
" return row\n",
" else:\n",
" return None\n",
" \n",
"# def append_latest_data():\n",
" \n",
"# global stats_df\n",
"# global df_norm\n",
"# newData = load_next()\n",
"# if newData is None:\n",
"# return\n",
"\n",
"# firstData = stats_df.iloc[0].values\n",
"# normNewData = [newData[i]/firstData[i] for i in range(len(stats_df.columns))]\n",
"# normNewDf = pd.DataFrame([normNewData], columns=stats_df.columns)\n",
"\n",
"# df_norm = pd.concat((df_norm, normNewDf))\n",
"# df_norm.index = stats_df.index[:len(df_norm)]\n",
"\n",
"def next_button_clicked(event):\n",
" # global stats_df\n",
" global norm_df\n",
" global idx\n",
" global heated\n",
" global frames\n",
" if idx < len(norm_df):\n",
" # row = stats_df.iloc[idx].values\n",
" heated.update(frames[norm_df.index[idx]])\n",
" idx = idx + 1\n",
" # append_latest_data()\n",
" # global df_norm\n",
" draw_one(norm_df.iloc[:idx])\n",
"\n",
"def clear_button_clicked(event):\n",
" global idx\n",
" idx = 0\n",
" df_norm = pd.DataFrame(columns=['mean_q_h_kwh_m2', 'mean_ww_h_kwh_m2', 'sum_q_h_kwh', 'sum_q_ww_kwh',\n",
" 'sum_co2eq_hww_kg', 'co2eq_kg_per_kwh', 'nb_buildings'])\n",
" draw_one(df_norm)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"executionInfo": {
"elapsed": 363,
"status": "ok",
"timestamp": 1663413093819,
"user": {
"displayName": "Jon Chambers",
"userId": "14157858456610911265"
},
"user_tz": -120
},
"id": "SFmKdw_Z7A0R",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
"\n",
"from ipyleaflet import (basemap_to_tiles, basemaps, Layer, LayerGroup,\n",
" Map, GeoJSON, WMSLayer, TileLayer, MarkerCluster,\n",
" FullScreenControl, DrawControl, LayersControl, Marker, AwesomeIcon,\n",
" WidgetControl)\n",
"from ipywidgets import Layout, Widget, Image, VBox, Label\n",
"from traitlets import CaselessStrEnum, Unicode, Instance, observe, List, Dict, Bool, Float\n",
"\n",
"class BuildingPointsLayer(MarkerCluster):\n",
" # NOTE: could make these configurable\n",
" color_var = 'co2eq_hww_kg'\n",
"\n",
" fallback_color = (1.0, 1.0, 1.0)\n",
" cmap = Unicode(default_value='RdYlGn_r')\n",
" point_style = Dict(default_value={\n",
" \"radius\": 5,\n",
" \"fillOpacity\": 1.0,\n",
" \"weight\": 3,\n",
" })\n",
"\n",
" def __init__(self, buildings=None, **kwargs):\n",
" if buildings is None:\n",
" markers = []\n",
" else:\n",
" markers = self._transform_points(buildings)\n",
" super().__init__(\n",
" markers=markers, disable_clustering_at_zoom=16, **kwargs)\n",
"\n",
" @observe('buildings')\n",
" def _on_buildings_update(self, change):\n",
" self.update(change.new)\n",
"\n",
" def update(self, buildings):\n",
" if buildings is None:\n",
" self.markers = []\n",
" else:\n",
" self.markers = self._transform_points(buildings)\n",
"\n",
" def _transform_points(self, buildings):\n",
" if self.color_var is not None:\n",
" vals = buildings[self.color_var]\n",
" # vals = vals[np.isfinite(vals)]\n",
" min_val = vals.dropna().quantile(0.02)\n",
" max_val = vals.dropna().quantile(0.98)\n",
" norm = mpl.colors.Normalize(vmin=min_val, vmax=max_val)\n",
" # norm = mpl.colors.LogNorm(vmin=min_val, vmax=max_val)\n",
" cmap = mpl.cm.get_cmap('RdYlGn_r')\n",
" cmap_val = cmap(norm(vals))\n",
" else:\n",
" class DummyVal:\n",
" def __init__(self, color):\n",
" self.color = color\n",
" def __getitem__(self, item):\n",
" return self.color\n",
"\n",
" cmap_val = DummyVal(self.fallback_color)\n",
"\n",
" features = []\n",
" for i, (row) in enumerate(buildings[[self.color_var, 'point_geojson']].itertuples()):\n",
" color = mpl.colors.to_hex(cmap_val[i])\n",
" features.append({'id': row.Index,\n",
" 'type': 'Feature',\n",
" 'properties': {\n",
" 'style': {'color': color,\n",
" 'fillColor': color}\n",
" },\n",
" 'geometry': row.point_geojson}\n",
" )\n",
"\n",
" geojson = {'type': 'FeatureCollection',\n",
" 'features': features\n",
" }\n",
"\n",
" return [GeoJSON(data=geojson,\n",
" point_style=self.point_style, )]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"executionInfo": {
"elapsed": 5137,
"status": "ok",
"timestamp": 1663413098952,
"user": {
"displayName": "Jon Chambers",
"userId": "14157858456610911265"
},
"user_tz": -120
},
"id": "wUEuMlNnUGDQ",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# fetch data from Riniken\n",
"\n",
"code=\"https://api3.geo.admin.ch/rest/services/all/MapServer/identify?geometry=8.1,47.4,8.3,47.6&geometryFormat=geojson&geometryType=esriGeometryEnvelope&imageDisplay=500,600,96&lang=en&layers=all:ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill&returnGeometry=true&tolerance=0&sr=4326\"\n",
"\n",
"text=requests.get(code)\n",
"data=pd.DataFrame.from_dict(text.json()[\"results\"])\n",
"properties=pd.DataFrame(data[\"properties\"].to_dict()).T\n",
"superData=data.merge(properties,left_index=True,right_index=True)\n",
"\n",
"community=\"Riniken\"\n",
"\n",
"gemeindeKoordinaten=pd.DataFrame(superData.loc[superData[\"gemname\"]==community,\"geometry\"].to_dict()).T[\"coordinates\"].values[0][0][0] # this line does not work\n",
"\n",
"#switch long lat\n",
"#gemeindeKoordinaten=[[x[0],x[0]] for x in gemeindeKoordinaten]\n",
"\n",
"polygon= {\n",
" \"id\": 1,\n",
" \"type\": \"Feature\",\n",
" \"properties\": {},\n",
" \"geometry\": {\n",
" \"type\": \"Polygon\",\n",
" \"coordinates\": [gemeindeKoordinaten]\n",
" }\n",
"}\n",
"\n",
"\n",
"url = 'https://edelweiss.unige.ch/api/v1/buildings/from_polygon/'\n",
"headers = {'content-type': 'application/json'}\n",
"r = requests.post(url, headers=headers, auth=HTTPBasicAuth('tessa-demo', 'password-to-be-provided'), json=polygon)\n",
"\n",
"data = pd.DataFrame(r.json())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"executionInfo": {
"elapsed": 371,
"status": "ok",
"timestamp": 1663413192460,
"user": {
"displayName": "Jon Chambers",
"userId": "14157858456610911265"
},
"user_tz": -120
},
"id": "mkAClkXszs-A",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"stats_df, norm_df, frames = generate_filtered(data)\n",
"df_norm_empty = pd.DataFrame(columns=['mean_q_h_kwh_m2', 'mean_ww_h_kwh_m2', 'sum_q_h_kwh', 'sum_q_ww_kwh',\n",
" 'sum_co2eq_hww_kg', 'co2eq_kg_per_kwh', 'nb_buildings'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"executionInfo": {
"elapsed": 700,
"status": "ok",
"timestamp": 1663413574356,
"user": {
"displayName": "Jon Chambers",
"userId": "14157858456610911265"
},
"user_tz": -120
},
"id": "3HkotAyDT2bh",
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"code=\"https://api3.geo.admin.ch/rest/services/all/MapServer/identify?geometry=8.1,47.4,8.3,47.6&geometryFormat=geojson&geometryType=esriGeometryEnvelope&imageDisplay=500,600,96&lang=en&layers=all:ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill&returnGeometry=true&tolerance=0&sr=4326\"\n",
"text=requests.get(code)\n",
"comm_data=pd.DataFrame.from_dict(text.json()[\"results\"])\n",
"properties=pd.DataFrame(comm_data[\"properties\"].to_dict()).T\n",
"superData=comm_data.merge(properties,left_index=True,right_index=True)\n",
"community=\"Riniken\"\n",
"gemeindeKoordinaten=pd.DataFrame(superData.loc[superData[\"gemname\"]==community,\"geometry\"].to_dict()).T[\"coordinates\"].values[0][0][0] # this line does not work\n",
"\n",
"#switch long lat\n",
"gemeindeKoordinaten=[[x[1],x[0]] for x in gemeindeKoordinaten]\n",
"\n",
"center = (47.48501, 8.20683)\n",
"\n",
"map_geo = ipyleaflet.Map(center=center, zoom=15)\n",
"\n",
"geo_j = ipyleaflet.GeoJSON(data=polygon,\n",
" # style_function=lambda x: {'fillColor': 'orange'}\n",
" )\n",
"\n",
"# marker = Marker(location=center, draggable=True)\n",
"# map_geo.add_layer(geo_j)\n",
"\n",
"communityLine = ipyleaflet.LayerGroup(name='Community', show=True,control=True)\n",
"# folium.PolyLine(gemeindeKoordinaten,color=\"red\").add_to(communityLine)\n",
"\n",
"map_geo.add(communityLine)\n",
"\n",
"plot_data=data\n",
"\n",
"# heated=ipyleaflet.LayerGroup(name='Heated houses', show=True,control=True)\n",
"heated = BuildingPointsLayer(plot_data)\n",
"\n",
"map_geo.add(heated);\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 553,
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\n",
"text/plain": "<Figure size 648x648 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
}
},
"d07ba82e99c848579c75c5d3964858e6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ButtonModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ButtonModel",
"_view_count": null,
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