From 4459ad5a73af304bc4baf5abe0001d26190b3bff Mon Sep 17 00:00:00 2001 From: anuShrestha83 <57894222+anuShrestha83@users.noreply.github.com> Date: Thu, 16 Apr 2020 15:52:19 +0200 Subject: [PATCH 1/4] Modified notebook and added statistics --- .../api_request_deckgl_anuShrestha83.ipynb | 1037 +++++++++++++++++ 1 file changed, 1037 insertions(+) create mode 100644 examples/api_request_deckgl_anuShrestha83.ipynb diff --git a/examples/api_request_deckgl_anuShrestha83.ipynb b/examples/api_request_deckgl_anuShrestha83.ipynb new file mode 100644 index 0000000..4df451f --- /dev/null +++ b/examples/api_request_deckgl_anuShrestha83.ipynb @@ -0,0 +1,1037 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Package loading and basic configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "conda 4.8.3\n", + "\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "conda --version" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# load dependencies'\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import seaborn as sns\n", + "\n", + "from envirocar import TrackAPI, DownloadClient, BboxSelector, ECConfig\n", + "\n", + "# create an initial but optional config and an api client\n", + "config = ECConfig()\n", + "track_api = TrackAPI(api_client=DownloadClient(config=config))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Querying enviroCar Tracks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell queries tracks from the enviroCar API. It defines a bbox for the area of Münster (Germany) and requests 50 tracks. The result is a GeoDataFrame, which is a geo-extended Pandas dataframe from the GeoPandas library. It contains all information of the track in a flat dataframe format including a specific geometry column. " + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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idtimegeometryGPS VDOP.valueGPS VDOP.unitIntake Temperature.valueIntake Temperature.unitO2 Lambda Voltage.valueO2 Lambda Voltage.unitGPS PDOP.value...track.endsensor.typesensor.engineDisplacementsensor.modelsensor.idsensor.fuelTypesensor.constructionYearsensor.manufacturertrack.appVersiontrack.touVersion
05a9bad3844ea851012e06c092018-02-24T16:21:20POINT (6.77675 51.22546)1.722027precision32.999999c2.774379V2.022027...2018-02-24T16:44:46Zcar1800Avensis574e78cbe4b09078f97bbb4agasoline2011ToyotaNaNNaN
15a9bad3844ea851012e06c0b2018-02-24T16:21:25POINT (6.77668 51.22518)2.919473precision32.000000c4.589390V3.281339...2018-02-24T16:44:46Zcar1800Avensis574e78cbe4b09078f97bbb4agasoline2011ToyotaNaNNaN
25a9bad3844ea851012e06c0c2018-02-24T16:21:30POINT (6.77635 51.22497)1.700000precision34.227160c3.118953V2.157822...2018-02-24T16:44:46Zcar1800Avensis574e78cbe4b09078f97bbb4agasoline2011ToyotaNaNNaN
35a9bad3844ea851012e06c0d2018-02-24T16:21:35POINT (6.77594 51.22484)1.951852precision32.000000c3.282321V2.351852...2018-02-24T16:44:46Zcar1800Avensis574e78cbe4b09078f97bbb4agasoline2011ToyotaNaNNaN
45a9bad3844ea851012e06c0e2018-02-24T16:21:40POINT (6.77566 51.22471)1.905823precision32.397662c3.358742V2.188153...2018-02-24T16:44:46Zcar1800Avensis574e78cbe4b09078f97bbb4agasoline2011ToyotaNaNNaN
..................................................................
37357a35e90e4b086b281cf6bd82016-08-04T15:24:17POINT (6.46094 51.22493)1.738716precision26.000000cNaNNaN2.111836...2016-08-04T15:24:37Zcar1400Corsa D578c86a7e4b086b281ada0d0gasoline2013OpelNaNNaN
37457a35e90e4b086b281cf6bd92016-08-04T15:24:22POINT (6.46095 51.22494)1.900000precision27.000000cNaNNaN2.327154...2016-08-04T15:24:37Zcar1400Corsa D578c86a7e4b086b281ada0d0gasoline2013OpelNaNNaN
37557a35e90e4b086b281cf6bda2016-08-04T15:24:27POINT (6.46095 51.22494)1.827628precision28.000000cNaNNaN2.198899...2016-08-04T15:24:37Zcar1400Corsa D578c86a7e4b086b281ada0d0gasoline2013OpelNaNNaN
37657a35e90e4b086b281cf6bdb2016-08-04T15:24:32POINT (6.46097 51.22496)1.900000precision29.000000cNaNNaN2.368122...2016-08-04T15:24:37Zcar1400Corsa D578c86a7e4b086b281ada0d0gasoline2013OpelNaNNaN
37757a35e90e4b086b281cf6bdc2016-08-04T15:24:37POINT (6.46097 51.22496)3.300000precision30.000000cNaNNaN3.900000...2016-08-04T15:24:37Zcar1400Corsa D578c86a7e4b086b281ada0d0gasoline2013OpelNaNNaN
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7440 rows × 52 columns

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" + ], + "text/plain": [ + " id time geometry \\\n", + "0 5a9bad3844ea851012e06c09 2018-02-24T16:21:20 POINT (6.77675 51.22546) \n", + "1 5a9bad3844ea851012e06c0b 2018-02-24T16:21:25 POINT (6.77668 51.22518) \n", + "2 5a9bad3844ea851012e06c0c 2018-02-24T16:21:30 POINT (6.77635 51.22497) \n", + "3 5a9bad3844ea851012e06c0d 2018-02-24T16:21:35 POINT (6.77594 51.22484) \n", + "4 5a9bad3844ea851012e06c0e 2018-02-24T16:21:40 POINT (6.77566 51.22471) \n", + ".. ... ... ... \n", + "373 57a35e90e4b086b281cf6bd8 2016-08-04T15:24:17 POINT (6.46094 51.22493) \n", + "374 57a35e90e4b086b281cf6bd9 2016-08-04T15:24:22 POINT (6.46095 51.22494) \n", + "375 57a35e90e4b086b281cf6bda 2016-08-04T15:24:27 POINT (6.46095 51.22494) \n", + "376 57a35e90e4b086b281cf6bdb 2016-08-04T15:24:32 POINT (6.46097 51.22496) \n", + "377 57a35e90e4b086b281cf6bdc 2016-08-04T15:24:37 POINT (6.46097 51.22496) \n", + "\n", + " GPS VDOP.value GPS VDOP.unit Intake Temperature.value \\\n", + "0 1.722027 precision 32.999999 \n", + "1 2.919473 precision 32.000000 \n", + "2 1.700000 precision 34.227160 \n", + "3 1.951852 precision 32.000000 \n", + "4 1.905823 precision 32.397662 \n", + ".. ... ... ... \n", + "373 1.738716 precision 26.000000 \n", + "374 1.900000 precision 27.000000 \n", + "375 1.827628 precision 28.000000 \n", + "376 1.900000 precision 29.000000 \n", + "377 3.300000 precision 30.000000 \n", + "\n", + " Intake Temperature.unit O2 Lambda Voltage.value O2 Lambda Voltage.unit \\\n", + "0 c 2.774379 V \n", + "1 c 4.589390 V \n", + "2 c 3.118953 V \n", + "3 c 3.282321 V \n", + "4 c 3.358742 V \n", + ".. ... ... ... \n", + "373 c NaN NaN \n", + "374 c NaN NaN \n", + "375 c NaN NaN \n", + "376 c NaN NaN \n", + "377 c NaN NaN \n", + "\n", + " GPS PDOP.value ... track.end sensor.type \\\n", + "0 2.022027 ... 2018-02-24T16:44:46Z car \n", + "1 3.281339 ... 2018-02-24T16:44:46Z car \n", + "2 2.157822 ... 2018-02-24T16:44:46Z car \n", + "3 2.351852 ... 2018-02-24T16:44:46Z car \n", + "4 2.188153 ... 2018-02-24T16:44:46Z car \n", + ".. ... ... ... ... \n", + "373 2.111836 ... 2016-08-04T15:24:37Z car \n", + "374 2.327154 ... 2016-08-04T15:24:37Z car \n", + "375 2.198899 ... 2016-08-04T15:24:37Z car \n", + "376 2.368122 ... 2016-08-04T15:24:37Z car \n", + "377 3.900000 ... 2016-08-04T15:24:37Z car \n", + "\n", + " sensor.engineDisplacement sensor.model sensor.id \\\n", + "0 1800 Avensis 574e78cbe4b09078f97bbb4a \n", + "1 1800 Avensis 574e78cbe4b09078f97bbb4a \n", + "2 1800 Avensis 574e78cbe4b09078f97bbb4a \n", + "3 1800 Avensis 574e78cbe4b09078f97bbb4a \n", + "4 1800 Avensis 574e78cbe4b09078f97bbb4a \n", + ".. ... ... ... \n", + "373 1400 Corsa D 578c86a7e4b086b281ada0d0 \n", + "374 1400 Corsa D 578c86a7e4b086b281ada0d0 \n", + "375 1400 Corsa D 578c86a7e4b086b281ada0d0 \n", + "376 1400 Corsa D 578c86a7e4b086b281ada0d0 \n", + "377 1400 Corsa D 578c86a7e4b086b281ada0d0 \n", + "\n", + " sensor.fuelType sensor.constructionYear sensor.manufacturer \\\n", + "0 gasoline 2011 Toyota \n", + "1 gasoline 2011 Toyota \n", + "2 gasoline 2011 Toyota \n", + "3 gasoline 2011 Toyota \n", + "4 gasoline 2011 Toyota \n", + ".. ... ... ... \n", + "373 gasoline 2013 Opel \n", + "374 gasoline 2013 Opel \n", + "375 gasoline 2013 Opel \n", + "376 gasoline 2013 Opel \n", + "377 gasoline 2013 Opel \n", + "\n", + " track.appVersion track.touVersion \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + ".. ... ... \n", + "373 NaN NaN \n", + "374 NaN NaN \n", + "375 NaN NaN \n", + "376 NaN NaN \n", + "377 NaN NaN \n", + "\n", + "[7440 rows x 52 columns]" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bbox = BboxSelector([\n", + " 6.7559051513671875, # min_x\n", + " 51.21226046094158, # min_y\n", + " 6.792297363281249, # max_x\n", + " 51.22817289705297 # max_y\n", + "])\n", + "\n", + "# issue a query\n", + "track_df = track_api.get_tracks(bbox=bbox, num_results=20) # requesting 20 tracks inside the bbox\n", + "track_df" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id\n", + "time\n", + "geometry\n", + "GPS VDOP.value\n", + "GPS VDOP.unit\n", + "Intake Temperature.value\n", + "Intake Temperature.unit\n", + "O2 Lambda Voltage.value\n", + "O2 Lambda Voltage.unit\n", + "GPS PDOP.value\n", + "GPS PDOP.unit\n", + "Consumption.value\n", + "Consumption.unit\n", + "Throttle Position.value\n", + "Throttle Position.unit\n", + "O2 Lambda Voltage ER.value\n", + "O2 Lambda Voltage ER.unit\n", + "Intake Pressure.value\n", + "Intake Pressure.unit\n", + "GPS Accuracy.value\n", + "GPS Accuracy.unit\n", + "Engine Load.value\n", + "Engine Load.unit\n", + "GPS Altitude.value\n", + "GPS Altitude.unit\n", + "GPS HDOP.value\n", + "GPS HDOP.unit\n", + "Rpm.value\n", + "Rpm.unit\n", + "GPS Speed.value\n", + "GPS Speed.unit\n", + "Speed.value\n", + "Speed.unit\n", + "MAF.value\n", + "MAF.unit\n", + "GPS Bearing.value\n", + "GPS Bearing.unit\n", + "CO2.value\n", + "CO2.unit\n", + "track.id\n", + "track.length\n", + "track.begin\n", + "track.end\n", + "sensor.type\n", + "sensor.engineDisplacement\n", + "sensor.model\n", + "sensor.id\n", + "sensor.fuelType\n", + "sensor.constructionYear\n", + "sensor.manufacturer\n", + "track.appVersion\n", + "track.touVersion\n", + "Calculated MAF.value\n", + "Calculated MAF.unit\n" + ] + } + ], + "source": [ + "# Printing the column names in track_df geodataframe\n", + "for col in track_df.columns:\n", + " print(col)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(track_df.groupby('track.id'))" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "track_df.plot(figsize=(10, 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inspecting a single Track" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "some_track_id = track_df['track.id'].unique()[0]\n", + "some_track = track_df[track_df['track.id'] == some_track_id]\n", + "some_track.plot(figsize=(10,10))" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "275" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(some_track['Speed.value'])" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "some_track = some_track[some_track['Speed.value'].notna()]" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "275" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(some_track['Speed.value'])" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 275.000000\n", + "mean 77.515545\n", + "std 43.094688\n", + "min 0.000000\n", + "25% 38.954023\n", + "50% 77.999998\n", + "75% 121.000000\n", + "max 132.999996\n", + "Name: Speed.value, dtype: float64" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# summary statistics of speed value for the selected track\n", + "some_track['Speed.value'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 275.000000\n", + "mean 12.655975\n", + "std 8.070626\n", + "min 1.506369\n", + "25% 4.919652\n", + "50% 12.343069\n", + "75% 19.617727\n", + "max 31.688137\n", + "Name: CO2.value, dtype: float64" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# summary statistics of co2 value for the selected track\n", + "some_track['CO2.value'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = some_track['Speed.value'].plot()\n", + "ax.set_title(\"Speed\")\n", + "ax.set_ylabel(some_track['Speed.unit'][0])\n", + "ax" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ay = some_track['CO2.value'].plot()\n", + "ay.set_title(\"CO2\")\n", + "ay.set_ylabel(some_track['CO2.unit'][0])\n", + "ay" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scatterplot\n", + "Using a function from the seaborn package to create a scatter plot of Speed and CO2 value for the chosen track." + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(y='CO2.value', x='Speed.value', data=some_track);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interactive Map\n", + "The following map-based visualization makes use of folium. It allows to visualizate geospatial data based on an interactive leaflet map. Since the data in the GeoDataframe is modelled as a set of Point instead of a LineString, we have to manually create a polyline" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import folium\n", + "\n", + "lats = list(some_track['geometry'].apply(lambda coord: coord.y))\n", + "lngs = list(some_track['geometry'].apply(lambda coord: coord.x))\n", + "\n", + "avg_lat = sum(lats) / len(lats)\n", + "avg_lngs = sum(lngs) / len(lngs)\n", + "\n", + "m = folium.Map(location=[avg_lat, avg_lngs], zoom_start=13)\n", + "folium.PolyLine([coords for coords in zip(lats, lngs)], color='blue').add_to(m)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example: Visualization with pydeck (deck.gl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The pydeck library makes use of the basemap tiles from Mapbox. In case you want to visualize the map with basemap tiles, you need to register with MapBox, and configure a specific access token. The service is free until a certain level of traffic is esceeded.\n", + "\n", + "You can either configure it via your terminal (i.e. `export MAPBOX_API_KEY=`), which pydeck will automatically read, or you can pass it as a variable to the generation of pydeck (i.e. `pdk.Deck(mapbox_key=, ...)`." + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'C:\\\\Users\\\\shres\\\\OneDrive - Universitat Jaume I\\\\II_sem\\\\Floating car project\\\\envirocar-py\\\\examples\\\\tracks_dusseldorf.html'" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pydeck as pdk\n", + "\n", + "# for pydeck the attributes have to be flat\n", + "track_df['lat'] = track_df['geometry'].apply(lambda coord: coord.y)\n", + "track_df['lng'] = track_df['geometry'].apply(lambda coord: coord.x)\n", + "vis_df = pd.DataFrame(track_df)\n", + "vis_df['speed'] = vis_df['Speed.value']\n", + "\n", + "# omit unit columns\n", + "vis_df_cols = [col for col in vis_df.columns if col.lower()[len(col)-4:len(col)] != 'unit']\n", + "vis_df = vis_df[vis_df_cols]\n", + "\n", + "layer = pdk.Layer(\n", + " 'ScatterplotLayer',\n", + " data=vis_df,\n", + " get_position='[lng, lat]',\n", + " auto_highlight=True,\n", + " get_radius=10, # Radius is given in meters\n", + " get_fill_color='[speed < 20 ? 0 : (speed - 20)*8.5, speed < 50 ? 255 : 255 - (speed-50)*8.5, 0, 140]', # Set an RGBA value for fill\n", + " pickable=True\n", + ")\n", + "\n", + "# Set the viewport location\n", + "view_state = pdk.ViewState(\n", + " longitude=6.7559051513671875, #changed to min lat and lon of bbox\n", + " latitude=51.21226046094158,\n", + " zoom=10,\n", + " min_zoom=5,\n", + " max_zoom=15,\n", + " pitch=40.5,\n", + " bearing=-27.36)\n", + "\n", + "r = pdk.Deck(\n", + " width=200, \n", + " layers=[layer], \n", + " initial_view_state=view_state, mapbox_key=\"pk.eyJ1IjoiYW51c2hyZXN0aGE4MyIsImEiOiJjazh6cmYyenowZTJvM25wcmdma3YwamNxIn0.Hr0pkYHkHlCdgf-FmZTjbw\"\n", + ")\n", + "r.to_html('tracks_dusseldorf.html', iframe_width=900)" + ] + } + ], + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 962465d4b93dd32241e42be3106eea7e7ed81b87 Mon Sep 17 00:00:00 2001 From: anuShrestha83 <57894222+anuShrestha83@users.noreply.github.com> Date: Tue, 9 Jun 2020 20:14:14 +0200 Subject: [PATCH 2/4] Solved esda module error --- examples/Data/Monchengladbach.feather | Bin 0 -> 4670002 bytes 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