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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Package loading and basic configurations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Querying enviroCar Tracks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%reload_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "# load dependencies'\n",
+ "import pandas as pd\n",
+ "import geopandas as gpd\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": [
+ "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": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " CO2.unit | \n",
+ " CO2.value | \n",
+ " Calculated MAF.unit | \n",
+ " Calculated MAF.value | \n",
+ " Consumption.unit | \n",
+ " Consumption.value | \n",
+ " Engine Load.unit | \n",
+ " Engine Load.value | \n",
+ " GPS Accuracy.unit | \n",
+ " GPS Accuracy.value | \n",
+ " ... | \n",
+ " sensor.fuelType | \n",
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+ " sensor.type | \n",
+ " time | \n",
+ " track.begin | \n",
+ " track.end | \n",
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\n",
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+ " % | \n",
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+ " Dodge | \n",
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+ " % | \n",
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+ " BMW | \n",
+ " 525 | \n",
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+ " 2019-12-10T08:11:37Z | \n",
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+ " ... | \n",
+ " diesel | \n",
+ " 523c72d1e4b060b8865cf1d4 | \n",
+ " BMW | \n",
+ " 525 | \n",
+ " car | \n",
+ " 2019-12-10T08:11:37 | \n",
+ " 2019-12-10T07:22:05Z | \n",
+ " 2019-12-10T08:11:37Z | \n",
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+ "
\n",
+ " \n",
+ "
\n",
+ "
12332 rows × 52 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " CO2.unit CO2.value Calculated MAF.unit Calculated MAF.value \\\n",
+ "0 kg/h 5.626691 g/s 7.283771 \n",
+ "1 kg/h 7.929576 g/s 10.264864 \n",
+ "2 kg/h 4.898223 g/s 6.340767 \n",
+ "3 kg/h 5.425340 g/s 7.023122 \n",
+ "4 kg/h 4.882005 g/s 6.319773 \n",
+ ".. ... ... ... ... \n",
+ "585 NaN NaN NaN NaN \n",
+ "586 NaN NaN NaN NaN \n",
+ "587 NaN NaN NaN NaN \n",
+ "588 NaN NaN NaN NaN \n",
+ "589 NaN NaN NaN NaN \n",
+ "\n",
+ " Consumption.unit Consumption.value Engine Load.unit Engine Load.value \\\n",
+ "0 l/h 2.394336 % 47.156864 \n",
+ "1 l/h 3.374287 % 57.130478 \n",
+ "2 l/h 2.084350 % 22.004620 \n",
+ "3 l/h 2.308655 % 35.564430 \n",
+ "4 l/h 2.077449 % 40.345959 \n",
+ ".. ... ... ... ... \n",
+ "585 NaN NaN % 40.213574 \n",
+ "586 NaN NaN % 21.583306 \n",
+ "587 NaN NaN % 60.610872 \n",
+ "588 NaN NaN % 63.246544 \n",
+ "589 NaN NaN % 1.178107 \n",
+ "\n",
+ " GPS Accuracy.unit GPS Accuracy.value ... sensor.fuelType \\\n",
+ "0 % 3.618905 ... gasoline \n",
+ "1 % 3.386111 ... gasoline \n",
+ "2 % 3.000000 ... gasoline \n",
+ "3 % 3.500000 ... gasoline \n",
+ "4 % 4.500000 ... gasoline \n",
+ ".. ... ... ... ... \n",
+ "585 % 4.026874 ... diesel \n",
+ "586 % 3.447538 ... diesel \n",
+ "587 % 3.000000 ... diesel \n",
+ "588 % 3.457266 ... diesel \n",
+ "589 % 4.791479 ... diesel \n",
+ "\n",
+ " sensor.id sensor.manufacturer sensor.model sensor.type \\\n",
+ "0 58395f40e4b0a979d45bd61b Dodge Caliber car \n",
+ "1 58395f40e4b0a979d45bd61b Dodge Caliber car \n",
+ "2 58395f40e4b0a979d45bd61b Dodge Caliber car \n",
+ "3 58395f40e4b0a979d45bd61b Dodge Caliber car \n",
+ "4 58395f40e4b0a979d45bd61b Dodge Caliber car \n",
+ ".. ... ... ... ... \n",
+ "585 523c72d1e4b060b8865cf1d4 BMW 525 car \n",
+ "586 523c72d1e4b060b8865cf1d4 BMW 525 car \n",
+ "587 523c72d1e4b060b8865cf1d4 BMW 525 car \n",
+ "588 523c72d1e4b060b8865cf1d4 BMW 525 car \n",
+ "589 523c72d1e4b060b8865cf1d4 BMW 525 car \n",
+ "\n",
+ " time track.begin track.end \\\n",
+ "0 2020-04-06T20:43:35 2020-04-06T20:43:35Z 2020-04-06T22:31:45Z \n",
+ "1 2020-04-06T20:43:40 2020-04-06T20:43:35Z 2020-04-06T22:31:45Z \n",
+ "2 2020-04-06T20:43:45 2020-04-06T20:43:35Z 2020-04-06T22:31:45Z \n",
+ "3 2020-04-06T20:43:50 2020-04-06T20:43:35Z 2020-04-06T22:31:45Z \n",
+ "4 2020-04-06T20:43:55 2020-04-06T20:43:35Z 2020-04-06T22:31:45Z \n",
+ ".. ... ... ... \n",
+ "585 2019-12-10T08:11:17 2019-12-10T07:22:05Z 2019-12-10T08:11:37Z \n",
+ "586 2019-12-10T08:11:22 2019-12-10T07:22:05Z 2019-12-10T08:11:37Z \n",
+ "587 2019-12-10T08:11:27 2019-12-10T07:22:05Z 2019-12-10T08:11:37Z \n",
+ "588 2019-12-10T08:11:32 2019-12-10T07:22:05Z 2019-12-10T08:11:37Z \n",
+ "589 2019-12-10T08:11:37 2019-12-10T07:22:05Z 2019-12-10T08:11:37Z \n",
+ "\n",
+ " track.id track.length \n",
+ "0 5e8baea465b80c5d6b4dbfbd 169.237435 \n",
+ "1 5e8baea465b80c5d6b4dbfbd 169.237435 \n",
+ "2 5e8baea465b80c5d6b4dbfbd 169.237435 \n",
+ "3 5e8baea465b80c5d6b4dbfbd 169.237435 \n",
+ "4 5e8baea465b80c5d6b4dbfbd 169.237435 \n",
+ ".. ... ... \n",
+ "585 5df148693bdb69186846bad7 40.461526 \n",
+ "586 5df148693bdb69186846bad7 40.461526 \n",
+ "587 5df148693bdb69186846bad7 40.461526 \n",
+ "588 5df148693bdb69186846bad7 40.461526 \n",
+ "589 5df148693bdb69186846bad7 40.461526 \n",
+ "\n",
+ "[12332 rows x 52 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bbox = BboxSelector([\n",
+ " 7.611165771484375, # min_x\n",
+ " 51.94807412325402, # min_y\n",
+ " 7.648200988769531, # max_x\n",
+ " 51.97261482608728 # max_y\n",
+ "])\n",
+ "\n",
+ "# issue a query\n",
+ "track_df = track_api.get_tracks(bbox=bbox, num_results=20) # requesting 50 tracks inside the bbox\n",
+ "track_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 55,
+ "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=(8, 10))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# summary statistics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['CO2.unit', 'CO2.value', 'Calculated MAF.unit', 'Calculated MAF.value',\n",
+ " 'Consumption.unit', 'Consumption.value', 'Engine Load.unit',\n",
+ " 'Engine Load.value', 'GPS Accuracy.unit', 'GPS Accuracy.value',\n",
+ " 'GPS Altitude.unit', 'GPS Altitude.value', 'GPS Bearing.unit',\n",
+ " 'GPS Bearing.value', 'GPS HDOP.unit', 'GPS HDOP.value', 'GPS PDOP.unit',\n",
+ " 'GPS PDOP.value', 'GPS Speed.unit', 'GPS Speed.value', 'GPS VDOP.unit',\n",
+ " 'GPS VDOP.value', 'Intake Pressure.unit', 'Intake Pressure.value',\n",
+ " 'Intake Temperature.unit', 'Intake Temperature.value', 'MAF.unit',\n",
+ " 'MAF.value', 'O2 Lambda Voltage ER.unit', 'O2 Lambda Voltage ER.value',\n",
+ " 'O2 Lambda Voltage.unit', 'O2 Lambda Voltage.value', 'Rpm.unit',\n",
+ " 'Rpm.value', 'Speed.unit', 'Speed.value', 'Throttle Position.unit',\n",
+ " 'Throttle Position.value', 'geometry', 'id', 'sensor.constructionYear',\n",
+ " 'sensor.engineDisplacement', 'sensor.fuelType', 'sensor.id',\n",
+ " 'sensor.manufacturer', 'sensor.model', 'sensor.type', 'time',\n",
+ " 'track.begin', 'track.end', 'track.id', 'track.length', 'lat', 'lng',\n",
+ " 'speed'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "track_df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " CO2.value | \n",
+ " Calculated MAF.value | \n",
+ " Consumption.value | \n",
+ " Engine Load.value | \n",
+ " GPS Accuracy.value | \n",
+ " GPS Altitude.value | \n",
+ " GPS Bearing.value | \n",
+ " GPS HDOP.value | \n",
+ " GPS PDOP.value | \n",
+ " GPS Speed.value | \n",
+ " ... | \n",
+ " O2 Lambda Voltage.value | \n",
+ " Rpm.value | \n",
+ " Speed.value | \n",
+ " Throttle Position.value | \n",
+ " sensor.constructionYear | \n",
+ " sensor.engineDisplacement | \n",
+ " track.length | \n",
+ " lat | \n",
+ " lng | \n",
+ " speed | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count | \n",
+ " 11020.000000 | \n",
+ " 11020.000000 | \n",
+ " 11020.000000 | \n",
+ " 12315.000000 | \n",
+ " 12332.000000 | \n",
+ " 12332.000000 | \n",
+ " 12303.000000 | \n",
+ " 11237.000000 | \n",
+ " 11237.000000 | \n",
+ " 12332.000000 | \n",
+ " ... | \n",
+ " 1095.000000 | \n",
+ " 12315.000000 | \n",
+ " 12315.000000 | \n",
+ " 12115.000000 | \n",
+ " 12332.000000 | \n",
+ " 12332.000000 | \n",
+ " 12332.000000 | \n",
+ " 12332.000000 | \n",
+ " 12332.000000 | \n",
+ " 12315.000000 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 19.605949 | \n",
+ " 25.379971 | \n",
+ " 8.342957 | \n",
+ " 51.718541 | \n",
+ " 2.164513 | \n",
+ " 87.437767 | \n",
+ " 150.225603 | \n",
+ " 0.567556 | \n",
+ " 1.075705 | \n",
+ " 82.381859 | \n",
+ " ... | \n",
+ " 0.670338 | \n",
+ " 2311.836698 | \n",
+ " 81.470060 | \n",
+ " 29.067010 | \n",
+ " 2007.783814 | \n",
+ " 1803.041518 | \n",
+ " 149.163815 | \n",
+ " 52.453744 | \n",
+ " 7.887676 | \n",
+ " 81.470060 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 12.162856 | \n",
+ " 15.744860 | \n",
+ " 5.175683 | \n",
+ " 24.351280 | \n",
+ " 1.428757 | \n",
+ " 27.866227 | \n",
+ " 106.859615 | \n",
+ " 0.248852 | \n",
+ " 0.552975 | \n",
+ " 46.020793 | \n",
+ " ... | \n",
+ " 0.302914 | \n",
+ " 975.923946 | \n",
+ " 45.226743 | \n",
+ " 20.120237 | \n",
+ " 2.311758 | \n",
+ " 91.863559 | \n",
+ " 75.588258 | \n",
+ " 0.470145 | \n",
+ " 0.420753 | \n",
+ " 45.226743 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " -2.563380 | \n",
+ " -3.318304 | \n",
+ " -1.090800 | \n",
+ " -495.792866 | \n",
+ " 1.000000 | \n",
+ " 30.999999 | \n",
+ " 0.000000 | \n",
+ " 0.400000 | \n",
+ " 0.800000 | \n",
+ " 0.000000 | \n",
+ " ... | \n",
+ " 0.041240 | \n",
+ " -183.913050 | \n",
+ " 0.000000 | \n",
+ " 10.000000 | \n",
+ " 2007.000000 | \n",
+ " 1395.000000 | \n",
+ " 1.561091 | \n",
+ " 51.934327 | \n",
+ " 7.113174 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 8.549539 | \n",
+ " 11.067409 | \n",
+ " 3.638102 | \n",
+ " 34.221333 | \n",
+ " 1.500000 | \n",
+ " 69.000000 | \n",
+ " 45.702598 | \n",
+ " 0.400000 | \n",
+ " 0.837052 | \n",
+ " 47.539286 | \n",
+ " ... | \n",
+ " 0.428791 | \n",
+ " 1509.967685 | \n",
+ " 47.000001 | \n",
+ " 17.242937 | \n",
+ " 2007.000000 | \n",
+ " 1798.000000 | \n",
+ " 142.209935 | \n",
+ " 52.002597 | \n",
+ " 7.646013 | \n",
+ " 47.000001 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 18.091673 | \n",
+ " 23.419735 | \n",
+ " 7.698584 | \n",
+ " 52.111692 | \n",
+ " 1.500000 | \n",
+ " 93.000000 | \n",
+ " 170.776128 | \n",
+ " 0.600000 | \n",
+ " 1.000000 | \n",
+ " 83.917169 | \n",
+ " ... | \n",
+ " 0.587886 | \n",
+ " 2180.233333 | \n",
+ " 83.000000 | \n",
+ " 22.000001 | \n",
+ " 2007.000000 | \n",
+ " 1798.000000 | \n",
+ " 169.237435 | \n",
+ " 52.276080 | \n",
+ " 7.696748 | \n",
+ " 83.000000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 29.038578 | \n",
+ " 37.590541 | \n",
+ " 12.356841 | \n",
+ " 69.411764 | \n",
+ " 2.500000 | \n",
+ " 103.000000 | \n",
+ " 223.200000 | \n",
+ " 0.600000 | \n",
+ " 1.166860 | \n",
+ " 124.369941 | \n",
+ " ... | \n",
+ " 0.860665 | \n",
+ " 3240.377420 | \n",
+ " 122.999996 | \n",
+ " 29.000000 | \n",
+ " 2007.000000 | \n",
+ " 1798.000000 | \n",
+ " 219.767934 | \n",
+ " 52.897261 | \n",
+ " 8.173459 | \n",
+ " 122.999996 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 55.680724 | \n",
+ " 72.078894 | \n",
+ " 23.693925 | \n",
+ " 100.000003 | \n",
+ " 15.036181 | \n",
+ " 195.999997 | \n",
+ " 359.892290 | \n",
+ " 5.444747 | \n",
+ " 40.292816 | \n",
+ " 174.567824 | \n",
+ " ... | \n",
+ " 1.247910 | \n",
+ " 4530.827519 | \n",
+ " 373.333340 | \n",
+ " 89.000003 | \n",
+ " 2018.000000 | \n",
+ " 1995.000000 | \n",
+ " 233.951996 | \n",
+ " 53.551754 | \n",
+ " 8.864959 | \n",
+ " 373.333340 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
8 rows × 25 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " CO2.value Calculated MAF.value Consumption.value \\\n",
+ "count 11020.000000 11020.000000 11020.000000 \n",
+ "mean 19.605949 25.379971 8.342957 \n",
+ "std 12.162856 15.744860 5.175683 \n",
+ "min -2.563380 -3.318304 -1.090800 \n",
+ "25% 8.549539 11.067409 3.638102 \n",
+ "50% 18.091673 23.419735 7.698584 \n",
+ "75% 29.038578 37.590541 12.356841 \n",
+ "max 55.680724 72.078894 23.693925 \n",
+ "\n",
+ " Engine Load.value GPS Accuracy.value GPS Altitude.value \\\n",
+ "count 12315.000000 12332.000000 12332.000000 \n",
+ "mean 51.718541 2.164513 87.437767 \n",
+ "std 24.351280 1.428757 27.866227 \n",
+ "min -495.792866 1.000000 30.999999 \n",
+ "25% 34.221333 1.500000 69.000000 \n",
+ "50% 52.111692 1.500000 93.000000 \n",
+ "75% 69.411764 2.500000 103.000000 \n",
+ "max 100.000003 15.036181 195.999997 \n",
+ "\n",
+ " GPS Bearing.value GPS HDOP.value GPS PDOP.value GPS Speed.value \\\n",
+ "count 12303.000000 11237.000000 11237.000000 12332.000000 \n",
+ "mean 150.225603 0.567556 1.075705 82.381859 \n",
+ "std 106.859615 0.248852 0.552975 46.020793 \n",
+ "min 0.000000 0.400000 0.800000 0.000000 \n",
+ "25% 45.702598 0.400000 0.837052 47.539286 \n",
+ "50% 170.776128 0.600000 1.000000 83.917169 \n",
+ "75% 223.200000 0.600000 1.166860 124.369941 \n",
+ "max 359.892290 5.444747 40.292816 174.567824 \n",
+ "\n",
+ " ... O2 Lambda Voltage.value Rpm.value Speed.value \\\n",
+ "count ... 1095.000000 12315.000000 12315.000000 \n",
+ "mean ... 0.670338 2311.836698 81.470060 \n",
+ "std ... 0.302914 975.923946 45.226743 \n",
+ "min ... 0.041240 -183.913050 0.000000 \n",
+ "25% ... 0.428791 1509.967685 47.000001 \n",
+ "50% ... 0.587886 2180.233333 83.000000 \n",
+ "75% ... 0.860665 3240.377420 122.999996 \n",
+ "max ... 1.247910 4530.827519 373.333340 \n",
+ "\n",
+ " Throttle Position.value sensor.constructionYear \\\n",
+ "count 12115.000000 12332.000000 \n",
+ "mean 29.067010 2007.783814 \n",
+ "std 20.120237 2.311758 \n",
+ "min 10.000000 2007.000000 \n",
+ "25% 17.242937 2007.000000 \n",
+ "50% 22.000001 2007.000000 \n",
+ "75% 29.000000 2007.000000 \n",
+ "max 89.000003 2018.000000 \n",
+ "\n",
+ " sensor.engineDisplacement track.length lat lng \\\n",
+ "count 12332.000000 12332.000000 12332.000000 12332.000000 \n",
+ "mean 1803.041518 149.163815 52.453744 7.887676 \n",
+ "std 91.863559 75.588258 0.470145 0.420753 \n",
+ "min 1395.000000 1.561091 51.934327 7.113174 \n",
+ "25% 1798.000000 142.209935 52.002597 7.646013 \n",
+ "50% 1798.000000 169.237435 52.276080 7.696748 \n",
+ "75% 1798.000000 219.767934 52.897261 8.173459 \n",
+ "max 1995.000000 233.951996 53.551754 8.864959 \n",
+ "\n",
+ " speed \n",
+ "count 12315.000000 \n",
+ "mean 81.470060 \n",
+ "std 45.226743 \n",
+ "min 0.000000 \n",
+ "25% 47.000001 \n",
+ "50% 83.000000 \n",
+ "75% 122.999996 \n",
+ "max 373.333340 \n",
+ "\n",
+ "[8 rows x 25 columns]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "track_df.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Inspecting a single Track"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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