From ad7746e491d7bb95e35c7a3511498b002434a016 Mon Sep 17 00:00:00 2001 From: maneenp Date: Thu, 16 Apr 2020 20:14:47 +0200 Subject: [PATCH 1/5] Copying and renaming the file --- examples/api_request_deckgl_maneenp.ipynb | 739 ++++++++++++++++++++++ 1 file changed, 739 insertions(+) create mode 100644 examples/api_request_deckgl_maneenp.ipynb diff --git a/examples/api_request_deckgl_maneenp.ipynb b/examples/api_request_deckgl_maneenp.ipynb new file mode 100644 index 0000000..a0c5fc8 --- /dev/null +++ b/examples/api_request_deckgl_maneenp.ipynb @@ -0,0 +1,739 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Package loading and basic configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'geopandas'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# load dependencies'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0menvirocar\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTrackAPI\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDownloadClient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBboxSelector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mECConfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'geopandas'" + ] + } + ], + "source": [ + "%load_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": [ + "# 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": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idtimegeometryGPS PDOP.valueGPS PDOP.unitSpeed.valueSpeed.unitGPS Altitude.valueGPS Altitude.unitGPS Bearing.value...Consumption.valueConsumption.unittrack.appVersiontrack.touVersionO2 Lambda Voltage ER.valueO2 Lambda Voltage ER.unitMAF.valueMAF.unitO2 Lambda Voltage.valueO2 Lambda Voltage.unit
05e8b930965b80c5d6b4d7cd12020-03-07T12:33:15POINT (7.64069 51.95733)1.090631precision28.999999km/h110.381939m124.858622...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
15e8b930965b80c5d6b4d7cd32020-03-07T12:33:20POINT (7.64118 51.95712)1.000000precision28.000000km/h108.260375m125.020801...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
25e8b930965b80c5d6b4d7cd42020-03-07T12:33:26POINT (7.64162 51.95690)1.257198precision28.000001km/h105.826028m121.203960...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
35e8b930965b80c5d6b4d7cd52020-03-07T12:33:31POINT (7.64210 51.95672)1.000000precision30.000000km/h104.395998m123.412759...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
45e8b930965b80c5d6b4d7cd62020-03-07T12:33:36POINT (7.64264 51.95650)1.026727precision31.409419km/h101.516865m122.170479...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
2835dc986e844ea856b702e3e0b2019-10-28T16:34:55POINT (7.59523 51.96505)1.700000precision47.999999km/h109.652212m276.419653...3.122268l/hNaNNaNNaNNaNNaNNaNNaNNaN
2845dc986e844ea856b702e3e0c2019-10-28T16:35:00POINT (7.59425 51.96512)1.497088precision48.297297km/h110.122771m276.271049...2.853618l/hNaNNaNNaNNaNNaNNaNNaNNaN
2855dc986e844ea856b702e3e0d2019-10-28T16:35:05POINT (7.59327 51.96518)1.688911precision49.000001km/h110.573987m275.808021...4.657916l/hNaNNaNNaNNaNNaNNaNNaNNaN
2865dc986e844ea856b702e3e0e2019-10-28T16:35:10POINT (7.59225 51.96525)1.300000precision51.000000km/h111.140661m275.411387...3.445271l/hNaNNaNNaNNaNNaNNaNNaNNaN
2875dc986e844ea856b702e3e0f2019-10-28T16:35:15POINT (7.59123 51.96531)1.423253precision50.000001km/h111.891658m276.124438...3.248333l/hNaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

9944 rows × 54 columns

\n", + "
" + ], + "text/plain": [ + " id time geometry \\\n", + "0 5e8b930965b80c5d6b4d7cd1 2020-03-07T12:33:15 POINT (7.64069 51.95733) \n", + "1 5e8b930965b80c5d6b4d7cd3 2020-03-07T12:33:20 POINT (7.64118 51.95712) \n", + "2 5e8b930965b80c5d6b4d7cd4 2020-03-07T12:33:26 POINT (7.64162 51.95690) \n", + "3 5e8b930965b80c5d6b4d7cd5 2020-03-07T12:33:31 POINT (7.64210 51.95672) \n", + "4 5e8b930965b80c5d6b4d7cd6 2020-03-07T12:33:36 POINT (7.64264 51.95650) \n", + ".. ... ... ... \n", + "283 5dc986e844ea856b702e3e0b 2019-10-28T16:34:55 POINT (7.59523 51.96505) \n", + "284 5dc986e844ea856b702e3e0c 2019-10-28T16:35:00 POINT (7.59425 51.96512) \n", + "285 5dc986e844ea856b702e3e0d 2019-10-28T16:35:05 POINT (7.59327 51.96518) \n", + "286 5dc986e844ea856b702e3e0e 2019-10-28T16:35:10 POINT (7.59225 51.96525) \n", + "287 5dc986e844ea856b702e3e0f 2019-10-28T16:35:15 POINT (7.59123 51.96531) \n", + "\n", + " GPS PDOP.value GPS PDOP.unit Speed.value Speed.unit GPS Altitude.value \\\n", + "0 1.090631 precision 28.999999 km/h 110.381939 \n", + "1 1.000000 precision 28.000000 km/h 108.260375 \n", + "2 1.257198 precision 28.000001 km/h 105.826028 \n", + "3 1.000000 precision 30.000000 km/h 104.395998 \n", + "4 1.026727 precision 31.409419 km/h 101.516865 \n", + ".. ... ... ... ... ... \n", + "283 1.700000 precision 47.999999 km/h 109.652212 \n", + "284 1.497088 precision 48.297297 km/h 110.122771 \n", + "285 1.688911 precision 49.000001 km/h 110.573987 \n", + "286 1.300000 precision 51.000000 km/h 111.140661 \n", + "287 1.423253 precision 50.000001 km/h 111.891658 \n", + "\n", + " GPS Altitude.unit GPS Bearing.value ... Consumption.value \\\n", + "0 m 124.858622 ... NaN \n", + "1 m 125.020801 ... NaN \n", + "2 m 121.203960 ... NaN \n", + "3 m 123.412759 ... NaN \n", + "4 m 122.170479 ... NaN \n", + ".. ... ... ... ... \n", + "283 m 276.419653 ... 3.122268 \n", + "284 m 276.271049 ... 2.853618 \n", + "285 m 275.808021 ... 4.657916 \n", + "286 m 275.411387 ... 3.445271 \n", + "287 m 276.124438 ... 3.248333 \n", + "\n", + " Consumption.unit track.appVersion track.touVersion \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + ".. ... ... ... \n", + "283 l/h NaN NaN \n", + "284 l/h NaN NaN \n", + "285 l/h NaN NaN \n", + "286 l/h NaN NaN \n", + "287 l/h NaN NaN \n", + "\n", + " O2 Lambda Voltage ER.value O2 Lambda Voltage ER.unit MAF.value MAF.unit \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + ".. ... ... ... ... \n", + "283 NaN NaN NaN NaN \n", + "284 NaN NaN NaN NaN \n", + "285 NaN NaN NaN NaN \n", + "286 NaN NaN NaN NaN \n", + "287 NaN NaN NaN NaN \n", + "\n", + " O2 Lambda Voltage.value O2 Lambda Voltage.unit \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + ".. ... ... \n", + "283 NaN NaN \n", + "284 NaN NaN \n", + "285 NaN NaN \n", + "286 NaN NaN \n", + "287 NaN NaN \n", + "\n", + "[9944 rows x 54 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bbox = BboxSelector([\n", + " 7.601165771484375, # 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=50) # requesting 50 tracks inside the bbox\n", + "track_df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAI/CAYAAABTSLRUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3df5RV5X0u8OcZxiHeKVZJhlmKUlKkpqsBBztX4OKy2HS8KrlmjDHUQBKbFq6rSW8prY2EudEkTIPVEu5dWddE0qyki0lCjXLSFiTMasvtCmVIh8zIaGKDWCROLJCQFC6x4sD3/nH22ONwzux3n7N/7+ez1izO2WfvPe8GnrPf/e53vy/NDCJSDE1JF0BE4qPAixSIAi9SIAq8SIEo8CIFosCLFEhz0gUI4i1veYvNnj076WKIpNKBAwd+ZGZtk62TqcDPnj0bg4ODSRdDJJVIvui3jqr0IgWiwIsUiAIvUiAKvEiBKPAiBaLAixSIAi9SIAq8SIEo8CIFosCLFIgCL1IgCrxIgSjwIgWiwIsUiAIvUiAKvEiBKPAiBaLAixSIAi9SIAq8SIFkahDLPOgpjWDrwNELlq9cNAsbuuclUCIpEp3hY1Qr7ACwdeAo5j+wK+YSSdEo8DH66v4fTPr5qVfPoWvTnngKI4WkwMfonJnvOoeOn4mhJFJUCnyMppBO671t/c6ISyJFpcDH6O6FVzmt9+/nTNfzEgkFPkYbuudh5aJZTuueevUcFvb2R1wiKRqnwJM8QnKE5DDJQW/Zp0ge9JbtJnlFjW3PeesMk/yriuVvJbmf5PMkt5FsCeeQ0m1D9zwc2bjMad1jp88q9BKqIGf4m8ysw8w6vfcPm9l8M+sA8DcAPl5ju1e87TrM7PaK5Q8B+IyZXQ3gJwB+O3DpM8z1TK/QS5jqrtKb2amKt60A/JugPSQJ4NcBfN1b9GUA3fWWJYs2dM/DkjnTndZV6CUsroE3ALtJHiC5enwhyV6SPwCwArXP8G8iOUhygOR4qN8M4KdmNua9fwnAzDrKn2l9qxYHOtOrIU8a5Rr4G8zsOgC3AvgwyRsBwMzWm9lVAPoAfKTGtr/gXQa8D8BmknOCFJDkau8LY/DEiRNBNs2EoA15Cr00winwZjbq/XkcwHYA109YpQ/AnT7bvgBgD4AFAH4M4FKS4335rwQwWmP7x8ys08w629raXIqbOQq9xMU38CRbSU4bfw3gZgDPkJxbsdq7ADxXZdvLSE71Xr8FwBIA3zUzA/D3AN7jrfpBAN9o5ECyTqGXOLic4dsBfIvk0wC+DWCHme0CsJHkMyQPovwl8PsAQLKT5Be8bX8ZwKC37d8D2Ghm3/U++yiAtSSfR/ma/s9DO6qMChr62ffviLhEkjc0h/7dadHZ2WmDg4NJFyNypaFRrNk27LRuM4HnP+12X1/yjeSBitvmVamnXQp1L5iJzcs7nNYdy873taSABsBImZ7SCPoGjrp3ahAJQIFPgRVb9mHv4ZNJF0MKQIFPUGloFH+wbVhnc4mNAp+Qrk17QhnswrV7rgigwMdusnHtgloyZzr6Vi0OZV9SDAp8jBb29uPY6bMN7aMJwKblHeheULhHDyQECnwMwrhWv/iiJnz63fMVdGmIAh+xRlrgNVa9hE2Bj1A9DXPt01qwf31XRCWSolPgI1LP9brO6BI1BT4Cb1u/E/9+zv2KXa3tEhcFPkRBHnoZt1kt7hIjBT4kQRvndK0uSVDgQxC0cU5VeEmKAt+goI1zapiTJCnwdaqnM42u1yVpCnwdgjbOaVQaSQuNeFOHIGG/ZOoUhV1SQ4EPKMjAkXNntOLgJ26JsDQiwSjwAVy9LljY+9cuja4wInVQ4ANwHTByyZzpCrukkgIfsqnNTbir021seZG4KfAhe3XsPNZsG8Y1PU+hNFR19iyRxCjwERkP/oot+5IuisjrFPiI7T18UqGX1FDgY7D38ElV7yUV1NMuJvc9PuzcrbY0NIoH/+pZ/PSV1+r6XeqvL7VoMsmA8jBjKwGs0JdC7rhMJqnAh6A0NIq124ZxPumC1ElfAPmgwMcsrNlkkjS1uQkP3anhsLNI00XHLA+968ZvJ179sZ1qaMwhBT5kU5vz8Vc6dt7UjyCH8vG/M0UeunN+0kUI1d7DJ7Gwtz/pYkhIdFsuZOPXvuuePIhXXmu8Ga+1ZQp675jne00d5Rzzx06fxfwHdulR3xxQo11BlIZGG/4S0ki76aZWeqmp3i+AS6ZO0Zk+pRR4cVLPnPXqzZc+ui0nTjZ0z8ORjcswd0ar8zZbB46qMS+DFHh5Xf/apVgyZ7rz+sdOn8Uvrtuh+/UZosDLG/StWhwo9OetPIpvT2kkwlJJWBR4uUDQ0APlKr466aSfAi9V9a1ajM3LOwJts/fwSXRt2hNNgSQUCrzU1L1gJo5sXIb2aS3O2xw6fkaNeSmmwIuv/eu7sHKR+0i8x06fVehTSoEXJ0Fv3Sn06aTASyD9a5cGCr1u26WLAi+BBblfP37bTi346aDAS12CtuJruO50UOClbkFb8fcePqkOOglT4KVh+9d3OYd+68BRXdMnSIGXUAQJ/dptwxGXRmpR4CU0+9d3OTXmnQdUtU+IAi+h6lu12KmTTtDn7yUcCryEbkP3PKczvVrt46dBLKUhpaFRrN8+gjNnzwXeNqpBN6U2BV4C6ymNoG/gKLIzOJqMU+DFSSNnckkPp8CTPALgNIBzAMbMrJPkpwC8C+VG1+MA7jGzH07YrgPAowAu8bbtNbNt3mdfAvBrAP7NW/0eM9P9mhSKcsx7iVeQM/xNZvajivcPm9n/BACS/wPAxwHcO2GbnwH4gJkdInkFgAMkv2lmP/U+v8/Mvl5v4SVapaFRrP3LYZyPqO4eZNBMCUfdVXozO1XxthW48JLOzL5f8fqHJI8DaAPw04nrSrrUM3R1EO3TWnIx+WbWuAbeAOwmaQA+b2aPAQDJXgAfQLlaftNkOyB5PYAWAIcrFveS/DiAvwVwv5m9GrD8EoEoq/AXX9SET79b01EnxWkiCpIzzWyU5AwA/QB+z8z+oeLzdQDeZGYP1Nj+cgB7AHzQzAYqlv0ryl8CjwE4bGafrLLtagCrAWDWrFm/+uKLLwY7QnHWaBX+0osvwoO3/4rCnJBIZp4h+SCA/2dmj1QsmwVgp5m9vcr6l6Ac9j+pdb1OcimAPzKzd072uzXzTHTqOatf1AQ8fFeHAp4SLoH3rdKTbAXQZGanvdc3A/gkyblmdshb7V0AnquybQuA7QD+YmLYSV5uZi+TJIBuAM84HZWErqc0Ejjsm5cr6Fnkcg3fDmB7OZdoBvAVM9tF8gmS16B8W+5FeC30JDsB3GtmvwPgvQBuBPBmkvd4+xu//dZHsg0AAQzjwhZ+iUgj99Q1g2y2aTLJgugpjeAr+482dItt7oxWtaynWChVesmeMMI90ZI509G3anF4O5REKPAZF0eXV12v54cCnyFxP7SiOeDzR4FPubgfWlHI802BT7G4H1pR2PNPgU+phb39OHb6bOS/R11di0WBT6EVW/ZFFnYFvNgU+BQKqxqvcMtECnzK1Duwo8ItLhT4lHE9u6uBTeqhwKeIy+QM6gQjjdC49CniN8LMykWzFHZpiAKfEgt7+33XURVeGqXAp4DLbTiXmVxE/CjwCSsNjTo11OlJNQmDAp8wl6mTdXaXsCjwCeopjeC8zzqXTJ2is7uERoFPkMu47wc/cUsMJZGiUOATUhoa9V3HZZ51kSAU+ITc9/jk1+7NTdRtOAmdAp+Q13wu3h+569p4CiKFosAnwKU6rx51EgUFPgF+1Xldu0tUFPgE+FXnde0uUVHgY9a1ac+kn7e2TImnIFJICnyMSkOjOHT8zKTr9N6hs7tER4GP0RqHbrRqrJMoKfAx8avKA2qsk+gp8DFYsWWfb1W+iWqsk+gp8BFzffx103s7YiiNFJ0CHzGX6/a5M1p17S6xUOAj5DJsVROhOdclNgp8RLo27XGaPUZVeYmTAh+BntKIbyMdUB7JRlV5iZMCHwGXgS3mzmjVSDYSOwU+ZC5TRbVPa9F1uyRCgQ+Z3y24JgL713fFVBqRN1LgY6ZGOkmSAh8iv+q8GukkaQp8iPyq82qkk6Qp8DFh0gUQgQIfmxV6Ek5SQIGPiZ6EkzRQ4EUKRIEXKRAFXqRAFHiRAlHgRQpEgRcpEAU+Ji7zyYlETYGPid98ciJxUOBj4jefnEgcFPgQaV44STsFPkR+88K5jIYjEiUFPkR+z7q7TEghEiUFPmR6DFbSTIEPmd9jsD2lkZhKInIhBT5kfo/BugxhLRIVBV6kQJwCT/IIyRGSwyQHvWWfInnQW7ab5BU1tv0gyUPezwcrlv+qt8/nSf5vkrm5/NXtOUmrIGf4m8ysw8w6vfcPm9l8M+sA8DcAPj5xA5LTATwAYCGA6wE8QPIy7+NHAawCMNf7uaXOY0gdv9tzIkmpu0pvZqcq3rYCsCqr/VcA/WZ20sx+AqAfwC0kLwdwiZkNmJkB+AsA3fWWJW00FLWkVbPjegZgN0kD8HkzewwASPYC+ACAfwNwU5XtZgL4QcX7l7xlM73XE5eLSIRcz/A3mNl1AG4F8GGSNwKAma03s6sA9AH4SBQFJLma5CDJwRMnTkTxK0QKwynwZjbq/XkcwHaUr8cr9QG4s8qmowCuqnh/pbds1Hs9cXm13/2YmXWaWWdbW5tLcUWkBt/Ak2wlOW38NYCbATxDcm7Fau8C8FyVzb8J4GaSl3mNdTcD+KaZvQzgFMlFXuv8BwB8o8FjEREfLtfw7QC2e3fNmgF8xcx2kXyC5DUAzgN4EcC9AECyE8C9ZvY7ZnaS5KcA/JO3r0+a2XiH8t8F8CUAFwN4yvsRkQj5Bt7MXgBwbZXl1arwMLNBAL9T8f6LAL5YY723BymsiDRGPe1ECkSBFykQBV6kQBR4kQJR4BOgoa4kKQp8AjTUlSRFgY9Ibp71lVxR4CPiN9SVSBIU+Ij4DXUlkgQFXqRAFHiRAlHgRQpEgRcpEAVepEAUeJECUeBFCkSBFykQBT4ipaGqY3KKJEqBj8jabcNJF0HkAgp8BEpDozifdCFEqlDgI3Df45Of3TXZpCRFgQ9ZT2kEr/mc3jXZpCRFgQ9RaWgUWweOTrpOcxM12aQkRoEPkUtD3SN3XTDEv0hsFPiQLOzt922om9rcpLO7JEqBD8HC3n4cO33Wd72H7pwfQ2lEalPgG9S1aY9T2JfMma6zuyROgW9AT2kEh46f8V1v7oxW9K1aHEOJRCanwDfAr0UeKIe9f+3S6Asj4kCBr5PLZBLt01oUdkkVBb5OfpNJNBHYv74rptKIuFHg69BTGvFdZ9N7O2IoiUgwCnwd/K7d1SIvaaXAR0At8pJWCnzIVmqKKUkxBT4gv+t3TTElaabAB+Ry710krRT4APzO7poiWtJOgQ/A7+yuKaIl7RR4Ry4963T9LmmnwDvoKY349qxbMmd6TKURqZ8C76OnNOLUUKd775IFzUkXIM1WbNnne2YHdO9dskOBr6Fr0x6nZ92bm6hrd8kMVemrcA07oEEpJVsU+AmChF0PyUjWqEpfwXUwSqAcdjXUSdboDO8JEvaVi2Yp7JJJOsOj3BrvGvbNyztUjZfMKvwZvjQ06nTrDVDYJfsKH/h1Tx70XacJCrvkQ+Gr9K/4TPXaPq1Fg1FKbhT6DO/3QIxGnpW8KXTg/a7dNfKs5E2hA+9H1+ySNwp8DXogRvKosIHXYJRSRIUNvAajlCJyCjzJIyRHSA6THPSWPUzyOZIHSW4neWmV7a7xthn/OUVyjffZgyRHKz67LdxDq58Go5S8CnKGv8nMOsys03vfD+DtZjYfwPcBrJu4gZn9s7dNB4BfBfAzANsrVvnM+OdmtrPOYwidBqOUvKq7Sm9mu81szHs7AOBKn03eAeCwmb1Y7++Mi67fJa9cA28AdpM8QHJ1lc8/BOApn338JoCvTlj2Ee+S4IskL3Msi4jUyTXwN5jZdQBuBfBhkjeOf0ByPYAxAH21NibZAuB2AI9XLH4UwBwAHQBeBvBnNbZdTXKQ5OCJEycciysi1TgF3sxGvT+Po3wNfj0AkLwHwDsBrDAzm2QXtwL4jpkdq9jnMTM7Z2bnAWwZ32eV3/2YmXWaWWdbW5tLcUWkBt/Ak2wlOW38NYCbATxD8hYAfwzgdjP7mc9u7saE6jzJyyve3gHgmSAFj1JpaDTpIohEwuUM3w7gWySfBvBtADvMbBeAzwKYBqDfu632OQAgeQXJ11vcvS+JLgBPTtjvn3q3+g4CuAnAHzR+OOFweWRWJIt8H481sxcAXDA0q5ldXWP9HwK4reL9GQBvrrLe+wOVNEZ+j8yKZFVhe9q1tkxJuggisSts4Hvv0L12KZ7CBl6PvkoRFTbwIkWkwIsUiAIvUiAKvEiBKPAiBaLAixSIAi9SIAq8SIEo8CIFosDX4DeMtUgWKfA1aBhryaNCB95vOOr5D+yKpRwicSl04P2Goz716jks7O2PqTQi0St04F2Goz52+iy6Nu2JvjAiMSh04AG3SSMPHT+j0EsuFD7wG7rnoclhbqlDx89gxZZ90RdIJEKFDzwAbHpvh9N6ew+f1Ii2kmkKPMqj3yyZM91p3TXbhiMujUh0FHhP36rFmDuj1WldneUlqxT4Cv1rlzqF/r7HdZaXbFLgJ+hfuxTt01omXUfD1ktWKfBV7F/flXQRRCKhwIsUiAJfhZ6Uk7xS4Kvwe1LOpXeeSBop8HVw6YMvkkYKvEiBKPABqTovWabAB6TqvGSZAj+BWuglzxT4CTSWneSZAi9SIAp8BVXnJe8U+ArqcCN5p8AHoBZ6yToFXqRAFHhHqs5LHijwjlSdlzxQ4B1pHDvJAwXekcaxkzxQ4B29dl5neck+BT4AneUl6xT4Cn4t8TrLS9Yp8BVcWuLXPXkwhpKIREOBn8DvLP+KBqWXDFPgJ9jQPQ/NPtPJahZZySoFvopH7rp20s/3Hj4ZU0lEwqXAV9G9YGbSRRCJhAJfg9+1vFrrJYsU+Br8WuzVWi9ZpMDXSa31kkUK/CRaW6YkXQSRUCnwk+i9Q4/ESr4o8JNQa73kjQIvUiAKvEiBOAWe5BGSIySHSQ56yx4m+RzJgyS3k7zUdVtv+XSS/SQPeX9eFs4hxUf34iVrgpzhbzKzDjPr9N73A3i7mc0H8H0A6wJsCwD3A/hbM5sL4G+995mie/GSNXVX6c1st5mNeW8HAFwZcBfvAvBl7/WXAXTXW5ak6F68ZI1r4A3AbpIHSK6u8vmHADwVcNt2M3vZe/2vANodyxIrv3vxqtZLlrgG/gYzuw7ArQA+TPLG8Q9IrgcwBqAv6LbjzMxQ/mK4AMnVJAdJDp44ccKxuOHxuxevar1kiVPgzWzU+/M4gO0ArgcAkvcAeCeAFV5onbcFcIzk5d5+LgdwvMb2j5lZp5l1trW1OR5WePzuxataL1niG3iSrSSnjb8GcDOAZ0jeAuCPAdxuZj8Lsq338V8B+KD3+oMAvtHIgURpavPkf01dm/bEUxCRBrmc4dsBfIvk0wC+DWCHme0C8FkA0wD0e7fcPgcAJK8gudNnWwDYCKCL5CEAv+G9T6WH7pw/6eeHjp/RKDiSCaxRE0+lzs5OGxwc9F8xArPv3+G7zpI509G3anEMpRG5EMkDE259X0A97Ry5TCa59/BJVe8l1RR4Ry6DWwLl6v3VH9up23WSSgp8AH6DW44bO29Ys21Y1/WSOgp8AN0LZgaaJ15VfEkbBT6gDd3zAoVeVXxJEwW+Dhu652Hz8g7nv7zxKn5PaSTScon4UeDr1L1gJl7YuAxzZ7Q6b7N14KjO9JIoBb5B/WuXYsmc6c7rr9mmKaclOQp8CPpWLQ5UxV/Y2x9peURqUeBDEqSKf+z0WV3PSyIU+JC5VvG3DhyNoTQib6TAR6Bv1WKn0KsBT+KmwEekb9VitE9rmXQdDZ4hcVPgI7R/fdekn2vwDImbAh8xzU8naaLAR+yO6zRdlaSHAh+xJw68lHQRRF6nwEdM1+mSJgp8hPyeh/cfTkMkXAp8REpDo9h7+OSk66wI8JitSBgU+IisdXhIZkP35JNciIRNgY9AT2kEflfuQQbREAmLAh8Bv37yzU3U2V0SocAnwHUwTJGwKfAxm9rc5DtfnUhUFPiY+U1bJRIlBT5mOrtLkhT4mGmcekmSAh+zQ8fPaHgrSYwCnwANbyVJUeAj4NKpRsNbSRIU+Ai4zDR73+Man17ip8BHxK9zjZ6alSQo8BHpXjATU5v11yvpov+REVInG0kbBT5C6mQjaaPAixSIAi9SIAq8SIEo8CIFosCLFIgCL1IgCrxIgSjwIgWiwIsUiAIfIb+ppkTipsBHpKc04jvVlEjcmpMuQB6t2LLPN+yaSFKSoMCHqDQ0ij96/GmMnTffdTWRpCRBgW9QaWgUn/jrZ/GTn70WaDtNNSVJUODr0FMawVf2H4XDibwqTSQpSVHgHZSGRrF++wjOnD3X8L6WzJmus7skRoGfRGloFB994iBeHQtnALqVi2Yp7JIoBb4Gl5Z2V00ANi3v0Ag4kjgFfoIgLe0udFaXNFHgK5SGRrFmWzjjxSvokkYKfIW1DYb90osvwoO3/4qq7pJaCrxnxZZ9CNo0p4BL1ijwCNbvfcmc6ehbtTjiEolEw+nhGZJHSI6QHCY56C17mORzJA+S3E7y0irbXUXy70l+l+SzJH+/4rMHSY56+xwmeVt4h+WuNDTqNJtrE4DNyzsUdsm0IGf4m8zsRxXv+wGsM7Mxkg8BWAfgoxO2GQPwh2b2HZLTABwg2W9m3/U+/4yZPVJ36UPgct0+d0Yr+tcujb4wIhGr+/FYM9ttZmPe2wEAV1ZZ52Uz+473+jSA7wFIzQVv16Y9vtftzU1U2CU3XANvAHaTPEBydZXPPwTgqcl2QHI2gAUA9lcs/oh3SfBFkpc5liUUXZv24NDxM77r+c0CK5IlroG/wcyuA3ArgA+TvHH8A5LrUa6699XamOTPAXgCwBozO+UtfhTAHAAdAF4G8Gc1tl1NcpDk4IkTJxyLOznXsC+ZM10t8JIrToE3s1Hvz+MAtgO4HgBI3gPgnQBWmFnVrmkkL0I57H1m9mTFPo+Z2TkzOw9gy/g+q/zux8ys08w629ranA+smtLQKK7+2E6nsM+d0aoGOskd38CTbPUa3ECyFcDNAJ4heQuAPwZwu5n9rMa2BPDnAL5nZpsmfHZ5xds7ADxT3yG46SmNYM22Yacus2qkk7xyaaVvB7C9nF00A/iKme0i+TyAqQD6vc8GzOxeklcA+IKZ3QZgCYD3AxghOd4c/jEz2wngT0l2oNw+cATAfw/xuN6gpzTidOsNUCOd5Jtv4M3sBQAXtFyZ2dU11v8hgNu8199CjeHbzOz9gUpapyBhB9RIJ/mW61Frg4R9vGONGukkz3LbtTZI2HXNLkWRyzO8a3dZoHzrTWGXoshl4F2fadeDMFI0uQt816Y9Tusp7FJEuQp8aWjUuQedwi5FlKtGO9eq/N7DJzH7/h1VP2ttmYLeO+aptV5yiTV6xKZSZ2enDQ4OVv1sYW8/jp0+G3OJyp0MVmj8OkkBkgfMrHOydXJxhl+xZV8iYQfK3QS3Dhx9/a6AvgAkzXIR+DRNy1z5BaDLA0mbXDXapc2Zs+ewZtuw850Dkagp8DE4dPwMZt+/Az2lkaSLIgWnwMdo68BRBV8SlYtr+JWLZgV6Im7itXVpaBTrnjyIV14LZ9JIP5WNfLU0EXjfQjX+Sbhyc1uupzSCr+7/Ac6ZYQqJuxdeFWlYgj522wi1/IsLl9tyuQl80uL6AlDLv9SiwCcgzAkpg1JNoNgU+ASFOb98PfS8QPG4BF6t9BHpW7UYm5d34KKE/obHnxfQHQGppMBHqHvBTBz6k2XYvLwDFyeU/K0DR7Gwtz+R3y3poyp9ykTV+Nc+rQX713eFvl9JD1XpM2hD9zwc2bgMRzYuw5I500Pb77HTZ1W9FwU+zfpWLcaRjctCawuIq9+ApFcuetrlXfeCmZPed4+zE5Bkm87wOTB+GeByCbBiy74YSiRppcDnSN+qxVi5aNak66Rp7ACJnwKfM+plJ5NR4HMozNZ9yRcFPof8utSWhkZjKomkjQJfQEk93CPJU+ALav4Du5IugiRAgS+oU6+ew+z7d+BXPr5LVfwCUeBzyu/23LjxkXUV/mJQ4HNqQ/c8tE9rCbTNePiv/thOBT+nFPgc27++C6xju7HzhjXbhtUrL4cU+Jz7zPKOurfde/iknqXPGQU+57oXzMTmBkJ/7PRZnelzRIEvgO4FM3Fk4zLnhryJ1P8+PxT4Ahl/qq6eIbc0P14+aIirggsyrHZzE/HIXddqTPyU0hBX4mu8uu/ywM14672GysouBV4AlB+4aW5yu4m3deCo7tNnlAIvr3vkrmud19UDONmkwMvruhfMDPQsvW7XZY8CL2/Qt2qxc+h1uy57FHi5wPg0WS7/OXSWzxYFXqrqXjATL2xchkumTpl0PZ3ls0X34cXX7Pt31LXdkY3LQi6JTEb34SUU9Q6KWe8XhURHgRdfjcwz/1aFPlUUeHEytbm+/yoGaJ76FFHgxclDd85vaHvNU58OCrw46V4ws+7Ha8cdO31WZ/uEKfDibEP3vIYG0xi3deCohslOiG7LSV1WbNkX2j34lYtmaU68ELjcllPgpSFhzk1/8UVN+PS75+t5+zop8BKrMMOvs35wCrwkIsgoOn4uagIevqtDZ30HoQWe5BEApwGcAzBmZp0kHwbw3wCcBXAYwG+Z2U+rbHsLgP8FYAqAL5jZRm/5WwF8DcCbARwA8H4zOztZORT4bAnzOh9Q+P2EHfhOM/tRxbKbAfydmY2RfAgAzOyjE7abAuD7ALoAvATgnwDcbWbfJfmXAJ40s6+R/ByAp83s0cnKocBnT9ihr+Ra7b963Q6MVflvnre+/pH2pTez3WY25r0dAHBlldWuB/C8mb3gnb2/BuBdJAng1wF83VvvywC66y2LpFffqsWYO6M1kn1vHTjqe1+/VtiBYvb1dw28AdhN8gDJ1VU+/xCAp6osnwngBxXvX/KWvRnAT6zjwnIAAAoxSURBVCu+MMaXSw71r10aWeiB/wh+taG0a4V9XNH6+jc7rneDmY2SnAGgn+RzZvYPAEByPYAxAH1RFND7glkNALNmNdbTS5LTv3ZppNV7ADh0/Axm378DS+ZMd37gxwDMf2AXDn7ilsjKVY+uTXtw6PiZC5a3T2vB/vVdde/X6QxvZqPen8cBbEe5qg6S9wB4J4AVVr0xYBTAVRXvr/SW/RjApSSbJyyv9rsfM7NOM+tsa2tzKa6kVN+qxQ13z3Wx9/BJzL5/h/PIuqdePYfZ9+9ITRV/9v07qoYdKHdPbuSZBN9GO5KtAJrM7LT3uh/AJ72PNwH4NTM7UWPbZpQb7d6BcqD/CcD7zOxZko8DeKKi0e6gmf2fycqiRrt8KA2N4r7Hh/Ha+aRLUl3YjXm1ztaNqFZGl0Y7lyp9O4Dt5XY2NAP4ipntIvk8gKkoV/EBYMDM7iV5Bcq3327zWvA/AuCbKN+W+6KZPevt96MAvkZyA4AhAH/ucqCSfd0LZqJ7wczUBn/Fln2BxwAIs9NRlNTxRhJXGhrF2m3DSFnuJz3Tz39gF069ei7G0rxRlGd4kUhVnvHXPXkQr6TklJ+Wa/qJ2qe11L2tAi+pMR78cVmpJsep0VZ6Vekl89J6Jg7T5uX+XYpVpRfJgTDvGijwIjFzOVtHRYEXicCbphDP9d6WdDEuoMCLNKiZwPOfzsaTdwq8SABpPXO7UuAl845sXBZ5S31enp3XMNUiBaLASy74TWvdqLzc61fgJReS7NeeJQq8SIEo8CIFosBLLkR9DZ8XCrzkwsFP3BJp6BnZnuOlwEtuzLvy5xvafsmc6VWXE8C/5OQ+vDreSG64jojbPq0Fx06ffcP7Rp4xzxIFXnLhbet3Oq03d0Yr+tcujbYwKaYqvWTewt5+/Ps5hynTgEKHHVDgJeO6Nu15Q/W8lkumTsnNdXgjFHjJrJ7SiNN473NntKZuZpmkKPCSWS4DXL5pCgtfja+kwEsmuU63lOVn16OgVnrJHNdJIDYv74ihNNmiM7xkyoot+5zCvmTO9MQGikwzBV4yxbVzTdC54YpCVXpJtdLQKNZvH8GZs+7Pu9fqIisKvKRYaWgUa7YNB9rmkqlTdHafhKr0klpBw75kznTdb/ehwEsuNDdRZ3YHCrzkwiN3XZt0ETJBgZdU6tq0x3ndJOdqyxo12kkqufSRX7loFjZ0z4uhNPmhwEsm5WUmmLipSi+p41ednzujNZ6C5JACL6njV53X02/1U+BFCkSBl0xRt9nGKPCSKn6TNqpzTWMUeEmNt+ZkhtY0U+AlFXpKI/Afd1YapcBLKnx1/w+SLkIhKPCSCufM//y+ctGsGEqSbwq8pMIU+k/XqG60jVPgJRXuXnhV0kUoBAVeUmFD9zy0T2tJuhi5p8BLarhMGSWNUeBFCkSBFykQBV4yozQ0mnQRMk+Bl9Twu88edBRbuZACL6mh++zRU+AlU1xnjZXqFHhJFb9qvW7dNUaBl1RRtT5aCrykytvW70y6CLmmYaolcfXMECv1UeAlUfXMECv1c6rSkzxCcoTkMMlBb9ldJJ8leZ5kZ43trvG2Gf85RXKN99mDJEcrPrstvMOSrFgbMOx6Jr4xQc7wN5nZjyrePwPg3QA+X2sDM/tnAB0AQHIKgFEA2ytW+YyZPRKgDJIz5wOsu2TOdDXqNajuKr2ZfQ8A6DBwgecdAA6b2Yv1/k7Jl57SiPO6mjAyHK6BNwC7SRqAz5vZY3X8rt8E8NUJyz5C8gMABgH8oZn9pI79Ssas2LIPew+fdFr3TVOI53p1tRcW19tyN5jZdQBuBfBhkjcG+SUkWwDcDuDxisWPApiDcpX/ZQB/VmPb1SQHSQ6eOHEiyK+VFAoSdgAKe8icAm9mo96fx1G+Br8+4O+5FcB3zOxYxT6Pmdk5MzsPYEutfZrZY2bWaWadbW1tAX+tpE2QsEv4fANPspXktPHXAG5GucEuiLsxoTpP8vKKt3fUsU8RCcjlDN8O4FsknwbwbQA7zGwXyTtIvgRgMYAdJL8JACSvIPl6dynvS6ILwJMT9vun3q2+gwBuAvAHIRyP5IjGuAsfzWE88LTo7Oy0wcHBpIshAZSGRvHRJw7i1bEgN+DKYd+/viuiUuUTyQNmVrVPzDj1tJNIBG2cq3Rk47KQSyPjFHgJhfrDZ4MCL3Xr2rQHh46fCXWfmv89Wno8VuoSRdjbp7Vo/veIKfBSl7DDvnLRLDXSxUBVeknMxRc14dPvnq8+8jFS4CU2CnjyFHiJ3MpFs/RYa0roGl4ip7CnhwIvdXnTFOdxECRFFHipy3O9tzmF/pKpU2IojbjSNbzUbfxZ9VoDUV4ydQoOfuKWuIslk1DgpWHdC2aq5T0jVKUXKRAFXqRAFHiRAlHgRQpEgRcpEAVepEAUeJECUeBFCkSBFykQBV6kQBR4kQJR4EUKRIEXKRAFXqRAFHiRAlHgRQpEgRcpEAVepEAUeJECUeBFCoRmlnQZnJE8AeDFhIvxFgA/SrgMUdBxZUu14/oFM2ubbKNMBT4NSA6aWWfS5Qibjitb6j0uVelFCkSBFykQBT64x5IuQER0XNlS13HpGl6kQHSGFykQBX4CkteQHK74OUVyzYR1VpA8SHKE5D+SvDap8rpyOa6Kdf8zyTGS74m7nEG5HhfJpd7nz5L8v0mUNSjH/4s/T/KvST7tHdtvTbpTM9NPjR8AUwD8K8r3NyuX/xcAl3mvbwWwP+myhnFcFZ/9HYCdAN6TdFlD+ve6FMB3Aczy3s9IuqwhHtvHADzkvW4DcBJAS6396Aw/uXcAOGxmb+jsY2b/aGY/8d4OALgy9pI1pupxeX4PwBMAjsdbpFDUOq73AXjSzI4CgJnl6dgMwDSSBPBzKAd+rNZOFPjJ/SaAr/qs89sAnoqhLGGqelwkZwK4A8CjsZcoHLX+vX4JwGUk95A8QPIDMZcrDLWO7bMAfhnADwGMAPh9Mztfcy9JV1XS+gOgBeWui+2TrHMTgO8BeHPS5Q3juAA8DmCR9/pLyFCV3ue4PotyTawV5S6phwD8UtJlDunY3gPgMwAI4GoA/wLgklr7ag7yFVMwtwL4jpkdq/YhyfkAvgDgVjP7cawla8xkx9UJ4Gvl2iHeAuA2kmNmVoqzgHWa7LheAvBjMzsD4AzJfwBwLYDvx1nABkx2bL8FYKOV0/88yX8B8DYA3662I1Xpa7sbNarzJGcBeBLA+80sK/9pxtU8LjN7q5nNNrPZAL4O4HczEnZgkuMC8A0AN5BsJvmfACxEuWaWFZMd21GUr+9Bsh3ANQBeqLUjdbypgmQryn+Rv2hm/+YtuxcAzOxzJL8A4E78x5N7Y5aBBzT8jmvCul8C8Ddm9vW4yxmUy3GRvA/ls+F5AF8ws80JFTcQh/+LV6B8+XU5ytX6jWa2teb+FHiR4lCVXqRAFHiRAlHgRQpEgRcpEAVepEAUeJECUeBFCkSBFymQ/w8mmGO+lQWCQwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "track_df.plot(figsize=(8, 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inspecting a single Track" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "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()[5]\n", + "some_track = track_df[track_df['track.id'] == some_track_id]\n", + "some_track.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "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": "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": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "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": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'/home/hafenkran/dev/envirocar/envirocar-py/examples/tracks_muenster.html'" + ] + }, + "execution_count": 7, + "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=7.5963592529296875,\n", + " latitude=51.96246168188569,\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=\n", + ")\n", + "r.to_html('tracks_muenster.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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 7ff8b677ec9100ff90a3cc16e0b353001dbde4c6 Mon Sep 17 00:00:00 2001 From: maneenp Date: Fri, 17 Apr 2020 12:36:53 +0200 Subject: [PATCH 2/5] Customized to view data in moenchengladbach --- examples/api_request_deckgl_maneenp.ipynb | 500 +++++++++++----------- 1 file changed, 258 insertions(+), 242 deletions(-) diff --git a/examples/api_request_deckgl_maneenp.ipynb b/examples/api_request_deckgl_maneenp.ipynb index a0c5fc8..f470918 100644 --- a/examples/api_request_deckgl_maneenp.ipynb +++ b/examples/api_request_deckgl_maneenp.ipynb @@ -10,20 +10,10 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'geopandas'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# load dependencies'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0menvirocar\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTrackAPI\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDownloadClient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBboxSelector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mECConfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'geopandas'" - ] - } - ], + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -82,39 +72,39 @@ " id\n", " time\n", " geometry\n", - " GPS PDOP.value\n", - " GPS PDOP.unit\n", - " Speed.value\n", - " Speed.unit\n", - " GPS Altitude.value\n", - " GPS Altitude.unit\n", " GPS Bearing.value\n", + " GPS Bearing.unit\n", + " Intake Temperature.value\n", + " Intake Temperature.unit\n", + " MAF.value\n", + " MAF.unit\n", + " Intake Pressure.value\n", " ...\n", " Consumption.value\n", " Consumption.unit\n", + " Throttle Position.value\n", + " Throttle Position.unit\n", " track.appVersion\n", " track.touVersion\n", - " O2 Lambda Voltage ER.value\n", - " O2 Lambda Voltage ER.unit\n", - " MAF.value\n", - " MAF.unit\n", " O2 Lambda Voltage.value\n", " O2 Lambda Voltage.unit\n", + " O2 Lambda Voltage ER.value\n", + " O2 Lambda Voltage ER.unit\n", " \n", " \n", " \n", " \n", " 0\n", - " 5e8b930965b80c5d6b4d7cd1\n", - " 2020-03-07T12:33:15\n", - " POINT (7.64069 51.95733)\n", - " 1.090631\n", - " precision\n", - " 28.999999\n", - " km/h\n", - " 110.381939\n", - " m\n", - " 124.858622\n", + " 5b46294f44ea850910a1c1e0\n", + " 2018-07-11T06:07:23\n", + " POINT (6.41455 51.20328)\n", + " 117.943870\n", + " deg\n", + " 28.000001\n", + " c\n", + " 35.669282\n", + " l/s\n", + " 120.000001\n", " ...\n", " NaN\n", " NaN\n", @@ -129,16 +119,16 @@ " \n", " \n", " 1\n", - " 5e8b930965b80c5d6b4d7cd3\n", - " 2020-03-07T12:33:20\n", - " POINT (7.64118 51.95712)\n", - " 1.000000\n", - " precision\n", + " 5b46294f44ea850910a1c1e2\n", + " 2018-07-11T06:07:28\n", + " POINT (6.41512 51.20308)\n", + " 115.088323\n", + " deg\n", " 28.000000\n", - " km/h\n", - " 108.260375\n", - " m\n", - " 125.020801\n", + " c\n", + " 31.446508\n", + " l/s\n", + " 113.073678\n", " ...\n", " NaN\n", " NaN\n", @@ -153,16 +143,16 @@ " \n", " \n", " 2\n", - " 5e8b930965b80c5d6b4d7cd4\n", - " 2020-03-07T12:33:26\n", - " POINT (7.64162 51.95690)\n", - " 1.257198\n", - " precision\n", - " 28.000001\n", - " km/h\n", - " 105.826028\n", - " m\n", - " 121.203960\n", + " 5b46294f44ea850910a1c1e3\n", + " 2018-07-11T06:07:33\n", + " POINT (6.41564 51.20299)\n", + " 106.541059\n", + " deg\n", + " 28.000000\n", + " c\n", + " 28.072108\n", + " l/s\n", + " 112.068772\n", " ...\n", " NaN\n", " NaN\n", @@ -177,16 +167,16 @@ " \n", " \n", " 3\n", - " 5e8b930965b80c5d6b4d7cd5\n", - " 2020-03-07T12:33:31\n", - " POINT (7.64210 51.95672)\n", - " 1.000000\n", - " precision\n", - " 30.000000\n", - " km/h\n", - " 104.395998\n", - " m\n", - " 123.412759\n", + " 5b46294f44ea850910a1c1e4\n", + " 2018-07-11T06:07:38\n", + " POINT (6.41615 51.20291)\n", + " 99.885196\n", + " deg\n", + " 28.000000\n", + " c\n", + " 33.429661\n", + " l/s\n", + " 129.759303\n", " ...\n", " NaN\n", " NaN\n", @@ -201,16 +191,16 @@ " \n", " \n", " 4\n", - " 5e8b930965b80c5d6b4d7cd6\n", - " 2020-03-07T12:33:36\n", - " POINT (7.64264 51.95650)\n", - " 1.026727\n", - " precision\n", - " 31.409419\n", - " km/h\n", - " 101.516865\n", - " m\n", - " 122.170479\n", + " 5b46294f44ea850910a1c1e5\n", + " 2018-07-11T06:07:43\n", + " POINT (6.41671 51.20282)\n", + " 104.435649\n", + " deg\n", + " 27.999999\n", + " c\n", + " 25.774274\n", + " l/s\n", + " 106.020177\n", " ...\n", " NaN\n", " NaN\n", @@ -248,22 +238,22 @@ " ...\n", " \n", " \n", - " 283\n", - " 5dc986e844ea856b702e3e0b\n", - " 2019-10-28T16:34:55\n", - " POINT (7.59523 51.96505)\n", - " 1.700000\n", - " precision\n", - " 47.999999\n", - " km/h\n", - " 109.652212\n", - " m\n", - " 276.419653\n", - " ...\n", - " 3.122268\n", - " l/h\n", + " 139\n", + " 5b435e1944ea8509109ec020\n", + " 2018-07-05T14:54:40\n", + " POINT (6.37907 51.16716)\n", + " 306.505961\n", + " deg\n", + " 51.000000\n", + " c\n", " NaN\n", " NaN\n", + " 47.754251\n", + " ...\n", + " 3.018230\n", + " l/h\n", + " 16.373109\n", + " %\n", " NaN\n", " NaN\n", " NaN\n", @@ -272,22 +262,22 @@ " NaN\n", " \n", " \n", - " 284\n", - " 5dc986e844ea856b702e3e0c\n", - " 2019-10-28T16:35:00\n", - " POINT (7.59425 51.96512)\n", - " 1.497088\n", - " precision\n", - " 48.297297\n", - " km/h\n", - " 110.122771\n", - " m\n", - " 276.271049\n", - " ...\n", - " 2.853618\n", - " l/h\n", + " 140\n", + " 5b435e1944ea8509109ec021\n", + " 2018-07-05T14:54:45\n", + " POINT (6.37828 51.16748)\n", + " 301.887519\n", + " deg\n", + " 50.289967\n", + " c\n", " NaN\n", " NaN\n", + " 64.000001\n", + " ...\n", + " 4.207688\n", + " l/h\n", + " 19.000000\n", + " %\n", " NaN\n", " NaN\n", " NaN\n", @@ -296,22 +286,22 @@ " NaN\n", " \n", " \n", - " 285\n", - " 5dc986e844ea856b702e3e0d\n", - " 2019-10-28T16:35:05\n", - " POINT (7.59327 51.96518)\n", - " 1.688911\n", - " precision\n", - " 49.000001\n", - " km/h\n", - " 110.573987\n", - " m\n", - " 275.808021\n", - " ...\n", - " 4.657916\n", - " l/h\n", + " 141\n", + " 5b435e1944ea8509109ec022\n", + " 2018-07-05T14:54:50\n", + " POINT (6.37755 51.16790)\n", + " 320.367792\n", + " deg\n", + " 50.000000\n", + " c\n", " NaN\n", " NaN\n", + " 58.226904\n", + " ...\n", + " 3.994924\n", + " l/h\n", + " 17.712219\n", + " %\n", " NaN\n", " NaN\n", " NaN\n", @@ -320,22 +310,22 @@ " NaN\n", " \n", " \n", - " 286\n", - " 5dc986e844ea856b702e3e0e\n", - " 2019-10-28T16:35:10\n", - " POINT (7.59225 51.96525)\n", - " 1.300000\n", - " precision\n", - " 51.000000\n", - " km/h\n", - " 111.140661\n", - " m\n", - " 275.411387\n", - " ...\n", - " 3.445271\n", - " l/h\n", + " 142\n", + " 5b435e1944ea8509109ec023\n", + " 2018-07-05T14:54:55\n", + " POINT (6.37694 51.16839)\n", + " 324.743345\n", + " deg\n", + " 50.000001\n", + " c\n", " NaN\n", " NaN\n", + " 25.384494\n", + " ...\n", + " 1.648222\n", + " l/h\n", + " 14.000000\n", + " %\n", " NaN\n", " NaN\n", " NaN\n", @@ -344,22 +334,22 @@ " NaN\n", " \n", " \n", - " 287\n", - " 5dc986e844ea856b702e3e0f\n", - " 2019-10-28T16:35:15\n", - " POINT (7.59123 51.96531)\n", - " 1.423253\n", - " precision\n", - " 50.000001\n", - " km/h\n", - " 111.891658\n", - " m\n", - " 276.124438\n", - " ...\n", - " 3.248333\n", - " l/h\n", + " 143\n", + " 5b435e1944ea8509109ec024\n", + " 2018-07-05T14:55:00\n", + " POINT (6.37646 51.16890)\n", + " 330.838042\n", + " deg\n", + " 49.999999\n", + " c\n", " NaN\n", " NaN\n", + " 64.546853\n", + " ...\n", + " 4.344023\n", + " l/h\n", + " 18.472844\n", + " %\n", " NaN\n", " NaN\n", " NaN\n", @@ -369,89 +359,102 @@ " \n", " \n", "\n", - "

9944 rows × 54 columns

\n", + "

22006 rows × 54 columns

\n", "" ], "text/plain": [ " id time geometry \\\n", - "0 5e8b930965b80c5d6b4d7cd1 2020-03-07T12:33:15 POINT (7.64069 51.95733) \n", - "1 5e8b930965b80c5d6b4d7cd3 2020-03-07T12:33:20 POINT (7.64118 51.95712) \n", - "2 5e8b930965b80c5d6b4d7cd4 2020-03-07T12:33:26 POINT (7.64162 51.95690) \n", - "3 5e8b930965b80c5d6b4d7cd5 2020-03-07T12:33:31 POINT (7.64210 51.95672) \n", - "4 5e8b930965b80c5d6b4d7cd6 2020-03-07T12:33:36 POINT (7.64264 51.95650) \n", + "0 5b46294f44ea850910a1c1e0 2018-07-11T06:07:23 POINT (6.41455 51.20328) \n", + "1 5b46294f44ea850910a1c1e2 2018-07-11T06:07:28 POINT (6.41512 51.20308) \n", + "2 5b46294f44ea850910a1c1e3 2018-07-11T06:07:33 POINT (6.41564 51.20299) \n", + "3 5b46294f44ea850910a1c1e4 2018-07-11T06:07:38 POINT (6.41615 51.20291) \n", + "4 5b46294f44ea850910a1c1e5 2018-07-11T06:07:43 POINT (6.41671 51.20282) \n", ".. ... ... ... \n", - "283 5dc986e844ea856b702e3e0b 2019-10-28T16:34:55 POINT (7.59523 51.96505) \n", - "284 5dc986e844ea856b702e3e0c 2019-10-28T16:35:00 POINT (7.59425 51.96512) \n", - "285 5dc986e844ea856b702e3e0d 2019-10-28T16:35:05 POINT (7.59327 51.96518) \n", - "286 5dc986e844ea856b702e3e0e 2019-10-28T16:35:10 POINT (7.59225 51.96525) \n", - "287 5dc986e844ea856b702e3e0f 2019-10-28T16:35:15 POINT (7.59123 51.96531) \n", + "139 5b435e1944ea8509109ec020 2018-07-05T14:54:40 POINT (6.37907 51.16716) \n", + "140 5b435e1944ea8509109ec021 2018-07-05T14:54:45 POINT (6.37828 51.16748) \n", + "141 5b435e1944ea8509109ec022 2018-07-05T14:54:50 POINT (6.37755 51.16790) \n", + "142 5b435e1944ea8509109ec023 2018-07-05T14:54:55 POINT (6.37694 51.16839) \n", + "143 5b435e1944ea8509109ec024 2018-07-05T14:55:00 POINT (6.37646 51.16890) \n", "\n", - " GPS PDOP.value GPS PDOP.unit Speed.value Speed.unit GPS Altitude.value \\\n", - "0 1.090631 precision 28.999999 km/h 110.381939 \n", - "1 1.000000 precision 28.000000 km/h 108.260375 \n", - "2 1.257198 precision 28.000001 km/h 105.826028 \n", - "3 1.000000 precision 30.000000 km/h 104.395998 \n", - "4 1.026727 precision 31.409419 km/h 101.516865 \n", - ".. ... ... ... ... ... \n", - "283 1.700000 precision 47.999999 km/h 109.652212 \n", - "284 1.497088 precision 48.297297 km/h 110.122771 \n", - "285 1.688911 precision 49.000001 km/h 110.573987 \n", - "286 1.300000 precision 51.000000 km/h 111.140661 \n", - "287 1.423253 precision 50.000001 km/h 111.891658 \n", + " GPS Bearing.value GPS Bearing.unit Intake Temperature.value \\\n", + "0 117.943870 deg 28.000001 \n", + "1 115.088323 deg 28.000000 \n", + "2 106.541059 deg 28.000000 \n", + "3 99.885196 deg 28.000000 \n", + "4 104.435649 deg 27.999999 \n", + ".. ... ... ... \n", + "139 306.505961 deg 51.000000 \n", + "140 301.887519 deg 50.289967 \n", + "141 320.367792 deg 50.000000 \n", + "142 324.743345 deg 50.000001 \n", + "143 330.838042 deg 49.999999 \n", "\n", - " GPS Altitude.unit GPS Bearing.value ... Consumption.value \\\n", - "0 m 124.858622 ... NaN \n", - "1 m 125.020801 ... NaN \n", - "2 m 121.203960 ... NaN \n", - "3 m 123.412759 ... NaN \n", - "4 m 122.170479 ... NaN \n", - ".. ... ... ... ... \n", - "283 m 276.419653 ... 3.122268 \n", - "284 m 276.271049 ... 2.853618 \n", - "285 m 275.808021 ... 4.657916 \n", - "286 m 275.411387 ... 3.445271 \n", - "287 m 276.124438 ... 3.248333 \n", + " Intake Temperature.unit MAF.value MAF.unit Intake Pressure.value ... \\\n", + "0 c 35.669282 l/s 120.000001 ... \n", + "1 c 31.446508 l/s 113.073678 ... \n", + "2 c 28.072108 l/s 112.068772 ... \n", + "3 c 33.429661 l/s 129.759303 ... \n", + "4 c 25.774274 l/s 106.020177 ... \n", + ".. ... ... ... ... ... \n", + "139 c NaN NaN 47.754251 ... \n", + "140 c NaN NaN 64.000001 ... \n", + "141 c NaN NaN 58.226904 ... \n", + "142 c NaN NaN 25.384494 ... \n", + "143 c NaN NaN 64.546853 ... \n", "\n", - " Consumption.unit track.appVersion track.touVersion \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - ".. ... ... ... \n", - "283 l/h NaN NaN \n", - "284 l/h NaN NaN \n", - "285 l/h NaN NaN \n", - "286 l/h NaN NaN \n", - "287 l/h NaN NaN \n", + " Consumption.value Consumption.unit Throttle Position.value \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + ".. ... ... ... \n", + "139 3.018230 l/h 16.373109 \n", + "140 4.207688 l/h 19.000000 \n", + "141 3.994924 l/h 17.712219 \n", + "142 1.648222 l/h 14.000000 \n", + "143 4.344023 l/h 18.472844 \n", "\n", - " O2 Lambda Voltage ER.value O2 Lambda Voltage ER.unit MAF.value MAF.unit \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "283 NaN NaN NaN NaN \n", - "284 NaN NaN NaN NaN \n", - "285 NaN NaN NaN NaN \n", - "286 NaN NaN NaN NaN \n", - "287 NaN NaN NaN NaN \n", + " Throttle Position.unit track.appVersion track.touVersion \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + ".. ... ... ... \n", + "139 % NaN NaN \n", + "140 % NaN NaN \n", + "141 % NaN NaN \n", + "142 % NaN NaN \n", + "143 % NaN NaN \n", "\n", - " O2 Lambda Voltage.value O2 Lambda Voltage.unit \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - ".. ... ... \n", - "283 NaN NaN \n", - "284 NaN NaN \n", - "285 NaN NaN \n", - "286 NaN NaN \n", - "287 NaN NaN \n", + " O2 Lambda Voltage.value O2 Lambda Voltage.unit \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + ".. ... ... \n", + "139 NaN NaN \n", + "140 NaN NaN \n", + "141 NaN NaN \n", + "142 NaN NaN \n", + "143 NaN NaN \n", "\n", - "[9944 rows x 54 columns]" + " O2 Lambda Voltage ER.value O2 Lambda Voltage ER.unit \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + ".. ... ... \n", + "139 NaN NaN \n", + "140 NaN NaN \n", + "141 NaN NaN \n", + "142 NaN NaN \n", + "143 NaN NaN \n", + "\n", + "[22006 rows x 54 columns]" ] }, "execution_count": 2, @@ -461,10 +464,10 @@ ], "source": [ "bbox = BboxSelector([\n", - " 7.601165771484375, # min_x\n", - " 51.94807412325402, # min_y\n", - " 7.648200988769531, # max_x\n", - " 51.97261482608728 # max_y\n", + " 6.3239, # min_x\n", + " 51.1316, # min_y\n", + " 6.5502, # max_x\n", + " 51.2555 # max_y\n", "])\n", "\n", "# issue a query\n", @@ -480,7 +483,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -489,7 +492,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -519,7 +522,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -528,7 +531,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -540,29 +543,29 @@ } ], "source": [ - "some_track_id = track_df['track.id'].unique()[5]\n", + "some_track_id = track_df['track.id'].unique()[24]\n", "some_track = track_df[track_df['track.id'] == some_track_id]\n", "some_track.plot()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -590,19 +593,19 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
" + "
" ], "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -640,7 +643,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -656,7 +661,7 @@ " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -665,7 +670,7 @@ { "data": { "text/plain": [ - "'/home/hafenkran/dev/envirocar/envirocar-py/examples/tracks_muenster.html'" + "'C:\\\\Users\\\\User\\\\envirocar-py\\\\examples\\\\tracks_muenster.html'" ] }, "execution_count": 7, @@ -686,6 +691,9 @@ "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", + "MAPBOX_KEY = \"pk.eyJ1IjoibXByZW1hc2kiLCJhIjoiY2s5NDFueDhyMDFpODNnbjNoNzM1eWhvcCJ9.CqjZdNZJ4h8aejMWX4ZObA\"\n", + "\n", + "\n", "layer = pdk.Layer(\n", " 'ScatterplotLayer',\n", " data=vis_df,\n", @@ -698,8 +706,8 @@ "\n", "# Set the viewport location\n", "view_state = pdk.ViewState(\n", - " longitude=7.5963592529296875,\n", - " latitude=51.96246168188569,\n", + " longitude= 6.433333,\n", + " latitude=51.2,\n", " zoom=10,\n", " min_zoom=5,\n", " max_zoom=15,\n", @@ -708,18 +716,26 @@ "\n", "r = pdk.Deck(\n", " width=200, \n", - " layers=[layer], \n", - " initial_view_state=view_state #, mapbox_key=\n", + " layers=[layer], \n", + " initial_view_state=view_state, \n", + " mapbox_key = MAPBOX_KEY\n", ")\n", "r.to_html('tracks_muenster.html', iframe_width=900)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".env_full", "language": "python", - "name": "python3" + "name": ".env_full" }, "language_info": { "codemirror_mode": { @@ -731,7 +747,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" } }, "nbformat": 4, From 418820ed6a20e726473405b11467dc367b601cfd Mon Sep 17 00:00:00 2001 From: maneenp Date: Fri, 17 Apr 2020 14:57:55 +0200 Subject: [PATCH 3/5] Added plotly and view engine load data --- examples/api_request_deckgl_maneenp.ipynb | 383 +++++++--------------- 1 file changed, 126 insertions(+), 257 deletions(-) diff --git a/examples/api_request_deckgl_maneenp.ipynb b/examples/api_request_deckgl_maneenp.ipynb index f470918..14daf4c 100644 --- a/examples/api_request_deckgl_maneenp.ipynb +++ b/examples/api_request_deckgl_maneenp.ipynb @@ -213,248 +213,62 @@ " NaN\n", " NaN\n", " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 139\n", - " 5b435e1944ea8509109ec020\n", - " 2018-07-05T14:54:40\n", - " POINT (6.37907 51.16716)\n", - " 306.505961\n", - " deg\n", - " 51.000000\n", - " c\n", - " NaN\n", - " NaN\n", - " 47.754251\n", - " ...\n", - " 3.018230\n", - " l/h\n", - " 16.373109\n", - " %\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 140\n", - " 5b435e1944ea8509109ec021\n", - " 2018-07-05T14:54:45\n", - " POINT (6.37828 51.16748)\n", - " 301.887519\n", - " deg\n", - " 50.289967\n", - " c\n", - " NaN\n", - " NaN\n", - " 64.000001\n", - " ...\n", - " 4.207688\n", - " l/h\n", - " 19.000000\n", - " %\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 141\n", - " 5b435e1944ea8509109ec022\n", - " 2018-07-05T14:54:50\n", - " POINT (6.37755 51.16790)\n", - " 320.367792\n", - " deg\n", - " 50.000000\n", - " c\n", - " NaN\n", - " NaN\n", - " 58.226904\n", - " ...\n", - " 3.994924\n", - " l/h\n", - " 17.712219\n", - " %\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 142\n", - " 5b435e1944ea8509109ec023\n", - " 2018-07-05T14:54:55\n", - " POINT (6.37694 51.16839)\n", - " 324.743345\n", - " deg\n", - " 50.000001\n", - " c\n", - " NaN\n", - " NaN\n", - " 25.384494\n", - " ...\n", - " 1.648222\n", - " l/h\n", - " 14.000000\n", - " %\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 143\n", - " 5b435e1944ea8509109ec024\n", - " 2018-07-05T14:55:00\n", - " POINT (6.37646 51.16890)\n", - " 330.838042\n", - " deg\n", - " 49.999999\n", - " c\n", - " NaN\n", - " NaN\n", - " 64.546853\n", - " ...\n", - " 4.344023\n", - " l/h\n", - " 18.472844\n", - " %\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", " \n", "\n", - "

22006 rows × 54 columns

\n", + "

5 rows × 54 columns

\n", "" ], "text/plain": [ - " id time geometry \\\n", - "0 5b46294f44ea850910a1c1e0 2018-07-11T06:07:23 POINT (6.41455 51.20328) \n", - "1 5b46294f44ea850910a1c1e2 2018-07-11T06:07:28 POINT (6.41512 51.20308) \n", - "2 5b46294f44ea850910a1c1e3 2018-07-11T06:07:33 POINT (6.41564 51.20299) \n", - "3 5b46294f44ea850910a1c1e4 2018-07-11T06:07:38 POINT (6.41615 51.20291) \n", - "4 5b46294f44ea850910a1c1e5 2018-07-11T06:07:43 POINT (6.41671 51.20282) \n", - ".. ... ... ... \n", - "139 5b435e1944ea8509109ec020 2018-07-05T14:54:40 POINT (6.37907 51.16716) \n", - "140 5b435e1944ea8509109ec021 2018-07-05T14:54:45 POINT (6.37828 51.16748) \n", - "141 5b435e1944ea8509109ec022 2018-07-05T14:54:50 POINT (6.37755 51.16790) \n", - "142 5b435e1944ea8509109ec023 2018-07-05T14:54:55 POINT (6.37694 51.16839) \n", - "143 5b435e1944ea8509109ec024 2018-07-05T14:55:00 POINT (6.37646 51.16890) \n", + " id time geometry \\\n", + "0 5b46294f44ea850910a1c1e0 2018-07-11T06:07:23 POINT (6.41455 51.20328) \n", + "1 5b46294f44ea850910a1c1e2 2018-07-11T06:07:28 POINT (6.41512 51.20308) \n", + "2 5b46294f44ea850910a1c1e3 2018-07-11T06:07:33 POINT (6.41564 51.20299) \n", + "3 5b46294f44ea850910a1c1e4 2018-07-11T06:07:38 POINT (6.41615 51.20291) \n", + "4 5b46294f44ea850910a1c1e5 2018-07-11T06:07:43 POINT (6.41671 51.20282) \n", "\n", - " GPS Bearing.value GPS Bearing.unit Intake Temperature.value \\\n", - "0 117.943870 deg 28.000001 \n", - "1 115.088323 deg 28.000000 \n", - "2 106.541059 deg 28.000000 \n", - "3 99.885196 deg 28.000000 \n", - "4 104.435649 deg 27.999999 \n", - ".. ... ... ... \n", - "139 306.505961 deg 51.000000 \n", - "140 301.887519 deg 50.289967 \n", - "141 320.367792 deg 50.000000 \n", - "142 324.743345 deg 50.000001 \n", - "143 330.838042 deg 49.999999 \n", + " GPS Bearing.value GPS Bearing.unit Intake Temperature.value \\\n", + "0 117.943870 deg 28.000001 \n", + "1 115.088323 deg 28.000000 \n", + "2 106.541059 deg 28.000000 \n", + "3 99.885196 deg 28.000000 \n", + "4 104.435649 deg 27.999999 \n", "\n", - " Intake Temperature.unit MAF.value MAF.unit Intake Pressure.value ... \\\n", - "0 c 35.669282 l/s 120.000001 ... \n", - "1 c 31.446508 l/s 113.073678 ... \n", - "2 c 28.072108 l/s 112.068772 ... \n", - "3 c 33.429661 l/s 129.759303 ... \n", - "4 c 25.774274 l/s 106.020177 ... \n", - ".. ... ... ... ... ... \n", - "139 c NaN NaN 47.754251 ... \n", - "140 c NaN NaN 64.000001 ... \n", - "141 c NaN NaN 58.226904 ... \n", - "142 c NaN NaN 25.384494 ... \n", - "143 c NaN NaN 64.546853 ... \n", + " Intake Temperature.unit MAF.value MAF.unit Intake Pressure.value ... \\\n", + "0 c 35.669282 l/s 120.000001 ... \n", + "1 c 31.446508 l/s 113.073678 ... \n", + "2 c 28.072108 l/s 112.068772 ... \n", + "3 c 33.429661 l/s 129.759303 ... \n", + "4 c 25.774274 l/s 106.020177 ... \n", "\n", - " Consumption.value Consumption.unit Throttle Position.value \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - ".. ... ... ... \n", - "139 3.018230 l/h 16.373109 \n", - "140 4.207688 l/h 19.000000 \n", - "141 3.994924 l/h 17.712219 \n", - "142 1.648222 l/h 14.000000 \n", - "143 4.344023 l/h 18.472844 \n", + " Consumption.value Consumption.unit Throttle Position.value \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", "\n", - " Throttle Position.unit track.appVersion track.touVersion \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - ".. ... ... ... \n", - "139 % NaN NaN \n", - "140 % NaN NaN \n", - "141 % NaN NaN \n", - "142 % NaN NaN \n", - "143 % NaN NaN \n", + " Throttle Position.unit track.appVersion track.touVersion \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", "\n", - " O2 Lambda Voltage.value O2 Lambda Voltage.unit \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - ".. ... ... \n", - "139 NaN NaN \n", - "140 NaN NaN \n", - "141 NaN NaN \n", - "142 NaN NaN \n", - "143 NaN NaN \n", + " O2 Lambda Voltage.value O2 Lambda Voltage.unit O2 Lambda Voltage ER.value \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", "\n", - " O2 Lambda Voltage ER.value O2 Lambda Voltage ER.unit \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - ".. ... ... \n", - "139 NaN NaN \n", - "140 NaN NaN \n", - "141 NaN NaN \n", - "142 NaN NaN \n", - "143 NaN NaN \n", + " O2 Lambda Voltage ER.unit \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", "\n", - "[22006 rows x 54 columns]" + "[5 rows x 54 columns]" ] }, "execution_count": 2, @@ -472,7 +286,7 @@ "\n", "# issue a query\n", "track_df = track_api.get_tracks(bbox=bbox, num_results=50) # requesting 50 tracks inside the bbox\n", - "track_df" + "track_df.head()" ] }, { @@ -483,7 +297,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -504,7 +318,8 @@ } ], "source": [ - "track_df.plot(figsize=(8, 10))" + "track_df.plot(figsize=(8, 10))\n", + "\n" ] }, { @@ -514,24 +329,66 @@ "# Inspecting a single Track" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Single track is displayed with plotly express" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "line() got an unexpected keyword argument 'line'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"lats\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"longs\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Some track'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'firebrick'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdash\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dash'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 15\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: line() got an unexpected keyword argument 'line'" + ] + } + ], + "source": [ + "some_track_id = track_df['track.id'].unique()[24]\n", + "some_track = track_df[track_df['track.id'] == some_track_id]\n", + "#some_track.plot()\n", + "\n", + "import plotly.express as px\n", + "\n", + "dflats = list(some_track['geometry'].apply(lambda coord: coord.y))\n", + "dflngs = list(some_track['geometry'].apply(lambda coord: coord.x))\n", + "\n", + "d = {'lats':dflats,'longs':dflngs}\n", + "df = pd.DataFrame(d)\n", + "\n", + "\n", + "fig = px.line(df, x=\"lats\", y=\"longs\", title='Some track', line=dict(color='firebrick', width=4, dash='dash'))\n", + "fig.show()\n" + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -543,29 +400,30 @@ } ], "source": [ - "some_track_id = track_df['track.id'].unique()[24]\n", - "some_track = track_df[track_df['track.id'] == some_track_id]\n", - "some_track.plot()" + "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": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -577,9 +435,9 @@ } ], "source": [ - "ax = some_track['Speed.value'].plot()\n", - "ax.set_title(\"Speed\")\n", - "ax.set_ylabel(some_track['Speed.unit'][0])\n", + "ax = some_track['Engine Load.value'].plot()\n", + "ax.set_title(\"Engine Load\")\n", + "ax.set_ylabel(some_track['Engine Load.unit'][0])\n", "ax" ] }, @@ -591,21 +449,26 @@ "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": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
" + "
" ], "text/plain": [ - "" + "" ] }, - "execution_count": 70, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -621,7 +484,13 @@ "\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" + "m\n", + "\n", + "#d1 = {'lats':lats,'longs':lngs}\n", + "#df1 = pd.DataFrame(d1)\n", + "#fig1 = px.scatter_mapbox(df1, lat=\"lats\", lon=\"longs\", size_max=15, zoom=10)\n", + "#fig1.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n", + "#fig1.show()\n" ] }, { @@ -642,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "scrolled": true }, @@ -661,7 +530,7 @@ " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -673,7 +542,7 @@ "'C:\\\\Users\\\\User\\\\envirocar-py\\\\examples\\\\tracks_muenster.html'" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -685,7 +554,7 @@ "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", + "vis_df['speed'] = vis_df['Engine Load.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", @@ -708,7 +577,7 @@ "view_state = pdk.ViewState(\n", " longitude= 6.433333,\n", " latitude=51.2,\n", - " zoom=10,\n", + " zoom=12,\n", " min_zoom=5,\n", " max_zoom=15,\n", " pitch=40.5,\n", From bf38b67d67c7468b3f44c1ab26bf066dc942e97f Mon Sep 17 00:00:00 2001 From: maneenp Date: Fri, 17 Apr 2020 15:00:47 +0200 Subject: [PATCH 4/5] Fixed minor bug --- examples/api_request_deckgl_maneenp.ipynb | 1320 ++++++++++++++++++++- 1 file changed, 1304 insertions(+), 16 deletions(-) diff --git a/examples/api_request_deckgl_maneenp.ipynb b/examples/api_request_deckgl_maneenp.ipynb index 14daf4c..e8de471 100644 --- a/examples/api_request_deckgl_maneenp.ipynb +++ b/examples/api_request_deckgl_maneenp.ipynb @@ -338,19 +338,1307 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "line() got an unexpected keyword argument 'line'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"lats\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"longs\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Some track'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'firebrick'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdash\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dash'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 15\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: line() got an unexpected keyword argument 'line'" - ] + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "lats=%{x}
longs=%{y}", + "legendgroup": "", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "mode": "lines", + "name": "", + "showlegend": false, + "type": "scatter", + "x": [ + 51.166520238258684, + 51.16653832298039, + 51.16656264793261, + 51.16646182759749, + 51.166440519205835, + 51.16643880632864, + 51.16643880632865, + 51.16643880632865, + 51.16643880632865, + 51.166432333335905, + 51.166432333335905, + 51.1664324486022, + 51.16643015375647, + 51.166382393477804, + 51.166296877622536, + 51.1661877765454, + 51.16604121429204, + 51.16596060763668, + 51.16599229690948, + 51.166061852773055, + 51.1663254126309, + 51.166587540212916, + 51.166852345662974, + 51.16710924898567, + 51.16735346718181, + 51.167605360101454, + 51.16790627366481, + 51.16817891467156, + 51.16847893635425, + 51.168590493675495, + 51.16859203379417, + 51.16859203379417, + 51.16858974637992, + 51.16859050885134, + 51.16859050885134, + 51.16859203379417, + 51.16859050885134, + 51.168588983908506, + 51.168588983908506, + 51.168880156702514, + 51.168680669543335, + 51.16833300002915, + 51.16811276490024, + 51.16782644492167, + 51.167418838197094, + 51.16691452780074, + 51.16641839229939, + 51.166000422027786, + 51.165647922503844, + 51.16531773506742, + 51.16499674349526, + 51.16482514329071, + 51.164675477576715, + 51.16451643894863, + 51.16436130551978, + 51.16424717286806, + 51.164214959509685, + 51.16410572053105, + 51.16391591459434, + 51.163650041319805, + 51.1634026413052, + 51.163179742417896, + 51.16299908575811, + 51.162898944454014, + 51.16276534660681, + 51.16260393379502, + 51.16248508025819, + 51.16242891295164, + 51.16242732171028, + 51.16242427219207, + 51.16242579695117, + 51.16242579695118, + 51.16242427219207, + 51.16233130808528, + 51.162212372580306, + 51.162001990814986, + 51.161718640368136, + 51.16140202975698, + 51.161061213628656, + 51.16075747519888, + 51.160454049320656, + 51.16014239730554, + 51.15983244101062, + 51.159468943937604, + 51.15910112212867, + 51.158735836529935, + 51.158370848410264, + 51.157988558373866, + 51.15757011050944, + 51.15728814043668, + 51.156828020435775, + 51.15632128965406, + 51.15587058350168, + 51.15549072626955, + 51.15529622214812, + 51.155135510977054, + 51.15481862594824, + 51.15436267238311, + 51.154058407654496, + 51.15385765466693, + 51.1536662457653, + 51.15347680679291, + 51.15331476634056, + 51.153163281143726, + 51.15303042369066, + 51.152906610052696, + 51.152855686383084, + 51.1529006471725, + 51.153013644536756, + 51.15309103156373, + 51.15313643163974, + 51.15319505880371, + 51.1532263732897, + 51.15294065280713, + 51.152467745077985, + 51.152119209646514, + 51.15184870077682, + 51.1515229478147, + 51.151180146413, + 51.150833794232135, + 51.15037954713465, + 51.149964816684744, + 51.14970869958706, + 51.149296912111524, + 51.14860082609311, + 51.14782141803066, + 51.14716722975821, + 51.146558971732745, + 51.14627903619841, + 51.14629969920571, + 51.14638815316392, + 51.14637172963084, + 51.14623107562706, + 51.146140520901625, + 51.14606095706388, + 51.14605667294683, + 51.146254042000535, + 51.14658641089902, + 51.14689673207372, + 51.14675003975557, + 51.14635007490413, + 51.146050727893936, + 51.14578295664976, + 51.14572780904961, + 51.14566769236862, + 51.145343252126224, + 51.14483621668935, + 51.144591989586864, + 51.14457155053952, + 51.14457155053952, + 51.14457307476655, + 51.144573074766555, + 51.14457231265303, + 51.14447546719563, + 51.144205776497174, + 51.14397773184671, + 51.14368956493835, + 51.143530811017584, + 51.14315677581031, + 51.142758420466706, + 51.14273313356409, + 51.14273237147798, + 51.14273237147799, + 51.14254921273382, + 51.14206268626151, + 51.141404601766354, + 51.14072056862054, + 51.14006874818632, + 51.13985517704026, + 51.13981953426666, + 51.140124868564335, + 51.14049649587531, + 51.14102288747485, + 51.1418510911582, + 51.14279263781528, + 51.14373407972915, + 51.14452586170071, + 51.145156109371435, + 51.145556490139555, + 51.14565719667323, + 51.14559605567537, + 51.145416416234156, + 51.14521459648099, + 51.145050147063046, + 51.14490934956546, + 51.14479696151565, + 51.144666466213984, + 51.1445301237354, + 51.14440949248945, + 51.144134905379836, + 51.14356878183244, + 51.14297130081338, + 51.142366574913645, + 51.14175681303846, + 51.1412350155587 + ], + "xaxis": "x", + "y": [ + 6.44965563644609, + 6.45055935533521, + 6.451525548246278, + 6.452174481501801, + 6.452312430790405, + 6.452313888300575, + 6.452313888300575, + 6.452313888300575, + 6.452313888300575, + 6.452324120832352, + 6.452324120832352, + 6.452325832957797, + 6.452335823431408, + 6.452676354987936, + 6.45329537488239, + 6.454147821371768, + 6.455058178381915, + 6.456108524517946, + 6.4570548954579845, + 6.4578827975061355, + 6.458685614198814, + 6.459501926059821, + 6.460377479397283, + 6.461209760230249, + 6.461994939154116, + 6.462828938987607, + 6.463821658461972, + 6.464715768814235, + 6.465460145142524, + 6.465713755969625, + 6.465713965520415, + 6.465713965520415, + 6.465713676480484, + 6.465713772827128, + 6.465713772827128, + 6.465713965520414, + 6.465713772827128, + 6.465713580133841, + 6.4657135801338415, + 6.466105614892133, + 6.466639380084272, + 6.467399208075134, + 6.46780749312367, + 6.468360939959146, + 6.469213302098073, + 6.470220046284913, + 6.471220005698736, + 6.472003733087213, + 6.472436136773506, + 6.472805409874221, + 6.473148245965609, + 6.473756134973026, + 6.4745421699184025, + 6.4753227696458975, + 6.476126914770111, + 6.4766161843578525, + 6.47674353614436, + 6.477299828453029, + 6.478298935838744, + 6.479444070023083, + 6.480499878208304, + 6.481205053394329, + 6.481726402353355, + 6.4820698389386555, + 6.482490051986289, + 6.4829446501670445, + 6.483333348321977, + 6.483531443028704, + 6.483540767364044, + 6.483540380914912, + 6.483540574139478, + 6.483540574139478, + 6.483540380914911, + 6.483853115546298, + 6.484298338868039, + 6.485193486160471, + 6.486374541038783, + 6.487723869342826, + 6.489145161983988, + 6.490505983197281, + 6.491879087166426, + 6.493234732383994, + 6.494521471391984, + 6.495882969934129, + 6.497265322959509, + 6.498609585129135, + 6.499974041058769, + 6.501353443392771, + 6.5026776450957104, + 6.503651027808806, + 6.50402638009336, + 6.504442609992665, + 6.504770538252721, + 6.504927457043855, + 6.504945398519796, + 6.5048317566120515, + 6.504279580743473, + 6.50343650244639, + 6.502504026098893, + 6.5019303680268905, + 6.501501012475114, + 6.5018267082194265, + 6.502641198588001, + 6.503628743419604, + 6.504544084965584, + 6.505423634329172, + 6.5062955441909045, + 6.507120496681336, + 6.508028641756308, + 6.508868295044428, + 6.509701779376148, + 6.510638720444412, + 6.511509130093299, + 6.512002018799151, + 6.512380963404935, + 6.512767591902145, + 6.513357821674342, + 6.514139390021986, + 6.514943876078721, + 6.515709468597429, + 6.516236760154584, + 6.51682582683582, + 6.51774423535627, + 6.518604914241889, + 6.519346908901606, + 6.5200718325713165, + 6.520911945297207, + 6.5217312589888925, + 6.522585333446599, + 6.523947947771093, + 6.525443380750109, + 6.526918426650752, + 6.528554811307, + 6.530354006845208, + 6.532170998883419, + 6.533882575619683, + 6.535310601398695, + 6.536856328641533, + 6.5384338176168475, + 6.539935498504091, + 6.541609439432516, + 6.542878466144334, + 6.543867678070267, + 6.544567416843731, + 6.544831164760503, + 6.544895174069943, + 6.544682750735607, + 6.544583327184267, + 6.544571527221617, + 6.544571527221617, + 6.544571722265051, + 6.544571722265051, + 6.544571624743334, + 6.54452748977633, + 6.544357517280142, + 6.544151296100817, + 6.543887374417969, + 6.543763530000757, + 6.5435894849372715, + 6.543622054543165, + 6.543626022721208, + 6.543625925213581, + 6.543625925213581, + 6.543684706798793, + 6.543760819793374, + 6.54390658280006, + 6.544128294636792, + 6.544335893153744, + 6.544419650563794, + 6.5449910745236215, + 6.546087502390855, + 6.5474341846259225, + 6.549017918340255, + 6.5504474827946435, + 6.55181068733429, + 6.553028647376103, + 6.553834532612429, + 6.55444471321495, + 6.554873787058483, + 6.555026820885618, + 6.555513518182473, + 6.556476029624289, + 6.55746376289082, + 6.558373282721926, + 6.559389069249312, + 6.56027263915617, + 6.561201323519332, + 6.562161391716282, + 6.5629595010477395, + 6.5631488295032145, + 6.563238866774545, + 6.5633118291624015, + 6.5634244359643015, + 6.563533543388902, + 6.5639658706178015 + ], + "yaxis": "y" + } + ], + "layout": { + "legend": { + "tracegroupgap": 0 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Some track" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "lats" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "longs" + } + } + } + }, + "text/html": [ + "
\n", + " \n", + " \n", + "
\n", + " \n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -367,7 +1655,7 @@ "df = pd.DataFrame(d)\n", "\n", "\n", - "fig = px.line(df, x=\"lats\", y=\"longs\", title='Some track', line=dict(color='firebrick', width=4, dash='dash'))\n", + "fig = px.line(df, x=\"lats\", y=\"longs\", title='Some track')\n", "fig.show()\n" ] }, @@ -511,7 +1799,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -530,7 +1818,7 @@ " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -542,7 +1830,7 @@ "'C:\\\\Users\\\\User\\\\envirocar-py\\\\examples\\\\tracks_muenster.html'" ] }, - "execution_count": 8, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -554,7 +1842,7 @@ "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['Engine Load.value']\n", + "vis_df['load'] = vis_df['Engine Load.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", @@ -569,7 +1857,7 @@ " 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", + " get_fill_color='[load < 20 ? 0 : (load - 20)*8.5, load < 50 ? 255 : 255 - (load-50)*8.5, 0, 140]', # Set an RGBA value for fill\n", " pickable=True\n", ")\n", "\n", From e4d69215c2bfefe4fcd1d5fa8b12ca5cee9f1b63 Mon Sep 17 00:00:00 2001 From: Maneesha Date: Fri, 3 Jul 2020 11:47:22 +0200 Subject: [PATCH 5/5] Added Aggregating and visualizing statistics --- AggregatingStatistics.ipynb | 7708 +++++++++++++++++++++++++++++++++++ track_2.csv | 77 + 2 files changed, 7785 insertions(+) create mode 100644 AggregatingStatistics.ipynb create mode 100644 track_2.csv diff --git a/AggregatingStatistics.ipynb b/AggregatingStatistics.ipynb new file mode 100644 index 0000000..858a038 --- /dev/null +++ b/AggregatingStatistics.ipynb @@ -0,0 +1,7708 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import osmapi as osm\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotly as pt\n", + "import osmapi as osm\n", + "import plotly.graph_objects as go\n", + "import plotly.express as px\n", + "import pydeck as pdk\n", + "import folium\n", + "import os\n", + "import datetime\n", + "api = osm.OsmApi()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Unnamed: 0idtimegeometryEngine Load.valueEngine Load.unitCO2.valueCO2.unitIntake Pressure.valueIntake Pressure.unit...track.touVersionO2 Lambda Voltage ER.valueO2 Lambda Voltage ER.unitMAF.valueMAF.unitO2 Lambda Voltage.valueO2 Lambda Voltage.unitst_nodesend_nodesmax_speed
01335e42ccb63965f3689459b8f92020-02-03T17:23:04POINT (7.601057994636752 51.94497494499429)60.693559%12.532860kg/h57.808463kPa...2013-10-01NaNNaNNaNNaNNaNNaN64300140563492474950
11345e42ccb63965f3689459b8fa2020-02-03T17:23:09POINT (7.601532784823066 51.94552838110832)40.934116%10.056936kg/h40.449614kPa...2013-10-01NaNNaNNaNNaNNaNNaN349247493492477750
21355e42ccb63965f3689459b8fb2020-02-03T17:23:14POINT (7.602096892727208 51.94608124562291)30.783810%7.252608kg/h30.000000kPa...2013-10-01NaNNaNNaNNaNNaNNaN3492477746197031850
31365e42ccb63965f3689459b8fc2020-02-03T17:23:19POINT (7.602564980565499 51.94666702340365)29.590101%7.095522kg/h28.999999kPa...2013-10-01NaNNaNNaNNaNNaNNaN4619703183492478450
41375e42ccb63965f3689459b8fd2020-02-03T17:23:24POINT (7.602900041979403 51.94731436517897)27.168067%6.939990kg/h26.727273kPa...2013-10-01NaNNaNNaNNaNNaNNaN34924784643001403550
..................................................................
712045e42ccb63965f3689459b9412020-02-03T17:29:05POINT (7.595171614118939 51.96509090564787)84.810088%18.109086kg/h76.758218kPa...2013-10-01NaNNaNNaNNaNNaNNaN55122690512904140250
722055e42ccb63965f3689459b9422020-02-03T17:29:10POINT (7.594332430578294 51.96514638976937)51.213566%9.600139kg/h49.749305kPa...2013-10-01NaNNaNNaNNaNNaNNaN29041402551226905250
732065e42ccb63965f3689459b9432020-02-03T17:29:15POINT (7.593466547343575 51.96519774529605)35.804855%6.828497kg/h35.000001kPa...2013-10-01NaNNaNNaNNaNNaNNaN5512269052551226905350
742075e42ccb63965f3689459b9442020-02-03T17:29:20POINT (7.592625345160008 51.96525465733892)26.351722%4.562019kg/h25.027027kPa...2013-10-01NaNNaNNaNNaNNaNNaN5512269053551226905450
752085e42ccb63965f3689459b9452020-02-03T17:29:25POINT (7.591823222371572 51.96530573732579)40.995717%7.682926kg/h39.492537kPa...2013-10-01NaNNaNNaNNaNNaNNaN5512269054112024994250
\n", + "

76 rows × 58 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 id time \\\n", + "0 133 5e42ccb63965f3689459b8f9 2020-02-03T17:23:04 \n", + "1 134 5e42ccb63965f3689459b8fa 2020-02-03T17:23:09 \n", + "2 135 5e42ccb63965f3689459b8fb 2020-02-03T17:23:14 \n", + "3 136 5e42ccb63965f3689459b8fc 2020-02-03T17:23:19 \n", + "4 137 5e42ccb63965f3689459b8fd 2020-02-03T17:23:24 \n", + ".. ... ... ... \n", + "71 204 5e42ccb63965f3689459b941 2020-02-03T17:29:05 \n", + "72 205 5e42ccb63965f3689459b942 2020-02-03T17:29:10 \n", + "73 206 5e42ccb63965f3689459b943 2020-02-03T17:29:15 \n", + "74 207 5e42ccb63965f3689459b944 2020-02-03T17:29:20 \n", + "75 208 5e42ccb63965f3689459b945 2020-02-03T17:29:25 \n", + "\n", + " geometry Engine Load.value \\\n", + "0 POINT (7.601057994636752 51.94497494499429) 60.693559 \n", + "1 POINT (7.601532784823066 51.94552838110832) 40.934116 \n", + "2 POINT (7.602096892727208 51.94608124562291) 30.783810 \n", + "3 POINT (7.602564980565499 51.94666702340365) 29.590101 \n", + "4 POINT (7.602900041979403 51.94731436517897) 27.168067 \n", + ".. ... ... \n", + "71 POINT (7.595171614118939 51.96509090564787) 84.810088 \n", + "72 POINT (7.594332430578294 51.96514638976937) 51.213566 \n", + "73 POINT (7.593466547343575 51.96519774529605) 35.804855 \n", + "74 POINT (7.592625345160008 51.96525465733892) 26.351722 \n", + "75 POINT (7.591823222371572 51.96530573732579) 40.995717 \n", + "\n", + " Engine Load.unit CO2.value CO2.unit Intake Pressure.value \\\n", + "0 % 12.532860 kg/h 57.808463 \n", + "1 % 10.056936 kg/h 40.449614 \n", + "2 % 7.252608 kg/h 30.000000 \n", + "3 % 7.095522 kg/h 28.999999 \n", + "4 % 6.939990 kg/h 26.727273 \n", + ".. ... ... ... ... \n", + "71 % 18.109086 kg/h 76.758218 \n", + "72 % 9.600139 kg/h 49.749305 \n", + "73 % 6.828497 kg/h 35.000001 \n", + "74 % 4.562019 kg/h 25.027027 \n", + "75 % 7.682926 kg/h 39.492537 \n", + "\n", + " Intake Pressure.unit ... track.touVersion O2 Lambda Voltage ER.value \\\n", + "0 kPa ... 2013-10-01 NaN \n", + "1 kPa ... 2013-10-01 NaN \n", + "2 kPa ... 2013-10-01 NaN \n", + "3 kPa ... 2013-10-01 NaN \n", + "4 kPa ... 2013-10-01 NaN \n", + ".. ... ... ... ... \n", + "71 kPa ... 2013-10-01 NaN \n", + "72 kPa ... 2013-10-01 NaN \n", + "73 kPa ... 2013-10-01 NaN \n", + "74 kPa ... 2013-10-01 NaN \n", + "75 kPa ... 2013-10-01 NaN \n", + "\n", + " O2 Lambda Voltage ER.unit MAF.value MAF.unit O2 Lambda Voltage.value \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + ".. ... ... ... ... \n", + "71 NaN NaN NaN NaN \n", + "72 NaN NaN NaN NaN \n", + "73 NaN NaN NaN NaN \n", + "74 NaN NaN NaN NaN \n", + "75 NaN NaN NaN NaN \n", + "\n", + " O2 Lambda Voltage.unit st_nodes end_nodes max_speed \n", + "0 NaN 6430014056 34924749 50 \n", + "1 NaN 34924749 34924777 50 \n", + "2 NaN 34924777 461970318 50 \n", + "3 NaN 461970318 34924784 50 \n", + "4 NaN 34924784 6430014035 50 \n", + ".. ... ... ... ... \n", + "71 NaN 5512269051 29041402 50 \n", + "72 NaN 29041402 5512269052 50 \n", + "73 NaN 5512269052 5512269053 50 \n", + "74 NaN 5512269053 5512269054 50 \n", + "75 NaN 5512269054 1120249942 50 \n", + "\n", + "[76 rows x 58 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"track_2.csv\")\n", + "data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 int64\n", + "id object\n", + "time object\n", + "geometry object\n", + "Engine Load.value float64\n", + "Engine Load.unit object\n", + "CO2.value float64\n", + "CO2.unit object\n", + "Intake Pressure.value float64\n", + "Intake Pressure.unit object\n", + "GPS VDOP.value float64\n", + "GPS VDOP.unit object\n", + "GPS Speed.value float64\n", + "GPS Speed.unit object\n", + "Intake Temperature.value float64\n", + "Intake Temperature.unit object\n", + "Consumption.value float64\n", + "Consumption.unit object\n", + "Rpm.value float64\n", + "Rpm.unit object\n", + "GPS HDOP.value float64\n", + "GPS HDOP.unit object\n", + "GPS PDOP.value float64\n", + "GPS PDOP.unit object\n", + "GPS Bearing.value float64\n", + "GPS Bearing.unit object\n", + "Calculated MAF.value float64\n", + "Calculated MAF.unit object\n", + "GPS Accuracy.value float64\n", + "GPS Accuracy.unit object\n", + "Speed.value float64\n", + "Speed.unit object\n", + "Throttle Position.value float64\n", + "Throttle Position.unit object\n", + "GPS Altitude.value float64\n", + "GPS Altitude.unit object\n", + "track.id object\n", + "track.length float64\n", + "track.begin object\n", + "track.end object\n", + "sensor.type object\n", + "sensor.engineDisplacement int64\n", + "sensor.model object\n", + "sensor.id object\n", + "sensor.fuelType object\n", + "sensor.constructionYear int64\n", + "sensor.manufacturer object\n", + "track.appVersion object\n", + "track.touVersion object\n", + "O2 Lambda Voltage ER.value float64\n", + "O2 Lambda Voltage ER.unit float64\n", + "MAF.value float64\n", + "MAF.unit float64\n", + "O2 Lambda Voltage.value float64\n", + "O2 Lambda Voltage.unit float64\n", + "st_nodes int64\n", + "end_nodes int64\n", + "max_speed int64\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
st_nodeend_nodespeedtimespeed_limitCO2
064300140563492474946.2211062020-02-03T17:23:045012.532860
1349247493492477752.9999992020-02-03T17:23:095010.056936
23492477746197031851.9999982020-02-03T17:23:14507.252608
34619703183492478453.0000012020-02-03T17:23:19507.095522
434924784643001403556.0000022020-02-03T17:23:24506.939990
.....................
7155122690512904140238.3000002020-02-03T17:29:055018.109086
7229041402551226905241.8675662020-02-03T17:29:10509.600139
735512269052551226905342.2070582020-02-03T17:29:15506.828497
745512269053551226905440.0000012020-02-03T17:29:20504.562019
755512269054112024994242.0000002020-02-03T17:29:25507.682926
\n", + "

76 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " st_node end_node speed time speed_limit \\\n", + "0 6430014056 34924749 46.221106 2020-02-03T17:23:04 50 \n", + "1 34924749 34924777 52.999999 2020-02-03T17:23:09 50 \n", + "2 34924777 461970318 51.999998 2020-02-03T17:23:14 50 \n", + "3 461970318 34924784 53.000001 2020-02-03T17:23:19 50 \n", + "4 34924784 6430014035 56.000002 2020-02-03T17:23:24 50 \n", + ".. ... ... ... ... ... \n", + "71 5512269051 29041402 38.300000 2020-02-03T17:29:05 50 \n", + "72 29041402 5512269052 41.867566 2020-02-03T17:29:10 50 \n", + "73 5512269052 5512269053 42.207058 2020-02-03T17:29:15 50 \n", + "74 5512269053 5512269054 40.000001 2020-02-03T17:29:20 50 \n", + "75 5512269054 1120249942 42.000000 2020-02-03T17:29:25 50 \n", + "\n", + " CO2 \n", + "0 12.532860 \n", + "1 10.056936 \n", + "2 7.252608 \n", + "3 7.095522 \n", + "4 6.939990 \n", + ".. ... \n", + "71 18.109086 \n", + "72 9.600139 \n", + "73 6.828497 \n", + "74 4.562019 \n", + "75 7.682926 \n", + "\n", + "[76 rows x 6 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edgeset = pd.DataFrame({'st_node': data[\"st_nodes\"], 'end_node': data[\"end_nodes\"], 'speed':data[\"Speed.value\"], 'time':data[\"time\"], 'speed_limit': data[\"max_speed\"], 'CO2':data[\"CO2.value\"]})\n", + "edgeset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def aggregateStatsFromNodes(edgeset, isSpeed, isCO2):\n", + " if isSpeed:\n", + " edge_agg = edgeset.groupby(['st_node', 'end_node']).agg(mean_speed=pd.NamedAgg(column='speed', aggfunc=np.mean),\n", + " min_speed=pd.NamedAgg(column='speed', aggfunc=min),\n", + " max_speed=pd.NamedAgg(column='speed', aggfunc=max))\n", + " edgeset = pd.merge(edgeset, edge_agg, how='left', left_on=['st_node','end_node'], right_on = ['st_node','end_node'])\n", + " edgeset = edgeset.drop(['speed'], axis=1).drop_duplicates()\n", + " return edgeset\n", + " if isCO2:\n", + " edge_agg = edgeset.groupby(['st_node', 'end_node']).agg(mean_co2=pd.NamedAgg(column='CO2', aggfunc=np.mean),\n", + " min_co2=pd.NamedAgg(column='CO2', aggfunc=min),\n", + " max_co2=pd.NamedAgg(column='CO2', aggfunc=max))\n", + " edgeset = pd.merge(edgeset, edge_agg, how='left', left_on=['st_node','end_node'], right_on = ['st_node','end_node'])\n", + " edgeset = edgeset.drop(['CO2'], axis=1).drop_duplicates()\n", + " return edgeset\n", + "edgeset = aggregateStatsFromNodes(edgeset,False, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
st_nodeend_nodespeedtimespeed_limitmean_co2min_co2max_co2StNode_latStNode_lonEndNode_latEndNode_lon
064300140563492474946.2211062020-02-03T17:23:045012.53286012.53286012.53286051.9452047.60117451.9454647.601387
1349247493492477752.9999992020-02-03T17:23:095010.05693610.05693610.05693651.9454647.60138751.9456177.601554
23492477746197031851.9999982020-02-03T17:23:14507.2526087.2526087.25260851.9456177.60155451.9457917.601743
34619703183492478453.0000012020-02-03T17:23:19507.0955227.0955227.09552251.9457917.60174351.9461967.602167
434924784643001403556.0000022020-02-03T17:23:24506.9399906.9399906.93999051.9461967.60216751.9463747.602310
.......................................
7155122690512904140238.3000002020-02-03T17:29:055018.10908618.10908618.10908651.9615267.60254951.9616637.602535
7229041402551226905241.8675662020-02-03T17:29:10509.6001399.6001399.60013951.9616637.60253551.9617707.602520
735512269052551226905342.2070582020-02-03T17:29:15506.8284976.8284976.82849751.9617707.60252051.9619087.602493
745512269053551226905440.0000012020-02-03T17:29:20504.5620194.5620194.56201951.9619087.60249351.9620157.602471
755512269054112024994242.0000002020-02-03T17:29:25507.6829267.6829267.68292651.9620157.60247151.9621707.602432
\n", + "

76 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " st_node end_node speed time speed_limit \\\n", + "0 6430014056 34924749 46.221106 2020-02-03T17:23:04 50 \n", + "1 34924749 34924777 52.999999 2020-02-03T17:23:09 50 \n", + "2 34924777 461970318 51.999998 2020-02-03T17:23:14 50 \n", + "3 461970318 34924784 53.000001 2020-02-03T17:23:19 50 \n", + "4 34924784 6430014035 56.000002 2020-02-03T17:23:24 50 \n", + ".. ... ... ... ... ... \n", + "71 5512269051 29041402 38.300000 2020-02-03T17:29:05 50 \n", + "72 29041402 5512269052 41.867566 2020-02-03T17:29:10 50 \n", + "73 5512269052 5512269053 42.207058 2020-02-03T17:29:15 50 \n", + "74 5512269053 5512269054 40.000001 2020-02-03T17:29:20 50 \n", + "75 5512269054 1120249942 42.000000 2020-02-03T17:29:25 50 \n", + "\n", + " mean_co2 min_co2 max_co2 StNode_lat StNode_lon EndNode_lat \\\n", + "0 12.532860 12.532860 12.532860 51.945204 7.601174 51.945464 \n", + "1 10.056936 10.056936 10.056936 51.945464 7.601387 51.945617 \n", + "2 7.252608 7.252608 7.252608 51.945617 7.601554 51.945791 \n", + "3 7.095522 7.095522 7.095522 51.945791 7.601743 51.946196 \n", + "4 6.939990 6.939990 6.939990 51.946196 7.602167 51.946374 \n", + ".. ... ... ... ... ... ... \n", + "71 18.109086 18.109086 18.109086 51.961526 7.602549 51.961663 \n", + "72 9.600139 9.600139 9.600139 51.961663 7.602535 51.961770 \n", + "73 6.828497 6.828497 6.828497 51.961770 7.602520 51.961908 \n", + "74 4.562019 4.562019 4.562019 51.961908 7.602493 51.962015 \n", + "75 7.682926 7.682926 7.682926 51.962015 7.602471 51.962170 \n", + "\n", + " EndNode_lon \n", + "0 7.601387 \n", + "1 7.601554 \n", + "2 7.601743 \n", + "3 7.602167 \n", + "4 7.602310 \n", + ".. ... \n", + "71 7.602535 \n", + "72 7.602520 \n", + "73 7.602493 \n", + "74 7.602471 \n", + "75 7.602432 \n", + "\n", + "[76 rows x 12 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def appendNodeCoords(edgeset):\n", + " arrStartNodelat = np.array([])\n", + " arrStartNodelon = np.array([])\n", + " arrEndNodelat = np.array([])\n", + " arrEndNodelon = np.array([])\n", + "\n", + " \n", + " for row in edgeset.itertuples():\n", + " \n", + " stNode = api.NodeGet(row.st_node)\n", + " arrStartNodelat = np.append(arrStartNodelat, stNode[\"lat\"])\n", + " arrStartNodelon = np.append(arrStartNodelon, stNode[\"lon\"])\n", + " \n", + " endNode = api.NodeGet(row.end_node)\n", + " arrEndNodelat = np.append(arrEndNodelat,endNode[\"lat\"])\n", + " arrEndNodelon = np.append(arrEndNodelon, endNode[\"lon\"])\n", + " \n", + " edgeset['StNode_lat']=arrStartNodelat\n", + " edgeset['StNode_lon']=arrStartNodelon\n", + " edgeset['EndNode_lat']=arrEndNodelat\n", + " edgeset['EndNode_lon']=arrEndNodelon\n", + " \n", + " \n", + "appendNodeCoords(edgeset)\n", + "edgeset " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def FindCenterCoords(edgeset):\n", + " min_lat = edgeset['StNode_lat'].min()\n", + " max_lat = edgeset['StNode_lat'].max()\n", + " min_lon = edgeset['StNode_lon'].min()\n", + " max_lon = edgeset['StNode_lon'].max()\n", + " \n", + " \n", + " init_lat = (min_lat+max_lat)/2\n", + " init_long = (min_lon+max_lon)/2\n", + " \n", + " coord_list = []\n", + " coord_list.append(init_lat)\n", + " coord_list.append(init_long)\n", + " return coord_list" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def plotAggregatedStatistics(edgeset,speed, speedLimits, co2Emissions):\n", + " FindCenterCoords(edgeset)\n", + " coord_list= FindCenterCoords(edgeset)\n", + " init_lat = coord_list[0]\n", + " init_lon = coord_list[1]\n", + " \n", + " INITIAL_VIEW_STATE = pdk.ViewState(latitude=init_lat, longitude=init_lon, zoom=15, max_zoom=20, pitch=50, bearing=0)\n", + "\n", + " \n", + " if speed:\n", + " pointcolor='[mean_speed < 10 ? 50 : (mean_speed < 20 ? 100 :(mean_speed < 30 ? 150:200)),0,0,255]' \n", + " linecolor='[0,mean_speed < 10 ? 50 : (mean_speed < 20 ? 100 :(mean_speed < 30 ? 150:200)),0,255]'\n", + " \n", + " if speedLimits:\n", + " #Red = avg speed > speed limit\n", + " #Green = avg speed < speed limit\n", + " #Blue = avg speed = speed limit\n", + " min_val = edgeset['DiffBetweenSpeedLimit'].min()\n", + " max_val = edgeset['DiffBetweenSpeedLimit'].max()\n", + " edgeset['color'] = (edgeset['DiffBetweenSpeedLimit']/(max_val - min_val))*255\n", + " \n", + " pointcolor='[DiffBetweenSpeedLimit > 0? 255 : 0 ,DiffBetweenSpeedLimit < 0 ? 255 :0, DiffBetweenSpeedLimit == 0 ? 255 : 0,255]'\n", + " linecolor='[DiffBetweenSpeedLimit > 0? 255 : 0 ,DiffBetweenSpeedLimit < 0 ? 255 :0, DiffBetweenSpeedLimit == 0 ? 255 : 0,255]'\n", + " #linecolor = '[DiffBetweenSpeedLimit > 0? color : 0 ,DiffBetweenSpeedLimit < 0 ? color :0, DiffBetweenSpeedLimit == 0 ? 255 : 0,255]'\n", + " \n", + " if co2Emissions:\n", + " min_val = edgeset['mean_co2'].min()\n", + " max_val = edgeset['mean_co2'].max()\n", + " edgeset['color'] = (edgeset['mean_co2']/(max_val - min_val))*255\n", + " linecolor = '[color,0,0,255]'\n", + " pointcolor='[color,0,0,255]'\n", + "\n", + " \n", + " scatterplot = pdk.Layer(\n", + " \"ScatterplotLayer\",\n", + " edgeset,\n", + " radius_scale=10,\n", + " get_position=['StNode_lon', 'StNode_lat'],\n", + " get_fill_color=pointcolor,\n", + " get_radius=1,\n", + " pickable=True,\n", + " )\n", + "\n", + " \n", + " \n", + " line_layer = pdk.Layer(\n", + " \"LineLayer\",\n", + " edgeset,\n", + " get_source_position=['StNode_lon', 'StNode_lat'],\n", + " get_target_position=['EndNode_lon', 'EndNode_lat'],\n", + " get_color= linecolor,\n", + " get_width=10,\n", + " highlight_color=[255, 255, 0],\n", + " picking_radius=10,\n", + " auto_highlight=True,\n", + " pickable=True,\n", + " )\n", + "\n", + " layers = [line_layer,scatterplot]\n", + " MAPBOX_KEY = \"pk.eyJ1IjoibXByZW1hc2kiLCJhIjoiY2s5NDFueDhyMDFpODNnbjNoNzM1eWhvcCJ9.CqjZdNZJ4h8aejMWX4ZObA\"\n", + " r = pdk.Deck(layers=layers, initial_view_state=INITIAL_VIEW_STATE,mapbox_key = MAPBOX_KEY)\n", + " r.to_html(\"line_layer.html\", iframe_width=900)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotAggregatedStatistics(edgeset,False,False,True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Handling Multiple Tracks\n", + "\n", + "Use Case - Mean speed of streets on based on time\n", + "- Concatenate Map Matched Tracks\n", + "- Remove unwanted fields\n", + "- Convert time field to time object field\n", + "- Filter tracks by Month,day and hour\n", + "- Aggregates Nodes\n", + "- Mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
st_nodeend_nodespeedtimespeed_limitCO2
064300140563492474946.2211062020-02-03T17:23:045012.532860
1349247493492477752.9999992020-02-03T17:23:095010.056936
23492477746197031851.9999982020-02-03T17:23:14507.252608
34619703183492478453.0000012020-02-03T17:23:19507.095522
434924784643001403556.0000022020-02-03T17:23:24506.939990
564300140353492478555.0000012020-02-03T17:23:29508.082442
634924785643001405551.0000002020-02-03T17:23:34505.235873
764300140553492478647.5041782020-02-03T17:23:39503.467051
834924786643001405028.3192502020-02-03T17:23:44503.174234
9643001405063424624583.0000002020-02-03T17:23:50503.170922
106342462458643001405920.8663372020-02-03T17:23:555014.405008
1164300140593492478722.8148142020-02-03T17:24:005011.260461
1234924787634246245736.4035092020-02-03T17:24:05506.442676
1363424624573492478847.0000002020-02-03T17:24:10505.701738
1434924788634246246047.0000002020-02-03T17:24:15505.034212
1563424624603492478949.0000002020-02-03T17:24:20505.265162
16349247893492479051.9429922020-02-03T17:24:25507.930284
17349247903492479147.9999992020-02-03T17:24:30505.470158
1834924791149876652348.9135512020-02-03T17:24:355011.730917
19149876652335829906049.2645012020-02-03T17:24:40505.387822
20358299060715215427243.0000012020-02-03T17:24:45503.173990
217152154272715215427434.9539302020-02-03T17:24:51503.065228
22715215427471521542730.0000002020-02-03T17:24:56503.057912
237152154273338461850.0000002020-02-03T17:25:01504.155391
243384618546197034719.5367492020-02-03T17:25:065015.934264
25461970347642865508834.1468532020-02-03T17:25:11507.400763
26642865508840116381943.0000012020-02-03T17:25:165010.504903
274011638193384618448.0039152020-02-03T17:25:21508.466436
284011638193384618451.0000012020-02-03T17:25:26507.009605
293384618440116377548.0000012020-02-03T17:25:31503.222136
30401163775623687736544.4028582020-02-03T17:25:36503.126752
3162368773652151891437.2795472020-02-03T17:25:41503.045419
3221518914623687736623.0000012020-02-03T17:25:47503.174012
33623687736664311247360.0000002020-02-03T17:25:52503.071740
346431124736215189130.0000002020-02-03T17:25:575010.925399
3521518913643112475526.6456522020-02-03T17:26:025026.206483
366431124755643112473843.7308662020-02-03T17:26:075016.085216
37643112473812237508452.0769252020-02-03T17:26:125015.931978
381223750842151891250.9999992020-02-03T17:26:17505.688978
3921518912624237941649.0000012020-02-03T17:26:22505.607024
40624237941640116379348.9999992020-02-03T17:26:27506.745522
414011637932151891151.3284472020-02-03T17:26:325016.418607
422151891140116380154.0000012020-02-03T17:26:37509.341649
432151891140116380148.2555562020-02-03T17:26:43503.169324
444011638016067669029.7827232020-02-03T17:26:48503.161149
456067669062423794090.0000002020-02-03T17:26:53503.022734
46624237940962368773680.0000002020-02-03T17:26:58503.011477
47623687736832023522280.0000002020-02-03T17:27:03503.015020
48320235222862423794070.0000002020-02-03T17:27:08503.039808
49624237940762368773700.0000002020-02-03T17:27:13503.066515
50623687737062368773690.0000002020-02-03T17:27:18503.116671
516236877369215189100.0000002020-02-03T17:27:23503.010686
522151891054728674090.0000002020-02-03T17:27:28503.112850
532151891054728674090.0000002020-02-03T17:27:33503.043374
545472867409338461750.0000002020-02-03T17:27:39503.095505
555472867409338461750.0000002020-02-03T17:27:44503.047474
565472867409338461750.0000002020-02-03T17:27:49503.036022
575472867409338461750.0000002020-02-03T17:27:54503.075117
5833846175326845741516.8083992020-02-03T17:27:595018.000278
593268457415642507271126.3842112020-02-03T17:28:045011.176040
606425072711642507270539.0000012020-02-03T17:28:09504.046499
616425072705547429117532.1586202020-02-03T17:28:14503.075322
62547429117594592113110.4734302020-02-03T17:28:19503.025176
6394592113155122690430.0000002020-02-03T17:28:24502.973655
645512269043493270297711.2926832020-02-03T17:28:295018.879898
654932702977450264683232.3354442020-02-03T17:28:35504.900118
66450264683226807167139.7531172020-02-03T17:28:40504.713496
67268071671551226904833.9999992020-02-03T17:28:45504.862161
685512269048551226905026.9999992020-02-03T17:28:50505.141985
695512269050551226904930.0000002020-02-03T17:28:55505.362533
705512269049551226905137.5368422020-02-03T17:29:005012.651749
7155122690512904140238.3000002020-02-03T17:29:055018.109086
7229041402551226905241.8675662020-02-03T17:29:10509.600139
735512269052551226905342.2070582020-02-03T17:29:15506.828497
745512269053551226905440.0000012020-02-03T17:29:20504.562019
755512269054112024994242.0000002020-02-03T17:29:25507.682926
064300140563492474946.2211062020-02-03T17:23:045012.532860
1349247493492477752.9999992020-02-03T17:23:095010.056936
23492477746197031851.9999982020-02-03T17:23:14507.252608
34619703183492478453.0000012020-02-03T17:23:19507.095522
434924784643001403556.0000022020-02-03T17:23:24506.939990
564300140353492478555.0000012020-02-03T17:23:29508.082442
634924785643001405551.0000002020-02-03T17:23:34505.235873
764300140553492478647.5041782020-02-03T17:23:39503.467051
834924786643001405028.3192502020-02-03T17:23:44503.174234
9643001405063424624583.0000002020-02-03T17:23:50503.170922
106342462458643001405920.8663372020-02-03T17:23:555014.405008
1164300140593492478722.8148142020-02-03T17:24:005011.260461
1234924787634246245736.4035092020-02-03T17:24:05506.442676
1363424624573492478847.0000002020-02-03T17:24:10505.701738
1434924788634246246047.0000002020-02-03T17:24:15505.034212
1563424624603492478949.0000002020-02-03T17:24:20505.265162
16349247893492479051.9429922020-02-03T17:24:25507.930284
17349247903492479147.9999992020-02-03T17:24:30505.470158
1834924791149876652348.9135512020-02-03T17:24:355011.730917
19149876652335829906049.2645012020-02-03T17:24:40505.387822
20358299060715215427243.0000012020-02-03T17:24:45503.173990
217152154272715215427434.9539302020-02-03T17:24:51503.065228
22715215427471521542730.0000002020-02-03T17:24:56503.057912
237152154273338461850.0000002020-02-03T17:25:01504.155391
243384618546197034719.5367492020-02-03T17:25:065015.934264
25461970347642865508834.1468532020-02-03T17:25:11507.400763
26642865508840116381943.0000012020-02-03T17:25:165010.504903
274011638193384618448.0039152020-02-03T17:25:21508.466436
284011638193384618451.0000012020-02-03T17:25:26507.009605
293384618440116377548.0000012020-02-03T17:25:31503.222136
30401163775623687736544.4028582020-02-03T17:25:36503.126752
3162368773652151891437.2795472020-02-03T17:25:41503.045419
3221518914623687736623.0000012020-02-03T17:25:47503.174012
33623687736664311247360.0000002020-02-03T17:25:52503.071740
346431124736215189130.0000002020-02-03T17:25:575010.925399
3521518913643112475526.6456522020-02-03T17:26:025026.206483
366431124755643112473843.7308662020-02-03T17:26:075016.085216
37643112473812237508452.0769252020-02-03T17:26:125015.931978
381223750842151891250.9999992020-02-03T17:26:17505.688978
3921518912624237941649.0000012020-02-03T17:26:22505.607024
40624237941640116379348.9999992020-02-03T17:26:27506.745522
414011637932151891151.3284472020-02-03T17:26:325016.418607
422151891140116380154.0000012020-02-03T17:26:37509.341649
432151891140116380148.2555562020-02-03T17:26:43503.169324
444011638016067669029.7827232020-02-03T17:26:48503.161149
456067669062423794090.0000002020-02-03T17:26:53503.022734
46624237940962368773680.0000002020-02-03T17:26:58503.011477
47623687736832023522280.0000002020-02-03T17:27:03503.015020
48320235222862423794070.0000002020-02-03T17:27:08503.039808
49624237940762368773700.0000002020-02-03T17:27:13503.066515
50623687737062368773690.0000002020-02-03T17:27:18503.116671
516236877369215189100.0000002020-02-03T17:27:23503.010686
522151891054728674090.0000002020-02-03T17:27:28503.112850
532151891054728674090.0000002020-02-03T17:27:33503.043374
545472867409338461750.0000002020-02-03T17:27:39503.095505
555472867409338461750.0000002020-02-03T17:27:44503.047474
565472867409338461750.0000002020-02-03T17:27:49503.036022
575472867409338461750.0000002020-02-03T17:27:54503.075117
5833846175326845741516.8083992020-02-03T17:27:595018.000278
593268457415642507271126.3842112020-02-03T17:28:045011.176040
606425072711642507270539.0000012020-02-03T17:28:09504.046499
616425072705547429117532.1586202020-02-03T17:28:14503.075322
62547429117594592113110.4734302020-02-03T17:28:19503.025176
6394592113155122690430.0000002020-02-03T17:28:24502.973655
645512269043493270297711.2926832020-02-03T17:28:295018.879898
654932702977450264683232.3354442020-02-03T17:28:35504.900118
66450264683226807167139.7531172020-02-03T17:28:40504.713496
67268071671551226904833.9999992020-02-03T17:28:45504.862161
685512269048551226905026.9999992020-02-03T17:28:50505.141985
695512269050551226904930.0000002020-02-03T17:28:55505.362533
705512269049551226905137.5368422020-02-03T17:29:005012.651749
7155122690512904140238.3000002020-02-03T17:29:055018.109086
7229041402551226905241.8675662020-02-03T17:29:10509.600139
735512269052551226905342.2070582020-02-03T17:29:15506.828497
745512269053551226905440.0000012020-02-03T17:29:20504.562019
755512269054112024994242.0000002020-02-03T17:29:25507.682926
\n", + "
" + ], + "text/plain": [ + " st_node end_node speed time speed_limit \\\n", + "0 6430014056 34924749 46.221106 2020-02-03T17:23:04 50 \n", + "1 34924749 34924777 52.999999 2020-02-03T17:23:09 50 \n", + "2 34924777 461970318 51.999998 2020-02-03T17:23:14 50 \n", + "3 461970318 34924784 53.000001 2020-02-03T17:23:19 50 \n", + "4 34924784 6430014035 56.000002 2020-02-03T17:23:24 50 \n", + "5 6430014035 34924785 55.000001 2020-02-03T17:23:29 50 \n", + "6 34924785 6430014055 51.000000 2020-02-03T17:23:34 50 \n", + "7 6430014055 34924786 47.504178 2020-02-03T17:23:39 50 \n", + "8 34924786 6430014050 28.319250 2020-02-03T17:23:44 50 \n", + "9 6430014050 6342462458 3.000000 2020-02-03T17:23:50 50 \n", + "10 6342462458 6430014059 20.866337 2020-02-03T17:23:55 50 \n", + "11 6430014059 34924787 22.814814 2020-02-03T17:24:00 50 \n", + "12 34924787 6342462457 36.403509 2020-02-03T17:24:05 50 \n", + "13 6342462457 34924788 47.000000 2020-02-03T17:24:10 50 \n", + "14 34924788 6342462460 47.000000 2020-02-03T17:24:15 50 \n", + "15 6342462460 34924789 49.000000 2020-02-03T17:24:20 50 \n", + "16 34924789 34924790 51.942992 2020-02-03T17:24:25 50 \n", + "17 34924790 34924791 47.999999 2020-02-03T17:24:30 50 \n", + "18 34924791 1498766523 48.913551 2020-02-03T17:24:35 50 \n", + "19 1498766523 358299060 49.264501 2020-02-03T17:24:40 50 \n", + "20 358299060 7152154272 43.000001 2020-02-03T17:24:45 50 \n", + "21 7152154272 7152154274 34.953930 2020-02-03T17:24:51 50 \n", + "22 7152154274 7152154273 0.000000 2020-02-03T17:24:56 50 \n", + "23 7152154273 33846185 0.000000 2020-02-03T17:25:01 50 \n", + "24 33846185 461970347 19.536749 2020-02-03T17:25:06 50 \n", + "25 461970347 6428655088 34.146853 2020-02-03T17:25:11 50 \n", + "26 6428655088 401163819 43.000001 2020-02-03T17:25:16 50 \n", + "27 401163819 33846184 48.003915 2020-02-03T17:25:21 50 \n", + "28 401163819 33846184 51.000001 2020-02-03T17:25:26 50 \n", + "29 33846184 401163775 48.000001 2020-02-03T17:25:31 50 \n", + "30 401163775 6236877365 44.402858 2020-02-03T17:25:36 50 \n", + "31 6236877365 21518914 37.279547 2020-02-03T17:25:41 50 \n", + "32 21518914 6236877366 23.000001 2020-02-03T17:25:47 50 \n", + "33 6236877366 6431124736 0.000000 2020-02-03T17:25:52 50 \n", + "34 6431124736 21518913 0.000000 2020-02-03T17:25:57 50 \n", + "35 21518913 6431124755 26.645652 2020-02-03T17:26:02 50 \n", + "36 6431124755 6431124738 43.730866 2020-02-03T17:26:07 50 \n", + "37 6431124738 122375084 52.076925 2020-02-03T17:26:12 50 \n", + "38 122375084 21518912 50.999999 2020-02-03T17:26:17 50 \n", + "39 21518912 6242379416 49.000001 2020-02-03T17:26:22 50 \n", + "40 6242379416 401163793 48.999999 2020-02-03T17:26:27 50 \n", + "41 401163793 21518911 51.328447 2020-02-03T17:26:32 50 \n", + "42 21518911 401163801 54.000001 2020-02-03T17:26:37 50 \n", + "43 21518911 401163801 48.255556 2020-02-03T17:26:43 50 \n", + "44 401163801 60676690 29.782723 2020-02-03T17:26:48 50 \n", + "45 60676690 6242379409 0.000000 2020-02-03T17:26:53 50 \n", + "46 6242379409 6236877368 0.000000 2020-02-03T17:26:58 50 \n", + "47 6236877368 3202352228 0.000000 2020-02-03T17:27:03 50 \n", + "48 3202352228 6242379407 0.000000 2020-02-03T17:27:08 50 \n", + "49 6242379407 6236877370 0.000000 2020-02-03T17:27:13 50 \n", + "50 6236877370 6236877369 0.000000 2020-02-03T17:27:18 50 \n", + "51 6236877369 21518910 0.000000 2020-02-03T17:27:23 50 \n", + "52 21518910 5472867409 0.000000 2020-02-03T17:27:28 50 \n", + "53 21518910 5472867409 0.000000 2020-02-03T17:27:33 50 \n", + "54 5472867409 33846175 0.000000 2020-02-03T17:27:39 50 \n", + "55 5472867409 33846175 0.000000 2020-02-03T17:27:44 50 \n", + "56 5472867409 33846175 0.000000 2020-02-03T17:27:49 50 \n", + "57 5472867409 33846175 0.000000 2020-02-03T17:27:54 50 \n", + "58 33846175 3268457415 16.808399 2020-02-03T17:27:59 50 \n", + "59 3268457415 6425072711 26.384211 2020-02-03T17:28:04 50 \n", + "60 6425072711 6425072705 39.000001 2020-02-03T17:28:09 50 \n", + "61 6425072705 5474291175 32.158620 2020-02-03T17:28:14 50 \n", + "62 5474291175 945921131 10.473430 2020-02-03T17:28:19 50 \n", + "63 945921131 5512269043 0.000000 2020-02-03T17:28:24 50 \n", + "64 5512269043 4932702977 11.292683 2020-02-03T17:28:29 50 \n", + "65 4932702977 4502646832 32.335444 2020-02-03T17:28:35 50 \n", + "66 4502646832 268071671 39.753117 2020-02-03T17:28:40 50 \n", + "67 268071671 5512269048 33.999999 2020-02-03T17:28:45 50 \n", + "68 5512269048 5512269050 26.999999 2020-02-03T17:28:50 50 \n", + "69 5512269050 5512269049 30.000000 2020-02-03T17:28:55 50 \n", + "70 5512269049 5512269051 37.536842 2020-02-03T17:29:00 50 \n", + "71 5512269051 29041402 38.300000 2020-02-03T17:29:05 50 \n", + "72 29041402 5512269052 41.867566 2020-02-03T17:29:10 50 \n", + "73 5512269052 5512269053 42.207058 2020-02-03T17:29:15 50 \n", + "74 5512269053 5512269054 40.000001 2020-02-03T17:29:20 50 \n", + "75 5512269054 1120249942 42.000000 2020-02-03T17:29:25 50 \n", + "0 6430014056 34924749 46.221106 2020-02-03T17:23:04 50 \n", + "1 34924749 34924777 52.999999 2020-02-03T17:23:09 50 \n", + "2 34924777 461970318 51.999998 2020-02-03T17:23:14 50 \n", + "3 461970318 34924784 53.000001 2020-02-03T17:23:19 50 \n", + "4 34924784 6430014035 56.000002 2020-02-03T17:23:24 50 \n", + "5 6430014035 34924785 55.000001 2020-02-03T17:23:29 50 \n", + "6 34924785 6430014055 51.000000 2020-02-03T17:23:34 50 \n", + "7 6430014055 34924786 47.504178 2020-02-03T17:23:39 50 \n", + "8 34924786 6430014050 28.319250 2020-02-03T17:23:44 50 \n", + "9 6430014050 6342462458 3.000000 2020-02-03T17:23:50 50 \n", + "10 6342462458 6430014059 20.866337 2020-02-03T17:23:55 50 \n", + "11 6430014059 34924787 22.814814 2020-02-03T17:24:00 50 \n", + "12 34924787 6342462457 36.403509 2020-02-03T17:24:05 50 \n", + "13 6342462457 34924788 47.000000 2020-02-03T17:24:10 50 \n", + "14 34924788 6342462460 47.000000 2020-02-03T17:24:15 50 \n", + "15 6342462460 34924789 49.000000 2020-02-03T17:24:20 50 \n", + "16 34924789 34924790 51.942992 2020-02-03T17:24:25 50 \n", + "17 34924790 34924791 47.999999 2020-02-03T17:24:30 50 \n", + "18 34924791 1498766523 48.913551 2020-02-03T17:24:35 50 \n", + "19 1498766523 358299060 49.264501 2020-02-03T17:24:40 50 \n", + "20 358299060 7152154272 43.000001 2020-02-03T17:24:45 50 \n", + "21 7152154272 7152154274 34.953930 2020-02-03T17:24:51 50 \n", + "22 7152154274 7152154273 0.000000 2020-02-03T17:24:56 50 \n", + "23 7152154273 33846185 0.000000 2020-02-03T17:25:01 50 \n", + "24 33846185 461970347 19.536749 2020-02-03T17:25:06 50 \n", + "25 461970347 6428655088 34.146853 2020-02-03T17:25:11 50 \n", + "26 6428655088 401163819 43.000001 2020-02-03T17:25:16 50 \n", + "27 401163819 33846184 48.003915 2020-02-03T17:25:21 50 \n", + "28 401163819 33846184 51.000001 2020-02-03T17:25:26 50 \n", + "29 33846184 401163775 48.000001 2020-02-03T17:25:31 50 \n", + "30 401163775 6236877365 44.402858 2020-02-03T17:25:36 50 \n", + "31 6236877365 21518914 37.279547 2020-02-03T17:25:41 50 \n", + "32 21518914 6236877366 23.000001 2020-02-03T17:25:47 50 \n", + "33 6236877366 6431124736 0.000000 2020-02-03T17:25:52 50 \n", + "34 6431124736 21518913 0.000000 2020-02-03T17:25:57 50 \n", + "35 21518913 6431124755 26.645652 2020-02-03T17:26:02 50 \n", + "36 6431124755 6431124738 43.730866 2020-02-03T17:26:07 50 \n", + "37 6431124738 122375084 52.076925 2020-02-03T17:26:12 50 \n", + "38 122375084 21518912 50.999999 2020-02-03T17:26:17 50 \n", + "39 21518912 6242379416 49.000001 2020-02-03T17:26:22 50 \n", + "40 6242379416 401163793 48.999999 2020-02-03T17:26:27 50 \n", + "41 401163793 21518911 51.328447 2020-02-03T17:26:32 50 \n", + "42 21518911 401163801 54.000001 2020-02-03T17:26:37 50 \n", + "43 21518911 401163801 48.255556 2020-02-03T17:26:43 50 \n", + "44 401163801 60676690 29.782723 2020-02-03T17:26:48 50 \n", + "45 60676690 6242379409 0.000000 2020-02-03T17:26:53 50 \n", + "46 6242379409 6236877368 0.000000 2020-02-03T17:26:58 50 \n", + "47 6236877368 3202352228 0.000000 2020-02-03T17:27:03 50 \n", + "48 3202352228 6242379407 0.000000 2020-02-03T17:27:08 50 \n", + "49 6242379407 6236877370 0.000000 2020-02-03T17:27:13 50 \n", + "50 6236877370 6236877369 0.000000 2020-02-03T17:27:18 50 \n", + "51 6236877369 21518910 0.000000 2020-02-03T17:27:23 50 \n", + "52 21518910 5472867409 0.000000 2020-02-03T17:27:28 50 \n", + "53 21518910 5472867409 0.000000 2020-02-03T17:27:33 50 \n", + "54 5472867409 33846175 0.000000 2020-02-03T17:27:39 50 \n", + "55 5472867409 33846175 0.000000 2020-02-03T17:27:44 50 \n", + "56 5472867409 33846175 0.000000 2020-02-03T17:27:49 50 \n", + "57 5472867409 33846175 0.000000 2020-02-03T17:27:54 50 \n", + "58 33846175 3268457415 16.808399 2020-02-03T17:27:59 50 \n", + "59 3268457415 6425072711 26.384211 2020-02-03T17:28:04 50 \n", + "60 6425072711 6425072705 39.000001 2020-02-03T17:28:09 50 \n", + "61 6425072705 5474291175 32.158620 2020-02-03T17:28:14 50 \n", + "62 5474291175 945921131 10.473430 2020-02-03T17:28:19 50 \n", + "63 945921131 5512269043 0.000000 2020-02-03T17:28:24 50 \n", + "64 5512269043 4932702977 11.292683 2020-02-03T17:28:29 50 \n", + "65 4932702977 4502646832 32.335444 2020-02-03T17:28:35 50 \n", + "66 4502646832 268071671 39.753117 2020-02-03T17:28:40 50 \n", + "67 268071671 5512269048 33.999999 2020-02-03T17:28:45 50 \n", + "68 5512269048 5512269050 26.999999 2020-02-03T17:28:50 50 \n", + "69 5512269050 5512269049 30.000000 2020-02-03T17:28:55 50 \n", + "70 5512269049 5512269051 37.536842 2020-02-03T17:29:00 50 \n", + "71 5512269051 29041402 38.300000 2020-02-03T17:29:05 50 \n", + "72 29041402 5512269052 41.867566 2020-02-03T17:29:10 50 \n", + "73 5512269052 5512269053 42.207058 2020-02-03T17:29:15 50 \n", + "74 5512269053 5512269054 40.000001 2020-02-03T17:29:20 50 \n", + "75 5512269054 1120249942 42.000000 2020-02-03T17:29:25 50 \n", + "\n", + " CO2 \n", + "0 12.532860 \n", + "1 10.056936 \n", + "2 7.252608 \n", + "3 7.095522 \n", + "4 6.939990 \n", + "5 8.082442 \n", + "6 5.235873 \n", + "7 3.467051 \n", + "8 3.174234 \n", + "9 3.170922 \n", + "10 14.405008 \n", + "11 11.260461 \n", + "12 6.442676 \n", + "13 5.701738 \n", + "14 5.034212 \n", + "15 5.265162 \n", + "16 7.930284 \n", + "17 5.470158 \n", + "18 11.730917 \n", + "19 5.387822 \n", + "20 3.173990 \n", + "21 3.065228 \n", + "22 3.057912 \n", + "23 4.155391 \n", + "24 15.934264 \n", + "25 7.400763 \n", + "26 10.504903 \n", + "27 8.466436 \n", + "28 7.009605 \n", + "29 3.222136 \n", + "30 3.126752 \n", + "31 3.045419 \n", + "32 3.174012 \n", + "33 3.071740 \n", + "34 10.925399 \n", + "35 26.206483 \n", + "36 16.085216 \n", + "37 15.931978 \n", + "38 5.688978 \n", + "39 5.607024 \n", + "40 6.745522 \n", + "41 16.418607 \n", + "42 9.341649 \n", + "43 3.169324 \n", + "44 3.161149 \n", + "45 3.022734 \n", + "46 3.011477 \n", + "47 3.015020 \n", + "48 3.039808 \n", + "49 3.066515 \n", + "50 3.116671 \n", + "51 3.010686 \n", + "52 3.112850 \n", + "53 3.043374 \n", + "54 3.095505 \n", + "55 3.047474 \n", + "56 3.036022 \n", + "57 3.075117 \n", + "58 18.000278 \n", + "59 11.176040 \n", + "60 4.046499 \n", + "61 3.075322 \n", + "62 3.025176 \n", + "63 2.973655 \n", + "64 18.879898 \n", + "65 4.900118 \n", + "66 4.713496 \n", + "67 4.862161 \n", + "68 5.141985 \n", + "69 5.362533 \n", + "70 12.651749 \n", + "71 18.109086 \n", + "72 9.600139 \n", + "73 6.828497 \n", + "74 4.562019 \n", + "75 7.682926 \n", + "0 12.532860 \n", + "1 10.056936 \n", + "2 7.252608 \n", + "3 7.095522 \n", + "4 6.939990 \n", + "5 8.082442 \n", + "6 5.235873 \n", + "7 3.467051 \n", + "8 3.174234 \n", + "9 3.170922 \n", + "10 14.405008 \n", + "11 11.260461 \n", + "12 6.442676 \n", + "13 5.701738 \n", + "14 5.034212 \n", + "15 5.265162 \n", + "16 7.930284 \n", + "17 5.470158 \n", + "18 11.730917 \n", + "19 5.387822 \n", + "20 3.173990 \n", + "21 3.065228 \n", + "22 3.057912 \n", + "23 4.155391 \n", + "24 15.934264 \n", + "25 7.400763 \n", + "26 10.504903 \n", + "27 8.466436 \n", + "28 7.009605 \n", + "29 3.222136 \n", + "30 3.126752 \n", + "31 3.045419 \n", + "32 3.174012 \n", + "33 3.071740 \n", + "34 10.925399 \n", + "35 26.206483 \n", + "36 16.085216 \n", + "37 15.931978 \n", + "38 5.688978 \n", + "39 5.607024 \n", + "40 6.745522 \n", + "41 16.418607 \n", + "42 9.341649 \n", + "43 3.169324 \n", + "44 3.161149 \n", + "45 3.022734 \n", + "46 3.011477 \n", + "47 3.015020 \n", + "48 3.039808 \n", + "49 3.066515 \n", + "50 3.116671 \n", + "51 3.010686 \n", + "52 3.112850 \n", + "53 3.043374 \n", + "54 3.095505 \n", + "55 3.047474 \n", + "56 3.036022 \n", + "57 3.075117 \n", + "58 18.000278 \n", + "59 11.176040 \n", + "60 4.046499 \n", + "61 3.075322 \n", + "62 3.025176 \n", + "63 2.973655 \n", + "64 18.879898 \n", + "65 4.900118 \n", + "66 4.713496 \n", + "67 4.862161 \n", + "68 5.141985 \n", + "69 5.362533 \n", + "70 12.651749 \n", + "71 18.109086 \n", + "72 9.600139 \n", + "73 6.828497 \n", + "74 4.562019 \n", + "75 7.682926 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data1 = pd.read_csv(\"track_2.csv\")\n", + "data2 = pd.read_csv(\"track_2.csv\")\n", + "\n", + "frames = [data1, data2]\n", + "frames\n", + "\n", + "concatTracks = pd.concat(frames)\n", + "concatTracks = pd.DataFrame({'st_node': concatTracks[\"st_nodes\"], 'end_node': concatTracks[\"end_nodes\"], 'speed':concatTracks[\"Speed.value\"],'time':concatTracks[\"time\"], 'speed_limit': concatTracks[\"max_speed\"], 'CO2':concatTracks[\"CO2.value\"]})\n", + "concatTracks\n", + "\n", + "\n", + "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n", + "concatTracks\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
st_nodeend_nodespeedtimespeed_limitCO2
064300140563492474946.2211062020-02-03 17:23:045012.532860
1349247493492477752.9999992020-02-03 17:23:095010.056936
23492477746197031851.9999982020-02-03 17:23:14507.252608
34619703183492478453.0000012020-02-03 17:23:19507.095522
434924784643001403556.0000022020-02-03 17:23:24506.939990
564300140353492478555.0000012020-02-03 17:23:29508.082442
634924785643001405551.0000002020-02-03 17:23:34505.235873
764300140553492478647.5041782020-02-03 17:23:39503.467051
834924786643001405028.3192502020-02-03 17:23:44503.174234
9643001405063424624583.0000002020-02-03 17:23:50503.170922
106342462458643001405920.8663372020-02-03 17:23:555014.405008
1164300140593492478722.8148142020-02-03 17:24:005011.260461
1234924787634246245736.4035092020-02-03 17:24:05506.442676
1363424624573492478847.0000002020-02-03 17:24:10505.701738
1434924788634246246047.0000002020-02-03 17:24:15505.034212
1563424624603492478949.0000002020-02-03 17:24:20505.265162
16349247893492479051.9429922020-02-03 17:24:25507.930284
17349247903492479147.9999992020-02-03 17:24:30505.470158
1834924791149876652348.9135512020-02-03 17:24:355011.730917
19149876652335829906049.2645012020-02-03 17:24:40505.387822
20358299060715215427243.0000012020-02-03 17:24:45503.173990
217152154272715215427434.9539302020-02-03 17:24:51503.065228
22715215427471521542730.0000002020-02-03 17:24:56503.057912
237152154273338461850.0000002020-02-03 17:25:01504.155391
243384618546197034719.5367492020-02-03 17:25:065015.934264
25461970347642865508834.1468532020-02-03 17:25:11507.400763
26642865508840116381943.0000012020-02-03 17:25:165010.504903
274011638193384618448.0039152020-02-03 17:25:21508.466436
284011638193384618451.0000012020-02-03 17:25:26507.009605
293384618440116377548.0000012020-02-03 17:25:31503.222136
30401163775623687736544.4028582020-02-03 17:25:36503.126752
3162368773652151891437.2795472020-02-03 17:25:41503.045419
3221518914623687736623.0000012020-02-03 17:25:47503.174012
33623687736664311247360.0000002020-02-03 17:25:52503.071740
346431124736215189130.0000002020-02-03 17:25:575010.925399
3521518913643112475526.6456522020-02-03 17:26:025026.206483
366431124755643112473843.7308662020-02-03 17:26:075016.085216
37643112473812237508452.0769252020-02-03 17:26:125015.931978
381223750842151891250.9999992020-02-03 17:26:17505.688978
3921518912624237941649.0000012020-02-03 17:26:22505.607024
40624237941640116379348.9999992020-02-03 17:26:27506.745522
414011637932151891151.3284472020-02-03 17:26:325016.418607
422151891140116380154.0000012020-02-03 17:26:37509.341649
432151891140116380148.2555562020-02-03 17:26:43503.169324
444011638016067669029.7827232020-02-03 17:26:48503.161149
456067669062423794090.0000002020-02-03 17:26:53503.022734
46624237940962368773680.0000002020-02-03 17:26:58503.011477
47623687736832023522280.0000002020-02-03 17:27:03503.015020
48320235222862423794070.0000002020-02-03 17:27:08503.039808
49624237940762368773700.0000002020-02-03 17:27:13503.066515
50623687737062368773690.0000002020-02-03 17:27:18503.116671
516236877369215189100.0000002020-02-03 17:27:23503.010686
522151891054728674090.0000002020-02-03 17:27:28503.112850
532151891054728674090.0000002020-02-03 17:27:33503.043374
545472867409338461750.0000002020-02-03 17:27:39503.095505
555472867409338461750.0000002020-02-03 17:27:44503.047474
565472867409338461750.0000002020-02-03 17:27:49503.036022
575472867409338461750.0000002020-02-03 17:27:54503.075117
5833846175326845741516.8083992020-02-03 17:27:595018.000278
593268457415642507271126.3842112020-02-03 17:28:045011.176040
606425072711642507270539.0000012020-02-03 17:28:09504.046499
616425072705547429117532.1586202020-02-03 17:28:14503.075322
62547429117594592113110.4734302020-02-03 17:28:19503.025176
6394592113155122690430.0000002020-02-03 17:28:24502.973655
645512269043493270297711.2926832020-02-03 17:28:295018.879898
654932702977450264683232.3354442020-02-03 17:28:35504.900118
66450264683226807167139.7531172020-02-03 17:28:40504.713496
67268071671551226904833.9999992020-02-03 17:28:45504.862161
685512269048551226905026.9999992020-02-03 17:28:50505.141985
695512269050551226904930.0000002020-02-03 17:28:55505.362533
705512269049551226905137.5368422020-02-03 17:29:005012.651749
7155122690512904140238.3000002020-02-03 17:29:055018.109086
7229041402551226905241.8675662020-02-03 17:29:10509.600139
735512269052551226905342.2070582020-02-03 17:29:15506.828497
745512269053551226905440.0000012020-02-03 17:29:20504.562019
755512269054112024994242.0000002020-02-03 17:29:25507.682926
064300140563492474946.2211062020-02-03 17:23:045012.532860
1349247493492477752.9999992020-02-03 17:23:095010.056936
23492477746197031851.9999982020-02-03 17:23:14507.252608
34619703183492478453.0000012020-02-03 17:23:19507.095522
434924784643001403556.0000022020-02-03 17:23:24506.939990
564300140353492478555.0000012020-02-03 17:23:29508.082442
634924785643001405551.0000002020-02-03 17:23:34505.235873
764300140553492478647.5041782020-02-03 17:23:39503.467051
834924786643001405028.3192502020-02-03 17:23:44503.174234
9643001405063424624583.0000002020-02-03 17:23:50503.170922
106342462458643001405920.8663372020-02-03 17:23:555014.405008
1164300140593492478722.8148142020-02-03 17:24:005011.260461
1234924787634246245736.4035092020-02-03 17:24:05506.442676
1363424624573492478847.0000002020-02-03 17:24:10505.701738
1434924788634246246047.0000002020-02-03 17:24:15505.034212
1563424624603492478949.0000002020-02-03 17:24:20505.265162
16349247893492479051.9429922020-02-03 17:24:25507.930284
17349247903492479147.9999992020-02-03 17:24:30505.470158
1834924791149876652348.9135512020-02-03 17:24:355011.730917
19149876652335829906049.2645012020-02-03 17:24:40505.387822
20358299060715215427243.0000012020-02-03 17:24:45503.173990
217152154272715215427434.9539302020-02-03 17:24:51503.065228
22715215427471521542730.0000002020-02-03 17:24:56503.057912
237152154273338461850.0000002020-02-03 17:25:01504.155391
243384618546197034719.5367492020-02-03 17:25:065015.934264
25461970347642865508834.1468532020-02-03 17:25:11507.400763
26642865508840116381943.0000012020-02-03 17:25:165010.504903
274011638193384618448.0039152020-02-03 17:25:21508.466436
284011638193384618451.0000012020-02-03 17:25:26507.009605
293384618440116377548.0000012020-02-03 17:25:31503.222136
30401163775623687736544.4028582020-02-03 17:25:36503.126752
3162368773652151891437.2795472020-02-03 17:25:41503.045419
3221518914623687736623.0000012020-02-03 17:25:47503.174012
33623687736664311247360.0000002020-02-03 17:25:52503.071740
346431124736215189130.0000002020-02-03 17:25:575010.925399
3521518913643112475526.6456522020-02-03 17:26:025026.206483
366431124755643112473843.7308662020-02-03 17:26:075016.085216
37643112473812237508452.0769252020-02-03 17:26:125015.931978
381223750842151891250.9999992020-02-03 17:26:17505.688978
3921518912624237941649.0000012020-02-03 17:26:22505.607024
40624237941640116379348.9999992020-02-03 17:26:27506.745522
414011637932151891151.3284472020-02-03 17:26:325016.418607
422151891140116380154.0000012020-02-03 17:26:37509.341649
432151891140116380148.2555562020-02-03 17:26:43503.169324
444011638016067669029.7827232020-02-03 17:26:48503.161149
456067669062423794090.0000002020-02-03 17:26:53503.022734
46624237940962368773680.0000002020-02-03 17:26:58503.011477
47623687736832023522280.0000002020-02-03 17:27:03503.015020
48320235222862423794070.0000002020-02-03 17:27:08503.039808
49624237940762368773700.0000002020-02-03 17:27:13503.066515
50623687737062368773690.0000002020-02-03 17:27:18503.116671
516236877369215189100.0000002020-02-03 17:27:23503.010686
522151891054728674090.0000002020-02-03 17:27:28503.112850
532151891054728674090.0000002020-02-03 17:27:33503.043374
545472867409338461750.0000002020-02-03 17:27:39503.095505
555472867409338461750.0000002020-02-03 17:27:44503.047474
565472867409338461750.0000002020-02-03 17:27:49503.036022
575472867409338461750.0000002020-02-03 17:27:54503.075117
5833846175326845741516.8083992020-02-03 17:27:595018.000278
593268457415642507271126.3842112020-02-03 17:28:045011.176040
606425072711642507270539.0000012020-02-03 17:28:09504.046499
616425072705547429117532.1586202020-02-03 17:28:14503.075322
62547429117594592113110.4734302020-02-03 17:28:19503.025176
6394592113155122690430.0000002020-02-03 17:28:24502.973655
645512269043493270297711.2926832020-02-03 17:28:295018.879898
654932702977450264683232.3354442020-02-03 17:28:35504.900118
66450264683226807167139.7531172020-02-03 17:28:40504.713496
67268071671551226904833.9999992020-02-03 17:28:45504.862161
685512269048551226905026.9999992020-02-03 17:28:50505.141985
695512269050551226904930.0000002020-02-03 17:28:55505.362533
705512269049551226905137.5368422020-02-03 17:29:005012.651749
7155122690512904140238.3000002020-02-03 17:29:055018.109086
7229041402551226905241.8675662020-02-03 17:29:10509.600139
735512269052551226905342.2070582020-02-03 17:29:15506.828497
745512269053551226905440.0000012020-02-03 17:29:20504.562019
755512269054112024994242.0000002020-02-03 17:29:25507.682926
\n", + "
" + ], + "text/plain": [ + " st_node end_node speed time speed_limit \\\n", + "0 6430014056 34924749 46.221106 2020-02-03 17:23:04 50 \n", + "1 34924749 34924777 52.999999 2020-02-03 17:23:09 50 \n", + "2 34924777 461970318 51.999998 2020-02-03 17:23:14 50 \n", + "3 461970318 34924784 53.000001 2020-02-03 17:23:19 50 \n", + "4 34924784 6430014035 56.000002 2020-02-03 17:23:24 50 \n", + "5 6430014035 34924785 55.000001 2020-02-03 17:23:29 50 \n", + "6 34924785 6430014055 51.000000 2020-02-03 17:23:34 50 \n", + "7 6430014055 34924786 47.504178 2020-02-03 17:23:39 50 \n", + "8 34924786 6430014050 28.319250 2020-02-03 17:23:44 50 \n", + "9 6430014050 6342462458 3.000000 2020-02-03 17:23:50 50 \n", + "10 6342462458 6430014059 20.866337 2020-02-03 17:23:55 50 \n", + "11 6430014059 34924787 22.814814 2020-02-03 17:24:00 50 \n", + "12 34924787 6342462457 36.403509 2020-02-03 17:24:05 50 \n", + "13 6342462457 34924788 47.000000 2020-02-03 17:24:10 50 \n", + "14 34924788 6342462460 47.000000 2020-02-03 17:24:15 50 \n", + "15 6342462460 34924789 49.000000 2020-02-03 17:24:20 50 \n", + "16 34924789 34924790 51.942992 2020-02-03 17:24:25 50 \n", + "17 34924790 34924791 47.999999 2020-02-03 17:24:30 50 \n", + "18 34924791 1498766523 48.913551 2020-02-03 17:24:35 50 \n", + "19 1498766523 358299060 49.264501 2020-02-03 17:24:40 50 \n", + "20 358299060 7152154272 43.000001 2020-02-03 17:24:45 50 \n", + "21 7152154272 7152154274 34.953930 2020-02-03 17:24:51 50 \n", + "22 7152154274 7152154273 0.000000 2020-02-03 17:24:56 50 \n", + "23 7152154273 33846185 0.000000 2020-02-03 17:25:01 50 \n", + "24 33846185 461970347 19.536749 2020-02-03 17:25:06 50 \n", + "25 461970347 6428655088 34.146853 2020-02-03 17:25:11 50 \n", + "26 6428655088 401163819 43.000001 2020-02-03 17:25:16 50 \n", + "27 401163819 33846184 48.003915 2020-02-03 17:25:21 50 \n", + "28 401163819 33846184 51.000001 2020-02-03 17:25:26 50 \n", + "29 33846184 401163775 48.000001 2020-02-03 17:25:31 50 \n", + "30 401163775 6236877365 44.402858 2020-02-03 17:25:36 50 \n", + "31 6236877365 21518914 37.279547 2020-02-03 17:25:41 50 \n", + "32 21518914 6236877366 23.000001 2020-02-03 17:25:47 50 \n", + "33 6236877366 6431124736 0.000000 2020-02-03 17:25:52 50 \n", + "34 6431124736 21518913 0.000000 2020-02-03 17:25:57 50 \n", + "35 21518913 6431124755 26.645652 2020-02-03 17:26:02 50 \n", + "36 6431124755 6431124738 43.730866 2020-02-03 17:26:07 50 \n", + "37 6431124738 122375084 52.076925 2020-02-03 17:26:12 50 \n", + "38 122375084 21518912 50.999999 2020-02-03 17:26:17 50 \n", + "39 21518912 6242379416 49.000001 2020-02-03 17:26:22 50 \n", + "40 6242379416 401163793 48.999999 2020-02-03 17:26:27 50 \n", + "41 401163793 21518911 51.328447 2020-02-03 17:26:32 50 \n", + "42 21518911 401163801 54.000001 2020-02-03 17:26:37 50 \n", + "43 21518911 401163801 48.255556 2020-02-03 17:26:43 50 \n", + "44 401163801 60676690 29.782723 2020-02-03 17:26:48 50 \n", + "45 60676690 6242379409 0.000000 2020-02-03 17:26:53 50 \n", + "46 6242379409 6236877368 0.000000 2020-02-03 17:26:58 50 \n", + "47 6236877368 3202352228 0.000000 2020-02-03 17:27:03 50 \n", + "48 3202352228 6242379407 0.000000 2020-02-03 17:27:08 50 \n", + "49 6242379407 6236877370 0.000000 2020-02-03 17:27:13 50 \n", + "50 6236877370 6236877369 0.000000 2020-02-03 17:27:18 50 \n", + "51 6236877369 21518910 0.000000 2020-02-03 17:27:23 50 \n", + "52 21518910 5472867409 0.000000 2020-02-03 17:27:28 50 \n", + "53 21518910 5472867409 0.000000 2020-02-03 17:27:33 50 \n", + "54 5472867409 33846175 0.000000 2020-02-03 17:27:39 50 \n", + "55 5472867409 33846175 0.000000 2020-02-03 17:27:44 50 \n", + "56 5472867409 33846175 0.000000 2020-02-03 17:27:49 50 \n", + "57 5472867409 33846175 0.000000 2020-02-03 17:27:54 50 \n", + "58 33846175 3268457415 16.808399 2020-02-03 17:27:59 50 \n", + "59 3268457415 6425072711 26.384211 2020-02-03 17:28:04 50 \n", + "60 6425072711 6425072705 39.000001 2020-02-03 17:28:09 50 \n", + "61 6425072705 5474291175 32.158620 2020-02-03 17:28:14 50 \n", + "62 5474291175 945921131 10.473430 2020-02-03 17:28:19 50 \n", + "63 945921131 5512269043 0.000000 2020-02-03 17:28:24 50 \n", + "64 5512269043 4932702977 11.292683 2020-02-03 17:28:29 50 \n", + "65 4932702977 4502646832 32.335444 2020-02-03 17:28:35 50 \n", + "66 4502646832 268071671 39.753117 2020-02-03 17:28:40 50 \n", + "67 268071671 5512269048 33.999999 2020-02-03 17:28:45 50 \n", + "68 5512269048 5512269050 26.999999 2020-02-03 17:28:50 50 \n", + "69 5512269050 5512269049 30.000000 2020-02-03 17:28:55 50 \n", + "70 5512269049 5512269051 37.536842 2020-02-03 17:29:00 50 \n", + "71 5512269051 29041402 38.300000 2020-02-03 17:29:05 50 \n", + "72 29041402 5512269052 41.867566 2020-02-03 17:29:10 50 \n", + "73 5512269052 5512269053 42.207058 2020-02-03 17:29:15 50 \n", + "74 5512269053 5512269054 40.000001 2020-02-03 17:29:20 50 \n", + "75 5512269054 1120249942 42.000000 2020-02-03 17:29:25 50 \n", + "0 6430014056 34924749 46.221106 2020-02-03 17:23:04 50 \n", + "1 34924749 34924777 52.999999 2020-02-03 17:23:09 50 \n", + "2 34924777 461970318 51.999998 2020-02-03 17:23:14 50 \n", + "3 461970318 34924784 53.000001 2020-02-03 17:23:19 50 \n", + "4 34924784 6430014035 56.000002 2020-02-03 17:23:24 50 \n", + "5 6430014035 34924785 55.000001 2020-02-03 17:23:29 50 \n", + "6 34924785 6430014055 51.000000 2020-02-03 17:23:34 50 \n", + "7 6430014055 34924786 47.504178 2020-02-03 17:23:39 50 \n", + "8 34924786 6430014050 28.319250 2020-02-03 17:23:44 50 \n", + "9 6430014050 6342462458 3.000000 2020-02-03 17:23:50 50 \n", + "10 6342462458 6430014059 20.866337 2020-02-03 17:23:55 50 \n", + "11 6430014059 34924787 22.814814 2020-02-03 17:24:00 50 \n", + "12 34924787 6342462457 36.403509 2020-02-03 17:24:05 50 \n", + "13 6342462457 34924788 47.000000 2020-02-03 17:24:10 50 \n", + "14 34924788 6342462460 47.000000 2020-02-03 17:24:15 50 \n", + "15 6342462460 34924789 49.000000 2020-02-03 17:24:20 50 \n", + "16 34924789 34924790 51.942992 2020-02-03 17:24:25 50 \n", + "17 34924790 34924791 47.999999 2020-02-03 17:24:30 50 \n", + "18 34924791 1498766523 48.913551 2020-02-03 17:24:35 50 \n", + "19 1498766523 358299060 49.264501 2020-02-03 17:24:40 50 \n", + "20 358299060 7152154272 43.000001 2020-02-03 17:24:45 50 \n", + "21 7152154272 7152154274 34.953930 2020-02-03 17:24:51 50 \n", + "22 7152154274 7152154273 0.000000 2020-02-03 17:24:56 50 \n", + "23 7152154273 33846185 0.000000 2020-02-03 17:25:01 50 \n", + "24 33846185 461970347 19.536749 2020-02-03 17:25:06 50 \n", + "25 461970347 6428655088 34.146853 2020-02-03 17:25:11 50 \n", + "26 6428655088 401163819 43.000001 2020-02-03 17:25:16 50 \n", + "27 401163819 33846184 48.003915 2020-02-03 17:25:21 50 \n", + "28 401163819 33846184 51.000001 2020-02-03 17:25:26 50 \n", + "29 33846184 401163775 48.000001 2020-02-03 17:25:31 50 \n", + "30 401163775 6236877365 44.402858 2020-02-03 17:25:36 50 \n", + "31 6236877365 21518914 37.279547 2020-02-03 17:25:41 50 \n", + "32 21518914 6236877366 23.000001 2020-02-03 17:25:47 50 \n", + "33 6236877366 6431124736 0.000000 2020-02-03 17:25:52 50 \n", + "34 6431124736 21518913 0.000000 2020-02-03 17:25:57 50 \n", + "35 21518913 6431124755 26.645652 2020-02-03 17:26:02 50 \n", + "36 6431124755 6431124738 43.730866 2020-02-03 17:26:07 50 \n", + "37 6431124738 122375084 52.076925 2020-02-03 17:26:12 50 \n", + "38 122375084 21518912 50.999999 2020-02-03 17:26:17 50 \n", + "39 21518912 6242379416 49.000001 2020-02-03 17:26:22 50 \n", + "40 6242379416 401163793 48.999999 2020-02-03 17:26:27 50 \n", + "41 401163793 21518911 51.328447 2020-02-03 17:26:32 50 \n", + "42 21518911 401163801 54.000001 2020-02-03 17:26:37 50 \n", + "43 21518911 401163801 48.255556 2020-02-03 17:26:43 50 \n", + "44 401163801 60676690 29.782723 2020-02-03 17:26:48 50 \n", + "45 60676690 6242379409 0.000000 2020-02-03 17:26:53 50 \n", + "46 6242379409 6236877368 0.000000 2020-02-03 17:26:58 50 \n", + "47 6236877368 3202352228 0.000000 2020-02-03 17:27:03 50 \n", + "48 3202352228 6242379407 0.000000 2020-02-03 17:27:08 50 \n", + "49 6242379407 6236877370 0.000000 2020-02-03 17:27:13 50 \n", + "50 6236877370 6236877369 0.000000 2020-02-03 17:27:18 50 \n", + "51 6236877369 21518910 0.000000 2020-02-03 17:27:23 50 \n", + "52 21518910 5472867409 0.000000 2020-02-03 17:27:28 50 \n", + "53 21518910 5472867409 0.000000 2020-02-03 17:27:33 50 \n", + "54 5472867409 33846175 0.000000 2020-02-03 17:27:39 50 \n", + "55 5472867409 33846175 0.000000 2020-02-03 17:27:44 50 \n", + "56 5472867409 33846175 0.000000 2020-02-03 17:27:49 50 \n", + "57 5472867409 33846175 0.000000 2020-02-03 17:27:54 50 \n", + "58 33846175 3268457415 16.808399 2020-02-03 17:27:59 50 \n", + "59 3268457415 6425072711 26.384211 2020-02-03 17:28:04 50 \n", + "60 6425072711 6425072705 39.000001 2020-02-03 17:28:09 50 \n", + "61 6425072705 5474291175 32.158620 2020-02-03 17:28:14 50 \n", + "62 5474291175 945921131 10.473430 2020-02-03 17:28:19 50 \n", + "63 945921131 5512269043 0.000000 2020-02-03 17:28:24 50 \n", + "64 5512269043 4932702977 11.292683 2020-02-03 17:28:29 50 \n", + "65 4932702977 4502646832 32.335444 2020-02-03 17:28:35 50 \n", + "66 4502646832 268071671 39.753117 2020-02-03 17:28:40 50 \n", + "67 268071671 5512269048 33.999999 2020-02-03 17:28:45 50 \n", + "68 5512269048 5512269050 26.999999 2020-02-03 17:28:50 50 \n", + "69 5512269050 5512269049 30.000000 2020-02-03 17:28:55 50 \n", + "70 5512269049 5512269051 37.536842 2020-02-03 17:29:00 50 \n", + "71 5512269051 29041402 38.300000 2020-02-03 17:29:05 50 \n", + "72 29041402 5512269052 41.867566 2020-02-03 17:29:10 50 \n", + "73 5512269052 5512269053 42.207058 2020-02-03 17:29:15 50 \n", + "74 5512269053 5512269054 40.000001 2020-02-03 17:29:20 50 \n", + "75 5512269054 1120249942 42.000000 2020-02-03 17:29:25 50 \n", + "\n", + " CO2 \n", + "0 12.532860 \n", + "1 10.056936 \n", + "2 7.252608 \n", + "3 7.095522 \n", + "4 6.939990 \n", + "5 8.082442 \n", + "6 5.235873 \n", + "7 3.467051 \n", + "8 3.174234 \n", + "9 3.170922 \n", + "10 14.405008 \n", + "11 11.260461 \n", + "12 6.442676 \n", + "13 5.701738 \n", + "14 5.034212 \n", + "15 5.265162 \n", + "16 7.930284 \n", + "17 5.470158 \n", + "18 11.730917 \n", + "19 5.387822 \n", + "20 3.173990 \n", + "21 3.065228 \n", + "22 3.057912 \n", + "23 4.155391 \n", + "24 15.934264 \n", + "25 7.400763 \n", + "26 10.504903 \n", + "27 8.466436 \n", + "28 7.009605 \n", + "29 3.222136 \n", + "30 3.126752 \n", + "31 3.045419 \n", + "32 3.174012 \n", + "33 3.071740 \n", + "34 10.925399 \n", + "35 26.206483 \n", + "36 16.085216 \n", + "37 15.931978 \n", + "38 5.688978 \n", + "39 5.607024 \n", + "40 6.745522 \n", + "41 16.418607 \n", + "42 9.341649 \n", + "43 3.169324 \n", + "44 3.161149 \n", + "45 3.022734 \n", + "46 3.011477 \n", + "47 3.015020 \n", + "48 3.039808 \n", + "49 3.066515 \n", + "50 3.116671 \n", + "51 3.010686 \n", + "52 3.112850 \n", + "53 3.043374 \n", + "54 3.095505 \n", + "55 3.047474 \n", + "56 3.036022 \n", + "57 3.075117 \n", + "58 18.000278 \n", + "59 11.176040 \n", + "60 4.046499 \n", + "61 3.075322 \n", + "62 3.025176 \n", + "63 2.973655 \n", + "64 18.879898 \n", + "65 4.900118 \n", + "66 4.713496 \n", + "67 4.862161 \n", + "68 5.141985 \n", + "69 5.362533 \n", + "70 12.651749 \n", + "71 18.109086 \n", + "72 9.600139 \n", + "73 6.828497 \n", + "74 4.562019 \n", + "75 7.682926 \n", + "0 12.532860 \n", + "1 10.056936 \n", + "2 7.252608 \n", + "3 7.095522 \n", + "4 6.939990 \n", + "5 8.082442 \n", + "6 5.235873 \n", + "7 3.467051 \n", + "8 3.174234 \n", + "9 3.170922 \n", + "10 14.405008 \n", + "11 11.260461 \n", + "12 6.442676 \n", + "13 5.701738 \n", + "14 5.034212 \n", + "15 5.265162 \n", + "16 7.930284 \n", + "17 5.470158 \n", + "18 11.730917 \n", + "19 5.387822 \n", + "20 3.173990 \n", + "21 3.065228 \n", + "22 3.057912 \n", + "23 4.155391 \n", + "24 15.934264 \n", + "25 7.400763 \n", + "26 10.504903 \n", + "27 8.466436 \n", + "28 7.009605 \n", + "29 3.222136 \n", + "30 3.126752 \n", + "31 3.045419 \n", + "32 3.174012 \n", + "33 3.071740 \n", + "34 10.925399 \n", + "35 26.206483 \n", + "36 16.085216 \n", + "37 15.931978 \n", + "38 5.688978 \n", + "39 5.607024 \n", + "40 6.745522 \n", + "41 16.418607 \n", + "42 9.341649 \n", + "43 3.169324 \n", + "44 3.161149 \n", + "45 3.022734 \n", + "46 3.011477 \n", + "47 3.015020 \n", + "48 3.039808 \n", + "49 3.066515 \n", + "50 3.116671 \n", + "51 3.010686 \n", + "52 3.112850 \n", + "53 3.043374 \n", + "54 3.095505 \n", + "55 3.047474 \n", + "56 3.036022 \n", + "57 3.075117 \n", + "58 18.000278 \n", + "59 11.176040 \n", + "60 4.046499 \n", + "61 3.075322 \n", + "62 3.025176 \n", + "63 2.973655 \n", + "64 18.879898 \n", + "65 4.900118 \n", + "66 4.713496 \n", + "67 4.862161 \n", + "68 5.141985 \n", + "69 5.362533 \n", + "70 12.651749 \n", + "71 18.109086 \n", + "72 9.600139 \n", + "73 6.828497 \n", + "74 4.562019 \n", + "75 7.682926 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def TimeBasedAggreagating(concatTracks, hourly, daily, monthly, pTo = False):\n", + " concatTracks['time'] = pd.to_datetime(concatTracks['time'])\n", + " concatTracks.dtypes\n", + " \n", + " filterTracks = concatTracks \n", + " \n", + " if hourly:\n", + " if pTo:\n", + " filterTracks['hour'] = filterTracks['time'].dt.hour \n", + " maskHour = (filterTracks['hour'] >= hourly) & (filterTracks['hour'] <= pTo) \n", + " print(filteredConcatTracksDayTime)\n", + " \n", + " else:\n", + " filterTracks['hour'] = filterTracks['time'].dt.hour \n", + " maskHour = (filterTracks['hour'] == hourly)\n", + " filterTracks = filterTracks.loc[maskHour]\n", + " \n", + " if daily:\n", + " if pTo:\n", + " filterTracks['weekday'] = filterTracks['time'].dt.dayofweek \n", + " maskDay = (filterTracks['weekday'] >= daily) & (filterTracks['weekday'] <= pTo)\n", + " filterTracks = filterTracks.loc[maskDay]\n", + " else:\n", + " filterTracks['weekday'] = filterTracks['time'].dt.dayofweek \n", + " maskDay = (filterTracks['weekday'] == daily)\n", + " filterTracks = filterTracks.loc[maskDay]\n", + " \n", + " if monthly: \n", + " if pTo:\n", + " filterTracks['month'] = filterTracks['time'].dt.month \n", + " maskMonth = (filterTracks['month'] >= monthly) & (filterTracks['month'] <= pTo)\n", + " filterTracks = filterTracks.loc[maskMonth]\n", + " else:\n", + " filterTracks['month'] = filterTracks['time'].dt.month \n", + " maskMonth = (filterTracks['month'] == monthly)\n", + " filterTracks = filterTracks.loc[maskMonth]\n", + " \n", + " return filterTracks\n", + "\n", + "\n", + "concatTracks1 = concatTracks\n", + "hour = 0\n", + "day = 0\n", + "month = 0\n", + "to = 9\n", + "FilterTracks = TimeBasedAggreagating(concatTracks1, hour, day,month,to)\n", + "FilterTracks\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
st_nodeend_nodetimespeed_limitCO2mean_speedmin_speedmax_speedStNode_latStNode_lonEndNode_latEndNode_lon
06430014056349247492020-02-03 17:23:045012.53286046.22110646.22110646.22110651.9452047.60117451.9454647.601387
134924749349247772020-02-03 17:23:095010.05693652.99999952.99999952.99999951.9454647.60138751.9456177.601554
2349247774619703182020-02-03 17:23:14507.25260851.99999851.99999851.99999851.9456177.60155451.9457917.601743
3461970318349247842020-02-03 17:23:19507.09552253.00000153.00000153.00000151.9457917.60174351.9461967.602167
43492478464300140352020-02-03 17:23:24506.93999056.00000256.00000256.00000251.9461967.60216751.9463747.602310
56430014035349247852020-02-03 17:23:29508.08244255.00000155.00000155.00000151.9463747.60231051.9465627.602446
63492478564300140552020-02-03 17:23:34505.23587351.00000051.00000051.00000051.9465627.60244651.9467857.602576
76430014055349247862020-02-03 17:23:39503.46705147.50417847.50417847.50417851.9467857.60257651.9470247.602696
83492478664300140502020-02-03 17:23:44503.17423428.31925028.31925028.31925051.9470247.60269651.9471837.602777
9643001405063424624582020-02-03 17:23:50503.1709223.0000003.0000003.00000051.9471837.60277751.9473197.602835
10634246245864300140592020-02-03 17:23:555014.40500820.86633720.86633720.86633751.9473197.60283551.9474777.602923
116430014059349247872020-02-03 17:24:005011.26046122.81481422.81481422.81481451.9474777.60292351.9476267.603023
123492478763424624572020-02-03 17:24:05506.44267636.40350936.40350936.40350951.9476267.60302351.9478347.603200
136342462457349247882020-02-03 17:24:10505.70173847.00000047.00000047.00000051.9478347.60320051.9479767.603352
143492478863424624602020-02-03 17:24:15505.03421247.00000047.00000047.00000051.9479767.60335251.9481247.603530
156342462460349247892020-02-03 17:24:20505.26516249.00000049.00000049.00000051.9481247.60353051.9482737.603748
1634924789349247902020-02-03 17:24:25507.93028451.94299251.94299251.94299251.9482737.60374851.9484307.604013
1734924790349247912020-02-03 17:24:30505.47015847.99999947.99999947.99999951.9484307.60401351.9487367.604684
183492479114987665232020-02-03 17:24:355011.73091748.91355148.91355148.91355151.9487367.60468451.9488847.605039
1914987665233582990602020-02-03 17:24:40505.38782249.26450149.26450149.26450151.9488847.60503951.9489417.605177
2035829906071521542722020-02-03 17:24:45503.17399043.00000143.00000143.00000151.9489417.60517751.9491927.605727
21715215427271521542742020-02-03 17:24:51503.06522834.95393034.95393034.95393051.9491927.60572751.9493857.606137
22715215427471521542732020-02-03 17:24:56503.0579120.0000000.0000000.00000051.9493857.60613751.9494347.606228
237152154273338461852020-02-03 17:25:01504.1553910.0000000.0000000.00000051.9494347.60622851.9495067.606360
24338461854619703472020-02-03 17:25:065015.93426419.53674919.53674919.53674951.9495067.60636051.9496307.606209
2546197034764286550882020-02-03 17:25:11507.40076334.14685334.14685334.14685351.9496307.60620951.9496787.606151
2664286550884011638192020-02-03 17:25:165010.50490343.00000143.00000143.00000151.9496787.60615151.9498797.605897
27401163819338461842020-02-03 17:25:21508.46643649.50195848.00391551.00000151.9498797.60589751.9509147.604530
28401163819338461842020-02-03 17:25:26507.00960549.50195848.00391551.00000151.9498797.60589751.9509147.604530
29338461844011637752020-02-03 17:25:31503.22213648.00000148.00000148.00000151.9509147.60453051.9509537.604481
3040116377562368773652020-02-03 17:25:36503.12675244.40285844.40285844.40285851.9509537.60448151.9511097.604312
316236877365215189142020-02-03 17:25:41503.04541937.27954737.27954737.27954751.9511097.60431251.9512857.604142
322151891462368773662020-02-03 17:25:47503.17401223.00000123.00000123.00000151.9512857.60414251.9514607.603996
33623687736664311247362020-02-03 17:25:52503.0717400.0000000.0000000.00000051.9514607.60399651.9516307.603875
346431124736215189132020-02-03 17:25:575010.9253990.0000000.0000000.00000051.9516307.60387551.9518577.603737
352151891364311247552020-02-03 17:26:025026.20648326.64565226.64565226.64565251.9518577.60373751.9520207.603660
36643112475564311247382020-02-03 17:26:075016.08521643.73086643.73086643.73086651.9520207.60366051.9524187.603498
3764311247381223750842020-02-03 17:26:125015.93197852.07692552.07692552.07692551.9524187.60349851.9529827.603277
38122375084215189122020-02-03 17:26:17505.68897850.99999950.99999950.99999951.9529827.60327751.9540137.602906
392151891262423794162020-02-03 17:26:22505.60702449.00000149.00000149.00000151.9540137.60290651.9544147.602834
4062423794164011637932020-02-03 17:26:27506.74552248.99999948.99999948.99999951.9544147.60283451.9544647.602825
41401163793215189112020-02-03 17:26:325016.41860751.32844751.32844751.32844751.9544647.60282551.9545997.602805
42215189114011638012020-02-03 17:26:37509.34164951.12777848.25555654.00000151.9545997.60280551.9550597.602826
43215189114011638012020-02-03 17:26:43503.16932451.12777848.25555654.00000151.9545997.60280551.9550597.602826
44401163801606766902020-02-03 17:26:48503.16114929.78272329.78272329.78272351.9550597.60282651.9552897.602845
456067669062423794092020-02-03 17:26:53503.0227340.0000000.0000000.00000051.9552897.60284551.9555547.602867
46624237940962368773682020-02-03 17:26:58503.0114770.0000000.0000000.00000051.9555547.60286751.9557977.602896
47623687736832023522282020-02-03 17:27:03503.0150200.0000000.0000000.00000051.9557977.60289651.9558947.602907
48320235222862423794072020-02-03 17:27:08503.0398080.0000000.0000000.00000051.9558947.60290751.9561367.602932
49624237940762368773702020-02-03 17:27:13503.0665150.0000000.0000000.00000051.9561367.60293251.9562977.602944
50623687737062368773692020-02-03 17:27:18503.1166710.0000000.0000000.00000051.9562977.60294451.9565877.602944
516236877369215189102020-02-03 17:27:23503.0106860.0000000.0000000.00000051.9565877.60294451.9568107.602931
522151891054728674092020-02-03 17:27:28503.1128500.0000000.0000000.00000051.9568107.60293151.9585027.602758
532151891054728674092020-02-03 17:27:33503.0433740.0000000.0000000.00000051.9568107.60293151.9585027.602758
545472867409338461752020-02-03 17:27:39503.0955050.0000000.0000000.00000051.9585027.60275851.9594057.602668
555472867409338461752020-02-03 17:27:44503.0474740.0000000.0000000.00000051.9585027.60275851.9594057.602668
565472867409338461752020-02-03 17:27:49503.0360220.0000000.0000000.00000051.9585027.60275851.9594057.602668
575472867409338461752020-02-03 17:27:54503.0751170.0000000.0000000.00000051.9585027.60275851.9594057.602668
583384617532684574152020-02-03 17:27:595018.00027816.80839916.80839916.80839951.9594057.60266851.9595077.602659
59326845741564250727112020-02-03 17:28:045011.17604026.38421126.38421126.38421151.9595077.60265951.9595897.602653
60642507271164250727052020-02-03 17:28:09504.04649939.00000139.00000139.00000151.9595897.60265351.9596877.602647
61642507270554742911752020-02-03 17:28:14503.07532232.15862032.15862032.15862051.9596877.60264751.9597557.602641
6254742911759459211312020-02-03 17:28:19503.02517610.47343010.47343010.47343051.9597557.60264151.9600017.602626
6394592113155122690432020-02-03 17:28:24502.9736550.0000000.0000000.00000051.9600017.60262651.9603547.602602
64551226904349327029772020-02-03 17:28:295018.87989811.29268311.29268311.29268351.9603547.60260251.9607137.602584
65493270297745026468322020-02-03 17:28:35504.90011832.33544432.33544432.33544451.9607137.60258451.9609207.602579
6645026468322680716712020-02-03 17:28:40504.71349639.75311739.75311739.75311751.9609207.60257951.9611477.602571
6726807167155122690482020-02-03 17:28:45504.86216133.99999933.99999933.99999951.9611477.60257151.9612587.602567
68551226904855122690502020-02-03 17:28:50505.14198526.99999926.99999926.99999951.9612587.60256751.9613607.602561
69551226905055122690492020-02-03 17:28:55505.36253330.00000030.00000030.00000051.9613607.60256151.9614437.602556
70551226904955122690512020-02-03 17:29:005012.65174937.53684237.53684237.53684251.9614437.60255651.9615267.602549
715512269051290414022020-02-03 17:29:055018.10908638.30000038.30000038.30000051.9615267.60254951.9616637.602535
722904140255122690522020-02-03 17:29:10509.60013941.86756641.86756641.86756651.9616637.60253551.9617707.602520
73551226905255122690532020-02-03 17:29:15506.82849742.20705842.20705842.20705851.9617707.60252051.9619087.602493
74551226905355122690542020-02-03 17:29:20504.56201940.00000140.00000140.00000151.9619087.60249351.9620157.602471
75551226905411202499422020-02-03 17:29:25507.68292642.00000042.00000042.00000051.9620157.60247151.9621707.602432
\n", + "
" + ], + "text/plain": [ + " st_node end_node time speed_limit CO2 \\\n", + "0 6430014056 34924749 2020-02-03 17:23:04 50 12.532860 \n", + "1 34924749 34924777 2020-02-03 17:23:09 50 10.056936 \n", + "2 34924777 461970318 2020-02-03 17:23:14 50 7.252608 \n", + "3 461970318 34924784 2020-02-03 17:23:19 50 7.095522 \n", + "4 34924784 6430014035 2020-02-03 17:23:24 50 6.939990 \n", + "5 6430014035 34924785 2020-02-03 17:23:29 50 8.082442 \n", + "6 34924785 6430014055 2020-02-03 17:23:34 50 5.235873 \n", + "7 6430014055 34924786 2020-02-03 17:23:39 50 3.467051 \n", + "8 34924786 6430014050 2020-02-03 17:23:44 50 3.174234 \n", + "9 6430014050 6342462458 2020-02-03 17:23:50 50 3.170922 \n", + "10 6342462458 6430014059 2020-02-03 17:23:55 50 14.405008 \n", + "11 6430014059 34924787 2020-02-03 17:24:00 50 11.260461 \n", + "12 34924787 6342462457 2020-02-03 17:24:05 50 6.442676 \n", + "13 6342462457 34924788 2020-02-03 17:24:10 50 5.701738 \n", + "14 34924788 6342462460 2020-02-03 17:24:15 50 5.034212 \n", + "15 6342462460 34924789 2020-02-03 17:24:20 50 5.265162 \n", + "16 34924789 34924790 2020-02-03 17:24:25 50 7.930284 \n", + "17 34924790 34924791 2020-02-03 17:24:30 50 5.470158 \n", + "18 34924791 1498766523 2020-02-03 17:24:35 50 11.730917 \n", + "19 1498766523 358299060 2020-02-03 17:24:40 50 5.387822 \n", + "20 358299060 7152154272 2020-02-03 17:24:45 50 3.173990 \n", + "21 7152154272 7152154274 2020-02-03 17:24:51 50 3.065228 \n", + "22 7152154274 7152154273 2020-02-03 17:24:56 50 3.057912 \n", + "23 7152154273 33846185 2020-02-03 17:25:01 50 4.155391 \n", + "24 33846185 461970347 2020-02-03 17:25:06 50 15.934264 \n", + "25 461970347 6428655088 2020-02-03 17:25:11 50 7.400763 \n", + "26 6428655088 401163819 2020-02-03 17:25:16 50 10.504903 \n", + "27 401163819 33846184 2020-02-03 17:25:21 50 8.466436 \n", + "28 401163819 33846184 2020-02-03 17:25:26 50 7.009605 \n", + "29 33846184 401163775 2020-02-03 17:25:31 50 3.222136 \n", + "30 401163775 6236877365 2020-02-03 17:25:36 50 3.126752 \n", + "31 6236877365 21518914 2020-02-03 17:25:41 50 3.045419 \n", + "32 21518914 6236877366 2020-02-03 17:25:47 50 3.174012 \n", + "33 6236877366 6431124736 2020-02-03 17:25:52 50 3.071740 \n", + "34 6431124736 21518913 2020-02-03 17:25:57 50 10.925399 \n", + "35 21518913 6431124755 2020-02-03 17:26:02 50 26.206483 \n", + "36 6431124755 6431124738 2020-02-03 17:26:07 50 16.085216 \n", + "37 6431124738 122375084 2020-02-03 17:26:12 50 15.931978 \n", + "38 122375084 21518912 2020-02-03 17:26:17 50 5.688978 \n", + "39 21518912 6242379416 2020-02-03 17:26:22 50 5.607024 \n", + "40 6242379416 401163793 2020-02-03 17:26:27 50 6.745522 \n", + "41 401163793 21518911 2020-02-03 17:26:32 50 16.418607 \n", + "42 21518911 401163801 2020-02-03 17:26:37 50 9.341649 \n", + "43 21518911 401163801 2020-02-03 17:26:43 50 3.169324 \n", + "44 401163801 60676690 2020-02-03 17:26:48 50 3.161149 \n", + "45 60676690 6242379409 2020-02-03 17:26:53 50 3.022734 \n", + "46 6242379409 6236877368 2020-02-03 17:26:58 50 3.011477 \n", + "47 6236877368 3202352228 2020-02-03 17:27:03 50 3.015020 \n", + "48 3202352228 6242379407 2020-02-03 17:27:08 50 3.039808 \n", + "49 6242379407 6236877370 2020-02-03 17:27:13 50 3.066515 \n", + "50 6236877370 6236877369 2020-02-03 17:27:18 50 3.116671 \n", + "51 6236877369 21518910 2020-02-03 17:27:23 50 3.010686 \n", + "52 21518910 5472867409 2020-02-03 17:27:28 50 3.112850 \n", + "53 21518910 5472867409 2020-02-03 17:27:33 50 3.043374 \n", + "54 5472867409 33846175 2020-02-03 17:27:39 50 3.095505 \n", + "55 5472867409 33846175 2020-02-03 17:27:44 50 3.047474 \n", + "56 5472867409 33846175 2020-02-03 17:27:49 50 3.036022 \n", + "57 5472867409 33846175 2020-02-03 17:27:54 50 3.075117 \n", + "58 33846175 3268457415 2020-02-03 17:27:59 50 18.000278 \n", + "59 3268457415 6425072711 2020-02-03 17:28:04 50 11.176040 \n", + "60 6425072711 6425072705 2020-02-03 17:28:09 50 4.046499 \n", + "61 6425072705 5474291175 2020-02-03 17:28:14 50 3.075322 \n", + "62 5474291175 945921131 2020-02-03 17:28:19 50 3.025176 \n", + "63 945921131 5512269043 2020-02-03 17:28:24 50 2.973655 \n", + "64 5512269043 4932702977 2020-02-03 17:28:29 50 18.879898 \n", + "65 4932702977 4502646832 2020-02-03 17:28:35 50 4.900118 \n", + "66 4502646832 268071671 2020-02-03 17:28:40 50 4.713496 \n", + "67 268071671 5512269048 2020-02-03 17:28:45 50 4.862161 \n", + "68 5512269048 5512269050 2020-02-03 17:28:50 50 5.141985 \n", + "69 5512269050 5512269049 2020-02-03 17:28:55 50 5.362533 \n", + "70 5512269049 5512269051 2020-02-03 17:29:00 50 12.651749 \n", + "71 5512269051 29041402 2020-02-03 17:29:05 50 18.109086 \n", + "72 29041402 5512269052 2020-02-03 17:29:10 50 9.600139 \n", + "73 5512269052 5512269053 2020-02-03 17:29:15 50 6.828497 \n", + "74 5512269053 5512269054 2020-02-03 17:29:20 50 4.562019 \n", + "75 5512269054 1120249942 2020-02-03 17:29:25 50 7.682926 \n", + "\n", + " mean_speed min_speed max_speed StNode_lat StNode_lon EndNode_lat \\\n", + "0 46.221106 46.221106 46.221106 51.945204 7.601174 51.945464 \n", + "1 52.999999 52.999999 52.999999 51.945464 7.601387 51.945617 \n", + "2 51.999998 51.999998 51.999998 51.945617 7.601554 51.945791 \n", + "3 53.000001 53.000001 53.000001 51.945791 7.601743 51.946196 \n", + "4 56.000002 56.000002 56.000002 51.946196 7.602167 51.946374 \n", + "5 55.000001 55.000001 55.000001 51.946374 7.602310 51.946562 \n", + "6 51.000000 51.000000 51.000000 51.946562 7.602446 51.946785 \n", + "7 47.504178 47.504178 47.504178 51.946785 7.602576 51.947024 \n", + "8 28.319250 28.319250 28.319250 51.947024 7.602696 51.947183 \n", + "9 3.000000 3.000000 3.000000 51.947183 7.602777 51.947319 \n", + "10 20.866337 20.866337 20.866337 51.947319 7.602835 51.947477 \n", + "11 22.814814 22.814814 22.814814 51.947477 7.602923 51.947626 \n", + "12 36.403509 36.403509 36.403509 51.947626 7.603023 51.947834 \n", + "13 47.000000 47.000000 47.000000 51.947834 7.603200 51.947976 \n", + "14 47.000000 47.000000 47.000000 51.947976 7.603352 51.948124 \n", + "15 49.000000 49.000000 49.000000 51.948124 7.603530 51.948273 \n", + "16 51.942992 51.942992 51.942992 51.948273 7.603748 51.948430 \n", + "17 47.999999 47.999999 47.999999 51.948430 7.604013 51.948736 \n", + "18 48.913551 48.913551 48.913551 51.948736 7.604684 51.948884 \n", + "19 49.264501 49.264501 49.264501 51.948884 7.605039 51.948941 \n", + "20 43.000001 43.000001 43.000001 51.948941 7.605177 51.949192 \n", + "21 34.953930 34.953930 34.953930 51.949192 7.605727 51.949385 \n", + "22 0.000000 0.000000 0.000000 51.949385 7.606137 51.949434 \n", + "23 0.000000 0.000000 0.000000 51.949434 7.606228 51.949506 \n", + "24 19.536749 19.536749 19.536749 51.949506 7.606360 51.949630 \n", + "25 34.146853 34.146853 34.146853 51.949630 7.606209 51.949678 \n", + "26 43.000001 43.000001 43.000001 51.949678 7.606151 51.949879 \n", + "27 49.501958 48.003915 51.000001 51.949879 7.605897 51.950914 \n", + "28 49.501958 48.003915 51.000001 51.949879 7.605897 51.950914 \n", + "29 48.000001 48.000001 48.000001 51.950914 7.604530 51.950953 \n", + "30 44.402858 44.402858 44.402858 51.950953 7.604481 51.951109 \n", + "31 37.279547 37.279547 37.279547 51.951109 7.604312 51.951285 \n", + "32 23.000001 23.000001 23.000001 51.951285 7.604142 51.951460 \n", + "33 0.000000 0.000000 0.000000 51.951460 7.603996 51.951630 \n", + "34 0.000000 0.000000 0.000000 51.951630 7.603875 51.951857 \n", + "35 26.645652 26.645652 26.645652 51.951857 7.603737 51.952020 \n", + "36 43.730866 43.730866 43.730866 51.952020 7.603660 51.952418 \n", + "37 52.076925 52.076925 52.076925 51.952418 7.603498 51.952982 \n", + "38 50.999999 50.999999 50.999999 51.952982 7.603277 51.954013 \n", + "39 49.000001 49.000001 49.000001 51.954013 7.602906 51.954414 \n", + "40 48.999999 48.999999 48.999999 51.954414 7.602834 51.954464 \n", + "41 51.328447 51.328447 51.328447 51.954464 7.602825 51.954599 \n", + "42 51.127778 48.255556 54.000001 51.954599 7.602805 51.955059 \n", + "43 51.127778 48.255556 54.000001 51.954599 7.602805 51.955059 \n", + "44 29.782723 29.782723 29.782723 51.955059 7.602826 51.955289 \n", + "45 0.000000 0.000000 0.000000 51.955289 7.602845 51.955554 \n", + "46 0.000000 0.000000 0.000000 51.955554 7.602867 51.955797 \n", + "47 0.000000 0.000000 0.000000 51.955797 7.602896 51.955894 \n", + "48 0.000000 0.000000 0.000000 51.955894 7.602907 51.956136 \n", + "49 0.000000 0.000000 0.000000 51.956136 7.602932 51.956297 \n", + "50 0.000000 0.000000 0.000000 51.956297 7.602944 51.956587 \n", + "51 0.000000 0.000000 0.000000 51.956587 7.602944 51.956810 \n", + "52 0.000000 0.000000 0.000000 51.956810 7.602931 51.958502 \n", + "53 0.000000 0.000000 0.000000 51.956810 7.602931 51.958502 \n", + "54 0.000000 0.000000 0.000000 51.958502 7.602758 51.959405 \n", + "55 0.000000 0.000000 0.000000 51.958502 7.602758 51.959405 \n", + "56 0.000000 0.000000 0.000000 51.958502 7.602758 51.959405 \n", + "57 0.000000 0.000000 0.000000 51.958502 7.602758 51.959405 \n", + "58 16.808399 16.808399 16.808399 51.959405 7.602668 51.959507 \n", + "59 26.384211 26.384211 26.384211 51.959507 7.602659 51.959589 \n", + "60 39.000001 39.000001 39.000001 51.959589 7.602653 51.959687 \n", + "61 32.158620 32.158620 32.158620 51.959687 7.602647 51.959755 \n", + "62 10.473430 10.473430 10.473430 51.959755 7.602641 51.960001 \n", + "63 0.000000 0.000000 0.000000 51.960001 7.602626 51.960354 \n", + "64 11.292683 11.292683 11.292683 51.960354 7.602602 51.960713 \n", + "65 32.335444 32.335444 32.335444 51.960713 7.602584 51.960920 \n", + "66 39.753117 39.753117 39.753117 51.960920 7.602579 51.961147 \n", + "67 33.999999 33.999999 33.999999 51.961147 7.602571 51.961258 \n", + "68 26.999999 26.999999 26.999999 51.961258 7.602567 51.961360 \n", + "69 30.000000 30.000000 30.000000 51.961360 7.602561 51.961443 \n", + "70 37.536842 37.536842 37.536842 51.961443 7.602556 51.961526 \n", + "71 38.300000 38.300000 38.300000 51.961526 7.602549 51.961663 \n", + "72 41.867566 41.867566 41.867566 51.961663 7.602535 51.961770 \n", + "73 42.207058 42.207058 42.207058 51.961770 7.602520 51.961908 \n", + "74 40.000001 40.000001 40.000001 51.961908 7.602493 51.962015 \n", + "75 42.000000 42.000000 42.000000 51.962015 7.602471 51.962170 \n", + "\n", + " EndNode_lon \n", + "0 7.601387 \n", + "1 7.601554 \n", + "2 7.601743 \n", + "3 7.602167 \n", + "4 7.602310 \n", + "5 7.602446 \n", + "6 7.602576 \n", + "7 7.602696 \n", + "8 7.602777 \n", + "9 7.602835 \n", + "10 7.602923 \n", + "11 7.603023 \n", + "12 7.603200 \n", + "13 7.603352 \n", + "14 7.603530 \n", + "15 7.603748 \n", + "16 7.604013 \n", + "17 7.604684 \n", + "18 7.605039 \n", + "19 7.605177 \n", + "20 7.605727 \n", + "21 7.606137 \n", + "22 7.606228 \n", + "23 7.606360 \n", + "24 7.606209 \n", + "25 7.606151 \n", + "26 7.605897 \n", + "27 7.604530 \n", + "28 7.604530 \n", + "29 7.604481 \n", + "30 7.604312 \n", + "31 7.604142 \n", + "32 7.603996 \n", + "33 7.603875 \n", + "34 7.603737 \n", + "35 7.603660 \n", + "36 7.603498 \n", + "37 7.603277 \n", + "38 7.602906 \n", + "39 7.602834 \n", + "40 7.602825 \n", + "41 7.602805 \n", + "42 7.602826 \n", + "43 7.602826 \n", + "44 7.602845 \n", + "45 7.602867 \n", + "46 7.602896 \n", + "47 7.602907 \n", + "48 7.602932 \n", + "49 7.602944 \n", + "50 7.602944 \n", + "51 7.602931 \n", + "52 7.602758 \n", + "53 7.602758 \n", + "54 7.602668 \n", + "55 7.602668 \n", + "56 7.602668 \n", + "57 7.602668 \n", + "58 7.602659 \n", + "59 7.602653 \n", + "60 7.602647 \n", + "61 7.602641 \n", + "62 7.602626 \n", + "63 7.602602 \n", + "64 7.602584 \n", + "65 7.602579 \n", + "66 7.602571 \n", + "67 7.602567 \n", + "68 7.602561 \n", + "69 7.602556 \n", + "70 7.602549 \n", + "71 7.602535 \n", + "72 7.602520 \n", + "73 7.602493 \n", + "74 7.602471 \n", + "75 7.602432 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "concatAggregatedTracks = aggregateStatsFromNodes(FilterTracks,True,False)\n", + "appendNodeCoords(concatAggregatedTracks)\n", + "concatAggregatedTracks\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotAggregatedStatistics(concatAggregatedTracks,True,False,False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Speed difference with respect to speed limits.\n", + "edgset is aggregated to nodes with mean speeds and compare speed limits\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
st_nodeend_nodetimespeed_limitCO2mean_speedmin_speedmax_speedStNode_latStNode_lonEndNode_latEndNode_lonDiffBetweenSpeedLimit
06430014056349247492020-02-03T17:23:045012.53286046.22110646.22110646.22110651.9452047.60117451.9454647.601387-3.778894
134924749349247772020-02-03T17:23:095010.05693652.99999952.99999952.99999951.9454647.60138751.9456177.6015542.999999
2349247774619703182020-02-03T17:23:14507.25260851.99999851.99999851.99999851.9456177.60155451.9457917.6017431.999998
3461970318349247842020-02-03T17:23:19507.09552253.00000153.00000153.00000151.9457917.60174351.9461967.6021673.000001
43492478464300140352020-02-03T17:23:24506.93999056.00000256.00000256.00000251.9461967.60216751.9463747.6023106.000002
56430014035349247852020-02-03T17:23:29508.08244255.00000155.00000155.00000151.9463747.60231051.9465627.6024465.000001
63492478564300140552020-02-03T17:23:34505.23587351.00000051.00000051.00000051.9465627.60244651.9467857.6025761.000000
76430014055349247862020-02-03T17:23:39503.46705147.50417847.50417847.50417851.9467857.60257651.9470247.602696-2.495822
83492478664300140502020-02-03T17:23:44503.17423428.31925028.31925028.31925051.9470247.60269651.9471837.602777-21.680750
9643001405063424624582020-02-03T17:23:50503.1709223.0000003.0000003.00000051.9471837.60277751.9473197.602835-47.000000
10634246245864300140592020-02-03T17:23:555014.40500820.86633720.86633720.86633751.9473197.60283551.9474777.602923-29.133663
116430014059349247872020-02-03T17:24:005011.26046122.81481422.81481422.81481451.9474777.60292351.9476267.603023-27.185186
123492478763424624572020-02-03T17:24:05506.44267636.40350936.40350936.40350951.9476267.60302351.9478347.603200-13.596491
136342462457349247882020-02-03T17:24:10505.70173847.00000047.00000047.00000051.9478347.60320051.9479767.603352-3.000000
143492478863424624602020-02-03T17:24:15505.03421247.00000047.00000047.00000051.9479767.60335251.9481247.603530-3.000000
156342462460349247892020-02-03T17:24:20505.26516249.00000049.00000049.00000051.9481247.60353051.9482737.603748-1.000000
1634924789349247902020-02-03T17:24:25507.93028451.94299251.94299251.94299251.9482737.60374851.9484307.6040131.942992
1734924790349247912020-02-03T17:24:30505.47015847.99999947.99999947.99999951.9484307.60401351.9487367.604684-2.000001
183492479114987665232020-02-03T17:24:355011.73091748.91355148.91355148.91355151.9487367.60468451.9488847.605039-1.086449
1914987665233582990602020-02-03T17:24:40505.38782249.26450149.26450149.26450151.9488847.60503951.9489417.605177-0.735499
2035829906071521542722020-02-03T17:24:45503.17399043.00000143.00000143.00000151.9489417.60517751.9491927.605727-6.999999
21715215427271521542742020-02-03T17:24:51503.06522834.95393034.95393034.95393051.9491927.60572751.9493857.606137-15.046070
22715215427471521542732020-02-03T17:24:56503.0579120.0000000.0000000.00000051.9493857.60613751.9494347.606228-50.000000
237152154273338461852020-02-03T17:25:01504.1553910.0000000.0000000.00000051.9494347.60622851.9495067.606360-50.000000
24338461854619703472020-02-03T17:25:065015.93426419.53674919.53674919.53674951.9495067.60636051.9496307.606209-30.463251
2546197034764286550882020-02-03T17:25:11507.40076334.14685334.14685334.14685351.9496307.60620951.9496787.606151-15.853147
2664286550884011638192020-02-03T17:25:165010.50490343.00000143.00000143.00000151.9496787.60615151.9498797.605897-6.999999
27401163819338461842020-02-03T17:25:21508.46643649.50195848.00391551.00000151.9498797.60589751.9509147.604530-0.498042
28401163819338461842020-02-03T17:25:26507.00960549.50195848.00391551.00000151.9498797.60589751.9509147.604530-0.498042
29338461844011637752020-02-03T17:25:31503.22213648.00000148.00000148.00000151.9509147.60453051.9509537.604481-1.999999
3040116377562368773652020-02-03T17:25:36503.12675244.40285844.40285844.40285851.9509537.60448151.9511097.604312-5.597142
316236877365215189142020-02-03T17:25:41503.04541937.27954737.27954737.27954751.9511097.60431251.9512857.604142-12.720453
322151891462368773662020-02-03T17:25:47503.17401223.00000123.00000123.00000151.9512857.60414251.9514607.603996-26.999999
33623687736664311247362020-02-03T17:25:52503.0717400.0000000.0000000.00000051.9514607.60399651.9516307.603875-50.000000
346431124736215189132020-02-03T17:25:575010.9253990.0000000.0000000.00000051.9516307.60387551.9518577.603737-50.000000
352151891364311247552020-02-03T17:26:025026.20648326.64565226.64565226.64565251.9518577.60373751.9520207.603660-23.354348
36643112475564311247382020-02-03T17:26:075016.08521643.73086643.73086643.73086651.9520207.60366051.9524187.603498-6.269134
3764311247381223750842020-02-03T17:26:125015.93197852.07692552.07692552.07692551.9524187.60349851.9529827.6032772.076925
38122375084215189122020-02-03T17:26:17505.68897850.99999950.99999950.99999951.9529827.60327751.9540137.6029060.999999
392151891262423794162020-02-03T17:26:22505.60702449.00000149.00000149.00000151.9540137.60290651.9544147.602834-0.999999
4062423794164011637932020-02-03T17:26:27506.74552248.99999948.99999948.99999951.9544147.60283451.9544647.602825-1.000001
41401163793215189112020-02-03T17:26:325016.41860751.32844751.32844751.32844751.9544647.60282551.9545997.6028051.328447
42215189114011638012020-02-03T17:26:37509.34164951.12777848.25555654.00000151.9545997.60280551.9550597.6028261.127778
43215189114011638012020-02-03T17:26:43503.16932451.12777848.25555654.00000151.9545997.60280551.9550597.6028261.127778
44401163801606766902020-02-03T17:26:48503.16114929.78272329.78272329.78272351.9550597.60282651.9552897.602845-20.217277
456067669062423794092020-02-03T17:26:53503.0227340.0000000.0000000.00000051.9552897.60284551.9555547.602867-50.000000
46624237940962368773682020-02-03T17:26:58503.0114770.0000000.0000000.00000051.9555547.60286751.9557977.602896-50.000000
47623687736832023522282020-02-03T17:27:03503.0150200.0000000.0000000.00000051.9557977.60289651.9558947.602907-50.000000
48320235222862423794072020-02-03T17:27:08503.0398080.0000000.0000000.00000051.9558947.60290751.9561367.602932-50.000000
49624237940762368773702020-02-03T17:27:13503.0665150.0000000.0000000.00000051.9561367.60293251.9562977.602944-50.000000
50623687737062368773692020-02-03T17:27:18503.1166710.0000000.0000000.00000051.9562977.60294451.9565877.602944-50.000000
516236877369215189102020-02-03T17:27:23503.0106860.0000000.0000000.00000051.9565877.60294451.9568107.602931-50.000000
522151891054728674092020-02-03T17:27:28503.1128500.0000000.0000000.00000051.9568107.60293151.9585027.602758-50.000000
532151891054728674092020-02-03T17:27:33503.0433740.0000000.0000000.00000051.9568107.60293151.9585027.602758-50.000000
545472867409338461752020-02-03T17:27:39503.0955050.0000000.0000000.00000051.9585027.60275851.9594057.602668-50.000000
555472867409338461752020-02-03T17:27:44503.0474740.0000000.0000000.00000051.9585027.60275851.9594057.602668-50.000000
565472867409338461752020-02-03T17:27:49503.0360220.0000000.0000000.00000051.9585027.60275851.9594057.602668-50.000000
575472867409338461752020-02-03T17:27:54503.0751170.0000000.0000000.00000051.9585027.60275851.9594057.602668-50.000000
583384617532684574152020-02-03T17:27:595018.00027816.80839916.80839916.80839951.9594057.60266851.9595077.602659-33.191601
59326845741564250727112020-02-03T17:28:045011.17604026.38421126.38421126.38421151.9595077.60265951.9595897.602653-23.615789
60642507271164250727052020-02-03T17:28:09504.04649939.00000139.00000139.00000151.9595897.60265351.9596877.602647-10.999999
61642507270554742911752020-02-03T17:28:14503.07532232.15862032.15862032.15862051.9596877.60264751.9597557.602641-17.841380
6254742911759459211312020-02-03T17:28:19503.02517610.47343010.47343010.47343051.9597557.60264151.9600017.602626-39.526570
6394592113155122690432020-02-03T17:28:24502.9736550.0000000.0000000.00000051.9600017.60262651.9603547.602602-50.000000
64551226904349327029772020-02-03T17:28:295018.87989811.29268311.29268311.29268351.9603547.60260251.9607137.602584-38.707317
65493270297745026468322020-02-03T17:28:35504.90011832.33544432.33544432.33544451.9607137.60258451.9609207.602579-17.664556
6645026468322680716712020-02-03T17:28:40504.71349639.75311739.75311739.75311751.9609207.60257951.9611477.602571-10.246883
6726807167155122690482020-02-03T17:28:45504.86216133.99999933.99999933.99999951.9611477.60257151.9612587.602567-16.000001
68551226904855122690502020-02-03T17:28:50505.14198526.99999926.99999926.99999951.9612587.60256751.9613607.602561-23.000001
69551226905055122690492020-02-03T17:28:55505.36253330.00000030.00000030.00000051.9613607.60256151.9614437.602556-20.000000
70551226904955122690512020-02-03T17:29:005012.65174937.53684237.53684237.53684251.9614437.60255651.9615267.602549-12.463158
715512269051290414022020-02-03T17:29:055018.10908638.30000038.30000038.30000051.9615267.60254951.9616637.602535-11.700000
722904140255122690522020-02-03T17:29:10509.60013941.86756641.86756641.86756651.9616637.60253551.9617707.602520-8.132434
73551226905255122690532020-02-03T17:29:15506.82849742.20705842.20705842.20705851.9617707.60252051.9619087.602493-7.792942
74551226905355122690542020-02-03T17:29:20504.56201940.00000140.00000140.00000151.9619087.60249351.9620157.602471-9.999999
75551226905411202499422020-02-03T17:29:25507.68292642.00000042.00000042.00000051.9620157.60247151.9621707.602432-8.000000
\n", + "
" + ], + "text/plain": [ + " st_node end_node time speed_limit CO2 \\\n", + "0 6430014056 34924749 2020-02-03T17:23:04 50 12.532860 \n", + "1 34924749 34924777 2020-02-03T17:23:09 50 10.056936 \n", + "2 34924777 461970318 2020-02-03T17:23:14 50 7.252608 \n", + "3 461970318 34924784 2020-02-03T17:23:19 50 7.095522 \n", + "4 34924784 6430014035 2020-02-03T17:23:24 50 6.939990 \n", + "5 6430014035 34924785 2020-02-03T17:23:29 50 8.082442 \n", + "6 34924785 6430014055 2020-02-03T17:23:34 50 5.235873 \n", + "7 6430014055 34924786 2020-02-03T17:23:39 50 3.467051 \n", + "8 34924786 6430014050 2020-02-03T17:23:44 50 3.174234 \n", + "9 6430014050 6342462458 2020-02-03T17:23:50 50 3.170922 \n", + "10 6342462458 6430014059 2020-02-03T17:23:55 50 14.405008 \n", + "11 6430014059 34924787 2020-02-03T17:24:00 50 11.260461 \n", + "12 34924787 6342462457 2020-02-03T17:24:05 50 6.442676 \n", + "13 6342462457 34924788 2020-02-03T17:24:10 50 5.701738 \n", + "14 34924788 6342462460 2020-02-03T17:24:15 50 5.034212 \n", + "15 6342462460 34924789 2020-02-03T17:24:20 50 5.265162 \n", + "16 34924789 34924790 2020-02-03T17:24:25 50 7.930284 \n", + "17 34924790 34924791 2020-02-03T17:24:30 50 5.470158 \n", + "18 34924791 1498766523 2020-02-03T17:24:35 50 11.730917 \n", + "19 1498766523 358299060 2020-02-03T17:24:40 50 5.387822 \n", + "20 358299060 7152154272 2020-02-03T17:24:45 50 3.173990 \n", + "21 7152154272 7152154274 2020-02-03T17:24:51 50 3.065228 \n", + "22 7152154274 7152154273 2020-02-03T17:24:56 50 3.057912 \n", + "23 7152154273 33846185 2020-02-03T17:25:01 50 4.155391 \n", + "24 33846185 461970347 2020-02-03T17:25:06 50 15.934264 \n", + "25 461970347 6428655088 2020-02-03T17:25:11 50 7.400763 \n", + "26 6428655088 401163819 2020-02-03T17:25:16 50 10.504903 \n", + "27 401163819 33846184 2020-02-03T17:25:21 50 8.466436 \n", + "28 401163819 33846184 2020-02-03T17:25:26 50 7.009605 \n", + "29 33846184 401163775 2020-02-03T17:25:31 50 3.222136 \n", + "30 401163775 6236877365 2020-02-03T17:25:36 50 3.126752 \n", + "31 6236877365 21518914 2020-02-03T17:25:41 50 3.045419 \n", + "32 21518914 6236877366 2020-02-03T17:25:47 50 3.174012 \n", + "33 6236877366 6431124736 2020-02-03T17:25:52 50 3.071740 \n", + "34 6431124736 21518913 2020-02-03T17:25:57 50 10.925399 \n", + "35 21518913 6431124755 2020-02-03T17:26:02 50 26.206483 \n", + "36 6431124755 6431124738 2020-02-03T17:26:07 50 16.085216 \n", + "37 6431124738 122375084 2020-02-03T17:26:12 50 15.931978 \n", + "38 122375084 21518912 2020-02-03T17:26:17 50 5.688978 \n", + "39 21518912 6242379416 2020-02-03T17:26:22 50 5.607024 \n", + "40 6242379416 401163793 2020-02-03T17:26:27 50 6.745522 \n", + "41 401163793 21518911 2020-02-03T17:26:32 50 16.418607 \n", + "42 21518911 401163801 2020-02-03T17:26:37 50 9.341649 \n", + "43 21518911 401163801 2020-02-03T17:26:43 50 3.169324 \n", + "44 401163801 60676690 2020-02-03T17:26:48 50 3.161149 \n", + "45 60676690 6242379409 2020-02-03T17:26:53 50 3.022734 \n", + "46 6242379409 6236877368 2020-02-03T17:26:58 50 3.011477 \n", + "47 6236877368 3202352228 2020-02-03T17:27:03 50 3.015020 \n", + "48 3202352228 6242379407 2020-02-03T17:27:08 50 3.039808 \n", + "49 6242379407 6236877370 2020-02-03T17:27:13 50 3.066515 \n", + "50 6236877370 6236877369 2020-02-03T17:27:18 50 3.116671 \n", + "51 6236877369 21518910 2020-02-03T17:27:23 50 3.010686 \n", + "52 21518910 5472867409 2020-02-03T17:27:28 50 3.112850 \n", + "53 21518910 5472867409 2020-02-03T17:27:33 50 3.043374 \n", + "54 5472867409 33846175 2020-02-03T17:27:39 50 3.095505 \n", + "55 5472867409 33846175 2020-02-03T17:27:44 50 3.047474 \n", + "56 5472867409 33846175 2020-02-03T17:27:49 50 3.036022 \n", + "57 5472867409 33846175 2020-02-03T17:27:54 50 3.075117 \n", + "58 33846175 3268457415 2020-02-03T17:27:59 50 18.000278 \n", + "59 3268457415 6425072711 2020-02-03T17:28:04 50 11.176040 \n", + "60 6425072711 6425072705 2020-02-03T17:28:09 50 4.046499 \n", + "61 6425072705 5474291175 2020-02-03T17:28:14 50 3.075322 \n", + "62 5474291175 945921131 2020-02-03T17:28:19 50 3.025176 \n", + "63 945921131 5512269043 2020-02-03T17:28:24 50 2.973655 \n", + "64 5512269043 4932702977 2020-02-03T17:28:29 50 18.879898 \n", + "65 4932702977 4502646832 2020-02-03T17:28:35 50 4.900118 \n", + "66 4502646832 268071671 2020-02-03T17:28:40 50 4.713496 \n", + "67 268071671 5512269048 2020-02-03T17:28:45 50 4.862161 \n", + "68 5512269048 5512269050 2020-02-03T17:28:50 50 5.141985 \n", + "69 5512269050 5512269049 2020-02-03T17:28:55 50 5.362533 \n", + "70 5512269049 5512269051 2020-02-03T17:29:00 50 12.651749 \n", + "71 5512269051 29041402 2020-02-03T17:29:05 50 18.109086 \n", + "72 29041402 5512269052 2020-02-03T17:29:10 50 9.600139 \n", + "73 5512269052 5512269053 2020-02-03T17:29:15 50 6.828497 \n", + "74 5512269053 5512269054 2020-02-03T17:29:20 50 4.562019 \n", + "75 5512269054 1120249942 2020-02-03T17:29:25 50 7.682926 \n", + "\n", + " mean_speed min_speed max_speed StNode_lat StNode_lon EndNode_lat \\\n", + "0 46.221106 46.221106 46.221106 51.945204 7.601174 51.945464 \n", + "1 52.999999 52.999999 52.999999 51.945464 7.601387 51.945617 \n", + "2 51.999998 51.999998 51.999998 51.945617 7.601554 51.945791 \n", + "3 53.000001 53.000001 53.000001 51.945791 7.601743 51.946196 \n", + "4 56.000002 56.000002 56.000002 51.946196 7.602167 51.946374 \n", + "5 55.000001 55.000001 55.000001 51.946374 7.602310 51.946562 \n", + "6 51.000000 51.000000 51.000000 51.946562 7.602446 51.946785 \n", + "7 47.504178 47.504178 47.504178 51.946785 7.602576 51.947024 \n", + "8 28.319250 28.319250 28.319250 51.947024 7.602696 51.947183 \n", + "9 3.000000 3.000000 3.000000 51.947183 7.602777 51.947319 \n", + "10 20.866337 20.866337 20.866337 51.947319 7.602835 51.947477 \n", + "11 22.814814 22.814814 22.814814 51.947477 7.602923 51.947626 \n", + "12 36.403509 36.403509 36.403509 51.947626 7.603023 51.947834 \n", + "13 47.000000 47.000000 47.000000 51.947834 7.603200 51.947976 \n", + "14 47.000000 47.000000 47.000000 51.947976 7.603352 51.948124 \n", + "15 49.000000 49.000000 49.000000 51.948124 7.603530 51.948273 \n", + "16 51.942992 51.942992 51.942992 51.948273 7.603748 51.948430 \n", + "17 47.999999 47.999999 47.999999 51.948430 7.604013 51.948736 \n", + "18 48.913551 48.913551 48.913551 51.948736 7.604684 51.948884 \n", + "19 49.264501 49.264501 49.264501 51.948884 7.605039 51.948941 \n", + "20 43.000001 43.000001 43.000001 51.948941 7.605177 51.949192 \n", + "21 34.953930 34.953930 34.953930 51.949192 7.605727 51.949385 \n", + "22 0.000000 0.000000 0.000000 51.949385 7.606137 51.949434 \n", + "23 0.000000 0.000000 0.000000 51.949434 7.606228 51.949506 \n", + "24 19.536749 19.536749 19.536749 51.949506 7.606360 51.949630 \n", + "25 34.146853 34.146853 34.146853 51.949630 7.606209 51.949678 \n", + "26 43.000001 43.000001 43.000001 51.949678 7.606151 51.949879 \n", + "27 49.501958 48.003915 51.000001 51.949879 7.605897 51.950914 \n", + "28 49.501958 48.003915 51.000001 51.949879 7.605897 51.950914 \n", + "29 48.000001 48.000001 48.000001 51.950914 7.604530 51.950953 \n", + "30 44.402858 44.402858 44.402858 51.950953 7.604481 51.951109 \n", + "31 37.279547 37.279547 37.279547 51.951109 7.604312 51.951285 \n", + "32 23.000001 23.000001 23.000001 51.951285 7.604142 51.951460 \n", + "33 0.000000 0.000000 0.000000 51.951460 7.603996 51.951630 \n", + "34 0.000000 0.000000 0.000000 51.951630 7.603875 51.951857 \n", + "35 26.645652 26.645652 26.645652 51.951857 7.603737 51.952020 \n", + "36 43.730866 43.730866 43.730866 51.952020 7.603660 51.952418 \n", + "37 52.076925 52.076925 52.076925 51.952418 7.603498 51.952982 \n", + "38 50.999999 50.999999 50.999999 51.952982 7.603277 51.954013 \n", + "39 49.000001 49.000001 49.000001 51.954013 7.602906 51.954414 \n", + "40 48.999999 48.999999 48.999999 51.954414 7.602834 51.954464 \n", + "41 51.328447 51.328447 51.328447 51.954464 7.602825 51.954599 \n", + "42 51.127778 48.255556 54.000001 51.954599 7.602805 51.955059 \n", + "43 51.127778 48.255556 54.000001 51.954599 7.602805 51.955059 \n", + "44 29.782723 29.782723 29.782723 51.955059 7.602826 51.955289 \n", + "45 0.000000 0.000000 0.000000 51.955289 7.602845 51.955554 \n", + "46 0.000000 0.000000 0.000000 51.955554 7.602867 51.955797 \n", + "47 0.000000 0.000000 0.000000 51.955797 7.602896 51.955894 \n", + "48 0.000000 0.000000 0.000000 51.955894 7.602907 51.956136 \n", + "49 0.000000 0.000000 0.000000 51.956136 7.602932 51.956297 \n", + "50 0.000000 0.000000 0.000000 51.956297 7.602944 51.956587 \n", + "51 0.000000 0.000000 0.000000 51.956587 7.602944 51.956810 \n", + "52 0.000000 0.000000 0.000000 51.956810 7.602931 51.958502 \n", + "53 0.000000 0.000000 0.000000 51.956810 7.602931 51.958502 \n", + "54 0.000000 0.000000 0.000000 51.958502 7.602758 51.959405 \n", + "55 0.000000 0.000000 0.000000 51.958502 7.602758 51.959405 \n", + "56 0.000000 0.000000 0.000000 51.958502 7.602758 51.959405 \n", + "57 0.000000 0.000000 0.000000 51.958502 7.602758 51.959405 \n", + "58 16.808399 16.808399 16.808399 51.959405 7.602668 51.959507 \n", + "59 26.384211 26.384211 26.384211 51.959507 7.602659 51.959589 \n", + "60 39.000001 39.000001 39.000001 51.959589 7.602653 51.959687 \n", + "61 32.158620 32.158620 32.158620 51.959687 7.602647 51.959755 \n", + "62 10.473430 10.473430 10.473430 51.959755 7.602641 51.960001 \n", + "63 0.000000 0.000000 0.000000 51.960001 7.602626 51.960354 \n", + "64 11.292683 11.292683 11.292683 51.960354 7.602602 51.960713 \n", + "65 32.335444 32.335444 32.335444 51.960713 7.602584 51.960920 \n", + "66 39.753117 39.753117 39.753117 51.960920 7.602579 51.961147 \n", + "67 33.999999 33.999999 33.999999 51.961147 7.602571 51.961258 \n", + "68 26.999999 26.999999 26.999999 51.961258 7.602567 51.961360 \n", + "69 30.000000 30.000000 30.000000 51.961360 7.602561 51.961443 \n", + "70 37.536842 37.536842 37.536842 51.961443 7.602556 51.961526 \n", + "71 38.300000 38.300000 38.300000 51.961526 7.602549 51.961663 \n", + "72 41.867566 41.867566 41.867566 51.961663 7.602535 51.961770 \n", + "73 42.207058 42.207058 42.207058 51.961770 7.602520 51.961908 \n", + "74 40.000001 40.000001 40.000001 51.961908 7.602493 51.962015 \n", + "75 42.000000 42.000000 42.000000 51.962015 7.602471 51.962170 \n", + "\n", + " EndNode_lon DiffBetweenSpeedLimit \n", + "0 7.601387 -3.778894 \n", + "1 7.601554 2.999999 \n", + "2 7.601743 1.999998 \n", + "3 7.602167 3.000001 \n", + "4 7.602310 6.000002 \n", + "5 7.602446 5.000001 \n", + "6 7.602576 1.000000 \n", + "7 7.602696 -2.495822 \n", + "8 7.602777 -21.680750 \n", + "9 7.602835 -47.000000 \n", + "10 7.602923 -29.133663 \n", + "11 7.603023 -27.185186 \n", + "12 7.603200 -13.596491 \n", + "13 7.603352 -3.000000 \n", + "14 7.603530 -3.000000 \n", + "15 7.603748 -1.000000 \n", + "16 7.604013 1.942992 \n", + "17 7.604684 -2.000001 \n", + "18 7.605039 -1.086449 \n", + "19 7.605177 -0.735499 \n", + "20 7.605727 -6.999999 \n", + "21 7.606137 -15.046070 \n", + "22 7.606228 -50.000000 \n", + "23 7.606360 -50.000000 \n", + "24 7.606209 -30.463251 \n", + "25 7.606151 -15.853147 \n", + "26 7.605897 -6.999999 \n", + "27 7.604530 -0.498042 \n", + "28 7.604530 -0.498042 \n", + "29 7.604481 -1.999999 \n", + "30 7.604312 -5.597142 \n", + "31 7.604142 -12.720453 \n", + "32 7.603996 -26.999999 \n", + "33 7.603875 -50.000000 \n", + "34 7.603737 -50.000000 \n", + "35 7.603660 -23.354348 \n", + "36 7.603498 -6.269134 \n", + "37 7.603277 2.076925 \n", + "38 7.602906 0.999999 \n", + "39 7.602834 -0.999999 \n", + "40 7.602825 -1.000001 \n", + "41 7.602805 1.328447 \n", + "42 7.602826 1.127778 \n", + "43 7.602826 1.127778 \n", + "44 7.602845 -20.217277 \n", + "45 7.602867 -50.000000 \n", + "46 7.602896 -50.000000 \n", + "47 7.602907 -50.000000 \n", + "48 7.602932 -50.000000 \n", + "49 7.602944 -50.000000 \n", + "50 7.602944 -50.000000 \n", + "51 7.602931 -50.000000 \n", + "52 7.602758 -50.000000 \n", + "53 7.602758 -50.000000 \n", + "54 7.602668 -50.000000 \n", + "55 7.602668 -50.000000 \n", + "56 7.602668 -50.000000 \n", + "57 7.602668 -50.000000 \n", + "58 7.602659 -33.191601 \n", + "59 7.602653 -23.615789 \n", + "60 7.602647 -10.999999 \n", + "61 7.602641 -17.841380 \n", + "62 7.602626 -39.526570 \n", + "63 7.602602 -50.000000 \n", + "64 7.602584 -38.707317 \n", + "65 7.602579 -17.664556 \n", + "66 7.602571 -10.246883 \n", + "67 7.602567 -16.000001 \n", + "68 7.602561 -23.000001 \n", + "69 7.602556 -20.000000 \n", + "70 7.602549 -12.463158 \n", + "71 7.602535 -11.700000 \n", + "72 7.602520 -8.132434 \n", + "73 7.602493 -7.792942 \n", + "74 7.602471 -9.999999 \n", + "75 7.602432 -8.000000 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "edgeset = pd.DataFrame({'st_node': data[\"st_nodes\"], 'end_node': data[\"end_nodes\"], 'speed':data[\"Speed.value\"], 'time':data[\"time\"], 'speed_limit': data[\"max_speed\"], 'CO2':data[\"CO2.value\"]})\n", + "edgeset = aggregateStatsFromNodes(edgeset,True, False)\n", + "appendNodeCoords(edgeset)\n", + "edgeset[\"DiffBetweenSpeedLimit\"] = edgeset['mean_speed'] - edgeset['speed_limit']\n", + "edgeset" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotAggregatedStatistics(edgeset, False,True,False)\n", + "#Red = avg speed > speed limit\n", + "#Green = avg speed < speed limit\n", + "#Blue = avg speed = speed limit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/track_2.csv b/track_2.csv new file mode 100644 index 0000000..8b29853 --- /dev/null +++ b/track_2.csv @@ -0,0 +1,77 @@ +,id,time,geometry,Engine Load.value,Engine Load.unit,CO2.value,CO2.unit,Intake Pressure.value,Intake Pressure.unit,GPS VDOP.value,GPS VDOP.unit,GPS Speed.value,GPS Speed.unit,Intake Temperature.value,Intake Temperature.unit,Consumption.value,Consumption.unit,Rpm.value,Rpm.unit,GPS HDOP.value,GPS HDOP.unit,GPS PDOP.value,GPS PDOP.unit,GPS Bearing.value,GPS Bearing.unit,Calculated MAF.value,Calculated MAF.unit,GPS Accuracy.value,GPS Accuracy.unit,Speed.value,Speed.unit,Throttle Position.value,Throttle Position.unit,GPS Altitude.value,GPS Altitude.unit,track.id,track.length,track.begin,track.end,sensor.type,sensor.engineDisplacement,sensor.model,sensor.id,sensor.fuelType,sensor.constructionYear,sensor.manufacturer,track.appVersion,track.touVersion,O2 Lambda Voltage ER.value,O2 Lambda Voltage ER.unit,MAF.value,MAF.unit,O2 Lambda Voltage.value,O2 Lambda Voltage.unit,st_nodes,end_nodes,max_speed +133,5e42ccb63965f3689459b8f9,2020-02-03T17:23:04,POINT (7.601057994636752 51.94497494499429),60.69355933141469,%,12.532860344932155,kg/h,57.80846321582794,kPa,1.3439223706722259,precision,45.28837139923223,km/h,9.000000201165676,c,5.333132061673258,l/h,1605.3185840249062,u/min,0.9560776293277741,precision,1.6,precision,25.25371902809684,deg,16.223832159281855,g/s,2.999999910593033,%,46.221106216311455,km/h,18.000000469386578,%,109.99162413586706,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6430014056,34924749,50 +134,5e42ccb63965f3689459b8fa,2020-02-03T17:23:09,POINT (7.601532784823066 51.94552838110832),40.93411601343303,%,10.056936453367902,kg/h,40.44961363077164,kPa,1.91796875,precision,52.226194789453075,km/h,9.0,c,4.279547426965064,l/h,1840.9999862834811,u/min,1.11923828125,precision,2.21796875,precision,30.941130488639715,deg,13.018739901779973,g/s,3.0,%,52.999998815357685,km/h,16.395778357982635,%,110.07318333813822,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924749,34924777,50 +135,5e42ccb63965f3689459b8fb,2020-02-03T17:23:14,POINT (7.602096892727208 51.94608124562291),30.783809643502224,%,7.252607810751188,kg/h,30.000000447034836,kPa,1.7,precision,51.23649329088812,km/h,9.0,c,3.0862160896813564,l/h,1790.0921542569995,u/min,1.0,precision,1.9,precision,31.626054087038142,deg,9.388526529484825,g/s,3.0,%,51.999998450279236,km/h,15.392953932285309,%,109.69898013334335,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924777,461970318,50 +136,5e42ccb63965f3689459b8fc,2020-02-03T17:23:19,POINT (7.602564980565499 51.94666702340365),29.59010114638636,%,7.095521859501299,kg/h,28.99999913573265,kPa,1.8366870462894438,precision,51.929285551302314,km/h,8.99999986588955,c,3.019371004043106,l/h,1811.710553318262,u/min,1.0726626336574556,precision,2.1366870552301407,precision,21.705033300531,deg,9.185178208549464,g/s,2.0000000596046448,%,53.00000078976154,km/h,15.0,%,109.68730733925051,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,461970318,34924784,50 +137,5e42ccb63965f3689459b8fd,2020-02-03T17:23:24,POINT (7.602900041979403 51.94731436517897),27.168067040563614,%,6.9399903697300305,kg/h,26.727273121476173,kPa,1.363545775413513,precision,55.09502055791927,km/h,9.0,c,2.953187391374481,l/h,1922.6785140931606,u/min,0.8817728817462922,precision,1.5817728608846666,precision,17.91760663909008,deg,8.983842143510453,g/s,2.3968095779418945,%,56.000001668930054,km/h,15.763722911477089,%,109.24385765206021,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924784,6430014035,50 +138,5e42ccb63965f3689459b8fe,2020-02-03T17:23:29,POINT (7.603364394063977 51.94794367887403),34.72555072435898,%,8.082442412228376,kg/h,31.664671540260315,kPa,1.2251727908849717,precision,54.31702081342061,km/h,8.999999731779099,c,3.439337196692926,l/h,1890.0360507890582,u/min,0.8335636973381043,precision,1.4419546335935591,precision,31.067521640790275,deg,10.462750363772937,g/s,4.000000029802322,%,55.000000819563866,km/h,17.67543852329254,%,109.56927618388644,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6430014035,34924785,50 +139,5e42ccb63965f3689459b8ff,2020-02-03T17:23:34,POINT (7.604063252859018 51.9484543979432),22.619086448557425,%,5.235873316422841,kg/h,22.36956524848938,kPa,4.192600283771754,precision,50.67296795248393,km/h,9.999999720603228,c,2.2280311984778045,l/h,1739.2842102050781,u/min,1.6978417582809924,precision,4.4922919645905495,precision,47.359407703163384,deg,6.777856575036021,g/s,3.982248617336154,%,51.0,km/h,14.000000104308128,%,109.172062809753,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924785,6430014055,50 +140,5e42ccb63965f3689459b900,2020-02-03T17:23:39,POINT (7.604851437009421 51.94883231434704),28.627450289789078,%,3.4670509058720653,kg/h,27.685637265443802,kPa,2.0906126499176025,precision,47.34771154807456,km/h,9.315403699874878,c,1.4753408110093895,l/h,928.3107064068317,u/min,1.0635375499725341,precision,2.3784584999084473,precision,53.3695900839044,deg,4.488109692158147,g/s,2.9999999329447746,%,47.504178285598755,km/h,13.000000387430191,%,109.26378246543361,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6430014055,34924786,50 +141,5e42ccb63965f3689459b901,2020-02-03T17:23:44,POINT (7.605537336193051 51.94913977979754),31.372548122032015,%,3.1742344873770376,kg/h,31.0,kPa,1.0999999836087229,precision,31.738474385866198,km/h,10.0,c,1.3507380797349096,l/h,760.8801956176758,u/min,0.899999986588955,precision,1.4231755211949348,precision,55.54224382029105,deg,4.109057800060239,g/s,2.208577021956444,%,28.31924968957901,km/h,13.0,%,109.69209893429047,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924786,6430014050,50 +142,5e42ccb63965f3689459b902,2020-02-03T17:23:50,POINT (7.605749925214377 51.94922624573654),31.673201977349578,%,3.1709219407567115,kg/h,31.000000461935997,kPa,2.025121718645096,precision,4.140233888416333,km/h,11.0,c,1.3493284854283878,l/h,762.7705439329147,u/min,1.0977604687213898,precision,2.2592015624046327,precision,55.5062358747673,deg,4.104769696713608,g/s,3.0,%,2.999999910593033,km/h,12.999999612569809,%,109.6759702618723,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6430014050,6342462458,50 +143,5e42ccb63965f3689459b903,2020-02-03T17:23:55,POINT (7.605936140107922 51.94930194429372),66.89603843298846,%,14.405007852186497,kg/h,71.62907160818577,kPa,1.5190009832382203,precision,20.456292717927,km/h,9.718204498291016,c,6.129790575398509,l/h,1492.8999834433198,u/min,1.0642507374286652,precision,1.8737512290477754,precision,56.100321165887635,deg,18.647333746243547,g/s,3.0,%,20.866336941719055,km/h,19.87373796105385,%,109.71095344661867,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6342462458,6430014059,50 +144,5e42ccb63965f3689459b904,2020-02-03T17:24:00,POINT (7.606285289215467 51.94948310329912),51.862745129747054,%,11.26046052236476,kg/h,51.84816598892212,kPa,1.4413373470306396,precision,22.986718109644812,km/h,9.999999701976776,c,4.791685328665855,l/h,1613.8446296416223,u/min,1.0413373589515686,precision,1.7413373380899428,precision,22.81461096157807,deg,14.576706076912252,g/s,3.0,%,22.81481447815895,km/h,17.078590273857117,%,110.42758435887909,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6430014059,34924787,50 +145,5e42ccb63965f3689459b905,2020-02-03T17:24:05,POINT (7.606088444987175 51.94980878933633),39.3050392962798,%,6.442675925303332,kg/h,37.19799543172121,kPa,1.1399399399757386,precision,36.00134575437414,km/h,8.432801574468613,c,2.7415642235333326,l/h,1279.8977556228638,u/min,0.9,precision,1.4399399399757384,precision,320.5273449935521,deg,8.340066831673692,g/s,2.415338635444641,%,36.40350878238678,km/h,21.277778029441833,%,110.24962503215724,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924787,6342462457,50 +146,5e42ccb63965f3689459b906,2020-02-03T17:24:10,POINT (7.605542565869463 51.95022015674844),26.806193716973297,%,5.7017379614811325,kg/h,26.036010295152664,kPa,1.462589192390442,precision,46.214795888429194,km/h,7.9999998807907104,c,2.4262714729706945,l/h,1615.821228981018,u/min,1.0,precision,1.7969418942928312,precision,321.2341938814343,deg,7.380920010066266,g/s,2.999999910593033,%,47.0,km/h,14.00000036507845,%,110.85581367515701,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6342462457,34924788,50 +147,5e42ccb63965f3689459b907,2020-02-03T17:24:15,POINT (7.604950230605318 51.95067500453247),24.501100764002757,%,5.0342123893412545,kg/h,23.447761178016663,kPa,1.199999964237213,precision,45.93543549878166,km/h,8.99999986588955,c,2.142218038017555,l/h,1589.7638192176819,u/min,0.9260611772537232,precision,1.4999999552965164,precision,321.13692415664445,deg,6.516805789819237,g/s,3.000000089406967,%,47.0,km/h,14.999999552965164,%,110.2803587071354,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924788,6342462460,50 +148,5e42ccb63965f3689459b908,2020-02-03T17:24:20,POINT (7.604364836829564 51.95114844630615),24.07952065457448,%,5.265162134861478,kg/h,23.229109823703766,kPa,1.2843592524528504,precision,48.45519217334896,km/h,9.000000268220901,c,2.240494525472969,l/h,1678.346547178924,u/min,0.9156407803297043,precision,1.600000023841858,precision,326.6921304333996,deg,6.815771054365896,g/s,1.9999999701976776,%,49.00000036507845,km/h,14.0,%,109.9623592784438,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6342462460,34924789,50 +149,5e42ccb63965f3689459b909,2020-02-03T17:24:25,POINT (7.603908486534154 51.95170535956259),34.616512984709175,%,7.930283611522346,kg/h,33.16363537311554,kPa,1.1999999776482582,precision,50.56387030635452,km/h,8.999999932944775,c,3.3745887708605724,l/h,1770.6349999904633,u/min,0.8938631623983384,precision,1.3999999739229678,precision,337.6971646845127,deg,10.265780256688764,g/s,3.0000000670552254,%,51.942992486059666,km/h,16.0,%,110.52852613618006,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924789,34924790,50 +150,5e42ccb63965f3689459b90a,2020-02-03T17:24:30,POINT (7.603610602885801 51.95230612683978),25.544395205128183,%,5.470157752187384,kg/h,24.58823600411415,kPa,1.2986193366348744,precision,48.28268952757468,km/h,8.999999798834324,c,2.327726703058461,l/h,1647.3084823042154,u/min,0.900000005029142,precision,1.598619338311255,precision,344.76537762495246,deg,7.081138607929093,g/s,3.0000000838190317,%,47.999998927116394,km/h,16.088311195373535,%,111.30436317217435,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924790,34924791,50 +151,5e42ccb63965f3689459b90b,2020-02-03T17:24:35,POINT (7.603366995298014 51.95289372933314),55.068387296631954,%,11.730916603212558,kg/h,51.73228198289871,kPa,1.5094094097614288,precision,47.64374488488721,km/h,9.0,c,4.991879405622365,l/h,1679.0853333473206,u/min,1.0094094097614288,precision,1.8094094097614288,precision,346.4887818582479,deg,15.185713141853704,g/s,2.9150197207927704,%,48.913550674915314,km/h,19.759124100208282,%,111.90108308018826,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,34924791,1498766523,50 +152,5e42ccb63965f3689459b90c,2020-02-03T17:24:40,POINT (7.603124219523799 51.9535028933344),24.06002262000368,%,5.387821630192986,kg/h,23.6171875,kPa,1.615169686079025,precision,49.255543542015346,km/h,9.0,c,2.2926900554012706,l/h,1689.2249559871852,u/min,1.0461078003048896,precision,1.9151696905493736,precision,346.3697732702062,deg,6.974554206035282,g/s,3.146509289741516,%,49.26450115442276,km/h,14.000000417232513,%,112.63764511065085,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,1498766523,358299060,50 +153,5e42ccb63965f3689459b90d,2020-02-03T17:24:45,POINT (7.60293632061297 51.95407489098073),31.562657757611646,%,3.173990383429624,kg/h,30.638636350631714,kPa,1.3,precision,43.21888630919989,km/h,11.00000024586916,c,1.3506342057147336,l/h,772.5137846469879,u/min,0.9000000000000001,precision,1.6,precision,350.8949280846591,deg,4.108741806634695,g/s,1.9999999403953552,%,43.0000009611249,km/h,13.000000387430191,%,113.09713167843385,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,358299060,7152154272,50 +154,5e42ccb63965f3689459b90e,2020-02-03T17:24:51,POINT (7.602857650989836 51.95456559635377),31.17647075653076,%,3.0652280379536143,kg/h,30.0,kPa,1.3,precision,35.05293987087134,km/h,11.000000163912773,c,1.304352356576006,l/h,761.923901706934,u/min,0.9,precision,1.6,precision,356.6578838080295,deg,3.9679485647339248,g/s,3.616302192211151,%,34.95392967015505,km/h,13.000000387430191,%,113.35910729503928,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,7152154272,7152154274,50 +155,5e42ccb63965f3689459b90f,2020-02-03T17:24:56,POINT (7.602848860180807 51.95476898347223),30.631573688452477,%,3.057911633734512,kg/h,30.00000089406967,kPa,1.55015110373497,precision,0.8700702173517527,km/h,11.000000327825546,c,1.3012389930785155,l/h,760.1052404940128,u/min,1.0500503838062287,precision,1.850151112675667,precision,359.07198026561764,deg,3.9584774535276006,g/s,2.999999910593033,%,0.0,km/h,12.999999951571226,%,113.39910334724608,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,7152154274,7152154273,50 +156,5e42ccb63965f3689459b910,2020-02-03T17:25:01,POINT (7.602849086763367 51.95477053184505),36.69411767641702,%,4.155390776554653,kg/h,35.415648967027664,kPa,1.6414370119571686,precision,0.0,km/h,11.000000163912773,c,1.7682513942785756,l/h,874.9570404291153,u/min,1.094291341304779,precision,2.0414370119571688,precision,359.0719909667969,deg,5.379168095678283,g/s,2.9623762369155884,%,0.0,km/h,14.93069303035736,%,113.37957097683002,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,7152154273,33846185,50 +157,5e42ccb63965f3689459b911,2020-02-03T17:25:06,POINT (7.602847867603598 51.95484447942415),50.048888249322715,%,15.934263533177171,kg/h,47.983607947826385,kPa,2.0,precision,15.737290700318027,km/h,9.000000268220901,c,6.780537673692413,l/h,2458.9038839442655,u/min,1.1,precision,2.3,precision,0.34833558806500253,deg,20.62696064817846,g/s,2.5682730078697205,%,19.53674876689911,km/h,17.68981432914734,%,113.48368487295681,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,33846185,461970347,50 +158,5e42ccb63965f3689459b912,2020-02-03T17:25:11,POINT (7.60286313171177 51.95520100481636),53.339575556298314,%,7.400763338408577,kg/h,43.511165112257004,kPa,1.790229308605194,precision,34.55948220737628,km/h,9.676537573337555,c,3.1492609950674795,l/h,1262.462140351534,u/min,1.0634097695350646,precision,2.090229308605194,precision,2.760722554229801,deg,9.580314385411528,g/s,4.0,%,34.146853148937225,km/h,24.7619047164917,%,113.46809128728327,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,461970347,6428655088,50 +159,5e42ccb63965f3689459b913,2020-02-03T17:25:16,POINT (7.602908660144157 51.9556912186023),54.15211333821435,%,10.504903346324886,kg/h,52.62650603055954,kPa,1.6302463054656982,precision,41.97573023194332,km/h,10.000000298023224,c,4.470171636733994,l/h,1483.2914462983608,u/min,0.9604926109313966,precision,1.9302463054656982,precision,4.417795475531847,deg,13.598634633247872,g/s,2.0,%,43.00000064074993,km/h,17.408114552497864,%,114.066665714403,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6428655088,401163819,50 +160,5e42ccb63965f3689459b914,2020-02-03T17:25:21,POINT (7.602976892975983 51.95626530514606),38.67233538903611,%,8.466435658808185,kg/h,37.55511808395386,kPa,1.6590550914406776,precision,47.38619531670503,km/h,8.999999731779099,c,3.602738578216249,l/h,1669.2982456088066,u/min,1.238582655787468,precision,2.038582643866539,precision,3.506336407457834,deg,10.95983098314868,g/s,2.8000000417232513,%,48.00391460955143,km/h,16.52884614467621,%,114.82551774127609,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,401163819,33846184,50 +161,5e42ccb63965f3689459b915,2020-02-03T17:25:26,POINT (7.602979128773041 51.95689340713682),30.980392456054688,%,7.009604598268018,kg/h,29.99999910593033,kPa,1.3080447778105737,precision,49.83102428280387,km/h,8.99999986588955,c,2.9828104673480924,l/h,1730.1140607893467,u/min,0.8919551730155945,precision,1.5999999642372131,precision,357.8670742442366,deg,9.073958009211841,g/s,2.914609119296074,%,51.0000010211952,km/h,16.000000476837158,%,115.21970155174014,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,401163819,33846184,50 +162,5e42ccb63965f3689459b916,2020-02-03T17:25:31,POINT (7.602917032908413 51.95751324521013),28.685654232027787,%,3.222135878476224,kg/h,28.00000062584877,kPa,1.2999999822932298,precision,47.81509216102497,km/h,10.0,c,1.3711216504154145,l/h,855.1154989004135,u/min,0.9002055375953205,precision,1.6001027531339787,precision,356.022933743869,deg,4.171066320701224,g/s,3.0000000027939677,%,48.000001430511475,km/h,13.000000387430191,%,115.2188794651737,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,33846184,401163775,50 +163,5e42ccb63965f3689459b917,2020-02-03T17:25:36,POINT (7.602847186613463 51.95808976697941),30.958313472845106,%,3.126751565820957,kg/h,30.000000670552254,kPa,1.5641076892614365,precision,43.990850763112235,km/h,11.0,c,1.3305325812004072,l/h,777.21679058671,u/min,1.2730807676911355,precision,2.0551346123218535,precision,356.0779659084303,deg,4.047590989726738,g/s,4.000000089406967,%,44.40285846590996,km/h,13.000000193715096,%,115.1912225038862,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,401163775,6236877365,50 +164,5e42ccb63965f3689459b918,2020-02-03T17:25:41,POINT (7.602784031763604 51.95861351855358),31.081683166052983,%,3.0454191337850234,kg/h,29.999999776482582,kPa,2.4209073334932327,precision,37.50346909976611,km/h,10.999999672174454,c,1.2959230356532014,l/h,757.0000056400895,u/min,1.2000000178813934,precision,2.7432939261198044,precision,355.45588399628514,deg,3.9423058680433427,g/s,2.9999999552965164,%,37.279546558856964,km/h,12.999999612569809,%,114.87021027392797,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6236877365,21518914,50 +165,5e42ccb63965f3689459b919,2020-02-03T17:25:47,POINT (7.602744880447619 51.95898950100872),31.39308128318629,%,3.1740117385412745,kg/h,31.0,kPa,1.6392354130744933,precision,23.56861130923494,km/h,11.000000327825546,c,1.350643292996287,l/h,763.5138121843338,u/min,1.0,precision,1.9392354130744933,precision,356.4254994390358,deg,4.108769450902455,g/s,2.999999910593033,%,23.000000685453415,km/h,12.999999806284904,%,114.77164469684911,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,21518914,6236877366,50 +166,5e42ccb63965f3689459b91b,2020-02-03T17:25:52,POINT (7.602729211124434 51.9591408818487),30.81307230226355,%,3.0717400401316417,kg/h,30.0,kPa,1.8123092532157896,precision,0.8880683240934957,km/h,11.000000327825546,c,1.3071234213326135,l/h,763.5425908863544,u/min,1.1178026437759399,precision,2.1712105751037596,precision,355.6216324680554,deg,3.9763783746455874,g/s,3.384065270423889,%,0.0,km/h,13.000000387430191,%,114.31963689262778,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6236877366,6431124736,50 +167,5e42ccb63965f3689459b91c,2020-02-03T17:25:57,POINT (7.602729437703428 51.95914243035181),45.023343251377014,%,10.925398580738452,kg/h,49.73934908211231,kPa,1.8955823302268984,precision,0.0,km/h,12.0,c,4.649105779037639,l/h,1643.7394204158336,u/min,1.152208834886551,precision,2.247791165113449,precision,355.62164306640625,deg,14.142967205314086,g/s,4.0,%,0.0,km/h,15.526190012693405,%,114.50153724381413,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6431124736,21518913,50 +168,5e42ccb63965f3689459b91d,2020-02-03T17:26:02,POINT (7.602698676508187 51.9593163042151),84.4510223369698,%,26.206483074775264,kg/h,84.0,kPa,1.3291370183229447,precision,25.659421464080424,km/h,9.00000013411045,c,11.151694925436281,l/h,2310.1060280799866,u/min,1.0430456548929214,precision,1.7291370064020157,precision,356.18358763476135,deg,33.92438527108762,g/s,2.554884195327759,%,26.645651802420616,km/h,27.20408123731613,%,114.12635875586938,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,21518913,6431124755,50 +169,5e42ccb63965f3689459b91e,2020-02-03T17:26:07,POINT (7.602655272970264 51.95979709183395),76.44787192188937,%,16.085216136686416,kg/h,73.923274487257,kPa,1.6628713369369508,precision,41.85486658064792,km/h,10.450980693101883,c,6.844772824121879,l/h,1619.4809191673994,u/min,1.1000000327825548,precision,1.9628713458776472,precision,357.4629679628051,deg,20.8223693287141,g/s,3.0,%,43.73086550831795,km/h,21.64432990550995,%,113.90143670151912,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6431124755,6431124738,50 +170,5e42ccb63965f3689459b91f,2020-02-03T17:26:12,POINT (7.602617784300406 51.96038666634529),73.44309388379307,%,15.931977890972107,kg/h,65.90909196436405,kPa,1.3999999582767486,precision,49.474374922468996,km/h,10.584725856781006,c,6.77956505998813,l/h,1799.945107460022,u/min,0.9259333729743958,precision,1.6999999493360518,precision,357.9938511250366,deg,20.62400187623889,g/s,3.0,%,52.07692465186119,km/h,18.19346046447754,%,113.6004728686554,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6431124738,122375084,50 +171,5e42ccb63965f3689459b920,2020-02-03T17:26:17,POINT (7.602588679652994 51.96104114688706),24.937994811368526,%,5.688978288870342,kg/h,24.0,kPa,2.2694164991378782,precision,50.67169026992633,km/h,10.000000074505806,c,2.4208418250512094,l/h,1761.416267991066,u/min,1.1,precision,2.484708249568939,precision,358.82192244926046,deg,7.3644025686245325,g/s,3.0,%,50.99999886006117,km/h,14.000000208616257,%,113.54053994519745,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,122375084,21518912,50 +172,5e42ccb63965f3689459b921,2020-02-03T17:26:22,POINT (7.602554227822375 51.96167068733874),25.115615366967035,%,5.607023594239543,kg/h,24.759563207626343,kPa,2.333952994644642,precision,48.53473543495974,km/h,10.999999672174454,c,2.3859674869104435,l/h,1688.72707721591,u/min,1.455968664586544,precision,2.7266144365072247,precision,356.4029293622657,deg,7.258311925805478,g/s,2.0806773379445076,%,49.0000014603138,km/h,13.999999687075615,%,113.19832137838986,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,21518912,6242379416,50 +173,5e42ccb63965f3689459b922,2020-02-03T17:26:27,POINT (7.602425708378401 51.96228341786505),28.68704250508756,%,6.745521655180742,kg/h,29.968136429786682,kPa,1.306353861093521,precision,48.092943328261555,km/h,10.89928025007248,c,2.870434746885422,l/h,1677.9225056171417,u/min,0.9,precision,1.6,precision,350.1001048942194,deg,8.732101702921026,g/s,3.0000000335276127,%,48.9999985396862,km/h,15.0,%,112.89343259881204,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6242379416,401163793,50 +174,5e42ccb63965f3689459b923,2020-02-03T17:26:32,POINT (7.602218275352818 51.962901381994),69.26728195473106,%,16.418607028802068,kg/h,68.58947163820267,kPa,1.3703125,precision,50.2827334181809,km/h,10.000000298023224,c,6.986641288851943,l/h,1778.761904835701,u/min,0.9000000000000001,precision,1.58515625,precision,347.5796695902227,deg,21.253945020795012,g/s,3.0,%,51.3284472823143,km/h,19.902965262532234,%,112.90855441034338,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,401163793,21518911,50 +175,5e42ccb63965f3689459b924,2020-02-03T17:26:37,POINT (7.601976581832533 51.96355861602047),38.221926436704734,%,9.34164912350573,kg/h,37.631460666656494,kPa,1.5741871446371078,precision,52.84719891124098,km/h,9.999999925494194,c,3.975169839789672,l/h,1844.639241874218,u/min,1.074187159538269,precision,1.874187135696411,precision,347.5453166275238,deg,12.092797916793499,g/s,2.9999999552965164,%,54.00000120699406,km/h,16.323008835315704,%,113.03778095489002,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,21518911,401163801,50 +176,5e42ccb63965f3689459b925,2020-02-03T17:26:43,POINT (7.601769048959875 51.96419720982069),32.15686464543438,%,3.1693235695719846,kg/h,31.0,kPa,1.363914331793785,precision,48.62415644129703,km/h,10.999999672174454,c,1.3486483274774401,l/h,762.386063568294,u/min,0.9360855996608735,precision,1.6639143228530884,precision,350.7662060268103,deg,4.102700599546996,g/s,3.000000089406967,%,48.25555557012558,km/h,13.000000387430191,%,113.26529622628192,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,21518911,401163801,50 +177,5e42ccb63965f3689459b926,2020-02-03T17:26:48,POINT (7.601702782520512 51.96471351388309),31.764706249622733,%,3.1611493965635327,kg/h,31.0,kPa,1.692079210281372,precision,32.63210922721777,km/h,10.999999836087227,c,1.3451699559844819,l/h,760.419753074646,u/min,1.0,precision,1.9920792102813722,precision,358.1497848589406,deg,4.092119103601125,g/s,2.465551108121872,%,29.782722532749176,km/h,13.0,%,113.5350129645565,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,401163801,60676690,50 +178,5e42ccb63965f3689459b927,2020-02-03T17:26:53,POINT (7.601700169348174 51.96483595957651),30.588235513252364,%,3.0227341994029207,kg/h,30.000000447034836,kPa,1.8526987254619596,precision,1.1459867872225828,km/h,11.999999642372131,c,1.2862698720863492,l/h,754.0054347515106,u/min,1.0473012745380403,precision,2.1,precision,358.4834182714394,deg,3.9129401400426818,g/s,2.999999910593033,%,0.0,km/h,13.0,%,113.5151525837959,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,60676690,6242379409,50 +179,5e42ccb63965f3689459b928,2020-02-03T17:26:58,POINT (7.601700169348174 51.9648359595765),31.372548122032015,%,3.011477132139215,kg/h,30.000000447034836,kPa,2.1636091083288194,precision,0.0,km/h,12.000000357627869,c,1.2814796306975382,l/h,751.1974193453789,u/min,1.1636091381311418,precision,2.4636090993881226,precision,358.4834182714394,deg,3.898367826551136,g/s,2.999999910593033,%,0.0,km/h,13.0,%,113.44499881486881,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6242379409,6236877368,50 +180,5e42ccb63965f3689459b929,2020-02-03T17:27:03,POINT (7.6017003958965 51.96483750824935),30.98039291769851,%,3.0150196682902135,kg/h,30.000000223517418,kPa,1.5694586783647537,precision,0.0,km/h,12.000000357627869,c,1.2829870928894525,l/h,752.0810922980309,u/min,1.0000000298023224,precision,1.8694586873054506,precision,358.4834289550781,deg,3.902953652160788,g/s,3.0,%,0.0,km/h,12.999999612569809,%,113.4393052027061,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6236877368,3202352228,50 +181,5e42ccb63965f3689459b92a,2020-02-03T17:27:08,POINT (7.6017003958965 51.96483750824935),31.37254835577572,%,3.039807554811345,kg/h,30.00000089406967,kPa,1.754801219701767,precision,0.0,km/h,12.000000357627869,c,1.2935351297069553,l/h,758.2642856836319,u/min,1.0258002251386642,precision,2.029001033306122,precision,358.4834289550781,deg,3.9350416591627,g/s,3.0,%,0.0,km/h,13.0,%,113.40781275759915,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,3202352228,6242379407,50 +182,5e42ccb63965f3689459b92b,2020-02-03T17:27:13,POINT (7.6017003958965 51.96483750824935),31.372549524494246,%,3.066515352958964,kg/h,30.6649067401886,kPa,2.034611536562443,precision,0.0,km/h,12.0,c,1.304900150195304,l/h,748.340540766716,u/min,1.0163471385836602,precision,2.250958663225174,precision,358.4834289550781,deg,3.969614998573298,g/s,3.0,%,0.0,km/h,12.999999673105776,%,113.39244089364668,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6242379407,6236877370,50 +183,5e42ccb63965f3689459b92c,2020-02-03T17:27:18,POINT (7.601700225985256 51.96483634674473),31.372549524494246,%,3.116670592871897,kg/h,30.722222208976746,kPa,2.065897631645203,precision,0.0,km/h,11.999999821186066,c,1.326242805477403,l/h,759.1612902283669,u/min,1.186359041929245,precision,2.3658976227045057,precision,358.4834209423491,deg,4.03454113449605,g/s,2.9999999329447746,%,0.0,km/h,13.000000157393515,%,113.3873508962661,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6236877370,6236877369,50 +184,5e42ccb63965f3689459b92d,2020-02-03T17:27:23,POINT (7.601700537489203 51.96483847616988),30.980393263931376,%,3.010685633734927,kg/h,30.000000670552254,kPa,1.599900302453898,precision,0.0,km/h,12.0,c,1.2811428228659263,l/h,750.9999776184559,u/min,1.1997008998179808,precision,1.9997009014943614,precision,358.4834356323523,deg,3.8973432290600534,g/s,3.0000000558793545,%,0.0,km/h,13.0,%,113.36281788310457,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6236877369,21518910,50 +185,5e42ccb63965f3689459b92e,2020-02-03T17:27:28,POINT (7.601700339259418 51.96483712108115),31.372549057006836,%,3.1128504001382935,kg/h,30.727026104927063,kPa,1.1907723434269426,precision,0.0,km/h,12.0,c,1.32461719154821,l/h,758.1122222840786,u/min,0.8000000178813934,precision,1.4000000312924386,precision,358.48342628416844,deg,4.0295958814556165,g/s,1.9999999850988388,%,0.0,km/h,13.000000290572643,%,113.31821849606412,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,21518910,5472867409,50 +186,5e42ccb63965f3689459b92f,2020-02-03T17:27:33,POINT (7.601700282622337 51.96483673391293),30.98505053256034,%,3.0433737650739587,kg/h,30.000000447034836,kPa,1.4398010373115537,precision,0.0,km/h,12.0,c,1.2950526659889185,l/h,759.1538687646389,u/min,1.0000000298023224,precision,1.7398010462522506,precision,358.48342361325876,deg,3.939658130993789,g/s,1.9999999701976776,%,0.0,km/h,13.000000193715096,%,113.2696504252895,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,21518910,5472867409,50 +187,5e42ccb63965f3689459b930,2020-02-03T17:27:39,POINT (7.601700169348174 51.96483595957652),31.37254929075054,%,3.09550476324495,kg/h,30.78688570857048,kPa,1.640198016166687,precision,0.0,km/h,12.000000357627869,c,1.3172360694659362,l/h,752.4220184087753,u/min,1.0350495040416718,precision,1.9401980161666872,precision,358.4834182714394,deg,4.007141892987833,g/s,3.335643470287323,%,0.0,km/h,13.0,%,113.12698732446057,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5472867409,33846175,50 +188,5e42ccb63965f3689459b931,2020-02-03T17:27:44,POINT (7.601700395896501 51.96483750824935),31.372549057006836,%,3.047474346308862,kg/h,30.000000447034836,kPa,1.2303823292255402,precision,0.0,km/h,12.000000357627869,c,1.2967975941739838,l/h,760.1767385303974,u/min,0.8565392613410949,precision,1.4869215726852416,precision,358.4834289550781,deg,3.94496634794344,g/s,4.0,%,0.0,km/h,13.0,%,113.38956931902467,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5472867409,33846175,50 +189,5e42ccb63965f3689459b932,2020-02-03T17:27:49,POINT (7.601700622444826 51.96483905692219),31.356816912381873,%,3.0360219603490846,kg/h,30.000000447034836,kPa,2.04904904961586,precision,0.0,km/h,12.000000357627869,c,1.291924238446419,l/h,757.3199999928474,u/min,1.2,precision,2.4,precision,358.48343963871685,deg,3.93014119370721,g/s,4.0000001192092896,%,0.0,km/h,13.000000193715096,%,113.2960464053408,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5472867409,33846175,50 +190,5e42ccb63965f3689459b933,2020-02-03T17:27:54,POINT (7.601700622444826 51.96483905692219),31.372548589519425,%,3.075116670890806,kg/h,30.0,kPa,2.1000000625848774,precision,0.0,km/h,11.999999642372131,c,1.3085602854854492,l/h,767.0719831585884,u/min,1.2000000357627867,precision,2.4000000715255734,precision,358.48343963871685,deg,3.9807494351371933,g/s,3.000000089406967,%,0.0,km/h,13.0,%,113.0961694717813,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5472867409,33846175,50 +191,5e42ccb63965f3689459b934,2020-02-03T17:27:59,POINT (7.601697841180037 51.96489138438593),62.01153386797597,%,18.000277703343908,kg/h,60.26201927661896,kPa,1.7421471714973449,precision,13.689520993144697,km/h,12.000000178813934,c,7.659692639720811,l/h,2235.2767651975155,u/min,1.134592479467392,precision,2.1075547367334364,precision,359.5114609929451,deg,23.301423317750686,g/s,2.999999910593033,%,16.808398962020874,km/h,20.188171446323395,%,113.04287748515065,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,33846175,3268457415,50 +192,5e42ccb63965f3689459b935,2020-02-03T17:28:04,POINT (7.601641567705594 51.96517459356627),46.034157040901846,%,11.176040249856719,kg/h,45.29156011343002,kPa,1.2722331643104554,precision,25.86994955742091,km/h,12.000000357627869,c,4.755761808449668,l/h,1846.5728902816772,u/min,1.0722331702709198,precision,1.6722331523895264,precision,319.9694899104052,deg,14.467423734787925,g/s,2.297725021839142,%,26.384210526943207,km/h,18.0,%,113.29672032924702,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,3268457415,6425072711,50 +193,5e42ccb63965f3689459b936,2020-02-03T17:28:09,POINT (7.601011538841491 51.96516032763352),26.727894745906696,%,4.0464991987375765,kg/h,24.843903183937073,kPa,1.9970102518796922,precision,37.97997232840066,km/h,10.999999672174454,c,1.7219145526542876,l/h,1214.5899766683578,u/min,1.9757731378078458,precision,2.8818555861711506,precision,250.09596487578574,deg,5.238207562053731,g/s,3.9999999403953552,%,39.00000116229057,km/h,13.125261157751083,%,114.44699979750337,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6425072711,6425072705,50 +194,5e42ccb63965f3689459b937,2020-02-03T17:28:14,POINT (7.600307874225442 51.96505237196887),30.588236766703005,%,3.075322448767105,kg/h,30.000000447034836,kPa,1.291650089621544,precision,32.79163467771309,km/h,11.999999642372131,c,1.3086478505391936,l/h,767.1233019568026,u/min,1.2999999903142452,precision,1.7999999865889549,precision,259.96056244673855,deg,3.9810158153277717,g/s,6.000000089406967,%,32.15861973166466,km/h,13.0,%,115.21348410480118,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,6425072705,5474291175,50 +195,5e42ccb63965f3689459b938,2020-02-03T17:28:19,POINT (7.599851249077103 51.96502340642385),30.588236310902772,%,3.025175604432538,kg/h,29.999999664723873,kPa,1.8065990049391984,precision,13.708413840192918,km/h,12.000000357627869,c,1.2873087678436332,l/h,754.6144521981478,u/min,1.8039594110101462,precision,2.507918809726834,precision,267.34854772515115,deg,3.9161005475109856,g/s,3.0089464047923684,%,10.473430097103119,km/h,13.000000387430191,%,114.68967886182675,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5474291175,945921131,50 +196,5e42ccb63965f3689459b939,2020-02-03T17:28:24,POINT (7.599811849214809 51.96502144823425),31.107490300432914,%,2.9736546522221228,kg/h,30.000000447034836,kPa,2.333367504924536,precision,0.0,km/h,12.636363625526428,c,1.2653849583923926,l/h,743.4181735031307,u/min,1.5095189690589905,precision,2.7619243204593658,precision,267.96920976019805,deg,3.8494064921761906,g/s,3.000000022351742,%,0.0,km/h,13.0,%,114.76270845834489,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,945921131,5512269043,50 +197,5e42ccb63965f3689459b93a,2020-02-03T17:28:29,POINT (7.599779929045062 51.96502224307099),85.36387469757733,%,18.87989806172027,kg/h,84.19902920722961,kPa,1.827430859208107,precision,6.847176347723007,km/h,12.000000357627869,c,8.033999175200114,l/h,1677.9860255736858,u/min,1.5274308681488038,precision,2.410132992267609,precision,266.87229195276905,deg,24.44009499089001,g/s,3.5653966069221497,%,11.292682766914368,km/h,23.711864411830902,%,114.93620345130944,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5512269043,4932702977,50 +198,5e42ccb63965f3689459b93b,2020-02-03T17:28:35,POINT (7.599269007122817 51.96501909430118),26.90918539945801,%,4.900118071826993,kg/h,24.410256929695606,kPa,1.2696463644504548,precision,31.798537072584622,km/h,10.0,c,2.0851566263093586,l/h,1491.6713981702924,u/min,1.391060906648636,precision,1.8607072710990906,precision,271.11901618681804,deg,6.34322022028526,g/s,4.0,%,32.33544400334358,km/h,15.244680881500244,%,115.1395905447782,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,4932702977,4502646832,50 +199,5e42ccb63965f3689459b93c,2020-02-03T17:28:40,POINT (7.598547047119673 51.96501540828365),27.509589362200984,%,4.713495944240875,kg/h,25.22737744450569,kPa,1.3489960283041,precision,38.808194683911665,km/h,10.072681479156017,c,2.005742954996117,l/h,1388.7416646778584,u/min,0.9489960163831711,precision,1.6112450331449508,precision,269.5550552082914,deg,6.101637214344437,g/s,2.999999910593033,%,39.75311720371246,km/h,14.902612492442131,%,115.32292839172383,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,4502646832,268071671,50 +200,5e42ccb63965f3689459b93d,2020-02-03T17:28:45,POINT (7.597806633244129 51.96500970803048),23.915409336270216,%,4.8621606279005976,kg/h,23.848166823387146,kPa,2.0122388124465944,precision,33.961668143017164,km/h,12.000000178813934,c,2.0690045225108924,l/h,1525.7032794952393,u/min,2.4407960414886474,precision,3.1887562394142153,precision,269.09749625522,deg,6.294084174521677,g/s,3.9999998807907104,%,33.99999898672104,km/h,13.999999739229679,%,115.52747905230139,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,268071671,5512269048,50 +201,5e42ccb63965f3689459b93e,2020-02-03T17:28:50,POINT (7.597191286425139 51.964997743743),32.23799868351185,%,5.141985335632689,kg/h,31.7789471372962,kPa,1.4999999552965164,precision,26.761627197535745,km/h,12.0,c,2.188078866226676,l/h,1210.840970337391,u/min,1.3430483639240265,precision,2.043048343062401,precision,267.87186092825687,deg,6.656318250967067,g/s,3.4454732537269592,%,26.999999195337296,km/h,15.259154915809631,%,115.4295958267368,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5512269048,5512269050,50 +202,5e42ccb63965f3689459b93f,2020-02-03T17:28:55,POINT (7.596588580954894 51.96500132518667),30.71502967508991,%,5.362532872969307,kg/h,29.53050398826599,kPa,1.4393034428358078,precision,30.173552171489177,km/h,12.0,c,2.281928882114599,l/h,1358.923117429018,u/min,1.9169153571128845,precision,2.4910446971654894,precision,273.5921392243763,deg,6.941817820132785,g/s,3.367000102996826,%,30.0,km/h,14.999999552965164,%,115.42346350520327,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5512269050,5512269049,50 +203,5e42ccb63965f3689459b940,2020-02-03T17:29:00,POINT (7.595936426725089 51.96504007680178),57.04780830573236,%,12.651749395675305,kg/h,56.28455378115177,kPa,1.650455927848816,precision,35.060800488120776,km/h,11.60597825050354,c,5.383723147095874,l/h,1679.7962460517883,u/min,1.2504559278488159,precision,2.075683891773224,precision,275.7906172565108,deg,16.377734457061237,g/s,2.7384616136550903,%,37.53684210777283,km/h,16.78674352169037,%,115.54577799163607,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5512269049,5512269051,50 +204,5e42ccb63965f3689459b941,2020-02-03T17:29:05,POINT (7.595171614118939 51.96509090564787),84.81008849891964,%,18.109085805396095,kg/h,76.75821816921234,kPa,2.3102410063147545,precision,37.37039879469461,km/h,11.999999821186066,c,7.70599395974302,l/h,1765.4987721443176,u/min,1.6671687051653863,precision,2.8722892090678216,precision,276.6866667731297,deg,23.442275791701576,g/s,3.9999999403953552,%,38.30000001192093,km/h,26.50714335590601,%,115.72518277706277,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5512269051,29041402,50 +205,5e42ccb63965f3689459b942,2020-02-03T17:29:10,POINT (7.594332430578294 51.96514638976937),51.21356551837971,%,9.600139336349084,kg/h,49.749305099248886,kPa,2.3286306500434875,precision,40.725101164749844,km/h,11.999999642372131,c,4.085165675042163,l/h,1444.0635143965483,u/min,1.6821576379239558,precision,2.837551800906658,precision,275.7288163816554,deg,12.42741441395118,g/s,3.791013613343239,%,41.86756631731987,km/h,17.0,%,116.50826410019818,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,29041402,5512269052,50 +206,5e42ccb63965f3689459b943,2020-02-03T17:29:15,POINT (7.593466547343575 51.96519774529605),35.804854527151974,%,6.828497135766716,kg/h,35.00000052154064,kPa,1.9976072035729886,precision,41.89150864473085,km/h,12.0,c,2.9057434620283895,l/h,1460.0000108778477,u/min,3.2940179875586177,precision,3.8934197897091507,precision,275.9594355721745,deg,8.839513756778864,g/s,3.999999936670065,%,42.20705756545067,km/h,16.0,%,117.26792143755453,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5512269052,5512269053,50 +207,5e42ccb63965f3689459b944,2020-02-03T17:29:20,POINT (7.592625345160008 51.96525465733892),26.351721609218885,%,4.5620191463639985,kg/h,25.02702744677663,kPa,1.9341389447450636,precision,39.591011704859454,km/h,12.0,c,1.9412847431336162,l/h,1364.0918432027102,u/min,2.0203423649072647,precision,2.777240642905235,precision,276.2358506210269,deg,5.905549962341055,g/s,4.000000029802322,%,40.000001192092896,km/h,14.000000417232513,%,117.82000489142774,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5512269053,5512269054,50 +208,5e42ccb63965f3689459b945,2020-02-03T17:29:25,POINT (7.591823222371572 51.96530573732579),40.9957168178247,%,7.6829258634481175,kg/h,39.49253737926483,kPa,2.1141716212034227,precision,41.08016429914346,km/h,11.0,c,3.2693301546587734,l/h,1450.7135494798422,u/min,1.42145706564188,precision,2.557085788249969,precision,276.1517295955655,deg,9.945574774651543,g/s,2.0,%,42.0,km/h,15.470914125442505,%,118.41806562534488,m,5e42ccb63965f3689459b871,9.308336270978382,2020-02-03T17:11:48Z,2020-02-03T17:29:25Z,car,1999,Fusion SEL Ecoboost,5de3dba044ea85025c07072e,gasoline,2019,Ford,"Version 1.0.2 (38), 30.11.79 00:00",2013-10-01,,,,,,,5512269054,1120249942,50