lap.ipynb
1 { 2 "cells": [ 3 { 4 "cell_type": "code", 5 "execution_count": 1, 6 "metadata": {}, 7 "outputs": [ 8 { 9 "name": "stderr", 10 "output_type": "stream", 11 "text": [ 12 "2023-05-07 16:11:28,009 DEBUG Influx: Connected to http://telemetry.b4mad.racing/\n" 13 ] 14 } 15 ], 16 "source": [ 17 "import django_initializer\n", 18 "from telemetry.models import FastLap\n", 19 "from telemetry.analyzer import Analyzer\n", 20 "from telemetry.influx import Influx\n", 21 "\n", 22 "import plotly.express as px\n", 23 "import plotly.io as pio\n", 24 "\n", 25 "# pio.renderers.default = \"svg\" # comment this line to use interactive plots\n", 26 "import plotly.graph_objects as go\n", 27 "\n", 28 "import pandas as pd\n", 29 "\n", 30 "%load_ext autoreload\n", 31 "%autoreload 2\n", 32 "\n", 33 "analyzer = Analyzer()\n", 34 "influx = Influx()" 35 ] 36 }, 37 { 38 "cell_type": "code", 39 "execution_count": 2, 40 "metadata": {}, 41 "outputs": [], 42 "source": [ 43 "def lap_fig(df):\n", 44 " fig = go.Figure()\n", 45 "\n", 46 " fig.add_scatter(\n", 47 " x=df[\"DistanceRoundTrack\"],\n", 48 " y=df[\"Brake\"],\n", 49 " marker=dict(size=1),\n", 50 " name=\"Brake\",\n", 51 " line=dict(color=\"red\"),\n", 52 " )\n", 53 "\n", 54 " fig.add_scatter(\n", 55 " x=df[\"DistanceRoundTrack\"],\n", 56 " y=df[\"Throttle\"],\n", 57 " marker=dict(size=1),\n", 58 " name=\"Throttle\",\n", 59 " line=dict(color=\"green\"),\n", 60 " )\n", 61 " return fig" 62 ] 63 }, 64 { 65 "cell_type": "code", 66 "execution_count": 7, 67 "metadata": {}, 68 "outputs": [ 69 { 70 "name": "stderr", 71 "output_type": "stream", 72 "text": [ 73 "2023-05-07 16:13:51,093 INFO Processing iRacing - fuji nochicane - Ferrari 488 GT3 Evo 2020\n", 74 "2023-05-07 16:13:51,093 INFO track.id 409 car.id 9\n", 75 "2023-05-07 16:13:51,093 INFO session 1682107191 lap.id 37672 number 4\n", 76 "2023-05-07 16:13:51,094 INFO length 4459 time 97.0107 valid True\n", 77 "2023-05-07 16:13:51,094 INFO start 2023-04-21 22:04:53.148000+00:00 end 2023-04-21 22:06:30.157000+00:00\n" 78 ] 79 }, 80 { 81 "name": "stdout", 82 "output_type": "stream", 83 "text": [ 84 "iRacing Ferrari 488 GT3 Evo 2020 fuji nochicane\n" 85 ] 86 }, 87 { 88 "data": { 89 "application/vnd.plotly.v1+json": { 90 "config": { 91 "plotlyServerURL": "https://plot.ly" 92 }, 93 "data": [ 94 { 95 "line": { 96 "color": "red" 97 }, 98 "marker": { 99 "size": 1 100 }, 101 "name": "Brake", 102 "type": "scatter", 103 "x": [ 104 0.8923837, 105 1.89, 106 2.89, 107 3.89, 108 4.89, 109 5.89, 110 6.89, 111 7.89, 112 8.89, 113 9.89, 114 10.89, 115 11.89, 116 12.89, 117 13.89, 118 14.89, 119 15.89, 120 16.89, 121 17.89, 122 18.89, 123 19.89, 124 20.89, 125 21.89, 126 22.89, 127 23.89, 128 24.89, 129 25.89, 130 26.89, 131 27.89, 132 28.89, 133 29.89, 134 30.89, 135 31.89, 136 32.89, 137 33.89, 138 34.89, 139 35.89, 140 36.89, 141 37.89, 142 38.89, 143 39.89, 144 40.89, 145 41.89, 146 42.89, 147 43.89, 148 44.89, 149 45.89, 150 46.89, 151 47.89, 152 48.89, 153 49.89, 154 50.89, 155 51.89, 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"display_data" 18784 } 18785 ], 18786 "source": [ 18787 "kwargs = {\n", 18788 " \"game__name\": \"iRacing\",\n", 18789 " \"track__name\": \"fuji nochicane\",\n", 18790 " \"car__name\": \"Ferrari 488 GT3 Evo 2020\",\n", 18791 "}\n", 18792 "\n", 18793 "fast_laps = FastLap.objects.filter(**kwargs)\n", 18794 "\n", 18795 "data_frames = []\n", 18796 "for fast_lap in fast_laps:\n", 18797 " print(fast_lap)\n", 18798 " lap = fast_lap.laps.first()\n", 18799 " lap_df = influx.telemetry_for_laps([lap], measurement=\"fast_laps\", bucket=\"fast_laps\")[0]\n", 18800 " lap_df = analyzer.resample(lap_df, columns=[\"Brake\", \"Throttle\"])\n", 18801 " data_frames.append(\n", 18802 " {\n", 18803 " \"fast_lap\": fast_lap,\n", 18804 " \"df\": lap_df,\n", 18805 " }\n", 18806 " )\n", 18807 " fig = lap_fig(lap_df)\n", 18808 " fig.update_layout(title=dict(text=f\"{fast_lap}\"))\n", 18809 " fig.show()" 18810 ] 18811 }, 18812 { 18813 "cell_type": "code", 18814 "execution_count": 11, 18815 "metadata": {}, 18816 "outputs": [ 18817 { 18818 "data": { 18819 "text/html": [ 18820 "<div>\n", 18821 "<style scoped>\n", 18822 " .dataframe tbody tr th:only-of-type {\n", 18823 " vertical-align: middle;\n", 18824 " }\n", 18825 "\n", 18826 " .dataframe tbody tr th {\n", 18827 " vertical-align: top;\n", 18828 " }\n", 18829 "\n", 18830 " .dataframe thead th {\n", 18831 " text-align: right;\n", 18832 " }\n", 18833 "</style>\n", 18834 "<table border=\"1\" class=\"dataframe\">\n", 18835 " <thead>\n", 18836 " <tr style=\"text-align: right;\">\n", 18837 " <th></th>\n", 18838 " <th>DistanceRoundTrack</th>\n", 18839 " <th>Brake</th>\n", 18840 " <th>Throttle</th>\n", 18841 " </tr>\n", 18842 " </thead>\n", 18843 " <tbody>\n", 18844 " <tr>\n", 18845 " <th>3500</th>\n", 18846 " <td>3501.02</td>\n", 18847 " <td>0.197784</td>\n", 18848 " <td>0.000000</td>\n", 18849 " </tr>\n", 18850 " <tr>\n", 18851 " <th>1207</th>\n", 18852 " <td>1207.94</td>\n", 18853 " <td>0.099249</td>\n", 18854 " <td>0.003884</td>\n", 18855 " </tr>\n", 18856 " <tr>\n", 18857 " <th>4089</th>\n", 18858 " <td>4090.04</td>\n", 18859 " <td>0.000000</td>\n", 18860 " <td>1.000000</td>\n", 18861 " </tr>\n", 18862 " <tr>\n", 18863 " <th>1402</th>\n", 18864 " <td>1402.94</td>\n", 18865 " <td>0.000000</td>\n", 18866 " <td>1.000000</td>\n", 18867 " </tr>\n", 18868 " <tr>\n", 18869 " <th>1735</th>\n", 18870 " <td>1735.96</td>\n", 18871 " <td>0.000000</td>\n", 18872 " <td>0.598977</td>\n", 18873 " </tr>\n", 18874 " </tbody>\n", 18875 "</table>\n", 18876 "</div>" 18877 ], 18878 "text/plain": [ 18879 " DistanceRoundTrack Brake Throttle\n", 18880 "3500 3501.02 0.197784 0.000000\n", 18881 "1207 1207.94 0.099249 0.003884\n", 18882 "4089 4090.04 0.000000 1.000000\n", 18883 "1402 1402.94 0.000000 1.000000\n", 18884 "1735 1735.96 0.000000 0.598977" 18885 ] 18886 }, 18887 "execution_count": 11, 18888 "metadata": {}, 18889 "output_type": "execute_result" 18890 } 18891 ], 18892 "source": [ 18893 "df = data_frames[0][\"df\"]\n", 18894 "df.head()" 18895 ] 18896 } 18897 ], 18898 "metadata": { 18899 "kernelspec": { 18900 "display_name": "Python 3 (ipykernel)", 18901 "language": "python", 18902 "name": "python3" 18903 }, 18904 "language_info": { 18905 "codemirror_mode": { 18906 "name": "ipython", 18907 "version": 3 18908 }, 18909 "file_extension": ".py", 18910 "mimetype": "text/x-python", 18911 "name": "python", 18912 "nbconvert_exporter": "python", 18913 "pygments_lexer": "ipython3", 18914 "version": "3.10.10" 18915 }, 18916 "vscode": { 18917 "interpreter": { 18918 "hash": "ec0b82c30da6c40ee579e65aee6b1689f5f28a8e3a8da7f828e22b813a9dfaf5" 18919 } 18920 } 18921 }, 18922 "nbformat": 4, 18923 "nbformat_minor": 2 18924 }