soconnor/f1-race-prediction
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f1-race-prediction/project/Modeling.ipynb
Sean O'Connor 550649737d Remove race from sessions
2024-12-02 08:34:33 -05:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Formula One Project: Modeling\n",
"\n",
"DUE: December 4th, 2024 (Wed) \n",
"Name(s): Sean O'Connor, Connor Coles \n",
"Class: CSCI 349 - Intro to Data Mining \n",
"Semester: Fall 2024 \n",
"Instructor: Brian King "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assignment Description\n",
"\n",
"Copy over the important cells from the previous step that read in and cleaned your data to this new notebook file. You do not need to copy over all your EDA and plots describing your data, only the code that prepares your data for modeling. This notebook is about exploring the development of predictive models. Some initial preliminary work on applying some modeling techniques should be completed.\n",
"Be sure to commit and push all supporting code that you've completed in this file. Include in this notebook a summary cell at the top that details your accomplishments, challenges, and what you expect to accomplish for your final steps. Be sure to update your readme.md in your repository."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Importing Libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import os\n",
"\n",
"import fastf1\n",
"import fastf1.plotting\n",
"from fastf1.ergast.structure import FastestLap\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.ensemble import GradientBoostingRegressor\n",
"from sklearn.svm import SVR\n",
"import xgboost as xgb"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# FastF1 general setup\n",
"cache_dir = '../data/cache'\n",
"if not os.path.exists(cache_dir):\n",
" os.makedirs(cache_dir)\n",
"\n",
"fastf1.Cache.enable_cache(cache_dir)\n",
"fastf1.plotting.setup_mpl(misc_mpl_mods=False, color_scheme=None)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Define years and sessions of interest\n",
"years = [2021, 2022, 2023, 2024]\n",
"sessions = ['Race'] # Qualifying and Race sessions\n",
"# events = ['Brazil', 'Australia', 'Italy', 'United States'] \n",
"events = ['Great Britain', 'Mexico', 'Brazil', 'Bahrain', 'United States'] "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"core INFO \tLoading data for São Paulo Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"_api WARNING \tDriver 9: Ignoring late data for a previously processed lap.The data may contain errors (previous: 9; current 10)\n",
"_api WARNING \tDriver 10: Ignoring late data for a previously processed lap.The data may contain errors (previous: 16; current 17)\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['44', '33', '77', '11', '10', '55', '16', '4', '3', '14', '31', '5', '22', '7', '99', '18', '6', '63', '47', '9']\n",
"core INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['77', '33', '55', '11', '4', '16', '10', '31', '5', '44', '3', '14', '99', '18', '22', '6', '63', '47', '9', '7']\n",
"events WARNING \tCorrecting user input 'Australia' to 'Austrian Grand Prix'\n",
"core INFO \tLoading data for Austrian Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['33', '4', '11', '44', '77', '10', '22', '5', '63', '18', '55', '16', '3', '14', '99', '7', '31', '6', '47', '9']\n",
"events WARNING \tCorrecting user input 'Australia' to 'Austrian Grand Prix'\n",
"core INFO \tLoading data for Austrian Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['33', '4', '11', '44', '77', '10', '22', '63', '18', '55', '5', '16', '3', '14', '99', '7', '31', '6', '47', '9']\n",
"events WARNING \tCorrecting user input 'Italy' to 'Italian Grand Prix'\n",
"core INFO \tLoading data for Italian Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"_api WARNING \tDriver 16: Encountered 1 timing integrity error(s) near lap(s): [6].\n",
"This might be a bug and should be reported.\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"core WARNING \tDriver 16: Lap timing integrity check failed for 1 lap(s)\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['77', '44', '33', '4', '3', '10', '55', '16', '11', '99', '5', '18', '14', '31', '63', '6', '22', '47', '88', '9']\n",
"events WARNING \tCorrecting user input 'Italy' to 'Italian Grand Prix'\n",
"core INFO \tLoading data for Italian Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['33', '3', '4', '44', '16', '55', '99', '11', '18', '14', '5', '31', '6', '63', '22', '9', '88', '47', '77', '10']\n",
"core INFO \tLoading data for United States Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"_api WARNING \tDriver 22: Ignoring late data for a previously processed lap.The data may contain errors (previous: 10; current 11)\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['33', '44', '11', '77', '16', '55', '3', '4', '10', '22', '31', '5', '99', '63', '14', '18', '6', '7', '47', '9']\n",
"core INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"core WARNING \tDriver 7: Lap timing integrity check failed for 1 lap(s)\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['33', '44', '11', '16', '55', '3', '4', '10', '77', '22', '31', '99', '18', '6', '7', '47', '9', '5', '14', '63']\n",
"core INFO \tLoading data for São Paulo Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['20', '1', '63', '4', '55', '31', '14', '44', '11', '16', '23', '10', '5', '3', '18', '6', '24', '77', '22', '47']\n",
"core INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['63', '44', '1', '11', '16', '4', '55', '20', '5', '10', '3', '47', '24', '77', '18', '31', '14', '6', '23', '22']\n",
"core INFO \tLoading data for Australian Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['16', '1', '11', '4', '44', '63', '3', '31', '55', '14', '10', '77', '22', '24', '47', '23', '20', '5', '6', '18']\n",
"core INFO \tLoading data for Australian Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"_api WARNING \tDriver 3: Ignoring late data for a previously processed lap.The data may contain errors (previous: 28; current 29)\n",
"_api WARNING \tSkipping lap alignment (no suitable lap)!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['16', '1', '11', '4', '44', '63', '3', '31', '55', '14', '10', '77', '22', '24', '47', '20', '5', '6', '18', '23']\n",
"events WARNING \tCorrecting user input 'Italy' to 'Italian Grand Prix'\n",
"core INFO \tLoading data for Italian Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['16', '1', '55', '11', '44', '63', '4', '3', '10', '14', '31', '77', '45', '24', '22', '6', '5', '18', '20', '47']\n",
"events WARNING \tCorrecting user input 'Italy' to 'Italian Grand Prix'\n",
"core INFO \tLoading data for Italian Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['16', '63', '4', '3', '10', '14', '1', '45', '24', '6', '5', '18', '11', '31', '77', '20', '47', '55', '44', '22']\n",
"events WARNING \tCorrecting user input 'United States' to 'United States Grand Prix'\n",
"core INFO \tLoading data for United States Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['55', '16', '1', '11', '44', '63', '18', '4', '14', '77', '23', '5', '10', '24', '22', '20', '3', '31', '47', '6']\n",
"events WARNING \tCorrecting user input 'United States' to 'United States Grand Prix'\n",
"core INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"_api WARNING \tDriver 10: Ignoring late data for a previously processed lap.The data may contain errors (previous: 6; current 7)\n",
"_api WARNING \tDriver 20: Ignoring late data for a previously processed lap.The data may contain errors (previous: 31; current 32)\n",
"_api WARNING \tSkipping lap alignment (no suitable lap)!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['55', '1', '44', '63', '18', '4', '77', '23', '11', '5', '10', '16', '20', '14', '3', '47', '6', '24', '22', '31']\n",
"core INFO \tLoading data for São Paulo Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['1', '16', '18', '14', '44', '63', '4', '55', '11', '81', '27', '31', '10', '20', '23', '22', '3', '77', '2', '24']\n",
"core INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['1', '16', '18', '14', '44', '4', '55', '63', '11', '81', '27', '20', '23', '31', '10', '22', '3', '77', '2', '24']\n",
"core INFO \tLoading data for Australian Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['1', '63', '44', '14', '55', '18', '16', '23', '10', '27', '31', '22', '4', '20', '21', '81', '24', '2', '77', '11']\n",
"core INFO \tLoading data for Australian Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['1', '63', '44', '14', '55', '18', '16', '23', '10', '27', '31', '22', '4', '20', '21', '81', '24', '2', '77', '11']\n",
"core INFO \tLoading data for Italian Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['55', '1', '16', '63', '11', '23', '81', '44', '4', '14', '22', '40', '27', '77', '2', '24', '10', '31', '20', '18']\n",
"core INFO \tLoading data for Italian Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['55', '1', '16', '63', '11', '23', '81', '44', '4', '14', '22', '40', '27', '77', '2', '24', '10', '31', '20', '18']\n",
"events WARNING \tCorrecting user input 'United States' to 'United States Grand Prix'\n",
"core INFO \tLoading data for United States Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['16', '4', '44', '55', '63', '1', '10', '31', '11', '81', '22', '24', '77', '20', '3', '27', '14', '23', '18', '2']\n",
"events WARNING \tCorrecting user input 'United States' to 'United States Grand Prix'\n",
"core INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['16', '4', '44', '55', '63', '1', '10', '31', '11', '81', '22', '24', '77', '3', '23', '2', '20', '27', '14', '18']\n",
"core INFO \tLoading data for São Paulo Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['4', '63', '22', '31', '30', '16', '23', '81', '14', '18', '77', '1', '11', '55', '10', '44', '50', '43', '27', '24']\n",
"core INFO \tLoading data for São Paulo Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"core WARNING \tNo lap data for driver 23\n",
"core WARNING \tFailed to perform lap accuracy check - all laps marked as inaccurate (driver 23)\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['4', '63', '22', '31', '30', '16', '23', '81', '14', '18', '77', '11', '10', '44', '50', '43', '1', '27', '24', '55']\n",
"core INFO \tLoading data for Australian Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 19 drivers: ['1', '55', '11', '4', '16', '81', '63', '22', '18', '14', '44', '23', '77', '20', '31', '27', '10', '3', '24']\n",
"core INFO \tLoading data for Australian Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 19 drivers: ['1', '55', '4', '16', '81', '11', '63', '22', '18', '14', '44', '23', '77', '20', '31', '27', '10', '3', '24']\n",
"events WARNING \tCorrecting user input 'Italy' to 'Italian Grand Prix'\n",
"core INFO \tLoading data for Italian Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['4', '81', '63', '16', '55', '44', '1', '11', '23', '27', '14', '3', '20', '10', '31', '22', '18', '43', '77', '24']\n",
"events WARNING \tCorrecting user input 'Italy' to 'Italian Grand Prix'\n",
"core INFO \tLoading data for Italian Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['4', '81', '63', '16', '55', '44', '1', '11', '23', '27', '14', '3', '20', '10', '31', '22', '18', '43', '77', '24']\n",
"events WARNING \tCorrecting user input 'United States' to 'United States Grand Prix'\n",
"core INFO \tLoading data for United States Grand Prix - Qualifying [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['4', '1', '55', '16', '81', '63', '10', '14', '20', '11', '22', '27', '31', '18', '30', '23', '43', '77', '44', '24']\n",
"events WARNING \tCorrecting user input 'United States' to 'United States Grand Prix'\n",
"core INFO \tLoading data for United States Grand Prix - Race [v3.4.4]\n",
"req INFO \tNo cached data found for session_info. Loading data...\n",
"_api INFO \tFetching session info data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for driver_info. Loading data...\n",
"_api INFO \tFetching driver list...\n",
"req INFO \tData has been written to cache!\n",
"logger WARNING \tFailed to load result data from Ergast!\n",
"core WARNING \tNo result data for this session available on Ergast! (This is expected for recent sessions)\n",
"req INFO \tNo cached data found for session_status_data. Loading data...\n",
"_api INFO \tFetching session status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for lap_count. Loading data...\n",
"_api INFO \tFetching lap count data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for track_status_data. Loading data...\n",
"_api INFO \tFetching track status data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for _extended_timing_data. Loading data...\n",
"_api INFO \tFetching timing data...\n",
"_api INFO \tParsing timing data...\n",
"_api WARNING \tSkipping lap alignment (no suitable lap)!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for timing_app_data. Loading data...\n",
"_api INFO \tFetching timing app data...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tProcessing timing data...\n",
"logger WARNING \tFailed to add first lap time from Ergast!\n",
"req INFO \tNo cached data found for car_data. Loading data...\n",
"_api INFO \tFetching car data...\n",
"_api INFO \tParsing car data...\n",
"_api WARNING \tDriver 2: Car data is incomplete!\n",
"_api WARNING \tDriver 3: Car data is incomplete!\n",
"_api WARNING \tDriver 30: Car data is incomplete!\n",
"_api WARNING \tDriver 43: Car data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for position_data. Loading data...\n",
"_api INFO \tFetching position data...\n",
"_api INFO \tParsing position data...\n",
"_api WARNING \tDriver 2: Position data is incomplete!\n",
"_api WARNING \tDriver 3: Position data is incomplete!\n",
"_api WARNING \tDriver 30: Position data is incomplete!\n",
"_api WARNING \tDriver 43: Position data is incomplete!\n",
"_api WARNING \tDriver 241: Position data is incomplete!\n",
"_api WARNING \tDriver 242: Position data is incomplete!\n",
"_api WARNING \tDriver 243: Position data is incomplete!\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for weather_data. Loading data...\n",
"_api INFO \tFetching weather data...\n",
"req INFO \tData has been written to cache!\n",
"req INFO \tNo cached data found for race_control_messages. Loading data...\n",
"_api INFO \tFetching race control messages...\n",
"req INFO \tData has been written to cache!\n",
"core INFO \tFinished loading data for 20 drivers: ['4', '1', '55', '16', '81', '10', '14', '20', '11', '22', '27', '31', '18', '23', '43', '77', '44', '24', '30', '63']\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Weather Data:\n",
" Time AirTemp Humidity Pressure Rainfall TrackTemp \\\n",
"0 0 days 00:00:10.357000 16.0 79.7 930.2 False 26.1 \n",
"1 0 days 00:01:10.367000 16.0 80.0 930.3 False 25.9 \n",
"2 0 days 00:02:10.371000 16.0 80.0 930.4 False 26.0 \n",
"3 0 days 00:03:10.354000 16.0 80.1 930.3 False 25.9 \n",
"4 0 days 00:04:10.356000 15.9 80.1 930.3 False 25.9 \n",
"\n",
" WindDirection WindSpeed Year Session \n",
"0 154 0.4 2021 Q \n",
"1 129 0.3 2021 Q \n",
"2 150 0.4 2021 Q \n",
"3 190 0.2 2021 Q \n",
"4 190 0.2 2021 Q \n",
"Lap Data:\n",
" Time Driver DriverNumber LapTime \\\n",
"0 0 days 00:19:42.333000 RIC 3 NaT \n",
"1 0 days 00:20:52.007000 RIC 3 0 days 00:01:09.674000 \n",
"2 0 days 00:22:32.617000 RIC 3 0 days 00:01:40.610000 \n",
"3 0 days 00:23:48.713000 RIC 3 0 days 00:01:16.096000 \n",
"4 0 days 00:26:39.889000 RIC 3 NaT \n",
"\n",
" LapNumber Stint PitOutTime PitInTime \\\n",
"0 1.0 1.0 0 days 00:18:10.839000 NaT \n",
"1 2.0 1.0 NaT NaT \n",
"2 3.0 1.0 NaT NaT \n",
"3 4.0 1.0 NaT 0 days 00:23:45.604000 \n",
"4 5.0 2.0 0 days 00:25:19.110000 0 days 00:26:36.878000 \n",
"\n",
" Sector1Time Sector2Time ... LapStartDate \\\n",
"0 NaT 0 days 00:00:49.553000 ... 2021-11-12 19:03:10.848 \n",
"1 0 days 00:00:17.988000 0 days 00:00:35.404000 ... 2021-11-12 19:04:42.342 \n",
"2 0 days 00:00:27.817000 0 days 00:00:54.938000 ... 2021-11-12 19:05:52.016 \n",
"3 0 days 00:00:17.961000 0 days 00:00:35.501000 ... 2021-11-12 19:07:32.626 \n",
"4 NaT 0 days 00:00:39.997000 ... 2021-11-12 19:08:48.722 \n",
"\n",
" TrackStatus Position Deleted DeletedReason FastF1Generated IsAccurate \\\n",
"0 1 NaN False False False \n",
"1 1 NaN False False True \n",
"2 1 NaN False False True \n",
"3 1 NaN False False False \n",
"4 1 NaN False False False \n",
"\n",
" Year Event Session \n",
"0 2021 Brazil Q \n",
"1 2021 Brazil Q \n",
"2 2021 Brazil Q \n",
"3 2021 Brazil Q \n",
"4 2021 Brazil Q \n",
"\n",
"[5 rows x 34 columns]\n"
]
}
],
"source": [
"# Data holders\n",
"weather_data_list = []\n",
"lap_data_list = []\n",
"\n",
"# Loop through years and sessions\n",
"for year in years:\n",
" for event_name in events: \n",
" for session_name in sessions:\n",
" try:\n",
" # Load the session\n",
" session = fastf1.get_session(year, event_name, session_name)\n",
" session.load()\n",
" \n",
" # Process weather data\n",
" weather_data = session.weather_data\n",
" weather_df = pd.DataFrame(weather_data)\n",
" weather_df['Year'] = year\n",
" weather_df['Session'] = session_name\n",
" weather_data_list.append(weather_df)\n",
"\n",
" # Process lap data\n",
" lap_data = session.laps\n",
" lap_df = pd.DataFrame(lap_data)\n",
" lap_df['Year'] = year\n",
" lap_df['Event'] = event_name\n",
" lap_df['Session'] = session_name\n",
" lap_data_list.append(lap_df)\n",
" \n",
" except Exception as e:\n",
" print(f\"Error with {event_name} {session_name} ({year}): {e}\")\n",
"\n",
"# Combine weather and lap data into separate DataFrames\n",
"if weather_data_list:\n",
" weather_data_combined = pd.concat(weather_data_list, ignore_index=True)\n",
" print(\"Weather Data:\")\n",
" print(weather_data_combined.head())\n",
"\n",
"if lap_data_list:\n",
" lap_data_combined = pd.concat(lap_data_list, ignore_index=True)\n",
" print(\"Lap Data:\")\n",
" print(lap_data_combined.head())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Convert lap times to seconds for easier modeling\n",
"def convert_timedelta_to_seconds(td):\n",
" if pd.isna(td):\n",
" return None\n",
" return td.total_seconds()\n",
"\n",
"# Process lap times\n",
"lap_data_combined['LapTime_seconds'] = lap_data_combined['LapTime'].apply(convert_timedelta_to_seconds)\n",
"lap_data_combined['Sector1Time_seconds'] = lap_data_combined['Sector1Time'].apply(convert_timedelta_to_seconds)\n",
"lap_data_combined['Sector2Time_seconds'] = lap_data_combined['Sector2Time'].apply(convert_timedelta_to_seconds)\n",
"lap_data_combined['Sector3Time_seconds'] = lap_data_combined['Sector3Time'].apply(convert_timedelta_to_seconds)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Time Driver DriverNumber LapTime LapNumber Stint \\\n",
"0 2024-01-01 00:13:21.079 ZHO 24 NaT 1.0 1.0 \n",
"1 2024-01-01 00:13:25.515 OCO 31 NaT 1.0 1.0 \n",
"2 2024-01-01 00:13:28.581 GAS 10 NaT 1.0 1.0 \n",
"3 2024-01-01 00:13:33.760 MAG 20 NaT 1.0 1.0 \n",
"4 2024-01-01 00:13:39.639 HUL 27 NaT 1.0 1.0 \n",
"\n",
" PitOutTime PitInTime Sector1Time Sector2Time ... \\\n",
"0 0 days 00:11:34.445000 NaT NaT 0 days 00:00:36.406000 ... \n",
"1 0 days 00:11:43.198000 NaT NaT 0 days 00:00:33.336000 ... \n",
"2 0 days 00:11:47.409000 NaT NaT 0 days 00:00:33.379000 ... \n",
"3 0 days 00:11:50.367000 NaT NaT 0 days 00:00:35.836000 ... \n",
"4 0 days 00:11:56.096000 NaT NaT 0 days 00:00:36.690000 ... \n",
"\n",
" Sector3Time_seconds AirTemp Humidity Pressure Rainfall TrackTemp \\\n",
"0 34.895 14.7 59.0 1019.0 False 21.2 \n",
"1 35.205 30.3 37.0 991.2 False 36.8 \n",
"2 33.669 26.2 47.0 1014.5 False 40.3 \n",
"3 30.358 28.5 40.0 1005.4 False 38.6 \n",
"4 30.625 35.3 12.0 993.5 False 42.4 \n",
"\n",
" WindDirection WindSpeed Year_weather Session_weather \n",
"0 156 1.1 2023 Q \n",
"1 90 4.0 2022 Q \n",
"2 280 1.7 2022 Race \n",
"3 187 2.5 2024 Q \n",
"4 282 2.2 2023 Q \n",
"\n",
"[5 rows x 47 columns]\n"
]
}
],
"source": [
"# Merge weather data with lap data\n",
"# Convert timedelta to datetime for merging\n",
"def convert_time_for_merge(df, base_date='2024-01-01'):\n",
" if 'Time' in df.columns:\n",
" # Create a base datetime and add the timedelta\n",
" base = pd.Timestamp(base_date)\n",
" if df['Time'].dtype == 'timedelta64[ns]':\n",
" df['Time'] = base + df['Time']\n",
" return df\n",
"\n",
"# Process both dataframes\n",
"weather_data_combined = convert_time_for_merge(weather_data_combined)\n",
"lap_data_combined = convert_time_for_merge(lap_data_combined)\n",
"\n",
"# Now merge based on nearest timestamp\n",
"merged_data = pd.merge_asof(\n",
" lap_data_combined.sort_values('Time'),\n",
" weather_data_combined.sort_values('Time'),\n",
" on='Time',\n",
" suffixes=('_lap', '_weather')\n",
")\n",
"\n",
"print(merged_data.head())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Select features for modeling\n",
"feature_columns = [\n",
" 'TrackTemp', 'AirTemp', 'Humidity', 'Pressure', 'WindSpeed',\n",
" 'TyreLife', 'SpeedI1', 'SpeedI2', 'SpeedFL', 'SpeedST'\n",
"]\n",
"\n",
"target_column = 'LapTime_seconds'\n",
"\n",
"# Remove rows with missing values\n",
"model_data = merged_data[feature_columns + [target_column]].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Split the data into training and testing sets\n",
"X = model_data[feature_columns]\n",
"y = model_data[target_column]\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Scale the features\n",
"scaler = StandardScaler()\n",
"X_train_scaled = scaler.fit_transform(X_train)\n",
"X_test_scaled = scaler.transform(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# Train and evaluate multiple models\n",
"\n",
"# Initialize models\n",
"models = {\n",
" 'Linear Regression': LinearRegression(),\n",
" 'Random Forest': RandomForestRegressor(n_estimators=100, random_state=42),\n",
" 'Decision Tree': DecisionTreeRegressor(random_state=42),\n",
" 'Gradient Boosting': GradientBoostingRegressor(n_estimators=100, random_state=42),\n",
" 'Support Vector Regressor': SVR(kernel='rbf', C=1.0, epsilon=0.1),\n",
" 'XGBoost': xgb.XGBRegressor(n_estimators=100, random_state=42)\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Regression model trained.\n",
"Predictions made with Linear Regression model.\n",
"Metrics calculated with Linear Regression model.\n",
"Random Forest model trained.\n",
"Predictions made with Random Forest model.\n",
"Metrics calculated with Random Forest model.\n",
"Decision Tree model trained.\n",
"Predictions made with Decision Tree model.\n",
"Metrics calculated with Decision Tree model.\n",
"Gradient Boosting model trained.\n",
"Predictions made with Gradient Boosting model.\n",
"Metrics calculated with Gradient Boosting model.\n",
"Support Vector Regressor model trained.\n",
"Predictions made with Support Vector Regressor model.\n",
"Metrics calculated with Support Vector Regressor model.\n",
"XGBoost model trained.\n",
"Predictions made with XGBoost model.\n",
"Metrics calculated with XGBoost model.\n"
]
}
],
"source": [
"# Train and evaluate each model\n",
"results = {}\n",
"for name, model in models.items():\n",
" # Train the model\n",
" model.fit(X_train_scaled, y_train)\n",
" print(f\"{name} model trained.\")\n",
" \n",
" # Make predictions\n",
" y_pred = model.predict(X_test_scaled)\n",
" print(f\"Predictions made with {name} model.\")\n",
" \n",
" # Calculate metrics\n",
" mse = mean_squared_error(y_test, y_pred)\n",
" rmse = np.sqrt(mse)\n",
" r2 = r2_score(y_test, y_pred)\n",
" print(f\"Metrics calculated with {name} model.\")\n",
" \n",
" results[name] = {\n",
" 'RMSE': rmse,\n",
" 'R2': r2\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Model Performance:\n",
"\n",
"Linear Regression:\n",
"RMSE: 7.85 seconds\n",
"R2 Score: 0.703\n",
"\n",
"Random Forest:\n",
"RMSE: 3.86 seconds\n",
"R2 Score: 0.928\n",
"\n",
"Decision Tree:\n",
"RMSE: 5.71 seconds\n",
"R2 Score: 0.843\n",
"\n",
"Gradient Boosting:\n",
"RMSE: 4.37 seconds\n",
"R2 Score: 0.908\n",
"\n",
"Support Vector Regressor:\n",
"RMSE: 5.99 seconds\n",
"R2 Score: 0.827\n",
"\n",
"XGBoost:\n",
"RMSE: 3.96 seconds\n",
"R2 Score: 0.924\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Print results\n",
"print(\"\\nModel Performance:\")\n",
"for name, metrics in results.items():\n",
" print(f\"\\n{name}:\")\n",
" print(f\"RMSE: {metrics['RMSE']:.2f} seconds\")\n",
" print(f\"R2 Score: {metrics['R2']:.3f}\")\n",
"\n",
"# Visualize predictions vs actual values for the best model\n",
"plt.figure(figsize=(10, 6))\n",
"best_model_name = max(results.items(), key=lambda x: x[1]['R2'])[0]\n",
"best_model = models[best_model_name]\n",
"y_pred = best_model.predict(X_test_scaled)\n",
"\n",
"plt.scatter(y_test, y_pred, alpha=0.5)\n",
"plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
"plt.xlabel('Actual Lap Time (seconds)')\n",
"plt.ylabel('Predicted Lap Time (seconds)')\n",
"plt.title(f'Actual vs Predicted Lap Times\\n{best_model_name}')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Ftj09PV1nz5695eu5ublle6GTdO3/nF6v5a8sFkuO9b7xxhu5vom4IINDXpw8eTLHbSVLlpTVapWLi4ucnJyUnJycrd2JEyckKdsz+Pv938i3336rEydOKCEhwTa6Lem2vs+7XLly2rJli7KysnINh/fcc48sFos2b96c41u/zXgT+K3w9va+pTf25/YzFhsbq6ioqByPCQoKknTtDwO59fvN3MqzNUvZsmW1Y8eObNtvpe5btXz5cpUoUUKffvqpXXjN6TvHc7v29W05/QHmTnDPPffI3d1dixYtynX/dZGRkYqMjFR6erq2b9+uKVOmKDo6Wv7+/goLCyuskgHcwQjdAFBMRUREyMXFRUlJSTecymyxWGQYRrYA9c477ygzM9Nu2/U2OY1I+vv7a9++fXbbDh06pIMHD+YYuv+uadOm8vHx0YEDBzRgwICbti9Mq1ev1vTp020B4/z58/rkk0/08MMPy9nZWZ6enmrcuLFWr16tGTNmyN3dXZKUlZWlJUuWqEqVKqpZs+ZNr5Pb870eHv/eRwsWLMj3PbVt21bLli1TfHx8rtOgO3TooH/961/6/fff1blz53xf604RFBSkGjVqaO/evbaXAuYmPDxca9eu1R9//GH7Y09mZmaOL8X7u1t5tlLeRvdvVfPmzbVy5Up9/vnndlOoly9fXmDXuP71fs7OzrZtly5d0vvvv59j+/3792vv3r12U8w/+OADlSpVyvaiwDtNhw4dNHnyZJUtW1YBAQG3dIyrq6uaN28uHx8fffnll9q9ezehG4AkQjcAFFv+/v569dVXNXLkSP36669q06aNSpcurT/++EM7duyQp6enxo8fLy8vLzVr1kzTp0/XPffcI39/f23cuFHvvvuufHx87M5Zp04dSdLbb7+tUqVKyc3NTQEBASpbtqyeeeYZde/eXc8//7w6deqk//73v5o2bdotf0+z1WrVG2+8oZ49e+rs2bN68sknVb58eZ0+fVp79+7V6dOnNW/evIJ+TLfE2dlZrVu31pAhQ5SVlaWpU6cqNTVV48ePt7WZMmWKWrdurfDwcA0dOlQlS5bU3Llz9fPPP2vZsmW3NLJdt25dSdKcOXPUs2dPlShRQkFBQWrSpIlKly6t/v37a+zYsSpRooSWLl2qvXv35vueunbtqri4OPXv318HDx5UeHi4srKy9P333ys4OFhPP/20mjZtqueee07PPvusdu7cqWbNmsnT01PJycnasmWL6tatm+v3Yt+pFixYoLZt2yoiIkK9evVS5cqVdfbsWSUmJurHH3/UqlWrJEmjRo3S2rVr1bJlS40ZM0YeHh566623bum9ArfybKVr/Z2QkKBPPvlEFStWVKlSpWwj7fnVs2dPzZ49W927d9fEiRNVvXp1ff755/ryyy8l6ZZH3rdv357j9ubNm6t9+/aaNWuWoqOj9dxzz+nMmTOaMWNGrjMfKlWqpMcff1zjxo1TxYoVtWTJEn399deaOnWqPDw88nejJhs0aJA++ugjNWvWTIMHD9Z9992nrKwsHTt2TF999ZVeeuklNW7cWGPGjNFvv/2mVq1aqUqVKjp37pzmzJlj984FACB0A0AxFhsbq5CQEM2ZM0fLli1Tenq6fH199cADD6h///62dh988IFefPFFDRs2TFevXlXTpk319ddfZ3vRWEBAgF577TXNmTNHLVq0UGZmpuLi4tSrVy9FR0frxIkTmj9/vuLi4lSnTh3NmzfPLpjeTPfu3VW1alVNmzZN/fr10/nz51W+fHndf//9OX4HeGEZMGCALl++rBdeeEGnTp1S7dq1tW7dOjVt2tTWpnnz5vr22281duxY9erVS1lZWapXr57Wrl2b7WVOuWnRooViY2O1ePFiLVy4UFlZWdqwYYNatGihdevW6aWXXlL37t3l6empyMhIrVixIt8jhS4uLvrss880ZcoULVu2TK+99ppKlSqlevXq2b2Ea8GCBXrwwQe1YMECzZ07V1lZWapUqZKaNm2a7WVoRUF4eLh27NihSZMmadCgQfrzzz9VtmxZhYSE2I3m16lTR998841eeukl9ezZU6VLl9YzzzyjTp066bnnnrvhNW712c6ZM0f//Oc/9fTTT9u+oiq/L6e7ztPTU99++60GDRqkYcOGyWKx6NFHH9XcuXPVrl27bH9Iy83MmTNz3L5hwwa1bNlSixYt0tSpU/XYY4+pcuXK6tu3r8qXL68+ffpkO+b+++/Xs88+q7Fjx+qXX35RpUqVNGvWLA0ePPh2btVUnp6e2rx5s/71r3/p7bff1pEjR+Tu7q6qVavqkUcekb+/vySpcePG2rlzp4YPH67Tp0/Lx8dHDRs21LfffqvatWs79iYA3DEshvH/v2gQAADYOXr0qAICAjR9+vRcX7wFFAWTJ0/WqFGjdOzYMVWpUqXQruvv7686dero008/LbRrAsCdhpFuAACAYuTNN9+UJNWqVUsZGRn69ttv9frrr6t79+6FGrgBANcQugEAAIoRDw8PzZ49W0ePHlV6erqqVq2q4cOHa9SoUY4uDQDuSkwvBwAAAADAJIX75ZEAAAAAANxFCN0AAAAAAJiE0A0AAAAAgEl4kVoxlJWVpRMnTqhUqVKyWCyOLgcAAAAAih3DMHT+/HlVqlRJTk65j2cTuouhEydOyM/Pz9FlAAAAAECxd/z48Rt+JSOhuxgqVaqUpGud7+Xl5eBqAAAAAKD4SU1NlZ+fny1/5YbQXQxdn1Lu5eVF6AYAAAAAE93sI728SA0AAAAAAJMQugEAAAAAMAnTy4uxZqOWydnV3dFlAAAAAECe7Zrew9ElFAhGugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6C9nRo0dlsVi0Z88eR5cCAAAAADBZsQ3dp06dUr9+/VS1alW5urrK19dXERER2rZtm6NLs5NTCH/xxRfVoEEDubq66v7773dYbQAAAACA2+Pi6ALM0qlTJ2VkZGjx4sUKDAzUH3/8ofXr1+vs2bOOLu2mDMNQ79699f3332vfvn2OLgcAAAAAkE/FcqT73Llz2rJli6ZOnarw8HBVq1ZNjRo1UmxsrNq3by9Jslgsmjdvntq2bSt3d3cFBARo1apVduf5/fff1aVLF5UuXVply5ZVZGSkjh49atcmLi5OwcHBcnNzU61atTR37ly7/Tt27FBoaKjc3NzUsGFD7d69+6b1v/766/rnP/+pwMDA23sQAAAAAACHKpah22q1ymq1as2aNUpPT8+13ejRo9WpUyft3btX3bt3V9euXZWYmChJunjxosLDw2W1WrVp0yZt2bJFVqtVbdq00ZUrVyRJCxcu1MiRIzVp0iQlJiZq8uTJGj16tBYvXixJSktLU4cOHRQUFKRdu3Zp3LhxGjp0qPkPAAAAAABwRyiWodvFxUXx8fFavHixfHx81LRpU40YMSLbVO2nnnpKMTExqlmzpiZMmKCGDRvqjTfekCQtX75cTk5Oeuedd1S3bl0FBwcrLi5Ox44dU0JCgiRpwoQJmjlzpqKiohQQEKCoqCgNHjxYCxYskCQtXbpUmZmZWrRokWrXrq0OHTro5ZdfLvD7TU9PV2pqqt0CAAAAAHC8Yhm6pWuf6T5x4oTWrl2riIgIJSQkqH79+oqPj7e1CQsLszsmLCzMNtK9a9cuHT58WKVKlbKNnJcpU0aXL19WUlKSTp8+rePHj6tPnz62/VarVRMnTlRSUpIkKTExUfXq1ZOHh0eu1ywIU6ZMkbe3t23x8/Mr8GsAAAAAAPKu2L5ITZLc3NzUunVrtW7dWmPGjFFMTIzGjh2rXr165XqMxWKRJGVlZalBgwZaunRptjblypXT5cuXJV2bYt64cWO7/c7OzpKuvRCtMMTGxmrIkCG29dTUVII3AAAAANwBinXo/ruQkBCtWbPGtr59+3b16NHDbj00NFSSVL9+fa1YsULly5eXl5dXtnN5e3urcuXK+vXXX9WtW7dcr/f+++/r0qVLcnd3t12joLm6usrV1bXAzwsAAAAAuD3Fcnr5mTNn1LJlSy1ZskT79u3TkSNHtGrVKk2bNk2RkZG2dqtWrdKiRYt06NAhjR07Vjt27NCAAQMkSd26ddM999yjyMhIbd68WUeOHNHGjRv14osv6rfffpMkjRs3TlOmTNGcOXN06NAh/fTTT4qLi9OsWbMkSdHR0XJyclKfPn104MABffbZZ5oxY8ZN6z98+LD27NmjkydP6tKlS9qzZ4/27Nlje4EbAAAAAKBoKJYj3VarVY0bN9bs2bOVlJSkjIwM+fn5qW/fvhoxYoSt3fjx47V8+XI9//zz8vX11dKlSxUSEiJJ8vDw0KZNmzR8+HBFRUXp/Pnzqly5slq1amUb+Y6JiZGHh4emT5+uYcOGydPTU3Xr1tWgQYNsdXzyySfq37+/QkNDFRISoqlTp6pTp043rD8mJkYbN260rV8ffT9y5Ij8/f0L8EkBAAAAAMxkMQrrg8d3GIvFoo8//lgdO3Z0dCkFLjU1Vd7e3qo3cL6cXd0dXQ4AAAAA5Nmu6T1u3siBrueulJSUHD+SfF2xnF4OAAAAAMCdgNANAAAAAIBJiuVnum/FXTqrHgAAAABQiBjpBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCQuji4A5tk0sau8vLwcXQYAAAAA3LUY6QYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJC6OLgDmaTZqmZxd3R1dBoqRXdN7OLoEAAAAoEhhpBsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmh20RHjx6VxWLRnj17HF0KAAAAAMABikXoPnXqlPr166eqVavK1dVVvr6+ioiI0LZt2xxdWjYfffSRGjduLG9vb5UqVUq1a9fWSy+9JElq0aKFLBZLrou/v79jiwcAAAAA5ImLowsoCJ06dVJGRoYWL16swMBA/fHHH1q/fr3Onj3r6NLsfPPNN3r66ac1efJkPf7447JYLDpw4IDWr18vSVq9erWuXLkiSTp+/LgaNWqkb775RrVr15YkOTs7O6x2AAAAAEDeFfmR7nPnzmnLli2aOnWqwsPDVa1aNTVq1EixsbFq3769JMlisWjevHlq27at3N3dFRAQoFWrVtmd5/fff1eXLl1UunRplS1bVpGRkTp69Khdm7i4OAUHB8vNzU21atXS3Llz7fbv2LFDoaGhcnNzU8OGDbV79267/Z9++qkeeughvfzyywoKClLNmjXVsWNHvfHGG5KkMmXKyNfXV76+vipXrpwkqWzZstm2AQAAAACKhiIfuq1Wq6xWq9asWaP09PRc240ePVqdOnXS3r171b17d3Xt2lWJiYmSpIsXLyo8PFxWq1WbNm3Sli1bZLVa1aZNG9vI88KFCzVy5EhNmjRJiYmJmjx5skaPHq3FixdLktLS0tShQwcFBQVp165dGjdunIYOHWpXg6+vr/bv36+ff/65QJ9Benq6UlNT7RYAAAAAgOMV+dDt4uKi+Ph4LV68WD4+PmratKlGjBihffv22bV76qmnFBMTo5o1a2rChAlq2LChbYR5+fLlcnJy0jvvvKO6desqODhYcXFxOnbsmBISEiRJEyZM0MyZMxUVFaWAgABFRUVp8ODBWrBggSRp6dKlyszM1KJFi1S7dm116NBBL7/8sl0NAwcO1AMPPKC6devK399fTz/9tBYtWnTDPxbciilTpsjb29u2+Pn53db5AAAAAAAFo8iHbunaZ7pPnDihtWvXKiIiQgkJCapfv77i4+NtbcLCwuyOCQsLs41079q1S4cPH1apUqVsI+dlypTR5cuXlZSUpNOnT+v48ePq06ePbb/VatXEiROVlJQkSUpMTFS9evXk4eGR6zU9PT21bt06HT58WKNGjZLVatVLL72kRo0a6eLFi/m+/9jYWKWkpNiW48eP5/tcAAAAAICCUyxepCZJbm5uat26tVq3bq0xY8YoJiZGY8eOVa9evXI9xmKxSJKysrLUoEEDLV26NFubcuXK6fLly5KuTTFv3Lix3f7rLzczDOOWa7333nt17733KiYmRiNHjlTNmjW1YsUKPfvss7d8jr9ydXWVq6trvo4FAAAAAJinWIx05yQkJERpaWm29e3bt9vt3759u2rVqiVJql+/vn755ReVL19e1atXt1u8vb1VoUIFVa5cWb/++mu2/QEBAbbr7d27V5cuXcr1mjnx9/eXh4eHXa0AAAAAgOKhyIfuM2fOqGXLllqyZIn27dunI0eOaNWqVZo2bZoiIyNt7VatWqVFixbp0KFDGjt2rHbs2KEBAwZIkrp166Z77rlHkZGR2rx5s44cOaKNGzfqxRdf1G+//SZJGjdunKZMmaI5c+bo0KFD+umnnxQXF6dZs2ZJkqKjo+Xk5KQ+ffrowIED+uyzzzRjxgy7WseNG6dhw4YpISFBR44c0e7du9W7d29lZGSodevWhfTEAAAAAACFpchPL7darWrcuLFmz56tpKQkZWRkyM/PT3379tWIESNs7caPH6/ly5fr+eefl6+vr5YuXaqQkBBJkoeHhzZt2qThw4crKipK58+fV+XKldWqVSt5eXlJkmJiYuTh4aHp06dr2LBh8vT0VN26dTVo0CBbHZ988on69++v0NBQhYSEaOrUqerUqZOthubNm+utt95Sjx499Mcff6h06dIKDQ3VV199paCgoMJ7aAAAAACAQmEx8vJh5CLKYrHo448/VseOHR1dSqFITU2Vt7e36g2cL2dXd0eXg2Jk1/Qeji4BAAAAuCNcz10pKSm2wdqcFPnp5QAAAAAA3KkI3QAAAAAAmKTIf6b7VtwFM+gBAAAAAHcgRroBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwiYujC4B5Nk3sKi8vL0eXAQAAAAB3LUa6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADCJi6MLgHmajVomZ1d3R5eB27Breg9HlwAAAADgNjDSDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdhezo0aOyWCzas2ePo0sBAAAAAJis2IbuU6dOqV+/fqpatapcXV3l6+uriIgIbdu2zdGl2fl7CN+7d6+6du0qPz8/ubu7Kzg4WHPmzHFskQAAAACAfHFxdAFm6dSpkzIyMrR48WIFBgbqjz/+0Pr163X27FlHl3ZDu3btUrly5bRkyRL5+flp69ateu655+Ts7KwBAwY4ujwAAAAAQB4Uy5Huc+fOacuWLZo6darCw8NVrVo1NWrUSLGxsWrfvr0kyWKxaN68eWrbtq3c3d0VEBCgVatW2Z3n999/V5cuXVS6dGmVLVtWkZGROnr0qF2buLg4BQcHy83NTbVq1dLcuXPt9u/YsUOhoaFyc3NTw4YNtXv37hvW3rt3b73++utq3ry5AgMD1b17dz377LNavXr17T8YAAAAAEChKpah22q1ymq1as2aNUpPT8+13ejRo9WpUyft3btX3bt3V9euXZWYmChJunjxosLDw2W1WrVp0yZt2bJFVqtVbdq00ZUrVyRJCxcu1MiRIzVp0iQlJiZq8uTJGj16tBYvXixJSktLU4cOHRQUFKRdu3Zp3LhxGjp0aJ7vJyUlRWXKlMnHkwAAAAAAOFKxnF7u4uKi+Ph49e3bV/Pnz1f9+vXVvHlzPf3007rvvvts7Z566inFxMRIkiZMmKCvv/5ab7zxhubOnavly5fLyclJ77zzjiwWi6Rro9o+Pj5KSEjQo48+qgkTJmjmzJmKioqSJAUEBOjAgQNasGCBevbsqaVLlyozM1OLFi2Sh4eHateurd9++03/+Mc/bvletm3bppUrV2rdunW5tklPT7f740JqamqenhcAAAAAwBzFcqRbuvaZ7hMnTmjt2rWKiIhQQkKC6tevr/j4eFubsLAwu2PCwsJsI927du3S4cOHVapUKdvIeZkyZXT58mUlJSXp9OnTOn78uPr06WPbb7VaNXHiRCUlJUmSEhMTVa9ePXl4eOR6zRvZv3+/IiMjNWbMGLVu3TrXdlOmTJG3t7dt8fPzu+VrAAAAAADMUyxHuq9zc3NT69at1bp1a40ZM0YxMTEaO3asevXqlesx10e1s7Ky1KBBAy1dujRbm3Llyuny5cuSrk0xb9y4sd1+Z2dnSZJhGPmu/cCBA2rZsqX69u2rUaNG3bBtbGyshgwZYltPTU0leAMAAADAHaBYh+6/CwkJ0Zo1a2zr27dvV48ePezWQ0NDJUn169fXihUrVL58eXl5eWU7l7e3typXrqxff/1V3bp1y/V677//vi5duiR3d3fbNW5m//79atmypXr27KlJkybdtL2rq6tcXV1v2g4AAAAAULiK5fTyM2fOqGXLllqyZIn27dunI0eOaNWqVZo2bZoiIyNt7VatWqVFixbp0KFDGjt2rHbs2GH7Wq5u3brpnnvuUWRkpDZv3qwjR45o48aNevHFF/Xbb79JksaNG6cpU6Zozpw5OnTokH766SfFxcVp1qxZkqTo6Gg5OTmpT58+OnDggD777DPNmDHjhrXv379f4eHhat26tYYMGaKTJ0/q5MmTOn36tElPCwAAAABglmI50m21WtW4cWPNnj1bSUlJysjIkJ+fn/r27asRI0bY2o0fP17Lly/X888/L19fXy1dulQhISGSJA8PD23atEnDhw9XVFSUzp8/r8qVK6tVq1a2ke+YmBh5eHho+vTpGjZsmDw9PVW3bl0NGjTIVscnn3yi/v37KzQ0VCEhIZo6dao6deqUa+2rVq3S6dOntXTpUrup7dWqVcv2dWUAAAAAgDubxbidDx4XYRaLRR9//LE6duzo6FIKXGpqqry9vVVv4Hw5u7o7uhzchl3Te9y8EQAAAIBCdz13paSk5PiR5OuK5fRyAAAAAADuBIRuAAAAAABMUiw/030r7tJZ9QAAAACAQsRINwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACZxcXQBMM+miV3l5eXl6DIAAAAA4K7FSDcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJnFxdAEwT7NRy+Ts6u7oMky1a3oPR5cAAAAAALlipBsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhuxAcPXpUFotFe/bscXQpAAAAAIBCVKxC96lTp9SvXz9VrVpVrq6u8vX1VUREhLZt2+bo0uxcD+F/X7p37263n5AOAAAAAEWbi6MLKEidOnVSRkaGFi9erMDAQP3xxx9av369zp496+jScvTNN9+odu3atnV3d3cHVgMAAAAAKGjFZqT73Llz2rJli6ZOnarw8HBVq1ZNjRo1UmxsrNq3by9Jslgsmjdvntq2bSt3d3cFBARo1apVduf5/fff1aVLF5UuXVply5ZVZGSkjh49atcmLi5OwcHBcnNzU61atTR37ly7/Tt27FBoaKjc3NzUsGFD7d69O8eay5YtK19fX9vi7e1dcA8EAAAAAOBwxSZ0W61WWa1WrVmzRunp6bm2Gz16tDp16qS9e/eqe/fu6tq1qxITEyVJFy9eVHh4uKxWqzZt2qQtW7bIarWqTZs2unLliiRp4cKFGjlypCZNmqTExERNnjxZo0eP1uLFiyVJaWlp6tChg4KCgrRr1y6NGzdOQ4cONf8BAAAAAADuOMUmdLu4uCg+Pl6LFy+Wj4+PmjZtqhEjRmjfvn127Z566inFxMSoZs2amjBhgho2bKg33nhDkrR8+XI5OTnpnXfeUd26dRUcHKy4uDgdO3ZMCQkJkqQJEyZo5syZioqKUkBAgKKiojR48GAtWLBAkrR06VJlZmZq0aJFql27tjp06KCXX345x5qbNGli+2OB1WrNdUT8ZtLT05Wammq3AAAAAAAcr9iEbunaZ7pPnDihtWvXKiIiQgkJCapfv77i4+NtbcLCwuyOCQsLs41079q1S4cPH1apUqVsQbhMmTK6fPmykpKSdPr0aR0/flx9+vSxC8sTJ05UUlKSJCkxMVH16tWTh4dHrte8bsWKFdqzZ49tCQkJydd9T5kyRd7e3rbFz88vX+cBAAAAABSsYvUiNUlyc3NT69at1bp1a40ZM0YxMTEaO3asevXqlesxFotFkpSVlaUGDRpo6dKl2dqUK1dOly9flnRtinnjxo3t9js7O0uSDMO45Vr9/PxUvXr1W26fm9jYWA0ZMsS2npqaSvAGAAAAgDtAsQvdfxcSEqI1a9bY1rdv364ePXrYrYeGhkqS6tevrxUrVqh8+fLy8vLKdi5vb29VrlxZv/76q7p165br9d5//31dunTJ9jby7du3F+AdZefq6ipXV1dTrwEAAAAAyLtiE7rPnDmjp556Sr1799Z9992nUqVKaefOnZo2bZoiIyNt7VatWqWGDRvqoYce0tKlS7Vjxw69++67kqRu3bpp+vTpioyM1KuvvqoqVaro2LFjWr16tV5++WVVqVJF48aN0wsvvCAvLy+1bdtW6enp2rlzp/78808NGTJE0dHRGjlypPr06aNRo0bp6NGjmjFjRr7u6eDBg9m2hYSEqGTJkvl7SAAAAACAQlVsQrfValXjxo01e/ZsJSUlKSMjQ35+furbt69GjBhhazd+/HgtX75czz//vHx9fbV06VLbZ6k9PDy0adMmDR8+XFFRUTp//rwqV66sVq1a2Ua+Y2Ji5OHhoenTp2vYsGHy9PRU3bp1NWjQIFsdn3zyifr376/Q0FCFhIRo6tSp6tSpU57v6emnn8627ciRI/L398/7AwIAAAAAFDqLkZcPIRdxFotFH3/8sTp27OjoUkyVmpoqb29v1Rs4X86u7o4ux1S7pve4eSMAAAAAKGDXc1dKSkqOH0++rli9vRwAAAAAgDsJoRsAAAAAAJMUm89034q7aCY9AAAAAOAOwEg3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJnFxdAEwz6aJXeXl5eXoMgAAAADgrsVINwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmcXF0ATBPs1HL5Ozq7ugyCsSu6T0cXQIAAAAA5Bkj3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdhSghIUEWi0Xnzp2zbVuzZo2qV68uZ2dnDRo0yGG1AQAAAAAKXrEN3RaL5YZLr169TLmuv7+/XnvttRz3NWnSRMnJyfL29rZt69evn5588kkdP35cEyZMMKUmAAAAAIBjuDi6ALMkJyfb/r1ixQqNGTNGBw8etG1zd3fP0/kyMjJUokSJ26qpZMmS8vX1ta1fuHBBp06dUkREhCpVqnRb5wYAAAAA3HmK7Ui3r6+vbfH29pbFYpGvr68qVKighx56SAsXLrRr//PPP8vJyUlJSUmSro2Uz58/X5GRkfL09NTEiRMlSZ988okaNGggNzc3BQYGavz48bp69eot1fTX6eUJCQkqVaqUJKlly5ayWCxKSEiQJG3dulXNmjWTu7u7/Pz89MILLygtLa2AngwAAAAAoLAU29CdG4vFot69eysuLs5u+6JFi/Twww/r3nvvtW0bO3asIiMj9dNPP6l379768ssv1b17d73wwgs6cOCAFixYoPj4eE2aNCnPdTRp0sQ28v7RRx8pOTlZTZo00U8//aSIiAhFRUVp3759WrFihbZs2aIBAwbkeq709HSlpqbaLQAAAAAAx7vrQrckPfvsszp48KB27Ngh6drU8SVLlqh379527aKjo9W7d28FBgaqWrVqmjRpkl555RX17NlTgYGBat26tSZMmKAFCxbkuYaSJUuqfPnykqQyZcrI19dXJUuW1PTp0xUdHa1BgwapRo0aatKkiV5//XW99957unz5co7nmjJliry9vW2Ln59fnusBAAAAABS8uzJ0V6xYUe3bt9eiRYskSZ9++qkuX76sp556yq5dw4YN7dZ37dqlV199VVar1bb07dtXycnJunjxYoHUtmvXLsXHx9tdIyIiQllZWTpy5EiOx8TGxiolJcW2HD9+vEBqAQAAAADcnmL7IrWbiYmJ0TPPPKPZs2crLi5OXbp0kYeHh10bT09Pu/WsrCyNHz9eUVFR2c7n5uZWIHVlZWWpX79+euGFF7Ltq1q1ao7HuLq6ytXVtUCuDwAAAAAoOPkK3UlJSYqLi1NSUpLmzJmj8uXL64svvpCfn59q165d0DWaol27dvL09NS8efP0+eefa9OmTTc9pn79+jp48KCqV69uWl3169fX/v37Tb0GAAAAAKBw5Hl6+caNG1W3bl19//33Wr16tS5cuCBJ2rdvn8aOHVvgBZrF2dlZvXr1UmxsrKpXr66wsLCbHjNmzBi99957GjdunPbv36/ExEStWLFCo0aNsmv3+++/a8+ePXbL2bNnb6mu4cOHa9u2bfrnP/+pPXv26JdfftHatWs1cODAfN0nAAAAAMBx8hy6X3nlFU2cOFFff/21SpYsadseHh6ubdu2FWhxZuvTp4+uXLmS7QVquYmIiNCnn36qr7/+Wg888IAefPBBzZo1S9WqVbNrN2PGDIWGhtota9euvaVr3Hfffdq4caN++eUXPfzwwwoNDdXo0aNVsWLFPN8fAAAAAMCxLIZhGHk5wGq16qefflJAQIBKlSqlvXv3KjAwUEePHlWtWrVyfcP2nei7775TixYt9Ntvv6lChQqOLqfApKamytvbW/UGzpezq7ujyykQu6b3cHQJAAAAAGBzPXelpKTIy8sr13Z5Hun28fFRcnJytu27d+9W5cqV83o6h0hPT9fhw4c1evRode7cuVgFbgAAAADAnSPPoTs6OlrDhw/XyZMnZbFYlJWVpe+++05Dhw5Vjx5FYzRy2bJlCgoKUkpKiqZNm+bocgAAAAAAxVSep5dnZGSoV69eWr58uQzDkIuLizIzMxUdHa34+Hg5OzubVStuEdPLAQAAAMBctzq9PE9fGWYYhk6cOKGFCxdqwoQJ+vHHH5WVlaXQ0FDVqFHjtosGAAAAAKA4yXPorlGjhvbv368aNWooMDDQrLoAAAAAACjy8vSZbicnJ9WoUUNnzpwxqx4AAAAAAIqNPL9Ibdq0aXr55Zf1888/m1EPAAAAAADFRp6ml0tS9+7ddfHiRdWrV08lS5aUu7v9i7rOnj1bYMUBAAAAAFCU5Tl0v/baayaUAQAAAABA8ZPn0N2zZ08z6gAAAAAAoNjJc+g+duzYDfdXrVo138UAAAAAAFCc5Dl0+/v7y2Kx5Lo/MzPztgoCAAAAAKC4yHPo3r17t916RkaGdu/erVmzZmnSpEkFVhgAAAAAAEWdxTAMoyBOtG7dOk2fPl0JCQkFcTrchtTUVHl7eyslJUVeXl6OLgcAAAAAip1bzV15/p7u3NSsWVM//PBDQZ0OAAAAAIAiL8/Ty1NTU+3WDcNQcnKyxo0bpxo1ahRYYQAAAAAAFHV5Dt0+Pj7ZXqRmGIb8/Py0fPnyAisMAAAAAICiLs+he8OGDXbrTk5OKleunKpXry4XlzyfDgAAAACAYivPKdlisahJkybZAvbVq1e1adMmNWvWrMCKAwAAAACgKMvzi9TCw8N19uzZbNtTUlIUHh5eIEUBAAAAAFAc5Dl0G4aR7TPdknTmzBl5enoWSFEAAAAAABQHtzy9PCoqStK16eW9evWSq6urbV9mZqb27dunJk2aFHyFAAAAAAAUUbccur29vSVdG+kuVaqU3N3dbftKliypBx98UH379i34CgEAAAAAKKJuOXTHxcVJkvz9/TV06FCmkgMAAAAAcBMWwzAMRxeBgpWamipvb2/VGzhfzq7uubbbNb1HIVYFAAAAAMXH9dyVkpIiLy+vXNvl64u1P/zwQ61cuVLHjh3TlStX7Pb9+OOP+TklAAAAAADFTp7fXv7666/r2WefVfny5bV79241atRIZcuW1a+//qq2bduaUSMAAAAAAEVSnkP33Llz9fbbb+vNN99UyZIlNWzYMH399dd64YUXlJKSYkaNAAAAAAAUSXkO3ceOHbN9NZi7u7vOnz8vSXrmmWe0bNmygq0OAAAAAIAiLM+h29fXV2fOnJEkVatWTdu3b5ckHTlyRLyTDQAAAACA/5Pn0N2yZUt98sknkqQ+ffpo8ODBat26tbp06aInnniiwAsEAAAAAKCoyvPby99++21lZWVJkvr3768yZcpoy5Yteuyxx9S/f/8CLxAAAAAAgKIqz6HbyclJTk7/N0DeuXNnde7cuUCLAgAAAACgOMjz9HJJ2rx5s7p3766wsDD9/vvvkqT3339fW7ZsKdDiAAAAAAAoyvIcuj/66CNFRETI3d1du3fvVnp6uiTp/Pnzmjx5coEXCAAAAABAUZXn0D1x4kTNnz9fCxcuVIkSJWzbmzRpoh9//LFAiwMAAAAAoCjLc+g+ePCgmjVrlm27l5eXzp07VxA1AQAAAABQLOQ5dFesWFGHDx/Otn3Lli0KDAwskKIAAAAAACgO8hy6+/XrpxdffFHff/+9LBaLTpw4oaVLl2ro0KF6/vnnzajRNEePHpXFYtGePXscXQoAAAAAoBi6pdC9b98+23dzDxs2TB07dlR4eLguXLigZs2aKSYmRv369dOAAQPyXYjFYrnh0qtXr3yfOz8SEhJuWlN8fHyh1gQAAAAAKFpu6Xu6Q0NDlZycrPLlyyswMFA//PCDRowYocTERGVlZSkkJERWq/W2CklOTrb9e8WKFRozZowOHjxo2+bu7m7XPiMjw+5FbgWtSZMmdjW9+OKLSk1NVVxcnG2bt7e3adcHAAAAABR9tzTS7ePjoyNHjki6NiU7KytLnp6eatiwoRo1anTbgVuSfH19bYu3t7csFott/fLly/Lx8dHKlSvVokULubm5acmSJTpz5oy6du2qKlWqyMPDQ3Xr1tWyZcvszpuVlaWpU6eqevXqcnV1VdWqVTVp0qQca8jKylLfvn1Vs2ZNJScn29Xk7u4uV1dX23qFChX0xhtvKDAwUO7u7qpXr54+/PBD27muj5R/+eWXCg0Nlbu7u1q2bKlTp07p888/V3BwsLy8vNS1a1ddvHjRdlyLFi00YMAADRgwQD4+PipbtqxGjRolwzBu+xkDAAAAAArXLY10d+rUSc2bN1fFihVlsVjUsGFDOTs759j2119/LdAC/2r48OGaOXOm4uLi5OrqqsuXL6tBgwYaPny4vLy8tG7dOj3zzDMKDAxU48aNJUmxsbFauHChZs+erYceekjJycn6z3/+k+3cV65cUXR0tJKSkrRlyxaVL1/+hrWMGjVKq1ev1rx581SjRg1t2rRJ3bt3V7ly5dS8eXNbu3HjxunNN9+Uh4eHOnfurM6dO8vV1VUffPCBLly4oCeeeEJvvPGGhg8fbjtm8eLF6tOnj77//nvt3LlTzz33nKpVq6a+ffsW0JMEAAAAABSGWwrdb7/9tqKionT48GG98MIL6tu3r0qVKmV2bdkMGjRIUVFRdtuGDh1q+/fAgQP1xRdfaNWqVWrcuLHOnz+vOXPm6M0331TPnj0lSffee68eeughu3NcuHBB7du316VLl5SQkHDTaeNpaWmaNWuWvv32W4WFhUmSAgMDtWXLFi1YsMAudE+cOFFNmzaVJPXp00exsbFKSkqyven9ySef1IYNG+xCt5+fn2bPni2LxaKgoCD99NNPmj17dq6hOz09Xenp6bb11NTUG9YPAAAAACgctxS6JalNmzaSpF27dunFF190SOhu2LCh3XpmZqb+9a9/acWKFfr9999t4dPT01OSlJiYqPT0dLVq1eqG570+RX39+vXy8PC4aR0HDhzQ5cuX1bp1a7vtV65cUWhoqN22++67z/bvChUqyMPDw+6r1SpUqKAdO3bYHfPggw/KYrHY1sPCwjRz5kxlZmbmOMNgypQpGj9+/E3rBgAAAAAUrlsO3df99UVihe16mL5u5syZmj17tl577TXVrVtXnp6eGjRokK5cuSIp+8vXctOuXTstWbJE27dvV8uWLW/a/vqb3NetW6fKlSvb7XN1dbVb/+vL3iwWS7aXv1ksFtv58is2NlZDhgyxraempsrPz++2zgkAAAAAuH15Dt13ks2bNysyMlLdu3eXdC0M//LLLwoODpYk1ahRQ+7u7lq/fr1iYmJyPc8//vEP1alTR48//rjWrVtnNz08JyEhIXJ1ddWxY8du2jY/tm/fnm29Ro0auX6O3tXVNVvYBwAAAAA4XpEO3dWrV9dHH32krVu3qnTp0po1a5ZOnjxpC91ubm4aPny4hg0bppIlS6pp06Y6ffq09u/frz59+tida+DAgcrMzFSHDh30+eefZ/vc91+VKlVKQ4cO1eDBg5WVlaWHHnpIqamp2rp1q6xWq+3z4/l1/PhxDRkyRP369dOPP/6oN954QzNnzrytcwIAAAAACl+RDt2jR4/WkSNHFBERIQ8PDz333HPq2LGjUlJS7Nq4uLhozJgxOnHihCpWrKj+/fvneL5BgwYpKytL7dq10xdffKEmTZrkeu0JEyaofPnymjJlin799Vf5+Piofv36GjFixG3fV48ePXTp0iU1atRIzs7OGjhwoJ577rnbPi8AAAAAoHBZDL4A+o7SokUL3X///XrttdfyfY7U1FR5e3ur3sD5cnbN/XPtu6b3yPc1AAAAAOBudj13paSkyMvLK9d2ToVYEwAAAAAAdxVCNwAAAAAAJinSn+kujhISEhxdAgAAAACggDDSDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgElcHF0AzLNpYld5eXk5ugwAAAAAuGsx0g0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASVwcXQDM02zUMjm7uue6f9f0HoVYDQAAAADcfRjpBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMcleG7oSEBFksFp07d87RpQAAAAAAirFiHbq3bt0qZ2dntWnTxm57kyZNlJycLG9v71yP9ff3l8ViyXVp0aKFydUDAAAAAIo6F0cXYKZFixZp4MCBeuedd3Ts2DFVrVpVklSyZEn5+vrmelxmZqa+//57GYYh6Vp479Spkw4ePCgvLy/bOQAAAAAAuJFiO9KdlpamlStX6h//+Ic6dOig+Ph4276/Ty+Pj4+Xj4+PPv30U4WEhMjV1VUXL16Ur6+vfH19VaZMGUlS+fLlbdv+85//qFmzZnJ3d5efn59eeOEFpaWl2a7h7++viRMnqkePHrJarapWrZr+/e9/6/Tp04qMjJTValXdunW1c+dO2zHX61izZo1q1qwpNzc3tW7dWsePHy+UZwYAAAAAKFjFNnSvWLFCQUFBCgoKUvfu3RUXF2cbuc7JxYsXNWXKFL3zzjvav3+/ypcvn2vbn376SREREYqKitK+ffu0YsUKbdmyRQMGDLBrN3v2bDVt2lS7d+9W+/bt9cwzz6hHjx7q3r27fvzxR1WvXl09evSwq+vixYuaNGmSFi9erO+++06pqal6+umnb3iv6enpSk1NtVsAAAAAAI5XbEP3u+++q+7du0uS2rRpowsXLmj9+vW5ts/IyNDcuXPVpEkTBQUFydPTM9e206dPV3R0tAYNGqQaNWqoSZMmev311/Xee+/p8uXLtnbt2rVTv379VKNGDY0ZM0bnz5/XAw88oKeeeko1a9bU8OHDlZiYqD/++MOujjfffFNhYWFq0KCBFi9erK1bt2rHjh251jNlyhR5e3vbFj8/v7w8KgAAAACASYpl6D548KB27NhhGyF2cXFRly5dtGjRolyPKVmypO67775bOv+uXbsUHx8vq9VqWyIiIpSVlaUjR47Y2v31fBUqVJAk1a1bN9u2U6dO2ba5uLioYcOGtvVatWrJx8dHiYmJudYTGxurlJQU28J0dAAAAAC4MxTLF6m9++67unr1qipXrmzbZhiGSpQooT///DPHY9zd3WWxWG7p/FlZWerXr59eeOGFbPuuv6xNkkqUKGH79/Vz57QtKyvL7hw51XGj2lxdXeXq6npLtQMAAAAACk+xC91Xr17Ve++9p5kzZ+rRRx+129epUyctXbpUderUua1r1K9fX/v371f16tVv6zw5uXr1qnbu3KlGjRpJujZqf+7cOdWqVavArwUAAAAAMFexm17+6aef6s8//1SfPn1Up04du+XJJ5/Uu+++e9vXGD58uLZt26Z//vOf2rNnj3755RetXbtWAwcOvO1zlyhRQgMHDtT333+vH3/8Uc8++6wefPBBWwgHAAAAABQdxS50v/vuu3rkkUfk7e2dbV+nTp20Z88e/fjjj7d1jfvuu08bN27UL7/8oocfflihoaEaPXq0KlaseFvnlSQPDw8NHz5c0dHRCgsLk7u7u5YvX37b5wUAAAAAFD6LcaPv0UKhio+P16BBg2zfH55fqamp8vb2Vr2B8+Xs6p5ru13Te9zWdQAAAADgbnU9d6WkpMjLyyvXdsVupBsAAAAAgDsFoRsAAAAAAJMQuu8gvXr1uu2p5QAAAACAOwehGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJO4OLoAmGfTxK7y8vJydBkAAAAAcNdipBsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAk7g4ugCYp9moZXJ2dc9x367pPQq5GgAAAAC4+zDSDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJik3o7tWrlywWiywWi0qUKKHAwEANHTpUaWlpji4NAAAAAHCXcnF0AQWpTZs2iouLU0ZGhjZv3qyYmBilpaVp3rx5du0yMjJUokQJB1WZszuxJgAAAADA7Sk2I92S5OrqKl9fX/n5+Sk6OlrdunXTmjVrNG7cON1///1atGiRAgMD5erqKsMwlJKSoueee07ly5eXl5eXWrZsqb1799rOt3fvXoWHh6tUqVLy8vJSgwYNtHPnTknSf//7Xz322GMqXbq0PD09Vbt2bX322WeSpPj4ePn4+NjVtmbNGlksFtt6fmsCAAAAABQdxWqk++/c3d2VkZEhSTp8+LBWrlypjz76SM7OzpKk9u3bq0yZMvrss8/k7e2tBQsWqFWrVjp06JDKlCmjbt26KTQ0VPPmzZOzs7P27NljG43+5z//qStXrmjTpk3y9PTUgQMHZLVa81RffmrKSXp6utLT023rqampeX5WAAAAAICCV2xD944dO/TBBx+oVatWkqQrV67o/fffV7ly5SRJ3377rX766SedOnVKrq6ukqQZM2ZozZo1+vDDD/Xcc8/p2LFjevnll1WrVi1JUo0aNWznP3bsmDp16qS6detKkgIDA/NcY35qysmUKVM0fvz4PF8fAAAAAGCuYjW9/NNPP5XVapWbm5vCwsLUrFkzvfHGG5KkatWq2cKtJO3atUsXLlxQ2bJlZbVabcuRI0eUlJQkSRoyZIhiYmL0yCOP6F//+pdtuyS98MILmjhxopo2baqxY8dq3759ea43PzXlJDY2VikpKbbl+PHjea4FAAAAAFDwitVId3h4uObNm6cSJUqoUqVKdi8m8/T0tGublZWlihUrKiEhIdt5rn8ee9y4cYqOjta6dev0+eefa+zYsVq+fLmeeOIJxcTEKCIiQuvWrdNXX32lKVOmaObMmRo4cKCcnJxkGIbdOa9Pc/+r/NSUE1dXV9vIOAAAAADgzlGsQrenp6eqV69+S23r16+vkydPysXFRf7+/rm2q1mzpmrWrKnBgwera9euiouL0xNPPCFJ8vPzU//+/dW/f3/FxsZq4cKFGjhwoMqVK6fz588rLS3NFqz37NlTYDUBAAAAAIqGYjW9PC8eeeQRhYWFqWPHjvryyy919OhRbd26VaNGjdLOnTt16dIlDRgwQAkJCfrvf/+r7777Tj/88IOCg4MlSYMGDdKXX36pI0eO6Mcff9S3335r29e4cWN5eHhoxIgROnz4sD744APFx8ffdk0AAAAAgKLlrg3dFotFn332mZo1a6bevXurZs2aevrpp3X06FFVqFBBzs7OOnPmjHr06KGaNWuqc+fOatu2re2FZZmZmfrnP/+p4OBgtWnTRkFBQZo7d64kqUyZMlqyZIk+++wz1a1bV8uWLdO4ceNuuyYAAAAAQNFiMf7+4WMUeampqfL29la9gfPl7OqeY5td03sUclUAAAAAUHxcz10pKSny8vLKtd1dO9INAAAAAIDZCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJXBxdAMyzaWJXeXl5OboMAAAAALhrMdINAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmMTF0QXAPM1GLZOzq3uO+3ZN71HI1QAAAADA3YeRbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATFKkQndCQoIsFovOnTt3W+fp1auXOnbsWCA1me3o0aOyWCzas2ePo0sBAAAAAOSRw0L3/PnzVapUKV29etW27cKFCypRooQefvhhu7abN2+WxWJRpUqVlJycLG9v7wKt5dSpU+rXr5+qVq0qV1dX+fr6KiIiQtu2bSvQ6wAAAAAA7i4ujrpweHi4Lly4oJ07d+rBBx+UdC1c+/r66ocfftDFixfl4eEh6doId6VKlVSzZk1TaunUqZMyMjK0ePFiBQYG6o8//tD69et19uxZU64HAAAAALg7OGykOygoSJUqVVJCQoJtW0JCgiIjI3Xvvfdq69atdtvDw8OzTS+Pj4+Xj4+PvvzySwUHB8tqtapNmzZKTk62HZuZmakhQ4bIx8dHZcuW1bBhw2QYhm3/uXPntGXLFk2dOlXh4eGqVq2aGjVqpNjYWLVv397WzmKxaN68eWrbtq3c3d0VEBCgVatW2d3T77//ri5duqh06dIqW7asIiMjdfToUbs2cXFxCg4Olpubm2rVqqW5c+fa7d+xY4dCQ0Pl5uamhg0bavfu3fl9xAAAAAAAB3PoZ7pbtGihDRs22NY3bNigFi1aqHnz5rbtV65c0bZt2xQeHp7jOS5evKgZM2bo/fff16ZNm3Ts2DENHTrUtn/mzJlatGiR3n33XW3ZskVnz57Vxx9/bNtvtVpltVq1Zs0apaen37De0aNHq1OnTtq7d6+6d++url27KjEx0VZHeHi4rFarNm3apC1bttj+CHDlyhVJ0sKFCzVy5EhNmjRJiYmJmjx5skaPHq3FixdLktLS0tShQwcFBQVp165dGjdunN29AAAAAACKFoeH7u+++05Xr17V+fPntXv3bjVr1kzNmze3jYBv375dly5dyjV0Z2RkaP78+WrYsKHq16+vAQMGaP369bb9r732mmJjY9WpUycFBwdr/vz5dp8Jd3FxUXx8vBYvXiwfHx81bdpUI0aM0L59+7Jd66mnnlJMTIxq1qypCRMmqGHDhnrjjTckScuXL5eTk5Peeecd1a1bV8HBwYqLi9OxY8ds9zJhwgTNnDlTUVFRCggIUFRUlAYPHqwFCxZIkpYuXarMzEwtWrRItWvXVocOHfTyyy/f9Dmmp6crNTXVbgEAAAAAOJ5DQ3d4eLjS0tL0ww8/aPPmzapZs6bKly+v5s2b64cfflBaWpoSEhJUtWpVBQYG5ngODw8P3Xvvvbb1ihUr6tSpU5KklJQUJScnKywszLbfxcVFDRs2tDtHp06ddOLECa1du1YRERFKSEhQ/fr1FR8fb9fur+e5vn59pHvXrl06fPiwSpUqZRs9L1OmjC5fvqykpCSdPn1ax48fV58+fWz7rVarJk6cqKSkJElSYmKi6tWrZ/sse07XzMmUKVPk7e1tW/z8/G56DAAAAADAfA57kZokVa9eXVWqVNGGDRv0559/qnnz5pIkX19fBQQE6LvvvtOGDRvUsmXLXM9RokQJu3WLxWL3me1b5ebmptatW6t169YaM2aMYmJiNHbsWPXq1euGx1ksFklSVlaWGjRooKVLl2ZrU65cOV2+fFnStSnmjRs3ttvv7OwsSfmqW5JiY2M1ZMgQ23pqairBGwAAAADuAA7/nu7rL0hLSEhQixYtbNubN2+uL7/8Utu3b891avnNeHt7q2LFitq+fbtt29WrV7Vr166bHhsSEqK0tDS7bX89z/X1WrVqSZLq16+vX375ReXLl1f16tXtFm9vb1WoUEGVK1fWr7/+mm1/QECA7Zp79+7VpUuXcr1mTlxdXeXl5WW3AAAAAAAc744I3Vu2bNGePXtsI93StdC9cOFCXb58Od+hW5JefPFF/etf/9LHH3+s//znP3r++edtbz+XpDNnzqhly5ZasmSJ9u3bpyNHjmjVqlWaNm2aIiMj7c61atUqLVq0SIcOHdLYsWO1Y8cODRgwQJLUrVs33XPPPYqMjNTmzZt15MgRbdy4US+++KJ+++03SdK4ceM0ZcoUzZkzR4cOHdJPP/2kuLg4zZo1S5IUHR0tJycn9enTRwcOHNBnn32mGTNm5PveAQAAAACO5dDp5dK10H3p0iXVqlVLFSpUsG1v3ry5zp8/r3vvvfe2pkq/9NJLSk5OVq9eveTk5KTevXvriSeeUEpKiqRrby9v3LixZs+eraSkJGVkZMjPz099+/bViBEj7M41fvx4LV++XM8//7x8fX21dOlShYSESLr22fJNmzZp+PDhioqK0vnz51W5cmW1atXKNvIcExMjDw8PTZ8+XcOGDZOnp6fq1q2rQYMG2Wr55JNP1L9/f4WGhiokJERTp05Vp06d8n3/AAAAAADHsRj5/SDxXcZisejjjz9Wx44dHV3KTaWmpsrb21v1Bs6Xs6t7jm12Te9RyFUBAAAAQPFxPXelpKTc8CO+Dp9eDgAAAABAcUXoBgAAAADAJA7/THdRwSx8AAAAAEBeMdINAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASVwcXQDMs2liV3l5eTm6DAAAAAC4azHSDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJXBxdAMzTbNQyObu6Z9u+a3oPB1QDAAAAAHcfRroBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQuvPh6NGjslgs2rNnT65tEhISZLFYdO7cOUlSfHy8fHx8CqU+AAAAAMCdociG7l69eqljx47Ztv897JrBz89PycnJqlOnzi0f06VLFx06dMi2Pm7cON1///0mVAcAAAAAuFO4OLqAosjZ2Vm+vr55Osbd3V3u7u4mVQQAAAAAuBMV2ZHuW5HTaPJrr70mf39/2/r1EfPJkyerQoUK8vHx0fjx43X16lW9/PLLKlOmjKpUqaJFixbZjslpevlnn32mmjVryt3dXeHh4Tp69Kjddf86vTw+Pl7jx4/X3r17ZbFYZLFYFB8fr969e6tDhw52x129elW+vr521wcAAAAAFA2MdEv69ttvVaVKFW3atEnfffed+vTpo23btqlZs2b6/vvvtWLFCvXv31+tW7eWn59ftuOPHz+uqKgo9e/fX//4xz+0c+dOvfTSS7ler0uXLvr555/1xRdf6JtvvpEkeXt7q2bNmmrWrJmSk5NVsWJFSdfC/IULF9S5c+dcz5eenq709HTbempqan4fBQAAAACgABXpke5PP/1UVqvVbmnbtm2ez1OmTBm9/vrrCgoKUu/evRUUFKSLFy9qxIgRqlGjhmJjY1WyZEl99913OR4/b948BQYGavbs2QoKClK3bt3Uq1evXK/n7u4uq9UqFxcX+fr6ytfXV+7u7mrSpImCgoL0/vvv29rGxcXpqaeektVqzfV8U6ZMkbe3t23J6Q8DAAAAAIDCV6RDd3h4uPbs2WO3vPPOO3k+T+3ateXk9H+PokKFCqpbt65t3dnZWWXLltWpU6dyPD4xMVEPPvigLBaLbVtYWFie65CkmJgYxcXFSZJOnTqldevWqXfv3jc8JjY2VikpKbbl+PHj+bo2AAAAAKBgFenp5Z6enqpevbrdtt9++832bycnJxmGYbc/IyMj23lKlChht26xWHLclpWVlWMdf7/G7ejRo4deeeUVbdu2Tdu2bZO/v78efvjhGx7j6uoqV1fXAqsBAAAAAFAwinTovply5crp5MmTMgzDNgp9o+/Wzq+QkBCtWbPGbtv27dtveEzJkiWVmZmZbXvZsmXVsWNHxcXFadu2bXr22WcLslQAAAAAQCEq0tPLb6ZFixY6ffq0pk2bpqSkJL311lv6/PPPC/w6/fv3V1JSkoYMGaKDBw/qgw8+UHx8/A2P8ff315EjR7Rnzx7973//s3sRWkxMjBYvXqzExET17NmzwOsFAAAAABSOYh26g4ODNXfuXL311luqV6+eduzYoaFDhxb4dapWraqPPvpIn3zyierVq6f58+dr8uTJNzymU6dOatOmjcLDw1WuXDktW7bMtu+RRx5RxYoVFRERoUqVKhV4vQAAAACAwmExCvIDySgQFy9eVKVKlbRo0SJFRUXl+fjU1FR5e3ur3sD5cnZ1z7Z/1/QeBVEmAAAAANy1rueulJQUeXl55dquWH+mu6jJysrSyZMnNXPmTHl7e+vxxx93dEkAAAAAgNtA6L6DHDt2TAEBAapSpYri4+Pl4kL3AAAAAEBRRqq7g/j7+xfo148BAAAAAByrWL9IDQAAAAAARyJ0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACZxcXQBMM+miV3l5eXl6DIAAAAA4K7FSDcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJuEz3cWQYRiSpNTUVAdXAgAAAADF0/W8dT1/5YbQXQydOXNGkuTn5+fgSgAAAACgeDt//ry8vb1z3U/oLobKlCkjSTp27NgNOx/FQ2pqqvz8/HT8+HHeVl/M0dd3F/r77kJ/313o77sHfV28GYah8+fPq1KlSjdsR+guhpycrn1U39vbm1/uu4iXlxf9fZegr+8u9Pfdhf6+u9Dfdw/6uvi6lUFOXqQGAAAAAIBJCN0AAAAAAJiE0F0Mubq6auzYsXJ1dXV0KSgE9Pfdg76+u9Dfdxf6++5Cf9896GtIksW42fvNAQAAAABAvjDSDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN1F1Ny5cxUQECA3Nzc1aNBAmzdvvmH7jRs3qkGDBnJzc1NgYKDmz59fSJXiduWlr5OTkxUdHa2goCA5OTlp0KBBhVcoCkRe+nv16tVq3bq1ypUrJy8vL4WFhenLL78sxGpxu/LS31u2bFHTpk1VtmxZubu7q1atWpo9e3YhVovbldf/7b7uu+++k4uLi+6//35zC0SByUtfJyQkyGKxZFv+85//FGLFuB15/d1OT0/XyJEjVa1aNbm6uuree+/VokWLCqlaOAKhuwhasWKFBg0apJEjR2r37t16+OGH1bZtWx07dizH9keOHFG7du308MMPa/fu3RoxYoReeOEFffTRR4VcOfIqr32dnp6ucuXKaeTIkapXr14hV4vbldf+3rRpk1q3bq3PPvtMu3btUnh4uB577DHt3r27kCtHfuS1vz09PTVgwABt2rRJiYmJGjVqlEaNGqW33367kCtHfuS1v69LSUlRjx491KpVq0KqFLcrv3198OBBJScn25YaNWoUUsW4Hfnp786dO2v9+vV69913dfDgQS1btky1atUqxKpR6AwUOY0aNTL69+9vt61WrVrGK6+8kmP7YcOGGbVq1bLb1q9fP+PBBx80rUYUjLz29V81b97cePHFF02qDGa4nf6+LiQkxBg/fnxBlwYTFER/P/HEE0b37t0LujSYIL/93aVLF2PUqFHG2LFjjXr16plYIQpKXvt6w4YNhiTjzz//LITqUNDy2t+ff/654e3tbZw5c6YwysMdgpHuIubKlSvatWuXHn30Ubvtjz76qLZu3ZrjMdu2bcvWPiIiQjt37lRGRoZpteL25KevUXQVRH9nZWXp/PnzKlOmjBklogAVRH/v3r1bW7duVfPmzc0oEQUov/0dFxenpKQkjR071uwSUUBu53c7NDRUFStWVKtWrbRhwwYzy0QByU9/r127Vg0bNtS0adNUuXJl1axZU0OHDtWlS5cKo2Q4iIujC0De/O9//1NmZqYqVKhgt71ChQo6efJkjsecPHkyx/ZXr17V//73P1WsWNG0epF/+elrFF0F0d8zZ85UWlqaOnfubEaJKEC3099VqlTR6dOndfXqVY0bN04xMTFmlooCkJ/+/uWXX/TKK69o8+bNcnHh/64VFfnp64oVK+rtt99WgwYNlJ6ervfff1+tWrVSQkKCmjVrVhhlI5/y09+//vqrtmzZIjc3N3388cf63//+p+eff15nz57lc93FGP8VL6IsFovdumEY2bbdrH1O23HnyWtfo2jLb38vW7ZM48aN07///W+VL1/erPJQwPLT35s3b9aFCxe0fft2vfLKK6pevbq6du1qZpkoILfa35mZmYqOjtb48eNVs2bNwioPBSgvv9tBQUEKCgqyrYeFhen48eOaMWMGobuIyEt/Z2VlyWKxaOnSpfL29pYkzZo1S08++aTeeustubu7m14vCh+hu4i555575OzsnO2vZ6dOncr2V7brfH19c2zv4uKismXLmlYrbk9++hpF1+3094oVK9SnTx+tWrVKjzzyiJllooDcTn8HBARIkurWras//vhD48aNI3Tf4fLa3+fPn9fOnTu1e/duDRgwQNK1/6NuGIZcXFz01VdfqWXLloVSO/KmoP63+8EHH9SSJUsKujwUsPz0d8WKFVW5cmVb4Jak4OBgGYah3377jRfoFVN8pruIKVmypBo0aKCvv/7abvvXX3+tJk2a5HhMWFhYtvZfffWVGjZsqBIlSphWK25PfvoaRVd++3vZsmXq1auXPvjgA7Vv397sMlFACur32zAMpaenF3R5KGB57W8vLy/99NNP2rNnj23p37+/goKCtGfPHjVu3LiwSkceFdTv9u7du/n4XxGQn/5u2rSpTpw4oQsXLti2HTp0SE5OTqpSpYqp9cKBHPQCN9yG5cuXGyVKlDDeffdd48CBA8agQYMMT09P4+jRo4ZhGMYrr7xiPPPMM7b2v/76q+Hh4WEMHjzYOHDggPHuu+8aJUqUMD788ENH3QJuUV772jAMY/fu3cbu3buNBg0aGNHR0cbu3buN/fv3O6J85FFe+/uDDz4wXFxcjLfeestITk62LefOnXPULSAP8trfb775prF27Vrj0KFDxqFDh4xFixYZXl5exsiRIx11C8iD/Pz3/K94e3nRkde+nj17tvHxxx8bhw4dMn7++WfjlVdeMSQZH330kaNuAXmQ1/4+f/68UaVKFePJJ5809u/fb2zcuNGoUaOGERMT46hbQCEgdBdRb731llGtWjWjZMmSRv369Y2NGzfa9vXs2dNo3ry5XfuEhAQjNDTUKFmypOHv72/MmzevkCtGfuW1ryVlW6pVq1a4RSPf8tLfzZs3z7G/e/bsWfiFI1/y0t+vv/66Ubt2bcPDw8Pw8vIyQkNDjblz5xqZmZkOqBz5kdf/nv8VobtoyUtfT5061bj33nsNNzc3o3Tp0sZDDz1krFu3zgFVI7/y+rudmJhoPPLII4a7u7tRpUoVY8iQIcbFixcLuWoUJoth/P83agEAAAAAgALFZ7oBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAuIu0aNFCgwYNcnQZAADcNSyGYRiOLgIAABSOs2fPqkSJEipVqpSjS8kmISFB4eHh+vPPP+Xj4+PocgAAKBAuji4AAAAUnjJlyji6hBxlZGQ4ugQAAEzB9HIAAO4if51e7u/vr4kTJ6pHjx6yWq2qVq2a/v3vf+v06dOKjIyU1WpV3bp1tXPnTtvx8fHx8vHx0Zo1a1SzZk25ubmpdevWOn78uN115s2bp3vvvVclS5ZUUFCQ3n//fbv9FotF8+fPV2RkpDw9PRUTE6Pw8HBJUunSpWWxWNSrVy9J0hdffKGHHnpIPj4+Klu2rDp06KCkpCTbuY4ePSqLxaLVq1crPDxcHh4eqlevnrZt22Z3ze+++07NmzeXh4eHSpcurYiICP3555+SJMMwNG3aNAUGBsrd3V316tXThx9+WCDPHABwdyN0AwBwF5s9e7aaNm2q3bt3q3379nrmmWfUo0cPde/eXT/++KOqV6+uHj166K+fRrt48aImTZqkxYsX67vvvlNqaqqefvpp2/6PP/5YL774ol566SX9/PPP6tevn5599llt2LDB7tpjx45VZGSkfvrpJ7366qv66KOPJEkHDx5UcnKy5syZI0lKS0vTkCFD9MMPP2j9+vVycnLSE088oaysLLvzjRw5UkOHDtWePXtUs2ZNde3aVVevXpUk7dmzR61atVLt2rW1bds2bdmyRY899pgyMzMlSaNGjVJcXJzmzZun/fv3a/Dgwerevbs2btxY8A8dAHBX4TPdAADcRVq0aKH7779fr732mvz9/fXwww/bRqFPnjypihUravTo0Xr11VclSdu3b1dYWJiSk5Pl6+ur+Ph4Pfvss9q+fbsaN24sSfrPf/6j4OBgff/992rUqJGaNm2q2rVr6+2337Zdt3PnzkpLS9O6deskXRvpHjRokGbPnm1rc6uf6T59+rTKly+vn376SXXq1NHRo0cVEBCgd955R3369JEkHThwQLVr11ZiYqJq1aql6OhoHTt2TFu2bMl2vrS0NN1zzz369ttvFRYWZtseExOjixcv6oMPPsjn0wYAgJFuAADuavfdd5/t3xUqVJAk1a1bN9u2U6dO2ba5uLioYcOGtvVatWrJx8dHiYmJkqTExEQ1bdrU7jpNmza17b/ur+e4kaSkJEVHRyswMFBeXl4KCAiQJB07dizXe6lYsaJd3ddHunNy4MABXb58Wa1bt5bVarUt7733nt00dgAA8oMXqQEAcBcrUaKE7d8WiyXXbX+fyn19e27b/r7fMIxs2zw9PW+pxscee0x+fn5auHChKlWqpKysLNWpU0dXrly56b1cr9vd3T3X819vs27dOlWuXNlun6ur6y3VCABAbhjpBgAAeXL16lW7l6sdPHhQ586dU61atSRJwcHB2aZxb926VcHBwTc8b8mSJSXJ9jlrSTpz5owSExM1atQotWrVSsHBwbaXn+XFfffdp/Xr1+e4LyQkRK6urjp27JiqV69ut/j5+eX5WgAA/BUj3QAAIE9KlCihgQMH6vXXX1eJEiU0YMAAPfjgg2rUqJEk6eWXX1bnzp1Vv359tWrVSp988olWr16tb7755obnrVatmiwWiz799FO1a9dO7u7uKl26tMqWLau3335bFStW1LFjx/TKK6/kuebY2FjVrVtXzz//vPr376+SJUtqw4YNeuqpp3TPPfdo6NChGjx4sLKysvTQQw8pNTVVW7duldVqVc+ePfP1nAAAkBjpBgAAeeTh4aHhw4crOjpaYWFhcnd31/Lly237O3bsqDlz5mj69OmqXbu2FixYoLi4OLVo0eKG561cubLGjx+vV155RRUqVNCAAQPk5OSk5cuXa9euXapTp44GDx6s6dOn57nmmjVr6quvvtLevXvVqFEjhYWF6d///rdcXK6NP0yYMEFjxozRlClTFBwcrIiICH3yySe2z48DAJBfvL0cAADcsvj4eA0aNEjnzp1zdCkAABQJjHQDAAAAAGASQjcAAAAAACZhejkAAAAAACZhpBsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAk/w/u4HO8fkd5aoAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Feature importance for Random Forest\n",
"if 'Random Forest' in models:\n",
" rf_model = models['Random Forest']\n",
" feature_importance = pd.DataFrame({\n",
" 'feature': feature_columns,\n",
" 'importance': rf_model.feature_importances_\n",
" }).sort_values('importance', ascending=False)\n",
" \n",
" plt.figure(figsize=(10, 6))\n",
" sns.barplot(x='importance', y='feature', data=feature_importance)\n",
" plt.title('Feature Importance in Predicting Lap Times')\n",
" plt.tight_layout()\n",
" plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "csci349",
"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": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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