From 0595b810d522ae82ff877180e34e2acc5b25d57b Mon Sep 17 00:00:00 2001 From: Aditya Pulipaka Date: Sun, 19 Oct 2025 00:23:17 -0500 Subject: [PATCH] redesign of simulated data functionality --- ALONSO_2023_MONZA_RACE | 16585 ++++++++++++++++++++++++++++++++ fetch_data.ipynb | 649 ++ scripts/simulate_pi_stream.py | 140 +- 3 files changed, 17331 insertions(+), 43 deletions(-) create mode 100644 ALONSO_2023_MONZA_RACE create mode 100644 fetch_data.ipynb diff --git a/ALONSO_2023_MONZA_RACE b/ALONSO_2023_MONZA_RACE new file mode 100644 index 0000000..43a7223 --- /dev/null +++ b/ALONSO_2023_MONZA_RACE @@ -0,0 +1,16585 @@ +,lap_number,total_laps,speed,overall_time,throttle,brake,tire_compound,tire_life_laps,track_temperature,rainfall +0,1.0,51.0,0.0,0 days 01:22:21.734000,23.0,False,MEDIUM,1.0,42.5,False +1,1.0,51.0,0.0,0 days 01:22:21.894000,23.0,False,MEDIUM,1.0,42.5,False +2,1.0,51.0,4.0,0 days 01:22:22.214000,26.0,False,MEDIUM,1.0,42.5,False +3,1.0,51.0,14.0,0 days 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[v3.6.1]\n", + "req INFO \tUsing cached data for session_info\n", + "req INFO \tUsing cached data for driver_info\n", + "req INFO \tUsing cached data for session_info\n", + "req INFO \tUsing cached data for driver_info\n", + "req INFO \tUsing cached data for session_status_data\n", + "req INFO \tUsing cached data for lap_count\n", + "req INFO \tUsing cached data for track_status_data\n", + "req INFO \tUsing cached data for session_status_data\n", + "req INFO \tUsing cached data for lap_count\n", + "req INFO \tUsing cached data for track_status_data\n", + "req INFO \tUsing cached data for _extended_timing_data\n", + "req INFO \tUsing cached data for timing_app_data\n", + "core INFO \tProcessing timing data...\n", + "req INFO \tUsing cached data for _extended_timing_data\n", + "req INFO \tUsing cached data for timing_app_data\n", + "core INFO \tProcessing timing data...\n", + "req INFO \tUsing cached data for car_data\n", + "req INFO \tUsing cached data for car_data\n", + "req INFO \tUsing cached data for position_data\n", + "req INFO \tUsing cached data for position_data\n", + "req INFO \tUsing cached data for weather_data\n", + "req INFO \tUsing cached data for race_control_messages\n", + "core WARNING \tDriver 1 completed the race distance 06:25.888000 before the recorded end of the session.\n", + "core WARNING \tDriver 11 completed the race distance 06:19.824000 before the recorded end of the session.\n", + "core WARNING \tDriver 55 completed the race distance 06:14.695000 before the recorded end of the session.\n", + "core WARNING \tDriver 16 completed the race distance 06:14.511000 before the recorded end of the session.\n", + "core WARNING \tDriver 63 completed the race distance 06:07.860000 before the recorded end of the session.\n", + "core WARNING \tDriver 44 completed the race distance 05:48.209000 before the recorded end of the session.\n", + "req INFO \tUsing cached data for weather_data\n", + "req INFO \tUsing cached data for race_control_messages\n", + "core WARNING \tDriver 1 completed the race distance 06:25.888000 before the recorded end of the session.\n", + "core WARNING \tDriver 11 completed the race distance 06:19.824000 before the recorded end of the session.\n", + "core WARNING \tDriver 55 completed the race distance 06:14.695000 before the recorded end of the session.\n", + "core WARNING \tDriver 16 completed the race distance 06:14.511000 before the recorded end of the session.\n", + "core WARNING \tDriver 63 completed the race distance 06:07.860000 before the recorded end of the session.\n", + "core WARNING \tDriver 44 completed the race distance 05:48.209000 before the recorded end of the session.\n", + "core WARNING \tDriver 23 completed the race distance 05:40.782000 before the recorded end of the session.\n", + "core WARNING \tDriver 4 completed the race distance 05:40.439000 before the recorded end of the session.\n", + "core WARNING \tDriver 14 completed the race distance 05:39.594000 before the recorded end of the session.\n", + "core WARNING \tDriver 23 completed the race distance 05:40.782000 before the recorded end of the session.\n", + "core WARNING \tDriver 4 completed the race distance 05:40.439000 before the recorded end of the session.\n", + "core WARNING \tDriver 14 completed the race distance 05:39.594000 before the recorded end of the session.\n", + "core INFO \tFinished loading data for 20 drivers: ['1', '11', '55', '16', '63', '44', '23', '4', '14', '77', '40', '81', '2', '24', '10', '18', '27', '20', '31', '22']\n", + "core INFO \tFinished loading data for 20 drivers: ['1', '11', '55', '16', '63', '44', '23', '4', '14', '77', '40', '81', '2', '24', '10', '18', '27', '20', '31', '22']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created dataframe with 16584 rows\n", + "Total laps in race: 51.0\n", + "Laps covered: 1.0 to 51.0\n", + "Tire life range: 1.0 to 33.0 laps\n" + ] + }, + { + "data": { + "text/html": [ + "
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lap_numbertotal_lapsspeedoverall_timethrottlebraketire_compoundtire_life_lapstrack_temperaturerainfall
01.051.00.00 days 01:22:21.73400023.0FalseMEDIUM1.042.5False
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91.051.071.00 days 01:22:23.97400055.0FalseMEDIUM1.042.5False
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" + ], + "text/plain": [ + " lap_number total_laps speed overall_time throttle brake \\\n", + "0 1.0 51.0 0.0 0 days 01:22:21.734000 23.0 False \n", + "1 1.0 51.0 0.0 0 days 01:22:21.894000 23.0 False \n", + "2 1.0 51.0 4.0 0 days 01:22:22.214000 26.0 False \n", + "3 1.0 51.0 14.0 0 days 01:22:22.494000 24.0 False \n", + "4 1.0 51.0 24.0 0 days 01:22:22.774000 24.0 False \n", + "5 1.0 51.0 31.0 0 days 01:22:22.974000 26.0 False \n", + "6 1.0 51.0 38.0 0 days 01:22:23.254000 36.0 False \n", + "7 1.0 51.0 50.0 0 days 01:22:23.494000 41.0 False \n", + "8 1.0 51.0 58.0 0 days 01:22:23.694000 44.0 False \n", + "9 1.0 51.0 71.0 0 days 01:22:23.974000 55.0 False \n", + "\n", + " tire_compound tire_life_laps track_temperature rainfall \n", + "0 MEDIUM 1.0 42.5 False \n", + "1 MEDIUM 1.0 42.5 False \n", + "2 MEDIUM 1.0 42.5 False \n", + "3 MEDIUM 1.0 42.5 False \n", + "4 MEDIUM 1.0 42.5 False \n", + "5 MEDIUM 1.0 42.5 False \n", + "6 MEDIUM 1.0 42.5 False \n", + "7 MEDIUM 1.0 42.5 False \n", + "8 MEDIUM 1.0 42.5 False \n", + "9 MEDIUM 1.0 42.5 False " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "FastF1 Data Fetcher for HPC F1 AI Strategy System\n", + "\n", + "Downloads telemetry and race data from a specific F1 session to simulate\n", + "live telemetry streaming from a Raspberry Pi \"racecar\" to the HPC layer.\n", + "\n", + "Usage:\n", + " python fetch_race_data.py --year 2024 --race \"Monaco\" --driver VER --output data/\n", + "\"\"\"\n", + "import fastf1\n", + "import pandas as pd\n", + "\n", + "# 1. Load the session\n", + "session = fastf1.get_session(2023, 'Monza', 'R')\n", + "session.load(telemetry=True, laps=True, weather=True)\n", + "\n", + "# 2. Pick the driver\n", + "driver_laps = session.laps.pick_drivers('ALO')\n", + "\n", + "# Get total number of laps in the race (maximum lap number from all drivers)\n", + "total_laps = session.laps['LapNumber'].max()\n", + "\n", + "# 3. Collect all telemetry data with lap information\n", + "telemetry_list = []\n", + "\n", + "for lap_idx in driver_laps.index:\n", + " lap = driver_laps.loc[lap_idx]\n", + " lap_number = lap['LapNumber']\n", + " tire_compound = lap['Compound']\n", + " tire_life = lap['TyreLife'] # Number of laps on current tires\n", + " \n", + " # Get telemetry for this lap\n", + " car_data = lap.get_car_data()\n", + " \n", + " if car_data is not None and len(car_data) > 0:\n", + " # Add lap number, tire compound, and tire life to each telemetry point\n", + " car_data['LapNumber'] = lap_number\n", + " car_data['Compound'] = tire_compound\n", + " car_data['TyreLife'] = tire_life\n", + " telemetry_list.append(car_data)\n", + "\n", + "# 4. Combine all telemetry data\n", + "all_telemetry = pd.concat(telemetry_list, ignore_index=True)\n", + "\n", + "# 5. Get weather data\n", + "weather = session.weather_data\n", + "\n", + "# 6. Merge telemetry with weather based on timestamp\n", + "# First, ensure both have SessionTime column\n", + "all_telemetry['SessionTime'] = pd.to_timedelta(all_telemetry['SessionTime'])\n", + "weather['SessionTime'] = pd.to_timedelta(weather['Time'])\n", + "\n", + "# Merge using merge_asof for time-based joining\n", + "all_telemetry = all_telemetry.sort_values('SessionTime')\n", + "weather = weather.sort_values('SessionTime')\n", + "\n", + "merged_data = pd.merge_asof(\n", + " all_telemetry,\n", + " weather[['SessionTime', 'TrackTemp', 'Rainfall']],\n", + " on='SessionTime',\n", + " direction='nearest'\n", + ")\n", + "\n", + "# 7. Create final dataframe with requested columns\n", + "final_df = pd.DataFrame({\n", + " 'lap_number': merged_data['LapNumber'],\n", + " 'total_laps': total_laps, # Total laps in the race\n", + " 'speed': merged_data['Speed'],\n", + " 'overall_time': merged_data['SessionTime'],\n", + " 'throttle': merged_data['Throttle'],\n", + " 'brake': merged_data['Brake'],\n", + " 'tire_compound': merged_data['Compound'],\n", + " 'tire_life_laps': merged_data['TyreLife'], # Number of laps on current tires\n", + " 'track_temperature': merged_data['TrackTemp'],\n", + " 'rainfall': merged_data['Rainfall']\n", + "})\n", + "\n", + "print(f\"Created dataframe with {len(final_df)} rows\")\n", + "print(f\"Total laps in race: {total_laps}\")\n", + "print(f\"Laps covered: {final_df['lap_number'].min()} to {final_df['lap_number'].max()}\")\n", + "print(f\"Tire life range: {final_df['tire_life_laps'].min()} to {final_df['tire_life_laps'].max()} laps\")\n", + "final_df.head(10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "45d27f05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataframe Info:\n", + "Total telemetry points: 16584\n", + "\n", + "Column types:\n", + "lap_number float64\n", + "total_laps float64\n", + "speed float64\n", + "overall_time timedelta64[ns]\n", + "throttle float64\n", + "brake bool\n", + "tire_compound object\n", + "tire_life_laps float64\n", + "track_temperature float64\n", + "rainfall bool\n", + "dtype: object\n", + "\n", + "Basic statistics:\n" + ] + }, + { + "data": { + "text/html": [ + "
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lap_numbertotal_lapsspeedoverall_timethrottletire_life_lapstrack_temperature
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std14.7109770.076.9489060 days 00:21:30.06594087540.5612378.5580180.897756
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50%26.00000051.0245.0000000 days 01:59:31.222000100.00000015.00000043.100000
75%39.00000051.0309.0000000 days 02:18:13.365000100.00000021.00000043.600000
max51.00000051.0351.0000000 days 02:36:49.228000100.00000033.00000044.400000
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" + ], + "text/plain": [ + " lap_number total_laps speed overall_time \\\n", + "count 16584.000000 16584.0 16584.000000 16584 \n", + "mean 25.891341 51.0 235.570188 0 days 01:59:34.577446394 \n", + "std 14.710977 0.0 76.948906 0 days 00:21:30.065940875 \n", + "min 1.000000 51.0 0.000000 0 days 01:22:21.734000 \n", + "25% 13.000000 51.0 180.000000 0 days 01:40:53.558000 \n", + "50% 26.000000 51.0 245.000000 0 days 01:59:31.222000 \n", + "75% 39.000000 51.0 309.000000 0 days 02:18:13.365000 \n", + "max 51.000000 51.0 351.000000 0 days 02:36:49.228000 \n", + "\n", + " throttle tire_life_laps track_temperature \n", + "count 16584.000000 16584.000000 16584.000000 \n", + "mean 72.291546 15.339243 42.908816 \n", + "std 40.561237 8.558018 0.897756 \n", + "min 0.000000 1.000000 40.800000 \n", + "25% 40.000000 8.000000 42.500000 \n", + "50% 100.000000 15.000000 43.100000 \n", + "75% 100.000000 21.000000 43.600000 \n", + "max 100.000000 33.000000 44.400000 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display dataframe info and sample statistics\n", + "print(\"Dataframe Info:\")\n", + "print(f\"Total telemetry points: {len(final_df)}\")\n", + "print(f\"\\nColumn types:\")\n", + "print(final_df.dtypes)\n", + "print(f\"\\nBasic statistics:\")\n", + "final_df.describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "2fbcd2f9", + "metadata": {}, + "outputs": [], + "source": [ + "final_df.to_csv(\"ALONSO_2023_MONZA_RACE\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "729fb12e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tire compound usage throughout the race:\n", + " lap_number tire_compound tire_life_laps\n", + "0 1.0 MEDIUM 1.0\n", + "1 2.0 MEDIUM 2.0\n", + "2 3.0 MEDIUM 3.0\n", + "3 4.0 MEDIUM 4.0\n", + "4 5.0 MEDIUM 5.0\n", + "5 6.0 MEDIUM 6.0\n", + "6 7.0 MEDIUM 7.0\n", + "7 8.0 MEDIUM 8.0\n", + "8 9.0 MEDIUM 9.0\n", + "9 10.0 MEDIUM 10.0\n", + "10 11.0 MEDIUM 11.0\n", + "11 12.0 MEDIUM 12.0\n", + "12 13.0 MEDIUM 13.0\n", + "13 14.0 MEDIUM 14.0\n", + "14 15.0 MEDIUM 15.0\n", + "15 16.0 MEDIUM 16.0\n", + "16 17.0 MEDIUM 17.0\n", + "17 18.0 MEDIUM 18.0\n", + "18 19.0 MEDIUM 19.0\n", + "19 20.0 MEDIUM 20.0\n", + "20 21.0 MEDIUM 21.0\n", + "21 22.0 HARD 4.0\n", + "22 23.0 HARD 5.0\n", + "23 24.0 HARD 6.0\n", + "24 25.0 HARD 7.0\n", + "25 26.0 HARD 8.0\n", + "26 27.0 HARD 9.0\n", + "27 28.0 HARD 10.0\n", + "28 29.0 HARD 11.0\n", + "29 30.0 HARD 12.0\n", + "30 31.0 HARD 13.0\n", + "31 32.0 HARD 14.0\n", + "32 33.0 HARD 15.0\n", + "33 34.0 HARD 16.0\n", + "34 35.0 HARD 17.0\n", + "35 36.0 HARD 18.0\n", + "36 37.0 HARD 19.0\n", + "37 38.0 HARD 20.0\n", + "38 39.0 HARD 21.0\n", + "39 40.0 HARD 22.0\n", + "40 41.0 HARD 23.0\n", + "41 42.0 HARD 24.0\n", + "42 43.0 HARD 25.0\n", + "43 44.0 HARD 26.0\n", + "44 45.0 HARD 27.0\n", + "45 46.0 HARD 28.0\n", + "46 47.0 HARD 29.0\n", + "47 48.0 HARD 30.0\n", + "48 49.0 HARD 31.0\n", + "49 50.0 HARD 32.0\n", + "50 51.0 HARD 33.0\n" + ] + } + ], + "source": [ + "# Show tire compound changes and stint information\n", + "print(\"Tire compound usage throughout the race:\")\n", + "tire_changes = final_df.groupby(['lap_number', 'tire_compound', 'tire_life_laps']).size().reset_index(name='count')\n", + "tire_changes = tire_changes.groupby(['lap_number', 'tire_compound', 'tire_life_laps']).first().reset_index()[['lap_number', 'tire_compound', 'tire_life_laps']]\n", + "print(tire_changes.drop_duplicates())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9ebc90c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/simulate_pi_stream.py b/scripts/simulate_pi_stream.py index 2499c3a..4370d4b 100644 --- a/scripts/simulate_pi_stream.py +++ b/scripts/simulate_pi_stream.py @@ -1,96 +1,137 @@ """ Raspberry Pi Telemetry Stream Simulator -Replays downloaded FastF1 data as if it's coming from a live Raspberry Pi sensor. -Sends data to the HPC simulation layer via HTTP POST. +Reads the ALONSO_2023_MONZA_RACE CSV file row by row and simulates +live telemetry streaming from a Raspberry Pi sensor. +Sends data to the HPC simulation layer via HTTP POST at intervals +determined by the time differences between consecutive rows. Usage: - python simulate_pi_stream.py --data data/race_data/VER_telemetry.json --speed 1.0 + python simulate_pi_stream.py --data ALONSO_2023_MONZA_RACE --speed 1.0 """ import argparse -import json import time import sys from pathlib import Path -from typing import Dict, List, Any - +from typing import Dict, Any +import pandas as pd import requests -def load_telemetry(filepath: Path) -> List[Dict[str, Any]]: - """Load telemetry data from JSON file.""" - with open(filepath, 'r') as f: - data = json.load(f) - print(f"āœ“ Loaded {len(data)} telemetry points from {filepath}") +def load_telemetry_csv(filepath: Path) -> pd.DataFrame: + """Load telemetry data from CSV file.""" + df = pd.read_csv(filepath, index_col=0) + + # Convert overall_time to timedelta if it's not already + if df['overall_time'].dtype == 'object': + df['overall_time'] = pd.to_timedelta(df['overall_time']) + + print(f"āœ“ Loaded {len(df)} telemetry points from {filepath}") + print(f" Laps: {df['lap_number'].min():.0f} ā†’ {df['lap_number'].max():.0f}") + print(f" Duration: {df['overall_time'].iloc[-1]}") + + return df + + +def row_to_json(row: pd.Series) -> Dict[str, Any]: + """Convert a DataFrame row to a JSON-compatible dictionary.""" + data = { + 'lap_number': int(row['lap_number']) if pd.notna(row['lap_number']) else None, + 'total_laps': int(row['total_laps']) if pd.notna(row['total_laps']) else None, + 'speed': float(row['speed']) if pd.notna(row['speed']) else 0.0, + 'throttle': float(row['throttle']) if pd.notna(row['throttle']) else 0.0, + 'brake': bool(row['brake']), + 'tire_compound': str(row['tire_compound']) if pd.notna(row['tire_compound']) else 'UNKNOWN', + 'tire_life_laps': float(row['tire_life_laps']) if pd.notna(row['tire_life_laps']) else 0.0, + 'track_temperature': float(row['track_temperature']) if pd.notna(row['track_temperature']) else 0.0, + 'rainfall': bool(row['rainfall']) + } return data def simulate_stream( - telemetry: List[Dict[str, Any]], + df: pd.DataFrame, endpoint: str, speed: float = 1.0, start_lap: int = 1, end_lap: int = None ): """ - Simulate live telemetry streaming. + Simulate live telemetry streaming based on actual time intervals in the data. Args: - telemetry: List of telemetry points + df: DataFrame with telemetry data endpoint: HPC API endpoint URL speed: Playback speed multiplier (1.0 = real-time, 2.0 = 2x speed) start_lap: Starting lap number end_lap: Ending lap number (None = all laps) """ # Filter by lap range - filtered = [p for p in telemetry if p['lap'] >= start_lap] + filtered_df = df[df['lap_number'] >= start_lap].copy() if end_lap: - filtered = [p for p in filtered if p['lap'] <= end_lap] + filtered_df = filtered_df[filtered_df['lap_number'] <= end_lap].copy() - if not filtered: + if len(filtered_df) == 0: print("āŒ No telemetry points in specified lap range") return + # Reset index for easier iteration + filtered_df = filtered_df.reset_index(drop=True) + print(f"\nšŸ Starting telemetry stream simulation") print(f" Endpoint: {endpoint}") print(f" Laps: {start_lap} ā†’ {end_lap or 'end'}") print(f" Speed: {speed}x") - print(f" Points: {len(filtered)}") - print(f" Duration: {filtered[-1]['timestamp_ms'] / 1000.0:.1f}s\n") + print(f" Points: {len(filtered_df)}") - start_time = time.time() - start_ts = filtered[0]['timestamp_ms'] + total_duration = (filtered_df['overall_time'].iloc[-1] - filtered_df['overall_time'].iloc[0]).total_seconds() + print(f" Duration: {total_duration:.1f}s (real-time) ā†’ {total_duration / speed:.1f}s (playback)\n") sent_count = 0 error_count = 0 + current_lap = start_lap try: - for i, point in enumerate(filtered): - # Calculate when this point should be sent - point_offset = (point['timestamp_ms'] - start_ts) / 1000.0 / speed - target_time = start_time + point_offset + for i in range(len(filtered_df)): + row = filtered_df.iloc[i] - # Wait until the right time - sleep_time = target_time - time.time() - if sleep_time > 0: - time.sleep(sleep_time) + # Calculate sleep time based on time difference to next row + if i < len(filtered_df) - 1: + next_row = filtered_df.iloc[i + 1] + time_diff = (next_row['overall_time'] - row['overall_time']).total_seconds() + sleep_time = time_diff / speed + + # Ensure positive sleep time + if sleep_time < 0: + sleep_time = 0 + else: + sleep_time = 0 + + # Convert row to JSON + telemetry_point = row_to_json(row) # Send telemetry point try: response = requests.post( endpoint, - json=point, + json=telemetry_point, timeout=2.0 ) if response.status_code == 200: sent_count += 1 - if sent_count % 100 == 0: - elapsed = time.time() - start_time - progress = (i + 1) / len(filtered) * 100 - print(f" šŸ“” Lap {point['lap']}: {sent_count} points sent " - f"({progress:.1f}% complete, {elapsed:.1f}s elapsed)") + + # Print progress updates + if row['lap_number'] > current_lap: + current_lap = row['lap_number'] + progress = (i + 1) / len(filtered_df) * 100 + print(f" šŸ“” Lap {int(current_lap)}: {sent_count} points sent " + f"({progress:.1f}% complete)") + elif sent_count % 500 == 0: + progress = (i + 1) / len(filtered_df) * 100 + print(f" šŸ“” Lap {int(row['lap_number'])}: {sent_count} points sent " + f"({progress:.1f}% complete)") else: error_count += 1 print(f" āš  HTTP {response.status_code}: {response.text[:50]}") @@ -98,35 +139,47 @@ def simulate_stream( except requests.RequestException as e: error_count += 1 if error_count % 10 == 0: - print(f" āš  Connection error ({error_count} total): {e}") + print(f" āš  Connection error ({error_count} total): {str(e)[:50]}") + + # Sleep until next point should be sent + if sleep_time > 0: + time.sleep(sleep_time) print(f"\nāœ… Stream complete!") print(f" Sent: {sent_count} points") print(f" Errors: {error_count}") - print(f" Duration: {time.time() - start_time:.1f}s") except KeyboardInterrupt: print(f"\nāø Stream interrupted by user") - print(f" Sent: {sent_count}/{len(filtered)} points") + print(f" Sent: {sent_count}/{len(filtered_df)} points") def main(): parser = argparse.ArgumentParser( - description="Simulate Raspberry Pi telemetry streaming" + description="Simulate Raspberry Pi telemetry streaming from CSV data" ) - parser.add_argument("--data", type=str, required=True, help="Path to telemetry JSON file") + parser.add_argument("--data", type=str, default="ALONSO_2023_MONZA_RACE", + help="Path to telemetry CSV file") parser.add_argument("--endpoint", type=str, default="http://localhost:8000/telemetry", help="HPC API endpoint") - parser.add_argument("--speed", type=float, default=1.0, help="Playback speed (1.0 = real-time)") + parser.add_argument("--speed", type=float, default=1.0, + help="Playback speed (1.0 = real-time, 10.0 = 10x speed)") parser.add_argument("--start-lap", type=int, default=1, help="Starting lap number") parser.add_argument("--end-lap", type=int, default=None, help="Ending lap number") args = parser.parse_args() try: - telemetry = load_telemetry(Path(args.data)) + # Handle relative paths from the project root + data_path = Path(args.data) + if not data_path.is_absolute(): + # Try relative to script location first + script_dir = Path(__file__).parent.parent + data_path = script_dir / args.data + + df = load_telemetry_csv(data_path) simulate_stream( - telemetry, + df, args.endpoint, args.speed, args.start_lap, @@ -134,6 +187,7 @@ def main(): ) except FileNotFoundError: print(f"āŒ File not found: {args.data}") + print(f" Tried: {data_path}") sys.exit(1) except Exception as e: print(f"āŒ Error: {e}")