pipeline works from pi simulation to control output and strategy generation.
This commit is contained in:
Aditya Pulipaka
@@ -1,13 +1,14 @@
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"""
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Raspberry Pi Telemetry Stream Simulator
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Raspberry Pi Telemetry Stream Simulator - Lap-Level Data
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Reads the ALONSO_2023_MONZA_RACE CSV file row by row and simulates
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Reads the ALONSO_2023_MONZA_LAPS.csv file lap by lap and simulates
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live telemetry streaming from a Raspberry Pi sensor.
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Sends data to the HPC simulation layer via HTTP POST at intervals
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determined by the time differences between consecutive rows.
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Sends data to the HPC simulation layer via HTTP POST at fixed
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1-minute intervals between laps.
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Usage:
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python simulate_pi_stream.py --data ALONSO_2023_MONZA_RACE --speed 1.0
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python simulate_pi_stream.py
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python simulate_pi_stream.py --interval 30 # 30 seconds between laps
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"""
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import argparse
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@@ -19,39 +20,32 @@ import pandas as pd
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import requests
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def load_telemetry_csv(filepath: Path) -> pd.DataFrame:
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"""Load telemetry data from CSV file."""
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df = pd.read_csv(filepath, index_col=0)
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def load_lap_csv(filepath: Path) -> pd.DataFrame:
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"""Load lap-level telemetry data from CSV file."""
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df = pd.read_csv(filepath)
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# Convert overall_time to timedelta if it's not already
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if df['overall_time'].dtype == 'object':
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df['overall_time'] = pd.to_timedelta(df['overall_time'])
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# Convert lap_time to timedelta if it's not already
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if 'lap_time' in df.columns and df['lap_time'].dtype == 'object':
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df['lap_time'] = pd.to_timedelta(df['lap_time'])
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print(f"✓ Loaded {len(df)} telemetry points from {filepath}")
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print(f"✓ Loaded {len(df)} laps from {filepath}")
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print(f" Laps: {df['lap_number'].min():.0f} → {df['lap_number'].max():.0f}")
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print(f" Duration: {df['overall_time'].iloc[-1]}")
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return df
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def row_to_json(row: pd.Series) -> Dict[str, Any]:
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"""Convert a DataFrame row to a JSON-compatible dictionary."""
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def lap_to_json(row: pd.Series) -> Dict[str, Any]:
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"""Convert a lap DataFrame row to a JSON-compatible dictionary."""
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data = {
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'lap_number': int(row['lap_number']) if pd.notna(row['lap_number']) else None,
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'total_laps': int(row['total_laps']) if pd.notna(row['total_laps']) else None,
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'speed': float(row['speed']) if pd.notna(row['speed']) else 0.0,
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'throttle': float(row['throttle']) if pd.notna(row['throttle']) else 0.0,
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'brake': bool(row['brake']),
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'lap_time': str(row['lap_time']) if pd.notna(row['lap_time']) else None,
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'average_speed': float(row['average_speed']) if pd.notna(row['average_speed']) else 0.0,
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'max_speed': float(row['max_speed']) if pd.notna(row['max_speed']) else 0.0,
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'tire_compound': str(row['tire_compound']) if pd.notna(row['tire_compound']) else 'UNKNOWN',
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'tire_life_laps': float(row['tire_life_laps']) if pd.notna(row['tire_life_laps']) else 0.0,
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'tire_life_laps': int(row['tire_life_laps']) if pd.notna(row['tire_life_laps']) else 0,
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'track_temperature': float(row['track_temperature']) if pd.notna(row['track_temperature']) else 0.0,
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'rainfall': bool(row['rainfall']),
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# Additional race context fields
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'track_name': 'Monza', # From ALONSO_2023_MONZA_RACE
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'driver_name': 'Alonso',
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'current_position': 5, # Mock position, could be varied
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'fuel_level': max(0.1, 1.0 - (float(row['lap_number']) / float(row['total_laps']) * 0.8)) if pd.notna(row['lap_number']) and pd.notna(row['total_laps']) else 0.5,
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'rainfall': bool(row['rainfall'])
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}
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return data
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@@ -59,17 +53,17 @@ def row_to_json(row: pd.Series) -> Dict[str, Any]:
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def simulate_stream(
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df: pd.DataFrame,
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endpoint: str,
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speed: float = 1.0,
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interval: int = 60,
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start_lap: int = 1,
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end_lap: int = None
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):
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"""
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Simulate live telemetry streaming based on actual time intervals in the data.
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Simulate live telemetry streaming with fixed interval between laps.
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Args:
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df: DataFrame with telemetry data
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df: DataFrame with lap-level telemetry data
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endpoint: HPC API endpoint URL
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speed: Playback speed multiplier (1.0 = real-time, 2.0 = 2x speed)
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interval: Fixed interval in seconds between laps (default: 60 seconds)
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start_lap: Starting lap number
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end_lap: Ending lap number (None = all laps)
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"""
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@@ -79,95 +73,84 @@ def simulate_stream(
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filtered_df = filtered_df[filtered_df['lap_number'] <= end_lap].copy()
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if len(filtered_df) == 0:
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print("❌ No telemetry points in specified lap range")
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print("❌ No laps in specified lap range")
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return
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# Reset index for easier iteration
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filtered_df = filtered_df.reset_index(drop=True)
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print(f"\n🏁 Starting telemetry stream simulation")
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print(f"\n🏁 Starting lap-level telemetry stream simulation")
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print(f" Endpoint: {endpoint}")
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print(f" Laps: {start_lap} → {end_lap or 'end'}")
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print(f" Speed: {speed}x")
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print(f" Points: {len(filtered_df)}")
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total_duration = (filtered_df['overall_time'].iloc[-1] - filtered_df['overall_time'].iloc[0]).total_seconds()
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print(f" Duration: {total_duration:.1f}s (real-time) → {total_duration / speed:.1f}s (playback)\n")
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print(f" Interval: {interval} seconds between laps")
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print(f" Total laps: {len(filtered_df)}")
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print(f" Est. duration: {len(filtered_df) * interval / 60:.1f} minutes\n")
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sent_count = 0
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error_count = 0
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current_lap = start_lap
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try:
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for i in range(len(filtered_df)):
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row = filtered_df.iloc[i]
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lap_num = int(row['lap_number'])
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# Calculate sleep time based on time difference to next row
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if i < len(filtered_df) - 1:
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next_row = filtered_df.iloc[i + 1]
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time_diff = (next_row['overall_time'] - row['overall_time']).total_seconds()
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sleep_time = time_diff / speed
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# Ensure positive sleep time
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if sleep_time < 0:
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sleep_time = 0
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else:
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sleep_time = 0
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# Convert lap to JSON
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lap_data = lap_to_json(row)
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# Convert row to JSON
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telemetry_point = row_to_json(row)
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# Send telemetry point
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# Send lap data
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try:
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response = requests.post(
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endpoint,
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json=telemetry_point,
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timeout=2.0
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json=lap_data,
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timeout=5.0
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)
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if response.status_code == 200:
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sent_count += 1
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progress = (i + 1) / len(filtered_df) * 100
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# Print progress updates
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if row['lap_number'] > current_lap:
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current_lap = row['lap_number']
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progress = (i + 1) / len(filtered_df) * 100
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print(f" 📡 Lap {int(current_lap)}: {sent_count} points sent "
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f"({progress:.1f}% complete)")
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elif sent_count % 500 == 0:
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progress = (i + 1) / len(filtered_df) * 100
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print(f" 📡 Lap {int(row['lap_number'])}: {sent_count} points sent "
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f"({progress:.1f}% complete)")
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# Print lap info
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print(f" 📡 Lap {lap_num}/{int(row['total_laps'])}: "
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f"Avg Speed: {row['average_speed']:.1f} km/h, "
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f"Tire: {row['tire_compound']} (age: {int(row['tire_life_laps'])} laps) "
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f"[{progress:.0f}%]")
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# Show response if it contains strategies
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try:
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response_data = response.json()
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if 'strategies_generated' in response_data:
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print(f" ✓ Generated {response_data['strategies_generated']} strategies")
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except:
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pass
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else:
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error_count += 1
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print(f" ⚠ HTTP {response.status_code}: {response.text[:50]}")
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print(f" ⚠ Lap {lap_num}: HTTP {response.status_code}: {response.text[:100]}")
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except requests.RequestException as e:
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error_count += 1
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if error_count % 10 == 0:
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print(f" ⚠ Connection error ({error_count} total): {str(e)[:50]}")
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print(f" ⚠ Lap {lap_num}: Connection error: {str(e)[:100]}")
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# Sleep until next point should be sent
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if sleep_time > 0:
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time.sleep(sleep_time)
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# Sleep for fixed interval before next lap (except for last lap)
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if i < len(filtered_df) - 1:
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time.sleep(interval)
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print(f"\n✅ Stream complete!")
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print(f" Sent: {sent_count} points")
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print(f" Sent: {sent_count} laps")
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print(f" Errors: {error_count}")
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except KeyboardInterrupt:
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print(f"\n⏸ Stream interrupted by user")
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print(f" Sent: {sent_count}/{len(filtered_df)} points")
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print(f" Sent: {sent_count}/{len(filtered_df)} laps")
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def main():
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parser = argparse.ArgumentParser(
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description="Simulate Raspberry Pi telemetry streaming from CSV data"
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description="Simulate Raspberry Pi lap-level telemetry streaming"
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)
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parser.add_argument("--endpoint", type=str, default="http://localhost:8000/telemetry",
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help="HPC API endpoint")
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parser.add_argument("--speed", type=float, default=1.0,
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help="Playback speed (1.0 = real-time, 10.0 = 10x speed)")
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parser.add_argument("--endpoint", type=str, default="http://localhost:8000/ingest/telemetry",
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help="HPC API endpoint (default: http://localhost:8000/ingest/telemetry)")
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parser.add_argument("--interval", type=int, default=60,
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help="Fixed interval in seconds between laps (default: 60)")
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parser.add_argument("--start-lap", type=int, default=1, help="Starting lap number")
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parser.add_argument("--end-lap", type=int, default=None, help="Ending lap number")
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@@ -176,19 +159,19 @@ def main():
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try:
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# Hardcoded CSV file location in the same folder as this script
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script_dir = Path(__file__).parent
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data_path = script_dir / "ALONSO_2023_MONZA_RACE.csv"
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data_path = script_dir / "ALONSO_2023_MONZA_LAPS.csv"
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df = load_telemetry_csv(data_path)
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df = load_lap_csv(data_path)
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simulate_stream(
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df,
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args.endpoint,
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args.speed,
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args.interval,
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args.start_lap,
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args.end_lap
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)
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except FileNotFoundError:
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print(f"❌ File not found: {data_path}")
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print(f" Make sure ALONSO_2023_MONZA_RACE.csv is in the scripts/ folder")
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print(f" Make sure ALONSO_2023_MONZA_LAPS.csv is in the scripts/ folder")
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sys.exit(1)
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except Exception as e:
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print(f"❌ Error: {e}")
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