redesign of simulated data functionality
This commit is contained in:
Aditya Pulipaka
@@ -1,96 +1,137 @@
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"""
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Raspberry Pi Telemetry Stream Simulator
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Replays downloaded FastF1 data as if it's coming from a live Raspberry Pi sensor.
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Sends data to the HPC simulation layer via HTTP POST.
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Reads the ALONSO_2023_MONZA_RACE CSV file row by row 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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Usage:
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python simulate_pi_stream.py --data data/race_data/VER_telemetry.json --speed 1.0
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python simulate_pi_stream.py --data ALONSO_2023_MONZA_RACE --speed 1.0
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"""
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import argparse
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import json
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import time
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import sys
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from pathlib import Path
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from typing import Dict, List, Any
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from typing import Dict, Any
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import pandas as pd
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import requests
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def load_telemetry(filepath: Path) -> List[Dict[str, Any]]:
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"""Load telemetry data from JSON file."""
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with open(filepath, 'r') as f:
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data = json.load(f)
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print(f"✓ Loaded {len(data)} telemetry points from {filepath}")
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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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# 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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print(f"✓ Loaded {len(df)} telemetry points 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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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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'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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'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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}
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return data
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def simulate_stream(
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telemetry: List[Dict[str, Any]],
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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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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.
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Simulate live telemetry streaming based on actual time intervals in the data.
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Args:
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telemetry: List of telemetry points
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df: DataFrame with 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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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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# Filter by lap range
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filtered = [p for p in telemetry if p['lap'] >= start_lap]
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filtered_df = df[df['lap_number'] >= start_lap].copy()
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if end_lap:
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filtered = [p for p in filtered if p['lap'] <= end_lap]
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filtered_df = filtered_df[filtered_df['lap_number'] <= end_lap].copy()
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if not filtered:
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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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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" 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)}")
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print(f" Duration: {filtered[-1]['timestamp_ms'] / 1000.0:.1f}s\n")
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print(f" Points: {len(filtered_df)}")
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start_time = time.time()
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start_ts = filtered[0]['timestamp_ms']
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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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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, point in enumerate(filtered):
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# Calculate when this point should be sent
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point_offset = (point['timestamp_ms'] - start_ts) / 1000.0 / speed
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target_time = start_time + point_offset
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for i in range(len(filtered_df)):
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row = filtered_df.iloc[i]
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# Wait until the right time
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sleep_time = target_time - time.time()
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if sleep_time > 0:
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time.sleep(sleep_time)
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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 row to JSON
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telemetry_point = row_to_json(row)
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# Send telemetry point
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try:
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response = requests.post(
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endpoint,
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json=point,
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json=telemetry_point,
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timeout=2.0
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)
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if response.status_code == 200:
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sent_count += 1
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if sent_count % 100 == 0:
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elapsed = time.time() - start_time
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progress = (i + 1) / len(filtered) * 100
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print(f" 📡 Lap {point['lap']}: {sent_count} points sent "
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f"({progress:.1f}% complete, {elapsed:.1f}s elapsed)")
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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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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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@@ -98,35 +139,47 @@ def simulate_stream(
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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): {e}")
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print(f" ⚠ Connection error ({error_count} total): {str(e)[:50]}")
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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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print(f"\n✅ Stream complete!")
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print(f" Sent: {sent_count} points")
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print(f" Errors: {error_count}")
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print(f" Duration: {time.time() - start_time:.1f}s")
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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)} points")
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print(f" Sent: {sent_count}/{len(filtered_df)} points")
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def main():
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parser = argparse.ArgumentParser(
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description="Simulate Raspberry Pi telemetry streaming"
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description="Simulate Raspberry Pi telemetry streaming from CSV data"
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)
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parser.add_argument("--data", type=str, required=True, help="Path to telemetry JSON file")
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parser.add_argument("--data", type=str, default="ALONSO_2023_MONZA_RACE",
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help="Path to telemetry CSV file")
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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, help="Playback speed (1.0 = real-time)")
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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("--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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args = parser.parse_args()
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try:
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telemetry = load_telemetry(Path(args.data))
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# Handle relative paths from the project root
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data_path = Path(args.data)
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if not data_path.is_absolute():
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# Try relative to script location first
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script_dir = Path(__file__).parent.parent
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data_path = script_dir / args.data
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df = load_telemetry_csv(data_path)
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simulate_stream(
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telemetry,
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df,
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args.endpoint,
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args.speed,
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args.start_lap,
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@@ -134,6 +187,7 @@ def main():
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)
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except FileNotFoundError:
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print(f"❌ File not found: {args.data}")
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print(f" Tried: {data_path}")
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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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