#!/usr/bin/env python3 """ Altitude Analysis from Pressure Data =================================== Calculates altitude from pressure readings, averages by minute, and plots overlay for matching time periods only. """ import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdates from datetime import datetime import numpy as np import os def calculate_altitude_from_pressure(pressure_hpa, sea_level_pressure=1013.25): """ Calculate altitude from barometric pressure using standard atmosphere formula Altitude (m) = 44330 * (1 - (P/P0)^(1/5.255)) """ return 44330 * (1 - (pressure_hpa / sea_level_pressure) ** (1/5.255)) def load_and_process_data(): """Load and process both CSV files""" # File paths file1 = "temps_humid_pressure.csv" # pool2 data (current directory) file2 = "../pool/temps_humid_pressure.csv" # pool data # Check if files exist for file in [file1, file2]: if not os.path.exists(file): print(f"Error: File not found: {file}") return None # Read the CSV files print(f"Loading {file1}...") df1 = pd.read_csv(file1, skipinitialspace=True) print(f"Loading {file2}...") df2 = pd.read_csv(file2, skipinitialspace=True) # Clean the data - remove any rows where DATE or TIME might be invalid df1 = df1[df1['DATE'].str.contains(r'^\d{4}-\d{2}-\d{2}$', na=False)] df2 = df2[df2['DATE'].str.contains(r'^\d{4}-\d{2}-\d{2}$', na=False)] # Convert numeric columns to float numeric_cols = ['ATH_T', 'ATH_H', 'BMP_T', 'BMP_P', 'BMP_SLP', 'ALT', 'AD'] for col in numeric_cols: if col in df1.columns: df1[col] = pd.to_numeric(df1[col], errors='coerce') if col in df2.columns: df2[col] = pd.to_numeric(df2[col], errors='coerce') # Calculate altitude from pressure for both datasets df1['Calc_Altitude'] = calculate_altitude_from_pressure(df1['BMP_P']) df2['Calc_Altitude'] = calculate_altitude_from_pressure(df2['BMP_P']) # Add source labels df1['Source'] = 'Pool2' df2['Source'] = 'Pool' # Combine DataFrames df = pd.concat([df1, df2], ignore_index=True) print(f"Combined data: {len(df)} total records") print(f"Pool data: {len(df2)} records") print(f"Pool2 data: {len(df1)} records") return df def round_to_minute_and_average(df): """Round timestamps to nearest minute and average readings""" # Create datetime column with error handling try: df['DateTime'] = pd.to_datetime(df['DATE'] + ' ' + df['TIME'], errors='coerce') except: df['DateTime'] = pd.to_datetime(df['DATE'] + ' ' + df['TIME'], format='mixed', errors='coerce') # Remove rows where datetime parsing failed df = df.dropna(subset=['DateTime']) # Round to nearest minute df['MinuteRounded'] = df['DateTime'].dt.round('min') # Group by source and rounded minute, then average numeric_cols = ['ATH_T', 'ATH_H', 'BMP_T', 'BMP_P', 'BMP_SLP', 'ALT', 'AD', 'Calc_Altitude'] averaged_df = df.groupby(['Source', 'MinuteRounded']).agg({ **{col: 'mean' for col in numeric_cols}, 'DATE': 'first', 'TIME': 'first' }).reset_index() print(f"After averaging by minute: {len(averaged_df)} records") return averaged_df def create_altitude_overlay_plot(df): """Create overlay plots for altitude data - only for matching minutes""" # Find common minutes between both sources pool_minutes = set(df[df['Source'] == 'Pool']['MinuteRounded']) pool2_minutes = set(df[df['Source'] == 'Pool2']['MinuteRounded']) common_minutes = pool_minutes.intersection(pool2_minutes) print(f"\\nAltitude Analysis:") print(f"Common minutes found: {len(common_minutes)}") print(f"Pool minutes: {len(pool_minutes)}, Pool2 minutes: {len(pool2_minutes)}") if len(common_minutes) == 0: print("❌ No overlapping time periods found between datasets!") return None # Filter data to only common minutes df_common = df[df['MinuteRounded'].isin(common_minutes)].copy() # Pivot data for easier plotting calc_altitude = df_common.pivot(index='MinuteRounded', columns='Source', values='Calc_Altitude') sensor_altitude = df_common.pivot(index='MinuteRounded', columns='Source', values='ALT') pressure = df_common.pivot(index='MinuteRounded', columns='Source', values='BMP_P') # Create figure with 4 subplots - 1200px wide (12 inches at 100 DPI) fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(12, 20)) # Color schemes pool_colors = {'calc': '#1f77b4', 'sensor': '#ff7f0e', 'pressure': '#2ca02c'} pool2_colors = {'calc': '#d62728', 'sensor': '#9467bd', 'pressure': '#8c564b'} # === CALCULATED ALTITUDE PLOT === if 'Pool' in calc_altitude.columns and 'Pool2' in calc_altitude.columns: calc_altitude_clean = calc_altitude.dropna() if not calc_altitude_clean.empty: ax1.plot(calc_altitude_clean.index, calc_altitude_clean['Pool'], 'o-', label='Pool (Calculated)', color=pool_colors['calc'], alpha=0.8, markersize=4, linewidth=2) ax1.plot(calc_altitude_clean.index, calc_altitude_clean['Pool2'], 's-', label='Pool2 (Calculated)', color=pool2_colors['calc'], alpha=0.8, markersize=4, linewidth=2) ax1.set_title(f'Calculated Altitude from Pressure: Synchronized Data ({len(common_minutes)} matching minutes)', fontsize=14, fontweight='bold') ax1.set_ylabel('Altitude (meters)', fontsize=12) ax1.grid(True, alpha=0.3) ax1.legend(loc='upper right') ax1.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d %H:%M')) ax1.xaxis.set_major_locator(mdates.HourLocator(interval=6)) plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45) # === SENSOR ALTITUDE PLOT === if 'Pool' in sensor_altitude.columns and 'Pool2' in sensor_altitude.columns: sensor_altitude_clean = sensor_altitude.dropna() if not sensor_altitude_clean.empty: ax2.plot(sensor_altitude_clean.index, sensor_altitude_clean['Pool'], 'o--', label='Pool (Sensor ALT)', color=pool_colors['sensor'], alpha=0.8, markersize=3, linewidth=1) ax2.plot(sensor_altitude_clean.index, sensor_altitude_clean['Pool2'], 's--', label='Pool2 (Sensor ALT)', color=pool2_colors['sensor'], alpha=0.8, markersize=3, linewidth=1) ax2.set_title(f'Sensor-Reported Altitude: Synchronized Data', fontsize=14, fontweight='bold') ax2.set_ylabel('Altitude (meters)', fontsize=12) ax2.grid(True, alpha=0.3) ax2.legend(loc='upper right') ax2.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d %H:%M')) ax2.xaxis.set_major_locator(mdates.HourLocator(interval=6)) plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45) # === PRESSURE PLOT === if 'Pool' in pressure.columns and 'Pool2' in pressure.columns: pressure_clean = pressure.dropna() if not pressure_clean.empty: ax3.plot(pressure_clean.index, pressure_clean['Pool'], 'o-', label='Pool Pressure', color=pool_colors['pressure'], alpha=0.8, markersize=3, linewidth=2) ax3.plot(pressure_clean.index, pressure_clean['Pool2'], 's-', label='Pool2 Pressure', color=pool2_colors['pressure'], alpha=0.8, markersize=3, linewidth=2) ax3.set_title(f'Barometric Pressure: Synchronized Data', fontsize=14, fontweight='bold') ax3.set_xlabel('Time', fontsize=12) ax3.set_ylabel('Pressure (hPa)', fontsize=12) ax3.grid(True, alpha=0.3) ax3.legend(loc='upper right') ax3.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d %H:%M')) ax3.xaxis.set_major_locator(mdates.HourLocator(interval=6)) plt.setp(ax3.xaxis.get_majorticklabels(), rotation=45) # === ALTITUDE DIFFERENCE PLOT (Pool2 - Pool) in FEET === if 'Pool' in calc_altitude.columns and 'Pool2' in calc_altitude.columns: altitude_diff_clean = calc_altitude.dropna() if not altitude_diff_clean.empty: # Calculate difference: Pool2 - Pool (in meters, then convert to feet) altitude_diff_meters = altitude_diff_clean['Pool2'] - altitude_diff_clean['Pool'] altitude_diff_feet = altitude_diff_meters * 3.28084 # Convert meters to feet # Plot the difference ax4.plot(altitude_diff_clean.index, altitude_diff_feet, 'o-', label='Pool2 - Pool (feet)', color='purple', alpha=0.8, markersize=4, linewidth=2) # Add a horizontal line at zero ax4.axhline(y=0, color='gray', linestyle='--', alpha=0.5) # Add statistics mean_diff = altitude_diff_feet.mean() std_diff = altitude_diff_feet.std() ax4.text(0.02, 0.98, f'Mean Difference: {mean_diff:+.1f} ft\\nStd Dev: {std_diff:.1f} ft', transform=ax4.transAxes, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.8)) ax4.set_title(f'Altitude Difference (Pool2 - Pool): Calculated from Pressure', fontsize=14, fontweight='bold') ax4.set_xlabel('Time', fontsize=12) ax4.set_ylabel('Altitude Difference (feet)', fontsize=12) ax4.grid(True, alpha=0.3) ax4.legend(loc='upper right') ax4.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d %H:%M')) ax4.xaxis.set_major_locator(mdates.HourLocator(interval=6)) plt.setp(ax4.xaxis.get_majorticklabels(), rotation=45) plt.tight_layout() # Save plot - 1200px wide (12 inches at 100 DPI) output_file = 'altitude_pressure_analysis.png' plt.savefig(output_file, dpi=100, bbox_inches='tight') print(f"\\nAltitude analysis plot saved as: {output_file}") return fig, df_common def print_altitude_statistics(df_common): """Print summary statistics for altitude analysis""" print("\\n=== ALTITUDE ANALYSIS STATISTICS ===") pool_data = df_common[df_common['Source'] == 'Pool'] pool2_data = df_common[df_common['Source'] == 'Pool2'] for source in df_common['Source'].unique(): source_data = df_common[df_common['Source'] == source] print(f"\\n{source.upper()} ALTITUDE DATA:") print(f" Records: {len(source_data)}") print(f" Time Range: {source_data['MinuteRounded'].min()} to {source_data['MinuteRounded'].max()}") print(f" Calculated Altitude: {source_data['Calc_Altitude'].mean():.1f}m ± {source_data['Calc_Altitude'].std():.1f}m") print(f" Sensor Altitude: {source_data['ALT'].mean():.1f}m ± {source_data['ALT'].std():.1f}m") print(f" Pressure: {source_data['BMP_P'].mean():.1f} hPa ± {source_data['BMP_P'].std():.1f} hPa") # Calculate altitude difference altitude_diff = source_data['Calc_Altitude'].mean() - source_data['ALT'].mean() altitude_diff_feet = altitude_diff * 3.28084 print(f" Altitude Difference (Calc - Sensor): {altitude_diff:+.1f}m ({altitude_diff_feet:+.1f}ft)") # Calculate difference between locations if len(pool_data) > 0 and len(pool2_data) > 0: pool_avg = pool_data['Calc_Altitude'].mean() pool2_avg = pool2_data['Calc_Altitude'].mean() location_diff_m = pool2_avg - pool_avg location_diff_ft = location_diff_m * 3.28084 print(f"\\n=== LOCATION COMPARISON ===") print(f"Pool2 vs Pool Altitude Difference:") print(f" {location_diff_m:+.1f} meters ({location_diff_ft:+.1f} feet)") print(f" Pool2 is {'HIGHER' if location_diff_m > 0 else 'LOWER'} than Pool") def main(): """Main processing function""" try: # Load and process data df = load_and_process_data() if df is None: return # Round to minutes and average averaged_df = round_to_minute_and_average(df) # Create altitude overlay plots fig, df_common = create_altitude_overlay_plot(averaged_df) if df_common is not None: # Print statistics print_altitude_statistics(df_common) # Save processed data output_csv = 'altitude_analysis_data.csv' df_common.to_csv(output_csv, index=False) print(f"\\nProcessed altitude data saved as: {output_csv}") print(f"\\n✅ Altitude analysis complete!") print(f" - Plot: altitude_pressure_analysis.png") except Exception as e: print(f"Error processing altitude data: {e}") import traceback traceback.print_exc() if __name__ == "__main__": main()