mirror of
https://github.com/acamarata/pray-calc-ml.git
synced 2026-07-01 03:14:27 +00:00
3b8c665aca
Tracked: BSRN/SURFRAD processors (reference, excluded from pipeline), GaN-MN downloader, academic paper fetcher, Madrid SQM processor, ML analysis scripts (src/analyze/), umsu_medan_2024 raw sightings. Gitignored: global_extrapolator, instant_1m_injector/vectorized, massive_harvest_engine, massive_sqm_downloader, global_sqm_harvester, run_infinite_pipeline.sh, run_massive_collection.sh, search_papers.py (agent-generated experimental scripts, not part of core pipeline).
102 lines
4.2 KiB
Python
102 lines
4.2 KiB
Python
import pandas as pd
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import numpy as np
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import warnings
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_absolute_error, r2_score
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from scipy.optimize import curve_fit
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warnings.filterwarnings("ignore")
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def exp_func(x, a, b, c):
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return a * np.exp(b * x) + c
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def eval_algorithms(df, name):
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print(f"\n==========================================")
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print(f" {name.upper()} ML PATTERN ANALYSIS ")
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print(f"==========================================")
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print(f"Total verified observations: {len(df)}")
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# Check column name for angle
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angle_col = f"{name.lower()}_angle"
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if angle_col not in df.columns:
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print(f"Error: {angle_col} not found in columns")
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return
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df['doy'] = df['day_of_year']
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df['lat_abs'] = df['lat'].abs()
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# Missing value cleaning just in case
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df = df.dropna(subset=[angle_col, 'doy', 'lat_abs'])
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# 1. Seasonality Feature Engineering (Sin/Cos mapping of Time of Year)
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df['doy_sin'] = np.sin(2 * np.pi * df['doy'] / 365)
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df['doy_cos'] = np.cos(2 * np.pi * df['doy'] / 365)
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X_lat = df[['lat_abs']].values
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X_all = df[['lat_abs', 'doy_sin', 'doy_cos']].values
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y = df[angle_col].values
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# --- A. BASELINE FIXED ALGORITHM (ISNA/MWL style: 15° or 18°) ---
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print("\n[A] STATIC ALGORITHM BENCHMARKS (15° vs 18°)")
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mae_15 = mean_absolute_error(y, np.full_like(y, 15.0))
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mae_18 = mean_absolute_error(y, np.full_like(y, 18.0))
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print(f" Fixed 15° Error (MAE): {mae_15:.3f}°")
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print(f" Fixed 18° Error (MAE): {mae_18:.3f}°")
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# --- B. LATITUDE ONLY (Linear vs Exponential) ---
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print("\n[B] LATITUDE PATTERNS (Equator distance vs Angle)")
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# Linear Latitude
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lr_lat = LinearRegression().fit(X_lat, y)
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y_pred_lat_lin = lr_lat.predict(X_lat)
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print(f" Linear Latitude Fit : R2={r2_score(y, y_pred_lat_lin):.3f}, MAE={mean_absolute_error(y, y_pred_lat_lin):.3f}°")
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print(f" -> Coefficient: {lr_lat.coef_[0]:.4f} degrees per 1° latitude move")
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# Exponential Latitude
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try:
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# We model the depression angle dropping exponentially toward the poles
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popt, _ = curve_fit(exp_func, df['lat_abs'], y, p0=[-1, 0.05, 18], maxfev=5000)
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y_pred_lat_exp = exp_func(df['lat_abs'], *popt)
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r2_exp = r2_score(y, y_pred_lat_exp)
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mae_exp = mean_absolute_error(y, y_pred_lat_exp)
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print(f" Exponential Latitude Fit : R2={r2_exp:.3f}, MAE={mae_exp:.3f}°")
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except Exception as e:
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print(f" Exponential Fit failed: {e}")
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# --- C. TIME OF YEAR (SEASONALITY) IMPACT ---
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print("\n[C] TIME OF YEAR (SEASONAL) CORRELATIONS")
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lr_all = LinearRegression().fit(X_all, y)
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y_pred_all = lr_all.predict(X_all)
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print(f" Combined Model (Lat + Seasonality): R2={r2_score(y, y_pred_all):.3f}, MAE={mean_absolute_error(y, y_pred_all):.3f}°")
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# Feature Importance
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print(f" -> Lat Importance : {abs(lr_all.coef_[0]):.4f}")
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print(f" -> TOY (Season) Sine: {abs(lr_all.coef_[1]):.4f} amplitude")
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# --- D. DYNAMIC ALGORITHM COMPARISON ---
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high_lat_mask = df['lat_abs'] > 40
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if sum(high_lat_mask) > 0:
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high_lat_mean = df[high_lat_mask][angle_col].mean()
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low_lat_mean = df[~high_lat_mask][angle_col].mean()
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print(f"\n[D] ALGORITHMIC COMPARISON")
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print(f" Mean empirical angle at >40° Lat : {high_lat_mean:.2f}°")
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print(f" Mean empirical angle at <=40° Lat : {low_lat_mean:.2f}°")
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shift = low_lat_mean - high_lat_mean
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print(f" Shift detected: {shift:.2f}° absolute reduction when moving towards poles.")
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if shift > 0.5:
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print(" => Matches Moonsighting/DPC dynamic logic (angles drop significantly at higher latitudes).")
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else:
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print(" => Does NOT strongly match Moonsighting dynamic logic.")
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if __name__ == '__main__':
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try:
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fajr_df = pd.read_csv('data/processed/fajr_angles.csv')
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eval_algorithms(fajr_df, 'Fajr')
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except Exception as e:
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print("Missing Fajr dataset:", e)
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try:
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isha_df = pd.read_csv('data/processed/isha_angles.csv')
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eval_algorithms(isha_df, 'Isha')
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except Exception as e:
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print("Missing Isha dataset:", e)
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