Timon
fabbf3214d
- Updated `detect_mosaic_mode` function to check for grid-size subdirectories in addition to tile-named files. - Added comprehensive tests for DOY reset logic in `test_doy_logic.py`. - Implemented feature extraction tests in `test_feature_extraction.py`. - Created tests for growing window method in `test_growing_window_only.py`. - Developed a complete model inference test in `test_model_inference.py`. - Added a debug script for testing two-step refinement logic in `test_script22_debug.py`.
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
"""
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Test DOY reset logic for harvest detection.
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Verify that DOY resets to 1, 2, 3, ... after harvest is detected.
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path.cwd()))
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import pandas as pd
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import numpy as np
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from harvest_date_pred_utils import extract_features, load_model_and_config
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import torch
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# Load sample data
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ci_data = pd.read_csv('../laravel_app/storage/app/angata/Data/extracted_ci/ci_data_for_python/ci_data_for_python.csv')
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ci_data['Date'] = pd.to_datetime(ci_data['Date'])
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# Get field 779 data
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field_779 = ci_data[ci_data['field'] == '779'].reset_index(drop=True)
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print(f"Field 779: {len(field_779)} days of data")
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print(f"Date range: {field_779['Date'].min().date()} to {field_779['Date'].max().date()}\n")
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# Load model config
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model, config, scalers = load_model_and_config(Path.cwd())
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# Test 1: First season (season_anchor_day = 0)
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print("=" * 80)
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print("TEST 1: First season (season_anchor_day=0, lookback_start=0)")
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print("=" * 80)
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window = field_779.iloc[0:20].copy().reset_index(drop=True)
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features = extract_features(window, config['features'], ci_column='FitData',
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season_anchor_day=0, lookback_start=0)
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# Extract DOY_normalized column (index 13 or find it)
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feature_names = config['features']
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doy_idx = feature_names.index('DOY_normalized') if 'DOY_normalized' in feature_names else -1
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if doy_idx >= 0:
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doy_values = (features[:, doy_idx] * 450).astype(int) # Denormalize
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print(f"Window size: {len(window)} days")
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print(f"DOY values: {doy_values[:10]}")
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print(f"Expected: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]")
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assert list(doy_values[:10]) == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "DOY not incrementing correctly!"
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print("✓ DOY incrementing correctly for first season\n")
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# Test 2: After harvest detected at day 100, next season starts
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print("=" * 80)
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print("TEST 2: After harvest at day 100, new season starts (season_anchor_day=101, lookback_start=101)")
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print("=" * 80)
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harvest_day = 100
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window = field_779.iloc[harvest_day:harvest_day+20].copy().reset_index(drop=True)
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features = extract_features(window, config['features'], ci_column='FitData',
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season_anchor_day=harvest_day+1, lookback_start=harvest_day+1)
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if doy_idx >= 0:
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doy_values = (features[:, doy_idx] * 450).astype(int) # Denormalize
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print(f"Window size: {len(window)} days (starting at day {harvest_day})")
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print(f"DOY values: {doy_values[:10]}")
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print(f"Expected: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] (fresh season)")
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assert list(doy_values[:10]) == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "DOY not reset after harvest!"
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print("✓ DOY reset correctly for new season\n")
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print("=" * 80)
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print("ALL TESTS PASSED! DOY logic is correct.")
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print("=" * 80)
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