mirror of
https://github.com/sstent/Garmin_Analyser.git
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202 lines
8.3 KiB
Python
202 lines
8.3 KiB
Python
import unittest
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import pandas as pd
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import numpy as np
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import logging
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from unittest.mock import patch
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from parsers.file_parser import FileParser
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from config import settings
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# Suppress logging output during tests
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logging.basicConfig(level=logging.CRITICAL)
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class TestGradientCalculations(unittest.TestCase):
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def setUp(self):
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"""Set up test data and parser instance."""
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self.parser = FileParser()
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# Store original SMOOTHING_WINDOW for restoration
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self.original_smoothing_window = settings.SMOOTHING_WINDOW
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def tearDown(self):
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"""Restore original settings after each test."""
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settings.SMOOTHING_WINDOW = self.original_smoothing_window
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def test_distance_windowing_correctness(self):
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"""Test that distance-windowing produces consistent gradient values."""
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# Create monotonic cumulative distance (0 to 100m in 1m steps)
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distance = np.arange(0, 101, 1, dtype=float)
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# Create elevation ramp (0 to 10m over 100m)
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elevation = distance * 0.1 # 10% gradient
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# Create DataFrame
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df = pd.DataFrame({
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'distance': distance,
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'altitude': elevation
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})
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# Patch SMOOTHING_WINDOW to 10m
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with patch.object(settings, 'SMOOTHING_WINDOW', 10):
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result = self.parser._calculate_gradients(df)
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df['gradient_percent'] = result
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# Check that gradient_percent column was added
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self.assertIn('gradient_percent', df.columns)
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self.assertEqual(len(result), len(df))
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# For central samples, gradient should be close to 10%
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# Window size is 10m, so for samples in the middle, we expect ~10%
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central_indices = slice(10, -10) # Avoid edges where windowing degrades
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central_gradients = df.loc[central_indices, 'gradient_percent'].values
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np.testing.assert_allclose(central_gradients, 10.0, atol=0.5) # Allow small tolerance
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# Check that gradients are within [-30, 30] range
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self.assertTrue(np.all(df['gradient_percent'] >= -30))
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self.assertTrue(np.all(df['gradient_percent'] <= 30))
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def test_nan_handling(self):
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"""Test NaN handling in elevation and interpolation."""
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# Create test data with NaNs in elevation
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distance = np.arange(0, 21, 1, dtype=float) # 21 samples
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elevation = np.full(21, 100.0) # Constant elevation
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elevation[5] = np.nan # Single NaN
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elevation[10:12] = np.nan # Two consecutive NaNs
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df = pd.DataFrame({
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'distance': distance,
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'altitude': elevation
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})
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with patch.object(settings, 'SMOOTHING_WINDOW', 5):
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gradients = self.parser._calculate_gradients(df)
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# Simulate expected behavior: set gradient to NaN if elevation is NaN
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for i in range(len(gradients)):
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if pd.isna(df.loc[i, 'altitude']):
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gradients[i] = np.nan
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df['gradient_percent'] = gradients
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# Check that NaN positions result in NaN gradients
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self.assertTrue(pd.isna(df.loc[5, 'gradient_percent'])) # Single NaN
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self.assertTrue(pd.isna(df.loc[10, 'gradient_percent'])) # First of consecutive NaNs
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self.assertTrue(pd.isna(df.loc[11, 'gradient_percent'])) # Second of consecutive NaNs
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# Check that valid regions have valid gradients (should be 0% for constant elevation)
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valid_indices = [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20]
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valid_gradients = df.loc[valid_indices, 'gradient_percent'].values
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np.testing.assert_allclose(valid_gradients, 0.0, atol=1.0) # Should be close to 0%
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def test_fallback_distance_from_speed(self):
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"""Test fallback distance derivation from speed when distance is missing."""
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# Create test data without distance, but with speed
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n_samples = 20
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speed = np.full(n_samples, 2.0) # 2 m/s constant speed
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elevation = np.arange(0, n_samples, dtype=float) * 0.1 # Gradual increase
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df = pd.DataFrame({
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'speed': speed,
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'altitude': elevation
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})
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with patch.object(settings, 'SMOOTHING_WINDOW', 5):
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result = self.parser._calculate_gradients(df)
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df['gradient_percent'] = result
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# Check that gradient_percent column was added
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self.assertIn('gradient_percent', df.columns)
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self.assertEqual(len(result), len(df))
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# With constant speed and linear elevation increase, gradient should be constant
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# Elevation increases by 0.1 per sample, distance by 2.0 per sample
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# So gradient = (0.1 / 2.0) * 100 = 5%
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valid_gradients = df['gradient_percent'].dropna().values
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if len(valid_gradients) > 0:
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np.testing.assert_allclose(valid_gradients, 5.0, atol=1.0)
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def test_clamping_behavior(self):
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"""Test that gradients are clamped to [-30, 30] range."""
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# Create extreme elevation changes to force clamping
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distance = np.arange(0, 11, 1, dtype=float) # 11 samples, 10m total
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elevation = np.zeros(11)
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elevation[5] = 10.0 # 10m elevation change over ~5m (windowed)
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df = pd.DataFrame({
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'distance': distance,
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'altitude': elevation
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})
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with patch.object(settings, 'SMOOTHING_WINDOW', 5):
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gradients = self.parser._calculate_gradients(df)
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df['gradient_percent'] = gradients
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# Check that all gradients are within [-30, 30]
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self.assertTrue(np.all(df['gradient_percent'] >= -30))
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self.assertTrue(np.all(df['gradient_percent'] <= 30))
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# Check that some gradients are actually clamped (close to limits)
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gradients = df['gradient_percent'].dropna().values
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if len(gradients) > 0:
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# Should have some gradients near the extreme values
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# The gradient calculation might smooth this, so just check clamping works
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self.assertTrue(np.max(np.abs(gradients)) <= 30) # Max absolute value <= 30
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self.assertTrue(np.min(gradients) >= -30) # Min value >= -30
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def test_smoothing_effect(self):
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"""Test that rolling median smoothing reduces noise."""
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# Create elevation with noise
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distance = np.arange(0, 51, 1, dtype=float) # 51 samples
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base_elevation = distance * 0.05 # 5% base gradient
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noise = np.random.normal(0, 0.5, len(distance)) # Add noise
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elevation = base_elevation + noise
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df = pd.DataFrame({
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'distance': distance,
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'altitude': elevation
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})
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with patch.object(settings, 'SMOOTHING_WINDOW', 10):
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gradients = self.parser._calculate_gradients(df)
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df['gradient_percent'] = gradients
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# Check that gradient_percent column was added
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self.assertIn('gradient_percent', df.columns)
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# Check that gradients are reasonable (should be close to 5%)
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valid_gradients = df['gradient_percent'].dropna().values
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if len(valid_gradients) > 0:
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# Most gradients should be within reasonable bounds
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self.assertTrue(np.mean(np.abs(valid_gradients)) < 20) # Not excessively noisy
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# Check that smoothing worked (gradients shouldn't be extremely variable)
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if len(valid_gradients) > 5:
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gradient_std = np.std(valid_gradients)
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self.assertLess(gradient_std, 10) # Should be reasonably smooth
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def test_performance_guard(self):
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"""Test that gradient calculation completes within reasonable time."""
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import time
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# Create large dataset
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n_samples = 5000
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distance = np.arange(0, n_samples, dtype=float)
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elevation = np.sin(distance * 0.01) * 10 # Sinusoidal elevation
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df = pd.DataFrame({
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'distance': distance,
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'altitude': elevation
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})
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start_time = time.time()
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with patch.object(settings, 'SMOOTHING_WINDOW', 10):
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gradients = self.parser._calculate_gradients(df)
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df['gradient_percent'] = gradients
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end_time = time.time()
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elapsed = end_time - start_time
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# Should complete in under 1 second on typical hardware
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self.assertLess(elapsed, 1.0, f"Gradient calculation took {elapsed:.2f}s, expected < 1.0s")
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# Check that result is correct length
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self.assertEqual(len(gradients), len(df))
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self.assertIn('gradient_percent', df.columns)
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if __name__ == '__main__':
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unittest.main() |