removing old endpoints etc

tests/test_analyzer_speed_and_normalized_naming.py

@@ -0,0 +1,106 @@
+"""
+Tests for speed_analysis and normalized naming in the workout analyzer.
+
+Validates that [WorkoutAnalyzer.analyze_workout()](analyzers/workout_analyzer.py:1)
+returns the expected `speed_analysis` dictionary and that the summary dictionary
+contains normalized keys with backward-compatibility aliases.
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+from datetime import datetime
+
+from analyzers.workout_analyzer import WorkoutAnalyzer
+from models.workout import WorkoutData, WorkoutMetadata, SpeedData, HeartRateData
+
+@pytest.fixture
+def synthetic_workout_data():
+    """Create a small, synthetic workout dataset for testing."""
+    timestamps = np.arange(60)
+    speeds = np.linspace(5, 10, 60)  # speed in m/s
+    heart_rates = np.linspace(120, 150, 60)
+
+    # Introduce some NaNs to test robustness
+    speeds[10] = np.nan
+    heart_rates[20] = np.nan
+
+    df = pd.DataFrame({
+        'timestamp': pd.to_datetime(timestamps, unit='s'),
+        'speed_mps': speeds,
+        'heart_rate': heart_rates,
+    })
+    
+    metadata = WorkoutMetadata(
+        activity_id="test_activity_123",
+        activity_name="Test Ride",
+        start_time=datetime(2023, 1, 1, 10, 0, 0),
+        duration_seconds=60.0,
+        distance_meters=1000.0,  # Adding distance_meters to resolve TypeError in template rendering tests
+        sport="cycling",
+        sub_sport="road"
+    )
+
+    distance_values = (df['speed_mps'].fillna(0) * 1).cumsum().tolist() # Assuming 1Hz sampling
+    speed_data = SpeedData(speed_values=df['speed_mps'].fillna(0).tolist(), distance_values=distance_values)
+    heart_rate_data = HeartRateData(heart_rate_values=df['heart_rate'].fillna(0).tolist(), hr_zones={}) # Dummy hr_zones
+
+    return WorkoutData(
+        metadata=metadata,
+        raw_data=df,
+        speed=speed_data,
+        heart_rate=heart_rate_data
+    )
+
+
+def test_analyze_workout_includes_speed_analysis_and_normalized_summary(synthetic_workout_data):
+    """
+    Verify that `analyze_workout` returns 'speed_analysis' and a summary with
+    normalized keys 'avg_speed_kmh' and 'avg_hr'.
+    """
+    analyzer = WorkoutAnalyzer()
+    analysis = analyzer.analyze_workout(synthetic_workout_data)
+
+    # 1. Validate 'speed_analysis' presence and keys
+    assert 'speed_analysis' in analysis
+    assert isinstance(analysis['speed_analysis'], dict)
+    assert 'avg_speed_kmh' in analysis['speed_analysis']
+    assert 'max_speed_kmh' in analysis['speed_analysis']
+    
+    # Check that values are plausible floats > 0
+    assert isinstance(analysis['speed_analysis']['avg_speed_kmh'], float)
+    assert isinstance(analysis['speed_analysis']['max_speed_kmh'], float)
+    assert analysis['speed_analysis']['avg_speed_kmh'] > 0
+    assert analysis['speed_analysis']['max_speed_kmh'] > 0
+
+    # 2. Validate 'summary' presence and normalized keys
+    assert 'summary' in analysis
+    assert isinstance(analysis['summary'], dict)
+    assert 'avg_speed_kmh' in analysis['summary']
+    assert 'avg_hr' in analysis['summary']
+
+    # Check that values are plausible floats > 0
+    assert isinstance(analysis['summary']['avg_speed_kmh'], float)
+    assert isinstance(analysis['summary']['avg_hr'], float)
+    assert analysis['summary']['avg_speed_kmh'] > 0
+    assert analysis['summary']['avg_hr'] > 0
+
+
+def test_backward_compatibility_aliases_present(synthetic_workout_data):
+    """
+    Verify that `analyze_workout` summary includes backward-compatibility
+    aliases for avg_speed and avg_heart_rate.
+    """
+    analyzer = WorkoutAnalyzer()
+    analysis = analyzer.analyze_workout(synthetic_workout_data)
+
+    assert 'summary' in analysis
+    summary = analysis['summary']
+
+    # 1. Check for 'avg_speed' alias
+    assert 'avg_speed' in summary
+    assert summary['avg_speed'] == summary['avg_speed_kmh']
+
+    # 2. Check for 'avg_heart_rate' alias
+    assert 'avg_heart_rate' in summary
+    assert summary['avg_heart_rate'] == summary['avg_hr']