99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
import pytest
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import numpy as np
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import time
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from datetime import datetime, timezone
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from unittest.mock import Mock, patch
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from src.core.csi_processor import CSIProcessor, CSIFeatures
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from src.hardware.csi_extractor import CSIData
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def make_csi_data(amplitude=None, phase=None, n_ant=3, n_sub=56):
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"""Build a CSIData test fixture."""
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if amplitude is None:
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amplitude = np.random.uniform(0.1, 2.0, (n_ant, n_sub))
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if phase is None:
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phase = np.random.uniform(-np.pi, np.pi, (n_ant, n_sub))
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return CSIData(
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timestamp=datetime.now(timezone.utc),
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amplitude=amplitude,
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phase=phase,
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frequency=5.21e9,
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bandwidth=17.5e6,
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num_subcarriers=n_sub,
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num_antennas=n_ant,
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snr=15.0,
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metadata={"source": "test"},
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)
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_PROCESSOR_CONFIG = {
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"sampling_rate": 100,
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"window_size": 56,
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"overlap": 0.5,
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"noise_threshold": -60,
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"human_detection_threshold": 0.8,
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"smoothing_factor": 0.9,
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"max_history_size": 500,
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"enable_preprocessing": True,
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"enable_feature_extraction": True,
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"enable_human_detection": True,
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}
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class TestCSIProcessor:
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"""Test suite for CSI processor following London School TDD principles"""
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@pytest.fixture
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def csi_processor(self):
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"""Create CSI processor instance for testing"""
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return CSIProcessor(config=_PROCESSOR_CONFIG)
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@pytest.fixture
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def sample_csi(self):
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"""Generate synthetic CSIData for testing"""
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return make_csi_data()
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def test_preprocess_returns_csi_data(self, csi_processor, sample_csi):
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"""Preprocess should return a CSIData instance"""
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result = csi_processor.preprocess_csi_data(sample_csi)
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assert isinstance(result, CSIData)
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assert result.num_antennas == sample_csi.num_antennas
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assert result.num_subcarriers == sample_csi.num_subcarriers
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def test_preprocess_normalises_amplitude(self, csi_processor, sample_csi):
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"""Preprocess should produce finite, non-negative amplitude with unit-variance normalisation"""
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result = csi_processor.preprocess_csi_data(sample_csi)
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assert np.all(np.isfinite(result.amplitude))
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assert result.amplitude.min() >= 0.0
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# Normalised to unit variance: std ≈ 1.0 (may differ due to Hamming window)
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std = np.std(result.amplitude)
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assert 0.5 < std < 5.0 # within reasonable bounds of unit-variance normalisation
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def test_preprocess_removes_nan(self, csi_processor):
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"""Preprocess should replace NaN amplitude with 0"""
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amp = np.ones((3, 56))
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amp[0, 0] = np.nan
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csi = make_csi_data(amplitude=amp)
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result = csi_processor.preprocess_csi_data(csi)
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assert not np.isnan(result.amplitude).any()
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def test_extract_features_returns_csi_features(self, csi_processor, sample_csi):
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"""extract_features should return a CSIFeatures instance"""
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preprocessed = csi_processor.preprocess_csi_data(sample_csi)
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features = csi_processor.extract_features(preprocessed)
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assert isinstance(features, CSIFeatures)
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def test_extract_features_has_correct_shapes(self, csi_processor, sample_csi):
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"""Feature arrays should have expected shapes"""
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preprocessed = csi_processor.preprocess_csi_data(sample_csi)
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features = csi_processor.extract_features(preprocessed)
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assert features.amplitude_mean.shape == (56,)
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assert features.amplitude_variance.shape == (56,)
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def test_preprocess_performance(self, csi_processor, sample_csi):
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"""Preprocessing a single frame must complete in < 10 ms"""
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start = time.perf_counter()
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csi_processor.preprocess_csi_data(sample_csi)
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elapsed = time.perf_counter() - start
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assert elapsed < 0.010 # < 10 ms
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