add tests

tests/transforms/test_iirfilter.py

@@ -0,0 +1,101 @@
+import numpy as np
+import pytest
+import torch
+from scipy.signal import butter, filtfilt
+
+from ml4gw.transforms.iirfilter import IIRFilter
+
+
+@pytest.fixture(params=[256, 512, 1024, 2048])
+def sample_rate(request):
+    return request.param
+
+
+@pytest.fixture(params=[2, 4, 6, 8])
+def order(request):
+    return request.param
+
+
+def test_butterworth(sample_rate, order):
+    t = np.linspace(0, 1.0, sample_rate, endpoint=False)
+    tone_freq = 50
+    noise_amplitude = 0.5
+
+    signal = np.sin(2 * np.pi * tone_freq * t)
+    noise = noise_amplitude * np.random.normal(size=t.shape)
+    combined_signal = signal + noise
+
+    low_cutoff = 100
+    high_cutoff = 20
+    slice_length = int(0.1 * sample_rate)
+
+    butterworth_low = IIRFilter(
+        order,
+        torch.tensor(low_cutoff),
+        btype="low",
+        analog=False,
+        output="ba",
+        fs=torch.tensor(sample_rate),
+    )
+    butterworth_high = IIRFilter(
+        order,
+        torch.tensor(high_cutoff),
+        btype="high",
+        analog=False,
+        output="ba",
+        fs=torch.tensor(sample_rate),
+    )
+
+    b, a = butter(
+        order,
+        low_cutoff,
+        btype="low",
+        analog=False,
+        output="ba",
+        fs=sample_rate,
+    )
+    scipy_filtered_data_low = filtfilt(b, a, combined_signal)[
+        slice_length:-slice_length
+    ]
+    b, a = butter(
+        order,
+        high_cutoff,
+        btype="high",
+        analog=False,
+        output="ba",
+        fs=sample_rate,
+    )
+    scipy_filtered_data_high = filtfilt(b, a, combined_signal)[
+        slice_length:-slice_length
+    ]
+
+    torch_filtered_data_low = butterworth_low(
+        torch.tensor(combined_signal).repeat(10, 1)
+    )[:, slice_length:-slice_length].numpy()
+    torch_filtered_data_high = butterworth_high(
+        torch.tensor(combined_signal).repeat(10, 1)
+    )[:, slice_length:-slice_length].numpy()
+
+    # test batch processing
+    for i in range(9):
+        assert np.allclose(
+            torch_filtered_data_low[0],
+            torch_filtered_data_low[i + 1],
+            atol=float(np.finfo(float).eps),
+        )
+        assert np.allclose(
+            torch_filtered_data_high[0],
+            torch_filtered_data_high[i + 1],
+            atol=float(np.finfo(float).eps),
+        )
+
+    assert np.allclose(
+        scipy_filtered_data_low,
+        torch_filtered_data_low[0],
+        atol=1e-1,
+    )
+    assert np.allclose(
+        scipy_filtered_data_high,
+        torch_filtered_data_high[0],
+        atol=1e-1,
+    )