Add resample subpackage with downsampling support

- Add transivent.resample subpackage for signal preprocessing
- Implement average_downsample() for block averaging downsampling
- Add downsample_to_interval() for interval-based downsampling
- Add target_sampling_interval parameter to detect_from_wfm()
- Preserves signal amplitude and improves SNR through averaging
- Creates uniform time arrays to avoid validation warnings

1 files changed, 98 insertions, 0 deletions

diff --git a/tests/test_resample.py b/tests/test_resample.py
new file mode 100644
index 0000000..6b85878
--- /dev/null
+++ b/tests/test_resample.py
@@ -0,0 +1,98 @@
+"""Test the resample module."""
+
+import numpy as np
+import pytest
+
+from transivent.resample import average_downsample, downsample_to_interval
+
+
+def test_average_downsample_basic():
+    """Test basic downsampling functionality."""
+    # Create test data
+    t = np.linspace(0, 1, 1000)
+    x = np.ones(1000)
+    
+    # Downsample by factor of 10
+    t_down, x_down = average_downsample(t, x, q=10)
+    
+    assert len(t_down) == 100
+    assert len(x_down) == 100
+    assert np.allclose(x_down, 1.0)  # Mean should be preserved
+    assert np.allclose(t_down[1] - t_down[0], t[10] - t[0])  # Check time step
+
+
+def test_average_downsample_q1():
+    """Test that q=1 returns unchanged data."""
+    t = np.linspace(0, 1, 100)
+    x = np.random.randn(100)
+    
+    t_out, x_out = average_downsample(t, x, q=1)
+    
+    np.testing.assert_array_equal(t_out, t)
+    np.testing.assert_array_equal(x_out, x)
+
+
+def test_average_downsample_invalid_q():
+    """Test error handling for invalid downsample factor."""
+    t = np.linspace(0, 1, 100)
+    x = np.random.randn(100)
+    
+    with pytest.raises(ValueError, match="q must be >= 1"):
+        average_downsample(t, x, q=0)
+    
+    with pytest.raises(ValueError, match="q must be >= 1"):
+        average_downsample(t, x, q=-1)
+
+
+def test_average_downsample_short_array():
+    """Test downsampling when array is shorter than factor."""
+    t = np.linspace(0, 1, 5)
+    x = np.ones(5)
+    
+    with pytest.raises(ValueError, match="Input length .* is less than downsample factor"):
+        average_downsample(t, x, q=10)
+
+
+def test_downsample_to_interval():
+    """Test interval-based downsampling."""
+    # 10 kHz data (exact interval)
+    t = np.arange(10000) * 1e-4
+    x = np.random.randn(10000)
+    
+    # Downsample to 1 kHz
+    t_down, x_down = downsample_to_interval(t, x, target_interval=1e-3)
+    
+    # Should downsample by factor of 10
+    assert len(t_down) == 1000
+    assert len(x_down) == 1000
+    # Original dt was 1e-4, new dt should be 1e-3
+    assert np.allclose(t_down[1] - t_down[0], 1e-3)
+
+
+def test_downsample_to_interval_upsampling():
+    """Test error for upsampling (not supported)."""
+    t = np.linspace(0, 1, 1000)  # dt = 0.001
+    x = np.random.randn(1000)
+    
+    with pytest.raises(ValueError, match="Upsampling not supported"):
+        downsample_to_interval(t, x, target_interval=5e-4)  # Smaller interval
+
+
+def test_average_downsample_preserves_amplitude():
+    """Test that amplitude is preserved through averaging."""
+    t = np.linspace(0, 1, 1000)
+    # Create a step function
+    x = np.concatenate([np.ones(500), 2 * np.ones(500)])
+    
+    # Downsample by factor of 10
+    t_down, x_down = average_downsample(t, x, q=10)
+    
+    # Check the transition is at the right place
+    transition_idx = 50  # 500 / 10
+    # First part should be ~1, second part should be ~2
+    assert np.allclose(x_down[:transition_idx], 1.0)
+    assert np.allclose(x_down[transition_idx:], 2.0)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])
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