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| from path_analysis.data_preprocess import * | |
| import numpy as np | |
| import pytest | |
| def test_thin_points(): | |
| # Define a sample point list | |
| points = [ | |
| PeakData([0, 0, 0], 10, 0), | |
| PeakData([1, 1, 1], 8, 1), | |
| PeakData([10, 10, 10], 12, 2), | |
| PeakData([10.5, 10.5, 10.5], 5, 3), | |
| PeakData([20, 20, 20], 15, 4) | |
| ] | |
| # Call the thin_points function with dmin=5 (for example) | |
| removed_indices = thin_peaks(points, dmin=5) | |
| # Check results | |
| # Point at index 1 ([1, 1, 1]) should be removed since it's within 5 units distance of point at index 0 and has lower intensity. | |
| # Similarly, point at index 3 ([10.5, 10.5, 10.5]) should be removed as it's close to point at index 2 and has lower intensity. | |
| assert set(removed_indices) == {1, 3} | |
| # Another simple test to check if function does nothing when points are far apart | |
| far_points = [ | |
| PeakData([0, 0, 0], 10, 0), | |
| PeakData([100, 100, 100], 12, 1), | |
| PeakData([200, 200, 200], 15, 2) | |
| ] | |
| removed_indices_far = thin_peaks(far_points, dmin=5) | |
| assert len(removed_indices_far) == 0 # Expect no points to be removed | |
| def test_find_peaks2(): | |
| # Basic test | |
| data = np.array([0, 0, 0, 0, 0, 0, 5, 0, 3, 0]) | |
| peaks, _ = find_peaks2(data) | |
| assert set(peaks) == {6} # Expected peaks at positions 6 | |
| # Basic test | |
| data = np.array([0, 2, 0, 0, 0, 0, 0, 0, 0, 0]) | |
| peaks, _ = find_peaks2(data) | |
| assert set(peaks) == {1} # Expected peaks at positions 1 | |
| # Test with padding impacting peak detection | |
| data = np.array([3, 2.9, 0, 0, 0, 3]) | |
| peaks, _ = find_peaks2(data) | |
| assert set(peaks) == {0,5} # Peaks at both ends | |
| # Test with close peaks | |
| data = np.array([3, 0, 3]) | |
| peaks, _ = find_peaks2(data) | |
| assert set(peaks) == {2} # Peak at right end only | |
| # Test with close peaks | |
| # Test with close peaks | |
| data = np.array([3, 0, 3]) | |
| peaks, _ = find_peaks2(data, distance=1) | |
| assert set(peaks) == {0,2} # Peaks at both ends | |
| # Test with close peaks | |
| data = np.array([0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3]) | |
| peaks, _ = find_peaks2(data, distance=1) | |
| assert set(peaks) == {2,7} # Peak at centre (rounded to the left) of groups of maximum values | |
| # Test with prominence threshold | |
| data = np.array([0, 1, 0, 0.4, 0]) | |
| peaks, _ = find_peaks2(data, prominence=0.5) | |
| assert peaks == [1] # Only the peak at position 1 meets the prominence threshold | |
| def test_focus_criterion(): | |
| pos = np.array([0, 1, 2, 3, 4, 6]) | |
| values = np.array([0.1, 0.5, 0.2, 0.8, 0.3, 0.9]) | |
| # Basic test | |
| assert np.array_equal(focus_criterion(pos, values), np.array([1, 3, 6])) # only values 0.8 and 0.9 exceed 0.4 times the max (which is 0.9) | |
| # Empty test | |
| assert np.array_equal(focus_criterion(np.array([]), np.array([])), np.array([])) | |
| # Test with custom alpha | |
| assert np.array_equal(focus_criterion(pos, values, alpha=0.5), np.array([1, 3, 6])) | |
| # Test with a larger alpha | |
| assert np.array_equal(focus_criterion(pos, values, alpha=1.0), [6]) # No values exceed the maximum value itself | |
| # Test with all values below threshold | |
| values = np.array([0.1, 0.2, 0.3, 0.4]) | |
| assert np.array_equal(focus_criterion(pos[:4], values), [1,2,3]) # All values are below 0.4 times the max (which is 0.4) | |
| def mock_data(): | |
| all_paths = [ [ (0,0,0), (0,2,0), (0,5,0), (0,10,0), (0,15,0), (0,20,0)], [ (1,20,0), (1,20,10), (1,20,20) ] ] # Mock paths | |
| path_lengths = [ 2.2, 2.3 ] # Mock path lengths | |
| measured_trace_fluorescence = [ [100, 8, 3, 2, 3, 49], [38, 2, 20] ] # Mock fluorescence data | |
| return all_paths, path_lengths, measured_trace_fluorescence | |
| def test_process_cell_traces_return_type(mock_data): | |
| all_paths, path_lengths, measured_trace_fluorescence = mock_data | |
| result = process_cell_traces(all_paths, path_lengths, measured_trace_fluorescence) | |
| assert isinstance(result, CellData), f"Expected CellData but got {type(result)}" | |
| def test_process_cell_traces_pathdata_list_length(mock_data): | |
| all_paths, path_lengths, measured_trace_fluorescence = mock_data | |
| result = process_cell_traces(all_paths, path_lengths, measured_trace_fluorescence) | |
| assert len(result.pathdata_list) == len(all_paths), f"Expected {len(all_paths)} but got {len(result.pathdata_list)}" | |
| def test_process_cell_traces_pathdata_path_lengths(mock_data): | |
| all_paths, path_lengths, measured_trace_fluorescence = mock_data | |
| result = process_cell_traces(all_paths, path_lengths, measured_trace_fluorescence) | |
| path_lengths = [p.SC_length for p in result.pathdata_list] | |
| expected_path_lengths = [2.2, 2.3] | |
| assert path_lengths == expected_path_lengths, f"Expected {expected_path_lengths} but got {path_lengths}" | |
| def test_process_cell_traces_peaks(mock_data): | |
| all_paths, path_lengths, measured_trace_fluorescence = mock_data | |
| result = process_cell_traces(all_paths, path_lengths, measured_trace_fluorescence) | |
| print(result) | |
| peaks = [p.peaks for p in result.pathdata_list] | |
| assert peaks == [[0,5],[]] | |
| # Mock data | |
| def mock_celldata(): | |
| pathdata1 = PathData(peaks=[0, 5], points=[(0,0,0), (0,2,0), (0,5,0), (0,10,0), (0,15,0), (0,20,0)], removed_peaks=[], o_intensity=[100, 8, 3, 2, 3, 69], SC_length=2.2) | |
| pathdata2 = PathData(peaks=[2], points=[(1,20,0), (1,20,10), (1,20,20) ], removed_peaks=[RemovedPeakData(0, (0,5))], o_intensity=[38, 2, 20], SC_length=2.3) | |
| return CellData(pathdata_list=[pathdata1, pathdata2]) | |
| def test_analyse_celldata(mock_celldata): | |
| data_frame, foci_absolute_intensity, foci_position_index, dominated_foci_data, trace_median_intensity, trace_thresholds = analyse_celldata(mock_celldata, {'peak_threshold': 0.4, 'threshold_type':'per-trace'}) | |
| assert len(data_frame) == len(mock_celldata.pathdata_list), "Mismatch in dataframe length" | |
| assert len(foci_absolute_intensity) == len(mock_celldata.pathdata_list), "Mismatch in relative intensities length" | |
| assert len(foci_position_index) == len(mock_celldata.pathdata_list), "Mismatch in positions length" | |
| assert list(map(list, foci_position_index)) == [[0, 5], [2]] | |
| def test_analyse_celldata_per_cell(mock_celldata): | |
| data_frame, foci_absolute_intensity, foci_position_index, dominated_foci_data, trace_median_intensity, trace_thresholds = analyse_celldata(mock_celldata, {'peak_threshold': 0.4, 'threshold_type':'per-cell'}) | |
| assert len(data_frame) == len(mock_celldata.pathdata_list), "Mismatch in relative intensities length" | |
| assert len(foci_absolute_intensity) == len(mock_celldata.pathdata_list), "Mismatch in positions length" | |
| assert len(foci_position_index) == len(mock_celldata.pathdata_list), "Mismatch in position indices length" | |
| assert list(map(list, foci_position_index)) == [[0, 5], []] | |