def test_img_to_metadata_extra_list(): # make some example data colnames = ['Correlation_Correlation_W2_W3', 'Correlation_Costes_W2_W3', 'Correlation_Costes_W3_W2', 'Correlation_K_W2_W3', 'Correlation_K_W3_W2', 'Correlation_Manders_W2_W3', 'Correlation_Manders_W3_W2', 'Correlation_Overlap_W2_W3', 'Correlation_RWC_W2_W3', 'Correlation_RWC_W3_W2', 'Correlation_Slope_W2_W3', 'Count_Cells', 'Count_Nuclei', 'ExecutionTime_01LoadData', 'ExecutionTime_02IdentifyPrimaryObjects', 'ExecutionTime_03ImageMath', 'ExecutionTime_04IdentifySecondaryObjects', 'ExecutionTime_05MeasureObjectSizeShape', 'ExecutionTime_06MeasureImageQuality', 'ExecutionTime_07MeasureObjectIntensity', 'ExecutionTime_08MeasureObjectIntensity', 'ExecutionTime_09MeasureObjectIntensityDistribution', 'ExecutionTime_10MeasureObjectNeighbors', 'ExecutionTime_11MeasureTexture', 'ExecutionTime_12MeasureTexture', 'ExecutionTime_13MeasureCorrelation', 'ExecutionTime_14MeasureGranularity', 'ExecutionTime_15MeasureGranularity', 'FileName_W1', 'FileName_W2', 'FileName_W3', 'FileName_W4', 'FileName_W5', 'Granularity_10_W1', 'Granularity_10_W4', 'Granularity_10_W5', 'Granularity_11_W1', 'Metadata_well', 'Cells_AreaShape_Area', "Nuclei_AreaShape_Area"] dat = np.random.randn(10, len(colnames)) test_df = pd.DataFrame(dat, columns=colnames) ans = utils.img_to_metadata(test_df, prefix="Metadata_", extra=["Cells", "Nuclei"]) expected = ['Correlation_Correlation_W2_W3', 'Correlation_Costes_W2_W3', 'Correlation_Costes_W3_W2', 'Correlation_K_W2_W3', 'Correlation_K_W3_W2', 'Correlation_Manders_W2_W3', 'Correlation_Manders_W3_W2', 'Correlation_Overlap_W2_W3', 'Correlation_RWC_W2_W3', 'Correlation_RWC_W3_W2', 'Correlation_Slope_W2_W3', 'Count_Cells', 'Count_Nuclei', 'Metadata_ExecutionTime_01LoadData', 'Metadata_ExecutionTime_02IdentifyPrimaryObjects', 'Metadata_ExecutionTime_03ImageMath', 'Metadata_ExecutionTime_04IdentifySecondaryObjects', 'Metadata_ExecutionTime_05MeasureObjectSizeShape', 'Metadata_ExecutionTime_06MeasureImageQuality', 'Metadata_ExecutionTime_07MeasureObjectIntensity', 'Metadata_ExecutionTime_08MeasureObjectIntensity', 'Metadata_ExecutionTime_09MeasureObjectIntensityDistribution', 'Metadata_ExecutionTime_10MeasureObjectNeighbors', 'Metadata_ExecutionTime_11MeasureTexture', 'Metadata_ExecutionTime_12MeasureTexture', 'Metadata_ExecutionTime_13MeasureCorrelation', 'Metadata_ExecutionTime_14MeasureGranularity', 'Metadata_ExecutionTime_15MeasureGranularity', 'Metadata_FileName_W1', 'Metadata_FileName_W2', 'Metadata_FileName_W3', 'Metadata_FileName_W4', 'Metadata_FileName_W5', 'Granularity_10_W1', 'Granularity_10_W4', 'Granularity_10_W5', 'Granularity_11_W1', "Metadata_well", "Cells_AreaShape_Area", "Nuclei_AreaShape_Area"] assert ans == expected
def test_img_to_metadata(): # make some example data colnames = [ "Correlation_Correlation_W2_W3", "Correlation_Costes_W2_W3", "Correlation_Costes_W3_W2", "Correlation_K_W2_W3", "Correlation_K_W3_W2", "Correlation_Manders_W2_W3", "Correlation_Manders_W3_W2", "Correlation_Overlap_W2_W3", "Correlation_RWC_W2_W3", "Correlation_RWC_W3_W2", "Correlation_Slope_W2_W3", "Count_Cells", "Count_Nuclei", "ExecutionTime_01LoadData", "ExecutionTime_02IdentifyPrimaryObjects", "ExecutionTime_03ImageMath", "ExecutionTime_04IdentifySecondaryObjects", "ExecutionTime_05MeasureObjectSizeShape", "ExecutionTime_06MeasureImageQuality", "ExecutionTime_07MeasureObjectIntensity", "ExecutionTime_08MeasureObjectIntensity", "ExecutionTime_09MeasureObjectIntensityDistribution", "ExecutionTime_10MeasureObjectNeighbors", "ExecutionTime_11MeasureTexture", "ExecutionTime_12MeasureTexture", "ExecutionTime_13MeasureCorrelation", "ExecutionTime_14MeasureGranularity", "ExecutionTime_15MeasureGranularity", "FileName_W1", "FileName_W2", "FileName_W3", "FileName_W4", "FileName_W5", "Granularity_10_W1", "Granularity_10_W4", "Granularity_10_W5", "Granularity_11_W1", "Metadata_well", ] dat = np.random.randn(10, len(colnames)) test_df = pd.DataFrame(dat, columns=colnames) ans = utils.img_to_metadata(test_df, prefix="Metadata_") expected = [ "Correlation_Correlation_W2_W3", "Correlation_Costes_W2_W3", "Correlation_Costes_W3_W2", "Correlation_K_W2_W3", "Correlation_K_W3_W2", "Correlation_Manders_W2_W3", "Correlation_Manders_W3_W2", "Correlation_Overlap_W2_W3", "Correlation_RWC_W2_W3", "Correlation_RWC_W3_W2", "Correlation_Slope_W2_W3", "Count_Cells", "Count_Nuclei", "Metadata_ExecutionTime_01LoadData", "Metadata_ExecutionTime_02IdentifyPrimaryObjects", "Metadata_ExecutionTime_03ImageMath", "Metadata_ExecutionTime_04IdentifySecondaryObjects", "Metadata_ExecutionTime_05MeasureObjectSizeShape", "Metadata_ExecutionTime_06MeasureImageQuality", "Metadata_ExecutionTime_07MeasureObjectIntensity", "Metadata_ExecutionTime_08MeasureObjectIntensity", "Metadata_ExecutionTime_09MeasureObjectIntensityDistribution", "Metadata_ExecutionTime_10MeasureObjectNeighbors", "Metadata_ExecutionTime_11MeasureTexture", "Metadata_ExecutionTime_12MeasureTexture", "Metadata_ExecutionTime_13MeasureCorrelation", "Metadata_ExecutionTime_14MeasureGranularity", "Metadata_ExecutionTime_15MeasureGranularity", "Metadata_FileName_W1", "Metadata_FileName_W2", "Metadata_FileName_W3", "Metadata_FileName_W4", "Metadata_FileName_W5", "Granularity_10_W1", "Granularity_10_W4", "Granularity_10_W5", "Granularity_11_W1", "Metadata_well", ] assert ans == expected