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