Corrected argument-forwarding error; set up job for porto data; split off...

Corrected argument-forwarding error; set up job for porto data; split off ecotaxa TSV generation into its own module

extensions/chaeo/batch_jobs/20231023_Porto_EtOHfixed.py

+from pathlib import Path
+
+from model_server.util import autonumber_new_directory, get_matching_files, loop_workflow
+from extensions.chaeo.ecotaxa import write_ecotaxa_tsv
+from extensions.chaeo.workflows import export_patches_from_multichannel_zstack
+from extensions.ilastik.models import IlastikPixelClassifierModel
+
+
+if __name__ == '__main__':
+    root = Path('c:/Users/rhodes/projects/proj0012-trec-handoff/owncloud-sync/TREC-HD/Images/')
+    where_czi = (root / 'TREC_STOP_26_Porto/231023_automic/20231023-175838_EtOHfixed/LowZoom').__str__()
+    where_output = autonumber_new_directory(
+        'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0020/output',
+        'batch-output'
+    )
+
+    px_ilp = Path('c:/Users/rhodes/projects/proj0011-plankton-seg/exp0017/pxAF405_dim8bit.ilp').__str__()
+
+    params = {
+        'pxmap_threshold': 0.25,
+        'pxmap_foreground_channel': 0,
+        'segmentation_channel': 0,
+        'zmask_zindex': None,
+        'patches_channel': 2,
+        'zmask_type': 'boxes',
+        'zmask_filters': {'area': (1e3, 1e8)},
+        'zmask_expand_box_by': (128, 3),
+        'export_pixel_probabilities': True,
+        'export_2d_patches_for_training': True,
+        'draw_bounding_box_on_2d_patch': True,
+        'export_2d_patches_for_annotation': True,
+        'export_3d_patches': False,
+        'export_annotated_zstack': True,
+        'export_patch_masks': True,
+        'zmask_clip': 0.01,
+        'rgb_overlay_channels': (1, None, None),
+        'rgb_overlay_weights': (0.2, 1.0, 1.0)
+    }
+
+    input_files = get_matching_files(where_czi, 'czi', coord_filter={})
+
+    loop_workflow(
+        input_files,
+        where_output,
+        export_patches_from_multichannel_zstack,
+        [IlastikPixelClassifierModel(params={'project_file': Path(px_ilp)})],
+        params,
+        catch_and_continue=False,
+    )
+
+    csv_path = (Path(where_output) / 'workflow_data.csv').__str__()
+    write_ecotaxa_tsv(csv_path, where_output, sample_id='20231023-porto', scope_id='EMBL-MS-Zeiss-LSM900')
+
+    print('Finished')
\ No newline at end of file

extensions/chaeo/ecotaxa.py

+from pathlib import Path
+
+import pandas as pd
+
+def write_ecotaxa_tsv(patches_csv_path: str, where: str, sample_id: str, scope_id: str):
+    # import patch output table
+    df_patches = pd.read_csv(patches_csv_path)
+    df_patches['img_file_name'] = df_patches['patch_filename'].apply(lambda x: '2d_patches_annotation/' + x)
+
+    # make second column index level to comply w/ EcoTaxa schema
+    df_patches.columns = pd.MultiIndex.from_frame(
+        pd.DataFrame([
+            df_patches.columns,
+            df_patches.dtypes.apply(
+                lambda x: '[f]' if x in ['float64'] else '[t]'
+            )
+        ]).T,
+        names=['variable', 'data type']
+    )
+
+    # add new columns for ecotaxa
+    df_patches.loc[:, ('acq_instrument', '[t]')] = 'Other microscope'
+    df_patches.loc[:, ('acq_instrument_microscope', '[t]')] = scope_id
+    df_patches.loc[:, ('sample_id', '[t]')] = sample_id
+
+    df_patches.loc[:, ('acq_id', '[t]')] = df_patches.loc[:, ('input_file', '[t]')]
+    df_patches.loc[:, ('object_id', '[t]')] = df_patches.loc[:, ('patch_id', '[t]')]
+    df_patches.loc[:, ('process_id', '[t]')] = df_patches.loc[:, ('patch_id', '[t]')]
+
+    cols_to_transfer = [
+        'img_file_name',
+        'object_id',
+        'acq_id',
+        'acq_instrument',
+        'acq_instrument_microscope',
+        'sample_id',
+        'process_id'
+    ]
+    df_export = df_patches.loc[:, pd.IndexSlice[cols_to_transfer, :]]
+    df_export.to_csv(Path(where) / 'ecotaxa.tsv', sep='\t', index=False)
\ No newline at end of file

extensions/chaeo/workflows.py

@@ -63,7 +63,7 @@ def get_zmask_meta(
         stack.get_one_channel_data(segmentation_channel),
         mask_type=zmask_type,
         filters=zmask_filters,
-        **kwargs,
+        expand_box_by=kwargs['zmask_expand_box_by'],
     )
     ti.click('generate_zmasks')