minimal changes from main

ark/utils/example_dataset_test.py

@@ -1,6 +1,5 @@
-from email.generator import Generator
 import pathlib
-from typing import Callable, Iterator
+from typing import Callable, Iterator, Generator
 import itertools
 import pytest
 from ark.utils.example_dataset import ExampleDataset, get_example_dataset

ark/utils/notebooks_test_utils.py

@@ -149,7 +149,7 @@ def flowsom_pixel_setup(tb, flowsom_dir, create_seg_dir=True, img_shape=(50, 50)
     tb.execute_cell('import')
 
     # create data which will be loaded into img_xr
-    tiff_dir = os.path.join(flowsom_dir, "image_data")
+    tiff_dir = os.path.join(flowsom_dir, "input_data")
     os.mkdir(tiff_dir)
 
     if is_mibitiff:
@@ -173,14 +173,11 @@ def flowsom_pixel_setup(tb, flowsom_dir, create_seg_dir=True, img_shape=(50, 50)
 
     # generate sample segmentation labels so we can load them in
     if create_seg_dir:
-        seg_dir = os.path.join(flowsom_dir, "segmentation")
-        deepcell_output_dir = os.path.join(seg_dir, "deepcell_output")
-        os.makedirs(seg_dir)
-        os.makedirs(deepcell_output_dir)
-        generate_sample_feature_tifs(fovs, deepcell_output_dir, img_shape)
+        seg_dir = os.path.join(flowsom_dir, 'deepcell_output')
+        os.mkdir(seg_dir)
+        generate_sample_feature_tifs(fovs, seg_dir, img_shape)
 
         seg_dir = "\"%s\"" % seg_dir
-        deepcell_output_dir = "\"%s\"" % deepcell_output_dir
     else:
         seg_dir = None
 
@@ -190,12 +187,10 @@ def flowsom_pixel_setup(tb, flowsom_dir, create_seg_dir=True, img_shape=(50, 50)
         tiff_dir = "%s"
         img_sub_folder = None
         segmentation_dir = %s
-        deepcell_output_dir = %s
         seg_suffix = '_feature_0.tif'
         MIBItiff = %s
         mibitiff_suffix = '%s'
-    """ % (flowsom_dir, tiff_dir, str(seg_dir), str(deepcell_output_dir), is_mibitiff,
-           mibitiff_suffix)
+    """ % (flowsom_dir, tiff_dir, str(seg_dir), is_mibitiff, mibitiff_suffix)
     tb.inject(define_data_paths, after='file_path')
 
     if fovs is not None:
@@ -233,15 +228,15 @@ def flowsom_pixel_setup(tb, flowsom_dir, create_seg_dir=True, img_shape=(50, 50)
 
     # if seg_dir is set, test filtering with exclude = True for the second channel,
     # and exclude = False for the third channel
-    if deepcell_output_dir:
+    if seg_dir:
         run_chan_filtering_exclude = """
             filter_channel = '%s'
             nuclear_exclude = True
 
             pixel_cluster_utils.filter_with_nuclear_mask(
                 fovs,
                 tiff_dir,
-                deepcell_output_dir,
+                segmentation_dir,
                 filter_channel,
                 img_sub_folder,
                 nuclear_exclude
@@ -256,7 +251,7 @@ def flowsom_pixel_setup(tb, flowsom_dir, create_seg_dir=True, img_shape=(50, 50)
             pixel_cluster_utils.filter_with_nuclear_mask(
                 fovs,
                 tiff_dir,
-                deepcell_output_dir,
+                segmentation_dir,
                 filter_channel,
                 img_sub_folder,
                 nuclear_exclude
@@ -312,9 +307,9 @@ def flowsom_pixel_cluster(tb, flowsom_dir, fovs, channels,
     """
 
     # get path to the data
-    data_path = os.path.join(flowsom_dir + "/segmentation/",
+    data_path = os.path.join(flowsom_dir,
                              '%s_pixel_output_dir' % pixel_prefix,
-                             'pixel_mat_data')
+                             '%s_pixel_mat_data' % pixel_prefix)
 
     # make sample consensus data for each fov
     for fov in fovs:
@@ -349,9 +344,9 @@ def flowsom_pixel_cluster(tb, flowsom_dir, fovs, channels,
     avg_channels_som['pixel_som_cluster'] = range(1, 101)
     avg_channels_som['pixel_meta_cluster'] = np.repeat(range(1, 21), 5)
     avg_channels_som.to_csv(
-        os.path.join(flowsom_dir + "/segmentation/",
+        os.path.join(flowsom_dir,
                      '%s_pixel_output_dir' % pixel_prefix,
-                     'pixel_channel_avg_som_cluster.csv'),
+                     '%s_pixel_channel_avg_som_cluster.csv' % pixel_prefix),
         index=False
     )
 
@@ -364,9 +359,9 @@ def flowsom_pixel_cluster(tb, flowsom_dir, fovs, channels,
     )
     avg_channels_meta['pixel_meta_cluster'] = range(1, 21)
     avg_channels_meta.to_csv(
-        os.path.join(flowsom_dir + "/segmentation/",
+        os.path.join(flowsom_dir,
                      '%s_pixel_output_dir' % pixel_prefix,
-                     'pixel_channel_avg_meta_cluster.csv'),
+                     '%s_pixel_channel_avg_meta_cluster.csv' % pixel_prefix),
         index=False
     )
 
@@ -398,9 +393,9 @@ def flowsom_pixel_visualize(tb, flowsom_dir, fovs, pixel_prefix='test'):
     remap_data['metacluster'] = np.repeat(range(1, 11), 10)
     remap_data['mc_name'] = np.repeat(['meta_' + str(i) for i in range(1, 11)], 10)
     remap_data.to_csv(
-        os.path.join(flowsom_dir + "/segmentation/",
+        os.path.join(flowsom_dir,
                      '%s_pixel_output_dir' % pixel_prefix,
-                     'pixel_meta_cluster_mapping.csv'),
+                     '%s_pixel_meta_cluster_mapping.csv' % pixel_prefix),
         index=False
     )
 
@@ -410,7 +405,7 @@ def flowsom_pixel_visualize(tb, flowsom_dir, fovs, pixel_prefix='test'):
     # generate the colormap to use
     tb.execute_cell('pixel_cmap_gen')
 
-    # define the FOVs to use for the cell-pixel overlay
+    # define the FOVs to use for the cpixelell overlay
     if len(fovs) <= 2:
         fovs_overlay = fovs
     else: