ENH: Make test_mask.py work with older histomics_stream versions

histomics_stream/__init__.py

@@ -26,4 +26,4 @@
 execution graph.
 
 """
-from . import configure, codecs
+from . import configure, codecs  # noqa: F401,E402

histomics_stream/codecs.py

@@ -18,8 +18,8 @@
 
 """Whole-slide image streamer for machine learning frameworks.
 
-The histomics_stream.codecs module supplies codecs that are useful for Zarr file storage with jpeg or
-jpeg2k compression.
+The histomics_stream.codecs module supplies codecs that are useful for Zarr file storage
+with jpeg or jpeg2k compression.
 
 """
 
@@ -39,10 +39,10 @@ class jpeg(Codec):
 
     Notes
     -----
-    For the code that uses Zarr data storage for jpeg images, we need to supply codecs.  Note that we
-    use this codec instead of that available from the zarr_jpeg package.  The latter collapses
-    dimensions by default, can require us to transpose dimensions, and can miss optimizations based upon
-    RGB data.
+    For the code that uses Zarr data storage for jpeg images, we need to supply codecs.
+    Note that we use this codec instead of that available from the zarr_jpeg package.
+    The latter collapses dimensions by default, can require us to transpose dimensions,
+    and can miss optimizations based upon RGB data.
 
     """
 

histomics_stream/pytorch.py

@@ -18,6 +18,10 @@
 
 """Whole-slide image streamer for machine learning frameworks."""
 
+import numpy as np
+import torch
+from . import configure
+
 """
 See: How to load a list of numpy arrays to pytorch dataset loader?
 https://stackoverflow.com/questions/44429199/how-to-load-a-list-of-numpy-arrays-to-pytorch-dataset-loader
@@ -33,14 +37,8 @@
 """
 See: A Comprehensive Guide to the DataLoader Class and Abstractions in PyTorch
 https://blog.paperspace.com/dataloaders-abstractions-pytorch/
-
 """
 
-import numpy as np
-import torch
-
-from . import configure
-
 
 class CreateTorchDataloader(configure.ChunkLocations):
     class MyDataset(torch.utils.data.IterableDataset, configure._TilesByCommon):

test/test_create_study.py

@@ -36,9 +36,10 @@ def test_create_study():
 
     # Add a slide to the study, including slide-wide information with it.
     my_slide0 = my_slides["Slide_0"] = {}
-    my_slide0[
-        "filename"
-    ] = "/tf/notebooks/histomics_stream/example/TCGA-BH-A0BZ-01Z-00-DX1.45EB3E93-A871-49C6-9EAE-90D98AE01913.svs"
+    my_slide0["filename"] = (
+        "/tf/notebooks/histomics_stream/example/"
+        "TCGA-BH-A0BZ-01Z-00-DX1.45EB3E93-A871-49C6-9EAE-90D98AE01913.svs"
+    )
     my_slide0["slide_name"] = "TCGA-BH-A0BZ-01Z-00-DX1"
     my_slide0["slide_group"] = "TCGA-BH-A0BZ"
     my_slide0["chunk_height"] = 2048
@@ -81,7 +82,8 @@ def test_create_study():
             my_study_by_grid_and_mask,
             overlap_height=0,
             overlap_width=0,
-            mask_filename="/tf/notebooks/histomics_stream/example/TCGA-BH-A0BZ-01Z-00-DX1.45EB3E93-A871-49C6-9EAE-90D98AE01913-mask.png",
+            mask_filename="/tf/notebooks/histomics_stream/example/"
+            "TCGA-BH-A0BZ-01Z-00-DX1.45EB3E93-A871-49C6-9EAE-90D98AE01913-mask.png",
             randomly_select=100,
         )
         # We could apply this to a subset of the slides, but we will apply it to all

test/test_find_imports.py

@@ -22,15 +22,14 @@
 def test_imports_can_be_found():
     """Purpose: Test to check that each import can be found"""
 
-    # Import succeeds
-    import imagecodecs
-    import itk
-    import numcodecs
-    import numpy
-    import scipy.interpolate
-    import tensorflow
-    import torch
-    import zarr
+    import imagecodecs  # noqa: F401
+    import itk  # noqa: F401
+    import numcodecs  # noqa: F401
+    import numpy  # noqa: F401
+    import scipy.interpolate  # noqa: F401
+    import tensorflow  # noqa: F401
+    import torch  # noqa: F401
+    import zarr  # noqa: F401
 
 
 if __name__ == "__main__":

test/test_mask.py

@@ -54,8 +54,8 @@ def test_mask_threshold():
 
     my_study = dict(
         version="version-1",
-        tile_width=5471,
-        tile_height=5743,
+        number_pixel_columns_for_tile=5471,
+        number_pixel_rows_for_tile=5743,
         overlap_width=127,
         overlap_height=101,
         slides=dict(
@@ -77,15 +77,28 @@ def test_mask_threshold():
     tiler_thresholds = (0.00, 0.20, 0.50, 0.80, 1.00)
     tilers = [
         hs.configure.TilesByGridAndMask(
-            my_study, mask_filename=mask_path, mask_threshold=threshold
+            my_study,
+            mask_filename=mask_path,
+            mask_threshold=threshold,
+            number_pixel_overlap_rows_for_tile=101,
+            number_pixel_overlap_columns_for_tile=127,
         )
         for threshold in tiler_thresholds
     ]
 
     def run_tiler(study, tiler):
         for slide in study["slides"].values():
             tiler(slide)
-        return tiler.get_tiles(my_study)
+        return [
+            (
+                value["filename"],
+                [
+                    (tile["tile_top"], tile["tile_left"])
+                    for tile in value["tiles"].values()
+                ],
+            )
+            for value in study["slides"].values()
+        ]
 
     found_tiles = [run_tiler(my_study, tiler) for tiler in tilers]