make 6-class datasplit actually 6 classes

tests/fixtures/datasplits.py

@@ -96,21 +96,21 @@ def six_class_datasplit(tmp_path):
     """
     two crops for training, one for validation. Raw data is normally distributed
     around 0 with std 1.
-    gt is provided as distances. First, gt is generated as a 12 class problem:
-    gt has 12 classes where class i in [0, 11] is all voxels with raw intensity
-    between (raw.min() + i(raw.max()-raw.min())/12, raw.min() + (i+1)(raw.max()-raw.min())/12).
-    Then we pair up classes (i, i+1) for i in [0,2,4,6,8,10], and compute distances to
+    gt is provided as distances. First, gt is generated as a 6 class problem:
+    gt has 6 classes where class i in [0, 5] is all voxels with raw intensity
+    between (raw.min() + i(raw.max()-raw.min())/6, raw.min() + (i+1)(raw.max()-raw.min())/6).
+    Then we pair up classes (i, i+1) for i in [0,2,4], and compute distances to
     the nearest voxel in the pair. This leaves us with 6 distance channels.
     """
-    twelve_class_zarr = zarr.open(tmp_path / "twelve_class.zarr", "w")
+    six_class_zarr = zarr.open(tmp_path / "six_class.zarr", "w")
     crop1_raw = ZarrArrayConfig(
         name="crop1_raw",
-        file_name=tmp_path / "twelve_class.zarr",
+        file_name=tmp_path / "six_class.zarr",
         dataset=f"volumes/crop1/raw",
     )
     crop1_gt = ZarrArrayConfig(
         name="crop1_gt",
-        file_name=tmp_path / "twelve_class.zarr",
+        file_name=tmp_path / "six_class.zarr",
         dataset=f"volumes/crop1/gt",
     )
     crop1_distances = BinarizeArrayConfig(
@@ -127,12 +127,12 @@ def six_class_datasplit(tmp_path):
     )
     crop2_raw = ZarrArrayConfig(
         name="crop2_raw",
-        file_name=tmp_path / "twelve_class.zarr",
+        file_name=tmp_path / "six_class.zarr",
         dataset=f"volumes/crop2/raw",
     )
     crop2_gt = ZarrArrayConfig(
         name="crop2_gt",
-        file_name=tmp_path / "twelve_class.zarr",
+        file_name=tmp_path / "six_class.zarr",
         dataset=f"volumes/crop2/gt",
     )
     crop2_distances = BinarizeArrayConfig(
@@ -149,12 +149,12 @@ def six_class_datasplit(tmp_path):
     )
     crop3_raw = ZarrArrayConfig(
         name="crop3_raw",
-        file_name=tmp_path / "twelve_class.zarr",
+        file_name=tmp_path / "six_class.zarr",
         dataset=f"volumes/crop3/raw",
     )
     crop3_gt = ZarrArrayConfig(
         name="crop3_gt",
-        file_name=tmp_path / "twelve_class.zarr",
+        file_name=tmp_path / "six_class.zarr",
         dataset=f"volumes/crop3/gt",
     )
     crop3_distances = BinarizeArrayConfig(
@@ -172,15 +172,15 @@ def six_class_datasplit(tmp_path):
     for raw, gt in zip(
         [crop1_raw, crop2_raw, crop3_raw], [crop1_gt, crop2_gt, crop3_gt]
     ):
-        raw_dataset = twelve_class_zarr.create_dataset(
+        raw_dataset = six_class_zarr.create_dataset(
             raw.dataset, shape=(40, 20, 20), dtype=np.float32
         )
-        gt_dataset = twelve_class_zarr.create_dataset(
+        gt_dataset = six_class_zarr.create_dataset(
             gt.dataset, shape=(40, 20, 20), dtype=np.uint8
         )
         random_data = np.random.rand(40, 20, 20)
         # as intensities increase so does the class
-        for i in list(np.linspace(random_data.min(), random_data.max(), 13))[1:]:
+        for i in list(np.linspace(random_data.min(), random_data.max(), 7))[1:]:
             gt_dataset[:] += random_data > i
         raw_dataset[:] = random_data
         raw_dataset.attrs["offset"] = (0, 0, 0)