black

viscy/evaluation/evaluation_metrics.py

@@ -1,4 +1,5 @@
 """Metrics for model evaluation"""
+
 from typing import Sequence, Union
 from warnings import warn
 

viscy/light/engine.py

@@ -268,19 +268,19 @@ def _log_segmentation_metrics(
         self.log_dict(
             {
                 # semantic segmentation
-                "test_metrics/accuracy": accuracy(
-                    pred_binary, target_binary, task="binary"
-                )
-                if compute
-                else -1,
-                "test_metrics/dice": dice(pred_binary, target_binary)
-                if compute
-                else -1,
-                "test_metrics/jaccard": jaccard_index(
-                    pred_binary, target_binary, task="binary"
-                )
-                if compute
-                else -1,
+                "test_metrics/accuracy": (
+                    accuracy(pred_binary, target_binary, task="binary")
+                    if compute
+                    else -1
+                ),
+                "test_metrics/dice": (
+                    dice(pred_binary, target_binary) if compute else -1
+                ),
+                "test_metrics/jaccard": (
+                    jaccard_index(pred_binary, target_binary, task="binary")
+                    if compute
+                    else -1
+                ),
                 "test_metrics/mAP": coco_metrics["map"] if compute else -1,
                 "test_metrics/mAP_50": coco_metrics["map_50"] if compute else -1,
                 "test_metrics/mAP_75": coco_metrics["map_75"] if compute else -1,

viscy/preprocessing/generate_masks.py

@@ -1,4 +1,5 @@
 """Generate masks from sum of flurophore channels"""
+
 import iohub.ngff as ngff
 
 import viscy.utils.aux_utils as aux_utils

viscy/utils/image_utils.py

@@ -21,9 +21,7 @@ def im_bit_convert(im, bit=16, norm=False, limit=[]):
             / (limit[1] - limit[0] + sys.float_info.epsilon)
             * (2**bit - 1)
         )
-    im = np.clip(
-        im, 0, 2**bit - 1
-    )  # clip the values to avoid wrap-around by np.astype
+    im = np.clip(im, 0, 2**bit - 1)  # clip the values to avoid wrap-around by np.astype
     if bit == 8:
         im = im.astype(np.uint8, copy=False)  # convert to 8 bit
     else:

viscy/utils/normalize.py

@@ -1,4 +1,5 @@
 """Image normalization related functions"""
+
 import sys
 
 import numpy as np