Comply with flake8 + testing debug

Author: MaximLippeveld

src/misc/plot.py

@@ -10,4 +10,4 @@
     x = df["feat_area_combined"]
     y = df["feat_eccentricity_combined"]
     plt.scatter(x, y, s=1, alpha=0.5)
-    plt.savefig("test.png")
+    plt.savefig("test.png")

src/scip/features/texture.py

@@ -68,11 +68,11 @@ def _row(pixels, maximum_pixel_value, num_features):
     )
 
     out = numpy.full(shape=(num_features,), fill_value=None, dtype=float)
-    step = len(distances)*2
+    step = len(distances) * 2
     for i, prop in enumerate(graycoprop_names):
         v = graycoprops(glcm, prop=prop)
-        out[i*step:i*step+len(distances)] = v.mean(axis=1)
-        out[i*step+len(distances):(i+1)*step] = v.std(axis=1)
+        out[i * step:i * step + len(distances)] = v.mean(axis=1)
+        out[i * step + len(distances):(i + 1) * step] = v.std(axis=1)
 
     s = sobel(pixels)
     out[-4] = s.mean()

src/scip/main.py

@@ -130,7 +130,7 @@ def channel_boundaries(quantiles, *, config, output):
         pandas.DataFrame(data=data, index=index).to_csv(str(output / "channel_boundaries.csv"))
 
 
-def main(
+def main(  # noqa: C901
     *,
     paths,
     output,

src/scip/masking/threshold.py

@@ -19,7 +19,6 @@
 from skimage.morphology import (
     closing, disk, remove_small_holes, remove_small_objects, label)
 from skimage.filters import threshold_otsu, sobel, gaussian
-from skimage.segmentation import expand_labels
 from scipy.stats import normaltest
 from scip.utils.util import check
 
@@ -35,9 +34,9 @@ def get_mask(el, main, main_channel, smooth):
             x = gaussian(x, sigma=smooth)
             x = sobel(x)
             x = closing(x, footprint=disk(2))
-            x = gaussian(x, sigma=smooth*2)
+            x = gaussian(x, sigma=smooth * 2)
             x = threshold_otsu(x) < x
-            x = remove_small_holes(x, area_threshold=(x.shape[0]*x.shape[1])/4)
+            x = remove_small_holes(x, area_threshold=(x.shape[0] * x.shape[1]) / 4)
             x = remove_small_objects(x, min_size=20)
             x = label(x)
             mask[main_channel], cc = x > 0, x.max()
@@ -56,9 +55,9 @@ def get_mask(el, main, main_channel, smooth):
             if (normaltest(x.ravel()).pvalue < 0.05):
                 x = gaussian(x, sigma=smooth)
                 x = sobel(x)
-                x = gaussian(x, sigma=smooth*2)
+                x = gaussian(x, sigma=smooth * 2)
                 x = threshold_otsu(x) < x
-                x = remove_small_holes(x, area_threshold=(x.shape[0]*x.shape[1])/4)
+                x = remove_small_holes(x, area_threshold=(x.shape[0] * x.shape[1]) / 4)
                 x = remove_small_objects(x, min_size=20)
                 x = label(x)
                 mask[dim], cc = x > 0, x.max()

src/scip/masking/util.py

@@ -78,6 +78,7 @@ def _regions_touching(arr):
     else:
         return idx[counts > limit]
 
+
 @check
 def remove_regions_touching_border(p, bbox_channel_index):
     mask = numpy.empty_like(p["mask"])

test/conftest.py

@@ -19,9 +19,9 @@
 
 import pytest
 import numpy
-from dask.distributed import (Client, LocalCluster)
 import dask.bag
 
+from dask.distributed import (Client, LocalCluster)
 from scip.utils import util
 
 

test/masking/test_bounding_box.py

@@ -26,7 +26,7 @@ def test_bounding_box(images_folder, cluster):
         images_folder, channels=[0, 1, 2], partition_size=2)
     bag = watershed.create_masks_on_bag(bag, noisy_channels=[0])
     bag = bag.filter(partial(util.mask_predicate, bbox_channel_index=0))
-    bag = bag.map_partitions(util.bounding_box_partition, bbox_channel_index=0)
+    bag = bag.map_partitions(util.bounding_box_partition)
 
     bag = bag.compute()