struct. docs

* flake8

docs/source/conf.py

@@ -128,7 +128,7 @@
     'data-images',
     '*tests.*', '*.test_*',
     '*.so', '*.dll',
-    'modules.rst',
+    'api/modules.rst',
     '*/transform-img-plane_inter-circle.ipynb'
 ]
 
@@ -258,8 +258,11 @@
 
 def run_apidoc(_):
     for pkg in PACKAGES:
-        argv = ['-e', '-o', PATH_HERE, os.path.join(PATH_HERE, PATH_ROOT, pkg),
-                'tests/*', '--force']
+        argv = ['-e',
+                '-o', os.path.join(PATH_HERE, 'api'),
+                os.path.join(PATH_HERE, PATH_ROOT, pkg),
+                'tests/*',
+                '--force']
         try:
             # Sphinx 1.7+
             from sphinx.ext import apidoc

docs/source/index.rst

@@ -8,7 +8,7 @@ Contents
    :maxdepth: 2
 
    readme
-   imsegm
+   api/imsegm
    examples
 
 Indices and tables

imsegm/classification.py

@@ -335,8 +335,8 @@ def compute_classif_metrics(y_true, y_pred, metric_averages=METRIC_AVERAGES):
     eval_str = 'EVALUATION: {:<2} PRE: {:.3f} REC: {:.3f} F1: {:.3f} S: {:>6}'
     try:
         p, r, f, s = metrics.precision_recall_fscore_support(y_true, y_pred)
-        for l, _ in enumerate(p):
-            logging.debug(eval_str.format(l, p[l], r[l], f[l], s[l]))
+        for lb, _ in enumerate(p):
+            logging.debug(eval_str.format(lb, p[lb], r[lb], f[lb], s[lb]))
     except Exception:
         logging.exception('metrics.precision_recall_fscore_support')
 

imsegm/labeling.py

@@ -386,8 +386,8 @@ def mask_segm_labels(img_labeling, labels, mask_init=None):
         mask = np.full(img_labeling.shape, False, dtype=bool)
     else:
         mask = mask_init.copy()
-    for l in labels:
-        mask = np.logical_or(mask, (img_labeling == l))
+    for lb in labels:
+        mask = np.logical_or(mask, (img_labeling == lb))
     return mask
 
 
@@ -422,7 +422,7 @@ def sequence_labels_merge(labels_stack, dict_colors, labels_free, change_label=-
     im_labels = np.full(labels_stack.shape[1:], change_label, dtype=np.int)
     labels_used = [lb for lb in dict_colors if lb not in labels_free]
     lb_all = labels_used + labels_free + [change_label]
-    assert all(l in lb_all for l in np.unique(labels_stack)), 'some extra labels in image stack'
+    assert all(lb in lb_all for lb in np.unique(labels_stack)), 'some extra labels in image stack'
     # generate mask of free labels
     mask_free = mask_segm_labels(labels_stack, labels_free)
     for lb in labels_used:

imsegm/region_growing.py

@@ -1371,7 +1371,7 @@ def prepare_graphcut_variables(candidates, slic_points, slic_neighbours,
     assert np.max(candidates) < len(slic_points), \
         'max candidate idx: %d for %d centres' \
         % (np.max(candidates), len(slic_points))
-    max_slic_neighbours = max(max(l) for l in slic_neighbours)
+    max_slic_neighbours = max(max(lb) for lb in slic_neighbours)
     assert max_slic_neighbours < len(slic_points), \
         'max slic neighbours idx: %d for %d centres' \
         % (max_slic_neighbours, len(slic_points))

imsegm/utilities/data_samples.py

@@ -82,8 +82,8 @@ def sample_segment_vertical_2d(seg_size=SAMPLE_SEG_SIZE_2D_SMALL,
     """
     cls_vals = []
     cls_size = (seg_size[1], int(seg_size[0] / nb_labels))
-    for l in range(nb_labels):
-        cls_vals.append(l * np.ones(cls_size))
+    for lb in range(nb_labels):
+        cls_vals.append(lb * np.ones(cls_size))
     seg = np.hstack(tuple(cls_vals))
     seg = np.array(seg, dtype=np.int)
     return seg
@@ -108,10 +108,10 @@ def sample_segment_vertical_3d(seg_size=SAMPLE_SEG_SIZE_3D_SMALL,
            [4, 4, 4, 4, 4]])
     """
     seg = []
-    for l in range(int(levels)):
+    for lb in range(int(levels)):
         seg_2d = sample_segment_vertical_2d(seg_size[:2], nb_labels)
         for _ in range(int(seg_size[2] / levels)):
-            seg.append(seg_2d.copy() + l * nb_labels)
+            seg.append(seg_2d.copy() + lb * nb_labels)
     seg = np.array(seg, dtype=np.int)
     return seg
 

setup.py

@@ -54,11 +54,11 @@ def finalize_options(self):
 def _parse_requirements(file_path):
     with open(file_path) as fp:
         reqs = [r.rstrip() for r in fp.readlines() if not r.startswith('#')]
-        # drop all proprietary packages missing from PiPy
-        reqs = filter(lambda r: not r.startswith('http'), reqs)
-        # parse egg names if there are paths
-        reqs = [r[r.index(TEMP_EGG) + len(TEMP_EGG):] if TEMP_EGG in r else r for r in reqs]
-        return reqs
+    # drop all proprietary packages missing from PiPy
+    reqs = filter(lambda r: not r.startswith('http'), reqs)
+    # parse egg names if there are paths
+    reqs = [r[r.index(TEMP_EGG) + len(TEMP_EGG):] if TEMP_EGG in r else r for r in reqs]
+    return reqs
 
 
 setup_reqs = ['Cython', 'numpy']  # numpy v1.17 drops support for py2