Clean up a variety of warnings and deprecations.

histomicstk/annotations_and_masks/polygon_merger.py

@@ -7,7 +7,7 @@
 import os
 
 import numpy as np
-from imageio import imread
+from imageio.v2 import imread
 from PIL import Image
 # import cv2
 from shapely.geometry.polygon import Polygon

histomicstk/annotations_and_masks/tests/test_annotation_and_mask_utils.py

@@ -107,8 +107,9 @@ def test_get_scale_factor_and_appendStr(self):
             [(None, 10.), (0.25, '&magnification=10.00000000')],
             [(None, None), (1.0, '')],
         ]
-        for (MPP, MAG), (sftrue, apstr) in in_out:
-            sf, appendStr = get_scale_factor_and_appendStr(
-                gc=cfg.gc, slide_id=cfg.iteminfo['_id'], MPP=MPP, MAG=MAG)
-            assert sf == sftrue
-            assert appendStr == apstr
+        with pytest.warns(RuntimeWarning):
+            for (MPP, MAG), (sftrue, apstr) in in_out:
+                sf, appendStr = get_scale_factor_and_appendStr(
+                    gc=cfg.gc, slide_id=cfg.iteminfo['_id'], MPP=MPP, MAG=MAG)
+                assert sf == sftrue
+                assert appendStr == apstr

histomicstk/annotations_and_masks/tests/test_annotation_database_parser.py

@@ -88,10 +88,10 @@ def test_dump_annotations_locally_1(self):
         dbcon = sql_engine.connect()
 
         result = pd.read_sql_query(text("""SELECT count(*) FROM 'folders';"""), dbcon)
-        assert int(result.loc[0, :]) == 2
+        assert int(result.iloc[0, 0]) == 2
 
         result = pd.read_sql_query(text("""SELECT count(*) FROM 'items';"""), dbcon)
-        assert int(result.loc[0, :]) == 4
+        assert int(result.iloc[0, 0]) == 4
 
         # cleanup
         shutil.rmtree(savepath)

histomicstk/annotations_and_masks/tests/test_annotations_to_object_mask_handler.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 import pytest
-from imageio import imread
+from imageio.v2 import imread
 from pandas import read_csv
 
 from histomicstk.annotations_and_masks.annotation_and_mask_utils import (

histomicstk/annotations_and_masks/tests/test_masks_to_annotations_handler.py

@@ -8,7 +8,7 @@
 
 import pandas as pd
 import pytest
-from imageio import imread
+from imageio.v2 import imread
 from pandas import read_csv
 
 from histomicstk.annotations_and_masks.annotation_and_mask_utils import \

histomicstk/features/compute_global_cell_graph_features.py

@@ -193,8 +193,14 @@ def _pop_stats(pop):
         if mask.all():
             break
         pop = pop[mask]
-    minmaxr = pop.min() / pop.max()
-    disorder = stddev / (mean + stddev)
+    if pop.max():
+        minmaxr = pop.min() / pop.max()
+    else:
+        minmaxr = 0.0
+    if mean + stddev:
+        disorder = stddev / (mean + stddev)
+    else:
+        disorder = 0.0
     return PopStats(mean, stddev, minmaxr, disorder)
 
 

histomicstk/features/compute_haralick_features.py

@@ -325,8 +325,10 @@ def compute_haralick_features(im_label, im_intensity, offsets=None,
             meanx = np.dot(n_Minus, px)
             variance = np.dot(px, np.square(n_Minus)) - np.square(meanx)
             nGLCMr = np.ravel(nGLCM)
-            Correlation = (np.dot(np.ravel(xy), nGLCMr) - np.square(meanx)) / variance
-
+            if variance:
+                Correlation = (np.dot(np.ravel(xy), nGLCMr) - np.square(meanx)) / variance
+            else:
+                Correlation = 0.0
             # computes sum of squares : variance
             SumOfSquares = variance
 

histomicstk/saliency/cellularity_detection_superpixels.py

@@ -506,7 +506,7 @@ def set_color_normalization_values(
         assert what in ('thumbnail', 'main')
 
         if ref_image_path is not None:
-            from imageio import imread
+            from imageio.v2 import imread
 
             ref_im = np.array(imread(ref_image_path, pilmode='RGB'))
             mu, sigma = lab_mean_std(ref_im)

histomicstk/saliency/cellularity_detection_thresholding.py

@@ -194,7 +194,7 @@ def find_potentially_cellular_regions(self):
         # Second, low-pass filter to dilate and smooth a bit
         self.maybe_cellular = gaussian(
             0 + (self.maybe_cellular > 0), sigma=self.cdt.cellular_step2_sigma,
-            output=None, mode='nearest', preserve_range=True)
+            out=None, mode='nearest', preserve_range=True)
 
         # find connected components
         self.maybe_cellular, _ = ndimage.label(self.maybe_cellular)
@@ -224,7 +224,7 @@ def find_top_cellular_regions(self):
         # get top n brightest regions from the largest areas
         intensity_feats.sort_values('Intensity.Mean', axis=0, inplace=True)
         discard = np.array(intensity_feats.index[:-self.cdt.cellular_top_n])
-        discard = np.in1d(top_cellular, discard).reshape(top_cellular.shape)
+        discard = np.isin(top_cellular, discard).reshape(top_cellular.shape)
         top_cellular[discard] = 0
 
         # integrate into labeled mask
@@ -614,7 +614,7 @@ def set_color_normalization_target(
         >    reinhard and macenko_pca are accepted.
 
         """
-        from imageio import imread
+        from imageioi.v2 import imread
 
         # read input image
         ref_im = np.array(imread(ref_image_path, pilmode='RGB'))

histomicstk/saliency/tissue_detection.py

@@ -105,7 +105,7 @@ def get_tissue_mask(
         if sigma > 0.0:
             thumbnail = gaussian(
                 thumbnail, sigma=sigma,
-                output=None, mode='nearest', preserve_range=True)
+                out=None, mode='nearest', preserve_range=True)
 
         # get threshold to keep analysis region
         try:
@@ -124,7 +124,7 @@ def get_tissue_mask(
 
     # only keep
     unique, counts = np.unique(labeled[labeled > 0], return_counts=True)
-    discard = np.in1d(labeled, unique[counts < min_size])
+    discard = np.isin(labeled, unique[counts < min_size])
     discard = discard.reshape(labeled.shape)
     labeled[discard] = 0
 
@@ -265,7 +265,7 @@ def _get_largest_regions(labeled, top_n=10):
     keep = unique[np.argsort(counts)[-top_n:]]
 
     mask = np.zeros(labeled_im.shape)
-    keep_pixels = np.in1d(labeled_im, keep)
+    keep_pixels = np.isin(labeled_im, keep)
     keep_pixels = keep_pixels.reshape(labeled_im.shape)
     mask[keep_pixels] = 1
     labeled_im[mask == 0] = 0

tests/test_global_cell_graph_features.py

@@ -26,9 +26,9 @@ def testSimple(self):
             density_distance_for_neighbors_1_mean=2.59272486435,
             density_distance_for_neighbors_1_min_max_ratio=0.75,
             density_distance_for_neighbors_1_stddev=0.333333333333,
-            density_neighbors_in_distance_0_disorder=np.nan,
+            density_neighbors_in_distance_0_disorder=0.0,
             density_neighbors_in_distance_0_mean=0.0,
-            density_neighbors_in_distance_0_min_max_ratio=np.nan,
+            density_neighbors_in_distance_0_min_max_ratio=0.0,
             density_neighbors_in_distance_0_stddev=0.0,
             density_neighbors_in_distance_1_disorder=0.414213562373,
             density_neighbors_in_distance_1_mean=0.666666666667,

tox.ini

@@ -11,6 +11,7 @@ passenv =
   DOCKER_*
   GENERATE_GROUNDTRUTH
   PYTEST_*
+  DASK_*
 # This adds the tests directory to the python path so we can import the test
 # utilities as needed.
 setenv =